{"image_path": "q-fin/image/2209.02637v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Election and Contribution Descriptive Statistics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\t\t\\toprule[1.5pt]\n\t\t& All & Union Loss & Union Win \\\\\n\t\t\\midrule\n\t\t& \\multicolumn{3}{c}{[A] Election characteristics} \\\\\n\t\tNumber of elections & 6,063 & 3,397 & 2,666 \\\\\n\t\tUnion vote share (average) & .4950 & .3204 & .7175 \\\\\n\t\tNumber of votes (average) & 119.37 & 135.31 & 99.06 \\\\\n\t\tNumber of votes (total) & 723,752 & 459,661 & 264,091 \\\\\n\t\t& \\multicolumn{3}{c}{[B] Contribution characteristics} \\\\\n\t\tAmount (total, in million 2010 USD) & 105.82 & 65.38 & 40.43 \\\\\n\t\tNumber of contributions (total) & 357,436 & 204,797 & 152,639 \\\\\n\t\tNumber of donors (total) & 46,719 & 26,661 & 20,243 \\\\\n\t\tNumber of recipients (total) & 9,942 & 7,208 & 5,681 \\\\\n\t\t\\hline\\hline\n\t\t\\multicolumn{4}{l}{\n\t\t\\begin{minipage}{0.8\\linewidth} \\smallskip \\scriptsize\n\t\t\\textbf{Notes:} Data from NLRB union certification elections, which have at least one employee contribution matched in any of seven election cycles around the union election (three before, cycle of union election, three after). Contribution characteristics refer to the total numbers over all these seven election cycles. \n\t\t\\end{minipage}} \\\\\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Do Unions Shape Political Ideologies at Work?", "authors": ["Johannes Matzat", "Aiko Schmeißer"], "url": "https://arxiv.org/abs/2209.02637v4", "attribution": "\"Do Unions Shape Political Ideologies at Work?\" by Johannes Matzat and Aiko Schmeißer, arXiv:2209.02637v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.04997v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Leave-one-out cross-validation results for DNN classifiers.} The values are averaged over 14 cross-validation folds.} % title name of the table\n\\begin{tabular}{ccccccc}\n\t\t\t\\hline\n\t\t\t{\\textbf{Frame}} & {\\textbf{Seg}} & \\textbf{Clas-} & \\textbf{Mean} & \\textbf{Mean} & \\textbf{Mean} & \\textbf{Mean} \\\\\n\t\t\t\\textbf{($\\Psi$)} & \\textbf{($C$)} & \\textbf{-sifier} & \\textbf{Spec} & \\textbf{Sens} & \\textbf{Accuracy} & \\textbf{AUC} \\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\hline\n\t\t\t16 & 5 & CNN & 83\\% & 86\\% & 84.55\\% & 0.9243 \\\\\n\t\t\t\\hline\n\t\t\t16 & 10 & CNN & 87\\% & 84\\% & 85.66\\% & 0.9358 \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t32 & 5 & CNN & 76\\% & 93\\% & 84.47\\% & 0.9272 \\\\\n\t\t\t\\hline\n\t\t\t32 & 10 & CNN & 84\\% & 86\\% & 85.25\\% & 0.9324 \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t64 & 5 & CNN & 85\\% & 87\\% & 86.31\\% & 0.9339 \\\\\n\t\t\t\\hline\n\t\t\t\\textit{64} & \\textit{10} & \\textit{CNN} & \\textit{91\\%} & \\textit{80\\%} & \\textit{85.82\\%} & \\textit{0.9499} \\\\\t\t\t\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\hline\n\t\t\t16 & 5 & LSTM & 84\\% & 91\\% & 87.58\\% & 0.9444 \\\\\n\t\t\t\\hline\n\t\t\t16 & 10 & LSTM & 85\\% & 92\\% & 88.32\\% & 0.9504 \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t32 & 5 & LSTM & 79\\% & 95\\% & 87.1\\% & 0.9457 \\\\\n\t\t\t\\hline\n\t\t\t\\textit{32} & \\textit{10} & \\textit{LSTM} & \\textit{86\\%} & \\textit{93\\%} & \\textit{89.21\\%} & \\textit{0.9572} \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t64 & 5 & LSTM & 84\\% & 93\\% & 88.68\\% & 0.954 \\\\\n\t\t\t\\hline\n\t\t\t64 & 10 & LSTM & 86\\% & 89\\% & 87.66\\% & 0.9489 \\\\\t\t\t\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\hline\n\t\t\t16 & 5 & Resnet50 & 93\\% & 98\\% & 95.43\\% & 0.9802 \\\\\n\t\t\t\\hline\n\t\t\t16 & 10 & Resnet50 & 94\\% & 99\\% & 96.35\\% & 0.9812 \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t32 & 5 & Resnet50 & 94\\% & 99\\% & 96.54\\% & 0.9810 \\\\\n\t\t\t\\hline\n\t\t\t\\textit{\\textbf{32}} & \\textit{\\textbf{10}} & \\textit{\\textbf{Resnet50}} & \\textit{\\textbf{94\\%}} & \\textit{\\textbf{99\\%}} & \\textit{\\textbf{96.71\\%}} & \\textit{\\textbf{0.9888}} \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t64 & 5 & Resnet50 & 94\\% & 99\\% & 96.35\\% & 0.9854 \\\\\n\t\t\t\\hline\n\t\t\t64 & 10 & Resnet50 & 95\\% & 98\\% & 96.46\\% & 0.9884 \\\\\t\t\t\n\t\t\t\\hline\n\t\t\t\n\t\t\t\n\t\t\t\\hline\n\t\t\t\n\t\t\t\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Neural Network based Cough Detection using Bed-mounted Accelerometer Measurements", "authors": ["Madhurananda Pahar", "Igor Miranda", "Andreas Diacon", "Thomas Niesler"], "url": "https://arxiv.org/abs/2102.04997v1", "attribution": "\"Deep Neural Network based Cough Detection using Bed-mounted Accelerometer Measurements\" by Madhurananda Pahar, Igor Miranda, Andreas Diacon, and Thomas Niesler, arXiv:2102.04997v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.21192v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|}\n\\hline\nPeriod & 1 & 2 & 3 & 4 & 5 & 6 \\\\\n\\hline\n S\\&P~500 30 day return & -4.9\\% & -5.1\\% & -5.2\\% & -5.2\\% & -5.3\\% & -4.8\\% \\\\\n\\hline\nStrategy decision & & Buy & Hold & Rollover & Hold & Sell \\\\\nDays to expiration & & 21 & 20 & 29 ($F_t=6379$) & 28 & \\\\\nOption price (sold) & & & & 80 & & 110 \\\\\nOption price (bought/held) & & 90 & 95 & 75 & 77 & \\\\\n\\hline\nExercise price & & & & 6190 & & \\\\\nNumber of options held & & $\\frac{1000u}{90}$ & $\\frac{1000u}{90}$& \n$ \\frac{80 \\times1000u}{90\\times 75}$\n& \n$ \\frac{80 \\times1000u}{90\\times 75}$ \n& \\\\\nCash & 1000 & & & & & $ \\frac{110\\times 80 \\times1000u}{90\\times 75}$ \\\\\nValuation & 1000 & $1000u$ & $\\frac{95(1000u)}{90}$ &\n$ \\frac{80 \\times1000u}{90}$ &\n$ \\frac{77 \\times 80 \\times1000u}{90\\times 75}$ \n & $ \\frac{110\\times 80 \\times1000u}{90\\times 75}$ \\\\\n\\hline\nValuation (1dp, $u=1$) & 1000 & 1000 & 1055.6 & 888.9 & 912.6 & 1303.7 \\\\\nReturn (\\%, 1dp) & - & 0 & 5.6 & -15.8 & 2.7 & 42.9 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Option strategy evaluation - example}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Enhanced indexation using both equity assets and index options", "authors": ["Cristiano Arbex Valle", "John E Beasley"], "url": "https://arxiv.org/abs/2508.21192v1", "attribution": "\"Enhanced indexation using both equity assets and index options\" by Cristiano Arbex Valle and John E Beasley, arXiv:2508.21192v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.10784v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison between our ASFDA, DKD+ASD, and other SOTA methods when adapting Med-VFMs to the Abdominal Atlas domain. The performance was evaluated using Dice scores, and the results were reported as Mean$\\pm$SD. \\textbf{Bold} and \\underline{underline} represent the best and the second best results.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n\\toprule\n\\multirow{2}{*}{Methods} & \\multicolumn{2}{c}{Query Budgets} \\\\\n\\cline{2-3}\n & $5\\%$ & $10\\%$ \\\\\n\\midrule\nRAND & 73.74$\\pm$11.98 & 76.72$\\pm$11.48 \\\\\nENPY & 77.39$\\pm$10.34 & 80.46$\\pm$9.54 \\\\\nLCON & 74.07$\\pm$10.66 & 77.88$\\pm$10.90 \\\\\nMMAR & 75.74$\\pm$11.17 & 78.33$\\pm$9.81 \\\\\nCore-set & 79.11$\\pm$10.20 & 81.44$\\pm$9.61 \\\\\nBADGE & 79.23$\\pm$10.01 & 81.48$\\pm$9.52 \\\\\nSANN & 79.56$\\pm$9.42 & 81.57$\\pm$9.16 \\\\\nUGTST & 79.43$\\pm$10.15 & 81.56$\\pm$9.75 \\\\\nCUP & 77.36$\\pm$10.46 & 79.35$\\pm$9.92 \\\\\n\\midrule\nDKD+ASD & \\underline{81.92}$\\pm$9.31 & \\underline{83.80}$\\pm$8.70 \\\\\nASFDA & \\textbf{83.12}$\\pm$8.16 & \\textbf{84.86}$\\pm$7.52 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning", "authors": ["Jin Yang", "Daniel S. Marcus", "Aristeidis Sotiras"], "url": "https://arxiv.org/abs/2509.10784v1", "attribution": "\"Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning\" by Jin Yang, Daniel S. Marcus, and Aristeidis Sotiras, arXiv:2509.10784v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.07567v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of Base matrices with $m=3, n=4$ for different circulant sizes}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{Circulant size ($L$)} & \\textbf{Exhaustive} & \\textbf{RCPC} & $d_{min}^{\\mathcal{C}} > 7$ & $d_{min}^{\\mathcal{Q}} > 7$ \\\\ \\hline\n4 & $4^{6} = 4096$ & 700 & 0 & 0 \\\\ \\hline\n5 & 15625 & 1993 & 0& 0 \\\\ \\hline\n6 & 46656 & 3876 & 1008 & 720 \\\\ \\hline\n7 & 117649 & 7725 & 55440 & 53856 \\\\ \\hline\n8 & 262144 & 12628 & 128592 & 124704 \\\\ \\hline\n9 & 531441 & 20961 & 189360 & 183744\\\\ \\hline\n10 & 1000000 & 31084 & 656784 & 644544 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the Minimum Distances of Finite-Length Lifted Product Quantum LDPC Codes", "authors": ["Nithin Raveendran", "David Declercq", "Bane Vasić"], "url": "https://arxiv.org/abs/2503.07567v1", "attribution": "\"On the Minimum Distances of Finite-Length Lifted Product Quantum LDPC Codes\" by Nithin Raveendran, David Declercq, and Bane Vasić, arXiv:2503.07567v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11891v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc}\n \n & \\multicolumn{2}{c}{Our Annotation}\\\\ \\cline{2-3}\nCLEF-2020 & Non-claim & Claim \\\\ \\hline\n\\hline\nNon-claim & 301 & 47 \\\\\nClaim & 64 & 550 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Confusion matrix highlighting the differences and similarities between and our annotation guidelines for CLEF-2020 claim dataset.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content", "authors": ["Shreya Gupta", "Parantak Singh", "Megha Sundriyal", "Md Shad Akhtar", "Tanmoy Chakraborty"], "url": "https://arxiv.org/abs/2101.11891v1", "attribution": "\"LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content\" by Shreya Gupta, Parantak Singh, Megha Sundriyal, Md Shad Akhtar, and Tanmoy Chakraborty, arXiv:2101.11891v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.21452v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|}\n \\hline\n $h$ & 1/8 & 1/16 & 1/32 & 1/64 & 1/128 & 1/256 \\\\\n \\hline\n $\\varepsilon_h$ & 2.20E-04 & 6.99E-05 & 1.91E-05 & 5.04E-06 & 1.30E-06 & 3.30E-07 \\\\\n \\hline\n $r$ & --- & 1.65 & 1.88 & 1.92 & 1.96 & 1.98 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{the magnitude of the error and order of convergence.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Numerical solution of locally loaded Volterra integral equations", "authors": ["Vladislav Byankin", "Aleksandr Tynda", "Denis Sidorov", "Aliona Dreglea"], "url": "https://arxiv.org/abs/2503.21452v1", "attribution": "\"Numerical solution of locally loaded Volterra integral equations\" by Vladislav Byankin, Aleksandr Tynda, Denis Sidorov, and Aliona Dreglea, arXiv:2503.21452v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.10031v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\multirow{2}{*}{Ablation Option} & \\multicolumn{2}{c}{FMD} & \\multicolumn{2}{c}{SRDTrans} \\\\ \n\\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n & PSNR & SSIM & PSNR & SSIM \\\\ \n\\midrule\n(a) w/o Region & 32.41 & 0.795 & 28.62 & 0.895 \\\\\n(b) w/o Overall & 33.89 & 0.876 & 28.65 & 0.910 \\\\\n(c) w/o Channel+ & 34.02 & 0.882 & 28.68 & 0.900 \\\\\n(d) w/ Stage 1 Only & 23.75 & 0.681 & 25.59 & 0.780 \\\\\n(e) w/ Stage 2 Only & 34.08 & 0.884 & 28.82 & 0.916 \\\\ \\midrule \\rowcolor{gray!20}\n(f) Full Conduction & \\textbf{34.15} & \\textbf{0.886} & \\textbf{28.93} & \\textbf{0.919} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Ablation study of Noise Injection components and training stages in FM2S. The `w/o' stands for `without,' and `/w' stands for `with.'}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "FM2S: Towards Spatially-Correlated Noise Modeling in Zero-Shot Fluorescence Microscopy Image Denoising", "authors": ["Jizhihui Liu", "Qixun Teng", "Qing Ma", "Junjun Jiang"], "url": "https://arxiv.org/abs/2412.10031v2", "attribution": "\"FM2S: Towards Spatially-Correlated Noise Modeling in Zero-Shot Fluorescence Microscopy Image Denoising\" by Jizhihui Liu, Qixun Teng, Qing Ma, and Junjun Jiang, arXiv:2412.10031v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.10776v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameter settings in (\\text{learning rate}, \\text{batch}) pairs for varying $\\delta_{in}$ and $\\delta_{out}$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc}\n \\toprule\n & \\multicolumn{5}{c}{$\\delta_{in}$} \\\\\n \\cmidrule(lr){2-6}\n $\\delta_{out}$ \n & 5 & 21 & 63 & 126 & 252 \\\\\n \\midrule\n 5 & ($10^{-4}$,\\,64) & ($10^{-5}$,\\,16) & ($10^{-4}$,\\,16) & ($10^{-4}$,\\,32) & ($10^{-5}$,\\,16) \\\\\n 21 & ($10^{-5}$,\\,32) & ($10^{-5}$,\\,16) & ($10^{-5}$,\\,64) & ($10^{-4}$,\\,32) & ($10^{-4}$,\\,32) \\\\\n 63 & ($10^{-4}$,\\,64) & ($10^{-4}$,\\,32) & ($10^{-4}$,\\,64) & ($10^{-4}$,\\,64) & ($10^{-4}$,\\,32) \\\\\n 126 & ($10^{-5}$,\\,64) & ($10^{-4}$,\\,32) & ($10^{-4}$,\\,64) & ($10^{-4}$,\\,64) & ($10^{-5}$,\\,16) \\\\\n 252 & ($10^{-3}$,\\,32) & ($10^{-5}$,\\,16) & ($10^{-3}$,\\,16) & ($10^{-4}$,\\,16) & ($10^{-5}$,\\,16) \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Estimating Covariance for Global Minimum Variance Portfolio: A Decision-Focused Learning Approach", "authors": ["Juchan Kim", "Inwoo Tae", "Yongjae Lee"], "url": "https://arxiv.org/abs/2508.10776v1", "attribution": "\"Estimating Covariance for Global Minimum Variance Portfolio: A Decision-Focused Learning Approach\" by Juchan Kim, Inwoo Tae, and Yongjae Lee, arXiv:2508.10776v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13448v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\toprule\n \\multirow{2}{*}{Coin color} & Reward & Reward for \\\\\n & for self & co-player \\\\\n \\midrule\n matching & $+3$ & $+2$ \\\\\n mismatching & $+3$ & $+0$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Alternative incentive structure for Coins.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Warmth and competence in human-agent cooperation", "authors": ["Kevin R. McKee", "Xuechunzi Bai", "Susan T. Fiske"], "url": "https://arxiv.org/abs/2201.13448v4", "attribution": "\"Warmth and competence in human-agent cooperation\" by Kevin R. McKee, Xuechunzi Bai, and Susan T. Fiske, arXiv:2201.13448v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.09372v3_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Real Data: Criteo Experiment Result.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|llll}\n \\hline\n Policy & Hyperparameters & Mean Regret & Standard Error & Max & Min \\\\ \\hline\n ACIDP & L=4, n=1 & 38915.97 & 15637.81 & 78699.4 & 26595.33 \\\\ \n ACIDP-w & window variant & 26723.16 & 2115.70 & 29568.53 & 23473.74 \\\\ \n EG & $\\epsilon$=0.05 & 83775.79 & 25410.65 & 127203.09 & 52124.14 \\\\ \n ~ & $\\epsilon$=0.1 & 106739.31 & 11245.25 & 120781.82 & 86027.40 \\\\ \n ~ & $\\epsilon$=0.15 & 133023.59 & 14774.32 & 158582.07 & 107559.25 \\\\ \n TS & ~ & 199509.26 & 74.01 & 199622.38 & 199384.65 \\\\ \n UCB-Tuned & ~ & 235209.70 & 88035.52 & 373923.95 & 132805.21 \\\\ \n UCB & c=1 & 277109.95 & 139415.34 & 434026.10 & 107524.25 \\\\ \n ~ & c=2 & 191249.57 & 101755.70 & 429739.28 & 107482.55 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing", "authors": ["Po-Yi Liu", "Chi-Hua Wang", "Henghsiu Tsai"], "url": "https://arxiv.org/abs/2208.09372v3", "attribution": "\"Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing\" by Po-Yi Liu, Chi-Hua Wang, and Henghsiu Tsai, arXiv:2208.09372v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.08827v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n\\hline\n\\multicolumn{8}{c}{$\\textbf{RMSE (SD)}$}\\\\ \\hline\n$s = 5$ & $k_1 = k_2 = 5$ & \\multicolumn{2}{c}{$\\sigma^2 = 0.1$}& \\multicolumn{2}{c}{$\\sigma^2 = 0.5$}& \\multicolumn{2}{c}{$\\sigma^2 = 1$}\\\\ \\hline\n$p + q$& $n$ & $\\text{Debiasing}$& $\\text{Naive}$& $\\text{Debiasing}$& $\\text{Naive}$& $\\text{Debiasing}$& $\\text{Naive}$\\\\ \\hline\n$100$& $250$ & 0.3427 (0.1744) & 0.6013 (0.0799) & 0.7686 (0.3521) & 1.8324 (0.2988) & 0.5406 (0.4121) & 1.7697 (0.3425) \\\\\n$100$& $500$ & 0.1021 (0.0709) & 0.5254 (0.0516) & 0.2569 (0.2159) & 1.3490 (0.1752) & 0.4262 (0.3794) & 1.5741 (0.2385) \\\\\n$100$& $750$ & 0.0699 (0.0527) & 0.4947 (0.0398) & 0.2179 (0.2067) & 1.1528 (0.1662) & 0.3804 (0.2906) & 1.3007 (0.1860) \\\\\n$100$& $1000$ & 0.0721 (0.0623) & 0.4611 (0.0401) & 0.2261 (0.1823) & 1.1777 (0.1317) & 0.3423 (0.3197) & 1.3400 (0.1904) \\\\\n$100$& $1250$ & 0.0655 (0.0513) & 0.3574 (0.0323) & 0.1309 (0.1260) & 0.8603 (0.1012) & 0.2854 (0.2511) & 1.2710 (0.1695) \\\\ \\hline\n$800$& $250$ & 0.2022 (0.1538) & 0.9979 (0.1613) & 1.5864 (0.3329) & 2.5851 (0.5604) & 0.6420 (0.3056) & 2.3841 (0.4128) \\\\\n$800$& $500$ & 0.0846 (0.0712) & 0.8335 (0.0771) & 0.3043 (0.2502) & 1.5744 (0.2129) & 0.4239 (0.2231) & 1.6983 (0.2480) \\\\\n$800$& $750$ & 0.0989 (0.0682) & 0.7680 (0.0690) & 0.1586 (0.1534) & 1.2404 (0.1553) & 0.4282 (0.2365) & 1.4553 (0.1822) \\\\\n$800$& $1000$ & 0.0802 (0.0724) & 0.7192 (0.0603) & 0.1576 (0.1416) & 1.1444 (0.1439) & 0.3656 (0.2024) & 1.5415 (0.2026) \\\\\n$800$& $1250$ & 0.0564 (0.0557) & 0.7300 (0.0504) & 0.1379 (0.1377) & 1.1339 (0.1294) & 0.2459 (0.1871) & 1.4200 (0.1586) \\\\ \\hline\n$1500$& $250$ & 0.2897 (0.2021) & 1.8804 (0.4664) & 0.2780 (0.1859) & 1.9778 (0.4542) & 0.9987 (0.3472) & 3.1874 (0.6424) \\\\\n$1500$& $500$ & 0.1639 (0.1175) & 0.8545 (0.0993) & 0.2474 (0.1802) & 1.1352 (0.1865) & 0.7018 (0.2680) & 1.7272 (0.2433) \\\\\n$1500$& $750$ & 0.0813 (0.0796) & 0.7858 (0.0783) & 0.2334 (0.1326) & 1.0232 (0.1435) & 0.3798 (0.2487) & 1.7840 (0.2349) \\\\\n$1500$& $1000$ & 0.0870 (0.0646) & 0.7276 (0.0635) & 0.1397 (0.1219) & 0.9412 (0.1267) & 0.2757 (0.1956) & 1.5012 (0.1804) \\\\\n$1500$& $1250$ & 0.0692 (0.0635) & 0.6697 (0.0584) & 0.1253 (0.1163) & 0.8814 (0.1075) & 0.2436 (0.1939) & 1.4856 (0.1703) \\\\ \\hline\n$2500$& $250$ & 0.4518 (0.2448) & 3.0100 (0.6146) & 0.3964 (0.1788) & 1.5023 (0.2777) & 0.5233 (0.3316) & 3.1644 (0.5719) \\\\\n$2500$& $500$ & 0.1096 (0.0679) & 0.7782 (0.0820) & 0.2751 (0.1556) & 1.2072 (0.1662) & 0.5395 (0.2453) & 2.1845 (0.3075) \\\\\n$2500$& $750$ & 0.1868 (0.0948) & 0.7804 (0.0701) & 0.1735 (0.1266) & 0.9912 (0.1318) & 0.3212 (0.2701) & 1.8911 (0.2386) \\\\\n$2500$& $1000$ & 0.0571 (0.0563) & 0.6631 (0.0593) & 0.1685 (0.1332) & 1.0357 (0.1252) & 0.2602 (0.2006) & 1.9865 (0.2359) \\\\\n$2500$& $1250$ & 0.0484 (0.0393) & 0.6325 (0.0495) & 0.1108 (0.1026) & 0.9615 (0.1080) & 0.1988 (0.1821) & 1.6966 (0.1944) \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Root-mean-square error (RMSE) with standard deviation (SD) in parentheses, for Debiasing and Naive estimators. Numbers are averaged over 400 replications with $K_1 = K_2 = 2.75$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Debiased Estimator for the Mediation Functional in Ultra-High-Dimensional Setting in the Presence of Interaction Effects", "authors": ["Shi Bo", "AmirEmad Ghassami", "Debarghya Mukherjee"], "url": "https://arxiv.org/abs/2412.08827v3", "attribution": "\"A Debiased Estimator for the Mediation Functional in Ultra-High-Dimensional Setting in the Presence of Interaction Effects\" by Shi Bo, AmirEmad Ghassami, and Debarghya Mukherjee, arXiv:2412.08827v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.06671v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Goodness of fit comparison: our model (left), the model without covariates (middle), and the percentage decrease in error (right).}\n\\begin{tabular}{lccc}\n \\toprule\n & \\textbf{With Covariates} & \\textbf{Without Covariates} & \\textbf{\\% Decrease} \\\\\n \\midrule\n \\textbf{ERROR} & $55,441.40$ & $59,643.38$ & $7.05\\%$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Covariates-Adjusted Mixed-Membership Estimation: A Novel Network Model with Optimal Guarantees", "authors": ["Jianqing Fan", "Jiawei Ge", "Jikai Hou"], "url": "https://arxiv.org/abs/2502.06671v1", "attribution": "\"Covariates-Adjusted Mixed-Membership Estimation: A Novel Network Model with Optimal Guarantees\" by Jianqing Fan, Jiawei Ge, and Jikai Hou, arXiv:2502.06671v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.12619v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\large Asymmetric treatment on subset of US coal counties}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\tabularnewline\\midrule\\midrule\n Dependent Variable: & \\multicolumn{3}{c}{Change Unemployment Rate}\\\\\n Model: & (1) & (2) & (3)\\\\\n \\midrule \\emph{Variables} & & & \\\\\n $\\Delta$ Active Mines\\textsubscript{t} & -0.0193 & -0.0752$^{**}$ & \\\\\n & (0.0201) & (0.0235) & \\\\\n $\\Delta$Active Mines\\textsubscript{t-1} & 0.0221 & -0.0301 & \\\\\n & (0.0285) & (0.0219) & \\\\\n $\\Delta$Active Mines\\textsubscript{t-2} & 0.0090 & 0.0546$^{***}$ & \\\\\n & (0.0236) & (0.0116) & \\\\\n $\\Delta$ (log) Real GDPPC\\textsubscript{t} & -0.9724$^{***}$ & -0.9724$^{***}$ & -0.9724$^{***}$\\\\\n & (0.2138) & (0.2138) & (0.2138)\\\\\n $\\Delta$ Active Mines\\textsubscript{t} $\\times$ $\\Delta_{negative_t}$ & -0.0559 & & -0.0752$^{**}$\\\\\n & (0.0370) & & (0.0235)\\\\\n $\\Delta$ Active Mines\\textsubscript{t-1} $\\times$ $\\Delta_{negative_{t-1}}$ & -0.0521 & & -0.0301\\\\\n & (0.0422) & & (0.0219)\\\\\n $\\Delta$ Active Mines\\textsubscript{t-2} $\\times$ $\\Delta_{negative_{t-2}}$ & 0.0457 & & 0.0546$^{***}$\\\\\n & (0.0277) & & (0.0116)\\\\\n $\\Delta$ Active Mines\\textsubscript{t} $\\times$ $\\Delta_{positive_{t}} $ & & 0.0559 & -0.0193\\\\\n & & (0.0370) & (0.0201)\\\\\n $\\Delta$ Active Mines\\textsubscript{t-1} $\\times$ $\\Delta_{positive_{t-1}}$ & & 0.0521 & 0.0221\\\\\n & & (0.0422) & (0.0285)\\\\\n $\\Delta$ Active Mines\\textsubscript{t-2} $\\times$ $\\Delta_{positive_{t-2}}$ & & -0.0457 & 0.0090\\\\\n & & (0.0277) & (0.0236)\\\\\n \\midrule \\emph{Fixed-effects} & & & \\\\\n County FIPS Code & Yes & Yes & Yes\\\\\n Year & Yes & Yes & Yes\\\\\n \\midrule \\emph{Fit statistics} & & & \\\\\n Observations & 55,295 & 55,295 & 55,295\\\\\n R$^2$ & 0.61355 & 0.61355 & 0.61355\\\\\n Within R$^2$ & 0.01513 & 0.01513 & 0.01513\\\\\n \\midrule\\midrule\\multicolumn{4}{l}{\\emph{Clustered (County FIPS Code \\& Year) standard-errors in parentheses}}\\\\\n \\multicolumn{4}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'", "authors": ["Ebba Mark", "Ryan Rafaty", "Moritz Schwarz"], "url": "https://arxiv.org/abs/2211.12619v1", "attribution": "\"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'\" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18130v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Same setup as Table~, but using \\textbf{dilation} annealing.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n& \\multicolumn{5}{c}{sample size $N$}\\\\\n& 100 & 300 & 1000 & 3000 & 10000 \\\\\n\\midrule\nSBTM (ours) & 0.34 & 0.25 & 0.19 & 0.15 & \\textbf{0.13} \\\\\nSDE & \\textbf{0.25} & \\textbf{0.19} & \\textbf{0.14} & \\textbf{0.13} & 0.13 \\\\\nSVGD & 0.96 & 1.4 & 1.1 & 1.3 & 1.2 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Score-Based Deterministic Density Sampling", "authors": ["Vasily Ilin", "Peter Sushko", "Jingwei Hu"], "url": "https://arxiv.org/abs/2504.18130v2", "attribution": "\"Score-Based Deterministic Density Sampling\" by Vasily Ilin, Peter Sushko, and Jingwei Hu, arXiv:2504.18130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.11496v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Data of test system}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n \\hline\n Ambient temperature ($T_a$) & K & 308\\\\\n Battery voltage rating ($V_{b}$) & V & 259 \\\\\n Buying/selling rate at grid ($P_{grid}$) & \\$/MW & 30/26 \\\\\n Buying/selling rate for ESS ($P_{ess}$) & \\$/MW & 26/32 \\\\\n Charging efficiency ($\\eta_c$) & \\% & 0.95\\\\\n Coefficient of conduction $(k_b)$ & W/mk & 205\\\\\n Coefficient of convection loss ($h_{0}$) & $W/m^2K$ & 5\\\\\n Initial SOC ($E_t$) & kWh& 40 \\\\\n Discharging efficiency ($\\eta_d$) & \\% & 0.95\\\\\n ESS rated voltage & V & 259\\\\\n Fan speed coefficient of h $(\\lambda)$ & min/rotations & 0.01814 \\\\\n Initial fan speed $(u_f)$ & rpm & 2000\\\\\n Initial current through each battery ($u_i$) & A & 50\\\\\n Maximum SOC ($\\overline{E}$) & kWh & 66.304\\\\\n Maximum charge power of ESS $(\\overline{P_{bc}})$ & kW & 60\\\\\n Maximum discharge power of ESS $(\\overline{P_{bd}})$ & kW & 60\\\\\n Maximum bus voltage ($\\overline{V}$) & p.u. & 1.1 \\\\\n Minimum SOC ($\\underline{E}$) & kWh & 5\\\\\n Minimum bus voltage ($\\underline{V}$) & p.u. & 0.9 \\\\\n Resistance of each module ($R_{ref}$) & ohm & 0.1\\\\\n Temperature coefficient of resistance $(\\alpha_T)$ & $K^{-1}$ & 0.004\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Two-Layer Framework with Battery Temperature Optimal Control and Network Optimal Power Flow", "authors": ["Anshuman Singh", "Wang Peng", "Hung D. Nguyen"], "url": "https://arxiv.org/abs/2011.11496v1", "attribution": "\"A Two-Layer Framework with Battery Temperature Optimal Control and Network Optimal Power Flow\" by Anshuman Singh, Wang Peng, and Hung D. Nguyen, arXiv:2011.11496v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.09598v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n\t\t\\hline\n\t\t$\\bf{Parameters}$ & $\\bf{Assumptions}$ \\\\ \\hline\n\t\tMacrocell Radius & $100$m \\\\ \\hline\n\t\tGS Coordinate & [50,50] \\\\ \\hline\n\t\tBandwidth $B$ & 1MHz \\\\ \\hline\n\t\tTransmitting Power $P_{ij}^T$ & 1W \\\\ \\hline\n\t\tInput Data Size ${D_i}$ & 100kB \\\\ \\hline\n\t\tRequired Number of CPU Cycles $f_i^{L}$ & $10^9$ cycles/s \\\\ \\hline\n\t\tLocal Computational Capability $F_{max}^{L}$ & $10^9$ cycles/s \\\\ \\hline\n\t\tLatency Request $T^{req}$ & 2s \\\\ \\hline\n\t\tUAV Altitude ${H^{UAV}}$ & 20m \\\\ \\hline\n\t\tRemote Computational Capability (UAV) & $10^{10}$ cycles/s \\\\ \\hline\n\t\tRemote Computational Capability (GV) & $10^{11}$ cycles/s \\\\ \\hline\n\t\tRemote Computational Capability (GS) & $10^{12}$ cycles/s \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks", "authors": ["Feibo Jiang", "Kezhi Wang", "Li Dong", "Cunhua Pan", "Wei Xu", "Kun Yang"], "url": "https://arxiv.org/abs/1911.09598v1", "attribution": "\"Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks\" by Feibo Jiang, Kezhi Wang, Li Dong, Cunhua Pan, Wei Xu, and Kun Yang, arXiv:1911.09598v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.03861v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|l}\nSymbol & PRMS symbol & Quantity & Type & Range & Units & Description \\\\\n\\hline\nTms & tmax\\_allsnow & 1 & constant & -10, 40 & $^{o}$F & temperature below which P is all snow \\\\\nTmr & tmax\\_allrain & 1 & monthly & -8, 60 & $^{o}$F & temperature above which P is all rain \\\\\n$\\alpha$1 & smidx\\_coef & per HRU & constant & 0.001, 0.06 & - & linear surface runoff (Fsr) coeficient \\\\\nAsr & carea\\_max & per HRU & constant & 0.0, 1.0 & - & max areal fraction contributing to surface runoff Fsr \\\\\n$\\beta$ & dday\\_intcp & 1 & monthly & -60.0, 10.0 & $^{o}$day & intercept of degree-day equation \\\\\njc & jh\\_coef & 1 & monthly & 0.005, 0.06 & /$^{o}$F & coef. used in ETp \\\\\nSszmax & soilmoist\\_max & per HRU & constant & 0.001, 60.0 & inches & maximum soilzone water holding capacity \\\\\n$\\alpha$4 & gwflow\\_coef & per GWR & constant & 0.001, 0.5 & -/day & linear coefficient routing Sgw to streamflow \\\\\nFzgwmax & soil2gw\\_max & per HRU & constant & 0.0, 5.0 & inches & max soil excess water routed to gwStr \\\\\n$\\alpha$5 & gwsink\\_coef & per GWR & constant & 0.0, 1.0 & -/day & linear coefficient for groundwater sink \\\\\n$\\alpha$2 & ssr2gw\\_rate & per SSR & constant & 0.05, 0.8 & -/day & linear coefficient routing Sss to Sgw \\\\\n$\\alpha$3 & ssrcoef\\_sq & per SSR & constant & 0.0, 1.0 & - & coeficient routing Sss to streamflow \\\\\n$\\beta$3 & ssrcoef\\_lin & per SSR & constant & 0.0, 1.0 & -/day & linear coeficient routing Sss to streamflow\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Perturbed and updated parameters.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter", "authors": ["Sami A. Malek", "Alexandre M. Bayen", "Steven D. Glaser"], "url": "https://arxiv.org/abs/1912.03861v1", "attribution": "\"Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter\" by Sami A. Malek, Alexandre M. Bayen, and Steven D. Glaser, arXiv:1912.03861v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.02694v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The True Positive Rate (TPR), True Negative Rate (TNR), and Balanced Accuracy (BA) from \\texttt{TULIK-VI} and the baselines on 13-node SADs testing data. The standard deviations of TPR, TNR, and BA over 10 training replicas are in parentheses. The largest metrics are bolded. The training data and testing data consist of 800 and 110 trajectories (patients), respectively. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\\toprule\n & {\\texttt{TULIK-VI}} & {\\texttt{TULIK-VI} (Stationary)} & {\\texttt{GLM-L}} & {\\texttt{GLM-S}} & {\\texttt{HP-E}}\\\\\n \\cmidrule(lr){2-6}\n\t\t\t\\multirow{2}{*} {TPR} &{\\bf 0.7068} &0.6929 &0.5408 &0.6678 &0.5044\\\\\n & (0.0251) & (0.0242) & (0.0249) & (0.0192) & (0.0202)\\\\\n\\multirow{2}{*} {TNR} &{\\bf 0.7070} &0.6919 &0.6989 &0.6320 &0.5061\\\\\n & (0.0204) & (0.0177) & (0.0175) & (0.0168) & (0.0112)\\\\\n\\multirow{2}{*} {BA} &{\\bf 0.7069} &0.6924 &0.6199 &0.6499 &0.5053\\\\\n & (0.0045) & (0.0054) & (0.0097) & (0.0088) & (0.0074)\n \\\\\n \\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Point processes with event time uncertainty", "authors": ["Xiuyuan Cheng", "Tingnan Gong", "Yao Xie"], "url": "https://arxiv.org/abs/2411.02694v1", "attribution": "\"Point processes with event time uncertainty\" by Xiuyuan Cheng, Tingnan Gong, and Yao Xie, arXiv:2411.02694v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.01647v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{HSD-test for pairwise comparison of features extraction (Imbalanced dataset)}\n\\begin{tabular}{rllrrrr}\n\t\t\\hline\n\t\t& term & comparison & estimate & conf.low & conf.high & adj.p.value \\\\ \n\t\t\\hline\n\t\t1 & feat\\_extr & mfcc-wfe & 5.31 & 4.29 & 6.34 & 0.00 \\\\ \n\t\t2 & feat\\_extr & coif1-wfe & 7.33 & 6.30 & 8.36 & 0.00 \\\\ \n\t\t3 & feat\\_extr & db2-wfe & 7.89 & 6.86 & 8.91 & 0.00 \\\\ \n\t\t4 & feat\\_extr & db4-wfe & 9.49 & 8.46 & 10.51 & 0.00 \\\\ \n\t\t5 & feat\\_extr & coif1-mfcc & 2.01 & 0.99 & 3.04 & 0.00 \\\\ \n\t\t6 & feat\\_extr & db2-mfcc & 2.57 & 1.54 & 3.60 & 0.00 \\\\ \n\t\t7 & feat\\_extr & db4-mfcc & 4.17 & 3.14 & 5.20 & 0.00 \\\\ \n\t\t8 & feat\\_extr & db2-coif1 & 0.56 & -0.47 & 1.59 & 0.58 \\\\ \n\t\t9 & feat\\_extr & db4-coif1 & 2.16 & 1.13 & 3.19 & 0.00 \\\\ \n\t\t10 & feat\\_extr & db4-db2 & 1.60 & 0.57 & 2.63 & 0.00 \\\\ \n\t\t\\hline\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG Data", "authors": ["Cyrille Feudjio", "Victoire Djimna Noyum", "Younous Perieukeu Mofendjou", "Rockefeller", "Ernest Fokoué"], "url": "https://arxiv.org/abs/2102.01647v1", "attribution": "\"A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG Data\" by Cyrille Feudjio, Victoire Djimna Noyum, Younous Perieukeu Mofendjou, Rockefeller, and Ernest Fokoué, arXiv:2102.01647v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.07016v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c||c|c|}\n\t\t\\hline\n\t\t$Algorithm$&$ISFLP1$&$ISFLP2$\\\\\n\t\t\\hline\n\t\t\\hline\n\t\t$C^*$&$85.7698$&$85.5271$\\\\\n\t\t\\hline\n\t\tk&$5$&$5$\\\\\n\t\t\\hline\n\t\t$\\mathbf{A}_{1}^{(k)}=(a^{(k)}_1,b^{(k)}_1)$&(0.7963 , 1.9825)&(0.8595 , 1.9825)\\\\\n\t\t$\\mathbf{A}_{2}^{(k)}=(a^{(k)}_2,b^{(k)}_2)$&(1.0000 , 1.9889)&(1.0000 ,1.9975)\\\\\n\t\t$\\mathbf{A}_{3}^{(k)}=(a^{(k)}_3,b^{(k)}_3)$&(1.9819 , 4.9462)&(1.9858 , 4.9376)\\\\\n\t\t$\\mathbf{A}_{4}^{(k)}=(a^{(k)}_4,b^{(k)}_4)$&(1.9988 , 6.2974)&(1.9864 , 6.2060)\\\\\n\t\t$\\mathbf{A}_{5}^{(k)}=(a^{(k)}_5,b^{(k)}_5)$&(2.2953 , 7.7026)&(1.9784 , 7.7940)\\\\\n\t\t$\\mathbf{A}_{6}^{(k)}=(a^{(k)}_6,b^{(k)}_6)$&(1.9887 , 1.0415)&(1.9887 , 1.0330)\\\\\n\t\t$\\mathbf{A}_{7}^{(k)}=(a^{(k)}_7,b^{(k)}_7)$&(1.9990 , 4.1863)&(2.0000 , 4.1279)\\\\\n\t\t$\\mathbf{A}_{8}^{(k)}=(a^{(k)}_8,b^{(k)}_8)$&(1.7001 , 8.6238)&(1.8043 , 8.7344)\\\\\n\t\t$\\mathbf{A}_{9}^{(k)}=(a^{(k)}_9,b^{(k)}_9)$&(1.9974 , 1.9441)&(1.9854 , 1.9556 )\\\\\n\t\t$\\mathbf{A}_{10}^{(k)}=(a^{(k)}_{10},b^{(k)}_{10})$&(1.9896 , 1.9573)&(1.9898 , 1.9749)\\\\\n\t\t$\\mathbf{A}_{11}^{(k)}=(a^{(k)}_{11},b^{(k)}_{11})$&(6.0592 , 6.1561)&(6.0477 , 6.1215)\\\\\n\t\t$\\mathbf{A}_{12}^{(k)}=(a^{(k)}_{12},b^{(k)}_{12})$&(6.0094 , 1.9468)&(5.9983 , 1.9546)\\\\\n\t\t$\\mathbf{A}_{13}^{(k)}=(a^{(k)}_{13},b^{(k)}_{13})$\n\t\t&(6.8854 , 1.1146)&(6.9115 , 1.0885)\\\\\n\t\t$\\mathbf{A}_{14}^{(k)}=(a^{(k)}_{14},b^{(k)}_{14})$\n\t\t&(7.0358 , 1.9804)&(7.0289 , 1.9629)\\\\\n\t\t$\\mathbf{A}_{15}^{(k)}=(a^{(k)}_{15},b^{(k)}_{15})$\n\t\t&(8.1381 , 2.0000)&(8.1073 , 2.0000)\\\\\n\t\t$\\mathbf{A}_{16}^{(k)}=(a^{(k)}_{16},b^{(k)}_{16})$\n\t\t&(8.0000 , 8.0565)&(8.0000 , 8.0445)\\\\\n\t\t$\\mathbf{A}_{17}^{(k)}=(a^{(k)}_{17},b^{(k)}_{17})$\n\t\t&(9.4815 , 6.6534)&(9.3488 , 6.7575)\\\\\n\t\t$\\mathbf{A}_{18}^{(k)}=(a^{(k)}_{18},b^{(k)}_{18})$\n\t\t&(9.0937 , 5.9642)&(9.0655 , 5.9711)\\\\\n\t\t\\hline\n\t\t$\\dfrac{|\\bar{F}-F_k|}{|\\bar{F}|}$\n\t\t&0.0054&0.0000\\\\\n\t\t\\hline\n\t\t$CPU( in \\ \\ sec)$&$22.1654$&$55.3114$\\\\\n\t\t\\hline\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Inverse single facility location problem in the plane with variable coordinates", "authors": ["Nazanin Tour-Savadkoohi", "Jafar Fathali"], "url": "https://arxiv.org/abs/2503.07016v1", "attribution": "\"Inverse single facility location problem in the plane with variable coordinates\" by Nazanin Tour-Savadkoohi and Jafar Fathali, arXiv:2503.07016v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.02476v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccc}\n \\toprule\n Dataset & CIFARBinary & CIFAR10/CIFAR10-N & CIFAR100 & SVHN & Places365 & IMDB\\\\\n \\midrule\n Class Number & 2&10&100&10&10&2\\\\\n \\hline\n Size of the probe set & 2,000 & 10,000& 20,000& 10,000&10,000&5,000\\\\\n Start learning rate 1 & 0.1 & 0.1 & 0.1 & 0.1 &0.1 &0.1\\\\\n Learning rate schedule 1 &schedule 1 & schedule 1 & schedule 1 & schedule 1 & \n schedule 1& no schedule\\\\\n Optimizer 1 & SGD& SGD& SGD& SGD& SGD& AdamW\\\\\n Epoch 1&100 & 100 & 100 & 100 & 100& 20 \\\\\n \n \\hline\n Size of the sampling set &8,000 & 40,000& 30,000 &63,257&40,000&20,000 \\\\\n Start learning rate 2 & 0.1 & 0.1 & 0.1 & 0.1 &0.1 &0.1\\\\\n Learning rate schedule 2 &schedule 2 & schedule 2 & schedule 2 & schedule 2 & \n schedule 1& no schedule\\\\\n Optimizer 2 & AdamW& AdamW& AdamW& AdamW& SGD& AdamW\\\\\n Epoch 2&150&150&150&150&100&20 \\\\\n \\hline\n Size of the testing set &2,000 &10,000& 10,000 & 26,032& 1,000 & 25,000\\\\\n \n \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{This table illustrates the training details. Here we set weight decay as 5e-4 for all the experiments. Here no schedule means using the start learning rate without modification during training. Schedule 1 stands for the decaying of the learning rate by 0.1 every 30 epochs. Schedule 2 means using the cosine learning schedule with $T_{max}=50$ and $eta_{min}=0$}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning", "authors": ["Yong Lin", "Chen Liu", "Chenlu Ye", "Qing Lian", "Yuan Yao", "Tong Zhang"], "url": "https://arxiv.org/abs/2309.02476v1", "attribution": "\"Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning\" by Yong Lin, Chen Liu, Chenlu Ye, Qing Lian, Yuan Yao, and Tong Zhang, arXiv:2309.02476v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.00308v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MDP2 Transition Function}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c||c}\\hline\n State & Action & Next State \\\\\\hline\n 0 & 0 & 0 \\\\\n 0 & 1 & 1 \\\\\n 1 & 0 & 1\\\\\n 1 & 1 & 1\\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings", "authors": ["Karine Karine", "Susan A. Murphy", "Benjamin M. Marlin"], "url": "https://arxiv.org/abs/2412.00308v1", "attribution": "\"BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings\" by Karine Karine, Susan A. Murphy, and Benjamin M. Marlin, arXiv:2412.00308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04213v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Identified physiological parameters of the specific subject (knee case)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\nParameter Indices & Muscle Indices & Estimations & Physiological Ranges \\\\ \\midrule\n\\multirow{2}{*}{$l^{m}_{o}$(m)} & BFS & 0.1819 & 0.1630 - 0.1830 \\\\\n & RF & 0.1143 & 0.1040 - 0.1240 \\\\ \\hline\n\\multirow{2}{*}{$F^{m}_{o}$(N)} & BFS & 805.7 & 402 - 1206 \\\\\n & RF & 1199.6 & 584.5 - 1753.5 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals", "authors": ["Shuhao Ma", "Jie Zhang", "Chaoyang Shi", "Pei Di", "Ian D. Robertson", "Zhi-Qiang Zhang"], "url": "https://arxiv.org/abs/2412.04213v1", "attribution": "\"Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals\" by Shuhao Ma, Jie Zhang, Chaoyang Shi, Pei Di, Ian D. Robertson, and Zhi-Qiang Zhang, arXiv:2412.04213v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.21204v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\textbf{Binary dihedral groups} & $\\mathsf{BD}_{2t}=\\langle 2,2,t\\rangle$, & order $4t$, & $t\\geq2$,\\\\\n\\textbf{Binary tetrahedral group} & $\\mathsf{BT}=\\langle 2,3,3\\rangle$, & order $24$, &\\\\\n\\textbf{Binary octahedral group} & $\\mathsf{BO}=\\langle 2,3,4\\rangle$, & order $48$, &\\\\\n\\textbf{Binary icosahedral group} & $\\mathsf{BI}=\\langle 2,3,5\\rangle$, & order $120$. &\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Binary polyhedral groups.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Classification of indecomposable reflexive modules on quotient singularities through Atiyah--Patodi--Singer theory", "authors": ["José Antonio Arciniega Nevárez", "José Luis Cisneros-Molina", "Agustín Romano Velázquez"], "url": "https://arxiv.org/abs/2504.21204v1", "attribution": "\"Classification of indecomposable reflexive modules on quotient singularities through Atiyah--Patodi--Singer theory\" by José Antonio Arciniega Nevárez, José Luis Cisneros-Molina, and Agustín Romano Velázquez, arXiv:2504.21204v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11002v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fonts sizes to be used for various parts of the manuscript. Table captions should be centered above the table. When the caption is too long to fit on one line, it should be justified to the right and left margins of the body of the text.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|} %% this creates two columns\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Article title & 16 pt., bold, centered \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Author names and affiliations & 12 pt., normal, centered \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Keywords & 10 pt., normal, left justified \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Abstract Title & 11 pt., bold, centered \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Abstract body text & 10 pt., normal, justified \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Section heading & 11 pt., bold, centered (all caps) \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Subsection heading & 11 pt., bold, left justified \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Sub-subsection heading & 10 pt., bold, left justified \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Normal text & 10 pt., normal, justified \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Figure and table captions & \\, 9 pt., normal \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Footnote & \\, 9 pt., normal \\\\\n\\hline \n\\rule[-1ex]{0pt}{3.5ex} Reference Heading & 11 pt., bold, centered \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Reference Listing & 10 pt., normal, justified \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Multi-rater Comparative Study of Automatic Target Localization Methods for Epilepsy Deep Brain Stimulation Procedures", "authors": ["Han Liu", "Kathryn L. Holloway", "Dario J. Englot", "Benoit M. Dawant"], "url": "https://arxiv.org/abs/2201.11002v1", "attribution": "\"A Multi-rater Comparative Study of Automatic Target Localization Methods for Epilepsy Deep Brain Stimulation Procedures\" by Han Liu, Kathryn L. Holloway, Dario J. Englot, and Benoit M. Dawant, arXiv:2201.11002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.01467v1_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Predicting Firm Sales Growth }}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n Variable & (1) & (2) & (3) \\\\\n \\midrule\n $a_{t-1}$ & 0.031** & 0.029** & 0.007** \\\\\n & (0.002) & (0.001) & (0.000) \\\\\n $g_{t-1}$ & -0.016** & -0.018** & 0.015 \\\\\n & (0.003) & (0.003) & (0.011) \\\\\n $ROA_{i,t-1}$ & 0.080** & 0.063** & 0.064** \\\\\n & (0.003) & (0.002) & (0.002) \\\\\n $size_{i,t-1}$ & -0.008** & -0.008** & -0.008** \\\\\n & (0.000) & (0.000) & (0.000) \\\\\n $age_i$ & 0.037** & 0.050** & 0.046** \\\\\n & (0.004) & (0.004) & (0.003) \\\\\n Constant & 0.44 & 0.372 & 0.39 \\\\\n Obs & 259,976 & 259,976 & 259,976 \\\\\n Adj R2 & 0.265 & 0.259 & 0.258 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Risk in Network Economies", "authors": ["Victor Sellemi"], "url": "https://arxiv.org/abs/2208.01467v1", "attribution": "\"Risk in Network Economies\" by Victor Sellemi, arXiv:2208.01467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06890v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyper-parameters of F2DDPG.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n \\toprule\n F2DDPG Hyper-Parameter & \\\\\n \\midrule\n \\# Policy network MLP units &(64, 64) \\\\\n \\# $Q$-network MLP units &(64, 64) \\\\\n Network parameter initialization &Xavier uniform \\\\\n Nonlinear activation &ReLU \\\\\n Policy network learning rate &$10^{-2}$ \\\\\n $Q$-network learning rate &$10^{-2}$ \\\\\n $\\tau$ for updating target networks &$10^{-2}$ \\\\\n $\\gamma$ &0.95 \\\\\n Replay buffer size &$10^{6}$ \\\\\n Mini-batch size &1024 \\\\\n Optimizer &Adam \\\\\n $\\delta^A$ &$10^{-5}$ \\\\\n $\\delta^E$ &$10^{-3}$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning", "authors": ["Heechang Ryu", "Hayong Shin", "Jinkyoo Park"], "url": "https://arxiv.org/abs/2101.06890v1", "attribution": "\"Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning\" by Heechang Ryu, Hayong Shin, and Jinkyoo Park, arXiv:2101.06890v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18455v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|l|c|c|c|}\n \\hline \n \\# & K & \\hspace{3.8 cm} Scenario & no reg. & VIB & \\textbf{GPM-MDL} \\\\ \\hline\n 1 & 2&CIFAR10, Enc.=CNN4, Dist.=(Light,Light) & 0.632 & 0.639 & \\textbf{0.675}\\\\\n \\hline\n 2 &2& CIFAR10, Enc.=CNN4, Dist.=(Light,Heavy) & 0.596 & 0.597 & \\textbf{0.621}\\\\\\hline\n 3 &2& CIFAR100, Enc.=ResNet18, Dist.=(Light,Light) & 0.426 & 0.441 & \\textbf{0.468}\\\\\n \\hline \n 4 &2& USPS, Enc.=CNN4, Dist.=(Light,Light) & 0.952 & 0.953 & \\textbf{0.957}\\\\\n \\hline\n 5 & 2&CIFAR10, Enc.=CNN4, Dist.=Occ.(L,R) & 0.607 & 0.610 & \\textbf{0.652}\\\\\n \\hline\n 6 & 2&CIFAR10, Enc.=CNN4, Dist.= Occ.(L,R) \n + (Light, Light) & 0.560 & 0.567 & \\textbf{0.606}\\\\\n \\hline\n 7 & 2& CIFAR10, Enc.=CNN4, Dist.= Occ.(L,R) + (Medium, Medium) & 0.548 & 0.553 & \\textbf{0.577}\\\\\n \\hline\n 8 & 2& USPS, Enc.=CNN4, Dist.=Occ.(L,R) + (Heavy, Heavy) & 0.507 & 0.515 & \\textbf{0.627}\\\\\n \\hline\n 9 & 3 & CIFAR100, Enc.=ResNet18, Dist.=(Medium, Medium, Medium) & 0.373 & 0.381 & \\textbf{0.412}\\\\\n \\hline\n 10 & 3 & CIFAR100, Enc.=ResNet18, Dist.=(Light, Heavy, Heavy) & 0.375 & 0.380 & \\textbf{0.427}\\\\\n \\hline\n 11 & 3 & CIFAR100, Enc.=ResNet18, Dist.=(Medium, Heavy, Heavy) & 0.324 & 0.325 & \\textbf{0.366}\\\\\n \\hline\n 12 & 4 & CIFAR10, Enc.=CNN4, Dist.=(Medium, Medium, Medium, Medium) & 0.602 & 0.605 & \\textbf{0.639}\\\\\n \\hline\n 13 & 4 & CIFAR10, Enc.=CNN4, Dist.=(Medium, Heavy, Heavy, Heavy) & 0.574 & 0.576 & \\textbf{0.600}\\\\\\hline\n 14 & 4 & USPS, Enc.=CNN4, Dist.= (Heavy, Heavy, Heavy, Heavy) & 0.587 & 0.588 & \\textbf{0.696}\\\\\n \\hline\n 15 & 4 & CIFAR10, Enc.=CNN4, Dist.= Occ.(L,R,U,B) & 0.646 & 0.647 & \\textbf{0.674}\\\\\n \\hline\n 16 & 4 & CIFAR10, Enc.=CNN4, Dist.=Occ.(L,R,U,B)+(Light,\\ldots,Light) & 0.599 & 0.601 & \\textbf{0.634}\\\\\n \\hline\n 17 & 4 & CIFAR10, CNN4, D =Occ.(LU,RU,LB,RB)& 0.620 & 0.621 & \\textbf{0.646}\\\\\n \\hline\n 18 & 4 & CIFAR10, Enc.=CNN4, Dist.=Occ.(LU,RU,LB,RB)+(Light,\\ldots,Light) & 0.585 & 0.590 & \\textbf{0.620}\\\\\n \\hline 19 & 8 & CIFAR10, Enc.=CNN4, Dist.=(Heavy,Heavy,\\ldots,Heavy) & 0.396 & 0.447 & \\textbf{0.529}\\\\\n \\hline 20 & 8 & CIFAR10, Enc.=CNN4, Dist.=(Heavy,Heavy,Ultimate,\\ldots,Ultimate) & 0.256 & 0.302 & \\textbf{0.335}\\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Test performance of Multi-view representation learning models with different encoder architectures, and trained on selected datasets using no regularizer, per-view VIB , and our proposed Gaussians-product Mixture MDL (GPM-MDL). Encoder and distortion choices are abbreviated as ``Enc.'' and ``Dist.'', respectively, and Occlusion as ``Occ.''. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior", "authors": ["Milad Sefidgaran", "Abdellatif Zaidi", "Piotr Krasnowski"], "url": "https://arxiv.org/abs/2504.18455v1", "attribution": "\"Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior\" by Milad Sefidgaran, Abdellatif Zaidi, and Piotr Krasnowski, arXiv:2504.18455v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.06271v6_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sub-groups by maternal location in the year of childbirth }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc|c|c|}\n & & \\multicolumn{2}{c}{mothers delivered in another municipality} \\\\\n & & yes & no \\\\ \\cline{3-4} \n & \\multirow{2}{*}{yes} & Group (1) move after delivery & Group (2) move before delivery \\\\ \nmothers lived in another & & 4.2\\% & 10.3\\% \\\\ \\cline{3-4}\n\\textit{grunnkrets} last year & \\multirow{2}{*}{no} & Group (3) no move, far hospital & Group (4) no move, local hospital \\\\ \n & & 24.9\\% & 60.6\\% \\\\ \\cline{3-4} \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The effect of ambient air pollution on birth outcomes in Norway", "authors": ["Xiaoguang Ling"], "url": "https://arxiv.org/abs/2208.06271v6", "attribution": "\"The effect of ambient air pollution on birth outcomes in Norway\" by Xiaoguang Ling, arXiv:2208.06271v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12027v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the performance among different models combined with transformer based models on the validation dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n\\hline\nModel & Accuracy & \\multicolumn{2}{c|}{f1-score} & \\multicolumn{2}{c|}{Precision} & \\multicolumn{2}{c|}{Recall} \\\\ \\hline\n & & Fake & Real & Fake & Real & Fake & Real \\\\ \\hline\nBERT + RCNN & 0.967 & 0.965 & 0.969 & 0.980 & 0.956 & 0.950 & 0.982 \\\\ \\hline\nRoBERTa + RCNN & 0.968 & 0.966 & 0.970 & \\textbf{0.988} & 0.956 & 0.950 & \\textbf{0.989} \\\\ \\hline\nRoBERTa + SVM & 0.978 & 0.977 & 0.979 & 0.987 & 0.970 & 0.967 & 0.988 \\\\ \\hline\nRoBERTa + MLP & \\textbf{0.979} & \\textbf{0.977} & \\textbf{0.980} & 0.981 & \\textbf{0.976} & \\textbf{0.974} & 0.983 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A transformer based approach for fighting COVID-19 fake news", "authors": ["S. M. Sadiq-Ur-Rahman Shifath", "Mohammad Faiyaz Khan", "Md. Saiful Islam"], "url": "https://arxiv.org/abs/2101.12027v1", "attribution": "\"A transformer based approach for fighting COVID-19 fake news\" by S. M. Sadiq-Ur-Rahman Shifath, Mohammad Faiyaz Khan, and Md. Saiful Islam, arXiv:2101.12027v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18896v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n $d$ & $1500$ & $500$ & $100$ & $10$\\\\\n \\midrule\n Time (s) & $124.62$ & $58.98$ & $12.62$ & $6.54$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Time (seconds) required to construct the calibration bands on the mean of independent normal responses $(Y_i)_{i=1}^{n}$ using the set $\\mathcal{J}^{dist}_{d}$ for various distances $d$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Calibration Bands for Mean Estimates within the Exponential Dispersion Family", "authors": ["Łukasz Delong", "Selim Gatti", "Mario V. Wüthrich"], "url": "https://arxiv.org/abs/2503.18896v1", "attribution": "\"Calibration Bands for Mean Estimates within the Exponential Dispersion Family\" by Łukasz Delong, Selim Gatti, and Mario V. Wüthrich, arXiv:2503.18896v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.17967v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|ccc}\n\t\\toprule[1.1pt]\n\tID & Ticker & DPS & Closing Prices & Initial Quantity \\\\\n\t\\midrule\n\t1 & A & 22 & 454.17, \\dots, 445.60 & 1,200 \\\\\n\t2 & B & 23 & 354.17, \\dots, 465.80 & 1,000 \\\\\n\t3 & C & 25 & 500.47, \\dots, 440.60 & 1,600 \\\\\n\t\\dots & \\dots & \\dots & \\dots & \\dots \\\\\n\t\\bottomrule[1.1pt]\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{Stock Details.} \\texttt{DPS} denotes dividend per share; \\texttt{Closing Prices} are historical end-of-day closing prices; and \\texttt{Initial Quantity} specifies the starting number of shares.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents", "authors": ["Tianmi Ma", "Jiawei Du", "Wenxin Huang", "Wenjie Wang", "Liang Xie", "Xian Zhong", "Joey Tianyi Zhou"], "url": "https://arxiv.org/abs/2502.17967v2", "attribution": "\"Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents\" by Tianmi Ma, Jiawei Du, Wenxin Huang, Wenjie Wang, Liang Xie, Xian Zhong, and Joey Tianyi Zhou, arXiv:2502.17967v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14913v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|cc|}\n \\hline\n n & $L^{2}$error & order & $H^{1}$error & order & $H^{2}$error & order\\\\\n \\hline\n 5 & 7.608e-04& - &1.320e-02& - &4.284e-01 & - \\\\\n \\hline\n 10 & 4.861e-05&3.968&1.685e-03&2.971&1.092e-01&1.972 \\\\\n \\hline\n 20 & 3.054e-06&3.992&2.116e-04&2.993&2.743e-02&1.993 \\\\\n \\hline\n 40 & 1.911e-07&3.998&2.648e-05&2.998&6.865e-03&1.998 \\\\\n \\hline\n 80 & 1.193e-08&4.001&3.311e-06&3.000&1.717e-03&2.000 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{error and order of basis $V_{h}$, biharmonic equation in 1d.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A PINN-enriched finite element method for linear elliptic problems", "authors": ["Xiao Chen", "Yixin Luo", "Jingrun Chen"], "url": "https://arxiv.org/abs/2503.14913v1", "attribution": "\"A PINN-enriched finite element method for linear elliptic problems\" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18756v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average duration of a BO loop for each AF. We measure the runtime on the \\texttt{Branin}, \\texttt{Levy}, and \\texttt{Hartmann} benchmarks and average over benchmarks, BO iterations, and 10 random restarts. For $N=5$ outer repetitions, VES has a higher runtime than the other acquisition functions.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lr}\n \\toprule\n AF & average time per BO iteration\\\\\n \\midrule\n EI & $1.627s\\, (\\pm 0.916s)$ \\\\\n MES & $1.120s\\, (\\pm 0.472s)$ \\\\\n VES & $10.910s\\, (\\pm 12.323)$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization", "authors": ["Nuojin Cheng", "Leonard Papenmeier", "Stephen Becker", "Luigi Nardi"], "url": "https://arxiv.org/abs/2501.18756v2", "attribution": "\"A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization\" by Nuojin Cheng, Leonard Papenmeier, Stephen Becker, and Luigi Nardi, arXiv:2501.18756v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14699v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\n \\hline \n Optimizer setting & Name/value\\\\\n \\hline \n optimizer & AdamW \\\\\\\n $\\beta_{1}$ & 0.9 (default)\\\\\n $\\beta_{2}$ & 0.999 (default)\\\\\n weight decay & 0.01 (default)\\\\\n batch size & 8\\\\ \n learning rate scheduling & cosine annealing \\\\\n $T_{0}$ &10\\\\\n $T_{mult}$ & 2\\\\\n $\\eta_{min}$ & $1e^{-6}$\\\\\n initial learning rate & $1e^{-3}$\\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{CNN model training optimizer setting.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Acceleration of RANS Solver Convergence via Initialization with Wake Extension Models", "authors": ["Kazuko W. Fuchi", "Eric M. Wolf", "Christopher R. Schrock", "Philip S. Beran"], "url": "https://arxiv.org/abs/2501.14699v1", "attribution": "\"Acceleration of RANS Solver Convergence via Initialization with Wake Extension Models\" by Kazuko W. Fuchi, Eric M. Wolf, Christopher R. Schrock, and Philip S. Beran, arXiv:2501.14699v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.02255v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|c|c|}\\hline\n\t\t\\textbf{Number of orders} & $1$ & $2$ & $3$ & $4$ & $5$ & $6$ & $7$ & $8$ & $9$ & $10+$ \\\\\\hline \n\t\t\\textbf{Number of buyers} & $430,743$ & $21,688$ & $1,832$ & $284$ & $99$ & $42$ & $37$ & $22$ & $19$ & $131$ \\\\\\hline \n\t\t\\textbf{Percentage $\\%$} & $94.69$ & $4.77$ & $0.40$ & $0.06$ & $0.02$ & $0.01$ & $0.01$ & $< 0.01$ & $< 0.01$ & $0.03$ \\\\\\hline \n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Number of orders made by buyer users}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study", "authors": ["Saed Alizamir", "Kasun Bandara", "Ali Eshragh", "Foaad Iravani"], "url": "https://arxiv.org/abs/2212.02255v1", "attribution": "\"A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study\" by Saed Alizamir, Kasun Bandara, Ali Eshragh, and Foaad Iravani, arXiv:2212.02255v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.02659v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n$\\hat{\\mu}$ & $\\hat{a}_1$ & $\\hat{b}_0$ & $T$ \\\\ \\hline\n0.2440 & 0.7540 & 0.4877 & 5000\\\\\n0.2121 & 0.7127 & 0.4954 & 15000\\\\\n0.2044 & 0.7016 & 0.4951 & 25000\\\\\n0.1992 & 0.7042 & 0.4990 & 50000\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Parameter estimates from MME for a Hawkes process. True parameters are $\\mu=0.2$, $a_1=0.7$ and $b_0=0.5$.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Hawkes model with CARMA(p,q) intensity", "authors": ["Lorenzo Mercuri", "Andrea Perchiazzo", "Edit Rroji"], "url": "https://arxiv.org/abs/2208.02659v3", "attribution": "\"A Hawkes model with CARMA(p,q) intensity\" by Lorenzo Mercuri, Andrea Perchiazzo, and Edit Rroji, arXiv:2208.02659v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.01863v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of UrbanSound8K by classes and salience.}\n\\begin{tabular}{llll}\nClass & Foreground & Background & Total\\\\\n\\hline\nAir Conditioner & 569 & 431 & 1000\\\\\nCar Horn & 153 & 276 & 429\\\\\nChildren Playing & 588 & 412 & 1000\\\\\nDog Bark & 645 & 355 & 1000\\\\\nDrilling & 902 & 98 & 1000\\\\\nEngine Idling & 916 & 84 & 1000\\\\\nGun Shot & 304 & 70 & 374\\\\\nJackhammer & 731 & 269 & 1000\\\\\nSiren & 269 & 660 & 929\\\\\nStreet Music & 625 & 375 & 1000\\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Environment Transfer for Distributed Systems", "authors": ["Chunheng Jiang", "Jae-wook Ahn", "Nirmit Desai"], "url": "https://arxiv.org/abs/2101.01863v1", "attribution": "\"Environment Transfer for Distributed Systems\" by Chunheng Jiang, Jae-wook Ahn, and Nirmit Desai, arXiv:2101.01863v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.05488v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Inference Time Comparison Between the Baseline and the Proposed NLC Method. For the CIFAR-10 experiment, the baseline is DDPM and the proposed method is DDPM+NLC. For the ImageNet experiment, the baseline is DDNM and the proposed method is DDNM+NLC (inpainting experiments). }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc}\n\\hline\n$\\epsilon$ model & CIFAR10-DDPM & ImageNet - DDNM \\\\ \\hline\nbaseline & 0.32 & 0.93 \\\\\nbaseline + NLC & 0.34 & 0.95 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Enhancing Sample Generation of Diffusion Models using Noise Level Correction", "authors": ["Abulikemu Abuduweili", "Chenyang Yuan", "Changliu Liu", "Frank Permenter"], "url": "https://arxiv.org/abs/2412.05488v3", "attribution": "\"Enhancing Sample Generation of Diffusion Models using Noise Level Correction\" by Abulikemu Abuduweili, Chenyang Yuan, Changliu Liu, and Frank Permenter, arXiv:2412.05488v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.07924v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Scale of the Experiment}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\tNumber of Nodes & 10 & 20 & 30 & 40 & 50 & 60 \\\\ \\hline\n\t\t\tAverage Number of Edges & 30 & 125 & 280 & 420 & 700 & 1000 \\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Multi-Objective Routing Optimization Using Coherent Ising Machine in Wireless Multihop Networks", "authors": ["Yu-Xuan Lin", "Chu-Yao Xu", "Chuan Wang"], "url": "https://arxiv.org/abs/2503.07924v1", "attribution": "\"Multi-Objective Routing Optimization Using Coherent Ising Machine in Wireless Multihop Networks\" by Yu-Xuan Lin, Chu-Yao Xu, and Chuan Wang, arXiv:2503.07924v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02305v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{predictors and their corresponding coefficients for lasso regression}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n \\hline\n \\textbf{predictors} & \\textbf{~~~~~~Coefficients~~~~~~} & \\textbf{~~~~~~$95\\%$ Confidence Interval~~~~~~} \\\\ \\hline\n abnormal\\_ALP & -0.02 & (-0.08 , 0.04) \\\\ \\hline\n abnormal\\_MPV\t& 0.01\t& (-0.06 , 0.11) \\\\ \\hline\n abnormal\\_hematocrit\t& 0.00 & (-0.11 , 0.14) \\\\ \\hline\n abnormal\\_PO2 & -0.11 & (-0.19 , 0.00) \\\\ \\hline\n abnormal\\_creatinine\t& 0.03 & (-0.03 , 0.11) \\\\ \\hline\n abnormal\\_INR & 0.06 & (-0.02 , 0.22) \\\\ \\hline\n abnormal\\_MCHb & \t-0.03 & (-0.10 , 0.04) \\\\ \\hline\n abnormal\\_MCHb\\_conc & -0.03 & (-0.10 , 0.04) \\\\ \\hline\n abnormal\\_hb\t& 0.05\t& (-0.04 , 0.19) \\\\ \\hline\n abnormal\\_mcv\t& -0.03\t& (-0.11 , 0.04) \\\\ \\hline\n abnormal\\_plt\t& 0.23\t& (0.02 , 0.36) \\\\ \\hline\n abnormal\\_redcellwidth & 0.07\t& (0.00 , 0.15) \\\\ \\hline\n abnormal\\_wbc\t& -0.02\t& (-0.09 , 0.03) \\\\ \\hline\n abnormal\\_ALC\t& 0.01\t& (-0.05 , 0.08) \\\\ \\hline\n location\\_GeneralMedicine & -0.11 & (-0.21 , 0.00) \\\\ \\hline\n location\\_Hematology & 0.04 & (-0.02 , 0.16) \\\\ \\hline\n location\\_IntensiveCare & 0.05 & (-0.01 , 0.15) \\\\ \\hline\n location\\_CardiovascularSurgery & 0.04 & (-0.03 , 0.11) \\\\ \\hline\n location\\_Pediatric\t& 0.04\t& (-0.02 , 0.10) \\\\ \\hline\n Monday & 0.07 & (0.00 , 0.16) \\\\ \\hline\n Tuesday & 0.07 & (0.00 , 0.14) \\\\ \\hline\n Wednesday & 0.00 & (-0.04 , 0.07) \\\\ \\hline\n Thursday & 0.01 & (-0.03 , 0.09) \\\\ \\hline\n Friday & -0.39 & (-0.46 , -0.31) \\\\ \\hline\n Saturday & -0.31 & (-0.39 , -0.23) \\\\ \\hline\n Sunday & 0.10 & (0.03 , 0.18) \\\\ \\hline\n lastWeek\\_Usage & 0.12 & (0.05 , 0.19) \\\\ \\hline\n yesterday\\_Usage & 0.10 & (0.02 , 0.17) \\\\ \\hline\n yesterday\\_ReceivedUnits & 0.06 & (0.00 , 0.14) \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models", "authors": ["Maryam Motamedi", "Jessica Dawson", "Na Li", "Douglas G. Down", "Nancy M. Heddle"], "url": "https://arxiv.org/abs/2101.02305v2", "attribution": "\"Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models\" by Maryam Motamedi, Jessica Dawson, Na Li, Douglas G. Down, and Nancy M. Heddle, arXiv:2101.02305v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.04686v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The size of communicated messages (in million) in each update of FedAvg and FedRF-TCA (with $N = 1\\,000$).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n & Data-dependent features & Model parameters & Sum \\\\ \n \\midrule \n FedAvg & 0 & 25.637 & 25.637 \\\\\n FedRF-TCA & 0.001 & 1.691 & 1.692 \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Robust and Communication-Efficient Federated Domain Adaptation via Random Features", "authors": ["Zhanbo Feng", "Yuanjie Wang", "Jie Li", "Fan Yang", "Jiong Lou", "Tiebin Mi", "Robert. C. Qiu", "Zhenyu Liao"], "url": "https://arxiv.org/abs/2311.04686v2", "attribution": "\"Robust and Communication-Efficient Federated Domain Adaptation via Random Features\" by Zhanbo Feng, Yuanjie Wang, Jie Li, Fan Yang, Jiong Lou, Tiebin Mi, Robert. C. Qiu, and Zhenyu Liao, arXiv:2311.04686v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.09436v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Nodes on the designed ISIG BNs with assumed states}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\hline\nnode & description & states \\\\ \n \\hline\n SC & signal controller & N,L,M,H,C \\\\ \n RSE & roadside equipment & L,M,H \\\\ \n TC & traffic composition & UP,EP,UV,CR,EM,FR,TR \\\\ \n UP & unequipped pedestrians & N,L,M,H,C \\\\ \n EP & equipped pedestrians & N,L,M,H,C \\\\ \n UV & unequipped vehicles & N,L,M,H,C \\\\ \n CR & regular equipped vehicles & N,L,M,H,C \\\\ \n EM & emergency vehicles & N,L,M,H,C \\\\ \n FR & freight trucks & N,L,M,H,C \\\\ \n TR & transit vehicles & N,L,M,H,C \\\\ \n ITS & other ITS devices & L,M,H \\\\ \n TMC & traffic management center & L,M,H \\\\ \n S & sensor & True,False \\\\ \n DD & detection by driver & L,M,H \\\\ \n DP & detection by pedestrian & L,M,H \\\\ \n DS & detection by system (vehs) & L,M,H \\\\ \n PDS & detection by system (pedes) & L,M,H \\\\ \n RDS & detection by system (RSE) & L,M,H \\\\ \n SDS & detection by system (SA) & L,M,H \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks", "authors": ["Gurcan Comert", "Mashrur Chowdhury", "David M. Nicol"], "url": "https://arxiv.org/abs/2011.09436v1", "attribution": "\"Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks\" by Gurcan Comert, Mashrur Chowdhury, and David M. Nicol, arXiv:2011.09436v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.01997v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Diarization performance on LibriCSS evaluation set (sessions 2-10), evaluated condition-wise, in terms of \\% DER. 0S and 0L refer to 0\\% overlap with short and long inter-utterance silences, respectively. The DL results are using rank-based weighting.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Method}} & \\multicolumn{6}{c}{\\textbf{Overlap ratio in \\%}} & \\multirow{2}{*}{\\textbf{Average}} \\\\\n\\cmidrule(r{4pt}){2-7}\n & \\textbf{0L} & \\textbf{0S} & \\textbf{10} & \\textbf{20} & \\textbf{30} & \\textbf{40} & \\\\\n\\midrule\nVB & 3.9 & 3.8 & 6.5 & 8.2 & 12.6 & 13.4 & 8.6 \\\\\nSC & 2.6 & 3.4 & 6.8 & 10.0 & 13.9 & 15.2 & 9.3 \\\\\nRPN & 4.5 & 9.1 & 8.3 & 6.7 & 11.6 & 14.2 & 9.5 \\\\\nTS-VAD & 6.0 & 4.6 & 6.6 & 7.3 & 10.3 & 9.5 & 7.4 \\\\\n\\midrule\nDL & \\textbf{2.3} & \\textbf{2.2} & \\textbf{4.0} & \\textbf{5.3} & \\textbf{7.9} & \\textbf{9.0} & \\textbf{5.4} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs", "authors": ["Desh Raj", "Leibny Paola Garcia-Perera", "Zili Huang", "Shinji Watanabe", "Daniel Povey", "Andreas Stolcke", "Sanjeev Khudanpur"], "url": "https://arxiv.org/abs/2011.01997v1", "attribution": "\"DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs\" by Desh Raj, Leibny Paola Garcia-Perera, Zili Huang, Shinji Watanabe, Daniel Povey, Andreas Stolcke, and Sanjeev Khudanpur, arXiv:2011.01997v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.03287v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Level 1 Crypto TVL Change Portfolio Descriptive Statistics. Values are portfolio returns in percentage points and are at the weekly frequency. TVL is adjusted for staking, pool2, governance tokens, borrows, double count, liquid staking, and vesting. There are 104 weekly observations. \\(^{***}\\), \\(^{**}\\), and \\(^{*}\\), denote statistical significance at the 1\\%, 5\\%, and 10\\% levels respectively.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\\hline\n & HML & Q1 & Q2 & Q3 & Q4\\\\\n\\hline\nmean & -0.97 & 2.34\\(^{*}\\) & 2.83\\(^{**}\\) & 1.59 & 1.37\\(^{*}\\)\\\\\nstd & 8.92 & 13.27 & 13.72 & 12.58 & 8.36\\\\\nmin & -36.89 & -26.24 & -28.50 & -47.72 & -17.73\\\\\n25\\% & -3.38 & -4.87 & -5.59 & -5.43 & -3.93\\\\\n50\\% & 0.25 & -0.38 & 0.21 & 0.84 & 0.29\\\\\n75\\% & 3.45 & 8.05 & 10.71 & 8.11 & 6.13\\\\\nmax & 22.76 & 65.89 & 48.46 & 56.44 & 28.99\\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns", "authors": ["Matt Brigida"], "url": "https://arxiv.org/abs/2506.03287v3", "attribution": "\"The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns\" by Matt Brigida, arXiv:2506.03287v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.16207v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\toprule\n \\textbf{Metric} & \\textbf{JSD} & \\textbf{MMD} \\\\ \n \\midrule\n \\textbf{Score } & 0.015 & 0.0001 \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study", "authors": ["Ainaz Jamshidi", "Muhammad Arif", "Sabir Ali Kalhoro", "Alexander Gelbukh"], "url": "https://arxiv.org/abs/2412.16207v1", "attribution": "\"Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study\" by Ainaz Jamshidi, Muhammad Arif, Sabir Ali Kalhoro, and Alexander Gelbukh, arXiv:2412.16207v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13444v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ASR (\\%) of Untargeted Soft-Label Attacks. $\\sigma$ = 0.1.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|ccc}\n\t\n\t\t\\toprule\n\t\tDataset & Attacks %& & ASR (\\%) &\\\\ & \n\t\t& No defense & $\\theta_1$ = 0.5 & $\\theta_2$ = 0.7 \\\\\n\t\t\\midrule\n\t\t & AZ & 97 & 0 & 0 \\\\\n\t\t MNIST & SA & 92 & 89 & 10\\\\\n\t\t & SimBA & 82 & 31 & 0 \\\\\n\t\t \\midrule \\midrule\n\t\t Dataset & Attacks %& &ASR (\\%) &\\\\\n\t\t & No Defense & $\\theta_1$ = 0.3 & $\\theta_2$ = 0.4 \\\\\n\t\t \\midrule\n\t\t & AZ & 100 & 0 & 0 \\\\\n\t\t IMAGENET & SA & 100 & 38 & 30\\\\\n\t\t & SimBA & 100 & 96 & 0 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Boundary Defense Against Black-box Adversarial Attacks", "authors": ["Manjushree B. Aithal", "Xiaohua Li"], "url": "https://arxiv.org/abs/2201.13444v1", "attribution": "\"Boundary Defense Against Black-box Adversarial Attacks\" by Manjushree B. Aithal and Xiaohua Li, arXiv:2201.13444v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.07836v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ordinary least squares and instrumental variables estimates of the simple specification (left panel) and the extended specification including additional covariates (right panel) for the recent subsample (1992-2022). The 95\\% confidence intervals for the OLS and IV regression are based on the Gaussian distribution. The Deming regression estimates are obtained using $\\delta \\in \\{0.2, 0.5, 1, 2, 5\\}$. Standard errors and confidence intervals for Deming regression are computed using 9999 bootstrap replications.\\\\}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|lll|lll}\n \\toprule\n Recent subsample &\\multicolumn{3}{l|}{Simple specification, eqn. ()}&\\multicolumn{3}{l}{Extended specification, eqn. ()}\\\\\n Model\t\t\t&\tEst.\t&\tS. e.\t&\tConf.\tinterval\t&\tEst.\t&\tS. e.\t&\tConf.\tinterval \\\\\n \\midrule\t\t\t\t\t\t\t\t\t\t\t\t\n OLS Regression\t\t&\t0.4497\t&\t0.0173\t&\t[0.4157, 0.4836]\t\t&\t0.4613\t&\t0.0112\t&\t[0.4392, 0.4833]\t\t\\\\\n IV\treg.\t(H\\&N)\t&\t0.4496\t&\t0.0173\t&\t[0.4157, 0.4836]\t\t&\t0.4622\t&\t0.0112\t&\t[0.4402, 0.4842]\t\t\\\\\n IV\treg.\t(vMA)\t&\t0.4502\t&\t0.0245\t&\t[0.4022, 0.4982]\t\t&\t0.4623\t&\t0.0112\t&\t[0.4402, 0.4843]\t\t\\\\\n IV\treg.\t(H\\&N-vMA)\t&\t0.4495\t&\t0.0173\t&\t[0.4156, 0.4834]\t\t&\t0.4622\t&\t0.0112\t&\t[0.4401, 0.4842]\t\t\\\\\n Deming\treg.\t(0.2)\t&\t0.4598\t&\t0.0176\t&\t[0.4509, 0.4675]\t\t&\t0.4662\t&\t0.0105\t&\t[0.4611, 0.4709]\t\t\\\\\n Deming\treg.\t(0.5)\t&\t0.4555\t&\t0.0175\t&\t[0.4468, 0.4630]\t\t&\t0.4645\t&\t0.0106\t&\t[0.4594, 0.4692]\t\t\\\\\n Deming\treg.\t(1)\t&\t0.4531\t&\t0.0172\t&\t[0.4446, 0.4603]\t\t&\t0.4636\t&\t0.0107\t&\t[0.4584, 0.4683]\t\t\\\\\n Deming\treg.\t(2)\t&\t0.4515\t&\t0.0172\t&\t[0.4431, 0.4587]\t\t&\t0.4630\t&\t0.0107\t&\t[0.4578, 0.4677]\t\t\\\\\n Deming\treg.\t(5)\t&\t0.4504\t&\t0.0171\t&\t[0.4422, 0.4576]\t\t&\t0.4625\t&\t0.0106\t&\t[0.4574, 0.4672]\t\t\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Robust estimation of carbon dioxide airborne fraction under measurement errors", "authors": ["J. Eduardo Vera-Valdés", "Charisios Grivas"], "url": "https://arxiv.org/abs/2411.07836v1", "attribution": "\"Robust estimation of carbon dioxide airborne fraction under measurement errors\" by J. Eduardo Vera-Valdés and Charisios Grivas, arXiv:2411.07836v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.01000v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n \\textbf{Model}& \\textbf{Error Rate}\\\\\n \\midrule\n XD Gradient& 35 \\%\\\\\n M/D/1 Queue& 30 \\%\\\\\n Random Forest& 24 \\%\\\\\n Random Forest M/D/1 Hybrid& 19 \\% \\\\\n \\bottomrule \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Distributed Edge Provisioning Techniques}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Characterizing and Modeling AI-Driven Animal Ecology Studies at the Edge", "authors": ["Jenna Kline", "Austin O'Quinn", "Tanya Berger-Wolf", "Christopher Stewart"], "url": "https://arxiv.org/abs/2412.01000v1", "attribution": "\"Characterizing and Modeling AI-Driven Animal Ecology Studies at the Edge\" by Jenna Kline, Austin O'Quinn, Tanya Berger-Wolf, and Christopher Stewart, arXiv:2412.01000v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10952v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\nModel & Precision & Recall & F1 \\\\\n\\hline\n{LR$_{SarcF}$} & 60.55 &\t60.45 &\t60.2 \\\\\n{LR$_{SarcF+ArgF}$} & 60.75 &\t60.75\t& \\textbf{60.75} \\\\\n\\hline\n{LSTM$_{Attn}$} & 52.85 &\t52.80&\t 52.90 \\\\\n{LSTM$_{MT}$} & 59.50 &\t59.25&\t\\textbf{59.35} \\\\\n\\hline\n \n{BERT$_{orig}$} & 57.39 & 57.39 & 57.39\\\\\n{BERT$_{MT}$} & 61.79 & 61.74 & \\textbf{61.76} \\\\\n{BERT$_{{MT}_{uncert}}$} & 64.05 & 63.5 & \\textbf{64.00} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Evaluations of sarcasm detection on the test set of $IAC_{orig}$.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "\"Laughing at you or with you\": The Role of Sarcasm in Shaping the Disagreement Space", "authors": ["Debanjan Ghosh", "Ritvik Shrivastava", "Smaranda Muresan"], "url": "https://arxiv.org/abs/2101.10952v1", "attribution": "\"\"Laughing at you or with you\": The Role of Sarcasm in Shaping the Disagreement Space\" by Debanjan Ghosh, Ritvik Shrivastava, and Smaranda Muresan, arXiv:2101.10952v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2304.06484v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|r|r|r|}\n\\hline\nOutlier Percentile & 0.1\\% & 2.5\\% & 5.0\\% \\\\ \\hline\nPaired t-test (marked to day) & \\textbf{0.027} & {0.719} & 0.803 \\\\ \\hline\nPaired t-test (marked to week) & 0.103 & 0.815 & 0.778 \\\\ \\hline\nUnpaired t-test & 0.647 & 0.978 & 0.886 \\\\ \\hline\nLog Paired t-test (marked to day) & \\textbf{0.000} & {0.144} & 0.250 \\\\ \\hline\nLog Paired t-test (marked to week) & 0.114 & 0.240 & 0.129 \\\\ \\hline\nLog Unpaired t-test & \\textbf{0.005} & {0.623} & 0.549 \\\\ \\hline\n\\end{tabular}\n\\caption{P-values varying outlier detection methods and types of one-sided t-tests for hypothesis that price of Male CryptoPunks $>$ Female CryptoPunks. \\textbf{Bold} indicates p-value$<0.05$. }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Exploring Gender and Race Biases in the NFT Market", "authors": ["Howard Zhong", "Mark Hamilton"], "url": "https://arxiv.org/abs/2304.06484v1", "attribution": "\"Exploring Gender and Race Biases in the NFT Market\" by Howard Zhong and Mark Hamilton, arXiv:2304.06484v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.09760v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|}\n \\toprule\n & JUN20 & SEP20 & DEC20 & MAR21 & JUN21 & SEP21 & DEC21 & JUN22 & DEC22 \\\\ \n \\bottomrule\n \\toprule\n Mean & 148 & 104 & 100 & 19 & 22 & 4 & 24 & 3 & 7 \\\\\n Median & 148 & 107 & 114 & 18 & 21 & 4 & 24 & 3 & 8 \\\\\n Std & 0 & 17 & 30 & 7 & 5 & 2 & 5 & 1 & 2 \\\\\n $q_{0.05}$ & 148 & 96 & 28 & 11 & 17 & 1 & 16 & 0 & 4 \\\\\n $q_{0.95}$ & 148 & 114 & 117 & 31 & 30 & 6 & 29 & 3 & 9 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\small{We report the number of call-put options' couples traded for each maturity and value date: mean, median, standard deviation (std), and quantiles (q) 5\\%, 95\\% considering each value date.}}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Additive Bachelier model with an application to the oil option market in the Covid period", "authors": ["Roberto Baviera", "Michele Domenico Massaria"], "url": "https://arxiv.org/abs/2506.09760v1", "attribution": "\"The Additive Bachelier model with an application to the oil option market in the Covid period\" by Roberto Baviera and Michele Domenico Massaria, arXiv:2506.09760v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12054v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\n$n$ & Galerkin & Riesz \\\\\n\\hline\n6 & $3.12\\cdot 10^{-1\\strut}$ & $7.44\\cdot 10^{-9}$ \\\\\n10 & $8.78\\cdot 10^{-1}$ & $5.16\\cdot 10^{-9}$ \\\\\n20 & $4.38\\cdot 10^{4\\phantom{-}}$ & $1.94\\cdot 10^{-8}$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Regularized minimal-norm solution of an overdetermined system of first kind integral equations", "authors": ["Patricia Díaz de Alba", "Luisa Fermo", "Federica Pes", "Giuseppe Rodriguez"], "url": "https://arxiv.org/abs/2201.12054v2", "attribution": "\"Regularized minimal-norm solution of an overdetermined system of first kind integral equations\" by Patricia Díaz de Alba, Luisa Fermo, Federica Pes, and Giuseppe Rodriguez, arXiv:2201.12054v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.05013v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rrrr|rrrr|rrrr}\n\\toprule\n\\multirow{2}{*}{$k$} & \\multicolumn{4}{c}{Window 1} & \\multicolumn{4}{c}{Window 2} & \\multicolumn{4}{c}{Window 3} \\\\\n\\cmidrule{2-5} \\cmidrule{6-9} \\cmidrule{10-13}\n & PVE$_k$ & ADF $p$ & $\\text{sd}(\\beta_{t,k})$ & $\\tau$ & PVE$_k$ & ADF $p$ & $\\text{sd}(\\beta_{t,k})$ & $\\tau$ & PVE$_k$ & ADF $p$ & $\\text{sd}(\\beta_{t,k})$ & $\\tau$ \\\\\n\\midrule\n1 & 0.582 & 0.385 & 3.068 & 17.055 & 0.430 & 0.130 & 6.396 & 29.026 & 0.591 & 0.358 & 7.878 & 123.980 \\\\\n2 & 0.166 & 0.431 & 1.636 & 64.624 & 0.150 & 0.259 & 3.783 & 36.975 & 0.120 & 0.001 & 3.553 & 12.187 \\\\\n3 & 0.049 & 0.000 & 0.890 & 9.819 & 0.110 & 0.100 & 3.239 & 20.934 & 0.087 & 0.009 & 3.030 & 15.286 \\\\\n4 & 0.038 & 0.049 & 0.787 & 11.956 & 0.064 & 0.013 & 2.469 & 13.638 & 0.025 & 0.014 & 1.623 & 7.152 \\\\\n5 & 0.030 & 0.000 & 0.702 & 5.480 & 0.040 & 0.000 & 1.948 & 5.334 & 0.020 & 0.000 & 1.461 & 5.360 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Summary statistics for principal component time series of ETH30 for each window. $\\tau$ is mean reversion time $-1/\\log|\\phi|$ from AR(1) fit.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Dynamics of Liquidity Surfaces in Uniswap v3", "authors": ["Jimmy Risk", "Shen-Ning Tung", "Tai-Ho Wang"], "url": "https://arxiv.org/abs/2509.05013v1", "attribution": "\"Dynamics of Liquidity Surfaces in Uniswap v3\" by Jimmy Risk, Shen-Ning Tung, and Tai-Ho Wang, arXiv:2509.05013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11949v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The CPU time of three compared methods on Caltech101-7 in terms of both ranks and views.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|ccc|ccc} \\hline\n\t\t\t\\multirow{2}{*}{rank} & \\multicolumn{3}{c}{3 views} &\n\t\t\t\\multicolumn{3}{c}{4 views} & \\multicolumn{3}{c}{5 views} \\\\ \\cline{2-10}\n\t\t\t& mcca & als & Alg.~ & mcca & als & Alg.~\n\t\t\t& mcca & als & Alg.~ \\\\ \\hline\n\t\t\t3 & 0.0016& 0.0464& 0.0578& 0.0022& 0.0520& 0.0863& 0.0040& 0.2463& 0.7044 \\\\\n\t\t\t4 & 0.0013& 0.0450& 0.0656& 0.0022& 0.0517& 0.0916& 0.0037& 0.2388& 0.8510 \\\\\n\t\t\t5 & 0.0014& 0.0464& 0.0713& 0.0021& 0.0537& 0.1216& 0.0040& 0.2434& 1.0633 \\\\\n\t\t\t6 & 0.0013& 0.0459& 0.0781& 0.0021& 0.0522& 0.1431& 0.0041& 0.2461& 1.2318 \\\\\n\t\t\t7 & 0.0013& 0.0464& 0.0797& 0.0022& 0.0537& 0.1519& 0.0036& 0.2435& 1.3059 \\\\\n\t\t\t8 & 0.0013& 0.0478& 0.0843& 0.0020& 0.0520& 0.1578& 0.0037& 0.2563& 1.3738 \\\\\n\t\t\t9 & 0.0014& 0.0454& 0.0942& 0.0022& 0.0536& 0.1665& 0.0035& 0.2556& 1.4235 \\\\\n\t\t\t10 & 0.0013& 0.0461& 0.0961& 0.0023& 0.0528& 0.1778& 0.0036& 0.2609& 1.6305 \\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Higher Order Correlation Analysis for Multi-View Learning", "authors": ["Jiawang Nie", "Li Wang", "Zequn Zheng"], "url": "https://arxiv.org/abs/2201.11949v1", "attribution": "\"Higher Order Correlation Analysis for Multi-View Learning\" by Jiawang Nie, Li Wang, and Zequn Zheng, arXiv:2201.11949v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19866v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lcccccccccc}\n\\toprule\n\\textbf{Dataset} & {Aircraft} & {Caltech-101} & {Cars} & {CUB} & DTD & {Flowers} & {Food} & {Pets} & {Dogs} & {SUN397}\\\\\n\\midrule\n\\textbf{Vanilla Transfer} & 79.7$\\pm$0.4 & 90.5$\\pm$0.4 & 83.8$\\pm$0.1 & 77.6$\\pm$0.1 & 71.6$\\pm$0.3 & 95.1$\\pm$0.0 & 83.5$\\pm$0.1 & 91.3$\\pm$0.0 & 83.9$\\pm$0.1 & 57.9$\\pm$0.2\\\\\n\\textbf{Mixed Transfer} & 70.7$\\pm$0.3 & 81.3$\\pm$0.5 & 80.1$\\pm$0.4& 69.8$\\pm$0.3 & 62.4$\\pm$0.5 & 87.8$\\pm$0.1 & 82.9$\\pm$0.2 & 82.5$\\pm$0.2 & 76.4$\\pm$0.4 & 50.6$\\pm$0.5\\\\\n\\textbf{Bridged Transfer} & 83.9$\\pm$0.2 & 89.7$\\pm$0.3 & 90.0$\\pm$0.2 & 79.7$\\pm$0.1 & 69.2$\\pm$0.1 & 96.0$\\pm$0.0 & 83.5$\\pm$0.1 & 91.1$\\pm$0.0 & 79.3$\\pm$0.2 & 60.3$\\pm$0.2\\\\\n\\textbf{+ FC Reinit} & 84.6$\\pm$0.5 & 90.7$\\pm$0.2 & 90.0$\\pm$0.0 & 79.7$\\pm$0.0\n& 71.7$\\pm$0.3 & 96.5$\\pm$0.0 & 83.9$\\pm$0.1 & 90.2$\\pm$0.1 & 79.9$\\pm$0.1 & 60.4$\\pm$0.2 \\\\\n\\textbf{+ FC Reinit \\& Mixup} & \\textbf{85.2$\\pm$0.0} & \\bf 91.3$\\pm$0.1 & \\bf 91.6$\\pm$0.1 & \\bf 80.1$\\pm$0.1 &\\bf 72.3$\\pm$0.3 & \\textbf{96.9$\\pm$0.1} & \\bf 84.2$\\pm$0.0 & \\bf 91.8$\\pm$0.0 & \\bf 86.6$\\pm$0.2 & \\bf 60.8$\\pm$0.2\\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization", "authors": ["Yuhang Li", "Xin Dong", "Chen Chen", "Jingtao Li", "Yuxin Wen", "Michael Spranger", "Lingjuan Lyu"], "url": "https://arxiv.org/abs/2403.19866v2", "attribution": "\"Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization\" by Yuhang Li, Xin Dong, Chen Chen, Jingtao Li, Yuxin Wen, Michael Spranger, and Lingjuan Lyu, arXiv:2403.19866v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2310.09844v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c|c|c|c|c|c}\n\t\t\t\\hline\n\t\t\t\\multirow{2}{*}{rule} \n\t\t\t& training & test\t&number \n\t\t\t&\\multicolumn{3}{c}{suboptimality}\\\\ \n\t\t\t\\cline{5-7} \n\t\t\t& data & data & feasible & min &avg & max\\\\\n\t\t\t\\hline\n\t\t\t$B^\\nu,b^\\nu$ & beta & unif & 33 &0.010 &0.048 &0.076\\\\\t\n\t\t\tMDR & beta & unif & 100 &0.033 &0.052 &0.095\\\\\t\n\t\t\tAMDR & beta & unif & 100 &0.000 &0.000 &0.007\\\\\t\t\t\t\n \\hline\n\t\t\\end{tabular}\n\\caption{Performance of decision rules obtain from ($\\beta$-SP1)$^\\nu$ against dispersed target. Suboptimality statistics are computed over feasible decisions.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Risk-Adaptive Local Decision Rules", "authors": ["Johannes O. Royset", "Miguel A. Lejeune"], "url": "https://arxiv.org/abs/2310.09844v2", "attribution": "\"Risk-Adaptive Local Decision Rules\" by Johannes O. Royset and Miguel A. Lejeune, arXiv:2310.09844v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.01048v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rrrr|rrrr|rrrr}\n & \\multicolumn{4}{c}{EW} & \\multicolumn{4}{c}{VW} & \\multicolumn{4}{c}{PW} \\\\\n \\hline\n & Pred & Avg & Std & SR & Pred & Avg & Std & SR & Pred & Avg & Std & SR \\\\\n \\hline\n $D1$ & -0.27 & -0.18 & 0.29 & -0.62 & -0.24 & -0.10 & 0.29 & -0.35 & -0.37 & -0.29 & 0.31 & -0.93 \\\\\n $D2$ & -0.11 & -0.03 & 0.23 & -0.14 & -0.11 & -0.03 & 0.22 & -0.12 & -0.13 & -0.04 & 0.24 & -0.18 \\\\\n $D3$ & -0.06 & 0.01 & 0.19 & 0.04 & -0.05 & 0.01 & 0.18 & 0.04 & -0.06 & 0.00 & 0.19 & 0.00 \\\\\n $D4$ & -0.02 & 0.04 & 0.17 & 0.25 & -0.02 & 0.04 & 0.17 & 0.22 & -0.02 & 0.04 & 0.17 & 0.23 \\\\\n $D5$ & 0.02 & 0.06 & 0.16 & 0.38 & 0.02 & 0.05 & 0.15 & 0.32 & 0.01 & 0.06 & 0.16 & 0.37 \\\\\n $D6$ & 0.04 & 0.08 & 0.16 & 0.49 & 0.04 & 0.06 & 0.15 & 0.44 & 0.05 & 0.08 & 0.16 & 0.50 \\\\\n $D7$ & 0.07 & 0.10 & 0.16 & 0.60 & 0.07 & 0.08 & 0.15 & 0.52 & 0.08 & 0.10 & 0.16 & 0.62 \\\\\n $D8$ & 0.11 & 0.12 & 0.16 & 0.76 & 0.11 & 0.11 & 0.15 & 0.69 & 0.12 & 0.13 & 0.17 & 0.78 \\\\\n $D9$ & 0.16 & 0.16 & 0.18 & 0.90 & 0.15 & 0.12 & 0.16 & 0.71 & 0.17 & 0.17 & 0.19 & 0.92 \\\\\n $D10$ & 0.29 & 0.37 & 0.26 & 1.46 & 0.23 & 0.19 & 0.21 & 0.89 & 0.43 & 0.69 & 0.36 & 1.93 \\\\\n LS & 0.50 & 0.50 & 0.16 & 3.18 & 0.42 & 0.24 & 0.21 & 1.12 & 0.75 & 0.93 & 0.25 & 3.68 \\\\\n \\hline\n & \\multicolumn{4}{c}{UW} & \\multicolumn{4}{c}{PUW1} & \\multicolumn{4}{c}{PUW10} \\\\\n \\hline\n & Pred & Avg & Std & SR & Pred & Avg & Std & SR & Pred & Avg & Std & SR \\\\\n \\hline\n $D1$ & -0.22 & -0.15 & 0.17 & -0.93 & -0.72 & -0.72 & 0.58 & -1.24 & -0.45 & -0.40 & 0.25 & -1.56 \\\\\n $D2$ & -0.11 & -0.05 & 0.13 & -0.36 & -0.15 & -0.08 & 0.18 & -0.43 & -0.13 & -0.06 & 0.14 & -0.44 \\\\\n $D3$ & -0.05 & -0.01 & 0.12 & -0.10 & -0.07 & -0.01 & 0.14 & -0.06 & -0.06 & -0.02 & 0.12 & -0.14 \\\\\n $D4$ & -0.02 & 0.02 & 0.11 & 0.19 & -0.03 & 0.03 & 0.12 & 0.25 & -0.02 & 0.02 & 0.11 & 0.19 \\\\\n $D5$ & 0.02 & 0.04 & 0.10 & 0.39 & 0.00 & 0.05 & 0.12 & 0.39 & 0.01 & 0.04 & 0.11 & 0.37 \\\\\n $D6$ & 0.04 & 0.06 & 0.10 & 0.63 & 0.06 & 0.07 & 0.11 & 0.63 & 0.05 & 0.07 & 0.10 & 0.65 \\\\\n $D7$ & 0.07 & 0.09 & 0.11 & 0.85 & 0.09 & 0.10 & 0.12 & 0.85 & 0.08 & 0.09 & 0.11 & 0.88 \\\\\n $D8$ & 0.11 & 0.11 & 0.11 & 1.02 & 0.12 & 0.13 & 0.12 & 1.06 & 0.12 & 0.12 & 0.11 & 1.12 \\\\\n $D9$ & 0.15 & 0.15 & 0.11 & 1.29 & 0.18 & 0.18 & 0.15 & 1.24 & 0.17 & 0.16 & 0.12 & 1.37 \\\\\n $D10$ & 0.23 & 0.24 & 0.13 & 1.76 & 0.91 & 2.01 & 1.42 & 1.41 & 0.40 & 0.50 & 0.24 & 2.06 \\\\\n LS & 0.39 & 0.34 & 0.11 & 2.97 & 1.57 & 2.67 & 1.48 & 1.80 & 0.80 & 0.84 & 0.26 & 3.27 \\\\\n \\hline\n & \\multicolumn{4}{c}{PUW20} & \\multicolumn{4}{c}{PUW100} & \\multicolumn{4}{c}{PUW250} \\\\\n \\hline\n & Pred & Avg & Std & SR & Pred & Avg & Std & SR & Pred & Avg & Std & SR \\\\\n \\hline\n$D1$ &-0.32 & -0.27 & 0.19 & -1.41 & -0.23 & -0.16 & 0.17 & -0.98 & -0.22 & -0.16 & 0.17 & -0.95 \\\\\n $D2$ & -0.12 & -0.06 & 0.14 & -0.40 & -0.11 & -0.05 & 0.14 & -0.37 & -0.11 & -0.05 & 0.14 & -0.36 \\\\\n $D3$ & -0.06 & -0.02 & 0.12 & -0.13 & -0.05 & -0.01 & 0.12 & -0.11 & -0.05 & -0.01 & 0.12 & -0.10 \\\\\n $D4$ & -0.02 & 0.02 & 0.11 & 0.19 & -0.02 & 0.02 & 0.11 & 0.19 & -0.02 & 0.02 & 0.11 & 0.19 \\\\\n $D5$ & 0.01 & 0.04 & 0.10 & 0.38 & 0.02 & 0.04 & 0.10 & 0.39 & 0.02 & 0.04 & 0.10 & 0.39 \\\\\n $D6$ & 0.05 & 0.07 & 0.10 & 0.64 & 0.05 & 0.06 & 0.10 & 0.63 & 0.04 & 0.06 & 0.10 & 0.63 \\\\\n $D7$ & 0.08 & 0.09 & 0.11 & 0.86 & 0.07 & 0.09 & 0.11 & 0.85 & 0.07 & 0.09 & 0.11 & 0.85 \\\\\n $D8$ & 0.11 & 0.12 & 0.11 & 1.09 & 0.11 & 0.11 & 0.11 & 1.04 & 0.11 & 0.11 & 0.11 & 1.03 \\\\\n $D9$ & 0.16 & 0.16 & 0.11 & 1.36 & 0.15 & 0.15 & 0.11 & 1.31 & 0.15 & 0.15 & 0.11 & 1.30 \\\\\n $D10$ & 0.29 & 0.32 & 0.16 & 2.00 & 0.24 & 0.24 & 0.14 & 1.81 & 0.23 & 0.24 & 0.13 & 1.78 \\\\\n LS & 0.56 & 0.53 & 0.15 & 3.66 & 0.41 & 0.35 & 0.12 & 3.08 & 0.40 & 0.34 & 0.11 & 3.01 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{In this table, we compare the economic performance of prediction sorted portfolios over the 30-year out-of-sample testing period for the ensemble GPR model with $\\gamma$-exponential kernel. We compare the performance of the decile portfolios corresponding to EW, VW, PW, UW and PUW strategies. We report the performance of PUW portfolios for different values, $\\{1, 10 20, 100, 250\\}$ of $\\zeta$, the uncertainty-aversion parameter. We also compare the long-short portfolios. For each portfolio, we report the predicted monthly returns (``Pred\"), the average realized monthly returns (``Avg\"), their standard deviations (``Std\"), and Sharpe ratios (``SR\"). We calculate these measures using realized simple excess returns of the portfolios over the test sample. The values of ``Avg\", ``Std\" and ``SR\" for the S\\&P 500 are 0.054, 0.150 and 0.360 respectively. All measures are annualized.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Empirical Asset Pricing via Ensemble Gaussian Process Regression", "authors": ["Damir Filipović", "Puneet Pasricha"], "url": "https://arxiv.org/abs/2212.01048v2", "attribution": "\"Empirical Asset Pricing via Ensemble Gaussian Process Regression\" by Damir Filipović and Puneet Pasricha, arXiv:2212.01048v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18002v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{soul}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll|lllll|lllll|lllll}\n\\hline\n\\multicolumn{20}{c}{\\textbf{A Uniform}}\\\\\n\\hline\n\\multicolumn{5}{c|}{Shifted sinusoidal $d=2$}\n&\\multicolumn{5}{c|}{Shifted sinusoidal $d=5$}\n& \\multicolumn{5}{c|}{Rosenbrock $d=2$}\n& \\multicolumn{5}{c}{Rosenbrock $d=5$}\n\\\\\\hline\n$k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \\\\ \n\\hline\n 10 & {\\hl {0.393}} & {\\hl {0.456}} & 1.63 & 2.769 & \n 10 & 0.149 & 0.092 & 2.811 & 3.411 & \n 10 & 0.491 & 0.489 & 8.298 & 35.649 & \n 10 & 0.07 & 0.07 & 157.848 & 1012.868 \\\\ \n 18 & 0.135 & 0 & 0.998 & 2.229 & \n 15 & {\\hl {0.552}} & {\\hl {0.571}} & 2.67 & 2.961 & \n 15 & 0.41 & 0.406 & 8.298 & 35.649 & \n 15 & {\\hl {0.171}} & {\\hl {0.172}} & 105.59 & 287.408 \\\\ \n 25 & 0.23 & 0.129 & 0.998 & 1.912 & \n 32 & 0.176 & 0.173 & 2.594 & 3.347 & \n 25 & 0.145 & 0.113 & 3.834 & 35.649 & \n 25 & 0.157 & 0.156 & 105.59 & 305.584 \\\\ \n 35 & 0.014 & 0 & 0.108 & 1.847 &\n 38 & 0.202 & 0.182 & 2.594 & 3.287 & \n 45 & 0.087 & 0.069 & 3.017 & 60.589 & \n 35 & 0.146 & 0.135 & 105.59 & 305.584 \\\\ \n 87 & 0.018 & 0 & 0.108 & 1.762 & \n 47 & 0.199 & 0.176 & 2.594 & 3.284 & \n 51 & 0.057 & 0.037 & 2.17 & 58.605 & \n 45 & 0.072 & 0.065 & 105.59 & 497.471 \\\\ \n 105 & 0.001 & 0 & 0.008 & 1.722 & \n 57 & 0.197 & 0.181 & 2.594 & 3.287 & \n 99 & {\\hl {0.01}} & {\\hl {0.012}} & 0.015 & 57.96 & \n 55 & 0.072 & 0.064 & 105.59 & 509.156 \\\\ \n 113 & 0.001 & 0 & 0.008 & 1.56 & \n 67 & 0.066 & 0.043 & 1.92 & 3.284 & \n 115 & {\\hl {0.009}} & {\\hl {0.01}} & 0.015 & 29.507 & \n 65 & 0.072 & 0.066 & 105.59 & 497.471 \\\\ \n 123 & 0.001 & 0 & 0.008 & 1.108 & \n 120 & 0.042 & 0.001 & 1.92 & 3.272 & \n 128 & {\\hl {0.016}} &{\\hl { 0.019}} & 0.015 & 14.095 & \n 85 & 0.016 & 0.014 & 43.581 & 384.793 \\\\ \n 136 & 0.002 & 0 & 0.008 & 0.838 & \n 143 & 0.015 & 0 & 1.505 & 3.082 & \n 188 & {\\hl {0.018}} &{\\hl { 0.022}} & 0.015 & 12.251 & \n 149 & 0.013 & 0.013 & 40.763 & 385.923 \\\\ \n 151 & 0 & 0 & 0.008 & 0.521 & \n 156 & 0.003 & 0 & 1.168 & 2.978 & \n 252 & {\\hl {0.016}} & {\\hl {0.019}} & 0.015 & 14.095 & \n 169 & 0.012 & 0.012 & 39.685 & 384.793 \\\\ \n 166 & 0 & 0 & 0.002 & 0.361 & \n 214 & 0.004 & 0 & 1.168 & 2.871 & & & & & & \n 185 & 0.002 & 0.002 & 18.616 & 340.773 \\\\ \n 232 & 0 & 0 & 0.001 & 0.398 & \n 257 & 0.004 & 0 & 1.168 & 2.742 & & & & & & \n 246 & 0.002 & 0.002 & 18.616 & 379.67 \\\\ \n 264 & 0 & 0 & 0.001 & 0.348 & & & & & & & & & & & \n 268 & 0.002 & 0.002 & 18.616 & 332.86 \\\\ \n & & & & & & & & & & & & & & & & & & & \\\\ \n \\hline\n\\multicolumn{20}{c}{\\textbf{B Gaussian Process}}\\\\ \n \\hline\n\\multicolumn{5}{c|}{Shifted sinusoidal $d=2$}\n&\\multicolumn{5}{c|}{Shifted sinusoidal $d=5$}\n& \\multicolumn{5}{c|}{Rosenbrock $d=2$}\n& \\multicolumn{5}{c}{Rosenbrock $d=5$}\n\\\\\\hline\n$k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \\\\ \n\\hline\n 10 & {\\hl {0.393}} & {\\hl {0.456}} & 1.63 & 2.769 & \n 20 & 0.077 & 0.055 & 1.883 & 3.209 & \n 10 & 0.491 & 0.489 & 8.298 & 35.649 & \n 10 & 0.07 & 0.07 & 157.848 & 1012.868 \\\\ \n 64 & 0.094 & 0 & 1.002 & 2.636 & \n 74 & 0.04 & 0.015 & 1.883 & 3.517 & \n 15 & 0.41 & 0.406 & 8.298 & 35.649 & \n 60 & 0.032 & 0.03 & 157.848 & 2414.04 \\\\ \n 116 & 0.161 & 0.009 & 1.002 & 2.116 & \n 140 & 0.002 & 0.001 & 1.207 & 3.533 &\n 25 & 0.145 & 0.113 & 3.834 & 35.649 & \n 117 & 0.028 & 0.024 & 157.848 & 2207.16 \\\\ \n 174 & 0.158 & 0.101 & 1.002 & 2.116 & \n 202 & 0.002 & 0 & 1.207 & 3.637 & \n 53 & 0.073 & 0.055 & 3.62 & 72.723 &\n 175 & 0.009 & 0.005 & 82.823 & 2085.52 \\\\ \n 227 & 0.158 & 0.088 & 1.002 & 2.145 & \n 261 & 0.001 & 0 & 1.207 & 3.686 & \n 59 & 0.189 & 0.161 & 3.62 & 35.649 & \n 230 & 0.009 & 0.006 & 82.823 & 1988.828 \\\\ \n 259 & 0.182 & 0.13 & 1.002 & 2.116 & & & & & & \n 68 & 0.067 & 0.061 & 0.416 & 15.787 & \n 256 & 0.009 & 0.005 & 82.823 & 2045.24 \\\\ \n & & & & & & & & & & \n 122 & 0.049 & 0.048 & 0.416 & 35.649 & & & & & \\\\ \n & & & & & & & & & & \n 186 & 0.061 & 0.062 & 0.416 & 33.315 & & & & & \\\\ \n & & & & & & & & & & \n 256 & {\\hl {0.064}} & {\\hl {0.065}} & 0.416 & 31.607 & & & & & \\\\ \n & & & & & & & & & & & & & & & & & & & \\\\ \n \\hline\n\\multicolumn{20}{c}{\\textbf{C Quadratic Regression}}\\\\ \n \\hline\n\\multicolumn{5}{c|}{Shifted sinusoidal $d=2$}\n&\\multicolumn{5}{c|}{Shifted sinusoidal $d=5$}\n& \\multicolumn{5}{c|}{Rosenbrock $d=2$}\n& \\multicolumn{5}{c}{Rosenbrock $d=5$}\n\\\\\\hline\n$k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \\\\ \n\\hline\n 10 & {\\hl {0.393}} & {\\hl {0.456}} & 1.63 & 2.769 & \n 10 & 0.149 & 0.092 & 2.811 & 3.411 & \n 10 & 0.491 & 0.489 & 8.298 & 35.649 & \n 10 & 0.07 & 0.07 & 157.848 & 1012.868 \\\\ \n 18 & 0.135 & 0 & 0.998 & 2.229 & \n 15 & {\\hl {0.552}}& {\\hl {0.571}} & 2.67 & 2.961 & \n 15 & 0.41 & 0.406 & 8.298 & 35.649 & \n 15 & {\\hl {0.171}} & {\\hl {0.172}} & 105.59 & 287.408 \\\\ \n 25 & 0.23 & 0.129 & 0.998 & 1.912 & \n 32 & 0.176 & 0.173 & 2.594 & 3.347 & \n 25 & 0.145 & 0.113 & 3.834 & 35.649 & \n 25 & 0.157 & 0.156 & 105.59 & 305.584 \\\\ \n 35 & 0.014 & 0 & 0.108 & 1.847 & \n 38 & 0.202 & 0.182 & 2.594 & 3.287 & \n 45 & 0.087 & 0.069 & 3.017 & 60.589 & \n 35 & 0.146 & 0.135 & 105.59 & 305.584 \\\\ \n 87 & 0.018 & 0 & 0.108 & 1.762 &\n 47 & 0.199 & 0.176 & 2.594 & 3.284 & \n 51 & 0.057 & 0.037 & 2.17 & 58.605 & \n 45 & 0.072 & 0.065 & 105.59 & 497.471 \\\\ \n 105 & 0.001 & 0 & 0.008 & 1.722 & \n 57 & 0.197 & 0.181 & 2.594 & 3.287 & \n 99 &{\\hl { 0.01}} & {\\hl {0.012}} & 0.015 & 57.96 & \n 55 & 0.072 & 0.064 & 105.59 & 509.156 \\\\ \n 113 & 0.001 & 0 & 0.008 & 1.56 & \n 67 & 0.066 & 0.043 & 1.92 & 3.284 & \n 115 & {\\hl {0.009}} & {\\hl {0.01}} & 0.015 & 29.507 &\n 65 & 0.072 & 0.066 & 105.59 & 497.471 \\\\ \n 123 & 0.001 & 0 & 0.008 & 1.108 &\n 120 & 0.042 & 0.001 & 1.92 & 3.272 & \n 128 & {\\hl {0.016}} & {\\hl {0.019}} & 0.015 & 14.095 & \n 85 & 0.016 & 0.014 & 43.581 & 384.793 \\\\ \n 136 & 0.002 & 0 & 0.008 & 0.838 & \n 143 & 0.015 & 0 & 1.505 & 3.082 & \n 188 & {\\hl {0.016}} & {\\hl {0.018}} & 0.015 & 14.4 & \n 149 & 0.013 & 0.013 & 40.763 & 385.923 \\\\ \n 151 & 0 & 0 & 0.008 & 0.521 & \n 156 & 0.003 & 0 & 1.168 & 2.978 & \n 248 & {\\hl {0.016}} & {\\hl {0.018}} & 0.015 & 14.825 & \n 169 & 0.012 & 0.012 & 39.685 & 384.793 \\\\ \n 166 & 0 & 0 & 0.002 & 0.361 & \n 214 & 0.004 & 0 & 1.168 & 2.871 & \n 261 & 0.02 & 0.022 & 0.015 & 9.111 & \n 185 & 0.002 & 0.002 & 18.616 & 340.773 \\\\ \n 232 & 0 & 0 & 0.001 & 0.398 & \n 257 & 0.004 & 0 & 1.168 & 2.742 & & & & & & \n 246 & 0.002 & 0.002 & 18.616 & 379.67 \\\\ \n 264 & 0 & 0 & 0.001 & 0.348 & & & & & & & & & & & \n 268 & 0.002 & 0.002 & 18.616 & 332.86 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Branching Adaptive Surrogate Search Optimization (BASSO)", "authors": ["Pariyakorn Maneekul", "Zelda B. Zabinsky", "Giulia Pedrielli"], "url": "https://arxiv.org/abs/2504.18002v1", "attribution": "\"Branching Adaptive Surrogate Search Optimization (BASSO)\" by Pariyakorn Maneekul, Zelda B. Zabinsky, and Giulia Pedrielli, arXiv:2504.18002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17626v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Example table.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|} \\hline\n Species & \\bf Mean & \\bf Std.~Dev. \\\\ \\hline\n 1 & 3.4 & 1.2 \\\\\n 2 & 5.4 & 0.6 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Preconditioned normal equations for solving discretised partial differential equations", "authors": ["Lorenzo Lazzarino", "Yuji Nakatsukasa", "Umberto Zerbinati"], "url": "https://arxiv.org/abs/2502.17626v2", "attribution": "\"Preconditioned normal equations for solving discretised partial differential equations\" by Lorenzo Lazzarino, Yuji Nakatsukasa, and Umberto Zerbinati, arXiv:2502.17626v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table20.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc|cc|cc}\nIteration & $G^\\uparrow$ (s) & $G^\\downarrow$ & $x^\\uparrow$ (v/min) & $x^\\downarrow$ & $t^\\uparrow$ (min) & $t^\\downarrow$ & $X^\\uparrow$ & $X^\\downarrow$ \\\\\n\\hline\n0 &48 &12 &23.6 &11.4 &2.11 &2.11 &0.982 &0.953 \\\\\n1 &48.3 &11.7 &23.8 &11.2 &2.26 &2.26 &0.984 &0.959 \\\\\n2 &48.5 &11.5 &23.9 &11.1 &2.43 &2.43 &0.986 &0.965 \\\\\n3 &48.7 &11.3 &24.1 &10.9 &2.63 &2.63 &0.988 &0.969 \\\\\n4 &48.9 &11.1 &24.2 &10.8 &2.86 &2.86 &0.99 &0.974 \\\\\n5 &49.1 &10.9 &24.3 &10.7 &3.12 &3.12 &0.991 &0.977 \\\\\n10 &49.5 &10.5 &24.7 &10.3 &5.11 &5.11 &0.996 &0.989 \\\\\n20 &49.88 &10.12 &24.91 &10.09 &16.58 &16.58 &0.9988 &0.9971 \\\\\n50 &49.998 &10.002 &24.998 &10.002 &855.92 &855.93 &0.99998 &0.99995 \\\\\n\\hline\n$\\infty$ &50 &10 &25 &10 &$\\infty$&$\\infty$ &1 &1 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09574v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{rotating}\n\\usepackage{amsfonts}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\\hline\n\\hline\n & &PCF&TANet&CPFP&DMRA&D3Net&MCI-Net&Ours\\\\\n &Metric&CVPR18&TIP19\tCVPR19&ICCV19&TNNLS20&Neurocomputing21&21\\\\\n & &[7]&[38]&[6]&[5]&[50]&[58]& \\\\\n\\hline\n\\hline\n\\multirow{8}*{\\rotatebox{90}{\\emph{SSD} \n}} &$S_\\alpha$↑ &0.841 & 0.839 & 0.807 & 0.857 & 0.865 & 0.860 & \\textbf{0.880} \\\\\n &max $F_\\beta$↑ &0.807 & 0.810 & 0.766 & 0.844 & 0.846 & - & \\textbf{0.868} \\\\\n &mean $F_\\beta$↑ &0.777 & 0.773 & 0.747 & 0.828 & 0.815 & 0.820 & \\textbf{0.843} \\\\\n &adp $F_\\beta$↑ &0.791 & 0.767 & 0.726 & 0.821 & 0.790 & - & \\textbf{0.836} \\\\\n &max $E_\\xi$↑ &0.894 & 0.897 & 0.852 & 0.906 & 0.907 & - & \\textbf{0.918} \\\\\n &mean $E_\\xi$↑ &0.856 & 0.861 & 0.839 & 0.897 & 0.886 & 0.901 & \\textbf{0.902} \\\\\n &adp $E_\\xi$↑ &0.886 & 0.879 & 0.832 & 0.892 & 0.885 & - & \\textbf{0.901} \\\\\n &$\\mathcal{M}$↓ &0.062 & 0.063 & 0.082 & 0.058 & 0.059 & 0.052 & \\textbf{0.050} \\\\\n\\hline\n\\multirow{8}*{\\rotatebox{90}{\\emph{SIP} \n}} &$S_\\alpha$↑ &0.842 & 0.835 & 0.850 & 0.806 & 0.864 & 0.867 & \\textbf{0.879} \\\\\n &max $F_\\beta$↑ &0.838 & 0.830 & 0.851 & 0.821 & 0.861 & - & \\textbf{0.884} \\\\\n &mean $F_\\beta$↑ &0.814 & 0.803 & 0.821 & 0.811 & 0.830 & 0.840 & \\textbf{0.867} \\\\\n &adp $F_\\beta$↑ &0.825 & 0.809 & 0.819 & 0.819 & 0.829 & - & \\textbf{0.870} \\\\\n &max $E_\\xi$↑ &0.901 & 0.895 & 0.903 & 0.875 & 0.910 & - & \\textbf{0.920} \\\\\n &mean $E_\\xi$↑ &0.878 & 0.870 & 0.893 & 0.844 & 0.893 & \\textbf{0.909} & 0.903 \\\\\n &adp $E_\\xi$↑ &0.899 & 0.893 & 0.899 & 0.863 & 0.901 & - & \\textbf{0.914} \\\\\n &$\\mathcal{M}$↓ &0.071 & 0.075 & 0.064 & 0.085 & 0.063 & 0.056 & \\textbf{0.056} \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection", "authors": ["Ze-yu Liu", "Jian-wei Liu", "Xin Zuo", "Ming-fei Hu"], "url": "https://arxiv.org/abs/2201.09574v1", "attribution": "\"Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection\" by Ze-yu Liu, Jian-wei Liu, Xin Zuo, and Ming-fei Hu, arXiv:2201.09574v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.07363v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Robust mean estimation for $n =200$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nMethod & Estimation error & Run-time\\\\\n\\hline \nROBOT-Sinkhorn & $0.196 \\pm 0.1$ & $35s \\pm 0.6s$\\\\\n & $0.212 \\pm 0.074$ & $7745s \\pm 1670s$\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Outlier-Robust Optimal Transport", "authors": ["Debarghya Mukherjee", "Aritra Guha", "Justin Solomon", "Yuekai Sun", "Mikhail Yurochkin"], "url": "https://arxiv.org/abs/2012.07363v2", "attribution": "\"Outlier-Robust Optimal Transport\" by Debarghya Mukherjee, Aritra Guha, Justin Solomon, Yuekai Sun, and Mikhail Yurochkin, arXiv:2012.07363v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.13777v3_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n\t\t\\cmidrule[\\heavyrulewidth]{2-3} & $\\alpha = 0.05$ & $\\alpha = 0.01$ \\\\\n\t\t\\toprule\n\t\tbenchmark & 0\\% & 0\\% \\\\\n\t\t\\midrule\n\t\tdeepLOB(L1, universal) & 0\\% & 0\\% \\\\\n\t\t\\midrule\n\t\tdeepOF(L1, universal) & 0\\% & 0\\% \\\\\n\t\t\\midrule\n\t\tdeepLOB(L2, universal) & 8\\% & 24\\% \\\\\n\t\t\\midrule\n\t\tdeepOF(L2, universal) & 42\\% & 62\\% \\\\\n\t\t\\midrule\n\t\tdeepVOL(L2, universal) & 71\\% & 81\\% \\\\\n\t\t\\midrule\n\t\tdeepVOL(L3, universal) & 92\\% & 100\\% \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{\\% of times each model is in the $\\alpha$-MCS when predictability is identified at the corresponding level $\\alpha$.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective", "authors": ["Lorenzo Lucchese", "Mikko Pakkanen", "Almut Veraart"], "url": "https://arxiv.org/abs/2211.13777v3", "attribution": "\"The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective\" by Lorenzo Lucchese, Mikko Pakkanen, and Almut Veraart, arXiv:2211.13777v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2007.07013v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|}\n\\hline\nMethod & Frobnability \\\\\n\\hline\\hline\nTheirs & Frumpy \\\\\nYours & Frobbly \\\\\nOurs & Makes one's heart Frob\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results. Ours is better.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Pose2RGBD. Generating Depth and RGB images from absolute positions", "authors": ["Mihai Cristian Pîrvu"], "url": "https://arxiv.org/abs/2007.07013v1", "attribution": "\"Pose2RGBD. Generating Depth and RGB images from absolute positions\" by Mihai Cristian Pîrvu, arXiv:2007.07013v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.01502v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Power Delay Profile for Modified Echo Channel}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c||} \n\t\t\\hline\n\t\tPath & Amplitude(dB) & Delay($\\mu s$) \\\\\n\t\t\\hline\\hline\n\t\t1 & 0 & 0\\\\ \n\t 2 & 0 & $\\alpha$*$T_{cp}$ \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Mobility and Coverage Evaluation of New Radio PDCCH for Point-to-Multipoint Scenario", "authors": ["Hongzhi Chen", "De Mi", "Belkacem Mouhouche", "Pei Xiao", "Rahim Tafazolli"], "url": "https://arxiv.org/abs/1912.01502v1", "attribution": "\"Mobility and Coverage Evaluation of New Radio PDCCH for Point-to-Multipoint Scenario\" by Hongzhi Chen, De Mi, Belkacem Mouhouche, Pei Xiao, and Rahim Tafazolli, arXiv:1912.01502v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2010.13892v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n & Predicted NO & Predicted YES\\tabularnewline\n\\hline\n\\hline\nTrue NO & 2101 & 4\\tabularnewline\n\\hline\nTrue YES & 47 & 0\\tabularnewline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction", "authors": ["Amir Mukeri", "Habibullah Shaikh", "D. P. Gaikwad"], "url": "https://arxiv.org/abs/2010.13892v2", "attribution": "\"Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction\" by Amir Mukeri, Habibullah Shaikh, and D. P. Gaikwad, arXiv:2010.13892v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14565v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n \\textbf{asset} & \\textbf{Our approach}& \\\\ \\midrule\n 1 & 0.0& 0.0\\\\\n 2 & 0.0 & 0.0 \\\\\n 3 & 0.2549& 0.2621 \\\\\n 4 & 0.0022 & 0.0 \\\\\n 5 & 0.0& 0.0\\\\\n 6 & 0.3763 & 0.4081 \\\\\n 7 & 0.2485 & 0.2403 \\\\\n 8 &0.1180 & 0.0892 \\\\ \\midrule\n \\multicolumn{1}{c}\\textbf{objective}&11.03 $\\%$&11.02$\\%$ \\\\ \\midrule\n \\multicolumn{1}{c}\\textbf{number of TSD constraints}&\\textbf{3}& 508\\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Third order stochastic dominance (case $p=2$): a comparison with }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Higher-Order Stochastic Dominance Constraints in Optimization", "authors": ["Rajmadan Lakshmanan", "Alois Pichler", "Miloš Kopa"], "url": "https://arxiv.org/abs/2501.14565v3", "attribution": "\"Higher-Order Stochastic Dominance Constraints in Optimization\" by Rajmadan Lakshmanan, Alois Pichler, and Miloš Kopa, arXiv:2501.14565v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.07482v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\bfseries Dataset & \\bfseries Result\\\\\n Data1 & 0.12345\\\\\n Data2 & 0.67890\\\\\n Data3 & 0.54321\\\\\n Data4 & 0.09876\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Towards Trainable Saliency Maps in Medical Imaging", "authors": ["Mehak Aggarwal", "Nishanth Arun", "Sharut Gupta", "Ashwin Vaswani", "Bryan Chen", "Matthew Li", "Ken Chang", "Jay Patel", "Katherine Hoebel", "Mishka Gidwani", "Jayashree Kalpathy-Cramer", "Praveer Singh"], "url": "https://arxiv.org/abs/2011.07482v1", "attribution": "\"Towards Trainable Saliency Maps in Medical Imaging\" by Mehak Aggarwal, Nishanth Arun, Sharut Gupta, Ashwin Vaswani, Bryan Chen, Matthew Li, Ken Chang, Jay Patel, Katherine Hoebel, Mishka Gidwani, Jayashree Kalpathy-Cramer, and Praveer Singh, arXiv:2011.07482v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.02357v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|}\nRibbon Graph Property & Combinatorial Map Property\\\\\n\\hline\nNumber of Vertices & $c(\\sigma)$\\\\\n\\hline\nNumber of Edges & $c(\\alpha)$\\\\\n\\hline\nNumber of Faces & $c(\\varphi)$\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Classification of Minimal Separating Sets of Low Genus Surfaces", "authors": ["Christopher N. Aagaard", "J. J. P. Veerman"], "url": "https://arxiv.org/abs/2312.02357v2", "attribution": "\"Classification of Minimal Separating Sets of Low Genus Surfaces\" by Christopher N. Aagaard and J. J. P. Veerman, arXiv:2312.02357v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18896v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n Contamination $\\delta$ & $0$ & $0.5$ & $1$\\\\\n \\midrule\n Global shift &$5/1000$& $291/1000$ & $1000/1000$ \\\\\n Local shift of level $l = 10$ & - & $13/1000$ & $612/1000$ \\\\\n Local shift of level $l = 13$ & - &$128/1000$& $994/1000$ \\\\\n Local shift of level $l = 15$ & - & $3/1000$ & $270/1000$ \\\\\n \\bottomrule \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Power of the performed statistical tests with confidence level $1-\\alpha = 0.95$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Calibration Bands for Mean Estimates within the Exponential Dispersion Family", "authors": ["Łukasz Delong", "Selim Gatti", "Mario V. Wüthrich"], "url": "https://arxiv.org/abs/2503.18896v1", "attribution": "\"Calibration Bands for Mean Estimates within the Exponential Dispersion Family\" by Łukasz Delong, Selim Gatti, and Mario V. Wüthrich, arXiv:2503.18896v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12784v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lll}\n\t\t\tSymbol & Value & Dimension \\\\\n\t\t\t\\hline\n\t\t\t$\\phi$ & 0.41 & - \\\\\n\t\t\t$K$ & $2.89\\cdot 10^{-11}$ & m$^2$ \\\\\n\t\t\t$D_\\mathrm{w}^\\mathrm{NaCl}$ & $1.5\\cdot10^{-9}$ & m$^{2}$ s$^{-1}$\\\\ \n\t\t\t$n$ & 10.8 & - \\\\\n\t\t\t$m$ & 0.907 & -\\\\\n\t\t\t$S_{wr}$ & 0.122& - \\\\\n\t\t\t$L$ & 0.73 & - \\\\\n\t\t\t$p_B$ & 2464 & kg m$^{-1}$ s$^{-2}$\\\\\n\t\t\t$\\alpha$ & $1.77\\cdot10^{-4}$ & (kg m$^{-1}$ s$^{-2}$)$^{-1}$\\\\\n\t\t\t$E$ & $1.08\\cdot10^{-8}$ & m s$^{-1}$\\\\\n\t\t\t$H$ & 0.2 & m\\\\\n\t\t\t$\\ell$ & 0.6 & m\\\\\n\t\t\t$g$ & 9.8 & m s$^{-2}$\\\\\n\t\t\t$\\mathsf x_\\mathrm{w}^\\mathrm{NaCl}|_{t=0}$ & 0.0036 & mol mol$^{-1}$\\\\ % 0.0116 in kg/kg\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\caption{Overview of values used in base case.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Impact of saturation on evaporation-driven density instabilities in porous media: mathematical and numerical analysis", "authors": ["C. Bringedal", "S. Kiemle", "C. J. van Duijn", "R. Helmig"], "url": "https://arxiv.org/abs/2501.12784v1", "attribution": "\"Impact of saturation on evaporation-driven density instabilities in porous media: mathematical and numerical analysis\" by C. Bringedal, S. Kiemle, C. J. van Duijn, and R. Helmig, arXiv:2501.12784v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08246v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Frequency of Special Characters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccl}\n \\toprule\n Non-English or Math&Frequency&Comments\\\\\n \\midrule\n \\O & 1 in 1,000& For Swedish names\\\\\n $\\pi$ & 1 in 5& Common in math\\\\\n \\$ & 4 in 5 & Used in business\\\\\n $\\Psi^2_1$ & 1 in 40,000& Unexplained usage\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "\"Project smells\" -- Experiences in Analysing the Software Quality of ML Projects with mllint", "authors": ["Bart van Oort", "Luís Cruz", "Babak Loni", "Arie van Deursen"], "url": "https://arxiv.org/abs/2201.08246v1", "attribution": "\"\"Project smells\" -- Experiences in Analysing the Software Quality of ML Projects with mllint\" by Bart van Oort, Luís Cruz, Babak Loni, and Arie van Deursen, arXiv:2201.08246v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12565v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|l|l}\n $n$ & Max sets with $n$ cards & $C(81,n)$ configurations to check & Computational Time \\\\ \\hline \n $3$ & $1$ & $85320$ & $<1$ second \\\\\n $4$ & $1$ & $1,663,740$ & ~ $6$ seconds \\\\\n $5$ & $2$ & $25,621,596$ & ~ $4$ minutes \\\\\n $6$ & $3$ & $324,540,216$ & ~ $1$ hour, $33$ minutes \\\\\n $7$ & $5$ & $3,477,216,600$ & $29$ hours, $12$ minutes\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The maximum number of sets for $3\\le n\\le 7$ by computer proof.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Maximum Number of Sets for 12 Cards is 14", "authors": ["Justin Stevens", "Duncan Wilson"], "url": "https://arxiv.org/abs/2501.12565v1", "attribution": "\"The Maximum Number of Sets for 12 Cards is 14\" by Justin Stevens and Duncan Wilson, arXiv:2501.12565v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06560v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule \n Attackers & 0 & 1 & 2 & 3 \\\\\n \\midrule \n Train On 0 & 89.93 & 52.31 & 28.31 & 12.07 \\\\\n Train On 1 & \\textbf{90.09} & \\textbf{90.00} & 81.95 & 75.28 \\\\\n Train On 2 & 89.71 & 89.68 & 88.91 & 88.33 \\\\\n Train On 3 & 89.55 & 89.51 & \\textbf{88.94} & \\textbf{88.51} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Adversarial training with multiple attackers in the V2V setting. We train on settings with various number of attackers and evaluate the models across the settings.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Adversarial Attacks On Multi-Agent Communication", "authors": ["James Tu", "Tsunhsuan Wang", "Jingkang Wang", "Sivabalan Manivasagam", "Mengye Ren", "Raquel Urtasun"], "url": "https://arxiv.org/abs/2101.06560v2", "attribution": "\"Adversarial Attacks On Multi-Agent Communication\" by James Tu, Tsunhsuan Wang, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren, and Raquel Urtasun, arXiv:2101.06560v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09103v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The first value in brackets denotes Player 1's payoff, and the second Player 2's. The bold values correspond to the payoffs of the typical NE strategies.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|cc|} \n \\cline{3-4}\n & & \\multicolumn{2}{c|} {Player 2} \\\\\n & & a & b \\\\ \\cline{1-4}\n \\multirow{2}{*}{Player 1} & a & (p, p) & (\\textbf{q}, \\textbf{r}) \\\\\n & b & (\\textbf{r}, \\textbf{q}) & (s, s) \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Nash Equilibria in certain two-choice multi-player games played on the ladder graph", "authors": ["Victoria Sánchez Muñoz", "Michael Mc Gettrick"], "url": "https://arxiv.org/abs/2101.09103v1", "attribution": "\"Nash Equilibria in certain two-choice multi-player games played on the ladder graph\" by Victoria Sánchez Muñoz and Michael Mc Gettrick, arXiv:2101.09103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12491v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\nMethod & Year & Test Error {[}\\%{]} \\\\ \\hline\nMulti-Column Deep Neural Networks for Image Classification & 2012 & 0.23 \\\\\nRegularization of Neural Networks using DropConnect & 2013 & 0.21 \\\\ \nRMDL:Random Multimodel Deep Learning for Classification & 2018 & 0.18 \\\\ \nBase-Branching \\& Merging CNNw/HFCs & 2020 & 0.16 \\\\\nEfficient-CapsNet & 2021 & 0.16 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Test error (\\%) on the MNIST classification task of state-of-the-art methodologies based on ensemble over the years.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Efficient-CapsNet: Capsule Network with Self-Attention Routing", "authors": ["Vittorio Mazzia", "Francesco Salvetti", "Marcello Chiaberge"], "url": "https://arxiv.org/abs/2101.12491v2", "attribution": "\"Efficient-CapsNet: Capsule Network with Self-Attention Routing\" by Vittorio Mazzia, Francesco Salvetti, and Marcello Chiaberge, arXiv:2101.12491v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.14130v2_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n(n,m)&$ (1000,10)$& $(3000,20)$ & $(5000,20)$\\\\ \\hline\nBenchmark & 15.27s (3.48s) & 194.8s (24.4s) & 744.5s (107.5s) \\\\\n Random & 2.37s (1.39s) & 6.4s (2.0s) & 11.3s (2.5s)\\\\\nSpatial & 2.36s (1.60s) & 6.0s (1.5s) & 10.7s (2.4s)\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\scriptsize Data-based rescaled (MMLE) squared exponential Gaussian process prior. Average run time for the computation of the posterior. Benchmark: Non-distributed method. Method 1: Random partitioning, Method 2: Spatial partitioning.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Adaptation using spatially distributed Gaussian Processes", "authors": ["Botond Szabo", "Amine Hadji", "Aad van der Vaart"], "url": "https://arxiv.org/abs/2312.14130v2", "attribution": "\"Adaptation using spatially distributed Gaussian Processes\" by Botond Szabo, Amine Hadji, and Aad van der Vaart, arXiv:2312.14130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2401.08663v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{} & \\textbf{P-mse ($rad^2/s^2$)} & \\textbf{Q-mse ($rad^2/s^2$)} & \\textbf{R-mse ($rad^2/s^2$)} & \\textbf{PQR-mse ($rad^2/s^2$)} \\\\ \\hline\n\\textbf{Split-S Maneuver} & 0.0125 & 0.0030 & 7.5693e-5 & 0.0052 \\\\ \\hline\n\\textbf{Chandelle Maneuver} & 0.0481 & 0.0174 & 0.0021 & 0.0226 \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data", "authors": ["Gulay Goktas Sever", "Umut Demir", "Abdullah Sadik Satir", "Mustafa Cagatay Sahin", "Nazim Kemal Ure"], "url": "https://arxiv.org/abs/2401.08663v1", "attribution": "\"An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data\" by Gulay Goktas Sever, Umut Demir, Abdullah Sadik Satir, Mustafa Cagatay Sahin, and Nazim Kemal Ure, arXiv:2401.08663v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.16403v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Uncertainty in imputed values when the data was generated with an assumed rank of 15.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\hline\ns2n & Missingness Type & Mean RSE & Mean Coverage & Mean CI Width \\\\ \n \\hline\n9 & Entrywise & 0.2261 & 0.9350 & 5.5594 \\\\ \n 9 & Blockwise & 0.6384 & 0.6283 & 4.2770 \\\\ \n 9 & MNAR & 0.5729 & 0.3897 & 6.3075 \\\\ \n \\hline \n 3 & Entrywise & 0.4602 & 0.9133 & 4.6483 \\\\ \n 3 & Blockwise & 0.7633 & 0.7656 & 4.0584 \\\\ \n 3 & MNAR & 0.8706 & 0.2846 & 4.5944 \\\\ \n \\hline \n 1 & Entrywise & 0.7903 & 0.9039 & 4.1939 \\\\ \n 1 & Blockwise & 0.9166 & 0.8533 & 3.9010 \\\\ \n 1 & MNAR & 1.3614 & 0.2599 & 3.3007 \\\\ \n \\hline \n 0.333 & Entrywise & 0.9773 & 0.9156 & 3.9481 \\\\ \n 0.333 & Blockwise & 0.9923 & 0.8997 & 3.7835 \\\\ \n 0.333 & MNAR & 1.2615 & 0.3140 & 3.0191 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data", "authors": ["Sarah Samorodnitsky", "Chris H. Wendt", "Eric F. Lock"], "url": "https://arxiv.org/abs/2211.16403v2", "attribution": "\"Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data\" by Sarah Samorodnitsky, Chris H. Wendt, and Eric F. Lock, arXiv:2211.16403v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11653v3_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c||} \n \\hline\n Name & Conditions\\\\ \n \\hline\n \\hline\n SGD combined & SGD batch size = 1 and SGD momentum = 0.9\\\\\n \\hline \n Adam combined & Adam single and Adam 768 neurons in the single hidden layer\\\\\n \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Experimental details for the combined model of SGD and Adam}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Representations learnt by SGD and Adaptive learning rules: Conditions that vary sparsity and selectivity in neural networks", "authors": ["Jin Hyun Park"], "url": "https://arxiv.org/abs/2201.11653v3", "attribution": "\"Representations learnt by SGD and Adaptive learning rules: Conditions that vary sparsity and selectivity in neural networks\" by Jin Hyun Park, arXiv:2201.11653v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09827v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Matrices from used for numerical experiments and their properties.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\t\t\\hline\n\t\tName & Size & nnz & $\\kappa_\\infty(A)$ & Group & Kind \\\\ \\hline\n\t\torsirr\\_1 & 1030 & 1030 & 9.96E+04 & HB & Computational Fluid Dynamics Problem \\\\\n\t\tcomsol & 1500 & 1500 & 3.42E+06 & Langemyr & Structural Problem \\\\\n\t\tcircuit204 & 1020 & 1020 & 9.03E+09 & Yzhou & Circuit Simulation Problem \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Mixed Precision GMRES-based Iterative Refinement with Recycling", "authors": ["Eda Oktay", "Erin Carson"], "url": "https://arxiv.org/abs/2201.09827v2", "attribution": "\"Mixed Precision GMRES-based Iterative Refinement with Recycling\" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.12129v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\\hline\n Model & Kolmogorov distance & P-value \\\\\n\\hline\nARIMA-GARCH & 0.495 & $2.861e-10$ \\\\\n\\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Test of model residuals of ARIMA-GARCH process.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A first econometric analysis of the CRIX family", "authors": ["Shi Chen", "Cathy Yi-Hsuan Chen", "Wolfgang Karl Härdle"], "url": "https://arxiv.org/abs/2009.12129v1", "attribution": "\"A first econometric analysis of the CRIX family\" by Shi Chen, Cathy Yi-Hsuan Chen, and Wolfgang Karl Härdle, arXiv:2009.12129v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11316v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\emph{Effect of regional features.} Mean and standard deviation of accuracy (\\%) on CLEVR-CoGenT validation condition B with grid features and regional features. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\nMethods & Grid features & Regional features \\\\ \\hline\nTransformer & 78.9 $\\pm$ 0.80 & 77.4 $\\pm$ 0.68 \\\\ \nTransformer w/ PR & \\textbf{81.7} $\\pm$ 1.1 & 78.2 $\\pm$ 0.44 \\\\\nTMN-Stack (ours) & 80.6 $\\pm$ 0.21 & 79.4 $\\pm$ 0.37 \\\\ \nTMN-Tree (ours) & 80.1 $\\pm$ 0.72 & \\textbf{80.9} $\\pm$ 0.25 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Transformer Module Networks for Systematic Generalization in Visual Question Answering", "authors": ["Moyuru Yamada", "Vanessa D'Amario", "Kentaro Takemoto", "Xavier Boix", "Tomotake Sasaki"], "url": "https://arxiv.org/abs/2201.11316v2", "attribution": "\"Transformer Module Networks for Systematic Generalization in Visual Question Answering\" by Moyuru Yamada, Vanessa D'Amario, Kentaro Takemoto, Xavier Boix, and Tomotake Sasaki, arXiv:2201.11316v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.02473v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{C-OPF vs. ENApp D-OPF for IEEE 8500-node Test System}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\n\\multirow{2}{*}{Test System}& \\multicolumn{2}{c|}{Total Loss (kW)} & \\multicolumn{2}{c|}{Time (sec)}& \\#Iterations\\\\\\cline{2-6}\n &{C-OPF{\\color{black}$^*$}}&{D-OPF}&{C-OPF}&{D-OPF}&{D-OPF}\\\\\n\\hline\n{TS-3} &{249}&{249.1} & {NA}& {180} & 17\\\\\n\\hline\n{TS-4} &{152} & {152.8} & {NA} & {700} & 17\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Distributed Optimization using Reduced Network Equivalents for Radial Power Distribution Systems", "authors": ["Rabayet Sadnan", "Anamika Dubey"], "url": "https://arxiv.org/abs/2012.02473v1", "attribution": "\"Distributed Optimization using Reduced Network Equivalents for Radial Power Distribution Systems\" by Rabayet Sadnan and Anamika Dubey, arXiv:2012.02473v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.19200v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n\\toprule \n{\\small{}{}Rebalancing} & {\\small{}E{[}$R_{T}${]}} & {\\small{}Median{[}$R_{T}${]}} & {\\small{}$R_{T}:5^{th}$} & {\\small{}$R_{T}:95^{th}$} & {\\small{}ES( 5\\% )} & {\\small{}$\\Omega(1)$} & {\\small{}$Prob[R_{T}>1]$}\\tabularnewline\n{\\small{}interval} & & & {\\small{}percentile} & {\\small{}percentile} & & & \\tabularnewline\n\\midrule \n & \\multicolumn{7}{c}{{\\small{}Using optimal $\\alpha^{\\ell\\ast}$ for $CD\\left(\\delta=0.02\\right)$}}\\tabularnewline\n\\midrule \n{\\small{}Quarterly} & {\\small{}1.1153 (<0.001)} & {\\small{}1.1423} & {\\small{}0.8857} & {\\small{}1.2407} & {\\small{}0.7134} & {\\small{}7.8316} & {\\small{}0.8956}\\tabularnewline\n\\midrule \n & \\multicolumn{7}{c}{{\\small{}Using optimal $\\alpha^{\\ell\\ast}$ for $CD\\left(\\delta=0.04\\right)$}}\\tabularnewline\n\\midrule \n{\\small{}Quarterly} & {\\small{}1.2419 (<0.001)} & {\\small{}1.3073} & {\\small{}0.6995} & {\\small{}1.4576} & {\\small{}0.4919} & {\\small{}8.0831} & {\\small{}0.8814}\\tabularnewline\n\\midrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Making Leveraged Exchange-Traded Funds Work for your Portfolio", "authors": ["Peter Forsyth", "Pieter van Staden", "Yuying Li"], "url": "https://arxiv.org/abs/2506.19200v1", "attribution": "\"Making Leveraged Exchange-Traded Funds Work for your Portfolio\" by Peter Forsyth, Pieter van Staden, and Yuying Li, arXiv:2506.19200v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00113v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\t\t\\textbf{Hyperparameter}\t& \\textbf{Value} \\\\\n\t\tOptimiser & Adam \\\\\n\t\tBatch size & $25$ \\\\\n\t\tInitial learning rate & $0.001$ \\\\\n\t\tEpochs & $100$ \\\\\n\t\tDropout value & $0.3$\\\\\n\t\tInflation factor & $2$\\\\\n\t\tDimension of latent space & $50$ \\\\\n\t\tLearning rate schedule & $0.5$ [decaying*]\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Hyperparameters for the bouncing balls experiment. (*) The learning rate decays exponentially at steps of 30 epochs.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Imbedding Deep Neural Networks", "authors": ["Andrew Corbett", "Dmitry Kangin"], "url": "https://arxiv.org/abs/2202.00113v2", "attribution": "\"Imbedding Deep Neural Networks\" by Andrew Corbett and Dmitry Kangin, arXiv:2202.00113v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16448v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Details of \\texttt{ComTree} in Evaluation}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\n\\hline\n\\texttt{ComTree}(P)& The optimal tree generated by teacher Pensieve\\\\\n\\hline\n\\texttt{ComTree}\\_C&The most comprehensible tree in Rashomon\\\\\n\\hline\n'-L' suffix& Adjusted by LLM\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Beyond Interpretability: Exploring the Comprehensibility of Adaptive Video Streaming through Large Language Models", "authors": ["Lianchen Jia", "Chaoyang Li", "Ziqi Yuan", "Jiahui Chen", "Tianchi Huang", "Jiangchuan Liu", "Lifeng Sun"], "url": "https://arxiv.org/abs/2508.16448v1", "attribution": "\"Beyond Interpretability: Exploring the Comprehensibility of Adaptive Video Streaming through Large Language Models\" by Lianchen Jia, Chaoyang Li, Ziqi Yuan, Jiahui Chen, Tianchi Huang, Jiangchuan Liu, and Lifeng Sun, arXiv:2508.16448v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.02914v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|lll|}\n\\hline\nApproximating Exchangeable RGN & Parameter Specification & Features\\\\\n\\hline \n$ER$ & $K$=1, $N=100$ (fig. 3) & Blocks and Density\\\\\n\\hline\n$EG_1$ & Point of ER after 100 moves (fig. 5) & Blocks and Density \\\\\n\\hline\n$EG_2$ & $\\lambda=0.5$, $\\epsilon=0.5$, $N=100$ & Blocks and Density \\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Approximating Exchangeable Random graph models, parameter vectors and graph features considered for setting up simulation regimes.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Robustness on Networks", "authors": ["Marios Papamichalis", "Simon Lunagomez", "Patrick J. Wolfe"], "url": "https://arxiv.org/abs/2012.02914v1", "attribution": "\"Robustness on Networks\" by Marios Papamichalis, Simon Lunagomez, and Patrick J. Wolfe, arXiv:2012.02914v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.00305v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lc|cccc}\n\t\t\\hline\\hline\n\t\tLevel $\\alpha$ & Method & False discoveries & True discoveries & FDP \\\\ \\hline\n\t\t$\\alpha = 0.01$ & Multiplication & 13 & 59 & 0.1806 \\\\\n\t\t& Minimum & 13 & 58 & 0.1831 \\\\\n\t\t& Addition & 13 & 60 & 0.1781 \\\\ \n & SDA & \\textbf{0} & {18} & \\textbf{0}\\\\ \n & BHq & 1 & 26 & 0.0370 \\\\ \n \\hline\n \t\t$\\alpha = 0.05$ & Multiplication & 20 & 84 & 0.1923 \\\\\n\t\t& Minimum & 20 & 83 & 0.1942 \\\\\n\t\t& Addition & 20 & 84 & 0.1923 \\\\ \n & SDA & \\textbf{2} & {25} & \\textbf{0.0741}\\\\ \n & BHq & 10 & 53 & 0.1587 \\\\ \n \\hline\n \t\t$\\alpha = 0.1$ & Multiplication & 24 & \\textbf{95} & \\textbf{0.2017} \\\\\n\t\t& Minimum & 24 & 94 & 0.2034 \\\\\n\t\t& Addition & 25 & 95 & 0.2083 \\\\ \n & SDA & \\textbf{8} & {49} & \\textbf{0.1404} \\\\ \n & BHq & 22 & 76 & 0.2245 \\\\ \n \\hline\n $\\alpha = 0.2$ & Multiplication & 33 & 108 & 0.2340 \\\\\n\t\t& Minimum & 33 & 108 & 0.2340 \\\\\n\t\t& Addition & 33 & 108 & 0.2340 \\\\ \n & SDA & \\textbf{23} & 89 & \\textbf{0.2054} \\\\ \n & BHq & 36 & 115 & 0.2384 \\\\ \n \\hline \\hline\n\t\\end{tabular}\n\\caption{Numbers of the discovered entry pairs with FDP by different data aggregation methods under various levels on MovieLens data.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Multiple Testing of Linear Forms for Noisy Matrix Completion", "authors": ["Wanteng Ma", "Lilun Du", "Dong Xia", "Ming Yuan"], "url": "https://arxiv.org/abs/2312.00305v2", "attribution": "\"Multiple Testing of Linear Forms for Noisy Matrix Completion\" by Wanteng Ma, Lilun Du, Dong Xia, and Ming Yuan, arXiv:2312.00305v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11852v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Confusion matrix for the random forest algorithm on the metrics dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|ccc|}\n \\cline{3-5}\n \\multicolumn{2}{c|}{\\multirow{2}{*}{Diagnosis}} & \\multicolumn{3}{|c|}{Actual} \\\\\n \\cline{3-5}\n \\multicolumn{2}{c|}{} & P & C & H \\\\\n \\hline\n & P & 95.1\\,\\% & 55.0\\,\\% & 0\\,\\% \\\\\n & C & 4.9\\,\\% & 37.5\\,\\% & 0\\,\\% \\\\\n \\multirow{-3}{*}{Predicted} & H & 0\\,\\% & 7.5\\,\\% & 100\\,\\% \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features", "authors": ["C. V. Vletter", "H. L. Burger", "H. Alers", "N. Sourlos", "Z. Al-Ars"], "url": "https://arxiv.org/abs/2201.11852v1", "attribution": "\"Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features\" by C. V. Vletter, H. L. Burger, H. Alers, N. Sourlos, and Z. Al-Ars, arXiv:2201.11852v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.12530v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Expected Shortfall at Level $p=0.9$ of the Normalized Total Loss.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrrrrrrrrr} \\toprule \n& \\multicolumn{6}{c}{Binomial Model} & \\quad\\quad & \\multicolumn{6}{c}{Poisson Model} \\\\ \\midrule \n& \\multicolumn{3}{c}{Gamma} & \\multicolumn{3}{c}{Log-Normal} & & \\multicolumn{3}{c}{Gamma} & \\multicolumn{3}{c}{Log-Normal} \\\\ \\midrule \n& $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ & $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ & & $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ & $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ \\\\ \\toprule \n \\\\ \n&\\multicolumn{13}{c}{\\textbf{Uniform Accident Occurrence}} \\\\ \n$\\mathbf{\\rho^\\Phi=0.1:}$ & & & & & & & & & & & & & \\\\ \n$\\xi^{1a}$ & 206.4 & 223.0 & 271.7 & 206.9 & 224.4 & 282.2 & & 205.3 & 223.4 & 272.5 & 207.3 & 225.4 & 277.7 \\\\ \n$\\xi^{1b}$ & 200.3 & 216.3 & 263.9 & 202.5 & 217.4 & 272.4 & & 200.1 & 216.9 & 263.3 & 199.1 & 219.7 & 271.4 \\\\ \n$\\xi^{2a}$ & 560.1 & 612.2 & 771.9 & 562.2 & 613.9 & 779.9 & & 556.8 & 610.7 & 779.7 & 558.7 & 622.6 & 774.8 \\\\ \n$\\xi^{2b}$ & 567.7 & 617.8 & 779.1 & 569.7 & 628.4 & 796.9 & & 562.8 & 625.4 & 780.1 & 563.2 & 620.8 & 788.3 \\\\ \n$\\xi^{3a}$ & 863.9 & 942.5 & 1218.4 & 873.9 & 959.3 & 1225.4 & & 866.8 & 957.0 & 1241.1 & 865.2 & 956.7 & 1213.7 \\\\ \n$\\xi^{3b}$ & 904.4 & 983.1 & 1261.2 & 904.3 & 998.5 & 1280.9 & & 899.5 & 988.4 & 1262.5 & 897.0 & 1010.1 & 1263.2 \\\\ \n$\\mathbf{\\rho^\\Phi=0.5:}$ & & & & & & & & & & & & & \\\\ \n$\\xi^{1a}$ & 165.9 & 171.2 & 193.8 & 165.1 & 173.9 & 196.7 & & 165.9 & 172.2 & 194.3 & 165.4 & 171.8 & 194.4 \\\\ \n$\\xi^{1b}$ & 183.0 & 191.1 & 219.4 & 183.2 & 192.5 & 216.1 & & 183.8 & 192.5 & 219.6 & 183.6 & 193.2 & 217.4 \\\\ \n$\\xi^{2a}$ & 445.1 & 467.1 & 534.4 & 444.4 & 468.6 & 540.9 & & 443.9 & 466.1 & 527.9 & 446.2 & 469.7 & 536.2 \\\\ \n$\\xi^{2b}$ & 557.8 & 583.2 & 652.7 & 559.1 & 582.6 & 666.0 & & 560.0 & 581.8 & 653.8 & 558.3 & 581.9 & 659.9 \\\\ \n$\\xi^{3a}$ & 781.2 & 820.8 & 936.8 & 783.9 & 822.0 & 957.9 & & 780.3 & 823.0 & 938.9 & 784.8 & 821.9 & 948.8 \\\\ \n$\\xi^{3b}$ & 1016.6 & 1057.8 & 1183.0 & 1018.9 & 1063.0 & 1215.6 & & 1020.4 & 1056.8 & 1190.8 & 1014.8 & 1056.8 & 1201.1 \\\\ \n$\\mathbf{\\rho^\\Phi=0.9:}$ & & & & & & & & & & & & & \\\\ \n$\\xi^{1a}$ & 132.1 & 137.1 & 152.2 & 132.2 & 137.5 & 152.6 & & 131.5 & 137.4 & 152.0 & 132.0 & 138.0 & 152.6 \\\\ \n$\\xi^{1b}$ & 162.5 & 169.0 & 186.4 & 162.1 & 169.4 & 185.8 & & 162.6 & 168.2 & 185.0 & 161.6 & 168.8 & 185.1 \\\\ \n$\\xi^{2a}$ & 437.1 & 452.2 & 494.7 & 439.1 & 453.2 & 499.2 & & 438.2 & 453.3 & 494.6 & 436.0 & 452.7 & 503.7 \\\\ \n$\\xi^{2b}$ & 540.9 & 557.3 & 603.8 & 541.0 & 557.1 & 611.0 & & 539.8 & 552.9 & 605.8 & 538.0 & 556.9 & 607.6 \\\\ \n$\\xi^{3a}$ & 883.3 & 912.1 & 982.9 & 883.5 & 914.7 & 1000.7 & & 882.2 & 907.2 & 994.3 & 881.0 & 907.8 & 1004.8 \\\\ \n$\\xi^{3b}$ & 1209.5 & 1246.5 & 1338.6 & 1210.4 & 1249.8 & 1354.3 & & 1208.6 & 1242.4 & 1340.3 & 1210.9 & 1249.0 & 1354.9 \\\\ \n\\\\ \n&\\multicolumn{13}{c}{\\textbf{Non-Uniform Accident Occurrence}} \\\\ \n$\\mathbf{\\rho^\\Phi=0.1:}$ & & & & & & & & & & & & & \\\\ \n$\\xi^{1a}$ & 33.6 & 40.3 & 62.1 & 33.9 & 40.3 & 58.8 & & 33.5 & 40.7 & 60.3 & 34.0 & 42.1 & 56.6 \\\\ \n$\\xi^{1b}$ & 33.9 & 41.4 & 61.6 & 33.7 & 40.8 & 56.8 & & 34.3 & 40.8 & 60.6 & 33.5 & 41.2 & 59.2 \\\\ \n$\\xi^{2a}$ & 169.4 & 201.5 & 285.7 & 170.5 & 202.9 & 273.8 & & 167.8 & 202.1 & 292.2 & 170.1 & 203.8 & 274.4 \\\\ \n$\\xi^{2b}$ & 178.2 & 209.9 & 312.7 & 177.0 & 214.9 & 283.4 & & 179.0 & 211.2 & 304.0 & 177.8 & 213.7 & 285.9 \\\\ \n$\\xi^{3a}$ & 473.2 & 547.9 & 748.0 & 475.3 & 557.2 & 727.9 & & 475.5 & 540.1 & 771.1 & 478.5 & 561.9 & 742.7 \\\\ \n$\\xi^{3b}$ & 550.7 & 623.1 & 850.9 & 547.4 & 632.0 & 842.4 & & 542.4 & 627.4 & 838.9 & 550.1 & 622.8 & 856.8 \\\\ \n$\\mathbf{\\rho^\\Phi=0.5:}$ & & & & & & & & & & & & & \\\\ \n$\\xi^{1a}$ & 20.1 & 22.7 & 31.0 & 20.3 & 23.2 & 31.3 & & 20.2 & 22.9 & 30.8 & 20.3 & 23.0 & 31.3 \\\\ \n$\\xi^{1b}$ & 22.5 & 25.2 & 33.6 & 22.8 & 25.8 & 33.2 & & 22.4 & 25.4 & 33.5 & 22.5 & 25.6 & 34.2 \\\\ \n$\\xi^{2a}$ & 138.2 & 150.4 & 191.6 & 138.8 & 151.6 & 194.4 & & 137.7 & 151.4 & 191.6 & 138.5 & 152.0 & 193.6 \\\\ \n$\\xi^{2b}$ & 163.2 & 176.6 & 219.6 & 163.4 & 178.4 & 224.7 & & 161.8 & 176.4 & 222.4 & 162.3 & 179.6 & 221.7 \\\\ \n$\\xi^{3a}$ & 531.4 & 572.2 & 670.4 & 530.7 & 572.1 & 687.0 & & 530.9 & 568.6 & 669.8 & 528.7 & 569.9 & 691.3 \\\\ \n$\\xi^{3b}$ & 709.9 & 751.6 & 863.5 & 714.0 & 755.6 & 882.8 & & 714.9 & 752.5 & 871.4 & 715.3 & 752.0 & 876.7 \\\\ \n$\\mathbf{\\rho^\\Phi=0.9:}$ & & & & & & & & & & & & & \\\\ \n$\\xi^{1a}$ & 17.2 & 18.8 & 24.0 & 17.0 & 19.1 & 24.0 & & 16.9 & 18.8 & 24.2 & 17.2 & 19.1 & 24.4 \\\\ \n$\\xi^{1b}$ & 19.0 & 21.2 & 26.7 & 19.1 & 21.0 & 27.2 & & 19.1 & 21.0 & 26.6 & 19.2 & 21.3 & 26.9 \\\\ \n$\\xi^{2a}$ & 95.9 & 103.0 & 127.6 & 95.6 & 104.5 & 131.5 & & 95.1 & 103.5 & 128.0 & 95.6 & 104.4 & 130.1 \\\\ \n$\\xi^{2b}$ & 115.1 & 124.4 & 150.7 & 115.2 & 124.9 & 152.6 & & 115.6 & 124.5 & 151.3 & 115.5 & 126.1 & 154.8 \\\\ \n$\\xi^{3a}$ & 526.2 & 553.8 & 621.5 & 526.2 & 553.2 & 630.1 & & 525.5 & 551.2 & 621.1 & 529.1 & 553.1 & 631.7 \\\\ \n$\\xi^{3b}$ & 631.4 & 659.8 & 737.3 & 631.3 & 661.4 & 748.1 & & 627.7 & 658.5 & 739.6 & 629.9 & 658.0 & 755.8 \\\\ \n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Microscopic Traffic Models, Accidents, and Insurance Losses", "authors": ["Sojung Kim", "Marcel Kleiber", "Stefan Weber"], "url": "https://arxiv.org/abs/2208.12530v2", "attribution": "\"Microscopic Traffic Models, Accidents, and Insurance Losses\" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.13927v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Numerical summary of implementation details.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\t\t\\hline\n\t\t\\bfseries Implementation Detail & \\bfseries Value\\\\ \n\t\t\\hline\n\t\tBatch size ($b$) & $10$\\\\\n\t\t\\hline\n\t\tKernel initialization mean(std.) & $0(0.001)$ \\\\\n\t\t\\hline\n\t\tLearning rate& $10^{-4}$\\\\\n\t\t\\hline\n\t\tL1, L2 coefficients & $10^{-8}, 10^{-6}$ \\\\\n\t\t\\hline\n\t\tDropout & $0.5$\\\\\n\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Modelling brain lesion volume in patches with CNN-based Poisson Regression", "authors": ["Kevin Raina"], "url": "https://arxiv.org/abs/2011.13927v1", "attribution": "\"Modelling brain lesion volume in patches with CNN-based Poisson Regression\" by Kevin Raina, arXiv:2011.13927v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10927v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\hline\n \\textbf{Model} & \\textbf{SNLI} & \\textbf{MultiNLI(m)} & \\textbf{MultiNLI(mm)} \\\\ \\hline\n ESIM & 88.0 & 72.3 & 72.1 \\\\\n KIM & 88.6 & 77.2 & 76.4 \\\\\n ADIN & 88.8 & 78.8 & 77.9 \\\\ \\hline\n BERT & 89.8 & 83.3 & 82.7 \\\\\n PairSCL-BERT$_\\texttt{base}$ & \\textbf{91.9} & \\textbf{85.5} & \\textbf{84.6} \\\\ \\hline\n RoBERTa$_\\texttt{base}$ & $91.2$ & $90.8$ & $90.2$ \\\\ \n PairSCL-RoBERTa$_\\texttt{base}$ & \\textbf{93.2} & \\textbf{92.7} & \\textbf{92.3} \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance on the test dataset. MultiNLI(m) and MultiNLI(mm) represent the accuracy on matched and mismatched datasets. The best performance is in \\textbf{bold} among models with the same pre-trained encoder.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Pair-Level Supervised Contrastive Learning for Natural Language Inference", "authors": ["Shu'ang Li", "Xuming Hu", "Li Lin", "Lijie Wen"], "url": "https://arxiv.org/abs/2201.10927v3", "attribution": "\"Pair-Level Supervised Contrastive Learning for Natural Language Inference\" by Shu'ang Li, Xuming Hu, Li Lin, and Lijie Wen, arXiv:2201.10927v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.10480v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|ccc|c}\n\t\t\t\\toprule\n\t\t\tDataset\t& Observation\t&\t1 robot\t&\t5 robots\t&\t10 robots & VGG-19 \\\\\n\t\t\t\t& size (\\%)\t&\t \t&\t \t&\t & w/ full image \\\\\n\t\t\t\\midrule\n\t\t\tMNIST & 2.04 & 91.27 & 94.98 & 98.31 & 99.33 \\\\ \n\t\t\t\\midrule\n\t\t\tMap & 0.52 & 85.19 & 98.94 & 99.71 & 99.84 \\\\ \n\t\t\tw/o clouds & 2.08 & 88.59 & 99.27 & 99.84 & 99.84 \\\\ \n\t\t\t\\midrule\n\t\t\tMap & 0.52 & 72.42 & 97.30 & 97.56 & 99.43 \\\\ \n\t\t\tw/ clouds & 2.08 & 77.62 & 98.21 & 98.98 & 99.43 \\\\ \n\t\t\t\\toprule\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{The classification accuracy (\\%) over the MNIST and the satellite map dataset.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Distributed Map Classification using Local Observations", "authors": ["Guangyi Liu", "Arash Amini", "Martin Takáč", "Héctor Muñoz-Avila", "Nader Motee"], "url": "https://arxiv.org/abs/2012.10480v2", "attribution": "\"Distributed Map Classification using Local Observations\" by Guangyi Liu, Arash Amini, Martin Takáč, Héctor Muñoz-Avila, and Nader Motee, arXiv:2012.10480v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.02900v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Diarization results on AMI mixed-headset eval set, in terms of missed speech (MS), false alarm (FA), speaker confusion (Conf.), and diarization error rate (DER). For all the recordings in the test set, the NME-based speaker counting approach estimated between 3 and 6 speakers, which is close to the oracle count of 4 speakers.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\textbf{Method} & \\textbf{MS} & \\textbf{FA} & \\textbf{Conf.} & \\textbf{DER} \\\\ \\midrule\nAHC/PLDA & 19.9 & 0.0 & 8.4 & 28.3 \\\\\nSpectral/cosine & 19.9 & 0.0 & 7.0 & 26.9 \\\\\nVBx~ & 19.9 & 0.0 & 6.3 & 26.2 \\\\ \\midrule\nVB resegmentation~ & 13.0 & 3.6 & 7.2 & 23.8 \\\\\nRPN~ & 9.5 & 7.7 & 8.3 & 25.5 \\\\ \\midrule\nOur method & 11.3 & 2.2 & 10.5 & 24.0 \\\\\nOur method + oracle OD & 7.4 & 1.8 & 12.3 & \\underline{21.5} \\\\ \nOur method + noise aug. & 11.3 & 2.2 & 10.1 & \\textbf{23.6} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Multi-class Spectral Clustering with Overlaps for Speaker Diarization", "authors": ["Desh Raj", "Zili Huang", "Sanjeev Khudanpur"], "url": "https://arxiv.org/abs/2011.02900v1", "attribution": "\"Multi-class Spectral Clustering with Overlaps for Speaker Diarization\" by Desh Raj, Zili Huang, and Sanjeev Khudanpur, arXiv:2011.02900v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.11765v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Frequency of Special Characters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccl}\n \\toprule\n Non-English or Math&Frequency&Comments\\\\\n \\midrule\n \\O & 1 in 1,000& For Swedish names\\\\\n $\\pi$ & 1 in 5& Common in math\\\\\n \\$ & 4 in 5 & Used in business\\\\\n $\\Psi^2_1$ & 1 in 40,000& Unexplained usage\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis", "authors": ["Tanja Aue", "Adam Jatowt", "Michael Färber"], "url": "https://arxiv.org/abs/2212.11765v1", "attribution": "\"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis\" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09403v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the expressive power of PCA on the original space of curves and on the space of TSRVFs. The lower the values are, the better.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n \\hline\n & \\multicolumn{3}{c|}{\\textbf{PCA on curves}} & \\multicolumn{3}{c|}{\\textbf{PCA on TSRVFs}} \\\\\n \\cline{2-7}\n & \\textbf{Mean} & \\textbf{Std} & \\textbf{Median} & \\textbf{Mean} & \\textbf{Std} & \\textbf{Median} \\\\\n \\hline\n DFAUST & $0.926$ & $1.110$ & $0.693$ & $\\textbf{0.756}$ & $0.104$ & $\\textbf{0.715}$ \\\\\n \\hline\n VOCA & $0.676$ & $0.182$ & $0.64$ & $\\textbf{0.486}$ & $0.318$ & $\\textbf{0.603}$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "4D Atlas: Statistical Analysis of the Spatiotemporal Variability in Longitudinal 3D Shape Data", "authors": ["Hamid Laga", "Marcel Padilla", "Ian H. Jermyn", "Sebastian Kurtek", "Mohammed Bennamoun", "Anuj Srivastava"], "url": "https://arxiv.org/abs/2101.09403v2", "attribution": "\"4D Atlas: Statistical Analysis of the Spatiotemporal Variability in Longitudinal 3D Shape Data\" by Hamid Laga, Marcel Padilla, Ian H. Jermyn, Sebastian Kurtek, Mohammed Bennamoun, and Anuj Srivastava, arXiv:2101.09403v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16246v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n \\toprule\n $ n $ & $ a_n = \\frac{1}{n!} $ \\\\\n \\midrule\n 0 & 1.00000000 \\\\\n 1 & 1.00000000 \\\\\n 2 & 0.50000000 \\\\\n 3 & 0.16666667 \\\\\n 4 & 0.04166667 \\\\\n 5 & 0.00833333 \\\\\n 6 & 0.00138889 \\\\\n 7 & 0.00019841 \\\\\n 8 & 0.00002480 \\\\\n 9 & 0.00000276 \\\\\n 10 & 0.00000028 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Taylor coefficients $ a_n = \\frac{1}{n!} $ for the expansion of $ e^z $.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Numerical Derivatives, Projection Coefficients, and Truncation Errors in Analytic Hilbert Space With Gaussian Measure", "authors": ["M. W. AlMasri"], "url": "https://arxiv.org/abs/2504.16246v2", "attribution": "\"Numerical Derivatives, Projection Coefficients, and Truncation Errors in Analytic Hilbert Space With Gaussian Measure\" by M. W. AlMasri, arXiv:2504.16246v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00698v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c||} \n \\hline\n Iteration & $L_2$ error \\\\ \n \\hline\\hline\n 1 & 9.6448e-02 \\\\ \n 2 & 2.7334-02 \\\\\n 3 & 2.7342e-02 \\\\\n 4 & 2.9523e-02 \\\\\n 5 & 8.8202e-02 \\\\ \n 6 & 5.7279e-02 \\\\\n 7 & 5.6871e-02 \\\\\n 8 & 8.7658e-02 \\\\\n 9 & 2.7334e-02 \\\\\n 10 & 8.7835e-02 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Runge $\\text{deg}(p)=\\text{deg}(q)=4$, $n=10$ random start $L_2$ error}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Deep Univariate Polynomial and Conformal Approximation", "authors": ["Kingsley Yeon"], "url": "https://arxiv.org/abs/2503.00698v2", "attribution": "\"Deep Univariate Polynomial and Conformal Approximation\" by Kingsley Yeon, arXiv:2503.00698v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.19859v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of setups of experiments in \\S .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\nExperiment & Architecture & Dataset & Loss Function & Batch Size \\\\ \\midrule\n1 & Deep Linear Network & Synthetic & MSE & 1024 \\\\\n2 & Multi-Layer Perceptron & Synthetic & MSE & 1024 \\\\\n3 & ResNet & CIFAR-10 & Cross-Entropy & 768 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An Overview of Low-Rank Structures in the Training and Adaptation of Large Models", "authors": ["Laura Balzano", "Tianjiao Ding", "Benjamin D. Haeffele", "Soo Min Kwon", "Qing Qu", "Peng Wang", "Zhangyang Wang", "Can Yaras"], "url": "https://arxiv.org/abs/2503.19859v1", "attribution": "\"An Overview of Low-Rank Structures in the Training and Adaptation of Large Models\" by Laura Balzano, Tianjiao Ding, Benjamin D. Haeffele, Soo Min Kwon, Qing Qu, Peng Wang, Zhangyang Wang, and Can Yaras, arXiv:2503.19859v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.17618v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\n \\hline\n \\multicolumn{2}{|c|}{}& \\multicolumn{4}{|c|}{Frequentist}& \\multicolumn{3}{|c|}{Bayesian} & \\\\\n \\hline\nQunatities & $\\theta_0$ & DS & NAIVE & WLP & LSW & BMA & BLASSO & CB & ORACLE \\\\\n \\hline\n & 0.0 & 0.556 & 0.904 & 0.858 & 0.982 & 1.000 & 0.996 & 0.949 & 0.936 \\\\ \n & 0.1 & 0.583 & 0.902 & 0.902 & 0.982 & 0.000 & 0.996 & 0.933 & 0.947 \\\\ \n & 0.2 & 0.557 & 0.872 & 0.949 & 0.978 & 0.000 & 0.998 & 0.951 & 0.946 \\\\ \n & 0.3 & 0.559 & 0.875 & 0.955 & 0.971 & 0.007 & 1.000 & 0.955 & 0.944 \\\\ \n Coverage & 0.4 & 0.517 & 0.871 & 0.967 & 0.973 & 0.016 & 0.993 & 0.951 & 0.956 \\\\ \n & 0.5 & 0.512 & 0.827 & 0.960 & 0.953 & 0.027 & 0.958 & 0.956 & 0.944 \\\\ \n & 0.6 & 0.482 & 0.840 & 0.933 & 0.976 & 0.082 & 0.964 & 0.958 & 0.960 \\\\ \n & 0.7 & 0.458 & 0.816 & 0.929 & 0.967 & 0.202 & 0.944 & 0.962 & 0.951 \\\\ \n & 0.8 & 0.484 & 0.757 & 0.845 & 0.969 & 0.295 & 0.940 & 0.945 & 0.924 \\\\ \n & 0.9 & 0.465 & 0.716 & 0.869 & 0.957 & 0.494 & 0.940 & 0.950 & 0.940 \\\\ \n \\hline\n\\hline\n & 0.0 & 0.640 & 1.414 & 0.584 & 1.515 & 0.001 & 0.908 & 1.150 & 1.036 \\\\ \n & 0.1 & 0.644 & 1.349 & 0.585 & 1.517 & 0.001 & 0.992 & 1.144 & 1.030 \\\\ \n & 0.2 & 0.644 & 1.459 & 0.589 & 1.520 & 0.002 & 1.096 & 1.148 & 1.030 \\\\ \n Interval & 0.3 & 0.648 & 1.401 & 0.594 & 1.510 & 0.016 & 1.205 & 1.152 & 1.032 \\\\ \n Length & 0.4 & 0.650 & 1.440 & 0.595 & 1.507 & 0.040 & 1.329 & 1.146 & 1.033 \\\\ \n & 0.5 & 0.655 & 1.428 & 0.601 & 1.484 & 0.082 & 1.400 & 1.160 & 1.036 \\\\ \n & 0.6 & 0.659 & 1.449 & 0.607 & 1.563 & 0.208 & 1.540 & 1.161 & 1.040 \\\\ \n & 0.7 & 0.665 & 1.476 & 0.606 & 1.513 & 0.432 & 1.650 & 1.167 & 1.043 \\\\ \n & 0.8 & 0.670 & 1.533 & 0.617 & 1.565 & 0.606 & 1.745 & 1.172 & 1.051 \\\\ \n & 0.9 & 0.675 & 1.544 & 0.626 & 1.569 & 0.807 & 1.903 & 1.187 & 1.055 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Coverage and interval length corresponding to sample-based standard errors for each method considered for $n=500, d=600$ under signal strengths $\\theta_0 \\in \\{0,0.1,0.2,\\dots,0.9\\}$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates", "authors": ["Abhishek Ojha", "Naveen N. Narisetty"], "url": "https://arxiv.org/abs/2411.17618v1", "attribution": "\"Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates\" by Abhishek Ojha and Naveen N. Narisetty, arXiv:2411.17618v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.04520v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{{ Average effects of the model chosen in Figure , in which all effects and their interactions, but the interaction of the $\"$common colonizer$\"$ and the $\"$same region$\"$ effect are included. All effects but the regional effect were rounded to the third decimal place.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c|c}\n &comcol&sameRegion&intercept&contig&comcol and contig&sameRegion and contig&\\\\ \\hline &0.008&2e-06&0.061&0.045&0.218&0.146 &\\\\ \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Structured Estimator for large Covariance Matrices in the Presence of Pairwise and Spatial Covariates", "authors": ["Martin Metodiev", "Marie Perrot-Dockès", "Sarah Ouadah", "Bailey K. Fosdick", "Stéphane Robin", "Pierre Latouche", "Adrian E. Raftery"], "url": "https://arxiv.org/abs/2411.04520v1", "attribution": "\"A Structured Estimator for large Covariance Matrices in the Presence of Pairwise and Spatial Covariates\" by Martin Metodiev, Marie Perrot-Dockès, Sarah Ouadah, Bailey K. Fosdick, Stéphane Robin, Pierre Latouche, and Adrian E. Raftery, arXiv:2411.04520v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06423v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison results with other word-level noise filtering strategies.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\t\t\\toprule\n\t\t & \\multicolumn{2}{c}{CNSE} & \\multicolumn{2}{c}{CNSS} \\\\\n\t\tWord-level Filter & Acc & F1 & Acc & F1 \\\\\n\t\t\\midrule\n\t\tRandom & 80.38 & 79.19 & 87.68 & 88.20 \\\\\n\t\tEmbedding Norm & 80.54 & 78.15 & 84.92 & 85.03 \\\\\n\t\tAttention Weight & 85.52 & 83.34 & 89.91 & 89.88 \\\\\n\t\tPageRank & 86.32 & 84.55 & 91.28 & 91.39 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching", "authors": ["Liang Pang", "Yanyan Lan", "Xueqi Cheng"], "url": "https://arxiv.org/abs/2101.06423v2", "attribution": "\"Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching\" by Liang Pang, Yanyan Lan, and Xueqi Cheng, arXiv:2101.06423v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.09865v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The ranges of markups and quantities satisfying $q \\ge 0$, $u' > 0$, $u'' < 0$, $0<\\mu \\le 1$, and the second-order condition .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c|cc}\n\\multicolumn{5}{c}{Case 1: $\\alpha >0$.} \\\\\n\\hline\n & \\multicolumn{2}{c|}{Markups} & \\multicolumn{2}{c}{Quantities} \\\\ \\cline{2-5}\n & $\\sigma \\le 1$ & $\\sigma > 1 $ & $\\sigma \\le 1$ & $\\sigma > 1 $ \\\\ \\hline\n\\multirow{2}{*}{Case 1a. $1-\\beta \\sigma>0$ and $\\beta \\neq 1$} &\n\\multirow{2}{*}{$\\frac{1}{1-\\beta} \\le \\frac{1}{\\mu} < \\infty$} &\n\\multirow{2}{*}{$\\frac{1}{1-\\beta} \\le \\frac{1}{\\mu} < \\frac{\\sigma}{\\sigma-1}$} &\n\\multirow{2}{*}{$0 \\le q < -\\frac{1-\\beta}{\\alpha (\\sigma-1)}$$^\\dagger$} &\n\\multirow{2}{*}{$0 \\le q < \\infty$} \\\\\n&\n&\n&\n&\n\\\\ \\hline\n&\n&\n&\n&\n\\\\\n\\multirow{2}{*}{Case 1b.} \\hskip .05cm $1-\\beta \\sigma<0$ and $\\beta\\in(0,1)$ &\n\\multirow{2}{*}{n.a.} &\n$\\frac{\\sigma}{\\sigma-1} < \\frac{1}{\\mu } \\le \\frac{1}{1-\\beta}$ &\n\\multirow{2}{*}{n.a.} & $0 \\le q < \\infty$ \\\\ \n\\hskip 1.73cm\n$1-\\beta \\sigma<0$ and $\\beta=1$\n&\n& $\\frac{\\sigma}{\\sigma-1} < \\frac{1}{\\mu} < \\infty$\n&\n& $0 < q < \\infty$\n\\\\ \\hline \n\\multicolumn{5}{c}{} \\\\\n\\multicolumn{5}{c}{Case 2: $\\alpha <0$.} \\\\ \\hline\n & \\multicolumn{2}{c|}{Markups} & \\multicolumn{2}{c}{Quantities} \\\\ \\cline{2-5}\n & $\\sigma \\le 1$ & $\\sigma > 1 $ & $\\sigma \\le 1$ & $\\sigma > 1 $ \\\\ \\hline \n\\multirow{2}{*}{Case 2a. $1-\\beta \\sigma>0$ and $\\beta \\in ( 0, 1)$} &\n\\multirow{2}{*}{$\\frac{(1-\\beta)\\sigma+1}{(1-\\beta)\\sigma+\\Delta} < \\frac{1}{\\mu} \\le \\frac{1}{1-\\beta}$$^\\ddagger$} &\n\\multirow{2}{*}{$\\frac{(1-\\beta)\\sigma+1}{(1-\\beta)\\sigma+\\Delta} < \\frac{1}{\\mu} \\le \\frac{1}{1-\\beta}$} &\n\\multirow{2}{*}{$ 0 \\le q < -\\frac{1 - \\beta - \\Delta}{\\alpha (\\sigma-1)}$$^\\S$} & \n\\multirow{2}{*}{$ 0 \\le q < -\\frac{1 - \\beta - \\Delta}{\\alpha (\\sigma-1)} $} \\\\\n&\n&\n&\n&\n\\\\ \\hline \n\\multirow{2}{*}{Case 2b. $1-\\beta \\sigma<0$ and $\\beta \\in ( 0, 1)$}\n&\n\\multirow{2}{*}{n.a.} &\n\\multirow{2}{*}{$\\frac{1}{1-\\beta} \\le \\frac{1}{\\mu } < \\infty$} &\n\\multirow{2}{*}{n.a.} &\n\\multirow{2}{*}{$0 \\le q < -\\frac{1-\\beta}{\\alpha (\\sigma-1)}$} \\\\ \n&\n&\n&\n&\n\\\\ \\hline\n\\multicolumn{5}{p{21.4cm}}{\\scriptsize {\\it Notes}: We let $\\Delta \\equiv \\sqrt{(1-\\beta)(1-\\beta \\sigma)}$.\n$^{\\dagger}$ means that when $\\sigma=1$, $0 \\le q < -\\frac{1-\\beta}{\\alpha (\\sigma-1)}$ must be replaced with $0 \\le q < \\infty$.\n$^{\\ddagger}$ means that when $\\sigma=1$, $ \\frac{(1-\\beta)\\sigma+1}{(1-\\beta)\\sigma+\\Delta}$ must be replaced with $ \\frac{2-\\beta}{2(1-\\beta)}$.\n$^\\S$ means that when $\\sigma=1$, $ -\\frac{1 - \\beta - \\Delta}{\\alpha (\\sigma-1)} $ must be replaced with $-\\frac{\\beta}{2\\alpha}$.\nMarginal cost pricing $\\frac{1}{\\mu}=1$ is admissible only when $\\beta=0$ in Case 1a because $\\frac{\\sigma}{\\sigma-1} > 1$ holds in Case 1b, $\\frac{(1-\\beta)\\sigma+1}{(1-\\beta)\\sigma+\\Delta} >1$ holds in Case 2a, and $\\beta=0$ is not admissible in Case 2b.\nThe case with $\\alpha=0$ corresponds to the CES and requires that $\\beta \\in (0,1)$.\nThe case with $1-\\beta \\sigma=0$ also corresponds to the CES and requires that $\\sigma > 1 $.\nIf $\\alpha=0$ or $\\beta =\\frac{1}{\\sigma}$, then the markup is given by $\\frac{1}{\\mu} =\\frac{1}{1-\\beta} \\in (1, \\infty)$ or $\\frac{1}{\\mu} =\\frac{\\sigma}{\\sigma-1} \\in (1, \\infty)$, respectively, and the range of quantities is given by $0\\le q<\\infty$.\nSince these two cases are well known, we omit them from the table.}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Linear fractional relative risk aversion", "authors": ["Kristian Behrens", "Yasusada Murata"], "url": "https://arxiv.org/abs/2509.09865v1", "attribution": "\"Linear fractional relative risk aversion\" by Kristian Behrens and Yasusada Murata, arXiv:2509.09865v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.11393v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the coverage probabilities and average width (standard error) for the bootstrap confidence interval and standard error estimation under SC8. The true standard errors of the pseudo-true estimations are in bold italics.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc} \n\\toprule\n&Confidence &Parametric &Pairwise &Wild &Multiplier &LRB- \\\\ \n&level &Bootstrap &Bootstrap &Bootstrap &Bootstrap &Pearson \\\\\n\\midrule\n\\multirow{5}{*}{Coverage rate of $\\mbox{CI}_{\\mbox{nor}}$} \n&\\multirow{2}{*}{0.99} &1.00 &1.00 &1.00 &1.00 &1.00\\\\\n& &0.016 (0.001) &0.078 (0.002) &0.077 (0.003) &0.077 (0.003) &0.009 (0.000) \\\\\n&\\multirow{2}{*}{0.95} &1.00 &1.00 &1.00 &1.00 &0.94\\\\\n& &0.012 (0.000) &0.059 (0.002) &0.059 (0.002) &0.059 (0.002) &0.007 (0.000) \\\\\n&\\multirow{2}{*}{0.90} &1.00 &1.00 &1.00 &1.00 &0.88\\\\\n& &0.010 (0.000) &0.050 (0.002) &0.049 (0.002) &0.049 (0.002) &0.006 (0.000) \\\\\n&\\multirow{2}{*}{0.80} &0.96 &1.00 &1.00 &1.00 &0.81\\\\\n& &0.008 (0.000) &0.039 (0.001) &0.038 (0.001) &0.038 (0.001) &0.005 (0.000) \\\\\n&\\multirow{2}{*}{0.75} &0.93 &1.00 &1.00 &1.00 &0.76\\\\\n& &0.007 (0.000) &0.035 (0.001) &0.035 (0.001) &0.035 (0.001) &0.004 (0.000) \\\\\n\\cline{2-7}\n\\multirow{5}{*}{Coverage rate of $\\mbox{CI}_{\\mbox{per}}$} \n&\\multirow{2}{*}{0.99} &1.00 &1.00 &1.00 &1.00 &0.98\\\\\n& &0.016 (0.001) &0.081 (0.005) &0.079 (0.005) &0.080 (0.005) &0.010 (0.001) \\\\\n&\\multirow{2}{*}{0.95} &1.00 &1.00 &1.00 &1.00 &0.94\\\\\n& &0.012 (0.001) &0.059 (0.002) &0.059 (0.002) &0.059 (0.003) &0.007 (0.000) \\\\\n&\\multirow{2}{*}{0.90} &0.99 &1.00 &1.00 &1.00 &0.85\\\\\n& &0.010 (0.000) &0.049 (0.002) &0.050 (0.002) &0.049 (0.002) &0.006 (0.000) \\\\\n&\\multirow{2}{*}{0.80} &0.96 &1.00 &1.00 &1.00 &0.76\\\\\n& &0.008 (0.000) &0.039 (0.001) &0.039 (0.001) &0.038 (0.002) &0.005 (0.000) \\\\\n&\\multirow{2}{*}{0.75} &0.92 &1.00 &1.00 &1.00 &0.72\\\\\n& &0.007 (0.000) &0.035 (0.001) &0.035 (0.001) &0.035 (0.001) &0.004 (0.000) \\\\\n\\cline{2-7}\nEstimated SE $(\\times 10^{-3})$ &\\textbf{\\emph{1.973}} &3.062 &15.093 &15.032 &15.027 &1.820 \\\\\n(Estimated SE)/(true SE) &- &1.552 &7.651 &7.621 &7.618 &0.923 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Assessing Estimation Uncertainty under Model Misspecification", "authors": ["Rong Li", "Yichen Qin", "Yang Li"], "url": "https://arxiv.org/abs/2312.11393v1", "attribution": "\"Assessing Estimation Uncertainty under Model Misspecification\" by Rong Li, Yichen Qin, and Yang Li, arXiv:2312.11393v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10516v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Discriminating Power of Invariants for Higher Orders}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\t\t\\toprule\n\t\t& & & \\multicolumn{2}{c}{Non-isomorphic Models} \\\\ \\cmidrule(lr){4-5}\n\t\t& Order & \\multicolumn{1}{p{1.7cm}}{\\centering \\#Blocks} & \\multicolumn{1}{p{1.4cm}}{\\centering Total} & \\multicolumn{1}{p{1.9cm}}{\\centering Avg per Block} \\\\ \\cmidrule{1-5}\n\t\t\\#88 Quasi-MV-algebra & 7 & 567 & 477 & 1.19 \\\\\n\t\t& 8 & 153,163 & 55,544 & 2.76 \\\\ \n\t\t& 9 & 264,972 & 141,750 & 1.87 \\\\ \n\t\t\\#15 Brouwerian semilattices & 6 & 745 & 745 & 1.00 \\\\\n\t\t& 7 & 8,272 & 8,272 & 1.00 \\\\ \n\t\t& 8 & 115,801 & 114,943 & 1.01 \\\\ \n\t\t\\#4 BCK-join-semilattice & 9 & 26 & 26 & 1.00 \\\\\n\t\t& 10 & 47 & 47 & 1.00 \\\\ \n\t\t& 11 & 82 & 82 & 1.00 \\\\ \\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Boosting Isomorphic Model Filtering with Invariants", "authors": ["João Araújo", "Choiwah Chow", "Mikoláš Janota"], "url": "https://arxiv.org/abs/2201.10516v1", "attribution": "\"Boosting Isomorphic Model Filtering with Invariants\" by João Araújo, Choiwah Chow, and Mikoláš Janota, arXiv:2201.10516v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.03710v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classical orbital elements and orbital period.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\hline \\hline\n$a$ (km) & $e$ (-)& $i$ (deg)& $\\Omega$ (deg)& $\\omega$ (deg)& $\\nu$ (deg) & (hrs) \\\\\n\\hline\n26646.6808 & 0.74& 62.8& 0.0& 280 & 0 & 12.0247 \\\\\n\\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor", "authors": ["Taehyeun Kim", "Anouck Girard", "Ilya Kolmanovsky"], "url": "https://arxiv.org/abs/2412.03710v2", "attribution": "\"CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor\" by Taehyeun Kim, Anouck Girard, and Ilya Kolmanovsky, arXiv:2412.03710v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.07825v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lc}\n \\toprule\n \\textbf{Step} & \\textbf{Setup} \\\\\n \\midrule\n \\multirow{2}{*}{Step 1} & 2000 starting points within \\( [-8,8]^{20} \\) \\\\\n & 10000 SGD iterations with step size \\( \\lambda = 0.02 \\) \\\\\n \\midrule\n \\multirow{2}{*}{Step 2} & 5000 samples per component \\\\\n & 40000 Langevin iterations with step size \\( \\eta = 0.002 \\) \\\\\n \\midrule\n \\multirow{2}{*}{Step 3} & explicit Euler scheme for reverse ODE with 100 time steps \\\\\n & 5000 labeled samples generated per component \\\\\n \\midrule\n \\multirow{4}{*}{Step 4} & Feedforward model with three hidden layers \\\\\n & Each layer has 1000 nodes with \\texttt{tanh} activation \\\\\n & Adam with initial learning rate 0.001, halved every 500 epochs \\\\\n & Training over $2000$ epochs with batch size $1500$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions", "authors": ["Hoang Tran", "Zezhong Zhang", "Feng Bao", "Dan Lu", "Guannan Zhang"], "url": "https://arxiv.org/abs/2505.07825v1", "attribution": "\"Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions\" by Hoang Tran, Zezhong Zhang, Feng Bao, Dan Lu, and Guannan Zhang, arXiv:2505.07825v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.03260v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classification accuracy of the GRAND-l and KuramotoGNN trained with different depth on the OGBN-arXiv graph node classification task.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\hline\n \\hline\n Model & $T=1$ & $T=8$ & $T=32$ & $T=64$\\\\\n \\hline\n KuramotoGNN& \\textbf{66.00$\\pm$0.8} & \\textbf{69.87$\\pm$0.2} & \\textbf{69.31$\\pm$0.3} & \\textbf{68.32$\\pm$0.2}\\\\\n \\hline\n GRAND-l & 64.43$\\pm$0.5 & 69 .02$\\pm$0.4 & 67.81$\\pm$0.4&66.58$\\pm$1.2\\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach", "authors": ["Tuan Nguyen", "Hirotada Honda", "Takashi Sano", "Vinh Nguyen", "Shugo Nakamura", "Tan M. Nguyen"], "url": "https://arxiv.org/abs/2311.03260v2", "attribution": "\"From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach\" by Tuan Nguyen, Hirotada Honda, Takashi Sano, Vinh Nguyen, Shugo Nakamura, and Tan M. Nguyen, arXiv:2311.03260v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06775v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Numerical comparison of four methods using BraTS 2018 testing set. Note that smaller mean L1 error and larger SSIM mean error indicate higher similarity.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|cccc|ccc}\n\\hline\nMethods& ~mean L1 error~$\\downarrow$ ~~&~~~SSIM~$\\uparrow$~~&\t~~~PSNR~$\\uparrow$~~\t& Inception Score $\\uparrow$\\\\\\hline\\hline\nPatch-match \t&445.8&\t0.9460&\t29.55&\t9.13\\\\\nGLC \t&432.6&\t0.9506&\t30.34&\t9.68\\\\\nPartial Conv \t&373.2&\t0.9512&\t33.57&\t9.77\\\\\\hline\nProposed\t&292.5&\t0.9667&\t34.26&\t10.26\\\\\nProposed+symmetry&\t\\textbf{254.8}&\t\\textbf{0.9682}\t&\\textbf{34.52}&\t\\textbf{10.58}\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology", "authors": ["Xiaofeng Liu", "Fangxu Xing", "Chao Yang", "C. -C. Jay Kuo", "Georges ElFakhri", "Jonghye Woo"], "url": "https://arxiv.org/abs/2101.06775v1", "attribution": "\"Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology\" by Xiaofeng Liu, Fangxu Xing, Chao Yang, C. -C. Jay Kuo, Georges ElFakhri, and Jonghye Woo, arXiv:2101.06775v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.14875v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lcccccc}\n\t\t\\toprule\n\t\t& \\multicolumn{2}{c}{Iterations} & \\multicolumn{2}{c}{Broadwell (ms)} & \\multicolumn{2}{c}{Ivy Bridge (ms)} \\\\\n\t\t\\cmidrule(r){2-3} \\cmidrule(r){4-5} \\cmidrule(r){6-7}\n\t\t$l_{max}$ & $7$& $8$ & $7$ & $8$ & $7$ & $8$\\\\\n\t\t\\midrule\n\t\tV(1, 0) & 29 & 30 & 121.3 &1221 & 134.6 & 1470 \\\\\n\t\t\\midrule\n\t\tV(1, 1) & 13 & 13 & 70.8 & 682 & 79.9 & 838 \\\\\n\t\t\\midrule\n\t\tV(2, 1) & 9 & 9 & 59.0 & 582 & 66.2 & 708 \\\\\n\t\t\\midrule\n\t\tV(2, 2) & 7 & 7 & 54.6 & 531 & 65.4 & 654 \\\\\n\t\t\\midrule\n\t\tV(3, 2) & 7 & 7 & 61.9 & 610 & 74.6 & 757 \\\\\n\t\t\\midrule\n\t\tV(3, 3) & 7 & 7 & 72.6 & 690 & 86.6 & 857 \\\\\n\t\t\\midrule\n\t\tV(4, 3) & 7 & 6 & 77.9 & 656 & 87.3 & 825 \\\\\n\t\t\\midrule\n\t\tV(4, 4) & 6 & 6 & 73.2 & 725 & 82.5 & 906 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Automating the Design of Multigrid Methods with Evolutionary Program Synthesis", "authors": ["Jonas Schmitt"], "url": "https://arxiv.org/abs/2312.14875v1", "attribution": "\"Automating the Design of Multigrid Methods with Evolutionary Program Synthesis\" by Jonas Schmitt, arXiv:2312.14875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07434v5_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c} \n\t\t\\toprule[1pt]\n\t\tMethods&OS& MF &Aux& mIOU (\\%) & FLOPs\\\\ \n\t\t\\midrule[0.5pt]\n\t\t\\midrule[0.5pt]\n\t\tResNet& 16 & & - & - & 59.85G \\\\\n\t\t-101& 8 & & - & - & 190.70G \\\\\n\t\t\\midrule\n\t\tDANet& 8 & & & & +101.25G\\\\ \n\t\t & 8 & \\checkmark & \\checkmark &52.60 & - \\\\ \n\t\t\\midrule\n\t\tAxial & 16 & & & 50.27($\\pm$0.2)& +8.85G\\\\ \n\t\tAttention & 16 & \\checkmark & & 52.01($\\pm$0.2) & -\\\\ \n\t\t & 8 & & & 51.24($\\pm$0.2) & +34.33G\\\\ \n\t\t & 8 & \\checkmark & &52.51($\\pm$0.2) & - \\\\ \n\t\t\\midrule\n\t\tOur & 16 & & & 51.06($\\pm$0.2)& +8.85G\\\\\n\t\tCAA & 16 & \\checkmark & &53.09($\\pm$0.3) & - \\\\\n\t\t & 8 & & & 52.73($\\pm$0.1) & +34.33G\\\\ \n\t\t & 8 & \\checkmark & & 54.05($\\pm$0.1) & - \\\\ \n\t\t\\midrule\n\t\tOur & 16 & & \\checkmark & 51.80($\\pm$0.2)& +8.85G\\\\\n\t\t CAA & 16 &\\checkmark& \\checkmark & 53.52($\\pm$0.2)& - \\\\\n\t\t + & 8 & & \\checkmark& 53.48($\\pm$0.3) & +34.33G\\\\\n\t\t Aux loss & 8 & \\checkmark &\\checkmark &54.65($\\pm$0.4) & - \\\\\n\t\t\\bottomrule[1pt]\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation", "authors": ["Ye Huang", "Di Kang", "Wenjing Jia", "Xiangjian He", "Liu Liu"], "url": "https://arxiv.org/abs/2101.07434v5", "attribution": "\"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation\" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.11507v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\n\\textbf{Genre} & \\textbf{Examples} \\\\\n\\hline\nBass Music & Dubstep, Drum and Bass, Jungle \\\\\nLive Sounds & Rock, Jazz, Disco \\\\\nCinematic & Sound FX, Filmscore, Sci-Fi \\\\\nGlobal & Reggae, Dancehall, Indian Music \\\\\nHip Hop & Trap, Boom Bap, Lofi Hip Hop \\\\\nElectronic & Ambient, IDM, Chill Out \\\\\nHouse / Techno & Deep House, Electro, Tech House \\\\\nOther Dance Music & EDM, Psy Trance, Hardstyle \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Taxonomy of genres used for the annotation and examples for each category.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "The Freesound Loop Dataset and Annotation Tool", "authors": ["Antonio Ramires", "Frederic Font", "Dmitry Bogdanov", "Jordan B. L. Smith", "Yi-Hsuan Yang", "Joann Ching", "Bo-Yu Chen", "Yueh-Kao Wu", "Hsu Wei-Han", "Xavier Serra"], "url": "https://arxiv.org/abs/2008.11507v2", "attribution": "\"The Freesound Loop Dataset and Annotation Tool\" by Antonio Ramires, Frederic Font, Dmitry Bogdanov, Jordan B. L. Smith, Yi-Hsuan Yang, Joann Ching, Bo-Yu Chen, Yueh-Kao Wu, Hsu Wei-Han, and Xavier Serra, arXiv:2008.11507v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.15733v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance metrics for the first eigenvalue prediction with different settings. $d\\times d$+dp+ma stands for $d\\times d$ image with detailed pixelization on the boundaries and main axis alignment. $d\\times d$+ma stands for $d\\times d$ image with main axis alignment.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|} \\hline\n\t\t\t\tSetting & RMSE & $R^2$ & MAPE \\\\ \\hline\n\t\t\t\t$32 \\times 32$+dp+ma & 2.2121 & 0.9988 & 1.15\\% \\\\\n\t\t\t\t$32 \\times 32$+ma & 5.9933 & 0.9917 & 1.75\\%\\\\\n\t\t\t\t$32 \\times 32$ & 7.3343 & 0.9864 & 1.67\\% \\\\\n\t\t\t\t$64 \\times 64$+dp+ma & 2.2113 & 0.9988 & 0.73\\% \\\\\n\t\t\t\t$64 \\times 64$+ma & 3.0124 & 0.9979 & 1.57\\% \\\\\n\t\t\t\t$64 \\times 64$ & 3.6447 & 0.9966 & 1.78\\% \\\\ \\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Operator Inference for Elliptic Eigenvalue Problems", "authors": ["Haoqian Li", "Jiguang Sun", "Zhiwen Zhang"], "url": "https://arxiv.org/abs/2504.15733v1", "attribution": "\"Operator Inference for Elliptic Eigenvalue Problems\" by Haoqian Li, Jiguang Sun, and Zhiwen Zhang, arXiv:2504.15733v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15444v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mutually unbiased weighing matrices of order $16$ and weight $9$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l}\n\\noalign{\\hrule height1pt}\n$W_{16,562}$\\\\\n\\hline\n1102100100020111\n1001010111202002\n1001010220120210\n1201020112101000\\\\\n0120202010010211\n0220212000020121\n0210201001201011\n2101001100020221\\\\\n2001020210022110\n0021122021201000\n2001010122010110\n0120201001101102\\\\\n1111000200010121\n1020221022202000\n0220101001112001\n0010222121102000\\\\\n\\hline\n$ A_{16,562,2}$\\\\\n\\hline\n0100122110021020\n0120201110022010\n0111020022002102\n0111010010211010\\\\\n0121010021000222\n0102101210012020\n0112010022020201\n1011021001100201\\\\\n1021110002100101\n1122020020011010\n1012010101100102\n1200101102200202\\\\\n1000102201222010\n1000201200221120\n1000202100212021\n1000202212100202\\\\\n\\hline\n$ A_{16,562,3}$\\\\\n\\hline\n0100012202210220\n0100012120022012\n0100021120201022\n0100022211001012\\\\\n0110011201120020\n0120120220002101\n0112020110002201\n1101201002110010\\\\\n1011100012220100\n1021200001220201\n1001102101110020\n1012202020001101\\\\\n1022200010002122\n1022100002121200\n1002111001210010\n1210020220002202\\\\\n\\hline\n$ A_{16,562,4}$\\\\\n\\hline\n0001111011020011\n0001112020202120\n0001211022201200\n0001122002020212\\\\\n0111202011000220\n0121021012000120\n0122002200221001\n1001220221000110\\\\\n1112001000222002\n1120000120102201\n1100110200111002\n1210000202120021\\\\\n1220000210212200\n1220000101021022\n1010120100211001\n1000212112000110\\\\\n\\hline\n$ A_{16,562,5}$\\\\\n\\hline\n0011120210100022\n0101200200202212\n0121100102011200\n0012102110200012\\\\\n0102100201100211\n0102200212011100\n0102200100120222\n1101012001021100\\\\\n1022100220200022\n1100021101012100\n1220011010100012\n1200222001011200\\\\\n1010012022112000\n1010011010200221\n1010021022021010\n1020022012022001\\\\\n\\hline\n$ A_{16,562,6}$\\\\\n\\hline\n0010210121220020\n0010110110012101\n0010110222200012\n0010120201021101\\\\\n0101210212020001\n0102222000202110\n0122110011020002\n1101120000202220\\\\\n1201002000122102\n1021001100201110\n1102001022100120\n1202001000022211\\\\\n1202002012201020\n1110002100101210\n1010201211010002\n1020012221010001\\\\\n\\hline\n$ A_{16,562,7}$\\\\\n\\hline\n0100011111110200\n0100011220001111\n0100021202122200\n0100022102110101\\\\\n0110012210002102\n0120220201210020\n0111020101220010\n1201201010002101\\\\\n1011200020101022\n1021100020012012\n1001102210001201\n1012100021002021\\\\\n1012200002210210\n1022202001120010\n1002121010001102\n1120010102220020\\\\\n\\hline\n$ A_{16,562,8}$\\\\\n\\hline\n0001121102002011\n0011001111001120\n0011002202101110\n0011002120210202\\\\\n0012002102022120\n0012021220210100\n0102011102001012\n1001011202002022\\\\\n1212100010110200\n1110210001002011\n1120102010210100\n1100220002001221\\\\\n1100120021120002\n1220200120110100\n1200220010220012\n1200110020221001\\\\\n\\noalign{\\hrule height1pt}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unbiased weighing matrices of weight $9$", "authors": ["Makoto Araya", "Masaaki Harada", "Hadi Kharaghani", "Sho Suda", "Wei-Hsuan Yu"], "url": "https://arxiv.org/abs/2501.15444v2", "attribution": "\"Unbiased weighing matrices of weight $9$\" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.04093v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model results for the unconditional output}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\t\t\t\\hline\n\t\t\t& $\\gamma=1$ & $\\gamma=5$ & $\\gamma=10$ \\\\\n\t\t\t\\hline\n\t\t\t&\t\\multicolumn{3}{c}{$\\alpha=0$} \\\\\n\t\t\t\\hline\n\t\t\t$E[\\mu_s(\\omega)-r_f]$ & -0.0043 & 0.0029 & 0.0136 \\\\\n\t\t\t$\\sqrt{E[\\sigma^2(\\omega)]}$ & 0.0626 & 0.0857 & 0.101 \\\\\n\t\t\t$\\exp\\big[E\\big(\\ln (S_t/D_t)\\big)\\big]$ & 10.2623 & 4.8255 & 3.0452 \\\\\n\t\t\t$\\sqrt{\\mathrm{var}(\\ln (S_t/D_t))}$ & 0.1451 & 0.1089 & 0.0859 \\\\\n\t\t\t\\hline\n\t\t\t&\\multicolumn{3}{c}{$\\alpha=0.25$} \\\\\n\t\t\t\\hline\n\t\t\t$E[\\mu_S(\\omega)-r_f]$ & -0.0015 & 0.0062 & 0.0169 \\\\\n\t\t\t$\\sqrt{E[\\sigma^2(\\omega)]}$ & 0.0626 & 0.0834 & 0.0972 \\\\\n\t\t\t$\\exp\\big[E\\big(\\ln (S_t/D_t)\\big)\\big]$ & 10.2623 & 5.1687 & 3.3988 \\\\\n\t\t\t$\\sqrt{\\mathrm{var}(\\ln (S_t/D_t))}$ & 0.1451 & 0.1125 & 0.0915 \\\\\n\t\t\t\\hline\n\t\t\t&\\multicolumn{3}{c}{$\\alpha=0.5$} \\\\\n\t\t\t\\hline\n\t\t\t$E[\\mu_S(\\omega)-r_f]$ & 0.0014 & 0.009 & 0.0195 \\\\\n\t\t\t$\\sqrt{E[\\sigma^2(\\omega)]}$ & 0.0626 & 0.0812 & 0.0938 \\\\\n\t\t\t$\\exp\\big[E\\big(\\ln (S_t/D_t)\\big)\\big]$ & 10.2623 & 5.5116 & 3.7516 \\\\\n\t\t\t$\\sqrt{\\mathrm{var}(\\ln (S_t/D_t))}$ & 0.1451 & 0.1158 & 0.0967 \\\\\n\t\t\t\\hline\n\t\t\t&\\multicolumn{3}{c}{$\\alpha=0.75$} \\\\\n\t\t\t\\hline\n\t\t\t$E[\\mu_S(\\omega)-r_f]$ & 0.0043 & 0.0115 & 0.0215 \\\\\n\t\t\t$\\sqrt{E[\\sigma^2(\\omega)]}$ & 0.0626 & 0.0792 & 0.0907 \\\\\n\t\t\t$\\exp\\big[E\\big(\\ln (S_t/D_t)\\big)\\big]$ & 10.2623 & 5.8543 & 4.1038 \\\\\n\t\t\t$\\sqrt{\\mathrm{var}(\\ln (S_t/D_t))}$ & 0.1451 & 0.1189 & 0.1014 \\\\\n\t\t\t\\hline\n\t\t\t&\\multicolumn{3}{c}{$\\alpha=1$} \\\\\n\t\t\t\\hline\n\t\t\t$E[\\mu_S(\\omega)-r_f]$ & 0.0071 & 0.0138 & 0.0233 \\\\\n\t\t\t$\\sqrt{E[\\sigma^2(\\omega)]}$ & 0.0626 & 0.0773 & 0.0878 \\\\\n\t\t\t$\\exp\\big[E\\big(\\ln (S_t/D_t)\\big)\\big]$ & 10.2623 & 6.1969 & 4.4553 \\\\\n\t\t\t$\\sqrt{\\mathrm{var}(\\ln (S_t/D_t))}$ & 0.1451 & 0.1219 & 0.1057 \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Dynamic Asset Pricing with α-MEU Model", "authors": ["Jiacheng Fan", "Xue Dong He", "Ruocheng Wu"], "url": "https://arxiv.org/abs/2507.04093v1", "attribution": "\"Dynamic Asset Pricing with α-MEU Model\" by Jiacheng Fan, Xue Dong He, and Ruocheng Wu, arXiv:2507.04093v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.05604v3_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Correct Frequency and running time (second) of different optimization methods (Matlab) in finding the global maximum of different functions in Subsection . SMCOtree has highest correct frequency with low running time.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccccccccc}\n\\cline{1-15}\n\\multicolumn{3}{c}{Case} && \\multicolumn{3}{c}{Method (\\texttt{Matlab})} && \\multicolumn{3}{c}{Correct Frequency} && \\multicolumn{3}{c}{Time$\\times10^{2}~$(second)} \\\\\n\\cline{1-15} \n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SMCO} && \\multicolumn{3}{c}{\\textbf{0.95}} && \\multicolumn{3}{c}{\\textbf{0.18}}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SMCOtree} && \\multicolumn{3}{c}{\\textbf{0.97}} && \\multicolumn{3}{c}{1.20}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{GD} && \\multicolumn{3}{c}{0.26} && \\multicolumn{3}{c}{2.51}\\\\\n\\multicolumn{3}{c}{Case 1, $d=1$} && \\multicolumn{3}{c}{SA($T$= 80)} && \\multicolumn{3}{c}{0.87} && \\multicolumn{3}{c}{6.29}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SA($T$= 100)} && \\multicolumn{3}{c}{0.83} && \\multicolumn{3}{c}{6.79}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{PSO($S$= 100)} && \\multicolumn{3}{c}{0.88} && \\multicolumn{3}{c}{2.07}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{PSO($S$= 150)} && \\multicolumn{3}{c}{0.92} && \\multicolumn{3}{c}{2.76}\\\\\n\\cline{1-15} \n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SMCO} && \\multicolumn{3}{c}{\\textbf{1.00}} && \\multicolumn{3}{c}{\\textbf{0.21}}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SMCOtree} && \\multicolumn{3}{c}{\\textbf{1.00}} && \\multicolumn{3}{c}{3.38}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SPSA} && \\multicolumn{3}{c}{0.88} && \\multicolumn{3}{c}{10.55}\\\\\n\\multicolumn{3}{c}{Case 2, $d=2$} && \\multicolumn{3}{c}{SA($T$= 80)} && \\multicolumn{3}{c}{\\textbf{0.97}} && \\multicolumn{3}{c}{20.35}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SA($T$= 100)} && \\multicolumn{3}{c}{\\textbf{0.98}} && \\multicolumn{3}{c}{20.48}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{PSO($S$= 100)} && \\multicolumn{3}{c}{\\textbf{1.00}} && \\multicolumn{3}{c}{3.97}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{PSO($S$= 150)} && \\multicolumn{3}{c}{\\textbf{1.00}} && \\multicolumn{3}{c}{4.62}\\\\\n\\cline{1-15} \n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SMCO} && \\multicolumn{3}{c}{\\textbf{1.00}} && \\multicolumn{3}{c}{\\textbf{0.11}}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SMCOtree} && \\multicolumn{3}{c}{\\textbf{1.00}} && \\multicolumn{3}{c}{1.32}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SPSA} && \\multicolumn{3}{c}{0.78} && \\multicolumn{3}{c}{11.46}\\\\\n\\multicolumn{3}{c}{Case 3, Griewank, $d=2$} && \\multicolumn{3}{c}{SA($T$= 80)} && \\multicolumn{3}{c}{0.43} && \\multicolumn{3}{c}{13.13}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SA($T$= 100)} && \\multicolumn{3}{c}{0.40} && \\multicolumn{3}{c}{13.21}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{PSO($S$= 100)} && \\multicolumn{3}{c}{\\textbf{1.00}} && \\multicolumn{3}{c}{1.99}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{PSO($S$= 150)} && \\multicolumn{3}{c}{\\textbf{1.00}} && \\multicolumn{3}{c}{2.37}\\\\\n\\cline{1-15} \n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SMCO} && \\multicolumn{3}{c}{0.00} && \\multicolumn{3}{c}{0.06}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SMCOtree} && \\multicolumn{3}{c}{\\textbf{0.93}} && \\multicolumn{3}{c}{\\textbf{2.85}}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SPSA} && \\multicolumn{3}{c}{0.75} && \\multicolumn{3}{c}{11.30}\\\\\n\\multicolumn{3}{c}{Case 4, Rastrigin, $d=2$} && \\multicolumn{3}{c}{SA($T$= 80)} && \\multicolumn{3}{c}{0.65} && \\multicolumn{3}{c}{14.07}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SA($T$= 100)} && \\multicolumn{3}{c}{0.58} && \\multicolumn{3}{c}{14.48}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{PSO($S$= 100)} && \\multicolumn{3}{c}{0.62} && \\multicolumn{3}{c}{4.33}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{PSO($S$= 150)} && \\multicolumn{3}{c}{0.68} && \\multicolumn{3}{c}{5.94}\\\\\n\\cline{1-15} \n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SMCOtree} && \\multicolumn{3}{c}{\\textbf{0.99}} && \\multicolumn{3}{c}{\\textbf{80.57}}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SPSA} && \\multicolumn{3}{c}{0.01} && \\multicolumn{3}{c}{62.14}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{SA($T$= 1000)} && \\multicolumn{3}{c}{0.00} && \\multicolumn{3}{c}{71.20}\\\\\n\\multicolumn{3}{c}{Case 5, Rastrigin, $d=10$} && \\multicolumn{3}{c}{SA($T$= 1500)} && \\multicolumn{3}{c}{0.00} && \\multicolumn{3}{c}{76.06}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{PSO($S$= 1000)} && \\multicolumn{3}{c}{0.47} && \\multicolumn{3}{c}{98.84}\\\\\n\\multicolumn{3}{c}{} && \\multicolumn{3}{c}{PSO($S$= 1500)} && \\multicolumn{3}{c}{0.60} && \\multicolumn{3}{c}{207.17}\\\\\n\\cline{1-15} \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Optimization via Strategic Law of Large Numbers", "authors": ["Xiaohong Chen", "Zengjing Chen", "Wayne Yuan Gao", "Xiaodong Yan", "Guodong Zhang"], "url": "https://arxiv.org/abs/2412.05604v3", "attribution": "\"Optimization via Strategic Law of Large Numbers\" by Xiaohong Chen, Zengjing Chen, Wayne Yuan Gao, Xiaodong Yan, and Guodong Zhang, arXiv:2412.05604v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.17011v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\toprule\n\\textbf{Method} & \\textbf{Bond Type} & \\textbf{MAE} $\\downarrow$ \\\\\n\\midrule\nGAN & US\\_10Y\\_Yield & 0.437 \\\\\nGAN & AAA\\_Bond\\_Yield & 0.343 \\\\\nGAN & BAA\\_Bond\\_Yield & 0.372 \\\\\nGAN & Junk\\_Bond\\_Yield & 0.594 \\\\\nRL & US\\_10Y\\_Yield & \\textbf{0.103} \\\\\nRL & AAA\\_Bond\\_Yield & 0.124 \\\\\nRL & BAA\\_Bond\\_Yield & 0.174 \\\\\nRL & Junk\\_Bond\\_Yield & 0.458 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean Absolute Error for Different Bond Yields. Best score denoted by \\textbf{bold}.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation", "authors": ["Jaskaran Singh Walia", "Aarush Sinha", "Srinitish Srinivasan", "Srihari Unnikrishnan"], "url": "https://arxiv.org/abs/2502.17011v1", "attribution": "\"Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation\" by Jaskaran Singh Walia, Aarush Sinha, Srinitish Srinivasan, and Srihari Unnikrishnan, arXiv:2502.17011v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.15744v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{High-confidence $k$-means lower bounds with $\\ell=30$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|lll|llll|lll|}\\hline\nDataset & $k$ & $\\min v_i$ & $\\operatorname{avg}L_i$ & \\phantom{-}$L_H$ & $L_M$ & \\phantom{-}$B_H$ & $B_M$ & $T_{\\mathrm{init}}$ & $T_{\\mathrm{k++}}$ & $T_{\\mathrm{SDP}}$ \\\\ \\hline\\hline\nMNIST & 10 & 3.92e1 & 1.26e0 & -9.59e0 & 1.06e0 & \\phantom{-}2.56e1 & \\textbf{2.96e1} & 1.71e1 & 4.18e2 & 2.47e2\\\\\\hline\nNORM-10 & 10 & 4.97e0 & 1.10e0 & -1.07e0 & 1.24e-1 & \\phantom{-}3.42e0 & \\textbf{3.72e0} & 2.24e-1 & 1.48e-1 & 2.92e1\\\\\nNORM-10 & 25 & 4.05e0 & 1.02e-1 & -1.01e0 & 8.63e-2 & \\phantom{-}1.43e0 & \\textbf{2.11e0} & 4.00e-1 & 1.97e0 & 3.98e2\\\\\nNORM-10 & 50 & 3.18e0 & 7.58e-2 & -8.05e-1 & 6.33e-2 & \\phantom{-}5.24e-1 & \\textbf{1.12e0} & 6.30e-1 & 3.71e0 & 1.93e2\\\\\\hline\nNORM-25 & 10 & 1.18e5 & 3.94e3 & -2.90e4 & 3.03e3 & \\phantom{-}6.99e4 & \\textbf{8.08e4} & 2.58e-1 & 1.95e-1 & 1.67e2\\\\\nNORM-25 & 25 & 1.50e1 & 6.37e0 & -3.31e0 & 3.08e-1 & \\phantom{-}9.61e0 & \\textbf{1.15e1} & 4.42e-1 & 3.63e-1 & 3.44e1\\\\\nNORM-25 & 50 & 1.41e1 & 3.04e-1 & -3.61e0 & 2.60e-1 & \\phantom{-}5.23e0 & \\textbf{7.48e0} & 6.88e-1 & 2.50e0 & 4.64e1\\\\\\hline\nCLOUD & 10 & 5.62e3 & 2.41e2 & -1.31e3 & 1.62e2 & \\phantom{-}2.70e3 & \\textbf{3.06e3} & 1.01e-1 & 1.75e-1 & 1.46e2\\\\\nCLOUD & 25 & 1.94e3 & 6.31e1 & -4.75e2 & 4.91e1 & \\phantom{-}8.24e2 & \\textbf{9.43e2} & 1.54e-1 & 1.90e-1 & 3.48e2\\\\\nCLOUD & 50 & 1.09e3 & 2.99e1 & -2.72e2 & 2.33e1 & \\phantom{-}2.57e2 & \\textbf{4.54e2} & 2.19e-1 & 3.67e-1 & 4.99e2\\\\\\hline\nINTRUSION & 10 & 2.36e7 & 1.00e6 & -5.55e6 & \\textbf{6.03e5} & -6.43e6 & 2.93e4 & 9.46e0 & 3.76e1 & 3.43e2\\\\\nINTRUSION & 25 & 2.19e6 & 7.64e4 & -5.31e5 & \\textbf{5.31e4} & -6.03e5 & 1.67e3 & 1.86e1& 1.04e2 & 4.58e2\\\\\nINTRUSION & 50 & 4.52e5 & 1.36e4 & -1.11e5 & \\textbf{9.27e3} & -1.25e5 & 4.56e1 & 3.43e1 & 1.51e2 & 2.16e3 \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Sketch-and-solve approaches to k-means clustering by semidefinite programming", "authors": ["Charles Clum", "Dustin G. Mixon", "Soledad Villar", "Kaiying Xie"], "url": "https://arxiv.org/abs/2211.15744v1", "attribution": "\"Sketch-and-solve approaches to k-means clustering by semidefinite programming\" by Charles Clum, Dustin G. Mixon, Soledad Villar, and Kaiying Xie, arXiv:2211.15744v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.11354v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dataset of 3178 images: Segmentation performance metrics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrr}\n\\hline\n & \\textbf{Dice} & \\textbf{IoU} & \\textbf{Accuracy} & \\textbf{Precision} & \\textbf{Recall} & \\textbf{F1 Score} & \\textbf{SSIM} & \\textbf{Hausdorff} \\\\\n\\hline\n\\textbf{Mean} & 0.814431 & 0.694019 & 0.872015 & 0.803413 & 0.851245 & 0.814431 & 0.502930 & 57.287395 \\\\\n\\textbf{Std} & 0.078670 & 0.106993 & 0.096104 & 0.112562 & 0.124639 & 0.078670 & 0.245851 & 34.920233 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Introduction to a Low-Cost AI-Powered GUI for Unstained Cell Culture Analysis", "authors": ["Surajit Das", "Pavel Zun"], "url": "https://arxiv.org/abs/2509.11354v1", "attribution": "\"Introduction to a Low-Cost AI-Powered GUI for Unstained Cell Culture Analysis\" by Surajit Das and Pavel Zun, arXiv:2509.11354v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11985v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters used for the simulation of the analytical benchmark.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\hline\n \\textbf{Parameter} & \\textbf{Value}\\\\\n \\hline\n Thermal conductivity, $k$ \t& $3.0~ W/(m K)$ \\\\\n Heat transfer coefficient, $h$ \t& $5.0~W/(m^2 K)$\\\\\n $a$\t & $5 ~ K/m^2$ \\\\\n $b$\t & $10~ K/m^2$ \\\\\n $c$ \t & $15~ K/m^2$ \\\\\n $L$ \t & $1~m$\\\\\n $W$\t & $1~m$\\\\\n $H$\t & $1~m$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation", "authors": ["Umberto Emil Morelli", "Patricia Barral", "Peregrina Quintela", "Gianluigi Rozza", "Giovanni Stabile"], "url": "https://arxiv.org/abs/2101.11985v1", "attribution": "\"A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation\" by Umberto Emil Morelli, Patricia Barral, Peregrina Quintela, Gianluigi Rozza, and Giovanni Stabile, arXiv:2101.11985v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.11606v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Robustness specifications on the effect of school closures on test scores in Sicily}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\t\t\t\t\t\n\t\t\t\t\t\\hline\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t& Math (ln) & Math (IHS) & Math (Rasch) & Math (std. - PSM) \\\\ \n\t\t\t\t\t\n\t\t\t\t\t& (1) & (2) & (3) & (4) \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\\textbf{Days of school closure} (ln) & -0.001*** & & & \\\\\n\t\t\t\t& (0.000) & & & \\\\ \n\t\t\t\t\\textbf{Days of school closure} (IHS) & & -0.001*** & & \\\\\n\t\t\t& & (0.000) & & \\\\ \n\t\t\t\n\t\t\t\\textbf{Days of school closure} (level) & & & -0.006*** & \\\\\n\t\t & & & (0.002) & \\\\ \n\t\t \n\t\t \n\t\t \n\t\t\\textbf{Days of school closure} (std.) & & & & -0.006*** \\\\\n\t& & & & (0.002) \\\\\n\t& & & & \\\\ \\hline\n\t\\textbf{Other controls} & Yes & Yes & Yes & Yes \\\\\n\t\\textbf{Student FEs} & Yes & Yes & Yes & Yes \\\\\n\t\\textbf{Relative time dummies} & Yes & Yes & Yes & Yes \\\\ \n\t\\textbf{Grade dummies}& Yes & Yes & Yes & Yes \\\\ \\hline \n\t\n\t\\textbf{Observations} & 334,366 & 334,366 & 334,366 & 334,366 \\\\\n\tR-squared & 0.553 & 0.554 & 0.563 & 0.534 \\\\\n\t\\textbf{Number of students} & 167,183 & 167,183 & 167,183 & 167,183 \\\\ \\hline\n\t\n\t\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Will the last be the first? School closures and educational outcomes", "authors": ["Michele Battisti", "Giuseppe Maggio"], "url": "https://arxiv.org/abs/2208.11606v1", "attribution": "\"Will the last be the first? School closures and educational outcomes\" by Michele Battisti and Giuseppe Maggio, arXiv:2208.11606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04807v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Convergence rate comparison between different methods. The rows of $\\mathbf{ A}$ are normalized, $\\|\\mathbf{ e}_k\\|^2_2=\\|\\mathbf{A}\\mathbf{x}^k-\\mathbf{b}\\|^2_2$, $|\\hat{\\mathbf{x}}|_{\\min}$ means the smallest nonzero absolute element, $\\tilde{\\sigma}_{\\min}(\\mathbf{A})$ is the non-zero smallest singular value, and $\\beta_k/\\gamma_k\\geq 1$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\t\t\\hline & \\text { Selection Rule } & \\text { Convergence Rate } \\\\\n\t\t\\hline $\\mathrm{RK}$ &$ \\mathbb{P}\\left(t_{j}=i\\right)=\\frac{\\left\\|\\mathbf{a}_{i}\\right\\|^{2}_2}{\\|\\mathbf{A}\\|_{\\text{F}}^{2}}$ &$ \\mathbb{E}\\left\\|\\mathbf{e}_{k}\\right\\|^{2} \\leq\\left(1-\\frac{\\sigma_{\\min }^{2}(\\mathbf{A})}{\\|\\mathbf{A}\\|_{\\text{F}}^{2}}\\right)^{k}\\left\\|\\mathbf{e}_{0}\\right\\|^{2}_2$ \\\\\n\t\t\\hline \n\t\t$\\mathrm{SRK}$\n\t\t&$\\mathbb{P}\\left(t_{j}=i\\right)=\\frac{\\left\\|\\mathbf{a}_{i}\\right\\|^{2}_2}{\\|\\mathbf{A}\\|_{\\text{F}}^{2}}$& \n\t\t$\\mathbb{E}\\left\\|\\mathbf{e}_{k}\\right\\|^{2} \\leq\\left(1-\\frac{\\tilde{\\sigma}_{\\min }^{2}(A)}{2m}\\cdot\\frac{|\\hat{\\mathbf{ x}}|_{\\min}}{|\\hat{\\mathbf{ x}}|_{\\min}+2\\lambda}\\right)^{k}\\left\\|\\mathbf{e}_{0}\\right\\|^{2}_2$ \n\t\t\\\\\n\t\t\\hline \n\t\t\t\\multirow{2}{*}{$\\mathrm{SSKM}$}\n\t & $\\tau_j\\sim\\binom{[m]}{\\beta}$&\t \\multirow{2}{*}{$\\mathbb{E}\\left\\|\\mathbf{e}_{k}\\right\\|^{2} \\leq\\prod_{i=0}^{k}\\left(1-\\frac{\\beta_k\\tilde{\\sigma}_{\\min }^{2}(A)}{2\\gamma_km}\\cdot\\frac{|\\hat{\\mathbf{ x}}|_{\\min}}{|\\hat{\\mathbf{ x}}|_{\\min}+2\\lambda}\\right)\\left\\|\\mathbf{e}_{0}\\right\\|^{2}_2$ }\\\\\n\t & $t_{j}=\\arg \\max _{i\\in\\tau_j}(\\mathbf{a}_{i}^{\\top} \\mathbf{x}_{j-1}-b_{i})^2$ &\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Sparse Sampling Kaczmarz-Motzkin Method with Linear Convergence", "authors": ["Ziyang Yuan", "Hui Zhang", "Hongxia Wang"], "url": "https://arxiv.org/abs/2101.04807v2", "attribution": "\"Sparse Sampling Kaczmarz-Motzkin Method with Linear Convergence\" by Ziyang Yuan, Hui Zhang, and Hongxia Wang, arXiv:2101.04807v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.09061v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n& Auditory task & Visual task & Rest&P-value \\\\ \\hline\n \\multirow{2}{*}{Auditory task} & 0.000 & 0.104 & 0.029& \\\\\n&-- & (0.98) & (0.98) & \\multirow{2}{*}{0.451}\\\\\n\\cline{1-4}\n \\multirow{2}{*}{Visual task} & 0.104 & 0.000 & 0.080 & \\\\ \n&(0.98) & -- & (0.98) & \\\\ \\hline\n\\end{tabular}\n\\caption{Statistical analysis of the intrinsic discrepancy between the tasks.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Analysis of EEG data using complex geometric structurization", "authors": ["Eddy Kwessi", "Lloyd Edwards"], "url": "https://arxiv.org/abs/2102.09061v1", "attribution": "\"Analysis of EEG data using complex geometric structurization\" by Eddy Kwessi and Lloyd Edwards, arXiv:2102.09061v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2001.02048v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|}\n\t\t\\hline\n\t\t& Power Consumption & Total \\\\ \\hline\n\t\t$2\\times$ AP7312 & 45 mW & \\multirow{4}{*}{1.5695 W} \\\\ \\cline{1-2}\n\t\t$2\\times$ TVP5150 & 230 mW & \\\\ \\cline{1-2}\n\t\tMCP1700 & 44.5 mW & \\\\ \\cline{1-2}\n\t\tADV7171 & 1.25 W & \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Power consumption values for the main devices of the implemented system according to their corresponding datasheets.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Flexible Architecture for Real-time Processing of Multiple Video Signals", "authors": ["Mohamed Awad", "Islam T. Abougindia", "Ahmed Elliethy", "Hussein A. Aly"], "url": "https://arxiv.org/abs/2001.02048v1", "attribution": "\"Flexible Architecture for Real-time Processing of Multiple Video Signals\" by Mohamed Awad, Islam T. Abougindia, Ahmed Elliethy, and Hussein A. Aly, arXiv:2001.02048v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.06601v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Bias models with corresponding functions and parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\n Model & Equation & Parameters \\\\\n \\hline\n Static & $H_b(\\theta,t_s,t_k) = A$ & $\\theta = [A]$ \\\\\n \\hline\n Linear & $H_b(\\theta,t_s,t_k) = A + B(t_k-t_s)$ & $\\theta = [A,B]$ \\\\\n \\hline\n Quadratic & $\\!\\begin{aligned}[t]\n H_b(\\theta,t_s,t_k) &=A + B(t_k-t_s) \\\\\n &+ C(t_k-t_s)^2 \n \\end{aligned}$ & $\\theta = [A,B,C]$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A switching Kalman filter approach to online mitigation and correction of sensor corruption for inertial navigation", "authors": ["Artem Mustaev", "Nicholas Galioto", "Matt Boler", "John D. Jakeman", "Cosmin Safta", "Alex Gorodetsky"], "url": "https://arxiv.org/abs/2412.06601v2", "attribution": "\"A switching Kalman filter approach to online mitigation and correction of sensor corruption for inertial navigation\" by Artem Mustaev, Nicholas Galioto, Matt Boler, John D. Jakeman, Cosmin Safta, and Alex Gorodetsky, arXiv:2412.06601v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.15390v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{graphicx}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|c|}\n\t\t\t\t\\hline\n\t\t\t\t\\raisebox{-1ex}{}\n\t\t\t\t$(\\underline{d_1},\\underline{d_2},\\dots,\\underline{d_l})$&1-ps $\\lambda$ & \\ Weights of $\\tilde{\\mathcal{U}}_1$ \\ & \\ Weights of $\\tilde{\\mathcal{U}}_2$ \\ & \\ Weight of $\\det \\tilde{\\mathcal U}_1$ \\ &$\\eta_{\\lambda}$ \\\\\n\t\t\t\t\\hline\n\t\t\t\t(1,1),(1,2) &\n\t\t\t\t\\scalebox{.7}{\\raisebox{-4.5ex}{}\n\t\t\t\t\t$\\begin{pmatrix}\n\t\t\t\t\t\tt^3 & 0\\\\\n\t\t\t\t\t\t0& t^{-2}\n\t\t\t\t\t\\end{pmatrix},\\begin{pmatrix}\n\t\t\t\t\t\tt^3 & 0&0\\\\\n\t\t\t\t\t\t0& t^{-2}&0\\\\\n\t\t\t\t\t\t0&0&t^{-2}\n\t\t\t\t\t\\end{pmatrix}$}\n\t\t\t\t&(5,0)&(5,0,0)&5&15 \\\\ \n\t\t\t\t\\hline (2,2),(0,1) &\n\t\t\t\t\\scalebox{.7}{\\raisebox{-4.5ex}{}$\\begin{pmatrix}\n\t\t\t\t\t\tt & 0\\\\\n\t\t\t\t\t\t0& t\n\t\t\t\t\t\\end{pmatrix},\\begin{pmatrix}\n\t\t\t\t\t\tt & 0&0\\\\\n\t\t\t\t\t\t0& t&0\\\\\n\t\t\t\t\t\t0&0&t^{-4}\n\t\t\t\t\t\\end{pmatrix}$}\n\t\t\t\t&(5,5)&(5,5,0)&10&20 \\\\ \n\t\t\t\t\\hline\n\t\t\t\t(2,1),(0,2) &\n\t\t\t\t\\scalebox{.7}{\\raisebox{-4.5ex}{}$\\begin{pmatrix}\n\t\t\t\t\t\tt^4 & 0\\\\\n\t\t\t\t\t\t0& t^4\n\t\t\t\t\t\\end{pmatrix},\\begin{pmatrix}\n\t\t\t\t\t\tt^4 & 0&0\\\\\n\t\t\t\t\t\t0& t^{-6}&0\\\\\n\t\t\t\t\t\t0&0&t^{-6}\n\t\t\t\t\t\\end{pmatrix}$}\n\t\t\t\t&(20,20)&(20,10,10)&40&100 \\\\ \n\t\t\t\t\\hline\n\t\t\t\t(1,0),(1,3) &\n\t\t\t\t\\scalebox{.7}{\\raisebox{-4.5ex}{}$\\begin{pmatrix}\n\t\t\t\t\t\tt^{12} & 0\\\\\n\t\t\t\t\t\t0& t^{-3}\n\t\t\t\t\t\\end{pmatrix},\\begin{pmatrix}\n\t\t\t\t\t\tt^{-3} & 0&0\\\\\n\t\t\t\t\t\t0& t^{-3}&0\\\\\n\t\t\t\t\t\t0&0&t^{-3}\n\t\t\t\t\t\\end{pmatrix}$}\n\t\t\t\t&(30,15)&(15,15,15)&45& \\ 120 \\ \\\\\n\t\t\t\t\\hline\n\t\t\t\t(1,0),(1,2),(0,1) \\ &\n\t\t\t\t\\scalebox{.7}{\\raisebox{-4.5ex}{}$\\begin{pmatrix}\n\t\t\t\t\t\tt^9 & 0\\\\\n\t\t\t\t\t\t0& t^{-1}\n\t\t\t\t\t\\end{pmatrix},\\begin{pmatrix}\n\t\t\t\t\t\tt^{-1} & 0&0\\\\\n\t\t\t\t\t\t0& t^{-1}&0\\\\\n\t\t\t\t\t\t0&0&t^{-6}\n\t\t\t\t\t\\end{pmatrix}$}\n\t\t\t\t&(25,15)&(15,15,10)&40&100 \\\\ \n\t\t\t\t\\hline\n\t\t\t\t(1,0),(1,1),(0,2) &\n\t\t\t\t\\scalebox{.7}{\\raisebox{-4.5ex}{}$\\begin{pmatrix}\n\t\t\t\t\t\tt^6 & 0\\\\\n\t\t\t\t\t\t0& t\n\t\t\t\t\t\\end{pmatrix},\\begin{pmatrix}\n\t\t\t\t\t\tt & 0&0\\\\\n\t\t\t\t\t\t0& t^{-4}&0\\\\\n\t\t\t\t\t\t0&0&t^{-4}\n\t\t\t\t\t\\end{pmatrix}$}\n\t\t\t\t&(20,15)&(15,10,10)&35&90 \\\\ \n\t\t\t\t\\hline\n\t\t\t\t(2,0),(0,3) &\n\t\t\t\t\\scalebox{.7}{\\raisebox{-4.5ex}{}$\\begin{pmatrix}\n\t\t\t\t\t\tt^3 & 0\\\\\n\t\t\t\t\t\t0& t^3\n\t\t\t\t\t\\end{pmatrix},\\begin{pmatrix}\n\t\t\t\t\t\tt^{-2} & 0&0\\\\\n\t\t\t\t\t\t0& t^{-2}&0\\\\\n\t\t\t\t\t\t0&0&t^{-2}\n\t\t\t\t\t\\end{pmatrix}$}\n\t\t\t\t&(15,15)&(10,10,10)&30& 90 \\\\ \\hline\n\t\t\t\\end{tabular}\n\\caption{Weights of universal representations on strata}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Full Exceptional Sequence for a Fine Quiver Moduli Space", "authors": ["Svetlana Makarova", "Junyu Meng"], "url": "https://arxiv.org/abs/2412.15390v1", "attribution": "\"Full Exceptional Sequence for a Fine Quiver Moduli Space\" by Svetlana Makarova and Junyu Meng, arXiv:2412.15390v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.04166v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|} \n \\hline\n & \\multicolumn{2}{|c|}{top-1} & \\multicolumn{2}{|c|}{top-5}\\\\ \n \\hline\n Model & CIFAR10 & Flowers102 & CIFAR10 & Flowers102\\\\\n \\hline\n AdaBoost & 31.08 & 2.1 & 77.2 & 4.8 \\\\ \n \\hline\n LigthGBM & 53.1 & 12.2 & 96.3 & 20.0\\\\\n \\hline\n logistic regression & 37.5 & 18.2 & 77.5 & 71.8 \\\\\n \\hline\n random forest & 47.2 & 15.6 & 98.2 & 83.2\\\\\n \\hline\n XGBoost & 53.8 & 12.9 & 94.3& 94.6\\\\\n \\hline\n DenseNet121 & 86.9 & 41.7 & 97.5 & 96.4\\\\\n \\hline\n resNet18 & 84.6 & 34.01 & 95.5 & 95.1\\\\\n \\hline\n resNet34 & 84.9 & 35.98 & 95.8 & 95.6\\\\\n \\hline\n resNet50 & 87.8 & 31.07 & 98.6 & 97.7\\\\\n \\hline\n VGG11 & 85.3 & 42.3 & 96.2& 96.1\\\\\n \\hline\n VGG16 & 86.1 & 43.6 & 97.3 & 96.5\\\\\n \\hline\n\\end{tabular}\n\\caption{Accuracy of different models for different datasets.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms", "authors": ["Disha Ghandwani", "Neeraj Sarna", "Yuanyuan Li", "Yang Lin"], "url": "https://arxiv.org/abs/2412.04166v1", "attribution": "\"An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms\" by Disha Ghandwani, Neeraj Sarna, Yuanyuan Li, and Yang Lin, arXiv:2412.04166v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11864v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\nSource & Accuracy & 95\\%CI & Data\\\\\n\\hline\n ALL-IDB2 & 1.00 & (0.9824, 1.0000) & Training \\\\ \\hline\n C-NMC & 1.00 & (0.9996, 1.0000) & Training \\\\ \\hline \n \\hline\n ALL-IDB2 & 1.00 & (0.9315, 1.000) & Validation \\\\ \\hline\n C-NMC & 0.8645 & (0.8493, 0.8788) & Validation \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The accuracy and 95\\% confidence interval (CI) for the various datasets from our RF model.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features", "authors": ["William Franz Lamberti"], "url": "https://arxiv.org/abs/2201.11864v1", "attribution": "\"Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features\" by William Franz Lamberti, arXiv:2201.11864v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.06553v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|}\n\t\t\\hline\n\t\t\\hline \n\t\t\\textbf{Step 1} & Traffic event matrix (TEM)\\\\ \n\t\t\\textbf{Step 2} & Event trigger conditions\\\\\n\t\t\\textbf{Step 3} & Event counter\\\\ \n\t\t\\textbf{Step 4} & Scenario time frame\\\\ \n\t\t\\textbf{Step 5} & Storage of vehicle states\\\\ \n\t\t\\hline\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Definitions of required implementation steps for STM.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Stress Testing Method for Scenario Based Testing of Automated Driving Systems", "authors": ["Demin Nalic", "Hexuan Li", "Arno Eichberger", "Christoph Wellershaus", "Aleksa Pandurevic", "Branko Rogic"], "url": "https://arxiv.org/abs/2011.06553v2", "attribution": "\"Stress Testing Method for Scenario Based Testing of Automated Driving Systems\" by Demin Nalic, Hexuan Li, Arno Eichberger, Christoph Wellershaus, Aleksa Pandurevic, and Branko Rogic, arXiv:2011.06553v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.22936v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model Rankings Based on Average Metrics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{clccccc}\n\\toprule\n\\textbf{Rank} & \\textbf{Model} & \\textbf{Avg R-1} & \\textbf{Avg R-2} & \\textbf{Avg R-L} & \\textbf{Avg Cosine} & \\textbf{Avg Jaccard} \\\\\n\\midrule\n1 & Gemini & 0.56 & 0.22 & 0.16 & 0.63 & 0.22 \\\\\n2 & GPT & 0.31 & 0.08 & 0.10 & 0.68 & 0.13 \\\\\n3 & Perplexity & 0.29 & 0.08 & 0.10 & 0.71 & 0.13 \\\\\n4 & Claude & 0.27 & 0.08 & 0.09 & 0.67 & 0.12 \\\\\n5 & DeepSeek & 0.16 & 0.03 & 0.06 & 0.59 & 0.09 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis", "authors": ["Md Talha Mohsin"], "url": "https://arxiv.org/abs/2507.22936v1", "attribution": "\"Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis\" by Md Talha Mohsin, arXiv:2507.22936v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.17839v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Deviation in installed capacity across approaches relative to perfect information for different profiles and cases.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccc}\n\\hline\nModel & Tech & Profile & PI & \\textbf{$ARSO_{\\beta=0}$} & \\textbf{$ARO_{\\beta=0}$} & \\textbf{$ARSO_{\\beta=10}$} & \\textbf{$ARO_{\\beta=10}$} & \\textbf{$ARSO_{\\beta=20}$} & \\textbf{$ARO_{\\beta=20}$} \\\\ \\hline\n\\multirow{2}{*}{Only PV} & PV & A & 0.053 & 0.001 & -0.003 & 0.005 & 0.014 & 0.048 & 0.084 \\\\\n & PV & B & 0.050 & -0.050 & 0.000 & 0.005 & 0.020 & 0.027 & 0.087 \\\\ \\hline\n\\multirow{4}{*}{PV and BESS} & PV & A & 0.053 & 0.004 & -0.002 & 0.012 & 0.034 & 0.050 & 0.089 \\\\\n & BESS & A & 0.000 & 0.038 & 0.069 & 0.060 & 0.053 & 0.087 & 0.090 \\\\\n & PV & B & 0.050 & 0.000 & 0.000 & 0.007 & 0.055 & 0.031 & 0.085 \\\\\n & BESS & B & 0.000 & 0.000 & 0.000 & 0.040 & 0.058 & 0.051 & 0.036 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Adaptive Robust Optimization Models for DER Planning in Distribution Networks under Long- and Short-Term Uncertainties", "authors": ["Fernando García-Muñoz", "Cristian Duran-Mateluna"], "url": "https://arxiv.org/abs/2503.17839v1", "attribution": "\"Adaptive Robust Optimization Models for DER Planning in Distribution Networks under Long- and Short-Term Uncertainties\" by Fernando García-Muñoz and Cristian Duran-Mateluna, arXiv:2503.17839v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17106v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n Resolution & Error & Order \\\\\n \\hline\n $h_0$ & 6.38E$-$4 & $-$ \\\\\n \\hline\n $h_1$ & 7.99E$-$5 & $3.00$ \\\\\n \\hline\n $h_2$ & 1.00E$-$5 & $3.00$ \\\\\n \\hline\n $h_3$ & 1.25E$-$6 & $3.00$ \\\\\n \\hline\n $h_4$ & 1.56E$-$7 & $3.00$ \\\\\n \\hline\n $h_5$ & 1.95E$-$8 & $3.00$ \\\\\n \\hline\n $h_6$ & 2.44E$-$9 & $3.00$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Improved weights (), $\\beta=h^2$, example 2.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17106v1", "attribution": "\"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10650v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{PAD-UFES dataset classification results}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\n\\textbf{Experiment} & $\\textbf{\\textit{n}}_f$ & \\textbf{SE} & \\textbf{SP} & \\textbf{AC} & \\textbf{BAC} \\\\ \\hline\nImage features & 59 & 0.664 & 0.816 & 0.640 & 0.740 \\\\\nContext information & 7 & 0.684 & 0.836 & 0.692 & 0.760 \\\\\nCombined & 59+7 & \\textbf{0.744} & \\textbf{0.872} & \\textbf{0.753} & \\textbf{0.808} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data", "authors": ["José G. M. Esgario", "Renato A. Krohling"], "url": "https://arxiv.org/abs/2201.10650v1", "attribution": "\"Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data\" by José G. M. Esgario and Renato A. Krohling, arXiv:2201.10650v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.20474v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Lid-driven cavity flow: steady-state location $(x,y)$ of the primary vortex.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n \\textbf{} & \\textbf{Present (Taylor--Hood)} & \\textbf{Present (equal-order)}\\\\\n \\hline\n $(0.5547,0.6055)$ & $(0.555,0.605)$ & $(0.555,0.605)$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Consistent splitting SAV schemes for finite element approximations of incompressible flows", "authors": ["Douglas R. Q. Pacheco"], "url": "https://arxiv.org/abs/2503.20474v1", "attribution": "\"Consistent splitting SAV schemes for finite element approximations of incompressible flows\" by Douglas R. Q. Pacheco, arXiv:2503.20474v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.18600v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the temporal model performances with and without explicit multi-scale modeling}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|rrrrrr}\n \\toprule\n \\multirow{2}[2]{*}{Model} & \\multirow{2}[2]{*}{Multiscale?} & \\multicolumn{3}{c}{CSI300} & \\multicolumn{3}{c}{SP500} \\\\\n & & IC & AR & Sharpe & IC & AR & Sharpe \\\\\n \\midrule\n \\multirow{2}[1]{*}{RNN} & Single-scale & & & & & & \\\\\n & Multi-scale & & & & & & \\\\\n \\multirow{2}[0]{*}{CNN} & Single-scale & & & & & & \\\\\n & Multi-scale & & & & & & \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "QuantBench: Benchmarking AI Methods for Quantitative Investment", "authors": ["Saizhuo Wang", "Hao Kong", "Jiadong Guo", "Fengrui Hua", "Yiyan Qi", "Wanyun Zhou", "Jiahao Zheng", "Xinyu Wang", "Lionel M. Ni", "Jian Guo"], "url": "https://arxiv.org/abs/2504.18600v1", "attribution": "\"QuantBench: Benchmarking AI Methods for Quantitative Investment\" by Saizhuo Wang, Hao Kong, Jiadong Guo, Fengrui Hua, Yiyan Qi, Wanyun Zhou, Jiahao Zheng, Xinyu Wang, Lionel M. Ni, and Jian Guo, arXiv:2504.18600v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10253v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The caption to the table with multiple columns.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|rr|rr|}\\hline\n Row $1$ & c1 & c2 & c3 & c4 \\\\\n Row $2$ & d1 & d2 & d3 & d4\\\\\n Row $3$ & e1 & e2 & e3 & e4 \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "The emergence of visual semantics through communication games", "authors": ["Daniela Mihai", "Jonathon Hare"], "url": "https://arxiv.org/abs/2101.10253v1", "attribution": "\"The emergence of visual semantics through communication games\" by Daniela Mihai and Jonathon Hare, arXiv:2101.10253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2301.09722v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lrrrrrrrrrr}\n$\\tau$ & 0.10 & & 0.25 & & 0.50 & & 0.75 & & 0.90 & \\\\\n\\midrule\n & Bias & Std.Err & Bias & Std.Err & Bias & Std.Err & Bias & Std.Err & Bias & Std.Err \\\\\n\\midrule\nPanel A: T=500 & & & & & & & & & & \\\\\nState 1 & & & & & & & & & & \\\\\n$\\beta_{1,1}$ = -1 & -0.01841 & 0.03157 & -0.03955 & 0.05333 & -0.09216 & 0.09981 & -0.20451 & 0.19438 & -0.45862 & 0.36374 \\\\\n$\\beta_{2,1}$ = 2 & -0.00277 & 0.03319 & 0.00136 & 0.04505 & 0.01644 & 0.06219 & 0.05378 & 0.09196 & 0.1013 & 0.12821 \\\\\nState 2 & & & & & & & & & & \\\\\n$\\beta_{1,2}$ = 1 & -0.0129 & 0.02844 & -0.02082 & 0.04301 & -0.0259 & 0.07011 & -0.01308 & 0.12864 & 0.06959 & 0.24556 \\\\\n$\\beta_{2,2}$ = -2 & 0.0048 & 0.03329 & 0.0137 & 0.05402 & 0.04164 & 0.09423 & 0.10806 & 0.18734 & 0.27889 & 0.36313 \\\\\n\\midrule\n & & & & & & & & & & \\\\\nPanel B: T = 1000 & & & & & & & & & & \\\\\nState 1 & & & & & & & & & & \\\\\n$\\beta_{1,1}$ = -1 & -0.01893 & 0.02223 & -0.0407 & 0.03742 & -0.09205 & 0.07219 & -0.20502 & 0.14815 & -0.48193 & 0.31102 \\\\\n$\\beta_{2,1}$ = 2 & -0.00173 & 0.02268 & 0.0029 & 0.03231 & 0.01791 & 0.04616 & 0.05418 & 0.06687 & 0.10616 & 0.10064 \\\\\nState 2 & & & & & & & & & & \\\\\n$\\beta_{1,2}$ = 1 & -0.01307 & 0.02039 & -0.01932 & 0.0304 & -0.02164 & 0.05015 & -0.00369 & 0.09865 & 0.0976 & 0.2139 \\\\\n$\\beta_{2,2}$ = -2 & 0.00362 & 0.02197 & 0.01112 & 0.03502 & 0.03628 & 0.06447 & 0.10502 & 0.14078 & 0.30792 & 0.32359 \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{Bias and standard error values of the state-regression parameter estimates with skew-Ged distributed errors for $T=500$ (Panel A) and $T=1000$ (Panel B) for each expectile level considered.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Expectile hidden Markov regression models for analyzing cryptocurrency returns", "authors": ["Beatrice Foroni", "Luca Merlo", "Lea Petrella"], "url": "https://arxiv.org/abs/2301.09722v2", "attribution": "\"Expectile hidden Markov regression models for analyzing cryptocurrency returns\" by Beatrice Foroni, Luca Merlo, and Lea Petrella, arXiv:2301.09722v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.13177v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The result of ablation study.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c||c|}\n\\hline\nCondition & NIQE($\\downarrow$) & ENTROPY($\\uparrow$)\\\\\n\\hline\nwith $D_C$, w/o $D_{T}$, w/o $D_{M}$ & 5.416 & 7.083 \\\\\n\\hline\nwith $D_T$, w/o $D_{C}$, w/o $D_{M}$ & 5.656 & 6.910 \\\\\n\\hline\nwith $D_M$, w/o $D_{T}$, w/o $D_{C}$ & 4.840 & 6.743\\\\\n\\hline\nwith $D_{T}$, with $D_{M}$, w/o $D_C$ & 4.589 & 7.132 \\\\\n\\hline\nwith $D_{C}$, with $D_{M}$, w/o $D_T$ & 3.789 & 7.291 \\\\\n\\hline\nwith $D_{T}$, with $D_{C}$, w/o $D_M$ & 3.929 & 6.992 \\\\\n\\hline\nwith $D_M$, $D_{T}$, $D_{C}$, w/o $CPAF$ & 6.625 & 7.067 \\\\\n\\hline\ndefault configuration & 3.485 & 7.506 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "UMLE: Unsupervised Multi-discriminator Network for Low Light Enhancement", "authors": ["Yangyang Qu", "Kai Chen", "Chao Liu", "Yongsheng Ou"], "url": "https://arxiv.org/abs/2012.13177v2", "attribution": "\"UMLE: Unsupervised Multi-discriminator Network for Low Light Enhancement\" by Yangyang Qu, Kai Chen, Chao Liu, and Yongsheng Ou, arXiv:2012.13177v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.12395v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n \\hline\n Training & Top & Before & After \\\\\n Set & Features & Aug. (\\%) & Aug. (\\%) \\\\\n \\hline\n Average & 10 & 59.090 & 64.224 \\\\\n Average & 20 & 59.005 & 60.368 \\\\\n Average & 30 & 60.808 & 61.060 \\\\\n Average & 40 & 60.531 & 65.446 \\\\\n 1,2,3,4 & 10 & 98.263 & 95.037 \\\\\n 1,2,3,4 & 20 & 96.278 & 96.386 \\\\\n 1,2,3,4 & 30 & 97.643 & 98.325 \\\\\n 1,2,3,4 & 40 & 99.752 & 97.519 \\\\\n 1,2,3,5 & 10 & 73.718 & 72.716 \\\\\n 1,2,3,5 & 20 & 78.205 & 76.923 \\\\\n 1,2,3,5 & 30 & 83.974 & 78.846 \\\\\n 1,2,3,5 & 40 & 79.808 & 81.851 \\\\\n 1,2,4,5 & 10 & 41.088 & 54.305 \\\\\n 1,2,4,5 & 20 & 40.785 & 54.381 \\\\\n 1,2,4,5 & 30 & 42.296 & 54.343 \\\\\n 1,2,4,5 & 40 & 41.692 & 54.192 \\\\\n 1,3,4,5 & 10 & 57.249 & 76.069 \\\\\n 1,3,4,5 & 20 & 58.364 & 51.487 \\\\\n 1,3,4,5 & 30 & 58.736 & 51.394 \\\\\n 1,3,4,5 & 40 & 59.480 & 73.281 \\\\\n 2,3,4,5 & 10 & 25.134 & 22.995 \\\\\n 2,3,4,5 & 20 & 21.390 & 22.660 \\\\\n 2,3,4,5 & 30 & 21.390 & 22.393 \\\\\n 2,3,4,5 & 40 & 21.925 & 20.388 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sound Classification of Four Insect Classes", "authors": ["Yinxuan Wang", "Sudip Vhaduri"], "url": "https://arxiv.org/abs/2412.12395v1", "attribution": "\"Sound Classification of Four Insect Classes\" by Yinxuan Wang and Sudip Vhaduri, arXiv:2412.12395v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11106v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Experiment} & \\textbf{SSIM$\\uparrow$} & \\textbf{FID$\\downarrow$} \\\\\n\\midrule\nbaseline & 0.8015 & 268.491 \\\\\nbaseline+\\textbf{StainStylePrompt} & \\textbf{0.8121} & \\textbf{255.261} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Evaluation of effectiveness of StainStylePrompt using FID, SSIM.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion", "authors": ["Bing Xiong", "Yue Peng", "RanRan Zhang", "Fuqiang Chen", "JiaYe He", "Wenjian Qin"], "url": "https://arxiv.org/abs/2412.11106v1", "attribution": "\"Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion\" by Bing Xiong, Yue Peng, RanRan Zhang, Fuqiang Chen, JiaYe He, and Wenjian Qin, arXiv:2412.11106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.07037v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Heterogeneous Effects of XBRL Implementation}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n& \\multicolumn{4}{c}{Incorporation Speed (Log Days)} \\\\\n& (1) & (2) & (3) & (4) \\\\\n\\midrule\nXBRL Implementation & -0.089* & -0.067 & -0.045 & -0.078 \\\\\n& (0.0456) & (0.0423) & (0.0398) & (0.0434) \\\\\n\\\\\nXBRL × Small Firm & -0.145*** & & & \\\\\n& (0.0378) & & & \\\\\nXBRL × High Retail & & -0.156*** & & \\\\\n& & (0.0392) & & \\\\\nXBRL × Low Institutional & & & -0.134*** & \\\\\n& & & (0.0367) & \\\\\nXBRL × Low Analyst Coverage & & & & -0.142*** \\\\\n& & & & (0.0383) \\\\\n\\\\\nControls & Yes & Yes & Yes & Yes \\\\\nFixed Effects & Yes & Yes & Yes & Yes \\\\\n\\\\\nObservations & 847,523 & 847,523 & 847,523 & 847,523 \\\\\nR-squared & 0.371 & 0.369 & 0.368 & 0.370 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Cognitive Load and Information Processing in Financial Markets: Theory and Evidence from Disclosure Complexity", "authors": ["Yimin Du", "Guolin Tang"], "url": "https://arxiv.org/abs/2507.07037v1", "attribution": "\"Cognitive Load and Information Processing in Financial Markets: Theory and Evidence from Disclosure Complexity\" by Yimin Du and Guolin Tang, arXiv:2507.07037v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.06685v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Voltage regulation and var sharing index comparison }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\hline\n\\multirow{2}{*}{Case Type} & \\multicolumn{2}{c}{Event 1} & \\multicolumn{2}{c}{Event 2}\\\\\n & $V_{error}$ & $MQSI$ & $V_{error}$ & $MQSI$ \\\\\n\\hline\nno-control & 0.015 & 0.22 & 0.006 & 0.30\\\\\nuncoordinated & 0.001 & 2.03 & 0.000 & 1.66\\\\\nGFM-coordinated & 0.002 & 1.77 & 0.002 & 1.07\\\\\nfully-coordinated & 0.002 & 0.04 & 0.001 & 0.05\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Coordinated Frequency and Voltage Regulation of Grid-Following and Grid-Forming Inverters", "authors": ["Ankit Singhal", "Thanh Long Vu", "Wei Du"], "url": "https://arxiv.org/abs/2012.06685v2", "attribution": "\"Coordinated Frequency and Voltage Regulation of Grid-Following and Grid-Forming Inverters\" by Ankit Singhal, Thanh Long Vu, and Wei Du, arXiv:2012.06685v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.06276v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameter estimates for 12/31/2021 for the ESG-valued models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc|cccc|cccc}\n\t\\toprule\n\tEquity & $\\hat{\\mu}_0$ & $\\hat{\\mu}_{0.25}$ & $\\hat{\\mu}_{0.5}$ & $\\hat{\\mu}_{0.75}$\n\t\t & $\\hat{\\sigma}_0$ & $\\hat{\\sigma}_{0.25}$ & $\\hat{\\sigma}_{0.5}$ & $\\hat{\\sigma}_{0.75}$\n\t\t & $\\hat{p}_0$ & $\\hat{p}_{0.25}$ & $\\hat{p}_{0.5}$ & $\\hat{p}_{0.75}$\\\\\n\t\\omit & \\multicolumn{4}{| c}{$\\left(\\times 10^{-3}\\right)$}\n\t\t & \\multicolumn{4}{| c |}{$\\left(\\times 10^{-2}\\right)$} &\\multicolumn{4}{c}{$\\ $} \\\\\n\t\\midrule\n\t\\omit & \\multicolumn{12}{c}{arithmetic return} \\\\\n\tMSFT & 1.76 & 2.24 & 2.72 & 3.19 & 1.33 & 0.99 & 0.66 & 0.33 & 0.52 & 0.57 & 0.66 & 0.85 \\\\\n\tAMZN & 0.21 & 0.38 & 0.55 & 0.72 & 1.52 & 1.14 & 0.76 & 0.38 & 0.51 & 0.52 & 0.54 & 0.60 \\\\\n\tAAPL & 1.31 & 0.50 & -0.30 & -1.11 & 1.58 & 1.19 & 0.79 & 0.40 & 0.52 & 0.51 & 0.46 & 0.36 \\\\\n\t\\omit & \\multicolumn{12}{c}{log-return} \\\\\n\tMSFT & 1.76 & 2.22 & 2.69 & 3.18 & 1.32 & 0.99 & 0.66 & 0.33 & 0.52 & 0.57 & 0.66 & 0.85 \\\\\n\tAMZN & 0.21 & 0.36 & 0.52 & 0.70 & 1.52 & 1.14 & 0.76 & 0.38 & 0.51 & 0.52 & 0.54 & 0.60 \\\\\n\tAAPL & 1.30 & 0.48 & -0.33 & -1.12 & 1.58 & 1.19 & 0.79 & 0.40 & 0.52 & 0.51 & 0.46 & 0.36 \\\\\n\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "ESG-valued discrete option pricing in complete markets", "authors": ["Yuan Hu", "W. Brent Lindquist", "Svetlozar T. Rachev"], "url": "https://arxiv.org/abs/2209.06276v1", "attribution": "\"ESG-valued discrete option pricing in complete markets\" by Yuan Hu, W. Brent Lindquist, and Svetlozar T. Rachev, arXiv:2209.06276v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.11471v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lccc}\n \\toprule\n MLE parameters & $b$ & $g$ & $q$\\\\\n \\midrule\n Standard MLE density (Green) & $5.66 \\cdot 10^{-3}$& $50.5$ & - \\\\\n Flexible MLE density (Red) & $2.29 \\cdot 10^{-3}$ & $94.9$ & $0.0339$ \\\\\n \\bottomrule \n \\end{tabular}\n\\caption{MLE parameters of the MBBEFD example rounded to three significant digits (Section~).}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modeling lower-truncated and right-censored insurance claims with an extension of the MBBEFD class", "authors": ["Selim Gatti", "Mario V. Wüthrich"], "url": "https://arxiv.org/abs/2310.11471v2", "attribution": "\"Modeling lower-truncated and right-censored insurance claims with an extension of the MBBEFD class\" by Selim Gatti and Mario V. Wüthrich, arXiv:2310.11471v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2010.08259v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model comparisons via Information Criteria (AIC and BIC) and forecasting capability (MSE and MAE) -- Sample period: June 1, 2009 - December 31, 2019. Best model in bold. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\t\t\t & \\textbf{AMEM} & \\textbf{X-MAP} & \\textbf{MAP} & \\textbf{L-MAP} & \\textbf{P-MAP} \\\\\n\t\t\t\\multicolumn{6}{c}{\\textbf{France}} \\\\\n\t\t\t\\hline\n\t\t\tAIC & 5.830 & 5.809 & 5.797 & \\textbf{5.796} & 5.797 \\\\\n\t\t\tBIC & 5.841 & 5.824 & 5.815 & \\textbf{5.814} & 5.815 \\\\\n\t\t\tMSE & 29.531 & 29.117 & 28.844 & \\textbf{28.627} & 28.639 \\\\\n\t\t\tQLike & 0.068 & 0.066 & \\textbf{0.065} & \\textbf{0.065} & \\textbf{0.065} \\\\\n\t\t\t & & & & & \\\\\n\t\t\t\\multicolumn{6}{c}{\\textbf{Germany}} \\\\\n\t\t\t\\hline\n\t\t\tAIC & 5.657 & 5.642 & 5.633 & \\textbf{5.631} & 5.632 \\\\\n\t\t\tBIC & 5.668 & 5.658 & 5.651 & \\textbf{5.649} & \\textbf{5.649} \\\\\n\t\t\tMSE & 23.551 & 23.269 & 23.096 & \\textbf{22.93} & 22.939 \\\\\n\t\t\tQLike & 0.054 & 0.053 & \\textbf{0.052} & \\textbf{0.052} & \\textbf{0.052} \\\\ \n\t\t\t & & & & & \\\\\n\t\t\t\\multicolumn{6}{c}{\\textbf{Italy}} \\\\\n\t\t\t\\hline\n\t\t\tAIC & 5.753 & 5.713 & \\textbf{5.699} & \\textbf{5.699} & \\textbf{5.699} \\\\\n\t\t\tBIC & 5.764 & 5.728 & \\textbf{5.717} & \\textbf{5.717} & \\textbf{5.717} \\\\\n\t\t\tMSE & 28.171 & 27.634 & 27.209 & \\textbf{27.191} & 27.203 \\\\\n\t\t\tQLike & 0.048 & 0.046 & \\textbf{0.045} & \\textbf{0.045} & \\textbf{0.045}\\\\\n\t\t\t & & & & & \\\\\n\t\t\t\\multicolumn{6}{c}{\\textbf{Spain}} \\\\\n\t\t\t\\hline\n\t\t\tAIC & 6.048 & 6.011 & 6.002 & \\textbf{5.997} & \\textbf{5.997} \\\\\n\t\t\tBIC & 6.059 & 6.027 & 6.02 & \\textbf{6.015} & \\textbf{6.015} \\\\\n\t\t\tMSE & 42.048 & 40.918 & 40.462 & 39.461 & \\textbf{39.439} \\\\\n\t\t\tQLike & 0.056 & 0.054 & \\textbf{0.053} & \\textbf{0.053} & \\textbf{0.053} \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Unconventional Policies Effects on Stock Market Volatility: A MAP Approach", "authors": ["Demetrio Lacava", "Giampiero M. Gallo", "Edoardo Otranto"], "url": "https://arxiv.org/abs/2010.08259v2", "attribution": "\"Unconventional Policies Effects on Stock Market Volatility: A MAP Approach\" by Demetrio Lacava, Giampiero M. Gallo, and Edoardo Otranto, arXiv:2010.08259v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.08983v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Different Pooling Options}\n\\begin{tabular}{|c|c|c|}\n\t\t\t\\hline\n\t\t\t\\textbf{Pooling [$p_{ant}$, $p_{sc}$]}&\\textbf{Antenna Configuration} & \\textbf{ME (m)}\\\\\n\t\t\t\\hline \n\t\t\t\n\t\t\t[1,4] & ULA & 0.00612 \\\\\n\t\t\t\\hline\n\t\t\t[2,2] & ULA & 0.01010 \\\\\n\t\t\t\\hline\n\t\t\t[4,1] & ULA & 0.01633 \\\\\n\t\t\t\\hline\n\t\t\t[1,4] & distributed & 0.00521 \\\\\n\t\t\t\\hline\n\t\t\t[2,2] & distributed &0.00732 \\\\\n\t\t\t\\hline\n\t\t\t[4,1] & distributed & 0.00982 \\\\\n\t\t\t\\hline\n\t\t\t[1,4] & URA & 0.01096 \\\\\n\t\t\t\\hline\n\t\t\t[2,2] & URA & 0.01183 \\\\\n\t\t\t\\hline\n\t\t\t[4,1] & URA & 0.01908 \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "CSI-Based Localization with CNNs Exploiting Phase Information", "authors": ["Anastasios Foliadis", "Mario H. Castañeda Garcia", "Richard A. Stirling-Gallacher", "Reiner S. Thomä"], "url": "https://arxiv.org/abs/2101.08983v1", "attribution": "\"CSI-Based Localization with CNNs Exploiting Phase Information\" by Anastasios Foliadis, Mario H. Castañeda Garcia, Richard A. Stirling-Gallacher, and Reiner S. Thomä, arXiv:2101.08983v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.11315v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{different choices of phase features $x(t)$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n\t\t\t\\hline\n\t\t\tfeature name & phase features $x(t)$ \\\\\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\tphase & $\\phi_p(t)$ \\\\\n\t\t\t\\hline\n\t\t\tphase delta & $\\Delta \\phi_p(t)$ \\\\\n\t\t\t\\hline\n\t\t\tphase delta + double-delta & $[\\Delta \\phi_p(t), \\Delta \\Delta\\phi_p(t))]$ \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "End-to-end Silent Speech Recognition with Acoustic Sensing", "authors": ["Jian Luo", "Jianzong Wang", "Ning Cheng", "Guilin Jiang", "Jing Xiao"], "url": "https://arxiv.org/abs/2011.11315v1", "attribution": "\"End-to-end Silent Speech Recognition with Acoustic Sensing\" by Jian Luo, Jianzong Wang, Ning Cheng, Guilin Jiang, and Jing Xiao, arXiv:2011.11315v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.09878v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ \\small In-sample validation of age-specific net migration projection on the scale of counts and rates (migrants per 100 people). The three methods were validated over seven time periods (between 1950-1960 and 2010-2020), 39 counties and 20 5-year age groups, i.e., 5460 values. MAE is mean absolute error. RMSE is root mean squared error. The cov80 and cov95 columns refer to the percentage of the observations that fell within their probability interval.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|rrrrr}\\hline\n\\bf Scale & \\bf Method & \\bf MAE & \\bf RMSE & \\bf Bias & \\bf cov80 & \\bf cov95\\\\\\hline\nCounts & Basic Rogers-Castro & 585.4 & 2054.0 & \\bf 0.00 & --- & --- \\\\\n& Deterministic FDM & 349.2 & 1207.3 & -0.04 & --- & --- \\\\\n& Bayesian FDM & \\bf 319.7 & \\bf 1118.3 & 1.00 & \\bf 86.7 & \\bf 95.6 \\\\\\hline\nRates & Basic Rogers-Castro & 12.14 & 24.91 & 1.61 & --- & --- \\\\\n& Deterministic FDM & 8.39 & 16.32 & \\bf -0.18 & --- & --- \\\\\n& Bayesian FDM & \\bf 7.76 & \\bf 13.83 & -1.03 & \\bf 86.7 & \\bf 95.6 \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Forecasting Net Migration By Age: The Flow-Difference Approach", "authors": ["Hana Ševčíková", "James Raymer", "Adrian E. Raftery"], "url": "https://arxiv.org/abs/2411.09878v1", "attribution": "\"Forecasting Net Migration By Age: The Flow-Difference Approach\" by Hana Ševčíková, James Raymer, and Adrian E. Raftery, arXiv:2411.09878v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15554v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overview of the analytical test functions from BoTorch that were used for benchmarking the described optimization strategies. For each function, the first row describes the analytical function with only output-dependent objectives, and the second row includes an additional input-dependent objective. Visualizations of the objective ranges and inter-objective correlations are provided in the following figures.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\toprule\nFunction & Number of Inputs & Number of Outputs & Objective \\#1 & Objective \\#2 & Objective \\#3 \\\\ \\midrule \\midrule \nBNH & \\multirow{2}{*}{2} & \\multirow{2}{*}{2} & $y_0 > -60.0$ & $y_1 > -11.0$ & \\\\\nBNH* & & & $y_0 > -60.0$ & $x_0 - x_1 > 2.0$ & $y_1 > -15.0$ \\\\ \\midrule\nDH4 & \\multirow{2}{*}{6} & \\multirow{2}{*}{2} & $y_0 > -0.15$ & $y_1 > -15.0$ & \\\\\nDH4* & & & $y_0 > -0.15$ & $x_1 > 0.6$ & $y_1 > -15.0$ \\\\ \\midrule\nDTLZ5 & \\multirow{2}{*}{4} & \\multirow{2}{*}{2} & $y_0 > -0.5$ & $y_1 > -0.95$ & \\\\\nDTLZ5* & & & $y_0 > -0.5$ & $x_2 + x_3 < 1.0$ & $y_1 > -0.95$ \\\\ \\midrule\nZDT1 & \\multirow{2}{*}{10} & \\multirow{2}{*}{2} & $y_0 > -0.18$ & $y_1 > -2.5$ & \\\\\nZDT1* & & & $y_0 > -0.18$ & $x_1 + x_5 < 0.5$ & $y_1 > -2.5$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives", "authors": ["Mohammad Haddadnia", "Leonie Grashoff", "Felix Strieth-Kalthoff"], "url": "https://arxiv.org/abs/2501.15554v1", "attribution": "\"BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives\" by Mohammad Haddadnia, Leonie Grashoff, and Felix Strieth-Kalthoff, arXiv:2501.15554v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14416v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|l|}\n\t\t\t\\hline\n\t\t\t\\textbf{Class}& \\textbf{Nº finite singular points} & \\textbf{$\\boldsymbol{ind_F}$} & \\textbf{Global phase portraits} \\\\\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\t1\n\t\t\t&\n\t\t\t3\n\t\t\t&\n\t\t\t1\n\t\t\t&\n\t\t\tG1, G2, G3, G4, G5, G6, G7, G8, G9, G10.\n\t\t\t\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t2\n\t\t\t&\n\t\t\t\\multirow{3}{1cm}{\\centering{2}}\n\t\t\t&\n\t\t\t1\n\t\t\t&\n\t\t\tG11, G12, G13, G14.\n\t\t\t\\\\\n\t\t\t\\cline{1-1}\\cline{3-4}\n\t\t\t\n\t\t\t\n\t\t\t3\n\t\t\t&\n\t\t\t\\multirow{3}{*}\n\t\t\t&\n\t\t\t0\n\t\t\t&\n\t\t\tG15, G16, G17, G18, G23, G24, G25, G26.\n\t\t\t\\\\\n\t\t\t\\cline{1-1}\\cline{3-4}\n\t\t\t\n\t\t\t\n\t\t\t4\n\t\t\t&\n\t\t\t\\multirow{3}{*}\n\t\t\t&\n\t\t\t2\n\t\t\t&\n\t\t\tG19, G20, G27, G28.\n\t\t\t\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\n\t\t\t5\n\t\t\t&\n\t\t\t\\multirow{3}{1cm}{\\centering{1}}\n\t\t\t&\n\t\t\t0\n\t\t\t&\n\t\t\tG21, G22, G29, G30.\n\t\t\t\\\\\n\t\t\t\\cline{1-1}\\cline{3-4}\n\t\t\t\n\t\t\t\n\t\t\t6\n\t\t\t&\n\t\t\t\\multirow{3}{*}\n\t\t\t&\n\t\t\t-1\n\t\t\t&\n\t\t\tG31, G32.\n\t\t\t\\\\\n\t\t\t\\cline{1-1}\\cline{3-4}\n\t\t\t\n\t\t\t\n\t\t\t7\n\t\t\t&\n\t\t\t\\multirow{3}{*}\n\t\t\t&\n\t\t\t1\n\t\t\t&\n\t\t\tG33, G34, G35, G36.\n\t\t\t\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Classes of equivalence according to the number of finite singular points and to the $ind_F$. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Planar Kolmogorov systems with infinitely many singular points at infinity", "authors": ["Érika Diz-Pita", "Jaume Llibre", "M. Victoria Otero-Espinar"], "url": "https://arxiv.org/abs/2501.14416v1", "attribution": "\"Planar Kolmogorov systems with infinitely many singular points at infinity\" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.14416v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19259v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Optimal Learning Rates and Convergence Times from Figure~}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline \n & \\textbf{optimal learning rate} & \\textbf{optimal convergence time} \\\\\n\\hline\n$\\eta$ coordinates & $\\alpha_{\\eta}=0.0036$ & \\hspace{0.2cm}$k=29$ \\\\\nnatural gradient & $\\alpha_{ng}\\in [0.7141,1.16]$ & $k=2$\\\\\n$\\theta$ coordinates & $\\alpha_{\\theta}\\in [11.96,14.24]$ & \\hspace{0.2cm}$k=12$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Convergence Properties of Natural Gradient Descent for Minimizing KL Divergence", "authors": ["Adwait Datar", "Nihat Ay"], "url": "https://arxiv.org/abs/2504.19259v2", "attribution": "\"Convergence Properties of Natural Gradient Descent for Minimizing KL Divergence\" by Adwait Datar and Nihat Ay, arXiv:2504.19259v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.10948v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n Method & Precision & Recall & $F_1$ Score \\\\ \\hline\n No Augmentation & \\textbf{0.8901} & 0.8265 & 0.8571 \\\\ \\hline\n Diffusion & 0.8737 & \\textbf{0.8469} & \\textbf{0.8601} \\\\ \\hline\n \\end{tabular}\n\\caption{Precision, recall, and $F_1$ score for XGBoost on a test set after being trained with and without data augmentation with diffusion models. The maximum value in each column is bolded.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Generative Modeling with Diffusion", "authors": ["Justin Le"], "url": "https://arxiv.org/abs/2412.10948v2", "attribution": "\"Generative Modeling with Diffusion\" by Justin Le, arXiv:2412.10948v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10245v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{soul}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average true positive rate per class for differing IR sensor activation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l}\n & \\multicolumn{2}{l}{Per Class True Positive Rate} \\\\\nIR Sensor Activation & Average & STD Error \\\\\n\\hline\n\\textbf{Required}, N=13 & \\hl{38.92}\\% & \\hl{0.01} \\\\\nNot Required, N=8 & \\hl{13.71}\\% & \\hl{0.02} \\\\\nMajority & 5.34\\% & N/A \\\\\nChance & 4.76\\% & N/A \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "AirWare: Utilizing Embedded Audio and Infrared Signals for In-Air Hand-Gesture Recognition", "authors": ["Nibhrat Lohia", "Raunak Mundada", "Arya D. McCarthy", "Eric C. Larson"], "url": "https://arxiv.org/abs/2101.10245v1", "attribution": "\"AirWare: Utilizing Embedded Audio and Infrared Signals for In-Air Hand-Gesture Recognition\" by Nibhrat Lohia, Raunak Mundada, Arya D. McCarthy, and Eric C. Larson, arXiv:2101.10245v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08474v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Detection accuracy of ET with RE-AP on the 10 five-class domains with 1 attack, 2 attacks, and no attack, compared with RED-AP (original) and RED-AP (MAD).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n\t\t\t\t&1 attack &2 attacks &clean\n\t\t\t\t\\\\ \\hline\n\t\t\t\tET (RE-AP) &10/10 &10/10 &10/10\\\\\n\t\t\t\tRED-AP (original) &10/10 &0/10 &9/10\\\\\n\t\t\t\tRED-AP (MAD) &6/10 &2/10 &7/10\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios", "authors": ["Zhen Xiang", "David J. Miller", "George Kesidis"], "url": "https://arxiv.org/abs/2201.08474v2", "attribution": "\"Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios\" by Zhen Xiang, David J. Miller, and George Kesidis, arXiv:2201.08474v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10500v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{rotating}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Datasets vs. Security services}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|}\n\\hline\nDataset & \\rotatebox{90}{~\\parbox{2cm}{Integrity~}} & \\rotatebox{90}{~\\parbox{2cm}{Authentication~}} & \\rotatebox{90}{~\\parbox{2cm}{Availability~}}& \\rotatebox{90}{~\\parbox{2cm}{Confidentiality~}} \\\\ \\hline\nVeReMi & X & & & \\\\ \\hline\nVeReMi extension & X & & X & \\\\ \\hline\nDARPA & & X & X & \\\\ \\hline\nCAIDA DDos 2007 & & & X & \\\\ \\hline\nAWID2 & & X & X & X \\\\ \\hline\nKDD CUP 99 & & X & X & \\\\ \\hline\nNSL-KDD & & X & X & \\\\ \\hline\nKyoto & & X & X & \\\\ \\hline\nUNSW-NB15 & & X & X & \\\\ \\hline\nCICIDS2017 & X & X & X & \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks", "authors": ["Abdelwahab Boualouache", "Thomas Engel"], "url": "https://arxiv.org/abs/2201.10500v1", "attribution": "\"A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks\" by Abdelwahab Boualouache and Thomas Engel, arXiv:2201.10500v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13229v2_tex_table16.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The results of the Chi-square for one day vs year with sideswipe crashes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrr}\n & Sun & M & T & W & T & F & Sat \\\\\nstatistic & 8.58E-01 & 1.50E+00 & 2.73E+00 & 2.81E+00 & 3.40E+00 & 5.18E+00 & 3.72E+00 \\\\\nP-value & 9.31E-01 & 8.27E-01 & 6.05E-01 & 5.90E-01 & 4.93E-01 & 2.69E-01 & 4.46E-01 \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study", "authors": ["Xiwen Chen", "Hao Wang", "Abolfazl Razi", "Brendan Russo", "Jason Pacheco", "John Roberts", "Jeffrey Wishart", "Larry Head", "Alonso Granados Baca"], "url": "https://arxiv.org/abs/2201.13229v2", "attribution": "\"Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study\" by Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head, and Alonso Granados Baca, arXiv:2201.13229v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.21099v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of out-of-sample validation with simulations ($a_{1|0}=3, \\psi=1$, $\\Delta=0.5$).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\toprule\n & \\multicolumn{3}{c}{RMSE} & \\multicolumn{3}{c}{GDEV} \\\\\n\\cmidrule(l{3pt}r{3pt}){2-4} \\cmidrule(l{3pt}r{3pt}){5-7}\n & Independent & B{\\\"u}hlmann & SSM & Independent & B{\\\"u}hlmann & SSM\\\\\n\\midrule\nHomogeneous & 6067.64 & 6133.54 & 5981.14 & 6680.86 & 6764.54 & 6499.17\\\\\n & (724.26) & (690.46) & (701.65) & (180.72) & (189.24) & (175.27)\\\\\n \\hline\nHeterogeneous & 5974.26 & 6025.61 & 5878.33 & 6417.36 & 6473.82 & 6229.60\\\\\n & (727.88) & (695.29) & (704.12) & (176.98) & (190.43) & (175.06)\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An Observation-Driven State-Space Model for Claims Size Modeling", "authors": ["Jae Youn Ahn", "Himchan Jeong", "Mario V. Wüthrich"], "url": "https://arxiv.org/abs/2412.21099v1", "attribution": "\"An Observation-Driven State-Space Model for Claims Size Modeling\" by Jae Youn Ahn, Himchan Jeong, and Mario V. Wüthrich, arXiv:2412.21099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01853v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of the clean simulated data set}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c|c|c}\n \\toprule\n & Train & \\multicolumn{2}{c|}{Dev} & \\multicolumn{2}{c}{Test} \\\\\n & & short & long & short & long \\\\\n \\midrule\n Source & train\\_960 & dev\\_clean & dev\\_clean & test\\_clean & test\\_clean \\\\\n \\# of samples & 50,083 & 513 & 319 & 496 & 310 \\\\\n Avg. words. & 193 & 106 & 171 & 106 & 170 \\\\\n Avg. dur. (sec) & 61.9 & 34.1 & 54.1 & 35.4 & 56.1 \\\\ \\midrule\n Total dur. (hr) & 861.6 & 4.9 & 4.8 & 4.9 & 4.8 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Hypothesis Stitcher for End-to-End Speaker-attributed ASR on Long-form Multi-talker Recordings", "authors": ["Xuankai Chang", "Naoyuki Kanda", "Yashesh Gaur", "Xiaofei Wang", "Zhong Meng", "Takuya Yoshioka"], "url": "https://arxiv.org/abs/2101.01853v1", "attribution": "\"Hypothesis Stitcher for End-to-End Speaker-attributed ASR on Long-form Multi-talker Recordings\" by Xuankai Chang, Naoyuki Kanda, Yashesh Gaur, Xiaofei Wang, Zhong Meng, and Takuya Yoshioka, arXiv:2101.01853v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.07616v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{kNN val accuracy for several ways of sampling TF patches.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc|lc}\n\\toprule\n\\textbf{Sampling method} & \\textbf{kNN} & \\textbf{Sampling method} & \\textbf{kNN} \\\\\n\\midrule\nSampling at random & \\textbf{70.1} & $d = 125$ & 67.9 \\\\\n$d = 0$ (same patch) & 51.1 & $d = 200$ & 69.9 \\\\\n$d = 25$ & 61.5 & $d = 300$ & 68.5 \\\\\n$d = 75$ & 65.1 & $d = 400$ & 69.7 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Unsupervised Contrastive Learning of Sound Event Representations", "authors": ["Eduardo Fonseca", "Diego Ortego", "Kevin McGuinness", "Noel E. O'Connor", "Xavier Serra"], "url": "https://arxiv.org/abs/2011.07616v1", "attribution": "\"Unsupervised Contrastive Learning of Sound Event Representations\" by Eduardo Fonseca, Diego Ortego, Kevin McGuinness, Noel E. O'Connor, and Xavier Serra, arXiv:2011.07616v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2304.00544v1_tex_table34.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{OLS regression - Cycalility of occupational mobility}\n\\begin{tabular}{rlllll}\n \\toprule\n \\multicolumn{6}{c}{Level and Responsiveness of Mobility to the Unemployment Rate} \\\\\n \\midrule\n & Level & \\multicolumn{4}{c}{Cyclical responsiveness} \\\\ \\cmidrule(lr){2-2} \\cmidrule(lr){3-6}\n & Ave. & \\multicolumn{2}{c}{HP Filtered (1600)} & \\multicolumn{2}{c}{Band-Pass Filtered} \\\\\n \\cmidrule(lr){3-4} \\cmidrule(lr){5-6}\n& Mob. & up to '21 & up to `19 & up to '21 & up to '19 \\\\\n \\midrule\n \\multicolumn{6}{c}{Quarterly Occupational Mobility Rate, Hires from U (2010 MOG)} \\\\\n \\midrule\n All unemployed, 1976- & 0.475 & -0.223$^{\\star\\star\\star}$ & -0.105$^{\\star\\star}$ & -0.105 $^{\\star\\star\\star}$ & -0.104$^{\\star\\star\\star}$ \\\\\n & & (0.030) & (0.039) & (0.016) & (0.017) \\\\\n All unemployed, 1994- & 0.464 & -0.254$^{\\star\\star\\star}$ & -0.103$^{\\star\\star}$ & -0.096$^{\\star\\star\\star}$ & -0.104$^{\\star\\star\\star}$ \\\\\n & & (0.036) & (0.050) & (0.018) & (0.019) \\\\\n Unemployed, not on temporary layoffs, 1994- & 0.569 & -0.044$^{\\diamond}$ & -0.091$^{\\star}$ & -0.063$^{\\star\\star}$ & -0.085$^{\\star\\star\\star}$ \\\\\n & & (0.024) & (0.037) & (0.019) & (0.017) \\\\\n \\bottomrule\n \\bottomrule\n \\multicolumn{5}{l}{\\footnotesize $^{\\star\\star\\star}$ p-value<0.001; $^{\\star\\star}$ p-value<0.01; $^{\\star}$ p-value<0.05; $^{\\diamond}$ p-value<0.1.}\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Unemployment and Endogenous Reallocation over the Business Cycle", "authors": ["Carlos Carrillo-Tudela", "Ludo Visschers"], "url": "https://arxiv.org/abs/2304.00544v1", "attribution": "\"Unemployment and Endogenous Reallocation over the Business Cycle\" by Carlos Carrillo-Tudela and Ludo Visschers, arXiv:2304.00544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.00771v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\t\t\\toprule\n\t\t&\tRisk & Estimation function class& Error bound\\\\\n\t\t\\midrule\n\t\\tiny{\t}&\tnatural & FNNs& $n^{-2\\alpha/(d+2\\alpha)}$\\\\\n\t\t&\tnatural & FNNs with norm constraints&$n^{-\\alpha/(d+2\\alpha+1)}$\\\\\n\t\tThis paper &\t\tadversarial & FNNs with norm constraints& $n^{-2\\alpha/(2d+5\\alpha)}$\\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models", "authors": ["Changyu Liu", "Yuling Jiao", "Junhui Wang", "Jian Huang"], "url": "https://arxiv.org/abs/2309.00771v1", "attribution": "\"Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models\" by Changyu Liu, Yuling Jiao, Junhui Wang, and Jian Huang, arXiv:2309.00771v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18092v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Configurations with Different Scales}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\hline\n \\textbf{Index} & \\textbf{$d$} & \\textbf{$b$} & \\textbf{Dimension of $L-1$ Layer's Output} & \\textbf{Training Samples} \\\\\n \\hline\n 1 & 32 & 25 & 1024 & 32 \\\\ \n 2 &512 & 400 & 5120 & 10 \\\\\n 3 &512 & 400 & 20 & 2048 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Learning Provably Improves the Convergence of Gradient Descent", "authors": ["Qingyu Song", "Wei Lin", "Hong Xu"], "url": "https://arxiv.org/abs/2501.18092v4", "attribution": "\"Learning Provably Improves the Convergence of Gradient Descent\" by Qingyu Song, Wei Lin, and Hong Xu, arXiv:2501.18092v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.13663v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results of the Diebold-Mariano-like test for different method pairs in the North zone. Each cell contains the interval of values of \\(\\mu_0\\) leading to not reject the null hypothesis that $E(d_{t,ij})=\\mu_0$ at the \\(5\\%\\) significance level.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc} \n\\hline & Method 1 & Method 2 & Method 3 & Method 4 & Method 5 & Method 6 \\\\ \\toprule Method 1 & - & & & & & \\\\ Method 2 & \\([-9, -5]\\) & - & & & & \\\\ Method 3 & \\( [17, 26] \\) & \\( [23, 34] \\) & - & & & \\\\ Method 4 & \\( [-3917, -2870] \\) & \\( [-3909, -2865] \\) & \\( [-3942, -2889] \\) & - & & \\\\ Method 5 & \\( [-4037, -2959] \\) & \\( [-4028, -2954] \\) & \\( [-4061, -2978] \\) & \\( [-123, -85] \\) & - & \\\\ Method 6 & \\( [-3187, -2338] \\) & \\( [-3179, -2333] \\) & \\( [-3212, -2357] \\) & \\( [531, 731] \\) & \\( [621, 849] \\) & - \\\\ \\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Accounting carbon emissions from electricity generation: a review and comparison of emission factor-based methods", "authors": ["Marina Bertolini", "Pierdomenico Duttilo", "Francesco Lisi"], "url": "https://arxiv.org/abs/2411.13663v1", "attribution": "\"Accounting carbon emissions from electricity generation: a review and comparison of emission factor-based methods\" by Marina Bertolini, Pierdomenico Duttilo, and Francesco Lisi, arXiv:2411.13663v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10459v3_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{XGBoost Classification Hyperparameter Selection (Reduction=5)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|}\n\\hline\nHyperparameter & Datatype & Values/{[}Min, Max{]} & Log Scaling & Gridpoint Count & Best Value \\\\ \\hline\nMaximum Tree Depth & Integer & {[}2, 8{]} & FALSE & 3 & 2 \\\\ \\hline\nL2 Weight Regularization (lambda) & Continuous & {[}0.1, 100{]} & TRUE & 3 & 0.37363 \\\\ \\hline\nLearning Rate (eta) & Continuous & {[}0.0, 1.0{]} & FALSE & 3 & 0.688255 \\\\ \\hline\nMinimum Split Loss (gamma) & Continuous & {[}0, 1{]} & FALSE & 3 & 0.695424 \\\\ \\hline\nMinimum Child Weight & Continuous & {[}0.5, 10{]} & TRUE & 3 & 0.694833 \\\\ \\hline\nEarly Stopping Patience & Integer & {[}1, 100{]} & TRUE & 3 & 33 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames", "authors": ["Lyle Regenwetter", "Colin Weaver", "Faez Ahmed"], "url": "https://arxiv.org/abs/2201.10459v3", "attribution": "\"FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames\" by Lyle Regenwetter, Colin Weaver, and Faez Ahmed, arXiv:2201.10459v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.18392v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Preprocessing pipelines considered in this work.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\Xhline{2\\arrayrulewidth} \\\\\n preprocessing step & Raw & Filt & ICA & ICA+ASR\\\\\n \\\\ \\Xhline{2\\arrayrulewidth}\n non-EEG channels removal & \\checkmark & \\checkmark & \\checkmark & \\checkmark \\\\\n \\hline\n time segments removal & & & \\checkmark & \\checkmark \\\\\n \\hline\n baseline removal & & \\checkmark & \\checkmark & \\checkmark \\\\\n \\hline\n resampling & \\checkmark & \\checkmark & \\checkmark & \\checkmark \\\\\n \\hline\n filtering & & \\checkmark & \\checkmark & \\checkmark \\\\\n \\hline\n independent component analysis & & & \\checkmark & \\checkmark \\\\\n \\hline\n automatic component rejection & & & \\checkmark & \\checkmark \\\\\n \\hline\n bad-channel removal & & & & \\checkmark \\\\\n \\hline\n bad-time windows correction (ASR) & & & & \\checkmark \\\\\n \\hline\n spherical interpolation & & & & \\checkmark \\\\\n \\hline \n re-reference & & \\checkmark & \\checkmark & \\checkmark \\\\\n \\hline\n template alignment & \\checkmark & \\checkmark & \\checkmark & \\checkmark \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "The more, the better? Evaluating the role of EEG preprocessing for deep learning applications", "authors": ["Federico Del Pup", "Andrea Zanola", "Louis Fabrice Tshimanga", "Alessandra Bertoldo", "Manfredo Atzori"], "url": "https://arxiv.org/abs/2411.18392v1", "attribution": "\"The more, the better? Evaluating the role of EEG preprocessing for deep learning applications\" by Federico Del Pup, Andrea Zanola, Louis Fabrice Tshimanga, Alessandra Bertoldo, and Manfredo Atzori, arXiv:2411.18392v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16302v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|r|rrrrr|}\n \\hline\nNumber of & Total number & \\multicolumn{5}{c|}{Number of trees with a fixed number of galls ($e_{n,g}$)} \\\\ \nleaves ($n$) & of trees ($a_n$) & $g=0$ & $g=1$ & $g=2$ & $g=3$ & $g=4$ \\\\ \\hline\n 1 & 1 & 1 & - & - & - & - \\\\\n 2 & 1 & 1 & - & - & - & - \\\\\n 3 & 6 & 3 & 3 & - & - & - \\\\\n 4 & 69 & 15 & 54 & - & - & - \\\\\n 5 & 1,050 & 105 & 855 & 90 & - & - \\\\\n 6 & 20,025 & 945 & 14,040 & 5040 & - & - \\\\\n 7 & 464,310 & 10,395 & 248,535 & 197,820 & 7,560 & - \\\\\n 8 & 12,709,305 & 135,135 & 4,787,370 & 6,917,400 & 869,400 & - \\\\\n 9 & 401,112,810 & 2,027,025 & 100,361,835 & 233,859,150 & 63,617,400 & 1,247,400 \\\\\n10 & 14,338,565,325 & 34,459,425 & 2,282,912,100 & 7,927,227,000 & 3,850,723,800 & 243,243,000 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Numbers of labeled time-consistent galled trees with specified numbers of leaves and galls. Entries $e_{n,g}$ are computed recursively ().}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Enumerative combinatorics of unlabeled and labeled time-consistent galled trees", "authors": ["Lily Agranat-Tamir", "Michael Fuchs", "Bernhard Gittenberger", "Noah A. Rosenberg"], "url": "https://arxiv.org/abs/2504.16302v1", "attribution": "\"Enumerative combinatorics of unlabeled and labeled time-consistent galled trees\" by Lily Agranat-Tamir, Michael Fuchs, Bernhard Gittenberger, and Noah A. Rosenberg, arXiv:2504.16302v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.11346v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Transducer characteristics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c} \n\\hline\nParameter & Value\\\\ \n\\hline\\hline\nResistance ($R_t$) & 10.6 $\\Omega$ \\\\ \nInductance ($L$) & 0.0219 H \\\\\nPermanent-magnet flux linkage ($\\Lambda_{PM}$) & 0.1603 V-s \\\\\nNo. of poles ($N_p$) & 6 \\\\\nRotational inertia ($J_t$) & 3.54 $\\times 10^{-5}$ kg-m$^2$\\\\ \nRotational viscous damping ($B_t$) & 3.25 $\\times 10^{-4}$ N-m-s\\\\ \nCoulomb friction ($f_c$) & 35 N\\\\ \nLead length ($\\ell$) & 1.27 $\\times 10^{-3}$ m-rad$^{-1}$\\\\\nEfficiency ($\\eta$) & 0.91\\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Large-Scale Self-Powered Vibration Control: Theory and Experiment", "authors": ["Connor Ligeikis", "Heath Hofmann", "Jeff Scruggs"], "url": "https://arxiv.org/abs/2509.11346v1", "attribution": "\"Large-Scale Self-Powered Vibration Control: Theory and Experiment\" by Connor Ligeikis, Heath Hofmann, and Jeff Scruggs, arXiv:2509.11346v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.11161v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameter $\\alpha$ for different lens designs.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\t& Design 1 & Design 2 \\\\\n\t\t\t\\hline\n\t\t\t$\\alpha$ & $\\dfrac{{\\pi {{\\sin }^{\\rm{2}}}\\theta }}{{\\lambda F}} - \\dfrac{{\\pi {{\\cos }^{\\rm{2}}}\\phi }}{{\\lambda d}}$ & $\\dfrac{{\\pi {{\\sin }^{\\rm{2}}}\\theta }}{{\\lambda F}} - \\dfrac{{\\pi {{\\cos }^{\\rm{2}}}\\phi }}{{\\lambda d}} + \\dfrac{\\pi }{{\\lambda {F_0}}}$ \\\\\n\t\t\t\\hline\t\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Communication and Localization with Extremely Large Lens Antenna Array", "authors": ["Jie Yang", "Yong Zeng", "Shi Jin", "Chao-Kai Wen", "Pingping Xu"], "url": "https://arxiv.org/abs/2012.11161v1", "attribution": "\"Communication and Localization with Extremely Large Lens Antenna Array\" by Jie Yang, Yong Zeng, Shi Jin, Chao-Kai Wen, and Pingping Xu, arXiv:2012.11161v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.14361v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ccc}\n \\hline\\hline\n Methods & $\\|\\nabla f\\|$ & Time \\\\\n \\hline\n ASK & 5.3550e-07 & 9.3056e-03 \\\\\n GD & 9.9561e-07 & 7.4152e-03 \\\\\n NAG & 2.9041e-03 & 6.5248e-03 \\\\\n HB & 9.8639e-07 & 5.1908e-03 \\\\\n \\hline\\hline\n \\end{tabular}\n\\caption{Six-hump Camel Function, init=$6\\times\\text{rand}(2,1)-3$. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Gradient-Based Optimization Method Using the Koopman Operator", "authors": ["Mengqi Hu", "Bian Li", "Yi-An Ma", "Yifei Lou", "Xiu Yang"], "url": "https://arxiv.org/abs/2312.14361v1", "attribution": "\"A Gradient-Based Optimization Method Using the Koopman Operator\" by Mengqi Hu, Bian Li, Yi-An Ma, Yifei Lou, and Xiu Yang, arXiv:2312.14361v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.05387v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{||c|c|c|c|c||} \n \\hline\n $(\\alpha,s)$ & $(0.3,0.2)$ & $(0.3,0.9)$ & $(0.8,0.2)$ & $(0.8,0.9)$ \\\\ \n \\hline\n$E_k$ & $0.00654$ & $0.00811$ & $0.00783$ & $0.00827$ \\\\ \n \\hline\n\\end{tabular}\n\\caption{Choices of the fractional derivatives orders $\\alpha$ and $s$ along with the errors $E_k$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Identification of the initial value for a space-time fractional diffusion equation", "authors": ["Mohamed BenSalah", "Salih Tatar"], "url": "https://arxiv.org/abs/2412.05387v1", "attribution": "\"Identification of the initial value for a space-time fractional diffusion equation\" by Mohamed BenSalah and Salih Tatar, arXiv:2412.05387v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.06845v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Prediction Accuracy by Component Type}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\nComponent Type & MAE (mm) & RMSE (mm) & $R^2$ Score & 95\\% CI (mm) \\\\\n\\midrule\nHydraulic Manifold & 0.0068 & 0.0083 & 0.912 & $\\pm$0.011 \\\\\nTurbine Blade & 0.0074 & 0.0092 & 0.893 & $\\pm$0.014 \\\\\nMounting Bracket & 0.0052 & 0.0063 & 0.934 & $\\pm$0.009 \\\\\nGear Housing & 0.0071 & 0.0087 & 0.905 & $\\pm$0.012 \\\\\nMedical Implant & 0.0085 & 0.0104 & 0.881 & $\\pm$0.016 \\\\\nOverall & 0.0070 & 0.0086 & 0.907 & $\\pm$0.012 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Hybrid Machine Learning Framework for Predicting Geometric Deviations from 3D Surface Metrology", "authors": ["Hamidreza Samadi", "Md Manjurul Ahsan", "Shivakumar Raman"], "url": "https://arxiv.org/abs/2508.06845v1", "attribution": "\"Hybrid Machine Learning Framework for Predicting Geometric Deviations from 3D Surface Metrology\" by Hamidreza Samadi, Md Manjurul Ahsan, and Shivakumar Raman, arXiv:2508.06845v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.15435v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllll}\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multicolumn{7}{c}{$\\Delta\\ln(s_{it})$} \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t& (1) & (2) & (3) & (4) & (5) & (6)\\\\\n\t\t\t\n\t\t\t\n\t\t\t$\\Delta\\ln(y_{it}) $ & 6.16*** & 6.08*** & 4.82*** & 4.77*** & 6.81*** &6.74***\\\\\n\t\t\t\n\t\t\t& (1.33)&(1.33)&(1.22)&(1.22)&(1.27)&(1.25)\\\\\n\t\t\t\n\t\t\t$\\Delta\\ln(y_{it})*\\lambda_i$ & -10.82*** & -10.87*** & & & -9.23*** & -9.25***\\\\\n\t\t\t\n\t\t\t& (3.09)&(3.05)&&&(3.01)&(2.94)\\\\\n\t\t\t\n\t\t\t$\\Delta\\ln(y_{it})*y_i$ & & & -0.13*** & -0.13*** & -0.06** &-0.06**\\\\\n\t\t\t\n\t\t\t& &&(0.05)&(0.05)&(0.03)&(0.03)\\\\\n\t\t\t\n\t\t\t$\\Delta\\ln(\\lambda_{it})$ & & -0.29** & & -0.20** & &-0.27**\\\\\n\t\t\t\n\t\t\t& &(0.12)&&(0.09)&&(0.12)\\\\\n\t\t\t\n\t\t\tKleibergen-Paap & 18.82 & 18.85 &12.80 & 12.53 & 14.08 &14.05\\\\\n\t\t\tF-stat &&&&&\\\\\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\tEndogenous & $\\Delta\\ln(y_{it})$, & $\\Delta\\ln(y_{it})$, & $\\Delta\\ln(y_{it})$, & $\\Delta\\ln(y_{it})$, & $\\Delta\\ln(y_{it})$,& $\\Delta\\ln(y_{it})$, \\\\\n\t\t\t\n\t\t\t\n\t\t\tRegressors& $\\Delta\\ln(y_{it})^*\\lambda_i$, & $\\Delta\\ln(y_{it})^*\\lambda_i$ &$\\Delta\\ln(y_{it})^*y_i$ & $\\Delta\\ln(y_{it})^*y_i$ &\n\t\t\t$\\ln(y_{it})^*\\lambda_i$,& $\\ln(y_{it})^*\\lambda_i$, \\\\\n\t\t\t\n\t\t\t& & & & & $\\Delta\\ln(y_{it})^*y_i$ & $\\Delta\\ln(y_{it})^*y_i$\\\\\n\t\t\t\n\t\t\t\n\t\t\tInstruments & $OPS_{it}$, & $OPS_{it}$,& $OPS_{it}$, & $OPS_{it}$, & $OPS_{it}$, &$OPS_{it}$, \\\\\n\t\t\t\n\t\t\t& $OPS_{it}{}^*\\lambda_i$ & $OPS_{it}{}^*\\lambda_i$& $OPS_{it}{}^*y_i$ & $OPS_{it}{}^*y_i$ & $OPS_{it}{}^*\\lambda_i$, & $OPS_{it}{}^*\\lambda_i$, \\\\\n\t\t\t\n\t\t\t\n\t\t\t& &&&& $OPS_{it}{}^*y_i$ & $OPS_{it}{}^*y_i$ \\\\\n\t\t\t\n\t\t\tCountry FE & Yes & Yes & Yes & Yes & Yes & Yes \\\\\n\t\t\t\n\t\t\tYear FE & Yes & Yes & Yes & Yes & Yes & Yes \\\\\n\t\t\t\n\t\t\tObservations & 3850 & 3850 & 3850 & 3850 &3850 & 3850 \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Transitional Dynamics of the Saving Rate and Economic Growth", "authors": ["Markus Brueckner", "Tomoo Kikuchi", "George Vachadze"], "url": "https://arxiv.org/abs/2012.15435v3", "attribution": "\"Transitional Dynamics of the Saving Rate and Economic Growth\" by Markus Brueckner, Tomoo Kikuchi, and George Vachadze, arXiv:2012.15435v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2306.12446v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Description of data}\n\\begin{tabular}{ccc}\n\\toprule\n Asset & Start date & End date \\\\\n\\midrule\n S\\&P500 & 22/Sep/2003 & 31/Dec/2018 \\\\\n NASDAQ100 & 01/Jan/2003 & 31/Dec/2018 \\\\\n Gold & 03/Jun/2008 & 31/Dec/2018 \\\\\n Silver & 16/Mar/2011 & 31/Dec/2018 \\\\\n WTI Crude Oil & 10/May/2007 & 31/Dec/2018 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Data", "authors": ["Wenbo Ge", "Pooia Lalbakhsh", "Leigh Isai", "Artem Lensky", "Hanna Suominen"], "url": "https://arxiv.org/abs/2306.12446v2", "attribution": "\"Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Data\" by Wenbo Ge, Pooia Lalbakhsh, Leigh Isai, Artem Lensky, and Hanna Suominen, arXiv:2306.12446v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03847v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c||} \n \\hline\\hline\n $Q^S_1 = \\frac{(k_9 [13][2])}{(K_{9M} + [2])} -h_9[1][15]$ \\\\ \n $Q^S_2 = -\\frac{(k_9 [13][2]}{(K_{9M} + [2])}$ \\\\ \n $Q^S_3 = \\frac{(k_8 [9][4]}{(K_{8M} + [4])} - h_8[3] - \\frac{(h_{C8}[17][3])}{H_{C8M} +[3]} $ \\\\ \n $Q^S_4 = \\frac{(k_8 [9][4])}{(K_{8M} + [4])}$ \\\\ \n $Q^S_5 = \\frac{(k5 [9][6]}{(K_{5M} + [6])} - h_5[5]- \\frac{(h_{C5}[17][5])}{(H_{C5M} +[5]}) $ \\\\ \n $Q^S_6 = -\\frac{(k_5 [9][6]}{(K_{5M} + [6])}$ \\\\\n $Q^S_7 = \\frac{(k_{10} [24][8]}{(K_{10M} + [8])} - h_{10}[7][15] -h_{TFPI}[16][7]$ \\\\\n $Q^S_8 = -\\frac{(k_{10} [24][8])}{(K_{10M} + [8])}$ \\\\ \n $Q^S_9 = \\frac{(k_2 [25][10])}{(K_{2M} + [10])} -h_2[9][15]$ \\\\ \n $Q^S_{10} = -\\frac{(k_2 [25][10])}{(K_{2M} + [10])}$ \\\\ \n $Q^S_{11} = \\frac{(k_1 [9][12])}{(K_{1M} + [12])} - \\frac{(h_{1}[21][11])}{H_{1M} +[11]} $ \\\\ \n $Q^S_{12} = -\\frac{(k_1 [9][12])}{(K_{1M} + [12])}$ \\\\\n $Q^S_{13} = \\frac{(k_{11} [9][14])}{(K_{11M} + [14])} - h_{11}^{A3}[13][15] -h_{11}^{L1}[13][19]$ \\\\\n $Q^S_{14} = -\\frac{(k_{11} [9][14])}{(K_{11M} + [14])}$ \\\\\n $Q^S_{15} = -(h_9[1] + h_{10}[7] + h_2[9] +h_{11}^{A3}[13])[15]$ \\\\\n $Q^S_{16} = -h_{TFPI}[16][7]$ \\\\ \n $Q^S_{17} = \\frac{(k_{PC} [9][18])}{(K_{PCM} + [18])} -h_{PC}[17][19]$ \\\\ \n $Q^S_{18} = -\\frac{(k_{PC} [9][18]}{(K_{PCM} + [18])}$ \\\\\n $Q^S_{19} = -h_{PC}[17][19] -h_{11}^{L1}[13][19]$ \\\\ \n $Q^S_{20} =0$\\\\\n $Q^S_{21} = \\frac{(k_{PLA} [20][22])}{(K_{PLAM} + [22])} -h_{PLA}[21][23]$ \\\\ \n $Q^S_{22} = -\\frac{(k_{PLA} [20][22])}{(K_{PLAM} + [22])}$ \\\\ \n $Q^S_{23} = -h_{PLA}[21][23]$ \\\\ \n \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Reaction equations for source terms. The parameters are as follows: $k_9 = 2.54\\times 10^{-2}$, $K_{9M} = 160$, $h_9 = 3.74\\times10^{-5} $, $k_8 = 0.449$, $K_{8M} = 1.12 \\times 10^{5}$, $h_8 = 5.13 \\times 10^{-4}$, $h_{C8} = 2.36 \\times 10^{-2}$, $H_{C8M} = 14.6$, $k_5 = 6.24\\times 10^{-2}$, $K_{5M} = 140.5$, $h_5 = 3.93 \\times 10^{-4}$, $h_{C5} = 2.36 \\times 10^{-2}$, $H_{C5M} = 14.6$, $k_{10} = 5.523$, $K_{10M} = 160$, $h_{10} = 8.01 \\times 10^{-4}$, $h_{TFPI} = 1.11 \\times 10^{-3}$, $k_2 = 3.105$, $K_{2M} = 1060$, $h_2 = 1.65 \\times 10^{-3}$, $k_1 = 8.177$, $K_{1M} = 3160$, $h_1 = 3.456$, $H_{1M} = 2.50 \\times 10^{5}$, $k_{11} = 1.80 \\times 10^{-5}$, $K_{11M} = 50$, $h_{11}^{A3} = 3.70\\times 10^{-6}$, $h_{11}^{L1} = 3.00 \\times 10^{-8}$, $k_{PC} = 9.01 \\times 10^{-2}$, $K_{PCM} = 3190$, $h_{PC} = 1.52 \\times 10^{-9}$, $k_{PLA} = 2.77 \\times 10^{-2}$, $K_{PLAM} = 18$, and $h_{PLA} = 2.22 \\times 10^{-4}$}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "On-the-fly Reduced Order Modeling of Passive and Reactive Species via Time-Dependent Manifolds", "authors": ["Donya Ramezanian", "Arash G. Nouri", "Hessam Babaee"], "url": "https://arxiv.org/abs/2101.03847v1", "attribution": "\"On-the-fly Reduced Order Modeling of Passive and Reactive Species via Time-Dependent Manifolds\" by Donya Ramezanian, Arash G. Nouri, and Hessam Babaee, arXiv:2101.03847v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.11308v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The results of experiments on the \\texttt{Elec2} dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccccc}\\toprule\n && \\multicolumn{6}{c}{\\textbf{\\#batch}}\\\\\\cmidrule(lr){3-8}\n && \\multicolumn{2}{c}{\\textbf{10}} & \\multicolumn{2}{c}{\\textbf{20}} & \\multicolumn{2}{c}{\\textbf{30}} \\\\\\cmidrule(lr){3-8}\n \\textbf{Method} & \\textbf{Model} & \\textbf{accuracy} & \\textbf{\\#drifts} & \\textbf{accuracy} & \\textbf{\\#drifts} & \\textbf{accuracy} & \\textbf{\\#drifts} \\\\\\midrule\n HDDM-A & LR & 0.6983 & 10 & 0.6836 & 20 & 0.7099 & 30 \\\\\n & DT & 0.7255 & 10 & 0.7255 & 20 & 0.7255 & 30 \\\\\n & RF & 0.7029 & 10 & 0.7253 & 20 & 0.7356 & 30 \\\\\n \\midrule\n HDDM-W & LR & 0.6983 & 10 & 0.6836 & 20 & 0.7099 & 30 \\\\\n & DT & 0.7255 & 10 & 0.7255 & 20 & 0.7255 & 30 \\\\\n & RF & 0.7029 & 10 & 0.7253 & 20 & 0.7356 & 30 \\\\\n \\midrule\n KSWIN & LR & 0.6983 & 10 & 0.6836 & 20 & 0.7099 & 30 \\\\\n & DT & 0.7255 & 10 & 0.7255 & 20 & 0.7255 & 30 \\\\\n & RF & 0.7029 & 10 & 0.7253 & 20 & 0.7356 & 30 \\\\\n \\midrule\n PH & LR & 0.7215 & 9 & 0.7342 & 13 & 0.7390 & 16 \\\\\n & DT & 0.7342 & 9 & 0.7417 & 15 & 0.7502 & 16 \\\\\n & RF & 0.6992 & 8 & 0.7131 & 12 & 0.7359 & 16 \\\\\n \\midrule\n DDM & LR & 0.6983 & 10 & 0.6836 & 20 & 0.7099 & 30 \\\\\n & DT & 0.7255 & 10 & 0.7255 & 20 & 0.7255 & 30 \\\\\n & RF & 0.7029 & 10 & 0.7253 & 20 & 0.7356 & 30 \\\\\n \\midrule\n EDDM & LR & 0.7255 & 10 & 0.7255 & 20 & 0.7255 & 30 \\\\\n & DT & 0.7255 & 10 & 0.7255 & 20 & 0.7255 & 30 \\\\\n & RF & 0.7029 & 10 & 0.7253 & 20 & 0.7356 & 30 \\\\\n \\midrule\n PDD & LR & 0.6740 & 8 & 0.7349 & 4 & 0.7402 & 12 \\\\\n & DT & 0.7253 & 1 & 0.7392 & 1 & 0.7429 & 5 \\\\\n & RF & 0.7093 & 3 & 0.7311 & 0 & 0.7393 & 1 \\\\\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "datadriftR: An R Package for Concept Drift Detection in Predictive Models", "authors": ["Ugur Dar", "Mustafa Cavus"], "url": "https://arxiv.org/abs/2412.11308v1", "attribution": "\"datadriftR: An R Package for Concept Drift Detection in Predictive Models\" by Ugur Dar and Mustafa Cavus, arXiv:2412.11308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.06270v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance measures of imputations using state space model and Random Forest in `mice'. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\hline\n\\multirow{2}{*}{\\textbf{Variable}} & \\multicolumn{3}{c}{\\textbf{State space model}}& %\n \\multicolumn{3}{c}{\\textbf{Random Forest in mice}}\\\\\n\\cline{2-7}\n & SMAPE(60/40) & SMAPE(70/30) & SMAPE(80/20) & SMAPE(60/40) & SMAPE(70/30) & SMAPE(80/20) \\\\\\hline\n\\% patients with septicemia &0.162 &0.140&0.093&0.466&0.476&0.443\\\\\\hline\nNumber of staff working for the facility&0.167 &0.125&0.063&0.485&0.512&0.493 \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Missing Data Imputation and Multilevel Conditional Autoregressive Modeling of Spatial End-Stage Renal Disease Incidence", "authors": ["Supraja Malladi", "Indranil Sahoo", "QiQi Lu"], "url": "https://arxiv.org/abs/2309.06270v1", "attribution": "\"Missing Data Imputation and Multilevel Conditional Autoregressive Modeling of Spatial End-Stage Renal Disease Incidence\" by Supraja Malladi, Indranil Sahoo, and QiQi Lu, arXiv:2309.06270v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.07770v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline \n\\multirow{2}{*}{\\textbf{Political position}} & \\multicolumn{2}{c|}{\\textbf{Share in 2004}} & \\multicolumn{2}{c|}{\\textbf{Share in 2016}}\\tabularnewline\n\\cline{2-5}\n & \\textbf{Before} & \\textbf{After} & \\textbf{Before} & \\textbf{After}\\tabularnewline\n\\hline \n\\textbf{1 (Extremely liberal)} & 3.04\\% & 2.54\\% & 4.27\\% & 3.62\\%\\tabularnewline\n\\textbf{2} & 11.63\\% & 10.84\\% & 15.00\\% & 14.52\\%\\tabularnewline\n\\textbf{3} & 10.39\\% & 14.31\\% & 12.29\\% & 13.42\\%\\tabularnewline\n\\textbf{4} & 33.49\\% & 32.44\\% & 27.11\\% & 29.11\\%\\tabularnewline\n\\textbf{5} & 15.82\\% & 16.68\\% & 15.44\\% & 14.35\\%\\tabularnewline\n\\textbf{6} & 21.62\\% & 19.88\\% & 21.20\\% & 20.70\\%\\tabularnewline\n\\textbf{7 (Extremely conservative)} & 4.01\\% & 3.30\\% & 4.69\\% & 4.28\\%\\tabularnewline\n\\hline \n\\textbf{Average position} & 4.283 & 4.227 & 4.168 & 4.153\\tabularnewline\n\\textbf{Variance of positions} & 2.147 & 2.013 & 2.503 & 2.375\\tabularnewline\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Shares of respondents by self-reported position on a 1-7 liberal-conservative scale, by year. Before and After refer to surveys taken before and after the election, respectively. Source: ANES dataset, https://electionstudies.org/data-tools/anes-continuity-guide/\\#liberal-conservative. Answers are weighted using the weights provided by ANES. Respondents who did not know how to answer or refused to answer are omitted.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Flexible Measure of Voter Polarization", "authors": ["Boris Ginzburg"], "url": "https://arxiv.org/abs/2507.07770v1", "attribution": "\"A Flexible Measure of Voter Polarization\" by Boris Ginzburg, arXiv:2507.07770v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.01026v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c||c|c||c|c}\n$i$ & $(i)_N$ & $i$ & $(i)_N$ & $i$ & $(i)_N$ \\\\\n\\hline\n0& $\\epsilon$& 7& 10001& 14& 1000001 \\\\\n 1& 1& 8& 10010& 15& 1000010 \\\\\n 2& 10& 9& 100000& 16& 1000100 \\\\\n 3& 100& 10& 100001& 17& 1001000 \\\\\n 4& 1000& 11& 100010& 18& 1001001 \\\\\n 5& 1001& 12& 100100& 19&10000000\\\\\n 6& 10000& 13& 1000000& 20&10000001\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Narayana representations.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Narayana Morphism and Related Words", "authors": ["Jeffrey Shallit"], "url": "https://arxiv.org/abs/2503.01026v3", "attribution": "\"The Narayana Morphism and Related Words\" by Jeffrey Shallit, arXiv:2503.01026v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12176v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The averaged results for solving optimal portfolio selection model. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\t\t\t\\toprule\n\t\t\t\\multicolumn{2}{c}{n} &\\multicolumn{5}{c}{n = 200} \\\\\n\t\t\t\\cmidrule[\\heavyrulewidth](lr){1-2} \\cmidrule[\\heavyrulewidth](lr){3-7}\n\t\t\t\\multicolumn{2}{c}{m} & m=1 & m=5 & m=20 & m=40 & m=50 \\\\ \\toprule\n\t\t\t\\multirow{3}{*}{ObjVal}&FPSA-nl& 1.89e-02 & 1.93e-02 & 2.08e-02 & 2.36e-02 & 2.55e-02 \\\\\n\t\t\t&PGSA\\_BE& 1.89e-02 & 1.93e-02 & 2.08e-02 & 2.36e-02 & 2.55e-02 \\\\\n\t\t\t&e-PSG& 1.89e-02 & 1.93e-02 & 2.08e-02 & 2.36e-02 & 2.55e-02 \\\\ \\hline\n\t\t\t\\multirow{3}{*}{StatRes}&FPSA-nl& 1.84e-08 & 1.20e-06 & 9.40e-06 & 8.49e-05 & 3.47e-04 \\\\\n\t\t\t&PGSA\\_BE& 1.63e-07 & 1.26e-06 & 9.40e-06 & 8.49e-05 & 3.47e-04 \\\\\n\t\t\t&e-PSG& 1.93e-07 & 1.32e-06 & 9.40e-06 & 8.49e-05 & 3.47e-04 \\\\ \\hline\n\t\t\t\\multirow{3}{*}{Infeas}&FPSA-nl& 4.60e-09 & 3.86e-09 & 4.12e-09 & 2.61e-09 & 2.24e-09 \\\\\n\t\t\t&PGSA\\_BE& 4.40e-09 & 4.52e-09 & 3.50e-09 & 4.47e-09 & 3.56e-09 \\\\\n\t\t\t&e-PSG& 4.40e-09 & 4.52e-09 & 3.50e-09 & 4.47e-09 & 3.56e-09 \\\\ \\hline\n\t\t\t\\multirow{3}{*}{CPU}&FPSA-nl& 4.52e-02 & 4.56e-02 & 6.39e-02 & 7.44e-02 & 8.38e-02 \\\\\n\t\t\t&PGSA\\_BE& 1.44e-01 & 1.58e-01 & 1.89e-01 & 2.66e-01 & 2.66e-01 \\\\\n\t\t\t&e-PSG& 1.68e-01 & 1.62e-01 & 1.92e-01 & 2.44e-01 & 2.56e-01 \\\\\n\t\t\t\\toprule\n\t\t\t\\multicolumn{2}{c}{n} &\\multicolumn{5}{c}{n = 800} \\\\\n\t\t\t\\cmidrule[\\heavyrulewidth](lr){1-2} \\cmidrule[\\heavyrulewidth](lr){3-7}\n\t\t\t\\multicolumn{2}{c}{m} & m=4 & m=20 & m=80 & m=160 & m=200 \\\\ \\toprule\n\t\t\t\\multirow{3}{*}{ObjVal}&FPSA-nl& 4.70e-03 & 4.76e-03 & 5.17e-03 & 5.92e-03 & 6.55e-03 \\\\\n\t\t\t&PGSA\\_BE& 4.70e-03 & 4.76e-03 & 5.17e-03 & 5.92e-03 & 6.55e-03 \\\\\n\t\t\t&e-PSG& 4.70e-03 & 4.76e-03 & 5.17e-03 & 5.92e-03 & 6.55e-03 \\\\ \\hline\n\t\t\t\\multirow{3}{*}{StatRes}&FPSA-nl& 1.09e-07 & 1.79e-06 & 1.44e-05 & 2.30e-04 & 1.76e-03 \\\\\n\t\t\t&PGSA\\_BE& 5.04e-07 & 1.97e-06 & 1.44e-05 & 2.30e-04 & 1.76e-03 \\\\\n\t\t\t&e-PSG& 1.56e-07 & 1.80e-06 & 1.44e-05 & 2.30e-04 & 1.76e-03 \\\\ \\hline\n\t\t\t\\multirow{3}{*}{Infeas}&FPSA-nl& 2.86e-09 & 4.10e-09 & 5.99e-09 & 4.27e-09 & 4.10e-09 \\\\\n\t\t\t&PGSA\\_BE& 4.80e-09 & 4.59e-09 & 3.71e-09 & 4.30e-09 & 2.79e-09 \\\\\n\t\t\t&e-PSG& 4.80e-09 & 4.59e-09 & 3.71e-09 & 4.30e-09 & 2.79e-09 \\\\ \\hline\n\t\t\t\\multirow{3}{*}{CPU}&FPSA-nl& 5.88e-01 & 7.19e-01 & 8.67e-01 & 8.66e-01 & 9.00e-01 \\\\\n\t\t\t&PGSA\\_BE& 4.09e+00 & 4.29e+00 & 3.82e+00 & 4.88e+00 & 4.87e+00 \\\\\n\t\t\t&e-PSG& 3.41e+00 & 3.36e+00 & 3.39e+00 & 3.35e+00 & 3.23e+00 \\\\ \\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Single-loop Proximal Subgradient Algorithm for A Class Structured Fractional Programs", "authors": ["Deren Han", "Min Tao", "Zihao Xia"], "url": "https://arxiv.org/abs/2503.12176v1", "attribution": "\"A Single-loop Proximal Subgradient Algorithm for A Class Structured Fractional Programs\" by Deren Han, Min Tao, and Zihao Xia, arXiv:2503.12176v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.15589v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Scenario Parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n Parameter & Sub-6GHz & THz\\\\\n \\hline\n No. of Users & 100,000 & 100,000\\\\\n BS Antenna Array Length & 4 & 128\\\\\n BS Antenna Height (m) & 8 & 8\\\\\n User Antenna Height (m) & 2 & 2\\\\\n Frequency (GHz) & 2.4 & 100\\\\\n Propagation Model & sbr+gas+cloud & sbr+gas+cloud\\\\\n Max No. of Paths & 8 & 4\\\\\n Bandwidth (MHz) & 20 & 500\\\\\n No. of OFDM Subcarriers & 64 & 64\\\\\n Codebook size (X,Y,Z) & $4\\times 64\\times 4$ & $4 \\times 128\\times 4$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Learning for THz Channel Estimation and Beamforming Prediction via Sub-6GHz Channel", "authors": ["Sagnik Bhattacharya", "Abhishek K. Gupta"], "url": "https://arxiv.org/abs/2411.15589v1", "attribution": "\"Deep Learning for THz Channel Estimation and Beamforming Prediction via Sub-6GHz Channel\" by Sagnik Bhattacharya and Abhishek K. Gupta, arXiv:2411.15589v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15444v2_tex_table21.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mutually unbiased weighing matrices of order $23$ and weight $9$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l}\n\\noalign{\\hrule height1pt}\n$W_{23}$\\\\\n\\hline\n01000100010020201200101\n01221012000010010000010\n01102001002002010000012\\\\\n10022100010010000002220\n00100000011201000011210\n12000010000022010110001\\\\\n00001121101000010120000\n00120110120100020001000\n10000000201002222020010\\\\\n01202200101120000000200\n00012102020001212000000\n01100022200100100100001\\\\\n01001001220000200010220\n10000001020001100202011\n00100210000001201122000\\\\\n00111002100002000202200\n10001000012121002000002\n10020022020220001000002\\\\\n00120200001000012200120\n10000220102010200001001\n11010010000200100021020\\\\\n01000000100200022112100\n10010000001110001010102\\\\\n\\hline\n$ A_{23,2}$\\\\\n\\hline\n00110001200011020210000\n00000110012010212000002\n10001020200010010020011\\\\\n00000102211000100001220\n11010022100100002010000\n01000012000000020221110\\\\\n10000010020001200101021\n10210211011002000000000\n01002200202200102100000\\\\\n01002101001021010000010\n10121000010220000010100\n10110000002020001020202\\\\\n00000012200100001112010\n12202002000201000202000\n10122000021012000000002\\\\\n00102000010100000022121\n10220101002100120000000\n01000100000202220002201\\\\\n01200020200000201000122\n01001010020000110202020\n01020200110011001000200\\\\\n00010100100210101100100\n00001000001001022122002\\\\\n\\hline\n\\noalign{\\hrule height1pt}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unbiased weighing matrices of weight $9$", "authors": ["Makoto Araya", "Masaaki Harada", "Hadi Kharaghani", "Sho Suda", "Wei-Hsuan Yu"], "url": "https://arxiv.org/abs/2501.15444v2", "attribution": "\"Unbiased weighing matrices of weight $9$\" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.17168v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Calibration of probabilistic arrival time forecasts for lead times $l=1$ to $l=8$ on treatment arm of real-world A/B test}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\t\t\t\\toprule\n\t\t\tProbability & $l=1$ & $l=2$ & $l=3$ & $l=4$ \\\\\n\t\t\t\\midrule\n\t\t\t0.0-0.1 & 0.137 ± 0.005 & 0.123 ± 0.004 & 0.147 ± 0.002 & 0.141 ± 0.001 \\\\\n\t\t\t0.1-0.2 & 0.388 ± 0.010 & 0.295 ± 0.006 & 0.291 ± 0.002 & 0.277 ± 0.001 \\\\\n\t\t\t0.2-0.3 & 0.453 ± 0.013 & 0.344 ± 0.004 & 0.410 ± 0.002 & 0.382 ± 0.002 \\\\\n\t\t\t0.3-0.4 & 0.483 ± 0.013 & 0.481 ± 0.003 & 0.485 ± 0.002 & 0.484 ± 0.002 \\\\\n\t\t\t0.4-0.5 & 0.687 ± 0.010 & 0.597 ± 0.002 & 0.575 ± 0.002 & 0.562 ± 0.002 \\\\\n\t\t\t0.5-0.6 & 0.741 ± 0.007 & 0.668 ± 0.002 & 0.648 ± 0.002 & 0.659 ± 0.002 \\\\\n\t\t\t0.6-0.7 & 0.740 ± 0.007 & 0.752 ± 0.002 & 0.729 ± 0.002 & 0.725 ± 0.002 \\\\\n\t\t\t0.7-0.8 & 0.826 ± 0.004 & 0.813 ± 0.001 & 0.812 ± 0.002 & 0.801 ± 0.002 \\\\\n\t\t\t0.8-0.9 & 0.908 ± 0.002 & 0.887 ± 0.001 & 0.884 ± 0.001 & 0.887 ± 0.002 \\\\\n\t\t\t0.9-1.0 & 0.988 ± 0.000 & 0.958 ± 0.000 & 0.951 ± 0.001 & 0.950 ± 0.001 \\\\\n\t\t\t\\cmidrule{2-5}\n\t\t\t & $l=5$ & $l=6$ & $l=7$ & $l=8$ \\\\\n\t\t\t\\cmidrule{2-5}\n\t\t\t0.0-0.1 & 0.126 ± 0.001 & 0.113 ± 0.001 & 0.097 ± 0.001 & 0.078 ± 0.000 \\\\\n\t\t\t0.1-0.2 & 0.260 ± 0.001 & 0.263 ± 0.001 & 0.254 ± 0.001 & 0.229 ± 0.001 \\\\\n\t\t\t0.2-0.3 & 0.371 ± 0.002 & 0.377 ± 0.002 & 0.370 ± 0.002 & 0.329 ± 0.002 \\\\\n\t\t\t0.3-0.4 & 0.481 ± 0.002 & 0.476 ± 0.003 & 0.442 ± 0.003 & 0.392 ± 0.002 \\\\\n\t\t\t0.4-0.5 & 0.564 ± 0.003 & 0.537 ± 0.003 & 0.515 ± 0.003 & 0.474 ± 0.003 \\\\\n\t\t\t0.5-0.6 & 0.624 ± 0.003 & 0.617 ± 0.003 & 0.618 ± 0.003 & 0.576 ± 0.004 \\\\\n\t\t\t0.6-0.7 & 0.726 ± 0.003 & 0.704 ± 0.003 & 0.684 ± 0.003 & 0.707 ± 0.008 \\\\\n\t\t\t0.7-0.8 & 0.796 ± 0.003 & 0.813 ± 0.002 & 0.749 ± 0.003 & -- \\\\\n\t\t\t0.8-0.9 & 0.869 ± 0.002 & 0.863 ± 0.002 & 0.833 ± 0.017 & -- \\\\\n\t\t\t0.9-1.0 & 0.930 ± 0.001 & 0.852 ± 0.005 & --\t\t\t& -- \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Learning an Inventory Control Policy with General Inventory Arrival Dynamics", "authors": ["Sohrab Andaz", "Carson Eisenach", "Dhruv Madeka", "Kari Torkkola", "Randy Jia", "Dean Foster", "Sham Kakade"], "url": "https://arxiv.org/abs/2310.17168v2", "attribution": "\"Learning an Inventory Control Policy with General Inventory Arrival Dynamics\" by Sohrab Andaz, Carson Eisenach, Dhruv Madeka, Kari Torkkola, Randy Jia, Dean Foster, and Sham Kakade, arXiv:2310.17168v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12027v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative comparison of ensemble models on the validation dataset. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n\\hline\nModel & Accuracy & \\multicolumn{2}{c|}{f1-score} & \\multicolumn{2}{c|}{Precision} & \\multicolumn{2}{c|}{Recall} \\\\ \\hline\n & & Fake & Real & Fake & Real & Fake & Real \\\\ \\hline\nEnsemble-v1 & 0.981 & 0.980 & 0.982 & 0.980 & 0.983 & 0.981 & 0.981 \\\\ \\hline\nEnsemble-v2 & 0.983 & 0.982 & 0.984 & \\textbf{0.986} & 0.980 & 0.978 & \\textbf{0.988} \\\\ \\hline\nEnsemble-v3 & \\textbf{0.984} & \\textbf{0.983} & \\textbf{0.984} & 0.983 & \\textbf{0.984} & \\textbf{0.982} & 0.985 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A transformer based approach for fighting COVID-19 fake news", "authors": ["S. M. Sadiq-Ur-Rahman Shifath", "Mohammad Faiyaz Khan", "Md. Saiful Islam"], "url": "https://arxiv.org/abs/2101.12027v1", "attribution": "\"A transformer based approach for fighting COVID-19 fake news\" by S. M. Sadiq-Ur-Rahman Shifath, Mohammad Faiyaz Khan, and Md. Saiful Islam, arXiv:2101.12027v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.06830v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MARs (\\%) based on autosuspension data}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\hline\n\\hline\nApproach &K-means &FPCM &KPCA & I-MPPCA\\\\\n\\hline\nFault 1 &2.82 &1.41 &0 &\\textbf{0}\\\\\nFault 2 &2.85 &1.90 &0 &\\textbf{0}\\\\\nFault 3 &7.14 &6.52 &6.21 &\\textbf{0.62}\\\\\nFault 4 &8.11 &7.66 &6.76 &\\textbf{0.45}\\\\\nFault 5 &13.66 &13.04 &12.11 &\\textbf{1.86}\\\\\nFault 6 &15.77 &13.96 &13.06 &\\textbf{3.15}\\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring", "authors": ["Jingxin Zhang", "Hao Chen", "Songhang Chen", "Xia Hong"], "url": "https://arxiv.org/abs/2012.06830v1", "attribution": "\"An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring\" by Jingxin Zhang, Hao Chen, Songhang Chen, and Xia Hong, arXiv:2012.06830v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.10392v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|cc|cc}\n\t\t\\hline \\hline\n\t\t\\multicolumn{2}{c|}{BL and LM} & \t\\multicolumn{2}{c|}{BL and MPQA} & \t\\multicolumn{2}{c}{LM and MPQA} \\\\\n\t\t{Positive (131)} & {Negative (322)} & {Positive (971)} & {Negative (1164)} & {Positive (32)} & {Negative (30)}\\\\\n\t\t\\hline\n{Gains} & {Losses} & {Free} & {Gross} & {Despite} & {Against} \\\\\n(7,604) & (5,938) & (133,395) & (8,228) & (7,413) & (8,877) \\\\\n{Gained} & {Missed} & {Well} & {Risk} & {Able} & {Cut} \\\\\n(7,493) & (3,165) & (3,0270) & (7,471) & (5,246) & (3,401) \\\\\n{Improved} & {Declining} & {Like} & {Limited} & {Opportunity} & {Challenge} \\\\\n(7,407) & (3,053) & (24,617) & (5,884) & (4,398) & (1,042) \\\\\n{Improve} & {Failed} & {Top} & {Motley} & {Profitable} & {Serious} \\\\\n(5,726) & (2,421) & (14,899) & (5,165) & (3,580) & (1,022) \\\\\n{Restructuring} & {Concerned} & {Guidance} & {Crude} & {Efficiency} & {Contrary} \\\\\n(3,210) & (1,991) & (11,715) & (5,109) & (2,615) & (401) \\\\\n{Gaining} & {Declines} & {Significant} & {Cloud} & {Popularity} & {Severely} \\\\\n(3,150) & (1,654) & (10,576) & (4,906) & (1,588) & (348) \\\\\n{Enhance} & {Suffered} & {Worth} & {Fall} & {Exclusive} & {Despite} \\\\\n(2,753) & (1,435) & (10,503) & (4,732) & (1,225) & (342) \\\\\n{Outperform} & {Weaker} & {Gold} & {Mar} & {Tremendous} & {Argument} \\\\\n(2,518) & (1,288) & (9,303) & (3,190) & (611) & (324) \\\\\n{Stronger} & {Critical} & {Support} & {Hard} & {Dream} & {Seriously} \\\\\n(1,657) & (1,131) & (9,120) & (2,957) & (581) & (240) \\\\\n{Win} & {Drag} & {Recommendation} & {Cancer} & {Satisfaction} & {Staggering} \\\\\n(1,491) & (1,095) & (8,993) & (2,521) & (410) & (209) \\\\\n\t\t\\hline \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Lists of ten most frequent positive words and ten most frequent negative words that are shared only by BL and LM lexica, only by BL and MPQA lexica, or only by LM and MPQA lexica, along with their frequencies given in parentheses.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Distillation of News Flow into Analysis of Stock Reactions", "authors": ["Junni L. Zhang", "Wolfgang Karl Härdle", "Cathy Y. Chen", "Elisabeth Bommes"], "url": "https://arxiv.org/abs/2009.10392v1", "attribution": "\"Distillation of News Flow into Analysis of Stock Reactions\" by Junni L. Zhang, Wolfgang Karl Härdle, Cathy Y. Chen, and Elisabeth Bommes, arXiv:2009.10392v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00159v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The architecture of CNN network used for metric testing. (*) This activation function is changed in some experiments.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\nlayer & size & activation function \\\\ \n\\midrule\n(input) & - & -\\\\\nConv 2D & 3,3 X 32 & -\\\\\nMaxpool & 2,2 & -\\\\\nflatten & - & - \\\\\nDense & 128 & ReLu*\\\\\nDense & 64 & ReLu\\\\\nDense & 10 & Softmax\\\\\n\\midrule\n\\multicolumn{3}{l}{learning-rate: 0.01 }\\\\\n\\multicolumn{3}{l}{Loss function: categorical crossentropy }\\\\\n\\multicolumn{3}{l}{Optimizer: SGD with batch size 50}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Fidel: Reconstructing Private Training Samples from Weight Updates in Federated Learning", "authors": ["David Enthoven", "Zaid Al-Ars"], "url": "https://arxiv.org/abs/2101.00159v2", "attribution": "\"Fidel: Reconstructing Private Training Samples from Weight Updates in Federated Learning\" by David Enthoven and Zaid Al-Ars, arXiv:2101.00159v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11108v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\nParameter & meaning in BLV model & meaning in synthetic neural data\\\\\n\\hline \\hline\n$|\\mathbf{z}| \\sim \\lbrack \\mathcal{\\text{Bin}}(M,Q)\\rbrack_{K_{min}}^{K_{max}}$ & distribution of \\# active latent variables & distribution \\# coactive assemblies \\\\\n\\hline\n\\hspace{0.4cm} $Q \\sim \\lbrack \\mathcal{N}(K/M,\\sigma_{Q})\\rbrack_0^1$ & prob. that latent variable is active & prob. that cell assembly is active \\\\\n\\hline\n\\hspace{0.4cm} $M$ & dimension of latent vector $\\mathbf{z}$ & \\# different cell assemblies \\\\\n\\hline\n\\hspace{0.4cm} $K$ & most probable \\# active latent variables & most probable \\# coactive assemblies \\\\\n\\hline\n\\hspace{0.4cm} $K_{min}$ & minimum allowed |$\\mathbf{z}$| & minimum \\# coactive assemblies \\\\\n\\hline\n\\hspace{0.4cm} $K_{max}$ & maximum allowed |$\\mathbf{z}$| & maximum \\# coactive assemblies \\\\\n\\hline \\hline\n$P_{ia} \\sim [\\mathcal{N}(S_{ia}(1 - \\mu_{P}),\\sigma_{P})]_0^1$ & membership prob. of visible in group & membership prob. of neuron in CA\\\\\n\\hline\n\\hspace{0.4cm} $\\mathbf{S}$ binary membership & between visible and groups & between neurons and cell assemblies\\\\\n\\hline\n\\hspace{0.4cm} $|\\mathbf{S}_{\\cdot a}| \\sim \\lbrack \\mathcal{\\text{Bin}}(N,C/N)\\rbrack_{C_{min}}^{C_{max}}$ & distribution of group size & distribution of cell assembly size\\\\\n\\hline\n\\hspace{0.4cm} $N$ & dimension of observation vector $\\mathbf{y}$ & \\# neurons \\\\\n\\hline\n\\hspace{0.4cm} $C$ & \\# most probable group size & most probable cell assembly size \\\\\n\\hline\n\\hspace{0.4cm} $C_{min}$ & minimal group size $|\\mathbf{S}_{\\cdot a}|$ & size of smallest cell assembly\\\\\n\\hline\n\\hspace{0.4cm} $C_{max}$ & maximal group size $|\\mathbf{S}_{\\cdot a}|$ & size of largest cell assembly\\\\\n\\hline \\hline $R_i \\sim \\lbrack \\mathcal{N}(1-\\mu_{R},\\sigma_{R})\\rbrack_0^1$ & prob. of spurious activity & spurious firing, chattery-ness \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Generation of parameters for creating synthetic datasets. The three vertical sections of the table describe how we generate latent vectors, probabilistic cell assembly structure and spontaneous firing in synthetic datasets, respectively. Expressions use definitions (), (), further $[p(x)]_{l}^{u}$ means the distribution $p(x)$ truncated at the lower and upper bounds $l$ and $u$.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A probabilistic latent variable model for detecting structure in binary data", "authors": ["Christopher Warner", "Kiersten Ruda", "Friedrich T. Sommer"], "url": "https://arxiv.org/abs/2201.11108v1", "attribution": "\"A probabilistic latent variable model for detecting structure in binary data\" by Christopher Warner, Kiersten Ruda, and Friedrich T. Sommer, arXiv:2201.11108v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.09154v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ll|l}\n\\textbf{Beam horizontal component} & \\textbf{Beam vertical component} & \\textbf{Hit probability $g_L$} \\\\ \\hline\n1.000000 & 0.000000 & 0.424413 \\\\\n0.999683 & 0.025180 & 0.424682 \\\\\n0.998732 & 0.050345 & 0.425489 \\\\\n$\\vdots$ & $\\vdots$ & $\\vdots$ \\\\\n0.009444 & 0.999955 & 0.848789 \\\\\n0.000000 & 1.000000 & 0.848822 \n\\end{tabular}\n\\caption{Values from a look-up table (LUT) for the hit probability $g_L$ at a given beam direction $L$, represented by horizontal and vertical component. The numbers here correspond to an erectophile distribution and are obtained using a numerical integration method on the formulae from . The probability $g_L$ is normalised over $2\\pi$.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic 3D laser scanning", "authors": ["Lukas Winiwarter", "Alberto Manuel Esmorís Pena", "Hannah Weiser", "Katharina Anders", "Jorge Martínez Sanchez", "Mark Searle", "Bernhard Höfle"], "url": "https://arxiv.org/abs/2101.09154v1", "attribution": "\"Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic 3D laser scanning\" by Lukas Winiwarter, Alberto Manuel Esmorís Pena, Hannah Weiser, Katharina Anders, Jorge Martínez Sanchez, Mark Searle, and Bernhard Höfle, arXiv:2101.09154v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03872v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of computation time for MIQP and MILP formulations, $n=15$.}\n\\begin{tabular}{l|c|c|c|c|c|c}\n\\hline\nFormulation & F0Q & F1Q & F2Q & F0L & F1L & F2L \\\\\n\\hline\nMedian computation time, sec. & 24.86 & 21.59 & 8.95 & 34.80 & 1.16 & 3.14 \\\\\n\\hline\nMedian time rank & V & IV & III & VI & I & II \\\\\n\\hline\nAverage computation time, sec. & 79.40 & 683.23 & 25.08 & 99.15 & 7.06 & 8.35 \\\\\n\\hline\nAverage time rank & IV & VI & III & V & I & II \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Mixed-Integer Approaches to Constrained Optimum Communication Spanning Tree Problem", "authors": ["Alexander Veremyev", "Mikhail Goubko"], "url": "https://arxiv.org/abs/2101.03872v1", "attribution": "\"Mixed-Integer Approaches to Constrained Optimum Communication Spanning Tree Problem\" by Alexander Veremyev and Mikhail Goubko, arXiv:2101.03872v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.12530v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crrrrrrccrrrrrrr} \\toprule \nScenario & $\\xi^{1a}$ & $\\xi^{1b}$ & $\\xi^{2a}$ & $\\xi^{2b}$ & $\\xi^{3a}$ & $\\xi^{3b}$ & \\quad\\quad & Scenario & $\\xi^{1a}$ & $\\xi^{1b}$ & $\\xi^{2a}$ & $\\xi^{2b}$ & $\\xi^{3a}$ & $\\xi^{3b}$ \\\\ \\toprule \n 1 & 0.063 & 0.067 & 0.158 & 0.178 & 0.424 & 0.512 & & 51 & 0.087 & 0.094 & 0.244 & 0.261 & 0.613 & 0.888\\\\ \n 2 & 0.128 & 0.126 & 0.287 & 0.312 & 0.926 & 1.236 & & 52 & 0.165 & 0.172 & 0.512 & 0.509 & 1.277 & 1.791\\\\ \n 3 & 0.076 & 0.088 & 0.317 & 0.362 & 0.753 & 1.004 & & 53 & 0.133 & 0.131 & 0.407 & 0.563 & 1.539 & 2.188\\\\ \n 4 & 0.070 & 0.063 & 0.340 & 0.343 & 0.957 & 1.228 & & 54 & 0.149 & 0.141 & 0.526 & 0.650 & 1.617 & 1.980\\\\ \n 5 & 0.136 & 0.126 & 0.270 & 0.289 & 0.747 & 0.934 & & 55 & 0.191 & 0.156 & 0.563 & 0.593 & 1.808 & 2.274\\\\ \n 6 & 0.078 & 0.102 & 0.264 & 0.284 & 0.971 & 0.930 & & 56 & 0.218 & 0.231 & 0.522 & 0.639 & 1.959 & 2.337\\\\ \n 7 & 0.102 & 0.107 & 0.211 & 0.235 & 0.533 & 0.702 & & 57 & 0.204 & 0.233 & 0.684 & 0.735 & 1.429 & 2.032\\\\ \n 8 & 0.119 & 0.122 & 0.225 & 0.194 & 0.561 & 0.705 & & 58 & 0.223 & 0.173 & 0.561 & 0.652 & 1.922 & 2.272\\\\ \n 9 & 0.138 & 0.121 & 0.217 & 0.271 & 0.628 & 0.772 & & 59 & 0.213 & 0.268 & 0.707 & 0.941 & 2.604 & 2.524\\\\ \n 10 & 0.130 & 0.123 & 0.299 & 0.251 & 0.606 & 0.999 & & 60 & 0.240 & 0.214 & 0.784 & 0.858 & 2.953 & 2.798\\\\ \n 11 & 0.111 & 0.123 & 0.265 & 0.286 & 1.073 & 1.048 & & 61 & 0.278 & 0.217 & 0.817 & 1.036 & 2.687 & 2.537\\\\ \n 12 & 0.122 & 0.133 & 0.211 & 0.294 & 1.050 & 0.819 & & 62 & 0.272 & 0.349 & 0.898 & 0.958 & 2.813 & 3.100\\\\ \n 13 & 0.168 & 0.196 & 0.305 & 0.264 & 0.840 & 0.726 & & 63 & 0.297 & 0.350 & 0.873 & 0.924 & 3.373 & 2.500\\\\ \n 14 & 0.158 & 0.124 & 0.205 & 0.214 & 0.980 & 0.789 & & 64 & 0.404 & 0.346 & 0.840 & 1.063 & 2.989 & 2.898\\\\ \n 15 & 0.170 & 0.123 & 0.323 & 0.214 & 0.799 & 0.632 & & 65 & 0.360 & 0.329 & 0.836 & 1.045 & 3.003 & 2.649\\\\ \n 16 & 0.148 & 0.134 & 0.352 & 0.243 & 1.009 & 0.621 & & 66 & 0.319 & 0.384 & 0.963 & 1.025 & 2.705 & 3.167\\\\ \n 17 & 0.159 & 0.121 & 0.326 & 0.199 & 0.933 & 0.666 & & 67 & 0.376 & 0.332 & 1.340 & 1.066 & 3.366 & 3.661\\\\ \n 18 & 0.206 & 0.164 & 0.365 & 0.315 & 1.006 & 0.945 & & 68 & 0.411 & 0.292 & 1.028 & 1.117 & 3.318 & 3.275\\\\ \n 19 & 0.148 & 0.154 & 0.351 & 0.428 & 0.994 & 0.770 & & 69 & 0.380 & 0.464 & 0.810 & 1.245 & 3.744 & 3.216\\\\ \n 20 & 0.180 & 0.110 & 0.360 & 0.362 & 0.912 & 0.929 & & 70 & 0.513 & 0.483 & 1.062 & 1.217 & 2.970 & 3.024\\\\ \n 21 & 0.164 & 0.179 & 0.330 & 0.330 & 1.054 & 0.646 & & 71 & 0.401 & 0.437 & 1.452 & 1.598 & 2.989 & 4.208\\\\ \n 22 & 0.189 & 0.139 & 0.490 & 0.409 & 1.001 & 0.918 & & 72 & 0.391 & 0.401 & 1.032 & 1.269 & 2.794 & 3.274\\\\ \n 23 & 0.208 & 0.221 & 0.557 & 0.380 & 1.132 & 0.839 & & 73 & 0.512 & 0.437 & 1.600 & 1.558 & 3.549 & 2.930\\\\ \n 24 & 0.158 & 0.178 & 0.429 & 0.278 & 0.755 & 0.672 & & 74 & 0.336 & 0.520 & 1.319 & 1.372 & 2.913 & 3.336\\\\ \n 25 & 0.214 & 0.208 & 0.575 & 0.365 & 1.175 & 0.881 & & 75 & 0.478 & 0.481 & 1.127 & 1.616 & 3.185 & 2.219\\\\ \n 26 & 0.233 & 0.224 & 0.507 & 0.420 & 0.798 & 0.777 & & 76 & 0.410 & 0.561 & 1.271 & 1.367 & 2.591 & 2.516\\\\ \n 27 & 0.120 & 0.176 & 0.458 & 0.426 & 1.130 & 0.946 & & 77 & 0.354 & 0.433 & 1.284 & 1.130 & 2.289 & 3.011\\\\ \n 28 & 0.263 & 0.199 & 0.549 & 0.317 & 1.305 & 1.108 & & 78 & 0.551 & 0.416 & 1.220 & 0.890 & 2.830 & 2.836\\\\ \n 29 & 0.149 & 0.164 & 0.361 & 0.401 & 0.957 & 0.659 & & 79 & 0.389 & 0.523 & 0.990 & 1.293 & 2.546 & 4.131\\\\ \n 30 & 0.158 & 0.190 & 0.353 & 0.337 & 0.960 & 0.763 & & 80 & 0.357 & 0.552 & 1.213 & 1.126 & 2.879 & 3.642\\\\ \n 31 & 0.155 & 0.190 & 0.514 & 0.361 & 1.059 & 0.796 & & 81 & 0.351 & 0.515 & 1.200 & 0.961 & 3.724 & 2.900\\\\ \n 32 & 0.154 & 0.215 & 0.683 & 0.385 & 0.611 & 0.711 & & 82 & 0.390 & 0.428 & 1.186 & 1.323 & 3.836 & 3.237\\\\ \n 33 & 0.127 & 0.142 & 0.525 & 0.401 & 0.483 & 0.736 & & 83 & 0.327 & 0.394 & 1.577 & 1.278 & 3.975 & 3.375\\\\ \n 34 & 0.100 & 0.172 & 0.571 & 0.384 & 0.873 & 0.810 & & 84 & 0.371 & 0.427 & 1.633 & 1.169 & 3.878 & 3.206\\\\ \n 35 & 0.185 & 0.149 & 0.599 & 0.286 & 0.676 & 0.750 & & 85 & 0.376 & 0.219 & 1.395 & 1.258 & 4.207 & 2.876\\\\ \n 36 & 0.177 & 0.206 & 0.521 & 0.416 & 0.980 & 0.681 & & 86 & 0.433 & 0.352 & 1.616 & 0.914 & 3.997 & 3.670\\\\ \n 37 & 0.152 & 0.126 & 0.551 & 0.290 & 1.049 & 0.743 & & 87 & 0.443 & 0.214 & 1.480 & 1.073 & 3.792 & 4.043\\\\ \n 38 & 0.160 & 0.174 & 0.446 & 0.349 & 0.922 & 0.694 & & 88 & 0.403 & 0.253 & 1.307 & 0.911 & 2.677 & 4.091\\\\ \n 39 & 0.220 & 0.198 & 0.442 & 0.328 & 0.760 & 0.776 & & 89 & 0.386 & 0.196 & 0.987 & 1.118 & 3.054 & 4.438\\\\ \n 40 & 0.139 & 0.150 & 0.444 & 0.333 & 0.937 & 0.730 & & 90 & 0.374 & 0.408 & 1.080 & 1.298 & 3.068 & 3.832\\\\ \n 41 & 0.143 & 0.213 & 0.515 & 0.410 & 1.088 & 1.004 & & 91 & 0.411 & 0.320 & 1.053 & 1.053 & 3.353 & 4.813\\\\ \n 42 & 0.135 & 0.147 & 0.624 & 0.482 & 0.961 & 0.744 & & 92 & 0.374 & 0.481 & 1.105 & 1.343 & 3.303 & 5.189\\\\ \n 43 & 0.139 & 0.150 & 0.494 & 0.723 & 0.958 & 0.926 & & 93 & 0.380 & 0.496 & 1.243 & 1.317 & 3.400 & 4.766\\\\ \n 44 & 0.169 & 0.162 & 0.441 & 0.539 & 0.922 & 0.887 & & 94 & 0.408 & 0.450 & 1.172 & 1.382 & 4.417 & 5.476\\\\ \n 45 & 0.227 & 0.180 & 0.584 & 0.489 & 1.168 & 0.837 & & 95 & 0.265 & 0.432 & 1.132 & 1.571 & 4.071 & 4.528\\\\ \n 46 & 0.122 & 0.127 & 0.481 & 0.320 & 1.455 & 0.679 & & 96 & 0.274 & 0.440 & 1.029 & 1.450 & 3.714 & 4.625\\\\ \n 47 & 0.186 & 0.159 & 0.519 & 0.490 & 0.990 & 0.740 & & 97 & 0.416 & 0.391 & 0.776 & 1.287 & 3.569 & 3.219\\\\ \n 48 & 0.253 & 0.164 & 0.460 & 0.447 & 0.852 & 0.716 & & 98 & 0.276 & 0.584 & 1.393 & 1.487 & 3.659 & 3.135\\\\ \n 49 & 0.155 & 0.138 & 0.455 & 0.476 & 0.804 & 0.954 & & 99 & 0.555 & 0.467 & 1.267 & 1.379 & 3.765 & 2.717\\\\ \n 50 & 0.077 & 0.066 & 0.301 & 0.292 & 0.563 & 0.238 & & 100 & 0.145 & 0.157 & 0.598 & 0.986 & 1.552 & 1.396\\\\ \n \n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Microscopic Traffic Models, Accidents, and Insurance Losses", "authors": ["Sojung Kim", "Marcel Kleiber", "Stefan Weber"], "url": "https://arxiv.org/abs/2208.12530v2", "attribution": "\"Microscopic Traffic Models, Accidents, and Insurance Losses\" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.07687v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l}\n & \\textbf{Voisard et al.} & \\textbf{PerCDL} \\\\ \\hline\n\\textbf{Sensitivity} & 0.959 & 0.988 \\\\\n\\textbf{FPR} & 0.004 & 0.033 \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Personalized Convolutional Dictionary Learning of Physiological Time Series", "authors": ["Axel Roques", "Samuel Gruffaz", "Kyurae Kim", "Alain Oliviero-Durmus", "Laurent Oudre"], "url": "https://arxiv.org/abs/2503.07687v1", "attribution": "\"Personalized Convolutional Dictionary Learning of Physiological Time Series\" by Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero-Durmus, and Laurent Oudre, arXiv:2503.07687v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12409v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\t\t\\hline\n\t\tCorpus & \\#Sentence &\\#SrcToken &\\#TgtToken \\\\\n\t\t\\hline\n\t\tLang-8 & 1.09M & 14M & 15M \\\\\n\t\tHSK & 88K & 1.78M & 1.76M \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Data statistics for the Lang-8 and HSK datasets}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning", "authors": ["Shengsheng Zhang", "Yaping Huang", "Yun Chen", "Liner Yang", "Chencheng Wang", "Erhong Yang"], "url": "https://arxiv.org/abs/2101.12409v1", "attribution": "\"Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning\" by Shengsheng Zhang, Yaping Huang, Yun Chen, Liner Yang, Chencheng Wang, and Erhong Yang, arXiv:2101.12409v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18200v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc|cc}\n\\hline\n $G_i$ & $\\|Q_0 u- u_0\\|_0$ & $ h^r $ & $\\|\\nabla_w(Q_h u- u_h)\\|_0$ & $ h^r $ &\n $\\|E_w(Q_h u- u_h)\\|_0$ & $ h^r $ \\\\\n\\hline&\\multicolumn{6}{c}{ By the $P_2$/$P_2$/$P_{1}$ WG finite element .}\\\\\n\\hline \n 4& 0.227E-3 & 1.3& 0.225E-2 & 1.4& 0.288E+0 & 0.9 \\\\\n 5& 0.676E-4 & 1.7& 0.657E-3 & 1.8& 0.148E+0 & 1.0 \\\\\n 6& 0.178E-4 & 1.9& 0.172E-3 & 1.9& 0.743E-1 & 1.0 \\\\\n\\hline&\\multicolumn{6}{c}{ By the $P_3$/$P_3$/$P_{2}$ WG finite element .}\\\\\n\\hline \n 3& 0.328E-4 & 3.3& 0.113E-2 & 2.0& 0.770E-1 & 2.1 \\\\\n 4& 0.231E-5 & 3.8& 0.158E-3 & 2.8& 0.209E-1 & 1.9 \\\\\n 5& 0.148E-6 & 4.0& 0.197E-4 & 3.0& 0.537E-2 & 2.0 \\\\\n\\hline&\\multicolumn{6}{c}{ By the $P_4$/$P_4$/$P_{3}$ WG finite element .}\\\\\n\\hline \n 2& 0.157E-4 & 5.2& 0.112E-2 & 5.2& 0.104E+0 & 4.8 \\\\\n 3& 0.702E-6 & 4.5& 0.128E-3 & 3.1& 0.112E-1 & 3.2 \\\\\n 4& 0.201E-7 & 5.1& 0.919E-5 & 3.8& 0.152E-2 & 2.9 \\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Simple Weak Galerkin Finite Element Method for a Class of Fourth-Order Problems in Fluorescence Tomography", "authors": ["Chunmei Wang", "Shangyou Zhang"], "url": "https://arxiv.org/abs/2503.18200v1", "attribution": "\"A Simple Weak Galerkin Finite Element Method for a Class of Fourth-Order Problems in Fluorescence Tomography\" by Chunmei Wang and Shangyou Zhang, arXiv:2503.18200v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.10509v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc} \\toprule\nRetiree & 65-year old Canadian male\\\\\nTontine Gain $\\mathbb{T}^g$ & equation () \\\\\nGroup Gain $G$ ( see equation ( ) ) & 1.0\\\\\nMortality table & CPM 2014\\\\\nInvestment horizon $T$ (years) & 30.0 \\\\\nEquity market index & CRSP Cap-weighted index (real) \\\\\nBond index & 30-day T-bill (US) (real) \\\\\nInitial portfolio value $W_0$ & 1000 \\\\\nCash withdrawal$/$rebalancing times & $t=0,1.0, 2.0,\\ldots, 29.0$\\\\\nMaximum withdrawal (per year) & $ q_{\\max} = 80$\\\\\nMinimum withdrawal (per year) & $ q_{\\min} = 40$\\\\\nEquity fraction range & $[0,1]$\\\\\nBorrowing spread $\\mu_c^{b}$ & 0.02 \\\\\nRebalancing interval (years) & 1.0 \\\\\n$\\alpha$ (EW-ES) & .05 \\\\\nFees $\\mathbb{T}^f$ ( see equation () ) & 50 bps per year\\\\\nStabilization $\\epsilon$ ( see equation () ) & $ -10^{-4} $ \\\\\nMarket parameters & See Table~ \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Optimal performance of a tontine overlay subject to withdrawal constraints", "authors": ["Peter A. Forsyth", "Kenneth R. Vetzal", "G. Westmacott"], "url": "https://arxiv.org/abs/2211.10509v1", "attribution": "\"Optimal performance of a tontine overlay subject to withdrawal constraints\" by Peter A. Forsyth, Kenneth R. Vetzal, and G. Westmacott, arXiv:2211.10509v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.05393v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Data set v3 vs v5}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc} \\toprule\n\\textbf{Dataset} & \\textbf{Val acc [\\%]} & \\textbf{Test acc [\\%]} \\\\ \\midrule\nroeiboat-v3 & 90.09 & 87.40 \\\\\nroeiboat-v5 & 95.46 & 90.10 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fine-grained Classification of Rowing teams", "authors": ["M. J. A. van Wezel", "L. J. Hamburger", "Y. Napolean"], "url": "https://arxiv.org/abs/1912.05393v1", "attribution": "\"Fine-grained Classification of Rowing teams\" by M. J. A. van Wezel, L. J. Hamburger, and Y. Napolean, arXiv:1912.05393v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.05326v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Verhoeff's Irregular Code}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccccccc}\n$S$&0&1&2&3&4&5&6&7&8&9\\\\\n\\hline\n \n0&0&3&4&9&6&7&5&8&2&1\\\\\n1&5&1&0&2&8&3&9&6&7&4\\\\\n2&7&6&2&4&1&0&8&9&3&5\\\\\n3&1&5&8&3&7&6&4&0&9&2\\\\\n4&2&9&7&5&4&8&1&3&0&6\\\\\n5&6&7&9&0&3&5&2&4&1&8\\\\\n6&3&8&1&7&5&9&6&2&4&0\\\\\n7&9&4&5&8&2&1&0&7&6&3\\\\\n8&4&0&6&1&9&2&3&5&8&7\\\\\n9&8&2&3&6&0&4&7&1&5&9\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Totally Disjoint 3-Digit Decimal Check Digit Codes", "authors": ["Larry A. Dunning"], "url": "https://arxiv.org/abs/2504.05326v2", "attribution": "\"Totally Disjoint 3-Digit Decimal Check Digit Codes\" by Larry A. Dunning, arXiv:2504.05326v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.06422v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average Accuracy Rate by Model Type}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|lc|}\n\t\t\t\\hline\n\t\t\tModel & Average accuracy rate \\\\\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\tCOMPAS (baseline) & 65.4\\% \\\\\n\t\t\tLASSO & 67.2\\% \\\\\n\t\t\tRidge & 67.1\\% \\\\\n\t\t\tElastic net & 67.4\\%\\\\\n\t\t\t\\hline\n\t\t\t\\multicolumn{2}{r}{\\textit{Note: Average accuracy rate from 1,000 iterations of model fit and evaluation.}}\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Pursuing Open-Source Development of Predictive Algorithms: The Case of Criminal Sentencing Algorithms", "authors": ["Philip D. Waggoner", "Alec Macmillen"], "url": "https://arxiv.org/abs/2011.06422v1", "attribution": "\"Pursuing Open-Source Development of Predictive Algorithms: The Case of Criminal Sentencing Algorithms\" by Philip D. Waggoner and Alec Macmillen, arXiv:2011.06422v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.20204v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{SW Model: Specification Testing With(out) Stochastic Singularity}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc||ccc||ccc||ccc}\n \\hline\\hline\n & \\multicolumn{3}{c||}{7 shocks} & \\multicolumn{3}{c||}{6 shocks} & \n \\multicolumn{3}{c||}{5 shocks} & \\multicolumn{3}{c}{4 shocks} \\\\ \n & \\textsc{stat} & 10\\% & 5\\% & \\textsc{stat} & 10\\% & 5\\% & \\textsc{stat} & \n 10\\% & 5\\% & \\textsc{stat} & 10\\% & 5\\% \\\\ \\hline\n All & 256.6 & 320.7 & 395.0 & 411.4 & 282.1 & 364.5 & 597.7 & 291.4 & 357.0\n & 632.0 & 288.6 & 353.3 \\\\ \\hline\n Cons. & 60.4 & 31.9 & 36.8 & 56.5 & 32.6 & 39.9 & 192.9 & 6.3 & 8.9 & 192.7\n & 6.1 & 8.5 \\\\ \n Invest. & 25.6 & 33.2 & 38.6 & 34.1 & 31.9 & 36.6 & 45.6 & 41.0 & 47.3 & 45.0\n & 41.1 & 47.7 \\\\ \n Output & 34.3 & 37.0 & 41.8 & 34.9 & 32.4 & 36.7 & 54.4 & 39.8 & 45.1 & 57.9\n & 39.2 & 44.8 \\\\ \n Labor & 16.3 & 35.4 & 47.2 & 14.7 & 48.4 & 66.8 & 86.9 & 49.3 & 63.5 & 97.6\n & 47.8 & 60.0 \\\\ \n Infl. & 52.8 & 92.5 & 122.7 & 75.1 & 93.5 & 124.2 & 67.6 & 101.1 & 137.8 & \n 69.6 & 104.1 & 146.1 \\\\ \n Wage & 22.6 & 64.8 & 80.3 & 108.3 & 26.2 & 33.3 & 76.4 & 30.9 & 37.6 & 98.3 & \n 27.4 & 33.1 \\\\ \n Int. Rate & 44.6 & 47.9 & 63.3 & 87.9 & 33.8 & 45.2 & 73.9 & 49.9 & 67.3 & \n 70.9 & 48.8 & 65.6 \\\\ \\hline\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fitting Dynamically Misspecified Models: An Optimal Transportation Approach", "authors": ["Jean-Jacques Forneron", "Zhongjun Qu"], "url": "https://arxiv.org/abs/2412.20204v2", "attribution": "\"Fitting Dynamically Misspecified Models: An Optimal Transportation Approach\" by Jean-Jacques Forneron and Zhongjun Qu, arXiv:2412.20204v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.14318v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Correlation coefficients and p-value with RUL under constant charging/discharging current}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\toprule\n & Correlation coefficient & p-value$^\\mathrm{a}$ \\\\ \\midrule\n$\\textbf{V}_{max}$ & -0.5097 & 1.9e-34 \\\\\n$\\textbf{V}_{min}$ & 0.2856 & 7.7e-11 \\\\\n$\\Delta\\textbf{V}$ & -0.4108 & 8.8e-22 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Framework for Health-informed RUL-constrained Optimal Power Flow with Li-ion Batteries", "authors": ["Jiahang Xie", "Yu Weng", "Hung D. Nguyen"], "url": "https://arxiv.org/abs/2011.14318v1", "attribution": "\"A Framework for Health-informed RUL-constrained Optimal Power Flow with Li-ion Batteries\" by Jiahang Xie, Yu Weng, and Hung D. Nguyen, arXiv:2011.14318v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.04929v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Video prediction results on KTH ($64\\times64$), predicting 30 and 40 frames using models trained to predict $k$ frames at a time. All models condition on 10 past frames on 256 test videos.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rc|lcl}\n\\toprule\n\\textbf{KTH} [10 $\\rightarrow$ $\\# \\text{pred}$; trained on $k$] & $k$ & $\\# \\text{pred}$ & FVD$\\downarrow$ & PSNR$\\uparrow$ & SSIM$\\uparrow$ \\\\ \\hline\nSVG-LP~ & 10 & 30 & 377 & 28.1 & 0.844 \\\\\nSAVP~ & 10 & 30 & 374 & 26.5 & 0.756 \\\\\nMCVD~ & 5 & 30 & 323 & 27.5 & 0.835 \\\\\nSLAMP~ & 10 & 30 & 228 & 29.4 & 0.865 \\\\\nSRVP~ & 10 & 30 & 222 & 29.7 & 0.870 \\\\\nRIVER~& 10&30& 180& \\textbf{30.4} & 0.86\\\\\n\\textbf{CVP (Ours) } & \\textbf{1} & 30 & \\textbf{140.6}& 29.8 & \\textbf{0.872} \\\\\n\\midrule\nStruct-vRNN~ & 10 & 40 & 395.0 & 24.29 & 0.766 \\\\\nSVG-LP~ & 10 & 40 & 157.9 & 23.91 & 0.800 \\\\\nMCVD~& 5 & 40 & 276.7 & 26.40 & 0.812 \\\\\nSAVP-VAE~ & 10 & 40 & 145.7 & 26.00 & 0.806 \\\\\nGrid-keypoints~& 10 & 40 & 144.2 & 27.11 & 0.837 \\\\\nRIVER~& 10&40& 170.5& 29.0 & 0.82\\\\\n\\textbf{CVP (Ours)} & \\textbf{1} & 40 & \\textbf{120.1} & \\textbf{29.2} & \\textbf{0.841} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Prediction", "authors": ["Gaurav Shrivastava", "Abhinav Shrivastava"], "url": "https://arxiv.org/abs/2412.04929v2", "attribution": "\"Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Prediction\" by Gaurav Shrivastava and Abhinav Shrivastava, arXiv:2412.04929v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.08430v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Percentage of claims with different activation delays for the four coverages}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\\toprule\n& \\multicolumn{5}{c}{Activation delays} \\\\\n\\midrule\n Coverage & No delay & 1 period & 2 periods & 3 periods & $\\geq 4$ periods \\\\ \n \\midrule\n Accident Benefits & 93.84 & 5.73 & 0.29 & 0.08 & 0.06\\\\\n Bodily Injury & 85.86 & 9.86 & 1.46 & 1.13 & 1.69\\\\\n Vehicle Damage & 94.14 & 5.65 & 0.13 & 0.04 & 0.04 \\\\\n Loss of Use & 92.10 & 7.73 & 0.13 & 0.03 & 0.01\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Individual Claims Reserving using Activation Patterns", "authors": ["Marie Michaelides", "Mathieu Pigeon", "Hélène Cossette"], "url": "https://arxiv.org/abs/2208.08430v3", "attribution": "\"Individual Claims Reserving using Activation Patterns\" by Marie Michaelides, Mathieu Pigeon, and Hélène Cossette, arXiv:2208.08430v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.15785v5_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Filter design equations ...}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\nOrder & Arbitrary coefficients & \ncoefficients\\\\\nof filter & $e_m$ & $b_{ij}$ \\\\\n\\hline\n1& $b_{ij}=\\hat{e}.\\hat{\\beta_{ij}}$, \n& $b_{00}=0$\\\\\n\\hline\n2&$\\beta_{22}=(~1,-1,-1,~~1,~~1,~~1)$ &\\\\ \n\\hline\n3& $b_{ij}=\\hat{e}.\\hat{\\beta_{ij}}$, \n& $b_{00}=0$,\\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Supply Chain Characteristics as Predictors of Cyber Risk: A Machine-Learning Assessment", "authors": ["Kevin Hu", "Retsef Levi", "Raphael Yahalom", "El Ghali Zerhouni"], "url": "https://arxiv.org/abs/2210.15785v5", "attribution": "\"Supply Chain Characteristics as Predictors of Cyber Risk: A Machine-Learning Assessment\" by Kevin Hu, Retsef Levi, Raphael Yahalom, and El Ghali Zerhouni, arXiv:2210.15785v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16490v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{An Example of a Table}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c||c||c|}\n\\hline\nPolicy & Steps When No Noise & Steps when encounter random noise\\\\\n\\hline\nNon-smooth & & Steps when encounter random noise\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Multi-agent Robust and Optimal Policy Learning for Data Harvesting", "authors": ["Shili Wu", "Yancheng Zhu", "Aniruddha Datta", "Sean B. Andersson"], "url": "https://arxiv.org/abs/2508.16490v1", "attribution": "\"Multi-agent Robust and Optimal Policy Learning for Data Harvesting\" by Shili Wu, Yancheng Zhu, Aniruddha Datta, and Sean B. Andersson, arXiv:2508.16490v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.13454v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\hline \nMethod & $< 0.5\\%$ & $< 1.25\\%$ & $< 2.5\\%$ & $>97.5\\%$ & $> 98.75\\%$ & $>99.5\\%$ \\\\\n\\hline \n1st order & 5.00 & 5.73 & 8.53 & 1.52 & 0.74 & 0.35 \\\\\n & 0.28 & 0.70 & 1.19 & 1.20 & 0.67 & 0.30 \\\\\n & 0.29 & 0.69 & 2.18 & 2.05 & 0.95 & 0.41 \\\\\n3rd order (a) & 0.37 & 0.90 & 2.30 & 2.41 & 1.13 & 0.47 \\\\\n3rd order (b) & 0.37 & 0.91 & 2.30 & 2.41 & 1.13 & 0.47 \\\\\n{\\em Profile marginal IM} & 0.22 & 0.65 & 1.57 & 2.71 & 1.31 & 0.59 \\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results for the gamma mean simulation in Example~. The table shows the percentage of $p$-values in the stated bins (across 10000 simulations) for the various methods which relates to the coverage probability of the corresponding confidence intervals for the mean $\\Phi$. All the values except for the last row are taken from Table~2 in . The standard errors across the board are all less than 0.16\\%.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Valid and efficient imprecise-probabilistic inference with partial priors, III. Marginalization", "authors": ["Ryan Martin"], "url": "https://arxiv.org/abs/2309.13454v1", "attribution": "\"Valid and efficient imprecise-probabilistic inference with partial priors, III. Marginalization\" by Ryan Martin, arXiv:2309.13454v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17103v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n $n$ & Error $\\|\\cdot\\|_1$ & Order $\\|\\cdot\\|_1$ & Error\n $\\|\\cdot\\|_{\\infty}$ & Order $\\|\\cdot\\|_{\\infty}$ & $\\%$\n Success \\\\\n \\hline\n 40 & 1.95E$-5$ & $-$ & 1.38E$-4$ & $-$ & 98.75 $\\%$ \\\\\n \\hline\n 80 & 2.70E$-7$ & 6.17 & 7.35E$-7$ & 7.55 & 100.00 $\\%$ \\\\\n \\hline\n 160 & 8.45E$-9$ & 5.00 & 2.31E$-8$ & 4.99 & 100.00 $\\%$ \\\\\n \\hline\n 320 & 2.64E$-10$ & 5.00 & 6.95E$-10$ & 5.06 & 100.00 $\\%$ \\\\\n \\hline\n 640 & 8.26E$-12$ & 5.00 & 2.13E$-11$ & 5.03 & 100.00 $\\%$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17103v1", "attribution": "\"High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.22779v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{{\\small States of different facilities in microgrids}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\hline\n \\toprule \n State & 1 & 2 & 3 & 4 & 5 & 6\\\\ \\hline\n Wind power/MW & 0 & 1 & 2 & 3 & 4 & 5 \\\\ \\hline\n Demand load/MW & 0.6 & 1.2 & 1.8 & 2.4 & 3.0 & 3.6 \\\\ \\hline\n Storage energy level/MWh & 0 & 1 & 2 & 3 & 4 & 5 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games", "authors": ["Junkai Hu", "Li Xia"], "url": "https://arxiv.org/abs/2503.22779v2", "attribution": "\"Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games\" by Junkai Hu and Li Xia, arXiv:2503.22779v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.11910v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quality of data manifold global structure preservation at projection Spheres dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n & \\multicolumn{4}{c}{Quality measure} \\\\\n \\cmidrule(r){2-5}\n Method & L. C. & W. D. $H_{0}$ & T. A. & RTD \\\\\n \\midrule\n \\multicolumn{5}{c}{\\textbf{into 2D space}} \\\\\n UMAP & 0.021 & {44.90 $\\pm$ 1.8} & 0.54 $\\pm$ 0.01 & 42.60 $\\pm$ 1.8 \\\\\n AE & 0.311 & {46.07 $\\pm$ 1.5} & 0.41 $\\pm$ 0.01 & 41.02 $\\pm$ 1.4 \\\\\n TopoAE & {0.495} & \\underline{43.92 $\\pm$ 2.5} & {0.54 $\\pm$ 0.02} & \\underline{39.69 $\\pm$ 1.4} \\\\\n RTD & \\underline{0.626} & {45.29 $\\pm$ 2.2} &\\underline{0.68 $\\pm$ 0.02} & \\underline{39.60 $\\pm$ 1.9}\\\\\n RTD-L& {0.570} & {45.72 $\\pm$ 2.0} & {0.64 $\\pm$ 0.02} & \\underline{40.00 $\\pm$ 1.7} \\\\\n \\midrule\n \\multicolumn{5}{c}{\\textbf{into 3D space} } \\\\\n UMAP & 0.041 & 45.56 $\\pm$ 2.2 & 0.54 $\\pm$ 0.01 & 41.79 $\\pm$ 2.2 \\\\\n AE & 0.376 & {46.06 $\\pm$ 2.3} & 0.41 $\\pm$ 0.02 & 41.05 $\\pm$ 2.2 \\\\\n TopoAE & {0.622} & \\underline{40.41 $\\pm$ 2.7} & 0.71 $\\pm$ 0.01 & 34.74 $\\pm$ 1.9 \\\\\n RTD & \\underline{0.687} & {41.39 $\\pm$ 2.3} & \\underline{0.74 $\\pm$ 0.02} & \\underline{33.74 $\\pm$ 1.6} \\\\\n RTD-L & 0.615 & {41.76 $\\pm$ 1.8} & 0.66 $\\pm$ 0.02 & 35.80 $\\pm$ 1.5 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks", "authors": ["Eduard Tulchinskii", "Daria Voronkova", "Ilya Trofimov", "Evgeny Burnaev", "Serguei Barannikov"], "url": "https://arxiv.org/abs/2503.11910v1", "attribution": "\"RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks\" by Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, and Serguei Barannikov, arXiv:2503.11910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03806v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative comparison with state-of-the-art: we compare the Frechet Inception Distance (FID) obtained with the different methods. The lower the better.}\n\\begin{tabular}{cccc}\n \\toprule\n\t\\textbf{Artist} & \\textbf{ComboGAN} & \\textbf{StarGAN} & \\textbf{\\textit{Ada$^2$Net}}\\\\\n \\midrule\n\tBeksinski & 179.05 & 146.05 & \\bf 111.5\\\\\n\tBoudin & 160.3 & 148.54 & \\bf 107.07\\\\\n\tBurliuk & 142.84 & 136.57 & \\bf 105.16\\\\\n\tCezanne & 146.44 & 129.98 & \\bf 92.28\\\\\n\tChagall & 142.66 & 109.96 & \\bf 102.43\\\\\n\tCorot & 173.73 & 165.66 & \\bf 106.37\\\\\n\tEarle & 178.68 & 172.73 & \\bf 147.95\\\\\n\tGauguin & 149.87 & 141.56 & \\bf 98.51\\\\\t\n\tHassam & 140.98 & 141.24 & \\bf 98.49\\\\\n\tLevitan & 151.83 & 186.92 & \\bf 112.44\\\\\n\tMonet & 144.96 & 131.48 & \\bf 78.81\\\\\n\tPicasso & 145.1 & 134.1 & \\bf123.69\\\\\n\tUkiyo-e & 151.81 & 110.22 & \\bf 99.97\\\\\n\tVan Gogh & 151.91 & 135.45 & \\bf 97.25\\\\\n\\midrule\n\t\\textbf{Average$\\pm$Std} & 154.3$\\pm$12.91 & 142.18$\\pm$20.72 & \\textbf{105.85$\\pm$15.38}\\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multi-Domain Image-to-Image Translation with Adaptive Inference Graph", "authors": ["The-Phuc Nguyen", "Stéphane Lathuilière", "Elisa Ricci"], "url": "https://arxiv.org/abs/2101.03806v1", "attribution": "\"Multi-Domain Image-to-Image Translation with Adaptive Inference Graph\" by The-Phuc Nguyen, Stéphane Lathuilière, and Elisa Ricci, arXiv:2101.03806v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08912v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\toprule[1.5pt]\n\\multicolumn{7}{c}{Single grid} \\\\\n\\midrule\n& $N_h$ & $L^1$ Error & Order & $L^\\infty$ Error & Order & CPU(s) \\\\\n\\midrule\n& 160 & $1.27 \\times 10^{-5}$ & - & $4.91 \\times 10^{-5}$ & - & 1.83 \\\\\n& 320 & $1.59 \\times 10^{-6}$ & 3.00 & $6.14 \\times 10^{-6}$ & 3.00 & 11.74\\\\\n& 640 & $1.98 \\times 10^{-7}$ & 3.00 & $7.68 \\times 10^{-7}$ & 3.00 & 80.38\\\\\n& 1280 & $2.47 \\times 10^{-8}$ & 3.00 & $9.60 \\times 10^{-8}$ & 3.00 & 748.34\\\\\n\\midrule\n\\multicolumn{7}{c}{Sparse grid} \\\\\n\\midrule\n$N_r$ & $N_h$ & $L^1$ Error & Order & $L^\\infty$ Error & Order & CPU(s) \\\\\n\\midrule\n20 & 160 & $4.56 \\times 10^{-5}$ & - & $5.21 \\times 10^{-4}$ & - & 1.51 \\\\\n40 & 320 & $2.11 \\times 10^{-6}$ & 4.43 & $1.63 \\times 10^{-4}$ & 1.67 & 6.08 \\\\\n80 & 640 & $2.73 \\times 10^{-7}$ & 2.95 & $1.35 \\times 10^{-5}$ & 3.60 & 22.63 \\\\\n160 & 1280 & $3.02 \\times 10^{-8}$ & 3.18 & $1.56 \\times 10^{-6}$ & 3.12 & 159.19 \\\\\n\\bottomrule[1.5pt]\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Sparse grid implementation of a fixed-point fast sweeping WENO scheme for Eikonal equations", "authors": ["Zachary M. Miksis", "Yong-Tao Zhang"], "url": "https://arxiv.org/abs/2201.08912v1", "attribution": "\"Sparse grid implementation of a fixed-point fast sweeping WENO scheme for Eikonal equations\" by Zachary M. Miksis and Yong-Tao Zhang, arXiv:2201.08912v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.15087v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The RMSE and RSB values of the five estimators for $n=500$ under various settings. The results are based on 1000 simulations.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cclrrrrr} \\hline \n\t\t\t\t$g$\t & $\\sigma$& &\n\t\t\t\t$\\quad\\hat \\sigma^2_{\\text{ord}}(2)$ & $\\quad\\hat \\sigma^2_{\\text{opt}}(2)$ &\n\t\t\t\t$\\quad\\hat \\sigma^2_{\\text{ord}}(3)$ & $\\quad\\hat \\sigma^2_{\\text{opt}}(3)$ &\n\t\t\t\t$\\quad\\hat \\sigma^2_{\\text{opt-k}}$ \\\\ \\hline\n\t\t\t\t$g_1$ &\t0.2 & RMSE & 1.99 & 1.34& 2.39 & 1.40 & {\\bf 1.24} \\\\\n\t\t\t\t& & RSB & 0.00 & 0.03& 0.00 & 0.16 & 0.00 \\\\\n\t\t\t\t&\t0.5 & RMSE & 1.99 & 1.31& 2.39 & {\\bf 1.24} & {\\bf 1.24} \\\\\n\t\t\t\t& & RSB & 0.00 & 0.00& 0.00 & 0.00 & 0.00\\\\\t\t\t\t\n\t\t\t\t&\t1.5 & RMSE & 1.99 & 1.31& 2.39 & {\\bf 1.24} & {\\bf 1.24} \\\\\n\t\t\t\t& & RSB & 0.00 & 0.00& 0.00 & 0.00 & 0.00 \\\\\t%[5pt]\t\t\t\n\t\t\t\t$g_2$ &\t0.2 & RMSE & 1.99 & 15.98& 2.39 & 52.87 & {\\bf 1.24} \\\\\n\t\t\t\t& & RSB & 0.00 & 14.68& 0.00 & 51.64 & 0.00 \\\\\n\t\t\t\t&\t0.5 & RMSE & 1.99 & 1.62& 2.39 & 2.46 & {\\bf 1.31} \\\\\n\t\t\t\t& & RSB & 0.00 & 0.32& 0.00 & 1.22 & 0.00 \\\\ \n\t\t\t\t&\t1.5 & RMSE & 1.99 & 1.31& 2.39 & {\\bf 1.24} & 1.27 \\\\\n\t\t\t\t& & RSB & 0.00 & 0.00& 0.00 & 0.00 & 0.00 \\\\ \\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Optimal-$k$ difference sequence in nonparametric regression", "authors": ["Wenlin Dai", "Xingwei Tong", "Tiejun Tong"], "url": "https://arxiv.org/abs/2211.15087v1", "attribution": "\"Optimal-$k$ difference sequence in nonparametric regression\" by Wenlin Dai, Xingwei Tong, and Tiejun Tong, arXiv:2211.15087v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.03021v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crrrrr}\n\\hline\n\\multirow{2}{*}{\\textbf{Sample Size}} & \\multicolumn{5}{c}{$\\boldsymbol{D}$}\\\\\n& $\\mathbf{10}$ & $\\mathbf{25}$ & $\\mathbf{50}$ & $\\mathbf{100}$ & $\\mathbf{200}$ \\\\\n\\hline\n10 & 0.13 $\\pm$ 0.01 & 0.90 $\\pm$ 0.25 & 3.5 $\\pm$ 0.04 & 14 $\\pm$ 0.07 & 61 $\\pm$ 1.40 \\\\\n25 & 0.21 $\\pm$ 0.01 & 1.40 $\\pm$ 0.04 & 5.7 $\\pm$ 0.09 & 23 $\\pm$ 0.07 & 94 $\\pm$ 0.36 \\\\\n50 & 0.37 $\\pm$ 0.01 & 2.45 $\\pm$ 0.05 & 10.0 $\\pm$ 0.09 & 41 $\\pm$ 0.25 & 169 $\\pm$ 4.56 \\\\\n100 & 0.71 $\\pm$ 0.07 & 4.66 $\\pm$ 0.09 & 19.1 $\\pm$ 0.23 & 77 $\\pm$ 0.46 & 314 $\\pm$ 2.54 \\\\\n200 & 1.50 $\\pm$ 0.10 & 9.62 $\\pm$ 0.16 & 39.4 $\\pm$ 0.41 & 158 $\\pm$ 3.19 & 625 $\\pm$ 2.88 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Computation time (seconds) for vine copula fitting across dimensions $D$ and sample sizes, on a MacBook Pro M1 Pro (2023). The results were averaged over 10 different datasets, per dimension/datasize pair.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Testing Generalizability in Causal Inference", "authors": ["Daniel de Vassimon Manela", "Linying Yang", "Robin J. Evans"], "url": "https://arxiv.org/abs/2411.03021v2", "attribution": "\"Testing Generalizability in Causal Inference\" by Daniel de Vassimon Manela, Linying Yang, and Robin J. Evans, arXiv:2411.03021v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2207.12494v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Most Commonly Excluded and Included Expenditure Categories}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n \\hline \\hline\n & Median & Trimmed Mean & Middle 80\\% \\\\\n & & & (10, 10) Trim \\\\\n \\multicolumn{4}{c}{Most Commonly Excluded}\\\\\n \\hline \n 1 & & Eggs & Eggs \\\\\n 2 & 66 series are & Food on farms & Vegetables \\\\\n 3 & never median & Vegetables & Food on farms \\\\\n 4 & & Fruit & Fuel Oil \\\\\n 5 & & Gasoline & Gasoline \\\\\n \\hline\n \\multicolumn{4}{c}{Most Commonly Included}\\\\ \\hline\n 1 & Owner-occ homes & Owner-occ homes & Owner-occ homes \\\\\n 2 & Other purchased meals & Other purchased meals & Other purch meals \\\\\n 3 & Tenant-occ homes & Casino gambling & Tenant-occ homes \\\\\n 4 & Nonprofit hospitals & Owner-occ mobile homes & Casino gambling \\\\\n 5 & Physician services & Tenant-occ homes & Lotteries \\\\\n \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Extending the Range of Robust PCE Inflation Measures", "authors": ["Sergio Ocampo", "Raphael Schoenle", "Dominic A. Smith"], "url": "https://arxiv.org/abs/2207.12494v3", "attribution": "\"Extending the Range of Robust PCE Inflation Measures\" by Sergio Ocampo, Raphael Schoenle, and Dominic A. Smith, arXiv:2207.12494v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.17472v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|}\n \\hline\n \\textbf{Model} & \\textbf{Score} & \\textbf{Change (\\%)} \\\\\n \\hline\n Ours & 0.344 & -- \\\\\n \\hline\n - $\\mathcal{R}_{PAC}$ & 0.338 & \\textcolor{red!70!black}{-1.74\\% $\\downarrow$} \\\\\n \\hline\n - $\\mathcal{L}_{out}$ & 0.336 & \\textcolor{red!70!black}{-0.59\\% $\\downarrow$} \\\\\n \\hline\n - $\\mathcal{L}_{sim}$ & 0.331 & \\textcolor{red!70!black}{-1.48\\% $\\downarrow$} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{ \\textbf{Impact of Removing Individual Loss Components on Full Similarity Scores} in the ABC Dataset. This table presents the Full Sim. scores for the complete model and each ablated variant, along with the corresponding percentage decrease in performance.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory", "authors": ["Eric Hanchen Jiang", "Yasi Zhang", "Zhi Zhang", "Yixin Wan", "Andrew Lizarraga", "Shufan Li", "Ying Nian Wu"], "url": "https://arxiv.org/abs/2411.17472v1", "attribution": "\"Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory\" by Eric Hanchen Jiang, Yasi Zhang, Zhi Zhang, Yixin Wan, Andrew Lizarraga, Shufan Li, and Ying Nian Wu, arXiv:2411.17472v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13714v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\t\\small\\textbf{Case}& \\small\\textbf{Conditions}& \\small\\textbf{$\\boldsymbol{O_1}$}& \\small\\textbf{$\\boldsymbol{O_2}$} & \\small\\textbf{Global} \\\\\n\t\t\t\\hline\n\t\t\t\\hline\t\n\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\\multirow{4}{1cm}{\\centering{4.1}}\n\t\t\t\t&\n\t\t\t\t$\\mu=0$\n\t\t\t\t&\n\t\t\t\tL14\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{3cm}{\\centering{Unstable node}}\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{1cm}{\\centering{G84}} \\\\\n\t\t\t\t\\cline{2-3}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu>-1$, $a_0+c_0\\mu>0$\n\t\t\t\t&\n\t\t\t\tL29\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{*}\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{*} {\\phantom{h}} \n\t\t\t\t\\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu>-1$, $a_0+c_0\\mu<0$\n\t\t\t\t&\n\t\t\t\tL30\n\t\t\t\t&\n\t\t\t\tUnstable node\n\t\t\t\t&\n\t\t\t\tG85\\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu<-1$\n\t\t\t\t&\n\t\t\t\tL33\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG86 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\\multirow{5}{1cm}{\\centering{4.2}}\n\t\t\t\t&\n\t\t\t\t$\\mu=0$\n\t\t\t\t&\n\t\t\t\tL5\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t G87\\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{5}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu>-1$, $a_0+c_0\\mu>0$\n\t\t\t\t&\n\t\t\t\tL27\n\t\t\t\t&\n\t\t\t Stable node\n\t\t\t\t&\n\t\t\t\tG88 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{5}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu>-1$, $a_0+c_0\\mu<0$\n\t\t\t\t&\n\t\t\t\tL28\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG89 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{5}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu<-1$, $a_0+c_0\\mu>0$\n\t\t\t\t&\n\t\t\t\tL31\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG90 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{5}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu<-1$, $a_0+c_0\\mu<0$\n\t\t\t\t&\n\t\t\t\tL32\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG91 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\\multirow{3}{1cm}{\\centering{4.3}}\n\t\t\t\t&\n\t\t\t\t$\\mu=0$\n\t\t\t\t&\n\t\t\t\tL14\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{3cm}{\\centering{Unstable node}}\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{1cm}{\\centering{G92}} \\\\\n\t\t\t\t\\cline{2-3}\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu>-1$\n\t\t\t\t&\n\t\t\t\tL20\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{*}\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{*} {\\phantom{h}} \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu<-1$\n\t\t\t\t&\n\t\t\t\tL22\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG93 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\\multirow{3}{1cm}{\\centering{4.4}}\n\t\t\t\t&\n\t\t\t\t$\\mu=0$\n\t\t\t\t&\n\t\t\t\tL5\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG94 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu>-1$\n\t\t\t\t&\n\t\t\t\tL19\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG95 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu<-1$\n\t\t\t\t&\n\t\t\t\tL21\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG96 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\\multirow{3}{1cm}{\\centering{5.1}}\n\t\t\t\t&\n\t\t\t\t$\\mu>0$\n\t\t\t\t&\n\t\t\t\tL3\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG97 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-1,0)$\n\t\t\t\t&\n\t\t\t\tL10\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG98 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu<-1$\n\t\t\t\t&\n\t\t\t\tL11\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG99 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t5.2\n\t\t\t\t&\n\t\t\t\t\n\t\t\t\t&\n\t\t\t\tL12\n\t\t\t\t&\n\t\t\t\tUnstable node\n\t\t\t\t&\n\t\t\t\tG76 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\\multirow{3}{1cm}{\\centering{5.3}}\n\t\t\t\t&\n\t\t\t\t$\\mu>0$\n\t\t\t\t&\n\t\t\t\tL6\n\t\t\t\t&\n\t\t\t\tUnstable node\n\t\t\t\t&\n\t\t\t\tG100 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu<-1$\n\t\t\t\t&\n\t\t\t\tL8\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG77 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-1,0)$\n\t\t\t\t&\n\t\t\t\tL13\n\t\t\t\t&\n\t\t\t\tUnstable node\n\t\t\t\t&\n\t\t\t\tG78 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\\multirow{3}{1cm}{\\centering{5.4}}\n\t\t\t\t&\n\t\t\t\t$\\mu>0$\n\t\t\t\t&\n\t\t\t\tL3\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG79 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t $\\mu\\in(-1,0)$\n\t\t\t\t&\n\t\t\t\tL10\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG101 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu<-1$\n\t\t\t\t&\n\t\t\t\tL11\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG102 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\\multirow{4}{1cm}{\\centering{5.5}}\n\t\t\t\t&\n\t\t\t\t$\\mu<-2$\n\t\t\t\t&\n\t\t\t\tL1\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG80 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-1,0)$\n\t\t\t\t&\n\t\t\t\tL4\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG81 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-2,-1)$\n\t\t\t\t&\n\t\t\t\tL15\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG82 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu=-2$\n\t\t\t\t&\n\t\t\t\tL17\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG83 \\\\\n\t\t\t\t\\hline\n\t\n\t\t\t\t\\multirow{3}{1cm}{\\centering{6.1}}\n\t\t\t\t&\n\t\t\t\t$\\mu=0$\n\t\t\t\t&\n\t\t\t\tL14\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{3cm}{\\centering{Unstable node}}\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{1cm}{\\centering{G92}}\n\t\t\t\t\\\\\n\t\t\t\t\\cline{2-3}\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu>-1$\n\t\t\t\t&\n\t\t\t\tL20\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{*}\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{*} {\\phantom{h}}\n\t\t\t\t\\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu<-1$\n\t\t\t\t&\n\t\t\t\tL22\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG93 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\\multirow{3}{1cm}{\\centering{6.2}}\n\t\t\t\t&\n\t\t\t\t$\\mu=0$\n\t\t\t\t&\n\t\t\t\tL5\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG94\n\t\t\t\t\\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu>-1$\n\t\t\t\t&\n\t\t\t\tL19\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG95\n\t\t\t\t\\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu<-1$\n\t\t\t\t&\n\t\t\t\tL21\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG96 \\\\\n\t\t\t\t\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Classification of global phase portrais of system .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Phase portraits of a family of Kolmogorov systems depending on six parameters", "authors": ["Érika Diz-Pita", "Jaume Llibre", "M. Victoria Otero-Espinar"], "url": "https://arxiv.org/abs/2501.13714v1", "attribution": "\"Phase portraits of a family of Kolmogorov systems depending on six parameters\" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.13714v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.18314v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lccc}\n\\hline\n\\textbf{Model} & \\textbf{Precision} & \\textbf{Recall} & \\textbf{Test ACC} \\\\\n\\hline\nBERT & 0.886 & 0.885 & 0.886 \\\\\nBiGRUx3 & 0.900 & 0.830 & 0.806 \\\\\nSVM & 0.910 & 0.762 & 0.829 \\\\\n\\hline\n\\end{tabular}\n\\caption{Offensive language detection system's performance using the TOCAB benchmark}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Chinese Offensive Language Detection:Current Status and Future Directions", "authors": ["Yunze Xiao", "Houda Bouamor", "Wajdi Zaghouani"], "url": "https://arxiv.org/abs/2403.18314v3", "attribution": "\"Chinese Offensive Language Detection:Current Status and Future Directions\" by Yunze Xiao, Houda Bouamor, and Wajdi Zaghouani, arXiv:2403.18314v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.16026v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|}\n\\hline \nParameters& $p$ & $m$ & $n$ & $\\sigma_k$& $\\sigma_\\psi$ &$c_\\mu^0$& $c_1$ &$c_2$&$c_3^+$&$c_3^-$&$k_{\\textrm{min}}$&$\\psi_{\\textrm{min}}$&$F_{\\textrm{wall}}$&$c$& $\\nu$&$\\nu_{\\theta}$\\\\ \\hline\nValues for & & & & & & & & & & & & & & & &\\\\ \n$k-\\varepsilon$ model & 3 & 1.5 & -1 & 1.0 & 1.3 & 0.5544 & 1.44 & 1.92 & 1.0 & -0.518 & $7.6\\cdot 10^{-6}$ & $1.0 \\cdot 10^{-12}$ & 1.0 & 1 & $5.0 \\cdot 10^{-6}$ & $5.0 \\cdot 10^{-6}$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Parameters for the generic length scale turbulence- $k-\\varepsilon$ model and Kantha-Clayson stability functions (see ).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Dynamical systems for remote validation of very high-resolution ocean models", "authors": ["G. Garcia-Sanchez", "A. M. Mancho", "A. G. Ramos", "J. Coca", "J. A. Jimenez-Madrid"], "url": "https://arxiv.org/abs/2501.16026v1", "attribution": "\"Dynamical systems for remote validation of very high-resolution ocean models\" by G. Garcia-Sanchez, A. M. Mancho, A. G. Ramos, J. Coca, and J. A. Jimenez-Madrid, arXiv:2501.16026v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.03757v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mechanism Decomposition by CEO Age}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n & \\multicolumn{2}{c}{Young CEOs (Under 50)} & \\multicolumn{2}{c}{Older CEOs (60+)} & \\\\\n & Effect & \\% of Total & Effect & \\% of Total & Difference \\\\\n\\midrule\nCost Reduction & 0.011*** & 35\\% & 0.009** & 50\\% & 0.002 \\\\\n & (0.003) & & (0.004) & & (0.005) \\\\\nRevenue Enhancement & 0.013*** & 42\\% & 0.006* & 33\\% & 0.007** \\\\\n & (0.004) & & (0.003) & & (0.003) \\\\\nInnovation Output & 0.007** & 23\\% & 0.003 & 17\\% & 0.004* \\\\\n & (0.003) & & (0.002) & & (0.002) \\\\\n\\midrule\nTotal Effect & 0.031*** & 100\\% & 0.018** & 100\\% & 0.013** \\\\\n & (0.009) & & (0.009) & & (0.006) \\\\\n\\midrule\nObservations & 892 & & 697 & & 1,589 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises", "authors": ["Tatsuru Kikuchi"], "url": "https://arxiv.org/abs/2508.03757v1", "attribution": "\"AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises\" by Tatsuru Kikuchi, arXiv:2508.03757v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.06045v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\hline\n\\multicolumn{5}{c}{\\textbf{PLN estimates}}\\tabularnewline\n\\hline\n\\multirow{2}{*}{\\textbf{Parameter}} & \\multirow{2}{*}{\\textbf{M-H}} & \\multicolumn{3}{c}{\\textbf{INLA}}\\tabularnewline\n & & \\textbf{Gaussian} & \\textbf{Simplified Laplace} & \\textbf{Laplace}\\tabularnewline\n\\hline\n$\\beta_{0}$ intercept & -1.031 (2.288) & -1.022 (2.623) & -1.061 (2.623) & -1.061 (2.623)\\tabularnewline\n$\\beta_{1}$ ER (o) & 0.763 (0.865) & 0.749 (0.985) & 0.755 (0.985) & 0.755 (0.985)\\tabularnewline\n$\\beta_{2}$ ER (d) & 1.826 (0.883) & 1.802 (0.979) & 1.824 (0.979) & 1.824 (0.979)\\tabularnewline\n$\\beta_{3}$ PD (o) & 0.406 (0.420) & 0.391 (0.476) & 0.401 (0.476) & 0.401 (0.476)\\tabularnewline\n$\\beta_{4}$ PD (d) & 1.414 (0.425) & 1.401 (0.477) & 1.420 (0.477) & 1.420 (0.477)\\tabularnewline\n$\\beta_{5}$ RL (o) & 0.697 (0.779) & 0.684 (0.892) & 0.689 (0.891) & 0.689 (0.891)\\tabularnewline\n$\\beta_{6}$ RL (d) & -0.060 (0.805) & -0.048 (0.895) & -0.057 (0.894) & -0.057 (0.894)\\tabularnewline\n$\\beta_{7}$ HT (o) & -0.297 (0.154) & -0.291 (0.180) & -0.291 (0.180) & -0.291 (0.178)\\tabularnewline\n$\\beta_{8}$ HT (d) & 0.194 (0.156) & 0.181 (0.180) & 0.187 (0.180) & 0.187 (0.180)\\tabularnewline\n$\\beta_{9}$ PMT (o) & 0.891 (0.225) & 0.897 (0.260) & 0.901 (0.260) & 0.901 (0.260)\\tabularnewline\n$\\beta_{10}$PMT (d) & 0.886 (0.220) & 0.889 (0.260) & 0.890 (0.260) & 0.890 (0.259)\\tabularnewline\n$\\beta_{11}$D & -1.129 (0.048) & -1.131 (0.057) & -1.135 (0.057) & -1.135 (0.057)\\tabularnewline\n$\\tau (1/\\sigma^2)$ & 0.989 (0.139) & 0.906 (0.139) & 0.906 (0.139) & 0.906 (0.139)\\tabularnewline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures", "authors": ["Konstantinos Perrakis", "Dimitris Karlis", "Mario Cools", "Davy Janssens"], "url": "https://arxiv.org/abs/2011.06045v1", "attribution": "\"Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures\" by Konstantinos Perrakis, Dimitris Karlis, Mario Cools, and Davy Janssens, arXiv:2011.06045v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.07651v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Uniform prior: RMSE and MAD for NP-EB, NP-ML, NP-MD, P-EB and QB-EB.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\\hline\n\\hline\n&&&NP-EB &NP-ML&NP-MD & P-EB && QB-EB \\\\\n\\hline\nRMSE\\hspace{0.2cm} $n=50$&&& 1.354& 0.727 &0.702 & 0.703&& \\textbf{0.724}\\\\\nRMSE\\hspace{0.2cm} $n=100$&&& 1.253&0.818 &0.841 & 0.763&& \\textbf{0.804}\\\\\nRMSE\\hspace{0.2cm} $n=200$&&&0.88&0.718 &0.729 & 0.726&& \\textbf{0.73}\\\\\nRMSE\\hspace{0.2cm} $n=500$&&&0.88&0.725 &0.746 & 0.72&& \\textbf{0.719}\\\\\nMAD\\hspace{0.45cm}$n=50$&&&0.996& 0.608 &0.562 & 0.581&& \\textbf{ 0.615}\\\\\nMAD\\hspace{0.45cm}$n=100$&&& 0.998&0.673 &0.685 & 0.628&& \\textbf{0.653}\\\\\nMAD\\hspace{0.45cm}$n=200$&&&0.742&0.613 &0.605 & 0.623&& \\textbf{0.619}\\\\\nMAD\\hspace{0.45cm}$n=500$&&&0.719&0.606 &0.633 & 0.607&& \\textbf{ 0.612}\\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem", "authors": ["Stefano Favaro", "Sandra Fortini"], "url": "https://arxiv.org/abs/2411.07651v2", "attribution": "\"Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem\" by Stefano Favaro and Sandra Fortini, arXiv:2411.07651v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2308.16438v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Test outcomes for Models 1--4 in Section .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc} \n \\toprule \n {\\bf Model A} & {\\bf Model B} & {\\bf S-W statistic} & {\\bf In favor}\\\\\n \\midrule\n1 & 2 & -4.433 &2\\\\\n1 & 3 & -1.827 &-\\\\\n1 & 4 & -1.374 &-\\\\\n2 & 3 & 0.908 &-\\\\\n2 & 4 & 5.802 &2\\\\\n3 & 4 & 1.680 &-\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Model Selection for Ordinary Differential Equations: a Statistical Testing Approach", "authors": ["Itai Dattner", "Shota Gugushvili", "Oleksandr Laskorunskyi"], "url": "https://arxiv.org/abs/2308.16438v1", "attribution": "\"Model Selection for Ordinary Differential Equations: a Statistical Testing Approach\" by Itai Dattner, Shota Gugushvili, and Oleksandr Laskorunskyi, arXiv:2308.16438v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.14813v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cccc|}\n \\hline\n \\textbf{beta} & \\textbf{0.5} & \\textbf{1} & \\textbf{2} & \\textbf{5} \\\\\n \\hline\n \\textbf{N=2} & 0.37\\% & 0.32\\% & 0.24\\% & 0.03\\% \\\\\n \\textbf{N=3} & 0.03\\% & 0.05\\% & 0.10\\% & 0.21\\% \\\\\n \\textbf{N=5} & 0.00\\% & 0.00\\% & 0.00\\% & 0.00\\% \\\\\n \\textbf{N=8} & 0.00\\% & 0.00\\% & 0.00\\% & 0.00\\% \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Relative absolute difference for different $N$ and $\\beta$ on the option price compared to $N=10$, with $Y_0=D, h_0=1, \\alpha=0.1, T_0=2$, strike $K=2$ and the time to maturity $\\vartheta=1$. BNS model.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Pricing Options on Forwards in Function-Valued Affine Stochastic Volatility Models", "authors": ["Jian He", "Sven Karbach", "Asma Khedher"], "url": "https://arxiv.org/abs/2508.14813v1", "attribution": "\"Pricing Options on Forwards in Function-Valued Affine Stochastic Volatility Models\" by Jian He, Sven Karbach, and Asma Khedher, arXiv:2508.14813v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.05923v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Run times (wallclock seconds) for unos and heart\\_failure. Times for the other datasets are given in Table in the Appendix.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\nmodel & unos & heart\\_failure \\\\ \n\\midrule\nCoxPH & 22.895 ± 1.257 & 17.920 ± 2.906 \\\\ \nAFT & 12.073 ± 0.282 & 9.378 ± 0.201 \\\\ \nSATransformer & 5414.365 ± 602.512 & 2100.597 ± 95.863 \\\\ \nDRSA & 1157.576 ± 76.178 & 360.696 ± 26.253 \\\\ \nRSF & * & * \\\\ \nCoxNAM & 657.999 ± 89.798 & 165.809 ± 31.627 \\\\ \nPseudoNAM & * & * \\\\ \nDyS & 1517.205 ± 86.914 & 783.335 ± 165.016 \\\\ \nDNAMite & 2690.676 ± 103.465 & 819.684 ± 78.972 \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive Models", "authors": ["Mike Van Ness", "Billy Block", "Madeleine Udell"], "url": "https://arxiv.org/abs/2411.05923v1", "attribution": "\"DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive Models\" by Mike Van Ness, Billy Block, and Madeleine Udell, arXiv:2411.05923v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.09090v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Optimal solution for a small-scale example}\n\\begin{tabular}{ccc|ccc}\n \\hline\n Dock & Mode & Truck order & Dock & Mode & Truck order \\\\\n \\hline\n $D_1$ & Unloading-only & 1 $\\rightarrow$ 6 & $D_4$ & Loading-only & 13 $\\rightarrow$ 15 $\\rightarrow$ 17 $\\rightarrow$ 12 $\\rightarrow$ 10 \\\\\n $D_2$ & Unloading-only & 7 $\\rightarrow$ 2 & $D_5$ & Loading-only & 16 $\\rightarrow$ 9 $\\rightarrow$ 11 $\\rightarrow$ 20 \\\\\n $D_3$ & Mixed-mode & 3 $\\rightarrow$ 5 $\\rightarrow$ 8 $\\rightarrow$ 4 & $D_6$ & Loading-only & 14 $\\rightarrow$ 18 $\\rightarrow$ 19 \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm", "authors": ["Yueyi Li", "Mehrdad Mohammadi", "Xiaodong Zhang", "Yunxing Lan", "Willem van Jaarsveld"], "url": "https://arxiv.org/abs/2412.09090v1", "attribution": "\"Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm\" by Yueyi Li, Mehrdad Mohammadi, Xiaodong Zhang, Yunxing Lan, and Willem van Jaarsveld, arXiv:2412.09090v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.16566v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|} \n \\hline\n \\multicolumn{4}{|c|}{Table 1: Percentages of stable simulations (out of 1,000 simulations)} \\\\\n \\hline\n LQR about the origin & Robust control: Eq. & Data-conforming robust control: Eq. \\\\\n \\hline\n 0.0\\% & 61.8\\% & 94.3\\% \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Dampening parameter distributional shifts under robust control and gain scheduling", "authors": ["Mohammad Ramadan", "Mihai Anitescu"], "url": "https://arxiv.org/abs/2411.16566v1", "attribution": "\"Dampening parameter distributional shifts under robust control and gain scheduling\" by Mohammad Ramadan and Mihai Anitescu, arXiv:2411.16566v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.12114v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameter search range in numerical experiments.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc} \n\t\t\\toprule\n\t\t\\multirow{2}{*}{Parameter} & \\multicolumn{2}{c}{RT} & \\multicolumn{2}{c}{PW} \\\\ \n\t\t\\cmidrule{2-5}\n\t\t& Lower & Upper & Lower & Upper \\\\ \n\t\t\\midrule\n\t\t$I_{ph}$ (A) & 0 & 1 & 0 & 2 \\\\\n\t\t$ I_0, I_{01}, I_{02} $ ($ \\mu $A) & 0 & 1 & 0 & 50 \\\\\n\t\t$ n, n_1, n_2 $ & 1 & 2 & 1 & 50 \\\\\n\t\t$ R_s $ ($ \\Omega $) & 0 & 0.5 & 0 & 2 \\\\\n\t\t$ R_p $ ($ \\Omega $) & 0 & 100 & 0 & 2000 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "On Solar Photovoltaic Parameter Estimation: Global Optimality Analysis and a Simple Efficient Differential Evolution Method", "authors": ["Shuhua Gao", "Yunyi Zhao", "Cheng Xiang", "Yu Ming", "Tan Kuan Tak", "Tong Heng Lee"], "url": "https://arxiv.org/abs/2011.12114v4", "attribution": "\"On Solar Photovoltaic Parameter Estimation: Global Optimality Analysis and a Simple Efficient Differential Evolution Method\" by Shuhua Gao, Yunyi Zhao, Cheng Xiang, Yu Ming, Tan Kuan Tak, and Tong Heng Lee, arXiv:2011.12114v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.15301v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Misclassification rates of $k$NN, tree, random forest, node harvest, SVM (with four different kernels), random projection with quadratic and linear discriminant analyses, OTE, OTE$_{oob}$ and OTE$_{sub}$. Results are based on 50\\% training and 50\\% testing parts of the data. Overall best performing method result is shown in bold. The results are italicised when OTE$_{oob}$ and/or OTE$_{sub}$ are/is better than \\emph{OTE}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccccccc}\n\t\t\t\t\\toprule\n\t\t\t\tDataset & $n$ & $p$ & kNN & Tree & NH & SVM & SVM & SVM & SVM & RP & RP & RF & OTE & OTE$_{oob}$ & OTE$_{sub}$ \\\\\n\t\t\t\t& & & & & & (Radial) & (Linear) & (Bessel) & (Laplacian) & (LDA) & (QDA) & & & & \\\\\n\t\t\t\t\\midrule\n\t\t\t\tMammographic & 830 & 5 & 0.2100 & 0.1661 & 0.1760 & 0.1882 & 0.1772 & 0.1836 & 0.1845 & 0.1890 & 0.1993 & \\textbf{0.1614} & 0.1793 & 0.1824 & 0.1916 \\\\\n\t\t\t\tDystrophy & 209 & 5 & 0.1354 & 0.1831 & 0.1632 & 0.1101 & 0.1191 & 0.1112 & 0.1087 & 0.1233 & \\textbf{0.0959} & 0.1316 & 0.1311 & \\textit{0.1285} & 0.1308 \\\\\n\t\t\t\tMonk3 & 122 & 6 & 0.1421 & 0.0773 & 0.2855 & 0.1228 & 0.2367 & 0.1137 & 0.1731 & 0.2308 & 0.1352 & 0.0790 & 0.0887 & \\textit{\\textbf{0.0735}} & 0.0833 \\\\\n\t\t\t\tAppendicitis & 106 & 7 & 0.1624 & 0.1777 & 0.1569 & 0.1937 & 0.2026 & 0.1885 & 0.1631 & \\textbf{0.1358} & 0.1692 & 0.1418 & 0.1681 & \\textit{0.1493} & 0.1526 \\\\\n\t\t\t\tSAHeart & 462 & 9 & 0.3499 & 0.3409 & \\textbf{0.2876} & 0.3179 & 0.3153 & 0.3430 & 0.3249 & 0.3057 & 0.3140 & 0.2979 & 0.3184 & 0.3202 & 0.3282 \\\\\n\t\t\t\tTic-Tac-Toe & 958 & 9 & 0.3790 & 0.1884 & 0.3109 & 0.2771 & 0.4073 & 0.2233 & 0.4462 & 0.3266 & 0.2536 & 0.0751 & 0.0791 & 0.0826 & \\textit{\\textbf{0.0642}} \\\\\n\t\t\t\tHeart & 303 & 13 & 0.3701 & 0.2370 & 0.2140 & 0.1761 & 0.1830 & 0.3807 & \\textbf{0.1703} & 0.2120 & 0.2288 & 0.1790 & 0.2037 & \\textit{0.1899} & 0.1929 \\\\\n\t\t\t\tHouse Vote & 232 & 16 & 0.0987 & 0.0472 & 0.1211 & 0.0514 & 0.0467 & 0.0497 & 0.0655 & 0.0699 & 0.0717 & \\textbf{0.0418} & 0.0441 & \\textit{0.0434} & 0.0438 \\\\\n\t\t\t\tBands & 365 & 19 & 0.3299 & 0.3289 & 0.3827 & 0.3432 & 0.2969 & 0.4397 & 0.5185 & 0.3413 & 0.3184 & \\textbf{0.2522} & 0.2580 & \\textit{0.2557} & \\textit{0.2525} \\\\\n\t\t\t\tParkinson & 195 & 22 & 0.1822 & 0.1720 & 0.1430 & 0.1831 & 0.2071 & 0.2564 & 0.2234 & 0.1842 & 0.1671 & 0.1200 & 0.1143 & \\textit{0.1135} & \\textit{\\textbf{0.1093}} \\\\\n\t\t\t\tBody & 507 & 23 & 0.0410 & 0.1006 & 0.0887 & 0.0229 & 0.0187 & 0.5273 & 0.0420 & \\textbf{0.0214} & 0.0240 & 0.0496 & 0.0473 & \\textit{0.0472} & \\textit{0.0454} \\\\\n\t\t\t\tThyroid & 9172 & 27 & 0.0401 & 0.0130 & 0.0302 & 0.1153 & 0.0370 & 0.3546 & 0.0718 & 0.0500 & 0.0454 & 0.0110 & 0.0111 & \\textit{0.0110} & \\textit{\\textbf{0.0108}} \\\\\n\t\t\t\tWDBC & 569 & 29 & 0.0790 & 0.0749 & 0.0754 & 0.0503 & 0.3020 & 0.5979 & 0.0524 & 0.0584 & 0.0567 & 0.0461 & 0.0468 & \\textit{0.0445} & \\textit{\\textbf{0.0443}} \\\\\n\t\t\t\tWPBC & 198 & 32 & 0.2712 & 0.2836 & 0.2483 & 0.3117 & 0.2970 & 0.5208 & 0.4181 & \\textbf{0.2242} & 0.2463 & 0.2246 & 0.2276 & \\textit{0.2260} & 0.2324 \\\\\n\t\t\t\tOil-Spill & 937 & 49 & 0.0654 & 0.0465 & 0.0521 & 0.0799 & 0.0933 & 0.3087 & 0.1273 & 0.0446 & 0.0435 & 0.0490 & 0.0380 & \\textit{\\textbf{0.0379}} & \\textit{\\textbf{0.0379}} \\\\\n\t\t\t\tSpam base & 4601 & 58 & 0.1912 & 0.1059 & 0.1124 & 0.0941 & 0.0758 & 0.4895 & 0.1190 & 0.2180 & 0.3149 & 0.0527 & 0.0522 & \\textit{0.0520} & \\textit{\\textbf{0.0506}} \\\\\n\t\t\t\tSonar & 208 & 60 & 0.1900 & 0.2984 & 0.2568 & 0.2045 & 0.2596 & 0.5393 & 0.4000 & 0.2689 & 0.2249 & 0.2075 & 0.2046 & \\textit{0.2035} & \\textit{\\textbf{0.1920}} \\\\\n\t\t\t\tGlaucoma & 196 & 62 & 0.2102 & 0.1526 & 0.1375 & 0.1374 & 0.1663 & 0.6430 & 0.2024 & \\textbf{0.1182} & 0.1439 & 0.1200 & 0.1279 & \\textit{0.1255} & 0.1279 \\\\\n\t\t\t\tNki 70 & 144 & 76 & 0.1912 & 0.1508 & 0.1648 & 0.2000 & 0.3281 & 0.3780 & 0.5097 & 0.1922 & 0.1984 & 0.1561 & \\textbf{0.1545} & 0.1635 & 0.1865 \\\\\n\t\t\t\tMusk & 476 & 166 & 0.1599 & 0.2583 & 0.2654 & 0.1700 & 0.1896 & 0.4946 & 0.5152 & 0.1128 & \\textbf{0.0964} & 0.1415 & 0.1412 & \\textit{0.1373} & \\textit{0.1341} \\\\\n\t\t\t\t\\bottomrule\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Optimal trees selection for classification via out-of-bag assessment and sub-bagging", "authors": ["Zardad Khan", "Naz Gul", "Nosheen Faiz", "Asma Gul", "Werner Adler", "Berthold Lausen"], "url": "https://arxiv.org/abs/2012.15301v1", "attribution": "\"Optimal trees selection for classification via out-of-bag assessment and sub-bagging\" by Zardad Khan, Naz Gul, Nosheen Faiz, Asma Gul, Werner Adler, and Berthold Lausen, arXiv:2012.15301v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.19583v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Cost of 1 iteration for each of the methods tested. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|r|r|r|r}\nMethod & Residuals & Jacobians & Solves & O(n) \\\\\\hline\nDogleg* & 6 & 6 & 0 & O($n^2$)\\\\ \nSteihaug* & 6 & 6 & 0 & O($n^2$)\\\\\nNewton (RF) & 1 & 1 & 1 & O($n^3$)\\\\ \nL-BFGS-B* & 1 & 3 & 0 & O($n^2$)\\\\\nBroyden & 1 & 0 & 1 & O($n^3$)\\\\\nBroyden (inv) & 1 & 0 & 0 & O($n^2$)\\\\\nADAM* & 3 & 4 & 0 & O($n^2$)\\\\\nADAGRAD* & 3 & 4 & 0 & O($n^2$)\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Rootfinding and Optimization Techniques for Solving Nonlinear Systems of Equations Arising from Cohesive Zone Models", "authors": ["Alberto Cattaneo", "Varun Shankar", "M. Keith Ballard"], "url": "https://arxiv.org/abs/2502.19583v1", "attribution": "\"Rootfinding and Optimization Techniques for Solving Nonlinear Systems of Equations Arising from Cohesive Zone Models\" by Alberto Cattaneo, Varun Shankar, and M. Keith Ballard, arXiv:2502.19583v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.14810v4_tex_table4.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c||c|}\n \\hline\n Problem & Bases & PtO & Jacobian & Total & Train (GPU) \\\\ \\hline\n 2D-CDR & 437 & 6,554 & 492 & 7,482 & 145 \\\\ \\hline\n 3D-CDR & 31,241 & 52,224 & 10,076 & 93,541 & 413 \\\\ \\hline\n \\end{tabular}\n\\caption{Offline time (in seconds) in computing the input and output projection bases, PtO maps, Jacobians, and training (GPU) the neural networks, averaged over 5 times.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Accurate, scalable, and efficient Bayesian optimal experimental design with derivative-informed neural operators", "authors": ["Jinwoo Go", "Peng Chen"], "url": "https://arxiv.org/abs/2312.14810v4", "attribution": "\"Accurate, scalable, and efficient Bayesian optimal experimental design with derivative-informed neural operators\" by Jinwoo Go and Peng Chen, arXiv:2312.14810v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19548v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{makecell}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|}\n \\hline\n Use case & Prompt \\\\\n \\Xhline{2pt}\n \\multirow{7}{*}{\\parbox{2cm}{Zephyr \\\\ summarization}} & \\multirow{7}{*}[-1ex]{\\parbox{4cm}{<|system|>\\\\\nYou are a tool providing a short text summary.\\\\\n<|user|>\\\\\nWrite a short summary of the following text: {context}\\\\\n<|assistant|>}} \\\\\n & \\\\\n & \\\\\n & \\\\\n & \\\\\n & \\\\\n & \\\\\n & \\\\\n \\hline\n \\multirow{6}{*}{\\parbox{2cm}{FLAN-T5 \\\\ summarization \\\\ Comparative \\\\ Assessment}} & \\multirow{6}{*}[-1ex]{\\parbox{4cm}{Passage: \\{passage\\}\\\\ Summary A: \\{summary 1\\}\\\\ Summary B: \\{summary 2\\}\\\\ Between Summary A and Summary B, which text summarises the passage better?}} \\\\\n & \\\\\n & \\\\\n & \\\\\n & \\\\\n & \\\\\n & \\\\\n \\hline\n \\multirow{6}{*}{\\parbox{2cm}{FLAN-T5 \\\\ translation \\\\ Comparative \\\\ Assessment}} & \\multirow{6}{*}[-1ex]{\\parbox{4cm}{Original text: \\{context\\}\\\\ Translation A: \\{translation 1\\}\\\\ Translation B: \\{translation 2\\}\\\\ Between Translation A and Translation B, which is the better translation of original text?}} \\\\\n & \\\\\n & \\\\\n & \\\\\n & \\\\\n & \\\\\n & \\\\\n \\hline\n \\end{tabular}\n\\caption{prompts used for experiments.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "WaterJudge: Quality-Detection Trade-off when Watermarking Large Language Models", "authors": ["Piotr Molenda", "Adian Liusie", "Mark J. F. Gales"], "url": "https://arxiv.org/abs/2403.19548v1", "attribution": "\"WaterJudge: Quality-Detection Trade-off when Watermarking Large Language Models\" by Piotr Molenda, Adian Liusie, and Mark J. F. Gales, arXiv:2403.19548v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2308.11138v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lccccccc}\n\\hline\\hline \nModel & \n\\multicolumn{3}{c}{TF-IDF} & \n & \n\\multicolumn{3}{c}{TF-IDF-VADER} \n\\\\ \n\\cline{2-4} \\cline{6-8} \n & Accuracy & Merit & F1 & & Accuracy & Merit & F1 \\\\ \\hline \nLR & 67.37\\% & 15.41\\% & 78.00\\% & & 63.96\\% & ~~8.52\\% & 76.80\\% \\\\ \nSVM & 67.30\\% & 13.24\\% & 78.24\\% & & 63.72\\% & ~~7.29\\% & 76.65\\% \\\\ \nGB & 64.61\\% & 16.43\\% & 76.03\\% & & 62.75\\% & 11.31\\% & 75.44\\% \\\\\nMLP & 58.08\\% & 36.04\\% & 71.22\\% & & 56.50\\% & 34.96\\% & 66.38\\% \\\\ \nRF & 65.56\\% & ~~9.80\\% & 77.64\\% & & 60.67\\% & 18.71\\% & 73.28\\% \\\\ \n\\hline\n\\end{tabular}\n\\caption{Average classification accuracy, merit, and F1 scores for 500 random splits of training and testing sets under the TF-IDF and TF-IDF-VADER featurizations.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "NLP-based detection of systematic anomalies among the narratives of consumer complaints", "authors": ["Peiheng Gao", "Ning Sun", "Xuefeng Wang", "Chen Yang", "Ričardas Zitikis"], "url": "https://arxiv.org/abs/2308.11138v3", "attribution": "\"NLP-based detection of systematic anomalies among the narratives of consumer complaints\" by Peiheng Gao, Ning Sun, Xuefeng Wang, Chen Yang, and Ričardas Zitikis, arXiv:2308.11138v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11223v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc|ccc|ccc}\n \\hline\n \\multirow{2}{*}{Method} & \\multicolumn{3}{c|}{COCO} & \\multicolumn{3}{c|}{CrowdPose} & \\multicolumn{3}{c}{OCHuman}\\\\\n & $\\text{AP}$ & $\\text{AP}^{50}$ & $\\text{AP}^{75}$ & $\\text{AP}$ & $\\text{AP}^{50}$ & $\\text{AP}^{75}$ & $\\text{AP}$ & $\\text{AP}^{50}$ & $\\text{AP}^{75}$ \\\\\n \\hline\n CSM+SCARB [2] & 73.8 & 91.7 & 81.4 & - & - & - & - & - & - \\\\\n MDN [4] & 52.3 & 77.2 & 58.0 & - & - & - & - & - & - \\\\\n MaskRCNN & 64.8 & - & - & 57.2 & 83.5 & 60.3 & 20.2 & 33.2 & 24.5 \\\\\n SBL & 73.7 & 91.9 & 81.8 &60.8 & 81.4 & 65.7 & 24.1 & 37.4 & 26.8 \\\\\n AlphaPose & 70.1 & - & - & 61.0 & 81.3 & 66.0 & - & - & - \\\\\n CrowdPose[5] & 70.9 & - & - & 66.0 & 81.4 & 65.7 & - & - & - \\\\\n AlphaPose+ & 72.2 & 90.1 & 79.3 & 68.5 & 86.7 & 73.2 & 27.5 & 40.8 & 29.9 \\\\\n OPEC-Net[6] & 73.9 & 91.9 & \\textbf{82.2} & \\textbf{70.6} & \\textbf{86.8} & \\textbf{75.6} & 29.1 & 41.3 & 31.4\\\\\n MIPNet (Ours) & \\textbf{74.2} & \\textbf{92.4} & 81.9 & 68.1 & 86.3 & 73.4 & \\textbf{40.0} & \\textbf{51.0} & \\textbf{44.0} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{For a fair comparison, all methods use ResNet-101 backbone (except MDN) and YOLO-v3 detector (except CSM+SCARB and MDN).}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation", "authors": ["Rawal Khirodkar", "Visesh Chari", "Amit Agrawal", "Ambrish Tyagi"], "url": "https://arxiv.org/abs/2101.11223v3", "attribution": "\"Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation\" by Rawal Khirodkar, Visesh Chari, Amit Agrawal, and Ambrish Tyagi, arXiv:2101.11223v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.02029v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrr}\n \\hline\nlag & stage 0 & stage 1 & stage 2 & stage 3 \\\\ \n \\hline\n 1 & 0.97 & 0.97 & 0.97 & 0.97 \\\\ \n 2 & 0.97 & 0.97 & 0.91 & 0.97 \\\\ \n 3 & 0.94 & 0.91 & 0.94 & 0.94 \\\\ \n 4 & 0.94 & 0.88 & 0.94 & 0.94 \\\\ \n 5 & 0.94 & 0.97 & 0.97 & 0.91 \\\\ \n 6 & 0.91 & 0.94 & 0.94 & 0.91 \\\\ \n 7 & 0.84 & 0.91 & 0.91 & 0.91 \\\\ \n 8 & 0.91 & 0.91 & 0.84 & 0.91 \\\\ \n 9 & 0.91 & 0.91 & 0.94 & 0.88 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "GDP nowcasting with large-scale inter-industry payment data in real time -- A network approach", "authors": ["Anastasia Mantziou", "Kerstin Hotte", "Mihai Cucuringu", "Gesine Reinert"], "url": "https://arxiv.org/abs/2411.02029v1", "attribution": "\"GDP nowcasting with large-scale inter-industry payment data in real time -- A network approach\" by Anastasia Mantziou, Kerstin Hotte, Mihai Cucuringu, and Gesine Reinert, arXiv:2411.02029v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.14420v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of Accuracy, AUC, and Weighted Average F1 Between Gemma 2-2B and Gemma 2-9B}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n \\textbf{Model} \n & \\textbf{Accuracy} \n & \\textbf{AUC} \n & \\textbf{F1} \\\\\n \\midrule\n Last Hidden State + LR \n & 0.761 \n & 0.628 \n & 0.737 \\\\\n Last Hidden State + MLP \n & 0.770 \n & 0.634 \n & 0.731 \\\\\n \\textbf{SAE-FiRE (Gemma2-2B)} \n & \\textbf{0.793} \n & \\textbf{0.657} \n & \\textbf{0.743} \\\\\n \\textbf{SAE-FiRE (Gemma2-9B)} \n & \\textbf{0.801} \n & \\textbf{0.668} \n & \\textbf{0.757} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection", "authors": ["Huopu Zhang", "Yanguang Liu", "Mengnan Du"], "url": "https://arxiv.org/abs/2505.14420v1", "attribution": "\"SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection\" by Huopu Zhang, Yanguang Liu, and Mengnan Du, arXiv:2505.14420v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.03861v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|r|r|r|r|r}\nWater Year & no assim. & SWE update & $\\Delta$(\\%) & Joint SWE \\& Stream & $\\Delta$(\\%) \\\\ \n\\hline\n2006 & 1.08 & 0.02 & -98 & 0.04 & -96 \\\\\n2011 & 1.97 & 0.05 & -97 & 0.05 & -98 \\\\\n2014 & 0.20 & 0.01 & -93 & 0.01 & -95 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Experiments validation in terms of basin-mean SWE RMSE.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter", "authors": ["Sami A. Malek", "Alexandre M. Bayen", "Steven D. Glaser"], "url": "https://arxiv.org/abs/1912.03861v1", "attribution": "\"Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter\" by Sami A. Malek, Alexandre M. Bayen, and Steven D. Glaser, arXiv:1912.03861v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.03164v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c}\n & 1 m & 3 m & 6 m & 12 m & 18 m \\\\\n\\hline\nSPX & 99.82\\% & 99.74\\% & 99.79\\% & 99.94\\% & 99.89\\% \\\\\nEurostoxx & 99.15\\% & 99.38\\% & 99.12\\% & 98.97\\% & 98.25\\% \\\\\nUKX & 99.52\\% & 99.51\\% & 99.41\\% & 99.17\\% & 98.67\\% \\\\\nNikkei & 98.24\\% & 98.00\\% & 97.32\\% & 97.38\\% & 97.36\\% \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Correlation between the time series of the square root of the Variance Swap and the At The Money Volatility.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Estimation of Historical volatility and Allocation strategies using Variance Swaps", "authors": ["Lucio Fiorin"], "url": "https://arxiv.org/abs/2208.03164v1", "attribution": "\"Estimation of Historical volatility and Allocation strategies using Variance Swaps\" by Lucio Fiorin, arXiv:2208.03164v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.05941v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n \\textbf{Technique Used} &$T_{tot} (ms)$&$T_{inv}(ms)$ & $N$\\\\\n \\hline\n Algorithm \n ($\\varrho$ initialised with $\\varrho_{off}$) & $0.3236$ & $0.0036$ & $ 33$ \\\\ \n \\hline\n Proposed Algorithm & $0.2913$ & $0.0026$ & $33$ \\\\ \n \\hline\n\\multicolumn{4}{|c|}{ms=milliseconds} \\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of different approaches}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Choosing Augmentation Parameters in OSQP- A New Approach based on Conjugate Directions", "authors": ["Avinash Kumar"], "url": "https://arxiv.org/abs/2503.05941v2", "attribution": "\"Choosing Augmentation Parameters in OSQP- A New Approach based on Conjugate Directions\" by Avinash Kumar, arXiv:2503.05941v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2410.24003v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimated AR-Gaussian HMMs with NASDAQ as a covariate, for Apple, Intel and HP daily returns.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|cc|c|ccc}\n& & AR & \\multicolumn{2}{c|}{Covariates}&Sigma&\\multicolumn{3}{c}{Transition probabilities}\\\\ \\hline\nSeries& Regime j& $\\phi_{1,j}$ & $\\theta_{1,j}$ & $\\theta_{2,j}$& $\\sigma_j$ & $Q_{1,j}$ & $Q_{2,j}$& $Q_{3,j}$ \\\\ \\hline\nApple&1& 0.1978 & 0.0035 & 0.8650 & 0.0178 & 0.6735 & 0.3265 & \\\\\n &2& -0.0077 & -0.0000 & 1.0288 & 0.0063 & 0.1965 & 0.8035 & \\\\ \\hline\n& 1& 0 & 0 & 0 & 0 & 0 & 1.0000 & 0.0000 \\\\\nIntel &2& -0.0039 & 0.0016 & 1.0783 & 0.0150 & 0.0303 & 0.7068 & 0.2629 \\\\\\\n & 3& -0.0104 & -0.0013 & 1.0219 & 0.0076 & 0.0238 & 0.1700 & 0.8062 \\\\ \\hline\n HP & 1& 0.0931 & -0.0019 & 0.8825 & 0.0208 & 0.9738 & 0.0262 & \\\\\n & 2& -0.0502 & 0.0001 & 0.9354 & 0.0080 & 0.0128 & 0.9872 & \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On testing for independence between generalized error models of several time series", "authors": ["Kilani Ghoudi", "Bouchra R. Nasri", "Bruno N. Remillard"], "url": "https://arxiv.org/abs/2410.24003v2", "attribution": "\"On testing for independence between generalized error models of several time series\" by Kilani Ghoudi, Bouchra R. Nasri, and Bruno N. Remillard, arXiv:2410.24003v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.05463v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|r|lllr|}\n \\hline\nrank & ROPE & posterior CI & combination rule & AUC \\\\ \n \\hline\n1 & 80\\% ETI & 80\\% HDI & option 2 & 0.890 \\\\ \n2 & 80\\% ETI & 80\\% ETI & option 2 & 0.873 \\\\ \n3 & 80\\% ETI & 80\\% ETI & option 3 & 0.838 \\\\ \n4 & 80\\% ETI & 80\\% HDI & option 3 & 0.834 \\\\ \n5 & 75\\% ETI & 75\\% HDI & option 3 & 0.823 \\\\ \n6 & 75\\% ETI & 75\\% ETI & option 3 & 0.802 \\\\ \n7 & 85\\% ETI & 85\\% HDI & option 2 & 0.693 \\\\ \n8 & 85\\% ETI & 85\\% ETI & option 2 & 0.691 \\\\ \n9 & 90\\% ETI & 90\\% HDI & option 1 & 0.653 \\\\ \n10 & 85\\% ETI & 85\\% HDI & option 1 & 0.643 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Ranking of top ten BPgWSP test specifications according to AUC averaged over all sample scenarios given a correct prior belief.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "The BPgWSP test: a Bayesian Weibull Shape Parameter signal detection test for adverse drug reactions", "authors": ["Julia Dyck", "Odile Sauzet"], "url": "https://arxiv.org/abs/2412.05463v2", "attribution": "\"The BPgWSP test: a Bayesian Weibull Shape Parameter signal detection test for adverse drug reactions\" by Julia Dyck and Odile Sauzet, arXiv:2412.05463v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.08798v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cccc|cccc|}\n\t\t\\hline\n\t\t-\t& &Acute &Patients & \t& &Chronic &Patients & \\\\\n\t\t\\hline\n\t\tScale (k)\t& $SAD^p_k$ & $SAD^{np}_k$ & $PNP^1_k$ & $PNP^2_k$ & $SAD^p_k$ & $SAD^{np}_k$ & $PNP^1_k$ & $PNP^2_k$ \\\\\n\t\t\\hline\n\t\tk=1.1 & -0.41 & 0.32 & 0.68 & -0.70 & 0.22 & 0.49 & 0.56 & -0.56\\\\\n\t\tk=1.2 & -0.42 & 0.33 & 0.69 & -0.71 & 0.24 & 0.50 & 0.57 & -0.56\\\\\n\t\tk=1.3 & -0.43 & 0.32 & 0.70 & -0.72 & 0.23 & 0.51 & 0.58 & -0.57\\\\\n\t\tk=1.4 & -0.42 & 0.33 & 0.69 & -0.71 & 0.24 & 0.51 & 0.57 & -0.57\\\\\n\t\tk=2 & -0.42 & 0.31 & 0.69 & -0.71 & 0.23 & 0.50 & 0.56 & -0.55\\\\\n\t\tk=3 & -0.42 & 0.27 & 0.67 & -0.68 & 0.25 & 0.50 & 0.53 & -0.52\\\\\n\t\tk=4 & -0.43 & 0.20 & 0.60 & -0.63 & 0.26 & 0.50 & 0.48 & -0.47\\\\\n\t\tk=5 & -0.42 & 0.10 & 0.49 & -0.52 & 0.27 & 0.50 & 0.43 & -0.42\\\\\n\t\tk=6 & -0.37 & -0.01 & 0.35 & -0.38 & 0.27 & 0.48 & 0.35 & -0.34\\\\\n\t\tk=7 & -0.30 & -0.10 & 0.19 & -0.20 & 0.28 & 0.45 & 0.25 & -0.24\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Correlation coefficients of the wavelet features and CAHAI score.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Designing Compact Features for Remote Stroke Rehabilitation Monitoring using Wearable Accelerometers", "authors": ["Xi Chen", "Yu Guan", "Jian Qing Shi", "Xiu-Li Du", "Janet Eyre"], "url": "https://arxiv.org/abs/2009.08798v3", "attribution": "\"Designing Compact Features for Remote Stroke Rehabilitation Monitoring using Wearable Accelerometers\" by Xi Chen, Yu Guan, Jian Qing Shi, Xiu-Li Du, and Janet Eyre, arXiv:2009.08798v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.03486v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{rotating}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\specialrule{1.5pt}{0pt}{0pt}\n\\multirow{4}{*}[-12ex]{\\rotatebox{90}{Risk Certificate}} & & \\multicolumn{4}{c}{Contrastive 0-1 Risk} \\\\\n\\cmidrule(lr){3-6}\n & & $\\tau=1 $ & $\\tau=0.7$ & $\\tau=0.5$ & $\\tau=0.2$ \\\\\n\\specialrule{1.5pt}{0pt}{0pt}\n& Test Loss & 0.0571 & 0.0529 & 0.0478 & 0.0357 \\\\\n \\midrule\n & kl bound (iid) & 0.408 & 0.327 & 0.324 & 0.319 \\\\\n & Catoni's bound (iid) & 0.419 & 0.333 & 0.331 & 0.309 \\\\\n & Classic bound (iid) & 0.466 & 0.396 & 0.394 & 0.399 \\\\\n & Nozawa et al. & 2.535 & 1.95 & 2.097 & 2.097 \\\\\n & Th. 4 (ours) & 0.356 & 0.347 & 0.343 & 0.333 \\\\\n & Th. 5 (ours) & 0.113 & 0.101 & 0.098 & 0.088 \\\\ \n\\specialrule{1.5pt}{0pt}{0pt}\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of risk certificates for the contrastive zero-one risk on the MNIST dataset. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Tight PAC-Bayesian Risk Certificates for Contrastive Learning", "authors": ["Anna Van Elst", "Debarghya Ghoshdastidar"], "url": "https://arxiv.org/abs/2412.03486v2", "attribution": "\"Tight PAC-Bayesian Risk Certificates for Contrastive Learning\" by Anna Van Elst and Debarghya Ghoshdastidar, arXiv:2412.03486v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.18070v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average PS and ACR for M1 and M2 models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Model} & \\textbf{Average PS} & \\textbf{Average ACR} \\\\\n\\midrule\nM1 & 61.405091 & 0.552943 \\\\\nM2 & 29.420010 & 0.550404 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Large Scale Evaluation of Deep Learning-based Explainable Solar Flare Forecasting Models with Attribution-based Proximity Analysis", "authors": ["Temitope Adeyeha", "Chetraj Pandey", "Berkay Aydin"], "url": "https://arxiv.org/abs/2411.18070v1", "attribution": "\"Large Scale Evaluation of Deep Learning-based Explainable Solar Flare Forecasting Models with Attribution-based Proximity Analysis\" by Temitope Adeyeha, Chetraj Pandey, and Berkay Aydin, arXiv:2411.18070v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11469v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Demographics of VOTE400 Dialog Speech}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcr}\n\\hline\nRegion(\\texttt{R}) & No. Participants & Age ($\\mu/\\sigma$) \\\\ \\hline \\hline\nSeoul-si(SE) & 251(F:210,M:41) & 78.98/5.13 \\\\ \\hline\nDaegu-si(DG) & 108(F:95,M:13) & 80.33/6.08 \\\\ \\hline\nGyoungki-do(GG) & 110(F:83,M:27) & 80.17/5.41 \\\\ \\hline\nChungcheongnam-do(CN) & 6(F:6,M:0) & 77.00/3.69 \\\\ \\hline\nJeollanam-do(JN) & 70(F:56,M:14) & 80.76/4.90 \\\\ \\hline\nBusan-si(PS) & 160(F:137,M:23) & 78.70/5.51 \\\\ \\hline\nDaejeon-si(DJ) & 96(F:72,M:24) & 78.81/5.24 \\\\ \\hline\nGangwon-do(GW) & 109(F:94,M:15) & 80.07/5.50 \\\\ \\hline\nGyeongsangbuk-do(GB) & 98(F:95,M:3) & 80.87/4.48 \\\\ \\hline\nGwangju-si(GJ) & 87(F:70,M:17) & 79.39/5.77 \\\\ \\hline\nChungcheongbuk-do(CB) & 17(F:17,M:0) & 80.47/5.51 \\\\ \\hline\nUlsan-si(WS) & 58(F:49,M:9) & 76.97/4.48 \\\\ \\hline \\hline\nTotal & 1,170(F:984,M:186) & 79.47/5.37 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "VOTE400(Voide Of The Elderly 400 Hours): A Speech Dataset to Study Voice Interface for Elderly-Care", "authors": ["Minsu Jang", "Sangwon Seo", "Dohyung Kim", "Jaeyeon Lee", "Jaehong Kim", "Jun-Hwan Ahn"], "url": "https://arxiv.org/abs/2101.11469v1", "attribution": "\"VOTE400(Voide Of The Elderly 400 Hours): A Speech Dataset to Study Voice Interface for Elderly-Care\" by Minsu Jang, Sangwon Seo, Dohyung Kim, Jaeyeon Lee, Jaehong Kim, and Jun-Hwan Ahn, arXiv:2101.11469v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.03481v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{siunitx}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Resource utilization of target ZYNQ device XC7Z045 for different \\mbox{BinArray} configurations in \\%. [1,32,2] means $N_{SA}=1$, $D_{arch}=32$, $M_{arch}=2$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\t\t\\hline\n\t & & \\multicolumn{4}{c}{BinArray} \\\\\n\t\t & Total & [1,8,2] & [1,32,2] & [4,32,4] & [16,32,4] \\\\\n\t\t\\hline\n\t\tLUT \t\t& 218,600 & 0.78 & 1.68 & 13.32 & 52.74 \\\\\n\t\tFF \t\t& 437,200 & 0.53 & 1.22 & 8.11 & 32.01 \\\\\n\t\tBRAM CNN-A\t& \\SI{19.2}{\\mega\\bit} & 1.15 & 1.15 & 6.19 & 24.2 \\\\\n\t\tBRAM CNN-B\t& \\SI{19.2}{\\mega\\bit} & 23.72 & 23.94 & 28.85 & 46.90 \\\\\n\t\tDSP \t\t& 900 & 0.22 & 0.22 & 1.78 & 7.11 \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "BinArray: A Scalable Hardware Accelerator for Binary Approximated CNNs", "authors": ["Mario Fischer", "Juergen Wassner"], "url": "https://arxiv.org/abs/2012.03481v1", "attribution": "\"BinArray: A Scalable Hardware Accelerator for Binary Approximated CNNs\" by Mario Fischer and Juergen Wassner, arXiv:2012.03481v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2501.00024v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{AUC Comparison between LoRaFlow and NELoRa}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\nSF & LoRaFlow AUC & NELoRa AUC & Improvement over NELoRa \\\\\n\\hline\n7 & \\textbf{2.922} & 2.227 & 31.2\\% \\\\\n8 & \\textbf{3.143} & 2.409 & 30.5\\% \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "LoRaFlow: High-Quality Signal Reconstruction using Rectified Flow", "authors": ["Mohamed Osman", "Tamer Nadeem"], "url": "https://arxiv.org/abs/2501.00024v1", "attribution": "\"LoRaFlow: High-Quality Signal Reconstruction using Rectified Flow\" by Mohamed Osman and Tamer Nadeem, arXiv:2501.00024v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14599v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ccc}\n Element & Rows & Nonzeros per row \\\\ \\hline\n Morley & 289 & 10.63 \\\\\n PS6 & 243 & 18.41 \\\\\n PS12 & 451 & 22.73 \\\\\n HCT-red & 243 & 18.41 \\\\\n HCT3 & 451 & 22.73 \\\\\n HCT4 & 995 & 32.56 \\\\\n Bell & 486 & 36.81 \\\\\n Argyris & 694 & 41.02\n \\end{tabular}\n\\caption{The biharmonic equation was discretized on an $8 \\times 8$ mesh, divided into right triangles. This table shows the total number of degrees of freedom (rows in the matrix) and the average sparsity over all rows of the matrix.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "FIAT: enabling classical and modern macroelements", "authors": ["Pablo D. Brubeck", "Robert C. Kirby"], "url": "https://arxiv.org/abs/2501.14599v2", "attribution": "\"FIAT: enabling classical and modern macroelements\" by Pablo D. Brubeck and Robert C. Kirby, arXiv:2501.14599v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.15103v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Adopters Sample Size by Adoption Time - By Product Category}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\nAdoption Month & Dry Dog Food & Dry Cat Food & Dog Hygiene & Cat Hygiene \\\\\n\\midrule\n2019-07 & 85 & 53 & 52 & 36 \\\\\n2019-08 & 111 & 46 & 71 & 41 \\\\\n2019-09 & 100 & 34 & 96 & 44 \\\\\n2019-10 & 110 & 42 & 82 & 47 \\\\\n2019-11 & 174 & 51 & 143 & 67 \\\\\n2019-12 & 144 & 78 & 120 & 58 \\\\\n2020-01 & 138 & 57 & 107 & 47 \\\\\n2020-02 & 143 & 52 & 106 & 55 \\\\\n2020-03 & 334 & 160 & 382 & 190 \\\\\n2020-04 & 438 & 208 & 442 & 241 \\\\\n2020-05 & 357 & 143 & 353 & 209 \\\\\n2020-06 & 276 & 113 & 260 & 157 \\\\ \\midrule\nTotal & 2410 & 1037 & 2214 & 1192 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "How does online shopping affect offline price sensitivity?", "authors": ["Shirsho Biswas", "Hema Yoganarasimhan", "Haonan Zhang"], "url": "https://arxiv.org/abs/2506.15103v1", "attribution": "\"How does online shopping affect offline price sensitivity?\" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.17706v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average token costs for each topic.}\n\\begin{tabular}{|c|cc|cc|}\n\\hline\n\\multirow{2}{*}{\\textbf{Datasets}} & \\multicolumn{2}{c|}{\\textbf{Ours}} & \\multicolumn{2}{c|}{\\textbf{LLM-TM}} \\\\\n & \\textbf{\\#Input} & \\textbf{\\#Output} & \\textbf{\\#Input} & \\textbf{\\#Output} \\\\ \\hline\n\\textit{Tweet} & 1591.01 & 274.90 & 4958.73 & 1214.07 \\\\\n\\textit{AGNews} & 1588.60 & 264.22 & 33382.61 & 7541.41 \\\\\n\\textit{TagMyNews} & 1593.70 & 275.83 & 61872.93 & 14448.79 \\\\\n\\textit{YahooAnswer} & 1578.43 & 231.50 & 27358.56 & 5913.36 \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Large Language Model Guided Topic Refinement Mechanism for Short Text Modeling", "authors": ["Shuyu Chang", "Rui Wang", "Peng Ren", "Qi Wang", "Haiping Huang"], "url": "https://arxiv.org/abs/2403.17706v2", "attribution": "\"A Large Language Model Guided Topic Refinement Mechanism for Short Text Modeling\" by Shuyu Chang, Rui Wang, Peng Ren, Qi Wang, and Haiping Huang, arXiv:2403.17706v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.15531v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\t\t\t\\hline\n\t\t\tMethod & Training Set & AP on Image-test & AP on Video-test \\\\\n\t\t\t\\hline\n\t\t\tBase & I & 0.770 & 0.631 \\\\\n\t\t\tMixupDet & I + V & 0.778 & 0.714 \\\\ % TODO\n\t\t\tTCR & I + V & 0.774 & 0.654 \\\\\n\t\t\tMixupDet + TCR & I + V & \\textbf{0.791} & \\textbf{0.717} \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Colonoscopy Polyp Detection: Domain Adaptation From Medical Report Images to Real-time Videos", "authors": ["Zhi-Qin Zhan", "Huazhu Fu", "Yan-Yao Yang", "Jingjing Chen", "Jie Liu", "Yu-Gang Jiang"], "url": "https://arxiv.org/abs/2012.15531v1", "attribution": "\"Colonoscopy Polyp Detection: Domain Adaptation From Medical Report Images to Real-time Videos\" by Zhi-Qin Zhan, Huazhu Fu, Yan-Yao Yang, Jingjing Chen, Jie Liu, and Yu-Gang Jiang, arXiv:2012.15531v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.17540v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison between training strategies with Thin ResNet-34 on VoxCeleb1-H.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n \\textbf{Training strategy} & \\textbf{EER{[}\\%{]}} & \\textbf{minDCF} \\\\\n \\midrule\n AAMSoftmax & 3.17 & 0.1882 \\\\\n SupCon & 3.42 & 0.2107 \\\\\n H-SCL & 3.25 & 0.2006 \\\\\n SupCon + CHNS (ours) & 2.94 & 0.1845 \\\\\n H-SCL + CHNS (ours) & \\textbf{2.88} & \\textbf{0.1784} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Clustering-based hard negative sampling for supervised contrastive speaker verification", "authors": ["Piotr Masztalski", "Michał Romaniuk", "Jakub Żak", "Mateusz Matuszewski", "Konrad Kowalczyk"], "url": "https://arxiv.org/abs/2507.17540v1", "attribution": "\"Clustering-based hard negative sampling for supervised contrastive speaker verification\" by Piotr Masztalski, Michał Romaniuk, Jakub Żak, Mateusz Matuszewski, and Konrad Kowalczyk, arXiv:2507.17540v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10000v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Result from synthetic experiment. Accuracy is in \\% (chance level is 50\\%), z-sim is the average absolute value of cosine similarity between feature vectors $z$ for different pairs of $z$. True Cluster: pairs of $z$ are from different ground truth clusters. Found Clusters: pairs of $z$ are from two different found clusters. Within Cluster: pairs of $z$ are randomly picked from the same found cluster, averaged between two found clusters.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|llll}\n\\bf \\small Dataset &\\bf \\small Accuracy &\\bf \\small z-sim: True Cluster &\\bf \\small z-sim: Found Clusters &\\bf \\small z-sim: Within Cluster\n\\\\ \\hline \\\\\nIdentifiable &100.0 &0.017 &0.017 &0.503 \\\\\nNot-identifiable &69.8 &0.717 &0.287 &0.770 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Neural Manifold Clustering and Embedding", "authors": ["Zengyi Li", "Yubei Chen", "Yann LeCun", "Friedrich T. Sommer"], "url": "https://arxiv.org/abs/2201.10000v1", "attribution": "\"Neural Manifold Clustering and Embedding\" by Zengyi Li, Yubei Chen, Yann LeCun, and Friedrich T. Sommer, arXiv:2201.10000v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.09449v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n\t\t& MAFDS \\\\ \n\t\tcomputer model 1 & $ 0.00138 $ \\\\\n\t\tcomputer model 2 & $ 0.00326 $ \\\\ \n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{ Median of the bootstrap estimates of the $ 0.95 $-quantiles of the $ n=100 $ maximal model errors for both computer models of the MAFDS.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Uncertainty Quantification in Case of Imperfect Models: A Review", "authors": ["Sebastian Kersting", "Michael Kohler"], "url": "https://arxiv.org/abs/2012.09449v1", "attribution": "\"Uncertainty Quantification in Case of Imperfect Models: A Review\" by Sebastian Kersting and Michael Kohler, arXiv:2012.09449v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11307v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[table]{xcolor}\n\\usepackage{rotating}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|l|c|c}\n & TripletWeight & $P^{con}$ & $P^{lin}$ \\\\\n\\hline\n & \\cellcolor[HTML]{FFFFFF}$T^{con}$ & \\cellcolor[HTML]{FCFDFF}75.5 $\\pm$ 0.2 & \\cellcolor[HTML]{7FACF8}77.8 $\\pm$ 0.9 \\\\\n & \\cellcolor[HTML]{FFFFFF}$T^{cos}$ & \\cellcolor[HTML]{EFF5FF}75.8 $\\pm$ 0.2 & \\cellcolor[HTML]{8EB6F9}77.5 $\\pm$ 0.5 \\\\\n & \\cellcolor[HTML]{FFFFFF}$T^{cos}$ \\& $T^{sc1}$ & \\cellcolor[HTML]{AAC8FB}77.0 $\\pm$ 0.2 & \\cellcolor[HTML]{4587F5}78.8 $\\pm$ 0.6 \\\\\n & \\cellcolor[HTML]{FFFFFF}$T^{cos}$ \\& $T^{sc2}$ & \\cellcolor[HTML]{A0C2FA}77.2 $\\pm$ 0.5 & \\cellcolor[HTML]{5B95F6}78.4 $\\pm$ 0.5 \\\\\n & \\cellcolor[HTML]{FFFFFF}$T^{cir}$ & \\cellcolor[HTML]{FFFFFF}75.5 $\\pm$ 0.3 & \\cellcolor[HTML]{6098F6}78.3 $\\pm$ 0.2 \\\\\n & \\cellcolor[HTML]{FFFFFF}$T^{cir}$ \\& $T^{sc1}$ & \\cellcolor[HTML]{A9C8FA}77.0 $\\pm$ 0.4 & \\cellcolor[HTML]{6EA1F7}78.1 $\\pm$ 0.6 \\\\\n\\multirow{-7}{*}{\\rotatebox[origin=c]{90}{CAR}} & \\cellcolor[HTML]{FFFFFF}$T^{cir}$ \\& $T^{sc2}$ & \\cellcolor[HTML]{BFD6FC}76.6 $\\pm$ 0.8 & \\cellcolor[HTML]{4285F4}78.8 $\\pm$ 0.3 \\\\\n\\hline\n & \\cellcolor[HTML]{FFFFFF}$T^{con}$ & \\cellcolor[HTML]{C9DDFC}85.2 $\\pm$ 0.1 & \\cellcolor[HTML]{5D97F6}87.3 $\\pm$ 0.2 \\\\\n & \\cellcolor[HTML]{FFFFFF}$T^{cos}$ & \\cellcolor[HTML]{A4C4FA}86.0 $\\pm$ 0.3 & \\cellcolor[HTML]{4285F4}87.9 $\\pm$ 0.4 \\\\\n & \\cellcolor[HTML]{FFFFFF}$T^{cos}$ \\& $T^{sc1}$ & \\cellcolor[HTML]{D0E1FD}85.1 $\\pm$ 0.3 & \\cellcolor[HTML]{5E97F6}87.3 $\\pm$ 0.3 \\\\\n & \\cellcolor[HTML]{FFFFFF}$T^{cos}$ \\& $T^{sc2}$ & \\cellcolor[HTML]{EFF5FF}84.5 $\\pm$ 0.2 & \\cellcolor[HTML]{76A7F7}86.9 $\\pm$ 0.2 \\\\\n & \\cellcolor[HTML]{FFFFFF}$T^{cir}$ & \\cellcolor[HTML]{A0C2FA}86.0 $\\pm$ 0.1 & \\cellcolor[HTML]{4D8CF5}87.7 $\\pm$ 0.2 \\\\\n & \\cellcolor[HTML]{FFFFFF}$T^{cir}$ \\& $T^{sc1}$ & \\cellcolor[HTML]{DCE9FD}84.9 $\\pm$ 0.2 & \\cellcolor[HTML]{649BF6}87.2 $\\pm$ 0.1 \\\\\n\\multirow{-7}{*}{\\rotatebox[origin=c]{90}{In-shop}} & \\cellcolor[HTML]{FFFFFF}$T^{cir}$ \\& $T^{sc2}$ & \\cellcolor[HTML]{FFFFFF}84.2 $\\pm$ 0.1 & \\cellcolor[HTML]{73A5F7}86.9 $\\pm$ 0.2\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparing recall@1 performance of different triplet weights with constant and linear pair weight on CAR and In-shop dataset}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Dissecting the impact of different loss functions with gradient surgery", "authors": ["Hong Xuan", "Robert Pless"], "url": "https://arxiv.org/abs/2201.11307v1", "attribution": "\"Dissecting the impact of different loss functions with gradient surgery\" by Hong Xuan and Robert Pless, arXiv:2201.11307v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2307.00251v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ASC Results: Cases \\& Deaths }\n\\begin{tabular}{rrrrr|rrrrr}\n \\hline\n \\multicolumn{5}{c}{\\textbf{Cases}} & \\multicolumn{5}{c}{\\textbf{Deaths}}\\\\\nEvent Time & Estimate & SE & Upper & Lower & Event Time & Estimate & SE & Upper & Lower \\\\ \n \\hline\n-8 & -0.27 & 0.22 & -0.71 & 0.13 & -8 & -0.04 & 0.34 & -0.74 & 0.49 \\\\ \n -7 & -0.15 & 0.20 & -0.58 & 0.21 & -7 & -0.14 & 0.26 & -0.65 & 0.29 \\\\ \n -6 & -0.07 & 0.17 & -0.41 & 0.21 & -6 & -0.11 & 0.15 & -0.41 & 0.17 \\\\ \n -5 & 0.00 & 0.16 & -0.32 & 0.29 & -5 & -0.12 & 0.19 & -0.53 & 0.23 \\\\ \n -4 & 0.02 & 0.14 & -0.27 & 0.25 & -4 & -0.14 & 0.27 & -0.73 & 0.28 \\\\ \n -3 & -0.01 & 0.10 & -0.22 & 0.17 & -3 & -0.03 & 0.34 & -0.83 & 0.46 \\\\ \n -2 & -0.00 & 0.13 & -0.28 & 0.24 & -2 & -0.06 & 0.26 & -0.63 & 0.35 \\\\ \n -1 & 0.02 & 0.17 & -0.33 & 0.33 & -1 & -0.05 & 0.26 & -0.59 & 0.38 \\\\ \n 0 & -0.01 & 0.18 & -0.36 & 0.36 & 0 & -0.17 & 0.27 & -0.71 & 0.30 \\\\ \n 1 & 0.13 & 0.25 & -0.41 & 0.59 & 1 & 0.19 & 0.33 & -0.50 & 0.78 \\\\ \n 2 & 0.09 & 0.24 & -0.36 & 0.58 & 2 & 0.14 & 0.29 & -0.45 & 0.67 \\\\ \n 3 & 0.28 & 0.31 & -0.34 & 0.84 & 3 & -0.02 & 0.32 & -0.65 & 0.58 \\\\ \n 4 & 0.29 & 0.29 & -0.27 & 0.85 & 4 & -0.25 & 0.30 & -0.86 & 0.33 \\\\ \n 5 & 0.54 & 0.28 & -0.00 & 1.10 & 5 & -0.15 & 0.41 & -0.97 & 0.62 \\\\ \n 6 & $0.56^{*}$ & 0.24 & 0.09 & 1.04 & 6 & 0.18 & 0.40 & -0.66 & 0.90 \\\\ \n 7 & 0.56 & 0.34 & -0.14 & 1.17 & 7 & 0.24 & 0.46 & -0.71 & 1.06 \\\\ \n 8 & 0.68 & 0.35 & -0.04 & 1.29 & 8 & 0.23 & 0.41 & -0.61 & 1.00 \\\\ \n 9 & 0.64 & 0.33 & -0.04 & 1.22 & 9 & 0.39 & 0.43 & -0.51 & 1.20 \\\\ \n 10 & 0.72* & 0.34 & 0.00 & 1.30 & 10 & 0.55 & 0.46 & -0.42 & 1.40 \\\\ \n 11 & 0.77* & 0.32 & 0.12 & 1.32 & 11 & 0.61 & 0.40 & -0.21 & 1.34 \\\\ \n 12 & 0.60* & 0.29 & 0.05 & 1.17 & 12 & 0.44 & 0.40 & -0.38 & 1.16 \\\\ \n \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Local Eviction Moratoria and the Spread of COVID-19", "authors": ["Julia Hatamyar", "Christopher F. Parmeter"], "url": "https://arxiv.org/abs/2307.00251v1", "attribution": "\"Local Eviction Moratoria and the Spread of COVID-19\" by Julia Hatamyar and Christopher F. Parmeter, arXiv:2307.00251v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.12771v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\n\\textbf{Method} & \\textbf{Train Accuracy} & \\textbf{Test Accuracy} \\\\ \\hline\nMLP & 49.98\\% & 50.00\\% \\\\ \nCNN & 55.28\\% & 52.98\\% \\\\ \nResNet & 77.35\\% & 50.64\\% \\\\ \nRNN & 53.70\\% & 50.72\\% \\\\ \nGRU & 49.65\\% & 50.00\\% \\\\ \nLSTM & 49.80\\% & 50.00\\% \\\\ \\\nAutoencoder & 50.12\\% & 50.00\\% \\\\ \nt-CNN & 55.08\\% & 52.29\\% \\\\ \nm-CNN & 96.01\\% & 50.67\\% \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Average results of the complementary experiment using stock price time series. The average test accuracy in all machine learning models is approximately 50\\%.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks", "authors": ["Yu Zhang", "Zelin Wu", "Claudio Tessone"], "url": "https://arxiv.org/abs/2504.12771v1", "attribution": "\"Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks\" by Yu Zhang, Zelin Wu, and Claudio Tessone, arXiv:2504.12771v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.09544v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Llama3 MT-Bench results on Helpsteer2 and Zephyr settings.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n & \\multicolumn{2}{c}{\\textbf{Helpsteer2}} & \\multicolumn{2}{c}{\\textbf{Zephyr}} \\\\\n\\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n & \\textbf{Llama3-8B-Base} & \\textbf{Llama3-8B-Instruct} & \\textbf{Llama3-8B-Base} & \\textbf{Llama3-8B-Instruct} \\\\\n\\midrule \n\\textbf{Method} & {GPT-4 Score} & {GPT-4 Score} & {GPT-4 Score} & {GPT-4 Score} \\\\\n\\midrule\nInitial Model & 4.9 & 8.3 & 6.1 & {8.3} \\\\\n\\midrule \nDPO & 5.6 & \\textbf{8.2} & \\underline{7.0} & \\textbf{8.3} \\\\\nDPO+SFT & 5.5 & \\underline{8.1} & \\underline{7.0} & 8.0 \\\\\ncDPO & 5.8 & \\underline{8.1} & 6.9 & \\underline{8.2} \\\\\nR-DPO & 5.4 & \\textbf{8.2} & 6.9 & 8.1 \\\\\nIPO & 4.9 & 8.0 & 6.3 & 8.0 \\\\\n$\\chi$PO & 5.5 & 8.0 & 6.9 & 8.1 \\\\\nSPPO & 5.8 & \\textbf{8.2} & \\textbf{7.1} & \\underline{8.2} \\\\\nCPO & \\underline{5.9} & 7.2 & 6.3 & 8.0 \\\\\nRRHF & 5.7 & 7.9 & 6.2 & 8.1 \\\\\nSLiC-HF & \\underline{5.9} & 7.8 & 6.8 & 7.9 \\\\\nORPO & 5.7 & \\underline{8.1} & 6.6 & \\textbf{8.3} \\\\\nSimPO & \\textbf{6.0} & 8.0 & 6.8 & \\underline{8.2} \\\\\nROPO & 5.8 & \\underline{8.1} & \\textbf{7.1} & \\underline{8.2} \\\\\n\\midrule \nPOWER-DL & \\textbf{6.0} & \\textbf{8.2} & \\textbf{7.1} & \\underline{8.2} \\\\\nPOWER & \\underline{5.9} & \\textbf{8.2} & \\underline{7.0} & \\underline{8.2} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Sail into the Headwind: Alignment via Robust Rewards and Dynamic Labels against Reward Hacking", "authors": ["Paria Rashidinejad", "Yuandong Tian"], "url": "https://arxiv.org/abs/2412.09544v1", "attribution": "\"Sail into the Headwind: Alignment via Robust Rewards and Dynamic Labels against Reward Hacking\" by Paria Rashidinejad and Yuandong Tian, arXiv:2412.09544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01543v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Energy analysis for single MAC and data-memory access.}\n\\begin{tabular}{lllll}\n \\toprule\n \\cmidrule(r){2-3}\n Operation & Notation & Energy (pJ) \\\\\n \\midrule\n $k_b$ bit Memory access & $E_{A,k_b}$ & 2.5$k_b$ \\\\\n 32 bit MULT 32 bit & $E_{M,I}$ & 3.1 \\\\\n 32 bit ADD 32 bit & $E_{Add,I}$ & 0.1 \\\\\n $k_b$ bit MAC INT & $E_{C,k_b}$ & (3.1 * $k_b$)/ 32) + 0.1 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks", "authors": ["Rachel Sterneck", "Abhishek Moitra", "Priyadarshini Panda"], "url": "https://arxiv.org/abs/2101.01543v2", "attribution": "\"Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks\" by Rachel Sterneck, Abhishek Moitra, and Priyadarshini Panda, arXiv:2101.01543v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.15000v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n & \\multicolumn{4}{c}{micro (label average)} & \\multicolumn{4}{c}{macro (instance average)} \\\\\n\t\\cmidrule(lr){2-5} \\cmidrule(lr){6-9}\n & P@N & R@N & F1@N & mAP & P@N & R@N & F1@N & mAP \\\\\n\t\\toprule\n ($b=128$) & 0.701 & 0.711 & 0.699 & 0.676 & - & - & - & - \\\\\n (simplified, $b=64$) & 0.704 & 0.701 & 0.696 & 0.665 & 0.430 & 0.480 & 0.429 & 0.385 \\\\\n ($b=64$) & 0.717 & 0.737 & - & 0.685 & 0.450 & 0.550 & - & 0.444 \\\\\n DeepSphere (equiangular $b=64$) & 0.709 & 0.700 & 0.698 & 0.665 & 0.439 & 0.489 & 0.439 & 0.403 \\\\\n DeepSphere (HEALPix $N_{side}=32$) & 0.725 & 0.717 & 0.715 & 0.686 & 0.475 & 0.508 & 0.468 & 0.428\\\\\n\t\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Official metrics from the SHREC'17 object retrieval competition.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DeepSphere: a graph-based spherical CNN", "authors": ["Michaël Defferrard", "Martino Milani", "Frédérick Gusset", "Nathanaël Perraudin"], "url": "https://arxiv.org/abs/2012.15000v1", "attribution": "\"DeepSphere: a graph-based spherical CNN\" by Michaël Defferrard, Martino Milani, Frédérick Gusset, and Nathanaël Perraudin, arXiv:2012.15000v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18792v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Computation Time (seconds)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n & {GP} & {MoNNE} & {BMNN (VI)}\\\\\n\\midrule\nDTLZ2 & $0.39 (\\pm 0.02)$ & $3.53 (\\pm 0.91)$& $61.34 (\\pm 0.17)$ \\\\\nVLMOP3 & $0.42 (\\pm 0.02)$ & $4.19 (\\pm 0.91)$ & $60.66 (\\pm 0.01)$\\\\\nVehicleSafety & $0.49 (\\pm 0.04)$ & $5.58 (\\pm 1.05)$ & $60.88 (\\pm 0.02)$\\\\\nOSY & $0.49 (\\pm 0.03)$ & $3.83 (\\pm 0.8)$ & $123.59 (\\pm 0.04)$\\\\\nCarCabDesign & $0.28(\\pm 0.01)$ & $4.92 (\\pm 0.98)$ & $124.32 (\\pm 0.11)$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble", "authors": ["Hanyang Wang", "Juergen Branke", "Matthias Poloczek"], "url": "https://arxiv.org/abs/2501.18792v1", "attribution": "\"Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble\" by Hanyang Wang, Juergen Branke, and Matthias Poloczek, arXiv:2501.18792v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.07596v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison between deformed and real images - mean squared error (MSE) and Dice coefficient of different tissues. For our model, we report error between prescribed $a_{FNIRT}$ and the computed atrophy $\\rm{det}(\\textbf{F)}$. We report average values and corresponding standard deviation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n \\toprule\n Method & Input & MSE$_{atrophy}$ & MSE$_{Image}$ & Dice$_{CSF}$ & Dice$_{WM}$ & Dice$_{GM}$ & Dice$_{DGM}$ \\\\\n \\midrule\n FNIRT &- & - & \\( 9.0\\times10^{-4}\\) & 0.878 & 0.922 & 0.839 & 0.780 \\\\\n \n & & & ($4.8\\times10^{-4}$) & (0.021) & (0.008) & (0.012) & (0.028)\\\\\n Ours & a$_{FNIRT}$ & \\(5.1\\times10^{-5}\\) & \\( 1.0\\times10^{-3} \\) & 0.877 & 0.921 & 0.837 & 0.775 \\\\\n \n & & \\( (1.8\\times10^{-5})\\) & \\( (5.1\\times10^{-4}) \\) & $ (0.021)$ & $(0.009)$ & $(0.013)$ & $(0.032)$\\\\\n & a$_{FNIRT}$ & \\(8.2\\times10^{-4}\\) & \\(1.2\\times10^{-3}\\) & 0.874 & 0.920 & 0.836 & 0.767 \\\\\n & brain only & \\( (3.9\\times10^{-5})\\) & \\( (6.9\\times10^{-4})\\) & $ (0.022)$ & $(0.009)$ & $(0.014)$ & $(0.044)$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Biomechanical modelling of brain atrophy through deep learning", "authors": ["Mariana da Silva", "Kara Garcia", "Carole H. Sudre", "Cher Bass", "M. Jorge Cardoso", "Emma Robinson"], "url": "https://arxiv.org/abs/2012.07596v1", "attribution": "\"Biomechanical modelling of brain atrophy through deep learning\" by Mariana da Silva, Kara Garcia, Carole H. Sudre, Cher Bass, M. Jorge Cardoso, and Emma Robinson, arXiv:2012.07596v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.12543v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation Parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc} % 去掉了竖线\n\t\t\t\\toprule % 使用 top rule\n\t\t\t\\textbf{Parameter} & \\textbf{Value} \\\\\n\t\t\t\\midrule % 使用 mid rule\n\t\t\tNumber of MEC servers $M$ & 5 \\\\\n\t\t\tPlateau factors of MZipf $q_m$ & [100, 200, 90, 40, 80] \\\\\n\t\t\tZipf factors of MZipf $k_m$ & [0.60, 0.60, 0.75, 0.90, 0.90] \\\\\n\t\t\tData size of contents $\\eta_c$ & 1$\\sim$8 GB \\\\\n\t\t\tDownloading payment of contents $\\phi_c$ & 0.05$\\sim$0.5 HKD \\\\\n\t\t\tNumber of DNN layers & 6 \\\\\n\t\t\tNumber of neurons per hidden layer & 128 \\\\\n\t\t\tLearning rate $\\xi$ & 0.002 \\\\\n\t\t\tUpdate coefficient $\\tau$ & 0.005 \\\\\n\t\t\tDiscounted factor $\\gamma$ & 0.99 \\\\\n\t\t\t\\bottomrule % 使用 bottom rule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Personalized Federated Deep Reinforcement Learning for Heterogeneous Edge Content Caching Networks", "authors": ["Zhen Li", "Tan Li", "Hai Liu", "Tse-Tin Chan"], "url": "https://arxiv.org/abs/2412.12543v1", "attribution": "\"Personalized Federated Deep Reinforcement Learning for Heterogeneous Edge Content Caching Networks\" by Zhen Li, Tan Li, Hai Liu, and Tse-Tin Chan, arXiv:2412.12543v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.11501v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Event-Study Regression Results: Log Wages}\n\\begin{tabular}{lcccc}\n\\hline\nVariable & Estimate & Std. Error & t value & Pr($>|t|$) \\\\\n\\hline\ntime\\_to\\_treat::-4:treated & 0.025571 & 0.028379 & 0.901 & 0.370 \\\\\ntime\\_to\\_treat::-3:treated & 0.024513 & 0.028702 & 0.854 & 0.395 \\\\\ntime\\_to\\_treat::-2:treated & 0.036410 & 0.024402 & 1.492 & 0.139 \\\\\ntime\\_to\\_treat::-1:treated & 0.023343 & 0.021267 & 1.098 & 0.275 \\\\\ntime\\_to\\_treat::1:treated & 0.020747 & 0.019858 & 1.045 & 0.299 \\\\\ntime\\_to\\_treat::2:treated & 0.072504 & 0.020958 & 3.459 & 0.001*** \\\\\ntime\\_to\\_treat::3:treated & 0.059044 & 0.021793 & 2.709 & 0.008** \\\\\ntime\\_to\\_treat::4:treated & 0.030442 & 0.020754 & 1.467 & 0.146 \\\\\ntime\\_to\\_treat::5:treated & 0.052742 & 0.029291 & 1.801 & 0.075. \\\\\ntime\\_to\\_treat::6:treated & 0.053434 & 0.027038 & 1.976 & 0.051. \\\\\ntime\\_to\\_treat::7:treated & 0.061368 & 0.026183 & 2.344 & 0.021* \\\\\ntime\\_to\\_treat::8:treated & 0.029082 & 0.023458 & 1.240 & 0.218 \\\\\ntime\\_to\\_treat::9:treated & 0.058933 & 0.035223 & 1.673 & 0.098. \\\\\n\\hline\n\\multicolumn{5}{l}{\\footnotesize Standard errors clustered by area\\_fips. Significance: *** $p<0.001$, ** $p<0.01$, * $p<0.05$, . $p<0.1$} \\\\\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Price of Disaster: Estimating the Impact of Hurricane Harvey on the Texas Construction Labor Market", "authors": ["Kartik Ganesh"], "url": "https://arxiv.org/abs/2509.11501v1", "attribution": "\"The Price of Disaster: Estimating the Impact of Hurricane Harvey on the Texas Construction Labor Market\" by Kartik Ganesh, arXiv:2509.11501v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.12016v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CART Simulation Results - BART Predictions}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\toprule\nType & $M$\\textsuperscript{a} & Bias\\textsuperscript{b} & RMSE\\textsuperscript{b} & Coverage\\textsuperscript{b} & Width\\textsuperscript{b}\\\\\n\\midrule\noracle & & 0.002 (0.001) & 0.036 (0.001) & 0.940 (0.017) & 0.144 (0.000)\\\\\n\\midrule\nclbart & 1 & 0.000 (0.001) & 0.067 (0.001) & 0.819 (0.027) & 0.187 (0.003)\\\\\nclbart & 5 & 0.002 (0.001) & 0.056 (0.001) & 0.933 (0.018) & 0.211 (0.002)\\\\\nclbart & 10 & 0.002 (0.001) & 0.058 (0.001) & 0.952 (0.015) & 0.235 (0.002)\\\\\nclbart & 25 & 0.002 (0.001) & 0.063 (0.001) & 0.960 (0.014) & 0.266 (0.001)\\\\\nclbart & 50 & 0.002 (0.001) & 0.069 (0.001) & 0.958 (0.014) & 0.286 (0.001)\\\\\n\\bottomrule\n\\multicolumn{6}{l}{\\textsuperscript{a} $M$: Number of trees.}\\\\\n\\multicolumn{6}{l}{\\textsuperscript{b} Monte Carlo mean and standard errors across 200 simulations reported.}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Estimating Heterogeneous Exposure Effects in the Case-Crossover Design using BART", "authors": ["Jacob Englert", "Stefanie Ebelt", "Howard Chang"], "url": "https://arxiv.org/abs/2311.12016v2", "attribution": "\"Estimating Heterogeneous Exposure Effects in the Case-Crossover Design using BART\" by Jacob Englert, Stefanie Ebelt, and Howard Chang, arXiv:2311.12016v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.19315v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\n \\toprule \\midrule\n Category & Percentage (\\# speakers) \\\\\n \\midrule\n \\multirow{2}{*}{Gender} & male: 69\\% (20) \\\\\n & female: 31\\% (9) \\\\\n \\midrule\n \\multirow{3}{*}{Severity} & mild: 24.1\\% (7) \\\\\n & moderate: 37.9\\% (11) \\\\\n & severe: 37.9\\% (11) \\\\\n \\midrule\n Etiology & ALS: 41.4\\% (12) \\\\\n (top-3) & Cerebral Palsy: 17.2\\% (5) \\\\\n & Vocal Cord Paralysis: 6.9\\% (2) \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Diversity of our \\textbf{conversational speech test set}.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Towards a Single ASR Model That Generalizes to Disordered Speech", "authors": ["Jimmy Tobin", "Katrin Tomanek", "Subhashini Venugopalan"], "url": "https://arxiv.org/abs/2412.19315v1", "attribution": "\"Towards a Single ASR Model That Generalizes to Disordered Speech\" by Jimmy Tobin, Katrin Tomanek, and Subhashini Venugopalan, arXiv:2412.19315v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.02411v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n{Set} & {$b$} & Exact entropy or bounds\\\\ \\hline\n$S_0$ & $\\left(4,\\frac{14}{3}\\right)$ & $\\left[0.14717,0.28888\\right]$ \\\\ \\hline\n$T_0$ & $\\left[\\frac{14}{3},\\frac{21}{4}\\right]$ & $0.20844$ \\\\ \\hline\n$U_0$ & $\\left(\\frac{21}{4},\\frac{11}{2}\\right)$ & $\\left[0.14717,023031\\right]$ \\\\ \\hline\n$V_0$ & $\\left[\\frac{11}{2},\\frac{20}{3}\\right]$ & $0.18600$ \\\\ \\hline\n$S_1$ & $\\left(\\frac{20}{3},\\frac{34}{5}\\right)$ & $\\left[0.11977,0.21132\\right]$ \\\\ \\hline\n$T_1$ & $\\left[\\frac{34}{5},\\frac{85}{12}\\right]$ & $ 0.16389$ \\\\ \\hline\n$U_1$ & $\\left(\\frac{85}{12},\\frac{43}{6}\\right)$ & $\\left[0.11977,0.18155\\right]$ \\\\ \\hline\n$V_1$ & $\\left[\\frac{43}{6},\\frac{84}{11}\\right]$ & $0.15051$ \\\\ \\hline\n$S_2$ & $\\left(\\frac{84}{11},\\frac{682}{89}\\right)$ & $\\left[0.10238,0.17042\\right]$ \\\\ \\hline\n$T_2$ & $\\left[\\frac{682}{89},\\frac{31}{4}\\right]$ & $0.13698$ \\\\ \\hline\n$U_2$ & $\\left(\\frac{31}{4},\\frac{171}{22}\\right)$ & $\\left[0.10238,0.15186\\right]$ \\\\ \\hline\n$V_2$ & $\\left[\\frac{171}{22},\\frac{340}{43}\\right]$ & $0.12795$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{First sets of the partition of the parameter interval $(4,8)$ and exact value (with five significant digits) or bounds of the entropy according to Proposition .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Further results for a family of continuous piecewise linear planar maps", "authors": ["Anna Cima", "Armengol Gasull", "Víctor Mañosa", "Francesc Mañosas"], "url": "https://arxiv.org/abs/2503.02411v1", "attribution": "\"Further results for a family of continuous piecewise linear planar maps\" by Anna Cima, Armengol Gasull, Víctor Mañosa, and Francesc Mañosas, arXiv:2503.02411v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Forward star representation of a network with more nodes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rc|c|crc|c|c|}\n\t\tNode & \n\t\t & \n\t\tPoint & \n\t\t & \n\t\tLink & \n\t\t & \n\t\ttail & \n\t\thead \\\\ \n\t\t\\cline{1-1}\n\t\t\\cline{3-3}\n\t\t\\cline{5-5}\n\t\t\\cline{7-8}\n\t\t1 & & 1 & & 1 & & 1 & 2 \\\\\n\t\t\\cline{3-3}\n\t\t\\cline{7-8}\n\t\t2 & & 3 & & 2 & & 1 & 4 \\\\\n\t\t\\cline{3-3}\n\t\t\\cline{7-8}\n\t\t3 & & 4 & & 3 & & 2 & 5 \\\\\n\t\t\\cline{3-3}\n\t\t\\cline{7-8}\n\t\t4 & & 6 & & 4 & & 3 & 5 \\\\\n\t\t\\cline{3-3}\n\t\t\\cline{7-8}\n\t\t5 & & 8 & & 5 & & 3 & 6 \\\\\n\t\t\\cline{3-3}\n\t\t\\cline{7-8}\n\t\t6 & & 8 & & 6 & & 4 & 5 \\\\\n\t\t\\cline{3-3}\n\t\t\\cline{7-8}\n\t\t7 & & 9 & & 7 & & 4 & 7 \\\\\n\t\t\\cline{3-3}\n\t\t\\cline{7-8}\n\t\t8 & & 10 & & 8 & & 6 & 8 \\\\\n\t\t\\cline{3-3}\n\t\t\\cline{7-8}\n\t\t9 & & 12 & & 9 & & 7 & 5 \\\\\n\t\t\\cline{3-3}\n\t\t\\cline{7-8}\n\t\t & & & & 10 & & 8 & 3 \\\\\n\t\t\\cline{7-8}\n\t\t & & & & 11 & & 8 & 5 \\\\\n\t\t\\cline{7-8}\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.14930v1_tex_table39.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Evaluation of kernel function combinations for the diffusion equation}%\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n\t\t\t\\hline \\text { Kernel function } & \\text { MSE } & \\text { RMSE } & \\text { MAE } & R$^{ 2 }$ & \\text { BIC } & \\text { Corr } & \\text { MdAPE } & \\text { LOO-SPE } \\\\\\hline\n\t\t\t\\text { Gaussian+Laplacian } & $5.1681 \\times 10^{-5} $& 0.0072 & 0.0055 & 0.0785 & -171.0345 & -0.1125 & 0.0247 &$ 2.3654 \\times 10^{-5} $\\\\\\hline\n\t\t\t\\text { Gaussian+Exponential } & $5.4004 \\times 10^{-5} $& 0.0073 & 0.0060 & 0.1270 & -187.9657 & -0.3237 & 0.0302 & $1.7986 \\times 10^{-5}$ \\\\\\hline\n\t\t\t\\text { Gaussian } &$ 5.1842 \\times 10^{-5} $& 0.0072 & 0.0058 & 0.0819 & -206.6511 & 0.0813 & 0.0346 &$ 2.3772\\times 10^{-5}$ \\\\\\hline\n\t\t\t\\text { Rational Quadratic+Laplacian } & $5.4985 \\times 10^{-5}$ & 0.0074 & 0.0061 & 0.1475 & -182.9859 & -0.3589 & 0.0309 &$1.6554 \\times 10^{-5}$ \\\\\\hline\n\t\t\t\\text { Matérn+Laplacian } &$ 5.2654 \\times 10^{-5} $& 0.0072 & 0.0055 & 0.0988 & -167.8611 & -0.1321 & 0.0242 &$2.3381 \\times 10^{-5}$ \\\\\\hline\n\t\t\t\\text { Rational Quadratic+Gaussian } &$ 5.2787 \\times 10^{-5} $& 0.0073 & 0.0059 & 0.1016 & -180.7563 & -0.3011 & 0.0274 & $1.8043 \\times 10^{-5}$ \\\\\\hline\n\t\t\t\\text { Matérn+Gaussian+Laplacian } & $5.2466\\times 10^{-5}$ & 0.0072 & 0.0055 & 0.0949 & -161.4881 & -0.1296 & 0.0243 &$ 2.3103 \\times 10^{-5} $\\\\\\hline\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs", "authors": ["Juan Zhang", "Junyue Luo", "Fangfang Zhang", "Xiaoqiang Yue"], "url": "https://arxiv.org/abs/2504.14930v1", "attribution": "\"Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs\" by Juan Zhang, Junyue Luo, Fangfang Zhang, and Xiaoqiang Yue, arXiv:2504.14930v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.12321v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|ccc}\n\\hline\n$\\delta$ & & \\textsc{ME} & \\textsc{Smart-ME} \\\\\n\\hline\\hline\n0.5 & 2.66 & 377.21 & 279.24 \\\\\n1.0 & 3.95 & 313.92 & 160.79 \\\\\n2.0 & 5.19 & 265.27 & 107.84 \\\\\n4.0 & 37.93 & 276.15 & 107.62 \\\\\n\\hline\n\\end{tabular}\n\\caption{\\centering Relative deviation cost difference (in \\%) for three benchmark policies vs. \\textsc{Off}, for $\\delta \\in \\{0.5, 1, 2, 4\\}$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Target-Following Online Resource Allocation Using Proxy Assignments", "authors": ["Chamsi Hssaine", "Huseyin Topaloglu", "Garrett van Ryzin"], "url": "https://arxiv.org/abs/2412.12321v1", "attribution": "\"Target-Following Online Resource Allocation Using Proxy Assignments\" by Chamsi Hssaine, Huseyin Topaloglu, and Garrett van Ryzin, arXiv:2412.12321v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.11737v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n Method & F1-Score & Deviation & Duration\\\\\n \\midrule\n \\textbf{CNN} & 0.89 & \\textbf{9} ms & 267 s\\\\\n \\textbf{RNN} & 0.89 & 10 ms & 266 s\\\\\n \\textbf{HFC} & 0.94 & 17 ms & \\textbf{7} s\\\\\n \\textbf{Complex} & \\textbf{0.95} & 21 ms & 10 s\\\\\n \\midrule\n \\textbf{CNN-SF} & 0.89 & \\textbf{6} ms & -\\\\\n \\textbf{RNN-SF} & 0.88 & 9 ms & -\\\\\n \\textbf{HFC-SF} & 0.93 & 11 ms & -\\\\\n \\textbf{Complex-SF} & \\textbf{0.94} & 11 ms & -\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Final results of the onset detection study for vocal percussion. The '-SF' suffix indicates the inclusion of spectral flux refinement in the process. From left to right, it is displayed the F1-Score, the mean absolute deviation of the predicted onsets with respect to the real ones, and the duration of the analysis for all onsets using the same laptop.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A New Dataset for Amateur Vocal Percussion Analysis", "authors": ["Alejandro Delgado", "SKoT McDonald", "Ning Xu", "Mark Sandler"], "url": "https://arxiv.org/abs/2009.11737v1", "attribution": "\"A New Dataset for Amateur Vocal Percussion Analysis\" by Alejandro Delgado, SKoT McDonald, Ning Xu, and Mark Sandler, arXiv:2009.11737v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.12395v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n \\hline\n Training & Top & Before & After \\\\\n Set & Features & Aug. (\\%) & Aug. (\\%) \\\\\n \\hline\n Average & 10 & 41.024 & 60.034 \\\\\n Average & 20 & 39.539 & 61.397 \\\\\n Average & 30 & 39.614 & 59.931 \\\\\n Average & 40 & 41.449 & 61.333 \\\\\n 1,2,3,4 & 10 & 20.223 & 94.944 \\\\\n 1,2,3,4 & 20 & 24.442 & 93.734 \\\\\n 1,2,3,4 & 30 & 25.558 & 95.053 \\\\\n 1,2,3,4 & 40 & 22.208 & 96.262 \\\\\n 1,2,3,5 & 10 & 71.474 & 84.255 \\\\\n 1,2,3,5 & 20 & 71.474 & 90.665 \\\\\n 1,2,3,5 & 30 & 71.474 & 81.811 \\\\\n 1,2,3,5 & 40 & 71.474 & 84.896 \\\\\n 1,2,4,5 & 10 & 40.785 & 53.852 \\\\\n 1,2,4,5 & 20 & 28.399 & 53.210 \\\\\n 1,2,4,5 & 30 & 28.399 & 53.248 \\\\\n 1,2,4,5 & 40 & 40.181 & 53.248 \\\\\n 1,3,4,5 & 10 & 28.253 & 45.864 \\\\\n 1,3,4,5 & 20 & 28.996 & 46.515 \\\\\n 1,3,4,5 & 30 & 28.253 & 48.420 \\\\\n 1,3,4,5 & 40 & 28.996 & 51.069 \\\\\n 2,3,4,5 & 10 & 44.385 & 21.257 \\\\\n 2,3,4,5 & 20 & 44.385 & 22.861 \\\\\n 2,3,4,5 & 30 & 44.385 & 21.123 \\\\\n 2,3,4,5 & 40 & 44.385 & 21.190 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sound Classification of Four Insect Classes", "authors": ["Yinxuan Wang", "Sudip Vhaduri"], "url": "https://arxiv.org/abs/2412.12395v1", "attribution": "\"Sound Classification of Four Insect Classes\" by Yinxuan Wang and Sudip Vhaduri, arXiv:2412.12395v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.15979v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dataset-2: Segmentation performance metrics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\hline\n\\textbf{Metric} & \\textbf{Mean} & \\textbf{Std} \\\\\n\\hline\nDice & 0.814431 & 0.078670 \\\\\nIoU & 0.694019 & 0.106993 \\\\\nAccuracy & 0.872015 & 0.096104 \\\\\nPrecision & 0.803413 & 0.112562 \\\\\nRecall & 0.851245 & 0.124639 \\\\\nF1 Score & 0.814431 & 0.078670 \\\\\nSSIM & 0.502930 & 0.245851 \\\\\nHausdorff & 57.287395 & 34.920233 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "GUI Based Fuzzy Logic and Spatial Statistics for Unsupervised Microscopy Segmentation", "authors": ["Surajit Das", "Pavel Zun"], "url": "https://arxiv.org/abs/2508.15979v1", "attribution": "\"GUI Based Fuzzy Logic and Spatial Statistics for Unsupervised Microscopy Segmentation\" by Surajit Das and Pavel Zun, arXiv:2508.15979v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.13629v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The Number of Articles by Field and Year in arVix}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrrr}\n\\hline\n\\hline\nYear & Maths & Phys & CS & EE\\&SS & Stats & Bio & Econ & Fin & Total \\\\\n\\hline\n2021 & 36280 & 69824 & 60467 & 8185 & 5103 & 2187 & 1251 & 931 & 184228 \\\\\n2022 & 38578 & 69928 & 65631 & 8970 & 5233 & 1979 & 1331 & 874 & 192524 \\\\\n2023 & 49210 & 84966 & 92830 & 10731 & 7028 & 2655 & 2054 & 1158 & 250632 \\\\\n\\hline\nTotal & 124068 & 224718 & 218928 & 27886 & 17364 & 6821 & 4636 & 2963 & 627384 \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing", "authors": ["Cong William Lin", "Wu Zhu"], "url": "https://arxiv.org/abs/2504.13629v1", "attribution": "\"Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing\" by Cong William Lin and Wu Zhu, arXiv:2504.13629v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.11153v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The $\\gamma$ coefficients for biAA, BMM, and FDkMpf of the illustrative example.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|ccc|ccc|ccc|}\n\\hline\n1&0&0&1&0&0&1&0&0\\\\\n0.71&0.12&0.17&1&0&0&1&0&0\\\\\n0.57&0.42&0.01&1&0&0&0.85& 0.08&0.08\\\\\n0&0.80&0.20&0&0.01&0.99&0&0.5&0.5\\\\\n0.13&0.76&0.11&0&1&0&0&0.5&0.5\\\\\n0.14&0.86&0&0&1&0&0.29&0.35&0.35\\\\\n0&0.80&0.20&0&1&0&0&0.5&0.5\\\\\n0.01&0.99&0&0&0&1&0.2&0.4&0.4\\\\\n0&0.90&0.11&0&1&0&0&0.5&0.5\\\\\n0&0.89&0.11&0&1&0&0&0.5&0.5\\\\\n0&0.42&0.58&0&1&0&0&0.5&0.5\\\\\n0.05&0.45&0.5&0&0.96&0.04&0&0.5&0.5\\\\\n0&0.62&0.38&0&1&0&0&0.5&0.5\\\\\n0.05&0&0.95&0&0&1&0&0.5&0.5\\\\\n0&0.04&0.96&0&0&1&0&0.5&0.5\\\\\n0&0.10&0.91&0&0&1&0&0.5&0.5\\\\\n0.13&0&0.87&0&0&1&0&0.5&0.5\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Biarchetype analysis: simultaneous learning of observations and features based on extremes", "authors": ["Aleix Alcacer", "Irene Epifanio", "Ximo Gual-Arnau"], "url": "https://arxiv.org/abs/2311.11153v2", "attribution": "\"Biarchetype analysis: simultaneous learning of observations and features based on extremes\" by Aleix Alcacer, Irene Epifanio, and Ximo Gual-Arnau, arXiv:2311.11153v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.14267v4_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Lower-Tail $\\Delta$CIQ Premium across Different Specifications}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n \\toprule\n \\midrule\n & \\multicolumn{4}{c}{Equal-Weighted} & \\multicolumn{4}{c}{Value-Weighted} \\\\ % align: l,c,r\n \\cmidrule(lr){2-5} \\cmidrule(lr){6-9} \n & Premium & $t$-stat & $\\alpha^{FF6}$ & $t$-stat & Premium & $t$-stat & $\\alpha^{FF6}$ & $t$-stat \\\\ \n \\midrule\n \\textit{\\textbf{Panel A:}} Volatility Control &&&&&&& \\\\\n \\midrule\n TV volatility std. & 5.74 & 3.18 & 6.55 & 3.65 & 7.27 & 2.78 & 8.58 & 3.37 \\\\ \n PCA-SQ control & 5.78 & 3.47 & 4.87 & 2.96 & 7.43 & 2.95 & 6.19 & 2.57 \\\\\n \\midrule\n \\textit{\\textbf{Panel B:}} Lower-tail $\\tau$ &&&&&&& \\\\\n \\midrule\n $\\tau=0.10$ & 4.85 & 2.68 & 5.06 & 2.83 & 5.41 & 2.17 & 5.00 & 2.16 \\\\ \n $\\tau=0.15$ & 5.62 & 3.03 & 5.97 & 3.29 & 6.41 & 2.41 & 6.61 & 2.68 \\\\ \n $\\tau=0.30$ & 5.71 & 3.33 & 6.70 & 4.25 & 7.00 & 2.56 & 8.35 & 3.20 \\\\ \n $\\tau=0.40$ & 4.95 & 3.20 & 5.60 & 3.76 & 5.62 & 2.31 & 7.75 & 3.22 \\\\ \n \\midrule\n \\textit{\\textbf{Panel C:}} Linear Specification &&&&&&& \\\\\n \\midrule\n FF5 & 5.88 & 3.47 & 7.19 & 4.20 & 6.90 & 2.82 & 8.33 & 3.40 \\\\ \n FF6 & 4.21 & 2.54 & 5.98 & 3.29 & 4.33 & 1.78 & 5.83 & 2.18 \\\\ \n \\midrule\n \\textit{\\textbf{Panel D:}} Stock Price &&&&&&& \\\\\n \\midrule\n All stocks & 6.61 & 3.05 & 6.85 & 3.63 & 6.47 & 2.60 & 5.76 & 2.60 \\\\ \n Price $>$ \\$5 & 6.01 & 3.35 & 6.31 & 4.32 & 7.04 & 2.91 & 6.51 & 2.98 \\\\ \n \\midrule\n \\textit{\\textbf{Panel E:}} Firm Size &&&&&&& \\\\\n \\midrule\n Market cap $>$ $q_{NYSE}$(10\\%) & 6.68 & 3.09 & 6.91 & 3.71 & 6.58 & 2.64 & 5.84 & 2.64 \\\\\n Market cap $>$ $q_{NYSE}$(20\\%)& 6.32 & 2.83 & 6.76 & 3.50 & 6.16 & 2.54 & 5.56 & 2.77 \\\\ \n Market cap $>$ $q_{NYSE}$(50\\%) & 5.09 & 2.26 & 5.12 & 2.77 & 5.69 & 2.41 & 5.13 & 2.60 \\\\ \n \\midrule\n \\textit{\\textbf{Panel F:}} Time Split &&&&&&& \\\\\n \\midrule\n 01/1968 - 12/1993 & 4.83 & 1.88 & 4.23 & 1.97 & 6.19 & 1.60 & 7.68 & 2.17 \\\\ \n 01/1994 - 12/2018 & 8.01 & 3.17 & 7.15 & 2.69 & 10.06 & 2.84 & 8.09 & 2.47 \\\\ \n \\midrule\n \\textit{\\textbf{Panel G:}} Multi-Period Returns &&&&&&& \\\\\n \\midrule\n Next 3 months & 4.80 & 3.03 & 5.71 & 3.15 & 6.66 & 2.84 & 7.64 & 3.08 \\\\ \n Next 6 months & 4.41 & 3.09 & 5.40 & 2.88 & 6.44 & 3.10 & 7.74 & 3.06 \\\\ \n Next 12 months & 4.00 & 3.56 & 3.13 & 2.06 & 5.75 & 3.62 & 4.04 & 1.91 \\\\ \n $t+2$ to $t+12$ & 3.91 & 3.38 & 3.52 & 2.71 & 5.52 & 3.37 & 5.01 & 2.54 \\\\\n \\midrule\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Beyond Volatility: Common Factors in Idiosyncratic Quantile Risks", "authors": ["Jozef Barunik", "Matej Nevrla"], "url": "https://arxiv.org/abs/2208.14267v4", "attribution": "\"Beyond Volatility: Common Factors in Idiosyncratic Quantile Risks\" by Jozef Barunik and Matej Nevrla, arXiv:2208.14267v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.11257v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameter Setup in HJM Model}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccccccccc}\n \\hline\n \\textbf{Mode} & $\\sigma_0$ & $\\alpha_\\sigma$ & $f_0$ & $c_f$ & $\\alpha_f$ & $R$ & $C$ & $t'_0$ & $\\Delta t'$ & $n_p$ & $\\Delta t$ & $T^*$ \\\\\n \\hline\n $N_{\\text{train}}=3,000,000$ & $[0.01, 0.03]$ & $[0.001, 0.9]$ & $[0.01, 0.03]$ & $[0.01, 0.05]$ & $[0.001, 0.9]$ & \n \\multirow{2}{*}{$\\exp(-\\frac{\\sum_{i=0}^{n-1} f(0, t'_i)}{T^* - t'_0})$} &\n \\multirow{2}{*}{$100$} &\n \\multirow{2}{*}{$5$} & \n \\multirow{2}{*}{$1$} &\n \\multirow{2}{*}{$20$} &\n \\multirow{2}{*}{$1/52$} &\n \\multirow{2}{*}{$25$} \\\\\n evaluation & $0.0015$ & $100$ & $0.02$ & $3.0$ & $0.5$ & & & & & & & \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate", "authors": ["Fengpei Li", "Haoxian Chen", "Jiahe Lin", "Arkin Gupta", "Xiaowei Tan", "Honglei Zhao", "Gang Xu", "Yuriy Nevmyvaka", "Agostino Capponi", "Henry Lam"], "url": "https://arxiv.org/abs/2412.11257v3", "attribution": "\"Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate\" by Fengpei Li, Haoxian Chen, Jiahe Lin, Arkin Gupta, Xiaowei Tan, Honglei Zhao, Gang Xu, Yuriy Nevmyvaka, Agostino Capponi, and Henry Lam, arXiv:2412.11257v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.16175v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{llll}\n\t\\toprule\n\t{Target Return} & Mean Realized Return& Standard Error & Confidence Interval \\\\\n\t\\midrule\n\t0.05 & 0.05002 & 7.43909e-05 & [0.05, 0.05004] \\\\\n\t0.10 & 0.10008 & 0.000311455 & [0.1, 0.10016] \\\\\n\t0.15 & 0.15015 & 0.000645542 & [0.14998, 0.15032] \\\\\n\t0.20 & 0.20026 & 0.0011176 & [0.19997, 0.20054] \\\\\n\t0.25 & 0.25041 & 0.00193245 & [0.24991, 0.25091] \\\\\n\t0.30 & 0.30053 & 0.00239679 & [0.29992, 0.30115] \\\\\n\t0.35 & 0.35074 & 0.0045035 & [0.34957, 0.3519] \\\\\n\t0.40 & 0.40096 & 0.0057096 & [0.39948, 0.40243] \\\\\n\t0.45 & 0.45115 & 0.00664192 & [0.44943, 0.45286] \\\\\n\t0.50 & 0.50128 & 0.00563606 & [0.49983, 0.50274] \\\\\n\t\\bottomrule\n\\end{tabular}\n\\caption{\\textbf{Performance of vCTRL with different target returns.} Mean Realized Return and Standard Error represent the average and standard error of realized annual returns over 100 independent experiments. Confidence Interval is calculated with 99\\% confidence.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study", "authors": ["Yilie Huang", "Yanwei Jia", "Xun Yu Zhou"], "url": "https://arxiv.org/abs/2412.16175v1", "attribution": "\"Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study\" by Yilie Huang, Yanwei Jia, and Xun Yu Zhou, arXiv:2412.16175v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12251v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ccc|c|}\n \\hline\n Criteria & $E_1$ & $E_2$ & $E_3$& Aggregated results \\\\ \\hline\n $T_1$ & $\\langle (0.8,0.2);0.8\\rangle$ & $\\langle (0.7,0.3);0.7\\rangle$ &$\\langle (0.5,0.5);0.5\\rangle$& $\\langle (0.72,0.28);0.69 \\rangle$ \\\\\n $T_2$ & $\\langle (0.6,0.4);0.6\\rangle$ & $\\langle (0.6,0.4);0.6\\rangle$ &$\\langle (0.4,0.6);0.4\\rangle$&$\\langle (0.57,0.43);0.55 \\rangle$ \\\\\n $T_3$ & $\\langle (0.6,0.4);0.6\\rangle$ & $\\langle (0.5,0.5);0.5\\rangle$ &$\\langle (0.3,0.7);0.3\\rangle$&$\\langle (0.51,0.49);0.49 \\rangle$ \\\\\n $T_4$ & $\\langle (0.5,0.5);0.5\\rangle$ & $\\langle (0.4,0.6);0.4\\rangle$ &$\\langle (0.2,0.8);0.2\\rangle$&$\\langle (0.41,0.59);0.38 \\rangle$ \\\\ \\hline\n \n \\end{tabular}\n\\end{adjustbox}\n\\caption{The importance weights matrix of criteria and its aggregated results}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology", "authors": ["Mahmut Can Bozyiğit", "Mehmet Ünver"], "url": "https://arxiv.org/abs/2501.12251v1", "attribution": "\"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology\" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.06442v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The comparison of computational efficiency, with the fastest and second-fastest models highlighted in \\textcolor{red}{red} and \\textcolor{blue}{blue}, respectively. And the best and second-best performances are highlighted in \\textbf{bold} and \\underline{underlined}, respectively. FLOPs are calculated using an input tensor of shape $3 \\times 512 \\times 512$, and inference speed is averaged over 100 runs. All evaluations are conducted on the QADS database.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|cc}\n\\toprule\nModel & Params/M & Flops/G & Speed/ms & SRCC $\\uparrow$& PLCC $\\uparrow$ \\\\\n\\midrule\nWaDIQaM~ & 6.287 & 30.479 & \\textcolor{blue}{8.170} & 0.871 & 0.887 \\\\\nHyperIQA~ & 27.375 & 107.831 & 29.980 & 0.954 & 0.957 \\\\\nCLIP-IQA~ &\\makebox[1em][c]{\\textemdash} & 61.065 & 21.100 & 0.931 & 0.833 \\\\\nTOPIQ~ & 36.039 & 50.138 & 22.140 & 0.969 & 0.970 \\\\\nDeepSRQ~ & \\textcolor{red}{1.317} & \\textcolor{red}{1.589} & \\textcolor{red}{2.291} & 0.953 & 0.956 \\\\\nTADSRNet~ & 2.119 & 303.076 & 87.568 & 0.972 & 0.974 \\\\\nPFIQA~ & 38.772 & 127.109 & 18.14 & \\underline{0.982} & \\underline{0.983} \\\\\nPBAN & \\textcolor{blue}{2.222} & \\textcolor{blue}{26.278} & 16.995 & \\textbf{0.986} & \\textbf{0.987}\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment", "authors": ["Yixiao Li", "Xiaoyuan Yang", "Guanghui Yue", "Jun Fu", "Qiuping Jiang", "Xu Jia", "Paul L. Rosin", "Hantao Liu", "Wei Zhou"], "url": "https://arxiv.org/abs/2509.06442v1", "attribution": "\"Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment\" by Yixiao Li, Xiaoyuan Yang, Guanghui Yue, Jun Fu, Qiuping Jiang, Xu Jia, Paul L. Rosin, Hantao Liu, and Wei Zhou, arXiv:2509.06442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.02328v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Discrete Actions}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\textbf{\\#} & \\textbf{Action} & \\textbf{\\#} & \\textbf{Action} \\\\\n\\hline\n0 & 0.0 & 4 & -1.0 \\\\\n\\hline\n1 & 1.0 & 5 & -0.5 \\\\\n\\hline\n2 & 0.5 & 6 & -0.25 \\\\\n\\hline\n3 & 0.25 & & \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Neurosymbolic Meta-Reinforcement Lookahead Learning Achieves Safe Self-Driving in Non-Stationary Environments", "authors": ["Haozhe Lei", "Quanyan Zhu"], "url": "https://arxiv.org/abs/2309.02328v1", "attribution": "\"Neurosymbolic Meta-Reinforcement Lookahead Learning Achieves Safe Self-Driving in Non-Stationary Environments\" by Haozhe Lei and Quanyan Zhu, arXiv:2309.02328v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10231v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n \\toprule\n 12 months ending & 2018-09-01 & 2019-09-01 & 2020-09-01 \\\\\n \\midrule\n Commits & 4394 & 4702 & 5538 \\\\\n Commits per day & 12.0 & 12.9 & 15.2 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Creating a Virtuous Cycle in Performance Testing at MongoDB", "authors": ["David Daly"], "url": "https://arxiv.org/abs/2101.10231v2", "attribution": "\"Creating a Virtuous Cycle in Performance Testing at MongoDB\" by David Daly, arXiv:2101.10231v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.00265v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Non-fluent In-context Learning: Zero-Shot ASR tasks Transfer}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc} \n \\toprule \n \n & \\textit{Libri-Dys} & \\textit{Libri-Co-Dys (Multi-types)} \\\\\n \\hline\n \\hline\n WER (Whisper) ($\\% \\downarrow$) & 4.167 & 8.89 \\\\\nWER-Zero-Shot (SSDM) ($\\% \\downarrow$) &10.08 & 17.45 \\\\\n WER-Zero-Shot (SSDM 2.0) ($\\% \\downarrow$) &\\textbf{3.92} & \\textbf{7.10} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SSDM 2.0: Time-Accurate Speech Rich Transcription with Non-Fluencies", "authors": ["Jiachen Lian", "Xuanru Zhou", "Zoe Ezzes", "Jet Vonk", "Brittany Morin", "David Baquirin", "Zachary Mille", "Maria Luisa Gorno Tempini", "Gopala Krishna Anumanchipalli"], "url": "https://arxiv.org/abs/2412.00265v1", "attribution": "\"SSDM 2.0: Time-Accurate Speech Rich Transcription with Non-Fluencies\" by Jiachen Lian, Xuanru Zhou, Zoe Ezzes, Jet Vonk, Brittany Morin, David Baquirin, Zachary Mille, Maria Luisa Gorno Tempini, and Gopala Krishna Anumanchipalli, arXiv:2412.00265v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.12462v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{$C_5$ Part 2 Summary}\n\\begin{tabular}{|l|l|l|} \\hline\nTerm & Symmetric Terms & Table Name \\\\ \\hline\n $- f (X)_{i_0,i_0} \\cdot f(X)_{i_0,i_2} \\cdot w(X)_{i_0,j_2} \\cdot v_{j_1,j_0}$ & No & na \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights", "authors": ["Yichuan Deng", "Zhao Song", "Shenghao Xie", "Chiwun Yang"], "url": "https://arxiv.org/abs/2310.12462v1", "attribution": "\"Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights\" by Yichuan Deng, Zhao Song, Shenghao Xie, and Chiwun Yang, arXiv:2310.12462v1, licensed under CC BY-NC-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-NC-SA 4.0", "url": "https://creativecommons.org/licenses/by-nc-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08533v5_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The performance of different models is evaluated on cross-domain dataset. M→D means that we train the model on Market1501 and evaluate it on DukeMTMC.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cclcl}\n\t\t\\hline\n\t\t\\multirow{3}{*}{Methods} & \\multicolumn{4}{c}{Cross-Domain} \\\\ \\cline{2-5} \n\t\t& \\multicolumn{2}{c}{M→D} & \\multicolumn{2}{c}{D→M} \\\\ \\cline{2-5} \n\t\t& \\multicolumn{2}{c}{Rank-1/mAP(\\%)} & \\multicolumn{2}{c}{Rank-1/mAP(\\%)} \\\\ \\hline\n\t\tSB+REA+RK & \\multicolumn{2}{c}{33.6/24.3} & \\multicolumn{2}{c}{51.6/32.3} \\\\ \n\t\tSB+REA+GGT+RK(ours) & \\multicolumn{2}{c}{\\textbf{37.8/27.8}} & \\multicolumn{2}{c}{\\textbf{55.4/35.7}} \\\\ \n\t\tSB-REA+RK & \\multicolumn{2}{c}{45.5/37.0} & \\multicolumn{2}{c}{58.2/37.8} \\\\ \n\t\tSB-REA+GGT+RK(ours) & \\multicolumn{2}{c}{\\textbf{48.2/37.9}} & \\multicolumn{2}{c}{\\textbf{65.0/43.7}} \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method", "authors": ["Yunpeng Gong", "Liqing Huang", "Lifei Chen"], "url": "https://arxiv.org/abs/2101.08533v5", "attribution": "\"Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method\" by Yunpeng Gong, Liqing Huang, and Lifei Chen, arXiv:2101.08533v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13148v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllllllll}\n\\hline\n$\\lambda_F = \\lambda_G$ & $\\pi_F = \\pi_G$ & $\\alpha$ & True $\\rho_S$ & & $\\hat \\rho_S$ & & & $\\hat \\rho_M$ & & & $\\hat \\rho_A$ & \\\\ \\cline{6-7} \\cline{9-10} \\cline{12-13} \n & & & & & Mean & MSE & & Mean & MSE & & Mean & MSE \\\\ \\hline\n2 & 0.2 & 0.2 & 0.09 & & 0.20 & 2.31 & & 0.10 & 0.24 & & 0.09 & \\textbf{0.19} \\\\\n & & 0.5 & 0.22 & & 0.51 & 9.30 & & 0.24 & 0.44 & & 0.22 & \\textbf{0.29} \\\\\n & & 0.8 & 0.35 & & 0.80 & 20.8 & & 0.38 & 0.59 & & 0.34 & \\textbf{0.35} \\\\ \\cline{2-13} \n & 0.8 & 0.2 & 0.19 & & 0.20 & 0.68 & & 0.27 & 1.49 & & 0.19 & \\textbf{0.60} \\\\\n & & 0.5 & 0.47 & & 0.50 & 0.67 & & 0.61 & 2.51 & & 0.48 & \\textbf{0.56} \\\\\n & & 0.8 & 0.76 & & 0.80 & 0.46 & & 0.94 & 3.73 & & 0.77 & \\textbf{0.28} \\\\ \\hline\n8 & 0.2 & 0.2 & 0.10 & & 0.20 & 2.02 & & 0.11 & 0.26 & & 0.10 & \\textbf{0.23} \\\\\n & & 0.5 & 0.24 & & 0.50 & 7.45 & & 0.26 & 0.37 & & 0.24 & \\textbf{0.31} \\\\\n & & 0.8 & 0.39 & & 0.80 & 16.8 & & 0.41 & 0.45 & & 0.39 & \\textbf{0.37} \\\\ \\cline{2-13}\n & 0.8 & 0.2 & 0.20 & & 0.20 & 0.74 & & 0.22 & 0.90 & & 0.20 & \\textbf{0.72} \\\\\n & & 0.5 & 0.49 & & 0.50 & 0.60 & & 0.55 & 0.93 & & 0.50 & \\textbf{0.58} \\\\\n & & 0.8 & 0.79 & & 0.80 & 0.29 & & 0.86 & 0.85 & & 0.79 & \\textbf{0.27} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Simulation results for a sample size $N = 150$ and 1000 repetitions. The MSE lowest value is highlighted in bold.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Spearman's rho for bivariate zero-inflated data", "authors": ["Jasper Arends", "Elisa Perrone"], "url": "https://arxiv.org/abs/2503.13148v1", "attribution": "\"Spearman's rho for bivariate zero-inflated data\" by Jasper Arends and Elisa Perrone, arXiv:2503.13148v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.03155v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n &\\textbf{Group} $X$ &\\textbf{Group} $Y$ \\\\\n \\hline\n\\textbf{False Positive Rate}& 0 & $1-\\phi$ \\\\\n\\textbf{False Negative Rate}& $F_X(0)$ & $1-\\phi$ \\\\\n \\hline\n\\textbf{Positive Predictive Value}& Undefined & $\n\\frac{F_Y\\left(2\\phi-1\\right)\\phi}{F_Y\\left(2\\phi-1\\right)\\phi+(1-F_Y\\left(2\\phi-1\\right))\\left(1-\\phi\\right)}\n$\\\\\n\\textbf{Negative Predictive Value}&$1-F_X(0)$ & $\n\\frac{\\left(1-F_Y\\left(2\\phi-1\\right)\\right)\\phi}{\\left(1-F_Y\\left(2\\phi-1\\right)\\right)\\phi+F_Y\\left(2\\phi-1\\right)\\left(1-\\phi\\right)}\n$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Error Rates \\& Predictive Values With Discrimination}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Algorithmic Fairness with Feedback", "authors": ["John W. Patty", "Elizabeth Maggie Penn"], "url": "https://arxiv.org/abs/2312.03155v1", "attribution": "\"Algorithmic Fairness with Feedback\" by John W. Patty and Elizabeth Maggie Penn, arXiv:2312.03155v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07282v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n & {[}1, 3) & {[}3, 5) & {[}5, 7) \\\\\n \\hline\nour best & 0.0553 & \\textbf{0.4492} & 0.1956 \\\\\n\\hline\nbaseline & 0.0672 & 0.3947 & 0.1975 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{valence CCC among certain ranges}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Lessons Learnt: Revisit Key Training Strategies for Effective Speech Emotion Recognition in the Wild", "authors": ["Jing-Tong Tzeng", "Bo-Hao Su", "Ya-Tse Wu", "Hsing-Hang Chou", "Chi-Chun Lee"], "url": "https://arxiv.org/abs/2508.07282v1", "attribution": "\"Lessons Learnt: Revisit Key Training Strategies for Effective Speech Emotion Recognition in the Wild\" by Jing-Tong Tzeng, Bo-Hao Su, Ya-Tse Wu, Hsing-Hang Chou, and Chi-Chun Lee, arXiv:2508.07282v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.15007v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll}\nmeta-estimator & outcome\\_model & mean\\_pehe & ate\\_rmse & ate\\_abs\\_bias & ate\\_std\\_error \\\\\nstandardization & Standardized\\_SVM\\_rbf & 3058.74 & 2052.88 & 1882.01 & 819.98 \\\\\nstratified\\_standardization & ElasticNet & 4889.81 & 3489.11 & 2159.56 & 2740.47 \\\\\nstratified\\_standardization & Ridge & 4927.52 & 3936.74 & 2904.55 & 2657.35 \\\\\nstratified\\_standardization & Lasso & 5064.64 & 3360.80 & 1826.03 & 2821.45 \\\\\nstratified\\_standardization & LinearRegression & 5124.72 & 3383.14 & 1813.37 & 2856.10 \\\\\nstratified\\_standardization & LinearSVM & 5209.82 & 4902.21 & 2173.07 & 4394.25 \\\\\nstratified\\_standardization & Standardized\\_SVM\\_rbf & 5326.08 & 4639.97 & 4406.92 & 1452.04 \\\\\nstandardization & LinearRegression\\_degree2 & 5488.03 & 3593.31 & 1201.96 & 3386.32 \\\\\nstandardization & Standardized\\_SVM\\_sigmoid & 5633.50 & 1678.65 & 1478.32 & 795.26 \\\\\nstandardization & LinearRegression\\_interact & 5637.26 & 3789.72 & 1560.70 & 3453.43 \\\\\nstratified\\_standardization & Standardized\\_LinearSVM & 5995.41 & 3980.05 & 2405.59 & 3170.80 \\\\\nstratified\\_standardization & SVM\\_rbf & 6240.25 & 5281.01 & 5212.29 & 849.16 \\\\\nstratified\\_standardization & SVM\\_sigmoid & 6883.21 & 5510.08 & 5451.39 & 802.03 \\\\\nstratified\\_standardization & DecisionTree & 6953.37 & 4726.51 & 372.14 & 4711.83 \\\\\nstratified\\_standardization & Standardized\\_SVM\\_sigmoid & 7895.10 & 4708.39 & 4613.74 & 939.35 \\\\\nstandardization & Standardized\\_LinearSVM & 9157.57 & 6530.70 & 6488.87 & 737.98 \\\\\nstandardization & LinearRegression & 9467.81 & 6971.02 & 6910.01 & 920.23 \\\\\nstandardization & Ridge & 9508.04 & 6998.69 & 6987.78 & 390.59 \\\\\nstandardization & ElasticNet & 9550.25 & 7050.64 & 7049.30 & 137.34 \\\\\nstandardization & Lasso & 9552.70 & 7053.98 & 7052.56 & 141.86 \\\\\nstandardization & KernelRidge & 9577.83 & 7092.93 & 7082.44 & 385.52 \\\\\nstandardization & kNN & 9613.85 & 7135.82 & 7135.82 & 0.41 \\\\\nstandardization & DecisionTree & 9613.87 & 7135.67 & 7135.67 & 0.00 \\\\\nstandardization & LinearSVM & 9613.87 & 7135.67 & 7135.67 & 0.00 \\\\\nstandardization & SVM\\_rbf & 9613.87 & 7135.67 & 7135.67 & 0.00 \\\\\nstandardization & SVM\\_sigmoid & 9613.87 & 7135.67 & 7135.67 & 0.00 \\\\\nstratified\\_standardization & LinearRegression\\_degree2 & 100705.35 & 57156.93 & 15734.67 & 54948.48 \\\\\nstratified\\_standardization & LinearRegression\\_interact & 105675.94 & 44091.19 & 11020.68 & 42691.66 \\\\\nstratified\\_standardization & kNN & & & & \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "RealCause: Realistic Causal Inference Benchmarking", "authors": ["Brady Neal", "Chin-Wei Huang", "Sunand Raghupathi"], "url": "https://arxiv.org/abs/2011.15007v2", "attribution": "\"RealCause: Realistic Causal Inference Benchmarking\" by Brady Neal, Chin-Wei Huang, and Sunand Raghupathi, arXiv:2011.15007v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.10670v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n $\\lambda_1$ &$\\lambda_2$ & $b$ &$d$ \\\\\n $day^{-1}$$ $& $day^{-1}$$ $ & $day^{-1}$ & $day^{-1}mm^{-2}$ \\\\\n \\hline $0.0741$&$0.0021$&$1.3383$&$0.002$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Parameter values for the ODE system ().}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A mathematical perspective on the paradox that chemotherapy sometimes works backwards", "authors": ["Luis A. Fernández", "Isabel Lasheras", "Cecilia Pola"], "url": "https://arxiv.org/abs/2503.10670v1", "attribution": "\"A mathematical perspective on the paradox that chemotherapy sometimes works backwards\" by Luis A. Fernández, Isabel Lasheras, and Cecilia Pola, arXiv:2503.10670v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.08578v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of Weight Priorities, Quadratic Forms, and Offset Terms in GFL and GFM Behavior Realization}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\textbf{Mode} & \\textbf{$\\alpha$‐Weights} & {\\boldmath$Q_{P\\omega}$} & {\\boldmath$Q_{QV}$} & {\\boldmath$\\boldsymbol{\\phi}_N$} \\\\ \\midrule\nGFL &\n$\\alpha_3 > \\alpha_1 = \\alpha_2$ &\n$\\begin{bmatrix} 0 & 0 \\\\ 0 & 1 \\end{bmatrix}\\!\\otimes\\! I_N$ &\n$\\begin{bmatrix} 0 & 0 \\\\ 0 & 1 \\end{bmatrix}\\!\\otimes\\! I_N$ &\n$\\boldsymbol{\\phi}_N\\!\\in\\!\\mathbb{R}^N$ \\\\\nGFM &\n$\\alpha_2 = \\alpha_3 > \\alpha_1$ &\n$\\begin{bmatrix} M^{\\!\\top}M & M^{\\!\\top} \\\\ M & I_N \\end{bmatrix}$ &\n$\\begin{bmatrix} 1 & 0 \\\\ 0 & 0 \\end{bmatrix}\\!\\otimes\\! I_N$ &\n$\\dfrac{JM^{-1} d_0}{T_{\\rm S}}$ \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "DeePConverter: A Data-Driven Optimal Control Architecture for Grid-Connected Power Converters", "authors": ["Ruohan Leng", "Linbin Huang", "Huanhai Xin", "Ping Ju", "Xiongfei Wang", "Eduardo Prieto-Araujo", "Florian Dörfler"], "url": "https://arxiv.org/abs/2508.08578v1", "attribution": "\"DeePConverter: A Data-Driven Optimal Control Architecture for Grid-Connected Power Converters\" by Ruohan Leng, Linbin Huang, Huanhai Xin, Ping Ju, Xiongfei Wang, Eduardo Prieto-Araujo, and Florian Dörfler, arXiv:2508.08578v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08699v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline \nAlgorithm & $K=10$ & $K=20$ & $K=40$ & $K=80$ \\\\ \n\\hline \nUCB & 0.038 & 0.039 & 0.042 & 0.042 \\\\ \n\\hline \nB-UCB & 0.963 & 0.980 & 1.052 & 1.185 \\\\ \n\\hline \nTS & 0.992 & 0.967 & 1.207 & 1.864 \\\\ \n\\hline \nO-TS & 0.968 & 0.939 & 1.034 & 1.609 \\\\ \n\\hline \nG-AI & 0.041 & 0.041 & 0.054 & 0.072 \\\\ \n\\hline\nA-AI & 0.034 & 0.036 & 0.039 & 0.040 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Compute times per decision presented in milliseconds. The compute times were estimated as an average over ten repetitions of a $T=10000$ long loop, consisting of only action selection and learning algorithm. Outcomes were kept fixed on all trials. Each algorithm was executed with $N=1000$ parallel simulations. For comparison, we show run times of classical variants of UCB and TS .}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An empirical evaluation of active inference in multi-armed bandits", "authors": ["Dimitrije Markovic", "Hrvoje Stojic", "Sarah Schwoebel", "Stefan J. Kiebel"], "url": "https://arxiv.org/abs/2101.08699v4", "attribution": "\"An empirical evaluation of active inference in multi-armed bandits\" by Dimitrije Markovic, Hrvoje Stojic, Sarah Schwoebel, and Stefan J. Kiebel, arXiv:2101.08699v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08977v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Evaluation for the classification accuracy with different training strategies and ResNet as backbones. For the strategy names, suffix \\textit{CR} indicates training for both classifiers and regressors, \\textit{GAN} for semi-supervised training strategy using unlabeled data, \\textit{WP} for using semi-supervised windows dataset as pre-training weights, e.g., results of ResNetCR+GAN, and \\textit{NP} for using no pre-trained weights.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\t\t\t\\hline\n\t\t\t\\multirow{2}{*}{Name} & \\multicolumn{2}{c}{Labeled Data} & Unlabeled & \\multicolumn{2}{c}{Pre-trained Weights} & \\multirow{2}{*}{Top-1 Accurary} \\\\\n\t\t\t& Classes & Parameters & Data & ImageNet & Window & \\\\ \\hline\n\t\t\tResNetCR+GAN & \\checkmark & \\checkmark & \\checkmark & \\checkmark & & 75.54 \\\\\n\t\t\tResNet+GAN+WP & \\checkmark & & \\checkmark & & \\checkmark & 76.14 \\\\\n\t\t\tResNet+WP & \\checkmark & & & & \\checkmark & 78.91 \\\\\n\t\t\tResNet & \\checkmark & & & \\checkmark & & 67.74 \\\\ \\hline\n\t\t\tResNet+NP & \\checkmark & & & & & 38.75 \\\\\n\t\t\tResNet+GAN+NP & \\checkmark & & \\checkmark & & & 61.25 \\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Façade Modeling", "authors": ["Han Hu", "Xinrong Liang", "Yulin Ding", "Qisen Shang", "Bo Xu", "Xuming Ge", "Min Chen", "Ruofei Zhong", "Qing Zhu"], "url": "https://arxiv.org/abs/2201.08977v2", "attribution": "\"Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Façade Modeling\" by Han Hu, Xinrong Liang, Yulin Ding, Qisen Shang, Bo Xu, Xuming Ge, Min Chen, Ruofei Zhong, and Qing Zhu, arXiv:2201.08977v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.18969v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of 2-branch and 3-branch MBRB designs on two datasets. While the 3-branch structure slightly improves compression ratio, it incurs higher memory and computational cost(batchsize = 8192).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\textbf{Dataset} & \\textbf{Branch} & \\textbf{Ratio ↑} & \\textbf{Speed (kB/min) ↑} & \\textbf{Mem (MB) ↓} \\\\\n\\midrule\n\\multirow{2}{*}{\\textit{Backup}} \n& 2-branch & 1.94 & 10214.8 & 2592 \\\\\n& 3-branch & 1.96 & 7826.4 & 2820 \\\\\n\\midrule\n\\multirow{2}{*}{\\textit{Silesia}} \n& 2-branch & 5.31 & 10113.5 & 2592 \\\\\n& 3-branch & 5.34 & 7817.8 & 2820 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "EDPC: Accelerating Lossless Compression via Lightweight Probability Models and Decoupled Parallel Dataflow", "authors": ["Zeyi Lu", "Xiaoxiao Ma", "Yujun Huang", "Minxiao Chen", "Bin Chen", "Baoyi An", "Shu-Tao Xia"], "url": "https://arxiv.org/abs/2507.18969v1", "attribution": "\"EDPC: Accelerating Lossless Compression via Lightweight Probability Models and Decoupled Parallel Dataflow\" by Zeyi Lu, Xiaoxiao Ma, Yujun Huang, Minxiao Chen, Bin Chen, Baoyi An, and Shu-Tao Xia, arXiv:2507.18969v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.23025v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experiment results for capital control binary and hierarchical classification}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|ccccc}\n\\toprule\n\\textbf{Model} & \\multirow{2}{*}{\\textbf{Binary Acc}} & \\multicolumn{5}{c}{\\textbf{Hierarchical Acc}} \\\\\n\\cmidrule(lr){3-7}\n & & \\textbf{L3} & \\textbf{L4} & \\textbf{L5} & \\textbf{L6} & \\textbf{Avg} \\\\\n\\midrule\nLlama 3.1-8B & 90.67 & 84.00 & 59.05 & 44.68 & 26.42 & 53.54 \\\\\nGPT-4o & 76.07 & 70.09 & 59.63 & 42.07 & 40.74 & 53.13 \\\\\nGPT-4.1 & 94.44 & 82.91 & 68.81 & 56.55 & 48.15 & 64.10 \\\\\n\\midrule\nCCM-Llama & \\textbf{99.55} & \\textbf{90.09} & \\textbf{75.85} & \\textbf{65.19} & \\textbf{60.78} & \\textbf{72.98} \\\\\n\\rowcolor{gray!10}\n$\\Delta$ over Best Baseline& \\textbf{+5.11} & \\textbf{+7.18} & \\textbf{+7.04} & \\textbf{+8.64} & \\textbf{+12.63} & \\textbf{+8.88} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Learning to Regulate: A New Event-Level Dataset of Capital Control Measures", "authors": ["Geyue Sun", "Xiao Liu", "Tomas Williams", "Roberto Samaniego"], "url": "https://arxiv.org/abs/2505.23025v1", "attribution": "\"Learning to Regulate: A New Event-Level Dataset of Capital Control Measures\" by Geyue Sun, Xiao Liu, Tomas Williams, and Roberto Samaniego, arXiv:2505.23025v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.15785v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Demand Estimation, by Merger}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n Variables$\\backslash$Merger & DL-NW & UA-CO & AA-US \\\\\n \\midrule\n Airfare & -1.0219$^{***}$ & -1.0373$^{***}$ & -1.4985$^{***}$ \\\\\n & (0.0136) & (0.0153) & (0.0256) \\\\\n M$_{lsjh}$ & 0.5814$^{***}$ & 0.4818$^{***}$ & 0.2572$^{***}$ \\\\\n & (0.0064) & (0.0064) & (0.0092) \\\\\n M$_{lshg}$ & 0.0246$^{***}$ & 0.0817$^{***}$ & 0.1676$^{***}$ \\\\\n & (0.0064) & (0.0066) & (0.0098) \\\\\n Nonstop & 2.4955$^{***}$ & 2.6256$^{***}$ & 3.0311$^{***}$ \\\\\n & (0.0167) & (0.0170) & (0.0225) \\\\\n Ticketing Carrier & 0.6289$^{***}$ & 0.7185$^{***}$ & 0.6426$^{***}$ \\\\\n & (0.0143) & (0.0147) & (0.0176) \\\\\n Distance & 0.6659$^{***}$ & 0.6591$^{***}$ & 0.8713$^{***}$ \\\\\n & (0.0064) & (0.0071) & (0.0118) \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Bridging Retrospective and Prospective Merger Analyses: The Case of US Airline Mergers", "authors": ["Gaurab Aryal", "Anirban Chattopadhyaya", "Federico Ciliberto"], "url": "https://arxiv.org/abs/2503.15785v3", "attribution": "\"Bridging Retrospective and Prospective Merger Analyses: The Case of US Airline Mergers\" by Gaurab Aryal, Anirban Chattopadhyaya, and Federico Ciliberto, arXiv:2503.15785v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16403v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n \\textbf{Poisson Brackets} & \\textbf{Result} & \\textbf{Description} \\\\\n \\hline\n $\\{ L_{12}, L_{13} \\}$ & $L_{23}$ & $SO(4)$ angular momentum algebra \\\\\n $\\{ L_{12}, L_{14} \\}$ & $L_{24}$ & $SO(4)$ angular momentum algebra \\\\\n $\\{ L_{13}, L_{14} \\}$ & $L_{34}$ & $SO(4)$ angular momentum algebra \\\\\n $\\{ I_{ii}, I_{jj} \\}$ & $0$ & Commuting quadratic integrals \\\\\n $\\{ I_{ii}, L_{ij} \\}$ & $2 \\,\\omega^2 \\,I_{ij}$ & Fradkin tensor coupling to $SO(4)$ \\\\\n $\\{ H_{4D}, L_{ij} \\}$ & $0$ & Energy is a Casimir element \\\\\n $\\{ H_{4D}, I_{ii} \\}$ & $0$ & Energy commutes with quadratic integrals \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Poisson algebra of the 7 algebraically independent integrals of motion ($H_{4D},L_{12}, L_{13},L_{14}, I_{11}, I_{22}, I_{33}$) in the 4D isotropic harmonic oscillator.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the four-body limaçon choreography: maximal superintegrability and choreographic fragmentation", "authors": ["Adrian M Escobar-Ruiz", "Manuel Fernandez-Guasti"], "url": "https://arxiv.org/abs/2504.16403v3", "attribution": "\"On the four-body limaçon choreography: maximal superintegrability and choreographic fragmentation\" by Adrian M Escobar-Ruiz and Manuel Fernandez-Guasti, arXiv:2504.16403v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01887v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Test MSE on UCI Regression Tasks}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrr}\\toprule\n& \\multicolumn{4}{c}{Test MSE $\\downarrow$} \\\\ \n\\cmidrule{2-5}\nDataset & Ensembles & WGD & SVGD & SVN \\\\ \\midrule\nautompg ($n$ = 392) & $2.53_{0.04}$ & $\\mathbf{0.77_{0.28}}$ & $3.22_{0.15}$ & $1.61_{0.28}$ \\\\\nconcrete ($n$ = 1030) & $\\mathbf{2.68_{0.66}}$ & $\\mathbf{2.61_{0.31}}$ & $\\mathbf{2.42_{0.81}}$ & $\\mathbf{2.45_{0.72}}$ \\\\\nenergy ($n$ = 768) & $0.19_{0.04}$ & $0.19_{0.05}$ & $0.14_{0.02}$ & $\\mathbf{0.07_{0.01}}$ \\\\\nkin8nm ($n$ = 8192) & $\\mathbf{7.4e^{-6} \\pm 2.36 e^{-6}}$ & $4.5 e^{-5} \\pm 1.3e^{-5}$ & $1.9e^{-5} \\pm 0.3 e^{-5}$ & $7.06 e^{-5} \\pm 1.42 e^{-5}$ \\\\\nnaval ($n$ = 11934) & $\\mathbf{6.2e^{-8} \\pm 1.4e^{-8}}$ & $5.4e^{-7} \\pm 1.2e^{-7}$ & $2.8e^{-7} \\pm 0.7e^{-7}$ & $1.2e^{-7} \\pm 0.3e^{-7}$ \\\\\npower ($n$ = 9568) & $0.16_{0.02}$ & $0.49_{0.09}$ & $0.77_{0.06}$ & $\\mathbf{0.08_{0.02}}$ \\\\\nprotein ($n$ = 45730) & $\\mathbf{0.11_{0.11}}$ & $\\mathbf{0.06_{0.01}}$ & $\\mathbf{0.11_{0.11}}$ & $0.36_{0.10}$ \\\\\nwine ($n$ = 1599) & $0.05_{0.01}$ & $\\mathbf{0.02_{0.01}}$ & $\\mathbf{0.04_{0.01}}$ & $\\mathbf{0.02_{0.01}}$ \\\\\nyacht ($n$ = 308) & $\\mathbf{0.51_{0.20}}$ & $\\mathbf{0.26_{0.08}}$ & $0.63_{0.10}$ & $\\mathbf{0.61_{0.47}}$ \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Stein Variational Newton Neural Network Ensembles", "authors": ["Klemens Flöge", "Mohammed Abdul Moeed", "Vincent Fortuin"], "url": "https://arxiv.org/abs/2411.01887v1", "attribution": "\"Stein Variational Newton Neural Network Ensembles\" by Klemens Flöge, Mohammed Abdul Moeed, and Vincent Fortuin, arXiv:2411.01887v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00270v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{xcolor}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The execution time and speedup of different algorithms across 13 graphs. The R0-R10 graphs are the real-world network from SNAP, while the S0-S1 are the synthesis network generated from 1st DIMACS Challenge . The edge capacity of graphs in SNAP is set to 1. The bold font time represents the best execution time among these four algorithms.}\n\\begin{tabular}{|l|r|r|rrrr|rr|}\n\\hline\n\\rowcolor[HTML]{EFEFEF} \n\\cellcolor[HTML]{EFEFEF} & \\cellcolor[HTML]{EFEFEF} & \\cellcolor[HTML]{EFEFEF} & \\multicolumn{4}{c|}{\\cellcolor[HTML]{EFEFEF}Execution time (ms)} & \\multicolumn{2}{c|}{\\cellcolor[HTML]{EFEFEF}Speedup (TC/VC)} \\\\ \\cline{4-9} \n\\rowcolor[HTML]{EFEFEF} \n\\multirow{-2}{*}{\\cellcolor[HTML]{EFEFEF}Graph} & \\multirow{-2}{*}{\\cellcolor[HTML]{EFEFEF}$|V|$} & \\multirow{-2}{*}{\\cellcolor[HTML]{EFEFEF}$|E|$} & \\cellcolor[HTML]{EFEFEF}TC+RCSR & \\cellcolor[HTML]{EFEFEF}TC+BCSR & \\cellcolor[HTML]{EFEFEF}VC+RCSR & \\cellcolor[HTML]{EFEFEF}VC+BCSR & \\cellcolor[HTML]{EFEFEF}RCSR & \\cellcolor[HTML]{EFEFEF}BCSR \\\\ \\hline\n{\\color[HTML]{000000} Amazon0302 (R0)} & 262,111 & 1,234,877 & 5,728 & \\textbf{2,307} & 8,477 & 5,191 & 0.67x & 0.44x \\\\ \\hline\n{\\color[HTML]{000000} roadNet-CA (R1)} & 1,965,206 & 2,766,607 & 57,966 & 70,468 & 74,707 & \\textbf{32,842} & 0.78x & 2.15x \\\\ \\hline\n{\\color[HTML]{000000} roadNet-PA (R2)} & 1,088,092 & 1,541,898 & 27,667 & \\textbf{14,822} & 43,283 & 18,078 & 0.64x & 0.82x \\\\ \\hline\n{\\color[HTML]{000000} web-BerkStan (R3)} & 685,230 & 7,600,595 & 82,984 & 23,959 & 35,129 & \\textbf{23,596} & 2.36x & 1.02x \\\\ \\hline\n{\\color[HTML]{000000} web-Google (R4)} & 875,713 & 5,105,039 & 28,053 & 12,165 & 17,664 & \\textbf{7,927} & 1.58x & 1.53x \\\\ \\hline\n{\\color[HTML]{000000} cit-Patents (R5)} & 3,774,768 & 16,518,948 & 80,223 & 237,968 & 4,879 & \\textbf{2,992} & 16.44x & 79.53x \\\\ \\hline\ncit-HepPh (R6) & 34,546 & 421,578 & 651 & 214 & 312 & \\textbf{141} & 2.09x & 1.52x \\\\ \\hline\n{\\color[HTML]{000000} soc-LiveJounal1 (R7)} & 4,847,571 & 68,993,773 & 833,189 & 703,077 & 572,705 & \\textbf{385,912} & 1.45x & 1.82x \\\\ \\hline\n{\\color[HTML]{000000} soc-Pokec (R8)} & 81,306 & 1,768,149 & 82,525 & 66,319 & 73,060 & \\textbf{34,214} & 1.13x & 1.94x \\\\ \\hline\n{\\color[HTML]{000000} com-YouTube (R9)} & 1,134,890 & 2,987,624 & 398,794 & \\textbf{91,859} & 177,555 & 114,003 & 2.25x & 0.81x \\\\ \\hline\n{\\color[HTML]{000000} com-Orkut (R10)} & 3,072,441 & 117,185,083 & 5,001,263 & 326,534 & 3,141,124 & \\textbf{325,351} & 1.59x & 1.00x \\\\ \\hline\nWashington-RLG (S0) & 262,146 & 785,920 & 162,792 & 132,482 & 287,390 & \\textbf{99,410} & 0.56x & 1.33x \\\\ \\hline\nGenrmf (S1) & 2,097,152 & 10,403,840 & \\textbf{2,138} & 2,900 & 2,685 & 2,503 & 0.79x & 1.15x \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Engineering A Workload-balanced Push-Relabel Algorithm for Massive Graphs on GPUs", "authors": ["Chou-Ying Hsieh", "Po-Chieh Lin", "Sy-Yen Kuo"], "url": "https://arxiv.org/abs/2404.00270v1", "attribution": "\"Engineering A Workload-balanced Push-Relabel Algorithm for Massive Graphs on GPUs\" by Chou-Ying Hsieh, Po-Chieh Lin, and Sy-Yen Kuo, arXiv:2404.00270v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.17124v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Table showing correlation analyses determining the relationship between Utterance Frequency, Average Pointing Time, Stationary Time Ratio and Task Completion Time (\\textit{* = $p < .05$; ** = $p < .01$; *** = $p < .001$})}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|l|l|l|}\n\\hline\n\\textbf{\\textit{Modality}} & \\textbf{\\textit{Name}} & \\textbf{\\textit{r}} & \\textbf{\\textit{p}} & \\textbf{\\textit{Sig}} & \\textbf{\\textit{$M_1$}} & \\textbf{\\textit{$SD_1$}} & \\textbf{\\textit{$M_2$}} & \\textbf{\\textit{$SD_2$}} \\\\ \\hline\nVoice & Utterance Frequency (Task 1) & 0.39 & $< .05$ & * & 11.94 & 5.07 & 382.84 & 148.7 \\\\ \\hline\nVoice & Utterance Frequency (Task 2) & 0.85 & $< .05$ & * & 17.32 & 5.67 & 451.4 & 133.77 \\\\ \\hline\nHand & Avg. Pointing Time (Task 1) & 0.18 & 0.32 & No & 1.93 & 1.00 & 382.84 & 148.7 \\\\ \\hline\nHand & Avg. Pointing Time (Task 2) & 0.15 & 0.41 & No & 4.24 & 2.42 & 451.4 & 133.77 \\\\ \\hline\nEye Gaze & Stationary Time Ratio (Task 1) & 0.37 & $< .05$ & * & 0.29 & 0.12 & 382.84 & 148.7 \\\\ \\hline\nEye Gaze & Stationary Time Ratio (Task 2) & -0.025 & 0.88 & No & 0.39 & 0.14 & 451.4 & 133.77 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks", "authors": ["Ryan Ghamandi", "Yahya Hmaiti", "Mykola Maslych", "Ravi Kiran Kattoju", "Joseph J. LaViola"], "url": "https://arxiv.org/abs/2508.17124v1", "attribution": "\"Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks\" by Ryan Ghamandi, Yahya Hmaiti, Mykola Maslych, Ravi Kiran Kattoju, and Joseph J. LaViola, arXiv:2508.17124v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.12215v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average Number of Data Samples per Class for Training and Testing.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n \\hline\n Class & Training Samples & Testing Samples \\\\ \\hline\n Imagined Speech & 53,293 & 13,325 \\\\ \\hline\n Idle State & 25,580 & 10,492 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Imagined Speech State Classification for Robust Brain-Computer Interface", "authors": ["Byung-Kwan Ko", "Jun-Young Kim", "Seo-Hyun Lee"], "url": "https://arxiv.org/abs/2412.12215v1", "attribution": "\"Imagined Speech State Classification for Robust Brain-Computer Interface\" by Byung-Kwan Ko, Jun-Young Kim, and Seo-Hyun Lee, arXiv:2412.12215v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.10403v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Relative errors of the final regularized solutions and corresponding early stopping iterations (in parentheses), where $\\varepsilon=10^{-3}$}\n\\begin{tabular}{lllll}\n\t\t\\toprule\n\t\tProblem & Example 1 & Example 2 & Example 3 & Example 4 \\\\\n\t\t\\midrule\n\t\tTikh-opt & 0.0361 & 0.0060 & 0.0062 & 0.0038 \\\\\n\t\tWLSQR-opt & 0.031 (9) & 0.0057 (11) & 0.0037 (3) & 0.0029 (7) \\\\\n WLSQR-DP & 0.0474 (7) & 0.0089 (8) & 0.0538 (2) & 0.0066 (5) \\\\\n WLSQR-LC & 0.0451 (8) & 0.0186 (14) & 0.0037 (3) & 0.0233 (12) \\\\\n LSQR-opt & 0.3178 (9) & 0.3163 (11) & 0.3166 (3) & 0.3162 (7) \\\\\n LSQR-DP & 0.3194 (7) & 0.3163 (8) & 0.3206 (2) & 0.3163 (5) \\\\\n LSQR-LC & 0.3191 (8) & 0.3164 (14) & 0.3166 (3) & 0.3170 (12) \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Generalizing the SVD of a matrix under non-standard inner product and its applications to linear ill-posed problems", "authors": ["Haibo Li"], "url": "https://arxiv.org/abs/2312.10403v1", "attribution": "\"Generalizing the SVD of a matrix under non-standard inner product and its applications to linear ill-posed problems\" by Haibo Li, arXiv:2312.10403v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10459v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Physical validation study demonstrates modest error in FEA simulation results.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|ccc|ccc|ccc|}\n\\hline\n & \\multicolumn{3}{c|}{DeRosa SLX} & \\multicolumn{3}{c|}{Casati Gold Line} & \\multicolumn{3}{c|}{Holland SL/SP} \\\\ \\hline\n & Front & Rear & Model & Front & Rear & Model & Front & Rear & Model \\\\\n & Defl. & Defl. & Mass & Defl. & Defl. & Mass & Defl. & Defl. & Mass \\\\ \\hline\nActual & 0.40 & 0.15 & 1.966 & 0.44 & 0.15 & 1.966 & 0.38 & 0.13 & 1.962 \\\\\nSimulated & 0.297 & 0.116 & 1.69 & 0.3028 & 0.124 & 1.80 & 0.26 & 0.107 & 1.77 \\\\ \\hline\nError & 26\\% & 23\\% & 14\\% & 31\\% & 17\\% & 8\\% & 32\\% & 18\\% & 10\\% \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames", "authors": ["Lyle Regenwetter", "Colin Weaver", "Faez Ahmed"], "url": "https://arxiv.org/abs/2201.10459v3", "attribution": "\"FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames\" by Lyle Regenwetter, Colin Weaver, and Faez Ahmed, arXiv:2201.10459v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.12919v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Top 10 institutions' impact on acceptance, separate}, ICLR 2020. Logistic regression summary for predicting paper acceptance. The top 10 institution ranks each have their own indicator variable. Statistically significant effects are in bold. The accuracy of the classifier was 92\\% on a hold-out set containing 30\\% of 2020 papers}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc} \n\\bf Variable &\\bf Coefficient &\\bf Std. Error &\\bf Z-score &\\bf p-value\n\\\\ \\hline \\\\\n\\texttt{mean reviewer score} & 2.4600 & 0.099 & 24.949 & 0.000\\\\ %2.267 2.653\n\\bf \\texttt{Carnegie Mellon} & $\\mathbf{0.7510}$ & 0.363 & 2.071 & $\\mathbf{0.038}$\\\\ %0.040 1.462\n\\bf \\texttt{MIT} & $\\mathbf{0.7498}$ & 0.357 & 2.100 & $\\mathbf{0.036}$\\\\ %0.050 1.450\n\\texttt{U. Illinois, Urbana-Champaign} & 0.6698 & 0.535 & 1.252 & 0.211\\\\ %-0.379 1.718\n\\texttt{Stanford} & -0.0312 & 0.371 & -0.084 & 0.933\\\\ %-0.759 0.696\n\\texttt{U.C. Berkeley} & 0.2299 & 0.337 & 0.681 & 0.496\\\\ %-0.432 0.891\n\\texttt{U. Washington} & -0.8189 & 0.684 & -1.198 & 0.231\\\\ %-2.159 0.521\n\\bf \\texttt{Cornell} & $\\mathbf{1.4267}$ & 0.607 & 2.350 & $\\mathbf{0.019}$\\\\ %0.237 2.617\n\\texttt{Tsinghua U. \\& U. Michigan (tied)} & -0.0118 & 0.369 & -0.032 & 0.974\\\\ %-0.735 0.711\n\\texttt{ETH Zurich} & 0.0608 & 0.640 & 0.095 & 0.924\\\\ %-1.193 1.314\n\\texttt{constant} & -13.4048 & 0.535 & -25.038 & 0.000\\\\ %-14.454 -12.356\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Analyzing the Machine Learning Conference Review Process", "authors": ["David Tran", "Alex Valtchanov", "Keshav Ganapathy", "Raymond Feng", "Eric Slud", "Micah Goldblum", "Tom Goldstein"], "url": "https://arxiv.org/abs/2011.12919v2", "attribution": "\"Analyzing the Machine Learning Conference Review Process\" by David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, and Tom Goldstein, arXiv:2011.12919v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.04375v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average width and coverage of $90\\%$-confidence intervals for PATE. Constant treatment effect with $n=10^3$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n\\toprule\n\\multirow{2}{*}{Non-private} \n& \\multicolumn{7}{c}{0.901}\\\\\n& \\multicolumn{7}{c}{0.002 $\\pm$ 3.24e-05}\n\\\\ \\midrule\n &\n $\\varepsilon = 0.1$ &\n $\\varepsilon = 0.4$ &\n $\\varepsilon = 0.7$ &\n $\\varepsilon = 1.0$ &\n $\\varepsilon = 1.3$ &\n $\\varepsilon = 1.6$ &\n $\\varepsilon = 1.9$ \\\\ \\midrule\n\\multirow{2}{*}{Central Gaussian} \n& 0.905\n& 0.895\n& 0.899\n& 0.902\n& 0.904\n& 0.899\n& 0.899\n\\\\\n& 0.771 $\\pm$ 1.24e-07\n& 0.199 $\\pm$ 4.85e-07\n& 0.118 $\\pm$ 8.20e-07\n& 0.084 $\\pm$ 1.16e-06\n& 0.066 $\\pm$ 1.47e-06\n& 0.055 $\\pm$ 1.78e-06\n& 0.047 $\\pm$ 2.07e-06\n \\\\\\midrule\n \\multirow{2}{*}{PBM (m=256)} \n& 0.902\n& 0.900\n& 0.900\n& 0.903\n& 0.906\n& 0.900\n& 0.903 \n \\\\\n& 0.772 $\\pm$ 1.25e-07\n& 0.200 $\\pm$ 4.84e-07\n& 0.119 $\\pm$ 8.15e-07\n& 0.085 $\\pm$ 1.15e-06\n& 0.067 $\\pm$ 1.43e-06\n& 0.056 $\\pm$ 1.72e-06\n& 0.048 $\\pm$ 2.02e-06\n \\\\\\midrule\n \n \\multirow{2}{*}{PBM (m=1024)} \n& 0.900\n& 0.897\n& 0.902\n& 0.900\n& 0.904\n& 0.898\n& 0.896\n \\\\\n& 0.772 $\\pm$ 1.26e-07\n& 0.199 $\\pm$ 4.85e-07\n& 0.118 $\\pm$ 8.28e-07\n& 0.085 $\\pm$ 1.17e-06\n& 0.066 $\\pm$ 1.46e-06\n& 0.055 $\\pm$ 1.77e-06\n& 0.047 $\\pm$ 2.05e-06\n\\\\\\midrule\n \\multirow{2}{*}{PBM (m=2048)} \n& 0.897\n& 0.902\n& 0.901\n& 0.901\n& 0.899\n& 0.902\n& 0.898\n\\\\\n& 0.772 $\\pm$ 1.24e-07\n& 0.199 $\\pm$ 4.85e-07\n& 0.118 $\\pm$ 8.19e-07\n& 0.084 $\\pm$ 1.16e-06\n& 0.066 $\\pm$ 1.47e-06\n& 0.055 $\\pm$ 1.77e-06\n& 0.047 $\\pm$ 2.06e-06\n\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Federated Experiment Design under Distributed Differential Privacy", "authors": ["Wei-Ning Chen", "Graham Cormode", "Akash Bharadwaj", "Peter Romov", "Ayfer Özgür"], "url": "https://arxiv.org/abs/2311.04375v1", "attribution": "\"Federated Experiment Design under Distributed Differential Privacy\" by Wei-Ning Chen, Graham Cormode, Akash Bharadwaj, Peter Romov, and Ayfer Özgür, arXiv:2311.04375v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04201v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Main notations in this paper.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n \\hline\n Variable & Denotation \\\\ \\hline\n $P$ & High-resolution PAN (HRPAN) image\\\\\n $Q$ & Low-resolution PAN (LRPAN) image\\\\\n $H$ & High-resolution HS (HRHS) image\\\\\n $L$ & Low-resolution HS (LRHS) image\\\\\n $N$ & Noisy LRHS (NLRHS) image\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Hipandas: Hyperspectral Image Joint Denoising and Super-Resolution by Image Fusion with the Panchromatic Image", "authors": ["Shuang Xu", "Zixiang Zhao", "Haowen Bai", "Chang Yu", "Jiangjun Peng", "Xiangyong Cao", "Deyu Meng"], "url": "https://arxiv.org/abs/2412.04201v1", "attribution": "\"Hipandas: Hyperspectral Image Joint Denoising and Super-Resolution by Image Fusion with the Panchromatic Image\" by Shuang Xu, Zixiang Zhao, Haowen Bai, Chang Yu, Jiangjun Peng, Xiangyong Cao, and Deyu Meng, arXiv:2412.04201v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.03638v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\footnotesize Results benchmark (BM) replication (see, Table 5 from ).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll}\n\\hline\\hline\n & \\multicolumn{7}{c}{Set BM} \\\\\\cline{1-8}\n & $r$ & $\\kappa$ & $\\gamma$ & $\\rho$ & $\\Bar{v}$ & $v_0$ & $T$\\\\\n & \\multirow{1}{*}{0.05} & \\multirow{1}{*}{3.0} & \\multirow{1}{*}{0.1} & \\multirow{1}{*}{-0.1} & \\multirow{1}{*}{0.04} & \\multirow{1}{*}{0.04} & \\multirow{1}{*}{0.25}\\\\\\hline\\hline\n&\\multicolumn{3}{c}{BM}&\\multicolumn{3}{c}{SC}&SA\\\\\\cline{1-8}\n$K_2$ & V & \\multicolumn{2}{c|}{95CI} & V & \\multicolumn{2}{c|}{95CI} & V\\\\\\cline{1-8}\n90 & 10.5439 & \\multicolumn{2}{c|}{[10.5329, 10.5550]} & 10.5459 & \\multicolumn{2}{c|}{[10.5349, 10.5570]} & 10.5486\\\\\n95 & 6.0168 & \\multicolumn{2}{c|}{[6.0069, 6.0267]} & 6.0173 & \\multicolumn{2}{c|}{[6.0074, 6.0271]} & 6.0196\\\\\n100 & 2.6026 & \\multicolumn{2}{c|}{[2.5953, 2.6098]} & 2.5992 & \\multicolumn{2}{c|}{[2.5920, 2.6064]} & 2.5996\\\\\n105 & 0.7902 & \\multicolumn{2}{c|}{[0.7862, 0.7943]} & 0.7867 & \\multicolumn{2}{c|}{[0.7827, 0.7907]} & 0.7865\\\\\n110 & 0.1622 & \\multicolumn{2}{c|}{[0.1604, 0.1639]} & 0.1612 & \\multicolumn{2}{c|}{[0.1595, 0.1630]} & 0.1615\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "On Pricing of Discrete Asian and Lookback Options under the Heston Model", "authors": ["Leonardo Perotti", "Lech A. Grzelak"], "url": "https://arxiv.org/abs/2211.03638v2", "attribution": "\"On Pricing of Discrete Asian and Lookback Options under the Heston Model\" by Leonardo Perotti and Lech A. Grzelak, arXiv:2211.03638v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.01880v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Cointegrating Vector Coefficients for Crypto}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rrrrr}\n \\toprule\n & \\textbf{ADA} & \\textbf{BTC} & \\textbf{ETH} & \\textbf{LTC} & \\textbf{XRP} \\\\\n \\midrule\n CEV$_1$ & $+2.62$ & $+3.69$ & $-4.60$ & $-0.99$ & $+2.23$ \\\\\n CEV$_2$ & $+5.15$ & $-0.19$ & $-5.64$ & $+1.82$ & $-3.51$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Time-Varying Factor-Augmented Models for Volatility Forecasting", "authors": ["Duo Zhang", "Jiayu Li", "Junyi Mo", "Elynn Chen"], "url": "https://arxiv.org/abs/2508.01880v1", "attribution": "\"Time-Varying Factor-Augmented Models for Volatility Forecasting\" by Duo Zhang, Jiayu Li, Junyi Mo, and Elynn Chen, arXiv:2508.01880v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.20834v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results of average part-worth utilities from Quantification Theory Type 1 and average ranges for each components.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccccc}\n\\toprule\nAttribute & Level & \\multicolumn{3}{c}{Dummy} & Part-Worth Utility & Range \\\\\n\\cmidrule{3-5}\n & & D1 & D2 & D3 & & \\\\\n\\midrule\\midrule\ntitle & short & 1 & 0 & - & 0.44 & 0.44 \\\\\n & long & 0 & 1 & - & 0.82 & \\\\ \\midrule\nprice & cheap & 1 & 0 & - & 0.67 & 0.38\\\\\n & expensive & 0 & 1 & - & 0.59 & \\\\ \\midrule\nproduct description & short & 1 & 0 & 0 & -0.42 & 1.85 \\\\\n & middle & 0 & 1 & 0 & 0.35 & \\\\\n & long & 0 & 0 & 1 & 1.34 & \\\\ \\midrule\nimage & 1 image & 1 & 0 & - & 0.27 & 0.73 \\\\\n & 3 images & 0 & 1 & - & 0.99 & \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Analysis of Information Digestion Differences among Players in Online C2C Markets", "authors": ["Jun Sashihara", "Teruaki Hayashi"], "url": "https://arxiv.org/abs/2503.20834v1", "attribution": "\"Analysis of Information Digestion Differences among Players in Online C2C Markets\" by Jun Sashihara and Teruaki Hayashi, arXiv:2503.20834v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06635v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Our model’s accuracy (\\%) on the \\textbf{NABirds} dataset with different SotA base CNN architectures. Previous best accuracy is 86.4\\% (Luo et al. 2019) for primary only and 87.9\\% (Cui et al. 2018) for combined primary and secondary datasets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c}\n \\toprule\n \n {Base CNN} & {Accuracy(\\%)} \\\\\n \\midrule\n ResNet-50 &\t88.8 \\\\\n Inception V3 & 89.1 \\\\ \n Xception & \\textbf{91.0} \\\\\n DenseNet-121 &88.3 \\\\\nNASNet-Mobile &88.7 \\\\\nMobileNet V2 &89.1 \\\\\n \\bottomrule \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification", "authors": ["Ardhendu Behera", "Zachary Wharton", "Pradeep Hewage", "Asish Bera"], "url": "https://arxiv.org/abs/2101.06635v1", "attribution": "\"Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification\" by Ardhendu Behera, Zachary Wharton, Pradeep Hewage, and Asish Bera, arXiv:2101.06635v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.01997v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Diarization result break-down on LibriCSS evaluation set, in terms of \\% missed speech (MS), false alarm (FA), and speaker confusion (Conf.).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\textbf{Method} & \\textbf{MS} & \\textbf{FA} & \\textbf{Conf.} & \\textbf{DER} \\\\ \\midrule\nVB & \\textbf{1.7} & \\textbf{0.5} & 6.4 & 8.6 \\\\\nSC & 2.5 & 1.1 & 5.7 & 9.3 \\\\\nRPN & 2.9 & 3.3 & 3.3 & 9.5 \\\\\nTS-VAD & 3.2 & 1.3 & 2.9 & 7.4 \\\\\n\\midrule\nDL & 2.7 & 0.7 & \\textbf{2.0} & \\textbf{5.4} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs", "authors": ["Desh Raj", "Leibny Paola Garcia-Perera", "Zili Huang", "Shinji Watanabe", "Daniel Povey", "Andreas Stolcke", "Sanjeev Khudanpur"], "url": "https://arxiv.org/abs/2011.01997v1", "attribution": "\"DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs\" by Desh Raj, Leibny Paola Garcia-Perera, Zili Huang, Shinji Watanabe, Daniel Povey, Andreas Stolcke, and Sanjeev Khudanpur, arXiv:2011.01997v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00356v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Time comparison between constraint in the STL formula and actual path duration. Time in [s].}\n\\begin{tabular}{c|c|c|c|c|c}\n \t& $\\phi'$ & $\\phi''$ & $\\phi'''$ & $\\phi''''$ & $t_{total}$\\\\\n\t\\hline\n\t\\textbf{STL constraint} & 10 & 30 & 10 & 10 & 60\\\\\n \\hline\n \\textbf{Actual path duration} & 6.51 & 25.06 & 9.01 & 6.50 & 47.08 \n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "CBF-Based Motion Planning for Socially Responsible Robot Navigation Guaranteeing STL Specification", "authors": ["Andrea Ruo", "Lorenzo Sabattini", "Valeria Villani"], "url": "https://arxiv.org/abs/2404.00356v1", "attribution": "\"CBF-Based Motion Planning for Socially Responsible Robot Navigation Guaranteeing STL Specification\" by Andrea Ruo, Lorenzo Sabattini, and Valeria Villani, arXiv:2404.00356v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.10892v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Diffusion models used for numerical experiments}. \\\\$\\dag$: The MNIST diffusion model uses the upsampled $3\\times 32\\times 32$ RGB pixel space as sample space, while the original MNIST data set consists of $28\\times 28$ single channel black and white images. Thus the effective dimensionality of these images is around $784$. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\nData set & Hugging Face model\\_id & Dimensionality\\\\\n\\midrule\nMNIST & \\text{dimpo/ddpm-mnist} & 3x32x32=3072 $\\dag$ \\\\\nCIFAR-10 & \\text{google/ddpm-cifar10-32} & 3x32x32=3072 \\\\\nLsun-Church & \\text{google/ddpm-church-256} & 3x256x256 = 196,608\\\\\nCelebA-HQ & \\text{google/ddpm-celebahq-256} & 3x256x256 = 196,608 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "The Hidden Linear Structure in Score-Based Models and its Application", "authors": ["Binxu Wang", "John J. Vastola"], "url": "https://arxiv.org/abs/2311.10892v1", "attribution": "\"The Hidden Linear Structure in Score-Based Models and its Application\" by Binxu Wang and John J. Vastola, arXiv:2311.10892v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.05224v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c}\n\\hline\n & UCF101~ & ActNet 100~ & HMDB51~ \\\\\n\\textbf{Pre-trained} & Top-1 & Top-1 & Top-1 \\\\\n\\hline\nNone & 58.87\\% & 43.54\\% & 28.56\\% \\\\ \nAVA~ & 48.54\\% & 30.51\\% & 25.28\\% \\\\ \nGym288~ & \\textbf{69.94\\%} & 43.79\\% & 36.24\\% \\\\ \nUCF101~ & - & 42.94\\% & 32.37\\% \\\\ \nActNet 100~ & 57.52\\% & - & 28.63\\% \\\\\nHMDB51~ & 53.36\\% & 39.33\\% & - \\\\\n\\hline\nHAA500 & 68.70\\% & \\textbf{47.75\\%} & \\textbf{40.45\\%}\\\\\n\\:\\:\\:Relaxed & 62.24\\% & 38.30\\% & 33.29\\% \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Fine-tuning performance on I3D.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "HAA500: Human-Centric Atomic Action Dataset with Curated Videos", "authors": ["Jihoon Chung", "Cheng-hsin Wuu", "Hsuan-ru Yang", "Yu-Wing Tai", "Chi-Keung Tang"], "url": "https://arxiv.org/abs/2009.05224v2", "attribution": "\"HAA500: Human-Centric Atomic Action Dataset with Curated Videos\" by Jihoon Chung, Cheng-hsin Wuu, Hsuan-ru Yang, Yu-Wing Tai, and Chi-Keung Tang, arXiv:2009.05224v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.10435v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison with existing Bayesian methods: AP, CG, and PK are acronyms for Analytic form of Posterior, Consistency Guarantee, and Prior Knowledge incorporation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule \n Method & AP & CG & PK \\\\\n \\midrule\n & $\\checkmark$ & $\\checkmark$ & \\\\\n & & & $\\checkmark$ \\\\\n \\textbf{Proposed method} & $\\checkmark$ & $\\checkmark$ & $\\checkmark$ \\\\\n \\bottomrule \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model", "authors": ["Shunsuke Horii", "Yoichi Chikahara"], "url": "https://arxiv.org/abs/2312.10435v1", "attribution": "\"Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model\" by Shunsuke Horii and Yoichi Chikahara, arXiv:2312.10435v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19280v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The summary of performance test with Neumann condition. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\t\t$\\theta$ & $\\max|y''-f|$ & $\\max(|y_{opt}-y_{b}|)$ & $\\max(|y_{rk4}-y_{b}|)$ \\\\ \\hline\\hline\n\t\t$\\pi/2$ & 1.1E-07 & 8.8E-10 & 2.4E-07 \\\\\n\t\t\t$3\\pi/2$ & 1.1E-06 & 1.8E-08 & 1.1E-04 \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions", "authors": ["Xiaorong Zou"], "url": "https://arxiv.org/abs/2504.19280v1", "attribution": "\"Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions\" by Xiaorong Zou, arXiv:2504.19280v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09972v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Recognition accuracy on the ETH-$80$ database.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\n\\textbf{Method} & \\textbf{Accuracy}\\\\\n\\hline\n\\textbf{LLP}+\\textbf{ARSRG}emb & \\textbf{89.26}\\%\\\\\n\\hline\n\\textbf{LLP}+BoW & 58.83\\%\\\\\n\\hline\ngdFil& 47.59\\%\\\\\n\\hline\nAPGM & 84.39\\%\\\\\n\\hline\nVEAM & 82.68\\%\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications", "authors": ["Mario Manzo"], "url": "https://arxiv.org/abs/1912.09972v1", "attribution": "\"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications\" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.12016v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Extended CART Simulation Results - Confounder Estimates (Bias)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\\toprule\n\\multicolumn{4}{c}{ } & \\multicolumn{5}{c}{Bias $\\times$ 1,000\\textsuperscript{d}} \\\\\n\\cmidrule(l{3pt}r{3pt}){5-9}\nType & $M$\\textsuperscript{a} & $k$\\textsuperscript{b} & $(\\gamma, \\xi)$\\textsuperscript{c} & $\\beta_1$ & $\\beta_2$ & $\\beta_3$ & $\\beta_4$ & $\\beta_5$\\\\\n\\midrule\noracle & & & & 6.54 (4.49) & -11.05 (4.77) & 1.59 (5.10) & -3.90 (4.65) & -0.46 (4.69)\\\\\n\\midrule\nclbart & 1 & 0.1 & (0.5, 3) & 5.60 (4.51) & -11.30 (4.78) & 1.56 (5.13) & -3.85 (4.66) & -0.06 (4.65)\\\\\nclbart & 1 & 0.1 & (0.95, 2) & 5.68 (4.49) & -11.56 (4.80) & 2.08 (5.10) & -4.17 (4.66) & 0.16 (4.66)\\\\\nclbart & 1 & 0.5 & (0.5, 3) & 5.96 (4.50) & -11.90 (4.79) & 1.55 (5.09) & -4.39 (4.67) & 0.31 (4.70)\\\\\nclbart & 1 & 0.5 & (0.95, 2) & 5.53 (4.48) & -11.76 (4.75) & 1.56 (5.10) & -3.80 (4.66) & 0.22 (4.69)\\\\\nclbart & 1 & 1.0 & (0.5, 3) & 6.56 (4.48) & -11.50 (4.77) & 1.46 (5.11) & -4.22 (4.65) & -0.06 (4.64)\\\\\nclbart & 1 & 1.0 & (0.95, 2) & 6.06 (4.51) & -11.43 (4.80) & 1.30 (5.09) & -3.48 (4.66) & 0.07 (4.63)\\\\\n\\midrule\nclbart & 5 & 0.1 & (0.5, 3) & 5.63 (4.49) & -10.93 (4.79) & 1.64 (5.10) & -4.34 (4.65) & -0.38 (4.71)\\\\\nclbart & 5 & 0.1 & (0.95, 2) & 5.90 (4.50) & -11.43 (4.76) & 1.77 (5.11) & -4.02 (4.65) & 0.20 (4.69)\\\\\nclbart & 5 & 0.5 & (0.5, 3) & 6.17 (4.48) & -11.10 (4.81) & 1.59 (5.11) & -3.98 (4.65) & 0.02 (4.70)\\\\\nclbart & 5 & 0.5 & (0.95, 2) & 5.43 (4.50) & -11.48 (4.76) & 1.42 (5.15) & -3.74 (4.65) & 0.76 (4.69)\\\\\nclbart & 5 & 1.0 & (0.5, 3) & 6.24 (4.50) & -11.31 (4.77) & 1.55 (5.10) & -3.71 (4.67) & 0.30 (4.69)\\\\\nclbart & 5 & 1.0 & (0.95, 2) & 5.86 (4.50) & -11.22 (4.79) & 1.81 (5.11) & -3.64 (4.67) & 0.02 (4.68)\\\\\n\\midrule\nclbart & 10 & 0.1 & (0.5, 3) & 6.11 (4.46) & -11.37 (4.78) & 0.80 (5.11) & -4.06 (4.69) & 0.25 (4.67)\\\\\nclbart & 10 & 0.1 & (0.95, 2) & 5.95 (4.51) & -11.78 (4.80) & 1.62 (5.13) & -4.00 (4.63) & 0.69 (4.71)\\\\\nclbart & 10 & 0.5 & (0.5, 3) & 6.28 (4.49) & -11.31 (4.80) & 1.78 (5.11) & -4.04 (4.65) & 0.53 (4.71)\\\\\nclbart & 10 & 0.5 & (0.95, 2) & 5.44 (4.49) & -11.57 (4.79) & 0.75 (5.13) & -4.04 (4.66) & 0.74 (4.71)\\\\\nclbart & 10 & 1.0 & (0.5, 3) & 6.16 (4.49) & -11.39 (4.79) & 1.35 (5.14) & -4.21 (4.64) & 0.21 (4.67)\\\\\nclbart & 10 & 1.0 & (0.95, 2) & 5.54 (4.49) & -11.01 (4.82) & 1.86 (5.08) & -3.93 (4.69) & 0.96 (4.69)\\\\\n\\midrule\nclbart & 25 & 0.1 & (0.5, 3) & 5.78 (4.51) & -11.56 (4.79) & 1.64 (5.11) & -3.41 (4.64) & 0.21 (4.71)\\\\\nclbart & 25 & 0.1 & (0.95, 2) & 5.80 (4.50) & -10.96 (4.81) & 1.66 (5.13) & -3.94 (4.68) & 0.84 (4.72)\\\\\nclbart & 25 & 0.5 & (0.5, 3) & 6.25 (4.51) & -11.64 (4.78) & 1.59 (5.13) & -3.80 (4.68) & 0.23 (4.71)\\\\\nclbart & 25 & 0.5 & (0.95, 2) & 5.19 (4.53) & -11.45 (4.78) & 1.54 (5.10) & -3.87 (4.66) & 0.44 (4.73)\\\\\nclbart & 25 & 1.0 & (0.5, 3) & 5.71 (4.49) & -11.49 (4.80) & 1.72 (5.14) & -3.69 (4.67) & 0.34 (4.70)\\\\\nclbart & 25 & 1.0 & (0.95, 2) & 5.65 (4.51) & -11.34 (4.79) & 1.97 (5.13) & -3.98 (4.63) & 0.86 (4.72)\\\\\n\\midrule\nclbart & 50 & 0.1 & (0.5, 3) & 5.58 (4.50) & -11.22 (4.80) & 1.60 (5.14) & -3.79 (4.66) & 0.21 (4.71)\\\\\nclbart & 50 & 0.1 & (0.95, 2) & 5.64 (4.56) & -11.51 (4.78) & 1.70 (5.11) & -3.64 (4.68) & 1.06 (4.68)\\\\\nclbart & 50 & 0.5 & (0.5, 3) & 5.86 (4.49) & -11.21 (4.80) & 1.01 (5.09) & -3.96 (4.68) & -0.27 (4.74)\\\\\nclbart & 50 & 0.5 & (0.95, 2) & 5.97 (4.53) & -11.80 (4.77) & 1.47 (5.11) & -3.61 (4.68) & 1.16 (4.68)\\\\\nclbart & 50 & 1.0 & (0.5, 3) & 5.87 (4.55) & -11.56 (4.82) & 1.30 (5.11) & -3.64 (4.64) & 0.40 (4.69)\\\\\nclbart & 50 & 1.0 & (0.95, 2) & 5.33 (4.50) & -11.11 (4.78) & 1.50 (5.13) & -3.90 (4.67) & 0.21 (4.74)\\\\\n\\bottomrule\n\\multicolumn{9}{l}{\\textsuperscript{a} $M$: Number of trees.}\\\\\n\\multicolumn{9}{l}{\\textsuperscript{b} $k$: Numerator of scale parameter for half-Cauchy hyper-prior.}\\\\\n\\multicolumn{9}{l}{\\textsuperscript{c} $(\\gamma, \\xi)$: Hyperparameters for tree depth prior.}\\\\\n\\multicolumn{9}{l}{\\textsuperscript{d} Monte Carlo mean and standard errors across 200 simulations reported.}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Estimating Heterogeneous Exposure Effects in the Case-Crossover Design using BART", "authors": ["Jacob Englert", "Stefanie Ebelt", "Howard Chang"], "url": "https://arxiv.org/abs/2311.12016v2", "attribution": "\"Estimating Heterogeneous Exposure Effects in the Case-Crossover Design using BART\" by Jacob Englert, Stefanie Ebelt, and Howard Chang, arXiv:2311.12016v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.14930v1_tex_table32.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Training set, test set, and retraining set for using GPR to predict the optimal parameters for AMG solving the variable coefficient Poisson equation}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\tTraining set & $n:128\\sim512,\\Delta n=16$ \n\t\t\t&Test set & $n:150\\sim600, \\Delta n=32$ \n\t\t\t&Retraining set & $n:150\\sim600, \\Delta n=32$ \\\\ \n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs", "authors": ["Juan Zhang", "Junyue Luo", "Fangfang Zhang", "Xiaoqiang Yue"], "url": "https://arxiv.org/abs/2504.14930v1", "attribution": "\"Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs\" by Juan Zhang, Junyue Luo, Fangfang Zhang, and Xiaoqiang Yue, arXiv:2504.14930v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.00180v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The mean of the MESEs for each nonparametric estimator fitted to the simulated continuous regression function data.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|r|rrr|rrr|rrr|}\n \\hline\n & & $n=400$ & & & $n=800$ & & & $n=1600$ & \\\\ \n \\hline\n$\\sigma$ & LC & ALC & ALCT & LC & ALC & ALCT & LC & ALC & ALCT \\\\ \n 0.1 & 0.00126 & 0.00189 & 0.00101 & 0.00074 & 0.00111 & 0.00057 & 0.00043 & 0.00065 & 0.00032 \\\\ \n 0.5 & 0.01473 & 0.01938 & 0.01239 & 0.00861 & 0.01176 & 0.00684 & 0.00513 & 0.00723 & 0.00414 \\\\ \n 1 & 0.0416 & 0.05359 & 0.03765 & 0.02364 & 0.03154 & 0.02078 & 0.01454 & 0.01959 & 0.01191 \\\\ \n 2 & 0.12214 & 0.15806 & 0.11457 & 0.0712 & 0.08885 & 0.06197 & 0.04204 & 0.05613 & 0.0363 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Anisotropic local constant smoothing for change-point regression function estimation", "authors": ["John R. J. Thompson", "W. John Braun"], "url": "https://arxiv.org/abs/2012.00180v2", "attribution": "\"Anisotropic local constant smoothing for change-point regression function estimation\" by John R. J. Thompson and W. John Braun, arXiv:2012.00180v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19718v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|cccc|} \n\\hline\nFingerprint & logistic regression & Random Forest & LightGBM \\\\ \n\\hline\nECFP & 58.00 & 67.48 ± 0.97 & 60.26\\\\\n\\hline\nAtom Pair & 61.02 & \\textbf{71.76 ± 0.82} & 68.55\\\\\n\\hline\nTopological Torsion & 57.20 & 66.23 ± 0.91 & 63.41\\\\\n\\hline\nMACCS Keys & 71.02 & 69.56 ± 0.78 & 68.17\\\\\n\\hline\nErG & 53.41 & 71.42 ± 0.82 & 71.52\\\\\n\\hline\nMAP4 & 61.00 & 59.54 ± 1.54 & 59.23\\\\\n\\hline\nMHFP & 53.16 & 56.19 ± 3.50 & 57.38\\\\\n\\hline\n\\end{tabular}\n\\caption{Test AUROC scores for BBBP dataset.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Python library for efficient computation of molecular fingerprints", "authors": ["Michał Szafarczyk", "Piotr Ludynia", "Przemysław Kukla"], "url": "https://arxiv.org/abs/2403.19718v1", "attribution": "\"A Python library for efficient computation of molecular fingerprints\" by Michał Szafarczyk, Piotr Ludynia, and Przemysław Kukla, arXiv:2403.19718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23430v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The performance of DGSAM with 20 baseline algorithms on VLCS}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc|ccc}\n\\hline\nAlgorithm & C & L & S & V & Avg & SD & (s/iter) \\\\ \\toprule\nRSC\\(^\\dag\\) & {97.9\\scriptsize$\\pm$0.1} & {62.5\\scriptsize$\\pm$0.7} & {72.3\\scriptsize$\\pm$1.2} & {75.6\\scriptsize$\\pm$0.8} & 77.1 & 13.0 & 0.13 \\\\\nMLDG\\(^\\dag\\) & {97.4\\scriptsize$\\pm$0.2} & {65.2\\scriptsize$\\pm$0.7} & {71.0\\scriptsize$\\pm$1.4} & {75.3\\scriptsize$\\pm$1.0} & 77.2 & 12.2 & 0.12 \\\\\nMTL\\(^\\dag\\) & {97.8\\scriptsize$\\pm$0.4} & {64.3\\scriptsize$\\pm$0.3} & {71.5\\scriptsize$\\pm$0.7} & {75.3\\scriptsize$\\pm$1.7} & 77.2 & 12.5 & 0.12 \\\\\nERM\\(^\\dag\\) & {98.0\\scriptsize$\\pm$0.3} & {64.7\\scriptsize$\\pm$1.2} & {71.4\\scriptsize$\\pm$1.2} & {75.2\\scriptsize$\\pm$1.6} & 77.3 & 12.5 & 0.11 \\\\\nCDANN\\(^\\dag\\) & {97.1\\scriptsize$\\pm$0.3} & {65.1\\scriptsize$\\pm$1.2} & {70.7\\scriptsize$\\pm$0.8} & {77.1\\scriptsize$\\pm$1.5} & 77.5 & 12.1 & 0.11 \\\\\nARM\\(^\\dag\\) & {98.7\\scriptsize$\\pm$0.2} & {63.6\\scriptsize$\\pm$0.7} & {71.3\\scriptsize$\\pm$1.2} & {76.7\\scriptsize$\\pm$0.6} & 77.6 & 13.1 & 0.11 \\\\\nSagNet\\(^\\dag\\) & {97.9\\scriptsize$\\pm$0.4} & {64.5\\scriptsize$\\pm$0.5} & {71.4\\scriptsize$\\pm$1.3} & {77.5\\scriptsize$\\pm$0.5} & 77.8 & 12.5 & 0.32 \\\\\nVREx\\(^\\dag\\) & {98.4\\scriptsize$\\pm$0.3} & {64.4\\scriptsize$\\pm$1.4} & {74.1\\scriptsize$\\pm$0.4} & {76.2\\scriptsize$\\pm$1.3} & 78.3 & 12.4 & 0.11 \\\\\nDANN\\(^\\dag\\) & {99.0\\scriptsize$\\pm$0.3} & {65.1\\scriptsize$\\pm$1.4} & {73.1\\scriptsize$\\pm$0.3} & {77.2\\scriptsize$\\pm$0.6} & 78.6 & 12.6 & 0.11 \\\\\nIRM\\(^\\dag\\) & {98.6\\scriptsize$\\pm$0.1} & {64.9\\scriptsize$\\pm$0.9} & {73.4\\scriptsize$\\pm$0.6} & {77.3\\scriptsize$\\pm$0.9} & 78.6 & 12.4 & 0.12 \\\\\nCORAL\\(^\\dag\\) & {98.3\\scriptsize$\\pm$0.1} & {66.1\\scriptsize$\\pm$1.2} & {73.4\\scriptsize$\\pm$0.3} & {77.5\\scriptsize$\\pm$1.2} & 78.8 & 12.0 & 0.12 \\\\\nSWAD & {98.8\\scriptsize$\\pm$0.1} & {63.3\\scriptsize$\\pm$0.3} & {75.3\\scriptsize$\\pm$0.5} & {79.2\\scriptsize$\\pm$0.6} & 79.1 & 12.8 & 0.11 \\\\ \\midrule\nGAM\\(^\\ddag\\) & {98.8\\scriptsize$\\pm$0.6} & {65.1\\scriptsize$\\pm$1.2} & {72.9\\scriptsize$\\pm$1.0} & {77.2\\scriptsize$\\pm$1.9} & 78.5 & 12.5 & 0.43 \\\\\nLookbehind-SAM & {98.7\\scriptsize$\\pm$0.6} & {65.1\\scriptsize$\\pm$1.1} & {73.1\\scriptsize$\\pm$0.4} & {78.7\\scriptsize$\\pm$0.9} & 78.9 & 12.4 & 0.50 \\\\\nFAD & {99.1\\scriptsize$\\pm$0.5} & {66.8\\scriptsize$\\pm$0.9} & {73.6\\scriptsize$\\pm$1.0} & {76.1\\scriptsize$\\pm$1.3} & 78.9 & 12.1 & 0.38 \\\\\nGSAM\\(^\\dag\\) & {98.7\\scriptsize$\\pm$0.3} & {64.9\\scriptsize$\\pm$0.2} & {74.3\\scriptsize$\\pm$0.0} & {78.5\\scriptsize$\\pm$0.8} & 79.1 & 12.3 & 0.22 \\\\\nSAM\\(^\\dag\\) & {99.1\\scriptsize$\\pm$0.2} & {65.0\\scriptsize$\\pm$1.0} & {73.7\\scriptsize$\\pm$1.0} & {79.8\\scriptsize$\\pm$0.1} & 79.4 & 12.5 & 0.22 \\\\\nDISAM & {99.3\\scriptsize$\\pm$0.0} & {66.3\\scriptsize$\\pm$0.5} & {81.0\\scriptsize$\\pm$0.1} & {73.2\\scriptsize$\\pm$0.1} & 79.9 & 12.3 & 0.33 \\\\\nSAGM & {99.0\\scriptsize$\\pm$0.2} & {65.2\\scriptsize$\\pm$0.4} & {75.1\\scriptsize$\\pm$0.3} & {80.7\\scriptsize$\\pm$0.8} & 80.0 & 12.3 & 0.22 \\\\\nDGSAM + SWAD & {99.3\\scriptsize$\\pm$0.7} & {67.2\\scriptsize$\\pm$0.3} & {77.7\\scriptsize$\\pm$0.6} & {79.2\\scriptsize$\\pm$0.5} & 80.9 & 11.6 & 0.17 \\\\\nDGSAM (Ours) & {99.0\\scriptsize$\\pm$0.5} & {67.0\\scriptsize$\\pm$0.5} & {77.9\\scriptsize$\\pm$0.5} & {81.8\\scriptsize$\\pm$0.4} & 81.4 & 11.5 & 0.17 \\\\\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization", "authors": ["Youngjun Song", "Youngsik Hwang", "Jonghun Lee", "Heechang Lee", "Dong-Young Lim"], "url": "https://arxiv.org/abs/2503.23430v2", "attribution": "\"DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization\" by Youngjun Song, Youngsik Hwang, Jonghun Lee, Heechang Lee, and Dong-Young Lim, arXiv:2503.23430v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.11606v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Augmented dataset single channel model hyperparameters.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n\\toprule\n\\textbf{Hyperparameter} & \\textbf{CinC PCG Model} \\\\\n\\hline\n\\midrule\nLearning rate & 0.001 \\\\\nWeight decay & 4.11e-5 \\\\\nMomentum & 0.57562 \\\\\nGamma & 0.167 \\\\\nStep size & 2 \\\\\nBatch size & 32 \\\\\nNumber of hidden layers & 3 \\\\\nHidden layer size & 512 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Scaling to Multimodal and Multichannel Heart Sound Classification: Fine-Tuning Wav2Vec 2.0 with Synthetic and Augmented Biosignals", "authors": ["Milan Marocchi", "Matthew Fynn", "Kayapanda Mandana", "Yue Rong"], "url": "https://arxiv.org/abs/2509.11606v1", "attribution": "\"Scaling to Multimodal and Multichannel Heart Sound Classification: Fine-Tuning Wav2Vec 2.0 with Synthetic and Augmented Biosignals\" by Milan Marocchi, Matthew Fynn, Kayapanda Mandana, and Yue Rong, arXiv:2509.11606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.05676v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc} \n\\Xhline{1.2pt}\n & \\multicolumn{3}{c}{Pure Endowment} \\\\ \n\\hline\n & No Hedging & Static Hedging & Continuos Hedging \\\\ \n\\hline\nMean & $5.842$ & $2.186$ & $-0.0016$ \\\\\nSt. Dev. & $2.84$ & $1.807$ & $0.1184$ \\\\ \n\\Xhline{1.2pt}\n & \\multicolumn{3}{c}{Term Insurance} \\\\ \n\\hline\n & No Hedging & Static Hedging & Continuos Hedging \\\\ \n\\hline\nMean & $1.863$ & $0.503$ & $-0.0004$\\\\\nSt. Dev. & $0.184$ & $0.474$ & $0.121$ \\\\ \n\\Xhline{1.2pt}\n & \\multicolumn{3}{c}{Endowment Insurance} \\\\ \n\\hline\n & No Hedging & Static Hedging & Continuos Hedging \\\\ \n\\hline\nMean & $7.721$ & $2.708$ & $0.0006$ \\\\\nSt. Dev. & $2.846$ & $1.727$ & $0.012$ \\\\\n\\Xhline{1.2pt}\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean and standard deviation of the distribution of the hedging cost for a portfolio of 1000 pure endowment policies (top panel), term insurance policies (middle panel), and endowment insurance policies (bottom panel) under no hedging, static hedging, and continuous hedging.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Design and hedging of unit linked life insurance with environmental factors", "authors": ["Katia Colaneri", "Alessandra Cretarola", "Edoardo Lombardo", "Daniele Mancinelli"], "url": "https://arxiv.org/abs/2509.05676v1", "attribution": "\"Design and hedging of unit linked life insurance with environmental factors\" by Katia Colaneri, Alessandra Cretarola, Edoardo Lombardo, and Daniele Mancinelli, arXiv:2509.05676v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10459v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Decision Tree Regression Hyperparameter Selection (Reduction=5)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|}\n\\hline\nHyperparameter & Datatype & Values/{[}Min, Max{]} & Log Scaling & Gridpoint Count & Best Value \\\\ \\hline\nMaximum Tree Depth & Integer & {[}2, 10{]} & FALSE & 5 & 7 \\\\ \\hline\nSplitting Strategy & Categorical & {[}'best', 'random'{]} & FALSE & N/A & best \\\\ \\hline\nSplit Quality Criterion & Categorical & {[}'squared\\_error', 'friedman\\_mse', 'absolute\\_error'{]} & FALSE & N/A & absolute\\_error \\\\ \\hline\nMin. Samples to Split Internal Node & Integer & {[}2, 20{]} & TRUE & 3 & 5 \\\\ \\hline\nMin. Samples to Split Leaf Node & Integer & {[}1, 20{]} & TRUE & 3 & 11 \\\\ \\hline\nFeatures Considered During Split & Integer & {[}1, 39{]} & TRUE & 3 & 32 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames", "authors": ["Lyle Regenwetter", "Colin Weaver", "Faez Ahmed"], "url": "https://arxiv.org/abs/2201.10459v3", "attribution": "\"FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames\" by Lyle Regenwetter, Colin Weaver, and Faez Ahmed, arXiv:2201.10459v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23340v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{Cardinality \\(m\\)} \n & \\textbf{Subset \\(S_{m,1}\\)} \n & \\textbf{Subset \\(S_{m,2}\\)} \n & \\textbf{Subset \\(S_{m,3}\\)} \n & \\textbf{\\(H(\\otimes_{i=1}^3 P^{(S_{m,i})})\\)} \\\\\n\\hline\n1 & \\(\\emptyset\\) & \\(\\emptyset\\) & \\(\\{10\\}\\) & 0.46094 \\\\\n2 & \\(\\emptyset\\) & \\(\\{7\\}\\) & \\(\\{10\\}\\) & 0.90046 \\\\\n3 & \\(\\emptyset\\) & \\(\\{7\\}\\) & \\(\\{8,\\,9\\}\\) & 1.26966 \\\\\n4 & \\(\\{4\\}\\) & \\(\\{7\\}\\) & \\(\\{8,\\,9\\}\\) & 1.70072 \\\\\n5 & \\(\\{4\\}\\) & \\(\\{5,\\,7\\}\\) & \\(\\{8,\\,9\\}\\) & 2.08692 \\\\\n6 & \\(\\{4\\}\\) & \\(\\{5,\\,6,\\,7\\}\\) & \\(\\{8,\\,9\\}\\) & 2.43035 \\\\\n7 & \\(\\{4\\}\\) & \\(\\{5,\\,6,\\,7\\}\\) & \\(\\{8,\\,9,\\,10\\}\\) & 2.71405 \\\\\n8 & \\(\\{3,\\,4\\}\\) & \\(\\{5,\\,6,\\,7\\}\\) & \\(\\{8,\\,9,\\,10\\}\\) & 3.10451 \\\\\n9 & \\(\\{1,\\,2,\\,4\\}\\) & \\(\\{5,\\,6,\\,7\\}\\) & \\(\\{8,\\,9,\\,10\\}\\) & 3.46267 \\\\\n10 & \\(\\{1,\\,2,\\,3,\\,4\\}\\) & \\(\\{5,\\,6,\\,7\\}\\) & \\(\\{8,\\,9,\\,10\\}\\) & 3.78968 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Performance evaluation of the generalized distorted greedy algorithm. Entropy rate of the full chain of the Bernoulli-Laplace level model is $H(P) = 1.96068$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Information-theoretic subset selection of multivariate Markov chains via submodular optimization", "authors": ["Zheyuan Lai", "Michael C. H. Choi"], "url": "https://arxiv.org/abs/2503.23340v1", "attribution": "\"Information-theoretic subset selection of multivariate Markov chains via submodular optimization\" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.12771v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\hline\n\\textbf{Method} & \\textbf{Architecture} \\\\ \\hline\nMLP & FC(32)-FC(64)-FC(64)-FC(128)-FC(1) \\\\ \nCNN & CONV(32)-CONV(64)-CONV(64)-CONV(128)-FC(1) \\\\ \nResNet & CONV(64)- Resblock(CONV(64)-CONV(64)*6-FC(1) \\\\ \nRNN & RNN(32)-RNN(32)-FC(1) \\\\ \nGRU & GRU(32)-GRU(32)-FC(1) \\\\ \nLSTM & LSTM(32)-LSTM(32)-FC(1) \\\\ \nAutoencoder & CONV(64)-CONV(128)-CONV(256)-FC(256)-CONV(256)-CONV(128)-CONV(64)-FC(1)\\\\ \nTime-CNN & CONV(6)-CONV(12)-FC(1) \\\\ \nMulti-Channel-CNN & (concatenate CNN(32), CNN(64), CNN(128)-FC(64)-FC(1) \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks", "authors": ["Yu Zhang", "Zelin Wu", "Claudio Tessone"], "url": "https://arxiv.org/abs/2504.12771v1", "attribution": "\"Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks\" by Yu Zhang, Zelin Wu, and Claudio Tessone, arXiv:2504.12771v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.00760v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc}\n& \\textbf{Pred: False} & \\textbf{Pred: True} \\\\\n\\hline\n\\textbf{Label: False} & 502 & 197 \\\\\n\\textbf{Label: True} & 180 & 500 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Hallucination Removal (our) dialogue refinement feedback detection confusion matrix.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment", "authors": ["Firdavs Nasriddinov", "Rafal Kocielnik", "Arushi Gupta", "Cherine Yang", "Elyssa Wong", "Anima Anandkumar", "Andrew Hung"], "url": "https://arxiv.org/abs/2412.00760v1", "attribution": "\"Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment\" by Firdavs Nasriddinov, Rafal Kocielnik, Arushi Gupta, Cherine Yang, Elyssa Wong, Anima Anandkumar, and Andrew Hung, arXiv:2412.00760v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2401.00395v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Standardized RMSPE of the Borehole Example. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|} \n\\hline\nType of mean & EVIGP & mlegp \\\\\\hline\nConstant & 0.01151 & 0.3354 \\\\ \nLinear & 0.04191 & 0.1810 \\\\ \nQuadratic & 0.01212 & 0.2019\\\\\nQuadratic, after selection & 0.01019 & N/A\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Energetic Variational Gaussian Process Regression for Computer Experiments", "authors": ["Lulu Kang", "Yuanxing Cheng", "Yiwei Wang", "Chun Liu"], "url": "https://arxiv.org/abs/2401.00395v2", "attribution": "\"Energetic Variational Gaussian Process Regression for Computer Experiments\" by Lulu Kang, Yuanxing Cheng, Yiwei Wang, and Chun Liu, arXiv:2401.00395v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.21174v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|lll}\n\\# & $\\tau_{large}$ &$\\tau_{mix}$ & $\\tau_{small}$ \\\\\\hline\n1 & 36,413 & 1,642 & 73,858 \\\\\n2 & 142,321 &1,590 & $\\S$ \\\\\n3 & 162,901 &2,061 & 1,272,471 \\\\\n4 & 45,250 & 27,759& 50,769 \\\\\n5 & 881,668 & 94,735 & 1,218,169 \\\\\n6 & 183 & 87 & 185 \\\\\n7 &4,681 & 2,276 & 8,145 \\\\\n8 &6,045 & 1,952& 24,301 \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Number of nonzeros of the new scheme (symmetric case) with $M$-orthogonalization}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust iterative methods for linear systems with saddle point structure", "authors": ["Murat Manguoğlu", "Volker Mehrmann"], "url": "https://arxiv.org/abs/2502.21174v1", "attribution": "\"Robust iterative methods for linear systems with saddle point structure\" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.10761v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrlllll}\n \\hline\n & N & Without $\\tau$ & $\\tau=0.1$ & $\\tau=0.01$ & $\\tau=0.0001$ \\\\\n \\hline\n At DGP (0) & 100 & -0.0023(8e-05) & -0.0042(0.00436) & -0.0038(0.00941) & -0.0026(0.00044) \\\\\n Not at DGP (0) & 100 & 0.0093(0.00021) & 0.0183(0.00278) & 0.0163(0.00938) & 0.012(0.00324) \\\\\n At DGP (0) & 1000 & -0.0006(8e-05) & -0.0011(0.00116) & -0.0019(0.00794) & -0.0009(0.00113) \\\\\n Not at DGP (0) & 1000 & 0.0289(0.00027) & 0.057(0.0051) & 0.0521(0.01858) & 0.0394(0.01097) \\\\\n At DGP (1) & 100 & 0.0005(2e-05) & 1e-04(0.00179) & 0.0013(0.00679) & 0.0005(0.00033) \\\\\n Not at DGP (1) & 100 & -0.0011(0.00017) & -0.0024(0.00125) & -0.0028(0.00369) & -0.0022(0.00426) \\\\\n At DGP (1) & 1000 & -0.0002(2e-05) & -0.0003(0.00054) & -0.0005(0.00223) & -0.0003(0.00055) \\\\\n Not at DGP (1) & 1000 & -0.0188(9e-05) & -0.0187(0.00179) & -0.0197(0.01062) & -0.0204(0.00806) \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Gradients estimates for $\\beta_{\\lambda}$ in different scenarios. $(0)$ refers to the first component. $(1)$ refers to the second component.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Simulation Based Composite Likelihood", "authors": ["Lorenzo Rimella", "Chris Jewell", "Paul Fearnhead"], "url": "https://arxiv.org/abs/2310.10761v2", "attribution": "\"Simulation Based Composite Likelihood\" by Lorenzo Rimella, Chris Jewell, and Paul Fearnhead, arXiv:2310.10761v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.01467v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Network Determinants of Industry Variance (TFP growth shocks)}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n \\multicolumn{7}{c}{Panel A: Market Return Variance} \\\\\n \\midrule\n & (1) & (2) & (3) & (4) & (5) & (6) \\\\\n \\midrule\n Self-origin (demand) & 0.365*** & 0.246*** & & & 0.217*** & 0.119*** \\\\\n & (0.038) & (0.039) & & & (0.021) & (0.022) \\\\\n Across (demand) & 0.100*** & 0.084*** & & & 0.085*** & 0.119*** \\\\\n & (0.020) & (0.018) & & & (0.020) & (0.019) \\\\\n Between (demand) & 0.043** & 0.010 & & & 0.091*** & -0.076*** \\\\\n & (0.018) & (0.022) & & & (0.019) & (0.020) \\\\\n Self-origin (supply) & & & 0.039* & 0.149*** & 0.217*** & 0.119*** \\\\\n & & & (0.029) & (0.038) & (0.021) & (0.022) \\\\\n Across (supply) & & & 0.119*** & 0.147*** & 0.270*** & 0.170*** \\\\\n & & & (0.021) & (0.026) & (0.027) & (0.037) \\\\\n Between (supply) & & & 0.085*** & 0.042** & 0.086*** & 0.179*** \\\\\n & & & (0.019) & (0.019) & (0.025) & (0.034) \\\\\n Size & & -0.202*** & & -0.325*** & & -0.183*** \\\\\n & & (0.030) & & (0.023) & & (0.034) \\\\\n Upstream centrality & & -0.145** & & 0.129** & & -0.238*** \\\\\n & & (0.062) & & (0.051) & & (0.066) \\\\\n Downstream centrality & & 1.956*** & & 0.296** & & 2.137*** \\\\\n & & (0.145) & & (0.125) & & (0.165) \\\\\n Durability & & 1.088*** & & 0.389*** & & 1.164*** \\\\\n & & (0.153) & & (0.108) & & (0.156) \\\\\n Vertical position & & 3.890*** & & 0.243*** & & 4.063*** \\\\\n & & (0.116) & & (0.083) & & (0.134) \\\\\n Constant & -2.123 & -0.634 & -2.821 & -0.096 & -1.473 & -0.693 \\\\\n Obs & 2359 & 1626 & 2861 & 2138 & 2114 & 1471 \\\\\n Adj $R^2$ & 0.225 & 0.607 & 0.083 & 0.191 & 0.288 & 0.633 \\\\\n \\midrule\n \\multicolumn{7}{c}{Panel B: Cash Flow Variance} \\\\\n \\midrule\n & (1) & (2) & (3) & (4) & (5) & (6) \\\\\n \\midrule\n Self-origin (demand) & 0.363*** & 0.009 & & & 0.190*** & 0.017 \\\\\n & (0.072) & (0.093) & & & (0.037) & (0.049) \\\\\n Across (demand) & 0.038 & -0.050* & & & 0.011 & 0.099** \\\\\n & (0.039) & (0.039) & & & (0.040) & (0.042) \\\\\n Between (demand) & 0.136*** & 0.128*** & & & 0.156*** & 0.184*** \\\\\n & (0.037) & (0.042) & & & (0.039) & (0.047) \\\\\n Self-origin (supply) & & & 0.026 & 0.286*** & 0.190*** & 0.017 \\\\\n & & & (0.065) & (0.076) & (0.037) & (0.049) \\\\\n Across (supply) & & & 0.250*** & 0.384*** & 0.309*** & 0.107* \\\\\n & & & (0.051) & (0.062) & (0.061) & (0.091) \\\\\n Between (supply) & & & 0.093** & 0.169*** & 0.202*** & 0.162** \\\\\n & & & (0.044) & (0.055) & (0.057) & (0.086) \\\\\n Size & & -0.161** & & -0.333*** & & -0.155** \\\\\n & & (0.064) & & (0.052) & & (0.075) \\\\\n Upstream centrality & & -0.069 & & 0.562*** & & -0.100 \\\\\n & & (0.150) & & (0.132) & & (0.162) \\\\\n Downstream centrality & & 2.193*** & & -0.070 & & 2.315*** \\\\\n & & (0.343) & & (0.305) & & (0.379) \\\\\n Durability & & 2.204*** & & 1.172*** & & 2.307*** \\\\\n & & (0.272) & & (0.182) & & (0.285) \\\\\n Vertical position & & 3.502*** & & 1.142* & & 3.590*** \\\\\n & & (0.268) & & (0.181) & & (0.320) \\\\\n Constant & -4.201 & -0.711 & -5.246 & 0.023 & -4.123 & -0.75 \\\\\n Obs & 2359 & 1626 & 2861 & 2138 & 2114 & 1471 \\\\\n Adj $R^2$ & 0.074 & 0.248 & 0.017 & 0.08 & 0.087 & 0.271 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Risk in Network Economies", "authors": ["Victor Sellemi"], "url": "https://arxiv.org/abs/2208.01467v1", "attribution": "\"Risk in Network Economies\" by Victor Sellemi, arXiv:2208.01467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02875v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of WSD gold standard dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\t\t\t\\hline\n\t\t\tCriteria\t\t&SE2\t&SE3\t&SE07\t&SE13\t&SE15\\\\\n\t\t\t\\hline\n\t\t\t\\#Doc\t\t\t&\t3\t&\t3\t&\t3\t&\t13\t&\t4\t\\\\\n\t\t\t\\#Sent*\t\t\t&\t242\t&\t297\t&\t120\t&\t301\t&\t133\t\\\\\n\t\t\t\\#Terms\t\t\t&2282\t&1850\t&\t455\t&\t1644&\t1022\\\\\n\t\t\tAvgSentSize\t\t&\t9\t&\t6\t&\t3\t&\t5\t&\t7\t\\\\\n\t\t\tSingle sense\t&\t442\t&311\t&\t26\t&\t348\t&189\\\\\n\t\t\tAmbiguity rate\t&81\\%\t&83\\%\t&94\\%\t&79\\%\t&82\\% \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph", "authors": ["Mohannad AlMousa", "Rachid Benlamri", "Richard Khoury"], "url": "https://arxiv.org/abs/2101.02875v1", "attribution": "\"A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph\" by Mohannad AlMousa, Rachid Benlamri, and Richard Khoury, arXiv:2101.02875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.13777v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{hhline}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l||c|c|c|c|c|c|c|c|c|}\n\\hline\n & $h=10$ & $h=20$ & $h=30$ & $h=50$ & $h=100$ & $h=200$ & $h=300$ & $h=500$ & $h=1000$ \\\\\n \\hhline{|=#=|=|=|=|=|=|=|=|=|}\n LILAK & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & 0.42 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\\\\n \\hline\n QRTEA & 1.00 & \\cellcolor[rgb]{ .776, .878, .706}0.01 & \\cellcolor[rgb]{ .776, .878, .706}0.01 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & 0.08 & 0.77 & 0.54 \\\\\n \\hline\n XRAY & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .776, .878, .706}0.01 & 0.10 & 0.23 & 0.48 & 1.00 & 0.93 \\\\\n \\hline\n CHTR & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .776, .878, .706}0.04 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & 0.42 & 1.00 & 1.00 & 1.00 & 0.97 \\\\\n \\hline\n PCAR & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & 1.00 & 1.00 & 0.60 & 1.00 & 1.00 \\\\\n \\hline\n EXC & 0.26 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & 0.51 & 1.00 \\\\\n \\hline\n AAL & 0.11 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & 0.71 & 1.00 & 1.00 & 1.00 \\\\\n \\hline\n WBA & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .776, .878, .706}0.02 & 0.27 & 0.13 & \\cellcolor[rgb]{ .776, .878, .706}0.01 & 1.00 \\\\\n \\hline\n ATVI & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & 0.78 & 1.00 & 1.00 & 1.00 \\\\\n \\hline\n AAPL & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & \\cellcolor[rgb]{ .663, .816, .557}0.00 & 0.23 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{MCS p-values of the \\textit{unpredictive benchmark} model for the 10 tickers and 9 horizons under consideration. When the p-value is low at least one of the order book-driven models statistically outperforms the \\textit{unpredictive benchmark}, i.e.\\ there is order book-driven predictability according to Definition .}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective", "authors": ["Lorenzo Lucchese", "Mikko Pakkanen", "Almut Veraart"], "url": "https://arxiv.org/abs/2211.13777v3", "attribution": "\"The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective\" by Lorenzo Lucchese, Mikko Pakkanen, and Almut Veraart, arXiv:2211.13777v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03326v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{F1-scores for different models explained in Sec.~. We train each model 3 times. Note that U-Net* stands for the modified U-Net.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc|c}\n \\hline\n Model Name & Run1 & Run2 & Run3 & Average \\\\ \\hline\n U-Net & 0.683 & 0.672 & 0.669 & 0.675 \\\\\n U-Net + BN & 0.906 & 0.903 & 0.904 & 0.904 \\\\\n U-Net* & 0.860 & 0.847 & 0.852 & 0.853 \\\\\n U-Net* + BN & 0.908 & 0.905 & 0.902 & 0.905 \\\\\n U-Net* + Res1 & 0.910 & 0.906 & 0.901 & 0.906 \\\\\n U-Net* + Res2 & 0.908 & 0.911 & 0.909 & 0.910 \\\\\n U-Net* + SE-Res1 & 0.911 & 0.908 & 0.909 & 0.909 \\\\\n U-Net* + SE-Res2 & 0.912 & 0.911 & 0.911 & \\textbf{0.911} \\\\\n SegNet & 0.907 & 0.910 & 0.909 & 0.909 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Pushing the Envelope of Thin Crack Detection", "authors": ["Liang Xu", "Taro Hatsutani", "Xing Liu", "Engkarat Techapanurak", "Han Zou", "Takayuki Okatani"], "url": "https://arxiv.org/abs/2101.03326v1", "attribution": "\"Pushing the Envelope of Thin Crack Detection\" by Liang Xu, Taro Hatsutani, Xing Liu, Engkarat Techapanurak, Han Zou, and Takayuki Okatani, arXiv:2101.03326v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.17839v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of the computation times in seconds of all iterations for the ARSO and ARO formulation, and BoU values}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{clrrrrr}\n\\hline\n\\textbf{Problem} & \\textbf{Formulation} & \\textbf{Min} & \\textbf{Max} & \\textbf{Average} & \\textbf{First} & \\textbf{Last} \\\\ \\hline\nMP & $ARSO_{\\beta=0}$ & 2.40 & 7.49 & 4.30 & 2.76 & 5.17 \\\\\n & $ARSO_{\\beta=10}$ & 2.89 & 7.40 & 4.49 & 2.89 & 6.57 \\\\\n & $ARSO_{\\beta=20}$ & 2.57 & 7.33 & 4.42 & 2.78 & 6.84 \\\\\n & $ARO_{\\beta=0}$ & 0.72 & 1.16 & 1.00 & 0.82 & 1.11 \\\\\n & $ARO_{\\beta=10}$ & 0.76 & 1.20 & 1.03 & 0.78 & 1.15 \\\\\n & $ARO_{\\beta=20}$ & 0.76 & 1.21 & 1.00 & 0.76 & 1.07 \\\\ \\hline\nSP & $ARSO_{\\beta=0}$ & 2.68 & 3.34 & 2.85 & 3.26 & 2.74 \\\\\n & $ARSO_{\\beta=10}$ & 52.8 & 71.90 & 59.60 & 58.60 & 56.20 \\\\\n & $ARSO_{\\beta=20}$ & 55.4 & 79.70 & 63.76 & 62.70 & 57.90 \\\\\n & $ARO_{\\beta=0}$ & 0.89 & 1.30 & 1.07 & 0.91 & 1.07 \\\\\n & $ARO_{\\beta=10}$ & 3.90 & 5.80 & 4.63 & 5.78 & 5.80 \\\\\n & $ARO_{\\beta=20}$ & 5.30 & 32.40 & 12.80 & 15.10 & 31.60 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Adaptive Robust Optimization Models for DER Planning in Distribution Networks under Long- and Short-Term Uncertainties", "authors": ["Fernando García-Muñoz", "Cristian Duran-Mateluna"], "url": "https://arxiv.org/abs/2503.17839v1", "attribution": "\"Adaptive Robust Optimization Models for DER Planning in Distribution Networks under Long- and Short-Term Uncertainties\" by Fernando García-Muñoz and Cristian Duran-Mateluna, arXiv:2503.17839v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10266v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of optimal, sub-optimal, and incorrect plans expressed as a fraction of the total number of plans. Reasoning with the learned axioms improves performance.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n \\textbf{Plans} & \\textbf{Without} & \\textbf{With}\\\\\n \\hline\n Optimal & 0.15 & 0.49 \\\\\n \\hline\n Sub-optimal & 0.31 & 0.51 \\\\\n \\hline\n Incorrect & 0.54 & 0 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Combining Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning in Robotics", "authors": ["Mohan Sridharan", "Tiago Mota"], "url": "https://arxiv.org/abs/2201.10266v1", "attribution": "\"Combining Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning in Robotics\" by Mohan Sridharan and Tiago Mota, arXiv:2201.10266v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.01846v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrr}\n\\toprule\n{} & maize & pork & poultry & eggs & maize g. oil & beer & alc. beverages & sweeteners \\\\\n\\midrule\nAfrica N & 17.1 & 0.0 & 0.2 & 0.1 & 0.1 & 0.0 & 0.1 & 0.1 \\\\\nAfrica S & 1.9 & 0.0 & 0.1 & 0.2 & 0.0 & 0.0 & 0.1 & 0.1 \\\\\nAmerica S & 1.7 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.1 \\\\\nAmerica N & 0.1 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.1 & 0.1 \\\\\nAsia C & 2.2 & 0.0 & 7.2 & 0.0 & 1.4 & 0.4 & 1.5 & 28.3 \\\\\nAsia E & 1.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\\\\nAsia S & 5.9 & 0.0 & 0.0 & 0.0 & 1.2 & 0.0 & 0.0 & 0.0 \\\\\nAsia SE & 2.6 & 0.0 & 0.0 & 0.0 & 0.1 & 0.0 & 0.0 & 0.0 \\\\\nAsia W & 22.2 & 0.0 & 0.5 & 1.6 & 1.1 & 0.1 & 0.5 & 1.2 \\\\\nAustralia & 9.3 & 0.0 & 0.0 & 0.0 & 0.1 & 0.0 & 0.2 & 0.1 \\\\\nEurope E & 5.4 & 0.1 & 0.6 & 0.1 & 4.6 & 1.3 & 2.8 & 1.7 \\\\\nEurope N & 39.1 & 0.0 & 0.0 & 0.0 & 0.0 & 0.1 & 0.1 & 0.3 \\\\\nEurpe S & 30.1 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.1 & 0.0 \\\\\nEurope W & 15.7 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.1 & 0.1 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Relative loss in \\% of different food products in different world regions after a shock to the respective product. These numbers correspond to the right half of each cell in the upper part of fig. 5 in the main text.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability", "authors": ["Moritz Laber", "Peter Klimek", "Martin Bruckner", "Liuhuaying Yang", "Stefan Thurner"], "url": "https://arxiv.org/abs/2210.01846v3", "attribution": "\"Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability\" by Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang, and Stefan Thurner, arXiv:2210.01846v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19718v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|ccccc|} \n \\hline\n Dataset & \\# Graphs & Avg. \\# Nodes & Avg. \\# Edges & \\# Classes \\\\ \n \\hline\\hline\n HIV & 41127 & 25.5 & 27.5 & 2 \\\\\n \\hline\n\\end{tabular}\n\\caption{The properties of HIV dastaset from MoleculeNet benchmark.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Python library for efficient computation of molecular fingerprints", "authors": ["Michał Szafarczyk", "Piotr Ludynia", "Przemysław Kukla"], "url": "https://arxiv.org/abs/2403.19718v1", "attribution": "\"A Python library for efficient computation of molecular fingerprints\" by Michał Szafarczyk, Piotr Ludynia, and Przemysław Kukla, arXiv:2403.19718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.07301v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c||c|c||c|c}\\toprule\n\t\t\t$\\Delta t$ & $10^{-6}$ &\n\t\t\t$T$ & $3\\cdot10^4$ &\n\t\t\t$T_{\\mathrm{trans}}$ & $2\\cdot10^3$ \n\t\t\t\\\\\n\t\t\t\\midrule\n\t\t\t$\\beta$ & $0.1$ & \n\t\t\t$\\nu_0$ & $10^{-1}$ & \n\t\t\t$ $ & \\\\\\bottomrule\n\t\t\\end{tabular}\n\\caption{Simulation parameters and hyperparameters. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Reconstructing the system coefficients for coupled harmonic oscillators", "authors": ["Jan Bartsch", "Ahmed A. Barakat", "Simon Buchwald", "Gabriele Ciaramella", "Stefan Volkwein", "Eva M. Weig"], "url": "https://arxiv.org/abs/2412.07301v1", "attribution": "\"Reconstructing the system coefficients for coupled harmonic oscillators\" by Jan Bartsch, Ahmed A. Barakat, Simon Buchwald, Gabriele Ciaramella, Stefan Volkwein, and Eva M. Weig, arXiv:2412.07301v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.04414v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The lowest average out-degree when there is no critical nodes or edges found in the network.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\n\t\t\\hline\n\t\t&ER&SW&SF&QS&QR&RT&RR&HO&OL \\\\ \\hline\n\t\tNode&9&3& 23&6&4&8&6&3&22 \\\\ \\hline\n\t\tEdge&10&3&24&5&6&8&6&3&22 \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Framework of Hierarchical Attacks to Network Controllability", "authors": ["Yang Lou", "Lin Wang", "Guanrong Chen"], "url": "https://arxiv.org/abs/2008.04414v1", "attribution": "\"A Framework of Hierarchical Attacks to Network Controllability\" by Yang Lou, Lin Wang, and Guanrong Chen, arXiv:2008.04414v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10231v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n \\toprule\n \\textbf{Release Cycle} & \\textbf{4.2.0} & \\textbf{4.4.0} \\\\\n \\midrule\n Performance improvements & 21 & 40 \\\\\n Percentage of tickets that are improvements & 7.69\\% & 10.18\\% \\\\\n Days per performance improvement & 19.62 & 8.80 \\\\\n Performance regressions & 15 & 13 \\\\\n Percentage of tickets that are regressions & 5.49\\% & 3.31\\% \\\\\n Days per performance regression & 27.47 & 27.08 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Creating a Virtuous Cycle in Performance Testing at MongoDB", "authors": ["David Daly"], "url": "https://arxiv.org/abs/2101.10231v2", "attribution": "\"Creating a Virtuous Cycle in Performance Testing at MongoDB\" by David Daly, arXiv:2101.10231v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10242v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{WIN / DRAW / LOSE}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\t\t\\hline\n\t\t\\multirow{2}{*}{Dataset} & \\multirow{2}{*}{Method} & \\multicolumn{2}{c}{Win/Draw/Lose}\\\\\n\t\t\\cline{3-4} & ~ & Symmetric & \tAsymmetric\\\\\n\t\t\\hline\n\t\t\\multirow{5}{*}{Synth1} & Ada & \t0/0/6 & \t0/0/6\\\\\n\t\t~ & rNDA & \t0/0/6 & \t0/0/6\\\\\n\t\t~ & GMDA & \t6/0/0 & \t6/0/0\\\\\n\t\t~ & rmLR & \t0/0/6 & \t0/0/6\\\\\n\t\t~ & rLR & \t1/0/5 & \t0/0/6\\\\\n\t\t\\hline\n\t\t\\multirow{3}{*}{Synth2} & Ada & \t0/0/6 & \t0/0/6\\\\\n\t\t~ & rNDA & \t0/0/6 &\t1/1/4\\\\\n\t\t~ & GMDA & \t5/0/1 &\t4/1/1\\\\\n\t\t\\hline\n\t\t\\multirow{3}{*}{Breast Issue} & Ada & \t0/0/6 &\t0/0/6\\\\\n\t\t~ & rNDA & \t1/1/4 & \t2/0/4\\\\\n\t\t~ & GMDA & \t4/1/1 &\t4/0/2\\\\\n\t\t\\hline\n\t\t\\multirow{3}{*}{Iris} & Ada & \t0/0/6 & \t0/0/5\\\\\n\t\t~ & rNDA & \t0/3/3 &\t0/4/1\\\\\n\t\t~ & GMDA & \t3/3/0 & \t1/4/0\\\\\n\t\t\\hline\n\t\t\\multirow{5}{*}{Wine} & Ada & \t0/0/6 &\t0/0/5\\\\\n\t\t~ & rNDA & \t1/2/3 &\t0/2/3\\\\\n\t\t~ & GMDA &\t\t3/2/1 & \t3/2/0\\\\\n\t\t~ & rmLR & \t0/0/6 & \t0/0/5\\\\\n\t\t~ & rLR & \t0/0/6 &\t0/0/5\\\\\n\t\t\\hline\n\t\t\\multirow{5}{*}{Heart} & Ada & \t0/1/5 & \t0/0/5\\\\\n\t\t~ & rNDA & \t0/0/6 & \t0/0/5\\\\\n\t\t~ & GMDA & \t5/1/0 & \t5/0/0\\\\\n\t\t~ & rmLR & \t1/1/4 &\t1/2/3\\\\\n\t\t~ & rLR & \t1/1/4 &\t1/2/3\\\\\n\t\t\\hline\n\t\t\\multirow{3}{*}{Boston} & Ada & \t0/1/5 &\t0/0/6\\\\\n\t\t~ & rNDA & \t0/0/6 &\t0/0/6\\\\\n\t\t~ & GMDA & \t2/1/3 & \t2/0/4\\\\\n\t\t\\hline\n\t\t\\multirow{3}{*}{Wave form} & Ada & \t4/0/2 & \t2/0/3\\\\\n\t\t~ & rNDA &\t 0/0/6 & \t0/0/5\\\\\n\t\t~ & GMDA & \t2/0/4 & \t3/0/2\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "GMM Discriminant Analysis with Noisy Label for Each Class", "authors": ["Jian-wei Liu", "Zheng-ping Ren", "Run-kun Lu", "Xiong-lin Luo"], "url": "https://arxiv.org/abs/2201.10242v1", "attribution": "\"GMM Discriminant Analysis with Noisy Label for Each Class\" by Jian-wei Liu, Zheng-ping Ren, Run-kun Lu, and Xiong-lin Luo, arXiv:2201.10242v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00644v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Optimal AMMs for Trading Pairs with Various LST Types Based on Capital Efficiency. Concentrated Liquidity Market Maker (CLMM)~ Requires Periodic Rebalancing~.}\n\\begin{tabular}{|l|l|l|}\n\\hline\n\\textbf{Token 1} & \\textbf{Token 2} & \\textbf{AMMs} \\\\ \\hline\nrebase-LST & ETH & Stableswap~ \\\\ \nrebase-LST & rebase-LST & Stableswap \\\\ \nreward-LST & ETH & Cryptoswap~, CLMM(*) \\\\ \nreward-LST & reward-LST & Cryptoswap, CLMM(*) \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "SoK: Liquid Staking Tokens (LSTs) and Emerging Trends in Restaking", "authors": ["Krzysztof Gogol", "Yaron Velner", "Benjamin Kraner", "Claudio Tessone"], "url": "https://arxiv.org/abs/2404.00644v3", "attribution": "\"SoK: Liquid Staking Tokens (LSTs) and Emerging Trends in Restaking\" by Krzysztof Gogol, Yaron Velner, Benjamin Kraner, and Claudio Tessone, arXiv:2404.00644v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.00052v3_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|}\n\\hline\nImage Size & Encoder & \\textit{Recall} & Accuracy & IOU \\\\ \\hline\n100 x 100 & Resnet 50 & 0.96 & 0.98 & 0.95 \\\\ \\hline\n150 x 150 & Resnet 50 & 0.96 & 0.98 & 0.95 \\\\ \\hline\n224 x 224 & Resnet 32 & 0.96 & 0.98 & 0.95 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Face Mask Fit Analyser Model Results. IOU refers to the Intersection Over Union (IOU) score.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "(Un)Masked COVID-19 Trends from Social Media", "authors": ["Asmit Kumar Singh", "Paras Mehan", "Divyanshu Sharma", "Rohan Pandey", "Tavpritesh Sethi", "Ponnurangam Kumaraguru"], "url": "https://arxiv.org/abs/2011.00052v3", "attribution": "\"(Un)Masked COVID-19 Trends from Social Media\" by Asmit Kumar Singh, Paras Mehan, Divyanshu Sharma, Rohan Pandey, Tavpritesh Sethi, and Ponnurangam Kumaraguru, arXiv:2011.00052v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.10663v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|r|}\n\\hline\n\\textbf{Negative Pairs Strategy} & \\textbf{Linear} \\\\ \\hline \\hline\nDefault & $\\mathbf{0.876 \\pm 0.013}$ \\\\ \\hline\nSame Laterality only & $0.872 \\pm 0.011$ \\\\ \\hline\nSame Laterality (reweighted) & $0.864 \\pm 0.006$ \\\\ \\hline\nSame Laterality (appended) & $0.875 \\pm 0.006$ \\\\ \\hline\nSame Laterality (synthetic) & $0.870 \\pm 0.004$ \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation", "authors": ["Yen Nhi Truong Vu", "Richard Wang", "Niranjan Balachandar", "Can Liu", "Andrew Y. Ng", "Pranav Rajpurkar"], "url": "https://arxiv.org/abs/2102.10663v2", "attribution": "\"MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation\" by Yen Nhi Truong Vu, Richard Wang, Niranjan Balachandar, Can Liu, Andrew Y. Ng, and Pranav Rajpurkar, arXiv:2102.10663v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.24078v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\hline\n \\textbf{Variable} & \\textbf{Estimate} & \\textbf{Std. Error} & \\textbf{p-value} \\\\\n \\hline\n Gender (Female) & -0.0321 & 0.0039 & 2.32e-16 *** \\\\\n University Fixed Effects & Yes & - & - \\\\\n Department Fixed Effects & Yes & - & - \\\\\n log$_{10}$(i10-index) & 0.0094 & 0.0031 & 0.00256 ** \\\\\n Titles (Associate Professor) & 0.0526 & 0.0049 & $<$2e-16 *** \\\\\n Titles (Professor) & 0.1760 & 0.0057 & $<$2e-16 *** \\\\\n Working Years & -0.0015 & 0.0002 & 6.31e-10 *** \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Fixed Effects Regression Controlling for Institutional and Department-Level Factors}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Estimation of Gender Wage Gap in the University of North Carolina System", "authors": ["Zihan Zhang", "Jan Hannig"], "url": "https://arxiv.org/abs/2505.24078v1", "attribution": "\"Estimation of Gender Wage Gap in the University of North Carolina System\" by Zihan Zhang and Jan Hannig, arXiv:2505.24078v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09449v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of the Hard test sets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{Dataset} & \\textbf{Positive} & \\textbf{Negative} & \\textbf{Neutral} & \\textbf{Total (\\% of Test Set)} \\\\ \n\\hline\n\\textbf{Laptop} & 31 & 24 & 46 & 101 (15.8 \\%) \\\\\n\\hline\n\\textbf{Restaurants} & 81 & 60 & 83 & 224 (20.0 \\%) \\\\\n\\hline\n\\textbf{Men's T-shirt} & 23 & 24 & 1 & 48 (10.2 \\%) \\\\ \n\\hline\n\\textbf{Television} & 43 & 40 & 19 & 102 (10.8 \\%) \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild", "authors": ["Rajdeep Mukherjee", "Shreyas Shetty", "Subrata Chattopadhyay", "Subhadeep Maji", "Samik Datta", "Pawan Goyal"], "url": "https://arxiv.org/abs/2101.09449v1", "attribution": "\"Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild\" by Rajdeep Mukherjee, Shreyas Shetty, Subrata Chattopadhyay, Subhadeep Maji, Samik Datta, and Pawan Goyal, arXiv:2101.09449v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2106.05847v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Clustering algorithms used for the \\textit{C. elegans} homeobox gene network}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l}\n\\textbf{Method} & \\textbf{Source} & \\textbf{Parameters} \\\\ \\hline\nGeometric modularity & this paper & $-0.5<log_{10}\\tau<0$, $\\kappa_0$=0 \\\\\nModularity & & Resolution parameter = = 1, 0.1, 0.01 \\\\\nWavelet & & $2<s<5$ \\\\\nMarkov stability & & $-0.5<log_{10}\\tau<0$ \\\\\nMarkov clustering & & expansion=2, inflation=2, loop=1, iterations=100, threshold=0.001\\\\\nPercoMVC & & \\\\\nLabel propagation & & \\\\\nwalktrap & & \\\\\nSpinglass & & \\\\\nEdMot & & \\\\\nNon-negative matrix factorisation & & \\\\\nGreedy modularity & & \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature", "authors": ["Adam Gosztolai", "Alexis Arnaudon"], "url": "https://arxiv.org/abs/2106.05847v3", "attribution": "\"Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature\" by Adam Gosztolai and Alexis Arnaudon, arXiv:2106.05847v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23211v2_tex_table22.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Location of estimated change points from the video data set for the ``\\textit{first person walks out of the lobby} (so-called action 1)\" and ``\\textit{second person walks into the lobby} (so-called action 2)\" using LOCAL method. Note that the true value is 116 for action 1 and 174 for action 2.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\nAction & Pixel & Estimated change points based on \\textbf{LOCAL} method \\\\ \\hline\\hline\n1 & 700 & 84 \\\\\n& 702 & 79 \\\\\n & 731 & 91 \\\\\n& 762 & 96 \\\\\n& 764 & 98 \\\\\n\\hline\n2 & 48 & 260 \\\\\n& 78 & 278 \\\\\n& 110 & 276 \\\\\n& 174 & 170 \\\\\n& 209 & 203 \\\\\n& 241 & 142 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models", "authors": ["Sepideh Mosaferi", "Abolfazl Safikhani", "Peiliang Bai"], "url": "https://arxiv.org/abs/2503.23211v2", "attribution": "\"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models\" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.15743v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l}\n authblk & babel & cjk & dvips \\\\\n epsf & epsfig & euler & float \\\\\n fullpage & geometry & graphics & hyperref \\\\\n layout & linespread & lmodern & maltepaper \\\\\n navigator & pdfcomment & pgfplots & psfig \\\\\n pstricks & t1enc & titlesec & tocbind \\\\\n ulem\n\\end{tabular}\n\\end{adjustbox}\n\\caption{LaTeX style packages that must not be used.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Towards Reliable Item Sampling for Recommendation Evaluation", "authors": ["Dong Li", "Ruoming Jin", "Zhenming Liu", "Bin Ren", "Jing Gao", "Zhi Liu"], "url": "https://arxiv.org/abs/2211.15743v3", "attribution": "\"Towards Reliable Item Sampling for Recommendation Evaluation\" by Dong Li, Ruoming Jin, Zhenming Liu, Bin Ren, Jing Gao, and Zhi Liu, arXiv:2211.15743v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.18462v2_tex_table16.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|ccc|ccc}\n\\hline\n\\multirow{2}{*}{Run (Retriever)} & \\multicolumn{3}{c|}{Cutoff = 10} & \\multicolumn{3}{c}{Cutoff = 20} \\\\ \\cline{2-7} \n & DEJA-VU & nDCG & Recall & DEJA-VU & nDCG & Recall \\\\ \\hline\nANCE & 0.947 & 0.645 & 0.145 & 0.939 & 0.616 & 0.240 \\\\\nBM25 & 0.885 & 0.506 & 0.129 & 0.926 & 0.491 & 0.201 \\\\\nSBERT & 0.940 & 0.693 & 0.148 & 0.956 & 0.656 & 0.232 \\\\\nSPLADE++ & \\underline{0.948} & \\textbf{0.731} & \\textbf{0.172} & \\underline{0.974} & \\textbf{0.717} & \\textbf{0.289} \\\\\nTCT-ColBERT &\\textbf{0.974} & \\underline{0.720} & \\underline{0.167} & \\textbf{0.981} & \\underline{0.680} & \\underline{0.261} \\\\\nuniCOIL & 0.914 & 0.643 & 0.162 & 0.966 & 0.614 & 0.247 \\\\ \\hline\n\\end{tabular}\n\\caption{Scores of DEJA-VU, nDCG, and Recall for the six runs on TRDL 19 at cutoffs of 10 and 20 respectively. Bold indicates the highest score for a run, while underscore denotes the second highest score.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability", "authors": ["Nuo Chen", "Jiqun Liu", "Hanpei Fang", "Yuankai Luo", "Tetsuya Sakai", "Xiao-Ming Wu"], "url": "https://arxiv.org/abs/2403.18462v2", "attribution": "\"Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability\" by Nuo Chen, Jiqun Liu, Hanpei Fang, Yuankai Luo, Tetsuya Sakai, and Xiao-Ming Wu, arXiv:2403.18462v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.06010v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|rrrrrrrr|}\n\\hline\n$Z$ & $Z_V$ & $Z_S$ & $Z_R$ & $Z_G$ & $U$ & $Z_Q$ & $Z_I$ & $Z_B$ \\\\\n\\hline\n$Z_V$ & 1.0000 & 0.0538 & 0.2978 & -0.1800 & -0.4234 & -0.0516 & -0.0753 & 0.0725 \\\\\n$Z_S$ & 0.0538 & 1.0000 & -0.1817 & -0.0661 & -0.0608 & -0.1581 & -0.0397 & -0.0951 \\\\\n$Z_R$ & 0.2978 & -0.1817 & 1.0000 & -0.1628 & -0.5177 & -0.0979 & -0.0265 & -0.1470 \\\\\n$Z_G$ & -0.1800 & -0.0661 & -0.1628 & 1.0000 & 0.1524 & 0.1204 & 0.0724 & -0.1878 \\\\\n$U$ & -0.4234 & -0.0608 & -0.5177 & 0.1524 & 1.0000 & 0.7038 & 0.3228 & 0.2714 \\\\\n$Z_Q$ & -0.0516 & -0.1581 & -0.0979 & 0.1204 & 0.7038 & 1.0000 & 0.2768 & 0.2817 \\\\\n$Z_I$ & -0.0753 & -0.0397 & -0.0265 & 0.0724 & 0.3228 & 0.2768 & 1.0000 & -0.0948 \\\\\n$Z_B$ & 0.0725 & -0.0951 & -0.1470 & -0.1878 & 0.2714 & 0.2817 & -0.0948 & 1.0000 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Correlation matrix of series of residuals, see \\texttt{corrMatrix.xlsx}}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Time Series Model for Three Asset Classes used in Financial Simulator", "authors": ["Andrey Sarantsev", "Angel Piotrowski", "Ian Anderson"], "url": "https://arxiv.org/abs/2508.06010v1", "attribution": "\"A Time Series Model for Three Asset Classes used in Financial Simulator\" by Andrey Sarantsev, Angel Piotrowski, and Ian Anderson, arXiv:2508.06010v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.13824v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Correlation coefficients for the logarithmic returns of media attention and absolute values of logarithmic returns of the Bitcoin price, computed only for the time moments classified as jumps.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n& Pearson & Kendall's \\(\\tau\\) & Spearman's \\(\\rho\\) \\\\ \\hline\nCoefficient & 0.44357 & 0.3026 & 0.43869 \\\\ \np-value & 1.1204\\(\\cdot 10^{-17}\\) & 1.2176\\(\\cdot 10^{-16}\\) & 2.77314\\(\\cdot 10^{-17}\\) \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Modelling the Bitcoin prices and the media attention to Bitcoin via the jump-type processes", "authors": ["Ekaterina Morozova", "Vladimir Panov"], "url": "https://arxiv.org/abs/2210.13824v1", "attribution": "\"Modelling the Bitcoin prices and the media attention to Bitcoin via the jump-type processes\" by Ekaterina Morozova and Vladimir Panov, arXiv:2210.13824v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.16951v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Running Time Summary in Seconds (Numerical Features: Standardized; Categorical Features: One-hot encoding).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\n{Models} & Total Fits & Tuning Time& Average Tuning & Fitting Time (Best Model) \\\\\n \\midrule\nLightGBM & 2000 & 213.44 & 0.11 & 0.46\\\\\nLinear Regression & 150 & 44.39 & 0.30 & 0.82\\\\\nMLP & 40 & 2147.05 & 53.68 & 65.81\\\\\nRandom Forest & 1200 & 3633.05 & 3.03 & 115.67 \\\\\nXGBoost & 5760 & 13320.13 & 2.31 & 4.16 \\\\\n\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Beyond Tides and Time: Machine Learning Triumph in Water Quality", "authors": ["Yinpu Li", "Siqi Mao", "Yaping Yuan", "Ziren Wang", "Yixin Kang", "Yuanxin Yao"], "url": "https://arxiv.org/abs/2309.16951v2", "attribution": "\"Beyond Tides and Time: Machine Learning Triumph in Water Quality\" by Yinpu Li, Siqi Mao, Yaping Yuan, Ziren Wang, Yixin Kang, and Yuanxin Yao, arXiv:2309.16951v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11327v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n \\textbf{Aspect} & \\textbf{\\# (\\%) of Instances}\\\\\n \\midrule\n Performance& 348 (7.7) \\\\\n Usability &1,437 (31.8)\\\\\n Security &163 (3.6)\\\\\n Bug&189 (4.2)\\\\\n Community&93 (2.1)\\\\\n Compatibility&93 (2.1)\\\\\n Documentation&256 (5.6)\\\\\n Legal&50 (1.1)\\\\\n Portability&70 (1.5)\\\\\n OnlySentiment&348 (7.7)\\\\\n Others&1,699 (37.6)\\\\\\hline\n Total &4,522\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?", "authors": ["chengran Yang", "Bowen Xu", "Junaed younus Khan", "Gias Uddin", "Donggyun Han", "Zhou Yang", "David Lo"], "url": "https://arxiv.org/abs/2201.11327v1", "attribution": "\"Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?\" by chengran Yang, Bowen Xu, Junaed younus Khan, Gias Uddin, Donggyun Han, Zhou Yang, and David Lo, arXiv:2201.11327v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.13279v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Properties of audio samples from dataset Part 1.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|} \n \\hline\t\n Label & \\thead{No.\\\\ of samples} & \\thead{Total\\\\ duration (s)} & \\thead{Mean\\\\ duration (s)} & \\thead{Std.\\\\ deviation of\\\\ duration (s)} \\\\\n \\hline\n 0 & 1000 & 8255.93 & 8.26 & 2.75 \\\\ \n \\hline\n 1 & 1000 & 6426.55 & 6.43 & 2.08\t \\\\\n \\hline\n 2 & 1000 & 6359.18\t& 6.36\t & \t2.12 \\\\ \n \\hline\n 3 & 1000 & 8136.84 & 8.14 & \t2.56 \\\\\n \\hline\n 4 & 1000 & 5615.91 & \t5.62 & 1.91 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Synthetic Speech Classification: IEEE Signal Processing Cup 2022 challenge", "authors": ["Mahieyin Rahmun", "Rafat Hasan Khan", "Tanjim Taharat Aurpa", "Sadia Khan", "Zulker Nayeen Nahiyan", "Mir Sayad Bin Almas", "Rakibul Hasan Rajib", "Syeda Sakira Hassan"], "url": "https://arxiv.org/abs/2412.13279v1", "attribution": "\"Synthetic Speech Classification: IEEE Signal Processing Cup 2022 challenge\" by Mahieyin Rahmun, Rafat Hasan Khan, Tanjim Taharat Aurpa, Sadia Khan, Zulker Nayeen Nahiyan, Mir Sayad Bin Almas, Rakibul Hasan Rajib, and Syeda Sakira Hassan, arXiv:2412.13279v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17042v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l}\nModel & $r_ \\kappa(SBM)$* & $r_ \\kappa(IM)$* \\\\\n\\hline\n${\\rm LS}(d=1)$ & 81 & 222\\\\\n${\\rm LS}(d=2)$ & 77 & 212\\\\\n${\\rm LS}(d=3)$ & 87 & 297\\\\\n${\\rm DS}(q=0.3)$ & 239 & 1637\\\\\n${\\rm DS}(q=0.5)$ & 98 & 622\\\\\n${\\rm DS}(q=0.7)$ & 55 & 707\\\\\n${\\rm WS}(K=\\{1\\}, p)$ & $-$ & $-$\\\\\n${\\rm WS}(K=\\{1,2\\}, p=0.1)$ & 115 & 184\\\\\n${\\rm WS}(K=\\{1,2\\}, p=0.2)$ & 271 & 574\\\\\n${\\rm WS}(K=\\{1,2\\}, p=0.3)$ & 2377 & 2510\\\\\n${\\rm WS}(K=\\{1,2,3\\}, p=0.1)$ & 59 & 75\\\\\n${\\rm WS}(K=\\{1,2,3\\}, p=0.2)$ & 81 & 151\\\\\n${\\rm WS}(K=\\{1,2,3\\}, p=0.3)$ & 168 & 418\\\\\n${\\rm WS}(K=\\{1,2,3\\}, p=0.4)$ & 723 & 1783\\\\\n${\\rm BA}(m=2)$ & $-$ & $-$\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Ramsey community numbers for different network models and $\\epsilon=0.05$, when using the stochastic block model correcting for the degree sequence (SBM) or regularized Infomap (IM). $-$Does not have the emergent communities property. *An estimate, not an exact number, with variations in the last digit due to the stochastic nature of the network generative models and the communities detection method.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Emergence of network communities driven by local rules", "authors": ["Alexei Vazquez"], "url": "https://arxiv.org/abs/2501.17042v4", "attribution": "\"Emergence of network communities driven by local rules\" by Alexei Vazquez, arXiv:2501.17042v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.05603v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc} \n\\toprule\nMethod & CRPS & MAE & RMSE & Coverage & Width \\\\ \\hline\nRaw ensemble & 1.017 & 1.255 & 1.730 & 63.071 & 2.922 \\\\ \\hline\nEMOS & 0.718 & \\textbf{0.985} & 1.387 & 95.573 & \\textbf{5.441} \\\\ \nDVQR & 0.717 & 0.985 & 1.366 & \\textbf{96.339} & 5.990 \\\\ \\hline\nGAM-DVQR-C & 0.719 & 0.989 & 1.358 & 96.499 & 5.994 \\\\\nGAM-DVQR-T1 & \\textbf{0.713} & 0.985 & \\textbf{1.350} & 96.594 & 5.889\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Verification scores aggregated over all stations and time points in the validation period. Bold values represent the best value for each score.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "D-Vine GAM Copula based Quantile Regression with Application to Ensemble Postprocessing", "authors": ["David Jobst", "Annette Möller", "Jürgen Groß"], "url": "https://arxiv.org/abs/2309.05603v1", "attribution": "\"D-Vine GAM Copula based Quantile Regression with Application to Ensemble Postprocessing\" by David Jobst, Annette Möller, and Jürgen Groß, arXiv:2309.05603v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.01705v3_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Benjamini Hochberg (1995) adjusted p-values (original p-values in parenthesis)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrrrr}\n\\toprule \n&\\multicolumn{5}{c}{\\textbf{Uncertainty}}&\\multicolumn{5}{c}{\\textbf{Negative Sentiment}}\\\\\n\\toprule\n &Aggregate&Economics&Health&Politics&Policy&Aggregate&Economics&Health&Politics&Policy\\\\\n\\midrule\nred zone = 1 & 0.53 & 0.00 & 0.97 & 0.00 & 0.92 & 0.04 & 0.02 & 0.92 & 0.00 & 0.92 \\\\\n & (0.39) & (0.00) & (0.97)& (0.00) & (0.87) & (0.01) & (0.01) & (0.92)& (0.00) & (0.90)\\\\\npost = 1 & 0.01 & 0.01 & 0.00 & 0.66 & 0.00 & 0.38 & 0.00 & 0.00 & 0.17 & 0.02\\\\\n & (0.00) & (0.00) & (0.00)& (0.53) & (0.00) & (0.23) & (0.00) & (0.00)& (0.12) & (0.01)\\\\\npost = 2 & 0.00 & 0.00& 0.00 & 0.63 & 0.00 & 0.18 & 0.78 & 0.00 & 0.02 & 0.03\\\\\n & (0.00) & (0.00) & (0.00)& (0.47) & (0.00) & (0.08) & (0.66) & (0.00)& (0.01) & (0.02)\\\\\n\\textbf{red zone = 1 x post = 1} & 0.74 & 0.00 & 0.01 & 0.02 & 0.85 & 0.72 & 0.17 & 0.00 & 0.00 & 0.42 \\\\\n& (0.74) & (0.00) & (0.01)& (0.01) & (0.76) & (0.67) & (0.13) & (0.00)& (0.00) & (0.34)\\\\\nred zone = 1 x post = 2 & 0.74 & 0.36 & 0.85 & 0.16 & 0.09 & 0.72 & 0.86 & 0.06 & 0.00 & 0.07 \\\\\n & (0.73) & (0.25) & (0.72)& (0.10) & (0.06) & (0.66) & (0.77) & (0.03)& (0.00) & (0.05)\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic", "authors": ["C. Biliotti", "F. J. Bargagli-Stoffi", "N. Fraccaroli", "M. Puliga", "M. Riccaboni"], "url": "https://arxiv.org/abs/2212.01705v3", "attribution": "\"Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic\" by C. Biliotti, F. J. Bargagli-Stoffi, N. Fraccaroli, M. Puliga, and M. Riccaboni, arXiv:2212.01705v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.11691v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{True negative and positive rates (TNR/TPR) and negative and positive predicted values (NPV/PPV) estimated using 70/30\\% cross-validation and 1,000 Monte Carlo splits. The full model which yields larger estimated propensities for many units yields a marked improvement in the true positive rate with a negligble decline in the true negative rate. Furthermore, the full model yields marked improvements in both the negative and positive predicted values. This implies a betterment in the proportion of correctly classified treatments and the proportion of classified treatments (controls) which are actually treatments (controls).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\n & TNR & TPR & NPV & PPV\\\\\n\\midrule\nRestricted Model & 0.987 & 0.089 & 0.834 & 0.599\\\\\nFull Model & 0.974 & 0.185 & 0.847 & 0.612\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "The causal effect of a timeout at stopping an opposing run in the NBA", "authors": ["Connor Gibbs", "Ryan Elmore", "Bailey Fosdick"], "url": "https://arxiv.org/abs/2011.11691v3", "attribution": "\"The causal effect of a timeout at stopping an opposing run in the NBA\" by Connor Gibbs, Ryan Elmore, and Bailey Fosdick, arXiv:2011.11691v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.15103v1_tex_table16.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Descriptive Analysis - Online vs. Offline Prices at the SKU-Date Level}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\tabularnewline \\midrule \\midrule\n Dependent Variable: & \\multicolumn{3}{c}{ln(SKU\\_Price)}\\\\\n & Dry Dog Food & Dog Hygiene & Cat Hygiene \\\\ \n Model: & (1) & (2) & (3) \\\\ \n \\midrule\n \\emph{Variables}\\\\\n isOnline & -0.0322$^{***}$ & -0.0361$^{*}$ & -0.0607$^{***}$\\\\ \n & (0.0022) & (0.0135) & (0.0087)\\\\ \n \\midrule\n \\emph{Fixed-effects}\\\\\n SKU & Yes & Yes & Yes\\\\ \n DATE & Yes & Yes & Yes\\\\ \n \\midrule\n \\emph{Fit statistics}\\\\\n Observations & 554,191 & 328,323 & 244,723\\\\ \n R$^2$ & 0.95202 & 0.98493 & 0.91763\\\\ \n Within R$^2$ & 0.01819 & 0.00912 & 0.02441\\\\ \n \\midrule \\midrule\n \\multicolumn{4}{l}{\\emph{Clustered (SKU \\& DATE) standard-errors in parentheses}}\\\\\n \\multicolumn{4}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05}}\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "How does online shopping affect offline price sensitivity?", "authors": ["Shirsho Biswas", "Hema Yoganarasimhan", "Haonan Zhang"], "url": "https://arxiv.org/abs/2506.15103v1", "attribution": "\"How does online shopping affect offline price sensitivity?\" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.12785v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rcccc}\n \\toprule\n $M$ & 1\\textsuperscript{st} Hopf & 2\\textsuperscript{nd} Hopf & 1\\textsuperscript{st} PD & 2\\textsuperscript{nd} PD \\\\\n \\midrule\n linear chain trick: 10 & 1.98041101 & --- & --- & --- \\\\\n 20 & 1.48084288 & --- & --- & --- \\\\\n 40 & 1.32851441 & --- & 5.75976022 & --- \\\\\n 80 & 1.26550447 & --- & 4.27685322 & 6.85489462 \\\\\n \\midrule\n pseudospectral: \\ 5 & 1.20947760 & --- & 3.48977702 & 4.88889653 \\\\\n 10 & 1.20919957 & 4.83374221 & 3.55626313 & 4.85243716 \\\\\n 20 & 1.20919957 & 4.83679830 & 3.55716811 & 4.86050005 \\\\\n 40 & 1.20919957 & 4.83679830 & 3.55716813 & 4.86049811 \\\\\n \\midrule \n DDE-BIFTOOL & 1.20919957 & 4.83679830 & 3.55716847 & 4.86050131 \\\\\n \\bottomrule \\\\\n \\toprule\n \\textit{Computation times ($s$):} & & Equilibrium & Limit cycle & Doubled cycle \\\\\n \\midrule\n linear chain trick: 10 & & 0.2 & 21.1 & --- \\\\\n 20 & & 0.7 & 52.7 & --- \\\\\n 40 & & 2.7 & 1030.8 & 242.3 \\\\\n 80 & & 108.0 & 12455.3 & 12453.2 \\\\\n \\midrule\n pseudospectral: \\ 5 & & 0.7 & 11.8 & 11.9 \\\\\n 10 & & 0.2 & 31.0 & 31.0 \\\\\n 20 & & 0.7 & 114.5 & 94.4 \\\\\n 40 & & 4.7 & 484.7 & 371.3 \\\\\n \\midrule\n DDE-BIFTOOL & & 7.0 & 11.2 & 13.7 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "New developments in MatCont: delay equation importer and Lyapunov exponents", "authors": ["Davide Liessi", "Enrico Santi", "Rossana Vermiglio", "Mayank Thakur", "Hil G. E. Meijer", "Francesca Scarabel"], "url": "https://arxiv.org/abs/2504.12785v2", "attribution": "\"New developments in MatCont: delay equation importer and Lyapunov exponents\" by Davide Liessi, Enrico Santi, Rossana Vermiglio, Mayank Thakur, Hil G. E. Meijer, and Francesca Scarabel, arXiv:2504.12785v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.23409v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary statistics for the ARBAE of VIX calls across all calibration dates. The table reports key metrics of ARBAE, including the mean, standard deviation (SD), minimum, maximum, and the 95th and 99th percentiles. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n \\toprule \n Model & mean ARBAE & SD & min & max & $95\\%$ & $99\\%$ \\\\ \\midrule\n One-factor & $9.08\\times 10^{-5}$ & $0.00051$ & $0$ & $0.00987$ & $5.0\\times 10^{-4}$ & $0.00225$\\\\\n Two-factor & $7.56\\times 10^{-5}$ & $ 0.00051$ & $0$ & $0.00992$ & $2.33\\times 10^{-4}$ & $0.00174$\\\\\n \\bottomrule\n \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Pricing and Calibration of VIX Derivatives in Mixed Bergomi Models via Quantisation", "authors": ["Nelson Kyakutwika", "Mesias Alfeus", "Erik Schlögl"], "url": "https://arxiv.org/abs/2506.23409v1", "attribution": "\"Pricing and Calibration of VIX Derivatives in Mixed Bergomi Models via Quantisation\" by Nelson Kyakutwika, Mesias Alfeus, and Erik Schlögl, arXiv:2506.23409v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02047v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The list of the number of images used in the test, validation, and the training sets of the generic database}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\nGesture Class & Test Set & Validation Set & Training Set & Total\\\\ \\hline\nSingleOne & 337 & 151 & 2886 & 3374 \\\\ \\hline\nSingleTwo & 376 & 169 & 3218 & 3763 \\\\ \\hline\nSingleThree & 376 & 169 & 3223 & 3768 \\\\ \\hline\nSingleFour & 376 & 169 & 3222 & 3767 \\\\ \\hline\nSingleFive & 375 & 169 & 3211 & 3755 \\\\ \\hline\nSingleSix & 375 & 169 & 3213 & 3757 \\\\ \\hline\nSingleSeven & 377 & 169 & 3227 & 3773 \\\\ \\hline\nSingleEight & 338 & 152 & 2890 & 3380 \\\\ \\hline\nTotal & 2930 & 1317 & 25090 & 29337 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection", "authors": ["Mohammad Mahmudul Alam", "Mohammad Tariqul Islam", "S. M. Mahbubur Rahman"], "url": "https://arxiv.org/abs/2101.02047v3", "attribution": "\"Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection\" by Mohammad Mahmudul Alam, Mohammad Tariqul Islam, and S. M. Mahbubur Rahman, arXiv:2101.02047v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.03671v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|}\n\\cline{1-3}\nParameter & Symbol & Value \\\\\n\\hline\n\\multicolumn{3}{|l|}{\\textit{Fiber parameters}} \\\\\n\\hline\nAttenuation Coefficient @ 1550 nm & $\\alpha$ & $\\mathrm{0.165 dB/km}$\\\\\nAttenuation Coefficient @ 1590 nm & $\\alpha$ & $\\mathrm{0.171 dB/km}$\\\\\nAttenuation Coefficient @ 1495 nm & $\\alpha$ & $\\mathrm{0.177 dB/km}$\\\\\nAttenuation Coefficient @ 1410 nm & $\\alpha$ & $\\mathrm{0.217 dB/km}$\\\\\nDispersion Coefficient & $D$ & $\\mathrm{21.3~{ps}/{nm}/{km}}$\\\\\nDispersion Slope & $S$ & $\\mathrm{0.067~{ps}/{nm}^{2}/{km}}$ \\\\\nRaman gain coefficient @ 13 THz &$C_r$& $\\mathrm{0.028}$ $\\mathrm{(1/W/km)}$\\\\\nNonlinear coefficient & $\\gamma$ & $\\mathrm{1.2~ W^{-1}km^{-1}}$ \\\\\nOptical Reference Bandwidth & ${\\Delta}_{ref}$ & $\\mathrm{12.5 GHz}$ \\\\ \nSpan length & $L$ & $\\mathrm{100 km}$\\\\\n\\hline\n\\multicolumn{3}{|l|}{\\textit{Amplifier parameters}} \\\\\n\\hline\nNoise figure & ${F}$ & $\\mathrm{5 dB} $\\\\\n\\hline\n\\multicolumn{3}{|l|}{\\textit{Signal parameters}} \\\\\n\\hline\nCentral frequency WDM & $v$ & $\\mathrm{1480 nm (200.67 THz)}$\\\\\nSymbol Rate & $R$ & $\\mathrm{12.5 GBaud}$ \\\\\nChannel spacing & $\\Delta f$ & $\\mathrm{12.5 GHz}$\\\\ \nOptical bandwidth & BW & $\\mathrm{2720 FSUs (34 THz)}$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Fiber parameters for maximum reach calculation}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Maximum transmission reach for optical signals in elastic optical networks employing band division multiplexing", "authors": ["Esteban Paz", "Gabriel Saavedra"], "url": "https://arxiv.org/abs/2011.03671v3", "attribution": "\"Maximum transmission reach for optical signals in elastic optical networks employing band division multiplexing\" by Esteban Paz and Gabriel Saavedra, arXiv:2011.03671v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.18953v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The statistics comparison of several audio-caption datasets. Quan denotes the number of captions, Len represents the average caption length in words, and Voc indicates the vocabulary size. Caption generation methods include human annotation (H), audio model (A), visual model (V), and language model (L).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\toprule\n\\textbf{Dataset} & \\textbf{Quan} & \\textbf{Len} & \\textbf{Voc} & \\textbf{Source} \\\\ \\midrule\nClotho & $30$~K & $11$ & $4$~K & H \\\\\nAudioCaps & $57$~K & $9$ & $5$~K & H \\\\ \\midrule\nLAION-Audio-630K & $630$~K & $7$ & $311$~K & L\\\\\nWavCaps & $400$~K & $8$ & $24$~K & L \\\\\nAuto-ACD & $1.5$~M & $18$ & $20$~K & A+V+L \\\\\nSound-VECaps & $1.6$~M & $40$ & $50$~K & A+V+L \\\\\nAudioSetCaps & $1.9$~M & $28$ & $21$~K & A+L \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Large Audio and Language Models", "authors": ["Jisheng Bai", "Haohe Liu", "Mou Wang", "Dongyuan Shi", "Wenwu Wang", "Mark D. Plumbley", "Woon-Seng Gan", "Jianfeng Chen"], "url": "https://arxiv.org/abs/2411.18953v1", "attribution": "\"AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Large Audio and Language Models\" by Jisheng Bai, Haohe Liu, Mou Wang, Dongyuan Shi, Wenwu Wang, Mark D. Plumbley, Woon-Seng Gan, and Jianfeng Chen, arXiv:2411.18953v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2010.08259v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Marginal effects of policy variables $x_{t-1}$ and $\\Delta_t$ on $\\mu_{t+\\tau}$, $\\tau\\geq0$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\t\t\tModel & Marginal effect on $\\mu_{t+\\tau}$\\\\\n\t\t\t\\hline \\\\\n\t\t\t\n\t\t\tMAP& $\\kappa \\psi^\\tau$ \\\\\n\t\t\tX--MAP& $\\kappa \\beta^\\tau$ \\\\\n\t\t\tL-MAP & $2\\varsigma_{t+\\tau} \\kappa \\psi^\\tau \\frac{exp(\\xi_{t+\\tau})}{(1+exp(\\xi_{t+\\tau}))^2}$ \\\\\n\t\t\tP-MAP &$\\kappa \\psi^\\tau \\varsigma_{t+\\tau}$ \\\\\n\t\t\t\\hline \n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Unconventional Policies Effects on Stock Market Volatility: A MAP Approach", "authors": ["Demetrio Lacava", "Giampiero M. Gallo", "Edoardo Otranto"], "url": "https://arxiv.org/abs/2010.08259v2", "attribution": "\"Unconventional Policies Effects on Stock Market Volatility: A MAP Approach\" by Demetrio Lacava, Giampiero M. Gallo, and Edoardo Otranto, arXiv:2010.08259v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.18960v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The COVID-19 pandemic's impact on returns, absolute returns (AR), and volatility increment (VI) time series. The meanings of the symbols are as follows. $\\bigcirc$: impact observed, $\\times$: no impact observed, $\\triangle$: impact unclear. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccc}\n\\toprule\n & \\textbf{DAX} & \\textbf{Nikkei 225} & \\textbf{SSE} & \\textbf{VIX} & \\textbf{Bitcoin} & \\textbf{Ethereum} \\\\\n\\midrule\nReturns $h(2)$ &negative &$\\bigcirc$ &$\\times$ & positive & $\\times$ & $\\times$ \\\\\nReturns $\\Delta h(5)$ &$\\bigcirc$ &$\\bigcirc$ & negative & positive & negative & negative \\\\\nAR $h(2)$ &negative &negative & negative & negative & $\\times$ & $\\times$ \\\\\nAR $\\Delta h(5)$ &$\\bigcirc$ &negative & $\\triangle$ & positive & $\\triangle$ & negative \\\\\nVI $h(2)$ &$\\times$ &$\\times$ &$\\times$ & $\\times$ & $\\times$ & $\\times$\\\\\nVI $\\Delta h(5)$ &$\\times$ &$\\times$ &$\\times$ & $\\times$ & $\\times$ & $\\times$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Impact of the COVID-19 pandemic on the financial market efficiency of price returns, absolute returns, and volatility increment: Evidence from stock and cryptocurrency markets", "authors": ["Tetsuya Takaishi"], "url": "https://arxiv.org/abs/2504.18960v1", "attribution": "\"Impact of the COVID-19 pandemic on the financial market efficiency of price returns, absolute returns, and volatility increment: Evidence from stock and cryptocurrency markets\" by Tetsuya Takaishi, arXiv:2504.18960v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.09697v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Confounder balance in 1474 matched treated-control pairs. We report the mean values of confounders and standardized mean differences (SMDs) before and after matching.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcclcccl}\n\\hline \\hline\n\\multicolumn{2}{l}{} & \\multicolumn{3}{c}{Before Matching} & & \\multicolumn{3}{c}{After Matching} \\\\ \\cline{3-5} \\cline{7-9}\nConfounder & & Treated & Control & SMD & & Treated & Control & SMD \\\\\n\\hline\nAge && 59.17& 62.30& -0.305&& 59.17& 59.18&\t-0.001 \\\\\nAge $\\geq$ 65 && 0.315& 0.426& -0.237&&\t0.315& 0.315& 0.000 \\\\\nMale && 0.423&\t0.442& -0.037&&\t0.423& 0.423& 0.000 \\\\\nEducation &&&&&&&& \\\\\n\\hspace{0.5cm} $\\leq$ Elementary school && 0.308& 0.519& -0.457&& 0.308& 0.310& -0.004 \\\\\n\\hspace{0.5cm} Middle school && 0.134& 0.172& -0.111&& 0.134& 0.124& 0.030 \\\\\n\\hspace{0.5cm} High school && 0.341& 0.241& 0.211&& 0.341& 0.386& -0.096 \\\\\n\\hspace{0.5cm} $\\geq$ College && 0.215& 0.068& 0.357&& 0.215& 0.179& 0.088 \\\\\n\\hspace{0.5cm} Missing && 0.002& 0.000& 0.040&& 0.002& 0.001& 0.030 \\\\\nUrban Resident && 0.832& 0.794&\t0.101&&\t0.832& 0.827& 0.013 \\\\\nMarried && 0.830& 0.776& 0.144&& 0.830& 0.828& 0.004 \\\\\nIncome &&&&&&&& \\\\\n\\hspace{0.5cm} Low && 0.200& 0.265& -0.163&& 0.200&\t0.214& -0.034 \\\\\n\\hspace{0.5cm} Middle && 0.246& 0.259& -0.029&& 0.246& 0.242& 0.011 \\\\\n\\hspace{0.5cm} High && 0.305& 0.209& 0.209&& 0.305& 0.305& 0.000 \\\\\n\\hspace{0.5cm} Missing && 0.249& 0.267&\t-0.043&& 0.249&\t0.240& 0.020 \\\\\nEconomic Activity && 0.406&\t0.359& 0.094&& 0.406& 0.409& -0.007 \\\\\nAlcohol use && 0.428& 0.447& -0.038&& 0.428& 0.432&\t-0.008 \\\\\nSmoker && 0.244& 0.308&\t-0.149&& 0.244&\t0.254& -0.024 \\\\\nDepression && 0.097& 0.130&\t-0.112&& 0.097&\t0.087& 0.034 \\\\\nSelf-reported health status &&&&&&&& \\\\\n\\hspace{0.5cm} Bad && 0.177& 0.337&\t-0.418&& 0.177&\t0.189& -0.032 \\\\\n\\hspace{0.5cm} Moderate && 0.460& 0.468& -0.017&& 0.460& 0.467&\t-0.015 \\\\\n\\hspace{0.5cm} Good && 0.363& 0.195& 0.349&& 0.363&\t0.343& 0.041 \\\\\nNo. chronic diseases &&&&&&&& \\\\\n\\hspace{0.5cm} 0 && 0.540& 0.510& 0.060&& 0.540& 0.547&\t-0.015 \\\\\n\\hspace{0.5cm} 1 && 0.298& 0.291& 0.014&& 0.298& 0.286&\t0.025 \\\\\n\\hspace{0.5cm} $\\geq$ 2 && 0.162& 0.199& -0.099&& 0.162& 0.166&\t-0.011 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Randomization-Based Method for Evaluating Time-Varying Treatment Effects", "authors": ["Sangjin Lee", "Kwonsang Lee"], "url": "https://arxiv.org/abs/2412.09697v3", "attribution": "\"A Randomization-Based Method for Evaluating Time-Varying Treatment Effects\" by Sangjin Lee and Kwonsang Lee, arXiv:2412.09697v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03057v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\\toprule\n & Val. Acc. (\\%) & Diff (pp.) & Parameters \\\\ \\midrule\nResnet18 & 39.9 $\\pm$ 0.3 & 0.0 & 11.2M \\\\\nWideResnet18 & 42.3 $\\pm$ 0.3 & 2.4 & 25.3M \\\\\nDeepResnet18 & 43.1 $\\pm$ 0.4 & 3.2 & 13.3M \\\\\nDWResnet18 & 43.7 $\\pm$ 0.1 & 3.8 & 19.0M \\\\ \\midrule\nGapCatNoSSAL & 40.2 $\\pm$ 0.3 & 0.3 & 15.6M \\\\\nCatFCNoSSAL & 35.3 $\\pm$ 0.8 & -4.6 & 13.6M \\\\ \\midrule\n\\textbf{LinearComb} & \\textbf{44.1 $\\pm$ 0.1} & \\textbf{4.2} & 12.8M \\\\\n\\textbf{SSAL x1} & \\textbf{45.8 $\\pm$ 0.2} & \\textbf{5.9} & 12.6M \\\\\n\\textbf{SSAL x3} & \\textbf{50.0 $\\pm$ 0.4} & \\textbf{10.1} & 15.6M \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Baselines for SSAL models on TinyImagenet. Models that have a deeper architecture, wider layers, or lack the SSAL objective fail to reach the level of accuracy of SSAL models.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks", "authors": ["Sebastian Palacio", "Philipp Engler", "Jörn Hees", "Andreas Dengel"], "url": "https://arxiv.org/abs/2101.03057v1", "attribution": "\"Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks\" by Sebastian Palacio, Philipp Engler, Jörn Hees, and Andreas Dengel, arXiv:2101.03057v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2501.00452v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|}\n\\hline\nNetwork & Max Novelty Score \\\\\n\\hline\\hline\nGAN & 0.029\\\\\nCAN & 0.013 \\\\\nUnrolled CAN & \\textbf{0.043}\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results. Unrolled CAN is better.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Unrolled Creative Adversarial Network For Generating Novel Musical Pieces", "authors": ["Pratik Nag"], "url": "https://arxiv.org/abs/2501.00452v1", "attribution": "\"Unrolled Creative Adversarial Network For Generating Novel Musical Pieces\" by Pratik Nag, arXiv:2501.00452v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.06442v1_tex_table16.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The impact of channel shuffle, dot-product, and upsampling methods in SubEC on CVIU database, with the best performance values highlighted in bold. The Flops is calculated with input tensor shape $3\\times\\ 32\\times 32$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|cccc}\n\\toprule\nModel & Params/M & Flops/G & Speed/ms & SRCC $\\uparrow$ & KRCC $\\uparrow$ & PLCC $\\uparrow$ & RMSE $\\downarrow$ \\\\\n\\midrule \nw/o Channel Shuffle & 2.220 & 0.179 & 10.981 & 0.970 & 0.853 & 0.971 & 0.654 \\\\\nElement-wise Add & 2.220 & 0.179 & 10.690 & 0.963 & 0.838 & 0.966 & 0.699 \\\\\nTransposed Conv & 2.194 & 0.160 & 10.905 & 0.975 & 0.871 & 0.979 & 0.528\\\\\nBilinear & 2.193 & 0.156 & 9.695 & 0.974 & 0.864 & 0.978 & 0.602 \\\\\nBicubic & 2.193 & 0.156 & 10.336 & 0.971 & 0.857 & 0.976 & 0.655 \\\\\nPBAN (Sub-pixel Conv) & 2.220 & 0.179 & 11.010 & \\textbf{0.978} & \\textbf{0.872} & \\textbf{0.981} & \\textbf{0.397} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment", "authors": ["Yixiao Li", "Xiaoyuan Yang", "Guanghui Yue", "Jun Fu", "Qiuping Jiang", "Xu Jia", "Paul L. Rosin", "Hantao Liu", "Wei Zhou"], "url": "https://arxiv.org/abs/2509.06442v1", "attribution": "\"Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment\" by Yixiao Li, Xiaoyuan Yang, Guanghui Yue, Jun Fu, Qiuping Jiang, Xu Jia, Paul L. Rosin, Hantao Liu, and Wei Zhou, arXiv:2509.06442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.17489v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study of different filter dimensions N in the GDFG. \\textbf{Bold} represents the best result.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\nDimensions $N$ & PSNR$\\uparrow$ & SSIM$\\uparrow$ & LPIPS$\\downarrow$ & G-PSNR$\\uparrow$ & S-PSNR$\\uparrow$ \\\\ \\hline\n2 & 27.891 & 0.899 & 0.0426 & 24.257 & 23.138 \\\\\n3 & 27.958 & 0.901 & 0.0426 & 24.435 & \\textbf{23.355} \\\\\n4 & \\textbf{28.030} & \\textbf{0.903} & \\textbf{0.0422} & \\textbf{24.509} & 23.300 \\\\\n5 & 27.991 & 0.901 & 0.0423 & 24.415 & 23.275 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "DFDNet: Dynamic Frequency-Guided De-Flare Network", "authors": ["Minglong Xue", "Aoxiang Ning", "Shivakumara Palaiahnakote", "Mingliang Zhou"], "url": "https://arxiv.org/abs/2507.17489v1", "attribution": "\"DFDNet: Dynamic Frequency-Guided De-Flare Network\" by Minglong Xue, Aoxiang Ning, Shivakumara Palaiahnakote, and Mingliang Zhou, arXiv:2507.17489v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.19625v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{AI Teaching Impact and Workplace Skill Disruption, OLS}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n& \\textbf{(1)} & \\textbf{(2)} \\\\\n& \\textit{Dep. Var.:} & \\textit{Dep. Var.:} \\\\\n& \\textbf{AI Skill Disruption Index} & \\textbf{AI Teaching Impact} \\\\\n\\midrule\nAI Teaching Impact & 0.758*** & \\\\\n& (0.179) & \\\\\n\\addlinespace\nAI Workplace Skill Disruption & & 0.242*** \\\\\n& & (0.055) \\\\\n\\addlinespace\nSkill Type FE & Yes & Yes \\\\\n\\addlinespace\nIntercept & -0.828 & 0.155 \\\\\n& (0.528) & (0.189) \\\\\n\\midrule\nObservations & 90 & 90 \\\\\nAdj. $R^2$ & 0.470 & 0.273 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Training for Obsolescence? The AI-Driven Education Trap", "authors": ["Andrew J. Peterson"], "url": "https://arxiv.org/abs/2508.19625v1", "attribution": "\"Training for Obsolescence? The AI-Driven Education Trap\" by Andrew J. Peterson, arXiv:2508.19625v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.10317v6_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Regression Estimates of Risk vs Mispricing Effects on Predictability Decay}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc} \\toprule \n & \\multicolumn{5}{c}{LHS: Long-Short Strategy Return (bps pm, scaled) } \\\\\n \\hline\nRHS Variables & (1) & (2) & (3) & (4) & (5) \\\\ \n \\hline\nIntercept & 71.4 & 71.4 & 71.4 & 71.4 & 73.0 \\\\ \n & (3.7) & (3.7) & (3.7) & (3.7) & (3.9) \\\\ \n Post-Sample & -28.9 & -25.4 & -25.5 & -22.9 & -5.6 \\\\ \n & (5.6) & (7.1) & (6.6) & (10.7) & (8.2) \\\\ \n Post-Pub & & -4.2 & & -3.0 & \\\\ \n & & (7.8) & & (12.5) & \\\\ \n Post-Sample x Risk & -19.1 & -7.6 & -22.5 & -10.1 & -15.6 \\\\ \n & (7.7) & (10.2) & (8.5) & (12.9) & (7.6) \\\\ \n Post-Pub x Risk & & -15.2 & & -16.4 & \\\\ \n & & (13.3) & & (15.9) & \\\\ \n Post-Sample x Mispricing & & & -4.5 & -3.2 & \\\\ \n & & & (5.8) & (11.0) & \\\\ \n Post-Pub x Mispricing & & & & -1.8 & \\\\ \n & & & & (12.0) & \\\\ \n Post-2004 & & & & & -33.7 \\\\ \n & & & & & (9.9) \\\\ \n \\hline\nNull: Risk No Decay & $<$ 0.1\\% & $<$ 0.1\\% & $<$ 0.1\\% & $<$ 0.1\\% & $<$ 0.1\\% \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Does Peer-Reviewed Research Help Predict Stock Returns?", "authors": ["Andrew Y. Chen", "Alejandro Lopez-Lira", "Tom Zimmermann"], "url": "https://arxiv.org/abs/2212.10317v6", "attribution": "\"Does Peer-Reviewed Research Help Predict Stock Returns?\" by Andrew Y. Chen, Alejandro Lopez-Lira, and Tom Zimmermann, arXiv:2212.10317v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.17533v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Algorithm for Void Identification}\n\\begin{tabular}{rl}\n\\textbf{Step} & \\textbf{Action} \\\\\n1: & Identify Convex Hull \\(P^{CH}\\) of \\(P\\) \\\\\n2: & Select a starting point \\(P^{CH}_i\\) from \\(P^{CH}\\) \\\\\n3: & For each \\(P^{CH}_i\\), create and analyze segments \\(L_{ij}\\) \\\\\n4: & Calculate MDS(\\(L_{ij}\\)) for each segment \\\\\n5: & Select \\(L^{best}_{ij}\\) with minimum MDS value \\\\\n6: & Apply Delaunay Triangulation and Voronoi Diagrams to select DV point on \\(L^{best}_{ij}\\) \\\\\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Void Shape Identification in a 2D Point Distribution", "authors": ["Netzer Moriya"], "url": "https://arxiv.org/abs/2312.17533v1", "attribution": "\"Void Shape Identification in a 2D Point Distribution\" by Netzer Moriya, arXiv:2312.17533v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.13629v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Writing Rules: Within Paper Comparison}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccc}\n\\hline\n\\hline\n& \\textbf{Rule1a} & \\textbf{Rule1b} & \\textbf{Rule2} & \\textbf{Rule4} & \\textbf{Rule5} & \\textbf{Rule7a} & \\textbf{Rule7b} & \\textbf{Rule8} & \\textbf{Rule9} & \\textbf{Rule10a} & \\textbf{Rule10b} \\\\\n\\hline\n\\textbf{Version1} & -46.44*** & -1.290*** & 4.036*** & 4.830*** & 1.620*** & -5.546*** & 8.502*** & -4.937*** & -2.608*** & -1.080*** & -0.918*** \\\\\n& (0.0685) & (0.00266) & (0.0275) & (0.0160) & (0.00342) & (0.0335) & (0.0538) & (0.0310) & (0.0227) & (0.0159) & (0.0123) \\\\\n\\textbf{Version2} & -45.06*** & -1.346*** & 1.405*** & 3.849*** & 1.437*** & -5.313*** & 5.300*** & -5.473*** & -2.781*** & -1.197*** & -0.956*** \\\\\n& (0.0695) & (0.00269) & (0.0273) & (0.0169) & (0.00345) & (0.0354) & (0.0540) & (0.0322) & (0.0232) & (0.0164) & (0.0125) \\\\\n\\textbf{Version3} & -46.95*** & -1.466*** & 0.254*** & 1.214*** & 1.351*** & -6.183*** & 6.349*** & -5.624*** & -2.819*** & -1.230*** & -0.964*** \\\\\n& (0.0709) & (0.00275) & (0.0281) & (0.0183) & (0.00353) & (0.0362) & (0.0540) & (0.0321) & (0.0231) & (0.0166) & (0.0125) \\\\\n\\textbf{Version4} & -47.20*** & -1.456*** & 0.825*** & 3.535*** & 1.429*** & -5.974*** & 8.112*** & -5.505*** & -2.779*** & -1.206*** & -0.960*** \\\\\n& (0.0706) & (0.00274) & (0.0284) & (0.0173) & (0.00351) & (0.0360) & (0.0545) & (0.0320) & (0.0231) & (0.0165) & (0.0125) \\\\\n\\textbf{Version5} & -44.54*** & -1.387*** & 0.418*** & 3.235*** & 1.437*** & -5.541*** & 7.907*** & -5.111*** & -2.589*** & -1.127*** & -0.903*** \\\\\n& (0.0696) & (0.00269) & (0.0274) & (0.0166) & (0.00340) & (0.0347) & (0.0526) & (0.0311) & (0.0223) & (0.0160) & (0.0122) \\\\\n\\textbf{Version6} & -20.85*** & -0.632*** & -2.377*** & 4.154*** & 0.859*** & -2.620*** & 10.22*** & -4.902*** & -2.590*** & -0.931*** & -0.872*** \\\\\n& (0.0559) & (0.00235) & (0.0273) & (0.0162) & (0.00353) & (0.0320) & (0.0545) & (0.0309) & (0.0224) & (0.0154) & (0.0120) \\\\\n\\textbf{Baseline} & 185.9*** & 6.708*** & 25.31*** & 71.40*** & 14.91*** & 28.15*** & 14.60*** & 12.85*** & 4.392*** & 3.881*** & 1.953*** \\\\\n& (0.0557) & (0.00217) & (0.0217) & (0.0131) & (0.00275) & (0.0276) & (0.0411) & (0.0260) & (0.0191) & (0.0133) & (0.0104) \\\\\n\\textbf{Article FE} & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\\\\n\\hline\n\\textbf{R2} & 0.876 & 0.853 & 0.754 & 0.774 & 0.881 & 0.908 & 0.703 & 0.843 & 0.750 & 0.927 & 0.878 \\\\\n\\textbf{N} & 4,370,848 & 4,370,848 & 4,370,848 & 4,370,848 & 4,370,848 & 4,370,848 & 4,370,848 & 4,370,848 & 4,370,848 & 4,370,848 & 4,370,848 \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing", "authors": ["Cong William Lin", "Wu Zhu"], "url": "https://arxiv.org/abs/2504.13629v1", "attribution": "\"Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing\" by Cong William Lin and Wu Zhu, arXiv:2504.13629v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.15771v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\hline\n \\textbf{Method} & \\textbf{Error control} & \\textbf{Speed} \\\\\n \\hline\n AdaPT (2018) & Finite FDR & Slow \\\\\n AdaPT\\textsubscript{g} (2021) & Finite FDR & Slow \\\\\n AdaPT-GMM\\textsubscript{g} (2021) & Finite FDR & Slow \\\\\n ZAP-asymp (2022) & Asymptotic FDR & Fast \\\\\n AdaFDR (2019) & Asymptotic FDP & Fast \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Summary of some popular and recent p-value based multiple testing procedures that use side information, indicating each method's type-1 error control and speed.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A semi-supervised framework for diverse multiple hypothesis testing scenarios", "authors": ["Jack Freestone", "William Stafford Noble", "Uri Keich"], "url": "https://arxiv.org/abs/2411.15771v2", "attribution": "\"A semi-supervised framework for diverse multiple hypothesis testing scenarios\" by Jack Freestone, William Stafford Noble, and Uri Keich, arXiv:2411.15771v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11803v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccc}\n\\toprule\nCodename & PARAs(K) & \\% & FLOPs(K) & \\% & Testing Accuracy \\\\ \\midrule\n1111111111 & 512.80 & 1.00 & 653.8 & 1.00 & 53.63 \\\\\n1111111122 & 482.34 & 0.94 & 619.6 & 0.94 & 53.12 \\\\\n1111112222 & 451.936 & 0.88 & 587.0 & 0.89 & 52.66 \\\\\n1111112223 & 451.936 & 0.88 & 587.0 & 0.89 & 52.98 \\\\\n1111112233 & 451.936 & 0.88 & 587.0 & 0.89 & 54.20 \\\\\n1111113333 & 451.936 & 0.88 & 587.0 & 0.89 & 52.96 \\\\\n1111114444 & 451.936 & 0.88 & 587.0 & 0.89 & 51.61 \\\\\n1111222222 & 421.504 & 0.82 & 553.7 & 0.84 & 51.69 \\\\\n1111222334 & 421.504 & 0.82 & 553.7 & 0.84 & 52.20 \\\\\n1111223344 & 421.504 & 0.82 & 553.7 & 0.84 & 52.54 \\\\\n1222333444 & 375.856 & 0.73 & 503.6 & 0.77 & 49.15 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results For Weights Pruning on CIFAR 10}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning", "authors": ["Hanhan Zhou", "Tian Lan", "Guru Venkataramani", "Wenbo Ding"], "url": "https://arxiv.org/abs/2201.11803v2", "attribution": "\"On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning\" by Hanhan Zhou, Tian Lan, Guru Venkataramani, and Wenbo Ding, arXiv:2201.11803v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19648v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean and standard error across the 200 scenarios in the training dataset, \\textbf{controlling all vehicles in every scenario} (Self-play). The reported HR-PPO performance is with $\\lambda = 0.06$ (green square in the Figures above).}\n\\begin{tabular}{lrrrrr}\n\\toprule\n& \\textbf{GC-ADE} & \\textbf{Accel MAE} & \\textbf{Action Acc.} (\\%) & \\textbf{Speed MAE} & \\textbf{Steer MAE} \\\\\nAgent & & & & & \\\\ \\midrule\nBC & 0.31 ± 0.01 & 1.71 ± 0.02 & 5.61 ± 0.02 & 0.84 ± 0.02 & 0.02 ± 0.00 \\\\\nHR-PPO & 0.54 ± 0.01 & 2.09 ± 0.02& 3.25 ± 0.01 & 1.82 ± 0.03 & 0.02 ± 0.00 \\\\\nPPO & 1.32 ± 0.03 & 3.93 ± 0.02 & 0.20 ± 0.00 & 5.07 ± 0.08 & 0.08 ± 0.00 \\\\\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Human-compatible driving partners through data-regularized self-play reinforcement learning", "authors": ["Daphne Cornelisse", "Eugene Vinitsky"], "url": "https://arxiv.org/abs/2403.19648v2", "attribution": "\"Human-compatible driving partners through data-regularized self-play reinforcement learning\" by Daphne Cornelisse and Eugene Vinitsky, arXiv:2403.19648v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.12121v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Confusion matrix of classification for three methods (Naive Bayes, Class-weighted SVM and LDA)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc|cc|c}\n\\hline\n\\hline\n\\multirow{2}{*}{True\\textbackslash Pred} & \\multicolumn{2}{c|}{NB} & \\multicolumn{2}{c|}{SVM$_{cw}$} & \\multicolumn{2}{c|}{LDA} & \\multirow{2}{*}{Total} \\\\\n & 1 & 0 & 1 & 0 & 1 & 0 & \\\\\n \\hline\n1 & 0 & 582 & 0 & 582 & 361 & 221 & 582 \\\\\n0 & 0 & 4004 & 0 & 4004 & 188 & 3816 & 4004 \\\\\nTotal & 0 & 4586 & 0 & 4586 & 549 & 4037 & 4586 \\\\\n\\hline\nAccuracy & \\multicolumn{2}{c|}{0.873} & \\multicolumn{2}{c|}{0.873} & \\multicolumn{2}{c|}{0.907} & \\\\\n\\hline\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies", "authors": ["Xinwen Ni", "Wolfgang Karl Härdle", "Taojun Xie"], "url": "https://arxiv.org/abs/2009.12121v3", "attribution": "\"A Machine Learning Based Regulatory Risk Index for Cryptocurrencies\" by Xinwen Ni, Wolfgang Karl Härdle, and Taojun Xie, arXiv:2009.12121v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04898v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Clean test accuracy of RN-18 models trained on unlearnable CIFAR-10 by either small patch noise applied at random location or a mixture of two type of noises.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc|cc}\n\\hline\n\\multirow{2}{*}{\\textbf{Methods}} & \\multicolumn{4}{c|}{\\textbf{Patch Size}} & \\multicolumn{2}{c}{\\textbf{Noise Mixture}} \\\\\n & \\textbf{$8\\times8$} & \\textbf{$16\\times16$} & \\textbf{$24\\times24$} & \\textbf{$32\\times32$} & \\textbf{$\\Delta_{c1}\\lor\\Delta_{c2}$} & \\textbf{$\\Delta_{c1}+\\Delta_u$} \\\\ \\hline\nStandard Training & 87.23 & 19.19 & 26.42 & 16.42 & 25.41 & 14.94 \\\\\nFast Autoaugment & 90.66 & 36.69 & 50.60 & 22.89 & 56.20 & 30.40 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Unlearnable Examples: Making Personal Data Unexploitable", "authors": ["Hanxun Huang", "Xingjun Ma", "Sarah Monazam Erfani", "James Bailey", "Yisen Wang"], "url": "https://arxiv.org/abs/2101.04898v2", "attribution": "\"Unlearnable Examples: Making Personal Data Unexploitable\" by Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, and Yisen Wang, arXiv:2101.04898v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.06711v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Metadata of the processed datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n \t\t\t\\hline\n \t\t\t\\textbf{Datasets} & \\# records & $|F^S|$ & \\# subgroups \\\\\n \\hline\n Attrition & 59,598 & 19 & 8 \\\\\n \t\t\t\\hline\n \t\t\tMobile & 2,000 & 15 & 2 \\\\\n \t\t\t\\hline\n \t\t\tLoan & 67,463 & 15 & 3 \\\\\n \t\t\t\\hline\n \t\t\t$SD1$ & 50,000 & 15 & 4 \\\\\n \t\t\t\\hline\n \t\t\t$SD2$ & 50,000 & 20 & 4 \\\\\n \t\t\t\\hline\n \t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "MISFEAT: Feature Selection for Subgroups with Systematic Missing Data", "authors": ["Bar Genossar", "Thinh On", "Md. Mouinul Islam", "Ben Eliav", "Senjuti Basu Roy", "Avigdor Gal"], "url": "https://arxiv.org/abs/2412.06711v1", "attribution": "\"MISFEAT: Feature Selection for Subgroups with Systematic Missing Data\" by Bar Genossar, Thinh On, Md. Mouinul Islam, Ben Eliav, Senjuti Basu Roy, and Avigdor Gal, arXiv:2412.06711v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08720v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\nHeading level & Example & Font size and style\\\\\n\\hline\nTitle (centered) & {\\Large\\bfseries Lecture Notes} & 14 point, bold\\\\\n1st-level heading & {\\large\\bfseries 1 Introduction} & 12 point, bold\\\\\n2nd-level heading & {\\bfseries 2.1 Printing Area} & 10 point, bold\\\\\n3rd-level heading & {\\bfseries Run-in Heading in Bold.} Text follows & 10 point, bold\\\\\n4th-level heading & {\\itshape Lowest Level Heading.} Text follows & 10 point, italic\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Leafy Automata for Higher-Order Concurrency", "authors": ["Alex Dixon", "Ranko Lazić", "Andrzej S. Murawski", "Igor Walukiewicz"], "url": "https://arxiv.org/abs/2101.08720v1", "attribution": "\"Leafy Automata for Higher-Order Concurrency\" by Alex Dixon, Ranko Lazić, Andrzej S. Murawski, and Igor Walukiewicz, arXiv:2101.08720v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2302.13429v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimates of the Production Function and Productivity Parameters}\n\\begin{tabular}{ccccc}\n\\toprule[1pt]\n\\multicolumn{5}{c}{\\bf Panel A: Elasticities and Returns to Scale} \\\\\n & Mean & 1st Qu. & Median & 3rd Qu. \\\\ \\cline{2-5} \nCapital elasticity & 0.0361 & 0.0213 & 0.0368 & 0.0507 \\\\\n & (0.0080) & (0.0094) & (0.0081) & (0.0099) \\\\\nLabor elasticity & 0.0950 & 0.0458 & 0.0864 & 0.1287 \\\\\n & (0.0003) & (0.0001) & (0.0002) & (0.0004) \\\\\nMaterial elasticity & 0.7377 & 0.7041 & 0.7463 & 0.7869 \\\\\n & (0.0020) & (0.0019) & (0.0021) & (0.0022) \\\\\nRTS & 0.8688 & 0.8540 & 0.8695 & 0.8834 \\\\\n & (0.0081) & (0.0095) & (0.0082) & (0.0100) \\\\\n\\midrule\n\\multicolumn{5}{c}{\\bf Panel B: Productivity Parameters} \\\\ \n\\multicolumn{2}{c}{\\sl Labor-Augmenting} & & \\multicolumn{2}{c}{\\sl Factor-Neutral} \\\\ \\cline{1-2} \\cline{4-5}\n$\\rho_{\\varphi,0}$& 0.0000 &&$\\rho_{\\omega,0}$ & 0.6525 \\\\\n & --- & & & (0.1026) \\\\\n$\\rho_{\\varphi,1}$ & 0.5723 & & $\\rho_{\\omega,1}$ & 0.7562 \\\\\n & (0.0244) & & & (0.0307) \\\\\n$\\rho_{\\varphi,2}$ & 0.0726 & & $\\rho_{\\omega,2}$ & 0.0020 \\\\\n & (0.0183) & & & (0.0084) \\\\ \n\\midrule\n\\multicolumn{5}{p{9cm}}{\\footnotesize {\\sc Notes:} The productivity processes are parameterized as follows: $\\varphi_{it}=\\rho_{\\varphi,1}\\varphi_{it-1}+\\rho_{\\varphi,2}Z_{it-1}+\\zeta_{\\varphi,it}$ with $\\rho_{\\varphi,0}$ normalized to 0, and $\\omega_{it}=\\rho_{\\omega,0}+\\rho_{\\omega,1}\\omega_{it-1}+\\rho_{\\omega,2}Z_{it-1}+\\zeta_{\\omega,it}$. Bootstrap standard errors are in parentheses.} \\\\\n\\bottomrule[1pt]\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A System Approach to Structural Identification of Production Functions with Multi-Dimensional Productivity", "authors": ["Emir Malikov", "Shunan Zhao", "Jingfang Zhang"], "url": "https://arxiv.org/abs/2302.13429v1", "attribution": "\"A System Approach to Structural Identification of Production Functions with Multi-Dimensional Productivity\" by Emir Malikov, Shunan Zhao, and Jingfang Zhang, arXiv:2302.13429v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.00213v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{siunitx}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Scenario Details}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n & 4-Player Scenario & 10-Player Scenario & CITR \\\\\n \\midrule\n \\midrule\n Scenario size (position limit) & $\\SI{5}{\\meter}\\times\\SI{5}{\\meter}$ & $\\SI{7}{\\meter}\\times\\SI{7}{\\meter}$ & $\\SI{7.5}{\\meter}\\times\\SI{25.5}{\\meter}$ \\\\\n \\midrule\n Observation interval & \\multicolumn{3}{c}{10 Steps $=\\SI{1}{\\second}$} \\\\\n \\midrule\n Receding horizon steps & \\multicolumn{2}{c}{50 Steps $=\\SI{5}{\\second}$} & Vary by data \\\\\n \\midrule\n Velocity limit & \\multicolumn{2}{c}{$v_{x,k}^i, v_{y,k}^i\\in[-2,2]$} & $v_{x,k}^i\\in[-1, 1.5],\\ v_{y,k}^i\\in[-2.5,2.5]$ \\\\\n \\midrule\n Control limit & \\multicolumn{2}{c}{$a_{x,k}^i, a_{y,k}^i\\in[-1,1]$} & $a_{x,k}^i, a_{y,k}^i\\in[-0.5,0.5]$ \\\\\n \\midrule\n Game weight & \\multicolumn{2}{c}{$\\textbf{w}^i=[1, 1, 0.1, 2]$} & $\\textbf{w}^i=[0.3, 1, 0.1, 1]$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "PSN Game: Game-theoretic Planning via a Player Selection Network", "authors": ["Tianyu Qiu", "Eric Ouano", "Fernando Palafox", "Christian Ellis", "David Fridovich-Keil"], "url": "https://arxiv.org/abs/2505.00213v1", "attribution": "\"PSN Game: Game-theoretic Planning via a Player Selection Network\" by Tianyu Qiu, Eric Ouano, Fernando Palafox, Christian Ellis, and David Fridovich-Keil, arXiv:2505.00213v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.14111v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule \n$f$~[Hz] & 5 & 10 & 20 & 100 \\\\\n\\midrule\nARE [$^\\circ$] & 0.662 & 0.635 & 0.449 & 0.505 \\\\\nPhE [$\\cdot 10^5$] & 1.336 & 1.284 & 1.123 & 1.124 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Event-based Photometric Bundle Adjustment", "authors": ["Shuang Guo", "Guillermo Gallego"], "url": "https://arxiv.org/abs/2412.14111v2", "attribution": "\"Event-based Photometric Bundle Adjustment\" by Shuang Guo and Guillermo Gallego, arXiv:2412.14111v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.19307v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\t\t\\toprule\n\t\t& \\textbf{Low} & \\textbf{Medium}&\\textbf{High}&\\textbf{Extreme}\\\\\n\t\t\\midrule\n\t\t\\rowcolor{gray!15}\n\t\tLow & 1.51 & 3.75 & 6.62 & 11.57 \\\\ \n\t\t\\rowcolor{gray!15}\n\t\t& (1.69) & (4.42) & (7.76) & (13.00) \\\\ \n\t\tMedium & 6.72 & 9.34 & 15.37 & 27.17 \\\\ \n\t\t& (5.56) & (12.19) & (18.57) & (29.98) \\\\ \n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Climate Physical Risk Assessment in Asset Management", "authors": ["Michele Azzone", "Matteo Ghesini", "Davide Stocco", "Lorenzo Viola"], "url": "https://arxiv.org/abs/2504.19307v1", "attribution": "\"Climate Physical Risk Assessment in Asset Management\" by Michele Azzone, Matteo Ghesini, Davide Stocco, and Lorenzo Viola, arXiv:2504.19307v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.17536v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lll}\n \\toprule\n $P_1$ & instruction & Given the possible intents: \\{$L$\\} \\\\\n \\cmidrule{2-3}\n & input & What is the user's intent in '\\texttt{\\bf x}'? Intent: \\\\ \n \\toprule\n $P_2$ & instruction & Given the following options: \\{$L$\\} \\\\\n \\cmidrule{2-3}\n & input & What did the user want when the user said, '\\texttt{\\bf x}'? Answer:\\\\\n \\toprule\n $P_3$ & instruction & Classify the USER's utterances into one of the following\\\\\n & & intent options: \\{$L$\\} \\\\\n \\cmidrule{2-3}\n & input & USER: '\\texttt{\\bf x}' Intent: \\\\\n \\toprule\n $P_4$ & instruction & Given a USER's utterance, choose one of the following\\\\\n & & intents: \\{$L$\\} \\\\\n \\cmidrule{2-3}\n & input & USER: '\\texttt{\\bf x}' Intent: \\\\\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template variations for IC.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler", "authors": ["Paramita Mirza", "Viju Sudhi", "Soumya Ranjan Sahoo", "Sinchana Ramakanth Bhat"], "url": "https://arxiv.org/abs/2403.17536v1", "attribution": "\"ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler\" by Paramita Mirza, Viju Sudhi, Soumya Ranjan Sahoo, and Sinchana Ramakanth Bhat, arXiv:2403.17536v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16385v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\n\\multicolumn{3}{|c|}{\\textbf{ISA}} & \\multicolumn{3}{|c|}{\\textbf{Collisional OT}}\\\\\n\\hline\nNo. Coll. & Time [s] & Rel. Error [-] & No. Coll. & Time [s] & Rel. Error [-] \\\\\n\\hline\n$N_p^2/2$ &\n\\textbf{0.146 ± 0.021}&\n\\textbf{9.164e-03 ±3.611e-04}&\n500$(N_p/2)$ &\n{0.037 ± 0.003}&\n{3.706e-02 ± 4.738e-04}\n\\\\ \\hline \n2$(N_p^2/2)$ &\n0.287 ± 0.020 &\n9.972e-04 ± 1.017e-04 &\n1000$(N_p/2)$ &\n{0.066 ± 0.001} &\n{1.927e-02 ± 8.256e-05} \n\\\\ \\hline \n3$(N_p^2/2)$ &\n0.423 ± 0.020&\n5.659e-04 ± 6.927e-05&\n1500$(N_p/2)$ &\n\\textbf{0.114 ± 0.010} &\n\\textbf{1.239e-02 ± 7.734e-04}\n\\\\ \\hline \n4$(N_p^2/2)$ &\n0.529 ± 0.006 &\n5.366e-04 ± 2.189e-05 &\n2000$(N_p/2)$&\n{0.149 ± 0.006} &\n{9.247e-03 ± 3.580e-04} \n\\\\ \\hline \n5$(N_p^2/2)$ &\n0.651 ± 0.017&\n5.109e-04 ± 9.676e-05 &\n10000$(N_p/2)$&\n0.699 ± 0.010&\n1.820e-03 ± 1.494e-04\n\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Evolution of relative error and the execution time in finding the Wasserstein distance between Banana and Normal distribution given $N_p=8000$ samples repeated $20$ times using ISA and collisional OT.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Collision-based Dynamics for Multi-Marginal Optimal Transport", "authors": ["Mohsen Sadr", "Hossein Gorji"], "url": "https://arxiv.org/abs/2412.16385v2", "attribution": "\"Collision-based Dynamics for Multi-Marginal Optimal Transport\" by Mohsen Sadr and Hossein Gorji, arXiv:2412.16385v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.03168v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ccccc} \n \\hline\n $z $& $(3,4)$& $(3,3 )$&$(2,4)$ &otherwise\\\\\n\\hline\n$P^z_{(1,3),(2,1)}$& $\\frac{2}{7}$ & $\\frac{3}{7}$ & $\\frac{2}{7}$ & 0\\\\\n\\hline \n \\end{tabular}\n\\caption{Results of aggregation $(1,3)+(2,1)$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Lattice aggregations of boxes and symmetric functions", "authors": ["Natasha Rozhkovskaya"], "url": "https://arxiv.org/abs/2312.03168v1", "attribution": "\"Lattice aggregations of boxes and symmetric functions\" by Natasha Rozhkovskaya, arXiv:2312.03168v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.00210v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{SNPs and its associated gene selected by the GPLM-BAR method.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n\\hline\nSNP & Gene \\\\\n\\hline\n rs1407961 & ZMYM2 \\\\\n rs8037353 & GABRG3 \\\\\n rs7107322 & PRCP \\\\\n rs2387952 & ADARB2 \\\\\n rs6680365 & CAMTA1 \\\\\n rs3136558 & IL1B \\\\\n rs4131888 & SLC7A11 \\\\\n rs3845439 & CACNA1E\\\\\n rs769449 & APOE \\\\\n rs9549675 & F10 \\\\\n rs2805543 & ADARB2 \\\\\n rs821292 & GFOD1 \\\\\n rs12612481 & PDE11A \\\\\n rs7188981 & RBFOX1 \\\\\n rs9932172 & RBFOX1 \\\\\n rs9282537 & ABCA1 \\\\\n rs244072 & ADA \\\\\n rs11859718 & CDH13 \\\\\n rs17585580 & CNTN5 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Broken Adaptive Ridge Method for Variable Selection in Generalized Partly Linear Models with Application to the Coronary Artery Disease Data", "authors": ["Christian Chan", "Xiaotian Dai", "Thierry Chekouo", "Quan Long", "Xuewen Lu"], "url": "https://arxiv.org/abs/2311.00210v1", "attribution": "\"Broken Adaptive Ridge Method for Variable Selection in Generalized Partly Linear Models with Application to the Coronary Artery Disease Data\" by Christian Chan, Xiaotian Dai, Thierry Chekouo, Quan Long, and Xuewen Lu, arXiv:2311.00210v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.06919v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Relative errors and the number of breaking points for Burgers' equation with sinusoidal initial data}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\nTime & $\\frac{\\|\\tilde{u}(\\cdot, t_k)- u^{(k)}_{N}\\|_{L^2(\\Omega)}}{\\|\\tilde{u}(\\cdot, t_k)\\|_{L^2(\\Omega)}}$ & $n_k$ \\\\ \\hline \n0.0 & $6.6923\\times 10^{-4}$ & 78\\\\ \\hline\n0.1 & $7.8352\\times 10^{-4}$ & 78\\\\ \\hline\n0.2 & $4.0166\\times 10^{-2}$ & 56\\\\ \\hline\n0.3 & $5.1491\\times 10^{-2}$ & 38 \\\\ \\hline\n0.4 & $5.3515\\times 10^{-2}$ & 30\\\\ \\hline\n0.5 & $5.4162\\times 10^{-2}$ & 25\\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Evolving Neural Network (ENN) Method for One-Dimensional Scalar Hyperbolic Conservation Laws: I Linear and Quadratic Fluxes", "authors": ["Zhiqiang Cai", "Brooke Hejnal"], "url": "https://arxiv.org/abs/2312.06919v1", "attribution": "\"Evolving Neural Network (ENN) Method for One-Dimensional Scalar Hyperbolic Conservation Laws: I Linear and Quadratic Fluxes\" by Zhiqiang Cai and Brooke Hejnal, arXiv:2312.06919v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.16693v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllllll}\n\\hline\n&&&&&&&&&\\\\\n$\\vartheta_A$ & $\\vartheta_U$ &$\\mathcal{T}$ &$\\alpha$&$\\beta$&$\\gamma$& $t_1$ &$t_2$&$n$&ETC\\\\\n&&&&&&&&&\\\\\n\\hline \n&&&&&&&&&\\\\\n200&100&100&0.05&0.05&28&186.2786&186.2793&34&228.1767\\\\\n&&&&&&&&&\\\\\n&&&0.01&0.05&26&178.3335&178.3342&29&232.5816\\\\\n&&&&&&&&&\\\\\n&&&0.01&0.01&23&177.5453&177.5455&45&220.3530\\\\\n&&&&&&&&&\\\\\n500&200&50&0.05&0.05&12&400.025&400.0248&25&627.7466\\\\\n&&&&&&&&&\\\\\n&&&0.01&0.05&15&396.9158&396.9158&26&635.9504\\\\\n&&&&&&&&&\\\\\n&&&0.01&0.01&14&405.41&405.4198&26&636.7619\\\\\n&&&&&&&&&\\\\\n500&200&100&0.05&0.05&18&423.5114&423.5116&43&600.3209\\\\\n&&&&&&&&&\\\\\n&&&0.01&0.05&22&405.5332&405.5335&38&620.4932\\\\\n&&&&&&&&&\\\\\n&&&0.01&0.01&24&411.5643&411.5644&37&623.4914\\\\\n&&&&&&&&&\\\\\n3000&1500&1000&0.05&0.05&19&3783.5928&3783.5929&29&3449.2903\\\\\n&&&&&&&&&\\\\\n&&&0.01&0.05&11&2507.4709&2507.471&30&3411.8618\\\\\n&&&&&&&&&\\\\\n&&&0.01&0.01&9&2557.26&2557.2699&27&3450.2116\\\\\n&&&&&&&&&\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ASP under Type I HCS using Linex Loss for $c=-0.5$ and C=1.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Optimal variable acceptance sampling plan for exponential distribution using Bayesian estimate under Type I hybrid censoring", "authors": ["Ashlyn Maria Mathai", "Mahesh Kumar"], "url": "https://arxiv.org/abs/2311.16693v1", "attribution": "\"Optimal variable acceptance sampling plan for exponential distribution using Bayesian estimate under Type I hybrid censoring\" by Ashlyn Maria Mathai and Mahesh Kumar, arXiv:2311.16693v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.12510v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccccc|ccccc|}\n\\hline \\hline\n & \\multicolumn{3}{c}{\\textbf{VaR}} & $\\mathbf{HHI}_g$ &$\\mathbf{HHI}_b$ & & {\\textbf{VaR}} & &$\\mathbf{HHI}_g$ &$\\mathbf{HHI}_b$\\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-6} \\cmidrule(lr){7-9} \\cmidrule(lr){10-11}\n $n$ & 99\\% & 99.5\\% & 99.9\\% & \\multicolumn{2}{c|}{$a=0$} & 99\\% &99.5\\% & 99.9\\% & \\multicolumn{2}{c|}{$a=0.6$} \\\\\n\\midrule\n\\multirow{2}{*}{$500$} \n & 0.0320 & 0.0400 & 0.0520 & & & 0.0354 & 0.0484 & 0.0649 & &\\\\\n & (0.0048) & (0.0059) & (0.0084) & 0.0066 & 0.0028 & (0.0091) & (0.0159) & (0.0241) & 0.0136 & 0.0070\\\\\n\\midrule\n\\multirow{2}{*}{$1000$} \n & 0.0290 & 0.0370 & 0.0480 & & & 0.0321 & 0.0425 & 0.0558 & &\\\\\n & (0.0018) & (0.0029) & (0.0044) & 0.0033 & 0.0014 & (0.0058) & (0.0100) & (0.0150) & 0.0079 & 0.0040\\\\\n\\midrule\n\\multirow{2}{*}{$2000$} \n & 0.0280 & 0.0355 & 0.0455 & & & 0.0299 & 0.0384 & 0.0499 & &\\\\\n &(0.0008) & (0.0014) & (0.0019) & 0.0017 & 0.0007 & (0.0036) & (0.0060) & (0.0091) & 0.0046 & 0.0023\\\\\n\\midrule\n\\multirow{2}{*}{$3000$} \n & 0.0280 & 0.0353 & 0.0453 & & & 0.0290 & 0.0369 & 0.0473& &\\\\\n & (0.0008) & (0.0012) & (0.0017) & 0.0011 & 0.0005 & (0.0027) & (0.0044) & (0.0065) & 0.0033 & 0.0017 \\\\\n\\midrule\n\\multirow{2}{*}{$4000$} \n & 0.0277 & 0.0348 & 0.0447 & & & 0.0285 & 0.0359 & 0.0459& &\\\\\n & (0.0006) & (0.0006) & (0.0012) & 0.0008 & 0.0004 & (0.0022) & (0.0034) & (0.0051) & 0.0026 & 0.0014\\\\\n\\midrule\n\\multirow{2}{*}{$5000$} \n & 0.0276 & 0.0348 & 0.0446 & & & 0.0281 & 0.0355 & 0.0452 & &\\\\\n & (0.0004) & (0.0007) & (0.0010) & 0.0007 & 0.0003 & (0.0018) & (0.0030) & (0.0044) & 0.0022 & 0.0011\\\\\n\\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Numerical results related to converge analysis for scenario 2, with $a=0$ and $a=0.6$. Empirical quantiles are reported, and, in parentheses, the error with respect to the theoretical value, $\\widehat{VaR_{\\beta}} - VaR_{\\beta}$. }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Credit risk for large portfolios of green and brown loans: extending the ASRF model", "authors": ["Alessandro Ramponi", "Sergio Scarlatti"], "url": "https://arxiv.org/abs/2506.12510v1", "attribution": "\"Credit risk for large portfolios of green and brown loans: extending the ASRF model\" by Alessandro Ramponi and Sergio Scarlatti, arXiv:2506.12510v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table21.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc}\n & \\multicolumn{5}{c}{Sensitivity problem} \\\\\nLink & (1,2) & (1,3) & (2,3) & (2,4) & (3,4) \\\\\n\\hline\n(1,2) & 3.36 & $-0.17$ & 0.15 & 0.014 & $-0.28$ \\\\\n(1,3) & $-3.36$ & 0.17 & $-0.15$ & $-0.014$ & 0.28 \\\\\n(2,3) & 3.08 & $-0.15$ & 0.31 & $-0.15$ & 3.08 \\\\\n(2,4) & 0.28 & $-0.014$ & $-0.15$ & 0.17 & $-3.36$ \\\\\n(3,4) & $-0.28$ & 0.014 & 0.15 & $-0.17$ & 3.36 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.02873v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n \\hline\n & $D^1_N$& Roots of $D^1_N$\\\\\n \\hline \\hline\n &&\\\\\n $N=2$& $-B_1$& 2.0600\\\\\n \\hline \n &&\\\\\n $N=3$& $B_1B_2 - A_1C_2$ & 7.9419\\\\\n & & 2.1218\\\\\n \\hline\n &&\\\\\n $N=4$& $A_2B_1C_3+A_1B_3C_2-B_1B_2B_3$ & 17.7303\\\\\n & &8.0194\\\\ \n & &2.1423\\\\\n \\hline\n &&\\\\\n $N=5$& $A_1A_3C_2C_4 - A_1B_3B_4C_2 - A_2B_1B_4C_3$ &31.4791\\\\\n & $ - A_3B_1B_2C_4 + B_1B_2B_3B_4$ &17.5329\\\\\n & &8.1618\\\\\n & &2.1491\\\\\n \\hline\n &&\\\\\n $N=6$& $A_1B_3B_4B_5C_2 - A_1A_4B_3C_2C_5 - A_1A_3B_5C_2C_4$ & 49.3158\\\\\n & $- A_2A_4B_1C_3C_5 + A_2B_1B_4B_5C_3 + A_3B_1B_2B_5C_4$ & 30.5320\\\\\n & $+A_4B_1B_2B_3C_5 - B_1B_2B_3B_4B_5$ &17.8975\\\\\n & & 8.2353\\\\\n & &2.1517\\\\ \n \\hline\n &&\\\\\n $N=7$& $A_1A_3B_5B_6C_2C_4 - A_1A_3A_5C_2C_4C_6+A_1A_4B_3B_6C_2C_5$ & 71.3459\\\\\n & $ + A_1A_5B_3B_4C_2C_6-A_1B_3B_4B_5B_6C_2 + A_2A_4B_1B_6C_3C_5$ &47.0817\\\\\n & $-A_1B_3B_4B_5B_6C_2 + A_2A_4B_1B_6C_3C_5 + A_2A_5B_1B_4C_3C_6$ &31.0721\\\\\n & $- A_2B_1B_4B_5B_6C_3 + A_3A_5B_1B_2C_4C_6 - A_3B_1B_2B_5B_6C_4$ &18.1722\\\\\n & $- A_4B_1B_2B_3B_6C_5 - A_5B_1B_2B_3B_4C_6 + B_1B_2B_3B_4B_5B_6$ &8.2712\\\\\n & &2.1528\\\\\n \\hline\n &&\\\\\n $N=8$& $A_1A_3A_5B_7C_2C_4C_6 + A_1A_3A_6B_5C_2C_4C_7 - A_1A_3B_5B_6B_7C_2C_4$&97.6417\\\\\n & $+ A_1A_4A_6B_3C_2C_5C_7 - A_1A_4B_3B_6B_7C_2C_5 - A_1A_5B_3B_4B_7C_2C_6$& 67.3382\\\\\n &$- A_1A_6B_3B_4B_5C_2C_7 + A_1B_3B_4B_5B_6B_7C_2 + A_2A_4A_6B_1C_3C_5C_7$& 47.5424\\\\\n &$- A_2A_4B_1B_6B_7C_3C_5 - A_2A_5B_1B_4B_7C_3C_6 - A_2A_6B_1B_4B_5C_3C_7$&31.6907\\\\\n &$+ A_2B_1B_4B_5B_6B_7C_3 - A_3A_5B_1B_2B_7C_4C_6 - A_3A_6B_1B_2B_5C_4C_7$& 18.3334\\\\\n &$+ A_3B_1B_2B_5B_6B_7C_4 - A_4A_6B_1B_2B_3C_5C_7 + A_4B_1B_2B_3B_6B_7C_5$& 8.2896\\\\\n &$+ A_5B_1B_2B_3B_4B_7C_6 + A_6B_1B_2B_3B_4B_5C_7 - B_1B_2B_3B_4B_5B_6B_7$ & 2.1534\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Expressions and roots of the polynomial $D^1_N$ for different choices of $N,$ with parameters $a(z)=z+1$, $b(z)=(z+1)^2$, $z_l=0$, $z_r=1$, and $\\delta v=0.1$.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A novel difference equation approach for the stability and robustness of compact schemes for variable coefficient PDEs", "authors": ["Anindya Goswami", "Kuldip Singh Patel", "Pradeep Kumar Sahu"], "url": "https://arxiv.org/abs/2209.02873v3", "attribution": "\"A novel difference equation approach for the stability and robustness of compact schemes for variable coefficient PDEs\" by Anindya Goswami, Kuldip Singh Patel, and Pradeep Kumar Sahu, arXiv:2209.02873v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.11765v1_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overview of the News Article Dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n& \\textbf{2018} & \\textbf{2019} & \\textbf{2020}\n\\\\ \n\\hline\n\\textbf{\\# of companies} & 2,665 & 2,940 & 2,879 \\\\\n\\textbf{\\# of articles} & 1,165,840 & 1,399,277 & 1,174,754 \\\\ \n\\hline\n\\textbf{\\# of articles per company} & & & \\\\\n\\textit{\\textbf{average}} & 435 & 473 & 406 \\\\\n\\textit{\\textbf{max}} & 1,984 & 2,154 & 2,005 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis", "authors": ["Tanja Aue", "Adam Jatowt", "Michael Färber"], "url": "https://arxiv.org/abs/2212.11765v1", "attribution": "\"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis\" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.12510v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Unlabelled PPG corpora used for pipeline development.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\textbf{Corpus} & \\textbf{Site} & \\textbf{Native $f_s$} & \\textbf{Hours} & \\textbf{LED}\\\\\n \\midrule\n WildPPG~ & wrist & 128 Hz & 216 & green \\\\\n We-Be~ & wrist & 25 Hz & 60 & green \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Self-Supervised and Topological Signal-Quality Assessment for Any PPG Device", "authors": ["Wei Shao", "Ruoyu Zhang", "Zequan Liang", "Ehsan Kourkchi", "Setareh Rafatirad", "Houman Homayoun"], "url": "https://arxiv.org/abs/2509.12510v1", "attribution": "\"Self-Supervised and Topological Signal-Quality Assessment for Any PPG Device\" by Wei Shao, Ruoyu Zhang, Zequan Liang, Ehsan Kourkchi, Setareh Rafatirad, and Houman Homayoun, arXiv:2509.12510v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2303.04236v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c||c|c|}\n\t\t\\hline\n\t\tParameter & $B_1$ & $B_2$ & $C_1$ & $C_2$ \\\\\n\\hline\n\t\t$\\beta$ & $8$ & $6$ & $8$ & $8$ \\\\\n\t\t$b$ & $0.4$ & $0.4$ & $0.8$ & $0.4$ \\\\\n\t\t$\\mu$ & $16\\%$ & $10 \\%$ & $-5 \\%$ & $16 \\%$ \\\\\n\t\t$\\eta$ & $2$ & $4$ & $4$ & $4$ \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\caption{Parameter sets $B_1,B_2,C_1$ and $C_2.$}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Optimal investment with insurable background risk and nonlinear portfolio allocation frictions", "authors": ["Hugo E. Ramirez", "Rafael Serrano"], "url": "https://arxiv.org/abs/2303.04236v1", "attribution": "\"Optimal investment with insurable background risk and nonlinear portfolio allocation frictions\" by Hugo E. Ramirez and Rafael Serrano, arXiv:2303.04236v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.01467v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Simulation t-statistics}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n n=2 & n=3 & n=4 & n=5 \\\\\n \\midrule\n 121 & -27 & -54 & -118 \\\\\n 863 & 48 & 133 & 201 \\\\\n 861 & 60 & 163 & 152 \\\\\n -504 & 48 & 2 & -502 \\\\\n & -68 & 133 & -152 \\\\\n & 80 & -247 & 201 \\\\\n & 60 & -26 & -583 \\\\\n & 80 & 190 & 172 \\\\\n & -91 & 163 & 205 \\\\\n & & -26 & 61 \\\\\n & & -255 & 152 \\\\\n & & 204 & 172 \\\\\n & & 2 & -177 \\\\\n & & 190 & 174 \\\\\n & & 204 & 138 \\\\\n & & -60 & -502 \\\\\n & & & 205 \\\\\n & & & 174 \\\\\n & & & -89 \\\\\n & & & -334 \\\\\n & & & -152 \\\\\n & & & 61 \\\\\n & & & 138 \\\\\n & & & -334 \\\\\n & & & -75 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Risk in Network Economies", "authors": ["Victor Sellemi"], "url": "https://arxiv.org/abs/2208.01467v1", "attribution": "\"Risk in Network Economies\" by Victor Sellemi, arXiv:2208.01467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.09782v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rr}\n \\hline\n\\hline\n & MSE & MDA \\\\ \n \\hline\nFIPC vs. TMI & 24.97 & 0.97 \\\\ \n IPC vs. TMI & 4743.50 & 0.91 \\\\ \n \\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of IPC with CRIX methodology (FIPC) and rescaled IPC against TMI}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "CRIX an index for cryptocurrencies", "authors": ["Simon Trimborn", "Wolfgang Karl Härdle"], "url": "https://arxiv.org/abs/2009.09782v1", "attribution": "\"CRIX an index for cryptocurrencies\" by Simon Trimborn and Wolfgang Karl Härdle, arXiv:2009.09782v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01931v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dataset statistics after preprocessing: number of users, items, interactions.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\t\t\\toprule\n\t\tDataset & Users & Items & Interactions \\\\\n\t\t\\midrule\n\t\tMovieLens-10M & 71{,}567 & 10{,}677 & 7{,}972{,}582 \\\\\n\t\tEachMovie & 74{,}425 & 1{,}649 & 2{,}216{,}887 \\\\\n\t\tJester & 54{,}906 & 151 & 1{,}450{,}010 \\\\\n\t\tAmazon-book & 52{,}643 & 91{,}599 & 2{,}984{,}108 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Differentially private and decentralized randomized power method", "authors": ["Julien Nicolas", "César Sabater", "Mohamed Maouche", "Sonia Ben Mokhtar", "Mark Coates"], "url": "https://arxiv.org/abs/2411.01931v3", "attribution": "\"Differentially private and decentralized randomized power method\" by Julien Nicolas, César Sabater, Mohamed Maouche, Sonia Ben Mokhtar, and Mark Coates, arXiv:2411.01931v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11653v3_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c|c||} \n \\hline\n Name & How many different digits in the batch & batch size and hyper-parameters\\\\ \n \\hline\n \\hline\n single & 1 & $\\cdot$ \\\\\n \\hline \n pair & 2 & $\\cdot$ \\\\ \n \\hline \n 5nums & 5 & $\\cdot$ \\\\\n \\hline \n original & random & $\\cdot$ \\\\\n \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Experiment details. $\\cdot$ indicates the baseline setting.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Representations learnt by SGD and Adaptive learning rules: Conditions that vary sparsity and selectivity in neural networks", "authors": ["Jin Hyun Park"], "url": "https://arxiv.org/abs/2201.11653v3", "attribution": "\"Representations learnt by SGD and Adaptive learning rules: Conditions that vary sparsity and selectivity in neural networks\" by Jin Hyun Park, arXiv:2201.11653v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.13777v3_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{hhline}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l||c|c|c|c|c|c|c|c|c|}\n \\hline\n & $h=10$ & $h=20$ & $h=30$ & $h=50$ & $h=100$ & $h=200$ & $h=300$ & $h=500$ & $h=1000$ \\\\\n \\hhline{|=#=|=|=|=|=|=|=|=|=|}\n LILAK & \\cellcolor[rgb]{ .557, .663, .859} & \\cellcolor[rgb]{ .557, .663, .859} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .851, .882, .949} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} \\\\\n \\hline\n QRTEA & \\cellcolor[rgb]{ .851, .882, .949} & \\cellcolor[rgb]{ .851, .882, .949} & \\cellcolor[rgb]{ .682, .667, .667} & & & & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} \\\\\n \\hline\n XRAY & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .557, .663, .859} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} \\\\\n \\hline\n CHTR & & \\cellcolor[rgb]{ .557, .663, .859} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .851, .882, .949} & \\cellcolor[rgb]{ .851, .882, .949} & \\cellcolor[rgb]{ .851, .882, .949} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} \\\\\n \\hline\n PCAR & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} \\\\\n \\hline\n EXC & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} \\\\\n \\hline\n AAL & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} \\\\\n \\hline\n WBA & \\cellcolor[rgb]{ .557, .663, .859} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .557, .663, .859} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .557, .663, .859} & \\cellcolor[rgb]{ .682, .667, .667} \\\\\n \\hline\n ATVI & \\cellcolor[rgb]{ .557, .663, .859} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} \\\\\n \\hline\n AAPL & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .706, .776, .906} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} & \\cellcolor[rgb]{ .682, .667, .667} \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison between emprical AR model and deepVOL(L3) MCS results, $\\alpha=0.01$. Shading indicates whether the empirical AR model and/or deepVOL(L3) is in the set of best models. White: neither, light blue: empirical AR model only, blue: deepVOL(L3), dark blue: both. Grey shading indicates the ticker-horizon combination was not deemed predictable at the 99\\% confidence level.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective", "authors": ["Lorenzo Lucchese", "Mikko Pakkanen", "Almut Veraart"], "url": "https://arxiv.org/abs/2211.13777v3", "attribution": "\"The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective\" by Lorenzo Lucchese, Mikko Pakkanen, and Almut Veraart, arXiv:2211.13777v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06323v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Example of a query from with Semantic ANN: proxy neighbor are quite relevant to the original query}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n \\toprule\n &ANN&Clicked Neigh&Neigh&Neigh\\\\\n Query&Query&Keyword&\\# Impress&\\# Click\\\\\n \\midrule\n video& & free games & 58&1\\\\\n \\cline{3-5}\n games & no & online games & 260 & 4\\\\\n \\cline{3-5}\n computers & internet &online & & \\\\\n free & games & computer games & 67 & 1\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search", "authors": ["Jason Yue Zhu", "Yanling Cui", "Yuming Liu", "Hao Sun", "Xue Li", "Markus Pelger", "Tianqi Yang", "Liangjie Zhang", "Ruofei Zhang", "Huasha Zhao"], "url": "https://arxiv.org/abs/2101.06323v3", "attribution": "\"TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search\" by Jason Yue Zhu, Yanling Cui, Yuming Liu, Hao Sun, Xue Li, Markus Pelger, Tianqi Yang, Liangjie Zhang, Ruofei Zhang, and Huasha Zhao, arXiv:2101.06323v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09519v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The convergence in Example 1. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline \n$N$ &\t$h^2$ &\t$K$\t&$Error$ \\\\\n\\hline \n50\t& 4.0000e-04 &\t3 &\t6.1899e-05\\\\\n100\t& 1.0000e-04 &\t3&\t1.5475e-05\\\\\n150\t&4.4444e-05\t& 3&6.877 -06\\\\\n200\t&2.5000e-05\t&3\t&3.8688e-06\\\\\n300\t&1.1111e-05\t&3\t&1.7195e-06\\\\\n400&\t6.2500e-06&\t3\t&9.6721e-07\\\\\n500\t&4.0000e-06\t&3\t&6.1901e-07\\\\\n800\t&1.5625e-06&\t3&\t6.1901e-07\\\\\n1000&\t1.0000e-06\t&3\t&1.5475e-07\n\\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A unified approach to study the existence and numerical solution of functional differential equation", "authors": ["Dang Quang A", "Dang Quang Long"], "url": "https://arxiv.org/abs/2101.09519v1", "attribution": "\"A unified approach to study the existence and numerical solution of functional differential equation\" by Dang Quang A and Dang Quang Long, arXiv:2101.09519v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.16201v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n \\hline\nModel & AS & MWIS & IAS centered at 15k & IAS uniform \\\\ \n \\hline\n\\texttt{CU-select} & 669 & 133 & 774 & 326 \\\\ \n \\texttt{per-capita} & 865 & - & 1029 & 366 \\\\ \n \\texttt{COVIDhub-ensemble} & 873 & 159 & 1067 & 438 \\\\ \n \\texttt{USC-SI\\_kJalpha} & 995 & 91 & 1216 & 1097 \\\\ \n \\texttt{JHUAPL-Gecko} & 1034 & 164 & 1141 & 418 \\\\ \n \\texttt{MUNI-ARIMA} & 1084 & 169 & 1248 & 440 \\\\ \n \\texttt{COVIDhub-trained\\_ensemble} & 1089 & 169 & 1271 & 823 \\\\ \n \\texttt{COVIDhub-baseline} & 1175 & 170 & 1317 & 535 \\\\ \n \\texttt{JHUAPL-Bucky} & 1358 & 102 & 1566 & 1214 \\\\ \n \\texttt{JHUAPL-SLPHospEns} & 1540 & 129 & 1604 & 1102 \\\\ \n \\texttt{UVA-Ensemble} & 2469 & 213 & 2635 & 2494 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{For one illustrative week, a comparison of allocation scores (AS), mean weighted interval scores (MWIS), and two varieties of Integrated Allocation Scores (IAS), (see Section ). All metrics are shown for 10 models that made forecasts of hospital admissions for 2022-01-03. Results are sorted by AS. For all metrics, lower scores indicate better accuracy.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Evaluating infectious disease forecasts with allocation scoring rules", "authors": ["Aaron Gerding", "Nicholas G. Reich", "Benjamin Rogers", "Evan L. Ray"], "url": "https://arxiv.org/abs/2312.16201v3", "attribution": "\"Evaluating infectious disease forecasts with allocation scoring rules\" by Aaron Gerding, Nicholas G. Reich, Benjamin Rogers, and Evan L. Ray, arXiv:2312.16201v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12733v5_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small Inference time of TPC for different transformations}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llc}\n\\toprule\n Transformation & Attack radius & Inference time ($\\mathrm {ms}$)\\\\\n \\midrule\n General rotation & 15$^\\circ$ & 9.56\\\\\n Z-rotation & 180$^\\circ$ & 3.12\\\\\n Z-shear & 0.2 & 3.46\\\\\n Z-twist & 180$^\\circ$ & 3.76\\\\\n Z-taper & 0.5 & 10.61 \\\\\n Linear & 0.2 & 3.94 \\\\\n Z-twist $\\circ$ Z-rotation & 50$^\\circ$, 5$^\\circ$& 5.34\\\\\n Z-taper $\\circ$ Z-rotation & 0.2, 1$^\\circ$ & 8.72\\\\\n Z-twist $\\circ$ Z-taper $\\circ$ Z-rotation & 20$^\\circ$, 0.2, 1$^\\circ$ & 9.40\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "TPC: Transformation-Specific Smoothing for Point Cloud Models", "authors": ["Wenda Chu", "Linyi Li", "Bo Li"], "url": "https://arxiv.org/abs/2201.12733v5", "attribution": "\"TPC: Transformation-Specific Smoothing for Point Cloud Models\" by Wenda Chu, Linyi Li, and Bo Li, arXiv:2201.12733v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.16701v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Distribution of State-Dependent Factors Across Market States}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n\\textbf{Parameter} & \\textbf{Mean} & \\textbf{Std Dev} & \\textbf{Min} & \\textbf{Max} & \\textbf{Range} \\\\\n\\midrule\n\\textbf{Movement Factors} & & & & & \\\\\nUp Factor $u(s)$ & 1.000676 & 0.000261 & 1.000410 & 1.001577 & 0.001167 \\\\\nDown Factor $d(s)$ & 0.999335 & 0.000220 & 0.998683 & 0.999576 & 0.000893 \\\\\n& & & & & \\\\\n\\textbf{Probabilities} & & & & & \\\\\nPhysical $p_{RF}(s)$ & 0.498 & 0.277 & 0.059 & 0.939 & 0.881 \\\\\nRisk-Neutral $p_{MMM}(s)$ & 0.499 & 0.054 & 0.419 & 0.572 & 0.153 \\\\\nAbsolute Difference & 0.217 & 0.106 & 0.063 & 0.369 & 0.306 \\\\\n& & & & & \\\\\n\\textbf{Calibration Metrics} & & & & & \\\\\nImplied Volatility & 0.000551 & 0.000353 & 0.000322 & 0.001405 & 0.001083 \\\\\nKL Divergence & 0.177 & 0.142 & 0.001 & 0.560 & 0.559 \\\\\nSample Size & 465 & 219 & 43 & 689 & 646 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach", "authors": ["Akash Deep", "Chris Monico", "W. Brent Lindquist", "Svetlozar T. Rachev", "Frank J. Fabozzi"], "url": "https://arxiv.org/abs/2507.16701v1", "attribution": "\"Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach\" by Akash Deep, Chris Monico, W. Brent Lindquist, Svetlozar T. Rachev, and Frank J. Fabozzi, arXiv:2507.16701v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.17792v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The logarithmic estimation errors of the naive divide-and-conquer estimate (N-DC), the whitening divide-and-conquer estimate (W-DC), and our proposed distributed estimate (DIST) for different numbers of sub-samples $K$. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccccc} \n\\hline \n & $K=2$ & $K=4$ & $K=8$ & $K=16$ & $K=25$ & $K=50$ & $K=80$ \\\\ \n\\hline\n& \\multicolumn{7}{c}{MMILL}\\\\ \n\\cline{2-8}\nN-DC & -4.2752 & -4.2712 & -4.2673 & -4.2634 & -4.2477 & -4.2323 & -4.1880 \\\\ \nW-DC & -9.7303 & -8.4329 & -7.4726 & -6.5812 & -6.0755 & -5.6983 & -5.2458 \\\\ \nDIST & -24.6840 & -17.6880 & -19.2427 & -12.6477 & -9.4220 & -8.0301 & -9.2124 \\\\ \n\\cline{2-8} \n& \\multicolumn{7}{c}{MNIST}\\\\ \n\\cline{2-8} \nN-DC & -1.8975 & -1.8966 & -1.8951 & -1.8926 & -1.8901 & -1.8831 & -1.8718 \\\\ \nW-DC & -9.1328 & -8.1677 & -7.1788 & -6.3508 & -5.9515 & -5.0023 & -4.5328 \\\\ \nDIST & -41.5599 & -35.1307 & -26.3739 & -21.4233 & -21.7905 & -12.4132 & -9.0029 \\\\\n\\cline{2-8} \n& \\multicolumn{7}{c}{MFEAT}\\\\ \n\\cline{2-8}\nN-DC & -2.1189 & -2.1130 & -2.1535 & -2.0217 & -1.9283 & -1.8018 & -1.7706 \\\\ \nW-DC & -7.7868 & -6.8604 & -6.1246 & -5.0453 & -4.7029 & -4.1052 & -3.2374 \\\\ \nDIST & -24.2076 & -17.1580 & -14.1297 & -10.8736 & -9.4998 & -7.5504 & -6.1650 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Distributed Estimation and Gap-Free Analysis of Canonical Correlations", "authors": ["Canyi Chen", "Liping Zhu"], "url": "https://arxiv.org/abs/2412.17792v1", "attribution": "\"Distributed Estimation and Gap-Free Analysis of Canonical Correlations\" by Canyi Chen and Liping Zhu, arXiv:2412.17792v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.17239v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\toprule\n \\textbf{S.No.} & $\\alpha$ & $\\varepsilon$ & $\\delta$ & \\textbf{NEs} \\\\\n \\midrule\n 1 & 2 & 0.9 & 4 & 49 \\\\\n 2 & 2 & 0.9 & 0.4 & 32 \\\\\n 3 & 2 & 0.54 & 4 & 6 \\\\\n 4 & 2 & 0.54 & 0.4 & 12 \\\\\n 5 & 0.2 & 0.9 & 4 & 557 \\\\\n 6 & 0.2 & 0.9 & 0.4 & 262 \\\\\n 7 & 0.2 & 0.54 & 4 & 142 \\\\\n 8 & 0.2 & 0.54 & 0.4 & 31 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Number of NEs.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Price equilibria with positive margins in loyal-strategic markets with discrete prices", "authors": ["Gurkirat Wadhwa", "Akansh Verma", "Veeraruna Kavitha", "Priyank Sinha"], "url": "https://arxiv.org/abs/2506.17239v1", "attribution": "\"Price equilibria with positive margins in loyal-strategic markets with discrete prices\" by Gurkirat Wadhwa, Akansh Verma, Veeraruna Kavitha, and Priyank Sinha, arXiv:2506.17239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11711v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Two most common codes per category. To give examples of the codes in each categories, we list the two most common codes per category. A full list of codes, their rate of occurrence, and their category can be found in the appendix.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrl}\n \\toprule\n Code & BMC & BMC+K & SymEx & CEGAR & Category \\\\ \\midrule\n Assert Condition & 0.115 & 0.182 & 0.155 & 0.118 & Assertion Satisfiability \\\\\n Branch Condition & 0.019 & 0.027 & 0.100 & 0.064 & Branching (No Loops) \\\\\n Conditional Statement & 0.010 & 0.036 & 0.009 & 0.018 & Branching (No Loops) \\\\\n Directly Dependent Function Return & 0.096 & 0.000 & 0.100 & 0.082 & Error Reachability \\\\\n Dead Code & 0.019 & 0.018 & 0.009 & 0.018 & Misc. \\\\\n Error Call & 0.019 & 0.055 & 0.045 & 0.027 & Error Reachability \\\\\n Input Fun & 0.000 & 0.000 & 0.018 & 0.018 & Misc. \\\\\n Loop Condition (Large Bound) & 0.010 & 0.027 & 0.000 & 0.045 & Loop (Large Bound) \\\\\n Loop Condition (Small Bound) & 0.038 & 0.027 & 0.027 & 0.000 & Loop (Small Bound) \\\\\n Update Of Directly Dependent Var & 0.087 & 0.182 & 0.136 & 0.073 & Assertion Satisfiability \\\\\n Update Of Loop Condition Var (Large Bound) & 0.019 & 0.064 & 0.000 & 0.009 & Loop (Large Bound) \\\\\n Update Of Recursion Condition Var (Small Bound) & 0.000 & 0.000 & 0.055 & 0.036 & Loop (Small Bound) \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Algorithm Selection for Software Verification using Graph Neural Networks", "authors": ["Will Leeson", "Matthew B Dwyer"], "url": "https://arxiv.org/abs/2201.11711v3", "attribution": "\"Algorithm Selection for Software Verification using Graph Neural Networks\" by Will Leeson and Matthew B Dwyer, arXiv:2201.11711v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.11861v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of topic modeling results (iOS Data)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\hline\n n-gram & S$^1$ & $p^2$ & $c^3$ & $|T|$ & score\\\\\n \\hline\n uni & neg & $-9.536$ & $0.389$ & $15$ & --- \\\\\n bi & neg & $-23.903$ & $0.703$ & $15$ & $12.80$ \\\\\n tri & neg & $-21.000$ & $0.779$ & $7$ & $11.39$ \\\\\n \\hline\n uni & neu & $-9.076$ & $0.355$ & $15$ & --- \\\\\n bi & neu & $-20.940$ & $0.730$ & $15$ & $11.34$ \\\\\n tri & neu & $-29.120$ & $0.569$ & $15$ & $15.34$ \\\\\n \\hline \n uni & pos & $-9.379$ & $0.371$ & $15$ & --- \\\\\n bi & pos & $-23.302$ & $0.724$ & $15$ & $12.51$ \\\\\n tri & pos & $-30.647$ & $0.686$ & $13$ & $16.17$\\\\\n \\hline\n \\emph{Notes:} & \\multicolumn{5}{l}{1: Sentiment \\ \\ \\ 2: Perplexity \\ \\ \\ 3: Coherence}\\\\\n \\multicolumn{6}{l}{Score is not calculated for the worst performing topic model.}\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Banking on Feedback: Text Analysis of Mobile Banking iOS and Google App Reviews", "authors": ["Yekta Amirkhalili", "Ho Yi Wong"], "url": "https://arxiv.org/abs/2503.11861v1", "attribution": "\"Banking on Feedback: Text Analysis of Mobile Banking iOS and Google App Reviews\" by Yekta Amirkhalili and Ho Yi Wong, arXiv:2503.11861v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.21377v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results for the control task for $200$ s.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n Training dataset & Model (A) & Model (B) & Model (C) \\\\\n \\hline\n Control error Eq.~\n & 5.05E-03\t& 1.36E-03 & 4.19E-04 \\\\\n Mean Control input Eq.~\n & 3.40E-05 & 4.01E-05 & 1.18E-05 \\\\\n Constraint error Eq.~\n & 0.0 & 0.0 & 4.3E-04 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Physics-informed Gaussian Processes for Model Predictive Control of Nonlinear Systems", "authors": ["Adrian Lepp", "Jörn Tebbe", "Andreas Besginow"], "url": "https://arxiv.org/abs/2504.21377v1", "attribution": "\"Physics-informed Gaussian Processes for Model Predictive Control of Nonlinear Systems\" by Adrian Lepp, Jörn Tebbe, and Andreas Besginow, arXiv:2504.21377v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.16470v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrcr}\n\\hline\n$i$ & Time to Post [min] & $t[r,i]$ & $O[r,i,1]$ & $O[r,i,2]$ & $O[r,i,3]$ & $\\cdots$ & $O[r,i,10]$ \\\\\n\\hline\n1 & 358 & 1 & 0.0 & 0.0 & 0.0 & $\\cdots$ & 0.0 \\\\ \n2 & 351 & 169 & 1.6 & 33.3 & 7.9 & $\\cdots$ & 33.3 \\\\ \n3 & 343 & 314 & 1.8 & 11.3 & 8.0 & $\\cdots$ & 6.7 \\\\ \n4 & 336 & 812 & 2.9 & 17.8 & 14.6 & $\\cdots$ & 9.9 \\\\ \n5 & 329 & 1400 & 3.3 & 8.6 & 10.6 & $\\cdots$ & 5.7 \\\\ \n6 & 322 & 1587 & 2.7 & 9.2 & 11.3 & $\\cdots$ & 6.1 \\\\ \n$\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ \\\\\n51 & 10 & 80064 & 2.4 & 6.4 & 13.4 & $\\cdots$ & 8.0 \\\\ \n52 & 4 & 148289 & 2.4 & 4.9 & 16.1 & $\\cdots$ & 8.2 \\\\ \n53 & -2 & 211653 & 2.4 & 5.3 & 17.0 & $\\cdots$ & 7.9 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Ornstein-Uhlenbeck Process for Horse Race Betting: A Micro-Macro Analysis of Herding and Informed Bettors", "authors": ["Tomoya Sugawara", "Shintaro Mori"], "url": "https://arxiv.org/abs/2503.16470v2", "attribution": "\"Ornstein-Uhlenbeck Process for Horse Race Betting: A Micro-Macro Analysis of Herding and Informed Bettors\" by Tomoya Sugawara and Shintaro Mori, arXiv:2503.16470v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.19708v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\nRPC & Inclusion Delay & Price Improvement & Average Rebate & Success Rate \\\\\n\\midrule\nMEV Blocker & 1.34 blocks & -- & 0.0035 ETH & 85\\% \\\\\nFlashbots & 1.49 blocks & 21bps less & 0.0016 ETH & 84\\% \\\\\nMerkle & 1.86 blocks & 9bps less & 0.0009 ETH & 70\\% \\\\\nBlink & 1.56 blocks & 9bps less & 0.0000 ETH & 84\\% \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Net Average Price Difference vs MEV Blocker.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Private MEV Protection RPCs: Benchmark Stud", "authors": ["Paul Janicot", "Alex Vinyas"], "url": "https://arxiv.org/abs/2505.19708v1", "attribution": "\"Private MEV Protection RPCs: Benchmark Stud\" by Paul Janicot and Alex Vinyas, arXiv:2505.19708v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.09239v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\hline\n\\hline\ndate & n & climate change & covid & price & taxes & inequality \\\\ \n \\hline\n1997-02-01 & 1 & 0 & 0 & 1 & 0 & 0 \\\\ \n 1997-03-01 & 1 & 0 & 0 & 1 & 0 & 0 \\\\ \n 1997-04-01 & 2 & 0 & 0 & 2 & 0 & 0 \\\\ \n 1997-05-01 & 1 & 0 & 0 & 1 & 0 & 0 \\\\ \n 1997-06-01 & 3 & 0 & 0 & 2 & 1 & 0 \\\\ \n 1997-09-01 & 2 & 0 & 0 & 2 & 0 & 0 \\\\ \n 1997-10-01 & 4 & 0 & 0 & 4 & 1 & 0 \\\\ \n 1997-11-01 & 5 & 0 & 0 & 5 & 0 & 0 \\\\ \n 1998-01-01 & 4 & 0 & 0 & 4 & 1 & 0 \\\\ \n 1998-02-01 & 2 & 0 & 0 & 2 & 0 & 0 \\\\ \n 1998-03-01 & 1 & 0 & 0 & 1 & 0 & 0 \\\\ \n 1998-06-01 & 1 & 0 & 0 & 1 & 0 & 0 \\\\ \n 1998-07-01 & 3 & 0 & 0 & 3 & 0 & 0 \\\\ \n 1998-09-01 & 5 & 0 & 0 & 4 & 1 & 0 \\\\ \n 1998-10-01 & 4 & 0 & 0 & 4 & 1 & 0 \\\\ \n 1998-11-01 & 9 & 0 & 0 & 9 & 2 & 0 \\\\ \n 1998-12-01 & 9 & 0 & 0 & 8 & 3 & 0 \\\\ \n 1999-01-01 & 6 & 0 & 0 & 6 & 0 & 0 \\\\ \n 1999-02-01 & 7 & 0 & 0 & 7 & 2 & 0 \\\\ \n 1999-03-01 & 7 & 0 & 0 & 7 & 2 & 0 \\\\ \n 1999-04-01 & 7 & 0 & 0 & 6 & 0 & 0 \\\\ \n 1999-05-01 & 10 & 0 & 0 & 10 & 2 & 0 \\\\ \n 1999-06-01 & 10 & 0 & 0 & 9 & 1 & 0 \\\\ \n 1999-07-01 & 5 & 0 & 0 & 5 & 1 & 0 \\\\ \n 1999-08-01 & 2 & 0 & 0 & 2 & 1 & 0 \\\\ \n 1999-09-01 & 11 & 0 & 0 & 11 & 1 & 0 \\\\ \n 1999-10-01 & 7 & 0 & 0 & 7 & 0 & 0 \\\\ \n 1999-11-01 & 16 & 0 & 0 & 14 & 3 & 0 \\\\ \n 1999-12-01 & 4 & 0 & 0 & 4 & 0 & 0 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\ ECB Speeches. 1997-2022}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Interactions of Social Norms about Climate Change: Science, Institutions and Economics", "authors": ["Antonio Cabrales", "Manu García", "David Ramos Muñoz", "Angel Sánchez"], "url": "https://arxiv.org/abs/2208.09239v1", "attribution": "\"The Interactions of Social Norms about Climate Change: Science, Institutions and Economics\" by Antonio Cabrales, Manu García, David Ramos Muñoz, and Angel Sánchez, arXiv:2208.09239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02722v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{rotating}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllcccccc}\n \\toprule\n & Difficulty & Method & Camera & Color & Backgr. & Product & & Benchmark\\\\\n \\midrule\n \\parbox[t]{1mm}{\\multirow{8}{*}{\\rotatebox[origin=c]{90}{Static setting}}} \n & Easy & SAC+RAD & 0.79 & 0.74 & 0.41 & 0.24 & $>$ & 0.22 \\\\\n & Easy & SAC+DrQ & 0.81 & 1.01 & 0.41 & 0.33 & $>$ & 0.21 \\\\\n & Easy & QT-Opt+RAD & 0.88 & 0.92 & 0.60 & 0.48 & $>$ & 0.39 \\\\\n & Easy & QT-Opt+DrQ & 0.91 & 0.95 & 0.59 & 0.51 & $>$ & 0.37 \\\\\n & Medium & SAC+RAD & 0.58 & 0.64 & 0.46 & 0.17 & $>$ & 0.14 \\\\\n & Medium & SAC+DrQ & 0.55 & 0.76 & 0.42 & 0.18 & $>$ & 0.16 \\\\\n & Medium & QT-Opt+RAD & 0.57 & 0.92 & 0.54 & 0.28 & $>$ & 0.2 \\\\\n & Medium & QT-Opt+DrQ & 0.61 & 0.84 & 0.54 & 0.28 & $>$ & 0.21 \\\\\n \\midrule\n \\parbox[t]{1mm}{\\multirow{8}{*}{\\rotatebox[origin=c]{90}{Dynamic setting}}} \n & Easy & SAC+RAD & 0.72 & 0.80 & 0.75 & 0.43 & $>$ & 0.32 \\\\\n & Easy & SAC+DrQ & 0.72 & 1.02 & 0.63 & 0.46 & $>$ & 0.25 \\\\\n & Easy & QT-Opt+RAD & 0.80 & 0.97 & 0.87 & 0.68 & $>$ & 0.42 \\\\\n & Easy & QT-Opt+DrQ & 0.84 & 0.93 & 0.83 & 0.65 & $>$ & 0.33 \\\\\n & Medium & SAC+RAD & 0.45 & 0.71 & 0.81 & 0.26 & $>$ & 0.11 \\\\\n & Medium & SAC+DrQ & 0.49 & 0.69 & 0.76 & 0.26 & $>$ & 0.11 \\\\\n & Medium & QT-Opt+RAD & 0.52 & 0.91 & 0.83 & 0.39 & $>$ & 0.13 \\\\\n & Medium & QT-Opt+DrQ & 0.54 & 0.90 & 0.79 & 0.38 & $>$ & 0.13 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels", "authors": ["Austin Stone", "Oscar Ramirez", "Kurt Konolige", "Rico Jonschkowski"], "url": "https://arxiv.org/abs/2101.02722v1", "attribution": "\"The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels\" by Austin Stone, Oscar Ramirez, Kurt Konolige, and Rico Jonschkowski, arXiv:2101.02722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.20608v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fama-MacBeth Regression Results for Proxy $= \\text{CFVOL}$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n Variable & (1) & (2) & (3) & (4) \\\\\n \\midrule\n CGO & 0.0023 & 0.0022 & 0.0561 & 0.0366 \\\\\n & (0.89) & (0.80) & (6.50) & (4.35) \\\\\n PROXY & & -0.1417 & -0.2395 & -0.2010 \\\\\n & & (-3.17) & (-4.62) & (-3.64) \\\\\n PROXY $\\times$ CGO & & & -0.1579 & -0.1186 \\\\\n & & & (-6.58) & (-4.52) \\\\\n PROXY $\\times$ MOM(-12,-1) & & & & -0.0584 \\\\\n & & & & (-3.14) \\\\\n MOM(-1,0) & 0.0064 & 0.0056 & 0.0053 & 0.0057 \\\\\n & (2.03) & (1.72) & (1.62) & (1.70) \\\\\n MOM(-12,-1) & -0.0178 & -0.0242 & -0.0255 & -0.0122 \\\\\n & (-19.67) & (-23.90) & (-25.15) & (-3.82) \\\\\n TURNOVER & -0.0149 & -0.0139 & -0.0138 & -0.0125 \\\\\n & (-3.57) & (-5.50) & (-5.15) & (-4.69) \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Replication of Reference-Dependent Preferences and the Risk-Return Trade-Off in the Chinese Market", "authors": ["Penggan Xu"], "url": "https://arxiv.org/abs/2505.20608v1", "attribution": "\"Replication of Reference-Dependent Preferences and the Risk-Return Trade-Off in the Chinese Market\" by Penggan Xu, arXiv:2505.20608v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.10720v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Type I error estimation for the Pocock et al. and our proposed tests statistic}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{clccccc}\n\t\t\t\\toprule\n\t\t\t& & \\multicolumn{5}{c}{Sample size} \\\\\\cmidrule(lr){3-7}\n\t\t\t$p_w $ & Test & 30 & 40 & 50 & 100 & 200\\\\\n\t\t\t\\midrule\n\t\t\t\\multirow{2}{*}{$0.1$}\n\t\t\t& $Z_P$ & 0.16 & 0.13 & 0.11 & 0.07 & 0.05 \\\\ \n\t\t\t& $Z$ & 0.05 & 0.05 & 0.04 & 0.05 & 0.04\\\\ \n\t\t\t\\midrule\n\t\t\t\\multirow{2}{*}{$0.2$}\n\t\t\t& $Z_P$ & 0.11 & 0.08 & 0.07 & 0.06 & 0.06 \\\\ \n\t\t\t& $Z$ & 0.05 & 0.05 & 0.05 & 0.05 & 0.05 \\\\ \n\t\t\t\\midrule\n\t\t\t\\multirow{2}{*}{$0.3$}\n\t\t\t& $Z_P$ & 0.07 & 0.06 & 0.06 & 0.06 & 0.05 \\\\\n\t\t\t& $Z$ & 0.05& 0.05 & 0.05 & 0.05 & 0.05 \\\\\n\t\t\t\\midrule\n\t\t\t\\multirow{2}{*}{$0.4$} \n\t\t\t& $Z_P$ & 0.07 & 0.06 & 0.06 & \t0.05 & \t0.05\\\\\n\t\t\t& $Z$ & 0.07 & 0.05 & 0.05 & \t0.05 & \t0.05\\\\\n\t\t\t\\midrule\n\t\t\t\\multirow{2}{*}{$0.5$}\n\t\t\t& $Z_P$ & 0.05 & 0.09 & 0.07 & 0.06 & 0.06\\\\\n\t\t\t& $Z$ & 0.04 & 0.04 & 0.07 & 0.06 & 0.06\\\\\t\n\t\t\t\\bottomrule\n\t\t\t\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Robust statistical inference for the matched net benefit and the matched win ratio using prioritized composite endpoints", "authors": ["Roland A. Matsouaka", "Adrian Coles"], "url": "https://arxiv.org/abs/2011.10720v1", "attribution": "\"Robust statistical inference for the matched net benefit and the matched win ratio using prioritized composite endpoints\" by Roland A. Matsouaka and Adrian Coles, arXiv:2011.10720v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2308.11978v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the top-1 DRD2, Median1, Median2 and QED scores with the selected non-GNN-based generative models. The full table can be found in Table in Appendix .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\t\t\\toprule\n\t\\multirow{1}{*}{Model} & DRD2 & Median1 & Median2 & QED\\\\\n \\midrule\n GCPN (R-GCN) & 0.479 & 0.337 & 0.192 & 0.948\\\\\n GCPN (GearNet) & 0.970 \\textbf{($+$102.51$\\%$)} & 0.337 \\textbf{($+$0.00$\\%$)} & 0.286 \\textbf{($+$48.96$\\%$)} & 0.948 \\textbf{($+$0.00$\\%$)}\\\\\n \\midrule\n GraphAF (R-GCN) & 0.928 & 0.281 & 0.143 & 0.946\\\\\n GraphAF (GearNet) & 0.987 \\textbf{($+$6.36$\\%$)} & 0.290 \\textbf{($+$3.20$\\%$)} & 0.183 \\textbf{($+$27.97$\\%$)} & 0.947 \\textbf{($+$0.11$\\%$)}\\\\\n \\midrule\n GraphEBM (R-GCN) & 0.691 & 0.281 & 0.148 & 0.948\\\\\n GraphEBM (GearNet) & 0.944 \\textbf{($+$36.61$\\%$)} & 0.400 \n \\textbf{($+$42.35$\\%$)} & 0.207 \\textbf{($+$39.86$\\%$)} & 0.948 \\textbf{($+$0.00$\\%$)}\\\\\n \\midrule\n LSTM HC (SMILES)~& 0.999 & 0.388 & 0.339 & 0.948 \\\\\n DoG-Gen~ & 0.999 & 0.322 & 0.297 & 0.948 \\\\\n GP BO~ & 0.999 & 0.345 & 0.337 & 0.947 \\\\\n SynNet~ & 0.999 & 0.244 & 0.259 & 0.948 \\\\\n GA+D~ & 0.836 & 0.219 & 0.161 & 0.945 \\\\\n VAE BO (SMILES)~ & 0.940 & 0.231 & 0.206 & 0.947 \\\\\n Graph MCTS~ & 0.586 & 0.242 & 0.148 & 0.928 \\\\\n MolDQN~ & 0.049 & 0.188 & 0.108 & 0.871 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Will More Expressive Graph Neural Networks do Better on Generative Tasks?", "authors": ["Xiandong Zou", "Xiangyu Zhao", "Pietro Liò", "Yiren Zhao"], "url": "https://arxiv.org/abs/2308.11978v4", "attribution": "\"Will More Expressive Graph Neural Networks do Better on Generative Tasks?\" by Xiandong Zou, Xiangyu Zhao, Pietro Liò, and Yiren Zhao, arXiv:2308.11978v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10260v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Testing Results. Last row reflects overall mean performance.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n \\toprule\n \\multicolumn{4}{c}{RMSE} & \\multicolumn{4}{c}{$R^2$}\\\\\n \\cmidrule(r){1-4}\n \\cmidrule(r){5-8}\n \\multicolumn{2}{c}{Victoria Coast} & \\multicolumn{2}{c}{Fraser River Mouth} & \\multicolumn{2}{c}{Victoria Coast} & \\multicolumn{2}{c}{Fraser River Mouth}\\\\\n \\cmidrule(r){1-4}\n \\cmidrule(r){5-8}\n DINEOF & VConstruct & DINEOF & VConstruct & DINEOF & VConstruct & DINEOF & VConstruct\\\\\n \\cmidrule(r){1-4}\n \\cmidrule(r){5-8}\n .104 & .125 & .183 & .152 & .247 & -.089 & .759 & .834\\\\\n .093 & .096 & .209 & .234 & .667 & .646 & .788 & .736\\\\\n .078 & .08 & .131 & .119 & .569 & .552 & .797 & .833\\\\\n .071 & .086 & .154 & .193 & .736 & .614 & .789 & .688\\\\\n .067 & .068 & .164 & .176 & .499 & .472 & .898 & .883\\\\\n \\cmidrule(r){1-4}\n \\cmidrule(r){5-8}\n .0826 & .091 & .1684 & .1748 & .544 & .439 & .806 & .791\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "VConstruct: Filling Gaps in Chl-a Data Using a Variational Autoencoder", "authors": ["Matthew Ehrler", "Neil Ernst"], "url": "https://arxiv.org/abs/2101.10260v1", "attribution": "\"VConstruct: Filling Gaps in Chl-a Data Using a Variational Autoencoder\" by Matthew Ehrler and Neil Ernst, arXiv:2101.10260v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17103v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n $n$ & Error $\\|\\cdot\\|_1$ & Order $\\|\\cdot\\|_1$ & Error\n $\\|\\cdot\\|_{\\infty}$ & Order $\\|\\cdot\\|_{\\infty}$ \\\\\n \\hline\n 40 & 3.66E$-5$ & $-$ & 7.45E$-4$ & $-$ \\\\\n \\hline\n 80 & 6.96E$-7$ & 5.72 & 1.73E$-5$ & 5.43 \\\\\n \\hline\n 160 & 1.33E$-8$ & 5.70 & 3.58E$-7$ & 5.59 \\\\\n \\hline\n 320 & 3.34E$-10$ & 5.32 & 1.15E$-8$ & 4.96 \\\\\n \\hline\n 640 & 1.02E$-11$ & 5.04 & 3.43E$-10$ & 5.06 \\\\\n \\hline\n 1280 & 3.19E$-13$ & 4.99 & 1.03E$-11$ & 5.06 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Error table for Burgers equation, $t=0.3$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17103v1", "attribution": "\"High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01576v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of {\\tt Ex-Greedy} and {\\tt IMM} over 10 datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\nDataset \t& k & {\\tt IMM} &\t{\\tt Ex-Greedy} \\\\ \\hline \n{\\tt breast\\_cancer} & 2 & 1,00 & 1,00 \\\\\n{\\tt iris} & 3 & 1,04 & 1,04 \\\\\n{\\tt wine} & 3 & 1,00 & 1,00 \\\\\n{\\tt covtype} & 7 & 1,03 & 1,03 \\\\\n{\\tt mice} & 8 & 1,12 & {\\bf 1,08} \\\\\n{\\tt digits } & 10 & {\\bf 1,23 } & 1,24 \\\\\n{\\tt anuran} & 10 & 1,30 & {\\bf 1,15} \\\\\n{\\tt CIFAR-10} & 10 & 1,23 & {\\bf 1,17} \\\\\n{\\tt avila} & 12 & 1,10 & 1,10 \\\\\n{\\tt newgroup} & 20 & 1,01 & 1,01 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "On the price of explainability for some clustering problems", "authors": ["Eduardo Laber", "Lucas Murtinho"], "url": "https://arxiv.org/abs/2101.01576v2", "attribution": "\"On the price of explainability for some clustering problems\" by Eduardo Laber and Lucas Murtinho, arXiv:2101.01576v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.00304v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hall sensitivity values for different materials}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\hline\nMaterial & $H_c$ ($\\Omega$/T) \\\\\n\\hline\nInAs & 370 \\\\\nInSb & 370 \\\\\nGraphene & 800 \\\\\nGraphene & 1200 \\\\\nGraphene & 2093 \\\\\nGraphene & 2745 \\\\\nMoS$_2$/h-BN & 2996 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Reconsideration of feasibility of Hall amplifier", "authors": ["Abhimanyu Kumar", "Om Prakash Pandey"], "url": "https://arxiv.org/abs/2012.00304v1", "attribution": "\"Reconsideration of feasibility of Hall amplifier\" by Abhimanyu Kumar and Om Prakash Pandey, arXiv:2012.00304v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.06019v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\multirow{2}{*}{} & \\multicolumn{3}{c}{Part 1 violent crimes}\\\\\n\\cmidrule(rl){2-4}\n&\\% Change& \\# of Crimes & \\shortstack{Est. Costs\\\\Avoided*}\\\\\n\\midrule\nChronic Hot Spots & -23.8\\% & -19 &\\$2,701,891 \\\\%\\hline\nTemporary Hot Spots& -33.3\\% & -5 &\\$709,437 \\\\\\hline\nAll Hot Spots& -25.3\\% & -24 &\\$3,411,328 \\\\%\\hline\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Policing Chronic and Temporary Hot Spots of Violent Crime: A Controlled Field Experiment", "authors": ["Dylan J. Fitzpatrick", "Wilpen L. Gorr", "Daniel B. Neill"], "url": "https://arxiv.org/abs/2011.06019v1", "attribution": "\"Policing Chronic and Temporary Hot Spots of Violent Crime: A Controlled Field Experiment\" by Dylan J. Fitzpatrick, Wilpen L. Gorr, and Daniel B. Neill, arXiv:2011.06019v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08498v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{ $n$} & \\textbf{$p(z)$}& $g(z)$& $a$, $b$& $N_{\\frac{z^4}{p(z)}}$ \\\\ \n\\hline\n\\hline\n4 \t\t\t& $z^4 -1$\t\t\t&-\t\t & - &$\\frac{z(z^4 +3)}{4}$\\\\ \n\\hline\n3 \t\t\t& $(z-1)^2(z^2+az+b)$&$(a-2)z^2+$&$a=2$& \\\\\n\t\t & &$ (-3a+2b)z-4b$& $ b=3$ &$\\frac{z(z^3+z^2+z+9)}{12}$ \\\\ \n\\hline\n2&(i) $(z-1)^2(z-a)^2$& $2(2a-(a+1)z)$&$a=-1$&$\\frac{z(z^2 +3)}{4}$ \\\\ \n\\hline\n2 &(ii) $(z-1)^3(z-a) $& $-(a+3)z+4a$&$a=-3$&$\\frac{z(z^2+2z+9)}{12}$\\\\ \n\\hline\n1 & $(z-1)^4$&4&- &$\\frac{z(z+3)}{4}$\\\\ \n\\hline\t\t\n\\end{tabular}\n\\caption{Newton maps $N_{\\frac{z^4}{p(z)}}$ with an exceptional point}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Newton's method applied to rational functions: Fixed points and Julia sets", "authors": ["Tarakanta Nayak", "Soumen Pal", "Pooja Phogat"], "url": "https://arxiv.org/abs/2503.08498v1", "attribution": "\"Newton's method applied to rational functions: Fixed points and Julia sets\" by Tarakanta Nayak, Soumen Pal, and Pooja Phogat, arXiv:2503.08498v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.20659v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{SPICEsat Component Overview} % title of Table\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}% centered columns (3 columns)\n \\hline\\hline %inserts double horizontal lines\n \\textbf{Component} & \\textbf{Description} & \\textbf{Vendor} \\\\ % inserts table\n \\hline % inserts a single horizontal line\n ADCS (MSS) & Satellite attitude and control & Blue Canyon XACT-50\\\\\n Camera (VSS) & Sloshing video recording & ArduCam OV5647 \\\\\n Tank & Primary experiment with fluid & Polycarbonate \\\\\n Pressure Sensors (LSS) & Pressure sensor mapping & Sensitronics \\\\\n Chassis \\& Panel & Satellite main chassis & SpaceMind 6U \\\\\n Electrical Power & Power for satellite & SpaceMind \\\\\n Payload Computer & Payload computer / control & RasberryPi 4B+ \\\\\n Onboard Computer & Flight computer & GOMSpace NanoMind A3200 \\\\\n UHF Transceiver & Omni-directional comms & Ultra High Frequency (UHF) Transceiver \\\\\n S-band Transceiver & High data rate comms & GOMSpace Software Defined Radio (SDR) \\\\\n \\hline %inserts a single line\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Nanosatellite Design Considerations for a Mission to Explore the Propellant Sloshing Problem", "authors": ["Michael fogel", "Snigdha Sushil Mishra", "Laurent Burlion"], "url": "https://arxiv.org/abs/2412.20659v1", "attribution": "\"Nanosatellite Design Considerations for a Mission to Explore the Propellant Sloshing Problem\" by Michael fogel, Snigdha Sushil Mishra, and Laurent Burlion, arXiv:2412.20659v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11248v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Resulting RMSE and MAPE after personalization over 180 clients}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n \t\t\\hline\n \t\t\t &\\multicolumn{3}{c|}{RMSE} & \\multicolumn{3}{c|}{MAPE}\\\\ \n \t\t\t\\hline\n \t\t\tScenario & Min & Max & Mean & Min & Max & Mean \\\\\n \t\t\t\\hline\n \t\t\t1 & ~0.0 & 2.47 & 0.550 & 8.13\\% & 99.16\\% & 36.33\\% \\\\ \\hline\n \t\t\t2 & ~0.0 & 2.47 & 0.551 & 7.89\\% & 91.23\\% & 36.39\\% \\\\ \\hline\n \t\t\t3 & ~0.0 & 2.371 & 0.536 & 7.64\\% & 88.76\\% & 34.27\\% \\\\ \\hline\n \t \t4 & ~0.0 & 2.375 & 0.536 & 8.00\\% & 82.14\\% & 34.14\\% \\\\ \\hline\n \t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Electrical Load Forecasting Using Edge Computing and Federated Learning", "authors": ["Afaf Taik", "Soumaya Cherkaoui"], "url": "https://arxiv.org/abs/2201.11248v1", "attribution": "\"Electrical Load Forecasting Using Edge Computing and Federated Learning\" by Afaf Taik and Soumaya Cherkaoui, arXiv:2201.11248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.20376v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The first two columns report the mean and the standard deviation (in brackets) of the ARI for the two groups over the $50$ simulated datasets under Experiment 1. The last column shows the percentage of times that each method gathers all observations of the second group in a single cluster, hence, lower values are better.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\n& Group 1 & Group 2 & \\% 1 cluster \\\\ \\hline\nMFM & 0.992 (0.011) & 0.002 (0.037) & 98\\% \\\\\nHDP & 0.992 (0.011) & 0.122 (0.183) & 68\\% \\\\\nHMFM - Marginal & 0.990 (0.012) & 0.216 (0.191) & 42\\% \\\\\nHMFM - Conditional & 0.991 (0.012) & 0.177 (0.190) & 52\\% \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Hierarchical Mixture of Finite Mixtures", "authors": ["Alessandro Colombi", "Raffaele Argiento", "Federico Camerlenghi", "Lucia Paci"], "url": "https://arxiv.org/abs/2310.20376v2", "attribution": "\"Hierarchical Mixture of Finite Mixtures\" by Alessandro Colombi, Raffaele Argiento, Federico Camerlenghi, and Lucia Paci, arXiv:2310.20376v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.11308v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The results of experiments on the \\texttt{Ozone} dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccccc}\\toprule\n && \\multicolumn{6}{c}{\\textbf{\\#batch}}\\\\\\cmidrule(lr){3-8}\n && \\multicolumn{2}{c}{\\textbf{10}} & \\multicolumn{2}{c}{\\textbf{20}} & \\multicolumn{2}{c}{\\textbf{30}} \\\\\\cmidrule(lr){3-8}\n \\textbf{Method} & \\textbf{Model} & \\textbf{accuracy} & \\textbf{\\#drifts} & \\textbf{accuracy} & \\textbf{\\#drifts} & \\textbf{accuracy} & \\textbf{\\#drifts} \\\\\\midrule\n HDDM-A & LR & 0.9294 & 0 & 0.8942 & 2 & 0.8116 & 0 \\\\\n & DT & 0.9487 & 0 & 0.9101 & 1 & 0.9036 & 2 \\\\\n & RF & 0.9497 & 0 & 0.9427 & 1 & 0.9374 & 0 \\\\\n \\midrule\n HDDM-W & LR & 0.9294 & 0 & 0.8907 & 1 & 0.8526 & 1 \\\\\n & DT & 0.9487 & 0 & 0.9101 & 1 & 0.9122 & 2 \\\\\n & RF & 0.9519 & 1 & 0.9427 & 1 & 0.9401 & 1 \\\\\n \\midrule\n KSWIN & LR & 0.9396 & 3 & 0.9164 & 2 & 0.9144 & 5 \\\\\n & DT & 0.9444 & 3 & 0.9242 & 4 & 0.9099 & 5 \\\\\n & RF & 0.9519 & 1 & 0.9383 & 4 & 0.9437 & 3 \\\\\n \\midrule\n PH & LR & 0.9294 & 0 & 0.8502 & 0 & 0.8116 & 0 \\\\\n & DT & 0.9487 & 0 & 0.8911 & 0 & 0.8725 & 0 \\\\\n & RF & 0.9497 & 0 & 0.9422 & 0 & 0.9374 & 0 \\\\\n \\midrule\n DDM & LR & 0.9406 & 4 & 0.9155 & 6 & 0.8706 & 2 \\\\\n & DT & 0.9358 & 7 & 0.9301 & 6 & 0.9329 & 4 \\\\\n & RF & 0.9503 & 2 & 0.9417 & 2 & 0.9437 & 3 \\\\\n \\midrule\n EDDM & LR & 0.9396 & 2 & 0.9183 & 4 & 0.8842 & 6 \\\\\n & DT & 0.9412 & 2 & 0.9164 & 4 & 0.9185 & 5 \\\\\n & RF & 0.9513 & 2 & 0.9441 & 3 & 0.9437 & 3 \\\\\n \\midrule\n PDD & LR & 0.9353 & 1 & 0.8984 & 2 & 0.8724 & 2 \\\\\n & DT & 0.9449 & 1 & 0.8915 & 1 & 0.9198 & 10 \\\\\n & RF & 0.9497 & 2 & 0.9422 & 0 & 0.9455 & 12 \\\\\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "datadriftR: An R Package for Concept Drift Detection in Predictive Models", "authors": ["Ugur Dar", "Mustafa Cavus"], "url": "https://arxiv.org/abs/2412.11308v1", "attribution": "\"datadriftR: An R Package for Concept Drift Detection in Predictive Models\" by Ugur Dar and Mustafa Cavus, arXiv:2412.11308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05866v4_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Illustration of $R^2$ XPER values in a three-fold standard linear model}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n \\hline\n & $G(y_i; \\mathbf{x}_i; \\hat{\\delta}_n)$ & $\\hat{\\phi}_0$ & $\\hat{\\phi}_1$ & $\\hat{\\phi}_2$ & $\\hat{\\phi}_3$ & $y_i$ & $\\hat{y}_i$ & $(y_i - \\hat{y}_i)^2$ \\\\\n \\hline\n i = 1 & 0.7875 & -0.3388 & 0.9287 & -0.2303 & 0.4279 & 3.2871 & 1.3815 & 3.6312 \\\\\n i = 2 & 0.0151 & 0.2491 & -0.2697 & 0.0454 & -0.0097 & -0.3773 & -4.4793 & 16.8266 \\\\\n i = 3 & 0.5367 & 0.0830 & -0.0161 & 0.1953 & 0.2745 & -1.6622 & 1.1513 & 7.9157 \\\\\n i = 4 & 0.9992 & -1.0697 & 1.8839 & -0.0317 & 0.2166 & 4.8534 & 4.7354 & 0.0139 \\\\\n i = 5 & 0.8478 & 0.1834 & 0.4011 & 0.0691 & 0.1942 & 1.2401 & -0.3725 & 2.6007 \\\\\n ... & ... & ... & ... & ... & ... & ... & ... & ... \\\\\n i = 996 & 0.4523 & 0.2511 & 0.1402 & 0.0947 & -0.0336 & -0.3395 & -3.3984 & 9.3570 \\\\\n i = 997 & 0.8812 & 0.2219 & 0.2834 & 0.2266 & 0.1494 & -0.7394 & 0.6851 & 2.0290 \\\\\n i = 998 & 0.9627 & -1.1944 & 0.9645 & 0.6049 & 0.5876 & -4.9032 & -4.1044 & 0.6380 \\\\\n i = 999 & 0.9607 & 0.2520 & 0.3705 & 0.2684 & 0.0698 & -0.3202 & 0.4997 & 0.6722 \\\\\n i = 1,000 & 0.9203 & 0.2449 & 0.5788 & 0.0221 & 0.0745 & 0.6174 & -0.5495 & 1.3617 \\\\\n \\hline\n & 0.7629 & -0.7385 & 0.9649 & 0.4202 & 0.1163 & 0.1138 & 0.0843 & 4.0504 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring", "authors": ["Hué Sullivan", "Hurlin Christophe", "Pérignon Christophe", "Saurin Sébastien"], "url": "https://arxiv.org/abs/2212.05866v4", "attribution": "\"Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring\" by Hué Sullivan, Hurlin Christophe, Pérignon Christophe, and Saurin Sébastien, arXiv:2212.05866v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2105.10402v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Line characteristics of 5 bus network}\n\\begin{tabular}{cccc}\n\\hline\nfrom bus\t&\tto bus\t&\tX (PU)\t&\tLimit (MW)\t\\\\\\hline\n1\t&\t2\t&\t0.030\t&\t240\t\\\\\n1\t&\t4\t&\t0.050\t&\t270\t\\\\\n1\t&\t5\t&\t0.060\t&\t250\t\\\\\n2\t&\t3\t&\t0.025\t&\t270\t\\\\\n3\t&\t4\t&\t0.030\t&\t270\t\\\\\n4\t&\t5\t&\t0.020\t&\t270\t\\\\ \\hline \n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Controllable Transmission Networks under Demand Uncertainty with Modular FACTS", "authors": ["Alireza Soroudi"], "url": "https://arxiv.org/abs/2105.10402v1", "attribution": "\"Controllable Transmission Networks under Demand Uncertainty with Modular FACTS\" by Alireza Soroudi, arXiv:2105.10402v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11427v5_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study of partitioned normalization (PN) and star topology fully-connected neural networks (STAR FCN). All models are trained with the proposed auxiliary network.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n\t\\toprule\n\t & Base (BN) & Base (PN) & STAR FCN (BN) & STAR FCN (PN) \\\\\n\t\\midrule\n\tOverall AUC & 0.6364 &0.6485 & 0.6455 & \\textbf{0.6506} \\\\\n\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction", "authors": ["Xiang-Rong Sheng", "Liqin Zhao", "Guorui Zhou", "Xinyao Ding", "Binding Dai", "Qiang Luo", "Siran Yang", "Jingshan Lv", "Chi Zhang", "Hongbo Deng", "Xiaoqiang Zhu"], "url": "https://arxiv.org/abs/2101.11427v5", "attribution": "\"One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction\" by Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, and Xiaoqiang Zhu, arXiv:2101.11427v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2306.09437v2_tex_table33.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameter settings for Experiment~2 (Affiliated Valuations + Q-learning).}\n\\begin{tabular}{ll}\n\\toprule\n\\textbf{Parameter} & \\textbf{Possible Values}\\\\\n\\midrule\nNumber of bidders ($n$) & $\\{2,4,6\\}$\\\\\nReserve price ($r$) & $\\{0.0,0.1,0.2,0.3\\}$\\\\\nAffiliation ($\\eta$) & e.g.\\ $\\{0.0,0.25,0.5,0.75,1.0\\}$\\\\\nLearning rate ($\\alpha$) & e.g.\\ $\\{0.001,0.005,0.01,0.05,0.1\\}$\\\\\nDiscount factor ($\\gamma$) & e.g.\\ $\\{0.0,0.5,0.9,0.99\\}$\\\\\nExploration & $\\varepsilon$-greedy or Boltzmann\\\\\nQ-update mode & Asynchronous or synchronous\\\\\nState features & e.g.\\ $s_i,\\,\\text{median-of-others},\\,\\text{winner-bid}$\\\\\nNumber of episodes & e.g.\\ $10^5$\\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Algorithmic Collusion in Auctions: Evidence from Controlled Laboratory Experiments", "authors": ["Pranjal Rawat"], "url": "https://arxiv.org/abs/2306.09437v2", "attribution": "\"Algorithmic Collusion in Auctions: Evidence from Controlled Laboratory Experiments\" by Pranjal Rawat, arXiv:2306.09437v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.23992v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary statistics for PL- and ML- portfolios over the full and downturn periods}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\n& $n$ & $\\bar{L}$ & $\\hat{\\sigma}$ & Max workout period & Mean workout period & $r_f$ \\\\\n\\midrule\n\\multicolumn{7}{c}{Personal Loans} \\\\\n\\midrule\nFull & $121,151$ & $0.749$ & $0.290$ & $87$ months & $4.67$ months & $6.37\\%$\\\\\nDownturn & $60,032$ & $0.760$ & $0.273$ & $80$ months & $4.57$ months & $8.93\\%$\\\\\n\\midrule\n\\multicolumn{7}{c}{Mortgage Loans}\\\\\n\\midrule\nFull & $126,580$ & $0.256$ & $0.366$ & $113$ months & $25.64$ months & $6.88\\%$\\\\\nDownturn & $60,779$ & $0.293$ & $0.372$ & $90$ months & $33.59$ months & $8.49\\%$\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A cost of capital approach to determining the LGD discount rate", "authors": ["Janette Larney", "Arno Botha", "Gerrit Lodewicus Grobler", "Helgard Raubenheimer"], "url": "https://arxiv.org/abs/2503.23992v1", "attribution": "\"A cost of capital approach to determining the LGD discount rate\" by Janette Larney, Arno Botha, Gerrit Lodewicus Grobler, and Helgard Raubenheimer, arXiv:2503.23992v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.06445v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Cardiologists' assessments of the quality of synthetic cardiac MRI compared to original cardiac MRI. The table shows the percentage of the responses where the synthetic cardiac MRI was evaluated as better, similar, or worse than the original.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n\\textbf{Better} & \\textbf{Similar} & \\textbf{Worse} \\\\ \\hline\n\\textbf{14.58\\% } & \\textbf{30.21\\%} & \\textbf{55.21\\%} \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Echocardiography to Cardiac MRI View Transformation for Real-Time Blind Restoration", "authors": ["Ilke Adalioglu", "Serkan Kiranyaz", "Mete Ahishali", "Aysen Degerli", "Tahir Hamid", "Rahmat Ghaffar", "Ridha Hamila", "Moncef Gabbouj"], "url": "https://arxiv.org/abs/2412.06445v1", "attribution": "\"Echocardiography to Cardiac MRI View Transformation for Real-Time Blind Restoration\" by Ilke Adalioglu, Serkan Kiranyaz, Mete Ahishali, Aysen Degerli, Tahir Hamid, Rahmat Ghaffar, Ridha Hamila, and Moncef Gabbouj, arXiv:2412.06445v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.09539v3_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccr} \\hline \\hline \\\\ \n & $\\theta_1$ & $\\theta_2$ & $\\theta_1$ \\\\ \\hline \\\\\n Model & \\multicolumn{2}{c}{FRA} & GUM \\\\\n mean & 8.60 & -13.92 & 2.05\\\\\n $q_{2.5}$ & 7.50 & -17.80 & 1.86\\\\\n $q_{97.5}$ & 9.78 & -10.38 & 2.24\\\\ \n weight & 0.86 & 0.14 & 1 \\\\ \\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Occupancy data. Post MCMC summaries given chosen cluster configuration.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian nonparametric mixtures of Archimedean copulas", "authors": ["Ruyi Pan", "Luis E. Nieto-Barajas", "Radu V. Craiu"], "url": "https://arxiv.org/abs/2412.09539v3", "attribution": "\"Bayesian nonparametric mixtures of Archimedean copulas\" by Ruyi Pan, Luis E. Nieto-Barajas, and Radu V. Craiu, arXiv:2412.09539v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13833v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n &$r_c$ (\\AA)&$r_0$ (\\AA)&$\\kappa r_0$&\n &$r_c$ (\\AA) &$r_0$ (\\AA)&$\\kappa r_0$\\\\\n\\hline\nCu& 0.800 & 14.10 & 2.550 &Sn[1]\n& 0.680 & 1.870 & 3.700 \\\\\nAg& 0.990 & 15.90 & 2.710 &Pb[2]\n& 0.450 & 1.930 & 3.760 \\\\\nAu& 1.150 & 15.90 & 2.710 &Ca[3]\n& 0.750 & 2.170 & 3.560 \\\\\nMg& 0.490 & 17.60 & 3.200 &Sr[4]\n& 0.900 & 2.370 & 3.720 \\\\\nZn& 0.300 & 15.20 & 2.970 &Li[2]\n& 0.380 & 1.730 & 2.830 \\\\\nCd& 0.530 & 17.10 & 3.160 &Na[5]\n& 0.760 & 2.110 & 3.120 \\\\\nHg& 0.550 & 17.80 & 3.220 &K[5]\n& 1.120 & 2.620 & 3.480 \\\\\nAl& 0.230 & 15.80 & 3.240 &Rb[3]\n& 1.330 & 2.800 & 3.590 \\\\\nGa& 0.310 & 16.70 & 3.330 &Cs[4]\n& 1.420 & 3.030 & 3.740 \\\\\nIn& 0.460 & 18.40 & 3.500 &Ba[5]\n& 0.960 & 2.460 & 3.780 \\\\\nTl& 0.480 & 18.90 & 3.550 & & & & \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the Reasoning Capacity of AI Models and How to Quantify It", "authors": ["Santosh Kumar Radha", "Oktay Goktas"], "url": "https://arxiv.org/abs/2501.13833v1", "attribution": "\"On the Reasoning Capacity of AI Models and How to Quantify It\" by Santosh Kumar Radha and Oktay Goktas, arXiv:2501.13833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.01791v4_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n & \\multicolumn{2}{c}{Log rents} \\\\ \\cmidrule(l){2-3}\n & \\shortstack{Levels\\\\(1)} \n & \\shortstack{First Differences\\\\(2)} \\\\ \\midrule\n Residence MW & -0.0432 & -0.0199 \\\\\n & (0.1751) & (0.0195) \\\\\n Workplace MW & 0.0376 & 0.0687 \\\\\n & (0.2033) & (0.0298) \\\\ \\midrule\n Economic controls & Yes & Yes \\\\\n P-value autocorrelation test & & $<0.0001$ \\\\\n R-squared & 0.9924 & 0.0216 \\\\\n Observations & 80,340 & 78,912 \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "From Workplace to Residence: The Spillover Effects of Minimum Wage Policies on Local Housing Markets", "authors": ["Gabriele Borg", "Diego Gentile Passaro", "Santiago Hermo"], "url": "https://arxiv.org/abs/2208.01791v4", "attribution": "\"From Workplace to Residence: The Spillover Effects of Minimum Wage Policies on Local Housing Markets\" by Gabriele Borg, Diego Gentile Passaro, and Santiago Hermo, arXiv:2208.01791v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.08032v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Example of visiting order}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n \t\t\t\\hline\n \t\t\tPatients & 5 (2) & 1 (3) & 3 (1) &1 (2)& 4 (3) & 2 (1) & 6 (2) \\\\ \n \t\t\t\\hline\n \t\t\tCaregivers & & & & & & & \\\\ \n \t\t\t\\hline\n \t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Mathematical models and heuristics for the home health care routing and scheduling problem", "authors": ["Mohammed Bazirha"], "url": "https://arxiv.org/abs/2312.08032v1", "attribution": "\"Mathematical models and heuristics for the home health care routing and scheduling problem\" by Mohammed Bazirha, arXiv:2312.08032v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.00493v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Structure of encoder. Regularisation layers are not shown.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{Type} & \\textbf{Parameters} & \\textbf{Input size} \\\\ \\hline\nconv & 100x1/30 & 512x1 \\\\ \\hline\npool & 2 & 512x30 \\\\ \\hline\nconv & 100x1/15 & 256x30 \\\\ \\hline\npool & 2 & 256x15 \\\\ \\hline\nconv & 30x1/15 & 128x15 \\\\ \\hline\npool & 2 & 128x15 \\\\ \\hline\nconv & 20x1/5 & 64x15 \\\\ \\hline\npool & 2 & 64x5 \\\\ \\hline\ndense & 30 & 32x5 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Problems of representation of electrocardiograms in convolutional neural networks", "authors": ["Iana Sereda", "Sergey Alekseev", "Aleksandra Koneva", "Alexey Khorkin", "Grigory Osipov"], "url": "https://arxiv.org/abs/2012.00493v1", "attribution": "\"Problems of representation of electrocardiograms in convolutional neural networks\" by Iana Sereda, Sergey Alekseev, Aleksandra Koneva, Alexey Khorkin, and Grigory Osipov, arXiv:2012.00493v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.14532v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\hline \nMethods & Highest RankIC & Randomized ID & BID with GBT & BID with IID \\\\\n\\hline\nMean RankIC & \\textbf{0.1035} & 0.0651 & 0.0553 & \\textbf{0.0752} \\\\\nMean Correlation & 0.2276$\\downarrow$ & 0.5741$\\downarrow$ & \\textbf{0.1132} & \\textbf{0.1497} \\\\\n\\hline\nSharpe Ratio (OS) & 1.0276 & 1.0544 & 0.5045 & \\textbf{1.5721} \\\\\nSharpe Ratio (IS) & \\textbf{2.6511} & 1.3019 & 1.4965 & 2.3231 \\\\\n\\hline\nAnnual Return (OS) & 0.1043 & 0.0932 & 0.0484 & \\textbf{0.1633} \\\\\nAnnual Return (IS) & \\textbf{0.4390} & 0.2281 & 0.2425 & 0.3805 \\\\\n\\hline\nMax Drawdown (OS) & 0.0632 & \\textbf{0.0373} & \\textbf{0.0484} & \\textbf{0.0552} \\\\\nMax Drawdown (IS) & \\textbf{0.0892} & 0.1548 & 0.1232 & \\textbf{0.0975} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies", "authors": ["Jun Lu", "Joerg Osterrieder"], "url": "https://arxiv.org/abs/2209.14532v1", "attribution": "\"Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies\" by Jun Lu and Joerg Osterrieder, arXiv:2209.14532v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13025v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MDP formulation of the capacity-distortion trade-off}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|} \n \\hline\n state $\\delta_{i-1}$& $P_{S_{i-1}|Z^{i-1}}(\\cdot|y^{i-1})$ \\\\ \n \\hline\n action $a_i$ & $P_{X_i|S_{i-1} Z^{i-1}} (\\cdot|\\cdot, y^{i-1})$ \\\\ \n \\hline\n reward $r_i$ & $I(X_i, S_{i-1};Y_i|Z^{i-1}) - \\beta \\mathbb E[ d(S_i, \\hat S_i)]$.\\\\ \n \\hline\n disturbance & $z_i$ \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A MIMO ISAC System for Ultra-Reliable and Low-Latency Communications", "authors": ["Homa Nikbakht", "Yonina C. Eldar", "H. Vincent Poor"], "url": "https://arxiv.org/abs/2501.13025v2", "attribution": "\"A MIMO ISAC System for Ultra-Reliable and Low-Latency Communications\" by Homa Nikbakht, Yonina C. Eldar, and H. Vincent Poor, arXiv:2501.13025v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10685v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dynamic analysis data of the SWATH style ship ‘wasp’~)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\\toprule\n & Freeboard&\tCycles& \\multicolumn{2}{c}{Displacement}\t&\tTime&\tPeriod\\\\\\midrule\n &in&\tn&\tB1&\t B(n+1)& $\\Delta$ t(s)&\tP\\\\\\midrule \n \\multirow{3}{*}{Roll}&\t16&\t6&\t 7&\t3.5&\t37.90&\t6.32\\\\\n\t&12&\t5&\t10.5&\t4.5&\t36.33&\t7.27\\\\\n\t&8\t&6\t& 13\t&4.5\t&41.60\t&6.93\\\\\n\\multirow{3}{*}{Pitch\n(Black)}\t&16&\t2&\t1.5&\t0.5&\t12.61&\t6.31\\\\\n\t&12&\t4&\t3.5&\t0.75&\t27.66&\t6.91\\\\\n\t&8&\t4&\t3.25&\t0.75&\t25.74&\t6.44\\\\\n\\multirow{3}{*}{Pitch\n(Front)}&\t16&\t2&1.5&\t0.5&\t12.26&\t6.13\\\\\n\t&12&\t5&\t2.5&\t1&\t34.46&\t6.89\\\\\n\t&8&\t6&\t2.75&\t1&\t38.48&\t6.41\\\\\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Design and Development of an Autonomous Surface Vehicle for Water Quality Monitoring", "authors": ["MM Rashid", "Rupal Roy", "Md Manjurul Ahsan", "Zahed Siddique"], "url": "https://arxiv.org/abs/2201.10685v1", "attribution": "\"Design and Development of an Autonomous Surface Vehicle for Water Quality Monitoring\" by MM Rashid, Rupal Roy, Md Manjurul Ahsan, and Zahed Siddique, arXiv:2201.10685v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.11459v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ratings of the best South American clubs}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\n \\hline\n \\# & team & rating & league \\\\ \\hline\n 1 & Palmeiras & 1961.0 & Brazil. First \\\\ \\hline\n 2 & Flamengo RJ & 1883.1 & Brazil. First \\\\ \\hline\n 3 & Atletico-MG & 1849.4 & Brazil. First \\\\ \\hline\n 4 & River Plate Argentina & 1842.7 & Argentina. First \\\\ \\hline\n 5 & Athletico-PR & 1826.8 & Brazil. First \\\\ \\hline\n 6 & Ind. del Valle & 1823.2 & Ecuador. First \\\\ \\hline\n 7 & Fluminense & 1821.5 & Brazil. First \\\\ \\hline\n 8 & Libertad Asuncion & 1815.7 & Paraguay. First \\\\ \\hline\n 9 & Internacional & 1810.3 & Brazil. First \\\\ \\hline\n 10 & Sao Paulo & 1809.9 & Brazil. First \\\\ \\hline\n 11 & Cerro Porteno & 1793.6 & Paraguay. First \\\\ \\hline\n 12 & Defensa y Justicia & 1787.2 & Argentina. First \\\\ \\hline\n 13 & Fortaleza Brazil & 1782.6 & Brazil. First \\\\ \\hline\n 14 & Botafogo RJ & 1780.7 & Brazil. First \\\\ \\hline\n 15 & Talleres Cordoba & 1775.5 & Argentina. First \\\\ \\hline\n 16 & Nacional Uruguay & 1772.8 & Uruguay. First \\\\ \\hline\n 17 & LDU Quito & 1749.1 & Ecuador. First \\\\ \\hline\n 18 & Boca Juniors & 1748.4 & Argentina. First \\\\ \\hline\n 19 & Olimpia Asuncion & 1747.6 & Paraguay. First \\\\ \\hline\n 20 & Estudiantes L.P. & 1745.8 & Argentina. First \\\\ \\hline\n 21 & Bragantino & 1742.5 & Brazil. First \\\\ \\hline\n 22 & Gremio & 1740.8 & Brazil. First \\\\ \\hline\n 23 & Corinthians & 1737.5 & Brazil. First \\\\ \\hline\n 24 & San Lorenzo Argentina & 1736.6 & Argentina. First \\\\ \\hline\n 25 & Millonarios & 1732.7 & Colombia. First \\\\ \\hline\n 26 & Newells Old Boys & 1728.4 & Argentina. First \\\\ \\hline\n 27 & Racing Club & 1723.9 & Argentina. First \\\\ \\hline\n 28 & Atl. Nacional & 1718.7 & Colombia. First \\\\ \\hline\n 29 & Belgrano & 1717.7 & Argentina. First \\\\ \\hline\n 30 & Argentinos Jrs & 1712.4 & Argentina. First \\\\ \\hline\n 31 & Rosario Central & 1704.5 & Argentina. First \\\\ \\hline\n 32 & America MG & 1704.4 & Brazil. First \\\\ \\hline\n 33 & Cruzeiro & 1701.2 & Brazil. First \\\\ \\hline\n 34 & Atletico GO & 1699.1 & Brazil. Second \\\\ \\hline\n 35 & Sport Recife & 1697.7 & Brazil. Second \\\\ \\hline\n 36 & Barcelona SC & 1694.3 & Ecuador. First \\\\ \\hline\n 37 & Ceara & 1688.8 & Brazil. Second \\\\ \\hline\n 38 & Penarol & 1688.2 & Uruguay. First \\\\ \\hline\n 39 & Godoy Cruz & 1685.2 & Argentina. First \\\\ \\hline\n 40 & Goias & 1681.7 & Brazil. First \\\\ \\hline\n 41 & Nacional Asuncion & 1681.1 & Paraguay. First \\\\ \\hline\n 42 & Guarani Paraguay & 1681.0 & Paraguay. First \\\\ \\hline\n 43 & Cuiaba & 1680.3 & Brazil. First \\\\ \\hline\n 44 & Lanus & 1680.2 & Argentina. First \\\\ \\hline\n 45 & Tigre & 1677.0 & Argentina. First \\\\ \\hline\n 46 & Sportivo Trinidense & 1676.8 & Paraguay. First \\\\\\hline\n 47 & Santos & 1674.6 & Brazil. First \\\\ \\hline\n 48 & U. Catolica Ecuador & 1667.5 & Ecuador. First \\\\ \\hline\n 49 & Aucas & 1667.0 & Ecuador. First \\\\ \\hline\n 50 & Bahia & 1664.5 & Brazil. First \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Ratings of European and South American Football Leagues Based on Glicko-2 with Modifications", "authors": ["Andrei Shelopugin", "Alexander Sirotkin"], "url": "https://arxiv.org/abs/2310.11459v1", "attribution": "\"Ratings of European and South American Football Leagues Based on Glicko-2 with Modifications\" by Andrei Shelopugin and Alexander Sirotkin, arXiv:2310.11459v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.11765v1_tex_table18.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classification Report}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rcccc}\n\\hline\n\\textit{Max Length} & \\textit{512} & \\textit{Training Epochs} & \\textit{4} & \\textit{2} \\\\\n\\textit{Batch Size} & \\textit{32} & \\textit{Learning Rate} & \\textit{1e-4} & \\textit{2e-5} \\\\ \\hline\n\\textbf{} & \\textbf{precision} & \\textbf{recall} & \\textbf{f1-score} & \\textbf{support} \\\\ \\hline\n\\textbf{noise} & 0.80 & 0.79 & 0.80 & 23,938 \\\\\n\\textbf{ESG relevant} & 0.85 & 0.86 & 0.85 & 33,150 \\\\ \\hline\n\\textbf{accuracy} & & & 0.83 & 57,088 \\\\\n\\textbf{macro avg} & 0.83 & 0.83 & 0.83 & 57,088 \\\\\n\\textbf{weighted avg} & 0.83 & 0.83 & 0.83 & 57,088 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis", "authors": ["Tanja Aue", "Adam Jatowt", "Michael Färber"], "url": "https://arxiv.org/abs/2212.11765v1", "attribution": "\"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis\" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2501.00009v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c||c|c}\n\t\t\\hline\n\t\t& & &\\\\\n\t\tSymbol&Value&Symbol&Value\\\\\n\t\t\\hline\n\t\t& & &\\\\\n\t\t$f_{\\text{c}}$&$4.8498\\;\\mathrm{GHz}$&$R$&$10\\;\\mathrm{m}$\\\\\n\t\t& & &\\\\\n\t\t$\\Delta f$&$60\\;\\text{KHz}$&$P_{UE}$&$23\\;\\mathrm{dBm}$\\\\\n\t\t& & &\\\\\n\t\t$B$&$100\\;\\text{MHz}$&$\\Delta \\theta$&$0.1^{\\circ}$\\\\\n\t\t& & &\\\\\n\t\t$M$&4&$\\boldsymbol{\\theta}$&$[-60^{\\circ},60^{\\circ}]$\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Simulation settings.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB", "authors": ["Shengheng Liu", "Xingkang Li", "Zihuan Mao", "Peng Liu", "Yongming Huang"], "url": "https://arxiv.org/abs/2501.00009v1", "attribution": "\"Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB\" by Shengheng Liu, Xingkang Li, Zihuan Mao, Peng Liu, and Yongming Huang, arXiv:2501.00009v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.21845v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\n\\textbf{Code $\\backslash$ Erasure Rate} & $\\mathbf{0.2}$ & $\\mathbf{0.225}$ & $\\mathbf{0.25}$ & $\\mathbf{0.275}$ & $\\mathbf{0.3}$ & $\\mathbf{0.325}$ \\\\\n\\hline\n$[[1525,25]]$ & $20$ & $19$ & $30$ & $42$ & $250$ & $289$ \\\\\n\\hline\n$[[3904,64]]$ & $15$ & $16$ & $33$ & $38$ & $534$ & $663$ \\\\\n\\hline\n$[[6100,100]]$ & $15$ & $20$ & $21$ & $41$ & $738$ & $994$ \\\\\n\\hline\n$[[8784,144]]$ & $13$ & $21$ & $21$ & $24$ & $38$ & $1378$ \\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Maximum weights of residual error after peeling for the $[[1525,25]]$, $[[3904,64]]$, $[[6100,100]]$, and $[[8784,144]]$ quantum expander codes.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the Efficacy of the Peeling Decoder for the Quantum Expander Code", "authors": ["Jefrin Sharmitha Prabhu", "Abhinav Vaishya", "Shobhit Bhatnagar", "Aryaman Manish Kolhe", "V. Lalitha", "P. Vijay Kumar"], "url": "https://arxiv.org/abs/2504.21845v2", "attribution": "\"On the Efficacy of the Peeling Decoder for the Quantum Expander Code\" by Jefrin Sharmitha Prabhu, Abhinav Vaishya, Shobhit Bhatnagar, Aryaman Manish Kolhe, V. Lalitha, and P. Vijay Kumar, arXiv:2504.21845v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19259v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Optimal Learning Rates and Convergence Times from Figure~}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline \n & \\textbf{optimal learning rate} & \\textbf{optimal convergence time} \\\\\n\\hline\n$\\eta$ coordinates & $\\alpha_{\\eta}=0.0025$ & \\hspace{0.2cm}$k=41$ \\\\\nnatural gradient & $\\alpha_{ng}\\in [0.541,0.695]$ & $k=3$\\\\\n$\\theta$ coordinates & $\\alpha_{\\theta}\\in 6.93$ & \\hspace{0.2cm}$k=20$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Convergence Properties of Natural Gradient Descent for Minimizing KL Divergence", "authors": ["Adwait Datar", "Nihat Ay"], "url": "https://arxiv.org/abs/2504.19259v2", "attribution": "\"Convergence Properties of Natural Gradient Descent for Minimizing KL Divergence\" by Adwait Datar and Nihat Ay, arXiv:2504.19259v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00561v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc|ccc|ccc|c}\n\\multirow{2}{*}{Instance} & \\multirow{2}{*}{$n$} & \\multirow{2}{*}{$p$} & \\multicolumn{3}{c|}{\\textbf{Dev. Best vs. Average}} & \\multicolumn{3}{c|}{\\textbf{Dev. Best vs. Worst}} & \\multirow{2}{*}{\\textbf{Avg. CPU Time} } \\\\\n & & & Min & Average & Max & Min & Average & Max & \\\\ \\hline\n \\multirow{11}{*}{CAB} & \\multirow{2}{*}{10} & 3 & 0.13 & 0.18 & 0.21 & 0.32 & 0.39 & 0.53 & 611.12 \\\\\n & & 5 & 0.07 & 0.16 & 0.25 & 0.21 & 0.57 & 0.38 & 714.14 \\\\ \\cline{2-10} \n & \\multirow{3}{*}{15} & 3 & 0.26 & 0.64 & 1.60 & 0.52 & 1.36 & 3.44 & 1384.05 \\\\\n & & 5 & 0.54 & 1.05 & 1.53 & 1.28 & 2.15 & 4.20 & 1673.41 \\\\\n & & 7 & 0.46 & 0.91 & 1.66 & 1.06 & 1.90 & 3.04 & 2089.76 \\\\ \\cline{2-10} \n & \\multirow{3}{*}{20} & 3 & 0.86 & 3.15 & 6.60 & 1.93 & 6.50 & 11.28 & 2039.86 \\\\\n & & 5 & 0.63 & 2.37 & 6.61 & 1.61 & 4.40 & 12.15 & 2392.64 \\\\\n & & 7 & 0.71 & 2.23 & 3.58 & 1.63 & 4.52 & 6.97 & 2730.39 \\\\ \\cline{2-10} \n & \\multirow{3}{*}{25} & 3 & 2.71 & 4.57 & 7.12 & 5.72 & 9.33 & 13.09 & 3560.84 \\\\\n & & 5 & 3.86 & 5.27 & 8.21 & 8.04 & 10.69 & 13.70 & 4530.15 \\\\\n & & 7 & 1.81 & 5.26 & 7.72 & 4.32 & 10.89 & 14.02 & 4361.13 \\\\ \\hline\n\\multirow{11}{*}{AP} & \\multirow{2}{*}{10} & 3 & 0.17 & 0.21 & 0.26 & 0.32 & 0.47 & 0.54 & 595.57 \\\\\n & & 5 & 0.12 & 0.17 & 0.25 & 0.28 & 0.34 & 0.39 & 907.97 \\\\ \\cline{2-10} \n & \\multirow{3}{*}{15} & 3 & 0.74 & 1.22 & 1.52 & 1.68 & 2.76 & 3.53 & 1220.54 \\\\\n & & 5 & 0.56 & 1.70 & 2.66 & 1.83 & 3.46 & 4.77 & 1312.93 \\\\\n & & 7 & 0.67 & 1.45 & 3.05 & 1.35 & 2.90 & 4.83 & 1577.48 \\\\ \\cline{2-10} \n & \\multirow{3}{*}{20} & 3 & 0.44 & 1.54 & 2.38 & 0.79 & 3.11 & 4.60 & 1824.92 \\\\\n & & 5 & 0.55 & 2.86 & 4.72 & 1.22 & 5.24 & 8.65 & 1881.68 \\\\\n & & 7 & 1.60 & 2.23 & 3.39 & 2.85 & 3.82 & 4.94 & 1933.42 \\\\ \\cline{2-10} \n & \\multirow{3}{*}{25} & 3 & 2.82 & 6.62 & 9.58 & 5.68 & 13.49 & 18.83 & 2488.75 \\\\\n & & 5 & 2.18 & 6.51 & 12.30 & 5.98 & 11.50 & 18.46 & 2714.34 \\\\\n & & 7 & 1.30 & 5.35 & 9.02 & 2.81 & 10.85 & 18.96 & 2586.29 \\\\ \\hline\n\\multirow{11}{*}{TR} & \\multirow{2}{*}{10} & 3 & 0.10 & 0.16 & 0.32 & 0.19 & 0.38 & 0.95 & 686.01 \\\\\n & & 5 & 0.03 & 0.60 & 0.97 & 0.14 & 0.44 & 0.88 & 755.87 \\\\ \\cline{2-10} \n & \\multirow{3}{*}{15} & 3 & 0.32 & 1.12 & 2.12 & 1.19 & 2.02 & 3.23 & 1721.21 \\\\\n & & 5 & 1.28 & 1.90 & 2.39 & 2.06 & 3.03 & 4.06 & 2034.02 \\\\\n & & 7 & 0.95 & 1.52 & 2.14 & 1.57 & 2.45 & 3.43 & 2124.59 \\\\ \\cline{2-10} \n & \\multirow{3}{*}{20} & 3 & 1.26 & 4.15 & 6.43 & 3.58 & 8.73 & 12.15 & 2414.11 \\\\\n & & 5 & 1.38 & 2.61 & 4.13 & 3.05 & 5.29 & 9.02 & 2827.01 \\\\\n & & 7 & 1.83 & 3.53 & 4.99 & 3.70 & 5.96 & 6.81 & 3612.12 \\\\ \\cline{2-10} \n & \\multirow{3}{*}{25} & 3 & 1.43 & 7.86 & 14.64 & 2.68 & 17.70 & 27.97 & 5147.35 \\\\\n & & 5 & 1.22 & 6.66 & 10.49 & 2.48 & 12.20 & 18.09 & 8404.67 \\\\\n & & 7 & 4.19 & 5.07 & 5.89 & 7.94 & 9.69 & 11.58 & 6264.35 \\\\ \\cline{2-10} \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Summary of the deviations computed.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology", "authors": ["Víctor Blanco", "José-Fernando Camacho-Vallejo", "Carlos Corpus"], "url": "https://arxiv.org/abs/2503.00561v1", "attribution": "\"A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology\" by Víctor Blanco, José-Fernando Camacho-Vallejo, and Carlos Corpus, arXiv:2503.00561v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.01547v5_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{RTF benchmark}\n\\begin{tabular}{lllll}\n\\toprule\nmodel/decoding\\_chunk & full & 16 & 8 & 4 \\\\ \\midrule\nserver (x86) float32 & 0.079 & 0.095 & 0.128 & 0.186 \\\\\nserver (x86) int8 & 0.072 & 0.081 & 0.098 & 0.134 \\\\\non-device (Android) float32 & 0.164 & 0.251 & 0.350 & 0.505 \\\\\non-device (Android) int8 & 0.082 & 0.114 & 0.130 & 0.201 \\\\ \\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit", "authors": ["Zhuoyuan Yao", "Di Wu", "Xiong Wang", "Binbin Zhang", "Fan Yu", "Chao Yang", "Zhendong Peng", "Xiaoyu Chen", "Lei Xie", "Xin Lei"], "url": "https://arxiv.org/abs/2102.01547v5", "attribution": "\"WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit\" by Zhuoyuan Yao, Di Wu, Xiong Wang, Binbin Zhang, Fan Yu, Chao Yang, Zhendong Peng, Xiaoyu Chen, Lei Xie, and Xin Lei, arXiv:2102.01547v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11211v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|} \\hline \n\\textbf{Separation}\t&\t\\textbf{Approx. \\#Hops}\t&\t\\textbf{Latency Gain} \\\\ \\hline\n1\t&\t1\t&\t14.8\\% \\\\ \n2\t&\t1\t&\t27.9\\% \\\\\n4\t&\t2\t&\t30.25\\% \\\\\n6\t&\t3\t&\t8.1\\% \\\\\n8\t&\t4\t&\t-152\\%\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Scalability of latency gain}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Harvest: A Reliable and Energy Efficient Bulk Data Collection Service for Large Scale Wireless Sensor Networks", "authors": ["Vinayak Naik", "Anish Arora"], "url": "https://arxiv.org/abs/2101.11211v1", "attribution": "\"Harvest: A Reliable and Energy Efficient Bulk Data Collection Service for Large Scale Wireless Sensor Networks\" by Vinayak Naik and Anish Arora, arXiv:2101.11211v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.14100v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sensitivity and Specificity of BraTS-Africa Without Fine-Tuning, With Full Fine-Tuning, and With PEFT Fine-Tuning}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\n \\hline\n & \\multicolumn{3}{c|}{Without Fine-Tuning} & \\multicolumn{3}{c|}{With Full Fine-Tuning} & \\multicolumn{3}{c|}{With PEFT Fine-Tuning} \\\\ \\hline\n & ET & TC & WT & ET & TC & WT & ET & TC & WT \\\\ \\hline\n Sensitivity $\\uparrow$ & \n 0.40 & 0.27 & 0.36 & 0.64 & 0.59 & 0.68 & \\textbf{0.67} & \\textbf{0.66} & \\textbf{0.75} \\\\ \n \\textit{Std. Dev.} & \n (0.37) & (0.34) & (0.41) & (0.31) & (0.38) & (0.38) & \\textbf(0.27) & \\textbf(0.33) & \\textbf(0.35) \\\\ \\hline \n Specificity $\\uparrow$ & \n 0.99 & 0.98 & 0.96 & \\textbf{0.99} & \\textbf{0.99} & \\textbf{0.99} & \\textbf{0.99} & \\textbf{0.99} & \\textbf{0.99} \\\\ \n \\textit{Std. Dev.} & \n (0.01) & (0.03) & (0.06) & \\textbf(0.002) & \\textbf(0.004) & (0.01) & (0.001) & \\textbf(0.004) & \\textbf(0.007) \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset", "authors": ["Bijay Adhikari", "Pratibha Kulung", "Jakesh Bohaju", "Laxmi Kanta Poudel", "Confidence Raymond", "Dong Zhang", "Udunna C Anazodo", "Bishesh Khanal", "Mahesh Shakya"], "url": "https://arxiv.org/abs/2412.14100v1", "attribution": "\"Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset\" by Bijay Adhikari, Pratibha Kulung, Jakesh Bohaju, Laxmi Kanta Poudel, Confidence Raymond, Dong Zhang, Udunna C Anazodo, Bishesh Khanal, and Mahesh Shakya, arXiv:2412.14100v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18992v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c|c}\n\\toprule\n\\textbf{Object Type} & \\textbf{Numerical Independence Condition} & \\textbf{Implication} \\\\\n\\midrule\n\\textbf{Propositions} & $i(AB)=i(A)+i(B)$ & $P(AB)=P(A)P(B)$ \\\\\n\\textbf{Questions} & $d(a*b)=d(a)+d(b)$ & $P(AB)=P(A)P(B)$ \\\\\n\\textbf{Subjects} & $i(ab)=i(a)+i(b)$ & $P(AB)=P(A)P(B)\\;\\text{or}\\;A\\sim B$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Mathematics of Questions", "authors": ["R. O'Flanagan"], "url": "https://arxiv.org/abs/2503.18992v2", "attribution": "\"The Mathematics of Questions\" by R. O'Flanagan, arXiv:2503.18992v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.10796v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\t\t\t\t\\hline\n\t\t\t\t\\textbf{Methods} & \\textbf{Within Group} & \\textbf{Between Groups}\\\\\n\t\t\t\t\\hline\n\t\t\t\tSY2010 & Accept & Accept \\\\ \\hline\n\t\t\t\tLC2012 & Accept & Accept \\\\ \\hline\n\t\t\t\tCLX2013 & Reject & Reject \\\\ \\hline\n\t\t\t\tHC2018 & Accept & Accept \\\\ \\hline\n\t\t\t\tProposed & Accept & Reject \\\\ \\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of results for the ALL data.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Two sample test for covariance matrices in ultra-high dimension", "authors": ["Xiucai Ding", "Yichen Hu", "Zhenggang Wang"], "url": "https://arxiv.org/abs/2312.10796v1", "attribution": "\"Two sample test for covariance matrices in ultra-high dimension\" by Xiucai Ding, Yichen Hu, and Zhenggang Wang, arXiv:2312.10796v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.14552v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Default configuration for all experiments.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cl}\n \\toprule\n \\textbf{Category} & \\textbf{Value} \\\\\n \\midrule\n Input resolution & 256$\\times$256 px \\\\\n Slices per sweep & 100 \\\\\n Number of Gaussians & 120 per slice ($\\approx$ 12k) \\\\\n Loss & Hybrid L1 (80\\%) + SSIM (20\\%) \\\\\n Learning rates & \\parbox{3cm}{Means:0.20, Opacity:0.03, Scale:0.01, Intensity:0.008} \\\\ \n Batch size & 32 slices \\\\\n Epochs & 60 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound", "authors": ["Max Krähenmann", "Sergio Tascon-Morales", "Fabian Laumer", "Julia E. Vogt", "Ece Ozkan"], "url": "https://arxiv.org/abs/2508.14552v1", "attribution": "\"From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound\" by Max Krähenmann, Sergio Tascon-Morales, Fabian Laumer, Julia E. Vogt, and Ece Ozkan, arXiv:2508.14552v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.12160v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{BESS utilization and losses in Approaches 1 and 2}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\t \t & \\multicolumn{2}{|c|}{Number of cycles} & \\multicolumn{2}{|c|}{BESS losses (kWh)}\\\\\n\t\t\t \\hline\n\t\t\tYear \t& Approach 1\t& Approach 2 & Approach 1 & Approach 2 \\\\\n\t\t\t\\hline\n\t\t\t\\hline\n\t 2027 & 4.8 & 2.9\t& 7,479.4\t& 3,838.3 \\\\ \n\t 2032\t& 6.4\t& 2.8\t& 7,536.3\t& 2,318.1 \\\\\n\t 2037\t& 7.5\t& 3.3\t& 11,293.7\t& 4,108.0\\\\\n\t 2042 & 8.4\t& 5.2\t& 16,661.3\t& 9,709.9 \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Design and Optimization of EV Charging Infrastructure with Battery in Commercial Buildings", "authors": ["Quan Nguyen", "Christine Holland", "Siddharth Sridhar"], "url": "https://arxiv.org/abs/2509.12160v1", "attribution": "\"Design and Optimization of EV Charging Infrastructure with Battery in Commercial Buildings\" by Quan Nguyen, Christine Holland, and Siddharth Sridhar, arXiv:2509.12160v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.13685v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\nAudio Feature&Variability&Inertia\\\\\n\\hline\nDanceability&-0.03&0.03\\\\\nLoudness&-0.01&0.05\\\\\nSpeechiness&0.05&0.09*\\\\\nAcousticness&0.02&0.05\\\\\nInstrumentalness&-0.11**&0.02\\\\\nLiveness&-0.05&0.05\\\\\nTempo&-0.05&0.03\\\\\nMode&-0.05&0.07\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Spearman correlations between users' dynamic measures represented by Variability and Inertia of audio features and K10 scores. (*p\\textless0.05, **p\\textless0.01)}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Static and Dynamic Measures of Active Music Listening as Indicators of Depression Risk", "authors": ["Aayush Surana", "Yash Goyal", "Vinoo Alluri"], "url": "https://arxiv.org/abs/2009.13685v1", "attribution": "\"Static and Dynamic Measures of Active Music Listening as Indicators of Depression Risk\" by Aayush Surana, Yash Goyal, and Vinoo Alluri, arXiv:2009.13685v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09972v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|l|l|}\n\\hline\n $\\rho$ & \\emph{SIFT} & \\emph{SURF} & \\emph{ORB} & \\emph{FREAK} & \\emph{BRIEF} & \\textbf{ARSRG}$_{1^{st}}$ & \\textbf{ARSRG}$_{2^{nd}}$\\\\ \\hline\n $0.6$ & 0.0674 & 0.0820 & 0.2051 & 0.05584& 0.10689 & 1.0 & 1.0\\\\ \\hline\n $0.7$ & 0.0401 & 0.0441 & 0.0742 & 0.04671& 0.05664 & 1.0 & 1.0\\\\ \\hline %old values 0.6571 & 1.0\n $0.8$ & 0.0312 & 0.0338 & 0.0348 & 0.04072& 0.03452 & 1.0 & 1.0\\\\ \\hline %old values 0.1428 & 0.6666\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications", "authors": ["Mario Manzo"], "url": "https://arxiv.org/abs/1912.09972v1", "attribution": "\"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications\" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.12834v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c|c}\nWindow side length & 1 & 2 & 4 & 8 \\\\ \\hline\nPoisson (assuming estm.\\ intensity) & 0.053 & 0.053 & 0.052 & 0.051\\\\\nPoisson (assuming known intensity) & 0.0015 & 0.0011 & 0.0008 & 0.0008 \\\\\\hline\nMat{\\'e}rn (assuming estm.\\ intensity) & 0.63 & 1.00 & 1.00 & 1.00\\\\\nMat{\\'e}rn (assuming known intensity) & 0.31 & 0.96 & 1.00 & 1.00 \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A functional central limit theorem for the K-function with an estimated intensity function", "authors": ["Anne Marie Svane", "Christophe Biscio", "Rasmus Waagepetersen"], "url": "https://arxiv.org/abs/2309.12834v1", "attribution": "\"A functional central limit theorem for the K-function with an estimated intensity function\" by Anne Marie Svane, Christophe Biscio, and Rasmus Waagepetersen, arXiv:2309.12834v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.09307v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\n\\textbf{Kernel } & $\\mathbf{h}$ & $\\mathbf{\\mathcal{C}_{k}}$ & \\textbf{Error1} & \\textbf{Error2} & \\textbf{Error3}\\\\ \n\\hline\nGaussian & 4.981171 & 0.002179295 & 7.90000005 & 7.6266667 & 7.95026705\\\\\nEpanechnikov & 10.52929 & 1.02x$10^{-05}$ & 6.9500001 & 6.5284985 & 6.8285046\\\\\nCosine & 10.23305 & 1.26x$10^{-07}$ & 7.4 & 7.2599999 & 7.4782377\\\\\nUniform & 2.675163 & 0.004355385 & 8.4401727 & 8.19666675 & 8.45300395\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Average test error for each kernel as calculated for infant 5 through Monte Carlo simulation}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Computational Challenges in Non-parametric Prediction of Bradycardia in Preterm Infants", "authors": ["Sinjini Mitra"], "url": "https://arxiv.org/abs/2011.09307v1", "attribution": "\"Computational Challenges in Non-parametric Prediction of Bradycardia in Preterm Infants\" by Sinjini Mitra, arXiv:2011.09307v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12389v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\centering The comparison of precision, recall and F1-score for our model and Double-net in percentage from three different views for xVertSeg dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\hline\n\\textbf{Plane} & \\textbf{Model}& \\textbf{Phase }& \\textbf{Precision(\\%)} &\\textbf{Recall(\\%)}&\\textbf{F1-score(\\%)} \\\\\\hline\n\\multirow{4}{*}{Sagittal} & \\multirow{2}{*}{DoubleU-Net } &Valid& 93.14 & 93.49 & 93.31 \\\\ & & Test & 92.23 & 92.94 & 92.58 \\\\\\cline{2-6}\n& \\multirow{2}{*}{\\textbf{DoubleU-Net ++} } &Valid& \\textbf{98.49} & \\textbf{98.23} & \\textbf{98.35} \\\\ & & Test & \\textbf{97.95} & \\textbf{97.54} & \\textbf{97.74} \\\\ \n\\hline\n\\multirow{4}{*}{Coronal } & \\multirow{2}{*}{DoubleU-Net } &Valid& 91.48 & 91.34 & 91.40 \\\\ & & Test & 90.74 & 90.01 & 90.37 \\\\\\cline{2-6}\n& \\multirow{2}{*}{\\textbf{DoubleU-Net ++ }} &Valid&\\textbf{94.48} &\\textbf{94.83} & \\textbf{94.65} \\\\ & & Test & \\textbf{93.79} & \\textbf{93.64} &\\textbf{93.71} \\\\ \\hline\n\\multirow{4}{*}{Axial } & \\multirow{2}{*}{DoubleU-Net } &Valid& 92.81 & 92.94 & 92.87 \\\\ & & Test & 92.16 & 92.11 & 92.13 \\\\\\cline{2-6}\n& \\multirow{2}{*}{\\textbf{DoubleU-Net ++}} &Valid& \\textbf{96.49} & \\textbf{96.83} & \\textbf{96.65} \\\\ & & Test & \\textbf{96.02 } & \\textbf{96.17}& \\textbf{96.09} \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "DoubleU-Net++: Architecture with Exploit Multiscale Features for Vertebrae Segmentation", "authors": ["Simindokht Jahangard", "Mahdi Bonyani", "Abbas Khosravi"], "url": "https://arxiv.org/abs/2201.12389v1", "attribution": "\"DoubleU-Net++: Architecture with Exploit Multiscale Features for Vertebrae Segmentation\" by Simindokht Jahangard, Mahdi Bonyani, and Abbas Khosravi, arXiv:2201.12389v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08550v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Clustering-based Methods for Segmentation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\nReference & Year & Team & Segmentation Method\\\\\n\\hline\n & 2008 & O Sertel & $k$-means clustering\\\\\n & 2008 & O Sertel & $k$-means clustering, eccentricity\\\\\n & 2009 & O Sertel & $k$-means clustering\\\\\n & 2010 & S Samsi & $k$-means clustering\\\\\n & 2010 & J Han & $k$-means clustering\\\\\n & 2012 & S Samsi & $k$-means clustering\\\\\n & 2012 & B Oztan & $k$-means clustering\\\\\n & 2016 & P Shi & Hierarchical $k$-means clustering\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review", "authors": ["Xiaoqi Li", "Haoyuan Chen", "Chen Li", "Md Mamunur Rahaman", "Xintong Li", "Jian Wu", "Xiaoyan Li", "Hongzan Sun", "Marcin Grzegorzek"], "url": "https://arxiv.org/abs/2201.08550v2", "attribution": "\"What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review\" by Xiaoqi Li, Haoyuan Chen, Chen Li, Md Mamunur Rahaman, Xintong Li, Jian Wu, Xiaoyan Li, Hongzan Sun, and Marcin Grzegorzek, arXiv:2201.08550v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13822v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Error profile for computing on Figure meshes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|rr|rr} \n \\hline \n$G_i$ & % \\multicolumn{2}{c|}{$\\|\\b u-\\b u_h\\|_{0}$ $O(h^r)$} & \n $\\|\\b u-\\b u_h\\|_{0}$&$O(h^r)$ & $\\|\\nabla_w(\\b u-\\b u_h)\\|_0$ & $O(h^r)$ \\\\ \\hline \n & \\multicolumn{4}{c}{By the $P_1$ WG element} \\\\ \n 5& 0.202E-02 & 2.3& 0.238E+00 & 1.6 \\\\\n 6& 0.462E-03 & 2.1& 0.992E-01 & 1.3 \\\\\n 7& 0.112E-03 & 2.0& 0.466E-01 & 1.1 \\\\\n \\hline \n & \\multicolumn{4}{c}{By the $P_2$ WG element} \\\\ \n 4& 0.230E-02 & 4.0& 0.298E+00 & 3.0 \\\\\n 5& 0.154E-03 & 3.9& 0.374E-01 & 3.0 \\\\\n 6& 0.122E-04 & 3.7& 0.493E-02 & 2.9 \\\\\n \\hline \n & \\multicolumn{4}{c}{By the $P_3$ WG element} \\\\ \n 4& 0.399E-03 & 5.0& 0.822E-01 & 4.0 \\\\\n 5& 0.123E-04 & 5.0& 0.508E-02 & 4.0 \\\\\n 6& 0.384E-06 & 5.0& 0.316E-03 & 4.0 \\\\\n \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An Auto-Stabilized Weak Galerkin Method for Elasticity Interface Problems on Nonconvex Meshes", "authors": ["Chunmei Wang", "Shangyou Zhang"], "url": "https://arxiv.org/abs/2501.13822v1", "attribution": "\"An Auto-Stabilized Weak Galerkin Method for Elasticity Interface Problems on Nonconvex Meshes\" by Chunmei Wang and Shangyou Zhang, arXiv:2501.13822v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2102.00142v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experimental testbed}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|}\n\\hline\nCPU & Intel(R) Core(TM) i7-6700K @ 4.00GHz\\\\\n\\hline\nGPU & NVIDIA GeForce GTX Titan X \\\\\n\\hline\nCUDA & 10.2 \\\\\n\\hline\nRAM & 64 GB\\\\\n\\hline\nOprating system & Ubuntu 18.04 LTS\\\\\n\\hline\nLanguage & Python 3.5.6\\\\\n\\hline\nTensorflow & 2.2.0 \\\\\n\\hline\nKeras & 2.3.1 \\\\\n\\hline\nOpenCV & 3.1.0 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Latent-Space Inpainting for Packet Loss Concealment in Collaborative Object Detection", "authors": ["Ivan V. Bajić"], "url": "https://arxiv.org/abs/2102.00142v1", "attribution": "\"Latent-Space Inpainting for Packet Loss Concealment in Collaborative Object Detection\" by Ivan V. Bajić, arXiv:2102.00142v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12733v5_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small Inference time of TPC for different transformations}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llc}\n\\toprule\n Transformation & Attack radius & Inference time ($\\mathrm {ms}$)\\\\\n \\midrule\n General rotation & 15$^\\circ$ & 9.56\\\\\n Z-rotation & 180$^\\circ$ & 3.12\\\\\n Z-shear & 0.2 & 3.46\\\\\n Z-twist & 180$^\\circ$ & 3.76\\\\\n Z-taper & 0.5 & 10.61 \\\\\n Linear & 0.2 & 3.94 \\\\\n Z-twist $\\circ$ Z-rotation & 50$^\\circ$, 5$^\\circ$& 5.34\\\\\n Z-taper $\\circ$ Z-rotation & 0.2, 1$^\\circ$ & 8.72\\\\\n Z-twist $\\circ$ Z-taper $\\circ$ Z-rotation & 20$^\\circ$, 0.2, 1$^\\circ$ & 9.40\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "TPC: Transformation-Specific Smoothing for Point Cloud Models", "authors": ["Wenda Chu", "Linyi Li", "Bo Li"], "url": "https://arxiv.org/abs/2201.12733v5", "attribution": "\"TPC: Transformation-Specific Smoothing for Point Cloud Models\" by Wenda Chu, Linyi Li, and Bo Li, arXiv:2201.12733v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.14345v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrr}\n \\hline\n Metric & S\\&P500 & EW & MVO & BLM-Llama & BLM-Gemma & BLM-Qwen & BLM-GPT \\\\\n \\hline\n CAGR \\(\\uparrow\\) & 0.1159 & \\underline{0.3595} & -0.0189 & \\textbf{0.6731} & -0.2593 & 0.1494 & -0.1276 \\\\\n mean \\(\\uparrow\\) & 0.0005 & \\underline{0.0012} & 0.0001 & \\textbf{0.0022} & -0.0011 & 0.0007 & -0.0004 \\\\\n std \\(\\downarrow\\) & \\underline{0.0086} & \\textbf{0.0074} & 0.0182 & 0.0151 & 0.0128 & 0.0160 & 0.0157 \\\\\n Sharpe \\(\\uparrow\\) & 0.0455 & \\textbf{0.1580} & 0.0006 & \\underline{0.1374} & -0.0931 & 0.0375 & -0.0318 \\\\\n mean (ann.) \\(\\uparrow\\) & 0.1191 & \\underline{0.3142} & 0.0225 & \\textbf{0.5432} & -0.2795 & 0.1710 & -0.1056 \\\\\n std (ann.) \\(\\downarrow\\) & \\underline{0.1371} & \\textbf{0.1173} & 0.2882 & 0.2398 & 0.2026 & 0.2534 & 0.2486 \\\\\n Sharpe (ann.) \\(\\uparrow\\) & 0.7228 & \\textbf{2.5075} & 0.0087 & \\underline{2.1817} & -1.4778 & 0.5959 & -0.5054 \\\\\n MDD \\(\\uparrow\\) & \\underline{-0.0849} & \\textbf{-0.0519} & -0.1828 & -0.1245 & -0.2124 & -0.1509 & -0.1157 \\\\\n VaR95\\% \\(\\uparrow\\) & \\underline{-0.0155} & \\textbf{-0.0116} & -0.0285 & -0.0201 & -0.0203 & -0.0215 & -0.0251 \\\\\n CVaR95\\% \\(\\uparrow\\) & \\underline{-0.0212} & \\textbf{-0.0164} & -0.0480 & -0.0305 & -0.0327 & -0.0355 & -0.0392 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Out-of-sample Investment Performance Metrics of Portfolios}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Integrating LLM-Generated Views into Mean-Variance Optimization Using the Black-Litterman Model", "authors": ["Youngbin Lee", "Yejin Kim", "Suin Kim", "Yongjae Lee"], "url": "https://arxiv.org/abs/2504.14345v1", "attribution": "\"Integrating LLM-Generated Views into Mean-Variance Optimization Using the Black-Litterman Model\" by Youngbin Lee, Yejin Kim, Suin Kim, and Yongjae Lee, arXiv:2504.14345v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.20987v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccc}\n & $0$ & $1$ & $2$ & $\\cdots$ & $c$ \\\\\n \\hline\n $0$ & $1$ & . & . & $\\cdots$ & . \\\\\n $q$ & . & $\\beta_{1,q}$ & $\\beta_{2,q}$ & $\\cdots$ & $\\beta_{c,q}$ \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Betti tables of $\\sigma_q\\Delta(\\overline{K}_{r+1})$ and $\\sigma_q\\Delta(P_{r+1})$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Fröberg type theorem for higher secant complexes", "authors": ["Junho Choe", "Jaewoo Jung"], "url": "https://arxiv.org/abs/2502.20987v2", "attribution": "\"A Fröberg type theorem for higher secant complexes\" by Junho Choe and Jaewoo Jung, arXiv:2502.20987v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17103v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n $n$ & Error $\\|\\cdot\\|_1$ & Order $\\|\\cdot\\|_1$ & Error\n $\\|\\cdot\\|_{\\infty}$ & Order $\\|\\cdot\\|_{\\infty}$ \\\\\n \\hline\n 40 & 2.07E$-3$ & $-$ & 3.87E$-2$ & $-$ \\\\\n \\hline\n 80 & 5.32E$-4$ & 1.96 & 1.96E$-2$ & 0.98 \\\\\n \\hline\n 160 & 1.34E$-4$ & 1.99 & 9.81E$-3$ & 1.00 \\\\\n \\hline\n 320 & 3.38E$-5$ & 1.99 & 4.91E$-3$ & 1.00 \\\\\n \\hline\n 640 & 8.48E$-6$ & 1.99 & 2.45E$-3$ & 1.00 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Constant extrapolation (first order).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17103v1", "attribution": "\"High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16980v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Complexity in number of multiplications or divisions}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|}\n\\hline\n\\multirow{2}{*}{Stage} & \\multicolumn{2}{c|}{General} & \\multicolumn{2}{c|}{Simulated$^*$} \n\\\\ %\\cline{2-5} \n & OPS & OPA & OPS & OPA \n \\\\ \\hline\nObserver & $\\frac{3}{T_{\\textsc{m}}}$ & $\\frac{6}{T_{\\textsc{m}}}$ & $30$ & $60$ \n\\\\ \\hline\nPredictor & $\\frac{5}{T}$ & $\\frac{10}{T}$ & $5\\times10^{3}$ & $10\\times10^{3}$ \n\\\\ \\hline\n & \\multicolumn{2}{c|}{$2\\frac{MN}{T}$} & \\multicolumn{2}{c|}{$1.8\\times10^{6}$} \n\\\\ \\hline\nLUT & \\multicolumn{2}{c|}{$\\frac{MN}{T}$} & \\multicolumn{2}{c|}{$0.9\\times10^{6}$} \n\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Beamforming Control in RIS-Aided Wireless Communications: A Predictive Physics-Based Approach", "authors": ["Luis C. Mathias", "Atefeh Termehchi", "Taufik Abrão", "Ekram Hossain"], "url": "https://arxiv.org/abs/2508.16980v1", "attribution": "\"Beamforming Control in RIS-Aided Wireless Communications: A Predictive Physics-Based Approach\" by Luis C. Mathias, Atefeh Termehchi, Taufik Abrão, and Ekram Hossain, arXiv:2508.16980v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13085v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\n \\hline\n \\textit{Parameter} & $t_0$ & $t_f$ & $k_1$ & $k_{-1}$ & $k_2$ & $E_1^a / R$ & $E_{-1}^a / R$ & $E_2^a / R$ & $e_{\\text{tot}}$ \\\\\n \\hline\n \\textit{Value} & 0 & 30 & 0.04 & 0.03 & 0.035 & 200 & 220 & 190 & 0.3 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Parameter values used for the numerical simulation and control of the CPDS .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Modified Patankar Semi-Lagrangian Scheme for the Optimal Control of Production-Destruction systems", "authors": ["Simone Cacace", "Alessio Oliviero", "Mario Pezzella"], "url": "https://arxiv.org/abs/2501.13085v1", "attribution": "\"Modified Patankar Semi-Lagrangian Scheme for the Optimal Control of Production-Destruction systems\" by Simone Cacace, Alessio Oliviero, and Mario Pezzella, arXiv:2501.13085v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.11765v1_tex_table24.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overview of assigned Sentiment Labels}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccllll}\n & \\textbf{year} & \\textbf{positive} & \\textbf{negative} & \\textbf{pos/neg} & \\textbf{pos-neg} \\\\ \\hline\n\\textbf{ESG} & \\textbf{2018} & 78.1\\% & 21.9\\% & 3.570 & 0.562 \\\\\n & \\textbf{2019} & 78.7\\% & 21.3\\% & 3.687 & 0.573 \\\\\n & \\textbf{2020} & 75.3\\% & 24.7\\% & 3.057 & 0.507 \\\\ \\hline\n\\textbf{Noise} & \\textbf{2018} & 77.9\\% & 22.1\\% & 3.523 & 0.558 \\\\\n & \\textbf{2019} & 77.5\\% & 22.5\\% & 3.445 & 0.550 \\\\\n & \\textbf{2020} & 74.9\\% & 25.1\\% & 2.987 & 0.498 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis", "authors": ["Tanja Aue", "Adam Jatowt", "Michael Färber"], "url": "https://arxiv.org/abs/2212.11765v1", "attribution": "\"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis\" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11479v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Confusion Matrix obtained during cross-validation of our final detector on our Bell's Palsy Video dataset.} %\\tabcolsep{4}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\n\t\t\t\\hline\n\t\t\t\\multirow{3}{*}{Bell's Palsy}&&Predicted No&Predicted Yes\\\\\\cline{2-4}\n\t\t\t&Actual No& 32&2\\\\\n\t\t\t&Actual Yes& 2&39\\\\\n\t\t\t\\hline\n\t\t\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Eye-focused Detection of Bell's Palsy in Videos", "authors": ["Sharik Ali Ansari", "Koteswar Rao Jerripothula", "Pragya Nagpal", "Ankush Mittal"], "url": "https://arxiv.org/abs/2201.11479v1", "attribution": "\"Eye-focused Detection of Bell's Palsy in Videos\" by Sharik Ali Ansari, Koteswar Rao Jerripothula, Pragya Nagpal, and Ankush Mittal, arXiv:2201.11479v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.15594v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Data set details: the number of crops per subset, their classes and the percentage of the total number of image crops in each set. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\t\\hline\n\tSet & Non-malignant & Malignant & Total crops & Percentage\\\\\n\t\\hline\n\tTrain & 561 & 397& 958 & 70.3\\\\\n\tValidation & 104 & 95& 199 & 14.6\\\\\n\tTest & 116& 89& 205 & 15.1\\\\ \\hline\n\tTotal & 781& 581&1362 & 100\\\\\n\t\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Are Virtual DES Images a Valid Alternative to the Real Ones?", "authors": ["Ana C. Perre", "Luís A. Alexandre", "Luís C. Freire"], "url": "https://arxiv.org/abs/2508.15594v1", "attribution": "\"Are Virtual DES Images a Valid Alternative to the Real Ones?\" by Ana C. Perre, Luís A. Alexandre, and Luís C. Freire, arXiv:2508.15594v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18792v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Surrogate Model Accuracy (accuracy is measured as the percentage of correct predictions)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n & {GP} & {MoNNE} & {BMNN (VI)}\\\\\n\\midrule\nDTLZ2 & $0.9$ & $0.92$& $0.9$ \\\\\nVLMOP3 & $0.86$ & $0.92$ & $0.84$\\\\\nVehicleSafety & $0.82$ & $0.86$ & $0.86$\\\\\nOSY & $0.82$ & $0.88$ & $0.88$\\\\\nCarCabDesign & $0.84$ & $0.96$ & $0.94$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble", "authors": ["Hanyang Wang", "Juergen Branke", "Matthias Poloczek"], "url": "https://arxiv.org/abs/2501.18792v1", "attribution": "\"Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble\" by Hanyang Wang, Juergen Branke, and Matthias Poloczek, arXiv:2501.18792v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13232v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Cases when $P_I=1$ is the equilibrium.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n Scenario & Range of $R$ & Condition on $C_I$\\\\\n \\hline & & \\\\\n 1 \\& 2 & $R > c_w\\left(\\dfrac{2}{\\mu}-\\dfrac{1}{\\mu+\\lambda}\\right) $ & $C_I\\leq C_{I,1}^{1,2}(R)\\equiv \\dfrac{(1-\\rho)\\rho^{n_e}}{1-\\rho^{n_e+1}}\\left[\\dfrac{c_wn_e}{\\mu}+\\dfrac{c_w}{\\mu}-R\\right]$ \\\\\n 3 & $R \\in \\left(\\dfrac{c_w}{\\mu},c_w\\left(\\dfrac{2}{\\mu}-\\dfrac{1}{\\mu+\\lambda}\\right)\\right]$ & $C_I\\leq C_{I,1}^{3}(R) \\equiv \\dfrac{R\\mu-c_w}{\\mu+\\lambda}$ \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Queues with inspection cost: To see or not to see?", "authors": ["Jake Clarkson", "Konstantin Avrachenkov", "Eitan Altman"], "url": "https://arxiv.org/abs/2503.13232v1", "attribution": "\"Queues with inspection cost: To see or not to see?\" by Jake Clarkson, Konstantin Avrachenkov, and Eitan Altman, arXiv:2503.13232v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10650v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{TP, TN, FP and FN at the pixel level}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\n& \\textbf{\\textit{Predicted Pixel}} & \\textbf{\\textit{Actual Pixel}} \\\\\n\\hline\nTP & 1 & 1 \\\\\nTN & 0 & 0 \\\\\nFP & 1 & 0 \\\\\nFN & 0 & 1 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data", "authors": ["José G. M. Esgario", "Renato A. Krohling"], "url": "https://arxiv.org/abs/2201.10650v1", "attribution": "\"Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data\" by José G. M. Esgario and Renato A. Krohling, arXiv:2201.10650v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18455v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Test performance of single-view representation learning models with different encoder architectures, and trained on selected datasets using VIB , Category-dependent VIB (CDVIB) , and our proposed Gaussian Mixture MDL (GM-MDL).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\n\\# & Encoder & Dataset & no reg. & VIB & CDVIB & GM-MDL \\\\ \\hline\n1 & CNN4 & CIFAR10 & 0.612 & 0.626 & 0.649 & \\textbf{0.681} \\\\ \\hline\n2 & CNN4 & USPS & 0.948 & 0.952 & 0.955 & \\textbf{0.963} \\\\ \\hline\n3 & CNN4 & INTEL & 0.756 & 0.759 & 0.763 & \\textbf{0.776} \\\\ \\hline\n4 & ResNet18 & CIFAR10 & 0.824 & 0.829 & 0.835 & \\textbf{0.848} \\\\ \\hline\n5 & ResNet18 & CIFAR100 & 0.454 & 0.458 & 0.463 & \\textbf{0.497} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior", "authors": ["Milad Sefidgaran", "Abdellatif Zaidi", "Piotr Krasnowski"], "url": "https://arxiv.org/abs/2504.18455v1", "attribution": "\"Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior\" by Milad Sefidgaran, Abdellatif Zaidi, and Piotr Krasnowski, arXiv:2504.18455v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18200v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc|cc}\n\\hline\n $G_i$ & $\\|Q_0 u- u_0\\|_0$ & $ h^r $ & $\\|\\nabla_w(Q_h u- u_h)\\|_0$ & $ h^r $ &\n $\\|E_w(Q_h u- u_h)\\|_0$ & $ h^r $ \\\\\n\\hline&\\multicolumn{6}{c}{ By the $P_2$/$P_2$/$P_{1}$ WG finite element .}\\\\\n\\hline \n 4& 0.183E-3 & 1.5& 0.196E-2 & 1.6& 0.247E+0 & 0.9 \\\\\n 5& 0.502E-4 & 1.9& 0.536E-3 & 1.9& 0.125E+0 & 1.0 \\\\\n 6& 0.128E-4 & 2.0& 0.137E-3 & 2.0& 0.629E-1 & 1.0 \\\\\n\\hline&\\multicolumn{6}{c}{ By the $P_3$/$P_3$/$P_{2}$ WG finite element .}\\\\\n\\hline \n 3& 0.360E-4 & 3.2& 0.660E-3 & 2.9& 0.835E-1 & 1.8 \\\\\n 4& 0.277E-5 & 3.7& 0.808E-4 & 3.0& 0.221E-1 & 1.9 \\\\\n 5& 0.184E-6 & 3.9& 0.982E-5 & 3.0& 0.561E-2 & 2.0 \\\\\n\\hline&\\multicolumn{6}{c}{ By the $P_4$/$P_4$/$P_{3}$ WG finite element .}\\\\\n\\hline \n 2& 0.243E-4 & 3.8& 0.718E-3 & 3.2& 0.669E-1 & 2.4 \\\\\n 3& 0.140E-5 & 4.1& 0.777E-4 & 3.2& 0.126E-1 & 2.4 \\\\\n 4& 0.351E-7 & 5.3& 0.528E-5 & 3.9& 0.174E-2 & 2.9 \\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Simple Weak Galerkin Finite Element Method for a Class of Fourth-Order Problems in Fluorescence Tomography", "authors": ["Chunmei Wang", "Shangyou Zhang"], "url": "https://arxiv.org/abs/2503.18200v1", "attribution": "\"A Simple Weak Galerkin Finite Element Method for a Class of Fourth-Order Problems in Fluorescence Tomography\" by Chunmei Wang and Shangyou Zhang, arXiv:2503.18200v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11608v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c|c|c|c|c||}\n\\hline\nName & mass ratio & $\\chi_{1,2} $ & precession & {\\tt dim} & $\\ell $ & GW cyles \\\\\n\\hline \\hline\n{\\tt SpEC\\_q1\\_10\\_NoSpin} & $\\leq 10$ & 0 & no & 1 & $\\leq 8$ & $\\sim 25-31$ \\\\\n\\hline\n{\\tt NRSur4d2s\\_TDROM\\_grid12} & $\\leq 2$ & $\\leq 0.8$ & yes & 4 & $\\leq 3$ & $\\sim 25-35$ \\\\ \n\\hline \n{\\tt NRSur7dq2} & $\\leq 2$ & $\\leq 0.8$ & yes & 7 & $\\leq 4$ & $\\sim 40$ \\\\\n\\hline\n{\\tt NRSur7dq4} & $\\leq 4$ & $\\leq 0.8$ & yes & 7 & $\\leq 4$ & $\\sim 40$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Characterization of the waveform surrogates described so far corresponding to NR binary black holes.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Reduced Order and Surrogate Models for Gravitational Waves", "authors": ["Manuel Tiglio", "Aarón Villanueva"], "url": "https://arxiv.org/abs/2101.11608v1", "attribution": "\"Reduced Order and Surrogate Models for Gravitational Waves\" by Manuel Tiglio and Aarón Villanueva, arXiv:2101.11608v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.10648v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary statistics of cases by gender and race}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n & \\multicolumn{2}{c}{\\textbf{Child}} & \\multicolumn{2}{c}{\\textbf{Defendant}} \\\\\n \\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n & \\textbf{Count} & \\textbf{Percentage} & \\textbf{Count} & \\textbf{Percentage} \\\\\n \\midrule\n \\textbf{Gender} \\\\\n \\hspace{5mm} Female & 2359 & 40\\% & 1477 & 25\\% \\\\\n \\hspace{5mm} Male & 3277 & 55\\% & 4332 & 73\\% \\\\\n \\hspace{5mm} Unknown & 292 & 5\\% & 119 & 2\\% \\\\\n \\midrule\n \\textbf{Race} \\\\\n \\hspace{5mm} White & 1954 & 33\\% & 2381 & 40\\% \\\\\n \\hspace{5mm} Black & 669 & 11\\% & 890 & 15\\% \\\\\n \\hspace{5mm} Hispanic & 491 & 8\\% & 666 & 11\\% \\\\\n \\hspace{5mm} Asian & 58 & 1\\% & 67 & 1\\% \\\\\n \\hspace{5mm} Unknown & 2756 & 46\\% & 1924 & 32\\% \\\\\n \\midrule\n \\textbf{Total cases} & 5928 & 100\\% & 5928 & 100\\% \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Statistical Issues in the Diagnosis of Shaken Baby Syndrome/Abusive Head Trauma", "authors": ["Maria Cuellar"], "url": "https://arxiv.org/abs/2412.10648v2", "attribution": "\"Statistical Issues in the Diagnosis of Shaken Baby Syndrome/Abusive Head Trauma\" by Maria Cuellar, arXiv:2412.10648v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.13576v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Objective evaluation of the ECS model}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n & \\multicolumn{3}{c}{} \\\\\n Input Speech & STOI${\\tiny \\uparrow}$ & ESTOI${\\tiny \\uparrow}$ & NCM${\\tiny \\uparrow}$ \\\\ \n \\midrule\n \n Clean & 0.7604 & 0.5866 & 0.6809 \\\\\n Noisy & 0.4870 & 0.2073 & 0.3258 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "End-to-End Audio-Visual Learning for Cochlear Implant Sound Coding in Noisy Environments", "authors": ["Meng-Ping Lin", "Enoch Hsin-Ho Huang", "Shao-Yi Chien", "Yu Tsao"], "url": "https://arxiv.org/abs/2508.13576v1", "attribution": "\"End-to-End Audio-Visual Learning for Cochlear Implant Sound Coding in Noisy Environments\" by Meng-Ping Lin, Enoch Hsin-Ho Huang, Shao-Yi Chien, and Yu Tsao, arXiv:2508.13576v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07558v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{pDNSMOS scores on the ICASSP 2023 DNS Challenge blind test set.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc} \n\\toprule\n\\textbf{Model} & \\multicolumn{3}{c}{\\textbf{Track 1}} & \\multicolumn{3}{c}{\\textbf{Track 2}} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7}\n& SIG & BAK & OVRL & SIG & BAK & OVRL \\\\\n\\midrule\nNoisy & 4.15 & 2.37 & 2.71 & 4.05 & 2.16 & 2.50 \\\\\nTEA-PSE 3.0 & 4.12 & \\textbf{4.05} & 3.65 & 3.99 & \\textbf{3.95} & 3.49 \\\\\nNAPSE & 3.81 & 3.99 & 3.38 & 3.92 & 4.17 & \\textbf{3.56} \\\\\n\\rowcolor{gray!20} LLaSE-G1 & 4.21 & 3.99 & 3.72 & 4.08 & 3.84 & 3.55 \\\\\n\\midrule\n\\rowcolor{gray!20} UniFlow$_\\text{DDPM}$ & \\textbf{4.24} & 3.99 & \\textbf{3.73} & \\textbf{4.09} & 3.88 & \\textbf{3.56} \\\\\n\\rowcolor{gray!20} UniFlow$_\\text{FM}$ & 4.20 & 4.01 & 3.70 & 4.06 & 3.89 & 3.54 \\\\\n\\rowcolor{gray!20} UniFlow$_\\text{MF}$ & 4.18 & 3.99 & 3.67 & 4.04 & 3.87 & 3.51 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling", "authors": ["Ziqian Wang", "Zikai Liu", "Yike Zhu", "Xingchen Li", "Boyi Kang", "Jixun Yao", "Xianjun Xia", "Chuanzeng Huang", "Lei Xie"], "url": "https://arxiv.org/abs/2508.07558v1", "attribution": "\"UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling\" by Ziqian Wang, Zikai Liu, Yike Zhu, Xingchen Li, Boyi Kang, Jixun Yao, Xianjun Xia, Chuanzeng Huang, and Lei Xie, arXiv:2508.07558v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.09361v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n \\textbf{Level of Distortion} & \\textbf{PLCC} & \\textbf{SROCC} & \\textbf{KROCC}\\\\\n\\midrule\n Level $1$ & 0.889 & 0.843 & 0.659 \\\\\n Level $2$ & 0.924 & 0.918 & 0.748 \\\\\n Level $3$ & 0.935 & 0.933 & 0.777 \\\\\n Level $4$ & 0.937 & 0.922 & 0.765 \\\\\n Level $5$ & 0.931 & 0.897 & 0.725 \\\\\n\\midrule\n \\textbf{All}& 0.959 & 0.957 & 0.819 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A combined full-reference image quality assessment approach based on convolutional activation maps", "authors": ["Domonkos Varga"], "url": "https://arxiv.org/abs/2010.09361v3", "attribution": "\"A combined full-reference image quality assessment approach based on convolutional activation maps\" by Domonkos Varga, arXiv:2010.09361v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14598v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n \\hline\n\t\\textbf{Parameter}&\\textbf{binary16} & \n\t\t\\textbf{binary32} & \n\t\\textbf{binary64} &\\textbf{binary128} \\\\\n \\hline\n\tp & 11 & 24 & 53 & 113 \\\\\n\temax & 31 & 127 & 1023 & 16383 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Type-Based Approaches to Rounding Error Analysis", "authors": ["Ariel Eileen Kellison"], "url": "https://arxiv.org/abs/2501.14598v2", "attribution": "\"Type-Based Approaches to Rounding Error Analysis\" by Ariel Eileen Kellison, arXiv:2501.14598v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13833v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n &$r_c$ (\\AA)&$r_0$ (\\AA)&$\\kappa r_0$&\n &$r_c$ (\\AA) &$r_0$ (\\AA)&$\\kappa r_0$\\\\\n\\hline\nCu& 0.800 & 14.10 & 2.550 &Sn[1]\n& 0.680 & 1.870 & 3.700 \\\\\nAg& 0.990 & 15.90 & 2.710 &Pb[2]\n& 0.450 & 1.930 & 3.760 \\\\\nAu& 1.150 & 15.90 & 2.710 &Ca[3]\n& 0.750 & 2.170 & 3.560 \\\\\nMg& 0.490 & 17.60 & 3.200 &Sr[4]\n& 0.900 & 2.370 & 3.720 \\\\\nZn& 0.300 & 15.20 & 2.970 &Li[2]\n& 0.380 & 1.730 & 2.830 \\\\\nCd& 0.530 & 17.10 & 3.160 &Na[5]\n& 0.760 & 2.110 & 3.120 \\\\\nHg& 0.550 & 17.80 & 3.220 &K[5]\n& 1.120 & 2.620 & 3.480 \\\\\nAl& 0.230 & 15.80 & 3.240 &Rb[3]\n& 1.330 & 2.800 & 3.590 \\\\\nGa& 0.310 & 16.70 & 3.330 &Cs[4]\n& 1.420 & 3.030 & 3.740 \\\\\nIn& 0.460 & 18.40 & 3.500 &Ba[5]\n& 0.960 & 2.460 & 3.780 \\\\\nTl& 0.480 & 18.90 & 3.550 & & & & \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the Reasoning Capacity of AI Models and How to Quantify It", "authors": ["Santosh Kumar Radha", "Oktay Goktas"], "url": "https://arxiv.org/abs/2501.13833v1", "attribution": "\"On the Reasoning Capacity of AI Models and How to Quantify It\" by Santosh Kumar Radha and Oktay Goktas, arXiv:2501.13833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17973v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Size and Power of the Cross-Fit Tests for testing $H_0:\\theta^{(j)}=0,j=1,2$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccccccc}\n \\toprule\n & Size & \\multicolumn{14}{c}{Power (values of $h$ below)} \\\\\n \n \\cmidrule{3-16}\n & & 0.069 & 0.138 & 0.207 & 0.276 & 0.345 & 0.414 & 0.483 & 0.552 & 0.621 & 0.690 & 0.759 & 0.828 & 0.897 & 0.966 \\\\\n \\hline\n Panel A: ($n=50$) & \\multicolumn{15}{c}{}\\\\\n LR-test (MLE $\\hat\\theta_1$) & 0 & 0.000 & 0.002 & 0.013 & 0.049 & 0.099 & 0.175 & 0.267 & 0.387 & 0.521 & 0.631 & 0.744 & 0.821 & 0.887 & 0.928 \\\\\n LR-test (moment-based $\\hat\\theta_1$) & 0 & 0.002 & 0.006 & 0.018 & 0.056 & 0.103 & 0.184 & 0.280 & 0.391 & 0.517 & 0.631 & 0.755 & 0.825 & 0.891 & 0.933 \\\\\n & \\multicolumn{15}{c}{}\\\\\n Panel B: ($n=100$) & \\multicolumn{15}{c}{}\\\\\n LR-test (MLE $\\hat\\theta_1$) & 0 & 0.001 & 0.011 & 0.073 & 0.196 & 0.370 & 0.576 & 0.760 & 0.885 & 0.948 & 0.973 & 0.992 & 0.996 & 1.000 & 1.000 \\\\\n LR-test (moment-based $\\hat\\theta_1$) & 0 & 0.002 & 0.016 & 0.081 & 0.209 & 0.383 & 0.582 & 0.762 & 0.877 & 0.942 & 0.976 & 0.988 & 0.993 & 0.998 & 1.000 \\\\\n & \\multicolumn{15}{c}{}\\\\\n Panel C: ($n=200$) & \\multicolumn{15}{c}{}\\\\\n LR-test (MLE $\\hat\\theta_1$) & 0.001 & 0.007 & 0.060 & 0.235 & 0.522 & 0.794 & 0.948 & 0.988 & 0.997 & 1 & 1 & 1 & 1 & 1 & 1 \\\\\n LR-test (moment-based $\\hat\\theta_1$) & 0.002 & 0.008 & 0.066 & 0.246 & 0.521 & 0.776 & 0.940 & 0.988 & 0.996 & 1 & 1 & 1 & 1 & 1 & 1 \\\\\n \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Universal Inference for Incomplete Discrete Choice Models", "authors": ["Hiroaki Kaido", "Yi Zhang"], "url": "https://arxiv.org/abs/2501.17973v1", "attribution": "\"Universal Inference for Incomplete Discrete Choice Models\" by Hiroaki Kaido and Yi Zhang, arXiv:2501.17973v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09972v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results on the ALOI dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}\n\\hline\n\\textbf{Method} & \\textbf{200} & \\textbf{400} & \\textbf{800} & \\textbf{1200} & \\textbf{1600} & \\textbf{2000} & \\textbf{2400} & \\textbf{2800} & \\textbf{3200} & \\textbf{3600}\\\\\n\\hline\n\\multirow{1}{*}{\\textbf{BoAW} } & \\textbf{98.29%Please check if bold is necessary, if yes, please add explanation for the bold format. \n}\\% & \\textbf{92.83}\\% & \\textbf{98.80}\\% & \\textbf{96.80}\\% & \\textbf{96.76}\\% & \\textbf{98.15}\\% & \\textbf{89.52}\\% & \\textbf{82.65}\\% & \\textbf{79.96}\\% & \\textbf{79.88}\\%\\\\\n\\hline\n\\multirow{1}{*}{\\textbf{ARSRG}$emb$} & 86.00\\% & 90.00\\% & 93.00\\% & 96.00\\% & 95.62\\% & 96.00\\% & 88.00\\% & 81.89\\% & 79.17\\% & 79.78\\%\\\\\n\\hline\n\\multirow{1}{*}{BoVW} & 49.60\\% & 55.00\\% & 50.42\\% & 50.13\\% & 49.81\\% & 48.88\\% & 49.52\\% & 49.65\\% & 48.96\\% & 49.10\\%\\\\\n\\hline\n\\multirow{1}{*}{batchLDA} & 51.00\\% & 52.00\\% & 62.00\\% & 62.00\\% & 70.00\\% & 71.00\\% & 74.00\\% & 75.00\\% & 75.00\\% & 77.00\\%\\\\\n \\hline\n\\multirow{1}{*}{ILDAaPCA} & 51.00\\% & 42.00\\% & 53.00\\% & 48.00\\% & 45.00\\% & 50.00\\% & 51.00\\% & 49.00\\% & 49.00\\% & 50.00\\%\\\\\n\\multirow{1}{*}{ILDAonK} & 42.00\\% & 45.00\\% & 53.00\\% & 48.00\\% & 45.00\\% & 51.00\\% & 51.00\\% & 49.00\\% & 49.00\\% & 50.00\\%\\\\\n \\hline\n\\multirow{1}{*}{ILDAonL} & 51.00\\% & 52.00\\% & 61.00\\% & 61.00\\% & 65.00\\% & 69.00\\% & 71.00\\% & 70.00\\% & 71.00\\% & 72.00\\%\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications", "authors": ["Mario Manzo"], "url": "https://arxiv.org/abs/1912.09972v1", "attribution": "\"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications\" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.09085v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n \\toprule\n variable & mean & std & q25 & median & q75 \\\\ \\midrule\n home goals & 1.493 & 1.267 & 1 & 1 & 2 \\\\\n away goals & 1.222 & 1.149 & 0 & 1 & 2 \\\\\n home win odds & 2.534 & 1.368 & 1.84 & 2.24 & 2.75 \\\\\n draw odds & 3.658 & 0.807 & 3.29 & 3.42 & 3.68 \\\\\n away win odds & 3.756 & 2.523 & 2.49 & 3.12 & 4.13 \\\\\n lineup value & 4.9e+07 & 1.2e+08 & 2.6e+06 & 7.2e+06 & 3.4e+07 \\\\\n wins & 14.731 & 5.555 & 11 & 14 & 18 \\\\\n draws & 10.189 & 3.357 & 8 & 10 & 12 \\\\\n losses & 14.731 & 5.426 & 11 & 15 & 18 \\\\\n goals for & 53.828 & 15.032 & 43 & 52 & 63 \\\\\n goals against & 53.828 & 13.580 & 45 & 54 & 63 \\\\\n goal diff & 0 & 22.614 & -15 & -3 & 14 \\\\\n points & 54.264 & 16.815 & 42 & 52 & 65 \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Summary statistics of all leagues in dataset}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Predictive Modeling of Lower-Level English Club Soccer Using Crowd-Sourced Player Valuations", "authors": ["Josh Brown", "Yutong Bu", "Zachary Cheesman", "Benjamin Orman", "Iris Horng", "Samuel Thomas", "Amanda Harsy", "Adam Schultze"], "url": "https://arxiv.org/abs/2411.09085v1", "attribution": "\"Predictive Modeling of Lower-Level English Club Soccer Using Crowd-Sourced Player Valuations\" by Josh Brown, Yutong Bu, Zachary Cheesman, Benjamin Orman, Iris Horng, Samuel Thomas, Amanda Harsy, and Adam Schultze, arXiv:2411.09085v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table28.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|r|c|c|r|r}\n \\hline\n \\multicolumn{8}{c}{PH algorithm using 128 scenario paths for fixed carr-over case} \\\\\n \\hline\n \\multicolumn{8}{c}{Instances with only end item demands} \\\\\n \\hline\n Distribution & Util. & $|\\Omega|$ & $|\\Phi|$ & Converged & Gap (\\%) & Runtime (s) & Iterations \\\\\n \\hline\n \\multirow{10}{*}{Non stationary} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & Yes & 0.0 & 26.4 & 4\\\\\n & & 3 & 2,187 & Yes & 0.0 & 27.3 & 4\\\\\n & & 4 & 16,384 & Yes & 0.0 & 29.3 & 4\\\\\n & & 5 & 78,125 & Yes & 0.1 & 24.2 & 2\\\\\n & & 6 & 279,936 & & & &\\\\\\cline{2-8}\n &\\multirow{5}{*}{90\\%} \n & 2 & 128 & Yes & 0.0 & 26.4 & 4\\\\\n & & 3 & 2,187 & Yes & 1.1 & 49.7 & 7\\\\\n & & 4 & 16,384 & Yes & 0.0 & 29.3 & 4\\\\\n & & 5 & 78,125 & Yes & - & 41.1 & 4\\\\\n & & 6 & 279,936 & & & &\\\\\n \\hline\n \\multirow{10}{*}{Uniform} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & Yes & 0.0 & 11.5 & 2\\\\\n & & 3 & 2,187 & Yes & 0.0 & 12.2 & 2 \\\\\n & & 4 & 16,384 & Yes & 0.0 & 21.4 & 3 \\\\\n & & 5 & 78,125 & Yes & 0.0 & 39.8 & 4\\\\\n & & 6 & 279,936 & & & & \\\\\\cline{2-8}\n & \\multirow{5}{*}{90\\%}\n & 2 & 128 & Yes & 0.0 & 26.9 & 4\\\\\n & & 3 & 2,187 & Yes & 0.0 & 27.8 & 4 \\\\\n & & 4 & 16,384 & Yes & 0.0 & 28.2 & 4 \\\\\n & & 5 & 78,125 & Yes & 0.5 & 40.4 & 4 \\\\\n & & 6 & 279,936 & & & & \\\\\n \\hline\n \\multirow{10}{*}{Lumpy} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & Yes & 1.6 & 151.4 & 21 \\\\\n & & 3 & 2,187 & Yes & 3.9 & 395.4 & 54 \\\\\n & & 4 & 16,384 & Yes & 3.7 & 265.7 & 34 \\\\\n & & 5 & 78,125 & Yes & 7.5 & 619.8 & 73\\\\\n & & 6 & 279,936 & & & & \\\\\\cline{2-8}\n & \\multirow{5}{*}{90\\%} \n & 2 & 128 & No & Infeasible & 831.7 & 100 \\\\\n & & 3 & 2,187 & Yes & 2.7 & 536.6 & 61\\\\\n & & 4 & 16,384 & Yes & 1.7 & 283.6 & 30\\\\\n & & 5 & 78,125 & Yes & 6.0 & 550.9 & 61\\\\\n & & 6 & 279,936 & & & & \\\\\n \\hline\n \\multirow{10}{*}{Slow moving} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & No & Infeasible & 741.4 & 100\\\\\n & & 3 & 2,187 & Yes & 2.7 & 494.4 & 65 \\\\\n & & 4 & 16,384 & Yes & 2.7* & 554.6 & 72 \\\\\n & & 5 & 78,125 & Yes & - & 643.7 & 76 \\\\\n & & 6 & 279,936 & & & & \\\\\\cline{2-8}\n & \\multirow{5}{*}{90\\%}\n & 2 & 128 & Yes & 4.7 & 603.4 & 73 \\\\\n & & 3 & 2,187 & Yes & 6.6 & 738.6 & 95 \\\\\n & & 4 & 16,384 & Yes & 9.3* & 531.5 & 64\\\\\n & & 5 & 78,125 & Yes & - & 781.5 & 86 \\\\\n & & 6 & 279,936 & & & & \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Runtimes and gaps for the progressive hedging algorithm using 128 scenario paths on the instances including component demands for different distributions and numbers of scenarios per branch with a time limit of 3 hours (10,800 s) and maximum of 100 iterations}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.08061v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc} % centered columns (4 columns)\n\t\t\t\\hline\\hline %inserts double horizontal lines\n\t\t\tCopula & Copula function $(C(u,v))$ & Dual copula extropy ($J_C^*(X,Y)$) \\\\\n\t\t\t\\hline\n\t\t\t\tProduct copula& $uv$ &$\\frac{11}{72}$\t\\\\\n\tMarshall and Olkin copula & \n $uv\\text{min}(u^{-\\alpha},v^{-\\beta})$=\t$\\begin{cases} \n\t\t\t\tu^{1-\\alpha}v,~u^{-\\alpha}\\le v^{-\\beta}\n\t\t\t\t\\\\\n\t\t\t\tuv^{1-\\beta},~ u^{-\\alpha}\\ge v^{-\\beta}\n\t\t\t\\end{cases}$\n\t\t\t\t&\n\t\t\t$\\begin{cases} \n\t\t\\frac{1}{4}\\left[\\frac{7}{6}-\\frac{12-5\\alpha}{3(2-\\alpha)(3-\\alpha)}-\\frac{1}{3(3-2\\alpha)}\\right]\\\\\n\t\\frac{1}{4}\\left[\\frac{7}{6}-\\frac{12-5\\beta}{3(2-\\beta)(3-\\beta)}-\\frac{1}{3(3-2\\beta)}\\right]\n\t\\end{cases}$\\\\\t\t\t\t\n\t\t\t\n\t\\hline\t \t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Copula-based extropy measures, properties and dependence in bivariate distributions", "authors": ["Shital Saha", "Suchandan Kayal"], "url": "https://arxiv.org/abs/2311.08061v2", "attribution": "\"Copula-based extropy measures, properties and dependence in bivariate distributions\" by Shital Saha and Suchandan Kayal, arXiv:2311.08061v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14416v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|}\n\t\t\t\\hline\n\t\t\t\\textbf{Conditions} & \\textbf{ Phase portrait $O_2$} \\\\\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\t$ b_3 \\neq 0$, $b_1>0$, $b_0-c_0>0$. & $L^2_1$ \\\\\n\t\t\t\\hline\n\t\t\t$ b_3 \\neq 0$, $b_1>0$, $b_0-c_0<0$, $c_0>0$. & $L^2_2$ \\\\\t\n\t\t\t\\hline\n\t\t\t$ b_3 \\neq 0$, $b_1<0$, $b_0-c_0>0$. & $L^2_3$ \\\\\t\n\t\t\t\\hline\n\t\t\t$ b_3 \\neq 0$, $b_1<0$, $b_0-c_0<0$, $c_0>0$. & $L^2_4$ \\\\\t\n\t\t\t\\hline\n\t\t\t$ b_3 \\neq 0$, $c_0\\neq0$, $b_1>0$, $b_0-c_0\\neq 2b_1c_0$, $b_0-c_0<0$, $c_0>0$. & $L^2_5$ \\\\\t\n\t\t\t\\hline\n\t\t\t$ b_3 \\neq 0$, $c_0\\neq0$, $b_1>0$, $b_0-c_0\\neq 2b_1c_0$, $b_0-c_0>0$, $c_0<0$. & $L^2_6$ \\\\\t\n\t\t\t\\hline\n\t\t\t$ b_3 \\neq 0$, $c_0\\neq0$, $b_1>0$, $b_0-c_0\\neq 2b_1c_0$, $b_0-c_0>0$, $c_0>0$. & \t\\multirow{2}{1cm}{\\centering{$L^2_9$}} \\\\\t\n\t\t\t\\cline{1-1}\n\t\t\t$ b_3 \\neq 0$, $c_0\\neq0$, $b_1>0$, $b_0-c_0=2b_1c_0$, $c_0>0$. & \t\\multirow{2}{*} {\\phantom{a}} \\\\\t\n\t\t\t\\hline\n\t\t\t$ b_3 \\neq 0$, $c_0\\neq0$, $b_1<0$, $b_0-c_0\\neq 2b_1c_0$, $b_0-c_0<0$, $c_0>0$. & \t\\multirow{2}{1cm}{\\centering{$L^2_{10}$}} \\\\\t\n\t\t\t\\cline{1-1}\n\t\t\t$ b_3 \\neq 0$, $c_0\\neq0$, $b_1<0$, $b_0-c_0=2b_1c_0$, $c_0>0$. & \t\\multirow{2}{*} {\\phantom{a}} \\\\\t\n\t\t\t\\hline\n\t\t\t$ b_3 \\neq 0$, $c_0\\neq0$, $b_1<0$, $b_0-c_0\\neq 2b_1c_0$, $b_0-c_0>0$, $c_0>0$. & $L^2_7$ \\\\\t\n\t\t\t\\hline\n\t\t\t$ b_3 \\neq 0$, $c_0\\neq0$, $b_1<0$, $b_0-c_0\\neq 2b_1c_0$, $b_0-c_0<0$, $c_0<0$. & $L^2_{8}$ \\\\\t\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Conditions for each local phase portrait of $O_2$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Planar Kolmogorov systems with infinitely many singular points at infinity", "authors": ["Érika Diz-Pita", "Jaume Llibre", "M. Victoria Otero-Espinar"], "url": "https://arxiv.org/abs/2501.14416v1", "attribution": "\"Planar Kolmogorov systems with infinitely many singular points at infinity\" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.14416v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.14875v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lcccccc}\n\t\t\\toprule\n\t\t& \\multicolumn{2}{c}{Iterations} & \\multicolumn{2}{c}{Broadwell (ms)} & \\multicolumn{2}{c}{Ivy Bridge (ms)} \\\\\n\t\t\\cmidrule(r){2-3} \\cmidrule(r){4-5} \\cmidrule(r){6-7}\n\t\t$l_{max}$ & $10$& $11$ & $10$ & $11$ & $10$ & $11$\\\\\n\t\t\\midrule\n\t\tES-1 & 6 & 6 & 234 & 1117 & 235 & 1137 \\\\\n\t\t\\midrule\n\t\tES-2 & 6 & 6 & 216 & 1033 & 211 & 1035 \\\\\n\t\t\\midrule\n\t\tES-3 & 7 & 7 & 258 & 1225 & 259 & 1231 \\\\\n\t\t\\midrule\n\t\tES-4 & 6 & 6 & 226 & 1077 & 219 & 1093 \\\\\n\t\t\\midrule\n\t\tES-5 & 6 & 6 & 235 & 1121 & 229 & 1139 \\\\\n\t\t\\midrule\n\t\tES-6 & 6 & 6 & 220 & 1083 & 213 & 1093 \\\\\n\t\t\\midrule\n\t\tES-7 & 7 & 7 & 238 & 1191 & 236 & 1186 \\\\\n\t\t\\midrule\n\t\tES-8 & 6 & 6 & 217 & 1037 & 223 & 1039 \\\\\n\t\t\\midrule\n\t\tES-9 & 6 & 6 & 224 & 1039 & 222 & 1058 \\\\\n\t\t\\midrule\n\t\tES-10 & 7 & 7 & 243 & 1188 & 238 & 1188 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Automating the Design of Multigrid Methods with Evolutionary Program Synthesis", "authors": ["Jonas Schmitt"], "url": "https://arxiv.org/abs/2312.14875v1", "attribution": "\"Automating the Design of Multigrid Methods with Evolutionary Program Synthesis\" by Jonas Schmitt, arXiv:2312.14875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.09782v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|cc}\n\t\t\\hline \\hline\n\t\t& market cap weighting & liquidity weighting \\\\\n\t\t\\hline\n\t\t$\\beta_{i,t^-_l}$ & 1 & $\\frac{Vol_{i, t^-_{l}} }{ P_{i, t^-_{l}}Q_{i, t^-_{l}} }$ \\\\\n\t\t\\hline \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Weighting schemes for derivation of CRIX}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "CRIX an index for cryptocurrencies", "authors": ["Simon Trimborn", "Wolfgang Karl Härdle"], "url": "https://arxiv.org/abs/2009.09782v1", "attribution": "\"CRIX an index for cryptocurrencies\" by Simon Trimborn and Wolfgang Karl Härdle, arXiv:2009.09782v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.16928v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of GFLOPs and Params based on Audio-Visual fusion model.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\hline\nNetwork & GFLOPs & Params(M) \\\\\n\\hline\nTalkNet &\\textbf{1.53} & \\underline{15.80} \\\\\nAV-ped & 5.67 & 32.36 \\\\\nAV-FDTI & 11.37 & 92.28 \\\\\n\\textbf{AV-DTEC}(Ours)& \\underline{1.98} &\\textbf{10.16} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "AV-DTEC: Self-Supervised Audio-Visual Fusion for Drone Trajectory Estimation and Classification", "authors": ["Zhenyuan Xiao", "Yizhuo Yang", "Guili Xu", "Xianglong Zeng", "Shenghai Yuan"], "url": "https://arxiv.org/abs/2412.16928v1", "attribution": "\"AV-DTEC: Self-Supervised Audio-Visual Fusion for Drone Trajectory Estimation and Classification\" by Zhenyuan Xiao, Yizhuo Yang, Guili Xu, Xianglong Zeng, and Shenghai Yuan, arXiv:2412.16928v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.04859v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|l|l|} \\hline\n31/12/2024 & 29/12/2023 & 30/12/2022 & 31/12/2021 & 31/12/2020 & 31/12/2019 & 31/12/2018 & 29/12/2017 \\\\ \\hline\n30/12/2016 & 31/12/2015 & 31/12/2014 & 31/12/2013 & 31/12/2012 & 30/12/2011 & 31/12/2010 & 31/12/2009 \\\\ \\hline\n31/12/2008 & 31/12/2007 & 29/12/2006 & 30/12/2005 & 31/12/2004 & 31/12/2003 & & \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Last trading date of year (2003-2024)}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "F&O Expiry vs. First-Day SIPs: A 22-Year Analysis of Timing Advantages in India's Nifty 50", "authors": ["Siddharth Gavhale"], "url": "https://arxiv.org/abs/2507.04859v2", "attribution": "\"F&O Expiry vs. First-Day SIPs: A 22-Year Analysis of Timing Advantages in India's Nifty 50\" by Siddharth Gavhale, arXiv:2507.04859v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.09508v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\\toprule\n & \\textbf{Beijing} & \\textbf{Chengdu} & \\textbf{Harbin} & \\textbf{Shenyang} & \\textbf{Shijiazhuang} & \\textbf{Xian} & \\textbf{Yuncheng} & \\textbf{Zhengzhou} \\\\\n\\midrule\nmean $T_o$ & 6.92 & 12.36 & -3.37 & 1.87 & 8.27 & 8.40 & 2.53 & 8. 81 \\\\ \n(std $T_o$) & (7.58) & (4.78) & (10.01) & (1.87) & (7.20) & (6.67) & (5.81) & (6.69) \\\\\nmax $\\phi$ & 634 & 374 & 533 & 582 & 555 & 510 & 631 & 580 \\\\\n(std max $\\phi$) & (186) & (261) & (167) & (198) & (211) & (226) & (315) & (230) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Towards Optimal District Heating Temperature Control in China with Deep Reinforcement Learning", "authors": ["Adrien Le-Coz", "Tahar Nabil", "Francois Courtot"], "url": "https://arxiv.org/abs/2012.09508v2", "attribution": "\"Towards Optimal District Heating Temperature Control in China with Deep Reinforcement Learning\" by Adrien Le-Coz, Tahar Nabil, and Francois Courtot, arXiv:2012.09508v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.04891v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{GCN hyperparameters used in the experiments.}\n\\begin{tabular}{cccc}\n\\toprule\nDataset & LR & Dropout & HiddenDimension \\\\ \\midrule\nCora & 0.01 & 0.41 & 128 \\\\\nCiteseer & 0.01 & 0.31 & 32 \\\\\nPubmed & {0.01} & {0.41} & {32} \\\\\nCornell & 0.001 & 0.51 & {128} \\\\\nTexas & 0.001 & {0.51} & 128 \\\\\nWisconsin & 0.001 & 0.51 & 128 \\\\\nChameleon & 0.001 & 0.21 & 128 \\\\\nSquirrel & 0.001 & 0.51 & 128 \\\\\nActor & 0.001 & 0.51 & 128 \\\\\nCS & 0.001 & 0.51 & 512 \\\\\nPhoto & 0.01 & 0.51 & 512 \\\\\nPhysics & 0.01 & 0.51 & 512 \\\\\nRoman-empire & 0.003 & 0.31 & 512 \\\\\nAmazon-ratings & 0.003 & 0.31 & 512 \\\\\nMinesweeper & 0.003 & 0.31 & 512 \\\\ \\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring", "authors": ["Celia Rubio-Madrigal", "Adarsh Jamadandi", "Rebekka Burkholz"], "url": "https://arxiv.org/abs/2502.04891v1", "attribution": "\"GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring\" by Celia Rubio-Madrigal, Adarsh Jamadandi, and Rebekka Burkholz, arXiv:2502.04891v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.20279v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Character degrees for $\\mathrm{Sp}_{2}\\left(q^2\\right)$ with $q$ even}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc}\nCharacter & Degree&Multiplicity\\\\\n\\hline\n Tr & $1$& $1$\\\\\n$\\psi$ & $q^2$& $1$\\\\\n$\\chi_s$ & $q^2+1$ & $(q^2-2)/2$\\\\\n$\\theta_j$ & $q^2-1$&$q^2/2$\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Strong Gelfand Pairs of the Symplectic Group $\\boldsymbol{\\rm Sp}_4(q)$ where $q$ is even", "authors": ["Stephen P. Humphries", "Joseph E. Marrow"], "url": "https://arxiv.org/abs/2504.20279v2", "attribution": "\"Strong Gelfand Pairs of the Symplectic Group $\\boldsymbol{\\rm Sp}_4(q)$ where $q$ is even\" by Stephen P. Humphries and Joseph E. Marrow, arXiv:2504.20279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.00069v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MM estimates of regression parameters under Model S1.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\n Variable & Estimate & s.e. & $z$-value & $p$-value \\\\\n\\midrule\n $Intercept$ & -1.8803 & 0.1515 &-12.4086 & $<$ 0.001 \\\\\n $lab2$ & 2.9848 & 1.2097 & 2.4689 & 0.0136 \\\\\n $edu3$ & -1.3809 & 0.5033 & -2.7445 & 0.0061 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Area-level spatio-temporal Poisson mixed models for predicting domain counts and proportions", "authors": ["M. Boubeta", "M. J. Lombardía", "F. Marey-Pérez", "D. Morales"], "url": "https://arxiv.org/abs/2012.00069v1", "attribution": "\"Area-level spatio-temporal Poisson mixed models for predicting domain counts and proportions\" by M. Boubeta, M. J. Lombardía, F. Marey-Pérez, and D. Morales, arXiv:2012.00069v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.11606v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary statistics for the national sample by relative time}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc|cccccc}\n\t\t\\hline\n\t\t & \\multicolumn{6}{c}{\\textbf{Relative time=0}} & \\multicolumn{6}{c}{\\textbf{Relative time=1}} \\\\ \\hline\n\t\t & \\multicolumn{2}{c}{\\textbf{Full Sample}} & \\multicolumn{2}{c}{\\textbf{Treated}} & \\multicolumn{2}{c}{\\textbf{Control}} & \\multicolumn{2}{c}{\\textbf{Full Sample}} & \\multicolumn{2}{c}{\\textbf{Treated}} & \\multicolumn{2}{c}{\\textbf{Control}} \\\\ \n Variables & Mean & SD & Mean & SD & Mean & SD & Mean & SD & Mean & SD & Mean & SD \\\\ \\hline\nScore in Math test & 211.14 & 40.87 & 209.87 & 37.89 & 212.17 & 43.10 & 202.18 & 38.84 & 198.22 & 39.71 & 205.97 & 37.61 \\\\ \nScore in Italian test & 208.42 & 39.81 & 206.65 & 37.72 & 209.85 & 41.35 & 202.96 & 37.66 & 200.67 & 37.93 & 205.15 & 37.27 \\\\ \nPeers' score in Math test & 210.96 & 24.67 & 208.16 & 20.23 & 213.22 & 27.52 & 202.01 & 23.97 & 198.21 & 24.55 & 205.64 & 22.83 \\\\ \nPeers' score in Italian test & 208.21 & 21.33 & 204.84 & 18.01 & 210.92 & 23.31 & 202.78 & 21.72 & 200.66 & 22.76 & 204.80 & 20.47 \\\\ \nParents' year of education & 10.78 & 6.02 & 11.49 & 5.73 & 10.21 & 6.18 & 10.90 & 6.01 & 11.46 & 5.74 & 10.37 & 6.20 \\\\ \nStudent repeating the year (1=yes) & 0.02 & 0.14 & 0.02 & 0.13 & 0.02 & 0.14 & 0.02 & 0.13 & 0.02 & 0.13 & 0.02 & 0.13 \\\\ \n5th grade - Low School (1=yes) & 0.36 & 0.48 & 0.39 & 0.49 & 0.34 & 0.47 & 0.33 & 0.47 & 0.32 & 0.47 & 0.35 & 0.48 \\\\ \n8th grade - Middle School (1=yes) & 0.32 & 0.47 & 0.28 & 0.45 & 0.36 & 0.48 & 0.39 & 0.49 & 0.40 & 0.49 & 0.37 & 0.48 \\\\ \n13th grade - High School (1=yes) & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.28 & 0.45 & 0.28 & 0.45 & 0.28 & 0.45 \\\\ \\hline\nObservations & \\multicolumn{2}{c}{2,306,857} & \\multicolumn{2}{c}{1,127,790} &\\multicolumn{2}{c}{1,179,067} & \\multicolumn{2}{c}{2,306,857} & \\multicolumn{2}{c}{1,127,790} &\\multicolumn{2}{c}{1,179,067} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Will the last be the first? School closures and educational outcomes", "authors": ["Michele Battisti", "Giuseppe Maggio"], "url": "https://arxiv.org/abs/2208.11606v1", "attribution": "\"Will the last be the first? School closures and educational outcomes\" by Michele Battisti and Giuseppe Maggio, arXiv:2208.11606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08887v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n\\toprule\nMethod & top1 & top5 & mAP \\\\\n\\midrule\nSTE-NVAN~ & 42.2 & - & 41.3 \\\\\nTKP~ & 77.9 & - & 75.9 \\\\\nNVAN~ & 78.4 & - & 76.7 \\\\\nVKD~ & 85.6 & 93.9 & 83.8 \\\\\nREAD~ & 86.3 & 94.4 & 83.4 \\\\\n\\midrule\nMDKT & \\textbf{86.8} & \\textbf{94.9} & \\textbf{84.8} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison with SOTA methods on Duke-video dataset.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Image-to-Video Re-Identification via Mutual Discriminative Knowledge Transfer", "authors": ["Pichao Wang", "Fan Wang", "Hao Li"], "url": "https://arxiv.org/abs/2201.08887v1", "attribution": "\"Image-to-Video Re-Identification via Mutual Discriminative Knowledge Transfer\" by Pichao Wang, Fan Wang, and Hao Li, arXiv:2201.08887v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11843v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summarization of compared methods.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\hline\nMethods & Unsupervised & Supervised & Kernel-based & Correlation-based \\\\\n\\hline\n\\textbf{CCA} & Y & N & N & Y \\\\\n\\textbf{KCCA} & Y & N & Y & Y \\\\\n\\textbf{ml-CCA} & N & Y & N & Y \\\\\n\\textbf{KDM} & N & Y & Y & Y \\\\\n\\textbf{CKD} & N & Y & Y & Y \\\\\n\\textbf{DS²L} & N & Y & Y & Y \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Discriminative Supervised Subspace Learning for Cross-modal Retrieval", "authors": ["Haoming Zhang", "Xiao-Jun Wu", "Tianyang Xu", "Donglin Zhang"], "url": "https://arxiv.org/abs/2201.11843v1", "attribution": "\"Discriminative Supervised Subspace Learning for Cross-modal Retrieval\" by Haoming Zhang, Xiao-Jun Wu, Tianyang Xu, and Donglin Zhang, arXiv:2201.11843v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.14318v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\tFidelity & Training Samples & Epochs & Model Error & Filter Error \\\\ \\hline\n\t\t Low & $10^3$ & 20 & $\\delta\\approx 2.16$ & 2.59 \\\\ \\hline\n Medium & $10^4$ & 50 & $\\delta\\approx 1.02 $ & 1.16 \\\\ \\hline\n High & $10^6$ & 200 & $\\delta\\approx 0.35$ & 0.99 \\\\ \\hline\n\t\t\\end{tabular}\n\\caption{Summary of the training details, estimated model error, and average filter error for each model. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems", "authors": ["Daniel Sanz-Alonso", "Nathan Waniorek"], "url": "https://arxiv.org/abs/2412.14318v1", "attribution": "\"Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems\" by Daniel Sanz-Alonso and Nathan Waniorek, arXiv:2412.14318v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.04970v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{HisarMod2019.1 includes 26 different modulation types from 5 different modulation families.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cl}\n\\toprule\nModulation Family & Modulation Types \\\\ \\midrule\n\\multirow{6}{*}{Analog} & AM--DSB \\\\\n & AM--SC \\\\\n & AM--USB \\\\\n & AM--LSB \\\\\n & FM \\\\\n & PM \\\\ \\midrule\n\\multirow{4}{*}{FSK} & 2--FSK \\\\\n & 4--FSK \\\\\n & 8--FSK \\\\\n & 16--FSK \\\\ \\midrule\n\\multirow{3}{*}{PAM} & 4--PAM \\\\\n & 8--PAM \\\\\n & 16--PAM \\\\ \\midrule\n\\multirow{6}{*}{PSK} & BPSK \\\\\n & QPSK \\\\\n & 8--PSK \\\\\n & 16--PSK \\\\\n & 32--PSK \\\\\n & 64--PSK \\\\ \\midrule\n\\multirow{7}{*}{QAM} & 4--QAM \\\\\n & 8--QAM \\\\\n & 16--QAM \\\\\n & 32--QAM \\\\\n & 64--QAM \\\\\n & 128--QAM \\\\\n & 256--QAM \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Robust and Fast Automatic Modulation Classification with CNN under Multipath Fading Channels", "authors": ["Kürşat Tekbıyık", "Ali Rıza Ekti", "Ali Görçin", "Güneş Karabulut Kurt", "Cihat Keçeci"], "url": "https://arxiv.org/abs/1911.04970v2", "attribution": "\"Robust and Fast Automatic Modulation Classification with CNN under Multipath Fading Channels\" by Kürşat Tekbıyık, Ali Rıza Ekti, Ali Görçin, Güneş Karabulut Kurt, and Cihat Keçeci, arXiv:1911.04970v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.02225v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Test accuracy (\\%) of on various neural networks trained on CIFAR10.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc}\n \\textbf{Model} & $\\l=0.0$ & $\\l=0.5$ & $\\l=1.0$ & $\\l=1/t$ & $\\l=1-1/t$ \\\\\n \\hline\n ResNet-18 & $96.12_{\\pm 0.02}$ & $96.10_{\\pm 0.05}$ & $96.22_{\\pm 0.11}$ & $96.03_{\\pm 0.04}$ & $\\boldsymbol{96.34}_{\\pm 0.01}$ \\\\\n WideResNet-28-10 & $96.77_{\\pm 0.07}$ & $96.93_{\\pm 0.03}$ & $97.03_{\\pm 0.06}$ & $96.71_{\\pm 0.06}$ & $\\boldsymbol{97.06}_{\\pm 0.09}$ \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Sharpness-Aware Minimization: General Analysis and Improved Rates", "authors": ["Dimitris Oikonomou", "Nicolas Loizou"], "url": "https://arxiv.org/abs/2503.02225v1", "attribution": "\"Sharpness-Aware Minimization: General Analysis and Improved Rates\" by Dimitris Oikonomou and Nicolas Loizou, arXiv:2503.02225v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.15568v1_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Subjects Performance Predictions and Ratio Before the Test}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\hline\n \\hline\n & & \\multicolumn{2}{c}{Pooled sample} \\\\\\cline{3-4} \n Variable & & Mean & Std. dev. \\\\\n \\hline\n Predicted own performance & & 13.94 & 3.612 \\\\\n Predicted group performance & & 12.99 & 2.594 \\\\\n Better & & 1.088 & 0.269 \\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)", "authors": ["Marius Protte"], "url": "https://arxiv.org/abs/2507.15568v1", "attribution": "\"Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)\" by Marius Protte, arXiv:2507.15568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10459v3_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Neural Network Classification Hyperparameter Selection (Reduction=5)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|}\n\\hline\nHyperparameter & Datatype & Values/{[}Min, Max{]} & Log Scaling & Gridpoint Count & Best Value \\\\ \\hline\nBatch Size & Integer & {[}32, 1024{]} & TRUE & 2 & 775 \\\\ \nEarly Stopping Patience & Integer & {[}1, 100{]} & TRUE & 2 & 100 \\\\ \nDropout Rate & Continuous & {[}0.0, 1.0{]} & FALSE & 2 & 0.70054 \\\\ \nLearning Rate & Continuous & {[}1e-05, 1.0{]} & TRUE & 2 & 0.015856 \\\\ \nNumber of Layers & Integer & {[}1, 6{]} & FALSE & 2 & 3 \\\\ \nInclude Batchnormalization & Categorical & {[}False, True{]} & FALSE & N/A & TRUE \\\\ \nActivation Function & Categorical & {[}'ReLU', 'Leaky ReLU'{]} & FALSE & N/A & ReLU \\\\ \nLayer Size & Integer & {[}4, 100{]} & TRUE & 2 & 18 \\\\ \nFeasible Sample Weight & Continuous & {[}0.01, 100.0{]} & TRUE & 2 & 0.447665 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames", "authors": ["Lyle Regenwetter", "Colin Weaver", "Faez Ahmed"], "url": "https://arxiv.org/abs/2201.10459v3", "attribution": "\"FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames\" by Lyle Regenwetter, Colin Weaver, and Faez Ahmed, arXiv:2201.10459v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00436v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameters for Baleen's condensers on HoVer and HotPotQA.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Hyperparameter} & \\textbf{HoVer} & \\textbf{HotPotQA} \\\\ \\midrule\nLearning rate & $1 \\times 10^{-5}$ & $1 \\times 10^{-5}$ \\\\\nBatch size & 64 & 64 \\\\\nMaximum Sequence Length & 512 & 512 \\\\\nWarmup Steps & 1000 & 1000 \\\\\nTraining steps (stage \\#1) & 5k & 10k \\\\\nTraining steps (stage \\#2) & 5k & 10k \\\\\nNegative Sampling Depth (stage \\#1; per hop) & 20, 20, 20, 20 & 10, 30 \\\\\nNegative Sampling Depth (stage \\#2; for each hop) & 10 & 10 \\\\\nContext Sampling Depth (from each hop) & 5 & 5 \\\\\nPositive Sampling Depth (stage \\#1; per hop) & 10, 10, 10, all & 10, all \\\\\nPositive Sampling Depth (stage \\#2; for each hop) & 10 & 10 \\\\\nFacts fed to stage \\# 2: training (inference) & 7--9 (9) & 7--9 (9) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval", "authors": ["Omar Khattab", "Christopher Potts", "Matei Zaharia"], "url": "https://arxiv.org/abs/2101.00436v3", "attribution": "\"Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval\" by Omar Khattab, Christopher Potts, and Matei Zaharia, arXiv:2101.00436v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.05672v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{hhline}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n\\hhline{-|-|-|-|}\n\\rowcolor{lightgray!10} Solver &Outer loop & Linear system solves & Run time (s) \\\\\n\\hline\nProximal Galerkin & 13 & 20 & 61.70 \\\\\nMoreau--Yosida Penalty & 14 & 51 & 78.01 \\\\\nSemismooth Active Set & 7 & 236 & 112.60 \\\\\nFixed Point & 164 & 8493 & 3633.72\\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Example 5. The performance of four solvers, terminating when $\\|u^{k} - u^{k-1}\\|_{H^1(\\Omega)} \\leq 10^{-5}$. The Moreau--Yosida penalty solver and fixed point approach deliver an infeasible solution, whereas proximal Galerkin and the semismooth active set solver provide a feasible one.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The latent variable proximal point algorithm for variational problems with inequality constraints", "authors": ["Jørgen S. Dokken", "Patrick E. Farrell", "Brendan Keith", "Ioannis P. A. Papadopoulos", "Thomas M. Surowiec"], "url": "https://arxiv.org/abs/2503.05672v2", "attribution": "\"The latent variable proximal point algorithm for variational problems with inequality constraints\" by Jørgen S. Dokken, Patrick E. Farrell, Brendan Keith, Ioannis P. A. Papadopoulos, and Thomas M. Surowiec, arXiv:2503.05672v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16608v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cccc|c|}\n \\hline\n unknowns & cell & face & grad & edge & $k$\\\\\n \\hline\n & $k$ & $k$ & $k^n$ & -- & $\\geq 1$\\\\\n \\hline\n (2D) & $k+2$ & $k+1$ & $k$ & -- & $\\geq 0$\\\\\n (3D) & $k+2$ & $k+2$ & $k$ & -- & $\\geq 0$\\\\\n \\hline\n \\shortstack{present \\\\ = ($k \\geq 1$)}) & $\\geq \\min\\{k-2,0\\}$ & $\\min\\{k-1,0\\}$ & $k$ & $k$ & $\\geq 0$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Degrees of freedom of HHO methods in the literature and the present method. The degrees associated with grad are faced-based.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A hybrid high-order method for the biharmonic problem", "authors": ["Yizhou Liang", "Ngoc Tien Tran"], "url": "https://arxiv.org/abs/2504.16608v2", "attribution": "\"A hybrid high-order method for the biharmonic problem\" by Yizhou Liang and Ngoc Tien Tran, arXiv:2504.16608v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.00934v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results of comparative statics after telework firm appears around $ b $}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c||c|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\t& $ \\theta^* $\n\t\t\t& $ w^* $\n\t\t\t& $ z^* $\n\t\t\t& $ b_1(\\theta )$ \n\t\t\t& $ b_2(\\theta ) $ \n\t\t\t& $f(\\theta )$ \n\t\t\t& $\\phi_t(x) $ \n\t\t\t& $\\phi_o(x) $ \n\t\t\t& $ \\psi(x) $\\\\\n\t\t\t\\hline\n\t\t\t$ MC_t $ & + & $ - $ & $ - $ & + & + & + & + & + & + \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Shifting to Telework and Firms' Location: Does Telework Make Our Society Efficient?", "authors": ["Kazufumi Tsuboi"], "url": "https://arxiv.org/abs/2212.00934v1", "attribution": "\"Shifting to Telework and Firms' Location: Does Telework Make Our Society Efficient?\" by Kazufumi Tsuboi, arXiv:2212.00934v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.18063v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison of PEP-Net and baselines for 30-day mortality prediction. {\\color{blue}Input: Cardiac-ROI.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\hline\n\\textbf{Method} & \\textbf{Accuracy} & \\textbf{AUC} & \\textbf{Sensitivity} & \\textbf{Specificity} \\\\ \\hline\nResNet18 & \\(0.693 \\pm 0.159\\) & \\(0.733 \\pm 0.100\\) & \\(0.889 \\pm 0.106\\) & \\(0.582 \\pm 0.174\\) \\\\ \nResNet50 & \\(0.709 \\pm 0.176\\) & \\(0.563 \\pm 0.148\\) & \\(0.607 \\pm 0.334\\) & \\(0.654 \\pm 0.294\\) \\\\\nEfficientNetB0 & \\(0.765 \\pm 0.141\\) & \\(0.660 \\pm 0.131\\) & \\(0.746 \\pm 0.174\\) & \\(0.721 \\pm 0.207\\) \\\\ \\hline\n\\textbf{PEP-Net} & \\textbf{0.940 \\(\\pm\\) 0.007} & \\textbf{0.901 \\(\\pm\\) 0.008} & \\textbf{0.962 \\(\\pm\\) 0.004}& \\textbf{0.852 \\(\\pm\\) 0.013} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Mortality Prediction of Pulmonary Embolism Patients with Deep Learning and XGBoost", "authors": ["Yalcin Tur", "Vedat Cicek", "Tufan Cinar", "Elif Keles", "Bradlay D. Allen", "Hatice Savas", "Gorkem Durak", "Alpay Medetalibeyoglu", "Ulas Bagci"], "url": "https://arxiv.org/abs/2411.18063v1", "attribution": "\"Mortality Prediction of Pulmonary Embolism Patients with Deep Learning and XGBoost\" by Yalcin Tur, Vedat Cicek, Tufan Cinar, Elif Keles, Bradlay D. Allen, Hatice Savas, Gorkem Durak, Alpay Medetalibeyoglu, and Ulas Bagci, arXiv:2411.18063v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.19056v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Interaction Data With the Vaccine Information}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\t\\hline\\hline\n\t& Top-Ranked & Requested & All \\\\ \\hline\n\t\\textit{Bottom Clicked (\\%)} \\\\\n\tVaccine Platform &\t83.4\t&\t82.5\t&\t79.7\\\\\n\tCountries\t\t&\t82.3\t&\t82.2\t&\t79.3 \\\\\n\tDoses\t\t\t&\t82.3\t&\t82.1\t&\t79.1\\\\\n\tResearch Phase\t&\t82.7\t&\t82.3\t&\t79.1\\\\\n\tEfficacy\t\t&\t82.7\t&\t82.9\t&\t80.0\\\\\n\tHospital\t\t&\t82.1\t&\t81.9\t&\t79.1\\\\\n\tAdverse Event\t&\t82.3\t&\t81.9\t&\t78.7\\\\\n\tSevere AE\t\t&\t80.7\t&\t79.8\t&\t76.7\\\\\n\t\\textit{Time on page (sec)}\\\\\n\tMean\t\t\t& \t52.90\t&\t50.00 \t& \t47.06 \\\\\n\tMedian\t\t\t& \t45.44\t&\t39.55\t&\t37.44 \\\\ \\hline\n\t$N$ \t\t\t&\t481\t\t&\t1,150\t&\t1,762 \\\\ \\hline\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Self-selection of Information and Belief Update: An Experiment on COVID-19 Vaccine Information Acquisition", "authors": ["ChienHsun Lin", "Hans H. Tung"], "url": "https://arxiv.org/abs/2506.19056v1", "attribution": "\"Self-selection of Information and Belief Update: An Experiment on COVID-19 Vaccine Information Acquisition\" by ChienHsun Lin and Hans H. Tung, arXiv:2506.19056v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.13793v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Composition of the population dataset ($N=2,342$) and the expert-labeled validation cohort ($N=105$).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|ccc|cccc}\n\\hline\\hline\n\\multirow{2}{*}{\\textbf{Dataset}} & \\multirow{2}{*}{\\textbf{Labels}} & \\multicolumn{3}{c}{\\textbf{Gender}} & \\multicolumn{4}{c}{\\textbf{Age}} \\\\\n\\cline{3-9}\n& & M & F & N/A & 18-30 & 30-50 & 50-70 & $>$70 \\\\\n\\hline\nPopulation & - & 1064 & 1243 & 35 & 425 & 839 & 795 & 283\\\\\n\\hline\n\\hline\n\\multirow{3}{*}{Validation} & CN & 10 & 13 & 1 & - & - & 13 & 11\\\\\n & MCI & 12 & 18 & 1 & - & 2 & 21 & 8\\\\\n & AD & 6 & 42 & 2 & - & 1 & 25 & 24\\\\ \n\\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Tensor Decomposition of Large-scale Clinical EEGs Reveals Interpretable Patterns of Brain Physiology", "authors": ["Teja Gupta", "Neeraj Wagh", "Samarth Rawal", "Brent Berry", "Gregory Worrell", "Yogatheesan Varatharajah"], "url": "https://arxiv.org/abs/2211.13793v2", "attribution": "\"Tensor Decomposition of Large-scale Clinical EEGs Reveals Interpretable Patterns of Brain Physiology\" by Teja Gupta, Neeraj Wagh, Samarth Rawal, Brent Berry, Gregory Worrell, and Yogatheesan Varatharajah, arXiv:2211.13793v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.10375v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n \\toprule & CLEVR-TR & MSN-Hard\\\\\n \\midrule \n GTA-Kronecker & 38.32 & 24.52\\\\\n GTA-Euclid & 38.59 & 24.75 \\\\\n GTA & \\textbf{38.99} & \\textbf{24.80}\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers", "authors": ["Takeru Miyato", "Bernhard Jaeger", "Max Welling", "Andreas Geiger"], "url": "https://arxiv.org/abs/2310.10375v3", "attribution": "\"GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers\" by Takeru Miyato, Bernhard Jaeger, Max Welling, and Andreas Geiger, arXiv:2310.10375v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.07277v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n$ \\ $ & $q$ & $\\eta$ & $\\tau_{comp}$ \\\\ \\hline\nModel 1 & 5 & 1.0 & $10^{-3}$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Constructing Gaussian Processes via Samplets", "authors": ["Marcel Neugebauer"], "url": "https://arxiv.org/abs/2411.07277v1", "attribution": "\"Constructing Gaussian Processes via Samplets\" by Marcel Neugebauer, arXiv:2411.07277v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.14389v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary statistics of UK-SMEC dataset [kWh].}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\t\t\t\\toprule\n\t\t\t&H\\#1 &H\\#2 &H\\#3 &H\\#4 &H\\#5 &H\\#6 &H\\#7 &H\\#8\\\\\n\t\t\t\\midrule\n\t\t\tmean &0.704 &0.561 &0.453 &0.370 &0.276 &0.341 &0.357 &0.267\\\\\n\t\t\tstd &1.182 &0.959 &0.881 &0.846 &0.467 &0.423 &0.666 &0.526\\\\\n\t\t\t25\\% &0.166 &0.040 &0.090 &0.031 &0.055 &0.082 &0.044 &0.033\\\\\n\t\t\t50\\% &0.269 &0.103 &0.167 &0.089 &0.095 &0.177 &0.120 &0.059\\\\\n\t\t\t75\\% &0.578 &0.617 &0.293 &0.229 &0.201 &0.357 &0.219 &0.151\\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Probabilistic electric load forecasting through Bayesian Mixture Density Networks", "authors": ["Alessandro Brusaferri", "Matteo Matteucci", "Stefano Spinelli", "Andrea Vitali"], "url": "https://arxiv.org/abs/2012.14389v2", "attribution": "\"Probabilistic electric load forecasting through Bayesian Mixture Density Networks\" by Alessandro Brusaferri, Matteo Matteucci, Stefano Spinelli, and Andrea Vitali, arXiv:2012.14389v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2309.16679v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{FI-2010 evaluation of the RDAIN method : evaluating the performance of different normalization methods for the first prediction horizon (next 10 timesteps).}\n\\begin{tabular}{llll}\n \\toprule\n \\textbf{Model} & \\textbf{Method} & \\textbf{F1-score} & $\\boldsymbol{\\kappa}$ \\textbf{score} \\\\ \\midrule\n \\textbf{MLP} & Standardization & $56.79 \\pm 0.47$ & $0.3536 \\pm 0.0071$ \\\\\n \\textbf{MLP} & Sample Average & $56.21 \\pm 1.80$ & $0.3426 \\pm 0.0235$ \\\\\n \\textbf{MLP} & Sample Standardization & $63.86 \\pm 1.31$ & $0.4468 \\pm 0.0188$ \\\\\n \\textbf{MLP} & Batch Normalization & $56.26 \\pm 0.51$ & $0.3455 \\pm 0.0085$ \\\\\n \\textbf{MLP} & Instance Normalization & $60.77 \\pm 0.92$ & $0.4006 \\pm 0.0114$ \\\\\n \\textbf{MLP} & DAIN & $69.50 \\pm 0.35$ & $0.5346 \\pm 0.0051$ \\\\\n \\textbf{MLP} & Proposed & $\\underline{69.94 \\pm 0.55}$ & $\\underline{0.5413 \\pm 0.0086}$ \\\\\n \\textbf{CNN} & Standardization & $57.27 \\pm 1.12$ & $0.3563 \\pm 0.0183$ \\\\\n \\textbf{CNN} & Sample Average & $58.41 \\pm 0.63$ & $0.3663 \\pm 0.0101$ \\\\\n \\textbf{CNN} & Sample Standardization & $60.44 \\pm 1.02$ & $0.3972 \\pm 0.0167$ \\\\\n \\textbf{CNN} & Batch Normalization & $55.93 \\pm 1.08$ & $0.3372 \\pm 0.0182$ \\\\\n \\textbf{CNN} & Instance Normalization & $60.17 \\pm 0.69$ & $0.3917 \\pm 0.0120$ \\\\\n \\textbf{CNN} & DAIN & $48.87 \\pm 3.07$ & $0.2488 \\pm 0.0372$ \\\\\n \\textbf{CNN} & Proposed & $\\underline{66.77 \\pm 0.48}$ & $\\underline{0.4926 \\pm 0.0086}$ \\\\\n \\textbf{RNN} & Standardization & $55.48 \\pm 1.29$ & $0.3285 \\pm 0.0207$ \\\\\n \\textbf{RNN} & Sample Average & $54.24 \\pm 1.38$ & $0.3030 \\pm 0.0221$ \\\\\n \\textbf{RNN} & Sample Standardization & $59.62 \\pm 0.91$ & $0.3850 \\pm 0.0151$ \\\\\n \\textbf{RNN} & Batch Normalization & $54.85 \\pm 1.32$ & $0.3649 \\pm 0.0135$ \\\\\n \\textbf{RNN} & Instance Normalization & $58.33 \\pm 0.84$ & $0.3649 \\pm 0.0135$ \\\\\n \\textbf{RNN} & DAIN & $67.86 \\pm 0.52$ & $0.5102 \\pm 0.0085$ \\\\\n \\textbf{RNN} & Proposed & $\\underline{67.90 \\pm 0.80}$ & $\\underline{0.5106 \\pm 0.0122}$ \\\\ \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management", "authors": ["Paraskevi Nousi", "Loukia Avramelou", "Georgios Rodinos", "Maria Tzelepi", "Theodoros Manousis", "Konstantinos Tsampazis", "Kyriakos Stefanidis", "Dimitris Spanos", "Manos Kirtas", "Pavlos Tosidis", "Avraam Tsantekidis", "Nikolaos Passalis", "Anastasios Tefas"], "url": "https://arxiv.org/abs/2309.16679v2", "attribution": "\"Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management\" by Paraskevi Nousi, Loukia Avramelou, Georgios Rodinos, Maria Tzelepi, Theodoros Manousis, Konstantinos Tsampazis, Kyriakos Stefanidis, Dimitris Spanos, Manos Kirtas, Pavlos Tosidis, Avraam Tsantekidis, Nikolaos Passalis, and Anastasios Tefas, arXiv:2309.16679v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19499v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The averaged performance on the MNIST dataset. The standard deviation of the metric between clients is reported in parentheses. w. is the abbreviation of 'weighted', and the $\\uparrow$ denotes that the higher the metric is, the better performance a model achieved, and the best performance is highlighted.}\n\\begin{tabular}{l|c|c}\n ~ & w. AUC ($10^{-2}$) $\\uparrow$ & w. F1 ($10^{-2}$) $\\uparrow$ \\\\\n \\hline\n Local Only & 85.18(4.62) &47.80(10.12)\\\\\n FedAvg+FT & 96.97(2.54) &80.81(10.14) \\\\\n FedAvg+BN & 99.69(0.14) &93.38(1.76) \\\\\n FedBN & 99.32(0.75) &83.85(18.82)\\\\\n FedRep & 75.72(15.56) &38.53(20.52)\\\\\n PerFL & 99.48(0.20) &91.40(1.95)\\\\ \n \\hline\n FedCS-FC1(ours) & \\bf{99.72(0.15)} &\\bf{93.72}(1.71)\\\\\n FedCS-FC2(ours) & 99.71(0.16) &93.43(1.78)\\\\\n FedCS-FC3(ours) & 99.72(0.26) &93.64(1.56)\\\\\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Client-supervised Federated Learning: Towards One-model-for-all Personalization", "authors": ["Peng Yan", "Guodong Long"], "url": "https://arxiv.org/abs/2403.19499v1", "attribution": "\"Client-supervised Federated Learning: Towards One-model-for-all Personalization\" by Peng Yan and Guodong Long, arXiv:2403.19499v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.06742v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{arydshln}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small{Results across multiple sampling-distribution variances and state noise levels. Mean $\\pm$ std. dev. over 10 trials. Only successful runs are considered for total time and traveled distance statistics. Dashes indicate missing data due to incomplete task.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cl|cccccc}\n \\toprule\n & & \\multicolumn{2}{c}{Success Rate $\\uparrow$} & \\multicolumn{2}{c}{Total Time (s) $\\downarrow$} & \\multicolumn{2}{c}{Trav. Distance (m) $\\downarrow$} \\\\\n Noise $\\sigma^2$ & Model & Mean & Std. Dev. & Mean & Std. Dev. & Mean & Std. Dev. \\\\\n \\bottomrule\n \\rowcolor{gray!20} \\multicolumn{8}{c}{\\textit{Mission 1}} \\\\\n \\multirow{2}{*}{\\textbf{0.0}} & \\textbf{Ours} & \\textbf{1.00} & \\textbf{0.00} & 145.87 & 33.98 & 120.65 & 15.65 \\\\\n \\textbf{} & \\textbf{PETS} & 0.45 & 0.50 & \\textbf{143.00} & \\textbf{41.05} & \\textbf{116.27} & \\textbf{10.94} \\\\\n \\hdashline\n \\multirow{2}{*}{\\textbf{0.01}} & \\textbf{Ours} & \\textbf{1.00} & \\textbf{0.00} & \\textbf{131.68} & \\textbf{9.26} & \\textbf{114.13} & \\textbf{4.49} \\\\\n \\textbf{} & \\textbf{PETS} & 0.68 & 0.47 & 154.75 & 42.66 & 119.14 & 11.23 \\\\\n \\hdashline\n \\multirow{2}{*}{\\textbf{0.05}} & \\textbf{Ours} & \\textbf{1.00} & \\textbf{0.00} & \\textbf{143.44} & \\textbf{34.60} & 117.68 & 12.68 \\\\\n \\textbf{} & \\textbf{PETS} & 0.75 & 0.44 & 154.99 & 29.38 & \\textbf{116.79} & \\textbf{5.88} \\\\\n \\hdashline\n \\multirow{2}{*}{\\textbf{0.10}} & \\textbf{Ours} & \\textbf{0.97} & \\textbf{0.16} & \\textbf{152.40} & \\textbf{37.19} & \\textbf{122.06} & \\textbf{15.73} \\\\\n \\textbf{} & \\textbf{PETS} & 0.88 & 0.33 & 181.71 & 46.55 & 123.72 & 12.15 \\\\\n \\hdashline\n \\multirow{2}{*}{\\textbf{0.20}} & \\textbf{Ours} & \\textbf{1.00} & \\textbf{0.00} & 183.11 & 65.68 & 133.34 & 26.13 \\\\\n \\textbf{} & \\textbf{PETS} & 0.93 & 0.27 & \\textbf{170.18} & \\textbf{48.90} & \\textbf{119.32} & \\textbf{10.94} \\\\\n \\hdashline\n \\multirow{2}{*}{\\textbf{0.50}} & \\textbf{Ours} & \\textbf{0.72} & \\textbf{0.45} & \\textbf{237.05} & \\textbf{69.13} & \\textbf{149.97} & \\textbf{24.68} \\\\\n \\textbf{} & \\textbf{PETS} & 0.40 & 0.50 & 289.11 & 56.09 & 163.90 & 22.30 \\\\\n \\hdashline\n \\multirow{2}{*}{\\textbf{1.0}} & \\textbf{Ours} & \\textbf{0.15} & \\textbf{0.36} & \\textbf{283.11} & \\textbf{22.82} & \\textbf{160.15} & \\textbf{9.77} \\\\\n \\textbf{} & \\textbf{PETS} & 0.00 & 0.00 & - & - & - & - \\\\\n \\bottomrule\n \\rowcolor{gray!20} \\multicolumn{8}{c}{\\textit{Mission 2}} \\\\\n \\multirow{2}{*}{\\textbf{0.0}} & \\textbf{Ours} & \\textbf{0.68} & \\textbf{0.47} & \\textbf{218.30} & \\textbf{128.10} & \\textbf{146.31} & \\textbf{55.92} \\\\\n \\textbf{} & \\textbf{PETS} & 0.00 & 0.00 & - & - & - & - \\\\\n \\hdashline\n \\multirow{2}{*}{\\textbf{0.01}} & \\textbf{Ours} & \\textbf{0.65} & \\textbf{0.48} & \\textbf{228.47} & \\textbf{139.21} & \\textbf{145.85} & \\textbf{55.98} \\\\\n \\textbf{} & \\textbf{PETS} & 0.00 & 0.00 & - & - & - & - \\\\\n \\hdashline\n \\multirow{2}{*}{\\textbf{0.05}} & \\textbf{Ours} & \\textbf{0.28} & \\textbf{0.45} & \\textbf{189.16} & \\textbf{66.60} & \\textbf{128.77} & \\textbf{22.50} \\\\\n \\textbf{} & \\textbf{PETS} & 0.00 & 0.00 & - & - & - & - \\\\\n \\hdashline\n \\multirow{2}{*}{\\textbf{0.10}} & \\textbf{Ours} & \\textbf{0.17} & \\textbf{0.38} & \\textbf{275.12} & \\textbf{139.38} & \\textbf{173.95} & \\textbf{67.78} \\\\\n \\textbf{} & \\textbf{PETS} & 0.00 & 0.00 & - & - & - & - \\\\\n \\hdashline\n \\multirow{2}{*}{\\textbf{0.20}} & \\textbf{Ours} & 0.05 & 0.22 & 471.54 & 37.85 & 238.77 & 9.25 \\\\\n \\textbf{} & \\textbf{PETS} & \\textbf{0.07} & \\textbf{0.27} & \\textbf{176.64} & \\textbf{19.07} & \\textbf{129.45} & \\textbf{12.54} \\\\\n \\hdashline\n \\multirow{2}{*}{\\textbf{0.50}} & \\textbf{Ours} & 0.03 & 0.16 & 387.82 & - & 193.95 & - \\\\\n \\textbf{} & \\textbf{PETS} & \\textbf{0.15} & \\textbf{0.36} & \\textbf{277.98} & \\textbf{133.41} & \\textbf{162.35} & \\textbf{45.66} \\\\\n \\hdashline\n \\multirow{2}{*}{\\textbf{1.0}} & \\textbf{Ours} & 0.00 & 0.00 & - & - & - & - \\\\\n \\textbf{} & \\textbf{PETS} & 0.00 & 0.00 & - & - & - & - \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning Causal Structure Distributions for Robust Planning", "authors": ["Alejandro Murillo-Gonzalez", "Junhong Xu", "Lantao Liu"], "url": "https://arxiv.org/abs/2508.06742v1", "attribution": "\"Learning Causal Structure Distributions for Robust Planning\" by Alejandro Murillo-Gonzalez, Junhong Xu, and Lantao Liu, arXiv:2508.06742v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.11861v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of topic modeling results (Google Data)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\hline\n n-gram & S$^1$ & $p^2$ & $c^3$ & $|T|$ & score\\\\\n \\hline\n uni & neg & $-6.884$ & $0.474$ & 5 & --- \\\\\n bi & neg & $-25.687$ & $0.610$ & 15 & 13.65 \\\\\n tri & neg & $-18.014$ & $0.644$ & 5 & 9.83 \\\\\n \\hline\n uni & neu & $-8.119$ & $0.363$ & 14 & --- \\\\\n bi & neu & $-24.891$ & $0.679$ & 15 & $^*$ \\\\\n tri & neu & $-19.002$ & $0.630$ & 7 & --- \\\\\n \\hline\n uni & pos & $-6.856$ & $0.500$ & 5 & --- \\\\\n bi & pos & $-24.796$ & $0.612$ & 15 & 13.20 \\\\\n tri & pos & $-34.593$ & $0.464$ & 14 & 18.03\\\\\n \\hline\n \\emph{Notes:} & \\multicolumn{5}{l}{1: Sentiment \\ \\ \\ 2: Perplexity \\ \\ \\ 3: Coherence}\\\\\n \\multicolumn{6}{l}{*: Both $p$ and $c$ are best.}\\\\\n \\multicolumn{6}{l}{Score is not calculated for the worst performing topic model.}\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Banking on Feedback: Text Analysis of Mobile Banking iOS and Google App Reviews", "authors": ["Yekta Amirkhalili", "Ho Yi Wong"], "url": "https://arxiv.org/abs/2503.11861v1", "attribution": "\"Banking on Feedback: Text Analysis of Mobile Banking iOS and Google App Reviews\" by Yekta Amirkhalili and Ho Yi Wong, arXiv:2503.11861v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.12636v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\nLoan Type & Return & PD & LGD \\\\\n\\hline\nSafe Loan & $r_{rf}=3\\%$ & $0$ & $0$ \\\\\n\\hline\nLess Risky Loan & $r_{s}=9\\%$ & $p_{s}=6.1\\%$ & $lgd_{s}=10\\%$ \\\\\n\\hline\nMore Risky Loan & $r_{r}=13.2\\%$ & $p_{r}=12.2\\%$ & $lgd_r=9\\%$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Risk parameters for the three loans}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Does limited liability reduce leveraged risk?: The case of loan portfolio management", "authors": ["Deb Narayan Barik", "Siddhartha P. Chakrabarty"], "url": "https://arxiv.org/abs/2209.12636v1", "attribution": "\"Does limited liability reduce leveraged risk?: The case of loan portfolio management\" by Deb Narayan Barik and Siddhartha P. Chakrabarty, arXiv:2209.12636v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.08198v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\hline\n\\textbf{AI lab} & \\textbf{Year} & \\textbf{Labor Share} & \\textbf{Compute Share} \\\\ \\hline\nC3.ai & 2020 & 0.275 & 0.725 \\\\ \nOpenAI & 2018 & 0.335 & 0.665 \\\\ \nSplunk & 2021 & 0.808 & 0.192 \\\\ \nAlteryx & 2021 & 0.900 & 0.100 \\\\ \nDocuSign & 2021 & 0.911 & 0.089 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Economic impacts of AI-augmented R&D", "authors": ["Tamay Besiroglu", "Nicholas Emery-Xu", "Neil Thompson"], "url": "https://arxiv.org/abs/2212.08198v2", "attribution": "\"Economic impacts of AI-augmented R&D\" by Tamay Besiroglu, Nicholas Emery-Xu, and Neil Thompson, arXiv:2212.08198v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09113v4_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The average relative rollout error for the cylinder flow, with unit of $\\times 10^{-3}$. We compare MeshGraphNet with noise injection (NI) to our model with transformer. We train the model on the training trajectories with 400 steps, and test it on the unseen trajectories with 800 steps.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccc} \n \\hline\nrollout step &\\multicolumn{3}{c}{800} \\\\\n{Variable} &$u$ & $v$ & $p$ \\\\\n \\hline\n MeshGraphNet-NI &43 &1274 &258 \\\\\n\\hline\n Ours-Transformer & \\textbf{6} & \\textbf{158} & \\textbf{48} \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Predicting Physics in Mesh-reduced Space with Temporal Attention", "authors": ["Xu Han", "Han Gao", "Tobias Pfaff", "Jian-Xun Wang", "Li-Ping Liu"], "url": "https://arxiv.org/abs/2201.09113v4", "attribution": "\"Predicting Physics in Mesh-reduced Space with Temporal Attention\" by Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, and Li-Ping Liu, arXiv:2201.09113v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.00914v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n\\textbf{Method} & \\textbf{Type} & $\\lambda$ \\textbf{Sch.} & \\textbf{Obj. Func.} \\\\\n\\midrule\nForward KL & Forward & -- & $\\mathbb{E}_P[\\log(P/Q)]$ \\\\ \nBackward KL & Backward & -- & $\\mathbb{E}_Q[\\log(Q/P)]$ \\\\\nFixed-Param BiKL & Bidirectional & Constant & $\\lambda_{\\text{fix}}$(KL\\textsubscript{F} + KL\\textsubscript{R}) \\\\\nBiKL & Bidirectional & $\\lambda$=1 & KL\\textsubscript{F} + KL\\textsubscript{R} \\\\\nStepped BiKL & Bidirectional & Adaptive & $\\lambda(t)$KL\\textsubscript{F} + [1-$\\lambda(t)$]KL\\textsubscript{R} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{KL Divergence Method. a) Forward KL: Forward KL Divergence, b) Backward KL: Backward KL Divergence, Fixed-Param BiKL: Fixed-Parameter Bi-directional KL Divergence, BiKL: Bi-directional KL Divergence, and Stepped BiKL: Stepped Bi-directional KL Divergence. Specifically, Forward and Backward KL Divergence only have one direction. Bi-directional KL has both forward and backward, yet it realizes this through three distinct methods, as presented in the table (where the formulas in the “Obj. Func.” column correspond to these different realization logics)}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "TinyMusician: On-Device Music Generation with Knowledge Distillation and Mixed Precision Quantization", "authors": ["Hainan Wang", "Mehdi Hosseinzadeh", "Reza Rawassizadeh"], "url": "https://arxiv.org/abs/2509.00914v1", "attribution": "\"TinyMusician: On-Device Music Generation with Knowledge Distillation and Mixed Precision Quantization\" by Hainan Wang, Mehdi Hosseinzadeh, and Reza Rawassizadeh, arXiv:2509.00914v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2309.16679v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Test results comparison amongst different KD methods in supervised learning.}\n\\begin{tabular}{lll}\n \\toprule\n & \\textbf{PnL} & \\textbf{Accuracy} \\\\ \\midrule\n \\textbf{Baseline} & $0.128\\%$ & $40.091\\%$ \\\\\n \\textbf{KD} & $0.757\\%$ & $42.453\\%$ \\\\ \n \\textbf{OKD} & $0.687\\%$ & $42.328$ \\\\\n \\textbf{*Teachers SD} & $\\underline{2.322\\%}$ & $43.092\\%$ \\\\\n \\textbf{*Teachers-Student} SD & $2.268\\%$ & $\\underline{43.109\\%}$ \\\\ \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management", "authors": ["Paraskevi Nousi", "Loukia Avramelou", "Georgios Rodinos", "Maria Tzelepi", "Theodoros Manousis", "Konstantinos Tsampazis", "Kyriakos Stefanidis", "Dimitris Spanos", "Manos Kirtas", "Pavlos Tosidis", "Avraam Tsantekidis", "Nikolaos Passalis", "Anastasios Tefas"], "url": "https://arxiv.org/abs/2309.16679v2", "attribution": "\"Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management\" by Paraskevi Nousi, Loukia Avramelou, Georgios Rodinos, Maria Tzelepi, Theodoros Manousis, Konstantinos Tsampazis, Kyriakos Stefanidis, Dimitris Spanos, Manos Kirtas, Pavlos Tosidis, Avraam Tsantekidis, Nikolaos Passalis, and Anastasios Tefas, arXiv:2309.16679v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00113v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\t\t\\textbf{Hyperparameter}\t& \\textbf{Value} \\\\\n\t\tOptimiser & Adam \\\\\n\t\tBatch size & $2$ \\\\\n\t\tLearning rate & $0.001$ \\\\\n\t\tEpochs & $20$ \\\\\n\t\tLearning rate schedule & Constant\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Hyperparameters for the convolutional Bouncing balls experiment.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Imbedding Deep Neural Networks", "authors": ["Andrew Corbett", "Dmitry Kangin"], "url": "https://arxiv.org/abs/2202.00113v2", "attribution": "\"Imbedding Deep Neural Networks\" by Andrew Corbett and Dmitry Kangin, arXiv:2202.00113v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.09661v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c||c|c|}\n\t\t\t\\hline\n\t\t\t$m$ & $k$ & Talbot-Gaver-Stehfest & Euler-Gaver-Stehfest \\\\ \\hline \\hline\n\t\t\t30 & 15 & $0.0047812$ & $0.0047684$ \\\\ \\hline\n\t\t\t30 & 18& $0.0047812$ & $0.0047684$\\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Joint density $p(1,6,4)$.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Geometric Brownian motion with affine drift and its time-integral", "authors": ["Runhuan Feng", "Pingping Jiang", "Hans Volkmer"], "url": "https://arxiv.org/abs/2012.09661v1", "attribution": "\"Geometric Brownian motion with affine drift and its time-integral\" by Runhuan Feng, Pingping Jiang, and Hans Volkmer, arXiv:2012.09661v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.02289v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Similar to Tables - with SNR =20 (mean and standard deviation over 50 trials are reported)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n \\toprule\n \\multicolumn{6}{c}{Scheme 1 with SNR=20}\\\\\n \\midrule\n & (r, SR) & Max-norm & Hybrid & Nuclear & TL1\\\\\n \\midrule\n 300 &(5, 0.1)& 0.327 (0.012)& \\textit{0.064 (0.013)}& 0.152 (0.007)& \\textbf{0.029 (0.001)}\\\\\n &(5, 0.2)& 0.195 (0.005)& \\textbf{0.009 (0.002)}& 0.068 (0.001)& \\textit{0.013 (0.001)}\\\\\n &(10, 0.1)& 0.568 (0.011)& \\textit{0.495 (0.013)}& 0.505 (0.012)& \\textbf{0.102 (0.003)}\\\\\n &(10, 0.2)& 0.248 (0.005)& \\textbf{0.021 (0.002)}& 0.120 (0.002)& \\textit{0.024 (0.001)}\\\\\n \\midrule\n 500&(5, 0.1)& 0.204 (0.007)& 0.014 (0.000)& \\textit{0.013 (0.002)}& \\textbf{0.005 (0.000)}\\\\\n &(5, 0.2)& 0.129 (0.004)& 0.008 (0.000)& \\textit{0.007 (0.000)}& \\textbf{0.003 (0.000)}\\\\\n &(10, 0.1)& 0.270 (0.007)& 0.082 (0.006)& \\textit{0.081 (0.006)}& \\textbf{0.009 (0.001)}\\\\\n &(10, 0.2)& 0.155 (0.003)& 0.016 (0.000)& \\textit{0.011 (0.000)}& \\textbf{0.006 (0.000)}\\\\\n \\bottomrule\\\\\n \\toprule\n \\multicolumn{6}{c}{Scheme 2 with SNR=20}\\\\\n \\midrule\n & (r, SR) & Max-norm & Hybrid & Nuclear & TL1\\\\\n \\midrule\n 300 &(5, 0.1)& 0.305 (0.033)& \\textbf{0.281 (0.028)}& 0.759 (0.017)& \\textit{0.368 (0.049)}\\\\\n &(5, 0.2)& 0.174 (0.016)& \\textit{0.139 (0.022)}& 0.606 (0.020)& \\textbf{0.063 (0.043)}\\\\\n &(10, 0.1)& 0.480 (0.026)& \\textbf{0.477 (0.029)}& 0.798 (0.010)& \\textit{0.505 (0.031)}\\\\\n &(10, 0.2)& 0.217 (0.014)& \\textit{0.204 (0.026)}& 0.610 (0.016)& \\textbf{0.138 (0.029)}\\\\\n \\midrule\n 500&(5, 0.1)& 0.265 (0.027)& \\textit{0.209 (0.022)}& 0.753 (0.014)& \\textbf{0.048 (0.037)}\\\\\n &(5, 0.2)& 0.144 (0.007)& \\textit{0.126 (0.006)}& 0.606 (0.015)& \\textbf{0.006 (0.000)}\\\\\n &(10, 0.1)& 0.337 (0.015)& \\textit{0.286 (0.016)}& 0.762 (0.008)& \\textbf{0.130 (0.024)}\\\\\n &(10, 0.2)& 0.178 (0.007)& \\textit{0.145 (0.008)}& 0.609 (0.010)& \\textbf{0.012 (0.011)}\\\\\n \\bottomrule\\\\\n \\toprule\n \\multicolumn{6}{c}{Scheme 3 with SNR=20}\\\\\n \\midrule\n & (r, SR) & Max-norm & Hybrid & Nuclear & TL1\\\\\n \\midrule\n 300 &(5, 0.1)& 0.464 (0.021)& \\textit{0.451 (0.023)}& 0.784 (0.010)& \\textbf{0.446 (0.031)}\\\\\n &(5, 0.2)& 0.184 (0.018)& \\textit{0.152 (0.024)}& 0.611 (0.017)& \\textbf{0.065 (0.035)}\\\\\n &(10, 0.1)& 0.543 (0.018)& \\textit{0.533 (0.018)}& 0.820 (0.007)& \\textbf{0.521 (0.018)}\\\\\n &(10, 0.2)& 0.229 (0.019)& \\textit{0.221 (0.022)}& 0.625 (0.012)& \\textbf{0.147 (0.034)}\\\\\n \\midrule\n 500&(5, 0.1)& 0.393 (0.025)& \\textit{0.323 (0.026)}& 0.757 (0.014)& \\textbf{0.107 (0.031)}\\\\\n &(5, 0.2)& 0.148 (0.061)& \\textit{0.127 (0.053)}& 0.607 (0.014)& \\textbf{0.010 (0.008)}\\\\\n &(10, 0.1)& 0.508 (0.012)& \\textit{0.472 (0.013)}& 0.793 (0.007)& \\textbf{0.329 (0.015)}\\\\\n &(10, 0.2)& 0.182 (0.008)& \\textit{0.151 (0.009)}& 0.611 (0.010)& \\textbf{0.015 (0.015)}\\\\\n \\bottomrule\n \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Noisy Low-Rank Matrix Completion via Transformed $L_1$ Regularization and its Theoretical Properties", "authors": ["Kun Zhao", "Jiayi Wang", "Yifei Lou"], "url": "https://arxiv.org/abs/2503.02289v1", "attribution": "\"Noisy Low-Rank Matrix Completion via Transformed $L_1$ Regularization and its Theoretical Properties\" by Kun Zhao, Jiayi Wang, and Yifei Lou, arXiv:2503.02289v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19280v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The summary of performance test under $Mix$ type with $\\theta=3\\pi/2$. Eight scenarios (1,3,5,6,8,9,11,14) approach to $y_b$, three scenarios (2,4,7 ) go to the other solution $y_s$, fourteen scenarios (10,12-13,15-25) diverges. Note that $\\max|y_{rk4}-y_{b}|$ values are not presented and they are all larger than $10^{-2}$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\tstatus & number & $\\max|y''-f|$ & $\\max|y_{opt}-y_{b}|$ \\\\ \\hline\\hline\n\t\t $\\to y_b$ & 8\t & 1.1E-06 & 6.8E-08 \\\\\n\t\t$\\to y_s$ & 3 & 1.1E-06 & 2.0E-01 \\\\ \n\t\t$diverge$ & 14 & diverge & diverge \\\\ \\hline\n\t \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions", "authors": ["Xiaorong Zou"], "url": "https://arxiv.org/abs/2504.19280v1", "attribution": "\"Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions\" by Xiaorong Zou, arXiv:2504.19280v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.17510v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Speedup ratio of parallelized and SIMDized real SpMV}\n\\begin{tabular}{|c|c|c|c|c|c|}\\hline \n\\multicolumn{2}{|c|}{32 Threads}& mpfr & DD &\tTD & QD \\\\ \\hline\n$n=101$ to $1000$ &P& 9.6\\%\t & 51.1\\%\t& 62.6\\%\t& 64.4\\% \\\\ \n\\# matrices: 219 & & 0.34\t & 29.09\t& 5.23\t& 5.44 \\\\ \\cline{2-6}\n &M& 85.8\\%\t & 52.5\\%\t& 56.6\\%\t& 64.8\\% \\\\\n & & 2.68\t & 28.06\t & 4.87\t &5.44\\\\ \\hline\n$n=1001$ to $5000$ &P& 36.2\\% &\t61.2\\%\t&100.0\\%\t&100.0\\%\\\\\n\\# matrices: 309 & & 0.34\t & 29.09\t& 5.23\t& 5.44 \\\\ \\cline{2-6}\n &M& 99.7\\%\t & 98.4\\%\t& 99.7\\%\t& 100.0\\% \\\\\n & & 1.09\t &6.62\t &7.63\t &11.37 \\\\ \\hline\n$n=5001$ to $10000$ &P& 99.0\\%\t & 100.0\\%\t& 100.0\\%\t& 100.0\\% \\\\\n\\# matrices: 103 & & 2.90\t & 16.89\t& 9.44\t& 11.31 \\\\ \\cline{2-6}\n &M& 100.0\\%\t & 100.0\\%\t& 100.0\\%\t& 100.0\\% \\\\\n & & 6.32\t & 7.45\t&10.84\t &14.32 \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Performance evaluation of accelerated real and complex multiple-precision sparse matrix-vector multiplication", "authors": ["Tomonori Kouya"], "url": "https://arxiv.org/abs/2412.17510v1", "attribution": "\"Performance evaluation of accelerated real and complex multiple-precision sparse matrix-vector multiplication\" by Tomonori Kouya, arXiv:2412.17510v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2306.00718v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Demographic characteristics of the sample group (N)}\n\\begin{tabular}{llr} \\toprule\n\\multirow{2}{*}{Gender} & Male & 9 \\\\\n & Female & 7 \\\\ \\hline\n\\multirow{3}{*}{Age} & <40 & 2 \\\\\n & 40-50 & 6 \\\\\n & >50 & 8 \\\\ \\hline\n\\multirow{6}{*}{Dominant background} & Healthcare entities - managerial position & 5 \\\\\n & Healthcare sector- administrative position & 4 \\\\\n & Healthcare unit- medical staff & 2 \\\\\n & Insurance scheme & 1 \\\\\n & Government – department of healthcare; ministry & 3 \\\\\n & Third party payer & 2 \\\\ \\hline\n\\multirow{4}{*}{Education} & MBA & 5 \\\\\n & Ph.D. & 8 \\\\\n & Professor & 1 \\\\\n & M.Sc. & 2 \\\\ \\hline\n\\multirow{3}{*}{Workplace} & Public sector employee & 13 \\\\\n & Private sector employee & 2 \\\\\n & Entrepreneur, self-employed & 1 \\\\ \\hline\n\\multirow{3}{*}{Years of experience} & 10-15 & 2 \\\\\n & 16-20 & 5 \\\\\n & > 20 & 9 \\\\ \\hline\n\\multirow{11}{*}{Country of origin} & Poland & 2 \\\\\n & Hungary & 2 \\\\\n & Latvia & 2 \\\\\n & France & 1 \\\\\n & Belgium & 1 \\\\\n & Finland & 1 \\\\\n & Sweden & 1 \\\\\n & Norway & 1 \\\\\n & Slovenia & 2 \\\\\n & United Kingdom & 2 \\\\\n & Germany & 1 \\\\ \\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Strong Sustainability Paradigm Based Analytical Hierarchy Process (SSP-AHP) Method to Evaluate Sustainable Healthcare Systems", "authors": ["Jarosław Wątróbski", "Aleksandra Bączkiewicz", "Iga Rudawska"], "url": "https://arxiv.org/abs/2306.00718v1", "attribution": "\"A Strong Sustainability Paradigm Based Analytical Hierarchy Process (SSP-AHP) Method to Evaluate Sustainable Healthcare Systems\" by Jarosław Wątróbski, Aleksandra Bączkiewicz, and Iga Rudawska, arXiv:2306.00718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2401.08606v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrrrrr}\n \\hline\n$J-k$ & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\\\\n ARI & 0.779 & 1.656 & 2.662 & 3.847 & 5.278 & 7.048 & 9.285 & 12.149 & 15.745 \\\\\n rate ($\\rho_{J-k}$) & & 1.124 & 0.608 & 0.445 & 0.372 & 0.335 & 0.317 & 0.308 & 0.296 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{Interval expansion}. We report the average range of intervals (ARI, from Equation , along with its growth with the number of free mappings ($J-k$). }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Forking paths in financial economics", "authors": ["Guillaume Coqueret"], "url": "https://arxiv.org/abs/2401.08606v1", "attribution": "\"Forking paths in financial economics\" by Guillaume Coqueret, arXiv:2401.08606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01121v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classification accuracy on CIFAR-10. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\t\t\t\t\\toprule \n\t\t\t\t\\addlinespace\n\t\t\t\tModel & Params & Benign & PGD & CW & BSA & Rand \\tabularnewline\n\t\t\t\t\\addlinespace\n\t\t\t\t\\midrule\n\t\t\t\tTanh16(U=4) & 773,600 & .510 & .460 & .550 & .600 & .368 \\tabularnewline\n\t\t\t\tSoftmax(U=2) & \\textbf{772,628} & .869 & .814 & \\textbf{.860} & \\textbf{.870} & .652 \\tabularnewline\n\t\t\t\tTanh16(U=2) & 773,600 & .872 & \\textbf{.826} & .830 & \\textbf{.830} & .765 \\tabularnewline\n\t\t\t\tLogEns10(U=2) & 1,197,998 & .882 & .806 & .830 & .800 & \\textbf{1.0} \\tabularnewline\n\t\t\t\t\\midrule \n\t\t\t\tMadry & 45,901,914 & .871 & .470 & .080 & 0 & .981 \\tabularnewline\n\t\t\t\tTanhEns64 & 3,259,456 & \\textbf{.896} & .601 & .760 & .760 & \\textbf{1.0} \\tabularnewline\n\t\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Local Competition and Stochasticity for Adversarial Robustness in Deep Learning", "authors": ["Konstantinos P. Panousis", "Sotirios Chatzis", "Antonios Alexos", "Sergios Theodoridis"], "url": "https://arxiv.org/abs/2101.01121v2", "attribution": "\"Local Competition and Stochasticity for Adversarial Robustness in Deep Learning\" by Konstantinos P. Panousis, Sotirios Chatzis, Antonios Alexos, and Sergios Theodoridis, arXiv:2101.01121v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.08222v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CES Substitution Parameters Estimates}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n& $\\sigma^O$ & $\\sigma^M$ & $\\sigma^I$ \\\\ \n\\midrule\n Estimate & 0.501 & 0.773 & 0.222 \\\\ \n SE & (0.066) & (0.232) & (0.268) \\\\ \n \\bottomrule\n \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Do Temporary Workers Face Higher Wage Markdowns? Evidence from India's Automotive Sector", "authors": ["Davide Luparello"], "url": "https://arxiv.org/abs/2507.08222v1", "attribution": "\"Do Temporary Workers Face Higher Wage Markdowns? Evidence from India's Automotive Sector\" by Davide Luparello, arXiv:2507.08222v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.13157v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Original Dataset 5-class Output Distribution}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n\\toprule\nBin \\# & 0 & 1 & 2 & 3 & 4 \\\\ \\midrule\nCeiling (W) & 90 & 900 & 9000 & 90000 & 200000 \\\\\n\\# of samples & 73 & 94 & 100 & 126 & 149 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction", "authors": ["James Daniell", "Kazuma Kobayashi", "Ayodeji Alajo", "Syed Bahauddin Alam"], "url": "https://arxiv.org/abs/2211.13157v4", "attribution": "\"Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction\" by James Daniell, Kazuma Kobayashi, Ayodeji Alajo, and Syed Bahauddin Alam, arXiv:2211.13157v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15444v2_tex_table22.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mutually unbiased weighing matrices of order $24$ and weight $9$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l}\n\\noalign{\\hrule height1pt}\n$W_{24}$\\\\\n\\hline\n010201001200020000201002\n010102011002000011000020\n011101200000100002000101\n101002202000002200001002\n010020102002002100020010\\\\\n000010010111000110001010\n001000220012201100000200\n112100020001020000100200\n100000100010110002200220\n100021000000011211000010\\\\\n010210020000012001010001\n000010102002021200001001\n001000010201000021020201\n100002001020200002200011\n001000120001200010020120\\\\\n001200000120120010002200\n001020101100000020111000\n001110100200000000012012\n010010000120011020020002\n120011001002002000120000\\\\\n100202000200101100100100\n100000000110020021202100\n100001012020200100010020\n010200010010200202102000\\\\\n\\hline\n$ A_{24,2}$\\\\\n\\hline\n100000020022012010001010\n001000010001010010102210\n101200000000120020010012\n000101010100022000020110\n011000102100201100001000\\\\\n001022000010000211200100\n000120021110000002000202\n010000121001100000100101\n001001220201000100220000\n001200100002002002022020\\\\\n010001000000022011010220\n001100001202200021100000\n112002000000000101022002\n100020000020021200020201\n000001022100010221002000\\\\\n110110002210000202000000\n120100100000000100212001\n120001000000001010100122\n010020200020200002012100\n001112200120100000000020\\\\\n100210201110200000000001\n010001011002111000200000\n000010101021200200200002\n100020010001012020001020\\\\\n\\hline\n$ A_{24,3}$\\\\\n\\hline\n100001000200000220102210\n122200001210000000001020\n101000010021000211000020\n000010000000002001211212\n010000022200022000020022\\\\\n012200202000111001000000\n100010122111000000000001\n010122001011000021000000\n010000200000212202001001\n000100000012001210220200\\\\\n100000221120000100020200\n000001200110200000012022\n100020012102020020001000\n010200101100100202000002\n111000000200001102210000\\\\\n010011001002020001000101\n001220120002210001000000\n001202200010020010100010\n100112000002010000100102\n001001010010112100020000\\\\\n012000110000200110100200\n000001000001201000021112\n102020000000002010202110\n000121020000100010111000\\\\\n\\hline\n$ A_{24,4}$\\\\\n\\hline\n102010011000010222000000\n000000100221001000012120\n100000021000100010102012\n001111100010000000101100\n001200000111001002010200\\\\\n102000000011202101010000\n010022000210000002000111\n110102002100001201000000\n010001000201200210020200\n001120201000002200010020\\\\\n001012010021022000000010\n100001202000020002200102\n000001202020010000110011\n010001100100102000202001\n000110201000001110200001\\\\\n100200010002020010100021\n100120100020000102001200\n010010020202020020010200\n010000200001100120120020\n121000020000200020022001\\\\\n011000010002210100002002\n000012022000012012000020\n101200000200110001201000\n010200021120200000001100\\\\\n\\hline\n$ A_{24,5}$\\\\\n\\hline\n000100010000002202012011\n001120202001110001000000\n102020022010002002001000\n101021110000200000120000\n000111000012010020000202\\\\\n100010000000002101220011\n011000020002000100110110\n000110002100001012020100\n001010000101202000011020\n100000021101001220000010\\\\\n100210200200010210100000\n111000000002120200200020\n010102000200201000001201\n010001001001100112000200\n012000000110000001102021\\\\\n010202012100000000000212\n102100100020000011010002\n000001222020220000002200\n010000000211200000202102\n000010102201120020100000\\\\\n000102201000022000120002\n120000210010021100010000\n012001210020000020001100\n100002000020010122002020\\\\\n\\hline\n$ A_{24,6}$\\\\\n\\hline\n112102100000012000000001\n000000101120020000012110\n100200100010201100002020\n101001000000012001110002\n000000120200000011220012\\\\\n120020000000022200020120\n010000000012101210100100\n011110001201020000000020\n100002210200000002002012\n000000010001011221200100\\\\\n010000022101200202000002\n101002020022001020001000\n010020011100000100021002\n001101112002000002200000\n010020002000120021002200\\\\\n000001021000110022022000\n001000002001000100120111\n000120220010000100210100\n110201200020000010200001\n001020001010200200000211\\\\\n001212000110102000200000\n120100000101101010000200\n102011000010020020001010\n000110200102200001022000\\\\\n\\noalign{\\hrule height1pt}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unbiased weighing matrices of weight $9$", "authors": ["Makoto Araya", "Masaaki Harada", "Hadi Kharaghani", "Sho Suda", "Wei-Hsuan Yu"], "url": "https://arxiv.org/abs/2501.15444v2", "attribution": "\"Unbiased weighing matrices of weight $9$\" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.03152v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MCD scores on the test set.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l||c|c|c}\n & \\multicolumn{3}{c}{{Mel-Cepstral Distortion (dB)}} \\\\\n\\cline{2-4}\n & ~~Continuous~~ & WaveGlow- & WaveGlow- \\\\\nSpeaker & ~~Vocoder~~ & EN & HU \\\\\n\\hline\\hline\nSpeaker \\#048 & 5.54 & 5.27 & 5.34 \\\\\nSpeaker \\#049 & 5.67 & 5.66 & 5.65 \\\\\nSpeaker \\#102 & 5.26 & 5.20 & 5.18 \\\\\nSpeaker \\#103 & 5.41 & 5.34 & 5.37 \\\\\n\\hline\nMean & 5.47 & 5.37 & 5.38 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Ultrasound-based Articulatory-to-Acoustic Mapping with WaveGlow Speech Synthesis", "authors": ["Tamás Gábor Csapó", "Csaba Zainkó", "László Tóth", "Gábor Gosztolya", "Alexandra Markó"], "url": "https://arxiv.org/abs/2008.03152v1", "attribution": "\"Ultrasound-based Articulatory-to-Acoustic Mapping with WaveGlow Speech Synthesis\" by Tamás Gábor Csapó, Csaba Zainkó, László Tóth, Gábor Gosztolya, and Alexandra Markó, arXiv:2008.03152v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.01559v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rcc}\\toprule\n & Robust\n \n & Bilevel \n \\\\\n \\midrule\n Continuous variables & $n+1$ & $2n+1$\n \\\\\n Binary variables & $2n$ & $2n$\n \\\\\n Continuous auxiliary variables & $n$ & $2n$\n \\\\\n \\midrule\n Constraints & $2n+2$ & $4n+4$\n \\\\\n McCormick constraints & $4n$ & $8n$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solving Decision-Dependent Robust Problems as Bilevel Optimization Problems", "authors": ["Henri Lefebvre", "Martin Schmidt", "Simon Stevens", "Johannes Thürauf"], "url": "https://arxiv.org/abs/2503.01559v2", "attribution": "\"Solving Decision-Dependent Robust Problems as Bilevel Optimization Problems\" by Henri Lefebvre, Martin Schmidt, Simon Stevens, and Johannes Thürauf, arXiv:2503.01559v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.07814v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c|c|c|c}\n \\multicolumn{2}{c}{}&\\multicolumn{4}{c}{TV}&\\multicolumn{4}{c}{WTV}\\\\\n \\multicolumn{2}{c}{}&\\multicolumn{2}{c}{supervised}&\\multicolumn{2}{c|}{unsupervised}&\\multicolumn{2}{c|}{supervised}&\\multicolumn{2}{c}{unsupervised} \\\\\n Image&$\\sigma$&IPSNR&ISSIM&IPSNR&ISSIM&IPSNR&ISSIM&IPSNR&ISSIM\\\\\n \\hline\n \\multirow{3}{*}{\\#1}&$0.03$&2.848&0.147&2.729&0.129&6.438&0.194&3.176&0.176\\\\\n&$0.06$&4.257&0.266&4.210&0.247&8.354&0.372&4.546&0.322\\\\\n &$0.09$&5.345& 0.314&5.332&0.303&9.461&0.471&5.462&0.370\\\\ % &$0.13$&&&&&&&&\\\\ \n \\hline\n\\multirow{3}{*}{\\#2}&$0.03$&2.896&0.129 &2.805&0.117&6.689&0.179&3.265& 0.150\\\\\n&$0.06$&4.467& 0.258&4.447&0.248&8.707&0.362&4.817&0.286\\\\\n&$0.09$&5.624&0.319 &5.617&0.313&10.016&0.465&5.891&0.346\\\\\n \\hline\n \\multirow{3}{*}{\\#3}&$0.03$&3.309&0.193&3.289&0.199&7.471&0.241&3.881 &0.224\\\\\n &$0.06$&4.604&0.331&4.589& 0.343&9.063 &0.434&4.951&0.386\\\\ &$0.09$&5.554&0.376&5.529&0.395&10.018&0.525&5.794&0.439\n \\end{tabular}\n\\end{adjustbox}\n\\caption{IPSNR and ISSIM values achieved by the considered models.}% for the three test images corrupted by different levels of AWGN.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising", "authors": ["Monica Pragliola", "Luca Calatroni", "Alessandro Lanza"], "url": "https://arxiv.org/abs/2503.07814v1", "attribution": "\"Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising\" by Monica Pragliola, Luca Calatroni, and Alessandro Lanza, arXiv:2503.07814v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09717v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Architecture of the autoencoder used in our method}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n \\multicolumn{3}{|c|}{\\footnotesize Encoder}\\\\\n \\cline{1-3}\n \\footnotesize Layer & \\footnotesize Filter & \\footnotesize Output Size\\\\\n \\cline{2-3}\n & \\footnotesize $k\\times k, s$ &\\footnotesize $H\\times W\\times C$\\\\\n \\hline\n \\footnotesize Input & -- &\\footnotesize $1024\\times1024\\times1$\\\\\n \\hline\n \\footnotesize Conv-BN-ReLU & \\footnotesize $5\\times5,2$ & \\footnotesize $512\\times512\\times32$\\\\\n \\hline\n \\footnotesize Conv-BN-ReLU & \\footnotesize $5\\times5,2$ & \\footnotesize $256\\times256\\times64$\\\\\n \\hline\n \\footnotesize Conv-BN-ReLU & \\footnotesize $5\\times5,2$ & \\footnotesize $128\\times128\\times128$\\\\\n \\hline\n \\footnotesize Conv-BN-ReLU & \\footnotesize $5\\times5,2$ & \\footnotesize $64\\times64\\times128$\\\\\n \\hline\n \\hline\n \\multicolumn{3}{|c|}{\\footnotesize Decoder}\\\\ \n \\cline{1-3}\n \\footnotesize Upsample & \\footnotesize $2\\times2,1$ & \\footnotesize $128\\times128\\times128$\\\\\n \\hline\n \\footnotesize Conv-BN-LReLU & \\footnotesize $5\\times5,1$ & \\footnotesize $128\\times128\\times128$\\\\\n \\hline\n \\footnotesize Upsample & \\footnotesize $2\\times2,1$ & \\footnotesize $256\\times256\\times64$\\\\\n \\hline\n \\footnotesize Conv-BN-LReLU & \\footnotesize $5\\times5,1$ & \\footnotesize $256\\times256\\times64$\\\\\n \\hline\n \\footnotesize Upsample & \\footnotesize $2\\times2,1$ & \\footnotesize $512\\times512\\times32$\\\\\n \\hline\n \\footnotesize Conv-BN-LReLU & \\footnotesize $5\\times5,1$ & \\footnotesize $512\\times512\\times32$\\\\\n \\hline\n \\footnotesize Upsample & \\footnotesize $2\\times2,1$ & \\footnotesize $1024\\times1024\\times32$\\\\\n \\hline\n \\footnotesize Conv-BN-Sigmoid & \\footnotesize $5\\times5,1$ & \\footnotesize $1024\\times1024\\times1$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning", "authors": ["Hao-Chiang Shao", "Hsing-Lei Ping", "Kuo-shiuan Chen", "Weng-Tai Su", "Chia-Wen Lin", "Shao-Yun Fang", "Pin-Yian Tsai", "Yan-Hsiu Liu"], "url": "https://arxiv.org/abs/2201.09717v1", "attribution": "\"Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning\" by Hao-Chiang Shao, Hsing-Lei Ping, Kuo-shiuan Chen, Weng-Tai Su, Chia-Wen Lin, Shao-Yun Fang, Pin-Yian Tsai, and Yan-Hsiu Liu, arXiv:2201.09717v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14913v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n \\hline\n basis & \\multicolumn{4}{|c|}{$V_{h}$}& \\multicolumn{4}{|c|}{$V_{h}^{*}$}\\\\\n \\hline\n h &$L^{2}$error & order & $H^{1}$error & order & $L^{2}$error & order & $H^{1}$error & order \\\\\n \\hline\n $2^{-2}$ &8.944e-02&-&7.995e-01&-&2.478e-05&-&3.120e-04&-\\\\\n \\hline\n $2^{-3}$ &2.457e-02&1.864&4.088e-01&0.968&1.074e-05&1.207&2.257e-04&0.468\\\\\n \\hline\n $2^{-4}$ &6.300e-03&1.964&2.055e-01&0.992&3.273e-06&1.714&1.312e-04&0.783\\\\\n \\hline\n $2^{-5}$ &1.585e-03&1.991&1.029e-01&0.998&8.643e-07&1.921&6.838e-05&0.940\\\\\n \\hline\n $2^{-6}$ &3.969e-04&1.998&5.146e-02&1.000&2.192e-07&1.980&3.456e-05&0.984 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{$L^{2}$ and $H^{1}$error and corresponding order using $P^{1}$ Lagrange element, $c(x)=-5$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A PINN-enriched finite element method for linear elliptic problems", "authors": ["Xiao Chen", "Yixin Luo", "Jingrun Chen"], "url": "https://arxiv.org/abs/2503.14913v1", "attribution": "\"A PINN-enriched finite element method for linear elliptic problems\" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18051v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cccc|}\n \\hline\n number of scenarios & objective value & 95\\% upper bound & 95\\% lower bound & relative difference \\\\\n \\hline\n25 &351.85&356.46&331.94&6.97\\%\\\\\n50&365.61&369.94&353.50&4.50\\%\\\\\n100&369.72&374.05&358.54&4.19\\%\\\\\n250&371.87&376.14&359.84&4.38\\%\\\\\n500&390.50&394.49&381.79&3.25\\%%\\\\\n\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Objective values and 95\\% bounds with different number of scenarios with the sampled stochastic model}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications", "authors": ["Jiaqi Lei", "Akhil Singla", "Sanjay Mehrotra"], "url": "https://arxiv.org/abs/2501.18051v2", "attribution": "\"A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications\" by Jiaqi Lei, Akhil Singla, and Sanjay Mehrotra, arXiv:2501.18051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.16074v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|} \\hline\n & $n$ & Running time (seconds) & Memory cost (GiB) & Accuracy \\\\\n \\hline\n Split-weight & 4 & 1.906 & 0.850 & 0.942 \\\\ \\hline\n Robinson-Foulds & 4 & 11.193 & 5.295 & 0.927 \\\\ \\hline\n Split-weight & 8 & 5.929 & 4.572 & 0.996 \\\\ \\hline\n Robinson-Foulds & 8 & 37.146 & 25.557 & 0.999 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Running time in seconds, memory cost in gibibytes (GiB), and accuracy for hierarchical clustering under two types of distances: distance from the split-weight embeddings and Robinson-Foulds distance on trees directly.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Unsupervised Learning of Phylogenetic Trees via Split-Weight Embedding", "authors": ["Yibo Kong", "George P. Tiley", "Claudia Solis-Lemus"], "url": "https://arxiv.org/abs/2312.16074v2", "attribution": "\"Unsupervised Learning of Phylogenetic Trees via Split-Weight Embedding\" by Yibo Kong, George P. Tiley, and Claudia Solis-Lemus, arXiv:2312.16074v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.09540v1_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|}\\hline\n Location & 10min & 3h avrg. & 3h inst. & 6h inst. \\\\ \\hline\n Aachen& 0 & -40.28 & -3.47 &1.99 \\\\\\hline\n Zugspitze& 0 & -39.16& 4.76 & 6.06 \\\\\\hline\n Boltenhagen &0 & -111.23& 9.86 & 3.69 \\\\\\hline\n Fichtelberg & 0 & -62.71 & 1.83& -9.80 \\\\\\hline\n \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Errors (in $MW$) of wind power generation prediction when using 30 years of data of lower resolution (three-hourly average and three-hourly and six-hourly instantaneous) wind speed observations compared to 10min wind speed observations. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts", "authors": ["Nina Effenberger", "Nicole Ludwig", "Rachel H. White"], "url": "https://arxiv.org/abs/2309.09540v1", "attribution": "\"Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts\" by Nina Effenberger, Nicole Ludwig, and Rachel H. White, arXiv:2309.09540v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18259v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\hline\nParameter & Value & Type & Derivation \\\\\n\\hline\n$\\alpha$ & 0.62 & Direct & - \\\\\n$\\gamma$ & 0.1 & Direct & - \\\\\n$\\rho$ & -0.681 & Direct & - \\\\\n$\\nu$ & 0.331 & Direct & - \\\\\n$\\theta$ & 0.3156 & Direct & - \\\\\n$V_0$ & 0.0392 & Direct & - \\\\\n$S_0$ & 100 & Direct & - \\\\\n\\hline\n$\\beta$ & 27.5583 & Derived & Solved from $\\rho=\\frac{1-\\beta}{\\sqrt{2(1+\\beta^2)}}$ \\\\\n$\\mu$ & 26.8592 & Derived & Solved from $\\nu=\\sqrt{\\frac{\\theta(1+\\beta^2)}{\\gamma\\mu(1+\\beta)^2}}$ \\\\\n$\\xi$ & 0.124208 & Derived & Derived from $V_0=\\xi\\theta$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Model parameters used in numerical experiments. The table shows both direct input parameters and derived parameters with their corresponding calculation methods.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\\infty$) processes and applications", "authors": ["Yingli Wang", "Zhenyu Cui"], "url": "https://arxiv.org/abs/2503.18259v3", "attribution": "\"Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\\infty$) processes and applications\" by Yingli Wang and Zhenyu Cui, arXiv:2503.18259v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.05581v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Commanded velocity components in $c_{\\text{cmd}}$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n \\textbf{Description} & \\textbf{Command Range} & \\textbf{Units} \\\\\n\\midrule\nDesired forward velocity & $[-0.3, 0.9]$ & m/s \\\\\nDesired lateral velocity & $[-0.3, 0.3]$ & m/s \\\\\nDesired yaw rate & $[-0.3, 0.3]$ & rad/s \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning to Walk in Costume: Adversarial Motion Priors for Aesthetically Constrained Humanoids", "authors": ["Arturo Flores Alvarez", "Fatemeh Zargarbashi", "Havel Liu", "Shiqi Wang", "Liam Edwards", "Jessica Anz", "Alex Xu", "Fan Shi", "Stelian Coros", "Dennis W. Hong"], "url": "https://arxiv.org/abs/2509.05581v1", "attribution": "\"Learning to Walk in Costume: Adversarial Motion Priors for Aesthetically Constrained Humanoids\" by Arturo Flores Alvarez, Fatemeh Zargarbashi, Havel Liu, Shiqi Wang, Liam Edwards, Jessica Anz, Alex Xu, Fan Shi, Stelian Coros, and Dennis W. Hong, arXiv:2509.05581v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06395v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{5way1shot classification accuracy (\\%) on \\textit{mini}ImageNet with different backbones.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc}\n\\hline\n {Backbones} \n & without DC & with DC \\\\\n \\hline\nconv4~ & $42.11 \\pm 0.71$ & $\\textbf{54.62} \\pm \\textbf{0.64}$ ($\\uparrow \\textbf{12.51}$)\\\\\nconv6~ & $46.07 \\pm 0.26$ & $\\textbf{57.14} \\pm \\textbf{0.45}$ ($\\uparrow \\textbf{11.07}$)\\\\\nresnet18~ & $52.32 \\pm 0.82$&$\\textbf{61.50} \\pm \\textbf{0.47}$ ($\\uparrow \\textbf{9.180}$)\\\\\nWRN28~ & $54.53 \\pm 0.56$ & $\\textbf{64.38} \\pm \\textbf{0.63}$ ($\\uparrow \\textbf{9.850}$)\\\\\nWRN28 + Rotation Loss~ & $56.37\\pm 0.68$ & $\\textbf{68.57} \\pm \\textbf{0.55}$ ($\\uparrow \\textbf{12.20}$)\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Free Lunch for Few-shot Learning: Distribution Calibration", "authors": ["Shuo Yang", "Lu Liu", "Min Xu"], "url": "https://arxiv.org/abs/2101.06395v3", "attribution": "\"Free Lunch for Few-shot Learning: Distribution Calibration\" by Shuo Yang, Lu Liu, and Min Xu, arXiv:2101.06395v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.07262v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation of fusion design and continual learning strategy.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\textbf{Depth} & \\textbf{Adapter} & \\textbf{Encoder Freezing} & \\textbf{PSNR} & \\textbf{$\\Delta$PSNR} \\\\\n \\hline\\hline\n None & $\\times$ & $\\times$ & 34.52 dB & - \\\\\n \\hline\n NN $\\times 8$ & $\\checkmark$ & $\\times$ & 36.17 dB & +1.65 dB \\\\\n NN $\\times 8$ & concat. & $\\checkmark$ & 35.24 dB & +0.72 dB \\\\\n \\hline\n NN $\\times 8$ & $\\checkmark$ & $\\checkmark$ & \\textbf{36.62 dB} & \\textbf{+2.10 dB} \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Lidar-guided Image Deblurring", "authors": ["Ziyao Yi", "Diego Valsesia", "Tiziano Bianchi", "Enrico Magli"], "url": "https://arxiv.org/abs/2412.07262v1", "attribution": "\"Deep Lidar-guided Image Deblurring\" by Ziyao Yi, Diego Valsesia, Tiziano Bianchi, and Enrico Magli, arXiv:2412.07262v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11538v3_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ASR finetuning results for the NSC subset measured in WER(\\%). The ``finetuning'' column indicates the total duration of finetuning data used. The best results are displayed in bold while the second best results are underlined.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n \\toprule\n Model & Finetuning (hrs) & Part 1 & Part 2 &Part 3&Part 4&Part 5&Part 6 \\\\\n \\midrule\n MERaLiON-SpeechEncoder & 420 & 7.2 & \\underline{11.5} & 20.4 & \\textbf{27.4} & \\textbf{14.4} & \\textbf{10.4} \\\\\n \\noalign{\\vskip 1mm} \n \\hdashline\n \\noalign{\\vskip 1mm} \n WavLM large (finetuned in-house) & 420 & 8.5 & 18.1 & \\underline{20.3} & \\underline{29.1} & \\underline{14.8} & \\underline{10.7} \\\\\n Whisper large v3 (zero-shot) & - & \\underline{6.9} & 31.9 & 30.0 & 47.5 & 22.0 & 17.5 \\\\\n Whisper large v3 (finetuned in-house) & 8169 & \\textbf{4.4}&\\textbf{3.8} & \\textbf{18.8} & 29.8 & 20.6 & 24.0 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond", "authors": ["Muhammad Huzaifah", "Geyu Lin", "Tianchi Liu", "Hardik B. Sailor", "Kye Min Tan", "Tarun K. Vangani", "Qiongqiong Wang", "Jeremy H. M. Wong", "Jinyang Wu", "Nancy F. Chen", "Ai Ti Aw"], "url": "https://arxiv.org/abs/2412.11538v3", "attribution": "\"MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond\" by Muhammad Huzaifah, Geyu Lin, Tianchi Liu, Hardik B. Sailor, Kye Min Tan, Tarun K. Vangani, Qiongqiong Wang, Jeremy H. M. Wong, Jinyang Wu, Nancy F. Chen, and Ai Ti Aw, arXiv:2412.11538v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.03842v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc} \n\t\t\t\\toprule\n\t\t\t\\ \t\t\t\t & PSNR (dB) & SSIM\t & RMSE (\\%) \\\\ \\midrule\\midrule\n\t\t\t\\ CycleGAN & 40.1006\t& 0.9753 & 4.2208\t\\\\\n\t\t \\ CycleGAN with linear kernel & 41.9220\t& 0.9825\t& 3.4224\t\\\\\n\t\t \\ Proposed\t\t & 43.4656\t& 0.9890 & 2.8652\t\\\\ \\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "CycleQSM: Unsupervised QSM Deep Learning using Physics-Informed CycleGAN", "authors": ["Gyutaek Oh", "Hyokyoung Bae", "Hyun-Seo Ahn", "Sung-Hong Park", "Jong Chul Ye"], "url": "https://arxiv.org/abs/2012.03842v1", "attribution": "\"CycleQSM: Unsupervised QSM Deep Learning using Physics-Informed CycleGAN\" by Gyutaek Oh, Hyokyoung Bae, Hyun-Seo Ahn, Sung-Hong Park, and Jong Chul Ye, arXiv:2012.03842v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.08186v1_tex_table20.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Frequency of Special Characters}\n\\begin{tabular}{ccl}\n \\toprule\n Non-English or Math&Frequency&Comments\\\\\n \\midrule\n \\O & 1 in 1,000& For Swedish names\\\\\n $\\pi$ & 1 in 5& Common in math\\\\\n \\$ & 4 in 5 & Used in business\\\\\n $\\Psi^2_1$ & 1 in 40,000& Unexplained usage\\\\\n \\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Towards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations", "authors": ["Dinesh Parthasarathy", "Tommaso Bevilacqua", "Martin Lanser", "Axel Klawonn", "Harald Köstler"], "url": "https://arxiv.org/abs/2412.08186v1", "attribution": "\"Towards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations\" by Dinesh Parthasarathy, Tommaso Bevilacqua, Martin Lanser, Axel Klawonn, and Harald Köstler, arXiv:2412.08186v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11223v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc|ccc|ccc}\n \\hline\n \\multirow{2}{*}{Method} & \\multicolumn{3}{c|}{COCO} & \\multicolumn{3}{c|}{OCHuman} & \\multicolumn{3}{c}{OCPose [6]}\\\\\n & $\\text{AP}$ & $\\text{AP}^{50}$ & $\\text{AP}^{75}$ & $\\text{AP}$ & $\\text{AP}^{50}$ & $\\text{AP}^{75}$ & $\\text{AP}$ & $\\text{AP}^{50}$ & $\\text{AP}^{75}$ \\\\\n \\hline\n HRNet & 75.5 & 92.5 & 83.3 & 37.2 & 46.7 & 40.0 & 30.2 & 48.3 & 23.1\\\\\n HRNet* & \\textbf{75.8} & \\textbf{92.8} & \\textbf{83.6} & 37.8 & 46.9 & 41.2 & 31.1 & 49.1 & 24.7\\\\\n MHPNet & 75.3 & 92.1 & 83.0 & \\textbf{42.5} & \\textbf{51.8} & \\textbf{46.3} & \\textbf{39.2} & \\textbf{61.2} & \\textbf{32.6}\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of HRNet (single-scale, 1 inference), HRNet* (six-scale, 6 inferences) and MHPNet (single-scale, 2 inferences) on \\texttt{test} sets using HRNet-48-384 backbone and Faster-RCNN.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation", "authors": ["Rawal Khirodkar", "Visesh Chari", "Amit Agrawal", "Ambrish Tyagi"], "url": "https://arxiv.org/abs/2101.11223v3", "attribution": "\"Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation\" by Rawal Khirodkar, Visesh Chari, Amit Agrawal, and Ambrish Tyagi, arXiv:2101.11223v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05524v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{VSP and Bucket Order Bayes Factors }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|r|rrrrr|rrrrr|}\n\\multicolumn{11}{c}{}\\\\\n\\hline \n&\\multicolumn{5}{|c}{Bucket Orders} &\\multicolumn{5}{|c|}{VSP orders} \\\\\nPeriod & Prior & Post. & BF & ESS & BF std.err. & Prior & Post. & BF & ESS & BF std.err.\\\\ \\hline\n1080-1084 & 0.085 & 0 & 0 & - & - & 0.22 & 0 & 0 & - & - \\\\\n1086-1090 & 0.15 & 0.17 & 1.2 & 45 & 0.38 & 0.31 & 0.34 & 1.1 & 33 & 0.27 \\\\\n1092-1096 & 0.27 & 0.14 & 0.53 & 29 & 0.24 & 0.48 & 0.31 & 0.65 & 53 & 0.13 \\\\\n1104-1108 & 0.18 & 0.12 & 0.66 & 31 & 0.32 & 0.36 & 0.21 & 0.6 & 29 & 0.21 \\\\\n1110-1114 & 0.42 & 0.25 & 0.6 & 81 & 0.12 & 0.72 & 0.64 & 0.89 & 230 & 0.044 \\\\\n1118-1122 & 0.1 & 0.22 & 2.1 & 38 & 0.65 & 0.24 & 0.53 & 2.2 & 69 & 0.25 \\\\\n1126-1130 & 0.16 & 0.13 & 0.86 & 75 & 0.25 & 0.32 & 0.35 & 1.1 & 60 & 0.19 \\\\\n1128-1132 & 0.084 & 0.016 & 0.19 & 96 & 0.15 & 0.22 & 0.067 & 0.31 & 15 & 0.3 \\\\\n1132-1134 & 0.088 & 0.0053 & 0.06 & 950 & 0.027 & 0.22 & 0.045 & 0.21 & 230 & 0.063 \\\\\n1138-1142 & 0.13 & 0.024 & 0.18 & 80 & 0.13 & 0.28 & 0.11 & 0.37 & 69 & 0.13 \\\\\n1144-1148 & 0.4 & 0.59 & 1.5 & 99 & 0.12 & 0.68 & 0.87 & 1.3 & 330 & 0.027 \\\\\n1150-1154 & 0.2 & 0.17 & 0.83 & 32 & 0.32 & 0.39 & 0.36 & 0.93 & 45 & 0.19 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian inference for partial orders from random linear extensions: power relations from 12th Century Royal Acta", "authors": ["Geoff K. Nicholls", "Jeong Eun Lee", "Nicholas Karn", "David Johnson", "Rukuang Huang", "Alexis Muir-Watt"], "url": "https://arxiv.org/abs/2212.05524v3", "attribution": "\"Bayesian inference for partial orders from random linear extensions: power relations from 12th Century Royal Acta\" by Geoff K. Nicholls, Jeong Eun Lee, Nicholas Karn, David Johnson, Rukuang Huang, and Alexis Muir-Watt, arXiv:2212.05524v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.00027v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Accuracy of the model trained on labeled datasets of sizes $10$, $20$, $40$, and $10,000$ with varying amounts of unlabeled data from the same distribution with $\\alpha = 0$ (\\textbf{left}), and different distribution with $\\alpha = 0.5\\|\\boldsymbol{\\mu}_0\\|_2$ (\\textbf{right}).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cclccclc}\n \\toprule\n \\multicolumn{4}{c}{Same distribution} & \\multicolumn{4}{c}{Different distribution} \\\\\n \\cmidrule(lr){1-4} \\cmidrule(lr){5-8}\n Labeled size & Acc & Unlabeled size & Acc & Labeled size & Acc & Unlabeled size & Acc \\\\\n \\midrule \n & & 10 & 0.63 & & & 10 & 0.61 \\\\\n 10 & 0.59 & 100 & 0.66 & 10 & 0.59 & 100 & 0.65 \\\\\n & & 1,000 & 0.79 & & & 1,000 & 0.78 \\\\\n & & 10,000 & \\textbf{0.82} & & & 10,000 & \\textbf{0.81} \\\\\n \\midrule\n & & 20 & 0.64 & & & 20 & 0.65 \\\\\n 20 & 0.62 & 200 & 0.69 & 20 & 0.62 & 200 & 0.65 \\\\\n & & 2,000 & 0.80 & & & 2,000 & 0.79 \\\\\n & & 10,000 & \\textbf{0.82} & & & 10,000 & \\textbf{0.80} \\\\\n \\midrule \n & & 40 & 0.65 & & & 40 & 0.65 \\\\\n 40 & 0.65 & 400 & 0.71 & 40 & 0.65 & 400 & 0.73 \\\\\n & & 4,000 & 0.81 & & & 4,000 & 0.78 \\\\\n & & 10,000 & \\textbf{0.82} & & & 10,000 & \\textbf{0.80} \\\\\n \\midrule\n 10,000 & \\textbf{0.83} & - & - & 10,000 & \\textbf{0.83} & - & - \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Out-Of-Domain Unlabeled Data Improves Generalization", "authors": ["Amir Hossein Saberi", "Amir Najafi", "Alireza Heidari", "Mohammad Hosein Movasaghinia", "Abolfazl Motahari", "Babak H. Khalaj"], "url": "https://arxiv.org/abs/2310.00027v2", "attribution": "\"Out-Of-Domain Unlabeled Data Improves Generalization\" by Amir Hossein Saberi, Amir Najafi, Alireza Heidari, Mohammad Hosein Movasaghinia, Abolfazl Motahari, and Babak H. Khalaj, arXiv:2310.00027v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.01467v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Comovement in Industry Variance}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n \\multicolumn{7}{c}{Panel A: Loadings} \\\\\n \\midrule\n Factor / Outcome & Across (d) & Between (d) & Across (s) & Between (s) & Var (mkt) & Var (cf) \\\\\n \\midrule\n Across (d) & 0.788 & 1.576 & 0.364 & 0.507 & 0.410 & 0.574 \\\\\n Between (d) & 0.584 & 0.735 & 0.394 & 0.567 & 0.206 & 0.383 \\\\\n Across (s) & 0.701 & 1.185 & 0.700 & 0.518 & 0.156 & 0.497 \\\\\n Between (s) & 0.455 & 0.422 & 0.401 & 0.736 & 0.157 & 0.310 \\\\\n Var (mkt) & 0.091 & 0.124 & 0.016 & 0.035 & 0.880 & 0.185 \\\\\n Var (cf) & 0.109 & 0.181 & 0.156 & 0.218 & 0.283 & 0.721 \\\\\n \\midrule\n \\multicolumn{7}{c}{Panel B: $R^2$ (avg univariate)} \\\\\n \\midrule\n Factor / Outcome & Across (d) & Between (d) & Across (s) & Between (s) & Var (mkt) & Var (cf) \\\\\n \\midrule\n Across (d) & 0.201 & 0.258 & 0.217 & 0.318 & 0.170 & 0.079 \\\\\n Between (d) & 0.189 & 0.318 & 0.257 & 0.400 & 0.174 & 0.087 \\\\\n Across (s) & 0.164 & 0.274 & 0.229 & 0.344 & 0.168 & 0.082 \\\\\n Between (s) & 0.189 & 0.322 & 0.265 & 0.347 & 0.165 & 0.086 \\\\\n Var (mkt) & 0.042 & 0.046 & 0.041 & 0.042 & 0.343 & 0.068 \\\\\n Var (cf) & 0.063 & 0.097 & 0.085 & 0.115 & 0.147 & 0.104 \\\\\n \\midrule\n \\multicolumn{7}{c}{Panel C: $R^2$ (aggregate)} \\\\\n \\midrule\n Factor / Outcome & Across (d) & Between (d) & Across (s) & Between (s) & Var (mkt) & Var (cf) \\\\\n \\midrule\n Across (d) & 1 & 0.620 & 0.575 & 0.566 & 0.373 & 0.352 \\\\\n Between (d) & & 1 & 0.628 & 0.670 & 0.255 & 0.422 \\\\\n Across (s) & & & 1 & 0.668 & 0.196 & 0.469 \\\\\n Between (s) & & & & 1 & 0.229 & 0.428 \\\\\n Var (mkt) & & & & & 1 & 0.165 \\\\\n Var (cf) & & & & & & 1 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Risk in Network Economies", "authors": ["Victor Sellemi"], "url": "https://arxiv.org/abs/2208.01467v1", "attribution": "\"Risk in Network Economies\" by Victor Sellemi, arXiv:2208.01467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09997v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n \\textbf{Entity Type}& \\textbf{Precision} & \\textbf{Recall} & \\textbf{F1-score} \\\\\\midrule\n\\textsc{org}-\\textsc{loc}-\\textsc{fac} & 0.94 & 0.92 & 0.94 \\\\\n\\textsc{per}-\\textsc{char} & 0.89 & 0.81 & 0.82 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\texttt{ruRoBERTa} scores in the binary classification task}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Razmecheno: Named Entity Recognition from Digital Archive of Diaries \"Prozhito\"", "authors": ["Timofey Atnashev", "Veronika Ganeeva", "Roman Kazakov", "Daria Matyash", "Michael Sonkin", "Ekaterina Voloshina", "Oleg Serikov", "Ekaterina Artemova"], "url": "https://arxiv.org/abs/2201.09997v1", "attribution": "\"Razmecheno: Named Entity Recognition from Digital Archive of Diaries \"Prozhito\"\" by Timofey Atnashev, Veronika Ganeeva, Roman Kazakov, Daria Matyash, Michael Sonkin, Ekaterina Voloshina, Oleg Serikov, and Ekaterina Artemova, arXiv:2201.09997v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13833v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcr}\nLeft&Centered&Right\\\\\n\\hline\n1 & 2 & 3\\\\\n10 & 20 & 30\\\\\n100 & 200 & 300\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the Reasoning Capacity of AI Models and How to Quantify It", "authors": ["Santosh Kumar Radha", "Oktay Goktas"], "url": "https://arxiv.org/abs/2501.13833v1", "attribution": "\"On the Reasoning Capacity of AI Models and How to Quantify It\" by Santosh Kumar Radha and Oktay Goktas, arXiv:2501.13833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2007.07925v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{{\\bf Quantitative evaluation on private photos with 43 filters.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll}\n\\hline\\noalign{\\smallskip}\nMethods & Ours (w. R, AU) & Ours (w. R, U) & WCT2~ & Color Transfer~\\\\\n\\noalign{\\smallskip}\n\\hline\n\\noalign{\\smallskip}\nPSNR & {\\bf 25.814 } & {\\bf 25.850 } & 17.234 & 6.985 \\\\\n$\\Delta E_{00}^*$ & {\\bf 5.881 } & {\\bf 5.854 } & 15.723 & 35.123 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Filter Style Transfer between Photos", "authors": ["Jonghwa Yim", "Jisung Yoo", "Won-joon Do", "Beomsu Kim", "Jihwan Choe"], "url": "https://arxiv.org/abs/2007.07925v1", "attribution": "\"Filter Style Transfer between Photos\" by Jonghwa Yim, Jisung Yoo, Won-joon Do, Beomsu Kim, and Jihwan Choe, arXiv:2007.07925v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2302.14440v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Occupational Groups.}\n\\begin{tabular}{lll}\n\\toprule\nCode & Label & Examples \\\\ \\midrule\n00 & missing & Undefined or missing occupation \\\\\n01 & executives & Politicians, business executives, managers \\\\\n02 & professionals & Professions requiring advanced college degrees \\\\\n03 & nurses & Nurses, midwives, physical therapy \\\\\n04 & teacher\\_lower & Pre-school and elementary school teachers \\\\\n05 & law & Law professionals \\\\\n06 & lower\\_professionals & Professions requiring shorter college education \\\\\n07 & secretary/clerical & Secretary, bank clerks, administrators \\\\\n08 & other\\_administrative & Sales persons, customer service agents, postal workers, local politicians \\\\\n09 & services & Waiters, beauticians, security personnel, public transport workers \\\\\n10 & retail & Shop assistants, cashiers, phone marketing \\\\\n11 & care & Assistant nurse, personal assistant, nursery staff \\\\\n12 & agriculture & Farming, forestry, fishery \\\\\n13 & construction/craft & Carpenters, welders, printers, food processing, tailors \\\\\n14 & machine\\_operators & Mining workers, steelworkers, fitters, industry machine operators \\\\\n15 & transportation & Truck drivers, sailors, bus drivers, train drivers \\\\\n16 & cleaning & Cleaning and domestic services \\\\\n17 & military & Military personnel, officers \\\\\n18 & medecine & Medical doctors, veterinaries, dentists, psychologists \\\\\n19 & teaching\\_professionals & University teachers, high school teachers, vocational teachers \\\\\n20 & farm \\_help & Planters, croppers \\\\\n21 & manual & Dock workers, factory workers \\\\\n22 & other\\_services & Garbage collectors, market vendors, fast food workers, janitors \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Intergenerational Mobility Trends and the Changing Role of Female Labor", "authors": ["Ulrika Ahrsjö", "René Karadakic", "Joachim Kahr Rasmussen"], "url": "https://arxiv.org/abs/2302.14440v2", "attribution": "\"Intergenerational Mobility Trends and the Changing Role of Female Labor\" by Ulrika Ahrsjö, René Karadakic, and Joachim Kahr Rasmussen, arXiv:2302.14440v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.16705v5_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Illustration on effect of the MD iterates on the average prediction losses of BNNs on three datasets: 'Australian scale' (negative log-likelihood), 'Boston' and 'Concrete' (mean square error). The results compare the the average prediction losses after 1000 iterations of GFlowVI and NGFlowVI (with MD iterates) against their counterparts without MD iterates (w/o-MD), demonstrating that incorporating MD iterates enhances prediction accuracy.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccr}\n\\hline\n\\textbf{Methods}&\tAustralian &\tBoston &\tConcrete\\\\\n\\hline\nGFlowVI &\t0.51$\\pm$0.02 &\t1.73$\\pm$0.28\t& 1.49$\\pm$0.08\\\\\nGFlowVI-w/o-MD &\t0.52$\\pm$0.05\t& 1.81$\\pm$0.15 &\t1.74$\\pm$0.03\\\\\nNGFlowVI &\t0.6$\\pm$0.03\t& 1.71$\\pm$0.09 &\t1.25$\\pm$0.06\\\\\nNGFlowVI-w/o-MD &\t0.72$\\pm$0.05\t& 1.87$\\pm$0.15 &\t1.34$\\pm$0.11\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference", "authors": ["Dai Hai Nguyen", "Tetsuya Sakurai", "Hiroshi Mamitsuka"], "url": "https://arxiv.org/abs/2310.16705v5", "attribution": "\"Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference\" by Dai Hai Nguyen, Tetsuya Sakurai, and Hiroshi Mamitsuka, arXiv:2310.16705v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.07192v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Compariosn of $\\mu_4$ in fBm decy}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|c|}\n\\multicolumn{4}{c}{}\\\\ \\hline\n$r$ &0&0.1&0.2&0.3&0.4&0.5&0.6&0.7&0.8&0.9\\\\ \n \\hline \\hline\n$N=1024$ $\\mu_6$ &\n5.0111\t&\t5.092\t&\t5.350\t&\t5.832\t&\t6.644\t&\t8.001& 10.4469& 15.4136& 28.4742& 91.4928\\\\\n\\hline\nTheory $\\mu 6$ &\n5\t&\t5.081\t&\t5.342\t&\t5.839\t&\t6.686\t&\t8.111 &10.58 & 15.19712 & 25.2904& 57.5221\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deformation of semi-circle law for the correlated time series and Phase transition", "authors": ["Masato Hisakado", "Takuya Kaneko"], "url": "https://arxiv.org/abs/2508.07192v1", "attribution": "\"Deformation of semi-circle law for the correlated time series and Phase transition\" by Masato Hisakado and Takuya Kaneko, arXiv:2508.07192v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.09430v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l||c|cc|cc|}\n\\hline\n$M$ & \n$\\langle \\overline{K}_{\\rm Bayes}\\rangle$ & \n$\\langle \\overline{K}_{\\rm ML}\\rangle_{b=b_{\\rm opt}}$ & \n$\\langle \\overline{K}_{\\rm ML}\\rangle_{b=1}$ & \n$\\langle\\overline{K}_{1} \\rangle$ & \n$\\langle\\overline{K}_{1+2} \\rangle$ \\\\ \\hline \n45 & 0.015 & 0.172 & 0.196 & 0.045 & 0.042 \\\\ %\\hline\n65 & 0.014 & 0.157 & 0.180 & 0.042 & 0.035 \\\\ %\\hline\n85 & 0.014 & 0.145 & 0.164 & 0.040 & 0.031 \\\\ %\\hline\n241 & 0.013 & 0.087 & 0.091 & 0.038 & 0.024 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Maximum Entropy competes with Maximum Likelihood", "authors": ["A. E. Allahverdyan", "N. H. Martirosyan"], "url": "https://arxiv.org/abs/2012.09430v1", "attribution": "\"Maximum Entropy competes with Maximum Likelihood\" by A. E. Allahverdyan and N. H. Martirosyan, arXiv:2012.09430v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04188v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters of the approximation settings}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c||c|c|c|c|c|c}\n\\thead{Arrival \\\\ process} & \\thead{Service \\\\ process} & \\thead{approx. \\\\ formula \\\\ $v_A$} & \\thead{approx. \\\\ formula \\\\ $v_S$} & \\thead{service rate \\\\ per route \\\\ $\\mu_r$} & \\thead{total \\\\ train count \\\\ $n_{total}$} & \\thead{quality \\\\ threshold \\\\$L_{limit}$} & \\thead{share of \\\\ main traffic \\\\ $p_{main}$} \\\\ \\hline \\hline\nM & M & 0.8 & 0.3 & 0.3 & $4, \\dots, 40$ & $0.13$ & $0.5$ \\\\ \\hline\nPh & M & 1 & 0.3 & 0.3 & $4, \\dots, 40$ & $0.13$ & $0.5$ \\\\ \\hline\nM & Ph & 0.8 & 1 & 0.3 & $4, \\dots, 40$ & $0.13$ & $0.5$ \\\\ \\hline\nPh & Ph & 1 & 1 & 0.3 & $4, \\dots, 40$ & $0.13$ & $0.5$\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Utilizing phase-type distributions for queueing-based railway junction performance determination", "authors": ["Tamme Emunds", "Nils Nießen"], "url": "https://arxiv.org/abs/2412.04188v1", "attribution": "\"Utilizing phase-type distributions for queueing-based railway junction performance determination\" by Tamme Emunds and Nils Nießen, arXiv:2412.04188v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2010.03315v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccrrr}\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\t& & \\multicolumn{3}{c}{ \\text{ Prediction }} \\\\\n\t\t\t$\\alpha$ & $w$ (hours) & 0 & 1 & 2 \\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{3}{*}{0.01} & 24 & 0.01 & \\textcolor{red}{0.15} & \\textcolor{blue}{-0.15} \\\\\n\t\t\t& 2880 & 0.01 & \\textcolor{red}{0.14} & \\textcolor{blue}{-0.14} \\\\\n\t\t\t& 4320 & 0.01 & \\textcolor{red}{0.13} & \\textcolor{blue}{-0.14} \\\\\n\t\t\t\\\\\n\t\t\t\\multirow{3}{*}{0.025} & 24 & 0.01 & \\textcolor{red}{0.15} & \\textcolor{blue}{-0.15} \\\\\n\t\t\t& 2880 & 0.01 & \\textcolor{red}{0.14} & \\textcolor{blue}{-0.14} \\\\\n\t\t\t& 4320 & 0.01 & \\textcolor{red}{0.13} & \\textcolor{blue}{-0.14} \\\\\n\t\t\t\\\\\n\t\t\t\\multirow{3}{*}{0.05} & 24 & 0.01 & \\textcolor{red}{0.15} & \\textcolor{blue}{-0.15}\\\\\n\t\t\t& 2880 & 0.01 & \\textcolor{red}{0.14} & \\textcolor{blue}{-0.14}\\\\\n\t\t\t& 4320 & 0.01 & \\textcolor{red}{0.13} & \\textcolor{blue}{-0.14} \\\\\n\t\t\t\\\\\n\t\t\t\\multirow{3}{*}{0.1} & 24 & 0.01 & \\textcolor{red}{0.15} & \\textcolor{blue}{-0.15} \\\\\n\t\t\t& 2880 & 0.01 & \\textcolor{red}{0.14} & \\textcolor{blue}{-0.14}\\\\\n\t\t\t& 4320 & 0.01 & \\textcolor{red}{0.13} & \\textcolor{blue}{-0.14}\\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Class 2 misclassification costs for different $\\operatorname{TVaR}^{\\alpha, w}$}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Tail-risk protection: Machine Learning meets modern Econometrics", "authors": ["Bruno Spilak", "Wolfgang Karl Härdle"], "url": "https://arxiv.org/abs/2010.03315v4", "attribution": "\"Tail-risk protection: Machine Learning meets modern Econometrics\" by Bruno Spilak and Wolfgang Karl Härdle, arXiv:2010.03315v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.06104v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{rotating}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small Experiment 1: $5$-way, $1$-shot, $5$-shot, and $10$-shot classification accuracies on \\textit{new repetitions with few-shot observation}. The classification on new repetitions with few-shot observation are performed by using Meta-supervised Learning approach. This table also shows a comparison between our methodology (Meta-supervised) learning and previous works where Supervised learning methodology is used.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\t\\multirow{6}[2]{*}{\\rotatebox[origin=c]{90}{\\textbf{Meta-Supervised Learning}}} \\newline \\multirow{6}[2]{*}{\\rotatebox[origin=c]{90}{\\textbf{Proposed Method}}}\n\t\t\t& \\multirow{1}[5]{*}{\\textbf{The Embedding}}\n\t\t\t& \\multicolumn{3}{c}{\\textbf{5-way Accuracy}} \\\\\n\t\t\t\\cline{3-5}\n\t\t\t&\n\t\t\t\\multirow{1}[-7]{*}{\\textbf{Module}}\n\t\t\t& \\textbf{1-shot}\n\t\t\t& \\textbf{5-shot} & \\textbf{10-shot}\n\t\t\t\\\\\n\t\t\t\\cline{2-5}\n\t\t\t&\n\t\t\t\\textbf{FC Embedding} & 72.59$\\%$ & 85.13$\\%$ & 89.26$\\%$\n\t\t\t\\\\\n\t\t\t&\n\t\t\t\\textbf{LSTM Embedding} & \\textbf{75.03}$\\%$ & 84.06$\\%$ & 88.45$\\%$\n\t\t\t\\\\\n\t\t\t&\n\t\t\t\\textbf{T-Block Embedding I} & 73.46$\\%$ & \\textbf{85.94}$\\%$ & 89.40$\\%$\n\t\t\t\\\\\n\t\t\t&\n\t\t\t\\textbf{T-Block Embedding II} & 74.89$\\%$ & 85.88$\\%$ & \\textbf{89.70}$\\%$\n\t\t\t\\\\\n\t\t\t\\hline\n\t\t\t\\multirow{7}[4]{*}{\\rotatebox[origin=c]{90}{\\textbf{Supervised Learning}}} \\newline \\multirow{6}[10]{*}{\\rotatebox[origin=c]{90}{\\textbf{Previous Works}}}\n\t\t\t& \\textbf{Previous Works}\n\t\t\t& \\multicolumn{2}{|c}{\\multirow{2}[-5]{-12cm}{\\textbf{Accuracy}}}\n\t\t\t\\\\\n\t\t\t\\cline{2-5}\n\t\t\t&\n\t\t\tWei \\textit{et al.}~ & \\multicolumn{2}{|c}{\\multirow{2}[-5]{-12cm}{\\textbf{83.70$\\%$}}}\n\t\t\t\\\\\n\t\t\t&\n\t\t\tHu et al.~ & \\multicolumn{2}{|c}{\\multirow{2}[-5]{-12cm}{82.20$\\%$}}\n\t\t\t\\\\\n\t\t\t&\n\t\t\tDing \\textit{et al.}~& \\multicolumn{2}{|c}{\\multirow{2}[-5]{-12cm}{78.86$\\%$}}\n\t\t\t\\\\\n\t\t\t&\n\t\t\tZhai \\textit{et al.}~ & \\multicolumn{2}{|c}{\\multirow{2}[-5]{-12cm}{78.71$\\%$}}\n\t\t\t\\\\\n\t\t\t&\n\t\t\tGeng \\textit{et al.}~ & \\multicolumn{2}{|c}{\\multirow{2}[-5]{-12cm}{77.80$\\%$}}\n\t\t\t\\\\\n\t\t\t&\n\t\t\tAtzori \\textit{et al.}~ & \\multicolumn{2}{|c}{\\multirow{2}[-5]{-12cm}{75.27$\\%$}}\n\t\t\t\\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography", "authors": ["Elahe Rahimian", "Soheil Zabihi", "Amir Asif", "Dario Farina", "Seyed Farokh Atashzar", "Arash Mohammadi"], "url": "https://arxiv.org/abs/2011.06104v1", "attribution": "\"FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography\" by Elahe Rahimian, Soheil Zabihi, Amir Asif, Dario Farina, Seyed Farokh Atashzar, and Arash Mohammadi, arXiv:2011.06104v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.09539v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccrrrrr} \\hline\\hline \\\\\nData / Model & True & Emp & AMH & CLA & FRA & GUM & JOE \\\\ \n\\hline \\\\\nAMH & 0.04 & 0.01 & 0.02 & 0.04 & 0.02 & 0.05 & -0.04 \\\\\nCLA & 0.25 & 0.14 & 0.12 & 0.22 & 0.22 & 0.33 & 0.19\\\\\nFRA & 0.00 & -0.07 & -0.05 & -0.01 & -0.07 & 0.05 & -0.08\\\\\nGUM &0.85 & 0.84 & 0.33 & 0.78 & 0.83 & 0.85 & 0.81\\\\\nJOE & 0.59 & 0.54 & 0.30 & 0.48 & 0.58 & 0.61 & 0.59\\\\\n\\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Simulation study 1: Bivariate data from a mixture of Archimedean copulas. True, empirical and estimated Kendall's tau values with $n=500$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian nonparametric mixtures of Archimedean copulas", "authors": ["Ruyi Pan", "Luis E. Nieto-Barajas", "Radu V. Craiu"], "url": "https://arxiv.org/abs/2412.09539v3", "attribution": "\"Bayesian nonparametric mixtures of Archimedean copulas\" by Ruyi Pan, Luis E. Nieto-Barajas, and Radu V. Craiu, arXiv:2412.09539v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.02858v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average ARI scores between 30 GEMINI-trained models and the best Deep LPM and LPM clustering according to ELBO criterion. $H$ represents the number of hidden layers in the trained model.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n GEMINI&$H$&LPM&Deep LPM\\\\\n \\midrule\n\t\t\\multirow{3}{*}{MI}&1&0.47 (0.08)&{\\bf 0.31 (0.10)}\\\\\n\t\t&2&0.31 (0.07)&0.17 (0.09)\\\\\n\t\t&3&0.29 (0.04)&0.16 (0.05)\\\\ \n \\cmidrule(lr){3-4}\n\t\t\\multirow{3}{*}{OvO Wasserstein}&1&{\\bf 0.58 (0.04)}&0.25 (0.03)\\\\\n\t\t&2&0.54 (0.06)&0.22 (0.03)\\\\\n\t\t&3&0.49 (0.06)&0.21 (0.04)\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Generalised Mutual Information: a Framework for Discriminative Clustering", "authors": ["Louis Ohl", "Pierre-Alexandre Mattei", "Charles Bouveyron", "Warith Harchaoui", "Mickaël Leclercq", "Arnaud Droit", "Frédéric Precioso"], "url": "https://arxiv.org/abs/2309.02858v1", "attribution": "\"Generalised Mutual Information: a Framework for Discriminative Clustering\" by Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron, Warith Harchaoui, Mickaël Leclercq, Arnaud Droit, and Frédéric Precioso, arXiv:2309.02858v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08461v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lll}\n\\hline\nScenario & $\\delta$ & Runtime \\\\\n\\hline\nParis & 0.1s & 13.65ms \\\\\nParis & 0.2s & 0.01ms \\\\\nNew York & 0.1s & 92.50ms \\\\\nSingapore & 0.1s & 33.33ms \\\\\nSingapore & 0.2s & 23.01ms \\\\\n\\hline\n\\end{tabular}\n\\caption{Latex default table}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Segmenting Transparent Object in the Wild with Transformer", "authors": ["Enze Xie", "Wenjia Wang", "Wenhai Wang", "Peize Sun", "Hang Xu", "Ding Liang", "Ping Luo"], "url": "https://arxiv.org/abs/2101.08461v3", "attribution": "\"Segmenting Transparent Object in the Wild with Transformer\" by Enze Xie, Wenjia Wang, Wenhai Wang, Peize Sun, Hang Xu, Ding Liang, and Ping Luo, arXiv:2101.08461v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.03504v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||cccccc||} \n \\hline\n m & $n_s$ & $n_s/m!$ & Simulated Annealing & Bubble Sort & GRASP \\\\ \n \\hline\\hline\n 9 & 400 & 0.0011 & 0.9111 & 0.9723 & 0.9822 \\\\ \n \\hline\n & 500 & 0.0014 & 0.9277 & 0.9809 & 0.9885 \\\\ \n \\hline\n & 600 & 0.0017 & 0.9403 & 0.9861 & 0.9919 \\\\ \n \\hline\n 10 & 400 & 0.0001 & 0.8856 & 0.9565 & 0.9725\\\\ \n \\hline\n & 500 & 0.0001 & 0.9098 & 0.9699 & 0.9795 \\\\ \n \\hline\n & 600 & 0.0002 & 0.9253 & 0.9773 & 0.9853 \\\\ \n \\hline\n 11 & 400 & $<0.0001$ & 0.8579 & 0.9368 & 0.9430 \\\\ \n \\hline\n & 500 & $<0.0001$ & 0.8880 & 0.9554 & 0.9608 \\\\ \n \\hline\n & 600 & $<0.0001$ & 0.9075 & 0.9663 & 0.9707 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Median Relative $D-$ Efficiencies}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Exact Designs for OofA Experiments Under a Transition-Effect Model", "authors": ["Jiayi Zheng", "Nicholas Rios"], "url": "https://arxiv.org/abs/2411.03504v1", "attribution": "\"Exact Designs for OofA Experiments Under a Transition-Effect Model\" by Jiayi Zheng and Nicholas Rios, arXiv:2411.03504v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2203.12460v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|}\n\\hline\n & (1) & (2) & (3) & (4) & (5) \\\\ \\hline\nVariables & Model 1 & Model 2 & Model 3 & Model 4 & Model 5 \\\\ \\hline\n & & & & & \\\\ \\hline\npositive & 0.12*** & 0.12*** & 0.12*** & 0.12*** & 0.11** \\\\ \\hline\n & (0.02) & (0.02) & (0.02) & (0.02) & (0.04) \\\\ \\hline\nnegative & -0.13 & -0.13 & -0.14 & -0.15 & -0.15 \\\\ \\hline\n & (0.10) & (0.10) & (0.10) & (0.10) & (0.17) \\\\ \\hline\nanxiety & 0.32 & 0.32 & 0.18 & 0.17 & -0.04 \\\\ \\hline\n & (0.18) & (0.18) & (0.19) & (0.19) & (0.32) \\\\ \\hline\nanger & 0.10 & 0.11 & 0.25 & 0.25 & 0.67* \\\\ \\hline\n & (0.19) & (0.19) & (0.20) & (0.20) & (0.32) \\\\ \\hline\nsad & -0.16 & -0.18 & -0.16 & -0.16 & -0.06 \\\\ \\hline\n & (0.15) & (0.15) & (0.15) & (0.15) & (0.26) \\\\ \\hline\ncertain & 0.05 & 0.05 & 0.05 & 0.04 & 0.06 \\\\ \\hline\n & (0.06) & (0.06) & (0.06) & (0.06) & (0.10) \\\\ \\hline\n & & & & & \\\\ \\hline\nMAR(3 m) & & YES & YES & YES & YES \\\\ \\hline\nIndustry & & & YES & YES & YES \\\\ \\hline\nYear & & & & YES & YES \\\\ \\hline\nMAR(5 d) & & & & & YES \\\\ \\hline\nN & 28,068 & 28,068 & 28,068 & 28,068 & 10,632 \\\\ \\hline\nBIC & 38904 & 38943 & 39002 & 39038 & 14941 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Logistic Regression of Value Based Label Function ($y_I(T^c_d)(3)$) on Analysts' Recommendations within 3 months ($MAR(3\\,m)$) and Sentiment of Earnings Calls Transcripts. Standard errors in parentheses. Significance levels marked with stars: *** p<0.001, ** p<0.01, * p<0.05}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An Exploratory Study of Stock Price Movements from Earnings Calls", "authors": ["Sourav Medya", "Mohammad Rasoolinejad", "Yang Yang", "Brian Uzzi"], "url": "https://arxiv.org/abs/2203.12460v1", "attribution": "\"An Exploratory Study of Stock Price Movements from Earnings Calls\" by Sourav Medya, Mohammad Rasoolinejad, Yang Yang, and Brian Uzzi, arXiv:2203.12460v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.15560v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc} % \n \\toprule\n \\textbf{Step} & \\textbf{Predicted Class} \\\\\n \\midrule\n Window 1 & 8 \\\\\n Window 2 & 0 \\\\\n Window 3 & 7 \\\\\n \\cmidrule(lr){1-2} % \n \\textbf{Final Prediction} & 8 \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Majority method}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings", "authors": ["Taketo Akama", "Zhuohao Zhang", "Pengcheng Li", "Kotaro Hongo", "Hiroaki Kitano", "Shun Minamikawa", "Natalia Polouliakh"], "url": "https://arxiv.org/abs/2412.15560v1", "attribution": "\"Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings\" by Taketo Akama, Zhuohao Zhang, Pengcheng Li, Kotaro Hongo, Hiroaki Kitano, Shun Minamikawa, and Natalia Polouliakh, arXiv:2412.15560v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16534v2_tex_table19.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The performance of \\textbf{large-sized regression} task (\\emph{heterogeneous features}).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\n & \\textbf{361104} & \\textbf{361095} & \\textbf{361096} & \\textbf{361101} & \\textbf{361103} & \\textbf{Ranking} \\\\\n\\midrule\n\\multicolumn{7}{c}{Default} \\\\\n\\midrule\nDOFEN (ours) & 0.9998 & 0.6120 & 0.9923 & 0.5288 & 0.6823 & $4.00 \\pm 2.48$ \\\\\nTrompt & 0.9996 & 0.6097 & 0.9917 & 0.4035 & 0.7048 & $6.20 \\pm 1.86$ \\\\\nGRANDE & 0.9153 & 0.4275 & 0.9011 & 0.3544 & 0.6370 & $10.00 \\pm 3.99$ \\\\\nFT-Transformer & 0.9994 & 0.3514 & 0.9923 & 0.4061 & 0.6761 & $7.40 \\pm 2.79$ \\\\\nResNet & 0.9895 & 0.3370 & 0.9816 & 0.3971 & 0.6660 & $9.80 \\pm 3.27$ \\\\\nMLP & nan & nan & nan & nan & nan & $nan \\pm nan$ \\\\\nSAINT & 0.9997 & 0.3891 & 0.9918 & 0.5480 & 0.6874 & $5.80 \\pm 1.79$ \\\\\nNODE & 0.9997 & 0.4156 & 0.9875 & 0.0198 & 0.6626 & $8.80 \\pm 2.79$ \\\\\nCatBoost & 0.9998 & 0.6332 & \\underline{0.9928} & 0.6050 & \\underline{0.7068} & $2.00 \\pm 3.44$ \\\\\nLightGBM & 0.9998 & 0.6324 & 0.9916 & 0.5769 & 0.7037 & $4.00 \\pm 2.40$ \\\\\nXGBoost & \\underline{0.9998} & \\underline{0.6345} & 0.9922 & \\underline{0.6244} & 0.7060 & $1.80 \\pm 3.93$ \\\\\nHistGradientBoostingTree & nan & nan & nan & nan & nan & $nan \\pm nan$ \\\\\nGradientBoostingTree & 0.9998 & 0.6165 & 0.9857 & 0.4809 & 0.6773 & $6.20 \\pm 1.74$ \\\\\nRandomForest & nan & nan & nan & nan & nan & $nan \\pm nan$ \\\\\n\\midrule\n\\multicolumn{7}{c}{Searched} \\\\\n\\midrule\nDOFEN (ours) & 0.9998 & 0.6251 & \\underline{0.9937} & 0.5912 & 0.6980 & $4.00 \\pm 3.06$ \\\\\nTrompt & 0.9998 & 0.6286 & 0.9918 & 0.5479 & 0.7073 & $7.00 \\pm 2.34$ \\\\\nGRANDE & 0.9158 & 0.4348 & 0.9010 & 0.3899 & 0.6421 & $10.40 \\pm 4.02$ \\\\\nFT-Transformer & 0.9998 & 0.3899 & 0.9925 & 0.5694 & 0.7047 & $7.40 \\pm 2.64$ \\\\\nResNet & 0.9998 & 0.3936 & 0.9922 & 0.5390 & 0.6865 & $8.00 \\pm 2.71$ \\\\\nMLP & nan & nan & nan & nan & nan & $nan \\pm nan$ \\\\\nSAINT & 0.9998 & 0.3951 & 0.9925 & 0.5662 & 0.6975 & $6.40 \\pm 2.07$ \\\\\nNODE & 0.9998 & 0.5908 & 0.9918 & 0.4135 & 0.6668 & $8.20 \\pm 2.99$ \\\\\nCatBoost & 0.9998 & 0.6363 & 0.9932 & 0.6262 & 0.7117 & $3.20 \\pm 3.39$ \\\\\nLightGBM & 0.9998 & 0.6324 & 0.9924 & 0.5769 & 0.7091 & $5.40 \\pm 2.66$ \\\\\nXGBoost & \\underline{0.9998} & \\underline{0.6387} & 0.9932 & \\underline{0.6472} & \\underline{0.7122} & $1.40 \\pm 4.00$ \\\\\nHistGradientBoostingTree & nan & nan & nan & nan & nan & $nan \\pm nan$ \\\\\nGradientBoostingTree & 0.9998 & 0.6301 & 0.9918 & 0.6356 & 0.7057 & $4.60 \\pm 2.79$ \\\\\nRandomForest & nan & nan & nan & nan & nan & $nan \\pm nan$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DOFEN: Deep Oblivious Forest ENsemble", "authors": ["Kuan-Yu Chen", "Ping-Han Chiang", "Hsin-Rung Chou", "Chih-Sheng Chen", "Tien-Hao Chang"], "url": "https://arxiv.org/abs/2412.16534v2", "attribution": "\"DOFEN: Deep Oblivious Forest ENsemble\" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13382v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\n\\multicolumn{7}{|c|}{$G(250,p)$} \\\\\n\\hline\n$p$ & $\\varepsilon$ & $|E|$ & $K'''$ & $K$ & Relative Error & \\% $|E|$ Variation \\\\\n\\hline\n0.90 & 0.5 & 20772&249.02 & 248.11 & 0.003 & 70.373 \\\\\n0.90 & 1.0 &8832 &251.80 & 248.11 & 0.012 & 88.805 \\\\\n0.90 & 1.5 & 4430&256.66 & 248.11 & 0.028 & 94.647 \\\\\n0.90 & 2.0 & 2599&264.56 & 248.11 & 0.053 & 96.824 \\\\\n0.70 & 0.5 & 17649&249.36 & 248.42 & 0.003 & 64.342 \\\\\n0.70 & 1.0 &8315 &252.12 & 248.42 & 0.012 & 83.052 \\\\\n0.70 & 1.5 & 4268&257.36 & 248.42 & 0.029 & 91.732 \\\\\n0.70 & 2.0 &2583 &264.03 & 248.42 & 0.053 & 95.029 \\\\\n0.50 & 0.5 & 13770&249.94 & 248.98 & 0.003 & 58.341 \\\\\n0.50 & 1.0 &7493 &252.77 & 248.98 & 0.012 & 77.621 \\\\\n0.50 & 1.5 & 4083&258.47 & 248.98 & 0.028 & 89.517 \\\\\n0.50 & 2.0 &2469 &265.49 & 248.98 & 0.052 & 94.482 \\\\\n0.05 & 0.5 & 1598&270.53 & 269.19 & 0.003 & 0.275 \\\\\n0.05 & 1.0 &1571 &273.95 & 269.19 & 0.013 & 5.021 \\\\\n0.05 & 1.5 & 1416&279.57 & 269.19 & 0.028 & 20.912 \\\\\n0.05 & 2.0 & 1202&289.00 & 269.19 & 0.054 & 39.473 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Sparse approximations for different values of $\\epsilon$ on $G(250,p)$, $p=0.9, 0.7, 0.5,0.05$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Spectral Approach to Kemeny's Constant", "authors": ["Aida Abiad", "Ángeles Carmona", "Andrés M. Encinas", "Maria José Jiménez", "Álvaro Samperio"], "url": "https://arxiv.org/abs/2503.13382v1", "attribution": "\"A Spectral Approach to Kemeny's Constant\" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14699v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\n \\hline \n software & OpenFOAM v10 \\\\\n \\hline\\hline\n Solver setting \\\\\n \\hline\n solver, $p$ & PCG \\\\ \n preconditioner, $p$ & DIC\\\\ \n solver, $U$, $\\tilde{\\nu}$ & PBiCG \\\\ \n preconditioner, $U$, $\\tilde{\\nu}$ & DILU\\\\ \n tolerance & $1e^{-10}$\\\\ \n residual & $1e^{-7}$\\\\ \n relaxation factor & 0.3 \\\\ \n \\hline\n Numerical scheme setting \\\\\n \\hline\n time scheme & steadyState\\\\ \n gradient scheme & Gauss linear \\\\ \n divergence scheme & linear \\\\ \n div(phi,U) & bounded Gauss linearUpwind grad(U)\\\\ \n div(phi,nuTilda) & bounded Gauss upwind\\\\ \n div((nuEff*dev2(T(grad(U))))) & Gauss linear\\\\ \n Laplacian scheme & Gauss linear corrected\\\\ \n interpolation scheme & linear \\\\ \n surface normal gradient scheme & corrected\\\\ \n wall distance scheme & meshWave \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Solver settings used in NACA0012 demonstration.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Acceleration of RANS Solver Convergence via Initialization with Wake Extension Models", "authors": ["Kazuko W. Fuchi", "Eric M. Wolf", "Christopher R. Schrock", "Philip S. Beran"], "url": "https://arxiv.org/abs/2501.14699v1", "attribution": "\"Acceleration of RANS Solver Convergence via Initialization with Wake Extension Models\" by Kazuko W. Fuchi, Eric M. Wolf, Christopher R. Schrock, and Philip S. Beran, arXiv:2501.14699v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08442v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{AdaRound on DeepLabv3 Semantic Segmentation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n \\toprule\n Configuration & mIOU (Mean Intersection over Union)\\\\\n \\midrule\n FP32 & 72.94\\% \\\\\n Nearest Rounding (W4A8) & 6.09\\% \\\\\n AdaRound (W4A8)& 70.86\\% \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)", "authors": ["Sangeetha Siddegowda", "Marios Fournarakis", "Markus Nagel", "Tijmen Blankevoort", "Chirag Patel", "Abhijit Khobare"], "url": "https://arxiv.org/abs/2201.08442v1", "attribution": "\"Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)\" by Sangeetha Siddegowda, Marios Fournarakis, Markus Nagel, Tijmen Blankevoort, Chirag Patel, and Abhijit Khobare, arXiv:2201.08442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11014v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|} \n \\hline\n prompt & S/S & B/S & S/B & B/B \\\\ \n \\hline\n \\textcolor{red}{\"a red word label over a picture of a X\"} & 87.69 \\% & 44.16 \\% & 29.64 \\% & 66.23 \\% \\\\\n \\hline\n \\textcolor{red}{\"a word is printed in a red font over a picture of a X\"} & 91.34 \\% & 44.86 \\% & 35.29 \\% & 69.72 \\% \\\\\n \\hline\n \\textcolor{red}{\"a photo of a word written in a red font over a picture of a X\"} & 86.53 \\% & 43.07 \\% & 30.78 \\% & 62.69 \\% \\\\\n \\hline\n \\textcolor{blue}{\"a text that says X\"} & 96.78 \\% & 52.50 \\% & 54.58 \\% & 88.22 \\% \\\\\n \\hline\n \\textcolor{blue}{\"a word of a X is printed in a red font over a picture\"} & 95.48 \\% & 51.65 \\% & 46.28 \\% & 78.02 \\% \\\\\n \\hline\n \\textcolor{blue}{\"a photo of the word X written in a red font over a picture\"} & 96.22 \\% & 53.58 \\% & 42.91 \\% & 85.05 \\% \\\\\n \\hline\n \\textcolor{orange}{\"a photo of the word Y written in a red font over a picture of a X\"} & 34.34 \\% & 35.33 \\% & 31.04 \\% & 34.92 \\% \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{CLIP's label switching rate for word-superimposed images on different prompts. The red-colored texts are the prompts that specify that the image content is what should be predicted. The blue-colored texts are the prompts that specify that the superimposed word is what should be predicted. The X means the label to be answered by the model. The orange-colored text is the condition in which a prompt varies according to the superimposed word on each image.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Language-biased image classification: evaluation based on semantic representations", "authors": ["Yoann Lemesle", "Masataka Sawayama", "Guillermo Valle-Perez", "Maxime Adolphe", "Hélène Sauzéon", "Pierre-Yves Oudeyer"], "url": "https://arxiv.org/abs/2201.11014v2", "attribution": "\"Language-biased image classification: evaluation based on semantic representations\" by Yoann Lemesle, Masataka Sawayama, Guillermo Valle-Perez, Maxime Adolphe, Hélène Sauzéon, and Pierre-Yves Oudeyer, arXiv:2201.11014v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10459v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of parameters used to represent the bicycle frame design space.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|l}\n\\cline{1-3}\n\\textbf{Parameter Type} & \\textbf{Data Type} & \\textbf{Count} & \\\\ \\cline{1-3}\nFrame Geometry Relations & Continuous & 18 & \\\\ \\cline{1-3}\nTube Outer Diameters & Continuous & 9 & \\\\ \\cline{1-3}\nTube Thicknesses & Continuous & 7 & \\\\ \\cline{1-3}\nFrame Material & Categorical & 1 & \\\\ \\cline{1-3}\nSeat/Chain Stay Bridge Flags & Boolean & 2 & \\\\ \\cline{1-3}\n\\textbf{Total} & & \\textbf{37} & \\\\ \\cline{1-3}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames", "authors": ["Lyle Regenwetter", "Colin Weaver", "Faez Ahmed"], "url": "https://arxiv.org/abs/2201.10459v3", "attribution": "\"FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames\" by Lyle Regenwetter, Colin Weaver, and Faez Ahmed, arXiv:2201.10459v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.10009v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameter values assumed for the true kinetic model}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n \\hline\t\t\t\n $Parameter$ & Value \\\\\n \\hline\t\t\t\n $k_1^0 (\\text{min}^{-1})$ & 8 \\\\\n $E_{\\text{a}1} (\\text{kJ/mol})$ & 29 \\\\\n $k_2^0 (\\text{min}^{-1})$ & 5 \\\\\n $E_{\\text{a}2} (\\text{kJ/mol})$ & 35 \\\\\n $k_3^0 (\\text{min}^{-1})$ & 3 \\\\\n $E_{\\text{a}3} (\\text{kJ/mol})$ & 32\\\\\n \\hline\t\t\t\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Safe model-based design of experiments using Gaussian processes", "authors": ["Panagiotis Petsagkourakis", "Federico Galvanin"], "url": "https://arxiv.org/abs/2011.10009v2", "attribution": "\"Safe model-based design of experiments using Gaussian processes\" by Panagiotis Petsagkourakis and Federico Galvanin, arXiv:2011.10009v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.07157v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison with benchmark ($f$ known) and other baseline methods dealing with unknown $f$, $\\alpha=1\\%$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\n \\toprule\n Method & Average Detection Delay & False Alarm Rate(\\%) \\\\\n \\midrule\n \\midrule\n $f$ known & \\multirow{2}{*}{$3.67\\pm 0.65$} & \\multirow{2}{*}{$0.05\\pm 0.03$}\\\\\n (benchmark) & & \\\\\n \\midrule\n PGD & ${\\bf 4.12}\\pm 0.83$ & ${\\bf 1.06}\\pm 0.37$\\\\\n MLE & $4.56\\pm 0.77$ & $38.2\\pm 7.39$ \\\\\n GLRT & $5.03\\pm 1.17$ & $7.52\\pm 1.01$ \\\\\n Shewhart & $4.78\\pm 0.84$ & $3.84\\pm 0.95$ \\\\\n \\midrule\n BGS & $4.85\\pm 0.81$ & $4.95\\pm 0.81$ \\\\\n ULR & $4.69\\pm 1.01$ & $3.16\\pm 0.74$ \\\\\n DIS & $5.03\\pm 0.95$ & $4.28\\pm 0.91$ \\\\\n DCQ & $4.77\\pm 1.22$ & $5.16\\pm 1.08$ \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee", "authors": ["Chenhan Xiao", "Yizheng Liao", "Yang Weng"], "url": "https://arxiv.org/abs/2309.07157v1", "attribution": "\"Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee\" by Chenhan Xiao, Yizheng Liao, and Yang Weng, arXiv:2309.07157v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16730v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overview of model architectures, parameters, and pre-training datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcl}\n\\toprule\n\\textbf{Model Name} & \\textbf{Backbone} & \\textbf{Param} & \\textbf{Pre-training Dataset} \\\\\n\\midrule\nMetaFormer & CAFormer & 27.5M & ImageNet \\\\\nMetaFormer\\_in21k & CAFormer & 27.5M & ImageNet21k \\\\\nSurgeNet\\_Cholec \n & CAFormer & 27.5M & Cholecystectomy collection \\\\\nSurgeNet\\_Public & CAFormer & 27.5M & Public surgical videos \\\\\nSurgeNet\\_XL & CAFormer & 27.5M & Public \\& inhouse surgical videos \\\\\nTimeSformer & ViT Base & 121.4M & Kinetics-400 dataset \\\\\nConvNeXtV2 & ConvNeXtV2 & 31.9M & ImageNet \\\\\nSurgeNet\\_ConvNeXtV2 & ConvNeXtV2 & 31.9M & Public \\& inhouse surgical videos \\\\\nPVTv2 & PVTv2 & 28.0M & ImageNet \\\\\nSurgeNet\\_PVTv2 & PVTv2 & 28.0M & Public \\& inhouse surgical videos \\\\\nGastroNet\\_RN50 & ResNet-50 & 28.8M & Inhouse endoscopic videos \\\\\nGastroNet\\_ViTS & ViT Small & 23.5M & Inhouse endoscopic videos \\\\\nEndoFM & ViT Base & 123.9M & Large-scale surgical dataset \\\\\nGSViT & ViT Base & 89.2M & Large-scale surgical dataset \\\\\nEndoViT & ViT variant & 13.9M & Large-scale surgical dataset \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Analysis of Transferability Estimation Metrics for Surgical Phase Recognition", "authors": ["Prabhant Singh", "Yiping Li", "Yasmina Al Khalil"], "url": "https://arxiv.org/abs/2508.16730v1", "attribution": "\"Analysis of Transferability Estimation Metrics for Surgical Phase Recognition\" by Prabhant Singh, Yiping Li, and Yasmina Al Khalil, arXiv:2508.16730v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.03024v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Gender-dependent EER (\\%) comparison between the speaker embedding vectors extracted from the x-vector-based embedding systems and the state-of-the-art i-vector-based systems.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n\t\\hline\n\t\\multirow{2}{*}{Methods} & \\multicolumn{2}{c|}{EER [\\%]} \\\\ \\cline{2-3} \n\t& Male & Female \\\\ \\hline\n\t\\textit{x-vector (Softmax)} & 2.09 & 2.48 \\\\ \\hline\n\t\\textit{DNN i-vectors} & 1.70 & 2.69 \\\\\n\t\\textit{Uncertainty normalized HMM/i-vector} & 1.52 & 1.77 \\\\ \\hline\n\t\\textit{x-vector (GRL)} & 3.75 & 4.17 \\\\\n\t\\textit{x-vector (Anti-loss)} & 1.25 & 1.66 \\\\ \\hline\n\t\\textit{\\textbf{x-vector (JFE)}} & \\textbf{0.82} & \\textbf{1.29} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Disentangled speaker and nuisance attribute embedding for robust speaker verification", "authors": ["Woo Hyun Kang", "Sung Hwan Mun", "Min Hyun Han", "Nam Soo Kim"], "url": "https://arxiv.org/abs/2008.03024v1", "attribution": "\"Disentangled speaker and nuisance attribute embedding for robust speaker verification\" by Woo Hyun Kang, Sung Hwan Mun, Min Hyun Han, and Nam Soo Kim, arXiv:2008.03024v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2302.00586v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{China Stock 2018}\n\\begin{tabular}{ccccccccc}\n\\toprule\n Metrics & A2C & PPO & SAC & SARL & DeepTrader & EIIE & IMIT & AlphaMix+ \\\\\n\\midrule\nTR(\\%) &-6.86$\\pm$11.30 &-6.56$\\pm$3.66 &-4.05$\\pm$10.77 &-5.77$\\pm$4.04 & 5.67$\\pm$5.95 &-2.27$\\pm$1.99 & -7.14$\\pm$ 0.82 & -0.70$\\pm$1.38\\\\\nSR &-0.28$\\pm$0.61 &-0.31$\\pm$0.22 &-0.12$\\pm$0.50 &-0.25$\\pm$0.21 &0.37$\\pm$0.30 & -0.05$\\pm$ 0.12 & -0.34$\\pm$0.1 &0.03$\\pm$0.08 \\\\\nCR &-0.14$\\pm$0.50 &-0.25$\\pm$0.16 &-0.06$\\pm$0.42 &-0.20$\\pm$0.15 &0.40$\\pm$0.32 & -0.04$\\pm$0.11& -0.29$\\pm$0.07 &0.04$\\pm$0.10 \\\\\nSoR &-0.33$\\pm$0.78 &-0.41$\\pm$0.26 &-0.15$\\pm$0.75 &-0.35$\\pm$0.29 &0.54$\\pm$0.45 & -0.07$\\pm$ 0.17 & -0.37$\\pm$0.08 &0.04$\\pm$0.11 \\\\\nMDD(\\%) &22.3$\\pm$5.52 &20.5$\\pm$1.55 &25.9$\\pm$4.95 & 20.2$\\pm$3.49 &19.1$\\pm$3.39&17.5$\\pm$1.52&20.4$\\pm$0.63 & 16.1$\\pm$1.45 \\\\\nVOL(\\%) &0.67$\\pm$0.03 &0.59$\\pm$0.01 &0.75$\\pm$0.03 & 0.61$\\pm$0.03 &0.66$\\pm$0.02 &0.59$\\pm$ 0.03 & 0.77$\\pm$0.06 & 0.57$\\pm$0.02 \\\\\nENT &1.54$\\pm$0.01 &2.85$\\pm$0.007 &1.01$\\pm$0.03 & 2.41$\\pm$0.40 &1.53$\\pm$0.007 & 2.55$\\pm$0.15 &1.30$\\pm$ 0.85 &3.12$\\pm$0.02 \\\\\nENB &1.31$\\pm$0.01 &1.08$\\pm$0.005 & 1.57$\\pm$0.009 &1.11$\\pm$0.10 & 1.30$\\pm$0.009 &1.05$\\pm$ 0.01 &1.19 $\\pm$0.08 &1.03$\\pm$0.003 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets", "authors": ["Shuo Sun", "Molei Qin", "Xinrun Wang", "Bo An"], "url": "https://arxiv.org/abs/2302.00586v2", "attribution": "\"PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets\" by Shuo Sun, Molei Qin, Xinrun Wang, and Bo An, arXiv:2302.00586v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.00209v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The numerical error for different $\\delta$ by three methods}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|cccc||}\n\t \\hline\n\t & $\\delta=1e-4$ & $\\delta=1e-3$ &$\\delta=1e-2$&$\\delta=1e-1$\\\\\n\t \\hline\n\t Iterated Tikhonov & 0.2692 & 0.1035&0.0648&0.0051 \\\\\n\t \n\t Landweber & 0.8654 & 0.1598 &0.1336&0.1370\\\\\n\t \n\t New iterated Tikhonov & 0.2418 & 0.0734 &0.0222&0.0046\\\\\n\t \\hline\n\t \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A new iterated Tikhonov regularization method for Fredholm integral equation of first kind", "authors": ["Xiaowei Pang", "Jun Wang"], "url": "https://arxiv.org/abs/2504.00209v1", "attribution": "\"A new iterated Tikhonov regularization method for Fredholm integral equation of first kind\" by Xiaowei Pang and Jun Wang, arXiv:2504.00209v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.14946v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n & & \\textbf{MAR 1} & \\textbf{MAR 2} & \\textbf{MNAR 1} & \\textbf{MNAR 2} \\\\\n \\hline\n \\multirow{4}{*}{\\textbf{Data Model}} & $n$ & \\multicolumn{4}{c|}{$2000 \\quad (n_{train} = 1500, n_{test} = 500)$} \\\\\n \\cline{2-6}\n & $p$ & \\multicolumn{4}{c|}{$2$} \\\\\n \\cline{2-6}\n & $q$ & \\multicolumn{4}{c|}{$5$} \\\\\n \\cline{2-6}\n & \\#Data trees & \\multicolumn{4}{c|}{$8$} \\\\\n \\hline\n \\multirow{4}{*}{\\textbf{Missingness Model}} & Model & Probit Regression & Probit BART & Probit Regression & Probit BART \\\\\n \\cline{2-6}\n & Model covariates & X only & X only & Y only & Y only \\\\\n \\cline{2-6}\n & \\#Missing trees & - & 3 & - & 5 \\\\\n \\cline{2-6}\n & Observed proportion & (57.10\\%, 78.05\\%) & (86.50\\% 56.10\\%) & (66.00\\%, 50.45\\%) & (58.05\\%, 75.15\\%) \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Simulation recipes for the bivariate simulation studies. The complete dataset is consistent across all $4$ scenarios, while the missingness model follows a multivariate probit regression or probit BART model.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Joint Models for Handling Non-Ignorable Missing Data using Bayesian Additive Regression Trees: Application to Leaf Photosynthetic Traits Data", "authors": ["Yong Chen Goh", "Wuu Kuang Soh", "Andrew C. Parnell", "Keefe Murphy"], "url": "https://arxiv.org/abs/2412.14946v1", "attribution": "\"Joint Models for Handling Non-Ignorable Missing Data using Bayesian Additive Regression Trees: Application to Leaf Photosynthetic Traits Data\" by Yong Chen Goh, Wuu Kuang Soh, Andrew C. Parnell, and Keefe Murphy, arXiv:2412.14946v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.16984v10_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rlll}\n \\toprule\n & Apa-AA-P (n=418) & AA-P (n=421) & All (n=839) \\\\\n \\midrule\n Age \\\\\n Median (IQR) & 71.0 (66.0-78.0) & 71.0 (65.0-77.0) & 71.0 (66.0-77.0) \\\\\n Percent missing & 0.0\\% & 0.0\\% & 0.0\\% \\\\\n \\midrule\n Body Mass Index (BMI) \\\\\n Median (IQR) & 27.5 (24.9-30.9) & 27.9 (25.1-31.3) & 27.72 (24.9-31.14) \\\\\n Percent missing & 2.1\\% & 2.9\\% & 2.5\\% \\\\\n \\midrule\n Performance Status (ECOG) \\\\\n 0 & 290 (69.4\\%) & 299 (71.0\\%) & 589 (70.2\\%) \\\\\n 1 & 128 (30.6\\%) & 122 (29.0\\%) & 250 (29.8\\%) \\\\\n 2 & 0 (0\\%) & 0 (0\\%) & 0 (0\\%) \\\\\n Percent missing & 0 (0\\%) & 0 (0\\%) & 0 (0\\%) \\\\\n \\midrule\n Brief Pain Inventory (BPI) score \\\\\n $\\leq$ 1 & 317 (75.8\\%) & 295 (70.1\\%) & 612 (72.9\\%) \\\\\n $>$ 1 & 95 (22.7\\%) & 118 (28.0\\%) & 213 (25.4\\%) \\\\\n Percent missing & 6 (1.4\\%) & 8 (1.9\\%) & 14 (1.7\\%) \\\\\n \\midrule\n Bone metastasis only \\\\\n True & 207 (49.5\\%) & 205 (48.7\\%) & 412 (49.1\\%) \\\\\n False & 211 (50.5\\%) & 216 (51.3\\%) & 427 (50.9\\%) \\\\\n Percent missing & 0 (0.0\\%) & 0 (0.0\\%) & 0 (0.0\\%) \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Baseline characteristics of NCT02257736. Source data are provided as a Source Data file.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings", "authors": ["Jean Ogier du Terrail", "Quentin Klopfenstein", "Honghao Li", "Imke Mayer", "Nicolas Loiseau", "Mohammad Hallal", "Michael Debouver", "Thibault Camalon", "Thibault Fouqueray", "Jorge Arellano Castro", "Zahia Yanes", "Laëtitia Dahan", "Julien Taïeb", "Pierre Laurent-Puig", "Jean-Baptiste Bachet", "Shulin Zhao", "Remy Nicolle", "Jérome Cros", "Daniel Gonzalez", "Robert Carreras-Torres", "Adelaida Garcia Velasco", "Kawther Abdilleh", "Sudheer Doss", "Félix Balazard", "Mathieu Andreux"], "url": "https://arxiv.org/abs/2311.16984v10", "attribution": "\"FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings\" by Jean Ogier du Terrail, Quentin Klopfenstein, Honghao Li, Imke Mayer, Nicolas Loiseau, Mohammad Hallal, Michael Debouver, Thibault Camalon, Thibault Fouqueray, Jorge Arellano Castro, Zahia Yanes, Laëtitia Dahan, Julien Taïeb, Pierre Laurent-Puig, Jean-Baptiste Bachet, Shulin Zhao, Remy Nicolle, Jérome Cros, Daniel Gonzalez, Robert Carreras-Torres, Adelaida Garcia Velasco, Kawther Abdilleh, Sudheer Doss, Félix Balazard, and Mathieu Andreux, arXiv:2311.16984v10, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.15826v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccccc}\n Parameter & $\\gamma_0$ &\n $\\gamma_1$ & \n $\\gamma_{12}^0$ & \n $\\gamma_{13}^0$ & \\\n $\\gamma_{23}^0$ & \n $\\gamma_{12}^1$ & \n $\\gamma_{13}^1$ & \n $\\gamma_{23}^1$ & \n $\\theta_{23}^0$ & \n $\\theta_{23}^1$ \\\\ \\hline\n Posterior Mean & -0.036 & 0.076 & 0.018 & 0.018 & 0.172 & 0.013 & 0.015 & 0.266 & 0.097 & 0.035 \\\\\n \n SE & 0.059 & 0.030 & 0.002 & 0.002 & 0.180 & 0.002 & 0.002 & 0.371 & 0.248 & 0.243 \\\\\n \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Parameter estimates for the prostate cancer data example. The posterior mean and estimated standard error are shown for each parameter. All $\\alpha_{jk}$ and $\\kappa_{jk}$ are set to 1.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Surrogacy Validation for Time-to-Event Outcomes with Illness-Death Frailty Models", "authors": ["Emily K. Roberts", "Michael R. Elliott", "Jeremy M. G. Taylor"], "url": "https://arxiv.org/abs/2211.15826v1", "attribution": "\"Surrogacy Validation for Time-to-Event Outcomes with Illness-Death Frailty Models\" by Emily K. Roberts, Michael R. Elliott, and Jeremy M. G. Taylor, arXiv:2211.15826v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.03222v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|cc|cc|}\n\t\t\\toprule\n\t\t & \\multicolumn{2}{c|}{Standard} & \\multicolumn{2}{c|}{BMS} & \\multicolumn{2}{c|}{Standard} & \\multicolumn{2}{c|}{BMS}\\\\\nParameter & Poisson & Gamma& Poisson& Gamma &CPG & Tweedie& CPG & Tweedie\\\\\n\t\t\t\t \\midrule\n$\\beta_0$ &-4.409& 8.770&-13.541& 6.227& 4.361& 4.330& -7.314& -6.626\\\\\n$\\beta_1$ & 0.135& 0.067& 0.121& 0.062& 0.202& 0.207& 0.183& 0.189\\\\\n$\\beta_2$ & 0.031&-0.078& 0.044&-0.068 & -0.047& -0.047& -0.024& -0.032\\\\\n$\\beta_3$ &-0.154& 0.000& -0.144& - & -0.154& -0.157& -0.144& -0.145\\\\\n$\\beta_4$ & 0.192& - & 0.220& 0.005& 0.192& 0.187& 0.224& 0.225\\\\\n$\\beta_5$ & 0.474& 0.109& 0.405& 0.086& 0.583& 0.587& 0.491& 0.505\\\\\n$\\beta_6$ & 0.548& 0.186& 0.528& 0.184& 0.735& 0.735& 0.711& 0.716\\\\\n$\\beta_7$ & 0.231& 0.098& 0.188& 0.085& 0.329& 0.342& 0.273& 0.283\\\\\n$\\beta_8$ &-0.128&-0.150& -0.054&-0.113& 0.199& 0.238& 0.160& 0.176\\\\\n$\\beta_9$ & 0.181& 0.018& 0.160& - & 0.199& 0.238& 0.160& 0.176\\\\\n$\\beta_{10}$ & 0.343& 0.104& 0.316& 0.084& 0.447& 0.486& 0.400& 0.417\\\\\n$\\beta_{11}$ & 0.378& 0.160& 0.347& 0.136& 0.538& 0.576& 0.482& 0.499 \\\\\n$\\beta_{12}$ & 0.223& 0.190& 0.191& 0.165& 0.413& 0.450& 0.356& 0.371\\\\\n$\\beta_{13}$ &-0.095& 0.190& -0.151& 0.160& 0.095& 0.130& 0.009& 0.189\\\\\n$\\beta_{14}$ & 0.106& 0.150& 0.042& 0.126& 0.256& 0.298& 0.168& - \\\\\n$\\beta_{15}$ & - & - & 0.094& 0.026& - & - & 0.094& 0.112\\\\\n$\\beta_{16}$ &&&&& - & - & 0.026& -\\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Estimated parameters}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bonus-Malus scale premiums for tweedie's compound poisson models", "authors": ["Jean-Philippe Boucher", "Raïssa Coulibaly"], "url": "https://arxiv.org/abs/2311.03222v1", "attribution": "\"Bonus-Malus scale premiums for tweedie's compound poisson models\" by Jean-Philippe Boucher and Raïssa Coulibaly, arXiv:2311.03222v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.02687v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Test accuracy (\\%) of DGI in standard contrastive correspondence (Std) and disordered correspondence (Dis).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccc}\n \\toprule\n Method & Contrast & Cora & CiteSeer & PubMed \\\\\n \\midrule \n \\multirow{2}*{DGI} & Std. & 83.38 $\\pm$ 2.68 & 72.07 $\\pm$ 2.37 & 84.77 $\\pm$ 0.71 \\\\\n \\cmidrule(r){2-5}\n ~ & Dis. & 83.35 $\\pm$ 2.68 & 72.04 $\\pm$ 2.17 & 84.70 $\\pm$ 0.68 \\\\\n \\bottomrule \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning", "authors": ["Xiaojun Guo", "Yifei Wang", "Zeming Wei", "Yisen Wang"], "url": "https://arxiv.org/abs/2311.02687v1", "attribution": "\"Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning\" by Xiaojun Guo, Yifei Wang, Zeming Wei, and Yisen Wang, arXiv:2311.02687v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10148v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rlrrrrrr}\n \\hline\n & & fields & mean & mean (control) & sd & max & min \\\\ \n \\hline\n & Max Accuracy & 2840 & 0.84 & 0.87 & 0.36 & 1.00 & 0.00 \\\\ \n & Balanced Accuracy & 2840 & 0.63 & 0.67 & 0.48 & 1.00 & 0.00 \\\\ \n & Continuous & 2840 & 0.71 & 0.73 & 0.16 & 0.99 & 0.02 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Detecting Crop Burning in India using Satellite Data", "authors": ["Kendra Walker", "Ben Moscona", "Kelsey Jack", "Seema Jayachandran", "Namrata Kala", "Rohini Pande", "Jiani Xue", "Marshall Burke"], "url": "https://arxiv.org/abs/2209.10148v1", "attribution": "\"Detecting Crop Burning in India using Satellite Data\" by Kendra Walker, Ben Moscona, Kelsey Jack, Seema Jayachandran, Namrata Kala, Rohini Pande, Jiani Xue, and Marshall Burke, arXiv:2209.10148v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.05531v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the standard error and its estimators. The column labeled emp.se shows the empirical standard deviation of the ACE estimators over 2000 simulated replicates, eif.se shows the average of the standard error estimates based on only the efficient influence function, and infl.se shows the average of the standard error estimates that account for estimation of the propensity score and outcome models. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrr}\n \\hline\nsetting & type & emp.se & eif.se & infl.se \\\\ \n \\hline\nlinear & wrong outcome right weights & 0.067 & 0.110 & 0.064 \\\\ \n linear & right outcome wrong weights & 0.047 & 0.045 & 0.045 \\\\ \n linear & wrong both & 0.084 & 0.085 & 0.084 \\\\ \n linear & right both & 0.049 & 0.049 & 0.049 \\\\ \n log\\_poisson & wrong outcome right weights & 0.163 & 0.209 & 0.164 \\\\ \n log\\_poisson & right outcome wrong weights & 0.157 & 0.161 & 0.158 \\\\ \n log\\_poisson & wrong both & 0.211 & 0.212 & 0.211 \\\\ \n log\\_poisson & right both & 0.159 & 0.161 & 0.161 \\\\ \n logit\\_binomial & wrong outcome right weights & 0.013 & 0.019 & 0.014 \\\\ \n logit\\_binomial & right outcome wrong weights & 0.012 & 0.012 & 0.013 \\\\ \n logit\\_binomial & wrong both & 0.018 & 0.018 & 0.018 \\\\ \n logit\\_binomial & right both & 0.012 & 0.012 & 0.012 \\\\ \n inverse\\_gaussian & wrong outcome right weights & 0.001 & 0.001 & 0.001 \\\\ \n inverse\\_gaussian & right outcome wrong weights & 0.001 & 0.001 & 0.001 \\\\ \n inverse\\_gaussian & wrong both & 0.001 & 0.001 & 0.001 \\\\ \n inverse\\_gaussian & right both & 0.001 & 0.001 & 0.001 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation", "authors": ["Erin E Gabriel", "Michael C Sachs", "Torben Martinussen", "Ingeborg Waernbaum", "Els Goetghebeur", "Stijn Vansteelandt", "Arvid Sjölander"], "url": "https://arxiv.org/abs/2309.05531v1", "attribution": "\"Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation\" by Erin E Gabriel, Michael C Sachs, Torben Martinussen, Ingeborg Waernbaum, Els Goetghebeur, Stijn Vansteelandt, and Arvid Sjölander, arXiv:2309.05531v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.10541v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\\\Pulse width limit $w_{\\text{lim}}$ with respect to pulse length $L$ for binary SSB-FSK.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\toprule\n $L$ & 2 & 4 & 6 & 8 & 10 & 12 \\\\ \\midrule\n $w_{\\text{lim}}$ & 1.6 & 3.2 & 4.8 & 6.4 & 7.9 & 9.5 \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Performance vs. Spectral Properties For Single-Sideband Continuous Phase Modulation", "authors": ["Karim Kassan", "Haïfa Farès", "D. Christian Glattli", "Yves Louët"], "url": "https://arxiv.org/abs/2011.10541v2", "attribution": "\"Performance vs. Spectral Properties For Single-Sideband Continuous Phase Modulation\" by Karim Kassan, Haïfa Farès, D. Christian Glattli, and Yves Louët, arXiv:2011.10541v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.11461v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ccccccccccc|}\n\\hline\n$r=$& $n=$& 1&2&3&4&5&6&7&8&9&10 \\\\\n\\hline\n1&&1&1&1&1&1&1&1&1&1&1\\\\\n2&&&1 & 3&8&46&790&37829&4134939&?&?\\\\\n3&&&&1& 5& 27& 1063& 1434219&?&?&?\\\\\n\\hline\n$r\\le 3$&& 1& 2 & 5 &14&74&1854&1472049&?&?&?\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Numbers of isomorphism equivalence classes of loop-free affine oriented matroids with $n$ elements and rank $r\\le 3$, also called abstract dissection types, as found in . The values in the last row are also the numbers of face-combinatorial equivalence classes of plane arrangements in $\\mathbb{R}^3$, for $n\\le 7$. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Counting plane arrangements via oriented matroids", "authors": ["Stefan Forcey"], "url": "https://arxiv.org/abs/2504.11461v1", "attribution": "\"Counting plane arrangements via oriented matroids\" by Stefan Forcey, arXiv:2504.11461v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.01846v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccccc}\n\\toprule\nproduct $i$ & $N^i$ & $N^i_\\mathrm{SCC}$ & $N^i_\\mathrm{WCC}$ & $L^i$ & $\\langle k^i\\rangle$ & $\\langle (k^{i,\\mathrm{out}})^2\\rangle$ & {$k^{i,\\mathrm{out}}_\\mathrm{UKR}$} & {$r^{i,k_{out}}_\\mathrm{UKR}$} \\\\\n\\midrule\nmaize & 192 & 108 & 182 & 8,025 & 41.8 & 5,285.0 & 178 & 1 \\\\\nwheat & 192 & 111 & 181 & 12,487 & 65.0 & 10,331.4 & 180 & 1 \\\\\nbarley & 192 & 67 & 174 & 5,759 & 30.0 & 4,119.7 & 105 & 34 \\\\\nsorghum & 192 & 33 & 110 & 1,064 & 5.5 & 283.3 & 70\t & 5 \\\\\n{pork}& 192 & 96 & 172 & 5,940 & 30.9 & 3,993.5 & 77\t & 40 \\\\\n{poultry} & 192 & 105 & 182 & 7,864 & 41.0 & 5,889.3 & 113 & 42 \\\\\neggs & 192 & 89 & 165 & 3,962 & 20.6 & 1,915.2 & 123 & 11 \\\\\nsunflower seed & 192 & 51 & 136 & 1,922 & 10.0 & 729.0 & 67\t & 12 \\\\\nr. \\& m. seed & 192 & 50 & 151 & 2,815 & 14.7 & 1,386.6 & 146 & 1 \\\\\nsunflower oil & 192 & 59 & 156 & 4,105 & 21.4 & 2,610.6 & 153 & 1 \\\\\nr. \\& m. oil & 192 & 50 & 140 & 2,868 & 14.9 & 1,339.5 & 77\t & 21 \\\\\nmaize germ oil & 192 & 30 & 149 & 1,459 & 7.6 & 660.5 & 60\t & 12 \\\\\nbeer & 192 & 132 & 178 & 8,446 & 44.0 & 5,902.3 & 112 & 45 \\\\\nalc. beverages & 192 & 110 & 169 & 8,264 & 43.0 & 5,854.1 & 154 & 15 \\\\\nsweeteners & 192 & 69 & 172 & 7,145 & 37.2 & 5,625.4 & 149 & 29 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Basic properties of the trade network layers for different products $i$. We report the total number of nodes $N^i$, the number of nodes in the largest strongly (weakly) connected component $N^i_\\mathrm{SCC}$ ($N^i_\\mathrm{WCC}$), the number of links $L^i$, the average degree $\\langle k^i \\rangle$, the second moment of the out-degree distribution $\\langle (k^{i,\\mathrm{out}})^2 \\rangle$, the out-degree of Ukraine $k^{i,\\mathrm{out}}_\\mathrm{UKR}$ and its rank based on out-degree $r^{i,k_{out}}_\\mathrm{UKR}$. We abbreviate {rapeseed and mustardseed} as {r.\\& m.}.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability", "authors": ["Moritz Laber", "Peter Klimek", "Martin Bruckner", "Liuhuaying Yang", "Stefan Thurner"], "url": "https://arxiv.org/abs/2210.01846v3", "attribution": "\"Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability\" by Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang, and Stefan Thurner, arXiv:2210.01846v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.04724v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc|cc|cc|cc|cc|cc|cc}\n \\toprule\n \\multirow{2}[4]{*}{\\textbf{DMU}} & \\multicolumn{2}{c|}{\\textbf{1995}} & \\multicolumn{2}{c|}{\\textbf{1996}} & \\multicolumn{2}{c|}{\\textbf{1997}} & \\multicolumn{2}{c|}{\\textbf{1998}} & \\multicolumn{2}{c|}{\\textbf{1999}} & \\multicolumn{2}{c|}{\\textbf{2000}} & \\multicolumn{2}{c|}{\\textbf{2001}} & \\multicolumn{2}{c}{\\textbf{2002}} \\\\\n\\cmidrule{2-17} & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ \\\\\n \\midrule\n \\textbf{1} & 0.902 & 1.161 & 0.835 & 1.149 & 0.844 & 1.140 & 0.913 & 1.156 & 0.840 & 1.157 & 0.821 & 1.195 & 0.852 & 1.221 & 0.834 & 1.270 \\\\\n \\textbf{2} & \\multicolumn{2}{c|}{1.004} & \\multicolumn{2}{c|}{1.000} & \\multicolumn{2}{c|}{0.974} & \\multicolumn{2}{c|}{1.046} & 1.054 & 1.572 & 1.026 & 2.696 & 1.011 & $\\infty$ & 0.993 & 15.450 \\\\\n \\textbf{3} & 1.124 & 2.328 & 1.233 & 1.874 & 1.259 & 1.839 & 1.142 & 1.895 & 1.235 & 1.559 & \\multicolumn{2}{c|}{1.406} & 1.308 & 1.513 & 1.129 & 1.461 \\\\\n \\textbf{4} & 1.171 & 2.011 & 1.213 & 2.258 & 0.866 & 2.260 & 0.810 & 2.465 & 0.805 & 2.489 & 0.822 & 1.953 & 0.810 & 1.974 & 0.833 & 2.226 \\\\\n \\textbf{5} & 1.052 & $\\infty$ & 1.045 & $\\infty$ & 1.653 & $\\infty$ & 3.577 & $\\infty$ & 1.071 & $\\infty$ & 0.896 & $\\infty$ & 0.978 & 3.727 & \\multicolumn{2}{c}{$\\infty$} \\\\\n \\textbf{6} & \\multicolumn{2}{c|}{0.913} & \\multicolumn{2}{c|}{0.897} & \\multicolumn{2}{c|}{0.847} & \\multicolumn{2}{c|}{0.816} & 0.795 & 0.897 & 0.753 & 1.004 & 0.717 & 1.069 & 0.871 & 0.909 \\\\\n \\textbf{7} & 0.208 & 1.141 & 0.196 & 1.146 & 0.200 & 1.105 & 0.200 & 1.121 & 0.205 & 1.111 & 0.200 & 1.099 & 0.216 & 1.101 & 0.238 & 1.118 \\\\\n \\textbf{8} & 1.011 & 1.697 & 1.013 & 1.810 & 1.016 & 1.936 & 0.996 & 2.177 & 1.018 & 3.086 & 1.010 & 2.137 & 1.009 & 2.042 & 1.008 & 1.589 \\\\\n \\textbf{9} & 0.975 & 1.215 & 0.960 & 1.292 & 0.950 & 1.300 & 0.947 & 1.324 & 0.940 & 1.413 & 0.948 & 1.540 & 0.944 & 1.373 & 0.926 & 1.326 \\\\\n \\textbf{10} & \\multicolumn{2}{c|}{1.179} & \\multicolumn{2}{c|}{1.152} & \\multicolumn{2}{c|}{1.121} & 1.095 & 1.122 & 1.090 & 1.111 & 1.067 & 1.099 & 1.059 & 1.150 & 1.045 & 1.509 \\\\\n \\textbf{11} & 1.022 & 1.172 & 1.023 & 1.168 & 1.003 & 1.116 & 1.018 & 1.105 & 0.996 & 1.120 & 0.978 & 1.138 & 0.978 & 1.124 & 0.941 & 1.077 \\\\\n \\textbf{12} & \\multicolumn{2}{c|}{1.022} & \\multicolumn{2}{c|}{1.023} & \\multicolumn{2}{c|}{1.003} & \\multicolumn{2}{c|}{1.003} & \\multicolumn{2}{c|}{0.983} & \\multicolumn{2}{c|}{0.955} & \\multicolumn{2}{c|}{0.972} & \\multicolumn{2}{c}{0.961} \\\\\n \\textbf{13} & \\multicolumn{2}{c|}{1.160} & \\multicolumn{2}{c|}{1.017} & \\multicolumn{2}{c|}{0.992} & \\multicolumn{2}{c|}{0.973} & \\multicolumn{2}{c|}{0.973} & \\multicolumn{2}{c|}{0.931} & \\multicolumn{2}{c|}{0.922} & \\multicolumn{2}{c}{0.954} \\\\\n \\textbf{14} & \\multicolumn{2}{c|}{1.054} & \\multicolumn{2}{c|}{1.087} & \\multicolumn{2}{c|}{0.991} & \\multicolumn{2}{c|}{0.964} & \\multicolumn{2}{c|}{0.952} & \\multicolumn{2}{c|}{0.960} & \\multicolumn{2}{c|}{0.962} & \\multicolumn{2}{c}{1.020} \\\\\n \\textbf{15} & \\multicolumn{2}{c|}{1.108} & \\multicolumn{2}{c|}{1.047} & \\multicolumn{2}{c|}{1.050} & \\multicolumn{2}{c|}{0.999} & \\multicolumn{2}{c|}{1.005} & \\multicolumn{2}{c|}{1.080} & \\multicolumn{2}{c|}{1.049} & \\multicolumn{2}{c}{1.083} \\\\\n \\textbf{16} & \\multicolumn{2}{c|}{1.028} & \\multicolumn{2}{c|}{1.025} & \\multicolumn{2}{c|}{1.045} & \\multicolumn{2}{c|}{1.008} & \\multicolumn{2}{c|}{1.001} & \\multicolumn{2}{c|}{1.037} & \\multicolumn{2}{c|}{0.992} & \\multicolumn{2}{c}{0.996} \\\\\n \\textbf{17} & 1.097 & 22.830 & 1.096 & 11.518 & 1.060 & 7.508 & 1.061 & 6.154 & 1.053 & 4.195 & 1.053 & 4.222 & 1.044 & 3.022 & 1.001 & 2.390 \\\\\n \\textbf{18} & \\multicolumn{2}{c|}{1.006} & \\multicolumn{2}{c|}{0.995} & \\multicolumn{2}{c|}{0.998} & \\multicolumn{2}{c|}{1.010} & \\multicolumn{2}{c|}{1.024} & \\multicolumn{2}{c|}{1.152} & 1.054 & 1.054 & \\multicolumn{2}{c}{1.036} \\\\\n \\textbf{19} & 0.378 & 1.472 & 0.400 & 1.458 & 0.392 & 1.435 & 0.403 & 1.190 & 0.424 & 1.433 & 0.431 & 1.400 & 0.427 & 1.222 & 0.428 & 1.388 \\\\\n \\textbf{20} & 1.014 & 1.198 & 1.045 & 1.259 & 1.067 & 1.336 & 1.074 & 1.331 & 1.092 & 1.649 & 1.066 & 2.111 & 1.064 & 1.885 & 1.031 & 2.041 \\\\\n \\textbf{21} & 1.120 & 1.587 & 1.117 & 2.246 & 1.082 & 7.745 & 3.681 & 6.870 & 1.511 & 7.028 & 1.045 & $\\infty$ & 1.087 & $\\infty$ & 1.209 & $\\infty$ \\\\\n \\textbf{22} & 0.962 & 1.123 & 0.957 & 1.215 & 0.960 & 1.313 & 0.926 & 1.168 & 0.892 & 1.187 & 0.909 & 1.320 & 0.956 & 1.254 & 0.938 & 1.093 \\\\\n \\textbf{23} & \\multicolumn{2}{c|}{1.101} & \\multicolumn{2}{c|}{1.100} & \\multicolumn{2}{c|}{1.077} & \\multicolumn{2}{c|}{1.105} & \\multicolumn{2}{c|}{1.120} & \\multicolumn{2}{c|}{1.138} & \\multicolumn{2}{c|}{1.114} & \\multicolumn{2}{c}{1.028} \\\\\n \\textbf{24} & 0.397 & 1.962 & 0.402 & 1.943 & 0.390 & 2.064 & 0.399 & 2.210 & 0.395 & 2.343 & 0.404 & 2.364 & 0.404 & 2.352 & 0.400 & 2.361 \\\\\n \\textbf{25} & 0.530 & 1.144 & 0.553 & 1.140 & 0.516 & 1.150 & 0.534 & 1.109 & 0.527 & 1.123 & 0.539 & 1.099 & 0.555 & 1.035 & 0.542 & 1.041 \\\\\n \\textbf{26} & 0.989 & 1.211 & 0.957 & 1.242 & 0.934 & 1.245 & 0.909 & 1.219 & 0.916 & 1.200 & 0.915 & 1.244 & 0.926 & 1.204 & 0.931 & 1.172 \\\\\n \\textbf{27} & \\multicolumn{2}{c|}{0.731} & \\multicolumn{2}{c|}{0.699} & \\multicolumn{2}{c|}{0.690} & 0.642 & 0.847 & 0.680 & 0.816 & 0.618 & 0.842 & 0.624 & 0.868 & 0.608 & 0.915 \\\\\n \\textbf{28} & \\multicolumn{2}{c|}{0.779} & \\multicolumn{2}{c|}{0.747} & \\multicolumn{2}{c|}{0.720} & 0.577 & 0.813 & 0.626 & 0.792 & 0.618 & 0.793 & 0.597 & 0.818 & 0.593 & 0.859 \\\\\n \\textbf{29} & 1.038 & 1.151 & 1.097 & 1.246 & 1.066 & 1.212 & \\multicolumn{2}{c|}{1.023} & \\multicolumn{2}{c|}{0.999} & \\multicolumn{2}{c|}{0.920} & \\multicolumn{2}{c|}{0.882} & \\multicolumn{2}{c}{0.895} \\\\\n \\textbf{30} & \\multicolumn{2}{c|}{1.249} & \\multicolumn{2}{c|}{1.211} & \\multicolumn{2}{c|}{1.302} & \\multicolumn{2}{c|}{1.083} & \\multicolumn{2}{c|}{1.134} & 1.058 & 1.134 & \\multicolumn{2}{c|}{1.037} & 1.252 & 1.290 \\\\\n \\textbf{31} & 1.062 & $\\infty$ & 1.024 & $\\infty$ & 1.028 & $\\infty$ & 1.061 & $\\infty$ & 1.062 & $\\infty$ & 1.065 & $\\infty$ & 1.075 & $\\infty$ & 1.105 & $\\infty$ \\\\\n \\textbf{32} & 1.045 & 1.100 & 1.016 & 1.138 & 0.998 & 1.087 & 1.008 & 1.140 & 0.988 & 1.154 & 0.983 & 1.190 & 0.980 & 1.131 & 0.957 & 1.086 \\\\\n \\textbf{33} & 0.992 & $\\infty$ & 0.983 & $\\infty$ & 0.945 & $\\infty$ & 0.957 & $\\infty$ & 0.982 & $\\infty$ & 0.990 & $\\infty$ & 0.982 & $\\infty$ & 0.983 & $\\infty$ \\\\\n \\textbf{34} & 7.426 & $\\infty$ & 2.920 & $\\infty$ & 23.236 & $\\infty$ & 1.124 & $\\infty$ & 1.075 & $\\infty$ & 1.189 & $\\infty$ & 1.037 & $\\infty$ & 1.169 & $\\infty$ \\\\\n \\textbf{35} & 1.085 & 1.197 & 1.089 & 1.169 & \\multicolumn{2}{c|}{1.070} & \\multicolumn{2}{c|}{1.131} & \\multicolumn{2}{c|}{1.079} & \\multicolumn{2}{c|}{1.093} & \\multicolumn{2}{c|}{1.145} & 1.081 & 1.090 \\\\\n \\textbf{36} & 0.977 & $\\infty$ & 0.967 & $\\infty$ & 0.924 & $\\infty$ & 0.842 & $\\infty$ & 0.931 & $\\infty$ & 0.955 & $\\infty$ & 1.136 & $\\infty$ & 1.220 & $\\infty$ \\\\\n \\textbf{37} & \\multicolumn{2}{c|}{0.990} & \\multicolumn{2}{c|}{0.995} & \\multicolumn{2}{c|}{1.018} & \\multicolumn{2}{c|}{1.039} & 0.982 & 1.030 & \\multicolumn{2}{c|}{1.084} & \\multicolumn{2}{c|}{1.032} & \\multicolumn{2}{c}{1.001} \\\\\n \\textbf{38} & \\multicolumn{2}{c|}{1.898} & \\multicolumn{2}{c|}{1.595} & \\multicolumn{2}{c|}{1.869} & \\multicolumn{2}{c|}{1.389} & \\multicolumn{2}{c|}{1.225} & \\multicolumn{2}{c|}{0.984} & \\multicolumn{2}{c|}{1.029} & \\multicolumn{2}{c}{1.003} \\\\\n \\textbf{39} & \\multicolumn{2}{c|}{0.853} & \\multicolumn{2}{c|}{0.847} & \\multicolumn{2}{c|}{0.913} & \\multicolumn{2}{c|}{0.897} & \\multicolumn{2}{c|}{0.864} & \\multicolumn{2}{c|}{0.835} & \\multicolumn{2}{c|}{0.814} & \\multicolumn{2}{c}{0.833} \\\\\n \\textbf{40} & 1.164 & 2.160 & 1.387 & 1.771 & \\multicolumn{2}{c|}{1.275} & \\multicolumn{2}{c|}{1.109} & \\multicolumn{2}{c|}{1.357} & \\multicolumn{2}{c|}{1.238} & \\multicolumn{2}{c|}{1.089} & \\multicolumn{2}{c}{1.048} \\\\\n \\textbf{41} & \\multicolumn{2}{c|}{0.992} & \\multicolumn{2}{c|}{1.028} & \\multicolumn{2}{c|}{1.045} & \\multicolumn{2}{c|}{1.030} & \\multicolumn{2}{c|}{1.031} & \\multicolumn{2}{c|}{0.967} & \\multicolumn{2}{c|}{0.944} & \\multicolumn{2}{c}{0.924} \\\\\n \\textbf{42} & \\multicolumn{2}{c|}{1.285} & \\multicolumn{2}{c|}{1.183} & \\multicolumn{2}{c|}{1.160} & \\multicolumn{2}{c|}{1.597} & \\multicolumn{2}{c|}{0.975} & \\multicolumn{2}{c|}{1.025} & \\multicolumn{2}{c|}{0.868} & \\multicolumn{2}{c}{0.875} \\\\\n \\textbf{43} & 1.086 & 1.135 & 1.055 & 1.133 & 0.986 & 1.193 & \\multicolumn{2}{c|}{0.930} & \\multicolumn{2}{c|}{0.985} & 0.892 & 1.088 & 0.993 & 1.105 & \\multicolumn{2}{c}{1.055} \\\\\n \\textbf{44} & 1.035 & $\\infty$ & 1.041 & $\\infty$ & 1.050 & 23.862 & 0.999 & 10.261 & 0.999 & 5.878 & 1.035 & 4.282 & 1.049 & 2.808 & 0.998 & 2.147 \\\\\n \\textbf{45} & 0.057 & 1.509 & 0.051 & 1.475 & 0.052 & 1.473 & 0.054 & 1.471 & 0.053 & 1.497 & 0.045 & 1.508 & 0.048 & 1.512 & 0.044 & 1.548 \\\\\n \\textbf{46} & 0.779 & 1.813 & 0.762 & 1.877 & 0.795 & 1.869 & 0.796 & 1.389 & 0.823 & 1.348 & 0.873 & 1.261 & 0.920 & 1.174 & 0.951 & 1.821 \\\\\n \\textbf{47} & 0.608 & 96.449 & 0.601 & 33.229 & 0.601 & 11.707 & 0.615 & 5.756 & 0.634 & 3.597 & 0.672 & 2.635 & 0.694 & 2.173 & 0.713 & 2.296 \\\\\n \\textbf{48} & \\multicolumn{2}{c|}{1.281} & \\multicolumn{2}{c|}{1.285} & \\multicolumn{2}{c|}{1.126} & \\multicolumn{2}{c|}{1.432} & \\multicolumn{2}{c|}{1.418} & \\multicolumn{2}{c|}{1.399} & \\multicolumn{2}{c|}{1.355} & \\multicolumn{2}{c}{1.309} \\\\\n \\textbf{49} & \\multicolumn{2}{c|}{0.693} & \\multicolumn{2}{c|}{0.724} & \\multicolumn{2}{c|}{0.757} & 0.829 & 0.888 & 0.816 & 0.871 & 0.834 & 0.889 & 0.776 & 0.890 & \\multicolumn{2}{c}{0.915} \\\\\n \\textbf{50} & \\multicolumn{2}{c|}{0.802} & \\multicolumn{2}{c|}{0.783} & 0.829 & 0.847 & \\multicolumn{2}{c|}{0.847} & \\multicolumn{2}{c|}{0.815} & \\multicolumn{2}{c|}{0.832} & \\multicolumn{2}{c|}{0.862} & \\multicolumn{2}{c}{0.871} \\\\\n \\textbf{51} & \\multicolumn{2}{c|}{0.746} & \\multicolumn{2}{c|}{0.750} & \\multicolumn{2}{c|}{0.751} & \\multicolumn{2}{c|}{0.757} & \\multicolumn{2}{c|}{0.761} & \\multicolumn{2}{c|}{0.744} & \\multicolumn{2}{c|}{0.728} & \\multicolumn{2}{c}{0.725} \\\\\n \\textbf{52} & 1.078 & 1.356 & 1.000 & 1.473 & 0.992 & 1.511 & 0.984 & 1.624 & 0.971 & 1.854 & 0.974 & 1.868 & 0.992 & 1.186 & 0.993 & 1.306 \\\\\n \\textbf{53} & \\multicolumn{2}{c|}{1.145} & \\multicolumn{2}{c|}{1.183} & \\multicolumn{2}{c|}{1.185} & \\multicolumn{2}{c|}{1.339} & \\multicolumn{2}{c|}{1.357} & \\multicolumn{2}{c|}{1.399} & \\multicolumn{2}{c|}{1.355} & \\multicolumn{2}{c}{1.309} \\\\\n \\textbf{54} & \\multicolumn{2}{c|}{0.718} & \\multicolumn{2}{c|}{0.727} & \\multicolumn{2}{c|}{0.749} & \\multicolumn{2}{c|}{0.777} & \\multicolumn{2}{c|}{0.761} & \\multicolumn{2}{c|}{0.769} & \\multicolumn{2}{c|}{0.779} & \\multicolumn{2}{c}{0.782} \\\\\n \\textbf{55} & \\multicolumn{2}{c|}{0.677} & \\multicolumn{2}{c|}{0.699} & \\multicolumn{2}{c|}{0.693} & \\multicolumn{2}{c|}{0.758} & \\multicolumn{2}{c|}{0.784} & \\multicolumn{2}{c|}{0.775} & \\multicolumn{2}{c|}{0.740} & \\multicolumn{2}{c}{0.757} \\\\\n \\textbf{56} & 1.145 & $\\infty$ & 1.183 & $\\infty$ & 1.126 & $\\infty$ & 1.296 & $\\infty$ & 1.308 & $\\infty$ & 1.608 & 11.164 & 1.287 & 5.505 & 1.304 & 3.798 \\\\\n \\textbf{57} & \\multicolumn{2}{c|}{1.249} & \\multicolumn{2}{c|}{1.190} & \\multicolumn{2}{c|}{1.010} & 0.964 & 1.039 & \\multicolumn{2}{c|}{0.982} & \\multicolumn{2}{c|}{0.990} & 0.981 & 0.983 & 0.929 & 1.062 \\\\\n \\textbf{58} & 0.990 & 1.174 & 0.995 & 1.256 & 0.959 & 1.279 & 0.987 & 1.154 & 1.016 & 1.259 & 1.074 & 1.291 & 1.128 & 1.148 & \\multicolumn{2}{c}{1.082} \\\\\n \\textbf{59} & \\multicolumn{2}{c|}{0.992} & \\multicolumn{2}{c|}{1.005} & \\multicolumn{2}{c|}{0.989} & \\multicolumn{2}{c|}{0.987} & \\multicolumn{2}{c|}{1.016} & 1.012 & 1.190 & \\multicolumn{2}{c|}{1.079} & 0.987 & 1.095 \\\\\n \\textbf{60} & \\multicolumn{2}{c|}{1.116} & \\multicolumn{2}{c|}{1.104} & \\multicolumn{2}{c|}{1.105} & \\multicolumn{2}{c|}{1.111} & \\multicolumn{2}{c|}{1.064} & 1.074 & 1.078 & 1.055 & 1.133 & 1.047 & 1.167 \\\\\n \\textbf{61} & 0.677 & 1.194 & 0.699 & 1.211 & 0.693 & 1.157 & 0.708 & 0.882 & 0.723 & 0.866 & 0.722 & 0.886 & 0.829 & 0.896 & 0.787 & 0.880 \\\\\n \\textbf{62} & \\multicolumn{2}{c|}{0.956} & 0.928 & 0.958 & 0.996 & 1.002 & 0.871 & 1.049 & 0.963 & 1.020 & 0.891 & 0.971 & \\multicolumn{2}{c|}{0.938} & \\multicolumn{2}{c}{0.930} \\\\\n \\textbf{63} & 0 & 0.912 & 0 & 0.902 & 0 & 0.916 & 0 & 0.913 & 0 & 0.909 & 0 & 0.886 & 0 & 0.869 & 0 & 0.871 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Individual $\\alpha$-returns to scale for 1995-2002} %\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "$Λ$-Returns to Scale and Individual Minimum Extrapolation Principle", "authors": ["Jean-Philippe Boussemart", "Walter Briec", "Raluca Parvulescu", "Paola Ravelojaona"], "url": "https://arxiv.org/abs/2212.04724v2", "attribution": "\"$Λ$-Returns to Scale and Individual Minimum Extrapolation Principle\" by Jean-Philippe Boussemart, Walter Briec, Raluca Parvulescu, and Paola Ravelojaona, arXiv:2212.04724v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table27.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|r|c|c|r|r}\n \\hline\n \\multicolumn{8}{c}{PH algorithm using 128 scenario paths for fixed carry-over case} \\\\\n \\hline\n \\multicolumn{8}{c}{Instances including component demands} \\\\\n \\hline\n Distribution & Util. & $|\\Omega|$ & $|\\Phi|$ & Converged & Gap (\\%) & Runtime (s) & Iterations \\\\\n \\hline\n \\multirow{10}{*}{Non stationary} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & Yes & 0.0 & 26.3 & 4\\\\\n & & 3 & 2,187 & Yes & 0.0 & 35.2 & 5\\\\\n & & 4 & 16,384 & Yes & 0.0 & 21.3 & 3\\\\\n & & 5 & 78,125 & Yes & 0.0 & 144.5 & 17\\\\\n & & 6 & 279,936 & & & &\\\\\\cline{2-8}\n &\\multirow{5}{*}{90\\%} \n & 2 & 128 & Yes & 0.0 & 26.1 & 3\\\\\n & & 3 & 2,187 & Yes & 0.0 & 31.4 & 4\\\\\n & & 4 & 16,384 & Yes & 0.0 & 23.7 & 3\\\\\n & & 5 & 78,125 & Yes & 0.0 & 46.1 & 4\\\\\n & & 6 & 279,936 & & & &\\\\\n \\hline\n \\multirow{10}{*}{Uniform} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & Yes & 0.0 & 26.3 & 4\\\\\n & & 3 & 2,187 & Yes & 0.0 & 12.1 & 2 \\\\\n & & 4 & 16,384 & Yes & 0.0 & 13.5 & 2\\\\\n & & 5 & 78,125 & Yes & 0.0 & 35.4 & 2\\\\\n & & 6 & 279,936 & & & & \\\\\\cline{2-8}\n & \\multirow{5}{*}{90\\%}\n & 2 & 128 & Yes & 0.0 & 30.7 & 4\\\\\n & & 3 & 2,187 & Yes & 0.0 & 29.0 & 4 \\\\\n & & 4 & 16,384 & Yes & 0.0 & 23.3 & 3 \\\\\n & & 5 & 78,125 & Yes & 0.0 & 74.4 & 6\\\\\n & & 6 & 279,936 & & & & \\\\\n \\hline\n \\multirow{10}{*}{Lumpy} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & Yes & 0.9 & 174.6 & 22 \\\\\n & & 3 & 2,187 & Yes & 3.4 & 395.2 & 50 \\\\\n & & 4 & 16,384 & Yes & 3.7 & 506.5 & 37 \\\\\n & & 5 & 78,125 & Yes & 2.8 & 194.9 & 23\\\\\n & & 6 & 279,936 & & & & \\\\\\cline{2-8}\n & \\multirow{5}{*}{90\\%} \n & 2 & 128 & Yes & 2.7 & 561.1 & 20 \\\\\n & & 3 & 2,187 & Yes & 2.4 & 853.4 & 41 \\\\\n & & 4 & 16,384 & Yes & 1.7 & 787.1 & 60 \\\\\n & & 5 & 78,125 & Yes & 1.4 & 725.8 & 45 \\\\\n & & 6 & 279,936 & & & & \\\\\n \\hline\n \\multirow{10}{*}{Slow moving} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & Yes & 0.8 & 176.6 & 24\\\\\n & & 3 & 2,187 & Yes & 4.6 & 345.4 & 46 \\\\\n & & 4 & 16,384 & No & 4.3* & 646.4 & 100 \\\\\n & & 5 & 78,125 & Yes & - & 340.9 & 40 \\\\\n & & 6 & 279,936 & & & & \\\\\\cline{2-8}\n & \\multirow{5}{*}{90\\%}\n & 2 & 128 & No & 10.7 & 1,035.4 & 100 \\\\\n & & 3 & 2,187 & Yes & 7.3 & 758.4 & 80 \\\\\n & & 4 & 16,384 & Yes & -69.9* & 543.4 & 59\\\\\n & & 5 & 78,125 & No & - & 926.8 & 100\\\\\n & & 6 & 279,936 & & & & \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Runtimes and gaps for the progressive hedging algorithm using 128 scenario paths on the instances including component demands for different distributions and numbers of scenarios per branch with a time limit of 3 hours (10,800 s) and maximum of 100 iterations}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.00914v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \n \\toprule\n \\textbf{Model} & \\textbf{FADscore} $\\downarrow$ & \\textbf{CLAPscore} $\\uparrow$ \\\\\n \\midrule\n MusicGen-Small (Baseline) & 6.49 & 0.303 \\\\\n TinyMusician & \\textbf{6.44} & 0.301 \\\\\n MusicGen-Small + Quantization & 7.11 & \\textbf{0.352} \\\\\n TinyMusician + Quantization & 7.05 & 0.343 \\\\\n \\bottomrule\n \n\\end{tabular}\n\\end{adjustbox}\n\\caption{The Scores of Ablation Study. $\\boldsymbol{\\text{FADscore} \\downarrow}$ represents lower is better, $\\boldsymbol{\\text{CLAPscore} \\uparrow}$ represents higher is better}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "TinyMusician: On-Device Music Generation with Knowledge Distillation and Mixed Precision Quantization", "authors": ["Hainan Wang", "Mehdi Hosseinzadeh", "Reza Rawassizadeh"], "url": "https://arxiv.org/abs/2509.00914v1", "attribution": "\"TinyMusician: On-Device Music Generation with Knowledge Distillation and Mixed Precision Quantization\" by Hainan Wang, Mehdi Hosseinzadeh, and Reza Rawassizadeh, arXiv:2509.00914v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08125v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The mAP@20 results on image-to-text ($I \\to T$) and text-to-image ($T \\to I$) retrieval tasks for different values of $B$ for the UCMerced datasets. ``S'' and ``U'' represent the learning type of the considered methods as supervised and unsupervised, respectively.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|lc|cccc} \n\\hline\nTask & Method && $B$=16 & $B$=32 & $B$=64 & $B$=128 \\\\ \n\\hline\\hline\n\\multirow{4}{*}{$I\\to T$} \n & CPAH &S & 0.706 & \\textbf{0.802} & \\textbf{0.891} & \\textbf{0.914} \\\\\n & DJSRH &U & 0.686 & 0.711 & 0.735 & 0.754 \\\\\n & JDSH &U & 0.462 & 0.751 & 0.820 & 0.829 \\\\\n & DUCH \\textit{(proposed)}&U & \\textbf{0.760} & 0.794 & 0.844 & 0.870 \\\\\n\\hline\\hline\n\\multirow{4}{*}{$T\\to I$}\n & CPAH &S & 0.782 & \\textbf{0.891} & \\textbf{0.987} & \\textbf{0.982} \\\\\n & DJSRH &U & 0.738 & 0.755 & 0.776 & 0.800 \\\\\n & JDSH &U & 0.509 & 0.794 & 0.884 & 0.904 \\\\\n & DUCH \\textit{(proposed)}&U & \\textbf{0.799} & 0.851 & 0.916 & 0.927 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing", "authors": ["Georgii Mikriukov", "Mahdyar Ravanbakhsh", "Begüm Demir"], "url": "https://arxiv.org/abs/2201.08125v1", "attribution": "\"Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing\" by Georgii Mikriukov, Mahdyar Ravanbakhsh, and Begüm Demir, arXiv:2201.08125v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14913v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|cc|}\n \\hline\n n & $L^{2}$error & order & $H^{1}$error & order & $H^{2}$error & order\\\\\n \\hline\n 5 & 5.526e-08& - &1.119e-06& - &3.947e-05 & - \\\\\n \\hline\n 10 & 7.729e-09&2.838&2.755e-07&2.022&1.805e-05&1.129 \\\\\n \\hline\n 20 & 5.654e-10&3.773&3.956e-08&2.800&5.126e-06&1.816 \\\\\n \\hline\n 40 & 3.543e-11&3.996&4.929e-09&3.005&1.283e-06&1.999 \\\\\n \\hline\n 80 & 2.352e-12&3.913&6.526e-10&2.917&3.387e-07&1.921 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{error and order of basis $V_{h}^{+}$, biharmonic equation in 1d.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A PINN-enriched finite element method for linear elliptic problems", "authors": ["Xiao Chen", "Yixin Luo", "Jingrun Chen"], "url": "https://arxiv.org/abs/2503.14913v1", "attribution": "\"A PINN-enriched finite element method for linear elliptic problems\" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19474v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|cc|ccc} \\hline\n \\textbf{Methods} & \\textbf{Acc} & \\textbf{Comp} & \\textbf{Prec} & \\textbf{Recall} & \\textbf{F1} \\\\ \\hline \\hline\n GeoTr~ & 0.1213 & 0.0917 & 95.84 & 87.17 & 90.11 \\\\ \\hline\n SGA~ & 0.0094 & 0.0935 & 90.87 & 97.44 & 93.58 \\\\ \\hline\n SG-PGM \\small{(ours)} & \\underline{0.0033} & \\underline{0.0040} & \\underline{99.81} & \\underline{99.79} & \\underline{99.80}\\\\ \\hline\n SGPGM+R \\small {(ours)} & \\textbf{0.0024} & \\textbf{0.0026} & \\textbf{99.86} & \\textbf{99.85} & \\textbf{99.86} \\\\ \\hline\n \\end{tabular}\n\\caption{\\textbf{Point cloud mosaicking from multiple fragments.} Our method outperforms others even without using RANSAC.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks", "authors": ["Yaxu Xie", "Alain Pagani", "Didier Stricker"], "url": "https://arxiv.org/abs/2403.19474v1", "attribution": "\"SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks\" by Yaxu Xie, Alain Pagani, and Didier Stricker, arXiv:2403.19474v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08580v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of truth inference methods}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc|ccc}\n \\toprule\n Methods & MAE$\\,\\downarrow$ & RMSE$\\,\\downarrow$ & P$\\,\\uparrow$ & R$\\,\\uparrow$ & F1$\\,\\uparrow$ \\\\\n \\midrule\n Majority voting & 0.134 & 0.173 & 0.321 & 0.419 & 0.364 \\\\\n \\midrule\n TruthFinder & 0.129 & 0.153 & 0.279 & 0.374 & 0.320 \\\\\n PooledInvestment & 0.091 & 0.108 & 0.397 & 0.380 & 0.388 \\\\\n \\midrule\n CATD & 0.127 & 0.145 & 0.432 & 0.423 & 0.427 \\\\\n \\midrule\n LTM & 0.071 & 0.093 & 0.262 & 0.394 & 0.315 \\\\\n LCA & 0.106 & 0.130 & 0.364 & 0.404 & 0.383 \\\\\n MBM & 0.104 & 0.125 & 0.340 & \\textbf{0.539} & 0.417 \\\\\n BWA & 0.088 & 0.102 & 0.414 & 0.408 & 0.411 \\\\\n \\midrule\n TKGC & \\textbf{0.054} & \\textbf{0.062} & \\textbf{0.524} & 0.491 & \\textbf{0.507} \\\\ \n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data", "authors": ["Jiacheng Huang", "Yao Zhao", "Wei Hu", "Zhen Ning", "Qijin Chen", "Xiaoxia Qiu", "Chengfu Huo", "Weijun Ren"], "url": "https://arxiv.org/abs/2201.08580v1", "attribution": "\"Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data\" by Jiacheng Huang, Yao Zhao, Wei Hu, Zhen Ning, Qijin Chen, Xiaoxia Qiu, Chengfu Huo, and Weijun Ren, arXiv:2201.08580v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17533v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{$\\text{\\LaTeX }$ formula patterns.} Each pattern was paired with both ``$\\backslash pi =$'' and ``$= \\backslash pi$,'' and the \\texttt{\\textbackslash cfrac}-based variants of the \\texttt{\\textbackslash frac}-containing regular expressions were also included, resulting in a total of 10 patterns.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n \\toprule\n Pattern for - & Python \\textbf{re} pattern \\\\\n \\midrule\n Series & \\textbackslash{} sum \\textbackslash{} s*\\_\\{(?s:.)*\\}\\textbackslash{} s*\\textbackslash{} char`\\^ \\textbackslash{} s*\n \\\\ \\\\\n \n Sum of \\textbackslash{} frac\n & (\\textbackslash{} s*\\textbackslash{} frac\\textbackslash{} s*\\{\\textbackslash{} s*[\\char`\\^ \\{\\}]*\\textbackslash{} s*\\}\\textbackslash{}s*\\{\\textbackslash{} s*[\\char`\\^ \\{\\}]*\\textbackslash{} s*\\}) \\\\ & ((?:\\textbackslash{} s*\\textbackslash{}+\\textbackslash{} s*\\textbackslash{} frac\\textbackslash{} s*\\{\\textbackslash{} s*[\\char`\\^ \\{\\}]*\\textbackslash{} s*\\}\\textbackslash{} s*\\{\\textbackslash{} s*[\\char`\\^ \\{\\}]*\\textbackslash{} s*\\})+) \\\\ \\\\\n \n Nested \\textbackslash{} frac &\n (\\textbackslash{} frac\\textbackslash{} s*\\{\\textbackslash{} s*[\\char`\\^ \\{\\} ]*\\textbackslash{} s*\\}\\textbackslash{} s*\\{\\textbackslash{} s*[\\char`\\^ \\{\\} ]*) \\\\ & ((?:\\textbackslash{} frac\\textbackslash{} s*\\{\\textbackslash{} s*[\\char`\\^ \\{\\} ]*\\textbackslash{} s*\\}\\textbackslash{} s*\\{\\textbackslash{} s*[\\char`\\^ \\{\\} ]*)+\\textbackslash{} s*\\}\\textbackslash{} s*\\}) \\\\\n \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "From Euler to AI: Unifying Formulas for Mathematical Constants", "authors": ["Tomer Raz", "Michael Shalyt", "Elyasheev Leibtag", "Rotem Kalisch", "Shachar Weinbaum", "Yaron Hadad", "Ido Kaminer"], "url": "https://arxiv.org/abs/2502.17533v2", "attribution": "\"From Euler to AI: Unifying Formulas for Mathematical Constants\" by Tomer Raz, Michael Shalyt, Elyasheev Leibtag, Rotem Kalisch, Shachar Weinbaum, Yaron Hadad, and Ido Kaminer, arXiv:2502.17533v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.12258v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Forecasting performance of the proposed E-STGCN model in comparison to the temporal-only and spatiotemporal forecasting techniques for 30 30-day ahead forecast horizon of PM$_{10}$ pollutant (best results are \\underline{\\textbf{highlighted}}).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|cccccc|ccccccc|c|}\n\\hline Forecast & \\multirow{3}{*}{Metric} & \\multicolumn{6}{c|}{Temporal-only Model} & \\multicolumn{7}{c|}{Spatiotemporal Model} & Proposed \\\\ \\cline{3-15}\n \\multirow{2}{*}{Period} & & \\multirow{2}{*}{ARIMA} & \\multirow{2}{*}{LSTM} & \\multirow{2}{*}{TCN} & \\multirow{2}{*}{DeepAR} & \\multirow{2}{*}{Transformers} & \\multirow{2}{*}{NBeats} & \\multirow{2}{*}{STARMA} & \\multirow{2}{*}{GSTAR} & \\multirow{2}{*}{GpGp} & \\multirow{2}{*}{{\\color{black}STGCN}} & \\multirow{2}{*}{STNN} & Modified & \\multirow{2}{*}{DeepKrigging} & \\multirow{2}{*}{E-STGCN} \\\\ \n & & & & & & & & & & & & & STGCN & & \\\\ \\hline\n \n\\multirow{6}{*}{JAN} & MAE & 99.31 & 276.07 & 262.61 & 275.41 & 165.16 & 87.24 & 121.40 & 82.99 & 86.54 & {\\color{black} 94.94} & 115.31 & 89.89 & 233.97 & \\underline{\\textbf{82.67}} \\\\\n & MASE & 1.23 & 3.46 & 3.31 & 3.45 & 2.05 & 1.10 & 1.49 & \\underline{\\textbf{1.02}} & 1.09 & {\\color{black} 1.18} & 1.47 & 1.12 & 2.94 & 1.03 \\\\\n & RMSE & 126.85 & 294.14 & 307.81 & 293.53 & 192.87 & 112.07 & 147.81 & 110.33 & 109.86 & {\\color{black} 123.03} & 145.59 & 119.16 & 273.87 & \\underline{\\textbf{108.41}} \\\\\n & SMAPE & 35.90 & 175.50 & 124.70 & 174.60 & 70.50 & 30.80 & 47.10 & 29.70 & 30.30 & {\\color{black} 34.22} & 37.00 & 32.00 & 120.60 & \\underline{\\textbf{29.10}} \\\\ \n & {\\color{black}Pinball Loss\t} & {\\color{black}73.38} & {\\color{black}220.85} & {\\color{black}\t197.76\t} & {\\color{black}\t220.33\t} & {\\color{black}\t131.76\t} & {\\color{black}\t49.21\t} & {\\color{black}\t95.66\t} & {\\color{black}\t58.43\t} & {\\color{black}\t44.33\t} & {\\color{black} 66.80} & {\\color{black}\t\\underline{\\textbf{38.69}}\t} & {\\color{black}\t67.15\t} & {\\color{black}\t174.11\t} & {\\color{black}\t55.12\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t148.08\t} & {\\color{black}\t181.85\t} & {\\color{black}\t191.48\t} & {\\color{black}\t181.85\t} & {\\color{black}\t181.60\t} & {\\color{black}\t126.42\t} & {\\color{black}\t166.91\t} & {\\color{black}\t133.98\t} & {\\color{black}\t125.78\t} & {\\color{black} 140.99 } & {\\color{black}\t149.49\t} & {\\color{black}\t142.56\t} & {\\color{black}\t193.51\t} & {\\color{black}\t\\underline{\\textbf{123.22}}\t} \\\\ \\hline\n\\multirow{6}{*}{FEB} & MAE & 59.32 & 218.02 & 212.75 & 216.10 & 102.77 & 68.89 & 157.65 & 153.76 & 106.83 & {\\color{black} 57.98} & 77.69 & 70.35 & 201.06 & \\underline{\\textbf{56.49}} \\\\\n & MASE & 1.25 & 4.72 & 4.58 & 4.68 & 2.14 & 1.50 & 3.38 & 3.32 & 2.22 & {\\color{black} 1.23} & 1.75 & 1.48 & 4.41 & \\underline{\\textbf{1.21}} \\\\\n & RMSE & 77.99 & 228.33 & 247.69 & 226.50 & 122.29 & 83.55 & 175.50 & 171.40 & 125.72 & {\\color{black} 74.10} & 101.26 & 88.41 & 223.28 & \\underline{\\textbf{71.36}} \\\\\n & SMAPE & 25.40 & 172.10 & 116.00 & 169.00 & 49.80 & 29.50 & 96.70 & 93.30 & 52.60 & {\\color{black} 24.96} & 32.20 & 32.20 & 140.50 & \\underline{\\textbf{24.30}} \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t42.62\t} & {\\color{black}\t174.42\t} & {\\color{black}\t151.88\t} & {\\color{black}\t172.88\t} & {\\color{black}\t81.67\t} & {\\color{black}\t34.85\t} & {\\color{black}\t126.07\t} & {\\color{black}\t122.95\t} & {\\color{black}\t85.05\t} & {\\color{black} 38.28} & {\\color{black}\t36.88\t} & {\\color{black}\t51.30\t} & {\\color{black}\t158.45\t} & {\\color{black}\t\\underline{\\textbf{31.21}}\t} \\\\\n \n & {\\color{black}\tCRPS\t} & {\\color{black}\t96.23\t} & {\\color{black}\t143.51\t} & {\\color{black}\t157.80\t} & {\\color{black}\t143.52\t} & {\\color{black}\t135.78\t} & {\\color{black}\t101.07\t} & {\\color{black}\t144.43\t} & {\\color{black}\t144.32\t} & {\\color{black}\t137.02\t} & {\\color{black} 92.88} & {\\color{black}\t109.52\t} & {\\color{black}\t111.50\t} & {\\color{black}\t149.42\t} & {\\color{black}\t\\underline{\\textbf{90.31}}\t} \\\\ \\hline\n\\multirow{6}{*}{MAR} & MAE & 51.41 & 159.77 & 168.58 & 154.64 & 52.33 & 52.00 & 54.18 & 51.87 & 60.80 & {\\color{black} 49.95} & 252.19 & 56.17 & 167.16 & \\underline{\\textbf{42.54}} \\\\\n & MASE & 1.29 & 3.93 & 4.20 & 3.80 & 1.23 & 1.31 & 1.30 & 1.26 & 1.61 & {\\color{black} 1.26} & 6.54 & 1.39 & 4.12 & \\underline{\\textbf{1.05}} \\\\\n & RMSE & 62.16 & 166.91 & 203.62 & 162.02 & 63.41 & 64.42 & 64.10 & 61.11 & 71.00 & {\\color{black} 60.05} & 309.41 & 70.32 & 174.16 & \\underline{\\textbf{51.96}} \\\\\n & SMAPE & 29.60 & 169.10 & 115.00 & 158.10 & 31.30 & 29.50 & 33.40 & 31.60 & 34.40 & {\\color{black} 29.01} & 78.20 & 36.20 & 185.20 & \\underline{\\textbf{25.30}} \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t14.42\t} & {\\color{black}\t127.82\t} & {\\color{black}\t113.45\t} & {\\color{black}\t123.71\t} & {\\color{black}\t37.08\t} & {\\color{black}\t\\underline{\\textbf{14.23}}\t} & {\\color{black}\t34.13\t} & {\\color{black}\t30.48\t} & {\\color{black}\t18.40\t} & {\\color{black} 16.54} & {\\color{black}\t52.64\t} & {\\color{black}\t40.34\t} & {\\color{black}\t133.73\t} & {\\color{black}\t16.90\t} \\\\\n \n & {\\color{black}\tCRPS\t} & {\\color{black}\t73.61\t} & {\\color{black}\t105.90\t} & {\\color{black}\t117.70\t} & {\\color{black}\t105.90\t} & {\\color{black}\t82.38\t} & {\\color{black}\t75.46\t} & {\\color{black}\t80.74\t} & {\\color{black}\t77.35\t} & {\\color{black}\t78.00\t} & {\\color{black} 72.26} & {\\color{black}\t212.69\t} & {\\color{black}\t89.06\t} & {\\color{black}\t105.94\t} & {\\color{black}\t\\underline{\\textbf{66.12}}\t} \\\\ \\hline\n\\multirow{6}{*}{APR} & MAE & 59.82 & 181.01 & 171.35 & 179.36 & 71.49 & 67.72 & 151.14 & 106.86 & 61.34 & {\\color{black} 55.68} & 62.35 & \\underline{\\textbf{53.04}} & 229.48 & \\underline{\\textbf{53.04}} \\\\\n & MASE & 1.37 & 4.18 & 3.89 & 4.14 & 1.61 & 1.54 & 3.46 & 2.45 & 1.42 & {\\color{black} 1.31} & 1.45 & \\underline{\\textbf{1.24}} & 5.38 & \\underline{\\textbf{1.24}} \\\\\n & RMSE & 75.86 & 191.41 & 191.06 & 189.86 & 88.30 & 85.42 & 165.07 & 124.18 & 74.55 & {\\color{black} 66.76} & 77.04 & \\underline{\\textbf{62.40}} & 311.57 & \\underline{\\textbf{62.40}} \\\\\n & SMAPE & 30.90 & 164.50 & 130.50 & 161.40 & 37.90 & 36.10 & 117.10 & 68.50 & 31.90 & {\\color{black} 29.03} & 32.70 & \\underline{\\textbf{27.70}} & 143.40 & \\underline{\\textbf{27.70}} \\\\\n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t43.18\t} & {\\color{black}\t144.81\t} & {\\color{black}\t132.12\t} & {\\color{black}\t143.49\t} & {\\color{black}\t53.27\t} & {\\color{black}\t51.59\t} & {\\color{black}\t120.90\t} & {\\color{black}\t83.76\t} & {\\color{black}\t38.60\t} & {\\color{black} 27.75} & {\\color{black}\t39.20\t} & {\\color{black}\t\\underline{\\textbf{25.20}}\t} & {\\color{black}\t156.05\t} & {\\color{black}\t\\underline{\\textbf{25.20}}\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t96.36\t} & {\\color{black}\t122.26\t} & {\\color{black}\t126.72\t} & {\\color{black}\t122.26\t} & {\\color{black}\t108.53\t} & {\\color{black}\t108.55\t} & {\\color{black}\t122.69\t} & {\\color{black}\t122.69\t} & {\\color{black}\t92.03\t} & {\\color{black} 82.19} & {\\color{black}\t93.58\t} & {\\color{black}\t\\underline{\\textbf{78.66}}\t} & {\\color{black}\t145.23\t} & {\\color{black}\t\\underline{\\textbf{78.66}}\t} \\\\\n \\hline\n\\multirow{6}{*}{MAY} & MAE & \\underline{\\textbf{71.01}} & 176.32 & 167.71 & 170.78 & 79.39 & 71.75 & 117.99 & 88.82 & 78.77 & {\\color{black} 78.42} & 94.51 & 77.46 & 188.01 & 77.46 \\\\\n & MASE & \\underline{\\textbf{1.12}} & 2.81 & 2.67 & 2.72 & 1.24 & 1.15 & 1.86 & 1.41 & 1.23 & {\\color{black} 1.25} & 1.53 & 1.24 & 3.01 & 1.24 \\\\\n & RMSE & \\underline{\\textbf{91.48}} & 199.93 & 205.51 & 195.07 & 107.15 & 92.81 & 147.03 & 118.57 & 105.91 & {\\color{black} 97.83} & 117.57 & 96.88 & 211.09 & 96.88 \\\\\n & SMAPE & \\underline{\\textbf{38.00}} & 167.30 & 119.70 & 155.40 & 42.90 & 38.60 & 77.90 & 49.30 & 42.50 & {\\color{black} 41.91} & 47.90 & 41.40 & 194.50 & 41.40 \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t35.98\t} & {\\color{black}\t141.06\t} & {\\color{black}\t122.77\t} & {\\color{black}\t136.63\t} & {\\color{black}\t53.76\t} & {\\color{black}\t33.77\t} & {\\color{black}\t91.62\t} & {\\color{black}\t61.73\t} & {\\color{black}\t52.29\t} & {\\color{black} 36.63} & {\\color{black}\t38.56\t} & {\\color{black}\t\\underline{\\textbf{31.28}}\t} & {\\color{black}\t150.38\t} & {\\color{black}\t\\underline{\\textbf{31.28}}\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t\\underline{\\textbf{90.80}}\t} & {\\color{black}\t122.60\t} & {\\color{black}\t127.58\t} & {\\color{black}\t122.60\t} & {\\color{black}\t108.91\t} & {\\color{black}\t90.80\t} & {\\color{black}\t122.96\t} & {\\color{black}\t119.12\t} & {\\color{black}\t107.33\t} & {\\color{black} 96.06} & {\\color{black}\t111.76\t} & {\\color{black}\t96.87\t} & {\\color{black}\t122.88\t} & {\\color{black}\t96.87\t} \\\\ \\hline\n\\multirow{6}{*}{JUN} & MAE & 67.66 & 117.99 & 123.54 & 114.96 & 47.59 & \\underline{\\textbf{43.32}} & 87.20 & 74.67 & 69.23 & {\\color{black} 72.83} & 43.44 & 53.60 & 130.25 & 51.65 \\\\\n & MASE & 2.00 & 3.36 & 3.53 & 3.27 & 1.42 & 1.27 & 2.46 & 2.11 & 2.09 & {\\color{black} 2.17} & \\underline{\\textbf{1.25}} & 1.58 & 3.78 & 1.51 \\\\\n & RMSE & 81.88 & 129.68 & 148.78 & 126.93 & 58.79 & 56.31 & 100.34 & 89.19 & 82.67 & {\\color{black} 82.26} & \\underline{\\textbf{55.60}} & 65.15 & 154.60 & 65.44 \\\\\n & SMAPE & 45.10 & 150.10 & 124.00 & 142.20 & 36.00 & 33.40 & 90.00 & 70.90 & 47.00 & {\\color{black} 48.73} & \\underline{\\textbf{33.00}} & 39.00 & 137.00 & 37.90 \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t20.16\t} & {\\color{black}\t94.39\t} & {\\color{black}\t90.49\t} & {\\color{black}\t91.96\t} & {\\color{black}\t20.32\t} & {\\color{black}\t23.77\t} & {\\color{black}\t69.76\t} & {\\color{black}\t59.68\t} & {\\color{black}\t23.20\t} & {\\color{black} 19.06} & {\\color{black}\t23.70\t} & {\\color{black}\t23.90\t} & {\\color{black}\t98.71\t} & {\\color{black}\t\\underline{\\textbf{16.17}}\t} \\\\\n \n & {\\color{black}\tCRPS\t} & {\\color{black}\t80.27\t} & {\\color{black}\t84.39\t} & {\\color{black}\t87.31\t} & {\\color{black}\t84.39\t} & {\\color{black}\t63.00\t} & {\\color{black}\t62.06\t} & {\\color{black}\t83.63\t} & {\\color{black}\t83.14\t} & {\\color{black}\t81.28\t} & {\\color{black} 77.44} & {\\color{black}\t\\underline{\\textbf{61.75}}\t} & {\\color{black}\t67.01\t} & {\\color{black}\t91.18\t} & {\\color{black}\t69.55\t} \\\\ \n \\hline\n\\multirow{6}{*}{JUL} & MAE & 101.08 & 60.00 & 72.71 & 57.24 & 72.83 & \\underline{\\textbf{30.07}} & 38.87 & 31.80 & 96.52 & {\\color{black} 125.26} & 60.67 & 48.40 & 76.07 & 48.40 \\\\\n & MASE & 5.72 & 3.21 & 3.77 & 3.05 & 4.37 & \\underline{\\textbf{1.65}} & 2.01 & 1.66 & 5.83 & {\\color{black} 7.09} & 3.52 & 2.74 & 4.11 & 2.74 \\\\\n & RMSE & 108.53 & 65.60 & 88.01 & 63.07 & 77.47 & \\underline{\\textbf{36.78}} & 46.32 & 38.07 & 103.92 & {\\color{black} 128.13} & 75.32 & 55.25 & 80.74 & 55.25 \\\\\n & SMAPE & 81.10 & 120.10 & 117.10 & 110.20 & 69.50 & \\underline{\\textbf{36.90}} & 62.10 & 46.70 & 79.90 & {\\color{black} 93.13} & 56.40 & 51.70 & 186.90 & 51.70 \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t20.77\t} & {\\color{black}\t48.00\t} & {\\color{black}\t51.51\t} & {\\color{black}\t45.79\t} & {\\color{black}\t14.95\t} & {\\color{black}\t\\underline{\\textbf{10.36}}\t} & {\\color{black}\t30.43\t} & {\\color{black}\t22.67\t} & {\\color{black}\t20.41\t} & {\\color{black} 26.11} & {\\color{black}\t16.01\t} & {\\color{black}\t10.67\t} & {\\color{black}\t60.86\t} & {\\color{black}\t10.67\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t74.62\t} & {\\color{black}\t47.71\t} & {\\color{black}\t52.75\t} & {\\color{black}\t47.70\t} & {\\color{black}\t58.41\t} & {\\color{black}\t\\underline{\\textbf{38.46}}\t} & {\\color{black}\t46.71\t} & {\\color{black}\t43.11\t} & {\\color{black}\t72.39\t} & {\\color{black} 83.19} & {\\color{black}\t61.67\t} & {\\color{black}\t48.14\t} & {\\color{black}\t47.75\t} & {\\color{black}\t48.14\t} \\\\ \\hline\n\\multirow{6}{*}{AUG} & MAE & 60.84 & 108.73 & 112.74 & 105.64 & 48.29 & 47.00 & 71.27 & 84.91 & 60.04 & {\\color{black} 42.14} & 87.94 & 47.44 & 94.30 & \\underline{\\textbf{39.70}} \\\\\n & MASE & 2.86 & 4.88 & 5.02 & 4.73 & 2.23 & 2.03 & 3.16 & 3.80 & 2.88 & {\\color{black} 1.90} & 3.98 & 2.19 & 4.25 & \\underline{\\textbf{1.80}} \\\\\n & RMSE & 69.72 & 116.57 & 124.58 & 113.71 & 56.02 & 59.81 & 87.31 & 97.19 & 69.23 & {\\color{black} 51.87} & 97.95 & 55.36 & 114.38 & \\underline{\\textbf{47.97}} \\\\\n & SMAPE & 42.60 & 146.70 & 141.80 & 138.40 & 37.20 & 39.20 & 71.80 & 94.80 & 43.10 & {\\color{black} 32.29} & 104.10 & 36.00 & 93.00 & \\underline{\\textbf{31.00}} \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t\\underline{\\textbf{14.02}}\t} & {\\color{black}\t86.98\t} & {\\color{black}\t88.22\t} & {\\color{black}\t84.51\t} & {\\color{black}\t16.72\t} & {\\color{black}\t35.18\t} & {\\color{black}\t56.80\t} & {\\color{black}\t67.91\t} & {\\color{black}\t15.76\t} & {\\color{black} 18.73} & {\\color{black}\t69.87\t} & {\\color{black}\t14.09\t} & {\\color{black}\t59.35\t} & {\\color{black}\t14.25\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t67.48\t} & {\\color{black}\t77.86\t} & {\\color{black}\t79.92\t} & {\\color{black}\t77.86\t} & {\\color{black}\t60.84\t} & {\\color{black}\t68.49\t} & {\\color{black}\t79.02\t} & {\\color{black}\t78.59\t} & {\\color{black}\t67.32\t} & {\\color{black} 58.65} & {\\color{black}\t77.33\t} & {\\color{black}\t60.26\t} & {\\color{black}\t85.83\t} & {\\color{black}\t\\underline{\\textbf{54.41}}\t} \\\\ \\hline\n\\multirow{6}{*}{SEP} & MAE & 82.97 & 92.66 & 104.00 & 91.22 & 52.34 & 68.16 & 48.30 & 47.63 & 66.26 & {\\color{black} 89.70} & 79.02 & 44.58 & 119.67 & \\underline{\\textbf{35.90}} \\\\\n & MASE & 4.60 & 5.68 & 6.07 & 5.59 & 3.08 & 3.89 & 2.95 & 2.90 & 3.91 & {\\color{black} 5.15} & 4.93 & 2.54 & 7.04 & \\underline{\\textbf{2.05}} \\\\\n & RMSE & 94.06 & 102.86 & 122.03 & 101.53 & 63.62 & 81.38 & 60.46 & 58.86 & 78.06 & {\\color{black} 101.05} & 90.40 & 55.68 & 166.10 & \\underline{\\textbf{44.51}} \\\\\n & SMAPE & 60.70 & 130.70 & 122.30 & 126.70 & 46.50 & 53.40 & 48.50 & 46.60 & 53.70 & {\\color{black} 63.63} & 96.90 & 41.50 & 98.90 & \\underline{\\textbf{35.70}} \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t16.86\t} & {\\color{black}\t74.08\t} & {\\color{black}\t74.96\t} & {\\color{black}\t72.92\t} & {\\color{black}\t13.99\t} & {\\color{black}\t14.91\t} & {\\color{black}\t26.92\t} & {\\color{black}\t24.25\t} & {\\color{black}\t14.97\t} & {\\color{black} 19.36} & {\\color{black}\t59.08\t} & {\\color{black}\t14.96\t} & {\\color{black}\t63.87\t} & {\\color{black}\t\\underline{\\textbf{13.98}}\t} \\\\\n \n & {\\color{black}\tCRPS\t} & {\\color{black}\t78.86\t} & {\\color{black}\t70.71\t} & {\\color{black}\t76.44\t} & {\\color{black}\t70.71\t} & {\\color{black}\t62.09\t} & {\\color{black}\t69.93\t} & {\\color{black}\t65.91\t} & {\\color{black}\t64.31\t} & {\\color{black}\t69.96\t} & {\\color{black} 84.24} & {\\color{black}\t71.51\t} & {\\color{black}\t59.82\t} & {\\color{black}\t84.12\t} & {\\color{black}\t\\underline{\\textbf{50.39}}\t} \\\\ \\hline\n\\multirow{6}{*}{OCT} & MAE & 56.62 & 214.64 & 202.97 & 206.32 & 81.46 & 94.69 & 120.44 & 118.95 & 67.79 & {\\color{black} 60.10} & 178.89 & 62.18 & 218.93 & \\underline{\\textbf{56.42}} \\\\\n & MASE & \\underline{\\textbf{1.54}} & 6.00 & 5.57 & 5.75 & 2.17 & 2.61 & 3.27 & 3.29 & 1.80 & {\\color{black} 1.64} & 5.03 & 1.70 & 6.12 & 1.55 \\\\\n & RMSE & 76.41 & 225.67 & 223.43 & 217.78 & 101.38 & 114.30 & 148.51 & 147.03 & 88.08 & {\\color{black} 79.08} & 192.39 & 80.64 & 231.48 & \\underline{\\textbf{74.86}} \\\\\n & SMAPE & \\underline{\\textbf{25.00}} & 176.90 & 140.50 & 162.80 & 37.90 & 47.60 & 66.20 & 65.60 & 30.20 & {\\color{black} 26.74} & 134.70 & 27.80 & 181.60 & \\underline{\\textbf{25.00}} \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t38.14\t} & {\\color{black}\t171.71\t} & {\\color{black}\t160.34\t} & {\\color{black}\t165.06\t} & {\\color{black}\t62.31\t} & {\\color{black}\t74.59\t} & {\\color{black}\t95.89\t} & {\\color{black}\t94.70\t} & {\\color{black}\t47.51\t} & {\\color{black} 40.09} & {\\color{black}\t142.75\t} & {\\color{black}\t39.99\t} & {\\color{black}\t175.13\t} & {\\color{black}\t\\underline{\\textbf{33.52}}\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t93.92\t} & {\\color{black}\t139.86\t} & {\\color{black}\t142.87\t} & {\\color{black}\t139.86\t} & {\\color{black}\t119.26\t} & {\\color{black}\t133.38\t} & {\\color{black}\t141.47\t} & {\\color{black}\t141.89\t} & {\\color{black}\t105.70\t} & {\\color{black} 95.93} & {\\color{black}\t138.79\t} & {\\color{black}\t95.26\t} & {\\color{black}\t140.35\t} & {\\color{black}\t\\underline{\\textbf{90.50}}\t} \\\\ \\hline\n\\multirow{6}{*}{NOV} & MAE & 160.63 & 370.52 & 342.23 & 365.48 & 230.28 & 114.14 & 164.50 & 163.94 & \\underline{\\textbf{101.36}} & {\\color{black} 139.78} & 248.39 & 105.00 & 378.61 & 105.00 \\\\\n & MASE & 2.00 & 4.64 & 4.27 & 4.57 & 2.86 & 1.43 & 2.04 & 2.04 & \\underline{\\textbf{1.28}} & {\\color{black} 1.74} & 3.08 & 1.32 & 4.77 & 1.32 \\\\\n & RMSE & 181.25 & 387.20 & 370.87 & 382.37 & 255.63 & 137.63 & 183.15 & 182.67 & \\underline{\\textbf{123.56}} & {\\color{black} 161.36} & 273.40 & 125.82 & 406.14 & 125.82 \\\\\n & SMAPE & 49.20 & 182.20 & 140.80 & 176.90 & 78.10 & 31.90 & 51.80 & 51.80 & \\underline{\\textbf{27.60}} & {\\color{black} 40.51} & 88.70 & 29.40 & 169.70 & 29.40 \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t124.79\t} & {\\color{black}\t296.41\t} & {\\color{black}\t268.32\t} & {\\color{black}\t292.38\t} & {\\color{black}\t183.96\t} & {\\color{black}\t73.64\t} & {\\color{black}\t125.90\t} & {\\color{black}\t125.97\t} & {\\color{black}\t\\underline{\\textbf{49.25}}\t} & {\\color{black} 105.57} & {\\color{black}\t198.49\t} & {\\color{black}\t71.86\t} & {\\color{black}\t302.31\t} & {\\color{black}\t71.86\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t222.28\t} & {\\color{black}\t237.70\t} & {\\color{black}\t241.89\t} & {\\color{black}\t237.70\t} & {\\color{black}\t237.70\t} & {\\color{black}\t178.26\t} & {\\color{black}\t216.77\t} & {\\color{black}\t219.46\t} & {\\color{black}\t\\underline{\\textbf{159.67}}\t} & {\\color{black} 206.01} & {\\color{black}\t237.94\t} & {\\color{black}\t165.00\t} & {\\color{black}\t243.63\t} & {\\color{black}\t165.00\t} \\\\ \\hline\n\\multirow{6}{*}{DEC} & MAE & 111.36 & 320.05 & 303.66 & 311.54 & 174.14 & \\underline{\\textbf{82.85}} & 131.11 & 148.68 & 112.51 & {\\color{black} 134.13} & 100.10 & 117.10 & 326.28 & 134.80 \\\\\n & MASE & 2.02 & 5.87 & 5.58 & 5.71 & 3.12 & \\underline{\\textbf{1.54}} & 2.36 & 2.71 & 2.02 & {\\color{black} 2.43} & 1.95 & 2.14 & 6.03 & 2.50 \\\\\n & RMSE & 139.68 & 329.61 & 342.88 & 321.36 & 191.44 & \\underline{\\textbf{100.84}} & 163.30 & 183.15 & 139.38 & {\\color{black} 155.58} & 118.07 & 141.74 & 347.20 & 157.71 \\\\\n & SMAPE & 36.60 & 184.50 & 129.80 & 174.50 & 66.20 & \\underline{\\textbf{24.70}} & 45.00 & 55.20 & 36.10 & {\\color{black} 46.92} & 28.90 & 39.50 & 168.30 & 48.20 \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t83.53\t} & {\\color{black}\t256.04\t} & {\\color{black}\t228.05\t} & {\\color{black}\t249.23\t} & {\\color{black}\t139.30\t} & {\\color{black}\t\\underline{\\textbf{39.40}}\t} & {\\color{black}\t95.89\t} & {\\color{black}\t117.67\t} & {\\color{black}\t80.35\t} & {\\color{black} 108.21} & {\\color{black}\t32.82\t} & {\\color{black}\t93.29\t} & {\\color{black}\t259.98\t} & {\\color{black}\t107.55\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t174.92\t} & {\\color{black}\t202.04\t} & {\\color{black}\t219.05\t} & {\\color{black}\t202.04\t} & {\\color{black}\t200.90\t} & {\\color{black}\t\\underline{\\textbf{128.38}}\t} & {\\color{black}\t189.84\t} & {\\color{black}\t201.10\t} & {\\color{black}\t171.97\t} & {\\color{black} 190.48} & {\\color{black}\t140.95\t} & {\\color{black}\t177.56\t} & {\\color{black}\t209.45\t} & {\\color{black}\t189.76\t} \\\\\n \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "E-STGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting", "authors": ["Madhurima Panja", "Tanujit Chakraborty", "Anubhab Biswas", "Soudeep Deb"], "url": "https://arxiv.org/abs/2411.12258v2", "attribution": "\"E-STGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting\" by Madhurima Panja, Tanujit Chakraborty, Anubhab Biswas, and Soudeep Deb, arXiv:2411.12258v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00406v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lccc}\n \\toprule\n & Train & Validation & Test \\\\\n \\midrule\n Topics & 25 & 8 & 10 \\\\\n Docs & 594 & 196 & 206 \\\\\n Mentions & 3808/4758 & 1245/1476 & 1780/2055 \\\\\n Clusters & 411/472 & 129/125 & 182/196 \\\\\n \\bottomrule\n \\end{tabular}\n\\caption{ECB+ dataset statistics. The slash numbers for Mentions and Clusters represent event/entity statistics.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "CDLM: Cross-Document Language Modeling", "authors": ["Avi Caciularu", "Arman Cohan", "Iz Beltagy", "Matthew E. Peters", "Arie Cattan", "Ido Dagan"], "url": "https://arxiv.org/abs/2101.00406v2", "attribution": "\"CDLM: Cross-Document Language Modeling\" by Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew E. Peters, Arie Cattan, and Ido Dagan, arXiv:2101.00406v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19668v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|cc|cc|cc}\n\\hline\n$z$ & Exact & \\multicolumn{2}{c|}{$m=20$} & \\multicolumn{2}{c|}{$m=50$} & \\multicolumn{2}{c}{$m=100$} \\\\\n & & Approx & Error & Approx & Error & Approx & Error \\\\\n\\hline\n0.50 & 0.2591 & 0.2794 & 0.0203 & 0.2655 & 0.0064 & 0.2621 & 0.0031 \\\\\n1.00 & 0.4207 & 0.4163 & 0.0044 & 0.4191 & 0.0016 & 0.4200 & 0.0008 \\\\\n2.00 & 0.1228 & 0.1407 & 0.0179 & 0.1234 & 0.0006 & 0.1221 & 0.0008 \\\\\n4.00 & -0.0152 & 0.0033 & 0.0186 & 0.0000 & 0.0152 & 0.0000 & 0.0152 \\\\\n8.00 & 0.0029 & 0.0030 & 0.0001 & 0.0029 & 0.0000 & 0.0028 & 0.0000 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Weighted approximation By Max-product Kantrovich type Exponential Sampling Series", "authors": ["Satyaranjan Pradhan", "Madan Mohan Soren"], "url": "https://arxiv.org/abs/2504.19668v1", "attribution": "\"Weighted approximation By Max-product Kantrovich type Exponential Sampling Series\" by Satyaranjan Pradhan and Madan Mohan Soren, arXiv:2504.19668v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.20317v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l||ll||ll}\n \\hline\n & \\multicolumn{2}{c||}{\\bf $P\\!\\!\\rightarrow\\!\\!C$} & \\multicolumn{2}{c}{\\bf $C\\!\\!\\rightarrow\\!\\!P$} \\\\\n \\hline\n {\\bf Datasets} & {\\bf $A_T ( \\uparrow )$} & {\\bf $F_T ( \\downarrow )$} & {\\bf $A_T ( \\uparrow )$} & {\\bf $F_T ( \\downarrow )$} \\\\\n \\hline\n {\\bf CIFAR-100} & $88.87 \\pm 0.33$ & $4.75 \\pm 0.15$ & $88.24 \\pm 0.31$ & $3.86 \\pm 0.34$ \\\\\n {\\bf ImageNet-R} & $77.86 \\pm 0.25$ & $4.33 \\pm 0.24$ & $77.76 \\pm 0.28$ & $3.65 \\pm 0.27$ \\\\\n {\\bf CUB-200} & $80.2 \\pm 0.52$ & $5.6 \\pm 0.38$ & $80.1 \\pm 0.45$ & $5.7 \\pm 0.26$ \\\\ \\hline\n \\end{tabular}\n\\caption{{Prefixes before and after projection on 10-task setup.}}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Convolutional Prompting meets Language Models for Continual Learning", "authors": ["Anurag Roy", "Riddhiman Moulick", "Vinay K. Verma", "Saptarshi Ghosh", "Abir Das"], "url": "https://arxiv.org/abs/2403.20317v1", "attribution": "\"Convolutional Prompting meets Language Models for Continual Learning\" by Anurag Roy, Riddhiman Moulick, Vinay K. Verma, Saptarshi Ghosh, and Abir Das, arXiv:2403.20317v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.06685v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The LFC controller performance in an event of multiple microgrid switching }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\hline\n&$|f-60|$ & MPSI & $V_error$ & MQSI \\\\\n\\hline\nmicrogrid 1 & 0.00 & 0.00 & 0.000 & 0.00\\\\\nmicrogrid 2 & 0.00 & 0.00 & 0.000 & 0.00\\\\\nmicrogrid 3 & 0.00 & 0.00 & 0.000 & 0.00\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Coordinated Frequency and Voltage Regulation of Grid-Following and Grid-Forming Inverters", "authors": ["Ankit Singhal", "Thanh Long Vu", "Wei Du"], "url": "https://arxiv.org/abs/2012.06685v2", "attribution": "\"Coordinated Frequency and Voltage Regulation of Grid-Following and Grid-Forming Inverters\" by Ankit Singhal, Thanh Long Vu, and Wei Du, arXiv:2012.06685v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02632v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\n \\textbf{Individual} & \\textbf{Position} \\\\\n \\midrule\n Chris Odom & Founder of Open-Transactions \\\\\n David Hartley & CEO of Pacio \\\\\n G. Ken Holman & CTO at Crane Softwrights Ltd and former editor of ISO/IEC 15944-21 \\\\\n Ian Grigg & Co-founder at Solidus/Chamapesa \\\\\n Jason Meyers & Founder at Auditchain \\\\\n Robert Haugen & Developer at Mikorizal Software \\\\\n Todd Boyle & Founder at International Accounting Services \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Triple-entry Accounting, Blockchain and Next of Kin: Towards a Standardisation of Ledger Terminology", "authors": ["Juan Ignacio Ibañez", "Chris N. Bayer", "Paolo Tasca", "Jiahua Xu"], "url": "https://arxiv.org/abs/2101.02632v3", "attribution": "\"Triple-entry Accounting, Blockchain and Next of Kin: Towards a Standardisation of Ledger Terminology\" by Juan Ignacio Ibañez, Chris N. Bayer, Paolo Tasca, and Jiahua Xu, arXiv:2101.02632v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.04859v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|}\n\\hline\n\\textbf{Year} & \\textbf{Jan} & \\textbf{Feb} & \\textbf{Mar} & \\textbf{Apr} & \\textbf{May} & \\textbf{Jun} & \\textbf{Jul} & \\textbf{Aug} & \\textbf{Sep} & \\textbf{Oct} & \\textbf{Nov} & \\textbf{Dec} \\\\\n\\hline\n2002 & & & & & & & & & & & & (26)\\\\\n2003 & 1 (30) & 3 (27) & 3 (27) & 1 (24) & 2 (27) & 2 (24) & 1 (31) & 1 (28) & 1 (30) & 1 (30) & 3 (27) & 1 (29) \\\\\n2004 & 1 (30) & 3 (25) & 1 (29) & 1 (30) & 3 (27) & 1 (30) & 1 (31) & 2 (29) & 1 (30) & 1 (29) & 1 (29) & 1 (31) \\\\\n2005 & 1 (31) & 3 (28) & 1 (31) & 1 (29) & 2 (30) & 1 (29) & 1 (31) & 1 (31) & 1 (30) & 3 (28) & 1 (30) & 1 (31) \\\\\n2006 & 1 (31) & 3 (27) & 1 (31) & 3 (27) & 2 (31) & 1 (30) & 3 (30) & 1 (31) & 1 (29) & 3 (27) & 1 (30) & 1 (31) \\\\\n2007 & 1 (31) & 2 (26) & 3 (28) & 2 (30) & 3 (29) & 1 (30) & 2 (30) & 1 (31) & 3 (28) & 1 (31) & 1 (30) & 3 (29) \\\\\n2008 & 1 (31) & 3 (28) & 1 (31) & 1 (30) & 2 (30) & 2 (30) & 1 (31) & 1 (31) & 1 (30) & 1 (31) & 3 (27) & 1 (31) \\\\\n2009 & 1 (31) & 1 (28) & 3 (28) & 1 (30) & 3 (30) & 1 (30) & 1 (31) & 1 (31) & 1 (30) & 3 (28) & 1 (30) & 1 (31) \\\\\n2010 & 3 (28) & 1 (28) & 3 (27) & 1 (30) & 1 (29) & 1 (30) & 1 (31) & 1 (31) & 2 (30) & 1 (30) & 1 (30) & 1 (31) \\\\\n2011 & 1 (31) & 1 (28) & 2 (29) & 3 (30) & 3 (30) & 1 (30) & 1 (31) & 1 (31) & 1 (30) & 1 (30) & 2 (29) & 1 (31) \\\\\n2012 & 1 (31) & 1 (28) & 2 (29) & 1 (30) & 1 (30) & 3 (30) & 1 (31) & 1 (31) & 1 (30) & 2 (29) & 1 (30) & 1 (31) \\\\\n2013 & 1 (31) & 1 (28) & 1 (31) & 1 (30) & 3 (30) & 1 (30) & 1 (31) & 2 (30) & 1 (31) & 1 (30) & 1 (30) & 1 (31) \\\\\n2014 & 1 (31) & 3 (28) & 1 (31) & 1 (30) & 2 (30) & 1 (30) & 1 (31) & 1 (30) & 3 (29) & 1 (30) & 1 (29) & 1 (31) \\\\\n2015 & 3 (30) & 1 (29) & 1 (31) & 3 (27) & 1 (30) & 1 (29) & 1 (30) & 2 (30) & 1 (31) & 1 (30) & 3 (29) & 1 (31) \\\\\n2016 & 1 (31) & 1 (28) & 2 (30) & 1 (30) & 1 (29) & 3 (30) & 1 (30) & 1 (31) & 3 (29) & 1 (30) & 1 (30) & 1 (31) \\\\\n2017 & 1 (31) & 2 (29) & 3 (30) & 1 (30) & 3 (29) & 1 (30) & 1 (31) & 2 (30) & 1 (30) & 1 (31) & 1 (30) & 1 (31) \\\\\n2018 & 1 (31) & 1 (28) & 1 (31) & 1 (30) & 2 (30) & 3 (30) & 1 (31) & 1 (31) & 1 (30) & 2 (30) & 1 (30) & 1 (31) \\\\\n2019 & 3 (30) & 1 (28) & 2 (29) & 1 (30) & 1 (31) & 2 (30) & 1 (31) & 1 (30) & 1 (30) & 1 (30) & 1 (31) & 1 (30) \\\\\n2020 & 1 (31) & 3 (28) & 1 (31) & 1 (30) & 2 (30) & 1 (30) & 1 (31) & 1 (31) & 3 (29) & 1 (30) & 1 (30) & 1 (31) \\\\\n2021 & 1 (31) & 1 (29) & 3 (27) & 1 (30) & 2 (30) & 1 (30) & 1 (31) & 2 (30) & 1 (31) & 1 (31) & 1 (30) & 1 (31) \\\\\n2022 & 1 (31) & 2 (29) & 3 (28) & 1 (30) & 2 (30) & 1 (30) & 1 (31) & 1 (31) & 2 (30) & 1 (31) & 1 (30) & 1 (31) \\\\\n2023 & 3 (30) & 1 (28) & 2 (30) & 1 (31) & 1 (30) & 1 (31) & 2 (30) & 1 (30) & 1 (31) & 1 (30) & 1 (29) & 1 (31) \\\\\n2024 & 1 (31) & 1 (28) & 1 (31) & 1 (30) & 2 (30) & 3 (30) & 1 (31) & 1 (31) & 2 (30) & 1 (31) & 1 (30) & 2 (\\, \\, ) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{First trading date and F\\&O expiry date (in parentheses) for each month}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "F&O Expiry vs. First-Day SIPs: A 22-Year Analysis of Timing Advantages in India's Nifty 50", "authors": ["Siddharth Gavhale"], "url": "https://arxiv.org/abs/2507.04859v2", "attribution": "\"F&O Expiry vs. First-Day SIPs: A 22-Year Analysis of Timing Advantages in India's Nifty 50\" by Siddharth Gavhale, arXiv:2507.04859v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.22779v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lr}\n\\toprule\nHyper-parameter & Value \\\\\n\\midrule\n Total steps & 2e7 \\\\\n Number of envs & 8 \\\\\n Episode length $T$ & 1000 \\\\\n Number of mini-batch & 40 \\\\\n Actor/critic network & MLP \\\\\n Network hidden sizes & 64 \\\\\n Hidden layer & 1 \\\\\n Activation function & Relu \\\\\n Optimizer & Adam \\\\\n Network learning rate & 5e-3 \\\\\n Optimization epochs & 5 \\\\\n Max grad norm & 0.5 \\\\\n GAE parameter $\\lambda$ & 0.95 \\\\\n Learning rate of average performance $\\alpha$ & 0.1 \\\\\n Average value constraint coefficient in AVC & 0.01 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Hyper-parameters sheet}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games", "authors": ["Junkai Hu", "Li Xia"], "url": "https://arxiv.org/abs/2503.22779v2", "attribution": "\"Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games\" by Junkai Hu and Li Xia, arXiv:2503.22779v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09972v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|l|l|l|}\n \\hline\n $\\rho$ & \\emph{SIFT} & \\emph{SURF} & \\emph{ORB} & \\emph{FREAK} & \\emph{BRIEF} & \\textbf{ARSRG}$_{1^{st}}$ & \\textbf{ARSRG}$_{2^{nd}}$\\\\ \\hline\n $0.6$ & 0.7485 & 0.8400 & 0.6500 & 0.3558 & 0.4300 & 0.6700 & 0.6750\\\\ \\hline\n $0.7$ & 0.7051 & 0.6800 & 0.6116 & 0.3360 & 0.3995 & 0.7133 & 0.7500\\\\ \\hline\n $0.8$ & 0.6963 & 0.5997 & 0.5651 & 0.2645 & 0.4227 & 0.6115 & 0.8000\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications", "authors": ["Mario Manzo"], "url": "https://arxiv.org/abs/1912.09972v1", "attribution": "\"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications\" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.08391v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Values for the reliability measures of accuracy, precision and coverage for the capture-recapture population size estimators of Horvitz-Thompson, generalised Chao and generalised Zelterman, where $S=1000$, $N=1000$, $\\Bar{t}=900$, $\\lambda^C=0.0004$, $\\lambda^L=0.007$, $\\lambda^U=0.009$, $\\gamma=1.5$, $\\sigma=0.8$, $\\alpha=36$, $\\beta=8.5$ and $\\rho=0.4$ for various proportions of outliers.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\toprule\n & & \\multicolumn{5}{c}{Proportion of Outliers} \\\\ \\cline{3-7}\n Measure & Estimator & 0.0\\% & 0.1\\% & 0.5\\% & 1.0\\% & 2.0\\% \\\\ \\hline\n & Horvitz-Thompson & 16 & 30 & 211 & 677 & 2.1e+06 \\\\\n Accuracy & Generalised Chao & 25 & 27 & 27 & 27 & 26 \\\\\n & Generalised Zelterman & 29 & 32 & 31 & 32 & 32 \\\\ \\hline\n & Horvitz-Thompson & 95 & 100 & 136 & 290 & 6.7e+07 \\\\\n Precision & Generalised Chao & 162 & 162 & 163 & 162 & 162 \\\\\n & Generalised Zelterman & 181 & 181 & 185 & 184 & 187 \\\\ \\hline\n & Horvitz-Thompson & 95.5\\% & 69.6\\% & 7.0\\% & 7.4\\% & 60.6\\% \\\\\n Coverage & Generalised Chao & 96.4\\% & 96.0\\% & 96.4\\% & 96.7\\% & 95.6\\% \\\\\n & Generalised Zelterman & 95.7\\% & 94.7\\% & 95.8\\% & 96.7\\% & 94.8\\% \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Performance of capture-recapture population size estimators under covariate information", "authors": ["Layna Charlie Dennett", "Dankmar Böhning"], "url": "https://arxiv.org/abs/2312.08391v1", "attribution": "\"Performance of capture-recapture population size estimators under covariate information\" by Layna Charlie Dennett and Dankmar Böhning, arXiv:2312.08391v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.20254v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{List of input parameters and binary variables of ILP program.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n \\textbf{Input} & \\textbf{Meaning}\\\\\n \\midrule\n \\footnotesize\n $|B|=2$ & Number of BSs. \\\\\n$|I|=8$ & Number of RISs.\\\\\n $E=200$ & Number of RIS elements .\\\\\n $|N|=50$ & Number of time slots.\\\\\n $\\theta=10^o$ & Beamwidth .\\\\\n $V=20 \\cdot 10^6$ Hz & Bandwidth . \\\\\n $T=290$ Kelvin & Absolute temperature .\\\\\n $P_b=1$ mW & Power of BS.\\\\\n $f=28 \\cdot 10^9$ Hz & Frequency .\\\\\n \n\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "An Optimization Driven Link SINR Assurance in RIS-assisted Indoor Networks", "authors": ["Cao Vien Phung", "Max Franke", "Ehsan Tohidi", "June Heinemann", "Andre Drummond", "Stefan Schmid", "Slawomir Stanczak", "Admela Jukan"], "url": "https://arxiv.org/abs/2412.20254v1", "attribution": "\"An Optimization Driven Link SINR Assurance in RIS-assisted Indoor Networks\" by Cao Vien Phung, Max Franke, Ehsan Tohidi, June Heinemann, Andre Drummond, Stefan Schmid, Slawomir Stanczak, and Admela Jukan, arXiv:2412.20254v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.16137v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||l|l|l||}\n\\hline\n\\textbf{Parameter} & \\textbf{Value} & \\textbf{Description} \\tabularnewline \\hline\nHeight & $h$ & 60~cm \\tabularnewline\nDepression angle & $\\theta$ & $36^{\\circ}$ \\tabularnewline\nFocal length & $f$ & 0.0367~cm \\tabularnewline\nSquare tile sides & $s$ & 20~cm \\tabularnewline \\hline\nSignal-to-intrinsic noise ratio & SINR & 3~dB (IP), 10~dB (MI) \\tabularnewline\nSignal-to-sensor noise ratio & $\\sigma^2/N_0$ & 10--80, 45~dB (AR1) \\tabularnewline \\hline\nTile depth count & $N_d$ & 11 tiles \\tabularnewline\nTile width count & $N_w$ & 6 tiles \\tabularnewline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{These parameters are employed in the numerical simulations.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Camera-Based Localization and Enhanced Normalized Mutual Information", "authors": ["Vishnu Teja Kunde", "Jean-Francois Chamberland", "Siddharth Agarwal"], "url": "https://arxiv.org/abs/2412.16137v1", "attribution": "\"Camera-Based Localization and Enhanced Normalized Mutual Information\" by Vishnu Teja Kunde, Jean-Francois Chamberland, and Siddharth Agarwal, arXiv:2412.16137v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.08691v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of results for estimation of the outcome mean. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccccccc}\n\t\t\t\\toprule\n & & &\\multicolumn{2}{c}{(C1)} & \\multicolumn{2}{c}{(C2)}& \\multicolumn{2}{c}{(C3)} &\\multicolumn{2}{c}{(C4)}&\\multicolumn{2}{c}{(C5)}\\\\\n & & &\\multicolumn{2}{c}{All correct} & \\multicolumn{2}{c}{mis $p(z\\mid u)$}& \\multicolumn{2}{c}{mis $p(y\\mid u)$} &\\multicolumn{2}{c}{mis $\\eta(x)$}&\\multicolumn{2}{c}{All mis}\\\\\n&\t$\\hat{\\mu}_{{\\textup{cc}}}$ &$\\hat{\\mu}_{{\\textup{full}}}$ & $\\tilde{\\mu}$ & $\\hat{\\mu}_{{\\textup{dr}}}$ & $\\tilde{\\mu}$ & $\\hat{\\mu}_{{\\textup{dr}}}$ & $\\tilde{\\mu}$ & $\\hat{\\mu}_{{\\textup{dr}}}$ & $\\tilde{\\mu}$ & $\\hat{\\mu}_{{\\textup{dr}}}$& $\\tilde{\\mu}$ & $\\hat{\\mu}_{{\\textup{dr}}}$ \\\\\n\t \\hline\n& \\multicolumn{12}{c}{$n=500^\\dag$}\\\\\n$|\\text{Bias}|$ &.136 & .001 & .004 & .004 & .002 & .089 & .006 & .004 & .042 & .032 & .093 & .103\\\\\n$\\sqrt{\\text{Var}}$ & .024 & .021 & .055 & .050 & .055 & .046 & .057 & .050 & .049 & .097 & .047 & .040 \\\\\n$\\sqrt{\\text{EVar}}$ &.026 & .022 & .055 & .080 & .056 & 6.349 & .064 & .070 & .059 & .042 & .048 & .045\\\\\nCov95 & .000& .957 & .912 & .943 & .920 & .948 & .901 & .943 & .848 & .847 & .499 & .386 \\\\\\cline{2-13}\n& \\multicolumn{12}{c}{$n=1000$}\\\\\n$|\\text{Bias}|$ &.136 & .000 & .004 & .001 & .002 & .088 & .005 & .001 & .042 & .017 & .092 & .101\\\\\n$\\sqrt{\\text{Var}}$ & .017 & .016 & .039 & .036 & .039 & .032 & .041 & .036 & .034 & .034 & .033 & .028\\\\\n$\\sqrt{\\text{EVar}}$ & .018 & .016 & .037 & .039 & .038 & 1.813 & .039 & .039 & .034 & .029 & .034 & .032\\\\\nCov95 & .000 & .947 & .916 & .929 & .931 & .939 & .920 & .941 & .750 & .862 & .219 & .084\\\\\\cline{2-13}\n& \\multicolumn{12}{c}{$n=5000$}\\\\\n$|\\text{Bias}|$ &.136 & .000& .001 & .000 & .001& .090 & .002 & .000 & .044 & .013 & .093 & .102\\\\\n$\\sqrt{\\text{Var}}$ & .008 & .007 & .017 & .015 & .017 & .014 & .018 & .015 & .015 & .015 & .015 & .013\\\\\n$\\sqrt{\\text{EVar}}$ & .008 & .007 & .017 & .016 & .017 & .066 & .018 & .016 & .015 & .013 & .015 & .014 \\\\\nCov95 & .000& .954 & .939 & .950 & .943 & .578 & .933 & .951 & .169 & .806 & .000 & .000\\\\\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On Doubly Robust Estimation with Nonignorable Missing Data Using Instrumental Variables", "authors": ["Baoluo Sun", "Wang Miao", "Deshanee S. Wickramarachchi"], "url": "https://arxiv.org/abs/2311.08691v4", "attribution": "\"On Doubly Robust Estimation with Nonignorable Missing Data Using Instrumental Variables\" by Baoluo Sun, Wang Miao, and Deshanee S. Wickramarachchi, arXiv:2311.08691v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.19328v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative results for the XCAT and MAGIX datasets for 3, 4, and 9 training projections. Bolding is used to indicate the best score.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|ccc|ccc|ccc}\n\\hline\n\\multirow{2}{*}{Dataset} & \\multirow{2}{*}{Method} & \\multicolumn{3}{c|}{3-view} & \\multicolumn{3}{c|}{4-view} & \\multicolumn{3}{c}{9-view} \\\\ \\cline{3-11} \n & & DSC & PSNR & SSIM & DSC & PSNR & SSIM & DSC & PSNR & SSIM \\\\ \\hline\n & SAX-NeRF & 0.00 & 13.49 & 0.58 & 0.00 & 12.89 & 0.58 & 0.00 & 11.44 & 0.55 \\\\\n & X-Gaussian & 0.05 & 12.58 & 0.49 & 0.22 & 12.53 & 0.51 & 0.37 & 9.30 & 0.49 \\\\\nXCAT & R2-Gaussian & 0.01 & 13.02 & 0.57 & 0.00 & 12.87 & 0.58 & 0.49 & 15.77 & 0.71 \\\\\n & NeRF-CA & 0.41 & 11.69 & 0.65 & 0.76 & 11.66 & 0.72 & 0.74 & \\textbf{16.25} & 0.83 \\\\\n & Ours & \\textbf{0.75} & \\textbf{15.34} & \\textbf{0.79} & \\textbf{0.84} & \\textbf{15.16} & \\textbf{0.80} & \\textbf{0.87} & 14.12 & \\textbf{0.84} \\\\ \\hline\n & SAX-NeRF & 0.00 & 13.70 & 0.71 & 0.00 & 14.17 & 0.72 & 0.00 & 13.72 & 0.74 \\\\\n & X-Gaussian & 0.26 & 13.73 & 0.65 & 0.20 & 14.54 & 0.68 & 0.47 & 14.20 & 0.67 \\\\\nMAGIX & R2-Gaussian & 0.00 & 13.09 & 0.55 & 0.00 & 13.90 & 0.58 & 0.00 & 16.24 & 0.70 \\\\\n & NeRF-CA & 0.82 & \\textbf{18.46} & 0.80 & 0.81 & 13.34 & 0.72 & 0.90 & 18.49 & 0.85 \\\\\n & Ours & \\textbf{0.88} & 14.97 & \\textbf{0.82} & \\textbf{0.90} & \\textbf{16.70} & \\textbf{0.86} & \\textbf{0.90} & \\textbf{18.92} & \\textbf{0.88} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "NerT-CA: Efficient Dynamic Reconstruction from Sparse-view X-ray Coronary Angiography", "authors": ["Kirsten W. H. Maas", "Danny Ruijters", "Nicola Pezzotti", "Anna Vilanova"], "url": "https://arxiv.org/abs/2507.19328v1", "attribution": "\"NerT-CA: Efficient Dynamic Reconstruction from Sparse-view X-ray Coronary Angiography\" by Kirsten W. H. Maas, Danny Ruijters, Nicola Pezzotti, and Anna Vilanova, arXiv:2507.19328v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.01704v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccccc}\n \\toprule\n \\(a_{0,1}\\) & \\(a_{0,2}\\) & \\(a_{0,3}\\) & \\(a_{0,4}\\) & \\(a_{0,5}\\) & \\(a_{0,6}\\) &\n \\(a_{0,7}\\) & \\(a_{0,8}\\) & \\(a_{0,9}\\) & \\(a_{0,10}\\) & \\(a_{0,11}\\) & \\(a_{0,12}\\) \\\\\n \\midrule\n 0 & 0.002 & 0.009 & 0.021 & 0.037 & 0.056 & 0.076 & 0.095 & 0.111 & 0.093 & 0.070 & 0.039 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Initial asset distribution obtained from a deterministic simulation without climate change.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Using Machine Learning to Compute Constrained Optimal Carbon Tax Rules", "authors": ["Felix Kübler", "Simon Scheidegger", "Oliver Surbek"], "url": "https://arxiv.org/abs/2507.01704v1", "attribution": "\"Using Machine Learning to Compute Constrained Optimal Carbon Tax Rules\" by Felix Kübler, Simon Scheidegger, and Oliver Surbek, arXiv:2507.01704v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.05163v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||l|cc|cc||} % centered columns (4 columns)\n\t\t\t\\hline\\hline %inserts double horizontal lines\n\t\t\tAlgorithms & bad data & bad data & data attack & data attack \\\\\n& (EPFL) & (Texas) & (EPFL) & (Texas) \\\\\t\t\t\\hline\\hline\n\t\t\tAnomaly-free Meas.\t & 6.6e-07\t& 2.5 e-06 & 6.7e-07\t& 4.3 e-06\t\\\\\n\t\t\tAnomaly Meas.\t\t & 1.1e-02\t& 1.4 e-02 & 2.3e-02\t& 2.3 e-02\\\\\n\t\t\tCleaned by ICA-GAN\t\t& 1.7e-03 \t& 3.2 e-03 & 3.7e-03\t& 6.4 e-03 \\\\\n\t\t\tCleaned by OC-SVM \t & 3.2e-03\t& 6.2 e-03 & 1.5e-02\t& 1.2 e-02\t\\\\\n\t\t\tCleaned by F-AnoGAN\t & 6.1e-03\t& 8.2 e-03 \t& 1.3e-02 & 2.1 e-02\t\\\\\n\t\t\tCleaned by NRT\t & 8.7e-03 & 4.0 e-03\t& 2.1e-02 & 2.2 e-02\t\\\\\n\t\t\t\\hline\\hline %inserts single line\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Average squared error of sate estimation. }\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems", "authors": ["Kursat Rasim Mestav", "Xinyi Wang", "Lang Tong"], "url": "https://arxiv.org/abs/2012.05163v2", "attribution": "\"A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems\" by Kursat Rasim Mestav, Xinyi Wang, and Lang Tong, arXiv:2012.05163v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.09145v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The effect of $z$ on $\\delta_{min}$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\n $z$ &$\\delta_{min}$ \\\\\n\\hline\n1 & 0,2 \\\\\n4 & 0,2857143 \\\\\n10& 0,3125\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Exploring the stability of solar geoengineering agreements", "authors": ["Niklas V. Lehmann"], "url": "https://arxiv.org/abs/2210.09145v3", "attribution": "\"Exploring the stability of solar geoengineering agreements\" by Niklas V. Lehmann, arXiv:2210.09145v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.14548v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n\\hline\n\\textbf{} & \\textbf{a1 vs.~a2} & \\textbf{a1 vs.~GT} & \\textbf{a2 vs.~GT} \\\\ \\hline\n$\\kappa$ & 0.71 & 0.75 & 0.85 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Cohen's Kappa reliability between categorical labels from annotators 1 and 2 (a1, a2) and the predefined emotion (GT).}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "EmoTale: An Enacted Speech-emotion Dataset in Danish", "authors": ["Maja J. Hjuler", "Harald V. Skat-Rørdam", "Line H. Clemmensen", "Sneha Das"], "url": "https://arxiv.org/abs/2508.14548v1", "attribution": "\"EmoTale: An Enacted Speech-emotion Dataset in Danish\" by Maja J. Hjuler, Harald V. Skat-Rørdam, Line H. Clemmensen, and Sneha Das, arXiv:2508.14548v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.16162v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimated values of parameters in numerical simulations}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\n & $\\alpha$ & $\\beta$ & $\\epsilon$ & $\\rho$ & $\\eta$\\\\\n\\hline \nChina & 14.13 & 8.02 & 0 & 0.254 & -0.152\\\\\nUS & 23.52 & 21.21 & 0 & 0.331 & -0.178\\\\\nEU & 24.18 & 74.19 & 0 & 0.043 & -0.020\\\\\nJapan & 8.87 & 22.90 & 0 & 0.021 & -0.011\\\\\nRussia & 6.23 & 24.05 & 0 & 0.026 & -0.017\\\\\nIndia & 4.90 & 1.96 & 0 & 0.662 & -0.412\\\\\nMidEast & 6.92 & 4.41 & 0 & 0.452 & -0.299\\\\\nLatAm & 7.29 & 4.71 & 0 & 0.380 & -0.245\\\\\nOthAsia & 4.16 & 6.17 & 0 & 0.593 & -0.454\\\\\nEurasia & 9.09 & 9.06 & 0 & 0.211 & -0.137\\\\\nOHI & 14.17 & 26.12 & 0 & 0.107 & -0.054\\\\\nAfrica & 3.53 & 2.02 & 0 & 0.446 & -0.332\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Long Coalition Leads to Shrink? The Roles of Tipping and Technology-Sharing in Climate Clubs", "authors": ["Lei Zhu", "Zhihao Yan", "Hongbo Duan", "Yongyang Cai", "Xiaobing Zhang"], "url": "https://arxiv.org/abs/2506.16162v1", "attribution": "\"Long Coalition Leads to Shrink? The Roles of Tipping and Technology-Sharing in Climate Clubs\" by Lei Zhu, Zhihao Yan, Hongbo Duan, Yongyang Cai, and Xiaobing Zhang, arXiv:2506.16162v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06248v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experiment results.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n \\textbf{Error Measure}& \\textbf{$X$ Offset (cm)}& \\textbf{$Y$ Offset (cm)} & \\textbf{$\\theta$ Offset (deg)}\\\\ \n \\hline\n Max Abs. Error & 3.800 & 3.642 & 6.200\\\\\n \\hline\n Mean Abs. Error & 0.822 & 0.934 & 1.533\\\\\n \\hline\n RMSE & 0.896 & 1.182 & 1.661\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Local Navigation and Docking of an Autonomous Robot Mower using Reinforcement Learning and Computer Vision", "authors": ["Ali Taghibakhshi", "Nathan Ogden", "Matthew West"], "url": "https://arxiv.org/abs/2101.06248v3", "attribution": "\"Local Navigation and Docking of an Autonomous Robot Mower using Reinforcement Learning and Computer Vision\" by Ali Taghibakhshi, Nathan Ogden, and Matthew West, arXiv:2101.06248v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11223v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|ccc}\n \\hline\n MIMBs & \\multicolumn{3}{c|}{COCO} & \\multicolumn{3}{c}{OCHuman}\\\\\n Placement & $\\text{AP}$ & $\\text{AP}^{50}$ & $\\text{AP}^{75}$ & $\\text{AP}$ & $\\text{AP}^{50}$ & $\\text{AP}^{75}$ \\\\\n \\hline\n Stage 1 & 78.7 & 94.4 & 85.4 & 72.3 & 89.7 & 78.2 \\\\\n Stage 2 & 78.8 & 94.4 & 85.6 & 74.0 & 90.1 & 80.3\\\\\n Stage 3 & 78.8 & 94.4 & 85.8 & 74.4 & 90.7 & 80.9\\\\\n Stage 4 & 78.5 & 94.4 & 85.5 & 70.8 & 89.8 & 77.5\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{MIMBs placement at various stages in HRNet and its impact on COCO and OCHuman \\texttt{val} sets using ground truth bounding boxes.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation", "authors": ["Rawal Khirodkar", "Visesh Chari", "Amit Agrawal", "Ambrish Tyagi"], "url": "https://arxiv.org/abs/2101.11223v3", "attribution": "\"Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation\" by Rawal Khirodkar, Visesh Chari, Amit Agrawal, and Ambrish Tyagi, arXiv:2101.11223v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00561v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|rrrr|}\n \\hline\n & \\multicolumn{4}{c|}{CAB dataset} \\\\\\hline\n & $F_{best}$ & $F_{avg}$ & $F_{worst}$ & Time \\\\\n \\hline \n 10-3-A & 7579789.86 & 7569673.41 & 7550847.87 & 357.8 \\\\ \n 10-3-B & 7277978.87 & 7266106.82 & 7250203.92 & 417.2 \\\\ \n 10-3-C & 6009770.36 & 5997034.23 & 5978195.89 & 611.12 \\\\ \n 10-3-D & 7785161.46 & 7768830.46 & 7760021.75 & 509.19 \\\\ \n 10-3-E & 7499243.92 & 7486263.72 & 7473209.28 & 466.66 \\\\ \\hline\n 10-5-A & 9128063.70 & 9105145.39 & 9093104.78 & 714.14 \\\\ \n 10-5-B & 8952557.97 & 8933934.60 & 8920928.81 & 757.12 \\\\ \n 10-5-C & 9030787.48 & 9024203.19 & 9011723.64 & 746.48 \\\\ \n 10-5-D & 9538181.74 & 9525119.64 & 9509612.92 & 742.49 \\\\ \n 10-5-E & 9268103.90 & 9254889.63 & 9245576.18 & 735.09 \\\\ \\hline\n 15-3-A & 21858598.70 & 21508774.62 & 21107368.23 & 1383.22 \\\\ \n 15-3-B & 20409270.85 & 20355753.84 & 20303000.47 & 1349.67 \\\\ \n 15-3-C & 23579809.45 & 23441841.01 & 23240093.59 & 1264.34 \\\\ \n 15-3-D & 21853493.40 & 21792899.71 & 21703149.83 & 1263.45 \\\\ \n 15-3-E & 19887129.23 & 19788833.57 & 19740653.96 & 1368.98 \\\\ \\hline\n 15-5-A & 27163151.65 & 26923084.59 & 26739195.77 & 1549.77 \\\\ \n 15-5-B & 25583274.17 & 25245731.31 & 25026430.12 & 1721.55 \\\\ \n 15-5-C & 24983068.60 & 24731360.63 & 24606186.56 & 1507.26 \\\\ \n 15-5-D & 25507336.98 & 25370724.83 & 25180015.33 & 1597.90 \\\\ \n 15-5-E & 24630390.71 & 24254597.17 & 23594976.22 & 1650.06 \\\\ \\hline\n 15-7-A & 28179268.18 & 28049607.81 & 27880561.91 & 2034.02 \\\\ \n 15-7-B & 26355102.50 & 26228863.71 & 25936792.36 & 1969.28 \\\\ \n 15-7-C & 27330429.51 & 26979800.08 & 26692984.36 & 2174.41 \\\\ \n 15-7-D & 27223736.49 & 27041098.97 & 26814914.93 & 2064.19 \\\\ \n 15-7-E & 26599249.76 & 26158905.27 & 25790075.15 & 2049.82 \\\\ \\hline\n 20-3-A & 18941061.90 & 17690575.86 & 16803651.35 & 1918.88 \\\\ \n 20-3-B & 19028903.10 & 18422274.97 & 17737439.81 & 2093.41 \\\\ \n 20-3-C & 18216841.10 & 17779239.83 & 17340323.27 & 2016.73 \\\\ \n 20-3-D & 18569665.78 & 18410876.59 & 18211960.12 & 2124.59 \\\\ \n 20-3-E & 20070474.05 & 19532927.51 & 18523824.11 & 2088.11 \\\\ \\hline\n 20-5-A & 28875907.94 & 26965882.26 & 25366456.06 & 2230.35 \\\\ \n 20-5-B & 25549850.97 & 25330623.25 & 24931117.33 & 2331.58 \\\\ \n 20-5-C & 25026549.94 & 24743154.18 & 24576537.48 & 2414.11 \\\\ \n 20-5-D & 25399327.45 & 25240439.04 & 24989447.34 & 2232.33 \\\\ \n 20-5-E & 24608231.01 & 23963526.76 & 23622619.22 & 2366.48 \\\\ \\hline\n 20-7-A & 25865063.53 & 25681964.78 & 25442226.07 & 2827.01 \\\\ \n 20-7-B & 23939417.43 & 23353684.66 & 22956599.62 & 2472.59 \\\\ \n 20-7-C & 27709890.82 & 27204590.57 & 26744055.67 & 2755.12 \\\\ \n 20-7-D & 28147934.36 & 27141064.81 & 26348915.62 & 2845.75 \\\\ \n 20-7-E & 27145335.43 & 26435532.37 & 25253539.88 & 2798.04 \\\\ \\hline\n 25-3-A & 71417725.92 & 67459201.94 & 8292765.93 & 3560.84 \\\\\n 25-3-B & 57692278.39 & 55433416.67& 7022188.03 & 4026.15 \\\\ \n 25-3-C & 49847917.08 & 48496765.74 & 7779608.08 & 4472.61 \\\\ \n 25-3-D & 57147532.96 & 55122114.20 & 6334730.37 & 3892.51 \\\\ \n 25-3-E & 54113218.44 & 50262722.67 & 7745726.48 & 3910.67 \\\\ \\hline\n 25-5-A & 78529235.66 & 75106345.62 & 9401275.56 & 4530.15 \\\\ \n 25-5-B & 81321193.08 & 78179173.01 & 9633241.15 & 4053.66 \\\\ \n 25-5-C & 90962091.93 & 86229611.42 & 9989281.04 & 6415.76 \\\\ \n 25-5-D & 90321112.02 & 82902659.25 & 9465055.76 & 4886.71 \\\\ \n 25-5-E & 83194268.62 & 79276170.55 & 10605750.29 & 4229.60 \\\\ \\hline\n 25-7-A & 113301804.21 & 104572327.86 & 10801125.49 & 4579.74 \\\\ \n 25-7-B & 101703768.38 & 99866719.39 & 10912205.21 & 6435.04 \\\\ \n 25-7-C & 103676959.34 & 99590719.29 & 10738221.07 & 4361.13 \\\\ \n 25-7-D & 94979699.99 & 90101516.17 & 11083820.40 & 6565.95 \\\\ \n 25-7-E & 90672141.70 & 90672141.70 & 11016164.98 & 4839.47 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{{Profits obtained for the instances in the CAB dataset.}}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology", "authors": ["Víctor Blanco", "José-Fernando Camacho-Vallejo", "Carlos Corpus"], "url": "https://arxiv.org/abs/2503.00561v1", "attribution": "\"A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology\" by Víctor Blanco, José-Fernando Camacho-Vallejo, and Carlos Corpus, arXiv:2503.00561v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.08623v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{i-vector statistic in DAC 13 i-vector dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc} \n\\toprule\n & \\textbf{SWB} & \\textbf{SRE} & \\textbf{SRE-1phn} \\\\ \n\\hline\\hline\n\\textbf{\\#spks} & 3114 & 3790 & 3787 \\\\\n\\textbf{\\#calls} & 33039 & 36470 & 25640 \\\\\n\\textbf{\\#calls/spkrs} & 10.6 & 9.6 & 6.77 \\\\\n\\textbf{\\#phone\\_num/spkrs} & 3.8 & 2.8 & 1.0 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Adversarial Training for Multi-domain Speaker Recognition", "authors": ["Qing Wang", "Wei Rao", "Pengcheng Guo", "Lei Xie"], "url": "https://arxiv.org/abs/2011.08623v1", "attribution": "\"Adversarial Training for Multi-domain Speaker Recognition\" by Qing Wang, Wei Rao, Pengcheng Guo, and Lei Xie, arXiv:2011.08623v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.18836v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Understanding the influence of vocal cord articulators on linguistic learning}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccl}\n \\toprule\n Database & Model's input & CER $\\downarrow$ & WER $\\downarrow$ \\\\\n \\midrule\n & Lip only & 19.48 & 25.50\\\\\n USC-TIMIT & Masked Lip & 14.38 & 19.04\\\\\n & Full rtMRI & \\textbf{10.95} & \\textbf{14.38}\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MRI2Speech: Speech Synthesis from Articulatory Movements Recorded by Real-time MRI", "authors": ["Neil Shah", "Ayan Kashyap", "Shirish Karande", "Vineet Gandhi"], "url": "https://arxiv.org/abs/2412.18836v2", "attribution": "\"MRI2Speech: Speech Synthesis from Articulatory Movements Recorded by Real-time MRI\" by Neil Shah, Ayan Kashyap, Shirish Karande, and Vineet Gandhi, arXiv:2412.18836v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14510v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Upper bounds for $f(r,s,t)$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\n\\hline\n$(\\min\\{r,s\\}, t)$ & Bounds &\n$\\min\\{r,s,t\\}$ & Bounds \\\\ \n\\hline \n$(3,3)$ & $24626$ & $4$ & $2283$ \\\\\n$(3,4)$ & $14750$ & $5$& $907$ \\\\\n$(3,n)$, $n\\ge 5$ & $6648$ & $6$& $697$ \\\\\n$(4,3)$ & $7254$ & $7$& $635$ \\\\\n$(n,3)$, $n\\ge 5$ & $3406$ & $\\ge 8$& $573$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The inter-universal Teichmüller theory and new Diophantine results over the rational numbers. I", "authors": ["Zhong-Peng Zhou"], "url": "https://arxiv.org/abs/2503.14510v1", "attribution": "\"The inter-universal Teichmüller theory and new Diophantine results over the rational numbers. I\" by Zhong-Peng Zhou, arXiv:2503.14510v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.15872v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|ccc|cccccc|cccccc|}\n\\hline\n\\rowcolor{gray!20} \n\\textbf{Model} & \\textbf{Learning Rate} & \\multicolumn{3}{c|}{\\textbf{Min Size Th.}} & \\multicolumn{6}{c|}{\\textbf{Dice Scores}} & \\multicolumn{6}{c|}{\\textbf{HD 95}} \\\\\n\\cline{2-16}\n\\rowcolor{gray!20} \n & & \\textbf{ET} & \\textbf{TC} & \\textbf{WT} & \\textbf{ET} & \\textbf{TC} & \\textbf{WT} & \\textbf{NETC} & \\textbf{CC} & \\textbf{ED} & \\textbf{ET} & \\textbf{TC} & \\textbf{WT} & \\textbf{NETC} & \\textbf{CC} & \\textbf{ED} \\\\\n\\hline\n\\multirow{6}{*}{\\textbf{MedNeXt Base}} \n& \\multirow{3}{*}{0.0027} \n& 100 & 150 & 500 \n& 0.555 & 0.869 & 0.869 & 0.834 & 0.71 & \\cellcolor{green!30}0.967 \n& 122.219 & 20.595 & 20.595 & 23.842 & 91.287 & \\cellcolor{green!30}12.33 \\\\\n \n& & 50 & 75 & 250 \n& 0.543 & 0.838 & 0.838 & 0.798 & 0.655 & \\cellcolor{green!30}0.967 \n& 124.183 & 33.228 & 33.226 & 34.421 & 98.004 & \\cellcolor{green!30}12.33 \\\\\n \n& & 25 & 37 & 125 \n& 0.507 & 0.832 & 0.832 & 0.791 & 0.634 & \\cellcolor{green!30}0.967 \n& 122.545 & 37.176 & 37.173 & 38.199 & 104.089 & \\cellcolor{green!30}12.33 \\\\\n\\cline{2-16}\n \n& \\multirow{3}{*}{0.0005} \n& 100 & 150 & 500 \n& 0.654 & \\cellcolor{green!30}0.89 & \\cellcolor{green!30}0.89 & \\cellcolor{green!30}0.853 & \\cellcolor{green!30}0.723 & \\cellcolor{green!30}0.967 \n& 88.651 & \\cellcolor{green!30}17.269 & \\cellcolor{green!30}17.269 & 18.571 & \\cellcolor{green!30}83.234 & \\cellcolor{green!30}12.33 \\\\\n \n& & 50 & 75 & 250 \n& \\cellcolor{green!30}0.657 & \\cellcolor{green!30}0.89 & \\cellcolor{green!30}0.89 & \\cellcolor{green!30}0.853 & \\cellcolor{green!30}0.723 & \\cellcolor{green!30}0.967 \n& \\cellcolor{green!30}76.553 & \\cellcolor{green!30}17.269 & \\cellcolor{green!30}17.269 & \\cellcolor{green!30}18.391 & \\cellcolor{green!30}83.234 & \\cellcolor{green!30}12.33 \\\\\n \n& & 25 & 37 & 125 \n& 0.619 & 0.885 & 0.885 & 0.848 & 0.659 & \\cellcolor{green!30}0.967 \n& 86.829 & 19.312 & 19.312 & 20.398 & 104.002 & \\cellcolor{green!30}12.33 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{LesionWise Dice Scores, HD 95 Results, Minimum Size Thresholds, and Learning Rates for Various Runs}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics", "authors": ["Sarim Hashmi", "Juan Lugo", "Abdelrahman Elsayed", "Dinesh Saggurthi", "Mohammed Elseiagy", "Alikhan Nurkamal", "Jaskaran Walia", "Fadillah Adamsyah Maani", "Mohammad Yaqub"], "url": "https://arxiv.org/abs/2411.15872v2", "attribution": "\"Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics\" by Sarim Hashmi, Juan Lugo, Abdelrahman Elsayed, Dinesh Saggurthi, Mohammed Elseiagy, Alikhan Nurkamal, Jaskaran Walia, Fadillah Adamsyah Maani, and Mohammad Yaqub, arXiv:2411.15872v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.01467v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Network Propagation and Macroeconomic Factors}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n \\toprule\n Variable & $\\Delta c_{t}$ & $\\Delta c_{t}^{dur}$ & $\\Delta c_{t}^{nondur}$ & $\\Delta y_{t}$ & $\\Delta y_{t}^{dur}$ & $\\Delta y_{t}^{nondur}$ & $\\Delta D_{t}$ \\\\\n \\midrule\n $W_{ut}$ & 0.003 & -0.111** & -0.158 & -0.112 & -1.383** & -0.416 & -0.033* \\\\\n & (0.103) & (0.048) & (0.087) & (0.275) & (0.607) & (0.229) & (0.021) \\\\\n $W_{dt}$ & -0.174* & -0.031 & -0.022 & -0.598** & -0.387 & -0.058 & -0.010 \\\\\n & (0.074) & (0.054) & (0.101) & (0.222) & (0.684) & (0.267) & (0.021) \\\\\n $a_t$ & 0.321** & 0.128** & 0.197** & 1.018** & 1.607** & 0.520** & 0.018 \\\\\n & (0.114) & (0.055) & (0.108) & (0.271) & (0.689) & (0.285) & (0.010) \\\\\n $g_t$ & 0.271 & -0.056 & 0.604 & -0.011 & -0.704 & 1.592 & 0.029 \\\\\n & (0.332) & (0.276) & (0.638) & (0.956) & (3.450) & (1.682) & (0.109) \\\\\\midrule\n Intercept & -0.485 & -0.111 & -0.205 & 1.218 & 4.09 & 2.023 & 0.042 \\\\\n Obs & 24 & 24 & 24 & 24 & 24 & 24 & 24 \\\\\n $R^2$ & 0.56 & 0.537 & 0.376 & 0.679 & 0.537 & 0.376 & 0.257 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Risk in Network Economies", "authors": ["Victor Sellemi"], "url": "https://arxiv.org/abs/2208.01467v1", "attribution": "\"Risk in Network Economies\" by Victor Sellemi, arXiv:2208.01467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.11871v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Function describing angular transients, the M\\\"{o}bius transformation on graph function, Taylor expansion of sum of coordinate functions, and the initial value}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|l|l|}\n \\hline\n\\thead{Function} & \\thead{M\\\"{o}bius transformation $e^{\\mathrm{i}z}$}& \\thead{The sum of Taylor expansions $\\mathfrak{T}(\\mathfrak{Re})$ and $\\mathfrak{T}(\\mathfrak{Im})$} & \\thead{$\\theta=0$}\\\\\n \\hline\n$f(\\theta)$ & $\\widetilde{f}:=e^{-\\cos\\frac{\\theta}{2}}e^{\\mathrm{i}\\theta}$ & $\\mathfrak{T}(\\widetilde{f})=\\frac{1}{e}+\\frac{\\theta}{e}-\\frac{3\\theta^2}{8e}-\\frac{\\theta^3}{24e}+\\mathcal{O}(\\theta^4)$ & $1$\\\\\n \\hline\n$g(\\theta)$ & $\\widetilde{g}:=e^{-\\frac{1}{2}\\sin\\frac{\\theta}{2}\\sin\\theta}e^{\\mathrm{i}\\theta}$ & $\\mathfrak{T}(\\widetilde{g})=1+\\theta-\\frac{3\\theta^2}{4}-\\frac{5\\theta^3}{12}+O(\\theta^4)$ & $0$\\\\\n \\hline\n$p(\\theta)$ & $\\widetilde{p}:=e^{-\\cot\\frac{\\theta}{2}}e^{\\mathrm{i}\\theta}$ & $ \\mathfrak{T}(\\widetilde{p})=e^{-\\frac{2}{\\theta}+\\mathcal{O}(\\theta)}\\left(1+\\theta-\\frac{\\theta^2}{2}-\\frac{\\theta^3}{6}+\\mathcal{O}(\\theta^4)\\right)$ & $\\infty$\\\\\n \\hline\n$q(\\theta)$ & $\\widetilde{q}:=e^{-\\frac{1}{2}\\csc^4\\frac{\\theta}{2}\\sin\\theta}e^{\\mathrm{i}\\theta}$ & $\\mathfrak{T}(\\widetilde{q})=e^{-\\frac{8}{\\theta^3}+\\mathcal{O}(\\theta)}\\left(1+\\theta-\\frac{\\theta^2}{2}-\\frac{\\theta^3}{6}+\\mathcal{O}(\\theta^4)\\right)$ & $\\infty$\\\\\n \\hline\n$\\theta$ & $\\widetilde{\\theta}:=e^{\\mathrm{i}\\theta}$ & $\\mathfrak{T}(\\widetilde{\\theta})=1+\\theta-\\frac{\\theta^2}{2}-\\frac{\\theta^3}{6}+\\mathcal{O}(\\theta^4)$ & $0$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Associated Discrete Laplacian in $\\mathbb{R}^3$ and Mean Curvature with Higher order Approximations", "authors": ["Wei-Hung Liao"], "url": "https://arxiv.org/abs/2501.11871v1", "attribution": "\"The Associated Discrete Laplacian in $\\mathbb{R}^3$ and Mean Curvature with Higher order Approximations\" by Wei-Hung Liao, arXiv:2501.11871v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09773v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\toprule\n\\textbf{Disease\\_tag} & Bronchiolitis & \\\\ \\midrule\n\\textbf{Exp Sym} & Runny Nose: 1 & Cough: 1 \\\\\n\\textbf{Imp Sym} & Sore Throat: 1 & Emesis: 0 \\\\\n\\textbf{} & Harsh Breath: 1 & Fever: 0 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{An example of a user goal in the Muzhi corpus, containing explicit symptoms and implicit symptoms. 1 means a symptom is confirmed by the patient, while 0 means that a symptom is denied by the patient.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks", "authors": ["Hongyin Luo", "Shang-Wen Li", "James Glass"], "url": "https://arxiv.org/abs/2101.09773v1", "attribution": "\"Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks\" by Hongyin Luo, Shang-Wen Li, and James Glass, arXiv:2101.09773v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09745v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|ccc|ccc|ccc|ccc}\n \\multicolumn{13}{c}{\\textbf{Campus dataset} $\\ (\\alpha = 0.5)$} \\\\\n & \\multicolumn{3}{c}{} \n & \\multicolumn{3}{c}{} \n & \\multicolumn{3}{c}{} \n & \\multicolumn{3}{c}{Ours} \n & \\multicolumn{3}{c}{Ours\\textsuperscript{+}}\\\\ \n \\hline\n Actor & 1 & 2 & 3 & 1 & 2 & 3 & 1 & 2 & 3 & 1 & 2 & 3 & 1 & 2 & 3 \\\\\n \\hline\n ua & .83 & .90 & .78 & .97 & .97 & .90 \n & .97 & .94 & 93\n & .86 & .97 & .91\n & .99 & .98 & .98\\\\\n la & .78 & .40 & .62 & .86 & .43 & .75 \n & .87 & .79 & 70\n & .74 & .64 & .68 \n & .91 & .70 & .92 \\\\\n ul & .86 & .74 & .83 & .93 & .75 & .92 \n & .94 & .99 & 88 \n & 1.0 & .99 & .99 \n & 1.0 & .98 & 1.0\\\\\n ll & .91 & .89 & .70 & .97 & .89 & .76 \n & .97 & .95 & 81\n & 1.0 & .98 & .99 \n & 1.0 & .98 & .99 \\\\\n \\hline\n avg & .85 & .73 & .73 & .93 & .76 & .83 \n & .94 & .93 & .85 \n & .90 & .90 & .89 \n & .98 & .91 & .98 \\\\\n \\hline\n avg\\textsuperscript{*} & \\multicolumn{3}{c|}{.77} & \\multicolumn{3}{c|}{.84} \n & \\multicolumn{3}{c|}{.91} \n & \\multicolumn{3}{c|}{.90} \n & \\multicolumn{3}{c}{\\textbf{.96}}\\\\\n \\hline\n \\multicolumn{13}{c}{} \\\\\n \\multicolumn{13}{c}{\\textbf{Shelf dataset} $\\ (\\alpha = 0.5)$} \\\\\n & \\multicolumn{3}{c}{} \n & \\multicolumn{3}{c}{} \n & \\multicolumn{3}{c}{} \n & \\multicolumn{3}{c}{Ours} \n & \\multicolumn{3}{c}{Ours\\textsuperscript{+}}\\\\ \n \\hline\n Actor & 1 & 2 & 3 & 1 & 2 & 3 & 1 & 2 & 3 & 1 & 2 & 3 & 1 & 2 & 3 \\\\\n \\hline\n ua & .72 & .80 & .91 & .82 & .83 & .93 \n & .93 & .78 & .94\n & .99 & .93 & .97\n &.1.0 & .97 & .97 \\\\\n la & .61 & .44 & .89 & .82 & .83 & .93 \n & .83 & .33 & .90 \n & .97 & .57 & .95\n & .99 & .64 & .96 \\\\\n ul & .37 & .46 & .46 & .43 & .50 & .57 \n & .96 & .95 & .97\n & .998 & 1.0 & 1.0 \n & 1.0 & 1.0 & 1.0 \\\\\n ll & .71 & .72 & .95 & .86 & .79 & .97 \n & .97 & .93 & .96\n & .998 & .99 & 1.0\n & 1.0 & 1.0 & 1.0 \\\\\n \\hline\n avg & .60 & .61 & .80 & .73 & .74 & .85 \n & .92 & .75 & .94 \n & .99 & .87 & .98\n & .998 & .90 & .98 \\\\\n \\hline\n avg\\textsuperscript{*} & \\multicolumn{3}{c|}{.67} & \\multicolumn{3}{c|}{.77} \n & \\multicolumn{3}{c|}{.87} \n & \\multicolumn{3}{c|}{.95} \n & \\multicolumn{3}{c}{\\textbf{.96}}\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Iterative Greedy Matching for 3D Human Pose Tracking from Multiple Views", "authors": ["Julian Tanke", "Juergen Gall"], "url": "https://arxiv.org/abs/2101.09745v1", "attribution": "\"Iterative Greedy Matching for 3D Human Pose Tracking from Multiple Views\" by Julian Tanke and Juergen Gall, arXiv:2101.09745v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2311.09514v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation studies on CASAS datasets - Kyoto8, Milan, Kyoto7, Aruba and Cairo. The baseline is a 2 layer fully connected network comparable to the graph in terms of the network size (measured by the number of parameters). The graph approach involves the use of the neural message-passing framework (outlined in Section ). \\textit{Embedding} refers to adding the embedding layer (outlined in Section ). \\textit{Attn} refers to the inclusion of the attention mechanism (outlined in Section ). We report the mean and standard deviation of F1 scores across five runs.}\n\\begin{tabular}{l|l|l|l|l|l}\n\\toprule\n\\multirow{2}{*}{Methods \\textbackslash Datasets} & Kyoto8 & Milan & Kyoto7 & Aruba & Cairo \\\\ \\cmidrule{2-6}\n & F1 score & F1 score & F1 score & F1 score & F1 score \\\\ \\midrule\n Baseline & 20.2 $\\pm$ 2.47 & 41.3 $\\pm$ 1.71 & 54.9 $\\pm$ 1.84 & 66.7 $\\pm$ 3.07 & 72.6 $\\pm$ 0.88 \\\\\n Graph & 71.5 $\\pm$ 0.62 & 70.9 $\\pm$ 0.89 & 76.8 $\\pm$ 0.83 & 83.7 $\\pm$ 1.68 & 78.4 $\\pm$ 0.84\\\\\n Graph + Embedding & 73.5 $\\pm$ 0.24 & 72.2 $\\pm$ 2.11 & 80.6 $\\pm$ 0.09 & 87.3 $\\pm$ 0.94 & 82.2 $\\pm$ 1.11\\\\\nGraph + Embedding + Attn & \\textbf{78.3 $\\pm$ 0.95} & \\textbf{80.4 $\\pm$ 0.52} & \\textbf{88.7 $\\pm$ 0.60} & \\textbf{92.4 $\\pm$ 0.34} & \\textbf{88.7 $\\pm$ 0.22} \\\\\\midrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes", "authors": ["Srivatsa P", "Thomas Plötz"], "url": "https://arxiv.org/abs/2311.09514v1", "attribution": "\"Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes\" by Srivatsa P and Thomas Plötz, arXiv:2311.09514v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.12114v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameter values obtained by DE in a typical run.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll} \n\t\t\\toprule\n\t\t& \\multicolumn{2}{c}{SDM} & \\multicolumn{2}{c}{DDM} \\\\ \n\t\t\\cline{2-5}\n\t\t& RT & PW & RT & PW \\\\ \n\t\t\\midrule\n\t\t$I_{ph}$(A) & 0.760775 & 1.03051 & 0.760781 & 1.03051 \\\\\n\t\t$I_0/I_{01} $$ (\\mu\\text{A}) $ & 0.323021 & 3.48226 & 0.225974 & 9.8113E-3 \\\\\n\t\t$I_{02}$($ \\mu $A) & --- & --- & 0.749344 & 3.47245 \\\\\n\t\t$n/n_1$ & 1.481184 & 48.6428 & 1.45101 & 48.64282 \\\\\n\t\t$n_2$ & --- & --- & 1.99999 & 48.64283 \\\\\n\t\t$R_s$($ \\Omega $) & 0.036377 & 1.20127 & 0.0367404 & 1.20127 \\\\\n\t\t$R_p$($ \\Omega $) & 53.71852 & 981.982 & 55.4854 & 981.982 \\\\\n\t\tRMSE & 9.8602E-4 & 2.4250E-3 & 9.8248E-4 & 2.4250E-3 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "On Solar Photovoltaic Parameter Estimation: Global Optimality Analysis and a Simple Efficient Differential Evolution Method", "authors": ["Shuhua Gao", "Yunyi Zhao", "Cheng Xiang", "Yu Ming", "Tan Kuan Tak", "Tong Heng Lee"], "url": "https://arxiv.org/abs/2011.12114v4", "attribution": "\"On Solar Photovoltaic Parameter Estimation: Global Optimality Analysis and a Simple Efficient Differential Evolution Method\" by Shuhua Gao, Yunyi Zhao, Cheng Xiang, Yu Ming, Tan Kuan Tak, and Tong Heng Lee, arXiv:2011.12114v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.04092v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|cc|}\n\\hline\nAcceleration factor & Gaussian & Spiral\\\\\n\\hline\nR1/actual value & \\multicolumn{2}{c|}{15.526}\\\\\nR8 & 15.829 & 13.813 \\\\\nR16 & 18.716 & 13.791 \\\\\nR32 & 23.043 & 13.844\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Estimated standard deviation of the noise for each mask and acceleration factor, using the magnitude reconstructed from R2 for acceleration factors greater than 1}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Parameter estimation in fluid flow models from undersampled frequency space data", "authors": ["Miriam Löcke", "Pim van Ooij", "Cristóbal Bertoglio"], "url": "https://arxiv.org/abs/2503.04092v1", "attribution": "\"Parameter estimation in fluid flow models from undersampled frequency space data\" by Miriam Löcke, Pim van Ooij, and Cristóbal Bertoglio, arXiv:2503.04092v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01001v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameters values for the optimized computer vision models with different input sizes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\toprule\nHyperparameter & $32 \\times 32$ & $64 \\times 64$ & $128 \\times 128$\\\\\n\\midrule\nNumber of base filters & 32 & 64 & 64\\\\\nDropout rate for convolutional blocks & 0.4 & 0.3 & 0.4\\\\\nBatch normalization for convolutional blocks & true & true & true\\\\\nType of global pooling & max & average & average\\\\\nIgnore additional inputs & false & false & false\\\\\n\\addlinespace\nNumber of units for the fully-connected layer & 256 & 256 & 256\\\\\nBatch normalization for the fully-connected layer & false & true & true\\\\\nDropout rate for the fully-connected layer & 0.2 & 0.4 & 0.1\\\\\nLearning rate & 0.0003 & 0.0006 & 0.0052\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Automated Assessment of Residual Plots with Computer Vision Models", "authors": ["Weihao Li", "Dianne Cook", "Emi Tanaka", "Susan VanderPlas", "Klaus Ackermann"], "url": "https://arxiv.org/abs/2411.01001v1", "attribution": "\"Automated Assessment of Residual Plots with Computer Vision Models\" by Weihao Li, Dianne Cook, Emi Tanaka, Susan VanderPlas, and Klaus Ackermann, arXiv:2411.01001v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10060v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{A Comparison of the average accuracy for each repetition over $19$ participants, the overall accuracy and the overall STD for the LDA model.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\hline\n\\hline\n\\textbf{Acc. F1 (\\%)} \\!\\!\\!\\!&\\!\\!\\!\\! \\textbf{Acc. F2} \\!\\!\\!\\!&\\!\\!\\!\\! \\textbf{Acc. F3} \\!\\!\\!\\!&\\!\\!\\!\\! \\textbf{Acc. F4} \\!\\!\\!\\!&\\!\\!\\!\\! \\textbf{Acc. F5} \\!\\!\\!\\!&\\!\\!\\!\\! \\textbf{Avg. Acc.} \\!\\!\\!\\!&\\!\\!\\!\\! \\textbf{STD (\\%)} \\\\\n\\hline\n82.58 & 69.65 & 84.21 & 82.74 & 75.27 &\\textbf{78.89} & 11.15 \\\\ \\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals", "authors": ["Mansooreh Montazerin", "Soheil Zabihi", "Elahe Rahimian", "Arash Mohammadi", "Farnoosh Naderkhani"], "url": "https://arxiv.org/abs/2201.10060v1", "attribution": "\"ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals\" by Mansooreh Montazerin, Soheil Zabihi, Elahe Rahimian, Arash Mohammadi, and Farnoosh Naderkhani, arXiv:2201.10060v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.10009v2_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Case study 2, 95\\% $\\chi^2,$ $\\chi^2_{ref} = 132.14$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\hline\n Method & $\\chi^2$ \\\\\n \\hline\n GP-MBDoE & $1805$\\\\\n MC-MBDoE & $3892$\\\\\n DE-MBDoE & $3079$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Safe model-based design of experiments using Gaussian processes", "authors": ["Panagiotis Petsagkourakis", "Federico Galvanin"], "url": "https://arxiv.org/abs/2011.10009v2", "attribution": "\"Safe model-based design of experiments using Gaussian processes\" by Panagiotis Petsagkourakis and Federico Galvanin, arXiv:2011.10009v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2401.10251v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation parameters}\n\\begin{tabular}{|l|c|r|}\n \\hline\n \\textbf{Sl.} & \\textbf{Parameters} & \\textbf{Features} \\\\\n \\hline\n 1 & Satellite & GEO \\\\\n 2 & Satellite Spot Beams No & 1 and 4 \\\\\n 3 & Environment & Rural, clear sky, complete LOS\\\\\n 4 & Operating Frequency & 2 GHz and 20 GHz\\\\\n 5 & Satellite Altitude & 35786 km\\\\\n 6 & Beam diameter & 2 GHz: 250 km; 20 GHz: 110 km\\\\\n 7 & UE max height & 1.5 m\\\\\n 8 & No of UE per SB & varies - all outdoor\\\\\n 9 & EIRP & 2 GHz: 29 dBm/Hz; 20 GHz: 10 dBm/Hz\\\\\n 10 & Bandwidth & 2 GHz: 30 MHz; 20 GHz: 400 MHz\\\\\n 11 & Sub Carrier Spacing (SCS) & 15 KHz\\\\\n 12 & Number of RBs in RBG & 1\\\\\n 13 & Noise power density, $\\sigma^2$ & -174 dBm\\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Non-Terrestrial Network (NTN): a Novel Alternate Fractional Programming for the Downlink Channels Power Allocation", "authors": ["Mahfuzur Rahman", "Zoheb Hassan", "Jeffrey H. Reed", "Lingjia Liu"], "url": "https://arxiv.org/abs/2401.10251v1", "attribution": "\"Non-Terrestrial Network (NTN): a Novel Alternate Fractional Programming for the Downlink Channels Power Allocation\" by Mahfuzur Rahman, Zoheb Hassan, Jeffrey H. Reed, and Lingjia Liu, arXiv:2401.10251v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2311.14483v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Main characteristics of each subset of SER\\_AMPEL dataset.}\n\\begin{tabular}{|c|ccc|ccc|}\n \\hline\n \\multicolumn{1}{|p{4em}|}{\\centering \\textbf{Subset}} & \\multicolumn{1}{p{4.665em}}{\\centering \\textbf{No. of \\newline{} Speakers}} & \n \\multicolumn{1}{p{4.5em}}{\\centering \\textbf{No. of \\newline{} Female}} & \n \\multicolumn{1}{p{6.5em}|}{\\centering \\textbf{Age (in years): \\newline{}$\\mu \\pm \\sigma$}} & \\multicolumn{1}{p{6em}}{\\centering \\textbf{No of \\newline{} Recordings }} & \n \\multicolumn{1}{p{5em}}{\\centering \\textbf{Average duration}} & \\multicolumn{1}{p{7.3em}|}{\\centering \\textbf{Transcriptions}} \\\\\n \\hline\n NOLD & 81 & 31 & 73.03 ± 5.4 & 591 & 40 sec & manual \\\\\n NYNG & 10 & 6 & 31.09 ± 11,38 & 150 & 1 min & manual \\\\\n AOLD & 10 & 3 & 71.4 ± 5.3 & 120 & 15 sec & automatic \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults", "authors": ["Alessandra Grossi", "Francesca Gasparini"], "url": "https://arxiv.org/abs/2311.14483v2", "attribution": "\"SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults\" by Alessandra Grossi and Francesca Gasparini, arXiv:2311.14483v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11165v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|} \n \\hline\n $\\mathtt{client(ann)\\cong t}$ & $1.00$ \\\\\n $\\mathtt{loan(l\\_1)\\cong t}$ & $1.00$ \\\\\n $\\mathtt{loan(l\\_2)\\cong t}$ & $1.00$ \\\\\n $\\mathtt{has\\_loan(ann,l\\_1)\\cong t}$ & $0.20$ \\\\\n $\\mathtt{has\\_loan(ann,l\\_2)\\cong t}$ & $0.20$ \\\\\n $\\mathtt{status(l\\_1)\\cong a}$ & $0.30$ \\\\\n $\\mathtt{status(l\\_2)\\cong d}$ & $0.70$ \\\\\n $\\mathtt{credit\\_score(ann)\\cong 601.2}$ & $0.04$ \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs", "authors": ["Nitesh Kumar", "Ondrej Kuzelka", "Luc De Raedt"], "url": "https://arxiv.org/abs/2201.11165v2", "attribution": "\"First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs\" by Nitesh Kumar, Ondrej Kuzelka, and Luc De Raedt, arXiv:2201.11165v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.15882v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n\\toprule\n\\textbf{Category Pair} & \\textbf{Accuracy} \\\\\n\\midrule\nCountries vs. Tools & 100.0\\% \\\\\nCountries vs. Clothing & 100.0\\% \\\\\nMusical Instr. vs. Countries & 96.7\\% \\\\\nCountries vs. Weather & 93.3\\% \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Top Performing Category Pairs (Layer 31)}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Beyond Transcription: Mechanistic Interpretability in ASR", "authors": ["Neta Glazer", "Yael Segal-Feldman", "Hilit Segev", "Aviv Shamsian", "Asaf Buchnick", "Gill Hetz", "Ethan Fetaya", "Joseph Keshet", "Aviv Navon"], "url": "https://arxiv.org/abs/2508.15882v1", "attribution": "\"Beyond Transcription: Mechanistic Interpretability in ASR\" by Neta Glazer, Yael Segal-Feldman, Hilit Segev, Aviv Shamsian, Asaf Buchnick, Gill Hetz, Ethan Fetaya, Joseph Keshet, and Aviv Navon, arXiv:2508.15882v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12251v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n \\hline\n \n Solar Panels & $T_1$ &$T_2$&$T_3$&$T_4$ \\\\ \\hline\n $P_1$ &$\\langle (0.87,0.13);0.86\\rangle$&$\\langle (0.32,0.68);0.68\\rangle$&$\\langle (0.84,0.16);0.82\\rangle$ &$\\langle (0.49,0.51);0.51\\rangle$\\\\\n $P_2$ &$\\langle (0.71,0.29);0.69\\rangle$&$\\langle (0.52,0.48);0.48\\rangle$&$\\langle (0.73,0.27);0.72\\rangle$&$\\langle (0.46,0.54);0.54\\rangle$\\\\\n $P_3$ &$\\langle (0.53,0.47);0.51\\rangle$&$\\langle (0.66,0.34);0.34\\rangle$&$\\langle (0.44,0.56);0.44\\rangle$&$\\langle (0.65,0.35);0.35\\rangle$\\\\\n $P_4$ &$\\langle (0.78,0.22);0.78\\rangle$&$\\langle (0.38,0.62);0.62\\rangle$&$\\langle (0.73,0.27);0.72\\rangle$&$\\langle (0.52,0.48);0.48\\rangle$\\\\\n $P_5$ &$\\langle (0.75,0.25);0.74\\rangle$&$\\langle (0.32,0.68);0.68\\rangle$&$\\langle (0.78,0.22);0.78\\rangle$&$\\langle (0.42,0.58);0.58\\rangle$\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Aggregated disc intuitionistic fuzzy decision matrix}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology", "authors": ["Mahmut Can Bozyiğit", "Mehmet Ünver"], "url": "https://arxiv.org/abs/2501.12251v1", "attribution": "\"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology\" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06852v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\\hline\n\t\t\t\\multicolumn{7}{|c|}{Temporal convergence (fixed $h=1/64$)}\\\\\\hline\n\t\t\t&\\multicolumn{2}{|c|}{$\\epsilon=0.001$}\n\t\t\t& \\multicolumn{2}{|c|}{$\\epsilon=0.01$}& \\multicolumn{2}{|c|}{$\\epsilon=0.1$} \\\\ \\hline\n\t\t\t$\\Delta t$ & $\\|<e_v>\\|_{2,1}$ & rate &$\\|<e_v>\\|_{2,1}$ & rate & $\\|<e_v>\\|_{2,1}$ & rate \\\\ \\hline\n\t\t\t$\\frac{T}{4}$ & 2.8765e-1 & & 2.8767e-1 & & 2.9004e-1& \\\\ \\hline\n\t\t\t$\\frac{T}{8}$ & 8.4966e-2 & 1.76 & 8.4974e-2 &1.76 & 8.5986e-2 & 1.75 \\\\ \\hline\n\t\t\t$\\frac{T}{16}$& 2.3855e-2 & 1.83 & 2.3860e-2 & 1.83 & 2.4048e-2 & 1.84 \\\\ \\hline\n\t\t\t$\\frac{T}{32}$& 6.2895e-3 &1.92 & 6.2899e-3 &1.92 & 6.3445e-3 & 1.92 \\\\ \\hline\n\t\t\t$\\frac{T}{64}$&1.5801e-3 & 1.99 & 1.5801e-3 &1.99 & 1.5938e-3& 1.99\\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{\\footnotesize Errors and convergence rates for $v$ with $\\theta=1/9$, $\\nu=0.01$, and $\\nu_m=0.001$.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "High order efficient algorithm for computation of MHD flow ensembles", "authors": ["Muhammad Mohebujjaman"], "url": "https://arxiv.org/abs/2101.06852v1", "attribution": "\"High order efficient algorithm for computation of MHD flow ensembles\" by Muhammad Mohebujjaman, arXiv:2101.06852v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.03668v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrr}\n & & & $K = 2$ & & & \\\\\n & N & t & C & L & gh & vg \\\\\n\\midrule\nMean & 0.9989 & 0.9851 & 0.943 & 0.9909 & 0.9999 & 0.9833 \\\\\nStd. Err. & 0.0022 & 0.0076 & 0.0157 & 0.0064 & 0.0005 & 0.0081 \\\\\n & & & $K = 3$ & & & \\\\\nMean & 0.9322 & 0.9228 & 0.7709 & 0.9059 & 0.9786 & 0.8506 \\\\\nStd. Err. & 0.0171 & 0.0165 & 0.0746 & 0.0186 & 0.0087 & 0.0249 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean and standard error (Std. Err.) values of ARI for the posterior probabilities for Gaussian (N), t, Cauchy (C), Laplace (L), generalized hyperbolic (gh) and variance gamma (vg) with $T=1000$ for $K = 2$ and $K = 3$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Hidden Markov graphical models with state-dependent generalized hyperbolic distributions", "authors": ["Beatrice Foroni", "Luca Merlo", "Lea Petrella"], "url": "https://arxiv.org/abs/2412.03668v1", "attribution": "\"Hidden Markov graphical models with state-dependent generalized hyperbolic distributions\" by Beatrice Foroni, Luca Merlo, and Lea Petrella, arXiv:2412.03668v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.13622v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|r|r|r|r|r|r}\n\\hline\nSEX & household\\_size & pre\\_tax & pre\\_tax\\_sd & pre\\_tax\\_per & pre\\_tax\\_per\\_sd & n\\\\\n\\hline\nFEMALE & 2 & 37224.03 & 33871.03 & 18612.015 & 16935.514 & 15136\\\\\n\\hline\nFEMALE & 3 & 39683.16 & 34775.13 & 13227.720 & 11591.711 & 6309\\\\\n\\hline\nFEMALE & 4 & 42400.20 & 40474.50 & 10600.049 & 10118.625 & 6011\\\\\n\\hline\nFEMALE & 5 & 37572.39 & 34901.75 & 7514.477 & 6980.350 & 2037\\\\\n\\hline\nFEMALE & 6 & 35466.25 & 34037.52 & 5911.042 & 5672.921 & 563\\\\\n\\hline\nFEMALE & 7 & 35295.74 & 27746.34 & 5042.249 & 3963.763 & 223\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Statistical Inquiry into Gender-Based Income Inequality in Canada", "authors": ["Ali R. Kaazempur-Mofrad"], "url": "https://arxiv.org/abs/2212.13622v1", "attribution": "\"A Statistical Inquiry into Gender-Based Income Inequality in Canada\" by Ali R. Kaazempur-Mofrad, arXiv:2212.13622v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.08761v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary statistics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\t\t\\midrule\n\t\t\\midrule \n\t\t& N & Mean & SD & Min & Max \\\\\n\t\t\t\\midrule\n\\textbf{Panel 1: Individual level variables} & & & & & \\\\\nDepression (scale) & 29,541 &\t1.82& \t0.99\t& 1\t&5 \\\\\nSevere or extreme depression (1=yes) & 29,541 & 0.08 & 0.28 & 0 & 1 \\\\\nWorry/Anxiety (scale) & 29,494 & 2.01 & 1.08 & 1 & 5 \\\\\nSevere or extreme anxiety (1=yes) & 29,494 & 0.13 & 0.33 & 0 & 1 \\\\\nMale (1=yes) & 29,719&\t0.45&\t0.50&\t0\t&1 \\\\\nAge & 29,718&\t48.36\t&17.13 &\t15&\t106 \\\\\nYears of education & 28,124 & 4.61 & 5.07 & 0 & 30 \\\\\nMarried (1=yes) & 29,718 & 0.77 & 0.42 & 0 & 1 \\\\\nChronic condition (1=yes) & 29,514 & 0.43 & 0.49 & 0 & 1 \\\\\nEver diagnosed with depression (1=yes) & 29,299 & 0.06 & 0.25 & 0 & 1 \\\\\nRural (1=yes) & 30,300 & 0.75 & 0.43 & 0 & 1 \\\\\nAccess to electricity (1=yes) & 29,767 & 0.75 & 0.43 & 0 & 1 \\\\\nHousehold owns a bike (1=yes) & 30,177 & 0.61 & 0.49 & 0 & 1 \\\\\nHousehold owns a car (1=yes) & 29,058 & 0.11 & 0.31 & 0 & 1 \\\\\n& & & & & \\\\\n\\textbf{Panel 2: Temperature and climate variables (N=30,300) } & & & & & \\\\\nMean wet bulb temperature (°C) & & 21.86 & 3.99 & 3.62 & 28.37 \\\\\n& & & & & \\\\\n\\textit{Temperature bins (distribution in last 30 days) } & & & & & \\\\\n\\# Days \\textless 16.5°C & & 5.08 & 8.39 & 0 & 30 \\\\\n\\# Days 16.5°C-18°C & & 2.92 & 3.88 & 0 & 18 \\\\\n\\# Days 18°C-19.5°C & & 2.74 & 3.64 & 0 & 21 \\\\\n\\# Days 19.5°C-21°C & & 3.03 & 4.12 & 0 & 27 \\\\\n\\# Days 21°C-22.5°C & & 3.77 & 5.33 & 0 & 29 \\\\\n\\# Days 22.5°C-24°C & & 3.52 & 5.02 & 0 & 29 \\\\\n\\# Days 24°C-25.5°C & & 4.13 & 6.51 & 0 & 30 \\\\\n\\# Days 25.5°C-27°C & & 3.77 & 6.80 & 0 & 28 \\\\\n\\# Days \\textgreater 27°C & & 1.01 & 3.26 & 0 & 23 \\\\\n& & & & & \\\\\n\\# Consecutive days \\textgreater 27°C & & 0.57 & 1.69 & 0 & 11 \\\\\nHeatwave ($\\geq$ 2 days $\\geq$ 27°) & & 0.12 & 0.32 & 0 & 1 \\\\\nHeatwave ($\\geq$ 3 days $\\geq$ 27°) & & 0.08 & 0.28 & 0 & 1 \\\\\nHeatwave ($\\geq$ 2 days $\\geq$ 28°) & & 0.02 & 0.14 & 0 & 1 \\\\\n& & & & & \\\\\n& & & & & \\\\\nRainfall, last 30 days (mm) & & 111.71 & 184.94 & 0 & 1355.76 \\\\\nRainfall deviation from long-run mean, last 30 days (mm) & & 6.04 & 88.17 & -746.14 & 766.25 \\\\\nAverage wind speed, last 30 days (m/s) & & 2.97 & 1.06 & 0.93 & 7.50 \\\\\nAverage air pollution (PM2.5), last 30 days (\\textmu g/m\\textsuperscript{3}) & & 34.67 & 11.28 & 7.03 & 72.57 \\\\\n& & & & & \\\\\n\\textit{Koeppen-Geiger climate classifications } & & & & & \\\\\nAm & & 0.09 & 0.29 & 0 & 1 \\\\\nAw & & 0.22 & 0.42 & 0 & 1 \\\\\nBWh & & 0.05 & 0.22 & 0 & 1 \\\\\nBSh & & 0.28 & 0.45 & 0 & 1 \\\\\nCwa & & 0.35 & 0.48 & 0 & 1 \\\\\nCwb & & 0.01 & 0.07 & 0 & 1 \\\\\n& & & & & \\\\\n\\textbf{Survey waves } & & & & & \\\\\nSurvey wave 0, year 2003 & & 0.34 & 0.47 & 0 & 1 \\\\\nSurvey wave 1, year 2007 & & 0.37 & 0.48 & 0 & 1 \\\\\nSurvey wave 2, year 2015 & & 0.29 & 0.46 & 0 & 1 \\\\\n\t\t\\midrule \n\t\t\\midrule \n\t\t\\multicolumn{6}{p{16cm}}{\\textit{Notes:} The Koeppen-Geiger climate classification system describes the following climate regions: \\textit{Am}: tropical -- monsoon, \\textit{Aw}: tropical -- savanna, dry winter, \\textit{BWh}: dry -- arid desert -- hot, \\textit{BSh}: dry -- semi-arid steppe -- hot, \\textit{Cwa}: temperate -- dry winter -- hot summer, \\textit{Cwb}; temperate -- dry winter -- warm summer.} \n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Beyond the heat: The mental health toll of temperature and humidity in India", "authors": ["Manuela Fritz"], "url": "https://arxiv.org/abs/2503.08761v1", "attribution": "\"Beyond the heat: The mental health toll of temperature and humidity in India\" by Manuela Fritz, arXiv:2503.08761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2309.14044v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{List of covariates used to generate objective benchmarks}\n\\begin{tabular}{ll}\n\t\t\t\t\t\\hline\\hline \n\t\t\\\\\n\t\tType & Covariate \\\\\n\t\t\\\\\n\t\t\\multicolumn{2}{l}{\\textbf{Socio-demographic characteristics}}\\\\ \n\t\t\tContinuous \t& Age \\\\\n\t\t\tCategorical & Female \\\\\n\t\t\t& School degree (4 categories: none, lower sec. degree, middle sec. degree, upper sec. degree) \\\\\n\t\t\t& Further education (3 categories: none, vocational certificate, university degree) \\\\\n\t\t\t& German citizen \\\\\n\t\t\t& Married \\\\\n\t\t\t& \\# of children (3 categories: 0, 1, $\\geq$ 2) \\\\\n\t\t\t\\multicolumn{2}{l}{\\textbf{Last job}}\\\\ \n\t\t\tContinuous \t& Wage in last job (gross, per month) \\\\\t\t\n\t\t\tCategorical & Last job was quit by individual \\\\\n\t\t\t\\multicolumn{2}{l}{\\textbf{Labor market history}}\\\\ \t\t\t\t\n\t\t\tContinuous \t& Duration of last UE spell in months \\\\\t\t\n\t\t\t& \\# of months employed in year t-1 \\\\\n\t\t\t& \\# of months employed in year t-2 \\\\\t\t\n\t\t\t& \\# of months employed in year t-3 \\\\\n\t\t\t& \\# of months employed in years t-4 to t-10 \\\\\t\t\t\t\t\n\t\t\t& \\# of months unemployed in year t-1 \\\\\n\t\t\t& \\# of months unemployed in year t-2 \\\\\t\t\n\t\t\t& \\# of months unemployed in year t-3 \\\\\n\t\t\t& \\# of months unemployed in years t-4 to t-10 \\\\\t\n\t\t\t& \\# of months in ALMP in year t-1 \\\\\n\t\t\t& \\# of months in ALMP in year t-2 \\\\\t\t\n\t\t\t& \\# of months in ALMP in year t-3 \\\\\n\t\t\t& \\# of months in ALMP in years t-4 to t-10 \\\\\t\t\t\t\t\t\t\t\t\t\n\t\t\tCategorical & \\# of employers in last 2 years (5 categories) \\\\\t\t\t\n\t\t\t& \\# of employers in last 10 years (5 categories) \\\\\t\n\t\t\t& \\# of UE spells in last 2 years (5 categories) \\\\\n\t\t\t& \\# of ALMP programs in last 2 years (5 categories) \\\\\n\t\t\t& Average wage in year t-1 (5 categories) \\\\\n\t\t\t& Average wage in year t-2 (5 categories) \\\\\t\t\n\t\t\t& Average wage in year t-3 (5 categories) \\\\\t\t\t\n\t\t\t& Average wage in years t-4 to t-10 (5 categories) \\\\\t\t\t\t\t\n\t\t\t& All months regularly employed in year t-1 \\\\\n\t\t\t& Zero months regularly employed in year t-2 \\\\\t\t\n\t\t\t& All months regularly employed in year t-2 \\\\\t\t\n\t\t\t& Zero months regularly employed in year t-3 \\\\\n\t\t\t& All months regularly employed in year t-3 \\\\\n\t\t\t& Zero months regularly employed in years t-4 to t-10 \\\\\t\t\n\t\t\t& All months regularly employed in years t-4 to t-10 \\\\\t\t\t\t\t\t\t\n\t\t\t& Zero months unemployed in year t-1 \\\\\n\t\t\t& Zero months unemployed in year t-2 \\\\\t\t\n\t\t\t& Zero months unemployed in year t-3 \\\\\n\t\t\t& Zero months unemployed in years t-4 to t-10 \\\\\t\t\n\t\t\t& Zero months in ALMP in year t-1 \\\\\n\t\t\t& Zero months in ALMP in year t-2 \\\\\t\t\n\t\t\t& Zero months in ALMP in year t-3 \\\\\n\t\t\t& Zero months in ALMP in years t-4 to t-10 \\\\\n\t\t\t\\multicolumn{2}{l}{\\textbf{Local labor market}}\\\\ \n\t\t\tCategorical & Unemployment rate in employment agency district at time of UE entry \\\\\t\n\t\t\t& (8 categories: West $<$3\\%, 3-6\\%, 6-9\\%, $>$9\\%; East $<$12\\%, 12-14\\%, 14-16\\%, $>$16\\%)\t\t\\\\\n\t\t\t\\multicolumn{2}{l}{\\textbf{Timing of unemployment spell}}\\\\ \t\n\t\t\tCategorical & Calendar month of entry into unemployment (12 categories) \\\\\n\t\\\\\n\\hline \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Accuracy of Job Seekers' Wage Expectations", "authors": ["Marco Caliendo", "Robert Mahlstedt", "Aiko Schmeißer", "Sophie Wagner"], "url": "https://arxiv.org/abs/2309.14044v2", "attribution": "\"The Accuracy of Job Seekers' Wage Expectations\" by Marco Caliendo, Robert Mahlstedt, Aiko Schmeißer, and Sophie Wagner, arXiv:2309.14044v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.21174v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|lll|lll|lll}\n\\# &\\multicolumn{3}{c}{$\\tau_{large}$} &\\multicolumn{3}{c}{$\\tau_{mix}$} & \\multicolumn{3}{c}{$\\tau_{small}$} \\\\\n & fgmres & lsqr & mrs & fgmres & lsqr & mrs & fgmres & lsqr & mrs \\\\ \\hline\n1 & 3.0 & 1.5 & 2.8 & 3.5 & 1.5 & 4.0 & 2.0 & 1.5 & 4.0 \\\\\n2 & 2.0 & 1.0 &5.0 & 2.0 & 1.0 & 7.0 & 1.5 & 1.0 & 7.0 \\\\\n3 & 2.5 & 1.0 & 2.0 & 3.0 & 1.0 & 3.3 & 2.0 & 1.0 & 3.0 \\\\\n4 &2.0 & 19.8 & 18.5 & 3.0 & 22.1 & 27.8 & 2.0 & 29.5 & 27.5 \\\\\n5 & 2.3 & 20.0 & 32.0 & 3.0 & 21.8 & 48.4 & 2.0 & 29.0 & 48.5 \\\\\n6 & 3.0 & 18.2 &100.1 & 4.8 & 21.6 & 164.8 & 3.0 & 29.0 & 151.7 \\\\\n7 & 8.0 & 19.2 & 216.0 & 54.8 & 21.4 & 567.2 & 7.0 & 29.3 & 358.4 \\\\\n8 & 54.5 & 17.9 & 460.9 & 72.0 & 21.4 & 752.2 & 124.5 & 28.0 & 810.6\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Average number of inner fGMRES, LSQR and MRS iterations for the new scheme (generalized case) with $M$-orthogonalization}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust iterative methods for linear systems with saddle point structure", "authors": ["Murat Manguoğlu", "Volker Mehrmann"], "url": "https://arxiv.org/abs/2502.21174v1", "attribution": "\"Robust iterative methods for linear systems with saddle point structure\" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.00302v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Architecture Metrics for HSI+LiDAR, HSI, and LiDAR Based On Training Model (100Epochs)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|c|c|c|}\n\\hline\nArchitecture & Metrics & HSI+LiDAR & HSI & LiDAR \\\\ \n\\hline\n\\multirow{3}{*}{{Model 1}} & OA & \\underline{\\textbf{0.9668}} & 0.9613 & 0.2738 \\\\ \n & AA & \\underline{\\textbf{0.9722}} & 0.9685 & 0.2833 \\\\ \n & Kappa & \\underline{\\textbf{0.9639}} & 0.9583 & 0.2208 \\\\ \n\\hline\n\\multirow{3}{*}{Model 2} & OA & 0.9538 & 0.9430 & 0.2523 \\\\ \n & AA & 0.9609 & 0.9535 & 0.2657 \\\\ \n & Kappa & 0.9498 & 0.9381 & 0.2208 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "HSLiNets: Hyperspectral Image and LiDAR Data Fusion Using Efficient Dual Non-Linear Feature Learning Networks", "authors": ["Judy X Yang", "Jing Wang", "Chen Hong Sui", "Zekun Long", "Jun Zhou"], "url": "https://arxiv.org/abs/2412.00302v2", "attribution": "\"HSLiNets: Hyperspectral Image and LiDAR Data Fusion Using Efficient Dual Non-Linear Feature Learning Networks\" by Judy X Yang, Jing Wang, Chen Hong Sui, Zekun Long, and Jun Zhou, arXiv:2412.00302v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.05905v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Bardet-Biedl syndrome gene expression study. Posterior summary statistics for the five best models $(\\mathcal{M}_1,\\dots,\\mathcal{M}_5)$, as estimated by the RJ algorithm. For each probe the reported summary statistics are the mean, the standard deviation (in italics), and the marginal inclusion probability (MIP). The summary statistics are calculated using only the post burn-in draws corresponding to the $j$-th model.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccccc}\\\\\n\\toprule\n& &\\multicolumn{6}{c}{\\textit{Summary statistics}}\\\\\n\\cmidrule(lr){3-8}\nProbe & gene & $\\mathcal{M}_1$ & $\\mathcal{M}_2$ & $\\mathcal{M}_3$ & $\\mathcal{M}_4$ & $\\mathcal{M}_5$ & MIP \\\\ \n\\cmidrule(lr){1-1}\\cmidrule(lr){2-2}\\cmidrule(lr){3-8}\n\\multirow{2}{*}{1369660\\_at} & \\multirow{2}{*}{Defb1} & -0.35 & -0.31 & -0.31 & -0.37 & -0.27&\\multirow{2}{*}{0.76} \\\\ \n & & {\\it (0.08)} & {\\it (0.06)} & {\\it (0.08)} & {\\it (0.08)} &{\\it (0.08)} \\\\ \n\\multirow{2}{*}{1376429\\_at}& \\multirow{2}{*}{Actl7b} & -0.20 & -0.19 & -0.16 & -0.18 & -0.18 & \\multirow{2}{*}{0.99}\\\\ \n && {\\it (0.07)} & {\\it (0.07)} & {\\it (0.07)} & {\\it (0.08)} & {\\it (0.09)} \\\\ \n\\multirow{2}{*}{1389910\\_at}&\\multirow{2}{*}{Tmem230}& 0.64 & 0.64 & 0.59 & 0.57 & 0.55 & \\multirow{2}{*}{0.91}\\\\ \n && {\\it (0.11)} & {\\it (0.12)}& {\\it (0.11)} &{\\it (0.09)} & {\\it (0.11)} \\\\ \n\\midrule\n\\multirow{2}{*}{1368967\\_at} &\\multirow{2}{*}{Eif2b3}& -0.10 & -0.04 & -0.02 & -0.01 &&\\multirow{2}{*}{0.52}\\\\ \n&& {\\it (0.10)} & {\\it (0.10)} & {\\it (0.10)} &{\\it ( 0.09)} \\\\ \n\\multirow{2}{*}{1369028\\_at }&\\multirow{2}{*}{Six3}& -0.18 & -0.11 & -0.15 & -0.18 &&\\multirow{2}{*}{0.82}\\\\ \n&& {\\it (0.11)} & {\\it (0.11)} &{\\it (0.10)} & {\\it (0.10)} \\\\ \n\\multirow{2}{*}{1370570\\_at} & \\multirow{2}{*}{Nrp1}& -0.07 & -0.05 & -0.03 & -0.04&&\\multirow{2}{*}{0.67} \\\\ \n&& {\\it (0.10)} & {\\it (0.11)} & {\\it (0.09)} & {\\it (0.08)} \\\\ \n\\multirow{2}{*}{1376386\\_at} && -0.05 & -0.02 & -0.01 & -0.02&&\\multirow{2}{*}{0.93} \\\\ \n&& {\\it (0.09)} & {\\it (0.09)} & {\\it (0.08)} & {\\it (0.08)} \\\\ \n\\multirow{2}{*}{1382674\\_a\\_at} &\\multirow{2}{*}{Tnrc6c}& -0.07 & -0.11 & -0.07 & -0.07&&\\multirow{2}{*}{0.90} \\\\ \n && {\\it (0.09)} & {\\it (0.09)} & {\\it (0.09)} & {\\it (0.08)} \\\\ \n\\multirow{2}{*}{1382904\\_at} & \\multirow{2}{*}{Nsrp1} & 0.18 & 0.14 & 0.11 & 0.18 &&\\multirow{2}{*}{0.58}\\\\ \n && {\\it (0.09)} & {\\it (0.07)} & {\\it (0.08)} & {\\it (0.08)} \\\\ \n\\multirow{2}{*}{1385539\\_at}&& -0.22 & -0.14 && -0.15 & -0.18 &\\multirow{2}{*}{0.73} \\\\ \n && {\\it (0.10)} & {\\it (0.10)} && {\\it (0.11)} & {\\it (0.07)} \\\\\n\\midrule\n\\multirow{2}{*}{1367915\\_at} &\\multirow{2}{*}{Dgat1}& 0.06 & 0.12 & 0.11 &&&\\multirow{2}{*}{0.30}\\\\ \n && {\\it (0.10)} & {\\it (0.10)} & {\\it (0.10)} \\\\ \n\\multirow{2}{*}{1379541\\_at}&\\multirow{2}{*}{Tmtc4}& -0.01 & -0.10 & -0.06 &&&\\multirow{2}{*}{0.26}\\\\ \n&& {\\it (0.14)} & {\\it (0.14)} & {\\it (0.14)} \\\\ \n\\midrule\n\\multirow{2}{*}{1375354\\_at}&& &&& -0.17 & -0.16 &\\multirow{2}{*}{0.64}\\\\ \n && &&& {\\it (0.07)} & {\\it (0.07)} \\\\ \n\\multirow{2}{*}{1379495\\_at} && & 0.28 & 0.23 &&&\\multirow{2}{*}{0.28}\\\\ \n&& &{\\it (0.11)} & {\\it (0.13)} &&\\\\ \n\\midrule\n\\multirow{2}{*}{1371045\\_at} & \\multirow{2}{*}{Asic1} & &&&& -0.10& \\multirow{2}{*}{0.16}\\\\ \n && &&&& {\\it (0.08)} \\\\ \n1371452\\_at &\\multirow{2}{*}{Ubl7}& &&&& -0.12& \\multirow{2}{*}{0.50}\\\\ \n&& &&&& {\\it (0.09)} \\\\ \n\\multirow{2}{*}{1374479\\_at} &\\multirow{2}{*}{Ino80c}& &&&& 0.14 &\\multirow{2}{*}{0.19}\\\\ \n && &&&& {\\it (0.10)} \\\\ \n\\multirow{2}{*}{1376445\\_at} &\\multirow{2}{*}{Il17b}& &&& -0.10&&\\multirow{2}{*}{0.20} \\\\ \n&& &&& {\\it (0.08)}& \\\\ \n\\multirow{2}{*}{1376728\\_at} &\\multirow{2}{*}{Rbm47}& &&& 0.03&&\\multirow{2}{*}{0.19} \\\\ \n && &&& {\\it (0.07)} \\\\ \n\\multirow{2}{*}{1379029\\_at} &\\multirow{2}{*}{Zfp62}& && 0.14 &&&\\multirow{2}{*}{0.28}\\\\ \n&& && {\\it (0.10)} \\\\ \n\\multirow{2}{*}{1386770\\_x\\_at} &\\multirow{2}{*}{Kcne2}&&&& & -0.03 &\\multirow{2}{*}{0.17}\\\\ \n && &&& & {\\it (0.08)} \\\\ \n\\multirow{2}{*}{1386969\\_at} &\\multirow{2}{*}{Nrn1}& &&&& 0.07 &\\multirow{2}{*}{0.46}\\\\ \n&& &&&& {\\it (0.08)} \\\\ \n\\multirow{2}{*}{1387290\\_at} &\\multirow{2}{*}{Galnt10}& &&&& -0.00&\\multirow{2}{*}{0.48} \\\\ \n&& &&&& {\\it (0.09)} \\\\ \n\\multirow{2}{*}{1390394\\_at}&& &&&& 0.16&\\multirow{2}{*}{0.34} \\\\ \n && &&&& {\\it (0.10)} \\\\ \n\\multirow{2}{*}{1399050\\_at} &\\multirow{2}{*}{Adss}&&&& & -0.12&\\multirow{2}{*}{0.26} \\\\ \n&& &&&& {\\it (0.11)} \\\\ \n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fast QR updating methods for statistical applications", "authors": ["Mauro Bernardi", "Claudio Busatto", "Manuela Cattelan"], "url": "https://arxiv.org/abs/2412.05905v2", "attribution": "\"Fast QR updating methods for statistical applications\" by Mauro Bernardi, Claudio Busatto, and Manuela Cattelan, arXiv:2412.05905v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.00213v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{siunitx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean performance in \\textbf{ratio} for player selection methods across diverse scenarios}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n \\toprule\n 4-Player Scenarios & Parameter & $\\text{traj}_{S}\\downarrow$ & $\\text{traj}_L\\downarrow$ & $\\text{Dist}_m\\uparrow$ & Consistency $\\uparrow$ & Player Num. & Runtime \\\\\n \\midrule\n \\midrule\n \\textbf{PSN-Full} & $m_\\text{th}=0.5$ & 0.781 & 0.970 & 0.968 & 0.986 & 1.619 & 0.164 \\\\\n \\textbf{PSN-Partial} & $m_\\text{th}=0.5$ & \\textbf{0.750} & 0.971 & 0.973 & 0.986 & 1.806 & 0.233\\\\\n \\texttt{Distance} & $r_\\text{th}=\\SI{2.5}{\\meter}$ & 1.219 & 1.011 & 0.984 & 0.987 & 2.506 & 0.368\\\\\n \\midrule\n \\textbf{PSN-Full} & \\multirow{8}[0]{*}{$|U^i|=1$} & 1.094 & 0.990 & 0.978 & \\textbf{0.989} & \\multirow{8}[0]{*}{2.000} & 0.134 \\\\\n \\textbf{PSN-Partial} & & 1.000 & \\textbf{0.966} & 0.977 & 0.987 & & 0.113 \\\\\n \\texttt{kNNs} & & 1.438 & 1.021 & 0.993 & \\textbf{0.989} & & 0.128 \\\\\n \\texttt{Cost Evolution} & & 1.813 & 0.976 & 0.997 & 0.951 & & 0.120 \\\\\n \\texttt{Gradient} & & 1.781 & 1.012 & 0.987 & 0.953 & & 0.117 \\\\\n \\texttt{Hessian} & & 1.500 & 1.014 & 0.995 & 0.980 & & 0.134 \\\\\n \\texttt{BF} & & 1.406 & 1.017 & 0.992 & 0.983 & & 0.115 \\\\\n \\texttt{CBF} & & 1.344 & 1.004 & \\textbf{1.000} & 0.971 & & 0.121 \\\\\n \\toprule\n 10-Player Scenarios & Parameter & $\\text{traj}_{S}\\downarrow$ & $\\text{traj}_L\\downarrow$ & $\\text{Dist}_m\\uparrow$ & Consistency $\\uparrow$ & Num.P & Runtime\\\\\n \\midrule\n \\midrule\n \\textbf{PSN-Full} & $m_\\text{th}=0.5$ & \\textbf{0.857} & 0.994 & 0.958 & 0.994 & 4.473 & 0.124\\\\\n \\textbf{PSN-Partial} & $m_\\text{th}=0.5$ & \\textbf{0.857} & 0.991 & 0.970 & 0.994 & 4.033 & 0.095\\\\\n \\texttt{Distance} & $r_\\text{th}=\\SI{2.5}{\\meter}$ & 1.190 & 0.998 & 0.980 & 0.989 & 2.277 & 0.022\\\\\n \\midrule\n \\textbf{PSN-Full} & \\multirow{8}[0]{*}{$|U^i|=4$} & \\textbf{0.857} & 0.991 & 0.972 & \\textbf{0.995} & \\multirow{8}[0]{*}{5.000} & 0.102\\\\\n \\textbf{PSN-Partial} & & \\textbf{0.857} & 0.988 & 0.957 & \\textbf{0.995} & & 0.124 \\\\\n \\texttt{kNNs} & & 0.952 & 1.000 & 0.965 & 0.984 & & 0.123 \\\\\n \\texttt{Cost Evolution} & & 1.048 & \\textbf{0.980} & 0.974 & 0.954 & & 0.121 \\\\\n \\texttt{Gradient} & & 0.952 & 1.000 & 0.967 & 0.984 & & 0.115 \\\\\n \\texttt{Hessian} & & 0.952 & 0.999 & 0.979 & 0.987 & & 0.128 \\\\\n \\texttt{BF} & & 1.000 & 1.003 & 1.015 & 0.984 & & 0.145 \\\\\n \\texttt{CBF} & & 0.952 & 1.002 & 1.013 & 0.984 & & 0.135 \\\\\n \\toprule\n CITR & Parameter & $\\text{traj}_{S}\\downarrow$ & $\\text{traj}_L\\downarrow$ & $\\text{Dist}_m\\uparrow$ & Consistency $\\uparrow$ & Num.P & Runtime\\\\\n \\midrule\n \\midrule\n \\textbf{PSN-Full} & $m_\\text{th}=0.5$ & 1.222 & 0.996 & 0.938 & 0.994 & 5.523 & 0.193\\\\\n \\textbf{PSN-Partial} & $m_\\text{th}=0.5$ & 1.222 & 0.996 & 0.918 & \\textbf{0.996} & 4.633 & 0.112\\\\\n \\texttt{Distance} & $r_\\text{th}=\\SI{2.5}{\\meter}$ & 1.333 & 0.997 & 0.825 & 0.993 & 1.829 & 0.010\\\\\n \\midrule\n \\textbf{PSN-Full} & \\multirow{5}[0]{*}{$|U^i|=4$} & 1.222 & 0.996 & 0.922 & \\textbf{0.996} & \\multirow{5}[0]{*}{5.000} & 0.119\\\\\n \\textbf{PSN-Partial} & & 1.222 & 0.996 & 0.917 & 0.993 & & 0.098 \\\\\n \\texttt{kNNs} & & \\textbf{1.111} & 0.998 & 0.926 & 0.991 & & 0.109\\\\\n \\texttt{Cost Evolution} & & 1.222 & \\textbf{0.994} & \\textbf{0.944} & 0.969 & & 0.126\\\\\n \\texttt{BF} & & \\textbf{1.111} & 0.999 & 0.943 & 0.989 & & 0.121\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "PSN Game: Game-theoretic Planning via a Player Selection Network", "authors": ["Tianyu Qiu", "Eric Ouano", "Fernando Palafox", "Christian Ellis", "David Fridovich-Keil"], "url": "https://arxiv.org/abs/2505.00213v1", "attribution": "\"PSN Game: Game-theoretic Planning via a Player Selection Network\" by Tianyu Qiu, Eric Ouano, Fernando Palafox, Christian Ellis, and David Fridovich-Keil, arXiv:2505.00213v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.16143v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c|c|}\n epochs & m \\\\\n \\hline\n $\\theta_t - \\theta$ \n &\n $\n - m \\alpha + c \\sum_{i=1}^m \\nabla L(\\theta_i) \n $\n \\\\\n $\\nabla L(\\theta_t) - \\nabla L$ \n &\n $\n (- \\alpha)^{i-1} \\sum_{h=1}^{i} \\frac{1}{h!} \n \\left(1 + \\sum_{l=2}^{m} (l-1)^{i-h}l^{h-1} \\right)\n $\n \\\\\n $\\nabla^j L(\\theta_t) - \\nabla^j L$ \n &\n $\n (- \\alpha)^{i-j} \\sum_{h=j}^{i} \\frac{\\nabla^i L}{h!} \n \\left(1 + \\sum_{l=2}^{m} (l-1)^{i-h}l^{h-j} \\right)\n $\n \\\\\n $L(\\theta_t) - L$ \n &\n $\n (- \\alpha)^{i} \\sum_{h=0}^{i} \\frac{\\nabla^i L}{h!} \n \\left(1 + \\sum_{l=2}^{m} (l-1)^{i-h}l^{h-j} \\right)\n $\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the Trajectories of SGD Without Replacement", "authors": ["Pierfrancesco Beneventano"], "url": "https://arxiv.org/abs/2312.16143v2", "attribution": "\"On the Trajectories of SGD Without Replacement\" by Pierfrancesco Beneventano, arXiv:2312.16143v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09448v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n \\hline\n Sentence Type & Max Length & Min Length & Avg. Length \\\\\n \\hline\n \\hline\n PROCEDURE & $48$ & $4$ & $8.371$ \\\\\n PERFORM & $296$ & $4$ & $35.89$ \\\\ \n IF & $298$ & $4$ & $45.37$ \\\\ \n INITIALIZE & $20$ & $4$ & $7.64$ \\\\\n ACCEPT & $5$ & $4$ & $4.06$ \\\\ \n MOVE & $290$ & $4$ & $9.15$\\\\ \n OPEN & $167$ & $3$ & $23.42$ \\\\ \n STRING & $30$ & $13$ & $19.0$ \\\\ \n SET & $119$ & $4$ & $6.57$ \\\\ \n CLOSE & $42$ & $4$ & $10.28$ \\\\ \n ADD & $52$ & $4$ & $6.92$ \\\\\n CALL & $70$ & $4$ & $12.917$\\\\\n IDENTIFIER & $134$ & $3$ & $11.76$ \\\\\n EVALUATE & $287$ & $23$ & $87.94$ \\\\\n READ & $84$ & $8$ & $18.78$ \\\\ \n WRITE & $19$ & $4$ & $5.18$ \\\\ \n GO & $3$ & $3$ & $3.0$ \\\\ \n COMPUTE & $160$ & $4$ & $10.43$\\\\\n ENTRY & $48$ & $12$ & $36.0$\\\\\n SEARCH & $86$ & $25$ & $42.4$\\\\\n SUBTRACT & $10$ & $4$ & $4.35$\\\\\n DIVIDE & $10$ & $10$ & $10.0$\\\\\n INSPECT & $41$ & $8$ & $10.84$\\\\\n REWRITE & $20$ & $20$ & $20.0$\\\\\n START & $8$ & $8$ & $8.0$\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{COBOL2Vec sentence types and their lengths.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Cobol2Vec: Learning Representations of Cobol code", "authors": ["Ankit Kulshrestha", "Vishwas Lele"], "url": "https://arxiv.org/abs/2201.09448v1", "attribution": "\"Cobol2Vec: Learning Representations of Cobol code\" by Ankit Kulshrestha and Vishwas Lele, arXiv:2201.09448v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.02562v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lrrrrrrr}\n \\toprule \n & $\\ $ $\\ $ i2t@1 &\t$\\ $ i2t@5 &\t$\\ $ i2t@10 &\t$\\ $ t2i@1\t & $\\ $ t2i@5 & $\\ $ t2i@10 & $\\ $\t$\\ $ Mean \\\\ \\midrule\n ViT & 53.88 &\t73.17 &\t78.49 &\t53.98 &\t73.83 &\t79.29 &\t68.77 \\\\\n RoPE & 55.27 &\t74.27 &\t79.52 &\t55.22 &\t74.61 &\t80.25 &\t69.86 \\\\\n RoPE\\text{-}M & 55.30 &\t74.08 &\t79.47 &\t55.36 &\t74.73 &\t80.18 &\t69.85 \\\\\n \\midrule\n Circulant\\text{-}S & \\underline{55.52} &\t\\underline{74.69} &\t\\underline{79.91} &\t\\underline{55.68} &\t\\underline{75.03} &\t\\underline{80.45} &\t\\underline{70.21} \\\\\n Cayley\\text{-}S & \\textbf{55.70} &\t\\textbf{75.08} &\t\\textbf{80.24} &\t\\textbf{55.82} &\t\\textbf{75.40} &\t\\textbf{80.65} &\t\\textbf{70.48} \\\\\n \\bottomrule\n\\end{tabular}\n\\caption{Image-to-text (i2t) and text-to-image (t2i) WebLI recall @ rank (best numbers: in \\textbf{bold}, second-best: \\underline{underlined}.)}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Learning the RoPEs: Better 2D and 3D Position Encodings with STRING", "authors": ["Connor Schenck", "Isaac Reid", "Mithun George Jacob", "Alex Bewley", "Joshua Ainslie", "David Rendleman", "Deepali Jain", "Mohit Sharma", "Avinava Dubey", "Ayzaan Wahid", "Sumeet Singh", "René Wagner", "Tianli Ding", "Chuyuan Fu", "Arunkumar Byravan", "Jake Varley", "Alexey Gritsenko", "Matthias Minderer", "Dmitry Kalashnikov", "Jonathan Tompson", "Vikas Sindhwani", "Krzysztof Choromanski"], "url": "https://arxiv.org/abs/2502.02562v1", "attribution": "\"Learning the RoPEs: Better 2D and 3D Position Encodings with STRING\" by Connor Schenck, Isaac Reid, Mithun George Jacob, Alex Bewley, Joshua Ainslie, David Rendleman, Deepali Jain, Mohit Sharma, Avinava Dubey, Ayzaan Wahid, Sumeet Singh, René Wagner, Tianli Ding, Chuyuan Fu, Arunkumar Byravan, Jake Varley, Alexey Gritsenko, Matthias Minderer, Dmitry Kalashnikov, Jonathan Tompson, Vikas Sindhwani, and Krzysztof Choromanski, arXiv:2502.02562v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00376v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|cc}\n\\toprule\n & \\textbf{CSQA} & \\textbf{RS} \\\\ \\midrule\n\\# All Examples & 12,102 & 5,715 \\\\\n\\# Train Examples & 9,741 & 3,510 \\\\\n\\# Validation Examples & 1,221 & 1,021 \\\\\n\\# Test Examples & 1,140 & 1,184 \\\\ \\midrule\nAverage Question Length & 15.06 & 24.04 \\\\ \n\\% Long Qs ($>$20 tokens) & 16.5\\% & 47.3\\% \\\\\nDistinct Question Words & 6,822 & 7,110 \\\\\nDistinct Choice Words & 7,044 & 9,912 \\\\\nAvg PLL of Qs & -34.41 & -53.98 \\\\\n\\midrule\nQA-NLI Conflict & 12.7\\% & 39.6\\% \\\\\nQA-NLI Neutral & 71.6\\% & 44.9\\% \\\\\nQA-NLI Entailment & 15.7\\% & 15.5\\% \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Key statistics of the \\textsc{RiddleSense} dataset (v1.1) vs the CommonsenseQA (CSQA) dataset.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge", "authors": ["Bill Yuchen Lin", "Ziyi Wu", "Yichi Yang", "Dong-Ho Lee", "Xiang Ren"], "url": "https://arxiv.org/abs/2101.00376v2", "attribution": "\"RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge\" by Bill Yuchen Lin, Ziyi Wu, Yichi Yang, Dong-Ho Lee, and Xiang Ren, arXiv:2101.00376v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.17633v4_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Supervised results on the \\textit{LiDAR-CS}~ dataset using IA-SSD~. with different sampling settings.}% *: doubled points in SA layers, **: four doubled points in SA layers.}\n\\begin{tabular}{ccc|cccc}\n Sensor & Sampling & $n_{input}$ & $AP_{3D, V}$ & $AP_{3D, P}$ & $AP_{3D, C}$ & Speed \\\\\n & Setting & & & & & [it/s]\\\\\n \\hline\n \\textit{CS-64} & 1 & 65536 & 76.51 & 17.35 & 47.71 & 20.2 \\\\\n & 2 & 65536 & 87.81 & 50.41 & 78.55 & 3.9 \\\\\n & 3 & 65536 & \\textbf{90.51} & \\textbf{ 65.97} & \\textbf{84.67} & 1.9 \\\\\n \n \n \\hline\n \\textit{CS-32} & 1 & 32768 & 55.06 & 18.99 & 25.34 & 20.2 \\\\\n & 2 & 32768 & 63.41 & \\textbf{46.50} & 53.31 & 7.8 \\\\\n & 3 & 32768 & \\textbf{65.11} & 42.89 & \\textbf{54.03} & 3.6 \\\\\n \n \n \\hline\n\\textit{CS-16} & 1 & 16384 & 50.39 & 17.41 & 34.00 & 20.2 \\\\ \n & 2 & 16384 & \\textbf{53.54} & \\textbf{47.78} & \\textbf{53.11} & 13.9 \\\\ \n & 3 & 16384 & 56.76 & 43.29 & 52.88 & 10.6 \\\\ \n \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps", "authors": ["Maciej K Wozniak", "Mattias Hansson", "Marko Thiel", "Patric Jensfelt"], "url": "https://arxiv.org/abs/2403.17633v4", "attribution": "\"UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps\" by Maciej K Wozniak, Mattias Hansson, Marko Thiel, and Patric Jensfelt, arXiv:2403.17633v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.11767v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{{\\bf Discrete-space simulation study results:} The standard method with constant effect across frequency and the proposed spectral method are evaluated in terms of their estimate of $\\beta_x$ using root mean squared error (``RMSE''), bias (``Bias''), average posterior standard deviation (``SD'') and coverage of 95\\% posterior intervals (``Cov'') for data generated with dependence between exposure and confounder controlled by $\\beta_{xz}$ and kernel bandwidth $\\phi$. Standard errors are in parentheses and all results are multiplied by 100.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|ll|cccc}\nScenario & Method & $\\phi$ & $\\beta_{xz}$ & RMSE & Bias & SD & Coverage\\\\\\hline \n1 & Standard & -- & 0 & 1.3 ( 0.0) & 0.0 ( 0.1) & 1.3 ( 0.0) & 95.4 ( 0.9) \\\\ \n & Parametric & & & 1.3 ( 0.0) & -0.1 ( 0.1) & 1.3 ( 0.0) & 94.4 ( 1.0) \\\\\n & Semi - PCP & & & 4.1 ( 0.3) & 0.0 ( 0.2) & 2.6 ( 0.1) & 95.2 ( 1.0) \\\\\n & Semi - $R^2$ & & & 4.0 ( 0.3) & 0.1 ( 0.2) & 2.4 ( 0.1) & 95.0 ( 1.0) \\\\\n &&&&&&\\\\\n2 & Standard & 1 & 1 & 19.0 ( 0.1) & 18.9 ( 0.1) & 1.4 ( 0.0) & 0.0 ( 0.0) \\\\\n & Parametric & & & 16.1 ( 0.1) & 16.1 ( 0.1) & 1.5 ( 0.0) & 0.0 ( 0.0) \\\\\n & Semi - PCP & & & 7.9 ( 0.2) & -0.7 ( 0.4) & 8.2 ( 0.0) & 95.4 ( 0.9) \\\\\n & Semi - $R^2$ & & & 8.0 ( 0.3) & -0.4 ( 0.4) & 8.8 ( 0.1) & 96.6 ( 0.8) \\\\\n&&&&&&\\\\\n3 & Standard & 1 & 2 & 34.4 ( 0.1) & 34.4 ( 0.1) & 1.7 ( 0.0) & 0.0 ( 0.0) \\\\\n & Parametric & & & 27.1 ( 0.1) & 27.0 ( 0.1) & 1.9 ( 0.0) & 0.0 ( 0.0) \\\\\n & Semi - PCP & & & 9.0 ( 0.3) & 0.7 ( 0.4) & 9.3 ( 0.0) & 96.4 ( 0.8) \\\\\n & Semi - $R^2$ & & & 9.3 ( 0.3) & 1.0 ( 0.4) & 9.4 ( 0.1) & 96.0 ( 0.9) \\\\\n&&&&&&\\\\\n4 & Standard & 2 & 1 & 5.6 (0.1) & 5.4 (0.1) & 1.4 (0.0) & 4.0 (0.9) \\\\\n & Parametric & & & 1.6 ( 0.0) & 0.4 ( 0.1) & 1.4 ( 0.0) & 90.4 ( 1.3) \\\\\n & Semi - PCP & & & 8.4 ( 0.3) & -0.7 ( 0.4) & 9.3 ( 0.1) & 96.6 ( 0.8) \\\\\n & Semi - $R^2$ & & & 8.4 ( 0.3) & -0.2 ( 0.4) & 9.2 ( 0.1) & 95.8 ( 0.9) \\\\\n&&&&&&\\\\\n5 & Standard & 2 & 2 & 8.8 (0.1) & 8.7 (0.1) & 1.5 (0.0) & 0.0 (0.0) \\\\\n & Parametric & & & 1.5 ( 0.0) & -0.3 ( 0.1) & 1.5 ( 0.0) & 95.0 ( 1.0) \\\\\n & Semi - PCP & & & 9.9 ( 0.3) & -0.8 ( 0.4) & 10.2 ( 0.1) & 94.2 ( 1.0) \\\\\n & Semi - $R^2$ & & & 9.9 ( 0.3) & -0.8 ( 0.4) & 10.3 ( 0.1) & 95.0 ( 1.0)\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A spectral adjustment for spatial confounding", "authors": ["Yawen Guan", "Garritt L. Page", "Brian J Reich", "Massimo Ventrucci", "Shu Yang"], "url": "https://arxiv.org/abs/2012.11767v1", "attribution": "\"A spectral adjustment for spatial confounding\" by Yawen Guan, Garritt L. Page, Brian J Reich, Massimo Ventrucci, and Shu Yang, arXiv:2012.11767v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.22936v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Cosine Similarity Scores and Variability Across Prompts}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllrr}\n\\toprule\n\\textbf{Company} & \\textbf{Year} & \\textbf{Prompt ID} & \\textbf{Mean Cosine} & \\textbf{Std. Dev} \\\\\n\\midrule\nAlphabet & 2022 & Alphabet\\_2022 & 0.778 & 0.070 \\\\\n & 2023 & Alphabet\\_2023 & 0.787 & 0.041 \\\\\n & 2024 & Alphabet\\_2024 & 0.812 & 0.034 \\\\\nAmazon & 2022 & Amazon\\_2022 & 0.786 & 0.053 \\\\\n & 2023 & Amazon\\_2023 & 0.807 & 0.050 \\\\\n & 2024 & Amazon\\_2024 & 0.708 & 0.078 \\\\\nApple & 2022 & Apple\\_2022 & 0.767 & 0.063 \\\\\n & 2023 & Apple\\_2023 & 0.774 & 0.055 \\\\\n & 2024 & Apple\\_2024 & 0.820 & 0.035 \\\\\nMeta & 2022 & Meta\\_2022 & 0.750 & 0.055 \\\\\n & 2023 & Meta\\_2023 & 0.789 & 0.038 \\\\\n & 2024 & Meta\\_2024 & 0.802 & 0.043 \\\\\nMicrosoft & 2022 & Microsoft\\_2022 & 0.846 & 0.029 \\\\\n & 2023 & Microsoft\\_2023 & 0.851 & 0.042 \\\\\n & 2024 & Microsoft\\_2024 & 0.810 & 0.041 \\\\\nNvidia & 2023 & Nvidia\\_2023 & 0.765 & 0.069 \\\\\n & 2024 & Nvidia\\_2024 & 0.806 & 0.064 \\\\\n & 2025 & Nvidia\\_2025 & 0.804 & 0.056 \\\\\nTesla & 2022 & Tesla\\_2022 & 0.751 & 0.062 \\\\\n & 2023 & Tesla\\_2023 & 0.766 & 0.036 \\\\\n & 2024 & Tesla\\_2024 & 0.813 & 0.038 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis", "authors": ["Md Talha Mohsin"], "url": "https://arxiv.org/abs/2507.22936v1", "attribution": "\"Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis\" by Md Talha Mohsin, arXiv:2507.22936v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.09865v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{array}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The ranges of markups and quantities satisfying $q \\ge 0$, $u' > 0$, $u'' < 0$, and $0<\\mu \\le 1$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c|cc}\n\\multicolumn{5}{c}{Case 1: $\\alpha >0$.} \\\\\n\\hline\n & \\multicolumn{2}{c|}{Markups} & \\multicolumn{2}{c}{Quantities} \\\\ \\cline{2-5}\n & $\\sigma \\le 1$ & $\\sigma > 1 $ & $\\sigma \\le 1$ & $\\sigma > 1 $ \\\\ \\hline\n\\multirow{2}{*}{Case 1a. $1-\\beta \\sigma>0$ and $\\beta \\neq 1$} &\n\\multirow{2}{*}{$\\frac{1}{1-\\beta} \\le \\frac{1}{\\mu} < \\infty$} &\n\\multirow{2}{*}{$\\frac{1}{1-\\beta} \\le \\frac{1}{\\mu} < \\frac{\\sigma}{\\sigma-1}$} &\n\\multirow{2}{*}{$0 \\le q < -\\frac{1-\\beta}{\\alpha (\\sigma-1)}$$^\\dagger$} &\n\\multirow{2}{*}{$0 \\le q < \\infty$} \\\\\n&\n&\n&\n&\n\\\\ \\hline\n&\n&\n&\n&\n\\\\\n\\multirow{2}{*}{Case 1b.} \\hskip .05cm $1-\\beta \\sigma<0$ and $\\beta\\in(0,1)$ &\n\\multirow{2}{*}{n.a.} &\n$\\frac{\\sigma}{\\sigma-1} < \\frac{1}{\\mu } \\le \\frac{1}{1-\\beta}$ &\n\\multirow{2}{*}{n.a.} & $0 \\le q < \\infty$ \\\\ \n\\hskip 1.73cm\n$1-\\beta \\sigma<0$ and $\\beta=1$\n&\n& $\\frac{\\sigma}{\\sigma-1} < \\frac{1}{\\mu} < \\infty$\n&\n& $0 < q < \\infty$\n\\\\ \\hline \n\\multicolumn{5}{c}{} \\\\\n \\multicolumn{5}{c}{Case 2: $\\alpha <0$.} \\\\ \\hline\n & \\multicolumn{2}{c|}{Markups} & \\multicolumn{2}{c}{Quantities} \\\\ \\cline{2-5}\n & $\\sigma \\le 1$ & $\\sigma > 1 $ & $\\sigma \\le 1$ & $\\sigma > 1 $ \\\\ \\hline \n &\n&\n&\n&\n\\\\\n\\multirow{2}{*}{Case 2a.} \\hskip .05cm $1-\\beta \\sigma>0$ and $\\beta\\in(0,1)$ &\n$1 < \\frac{1}{\\mu } \\le \\frac{1}{1-\\beta}$ &\n$1 < \\frac{1}{\\mu } \\le \\frac{1}{1-\\beta}$ &\n$0 \\le q < -\\frac{\\beta}{\\alpha} $ &\n$0 \\le q < -\\frac{\\beta}{\\alpha} $ \\\\ \n\\hskip 1.73cm\n$1-\\beta \\sigma>0$ and $\\beta=1$\n& $1 < \\frac{1}{\\mu } <\\infty^{\\ddagger}$\n& n.a.\n& $0 < q < {-\\frac{1}{\\alpha}}^{\\ddagger}$\n& n.a.\n\\\\ \\hline \n\\multirow{2}{*}{Case 2b. $1-\\beta \\sigma<0$ and $\\beta \\in (0,1)$} &\n\\multirow{2}{*}{n.a.} &\n\\multirow{2}{*}{$\\frac{1}{1-\\beta} \\le \\frac{1}{\\mu } < \\infty$} &\n\\multirow{2}{*}{n.a.} &\n\\multirow{2}{*}{$0 \\le q < -\\frac{1-\\beta}{\\alpha (\\sigma-1)}$} \\\\\n&\n&\n&\n&\n\\\\ \\hline\n\\multicolumn{5}{p{18.8cm}}{\\scriptsize {\\it Notes}:\n$^{\\dagger}$ means that when $\\sigma=1$, $0 \\le q < -\\frac{1-\\beta}{\\alpha (\\sigma-1)}$ must be replaced with $0 \\le q < \\infty$.\n$^{\\ddagger}$ means that $\\sigma=1$ must be excluded.\nThe case with $\\alpha=0$ corresponds to the CES and requires that $\\beta \\in (0,1)$.\nThe case with $1-\\beta \\sigma=0$ also corresponds to the CES and requires that $\\sigma > 1$.\nIf $\\alpha=0$ or $\\beta =\\frac{1}{\\sigma}$, then the markup is given by $\\frac{1}{\\mu} =\\frac{1}{1-\\beta} \\in (1, \\infty)$ or $\\frac{1}{\\mu} =\\frac{\\sigma}{\\sigma-1} \\in (1, \\infty)$, respectively, and the range of quantities is given by $0\\le q<\\infty$.\nSince these two cases are well known, we omit them from the table.}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Linear fractional relative risk aversion", "authors": ["Kristian Behrens", "Yasusada Murata"], "url": "https://arxiv.org/abs/2509.09865v1", "attribution": "\"Linear fractional relative risk aversion\" by Kristian Behrens and Yasusada Murata, arXiv:2509.09865v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17996v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrr|rrrr} \n\\multicolumn{4}{c}{problem sizes} & \\multicolumn{3}{c}{timing (s)} & \n\\multirow{2}{*}{iterations} \\\\ \n$n$ & $q$ & $m$ & $nm$ & MOSEK & PDMCF (CPU) & PDMCF (GPU) \\\\\n\\hline\n$3000$ & $10$ & $34424$ & $1\\times 10^8$ & OOM & $7056$ & $96$ & $4140$ \\\\\n$5000$ & $10$ & $57338$ & $3\\times 10^8$ & OOM & $19152$ & $395$ & $3970$ \\\\\n$10000$ & $10$ & $114054$ & $1\\times 10^9$ & OOM & $87490$ & $1908$& $4380$ \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Runtime table for large size problems.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solving Large Multicommodity Network Flow Problems on GPUs", "authors": ["Fangzhao Zhang", "Stephen Boyd"], "url": "https://arxiv.org/abs/2501.17996v2", "attribution": "\"Solving Large Multicommodity Network Flow Problems on GPUs\" by Fangzhao Zhang and Stephen Boyd, arXiv:2501.17996v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.11765v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Selected ESG issues by }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll}\n\\hline\n\\multicolumn{9}{l}{\\textbf{Environmental Issues}} \\\\ \\hline\n\\multicolumn{9}{l}{\\textit{- Climate change and related risks}} \\\\\n\\multicolumn{9}{l}{\\textit{- The need to reduce toxic releases and waste}} \\\\\n\\multicolumn{9}{l}{\\textit{- New regulation expanding the boundaries of environmental liability with regard to products and services}} \\\\\n\\multicolumn{9}{l}{\\textit{- Increasing pressure by civil society to improve performance, transparency and accountability,}} \\\\\n\\multicolumn{9}{l}{\\textit{leading to reputational risks if not managed properly}} \\\\\n\\multicolumn{9}{l}{\\textit{- Emerging markets for environmental services and environment-friendly products}} \\\\\n\\multicolumn{9}{l}{} \\\\ \\hline\n\\multicolumn{9}{l}{\\textbf{Social Issues}} \\\\ \\hline\n\\multicolumn{9}{l}{\\textit{- Workplace health and safety}} \\\\\n\\multicolumn{9}{l}{\\textit{- Community relations}} \\\\\n\\multicolumn{9}{l}{\\textit{- Human rights issues at company and suppliers’ / contractors’ premises}} \\\\\n\\multicolumn{9}{l}{\\textit{- Government and community relations in the context of operations in developing countries}} \\\\\n\\multicolumn{9}{l}{\\textit{- Increasing pressure by civil society to improve performance,}} \\\\\n\\multicolumn{9}{l}{\\textit{ transparency and accountability, leading to reputational risks}} \\\\\n\\multicolumn{9}{l}{} \\\\ \\hline\n\\multicolumn{9}{l}{\\textbf{Corporate Governance Issues}} \\\\ \\hline\n\\multicolumn{9}{l}{\\textit{- Board structure and accountability}} \\\\\n\\multicolumn{9}{l}{\\textit{- Accounting and disclosure practices}} \\\\\n\\multicolumn{9}{l}{\\textit{- Audit committee structure and independence of auditors}} \\\\\n\\multicolumn{9}{l}{\\textit{- Executive compensation}} \\\\\n\\multicolumn{9}{l}{\\textit{- Management of corruption and bribery issues}} \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis", "authors": ["Tanja Aue", "Adam Jatowt", "Michael Färber"], "url": "https://arxiv.org/abs/2212.11765v1", "attribution": "\"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis\" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09972v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|}\n\\hline\n \\emph{HGM} & \\emph{RRWGM} & \\emph{TM} & \\textbf{ARSRG}$_{1^{st}}$ & \\textbf{ARSRG}$_{2^{nd}}$\\\\ \\hline\n 0.1000 & 0.0545 & 0.0545 & 0.20961 & 0.39803\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications", "authors": ["Mario Manzo"], "url": "https://arxiv.org/abs/1912.09972v1", "attribution": "\"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications\" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12856v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Error analysis for activator-inhibitor example using NLS method}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\nvariable&bias&MAPE&MAE&RMSE&R$^2$\\\\ \\hline\n$x_1$&0.1150&0.2125&0.1275&0.1723&0.6963\\\\\n$x_2$&-0.2376&0.3971&0.2435&0.2927&0.7120\\\\\n$f_1(\\cdot)$&-5.7007e-04&7.0178&0.0173&0.0202&0.2692\\\\\n$f_2(\\cdot)$&-0.0101&8.5119&0.0266&0.0369&0.6857\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods", "authors": ["S. Syafiie", "Aries Subiantoro", "Vivi Andasari", "Fernando Tadeo"], "url": "https://arxiv.org/abs/2501.12856v1", "attribution": "\"Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods\" by S. Syafiie, Aries Subiantoro, Vivi Andasari, and Fernando Tadeo, arXiv:2501.12856v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11852v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Confusion matrix for the decision tree algorithm on the metrics dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|ccc|}\n \\cline{3-5}\n \\multicolumn{2}{c|}{\\multirow{2}{*}{Diagnosis}} & \\multicolumn{3}{|c|}{Actual} \\\\\n \\cline{3-5}\n \\multicolumn{2}{c|}{} & P & C & H \\\\\n \\hline\n & P & 75.5\\,\\% & 45.0\\,\\% & 5.0\\,\\% \\\\\n & C & 19.6\\,\\% & 42.5\\,\\% & 5.0\\,\\% \\\\\n \\multirow{-3}{*}{Predicted} & H & 6.7\\,\\% & 12.5\\,\\% & 90.0\\,\\% \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features", "authors": ["C. V. Vletter", "H. L. Burger", "H. Alers", "N. Sourlos", "Z. Al-Ars"], "url": "https://arxiv.org/abs/2201.11852v1", "attribution": "\"Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features\" by C. V. Vletter, H. L. Burger, H. Alers, N. Sourlos, and Z. Al-Ars, arXiv:2201.11852v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.11308v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The most important variables in the batches}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccc}\\toprule\n & & \\multicolumn{3}{c}{\\textbf{\\#batch}}\\\\\\cmidrule(lr){3-5}\n \\textbf{Dataset} & \\textbf{Model} & \\textbf{10} & \\textbf{20} & \\textbf{30} \\\\\\midrule\n \\texttt{SEA} & LR & \\texttt{V2} & \\texttt{V2} & \\texttt{V2} \\\\\n & DT & \\texttt{V1} & \\texttt{V1} & \\texttt{V1} \\\\\n & RF & \\texttt{V2} & \\texttt{V2} & \\texttt{V2} \\\\\\midrule\n \\texttt{Hyperplane} & LR & \\texttt{variable\\_1} & \\texttt{variable\\_1} & \\texttt{variable\\_1} \\\\\n & DT & \\texttt{variable\\_1} & \\texttt{variable\\_1} & \\texttt{variable\\_2} \\\\\n & RF & \\texttt{variable\\_1} & \\texttt{variable\\_1} & \\texttt{variable\\_1} \\\\\\midrule\n \\texttt{NOAA} & LR & \\texttt{attribute1} & \\texttt{attribute4} & \\texttt{attribute1} \\\\\n & DT & \\texttt{attribute2} & \\texttt{attribute2} & \\texttt{attribute2} \\\\\n & RF & \\texttt{attribute2} & \\texttt{attribute4} & \\texttt{attribute2} \\\\\\midrule\n \\texttt{Ozone} & LR & \\texttt{V26} & \\texttt{V52} & \\texttt{V52} \\\\\n & DT & \\texttt{V31} & \\texttt{V36} & \\texttt{V36} \\\\\n & RF & \\texttt{V42} & \\texttt{V61} & \\texttt{V61} \\\\\\midrule\n \\texttt{Elec2} & LR & \\texttt{nswprice} & \\texttt{nswprice} & \\texttt{nswprice} \\\\\n & DT & \\texttt{nswprice} & \\texttt{nswprice} & \\texttt{nswprice} \\\\\n & RF & \\texttt{nswprice} & \\texttt{nswprice} & \\texttt{nswprice} \\\\\\midrule\n \\texttt{Friedman} & LR & \\texttt{variable\\_4} & \\texttt{variable\\_4} & \\texttt{variable\\_4} \\\\\n & DT & \\texttt{variable\\_4} & \\texttt{variable\\_4} & \\texttt{variable\\_4} \\\\\n & RF & \\texttt{variable\\_4} & \\texttt{variable\\_4} & \\texttt{variable\\_4} \\\\\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "datadriftR: An R Package for Concept Drift Detection in Predictive Models", "authors": ["Ugur Dar", "Mustafa Cavus"], "url": "https://arxiv.org/abs/2412.11308v1", "attribution": "\"datadriftR: An R Package for Concept Drift Detection in Predictive Models\" by Ugur Dar and Mustafa Cavus, arXiv:2412.11308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15444v2_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mutually unbiased weighing matrices of order $17$ and weight $9$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l}\n\\noalign{\\hrule height1pt}\n$W_{17,33}$\\\\\n\\hline\n02020202002110022\n02021120000102011\n10012020200012021\n11201012020002100\\\\\n01220120010001120\n02202001101002102\n10011220101101000\n12000102100220220\\\\\n00211101002010202\n01100001102122020\n10100000112210110\n10220211010000201\\\\\n00121001021210020\n12000001222021100\n10100110211100002\n00002110120111001\\\\\n11022020020000212\\\\\n\\hline\n$ A_{17,33,2}$\\\\\n\\hline\n11020000011200222\n10222022000102100\n10102021021001001\n11201000102011001\\\\\n01100010020110122\n01220011220020010\n10101201010102010\n12001012001021100\\\\\n00121120202200100\n10010100202120220\n01102112010000011\n00001002221012201\\\\\n10010100120202012\n01010200000222121\n12002210202210000\n01010222200001012\\\\\n00120202122020200\\\\\n\\hline\n$ A_{17,33,3}$\\\\\n\\hline\n01220110001210001\n12212110000001002\n10100011021022100\n00122011012110000\\\\\n11020002002021110\n10100202201011020\n01011210100010012\n01010102010102120\\\\\n01012000220100211\n01100100022200222\n00001010100121221\n12000002122012001\\\\\n10011001212200001\n11202221100000020\n00001121020111100\n10100120111000210\\\\\n10221000200102202\\\\\n\\hline\n$ A_{17,33,4}$\\\\\n\\hline\n10102201210020020\n01222210000012002\n01101010002222010\n00000111002211120\\\\\n00120000221001112\n12001002210012100\n01010020111200102\n01010212000101101\\\\\n11200100210001210\n10011010021000222\n10010221022010010\n11020022020200021\\\\\n00121200110011200\n10221001100120100\n10102102102100002\n01100101001112001\\\\\n12002010101200011\\\\\n\\hline\n$ A_{17,33,5}$\\\\\n\\hline\n01220000120102201\n10220102002210002\n10101110002100101\n01010110110020202\\\\\n00001120011202021\n12000201102222000\n10012002121000120\n11022001011000110\\\\\n01101020120201010\n01010011220212000\n00121212001002002\n11201200200021020\\\\\n10010202010010211\n00122010000201221\n10100021000110222\n12000100221020210\\\\\n01102022202022000\\\\\n\\noalign{\\hrule height1pt}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unbiased weighing matrices of weight $9$", "authors": ["Makoto Araya", "Masaaki Harada", "Hadi Kharaghani", "Sho Suda", "Wei-Hsuan Yu"], "url": "https://arxiv.org/abs/2501.15444v2", "attribution": "\"Unbiased weighing matrices of weight $9$\" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.15435v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|}\n \\hline\nCountry & GDP p.c. ($y$) & Credit/GDP ($\\lambda$) & Country & GDP p.c. ($y$) & Credit/GDP ($\\lambda$) \\\\\n &[in Thousands] & & & [in Thousands]&\\\\ \\hline\nAlbania & 3.171 & 0.092 &Indonesia &3.142 &0.319\\\\ \\hline\nAlgeria &2.912& 0.333 &Iran& 4.22 &0.227\\\\ \\hline\nAngola &3.053& 0.049 &Ireland & 13.204 & 0.604\\\\ \\hline\nArgentina &6.986& 0.185 & Israel & 10.699 & 0.566\\\\ \\hline\nArmenia& 5.062 &0.079 & Italy& 13.643& 0.644 \\\\ \\hline\nAustralia &14.364 &0.501 &Jamaica &4.439 &0.238\\\\ \\hline\nAustria &14.83& 0.738 &Japan& 14.311 &1.517\\\\ \\hline\nAzerbaijan &4.316& 0.064 &Mauritius &9.942& 0.428 \\\\ \\hline\nBahrain &14.837& 0.442&Mexico &5.268 &0.224 \\\\ \\hline\nBangladesh& 1.268& 0.175 & Mongolia& 1.87& 0.154 \\\\ \\hline\nBarbados& 13.165 &0.496 & Morocco& 2.468 &0.261 \\\\ \\hline\nBelarus &13.087 &0.127 & Mozambique &1.299 &0.131\\\\ \\hline\nBelize &5.672& 0.363 &Nepal &0.897& 0.129\\\\ \\hline\nBenin& 0.746& 0.156 &Netherlands &14.625& 0.863\\\\ \\hline\nBolivia& 1.915 &0.258&New Zealand& 11.354& 0.552\\\\ \\hline\nBosnia \\& Herz. &4.445 &0.426 & Nicaragua& 1.601& 0.25 \\\\ \\hline\nBrazil &4.604 &0.423 & Niger & 0.588 &0.094 \\\\ \\hline\nBulgaria & 6.262& 0.368&Nigeria & 0.849 &0.11 \\\\ \\hline\nBurkina Faso& 0.663& 0.108&Norway &16.985& 0.484\\\\ \\hline\nBurundi& 0.506 &0.097 & Oman& 10.919& 0.249\\\\ \\hline\nCambodia& 1.819 &0.074&Pakistan& 1.499 &0.245\\\\ \\hline\nCameroon &1.491 &0.167&Panama &3.426& 0.606 \\\\ \\hline\nCanada &15.04 &0.817&Papua New Guinea &1.459 &0.185\\\\ \\hline\nCentral Afr. Rep.& 0.651& 0.104&Paraguay &2.791& 0.198\\\\ \\hline\nChad &0.874 &0.079 &Peru& 2.984& 0.168 \\\\ \\hline\nChile &6.164& 0.442 & Philippines& 2.084& 0.271\\\\ \\hline\nChina &2.624 &0.874 &Poland& 8.417 &0.276\\\\ \\hline\nColombia& 3.42& 0.283 &Portugal& 8.506& 0.749\\\\ \\hline\nCongo, Rep. of &1.421 &0.145 &Qatar& 30.938& 0.299\\\\ \\hline\nCosta Rica &5.111& 0.234 &Romania& 6.685 &0.149\\\\ \\hline\nCroatia &9.394& 0.401 &Russia &8.37& 0.187 \\\\ \\hline\nCyprus &12.216 &1.238 & Rwanda& 0.794& 0.063 \\\\ \\hline\nCzech Republic& 15.303 &0.487 & Samoa &3.888 &0.243\\\\ \\hline\nDenmark &14.335 &0.642 &Senegal &1.144 &0.22\\\\ \\hline\nDjibouti &3.651 &0.354 &Sierra Leone &1.326& 0.048\\\\ \\hline\nDom. Republic &3.527& 0.231&Singapore& 14.802& 0.744 \\\\ \\hline\nEcuador &3.029 &0.217 &Slovenia& 16.958 &0.382 \\\\ \\hline\nEgypt &2.282 &0.28&South Africa& 5.125 &0.902 \\\\ \\hline\nEl Salvador &2.88& 0.303 & Spain & 11.61 & 0.79 \\\\ \\hline\nEq. Guinea &5.549 &0.096 &Tajikistan& 2.2 & 0.173\\\\ \\hline\nEstonia & 11.733 & 0.494 &Tanzania& 0.626 &0.087 \\\\ \\hline\nEthiopia &0.746 &0.151 & Thailand &3.498 &0.657\\\\ \\hline\nFiji &2.983 &0.286 & Togo &0.684& 0.185\\\\ \\hline\nFinland &13.084& 0.565 &Trinidad \\&Tobago& 7.552& 0.336\\\\ \\hline\nFrance& 13.184 &0.806& Tunisia& 3.919& 0.514 \\\\ \\hline\nGabon& 4.584 &0.147 & Turkey &3.179& 0.182\\\\ \\hline\nGambia, The& 0.823 & 0.134 &Turkmenistan &6.589 &0.017\\\\ \\hline\nGeorgia &4.983 &0.105& Uganda &0.58 &0.066 \\\\ \\hline\nGermany &17.353 &0.913 & Ukraine& 6.208 &0.191\\\\ \\hline\nGhana& 0.925 &0.071& United Arab Emir. &32.473& 0.29 \\\\ \\hline\nGreece& 10.546& 0.354& United Kingdom& 13.117 &0.737\\\\ \\hline\nGuatemala& 3.043& 0.167& United States &18.805 &1.219\\\\ \\hline\nGuinea-Bissau &0.641& 0.088 & Uruguay &5.732& 0.323\\\\ \\hline\nGuyana &1.562 &0.333& Venezuela &5.365 &0.29 \\\\ \\hline\nHaiti &1.431& 0.138& Vietnam &2.432& 0.43\\\\ \\hline\nHonduras& 1.898 &0.294 & Yemen& 0.928 &0.055\\\\ \\hline\nHungary& 10.36 &0.399& Zambia &1.006 &0.119\\\\ \\hline\nIceland &15.676& 0.628 & Zimbabwe &2.238& 0.298\\\\ \\hline\nIndia &1.307 &0.213 &&&\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ List of countries }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Transitional Dynamics of the Saving Rate and Economic Growth", "authors": ["Markus Brueckner", "Tomoo Kikuchi", "George Vachadze"], "url": "https://arxiv.org/abs/2012.15435v3", "attribution": "\"Transitional Dynamics of the Saving Rate and Economic Growth\" by Markus Brueckner, Tomoo Kikuchi, and George Vachadze, arXiv:2012.15435v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.09107v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\nAge & 18-29 & 30-49 & 50-64 & 65+ \\\\ \\hline\n18-29 & 1 & 0.9 & 0.8 & 0.7 \\\\ \\hline\n30-49 & 0.9 & 0.9 & 0.8 & 0.8 \\\\ \\hline\n50-64 & 0.8 & 0.8 & 0.9 & 0.8 \\\\ \\hline\n65+ & 0.7 & 0.8 & 0.8 & 0.8 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\small Experiment 1 Graphon}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Modeling of Rumor Propagation in Large Populations with Network via Graphon Games", "authors": ["Huaning Liu", "Gokce Dayanikli"], "url": "https://arxiv.org/abs/2503.09107v2", "attribution": "\"Modeling of Rumor Propagation in Large Populations with Network via Graphon Games\" by Huaning Liu and Gokce Dayanikli, arXiv:2503.09107v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.07016v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The results of method of Baroughi-Bonab et al. on instance of Example .}\n\\begin{tabular}{cccccc}\n\t\t\t\\hline\n\t\t\t $\\mathbf{x}_{B}=(x_{B},y_{B})$& $\\mathbf{P}_{1B}$& $\\mathbf{P}_{2B}$&$\\mathbf{P}_{3B}$&$\\mathbf{P}_{4B}$&$||\\bar{\\mathbf{x}}-\\mathbf{x}_{B}||$\\\\\n\t\t\t\\hline\n\t\t\t( 0.0000, 1.0000)&\n\t\t(1.3333,0.0000)&(-5.0000,3.0000)&(7.0000,2.0000)&\n\t\t(0.0000,0.5000)&\n\t\t0.00000\\\\\n\t\t\t\\hline\n\t\t$F_B$&154.1667&&&&\\\\\n\t\t\t\\hline\n\t\tCPU(in s)&0.0426&&&&\\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Inverse single facility location problem in the plane with variable coordinates", "authors": ["Nazanin Tour-Savadkoohi", "Jafar Fathali"], "url": "https://arxiv.org/abs/2503.07016v1", "attribution": "\"Inverse single facility location problem in the plane with variable coordinates\" by Nazanin Tour-Savadkoohi and Jafar Fathali, arXiv:2503.07016v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.09598v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{activation functions of DNN.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\t\t\\hline\n\t\tName & $\\sigma(v_i)$ & Range \\\\ \\hline\n\t\tReLU & $max(0,v_i)$ & $[0,\\infty)$ \\\\ \\hline\n\t\ttanh & $\\frac{e^{v_i}-e^{-v_i}}{e^{v_i}+e^{-v_i}}$ & $(-1,1)$ \\\\ \\hline\n\t\tsigmoid & $\\frac{1}{1+e^{-v_i}}$ & $(0,1)$ \\\\ \\hline\n\t softmax & $\\frac{e^{v_i}}{\\sum_je^{-v_i}}$ & $(0,1)$ \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks", "authors": ["Feibo Jiang", "Kezhi Wang", "Li Dong", "Cunhua Pan", "Wei Xu", "Kun Yang"], "url": "https://arxiv.org/abs/1911.09598v1", "attribution": "\"Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks\" by Feibo Jiang, Kezhi Wang, Li Dong, Cunhua Pan, Wei Xu, and Kun Yang, arXiv:1911.09598v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00431v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\it User Evaluation of Visualizing Visual Appearance Patterns}\t\t\t\\centering\n\\begin{tabular}{l|c|c} \\hline%\\noalign{\\smallskip}\t\t\n\\hspace{50pt} \\textbf{Questions} & \\textbf{Mean} & \\textbf{Std. } \\\\\t\\hspace{30pt} 0(poor) - 10(excellent) &\\textbf{Value} & \\textbf{Dev.} \\\\\n\\hline%\\noalign{\\smallskip}\t\t\t\t\nQ1: Do you often use map-based navigation services &9.2 & 1.2\\\\\t\t\n ~~(e.g. Google map, Apple map, etc.) when planning a new route? &&\\\\\n \\hline\n \nQ2: Is the new visualization of the streetview information &7.4& 1.5 \\\\\n~~ potentially useful for you or others? && \\\\\t\n \\hline\nQ3: How are you familiar with the geo-environment in this example? &7.6& 1.9 \\\\\n\\hline\nQ4: Do you think the four patterns can represent the visual &8.0& 1.3 \\\\\n~~ appearance of this area? &&\\\\\n\\hline\nQ5: Do you agree that the pattern distribution shown on the three &7.9&1.4\\\\\n~~routes is reasonable, based on your own experience? &&\\\\\n\\hline\nQ6: Can you quickly get the information from the visualizations? &7.9 &1.6 \\\\\n\\hline\nQ7: Does the map view convey useful information? &8.1 &1.4\\\\\n\\hline\nQ8: Does the RouteViewLine convey useful information? &7.4 &1.7\\\\\n\\hline\nQ9: Does the coordinated raw street-view images convey &8.4 &1.4 \\\\\n~~useful information? &&\\\\\n\\hline\n\\hline%\\noalign{\\smallskip}\t\t\t\t\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Visualizing Routes with AI-Discovered Street-View Patterns", "authors": ["Tsung Heng Wu", "Md Amiruzzaman", "Ye Zhao", "Deepshikha Bhati", "Jing Yang"], "url": "https://arxiv.org/abs/2404.00431v1", "attribution": "\"Visualizing Routes with AI-Discovered Street-View Patterns\" by Tsung Heng Wu, Md Amiruzzaman, Ye Zhao, Deepshikha Bhati, and Jing Yang, arXiv:2404.00431v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.00079v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccc}\n\\toprule\nFeature & Method & \\multicolumn{6}{c}{CIFAR-10N} & \\multicolumn{2}{c}{CIFAR-100N} \\\\ \n\\cmidrule(lr){3-8} \\cmidrule(l){9-10}\n & & Clean & Aggre & Rand1 & Rand2 & Rand3 & Worst & Clean & Noisy \\\\ \\midrule\n\\multirow{4}{*}{DINOv2 SSL} & CE (ours) & 99.25 & 98.69 & 98.8 & 98.65 & 98.67 & 95.71 & \\bf{92.85} & \\bf{83.17} \\\\ \n& MAE & \\bf{99.27} & \\bf{99.04} & \\bf{99.01} & \\bf{99.09} & \\bf{99.11} & 95.55 & 90.68 & 82.55 \\\\\n& Sigmoid & 99.26 & 98.86 & 98.91 & 98.87 & 98.96 & \\bf{96.66} & 92.82 & 82.03 \\\\\n& ELR & 99.09 & 98.49 & 98.62 & 98.53 & 98.56 & 95.60 & 89.99 & 82.75 \\\\\n& SAM & 99.09 & 97.66 & 98.47 & 98.53 & 98.47 & 95.47 & 89.97 & 82.85 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Label Noise: Ignorance Is Bliss", "authors": ["Yilun Zhu", "Jianxin Zhang", "Aditya Gangrade", "Clayton Scott"], "url": "https://arxiv.org/abs/2411.00079v1", "attribution": "\"Label Noise: Ignorance Is Bliss\" by Yilun Zhu, Jianxin Zhang, Aditya Gangrade, and Clayton Scott, arXiv:2411.00079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2103.00534v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Equivalent Circuit Parameters of Varactor Diode }\n\\begin{tabular}{|cccc|}\n\t\t\\hline\\hline\n\t\t\\textbf{VR (V)} & \\textbf{C (pF)} & \\textbf{R (ohm)} & \\textbf{L (nH)} \\\\ \\hline\n\t\t 0 & 2.31 & 4.51 & 0.70 \\\\ \\hline\n\t\t -4 & 0.84 & 4.04 & 0.70 \\\\ \\hline\n\t\t -7 & 0.55 & 3.66 & 0.70 \\\\ \\hline\n\t\t -11 & 0.38 & 3.18 & 0.70 \\\\ \\hline\n\t\t -14 & 0.31 & 2.86 & 0.70 \\\\ \\hline\n\t\t -16 & 0.27 & 2.65 & 0.70 \\\\ \\hline\n\t\t -19 & 0.24 & 2.38 & 0.70 \\\\ \\hline\\hline\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials", "authors": ["Xilong Pei", "Haifan Yin", "Li Tan", "Lin Cao", "Zhanpeng Li", "Kai Wang", "Kun Zhang", "Emil Björnson"], "url": "https://arxiv.org/abs/2103.00534v2", "attribution": "\"RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials\" by Xilong Pei, Haifan Yin, Li Tan, Lin Cao, Zhanpeng Li, Kai Wang, Kun Zhang, and Emil Björnson, arXiv:2103.00534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2007.07925v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{{\\bf Quantitative evaluation.} The MSCOCO validation set with 18 Instagram filters is used for evaluation. Our method supports a few variations. Our method can have (R): regularization, (AU): Aleatoric uncertainty, (U): combined uncertainty, (CC): color correlation. Note that for WCT2, we gave option that uses features from decoder and skip-connection since it performs the best. Note that lower $\\Delta E_{00}^*$ (a.k.a., Delta-E 2000) is better.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\hline\\noalign{\\smallskip}\nMethods & PSNR & $\\Delta E_{00}^*$\\\\\n\\noalign{\\smallskip}\n\\hline\n\\noalign{\\smallskip}\n{\\bf Ours } & {\\bf 25.226 } & {\\bf 6.660 } \\\\\n{\\bf Ours (w. U, CC) } & {\\bf 24.931 } & {\\bf 6.725 } \\\\\n{\\bf Ours (w. AU) } & {\\bf 25.438 } & {\\bf 6.427 } \\\\\n{\\bf Ours (w. U) } & {\\bf 25.495 } & {\\bf 6.394 } \\\\\n{\\bf Ours (w. R, U) } & {\\bf 26.093 } & {\\bf 6.148 }\\\\\nWCT2 & 16.473 & 17.516 \\\\\nColor Transfer & 7.325 & 34.914 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Filter Style Transfer between Photos", "authors": ["Jonghwa Yim", "Jisung Yoo", "Won-joon Do", "Beomsu Kim", "Jihwan Choe"], "url": "https://arxiv.org/abs/2007.07925v1", "attribution": "\"Filter Style Transfer between Photos\" by Jonghwa Yim, Jisung Yoo, Won-joon Do, Beomsu Kim, and Jihwan Choe, arXiv:2007.07925v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12262v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{No. Days to Achieve $90\\%$ of Infection} & \\textbf{No Pre-release Mitigation} & \\textbf{With Pre-release Mitigation} \\\\ \n\\textbf{} & \\textbf{} & \\textbf{(Thermal Fogging $35\\%$)} \\\\ \\hline\nSingle Release & 5 years & 185 days \\\\ \\hline\n2-Batch Release & 174 days & 146 days \\\\ \\hline\n3-Batch Release & 189 days & 145 days \\\\ \\hline\n4-Batch Release & 131 days & 120 days \\\\ \\hline\n5-Batch Release & 123 days & 113 days \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Improving Wolbachia-Based Control Programs in Urban Settings: Insights from Spatial Modeling", "authors": ["Daniela Florez", "Ricardo Cortez", "James M. Hyman", "Zhuolin Qu"], "url": "https://arxiv.org/abs/2503.12262v1", "attribution": "\"Improving Wolbachia-Based Control Programs in Urban Settings: Insights from Spatial Modeling\" by Daniela Florez, Ricardo Cortez, James M. Hyman, and Zhuolin Qu, arXiv:2503.12262v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.11459v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Interpretation of Ratings}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|r|c|c|c|}\n\\hline\n Ratings difference & Win & Draw & Loss \\\\ \n\\hline\n0 & 35.7 & 28.6 & 35.7 \\\\\n10 & 37.1 & 28.5 & 34.5 \\\\\n20 & 38.5 & 28.2 & 33.3 \\\\\n50 & 42.5 & 27.8 & 29.7 \\\\\n100 & 49.9 & 25.7 & 24.3 \\\\\n200 & 63.8 & 20.5 & 15.7 \\\\\n500 & 90.5 & 6.0 & 3.5 \\\\\n800 & 98.1 & 1.2 & 0.7 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Ratings of European and South American Football Leagues Based on Glicko-2 with Modifications", "authors": ["Andrei Shelopugin", "Alexander Sirotkin"], "url": "https://arxiv.org/abs/2310.11459v1", "attribution": "\"Ratings of European and South American Football Leagues Based on Glicko-2 with Modifications\" by Andrei Shelopugin and Alexander Sirotkin, arXiv:2310.11459v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.17607v2_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Training data breakdown of ASR task for 12.5Hz speech tokenizer. Tokens measured in billion.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\toprule\nLanguage & Speech Hours & Speech Tokens & Text Tokens & Total Tokens \\\\ \\midrule\nChinese & 21,624 & 0.97B & 0.42B & 1.39B \\\\\nEnglish & 68,733 & 3.09B & 1.06B & 4.16B \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Scaling Speech-Text Pre-training with Synthetic Interleaved Data", "authors": ["Aohan Zeng", "Zhengxiao Du", "Mingdao Liu", "Lei Zhang", "Shengmin Jiang", "Yuxiao Dong", "Jie Tang"], "url": "https://arxiv.org/abs/2411.17607v2", "attribution": "\"Scaling Speech-Text Pre-training with Synthetic Interleaved Data\" by Aohan Zeng, Zhengxiao Du, Mingdao Liu, Lei Zhang, Shengmin Jiang, Yuxiao Dong, and Jie Tang, arXiv:2411.17607v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15174v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The approximation errors $\\varepsilon$ ($\\varepsilon_1$)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n \\hline\n & $L = 4$ & $L = 8$ & $L = 16$ & $L = 32$ & $L = 64$ & $L = 128$ & $L = 256$ \\\\\n \\hline\n \\hline\n $x_4(t)$ & 0.035217 & 0.004319 & 0.000524 & 0.000065 & $8.21 \\cdot 10^{-6}$ & $1.06 \\cdot 10^{-6}$ & $1.39 \\cdot 10^{-7}$ \\\\\n & (0.005314) & (0.000531) & (0.000060) & ($7.14 \\cdot 10^{-6}$) & ($8.71 \\cdot 10^{-7}$) & ($1.08 \\cdot 10^{-7}$) & ($1.34 \\cdot 10^{-8}$) \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On Spectral Approach to the Synthesis of Shaping Filters", "authors": ["Konstantin A. Rybakov"], "url": "https://arxiv.org/abs/2501.15174v1", "attribution": "\"On Spectral Approach to the Synthesis of Shaping Filters\" by Konstantin A. Rybakov, arXiv:2501.15174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10239v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Final rank of the models for the heteroscedastic noise cases. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccl}\n\\cline{1-4}\n\\textbf{Model} & \\textbf{low5\\_high50} & \\textbf{low5\\_high100} &\\textbf{low5\\_high500} & \\\\\n\\cline{1-4}\nLM\t&\t\t\t3\t&\t\t4\t&\t\t4\t&\t\t\\\\\nGP\t&\t\t\t1\t&\t\t1\t&\t\t3\t&\t\\\\\nSVM\t&\t\t\t1\t&\t\t2\t&\t\t1\t&\t\t\\\\\nRF\t&\t\t\t5\t&\t\t5\t&\t\t2\t&\t\\\\\nANN\\_sh\t&\t\t4\t&\t\t3\t&\t\t6\t&\t\t\\\\\nANN\\_dp\t&\t\t6\t&\t\t7\t&\t\t6\t&\t\t\\\\\naml\t&\t\t\t6\t&\t\t6\t&\t\t5\t&\t\t\\\\\n\\cline{1-4}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Design choice and machine learning model performances", "authors": ["Rosa Arboretti", "Riccardo Ceccato", "Luca Pegoraro", "Luigi Salmaso"], "url": "https://arxiv.org/abs/2201.10239v2", "attribution": "\"Design choice and machine learning model performances\" by Rosa Arboretti, Riccardo Ceccato, Luca Pegoraro, and Luigi Salmaso, arXiv:2201.10239v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17854v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c||c|c||c|c||c|c}\n \\multicolumn{6}{c}{\\textbf{ODE Terms:}}\\\\\nEqn.~ & \\texttt{DDF.} & Eqn.~ & \\texttt{DDF.} & Eqn.~ & \\texttt{DDF.}& Eqn.~ & \\texttt{DDF.}\\\\\n\\hline\n$A_0$ & \\texttt{A0} & $B_{1}$ & \\texttt{B1} &$B_{2}$&\\texttt{B2}&$B_v$&\\texttt{Bv}\\\\ \n$C_{1}$ & \\texttt{C1} & $D_{11}$ &\\texttt{D11}&$D_{12}$&\\texttt{D12}&$D_{1v}$&\\texttt{D1v}\\\\ \n$C_{2}$ & \\texttt{C2} & $D_{21}$ &\\texttt{D21}&$D_{22}$&\\texttt{D22}&$D_{2v}$&\\texttt{D2v} \\\\\n$C_{ri}$ & \\texttt{Cri\\{i\\}} & $B_{r1i}$ &\\texttt{Br1i\\{i\\}}&$B_{r2i}$&\\texttt{Br2i\\{i\\}}&$D_{rvi}$&\\texttt{Drvi\\{i\\}} \\\\\n\\hline \\\\\n \\multicolumn{6}{c}{ \\textbf{Discrete Delay Terms:}}\\\\\nEqn.~ & \\texttt{DDF.} & & & & \\\\\n\\hline\n$C_{vi}$ & \\texttt{Cvi\\{i\\}} & & &&\\\\ \n\\hline \\\\ \\multicolumn{6}{c}{\\textbf{Distributed Delay Terms: May be functions of \\texttt{pvar s}}}\\\\\nEqn.~ & \\texttt{DDF.} & & & & \\\\\n\\hline\n$C_{vdi}(s) $ & \\texttt{Cvdi\\{i\\}} & & &&\\\\ \n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Equivalent names of Matlab elements of the \\texttt{DDF} structure terms for terms in Eqn.~. For example, to set term \\texttt{XX} to \\texttt{YY}, we use \\texttt{DDF.XX=YY}. In addition, the delay $\\tau_i$ is specified using the vector element \\texttt{DDF.tau(i)} so that if $\\tau_1=1, \\tau_2=2, \\tau_3=3$, then \\texttt{DDF.tau=[1 2 3]}. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "PIETOOLS 2024: User Manual", "authors": ["Sachin Shivakumar", "Declan Jagt", "Danilo Braghini", "Amritam Das", "Yulia Peet", "Matthew Peet"], "url": "https://arxiv.org/abs/2501.17854v1", "attribution": "\"PIETOOLS 2024: User Manual\" by Sachin Shivakumar, Declan Jagt, Danilo Braghini, Amritam Das, Yulia Peet, and Matthew Peet, arXiv:2501.17854v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11701v3_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{SIVAL model results. The mean performance was calculated over ten repeat trainings of each model, and the standard error of the mean is given.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\t\t\n\t\t\\toprule\n\t\tModel & Train Accuracy & Val Accuracy & Test Accuracy \\\\\n\t\t\\midrule\n\t\tMI-Net & \\textbf{0.984 $\\pm$ 0.004} & \\textbf{0.850 $\\pm$ 0.009} & \\textbf{0.819 $\\pm$ 0.012} \\\\\n\t\tmi-Net & 0.967 $\\pm$ 0.005 & 0.835 $\\pm$ 0.010 & 0.808 $\\pm$ 0.011 \\\\\n\t\tMI-Attn & 0.972 $\\pm$ 0.007 & 0.830 $\\pm$ 0.011 & 0.813 $\\pm$ 0.012 \\\\\n\t\tMI-GNN & 0.932 $\\pm$ 0.014 & 0.803 $\\pm$ 0.014 & 0.781 $\\pm$ 0.019 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Model Agnostic Interpretability for Multiple Instance Learning", "authors": ["Joseph Early", "Christine Evers", "Sarvapali Ramchurn"], "url": "https://arxiv.org/abs/2201.11701v3", "attribution": "\"Model Agnostic Interpretability for Multiple Instance Learning\" by Joseph Early, Christine Evers, and Sarvapali Ramchurn, arXiv:2201.11701v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10005v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n& MSMARCO & NQ & TriviaQA \\\\ \n\\Xhline{2.5\\arrayrulewidth}\nFine-tuned SOTA & 44.3 & 84.8, 89.8 & 84.1, 87.8 \\\\\n\\Xhline{2.5\\arrayrulewidth} \\\\\n\\multicolumn{4}{c}{Unsupervised} \\\\ \n\\Xhline{1\\arrayrulewidth} \\\\\nBM25 & 18.4 & 62.9, 78.3 & 76.4, 83.2 \\\\\nICT & - & 50.9, 66.8 & 57.5, 73.6 \\\\\nMSS & - & 59.8, 74.9 & 68.2, 79.4 \\\\\nContriever & - & 67.2, 81.3 & 74.2, 83.2 \\\\\n\\Xhline{1\\arrayrulewidth} \\\\\n\\texttt{cpt-text} S & 19.9 & 65.5, 77.2 & 75.1, 81.7 \\\\\n\\texttt{cpt-text} M & 20.6 & 68.7, 79.6 & 78.0, 83.8 \\\\\n\\texttt{cpt-text} L & 21.5 & 73.0, 83.4 & 80.0, 86.8 \\\\\n\\texttt{cpt-text} XL & \\textbf{22.7} & \\textbf{78.8, 86.8} & \\textbf{82.1, 86.9} \\\\\n\\Xhline{2.5\\arrayrulewidth} \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Evaluation of unsupervised \\texttt{cpt-text} models of different sizes on several large-scale text search benchmarks. We report MRR@10 on MSMARCO and Recall@20, Recall@100 for NQ and TriviaQA as done in prior work. Results for training with Inverse Cloze Task (ICT) and masked salient spans (MSS) objectives are taken from . \\texttt{cpt-text} achieves the best results among unsupervised methods, surpassing keyword search methods on MSMARCO and embedding based methods on NQ and TriviaQA.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Text and Code Embeddings by Contrastive Pre-Training", "authors": ["Arvind Neelakantan", "Tao Xu", "Raul Puri", "Alec Radford", "Jesse Michael Han", "Jerry Tworek", "Qiming Yuan", "Nikolas Tezak", "Jong Wook Kim", "Chris Hallacy", "Johannes Heidecke", "Pranav Shyam", "Boris Power", "Tyna Eloundou Nekoul", "Girish Sastry", "Gretchen Krueger", "David Schnurr", "Felipe Petroski Such", "Kenny Hsu", "Madeleine Thompson", "Tabarak Khan", "Toki Sherbakov", "Joanne Jang", "Peter Welinder", "Lilian Weng"], "url": "https://arxiv.org/abs/2201.10005v1", "attribution": "\"Text and Code Embeddings by Contrastive Pre-Training\" by Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, Johannes Heidecke, Pranav Shyam, Boris Power, Tyna Eloundou Nekoul, Girish Sastry, Gretchen Krueger, David Schnurr, Felipe Petroski Such, Kenny Hsu, Madeleine Thompson, Tabarak Khan, Toki Sherbakov, Joanne Jang, Peter Welinder, and Lilian Weng, arXiv:2201.10005v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04724v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Objective metrics comparison of zero-shot style transfer between StableVC and baseline systems.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\hline\nModel & Corr $\\uparrow$ & RMSE $\\downarrow$ & UTMOS $\\uparrow$ & WER $\\downarrow$ & SECS $\\uparrow$ \\\\ \\hline\nStyleVC & 0.71 & 14.98 & 3.63 & 3.54 & 0.21 \\\\\nNS2VC & 0.67 & 17.19 & 3.57 & 5.31 & 0.47 \\\\\nDDDM-VC & 0.69 & 15.68 & 3.71 & 3.56 & 0.50 \\\\\nStableVC & \\textbf{0.75} & \\textbf{12.87} & \\textbf{4.06} & \\textbf{2.12} & \\textbf{0.64} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "StableVC: Style Controllable Zero-Shot Voice Conversion with Conditional Flow Matching", "authors": ["Jixun Yao", "Yuguang Yang", "Yu Pan", "Ziqian Ning", "Jiaohao Ye", "Hongbin Zhou", "Lei Xie"], "url": "https://arxiv.org/abs/2412.04724v2", "attribution": "\"StableVC: Style Controllable Zero-Shot Voice Conversion with Conditional Flow Matching\" by Jixun Yao, Yuguang Yang, Yu Pan, Ziqian Ning, Jiaohao Ye, Hongbin Zhou, and Lei Xie, arXiv:2412.04724v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.10614v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\\hline\nTest & Acquisiton & Average Final & SD of Final & Best Final & Worst Final\\\\\nFunction & Function & Solution & Solution & Solution & Solution\\\\\n\\hline\n\\multirow{5}{*}{GRL} & EI & \\bf{-0.867} & \\bf{0.021} & \\bf{-0.869} & \\bf{-0.663}\\\\\n & PEI & -0.862 & 0.051 & \\bf{-0.869} & -0.489\\\\\n & SEI & -0.666 & 0.189 & \\bf{-0.869} & -0.232\\\\\n & VEI & -0.858 & 0.056 & \\bf{-0.869} & -0.527\\\\\n & UEI & -0.860 & 0.055 & \\bf{-0.869} & -0.402\\\\\n \\hline\n \\multirow{5}{*}{ROS} & EI & 0.002 & 0.005 & 2e-08 & 0.028\\\\\n & PEI & 0.002 & 0.006 & 1e-08 & 0.039\\\\\n & SEI & 0.103 & 0.136 & 8e-08 & 0.654\\\\\n & VEI & 0.125 & 0.326 & 1e-06 & 2.83\\\\\n & UEI & \\bf{0.001} & \\bf{0.002} & \\bf{1e-10} & \\bf{0.009}\\\\\n \\hline\n \\multirow{5}{*}{MOT} & EI & -2.871 & 0.279 & -2.969 & -1.660\\\\\n & PEI & -2.927 & 0.162 & -2.969 & -1.659\\\\\n & SEI & -2.080 & 0.574 & -2.969 & -1.639\\\\\n & VEI & -2.822 & 0.339 & -2.969 & -1.640\\\\\n & UEI & \\bf{-2.969} & \\bf{3e-06} & \\bf{-2.969} & -\\bf{2.969}\\\\\n\\hline\n \\multirow{5}{*}{ACY} & EI & 0.009 & 0.010 & 6e-05 & 0.044\\\\\n & PEI & \\bf{0.008} & \\bf{0.010} & 8e-05& 0.041\\\\\n & SEI & 0.009 & 0.010 & 6e-05 & 0.044\\\\\n & VEI & 0.012 & 0.011 & 0.000 & 0.054\\\\\n & UEI & 0.008 & 0.010 & \\bf{4e-06} & \\bf{0.039}\\\\\n\\hline\n \\multirow{5}{*}{RAS} & EI & 0.094 & 0.488 & 1e-12 & \\bf{4.000}\\\\\n & PEI & 0.040 & 0.398 & 1e-10 & 3.981\\\\\n & SEI & 2.103 & 1.481 & 2e-07 & 4.995\\\\\n & VEI & 1.602 & 1.149 & 7e-07 & 4.995\\\\\n & UEI & \\bf{0.057} & \\bf{0.431} & \\bf{3e-12} & 4.000\\\\\n \\hline\n \\multirow{5}{*}{HTN} & EI & \\bf{-3.280} & 0.059 & -3.322 & -3.137\\\\\n & PEI & -3.279 & 0.058 & -3.322 & -3.193\\\\\n & SEI & -3.281 & \\bf{0.057} & -3.322 & -3.196\\\\\n & VEI & -3.279 & 0.058 & \\bf{-3.322} & \\bf{-3.201}\\\\\n & UEI & -3.279 & 0.058 & -3.322 & -3.201\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The average, standard deviation (SD), best, and worst solutions found at the end of the 100 Monte Carlo experiments by each acquisition function on each optimization test function. Bolded values signify the best outcome in a given category for each test function.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Understanding an Acquisition Function Family for Bayesian Optimization", "authors": ["Jiajie Kong", "Tony Pourmohamad", "Herbert K. H. Lee"], "url": "https://arxiv.org/abs/2310.10614v1", "attribution": "\"Understanding an Acquisition Function Family for Bayesian Optimization\" by Jiajie Kong, Tony Pourmohamad, and Herbert K. H. Lee, arXiv:2310.10614v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11224v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Analysis for the sparsity of reciprocal loss.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\hline\n \\textbf{Loss rate} & 2 & 3 & 4 & 5\n \\\\\n \\hline\n Average LDE (cm)/ sequence & 0.46 & \\textbf{0.25} & 0.29 & 0.38\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Reciprocal Landmark Detection and Tracking with Extremely Few Annotations", "authors": ["Jianzhe Lin", "Ghazal Sahebzamani", "Christina Luong", "Fatemeh Taheri Dezaki", "Mohammad Jafari", "Purang Abolmaesumi", "Teresa Tsang"], "url": "https://arxiv.org/abs/2101.11224v1", "attribution": "\"Reciprocal Landmark Detection and Tracking with Extremely Few Annotations\" by Jianzhe Lin, Ghazal Sahebzamani, Christina Luong, Fatemeh Taheri Dezaki, Mohammad Jafari, Purang Abolmaesumi, and Teresa Tsang, arXiv:2101.11224v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.21845v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n &\n {\\textbf{Erasure Rate $\\backslash$ Code}} & $\\mathbf{[[1525, 25]]}$ & $\\mathbf{[[3904, 64]]}$ & $\\mathbf{[[6100, 100]]}$ & $\\mathbf{[[8784, 144]]}$ \\\\ \\hline\n \\multirow{6}{*}{\\textbf{Maximum number of isolated horizontal clusters}} & \n $0.2$ & $2$ & $1$ & $1$ & $1$ \\\\ \\cline{2-6}\n & $0.225$ & $3$ & $1$ & $1$ & $1$ \\\\ \\cline{2-6}\n & $0.25$ & $4$ & $3$ & $2$ & $1$ \\\\ \\cline{2-6}\n & $0.275$ & $5$ & $3$ & $3$ & $2$ \\\\ \\cline{2-6}\n & $0.3$ & $7$ & $4$ & $4$ & $3$ \\\\ \\cline{2-6}\n & $0.325$ & $7$ & $6$ & $5$ & $3$ \\\\ \\hline\n \\multirow{6}{*}{\\textbf{Maximum size of isolated horizontal clusters}} & \n $0.2$ & $18$ & $24$ & $26$ & $26$ \\\\ \\cline{2-6}\n & $0.225$ & $20$ & $26$ & $30$ & $29$ \\\\ \\cline{2-6}\n & $0.25$ & $22$ & $32$ & $31$ & $37$ \\\\ \\cline{2-6}\n & $0.275$ & $22$ & $33$ & $36$ & $39$ \\\\ \\cline{2-6}\n & $0.3$ & $23$ & $32$ & $36$ & $45$ \\\\ \\cline{2-6}\n & $0.325$ & $23$ & $34$ & $36$ & $44$ \\\\ \\hline\n \\multirow{6}{*}{\\textbf{Minimum size of isolated horizontal clusters}} &\n $0.2$ & $7$ & $10$ & $0$ & $21$ \\\\ \\cline{2-6}\n & $0.225$ & $7$ & $11$ & $16$ & $25$ \\\\ \\cline{2-6}\n & $0.25$ & $7$ & $10$ & $14$ & $19$ \\\\ \\cline{2-6}\n & $0.275$ & $7$ & $10$ & $13$ & $17$ \\\\ \\cline{2-6}\n & $0.3$ & $7$ & $10$ & $13$ & $14$ \\\\ \\cline{2-6}\n & $0.325$ & $7$ & $10$ & $13$ & $14$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Statistics on isolated horizontal clusters after peeling for the $[[1525,25]]$, $[[3904,64]]$, $[[6100,100]]$, and $[[8784,144]]$ quantum expander codes.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the Efficacy of the Peeling Decoder for the Quantum Expander Code", "authors": ["Jefrin Sharmitha Prabhu", "Abhinav Vaishya", "Shobhit Bhatnagar", "Aryaman Manish Kolhe", "V. Lalitha", "P. Vijay Kumar"], "url": "https://arxiv.org/abs/2504.21845v2", "attribution": "\"On the Efficacy of the Peeling Decoder for the Quantum Expander Code\" by Jefrin Sharmitha Prabhu, Abhinav Vaishya, Shobhit Bhatnagar, Aryaman Manish Kolhe, V. Lalitha, and P. Vijay Kumar, arXiv:2504.21845v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.03055v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Adaptive CTC Fusion in Mismatch Conditions. (\\% WER)}\n\\begin{tabular}{lccc}\n \t \\toprule\n \t \\toprule\n Decoding Strategy& \\multicolumn{3}{c}{Test Data}\\\\\n \t \\midrule\n {\\bf DIRHA (BLA-L2L)}& {\\it BLA-NoMic}& {\\it BLA-KA6}& {\\it L3L-L4L}\\\\\n \\midrule\n \t Pre-defined CTC [0.5; 0.5] & \\bf{26.9}&21&20.3\\\\\n \t Adaptive CTC Fusion & 27.1&\\bf{20.7}&\\bf{20} \\\\\n \t \\midrule\n {\\bf AMI (MDM-SMDM)}& {\\it MDM-NoMic} & {\\it MDM-IHM0} & -- \\\\\n \t \\midrule\n \t Pre-defined CTC [0.5; 0.5] &46.1&44&--\\\\\n \t Adaptive CTC Fusion &\\bf{43.1}&\\bf{41.9}&--\\\\\n \\bottomrule\n \\bottomrule\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR", "authors": ["Ruizhi Li", "Gregory Sell", "Hynek Hermansky"], "url": "https://arxiv.org/abs/2102.03055v1", "attribution": "\"Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR\" by Ruizhi Li, Gregory Sell, and Hynek Hermansky, arXiv:2102.03055v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.21132v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\t\\hline\n\t& \\textbf{IEPS} & \\textbf{WES} \\\\\n\t\\hline\n\t\\textbf{Data Loss} & $10^{8}$ & $10^{12}$\\\\\n\t\\textbf{PDE Loss} & $10^{-14}$ & $10^{-14}$\\\\\n\t\\textbf{BC Loss} & $10^{-12}$ & $10^{-12}$\\\\\n\t\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Weights of the loss functions}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "DeepF-fNet: a physics-informed neural network for vibration isolation optimization", "authors": ["A. Tollardo", "F. Cadini", "M. Giglio", "L. Lomazzi"], "url": "https://arxiv.org/abs/2412.21132v1", "attribution": "\"DeepF-fNet: a physics-informed neural network for vibration isolation optimization\" by A. Tollardo, F. Cadini, M. Giglio, and L. Lomazzi, arXiv:2412.21132v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2312.06144v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Chosen Buses in OPtimal Path for 14-bus system (K=5).}\n\\begin{tabular}{cccc}\n\\hline\nRound & Path & Reward & Time (s) \\\\ \\hline\n15 & [0] & -8981614.735 & 388.4 \\\\\n53 & [0, 4] & -8969392.451 & 1340.1 \\\\\n59 & [0, 4, 8] & -8827543.464 & 1440.6 \\\\\n63 & [0, 4, 8, 9] & -8844486.411 & 1541.2 \\\\\n69 & [0, 4, 8, 9, 11] & -8844486.411 & 1893.8 \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Long-Term Carbon-Efficient Planning for Geographically Shiftable Resources: A Monte Carlo Tree Search Approach", "authors": ["Xuan He", "Danny H. K. Tsang", "Yize Chen"], "url": "https://arxiv.org/abs/2312.06144v2", "attribution": "\"Long-Term Carbon-Efficient Planning for Geographically Shiftable Resources: A Monte Carlo Tree Search Approach\" by Xuan He, Danny H. K. Tsang, and Yize Chen, arXiv:2312.06144v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|r|r|r|r|r|r}\n \\hline\n \\multicolumn{2}{c}{} & \\multicolumn{3}{|c}{End item demand only} & \\multicolumn{3}{|c}{Component demand} \\\\\n \\hline \n Utilization & $\\lambda$ & Runtime (s) & Total costs PH & $\\Delta$ (\\%) & Runtime (s) & Total costs PH & $\\Delta$ (\\%)\\\\\n \\hline\n \\multirow{4}{*}{50\\%} \n & 0.1 & 801.8 & 31689.2 & 5.11 & 1651.4 & 29543.8 & 7.83 \\\\\n & 1 & 307.9 & 30360.0 & \\textbf{1.26} & 533.4 & 27957.7 & \\textbf{2.69} \\\\\n & 10 & 246.9 & 30626.4 & 2.08 & 354.4 & 28052.1 & 3.03 \\\\\n & 100 & 274.1 & 30670.6 & 2.23 & 377.3 & 28077.2 & 3.06 \\\\\n \\hline\n \\multirow{4}{*}{90\\%} \n & 0.1 & 841.8 & 79067.8 & 8.82 & 2023.4 & 65682.4 & 5.12\\\\\n & 1 & 379.5 & 74022.6 & \\textbf{4.31} & 1070.7 & 63633.3 & 2.46\\\\\n & 10 & 281.7 & 74096.6 & 4.32 & 644.6 & 63548.1 & 2.46\\\\\n & 100 & 329.3 & 74820.8 & 5.88 & 851.0 & 63103.2 & \\textbf{2.39} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Majority voting consensus calculation: Evaluation of the $\\lambda$-multiple for the penalty parameter $\\rho$ in terms of total costs and runtime on a scenario tree with $|\\Omega| = 2$ and therefore $|\\Phi| = 128$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.11532v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Error metrics of DNN on ANF and EOG stock \\textit{Close} price prediction (validation set)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\multicolumn{6}{c}{\\textbf{DNN}}\\\\\\hline\n & \\textbf{MSE} & \\textbf{RMSE} & \\textbf{MAE} & \\textbf{MAPE} & \\textbf{EVS}\\\\ \\hline\n\\textit{ANF} & 1.75 & 1.32 & 1.07 & 0.02 & 0.91\\\\ \\hline\n\\textit{EOG} & 2.39 & 1.55 & 1.23 & 0.01 & 0.7\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networks", "authors": ["Ivan Letteri", "Giuseppe Della Penna", "Giovanni De Gasperis", "Abeer Dyoub"], "url": "https://arxiv.org/abs/2210.11532v1", "attribution": "\"DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networks\" by Ivan Letteri, Giuseppe Della Penna, Giovanni De Gasperis, and Abeer Dyoub, arXiv:2210.11532v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.08100v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\nDays & Theory & GBM & Euler-Maruyama & Abs. Dif. GBM & Abs. Dif. Euler-Maruyama \\\\\n\\hline\n3 & 0.003288 & 0.00184 & 0.00231 & 0.001448 & 0.000978 \\\\\n7 & 0.054313 & 0.03556 & 0.04241 & 0.018753 & 0.011903 \\\\\n14 & 0.173605 & 0.13239 & 0.15020 & 0.041215 & 0.023405 \\\\\n30 & 0.352608 & 0.30196 & 0.33991 & 0.050648 & 0.012698 \\\\\n60 & 0.510997 & 0.46529 & 0.51849 & 0.045707 & 0.007493 \\\\\n90 & 0.591495 & 0.55254 & 0.61697 & 0.038955 & 0.025475 \\\\\n120 & 0.642095 & 0.60637 & 0.67139 & 0.035725 & 0.029295 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of liquidation probabilities and their absolute differences from the theoretical values}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "DeFi Liquidation Risk Modeling Using the Reflection Principle for Zero-Drift Brownian Motion", "authors": ["Timofei Belenko", "Georgii Vosorov"], "url": "https://arxiv.org/abs/2505.08100v1", "attribution": "\"DeFi Liquidation Risk Modeling Using the Reflection Principle for Zero-Drift Brownian Motion\" by Timofei Belenko and Georgii Vosorov, arXiv:2505.08100v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.05236v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Impact of \\textit{3DRF} Gain ($\\Delta 3DRF$) over Accuracy.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Network & $\\Delta 3DRF$ & Accuracy (\\%) & $\\Delta$Accuracy~(\\%)\\\\\n \\midrule\n \\midrule\n VGG-11 & 0 & 92.68 & 0 \\\\\n \\midrule\n Variant-1 & 1.73 & 93.56 & 0.88\\\\\n Variant-2 & 1.60 & 93.46 & 0.78\\\\\n \\midrule\n Variant-3 & 0.29 & 92.75 & 0.07\\\\\n \\midrule\n Variant-4 & 0.0 & 92.58 & -0.10\\\\\n Variant-5 & 0.0 & 92.41 & -0.27\\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks", "authors": ["Yuke Wang", "Boyuan Feng", "Xueqiao Peng", "Yufei Ding"], "url": "https://arxiv.org/abs/2009.05236v4", "attribution": "\"An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks\" by Yuke Wang, Boyuan Feng, Xueqiao Peng, and Yufei Ding, arXiv:2009.05236v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09244v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of results for various BERT models for the second case study (physical activity).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\hline\n & Precision &Recall & F1\\\\ \\hline\n BERT Base & 0.88 & 0.86 & 0.87\\\\\n PubmedBERT(Abs+Ft) & 0.92 & 0.90 & 0.91\\\\\n \n PubmedBERT(Abs) & \\textbf{0.96} & \\textbf{0.96} & \\textbf{0.96} \\\\\n Bio BERT & 0.88 & 0.90 & 0.88\\\\\n \n UMLS BERT& 0.93 & 0.92 & 0.93\\\\\n Bio-clinical BERT& 0.89 & 0.88 & 0.89\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Extracting Lifestyle Factors for Alzheimer's Disease from Clinical Notes Using Deep Learning with Weak Supervision", "authors": ["Zitao Shen", "Yoonkwon Yi", "Anusha Bompelli", "Fang Yu", "Yanshan Wang", "Rui Zhang"], "url": "https://arxiv.org/abs/2101.09244v2", "attribution": "\"Extracting Lifestyle Factors for Alzheimer's Disease from Clinical Notes Using Deep Learning with Weak Supervision\" by Zitao Shen, Yoonkwon Yi, Anusha Bompelli, Fang Yu, Yanshan Wang, and Rui Zhang, arXiv:2101.09244v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.11126v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Cognitive tests and primary references.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\\hline\n Test Name & References \\\\\\hline\n Adult Decision Making Competence & \\\\\n Bayesian Updating & \\\\\n Berlin Numeracy & \\\\\n Cognitive Reflection & \\\\\n Coherence Forecasting & \\\\\n Denominator Neglect & \\\\\n & \\\\\n Graph Literacy & \\\\\n Impossible Question & \\\\\n Leapfrog & \\\\\n Raven Matrices & \\\\\n Number Series & \\\\\n & \\\\\n Shipley General Intelligence & \\\\\n Time Series & \\\\\n & \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Identifying good forecasters via adaptive cognitive tests", "authors": ["Edgar C. Merkle", "Nikolay Petrov", "Sophie Ma Zhu", "Ezra Karger", "Philip E. Tetlock", "Mark Himmelstein"], "url": "https://arxiv.org/abs/2411.11126v2", "attribution": "\"Identifying good forecasters via adaptive cognitive tests\" by Edgar C. Merkle, Nikolay Petrov, Sophie Ma Zhu, Ezra Karger, Philip E. Tetlock, and Mark Himmelstein, arXiv:2411.11126v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.09598v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\hline\n \\multicolumn{5}{c}{Dependent variable: Self reported measure of attention to environment}\\\\\n \\hline\n \\hline\n Avg. variability & -6.291***& -7.095***& -9.194***& -9.877***\\\\\n & (1.587) & (1.902) & (2.087) & (2.003) \\\\\n Avg. variability sq.& 71.038***& 78.984***& 100.939***& 111.767***\\\\\n & (18.330) & (21.756) & (22.608) & (21.346) \\\\\n Income & & -0.001* & -0.001* & -0.001* \\\\\n & & (0.001) & (0.001) & (0.001) \\\\\n Male & & -0.013***& -0.013***& -0.013***\\\\\n & & (0.003) & (0.003) & (0.003) \\\\\n Age & & 0.002***& 0.002***& 0.002***\\\\\n & & (0.000) & (0.000) & (0.000) \\\\\n Education level & & 0.006***& 0.006***& 0.006***\\\\\n & & (0.001) & (0.001) & (0.001) \\\\\n Occupation category Controls & N & Y & Y & Y \\\\\n Historical ethnic group characteristics & N & N & Y & Y \\\\\n Historical topographic characteristics & N & N & N & Y \\\\\n Country-year Fixed effects & Y & Y & Y & Y \\\\\n \\hline\n Mean of dep var & 0.702 & 0.704 & 0.704 & 0.704 \\\\\n St. Dev. of dep var & 0.252 & 0.250 & 0.250 & 0.250 \\\\\n Min value Avg. Variability& 0.015 & 0.015 & 0.015 & 0.015 \\\\\n Max value Avg. Variability& 0.093 & 0.093 & 0.093 & 0.093 \\\\\n R-sq & 0.097 & 0.113 & 0.113 & 0.113 \\\\\n Adj. R-sq & 0.097 & 0.112 & 0.112 & 0.113 \\\\\n N & 157142 & 138067 & 138067 & 138067 \\\\\n \\hline\n \\multicolumn{5}{c}{ {*}{*}{*} p$<$0.01, {*}{*}p$<$0.05, {*} p$<$0.1, {+} p$<$0.15}\\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Coefficients for the impact of average variability of ancestral climatic conditions on individual's self-reported attention to the environment }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Ancestral origins of attention to environmental issues", "authors": ["César Barilla", "Palaash Bhargava"], "url": "https://arxiv.org/abs/2509.09598v1", "attribution": "\"Ancestral origins of attention to environmental issues\" by César Barilla and Palaash Bhargava, arXiv:2509.09598v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.07704v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Kolmogorov-Smirnov and Anderson-Darling Tests of distributions ($n=200$)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrr}\n\t\t\\Xhline{2pt}\n\t\tm1 & m2 & v & K-S Test Statistic & A-D Test statistic \\\\\n\t\t\\Xhline{2pt}\n\t\t3 & 2 & 50 & \\textit{\\textbf{0.070}} & \\textit{\\textbf{-0.4497}} \\\\\n\t\t3 & 2 & 150 & \\textit{\\textbf{0.120}} & \\textit{\\textbf{2.6462}} \\\\\n\t\t7 & 2 & 50 & \\textit{\\textbf{0.080}} & \\textit{\\textbf{-0.0472}} \\\\\n\t\t7 & 2 & 150 & \\textit{\\textbf{0.100}} & \\textit{\\textbf{0.0334}} \\\\\n\t\t17 & 2 & 50 & \\textit{\\textbf{0.105}} & \\textit{\\textbf{0.7747}} \\\\\n\t\t17 & 2 & 150 & \\textit{\\textbf{0.100}} & \\textit{\\textbf{1.1361}} \\\\\n\t\t6 & 5 & 50 & \\textit{\\textbf{0.165}} & 5.2803 \\\\\n\t\t6 & 5 & 150 & 0.180 & \\textit{\\textbf{4.0643}} \\\\\n\t\t10 & 5 & 50 & \\textit{\\textbf{0.110}} & \\textit{\\textbf{0.7065}} \\\\\n\t\t10 & 5 & 150 & \\textit{\\textbf{0.115}} & \\textit{\\textbf{2.1679}} \\\\\n\t\t20 & 5 & 50 & \\textit{\\textbf{0.095}} & \\textit{\\textbf{0.7067}} \\\\\n\t\t20 & 5 & 150 & \\textit{\\textbf{0.085}} & \\textit{\\textbf{0.4033}} \\\\\n\t\t11 & 10 & 50 & \\textit{\\textbf{0.085}} & \\textit{\\textbf{-0.3534}} \\\\\n\t\t11 & 10 & 150 & \\textit{\\textbf{0.100}} & \\textit{\\textbf{2.3397}} \\\\\n\t\t15 & 10 & 50 & \\textit{\\textbf{0.100}} & \\textit{\\textbf{-0.2185}} \\\\\n\t\t15 & 10 & 150 & \\textit{\\textbf{0.100}} & \\textit{\\textbf{0.0764}} \\\\\n\t\t25 & 10 & 50 & \\textit{\\textbf{0.100}} & \\textit{\\textbf{0.4349}} \\\\\n\t\t25 & 10 & 150 & \\textit{\\textbf{0.080}} & \\textit{\\textbf{-0.6399}} \\\\\n\t\t16 & 15 & 50 & \\textit{\\textbf{0.130}} & \\textit{\\textbf{1.8812}} \\\\\n\t\t16 & 15 & 150 & \\textit{\\textbf{0.110}} & \\textit{\\textbf{-0.2864}} \\\\\n\t\t20 & 15 & 50 & \\textit{\\textbf{0.085}} & \\textit{\\textbf{-0.3123}} \\\\\n\t\t20 & 15 & 150 & \\textit{\\textbf{0.140}} & \\textit{\\textbf{2.8565}} \\\\\n\t\t30 & 15 & 50 & \\textit{\\textbf{0.100}} & \\textit{\\textbf{-0.2265}} \\\\\n\t\t30 & 15 & 150 & \\textit{\\textbf{0.075}} & \\textit{\\textbf{-0.1306}} \\\\\n\t\t26 & 25 & 50 & \\textit{\\textbf{0.065}} & \\textit{\\textbf{-0.1677}} \\\\\n\t\t26 & 25 & 150 & \\textit{\\textbf{0.060}} & \\textit{\\textbf{-0.8979}} \\\\\n\t\t30 & 25 & 50 & \\textit{\\textbf{0.120}} & \\textit{\\textbf{0.4653}} \\\\\n\t\t30 & 25 & 150 & \\textit{\\textbf{0.120}} & \\textit{\\textbf{1.5277}} \\\\\n\t\t40 & 25 & 50 & \\textit{\\textbf{0.090}} & \\textit{\\textbf{-0.4905}} \\\\\n\t\t40 & 25 & 150 & \\textit{\\textbf{0.065}} & \\textit{\\textbf{-0.5917}} \\\\\n\t\t\\Xhline{2pt}\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Distribution of the elemental regression weights with t-distributed co-variate measurement errors", "authors": ["I. Seidu", "E. Nyarko", "S. Iddi", "E. Ranganai", "K. Doku-Amponsah"], "url": "https://arxiv.org/abs/2312.07704v3", "attribution": "\"Distribution of the elemental regression weights with t-distributed co-variate measurement errors\" by I. Seidu, E. Nyarko, S. Iddi, E. Ranganai, and K. Doku-Amponsah, arXiv:2312.07704v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.06172v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c||c|c|c|c||c|c}\n\\textbf{Step} & \\textsc{cnot} & $R_z(\\theta)$ & $\\sqrt{\\textsc{not}}$ & \\textsc{not} & \\textbf{Depth} & \\textbf{Cost} \\\\\n\\hline\n\\textbf{1} & 1 & 4 & 4 & 0 & 9 & 13 \\\\ \\hdashline\n\\textbf{2.1} & 47 & 47 & 26 & 2 & 122 & 310 \\\\\n\\textbf{2.2} & 485 & 467 & 133 & 1 & 1086 & 3026 \\\\\n\\textbf{2.3} & 488 & 471 & 134 & 1 & 1094 & 3046 \\\\ \\hdashline\n\\textbf{3.1} & 490 & 470 & 133 & 1 & 1094 & 3054 \\\\\n\\textbf{3.2} & 494 & 475 & 134 & 1 & 1104 & 3080 \\\\\n\\textbf{3.3} & 498 & 480 & 135 & 1 & 1114 & 3106 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Cumulative counts and costs for the longest path until and including step $N$. We can see that the distribution loading is relatively cheap while the stopping time is the most expensive part.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Potential Applications of Quantum Computing for the Insurance Industry", "authors": ["Michael Adam"], "url": "https://arxiv.org/abs/2210.06172v1", "attribution": "\"Potential Applications of Quantum Computing for the Insurance Industry\" by Michael Adam, arXiv:2210.06172v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.17618v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n \\hline\n \\multicolumn{2}{|c|}{}& \\multicolumn{3}{|c|}{Frequentist}& \\multicolumn{2}{|c|}{Bayesian} & \\\\\n \\hline\nQuantities & $\\theta_*$ & NAIVE & WLP & LSW & BMA & CB & ORACLE \\\\ \n \\hline\nCoverage & -0.25 & 0.925 & 0.488 & 0.959 & 0.000 & 0.959 & 0.945 \\\\ \n & -0.4 & 0.924 & 0.480 & 0.952 & 0.000 & 0.955 & 0.953 \\\\ \n \\hline\n\\hline\nInterval & -0.25 & 1.054 & 1.013 & 1.702 & 0.004 & 1.237 & 0.990 \\\\ \n Length & -0.4 & 1.054 & 1.589 & 1.705 & 0.001 & 1.226 & 0.992 \\\\ \n \\hline\n\\hline\nBias & -0.25 & 0.054 & 0.337 & 0.039 & 0.250 & 0.088 & 0.012 \\\\ \n & -0.4 & 0.088 & 0.339 & 0.065 & 0.400 & 0.093 & 0.011 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Coverage, interval length, and bias corresponding to sample-based standard errors for each method considered for $n=300, d=400$ under signal strengths $\\theta_* \\in \\{-0.25,-0.4\\}$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates", "authors": ["Abhishek Ojha", "Naveen N. Narisetty"], "url": "https://arxiv.org/abs/2411.17618v1", "attribution": "\"Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates\" by Abhishek Ojha and Naveen N. Narisetty, arXiv:2411.17618v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00105v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c||c|c|c|c|c|c|c}\n\t\\ & $C_1$ & $C_2$ & $C_3$ & $C_4$ & $C_5$ & $C_6$ & $C_7$ \\\\\n\t\\hline\n\tElement & () & $(1,2)(3,4)$ & (1,2,3) & $(1,2,3)(4,5,6)$ & $(1,2,3,4)(5,6)$ & $(1,2,3,4,5)$ & $(1,2,3,4,6)$ \\\\\n\t$|C_i|$ &1 & 45& 40 & 40 & 90 & 72 &72 \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Conjugacy classes of $A_6$ that corresponds to the character table.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Anomalies of Coset Non-Invertible Symmetries", "authors": ["Po-Shen Hsin", "Ryohei Kobayashi", "Carolyn Zhang"], "url": "https://arxiv.org/abs/2503.00105v2", "attribution": "\"Anomalies of Coset Non-Invertible Symmetries\" by Po-Shen Hsin, Ryohei Kobayashi, and Carolyn Zhang, arXiv:2503.00105v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.09993v4_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model performance measures reported for the Market Shift feature extraction component, 3 different labelling configurations: 7, 11 and 15 ticks rebounds. Null-Precision corresponds to the performance of an always-positive classifier.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rrrrr}\nContract & PR-AUC & F1-score & Precision & ROC-AUC & Null-Precision \\\\\n\\hline\n\\multicolumn{6}{c}{\\textbf{Rebound 7}} \\\\\n\\hline\nESH2017 & .20 & .26 & .20 & .51 & .19 \\\\\nESM2017 & .25 & .28 & .24 & .55 & .21 \\\\\nESU2017 & .25 & .31 & .25 & .56 & .20 \\\\\nESZ2017 & .17 & .26 & .18 & .52 & .16 \\\\\nESH2018 & .20 & .29 & .19 & .54 & .17 \\\\\nESM2018 & .21 & .33 & .22 & .55 & .19 \\\\\nESU2018 & .17 & .23 & .17 & .51 & .16 \\\\\nESZ2018 & .18 & .28 & .18 & .52 & .17 \\\\\nESH2019 & .19 & .28 & .19 & .53 & .18 \\\\\nESM2019 & .18 & .27 & .18 & .53 & .17 \\\\\n\\hline\n\\multicolumn{6}{c}{\\textbf{Rebound 11}} \\\\\n\\hline\nESH2017 & .18 & .24 & .19 & .51 & .18 \\\\\nESM2017 & .21 & .24 & .20 & .50 & .19 \\\\\nESU2017 & .21 & .31 & .23 & .56 & .19 \\\\\nESZ2017 & .16 & .12 & .16 & .50 & .16 \\\\\nESH2018 & .20 & .28 & .18 & .54 & .17 \\\\\nESM2018 & .20 & .32 & .21 & .55 & .19 \\\\\nESU2018 & .17 & .22 & .17 & .51 & .16 \\\\\nESZ2018 & .17 & .27 & .17 & .52 & .17 \\\\\nESH2019 & .18 & .26 & .18 & .52 & .17 \\\\\nESM2019 & .18 & .27 & .18 & .53 & .17 \\\\\n\\hline\n\\multicolumn{6}{c}{\\textbf{Rebound 15}} \\\\\n\\hline\nESH2017 & .15 & .22 & .15 & .48 & .15 \\\\\nESM2017 & .17 & .20 & .16 & .50 & .17 \\\\\nESU2017 & .16 & .17 & .15 & .51 & .15 \\\\\nESZ2017 & .16 & .11 & .16 & .50 & .16 \\\\\nESH2018 & .17 & .27 & .17 & .53 & .16 \\\\\nESM2018 & .18 & .28 & .18 & .54 & .17 \\\\\nESU2018 & .16 & .22 & .16 & .51 & .16 \\\\\nESZ2018 & .17 & .27 & .17 & .52 & .16 \\\\\nESH2019 & .17 & .27 & .17 & .52 & .17 \\\\\nESM2019 & .18 & .27 & .18 & .53 & .16 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components", "authors": ["Artur Sokolovsky", "Luca Arnaboldi"], "url": "https://arxiv.org/abs/2009.09993v4", "attribution": "\"A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components\" by Artur Sokolovsky and Luca Arnaboldi, arXiv:2009.09993v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.13992v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\toprule[2pt]\n & $\\rho = 0.00$ & $\\rho = 0.50$ & $\\rho = 0.90$ & $\\rho = 0.95$ & $\\rho = 0.99$ \\\\ \\midrule[2pt]\n $H = 2$ & 0.000 (0.000) & 0.000 (0.000) & 0.903 (0.068) & 0.975 (0.041) & 0.999 (0.005) \\\\ \\midrule\n $H = 4$ & 0.000 (0.000) & 0.323 (0.285) & 0.979 (0.099) & 0.977 (0.099) & 0.961 (0.134) \\\\ \\midrule\n $H = 6$ & 0.000 (0.000) & 0.831 (0.221) & 1.000 (0.000) & 1.000 (0.000) & 1.000 (0.000) \\\\ \\midrule\n $H = 8$ & 0.000 (0.000) & 0.968 (0.071) & 1.000 (0.000) & 1.000 (0.000) & 1.000 (0.000) \\\\ \\midrule\n $H = 10$ & 0.000 (0.000) & 1.000 (0.003) & 1.000 (0.000) & 1.000 (0.000) & 1.000 (0.000) \\\\ \\midrule\n $H - 1 \\sim Poi(1)$ & 0.000 (0.000) & 0.000 (0.000) & 0.962 (0.050) & 0.994 (0.022) & 1.000 (0.000) \\\\ \\bottomrule[2pt]\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Estimated specificity in the simulated scenario in of the manuscript under different value of $\\rho$ and $H$. All values are reported as \\textnormal{mean (standard deviation)} over 50 simulated datasets.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian nonparametric boundary detection for income areal data", "authors": ["Matteo Gianella", "Mario Beraha", "Alessandra Guglielmi"], "url": "https://arxiv.org/abs/2312.13992v2", "attribution": "\"Bayesian nonparametric boundary detection for income areal data\" by Matteo Gianella, Mario Beraha, and Alessandra Guglielmi, arXiv:2312.13992v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14913v1_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n \\hline\n basis & \\multicolumn{4}{|c|}{$V_{h}$}& \\multicolumn{4}{|c|}{$V_{h}^{*}$}\\\\\n \\hline\n h &$L^{2}$error & order & $H^{1}$error & order & $L^{2}$error & order & $H^{1}$error & order \\\\\n \\hline\n $2^{-3}$ &4.492e-01& - &1.999e+00& - &6.153e-06& - &7.643e-05& - \\\\\n \\hline\n $2^{-4}$ &4.311e-01&0.059&1.916e+00&0.061&8.718e-06&-0.503&6.221e-05&0.297\\\\\n \\hline\n $2^{-5}$ &3.746e-01&0.203&1.665e+00&0.203&8.383e-06&0.057&4.574e-05&0.444\\\\\n \\hline\n $2^{-6}$ &2.462e-01&0.606&1.094e+00&0.605&5.539e-06&0.598&2.811e-05&0.702\\\\\n \\hline\n $2^{-7}$ &1.039e-01&1.245&4.619e-01&1.244&2.293e-06&1.272&1.227e-05&1.196 \\\\\n \\hline\n $2^{-8}$ &3.135e-02&1.728&1.398e-01&1.724&6.844e-07&1.745&4.582e-06&1.421 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{$L^{2}$ and $H^{1}$error and corresponding order using $P^{1}$ Lagrange element, $c(x)=-2\\pi^{2}$+0.01.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A PINN-enriched finite element method for linear elliptic problems", "authors": ["Xiao Chen", "Yixin Luo", "Jingrun Chen"], "url": "https://arxiv.org/abs/2503.14913v1", "attribution": "\"A PINN-enriched finite element method for linear elliptic problems\" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2102.00225v20_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The experiment result of text classification. The test dataset is the 40,000 data. The accuracy reported by human is 5000 data that sampled from the 500 million data. Model-A, Model-B and Model-C are corresponding to Fig 1.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\nModel In Fig 1 & Test Dataset Accuracy & Human Evaluate Accuracy \\\\\n\\hline\nModel-A & 83.3\\% & 88.0\\% \\\\ \n\\hline\nModel-B & 91.7\\% & 97.2\\% \\\\ \n\\hline\nModel-C & 92.5\\% & 97.7\\% \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Learning From How Humans Correct", "authors": ["Tong Guo"], "url": "https://arxiv.org/abs/2102.00225v20", "attribution": "\"Learning From How Humans Correct\" by Tong Guo, arXiv:2102.00225v20, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2401.00395v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Standardized RMSPE of the OTL-Circuit Example. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|} \n \\hline\n Type of mean & EVIGP & mlegp \\\\ \\hline\n Constant & 0.01608 & 0.04882 \\\\ \n Linear & 0.01399 & 0.03584 \\\\ \n Quadratic & 0.01792 & 0.03006\\\\\n Quadratic, after selection & 0.01625 & N/A \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Energetic Variational Gaussian Process Regression for Computer Experiments", "authors": ["Lulu Kang", "Yuanxing Cheng", "Yiwei Wang", "Chun Liu"], "url": "https://arxiv.org/abs/2401.00395v2", "attribution": "\"Energetic Variational Gaussian Process Regression for Computer Experiments\" by Lulu Kang, Yuanxing Cheng, Yiwei Wang, and Chun Liu, arXiv:2401.00395v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.17260v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Pre-processing steps and parameters for each team}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\toprule\nTeam & Clipping & Cropping & Resize & Interpolation & Other \\\\ \\midrule\nSN & $-1000, 4000$ & $448\\times448$ & $321\\times244\\times244$ & Linear & 3D \\\\\nMH & $-1000, 3000$ & $300\\times300$ & No & Linear & 2.5D, Scaling \\\\\nEK & $500, 2000$ & $515\\times515$ & No & No & 2.5D, Scaling\\\\\nCW & $0, 1900$ & $322\\times307$ & No & No & No\\\\\nSV & No & No & $96\\times96\\times1$ & Linear & Scaling \\\\\nBM & $-100, 3171$ & $480\\times480$ & $240\\times240\\times1$ & Area & Scaling \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "MiceBoneChallenge: Micro-CT public dataset and six solutions for automatic growth plate detection in micro-CT mice bone scans", "authors": ["Nikolay Burlutskiy", "Marija Kekic", "Jordi de la Torre", "Philipp Plewa", "Mehdi Boroumand", "Julia Jurkowska", "Borjan Venovski", "Maria Chiara Biagi", "Yeman Brhane Hagos", "Roksana Malinowska-Traczyk", "Yibo Wang", "Jacek Zalewski", "Paula Sawczuk", "Karlo Pintarić", "Fariba Yousefi", "Leif Hultin"], "url": "https://arxiv.org/abs/2411.17260v1", "attribution": "\"MiceBoneChallenge: Micro-CT public dataset and six solutions for automatic growth plate detection in micro-CT mice bone scans\" by Nikolay Burlutskiy, Marija Kekic, Jordi de la Torre, Philipp Plewa, Mehdi Boroumand, Julia Jurkowska, Borjan Venovski, Maria Chiara Biagi, Yeman Brhane Hagos, Roksana Malinowska-Traczyk, Yibo Wang, Jacek Zalewski, Paula Sawczuk, Karlo Pintarić, Fariba Yousefi, and Leif Hultin, arXiv:2411.17260v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.20912v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{System parameters' values}\\centering%\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\\hline\n\\textbf{Parameter} & \\textbf{Value/Range} \\\\ \\hline\\hline\n$\\lambda$ & $8.5$ cm \\\\ \\hline\n$G_{R,U}$, $G_{R,E}$ & $12$ dBi \\\\ \\hline\n$G_{T,B}$, $G_{R,B}$ & $25$ dBi \\\\ \\hline\n$G_{T,V}$ & $17$ dBi \\\\ \\hline\n$d_{BR}$ & $22$ m \\\\ \\hline\n$d_{BU_{j}}(\\forall j)$ & $30$ m \\\\ \\hline\n$d_{BE_{l}},d_{BV_{k}}(\\forall l,k)$ & $20$ m \\\\ \\hline\n$\\Delta _{r},\\Delta _{a}$ & $\\lambda /2$ \\\\ \\hline\n$\\varphi _{BU}$ & $[-15,5]$ deg \\\\ \\hline\n$\\varphi _{VB}$ & $[20,30]$ deg \\\\ \\hline\n$\\varphi _{BR}$ & $-40$ deg \\\\ \\hline\n$\\varphi _{BE}$ & $[-35,-15]$ deg \\\\ \\hline\n$\\theta _{XZ}$ $\\left(\\forall X,Z\\right)$ & $0$ \\\\ \\hline\n$N$ & $25$ ($5\\times 5$ REs) \\\\ \\hline\n$N_{F}$ & $5$ dB \\\\ \\hline\n$P_{\\max }$ & $25$ dB \\\\ \\hline\n$N_{t}$, $N_{r}$ & $8$ \\\\ \\hline\n$B$ & $50$ MHz \\\\ \\hline\n$T$ & $298$ Kelvins \\\\ \\hline\n$J,L$ & $2$ \\\\ \\hline\n$K$ & $3$ \\\\ \\hline\n$\\gamma _{E,\\mathrm{DL}}^{(\\max )}$, $\\gamma _{E,\\mathrm{DL}}^{(\\max )}$ & $%\n5 $ dB \\\\ \\hline\n$\\left( \\gamma _{B}^{(\\min )},\\gamma _{U,\\mathrm{DL}}^{(\\min )}\\right) $ & $%\n(5,10)$ dB \\\\ \\hline\n$\\kappa _{XZ}$ $\\left(\\forall XZ \\in \\left\\{BR, RU_j, V_kR \\right\\}\\right)$ & $20$ dB \\\\ \\hline\n$\\kappa _{RE_l}$ $\\left(\\forall l \\right)$ & $\\infty$ (Pure LoS) \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the Secrecy-Sensing Optimization of RIS-assisted Full-Duplex Integrated Sensing and Communication Network", "authors": ["Elmehdi Illi", "Ahmad Bazzi", "Marwa Qaraqe", "Ali Ghrayeb"], "url": "https://arxiv.org/abs/2504.20912v1", "attribution": "\"On the Secrecy-Sensing Optimization of RIS-assisted Full-Duplex Integrated Sensing and Communication Network\" by Elmehdi Illi, Ahmad Bazzi, Marwa Qaraqe, and Ali Ghrayeb, arXiv:2504.20912v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.10173v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean $T_1, T_2, \\text{CBV}, R$ values and standard deviations, in white (WM) and grey (GM) matters ROIs. Values significantly departing from literature reference (last column) are in red.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccc}\n \\toprule\n Parameter & ROI & Full matching & HD-GMM & HD-STM & Literature \\\\\n \\midrule \n \\multirow{2}[0]{*}{$T_1$(ms)} & WM & $891 \\pm 6$ & $650 \\pm 23 $ & $869 \\pm 19$ & $690$-$1100$ \\\\\n & GM & $1566 \\pm 12$ & $1650 \\pm 57$& $1631 \\pm 56$ & 1286-1393\\\\\n \\midrule\n \\multirow{2}[0]{*}{$T_2$(ms)} & WM &\n $52 \\pm 0.0$ & \\textcolor{red}{$400 \\pm 15$} & $48 \\pm 2$ & 56-80 \\\\\n & GM & $95 \\pm 4$ & \\textcolor{red}{$370 \\pm 43$} & $96 \\pm 12$ & 78-117\\\\\n \\midrule\n \\multirow{2}[0]{*}{$\\text{CBV}$($\\%$)} & WM & $5.4 \\pm 0.6$ & $2.0 \\pm 0.1$ & $4.8 \\pm 0.8$ & $1.7$ - $3.6$ \\\\\n & GM & $9.2 \\pm 1.1$ & $4.2 \\pm 0.8$ & $7.6 \\pm 1$ & $3.0$ - $8.0$ \\\\\n \\midrule\n \\multirow{2}[0]{*}{$R$ ($\\mu s$)} & WM & $5.7 \\pm 0.1$ & $5.6 \\pm 0.1$ & $5.7 \\pm 0.1$ & $6.8 \\pm 0.3$ \\\\\n & GM & $6.2 \\pm 0.1$ & $5.4 \\pm 0.4$ & $6.1 \\pm 0.1$ & $7.3 \\pm 0.3$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Scalable magnetic resonance fingerprinting: Incremental inference of high dimensional elliptical mixtures from large data volumes", "authors": ["Geoffroy Oudoumanessah", "Thomas Coudert", "Carole Lartizien", "Michel Dojat", "Thomas Christen", "Florence Forbes"], "url": "https://arxiv.org/abs/2412.10173v1", "attribution": "\"Scalable magnetic resonance fingerprinting: Incremental inference of high dimensional elliptical mixtures from large data volumes\" by Geoffroy Oudoumanessah, Thomas Coudert, Carole Lartizien, Michel Dojat, Thomas Christen, and Florence Forbes, arXiv:2412.10173v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.08623v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Unsupervised multiple domain adaptation with adversarial training setup}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc} \n\\toprule\n\\textbf{~Adaptation Methods~} & \\textbf{~ ~ $N$~ ~~} & \\textbf{~ ~ $M$~ ~~} \\\\ \n\\hline\\hline\nDAT~ & 1 & 1 \\\\ \n\\hline\n~ MS-DAT (based on LDC code)~~ & 6 & 1 \\\\\nMT-DAT (based on LDC code) & 1 & 4 \\\\\nMDAT (based on LDC code) & 6 & 4 \\\\\nMS-DAT (based on k-means) & 3 & 1 \\\\\nMT-DAT (based on k-means) & 1 & 2 \\\\\nMDAT (based on k-means) & 3 & 2 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Adversarial Training for Multi-domain Speaker Recognition", "authors": ["Qing Wang", "Wei Rao", "Pengcheng Guo", "Lei Xie"], "url": "https://arxiv.org/abs/2011.08623v1", "attribution": "\"Adversarial Training for Multi-domain Speaker Recognition\" by Qing Wang, Wei Rao, Pengcheng Guo, and Lei Xie, arXiv:2011.08623v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16385v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\nProblem & & EMD & Sinkhorn ($\\lambda_1$) & Sinkhorn ($\\lambda_2$) & ISA & Collisional OT\n\\\\\n\\hline\nJAFFE & Rel. error &\n-&\n6.413e-1 ± 0.616&\n1.796e-1 ± 0.122&\n1.409e-2 ± 1.390e-2&\n8.456e-3 ± 1.541e-2\n\\\\\n & Time [s] & 9.134 ± 0.820&\n2.659 ± 0.172&\n3.665 ± 0.581&\n0.470 ± 0.034&\n\\textbf{0.022 ± 0.003}\n\\\\ \\hline\nButterfly & Rel. error &\n-& 2.824e-1 ± 1.652e-1&\n2.717e-1 ± 8.056e-2&\n9.796e-3 ± 2.636e-2&\n1.028e-2 ± 2.860e-2\n\\\\\n & Time [s] & 9.761 ±0.681&\n2.765 ± 2.122e-1&\n4.349 ± 1.713&\n0.217 ± 2.190e-2&\n\\textbf{3.494e-2 ± 7.219e-3}\n\\\\ \\hline\nCelebA & Rel. error &\n-& 2.220 ± 2.897e-1 &\n2.219 ± 2.896e-1 &\n4.405e-3 ± 3.455e-3 &\n4.643e-3 ± 1.926e-3\n\\\\\n & Time [s] & 12.717 ± 0.864 &\n2.479 ± 0.285 &\n2.571 ± 0.346 &\n0.212 ± 0.014 &\n\\textbf{0.120 ± 0.014}\n\\\\ \\hline\nSwiss Roll-Normal & Rel. error &\n-& 0.09 ± 0.01&\n0.03 ± 0.01&\n1.731e-2 ± 1.415e-3&\n1.665e-2 ± 5.533e-4\n\\\\\n & Time [s] & 14.235 ± 0.552&\n4.09 ± 0.73&\n5.30 ± 0.59&\n0.130 ± 0.001&\n\\textbf{0.098 ± 0.004}\n\\\\ \\hline\nBanana-Normal & Rel. error &\n-& 0.29 ± 0.001&\n0.11 ± 0.01&\n9.164e-3 ± 3.611e-4&\n1.239e-2 ± 7.734e-4\n\\\\\n & Time [s] & 13.730 ± 0.148&\n4.70 ± 0.06&\n6.81 ± 0.05&\n0.146 ± 0.021&\n\\textbf{0.114 ± 0.010}\n\\\\ \\hline\nFunnel-Normal & Rel. error &\n-& 0.43 ± 0.01&\n0.18 ± 0.01&\n1.497e-2 ± 7.107e-4&\n1.550e-2 ± 7.087e-4\n\\\\\n & Time [s] & 13.489 ± 0.431&\n4.65 ± 0.43&\n6.77 ± 0.56&\n0.155 ± 0.025&\n\\textbf{0.105 ± 0.001}\n\\\\ \\hline\nRing-Normal & Rel. error &\n-& 0.32 ± 0.01&\n0.29 ± 0.01&\n1.715e-2 ± 9.159e-4&\n1.735e-2 ± 5.948e-4\n\\\\\n & Time [s] & 10.391 ± 0.552&\n2.80 ± 0.15&\n3.43 ± 0.25&\n0.167 ± 0.001&\n\\textbf{0.119 ± 0.008}\n\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Execution time and relative error of Sinkhron with two regularization factors $\\lambda_1>\\lambda_2$, ISA, and collisional method for the considered test cases, each obtained using 8000 samples and repeated 20 times to obtain reasonable statistics. Here, we stop ISA and Collisional OT algorithms with a similar tolerance of convergence.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Collision-based Dynamics for Multi-Marginal Optimal Transport", "authors": ["Mohsen Sadr", "Hossein Gorji"], "url": "https://arxiv.org/abs/2412.16385v2", "attribution": "\"Collision-based Dynamics for Multi-Marginal Optimal Transport\" by Mohsen Sadr and Hossein Gorji, arXiv:2412.16385v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08115v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Feedforward Module, $\\pi^{\\{H, L\\}}_{(0)}$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n hidden layers & $(512, 512)$ \\\\\n hidden layer activation & relu \\\\\n output activation & linear\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning", "authors": ["Sasha Salter", "Kristian Hartikainen", "Walter Goodwin", "Ingmar Posner"], "url": "https://arxiv.org/abs/2201.08115v2", "attribution": "\"Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning\" by Sasha Salter, Kristian Hartikainen, Walter Goodwin, and Ingmar Posner, arXiv:2201.08115v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2207.14793v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|cccc|c}\n\t\t\t\\hline\n\t\t\t& $\\mathbf{\\tilde{m}^*= }$ & $\\mathbf{0.0000}$ & $\\mathbf{0.1500}$ & $\\mathbf{0.3000}$ & $\\mathbf{0.4345}$ & \\\\\n\t\t\t& $\\mathbf{c^*\\,\\,= }$ & $\\mathbf{1.5338\\%}$ & $\\mathbf{1.0036\\%}$ & $\\mathbf{0.4741\\%}$ & $\\mathbf{0.000\\%}$ & \\textbf{Computation Time (sec.)}\\\\\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\t\\multirow{3}{*}{\\textbf{VA without ES}} & $N\\,\\,=2,000$ & $99.99615$&$99.99611$ & $99.99607$& $99.99603$ & N/A \\\\\n\t\t\t& $N\\,\\,=1,100$ & $99.99619$&$99.99615$ & $99.99611$& $99.99607$ & N/A\\\\\n\t\t\t& $N\\,\\,=100$ & $100.00276$ & $100.00290$& $100.00304$& $100.00317$ & N/A\\\\\n\t\t\t\\hline\n\t\t\t\\multirow{3}{*}{\\textbf{VA with ES}} & $N\\,\\,=2,000$&$103.02361$ & $103.01360$ & $103.00815$ & $103.00789$ &$4,405$\\\\\n\t\t\t& $N\\,\\,=1,100$ &$103.02360$ & $103.01359$ & $103.00814$ & $103.00788$ & $1,244$\\\\\n\t\t\t& $N\\,\\,=100$ &$103.02237$ & $103.01251$ & $103.00715$ & $103.00680$ & $9.60$\\\\\n\t\t\t\\hline \n\t\t\t\\multirow{3}{*}{\\textbf{ES value}} & $N=2,000$ & $3.02745$ & $3.01748$ & $3.01208$ & $3.01186$ &N/A\\\\\n\t\t\t&$N\\,\\,=1,100$ & $3.02741$ & $3.01744$ & $3.01203$ & $3.01181$ & N/A\\\\ \n\t\t\t& $N\\,\\,=100$ & $3.01961$ & $3.00961$ & $3.00411$ & $3.00363$ & N/A\\\\\n\t\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Variable annuity with and without early surrenders (ES) using CTMC Approximation Fast Algorithms with $N=100$ and $1,100,$ with $M=500\\times 10$; and $N=2,000$ with $M=500\\times 10$.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Analysis of VIX-linked fee incentives in variable annuities via continuous-time Markov chain approximation", "authors": ["Zhenyu Cui", "Anne MacKay", "Marie-Claude Vachon"], "url": "https://arxiv.org/abs/2207.14793v1", "attribution": "\"Analysis of VIX-linked fee incentives in variable annuities via continuous-time Markov chain approximation\" by Zhenyu Cui, Anne MacKay, and Marie-Claude Vachon, arXiv:2207.14793v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06560v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccc}\n \\toprule\n \\multicolumn{2}{c}{} & 2 Agents & 4 Agents & 6 Agents \\\\\n \\midrule\n \\multirow{2}{*}{ShapeNet} & $-\\mathcal{L}_{task}$ & 6.10 & 20.07 & 29.00 \\\\\n & $\\mathcal{L}_{adv}$ & \\textbf{0.37} & \\textbf{4.45} & \\textbf{13.77} \\\\\n \\midrule\n \\multirow{2}{*}{V2V} & $-\\mathcal{L}_{task}$ & 20.8 & 63.82 & 79.11 \\\\\n & $\\mathcal{L}_{adv}$ & \\textbf{7.55} & \\textbf{52.31} & \\textbf{76.18} \\\\\n \\bottomrule \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Adversarial Attacks On Multi-Agent Communication", "authors": ["James Tu", "Tsunhsuan Wang", "Jingkang Wang", "Sivabalan Manivasagam", "Mengye Ren", "Raquel Urtasun"], "url": "https://arxiv.org/abs/2101.06560v2", "attribution": "\"Adversarial Attacks On Multi-Agent Communication\" by James Tu, Tsunhsuan Wang, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren, and Raquel Urtasun, arXiv:2101.06560v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.16026v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|}\n\\hline\n\\textbf{Experiments} & \\textbf{2019-11-23} & \\textbf{2019-11-25} & \\textbf{2019-12-17} & \\textbf{2019-12-18} \\\\ \\hline\nB.S & 0.49919 & 0.085 & 0.3669 & 0.2720 \\\\\n\\hline\nC1. Horizontal mixing exp 1 & 0.3953 & 0.1185 & 0.2065 & 0.2802 \\\\\n\\hline\nC1. Horizontal mixing exp 2 & 0.4654 & 0.3188 & 0.4875 & 0.3815 \\\\\n\\hline\nC2. Bottom drag exp 1 & 0.4224 & 0.1096 & 0.3618 & 0.4835 \\\\\n\\hline\nC2. Bottom drag exp 2 & 0.4375 & 0.3165 & 0.2282 & 0.3676 \\\\\n\\hline\nC3. Wind stress exp 1 & 0.4462 & 0.4343 & 0.3164 & 0.3224 \\\\\n\\hline\nC3. Wind stress exp 2 & 0.4883 & 0.087 & 0.3793 & 0.2848 \\\\\n\\hline\nC4. Modified bathymetry exp 1 & 0.4491 & 0.005& 0.1305 & 0.2363 \\\\\n\\hline\nC5. Sponge exp 1 & 0.3778 & 0.0278 & 0.29965 & 0.2528 \\\\\n\\hline\nC6. Mixed radiation-nudging exp 1 & 0.5315 & 0.3682 & 0.2337 & \n0.1660 \\\\\n\\hline\nC6. Mixed radiation-nudging exp 2 & 0.5555 & 0.7476 & 0.5192 & 0.2551 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results of measured index ${\\cal A}$ obtained for the sensitivity experiments. they include the first period (2019-11-23 and 2019-11-25), and the second period (2019-12-17 and 2019-12-18).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Dynamical systems for remote validation of very high-resolution ocean models", "authors": ["G. Garcia-Sanchez", "A. M. Mancho", "A. G. Ramos", "J. Coca", "J. A. Jimenez-Madrid"], "url": "https://arxiv.org/abs/2501.16026v1", "attribution": "\"Dynamical systems for remote validation of very high-resolution ocean models\" by G. Garcia-Sanchez, A. M. Mancho, A. G. Ramos, J. Coca, and J. A. Jimenez-Madrid, arXiv:2501.16026v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.01705v3_tex_table20.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Benjamini Hochberg (1995) adjusted p-values (original p-values in parenthesis)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrrrr}\n\\toprule \n&\\multicolumn{5}{c}{\\textbf{Uncertainty}}&\\multicolumn{5}{c}{\\textbf{Negative Sentiment}}\\\\\n\\toprule\n &Aggregate&Economics&Health&Politics&Policy&Aggregate&Economics&Health&Politics&Policy\\\\\n\\midrule\npost = 1 & 0.01 & 0.02 & 0.00 & 0.11 & 0.00 & 0.10 & 0.95 & 0.00 & 0.11 & 0.00\\\\\n & (0.00) & (0.01) & (0.00)& (0.07) & (0.00) & (0.08) & (0.95) & (0.00)& (0.07) & (0.01)\\\\\npost = 2 & 0.01 & 0.00& 0.00 & 0.72 & 0. & 0.00 & 0.28 & 0.00 & 0.02 & 0.00\\\\\n & (0.00) & (0.00) & (0.00)& (0.54) & (0.00) & (0.03) & (0.26) & (0.00)& (0.01) & (0.00)\\\\\n\\textbf{red zone = 1 x post = 1} & 0.03 & 0.84 & 0.00 & 0.00 & 0.01 & 0.63 & 0.17 & 0.00 & 0.00 & 0.02 \\\\\n& (0.02) & (0.78) & (0.00)& (0.00) & (0.00) & (0.63) & (0.14) & (0.00)& (0.00) & (0.01)\\\\\nred zone = 1 x post = 2 & 0.57 & 0.72 & 0.94 & 0.41 & 0.99 & 0.63 & 0.19 & 0.10 & 0.00 & 0.12 \\\\\n & (0.49) & (0.59) & (0.88)& (0.10) & (0.29) & (0.99) & (0.16) & (0.07)& (0.00) & (0.09)\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic", "authors": ["C. Biliotti", "F. J. Bargagli-Stoffi", "N. Fraccaroli", "M. Puliga", "M. Riccaboni"], "url": "https://arxiv.org/abs/2212.01705v3", "attribution": "\"Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic\" by C. Biliotti, F. J. Bargagli-Stoffi, N. Fraccaroli, M. Puliga, and M. Riccaboni, arXiv:2212.01705v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.00646v2_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n\\toprule\nn\\_epochs & acc. & top-$3$. \\\\\n\\midrule\n$1$ & $74.84$ & $95.76$ \\\\ \n$2$ & $76.96$ & $96.00$ \\\\\n$3$ & $75.88$ & $95.88$ \\\\\n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Source Attribution for Large Language Model-Generated Data", "authors": ["Jingtan Wang", "Xinyang Lu", "Zitong Zhao", "Zhongxiang Dai", "Chuan-Sheng Foo", "See-Kiong Ng", "Bryan Kian Hsiang Low"], "url": "https://arxiv.org/abs/2310.00646v2", "attribution": "\"Source Attribution for Large Language Model-Generated Data\" by Jingtan Wang, Xinyang Lu, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, and Bryan Kian Hsiang Low, arXiv:2310.00646v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11692v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{List of notations.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rl}\n\\toprule\n{\\bf Notation} & {\\bf Description} \\\\\n\\midrule\n$F$, $F_*$, $F_s$ & Clean/Watermarked/Shadow encoder \\\\\n$\\mathcal{D}_{t}$, $\\mathcal{D}_{s}$ & Target/Shadow dataset \\\\\n$\\mathcal{D}_{p}$, $\\mathcal{D}_{v}$ & Private/Verification dataset \\\\\n$T$, $M$ & Trigger, Mask \\\\\n$\\kappa$, $G$ &Key-tuple, Decoder \\\\\n $sk$, $sk_x'$ & Secret vector, Decoded vector \\\\\nDA & Downstream accuracy \\\\\nWR & Watermark rate \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders", "authors": ["Tianshuo Cong", "Xinlei He", "Yang Zhang"], "url": "https://arxiv.org/abs/2201.11692v4", "attribution": "\"SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders\" by Tianshuo Cong, Xinlei He, and Yang Zhang, arXiv:2201.11692v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.10096v2_tex_table16.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{AUC scores obtained by instance level training on Criteo}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|}\n\\hline\n\\textbf{Experiment} & \\textbf{Model} & \\textbf{Optimizer} & \\textbf{Loss Function} & \\textbf{AUC Score} \\\\ \\hline\n\\textit{E1} & 1-Layer MLP & Adam & BCE & 79.23 \\\\ \\hline\n\\textit{E2} & 1-Layer MLP & Adam & MSE & 79.2 \\\\ \\hline\n\\textit{E3} & 2-Layer MLP & Adam & BCE & 80.1 \\\\ \\hline\n\\textit{E4} & 2-Layer MLP & Adam & MSE & 79.94 \\\\ \\hline\n\\textit{E5} & 1-Layer MLP & SGD & BCE & 80.7 \\\\ \\hline\n\\textit{E6} & 1-Layer MLP & SGD & MSE & 80.54 \\\\ \\hline\n\\textit{E7} & 2-Layer MLP & SGD & BCE & 79.17 \\\\ \\hline\n\\textit{E8} & 2-Layer MLP & SGD & MSE & 80.56 \\\\ \\hline\n\\textit{E9} & AutoInt & Adam & BCE & 80.66 \\\\ \\hline\n\\textit{E10} & AutoInt & Adam & MSE & 80.7 \\\\ \\hline\n\\textit{E11} & AutoInt & Adam & MSE & 79.02 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions", "authors": ["Anand Brahmbhatt", "Mohith Pokala", "Rishi Saket", "Aravindan Raghuveer"], "url": "https://arxiv.org/abs/2310.10096v2", "attribution": "\"LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions\" by Anand Brahmbhatt, Mohith Pokala, Rishi Saket, and Aravindan Raghuveer, arXiv:2310.10096v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2302.00761v1_tex_table62.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{rrr}\n\\hline\\hline\nYears&Zero&Normal\\tabularnewline\n\\hline\n$1996$&$ 0.9\\%$&$ 3.1\\%$\\tabularnewline\n$1997$&$ 0.5\\%$&$ 2.0\\%$\\tabularnewline\n$1998$&$-5.3\\%$&$-1.5\\%$\\tabularnewline\n$1999$&$-6.1\\%$&$-1.1\\%$\\tabularnewline\n$2000$&$-10.8\\%$&$-1.1\\%$\\tabularnewline\n$2001$&$-14.8\\%$&$-2.6\\%$\\tabularnewline\n$2002$&$-9.1\\%$&$ 0.0\\%$\\tabularnewline\n$2003$&$-4.7\\%$&$ 1.6\\%$\\tabularnewline\n$2004$&$-3.3\\%$&$ 3.4\\%$\\tabularnewline\n$2005$&$-3.7\\%$&$ 2.4\\%$\\tabularnewline\n$2006$&$-4.4\\%$&$ 2.3\\%$\\tabularnewline\n$2007$&$-4.2\\%$&$ 1.8\\%$\\tabularnewline\n$2008$&$-5.1\\%$&$ 0.7\\%$\\tabularnewline\n$2009$&$-2.7\\%$&$ 1.3\\%$\\tabularnewline\n$2010$&$-1.3\\%$&$ 4.1\\%$\\tabularnewline\n$2011$&$-3.6\\%$&$ 3.3\\%$\\tabularnewline\n$2012$&$-6.7\\%$&$ 1.5\\%$\\tabularnewline\n$2013$&$-8.4\\%$&$ 0.9\\%$\\tabularnewline\n$2014$&$-11.0\\%$&$ 0.7\\%$\\tabularnewline\n$2015$&$-17.5\\%$&$-1.1\\%$\\tabularnewline\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Zero-Leverage Puzzle", "authors": ["Mykola Pinchuk"], "url": "https://arxiv.org/abs/2302.00761v1", "attribution": "\"Zero-Leverage Puzzle\" by Mykola Pinchuk, arXiv:2302.00761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01677v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{rotating}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|ccccc}\n\\toprule\n\\multicolumn{7}{c}{\\textbf{Out-of-Class Generalization}} \\\\\n\\toprule\n& \\textbf{Dataset} & \nAbs.Rel &\nRMSE &\nRMSE$_{log}$ &\nSILog & \n$\\delta<1.05$\n\\\\\n\\toprule\n\\parbox[t]{2mm}{\\multirow{4}{*}{\\rotatebox[origin=c]{90}{\\textit{ResNet}}}}\n& \\textit{Mug} & 0.036 & 0.013 & 0.046 & 4.317 & 0.735 \\\\\n& \\textit{Wine Glass} & 0.040 & 0.015 & 0.051 & 4.889 & 0.692 \\\\\n& \\textit{Fingers} & 0.040 & 0.018 & 0.056 & 5.395 & 0.715 \\\\\n& \\textit{Box} & 0.051 & 0.019 & 0.063 & 6.102 & 0.618 \\\\\n\\midrule\n\\parbox[t]{2mm}{\\multirow{4}{*}{\\rotatebox[origin=c]{90}{\\textit{BTS}}}}\n& \\textit{Mug} & 0.038 & 0.015 & 0.050 & 4.629 & 0.744 \\\\\n& \\textit{Wine Glass} & 0.038 & 0.015 & 0.050 & 4.579 & 0.730 \\\\\n& \\textit{Fingers} & 0.039 & 0.018 & 0.056 & 5.504 & 0.741 \\\\\n& \\textit{Box} & 0.048 & 0.018 & 0.062 & 5.899 & 0.673 \\\\\n\\midrule\n\\parbox[t]{2mm}{\\multirow{4}{*}{\\rotatebox[origin=c]{90}{\\textit{PackNet}}}}\n& \\textit{Mug} & 0.034 & 0.013 & 0.042 & 3.458 & 0.765 \\\\\n& \\textit{Wine Glass} & 0.034 & 0.014 & 0.047 & 4.338 & 0.769 \\\\\n& \\textit{Fingers} & 0.030 & 0.014 & 0.047 & 4.479 & 0.832 \\\\\n& \\textit{Box} & 0.047 & 0.019 & 0.061 & 5.687 & 0.652 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Monocular depth estimation results for cross-dataset generalization using different networks. Independent models trained on 3/4 object datasets, evaluated on the remaining one.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Monocular Depth Estimation for Soft Visuotactile Sensors", "authors": ["Rares Ambrus", "Vitor Guizilini", "Naveen Kuppuswamy", "Andrew Beaulieu", "Adrien Gaidon", "Alex Alspach"], "url": "https://arxiv.org/abs/2101.01677v1", "attribution": "\"Monocular Depth Estimation for Soft Visuotactile Sensors\" by Rares Ambrus, Vitor Guizilini, Naveen Kuppuswamy, Andrew Beaulieu, Adrien Gaidon, and Alex Alspach, arXiv:2101.01677v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10289v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\hline\nChatbot use cases & \\multicolumn{2}{c}{Yes} & \\multicolumn{2}{l}{No} \\\\ \\cline{2-5} \n & Frequency & \\% & Frequency & \\% \\\\ \\hline\nAnswering to students' FAQs & 148 & 52.5 & 134 & 47.5 \\\\\nAssigning student grades & 113 & 40.1 & 169 & 59.9 \\\\\nFacilitating agenda information & 171 & 60.6 & 111 & 39.4 \\\\\nSharing class materials & 136 & 48.2 & 146 & 51.8 \\\\\nOthers & 25 & 8.9 & 257 & 91.1 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Perceived useful chatbot use cases}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Chatbots and messaging platforms in the classroom: an analysis from the teacher's perspective", "authors": ["J. J. Merelo", "P. A. Castillo", "Antonio M. Mora", "Francisco Barranco", "Noorhan Abbas", "Alberto Guillen", "Olia Tsivitanidou"], "url": "https://arxiv.org/abs/2201.10289v1", "attribution": "\"Chatbots and messaging platforms in the classroom: an analysis from the teacher's perspective\" by J. J. Merelo, P. A. Castillo, Antonio M. Mora, Francisco Barranco, Noorhan Abbas, Alberto Guillen, and Olia Tsivitanidou, arXiv:2201.10289v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13230v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\\hline\n& $\\psi_{1}\\left( x,y\\right) $ & $\\psi_{2}\\left( x,y\\right) $ & $\\psi\n_{3}\\left( x,y\\right) $ & $\\psi_{4}\\left( x,y\\right) $ & $\\dot{V}\\left(\nx,y\\right) $\\\\\\hline\n$T_{1}^{I}$ & $\\leq0$ & $\\geq0$ & $\\leq0$ & $\\leq0$ & $\\psi_{1}-\\psi_{2}-\\psi\n_{3}-\\psi_{4}$\\\\\\hline\n$T_{1}^{II}$ & $\\leq0$ & $\\leq0$ & $\\geq0$ & $\\leq0$ & $\\psi_{1}+\\psi_{2}%\n+\\psi_{3}-\\psi_{4}$\\\\\\hline\n$T_{1}^{III}$ & $\\geq0$ & $\\leq0$ & $\\geq0$ & $\\geq0$ & $-\\psi_{1}+\\psi_{2}%\n+\\psi_{3}+\\psi_{4}$\\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Table of signs of the $\\psi_j$ ($j=1,2,3,4$) and corresponding expression for $\\dot{V}$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Lyapunov functions for Morse-Smale synchronisation diffeomorphisms", "authors": ["Jorge Buescu", "Henrique M. Oliveira"], "url": "https://arxiv.org/abs/2503.13230v1", "attribution": "\"Lyapunov functions for Morse-Smale synchronisation diffeomorphisms\" by Jorge Buescu and Henrique M. Oliveira, arXiv:2503.13230v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.09239v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{VAR: ARx(y,z) \"x\" is the lag, \"y\" is affecting variable, and \"z\" is affected variable\\\\}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\hline\n \\hline\n&Value&Standard Error&TStatistic&PValue\\\\\n \\hline\nConstant(1)&-101.51&56.43&-1.8&0.07\\\\\nConstant(2)&-92.19&78.69&-1.17&0.24\\\\\nConstant(3)&-93.88*&41.64&-2.25&0.02\\\\\nConstant(4)&1.54&2.59&0.6&0.55\\\\\nAR{1}(1,1)&0.82***&0.12&6.91&0\\\\\nAR{1}(2,1)&0.61***&0.17&3.69&0\\\\\nAR{1}(3,1)&0.01&0.09&0.15&0.88\\\\\nAR{1}(4,1)&0&0.01&-0.39&0.7\\\\\nAR{1}(1,2)&-0.07&0.08&-0.88&0.38\\\\\nAR{1}(2,2)&0.24**&0.11&2.08&0.04\\\\\nAR{1}(3,2)&0&0.06&0.01&0.99\\\\\nAR{1}(4,2)&-0.01&0&-1.55&0.12\\\\\nAR{1}(1,3)&0.06&0.14&0.42&0.68\\\\\nAR{1}(2,3)&-0.17&0.19&-0.88&0.38\\\\\nAR{1}(3,3)&0.39***&0.1&3.88&0\\\\\nAR{1}(4,3)&0.01&0.01&1.48&0.14\\\\\nAR{1}(1,4)&3.23&2.23&1.45&0.15\\\\\nAR{1}(2,4)&0.3&3.11&0.1&0.92\\\\\nAR{1}(3,4)&0.91&1.65&0.55&0.58\\\\\nAR{1}(4,4)&0.79***&0.1&7.76&0\\\\\nAR{2}(1,1)&0.23&0.15&1.57&0.12\\\\\nAR{2}(2,1)&-0.44*&0.21&-2.1&0.04\\\\\nAR{2}(3,1)&0.11&0.11&0.96&0.34\\\\\nAR{2}(4,1)&0&0.01&-0.57&0.57\\\\\nAR{2}(1,2)&-0.25&0.08&-2.97&0\\\\\nAR{2}(2,2)&0.16&0.12&1.35&0.18\\\\\nAR{2}(3,2)&-0.02&0.06&-0.38&0.71\\\\\nAR{2}(4,2)&0&0&-0.38&0.71\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Interactions of Social Norms about Climate Change: Science, Institutions and Economics", "authors": ["Antonio Cabrales", "Manu García", "David Ramos Muñoz", "Angel Sánchez"], "url": "https://arxiv.org/abs/2208.09239v1", "attribution": "\"The Interactions of Social Norms about Climate Change: Science, Institutions and Economics\" by Antonio Cabrales, Manu García, David Ramos Muñoz, and Angel Sánchez, arXiv:2208.09239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19280v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\t\t\tstatus & number & $\\max|y''-f|$ & $\\max(|y_{opt}-y_{b}|)$ \\\\ \\hline\\hline\n\t\t$\\to y_b$ & 13\t & 1.1E-06 & 2.9E-10 \\\\\n\t\t\t$\\to y_s$ & 0 & NA & NA \\\\ \n\t\t\t$diverge$ & 12 & diverge & diverge \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions", "authors": ["Xiaorong Zou"], "url": "https://arxiv.org/abs/2504.19280v1", "attribution": "\"Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions\" by Xiaorong Zou, arXiv:2504.19280v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14598v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc} \n\\hline\n{Benchmark}\n& {Bound} \n& {Timing (ms)} \n\\\\\n\\hline \n{PythagoreanSum}$^{\\text{b}}$ &{8.88e-16} & 2 \n\\\\\n{HammarlingDistance}$^{\\text{b}}$ &{1.11e-15} & 2 \n\\\\\n{squareRoot3}$^{*}$ &{4.44e-16} & 2 \n\\\\\n {{squareRoot3Invalid}}$^{*}$ &{4.44e-16} & 2\n\\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Type-Based Approaches to Rounding Error Analysis", "authors": ["Ariel Eileen Kellison"], "url": "https://arxiv.org/abs/2501.14598v2", "attribution": "\"Type-Based Approaches to Rounding Error Analysis\" by Ariel Eileen Kellison, arXiv:2501.14598v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.06721v3_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Noisy observation inpainting and super-resolution for VP DDPM on CIFAR-10 1k validation set.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllll}\n\\hline\nProblem & Method & FID $\\downarrow$ & LPIPS $\\downarrow$ & MSE $\\downarrow$ & PSNR $\\uparrow$ & SSIM $\\uparrow$ \\\\ \\hline\n$\\sigma_{y}=0.01$ & DTMPD-D & 33.7 & 0.090 \\tiny{$\\pm$ 0.048} & 0.007 \\tiny{$\\pm$ 0.034} & 23.8 \\tiny{$\\pm$ 3.7} & 0.784 \\tiny{$\\pm$ 0.073} \\\\\n`box' mask & DPS-D & \\textbf{31.5} & \\textbf{0.064} \\tiny{$\\pm$ 0.033} & 0.004 \\tiny{$\\pm$ 0.003} & 25.8 \\tiny{$\\pm$ 3.6} & 0.841 \\tiny{$\\pm$ 0.068} \\\\\ninpainting & $\\Pi$GDM-D & 37.1 & 0.316 \\tiny{$\\pm$ 0.108} & 0.012 \\tiny{$\\pm$ 0.029} & 19.9 \\tiny{$\\pm$ 1.9} & 0.546 \\tiny{$\\pm$ 0.143} \\\\ \\hline\n$\\sigma_{y}=0.01$ & DTMPD-D & 40.4 & 0.272 \\tiny{$\\pm$ 0.071} & 0.028 \\tiny{$\\pm$ 0.020} & 16.4 \\tiny{$\\pm$ 3.0} & 0.584 \\tiny{$\\pm$ 0.071} \\\\\n`half' mask & DPS-D & \\textbf{33.1} & 0.221 \\tiny{$\\pm$ 0.072} & 0.028 \\tiny{$\\pm$ 0.021} & 16.8 \\tiny{$\\pm$ 3.6} & 0.637 \\tiny{$\\pm$ 0.093} \\\\\ninpainting & $\\Pi$GDM-D & 35.8 & 0.397 \\tiny{$\\pm$ 0.103} & 0.031 \\tiny{$\\pm$ 0.020} & 15.9 \\tiny{$\\pm$ 2.8} & 0.419 \\tiny{$\\pm$ 0.132} \\\\ \\hline\n$\\sigma_{y}=0.05$ & DTMPD-D & 35.9 & 0.128 \\tiny{$\\pm$ 0.061} & 0.006 \\tiny{$\\pm$ 0.011} & 23.5 \\tiny{$\\pm$ 3.3} & 0.763 \\tiny{$\\pm$ 0.079} \\\\\n`box' mask & DPS-D & \\textbf{31.1} & \\textbf{0.078} \\tiny{$\\pm$ 0.037} & 0.004 \\tiny{$\\pm$ 0.003} & 25.5 \\tiny{$\\pm$ 3.3} & \\textbf{0.830} \\tiny{$\\pm$ 0.070} \\\\\ninpainting & $\\Pi$GDM-D & 36.3 & 0.319 \\tiny{$\\pm$ 0.110} & 0.012 \\tiny{$\\pm$ 0.031} & 20.0 \\tiny{$\\pm$ 1.9} & 0.545 \\tiny{$\\pm$ 0.142} \\\\ \\hline\n$\\sigma_{y}=0.05$ & DTMPD-D & 40.4 & 0.292 \\tiny{$\\pm$ 0.075} & 0.029 \\tiny{$\\pm$ 0.020} & 16.4 \\tiny{$\\pm$ 3.1} & 0.572 \\tiny{$\\pm$ 0.076} \\\\\n`half' mask & DPS-D & \\textbf{32.5} & 0.230 \\tiny{$\\pm$ 0.075} & 0.028 \\tiny{$\\pm$ 0.023} & 16.7 \\tiny{$\\pm$ 3.5} & 0.626 \\tiny{$\\pm$ 0.096} \\\\\ninpainting & $\\Pi$GDM-D & 35.6 & 0.398 \\tiny{$\\pm$ 0.109} & 0.032 \\tiny{$\\pm$ 0.021} & 15.9 \\tiny{$\\pm$ 2.8} & 0.421 \\tiny{$\\pm$ 0.133} \\\\ \\hline\n$\\sigma_{y}=0.1$ & DTMPD-D & 38.9 & 0.168 \\tiny{$\\pm$ 0.072} & 0.007 \\tiny{$\\pm$ 0.012} & 22.5 \\tiny{$\\pm$ 2.9} & 0.728 \\tiny{$\\pm$ 0.081} \\\\\n`box' mask & DPS-D & \\textbf{31.6} & \\textbf{0.101} \\tiny{$\\pm$ 0.043} & 0.004 \\tiny{$\\pm$ 0.003} & 24.5 \\tiny{$\\pm$ 2.9} & 0.807 \\tiny{$\\pm$ 0.078} \\\\\ninpainting & $\\Pi$GDM-D & 36.3 & 0.318 \\tiny{$\\pm$ 0.109} & 0.012 \\tiny{$\\pm$ 8.553} & 19.7 \\tiny{$\\pm$ 1.8} & 0.546 \\tiny{$\\pm$ 0.140} \\\\ \\hline\n$\\sigma_{y}=0.1$ & DTMPD-D & 43.8 & 0.350 \\tiny{$\\pm$ 0.088} & 0.030 \\tiny{$\\pm$ 0.028} & 16.2 \\tiny{$\\pm$ 2.9} & 0.547 \\tiny{$\\pm$ 0.076} \\\\\n`half' mask & DPS-D & \\textbf{33.0} & 0.522 \\tiny{$\\pm$ 0.097} & 0.031 \\tiny{$\\pm$ 0.022} & 16.3 \\tiny{$\\pm$ 3.3} & 0.602 \\tiny{$\\pm$ 0.097} \\\\\ninpainting & $\\Pi$GDM-D & 36.2 & 0.276 \\tiny{$\\pm$ 0.085} & 0.034 \\tiny{$\\pm$ 0.023} & 15.5 \\tiny{$\\pm$ 2.7} & 0.412 \\tiny{$\\pm$ 0.128} \\\\ \\hline\n$\\sigma_{y}=0.01$ & DTMPD-D & 33.2 & 0.117 \\tiny{$\\pm$ 0.051} & 0.004 \\tiny{$\\pm$ 0.004} & 24.7 \\tiny{$\\pm$ 3.0} & 0.835 \\tiny{$\\pm$ 0.071} \\\\\n$2 \\times$ `nearest' & DPS-D & \\textbf{32.5} & \\textbf{0.099} \\tiny{$\\pm$ 0.044} & 0.004 \\tiny{$\\pm$ 0.003} & 25.1 \\tiny{$\\pm$ 3.1} & 0.847 \\tiny{$\\pm$ 0.073} \\\\\nsuper-resolution & $\\Pi$GDM-D & 35.6 & 0.407 \\tiny{$\\pm$ 0.118} & 0.016 \\tiny{$\\pm$ 0.006} & 18.2 \\tiny{$\\pm$ 1.7} & 0.442 \\tiny{$\\pm$ 0.152} \\\\ \\hline\n$\\sigma_{y}=0.01$ & DTMPD-D & 41.5 & 0.278 \\tiny{$\\pm$ 0.084} & 0.011 \\tiny{$\\pm$ 0.006} & 20.4 \\tiny{$\\pm$ 2.7} & 0.563 \\tiny{$\\pm$ 0.114} \\\\\n$4 \\times$ `bicubic' & DPS-D & \\textbf{33.9} & 0.220 \\tiny{$\\pm$ 0.079} & 0.010 \\tiny{$\\pm$ 0.006} & 20.8 \\tiny{$\\pm$ 3.0} & 0.609 \\tiny{$\\pm$ 0.135} \\\\\nsuper-resolution & $\\Pi$GDM-D & 39.4 & 0.279 \\tiny{$\\pm$ 0.081} & 0.011 \\tiny{$\\pm$ 0.006} & 20.1 \\tiny{$\\pm$ 2.5} & 0.546 \\tiny{$\\pm$ 0.111} \\\\ \\hline\n$\\sigma_{y}=0.05$ & DTMPD-D & \\textbf{33.9} & 0.156 \\tiny{$\\pm$ 0.065} & 0.005 \\tiny{$\\pm$ 0.025} & 24.1 \\tiny{$\\pm$ 2.8} & 0.810 \\tiny{$\\pm$ 0.079} \\\\\n$2 \\times$ `nearest' & DPS-D & 34.4 & 0.127 \\tiny{$\\pm$ 0.048} & 0.004 \\tiny{$\\pm$ 0.003} & 24.4 \\tiny{$\\pm$ 2.7} & 0.825 \\tiny{$\\pm$ 0.070} \\\\\nsuper-resolution & $\\Pi$GDM-D & 35.1 & 0.407 \\tiny{$\\pm$ 0.118} & 0.016 \\tiny{$\\pm$ 0.006} & 18.2 \\tiny{$\\pm$ 1.7} & 0.440 \\tiny{$\\pm$ 0.153} \\\\ \\hline\n$\\sigma_{y}=0.05$ & DTMPD-D & 38.3 & 0.332 \\tiny{$\\pm$ 0.093} & 0.013 \\tiny{$\\pm$ 0.032} & 19.6 \\tiny{$\\pm$ 2.4} & 0.501 \\tiny{$\\pm$ 0.116} \\\\\n$4 \\times$ `bicubic' & DPS-D & 38.2 & 0.265 \\tiny{$\\pm$ 0.087} & 0.011 \\tiny{$\\pm$ 0.006} & 20.2 \\tiny{$\\pm$ 2.4} & 0.567 \\tiny{$\\pm$ 0.128} \\\\\nsuper-resolution & $\\Pi$GDM-D & \\textbf{23.2} & 0.522 \\tiny{$\\pm$ 0.101} & 0.034 \\tiny{$\\pm$ 0.012} & 15.0 \\tiny{$\\pm$ 1.7} & 0.215 \\tiny{$\\pm$ 0.114} \\\\ \\hline\n$\\sigma_{y}=0.1$ & DTMPD-D & 35.0 & 0.208 \\tiny{$\\pm$ 0.085} & 0.006 \\tiny{$\\pm$ 0.005} & 23.1 \\tiny{$\\pm$ 2.5} & 0.760 \\tiny{$\\pm$ 0.091} \\\\\n$2 \\times$ `nearest' & DPS-D & 32.1 & 0.152 \\tiny{$\\pm$ 0.058} & 0.005 \\tiny{$\\pm$ 0.003} & 23.8 \\tiny{$\\pm$ 2.5} & 0.802 \\tiny{$\\pm$ 0.074} \\\\\nsuper-resolution & $\\Pi$GDM-D & 35.0 & 0.407 \\tiny{$\\pm$ 0.122} & 0.017 \\tiny{$\\pm$ 0.006} & 18.1 \\tiny{$\\pm$ 1.8} & 0.435 \\tiny{$\\pm$ 0.150} \\\\ \\hline\n$\\sigma_{y}=0.1$ & DTMPD-D & 36.8 & 0.378 \\tiny{$\\pm$ 0.102} & 0.015 \\tiny{$\\pm$ 0.007} & 18.7 \\tiny{$\\pm$ 2.1} & 0.435 \\tiny{$\\pm$ 0.121} \\\\\n$4 \\times$ `bicubic' & DPS-D & 34.8 & 0.308 \\tiny{$\\pm$ 0.095} & 0.013 \\tiny{$\\pm$ 0.006} & 19.4 \\tiny{$\\pm$ 2.2} & 0.513 \\tiny{$\\pm$ 0.132} \\\\\nsuper-resolution & $\\Pi$GDM-D & \\textbf{23.0} & 0.521 \\tiny{$\\pm$ 0.104} & 0.035 \\tiny{$\\pm$ 0.013} & 14.9 \\tiny{$\\pm$ 1.7} & 0.212 \\tiny{$\\pm$ 0.117} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Tweedie Moment Projected Diffusions For Inverse Problems", "authors": ["Benjamin Boys", "Mark Girolami", "Jakiw Pidstrigach", "Sebastian Reich", "Alan Mosca", "O. Deniz Akyildiz"], "url": "https://arxiv.org/abs/2310.06721v3", "attribution": "\"Tweedie Moment Projected Diffusions For Inverse Problems\" by Benjamin Boys, Mark Girolami, Jakiw Pidstrigach, Sebastian Reich, Alan Mosca, and O. Deniz Akyildiz, arXiv:2310.06721v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.06451v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Baseline settings for the simulation of the synthetic dataset in Blender.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\hline\n\\textbf{Parameter} & \\textbf{Setting} \\\\\n\\hline\nCamera Height & 150 m \\\\\nCamera Angle & 90\\textdegree \\\\\nCamera Sensor Width & 36 mm \\\\\nOutput Resolution & 8192 x 8192 pixels \\\\\nRendering Engine & Cycles \\\\\nAdaptive Sampling Noise Threshold & 0.01 \\\\\nMaximum Samples & 2048 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning", "authors": ["Yuanyuan Wang", "Qian Song", "Dawood Wasif", "Muhammad Shahzad", "Christoph Koller", "Jonathan Bamber", "Xiao Xiang Zhu"], "url": "https://arxiv.org/abs/2412.06451v1", "attribution": "\"How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning\" by Yuanyuan Wang, Qian Song, Dawood Wasif, Muhammad Shahzad, Christoph Koller, Jonathan Bamber, and Xiao Xiang Zhu, arXiv:2412.06451v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.11692v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison with state-of-the-art methods}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc} \nParameter & Relative error$\\left(\\%\\right)$ & RMSE $\\left(\\%\\right)$\\\\\\hline\n\\hspace{1cm} & $5\\%$ & $1\\%$ \\\\\n\\hspace{1cm} & $30-50\\%$ & $1.18-8\\%$\\\\ \n\\hspace{1cm}& $4-8\\%$ & $2.31-5.89\\%$\\\\\n\\hspace{1cm}& - & $2.27\\%$\\\\\n\\hspace{1cm}& $2\\%$ & $6.3\\%$\\\\\nDistributed Averaging & $2.49\\%$ & $4.8685\\%$\\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Thermostatic control for demand response using distributed averaging and deep neural networks", "authors": ["Kshitij Singh", "Pratik K. Bajaria"], "url": "https://arxiv.org/abs/1912.11692v1", "attribution": "\"Thermostatic control for demand response using distributed averaging and deep neural networks\" by Kshitij Singh and Pratik K. Bajaria, arXiv:1912.11692v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01434v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cccc|}\n\\hline\nMaterial properties& Young's modulus $E$ & Poisson ratio $\\nu$ & Density $\\rho$& Fracture energy $G_0$\\\\\n\\hline\nValue&$200 GPa$&$0.3$&$7800 kg/m^3$& $1.125 \\times 10^5 J/m^2$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mechanical properties for the cylinder fragmentation under internal pressure example, following .}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture", "authors": ["Yue Yu", "Huaiqian You", "Nathaniel Trask"], "url": "https://arxiv.org/abs/2101.01434v1", "attribution": "\"An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture\" by Yue Yu, Huaiqian You, and Nathaniel Trask, arXiv:2101.01434v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1910.10071v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Test set SDR (in dB) for 20 epochs early stopping.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|c|c|c|c|}\n \\hline\n & Value of $\\lambda_h$ & Basel. & 1/(2L) & 1/L & 1 \\\\\n \\hline\n \\multirow{4}[2]{*}{Voc.} & Med & 3.65 & 3.50 & \\textbf{3.69} & 3.64 \\\\\n & MAD & 3.04 & 2.82 & 2.98 & 2.96 \\\\\n & Mean & -0.56 & -0.39 & \\textbf{0.01} & -0.10 \\\\\n & SD & 14.23 & 13.53 & 13.31 & 13.30 \\\\\n \\hline\n \\multirow{4}[2]{*}{Acc.} & Med & 7.37 & 7.32 & \\textbf{7.44} & 7.42 \\\\\n & MAD & 2.10 & 2.11 & 2.14 & 2.14 \\\\\n & Mean & 7.53 & 7.44 & \\textbf{7.56} & 7.54 \\\\\n & SD & 3.74 & 3.92 & 3.89 & 3.89 \\\\\n \\hline\n \\multicolumn{2}{|c|}{Epochs} & 120 & 76 & 167 & 120 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical Energy", "authors": ["Joaquin Perez-Lapillo", "Oleksandr Galkin", "Tillman Weyde"], "url": "https://arxiv.org/abs/1910.10071v1", "attribution": "\"Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical Energy\" by Joaquin Perez-Lapillo, Oleksandr Galkin, and Tillman Weyde, arXiv:1910.10071v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04727v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\t\t\t\\toprule\n\t\t\tDataset & Noise2Sim & Clip-denoising & Restormer & GAdap & GAdap-ldct-batch4-5k & GAdap-ldct-batch16-5k \\\\\n\t\t\t\\midrule\n\t\t\t\\midrule\n\t\t\t\\multirow{1}{*}{LDCT} & 45.98/0.978 & 45.88/0.976 & 41.18/0.968 & 42.32/0.964 & 45.47/0.974 & 46.06/0.977 \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning to Translate Noise for Robust Image Denoising", "authors": ["Inju Ha", "Donghun Ryou", "Seonguk Seo", "Bohyung Han"], "url": "https://arxiv.org/abs/2412.04727v1", "attribution": "\"Learning to Translate Noise for Robust Image Denoising\" by Inju Ha, Donghun Ryou, Seonguk Seo, and Bohyung Han, arXiv:2412.04727v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.02637v4_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Descriptive Statistics for Contributions by Workers and Managers}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\t\t\\toprule[1.5pt]\n\t\t& All employees & Workers & Managers \\\\\n \\midrule\n\t\tAmount (in million 2010 USD) & 105.82 & 13.31 & 56.84 \\\\\n\t\tNumber of contributions & 357,436 & 71,782 & 214,593 \\\\\n Average amount per contribution (in 2010 USD) & 296.04 & 185.53 & 264.89\\\\\n\t\tNumber of donors & 46,719 & 13,335 & 19,830 \\\\\n\t\tNumber of recipients & 9,942 & 3,977 & 6,108 \\\\\n\t\t\\hline\\hline\n\t\t\\multicolumn{4}{l}{\n\t\t\\begin{minipage}{0.95\\linewidth} \\smallskip \\scriptsize\n\t\t\\textbf{Notes:} The table reports descriptive statistics on all matched employee contributions that were donated three cycles before to three cycles after the union election. The difference in the amounts from all employees and the total from workers and managers is driven by contributions for which we were unable to classify the occupation. \n\t\t\\end{minipage}} \\\\\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Do Unions Shape Political Ideologies at Work?", "authors": ["Johannes Matzat", "Aiko Schmeißer"], "url": "https://arxiv.org/abs/2209.02637v4", "attribution": "\"Do Unions Shape Political Ideologies at Work?\" by Johannes Matzat and Aiko Schmeißer, arXiv:2209.02637v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.05916v2_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary Statistics: Untreated Outcomes}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n & & \\textbf{All Controls} & & & & & \\textbf{Treated, Before Implementation} & \\\\\n \\midrule\n \\textit{\\textbf{Labor Force Participation}} & & & & & & & & \\\\\n & \\textbf{Mean} & \\textbf{Std Dev} & \\textbf{N} & & & \\textbf{Mean} & \\textbf{Std Dev} & \\textbf{N} \\\\\n \\midrule\n \\midrule\n Wave 1 & 0.792 & 0.406 & 1420 & & \\textit{$(E_i \\geq 1$)} & 0.698 & 0.460 & 301 \\\\\n Wave 2 & 0.804 & 0.397 & 1421 & & \\textit{$(E_i \\geq 1$)} & 0.740 & 0.440 & 334 \\\\\n Wave 3 & 0.829 & 0.377 & 1188 & & \\textit{$(E_i \\geq 2$)} & 0.837 & 0.370 & 160 \\\\\n Wave 4 & 0.821 & 0.384 & 1187 & & \\textit{$(E_i \\geq 3$)} & 0.830 & 0.377 & 159 \\\\\n Wave 5 & 0.799 & 0.401 & 1187 & & \\textit{$(E_i \\geq 4)$} & 0.792 & 0.407 & 159 \\\\\n Wave 6 & 0.791 & 0.407 & 1087 & & \\textit{$(E_i \\geq 5$)} & 0.814 & 0.393 & 59 \\\\\n Wave 7 & 0.759 & 0.428 & 1028 & & & & & \\\\\n Wave 8 & 0.730 & 0.444 & 1028 & & & & & \\\\\n Wave 9 & 0.711 & 0.453 & 1028 & & & & & \\\\\n Wave 10 & 0.691 & 0.462 & 1028 & & & & & \\\\\n & & & & & & & & \\\\\n \\midrule\n \\textit{\\textbf{Female labor income}} & & & & & & & & \\\\\n Wave 1 & 12,864 & 12,728 & 1124 & & \\textit{$(E_i \\geq 1$)} & 13,761 & 14,362 & 210 \\\\\n Wave 2 & 13,657 & 11,032 & 1142 & & \\textit{$(E_i \\geq 1$)} & 13,880 & 11,589 & 247 \\\\\n Wave 3 & 14,394 & 11,069 & 985 & & \\textit{$(E_i \\geq 2$)} & 15,300 & 11,209 & 134 \\\\\n Wave 4 & 15,068 & 13,198 & 974 & & \\textit{$(E_i \\geq 3$)} & 15,237 & 10,645 & 132 \\\\\n Wave 5 & 15,374 & 13,065 & 949 & & \\textit{$(E_i \\geq 4)$} & 16,470 & 11,570 & 126 \\\\\n Wave 6 & 14,999 & 11,548 & 860 & & \\textit{$(E_i \\geq 5$)} & 13,396 & 7,021 & 48 \\\\\n Wave 7 & 16,592 & 13,550 & 780 & & & & & \\\\\n Wave 8 & 16,268 & 12,683 & 750 & & & & & \\\\\n Wave 9 & 16,313 & 13,154 & 731 & & & & & \\\\\n Wave 10 & 16,562 & 13,418 & 710 & & & & & \\\\\n & & & & & & & & \\\\\n \\midrule\n \\textit{\\textbf{Female wages}} & & & & & & & & \\\\\n Wave 1 & 12,862 & 13,011 & 1074 & & \\textit{$(E_i \\geq 1$)} & 13,832 & 14,705 & 200 \\\\\n Wave 2 & 13,593 & 11,271 & 1090 & & \\textit{$(E_i \\geq 1$)} & 13,864 & 11,794 & 236 \\\\\n Wave 3 & 14,385 & 10,956 & 938 & & \\textit{$(E_i \\geq 2$)} & 15,196 & 11,314 & 128 \\\\\n Wave 4 & 15,117 & 13,435 & 913 & & \\textit{$(E_i \\geq 3$)} & 15,358 & 10,838 & 126 \\\\\n Wave 5 & 15,385 & 12,778 & 884 & & \\textit{$(E_i \\geq 4)$} & 16,496 & 11,730 & 119 \\\\\n Wave 6 & 15,210 & 11,777 & 807 & & \\textit{$(E_i \\geq 5$)} & 13,627 & 6,910 & 47 \\\\\n Wave 7 & 16,794 & 13,837 & 731 & & & & & \\\\\n Wave 8 & 16,368 & 12,860 & 716 & & & & & \\\\\n Wave 9 & 16,375 & 13,409 & 698 & & & & & \\\\\n Wave 10 & 16,357 & 12,687 & 683 & & & & & \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Workplace Breastfeeding Legislation and Female Labor Force Participation in the United States", "authors": ["Julia Hatamyar"], "url": "https://arxiv.org/abs/2209.05916v2", "attribution": "\"Workplace Breastfeeding Legislation and Female Labor Force Participation in the United States\" by Julia Hatamyar, arXiv:2209.05916v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.12722v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Reconstruction quality on DTU dataset by AACVP-MVSNet with different parameter $H$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\toprule\n \\quad & Singe-headed & $H = 2$ & $H = 4$ & $H=8$ \\\\\n \\midrule\n Acc. (mm) & $\\textbf{0.357}$ & $0.362$ & $0.359$ & $0.375$ \\\\\n Comp. (mm) & $0.326$ & $\\textbf{0.325}$ & $0.332$ & $0.339$ \\\\\n OA (mm) & $\\textbf{0.341}$ & $0.343$ & $0.345$ & $0.357$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Attention Aware Cost Volume Pyramid Based Multi-view Stereo Network for 3D Reconstruction", "authors": ["Anzhu Yu", "Wenyue Guo", "Bing Liu", "Xin Chen", "Xin Wang", "Xuefeng Cao", "Bingchuan Jiang"], "url": "https://arxiv.org/abs/2011.12722v1", "attribution": "\"Attention Aware Cost Volume Pyramid Based Multi-view Stereo Network for 3D Reconstruction\" by Anzhu Yu, Wenyue Guo, Bing Liu, Xin Chen, Xin Wang, Xuefeng Cao, and Bingchuan Jiang, arXiv:2011.12722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.00697v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Entropy Measures of Nifty 50 P/E Ratio}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\toprule\n \\textbf{Library} & \\textbf{Entropy Type} & \\textbf{Normalized Value} \\\\\n \\midrule\n SciPy & Shannon Entropy & 0.86 \\\\\n Manual & Tsallis Entropy ($q=0.1$) & 0.92 \\\\\n Manual & Tsallis Entropy ($q=2$) & 0.98 \\\\\n nolds & Sample Entropy & 0.10 \\\\\n antropy & Permutation Entropy & 0.94 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Multi Scale Analysis of Nifty 50 Return Characteristics Valuation Dynamics and Market Complexity 1990 to 2024", "authors": ["Chandradew Sharma"], "url": "https://arxiv.org/abs/2509.00697v1", "attribution": "\"Multi Scale Analysis of Nifty 50 Return Characteristics Valuation Dynamics and Market Complexity 1990 to 2024\" by Chandradew Sharma, arXiv:2509.00697v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.15439v1_tex_table27.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Logit regression for likelihood of cheating - Marginal effects}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccc} \n\\hline\n\\hline\n & & \\multicolumn{5}{c}{Dependent variable: Likelihood of cheating} \\\\ \\cmidrule(l){3-7} \n & & (1) & (2) & (3) & (4) & (5) \\\\ \n\\midrule\n\\multicolumn{2}{l}{Treatment} & & & & & \\\\ \n & \\textit{Machine} & 0.009 & 0.027 & -0.082 & 0.166 & 0.040 \\\\ \n & & (0.935) & (0.779) & (0.789) & (0.434) & (0.912) \\\\ \n & \\textit{Human Black Box} & -0.089 & -0.076 & -0.089 & -0.065 & -0.070 \\\\ \n & & (0.420) & (0.431) & (0.377) & (0.509) & (0.460) \\\\ \n & \\textit{Machine Black Box} & 0.100 & 0.106 & -0.020& 0.242 & 0.117 \\\\ \n & & (0.356) & (0.335) & (0.952) & (0.237) & (0.764) \\\\ \n & & & & & & \\\\ \n\\multicolumn{2}{l}{Age} & & 0.010 & & & 0.006\\\\ \n & & & (0.383) & & & (0.665) \\\\ \n\\multicolumn{2}{l}{Female} & & $-0.269^{***}$ & & & $-0.296^{***}$ \\\\ \n & & & (0.000) & & & (0.000) \\\\ \n & & & & & & \\\\\n\\multicolumn{2}{l}{Field of Study} & & & & & \\\\ \n & \\textit{Cultural \\& social studies} & & 0.012 & & & 0.037 \\\\ \n & & & (0.881) & & & (0.665) \\\\ \n & \\textit{Natural science} & & -0.112 & & & -0.144 \\\\ \n & & & (0.371) & & & (0.185) \\\\ \n & & & & & & \\\\\n\\multicolumn{2}{l}{Risk} & & & $0.041^{*}$ & & $0.041^{*}$ \\\\ \n & & & & (0.017) & & (0.013) \\\\ \n\\multicolumn{2}{l}{Ethical sensitivity} & & & 0.025 & & 0.138 \\\\ \n & & & & (0.758) & & (0.079) \\\\ \n\\multicolumn{2}{l}{Closeness} & & & 0.007 & & -0.004 \\\\ \n & & & & (0.805) & & (0.889) \\\\ \n & & & & & & \\\\\n\\multicolumn{2}{l}{Verification by machine} & & & 0.085 & & 0.137 \\\\ \n & & & & (0.299) & & (0.632) \\\\ \n\\multicolumn{2}{l}{ATI} & & & 0.067 & & -0.002 \\\\ \n & & & & (0.100) & & (0.971) \\\\ \n & & & & & & \\\\ \n\\multicolumn{2}{l}{Verification by preferred entity} & & & & 0.044 & 0.089\\\\ \n & & & & & (0.578) & (0.233) \\\\ \n\\multicolumn{2}{l}{Verification by more error-prone entity} & & & & -0.100 & -0.054 \\\\ \n & & & & & (0.412) & (0.646) \\\\ \n\\multicolumn{2}{l}{Verification by higher discretion entity} & & & & 0.246 & 0.205 \\\\ \n & & & & & (0.219) & (0.282) \\\\ \n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty", "authors": ["Marius Protte", "Behnud Mir Djawadi"], "url": "https://arxiv.org/abs/2507.15439v1", "attribution": "\"Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty\" by Marius Protte and Behnud Mir Djawadi, arXiv:2507.15439v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.18656v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n\\textbf{Hyperbolic Tangent} & \\textbf{Logistic} & \\textbf{Rectified Linear Unit (ReLU)} \\\\\n\\hline\n$tanh(x)=\\frac{e^x - e^{-x}}{e^x + e^{-x}}$ & $\\sigma(x)=\\frac{1}{1+e^{-x}}$ & $ReLU(x)= max(0,x)$ \\\\\n\\hline\n \\textbf{Softplus} & \\textbf{Sigmoid} & \\textbf{Softmax} (vector output) \\\\\n\\hline\n$ln(1+e^x)$ & $\\frac{x}{1+e^{-x}}$ & $ \\forall i \\quad soft_i=\\frac{e^{x_i}}{\\sum_{j=1}^J e^{x_j}}$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Example of common activation functions: The $x_i$ are the inputs, and we have $x=\\sum_i w_i x_i$, with $w_i$ the neural weights.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?", "authors": ["Jérémie Sublime"], "url": "https://arxiv.org/abs/2411.18656v1", "attribution": "\"The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?\" by Jérémie Sublime, arXiv:2411.18656v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.16393v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary Statistics: Median and interquartile range presented for continuous features. Counts and proportions presented for discrete features. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l} \nFeatures & $N=292$ \\\\\n\\hline\nFollow-up (years) \t\t& 2.6 (1.3-3.7 ) \\\\\nDeath \t\t& 114 (40\\%) \t\t\\\\\nNum. Treatment Courses, ($\\kappa$) \t\t& \t\t\\\\\n1 \t\t& 22 (8\\%) \t\t\\\\\n2 \t\t& 36 (12\\%) \t\t \\\\\n3 \t\t& 46 (16\\%) \t\t \\\\\n4 \t\t& 188 (63\\%) \t\t \\\\\nAML Risk Classification (high) \t\t& 64 (23\\%) \t\t \\\\\nWBC (cells/uL) \t\t\t& 20 (6.8-65) \\\\\nMale \t\t\t& 152 (52\\%) \t\t \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian Semiparametric Model for Sequential Treatment Decisions with Informative Timing", "authors": ["Arman Oganisian", "Kelly D. Getz", "Todd A. Alonzo", "Richard Aplenc", "Jason A. Roy"], "url": "https://arxiv.org/abs/2211.16393v1", "attribution": "\"Bayesian Semiparametric Model for Sequential Treatment Decisions with Informative Timing\" by Arman Oganisian, Kelly D. Getz, Todd A. Alonzo, Richard Aplenc, and Jason A. Roy, arXiv:2211.16393v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.06457v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Architecture of the $f_l$ Network}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\nLayer\n&Filter size, stride\n&Output size$(C,F)$\n\\\\\n\\midrule\nInput\n& - & 1$\\times$128,1$\\times$3\n\\\\\nConcatenation\n& - & 1$\\times$131\n\\\\\n\\midrule\nDense\n& - & 1$\\times$131\n\\\\\nReLU\n& - & 1$\\times$131\n\\\\\n\\midrule\nDense\n& - & 1$\\times$131\n\\\\\nReLU\n& - & 1$\\times$131\n\\\\\n\\midrule\nDense\n& - & 1$\\times$128\n\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Context Matters: Graph-based Self-supervised Representation Learning for Medical Images", "authors": ["Li Sun", "Ke Yu", "Kayhan Batmanghelich"], "url": "https://arxiv.org/abs/2012.06457v1", "attribution": "\"Context Matters: Graph-based Self-supervised Representation Learning for Medical Images\" by Li Sun, Ke Yu, and Kayhan Batmanghelich, arXiv:2012.06457v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2406.11847v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Machine learning performances from predictive models for learning patterns under the integration framework}\n\\begin{tabular}{llccccc}\n\\hline\n Pattern& Method & Accuracy(\\%) & Precision(\\%) & Recall(\\%) & F1 Score(\\%) & AUC \\\\ \\hline\n &logistic regression & 95.64& 99.92& 95.64& 97.73& 0.9872\n\\\\\n &decision tree & 93.44& 99.96& 93.36& 96.55& 0.9735\n\\\\\nlow &random forest & 91.64& 99.86& 91.63& 95.57& 0.9193\n\\\\\n autonomy &K-Nearest neighbor & 97.20& 99.79& 97.36& 98.56& 0.9472\n\\\\\n &multilayer perceptron &96.45&98.79& 96.45&97.32&0.9915\n\\\\\n &support vector classifier & 96.59&98.77&96.59& 97.40&0.9854 \\\\\n&extreme gradient boosting & 98.68& 99.55& 99.10& 99.33& 0.9929\\\\\n\\hline\n & logistic regression \n& 71.17& 68.15& 71.88& 69.96&0.7880\\\\\n & decision tree \n& 70.80& 80.00& 50.00& 61.54&0.6565\\\\\n & random forest \n& 70.80& 68.18& 70.31& 69.23&0.7582\\\\\nmotivated& K-Nearest neighbor \n& 72.26& 75.00 & 60.94& 67.24&0.7892\\\\\n& multilayer perceptron \n& 70.44& 68.50& 67.97& 68.24&0.7900\\\\\n& support vector classifier & 73.72& 72.95& 69.53& 71.20&0.7694\\\\\n& extreme gradient boosting \n& 73.72& 73.33& 68.75& 70.97&0.7960\\\\\n \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study", "authors": ["Jin Yuan", "Xuelan Qiu", "Jinran Wu", "Jiesi Guo", "Weide Li", "You-Gan Wang"], "url": "https://arxiv.org/abs/2406.11847v1", "attribution": "\"Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study\" by Jin Yuan, Xuelan Qiu, Jinran Wu, Jiesi Guo, Weide Li, and You-Gan Wang, arXiv:2406.11847v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.13608v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|}\n\\hline\n\\textbf{Variable} & \\textbf{Suggested Weight $w_i$} \\\\\n\\hline\nHumidity (v1)& $w_1 = 0.05$ \\\\\nTemperature (v2) & $w_2 = 0.1$ \\\\\nCloud Cover (v3) & $w_3 = 0.2$ \\\\\nWindspeed (v4)& $w_4 = 0.05$ \\\\\nSolar (Radiation) (v5) & $w_5 = 0.4$ \\\\\nDiffuse Solar (v6) & $w_6 = 0.2$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Suggested Weights for Each Variable}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Integrating Dynamic Correlation Shifts and Weighted Benchmarking in Extreme Value Analysis", "authors": ["Dimitrios P. Panagoulias", "Elissaios Sarmas", "Vangelis Marinakis", "Maria Virvou", "George A. Tsihrintzis"], "url": "https://arxiv.org/abs/2411.13608v2", "attribution": "\"Integrating Dynamic Correlation Shifts and Weighted Benchmarking in Extreme Value Analysis\" by Dimitrios P. Panagoulias, Elissaios Sarmas, Vangelis Marinakis, Maria Virvou, and George A. Tsihrintzis, arXiv:2411.13608v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.15554v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of computation cost (wall-clock time in seconds)}\n\\begin{tabular}{cc} \n \\hline\n Method \\quad \\quad \\quad \\quad \\quad \\quad & Cost \\\\\n \\hline\n FEM (Fluid domain) \\quad \\quad \\quad \\quad \\quad \\quad & $46$ (CPU) \\\\ \n FEM (Extended domain*) \\quad \\quad \\quad \\quad \\quad \\quad & $558$ (CPU) \\\\\n FEM (Extended domain) \\quad \\quad \\quad \\quad \\quad \\quad & $10360$ (CPU) \\\\\n FFT \\quad \\quad \\quad \\quad \\quad \\quad & $52$ (CPU) \\\\\n FFT (Parallel) \\quad \\quad \\quad \\quad \\quad \\quad & $2.7$ (GPU) \\\\\n \\hline\n *without considering the auxiliary velocity \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Accelerated Computational Micromechanics for Reactive Flow in Porous Media", "authors": ["Mina Karimi", "Kaushik Bhattacharya"], "url": "https://arxiv.org/abs/2312.15554v1", "attribution": "\"Accelerated Computational Micromechanics for Reactive Flow in Porous Media\" by Mina Karimi and Kaushik Bhattacharya, arXiv:2312.15554v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.17244v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model Size Comparison Used in the Experiment}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|}\n\\hline\n\\textbf{Model} & \\textbf{Size (KB)} \\\\\n\\hline\nCMG & 2194 \\\\\nBiLSTM & 3557 \\\\\nGRU & 1354 \\\\\nLSTM & 1786 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data", "authors": ["Arif Pathan"], "url": "https://arxiv.org/abs/2506.17244v1", "attribution": "\"Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data\" by Arif Pathan, arXiv:2506.17244v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.11079v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Regression results for different mathematical abilities}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc} \n\\hline\n & (1) & (2) \\\\\n & Low & High \\\\\nVARIABLES & Cont. PGG & Cont. PGG \\\\\n\\hline\n & & \\\\\n$AR$ & 0.014 & -0.168 \\\\\n & (0.146) & (0.124) \\\\\n$RA$ & 0.011 & 0.033 \\\\\n & (0.146) & (0.132) \\\\\n$AA$ & 0.070 & -0.001 \\\\\n & (0.146) & (0.131) \\\\\n\\emph{risk aversion} & -0.629** & -0.466** \\\\\n & (0.211) & (0.184) \\\\\nConstant & 2.516*** & 2.310*** \\\\\n & (0.393) & (0.381) \\\\\n & & \\\\\nObservations & 808 & 619 \\\\\nR-squared & 0.025 & 0.058 \\\\\nControls & Yes & Yes \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The effect of ambiguity in strategic environments: an experiment", "authors": ["Pablo Brañas-Garza", "Antonio Cabrales", "María Paz Espinosa", "Diego Jorrat"], "url": "https://arxiv.org/abs/2209.11079v1", "attribution": "\"The effect of ambiguity in strategic environments: an experiment\" by Pablo Brañas-Garza, Antonio Cabrales, María Paz Espinosa, and Diego Jorrat, arXiv:2209.11079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17996v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrr|rrrr} \n\\multicolumn{4}{c}{problem sizes} & \\multicolumn{3}{c}{timing (s)} & \n\\multirow{2}{*}{iterations} \\\\ \n$n$ & $q$ & $m$ & $nm$ & MOSEK & PDMCF (CPU) & PDMCF (GPU) \\\\\n\\hline\n$100$ & $10$ & $1178$ & $1\\times 10^5$ & $5$ & $12$ & $5$ & $490$ \\\\\n$200$ & $10$ & $2316$ & $5\\times 10^5$ & $23$ & $57$ & $6$ & $690$ \\\\\n$300$ & $10$ & $3472$ & $1\\times 10^6$ & $95$ & $164$ & $6$ & $840$ \\\\\n$500$ & $10$ & $5738$ & $3\\times 10^6$ & $340$ & $548$ & $7$ & $950$ \\\\\n$500$ & $20$ & $11176$ & $6\\times 10^6$ & $1977$ & $890$ & $8$ & $790$ \\\\ \n$1000$ & $10$ & $11424$ & $1\\times 10^7$ & $2889$ & $18554$ & $26$ & $7150$ \\\\ \n$1000$ & $20$ & $22286$ & $2\\times 10^7$ & $16765$ & $5143$ & $15$ & $1040$ \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Runtime table for small and medium size problems (JAX).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solving Large Multicommodity Network Flow Problems on GPUs", "authors": ["Fangzhao Zhang", "Stephen Boyd"], "url": "https://arxiv.org/abs/2501.17996v2", "attribution": "\"Solving Large Multicommodity Network Flow Problems on GPUs\" by Fangzhao Zhang and Stephen Boyd, arXiv:2501.17996v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.13825v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\hline\n \\textbf{Parameter} & \\textbf{Value} &\\textbf{Ref.}\\\\\n \\hline\n $d_{max}$& 4 meters&\\\\\n $\\eta$&1&\\\\\n $\\Psi$&1&\\\\\n $\\xi$&$20\\times20$&$^*$\\\\\n $E_{\\text{max}}$&100 units&$^\\dag$\\\\\n $E_{\\text{idle}}$&1 units&$^\\dag$\\\\\n $E_{\\text{WuR}}$&1/14 units&$^\\dag$\\\\\n $E_{\\text{Tx}}$&10 units&$^\\dag$\\\\\n $p(d_{i,j})$&$e^{-\\eta d_{i,j}}$&\\\\\n $N$&[10, 250]&$^*$\\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Energy Management and Wake-up for IoT Networks Powered by Energy Harvesting", "authors": ["David Ernesto Ruiz-Guirola", "Samuel Montejo-Sanchez", "Israel Leyva-Mayorga", "Zhu Han", "Petar Popovski", "Onel L. A. Lopez"], "url": "https://arxiv.org/abs/2508.13825v1", "attribution": "\"Energy Management and Wake-up for IoT Networks Powered by Energy Harvesting\" by David Ernesto Ruiz-Guirola, Samuel Montejo-Sanchez, Israel Leyva-Mayorga, Zhu Han, Petar Popovski, and Onel L. A. Lopez, arXiv:2508.13825v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.14668v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{DDPG hyperparameter settings}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|} \n\t\t\\hline\n\t\tHyperparameter & Setting \\\\\n\t\t\\hline\n\t\tCritic learning rate\t& 1$e^{-03}$ \\\\\n\t\tActor learning rate\t\t& 1$e^{-04}$ \\\\\n\t\tCritic hidden layer-1\t& 50 fully-connected \\\\\n\t\tCritic hidden layer-2\t& 25 fully-connected \\\\\n\t\tAction-path neurons\t\t& 25 fully-connected \\\\\n\t\tAction-path bound\t\t& tanh layer\\\\\n\t\tGamma\t\t\t\t\t& 0.9 \\\\\n\t\tBatch size\t\t\t\t& 64 \\\\\n\t\tOUP Variance \t\t\t& 1.5 \\\\\n\t\tOUP Variance Decay Rate\t& 1$e^{-05}$ \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Reinforcement Learning for Control of Valves", "authors": ["Rajesh Siraskar"], "url": "https://arxiv.org/abs/2012.14668v2", "attribution": "\"Reinforcement Learning for Control of Valves\" by Rajesh Siraskar, arXiv:2012.14668v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.01969v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Resilience by Region: Average Failed Assets per Simulation (\\(n = 1000\\)).}\n\\begin{tabular}{lc}\\hline\n \\textbf{Region} & \\textbf{Average Failed Assets} \\\\\\hline\n Brazil & 2.000 \\\\\n US & 0.000 \\\\\n Europe & 0.000 \\\\\n Asia & 0.000 \\\\\\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Systemic Risk and Default Cascades in Global Equity Markets: Extending the Gai-Kapadia Framework with Stochastic Simulations and Network Analysis", "authors": ["Ana I. C. Pereda"], "url": "https://arxiv.org/abs/2504.01969v2", "attribution": "\"Systemic Risk and Default Cascades in Global Equity Markets: Extending the Gai-Kapadia Framework with Stochastic Simulations and Network Analysis\" by Ana I. C. Pereda, arXiv:2504.01969v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.15979v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model Performance Comparison (Averages)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\nModel & IoU & Accuracy & Precision & Recall & F1 Score \\\\\n\\midrule\nOur Model & 0.431 & 0.871 & 0.531 & 0.726 & 0.601 \\\\\nStarDist & 0.087 & 0.672 & 0.130 & 0.267 & 0.172 \\\\\nCellpose & 0.130 & 0.865 & 0.358 & 0.164 & 0.205 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "GUI Based Fuzzy Logic and Spatial Statistics for Unsupervised Microscopy Segmentation", "authors": ["Surajit Das", "Pavel Zun"], "url": "https://arxiv.org/abs/2508.15979v1", "attribution": "\"GUI Based Fuzzy Logic and Spatial Statistics for Unsupervised Microscopy Segmentation\" by Surajit Das and Pavel Zun, arXiv:2508.15979v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.02381v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n \\textbf{\\textrm{Splitting Type}} & \\textbf{ $C_{\\max}$} & \\textbf{f(a,b)} & \\textbf{$D(f,0)$}& \\textbf{$D(f,m), m\\geq 1$ } \\\\\n \\hline\n (1111),(22) & (111) & $ab(a+b)$ & $\\frac{(p-2)(p-1)}{p^2}$ & $3(p-1)^2p^{-m-2}$ \\\\\n \\hline\n (112), (4) & (12) & $a(a^2-\\epsilon b^2)$ & $\\frac{p-1}{p}$ & $(p-1)^2p^{-m-2}$\\\\\n \\hline\n (13) & (3)& $\\bullet$ & $\\frac{p^2-1}{p^2}$ & 0\\\\\n \\hline\n $(1^211),(1^22)$ & $(1^21)$ & $a(b^2-\\pi a^2)$ & $\\frac{(p-1)^2}{p^2}$ & $(p-1)^2p^{-m-2} +\\frac{p-1}{p^2}\\cdot\\delta_{m=1}$\\\\\n \\hline\n $(1^31)$ & $(1^3)$ & $a^3-\\pi$ & $\\frac{p-1}{p}$ & $\\frac{p-1}{p^2}\\cdot\\delta_{m=1}$\\\\\n \\hline\n $(2^2)_{C_2^2},(1^21^2)_{=}$ & (111) & $ab(a+pb)$ & $\\frac{(p-1)^2}{p^2}$ & $(p-1)^2p^{-m-2} + \\frac{(p-1)(p-2)}{p^3}\\delta_{m=2}+2(p-1)^2p^{-m-1}\\delta_{m\\geq 3}$\\\\\n \\hline\n $(2^2)_{C_4},(1^21^2)_{\\neq}$ & $(12)$ & $a(p^2a^2-\\epsilon b^2)$ & $\\frac{(p-1)^2}{p^2}$ & $(p-1)^2p^{-m-2} + \\frac{p-1}{p^2}\\delta_{m=2}$ \\\\\n \\hline\n $(1^4)$ & $(1^21)$ & $a(a^2-\\pi b^2)$ & $\\frac{p-1}{p}$ & $(p-1)^2p^{-m-2}\\delta_{m\\geq 2}$ \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Secondary terms in the counting functions of quartic fields", "authors": ["Arul Shankar", "Jacob Tsimerman"], "url": "https://arxiv.org/abs/2503.02381v2", "attribution": "\"Secondary terms in the counting functions of quartic fields\" by Arul Shankar and Jacob Tsimerman, arXiv:2503.02381v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.01924v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Execution time data for the double integrator example.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|} \\hline\n & FG($N = 10$) & MPC($N =10$) & MPC ($N = 236$) & CG \\\\\\hline\n TAVE [ms] & $0.0126$ & $0.0884$ & $255$ & $0.00833$ \\\\ \\hline\n TMAX [ms] & $0.063$ & $0.345$ & $2170$ & $0.0369$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Feasibility Governor for Enlarging the Region of Attraction of Linear Model Predictive Controllers", "authors": ["Dominic Liao-McPherson", "Terrence Skibik", "Torbjørn Cunis", "Ilya Kolmanovsky", "Marco M. Nicotra"], "url": "https://arxiv.org/abs/2011.01924v1", "attribution": "\"A Feasibility Governor for Enlarging the Region of Attraction of Linear Model Predictive Controllers\" by Dominic Liao-McPherson, Terrence Skibik, Torbjørn Cunis, Ilya Kolmanovsky, and Marco M. Nicotra, arXiv:2011.01924v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.03112v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|r|r|r|r|r}\n\\hline\nblack box & model & $R^2_{train}$ & sd $R^2_{train}$ & $R^2_{test}$ & sd $R^2_{test}$ & time in sec\\\\\n\\hline\nstandalone & basic lm & 0.9633 & 0.0035 & 0.9469 & 0.0048 & 102.2842\\\\\nstandalone & penalized poly & 0.9824 & 0.0015 & 0.9781 & 0.0020 & 195.4385\\\\\nstandalone & B-Splines & 0.9929 & 0.0013 & 0.9714 & 0.0029 & 113.2447\\\\\nstandalone & gam & 0.9880 & 0.0016 & 0.9835 & 0.0017 & 1544.3393\\\\\n\\hline\nXGBoost & basic lm & 0.9198 & 0.0516 & 0.9030 & 0.0417 & 72.3790\\\\\nXGBoost & penalized poly & 0.9750 & 0.0033 & 0.9634 & 0.0051 & 206.5870\\\\\nXGBoost & B-Splines & 0.9865 & 0.0141 & 0.9591 & 0.0092 & 97.9321\\\\\nXGBoost & gam & 0.9786 & 0.0127 & 0.9672 & 0.0115 & 1728.6021\\\\\n\\hline\nXGBoost & XGBoost & 0.9392 & 0.0342 & 0.9216 & 0.0405 & 3.0131\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean performance simulation results nonlinear effects variant 2}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation", "authors": ["Julia Herbinger", "Susanne Dandl", "Fiona K. Ewald", "Sofia Loibl", "Giuseppe Casalicchio"], "url": "https://arxiv.org/abs/2310.03112v1", "attribution": "\"Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation\" by Julia Herbinger, Susanne Dandl, Fiona K. Ewald, Sofia Loibl, and Giuseppe Casalicchio, arXiv:2310.03112v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.04456v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Running time comparison (execution was limited to 24 hours). For the flow cytometry and KDD Cup99 dataset, t-SNE cannot complete the task in 24 hours. LargeVis and UMAP ran out of memory.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccr}\n\\toprule\nDataset (Size) & t-SNE & LargeVis & UMAP & TriMap & PaCMAP & Speedup \\\\\n\\midrule\nOlivetti Faces (0.4K) & 00:00:04 & 00:08:13 & 00:00:02 & \\textbf{00:00:01} & \\textbf{00:00:01}\\\\\nCOIL-20 (1.4K) & 00:00:08 & 00:10:18 & 00:00:05 & 00:00:02 & \\textbf{00:00:01} & 2.00\\\\\nCOIL-100 (7.2K) & 00:00:49 & 00:09:53 & 00:00:10 & 00:00:06 & \\textbf{00:00:03} & 2.00\\\\\nS-Curve with Hole (9.5K) & 00:01:17 & 00:10:09 & 00:00:15 & 00:00:08 & \\textbf{00:00:05} & 1.60\\\\\nUSPS (9.5K) & 00:01:14 & 00:10:15 & 00:00:15 & 00:00:07 & \\textbf{00:00:05} & 1.40\\\\\nMammoth (10K) & 00:00:58 & 00:10:36 & 00:00:16 & 00:00:08 & \\textbf{00:00:05} & 1.60 \\\\\n20Newsgroups (18K) & 00:03:29 & 00:11:40 & 00:00:19 & 00:00:18 & \\textbf{00:00:12} & 1.50\\\\\nMouse scRNA-seq (20K) & 00:04:43 & 00:12:52 & 00:00:24 & 00:00:20 & \\textbf{00:00:13} & 1.54\\\\\nMNIST (70K) & 00:14:02 & 00:20:19 & 00:01:09 & 00:01:14 & \\textbf{00:00:52} & 1.33\\\\\nF-MNIST (70K) & 00:12:43 & 00:17:11 & 00:00:59 & 00:01:13 & \\textbf{00:00:47} & 1.26\\\\\nFlow Cytometry (3M) & - & - & - & 02:10:27 & \\textbf{00:58:28} & 2.23\\\\\nKDD Cup99 (4M) & - & - & - & 03:34:57 & \\textbf{02:05:19} & 1.72\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization", "authors": ["Yingfan Wang", "Haiyang Huang", "Cynthia Rudin", "Yaron Shaposhnik"], "url": "https://arxiv.org/abs/2012.04456v2", "attribution": "\"Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization\" by Yingfan Wang, Haiyang Huang, Cynthia Rudin, and Yaron Shaposhnik, arXiv:2012.04456v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.21377v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters of the water tank system.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n Parameter & Short form & Value & Unit \\\\ \\hline\n Cross-sectional area &\n $A$ &\n $0.015$ &\n $\\text{m}^2$ \\\\\n Maximum flow rate of pump one &\n $u_{1,{\\text{max}}}$ &\n $2\\cdot 10^{-4}$&\n ${\\text{m}^3}/{\\text{s}}$ \\\\\n Valve parameter $V_{12}$ &\n $c_{12}$ &\n $2.5\\cdot 10^{-5}$&\n $\\text{m}^2$ \\\\\n Valve parameter $V_{2R}$ &\n $c_{2\\text{R}}$& \n $2.5\\cdot 10^{-5}$&\n $\\text{m}^2$ \\\\ \n Gravitational force &\n $g$ &\n $9.81$ &\n ${\\text{m}}/{\\text{s}^2}$ \\\\ \n \\hline\t\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Physics-informed Gaussian Processes for Model Predictive Control of Nonlinear Systems", "authors": ["Adrian Lepp", "Jörn Tebbe", "Andreas Besginow"], "url": "https://arxiv.org/abs/2504.21377v1", "attribution": "\"Physics-informed Gaussian Processes for Model Predictive Control of Nonlinear Systems\" by Adrian Lepp, Jörn Tebbe, and Andreas Besginow, arXiv:2504.21377v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.09865v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{First-order condition whose solution yields the markup function $\\frac{1}{\\mu}=\\frac{1}{{\\cal W} (x, \\alpha, \\beta,\\sigma)}$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|lll}\n\\hline\n & \\multicolumn{2}{c}{ } \\\\\n& $\\beta=0$ & & $\\beta \\in (0,1]$ \\\\ \\hline\n\\\\\n$\\sigma = 0$ & $\\textstyle \n1-\\mu = \\ln \\left( \\frac{\\mu}{x} \\right)$ & & $\\textstyle 1-\\mu = \\ln \\left( \\frac{\\mu}{x} \\right) + \\beta \\left[ 1 - \\ln \\left( \\frac{1-\\beta-\\mu}{\\alpha} \\right) \\right]^\\dagger$\n\\\\\n$\\sigma \\neq 0$ & $\\textstyle 1-\\mu = \\frac{1- \\left( \\frac{\\mu}{x} \\right)^{-\\sigma}}{\\sigma}$ & & $\\textstyle 1-\\mu= \\frac{1- \\left( \\frac{\\mu}{x} \\right)^{-\\sigma} (1 -\\beta \\sigma) \\left[ \\frac{1-\\beta -\\mu}{\\alpha ( 1 -\\beta \\sigma) } \\right]^{\\beta \\sigma}}{\\sigma}$ \\\\ \\hline\n\\multicolumn{4}{p{12cm}}{\n\\scriptsize {\\it Note}: $^\\dagger$ means that $\\beta=1$ must be excluded since $\\beta=1$ is possible only when $\\sigma>1$ (see Case~1b in Table~). If $\\alpha=0$ or $\\beta=\\frac{1}{\\sigma}$, then we cannot use\n as it relies on $\\alpha\\neq0$ or $\\beta \\neq \\frac{1}{\\sigma}$.\nHowever,\nwe can combine and to obtain the first-order condition $1-\\mu = \\beta$\nor $1-\\mu = \\frac{1}{\\sigma}$.}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Linear fractional relative risk aversion", "authors": ["Kristian Behrens", "Yasusada Murata"], "url": "https://arxiv.org/abs/2509.09865v1", "attribution": "\"Linear fractional relative risk aversion\" by Kristian Behrens and Yasusada Murata, arXiv:2509.09865v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13382v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\toprule\n $G(600,p)$ & $p$ & $\\varepsilon$ & $|E|$ & $K'''$ \\\\\n \\midrule\n & 0.9 & 0.5 & 81979 & 599.95\\\\\n & 0.9 & 1.0 & 27430& 605.54 \\\\\n & 0.9 & 1.5 & 12957 & 615.09\\\\\n & 0.9 & 2.0 & 7442 & 629.93\\\\\n \\midrule\n & 0.7 & 0.5 & 74207 & 600.25 \\\\\n & 0.7 & 1.0 & 26638 & 605.90\\\\\n & 0.7 & 1.5 & 12813& 615.52 \\\\\n & 0.7 & 2.0 & 7408 & 629.97 \\\\\n \\midrule\n & 0.5 & 0.5 & 63061 & 600.82\\\\\n & 0.5 & 1.0 & 25315& 606.36 \\\\\n & 0.5 & 1.5 & 12506&616.06\\\\\n & 0.5 & 2.0 & 7336& 630.14 \\\\\n \\midrule\n & 0.05 & 0.5 &8896 & 620.25\\\\\n & 0.05 & 1.0 & 8253 & 626.20 \\\\\n & 0.05& 1.5 & 6525 & 635.89 \\\\\n & 0.05& 2.0 &4834& 651.49 \\\\\n \\bottomrule\n \\hspace{.2cm}\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Sparse approximations for different values of $\\varepsilon$ on $G(600,p)$, $p=0.9, 0.7, 0.5,0.05$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Spectral Approach to Kemeny's Constant", "authors": ["Aida Abiad", "Ángeles Carmona", "Andrés M. Encinas", "Maria José Jiménez", "Álvaro Samperio"], "url": "https://arxiv.org/abs/2503.13382v1", "attribution": "\"A Spectral Approach to Kemeny's Constant\" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.00535v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|c|c|c|c|c|}\n\\hline\nYear & CS & Math & Physics & Stat & Total \\\\ \\hline\n2007 & 31 & 162 & 35 & 0 & 228 \\\\ \\hline\n2008 & 20 & 111 & 32 & 2 & 165 \\\\ \\hline\n2009 & 10 & 45 & 19 & 0 & 74 \\\\ \\hline\n2010 & 37 & 143 & 44 & 8 & 232 \\\\ \\hline\n2011 & 44 & 138 & 38 & 5 & 225 \\\\ \\hline\n2012 & 73 & 176 & 60 & 7 & 316 \\\\ \\hline\n2013 & 87 & 161 & 47 & 12 & 307 \\\\ \\hline\n2014 & 57 & 160 & 53 & 12 & 282 \\\\ \\hline\n2015 & 51 & 91 & 44 & 2 & 188 \\\\ \\hline\n2016 & 93 & 121 & 46 & 12 & 272 \\\\ \\hline\n2017 & 110 & 132 & 53 & 14 & 309 \\\\ \\hline\n2018 & 129 & 139 & 51 & 10 & 329 \\\\ \\hline\n2019 & 149 & 126 & 46 & 16 & 337 \\\\ \\hline\n2020 & 162 & 111 & 63 & 17 & 353 \\\\ \\hline\n2021 & 177 & 105 & 53 & 17 & 352 \\\\ \\hline\n2022 & 198 & 115 & 42 & 20 & 375 \\\\ \\hline\n2023 & 200 & 114 & 44 & 13 & 371 \\\\ \\hline\n2024 & 209 & 119 & 41 & 10 & 379 \\\\ \\hline\n\\end{tabular}\n\\caption{Yearly distribution of papers by subjects with totals. For each year, we randomly selected 500 papers, with replacement. If a year contained fewer than 500 papers, we allowed sampling with replacement to reach the target number of samples.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Tensor Topic Modeling Via HOSVD", "authors": ["Yating Liu", "Claire Donnat"], "url": "https://arxiv.org/abs/2501.00535v1", "attribution": "\"Tensor Topic Modeling Via HOSVD\" by Yating Liu and Claire Donnat, arXiv:2501.00535v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18051v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ccc|c|c}\n \\hline\n $\\rho$& & & \\\\\n\\hline\n0.2& -10.44&-10.27&-10.19\\\\\n\\hline\n0.5& -11.23&-10.79&-10.57\\\\\n\\hline\n1.0&-12.23&-11.55&-11.13\\\\\n\\hline\n2.0&-13.84&-12.85&-12.12\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Objective values and 95\\% bounds with different sizes of uncertainty for different models}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications", "authors": ["Jiaqi Lei", "Akhil Singla", "Sanjay Mehrotra"], "url": "https://arxiv.org/abs/2501.18051v2", "attribution": "\"A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications\" by Jiaqi Lei, Akhil Singla, and Sanjay Mehrotra, arXiv:2501.18051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15444v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mutually unbiased weighing matrices of order $15$ and weight $9$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l}\n\\noalign{\\hrule height1pt}\n$W_{15,12}$\\\\\n\\hline\n101122010212000\n200012101211100\n200021210011021\n120200101110201\\\\\n110100221001011\n002202222210200\n112201011200002\n000000112221211\\\\\n022010210002111\n210222001022001\n012020102110110\n001202210101012\\\\\n122022000021120\n021221020200110\n222120001000212\\\\\n\\hline\n$ A_{15,12,2}$\\\\\n\\hline\n001220210222002\n012002211210001\n110222020100210\n110011201120020\\\\\n101012020210022\n120120220002101\n102021002201220\n100211100202011\\\\\n120102111020200\n012002102022120\n010121101012002\n122200010110102\\\\\n001220101001121\n111100012001110\n001000012112221\\\\\n\\hline\n$ A_{15,12,3}$\\\\\n\\hline\n101011010020212\n000102112201022\n122200122001010\n111220200211000\\\\\n001102121010210\n010210121220020\n010212002112002\n001002002222111\\\\\n100012011101101\n012111000210110\n112020110002201\n100110222000221\\\\\n121001100012120\n122002201202002\n110120020120102\\\\\n\\hline\n$ A_{15,12,4}$\\\\\n\\hline\n120200022222020\n010111012022001\n101012102101200\n012212020000111\\\\\n010011220201202\n101210211012000\n000102221122200\n120001200121110\\\\\n112022212000002\n001102200201121\n110021020110021\n102110101000122\\\\\n111020101220010\n122100000210211\n001100022012112\\\\\n\\hline\n$ A_{15,12,5}$\\\\\n\\hline\n101201020021201\n100120120211100\n011112001021002\n110001112100022\\\\\n001111200110101\n012220201101100\n112010021012200\n100210202200112\\\\\n011202110010011\n011022222002020\n100000011222121\n120002210011220\\\\\n102102002120011\n121020001102012\n010121210200210\\\\\n\\hline\n$ A_{15,12,6}$\\\\\n\\hline\n102011022102100\n102110210221000\n100021011010111\n101202221020100\\\\\n012122020202010\n120001121200202\n010221002021012\n111100200110202\\\\\n010201200202221\n011010110202102\n001010022211011\n010010101120211\\\\\n112202100011020\n101120102020021\n120202012002210\\\\\n\\hline\n$ A_{15,12,7}$\\\\\n\\hline\n010122002011201\n112001100021101\n011000021212101\n011221011100200\\\\\n102021220010012\n011010222120010\n100212001201210\n102202200102021\\\\\n110100000222222\n000122211020110\n121100021101020\n010010210011122\\\\\n101222102000102\n121001212200001\n100110110112010\\\\\n\\hline\n\\noalign{\\hrule height1pt}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unbiased weighing matrices of weight $9$", "authors": ["Makoto Araya", "Masaaki Harada", "Hadi Kharaghani", "Sho Suda", "Wei-Hsuan Yu"], "url": "https://arxiv.org/abs/2501.15444v2", "attribution": "\"Unbiased weighing matrices of weight $9$\" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.16019v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average false negative (standard error) for variable selection}\n\\begin{tabular}{lccccccccccc}\n\t\t\t\\hline\n\t\t\t$N$ & \\multicolumn{3}{c}{p=20} && \\multicolumn{3}{c}{p=50} && \\multicolumn{3}{c}{p=100} \\\\\n\t\t\t\\cline{2-4} \\cline{6-8} \\cline{10-12} \n\t\t\t& gQ-Lasso & rq-Lasso & GCQR && gQ-Lasso & rq-Lasso & GCQR && gQ-Lasso & rq-Lasso & GCQR\\\\\n\t\t\t\\hline\n\t\t\t\\multicolumn{3}{l}{Error: $\\epsilon\\sim N(0,1)$}&&&&&&&&&\\\\\n\t\t\t\\cline{1-2}\n\t\t\t200 & 0.000 & 0.000 & 0.000 && 0.000 & 0.000 & 0.000 && 0.000 & 0.000 & 0.000 \\\\ \n\t\t\t& \\textit{(0.000)} & \\textit{(0.000)} & \\textit{(0.000)} && \\textit{(0.000)} & \\textit{(0.000)} & \\textit{(0.000)} && \\textit{(0.000)} & \\textit{(0.000)} & \\textit{(0.000)} \\\\ \n\t\t\t500 & 0.000 & 0.000 & 0.000 && 0.000 & 0.000 & 0.000 && 0.000 & 0.000 & 0.000 \\\\ \n\t\t\t& \\textit{(0.000)} & \\textit{(0.000)} & \\textit{(0.000)} && \\textit{(0.000)} & \\textit{(0.000)} & \\textit{(0.000)} && \\textit{(0.000)} & \\textit{(0.000)} & \\textit{(0.000)} \\\\ \n\t\t\t1000 & 0.000 & 0.000 & 0.000 && 0.000 & 0.000 & 0.000 && 0.000 & 0.000 & 0.000 \\\\ \n\t\t\t& \\textit{(0.000)} & \\textit{(0.000)} & \\textit{(0.000)} && \\textit{(0.000)} & \\textit{(0.000)} & \\textit{(0.000)} && \\textit{(0.000)} & \\textit{(0.000)} & \\textit{(0.000)} \\\\ \t\t\t\n\t\t\t\\hline\n\t\t\t\\multicolumn{3}{l}{Error: $\\epsilon\\sim t(2)$}&&&&&&&&&\\\\\n\t\t\t\\cline{1-2}\n\t\t\t200 & 0.000 & 0.030 & 0.040 && 0.000 & 0.064 & 0.040 && 0.000 & 0.076 & 0.038 \\\\ \n\t\t\t& \\textit{(0.000)} & \\textit{(0.072)} & \\textit{(0.090)} && \\textit{(0.000)} & \\textit{(0.124)} & \\textit{(0.085)} && \\textit{(0.000)} & \\textit{(0.123)} & \\textit{(0.079)} \\\\ \n\t\t\t500 & 0.000 & 0.006 & 0.006 && 0.000 & 0.000 & 0.000 && 0.000 & 0.004 & 0.002 \\\\ \n\t\t\t& \\textit{(0.000)} & \\textit{(0.034)} & \\textit{(0.034)} && \\textit{(0.000)} & \\textit{(0.000)} & \\textit{(0.000)} && \\textit{(0.000)} & \\textit{(0.028)} & \\textit{(0.020)} \\\\ \n\t\t\t1000 & 0.000 & 0.000 & 0.000 && 0.000 & 0.000 & 0.000 && 0.000 & 0.000 & 0.000 \\\\ \n\t\t\t& \\textit{(0.000)} & \\textit{(0.000)} & \\textit{(0.000)} && \\textit{(0.000)} & \\textit{(0.000)} & \\textit{(0.000)} && \\textit{(0.000)} & \\textit{(0.000)} & \\textit{(0.000)} \\\\ \t\t\t\n\t\t\t\\hline\n\t\t\t\\multicolumn{3}{l}{Error: heteroskedasticity}&&&&&&&&&\\\\\n\t\t\t\\cline{1-2}\n\t\t\t200 & 0.500 & 1.616 & 1.154 && 0.810 & 2.066 & 1.158 && 1.620 & 2.674 & 1.640 \\\\ \n\t\t\t& \\textit{(0.835)} & \\textit{(0.783)} & \\textit{(0.667)} && \\textit{(1.022)} & \\textit{(0.724)} & \\textit{(0.783)} && \\textit{(1.179)} & \\textit{(0.601)} & \\textit{(0.869)} \\\\ \n\t\t\t500 & 0.000 & 0.486 & 0.188 && 0.020 & 0.830 & 0.242 && 0.040 & 1.094 & 0.256 \\\\ \n\t\t\t& \\textit{(0.000)} & \\textit{(0.283)} & \\textit{(0.220)} && \\textit{(0.141)} & \\textit{(0.428)} & \\textit{(0.238)} && \\textit{(0.197)} & \\textit{(0.460)} & \\textit{(0.318)} \\\\ \n\t\t\t1000 & 0.000 & 0.260 & 0.096 && 0.000 & 0.404 & 0.114 && 0.010 & 0.490 & 0.094 \\\\ \n\t\t\t& \\textit{(0.000)} & \\textit{(0.149)} & \\textit{(0.115)} && \\textit{(0.000)} & \\textit{(0.175)} & \\textit{(0.137)} && \\textit{(0.100)} & \\textit{(0.189)} & \\textit{(0.146)} \\\\ \n\t\t\t\\hline\n\t\t\t\\multicolumn{3}{l}{Error: asymmetric}&&&&&&&&&\\\\\n\t\t\t\\cline{1-2}\n\t\t\t200 & 0.000 & 0.130 & 0.096 && 0.000 & 0.202 & 0.056 && 0.000 & 0.330 & 0.084 \\\\ \n\t\t\t& \\textit{(0.000)} & \\textit{(0.194)} & \\textit{(0.143)} && \\textit{(0.000)} & \\textit{(0.227)} & \\textit{(0.121)} && \\textit{(0.000)} & \\textit{(0.281)} & \\textit{(0.137)} \\\\ \n\t\t\t500 & 0.000 & 0.010 & 0.012 && 0.000 & 0.014 & 0.010 && 0.000 & 0.026 & 0.004 \\\\ \n\t\t\t& \\textit{(0.000)} & \\textit{(0.044)} & \\textit{(0.048)} && \\textit{(0.000)} & \\textit{(0.051)} & \\textit{(0.044)} && \\textit{(0.000)} & \\textit{(0.073)} & \\textit{(0.028)} \\\\ \n\t\t\t1000 & 0.000 & 0.000 & 0.002 && 0.000 & 0.008 & 0.004 && 0.000 & 0.004 & 0.002 \\\\ \n\t\t\t& \\textit{(0.000)} & \\textit{(0.000)} & \\textit{(0.020)} && \\textit{(0.000)} & \\textit{(0.039)} & \\textit{(0.028)} && \\textit{(0.000)} & \\textit{(0.028)} & \\textit{(0.020)} \\\\ \n\t\t\t\\hline\n\t\t\t\\multicolumn{3}{l}{Error: heter. + asym.}&&&&&&&&&\\\\\n\t\t\t\\cline{1-2}\n\t\t\t200 & 1.180 & 2.250 & 1.770 && 1.950 & 2.716 & 2.014 && 2.440 & 2.960 & 2.176 \\\\ \n\t\t\t& \\textit{(0.989)} & \\textit{(0.654)} & \\textit{(0.670)} && \\textit{(1.067)} & \\textit{(0.506)} & \\textit{(0.630)} && \\textit{(0.903)} & \\textit{(0.399)} & \\textit{(0.663)} \\\\ \n\t\t\t500 & 0.220 & 1.520 & 1.036 && 0.540 & 1.882 & 1.010 && 0.780 & 2.086 & 0.904 \\\\ \n\t\t\t& \\textit{(0.462)} & \\textit{(0.520)} & \\textit{(0.546)} && \\textit{(0.784)} & \\textit{(0.511)} & \\textit{(0.686)} && \\textit{(0.949)} & \\textit{(0.468)} & \\textit{(0.652)} \\\\ \n\t\t\t1000 & 0.010 & 0.952 & 0.490 && 0.070 & 1.202 & 0.514 && 0.080 & 1.516 & 0.456 \\\\ \n\t\t\t& \\textit{(0.100)} & \\textit{(0.449)} & \\textit{(0.431)} && \\textit{(0.293)} & \\textit{(0.453)} & \\textit{(0.405)} && \\textit{(0.273)} & \\textit{(0.410)} & \\textit{(0.413)} \\\\ \t\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Quantile Predictions for Equity Premium using Penalized Quantile Regression with Consistent Variable Selection across Multiple Quantiles", "authors": ["Shaobo Li", "Ben Sherwood"], "url": "https://arxiv.org/abs/2505.16019v1", "attribution": "\"Quantile Predictions for Equity Premium using Penalized Quantile Regression with Consistent Variable Selection across Multiple Quantiles\" by Shaobo Li and Ben Sherwood, arXiv:2505.16019v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.13033v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|} \\hline\n Brushed DC motor & HPI Racing Saturn & Provides thrust to all wheels\\\\ \\hline\n Servo motor & & Drives the steering\\\\ \\hline\n Microcontroller unit & STM32F765VIT6 (ST Microcontrollers) & Onboard Matek f765-Wing autopilot\\\\ \\hline\n Inertial Measurement Unit & MPU-6000 and ICM20602 (InvenSense) & Onboard Matek f765-Wing autopilot\\\\ \\hline\n GPS & ublox GPS (SAM-M8Q) & Provides speed and positioning \\\\ \\hline\n RF transceiver & Digi Xbee S2C RF & Provides communication to ground station\\\\ \\hline\n Radio receiver controller & RC Futaba with Sbus protocol & Allows to remote control the rover \\\\ \\hline\n Battery & 7.4 V Li-Po 3000 mAh & Rover power\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Rover's hardware components}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Singularity-Free Guiding Vector Field over Bézier's Curves Applied to Rovers Path Planning and Path Following", "authors": ["Alfredo González-Calvin", "Lía García-Pérez", "Juan Jiménez"], "url": "https://arxiv.org/abs/2412.13033v1", "attribution": "\"Singularity-Free Guiding Vector Field over Bézier's Curves Applied to Rovers Path Planning and Path Following\" by Alfredo González-Calvin, Lía García-Pérez, and Juan Jiménez, arXiv:2412.13033v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.09865v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparative statics.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n\\multicolumn{4}{c}{Case 1: $\\alpha >0$.} \\\\\n\\hline\n&&& \\\\\n & markups & prices & quantities \\\\ \\hline\n &&& \\\\\nCase 1a. $1-\\beta \\sigma>0$ & $\\frac{\\partial}{\\partial m} \\bigl( \\frac{1}{\\mu} \\bigr)<0 $ & $\\frac{\\partial}{\\partial m} \\bigl( \\frac{m}{\\mu} \\bigr)>0$ & $ \\frac{\\partial}{\\partial m} \\left[\\frac{1}{\\alpha} \\frac{1-\\beta -\\mu}{1-\\sigma(1-\\mu)} \\right] <0$\\\\ \nCase 1b. $1-\\beta \\sigma<0$ & $\\frac{\\partial}{\\partial m} \\bigl( \\frac{1}{\\mu} \\bigr)>0 $ & $\\frac{\\partial}{\\partial m} \\bigl( \\frac{m}{\\mu} \\bigr) >0$ & $ \\frac{\\partial}{\\partial m} \\left[\\frac{1}{\\alpha} \\frac{1-\\beta -\\mu}{1-\\sigma(1-\\mu)} \\right] <0$ \\\\\n\\hline\n\\multicolumn{4}{c}{} \\\\ \n\\multicolumn{4}{c}{Case 2: $\\alpha <0$.} \\\\\n\\hline\n&&& \\\\\n & markups & prices & quantities \\\\ \\hline\n &&& \\\\\nCase 2a. $1-\\beta \\sigma>0$ & $\\frac{\\partial}{\\partial m} \\bigl( \\frac{1}{\\mu} \\bigr)>0 $ &$\\frac{\\partial}{\\partial m} \\bigl( \\frac{m}{\\mu} \\bigr)>0$ & $ \\frac{\\partial}{\\partial m} \\left[\\frac{1}{\\alpha} \\frac{1-\\beta -\\mu}{1-\\sigma(1-\\mu)} \\right] <0$ \\\\\nCase 2b. $1-\\beta \\sigma<0$ & $\\frac{\\partial}{\\partial m} \\bigl( \\frac{1}{\\mu} \\bigr)<0 $ & $\\frac{\\partial}{\\partial m} \\bigl( \\frac{m}{\\mu} \\bigr) > 0$ & $ \\frac{\\partial}{\\partial m} \\left[\\frac{1}{\\alpha} \\frac{1-\\beta -\\mu}{1-\\sigma(1-\\mu)} \\right] <0$ \\\\\n\\hline\n\\multicolumn{4}{p{12.5cm}}{\\scriptsize {\\it Notes}:\nThe case with $\\alpha=0$ corresponds to the CES and requires that $\\beta \\in (0,1)$.\nThe case with $1-\\beta \\sigma=0$ also corresponds to the CES and requires that $\\sigma > 1$.\nIf $\\alpha=0$ or $\\beta =\\frac{1}{\\sigma}$, then the price is given by $p(m) =\\frac{m}{1-\\beta}$ or $p (m) =\\frac{\\sigma m}{\\sigma-1}$, respectively, and thus\n$\\frac{\\partial}{\\partial m} \\bigl( \\frac{1}{\\mu} \\bigr)=0$, $\\frac{\\partial}{\\partial m} \\bigl( \\frac{m}{\\mu} \\bigr)>0$, and $\\frac{\\partial}{\\partial m} \\bigl( q(m) \\bigr)=\n\\frac{\\partial}{\\partial m} \\bigl( (u')^{-1} (\\nu p(m)) \\bigr)<0$.\nSince these two cases are well known, we omit them from the table.}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Linear fractional relative risk aversion", "authors": ["Kristian Behrens", "Yasusada Murata"], "url": "https://arxiv.org/abs/2509.09865v1", "attribution": "\"Linear fractional relative risk aversion\" by Kristian Behrens and Yasusada Murata, arXiv:2509.09865v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.13124v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Changes in steady-state values from the elimination of bribery only in the traditional sector.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccc}\n \\hline\n \\hline\n && {Low-income} & {Middle-income} & {High-income} \\\\\n && {countries} & {countries} & {countries} \\\\\n \\hline\n {Aggregate}& {Intensive margin} & 0.7\\% & 0.3\\% & 0.2\\% \\\\\n {output} & {Extensive margin} & -0.9\\% & -0.5\\% & -0.4\\% \\\\\n &{Net change} & -0.2\\% & -0.2\\% & -0.2\\% \\\\\n \\hline\n {Aggregate} & {Intensive margin} & 0.3\\% & 0.1\\% & 0.1\\% \\\\\n {consumption} & {Extensive margin} & -0.3\\% & -0.2\\% & -0.1\\% \\\\\n & {Net change} & 0.0\\% & -0.1\\% & 0.0\\% \\\\\n \\hline\n {Aggregate}& {Intensive margin} & 0.7\\% & 0.3\\% & 0.1\\% \\\\\n {capital} & {Extensive margin} & -2.4\\% & -1.4\\% & -0.9\\% \\\\\n & {Net change} & -1.7\\% & -1.1\\% & -0.8\\% \\\\\n \\hline\n {Average}&{Intensive margin} & 1.7\\% & 0.7\\% & 0.4\\% \\\\\n {wage} & {Extensive margin} & 0.1\\% & 0.1\\% & 0.1\\% \\\\\n & {Net change} & 1.8\\% & 0.8\\% & 0.5\\% \\\\\n \\hline\n {Modern firms'}&{Intensive margin} & -0.4\\% & -0.2\\% & -0.2\\% \\\\\n {share of output} & {Extensive margin} & -2.0\\% & -1.2\\% & -0.8\\% \\\\\n &{Net change} & -2.4\\% & -1.4\\% & -1.0\\%\\\\ \n \\hline\n {Firms' entry}& & & & \\\\\n {(Number of firms)} &{Net change} & 3.0\\% & 1.1\\% & 0.8\\% \\\\\n \\hline\n {Fraction of}& & & & \\\\\n {modern firms} & {Net change} & -2.1\\% & -1.7\\% & -1.4\\% \\\\ \n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Heterogeneous Bribery, Technology Choice, and Capital Accumulation", "authors": ["Jafar M. Olimov", "Yi-Chan Tsai", "Hao-Yu Yang"], "url": "https://arxiv.org/abs/2507.13124v2", "attribution": "\"Heterogeneous Bribery, Technology Choice, and Capital Accumulation\" by Jafar M. Olimov, Yi-Chan Tsai, and Hao-Yu Yang, arXiv:2507.13124v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13822v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Error profile for computing on Figure meshes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|rr|rr} \n \\hline \n$G_i$ & % \\multicolumn{2}{c|}{$\\|\\b u-\\b u_h\\|_{0}$ $O(h^r)$} & \n $\\|\\b u-\\b u_h\\|_{0}$&$O(h^r)$ & $\\|\\nabla_w(\\b u-\\b u_h)\\|_0$ & $O(h^r)$ \\\\ \\hline \n & \\multicolumn{4}{c}{By the $P_1$ WG element} \\\\ \n 6& 0.499E-03 & 1.9& 0.356E-01 & 1.1 \\\\\n 7& 0.126E-03 & 2.0& 0.177E-01 & 1.0 \\\\\n 8& 0.315E-04 & 2.0& 0.885E-02 & 1.0 \\\\\n \\hline \n & \\multicolumn{4}{c}{By the $P_2$ WG element} \\\\ \n 4& 0.250E-03 & 4.0& 0.333E-01 & 3.1 \\\\\n 5& 0.167E-04 & 3.9& 0.410E-02 & 3.0 \\\\\n 6& 0.135E-05 & 3.6& 0.543E-03 & 2.9 \\\\\n \\hline \n & \\multicolumn{4}{c}{By the $P_3$ WG element} \\\\ \n 4& 0.350E-04 & 5.1& 0.747E-02 & 4.1 \\\\\n 5& 0.107E-05 & 5.0& 0.457E-03 & 4.0 \\\\\n 6& 0.333E-07 & 5.0& 0.283E-04 & 4.0 \\\\\n \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An Auto-Stabilized Weak Galerkin Method for Elasticity Interface Problems on Nonconvex Meshes", "authors": ["Chunmei Wang", "Shangyou Zhang"], "url": "https://arxiv.org/abs/2501.13822v1", "attribution": "\"An Auto-Stabilized Weak Galerkin Method for Elasticity Interface Problems on Nonconvex Meshes\" by Chunmei Wang and Shangyou Zhang, arXiv:2501.13822v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.11459v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average South American league ratings of top 5 teams}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n \\hline\n \\# & league & rating \\\\ \\hline\n 1 & Brazil. First & 1868.4 \\\\ \\hline\n 2 & Argentina. First & 1779.9 \\\\ \\hline\n 3 & Paraguay. First & 1743.8 \\\\ \\hline\n 4 & Ecuador. First & 1720.2 \\\\ \\hline\n 5 & Brazil. Second & 1676.0 \\\\ \\hline\n 6 & Uruguay. First & 1673.1 \\\\ \\hline\n 7 & Colombia. First & 1665.6 \\\\ \\hline\n 8 & Chile. First & 1605.5 \\\\ \\hline\n 9 & Argentina. Second & 1604.4 \\\\ \\hline\n 10 & Peru. First & 1592.5 \\\\ \\hline\n 11 & Venezuela. First & 1557.8 \\\\ \\hline\n 12 & Bolivia. First & 1556.2 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Ratings of European and South American Football Leagues Based on Glicko-2 with Modifications", "authors": ["Andrei Shelopugin", "Alexander Sirotkin"], "url": "https://arxiv.org/abs/2310.11459v1", "attribution": "\"Ratings of European and South American Football Leagues Based on Glicko-2 with Modifications\" by Andrei Shelopugin and Alexander Sirotkin, arXiv:2310.11459v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2112.12621v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule \n & \\multicolumn{5}{c}{Period} & \\tabularnewline\n\\cmidrule{2-7} \nTreatment & 1 & 1-5 & 6-10 & 11-15 & 15 & Total\\tabularnewline\n\\midrule \nIT & 0.71 & 0.61 & 0.66 & 0.67 & 0.60 & 0.65\\tabularnewline\nTT & 0.69 & 0.75 & 0.77 & 0.76 & 0.73 & 0.76\\tabularnewline\nITex & 0.71 & 0.55 & 0.51 & 0.42 & 0.35 & 0.49\\tabularnewline\nTTex & 0.71 & 0.74 & 0.76 & 0.67 & 0.53 & 0.73\\tabularnewline\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Should transparency be (in-)transparent? On monitoring aversion and cooperation in teams", "authors": ["Michalis Drouvelis", "Johannes Jarke-Neuert", "Johannes Lohse"], "url": "https://arxiv.org/abs/2112.12621v1", "attribution": "\"Should transparency be (in-)transparent? On monitoring aversion and cooperation in teams\" by Michalis Drouvelis, Johannes Jarke-Neuert, and Johannes Lohse, arXiv:2112.12621v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.15439v1_tex_table25.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{OLS Regression for Likelihood of Cheating (Linear Probability Model)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccc}\n\\hline\n\\hline\n & & \\multicolumn{5}{c}{Dependent variable: Likelihood of cheating} \\\\ \\cmidrule{3-7} \n & & (1) & (2) & (3) & (4) & (5) \\\\ \n\\midrule\n \\multicolumn{2}{l}{Intercept} & $0.479^{***}$ & 0.419 & -0.079 & 0.322 & -0.441\\\\\n & & (0.073) & (0.265) & (0.419) & (0.217) & (0.527) \\\\\n & & & & & & \\\\ \n \\multicolumn{2}{l}{Treatment} & & & & & \\\\ \n & \\textit{Machine} & 0.009 & 0.026 & -0.082 & 0.147 & 0.030 \\\\ \n & & (0.108) & (0.101) & (0.311) & (0.201) & (0.366) \\\\ \n & \\textit{Human Black Box} & -0.084 & -0.075 & -0.089 & -0.069 & -0.074 \\\\ \n & & (0.105) & (0.099) & (0.104) & (0.106) & (0.098) \\\\ \n & \\textit{Machine Black Box} & 0.100 & 0.105 & -0.021 & 0.228 & 0.110 \\\\ \n & & (0.110) & (0.112) & (0.341) & (0.203) & (0.397) \\\\\n & & & & & & \\\\ \n \\multicolumn{2}{l}{Age} & & 0.010 & & & 0.006\\\\ \n & & & (0.012) & & & (0.011) \\\\ \n \\multicolumn{2}{l}{Female} & & $-0.283^{***}$ & & & $-0.307^{***}$ \\\\ \n & & & (0.076) & & & (0.090) \\\\ \n & & & & & & \\\\\n \\multicolumn{2}{l}{Field of Study} & & & & & \\\\ \n & \\textit{Cultural \\& social studies} & & 0.012 & & & 0.032 \\\\ \n & & & (0.086) & & & (0.091) \\\\ \n & \\textit{Natural science} & & -0.110 & & & -0.146 \\\\ \n & & & (0.129) & & & (0.121) \\\\ \n & & & & & & \\\\\n \\multicolumn{2}{l}{Risk} & & & $0.041^{*}$ & & $0.042^{*}$ \\\\ \n & & & & (0.018) & & (0.017) \\\\ \n \\multicolumn{2}{l}{Ethical sensitivity} & & & 0.024 & & 0.140 \\\\ \n & & & & (0.079) & & (0.081) \\\\ \n \\multicolumn{2}{l}{Closeness} & & & 0.007 & & -0.002 \\\\ \n & & & & (0.028) & & (0.026) \\\\ \n & & & & & & \\\\\n \\multicolumn{2}{l}{Verification by machine \\# ATI} & & & & & \\\\ \n & \\textit{$0$} & & & 0.057 & & -0.015 \\\\ \n & & & & (0.061) & & (0.068) \\\\ \n & \\textit{$1$} & & & 0.080 & & 0.021 \\\\ \n & & & & (0.055) & & (0.065) \\\\ \n & & & & & & \\\\ \n \\multicolumn{2}{l}{Verification by preferred entity} & & & & 0.044 & 0.089\\\\ \n & & & & & (0.081) & (0.078) \\\\ \n \\multicolumn{2}{l}{Verification by more error-prone entity} & & & & -0.099 & -0.053 \\\\ \n & & & & & (0.125) & (0.123) \\\\ \n \\multicolumn{2}{l}{Verification by higher discretion entity} & & & & 0.222 & 0.191 \\\\ \n & & & & & (0.158) & (0.165) \\\\ \n\\hline\n \\multicolumn{2}{l}{F-test} & 0.92 & $2.69^{*}$ & $2.14^{*}$ & 1.07 & $3.07^{***}$ \\\\\n \\multicolumn{2}{l}{$R^{2}$} & 0.0161 & 0.1008 & 0.0708 & 0.0304 & 0.1558\\\\\n \\multicolumn{2}{l}{Adj. $R^{2}$} & -0.0017 & 0.0620 & 0.0246 & -0.0052 & 0.0736\\\\\n \\multicolumn{2}{l}{N} & 170 & 170 & 170 & 170 & 170 \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty", "authors": ["Marius Protte", "Behnud Mir Djawadi"], "url": "https://arxiv.org/abs/2507.15439v1", "attribution": "\"Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty\" by Marius Protte and Behnud Mir Djawadi, arXiv:2507.15439v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.06010v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|ccccccccc|}\n\\hline\nReg & Size & Stdev & Skew & Kurt & SW & JB & L1O & L1A \\\\\n\\hline\n$Z_V$ & 96 & 0.3644 & 0.590 & 0.057 & 0.009 & 0.061 & 0.401 & 0.237 \\\\\n$Z_R$ & 97 & 0.1357 & 0.814 & 1.409 & 0.007 & 0.000 & 0.177 & 0.360 \\\\\n$Z_Q$ & 97 & 0.0149 & 0.226 & 0.068 & 0.420 & 0.655 & 0.480 & 0.435 \\\\\n$Z_I$ & 55 & 0.0183 & -0.059 & -0.501 & 0.754 & 0.738 & 0.401 & 0.519 \\\\\n$Z_B$ & 52 & 0.0263 & 0.193 & 0.238 & 0.857 & 0.800 & 0.878 & 0.706 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Analysis of regression residuals. Skew = skewness, Kurt = kurtosis, SW and JB = Shapiro-Wilk and Jarque-Bera $p$-values, L1O and L1A = L1 norm for the first 5 lags of the ACF for $Z$ and $|Z|$, Length = the size of $Z$}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Time Series Model for Three Asset Classes used in Financial Simulator", "authors": ["Andrey Sarantsev", "Angel Piotrowski", "Ian Anderson"], "url": "https://arxiv.org/abs/2508.06010v1", "attribution": "\"A Time Series Model for Three Asset Classes used in Financial Simulator\" by Andrey Sarantsev, Angel Piotrowski, and Ian Anderson, arXiv:2508.06010v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2311.11231v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{rccccc}\n \\hline\n & & C1 & C2 & C3 & C4 \\\\\n \\hline\n & R1 & 0.17 & 0.14 & 0.12 & 0.10 \\\\\n Race and & R2 & 0.75 & 0.65 & 0.56 & 0.47 \\\\\n Ethnicity & R3 & 0.33 & 0.29 & 0.25 & 0.21 \\\\\n & R4 & 1 & 0.88 &0.75 & 0.63 \\\\\n \\hline\n & R1 \\& G1 & 1 & 0.88 & 0.75 & 0.63 \\\\\n & R2 \\& G1 & 1 & 0.88 & 0.75 & 0.63 \\\\\n Race and & R3 \\& G1 & 1 & 0.88 & 0.75 & 0.63 \\\\\n Ethnicity & R4 \\& G1 & 1 & 0.88 & 0.75 & 0.63 \\\\\n\\cline{2-6} and & R1 \\& G2 & 0.17 & 0.15 & 0.13 & 0.11 \\\\\n Gender & R2 \\& G2 & 0.75 & 0.65 & 0.56 & 0.47 \\\\\n & R3 \\& G2 & 0.33 & 0.29 & 0.25 & 0.21 \\\\\n & R4 \\& G2 & 1 & 0.88 & 0.75 & 0.63 \\\\\n \\hline\n \\end{tabular}\n\\caption{$pDEI$ Scores in Industry $S_1$. }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Workforce pDEI: Productivity Coupled with DEI", "authors": ["Lanqing Du", "Jinwook Lee"], "url": "https://arxiv.org/abs/2311.11231v2", "attribution": "\"Workforce pDEI: Productivity Coupled with DEI\" by Lanqing Du and Jinwook Lee, arXiv:2311.11231v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.00821v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cccccc|}\n\\hline\n & \\multicolumn{5}{c|}{\\textbf{AISML2R}} \\\\ \\hline\n\\textbf{k} & \\textbf{N} & \\textbf{variance} & \\textbf{bias} & \\textbf{rmse} & \\textbf{time} \\\\ \\hline\n\\textbf{3} & 2.86E+03 & 1.44E-02 & 2.41E-02 & 1.22E-01 & 7.99E-01 \\\\ \\hline\n\\textbf{4} & 1.14E+04 & 3.51E-03 & 2.13E-03 & 5.93E-02 & 1.03E+00 \\\\ \\hline\n\\textbf{5} & 4.58E+04 & 7.14E-04 & 8.31E-03 & 2.80E-02 & 1.67E+00 \\\\ \\hline\n\\textbf{6} & 1.83E+05 & 1.85E-04 & 9.92E-03 & 1.68E-02 & 3.78E+00 \\\\ \\hline\n\\textbf{7} & 7.33E+05 & 6.73E-05 & 9.88E-03 & 1.28E-02 & 1.32E+01 \\\\ \\hline\n\\textbf{8} & 3.39E+06 & 1.45E-05 & 8.88E-03 & 9.66E-03 & 7.14E+01 \\\\ \\hline\n\\textbf{9} & 1.36E+07 & 5.06E-06 & 8.81E-03 & 9.09E-03 & 2.77E+02 \\\\ \\hline\n\\multicolumn{6}{|c|}{\\textbf{ML2R}} \\\\ \\hline\n\\textbf{k} & \\textbf{N} & \\textbf{variance} & \\textbf{bias} & \\textbf{rmse} & \\textbf{time} \\\\ \\hline\n\\textbf{3} & 6.43E+03 & 1.29E-02 & 1.45E-02 & 1.15E-01 & 2.97E-01 \\\\ \\hline\n\\textbf{5} & 2.57E+04 & 4.37E-03 & 1.17E-03 & 6.61E-02 & 6.39E-01 \\\\ \\hline\n\\textbf{5} & 1.03E+05 & 8.24E-04 & 1.19E-02 & 3.11E-02 & 1.91E+00 \\\\ \\hline\n\\textbf{6} & 4.12E+05 & 2.43E-04 & 8.33E-03 & 1.77E-02 & 7.42E+00 \\\\ \\hline\n\\textbf{7} & 1.65E+06 & 6.59E-05 & 9.35E-03 & 1.24E-02 & 3.02E+01 \\\\ \\hline\n\\textbf{8} & 7.93E+06 & 1.39E-05 & 9.05E-03 & 9.79E-03 & 1.76E+02 \\\\ \\hline\n\\textbf{9} & 3.17E+07 & 2.62E-06 & 9.08E-03 & 9.22E-03 & 7.10E+02 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Pricing Lookback Option using Euler Scheme}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Multilevel Richardson-Romberg and Importance Sampling in Derivative Pricing", "authors": ["Devang Sinha", "Siddhartha P. Chakrabarty"], "url": "https://arxiv.org/abs/2209.00821v1", "attribution": "\"Multilevel Richardson-Romberg and Importance Sampling in Derivative Pricing\" by Devang Sinha and Siddhartha P. Chakrabarty, arXiv:2209.00821v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.06835v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n \\textbf{Divergence} & $\\pmb{\\varphi(t)}$ & $\\pmb{D_\\varphi(q\\vert\\vert p)}$ & $\\pmb{\\varphi^*(s)}$ \\\\\n \\hline\n Chi-Square & $(t-1)^2$& $\\sum_{i=1}^n \\frac{(q_i-p_i)^2}{p_i}$ & $\\left\\{\\begin{aligned}\n &s + s^2/4 \\quad &s\\geq -2\\\\\n &-1 \\quad &s\\leq -2\n \\end{aligned}\n \\right.$\\\\\n \\hline\n Kullback-Leibler & $t\\log t -t +1$ & $\\sum_{i=1}^n q_i\\log(q_i/p_i)$& $e^s-1$ \\\\\n \\hline\n Burg entropy & $-\\log t + t -1$& $\\sum_{i=1}^n p_i\\log(p_i/q_i)$ & $-\\log(1-s)$, $s<1$ \\\\\n \\hline\n Hellinger distance & $(\\sqrt{t}-1)^2$& $\\sum_{i=1}^n \\left(\\sqrt{p_i}-\\sqrt{q_i}\\right)^2$ & $\\frac{s}{1-s}$, $s\\leq 1$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Some coherent $\\varphi$-divergences and their characterizations.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Improving Offline Contextual Bandits with Distributional Robustness", "authors": ["Otmane Sakhi", "Louis Faury", "Flavian Vasile"], "url": "https://arxiv.org/abs/2011.06835v1", "attribution": "\"Improving Offline Contextual Bandits with Distributional Robustness\" by Otmane Sakhi, Louis Faury, and Flavian Vasile, arXiv:2011.06835v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08822v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|c|}\n \\hline\n & \\#Nodes & & \\#Fans & & \\#Fans per page \\\\\n \\hline\n Pro-vaccines & 124 & 9.3\\% & 6.9M & 8.1\\% & 55.1k\\\\\n Neutral & 885 & 66.7\\% & 74.1M & 87\\% & 83.9k\\\\\n Anti-vaccines & 317 & 23.9\\% & 4.2M & 4.9\\% & 13.1k\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Summary table of the proportion of nodes and fans per faction.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Ensemble of Opinion Dynamics Models to Understand the Role of the Undecided in the Vaccination Debate", "authors": ["Jacopo Lenti", "Giancarlo Ruffo"], "url": "https://arxiv.org/abs/2201.08822v1", "attribution": "\"Ensemble of Opinion Dynamics Models to Understand the Role of the Undecided in the Vaccination Debate\" by Jacopo Lenti and Giancarlo Ruffo, arXiv:2201.08822v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06733v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{One-Way ANOVA Results}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccc}\n\t\\hline\\noalign{\\smallskip}\n\t\\textbf{} & \\textbf{Factor} & \\textbf{Df.} & \\textbf{Sum Sq.} & \\textbf{Mean Sq.} & \\textbf{F-value} & \\textbf{\\textit{p-value}}\\\\\n\t\\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\n\t\\multicolumn{7}{l}{\\cellcolor{gray!10}\\textbf{Analysis of Variance Test - Top5/Bottom 5/Others}}\\\\ \n \\textbf{\\multirow{1}{*}{\\shortstack[c]{Simplicity}}} & \\textbf{Performance} & 2 & 0.01150 & 0.005750 & 5.984 & \\textbf{0.00594*}\\\\ \n & \\textbf{Residuals} & 34 & 0.03267 & 0.000961 & & \\\\ \n \n \\multicolumn{7}{l}{\\cellcolor{gray!10}\\textbf{Post Hoc Test}}\\\\\n \n & \\textbf{Treatments} & \\textbf{Diff} & \\textbf{Lower} & \\textbf{Upper} & \\textbf{\\textit{p-adj}} & \\\\\n & Others-Top5 & 0.01418519 & -0.02279834 & 0.05116871 & 0.6192293 & \\\\\n & Bottom5-Top5 & 0.06160000 & 0.01355700 & 0.10964300 & \\textbf{0.0094695*} & \\\\\n & Bottom5-Others & 0.04741481 & 0.01043129 & 0.08439834 & \\textbf{0.0094774*} & \\\\\n \n \\textbf{\\multirow{1}{*}{\\shortstack[c]{Interactions}}} & \\textbf{Performance} & 2 & 1431864 & 715932 & 0.555 & 0.579\\\\ \n & \\textbf{Residuals} & 34 & 43821404 & 1288865 & & \\\\ \n \n \n \\multicolumn{7}{l}{\\cellcolor{gray!10}\\textbf{Analysis of Variance Test - $>$ Q3(Top 8)/Q1(Bottom 15)/Others}}\\\\\n \\textbf{\\multirow{1}{*}{\\shortstack[c]{Simplicity}}} & \\textbf{Performance} & 2 & 0.00335 & 0.001676 & 1.396 & 0.262\\\\ \n & \\textbf{Residuals} & 34 & 0.04082 & 0.001201 & & \\\\ \n \n \\textbf{\\multirow{1}{*}{\\shortstack[c]{Interactions}}} & \\textbf{Course} & 2 & 165352 & 82676 & 0.062 & 0.94\\\\ \n & \\textbf{Residuals} & 34 & 45087916 & 1326115 & & \\\\ \n \n \\multicolumn{7}{l}{\\cellcolor{gray!10}\\textbf{Analysis of Variance Test - Top8/Bottom 8/Others}}\\\\\n \\textbf{\\multirow{1}{*}{\\shortstack[c]{Simplicity}}} & \\textbf{Performance} & 2 & 0.00656 & 0.003279 & 2.963 & 0.0651\\\\ \n & \\textbf{Residuals} & 34 & 0.03762 & 0.001106 & & \\\\ \n \n \n \n \n \\textbf{\\multirow{1}{*}{\\shortstack[c]{Interactions}}} & \\textbf{Performance} & 2 & 34612 & 17306 & 0.013 & 0.987\\\\ \n & \\textbf{Residuals} & 34 & 45218656 & 1329960 & & \\\\ \n \n \n \\multicolumn{7}{l}{\\cellcolor{gray!10}\\textbf{Analysis of Variance Test - LEI/ETI/LCD/IGE}}\\\\\n \\textbf{\\multirow{1}{*}{\\shortstack[c]{Simplicity}}} & \\textbf{Course} & 3 & 0.00371 & 0.001237 & 1.009 & 0.401\\\\ \n & \\textbf{Residuals} & 33 & 0.04046 & 0.001226 & & \\\\ \n \n \\textbf{\\multirow{1}{*}{\\shortstack[c]{Interactions}}} & \\textbf{Course} & 3 & 3919333 & 1306444 & 1.043 & 0.386\\\\ \n & \\textbf{Residuals} & 33 & 41333934 & 1252543 & & \\\\ \n \\noalign{\\smallskip}\\hline\n \\multicolumn{7}{l}{\\textit{*Statistically significant if p-value $<$ 0.05}}\\\\\n \\multicolumn{7}{l}{\\shortstack[l]{\\textbf{Df.} - Degrees of freedom, \\textbf{Sum Sq.} - Sum of Square, \\textbf{Mean Sq.} - Mean of Square}}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Profiling Software Developers with Process Mining and N-Gram Language Models", "authors": ["João Caldeira", "Fernando Brito e Abreu", "Jorge Cardoso", "Ricardo Ribeiro", "Claudia Werner"], "url": "https://arxiv.org/abs/2101.06733v1", "attribution": "\"Profiling Software Developers with Process Mining and N-Gram Language Models\" by João Caldeira, Fernando Brito e Abreu, Jorge Cardoso, Ricardo Ribeiro, and Claudia Werner, arXiv:2101.06733v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05544v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Ensemble classification accuracy} (Top-1, \\%) on \\textit{CIFAR-100} and \\textit{CIFAR-10}}%\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc||cccc|c|cc|cc|c||c|c}\n \\toprule\n \\multicolumn{3}{c||}{Method} & \\multicolumn{10}{c||}{CIFAR-100} & \\multicolumn{2}{c}{CIFAR-10}\\\\\n \\midrule\n Name & \\multicolumn{2}{c||}{Components} & \\multicolumn{5}{c|}{ResNet-32} & \\multicolumn{2}{c|}{Resnet-110} & \\multicolumn{3}{c||}{WRN-28-2} & ResNet-32 & ResNet-110\\\\\n & Diversity & I.B. & 3-branches & 4-branches & 5-branches & 6-branches & 4-nets & 3-branches & 4-branches & 3-branches & 4-branches & 3-nets & 4-branches & 3-branches \\\\\n \\midrule\n Ind. & & & 76.28 {\\tiny $\\pm$ 0.12} & 76.71 {\\tiny $\\pm$ 0.19} & 77.17 {\\tiny $\\pm$ 0.25} & 77.44 {\\tiny $\\pm$ 0.16} & 77.38 {\\tiny $\\pm$ 0.12} & 80.54 {\\tiny $\\pm$ 0.09} & 80.76 {\\tiny $\\pm$ 0.31} & 78.83 {\\tiny $\\pm$ 0.12} & 79.10 {\\tiny $\\pm$ 0.08} & 80.01 {\\tiny $\\pm$ 0.15} & 94.75 {\\tiny $\\pm$ 0.08} & 95.62 {\\tiny $\\pm$ 0.06} \\\\\n \\midrule\n ONE-E & & & 75.17 {\\tiny $\\pm$ 0.35} & 75.13 {\\tiny $\\pm$ 0.25} & 75.25 {\\tiny $\\pm$ 0.22} & 75.32 {\\tiny $\\pm$ 0.28} & 76.25 {\\tiny $\\pm$ 0.32} & 78.97 {\\tiny $\\pm$ 0.24} & 79.86 {\\tiny $\\pm$ 0.25} & 78.38 {\\tiny $\\pm$ 0.45} & 78.47 {\\tiny $\\pm$ 0.32} & 77.53 {\\tiny $\\pm$ 0.36} & 94.41 {\\tiny $\\pm$ 0.05} & 95.25 {\\tiny $\\pm$ 0.08} \\\\\n OKDDip-E & Asym. Cod. & & 75.37 {\\tiny $\\pm$ 0.32} & 76.85 {\\tiny $\\pm$ 0.25} & 76.95 {\\tiny $\\pm$ 0.18} & 77.02 {\\tiny $\\pm$ 0.20} & 77.27 {\\tiny $\\pm$ 0.31} & 79.07 {\\tiny $\\pm$ 0.27} & 80.46 {\\tiny $\\pm$ 0.35} & 79.01 {\\tiny $\\pm$ 0.19} & 79.32 {\\tiny $\\pm$ 0.17} & 80.02 {\\tiny $\\pm$ 0.14} & 94.86 {\\tiny $\\pm$ 0.08} & 95.21 {\\tiny $\\pm$ 0.09} \\\\%\n \\midrule\n ADP & Non max. logits & & 76.37 {\\tiny $\\pm$ 0.11} & 77.21 {\\tiny $\\pm$ 0.21} & 77.67 {\\tiny $\\pm$ 0.25} & 78.17 {\\tiny $\\pm$ 0.14} & 77.51 {\\tiny $\\pm$ 0.25} & 80.73 {\\tiny $\\pm$ 0.38} & 81.40 {\\tiny $\\pm$ 0.27} & 79.21 {\\tiny $\\pm$ 0.19} & 79.71 {\\tiny $\\pm$ 0.18} & 80.01 {\\tiny $\\pm$ 0.17} & 94.92 {\\tiny $\\pm$ 0.04} & 95.43 {\\tiny $\\pm$ 0.12} \\\\\n \\midrule\n \\midrule\n IB & & VIB & 76.01 {\\tiny $\\pm$ 0.12} & 76.93 {\\tiny $\\pm$ 0.24} & 77.22 {\\tiny $\\pm$ 0.19} & 77.46 {\\tiny $\\pm$ 0.16} & 77.72 {\\tiny $\\pm$ 0.12} & 80.43 {\\tiny $\\pm$ 0.34} & 81.12 {\\tiny $\\pm$ 0.19} & 79.19 {\\tiny $\\pm$ 0.35} & 79.15 {\\tiny $\\pm$ 0.12} & 80.15 {\\tiny $\\pm$ 0.13} & 94.76 {\\tiny $\\pm$ 0.12} & 94.54 {\\tiny $\\pm$ 0.07} \\\\\n CEB & & VCEB & 76.36 {\\tiny $\\pm$ 0.06} & 76.98{\\tiny $\\pm$ 0.18} & 77.35 {\\tiny $\\pm$ 0.14} & 77.68 {\\tiny $\\pm$ 0.04} & 77.64 {\\tiny $\\pm$ 0.15} & 81.08 {\\tiny $\\pm$ 0.12} & 81.17 {\\tiny $\\pm$ 0.16} & 78.92 {\\tiny $\\pm$ 0.08} & 79.20 {\\tiny $\\pm$ 0.13} & 80.38 {\\tiny $\\pm$ 0.18} & 94.93 {\\tiny $\\pm$ 0.11} & 94.65 {\\tiny $\\pm$ 0.05} \\\\\n \\midrule\n IBMI (Ours ) & MI & VIB & 76.68 {\\tiny $\\pm$ 0.13} & 77.25 {\\tiny $\\pm$ 0.13} & 77.77 {\\tiny $\\pm$ 0.21} & 77.95 {\\tiny $\\pm$ 0.13} & 77.84 {\\tiny $\\pm$ 0.12} & 81.34 {\\tiny $\\pm$ 0.21} & 81.38 {\\tiny $\\pm$ 0.08} & 79.33 {\\tiny $\\pm$ 0.15} & 79.90 {\\tiny $\\pm$ 0.10} & 80.22 {\\tiny $\\pm$ 0.10} & 94.91 {\\tiny $\\pm$ 0.14} & 95.68 {\\tiny $\\pm$ 0.05} \\\\\n CEBMI (Ours ) & MI & VCEB & 76.72 {\\tiny $\\pm$ 0.08} & 77.30 {\\tiny $\\pm$ 0.12} & 77.81 {\\tiny $\\pm$ 0.10} & 77.98 {\\tiny $\\pm$ 0.15} & 77.82 {\\tiny $\\pm$ 0.11} & 81.52 {\\tiny $\\pm$ 0.11} & 81.55 {\\tiny $\\pm$ 0.33} & 79.25 {\\tiny $\\pm$ 0.15} & 79.98 {\\tiny $\\pm$ 0.07} & 80.35 {\\tiny $\\pm$ 0.15} & 94.94 {\\tiny $\\pm$ 0.12} & 95.67 {\\tiny $\\pm$ 0.06} \\\\\n \\midrule\n DICE (Ours )& CMI & VCEB & \\textbf{76.89} {\\tiny $\\pm$ 0.09} & \\textbf{77.51} {\\tiny $\\pm$ 0.17} & \\textbf{78.08} {\\tiny $\\pm$ 0.18} & \\textbf{78.21} {\\tiny $\\pm$ 0.21} & \\textbf{77.92} {\\tiny $\\pm$ 0.08} & \\textbf{81.67} {\\tiny $\\pm$ 0.14} & \\textbf{81.93} {\\tiny $\\pm$ 0.13} & \\textbf{79.59} {\\tiny $\\pm$ 0.13} & \\textbf{80.05} {\\tiny $\\pm$ 0.11} & \\textbf{80.55} {\\tiny $\\pm$ 0.12} & \\textbf{95.01} {\\tiny $\\pm$ 0.09} & \\textbf{95.74} {\\tiny $\\pm$ 0.08} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation", "authors": ["Alexandre Rame", "Matthieu Cord"], "url": "https://arxiv.org/abs/2101.05544v1", "attribution": "\"DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation\" by Alexandre Rame and Matthieu Cord, arXiv:2101.05544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12834v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|l|l|l|l|}\n \\hline\n & $L=10^{9}$ & $L=10^{10}$ & $L=10^{11}$ \\\\ \\hline\n $D_{\\text{E}}^{\\text{(G)}}[s,d]$ & $2.7 \\times 10^{-3}$ & $1.7 \\times 10^{-3}$ & $1.5 \\times 10^{-3}$\\\\ \\hline\n $D_{\\text{CLE}}^{\\text{(G)}}[s,d]$ & $0.6 \\times 10^{-3}$ & $0.2 \\times 10^{-3}$ & $0.7 \\times 10^{-4}$\\\\ \\hline\n $D_{\\text{CFE}}^{\\text{(G)}}[s,d]$ & $2.1 \\times 10^{-3}$ & $1.5 \\times 10^{-5}$ & $1.4 \\times 10^{-3}$\\\\\n \\hline\n \\end{tabular}\n\\caption{The evaulation of $D_{\\text{E}}^{\\text{(G)}}[s,d]$, $D_{\\text{CLE}}^{\\text{(G)}}[s,d]$ and $D_{\\text{CFE}}^{\\text{(G)}}[s,d]$ for various computational limit $L$ values considereing $(128,64)$-random tree codes under BSC with $p=0.03$ where $s$ is optimized using the SBP algorithm and $d$ is in the form where $\\gamma=0.9992$. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Optimization of Random Tree Codes for Limited Computational Resources", "authors": ["B. Tan Bacinoglu"], "url": "https://arxiv.org/abs/2501.12834v1", "attribution": "\"The Optimization of Random Tree Codes for Limited Computational Resources\" by B. Tan Bacinoglu, arXiv:2501.12834v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.07814v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|r|r|r|r|r|r|r|r}\n \\multicolumn{2}{c}{}&\\multicolumn{4}{c}{supervised}&\\multicolumn{4}{c}{unsupervised}\\\\\n \\multicolumn{2}{c}{}&\\multicolumn{2}{c}{TV}&\\multicolumn{2}{c|}{WTV}&\\multicolumn{2}{c|}{TV}&\\multicolumn{2}{c}{WTV} \\\\\n Image&$\\sigma$&IPSNR&ISSIM&IPSNR&ISSIM&IPSNR&ISSIM&IPSNR&ISSIM\\\\\n \\hline\n \\multirow{3}{*}{\\#1}&$0.03$&2.848&0.147&\\textbf{6.438}&\\textbf{0.194}&2.729&0.129&\\textbf{3.176}&\\textbf{0.176}\\\\\n&$0.06$&4.257&0.266&\\textbf{8.354}&\\textbf{0.372}&4.210&0.247&\\textbf{4.546}&\\textbf{0.322}\\\\\n &$0.09$&5.345& 0.314&\\textbf{9.461}&\\textbf{0.471}&5.332&0.303&\\textbf{5.462}&\\textbf{0.370}\\\\ % &$0.13$&&&&&&&&\\\\ \n \\hline\n\\multirow{3}{*}{\\#2}&$0.03$&2.896&0.129 &\\textbf{6.689}&\\textbf{0.179}&2.805&0.117&\\textbf{3.265}& \\textbf{0.150}\\\\\n&$0.06$&4.467& 0.258&\\textbf{8.707}&\\textbf{0.362}&4.447&0.248&\\textbf{4.817}&\\textbf{0.286}\\\\\n&$0.09$&5.624&0.319 &\\textbf{10.016}&\\textbf{0.465}&5.617&0.313&\\textbf{5.891}&\\textbf{0.346}\\\\\n \\hline\n \\multirow{3}{*}{\\#3}&$0.03$&3.309&0.193&\\textbf{7.471}&\\textbf{0.241}&3.289&0.199&\\textbf{3.881} &\\textbf{0.224}\\\\\n &$0.06$&4.604&0.331&\\textbf{9.063 }&\\textbf{0.434}&4.589& 0.343&\\textbf{4.951}&\\textbf{0.386}\\\\ &$0.09$&5.554&0.376&\\textbf{10.018}&\\textbf{0.525}&5.529&0.395&\\textbf{5.794}&\\textbf{0.439}\n \\end{tabular}\n\\end{adjustbox}\n\\caption{IPSNR and ISSIM values achieved by the considered models.}% for the three test images corrupted by different levels of AWGN.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising", "authors": ["Monica Pragliola", "Luca Calatroni", "Alessandro Lanza"], "url": "https://arxiv.org/abs/2503.07814v1", "attribution": "\"Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising\" by Monica Pragliola, Luca Calatroni, and Alessandro Lanza, arXiv:2503.07814v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.15877v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Uniform coverage probability based on different methods}%$/100$ of %simultaneous coverage band and pointwise interval}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccccc}\n\\toprule\n& & & \\multicolumn{2}{c}{$15\\%$\\ Censoring\\ rate\\ \\ } & & \\multicolumn{2}{c}{ $35\\%$ Censoring\\ rate\\ \\ }\\\\\n\\cline{4-5} \\cline{7-8}\n$n$ & Bandwidth & & SCB & CI & & SCB & CI \\\\\n\\midrule\n400 & $h_1=n^{-0.35}, h_2=n^{-0.35}$ & & 91.3 & 34.8 & & 90.4 & 34.3 \\\\\n & $h_1=n^{-0.35}, h_2=n^{-0.45}$ & & 92.0 & 32.5 & & 90.5 & 29.3\\\\\n & auto & & 92.4 & 31.9 & & 90.3 & 27.2\\\\\n 900 & $h_1=n^{-0.35}, h_2=n^{-0.35}$ & & 93.1 & 28.0 & & 93.4 & 26.0\\\\\n & $h_1=n^{-0.35}, h_2=n^{-0.45}$ & & 92.9 & 23.2 & & 93.0 & 21.0\\\\\n & auto & & 93.8 & 17.6 & & 91.9 & 16.6\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Regression analysis of multiplicative hazards model with time-dependent coefficient for sparse longitudinal covariates", "authors": ["Zhuowei Sun", "Hongyuan Cao"], "url": "https://arxiv.org/abs/2310.15877v3", "attribution": "\"Regression analysis of multiplicative hazards model with time-dependent coefficient for sparse longitudinal covariates\" by Zhuowei Sun and Hongyuan Cao, arXiv:2310.15877v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.10926v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\hline\n TS-RE&SM &WM &IVW & Egger\\\\\n \\hline\n 0.31 (0.41) & 2.42 (0.25)&2.42 (0.25)& 2.27 (0.17)& 2.63 (0.42)\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Causal effect of BMI on SBP for independent black British: TS-RE used all SNPs and the other MR methods used the selected top 20 significant SNPs}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Random Effects Model-based Method of Moments Estimation of Causal Effect in Mendelian Randomization Studies", "authors": ["Wenhao Cao", "Saonli Basu"], "url": "https://arxiv.org/abs/2312.10926v1", "attribution": "\"A Random Effects Model-based Method of Moments Estimation of Causal Effect in Mendelian Randomization Studies\" by Wenhao Cao and Saonli Basu, arXiv:2312.10926v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.09445v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{xcolor}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|ccc}\n \\toprule\n {\\textbf{Data}} & \\textbf{Race} & \\textbf{Sex} & \\textbf{Both} \\\\ \\midrule\n Employment & OCCP & COW & OCCP, COW, \\textcolor{red}{POB} \\\\ \\midrule\n \\multirow{2}{*}{Income} & \\multirow{2}{*}{MAR, ANC} & \\multirow{2}{*}{MAR, ANC, CIT, MIG} & MAR, ANC, CIT, \\\\\n & & & MIG, \\textcolor{red}{SCHL}, \\textcolor{red}{NAT} \\\\ \\bottomrule\n \\end{tabular}\n\\caption{ACS dataset features which were not selected when conditioned on race, sex or both, represented in first, second and last column, respectively.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Differentiable Rank-Based Objective For Better Feature Learning", "authors": ["Krunoslav Lehman Pavasovic", "David Lopez-Paz", "Giulio Biroli", "Levent Sagun"], "url": "https://arxiv.org/abs/2502.09445v1", "attribution": "\"A Differentiable Rank-Based Objective For Better Feature Learning\" by Krunoslav Lehman Pavasovic, David Lopez-Paz, Giulio Biroli, and Levent Sagun, arXiv:2502.09445v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06368v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr} \n Word & $I_{w, \\text{social media}}$ & $I_{w, \\text{newspaper}}$ & $\\Delta \\: I_{w}$ \\\\ \\toprule\n zap & 0.179 & 1.000 & -0.821 \\\\\n block & 0.153 & 0.857 & -0.704 \\\\\n hype & 0.393 & 0.995 & -0.602 \\\\\n link & 0.335 & 0.872 & -0.536 \\\\\n like & 0.115 & 0.649 & -0.534 \\\\\n ... & ... & ... & ... \\\\\n pitch & 0.998 & 0.988 & 0.011 \\\\\n host & 0.990 & 0.972 & 0.018 \\\\\n google & 0.561 & 0.531 & 0.030 \\\\\n rock & 0.787 & 0.648 & 0.139 \\\\\n DM & 1.000 & 0.120 & 0.880 \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Loanwords with biggest differences in integration between newspaper and social media.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Tuiteamos o pongamos un tuit? Investigating the Social Constraints of Loanword Integration in Spanish Social Media", "authors": ["Ian Stewart", "Diyi Yang", "Jacob Eisenstein"], "url": "https://arxiv.org/abs/2101.06368v1", "attribution": "\"Tuiteamos o pongamos un tuit? Investigating the Social Constraints of Loanword Integration in Spanish Social Media\" by Ian Stewart, Diyi Yang, and Jacob Eisenstein, arXiv:2101.06368v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08910v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrlr}\n\\toprule\n dataset & $\\sigma$ & $\\alpha$ & $\\rho_{SR}$ & $\\beta$ & N & $\\sigma_b$ & readout & $\\rho_A$ \\\\\n\\midrule\n CL63 & 0.004 & 0.98 & 0.66 & 5.216114e-10 & 1200 & 1.57 & linear & 0.98 \\\\\n CL63 & 0.014 & 0.88 & 0.01 & 1.572121e-05 & 1200 & 0.00 & linear & 0.98 \\\\\n L63 & 0.011 & 0.49 & 0.59 & 1.115693e-10 & 1200 & 0.28 & linear & 0.98 \\\\\n L63 & 0.014 & 0.89 & 1.02 & 2.390891e-08 & 1200 & 0.00 & linear & 0.98 \\\\\n L9610d & 0.017 & 0.95 & 0.10 & 1.443850e-09 & 1200 & 1.59 & linear & 0.98 \\\\\n L9610d & 0.001 & 0.41 & 0.92 & 4.553807e-03 & 1200 & 0.00 & linear & 0.98 \\\\\n L96-5D & 0.100 & 1.00 & 0.05 & 1.012426e-10 & 1200 & 0.55 & linear & 0.98 \\\\\n L96-5D & 0.096 & 0.80 & 0.40 & 4.087443e-08 & 1200 & 0.00 & linear & 0.98 \\\\\ncolpitts & 0.100 & 0.55 & 1.87 & 2.836222e-09 & 1200 & 2.85 & linear & 0.98 \\\\\ncolpitts & 0.022 & 0.91 & 1.11 & 3.177257e-07 & 1200 & 0.00 & linear & 0.98 \\\\\n rossler & 0.042 & 0.89 & 0.79 & 1.006571e-08 & 1200 & 0.71 & linear & 0.98 \\\\\n rossler & 0.028 & 0.48 & 0.81 & 1.000000e-08 & 1200 & 0.00 & linear & 0.98 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Fig }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics", "authors": ["Jason A. Platt", "Stephen G. Penny", "Timothy A. Smith", "Tse-Chun Chen", "Henry D. I. Abarbanel"], "url": "https://arxiv.org/abs/2201.08910v1", "attribution": "\"A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics\" by Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen, and Henry D. I. Abarbanel, arXiv:2201.08910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23340v1_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|}\n\\hline\n & \\multicolumn{2}{c|}{\\textbf{Bernoulli-Laplace level model}} & \\multicolumn{2}{c|}{\\textbf{Curie-Weiss model}} \\\\\n\\hline\n\\(m\\) & \\textbf{Subset \\(S_m\\)} & $D(P^{(-S_m)} \\| \\Pi^{(-S_m)})$ & \\textbf{Subset \\(S_m\\)} & $D(P^{(-S_m)} \\| \\Pi^{(-S_m)})$ \\\\\n\\hline\n1 & \\(\\{9\\}\\) & 5.46950 & \\(\\{10\\}\\) & 3.93568 \\\\\n2 & \\(\\{9,\\,10\\}\\) & 4.30094 & \\(\\{9,\\,10\\}\\) & 3.43908 \\\\\n3 & \\(\\{8,\\,9,\\,10\\}\\) & 3.39168 & \\(\\{8,\\,9,\\,10\\}\\) & 2.96487 \\\\\n4 & \\(\\{7,\\,8,\\,9,\\,10\\}\\) & 2.63821 & \\(\\{7,\\,8,\\,9,\\,10\\}\\) & 2.507645 \\\\\n5 & \\(\\{6,\\,7,\\,8,\\,9,\\,10\\}\\) & 1.99871 & \\(\\{6,\\,7,\\,8,\\,9,\\,10\\}\\) & 2.06420 \\\\\n6 & \\(\\{4,\\,6,\\,7,\\,8,\\,9,\\,10\\}\\) & 1.45314 & \\(\\{5,\\,6,\\,7,\\,8,\\,9,\\,10\\}\\) & 1.63242 \\\\\n7 & \\(\\{3,\\,4,\\,6,\\,7,\\,8,\\,9,\\,10\\}\\) & 0.98630 & \\(\\{4,\\,5,\\,6,\\,7,\\,8,\\,9,\\,10\\}\\) & 1.21075 \\\\\n8 & \\(\\{1,\\,3,\\,4,\\,6,\\,7,\\,8,\\,9,\\,10\\}\\) & 0.58961 & \\(\\{3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,9,\\,10\\}\\) & 0.79828 \\\\\n9 & \\(\\{1,\\,2,\\,3,\\,4,\\,6,\\,7,\\,8,\\,9,\\,10\\}\\) & 0.25830 & \\(\\{2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,9,\\,10\\}\\) & 0.39435 \\\\\n10 & \\(\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,9,\\,10\\}\\)& 0.00000 & \\(\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,9,\\,10\\}\\)& 0.00000 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Performance evaluation of the greedy algorithm.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Information-theoretic subset selection of multivariate Markov chains via submodular optimization", "authors": ["Zheyuan Lai", "Michael C. H. Choi"], "url": "https://arxiv.org/abs/2503.23340v1", "attribution": "\"Information-theoretic subset selection of multivariate Markov chains via submodular optimization\" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.02796v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Aggregated Descriptive Statistics of Daily Log Returns (\\%) for the Top 250 Japan Equities.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\hline\n Statistic & Minimum & Average & Maximum \\\\\n \\hline\n Mean Return (\\%) & -0.004 & 0.003 & 0.010 \\\\\n Standard Deviation (\\%) & 1.333 & 1.939 & 3.010 \\\\\n Minimum Return (\\%) & -30.544 & -13.010 & -7.085 \\\\\n Maximum Return (\\%) & 6.971 & 13.008 & 23.159 \\\\\n Skewness (log returns) & -1.110 & -0.010 & 0.856 \\\\\n Kurtosis (log returns) & 1.640 & 5.693 & 56.401 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deep Learning Enhanced Multivariate GARCH", "authors": ["Haoyuan Wang", "Chen Liu", "Minh-Ngoc Tran", "Chao Wang"], "url": "https://arxiv.org/abs/2506.02796v1", "attribution": "\"Deep Learning Enhanced Multivariate GARCH\" by Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, and Chao Wang, arXiv:2506.02796v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.16120v2_tex_table19.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{computational time results of different methods in each experiments under \\textbf{Noisy Decision} setting.}\n\\begin{tabular}{cc|ccccc}\n\\hline\n\\multicolumn{2}{c|}{Sample size} & 50 & 100 & 300 & 500 & 1000 \\\\ \\hline\n\\multirow{4}{*}{Example A} & FY & \\textbf{20.34} & \\textbf{21.54} & \\textbf{68.33} & \\textbf{64.13} & \\textbf{97.20} \\\\ \\cline{2-2}\n & SPA & 290.84 & 409.33 & 710.18 & 1858.89 & 3574.48 \\\\ \\cline{2-2}\n & KKA & 2.92 & 9.76 & 13.76 & 26.71 & 49.82 \\\\ \\cline{2-2}\n & VIA & 2.19 & 15.09 & 26.11 & 41.70 & 78.80 \\\\ \\hline\n\\multirow{4}{*}{Example B} & FY & \\textbf{0.75} & \\textbf{0.85} & \\textbf{1.54} & \\textbf{2.28} & \\textbf{4.39} \\\\ \\cline{2-2}\n & SPA & 62.54 & 122.73 & 504.12 & 1064.74 & 3906.52 \\\\ \\cline{2-2}\n & KKA & 3.09 & 6.52 & 20.73 & 37.43 & 70.86 \\\\ \\cline{2-2}\n & VIA & 2.41 & 3.42 & 32.94 & 37.02 & 36.83 \\\\ \\hline\n\\multirow{4}{*}{Example C} & FY & \\textbf{0.72} & \\textbf{0.77} & \\textbf{1.25} & \\textbf{1.85} & \\textbf{3.25} \\\\ \\cline{2-2}\n & SPA & 166.96 & 140.48 & 507.64 & 1024.51 & 3205.72 \\\\ \\cline{2-2}\n & KKA & 3.38 & 7.78 & 20.12 & 32.53 & 62.99 \\\\ \\cline{2-2}\n & VIA & 1.98 & 8.02 & 15.05 & 24.34 & 54.06 \\\\ \\hline\n\\multirow{4}{*}{Example D} & FY & \\textbf{0.65} & \\textbf{0.76} & \\textbf{1.30} & \\textbf{1.97} & \\textbf{3.43} \\\\ \\cline{2-2}\n & SPA & 86.24 & 159.85 & 590.19 & 1780.07 & 4481.71 \\\\ \\cline{2-2}\n & KKA & - & - & - & - & - \\\\ \\cline{2-2}\n & VIA & 13.62 & 9.93 & 38.16 & 76.80 & 204.85 \\\\ \\hline\n\\multirow{4}{*}{Example E} & FY & \\textbf{1.94} & \\textbf{3.73} & \\textbf{14.98} & \\textbf{35.67} & \\textbf{38.22} \\\\ \\cline{2-2}\n & SPA & - & - & - & - & - \\\\ \\cline{2-2}\n & KKA & 42.46 & 257.40 & 907.82 & 2486.15 & 21449.75 \\\\ \\cline{2-2}\n & VIA & - & - & - & - & - \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization", "authors": ["Zhehao Li", "Yanchen Wu", "Xiaojie Mao"], "url": "https://arxiv.org/abs/2502.16120v2", "attribution": "\"A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization\" by Zhehao Li, Yanchen Wu, and Xiaojie Mao, arXiv:2502.16120v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.08975v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of $\\mathbf{accuracy_{class}}$ and $\\mathbf{accuracy_{steps}}$ for all experimental setups.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|}\n\\hline\n{\\bfseries Approach} & {\\bfseries Training Data} & {\\bfseries Testing Data} & { $\\mathbf{accuracy_{class}}$} & {$\\mathbf{accuracy_{steps}}$}\\\\\n\\hline\nPAA + Merge (Baseline) & N/A & Pedometer & N/A & 89.73 \\\\\nPAA + Merge (Baseline) & N/A & AZ & N/A & 86.05 \\\\\nLSTM-General I & Pedometer & Pedometer & 85.98 $\\pm\\,2.46$ & 98.63 $\\pm\\,2.37$\\\\\nLSTM-General II & Pedometer & AZ & 60.01 & 94.49\\\\\nLSTM-Personalized & Pedometer + $\\sim$AZ & AZ & 91.76 & 98.81\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Personalized Step Counting Using Wearable Sensors: A Domain Adapted LSTM Network Approach", "authors": ["Arvind Pillai", "Halsey Lea", "Faisal Khan", "Glynn Dennis"], "url": "https://arxiv.org/abs/2012.08975v1", "attribution": "\"Personalized Step Counting Using Wearable Sensors: A Domain Adapted LSTM Network Approach\" by Arvind Pillai, Halsey Lea, Faisal Khan, and Glynn Dennis, arXiv:2012.08975v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.05646v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Horizon} \\& \\textbf{Cumulative VaR (1\\%)} \\& \\textbf{Cumulative ES (1\\%)} \\\\\n\\midrule\n1 Day (t+1) \\& 8.61\\%\\& 10.84\\%\\\\\n2 Days (t+2) \\& 12.37\\%\\& 17.44\\%\\\\\n3 Days (t+3) \\& 13.93\\%\\& 17.18\\%\\\\\n4 Days (t+4) \\& 16.65\\%\\& 19.57\\%\\\\\n5 Days (t+5) \\& 22.04\\%\\& 24.31\\%\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{5-day cumulative VaR and ES using GARCH + FHS at 1\\% confidence}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Comparative Evaluation of VaR Models: Historical Simulation, GARCH-Based Monte Carlo, and Filtered Historical Simulation", "authors": ["Xin Tian"], "url": "https://arxiv.org/abs/2505.05646v1", "attribution": "\"Comparative Evaluation of VaR Models: Historical Simulation, GARCH-Based Monte Carlo, and Filtered Historical Simulation\" by Xin Tian, arXiv:2505.05646v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.04457v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of performance on the Ackley function for dimensions $d=20, 40, 1000$: Each optimization method has ten independent runs. Accuracy is quantified using the average function log regret ($r_f$) and the average minima log regret ($r_m$) across these ten runs. Computational efficiency is represented by the average runtime ($t$) across all ten runs, measured in seconds.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crrrcrrrcrrr} \\toprule\n & \\multicolumn{3}{c}{$d=20$} & & \\multicolumn{3}{c}{$d=40$} & & \\multicolumn{3}{c}{$d=1000$} \\\\ \\cmidrule{2-4} \\cmidrule{6-8} \\cmidrule {10-12} \n Method &\\bf ${r_f}$ &\\bf ${r_m}$ \n &\\bf ${t~ (\\mathrm{s})}$ & &\\bf ${r_f}$ &\\bf ${r_m}$ \n &\\bf ${t~ (\\mathrm{s})}$ & &\\bf ${r_f}$ &${r_m}$ \n &\\bf ${t~ (\\mathrm{s})}$ \\\\ \\midrule\n ZoRD & 2.83 & 1.68 & 1265.43 & & 2.85 & 1.72 & 1092.04 & & 2.73 & 1.58 & 406.16\\\\\n GD & 2.85 & 1.72 & 0.12 & & 2.87 & 1.75 & 0.13 & & 2.89 & 1.75 & 0.14\\\\\n GLD & 2.73 & 1.65 & 0.38 & & 2.89 & 1.70 & 0.39 & & 2.97 & 1.75 & 0.40 \\\\\n PRGF & 2.85 & 1.70 & 0.06 & & 2.89 & 1.74 & 0.07 & & 2.97 & 1.75 & 0.08\\\\\n RGF & 2.86 & 1.66 & 0.06 & & 2.89 & 1.72 & 0.07 & & 2.97 & 1.74 & 0.08 \\\\\nGP-UCB & 2.01 & 1.62 & 132.13 & & 2.07 & 1.63 & 323.12 & & 2.93 & 1.74 & 1132.61 \\\\\n TuRBO & 1.73 & 1.60 & 30.83 & & 2.54 & 1.62 & 83.84 & & 2.95 & 1.73 & 272.56\\\\\n \\textbf{ProGO} & \\textbf{-35.35} & \\textbf{-35.86} & 6.77 & & \\textbf{-31.56} & \\textbf{-9.50} & 16.25 & & \\textbf{1.99} & \\textbf{0.52} & 280.05\\\\\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "ProGO: Probabilistic Global Optimizer", "authors": ["Xinyu Zhang", "Sujit Ghosh"], "url": "https://arxiv.org/abs/2310.04457v2", "attribution": "\"ProGO: Probabilistic Global Optimizer\" by Xinyu Zhang and Sujit Ghosh, arXiv:2310.04457v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.00472v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MobileNet statistic and pvalue (ISIC-2016)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n\\textbf{Model} & \\textbf{MobileNet} & \\textbf{MobileNet+Wavelet} & \\textbf{MobileNet+Wavelet+IGWO} & \\textbf{MobileNet+Wavelet+Fox} & \\textbf{MobileNet+Wavelet+MGTO} \\\\\n\\midrule\nMobileNet & 0.0, 1.0 & -0.2828, 0.7844 & -3.3737, 0.0097 & -0.2502, 0.8087 & -0.6540, 0.5314 \\\\\nMobileNet+Wavelet & 0.2828, 0.7844 & 0.0, 1.0 & -2.7292, 0.0258 & -0.0691, 0.9465 & -0.4482, 0.6658 \\\\\nMobileNet+Wavelet+IGWO & 3.3737, 0.0097 & 2.7292, 0.0258 & 0.0, 1.0 & 1.3575, 0.2116 & 1.0879, 0.3082 \\\\\nMobileNet+Wavelet+Fox & 0.2502, 0.8087 & 0.0691, 0.9465 & -1.3575, 0.2116 & 0.0, 1.0 & -0.2873, 0.7811 \\\\\nMobileNet+Wavelet+MGTO & 0.6540, 0.5314 & 0.4482, 0.6658 & -1.0879, 0.3082 & 0.2873, 0.7811 & 0.0, 1.0 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers", "authors": ["Ramin Mousa", "Saeed Chamani", "Mohammad Morsali", "Mohammad Kazzazi", "Parsa Hatami", "Soroush Sarabi"], "url": "https://arxiv.org/abs/2412.00472v1", "attribution": "\"Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers\" by Ramin Mousa, Saeed Chamani, Mohammad Morsali, Mohammad Kazzazi, Parsa Hatami, and Soroush Sarabi, arXiv:2412.00472v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.11844v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllr}\n \\toprule\n From & To & Duration & Starting in instance \\# ($k$ represents thousands) \\\\\n \\midrule\n \\multirow{5}{*}{1}\n & \\multirow{2}{*}{2} & \\multirow{2}{*}{100} & 5k, 245k, 485k, 725k, 965k, 1205k, 1445k, 905k, 185k, 425k, 665k, 1145k, 1385k, \\\\\n & & & 605k, 125k, 365k, 845k, 1085k, 1325k, 65k, 305k, 545k, 1025k, 785k, 1265k \\\\\n & \\multirow{2}{*}{3} & 100 & 1355k, 755k, 155k, 1475k, 875k, 275k, 995k, 395k, 1115k, 515k, 35k, 1235k, 635k \\\\\n & & 1k & 1055k, 455k, 1175k, 575k, 1295k, 695k, 1415k, 815k, 215k, 95k, 935k, 335k \\\\\n & \\multirow{2}{*}{4} & 100 & 1245k, 45k, 405k, 285k, 165k, 525k, 645k, 765k, 885k, 1005k, 1125k, 1365k, 1485k \\\\\n & & 1k & 105k, 585k, 465k, 225k, 345k, 705k, 825k, 945k, 1065k, 1185k, 1305k, 1425k \\\\\n \\midrule\n \\multirow{5}{*}{2}\n & \\multirow{2}{*}{1} & 100 & 1290k, 1410k, 90k, 330k, 210k, 570k, 450k, 690k, 810k, 930k, 1050k, 1170k \\\\\n & & 1k & 1470k, 30k, 510k, 150k, 390k, 270k, 630k, 750k, 870k, 990k, 1110k, 1230k, 1350k \\\\\n & \\multirow{2}{*}{3} & 100 & 70k, 190k, 550k, 430k, 310k, 670k, 790k, 910k, 1030k, 1150k, 1270k, 1390k \\\\\n & & 1k & 1210k, 1330k, 10k, 490k, 370k, 250k, 130k, 610k, 730k, 850k, 970k, 1090k, 1450k \\\\\n & \\multirow{2}{*}{4} & \\multirow{2}{*}{1k} & 1435k, 235k, 415k, 1135k, 655k, 175k, 895k, 1375k, 115k, 835k, 355k, 1075k, \\\\\n & & & 595k, 1195k, 1315k, 475k, 535k, 775k, 55k, 295k, 1015k, 1255k, 1495k, 955k, 715k \\\\\n \\midrule\n \\multirow{6}{*}{3} \n & \\multirow{2}{*}{1} & 100 & 100k, 460k, 340k, 220k, 580k, 700k, 820k, 940k, 1060k, 1180k, 1300k, 1420k \\\\\n & & 1k & 40k, 160k, 520k, 400k, 280k, 640k, 760k, 880k, 1000k, 1120k, 1360k, 1480k, 1240k \\\\\n & \\multirow{2}{*}{2} & 100 & 25k, 265k, 145k, 385k, 505k, 625k, 745k, 865k, 985k, 1105k, 1225k, 1345k, 1465k \\\\\n & & 1k & 1285k, 85k, 565k, 445k, 325k, 205k, 685k, 805k, 925k, 1045k, 1165k, 1405k \\\\\n & \\multirow{2}{*}{4} & 100 & 1095k, 495k, 1215k, 615k, 1335k, 735k, 135k, 1455k, 855k, 15k, 255k, 975k, 375k \\\\\n & & 1k & 1395k, 795k, 195k, 915k, 315k, 1035k, 435k, 555k, 1155k, 75k, 1275k, 675k \\\\\n \\midrule\n \\multirow{6}{*}{4} \n & \\multirow{2}{*}{1} & 100 & 120k, 480k, 360k, 240k, 600k, 720k, 840k, 960k, 1080k, 1200k, 1320k, 1440k \\\\\n & & 1k & 60k, 180k, 540k, 300k, 420k, 660k, 780k, 900k, 1020k, 1140k, 1260k, 1380k, 1500k \\\\\n & \\multirow{2}{*}{2} & 100 & 1430k, 110k, 350k, 470k, 230k, 590k, 710k, 830k, 950k, 1070k, 1190k, 1310k \\\\\n & & 1k & 1250k, 1370k, 50k, 170k, 530k, 410k, 290k, 650k, 770k, 890k, 1010k, 1130k, 1490k \\\\\n & \\multirow{2}{*}{3} & 100 & 80k, 320k, 200k, 560k, 440k, 680k, 800k, 920k, 1040k, 1160k, 1280k, 1400k \\\\\n & & 1k & 20k, 140k, 500k, 380k, 260k, 620k, 740k, 860k, 980k, 1100k, 1220k, 1340k, 1460k \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "ProteuS: A Generative Approach for Simulating Concept Drift in Financial Markets", "authors": ["Andrés L. Suárez-Cetrulo", "Alejandro Cervantes", "David Quintana"], "url": "https://arxiv.org/abs/2509.11844v1", "attribution": "\"ProteuS: A Generative Approach for Simulating Concept Drift in Financial Markets\" by Andrés L. Suárez-Cetrulo, Alejandro Cervantes, and David Quintana, arXiv:2509.11844v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08675v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|}\n\t\t\t\\hline\n\t\t\t& holds & does not hold \\\\ \n\t\t\t\\hline\n\t\t\t does not hold & $S_\\infty=\\infty$ a.s., $\\P{D=\\infty}\\in(0,1)$ & $S_\\infty<\\infty$ a.s., $\\P{D=\\infty}\\in(0,1)$ \\\\\n\t\t\t\\hline\n\t\t\t holds & \\multicolumn{2}{c|}{$D$ and $ S_{D+1}$ are finite a.s.} \\\\ \n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{An overview of the behaviour of the random variables $D$ and $S_{D+1}$, based on Assumptions~ and~.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Preferential Attachment Trees with Vertex Death: Lack of Persistence of the Maximum Degree", "authors": ["Markus Heydenreich", "Bas Lodewijks"], "url": "https://arxiv.org/abs/2503.08675v1", "attribution": "\"Preferential Attachment Trees with Vertex Death: Lack of Persistence of the Maximum Degree\" by Markus Heydenreich and Bas Lodewijks, arXiv:2503.08675v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01115v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n \\toprule\n & & Construct T & LP on T & k-means & LP on S & Rounding & Total \\\\ \\hline\n\\multirow{2}{*}{Bank} & Algorithm & 0.01 & 2.4 & $<$0.01 & 1.23 & \\textless{}0.01 & 3.78 \\\\\n & NIPS19 & / & / & 0.14 & 0.81 & \\textless{}0.01 & 1.11 \\\\ \\hline\n\\multirow{2}{*}{Creditcard} & Algorithm~ & 0.01 & 4.06 & $<$0.01 & 2.27 & \\textless{}0.01 & 6.51 \\\\\n & NIPS19 & / & / & 0.18 & 2.05 & \\textless{}0.01 & 2.39 \\\\ \\hline\n\\multirow{2}{*}{Census1990} & Algorithm~ & 0.01 & 7.51 & 0.02 & 5.19 & \\textless{}0.01 & 12.99 \\\\\n & NIPS19 & / & / & 0.30 & 3.94 & \\textless{}0.01 & 4.42 \\\\ \\hline\n\\multirow{2}{*}{Adult} & Algorithm~ & 0.01 & 4.14 & $<$0.01 & 1.80 & \\textless{}0.01 & 6.12 \\\\\n & NIPS19 & / & / & 0.18 & 1.23 & \\textless{}0.01 & 1.59 \\\\ \\hline\n\\multirow{2}{*}{Breastcancer} & Algorithm~ & 0.01 & 0.19 & $<$0.01 & 0.82 & \\textless{}0.01 & 1.33 \\\\\n & NIPS19 & / & / & 0.10 & 0.22 & \\textless{}0.01 & 0.45 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Running time (s) on non-strictly fair datasets}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems", "authors": ["Shihong Song", "Guanlin Mo", "Qingyuan Yang", "Hu Ding"], "url": "https://arxiv.org/abs/2411.01115v3", "attribution": "\"Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems\" by Shihong Song, Guanlin Mo, Qingyuan Yang, and Hu Ding, arXiv:2411.01115v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11315v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{List of the proposed standard NetFlow features}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n\\hline\n\\textbf{Feature} & \\textbf{Description} \\\\ \\hline\nIPV4\\_SRC\\_ADDR & IPv4 source address \\\\ \\hline\nIPV4\\_DST\\_ADDR & IPv4 destination address \\\\ \\hline\nL4\\_SRC\\_PORT & IPv4 source port number \\\\ \\hline\nL4\\_DST\\_PORT & IPv4 destination port number \\\\ \\hline\nPROTOCOL & IP protocol identifier byte \\\\ \\hline\nL7\\_PROTO & Layer 7 protocol (numeric) \\\\ \\hline\nIN\\_BYTES & Incoming number of bytes \\\\ \\hline\nOUT\\_BYTES & Outgoing number of bytes \\\\ \\hline\nIN\\_PKTS & Incoming number of packets \\\\ \\hline\nOUT\\_PKTS & Outgoing number of packets \\\\\n\\hline\nFLOW\\_DURATION\\_MILLISECONDS & Flow duration in milliseconds \\\\ \\hline\nTCP\\_FLAGS & Cumulative of all TCP flags \\\\ \\hline\nCLIENT\\_TCP\\_FLAGS & Cumulative of all client TCP flags \\\\ \\hline\nSERVER\\_TCP\\_FLAGS & Cumulative of all server TCP flags \\\\ \\hline\nDURATION\\_IN & Client to Server stream duration (msec) \\\\ \\hline\nDURATION\\_OUT & Client to Server stream duration (msec) \\\\ \\hline\nMIN\\_TTL & Min flow TTL \\\\ \\hline\nMAX\\_TTL & Max flow TTL \\\\ \\hline\nLONGEST\\_FLOW\\_PKT & Longest packet (bytes) of the flow \\\\ \\hline\nSHORTEST\\_FLOW\\_PKT & Shortest packet (bytes) of the flow \\\\ \\hline\nMIN\\_IP\\_PKT\\_LEN & Len of the smallest flow IP packet observed \\\\ \\hline\nMAX\\_IP\\_PKT\\_LEN & Len of the largest flow IP packet observed \\\\ \\hline\nSRC\\_TO\\_DST\\_SECOND\\_BYTES & Src to dst Bytes/sec \\\\ \\hline\nDST\\_TO\\_SRC\\_SECOND\\_BYTES & Dst to src Bytes/sec \\\\ \\hline\nRETRANSMITTED\\_IN\\_BYTES & Number of retransmitted TCP flow bytes (src-$>$dst) \\\\ \\hline\nRETRANSMITTED\\_IN\\_PKTS & Number of retransmitted TCP flow packets (src-$>$dst) \\\\ \\hline\nRETRANSMITTED\\_OUT\\_BYTES & Number of retransmitted TCP flow bytes (dst-$>$src) \\\\ \\hline\nRETRANSMITTED\\_OUT\\_PKTS & Number of retransmitted TCP flow packets (dst-$>$src) \\\\ \\hline\nSRC\\_TO\\_DST\\_AVG\\_THROUGHPUT & Src to dst average thpt (bps) \\\\ \\hline\nDST\\_TO\\_SRC\\_AVG\\_THROUGHPUT & Dst to src average thpt (bps) \\\\ \\hline\nNUM\\_PKTS\\_UP\\_TO\\_128\\_BYTES & Packets whose IP size $<$= 128 \\\\ \\hline\nNUM\\_PKTS\\_128\\_TO\\_256\\_BYTES & Packets whose IP size $>$ 128 and $<$= 256 \\\\ \\hline\nNUM\\_PKTS\\_256\\_TO\\_512\\_BYTES & Packets whose IP size $>$ 256 and $<$= 512 \\\\ \\hline\nNUM\\_PKTS\\_512\\_TO\\_1024\\_BYTES & Packets whose IP size $>$ 512 and $<$= 1024 \\\\ \\hline\nNUM\\_PKTS\\_1024\\_TO\\_1514\\_BYTES & Packets whose IP size $>$ 1024 and $<$= 1514 \\\\ \\hline\nTCP\\_WIN\\_MAX\\_IN & Max TCP Window (src-$>$dst) \\\\ \\hline\nTCP\\_WIN\\_MAX\\_OUT & Max TCP Window (dst-$>$src) \\\\ \\hline\nICMP\\_TYPE & ICMP Type * 256 + ICMP code \\\\ \\hline\nICMP\\_IPV4\\_TYPE & ICMP Type \\\\ \\hline\nDNS\\_QUERY\\_ID & DNS query transaction Id \\\\ \\hline\nDNS\\_QUERY\\_TYPE & DNS query type (e.g., 1=A, 2=NS..) \\\\ \\hline\nDNS\\_TTL\\_ANSWER & TTL of the first A record (if any) \\\\ \\hline\nFTP\\_COMMAND\\_RET\\_CODE & FTP client command return code \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Towards a Standard Feature Set for Network Intrusion Detection System Datasets", "authors": ["Mohanad Sarhan", "Siamak Layeghy", "Marius Portmann"], "url": "https://arxiv.org/abs/2101.11315v2", "attribution": "\"Towards a Standard Feature Set for Network Intrusion Detection System Datasets\" by Mohanad Sarhan, Siamak Layeghy, and Marius Portmann, arXiv:2101.11315v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03788v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{THE DATA ATTRIBUTES }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|} \n \\hline\n Serial & AttributeTitle & DataType \\\\ \n \\hline\n 1 & Model & String \\\\ \n \\hline\n 2 & Year & String \\\\ \n \\hline\n 3 & Battery & String \\\\ \n \\hline\n 4 & Price & Number \\\\ \n \\hline\n 5 & Miles & Number \\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Second Hand Price Prediction for Tesla Vehicles", "authors": ["Sayed Erfan Arefin"], "url": "https://arxiv.org/abs/2101.03788v1", "attribution": "\"Second Hand Price Prediction for Tesla Vehicles\" by Sayed Erfan Arefin, arXiv:2101.03788v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.08161v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[table]{xcolor}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparative experiments to evaluate the effectiveness of replacing the video feature with prediction text in propagation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\rowcolor[HTML]{FAFAFA} \n\\cellcolor[HTML]{FAFAFA}\n& \\multicolumn{2}{c|}{\\cellcolor[HTML]{FAFAFA}M3} & \\multicolumn{2}{c|}{\\cellcolor[HTML]{FAFAFA}S4} \\\\\n\\rowcolor[HTML]{FAFAFA}\n\\multirow{-2}{*}{\\cellcolor[HTML]{FAFAFA}Method} \n & \\cellcolor[HTML]{FAFAFA}$\\mathcal {M_J}$ & \\cellcolor[HTML]{FAFAFA}$\\mathcal {M_F}$ &\\cellcolor[HTML]{FAFAFA} $\\mathcal {M_J}$ & \\cellcolor[HTML]{FAFAFA}$\\mathcal {M_F}$ \\\\ \\hline\\hline\nCo-Prop (Direct-guided) & 63.6\t & 74 & 83.7 & 90.9 \\\\\nText-guided & 52.8(↓10.8) & 63.7(↓10.3) & 78.5(↓5.2) & 87.1(↓3.8) \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Collaborative Hybrid Propagator for Temporal Misalignment in Audio-Visual Segmentation", "authors": ["Kexin Li", "Zongxin Yang", "Yi Yang", "Jun Xiao"], "url": "https://arxiv.org/abs/2412.08161v1", "attribution": "\"Collaborative Hybrid Propagator for Temporal Misalignment in Audio-Visual Segmentation\" by Kexin Li, Zongxin Yang, Yi Yang, and Jun Xiao, arXiv:2412.08161v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18259v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc|cccc}\n\\hline\n\\multirow{2}{*}{Strike} & \\multicolumn{4}{c|}{European Options} & \\multicolumn{4}{c}{Asian Options} \\\\\n\\cline{2-9}\n & Call & 95\\% CI & Put & 95\\% CI & Call & 95\\% CI & Put & 95\\% CI \\\\\n\\hline\n80 & 22.0152(22.1366) & [21.8835, 22.1469] & 2.1932 & [2.1582, 2.2281] & 20.1682 & [20.0899, 20.2465] & 0.2239 & [0.2160, 0.2317] \\\\\n90 & 14.8856(14.9672) & [14.7699, 15.0013] & 5.0636 & [5.0079, 5.1194] & 11.4500 & [11.3815, 11.5185] & 1.5056 & [1.4827, 1.5286] \\\\\n100 & 9.4369(9.4737) & [9.3407, 9.5330] & 9.6149 & [9.5370, 9.6928] & 5.1607 & [5.1106, 5.2109] & 5.2164 & [5.1721, 5.2607] \\\\\n110 & 5.6300(5.6234) & [5.5542, 5.7058] & 15.8080 & [15.7096, 15.9065] & 1.8029 & [1.7728, 1.8330] & 11.8586 & [11.7953, 11.9218] \\\\\n120 & 3.1741(3.1424) & [3.1168, 3.2313] & 23.3521 & [23.2367, 23.4674] & 0.4883 & [0.4730, 0.5036] & 20.5440 & [20.4696, 20.6184] \\\\\n\\hline\n\\multirow{2}{*}{Strike} & \\multicolumn{4}{c|}{Lookback Options} & \\multicolumn{4}{c}{Barrier Options} \\\\\n\\cline{2-9}\n & Call & 95\\% CI & Put & 95\\% CI & Up-In Call($B=110$) & 95\\% CI & Down-Out Put($B=90$) & 95\\% CI \\\\\n\\hline\n80 & 39.1040 & [38.9999, 39.2080] & 3.9453 & [3.9014, 3.9891] & 20.0574 & [19.9170, 20.1979] & 0.0000 & [0.0000, 0.0000] \\\\\n90 & 29.1040 & [28.9999, 29.2080] & 9.1218 & [9.0573, 9.1863] & 14.3925 & [14.2740, 14.5110] & 0.0000 & [0.0000, 0.0000] \\\\\n100 & 19.1040 & [18.9999, 19.2080] & 17.3563 & [17.2805, 17.4322] & 9.5182 & [9.4213, 9.6151] & 0.1284 & [0.1232, 0.1335] \\\\\n110 & 11.1521 & [11.0590, 11.2452] & 27.3563 & [27.2805, 27.4322] & 5.7546 & [5.6784, 5.8309] & 0.8136 & [0.7958, 0.8314]\\\\\n120 & 6.1561 & [6.0820, 6.2303] & 37.3563 & [37.2805, 37.4322] & 3.2371 & [3.1796, 3.2945] & 2.2349 & [2.1992, 2.2706] \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Option prices under the rough Heston model with 95\\% confidence intervals.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\\infty$) processes and applications", "authors": ["Yingli Wang", "Zhenyu Cui"], "url": "https://arxiv.org/abs/2503.18259v3", "attribution": "\"Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\\infty$) processes and applications\" by Yingli Wang and Zhenyu Cui, arXiv:2503.18259v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.06442v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Validity for the number of groups in group convolution of GMDC on the CVIU database. As multi-scale kernels are utilized, the minimum number of groups is 2. In experiments with different numbers of groups, half of the groups had kernel sizes set to 3, while the other half had kernel sizes set to 7.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n \\toprule\n GMDC (Groups) & SRCC $\\uparrow$ & KRCC $\\uparrow$ & PLCC $\\uparrow$ & RMSE $\\downarrow$\\\\\n \\midrule\n Groups 2 & \\textbf{0.978} & \\textbf{0.872} & \\textbf{0.981} & \\textbf{0.397} \\\\\n Groups 4 & 0.974 & 0.866 & 0.977 & 0.551 \\\\\n Groups 8 & 0.972 & 0.864 & 0.976 & 0.541 \\\\\n Groups 16 & 0.967 & 0.849 & 0.971 & 0.681 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment", "authors": ["Yixiao Li", "Xiaoyuan Yang", "Guanghui Yue", "Jun Fu", "Qiuping Jiang", "Xu Jia", "Paul L. Rosin", "Hantao Liu", "Wei Zhou"], "url": "https://arxiv.org/abs/2509.06442v1", "attribution": "\"Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment\" by Yixiao Li, Xiaoyuan Yang, Guanghui Yue, Jun Fu, Qiuping Jiang, Xu Jia, Paul L. Rosin, Hantao Liu, and Wei Zhou, arXiv:2509.06442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23340v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|}\n\\hline\n & \\multicolumn{2}{c|}{\\textbf{Approach 1}} & \\multicolumn{2}{c|}{\\textbf{Approach 2}} \\\\\n\\hline\n\\(m\\) & \\textbf{Subset \\(S_l\\)} & $D(P^{(S_l)} \\| \\Pi^{(S_l)})$ & \\textbf{Subset \\(S_l\\)} & $D(P^{(S_l)} \\| \\Pi^{(S_l)})$ \\\\\n\\hline\n1 & \\(\\{6\\}\\) & 0.40245 & \\(\\{6\\}\\) & 0.40245 \\\\\n2 & \\(\\{3,\\,6\\}\\) & 0.81082 & \\(\\{5,\\,6\\}\\) & 0.80739 \\\\\n3 & \\(\\{3,\\,6,\\,8\\}\\) & 1.22606 & \\(\\{5,\\,6,\\,8\\}\\) & 1.22234 \\\\\n4 & \\(\\{3,\\,4,\\,6,\\,8\\}\\) & 1.64626 & \\(\\{3,\\,5,\\,6,\\,8\\}\\) & 1.64615 \\\\\n5 & \\(\\{3,\\,4,\\,6,\\,8,\\,9\\}\\) & 2.07613 & \\(\\{2,\\,3,\\,5,\\,6,\\,8\\}\\) & 2.07601 \\\\\n6 & \\(\\{2,\\,3,\\,4,\\,6,\\,8,\\,9\\}\\) & 2.51741 & \\(\\{2,\\,3,\\,5,\\,6,\\,8,\\,9\\}\\) & 2.51771 \\\\\n7 & \\(\\{2,\\,3,\\,4,\\,5,\\,6,\\,8,\\,9\\}\\) & 2.97051 & \\(\\{2,\\,3,\\,4,\\,5,\\,6,\\,8,\\,9\\}\\) & 2.97051 \\\\\n8 & \\(\\{1,\\,2,\\,3,\\,4,\\,6,\\,8,\\,9\\}\\) & 3.44141 & \\(\\{2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,9\\}\\) & 3.44085 \\\\\n9 & \\(\\{1,\\,2,\\,3,\\,4,\\,6,\\,8,\\,9,\\,10\\}\\) & 3.93647 & \\(\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,9\\}\\) & 3.93568 \\\\\n10 & \\(\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,9,\\,10\\}\\) & 4.46975 & \\(\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,9,\\,10\\}\\) & 4.46975 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of different configurations of the batch greedy algorithm (C-W model).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Information-theoretic subset selection of multivariate Markov chains via submodular optimization", "authors": ["Zheyuan Lai", "Michael C. H. Choi"], "url": "https://arxiv.org/abs/2503.23340v1", "attribution": "\"Information-theoretic subset selection of multivariate Markov chains via submodular optimization\" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.20923v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\t\t\t\\hline\n\t\t Coeffs & $A_0= 0.0995$ & $B= 2.3671$ &$\\sim$\\\\ \\hline\n $A_1^0= -0.2865$\t& $A_1^1= 0.1931$ & $A_1^2= 0.0015$ & $A_1^3= 1.6727$ \\\\ \\hline\n\t$A_2^0= -0.1449$& $A_2^1=-0.1312$ & $A_2^2=0.8725 $& $A_2^3= 3.7680 $\\\\ \\hline\n\t$A_3^0= -0.2036$ & $A_3^1= -0.2946 $& $A_3^2= 0.5836$ & $A_3^3= 2.0845$\\\\ \\hline\n $A_4^0= -0.0445$ & $A_4^1= -0.0778$& $A_4^2= 0.1903$ & $A_4^3= 0.4011$\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Coefficients that fit the universal $\\sqrt{C}-\\chi-Q$ surface for $n=1$ BSs, corresponding to the set of data and fitting surface shown in .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Spinning boson stars in nonlinear sigma models and Universal Relations", "authors": ["Christoph Adam", "Jorge Castelo Mourelle", "Alberto García Martín-Caro", "Andrzej Wereszczynski"], "url": "https://arxiv.org/abs/2502.20923v3", "attribution": "\"Spinning boson stars in nonlinear sigma models and Universal Relations\" by Christoph Adam, Jorge Castelo Mourelle, Alberto García Martín-Caro, and Andrzej Wereszczynski, arXiv:2502.20923v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13096v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n \\toprule\n Model & Target & CPU & Real \\\\\n \\midrule\n ResNet50 & $1.00\\times$ & AMD & 0.478s -- $1.00\\times$ \\\\\n ResNet50 & $2.00\\times$ & AMD & 0.234s -- $2.04\\times$ \\\\\n ResNet50 & $2.50\\times$ & AMD & 0.178s -- $2.69\\times$ \\\\\n ResNet50 & $3.00\\times$ & AMD & 0.143s -- $3.34\\times$ \\\\\n ResNet50 & $3.50\\times$ & AMD & 0.121s -- $3.95\\times$ \\\\\n \\midrule\n MobileNetV1 & $1.00\\times$ & Intel & 0.045s -- $1.00\\times$ \\\\\n MobileNetV1 & $1.50\\times$ & Intel & 0.031s -- $1.45\\times$ \\\\\n \\midrule\n YOLOv5s & $1.00\\times$ & Intel & 0.641s -- $1.00\\times$ \\\\\n YOLOv5s & $1.50\\times$ & Intel & 0.449s -- $1.43\\times$ \\\\\n YOLOv5s & $1.75\\times$ & Intel & 0.380s -- $1.68\\times$ \\\\\n YOLOv5m & $1.00\\times$ & Intel & 1.459s -- $1.00\\times$ \\\\\n YOLOv5m & $1.75\\times$ & Intel & 0.848s -- $1.72\\times$ \\\\\n YOLOv5m & $2.00\\times$ & Intel & 0.725s -- $2.01\\times$ \\\\\n \\midrule\n BERT SQuAD & $1.00\\times$ & Intel & 0.969s -- $1.00\\times$ \\\\\n BERT SQuAD & $3.00\\times$ & Intel & 0.320s -- $3.03\\times$ \\\\\n BERT SQuAD & $3.50\\times$ & Intel & 0.271s -- $3.58\\times$ \\\\\n BERT SQuAD & $4.00\\times$ & Intel & 0.223s -- $4.35\\times$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "SPDY: Accurate Pruning with Speedup Guarantees", "authors": ["Elias Frantar", "Dan Alistarh"], "url": "https://arxiv.org/abs/2201.13096v2", "attribution": "\"SPDY: Accurate Pruning with Speedup Guarantees\" by Elias Frantar and Dan Alistarh, arXiv:2201.13096v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.02706v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The ProxyFAUG Parameters Selected for this Setting}\n\\begin{tabular}{|c|c|}\n \\hline\n \\textbf{Parameter} & \\textbf{Explanation} \\\\ \\hline\n $r$ & 20 meters \\\\ \\hline\n $S_{max}$ & 2\\\\ \\hline\n $N$ & 8 \\\\ \\hline\n $p_m$ & 0.3\\\\ \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "ProxyFAUG: Proximity-based Fingerprint Augmentation", "authors": ["Grigorios G. Anagnostopoulos", "Alexandros Kalousis"], "url": "https://arxiv.org/abs/2102.02706v2", "attribution": "\"ProxyFAUG: Proximity-based Fingerprint Augmentation\" by Grigorios G. Anagnostopoulos and Alexandros Kalousis, arXiv:2102.02706v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.14060v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n \\multicolumn{2}{|c|}{BNF} & \\multicolumn{2}{|c|}{Filter Bank} & \\multicolumn{2}{|c|}{MFCC} \\\\\\hline\n iteration & SWER (\\%) & iteration & SWER (\\%) & iteration & SWER (\\%) \\\\\\hline\n iter 1 & 31.43 & iter 1 & 32.59 & iter 1 & 32.66 \\\\\\hline\n iter 2 & 6.26 & iter 2 & 7.36 & iter 2 & 7.12 \\\\\\hline\n iter 3 & 3.75 & iter 3 & 4.64 & iter 3 & 4.54 \\\\\\hline\n iter 4 & 2.97 & iter 4 & 4.27 & iter 4 & 4.53 \\\\\\hline\n iter 5 & 2.23 & iter 5 & 4.27 & iter 5 & 4.54 \\\\\\hline \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Difference in input and output labels on bottleneck and filter bank feature}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Unsupervised Spoken Term Discovery on Untranscribed Speech", "authors": ["Man-Ling Sung"], "url": "https://arxiv.org/abs/2011.14060v1", "attribution": "\"Unsupervised Spoken Term Discovery on Untranscribed Speech\" by Man-Ling Sung, arXiv:2011.14060v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.13158v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison of proposed framework with CST and CHR on each SIXray subset in terms of mAP. Bold indicates the best performance while the second-best performance is underlined. Here, CST and CHR are driven through ResNet-50 .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Subset & Proposed & CST & CHR \\\\\\hline\n SIXray10 & \\underline{0.9601} & \\textbf{0.9634} & 0.7794\\\\\n SIXray100 & \\underline{0.8749} & \\textbf{0.9318} & 0.5787\\\\\n SIXray1000 & \\underline{0.7814} & \\textbf{0.8903} & 0.3700\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion", "authors": ["Taimur Hassan", "Samet Akcay", "Mohammed Bennamoun", "Salman Khan", "Naoufel Werghi"], "url": "https://arxiv.org/abs/2009.13158v2", "attribution": "\"Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion\" by Taimur Hassan, Samet Akcay, Mohammed Bennamoun, Salman Khan, and Naoufel Werghi, arXiv:2009.13158v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.06914v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average returns per trade on the test set for \"downward/Non-downward\" predictions}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n \\toprule\n Varieties & \\multicolumn{4}{c}{LR-related classification models} & \\multicolumn{4}{c}{SVM-related classification models} \\\\\n & LR & SMOTE-LR & RUS-LR & Mean-uncertainty LR & SVM & SMOTE-SVM & RUS-SVM & Mean-uncertainty SVM \\\\\n \\midrule\n AU & -2.34e-05 & \\textbf{2.05e-06} & 7.58e-07 & 1.30e-06 & 0 & 1.07e-06 & 7.62e-07 & \\textbf{2.68e-06} \\\\\n CU & 3.30e-06 & 2.39e-05 & 2.03e-05 & \\textbf{2.62e-05} & 0 & 2.27e-05 & 1.94e-05 & \\textbf{3.38e-05} \\\\\n SN & 1.94e-06 & 1.61e-05 & 1.52e-05 & \\textbf{1.86e-05} & 0 & 1.11e-05 & 1.26e-05 & \\textbf{3.09e-05} \\\\\n NI & -9.81e-05 & 8.10e-06 & 7.05e-06 & \\textbf{8.30e-06} & 0 & \\textbf{8.46e-06} & 6.70e-06 & 7.24e-06 \\\\\n C & 1.50e-05 & 5.15e-05 & 4.53e-05 & \\textbf{5.68e-05} & 0 & 5.18e-05 & 4.17e-05 & \\textbf{6.21e-05} \\\\\n ZN & 5.60e-06 & \\textbf{2.46e-05} & 2.17e-05 & 2.41e-05 & 0 & 1.70e-05 & 1.69e-05 & \\textbf{2.45e-05} \\\\\n PR & 1.91e-05 & 4.28e-05 & 3.31e-05 & \\textbf{4.55e-05} & 0 & \\textbf{3.52e-05} & 3.19e-05 & 2.34e-05 \\\\\n RB & 6.89e-07 & 3.00e-05 & 2.68e-05 & \\textbf{3.01e-05} & 0 & 2.08e-05 & \\textbf{2.18e-05} & 1.16e-05 \\\\\n AG & 7.82e-07 & \\textbf{7.48e-06} & 5.42e-06 & 6.63e-06 & 0 & 5.76e-06 & 6.61e-06 & \\textbf{1.81e-05} \\\\\n AL & 9.42e-06 & 4.27e-05 & 3.89e-05 & \\textbf{4.79e-05} & 0 & \\textbf{4.40e-05} & 3.78e-05 & 3.98e-05 \\\\\n LH & -6.05e-06 & 4.42e-05 & 3.57e-05 & \\textbf{4.67e-05} & 0 & 4.44e-05 & 3.51e-05 & \\textbf{5.78e-05} \\\\\n CF & 9.55e-06 & 4.75e-05 & 4.29e-05 & \\textbf{4.84e-05} & 0 & \\textbf{4.93e-05} & 3.83e-05 & 4.56e-05 \\\\\n PB & 4.54e-05 & 5.82e-05 & 5.08e-05 & \\textbf{6.46e-05} & 0 & 5.96e-05 & 4.97e-05 & \\textbf{7.30e-05} \\\\\n CS & 1.14e-05 & 4.33e-05 & 5.14e-05 & \\textbf{5.46e-05} & 0 & 5.49e-05 & 4.16e-05 & \\textbf{6.80e-05} \\\\\n M & 5.71e-06 & 4.40e-05 & 3.84e-05 & \\textbf{4.71e-05} & 0 & 4.35e-05 & 3.54e-05 & \\textbf{5.37e-05} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Prediction of high-frequency futures return directions based on the mean uncertainty classification methods: An application in China's future market", "authors": ["Ying Peng", "Yifan Zhang", "Xin Wang"], "url": "https://arxiv.org/abs/2508.06914v1", "attribution": "\"Prediction of high-frequency futures return directions based on the mean uncertainty classification methods: An application in China's future market\" by Ying Peng, Yifan Zhang, and Xin Wang, arXiv:2508.06914v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11182v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\hline\\noalign{\\smallskip}\n Parameter & \nDQN & DDPG & TRPO & A2C\n \\\\\n\\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\nEpisodes\\\\\n(50, 200, 500) & $\\checkmark$ & $\\checkmark$ & $\\checkmark$ & $\\checkmark$\\\\\n\\hline\nGamma $\\gamma$\\\\\n(0.01,0.1,0.5,0.99)& $\\checkmark$ & $\\checkmark$ & $\\checkmark$ & $\\checkmark$\\\\\n\\hline\nLearning rate\\\\\n(0.1, 0.01, 0.001)& $\\checkmark$ & $\\checkmark$ & $\\checkmark$ & $\\checkmark$\\\\\n\\hline\nBatch Size& $\\checkmark$ & $\\checkmark$ & $\\checkmark$ & $\\checkmark$\\\\\n\\hline\nNeurons No.& $\\checkmark$ & $\\checkmark$ & $\\checkmark$ & $\\checkmark$\\\\\n\\hline\nLayers & $\\checkmark$ & $\\checkmark$ & $\\checkmark$ & $\\checkmark$\\\\\n\\hline\nOptimization function \\\\(Adam, CG LBFGS, LM)& $\\checkmark$ & $\\checkmark$ & $\\checkmark$ & $\\checkmark$\\\\\n\\hline Activation function \\\\(tanh, relu)& $\\checkmark$ & $\\checkmark$ & $\\checkmark$ & $\\checkmark$\\\\\n\\hline\nTrajectory Size \\\\(10, 20, 50, 100, 1000)& & & & $\\checkmark$ \\\\\n\\hline\nKL value \\\\(0.001, 0.01, 0.1)& & & & $\\checkmark$\\\\\n\\hline\n\\noalign{\\smallskip}\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Hyperparameters for deep RL algorithms.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Hyperparameter Tuning for Deep Reinforcement Learning Applications", "authors": ["Mariam Kiran", "Melis Ozyildirim"], "url": "https://arxiv.org/abs/2201.11182v1", "attribution": "\"Hyperparameter Tuning for Deep Reinforcement Learning Applications\" by Mariam Kiran and Melis Ozyildirim, arXiv:2201.11182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00524v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Description of the training modes and the loss functions used in this work.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|} \\hline\n\\textbf{Training mode} & \\multicolumn{2}{c|}{\\textbf{Loss function}} \\\\ \\hline \\hline\nClassical & \\multicolumn{2}{c|}{Cross entropy} \\\\ \\hline\n\\multirow{2}{*}{Siamese} & \\multicolumn{2}{c|}{Single margin contrastive loss (SMCL)} \\\\\n & \\multicolumn{2}{c|}{Double margin contrastive loss (DMCL)} \\\\ \\hline\n\\multirow{6}{*}{Triplet} & \\multicolumn{2}{c|}{Offline triplet mining} \\\\ \\cline{2-3}\n & \\multirow{3}{*}{Online triplet mining} & Random negative \\\\\n & & Semi-hard negative \\\\\n & & Hardest negative \\\\ \\cline{2-3}\n & \\multirow{2}{*}{Multi-class N-pair} & All positive pair \\\\\n & & Hard negative pair \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "One-shot Representational Learning for Joint Biometric and Device Authentication", "authors": ["Sudipta Banerjee", "Arun Ross"], "url": "https://arxiv.org/abs/2101.00524v1", "attribution": "\"One-shot Representational Learning for Joint Biometric and Device Authentication\" by Sudipta Banerjee and Arun Ross, arXiv:2101.00524v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.02496v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Compression ratios (sparsities) of sparsest model within $\\varepsilon_{acc}$ percentage points of the dense model test accuracy. Random pruning is left out for clarity.}\n\\begin{tabular}{lcccccc}\n\\hline\\hline\nCR ($\\uparrow$) & $\\varepsilon_{acc}$ & \\textbf{LeNet-300-100} & \\textbf{LeNet-5} & \\textbf{ResNet-18} & \\textbf{VGG-19} & \\textbf{ResNet-18} \\\\\n & & \\tiny{F-MNIST} & \\tiny{K-MNIST} & \\tiny{CIFAR10} & \\tiny{CIFAR100} & \\tiny{Tiny ImageNet} \\\\\n\\cline{2-7}\nDepth 2 & 5\\% & 141 \\scriptsize{(99.29\\%)} & 52 \\scriptsize{(98.08\\%)} & 466 \\scriptsize{(99.79\\%)} & \\textbf{484} \\scriptsize{(99.79\\%)} & 60 \\scriptsize{(98.34\\%)} \\\\\n & 10\\% & 362 \\scriptsize{(99.72\\%)} & 78 \\scriptsize{(98.71\\%)} & 1169 \\scriptsize{(99.91\\%)} & 939 \\scriptsize{(99.89\\%)} & 99 \\scriptsize{(98.99\\%)} \\\\\nDepth 3 & 5\\% & \\textbf{506} \\scriptsize{(99.80\\%)} & \\textbf{75} \\scriptsize{(98.67\\%)} & \\textbf{573} \\scriptsize{(99.83\\%)} & 440 \\scriptsize{(99.77\\%)} & \\textbf{67} \\scriptsize{(98.51\\%)} \\\\\n & 10\\% & 1422 \\scriptsize{(99.93\\%)} & 134 \\scriptsize{(99.25\\%)} & \\textbf{1456} \\scriptsize{(99.93\\%)} & \\textbf{1014} \\scriptsize{(99.90\\%)} & \\textbf{161} \\scriptsize{(99.38\\%)} \\\\\nDepth 4 & 5\\% & 486 \\scriptsize{(99.79\\%)} & \\textbf{75} \\scriptsize{(98.67\\%)} & 445 \\scriptsize{(99.78\\%)} & 215 \\scriptsize{(99.53\\%)} & 13 \\scriptsize{(92.39\\%)} \\\\\n & 10\\% & \\textbf{1442} \\scriptsize{(99.93\\%)} & \\textbf{139} \\scriptsize{(99.28\\%)} & 1161 \\scriptsize{(99.91\\%)} & 675 \\scriptsize{(99.85\\%)} & 113 \\scriptsize{(99.12\\%)} \\\\\nGMP & 5\\% & 156 \\scriptsize{(99.36\\%)} & 22 \\scriptsize{(95.37\\%)} & 211 \\scriptsize{(99.53\\%)} & 37 \\scriptsize{(97.27\\%)} & 60 \\scriptsize{(98.33\\%)} \\\\\n & 10\\% & 235 \\scriptsize{(99.58\\%)} & 32 \\scriptsize{(96.84\\%)} & 484 \\scriptsize{(99.79\\%)} & 68 \\scriptsize{(98.52\\%)} & 133 \\scriptsize{(99.25\\%)} \\\\\nSNIP & 5\\% & 76 \\scriptsize{(98.69\\%)} & 17 \\scriptsize{(94.10\\%)} & 140 \\scriptsize{(99.29\\%)} & 28 \\scriptsize{(96.47\\%)} & 18 \\scriptsize{(94.34\\%)} \\\\\n & 10\\% & 146 \\scriptsize{(99.32\\%)} & 24 \\scriptsize{(95.91\\%)} & 339 \\scriptsize{(99.70\\%)} & 42 \\scriptsize{(97.59\\%)} & 41 \\scriptsize{(97.56\\%)} \\\\\nSynflow & 5\\% & 141 \\scriptsize{(99.29\\%)} & 21 \\scriptsize{(95.23\\%)} & 210 \\scriptsize{(99.52\\%)} & 46 \\scriptsize{(97.81\\%)} & 24 \\scriptsize{(95.84\\%)} \\\\\n & 10\\% & 302 \\scriptsize{(99.67\\%)} & 37 \\scriptsize{(97.30\\%)} & 721 \\scriptsize{(99.86\\%)} & 218 \\scriptsize{(99.54\\%)} & 71 \\scriptsize{(98.60\\%)} \\\\\n\\hline\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries", "authors": ["Chris Kolb", "Tobias Weber", "Bernd Bischl", "David Rügamer"], "url": "https://arxiv.org/abs/2502.02496v2", "attribution": "\"Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries\" by Chris Kolb, Tobias Weber, Bernd Bischl, and David Rügamer, arXiv:2502.02496v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.16187v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|ccccc}\n Fire & Location & Ignited & Fully Contained & Acreage & \\# of Pixels \\\\\n \\hline\nKincade & Sonoma County, CA & Oct. 23, 2019 & Nov. 6, 2019 & 77,758 & 82,125 \\\\\nCZU & Santa Cruz \\& San Mateo, CA & Aug. 16, 2020 & Sept. 22, 2020 & 86,509 & 88,581 \\\\\nWindy & Sierra Nevada, CA & Sept. 9, 2021 & Nov. 15, 2021 & 97,528 & 99,458 \\\\\nKNP & Sierra Nevada, CA & Sept. 9, 2021 & Dec. 16, 2021 & 88,307 & 92,171 \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Summary of wildland fires used in the study.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modelling wildland fire burn severity in California using a spatial Super Learner approach", "authors": ["Nicholas Simafranca", "Bryant Willoughby", "Erin O'Neil", "Sophie Farr", "Brian J Reich", "Naomi Giertych", "Margaret Johnson", "Madeleine Pascolini-Campbell"], "url": "https://arxiv.org/abs/2311.16187v1", "attribution": "\"Modelling wildland fire burn severity in California using a spatial Super Learner approach\" by Nicholas Simafranca, Bryant Willoughby, Erin O'Neil, Sophie Farr, Brian J Reich, Naomi Giertych, Margaret Johnson, and Madeleine Pascolini-Campbell, arXiv:2311.16187v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.15209v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Selected datasets from the UEA Archive.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr} % Left for Dataset, right for numbers\n\\toprule\nDataset & Size & Dimension & Length \\\\\n\\midrule\nAtrialFibrillation & 30 & 2 & 640 \\\\\nBasicMotions & 80 & 6 & 100 \\\\\nCharacterTrajectories & 2,858 & 3 & 182 \\\\\nEpilepsy & 275 & 3 & 206 \\\\\nJapaneseVowels & 640 & 12 & 29 \\\\\nNATOPS & 360 & 24 & 51 \\\\\nUWaveGestureLibrary & 440 & 3 & 315 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Quantized symbolic time series approximation", "authors": ["Erin Carson", "Xinye Chen", "Cheng Kang"], "url": "https://arxiv.org/abs/2411.15209v2", "attribution": "\"Quantized symbolic time series approximation\" by Erin Carson, Xinye Chen, and Cheng Kang, arXiv:2411.15209v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05042v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{mpc.gmd\\_bus}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\textit{parent} & \\textit{status} & \\textit{g\\_gnd} & \\textit{name} \\\\\n\\hline\n\\hline\n1 & 1 & 5 & `dc\\_sub1' \\\\\n\\hline\n2 & 1 & 5 & `dc\\_sub2' \\\\\n\\hline\n1 & 1 & 0 & `dc\\_bus1' \\\\\n\\hline\n2 & 1 & 0 & `dc\\_bus2' \\\\\n\\hline\n3 & 1 & 0 & `dc\\_bus3' \\\\\n\\hline\n4 & 1 & 0 & `dc\\_bus4' \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Analyzing and Mitigating the Impacts of GMD and EMP Events on the Electrical Grid with PowerModelsGMD.jl", "authors": ["Adam Mate", "Arthur K. Barnes", "Russell W. Bent", "Eduardo Cotilla-Sanchez"], "url": "https://arxiv.org/abs/2101.05042v2", "attribution": "\"Analyzing and Mitigating the Impacts of GMD and EMP Events on the Electrical Grid with PowerModelsGMD.jl\" by Adam Mate, Arthur K. Barnes, Russell W. Bent, and Eduardo Cotilla-Sanchez, arXiv:2101.05042v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.01178v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n \\textbf{Dataset} & \\textbf{Total KIDs} & \\textbf{Size} & \\textbf{Manufacturers} \\\\ \\hline\n DATASET-1 & 1240 & ~250MB & 36 \\\\ \\hline\n DATASET-2 & 7736 & ~3GB & 52 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Dataset Info}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Information Extraction through AI techniques: The KIDs use case at CONSOB", "authors": ["Domenico Lembo", "Alessandra Limosani", "Francesca Medda", "Alessandra Monaco", "Federico Maria Scafoglieri"], "url": "https://arxiv.org/abs/2202.01178v1", "attribution": "\"Information Extraction through AI techniques: The KIDs use case at CONSOB\" by Domenico Lembo, Alessandra Limosani, Francesca Medda, Alessandra Monaco, and Federico Maria Scafoglieri, arXiv:2202.01178v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.10258v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average detection and localization performance on PASCAL VOC 2012.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\t\t\\hline\\noalign{\\smallskip}\n\t\tMethods & mAP & CorLoc\\\\\n\t\t\\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\n\t\tDSTL & 38.3 & 58.8 \\\\\n\t\tOICR &37.9 & 62.1 \\\\\n\t\tWCCN & 37.9 & - \\\\\n\t\tPCL & 40.6 & 63.2 \\\\\n\t\tTS2C & 40.0 & 64.4 \\\\\n\t\tC-WSL & 41.5 & 64.2 \\\\\n\t\tWSD+FSD1 & 42.4 & 65.5 \\\\\n\t\tWeakRPN & 40.8 & 64.9 \\\\\n\t\tMELM & 42.4 & - \\\\\n\t\tZLDN & 42.9 & 61.5 \\\\\n\t\tWSCDN & 43.3 & 65.2 \\\\\n\t\tC-MIL & 46.6 & 67.4 \\\\\n\t\tSDCN & 43.5 & 67.9 \\\\\n\t\tBOICR & 46.7 & 66.3 \\\\\n\t\tMIL-OICR+GAM+REG & 46.8 & \\textbf{69.5} \\\\\n\t\tOurs & \\textbf{46.9} & 66.5 \\\\\n\t\t\\noalign{\\smallskip}\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Cascade Attentive Dropout for Weakly Supervised Object Detection", "authors": ["Wenlong Gao", "Ying Chen", "Yong Peng"], "url": "https://arxiv.org/abs/2011.10258v1", "attribution": "\"Cascade Attentive Dropout for Weakly Supervised Object Detection\" by Wenlong Gao, Ying Chen, and Yong Peng, arXiv:2011.10258v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.11532v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ADF test stationarity with AIC optimization.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c|c|ccc|}\n\t\t\\cline{2-8}\n\t\t& \\textbf{Test Statistic} & \\textbf{p-value} & \\textbf{Lags} & \\textbf{Observations} & \\multicolumn{3}{c|}{\\textbf{Critical Value}} \\\\ \\cline{6-8} \n\t\t& & & & & \\textbf{1\\%} & \\textbf{5\\%} & \\textbf{10\\%} \\\\ \\hline\n\t\t\\textit{ANF} & -2.302 & 0.171 & 5 & 2529 & -3.432 & -2.863 & -2.567 \\\\ \\hline\n\t\t\\textit{EOG} & -2.422 & 0.135 & 5 & 2529 & -3.433 & -2.862 & -2.567 \\\\ \\hline\n\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networks", "authors": ["Ivan Letteri", "Giuseppe Della Penna", "Giovanni De Gasperis", "Abeer Dyoub"], "url": "https://arxiv.org/abs/2210.11532v1", "attribution": "\"DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networks\" by Ivan Letteri, Giuseppe Della Penna, Giovanni De Gasperis, and Abeer Dyoub, arXiv:2210.11532v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.06122v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\\multicolumn{2}{l}{} & \\multicolumn{2}{c}{Baselines} & \\multicolumn{5}{c}{Non-baselines} \\\\\nTargets & Matrix & BC\\textsubscript{w} & BF\\textsubscript{w} & RS & \nCS & \nAS & \nBOISE & total \\\\\n \\hline\n PKnB & PKIS1 & 1 & 7 & 7 & 2& 3 & \\textbf{7} & 8 \\\\\n BGLF4 & PKIS1 & 3 & 9 & 3 & 7 & 10 & \\textbf{10} & 11 \\\\\n ROP18 & PKIS1 & 4 & 7 & 4 & 4 & 2 & \\textbf{7} &16 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayes Optimal Informer Sets for Early-Stage Drug Discovery", "authors": ["Peng Yu", "Spencer S. Ericksen", "Anthony Gitter", "Michael A. Newton"], "url": "https://arxiv.org/abs/2011.06122v1", "attribution": "\"Bayes Optimal Informer Sets for Early-Stage Drug Discovery\" by Peng Yu, Spencer S. Ericksen, Anthony Gitter, and Michael A. Newton, arXiv:2011.06122v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.00197v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\nModel & MAPE\\\\\n\\hline\n*GAN-MC & 0.08\\%\\\\\nMC & 0.53\\% \\\\\nRBF Network(Gauss) & 0.90\\%\\\\\nRBF Network(Sqrt) & 2.33\\%\\\\\nMLP Regression & 1.11\\%\\\\\nPPR & 1.24\\%\\\\\nLR & 1.13\\%\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "GAN-MC: a Variance Reduction Tool for Derivatives Pricing", "authors": ["Weishi Wang"], "url": "https://arxiv.org/abs/2212.00197v1", "attribution": "\"GAN-MC: a Variance Reduction Tool for Derivatives Pricing\" by Weishi Wang, arXiv:2212.00197v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.00527v5_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{PCa grading comparison. For fairness, all models use proposed DRN network as a backbone. Bold indicates the best performance while the second-best scores are underlined. The abbreviations are: CC: Classification Category, PF: Proposed Framework, DL: Dual Super-Resolution Learning , PN: PSPNet , UN: UNet , and F8: FCN-8 .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\toprule\n CC & MC & PF & DL & PN & UN & F8 \\\\\\hline\n GrG1 & TPR & \\textbf{0.560} & 0.493 & \\underline{0.524} & 0.494 & 0.461\\\\\n & PPV & \\textbf{0.346} & 0.284 & 0.274 & \\underline{0.286} & 0.217\\\\\n & F1 & \\textbf{0.428} & 0.361 & 0.360 & \\underline{0.362} & 0.295\\\\\\hline\n \n GrG2 & TPR & \\textbf{0.723} & 0.630 & 0.598 & \\underline{0.702} & 0.462\\\\\n & PPV & \\textbf{0.564} & \\underline{0.511} & 0.484 & \\underline{0.511} & 0.401\\\\\n & F1 & \\textbf{0.634} & 0.564 & 0.535 & \\underline{0.592} & 0.429\\\\\\hline\n \n GrG3 & TPR & \\textbf{0.450} & \\underline{0.406} & 0.389 & 0.292 & 0.390\\\\\n & PPV & \\textbf{0.107} & \\underline{0.084} & 0.076 & 0.064 & 0.064\\\\\n & F1 & \\textbf{0.174} & \\underline{0.140} & 0.127 & 0.105 & 0.110\\\\\\hline\n \n GrG4 & TPR & \\underline{0.752} & \\textbf{0.770} & 0.706 & 0.727 & 0.678\\\\\n & PPV & \\textbf{0.335} & \\underline{0.300} & 0.273 & 0.294 & 0.232\\\\\n & F1 & \\textbf{0.463} & \\underline{0.431} & 0.394 & 0.418 & 0.346\\\\\\hline\n \n GrG5 & TPR & \\textbf{0.578} & \\underline{0.544} & 0.460 & 0.422 & 0.437\\\\\n & PPV & \\textbf{0.138} & \\underline{0.113} & 0.093 & 0.093 & 0.075\\\\\n & F1 & \\textbf{0.223} & \\underline{0.188} & 0.155 & 0.153 & 0.128\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images", "authors": ["Taimur Hassan", "Bilal Hassan", "Ayman El-Baz", "Naoufel Werghi"], "url": "https://arxiv.org/abs/2011.00527v5", "attribution": "\"A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images\" by Taimur Hassan, Bilal Hassan, Ayman El-Baz, and Naoufel Werghi, arXiv:2011.00527v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.08313v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overview of the data sets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\toprule\n Name&\\# samples&\\# features&Skewness\\ /\\ Kurtosis&Source\\\\\n \\midrule\n \\texttt{concrete}&1030&8&0.42\\ /\\ 2.68&\\\\\n \\texttt{turbine}&9568&4&0.31\\ /\\ 1.95&\\\\\n \\texttt{puma32H}&8192&32&0.02\\ /\\ 3.04&\\\\\n \\texttt{residential}&372&105&1.26\\ /\\ 5.15&\\\\\n \\texttt{crime2}&1994&123&1.52\\ /\\ 4.83&\\\\\n \\texttt{star}&2161&39&0.29\\ /\\ 2.63&\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Conditional validity of heteroskedastic conformal regression", "authors": ["Nicolas Dewolf", "Bernard De Baets", "Willem Waegeman"], "url": "https://arxiv.org/abs/2309.08313v2", "attribution": "\"Conditional validity of heteroskedastic conformal regression\" by Nicolas Dewolf, Bernard De Baets, and Willem Waegeman, arXiv:2309.08313v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.19890v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Evaluation of mean square error (MSE) of different depths in the domain $[0,20] \\times [0.01,10]$ with $N_x=2$ and $N_y=20$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c} \\hline\n Number of hidden layers & MSE of \\(u\\) & MSE of \\(u^{r_0} \\) \\\\\n \\hline\n 5 & \\(2.291\\times10^{-4}\\) & \\(1.982\\times10^{-4}\\) \\\\\n 1 & \\(5.877\\times10^{-3}\\) & \\(4.650\\times10^{-4}\\) \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Physics-Informed Neural Networks for Optimal Vaccination Plan in SIR Epidemic Models", "authors": ["Minseok Kim", "Yeongjong Kim", "Yeoneung Kim"], "url": "https://arxiv.org/abs/2502.19890v1", "attribution": "\"Physics-Informed Neural Networks for Optimal Vaccination Plan in SIR Epidemic Models\" by Minseok Kim, Yeongjong Kim, and Yeoneung Kim, arXiv:2502.19890v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.04929v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rcc|c}\n\\toprule\n\\textbf{Human3.6M} &p& $k$ & $\\# \\text{pred}$ & FVD$\\downarrow$ \\\\ \n\\midrule\n SVG-LP~ &5 & 10 & 30 &718 \\\\\n Struct-VRNN~ &5& 10 & 30 &523.4 \\\\ \n DVG~&5&10&30& 479.5 \\\\\n SRVP~ &5& 10 & 30 &416.5 \\\\\n Grid keypoint~ &8& 8 & 30 & 166.1\\\\ \n \\textbf{CVP (Ours)} &5& 1 & 30 &\\textbf{144.5} \\\\\n\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Prediction", "authors": ["Gaurav Shrivastava", "Abhinav Shrivastava"], "url": "https://arxiv.org/abs/2412.04929v2", "attribution": "\"Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Prediction\" by Gaurav Shrivastava and Abhinav Shrivastava, arXiv:2412.04929v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04243v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc|cc}\n & \\multicolumn{2}{c|}{\\textbf{CPR}} & \\multicolumn{2}{c}{\\textbf{DoGD}} \\\\\n\\toprule\n\\textbf{Model} & $\\tau$ & $\\rho$ & $\\tau$ & $\\rho$ \\\\\n\\midrule\nSAM, ViT-H & -0.77 & -0.93 & 0.61 & 0.80 \\\\ \nSAM, ViT-B & -0.75 & -0.93 & 0.66 & 0.84 \\\\\nSAM 2, ViT-L & -0.73 & -0.91 & 0.67 & 0.85 \\\\\nSAM 2, ViT-B+ & -0.71 & -0.90 & 0.69 & 0.86 \\\\\nHQ-SAM, ViT-H & -0.74 & -0.92 & 0.70 & 0.87 \\\\\nHQ-SAM, ViT-B & -0.81 & -0.95 & 0.66 & 0.84 \\\\ \\midrule \n\\textbf{Average} & \\textbf{-0.75} & \\textbf{-0.92} & \\textbf{0.67} & \\textbf{0.84} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Rank correlation between SFM IoU and object tree-likeness (CPR and DoGD), on the synthetic dataset.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects", "authors": ["Yixin Zhang", "Nicholas Konz", "Kevin Kramer", "Maciej A. Mazurowski"], "url": "https://arxiv.org/abs/2412.04243v2", "attribution": "\"Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects\" by Yixin Zhang, Nicholas Konz, Kevin Kramer, and Maciej A. Mazurowski, arXiv:2412.04243v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.01056v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc}\n \\toprule\nSetting & $\\sigma=1$ & $\\sigma=1.5$ & $\\sigma=2$ & $\\sigma=2.5$ & $\\sigma=3$ \\\\ \n \\hline \n (i) Both linear \n & 99.62 & 144.28 & 215.62 & 318.04 & 459.01 \\\\ \n \\hline\n (ii) Linear outcome & 88.23 & 136.14 & 204.60 & 302.97 & 444.19 \\\\ \n \\hline\n(iii) Log-linear density & 65.89 & 107.54 & 169.56 & 255.98 & 378.10 \\\\ \n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Sample mean of $n_2$ for the power-calculated approach. Results are evaluated over $500$ independent repeats in three sub-settings in Section~. $\\sigma$ is the noise level in~.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Diagnosing the role of observable distribution shift in scientific replications", "authors": ["Ying Jin", "Kevin Guo", "Dominik Rothenhäusler"], "url": "https://arxiv.org/abs/2309.01056v1", "attribution": "\"Diagnosing the role of observable distribution shift in scientific replications\" by Ying Jin, Kevin Guo, and Dominik Rothenhäusler, arXiv:2309.01056v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08845v7_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc|cc}\n \\hline\n & Extract$_{20k}$ & Rand$_{20k}$ & Extract$_{200k}$ & Rand$_{200k}$ \\\\ \\hline\n PSNR$\\uparrow$ & \\textbf{30.09} & 25.44 & \\textbf{33.00} & 32.01 \\\\\n SSIM$\\uparrow$ & \\textbf{0.963} & 0.932 & \\textbf{0.978} & 0.972 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Point-NeRF: Point-based Neural Radiance Fields", "authors": ["Qiangeng Xu", "Zexiang Xu", "Julien Philip", "Sai Bi", "Zhixin Shu", "Kalyan Sunkavalli", "Ulrich Neumann"], "url": "https://arxiv.org/abs/2201.08845v7", "attribution": "\"Point-NeRF: Point-based Neural Radiance Fields\" by Qiangeng Xu, Zexiang Xu, Julien Philip, Sai Bi, Zhixin Shu, Kalyan Sunkavalli, and Ulrich Neumann, arXiv:2201.08845v7, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.06027v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of model performances on the AudioCaps evaluation set. AC is short for AudioCaps dataset, CM is for Customized-Concatenation and CE is for Customized-Overlay. The dataset marked with $\\ast$ indicates fine-tuning on AudioCaps. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\\toprule\nModel & Dataset & FAD$\\downarrow$ & KL$\\downarrow$ & CLAP$\\uparrow$ & CLAP$_{A} \\uparrow$ \\\\\n\\midrule\nAudioLDM & AC+AS+2 others & $5.25$ & $1.90$ & $42.1$ & $53.5$ \\\\\nAudioGen & AC+AS+8 others & $2.87$ & $1.52$ & $46.4$ & $60.2$ \\\\\nMake-an-Audio & AC+AS+13 others & $2.39$ & $1.64$ & $45.4$ & $59.8$ \\\\\nTango & AudioCaps & $2.24$ & $1.04$ & $51.7$ & $66.2$ \\\\\nAudioLDM2 & AC+AS+6 others & $2.56$ & $1.75$ & $45.8$ & $55.5$ \\\\\nRe-AudioLDM & AudioCaps & $1.85$ & $1.46$ & $49.9$ & $62.0$ \\\\\n\\midrule\nDreamAudio & AC+CM+CE & $4.25$ & $2.48$ & $34.9$ & $43.6$ \\\\\nDreamAudio & AudioCaps$^\\ast$ & $1.92$ & $1.51$ & $47.5$ & $58.8$ \\\\\n\\midrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "DreamAudio: Customized Text-to-Audio Generation with Diffusion Models", "authors": ["Yi Yuan", "Xubo Liu", "Haohe Liu", "Xiyuan Kang", "Zhuo Chen", "Yuxuan Wang", "Mark D. Plumbley", "Wenwu Wang"], "url": "https://arxiv.org/abs/2509.06027v1", "attribution": "\"DreamAudio: Customized Text-to-Audio Generation with Diffusion Models\" by Yi Yuan, Xubo Liu, Haohe Liu, Xiyuan Kang, Zhuo Chen, Yuxuan Wang, Mark D. Plumbley, and Wenwu Wang, arXiv:2509.06027v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.06467v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overall measures of entropy $S_{J}$ for the 4 iterations.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n\\toprule\nIteration&$S_{J}$ \\\\\n\\midrule\n1&274\\\\\n\\textbf{2}&\\textbf{38} \\\\\n3&274 \\\\\n4&242 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Advances in Kth nearest-neighbour clutter removal", "authors": ["Nicoletta D'Angelo"], "url": "https://arxiv.org/abs/2310.06467v1", "attribution": "\"Advances in Kth nearest-neighbour clutter removal\" by Nicoletta D'Angelo, arXiv:2310.06467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.00760v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc}\n& \\textbf{Pred: False} & \\textbf{Pred: True} \\\\\n\\hline\n\\textbf{Label: False} & 2144 & 358 \\\\\n\\textbf{Label: True} & 474 & 613 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Dialogue (off-the-shelf dialogue reconstruction) feedback detection confusion matrix.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment", "authors": ["Firdavs Nasriddinov", "Rafal Kocielnik", "Arushi Gupta", "Cherine Yang", "Elyssa Wong", "Anima Anandkumar", "Andrew Hung"], "url": "https://arxiv.org/abs/2412.00760v1", "attribution": "\"Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment\" by Firdavs Nasriddinov, Rafal Kocielnik, Arushi Gupta, Cherine Yang, Elyssa Wong, Anima Anandkumar, and Andrew Hung, arXiv:2412.00760v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13019v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Robust Inception Score} against pixel perturbations on CIFAR10.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n\\toprule \n\\midrule\n$\\epsilon$ & 0.0 & $5\\times10^{-3}$ & $0.01$ & random noise \\\\\n\\midrule\nR-IS & 9.94 & 5.49 & 3.91 & 1.01 \\\\\n\\midrule\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "On the Robustness of Quality Measures for GANs", "authors": ["Motasem Alfarra", "Juan C. Pérez", "Anna Frühstück", "Philip H. S. Torr", "Peter Wonka", "Bernard Ghanem"], "url": "https://arxiv.org/abs/2201.13019v2", "attribution": "\"On the Robustness of Quality Measures for GANs\" by Motasem Alfarra, Juan C. Pérez, Anna Frühstück, Philip H. S. Torr, Peter Wonka, and Bernard Ghanem, arXiv:2201.13019v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.07664v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small{Sample statistics, JB and ADF test results of $GDP$ and $IR$ ($p$-values in brackets).}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\\toprule\nSeries & Mean & Sd & Skewness & Kurtosis & JB & ADF \\\\\n\\midrule\n$GDP$ & 1.22 & 2.37 & 2.22 & 0.73 &14.85 & -4.27\\\\\n& & & & & (0.00) & ($< 0.01$) \\\\\n$IR$ & -3.32 & 32.51 & 3.87 & 9.84 & 382.83 & -4.33 \\\\\n& & & & & (0.00) & ($< 0.01$) \\\\\n($GDP,IR$) & & & 1.76 & 18.78 & 179.54 \\\\\n& & & & &(0.00) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models", "authors": ["Ufuk Beyaztas", "Han Lin Shang"], "url": "https://arxiv.org/abs/2011.07664v1", "attribution": "\"Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models\" by Ufuk Beyaztas and Han Lin Shang, arXiv:2011.07664v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01383v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{12-Run Plackett-Burman Design Matrix and Data}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crrrrrrrrrrrr}\n \\toprule\n \\multirow{2}{*}{\\text{Run}} & \\multicolumn{11}{c}{\\text{Factor}} & \\multirow{2}{*}{\\text{$\\mathbf{Y}$}} \\\\\n \\cmidrule(r){2-12}\n \\text{} & \\text{A} & \\text{B} & \\text{C} & \\text{D} & \\text{E} & \\text{F} & \\text{G} & \\text{H} & \\text{I} & \\text{J} & \\text{K}\\\\\n \\midrule\n 1 & 1 & 1 & -1 & 1 & 1 & 1 & -1 & -1 & -1 & 1 & -1 & 25 \\\\\n 2 & 1 & -1 & 1 & 1 & 1 & 1 & -1 & -1 & 1 & 1 & -1 & 15 \\\\\n 3 & -1 & 1 & 1 & 1 & 1 & -1 & -1 & -1 & 1 & 1 & 1 & -35 \\\\\n 4 & 1 & 1 & 1 & -1 & -1 & -1 & -1 & 1 & 1 & 1 & -1 & 35 \\\\\n 5 & 1 & 1 & -1 & 1 & -1 & 1 & -1 & 1 & -1 & -1 & 1 & 25 \\\\\n 6 & 1 & -1 & -1 & 1 & 1 & -1 & 1 & -1 & 1 & 1 & 1 & 5 \\\\\n 7 & -1 & -1 & -1 & 1 & -1 & 1 & -1 & 1 & 1 & 1 & 1 & -5 \\\\\n 8 & -1 & 1 & -1 & 1 & -1 & 1 & 1 & 1 & 1 & 1 & -1 & -15 \\\\\n 9 & -1 & -1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & -1 & -1 & -25 \\\\\n 10 & 1 & -1 & 1 & 1 & -1 & -1 & -1 & 1 & 1 & -1 & -1 & 15 \\\\\n 11 & -1 & 1 & 1 & 1 & 1 & -1 & 1 & -1 & -1 & 1 & 1 & -35 \\\\\n 12 & -1 & -1 & -1 & -1 & -1 & -1 & -1 & -1 & -1 & -1 & -1 & -5 \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Automated Analysis of Experiments using Hierarchical Garrote", "authors": ["Wei-Yang Yu", "V. Roshan Joseph"], "url": "https://arxiv.org/abs/2411.01383v1", "attribution": "\"Automated Analysis of Experiments using Hierarchical Garrote\" by Wei-Yang Yu and V. Roshan Joseph, arXiv:2411.01383v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.03496v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{$|u_h - u_d|_{H^2(\\Omega)}$ in , $\\ell =6$, $k=1$, $p=2$, $\\Delta U_h\\subset F_h$.}\n\\begin{tabular}{|c||c|c|c|}\n \\hline\n $\\beta^2 \\;\\backslash\\; \\gamma^2$ & $10^0$ & $10^{2}$ & $10^{4}$ \\\\ \\hline \\hline\n $1$ & 9.76e-03 & 9.76e-03 & 9.76e-03 \\\\ \\hline\n $10^{-2}$ & 9.76e-03 & 9.76e-03 & 9.76e-03 \\\\ \\hline\n $10^{-4}$ & 9.76e-03 & 9.76e-03 & 9.88e-03 \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Variational Formulations of the Strong Formulation -- Forward and Inverse Modeling using Isogeometric Analysis and Physics-Informed Networks", "authors": ["Kent-Andre Mardal", "Jarle Sogn", "Marius Zeinhofer"], "url": "https://arxiv.org/abs/2312.03496v1", "attribution": "\"Variational Formulations of the Strong Formulation -- Forward and Inverse Modeling using Isogeometric Analysis and Physics-Informed Networks\" by Kent-Andre Mardal, Jarle Sogn, and Marius Zeinhofer, arXiv:2312.03496v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1910.09722v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Validation accuracies of the scene understanding model using the evaluation dataset in NTHU-DDD dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lclclclclcl}\n\t\t\t\n\t\t\t\\hline\\noalign{\\smallskip}\n\t\t\t\n\t\t\tScenario & Glasses and illumination & Head & Mouth & Eye \\\\\n\t\t\t\n\t\t\t\\noalign{\\smallskip}\n\t\t\t\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\noalign{\\smallskip}\n\t\t\t\n\t\t\t{Day bare face} & 0.99 & 0.99 & 0.98 & 0.89\\\\\n\t\t\t\n\t\t\t{Day glasses} & 0.97 & 0.93 & 0.95 & 0.81\\\\\n\t\t\t\n\t\t\t{Day sunglasses} & 0.98 & 0.97 & 0.78 & 0.78\\\\\n\t\t\t\n\t\t\t{Night bare face} & 0.99 & 0.95 & 0.97 & 0.82\\\\\n\t\t\t\n\t\t\t{Night glasses} & 0.97 & 0.96 & 0.88 & 0.92\\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t\n\t\t\t{Average} & 0.98 & 0.96 & 0.912 & 0.844\\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t\n\t\t\t{Total average} & & & & 0.924\\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework", "authors": ["Jongmin Yu", "Sangwoo Park", "Sangwook Lee", "Moongu Jeon"], "url": "https://arxiv.org/abs/1910.09722v1", "attribution": "\"Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework\" by Jongmin Yu, Sangwoo Park, Sangwook Lee, and Moongu Jeon, arXiv:1910.09722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.03096v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of the time-complexities of the parallel algorithms studied in this work.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc} \n \\toprule\n \\textbf{Parallel Algorithm} & \\textbf{time complexity} \\\\ \n \\midrule\n Imminent collisions & $O(d)$ \\\\\n End collisions & $O(\\frac{d^2}{p} + d \\log d)$ \\\\\n Search collisions & $O(\\frac{d \\log^2 d}{p} + \\log^3 d)$ \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Weight-Sharing Regularization", "authors": ["Mehran Shakerinava", "Motahareh Sohrabi", "Siamak Ravanbakhsh", "Simon Lacoste-Julien"], "url": "https://arxiv.org/abs/2311.03096v2", "attribution": "\"Weight-Sharing Regularization\" by Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, and Simon Lacoste-Julien, arXiv:2311.03096v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09827v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Choices of IEEE standard precisions for three-precision GMRES-IR presented in , and their convergence conditions.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cccc|ccc|}\n\t\t\\hline\n\t\t\\multirow{2}{*}{$u_f$} & \\multirow{2}{*}{$u$} & \\multirow{2}{*}{$u_r$} & \\multirow{2}{*}{$\\kappa_\\infty(A)$} & \\multicolumn{2}{c}{Backward error}& \\\\\n\t\t& & & & Normwise & Componentwise & Forward error \\\\ \\hline\n\t\thalf & half & single & $10^{4}$ & half & half & half \\\\\n\t\thalf & single & double & $10^{8}$ & single & single & single \\\\\n\t\thalf & double & quad & $10^{12}$ & double & double & double \\\\ \n\t\tsingle & single & double & $10^{8}$ & single & single & single \\\\\n\t\tsingle & double & quad & $10^{16}$ & double & double & double \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Mixed Precision GMRES-based Iterative Refinement with Recycling", "authors": ["Eda Oktay", "Erin Carson"], "url": "https://arxiv.org/abs/2201.09827v2", "attribution": "\"Mixed Precision GMRES-based Iterative Refinement with Recycling\" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.14060v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|} \n \\hline\n & \\multicolumn{2}{|c|}{Phone error rate}\\\\\\hline\nModel & WSJ & CUSENT \\\\\\hline\nCD-GMM-HMM & 6.98\\% & 9.39\\% \\\\\nSGMMs-HMM & 6.95\\% &8.78\\% \\\\%\\hline\nDNN/DBN-HMM & 6.26\\% &7.02\\% \\\\\\hline \nMultilingual DNN-BN & 5.37\\% & 8.32\\% \\\\%\\hline \nMultilingual DNN-SBN & 5.33\\% & 7.96\\% \\\\\\hline \n \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performances on WSJ and CUSENT corpora using different models}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Unsupervised Spoken Term Discovery on Untranscribed Speech", "authors": ["Man-Ling Sung"], "url": "https://arxiv.org/abs/2011.14060v1", "attribution": "\"Unsupervised Spoken Term Discovery on Untranscribed Speech\" by Man-Ling Sung, arXiv:2011.14060v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.09355v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{KL-Divergences for Different Trajectories.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\n\\textit{p} & \\textit{q} & \\textit{$D_{KL(p||q)}$} & \\textit{$D_{KL(p||q)}$} \\\\\n\\hline\nTrajectory 1 & Trajectory 2 & 7.21 & 2.82 \\\\\nTrajectory 1 & Trajectory 3 & 1.28 & 1.83 \\\\\nTrajectory 1 & Trajectory 4 & 19.07 & 1269.47 \\\\\nTrajectory 1 & Trajectory 5 & 3.08 & 996.17 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data", "authors": ["Weizhu Qian", "Fabrice Lauri", "Franck Gechter"], "url": "https://arxiv.org/abs/1911.09355v1", "attribution": "\"A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data\" by Weizhu Qian, Fabrice Lauri, and Franck Gechter, arXiv:1911.09355v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.00883v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\nRegime & $\\mathbb{M}$ & Log-Likelihood & $\\hat{p}$ & $1/\\hat{p}$\\\\\n\\hline \n\\hline\n$x^+$ & $8$ & $23.0935$ & $0.236527$ & $4.22785$\\\\ \n\\hline\n$x^-$ & $66$ & $130.306$ & $0.34617$ & $2.88876$\\\\ \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Maximum likelihood estimation output of the proportion $p$.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Keynesian Beauty Contest in Morocco's Public Procurement Reform", "authors": ["Nizar Riane"], "url": "https://arxiv.org/abs/2503.00883v1", "attribution": "\"Keynesian Beauty Contest in Morocco's Public Procurement Reform\" by Nizar Riane, arXiv:2503.00883v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17215v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc} % centered columns (4 columns)\n\t\t\t\\hline %inserts double horizontal lines\n\t\tCopula & ~~~~~~~~~~~~~~~~~Parameter &~~~~~~~~~~~~~~~ p-value \\\\\n\t\t\t\\hline\n\t\tFrank& ~~~~~~~~~~~~~~~~~1.3776&~~~~~~~~~~~~~~~~~~0.488\\\\\n\t\tGumbel-Hougaarad& ~~~~~~~~~~~~~~~~~1.1542&~~~~~~~~~~~~~~~~~~0.036\\\\\n\tJoe& ~~~~~~~~~~~~~~~~~1.1977&~~~~~~~~~~~~~~~~~~0\\\\\n\tProduct& ~~~~~~~~~~~~~~~~~0&~~~~~~~~~~~~~~~~~~0\\\\\t\n\t\t\t\\hline\t \t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Multivariate Rényi inaccuracy measures based on copulas: properties and application", "authors": ["Shital Saha", "Suchandan Kayal"], "url": "https://arxiv.org/abs/2502.17215v1", "attribution": "\"Multivariate Rényi inaccuracy measures based on copulas: properties and application\" by Shital Saha and Suchandan Kayal, arXiv:2502.17215v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.06049v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rr|ll|cc}\nYear (Race) \t\t& D votes & R votes & D \\% &R \\% & \\# D Reps & \\# R Reps \\\\ \\hline\n1980 (Pres) & 367973 & 652264 \t&\t36.07\\%&\t63.93\\% & 3 & 2\\\\\n1982 (Gov) & 627960 & 392740 \t&\t61.52\\%&\t38.48\\% & 3 & 3\\\\\n1984 (Pres) & 454974 & 821818 \t&\t35.63\\%&\t64.37\\% & 2 & 4\\\\\n1986 (Gov) & 616325 & 434418 \t&\t58.66\\%&\t41.34\\% & 3 & 3\\\\\n1988 (Pres) & 621453 & 728177 \t&\t46.05\\%&\t53.95\\% & 3 & 3\\\\\n1990 (Gov) & 626032 & 358403 \t&\t63.59\\%&\t 36.41\\% & 3 & 3\\\\\n1992 (Pres) & 629681 & 562850 \t&\t52.80\\%&\t 47.20\\% & 2 & 4\\\\\n1994 (Gov) & 619205 & 432042 \t&\t58.90\\%&\t 41.10\\% & 2 & 4\\\\\n1996 (Pres) & 671152 & 691848 \t&\t49.24\\%&\t 50.76\\% & 2 & 4\\\\\n1998 (Gov) & 628846 & 633780 \t&\t49.80\\%& 50.20\\% & 2 & 4\t\\\\\n2000 (Pres) & 738227 & 883745 \t&\t45.51\\%&54.49\\%\t & 2 & 4\\\\\n2002 (Gov) & 475373 & 884583 \t&\t34.96\\%&\t65.04\\% & 2 & 5\\\\\n2004 (Pres) & 1001725 & 1101256 \t&\t47.63\\%&\t52.37\\% & 3 & 4\\\\\n2006 (Gov) & 887986 & 625886 \t&\t58.66\\%&\t41.34\\% & 4 & 3\\\\\n2008 (Pres) & 1288633 & 1073629 \t&\t54.55\\%&\t45.45\\% & 5 & 2\\\\\n2010 (Gov) & 915436 & 199792 \t&\t82.09\\%&\t17.91\\% & 3 & 4\\\\\n2012 (Pres) & 1323102 & 1185243 \t&\t52.75\\%&\t47.25\\% & 3 & 4\\\\\n2014 (Gov) & 1006433 & 938195 \t&\t51.75\\%&\t48.25\\% & 3 & 4\\\\\n2016 (Pres) & 1338870 & 1202484 \t&\t52.68\\%&\t47.32\\% & 3 & 4\\\\\n2018 (Gov) & 1348888 & 1080801\t&\t55.52\\%&\t44.48\\% & 4 & 3\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\footnotesize Votes Cast in Colorado Elections for Two Major Parties in Top of Ticket Races and Number of U.S. Congressional Representatives from Two Major Parties, 1980-2018. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Colorado in Context: Congressional Redistricting and Competing Fairness Criteria in Colorado", "authors": ["Jeanne Clelland", "Haley Colgate", "Daryl DeFord", "Beth Malmskog", "Flavia Sancier-Barbosa"], "url": "https://arxiv.org/abs/2011.06049v2", "attribution": "\"Colorado in Context: Congressional Redistricting and Competing Fairness Criteria in Colorado\" by Jeanne Clelland, Haley Colgate, Daryl DeFord, Beth Malmskog, and Flavia Sancier-Barbosa, arXiv:2011.06049v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.06588v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of computational complexity (run time in second) of the algorithms}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\t\t\t\t\\hline\n\t\t\t\tAlgorithm & This work & Linear & Sign & Finite & ADMM & DTAC-ADMM\\\\\n\t\t\t\t\\hline\n\t\t\t\tRun time & 0.0175 & 0.0171 & 0.0178 & 0.0188 & 0.0162 & 0.0165 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\\hline\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Distributed Automatic Generation Control subject to Ramp-Rate-Limits: Anytime Feasibility and Uniform Network-Connectivity", "authors": ["Mohammadreza Doostmohammadian", "Hamid R. Rabiee"], "url": "https://arxiv.org/abs/2509.06588v1", "attribution": "\"Distributed Automatic Generation Control subject to Ramp-Rate-Limits: Anytime Feasibility and Uniform Network-Connectivity\" by Mohammadreza Doostmohammadian and Hamid R. Rabiee, arXiv:2509.06588v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2106.05847v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Clustering algorithms used for the LFR benchmark}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l}\n\\textbf{Method} & \\textbf{Source} & \\textbf{Parameters} \\\\ \\hline\nGeometric modularity & this paper & $-0.5<log_{10}\\tau<0$, $\\kappa_0$=0 \\\\\nModularity & & Resolution parameter = 1 \\\\\nBelief propagation & & Max. iterations=100, Tolerance=$10^{-4}$, Re-runs=5, Threshold=0.005, $q_{max}$=7 \\\\\nSpectral & & Number of clusters = 40 \\\\\nInfomap & & \\\\\nRicci-flow & & Number of iterations = 100 \\\\\nSpinglass & & \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature", "authors": ["Adam Gosztolai", "Alexis Arnaudon"], "url": "https://arxiv.org/abs/2106.05847v3", "attribution": "\"Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature\" by Adam Gosztolai and Alexis Arnaudon, arXiv:2106.05847v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.00102v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\toprule {Parameters} & {Values} & {Units} \\\\ \\midrule% centered columns (4 columns) \n$b$ & 50 & $N s/m$\\\\ % inserting body of the table \n$m$ & 1000 & $kg$\\\\ \ninitial speed & 0 & $m/s$\\\\\ndesired speed & 15 & $m/s$\\\\\nrise time & $<5$ & $s$\\\\\nsteady-state error & $<5$\\% & -\\\\ \\midrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Vehicle parameters}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Verifying a Cruise Control System using Simulink and SpaceEx", "authors": ["Nikolaos Kekatos"], "url": "https://arxiv.org/abs/2101.00102v1", "attribution": "\"Verifying a Cruise Control System using Simulink and SpaceEx\" by Nikolaos Kekatos, arXiv:2101.00102v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.02273v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Labels included in the VLEngagement dataset with their variable type, value interval and category.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllc} \\hline\n Type&Label&Interval&Category\\\\ \\hline\n cont.&Mean Star Rating & $[1,5)$ & Explicit Rating\\\\ \n cont.&View Count & $(5,\\infty)$ & Popularity\\\\ \n \n cont.&SMNET (Eq. ) & $(0,1)$ & Watch Time \\\\ \n cont.&SANET (Eq. ) & $[0,1)$ & Watch Time \\\\ \n \n cont.&Std. of NET & $(0,1)$ & Watch Time \\\\ \n cont.&Number of User Sessions & $(5,\\infty)$ & Watch Time \\\\ \n cont.&Engagement Times (NET) & $[0,1)$ & Watch Time \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement", "authors": ["Sahan Bulathwela", "Maria Perez-Ortiz", "Emine Yilmaz", "John Shawe-Taylor"], "url": "https://arxiv.org/abs/2011.02273v1", "attribution": "\"VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement\" by Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, and John Shawe-Taylor, arXiv:2011.02273v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07511v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters of clustered federated learning (CFL) experiments.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|}\n\\hline\n\\textbf{Parameter (s)} & \\textbf{Value (s)} \\\\ \\hline\nCommunication Rounds & 30, 50, \\& 100 \\\\ \\hline\nEpochs & 5 \\& 10 \\\\ \\hline\nBatch Size & 16 \\& 32 \\\\ \\hline\nLearning Rate & $1e^{-3}$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge", "authors": ["Adnan Qayyum", "Kashif Ahmad", "Muhammad Ahtazaz Ahsan", "Ala Al-Fuqaha", "Junaid Qadir"], "url": "https://arxiv.org/abs/2101.07511v1", "attribution": "\"Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge\" by Adnan Qayyum, Kashif Ahmad, Muhammad Ahtazaz Ahsan, Ala Al-Fuqaha, and Junaid Qadir, arXiv:2101.07511v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.10467v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n \\textbf{GNN + MGM (Memory \\%)} & \\textbf{Fact} & \\textbf{Bias} \\\\\n \\midrule\n FAGCN + MGM (60\\%) & 39.07 & 43.09 \\\\\n FAGCN + MGM (80\\%) & 41.31 & \\textbf{46.02} \\\\\n FAGCN + MGM (90\\%) & 46.88 & 44.36 \\\\\n FAGCN + MGM (100\\%) & \\textbf{48.77} & 45.02 \\\\\n \\hdashline\n Gatv2 + MGM (60\\%) & 45.06 & 45.27 \\\\\n Gatv2 + MGM (80\\%) & 49.77 & \\textbf{52.44} \\\\\n Gatv2 + MGM (90\\%) & \\textbf{54.51} & 50.44 \\\\\n Gatv2 + MGM (100\\%) & 54.13 & 52.41 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance comparison of GNN + MGM at different memory allocations.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media", "authors": ["Muhammad Arslan Manzoor", "Ruihong Zeng", "Dilshod Azizov", "Preslav Nakov", "Shangsong Liang"], "url": "https://arxiv.org/abs/2412.10467v1", "attribution": "\"MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media\" by Muhammad Arslan Manzoor, Ruihong Zeng, Dilshod Azizov, Preslav Nakov, and Shangsong Liang, arXiv:2412.10467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.15568v1_tex_table19.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Subject Performance and Estimated Group Performance After the Test}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\hline\n \\hline\n & & \\multicolumn{2}{c}{Pooled sample} \\\\\\cline{3-4} \n Variable & & Mean & Std. dev. \\\\\n \\hline\n Actual own performance & & 12.84 & 3.551 \\\\\n Estimated group performance & & 12.52 & 2.594 \\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)", "authors": ["Marius Protte"], "url": "https://arxiv.org/abs/2507.15568v1", "attribution": "\"Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)\" by Marius Protte, arXiv:2507.15568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.11150v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Heterogeneous Response of Investment to $\\epsilon^{X}_t$ \\\\ \\footnotesize Long term Leverage }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n & \\multicolumn{4}{c}{Firm investment - $\\Delta \\log k_{i,t}$ } \\\\\n & (1) & (2) & (3) & (4) \\\\ \\hline \\hline\n & & & & \\\\\n$\\left(\\tilde{l}_{i,t-1} - \\mathbb{E}_{i} \\left[ \\tilde{l}_{t}\\right] \\right) \\epsilon^{X}_t$ & -0.278*** & -0.275*** & -0.205* & -0.202* \\\\\n & (0.0961) & (0.0936) & (0.114) & (0.112) \\\\\n & & & & \\\\\nFirm FE & yes & yes & yes & yes \\\\\nSector-Time FE & yes & yes & yes & yes \\\\\nControl for Lagged Inv. & no & yes & yes & yes \\\\\nControl for Lagged Currency Mismatch & no & no & yes & yes \\\\\nControl for $l_{i,t-1}$ & no & no & no & yes \\\\\n & & & & \\\\\nObservations & 11,670 & 11,516 & 10,064 & 9,995 \\\\ \\hline \n$R^{2}$ & 0.149 & 0.155 & 0.161 & 0.170 \\\\ \\hline \\hline\n\\multicolumn{5}{c}{ Clustered standard errors in parentheses} \\\\\n\\multicolumn{5}{c}{ *** p$<$0.01, ** p$<$0.05, * p$<$0.1} \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity", "authors": ["Santiago Camara", "Sebastian Ramirez Venegas"], "url": "https://arxiv.org/abs/2209.11150v1", "attribution": "\"The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity\" by Santiago Camara and Sebastian Ramirez Venegas, arXiv:2209.11150v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.09789v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small Comparison of average acoustic characteristic values. SILA outperforms the baseline, with scores within the expected range for each feature, indicating improved disentanglement between audio and its characteristics.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nCategories & Sub Categories & Baseline (w/o SILA) & SILA \\\\\n\\midrule \\midrule\n\\multirow{4}{*}{Reverb (RT60)} & \\textit{dry} & 0.55 & 0.11 \\\\\n & \\textit{slightly wet} & 0.52 & 0.52 \\\\\n & \\textit{wet} & 0.50 & 0.73 \\\\\n & \\textit{very wet} & 0.51 & 0.84 \\\\ \\midrule\n\\multirow{2}{*}{Noise (SNR)} & \\textit{silent background} & 4.31 & 6.78 \\\\\n & \\textit{noisy background} & 2.43 & 1.72 \\\\ \\midrule\n\\multirow{2}{*}{Brightness (spec. centroid)} & \\textit{dull} & 57.53 & 41.71 \\\\\n & \\textit{bright} & 59.24 & 71.37 \\\\ \\midrule\n\\multirow{2}{*}{Pitch (octave)} & \\textit{low} & 2.31 & 1.17 \\\\\n & \\textit{high} & 2.95 & 3.78 \\\\ \\midrule\n\\multirow{2}{*}{Duration (sec.)} & \\textit{3 seconds} & 4.87 & 3.12 \\\\\n & \\textit{4 seconds} & 4.94 & 3.97 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SILA: Signal-to-Language Augmentation for Enhanced Control in Text-to-Audio Generation", "authors": ["Sonal Kumar", "Prem Seetharaman", "Justin Salamon", "Dinesh Manocha", "Oriol Nieto"], "url": "https://arxiv.org/abs/2412.09789v1", "attribution": "\"SILA: Signal-to-Language Augmentation for Enhanced Control in Text-to-Audio Generation\" by Sonal Kumar, Prem Seetharaman, Justin Salamon, Dinesh Manocha, and Oriol Nieto, arXiv:2412.09789v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.17414v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{cllcl}\n \\toprule\n \\textbf{Label} & \\textbf{Purpose} &&\\textbf{Label} & \\textbf{Purpose}\\\\\n \\midrule\n p\\textsubscript1 & Improve service && p\\textsubscript{15} & Research \\\\\n p\\textsubscript2 & Provide service && p\\textsubscript{16} & Extract vocal and disorder relationships \\\\\n p\\textsubscript3 & Identify && p\\textsubscript{17} & Fighting spam/malware \\\\\n p\\textsubscript4 & Collect, measure, \\& process autism risk && p\\textsubscript{18} & Facilitate data collection \\\\\n p\\textsubscript5 & Extracting acoustic features && p\\textsubscript{19} & Identify web browser \\\\ \n p\\textsubscript6 & Send alert && p\\textsubscript{20} & Find site visit statistics \\\\ \n p\\textsubscript7 & Fulfill request && p\\textsubscript{21} & Marketing \\\\\n p\\textsubscript8 & Anti-fraud && p\\textsubscript{22} & Send research participation request \\\\ \n p\\textsubscript9 & Keep customer posted && p\\textsubscript{23} & Promote service \\\\\n p\\textsubscript{10} & Internal improvement && p\\textsubscript{24} & Any \\\\\n p\\textsubscript{11} & Send service information && p\\textsubscript{25} & Understand customer behavior \\\\\n p\\textsubscript{12} & Send notice && p\\textsubscript{26} & Facilitate interaction \\\\\n p\\textsubscript{13} & Auditing && p\\textsubscript{27} & Provide product \\\\\n p\\textsubscript{14} & Data analysis \\\\ \n \\bottomrule \n \\end{tabular}\n\\caption{ChatterBaby\\texttrademark's purposes}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "The Privacy Policy Permission Model: A Unified View of Privacy Policies", "authors": ["Maryam Majedi", "Ken Barker"], "url": "https://arxiv.org/abs/2403.17414v1", "attribution": "\"The Privacy Policy Permission Model: A Unified View of Privacy Policies\" by Maryam Majedi and Ken Barker, arXiv:2403.17414v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08459v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fire Segmentation Accuracy of Q-Rough Sets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n Sequence & $FP(\\%)$ & $FN(\\%)$ & $Precision$ & $Recall$\\\\\n \\hline\n PosVideo1 & 0.5 & 15 & 0.99 & 0.87\\\\\n \\hline\nPosVideo2 & 3 & 5 & 0.97 & 0.95\\\\\n\\hline\nPosVideo4 & 4 & 1 & 0.96 & 0.99\\\\\n\\hline\nPosVideo5 & 3 & 2 & 0.97 & 0.98\\\\\n\\hline\nPosVideo11 & 2 & 10 & 0.98 & 0.9\\\\\n\\hline\nWeddingDress & 2 & 8 & 0.98 & 0.91\\\\\n\\hline\nChristmasTree & 23 & 5 & 0.77 & 0.93\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Fire Threat Detection From Videos with Q-Rough Sets", "authors": ["Debarati B. Chakrabortya", "Vinay Detania", "Shah Parshv Jigneshkumar"], "url": "https://arxiv.org/abs/2101.08459v1", "attribution": "\"Fire Threat Detection From Videos with Q-Rough Sets\" by Debarati B. Chakrabortya, Vinay Detania, and Shah Parshv Jigneshkumar, arXiv:2101.08459v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04724v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Effect of Euler sample steps N on flow matching module. Objective metrics improve rapidly in the first 5 steps and continued qualitative improvements up to 20 steps.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\hline\nModel & N & UTMOS $\\uparrow$ & WER $\\downarrow$ & SECS $\\uparrow$ & RTF $\\downarrow$ \\\\ \\hline\n\\multirow{4}{*}{StableVC} & 1 & 3.42 & 3.87 & 0.54 & 0.031 \\\\\n & 2 & 3.49 & 3.94 & 0.55 & 0.043 \\\\\n & 5 & 3.82 & 2.98 & 0.61 & 0.068 \\\\\n & 10 & 4.12 & 2.03 & 0.67 & 0.146 \\\\\n & 20 & 4.15 & 2.07 & 0.66 & 0.215 \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "StableVC: Style Controllable Zero-Shot Voice Conversion with Conditional Flow Matching", "authors": ["Jixun Yao", "Yuguang Yang", "Yu Pan", "Ziqian Ning", "Jiaohao Ye", "Hongbin Zhou", "Lei Xie"], "url": "https://arxiv.org/abs/2412.04724v2", "attribution": "\"StableVC: Style Controllable Zero-Shot Voice Conversion with Conditional Flow Matching\" by Jixun Yao, Yuguang Yang, Yu Pan, Ziqian Ning, Jiaohao Ye, Hongbin Zhou, and Lei Xie, arXiv:2412.04724v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.06796v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n \\toprule\n Symbol & Value & Symbol & Value & \\\\\n \\midrule\n $\\alpha$ & 0.5 & $j_{0,\\text{ref}}$ & $1.08 \\cdot 10^{-17}$ A/m$^2$ \\\\\n $p_{H_2}$ & $1$ bar & $d_{\\text{membrane}}$ & $2.54\\cdot 10^{-4}$ m \\\\\n $p_{O_2}$ & $1$ bar & $d_{\\text{electrode}}$ & $8\\cdot 10^{-4}$ m \\\\\n $a_w$ & $1$ bar & $\\sigma_{\\text{electrode}}$ & $5.53\\cdot 10^{6}$ ($\\Omega\\cdot$ m)$^{-1}$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Parameters used in the PEM electrolyser electrochemical model }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Optimisation of Electrolyser Operation: Integrating External Heat", "authors": ["Matthias Derez", "Alexander Hoogsteyn", "Erik Delarue"], "url": "https://arxiv.org/abs/2507.06796v1", "attribution": "\"Optimisation of Electrolyser Operation: Integrating External Heat\" by Matthias Derez, Alexander Hoogsteyn, and Erik Delarue, arXiv:2507.06796v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13382v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\toprule\n Graph ($G$)& $\\gamma_2$ & $\\gamma_n$ & Lower bound & $K(G)$ & Upper bound \\\\\n \\midrule\n Complete bipartite $K_{10,15}$ & 10 & 25 & 23.47 & 23.50 & 23.50 \\\\\n Windmill $W(3,10)$ & 1 & 31 & 44.32 & 45.45&45.45 \\\\\n $W'(3,10,5)$ & 5 & 35 & 34.49 & 35.49&35.49 \\\\\n $W''(3,10,5)$ & 5 & 35 & 35.21 &35.54 &35.54 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Values of the lower and upper bounds from Eq. and $K(G)$ for several slightly regular graphs.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Spectral Approach to Kemeny's Constant", "authors": ["Aida Abiad", "Ángeles Carmona", "Andrés M. Encinas", "Maria José Jiménez", "Álvaro Samperio"], "url": "https://arxiv.org/abs/2503.13382v1", "attribution": "\"A Spectral Approach to Kemeny's Constant\" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.04457v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean squared errors of the estimates from for each of the methods}\n\\begin{tabular}{|c|c|c|}\n\t\t\t\t\t\\hline\n\t\t\t\t\tMethod & Mean Squared Error \\\\ \\hline \n\t\t\t\t\tFearnhead & $1.5\\cdot 10^{-5}$ \\\\\n\t\t\t\t\tLNA (with restart) & $6.5\\cdot 10^{-6}$ \\\\\n\t\t\t\t\tDiffusion guiding term & $2.1\\cdot 10^{-5}$ \\\\\n\t\t\t\t\tPoisson guiding term & $4.0\\cdot 10^{-7}$ \\\\ \\hline\n\t\t\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Guided simulation of conditioned chemical reaction networks", "authors": ["Marc Corstanje", "Frank van der Meulen"], "url": "https://arxiv.org/abs/2312.04457v3", "attribution": "\"Guided simulation of conditioned chemical reaction networks\" by Marc Corstanje and Frank van der Meulen, arXiv:2312.04457v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.11927v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\toprule\n\\textbf{Correlation} & \\textbf{Feature} \\\\ \\midrule\n-0.219 & sadness score from $s_{18}$ (E) \\\\ \n-0.196 & sadness score from $s_{17}$ (E) \\\\ \n-0.175 & positive score from $s_{18}$ (E) \\\\ \n0.173 & std-dev($\\Delta$ no. of words per turn) (T) \\\\ \n0.172 & std-dev(no. of words per turn) (T) \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Top 5 features most correlated with suicidal risk (feature set in parentheses). All correlations statistically significant $(p<0.05)$}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Automatic prediction of suicidal risk in military couples using multimodal interaction cues from couples conversations", "authors": ["Sandeep Nallan Chakravarthula", "Md Nasir", "Shao-Yen Tseng", "Haoqi Li", "Tae Jin Park", "Brian Baucom", "Craig J. Bryan", "Shrikanth Narayanan", "Panayiotis Georgiou"], "url": "https://arxiv.org/abs/1911.11927v1", "attribution": "\"Automatic prediction of suicidal risk in military couples using multimodal interaction cues from couples conversations\" by Sandeep Nallan Chakravarthula, Md Nasir, Shao-Yen Tseng, Haoqi Li, Tae Jin Park, Brian Baucom, Craig J. Bryan, Shrikanth Narayanan, and Panayiotis Georgiou, arXiv:1911.11927v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.04444v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textcolor{black}{Average running times (seconds) of one realisation of estimators for the $q$-GLE model.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc} \n\\toprule \\\\\n Experiment & $ \\hat{\\theta}_{2,n}$ & $\\hat{\\theta}_{3,n} $ \n \\\\ \\midrule\n \\\\ \n \\textbf{Design A} - \\textbf{Case I} \n & 38.25 & 44.33\n \\\\\n \\textbf{Design A} - \\textbf{Case II} \n & 24.46 & 28.34 \n \\\\\n \\textbf{Design B} - \\textbf{Case I} \n & 27.48 & 28.71 \n \\\\\n \\textbf{Design B} - \\textbf{Case II} \n & 90.26 & 104.12 \n \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Parameter Inference for Hypo-Elliptic Diffusions under a Weak Design Condition", "authors": ["Yuga Iguchi", "Alexandros Beskos"], "url": "https://arxiv.org/abs/2312.04444v2", "attribution": "\"Parameter Inference for Hypo-Elliptic Diffusions under a Weak Design Condition\" by Yuga Iguchi and Alexandros Beskos, arXiv:2312.04444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2101.10266v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l}\n authblk & babel & cjk & dvips \\\\\n epsf & epsfig & euler & float \\\\\n fullpage & geometry & graphics & hyperref \\\\\n layout & linespread & lmodern & maltepaper \\\\\n navigator & pdfcomment & pgfplots & psfig \\\\\n pstricks & t1enc & titlesec & tocbind \\\\\n ulem\n\\end{tabular}\n\\end{adjustbox}\n\\caption{LaTeX style packages that must not be used.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms", "authors": ["Rohan Sukumaran", "Parth Patwa", "T V Sethuraman", "Sheshank Shankar", "Rishank Kanaparti", "Joseph Bae", "Yash Mathur", "Abhishek Singh", "Ayush Chopra", "Myungsun Kang", "Priya Ramaswamy", "Ramesh Raskar"], "url": "https://arxiv.org/abs/2101.10266v2", "attribution": "\"COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms\" by Rohan Sukumaran, Parth Patwa, T V Sethuraman, Sheshank Shankar, Rishank Kanaparti, Joseph Bae, Yash Mathur, Abhishek Singh, Ayush Chopra, Myungsun Kang, Priya Ramaswamy, and Ramesh Raskar, arXiv:2101.10266v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12498v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n\\toprule\nDataset & \\multicolumn{2}{c|}{CIFAR-10} & \\multicolumn{2}{c}{CIFAR-100} \\\\ \n\\hline\nNoise & Sym 80 & Asym 40 & Sym 80 & Asym 40\\\\\n\\hline\nResNet-32 & 74.8 $\\pm$.3 & 87.5$\\pm$1.3 & 30.3$\\pm$.4 & 55.5$\\pm$.8 \\\\\nResNet-34 & 90.8$\\pm$.6 & 90.4$\\pm$.4 & 69.4$\\pm$.3& 65.2$\\pm$.2 \\\\\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Investigating Why Contrastive Learning Benefits Robustness Against Label Noise", "authors": ["Yihao Xue", "Kyle Whitecross", "Baharan Mirzasoleiman"], "url": "https://arxiv.org/abs/2201.12498v4", "attribution": "\"Investigating Why Contrastive Learning Benefits Robustness Against Label Noise\" by Yihao Xue, Kyle Whitecross, and Baharan Mirzasoleiman, arXiv:2201.12498v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.16874v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance (accuracy in \\%) comparison of the proposed multi-modality (speech-text) model with speech-only model for dysarthric detection and assessment tasks across different settings: speaker-dependent and unseen words (SD), speaker-independent seen words (SID-1), and unseen words (SID-2).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ccc||ccc|}\n\\hline\n & \\multicolumn{3}{c||}{Dysarthric detection} & \\multicolumn{3}{c|}{Severity assessment} \\\\ \\hline\n\\hline\nModality & SD & SID-1 & SID-2 & SD & SID-1 & SID-2 \\\\ \\hline\nSpeech-only & 95.38 & 89.61 & \\textbf{87.76} & 97.50 & 49.14 & 54.71 \\\\ \\hline\nSpeech-text &\n \\textbf{99.53} &\n \\textbf{93.20} &\n 85.30 &\n \\textbf{98.12} &\n \\textbf{51.97} &\n \\textbf{57.42} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Multi-modal Approach to Dysarthria Detection and Severity Assessment Using Speech and Text Information", "authors": ["Anuprabha M", "Krishna Gurugubelli", "V Kesavaraj", "Anil Kumar Vuppala"], "url": "https://arxiv.org/abs/2412.16874v4", "attribution": "\"A Multi-modal Approach to Dysarthria Detection and Severity Assessment Using Speech and Text Information\" by Anuprabha M, Krishna Gurugubelli, V Kesavaraj, and Anil Kumar Vuppala, arXiv:2412.16874v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2306.12964v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Tokens used in our framework.}\n\\begin{tabular}{c|c}\n\t\t\t\\toprule[1.5pt]\n\t\t\tCategory & Examples \\\\\n\t\t\t\\midrule[1pt]\n\t\t\tOperators & \\emph{CS-Log}, \\emph{CS-Add}, \\emph{TS-Mean}, \\emph, $\\dotsc$ \\\\\n Features & \\emph{\\$open}, \\emph{\\$volume}, $\\dotsc$ \\\\\n\t\t\tConstants & $-30, -10, -5, -2, -1, -0.5, -0.01, 0.01, 0.5, 1, 2, 5, 10, 30$ \\\\\n\t\t\tTime Deltas & $10d, 20d, 30d, 40d, 50d$ \\\\\n\t\t\tSequence Indicator & \\emph{BEG}(begin), \\emph{SEP}(end of expression) \\\\\n\t\t\t\\bottomrule[1.5pt]\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning", "authors": ["Shuo Yu", "Hongyan Xue", "Xiang Ao", "Feiyang Pan", "Jia He", "Dandan Tu", "Qing He"], "url": "https://arxiv.org/abs/2306.12964v1", "attribution": "\"Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning\" by Shuo Yu, Hongyan Xue, Xiang Ao, Feiyang Pan, Jia He, Dandan Tu, and Qing He, arXiv:2306.12964v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.00336v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{SOLs for different load configurations of the CLOD model}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\t\t\\toprule\n\t\t& \\multicolumn{6}{c}{\\textbf{Complex load (CLOD) model parameters}} & \\textbf{Scenario A} \\\\ \n\t\t\\cmidrule(lr){2-7}\\cmidrule(lr){8-8}\n \\textbf{Scenario} & LIM & SIM & DL & TS & MVA & Kp & \\textbf{SOL} \\\\ \n\t \\textbf{number} & [\\%] & [\\%] & [\\%] & [\\%] & [\\%] & [MW]\\\\ \n\t \\hline\n\t \\addlinespace\n\t 16 & 35 &\t35 & 5 & 5 & 10 & 2 & -\\\\ \n\t 17 & 30\t& 30 & 5 & 5 & 10 & 2 & 64\\\\ \n\t 18 & 20\t& 20 & 5 & 5 & 10 & 2 & 377\\\\ \n\t \\addlinespace\n\t 19 & 35\t& 30 & 5 & 5 & 10 & 2 & 12\\\\ \n\t 20 & 35\t& 20 & 5 & 5 & 10 & 2 & 92\\\\ \n\t 21 & 35 & 10 & 5 & 5 & 10 & 2 & 334\\\\ \n\t \\addlinespace\n\t 22 & 30\t& 35 & 5 & 5 & 10 & 2 & 28\\\\ \n\t 23 & 20\t& 35 & 5 & 5 & 10 & 2 & 198\\\\ \n\t 24 & 10\t& 35 & 5 & 5 & 10 & 2 & 375\\\\ \n\t \\addlinespace\n\t\t\\bottomrule\n\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Comparison of security margin estimation methods under various load configurations", "authors": ["Hannes Hagmar", "Robert Eriksson", "Le Anh Tuan"], "url": "https://arxiv.org/abs/2012.00336v1", "attribution": "\"Comparison of security margin estimation methods under various load configurations\" by Hannes Hagmar, Robert Eriksson, and Le Anh Tuan, arXiv:2012.00336v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15758v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The average increase in inference time per answer of ITI and RADIANT across base models. The unit is \\%.}\n\\begin{tabular}{lccc}\n\\toprule\nBase models & ITI & RADIANT & RADIANT-P \\\\ \\midrule\nGemma-2B & 2.53 & 6.82 & 1.75 \\\\\nGPT-2 Large & 2.43 & 3.01 & 1.65 \\\\\nLlama-7B & 2.46 & 3.09 & 1.45 \\\\\nLlama3-8B & 2.51 & 3.32 & 1.55 \\\\\nLlama2-chat-13B & 2.51 & 4.72 & 1.57 \\\\ \\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Risk-Aware Distributional Intervention Policies for Language Models", "authors": ["Bao Nguyen", "Binh Nguyen", "Duy Nguyen", "Viet Anh Nguyen"], "url": "https://arxiv.org/abs/2501.15758v1", "attribution": "\"Risk-Aware Distributional Intervention Policies for Language Models\" by Bao Nguyen, Binh Nguyen, Duy Nguyen, and Viet Anh Nguyen, arXiv:2501.15758v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08442v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Top-1 ImageNet validation accuracy (in $\\%$) for W4A8 post-training per-tensor quantization with base and extended variants of AdaRound.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n Method & ResNet-18 & ResNet-50 & InceptionV3 & MobileNetV2 \\\\\n \\midrule \n Full Precision & 69.68 & 76.07 & 77.40 & 71.72 \\\\\n \\midrule \n Nearest (W4A32) & 23.99 & 35.60 & 1.67 & 8.09 \\\\\n AdaRound & \n 68.48$\\pm{}$0.08 & \n 74.96$\\pm{}$0.05 & \n 75.66$\\pm{}$0.12 & \n 69.21$\\pm{}$0.07 \\\\ \n + extensions & \n \\textbf{68.74}$\\pm{}$\\textbf{0.07} & \n \\textbf{75.21}$\\pm{}$\\textbf{0.11} &\n \\textbf{75.87}$\\pm{}$\\textbf{0.05} & \n \\textbf{69.54}$\\pm{}$\\textbf{0.11} \\\\ \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)", "authors": ["Sangeetha Siddegowda", "Marios Fournarakis", "Markus Nagel", "Tijmen Blankevoort", "Chirag Patel", "Abhijit Khobare"], "url": "https://arxiv.org/abs/2201.08442v1", "attribution": "\"Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)\" by Sangeetha Siddegowda, Marios Fournarakis, Markus Nagel, Tijmen Blankevoort, Chirag Patel, and Abhijit Khobare, arXiv:2201.08442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.11393v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the coverage probabilities and average width (standard error) for the bootstrap confidence interval and standard error estimation under SC10. The true standard errors of the pseudo-true estimations are in bold italics.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\toprule\n&Confidence &Parametric &Pairwise &Wild &Multiplier &\\multirow{2}{*}{LRB} \\\\\n&level &Bootstrap &Bootstrap &Bootstrap &Bootstrap & \\\\\n\\midrule\n\\multirow{5}{*}{Coverage rate of $\\mbox{CI}_{\\mbox{nor}}$} \n&\\multirow{2}{*}{0.99} &1.00 &1.00 &1.00 &1.00 &0.99\\\\\n& &0.153 (0.007) &0.299 (0.019) &0.301 (0.018) &0.307 (0.023) &0.132 (0.011) \\\\\n&\\multirow{2}{*}{0.95} &0.98 &1.00 &1.00 &1.00 &0.92\\\\\n& &0.116 (0.006) &0.228 (0.015) &0.229 (0.014) &0.234 (0.018) &0.100 (0.008) \\\\\n&\\multirow{2}{*}{0.90} &0.94 &1.00 &1.00 &1.00 &0.89\\\\\n& &0.098 (0.005) &0.191 (0.012) &0.192 (0.012) &0.196 (0.015) &0.084 (0.007) \\\\\n&\\multirow{2}{*}{0.80} &0.86 &1.00 &1.00 &1.00 &0.78\\\\\n& &0.076 (0.004) &0.149 (0.009) &0.150 (0.009) &0.153 (0.012) &0.065 (0.005) \\\\\n&\\multirow{2}{*}{0.75} &0.81 &0.99 &0.99 &0.99 &0.75\\\\\n& &0.068 (0.003) &0.134 (0.009) &0.135 (0.008) &0.137 (0.010) &0.059 (0.005) \\\\\n\\cline{2-7}\n\\multirow{5}{*}{Coverage rate of $\\mbox{CI}_{\\mbox{per}}$} \n&\\multirow{2}{*}{0.99} &1.00 &1.00 &1.00 &1.00 &0.98\\\\\n& &0.149 (0.011) &0.289 (0.026) &0.284 (0.022) &0.301 (0.029) &0.126 (0.013) \\\\\n&\\multirow{2}{*}{0.96} &0.97 &1.00 &1.00 &1.00 &0.95\\\\\n& &0.116 (0.007) &0.224 (0.016) &0.225 (0.015) &0.233 (0.021) &0.099 (0.009) \\\\\n&\\multirow{2}{*}{0.90} &0.93 &1.00 &1.00 &1.00 &0.89\\\\\n& &0.098 (0.006) &0.191 (0.013) &0.193 (0.013) &0.196 (0.016) &0.084 (0.007) \\\\\n&\\multirow{2}{*}{0.80} &0.85 &0.99 &1.00 &0.99 &0.80\\\\\n& &0.076 (0.004) &0.149 (0.011) &0.151 (0.010) &0.152 (0.012) &0.066 (0.006) \\\\\n&\\multirow{2}{*}{0.75} &0.83 &0.99 &1.00 &0.98 &0.75\\\\\n& &0.068 (0.004) &0.133 (0.010) &0.135 (0.010) &0.135 (0.011) &0.059 (0.005) \\\\\n\\cline{2-7}\nEstimated SE $(\\times 10^{-2})$ &\\textbf{\\emph{2.548}} &2.975 &5.819 &5.860 &5.975 &2.558\\\\\n(Estimated SE)/(true SE) &- &1.168 &2.284 &2.300 &2.345 &1.004\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Assessing Estimation Uncertainty under Model Misspecification", "authors": ["Rong Li", "Yichen Qin", "Yang Li"], "url": "https://arxiv.org/abs/2312.11393v1", "attribution": "\"Assessing Estimation Uncertainty under Model Misspecification\" by Rong Li, Yichen Qin, and Yang Li, arXiv:2312.11393v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04884v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Modality}} & \\multicolumn{2}{c}{\\textbf{Sampling Scheme}} \\\\ \\cmidrule(l){2-3} \n & \\textbf{Contiguous} & \\textbf{Uniformly Dist.} \\\\ \\cmidrule(r){1-1} \\cmidrule(l){2-2} \\cmidrule(l){3-3} \nVideo & 65.55 & \\underline{73.95} \\\\\nAudio & 53.36 & \\underline{64.50} \\\\\n\\textsc{MMDL} & 61.55 & \\textbf{\\underline{74.60}} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{Performance} (accuracy in \\%) of single modalities vs a multimodal (\\textsc{MMDL}) assessment for contiguous and uniformly distributed sampling schemes.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Piano Skills Assessment", "authors": ["Paritosh Parmar", "Jaiden Reddy", "Brendan Morris"], "url": "https://arxiv.org/abs/2101.04884v2", "attribution": "\"Piano Skills Assessment\" by Paritosh Parmar, Jaiden Reddy, and Brendan Morris, arXiv:2101.04884v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12829v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Example 2oo3 vote structure function}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c||c|}\n\t\t\t\\hline Component & $c_1$ & $c_2$& $c_3$ & \t\\\\\n\t\t\t\\cline{1-4} State $\\mathbf x$ & $x_1 $ & $x_2$ & $x_3$ & $\\varphi(\\mathbf x)$\\\\ \n\t\t\t\\hline $\\mathbf x_0$ & 0 & 0 & 0 & 0 \\\\ \n\t\t\t\\hline $\\mathbf x_1$ & 1 & 0 & 0 & 0 \\\\\n\t\t\t\\hline $\\mathbf x_2$ & 0 & 1 & 0 & 0 \\\\\n\t\t\t\\hline $\\mathbf x_3$ & 0 & 0 & 1 & 0 \\\\\n\t\t\t\\hline $\\mathbf x_4$ & 1 & 1 & 0 & 1 \\\\ \n\t\t\t\\hline $\\mathbf x_5$ & 1 & 0 & 1 & 1 \\\\\n\t\t\t\\hline $\\mathbf x_6$ & 0 & 1 & 1 & 1 \\\\\n\t\t\t\\hline $\\mathbf x_7$ & 1 & 1 & 1 & 1 \\\\\t\t\t\n\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Optimising the reliability that can be claimed for a software-based system based on failure-free tests of its components", "authors": ["Peter Bishop", "Andrey Povyakalo"], "url": "https://arxiv.org/abs/2201.12829v1", "attribution": "\"Optimising the reliability that can be claimed for a software-based system based on failure-free tests of its components\" by Peter Bishop and Andrey Povyakalo, arXiv:2201.12829v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14503v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Data for the investigation of the variable dimension characteristic of the UAV problem.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll} \\hline \n & \\multicolumn{2}{c}{$DV = 5$} & \\multicolumn{2}{c}{$DV = 10$} & \\multicolumn{2}{c}{$DV = 15$} & \\multicolumn{2}{c}{$DV = 20$} \\\\\n & mean & number & mean & number & mean & number & mean & number \n \\\\ $B$ & rel. error & of wins & rel. error & of wins & rel. error & of wins & rel. error & of wins \\\\ \\hline \n$1e3$ & 0.008 & 45 & 0.136 & 7 & 0.292 & 3 & 0.524 & 1 \\\\\n$1e4$ & 0.020 & 50 & 0.106 & 5 & 0.237 & 1 & 0.579 & 0 \\\\\n$1e5$/3h & 0.005 & 51 & 0.155 & 1 & 0.127 & 2 & 0.222 & 2 \\\\ \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Benchmarking global optimization techniques for unmanned aerial vehicle path planning", "authors": ["Mhd Ali Shehadeh", "Jakub Kudela"], "url": "https://arxiv.org/abs/2501.14503v1", "attribution": "\"Benchmarking global optimization techniques for unmanned aerial vehicle path planning\" by Mhd Ali Shehadeh and Jakub Kudela, arXiv:2501.14503v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.00409v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\t\t\t\\toprule\n\t\t\t& 2015 & 2016 & 2017 & 2018 & 2019 & 2020\\\\ \n\t\t\t\\midrule\n\t\t\tShare of Sexual Offense Opinions & 0.361 & 0.356 & 0.353 & 0.353 & 0.357 & 0.352 \\\\ \n\t\t\t& & (0.503) & (0.646) & (0.865) & (0.888) & (0.275) \\\\ \n\t\t\t\\midrule\n\t\t\tComposition of Sexual Offenses: & & & & & & \\\\ \n\t\t\tSexual Assault & 0.834 & 0.815** & 0.786 & 0.817 & 0.824 & 0.827* \\\\ \n\t\t\t& & (0.932) & (0.015) & (0.644) & (0.773) & (0.098) \\\\ \n\t\t\tSexual Assault on a Minor/Child & 0.028 & 0.028 & 0.028 & 0.032 & 0.026 & 0.036 \\\\ \n\t\t\t& & (0.745) & (0.26) & (0.502) & (0.506) & (0.345) \\\\ \n\t\t\tStatutory Sexual Assault & 0.134 & 0.151 & 0.148 & 0.141 & 0.134 & 0.155 \\\\ \n\t\t\t& & (0.982) & (0.616) & (0.977) & (0.42) & (0.731) \\\\ \n\t\t\tSodomy & 0.091 & 0.114 & 0.102 & 0.084 & 0.103 & 0.113 \\\\ \n\t\t\t& & (0.034) & (0.316) & (0.964) & (0.639) & (0.361) \\\\ \n\t\t\tFondling & 0.306 & 0.315 & 0.325 & 0.339 & 0.32 & 0.338 \\\\ \n\t\t\t& & (0.483) & (0.533) & (0.241) & (0.49) & (0.454) \\\\ \n\t\t\tSexual Harassment & 0.002 & 0.006 & 0.007 & 0.006 & 0.004 & 0.006 \\\\ \n\t\t\t& & (0.57) & (0.405) & (0.553) & (0.846) & (0.438) \\\\ \n\t\t\t\\midrule\n\t\t\t\\# Opinions (Total) & 6474 & 7685 & 7111 & 7223 & 7883 & 6713 \\\\ \n\t\t\t\\# Sexual Offense Opinions & 2333 & 2737 & 2510 & 2548 & 2812 & 2367 \\\\\n\t\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach", "authors": ["Henrika Langen"], "url": "https://arxiv.org/abs/2209.00409v2", "attribution": "\"The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach\" by Henrika Langen, arXiv:2209.00409v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18775v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Comparison of execution times between Secant and Newton's methods for various test functions. Where $f$ is explicitly provided, the gradient function is computed via automatic differentiation. For the line search problems, the gradient is provided directly and not computed via automatic differentiation, as those are instances extracted from the line search routine. Timings are averaged over $1000$ runs.\\newline}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n Function & Secant Time (s) & Newton's Time (s) & Newton/Secant \\\\\n \\midrule\n $f(x) = x^2 - 2$ & 3.42e-06 & 4.32e-05 & 12.62 \\\\\n $f(x) = x^2 - 5$ & 3.14e-06 & 5.47e-05 & 17.42 \\\\\n $f(x) = x^2 - 10$ & 2.82e-06 & 4.13e-05 & 14.65 \\\\\n $f(x) = x^2 - x - 2$ & 3.70e-06 & 4.63e-05 & 12.52 \\\\\n $f(x) = x^2 + 2x - 7$ & 3.47e-06 & 4.11e-05 & 11.85 \\\\\n $f(x) = x^3 - 2$ & 4.81e-06 & 4.64e-05 & 9.65 \\\\\n $f(x) = x e^x - 7$ & 1.01e-05 & 5.89e-05 & 5.83 \\\\\n $f(x) = x - \\cos(x)$ & 6.39e-06 & 4.39e-05 & 6.88 \\\\\n \\midrule\n Line Search (quadratic) & 1.92e-05 & 7.97e-05 & 4.15 \\\\\n Line Search (arbitrary) & 3.75e-05 & 1.24e-04 & 3.31 \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Secant Line Search for Frank-Wolfe Algorithms", "authors": ["Deborah Hendrych", "Mathieu Besançon", "David Martínez-Rubio", "Sebastian Pokutta"], "url": "https://arxiv.org/abs/2501.18775v2", "attribution": "\"Secant Line Search for Frank-Wolfe Algorithms\" by Deborah Hendrych, Mathieu Besançon, David Martínez-Rubio, and Sebastian Pokutta, arXiv:2501.18775v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.10458v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{soul}\n\\usepackage{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\t\\multirow{2}{*}{Case} & $G_{\\text{PD}}$ & $G_{T}$ & $P_{\\text{cyc}}$ & Cycles & ExPB &SS & Waste \\\\ \n\t\t\t\t & Euro & Euro & & &(years) & (\\%) & (kWh) \\\\\n\t\t\t\\hline\n\t\t\tLoad +PV & - & - & - & - & - & 28.02 & 58.14 \\\\\n\t\t\t\\hline\n\t\t\t\\multicolumn{8}{|c|}{\\textbf{1 kWh} Battery} \\\\\n\t\t\t\\hline\n \\hl{\\textbf{0.25C-0.25C }}& 0 & 35.62 & \\textbf{0.8589} & 36.91 & \\textbf{0.99} & 28.71 & 40.68 \\\\\n \\hl{\\textbf{1C-1C }}\t\t& 0 & 39.42 & \\textbf{0.4543} & \\textcolor{red}{62.64} & \\textbf{1.48} & 29.42 & 22.69 \\\\\n \\hl{\\textbf{ 2C-2C }}\t\t& 0 & 40.02 & \\textbf{0.3765} & \\textcolor{red}{66.53} & \\textbf{1.87 } & 29.57 & 17.88 \\\\\n\t\t\t\\hline\n\t\t\t\\multicolumn{8}{|c|}{\\textbf{2 kWh} Battery}\\\\\n\t\t\t\\hline\n\t\t\\hl{\\textbf{\t0.25C-0.25C}} & 0 & 40.04 & \\textbf{0.2941} & 50.01 & \\textbf{1.77 } & 29.21 & 27.27 \\\\\n \\hl{\\textbf{1C-1C}} \t\t& 0 & 44.36 & \\textbf{0.1842} & \\textcolor{red}{61.75} & \\textbf{2.63} & 29.79 & 10.73 \\\\\n \\hl{\\textbf{2C-2C }}\t\t& 4.31 & 48.70 & \\textbf{0.1774} & \\textcolor{red}{60.51} & \\textbf{3.08} & 29.79 & 9.09 \\\\\n\t\t\t\\hline\n\t\t\t\\multicolumn{8}{|c|}{\\textbf{5 kWh} Battery} \\\\\n\t\t\t\\hline\n \\hl{\\textbf{0.25C-0.25C}} & 0 & 50.44 & {\\textbf{0.1117}} & 46.30 & \\textbf{3.51} & 29.90 & 4.80 \\\\\n \\hl{\\textbf{1C-1C }}\t\t& 4.31 & {58.64} & {\\textbf{0.1106}} & 41.06 & \\textbf{4.97} & 29.80 & 1.54 \\\\\n \\hl{\\textbf{2C-2C} }\t\t& 4.31 & 58.65 & \\textbf{0.0598} & 41.18 & \\textbf{6.39} & 29.80 & 1.36 \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sizing and Profitability of Energy Storage for Prosumers in Madeira, Portugal", "authors": ["Md Umar Hashmi", "Jonathan Cavaleiro", "Lucas Pereira", "Ana Bušić"], "url": "https://arxiv.org/abs/1911.10458v1", "attribution": "\"Sizing and Profitability of Energy Storage for Prosumers in Madeira, Portugal\" by Md Umar Hashmi, Jonathan Cavaleiro, Lucas Pereira, and Ana Bušić, arXiv:1911.10458v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08977v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Textured models used for fa\\c{c}ade modeling.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n\t\t\t\\toprule\n\t\t\tName & Model Source & Texture Source & GSD ($cm$) & Coverage & City \\\\ \\midrule\n\t\t\tBank & UAV Photogrammetry & UAV & 2 & Individual & Huzhou, CN \\\\\n\t\t\tCentral Business District (CBD) & 3D Warehouse & Aerial & 10 & Individual & London, UK \\\\\n\t\t\tState Capitol & 3D Warehouse & Ground & 2 & Individual & Denver, US \\\\\n\t\t\tBorough High Street & AccuCities & Ground & 2 & Block & London, UK \\\\\n\t\t\tBroad Street & 3D Warehouse & UAV & 5 & Block & Rome, US \\\\ \\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Façade Modeling", "authors": ["Han Hu", "Xinrong Liang", "Yulin Ding", "Qisen Shang", "Bo Xu", "Xuming Ge", "Min Chen", "Ruofei Zhong", "Qing Zhu"], "url": "https://arxiv.org/abs/2201.08977v2", "attribution": "\"Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Façade Modeling\" by Han Hu, Xinrong Liang, Yulin Ding, Qisen Shang, Bo Xu, Xuming Ge, Min Chen, Ruofei Zhong, and Qing Zhu, arXiv:2201.08977v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.11241v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrr}\n\\toprule\n\\textbf{track id} & \\textbf{tāḷa} \n & \\multicolumn{3}{c}{\\textbf{Beat}} \\\\\n\\cmidrule(lr){3-5}\n & & \\textbf{F-measure} & \\textbf{CML$_t$} & \\textbf{AML$_t$} \\\\\n\\midrule\n10047 & adi & 0.192975 & 0.100241 & 0.458864 \\\\\n11024 & rupakam & 0.605962 & 0.000145 & 0.903054 \\\\\n\\midrule\n\\textbf{track id} & \\textbf{tāḷa} \n & \\multicolumn{3}{c}{\\textbf{Downbeat}} \\\\\n\\cmidrule(lr){3-5}\n & & \\textbf{F-measure} & \\textbf{CML$_t$} & \\textbf{AML$_t$} \\\\\n\\midrule\n10048 & adi & 0.000000 & 0.0 & 1.000000 \\\\\n11040 & rupakam & 0.032501 & 0.0 & 0.000000 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{TCN-FS : Worst Performing Tracks by Beat and Downbeat F-Measure}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Revisiting Meter Tracking in Carnatic Music using Deep Learning Approaches", "authors": ["Satyajeet Prabhu"], "url": "https://arxiv.org/abs/2509.11241v1", "attribution": "\"Revisiting Meter Tracking in Carnatic Music using Deep Learning Approaches\" by Satyajeet Prabhu, arXiv:2509.11241v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.12084v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Distribution of Welfare Effects, 2020}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n& \\multicolumn{2}{c}{Welfare Ratio: Baseline/Counterfactual} \\\\\n\\cmidrule(lr){2-3}\n& Tariff Changes & Geopolitical Changes \\\\\n\\midrule\nMean & 1.0123 & 0.9943 \\\\\nMedian & 1.0069 & 0.9970 \\\\\nStd.\\ Deviation & 0.0134 & 0.0150 \\\\\nSkewness & 1.82 & $-3.41$ \\\\\n\\midrule\nCountries gaining & 70 (94.6\\%) & 27 (36.5\\%) \\\\\nCountries losing & 4 (5.4\\%) & 47 (63.5\\%) \\\\\n\\midrule\nRange of effects & [$-1.08\\%$, $+5.51\\%$] & [$-8.56\\%$, $+1.55\\%$] \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Geopolitical Barriers to Globalization", "authors": ["Tianyu Fan", "Mai Wo", "Wei Xiang"], "url": "https://arxiv.org/abs/2509.12084v2", "attribution": "\"Geopolitical Barriers to Globalization\" by Tianyu Fan, Mai Wo, and Wei Xiang, arXiv:2509.12084v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13525v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fit measures for the proposed functional random coefficient and its competitors including a simple linear effect. The fit is measured in terms of the log-likelihood (logLik), the integrated Brier score (IBS) and the AIC.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\hline & logLik & IBS & AIC \\\\\n \\hline\n Heterogeneous time-variation & -4099.36 & 0.1162 & 8238.87 \\\\\n Heterogeneity and time-variation & -4108.27 & 0.1174 & 8250.03 \\\\\n Heterogeneity only & -4111.04 & 0.1185 & 8250.50 \\\\\n Time-variation only & -4111.00 & 0.1184 & 8251.44 \\\\\n Linear effect & -4111.01 & 0.1184 & 8251.20 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Capturing heterogeneous time-variation in covariate effects in non-proportional hazard regression models", "authors": ["Niklas Hagemann", "Thomas Kneib", "Kathrin Möllenhoff"], "url": "https://arxiv.org/abs/2501.13525v1", "attribution": "\"Capturing heterogeneous time-variation in covariate effects in non-proportional hazard regression models\" by Niklas Hagemann, Thomas Kneib, and Kathrin Möllenhoff, arXiv:2501.13525v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.03247v2_tex_table22.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{OLS Estimation Results - Dependent Variable: Swim Out Times}\n\\begin{tabular}{lcccc}\n \\toprule\n & Estimate & Std. Error & t-value & Pr(>|t|) \\\\\n \\midrule\n Drafter & -23.8886 & 4.1184 & -5.8004 & $6.63 \\times 10^{-9}$ *** \\\\\n Pre-period $\\times$ Drafter & -8.5779 & 4.6013 & -1.8642 & 0.0623 . \\\\\n Covid-year $\\times$ Drafter & 19.4150 & 8.1469 & 2.3831 & 0.0172 * \\\\\n \\midrule\n Observations & \\multicolumn{4}{c}{168,391} \\\\\n Fixed-effects & \\multicolumn{4}{c}{Athlete ID: 29,194, Event ID: 1,339, Cluster (Swim Group): 224} \\\\\n Standard-errors & \\multicolumn{4}{c}{Heteroskedasticity-robust} \\\\\n RMSE & \\multicolumn{4}{c}{175.1} \\\\\n Adj. $R^2$ & \\multicolumn{4}{c}{0.9683} \\\\\n Within $R^2$ & \\multicolumn{4}{c}{0.0035} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Strategic Effort and Bandwagon Effects in Finite Multi-Stage Games with Non-Linear Externalities: Evidence from Triathlon", "authors": ["Felix Reichel"], "url": "https://arxiv.org/abs/2505.03247v2", "attribution": "\"Strategic Effort and Bandwagon Effects in Finite Multi-Stage Games with Non-Linear Externalities: Evidence from Triathlon\" by Felix Reichel, arXiv:2505.03247v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.12600v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean absolute error (MAE), root mean squared error (RMSE), coverage (Cov.), and length (Len.) of $95\\%$ CIs based on 500 simulated data sets in three (mis)specification settings.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr} \n \\toprule\n & Target & SS & IVW & AIPW-$L_1$ & MR-$L_1$ \\\\\n \\cmidrule{2-6} \n $C = 0$ & & & \\\\ \n \\hspace{.2cm} MAE & 0.109 & 1.933 & 0.177 & 0.110 & 0.050 \\\\\n \\hspace{.2cm} RMSE & 0.141 & 1.987 & 0.219 & 0.144 & 0.061 \\\\ \n \\hspace{.2cm} Cov. & 0.950 & 0.998 & 0.826 & 0.936 & 0.960 \\\\ \n \\hspace{.2cm} Len. & 0.551 & 7.035 & 0.567 & 0.547 & 0.234 \\\\\n \\midrule\n $C = 1/2$ & & &\\\\ \n \\hspace{.2cm} MAE & 0.109 & 1.111 & 0.107 & 0.109 & 0.050 \\\\\n \\hspace{.2cm} RMSE & 0.141 & 1.189 & 0.139 & 0.140 & 0.062 \\\\ \n \\hspace{.2cm} Cov. & 0.950 & 1.000 & 0.942 & 0.950 & 0.962 \\\\ \n \\hspace{.2cm} Len. & 0.551 & 6.010 & 0.540 & 0.547 & 0.242 \\\\\n \\midrule\n $C = 1$ & & &\\\\ \n \\hspace{.2cm} MAE & 0.109 & 0.036 & 0.035 & 0.050 & 0.049 \\\\\n \\hspace{.2cm} RMSE & 0.141 & 0.045 & 0.044 & 0.064 & 0.063 \\\\ \n \\hspace{.2cm} Cov. & 0.950 & 0.968 & 0.956 & 0.958 & 0.960 \\\\ \n \\hspace{.2cm} Len. & 0.551 & 0.195 & 0.191 & 0.260 & 0.253 \\\\ \n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Multiply Robust Federated Estimation of Targeted Average Treatment Effects", "authors": ["Larry Han", "Zhu Shen", "Jose Zubizarreta"], "url": "https://arxiv.org/abs/2309.12600v1", "attribution": "\"Multiply Robust Federated Estimation of Targeted Average Treatment Effects\" by Larry Han, Zhu Shen, and Jose Zubizarreta, arXiv:2309.12600v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.07590v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccccc}\n \\hline\n Cutoff & PSNR & SSIM & Lpips & FSIM & VIF & VSI & GMSD \\\\\n \\hline\n $\\pi/5$ & 25.89 & 0.711 & 0.283 & 0.826 & 0.306 & 0.950 & 0.112\\\\\n \n $\\pi/10$ & 27.60 & 0.829 & 0.147 & 0.896 & 0.426 & 0.970 & 0.061\\\\\n \n $\\pi/20$ & 26.34 & 0.779 & 0.180 & 0.867 & 0.343 & 0.962 & 0.085\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Experiment on the cutoff frequency of the filter.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model", "authors": ["Jiahua Xu", "Dawei Zhou", "Lei Hu", "Jianfeng Guo", "Feng Yang", "Zaiyi Liu", "Nannan Wang", "Xinbo Gao"], "url": "https://arxiv.org/abs/2412.07590v2", "attribution": "\"Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model\" by Jiahua Xu, Dawei Zhou, Lei Hu, Jianfeng Guo, Feng Yang, Zaiyi Liu, Nannan Wang, and Xinbo Gao, arXiv:2412.07590v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.11561v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Users' tendency to engage with more novel content between Group II vs. III}\n\\begin{tabular}{ccccc}\n\\toprule \n& \\multicolumn{4}{c}{Engagement (Group II vs Group III)} \\\\\n\\cmidrule(r){2-5}\n& (1) & (2) & (3) & (4) \\\\\n\\midrule % In-table horizontal line\n$\\text{SocialCue}$ $\\times$ NR & $0.042^{***}$ & $0.035^{***}$ & & \\\\\n& (0.008) & (0.008) & & \\\\\nNR & $-0.919^{***}$ & $-0.888^{***}$ & & \\\\\n& (0.006) & (0.006) & & \\\\\n$\\text{SocialCue}$ $\\times$ Diversity & & & $0.092^{***}$ & $0.086^{***}$ \\\\\n& & & (0.029) & (0.030) \\\\\nDiversity & & & $-1.710^{***}$ & $-1.679^{***}$ \\\\\n& & & (0.021) & (0.021) \\\\\n$\\text{SocialCue}$ & $0.069^{***}$ & $0.074^{***}$ & $0.084^{***}$ & $0.086^{***}$ \\\\\n& (0.007) & (0.006) & (0.006) & (0.006) \\\\\nConstant & $-1.993^{***}$ & $-3.037^{***}$ & $-2.240^{***}$ & $-3.435^{***}$ \\\\\n& (0.005) & (0.031) & (0.004) & (0.032) \\\\\nControls & & Yes & & Yes \\\\\n\\midrule % In-table horizontal line\nObservations & 7,529,256 & 7,529,256 & 7,529,256 & 7,529,256 \\\\\nPseudo $R^{2}$ & 0.025 & 0.044 & 0.008 & 0.029 \\\\\n\\bottomrule \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Algorithms vs. Peers: Shaping Engagement with Novel Content", "authors": ["Shan Huang", "Yi Ji", "Leyu Lin"], "url": "https://arxiv.org/abs/2503.11561v2", "attribution": "\"Algorithms vs. Peers: Shaping Engagement with Novel Content\" by Shan Huang, Yi Ji, and Leyu Lin, arXiv:2503.11561v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.05462v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Available elevation angle in different environment for frequencies between 1.5 and 3~GHz}\n\\begin{tabular}{|c|l|}\n\\hline\n\\textbf{Environment} & \\textbf{Available Elevation Angles (degrees)} \\\\\n\\hline\nUrban & 20, 30, 45, 60, 70 \\\\\n\\hline\nSuburban & 20, 30, 45, 60, 70 \\\\\n\\hline\nVillage & 20, 30, 45, 60, 70 \\\\\n\\hline\nRural wooded & 20, 30, 45, 60, 70 \\\\\n\\hline\nResidential & 20, 30, 60, 70 \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Evaluating the Influence of Satellite Systems on Terrestrial Networks: Analyzing S-Band Interference", "authors": ["Lingrui Zhang", "Zheng Li", "Sheng Yang"], "url": "https://arxiv.org/abs/2501.05462v1", "attribution": "\"Evaluating the Influence of Satellite Systems on Terrestrial Networks: Analyzing S-Band Interference\" by Lingrui Zhang, Zheng Li, and Sheng Yang, arXiv:2501.05462v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.01504v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Word error rate on development and evaluation sets of FS-2 ASR challenge track~2 (reference segmentation). Comparing models trained on the FS-2 data only, with Apollo 11 original noisy transcriptions (A11) and with semi-supervised training (SST) of the acoustic model (AM) and the language model (LM), using n-gram and recurrent neural network (RNN) LMs.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ll|ll|rr}\n\\toprule\n\\multirow{2}{*}{N\\textsuperscript{\\underline{o}}} & \\multicolumn{2}{|c|}{AM} & \\multicolumn{2}{|c|}{LM} & \\multicolumn{2}{|c}{WER, \\%} \\\\\n & Model & Data & Model & Data & Dev & Eval \\\\\n \\midrule\n1 & GMM & FS-2 & 3-gram & FS-2 & 53.8 & 55.6 \\\\\n2 & TDNN & FS-2 & 3-gram & FS-2 & 28.6 & 31.4 \\\\\n3 & TDNN & FS-2 & 3-gram & FS-2+A11 & 26.3 & 29.1 \\\\\n\\midrule\n4 & TDNN & +SST1 & 3-gram & FS-2+A11 & 23.7 & 26.0 \\\\\n5 & TDNN & +SST1 & 3-gram & FS-2+A11+SST1 & 23.5 & 25.8 \\\\\n6 & TDNN & +SST1 & 4-gram & FS-2+A11+SST1 & 23.0 & 25.6 \\\\\n7 & TDNN & +SST1 & +RNN & FS-2+A11+SST1 & 21.8 & 24.3 \\\\\n \\midrule\n8 & TDNN & +SST2 & +RNN & FS-2+A11+SST2 & 22.2 & 24.6 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "\"This is Houston. Say again, please\". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)", "authors": ["Arseniy Gorin", "Daniil Kulko", "Steven Grima", "Alex Glasman"], "url": "https://arxiv.org/abs/2008.01504v1", "attribution": "\"\"This is Houston. Say again, please\". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)\" by Arseniy Gorin, Daniil Kulko, Steven Grima, and Alex Glasman, arXiv:2008.01504v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2310.10992v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Objective results of different audio codecs. $^\\dag$ denotes that Encodec operates audio sampels in 24 kHz sampling rate.}\n\\begin{tabular}{l|ccccc}\n\t\t\t\\toprule\n\t\t\tMethod &Bitrate &Bandwidth &POLQA &ViSQOL &eSTOI(\\%) \\\\\n \\hline\n Opus &6 kbps &WB &1.82 &2.03 &58.45\\\\\n Opus &8 kbps &WB &2.96 &3.46 &76.03\\\\\n Opus &10 kbps &SWB &3.26 &3.71 &77.93\\\\\n Opus &16 kbps &WB &4.26 &4.30 &85.30\\\\\n \\hline\n Lyra-v2 &9.2 kbps &WB &3.49 &3.92 &90.81\\\\\n Encodec &12 kbps &SWB$^\\dag$ &3.72 &4.16 &94.21\\\\\n\t\t\t\\hline\n\t\t\tWB-NC &6 kbps &WB &3.66 &3.88 &91.59\\\\\n\t\t\t\\quad+PM loss &6 kbps &WB &3.86 &4.19 &93.00\\\\\n\t\t\t\\qquad+GAN-KD &6 kbps &WB &4.06 &4.22 &94.63\\\\\n \\quad\\qquad+SWB-BWE &8 kbps &SWB &4.16 &4.20 &94.37\\\\\n\t\t\t\\bottomrule\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A High Fidelity and Low Complexity Neural Audio Coding", "authors": ["Wenzhe Liu", "Wei Xiao", "Meng Wang", "Shan Yang", "Yupeng Shi", "Yuyong Kang", "Dan Su", "Shidong Shang", "Dong Yu"], "url": "https://arxiv.org/abs/2310.10992v1", "attribution": "\"A High Fidelity and Low Complexity Neural Audio Coding\" by Wenzhe Liu, Wei Xiao, Meng Wang, Shan Yang, Yupeng Shi, Yuyong Kang, Dan Su, Shidong Shang, and Dong Yu, arXiv:2310.10992v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.03035v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\t\tparameter & $ \\overline{\\text{AXI}} $ & $ \\sigma(\\text{AXI}) $ & $ \\bar{\\Delta} $ & $ \\sigma(\\Delta) $ \\\\\n\t\t\\hline\n\t\tvalue & 0.5141\\% & 0.2987\\% & -0.0020\\% & 0.3156\\% \\\\\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Assumed parameters for potential spread discount}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Case for AXI", "authors": ["Viktor Tsyrennikov"], "url": "https://arxiv.org/abs/2509.03035v1", "attribution": "\"A Case for AXI\" by Viktor Tsyrennikov, arXiv:2509.03035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.05224v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|c}\n\\hline \nDataset & Detectable Joints \\\\ \\hline\nKinetics 400~ & 41.0\\% \\\\ \\hline\nUCF101~ & 37.8\\% \\\\ \\hline\nHMDB51~ & 41.8\\% \\\\ \\hline\nFineGym~ & 44.7\\% \\\\ \\hline\n\\textbf{HAA500} & \\textbf{69.7\\%} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Detectable joints of video action datasets. We use AlphaPose~ to detect the largest person in the frame, and count the number of joints with a score higher than $0.5$. }\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "HAA500: Human-Centric Atomic Action Dataset with Curated Videos", "authors": ["Jihoon Chung", "Cheng-hsin Wuu", "Hsuan-ru Yang", "Yu-Wing Tai", "Chi-Keung Tang"], "url": "https://arxiv.org/abs/2009.05224v2", "attribution": "\"HAA500: Human-Centric Atomic Action Dataset with Curated Videos\" by Jihoon Chung, Cheng-hsin Wuu, Hsuan-ru Yang, Yu-Wing Tai, and Chi-Keung Tang, arXiv:2009.05224v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07434v5_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c} \n\t\t\\toprule\n\t\tAttention Base & Eval OS & Channelized & mIOU (\\%) \\\\\n\t\t\\midrule[0.5pt]\n\t\t\\midrule[0.5pt]\n\t\t\\multirow{2}{*}{Axial Attention }& 16 & & 50.27 \\\\\n\t\t& 16 \t & \\checkmark & 51.06 \\\\\n\t\t\\midrule\n\t\t\\multirow{2}{*}{ Self Attention} & 16 \t & & 50.42 \\\\\n\t\t& 16 \t & \\checkmark & 51.09 \\\\\n\t\t\\bottomrule[0.5pt]\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Ablation study of applying our Channelized Attention on self-attention with ResNet-101. \\textbf{Eval OS}: Output strides~ during evaluation.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation", "authors": ["Ye Huang", "Di Kang", "Wenjing Jia", "Xiangjian He", "Liu Liu"], "url": "https://arxiv.org/abs/2101.07434v5", "attribution": "\"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation\" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.10419v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fine-tuning hyperparameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\nLearning rate & Cosine annealing scheduler (lr=3e-7, T=10e3) \\\\ \\hline\nTraining steps & 50e3 \\\\ \\hline\nBatch size & 8 \\\\ \\hline\nGradient norm clipping & 0.5 \\\\ \\hline\nUpdate target network phase & 256 \\\\ \\hline\nOptimizer & AdamW (weight decay = 1e-2) \\\\ \\hline\n$\\kappa_2$ & 0.01 \\\\ \\hline\n$\\kappa_3$ & 1 \\\\ \\hline\n$\\kappa_4$ & 0.1 \\\\ \\hline\n$\\alpha_{prior}$ & 0.999 \\\\ \\hline\n$\\tau_{\\max}$ & 3 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Preference Adaptive and Sequential Text-to-Image Generation", "authors": ["Ofir Nabati", "Guy Tennenholtz", "ChihWei Hsu", "Moonkyung Ryu", "Deepak Ramachandran", "Yinlam Chow", "Xiang Li", "Craig Boutilier"], "url": "https://arxiv.org/abs/2412.10419v2", "attribution": "\"Preference Adaptive and Sequential Text-to-Image Generation\" by Ofir Nabati, Guy Tennenholtz, ChihWei Hsu, Moonkyung Ryu, Deepak Ramachandran, Yinlam Chow, Xiang Li, and Craig Boutilier, arXiv:2412.10419v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.15568v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|}\n \\hline\n & \\textbf{Option A} & \\textbf{Option B} \\\\\n \\hline\n 1) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €1 safe\\\\\n 2) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €2 safe \\\\\n 3) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €3 safe \\\\\n 4) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €4 safe \\\\\n 5) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €5 safe \\\\\n 6) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €6 safe \\\\\n 7) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €7 safe \\\\\n 8) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €8 safe \\\\\n 9) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €9 safe \\\\\n 10) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €10 safe \\\\\n 11) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €11 safe \\\\\n 12) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €12 safe \\\\\n 13) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €13 safe \\\\\n 14) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €14 safe \\\\\n 15) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €15 safe \\\\\n 16) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €16 safe \\\\\n 17) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €17 safe \\\\\n 18) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €18 safe \\\\\n 19) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €19 safe \\\\\n 20) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €20 safe \\\\\n 21) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €21 safe \\\\\n 22) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €22 safe \\\\\n 23) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €23 safe \\\\\n 24) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €24 safe \\\\\n 25) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €25 safe \\\\\n 26) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €26 safe \\\\\n 27) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €27 safe \\\\\n 28) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €28 safe \\\\\n 29) & €30 with a probability of 50\\%, €0 with a probability of 50\\% & €29 safe \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)", "authors": ["Marius Protte"], "url": "https://arxiv.org/abs/2507.15568v1", "attribution": "\"Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)\" by Marius Protte, arXiv:2507.15568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.10221v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\nOld Scheme&New Scheme&Ratio\\\\\n\\hline\n127.18&124.44&1.022\\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\sf Example 5: The CPU times (in seconds) consumed by the studied schemes.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "New More Efficient A-WENO Schemes", "authors": ["Shaoshuai Chu", "Alexander Kurganov", "Ruixiao Xin"], "url": "https://arxiv.org/abs/2503.10221v2", "attribution": "\"New More Efficient A-WENO Schemes\" by Shaoshuai Chu, Alexander Kurganov, and Ruixiao Xin, arXiv:2503.10221v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.15395v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Settings of the GPOPS-\\uppercase\\expandafter{\\romannumeral2} toolbox }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\toprule\n Stage & Parameter & Value \\\\\n \\midrule\n Problem 3 and 4 & setup.nlp.solver & snopt\\\\ \n Problem 3 and 4 & setup.derivatives.derivativelevel & second\\\\ \n Problem 3 and 4 & setup.derivatives.supplier & sparseCD\\\\\n Problem 3 and 4 & setup.method & RPMintegration\\\\\n Problem 3 and 4 & setup.mesh.method & hp1\\\\\n Problem 3 and 4 & setup.mesh.phase.colpoints & 4*ones(1,1)\\\\\n Problem 3 and 4 & setup.mesh.phase.fraction & ones(1,1)/1\\\\\n Problem 3 & setup.mesh.tolerance & $10^{-2}$\\\\\n Problem 4 & setup.mesh.tolerance & $10^{-6}$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Integrated Take-off Management and Trajectory Optimization for Merging Control in Urban Air Mobility Corridors", "authors": ["Yingqi Liu", "Tianlu Pan", "Jingjun Tan", "Renxin Zhong", "Can Chen"], "url": "https://arxiv.org/abs/2508.15395v1", "attribution": "\"Integrated Take-off Management and Trajectory Optimization for Merging Control in Urban Air Mobility Corridors\" by Yingqi Liu, Tianlu Pan, Jingjun Tan, Renxin Zhong, and Can Chen, arXiv:2508.15395v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.08488v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l} \n\t\t\\hline \\hline\nNum\t&Regions\t\t\t\t&Presence in the Gambia\t&Presence in the data\\\\ \n\\hline \\hline\n1\t&Kanifing \t\t\t\t&21.96\\%\t\t&32.95\\%\\\\\t\t\n2\t&Brikama \t\t\t\t&37.17\\%\t\t&39.17\\%\\\\\n3\t&Mansa Konko\t\t\t\t&4.38\\%\t\t\t&10.54\\%\\\\\n4\t&Kerewan \t\t\t\t&11.74\\%\t\t&5.29\\%\\\\\n5\t&Janjanburey-kuntaur\t\t\t&12.01\\%\t\t&7.83\\%\\\\\n6\t&Basse \t\t\t\t\t&12.74\\%\t\t&4.14\\%\\\\\n\\hline \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Population of the regions in \\% nationally and in the dat}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Overview description of the Gambian GABECE Educational Data and associated algorithms and unsupervized learning process", "authors": ["Ousmane Saine", "Soumaila Dembélé", "Gane Samb Lo", "Mohamed Cheikh Haidara"], "url": "https://arxiv.org/abs/2012.08488v1", "attribution": "\"Overview description of the Gambian GABECE Educational Data and associated algorithms and unsupervized learning process\" by Ousmane Saine, Soumaila Dembélé, Gane Samb Lo, and Mohamed Cheikh Haidara, arXiv:2012.08488v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11803v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccc}\n\\toprule\n\\multirow{2}{*}{codename} &\n \\multirow{2}{*}{100\\%} &\n \\multirow{2}{*}{75\\%} &\n \\multirow{2}{*}{50\\%} &\n \\multirow{2}{*}{PARAs} &\n \\multirow{2}{*}{FLOPs} &\n \\multirow{2}{*}{$\\Gamma_{min}$} &\n \\multirow{2}{*}{\\%PARA} &\n \\multirow{2}{*}{\\%FLOPS} &\n IID &\n \\multicolumn{2}{c}{Non-IID} \\\\ \\cmidrule(l){10-12} \n & & & & & & & & & Accuracy & Global & Local \\\\ \\midrule\n1111111111 & 10 & & & 159010 & 158800 & 10 & 1.00 & 1.00 & 97.67 & 94.12 & 94.45 \\\\\n1111114444 & 6 & 4 & & 143110 & 142920 & 6 & 0.9 & 0.90 & 97.76 & 92.33 & 92.55 \\\\\n1111144447 & 5 & 4 & 1 & 135160 & 134980 & 6 & 0.85 & 0.85 & 97.34 & 93.79 & 93.92 \\\\\n1111444444 & 4 & 6 & & 135160 & 134980 & 4 & 0.85 & 0.85 & 97.62 & 92.05 & 92.33 \\\\\n1111444477 & 4 & 4 & 2 & 127210 & 127040 & 4 & 0.80 & 0.8 & 97.32 & 92.67 & 92.95 \\\\\n1111444777 & 4 & 3 & 3 & 123235 & 123070 & 4 & 0.77 & 0.775 & 97.35 & 91.34 & 91.73 \\\\\n1111777777 & 4 & & 6 & 111310 & 111160 & 4 & 0.70 & 0.7 & 97.18 & 93.6 & 93.48 \\\\\n1114777777 & 3 & 1 & 6 & 107335 & 107190 & 3 & 0.67 & 0.675 & 97.12 & 93.7 & 93.57 \\\\\n1444777777 & 1 & 3 & 6 & 99385 & 99250 & 1 & 0.62 & 0.625 & 97.01 & 90.74 & 90.57 \\\\\n1477777777 & 1 & 1 & 8 & 91435 & 91310 & 1 & 0.57 & 0.575 & 96.88 & 90.73 & 90.67 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results For Fixed Sub-network on MNIST}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning", "authors": ["Hanhan Zhou", "Tian Lan", "Guru Venkataramani", "Wenbo Ding"], "url": "https://arxiv.org/abs/2201.11803v2", "attribution": "\"On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning\" by Hanhan Zhou, Tian Lan, Guru Venkataramani, and Wenbo Ding, arXiv:2201.11803v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.01705v3_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Chi-Squared tests of independence for difference in 14-days MA between (i) red zone and (ii) orange zone}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrr}\n \\hline\n Data & Statistic & P-value \\\\ \n \\hline\n Diff MA 19-20 & 26082.00 & 0.00 \\\\ \n Diff MA (divided by pop) 19-20 & 26082.00 & 0.00 \\\\\n Diff MA (divided by pop) 15/19-20 & 26082.00 & 0.00 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic", "authors": ["C. Biliotti", "F. J. Bargagli-Stoffi", "N. Fraccaroli", "M. Puliga", "M. Riccaboni"], "url": "https://arxiv.org/abs/2212.01705v3", "attribution": "\"Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic\" by C. Biliotti, F. J. Bargagli-Stoffi, N. Fraccaroli, M. Puliga, and M. Riccaboni, arXiv:2212.01705v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11188v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n \\hline\n Parameter & Value \\\\\n \\hline\n Cell radius & $500$ m \\\\\n Maximum D2D pair distance & $30$ m \\\\\n Carrier frequency & $2.1$ GHz \\\\\n RB bandwidth & $180$ kHz \\\\\n Number of RBs & $25$ \\\\\n Number of CUEs & $25$ \\\\\n Number of DUE pairs & $10, 20, 30, 40, 50$ \\\\\n CUE transmit power & $23$ dBm \\\\\n DUE min, max transmit power & $0, 20$ dBm \\\\\n Path loss model & Log-Distance Shadowing \\\\\n Path loss exponent & $2.0$ \\\\\n Shadowing SD $\\chi_{\\sigma}$ & 2.7 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platform", "authors": ["David Cotton", "Zenon Chaczko"], "url": "https://arxiv.org/abs/2101.11188v1", "attribution": "\"GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platform\" by David Cotton and Zenon Chaczko, arXiv:2101.11188v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.01846v3_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\n\\toprule\nregion & country \\\\ \\hline\nEurope E & Belarus \\\\\nEurope E & Bulgaria \\\\\nEurope E & Czech Republic \\\\\nEurope E & Hungary \\\\\nEurope E & Poland \\\\\nEurope E & Republic of Moldova \\\\\nEurope E & Romania \\\\\nEurope E & Russian Federation \\\\\nEurope E & Slovakia \\\\\nEurope E & Ukraine \\\\\nEurope N & Denmark \\\\\nEurope N & Estonia \\\\\nEurope N & Finland \\\\\nEurope N & Iceland \\\\\nEurope N & Ireland \\\\\nEurope N & Latvia \\\\\nEurope N & Lithuania \\\\\nEurope N & Norway \\\\\nEurope N & Sweden \\\\\nEurope N & United Kingdom \\\\\nEurope S & Albania \\\\\nEurope S & Bosnia and Herzegovina \\\\\nEurope S & Croatia \\\\\nEurope S & Greece \\\\\nEurope S & Italy \\\\\nEurope S & Malta \\\\\nEurope S & Montenegro \\\\\nEurope S & North Macedonia \\\\\nEurope S & Portugal \\\\\nEurope S & Serbia \\\\\nEurope S & Slovenia \\\\\nEurope S & Spain \\\\\nEurope W & Austria \\\\\nEurope W & Belgium \\\\\nEurope W & France \\\\\nEurope W & Germany \\\\\nEurope W & Luxembourg \\\\\nEurope W & Netherlands \\\\\nEurope W & Switzerland \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Countries and world regions of Europe}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability", "authors": ["Moritz Laber", "Peter Klimek", "Martin Bruckner", "Liuhuaying Yang", "Stefan Thurner"], "url": "https://arxiv.org/abs/2210.01846v3", "attribution": "\"Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability\" by Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang, and Stefan Thurner, arXiv:2210.01846v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.01319v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of performance of several R-peak detection methods using the MIT-BIH-AR database}\n\\begin{tabular}{cccc}\n\\toprule\n\\textbf{Method} & \\textbf{\\textit{Sen} (\\%)} & \\textbf{\\textit{PPR} (\\%)} & \\textbf{\\textit{DER} (\\%)}\\\\ \n\\midrule\n\\texttt Park et al. & 99.93 & 99.91 & 0.163\\\\\n\\texttt Farashi & 99.75 & 99.85 & 0.40 \\\\\n\\texttt Sharma and Sunkaria & 99.50 & 99.56 & 0.93\\\\\n\\texttt Castells-Rufas and Carrabina & 99.43 & 99.67 & 0.88\\\\\n\\textbf {Proposed Model} & \\textbf {99.64} & \\textbf {99.71} & \\textbf {0.19}\\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection", "authors": ["Atiyeh Fotoohinasab", "Toby Hocking", "Fatemeh Afghah"], "url": "https://arxiv.org/abs/2102.01319v2", "attribution": "\"A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection\" by Atiyeh Fotoohinasab, Toby Hocking, and Fatemeh Afghah, arXiv:2102.01319v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{A thesaurus of network terminology. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\nTerminology in this book & Alternative terms \\\\\n\\hline\nNetwork & Graph \\\\\nNode & Vertex \\\\\nLink & Arc, edge, line \\\\\nTree\t & Arborescence\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.13655v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Network Speed Measures for Polygon}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\n Measure & Pre- & Post- & t-stat & p-val \\\\\n\\hline \\hline\nTransactions per day & 2,821,632 & 2,729,498 & -1.49 & 0.14 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "An Event Study of the Ethereum Transition to Proof-of-Stake", "authors": ["Elie Kapengut", "Bruce Mizrach"], "url": "https://arxiv.org/abs/2210.13655v2", "attribution": "\"An Event Study of the Ethereum Transition to Proof-of-Stake\" by Elie Kapengut and Bruce Mizrach, arXiv:2210.13655v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.04035v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|cccc|cc|}\n \\hline\n \\multirow{2}{*}{Data} & \\multirow{2}{*}{\\# Rows} & \\multirow{2}{*}{\\# Cols} & \\multicolumn{4}{c|}{Column Missing Rates} & \\multicolumn{2}{c|}{\\% Rows Rated} \\\\\n & & & Avg & Med & Min & Max & One RP & All RPs \\\\ \\hline\n US Hospital & 4217 & 4 & 30.2\\% & 26.9\\% & 11.6\\% & 55.2\\% & 21.0\\% & 41.3\\% \\\\\n Journal & 944 & 11 & 36.0\\% & 37.7\\% & 5.2\\% & 65.8\\% & 1.2\\% & 13.5\\% \\\\\n ESG & 1356 & 4 & 47.5\\% & 53.9\\% & 3.8\\% & 78.4\\% & 44.0\\% & 14.0\\% \\\\\n Elementary School & 301 & 5 & 38.9\\% & 33.9\\% & 25.9\\% & 66.8\\% & 29.9\\% & 24.3\\% \\\\\n High School & 132 & 5 & 55.6\\% & 53.8\\% & 28.8\\% & 81.8\\% & 55.3\\% & 11.4\\% \\\\\n Movielens & 1102 & 12 & 62.7\\%\t& 64.8\\% & 53.0\\% & 67.3\\% & 23.1\\% & 3.0\\%\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Summary statistics of data sets}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Discordance Minimization-based Imputation Algorithms for Missing Values in Rating Data", "authors": ["Young Woong Park", "Jinhak Kim", "Dan Zhu"], "url": "https://arxiv.org/abs/2311.04035v1", "attribution": "\"Discordance Minimization-based Imputation Algorithms for Missing Values in Rating Data\" by Young Woong Park, Jinhak Kim, and Dan Zhu, arXiv:2311.04035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.14389v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Identification code of the households in UK-SMEC dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\t\t\t\\toprule\n\t\t\t&H\\#1 &H\\#2 &H\\#3 &H\\#4\\\\\n\t\t\t\\hline\n\t\t\tID &MAC005041 &MAC004970 &MAC004902 &MAC004897\\\\\n\t\t\t\\midrule\n\t\t\t&H\\#5 &H\\#6 &H\\#7 &H\\#8\\\\\n\t\t\t\\hline\n\t\t\tID &MAC004866 &MAC001477 &MAC000415 &MAC000032\\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Probabilistic electric load forecasting through Bayesian Mixture Density Networks", "authors": ["Alessandro Brusaferri", "Matteo Matteucci", "Stefano Spinelli", "Andrea Vitali"], "url": "https://arxiv.org/abs/2012.14389v2", "attribution": "\"Probabilistic electric load forecasting through Bayesian Mixture Density Networks\" by Alessandro Brusaferri, Matteo Matteucci, Stefano Spinelli, and Andrea Vitali, arXiv:2012.14389v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.24078v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcc}\n \\hline\n \\textbf{Method} & \\textbf{Dataset} & \\textbf{ATE Estimate} & \\textbf{Estimated Gap (\\%)} \\\\\n \\hline\n Unadjusted Analysis & Google Scholar Subset & -- & 11.71\\% \\\\\n & Full Dataset & -- & 12.80\\% \\\\\n \\hline\n OLS Regression & Google Scholar Subset & -0.0321 & 7.12\\% \\\\\n & Full Dataset & -0.0400 & 8.80\\% \\\\\n \\hline\n PSM Regression & Google Scholar Subset & -0.0297 & 6.61\\% \\\\\n & Full Dataset & -0.0293 & 6.52\\% \\\\\n \\hline\n Causal Forest & Google Scholar Subset & -0.0252 & 5.64\\% \\\\\n & Full Dataset & -0.0266 & 5.94\\% \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Estimated Gender Pay Gaps (log salary scale) by Method and Dataset.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Estimation of Gender Wage Gap in the University of North Carolina System", "authors": ["Zihan Zhang", "Jan Hannig"], "url": "https://arxiv.org/abs/2505.24078v1", "attribution": "\"Estimation of Gender Wage Gap in the University of North Carolina System\" by Zihan Zhang and Jan Hannig, arXiv:2505.24078v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06423v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Description of evaluation datasets, AvgWPerD denotes average number of words per document and AvgSPerD denotes the average number of sentences per document.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\t\t\t\\toprule\n\t\t\tDataset & AvgWPerD & AvgSPerD & Train & Dev & Test\\\\\n\t\t\t\\midrule\n\t\t\tCNSE & 982.7 & 20.1 & 17,438 & 5,813 & 5,812 \\\\\n\t\t\tCNSS & 996.6 & 20.4 & 20,102 & 6,701 & 6,700 \\\\\n\t\t\t\\hline\n\t\t\tAAN-Abs & 122.7 & 4.9 & 106,592 & 13,324 & 13,324 \\\\\n\t\t\tAAN-Body & 3270.1 & 111.6 & 104,371 & 12,818 & 12,696 \\\\\n\t\t\tOC & 190.4 & 7.0 & 240,000 & 30,000 & 30,000 \\\\\n\t\t\tS2ORC & 263.7 & 9.3 & 152,000 & 19,000 & 19,000 \\\\\n\t\t\t\\hline\n\t\t\tPAN & 1569.7 & 47.4 & 17,968 & 2,908 & 2,906 \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching", "authors": ["Liang Pang", "Yanyan Lan", "Xueqi Cheng"], "url": "https://arxiv.org/abs/2101.06423v2", "attribution": "\"Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching\" by Liang Pang, Yanyan Lan, and Xueqi Cheng, arXiv:2101.06423v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.17660v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|l|l}\n Bus type & Known Variables & Unknown variables \\\\\n \\hline\n PV & $P$, $|V|$ & $Q$, $\\theta$ \\\\\n PQ & $P$, $Q$ & $|V|$, $\\theta$ \\\\\n Slack & $V$, $\\theta$ & $P$, $Q$\n \\end{tabular}\n\\caption{Buses classification for power flow analysis.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations", "authors": ["Luis Piloto", "Sofia Liguori", "Sephora Madjiheurem", "Miha Zgubic", "Sean Lovett", "Hamish Tomlinson", "Sophie Elster", "Chris Apps", "Sims Witherspoon"], "url": "https://arxiv.org/abs/2403.17660v1", "attribution": "\"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations\" by Luis Piloto, Sofia Liguori, Sephora Madjiheurem, Miha Zgubic, Sean Lovett, Hamish Tomlinson, Sophie Elster, Chris Apps, and Sims Witherspoon, arXiv:2403.17660v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13229v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule \n \\textbf{Type} & All & Rear-end & Sideswipe \\\\ \\midrule\n \\textbf{P-value} & 2.12E-7 &1.38E-9 & 0.451 \\\\ \\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Contribution by road ID or merge condition for the current case. Segment 1 does not have merge lanes, while Segment 2 has merge lanes. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study", "authors": ["Xiwen Chen", "Hao Wang", "Abolfazl Razi", "Brendan Russo", "Jason Pacheco", "John Roberts", "Jeffrey Wishart", "Larry Head", "Alonso Granados Baca"], "url": "https://arxiv.org/abs/2201.13229v2", "attribution": "\"Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study\" by Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head, and Alonso Granados Baca, arXiv:2201.13229v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.02021v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimates of the NB model for STDs, after excluding the non-significant covariates}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\hline\nSTDs-NB & Estimate({ $\\hat{\\boldsymbol\\beta}_1$}) & StdDev & $p$-value \\\\\n\\hline\nIntercept & -2.0317 & 0.1567 & 0.0000 \\\\\nSmoke & 0.8100 & 0.3576 & 0.0235 \\\\\nDispersion & 0.1557 & &\\\\\n\\hline\n\\multicolumn{2}{l}{AIC = 570.87} & \\multicolumn{2}{l}{-2log-Lik. = 564.865} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modeling Count Data via Copulas", "authors": ["Hadi Safari-Katesari", "S. Yaser Samadi", "Samira Zaroudi"], "url": "https://arxiv.org/abs/2012.02021v1", "attribution": "\"Modeling Count Data via Copulas\" by Hadi Safari-Katesari, S. Yaser Samadi, and Samira Zaroudi, arXiv:2012.02021v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.09239v1_tex_table16.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\hline\n\\hline\ndate & n & climate change & covid & price & taxes & inequality \\\\ \n \\hline\n 2017-07-01 & 8 & 0 & 0 & 7 & 0 & 0 \\\\ \n 2017-08-01 & 3 & 0 & 0 & 2 & 1 & 2 \\\\ \n 2017-09-01 & 19 & 0 & 0 & 12 & 0 & 2 \\\\ \n 2017-10-01 & 15 & 0 & 0 & 10 & 0 & 2 \\\\ \n 2017-11-01 & 22 & 0 & 0 & 15 & 1 & 2 \\\\ \n 2017-12-01 & 2 & 0 & 0 & 2 & 1 & 0 \\\\ \n 2018-01-01 & 4 & 0 & 0 & 3 & 1 & 0 \\\\ \n 2018-02-01 & 15 & 0 & 0 & 10 & 0 & 2 \\\\ \n 2018-03-01 & 10 & 0 & 0 & 6 & 0 & 0 \\\\ \n 2018-04-01 & 9 & 0 & 0 & 8 & 2 & 1 \\\\ \n 2018-05-01 & 14 & 1 & 0 & 13 & 6 & 3 \\\\ \n 2018-06-01 & 5 & 0 & 0 & 5 & 0 & 0 \\\\ \n 2018-07-01 & 7 & 0 & 0 & 4 & 0 & 1 \\\\ \n 2018-08-01 & 1 & 0 & 0 & 1 & 0 & 0 \\\\ \n 2018-09-01 & 12 & 0 & 0 & 10 & 0 & 0 \\\\ \n 2018-10-01 & 11 & 2 & 0 & 11 & 0 & 1 \\\\ \n 2018-11-01 & 20 & 3 & 0 & 13 & 1 & 1 \\\\ \n 2018-12-01 & 5 & 0 & 0 & 2 & 0 & 0 \\\\ \n 2019-01-01 & 8 & 1 & 0 & 4 & 0 & 0 \\\\ \n 2019-02-01 & 11 & 0 & 0 & 8 & 0 & 1 \\\\ \n 2019-03-01 & 11 & 0 & 0 & 9 & 1 & 2 \\\\ \n 2019-04-01 & 4 & 1 & 0 & 2 & 0 & 0 \\\\ \n 2019-05-01 & 13 & 2 & 0 & 7 & 1 & 2 \\\\ \n 2019-06-01 & 9 & 1 & 0 & 5 & 0 & 0 \\\\ \n 2019-07-01 & 5 & 0 & 0 & 5 & 0 & 0 \\\\ \n 2019-08-01 & 1 & 0 & 0 & 1 & 0 & 0 \\\\ \n 2019-09-01 & 11 & 0 & 0 & 6 & 0 & 1 \\\\ \n 2019-10-01 & 10 & 3 & 0 & 9 & 3 & 3 \\\\ \n 2019-11-01 & 18 & 4 & 0 & 12 & 2 & 2 \\\\ \n 2019-12-01 & 6 & 3 & 0 & 5 & 1 & 1 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\ ECB Speeches. 1997-2022}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Interactions of Social Norms about Climate Change: Science, Institutions and Economics", "authors": ["Antonio Cabrales", "Manu García", "David Ramos Muñoz", "Angel Sánchez"], "url": "https://arxiv.org/abs/2208.09239v1", "attribution": "\"The Interactions of Social Norms about Climate Change: Science, Institutions and Economics\" by Antonio Cabrales, Manu García, David Ramos Muñoz, and Angel Sánchez, arXiv:2208.09239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.21306v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n \\toprule\n \\textbf{Technology} & \\textbf{Installed capacity [GW]} \\\\\n \\midrule\n Hard coal & 8.1 \\\\\n Lignite & 9 \\\\\n Gas & 33.4 \\\\\n Other conventionals & 5 \\\\\n Biomass & 8.5 \\\\\n Hydro & 10.4 \\\\\n Wind onshore & 85.5 \\\\\n Wind offshore & 18.3 \\\\\n Ground-mounted PV (solar park) & 31.2 \\\\\n Rooftop PV & Endogenous \\\\\n Home battery & Endogenous \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Installed Capacity by Technology.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Solar prosumage under different pricing regimes: Interactions with the transmission grid", "authors": ["Dana Kirchem", "Mario Kendziorski", "Enno Wiebrow", "Wolf-Peter Schill", "Claudia Kemfert", "Christian von Hirschhausen"], "url": "https://arxiv.org/abs/2502.21306v1", "attribution": "\"Solar prosumage under different pricing regimes: Interactions with the transmission grid\" by Dana Kirchem, Mario Kendziorski, Enno Wiebrow, Wolf-Peter Schill, Claudia Kemfert, and Christian von Hirschhausen, arXiv:2502.21306v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.08962v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc}\n\\hline\n\\textbf{model name} & \\textbf{$\\#$layers} & \\textbf{$\\#$dim} & \\textbf{$\\#$params} & \\textbf{train data} \\\\ \\hline\nw2v2\\_base & 12 & 768 & 94.4M & 960hr \\\\\nhubert\\_base & 12 & 768 & 94.4M & 960hr \\\\ \nwavlm\\_base & 12 & 768 & 94.4M & 960hr \\\\\nw2v2\\_xlsr\\_300m & 24 & 1024 & 315M & 436,000hr \\\\\nw2v2\\_xlsr\\_1b & 48 & 1280 & 962M & 436,000hr \\\\\nw2v2\\_xlsr\\_2b & 48 & 1920 & 2.16B & 436,000hr \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Specifications of selected SSL models}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Selection of Layers from Self-supervised Learning Models for Predicting Mean-Opinion-Score of Speech", "authors": ["Xinyu Liang", "Fredrik Cumlin", "Victor Ungureanu", "Chandan K. A. Reddy", "Christian Schuldt", "Saikat Chatterjee"], "url": "https://arxiv.org/abs/2508.08962v1", "attribution": "\"Selection of Layers from Self-supervised Learning Models for Predicting Mean-Opinion-Score of Speech\" by Xinyu Liang, Fredrik Cumlin, Victor Ungureanu, Chandan K. A. Reddy, Christian Schuldt, and Saikat Chatterjee, arXiv:2508.08962v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.05748v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Expected initial state of the Deputy relative to the Chief, e.g., \\( \\mathbb{E}\\big[X_d(t_{0}) - X_c(t_{0})\\big] \\).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\n\\( \\delta x_0 \\) [km] & \\( \\delta y_0 \\) [km] & \\( \\delta z_0 \\) [km] & \\( \\delta{\\dot x_0} \\) [km/s] & \\( \\delta{\\dot y_0} \\) [km/s] & \\( \\delta{\\dot z_0} \\) [km/s] \\\\\n\\hline\n-25.9809 & 27.8498 & 22.7715 & -0.0350 & -0.0066 & -0.0234 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governor", "authors": ["Taehyeun Kim", "Robin Inho Kee", "Ilya Kolmanovsky", "Anouck Girard"], "url": "https://arxiv.org/abs/2412.05748v1", "attribution": "\"Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governor\" by Taehyeun Kim, Robin Inho Kee, Ilya Kolmanovsky, and Anouck Girard, arXiv:2412.05748v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11381v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{pifont}\n\\usepackage{adjustbox}\n\\usepackage{marvosym}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|c|c|ccc|c|}\n \\hline\n No. & Name & Type & Size & \\multicolumn{4}{c|}{Characteristics} \\\\\n & & & & Artifact & Noise & Material & Reconstruction\\\\\\hline\n D1 & AFA & GAN & - & \\ding{51} & \\ding{51} & Steel & FDK\\\\\n \\hline\n \n D2 & Incl-718 & GAN & - & \\ding{51} & \\ding{51} & - & more defects, few inlcusions\\\\\\hline %synthetic in-dist\n \n D3 & InD & Experimental & 4020 & \\ding{51} & \\ding{55} & - & \\\\\\hline % test 3\n D4 & OoD, more pore, few Incl. & Experimental & 920 & \\ding{51} & \\ding{55} & Titanium & MBIR \\\\\\hline %test2\n \n D5 & OoD, no-Incl & GAN & 600 & \\ding{51}& \\ding{51} & - & More pores, No inclusions \\\\\\hline % test5\n \n D6 & OoD, less pore, more Incl. & Experimental & 920 & \\ding{51} & \\ding{51} & - & Less pore, many inclusion \\\\\\hline %test1\n \n D7 & OoD, no-Incl. & Experimental & 1080 & \\ding{51} & \\ding{55} & - & no inclusions, only pores \\\\\\hline %test4\n \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Dataset specification.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Adapting Segment Anything Model (SAM) to Experimental Datasets via Fine-Tuning on GAN-based Simulation: A Case Study in Additive Manufacturing", "authors": ["Anika Tabassum", "Amirkoushyar Ziabari"], "url": "https://arxiv.org/abs/2412.11381v1", "attribution": "\"Adapting Segment Anything Model (SAM) to Experimental Datasets via Fine-Tuning on GAN-based Simulation: A Case Study in Additive Manufacturing\" by Anika Tabassum and Amirkoushyar Ziabari, arXiv:2412.11381v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12733v5_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small Certified robust accuracy of TPC on CurveNet}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcc}\n \\toprule\n \\multirow{2}{*}[-2pt]{Transformation} & \\multirow{2}{*}[-2pt]{Attack radius} & \\multicolumn{2}{c}{Certified Accuracy ($\\%$)} \\\\ \\cline{3-4}\\\\\n & & PointNet& CurveNet \\\\\n \\midrule\n Z-rotation & 180$^\\circ$ & 81.3& \\textbf{85.4}\\\\\n Z-shear & 0.2 & 77.7 & \\textbf{87.8}\\\\\n Z-twist & 180$^\\circ$ & 64.3 & \\textbf{86.2}\\\\\n Z-taper & 0.2 & 76.5 & \\textbf{88.6}\\\\\n Linear & 0.2 & 59.9 & \\textbf{77.7} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "TPC: Transformation-Specific Smoothing for Point Cloud Models", "authors": ["Wenda Chu", "Linyi Li", "Bo Li"], "url": "https://arxiv.org/abs/2201.12733v5", "attribution": "\"TPC: Transformation-Specific Smoothing for Point Cloud Models\" by Wenda Chu, Linyi Li, and Bo Li, arXiv:2201.12733v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.08827v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{cccc}\n\\hline\n & Estimate & Bootstrap Mean&Quantile CI (95\\%)\\\\ \\hline\nIndirect Effect & 0.2600\n & 0.2531\n&(0.1279, 0.3752) \n\\\\ \\hline\nDirect Effect & -0.0223\n & -0.0178\n&(-0.2496, 0.1688) \n\\\\ \\hline\nTotal Effect & 0.2378 & 0.2353&(-0.0079, 0.4346) \\\\ \\hline\n\\end{tabular}\n\\caption{Summary of the effects in OLS (gene sites from other papers) with mean and confidence interval (CI) under 300-sample bootstrap.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Debiased Estimator for the Mediation Functional in Ultra-High-Dimensional Setting in the Presence of Interaction Effects", "authors": ["Shi Bo", "AmirEmad Ghassami", "Debarghya Mukherjee"], "url": "https://arxiv.org/abs/2412.08827v2", "attribution": "\"A Debiased Estimator for the Mediation Functional in Ultra-High-Dimensional Setting in the Presence of Interaction Effects\" by Shi Bo, AmirEmad Ghassami, and Debarghya Mukherjee, arXiv:2412.08827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.16988v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{xcolor}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimation results of $\\psi_2=(\\psi_{20},\\psi_{21})$ and $\\psi_1=(\\psi_{10},\\psi_{11})$ in STAR*D with the contrast function in stage $j$ specified as $\\psi_{j0}+\\psi_{j1}\\Delta S_{j}$ in Q- and A-learning for $j=1,2$. The results under AIPWE are normalized via $\\|\\psi_1\\|_2=1$ and $\\|\\psi_2\\|_2=1$.}\n\\begin{tabular}{crrrr}\n \\hline \\\\ \n& $\\psi_{20}$ & $\\psi_{21}$ & $\\psi_{10}$ & $\\psi_{11}$ \\\\ \\hline \\\\ \n Working treatment-free & \\multicolumn{2}{c}{$1+S_{2}+S_{1}+S_{0}$;} & \\multicolumn{2}{c}{\\quad $1+S_1+S_{0}$}\\\\\nterms & & & \\multicolumn{2}{c}{ \\textcolor{blue}{\\quad $1+S_1+\\Delta S_{1}$}}\\\\\nQ & $-1.5591$ (0.5686)& $0.1938$ (0.1515) & $-0.9078$ (0.3794)& $-0.0765$ (0.0872)\\\\ \n & & &\\textcolor{blue}{$-0.9725$ (0.3959)} &\\textcolor{blue}{$-0.2973$ (0.1337)}\\\\\nA & $-1.8081$ (0.5831) & 0.1609 (0.2056) & $-0.9868$ (0.4712)&$-0.1325$ (0.1271) \\\\ \n & & &\\textcolor{blue}{$-0.9651$ (0.5234)} &\\textcolor{blue}{$-0.1950$ (0.1677)}\\\\ \n Working treatment-free &\\multicolumn{2}{c}{$1+S_2+\\Delta S_{2}$; } & \\multicolumn{2}{c} {\\quad $1+S_1+S_{0}$} \\\\\n terms & & & \\multicolumn{2}{c} {\\textcolor{blue}{\\quad $1+S_1+\\Delta S_{1}$}} \\\\ \nQ & $-1.3489$ (0.4976)& $-0.0220$ (0.2990) & $-0.9678$ (0.4307) & $-0.0913$ (0.0917) \\\\ \n & & &\\textcolor{blue}{$-1.0199$ (0.4818)} &\\textcolor{blue}{$-0.2696$ (0.1525)}\\\\\nA & $-1.5855$ (0.5961) & 0.0400 (0.3538) & $-1.0044$ (0.4712)& $-0.1374$ (0.1270)\\\\ \n & & &\\textcolor{blue}{$-0.9838$ (0.5234)} &\\textcolor{blue}{$-0.2002$ (0.1676)}\\\\\nAIPWE &$-0.9782$ (0.1395)& $0.2075$ (0.3799)& $-0.9258$ (0.2336) & $0.3779$ (0.4085)\n\\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A tutorial on optimal dynamic treatment regimes", "authors": ["Chunyu Wang", "Brian DM Tom"], "url": "https://arxiv.org/abs/2502.16988v1", "attribution": "\"A tutorial on optimal dynamic treatment regimes\" by Chunyu Wang and Brian DM Tom, arXiv:2502.16988v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.03496v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{: Neural network approach using two shallow networks of width 32 for both $u$ and $f$. We use $\\operatorname{tanh}$ as activation function for $u_\\theta$ and $\\operatorname{tanh}'' = 2\\operatorname{tanh}^3 - 2\\operatorname{tanh}$ for $f_\\psi$, hence the Laplacian of $u_\\theta$ is representable by the network $f_\\psi$. We report relative $H^2$ errors.}\n\\begin{tabular}{|c||c|c|c|}\n \\hline\n $\\alpha^2 \\;\\backslash\\; \\gamma^2$ & $10^0$ & $10^{2}$ & $10^{4}$ \\\\ \\hline \\hline\n $1$ & 1.34e-05 & 7.09e-05 & 3.05e-04 \\\\ \\hline\n $10^{-2}$ & 1.44e-05 & 5.34e-05 & 3.56e-04 \\\\ \\hline\n $10^{-4}$ & 3.17e-05 & 1.78e-04 & 7.30e-04 \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Variational Formulations of the Strong Formulation -- Forward and Inverse Modeling using Isogeometric Analysis and Physics-Informed Networks", "authors": ["Kent-Andre Mardal", "Jarle Sogn", "Marius Zeinhofer"], "url": "https://arxiv.org/abs/2312.03496v1", "attribution": "\"Variational Formulations of the Strong Formulation -- Forward and Inverse Modeling using Isogeometric Analysis and Physics-Informed Networks\" by Kent-Andre Mardal, Jarle Sogn, and Marius Zeinhofer, arXiv:2312.03496v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12246v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results for the $AAUC_{5\\%}$ measure on the synthetic data.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l||c|c|c|c}\nname & rerun & min. $p$-value & permutation test & imported $p$-values \\\\\n\\hline\nEigenevent & 4.993 & -- & -- & 4.391\\\\\nWSARE 2.0 & -- & 2.963 & 3.805 & 4.925\\\\\nWSARE 2.5 & -- & 1.321 & 1.614 & 1.931\\\\\nWSARE 3.0 & -- & 0.899 & 1.325 & 1.610\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Revisiting Non-Specific Syndromic Surveillance", "authors": ["Moritz Kulessa", "Eneldo Loza Mencía", "Johannes Fürnkranz"], "url": "https://arxiv.org/abs/2101.12246v1", "attribution": "\"Revisiting Non-Specific Syndromic Surveillance\" by Moritz Kulessa, Eneldo Loza Mencía, and Johannes Fürnkranz, arXiv:2101.12246v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12251v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|}\n \\hline\n \n Solar Panels & $\\mathcal{S}(SD^q)$ &$\\mathcal{S}(SD^p)$ \\\\ \\hline\n $P_1$ &$0.596$&$0.600$\\\\\n $P_2$ &$0.565$&$0.569$\\\\\n $P_3$ &$0.501$&$0.502$\\\\\n $P_4$ &$0.567$&$0.568$\\\\\n $P_5$ &$0.540$&$0.541$\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The score values of significance degrees $SD^q$ and $SD^p$ }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology", "authors": ["Mahmut Can Bozyiğit", "Mehmet Ünver"], "url": "https://arxiv.org/abs/2501.12251v1", "attribution": "\"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology\" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10127v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\hline\n $n=30067$ & Predicted: Default & Predicted: Not default \\\\ \\hline\nActual: Default & 384 & 1754 \\\\ \\hline\nActual: Not default & 287 & 27642 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Confusion matrix for the neural network of the Kaggle dataset.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Interpretable Selective Learning in Credit Risk", "authors": ["Dangxing Chen", "Weicheng Ye", "Jiahui Ye"], "url": "https://arxiv.org/abs/2209.10127v1", "attribution": "\"Interpretable Selective Learning in Credit Risk\" by Dangxing Chen, Weicheng Ye, and Jiahui Ye, arXiv:2209.10127v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2503.05706v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{AIC and BIC comparison for Model 1 and Model 2}\n\\begin{tabular}{lcc}\n\\hline\n\\textbf{Model} & \\textbf{AIC} & \\textbf{BIC} \\\\\n\\hline\n\\hline\nModel 1 & 15406.98 & 5035.25 \\\\\nModel 2 & 15084.18 & 4717.32 \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "The Impact of Building-Induced Visibility Restrictions on Intersection Accidents", "authors": ["Hanlin Tian", "Yuxiang Feng", "Wei Zhou", "Anupriya", "Mohammed Quddus", "Yiannis Demiris", "Panagiotis Angeloudis"], "url": "https://arxiv.org/abs/2503.05706v1", "attribution": "\"The Impact of Building-Induced Visibility Restrictions on Intersection Accidents\" by Hanlin Tian, Yuxiang Feng, Wei Zhou, Anupriya, Mohammed Quddus, Yiannis Demiris, and Panagiotis Angeloudis, arXiv:2503.05706v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.14431v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The Effect of Path Number on Asian Options. Pricing Date:\\,20220926.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c|c|} \\hline\n\\rule[0pt]{0pt}{5mm}\n\\tt \\\tDeal \\ &\\tt 1Y\\_ATM &\\tt 1Y\\_10C &\\tt 6M\\_ATM\t&\\tt\t1M\\_ATM\t \t\\\\\n\\hline\n\\tt\tI=5000\t&\\tt 883.2436\t&\\tt 11.8253\t&\\tt 608.9964\t&\\tt 186.5733\t \\\\\n\\tt\tI=10000\t&\\tt 881.1290\t&\\tt 12.0545\t&\\tt 612.8797\t&\\tt 186.9654\t \\\\\n\\tt\tI=15000\t&\\tt 878.8648\t&\\tt 11.9601\t&\\tt 612.0276\t&\\tt 186.9369\t \\\\\n\\tt\tI=20000\t&\\tt 881.0790\t&\\tt 12.6660\t&\\tt 611.2084\t&\\tt 187.3059\t \\\\\n \\hline\n\\tt\tStrike\t&\\tt 7.00700\t&\\tt 7.51097\t&\\tt 7.04005\t&\\tt 7.08319\t \\\\\n\\tt\t~LastFix~ \t&\\tt 20230926\t&\\tt 20030926\t&\\tt 20030324\t&\\tt 20021026\t \\\\\n\\tt\tPayDate\t&\\tt 20230928\t&\\tt 20030928\t&\\tt 20030328\t&\\tt 20021028\t \\\\\n\\tt\tEuroPrc\t&\\tt 1177.4686\t&\\tt 168.8805\t&\\tt 847.7307\t&\\tt 328.0121\t \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Efficient and Accurate Calibration to FX Market Skew with Fully Parameterized Local Volatility Model", "authors": ["Dongli Wu", "Bufan Zhang", "Xiao Lin"], "url": "https://arxiv.org/abs/2211.14431v2", "attribution": "\"Efficient and Accurate Calibration to FX Market Skew with Fully Parameterized Local Volatility Model\" by Dongli Wu, Bufan Zhang, and Xiao Lin, arXiv:2211.14431v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.16919v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Functions used within the joint VaR and ES scoring function of () to give the five different versions of the score used in our study: QS, AL, NZ, FZG, and AS.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n & $G_1(x)$ & $G_2(x)$ & $\\zeta_2(x)$ & $a(x)$ \\\\\n \\midrule\n QS& $x$ & 0& 0 & $\\alpha x$ \\\\\n AL & 0 & $-1/x$ & $-\\ln(-x)$ & $1 - \\ln(1 - \\alpha)$ \\\\\n NZ & 0 & $1/2(-x)^{-1/2}$ & $-(-x)^{1/2}$ & 0 \\\\\n FZG & $x$ & $\\exp(x)/(1 + \\exp(x))$ & $\\ln(1 + \\exp(x))$ & $\\ln(2)$ \\\\\n AS & $-1/2Wx^2$ & $\\alpha x$ & $1/2\\alpha x^2$ & 0 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Combining a Large Pool of Forecasts of Value-at-Risk and Expected Shortfall", "authors": ["James W. Taylor", "Chao Wang"], "url": "https://arxiv.org/abs/2508.16919v1", "attribution": "\"Combining a Large Pool of Forecasts of Value-at-Risk and Expected Shortfall\" by James W. Taylor and Chao Wang, arXiv:2508.16919v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.08849v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Accuracies on each classification benchmark datasets, averaged across 20 training-testing splits for obliqueBART and axis-aligned methods. Best performing method is bolded and errors that are statistically significantly larger than obliqueBART's are marked with an asterisk.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\hline\ndata & ERT & obliqueBART & BART & RF & XGB \\\\\\hline\n\\texttt{banknote} & 0.999 & \\textbf{0.999} & 0.997* & 0.992* & 0.991* \\\\\n\\texttt{blood transfusion} & 0.776 & 0.768 & 0.795 & 0.768 & \\textbf{0.798}\\\\\n\\texttt{breast cancer diag.} & 0.965 & 0.922 & 0.966 & 0.961 & \\textbf{0.967}\\\\\n\\texttt{breast cancer} & 0.975 & 0.974 & 0.973 & \\textbf{0.975} & 0.964*\\\\\n\\texttt{breast cancer prog.} & 0.754 & 0.758 & \\textbf{0.760} & 0.756 & 0.733* \\\\\n\\texttt{climate crashes} & 0.919 & 0.917 & 0.944 & 0.926 & \\textbf{0.951}\\\\\n\\texttt{connectionist sonar} & \\textbf{0.871} & 0.796 & 0.824 & 0.835 & 0.832\\\\\n\\texttt{credit approval} & 0.874 & 0.866 & 0.87 & \\textbf{0.876} & 0.872\\\\\n\\texttt{echocardiogram} & \\textbf{0.733} & 0.714 & 0.712 & 0.707 & 0.692* \\\\\n\\texttt{fertility} & 0.846* & \\textbf{0.86} & \\textbf{0.860} & 0.842* & 0.846\\\\\n\\texttt{german credit} & \\textbf{0.765} & 0.751 & 0.76 & 0.763 & 0.745\\\\\n\\texttt{hepatitis} & 0.858 & 0.84 & 0.865 & \\textbf{0.868} & 0.855\\\\\n\\texttt{ILPD} & \\textbf{0.725} & 0.714 & 0.71 & 0.706 & 0.692*\\\\\n\\texttt{ionosphere} & \\textbf{0.936} & 0.902 & 0.926 & 0.928 & 0.924\\\\\n\\texttt{ozone1} & 0.969* & \\textbf{0.970} & 0.97 & 0.969* & 0.970\\\\\n\\texttt{ozone8} & \\textbf{0.940} & 0.933 & 0.938 & 0.939 & 0.938\\\\\n\\texttt{parkinsons} & \\textbf{0.910} & 0.866 & 0.861 & 0.899 & 0.880 \\\\\n\\texttt{planning relax} & 0.697* & \\textbf{0.717} & 0.708* & 0.686* & 0.621* \\\\\n\\texttt{qsar bio.} & \\textbf{0.868} & 0.826 & 0.845 & 0.862 & 0.855\\\\\n\\texttt{seismic bumps} & 0.930* & \\textbf{0.935} & 0.935 & 0.933* & 0.934* \\\\\n\\texttt{spambase} & \\textbf{0.956} & 0.765 & 0.932 & 0.953 & 0.945\\\\ \n\\texttt{spectf heart} & 0.802 & 0.799 & 0.813 & \\textbf{0.814} & 0.796\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Oblique Bayesian additive regression trees", "authors": ["Paul-Hieu V. Nguyen", "Ryan Yee", "Sameer K. Deshpande"], "url": "https://arxiv.org/abs/2411.08849v1", "attribution": "\"Oblique Bayesian additive regression trees\" by Paul-Hieu V. Nguyen, Ryan Yee, and Sameer K. Deshpande, arXiv:2411.08849v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.06714v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average relative gap (COMB) -- density=0.3}\n\\begin{tabular}{cccccccccccccc}\\toprule \n$\\Delta k$ & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & \\phantom{a} & avg time(s)\\\\\n\\midrule\n Shor1 & 1 & 1\n & 1\n & 1\n & 1\n & 1\n & 1\n & 1\n & 1\n & 1\n & &2.80 \\\\\n Shor2 & 2.13\t& 4.97&\t6.64\t&9.30\t&12.24&\t8.26\t&6.81&\t6.25\t&5.87&\t5.75 && 3.88 \\\\\n Our method & 1.35\t& 0.14& 0.04\t& 0.00\t& 0.07\t&0.26\t&0.35\t&0.45\t&0.50\t& 0.54 && 4.03\\\\\n Our method ($\\sigma =2$) & 0.99&\t0.00&\t\t0.43&\t\t1.10&\t\t2.06\t&\t1.87\t&\t1.84&\t\t1.91&\t\t1.95&\t\t2.02 && 3.63\\\\\n \n Our method ($\\sigma =4$) & 5.26 &\t1.22\t& 0.20 &\t0.00\t& 0.19 & \t0.48 &\t0.63 &\t0.79 &\t0.89 &\t0.98 && 3.89\\\\\n Our method ($\\sigma =6$) & 19.97 &\t7.47\t&2.75 &\t1.05\t & 0.18 &\t0.00 &\t0.05 &\t0.17\t &0.27&\t0.36 && 4.01\\\\\n Our method ($\\sigma =8$) & 35.30&15.82&\t6.46\t&3.28&\t1.45\t&0.29&\t0.03&\t0.00\t&0.03\t&0.06 && 3.48\\\\\n Our method ($\\sigma =10$) & 45.71\t&21.99\t&10.18\t&6.01\t&3.44\t&0.95\t&0.29\t&0.09\t&0.02 & 0.00 && 3.91\\\\\n Cont & 0.80 &\n 0.93 &\n 1.03 &\n 1.02 &\n 1.05 &\n 1.04 &\n 1.03 &\n 1.03 &\n 1.03 &\n 1.02 && 0.00 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Sensitivity analysis for mixed binary quadratic programming", "authors": ["Diego Cifuentes", "Santanu S. Dey", "Jingye Xu"], "url": "https://arxiv.org/abs/2312.06714v2", "attribution": "\"Sensitivity analysis for mixed binary quadratic programming\" by Diego Cifuentes, Santanu S. Dey, and Jingye Xu, arXiv:2312.06714v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.14767v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameter settings of XGBoost models.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\nHyperparameter & Setting \\\\ \\midrule\nTrees & 500 \\\\\nMin. node size & 20 \\\\\nMax. tree depth & 4 \\\\\nLearning rate & 0.1 \\\\\nMinimum loss reduction & 0 \\\\\nNo. randomly selected predictors & No. predictors \\\\\nProportion of sampled obs. & 0.5 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Predicting COVID-19 Infections Using Multi-layer Centrality Measures in Population-scale Networks", "authors": ["Christine Hedde-von Westernhagen", "Javier Garcia-Bernardo", "Ayoub Bagheri"], "url": "https://arxiv.org/abs/2310.14767v2", "attribution": "\"Predicting COVID-19 Infections Using Multi-layer Centrality Measures in Population-scale Networks\" by Christine Hedde-von Westernhagen, Javier Garcia-Bernardo, and Ayoub Bagheri, arXiv:2310.14767v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.20659v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ VSS Camera Parameters }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\hline\\hline %inserts double horizontal lines\n \\textbf{Parameter} & \\textbf{Measure} \\\\ % inserts table\n \\hline % inserts a single horizontal line\n No. of Cameras & 1 \\\\\n Resolution & 960H x 540V \\\\ \n Recording Time & 500 sec \\\\\n Frame/Sec & 24 fps \\\\\n No. of Experiments & 1 \\\\ \n Compression & H.264 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Nanosatellite Design Considerations for a Mission to Explore the Propellant Sloshing Problem", "authors": ["Michael fogel", "Snigdha Sushil Mishra", "Laurent Burlion"], "url": "https://arxiv.org/abs/2412.20659v1", "attribution": "\"Nanosatellite Design Considerations for a Mission to Explore the Propellant Sloshing Problem\" by Michael fogel, Snigdha Sushil Mishra, and Laurent Burlion, arXiv:2412.20659v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.15096v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll}\n \\toprule\n \\textbf{Order} &1&2 &3&4&5&6 &7 &8 \\\\\n \\midrule\n \\textbf{Task type} &$Q_0$ &$P$ &$B_R$&$B_{DR}$&$L_{DR}$&$ Q_1$&$L_{R}$&$Q_2$ \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A proposal and evaluation of new timbre visualisation methods for audio sample browsers", "authors": ["Etienne Richan", "Jean Rouat"], "url": "https://arxiv.org/abs/2011.15096v1", "attribution": "\"A proposal and evaluation of new timbre visualisation methods for audio sample browsers\" by Etienne Richan and Jean Rouat, arXiv:2011.15096v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.10221v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\nOld Scheme&New Scheme&Ratio\\\\\n\\hline\n4028.75&3785.95&1.064\\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\sf Example 8: The CPU times (in seconds) consumed by the studied schemes.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "New More Efficient A-WENO Schemes", "authors": ["Shaoshuai Chu", "Alexander Kurganov", "Ruixiao Xin"], "url": "https://arxiv.org/abs/2503.10221v2", "attribution": "\"New More Efficient A-WENO Schemes\" by Shaoshuai Chu, Alexander Kurganov, and Ruixiao Xin, arXiv:2503.10221v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13448v4_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\toprule\n Predictor & AIC & R\\textsuperscript{2}\\textsubscript{m} \\\\\n \\midrule\n Algorithm identities & $1661.9$ & $0.362$ \\\\\n Participant score & $1697.2$ & $0.363$ \\\\\n Social perception & $\\mathbf{1611.2}$ & $\\mathbf{0.436}$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Metrics for fractional response models predicting preferences in Study 1. Lower values of AIC and higher values of R\\textsuperscript{2}\\textsubscript{m} indicate stronger fits.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Warmth and competence in human-agent cooperation", "authors": ["Kevin R. McKee", "Xuechunzi Bai", "Susan T. Fiske"], "url": "https://arxiv.org/abs/2201.13448v4", "attribution": "\"Warmth and competence in human-agent cooperation\" by Kevin R. McKee, Xuechunzi Bai, and Susan T. Fiske, arXiv:2201.13448v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.00820v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small Heteroscedastic continuous data example. Frequentist coverage of credible intervals with nominal level $\\rho \\in \\{0.90, 0.95, 0.99\\}$ for Gaussian linear model (\\textsc{lm}) and quasi-posterior (\\textsc{qp}).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccccccc}\n & & \\multicolumn{4}{c}{$\\rho = 0.90$} & & \\multicolumn{4}{c}{$\\rho = 0.95$} & & \\multicolumn{4}{c}{$\\rho = 0.99$}\\\\\n & & $\\beta_1$ & $\\beta_2$ & $\\beta_3$ & $\\beta_4$ & & $\\beta_1$ & $\\beta_2$ & $\\beta_3$ & $\\beta_4$ & & $\\beta_1$ & $\\beta_2$ & $\\beta_3$ & $\\beta_4$ \\\\\n\\textsc{lm} & & 0.92 & 0.65 & 0.71 & 0.76 & & 0.95 & 0.71 & 0.82 & 0.84 & & 0.99 & 0.86 & 0.93 & 0.93 \\\\\n\\textsc{qp} & & 0.94 & 0.91 & 0.90 & 0.93 & & 0.96 & 0.95 & 0.96 & 0.95 & & 0.99 & 1.00 & 0.99 & 1.00 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian inference for generalized linear models via quasi-posteriors", "authors": ["Davide Agnoletto", "Tommaso Rigon", "David B. Dunson"], "url": "https://arxiv.org/abs/2311.00820v2", "attribution": "\"Bayesian inference for generalized linear models via quasi-posteriors\" by Davide Agnoletto, Tommaso Rigon, and David B. Dunson, arXiv:2311.00820v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.10481v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\toprule\n model&$H$&$df$& AIC & BIC & $\\sigma_{\\epsilon}$\\\\\n \\midrule\n\ttime FE&3&31&23757&23959&2.501\\\\\n\tlocal time FE&6&52&23112&23452&2.343\\\\\n\t\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Location-specific ANN panel model with time FE}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Regressing on distributions: The nonlinear effect of temperature on regional economic growth", "authors": ["Malte Jahn"], "url": "https://arxiv.org/abs/2309.10481v1", "attribution": "\"Regressing on distributions: The nonlinear effect of temperature on regional economic growth\" by Malte Jahn, arXiv:2309.10481v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.12269v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n & Num. cases & Num. sentences & Num. sent. with outcome & Prevalence \\\\\n \\midrule\nTraining & 388 & 99565 & 536 & 0.54\\% \\\\\nValidation & 100 & 29208 & 126 & 0.43\\% \\\\\nTesting & 150 & 44881 & 208 & 0.46\\% \\\\\n\\bottomrule \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Data statistics for the annotations used in the case outcome extraction task.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "The Cambridge Law Corpus: A Dataset for Legal AI Research", "authors": ["Andreas Östling", "Holli Sargeant", "Huiyuan Xie", "Ludwig Bull", "Alexander Terenin", "Leif Jonsson", "Måns Magnusson", "Felix Steffek"], "url": "https://arxiv.org/abs/2309.12269v4", "attribution": "\"The Cambridge Law Corpus: A Dataset for Legal AI Research\" by Andreas Östling, Holli Sargeant, Huiyuan Xie, Ludwig Bull, Alexander Terenin, Leif Jonsson, Måns Magnusson, and Felix Steffek, arXiv:2309.12269v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01697v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Importance of top 15 individual features}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n\\hline\n\\textbf{Feature Name} & \\textbf{Information Value (IV)} \\\\ \\hline\nis\\_collection & 0.010 \\\\\ngenome\\_0 & 0.005 \\\\\nkeywords\\_count & 0.004 \\\\\ngenome\\_2 & 0.004 \\\\\ngenome\\_13 & 0.004 \\\\\nmovies\\_per\\_month & 0.003 \\\\\nmale\\_count & 0.002 \\\\\ngenome\\_3 & 0.002 \\\\\ngenome\\_5 & 0.002 \\\\\ngenome\\_12 & 0.002 \\\\\nis\\_homepage & 0.001 \\\\\ntime\\_discounted\\_budget & 0.001 \\\\\nfemale\\_count & 0.001 \\\\\ngenome\\_1 & 0.001 \\\\\ngenome\\_10 & 0.001 \\\\ \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Analyzing movies to predict their commercial viability for producers", "authors": ["Devendra Swami", "Yash Phogat", "Aadiraj Batlaw", "Ashwin Goyal"], "url": "https://arxiv.org/abs/2101.01697v1", "attribution": "\"Analyzing movies to predict their commercial viability for producers\" by Devendra Swami, Yash Phogat, Aadiraj Batlaw, and Ashwin Goyal, arXiv:2101.01697v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.20631v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Annual projected EV and ET stock (thousands) for AT, HU, and SI (2026--2035)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crrrrrr}\n\\toprule\n\\textbf{Year} & \\multicolumn{3}{c}{\\textbf{EV Stock (thousands)}} & \\multicolumn{3}{c}{\\textbf{ET stock (thousands)}} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7}\n& Austria & Hungary & Slovenia & Austria & Hungary & Slovenia \\\\\n\\midrule\n2026 & 343.8 & 100.8 & 50.0 & 4.4 & 0.4 & 0.3 \\\\\n2027 & 398.8 & 118.8 & 57.7 & 4.9 & 0.5 & 0.3 \\\\\n2028 & 462.6 & 140.0 & 66.6 & 5.5 & 0.5 & 0.4 \\\\\n2029 & 536.6 & 165.1 & 76.9 & 6.2 & 0.6 & 0.5 \\\\\n2030 & 622.5 & 194.7 & 88.8 & 7.0 & 0.7 & 0.5 \\\\\n2031 & 722.1 & 229.6 & 102.6 & 7.9 & 0.8 & 0.6 \\\\\n2032 & 837.7 & 270.6 & 118.5 & 8.9 & 0.9 & 0.7 \\\\\n2033 & 971.7 & 318.9 & 136.9 & 10.1 & 1.0 & 0.8 \\\\\n2034 & 1127.2 & 375.9 & 158.2 & 11.4 & 1.1 & 0.9 \\\\\n2035 & 1307.5 & 443.3 & 182.7 & 12.8 & 1.3 & 1.0 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Cost-benefit analysis of an AI-driven operational digital platform for integrated electric mobility, renewable energy, and grid management", "authors": ["Arega Getaneh Abate", "Xiaobing Zhang", "Xiufeng Liu", "Dogan Keles"], "url": "https://arxiv.org/abs/2506.20631v1", "attribution": "\"Cost-benefit analysis of an AI-driven operational digital platform for integrated electric mobility, renewable energy, and grid management\" by Arega Getaneh Abate, Xiaobing Zhang, Xiufeng Liu, and Dogan Keles, arXiv:2506.20631v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10289v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccc}\n\\hline\n & & \\multicolumn{2}{c}{FAQs} & Total \\\\ \\cline{3-5} \n & & Yes & No & \\\\ \\cline{2-5} \n\\multirow{2}{*}{Agenda} & Yes & 103 & 68 & 171 \\\\\n & No & 45 & 66 & 111 \\\\\nTotal & & 148 & 134 & 282 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Agenda use case * FAQs use case crosstabulation }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Chatbots and messaging platforms in the classroom: an analysis from the teacher's perspective", "authors": ["J. J. Merelo", "P. A. Castillo", "Antonio M. Mora", "Francisco Barranco", "Noorhan Abbas", "Alberto Guillen", "Olia Tsivitanidou"], "url": "https://arxiv.org/abs/2201.10289v1", "attribution": "\"Chatbots and messaging platforms in the classroom: an analysis from the teacher's perspective\" by J. J. Merelo, P. A. Castillo, Antonio M. Mora, Francisco Barranco, Noorhan Abbas, Alberto Guillen, and Olia Tsivitanidou, arXiv:2201.10289v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05030v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|} \n\\hline\n{\\bf year} & {\\bf No trend} & {\\bf Increasing} & {\\bf Decreasing} \\\\\n\\hline\n2019 & 17 (34.7\\%) & 25 (51.0\\%) & 7 (14.3\\%)\n\\\\\n\\hline\n2020 & 21 (46.7\\%) & 6 (13.3\\%) & 18 (40.0\\%)\n\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Trend in daily update time series.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Multi-Perspective Study of Internet Performance during the COVID-19 Outbreak", "authors": ["Ahmed Elmokashfi", "Alfred Arouna", "Ioana Livadariu", "Mah-Rukh Fida", "Amund Kvalbein", "Anas Al-Selwi", "Thomas Dreibholz", "Haakon Bryhni"], "url": "https://arxiv.org/abs/2101.05030v1", "attribution": "\"A Multi-Perspective Study of Internet Performance during the COVID-19 Outbreak\" by Ahmed Elmokashfi, Alfred Arouna, Ioana Livadariu, Mah-Rukh Fida, Amund Kvalbein, Anas Al-Selwi, Thomas Dreibholz, and Haakon Bryhni, arXiv:2101.05030v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.16451v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of the VI-LSPM across different network sizes $n$, assessed through Procrustes correlation (PC), the area under the receiver operating characteristic curve (AUROC), and the area under the precision recall curve (AUPR) through comparison with the truth and with the MCMC-LSPM solutions. Standard deviations are given in brackets. *The results for $n=1000$ are based on only five sets of initial positions for VI-LSPM, and only on two networks with one set of initial positions for MCMC-LSPM. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ccc|ccc|}\n\\hline\n & \\multicolumn{3}{c|}{Comparison with truth} & \\multicolumn{3}{c|}{{ Comparison with MCMC}} \\\\ \\cline{2-7} \n\\multirow{-2}{*}{$n$} & PC & AUROC & AUPR & { PC} & { AUROC} & { AUPR} \\\\ \\hline\n20 & 0.78 (0.16) & { 0.920 (0.017)} & { 0.804 (0.059)} & { 0.77 (0.16)} & { 0.836 (0.060)} & { 0.850 (0.073)} \\\\ \\hline\n50 & 0.93 (0.04) & { 0.904 (0.009)} & { 0.783 (0.030)} & { 0.93 (0.04)} & { 0.885 (0.012)} & { 0.787 (0.041)} \\\\ \\hline\n100 & 0.95 (0.01) & { 0.904 (0.006)} & { 0.789 (0.018)} & { 0.95 (0.01)} & { 0.896 (0.010)} & { 0.800 (0.025)} \\\\ \\hline\n200 & 0.96 (0.01) & { 0.904 (0.004)} & { 0.796 (0.012)} & { 0.96 (0.01)} & { 0.904 (0.005)} & { 0.828 (0.025)} \\\\ \\hline\n{ 500} & { 0.97 (0.01)} & { 0.904 (0.002)} & { 0.805 (0.005)} & { 0.97 (0.01)} & { 0.910 (0.005)} & { 0.865 (0.020)} \\\\ \\hline\n{ 1000*} & { 0.97 (0.01)} & { 0.904 (0.001)} & { 0.806 (0.007)} & { 0.97 (0.01)} & { 0.912 (0.004)} & { 0.856 (0.003)} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Variational Inference for the Latent Shrinkage Position Model", "authors": ["Xian Yao Gwee", "Isobel Claire Gormley", "Michael Fop"], "url": "https://arxiv.org/abs/2311.16451v2", "attribution": "\"Variational Inference for the Latent Shrinkage Position Model\" by Xian Yao Gwee, Isobel Claire Gormley, and Michael Fop, arXiv:2311.16451v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.05646v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Horizon} \\& \\textbf{Cumulative VaR (1\\%)} \\& \\textbf{Cumulative ES (1\\%)} \\\\\n\\midrule\n1 Day (t+1) \\& 6.9\\%\\& 7.89\\%\\\\\n2 Days (t+2) \\& 10.60\\%\\& 11.76\\%\\\\\n3 Days (t+3) \\& 10.99\\%\\& 12.92\\%\\\\\n4 Days (t+4) \\& 13.82\\%\\& 15.07\\%\\\\\n5 Days (t+5) \\& 15.33\\%\\& 17.06\\%\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{5-day cumulative VaR and ES using GARCH + Normal + MCS at 1\\% confidence}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Comparative Evaluation of VaR Models: Historical Simulation, GARCH-Based Monte Carlo, and Filtered Historical Simulation", "authors": ["Xin Tian"], "url": "https://arxiv.org/abs/2505.05646v1", "attribution": "\"Comparative Evaluation of VaR Models: Historical Simulation, GARCH-Based Monte Carlo, and Filtered Historical Simulation\" by Xin Tian, arXiv:2505.05646v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.12502v3_tex_table21.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{1D GP Periodic Kernel Results. In a paired t-test, TNP-KR: SA had a lower NLL than TNP-D and ConvCNP with p-value $<0.001$ and TNP-KR: DKA had a lower NLL than TNP-KR: PERF with p-value $0.015$. ConvCNP, TNP-D, TNP-KR: DKA, and TNP-KR: SA are indistinguishable on regret.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n\\toprule\nModel & NLL & Regret \\\\\n\\midrule\nNP & $1.340\\pm0.001$ & $0.192\\pm0.015$ \\\\\nCNP & $1.119\\pm0.001$ & $0.247\\pm0.018$ \\\\\nBNP & $1.107\\pm0.001$ & $0.227\\pm0.016$ \\\\\nANP & $0.824\\pm0.013$ & $0.105\\pm0.011$\\\\\nCANP & $0.786\\pm0.007$ & $0.110\\pm0.011$\\\\\nBANP & $0.738\\pm0.008$ & $0.081\\pm0.009$\\\\\nConvCNP & $0.551\\pm0.002$ & $\\mathbf{0.028\\pm0.004}$ \\\\\nTNP-D & $0.536\\pm0.003$ & $\\mathbf{0.045\\pm0.006}$\\\\\nTNP-KR: PERF & $0.514\\pm0.001$ & $0.049\\pm0.006$ \\\\\nTNP-KR: DKA & $0.510\\pm0.001$ & $\\mathbf{0.038\\pm0.005}$\\\\\nTNP-KR: SA & $\\mathbf{0.491\\pm0.001}$ & $\\mathbf{0.036\\pm0.006}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Transformer Neural Processes - Kernel Regression", "authors": ["Daniel Jenson", "Jhonathan Navott", "Mengyan Zhang", "Makkunda Sharma", "Elizaveta Semenova", "Seth Flaxman"], "url": "https://arxiv.org/abs/2411.12502v3", "attribution": "\"Transformer Neural Processes - Kernel Regression\" by Daniel Jenson, Jhonathan Navott, Mengyan Zhang, Makkunda Sharma, Elizaveta Semenova, and Seth Flaxman, arXiv:2411.12502v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.06554v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n \\toprule \n \\textbf{Rank} & \\textbf{Time} & \\textbf{RMSE} &\\textbf{RMSD} & \\textbf{FPR} & \\textbf{FNR} & \\textbf{ACC}& \\textbf{RAI} \\\\\n \\midrule\n \\phantom{1}1. HB & 69.3s & 0.206 & 0.106 & 0.050 & 0.298 & 0.915 & 0.050 \\\\\n \\phantom{1}2. MAZ & \\phantom{2}2.2s & 0.212 & 0.119 & 0.051 & 0.482 & 0.888 & 0.078 \\\\\n \\phantom{1}3. NLL & \\phantom{2}7.2s & 0.217 & 0.114 & 0.085 & 0.498 & 0.856 & 0.083 \\\\\n \\phantom{1}4. ESQR & 91.2s & 0.292 & 0.165 & \\textbf{--} & \\textbf{--} & \\textbf{--} & 0.123 \\\\\n \\phantom{1}5. ZREC & \\phantom{2}3.2s & 0.350 & 0.312 & \\textbf{--} & \\textbf{--} & \\textbf{--} & 0.271 \\\\\n \\phantom{1}6. SUREAL & \\phantom{2}7.5s & 0.351 & 0.321 & \\textbf{--} & \\textbf{--} & \\textbf{--} & 0.246 \\\\\n \\phantom{1}7. NoOpt & \\textbf{--} & 0.372 & 0.358 & 0 & 1.000 & 0.857 & 0.143 \\\\\n \\phantom{1}8. KB & \\phantom{2}5.0s & 0.373 & 0.306 & 0.022 & 1.000 & 0.838 & 0.146 \\\\\n \\phantom{1}9. CB & \\phantom{2}8.9s & 0.432 & 0.344 & 0.230 & 0.973 & 0.664 & 0.175 \\\\\n 10. LPCC & 29.9s & 1.582 & 1.402 & 0.934 & 0.386 & 0.145 & 0.765 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Robustness and accuracy of mean opinion scores with hard and soft outlier detection", "authors": ["Dietmar Saupe", "Tim Bleile"], "url": "https://arxiv.org/abs/2509.06554v1", "attribution": "\"Robustness and accuracy of mean opinion scores with hard and soft outlier detection\" by Dietmar Saupe and Tim Bleile, arXiv:2509.06554v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15300v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|ccccc|}\n\t\t\t\t\t\n\t\t\t\t\t\\hline\n\t\t\t\t\tDimension & Method & itr& Tcpu& $Er_n$\\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\t25 & MDDLSCG&52&3.088055e-02 &5.691882e-07\\\\\n\t\t\t\t\t&MSCG&59 &3.947327e-02&6.931169e-07\\\\\n\t\t\t\t\t& ScCG& 65&4.730704e-02 &4.514777e-07\\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\t100& MDDLSCG&102& 5.552326e-02&8.162825e-07\\\\\n\t\t\t\t\t&MSCG&121 &6.512598e-02 &9.411660e-07\\\\\n\t\t\t\t\t& ScCG&134&6.823947e-02&8.609522e-07\\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\t1000& MDDLSCG&132& 3.481332e-02 &9.291368e-07\\\\\n\t\t\t\t\t&MSCG&146 &3.522395e-02&9.484382e-07\\\\\n\t\t\t\t\t& ScCG&171&4.053286e-02 &8.747078e-07\\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Modified Dai-Liao Spectral Conjugate Gradient Method with Application to Signal Processing", "authors": ["D. R. Sahu", "Shikher Sharma", "Pankaj Gautam"], "url": "https://arxiv.org/abs/2501.15300v2", "attribution": "\"Modified Dai-Liao Spectral Conjugate Gradient Method with Application to Signal Processing\" by D. R. Sahu, Shikher Sharma, and Pankaj Gautam, arXiv:2501.15300v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.17414v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n& $x$ $y$ $z$ $t$ & & $x$ $y$ $z$ $t$ \\\\\n\\hline\n$p_1$ & (0, 0, 0, 0) & $p_2$ & (1, 0, 0, 0) \\\\\n$p_3$ & (1, 1, 0, 0) & $p_4$ & (0, 1, 0, 0) \\\\\n$p_5$ & (0, 0, 1, 0) & $p_6$ & (1, 0, 1, 0) \\\\\n$p_7$ & (1, 1, 1, 0) & $p_8$ & (0, 1, 1, 0) \\\\\n$p_9$ & (0, 0, 0, 1) & $p_{10}$ & (1, 0, 0, 1) \\\\\n$p_{11}$ & (1, 1, 0, 1) & $p_{12}$ & (0, 1, 0, 1) \\\\\n$p_{13}$ & (0, 0, 1, 1) & $p_{14}$ & (1, 0, 1, 1) \\\\\n$p_{15}$ & (1, 1, 1, 1) & $p_{16}$ & (0, 1, 1, 1) \\\\\n\\hline\n\\end{tabular}\n\\caption{Ordering of coordinates for tesseract decompositions.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Anisotropic Delaunay hypervolume meshing for space-time applications: point insertion, quality heuristics, and bistellar flips", "authors": ["Jude T. Anderson", "David M. Williams"], "url": "https://arxiv.org/abs/2312.17414v2", "attribution": "\"Anisotropic Delaunay hypervolume meshing for space-time applications: point insertion, quality heuristics, and bistellar flips\" by Jude T. Anderson and David M. Williams, arXiv:2312.17414v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19559v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lrr}\n \\toprule\n \\textbf{Model} & \\textbf{0-Shot} & \\textbf{5-Shot} \\\\\n \\midrule\n LeoLM 7B Chat & 0.305 & 0.463 \\\\\n LeoLM 13B Chat & 0.341 & 0.655 \\\\\n LeoLM 70B Chat & 0.591 & 0.762 \\\\\n \\midrule\n GPT-3.5 & 0.790 & 0.783 \\\\\n GPT-4 & \\textbf{0.809} & \\textbf{0.833} \\\\\n \\midrule\n \\multicolumn{3}{c}{\\textbf{Content Moderation APIs}} \\\\\n \\midrule\n Perspective & \\multicolumn{2}{r}{0.610} \\\\\n OpenAI & \\multicolumn{2}{r}{\\textbf{0.695}} \\\\\n \\midrule\n \\multicolumn{3}{c}{\\textbf{Target Model}} \\\\\n \\midrule\n gelectra-large R0 & \\multicolumn{2}{r}{0.623} \\\\\n gelectra-large R4 & \\multicolumn{2}{r}{\\textbf{0.822}} \\\\\n \\bottomrule\n \\end{tabular}\n\\caption{Macro $F_1$ of LLMs and content moderation APIs on the GAHD test set. We include the results of gelectra-large, our target model, for comparison.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset", "authors": ["Janis Goldzycher", "Paul Röttger", "Gerold Schneider"], "url": "https://arxiv.org/abs/2403.19559v1", "attribution": "\"Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset\" by Janis Goldzycher, Paul Röttger, and Gerold Schneider, arXiv:2403.19559v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2302.14602v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimates of the Productivity Effects with Asymmetric Spillovers}\n\\begin{tabular}{lccc|c}\n\t\t\t\\toprule\n\t\t\t& \\multicolumn{3}{c}{\\textit{Point Estimates}} & {\\textit{Statistically $>0$}} \\\\ \n\t\t\tEstimand & 1st Qu. & Median & 3rd Qu. & (\\% Obs.) \\\\\n\t\t\t\\midrule\n\t\t\t$SP$ \t& 0.301 & 0.331 & 0.362 & 99.54 \\\\\n\t\t\t& (0.277, 0.328) & (0.306, 0.361) & (0.335, 0.400) & \\\\\n\t\t\t$DL$ \t& 0.083 & 0.133 & 0.169 & 87.01 \\\\\n\t\t\t& (0.058, 0.106) & (0.104, 0.156) & (0.135, 0.192) & \\\\\t\n\t\t\t\\midrule\n\t\t\t\\multicolumn{5}{p{11.1cm}}{\\footnotesize {\\sc Notes:} Reported are the results based on the productivity process formulation with asymmetric productivity spillovers in using our baseline specification of $\\mathcal{L}(i,t)$. The left panel summarizes point estimates of $SP_{it}$ and $DL_{it}$ with the corresponding two-sided 95\\% bootstrap percentile confidence intervals in parentheses. The last column reports the share of observations for which the point estimates are statistically positive at the 5\\% significance level using one-sided bootstrap percentile confidence intervals.} \\\\\n\t\t\t\\bottomrule[1pt]\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "On the Estimation of Cross-Firm Productivity Spillovers with an Application to FDI", "authors": ["Emir Malikov", "Shunan Zhao"], "url": "https://arxiv.org/abs/2302.14602v1", "attribution": "\"On the Estimation of Cross-Firm Productivity Spillovers with an Application to FDI\" by Emir Malikov and Shunan Zhao, arXiv:2302.14602v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06311v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Implemented TE algorithms }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l||l|}\n\\hline\nTE System & Description\\\\\n\\hline\nKSP+LB & \\textit{k-Shortest} Paths (KSP) for paths, LB for weights\\\\\nKSP+AD & \\textit{k-Shortest} Paths (KSP) for paths, AD for weights\\\\\nRACKE+LB & Räcke’s oblivious routing for paths, LB for weights\\\\\nRACKE+AD & Räcke’s oblivious routing for paths, AD for weights\\\\\nOPTIMAL(LB) & All paths, LB for weights \\\\\nOPTIMAL(AD) & All paths, AD for weights \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Boosting performance for software defined networks from traffic engineering perspective", "authors": ["Mohammed I. Salman", "Bin Wang"], "url": "https://arxiv.org/abs/2101.06311v1", "attribution": "\"Boosting performance for software defined networks from traffic engineering perspective\" by Mohammed I. Salman and Bin Wang, arXiv:2101.06311v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.06531v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n Model & RMSE & MdAE & PER10 ($\\%$) & PER20 ($\\%$) \\\\ \n \\midrule\n Scenario 1 & 1.11 & 0.75 & 30.0 & 55.0 \\\\ \n Scenario 2 & 1.27 & 0.81 & 28.0 & 51.5 \\\\ \n Scenario 3 & 0.99 & 0.43 & 49.0 & 72.5 \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Accuracy of the point predictions in the scenario with synthetically generated sale prices from the gradient boosted tree model. The table displays Root Mean Squared Error (RMSE), Median Absolute Error (MdAE), and Percentage Error Range (PER$10$ and PER$20$). \\textcolor{red}{Consider just writing this in the text, focusing on RMSE.}}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Uncertainty quantification in automated valuation models with spatially weighted conformal prediction", "authors": ["Anders Hjort", "Gudmund Horn Hermansen", "Johan Pensar", "Jonathan P. Williams"], "url": "https://arxiv.org/abs/2312.06531v2", "attribution": "\"Uncertainty quantification in automated valuation models with spatially weighted conformal prediction\" by Anders Hjort, Gudmund Horn Hermansen, Johan Pensar, and Jonathan P. Williams, arXiv:2312.06531v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.20196v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|l|l}\n\\hline\n & Acc. & F1 \\\\\n\\hline\n & 62.13 $\\pm$ 0.34 & 46.96$\\pm$0.43 \\\\ \n\\hline\nOur method & 63.13 $\\pm$ 1.12 & 47.95$\\pm$ 1.07 \\\\ \n\\hline\n-PDTB aug. & 63.82$\\pm$ 1.07 & 48.72$\\pm$ 0.11 \n\\\\ \\hline\n\\end{tabular}\n\\caption{ Results of extrinsic evaluation.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Automatic Alignment of Discourse Relations of Different Discourse Annotation Frameworks", "authors": ["Yingxue Fu"], "url": "https://arxiv.org/abs/2403.20196v2", "attribution": "\"Automatic Alignment of Discourse Relations of Different Discourse Annotation Frameworks\" by Yingxue Fu, arXiv:2403.20196v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.03164v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n & 1 month & 3 months & 6 months & 1 year \\\\\n \\hline\nSPX & -0.31 & -0.77 & -1.10 & -1.34 \\\\\nEUROSTOXX & -1.01 & -0.13 & -1.03 & - \\\\\nEURUSD & -0.09 & -0.48 & -0.44 & -0.87 \\\\\nGBPUSD & -0.48 & -0.51 & -0.57 & -0.31 \\\\\nUSDJPY & -0.26 & -0.31 & -0.34 & -0.68 \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Sharpe Ratio of the only long strategies on different assets, using Variance Swaps with different maturities.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Estimation of Historical volatility and Allocation strategies using Variance Swaps", "authors": ["Lucio Fiorin"], "url": "https://arxiv.org/abs/2208.03164v1", "attribution": "\"Estimation of Historical volatility and Allocation strategies using Variance Swaps\" by Lucio Fiorin, arXiv:2208.03164v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.05922v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Out-of-Sample Performance Comparison on the Hold-Out Test Set}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Model} & \\textbf{ROC AUC} & \\textbf{Brier Score} \\\\\n\\midrule\n\\textbf{Primary SVM (RF Select)} & \\textbf{0.8905} & \\textbf{0.0170} \\\\\n\\midrule\n\\textit{Benchmark Models} \\\\\nVanilla SVM (All Features) & 0.9061 & 0.0176 \\\\\nLassoCV & 0.9495 & 0.2528 \\\\\nHeuristic (VIX > 40) & 0.6656 & 0.0140 \\\\\nGaussian Naive Bayes & 0.4878 & 0.0180 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation", "authors": ["Peilin Rao", "Randall R. Rojas"], "url": "https://arxiv.org/abs/2509.05922v1", "attribution": "\"Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation\" by Peilin Rao and Randall R. Rojas, arXiv:2509.05922v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10175v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparisons with RFPose}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Model & Single-Person & Multi-Person & Action \\\\\n \\midrule\n RFPose(4) & 0.664 & 0.626 & 0.616 \\\\\n RFPose(12) & 0.675 & 0.631 & 0.614 \\\\\n RFPose(32) & 0.661 & 0.617 & 0.598 \\\\\n RFPose(64) & 0.641 & 0.589 & 0.604 \\\\\n RFMask(4) & 0.681 & 0.682 & 0.681 \\\\\n RFMask(12) & \\textbf{0.706} & \\textbf{0.711} & \\textbf{0.705} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signals", "authors": ["Zhi Wu", "Dongheng Zhang", "Chunyang Xie", "Cong Yu", "Jinbo Chen", "Yang Hu", "Yan Chen"], "url": "https://arxiv.org/abs/2201.10175v1", "attribution": "\"RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signals\" by Zhi Wu, Dongheng Zhang, Chunyang Xie, Cong Yu, Jinbo Chen, Yang Hu, and Yan Chen, arXiv:2201.10175v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.00140v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Demand and vehicle allocation costs per district for the Nepal instance}\n\\begin{tabular}{lllll}\n\\hline\nDistrict & Name & Demand per period & UAV costs & Truck costs \\\\ \\hline\n1 & Dolakha & 217 & 202 & 1256 \\\\\n2 & Gorkha & 305 & 178 & 1266 \\\\\n3 & Okhaldhunga & 55 & 266 & 1223 \\\\\n4 & Sindhupalchok & 278 & 108 & 667 \\\\\n5 & Bhaktapur & 117 & 26 & 169 \\\\\n6 & Rasuwa & 49 & 108 & 1928 \\\\\n7 & Ramechhap & 167 & 214 & 871 \\\\\n8 & Makwanpur & 156 & 197 & 1085 \\\\\n9 & Dhading & 352 & 113 & 731 \\\\\n10 & Sindhuli & 156 & 82 & 683 \\\\\n11 & Nuwakot & 333 & 67 & 437 \\\\\n12 & Kavrepalanchok & 308 & 62 & 365 \\\\\n13 & Lalitpur & 107 & 12 & 251 \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Stochastic Dynamic Post-Disaster Inventory Allocation Problem with Trucks and UAVs", "authors": ["Robert van Steenbergen", "Wouter van Heeswijk", "Martijn Mes"], "url": "https://arxiv.org/abs/2312.00140v1", "attribution": "\"The Stochastic Dynamic Post-Disaster Inventory Allocation Problem with Trucks and UAVs\" by Robert van Steenbergen, Wouter van Heeswijk, and Martijn Mes, arXiv:2312.00140v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.15440v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Top 10 Applications by Token Usage}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\nApplication & Usage (Millions of Tokens)\\\\\n\\midrule\n\\cellcolor{gray!10}{Cline} & \\cellcolor{gray!10}{314 583 M}\\\\\nRoo Code & 231 702 M\\\\\n\\cellcolor{gray!10}{shapes inc} & \\cellcolor{gray!10}{37 620 M}\\\\\nSillyTavern & 36 883 M\\\\\n\\cellcolor{gray!10}{Chub AI} & \\cellcolor{gray!10}{17 435 M}\\\\\n\\addlinespace\nDocsLoop & 14 264 M\\\\\n\\cellcolor{gray!10}{OpenRouter: Chatroom} & \\cellcolor{gray!10}{13 405 M}\\\\\nliteLLM & 11 461 M\\\\\n\\cellcolor{gray!10}{Fraction AI} & \\cellcolor{gray!10}{8 085 M}\\\\\nFish Audio & 6 445 M\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Demand for LLMs: Descriptive Evidence on Substitution, Market Expansion, and Multihoming", "authors": ["Andrey Fradkin"], "url": "https://arxiv.org/abs/2504.15440v1", "attribution": "\"Demand for LLMs: Descriptive Evidence on Substitution, Market Expansion, and Multihoming\" by Andrey Fradkin, arXiv:2504.15440v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.01704v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n \\textbf{Hyperparameter} & \\textbf{Value} \\\\ \\midrule\n Optimiser & Adam \\\\\n Initial learning rate & \\(10^{-4}\\) \\\\\n Minibatch size & 64 \\\\\n Hidden layers & 2 \\\\\n Neurons per hidden layer & 512 \\\\\n Activation function & GELU \\\\\n Path length ($N_{\\texttt{path length}}$ ) & 70 \\\\\n Parallel simulations & 512 \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Hyper-parameters of the DEQN training routine.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Using Machine Learning to Compute Constrained Optimal Carbon Tax Rules", "authors": ["Felix Kübler", "Simon Scheidegger", "Oliver Surbek"], "url": "https://arxiv.org/abs/2507.01704v1", "attribution": "\"Using Machine Learning to Compute Constrained Optimal Carbon Tax Rules\" by Felix Kübler, Simon Scheidegger, and Oliver Surbek, arXiv:2507.01704v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09972v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|}\n\\hline\n \\emph{HGM} & \\emph{RRWGM} & \\emph{TM} & \\textbf{ARSRG}$_{1^{st}}$ & \\textbf{ARSRG}$_{2^{nd}}$\\\\ \\hline\n 0.2600 & 0.1322 & 0.1348 & 0.6115 & 1.0\\\\ \\hline % old value 0.8000\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications", "authors": ["Mario Manzo"], "url": "https://arxiv.org/abs/1912.09972v1", "attribution": "\"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications\" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.23025v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Structure of the Capital Control Measures Dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\n\\textbf{Field} & \\textbf{Description} \\\\\n\\midrule\nYear & The implemented year of the capital control policies \\\\\nIFS code & Country code \\\\\nCountry & Name of the reporting country \\\\\nRegion & Region of the reporting country \\\\\nIncome group & Income group of the reporting country based on World Bank \\\\\nIncome subgroup & Income subgroup of the reporting country based on World Bank \\\\\nIndex Code & Index code provided by IMF \\\\\nCategory Index & Hierarchical index consistent with IMF AREAER classification \\\\\nCategory & Name of the capital control category (e.g., XI.A.2.a.1.i) \\\\\nDate & Effective date of the intervention \\\\\nDescription & Full text description of the capital control measure \\\\\nDate of Retroactive Changes & The date if there is a date for canceling this policy (if applicable) \\\\\nAction & Verbs describing the measure (e.g., prohibit, permit, require approval) \\\\\nAction Intensity & Restrictive / Conditional / Liberalizing / Neutral \\\\\nAction Direction & Inward / Outward / Both / Undefined \\\\\nAction Level & Supranational / National / Subnational / Undefined \\\\\nInstrument & Policy tool involved (e.g., foreign exchange, credit, reserve requirement) \\\\\nActor & Entity implementing the policy (e.g., central bank, government) \\\\\nCondition & With (e.g., subject to approval) / Without (e.g., no approval required) \\\\\nBeneficiary & Group intended to benefit or be affected (e.g., exporters, travelers) \\\\\nTarget Country & Country explicitly targeted by the measure (if applicable) \\\\\nTarget Industry & Sector targeted by the policy (if applicable) \\\\\nTarget Industry & Sector targeted by the policy (if applicable) \\\\\nLimit/Threshold & Limits number or threshold of the policy (if applicable) \\\\\nIs Trade Policy & True if measure relates to trade in goods or tariffs \\\\\nIs Sanction & True if measure involves bans, freezes, or restricted parties \\\\\nIs National Security & True if measure references security or national interest \\\\\nLLM Reasoning & Cited phrase and rationale reasoning extracted by LLMs \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Learning to Regulate: A New Event-Level Dataset of Capital Control Measures", "authors": ["Geyue Sun", "Xiao Liu", "Tomas Williams", "Roberto Samaniego"], "url": "https://arxiv.org/abs/2505.23025v1", "attribution": "\"Learning to Regulate: A New Event-Level Dataset of Capital Control Measures\" by Geyue Sun, Xiao Liu, Tomas Williams, and Roberto Samaniego, arXiv:2505.23025v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.04003v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model Estimates October 1, 2024}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n\\hline\nVariable & Value & Standard Error \\\\\n\\hline \\hline\nmempool quartile 1 & $-3.7229 \\times 10 ^{-3}$ & $1.069 \\times 10^{-2}$\\\\\nmempool quartile 2 & $3.5696 \\times 10 ^{-3}$ & $1.072 \\times 10^{-2}$ \\\\\nmempool quartile 3 & $-4.9224 \\times 10 ^{-3}$ & $1.073 \\times 10^{-2}$ \\\\\nmax fee per gas$^{***}$ & $-8.5780 \\times 10^{-4}$ & $3.117 \\times 10^{-5}$ \\\\\nto DEX$^{***}$ & $-7.7121 \\times 10^{-1}$ & $1.386 \\times 10^{-2}$ \\\\\nto MEV$^{***}$ & $-1.7074 \\times 10^0$ & $2.972 \\times 10^{-2}$ \\\\\nfrom DEX$^{***}$ & $-1.4161 \\times 10^0$ & $8.489 \\times 10^{-2}$ \\\\\nfrom MEV$^{***}$ & $1.9906 \\times 10^0$ & $3.238 \\times 10^{-2}$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering", "authors": ["Bruce Mizrach", "Nathaniel Yoshida"], "url": "https://arxiv.org/abs/2508.04003v1", "attribution": "\"The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering\" by Bruce Mizrach and Nathaniel Yoshida, arXiv:2508.04003v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table24.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|r|r|r|r|r|r|r}\n \\hline\n \\multicolumn{3}{c}{Problem size} & \\multicolumn{4}{|c|}{Solving subproblems} & Impl. sol. & Total 10 iter\\\\ \n \\hline\n $T$ & $|\\Omega|$ & $|\\Phi|$ & sec/$\\phi$ $(k=0)$ & sec/iter $(k=0)$ & sec/$\\phi$ & sec/iter & sec/iter & sec\\\\\n \\hline\n 7 & 2 & 128 & 0.03 & 3.84 & 0.01 & 1.28 & 0.2 & 16.26\\\\\n 7 & 3 & 2,187 & 0.03 & 65.61 & 0.01 & 21.87 & 1.5 & 275,94 \\\\\n 7 & 4 & 16,384 & 0.03 & 491.52 & 0.01 & 163.84 & 10.5 & 2,071.08\\\\\n 7 & 5 & 78,125 & 0.03 & 2,343.75 & 0.01 & 781.25 & 73.5 & 10,110.00\\\\\n 7 & 6 & 279,936 & 0.03 & 8,398.08 & 0.01 & 2799,36 & 514.5 & 38,737.32\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Runtime analysis for the progressive hedging algorithm}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.10553v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n \\toprule\n \\textbf{Benchmark Task} & \\textbf{Graph Learning Task} & \\textbf{Node Features} & \\textbf{Edge Features} & \\textbf{Global Features} \\\\\n \\midrule\n \\multirow{5}{*} & & \\ Player positions & Teammate or opponent & None \\\\\n & & \\ Player velocities & & \\\\\n Receiver Prediction & Node classification & \\ Player weights & & \\\\\n & & \\ Player heights & & \\\\\n & &\\ Ball possession & & \\\\\n \\midrule\n \\multirow{1}{*}{Shot Prediction} & Graph classification & \\ Same as above & Same as above & Receiver ID\\\\\n \\midrule\n Guided Generation & Node regression & \\ Same as above & Same as above & Shot indicator \\\\\n & & & & Receiver ID \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{{\\bf Summary of the features used in the corresponding tasks.}}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "TacticAI: an AI assistant for football tactics", "authors": ["Zhe Wang", "Petar Veličković", "Daniel Hennes", "Nenad Tomašev", "Laurel Prince", "Michael Kaisers", "Yoram Bachrach", "Romuald Elie", "Li Kevin Wenliang", "Federico Piccinini", "William Spearman", "Ian Graham", "Jerome Connor", "Yi Yang", "Adrià Recasens", "Mina Khan", "Nathalie Beauguerlange", "Pablo Sprechmann", "Pol Moreno", "Nicolas Heess", "Michael Bowling", "Demis Hassabis", "Karl Tuyls"], "url": "https://arxiv.org/abs/2310.10553v2", "attribution": "\"TacticAI: an AI assistant for football tactics\" by Zhe Wang, Petar Veličković, Daniel Hennes, Nenad Tomašev, Laurel Prince, Michael Kaisers, Yoram Bachrach, Romuald Elie, Li Kevin Wenliang, Federico Piccinini, William Spearman, Ian Graham, Jerome Connor, Yi Yang, Adrià Recasens, Mina Khan, Nathalie Beauguerlange, Pablo Sprechmann, Pol Moreno, Nicolas Heess, Michael Bowling, Demis Hassabis, and Karl Tuyls, arXiv:2310.10553v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10186v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n\\textit{Characteristic} & \\textit{Sensing} & \\textit{Data Processing} & \\textit{System Reaction} \\\\ \\hline \nEffect & None & Major & Major \\\\ \nTime & Major & Minor & None \\\\ \nFocus & Minor & Minor & None \\\\ \nSpace & Major & Minor & None \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Gesture-based Human-Machine Interaction: Taxonomy, Problem Definition, and Analysis", "authors": ["Alessandro Carfì", "Fulvio Mastrogiovanni"], "url": "https://arxiv.org/abs/2201.10186v1", "attribution": "\"Gesture-based Human-Machine Interaction: Taxonomy, Problem Definition, and Analysis\" by Alessandro Carfì and Fulvio Mastrogiovanni, arXiv:2201.10186v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23729v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n & PINNs & IR-PINNs1 & IR-PINNs2 \\\\\n \\midrule\n Relative $L_2$ error & 6.3784e-02 & 5.3724e-02 & 5.6185e-02 \\\\\n \\midrule\n Relative KL divergence & 2.0582e-03 & 2.0117e-03 & 1.8202e-03 \\\\\n \\midrule\n Running time (hours) & 9.612 & 21.06 & 24.47 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\textit{Time-dependent Fokker-Planck equation:} Relative $L_2$ errors and running time of different methods.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Integral regularization PINNs for evolution equations", "authors": ["Xiaodong Feng", "Haojiong Shangguan", "Tao Tang", "Xiaoliang Wan"], "url": "https://arxiv.org/abs/2503.23729v1", "attribution": "\"Integral regularization PINNs for evolution equations\" by Xiaodong Feng, Haojiong Shangguan, Tao Tang, and Xiaoliang Wan, arXiv:2503.23729v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.15733v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance metrics for the different eigenfunctions ($32 \\times 32$+dp+ma).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|} \\hline\n\t\t\t\tIndex of Eigenvalue & MaxAE & PSNR & RelL1 \\\\ \\hline\n\t\t\t\t1 & 0.11 & 53.04 & 1.22\\% \\\\\n\t\t\t\t2 & 0.37 & 42.13 & 7.04\\%\\\\\n\t\t\t\t3 & 0.68 & 35.88 & 13.01\\% \\\\ \\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Operator Inference for Elliptic Eigenvalue Problems", "authors": ["Haoqian Li", "Jiguang Sun", "Zhiwen Zhang"], "url": "https://arxiv.org/abs/2504.15733v1", "attribution": "\"Operator Inference for Elliptic Eigenvalue Problems\" by Haoqian Li, Jiguang Sun, and Zhiwen Zhang, arXiv:2504.15733v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04051v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc}\n\t\t\t\\hline\n Method & avg-min-CDR $\\downarrow$ & JDR $\\downarrow$ & Recall $\\uparrow$ & FPS $\\uparrow$\\\\\n \\hline\n w/o SBD& 14.18\\%& 2.261& 53.65\\%&\\textbf{33.4}\\\\\n w/o ST& \\textbf{4.76\\%}& 12.468& \\textbf{82.33\\%}&9.6\\\\\n \\hline\n Our Framework & 10.94\\%& \\textbf{0.865}& 69.53\\%&28.6\\\\\n \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Ablation study of our {\\it H2V} framework to investigate the contributions of shot boundary detection and subject tracking modules.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Horizontal-to-Vertical Video Conversion", "authors": ["Tun Zhu", "Daoxin Zhang", "Yao Hu", "Tianran Wang", "Xiaolong Jiang", "Jianke Zhu", "Jiawei Li"], "url": "https://arxiv.org/abs/2101.04051v2", "attribution": "\"Horizontal-to-Vertical Video Conversion\" by Tun Zhu, Daoxin Zhang, Yao Hu, Tianran Wang, Xiaolong Jiang, Jianke Zhu, and Jiawei Li, arXiv:2101.04051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09113v4_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of parameters for each model }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c}\n\\hline\nDataset & MeshGraphNet & GRU & LSTM & Transformer & GMR-GMU\\\\\n\\hline\nCylinder flow& 2.2M & 9.4M & 11.5M &14.1M &1.2M\\\\\n\\hline\nSonic flow& 2.2M & 9.4M & 11.5M &14.1M & 1.2M\\\\\n\\hline\nVascular flow& 2.2M & 5.8M & 7.0M & 8.5M & 1.2M \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Predicting Physics in Mesh-reduced Space with Temporal Attention", "authors": ["Xu Han", "Han Gao", "Tobias Pfaff", "Jian-Xun Wang", "Li-Ping Liu"], "url": "https://arxiv.org/abs/2201.09113v4", "attribution": "\"Predicting Physics in Mesh-reduced Space with Temporal Attention\" by Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, and Li-Ping Liu, arXiv:2201.09113v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.02695v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance metrics for ADHD classification.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\hline\n & \\textbf{Precision} & \\textbf{Recall} & \\textbf{F1-Score} \\\\\n \\hline\n \\textbf{0 (No ADHD)} & 0.98 & 0.79 & 0.88 \\\\\n \\textbf{1 (ADHD)} & 0.86 & 0.99 & 0.92 \\\\\n \\hline\n \\textbf{Accuracy} & - & - & 0.90 \\\\\n \\textbf{Macro avg} & 0.92 & 0.89 & 0.90 \\\\\n \\textbf{Weighted avg} & 0.91 & 0.90 & 0.90 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "An ADHD Diagnostic Interface Based on EEG Spectrograms and Deep Learning Techniques", "authors": ["Medha Pappula", "Syed Muhammad Anwar"], "url": "https://arxiv.org/abs/2412.02695v1", "attribution": "\"An ADHD Diagnostic Interface Based on EEG Spectrograms and Deep Learning Techniques\" by Medha Pappula and Syed Muhammad Anwar, arXiv:2412.02695v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03526v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameters of the models built in our experiments.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n\\hline\n\\textbf{Component} & \\textbf{Parameter} & \\textbf{Value} \\\\ \\hline\n\\multirow{2}{*}{Embedding} & Word embedding dimension $d_w$ & 50 \\\\ \\cline{2-3}\n & Position embedding dimension $d_p$ & 5 \\\\ \\hline\n\\multirow{3}{*}{Encoder} & Hidden layer dimension $d_h$ & 230 \\\\ \\cline{2-3}\n & Convolutional Window Size $u$ & 3 \\\\ \\cline{2-3}\n & Max length $T$ & 40 \\\\ \\hline\n\\multirow{2}{*}{Joint loss} & Margin $m$ & 0.5 \\\\ \\cline{2-3}\n & Alpha $\\alpha$ & 1 \\\\ \\hline\n\\multirow{3}{*}{Optimization} & Initial Learning Rate & 0.1 \\\\ \\cline{2-3}\n & Weight Decay & 10-5 \\\\ \\cline{2-3}\n & Dropout rate & 0.2 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification", "authors": ["Yan Xiao", "Yaochu Jin", "Kuangrong Hao"], "url": "https://arxiv.org/abs/2101.03526v1", "attribution": "\"Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification\" by Yan Xiao, Yaochu Jin, and Kuangrong Hao, arXiv:2101.03526v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.17932v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|rrr|rrr}\nCutoff & \\multicolumn{3}{|c|}{Abs} & \\multicolumn{3}{|c}{ReLU} \\\\\nChange & Acc (\\%) & T-stat & P-value & Acc (\\%) & T-stat & P-value \\\\\n\\hline\n1\\% & 52.90 & 28.1 & 6.1e-17 & 75.93 & 36.6 & 4.5e-19 \\\\\n5\\% & 60.13 & 24.8 & 6.4e-16 & 82.57 & 27.8 & 7.2e-17 \\\\\n10\\% & 71.05 & 21.9 & 6.0e-15 & 87.56 & 23.0 & 2.5e-15 \\\\\n20\\% & 88.55 & 21.0 & 1.3e-14 & 93.84 & 20.6 & 1.9e-14 \\\\\n30\\% & 96.11 & 24.5 & 7.8e-16 & 96.67 & 18.6 & 1.2e-13 \\\\\n40\\% & 98.70 & -25.3 & 4.3e-16 & 97.74 & -23.4 & 1.8e-15 \\\\\n50\\% & 99.60 & -26.7 & 1.6e-16 & 98.14 & -26.8 & 1.5e-16 \\\\\n75\\% & 99.98 & -26.5 & 1.8e-16 & 98.33 & -27.8 & 7.4e-17 \\\\\nBaseline & 99.99 & -26.5 & 1.8e-16 & 98.33 & -28.1 & 6.3e-17 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Effects of intensity cutoff on model accuracy. Cutoff values are shown as percentages of the maximum activation.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Neural Networks Use Distance Metrics", "authors": ["Alan Oursland"], "url": "https://arxiv.org/abs/2411.17932v1", "attribution": "\"Neural Networks Use Distance Metrics\" by Alan Oursland, arXiv:2411.17932v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.06276v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ESG values and stock closing prices $S_0$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcrcrr}\n\t\\toprule\n\tEquity & \\multicolumn{2}{c}{11/19/2020} & \\multicolumn{2}{c}{11/19/2021} & $S_0\\ \\ \\ $\\\\\n\t\\cline{2-5}\n\t\n\t\\strut & ESG$^{(\\text{R})}$ & $e_{n,k}\\ \\ \\ \\ $ & ESG$^{(\\text{R})}$ & $e_{n,k}\\ \\ \\ \\ $ & (USD) \\\\\n\t\\midrule\n\tMSFT & 96 & $\\ \\ 3.65 \\cdot 10^{-3}$ & 98 & $\\ \\ 3.81 \\cdot 10^{-3}$ & 336.32\\\\\n\tAMZN & 60 & $\\ \\ 0.79 \\cdot 10^{-3}$ & 71 & $\\ \\ 1.67 \\cdot 10^{-3}$ & 3,334.34\\\\\n\tAAPL & 25 & $ -1.98 \\cdot 10^{-3}$ & 34 & $ -1.27 \\cdot 10^{-3}$ & 177.57\\\\\n\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "ESG-valued discrete option pricing in complete markets", "authors": ["Yuan Hu", "W. Brent Lindquist", "Svetlozar T. Rachev"], "url": "https://arxiv.org/abs/2209.06276v1", "attribution": "\"ESG-valued discrete option pricing in complete markets\" by Yuan Hu, W. Brent Lindquist, and Svetlozar T. Rachev, arXiv:2209.06276v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.10562v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative comparison of axial strain estimation on simulated phantom.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\t\t\t\\hline\t\t\n\t\t\t&LF&Inter.&\tHF\\\\\n\t\t\t&OVERWIND&OVERWIND&\tOVERWIND\\\\\n\t\t\t\\hline\t\t \t \n\t\t\tME\t&$-3.13\\times 10^{-5}$ & $-3.93\\times 10^{-5}$\t & $-4.38\\times 10^{-5}$\\\\\n\t\t\tVE\t&$5.21\\times 10^{-7}$ & $4.97\\times 10^{-7}$ & $4.96\\times 10^{-7}$ \\\\\n\t\t\tRMSE& $7.99\\%$ & $7.82\\%$ & $7.81\\%$ \\\\\n\t\t\tCNR\t& $50.76$ & $52.56$ & $54.09$\\\\\t\t\t\t\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Virtual Source Synthetic Aperture for Accurate Lateral Displacement Estimation in Ultrasound Elastography", "authors": ["Morteza Mirzaei", "Amir Asif", "Hassan Rivaz"], "url": "https://arxiv.org/abs/2012.10562v2", "attribution": "\"Virtual Source Synthetic Aperture for Accurate Lateral Displacement Estimation in Ultrasound Elastography\" by Morteza Mirzaei, Amir Asif, and Hassan Rivaz, arXiv:2012.10562v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.16909v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Perplexity of held-out mutations for inferred mutational signature attributions. \\rm Signatures were based on COSMIC v3.3 signature weights. Super/subscripts indicate ninety-five per cent confidence intervals, computed by bootstrapping.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\toprule\n \\textbf{Method} & & \\textbf{Hold out perplexity} \\\\\n \\midrule\n SigProfilerExtractor$^{}$ & & 64.5$^{+0.7}_{-0.7}$ \\\\\n Zhou--Cong--Chen & (1 layer) & 62.0$^{+0.7}_{-0.7}$ \\\\\n & (2 layers) & 61.9$^{+0.7}_{-0.7}$ \\\\\n This work & (1 layer) & 62.0$^{+0.7}_{-0.7}$ \\\\\n & (2 layers) & 61.9$^{+0.7}_{-0.7}$ \\\\\n \\bottomrule \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Multinomial belief networks for healthcare data", "authors": ["H. C. Donker", "D. Neijzen", "J. de Jong", "G. A. Lunter"], "url": "https://arxiv.org/abs/2311.16909v3", "attribution": "\"Multinomial belief networks for healthcare data\" by H. C. Donker, D. Neijzen, J. de Jong, and G. A. Lunter, arXiv:2311.16909v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.06293v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Benchmark results: It details the performance of our finetuned model compared to other closed embedding models. The table shows HR@5, improvement (our model's improvement compared to benchmark models), and embedding size, illustrating our model's efficiency and effectiveness.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\toprule\n & HR@5 [\\%] & Improvement [\\%] & Embedding Size \\\\\n\\midrule\nGoogle Text-Embedding-004 & 84 & 5 & 768 \\\\\nCohere Embed-English-v3.0 & 85 & 4 & 1024 \\\\\nOpenAI Text-Embedding-3-Large & 86 & 2 & 3072 \\\\\nMistralAI Mistral-Embed & 87 & 1 & 1024 \\\\\nVoyageAI Voyage-Finance-2 & 88 & 0 & 1024 \\\\\nOurs & 88 & - & 768 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Generative AI Enhanced Financial Risk Management Information Retrieval", "authors": ["Amin Haeri", "Jonathan Vitrano", "Mahdi Ghelichi"], "url": "https://arxiv.org/abs/2504.06293v2", "attribution": "\"Generative AI Enhanced Financial Risk Management Information Retrieval\" by Amin Haeri, Jonathan Vitrano, and Mahdi Ghelichi, arXiv:2504.06293v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.01048v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\nModel & $R^2_{pool}$ (\\%) & $R^2_{avg} (\\%)$\\\\\n\\hline\nE-GPR ($\\gamma$-exp)&0.96& 0.58\\\\\nE-GPR (affine)&0.74& 0.42\\\\\nE-LR& 0.61 & 0.18\\\\\nLR & 0.37 & 0.003\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of predictive performance across all stocks, i.e., $R^2 (\\%)$, among various models}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Empirical Asset Pricing via Ensemble Gaussian Process Regression", "authors": ["Damir Filipović", "Puneet Pasricha"], "url": "https://arxiv.org/abs/2212.01048v2", "attribution": "\"Empirical Asset Pricing via Ensemble Gaussian Process Regression\" by Damir Filipović and Puneet Pasricha, arXiv:2212.01048v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.15671v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c|c|c||}\n\\hline\n$f_\\nu(X^*)$&$f_\\nu(Y^*)$&$f_\\nu(Z^*)$&$\\frac{f_\\nu(X^*)-f_\\nu(Y^*)}{f_\\nu(X^*)}\\leq \\frac{\\widehat{F}_\\nu(Y^*)-\\widehat{f}_\\nu(Y^*)}{\\widehat{f}_\\nu(Y^*)}$&$d_g(X^*,Y^*)\\leq N^{-\\frac{1}{\\nu}}2\\widehat{F}_\\nu(Y^*)$\\\\\n\\hline\n\\multicolumn{5}{||c||}{Clustered dataset, $(N,n, p, \\beta,\\nu) = (5,50,20,1,1)$}\\\\\n\\hline\n4.384 & 4.384 & 4.384 & \n $1.114$e$-6 \\leq 0.03344$ & \n $0.001613\\leq 1.811$ \\\\\n\\hline\n\\multicolumn{5}{||c||}{Spread dataset, $(N,n, p,\\beta,\\nu) = (5,50,20,1,2)$}\\\\\n\\hline\n11.59 & 11.66 & 11.66 & \n $0.005723 \\leq 0.4886$ &\n $0.7046\\leq 15.03$\\\\\n\\hline\n\\multicolumn{5}{||c||}{Clustered dataset, $(N,n, p,\\beta,\\nu) = (10,100,40,0.5,1)$}\\\\\n\\hline\n8.426 & 8.427 & 8.427 & \n $9.751$e$-5 \\leq 0.4707$ &\n $0.01176\\leq 1.953$\\\\\n\\hline\n\\multicolumn{5}{||c||}{Spread dataset, $(N,n, p, \\beta,\\nu) = (10,100,40,0.5,2)$}\\\\\n\\hline\n23.50 & 24.23 & 24.23 & \n $0.03116 \\leq 1.076$ &\n $3.274\\leq 23.52$\\\\\n\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Average results over 10 runs of the performances of algorithms (A1), (A2) and (A3) for the computation of an approximate Riemannian $l^\\nu$-barycenter.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the approximation of the Riemannian barycenter", "authors": ["Simon Mataigne", "P. -A. Absil", "Nina Miolane"], "url": "https://arxiv.org/abs/2504.15671v2", "attribution": "\"On the approximation of the Riemannian barycenter\" by Simon Mataigne, P. -A. Absil, and Nina Miolane, arXiv:2504.15671v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.14351v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Benchmarking attacks on the Loan Problem}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccccccc}\n\\hline\nAttack & $J$ & $z_1$ & $z_2$ & $z_3$ & $z_4$ & $z_5$ & $z_6$ & $z_7$ & Obj. Value$^\\ddag$ & $D_{KL}$$^{**}$ & Comp. Effort (sec) \\\\ \\hline\nWB & - & 81.000 & 16.209 & 38.272 & 12.210 & 77.874 & 25.200 & 102.190 & -0.004 & 0.007 & 0.124\\\\ \\cdashline{1-12}\n\\multirow{5}*{SAA}& 25&\t81.000&\t17.759&\t34.890&\t12.210&\t77.874&\t25.200&\t91.706&\t0.09504 & 5.67e-5 & 0.127 \\\\\n& 100&\t81.000&\t16.352&\t34.890&\t12.210&\t77.875&\t25.200&\t95.624&\t0.0501&\t 7.91e-5 & 0.226 \\\\\n& 500&\t81.000&\t16.209&\t34.890&\t12.210&\t77.874&\t25.200&\t94.742&\t0.0323&\t6.88e-4 & 0.873 \\\\\n& 2500&\t81.000&\t16.608&\t42.644&\t12.210&\t77.874&\t25.200&\t94.657&\t0.0476&\t9.32e-6 & 4.278\\\\\n& 10000&\t81.000&\t16.209&\t34.890&\t12.210&\t77.875&\t25.200&\t94.389&\t0.0555&\t5.52e-4 & 21.031 \\\\ \\cdashline{1-12} RN & - & 92.107 & 16.310 & 36.101 & 11.499 & 69.789 & 28.911 & 100.331 & -0.011& 0.0047 & 0.031 \\\\ \\cdashline{1-12} $\\mathbf{z}'$& - & 90.000 & 18.010 & 38.767 & 11.100 & 70.795 & 28.000 & 92.900 & -0.0144 & 0 & -\\\\\\hline\n\\multicolumn{15}{l}{$^\\ddag$ WB, RN and $\\mathbf{z}'$ values correspond to whitebox objective; SAA and SGA to grey-box}\\\\ \n\\multicolumn{15}{l}{$^{**}$ KL divergence between induced and true conditional treating the white-box model as the true joint}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Indiscriminate Disruption of Conditional Inference on Multivariate Gaussians", "authors": ["William N. Caballero", "Matthew LaRosa", "Alexander Fisher", "Vahid Tarokh"], "url": "https://arxiv.org/abs/2411.14351v1", "attribution": "\"Indiscriminate Disruption of Conditional Inference on Multivariate Gaussians\" by William N. Caballero, Matthew LaRosa, Alexander Fisher, and Vahid Tarokh, arXiv:2411.14351v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.14972v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Modeled Unobservables}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc} \n\\\\\\hline \n\\hline \\\\\n \\\\ Parameter & Estimate & Std. Error \\\\\n \n \\hline \\\\ \n \n Standard deviation of $q$, $\\sigma_{q}$ & $0.042^{***}$ & 0.004 \\\\\n \n \\\\\n \n Parameter on $\\sigma_q$, $\\delta$ & $0.512^{***}$ & 0.024 \\\\\n \n \\\\\n $\\gamma_{\\text{Signing Bonus}}$ & $0.217^{***}$ & 0.053 \\\\ \n \n \\\\\n $\\gamma_{\\text{Performance Bonus}}$ & $0.526^{***}$ & 0.049 \\\\ \n \n \\\\\n $\\gamma_{\\text{Medical Insurance}}$ & $0.017\\phantom{^{***}}$ & 0.079 \\\\ \n \\\\\n $\\gamma_{\\text{Relocation Allowance}}$ & $0.286^{***}$ & 0.051 \\\\ \n \\\\\n $\\gamma_{\\text{Restricted Stock Units}}$ & $0.487^{***}$ & 0.104 \\\\ \n\\hline \n\\hline \\\\ \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Making the Elite: Top Jobs, Disparities, and Solutions", "authors": ["Soumitra Shukla"], "url": "https://arxiv.org/abs/2208.14972v3", "attribution": "\"Making the Elite: Top Jobs, Disparities, and Solutions\" by Soumitra Shukla, arXiv:2208.14972v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10981v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experimental results of the proposed SWTR-Unet produced on in-house MRI data in comparison with state-of-the-art works. Stated are the Dice similarity coefficients (DSC) of the liver and liver lesion segmentations.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\n& $DSC_{liver}$ & $DSC_{lesion}$\\\\\n\\hline\n & $0.87$ & $0.70$ \\\\\n & $0.91 \\pm 0.01$ & $0.68 \\pm 0.03$ \\\\\n & & $0.81 \\pm 0.03$ \\\\\n & & $0.74 \\pm 0.19$ \\\\\n\\textbf{SWTR-Unet} & $\\mathbf{0.98 \\pm 0.02}$ & $\\mathbf{0.81 \\pm 0.28}$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers", "authors": ["Georg Hille", "Shubham Agrawal", "Pavan Tummala", "Christian Wybranski", "Maciej Pech", "Alexey Surov", "Sylvia Saalfeld"], "url": "https://arxiv.org/abs/2201.10981v3", "attribution": "\"Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers\" by Georg Hille, Shubham Agrawal, Pavan Tummala, Christian Wybranski, Maciej Pech, Alexey Surov, and Sylvia Saalfeld, arXiv:2201.10981v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.00208v1_tex_table47.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{DID Analysis of Offline Metrics (Natural Log Scale) -- \\textit{Black Friday Adopters} vs. \\textit{Organic Adopters}}\n\\begin{tabular}{lccccccc}\n \\midrule \\midrule\n DVs: & Spend & Quantity & Orders & Unique Items & Unique Brands & Subcategories & Categories\\\\ \n Model: & (1) & (2) & (3) & (4) & (5) & (6) & (7)\\\\ \n \\midrule\n \\emph{Variables}\\\\\n BlackFriday\\_Adopter * Post & 0.082 (0.059) & 0.003 (0.020) & 0.005 (0.010) & 0.009 (0.017) & 0.013 (0.015) & 0.014 (0.014) & 0.013 (0.013)\\\\\n & [0.170] & [0.882] & [0.639] & [0.582] & [0.383] & [0.308] & [0.325]\\\\\n \\midrule\n \\emph{Fixed-effects}\\\\\n Customer & Yes & Yes & Yes & Yes & Yes & Yes & Yes\\\\ \n YearMonth & Yes & Yes & Yes & Yes & Yes & Yes & Yes\\\\ \n \\midrule\n \\emph{Fit statistics}\\\\\n Observations & 76,842 & 76,842 & 76,842 & 76,842 & 76,842 & 76,842 & 76,842\\\\ \n R$^2$ & 0.35766 & 0.42251 & 0.41348 & 0.39672 & 0.38879 & 0.38097 & 0.37182\\\\ \n Within R$^2$ & $5.17\\times 10^{-5}$ & $5.66\\times 10^{-7}$ & $6.82\\times 10^{-6}$ & $9.35\\times 10^{-6}$ & $2.27\\times 10^{-5}$ & $2.87\\times 10^{-5}$ & $3.05\\times 10^{-5}$\\\\ \n \\midrule \\midrule\n \\multicolumn{8}{l}{\\emph{Clustered (Customer) standard‐errors are presented in standard brackets, while p‑values are shown in square brackets.}}\\\\\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Channel Choice and Customer Value", "authors": ["Shirsho Biswas", "Hema Yoganarasimhan", "Haonan Zhang"], "url": "https://arxiv.org/abs/2508.00208v1", "attribution": "\"Channel Choice and Customer Value\" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2508.00208v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.00290v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The Performance of Multimodal Information Retrieval on MusicTM-Dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccl}\n \\hline\n \\multicolumn{6}{c}{audio2lyrics retrieval}\\\\\\hline\n Methods &R@1 &R@5 &R@10 &MedR &MeanR\\\\\n \\hline\n Random Rank~ &0.028 &0.055 &0.076 &7312.0 &7257.2 \\\\\n CCA~ &0.306 &0.350 &0.353 &423.0 &639.4\\\\\n GCCA~ &0.040 &0.074 &0.093 &770.0 &881.1\\\\\n \\hline\n \\multicolumn{6}{c}{lyrics2audio retrieval}\\\\\n \\hline\n Random Rank &0.027 &0.055 &0.076 &7316.0 &7257.3 \\\\\n CCA &0.304 &0.349 &0.354 &427.0 &639.3\\\\\n GCCA &0.039 &0.078 &0.095 &774.0 &881.6 \\\\\n \\hline\n \\multicolumn{6}{c}{sheet music2lyrics retrieval}\\\\ \n \\hline\n Random Rank &0.027 &0.055 &0.075 &7311.0 &7257.3 \\\\\n CCA &0.093 &0.172 &0.203 &524.0 &708.7\\\\\n GCCA &0.089 &0.0142 &0.167 &573.0 &770.5 \\\\\n \\hline\n \\multicolumn{6}{c}{lyrics2sheet music retrieval}\\\\\n \\hline\n Random Rank &0.027 &0.055 &0.077 &7313.0 &7257.4 \\\\\n CCA &0.093 &0.168 &0.198 &522.0 &709.0\\\\\n GCCA &0.098 &0.014 &0.168 &578.0 &769.8\\\\\n \\hline\n \\multicolumn{6}{c}{audio2sheet music retrieval}\\\\ \n \\hline\n Random Rank &0.028 &5.57 &7.50 &7310.0 &7257.2 \\\\\n CCA &0.303 &0.349 &0.353 &341.0 &596.5\\\\\n GCCA &0.358 &0.403 &0.414 &271.0 &382.8 \\\\\n \\hline\n \\multicolumn{6}{c}{sheet music2audio retrieval}\\\\\n \\hline\n Random Rank &0.026 &0.055 &0.075 &7310.0 &7257.4 \\\\\n CCA &0.300 &0.350 &0.354 &332.0 &596.1\\\\\n GCCA &0.362 &0.407 &0.415 &271.0 &381.3 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MusicTM-Dataset for Joint Representation Learning among Sheet Music, Lyrics, and Musical Audio", "authors": ["Donghuo Zeng", "Yi Yu", "Keizo Oyama"], "url": "https://arxiv.org/abs/2012.00290v2", "attribution": "\"MusicTM-Dataset for Joint Representation Learning among Sheet Music, Lyrics, and Musical Audio\" by Donghuo Zeng, Yi Yu, and Keizo Oyama, arXiv:2012.00290v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.15007v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll}\nmeta-estimator & outcome\\_model & prop\\_score\\_model & ate\\_abs\\_bias & ate\\_rmse & mean\\_pehe & ate\\_std\\_error \\\\\nstratified\\_standardization & KernelRidge & & 258.97 & 3965.28 & 4584.82 & 3956.81 \\\\\nstratified\\_standardization & kNN & & 406.78 & 1683.85 & 3065.36 & 1633.97 \\\\\nstratified\\_standardization & SVM\\_sigmoid & & 435.80 & 1374.50 & 10744.12 & 1303.58 \\\\\nipw & & LDA & 1097.66 & 2549.87 & & 2301.52 \\\\\nipw & & LDA\\_shrinkage & 1101.51 & 2548.83 & & 2298.53 \\\\\nipw\\_trimeps.01 & & LDA & 1123.23 & 2561.22 & & 2301.78 \\\\\nipw\\_trimeps.01 & & LDA\\_shrinkage & 1127.30 & 2560.36 & & 2298.83 \\\\\nipw & & Standardized\\_SVM\\_rbf & 1327.76 & 1901.10 & & 1360.59 \\\\\nipw\\_trimeps.01 & & Standardized\\_SVM\\_rbf & 1332.77 & 1905.76 & & 1362.23 \\\\\nipw & & kNN & 1351.69 & 2388.52 & & 1969.26 \\\\\nipw\\_trimeps.01 & & kNN & 1394.47 & 2412.94 & & 1969.19 \\\\\nstratified\\_standardization & DecisionTree & & 1429.13 & 2894.01 & 8570.48 & 2516.52 \\\\\nipw\\_trimeps.01 & & LogisticRegression\\_l1\\_saga & 1520.06 & 2348.67 & & 1790.45 \\\\\nipw\\_trimeps.01 & & LogisticRegression\\_l2\\_liblinear & 1785.19 & 2481.44 & & 1723.55 \\\\\nipw\\_trimeps.01 & & LogisticRegression\\_l1\\_liblinear & 1798.03 & 2494.65 & & 1729.27 \\\\\nipw\\_trimeps.01 & & LogisticRegression\\_l2 & 1803.00 & 2488.65 & & 1715.39 \\\\\nipw & & LogisticRegression\\_l1\\_saga & 1850.84 & 2403.84 & & 1533.89 \\\\\nipw\\_trimeps.01 & & SVM\\_sigmoid & 1892.72 & 2905.19 & & 2204.02 \\\\\nipw & & LogisticRegression\\_l2\\_liblinear & 1933.00 & 2475.93 & & 1547.18 \\\\\nipw & & LogisticRegression\\_l1\\_liblinear & 1950.50 & 2484.97 & & 1539.68 \\\\\nipw & & LogisticRegression\\_l2 & 1952.25 & 2483.25 & & 1534.68 \\\\\nipw\\_trimeps.01 & & LogisticRegression & 1969.73 & 2582.76 & & 1670.57 \\\\\nstratified\\_standardization & LinearSVM & & 2077.53 & 6703.79 & 8962.51 & 6373.75 \\\\\nstandardization & Standardized\\_SVM\\_rbf & & 2098.06 & 2298.04 & 6375.94 & 937.60 \\\\\nipw & & LogisticRegression & 2127.49 & 2590.52 & & 1478.04 \\\\\nipw\\_trimeps.01 & & DecisionTree & 2257.44 & 2851.76 & & 1742.56 \\\\\nstratified\\_standardization & SVM\\_rbf & & 2320.72 & 2674.25 & 4416.33 & 1328.86 \\\\\nstratified\\_standardization & Standardized\\_SVM\\_sigmoid & & 2398.72 & 2787.82 & 5333.67 & 1420.60 \\\\\nstratified\\_standardization & Standardized\\_SVM\\_rbf & & 2438.89 & 2703.05 & 4312.85 & 1165.47 \\\\\nipw & & SVM\\_sigmoid & 2462.19 & 3423.57 & & 2378.76 \\\\\nipw & & GaussianNB & 2636.48 & 3049.13 & & 1531.73 \\\\\nstratified\\_standardization & Ridge & & 2706.11 & 4287.98 & 4825.75 & 3326.22 \\\\\nstratified\\_standardization & ElasticNet & & 2716.97 & 4240.31 & 4767.89 & 3255.51 \\\\\nstratified\\_standardization & Lasso & & 2797.95 & 4123.09 & 4614.18 & 3028.43 \\\\\nstratified\\_standardization & LinearRegression & & 2855.89 & 3694.49 & 5379.93 & 2343.74 \\\\\nstandardization & LinearRegression\\_degree2 & & 3122.71 & 3814.48 & 5961.98 & 2190.65 \\\\\nipw & & DecisionTree & 3152.58 & 3731.41 & & 1996.15 \\\\\nstandardization & LinearRegression\\_interact & & 3157.29 & 3877.79 & 6094.42 & 2251.40 \\\\\nipw\\_trimeps.01 & & QDA & 3521.42 & 4128.07 & & 2154.20 \\\\\nipw\\_trimeps.01 & & GaussianNB & 4643.51 & 5184.90 & & 2306.73 \\\\\nstratified\\_standardization & Standardized\\_LinearSVM & & 5676.84 & 6060.79 & 8109.11 & 2122.89 \\\\\nipw & & QDA & 7210.86 & 9170.70 & & 5666.14 \\\\\nipw\\_trimeps.01 & & Standardized\\_SVM\\_sigmoid & 10209.46 & 10482.60 & & 2377.36 \\\\\nstandardization & Standardized\\_SVM\\_sigmoid & & 10567.02 & 10574.70 & 15968.21 & 402.89 \\\\\nstandardization & DecisionTree & & 13124.53 & 13124.53 & 18066.16 & 0.00 \\\\\nstandardization & ElasticNet & & 13124.53 & 13124.53 & 18066.16 & 0.00 \\\\\nstandardization & Lasso & & 13124.53 & 13124.53 & 18066.16 & 0.00 \\\\\nstandardization & LinearSVM & & 13124.53 & 13124.53 & 18066.16 & 0.00 \\\\\nstandardization & SVM\\_rbf & & 13124.53 & 13124.53 & 18066.16 & 0.00 \\\\\nstandardization & SVM\\_sigmoid & & 13124.53 & 13124.53 & 18066.16 & 0.00 \\\\\nstandardization & Ridge & & 13125.30 & 13125.30 & 18066.73 & 1.18 \\\\\nstandardization & kNN & & 13125.92 & 13125.92 & 18065.93 & 1.29 \\\\\nstandardization & KernelRidge & & 13198.06 & 13198.56 & 18120.34 & 113.96 \\\\\nstandardization & LinearRegression & & 14097.20 & 14189.19 & 18818.59 & 1613.08 \\\\\nstandardization & Standardized\\_LinearSVM & & 14486.66 & 14556.98 & 19110.22 & 1429.18 \\\\\nipw & & Standardized\\_SVM\\_sigmoid & 16823.85 & 17323.20 & & 4129.33 \\\\\nstratified\\_standardization & LinearRegression\\_interact & & 37749.80 & 53972.80 & 105866.18 & 38574.81 \\\\\nstratified\\_standardization & LinearRegression\\_degree2 & & 93842.31 & 103618.34 & 201053.48 & 43936.11\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "RealCause: Realistic Causal Inference Benchmarking", "authors": ["Brady Neal", "Chin-Wei Huang", "Sunand Raghupathi"], "url": "https://arxiv.org/abs/2011.15007v2", "attribution": "\"RealCause: Realistic Causal Inference Benchmarking\" by Brady Neal, Chin-Wei Huang, and Sunand Raghupathi, arXiv:2011.15007v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08678v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Proportion of inactive crypto projects in each cluster in May 2020. Single feature (6 month).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n ID & \\# & N/A in CoinMarketCap & N/A in GitHub & All \\\\\\hline\n 1 & 628 & 225 (35.8\\%) & 14 (2.2\\%) & 237 (37.7\\%) \\\\\\hline\n 2 & 15 & 3 (20.0\\%) & 0 (1.7\\%) & 3 (20.0\\%) \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild", "authors": ["Jusop Choi", "Wonseok Choi", "William Aiken", "Hyoungshick Kim", "Jun Ho Huh", "Taesoo Kim", "Yongdae Kim", "Ross Anderson"], "url": "https://arxiv.org/abs/2201.08678v1", "attribution": "\"Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild\" by Jusop Choi, Wonseok Choi, William Aiken, Hyoungshick Kim, Jun Ho Huh, Taesoo Kim, Yongdae Kim, and Ross Anderson, arXiv:2201.08678v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.03779v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Point and 95\\% interval estimates of the rate of agreement of expert coding at 1, 2, 4, and 6 digits}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrr}\n \\hline\nExperts & 1 Digit & 2 Digits & 4 Digits & 6 Digits & Kappa \\\\ \n\\hline\n\\multicolumn{6}{c}{Before clerical review} \\\\ \n \\hline\n 1 \\& 2 & 87.7 (80.5, 93.4) & 84.2 (76.6, 90.5) & 78.7 (70.3, 86.1) & 74.2 (65.2, 82.2) & 85.4 (77.8, 93.0)\\\\ \n 1 \\& 3 & 86.2 (78.0, 92.7) & 86.2 (78.0, 92.7) & 74.7 (65.2, 83.2) & 66.2 (56.1, 75.7) & 83.5 (75.1, 92.0)\\\\ \n 2 \\& 3 & 85.9 (77.7, 92.5) & 84.5 (76.1, 91.3) & 75.6 (66.4, 83.8) & 68.5 (58.7, 77.6) & 83.1 (74.4, 91.8)\\\\ \n All & 79.9 (70.9, 87.6) & 78.5 (69.4, 86.3) & 66.1 (56.3, 75.3) & 59.2 (49.1, 69.0) & -- \\\\ \n \\hline\n\\multicolumn{6}{c}{After clerical review} \\\\\n \\hline\n 1 \\& 2 & 92.6 (86.2, 97.1) & 90.3 (83.4, 95.5) & 84.4 (76.4, 91.0) & 80.5 (72.0, 87.8) & 91.2 (84.8, 97.6) \\\\\n 1 \\& 3 & 90.2 (83.1, 95.5) & 90.2 (83.1, 95.5) & 84.7 (76.4, 91.5) & 77.5 (68.2, 85.6) & 88.3 (81.1, 95.6)\\\\\n 2 \\& 3 & 86.8 (79.0, 93.0) & 85.8 (77.8, 92.2) & 80.8 (72.0, 88.3) & 72.9 (63.3, 81.5) & 84.2 (76.1, 92.4)\\\\\n All & 84.8 (76.6, 91.5) & 83.8 (75.4, 90.7) & 75.6 (66.2, 83.9) & 68.5 (58.8, 77.5) & -- \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Multilingual hierarchical classification of job advertisements for job vacancy statistics", "authors": ["Maciej Beręsewicz", "Marek Wydmuch", "Herman Cherniaiev", "Robert Pater"], "url": "https://arxiv.org/abs/2411.03779v2", "attribution": "\"Multilingual hierarchical classification of job advertisements for job vacancy statistics\" by Maciej Beręsewicz, Marek Wydmuch, Herman Cherniaiev, and Robert Pater, arXiv:2411.03779v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2301.00248v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{This tables shows the different feature scenarios considered in our ablation study together with their total number of features. Note that the third column indicates the usage of both original and derived features from the given feature source. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{clc}\n \\toprule\n Scenario & Feature Source & Features\\\\\n \\midrule\n 1 & Stock Price & 2 \\\\\n 2 & Stock Price, Tweets & 8 \\\\\n 3 & Implied Volatility & 3 \\\\\n 4 & Implied Volatility, Tweets & 9 \\\\\n 5 & Tweets & 6\\\\\n 6 & Stock Price, Implied Volatility & 5 \\\\\n 7 & Stock Price, Implied Volatility, Tweets & 11 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Nowcasting Stock Implied Volatility with Twitter", "authors": ["Thomas Dierckx", "Jesse Davis", "Wim Schoutens"], "url": "https://arxiv.org/abs/2301.00248v1", "attribution": "\"Nowcasting Stock Implied Volatility with Twitter\" by Thomas Dierckx, Jesse Davis, and Wim Schoutens, arXiv:2301.00248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2001.02757v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{DCI Format 1C for M-RNTI in LTE}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c||} \n\t\t\\hline\n\t\tField Names& Occupied Bits \\\\\n\t\t\\hline\\hline\n\t\tMCCH Change Notification & 8 bits \\\\ \n\t\t\\multirow{6}{*}{Reserved}&N/A~ (1.4MHz)\\\\&2bits~(3MHz)\\\\&4bits~(5MHz)\\\\&5bits~(10MHz)\\\\&6bits~(15MHz)\\\\&7bits~(20MHz)\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "On the Performance of PDCCH in LTE and 5G New Radio", "authors": ["Hongzhi Chen", "De Mi", "Manuel Fuentes", "Eduardo Garro", "Jose Luis Carcel", "Belkacem Mouhouche", "Pei Xiao", "Rahim Tafazolli"], "url": "https://arxiv.org/abs/2001.02757v1", "attribution": "\"On the Performance of PDCCH in LTE and 5G New Radio\" by Hongzhi Chen, De Mi, Manuel Fuentes, Eduardo Garro, Jose Luis Carcel, Belkacem Mouhouche, Pei Xiao, and Rahim Tafazolli, arXiv:2001.02757v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.02496v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of datasets used in experiments.}\n\\begin{tabular}{lrrrr}\n\\toprule\nDataset & Training Samples & Test Samples & Classes & Input Features \\\\\n\\midrule\nMNIST & 60,000 & 10,000 & 10 & 784 (28$\\times$28$\\times$1) \\\\\nF-MNIST & 60,000 & 10,000 & 10 & 784 (28$\\times$28$\\times$1) \\\\\nK-MNIST & 60,000 & 10,000 & 10 & 784 (28$\\times$28$\\times$1) \\\\\nCIFAR-10 & 50,000 & 10,000 & 10 & 3,072 (32$\\times$32$\\times$3) \\\\\nCIFAR-100 & 50,000 & 10,000 & 100 & 3,072 (32$\\times$32$\\times$3) \\\\\nTiny ImageNet & 100,000 & 10,000 & 200 & 12,288 (64$\\times$64$\\times$3) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries", "authors": ["Chris Kolb", "Tobias Weber", "Bernd Bischl", "David Rügamer"], "url": "https://arxiv.org/abs/2502.02496v2", "attribution": "\"Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries\" by Chris Kolb, Tobias Weber, Bernd Bischl, and David Rügamer, arXiv:2502.02496v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.04837v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Efficient normal scoring mechanism.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\toprule\n \\toprule\n \\multirow{2}{*}{Normal scoring function}&\n \\multicolumn{2}{c}{CIFAR10}&\\multicolumn{2}{c}{CIFAR100}\\cr\n \\cmidrule(lr){2-3}\\cmidrule(lr){4-5}\n &AVG&STD&AVG&STD\\cr\n \\cmidrule(lr){1-5}\n $NormalScore_{rand}$ & 86.4 & 1.04 & 75.6 & 1.26\\cr\n $NormalScore_{mc}$ & 86.9 & 0.05 & 78.5 & 0.12\\cr\n $NormalScore_{ori}$ & 86.7 & $-$ & 77.4 & $-$\\cr\n \\bottomrule\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework", "authors": ["Fei Ye", "Huangjie Zheng", "Chaoqin Huang", "Ya Zhang"], "url": "https://arxiv.org/abs/2012.04837v1", "attribution": "\"Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework\" by Fei Ye, Huangjie Zheng, Chaoqin Huang, and Ya Zhang, arXiv:2012.04837v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.03481v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Compression factor (cf) and Top-1 accuracies with two different binary approximation procedures as function of $M$. For each network the single-precision floating-point accuracy is indicated for comparison.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crcccc}\n\t\t\\hline\n\t\t& & \\multicolumn{2}{c}{acc. w/ Algorithm~} & \\multicolumn{2}{c}{acc. w/ Algorithm~} \\\\\n\t\t$M$ & cf & no retrain & w/ retrain & no retrain & w/ retrain\\\\\n\t\t\\hline\n\t\t\\multicolumn{6}{c}{CNN-A (baseline acc.\\ 97.86\\%)} \\\\\n\t\t\\hline\n\t\t2\t& 15.8 & 84.68\\%\t&97.09\\%\t& 87.43\\%\t& 97.13\\%\\\\\n\t\t3\t& 10.6 & 93.40\\%\t&97.51\\% \t& 95.92\\%\t& 97.29\\%\\\\\n\t\t4\t& 7.9 & 95.64\\%\t&96.60\\% \t& 97.51\\%\t& 98.01\\%\\\\\n\t\t\\hline\n\t\t\\multicolumn{6}{c}{CNN-B1 (baseline acc. 56.3\\%)} \\\\\n\t\t\\hline\n\t\t4\t& 7.6 & 0.10\\% & 43.17\\% & 0.18\\% & 51.55\\% \\\\\n\t\t5\t& 6.1 & 0.08\\% & 46.29\\% & 0.64\\% & 54.46\\% \\\\\n\t\t6\t& 5.1 & 0.10\\% & 50.96\\% & 5.03\\% & 55.03\\% \\\\\n\t\t\\hline\n\t\t\\multicolumn{6}{c}{CNN-B2 (baseline acc.\\ 70.9\\%)} \\\\\n\t \\hline\n\t\t4\t& 7.9 & 0.11\\% & 46.90\\% & 0.2\\% & 47.82\\% \\\\\n\t\t5\t& 6.2 & 0.12\\% & 46.84\\% & 6.8\\%& 53.59\\% \\\\\n\t\t6\t& 5.2 & 0.08\\% & 51.23\\% & 25.2\\% & 69.10\\% \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "BinArray: A Scalable Hardware Accelerator for Binary Approximated CNNs", "authors": ["Mario Fischer", "Juergen Wassner"], "url": "https://arxiv.org/abs/2012.03481v1", "attribution": "\"BinArray: A Scalable Hardware Accelerator for Binary Approximated CNNs\" by Mario Fischer and Juergen Wassner, arXiv:2012.03481v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.15060v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Harder settings.} Mean HRP (\\%, $\\uparrow$) for ALOCC, NHP, and runner-ups, across all VS experiments. Best method in \\textbf{bold}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccc}\n\\toprule\n& \\multicolumn{2}{c}{Test set} & \\multicolumn{2}{c}{OOD} & \\multicolumn{2}{c}{Adv. Ex.} \\\\\nMethod & \\small{HRP $\\uparrow$} & \\small{Val. Gap} & \\small{HRP $\\uparrow$} & \\small{Val. Gap} & \\small{HRP $\\uparrow$} & \\small{Val. Gap} \\\\\n\\cmidrule(lr){1-1} \\cmidrule(lr){2-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-7} \nALOCC & -0.7\\textsubscript{±29.48} & - & -3.18\\textsubscript{±27.34} & - &-6.7\\textsubscript{±26.11} & - \\\\\nNHP (GMM) & 48.93\\textsubscript{±15.07} & 2.43\\textsubscript{±11.99} & 47.94\\textsubscript{±13.61} & 5.65\\textsubscript{±12.68} & 42.87\\textsubscript{±19.34} & 10.66\\textsubscript{±17.32} \\\\\nNHP (linear) & 51.58\\textsubscript{±14.75} & 1.23\\textsubscript{±10.08} & 49.57\\textsubscript{±12.52} & 4.25\\textsubscript{±10.58} & 43.52\\textsubscript{±17.15} & 10.3\\textsubscript{±16.66} \\\\\nNHP (Ours) & \\textbf{52.4\\textsubscript{±14.3}} & 1.42\\textsubscript{±9.61} & \n\\textbf{49.62\\textsubscript{±13.34}} & 3.99\\textsubscript{±11.34} & \\textbf{44.87\\textsubscript{±16.22}} & 8.75\\textsubscript{±16.92} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Detecting Hallucinations in Virtual Histology with Neural Precursors", "authors": ["Ji-Hun Oh", "Kianoush Falahkheirkhah", "Rohit Bhargava"], "url": "https://arxiv.org/abs/2411.15060v1", "attribution": "\"Detecting Hallucinations in Virtual Histology with Neural Precursors\" by Ji-Hun Oh, Kianoush Falahkheirkhah, and Rohit Bhargava, arXiv:2411.15060v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.01255v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n\\hline\n\\textbf{Real} & \\textbf{StyleGAN} & \\textbf{LDM} & \\textbf{Accuracy} & \\textbf{F1 Score} & \\textbf{Precision} & \\textbf{Recall} & \\textbf{MCC} \\\\\n\\hline\n0 & 0 & 250 & 47\\% & 0.52 & 0.60 & 0.47 & 0.40 \\\\\\hline\n0 & 0 & 500 & 67\\% & 0.69 & 0.74 & 0.66 & 0.61 \\\\\\hline\n0 & 0 & 1000 & 87\\% & 0.87 & 0.88 & 0.87 & 0.84 \\\\\\hline\n0 & 0 & 2000 & 90\\% & 0.90 & 0.90 & 0.90 & 0.88 \\\\\\hline\n0 & 0 & 3000 & 87\\% & 0.87 & 0.89 & 0.87 & 0.84 \\\\\\hline\n0 & 0 & 4000 & 89\\% & 0.89 & 0.89 & 0.90 & 0.85 \\\\\\hline\n0 & 0 & 5000 & 89\\% & 0.89 & 0.89 & 0.90 & 0.86 \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Classification results of the VGG16 model on the test dataset (consisting of real images), trained using various amounts of LDM-generated images.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Merging synthetic and real embryo data for advanced AI predictions", "authors": ["Oriana Presacan", "Alexandru Dorobantiu", "Vajira Thambawita", "Michael A. Riegler", "Mette H. Stensen", "Mario Iliceto", "Alexandru C. Aldea", "Akriti Sharma"], "url": "https://arxiv.org/abs/2412.01255v2", "attribution": "\"Merging synthetic and real embryo data for advanced AI predictions\" by Oriana Presacan, Alexandru Dorobantiu, Vajira Thambawita, Michael A. Riegler, Mette H. Stensen, Mario Iliceto, Alexandru C. Aldea, and Akriti Sharma, arXiv:2412.01255v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2311.05203v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of Trainable Parameters}\n\\begin{tabular}{lc}\n\\toprule\n\\textbf{Model} & \\textbf{Trainable Parameters (Millions)} \\\\\n\\midrule\nWav2vec2-base & 94.57 \\\\\nWhisper-base & 20.72 \\\\\n\\textbf{Whisper-base (Strategy 1)} & \\textbf{11.27} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Whisper in Focus: Enhancing Stuttered Speech Classification with Encoder Layer Optimization", "authors": ["Huma Ameer", "Seemab Latif", "Rabia Latif", "Sana Mukhtar"], "url": "https://arxiv.org/abs/2311.05203v1", "attribution": "\"Whisper in Focus: Enhancing Stuttered Speech Classification with Encoder Layer Optimization\" by Huma Ameer, Seemab Latif, Rabia Latif, and Sana Mukhtar, arXiv:2311.05203v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2007.13024v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{PESQ comparisons of different deep models for multi-channel speech enhancement on the WSJ0 corpus. The average PESQ score for unprocessed ch-1 noisy speech is 2.02.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l||c|c|c}\n \\hline\n \\hline\n Model \t\t& Channel \\# & Parameter \\# & PESQ\t \\\\\n \\hline\n \\hline\n DNN\t\t&\t1\t\t & \t27M\t & \t2.86\t\\\\\n DNN\t\t&\t2\t\t & \t33M\t & \t3.00\t\\\\\n \\hline\n CNN \t& 1\t\t &\t9.4M \t& \t3.03\\\\\n CNN \t& 2\t\t & \t9.4M \t& \t3.11\\\\\t\n \\hline\n CNN-TT \t\t&\t\t1\t &\t1.6M \t& \t2.99\t\\\\\t\n CNN-TT \t\t& \t1\t\t& \t2.8M\t\t& \t3.04\t\\\\\t\t\n CNN-TT \t\t&\t\t2\t &\t1.6M \t& \t3.08\t\\\\\t\n CNN-TT \t\t& \t2\t\t& \t2.8M\t\t& \t3.13\t\\\\\t\n \t\\hline\n CNN-Tucker-3 & 1 & 9.2M & 2.63 \\\\\n CNN-Tucker-3 & 2 & 9.2M & 2.56 \\\\\n \t\\hline\n \t\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement", "authors": ["Jun Qi", "Hu Hu", "Yannan Wang", "Chao-Han Huck Yang", "Sabato Marco Siniscalchi", "Chin-Hui Lee"], "url": "https://arxiv.org/abs/2007.13024v2", "attribution": "\"Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement\" by Jun Qi, Hu Hu, Yannan Wang, Chao-Han Huck Yang, Sabato Marco Siniscalchi, and Chin-Hui Lee, arXiv:2007.13024v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23211v2_tex_table20.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Location of estimated change points from the video data set for the ``\\textit{first person walks out of the lobby} (so-called action 1)\" and ``\\textit{second person walks into the lobby} (so-called action 2)\" using SDE method. Note that the true value is 116 for action 1 and 174 for action 2.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\nAction & Pixel & Estimated change points based on \\textbf{SDE} method \\\\ \\hline\\hline\n1 & 700 & 24 \\\\\n& 702 & 88 \\\\\n & 731 & 91 \\\\\n& 762 & 96 \\\\\n& 764 & 104 \\\\\n\\hline\n2 & 48 & 259 \\\\\n& 78 & 246 \\\\\n& 110 & 231 \\\\\n& 174 & 237 \\\\\n& 209 & 236 \\\\\n& 241 & 201 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models", "authors": ["Sepideh Mosaferi", "Abolfazl Safikhani", "Peiliang Bai"], "url": "https://arxiv.org/abs/2503.23211v2", "attribution": "\"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models\" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.20204v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{SW Model: parameters, bounds, and prior distribution}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|c|ccc}\n \\hline \\hline\n \\multirow{2}{*}{${\\theta }$} & \\multirow{2}{*}{Parameter Interpretation} & \\multirow{2}{*}{Bounds} & \\multicolumn{3}{c}{Prior Distribution} \\\\ \n & & & { \\textsc{density}} & { \\textsc{mean}} & { \\textsc{sd}} \\\\ \\hline\n ${ \\rho }_{ga}$ & { Corr.: tech. and exog. spending shocks} & \n { [0.01, 2]} & { Normal} & { 0.50} & { 0.25} \\\\ \n ${ \\mu }_{w}$ & { Wage mark-up shock MA} & { [0.01, 0.99]}\n & { Beta} & { 0.50} & { 0.20} \\\\ \n ${ \\mu }_{p}$ & { Price mark-up shock MA} & { [0.01, 0.99]}\n & { Beta} & { 0.50} & { 0.20} \\\\ \n ${ \\alpha }$ & { Share of capital in production} & { %\n [0.01, 1]} & { Normal} & { 0.30} & { 0.05} \\\\ \n ${ \\psi }$ & { Elast. of capital utilization adjustment cost} & \n { [0.01, 1]} & { Beta} & { 0.50} & { 0.15} \\\\ \n ${ \\varphi }$ & { Investment adjustment cost} & { [3,15]}\n & { Normal} & { 4.00} & { 1.50} \\\\ \n ${ \\sigma }_{c}$ & { Elast. of inertemporal substitution} & \n { [1, 3]} & { Normal} & { 1.50} & { 0.38} \\\\ \n ${ \\lambda }$ & { Habit persistence} & { [0.001, 0.99]} & \n { Beta} & { 0.70} & { 0.10} \\\\ \n ${ \\phi }_{p}$ & { Fixed costs in production} & { [1, 3]}\n & { Normal} & { 1.25} & { 0.13} \\\\ \n ${ \\iota }_{w}$ & { Wage indexation} & { [0.01, 0.99]} & \n { Beta} & { 0.50} & { 0.15} \\\\ \n ${ \\xi }_{w}$ & { Wage stickiness} & { [0.5, 0.95]} & \n { Beta} & { 0.50} & { 0.10} \\\\ \n ${ \\iota }_{p}$ & { Price indexation} & { [0.01, 0.99]} & \n { Beta} & { 0.50} & { 0.15} \\\\ \n ${ \\xi }_{p}$ & { Price stickiness} & { [0.1, 0.95]} & \n { Beta} & { 0.50} & { 0.10} \\\\ \n ${ \\sigma }_{l}$ & { Labor supply elasticity} & { [1, 10]}\n & { Normal} & { 2.00} & { 0.75} \\\\ \n ${ r}_{\\pi }$ & { Taylor rule: inflation weight} & { [1, 3]%\n } & { Normal} & { 1.50} & { 0.25} \\\\ \n ${ r}_{\\Delta y}$ & { Taylor rule: output gap change weight} & \n { [0.001, 0.5]} & { Normal} & { 0.13} & { 0.05} \\\\ \n ${ r}_{y}$ & { Taylor rule: output gap weight} & { [0.001,\n 0.5]} & { Normal} & { 0.13} & { 0.05} \\\\ \n ${ \\rho }$ & { Taylor rule: interest rate smoothing} & { %\n [0.5, 0.975]} & { Beta} & { 0.75} & { 0.10} \\\\ \n ${ \\rho }_{a}$ & { Productivity shock AR} & { [0.01, 0.99]}\n & { Beta} & { 0.50} & { 0.20} \\\\ \n ${ \\rho }_{b}$ & { Risk premium shock AR} & { [0.01, 0.99]}\n & { Beta} & { 0.50} & { 0.20} \\\\ \n ${ \\rho }_{g}$ & { Exogenous spending shock AR } & { %\n [0.01, 0.99]} & { Beta} & { 0.50} & { 0.20} \\\\ \n ${ \\rho }_{i}$ & { Investment shock AR} & { [0.01, 0.99]}\n & { Beta} & { 0.50} & { 0.20} \\\\ \n ${ \\rho }_{r}$ & { Monetary policy shock AR} & { [0.01,\n 0.99]} & { Beta} & { 0.50} & { 0.20} \\\\ \n ${ \\rho }_{p}$ & { Price mark-up shock AR} & { [0.01, 0.99]%\n } & { Beta} & { 0.50} & { 0.20} \\\\ \n ${ \\rho }_{w}$ & { Wage mark-up shock AR} & { [0.001, 0.99]%\n } & { Beta} & { 0.50} & { 0.20} \\\\ \n ${ \\sigma }_{a}$ & { Productivity shock std. dev.} & { %\n [0.01, 3]} & { IGamma} & { 0.10} & { 2.00} \\\\ \n ${ \\sigma }_{b}$ & { Risk premium shock std. dev.} & { %\n [0.025, 5]} & { IGamma} & { 0.10} & { 2.00} \\\\ \n ${ \\sigma }_{g}$ & { Exogenous spending shock std. dev.} & \n { [0.01, 3]} & { IGamma} & { 0.10} & { 2.00} \\\\ \n ${ \\sigma }_{i}$ & { Investment shock std. dev.} & { %\n [0.01, 3]} & { IGamma} & { 0.10} & { 2.00} \\\\ \n ${ \\sigma }_{r}$ & { Monetary policy shock std. dev.} & { %\n [0.01, 3]} & { IGamma} & { 0.10} & { 2.00} \\\\ \n ${ \\sigma }_{p}$ & { Price mark-up shock std. dev.} & { %\n [0.01, 3]} & { IGamma} & { 0.10} & { 2.00} \\\\ \n ${ \\sigma }_{w}$ & { Wage mark-up shock std. dev.} & { %\n [0.01, 3]} & { IGamma} & { 0.10} & { 2.00} \\\\ \n $\\overline{\\gamma }$ & { Trend growth: real GDP, Infl., Wages} & \n { [0.1, 0.8]} & { Normal} & { 0.40} & { 0.10} \\\\ \n ${ r}$ & { Discount rate} & { [0.01, 2]} & { Gamma}\n & { 0.25} & { 0.10} \\\\ \n $\\overline{\\pi }$ & { Steady state inflation rate} & { [0.1, 2]}\n & { Gamma} & { 0.62} & { 0.10} \\\\ \n $\\overline{l}$ & { Steady state hours worked} & { [-10,10]} & \n { Normal} & { 0.00} & { 2.00} \\\\ \\hline \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fitting Dynamically Misspecified Models: An Optimal Transportation Approach", "authors": ["Jean-Jacques Forneron", "Zhongjun Qu"], "url": "https://arxiv.org/abs/2412.20204v2", "attribution": "\"Fitting Dynamically Misspecified Models: An Optimal Transportation Approach\" by Jean-Jacques Forneron and Zhongjun Qu, arXiv:2412.20204v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11985v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters used in testing the inverse problem solvers for the analytical benchmark case.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\hline\n \\textbf{Parameter} & \\textbf{Value}\\\\\n \\hline\n N. of thermocouples\t& 16 \\\\\n Thermocouples plane\t& $y = 0.2~m$ \\\\\n $g^0$\t\t\t& $0~W/m^2$\\\\\n RBF kernel\t\t& Gaussian\\\\\n N. of RBF\t\t& 16 \\\\\n Shape parameter, $\\eta$\t& 0.7 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation", "authors": ["Umberto Emil Morelli", "Patricia Barral", "Peregrina Quintela", "Gianluigi Rozza", "Giovanni Stabile"], "url": "https://arxiv.org/abs/2101.11985v1", "attribution": "\"A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation\" by Umberto Emil Morelli, Patricia Barral, Peregrina Quintela, Gianluigi Rozza, and Giovanni Stabile, arXiv:2101.11985v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.15031v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation Parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\nSymbol & Description & Value\\\\\n\\hline\n$A_{sim}$& Simulation area &$20$m × $20$m\\\\\n\\hline\n$\\mathit{L}$& Number of RIS elements & $16$ \\\\ \n\\hline\n$C_X$ and $C_Y$ &LoS environmental constants & $9.61$ and $0.16$\\\\ \n\\hline\n$\\upsilon$ &Reference path loss at {\\footnotesize$d_{ref} = 1$} m & $-30$ dB\\\\ \n\\hline\n$\\sigma_k^2$& User $k$ noise power & $-102$ dBm \\\\ \n\\hline\n$\\varphi$& Non-LoS attenuation & $20$ dBm \\\\ \n\\hline\n$P_{BS}^{\\max}$& BS max transmit power & $500$ W \\\\ \n\\hline\n$\\alpha$& Path loss exponent for BS-RIS links & $3$ \\\\ \n\\hline\n$\\nu$& Path loss exponent for RIS-user links & $2.5$ \\\\ \n\\hline\n$R_{\\text{min}} $ & Minimum user QoS requirements& $70$ Mbps\\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Energy-Efficient Irregular RIS-aided UAV-Assisted Optimization: A Deep Reinforcement Learning Approach", "authors": ["Mahmoud M. Salim", "Khaled M. Rabie", "Ali H. Muqaibel"], "url": "https://arxiv.org/abs/2504.15031v1", "attribution": "\"Energy-Efficient Irregular RIS-aided UAV-Assisted Optimization: A Deep Reinforcement Learning Approach\" by Mahmoud M. Salim, Khaled M. Rabie, and Ali H. Muqaibel, arXiv:2504.15031v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10445v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|l|l|}\n\\hline\nHeading level & Example & Font size and style\\\\\n\\hline\nTitle (centered) & {\\Large\\bfseries Lecture Notes} & 14 point, bold\\\\\n1st-level heading & {\\large\\bfseries 1 Introduction} & 12 point, bold\\\\\n2nd-level heading & {\\bfseries 2.1 Printing Area} & 10 point, bold\\\\\n3rd-level heading & {\\bfseries Run-in Heading in Bold.} Text follows & 10 point, bold\\\\\n4th-level heading & {\\itshape Lowest Level Heading.} Text follows & 10 point, italic\\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Dairy Cow rumination detection: A deep learning approach", "authors": ["Safa Ayadi", "Ahmed ben said", "Rateb Jabbar", "Chafik Aloulou", "Achraf Chabbouh", "Ahmed Ben Achballah"], "url": "https://arxiv.org/abs/2101.10445v1", "attribution": "\"Dairy Cow rumination detection: A deep learning approach\" by Safa Ayadi, Ahmed ben said, Rateb Jabbar, Chafik Aloulou, Achraf Chabbouh, and Ahmed Ben Achballah, arXiv:2101.10445v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.06796v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n \\toprule\n Symbol & Value & Symbol & Value \\\\\n \\midrule\n $n_c$ & 5776 & $\\overline{T}$ & 1273 K \\\\\n $C_h$ & $173.28\\cdot 10^6$ J/K & $\\underline{T}$ & 1073 K \\\\\n $R_t$ & $1.3067\\cdot 10^{-3}$ K/W & $\\overline{j}$ & 10000 A/m$^2$ \\\\\n $U_{tn}$ & 1.2995 V & $\\underline{j}$ & 2000 A/m$^2$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Parameters used in SOE operational model}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Optimisation of Electrolyser Operation: Integrating External Heat", "authors": ["Matthias Derez", "Alexander Hoogsteyn", "Erik Delarue"], "url": "https://arxiv.org/abs/2507.06796v1", "attribution": "\"Optimisation of Electrolyser Operation: Integrating External Heat\" by Matthias Derez, Alexander Hoogsteyn, and Erik Delarue, arXiv:2507.06796v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.15979v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Cohen's Kappa Agreement Scores}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n\\toprule\n\\textbf{Comparison} & \\textbf{Kappa ($\\kappa$)} \\\\\n\\midrule\nExpert 1 vs. Expert 2 & 0.81 \\\\\nExpert 1 vs. Our Model & 0.78 \\\\\nExpert 2 vs. Our Model & 0.75 \\\\\nExpert vs. Cellpose & 0.42 \\\\\nExpert vs. StarDist & 0.35 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "GUI Based Fuzzy Logic and Spatial Statistics for Unsupervised Microscopy Segmentation", "authors": ["Surajit Das", "Pavel Zun"], "url": "https://arxiv.org/abs/2508.15979v1", "attribution": "\"GUI Based Fuzzy Logic and Spatial Statistics for Unsupervised Microscopy Segmentation\" by Surajit Das and Pavel Zun, arXiv:2508.15979v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16534v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{OpenML Task ID mappings for \\textbf{classification} datasets with \\textbf{numerical features only}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\toprule\n\\textbf{OpenML ID} & \\textbf{Dataset} \\\\ \\midrule\n361055 & credit \\\\\n361060 & electricity \\\\\n361061 & covertype \\\\\n361062 & pol \\\\\n361063 & house\\_16H \\\\\n361065 & MagicTelescope \\\\\n361066 & bank-marketing \\\\\n361068 & MiniBooNE \\\\\n361069 & Higgs \\\\\n361070 & eye\\_movements \\\\\n361273 & Diabetes130US \\\\\n361274 & jannis \\\\\n361275 & default-of-credit-card-clients \\\\\n361276 & Bioresponse \\\\\n361277 & california \\\\\n361278 & heloc \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DOFEN: Deep Oblivious Forest ENsemble", "authors": ["Kuan-Yu Chen", "Ping-Han Chiang", "Hsin-Rung Chou", "Chih-Sheng Chen", "Tien-Hao Chang"], "url": "https://arxiv.org/abs/2412.16534v2", "attribution": "\"DOFEN: Deep Oblivious Forest ENsemble\" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.04278v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative assessment on DEER-Lite ($\\mathrm{MEAN}\\pm \\mathrm{STD}$) for directly testing on 30-view data using a trained 75-view network. The measurements were obtained by averaging the values on the testing dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\toprule\n & FDK & DEER-Lite \\\\\n\\midrule\nPSNR & $22.1137\\pm6.3728$ & $28.0983\\pm8.4834$ \\\\\nSSIM & { }{ }$0.7795\\pm 0.1485$ & { }{ }$0.9018\\pm0.0985$ \\\\\nMAE & { }{ }$0.0289\\pm0.0270$ & { }{ }$0.0148\\pm0.0167$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction", "authors": ["Huidong Xie", "Hongming Shan", "Wenxiang Cong", "Chi Liu", "Xiaohua Zhang", "Shaohua Liu", "Ruola Ning", "Ge Wang"], "url": "https://arxiv.org/abs/1912.04278v3", "attribution": "\"Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction\" by Huidong Xie, Hongming Shan, Wenxiang Cong, Chi Liu, Xiaohua Zhang, Shaohua Liu, Ruola Ning, and Ge Wang, arXiv:1912.04278v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18002v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{soul}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll|lllll|lllll|lllll}\n\\hline\n\\multicolumn{20}{c}{\\textbf{A Uniform}}\\\\\n\\hline\n\\multicolumn{5}{c|}{Shifted sinusoidal $d=2$}\n&\\multicolumn{5}{c|}{Shifted sinusoidal $d=5$}\n& \\multicolumn{5}{c|}{Rosenbrock $d=2$}\n& \\multicolumn{5}{c}{Rosenbrock $d=5$}\n\\\\\\hline\n$k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \\\\ \n\\hline\n 10 & 0.475 & 0 & 1.63 & 2.769 & \n 10 & {\\hl {0.593}} & {\\hl {0.624}} & 3.429 & 3.583 & \n 4 & 0.249 & 0.203 & 0.971 & 4.073 & \n 4 & 0.918 & 0.918 & 1278.517 & 1398.628 \\\\ \n 15 & 0.351 & 0.33 & 1.259 & 2.442 & \n 22 & 0.126 & 0.042 & 2.068 & 3.443 & \n 104 & 0.025 & 0.013 & 0.167 & 27.401 & \n 106 & {\\hl {0.024}} & {\\hl {0.027}} & 68.333 & 648.264 \\\\ \n 25 & 0.113 & 0.089 & 0.63 & 2.15 & \n 32 & 0.035 & 0.03 & 1.513 & 3.331 & \n 204 & 0.029 & 0.018 & 0.167 & 41.594 & \n 206 & 0.007 & 0.003 & 30.144 & 636.586 \\\\ \n 48 & 0.099 & 0.043 & 0.229 & 1.685 & \n 42 & 0.049 & 0.006 & 1.513 & 3.246 & \n 304 & 0.030 & 0.021 & 0.167 & 46.290 & \n 308 & 0.010 & 0.007 & 30.144 & 623.913 \\\\ \n 54 & 0.189 & 0.066 & 0.229 & 1.685 & \n 52 & 0.052 & 0.006 & 1.513 & 3.246 & & & & & & & & & & \\\\ \n 63 & 0.148 & 0.007 & 0.096 & 1.4 & \n 60 & 0.025 & 0.005 & 1.306 & 2.972 & & & & & & & & & & \\\\ \n 78 & 0.092 & 0.078 & 0.053 & 0.788 & \n 110 & 0.013 & 0.012 & 1.115 & 2.33 & & & & & & & & & & \\\\ \n 93 & 0.111 & 0.005 & 0.053 & 0.653 & \n 120 & 0.013 & 0.004 & 1.115 & 2.395 & & & & & & & & & & \\\\ \n 110 & 0.023 & 0.001 & 0.006 & 0.512 & \n 137 & 0.014 & 0.006 & 1.115 & 2.33 & & & & & & & & & & \\\\ \n 172 & 0.02 & 0 & 0.006 & 0.535 & \n 155 & 0.014 & 0.008 & 1.115 & 2.249 & & & & & & & & & & \\\\ \n 227 & 0.013 & 0.006 & 0.006 & 0.512 & \n 175 & 0.019 & 0.01 & 1.115 & 2.1 & & & & & & & & & & \\\\ \n 262 & 0.024 & 0 & 0.006 & 0.377 & \n 195 & 0.004 & 0.001 & 0.708 & 1.967 & & & & & & & & & & \\\\ \n & & & & & \n 255 & 0.004 & 0.002 & 0.708 & 1.967 & & & & & & & & & & \\\\ \n \\hline\n\\multicolumn{20}{c}{\\textbf{B Gaussian Process}}\\\\ \n \\hline\n\\multicolumn{5}{c|}{Shifted sinusoidal $d=2$}\n&\\multicolumn{5}{c|}{Shifted sinusoidal $d=5$}\n& \\multicolumn{5}{c|}{Rosenbrock $d=2$}\n& \\multicolumn{5}{c}{Rosenbrock $d=5$}\n\\\\\\hline\n$k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \\\\ \n\\hline\n 10 & 0.515 & 0.515 & 1.209 & 1.778 & \n 10 & 0.402 & 0.125 & 2.727 & 3.222 & \n 10 & {\\hl {0.109}} & {\\hl {0.110}} & 1.382 & 23.492 & \n 10 & 0.325 & 0.315 & 196.646 & 416.047 \\\\ \n 65 & 0.215 & 0 & 1.209 & 4.479 &\n 18 & 0.176 & 0.06 & 2.627 & 3.29 &\n 20 & 0.114 & 0.072 & 1.382 & 23.492 & \n 20 & 0.141 & 0.139 & 120.619 & 416.047 \\\\ \n 76 & 0.2 & 0.014 & 1.1 & 3.229 & \n 28 & 0.386 & 0.053 & 2.627 & 3.322 & \n 30 & {\\hl {0.022}} & {\\hl {0.026}} & 0.102 & 34.613 & \n 130 & 0.101 & 0.098 & 91.088 & 404.833 \\\\ \n 86 & 0.2 & 0.099 & 1.1 & 2.524 &\n 33 & 0.334 & 0.203 & 2.627 & 3.322 & \n 140 & 0.043 & 0.016 & 0.102 & 47.682 & \n 240 & 0.104 & 0.093 & 91.088 & 400.131 \\\\ \n 96 & 0.102 & 0.034 & 0.242 & 2.163 & \n 43 & 0.206 & 0.184 & 2.441 & 3.29 & \n 250 & 0.019 & 0.001 & 0.102 & 31.743 &\n 350 & 0.098 & 0.076 & 91.088 & 400.131 \\\\ \n 156 & 0.37 & 0 & 0.242 & 2.516 & \n 100 & 0.097 & 0.01 & 1.549 & 3.369 & \n 360 & 0.045 & 0.032 & 0.102 & 17.118 & & & & & \\\\ \n 167 & 0.05 & 0 & 0.114 & 1.864 &\n 154 & 0.146 & 0.048 & 1.549 & 3.238 & & & & & & & & & & \\\\ \n 187 & 0.014 & 0.001 & 0.009 & 1.745 & \n 215 & 0.185 & 0.018 & 1.549 & 3.14 & & & & & & & & & & \\\\ \n 257 & 0.002 & 0.001 & 0.009 & 1.825 &\n 259 & 0.245 & 0.046 & 1.549 & 3.039 & & & & & & & & & & \\\\ \n\\hline\n\\multicolumn{20}{c}{\\textbf{C Quadratic Regression}}\\\\ \n\\hline\n\\multicolumn{5}{c|}{Shifted sinusoidal $d=2$}\n&\\multicolumn{5}{c|}{Shifted sinusoidal $d=5$}\n& \\multicolumn{5}{c|}{Rosenbrock $d=2$}\n& \\multicolumn{5}{c}{Rosenbrock $d=5$}\n\\\\\\hline\n$k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \\\\ \n\\hline\n 10 & 0.49 & 0 & 1.65 & 2.77 & \n 50 & 0.121 & 0.088 & 2.077 & 3.246 & \n 10 & 0.375 & 0.262 & 1.006 & 5.158 & \n 10 & 0.967 & 0.967 & 1143.565 & 1180.265 \\\\ \n 15 & 0.02 & 0.02 & 0.15 & 1.65 & \n 76 & 0.023 & 0.015 & 1.574 & 3.246 & \n 115 & 0.308 & 0.000 & 0.453 & 3.366 &\n 110 & 0.305 & 0.193 & 14.028 & 25.120 \\\\ \n 30 & 0 & 0 & 0.03 & 1.19 & \n 126 & 0.028 & 0.023 & 1.574 & 3.26 & \n 215 & 0.000 & 0.000 & 0.000 & 1.046 & \n 215 & {\\hl {0.166}} & {\\hl {0.167}} & 12.824 & 27.317 \\\\ \n 40 & 0.01 & 0.01 & 0.03 & 1.26 & \n 176 & 0.037 & 0 & 1.437 & 3.265 & \n 315 & 0.000 & 0.000 & 0.000 & 1.009 & \n 315 & 0.022 & 0.022 & 3.992 & 20.138 \\\\ \n 50 & 0.01 & 0.01 & 0.03 & 1.23 & \n 227 & 0.044 & 0.019 & 1.423 & 3.158 & & & & & & & & & & \\\\ \n 60 & 0.01 & 0.01 & 0.03 & 1.19 & & & & & & & & & & & & & & & \\\\ \n 66 & 0.01 & 0 & 0.01 & 0.9 & & & & & & & & & & & & & & & \\\\ \n 127 & 0.01 & 0 & 0.01 & 0.81 & & & & & & & & & & & & & & & \\\\ \n 182 & 0.01 & 0 & 0.01 & 0.77 & & & & & & & & & & & & & & & \\\\ \n 237 & 0.02 & 0.01 & 0.01 & 0.74 & & & & & & & & & & & & & & & \\\\ \n 257 & 0.01 & 0 & 0.01 & 0.62 & & & & & & & & & & & & & & & \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Branching Adaptive Surrogate Search Optimization (BASSO)", "authors": ["Pariyakorn Maneekul", "Zelda B. Zabinsky", "Giulia Pedrielli"], "url": "https://arxiv.org/abs/2504.18002v1", "attribution": "\"Branching Adaptive Surrogate Search Optimization (BASSO)\" by Pariyakorn Maneekul, Zelda B. Zabinsky, and Giulia Pedrielli, arXiv:2504.18002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00003v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\n\\textbf{Name} & \\textbf{Cost (USD)} & \\textbf{GHG emissions (kgCO\\textsubscript{2}eq)} \\\\ \\hline\nHardware & 650 & 54 \\\\ \\hline\nElectricity & 330 & 1225 \\\\ \\hline % Original: 1074\nTotal & 980 & 1279 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Final breakdown of costs and emissions for one RX 590 GPU over 2 years.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Green NFTs: A Study on the Environmental Impact of Cryptoart Technologies", "authors": ["Samuele Marro", "Luca Donno"], "url": "https://arxiv.org/abs/2202.00003v3", "attribution": "\"Green NFTs: A Study on the Environmental Impact of Cryptoart Technologies\" by Samuele Marro and Luca Donno, arXiv:2202.00003v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.01231v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc} \\hline\nCMM & LM & CENTR \\\\ \\hline\n2.42 $\\pm$ 0.98 & 1.67 $\\pm$ 0.86 & 0.20 $\\pm$ 0.12 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Dataset evaluation}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music", "authors": ["Sebastian Garcia-Valencia", "Alejandro Betancourt", "Juan G. Lalinde-Pulido"], "url": "https://arxiv.org/abs/2012.01231v1", "attribution": "\"Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music\" by Sebastian Garcia-Valencia, Alejandro Betancourt, and Juan G. Lalinde-Pulido, arXiv:2012.01231v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.16011v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Call spread option of maturity 1 year paying $(S_2-S_1-K)^+$, on two assets $S_1$ and $S_2$ with $S_1(0) = 200$, $S_2(0)=100$ and $\\sigma_1=60\\%, \\sigma_2=60\\%$ with correlation $\\rho_{1,2} = 28\\%$. This example has negative basket skewness}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrr}\\toprule\n\t\t\t$K$ & VG0 & VG1 & VG2 & VG3 & SLN & ICUB & MC \\\\ \\midrule\n\t\t\t-70 & 28.9222& 28.9251& 29.0906& 29.0853& 31.0619& 29.0854& 29.0854\\\\\n-80 & 33.4520& 33.4313& 33.6233& 33.6148& 35.8096& 33.6150& 33.6150\\\\\n-90 & 38.3631& 38.3258& 38.5397& 38.5276& 40.8772& 38.5281& 38.5281\\\\\n-100 & 43.6358& 43.5882& 43.8192& 43.8031& 46.2500& 43.8043& 43.8043\\\\\n-110 & 49.2485& 49.1961& 49.4394& 49.4194& 51.9127& 49.4212& 49.4212\\\\\n-120 & 55.1792& 55.1263& 55.3770& 55.3535& 57.8497& 55.3561& 55.3561\\\\\n-130 & 61.4056& 61.3551& 61.6093& 61.5826& 64.0453& 61.5861& 61.5861\\\\\n\t\t\t\\midrule\n\t\t\tRMSE & 0.1701 & 0.2084 & 0.0158 & 0.0018 & 2.3512 & 0.0000 & 0.0000 \t\t\t\\\\\n\t\t\tMAE & 0.1805 & 0.2310 & 0.0232 & 0.0034 & 2.4936 & 0.0000 & 0.0000\\\\\n\t\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Asian Basket Spread Options: A New Approximation Based on Stochastic Taylor Expansions", "authors": ["Fabien Le Floc'h"], "url": "https://arxiv.org/abs/2504.16011v3", "attribution": "\"Asian Basket Spread Options: A New Approximation Based on Stochastic Taylor Expansions\" by Fabien Le Floc'h, arXiv:2504.16011v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.20295v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Example: Color-Singlet States in QCD}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n\t\t\\hline\n\t\tState & Color Composition \\\\ \\hline\n\t\tMeson & $q\\bar{q}$ with $\\mathbf{3}\\otimes\\overline{\\mathbf{3}} \\rightarrow \\mathbf{1}$ \\\\ \\hline\n\t\tBaryon & $qqq$ with $\\mathbf{3}\\otimes\\mathbf{3}\\otimes\\mathbf{3} \\rightarrow \\mathbf{1}$ \\\\ \\hline\n\t\tTetraquark & $qq\\bar{q}\\bar{q}$ with proper contraction to yield $\\mathbf{1}$ \\\\ \\hline\n\t\tPentaquark & $qqqq\\bar{q}$ with proper contraction to yield $\\mathbf{1}$ \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Symmetry Packaging II: A Group-Theoretic Framework for Packaging Under Finite, Compact, Higher-Form, and Hybrid Symmetries", "authors": ["Rongchao Ma"], "url": "https://arxiv.org/abs/2503.20295v3", "attribution": "\"Symmetry Packaging II: A Group-Theoretic Framework for Packaging Under Finite, Compact, Higher-Form, and Hybrid Symmetries\" by Rongchao Ma, arXiv:2503.20295v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.04384v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{~ Definition of Variables and Their Expected Effect on the Net Interest Margin}\n\\begin{tabular}{l|llc}\n Variables & Notation & Definition & Expected Sign \\\\\n \\midrule\n \\midrule\n Net Interest Margin \\% & NIM & Net interest income divided by total assets & \\\\\n Risk Aversion \\% & RA & Equity over total assets & \\textbf{?} \\\\\n Credit Risk \\% & RBD & Non-performing loan over total loan & \\textbf{+} \\\\\n Operating Cost \\% & OC & Operation cost over total assets & \\textbf{?} \\\\\n Bank Size & LOGTA & Logarithm of total assets & \\textbf{?} \\\\\n Liquidity Ratio \\% & LQR & Ratio of liquid assets to total assets & \\textbf{-} \\\\\n Management Quality \\% & MNGMT & Total expenses over total generated revenues & \\textbf{-} \\\\\n Implicit Interest Payment \\% & IIP & Net non-interest income over total assets & \\textbf{+} \\\\\n Deposits Growth \\% & DPZTG & Quarterly growth of deposits & \\textbf{-} \\\\\n Operation Diversity \\% & DVRSTY & Non-interest income over operating income & \\textbf{-} \\\\\n Herfindahl Index \\% & HHI & Herfindahl index for assets & \\textbf{+} \\\\\n Real GDP Growth \\% & GDP & Quarterly real GDP growth & \\textbf{?} \\\\\n Inflation \\% & INF & CPI growth rate & \\textbf{+} \\\\\n \\bottomrule\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Determinants of Net Interest Margin in the Turkish Banking Sector: Does Bank Ownership Matter?", "authors": ["Fatih Kansoy"], "url": "https://arxiv.org/abs/2506.04384v2", "attribution": "\"The Determinants of Net Interest Margin in the Turkish Banking Sector: Does Bank Ownership Matter?\" by Fatih Kansoy, arXiv:2506.04384v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.17124v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Table showing coefficient of Multimodal Correlation Analyses determining the effect of Utterance Frequency, Average Pointing Time, And Gaze Stationary Time Ratio on Task Completion Time (\\textit{* = $p < .05$; ** = $p < .01$; *** = $p < .001$})}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|l|}\n\\hline\n\\textbf{\\textit{Modality}} & \\textbf{\\textit{Name}} & \\textbf{\\textit{F}} & \\textbf{\\textit{p}} & \\textbf{\\textit{Sig}} & \\textbf{\\textit{R}} & \\textbf{\\textit{$R^2$}} \\\\ \\hline\nMultimodal & Multimodal Correlation (Task 1) & $F_{3, 30} = 5.438$ & $< .01$ & ** & 0.535 & 0.287 \\\\ \\hline\nMultimodal & Multimodal Correlation (Task 2) & $F_{3, 30}$ = 27.62 & $< .001$ & *** & 0.841 & 0.708 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks", "authors": ["Ryan Ghamandi", "Yahya Hmaiti", "Mykola Maslych", "Ravi Kiran Kattoju", "Joseph J. LaViola"], "url": "https://arxiv.org/abs/2508.17124v1", "attribution": "\"Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks\" by Ryan Ghamandi, Yahya Hmaiti, Mykola Maslych, Ravi Kiran Kattoju, and Joseph J. LaViola, arXiv:2508.17124v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2307.08853v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Specifications of the forecasting~models.}\n\\begin{tabular}{lcrrrrr}\n\\toprule\n & & \\textbf{Year} & & & & \\\\\n\\textbf{Asset} & \\textbf{Model} & \\textbf{2018--2021} & \\textbf{2018} & \\textbf{2019} & \\textbf{2020} & \\textbf{2021} \\\\ \\midrule\nBTC-USD & ARIMA & (4, 1, 1) & (2, 1, 2) & (1, 0, 5) & (4, 1, 4) & (3, 0, 3) \\\\\nGDAXI & & (1, 1, 1) & (3, 1, 2) & (1, 1, 5) & (3, 0, 4) & (1, 1, 4) \\\\\nFTSE & & (4, 1, 3) & (5, 0, 2) & (5, 1, 1) & (3, 0, 5) & (2, 1, 4) \\\\\nN100 & & (5, 1, 0) & (3, 1, 2) & (2, 1, 1) & (1, 0, 0) & (1, 1, 5) \\\\\nFCHI & & (5, 1, 4) & (3, 0, 4) & (2, 1, 2) & (3, 0, 2) & (1, 1, 4) \\\\\nSSMI & & (1, 1, 5) & (2, 1, 5) & (5, 1, 0) & (4, 0, 2) & (4, 1, 5) \\\\\n\\multicolumn{6}{l}{{Note}%MDPI: We removed the empty row and added midrule. Please confirm.\n: optimal parameters based on information criterion `AIC'.} & \\\\\\midrule\nBTC-USD & kNN & k = 26 & k = 27 & k = 26 & k = 9 & k = 27 \\\\\nGDAXI & & k = 19 & k = 27 & k = 18 & k = 21 & k = 18 \\\\\nFTSE & & k = 25 & k = 27 & k = 27 & k = 16 & k = 25 \\\\\nN100 & & k = 27 & k = 20 & k = 23 & k = 27 & k = 21 \\\\\nFCHI & & k = 14 & k = 24 & k = 20 & k = 6 & k = 18 \\\\\nSSMI & & k = 27 & k = 22 & k = 15 & k = 14 & k = 27 \\\\\n\\multicolumn{6}{l}{{Note}%MDPI: We removed the empty row and added midrule. Please confirm.\n: weights: uniform; algorithm: brute; p: 2; k: optimal k.} & \\\\\\midrule\nETS-NN & ETS & NN & & & & \\\\\n & ETS(A, Ad, N) & \\multicolumn{2}{l}{LSTM layers: 50} & & & \\\\\n & & \\multicolumn{2}{l}{Dropout rate: 0.2} & & & \\\\\n & & \\multicolumn{2}{l}{Optimizer: Adam} & & & \\\\\n & & \\multicolumn{3}{l}{Loss Function: Mean Squared Error} & & \\\\\n & & \\multicolumn{2}{l}{Number of Epochs: 100} & & & \\\\\n & & \\multicolumn{2}{l}{Batch Size: 32} & & & \\\\ \\bottomrule \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins", "authors": ["Apostolos Ampountolas"], "url": "https://arxiv.org/abs/2307.08853v1", "attribution": "\"Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins\" by Apostolos Ampountolas, arXiv:2307.08853v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.20659v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccccc}\nm \\textbackslash n & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\\\ \n\\hline \n1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\\\\n2 & 1 & 3 & 7 & 15 &31 &63 & 127\\\\\n3 & 0 & 2 & 12 & 50 & 180 & 602 & 1932 \\\\\n4 & 0 & 0 & 6 & 60 & 390 & 2100 & 10206 \\\\\n5 & 0 & 0 & 0 & 24 &360 & 3360 & 25200\\\\\n6 & 0 & 0 & 0 & 0 & 120 & 2520 & 31920 \\\\\n7 & 0 & 0 & 0 & 0 & 0 & 720 & 20160 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Low Dimensional Homology of the Yang-Baxter Operators Yielding the HOMFLYPT Polynomial", "authors": ["Anthony Christiana", "Ben Clingenpeel", "Huizheng Guo", "Jinseok Oh", "Jozef H. Przytycki", "Xiao Wang", "Hongdae Yun"], "url": "https://arxiv.org/abs/2502.20659v1", "attribution": "\"Low Dimensional Homology of the Yang-Baxter Operators Yielding the HOMFLYPT Polynomial\" by Anthony Christiana, Ben Clingenpeel, Huizheng Guo, Jinseok Oh, Jozef H. Przytycki, Xiao Wang, and Hongdae Yun, arXiv:2502.20659v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10685v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{3 Malaysian Water Quality Index~}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\\toprule\n Parameters&\tUnit&\\multicolumn{6}{c}{\tClasses} \\\\\\midrule\n Ammoniacal Nitrogen&\tmg/l&\t0.1&\t0.3&\t0.3&\t0.9&\t2.7&\t>2.7\\\\\n BOD&\tmg/l&\t1&\t3&\t3&\t6&\t12&\t>12\\\\\nCOD&\tmg/l&\t10&\t25&\t25&\t50&\t100&\t>100\\\\\nDO&\tmg/l&\t7&\t5-7&\t5-7&\t3-5&\t<3&\t<1\\\\\npH&\tmg/l&\t6.5-8.5&\t6.5-9.0&\t6.5-9.0&\t5-9&\t5-9&\t-\\\\\nColor&\tTUC&\t15&\t150&\t150&\t-&\t-\\\\\t\nElec. Conductivity&\t$\\mu$ S/cm&\t1000&\t1000&\t-&\t-&\t6000&\t-\\\\\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Design and Development of an Autonomous Surface Vehicle for Water Quality Monitoring", "authors": ["MM Rashid", "Rupal Roy", "Md Manjurul Ahsan", "Zahed Siddique"], "url": "https://arxiv.org/abs/2201.10685v1", "attribution": "\"Design and Development of an Autonomous Surface Vehicle for Water Quality Monitoring\" by MM Rashid, Rupal Roy, Md Manjurul Ahsan, and Zahed Siddique, arXiv:2201.10685v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.11088v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Individual performance allowing hyperparameters to vary within environment/taskc; ``dog-trot', $n=3$. Figures are mean normalised scores, with 0 and 100 representing random and expert policies, respectively. Highest score highlighted in bold }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n Environment -dataset & & & & \\\\\n \\midrule\n DogTrot (BCQ) & $\\tau=0.025$ & $\\tau=0.05$ & $\\tau=0.1$ & $\\tau=0.25$ \\\\\n -expert & 57.8 & 85 & 93.6 & \\textbf{94.4} \\\\\n -medium-expert & 3.9 & 10.1 & 42.7 & \\textbf{74.9} \\\\\n -medium & 39.8 & 38.8 & 34.2 & \\textbf{49.1} \\\\\n -random-medium-expert & 5 & 5.6 & \\textbf{9} & 0.1 \\\\\n \\midrule\n DogTrot (CQL) & $\\alpha=0.25$ & $\\alpha=0.5$ & $\\alpha=1$ & $\\alpha=2$ \\\\\n -expert & 90.8 & 95.7 & 99.5 & \\textbf{100.2} \\\\\n -medium-expert & 76.6 & 81.7 & \\textbf{84.8} & 75.1 \\\\\n -medium & \\textbf{50.6} & 48.3 & 46.5 & 45.2 \\\\\n -random-medium-expert & 41.2 & 40.8 & \\textbf{43.4} & 38.6 \\\\\n \\midrule\n DogTrot (IQL $\\tau=0.5$) & $\\lambda=1$ & $\\lambda=2$ & $\\lambda=5$ & $\\lambda=10$ \\\\\n -expert & 37.9 & 82.5 & 98.9 & \\textbf{99.5} \\\\\n -medium-expert & 33 & 64 & 89.3 & \\textbf{98.6} \\\\\n -medium & \\textbf{58.8} & 56.5 & 52 & 47.3 \\\\\n -random-medium-expert & 10.6 & 28.6 & 44.1 & \\textbf{44.7} \\\\\n \\midrule\n DogTrot (OneStep) & $\\lambda=1$ & $\\lambda=2$ & $\\lambda=5$ & $\\lambda=10$ \\\\\n -expert & 53.3 & 91.7 & 101.2 & \\textbf{102} \\\\\n -medium-expert & 44.5 & 79.4 & 93.9 & \\textbf{96.6} \\\\\n -medium & \\textbf{59.3} & 57.5 & 50.2 & 48 \\\\\n -random-medium-expert & 23.8 & 43.9 & 44.9 & \\textbf{45.1} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces", "authors": ["Alex Beeson", "David Ireland", "Giovanni Montana"], "url": "https://arxiv.org/abs/2411.11088v1", "attribution": "\"An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces\" by Alex Beeson, David Ireland, and Giovanni Montana, arXiv:2411.11088v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2102.00167v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|rrr|rrr||rrr|rrr}\n &\\multicolumn{3}{c|}{Max \\# transplants} & \\multicolumn{3}{c}{Max total weight} & \\multicolumn{3}{c|}{Max \\# transplants} & \\multicolumn{3}{c}{Max total weight} \\\\\n $|V|$ & Core & Compet. & S.Core &Core & Compet. & S.Core & Core & Compet. & S.Core &Core & Compet. & S.Core \\\\\n \\cline{1-13}\n &\\multicolumn{6}{c|}{\\bf Strict preferences}&\\multicolumn{6}{c}{\\bf Weak preferences}\\\\\n20\t&\t0.00\t&\t0.03\t&\t0.01\t&\t0.00\t&\t0.02\t&\t0.01\t&\t0.00\t&\t0.04\t&\t0.01\t&\t0.00\t&\t0.03\t&\t0.01\t\\\\\n30\t&\t0.03\t&\t0.13\t&\t0.04\t&\t0.02\t&\t0.11\t&\t0.03\t&\t0.02\t&\t0.28\t&\t0.04\t&\t0.02\t&\t0.17\t&\t0.03\t\\\\\n40\t&\t0.08\t&\t0.48\t&\t0.12\t&\t0.06\t&\t0.25\t&\t0.11\t&\t0.09\t&\t0.63\t&\t0.10\t&\t0.06\t&\t0.44\t&\t0.08\t\\\\\n50\t&\t0.24\t&\t1.74\t&\t0.38\t&\t0.16\t&\t0.58\t&\t0.34\t&\t0.20\t&\t2.15\t&\t0.25\t&\t0.17\t&\t1.06\t&\t0.21\t\\\\\n60\t&\t0.47\t&\t2.39\t&\t0.87\t&\t0.28\t&\t0.91\t&\t0.79\t&\t0.52\t&\t6.03\t&\t0.44\t&\t0.26\t&\t2.87\t&\t0.39\t\\\\\n70\t&\t1.06\t&\t3.91\t&\t1.94\t&\t0.66\t&\t2.29\t&\t1.50\t&\t0.84\t&\t16.99\t&\t1.09\t&\t0.53\t&\t7.35\t&\t0.77\t\\\\\n80\t&\t1.62\t&\t6.54\t&\t3.26\t&\t0.82\t&\t3.39\t&\t2.32\t&\t1.41\t&\t32.21\t&\t1.63\t&\t0.76\t&\t17.47\t&\t1.01\t\\\\\n90\t&\t3.14\t&\t36.34\t&\t5.31\t&\t3.27\t&\t5.38\t&\t3.59\t&\t3.29\t&\t167.15\t&\t2.36\t&\t1.82\t&\t80.88\t&\t1.49\t\\\\\n100\t&\t3.53\t&\t16.19\t&\t19.26\t&\t2.43\t&\t6.15\t&\t9.81\t&\t4.51\t&\t188.35\t&\t8.87\t&\t3.08\t&\t95.39\t&\t4.62\t\\\\\n110\t&\t8.73\t&\t21.42\t&\t28.26\t&\t4.97\t&\t9.01\t&\t13.79\t&\t6.68\t&\t331.64\t&\t16.40\t&\t5.92\t&\t159.12\t&\t7.24\t\\\\\n120\t&\t17.84\t&\t72.87\t&\t57.36\t&\t6.81\t&\t15.36\t&\t24.32\t&\t20.14\t&\t392.88\t&\t19.60\t&\t6.79\t&\t218.58\t&\t10.87\t\\\\\n130\t&\t14.34\t&\t46.92\t&\t84.49\t&\t14.24\t&\t22.68\t&\t34.11\t&\t14.78\t&\t586.27\t&\t21.75\t&\t12.32\t&\t438.23\t&\t10.42\t\\\\\n140\t&\t29.50\t&\t61.99\t&\t110.82\t&\t21.51\t&\t34.33\t&\t46.67\t&\t41.59\t&\t708.92\t&\t40.97\t&\t16.43\t&\t539.56\t&\t14.89\t\\\\\n150\t&\t41.99\t&\t161.10\t&\t214.32\t&\t30.66\t&\t52.61\t&\t70.77\t&\t57.13\t&\t786.43\t&\t61.79\t&\t27.82\t&\t682.99\t&\t23.91\t\\\\\n \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Average CPU time (in seconds) for solving an instance of a given size with the proposed formulation.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Shapley-Scarf Housing Markets: Respecting Improvement, Integer Programming, and Kidney Exchange", "authors": ["Péter Biró", "Flip Klijn", "Xenia Klimentova", "Ana Viana"], "url": "https://arxiv.org/abs/2102.00167v1", "attribution": "\"Shapley-Scarf Housing Markets: Respecting Improvement, Integer Programming, and Kidney Exchange\" by Péter Biró, Flip Klijn, Xenia Klimentova, and Ana Viana, arXiv:2102.00167v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.02612v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Spoken Question Answering results.} Results for baselines are taken from .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\\toprule\n & \\textbf{Modality} & \\textbf{\\# Params} & \\textbf{Web Questions} & \\textbf{Llama Questions} & \\textbf{TriviaQA} \\\\ \\midrule\nTWIST & S$\\rightarrow$S & 7B & 1.5 & 4.0 & - \\\\\nSpeechGPT & S$\\rightarrow$T & 7B & 6.5 & 21.6 & 14.8 \\\\\nSpectron & S$\\rightarrow$T & 1B & 6.1 & 21.9 & - \\\\\nMoshi & S$\\rightarrow$T & 7B & 26.6 & 62.3 & 22.8 \\\\\nMoshi & S$\\rightarrow$S & 7B & 9.2 & 21.0 & 7.3 \\\\\n\\midrule\nGLM-4-Voice & S$\\rightarrow$T & 9B & \\textbf{32.2} & \\textbf{64.7} & \\textbf{39.1} \\\\ \nGLM-4-Voice & S$\\rightarrow$S & 9B & 15.9 & 50.7 & 26.5 \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot", "authors": ["Aohan Zeng", "Zhengxiao Du", "Mingdao Liu", "Kedong Wang", "Shengmin Jiang", "Lei Zhao", "Yuxiao Dong", "Jie Tang"], "url": "https://arxiv.org/abs/2412.02612v1", "attribution": "\"GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot\" by Aohan Zeng, Zhengxiao Du, Mingdao Liu, Kedong Wang, Shengmin Jiang, Lei Zhao, Yuxiao Dong, and Jie Tang, arXiv:2412.02612v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table26.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\nLink $(i,j)$ & $L_{ij}$ & $W_{ij}$ & $x_{ij}$ \\\\\n\\hline\n(1,2) & 1 & 1 & 1000 \\\\\n(1,3) & 1 & 1 & 1368 \\\\\n(2,3) & 0 & 0 & 0 \\\\\n(2,4) & 1 & 2 & 2000 \\\\\n(3,2) & 1 & 1 & 1000 \\\\\n(3,5) & $e^{-1}$ & $e^{-1}$ & 368 \\\\\n(4,5) & 1 & 2 & 2000 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.05044v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance Metrics of Pathfinding Algorithms}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Algorithm} & \\textbf{Accuracy} & \\textbf{Precision} & \\textbf{F1 Score} \\\\\n\\midrule\nAutoencoder & 0.98 & 0.97 & 0.97 \\\\\nTransformer & 0.98 & 0.97 & 0.98 \\\\\nGRU & 0.98 & 0.97 & 0.97 \\\\\nLSTM & 0.98 & 0.97 & 0.97 \\\\\nMLP & 0.98 & 0.96 & 0.97 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Deep Heuristic Learning for Real-Time Urban Pathfinding", "authors": ["Mohamed Hussein Abo El-Ela", "Ali Hamdi Fergany"], "url": "https://arxiv.org/abs/2411.05044v1", "attribution": "\"Deep Heuristic Learning for Real-Time Urban Pathfinding\" by Mohamed Hussein Abo El-Ela and Ali Hamdi Fergany, arXiv:2411.05044v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.16175v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{p-values of out-of-sample performance for top strategies (2010--2020).} Each entry shows the p-value for the null hypothesis that the Sharpe ratio of the column strategy is greater than that of the row strategy.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n& CTRL & min\\_v & drmv & pmv \\\\\n\\midrule\nCTRL & -- & 0.13 & 0.44 & 0.17 \\\\\nmin\\_v & 0.87 & -- & 0.94 & 0.63 \\\\\ndrmv & 0.56 & 0.06 & -- & 0.36 \\\\\npmv & 0.83 & 0.37 & 0.64 & -- \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study", "authors": ["Yilie Huang", "Yanwei Jia", "Xun Yu Zhou"], "url": "https://arxiv.org/abs/2412.16175v2", "attribution": "\"Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study\" by Yilie Huang, Yanwei Jia, and Xun Yu Zhou, arXiv:2412.16175v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.10096v2_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small Experiment Legend}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\n\\hline\n\\textbf{Expt.} & \\textbf{Model} & \\textbf{Opt.} & \\textbf{Loss} \\\\ \\hline\n\\textit{E1} & 1-Layer MLP & Adam & BCE \\\\ \\hline\n\\textit{E2} & 1-Layer MLP & Adam & MSE \\\\ \\hline\n\\textit{E3} & 2-Layer MLP & Adam & BCE \\\\ \\hline\n\\textit{E4} & 2-Layer MLP & Adam & MSE \\\\ \\hline\n\\textit{E5} & AutoInt & Adam & BCE \\\\ \\hline\n\\textit{E6} & AutoInt & Adam & MSE \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions", "authors": ["Anand Brahmbhatt", "Mohith Pokala", "Rishi Saket", "Aravindan Raghuveer"], "url": "https://arxiv.org/abs/2310.10096v2", "attribution": "\"LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions\" by Anand Brahmbhatt, Mohith Pokala, Rishi Saket, and Aravindan Raghuveer, arXiv:2310.10096v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.17971v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Simulation results for the mean and standard deviation (in parentheses) of the classification errors obtained with 200 repetitions of the experiment for different sample sizes and zero error variance. PC$_1$, first principal component; PC$_m$, principal component with lowest kurtosis coefficient; IC$_q$, $q$th independent component (minimal kurtosis); SIC$_q$, $q$th smoothed independent component; PCA, principal component analysis whitening; PCA-cor, principal component analysis correlated whitening; ZCA, zero-phase component analysis or Mahalanobis whitening; ZCA-cor, zero-phase component analysis or Mahalanobis correlated whitening; Cholesky, Cholesky whitening.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n & & \\multicolumn{5}{l}{Results (\\%) for the centroid classifiers:} \\\\ \n \\cmidrule{2-7}\nData & $n_k$ & PC$_1$ & PC$_m$ & Whitening & IC$_q$ & SIC$_q$ \\\\ \n \\midrule\n & & Scenario I (Gaussian) \\\\ \nExample 1 & 30 & 45.49 (3.295) & 6.167 (10.43) & PCA & 2.583 (3.698) & 2.520 (5.193) \\\\ \n & & & & PCA-cor & 2.583 (3.698) & 2.540 (5.869) \\\\ \n & & & & ZCA & 2.592 (3.709) & 1.590 (2.632) \\\\ \n & & & & ZCA-cor & 2.592 (3.709) & 1.955 (3.234) \\\\ \n & & & & Cholesky & 2.575 (3.702) & 1.219 (2.083) \\\\ \n & 50 & 45.47 (3.382) & 3.985 (7.754) & PCA & 0.970 (1.662) & 0.588 (1.058) \\\\ \n & & & & PCA-cor & 0.970 (1.662) & 0.885 (2.122) \\\\ \n & & & & ZCA & 0.965 (1.661) & 0.531 (1.051) \\\\ \n & & & & ZCA-cor & 0.970 (1.671) & 0.678 (1.066) \\\\ \n & & & & Cholesky & 0.965 (1.661) & 0.490 (1.266) \\\\ \n Example 2 & 30 & 44.83 (4.103) & 17.35 (15.10) & PCA & 4.500 (4.971) & 1.716 (2.318) \\\\ \n & & & & PCA-cor & 4.492 (4.960) & 1.766 (2.411) \\\\ \n & & & & ZCA & 4.475 (4.969) & 0.876 (1.431) \\\\ \n & & & & ZCA-cor & 4.475 (4.969) & 0.903 (1.455) \\\\ \n & & & & Chol & 4.492 (4.966) & 1.688 (2.342) \\\\ \n & 50 & 45.98 (3.047) & 11.35 (11.32) & PCA & 1.750 (2.198) & 0.758 (1.308) \\\\ \n & & & & PCA-cor & 1.755 (2.200) & 0.969 (1.451) \\\\ \n & & & & ZCA & 1.755 (2.200) & 0.397 (0.790) \\\\ \n & & & & ZCA-cor & 1.750 (2.203) & 0.420 (0.787) \\\\ \n & & & & Cholesky & 1.740 (2.202) & 0.942 (1.517) \\\\ \n Example 3 & 30 & 45.16 (3.810) & 2.975 (4.474) & PCA & 3.525 (4.031) & 1.667 (2.472) \\\\ \n & & & & PCA-cor & 3.533 (4.026) & 1.988 (4.355) \\\\ \n & & & & ZCA & 3.533 (4.026) & 2.264 (4.476) \\\\ \n & & & & ZCA-cor& 3.525 (4.018) & 2.004 (4.214) \\\\ \n & & & & Cholesky & 3.517 (4.023) & 1.286 (2.117) \\\\ \n & 50 & 45.54 (3.230) & 1.910 (1.560) & PCA & 1.530 (1.974) & 0.941 (1.541) \\\\ \n & & & & PCA-cor & 1.535 (1.974) & 1.129 (1.775) \\\\ \n & & & & ZCA & 1.535 (1.974) & 1.043 (1.700) \\\\ \n & & & & ZCA-cor & 1.535 (1.974) & 0.867 (1.505) \\\\ \n & & & & Cholesky & 1.530 (1.974) & 0.663 (1.044) \\\\\n & & \\\\ \n \\midrule\n & & Scenario II (non-Gaussian) \\\\ \nExample 1 & 30 & 45.40 (3.592) & 6.742 (10.45) & PCA & 3.917 (3.967) & 3.909 (5.068) \\\\ \n & & & & PCA-cor & 3.917 (3.967) & 4.167 (5.950) \\\\ \n & & & & ZCA & 3.900 (3.970) & 3.438 (2.643) \\\\ \n & & & & ZCA-cor & 3.900 (3.970) & 3.452 (2.602) \\\\ \n & & & & Cholesky & 3.908 (3.967) & 3.563 (2.543) \\\\ \n & 50 & 45.97 (3.271) & 4.875 (9.073) & PCA & 3.075 (3.547) & 2.920 (3.190) \\\\ \n & & & & PCA-cor & 3.075 (3.547) & 2.819 (2.889) \\\\ \n & & & & ZCA & 3.075 (3.547) & 3.010 (3.448) \\\\ \n & & & & ZCA-cor & 3.075 (3.547) & 3.071 (3.936) \\\\ \n & & & & Cholesky & 3.075 (3.547) & 2.678 (2.864) \\\\ \n Example 2 & 30 & 45.208 (4.058) & 7.45 (10.58) & PCA & 8.250 (9.897) & 8.349 (10.48) \\\\ \n & & & & PCA-cor & 8.250 (9.897) & 8.063 (10.77) \\\\ \n & & & & ZCA & 8.250 (9.889) & 7.155 (9.419) \\\\ \n & & & & ZCA-cor & 8.242 (9.892) & 6.800 (9.561) \\\\ \n & & & & Cholesky & 8.233 (9.897) & 7.343 (9.434) \\\\ \n & 50 & 46.29 (2.748) & 7.805 (11.97) & PCA & 7.145 (10.48) & 6.053 (8.575) \\\\ \n & & & & PCA-cor & 7.150 (10.50) & 5.840 (8.591) \\\\ \n & & & & ZCA & 7.155 (10.50) & 6.694 (10.56) \\\\ \n & & & & ZCA-cor & 7.155 (10.50) & 6.640 (10.69) \\\\ \n & & & & Cholesky & 7.150 (10.48) & 6.075 (8.519) \\\\ \n Example 3 & 30 & 45.15 (4.049) & 10.92 (11.87) & PCA & 6.858 (7.936) & 5.951 (6.266) \\\\ \n & & & & PCA-cor & 6.858 (7.936) & 6.516 (7.232) \\\\ \n & & & & ZCA & 6.867 (7.934) & 7.060 (8.419) \\\\ \n & & & & ZCA-cor & 6.867 (7.934) & 6.425 (7.626) \\\\ \n & & & & Cholesky & 6.858 (7.936) & 5.654 (6.642) \\\\ \n & 50 & 45.94 (3.092) & 10.92 (11.69) & PCA & 5.085 (6.098) & 4.832 (5.616) \\\\ \n & & & & PCA-cor & 5.085 (6.098) & 5.119 (6.249) \\\\ \n & & & & ZCA & 5.095 (6.120) & 4.935 (6.120) \\\\ \n & & & & ZCA-cor & 5.100 (6.119) & 4.873 (5.696) \\\\ \n & & & & Cholesky & 5.095 (6.095) & 4.173 (4.228) \\\\ \n\\midrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Functional independent component analysis by choice of norm: a framework for near-perfect classification", "authors": ["Marc Vidal", "Marc Leman", "Ana M. Aguilera"], "url": "https://arxiv.org/abs/2412.17971v2", "attribution": "\"Functional independent component analysis by choice of norm: a framework for near-perfect classification\" by Marc Vidal, Marc Leman, and Ana M. Aguilera, arXiv:2412.17971v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10524v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\hline\n\\textbf{Domain} & \\textbf{\\# intents} & \\textbf{\\# slots} & \\textbf{\\# utterances}\\\\\nWeather & 2 & 4 & 3692\\\\\nDevice & 17 & 6 & 2112\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{CSTOP Statistics}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing", "authors": ["Arash Einolghozati", "Abhinav Arora", "Lorena Sainz-Maza Lecanda", "Anuj Kumar", "Sonal Gupta"], "url": "https://arxiv.org/abs/2101.10524v3", "attribution": "\"El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing\" by Arash Einolghozati, Abhinav Arora, Lorena Sainz-Maza Lecanda, Anuj Kumar, and Sonal Gupta, arXiv:2101.10524v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2207.13319v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rrrrrrrrrrrr}\n\\toprule\nCovariance & \\multicolumn{4}{|c|}{$\\alpha$} & \n\\multicolumn{4}{|c|}{$\\beta_{PDR}$} & \\multicolumn{4}{|c}{$\\gamma$}\\\\\nEstimation & CC & FL & CRE & CI & CC & FL & CRE & CI & CC & FL & CRE & CI \\\\\n\\midrule\nbank clustered & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & & & & \\\\\ntime clustered & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & & & & \\\\ \\hline\nbank clustered & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\\\\ntime clustered & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Should Bank Stress Tests Be Fair?", "authors": ["Paul Glasserman", "Mike Li"], "url": "https://arxiv.org/abs/2207.13319v2", "attribution": "\"Should Bank Stress Tests Be Fair?\" by Paul Glasserman and Mike Li, arXiv:2207.13319v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12099v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison of DeepWORD and logic model method.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\hline\\noalign{\\smallskip}\nMethods & Easy & Hard & Mixed \\\\\n\\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\nLogic model & 92.91 & 71.83 & 79.90 \\\\\nDeepWORD & \\textbf{99.17} & \\textbf{94.35} & \\textbf{95.14} \\\\\n\\noalign{\\smallskip}\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Detecting Owner-member Relationship with Graph Convolution Network in Fisheye Camera System", "authors": ["Zizhang Wu", "Jason Wang", "Tianhao Xu", "Fan Wang"], "url": "https://arxiv.org/abs/2201.12099v1", "attribution": "\"Detecting Owner-member Relationship with Graph Convolution Network in Fisheye Camera System\" by Zizhang Wu, Jason Wang, Tianhao Xu, and Fan Wang, arXiv:2201.12099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.10258v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison on \\textbf{FakeParaEgg}. (A) single domain tests, (B) \\textcolor{blue}{out-of-domain generalizability tests} }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|c|c|c|c|c|}\n \\hline\n & Method & mF1 & mIoU & Prec. & Recall & Spec. \\\\\n \\hline\n & U-Net & .749 & .616 & .770 & .855 & .9934 \\\\\n (A) & RRU-Net & .777 & .652 & .788 & .898 & .9938\\\\\n & UCM-Net & .713 & .586 & .782 & .867 & .9937\\\\\n \n & Ours & \\textcolor{blue}{\\textbf{.922}} & \\textcolor{blue}{\\textbf{.885}} & .878 & .970 & .9972\\\\ \\hline \\hline\n \n & {ManTra-Net} & .099 & .060 & .094 & .280 & .9302\\\\\n & {DOA-GAN} & .390 & .315 & .792 & .603 & .9959\\\\\n (B) & {UCM-Net} & .478 & .396 & .714 & .742 & .9923\\\\\n & Ours & \\textcolor{blue}{\\textbf{.739}} & \\textcolor{blue}{\\textbf{.582}} & .828 & .667 & .9962\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Copy-Move Detection in Optical Microscopy: A Segmentation Network and A Dataset", "authors": ["Hao-Chiang Shao", "Yuan-Rong Liao", "Tse-Yu Tseng", "Yen-Liang Chuo", "Fong-Yi Lin"], "url": "https://arxiv.org/abs/2412.10258v1", "attribution": "\"Copy-Move Detection in Optical Microscopy: A Segmentation Network and A Dataset\" by Hao-Chiang Shao, Yuan-Rong Liao, Tse-Yu Tseng, Yen-Liang Chuo, and Fong-Yi Lin, arXiv:2412.10258v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03788v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dataset Sample}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|} \n \\hline\n Model & Year & Battery & Price & Miles \\\\ \n \\hline\n Model S & 2013 & Base & 34200 & 36800 \\\\ \n \\hline\n Model 3 & 2018 & 75 & 46995 & 2193 \\\\ \n \\hline\n Model S & 2018 & 75D & 64900 & 1095 \\\\ \n \\hline\n Model X & 2016 & P90D & 84984 & 20680 \\\\ \n \\hline\n Model S & 2016 & 75D & 58989 & 20303 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Second Hand Price Prediction for Tesla Vehicles", "authors": ["Sayed Erfan Arefin"], "url": "https://arxiv.org/abs/2101.03788v1", "attribution": "\"Second Hand Price Prediction for Tesla Vehicles\" by Sayed Erfan Arefin, arXiv:2101.03788v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11224v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics analysis for model trained by one frame only.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|cccccccc}\n \\hline\n \\textbf{Frame} & \\textbf{Criterion(cm)} & \\textbf{Mean$\\pm$ std} & \\textbf{min} & \\textbf{Medium}\n \\\\\n \\hline\n ED & LDE & 1.59$\\pm$1.85 & 0.04 & 0.95 \\\\\n & LE & 1.04$\\pm$1.20 & 0.02 & 0.68 \\\\\n \\hline\n ES & LDE & 1.76$\\pm$1.49 & 0.10 & 1.34 \\\\\n & LE & 1.77$\\pm$1.39 & 0.01 & 1.49\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Reciprocal Landmark Detection and Tracking with Extremely Few Annotations", "authors": ["Jianzhe Lin", "Ghazal Sahebzamani", "Christina Luong", "Fatemeh Taheri Dezaki", "Mohammad Jafari", "Purang Abolmaesumi", "Teresa Tsang"], "url": "https://arxiv.org/abs/2101.11224v1", "attribution": "\"Reciprocal Landmark Detection and Tracking with Extremely Few Annotations\" by Jianzhe Lin, Ghazal Sahebzamani, Christina Luong, Fatemeh Taheri Dezaki, Mohammad Jafari, Purang Abolmaesumi, and Teresa Tsang, arXiv:2101.11224v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.14939v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\MakeUppercase{Spatial characterization for test scenarios}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|llll|}\n\t\t\t\\hline\n\t\t\t& m & M & \\(\\nu\\) \\\\\n\t\t\t\\hline\n\t\t\tSensors & \\(1.0\\) & \\(10.0\\) & \\(4.0\\) \\\\\n\t\t\t\\hline\n\t\t\tActuators (nominal) & \\(1.0\\) & \\(0.0\\) & \\(4.0\\) \\\\\n\t\t\tActuators (realistic) & \\(1.0\\) & \\(30.0\\) & \\(4.0\\) \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Modeling of a multiple source heating plate", "authors": ["Stephan Scholz", "Lothar Berger"], "url": "https://arxiv.org/abs/2011.14939v1", "attribution": "\"Modeling of a multiple source heating plate\" by Stephan Scholz and Lothar Berger, arXiv:2011.14939v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11864v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\nPrediction/Truth & Healthy (Negative) & Malignant (Positive) \\\\\n\\hline\n Healthy (Negative) & 26& 0 \\\\ \\hline\n Malignant (Positive)& 0 & 26 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The ALL-IDB2 confusion matrix for the validation data.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features", "authors": ["William Franz Lamberti"], "url": "https://arxiv.org/abs/2201.11864v1", "attribution": "\"Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features\" by William Franz Lamberti, arXiv:2201.11864v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08030v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\n\\textbf{HopSkipJump} & \\textbf{1} & \\textbf{2} & \\textbf{3} & \\textbf{4} & \\textbf{5}\\\\\n\\hline\nSuccess Rate (\\%) & 0 & 100 & 100 & 100 & 100\\\\\n\\hline\n\\% of Unrealistic Values & - & 87.0 & 0 & 0 & 0\\\\\n\\hline\n\\# Checked Fields & - & - & 554 & 465 & 418\\\\\n\\hline\n\\# Non-Editable Fields & - & - & - & 159 & 0 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results obtained with each experiment configuration for HopSkipJump attack}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data", "authors": ["Francesco Cartella", "Orlando Anunciacao", "Yuki Funabiki", "Daisuke Yamaguchi", "Toru Akishita", "Olivier Elshocht"], "url": "https://arxiv.org/abs/2101.08030v1", "attribution": "\"Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data\" by Francesco Cartella, Orlando Anunciacao, Yuki Funabiki, Daisuke Yamaguchi, Toru Akishita, and Olivier Elshocht, arXiv:2101.08030v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00746v5_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experimental performance on 196 intersections in New York}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n \\toprule \n \\textbf{Model} & \\textbf{real} & \\textbf{mixed$_l$} & \\textbf{mixed$_h$} & mean \\\\\n \\midrule\n Random & 1869.00 & 1748.76 & 1892.73 & 1836.83 \\\\\n MaxPressure & 845.72 & 589.24 & 920.31 & 785.09 \\\\\n Fixedtime & 1831.65 & 1742.71 & 1205.97 & 1593.44 \\\\\n FixedtimeOffset & 1702.82 & 1732.37 & 1869.66 & 1768.28 \\\\\n\tSlidingFormula & 1185.64 & 921.28 & 1299.19 & 1135.37 \\\\\n SOTL & 1862.34 & 1793.37 & 1939.53 & 1865.08 \\\\\n \\midrule\n Individual RL & 1877.46 & 1369.64 & 1906.43 & 1717.84 \\\\\n MetaLight & 1285.74 & 1005.36 & 1368.96 & 1220.02 \\\\\n PressLight & 1586.75 & 1547.74 & 1685.74 & 1606.74 \\\\\n CoLight & 637.79 & 479.31 & 761.10 & 626.07 \\\\\n\tGeneraLight & 710.23 & 485.84 & 862.47 & 686.18 \\\\\n \\midrule \n MetaVIM & \\textbf{586.36} & \\textbf{396.57} & \\textbf{748.65} & \\textbf{577.19} \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control", "authors": ["Liwen Zhu", "Peixi Peng", "Zongqing Lu", "Xiangqian Wang", "Yonghong Tian"], "url": "https://arxiv.org/abs/2101.00746v5", "attribution": "\"MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control\" by Liwen Zhu, Peixi Peng, Zongqing Lu, Xiangqian Wang, and Yonghong Tian, arXiv:2101.00746v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2103.02654v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{NN Architectures used in our approach (CNN-based).}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\textbf{Name} & \\textbf{Encoder NN} & \\textbf{Decoder NN} & \\textbf{Generative Network} \\\\ \\hline\n\\multirow{4}{*}{\\textbf{Layer}} & \\textit{FC+eLU} & \\textit{Conv2d+ReLU} & \\textit{Conv1d+ReLU+BN} \\\\ \\cline{2-4} \n & \\textit{Conv1d+ReLU+Flatten} & \\textit{Conv2d+ReLU+Flatten} & \\textit{Cov1d+ReLU+BN+Flatten} \\\\ \\cline{2-4} \n & \\textit{FC+Linear} & \\textit{FC+ReLU} & \\textit{FC+Linear} \\\\ \\cline{2-4} \n & \\textit{Normalization ($\\ell_{2}$)} & \\textit{FC+Softmax} & \\textit{Normalization ($\\ell_{2}$)} \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Robust Adversarial Network-Based End-to-End Communications System With Strong Generalization Ability Against Adversarial Attacks", "authors": ["Yudi Dong", "Huaxia Wang", "Yu-Dong Yao"], "url": "https://arxiv.org/abs/2103.02654v1", "attribution": "\"A Robust Adversarial Network-Based End-to-End Communications System With Strong Generalization Ability Against Adversarial Attacks\" by Yudi Dong, Huaxia Wang, and Yu-Dong Yao, arXiv:2103.02654v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.07016v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ The parameters of the considered instance in Example }.\n\\begin{tabular}{ccccccc}\n\\hline\ni& $A_i=(a_i,b_i)$& $w_i$& $c_{i1}^{+}$&$c_{i2}^{+}$&$c_{i1}^{-}$&$c_{i2}^{-}$\\\\\n\\hline\n1& $(1,0)$&6&$\\sqrt{2}$&5&$1$&$6$\\\\\n2& $(-5,3)$&3&7&3&4&2\\\\ % \\\\\n3& $ (7,2)$&1&1&2&4&4\\\\\n4& $(0,-0.5)$&2&2&1&4&1\\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Inverse single facility location problem in the plane with variable coordinates", "authors": ["Nazanin Tour-Savadkoohi", "Jafar Fathali"], "url": "https://arxiv.org/abs/2503.07016v1", "attribution": "\"Inverse single facility location problem in the plane with variable coordinates\" by Nazanin Tour-Savadkoohi and Jafar Fathali, arXiv:2503.07016v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2203.12460v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|}\n\\hline\n & (1) & (2) & (3) & (4) & (5) \\\\ \\hline\nVariables & Model 1 & Model 2 & Model 3 & Model 4 & Model 5 \\\\ \\hline\n & & & & & \\\\ \\hline\npositive & 0.17*** & 0.17*** & 0.17*** & 0.15*** & 0.13* \\\\ \\hline\n & (0.03) & (0.03) & (0.03) & (0.04) & (0.06) \\\\ \\hline\nnegative & -0.11 & -0.11 & -0.09 & -0.07 & 0.19 \\\\ \\hline\n & (0.16) & (0.16) & (0.16) & (0.16) & (0.27) \\\\ \\hline\nanxiety & 0.10 & 0.10 & -0.28 & -0.32 & -1.05* \\\\ \\hline\n & (0.29) & (0.29) & (0.30) & (0.30) & (0.51) \\\\ \\hline\nanger & -0.09 & -0.08 & 0.16 & 0.16 & 0.54 \\\\ \\hline\n & (0.31) & (0.31) & (0.32) & (0.32) & (0.54) \\\\ \\hline\nsad & -0.43 & -0.47* & -0.54* & -0.58* & -0.48 \\\\ \\hline\n & (0.24) & (0.24) & (0.25) & (0.25) & (0.43) \\\\ \\hline\ncertain & 0.02 & 0.03 & 0.02 & 0.01 & 0.18 \\\\ \\hline\n & (0.09) & (0.09) & (0.10) & (0.10) & (0.16) \\\\ \\hline\n & & & & & \\\\ \\hline\nMAR(3 m) & & YES & YES & YES & YES \\\\ \\hline\nIndustry & & & YES & YES & YES \\\\ \\hline\nYear & & & & YES & YES \\\\ \\hline\nMAR(5 d) & & & & & YES \\\\ \\hline\nN & 10,663 & 10,663 & 10,663 & 10,663 & 4,071 \\\\ \\hline\nBIC & 14770 & 14802 & 14837 & 14873 & 5817 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Logistic Regression of Value Based Label Function ($y_I(T^c_d)(4)$) on Analysts' Recommendations within 3 months ($MAR(3\\,m)$) and Sentiment of Earnings Calls Transcripts. Standard errors in parentheses. Significance levels marked with stars: *** p<0.001, ** p<0.01, * p<0.05}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An Exploratory Study of Stock Price Movements from Earnings Calls", "authors": ["Sourav Medya", "Mohammad Rasoolinejad", "Yang Yang", "Brian Uzzi"], "url": "https://arxiv.org/abs/2203.12460v1", "attribution": "\"An Exploratory Study of Stock Price Movements from Earnings Calls\" by Sourav Medya, Mohammad Rasoolinejad, Yang Yang, and Brian Uzzi, arXiv:2203.12460v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.05428v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|} \n \\hline\n & Normal volumes & Normal slices & Abnormal volumes & Abnormal slices\\\\ \n \\hline\n Training & 119 & 16506 & - & - \\\\ \n \\hline\n Validation & 30 & 4126 &- &- \\\\\n \\hline\n Test & 30 & 4314 & 44 & 4805 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Distribution of the number of CT volumes and slices in this study.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Self-Supervised Out-of-Distribution Detection in Brain CT Scans", "authors": ["Abinav Ravi Venkatakrishnan", "Seong Tae Kim", "Rami Eisawy", "Franz Pfister", "Nassir Navab"], "url": "https://arxiv.org/abs/2011.05428v1", "attribution": "\"Self-Supervised Out-of-Distribution Detection in Brain CT Scans\" by Abinav Ravi Venkatakrishnan, Seong Tae Kim, Rami Eisawy, Franz Pfister, and Nassir Navab, arXiv:2011.05428v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.17129v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Pearson’s correlation test between MaFI scores and IWERs. ** ($p < 0.01$); ***($p < 0.001$). }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc||ccc} \n\\toprule\n\\textbf{Model} & \\multicolumn{3}{c||}{\\textbf{Auto-AVSR}} & \\multicolumn{3}{c}{\\textbf{AVEC}} \\\\ \n\\midrule\n\\textbf{SNR} & \\textbf{-5} & \\textbf{0} & \\textbf{5} & \\textbf{-5} & \\textbf{0} & \\textbf{5} \\\\ \n\\midrule\n\\textbf{\\textbf{AO}} & 0.002 & -0.021 & 0.039 & -0.019 & -0.020 & -0.052 \\\\\n\\textbf{VO} & \\multicolumn{3}{c||}{-0.097**} & \\multicolumn{3}{c}{-0.173***} \\\\\n\\textbf{\\textbf{AV}} & -0.040 & 0.055 & 0.035 & -0.151** & -0.150** & -0.080 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Uncovering the Visual Contribution in Audio-Visual Speech Recognition", "authors": ["Zhaofeng Lin", "Naomi Harte"], "url": "https://arxiv.org/abs/2412.17129v2", "attribution": "\"Uncovering the Visual Contribution in Audio-Visual Speech Recognition\" by Zhaofeng Lin and Naomi Harte, arXiv:2412.17129v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.01221v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n \\hline\n $\\lambda_{\\text{min}}$ \n & $\\lambda_{\\text{max}}$ & $\\Delta \\lambda$ & $h_{\\text{min}}$ & $h_{\\text{max}}$ & $\\Delta h$ \\\\\n \\hline\n $27$ & $216.6$ & $1$ & $0$ & $50$ & $0.5$ \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Meta-parameters for Algorithm .}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A stochastic Gordon-Loeb model for optimal cybersecurity investment under clustered attacks", "authors": ["Giorgia Callegaro", "Claudio Fontana", "Caroline Hillairet", "Beatrice Ongarato"], "url": "https://arxiv.org/abs/2505.01221v1", "attribution": "\"A stochastic Gordon-Loeb model for optimal cybersecurity investment under clustered attacks\" by Giorgia Callegaro, Claudio Fontana, Caroline Hillairet, and Beatrice Ongarato, arXiv:2505.01221v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.10218v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Median and IQR of the latent multi-ranks estimated through the eigenvalue ratio (ER) and the Growth Ratio (GR) for the 20 different simulations in the two scenarios for $30\\%$ and $50\\%$ of total entries missing.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|ccc|ccc}\n \n \\multicolumn{2}{}{} &\n \\multicolumn{3}{c}{ER} &\n \\multicolumn{3}{c}{GR} \\\\\n \n $(R_1^0, R_2^0, R_3^0)$ & NA (\\%) & $R_1$ & $R_2$ & $R_3$ &$R_1$ & $R_2$ &$R_3$ \\\\\n &&& median(IQR) &&& median(IQR)&\\\\\n \\hline\n (5,5,5) & 30 & 1(1) & 1(1) & 1(0.25)& 1.5(2)& 1(1) & 1(1)\\\\ \n & 50 & 1(0) & 1(0) & 1(1)& 1(0.25)& 1(0) & 1(0.25)\\\\ \n (10,7,3) & 30 & 1(1) & 1(1) & 1(0)& 1(1)& 1.5(1) & 1(0)\\\\\n & 50 & 1(1) & 1(1) & 1(0.25) & 1(1)& 1(1) & 1(0)\\\\\n \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian Adaptive Tucker Decompositions for Tensor Factorization", "authors": ["Federica Stolf", "Antonio Canale"], "url": "https://arxiv.org/abs/2411.10218v2", "attribution": "\"Bayesian Adaptive Tucker Decompositions for Tensor Factorization\" by Federica Stolf and Antonio Canale, arXiv:2411.10218v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23211v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of the model for scenario V with $T=500$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccccc}\n\\hline\nCoefficient & Truth & AB ($\\lfloor T\\hat{\\tau} \\rfloor$) & AB ($\\lfloor T\\tilde{\\tau} \\rfloor$) & RMSE ($\\lfloor T\\hat{\\tau} \\rfloor$) & RMSE ($\\lfloor T\\tilde{\\tau} \\rfloor$) & 90$\\%$ CP \n($\\lfloor T\\tilde{\\tau} \\rfloor$) & 95$\\%$ CP ($\\lfloor T \\tilde{\\tau} \\rfloor$) & 99$\\%$ CP ($\\lfloor T\\tilde{\\tau} \\rfloor$) \\\\\\hline\\hline\n& $\\lfloor T/3 \\rfloor$ & 16.690 & 17.640 & 38.977 & 39.789 & 0.890 & 0.920 & 0.960 \\\\\n$\\phi=-0.5$ & $\\lfloor T/2 \\rfloor$ & 10.840 & 9.490 & 20.429 & 18.639 & 0.930 & 0.960 & 0.970 \\\\\n& $\\lfloor 2T/3 \\rfloor$ & 8.260 & 8.390 & 13.854 & 14.798 & 0.910 & 0.950 & 0.990 \\\\\n& $\\lfloor 4T/5 \\rfloor$ & 8.500 & 7.550 & 14.310 & 11.985 & 0.890 & 0.920 & 0.960 \\\\\n\\hline\\hline\n& $\\lfloor T/3 \\rfloor$ & 15.100 & 14.070 & 30.370 & 29.970 & 0.890 & 0.900 & 0.950 \\\\\n$\\phi=0.5$ & $\\lfloor T/2 \\rfloor$ & 9.470 & 9.000 & 17.434 & 17.214 & 0.960 & 0.970 & 0.980 \\\\\n& $\\lfloor 2T/3 \\rfloor$ & 8.860 & 8.340 & 15.677 & 15.867 & 0.900 & 0.930 & 0.960 \\\\\n& $\\lfloor 4T/5 \\rfloor$ & 12.140 & 10.280 & 18.489 & 15.405 & 0.810 & 0.880 & 0.940 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models", "authors": ["Sepideh Mosaferi", "Abolfazl Safikhani", "Peiliang Bai"], "url": "https://arxiv.org/abs/2503.23211v2", "attribution": "\"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models\" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.01539v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n\\multirow{2}[2]{*}{Model} &\n\\multirow{2}[2]{*}{Method} & \n\\multicolumn{4}{c}{\\text{$\\epsilon=3$}} \\\\\n\\cmidrule(lr){3-6}\n& & $E=3$ & $E=10$ & $E=20$ & $E=30$ \\\\\n\\midrule\n\\multirow{2}{*}{RoBERTa-base} %\n& Flat clipping (tuned) & $88.70 (0.52)$ & $90.17 (1.10)$ & $91.47 (1.07)$ & $91.60 (0.95)$\\\\\n& Adaptive per-layer & $90.50 (1.21)$ & $91.90 (0.72)$ & $92.33 (0.42)$ & $92.23 (0.06)$ \\\\\n\\midrule\n\\multirow{2}{*}{RoBERTa-large} & Flat clipping (tuned) & $92.20 (0.26)$ & $93.07 (0.75)$ & $93.67 (0.40)$ & $94.23 (0.67)$ \\\\\n& Adaptive per-layer & $91.73 (0.59)$ & $93.27 (0.45)$ & $93.90 (0.26)$ & $94.13 (0.38)$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping", "authors": ["Jiyan He", "Xuechen Li", "Da Yu", "Huishuai Zhang", "Janardhan Kulkarni", "Yin Tat Lee", "Arturs Backurs", "Nenghai Yu", "Jiang Bian"], "url": "https://arxiv.org/abs/2212.01539v1", "attribution": "\"Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping\" by Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, and Jiang Bian, arXiv:2212.01539v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04773v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{}\n\\begin{tabular}{lll}\n \\toprule[1.5pt]\n Location & \\parbox[t]{2cm}{DE Iteration \\\\ Number} & \\parbox[t]{2.5cm}{One-time\\\\ Initialization Time}\\\\\n \\midrule\n Conference Room & 1 & 10.2 sec \\\\\n Dining Hall & 2 & 41.8 sec \\\\\n Gas Station & 2 & 18.4 sec \\\\\n \\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Practical Speech Re-use Prevention in Voice-driven Services", "authors": ["Yangyong Zhang", "Maliheh Shirvanian", "Sunpreet S. Arora", "Jianwei Huang", "Guofei Gu"], "url": "https://arxiv.org/abs/2101.04773v1", "attribution": "\"Practical Speech Re-use Prevention in Voice-driven Services\" by Yangyong Zhang, Maliheh Shirvanian, Sunpreet S. Arora, Jianwei Huang, and Guofei Gu, arXiv:2101.04773v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.05393v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Clothing Data sets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lr}\n \\toprule\n Name & Citation \\\\ \n \\midrule\n Fashionista data set & \\\\ \n Clothing Attributes Data set & \\\\ \n Paper Doll & \\\\ \n Color-Fashion Data set & \\\\ \n MVC Dataset & \\\\ \n Deep Fashion Data set & \\\\ \n Fashion-MNIST & \\\\ \n imat fashion & \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fine-grained Classification of Rowing teams", "authors": ["M. J. A. van Wezel", "L. J. Hamburger", "Y. Napolean"], "url": "https://arxiv.org/abs/1912.05393v1", "attribution": "\"Fine-grained Classification of Rowing teams\" by M. J. A. van Wezel, L. J. Hamburger, and Y. Napolean, arXiv:1912.05393v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10771v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|}\n \\hline\n Models & Put Price MAPE (\\%) \\\\ \n \\hline\\hline\n PINN & 10.8237 \\\\\n \\hline\n ConvLSTM & 4.0159 \\\\\n \\hline\n SA-ConvLSTM & 4.0352 \\\\\n \\hline\n ConvTF & 4.8325 \\\\\n \\hline\n PI-ConvTF & 2.9297 \\\\\n \\hline\n VAR & 6.7802 \\\\\n \\hline\n ARIMA & 23.4107 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Average put option price MAPE values across test dates for all models trained without outlier data. These inferred put option prices are derived from the call option prices previously estimated by our models. The MAPE values are computed after excluding outlier values of put options that fall below the $20^{th}$ percentile.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Physics-Informed Convolutional Transformer for Predicting Volatility Surface", "authors": ["Soohan Kim", "Seok-Bae Yun", "Hyeong-Ohk Bae", "Muhyun Lee", "Youngjoon Hong"], "url": "https://arxiv.org/abs/2209.10771v3", "attribution": "\"Physics-Informed Convolutional Transformer for Predicting Volatility Surface\" by Soohan Kim, Seok-Bae Yun, Hyeong-Ohk Bae, Muhyun Lee, and Youngjoon Hong, arXiv:2209.10771v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.01026v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccccccccccccccccccccccccccccccccc}\n$j$ & 1& 2& 3& 4& 5& 6& 7& 8& 9&10&11&12&13&14&15&16&17&18&19\\\\\n\\hline\n$a(j)$ & 1& 3& 4& 5& 7& 9&10&12&13&14&16&17&18&20&22&23&24&26&28\\\\\n$b(j)$ & 2& 6& 8&11&15&19&21&25&27&30&34&36&39&43&47&49&52&56&60\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The sequences $a(j)$ and $b(j)$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Narayana Morphism and Related Words", "authors": ["Jeffrey Shallit"], "url": "https://arxiv.org/abs/2503.01026v3", "attribution": "\"The Narayana Morphism and Related Words\" by Jeffrey Shallit, arXiv:2503.01026v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.10926v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\hline\n $M_a$ & Estimate & SE\\\\\n \\hline\n 1000& 0.3047400& 0.09359366\\\\\n 2000 &0.2962185& 0.12384585\\\\\n 5000& 0.2859627& 0.26879603\\\\\n 10000& 0.2915771& 0.21386340\\\\\n 20000& 0.2859134 &0.38467096\\\\\n 50000 &0.1124109 &2.40895732\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Change the number of null IVs from $\\mathbb{G}_a$ from 1000 to 50000 where the number of each other three groups is fixed to be 1000.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Random Effects Model-based Method of Moments Estimation of Causal Effect in Mendelian Randomization Studies", "authors": ["Wenhao Cao", "Saonli Basu"], "url": "https://arxiv.org/abs/2312.10926v1", "attribution": "\"A Random Effects Model-based Method of Moments Estimation of Causal Effect in Mendelian Randomization Studies\" by Wenhao Cao and Saonli Basu, arXiv:2312.10926v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.13915v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The MSD, MAD, and ABWD for S\\&P $500$ stock index dataset. The best bootstrapping technique with the least MSD, MAD, and ABWD is marked in bold.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\t\t\t\\hline\n\t\t\t& Bootstrap Methods & MSD & MAD & ABWD\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{3}{*}{Open Price}& \n\t\t\t$\\quad$NOBB$\\quad$ & $6946.36$ & $65.72$ & $18181.5$ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $4118.35$ & $48.47$ & $12993.9$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{2209.72}$ & $\\textbf{35.88}$ & $\\textbf{11545.4}$ \\\\\n\t\t\t\n\t\t\t\\hline \\multirow{3}{*}{High Price} & $\\quad$NOBB$\\quad$ & $6752.75$ & $65.76$ & $17270.2$ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $4073.18$ & $50.24$ & $12771.3$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{2788.50}$ & $\\textbf{42.34}$ & $\\textbf{11857.1}$ \\\\\n\t\t\t\n\t\t\t\\hline \\multirow{3}{*}{Low Price}& \n\t\t\t$\\quad$NOBB$\\quad$ & $8383.30$ & $73.70$ & $16886.9$ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $5736.40$ & $58.16$ & $15095.6$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{4027.22}$ & $\\textbf{50.35}$ & $\\textbf{11601.9}$ \\\\\n\t\t\t\n\t\t\t\\hline \\multirow{3}{*}{Closing Price} & $\\quad$NOBB$\\quad$ & $10198.64$ & $80.64$ & $15953.9$ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $8053.63$ & $72.07$ & $12183.8$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{6342.93}$ & $\\textbf{65.40}$ & $\\textbf{10855.8}$ \\\\\n\t\t\t\n\t\t\t\\hline \\multirow{3}{*}{Volume} & \n\t\t\t$\\quad$NOBB$\\quad$ & $4.33e+17\\quad$ & $\\textbf{4.36e+08}\\quad$ & $6.82e+10$ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $4.41e+17\\quad$ & $4.47e+08\\quad$ & $6.75e+10$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{6.50e+10}\\quad$ & $3.83e+17\\quad$ & $\\textbf{4.25e+08}$ \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network", "authors": ["Aryan Bhambu", "Arabin Kumar Dey"], "url": "https://arxiv.org/abs/2211.13915v1", "attribution": "\"Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network\" by Aryan Bhambu and Arabin Kumar Dey, arXiv:2211.13915v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.17633v4_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{GRL coefficients tests of IA-SSD, for \\textit{CS64} $\\rightarrow$ \\textit{CS16}.}\n\\begin{tabular}{c|ccc}\n $\\lambda$ & \\multicolumn{3}{c}{UADA3D/UADA3D\\textsubscript{$\\mathcal{L}m$}} \\\\ \\hline\n & $AP_{3D, V}$ & $AP_{3D, P}$ & $AP_{3D, C}$ \\\\ \\hline\n Source & 29.97 & 11.02 & 19.35 \\\\ \\hline\n 0.05 & 38.13/37.04 & 23.41/22.13 & 29.29/26.85 \\\\\n 0.1 & 42.96/\\textbf{48.48} & 25.48/25.08 & \\textbf{38.12}/\\textbf{37.91} \\\\\n 0.2 & \\textbf{43.24}/40.34 & 24.86/24.91 & 34.24/32.96 \\\\ \\hline\n $\\alpha=0.1$ & 42.76/41.06 & \\textbf{26.43}/\\textbf{26.80} & 33.77/31.83 \\\\\n $\\alpha=0.2$ & 40.14/38.11 & 24.55/24.7 & 34.22/30.01 \\\\\n $\\alpha=0.5$ & 40.24/37.55 & 23.58/21.42 & 33.16/27.43 \\\\\n $\\alpha=1$ & 41.65/40.09 & 23.68/22.58 & 31.72/29.39 \\\\ \\hline\n Oracle & 58.62 & 35.57 & 49.27 \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps", "authors": ["Maciej K Wozniak", "Mattias Hansson", "Marko Thiel", "Patric Jensfelt"], "url": "https://arxiv.org/abs/2403.17633v4", "attribution": "\"UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps\" by Maciej K Wozniak, Mattias Hansson, Marko Thiel, and Patric Jensfelt, arXiv:2403.17633v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.01291v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\nModel & Complexity$\\uparrow$ & Structure$\\uparrow$ & Musicality$\\uparrow$ \\\\ \\midrule\nOriginal & 3.22 & 3.47 & 3.56 \\\\ \\midrule\nInpaintNet & 2.98 & 3.01 & 3.09 \\\\ \\midrule\nSketchNet & 3.04 & 3.29 & 3.26 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results of the subjective listening test.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Music SketchNet: Controllable Music Generation via Factorized Representations of Pitch and Rhythm", "authors": ["Ke Chen", "Cheng-i Wang", "Taylor Berg-Kirkpatrick", "Shlomo Dubnov"], "url": "https://arxiv.org/abs/2008.01291v1", "attribution": "\"Music SketchNet: Controllable Music Generation via Factorized Representations of Pitch and Rhythm\" by Ke Chen, Cheng-i Wang, Taylor Berg-Kirkpatrick, and Shlomo Dubnov, arXiv:2008.01291v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.05177v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{DNN architecture} % title name of the table\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccrrrrrrr} % creating 10 columns\n\\hline\\hline % inserting double-line\n Layer &Type&Output Shape & Number of Parameters \n\\\\\n\\hline % inserts single-line\n1 & Dense(ReLu)& (None,1024) & 308224 \\\\\n2 & Dropout&(None,1024) & 0 \\\\\n3 & Dense(ReLu)& (None,1024) & 1049600 \\\\\n4 & Dropout&(None,1024) & 0 \\\\\n5 & dense(ReLu)&(None,512) & 524800 \\\\\n6 & Dense(ReLu)&(None,128) & 65664 \\\\\n7 & Dense(ReLu)&(None,64) & 8256 \\\\\n7 & Dense(Sigmoid)&(None,2) & 130 \\\\\n\\hline % inserts single-line\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network", "authors": ["Prateek Singh", "Ambalika Sharma", "Shreesha Maiya"], "url": "https://arxiv.org/abs/2202.05177v1", "attribution": "\"Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network\" by Prateek Singh, Ambalika Sharma, and Shreesha Maiya, arXiv:2202.05177v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12435v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Features and Outputs Description}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}%@{}ll@{}\n\\hline\n\\rowcolor{lightgray}\nFeature & Description \\\\ \n\\hline\n & Apple, Facebook, Facebook Messages, \\\\\nOTT Traffics & Facebook Video, HTTPS, Instagram, \\\\\n & Netflix, QUIC, Whatsapp, Youtube \\\\\n\\\\\nCQI & Channel quality indicator \\\\\n\\\\\nMIMO-FI & MIMO rank usage (\\%) \\\\ \n\\hline \n\\rowcolor{lightgray}\nOutput & Description \\\\ \n\\hline\nCPU Load & CPU resource consumption (\\%) \\\\ \\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing", "authors": ["Martino Chiarani", "Swastika Roy", "Christos Verikoukis", "Fabrizio Granelli"], "url": "https://arxiv.org/abs/2503.12435v1", "attribution": "\"XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing\" by Martino Chiarani, Swastika Roy, Christos Verikoukis, and Fabrizio Granelli, arXiv:2503.12435v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.17547v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n \\toprule \\hline\n Epoch & 0-4 &5-9 &10-14 &15-19 \\\\\n \\midrule\n Time(s), PMLP &748.4 &747.3 &747.1 &747.0 \\\\\n Time(s), KDE &3733.0 &4244.0 &4166.6 &4189.0 \\\\\n \\hline\n \\bottomrule \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Running time for using KDE and our PMLP to compute the density. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Probability-density-aware Semi-supervised Learning", "authors": ["Shuyang Liu", "Ruiqiu Zheng", "Yunhang Shen", "Ke Li", "Xing Sun", "Zhou Yu", "Shaohui Lin"], "url": "https://arxiv.org/abs/2412.17547v2", "attribution": "\"Probability-density-aware Semi-supervised Learning\" by Shuyang Liu, Ruiqiu Zheng, Yunhang Shen, Ke Li, Xing Sun, Zhou Yu, and Shaohui Lin, arXiv:2412.17547v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.10270v5_tex_table22.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccccc}\n\\hline\n\\textbf{} & \\multicolumn{3}{c|}{Diffusion+SVR} & \\multicolumn{3}{c|}{HGLET+SVR} & \\multicolumn{3}{c|}{GHWT+SVR} & \\multirow{2}{*}{SchNet} & \\multirow{2}{*}{PaiNN} & \\multirow{2}{*}{SO3Net I} & \\multirow{2}{*}{SO3Net II} \\\\ \\cline{1-10}\nFeature Type & Node & Edge & Both & Node & Edge & Both & Node & Edge & Both & & & & \\\\ \\hline\n\\multicolumn{14}{c}{Aspirin} \\\\ \\hline\nMAE & 4.856 & 3.132 & 3.267 & 4.884 & 3.135 & 3.285 & 4.928 & \\textbf{3.075} & 3.225 & 13.5 & 3.8 & 3.8 & \\textbf{2.6} \\\\\nRMSE & 6.181 & 4.144 & 4.314 & 6.215 & 4.129 & 4.407 & 6.213 & \\textbf{4.123} & 4.316 & 18.3 & 5.9 & 5.7 & \\textbf{3.8} \\\\\n\\# Parameters & 924 & 3784 & 4708 & 924 & 3784 & 4708 & 924 & 3784 & 4708 & $\\sim$ 432k & $\\sim$ 341k & $\\sim$ 283k & $\\sim$ 341k \\\\ \\hline\n\\multicolumn{14}{c}{Paracetamol} \\\\ \\hline\nMAE & 4.609 & 2.715 & 2.795 & 4.723 & 2.643 & 2.710 & 4.748 & \\textbf{2.624} & 2.699 & 8.4 & 2.1 & 2.2 & \\textbf{1.4} \\\\\nRMSE & 5.860 & 3.418 & 4.116 & 5.964 & 3.338 & 3.424 & 5.961 & \\textbf{3.299} & 3.408 & 11.2 & 2.9 & 3.0 & \\textbf{1.9} \\\\\n\\# Parameters & 924 & 3784 & 4444 & 924 & 3784 & 4444 & 924 & 3784 & 4444 & $\\sim$432k & $\\sim$ 341k & $\\sim$283k & $\\sim$341k \\\\ \\hline\n & & & & & & & & & & & & & \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of the performance of our MHSNs and the other state-of-the-art GNNs for potential energy prediction. We report the accuracy via MAE and RMSE as well as the number of trainable parameters in each network.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Multiscale Hodge Scattering Networks for Data Analysis", "authors": ["Naoki Saito", "Stefan C. Schonsheck", "Eugene Shvarts"], "url": "https://arxiv.org/abs/2311.10270v5", "attribution": "\"Multiscale Hodge Scattering Networks for Data Analysis\" by Naoki Saito, Stefan C. Schonsheck, and Eugene Shvarts, arXiv:2311.10270v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.09865v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Special cases of utility functions.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|lll}\n\\hline\n& \\multicolumn{3}{c}{} \\\\\n& HARA & Hypergeometric & CREMR \\\\\n& $\\beta = 0$ & $\\beta \\in (0,1)$ & $\\beta = 1$ \\\\\n\\hline \n& \\multicolumn{3}{c}{} \\\\\n$\\sigma=0$ & \\ $u(q)= \\frac{K}{\\alpha} (1-{\\rm e}^{-\\alpha q})$ & \\ \\ $u(q)= \\frac{K}{\\alpha^{1-\\beta}} \\gamma(1-\\beta, \\alpha q)$ \\quad & \\ \\ $u(q)= K\\, {\\rm Ei}(-\\alpha q) $ \\quad \\\\\n$\\sigma=1$ & \\ $u(q)= \\frac{K}{\\alpha} \\ln (\\alpha q+1)$ & \\ \\ $u(q)= \\frac{K}{(-\\alpha)^{1-\\beta}} B (-\\alpha q,1-\\beta,\\beta) $ \\quad & \\ \\ $u(q)= K \\ln q $ \\quad \\\\\n$\\sigma=-1$ & \\ $u(q)= - \\frac{K}{2} (\\alpha q^2- 2q) $ & & \\\\\n\\hline\n\\multicolumn{4}{p{16.5cm}}{\\scriptsize{{\\it Notes}: {\\rm We suppress the additive constant $C$ in each expression.\n}}}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Linear fractional relative risk aversion", "authors": ["Kristian Behrens", "Yasusada Murata"], "url": "https://arxiv.org/abs/2509.09865v1", "attribution": "\"Linear fractional relative risk aversion\" by Kristian Behrens and Yasusada Murata, arXiv:2509.09865v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.03166v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{array}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of Algorithm Performance}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c}\n\t\t\t\\hline\n\t\t\t & \n\t\t\t& \\multicolumn{1}{p{5em}<{\\centering}|}{{Time cost(s)}} \n\t\t\t& \\multicolumn{1}{p{5em}<{\\centering}|}{{Number of nodes}} \n\t\t\t& \\multicolumn{1}{p{4.78em}<{\\centering}|}{{Length of Ini. path}} \n\t\t\t& \\multicolumn{1}{p{5em}<{\\centering}}{{Length of Opt. path}} \\\\\n\t\t\t\\hline\n\t\t\t\\multirow{2}[2]{*}{\\text{Map1}} & \\text{CGAN-RRT*} & $\\textbf{136}$ & $\\textbf{7272}$ & $\\textbf{552}$ & \\multirow{2}[2]{*}{$527$} \\\\\n\t\t\t& \\text{RRT*} & $708$ & $17924$ & $589$ & \\\\\n\t\t\t\\hline\n\t\t\t\\multirow{2}[2]{*}{\\text{Map2}} & \\text{CGAN-RRT*} & $\\textbf{192}$ & $\\textbf{9312}$ & $\\textbf{504}$ & \\multirow{2}[2]{*}{$435$} \\\\\n\t\t\t& \\text{RRT*} & $419$ & $13680$ & $815$ & \\\\\n\t\t\t\\hline\n\t\t\t\\multirow{2}[2]{*}{\\text{Map3}} & \\text{CGAN-RRT*} & $\\textbf{206}$ & $\\textbf{8876}$ & $\\textbf{573}$ & \\multirow{2}[2]{*}{$535$} \\\\\n\t\t\t& \\text{RRT*} & $1229$ & $23006$ & $608$ & \\\\\n\t\t\t\\hline\n\t\t\t\\multirow{2}[2]{*}{\\text{Map4}} & \\text{CGAN-RRT*} & $\\textbf{75}$ & $\\textbf{5170}$ & $\\textbf{444}$ & \\multirow{2}[2]{*}{$420$} \\\\\n\t\t\t& \\text{RRT*} & $710$ & $18164$ & $513$ & \\\\\n\t\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Conditional Generative Adversarial Networks for Optimal Path Planning", "authors": ["Nachuan Ma", "Jiankun Wang", "Max Q. -H. Meng"], "url": "https://arxiv.org/abs/2012.03166v1", "attribution": "\"Conditional Generative Adversarial Networks for Optimal Path Planning\" by Nachuan Ma, Jiankun Wang, and Max Q. -H. Meng, arXiv:2012.03166v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00078v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|lc|c|}\n\\hline\nParameter & & Value \\\\\n\\hline\nYoung's modulus (GPa) & $E$ & 240 \\\\\nPoisson's ratio & $v$ & 0.27 \\\\\nYield strength (MPa) & $Y$ & 600 \\\\\nInitial void size ($\\mu$m$^3$) & $\\nu_0$ & 0.1 \\\\\nInitial void density ($\\mu$m$^{-3}$) & $\\eta_0$ & 0.001 \\\\\nFlow exponent & $n$ & 10 \\\\\nDamage exponent & $m$ & 2 \\\\\nFlow coefficient & $f$ & 10 \\\\\nIsotropic dynamic recovery & $R$ & 4\\\\\nIsotropic hardening (GPa) & $H$ & 5\\\\\nInitial hardening (MPa) & $\\kappa_0$ & 460\\\\\nInitial damage & $\\phi_0$ & 0.08\\\\\nShear nucleation & $N_1$ & 10 \\\\\nTriaxiality nucleation & $N_3$ & 13\\\\\nMaximum damage & $\\phi_\\text{max}$ & 0.5 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A heteroencoder architecture for prediction of failure locations in porous metals using variational inference", "authors": ["Wyatt Bridgman", "Xiaoxuan Zhang", "Greg Teichert", "Mohammad Khalil", "Krishna Garikipati", "Reese Jones"], "url": "https://arxiv.org/abs/2202.00078v1", "attribution": "\"A heteroencoder architecture for prediction of failure locations in porous metals using variational inference\" by Wyatt Bridgman, Xiaoxuan Zhang, Greg Teichert, Mohammad Khalil, Krishna Garikipati, and Reese Jones, arXiv:2202.00078v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11715v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Bosch dataset}\n\\begin{tabular}{cc|cccc}\n\t\t\\toprule\n\t\t\\multicolumn{2}{c |}{\\textbf{Sample Num (1184687)}} & \\multicolumn{4}{c}{\\textbf{Features Num (968)}} \\\\ \n\t\t\\textbf{Positive} & \\textbf{Negative} & \\textbf{L0} & \\textbf{L1} & \\textbf{L2} & \\textbf{L3}\\\\\n\t\t\\midrule\n\t\t1177808 & 6879 & 168 & 513 & 42 & 245 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Failure Prediction in Production Line Based on Federated Learning: An Empirical Study", "authors": ["Ning Ge", "Guanghao Li", "Li Zhang", "Yi Liu Yi Liu"], "url": "https://arxiv.org/abs/2101.11715v1", "attribution": "\"Failure Prediction in Production Line Based on Federated Learning: An Empirical Study\" by Ning Ge, Guanghao Li, Li Zhang, and Yi Liu Yi Liu, arXiv:2101.11715v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.17239v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\toprule\n \\textbf{S.No.} & $\\alpha$ & $\\varepsilon$ & $\\delta$ & \\textbf{NEs} \\\\\n \\midrule\n 1 & 2 & 0.9 & 4 & 49 \\\\\n 2 & 2 & 0.9 & 0.4 & 32 \\\\\n 3 & 2 & 0.54 & 4 & 6 \\\\\n 4 & 2 & 0.54 & 0.4 & 12 \\\\\n 5 & 0.2 & 0.9 & 4 & 557 \\\\\n 6 & 0.2 & 0.9 & 0.4 & 262 \\\\\n 7 & 0.2 & 0.54 & 4 & 142 \\\\\n 8 & 0.2 & 0.54 & 0.4 & 31 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The number of symmetric NEs }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Price equilibria with positive margins in loyal-strategic markets with discrete prices", "authors": ["Gurkirat Wadhwa", "Akansh Verma", "Veeraruna Kavitha", "Priyank Sinha"], "url": "https://arxiv.org/abs/2506.17239v1", "attribution": "\"Price equilibria with positive margins in loyal-strategic markets with discrete prices\" by Gurkirat Wadhwa, Akansh Verma, Veeraruna Kavitha, and Priyank Sinha, arXiv:2506.17239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08910v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrrrlr}\n\\toprule\n$N_\\text{output}$ & $N_\\text{halo}$ & $\\sigma$ & $\\alpha$ & $\\rho_{SR}$ & $\\beta$ & $N$ & $\\sigma_b$ & readout & $\\rho_A$ \\\\\n\\midrule\n 4 & 0 & 0.004 & 0.45 & 0.39 & 2.475150e-01 & 2000 & 0.29 & linear & 0.98 \\\\\n 4 & 2 & 0.021 & 0.77 & 1.03 & 1.372422e-08 & 2000 & 1.27 & linear & 0.98 \\\\\n 4 & 4 & 0.003 & 0.99 & 0.64 & 4.054118e-07 & 2000 & 1.17 & linear & 0.98 \\\\\n 4 & 6 & 0.003 & 0.99 & 0.64 & 4.054118e-07 & 2000 & 1.17 & linear & 0.98 \\\\\n 4 & 8 & 0.003 & 0.99 & 0.64 & 4.054118e-07 & 2000 & 1.17 & linear & 0.98 \\\\\n 4 & 0 & 0.004 & 0.45 & 0.39 & 2.475150e-01 & 1200 & 0.29 & linear & 0.98 \\\\\n 4 & 2 & 0.005 & 1.00 & 1.50 & 1.000000e-08 & 2400 & 2.00 & linear & 0.98 \\\\\n 4 & 4 & 0.005 & 0.36 & 0.48 & 1.000000e-08 & 3600 & 0.93 & linear & 0.98 \\\\\n 4 & 6 & 0.003 & 0.99 & 0.64 & 4.054118e-07 & 4800 & 1.17 & linear & 0.98 \\\\\n 4 & 8 & 0.003 & 0.99 & 0.64 & 4.054118e-07 & 6000 & 1.17 & linear & 0.98 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Fig. (b)}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics", "authors": ["Jason A. Platt", "Stephen G. Penny", "Timothy A. Smith", "Tse-Chun Chen", "Henry D. I. Abarbanel"], "url": "https://arxiv.org/abs/2201.08910v1", "attribution": "\"A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics\" by Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen, and Henry D. I. Abarbanel, arXiv:2201.08910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.11257v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MSE and MAE for mortality evaluation at various $\\tau$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\hline\n \\textbf{Threshold} & \\multicolumn{2}{c}{\\textbf{MSE} $\\downarrow$} & \\multicolumn{2}{c}{\\textbf{MAE} $\\downarrow$} \\\\\n & MC & PEMC & MC & PEMC \\\\\n \\hline\n $n=0$ & 10.974 & 4.142 & 2.765 & 1.587 \\\\\n $n=20$ & 9.535 & 3.266 & 2.497 & 1.416 \\\\\n $n=40$ & 9.174 & 3.122 & 2.428 & 1.376 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate", "authors": ["Fengpei Li", "Haoxian Chen", "Jiahe Lin", "Arkin Gupta", "Xiaowei Tan", "Honglei Zhao", "Gang Xu", "Yuriy Nevmyvaka", "Agostino Capponi", "Henry Lam"], "url": "https://arxiv.org/abs/2412.11257v3", "attribution": "\"Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate\" by Fengpei Li, Haoxian Chen, Jiahe Lin, Arkin Gupta, Xiaowei Tan, Honglei Zhao, Gang Xu, Yuriy Nevmyvaka, Agostino Capponi, and Henry Lam, arXiv:2412.11257v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18130v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n& \\multicolumn{5}{c}{sample size $N$}\\\\\n& 100 & 300 & 1000 & 3000 & 10000 \\\\\n\\midrule\nSBTM (ours) & \\textbf{0.24} & 0.19 & \\textbf{0.11} & \\textbf{0.089} & \\textbf{0.084} \\\\\nSDE & 0.24 & \\textbf{0.18} & 0.14 & 0.094 & 0.084 \\\\\nSVGD & 4.8 & 3.3 & 4.7 & 5.3 & 5.5 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Score-Based Deterministic Density Sampling", "authors": ["Vasily Ilin", "Peter Sushko", "Jingwei Hu"], "url": "https://arxiv.org/abs/2504.18130v2", "attribution": "\"Score-Based Deterministic Density Sampling\" by Vasily Ilin, Peter Sushko, and Jingwei Hu, arXiv:2504.18130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08533v5_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison on MSMT17 dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\t\t\\hline\n\t\t\\multirow{2}{*}{Methods} & \\multicolumn{2}{c}{MSMT17} \\\\ \\cline{2-3} \n\t\t& Rank-1(\\%) & mAP(\\%) \\\\ \\hline\n\t\tIANet & 75.5 & 46.8 \\\\ \n\t\tDGNet & 77.2 & 52.3 \\\\ \n\t\tRGA-SC & 80.3 & 57.5 \\\\ \n\t\tSCSN & 83.8 & 58.5 \\\\ \n\t\tAdaptiveReID & 81.7 & 62.2 \\\\ \\hline\n\t\tFastReID & 85.1 & 63.3 \\\\ \n\t\tFastReID + GGT(ours) & \\textbf{86.2} & \\textbf{65.3} \\\\ \n\t\tFastReID + GGT\\&LGT(ours) & \\textbf{86.2} & \\textbf{65.9} \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method", "authors": ["Yunpeng Gong", "Liqing Huang", "Lifei Chen"], "url": "https://arxiv.org/abs/2101.08533v5", "attribution": "\"Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method\" by Yunpeng Gong, Liqing Huang, and Lifei Chen, arXiv:2101.08533v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00133v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l}\n \\toprule\n \\textbf{\\em \\textcolor{blue}{Easy} questions} \\\\\n Q: What city in america has the highest population / Gold: New York City \\\\\n {\\em New York City}: New York City is the most populous city in the United States ... \\\\\n \\midrule\n Q: Who plays 11 in stranger things on netflix / Gold: Millie Bobby Brown \\\\\n {\\em Millie Bobby Brown}: She gained notability for her role as Eleven in the first season of Netflix science \\\\ fiction series Stranger Things. \\\\\n \\midrule\n Q: Where did leave it to beaver take place / Gold: Mayfield \\\\\n {\\em Leave It to Beaver}: Leave It to Beaver is set in the fictitious community of Mayfield and its environs. \\\\\n \\midrule\n Q: The substance that is dissolved in the solution / Gold: solute \\\\\n {\\em Solution}: A solute is a substance dissolved in another substance, known as a solvent. \\\\\n \\midrule\n Q: This means that in dna cytosine is always paired with / Gold: guanine \\\\\n {\\em Guanine}: In DNA, guanine is paired with cytosine. \\\\\n \\toprule\n \\textbf{\\em \\textcolor{red}{Hard} questions} \\\\\n Q: What is the area code for colombo sri lanka / Gold: 036 \\\\\n Telephone numbers in Sri Lanka: (Answer in the table) \\\\\n \\midrule\n Q: Which country is highest oil producer in the world / Gold: United States / Predictions: Russia \\\\\n {\\em History of the petroleum industry in the United States}: For much of the 19th and 20th centuries, the \\\\ US was the largest oil producing country in the world. \\\\\n {\\em List of countries by oil production}: (Answer in the table) \\\\\n {\\em Russia}: Russia is the world's leading natural gas exporter and second largest natural gas producer, \\\\ while also the second largest oil exporter and the third largest oil producer. \\\\\n \\midrule\n Q: Who did gibbs shoot in season 10 finale / Gold: Mendez \\\\\n Predictions: fbi agent tobias fornell (from all systems in unrestricted/6Gb track) %, pedro hernandez, aquaria, magnussen\n \\\\\n {\\em Past, Present and Future (NCIS)} Gibbs realizes that Mendez is .. The show then returns to the scene \\\\ depicted in a scene from season 10 finale's ... shoots and kills Mendez. \\\\\n \\midrule\n Q: The girl next door film based on a true story / Gold: loosely / Prediction: girl next door\t\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned", "authors": ["Sewon Min", "Jordan Boyd-Graber", "Chris Alberti", "Danqi Chen", "Eunsol Choi", "Michael Collins", "Kelvin Guu", "Hannaneh Hajishirzi", "Kenton Lee", "Jennimaria Palomaki", "Colin Raffel", "Adam Roberts", "Tom Kwiatkowski", "Patrick Lewis", "Yuxiang Wu", "Heinrich Küttler", "Linqing Liu", "Pasquale Minervini", "Pontus Stenetorp", "Sebastian Riedel", "Sohee Yang", "Minjoon Seo", "Gautier Izacard", "Fabio Petroni", "Lucas Hosseini", "Nicola De Cao", "Edouard Grave", "Ikuya Yamada", "Sonse Shimaoka", "Masatoshi Suzuki", "Shumpei Miyawaki", "Shun Sato", "Ryo Takahashi", "Jun Suzuki", "Martin Fajcik", "Martin Docekal", "Karel Ondrej", "Pavel Smrz", "Hao Cheng", "Yelong Shen", "Xiaodong Liu", "Pengcheng He", "Weizhu Chen", "Jianfeng Gao", "Barlas Oguz", "Xilun Chen", "Vladimir Karpukhin", "Stan Peshterliev", "Dmytro Okhonko", "Michael Schlichtkrull", "Sonal Gupta", "Yashar Mehdad", "Wen-tau Yih"], "url": "https://arxiv.org/abs/2101.00133v2", "attribution": "\"NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned\" by Sewon Min, Jordan Boyd-Graber, Chris Alberti, Danqi Chen, Eunsol Choi, Michael Collins, Kelvin Guu, Hannaneh Hajishirzi, Kenton Lee, Jennimaria Palomaki, Colin Raffel, Adam Roberts, Tom Kwiatkowski, Patrick Lewis, Yuxiang Wu, Heinrich Küttler, Linqing Liu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel, Sohee Yang, Minjoon Seo, Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Edouard Grave, Ikuya Yamada, Sonse Shimaoka, Masatoshi Suzuki, Shumpei Miyawaki, Shun Sato, Ryo Takahashi, Jun Suzuki, Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz, Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, and Wen-tau Yih, arXiv:2101.00133v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.08198v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\hline\n\\textbf{AI lab} & \\textbf{Year} & \\textbf{Human capital share} & \\textbf{Compute share} \\\\ \\hline\nC3.ai & 2020 & 0.275 & 0.725 \\\\ \nOpenAI & 2018 & 0.335 & 0.665 \\\\\n\\textbf{MS COCO 2017 (B1+trend)} & \\textbf{--} & \\textbf{0.581} & \\textbf{0.419} \\\\\n\\textbf{ImageNet (A3+trend)} & \\textbf{--} & \\textbf{0.675} & \\textbf{0.325} \\\\\nSplunk & 2021 & 0.808 & 0.192 \\\\ \nAlteryx & 2021 & 0.900 & 0.100 \\\\ \nDocuSign & 2021 & 0.911 & 0.089 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Economic impacts of AI-augmented R&D", "authors": ["Tamay Besiroglu", "Nicholas Emery-Xu", "Neil Thompson"], "url": "https://arxiv.org/abs/2212.08198v2", "attribution": "\"Economic impacts of AI-augmented R&D\" by Tamay Besiroglu, Nicholas Emery-Xu, and Neil Thompson, arXiv:2212.08198v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14715v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of results using various schemes and boundary conditions.}\n\\begin{tabular}{lcc}\n \\toprule\n Scheme & Re = 5000 \\\\ \\midrule\n Ghia et al. & $x = 0.5117; \\ y = 0.5352$ \\\\\n Medic et al. & $x = 0.53; \\ y = 0.53$ \\\\\n Stabilized FE (with slip bc) $\\mathbb{P}_1^2 \\times \\mathbb{P}_1$ (adapted mesh) & $x = 0.5018; \\ y = 0.5000$ \\\\\n Stabilized FE (with no-slip bc) $\\mathbb{P}_1^2 \\times \\mathbb{P}_1$ (adapted mesh) & $x = 0.5172; \\ y = 0.5351$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Equal order stabilized finite elements with Nitsche for stationary Navier-Stokes problem with slip boundary conditions : a priori and a posteriori error analysis", "authors": ["Aparna Bansal", "Nicolás Barnafi", "Rodolfo Araya", "Dwijendra Narain Pandey"], "url": "https://arxiv.org/abs/2501.14715v1", "attribution": "\"Equal order stabilized finite elements with Nitsche for stationary Navier-Stokes problem with slip boundary conditions : a priori and a posteriori error analysis\" by Aparna Bansal, Nicolás Barnafi, Rodolfo Araya, and Dwijendra Narain Pandey, arXiv:2501.14715v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.02143v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Characteristics by ESG Quintile}\n\\begin{tabular}{lccccc}\n\\toprule\nESG Quintile & Avg Return & Log Size & Leverage & Profitability & N \\\\\n\\midrule\nQ1 (Brown) & 0.027\\% & 22.04 & 0.386 & 0.122 & 13,017 \\\\\nQ2 & 0.038\\% & 22.56 & 0.419 & 0.164 & 13,008 \\\\\nQ3 & 0.038\\% & 22.92 & 0.439 & 0.189 & 13,034 \\\\\nQ4 & 0.004\\% & 23.31 & 0.512 & 0.133 & 13,033 \\\\\nQ5 (Green) & -0.002\\% & 23.65 & 0.510 & 0.196 & 13,010 \\\\\n\\midrule\nNo ESG Data & 0.030\\% & 21.40 & 0.430 & -0.000 & 18,459 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Green Shields: The Role of ESG in Uncertain Time", "authors": ["Fatih Kansoy", "Dominykas Stasiulaitis"], "url": "https://arxiv.org/abs/2506.02143v1", "attribution": "\"Green Shields: The Role of ESG in Uncertain Time\" by Fatih Kansoy and Dominykas Stasiulaitis, arXiv:2506.02143v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.19715v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\hline\nStrategy & $(1/K)\\sum_{k=1}^{K}\\log(V_{T_{k}})$ \\\\\n\\hline\nFGP & 0.078694 \\\\\nEWP & 0.0092019 \\\\\nMarket & -9.79007e-07 \\\\\nDWP $p=0.3$ & 0.00660325 \\\\\nDWP $p=0.5$ & 0.00478323 \\\\\nDWP $p=0.8$ & 0.0019446 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Neural Functionally Generated Portfolios", "authors": ["Michael Monoyios", "Olivia Pricilia"], "url": "https://arxiv.org/abs/2506.19715v1", "attribution": "\"Neural Functionally Generated Portfolios\" by Michael Monoyios and Olivia Pricilia, arXiv:2506.19715v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.12362v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Controllers from the Literature}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n \\hline\n Authors & Citation & Legend Code \\\\ \\hline\n Taylor et al. & & TAY \\\\ \\hline\n Black et al. & & BLA \\\\ \\hline\n Lopez et al. & (w/o SMID) & LOP \\\\ \\hline\n Lopez et al. & (w/ SMID) & LSM \\\\ \\hline\n Zhao et al. & & ZHA \\\\ \\hline\n Proposed Method & & PRO \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Fixed-Time Stable Adaptation Law for Safety-Critical Control under Parametric Uncertainty", "authors": ["Mitchell Black", "Ehsan Arabi", "Dimitra Panagou"], "url": "https://arxiv.org/abs/2011.12362v1", "attribution": "\"A Fixed-Time Stable Adaptation Law for Safety-Critical Control under Parametric Uncertainty\" by Mitchell Black, Ehsan Arabi, and Dimitra Panagou, arXiv:2011.12362v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table18.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Backnode vector for Exercise~. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline\n Node & Backnode \\\\\n \\hline\n 1 & 4 \\\\\n 2 & 6 \\\\\n 3 & $-1$ \\\\\n 4 & 7 \\\\\n 5 & 4 \\\\\n 6 & 5 \\\\\n 7 & 3 \\\\\n 8 & 10 \\\\\n 9 & 5 \\\\\n 10 & 6 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.12914v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Main Optimization Parameters with $N_d=10$ and $N_{APs}=2$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|}\n\\cline{2-3}\n & RF & RF-OC \\\\ \\hline\nAvg. Number of Variables & 1572 & 3521 \\\\ \\hline\nAvg. Number of Constraints & 3823 & 6246 \\\\ \\hline\nAvg. Running time (s) for 3\\% Duality Gap & 11 & 40 \\\\ \\hline\nAvg. Duality Gap for 60 (s) & 1.2\\% & 1.3\\% \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "AoI in Context-Aware Hybrid Radio-Optical IoT Networks", "authors": ["Aymen Hamrouni", "Sofie Pollin", "Hazem Sallouha"], "url": "https://arxiv.org/abs/2412.12914v3", "attribution": "\"AoI in Context-Aware Hybrid Radio-Optical IoT Networks\" by Aymen Hamrouni, Sofie Pollin, and Hazem Sallouha, arXiv:2412.12914v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.15000v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\t\t\\toprule\n\t\t& size & \\multicolumn{2}{c}{speed} \\\\\n \\cmidrule(lr){2-2} \\cmidrule(lr){3-4}\n\t\t& params & inference & training \\\\\n\t\t\\midrule\n\t\t & 330\\,k & 10\\,ms & 10\\,h \\\\ % 328'339\n\t\tDeepSphere (Jiang architecture) & 590\\,k & 5\\,ms & 3\\,h \\\\ % 590k\n DeepSphere & 13\\,M & 33\\,ms & 13\\,h \\\\ % 12'926'432\n\t\tDeepSphere (wider architecture) & 52\\,M & 50\\,ms & 20\\,h \\\\\n\t\t\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DeepSphere: a graph-based spherical CNN", "authors": ["Michaël Defferrard", "Martino Milani", "Frédérick Gusset", "Nathanaël Perraudin"], "url": "https://arxiv.org/abs/2012.15000v1", "attribution": "\"DeepSphere: a graph-based spherical CNN\" by Michaël Defferrard, Martino Milani, Frédérick Gusset, and Nathanaël Perraudin, arXiv:2012.15000v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.16370v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllll}\n\\toprule\nType & $\\zeta(x)$ & Exact & Source term & Kernel & PINN MAE & RISN MAE \\\\ \\midrule\nFredholm & $x$ & $\\sin(x t)$ & $x \\cos(y x) + \\frac{-1 + \\cos(x)}{x}$ & $1$ & $3.00 \\times 10^{-3}$ & $2.51 \\times 10^{-4}$ \\\\\nFredholm & $x$ & $\\sin(x t)$ & $x \\cos(y x) - x \\sin(y) + x \\sin(y) \\cos(x)$ & $x^2 \\sin(y)$ & $1.24 \\times 10^{-3}$& $1.95 \\times 10^{-4}$ \\\\\nFredholm & $x^2$ & $\\sin(x t)$ & $x \\cos(y x) + \\frac{\\cos(x) \\sin(x) - x}{2x}$ & $1$ & $1.89 \\times 10^{-3}$ & $1.05 \\times 10^{-4}$\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Numerical results for solving partial integro-differential equations using the proposed RISN and traditional PINN, comparing the MAE to showcase RISN's superior performance.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations", "authors": ["Mahdi Movahedian Moghaddam", "Kourosh Parand", "Saeed Reza Kheradpisheh"], "url": "https://arxiv.org/abs/2501.16370v2", "attribution": "\"Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations\" by Mahdi Movahedian Moghaddam, Kourosh Parand, and Saeed Reza Kheradpisheh, arXiv:2501.16370v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2308.16456v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Details of benchmark datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n ID & Name & m & n \\\\\n \\midrule\n (a) & Cleveland & 173 & 13 \\\\\n (b) & Ionosphere & 351 & 34 \\\\\n (c) & New-thyroid & 215 & 4 \\\\\n (d) & Parkinsons & 195 & 22 \\\\\n (e) & Sonar & 208 & 60 \\\\\n (f) & TicTacToe & 958 & 27 \\\\\n (g) & Vowel & 988 & 13 \\\\\n (h) & Wisconsin & 683 & 9 \\\\\n (i) & German & 1000 & 20 \\\\\n (j) & Shuttle & 1829 & 9 \\\\\n (k) & Segment & 2308 & 19 \\\\\n (l) & Waveform & 5000 & 21 \\\\\n (m) & TwoNorm & 7400 & 20 \\\\\n (n) & IJCNN01 & 49990 & 22 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Least Squares Maximum and Weighted Generalization-Memorization Machines", "authors": ["Shuai Wang", "Zhen Wang", "Yuan-Hai Shao"], "url": "https://arxiv.org/abs/2308.16456v1", "attribution": "\"Least Squares Maximum and Weighted Generalization-Memorization Machines\" by Shuai Wang, Zhen Wang, and Yuan-Hai Shao, arXiv:2308.16456v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.08287v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l|l|l}\n Char. & t-stat & p-value & average-1 & average-2 \\\\\n \\hline\n $Z^I$ & -100.40 & 0.00 & 0.16 & 0.78 \\\\\n $\\bar{Q}$ & 84.83 & 0.00 & 0.17 & 0.02 \\\\\n $Y_{tot}^\\alpha$ & 81.17 & 0.00 & 1.08 & 0.35 \\\\\n $Y_{tot}^I$ & 4.82 & $1.44\\times 10^{-6}$ & 1.42 & 1.35\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Two sample t-test for characteristics in Scenario 1 ($\\phi_1=2$) and Scenario 2 ($\\phi_1=20$). The other parameters are the same as in Figure .}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Liquidity Competition Between Brokers and an Informed Trader", "authors": ["Ryan Donnelly", "Zi Li"], "url": "https://arxiv.org/abs/2503.08287v1", "attribution": "\"Liquidity Competition Between Brokers and an Informed Trader\" by Ryan Donnelly and Zi Li, arXiv:2503.08287v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08024v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Online results. $\\uparrow$/$\\downarrow$ means that higher/lower is better. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Method & CVR ($\\uparrow$) & CTCVR ($\\uparrow$) & CPA ($\\downarrow$) \\\\\n \\midrule\n UKD & +3.4\\% & +5.0\\% & -4.3\\% \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation", "authors": ["Zixuan Xu", "Penghui Wei", "Weimin Zhang", "Shaoguo Liu", "Liang Wang", "Bo Zheng"], "url": "https://arxiv.org/abs/2201.08024v1", "attribution": "\"UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation\" by Zixuan Xu, Penghui Wei, Weimin Zhang, Shaoguo Liu, Liang Wang, and Bo Zheng, arXiv:2201.08024v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07690v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Graph datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\nGraphs & \\#vertices & \\#edges & Description \\\\ \\hline\\hline\nCiteSeer (CI)~ & 3264 & 4536 & Publication citation \\\\ \\hline\nMiCo (MI)~ & 100K & 1.1M & Co-authorship \\\\ \\hline\nOrkut (OK)~ & 3.1M & 117.2M & Social network \\\\ \\hline\nUK-2005 (UK)~ & 39M & 936M & Social network \\\\ \\hline\nFriendster (FR)~ & 65M & 1.8B & Social network \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Efficient Mining of Frequent Subgraphs with Two-Vertex Exploration", "authors": ["Peng Jiang", "Rujia Wang", "Bo Wu"], "url": "https://arxiv.org/abs/2101.07690v2", "attribution": "\"Efficient Mining of Frequent Subgraphs with Two-Vertex Exploration\" by Peng Jiang, Rujia Wang, and Bo Wu, arXiv:2101.07690v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19634v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Data provided by SdSv for speaker verification.}\n\\begin{tabular}{c|c}\n \\toprule\n enrollment & test\\\\\n \\hline\n 1 to 29 utterances (average of 7) & 1 utterance\\\\\n giving a net speech duration & (95\\% less than\\\\\n from 3 to 120 seconds & 5 seconds)\\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Asymmetric and trial-dependent modeling: the contribution of LIA to SdSV Challenge Task 2", "authors": ["Pierre-Michel Bousquet", "Mickael Rouvier"], "url": "https://arxiv.org/abs/2403.19634v1", "attribution": "\"Asymmetric and trial-dependent modeling: the contribution of LIA to SdSV Challenge Task 2\" by Pierre-Michel Bousquet and Mickael Rouvier, arXiv:2403.19634v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.03035v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n\t\tCredit line & \\$1 million \\\\\n\t\tRate & SOFR 30-day compounded average + AXI \\\\\n\t\tSpread & 1.00\\% \\\\\n\t\tDay count & 360 \\\\\n\t\tAmortized & No \\\\\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Case for AXI", "authors": ["Viktor Tsyrennikov"], "url": "https://arxiv.org/abs/2509.03035v1", "attribution": "\"A Case for AXI\" by Viktor Tsyrennikov, arXiv:2509.03035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2302.03694v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Data summary of the collected coverage}\n\\begin{tabular}{ccccc}\n\\hline \nMedia & Tot. collected files & Total hours & Avg. time per file \\\\\n\\hline\n{BLW} & {744} & {171.16} & 14~minutes \\\\\n{BSM} & {3885} & {398.15} & 6~minutes \\\\\n{YFM} & {318} & {2467} & 8~hours \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Characterizing Financial Market Coverage using Artificial Intelligence", "authors": ["Jean Marie Tshimula", "D'Jeff K. Nkashama", "Patrick Owusu", "Marc Frappier", "Pierre-Martin Tardif", "Froduald Kabanza", "Armelle Brun", "Jean-Marc Patenaude", "Shengrui Wang", "Belkacem Chikhaoui"], "url": "https://arxiv.org/abs/2302.03694v1", "attribution": "\"Characterizing Financial Market Coverage using Artificial Intelligence\" by Jean Marie Tshimula, D'Jeff K. Nkashama, Patrick Owusu, Marc Frappier, Pierre-Martin Tardif, Froduald Kabanza, Armelle Brun, Jean-Marc Patenaude, Shengrui Wang, and Belkacem Chikhaoui, arXiv:2302.03694v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.18061v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\hline\n \\textbf{Model} & \\textbf{HumanAccept-zh} & \\textbf{HumanChitchat-zh} \\\\ \\hline\n GLM-4-Voice & 92.55 & 59.50 \\\\\n MiniCPM-o-2.6 & 87.60 & 58.29 \\\\\n Baichuan-Omni-1.5 & 95.00 & 26.26 \\\\\n LLaMA-Omni & 49.16 & 9.21 \\\\\n SpeechGPT-2.0-preview & 76.41 & 41.22 \\\\\n Freeze-Omni & 87.57 & 30.9 \\\\\n Qwen2.5-Omni & 82.93 & 80.89 \\\\\n Kimi-Audio & 86.67 & 47.95 \\\\\n GPT4o-Audio (API) & 96.29 & 34.45 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Model scores (\\%) on Safety\\&Values and Chitchat\\&Human-likeness Tasks.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "TELEVAL: A Dynamic Benchmark Designed for Spoken Language Models in Chinese Interactive Scenarios", "authors": ["Zehan Li", "Hongjie Chen", "Yuxin Zhang", "Jing Zhou", "Xuening Wang", "Hang Lv", "Mengjie Du", "Yaodong Song", "Jie Lian", "Jian Kang", "Jie Li", "Yongxiang Li", "Zhongjiang He", "Xuelong Li"], "url": "https://arxiv.org/abs/2507.18061v1", "attribution": "\"TELEVAL: A Dynamic Benchmark Designed for Spoken Language Models in Chinese Interactive Scenarios\" by Zehan Li, Hongjie Chen, Yuxin Zhang, Jing Zhou, Xuening Wang, Hang Lv, Mengjie Du, Yaodong Song, Jie Lian, Jian Kang, Jie Li, Yongxiang Li, Zhongjiang He, and Xuelong Li, arXiv:2507.18061v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.10221v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\nOld Scheme&New Scheme&Ratio\\\\\n\\hline\n111.97&104.06&1.076\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\sf Example 2: The CPU times (in seconds) consumed by the studied schemes.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "New More Efficient A-WENO Schemes", "authors": ["Shaoshuai Chu", "Alexander Kurganov", "Ruixiao Xin"], "url": "https://arxiv.org/abs/2503.10221v2", "attribution": "\"New More Efficient A-WENO Schemes\" by Shaoshuai Chu, Alexander Kurganov, and Ruixiao Xin, arXiv:2503.10221v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13728v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cll|}\n\t\t\t\\hline\n\t\t\t\\textbf{Case} & \\textbf{Conditions} & \\textbf{Finite singular points} \\\\\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\t1& $b\\delta >c-\\delta$. & $P_0$ saddle, $P_1$ stable node. \\\\\t\\hline\n\t\t\t2& $b\\delta =c-\\delta$. & $P_0$ saddle, $P_1$ saddle-node.\\\\\t\\hline\n\t\t\t3& $0<b\\delta <c-\\delta$, $B\\geq0$, $A>0$. & $P_0$ saddle, $P_1$ saddle, $P_2$ unstable node. \\\\\t\\hline\n\t\t\t4& $0<b\\delta <c-\\delta$, $B\\geq0$, $A<0$. & $P_0$ saddle, $P_1$ saddle, $P_2$ stable node. \\\\\t\\hline\n\t\t\t5& $0<b\\delta <c-\\delta$, $B<0$, $A>0$. & $P_0$ saddle, $P_1$ saddle, $P_2$ unstable focus. \\\\\t\\hline\n\t\t\t6& $0<b\\delta <c-\\delta$, $B<0$, $A<0$. & $P_0$ saddle, $P_1$ saddle, $P_2$ stable focus. \\\\\t\\hline\n\t\t\t7& $0<b\\delta <c-\\delta$, $B<0$, $A=0$. & $P_0$ saddle, $P_1$ saddle, $P_2$ weak stable focus. \\\\\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{The finite singular points in the closed positive quadrant.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Global phase portraits of a predator-prey system", "authors": ["Érika Diz-Pita", "Jaume Llibre", "M. V. Otero-Espinar"], "url": "https://arxiv.org/abs/2501.13728v1", "attribution": "\"Global phase portraits of a predator-prey system\" by Érika Diz-Pita, Jaume Llibre, and M. V. Otero-Espinar, arXiv:2501.13728v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12037v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|}\n \\hline\n Dataset & Start (s) & Length (s) & Metric & Best & Model config. \\\\ \\hline \\hline\n MMI & 0 & 6 & BAC & 86.7 & Linear (2.) \\\\ \\hline\n BCIC & -2 & 6 & Accuracy & 42.6 & Linear (2.) \\\\ \\hline\n ERN & -0.7 & 2 & AUROC & 0.65 & Linear (2.) \\\\ \\hline\n SSC & 0 & 30 & BAC & 0.72 & Linear (2.) \\\\ \\hline\n P300 & -0.7 & 2 & AUROC & 0.72 & BENDR (1.) \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performances of downstream datasets. Start and length refer to length of trials and start with respect to event markers in seconds. Best performance specifies average performance across all subjects (and therefore folds) for best performing model configuration. BAC: class balanced accuracy; AUROC: area under the receiver operating characteristic curve. Model configurations are numbered in accordance with the list presented in section .}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data", "authors": ["Demetres Kostas", "Stephane Aroca-Ouellette", "Frank Rudzicz"], "url": "https://arxiv.org/abs/2101.12037v1", "attribution": "\"BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data\" by Demetres Kostas, Stephane Aroca-Ouellette, and Frank Rudzicz, arXiv:2101.12037v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table31.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|r|c|c|r|r|r}\n \\hline\n \\multicolumn{9}{c}{PH algorithm using 128 scenario paths for the case of adjustable carry-overs (constant $\\rho$)} \\\\\n \\hline\n \\multicolumn{9}{c}{Instances including component demands} \\\\\n \\hline\n Distribution & Util. & $|\\Omega|$ & $|\\Phi|$ & Converged & Gap (\\%) & Resolve (s) & Runtime (s) & Iterations \\\\\n \\hline\n \\multirow{10}{*}{Non stationary} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & Yes & - & - & 26.0 & 4\\\\\n & & 3 & 2,187 & Yes & - & - & 207.8 & 28\\\\\n & & 4 & 16,384 & Yes & - & - & 29.5 & 4 \\\\\n & & 5 & 78,125 & Yes & - & - & 374.5 & 41\\\\\n & & 6 & 279,936 & & & & & \\\\\\cline{2-9}\n &\\multirow{5}{*}{90\\%} \n & 2 & 128 & Yes & - & - & 48.7 & 4 \\\\\n & & 3 & 2,187 & Yes & - & - & 49.8 & 5 \\\\\n & & 4 & 16,384 & Yes & - & - & 33.8 & 4 \\\\\n & & 5 & 78,125 & Yes & - & - & 49.5 & 4 \\\\\n & & 6 & 279,936 & & & & & \\\\\n \\hline\n \\multirow{10}{*}{Uniform} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & Yes & - & - & 26.5 & 4 \\\\\n & & 3 & 2,187 & Yes & - & - & 12.5 & 2 \\\\\n & & 4 & 16,384 & Yes & - & - & 13.7 & 2 \\\\\n & & 5 & 78,125 & Yes & - & - & 25.0 & 2 \\\\\n & & 6 & 279,936 & & & & & \\\\\\cline{2-9}\n & \\multirow{5}{*}{90\\%}\n & 2 & 128 & Yes & - & - & 73.2 & 7\\\\\n & & 3 & 2,187 & Yes & - & - & 24.2 & 3 \\\\\n & & 4 & 16,384 & Yes & - & - & 24.4 & 3\\\\\n & & 5 & 78,125 & Yes & - & - & 46.8 & 4 \\\\\n & & 6 & 279,936 & & & & \\\\\n \\hline\n \\multirow{10}{*}{Lumpy} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & No & - & - & 10,800.0 & 639 \\\\\n & & 3 & 2,187 & No & - & - & 10,800.0 & 730 \\\\\n & & 4 & 16,384 & Yes & - & - & 1,141.3 & 95 \\\\\n & & 5 & 78,125 & Yes & - & - & 4517.0 & 382\\\\\n & & 6 & 279,936 & & & & & \\\\\\cline{2-9}\n & \\multirow{5}{*}{90\\%} \n & 2 & 128 & No & - & - & 10,800.0 & 87 \\\\\n & & 3 & 2,187 & No & - & - & 10,800.0 & 94 \\\\\n & & 4 & 16,384 & No & - & - & 10,800.0 & 127 \\\\\n & & 5 & 78,125 & No & - & - & 10,800.0 & 117 \\\\\n & & 6 & 279,936 & & & & & \\\\\n \\hline\n \\multirow{10}{*}{Slow moving} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & Yes & - & - & 2830.2 & 234 \\\\\n & & 3 & 2,187 & No & - & - & 10,800.0 & 691 \\\\\n & & 4 & 16,384 & Yes & - & - & 1059.7 & 112 \\\\\n & & 5 & 78,125 & Yes & - & - & 734.6 & 72\\\\\n & & 6 & 279,936 & & & & & \\\\\\cline{2-9}\n & \\multirow{5}{*}{90\\%}\n & 2 & 128 & No & - & - & 10,800.0 & 448 \\\\\n & & 3 & 2,187 & No & - & - & 10,800.0 & 412 \\\\\n & & 4 & 16,384 & No & - & - & 10,800.0 & 480\\\\\n & & 5 & 78,125 & No & - & - & 10,800.0 & 463\\\\\n & & 6 & 279,936 & & & & &\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Runtimes and gaps for the progressive hedging algorithm using 128 scenario paths on the instances including component demands for different distributions and numbers of scenarios per branch with a time limit of 3 hours (10,800 s) and maximum of 100 iterations}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.03862v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c|c|c|c}\n & $k = 2$ & $k = 3$ & $k = 4$ & $k \\geq 5$ \\\\ \\hline\n Conjectured bound & $f_k(\\mathcal{F})\\geq\\frac13$ & $f_k(\\mathcal{F})\\geq\\frac15$ & $f_k(\\mathcal{F})\\geq\\frac19$ & $f_k(\\mathcal{F})\\geq\\frac1{2^{k-1}+1}$\\\\ \\hline\n & $m\\leq 5$ & $m\\leq 9$ & $m\\leq 17$ & $ m \\le 2^k + 1$ \\\\\n & & & & $2^k + 2 \\leq m \\leq 2^{3(k-1)}$ \\\\\n & $m\\geq 4$ & $m\\geq 6$ & $m\\geq 14$ & $m \\geq 2^{2.71(k-1)}$ \\\\\n \\end{tabular}\n\\caption{Effective range of three propositions, with $|\\mathcal{F}|=m$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Frequent elements in union-closed set families", "authors": ["Shagnik Das", "Saintan Wu"], "url": "https://arxiv.org/abs/2412.03862v3", "attribution": "\"Frequent elements in union-closed set families\" by Shagnik Das and Saintan Wu, arXiv:2412.03862v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.09371v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n$p$ & Anti-corr Gaussian & NUTS & Comp-wise slice & EPC slice \\\\ \\hline\n10 & \\textbf{0.41} & 26.18 & 3.61 & 3.04 \\\\ \\hline\n50 & \\textbf{0.93} & 108.33 & 31.44 & 4.16 \\\\ \\hline\n500 & \\textbf{5.60} & 22875.74 & 1121.47 & 16.14 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Gibbs Sampling using Anti-correlation Gaussian Data Augmentation, with Applications to L1-ball-type Models", "authors": ["Yu Zheng", "Leo L. Duan"], "url": "https://arxiv.org/abs/2309.09371v2", "attribution": "\"Gibbs Sampling using Anti-correlation Gaussian Data Augmentation, with Applications to L1-ball-type Models\" by Yu Zheng and Leo L. Duan, arXiv:2309.09371v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.09712v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Linearity complexity metric values of the datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n Dataset & L1 & L2 & L3 \\\\\\midrule\n \\texttt{spambase} & 0.2591 & 0.1051 & 0.0875 \\\\\n \\texttt{MagicTelescope} & 0.3196 & 0.1533 & 0.1380 \\\\\n \\texttt{steel\\_plates\\_fault} & 0.0000 & 0.0000 & 0.0000 \\\\\n \\texttt{qsar-biodeg} & 0.2735 & 0.1127 & 0.0982 \\\\\n \\texttt{phoneme} & 0.3188 & 0.1560 & 0.1422 \\\\\n \\texttt{jm1} & 0.2813 & 0.1401 & 0.1364 \\\\\n \\texttt{SpeedDating} & 0.2301 & 0.0912 & 0.0826 \\\\\n \\texttt{kc1} & 0.2065 & 0.1021 & 0.0964 \\\\\n \\texttt{churn} & 0.2161 & 0.0990 & 0.0918 \\\\\n \\texttt{pc4} & 0.1730 & 0.0726 & 0.0672 \\\\\n \\texttt{pc3} & 0.1591 & 0.0737 & 0.0691 \\\\\n \\texttt{abalone} & 0.1681 & 0.0772 & 0.0763 \\\\\n \\texttt{us\\_crime} & 0.1227 & 0.0439 & 0.0383 \\\\\n \\texttt{yeast\\_ml8} & 0.1345 & 0.0638 & 0.0624 \\\\\n \\texttt{pc1} & 0.1177 & 0.0550 & 0.0527 \\\\\n \\texttt{ozone\\_level\\_8hr} & 0.1288 & 0.0487 & 0.0468 \\\\\n \\texttt{wilt} & 0.1047 & 0.0432 & 0.0418 \\\\\n \\texttt{wine\\_quality} & 0.0755 & 0.0333 & 0.0327 \\\\\n \\texttt{yeast\\_me2} & 0.0778 & 0.0293 & 0.0278 \\\\\n \\texttt{mammography} & 0.0458 & 0.0167 & 0.0153 \\\\\n \\texttt{abalone\\_19} & 0.0162 & 0.0075 & 0.0072 \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective", "authors": ["Mustafa Cavus", "Przemyslaw Biecek"], "url": "https://arxiv.org/abs/2412.09712v1", "attribution": "\"Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective\" by Mustafa Cavus and Przemyslaw Biecek, arXiv:2412.09712v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18013v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Error convergence of approximated $\\omega(t)$ for the $L_{\\infty}$-Norm and $N=4$, $5$ and $6$, respectively.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc} \\hline\n\\textbf{$t$} & \\textbf{$N=4$} & \\textbf{$N=5$} & \\textbf{$N=6$}\\\\ \\hline\n0.0 & 0.09217E-5 & 0.05309E-5 & 0.04012E-6 \\\\\n0.1 & 0.11259E-5 & 0.08000E-5 & 0.03015E-6 \\\\\n0.2 & 0.15822E-5 & 0.12443E-5 & 0.08928E-6 \\\\\n0.3 & 0.16448E-5 & 0.13418E-5 & 0.10092E-6 \\\\\n0.4 & 0.16449E-5 & 0.10919E-5 & 0.05097E-6 \\\\\n0.5 & 0.14476E-5 & 0.80021E-6 & 0.04902E-7 \\\\\n0.6 & 0.14445E-5 & 0.95708E-6 & 0.81000E-7 \\\\\n0.7 & 0.14409E-5 & 0.94712E-6 & 0.85011E-7 \\\\\n0.8 & 0.14319E-5 & 0.94810E-6 & 0.80031E-7 \\\\\n0.9 & 0.13318E-5 & 0.85701E-6 & 0.40122E-7 \\\\\n1.0 & 0.12231E-5 & 0.77500E-6 & 0.01884E-7 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A comprehensive numerical investigation of a coupled mathematical model of neuronal excitability", "authors": ["Burcu Gürbüz", "Aytül Gökçe", "Mahmut Modanlı"], "url": "https://arxiv.org/abs/2501.18013v1", "attribution": "\"A comprehensive numerical investigation of a coupled mathematical model of neuronal excitability\" by Burcu Gürbüz, Aytül Gökçe, and Mahmut Modanlı, arXiv:2501.18013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.01221v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c}\n \\hline\n $h_{\\text{min}}$ & $h_{\\text{max}}$ & $\\Delta h$ & $\\lambda_{\\text{min}}$ & $\\lambda_{\\text{max}}$ & $\\Delta \\lambda$ & $t_{\\text{init}}$ & $H_{\\text{init}}$ & $\\lambda_t(\\omega)$\\\\\n \\hline\n $0$ & $50$ & $0.5$ & $27$ & $216.6$ & $1$ & $t$ & $h$ & $\\lambda^P$ \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Meta-parameters for Algorithm .}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A stochastic Gordon-Loeb model for optimal cybersecurity investment under clustered attacks", "authors": ["Giorgia Callegaro", "Claudio Fontana", "Caroline Hillairet", "Beatrice Ongarato"], "url": "https://arxiv.org/abs/2505.01221v1", "attribution": "\"A stochastic Gordon-Loeb model for optimal cybersecurity investment under clustered attacks\" by Giorgia Callegaro, Claudio Fontana, Caroline Hillairet, and Beatrice Ongarato, arXiv:2505.01221v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.05558v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experiment results on the diffferent modalities.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc}\n\t\t\\toprule\n\t\tModality & ACC(\\%) & WF1(\\%) \\\\\n\t\t\\midrule\n\t\tA & 66.06 & 65.59 \\\\\n\t\tT & 58.74 & 58.63 \\\\\n\t\tV & 29.88 & 26.31 \\\\\n\t\tA+T & 67.75 & 67.45 \\\\\n\t\tA+V & 66.33 & 64.14 \\\\\n\t\tA+V+T & 70.53 & 70.6 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "WavFusion: Towards wav2vec 2.0 Multimodal Speech Emotion Recognition", "authors": ["Feng Li", "Jiusong Luo", "Wanjun Xia"], "url": "https://arxiv.org/abs/2412.05558v1", "attribution": "\"WavFusion: Towards wav2vec 2.0 Multimodal Speech Emotion Recognition\" by Feng Li, Jiusong Luo, and Wanjun Xia, arXiv:2412.05558v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.18417v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Weighted F1 scores of the evaluated sentiment-based methods on the FPB, TFNS, and NWGI financial sentiment datasets. HIV-4, VADER, and LMD are lexicon-based baselines. FinBERT and FinLlama are SFT classification LLMs, while Instruct-FinGPT, FinGPT v3.3 and FinSFT are instruction-tuned causal LLMs. FinDPO is our proposed DPO-aligned causal LLM. FinBERT is excluded from evaluation on FPB due to data leakage, as it was originally trained on the full FPB dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\hline\n\\textbf{Model} & \\textbf{FPB} & \\textbf{TFNS} & \\textbf{NWGI} & \\textbf{Average} \\\\ \\hline\nHIV-4 & 0.357 & 0.401& 0.384 & 0.385 \\\\\nVADER & 0.536 & 0.518 & 0.462 & 0.491 \\\\\nLMD & 0.546 & 0.572 & 0.440 & 0.498 \\\\\nFinBERT & --- & 0.733 & 0.538 & 0.611\\\\\nFinLlama & 0.707 & 0.904 & 0.538 & 0.679\\\\\nInstruct-FinGPT & 0.777 & 0.828 & 0.583 & 0.690\\\\\nFinGPT v3.3 & 0.879 & 0.903 & 0.643 & 0.762 \\\\\n\\textit{FinSFT} & 0.829 & 0.850 & 0.708 & 0.771 \\\\\n\\textbf{FinDPO (Ours)} & 0.865 & 0.872 & 0.833 & \\textbf{0.846} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMs", "authors": ["Giorgos Iacovides", "Wuyang Zhou", "Danilo Mandic"], "url": "https://arxiv.org/abs/2507.18417v1", "attribution": "\"FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMs\" by Giorgos Iacovides, Wuyang Zhou, and Danilo Mandic, arXiv:2507.18417v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16834v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small{Utilitarian and egalitarian HC for the 4 feeders with the Gini coefficient evaluating the inequality of the utilitarian solution and the PoF assessing the efficiency of the egalitarian solution.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n RF & $HC_{uti}$ [$kW$] & $Gini_{uti}$ & $HC_{ega}$ [$kW$] & $PoF_{ega}$ \\\\\n \\hline\n $1$ & 658 & 0.3062 & 520 & 0.21 \\\\\n $2$ & 1575 & 0.9876 & 937 & 0.41 \\\\\n $3$ & 922 & 0.8148 & 325 & 0.65 \\\\\n $4$ & 699 & 0.7247 & 442 & 0.37 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fairness for distribution network hosting capacity", "authors": ["Olivia Rubbers", "Sari Kerckhove", "Md Umar Hashmi", "Dirk Van Hertem"], "url": "https://arxiv.org/abs/2508.16834v1", "attribution": "\"Fairness for distribution network hosting capacity\" by Olivia Rubbers, Sari Kerckhove, Md Umar Hashmi, and Dirk Van Hertem, arXiv:2508.16834v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.11334v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l}\n\\toprule\n\\textbf{Top 15 selected unigrams and bigrams}\\\\ \n\\midrule\nwaiver (0.26), \\_UNK\\_ million (0.21), restructuring (0.21), \\\\severance (0.20), subordinated (0.20), financial covenant (0.15), \\\\ indenture (0.14), lender (0.14), interest payment (0.14), \\\\senior secured (0.14), asset sale (0.12), senior (0.09), \\\\ cross default (0.09), indebtedness (0.07), \\\\event default (0.05), credit facility (0.05) \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Top 15 tokens with largest logistic regression coefficients (shown in parentheses) of the Binary bag-of-words model with single year history.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines", "authors": ["Henri Arno", "Klaas Mulier", "Joke Baeck", "Thomas Demeester"], "url": "https://arxiv.org/abs/2208.11334v1", "attribution": "\"Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines\" by Henri Arno, Klaas Mulier, Joke Baeck, and Thomas Demeester, arXiv:2208.11334v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2312.13722v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Evaluation on the fluctuated effective bandwidth}\n\\begin{tabular}{l|c|cc} \n \\toprule\n Setting & method & LSD $\\downarrow$ & POLQA $\\uparrow$ \\\\\n \\midrule\n \\multirow{3}*{10-48 kHz} &unprocessed & 2.55 &3.96 \\\\\n \\cline{2-4} \n & AERO~{} & 0.82 & 4.26\\\\\n \\cline{2-4} \n & BAE-Net & \\textbf{0.69} & \\textbf{4.38}\\\\\n \\cline{1-4} \n \\multirow{3}*{14-48 kHz} &unprocessed & 2.03 &4.40\\\\\n \\cline{2-4} \n & AERO~{} & 0.69 & 4.42\\\\\n \\cline{2-4} \n & BAE-Net & \\textbf{0.56} & \\textbf{4.54}\\\\\n \\cline{1-4} \n \\multirow{3}*{20-48 kHz} &unprocessed & 1.05 &4.58\\\\\n \\cline{2-4} \n & AERO~{} & 0.51 & 4.57\\\\\n \\cline{2-4} \n & BAE-Net & \\textbf{0.42} & \\textbf{4.64}\\\\\n \\cline{1-4} \n \\multirow{3}*{24-48 kHz} &unprocessed & 0.08 &4.61\\\\\n \\cline{2-4} \n & AERO~{} & \\textbf{0.03} & 4.65\\\\\n \\cline{2-4} \n & BAE-Net & 0.04 & \\textbf{4.68}\\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "BAE-Net: A Low complexity and high fidelity Bandwidth-Adaptive neural network for speech super-resolution", "authors": ["Guochen Yu", "Xiguang Zheng", "Nan Li", "Runqiang Han", "Chengshi Zheng", "Chen Zhang", "Chao Zhou", "Qi Huang", "Bing Yu"], "url": "https://arxiv.org/abs/2312.13722v1", "attribution": "\"BAE-Net: A Low complexity and high fidelity Bandwidth-Adaptive neural network for speech super-resolution\" by Guochen Yu, Xiguang Zheng, Nan Li, Runqiang Han, Chengshi Zheng, Chen Zhang, Chao Zhou, Qi Huang, and Bing Yu, arXiv:2312.13722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09893v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n Model & P & R & F1 \\\\ \\hline\n MF & 89.03 & 42.2 & 57.26 \\\\\n ADE & 86.74 & 43.25 & 57.72 \\\\\n \\hline \\hline\n NOA & 78.14 & 35.06 & 48.40 \\\\\n UAD & 89.01 & 70.08 & 78.37 \\\\\n BEM & 86.75 & 35.94 & 50.82 \\\\\n DECBAE & 88.67 & 74.32 & 80.86 \\\\\n GAD & 89.27 & 76.66 & 81.90 \\\\ \\hline \\hline\n MadDog & 92.27 & 85.01 & 88.49 \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance of models for acronym disambiguation (AD)}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "MadDog: A Web-based System for Acronym Identification and Disambiguation", "authors": ["Amir Pouran Ben Veyseh", "Franck Dernoncourt", "Walter Chang", "Thien Huu Nguyen"], "url": "https://arxiv.org/abs/2101.09893v1", "attribution": "\"MadDog: A Web-based System for Acronym Identification and Disambiguation\" by Amir Pouran Ben Veyseh, Franck Dernoncourt, Walter Chang, and Thien Huu Nguyen, arXiv:2101.09893v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.19781v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Permutation prediction accuracy (\\%) on validation data for different segment counts and training strategies.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\toprule\n\\multirow{2}{*}{Training Strategy} & \\multicolumn{6}{c}{Number of Segments ($N$)} \\\\\n\\cmidrule{2-7}\n & 3 & 4 & 5 & 6 & 7 & 8 \\\\\n\\midrule\nDirect (Epoch 30) & 100.0 & 94.8 & 67.4 & 35.7 & 9.2 & 0.3 \\\\\nDirect (Epoch 100) & 100.0 & 100.0 & 99.5 & 70.2 & 32.5 & 1.7 \\\\\nDirect (Epoch 200) & 100.0 & 100.0 & 100.0 & 87.6 & 68.3 & 4.9 \\\\\n\\midrule\nCurriculum (Epoch 30) & 100.0 & - & - & - & - & - \\\\\nCurriculum (Epoch 50) & - & 100.0 & - & - & - & - \\\\\nCurriculum (Epoch 90) & - & - & 100.0 & - & - & - \\\\\nCurriculum (Epoch 140) & - & - & - & 100.0 & - & - \\\\\nCurriculum (Epoch 170) & - & - & - & - & 100.0 & - \\\\\nCurriculum (Epoch 200) & - & - & - & - & - & 84.2 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation", "authors": ["Daniel La'ah Ayuba", "Jean-Yves Guillemaut", "Belen Marti-Cardona", "Oscar Mendez Maldonado"], "url": "https://arxiv.org/abs/2507.19781v1", "attribution": "\"SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation\" by Daniel La'ah Ayuba, Jean-Yves Guillemaut, Belen Marti-Cardona, and Oscar Mendez Maldonado, arXiv:2507.19781v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09972v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results on the Caltech 101 dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\n\\textbf{Method} & \\textbf{Accuracy}\\\\\n\\hline\nBoAW & 74.00\\% \\\\\n\\hline\n BoVW & \\textbf{83.00\\%} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications", "authors": ["Mario Manzo"], "url": "https://arxiv.org/abs/1912.09972v1", "attribution": "\"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications\" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.18625v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of trading strategies, net of fees.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n \\toprule\n \\textbf{Strategy} & \\textbf{Trades} & \\textbf{Mean Return} & \\textbf{Std Dev Return} & \\textbf{Avg Holding Time (sec)} \\\\\n \\midrule\n Imbalance taker & 18927 & $-1.9621$ & 2.1653 & 15.04 \\\\\n Naive maker & 12623 & $-0.4307$ & 2.6486 & 22.56 \\\\\n Imbalance maker w/o cancel & 8851 & $-0.4705$ & 2.8529 & 32.17 \\\\\n Imbalance maker w/ cancel & 7333 & $-0.4921$ & 3.1143 & 38.83 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "To Make, or to Take, That Is the Question: Impact of LOB Mechanics on Natural Trading Strategies", "authors": ["Jakob Albers", "Mihai Cucuringu", "Sam Howison", "Alexander Y. Shestopaloff"], "url": "https://arxiv.org/abs/2502.18625v1", "attribution": "\"To Make, or to Take, That Is the Question: Impact of LOB Mechanics on Natural Trading Strategies\" by Jakob Albers, Mihai Cucuringu, Sam Howison, and Alexander Y. Shestopaloff, arXiv:2502.18625v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.01846v3_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\n\\toprule\nregion & country \\\\ \\hline\nAmerica S & Antigua and Barbuda \\\\\nAmerica S & Argentina \\\\\nAmerica S & Bahamas \\\\\nAmerica S & Barbados \\\\\nAmerica S & Belize \\\\\nAmerica S & Bolivia (Plurinational State of) \\\\\nAmerica S & Brazil \\\\\nAmerica S & Chile \\\\\nAmerica S & Colombia \\\\\nAmerica S & Costa Rica \\\\\nAmerica S & Cuba \\\\\nAmerica S & Dominica \\\\\nAmerica S & Dominican Republic \\\\\nAmerica S & Ecuador \\\\\nAmerica S & El Salvador \\\\\nAmerica S & Grenada \\\\\nAmerica S & Guatemala \\\\\nAmerica S & Guyana \\\\\nAmerica S & Haiti \\\\\nAmerica S & Honduras \\\\\nAmerica S & Jamaica \\\\\nAmerica S & Mexico \\\\\nAmerica S & Nicaragua \\\\\nAmerica S & Panama \\\\\nAmerica S & Paraguay \\\\\nAmerica S & Peru \\\\\nAmerica S & Puerto Rico \\\\\nAmerica S & Saint Kitts and Nevis \\\\\nAmerica S & Saint Lucia \\\\\nAmerica S & Saint Vincent and the Grenadines \\\\\nAmerica S & Suriname \\\\\nAmerica S & Trinidad and Tobago \\\\\nAmerica S & Uruguay \\\\\nAmerica S & Venezuela (Bolivarian Republic of) \\\\\nAmerica N & Canada \\\\\nAmerica N & United States of America \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Countries and world regions in the Americas}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability", "authors": ["Moritz Laber", "Peter Klimek", "Martin Bruckner", "Liuhuaying Yang", "Stefan Thurner"], "url": "https://arxiv.org/abs/2210.01846v3", "attribution": "\"Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability\" by Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang, and Stefan Thurner, arXiv:2210.01846v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.17099v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|}\n \\hline\n \\textbf{Irrep} & \\textbf{Multiplicative Equivariants} \\\\\n \\hline\n $ \\mathbb{S}^{((2),(0))} $ & $ 1 $ \\\\\n \\hline\n $ \\mathbb{S}^{((1),(1))} $ & $ y_1-y_1^{-1}, y_2-y_2^{-1} $ \\\\\n & $ (y_2+y_2^{-1})\\cdot (y_1-y_1^{-1}) , \\, (y_1+y_1^{-1})\\cdot (y_2-y_2^{-1}) $ \\\\\n \\hline\n $ \\mathbb{S}^{((0),(2))} $ & $ (y_1-y_1^{-1})\\cdot (y_2-y_2^{-1}) $ \\\\\n \\hline\n $ \\mathbb{S}^{((1,1),(0))} $ & $ (y_1+y_1^{-1})- (y_2+y_2^{-1}) $ \\\\\n \\hline\n $ \\mathbb{S}^{((0),(1,1))} $ & $ (y_1^2-y_1^{-2})(y_2-y_2^{-1})-(y_1-y_1^{-1})(y_2^2-y_2^{-2}) $ \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Additive and Multiplicative Coinvariant Spaces of Weyl Groups in the Light of Harmonics and Graded Transfer", "authors": ["Sebastian Debus", "Tobias Metzlaff"], "url": "https://arxiv.org/abs/2412.17099v1", "attribution": "\"Additive and Multiplicative Coinvariant Spaces of Weyl Groups in the Light of Harmonics and Graded Transfer\" by Sebastian Debus and Tobias Metzlaff, arXiv:2412.17099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.16544v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small\\emph{Names and wavelengths of spectral bands of Sentinel2 images. SWIR: Short Wave Infrared, NIR: Near Infrared. The bands are ordered in increasing wavelength.}}\n\\begin{tabular}{c|c|c|c}\n \\multirow[c]{2}{*}{Band Name} & Central & \\multirow[c]{2}{*}{Bandwidth (mm)} & \\multirow[c]{2}{*}{Description}\\\\\n & Wavelength (nm) & & \\\\\n \\hline\n B01 & 442.7 & 21 & Coastal aerosol\\\\\n B02 & 492.4 & 66 & Blue\\\\\n B03 & 559.8 & 36 & Green\\\\\n B04 & 664.6 & 31 & Red\\\\\n B05 & 704.1 & 15 & Vegetation Red Edge 1\\\\\n B06 & 740.5 & 15 & Vegetation Red Edge 2\\\\\n B07 & 782.8 & 20 & Vegetation Red Edge 3\\\\\n B08 & 832.8 & 106 & NIR\\\\\n B08A & 864.7 & 21 & Narrow NIR\\\\\n B09 & 945.1 & 20 & Water Vapor\\\\\n B11 & 1613.7 & 91 & SWIR 1\\\\\n B12 & 2202.4 & 175 & SWIR 2\\\\\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Incremental Hierarchical Tucker Decomposition", "authors": ["Doruk Aksoy", "Alex A. Gorodetsky"], "url": "https://arxiv.org/abs/2412.16544v1", "attribution": "\"Incremental Hierarchical Tucker Decomposition\" by Doruk Aksoy and Alex A. Gorodetsky, arXiv:2412.16544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.04251v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Active-power losses and optimality gaps.}\n\\begin{tabular}{lrrrrrrrrr}\n \\toprule\n & \\multicolumn{2}{c}{AC} & \\multicolumn{2}{c}{Jabr SOC} & & & \\\\ \n \\cmidrule(l{0.5em}r{0.40em}){2-3} \\cmidrule(l{0.5em}r{0.40em}){4-5}\n Case & Objective & Losses & Objective & Losses & Opt Gap & Loss Gap \\\\\n \\midrule\n 1354pegase & 74069.35 & 10.09 & 74008.58 & 9.48 & 0.08\\% & 6.04\\% & \\\\ %i2 SOC 74017.66/9.57\n ACTIVSg10k & 2485898.75 & 23.01 & 2466666.10 & 13.88 & 0.77\\% & 39.68\\% & \\\\\n ACTIVSg70k & 16439499.83 & 156.83 & 16217263.66 & 121.31 & 1.35\\% & 22.65\\% & \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Accurate Linear Cutting-Plane Relaxations for ACOPF", "authors": ["Daniel Bienstock", "Matias Villagra"], "url": "https://arxiv.org/abs/2312.04251v3", "attribution": "\"Accurate Linear Cutting-Plane Relaxations for ACOPF\" by Daniel Bienstock and Matias Villagra, arXiv:2312.04251v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.09542v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|}\n\\hline\n$k$ & $\\binom{23}{k-1}$ & $|\\mathcal{I}_{k}|$ & $|\\mathcal{G}_k|$ & $|\\mathcal{E}_k|$&$|\\mathcal{H}_k|$\\\\\n\\hline\n$1$ &$1$ &$1$ &$1$ &$1$& $1$ \\\\\n$2$ &$23$ &$23$ &$23$&$23$ & $4$\\\\\n$3$ &$253$ &$253$ &$244$&$172$& $6$\\\\\n$4$ &$1771$ &$1768$ &$1400$&$580$& $14$\\\\\n$5$ &$8855$ &$8780$ &$5275$ &$1040$& $11$\\\\\n$6$ &$33649$ &$32736$ &$11652$ &$1086$ &$14$\\\\\n$7$ &$100947$ &$93765$ &$17059$&$1589$& $6$\\\\\n$8$ &$245157$ &$204688$ &$14456$&$416$& $3$\\\\\n$9$ &$490314$ &$320304$ &$5286$&$300$& $1$\\\\\n$10$ &$817190$ &$287180$&$30$& $30$&$1$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Intermediate results from computation of Erd\\H{o}s matrices}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A note on Erdős matrices and Marcus\\unicode{x2013}Ree inequality", "authors": ["Aman Kushwaha", "Raghavendra Tripathi"], "url": "https://arxiv.org/abs/2503.09542v3", "attribution": "\"A note on Erdős matrices and Marcus\\unicode{x2013}Ree inequality\" by Aman Kushwaha and Raghavendra Tripathi, arXiv:2503.09542v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.05558v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The results of different methods on the MELD database.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n\t\t\\toprule\n\t\tMethod & ACC(\\%) & WF1(\\%) & Year \\\\\n\t\t\\midrule\n\t\tDialogueTRM & 65.66 & 63.55 & 2020 \\\\\n\t\tMMGCN & - & 58.65 & 2021 \\\\\n\t\tMM-DFN & 62.49 & 59.46 & 2022 \\\\\n\t\tUniMSE & 65.09 & 65.51 & 2022 \\\\\n\t\tHAAN-ERC & 66.5 & 65.66 & 2023 \\\\\n\t\t\\textbf{Ours} & \\textbf{66.93} & \\textbf{66.1} & \\textbf{2024} \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "WavFusion: Towards wav2vec 2.0 Multimodal Speech Emotion Recognition", "authors": ["Feng Li", "Jiusong Luo", "Wanjun Xia"], "url": "https://arxiv.org/abs/2412.05558v1", "attribution": "\"WavFusion: Towards wav2vec 2.0 Multimodal Speech Emotion Recognition\" by Feng Li, Jiusong Luo, and Wanjun Xia, arXiv:2412.05558v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.11910v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{GNN test accuracy on Amazon Computer Dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n \\toprule\n Architecture & Accuracy \\\\\n \\midrule\n GCN & $0.8677 \\pm 0.0004$\\\\\n SAGE & $0.9124 \\pm 0.0007$\\\\\n GAT & $0.8920 \\pm 0.0010$\\\\\n CGCN & $0.8689 \\pm 0.0011$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks", "authors": ["Eduard Tulchinskii", "Daria Voronkova", "Ilya Trofimov", "Evgeny Burnaev", "Serguei Barannikov"], "url": "https://arxiv.org/abs/2503.11910v1", "attribution": "\"RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks\" by Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, and Serguei Barannikov, arXiv:2503.11910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.01809v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc} \\hline \n & \n \\multicolumn{3}{c}{\\% (Not rejected by \\textit{Complete} $\\cap$ rejected by \\textit{method})} & \n \\multicolumn{3}{c}{\\% (Rejected by \\textit{Complete} $\\cap$ Rejected by \\textit{method})} \n \\\\ \\hline \n $n_r$ & \n Naive &\n Variability corrected&\n Full &\n Naive &\n Variability corrected&\n Full \n \\\\ \\hline \n500 & 0.169 & 0.013 & 0.036 & 0.598 & 0.086 & 0.512 \\\\\n1500 & 0.085 & 0.014 & 0.033 & 0.540 & 0.206 & 0.513 \\\\\n5000 & 0.052 & 0.024 & 0.035 & 0.517 & 0.366 & 0.509 \\\\ \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of methods on FTO gene analysis containing 33 SNPs. The \\textit{Naive} method reject a higher percentage of SNPs not rejected by the complete method (using all of the observations). Note, the parameter $n_r$ has no effect on the \\textit{Full} method, and the differences between rows are only due to different samples taken from the original study.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Inferring on joint associations from marginal associations and a reference sample", "authors": ["Tzviel Frostig", "Ruth Heller"], "url": "https://arxiv.org/abs/2212.01809v1", "attribution": "\"Inferring on joint associations from marginal associations and a reference sample\" by Tzviel Frostig and Ruth Heller, arXiv:2212.01809v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.14060v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\cline{1-3}\\cline{5-7} \n\\textbf{Trigram} & \\textbf{Count} & \\textbf{Match?} & & \\textbf{Unigram} & \\textbf{Count} & \\textbf{Match?} \\\\\\cline{1-3}\\cline{5-7} \ngreatest common divisor & 44 & partly & & jug & 80 & \\checkmark \\\\\nthe greatest common & 39 & \\checkmark & & times & 74 & \\checkmark \\\\\na and b & 31 & \\checkmark & & zero & 63 & \\checkmark \\\\\nis equal to & 26 & \\checkmark & & equal/equals & 63 & \\checkmark \\\\\nx plus y & 22 & \\checkmark & & y & 60 & \\checkmark \\\\\nlinear combination of & 20 & \\checkmark & & divides & 59 & \\checkmark \\\\\ncommon divisor of & 18 & partly & & gallon & 56 & \\checkmark \\\\\na linear combination & 16 & \\checkmark & & m & 54 & \\checkmark \\\\\nthe b jug & 12 & \\checkmark & & right & 54 & \\\\\nthree gallon jug & 11 & partly & & x & 53 & \\checkmark \\\\\\cline{1-3}\n\\textbf{Bigram} & \\textbf{Count} & \\textbf{Match?} & & gallons & 50 & \\checkmark \\\\\\cline{1-3}\ncommon divisor & 44 & partly & & plus & 48 & \\checkmark \\\\\ngreatest common & 44 & \\checkmark & & divisor & 45 & \\checkmark \\\\\nthe greatest & 39 & \\checkmark & & one & 45 & \\checkmark \\\\\ngallon jug & 39 & \\checkmark & & three & 44 & \\\\\nequal to & 39 & \\checkmark & & greatest & 44 & \\checkmark \\\\\nlinear combination & 38 & \\checkmark & & common & 44 & \\checkmark \\\\\na and & 38 & \\checkmark & & linear & 43 & \\checkmark \\\\\nand b & 32 & \\checkmark & & minus & 43 & \\checkmark \\\\\nis equal & 26 & \\checkmark & & combination & 38 & \\checkmark \\\\\nto prove & 25 & & & number & 37 & \\checkmark \\\\\nplus y & 22 & \\checkmark & & prime & 37 & \\\\\nx plus & 22 & \\checkmark & & prove & 30 & \\\\\nm divides & 21 & \\checkmark & & theorem & 27 & \\\\\ncombination of & 20 & \\checkmark & & five & 26 & \\checkmark \\\\\ns prime & 20 & & & jugs & 24 & \\checkmark \\\\\ntimes a & 19 & & & example & 23 & \\\\\ndivisor of & 19 & \\checkmark & & algorithm & 23 & \\checkmark \\\\\nthe remainder & 17 & \\checkmark & & state & 22 & \\\\\nthat m & 17 & & & remainder & 20 & \\checkmark \\\\\\cline{1-3}\\cline{5-7}\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Matching results for lecture ``Number Theory I''}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Unsupervised Spoken Term Discovery on Untranscribed Speech", "authors": ["Man-Ling Sung"], "url": "https://arxiv.org/abs/2011.14060v1", "attribution": "\"Unsupervised Spoken Term Discovery on Untranscribed Speech\" by Man-Ling Sung, arXiv:2011.14060v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.18446v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Latency setups with corresponding parameters.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc|c}\n\\Xhline{2\\arrayrulewidth}\n\\toprule\n\\multirow{2}{*}{\\textbf{Latency}} & Chunk & Right & FIFO & Update & Speaker & \\multirow{2}{*}{RTF} \\\\ \n & Size & Context & Queue & Period & Cache & \\\\ \n\\big[sec\\big] & \\multicolumn{5}{c|}{\\big[frame count\\big]} & \\\\ \\midrule\n10.0 & 124 & 1 & 124 & 124 & 188 & 0.005 \\\\\n1.04 & 6 & 7 & 188 & 144 & 188 & 0.093 \\\\\n0.32 & 3 & 1 & 188 & 144 & 188 & 0.180 \\\\ \n\\bottomrule\n\\Xhline{2\\arrayrulewidth}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Streaming Sortformer: Speaker Cache-Based Online Speaker Diarization with Arrival-Time Ordering", "authors": ["Ivan Medennikov", "Taejin Park", "Weiqing Wang", "He Huang", "Kunal Dhawan", "Jinhan Wang", "Jagadeesh Balam", "Boris Ginsburg"], "url": "https://arxiv.org/abs/2507.18446v1", "attribution": "\"Streaming Sortformer: Speaker Cache-Based Online Speaker Diarization with Arrival-Time Ordering\" by Ivan Medennikov, Taejin Park, Weiqing Wang, He Huang, Kunal Dhawan, Jinhan Wang, Jagadeesh Balam, and Boris Ginsburg, arXiv:2507.18446v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19280v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\t\tstatus & number & $\\max|y''-f|$ & $\\max|y_{opt}-y_{b}|$ \\\\ \\hline\\hline\n $\\to y_b$ & 12\t & 1.0E-07 & 1.3E-09 \\\\\n $\\to y_s$ & 11\t & 1.6E-07 & 2.7E+00 \\\\ \n $diverge$ & 2 & diverge & diverge \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions", "authors": ["Xiaorong Zou"], "url": "https://arxiv.org/abs/2504.19280v1", "attribution": "\"Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions\" by Xiaorong Zou, arXiv:2504.19280v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16355v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c|c}\n Size & Color & Blocks & $n_B$ & $t_B$ & Time $\\sigma$ & Time $\\sigma^{-1}$ & Time $\\mathsf{eval}_h$\\\\\n \\hline\\hline\n $224 \\times 224$\t& RGB & 1000 &\t150\t& 384 & $0.0235$ &\t$0.0963$ & $0.0128$ \\\\\n $128 \\times 128$ & RGB\t& 100 & 491\t& 1256\t& $0.1238$ & $0.3712$ &\t$0.0455$ \\\\\n $64\t\\times 64$ & RGB &\t100\t& 122\t& 312\t& $0.0185$ & $0.0777$ &\t$0.0101$ \\\\\n $28 \\times 28$ & RGB & 10 & 235\t& 601 &\t$0.0436$ &\t$0.1584$ & $0.0204$ \\\\\n $28\t\\times 28$ & Gray &\t1 &\t784\t& 2007 & $0.2596$ &\t$0.6667$ & $0.0784$ \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Total time in seconds taken by our scheme to produce the $\\sigma$-polynomial of an image, the $\\sigma$-polynomial and its inverse of an image, and the solution using the $\\mathsf{eval}_h$ function, where the images are divided into blocks with $t = 0.01qn$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Property-Preserving Hashing for $\\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks", "authors": ["Hassan Asghar", "Chenhan Zhang", "Dali Kaafar"], "url": "https://arxiv.org/abs/2504.16355v1", "attribution": "\"Property-Preserving Hashing for $\\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks\" by Hassan Asghar, Chenhan Zhang, and Dali Kaafar, arXiv:2504.16355v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.19555v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc} \n\t\t\n\t\t\\toprule\n\t\t\n\t\t& $\\omega_{1,0} = 0.025$ & $\\alpha_{1,0} = 0.05$ & $\\beta_{1,0} = 0.90$ & $\\omega_{2,0} = 0.50$ & $\\alpha_{2,0} = 0.40$ & $\\beta_{2,0} = 0.50$ & $p_{11,0} = 0.95$ & $p_{22,0} = 0.90$ \\\\\n\t\t\\cmidrule(lr){1-9}\n\t\t$T = 1250$ & $\\underset{(0.108)}{0.059}$ & $\\underset{(0.056)}{0.065}$ & $\\underset{(0.174)}{0.844}$ & $\\underset{(0.621)}{0.716}$ & $\\underset{(0.148)}{0.402}$ & $\\underset{(0.178)}{0.443}$ & $\\underset{(0.071)}{0.945}$ & $\\underset{(0.100)}{0.896}$ \\\\\n\t\t$T = 2500$ & $\\underset{(0.042)}{0.031}$ & $\\underset{(0.027)}{0.053}$ & $\\underset{(0.078)}{0.889}$ & $\\underset{(0.268)}{0.564}$ & $\\underset{(0.092)}{0.403}$ & $\\underset{(0.110)}{0.478}$ & $\\underset{(0.046)}{0.944}$ & $\\underset{(0.067)}{0.894}$ \\\\\n\t\t$T = 5000$ & $\\underset{(0.009)}{0.026}$ & $\\underset{(0.013)}{0.050}$ & $\\underset{(0.021)}{0.900}$ & $\\underset{(0.145)}{0.518}$ & $\\underset{(0.061)}{0.400}$ & $\\underset{(0.071)}{0.494}$ & $\\underset{(0.022)}{0.947}$ & $\\underset{(0.040)}{0.896}$ \\\\\n\t\t\\cmidrule(lr){1-9}\n\t\t\n\t\t& $\\omega_{1,0} = 0.025$ & $\\alpha_{1,0} = 0.05$ & $\\beta_{1,0} = 0.90$ & $\\omega_{2,0} = 0.25$ & $\\alpha_{2,0} = 0.30$ & $\\beta_{2,0} = 0.60$ & $p_{11,0} = 0.95$ & $p_{22,0} = 0.90$ \\\\\n\t\t\\cmidrule(lr){1-9}\n\t\t$T = 1250$ & $\\underset{(0.112)}{0.077}$ & $\\underset{(0.060)}{0.070}$ & $\\underset{(0.222)}{0.796}$ & $\\underset{(0.553)}{0.500}$ & $\\underset{(0.155)}{0.302}$ & $\\underset{(0.217)}{0.509}$ & $\\underset{(0.105)}{0.948}$ & $\\underset{(0.119)}{0.908}$ \\\\\n\t\t$T = 2500$ & $\\underset{(0.069)}{0.046}$ & $\\underset{(0.041)}{0.059}$ & $\\underset{(0.143)}{0.858}$ & $\\underset{(0.302)}{0.350}$ & $\\underset{(0.110)}{0.309}$ & $\\underset{(0.152)}{0.555}$ & $\\underset{(0.078)}{0.944}$ & $\\underset{(0.100)}{0.896}$ \\\\\n\t\t$T = 5000$ & $\\underset{(0.025)}{0.029}$ & $\\underset{(0.022)}{0.052}$ & $\\underset{(0.056)}{0.892}$ & $\\underset{(0.160)}{0.289}$ & $\\underset{(0.070)}{0.305}$ & $\\underset{(0.096)}{0.580}$ & $\\underset{(0.051)}{0.944}$ & $\\underset{(0.070)}{0.893}$ \\\\\n\t\t\\cmidrule(lr){1-9}\n\t\t\n\t\t& $\\omega_{1,0} = 0.025$ & $\\alpha_{1,0} = 0.05$ & $\\beta_{1,0} = 0.90$ & $\\omega_{2,0} = 0.125$ & $\\alpha_{2,0} = 0.20$ & $\\beta_{2,0} = 0.70$ & $p_{11,0} = 0.95$ & $p_{22,0} = 0.90$ \\\\\n\t\t\\cmidrule(lr){1-9}\n\t\t$T = 1250$ & $\\underset{(0.112)}{0.093}$ & $\\underset{(0.068)}{0.068}$ & $\\underset{(0.260)}{0.741}$ & $\\underset{(0.410)}{0.335}$ & $\\underset{(0.146)}{0.191}$ & $\\underset{(0.263)}{0.587}$ & $\\underset{(0.121)}{0.954}$ & $\\underset{(0.132)}{0.925}$ \\\\\n\t\t$T = 2500$ & $\\underset{(0.084)}{0.066}$ & $\\underset{(0.049)}{0.065}$ & $\\underset{(0.205)}{0.801}$ & $\\underset{(0.371)}{0.282}$ & $\\underset{(0.134)}{0.204}$ & $\\underset{(0.224)}{0.613}$ & $\\underset{(0.114)}{0.951}$ & $\\underset{(0.124)}{0.918}$ \\\\\n\t\t$T = 5000$ & $\\underset{(0.048)}{0.043}$ & $\\underset{(0.034)}{0.058}$ & $\\underset{(0.123)}{0.859}$ & $\\underset{(0.243)}{0.217}$ & $\\underset{(0.094)}{0.207}$ & $\\underset{(0.168)}{0.643}$ & $\\underset{(0.081)}{0.953}$ & $\\underset{(0.112)}{0.909}$ \\\\\n\t\t\\cmidrule(lr){1-9}\n\t\t\n\t\t\\bottomrule\n\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{The estimated means and standard deviations (in parentheses) of the estimated parameters of three two-state Markov-switching GARCH models together with the true parameters.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Asymptotic Properties of the Maximum Likelihood Estimator for Markov-switching Observation-driven Models", "authors": ["Frederik Krabbe"], "url": "https://arxiv.org/abs/2412.19555v2", "attribution": "\"Asymptotic Properties of the Maximum Likelihood Estimator for Markov-switching Observation-driven Models\" by Frederik Krabbe, arXiv:2412.19555v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2509.14198v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|}\n\\hline\n\\textbf{Hyperparameter} & \\textbf{Allen-Cahn} & \\textbf{Burgers} \\\\\n\\hline\nN. of Adam training iterations & 5e3 & 5e3 \\\\\nN. of SSBroyden training iterations & 6e4 & 6e4 \\\\\nNumber of hidden layers $N$ & 3 & 3 \\\\\nHidden layer dimension $H$ & 30 & 30 \\\\\nActivation function & $\\tanh(\\cdot)$ & $\\tanh(\\cdot)$ \\\\\nPeriodicity Encoding & $\\sin(\\cdot), \\cos(\\cdot)$ & $\\sin(\\cdot), \\cos(\\cdot)$ \\\\\n\\hline\n\\multicolumn{3}{|c|}{\\textbf{vRBA Parameters (Sampling)}} \\\\\n\\hline\nResampling Frequency & 100 iter. & 100 iter. \\\\\n$\\gamma$ (EMA memory) & 0.9 & 0.9 \\\\\n$\\eta$ (EMA learning rate) & 0.1 & 0.1 \\\\\n$\\phi$ (Smoothing) & 0.9 & 1.0 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Implementation details for the Allen-Cahn and Burgers' equations using a second-order SSBroyden optimizer, following the methodology in .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning", "authors": ["Juan Diego Toscano", "Daniel T. Chen", "Vivek Oommen", "George Em Karniadakis"], "url": "https://arxiv.org/abs/2509.14198v1", "attribution": "\"A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning\" by Juan Diego Toscano, Daniel T. Chen, Vivek Oommen, and George Em Karniadakis, arXiv:2509.14198v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.05922v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Complete Backtest Sensitivity Analysis Summary}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrrrr}\n\\toprule\n\\textbf{Holding Period} & \\textbf{Strategy} & \\textbf{Total Net P\\&L} & \\textbf{Sharpe Ratio (Ann.)} & \\textbf{Profit Factor} & \\textbf{Max Drawdown} & \\textbf{Max Drawdown (\\%)} \\\\\n\\midrule\n\\multirow{2}{*}{5 Days} & Fixed-Size & \\$31,247.50 & 0.38 & 1.22 & (\\$52,682.50) & 55.66\\% \\\\\n& Pyramiding & \\$797,222.50 & 1.62 & 2.77 & (\\$176,712.50) & 186.71\\% \\\\\n\\midrule\n\\multirow{2}{*}{7 Days} & Fixed-Size & \\$112,385.00 & 1.23 & 1.93 & (\\$39,287.50) & 41.00\\% \\\\\n& Pyramiding & \\$1,180,760.00 & 2.00 & 3.95 & (\\$135,325.00) & 141.21\\% \\\\\n\\midrule\n\\multirow{2}{*}{10 Days} & Fixed-Size & \\$200,985.00 & 2.01 & 3.00 & (\\$25,000.00) & 10.76\\% \\\\\n& Pyramiding & \\$1,404,622.50 & 2.18 & 4.42 & (\\$239,230.00) & 15.74\\% \\\\\n\\midrule\n\\multirow{2}{*}{12 Days} & Fixed-Size & \\$235,210.00 & 2.03 & 3.34 & (\\$56,052.50) & 18.37\\% \\\\\n& Pyramiding & \\$1,165,810.00 & 1.21 & 2.50 & (\\$694,205.00) & 40.40\\% \\\\\n\\midrule\n\\multirow{2}{*}{15 Days} & Fixed-Size & \\$249,235.00 & 1.68 & 2.79 & (\\$132,027.50) & 36.16\\% \\\\\n& Pyramiding & \\$772,910.00 & 0.63 & 1.56 & (\\$1,343,805.00) & 72.77\\% \\\\\n\\midrule\n\\multirow{2}{*}{17 Days} & Fixed-Size & \\$236,472.50 & 1.48 & 2.29 & (\\$180,727.50) & 47.02\\% \\\\\n& Pyramiding & \\$728,035.00 & 0.60 & 1.47 & (\\$1,551,217.50) & 78.94\\% \\\\\n\\midrule\n\\multirow{2}{*}{20 Days} & Fixed-Size & \\$217,385.00 & 1.23 & 1.95 & (\\$229,240.00) & 57.59\\% \\\\\n& Pyramiding & \\$735,522.50 & 0.63 & 1.45 & (\\$1,634,867.50) & 80.06\\% \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation", "authors": ["Peilin Rao", "Randall R. Rojas"], "url": "https://arxiv.org/abs/2509.05922v1", "attribution": "\"Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation\" by Peilin Rao and Randall R. Rojas, arXiv:2509.05922v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.06208v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ JIVR model parameter estimates. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrr}\n\\toprule\nParameter & $\\beta_1$ & $\\beta_2$ & $\\beta_3$ & $\\beta_4$ & $\\beta_5$ & & S\\&P500 \\\\\n\\cline{1-6}\\cline{8-8}\n$\\alpha$ & 0.000899 & 0.008400 & 0.000770 & -0.001393 & 0.000657 & $\\lambda$ & 2.711279 \\\\\n$\\theta_1$ & 0.996290 & -0.013869 & & 0.002841 & & &\\\\\n$\\theta_2$ & 0.003669 & 0.877813 & 0.001300 & & & &\\\\\n$\\theta_3$ & & -0.032640 & 0.997071 & 0.003722 & -0.004198 & &\\\\\n$\\theta_4$ & & & & 0.980269 & & &\\\\\n$\\theta_5$ & & -0.047789 & & & 0.986019 & &\\\\\n$\\nu$ & & 0.089445 & & & & &\\\\\n$\\sigma\\sqrt{252}$ & & 0.380279 & 0.052198 & 0.048641 & 0.051536 & &\\\\\n$\\omega$ & 0.267589 & & & & & & 0.977291 \\\\\n$\\kappa$ & 0.838220 & 0.965751 & 0.974251 & 0.945377 & 0.980844 & & 0.888977 \\\\\n$a$ & 0.134152 & 0.098272 & 0.092646 & 0.102201 & 0.100502 & & 0.056087 \\\\\n$\\gamma$ & -0.111813 & -1.482862 & 0.096766 & 0.060558 & -0.102996 & & 2.507796 \\\\\n$\\zeta$ & 0.143760 & 0.852943 & 0.029109 & -0.159051 & 0.092664 & & -0.641306 \\\\\n$\\varphi$ & 1.351070 & 1.538928 & 2.284780 & 1.449977 & 1.428477 & & 2.039669 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deep Hedging with Options Using the Implied Volatility Surface", "authors": ["Pascal François", "Geneviève Gauthier", "Frédéric Godin", "Carlos O. Pérez-Mendoza"], "url": "https://arxiv.org/abs/2504.06208v3", "attribution": "\"Deep Hedging with Options Using the Implied Volatility Surface\" by Pascal François, Geneviève Gauthier, Frédéric Godin, and Carlos O. Pérez-Mendoza, arXiv:2504.06208v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.24111v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Down--and--out call: finite–difference vs.\\ Monte–Carlo}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\\hline\nMethod & Price & Rel.\\ error (\\%) & CPU (s)\\\\\\hline\nGrünwald--Letnikov ($\\Delta x=0.012$) & 131.42 & 0.21 & 1.7\\\\\nGrünwald--Letnikov ($\\Delta x=0.008$) & 131.09 & 0.00 & 3.9\\\\\nMonte--Carlo ($10^{6}$) & 131.10 & -- & 82.0\\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Pricing Fractal Derivatives under Sub-Mixed Fractional Brownian Motion with Jumps", "authors": ["Nader Karimi"], "url": "https://arxiv.org/abs/2506.24111v1", "attribution": "\"Pricing Fractal Derivatives under Sub-Mixed Fractional Brownian Motion with Jumps\" by Nader Karimi, arXiv:2506.24111v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.02387v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\\hline\n{Dataset} & {MOM} & {NAG} & \\texttt{Grad-Avg} & {SGD} \\\\ \\hline \n \\textbf{Boston Housing} & 14.31 & 2.31 & \\textbf{3.35} & 3.36 \\\\\n \\textbf{CBM} & 0.76 & 0.48 & \\textbf{0.32} & 0.31 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{MSE on the test set for both the regression datasets using normal initialization.`MOM' stands for `SGD with Momentum'}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Variant of Gradient Descent Algorithm Based on Gradient Averaging", "authors": ["Saugata Purkayastha", "Sukannya Purkayastha"], "url": "https://arxiv.org/abs/2012.02387v2", "attribution": "\"A Variant of Gradient Descent Algorithm Based on Gradient Averaging\" by Saugata Purkayastha and Sukannya Purkayastha, arXiv:2012.02387v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13382v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n \\toprule\n $n$ & $p$ & $|E|$ & $\\gamma_2$ & $ \\gamma$ & \\text{Lower} & $K$ &\\text{Upper } & $K^{**}$ \\\\\n \\midrule\n 600 & 0.9 & 161690 & 513.07& 562.52 & 598.11 & 598.11& 598.11 & 597.83 \\\\\n 500 & 0.9 & 112373 & 428.18 & 471.34 & 498.11 & 498.11& 498.11&497.84\\\\\n 250 & 0.9 & 28068 & 203.97& 238.27& 248.11& 248.11& 248.11 & 247.81 \\\\\n 600 & 0.7 & 125691 & 380.80& 459.08& 598.43& 598.43& 598.43 & 597.64 \\\\\n 500 & 0.7 & 87303 & 313.29& 385.94& 498.43& 498.43& 498.43 & 497.62 \\\\\n 250 & 0.7 & 21942 & 149.61& 198.09& 248.42& 248.42& 248.42 & 247.56 \\\\\n 600 & 0.5 & 89818 & 252.62& 339.41& 599.00& 599.00& 599.00 & 598.00 \\\\\n 500 & 0.5 & 62670 & 215.95& 292.17& 498.99& 498.99& 498.99 & 498.07 \\\\\n 250 & 0.5 & 15698 & 100.97& 152.64& 248.98& 248.99& 248.99 & 248.06 \\\\\n 600 & 0.05 & 8912 & 13.06& 49.00& 618.34& 618.37& 618.39 & 635.00 \\\\\n 500 & 0.05 & 6226 & 12.48& 44.32& 518.51& 518.54& 518.56 & 532.59 \\\\\n 250 & 0.05 & 1599 & 2.66& 33.61& 269.19& 269.45 & 269.51& 287.82 \\\\\n \\bottomrule\n \\hspace{.2cm}\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Results for different values of $n$ and $p$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Spectral Approach to Kemeny's Constant", "authors": ["Aida Abiad", "Ángeles Carmona", "Andrés M. Encinas", "Maria José Jiménez", "Álvaro Samperio"], "url": "https://arxiv.org/abs/2503.13382v1", "attribution": "\"A Spectral Approach to Kemeny's Constant\" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.09916v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Modeling and sampling time in the \\textbf{CelebA} and \\textbf{MNIST} dataset of 2000 artificial samples based on a latent space of size $n=2000$ in [s]. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n & \\textbf{CelebA} & \\textbf{CelebA} & \\textbf{MNIST} &\\textbf{MNIST} \\\\\n Method &Learn &Sample&Learn &Sample\\\\\\hline\n Gauss &$ <$0.01 & 0.01 & 0.002 & 0.002 \\\\\n Indep. & 4.10 &0.07 & 0.393 &0.003 \\\\\n KDE & 75.25 & 0.01 & 13.958 & 0.001 \\\\\n GMM & 1.35 & 0.03 & 0.115 & 0.004 \\\\\n VCAE & 306.97 & 148.48 & 10.345 & 4.590 \\\\\n EBCAE & 3.41 & 59.36 & 0.328 & 5.738 \\\\\n Real NVP & 2541.19 &3.69 &341.608 & 0.477 \\\\ \\hline\n \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Learning Nonparametric High-Dimensional Generative Models: The Empirical-Beta-Copula Autoencoder", "authors": ["Maximilian Coblenz", "Oliver Grothe", "Fabian Kächele"], "url": "https://arxiv.org/abs/2309.09916v1", "attribution": "\"Learning Nonparametric High-Dimensional Generative Models: The Empirical-Beta-Copula Autoencoder\" by Maximilian Coblenz, Oliver Grothe, and Fabian Kächele, arXiv:2309.09916v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11803v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccc}\n\\toprule\n\\multirow{2}{*}{codename} &\n \\multirow{2}{*}{1} &\n \\multirow{2}{*}{0.75} &\n \\multirow{2}{*}{0.5} &\n \\multirow{2}{*}{PARAs} &\n \\multirow{2}{*}{FLOPs} &\n \\multirow{2}{*}{$\\Gamma_{min}$} &\n \\multirow{2}{*}{\\%PARA} &\n \\multirow{2}{*}{\\%FLOPS} &\n IID &\n \\multicolumn{2}{c}{Non-IID} \\\\ \\cmidrule(l){10-12} \n & & & & & & & & & Accuracy & Global & Local \\\\ \\midrule\n1111111111 & 10 & & & 159010 & 158800 & 10 & 1.00 & 1.00 & 98.045 & 93.59 & 93.82 \\\\\n1111114444 & 6 & 4 & & 143330 & 143120 & 6 & 0.90 & 0.90 & 98.18 & 95.15 & 95.49 \\\\\n1111144447 & 5 & 4 & 1 & 135490 & 135280 & 5 & 0.85 & 0.85 & 97.51 & 89.13 & 89.29 \\\\\n1111223344 & 4 & 6 & & 135490 & 135280 & 8 & 0.85 & 0.85 & 98.325 & 95.48 & 95.82 \\\\\n1111234444 & 4 & 6 & & 135490 & 135280 & 6 & 0.85 & 0.85 & 98.395 & 95.45 & 95.96 \\\\\n1111234567 & 4 & 3 & 3 & 123730 & 123520 & 7 & 0.77 & 0.77 & 96.735 & 88.99 & 88.9 \\\\\n1111444444 & 4 & 6 & & 135490 & 135280 & 4 & 0.85 & 0.85 & 97.85 & 89.13 & 89.29 \\\\\n1111444477 & 4 & 4 & 2 & 127650 & 127440 & 4 & 0.80 & 0.80 & 96.99 & 93.02 & 93.12 \\\\\n1111556677 & 4 & & 6 & 111970 & 111760 & 6 & 0.70 & 0.70 & 95.545 & 80.07 & 79.34 \\\\\n1114556677 & 3 & 1 & 6 & 108050 & 107840 & 5 & 0.67 & 0.67 & 95.80 & 79.3 & 79.75 \\\\\n1234556677 & 1 & 3 & 6 & 100210 & 100000 & 5 & 0.63 & 0.62 & 95.315 & 81.66 & 81.64 \\\\\n1455666777 & 1 & 1 & 8 & 92370 & 92160 & 3 & 0.58 & 0.58 & 94.795 & 79.15 & 79.08 \\\\\n2233445677 & 0 & 6 & 4 & 104130 & 103920 & 5 & 0.65 & 0.65 & 95.955 & 81.27 & 81.17 \\\\\n1444777777 & 1 & 3 & 6 & 92370 & 92160 & 6 & 0.65 & 0.65 & 95.10 & 72.19 & 71.64 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results For Weights Pruning on MNIST}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning", "authors": ["Hanhan Zhou", "Tian Lan", "Guru Venkataramani", "Wenbo Ding"], "url": "https://arxiv.org/abs/2201.11803v2", "attribution": "\"On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning\" by Hanhan Zhou, Tian Lan, Guru Venkataramani, and Wenbo Ding, arXiv:2201.11803v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10936v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\usepackage{csquotes}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|ccc|cccccc|}\n \\hline\n & \\multicolumn{3}{c|}{Accuracy} & \\multicolumn{6}{c|}{Fidelity} \\\\\n & I\\;$\\uparrow$ & C\\;$\\uparrow$ & TS\\;$\\uparrow$ & ND\\;$\\downarrow$ & P\\;$\\uparrow$ & V\\;$\\uparrow$ & D\\;$\\uparrow$ & $s_c\\uparrow$ & $s_g\\uparrow$ \\\\\n \\hline\n FIGARO & .960 & .593 & .997 & .238 & .827 & .735 & .748 & .790 & .853 \\\\\n \\hline\n Expert Similarity & \\textbf{.819} & \\textbf{.351} & \\textbf{.993} & \\textbf{.453} & .625 & .354 & .473 & \\textbf{.595} & .621 \\\\\n Learned Similarity & .288 & .057 & .753 & .696 & \\textbf{.699} & \\textbf{.670} & \\textbf{.623} & .398 & \\textbf{.741} \\\\\n \\hline\n Mean & .554 & .204 & .873 & .575 & .662 & .512 & .548 & .497 & .681 \\\\\n Best case & .819 & .351 & .993 & .453 & .699 & .670 & .623 & .595 & .741 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Description mixing performance on similarity metrics. \\enquote{FIGARO} refers to the performance on unaltered descriptions. Expert/learned similarity refers to similarity of generated samples to the sequence from which the expert/learned description was extracted from. Highlights indicate which of the two descriptions was more closely adhered to in terms of the respective metric.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control", "authors": ["Dimitri von Rütte", "Luca Biggio", "Yannic Kilcher", "Thomas Hofmann"], "url": "https://arxiv.org/abs/2201.10936v4", "attribution": "\"FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control\" by Dimitri von Rütte, Luca Biggio, Yannic Kilcher, and Thomas Hofmann, arXiv:2201.10936v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10803v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\nDataset & Majority Vote (\\%) & Fleiss' Kappa \\\\ \\hline\nAudioSet & 65.66 & 0.4385 \\\\\nVGG-Sound & 84.00 & 0.4634 \\\\\nOurs (2M) & 69.00 & 0.5110 \\\\ \nRandom & 44.00 & 0.6112 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{Human evaluation results} assessing the perceived audio-visual correspondence in videos from different datasets.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning", "authors": ["Sangho Lee", "Jiwan Chung", "Youngjae Yu", "Gunhee Kim", "Thomas Breuel", "Gal Chechik", "Yale Song"], "url": "https://arxiv.org/abs/2101.10803v3", "attribution": "\"ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning\" by Sangho Lee, Jiwan Chung, Youngjae Yu, Gunhee Kim, Thomas Breuel, Gal Chechik, and Yale Song, arXiv:2101.10803v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.15785v3_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimated Effects of Mergers (Synthetic DiD)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n& \\multicolumn{3}{c}{Merger Cases} \\\\\n\\cmidrule(lr){2-4}\nOutcomes & DL-NW & UA-CO & AA-US \\\\\n\\midrule\n\\multicolumn{4}{l}{\\textit{Panel A: Levels}} \\\\\nLog (Price) & -0.015 & -0.004 & -0.035 \\\\\n& (0.022) & (0.012) & (0.032) \\\\\nLog (Passengers) & 0.048 & 0.011 & 0.178 \\\\\n& (0.034) & (0.026) & (0.052) \\\\\nLog (Offered Seats) & 0.130 & 0.213$^{***}$ & 0.130$^{**}$ \\\\\n& (0.099) & (0.015) & (0.030) \\\\\n\\multicolumn{4}{l}{\\textit{Panel B: First Differences}} \\\\\nLog (Price) & -0.003 & -0.008$^{*}$ & -0.000 \\\\\n& (0.007) & (0.004) & (0.0006) \\\\\nLog (Passengers) & 0.005 & 0.005 & 0.018 \\\\\n& (0.034) & (0.013) & (0.011) \\\\\nLog (Offered Seats) & 0.020 & 0.053$^{***}$ & 0.020$^{**}$ \\\\\n& (0.017) & (0.013) & (0.009) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Bridging Retrospective and Prospective Merger Analyses: The Case of US Airline Mergers", "authors": ["Gaurab Aryal", "Anirban Chattopadhyaya", "Federico Ciliberto"], "url": "https://arxiv.org/abs/2503.15785v3", "attribution": "\"Bridging Retrospective and Prospective Merger Analyses: The Case of US Airline Mergers\" by Gaurab Aryal, Anirban Chattopadhyaya, and Federico Ciliberto, arXiv:2503.15785v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.19168v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Spring and damper conditions in the dataset. Only Condition \\#1 is considered as healthy, while all others are faulty. Two sub-conditions from Condition \\#2 (nSdD100 and nSdD120) are combined into nSdD and other two from condition \\#5 (lSdD100 and lSdD120) are lSdD.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n Condition \\# & Spring & Damper & Notation & \\# samples\\\\\n \\hline\n 1 & normal & normal & nSnD & 60 \\\\\n \\hline\n \\multirow{2}{*}{2} & \\multirow{2}{*}{normal} & degraded 100 & nSdD100 & 30 \\\\\n & & degraded 120 & nSdD120 & 30 \\\\\n \\hline\n 3 & high & normal & hSnD & 96 \\\\\n \\hline\n 4 & low & normal & lSnD & 65 \\\\\n \\hline\n \\multirow{2}{*}{5} & \\multirow{2}{*}{low} & degraded 100 & lSdD100 & 29 \\\\\n & & degraded 120 & lSdD120 & 30 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers", "authors": ["Chi-Ching Hsu", "Gaëtan Frusque", "Florent Forest", "Felipe Macedo", "Christian M. Franck", "Olga Fink"], "url": "https://arxiv.org/abs/2507.19168v1", "attribution": "\"Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers\" by Chi-Ching Hsu, Gaëtan Frusque, Florent Forest, Felipe Macedo, Christian M. Franck, and Olga Fink, arXiv:2507.19168v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.06317v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Calibration Errors for Sinusoidal Hull-White Model}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Maturity & Observed (\\%) & Fitted (\\%) & Error (\\%) \\\\\n \\midrule\n 1 & 1.22 & 1.43 & -0.21 \\\\\n 2 & 1.75 & 1.56 & 0.19 \\\\\n 3 & 1.91 & 1.79 & 0.12 \\\\\n 5 & 1.96 & 1.87 & 0.09 \\\\\n 7 & 2.01 & 2.00 & 0.01 \\\\\n 10 & 2.00 & 2.14 & -0.14 \\\\\n 20 & 2.45 & 2.25 & 0.20 \\\\\n 30 & 2.36 & 2.35 & 0.01 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Sinusoidal Hull-White Model for Interest Rate Dynamics: Capturing Long-Term Periodicity in U.S. Treasury Yields", "authors": ["Amit Kumar Jha"], "url": "https://arxiv.org/abs/2506.06317v1", "attribution": "\"A Sinusoidal Hull-White Model for Interest Rate Dynamics: Capturing Long-Term Periodicity in U.S. Treasury Yields\" by Amit Kumar Jha, arXiv:2506.06317v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.06540v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||cl||} \n \\hline\n Species Symbol & Definition \\\\ \n \\hline\\hline\n mRNA-Bax & Messenger mRNA for Bax \\\\ \n Bad-Bcl-xL & Bad bound to Bcl-xL \\\\ \n Casp-pro & Inactive caspase \\\\\n Bax-Bcl-xL & Bax bound to Bcl-xL \\\\\n Casp-act & Activated caspase \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Table of measurable species in the \\textit{PARP1} inhibited ODE model. As in , all initial values for these species is 0. The ODE model computes concentrations in dimensionless units \\textit{[Molec.]} representing molecules-per-cell.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models", "authors": ["Natalie M. Isenberg", "Susan D. Mertins", "Byung-Jun Yoon", "Kristofer Reyes", "Nathan M. Urban"], "url": "https://arxiv.org/abs/2309.06540v2", "attribution": "\"Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models\" by Natalie M. Isenberg, Susan D. Mertins, Byung-Jun Yoon, Kristofer Reyes, and Nathan M. Urban, arXiv:2309.06540v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10014v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|}\n\\hline \\bf Model & \\bf Aggreability Rate \\\\\n\\hline\n Word2Vec 2019 & 0.64 \\\\\n \\hline\n Word2Vec 2018 & 0.52 \\\\\n \\hline\n Word2Vec 2017 & 0.49 \\\\\n\\hline\n\\end{tabular}\n\\caption{ Expert validation for the knowledge base. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Simple Disaster-Related Knowledge Base for Intelligent Agents", "authors": ["Clark Emmanuel Paulo", "Arvin Ken Ramirez", "David Clarence Reducindo", "Rannie Mark Mateo", "Joseph Marvin Imperial"], "url": "https://arxiv.org/abs/2101.10014v1", "attribution": "\"A Simple Disaster-Related Knowledge Base for Intelligent Agents\" by Clark Emmanuel Paulo, Arvin Ken Ramirez, David Clarence Reducindo, Rannie Mark Mateo, and Joseph Marvin Imperial, arXiv:2101.10014v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.05136v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n\\hline\\hline\n& roc\\_auc\\_score \\\\ \n\\hline\nRandom Forest & 0.928\\\\\nLogistic Regression & 0.910 \\\\\n\\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{AUC Score for Logistic Regression and Random Forest}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Classification based credit risk analysis: The case of Lending Club", "authors": ["Aadi Gupta", "Priya Gulati", "Siddhartha P. Chakrabarty"], "url": "https://arxiv.org/abs/2210.05136v1", "attribution": "\"Classification based credit risk analysis: The case of Lending Club\" by Aadi Gupta, Priya Gulati, and Siddhartha P. Chakrabarty, arXiv:2210.05136v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08954v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{change detection results of different methods on the Florence dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccc}\n\\hline\\hline\nMethods & FP & FN & OE & PCC (\\%) & KC(\\%) & F1(\\%)\\\\ \\hline\nPCAKM & 1426 & 535 & 1961 & 97.01 & 79.13 & 82.20\\\\\nNR-ELM & 1273 & 520 & 1793 & 97.26 & 80.67 & 82.88\\\\\nGaborPCANet & 1055 & 564 & 1619 & 97.53 & 82.11 & 84.86\\\\\nLR-CNN & 776 & 714 & 1490 & 97.73 & 82.84 & 85.76\\\\\nMLFN & 702 & 397 & 1099 & 98.32 & 87.64 & 88.54\\\\\nDBN & 796 & \\textbf{371} & 1167 & 98.22 & 87.03 &88.53\\\\\nDCNet & 454 & 602 & 1056 & 98.39 & 87.58 & 88.67\\\\\nGKSNet & \\textbf{209} & 576 & \\textbf{785} & \\textbf{98.80} & \\textbf{90.56} &\\textbf{91.95} \\\\\n\\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network", "authors": ["Junjie Wang", "Feng Gao", "Junyu Dong", "Shan Zhang", "Qian Du"], "url": "https://arxiv.org/abs/2201.08954v2", "attribution": "\"Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network\" by Junjie Wang, Feng Gao, Junyu Dong, Shan Zhang, and Qian Du, arXiv:2201.08954v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16302v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Inclusion relationships for categories of networks.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline \nLine & First network class & Relationship & Second network class & Source \\\\ \\hline\n1 & Time-consistent galled trees & identical to & Normal galled trees & , p.~3 \\\\\n2 & Time-consistent galled trees & proper subset of & Normal networks & , p.~3 \\\\\n3 & Time-consistent galled trees & proper subset of & Galled trees & , p.~3 \\\\\n4 & Galled trees & proper subset of & Galled tree-child networks & , Table 1 \\#13 \\\\\n5 & Galled tree-child networks & proper subset of & Galled networks & , Remark 4\\\\ \n6 & Galled tree-child networks & proper subset of & Tree-child networks & , Remark 4 \\\\\n7 & Normal networks & proper subset of & Tree-child networks & , Fig.~12\\\\\n8 & Tree-child networks & proper subset of & Reticulation-visible networks & , Fig.~12 \\\\\n9 & Galled networks & proper subset of & Reticulation-visible networks & , Fig.~12 \\\\\n10 & Reticulation-visible networks & proper subset of & Phylogenetic networks & , Fig.~5\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Enumerative combinatorics of unlabeled and labeled time-consistent galled trees", "authors": ["Lily Agranat-Tamir", "Michael Fuchs", "Bernhard Gittenberger", "Noah A. Rosenberg"], "url": "https://arxiv.org/abs/2504.16302v1", "attribution": "\"Enumerative combinatorics of unlabeled and labeled time-consistent galled trees\" by Lily Agranat-Tamir, Michael Fuchs, Bernhard Gittenberger, and Noah A. Rosenberg, arXiv:2504.16302v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.16701v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Random Forest Classification Performance Metrics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n\\toprule\n\\textbf{Metric} & \\textbf{Value} \\\\\n\\midrule\n\\textbf{Predictive Performance} & \\\\\nAUC-ROC & 0.8825 \\\\\nAccuracy & 0.7968 \\\\\nPrecision (Up Moves) & 0.80 \\\\\nPrecision (Down Moves) & 0.80 \\\\\nRecall (Up Moves) & 0.80 \\\\\nRecall (Down Moves) & 0.80 \\\\\nF1-Score & 0.80 \\\\\n& \\\\\n\\textbf{Cross-Validation Results} & \\\\\nMean CV AUC & 0.8747 \\\\\nCV Standard Deviation & 0.0100 \\\\\nCV Range & [0.8678, 0.8792] \\\\\n& \\\\\n\\textbf{Feature Analysis} & \\\\\nNumber of Features & 17 \\\\\nTop Feature Importance & 0.432 (OFI) \\\\\nFeature Stability (CV) & 0.956 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach", "authors": ["Akash Deep", "Chris Monico", "W. Brent Lindquist", "Svetlozar T. Rachev", "Frank J. Fabozzi"], "url": "https://arxiv.org/abs/2507.16701v1", "attribution": "\"Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach\" by Akash Deep, Chris Monico, W. Brent Lindquist, Svetlozar T. Rachev, and Frank J. Fabozzi, arXiv:2507.16701v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.04878v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of the main dataset's statistics}\n\\begin{tabular}{lrr}\n\t& \\textbf{smartphones} & \\textbf{cameras} \\\\ \\hline\n\t\\# collected images & 24.908 & 8.347\\\\\n\t\\# devices & 372 & 114 \\\\\n\t\\# different models & 45 & 25 \\\\\n\t\\# reference images & 12.752 & 3.586\\\\\n\t\\# matching tests & 12.141 & 1.563 \\\\\n\t\\# mismatching tests & 61.655 & 19.382 \\\\\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A leak in PRNU based source identification. Questioning fingerprint uniqueness", "authors": ["Massimo Iuliani", "Marco Fontani", "Alessandro Piva"], "url": "https://arxiv.org/abs/2009.04878v2", "attribution": "\"A leak in PRNU based source identification. Questioning fingerprint uniqueness\" by Massimo Iuliani, Marco Fontani, and Alessandro Piva, arXiv:2009.04878v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.06730v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|l}\n \\hline\n & Top 10 most representative words per topic \\\\ \n \\hline\nComputer Science & ``learning\" ``network\" ``networks\" ``model\" \"can\" ``neural\" \\\\ &\n ``deep\" ``using\" \"models\" ``data\" \\\\ \n \n Physics & ``model\" ``can\" ``system\" ``field\" ``energy\" ``systems\" \\\\ &\n ``magnetic\" ``models\" ``using\" ``phase\" \n \\\\ \n Mathematics & ``problem\" ``can\" ``algorithm\" ``show\" ``method\" \n ``paper\" \\\\& ``results\" ``also\" ``time\" ``using\" \\\\ \n Statistics & ``data\" ``model\" ``can\" ``learning\" ``using\" ``models\" \\\\&\n ``method\" ``approach\" ``based\" ``paper\" \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Most common words found by our method}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Sparse topic modeling via spectral decomposition and thresholding", "authors": ["Huy Tran", "Yating Liu", "Claire Donnat"], "url": "https://arxiv.org/abs/2310.06730v1", "attribution": "\"Sparse topic modeling via spectral decomposition and thresholding\" by Huy Tran, Yating Liu, and Claire Donnat, arXiv:2310.06730v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04480v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\toprule\nDescription & Form.Name & Grand.Total\\\\\n\\midrule\nChanges in ownership & 4 & 6,757,053\\\\\nCurrent report filing & 8-K & 1,549,474\\\\\n5\\% passive ownership triggers amendments & SC 13G/A & 655,696\\\\\nInitial Ownership Report & 3 & 613,615\\\\\nQuarterly report & 10-Q & 574,774\\\\\n\\addlinespace\nDefinite materials & 497 & 417,177\\\\\nCurret report of Foreign issuer & 6-K & 379,844\\\\\n5\\% passive ownership triggers & SC 13G & 369,518\\\\\nChange on prospectus & 424B3 & 276,705\\\\\nQuarterly hodings, institutional managers & 13F-HR & 238,794\\\\\n\\addlinespace\nProspectus filed pursuant to Rule 424(b)(2) & 424B2 & 236,485\\\\\n5\\% active ownership triggers amendments & SC 13D/A & 222,739\\\\\nChanges in ownership amendments & 4/A & 217,095\\\\\nOffering made without registration & D & 211,275\\\\\nAnnual report on ownership changes & 5 & 199,887\\\\\n\\addlinespace\nA correspondence & CORRESP & 186,948\\\\\nUpload Notification & UPLOAD & 183,620\\\\\nAnnual report & 10-K & 183,148\\\\\nPost-effective amendment & 485BPOS & 171,995\\\\\nSummary Prospectus & 497K & 169,519\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Text analysis in financial disclosures", "authors": ["Sridhar Ravula"], "url": "https://arxiv.org/abs/2101.04480v1", "attribution": "\"Text analysis in financial disclosures\" by Sridhar Ravula, arXiv:2101.04480v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.11346v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Running time for Algorithm 1 and the algorithm in with $e_t =1$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\t\t\\hline\n\t\t& \\textbf{Algorithm 1} & Algorithm in \\\\\n\t\t\\hline\n\t\t$r=0.1$ & {2.03s} & 5.37s\\\\\n\t\t$r=0.3$ & {1.83s} & 4.44s\\\\\n\t\t$r=0.5$ & {1.83s} & 4.35s\\\\\n\t\t$r=0.8$ & {1.84s} & 4.39s\\\\\n\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MIMO Radar Waveform-Filter Design for Extended Target Detection from a View of Games", "authors": ["Zhou Xu", "Chongyi Fan", "Xiaotao Huang"], "url": "https://arxiv.org/abs/2011.11346v1", "attribution": "\"MIMO Radar Waveform-Filter Design for Extended Target Detection from a View of Games\" by Zhou Xu, Chongyi Fan, and Xiaotao Huang, arXiv:2011.11346v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.06888v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Values of estimators of $\\sigma_2$ under symmetric loss $L_3(t)$ for $\\sigma_2$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\t\t\t\\hline\\hline\n\t\t\t$(a_1,a_2)$ & $(b_1,b_2)$ & $\\delta_{02}^3$ & $\\delta_{Rmle}^*$ & $\\delta_{2S1}$ & $\\delta_{2S2}$ & $\\delta_{2S3}$ & $\\delta_{\\phi_{2}^{03}}$ & $\\delta^*_{\\phi_{1.5,3}}$ \\\\ \\hline\\hline\n\t\t\t$(1,1)$ & $(30,30)$ & 268.77 & 279.63 & 291.80 & 268.77 & 298.80 & 319.88 & 477.27 \\\\ \n\t\t\t$(2,3)$ & $(27,28)$ & 250.24 & 285.15 & 299.56 & 250.24 & 299.56 & 326.40 & 509.85 \\\\ \n\t\t\t$(1,1)$ & $(29,27)$ & 280.84 & 290.54 & 304.13 &280.84 & 304.13 & 336.45 & 511.73 \\\\ \n\t\t\t$(4,2)$ & $(30,30)$ & 237.64 & 262.42 & 272.70 & 237.64 & 274.70 & 298.16 & 449.35 \\\\ \\hline\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On the improved estimation of ordered parameters based on doubly type-II censored sample", "authors": ["Shrajal Bajpai", "Lakshmi Kanta Patra"], "url": "https://arxiv.org/abs/2411.06888v2", "attribution": "\"On the improved estimation of ordered parameters based on doubly type-II censored sample\" by Shrajal Bajpai and Lakshmi Kanta Patra, arXiv:2411.06888v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.03287v3_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Crypto Factor and Risk-Free Rate Descriptive Statistics. Values are returns in percentage points and are at the weekly frequency. There are 105 weekly observations.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\hline\n & Crypto Market & SMB & Momentum & Risk-Free Rate\\\\\n\\hline\nmean & 1.44 & -10.04 & 0.68 & 0.09\\\\\nstd & 6.39 & 33.46 & 7.68 & 0.01\\\\\nmin & -15.42 & -206.53 & -30.14 & 0.06\\\\\n25\\% & -1.89 & -6.41 & -4.54 & 0.09\\\\\n50\\% & 0.25 & -0.93 & 0.55 & 0.10\\\\\n75\\% & 5.72 & 2.18 & 4.33 & 0.10\\\\\nmax & 18.70 & 18.54 & 24.93 & 0.11\\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns", "authors": ["Matt Brigida"], "url": "https://arxiv.org/abs/2506.03287v3", "attribution": "\"The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns\" by Matt Brigida, arXiv:2506.03287v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.05013v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rrrr|rrrr|rrrr}\n\\toprule\n\\multirow{2}{*}{$k$} & \\multicolumn{4}{c}{Window 1} & \\multicolumn{4}{c}{Window 2} & \\multicolumn{4}{c}{Window 3} \\\\\n\\cmidrule{2-5} \\cmidrule{6-9} \\cmidrule{10-13}\n & PVE$_k$ & ADF $p$ & $\\text{sd}(\\beta_{t,k})$ & $\\tau$ & PVE$_k$ & ADF $p$ & $\\text{sd}(\\beta_{t,k})$ & $\\tau$ & PVE$_k$ & ADF $p$ & $\\text{sd}(\\beta_{t,k})$ & $\\tau$ \\\\\n\\midrule\n1 & 0.708 & 0.333 & 6.676 & 8.360 & 0.563 & 0.262 & 7.157 & 22.285 & 0.474 & 0.012 & 5.762 & 27.096 \\\\\n2 & 0.125 & 0.064 & 2.808 & 20.500 & 0.247 & 0.003 & 4.743 & 14.349 & 0.342 & 0.193 & 4.892 & 12.638 \\\\\n3 & 0.050 & 0.023 & 1.778 & 10.718 & 0.066 & 0.000 & 2.457 & 5.627 & 0.073 & 0.024 & 2.264 & 6.540 \\\\\n4 & 0.021 & 0.000 & 1.163 & 2.560 & 0.032 & 0.000 & 1.709 & 3.256 & 0.027 & 0.000 & 1.367 & 1.973 \\\\\n5 & 0.015 & 0.000 & 0.982 & 1.912 & 0.012 & 0.000 & 1.049 & 1.428 & 0.015 & 0.000 & 1.019 & 1.658 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Summary statistics for principal component time series of ETH5 for each window. $\\tau$ is mean reversion time $-1/\\log|\\phi|$ from AR(1) fit.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Dynamics of Liquidity Surfaces in Uniswap v3", "authors": ["Jimmy Risk", "Shen-Ning Tung", "Tai-Ho Wang"], "url": "https://arxiv.org/abs/2509.05013v1", "attribution": "\"Dynamics of Liquidity Surfaces in Uniswap v3\" by Jimmy Risk, Shen-Ning Tung, and Tai-Ho Wang, arXiv:2509.05013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2310.13267v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l}\n\\toprule\n A sound of \\texttt{label} \\\\\n a sound of \\texttt{label}. \\\\\n A constant sound of \\texttt{label}\\\\\n a constant sound of \\texttt{label}\\\\\n A constant sound of \\texttt{label}. \\\\\n a constant sound of \\texttt{label}. \\\\\n A big sound of \\texttt{label} \\\\\n a big sound of \\texttt{label} \\\\\n A big sound of \\texttt{label}. \\\\\n A small sound of \\texttt{label} \\\\\n a small sound of \\texttt{label} \\\\\n A small sound of \\texttt{label}. \\\\\n A \\texttt{label} is making a sound. \\\\\n a \\texttt{label} is making a sound \\\\\n An \\texttt{label} is making a sound. \\\\\n an \\texttt{label} is making a sound \\\\\n A sound of \\texttt{label} followed by a sound of \\texttt{label} \\\\\n A sound of \\texttt{label} followed by \\texttt{label} \\\\\n A \\texttt{label}@ \\\\\n A \\texttt{label} \\\\\n An \\texttt{label} \\\\\n an \\texttt{label} \\\\\n \\texttt{label}@ \\\\\n \\texttt{label} \\\\\n \\texttt{label} and \\texttt{label} \\\\\n A \\texttt{label} is running. \\\\\n A \\texttt{label} is running \\\\\n A \\texttt{label} is happening. \\\\\n A \\texttt{label} is happening \\\\ \\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "On the Language Encoder of Contrastive Cross-modal Models", "authors": ["Mengjie Zhao", "Junya Ono", "Zhi Zhong", "Chieh-Hsin Lai", "Yuhta Takida", "Naoki Murata", "Wei-Hsiang Liao", "Takashi Shibuya", "Hiromi Wakaki", "Yuki Mitsufuji"], "url": "https://arxiv.org/abs/2310.13267v1", "attribution": "\"On the Language Encoder of Contrastive Cross-modal Models\" by Mengjie Zhao, Junya Ono, Zhi Zhong, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Wei-Hsiang Liao, Takashi Shibuya, Hiromi Wakaki, and Yuki Mitsufuji, arXiv:2310.13267v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2308.16650v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|l|l|l|l|l|l|}\n\t\\hline \n\t & Method & WALD & AC & HS & AM1 & AM2 & BSG1 & BSG2 & Full1 & Full2 & Full3 \\\\ \\hline \\hline\n\t\\multirow{6}{*}{$\\alpha=0.01$} & AVG length & 0.851 & 0.9 & {\\bf 0.832} & 0.892 & 0.903 & 0.888 & 0.905 & 0.88 & 0.9 & 0.89 \\\\ \\cline{2-12} \n\t& PCT of under-coverage & 100.00\\% & 33.33\\% & 58.65\\% & 8.31\\% & 0.39\\% & 8.81\\% & 0.42\\% & 14.68\\% & {\\bf 0.00\\%} & 1.67\\% \\\\ \\cline{2-12} \n\t& PCT of substantial under-coverage & 98.58\\% & 0.95\\% & 15.50\\% & 0.02\\% & {\\bf 0.00\\%} & 0.02\\% & {\\bf 0.00\\%} & 0.02\\% & {\\bf 0.00\\%} & 0.01\\% \\\\ \\cline{2-12} \n\t& AVG deviation & 632.2 & 11.3 & 46.2 & 0.7 & {\\bf 0.0} & 0.8 & {\\bf 0.0} & 1.4 & {\\bf 0.0} & 0.1 \\\\ \\cline{2-12} \n\t& Min CL & 0.038 & 0.956 & 0.895 & 0.894 & 0.988 & 0.894 & 0.986 & 0.894 & 0.99 & 0.958 \\\\ \\cline{2-12} \n\t& AVG CL & 0.927 & 0.992 & 0.987 & 0.993 & 0.993 & 0.993 & 0.994 & 0.992 & 0.993 & 0.993 \\\\ \\hline \\hline\n\t\\multirow{6}{*}{$\\alpha=0.05$} & AVG length & {\\bf 0.658} & 0.69 & 0.666 & 0.704 & 0.716 & 0.693 & 0.708 & 0.682 & 0.702 & 0.692 \\\\ \\cline{2-12} \n\t& PCT of under-coverage & 99.60\\% & 18.51\\% & 47.75\\% & 6.16\\% & 0.24\\% & 7.14\\% & 0.63\\% & 14.90\\% & {\\bf 0.00\\%} & 1.47\\% \\\\ \\cline{2-12} \n\t& PCT of substantial under-coverage & 97.75\\% & 3.34\\% & 13.88\\% & 0.40\\% & {\\bf 0.00\\%} & 0.42\\% & 0.01\\% & 1.38\\% & {\\bf 0.00\\%} & {\\bf 0.00\\%} \\\\ \\cline{2-12} \n\t& AVG deviation & 738.9 & 11.6 & 39.3 & 2.4 & 0.1 & 2.5 & 0.2 & 6.3 & {\\bf 0.0} & 0.1 \\\\ \\cline{2-12} \n\t& Min CL & 0.038 & 0.907 & 0.863 & 0.894 & 0.941 & 0.894 & 0.939 & 0.894 & 0.95 & 0.946 \\\\ \\cline{2-12} \n\t& AVG CL & 0.876 & 0.961 & 0.954 & 0.963 & 0.966 & 0.961 & 0.965 & 0.958 & 0.963 & 0.961 \\\\ \\hline \\hline\n\t\\multirow{6}{*}{$\\alpha=0.1$} & AVG length & {\\bf 0.555} & 0.58 & 0.568 & 0.61 & 0.619 & 0.585 & 0.604 & 0.578 & 0.597 & 0.588 \\\\ \\cline{2-12} \n\t& PCT of under-coverage & 98.96\\% & 17.71\\% & 43.05\\% & 4.66\\% & 0.38\\% & 9.70\\% & 0.62\\% & 14.98\\% & {\\bf 0.00\\%} & 1.16\\% \\\\ \\cline{2-12} \n\t& PCT of substantial under-coverage & 97.48\\% & 7.05\\% & 23.90\\% & 0.52\\% & 0.02\\% & 1.75\\% & 0.08\\% & 3.64\\% & {\\bf 0.00\\%} & {\\bf 0.00\\%}\\\\ \\cline{2-12} \n\t& AVG deviation & 742.5 & 21.0 & 50.4 & 2.2 & 0.1 & 5.6 & 0.3 & 11.2 & {\\bf 0.0} & 0.2 \\\\ \\cline{2-12} \n\t& Min CL & 0.038 & 0.802 & 0.824 & 0.875 & 0.889 & 0.864 & 0.885 & 0.842 & 0.9 & 0.891 \\\\ \\cline{2-12} \n\t& AVG CL & 0.826 & 0.918 & 0.908 & 0.926 & 0.93 & 0.917 & 0.927 & 0.913 & 0.924 & 0.919 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Optimal confidence interval for the difference of proportions", "authors": ["Almog Peer", "David Azriel"], "url": "https://arxiv.org/abs/2308.16650v3", "attribution": "\"Optimal confidence interval for the difference of proportions\" by Almog Peer and David Azriel, arXiv:2308.16650v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.04724v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cl}\n \\toprule\n \\textbf{N} & \\textbf{Industry} \\\\\n \\midrule\n \\textbf{1} & Farms \\\\\n \\textbf{2} & Forestry, fishing, and related activities \\\\\n \\textbf{3} & Oil and gas extraction \\\\\n \\textbf{4} & Mining, except oil and gas \\\\\n \\textbf{5} & Support activities for mining \\\\\n \\textbf{6} & Utilities \\\\\n \\textbf{7} & Construction \\\\\n \\textbf{8} & Wood products \\\\\n \\textbf{9} & Nonmetallic mineral products \\\\\n \\textbf{10} & Primary metals \\\\\n \\textbf{11} & Fabricated metal products \\\\\n \\textbf{12} & Machinery \\\\\n \\textbf{13} & Computer and electronic products \\\\\n \\textbf{14} & Electrical equipment, appliances, and components \\\\\n \\textbf{15} & Motor vehicles, bodies and trailers, and parts \\\\\n \\textbf{16} & Other transportation equipment \\\\\n \\textbf{17} & Furniture and related products \\\\\n \\textbf{18} & Miscellaneous manufacturing \\\\\n \\textbf{19} & Food and beverage and tobacco products \\\\\n \\textbf{20} & Textile mills and textile product mills \\\\\n \\textbf{21} & Apparel and leather and allied products \\\\\n \\textbf{22} & Paper products \\\\\n \\textbf{23} & Printing and related support activities \\\\\n \\textbf{24} & Petroleum and coal products \\\\\n \\textbf{25} & Chemical products \\\\\n \\textbf{26} & Plastics and rubber products \\\\\n \\textbf{27} & Wholesale trade \\\\\n \\textbf{28} & Retail trade \\\\\n \\textbf{29} & Air transportation \\\\\n \\textbf{30} & Rail transportation \\\\\n \\textbf{31} & Water transportation \\\\\n \\textbf{32} & Truck transportation \\\\\n \\textbf{33} & Transit and ground passenger transportation \\\\\n \\textbf{34} & Pipeline transportation \\\\\n \\textbf{35} & Other transportation and support activities \\\\\n \\textbf{36} & Warehousing and storage \\\\\n \\textbf{37} & Publishing industries (includes software) \\\\\n \\textbf{38} & Motion picture and sound recording industries \\\\\n \\textbf{39} & Broadcasting and telecommunications \\\\\n \\textbf{40} & Information and data processing services \\\\\n \\textbf{41} & Federal Reserve banks, credit intermediation, and related activities \\\\\n \\textbf{42} & Securities, commodity contracts, and investments \\\\\n \\textbf{43} & Insurance carriers and related activities \\\\\n \\textbf{44} & Funds, trusts, and other financial vehicles \\\\\n \\textbf{45} & Real estate \\\\\n \\textbf{46} & Rental and leasing services and lessors of intangible assets \\\\\n \\textbf{47} & Legal services \\\\\n \\textbf{48} & Computer systems design and related services \\\\\n \\textbf{49} & Miscellaneous professional, scientific, and technical services \\\\\n \\textbf{50} & Management of companies and enterprises \\\\\n \\textbf{51} & Administrative and support services \\\\\n \\textbf{52} & Waste management and remediation services \\\\\n \\textbf{53} & Educational services \\\\\n \\textbf{54} & Ambulatory health care services \\\\\n \\textbf{55} & Hospitals and nursing and residential care facilities \\\\\n \\textbf{56} & Social assistance \\\\\n \\textbf{57} & Performing arts, spectator sports, museums, and related activities \\\\\n \\textbf{58} & Amusements, gambling, and recreation industries \\\\\n \\textbf{59} & Accommodation \\\\\n \\textbf{60} & Food services and drinking places \\\\\n \\textbf{61} & Other services, except government \\\\\n \\textbf{62} & Federal government \\\\\n \\textbf{63} & State and local government \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "$Λ$-Returns to Scale and Individual Minimum Extrapolation Principle", "authors": ["Jean-Philippe Boussemart", "Walter Briec", "Raluca Parvulescu", "Paola Ravelojaona"], "url": "https://arxiv.org/abs/2212.04724v2", "attribution": "\"$Λ$-Returns to Scale and Individual Minimum Extrapolation Principle\" by Jean-Philippe Boussemart, Walter Briec, Raluca Parvulescu, and Paola Ravelojaona, arXiv:2212.04724v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.16844v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small System-focused results under nodal, zonal, and copper-plate modeling. The results are reported for the base system and two market bidding REPs, both with 1000 MW electrolyzer installed.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrrr} \\toprule\n\\textbf{} &\n \\textbf{} &\n \\textbf{System} &\n \\textbf{Cost of} &\n \\textbf{System} &\n \\textbf{Renewable} \n \\\\\n\\textbf{} &\n \\textbf{} &\n \\textbf{load} &\n \\textbf{generation} &\n \\textbf{emissions } &\n \\textbf{curtailment} \n \\\\\n\\multicolumn{2}{c}{\\textbf{Case}} &\n [TWh] &\n [$\\$$/MWh] &\n [t/MWh] &\n [GWh] \n \\\\ \\toprule\n\\multirow{3}{*}{Base} & Copper & 37.66 & 18.54 & 0.36 & 348.75 \\\\\n & Zonal & 37.66 & 18.55 & 0.36 & 369.97 \\\\\n & Nodal & 37.66 & 18.67 & 0.37 & 608.09 \\\\\n \\midrule\n\\multirow{3}{*}{$\\$$1.5/kg} & Copper & 45.07 & 18.81 & 0.43 & 40.47 \\\\\n & Zonal & 44.94 & 18.80 & 0.42 & 40.77 \\\\\n & Nodal & 43.19 & 18.72 & 0.41 & 67.29 \\\\ \n \\midrule\n\\multirow{3}{*}{$\\$$6/kg}& Copper & 46.44 & 19.00 & 0.43 & 40.47 \\\\\n & Zonal & 46.44 & 19.04 & 0.43 & 40.77 \\\\\n & Nodal & 44.06 & 19.25 & 0.42 & 302.07 \\\\ \\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Electricity Market Bidding for Renewable Electrolyzer Plants: An Opportunity Cost Approach", "authors": ["Andrea Gloppen Johnsen", "Lesia Mitridati", "Jalal Kazempour", "Line Roald"], "url": "https://arxiv.org/abs/2501.16844v3", "attribution": "\"Electricity Market Bidding for Renewable Electrolyzer Plants: An Opportunity Cost Approach\" by Andrea Gloppen Johnsen, Lesia Mitridati, Jalal Kazempour, and Line Roald, arXiv:2501.16844v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16684v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Empirical power (in percent) with $\\alpha= 0.05$ and $m = n = 50$ under the difference pattern (i) full difference. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccccc}\n \\toprule\n $d$ & MATES & CM & GET & BD & GED & RF & MT & GPK & RISE & MMD & xMMD & aMMD & mMMD \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (I)} \\\\\n 200 & \\textbf{90.9} & 4.5 & 6.3 & 5.2 & 9.3 & 6.6 & 5.5 & 4.1 & 6.1 & 4.6 & 5.5 & 5.5 & 1.3 \\\\\n 500 & \\textbf{85.2} & 3.9 & 4.4 & 5.6 & 7.1 & 6.1 & 4.5 & 4.8 & 4.5 & 6.2 & 6.0 & 6.1 & 1.3 \\\\\n 1000 & \\textbf{85.8} & 3.3 & 4.7 & 5.5 & 6.8 & 6.6 & 5.7 & 4.8 & 4.3 & 5.2 & 6.3 & 5.7 & 0.9 \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (II)} \\\\\n 200 & \\textbf{81.2} & 3.4 & 5.2 & 3.6 & 9.8 & 5.7 & 5.2 & 4.1 & 5.2 & 5.5 & 5.2 & 6.0 & 2.1 \\\\\n 500 & \\textbf{71.3} & 2.9 & 5.2 & 5.6 & 8.0 & 5.4 & 5.4 & 4.5 & 4.1 & 4.9 & 6.3 & 6.1 & 1.5 \\\\\n 1000 & \\textbf{82.4} & 2.9 & 5.3 & 5.4 & 7.0 & 4.8 & 5.3 & 4.8 & 5.7 & 4.5 & 5.4 & 5.1 & 1.6 \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (III)} \\\\\n 200 & \\textbf{89.5} & 2.6 & 4.7 & 4.6 & 10.4 & 6.8 & 5.5 & 3.9 & 5.1 & 4.4 & 5.7 & 5.3 & 1.8 \\\\\n 500 & \\textbf{82.5} & 3.0 & 5.4 & 4.5 & 8.9 & 7.1 & 4.5 & 4.1 & 6.3 & 5.8 & 5.7 & 5.7 & 2.0 \\\\\n 1000 & \\textbf{82.7} & 3.2 & 4.8 & 5.2 & 5.4 & 5.7 & 5.5 & 4.1 & 4.4 & 4.6 & 6.3 & 4.1 & 1.2 \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (IV)} \\\\\n 200 & \\textbf{74.3} & 4.6 & 6.0 & 6.0 & 14.0 & 7.6 & 5.4 & 4.6 & 6.2 & 4.6 & 5.9 & 6.6 & 1.2 \\\\\n 500 & \\textbf{89.9} & 3.8 & 5.6 & 6.6 & 12.6 & 7.5 & 3.9 & 6.0 & 5.7 & 5.1 & 5.1 & 6.8 & 1.5 \\\\\n 1000 & \\textbf{84.7} & 3.8 & 4.9 & 4.8 & 10.3 & 6.9 & 3.9 & 4.8 & 5.1 & 5.0 & 6.1 & 6.1 & 2.4 \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (V)} \\\\\n 200 & \\textbf{77.9} & 5.1 & 10.0 & 6.2 & 13.4 & 7.5 & 5.0 & 4.3 & 9.6 & 5.4 & 6.4 & 7.4 & 1.3 \\\\\n 500 & \\textbf{88.3} & 4.2 & 7.6 & 5.1 & 11.7 & 5.7 & 4.5 & 4.3 & 8.6 & 4.6 & 5.1 & 6.4 & 0.9 \\\\\n 1000 & \\textbf{87.8} & 3.9 & 6.1 & 4.3 & 9.8 & 5.2 & 4.5 & 3.5 & 6.4 & 5.5 & 6.9 & 6.5 & 0.9 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "MATES: Multi-view Aggregated Two-Sample Test", "authors": ["Zexi Cai", "Wenbo Fei", "Doudou Zhou"], "url": "https://arxiv.org/abs/2412.16684v1", "attribution": "\"MATES: Multi-view Aggregated Two-Sample Test\" by Zexi Cai, Wenbo Fei, and Doudou Zhou, arXiv:2412.16684v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.17077v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{cccccc}\n\\hline\n\\multicolumn{6}{c}{Errors and convergence rates after $T/h$ iterations for $d=10$} \\\\ \\hline\n$h$\\textbackslash $\\phi$ & $\\phi_{1}(x)$ & $\\exp(-\\| x\\|)$ & $\\phi_{2}(x)$ & $\\arctan(\\| x\\|)$ & Iterations \\\\ \\hline\n$2^{-5}$ & 6.17e-02 & 3.98e-03 & 8.58e-02 & 5.56e-03 &\n192 \\\\ %\\hline\n$2^{-6}$ & 2.70e-02 & 1.96e-03 & 3.68e-02 & 2.73e-03 \n & 384 \\\\ %\\hline\n$2^{-7}$ & 1.53e-02 & 9.86e-04 & 2.15e-02 & 1.37e-03\n& 768 \\\\ %\\hline\n$2^{-8}$ & 6.33e-03 & 5.01e-04 & 9.00e-03 & 6.93e-04 \n& 1536\\\\ %\\hline\n$2^{-9}$ & 2.67e-03 & 2.36e-04 & 4.17e-03 & 3.27e-04 \n& 3072\\\\ \\hline\n\\text{Order} & 1.12 & \\ \\ 1.01 & 1.08 & \\ 1.02 \n& \\ \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Projected Langevin Monte Carlo algorithms in non-convex and super-linear setting", "authors": ["Chenxu Pang", "Xiaojie Wang", "Yue Wu"], "url": "https://arxiv.org/abs/2312.17077v4", "attribution": "\"Projected Langevin Monte Carlo algorithms in non-convex and super-linear setting\" by Chenxu Pang, Xiaojie Wang, and Yue Wu, arXiv:2312.17077v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.19294v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fitting Parameters \\( a \\) and \\( b \\) for the Function in Equation ()}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n \\toprule\n City & \\( a \\) & \\( b \\) \\\\\n \\midrule\n New York & $0.52$ & $38.7$ \\\\\n Tokyo & $0.51$ & $19.1$ \\\\\n Boston & $0.37$ & $4.5$ \\\\\n Chicago & $0.52$ & $14.6$ \\\\\n Washington D.C. & $0.37$ & $10.1$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Spatio-Temporal Differences in Bike Sharing Usage: A Tale of Six Cities", "authors": ["Shu-ichi Kinoshita", "Yuya Bando", "Hiroki Sayama"], "url": "https://arxiv.org/abs/2412.19294v1", "attribution": "\"Spatio-Temporal Differences in Bike Sharing Usage: A Tale of Six Cities\" by Shu-ichi Kinoshita, Yuya Bando, and Hiroki Sayama, arXiv:2412.19294v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09577v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Computational complexity of feature importance estimation. For Relief algorithms, we used $s = |I|$. The $t$ corresponds to the number of trees.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\hline \n Algorithm & time complexity & space complexity \\\\ \\hline\n ReliefE &$\\mathcal{O}(|\\nu|^2 \\cdot |F| + |I| \\cdot d \\cdot s)$ & $\\mathcal{O}(|\\nu|^2)$ \\\\\n ReliefF & $\\mathcal{O}(|F| \\cdot |I|^{2})$ & $\\mathcal{O}(|F|)$ \\\\\n Random Forest & $\\mathcal{O}(t \\cdot |F| \\cdot |I| \\cdot \\log^2 |I|)$ & $\\mathcal{O}(|I| + |F|)$\\\\\n Mutual Information & $\\mathcal{O}(|F| \\cdot |I|)$ & $\\mathcal{O}(|I|)$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings", "authors": ["Blaž Škrlj", "Sašo Džeroski", "Nada Lavrač", "Matej Petković"], "url": "https://arxiv.org/abs/2101.09577v1", "attribution": "\"ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings\" by Blaž Škrlj, Sašo Džeroski, Nada Lavrač, and Matej Petković, arXiv:2101.09577v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.06428v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameter settings for simulation}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{Symbols} & \\textbf{Value} \\\\\n\\hline\nCarrier frequency $f_c$ & $28$ GHz\\\\\n\\hline\nSubcarrier spacing $\\Delta f$ & $120$ kHz\\\\\n\\hline\nNumber of subcarriers $P$ & 1024 \\\\\n\\hline\nBandwidth & $122.88$ MHz \\\\\n\\hline\nNumber of OFDM symbols $Q$ & $1024$\\\\\n\\hline\nCP length $T_{\\mathrm{CP}}$ & $2.0833\\ \\mathrm{\\mu s}$ \\\\\n\\hline\nNoise power density $N_0$ & $-169$ dBm/Hz\\\\\n\\hline\nNumber of users $U$ & $4$\\\\\n\\hline\nNumber of subcarriers per user & 512\\\\\n\\hline\nNumber of OFDM symbols per user & 512\\\\\n\\hline\nNumber of sensing targets $K$ & 3\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Full-Dimensional Beamforming for Multi-User MIMO-OFDM ISAC for Low-Altitude UAV with Zero Sensing Resource Allocation", "authors": ["Zhiwen Zhou"], "url": "https://arxiv.org/abs/2508.06428v1", "attribution": "\"Full-Dimensional Beamforming for Multi-User MIMO-OFDM ISAC for Low-Altitude UAV with Zero Sensing Resource Allocation\" by Zhiwen Zhou, arXiv:2508.06428v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2311.06790v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results for $\\{u_t\\}_{t=0}^T \\subseteq [-1,1)$ }\n\\begin{tabular}{cccc}\n\t\t$M_t$ & MSE & Average $L^p$ & Average $L^*$ \\\\\n\t\t\\midrule\n\t\t$[0.01, 0.03)$ & 0.000000001980 & 0.480035256843 & 0.182249030442 \\\\\n\t\t$[0.03, 0.07)$ & 0.000000000030 & 0.480376371018 & 0.154875148169 \\\\\n\t\t$[0.07, 0.10)$ & 0.000000000058 & 0.480467501182 & 0.168214789041 \\\\\n\t\t$[0.01, 0.10)$ & 0.000000000037 & 0.479848646807 & 0.234253834963 \\\\\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The QLBS Model within the presence of feedback loops through the impacts of a large trader", "authors": ["Ahmet Umur Özsoy", "Ömür Uğur"], "url": "https://arxiv.org/abs/2311.06790v1", "attribution": "\"The QLBS Model within the presence of feedback loops through the impacts of a large trader\" by Ahmet Umur Özsoy and Ömür Uğur, arXiv:2311.06790v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11307v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\nDirection & CAR & In-shop \\\\\n\\hline\n$\\mathbf{e^{euc}}$ & \\cellcolor[HTML]{F9DEDC}69.5 $\\pm$ 0.7 & \\cellcolor[HTML]{FFFFFF}83.7 $\\pm$ 0.1 \\\\\n$\\mathbf{e^{cos}}$ & \\cellcolor[HTML]{EA9088}75.5 $\\pm$ 0.2 & \\cellcolor[HTML]{F3BCB7}85.2 $\\pm$ 0.3 \\\\\n$\\mathbf{e^{euc-orth}}$ & \\cellcolor[HTML]{FFFFFF}66.9 $\\pm$ 0.5 & \\cellcolor[HTML]{FCEFEE}84.1 $\\pm$ 0.1 \\\\\n$\\mathbf{e^{cos-orth}}$ & \\cellcolor[HTML]{E67C73}77.0 $\\pm$ 0.7 & \\cellcolor[HTML]{E67C73}86.6 $\\pm$ 0.2\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparing recall@1 performance of different gradient directions on CAR and In-shop dataset}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Dissecting the impact of different loss functions with gradient surgery", "authors": ["Hong Xuan", "Robert Pless"], "url": "https://arxiv.org/abs/2201.11307v1", "attribution": "\"Dissecting the impact of different loss functions with gradient surgery\" by Hong Xuan and Robert Pless, arXiv:2201.11307v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02028v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n topic 1 & topic 2 &topic 3 &topic 4 &topic 5\\\\\n ``laundry and other household\" &``baby and body care\" &``frozen products\" &``vegetable, fruits and milk\" &\"drinking and body care\"\\\\\n \\hline\nlaundry & baby food formula & frozen meals & fresh vegetables & body lotions soap \\\\ \ndeodorants & diapers wipes & frozen breakfast & fresh fruits & beers coolers \\\\ \nmint gum & baby bath body care & frozen pizza & packaged vegetables fruits & muscles joints pain relief \\\\ \ncleaning products & energy granola bars & instant foods & yogurt & dog food care \\\\ \ndish detergents & fruit vegetable snacks & cereal & packaged cheese & white wines \\\\ \ntrash bags liners & laundry & frozen appetizers sides & chips pretzels & air fresheners candles \\\\ \noral hygiene & canned fruit applesauce & plates bowls cups flatware & water seltzer sparkling water & ice cream toppings \\\\ \nbaby accessories & food storage & frozen dessert & frozen produce & red wines \\\\ \nmore household & cereal & frozen vegan vegetarian & ice cream ice & first aid \\\\ \npaper goods & hair care & hair care & milk & spirits \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{5 selected topics and the corresponding top 10 categories from derived from a 20-topic MCTM.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Multilayer Correlated Topic Model", "authors": ["Ye Tian"], "url": "https://arxiv.org/abs/2101.02028v1", "attribution": "\"A Multilayer Correlated Topic Model\" by Ye Tian, arXiv:2101.02028v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13833v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcr}\nLeft&Centered&Right\\\\\n\\hline\n1 & 2 & 3\\\\\n10 & 20 & 30\\\\\n100 & 200 & 300\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the Reasoning Capacity of AI Models and How to Quantify It", "authors": ["Santosh Kumar Radha", "Oktay Goktas"], "url": "https://arxiv.org/abs/2501.13833v1", "attribution": "\"On the Reasoning Capacity of AI Models and How to Quantify It\" by Santosh Kumar Radha and Oktay Goktas, arXiv:2501.13833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06560v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule \n \\multirow{3}{2.5cm}{\\centering } & \\multicolumn{2}{c}{ShapeNet} & \\multicolumn{2}{c}{V2V} \\\\\n \\cmidrule{2-3} \\cmidrule{4-5}\n & Clean & Perturbed & Clean & Perturbed \\\\\n \\midrule \n Original & 66.33 & 0.62 & 82.19 & 7.55 \\\\\n Adv Trained & \\textbf{67.29} & \\textbf{66.00} & \\textbf{82.60} & \\textbf{83.44} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Adversarial Attacks On Multi-Agent Communication", "authors": ["James Tu", "Tsunhsuan Wang", "Jingkang Wang", "Sivabalan Manivasagam", "Mengye Ren", "Raquel Urtasun"], "url": "https://arxiv.org/abs/2101.06560v2", "attribution": "\"Adversarial Attacks On Multi-Agent Communication\" by James Tu, Tsunhsuan Wang, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren, and Raquel Urtasun, arXiv:2101.06560v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.22932v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\hline\n \\textbf{Dataset} & \\textbf{Sharpe Ratio} & \\textbf{T-stat (p-value) }\\\\\n \\hline\n FinMarBa & 0.30 & 10 (0) \\\\\n Financial-Phrasebank & -0.13 & -4.36 (1.3 e-5) \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Sharpe Ratios of sentiment-based signals from FinMarBa and Financial-Phrasebank. Higher values indicate stronger risk-adjusted returns, with FinMarBa outperforming Financial-Phrasebank.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "FinMarBa: A Market-Informed Dataset for Financial Sentiment Classification", "authors": ["Baptiste Lefort", "Eric Benhamou", "Beatrice Guez", "Jean-Jacques Ohana", "Ethan Setrouk", "Alban Etienne"], "url": "https://arxiv.org/abs/2507.22932v1", "attribution": "\"FinMarBa: A Market-Informed Dataset for Financial Sentiment Classification\" by Baptiste Lefort, Eric Benhamou, Beatrice Guez, Jean-Jacques Ohana, Ethan Setrouk, and Alban Etienne, arXiv:2507.22932v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.02021v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Archimedean copulas and their corresponding generating functions $\\phi(t)$ }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\hline\nFamily & $\\phi(t)$ & $\\theta \\in$ &$ \\mathcal{C}(u_1,u_2;\\theta)$ \\\\\n\\hline\nFrank &-$\\ln \\dfrac{e^{-\\theta t}-1}{e^{-\\theta}-1}$ & $\\theta \\neq0$ &$ -\\frac{1}{\\theta}\\ln\\bigl[1+\\dfrac{(e^{-\\theta u_1}-1)(e^{-\\theta u_2}-1)}{e^{-\\theta}-1}\\bigr] $ \\\\\nClayton & $t^{-\\theta}-1 $ & $\\theta>0$ &$ (u_1^{-\\theta}+u_2^{-\\theta}-1)^{-\\frac{1}{\\theta}}$ \\\\\nGumbel-Hugard & $(-\\ln t)^\\theta $ & $\\theta\\geq 1$ &$\\exp \\Bigl\\lbrace -\\bigl[(-\\ln u_1)^\\theta+(-\\ln u_2)^\\theta\\bigr]^{\\frac{1}{\\theta}}\\Bigr\\rbrace$\\\\\nAli-Mikhail-Haq & $ \\ln \\dfrac{1-\\theta(1-t)}{t} $ &$-1\\leq \\theta <1$&$\\dfrac{u_1 u_2}{1-\\theta(1-u_1)(1-u_2)}$ \\\\\nJoe & $ -\\ln(1-(1-t)^{\\theta}) $ &$\\theta\\geq 1$ &$1-\\bigr[ (1-u_1)^{\\theta} + (1-u_2)^{\\theta} -(1-u_1)^{\\theta}(1-u_2)^{\\theta} \\bigr]^{1/ \\theta}$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modeling Count Data via Copulas", "authors": ["Hadi Safari-Katesari", "S. Yaser Samadi", "Samira Zaroudi"], "url": "https://arxiv.org/abs/2012.02021v1", "attribution": "\"Modeling Count Data via Copulas\" by Hadi Safari-Katesari, S. Yaser Samadi, and Samira Zaroudi, arXiv:2012.02021v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.15257v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Type of traces used to define membership by organization}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\t\t\\toprule\n\t\tshort\\_name & affectation & nomination & intégration & titularisation\\\\\n\t\t\\midrule\n\t\tcab & 0 & 1 & 0 & 0\\\\\n\t\tccomptes & 1 & 1 & 1 & 0\\\\\n\t\tcdiplo & 0 & 1 & 0 & 0\\\\\n\t\tce & 1 & 1 & 0 & 1\\\\\n\t\tcprefet & 0 & 1 & 0 & 1\\\\\n\t\tcsprefet & 0 & 1 & 0 & 1\\\\\n\t\tdgbudget & 1 & 1 & 1 & 1\\\\\n\t\tdgfip & 1 & 1 & 1 & 1\\\\\n\t\tdgtresor & 1 & 1 & 1 & 1\\\\\n\t\tena & 0 & 1 & 0 & 0\\\\\n\t\tiga & 1 & 1 & 1 & 1\\\\\n\t\tigas & 1 & 1 & 1 & 1\\\\\n\t\tigf & 1 & 1 & 1 & 1\\\\\n\t\tinsee & 0 & 1 & 0 & 0\\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Career Modeling with Missing Data and Traces", "authors": ["Théo Voldoire", "Robin J. Ryder", "Ryan Lahfa"], "url": "https://arxiv.org/abs/2311.15257v2", "attribution": "\"Career Modeling with Missing Data and Traces\" by Théo Voldoire, Robin J. Ryder, and Ryan Lahfa, arXiv:2311.15257v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10742v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\textbf{Graph class} & \\textbf{Problem type} & \\textbf{Algorithm} & \\textbf{Lower Bound} \\\\ \\hline \\hline\nGeneral graphs & Vertex-disjoint = edge-disjoint & ???? & NP-hard for $k=2$\\\\ \\hline\n\\multirow{3}{*}{DAGs} & \\multirow{3}{*}{Vertex-disjoint = edge-disjoint} & \\multirow{3}{*}{$n^{O(k)}$~} & $f(k)\\cdot n^{o(\\sqrt{k})}$~\\\\\n & & & $f(k)\\cdot n^{o(k/\\log k)}$~\\\\\n & & & $f(k)\\cdot n^{o(k)}$ \\textbf{[this paper]}\\\\ \\hline\n\\multirow{3}{*}{Planar graphs} & \\multirow{2}{*}{Vertex-disjoint} & $n^{O(k)}$~ & \\multirow{2}{*}{????} \\\\\n & & $2^{2^{O(k^2)}}\\cdot n^{O(1)}$~ & \\\\\\cline{2-4}\n & Edge-disjoint & ???? & $f(k)\\cdot n^{o(k)}$ \\textbf{[this paper]} \\\\ \\hline\n\\multirow{2}{*}{Planar DAGs} & Vertex-disjoint & $2^{2^{O(k^2)}}\\cdot n^{O(1)}$~ & ????\\\\\n & Edge-disjoint & $n^{O(k)}$~ & $f(k)\\cdot n^{o(k)}$ \\textbf{[this paper]} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Tight Lower Bound for Edge-Disjoint Paths on Planar DAGs", "authors": ["Rajesh Chitnis"], "url": "https://arxiv.org/abs/2101.10742v1", "attribution": "\"A Tight Lower Bound for Edge-Disjoint Paths on Planar DAGs\" by Rajesh Chitnis, arXiv:2101.10742v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.00486v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Performance on the VoxCeleb1 test data when only the WB VoxCeleb1 training data are given. $\\rightarrow$: sub-spectrogram selection operation. SR: Sampling rate.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n \\toprule\n \\textbf{ID}&\\textbf{Method}& \\textbf{Training SR and \\#Filters} & \\textbf{Testing SR}&\\textbf{Testing \\#Filters}&\\textbf{EER (\\%)}\\\\\n \\midrule\n 1&\\multirow{4}{*}{ WB baseline} &\\multirow{4}{*}{16k and 64}& 16k & 64 &\\textbf{4.35}&\\\\\n 2& &&8k&64&20.13&\\\\\n 3& &&8k&48&8.82&\\\\\n 4& &&16k&64$\\rightarrow$48&8.87&\\\\\n \\cmidrule(lr){1-6}\n 5& \\multirow{4}{*}{ NB baseline} &\\multirow{4}{*}{8k and 48}& 8k & 48 &\\textbf{4.92}&\\\\\n 6&&&16k&48&18.86\\\\\n 7&&&16k&64&8.01&\\\\\n 8&&&16k&64$\\rightarrow$48&4.95&\\\\\n \\cmidrule(lr){1-6}\n \\multirow{2}{*}{ \\textbf{9}}& \\multirow{2}{*}{ \\textbf{Proposed MB}}& \\multirow{2}{*}{16k and 64\\&48}& 16k&64&\\textbf{4.07}\\\\\n &&&8k&48&\\textbf{4.37}\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Unified Deep Speaker Embedding Framework for Mixed-Bandwidth Speech Data", "authors": ["Weicheng Cai", "Ming Li"], "url": "https://arxiv.org/abs/2012.00486v1", "attribution": "\"A Unified Deep Speaker Embedding Framework for Mixed-Bandwidth Speech Data\" by Weicheng Cai and Ming Li, arXiv:2012.00486v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.07350v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Path Loss Exponent of RIS Assisted Channels}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\t\t\\hline\n\t\tFrequency\t\t\t\t\t&$N$\t& $\\Delta P_L$ (dB) & PLE ($n$) \\\\ \\hline\n\t\t\\multirow{3}{*}{$2.4$ GHz} \t&64 & 5.6\t\t\t\t& 3.373 \t\\\\ \n\t\t&256\t& 13.3\t\t\t\t& 3.068 \t\\\\ \n\t\t&1024 \t& 23.8\t\t\t\t& 2.847 \t\\\\ \\hline \n\t\t\\multirow{3}{*}{$28$ GHz} \t&64 & 0.3 \t\t\t\t& 3.159 \t\\\\ \n\t\t&256\t& 1 \t\t\t\t& 3.130 \t\\\\ \n\t\t&1024 \t& 3.5\t\t\t\t& 3.038 \t\\\\ \\hline \n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Modeling and Analysis of Reconfigurable Intelligent Surfaces for Indoor and Outdoor Applications in Future Wireless Networks", "authors": ["I. Yildirim", "A. Uyrus", "E. Basar"], "url": "https://arxiv.org/abs/1912.07350v4", "attribution": "\"Modeling and Analysis of Reconfigurable Intelligent Surfaces for Indoor and Outdoor Applications in Future Wireless Networks\" by I. Yildirim, A. Uyrus, and E. Basar, arXiv:1912.07350v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2401.10235v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc} %\\hline\n\\multicolumn{5}{c}{$M=50$ individuals} \\\\ %\\hline\n& \\multicolumn{2}{c}{Cut degree 0} & \\multicolumn{2}{c}{VC-BART} \\\\\n Region & Covariate 1 & Covariates 2-10 & Covariate 1 & Covariates 2-10 \\\\\n$z \\in $(-3,-2] & 0.06 & 0.080 & 0.60 & 0.58 \\\\ \n$z \\in $(-2,-1] & 0.06 & 0.078 & 0.61 & 0.57 \\\\ \n$z \\in $(-1,0] & 0.056 & 0.072 & 0.61 & 0.58 \\\\ \n$z \\in $(0,1] & 0.73 & 0.084 & 0.82 & 0.56 \\\\ \n$z \\in $(1,2] & 0.873 & 0.070 & 0.97 & 0.56 \\\\ \n$z \\in $(2,3] & 1 & 0.057 & 0.98 & 0.54 \\\\ \n\\multicolumn{5}{c}{$M=100$ individuals} \\\\ %\\hline\n& \\multicolumn{2}{c}{Cut degree 0} & \\multicolumn{2}{c}{VC-BART} \\\\\n Region & Covariate 1 & Covariates 2-10 & Covariate 1 & Covariates 2-10 \\\\\n$z \\in $ (-3,-2] & 0.05 & 0.036 & 0.64 & 0.58 \\\\\n$z \\in $ (-2,-1] & 0.045 & 0.038 & 0.72 & 0.52 \\\\ \n$z \\in $ (-1,0] & 0.0376 & 0.039 & 0.59 & 0.48 \\\\ \n$z \\in $ (0,1] & 0.99 & 0.036 & 0.89 & 0.52 \\\\ \n$z \\in $ (1,2] & 0.995 & 0.031 & 1 & 0.54 \\\\ \n$z \\in $ (2,3] & 1 & 0.027 & 1 & 0.51 \\\\ \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Functional data simulation. Proportion of rejected null hypothesis for our 0-degree cut orthogonal basis, and VC-BART. For covariate 1 and $z>0$ this is the statistical power, otherwise it is the type I error}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Semi-parametric local variable selection under misspecification", "authors": ["David Rossell", "Arnold Kisuk Kseung", "Ignacio Saez", "Michele Guindani"], "url": "https://arxiv.org/abs/2401.10235v3", "attribution": "\"Semi-parametric local variable selection under misspecification\" by David Rossell, Arnold Kisuk Kseung, Ignacio Saez, and Michele Guindani, arXiv:2401.10235v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.13370v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\toprule\n\\multicolumn{3}{@{}l}{\\textbf{Variable}} & \\texttt{dropout3y}=0 & \\texttt{dropout3y}=1 \\\\\\cmidrule{1-3}\\cmidrule{4-5}\n\\textbf{Type} & \\textbf{Name} & & {\\textbf{Mean (sd)}} & {\\textbf{Mean (sd)}} \\\\\n\\midrule\n\\multirow{2}{*}{Numerical} \n& \\texttt{admission\\_score} & & 67.00 (11.46) & 63.61 (11.62) \\\\\n& \\texttt{ECTS1sem} & & 49.79 (14.74) & 10.38 (15.39) \\\\\n\\midrule\n& & \\textbf{Category} & \\textbf{N (Frequency)} & \\textbf{N (Frequency)} \\\\\n\\cmidrule{2-3}\\cmidrule{4-5}\n\\multirow{15}{*}{Categorical} & \\multirow{3}{*}{\\texttt{origins}} & \n \\texttt{OnSite\\textsuperscript{*}} & 1033 (21.55\\%) & 232 (26.61\\%) \\\\\n & & \\texttt{Commuter} & 3427 (71.49\\%) & 583 (66.86\\%) \\\\\n & & \\texttt{Offsite} & 334 (6.96\\%) & 57 (6.53\\%) \\\\\n \\cmidrule{2-5}\n & \\multirow{2}{*}{\\texttt{gender}} & \\texttt{Male\\textsuperscript{*}} & 3200 (66.75\\%) & 670 (76.83\\%)\\\\\n & & \\texttt{Female} & 31594 (33.25\\%) & 202 (23.17\\%) \\\\\n \\cmidrule{2-5}\n & \\multirow{4}{*}{\\texttt{highschool\\_type}} & \\texttt{Scientific\\textsuperscript{*}} & 3181 (66.36\\%) & 542 (62.15\\%) \\\\\n & & \\texttt{Classical} & 327 (6.82\\%) & 55 (6.31\\%) \\\\\n & & \\texttt{Others} &607 (12.66\\%) & 90 (10.32\\%) \\\\\n& & \\texttt{Technical} & 679 (14.16\\%) & 185 (21.22\\%) \\\\\n \\cmidrule{2-5}\n & \\multirow{4}{*}{\\texttt{income}} & \\texttt{Medium\\textsuperscript{*}} & 988 (20.61\\%) & 125 (14.33\\%) \\\\\n & & \\texttt{Grant} & 1404 (29.29\\%) & 287 (32.91\\%) \\\\\n & & \\texttt{High} & 1771 (36.94\\%) & 378 (43.35\\%) \\\\\n & & \\texttt{Low} & 631 (13.16\\%) & 82 (9.41\\%) \\\\\n \\cmidrule{2-5}\n & \\multirow{2}{*}{\\texttt{age19}} & \\texttt{0\\textsuperscript{*}} & 4276 (89.19\\%) &671 (76.95\\%) \\\\\n & & \\texttt{1} & 518 (10.81\\%) & 201 (23.05\\%) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Descriptive statistics for considered covariates after data pre-processing for \\texttt{career\\_start\\_ay}=\\lq 2017', according to the dropout status by the end of the third year.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Analysis of Higher Education Dropouts Dynamics through Multilevel Functional Decomposition of Recurrent Events in Counting Processes", "authors": ["Alessandra Ragni", "Chiara Masci", "Anna Maria Paganoni"], "url": "https://arxiv.org/abs/2411.13370v1", "attribution": "\"Analysis of Higher Education Dropouts Dynamics through Multilevel Functional Decomposition of Recurrent Events in Counting Processes\" by Alessandra Ragni, Chiara Masci, and Anna Maria Paganoni, arXiv:2411.13370v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2501.00509v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|lllll}\n & MG & SS & CV & FL & HL \\\\ \\hline\n\\#Utts & 8,423 & 19,266 & 516 & 548 & 198 \\\\\nDur (h) & 10.2 & 28.2 & 0.6 & 2.2 & 0.7 \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Number of utterances and total duration of test sets.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fotheidil: an Automatic Transcription System for the Irish Language", "authors": ["Liam Lonergan", "Ibon Saratxaga", "John Sloan", "Oscar Maharog", "Mengjie Qian", "Neasa Ní Chiaráin", "Christer Gobl", "Ailbhe Ní Chasaide"], "url": "https://arxiv.org/abs/2501.00509v1", "attribution": "\"Fotheidil: an Automatic Transcription System for the Irish Language\" by Liam Lonergan, Ibon Saratxaga, John Sloan, Oscar Maharog, Mengjie Qian, Neasa Ní Chiaráin, Christer Gobl, and Ailbhe Ní Chasaide, arXiv:2501.00509v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16684v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Empirical power (in percent) with $\\alpha= 0.05$ and $m = n = 50$ under the difference pattern (iii) correlated difference. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccccc}\n \\toprule\n $d$ & MATES & CM & GET & BD & GED & RF & MT & GPK & RISE & MMD & xMMD & aMMD & mMMD \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (I)} \\\\\n 200 & \\textbf{90.8} & 4.9 & 5.6 & 4.6 & 9.2 & 7.3 & 5.4 & 3.6 & 5.3 & 4.5 & 5.7 & 5.9 & 1.6 \\\\\n 500 & \\textbf{86.0} & 5.1 & 4.9 & 6.0 & 7.5 & 6.1 & 5.1 & 4.8 & 4.9 & 6.2 & 6.3 & 6.3 & 1.6 \\\\\n 1000 & \\textbf{85.8} & 4.5 & 3.8 & 5.3 & 7.2 & 5.9 & 5.2 & 4.8 & 4.4 & 5.6 & 6.1 & 5.8 & 0.9 \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (II)} \\\\\n 200 & \\textbf{90.0} & 4.5 & 5.3 & 3.7 & 10.2 & 7.1 & 4.2 & 4.1 & 6.0 & 6.0 & 4.9 & 6.9 & 2.0 \\\\\n 500 & \\textbf{75.1} & 4.2 & 5.4 & 5.3 & 8.3 & 7.0 & 5.5 & 4.6 & 4.9 & 5.3 & 6.0 & 6.1 & 1.4 \\\\\n 1000 & \\textbf{81.6} & 4.0 & 5.5 & 5.6 & 6.8 & 6.6 & 5.7 & 4.9 & 4.7 & 4.9 & 5.3 & 5.0 & 1.5 \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (III)} \\\\\n 200 & \\textbf{91.6} & 3.2 & 5.3 & 5.0 & 10.9 & 7.0 & 6.0 & 4.4 & 5.3 & 4.2 & 5.7 & 5.3 & 1.7 \\\\\n 500 & \\textbf{74.2} & 1.9 & 6.4 & 4.9 & 7.7 & 6.8 & 4.4 & 4.3 & 7.1 & 5.5 & 5.4 & 6.0 & 2.1 \\\\\n 1000 & \\textbf{81.5} & 4.1 & 4.0 & 5.0 & 6.4 & 5.9 & 5.0 & 4.1 & 4.8 & 4.8 & 6.3 & 4.5 & 1.2 \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (IV)} \\\\\n 200 & \\textbf{90.6} & 4.4 & 7.6 & 7.1 & 30.2 & 11.2 & 9.6 & 4.7 & 6.5 & 6.1 & 7.2 & 13.7 & 0.7 \\\\\n 500 & \\textbf{88.1} & 5.2 & 5.5 & 5.6 & 32.5 & 11.7 & 19.7 & 5.3 & 5.7 & 6.6 & 6.5 & 14.7 & 1.3 \\\\\n 1000 & \\textbf{82.2} & 4.4 & 6.2 & 5.8 & 43.2 & 14.2 & 31.4 & 7.7 & 5.9 & 10.4 & 10.7 & 24.4 & 2.5 \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (V)} \\\\\n 200 & \\textbf{80.5} & 4.3 & 9.8 & 6.3 & 15.7 & 8.4 & 4.0 & 4.6 & 9.2 & 5.6 & 5.9 & 7.4 & 1.4 \\\\\n 500 & \\textbf{87.6} & 3.6 & 6.7 & 6.2 & 10.4 & 6.7 & 5.0 & 4.5 & 7.4 & 4.3 & 6.4 & 6.0 & 1.3 \\\\\n 1000 & \\textbf{90.5} & 4.6 & 4.6 & 3.8 & 10.6 & 5.7 & 4.8 & 3.4 & 5.7 & 6.0 & 6.8 & 6.3 & 1.0 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "MATES: Multi-view Aggregated Two-Sample Test", "authors": ["Zexi Cai", "Wenbo Fei", "Doudou Zhou"], "url": "https://arxiv.org/abs/2412.16684v1", "attribution": "\"MATES: Multi-view Aggregated Two-Sample Test\" by Zexi Cai, Wenbo Fei, and Doudou Zhou, arXiv:2412.16684v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.04035v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n \\hline\n corr & Measure & cart & softImpute & missForest & QP-AS & dQP-SVAS \\\\ \\hline\n 0.3 & Time & 5.93 & 0.04 & 20.09 & 44.80 & 4.62 \\\\\n & Accuracy & 0.3905 & \\textbf{0.4367} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.4393} & \\textbf{0.4186} & \\textbf{0.4188} \\\\\n & RMSE & 1.0784 & 0.9770 & \\textbf{0.9009} & \\textbf{0.8843} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.8842} \\\\\n & MAD & 0.7845 & 0.6871 & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.6431} & \\textbf{0.6478} & \\textbf{0.6475} \\\\ \\hline\n 0.5 & Time & 5.92 & 0.04 & 20.10 & 44.89 & 4.60 \\\\\n & Accuracy & 0.3920 & \\textbf{0.4402} & \\textbf{0.4418} & \\textbf{0.4505} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.4509} \\\\\n & RMSE & 1.0776 & 0.9732 & \\textbf{0.8994} & \\textbf{0.8634} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.8631} \\\\\n & MAD & 0.7829 & 0.6823 & \\textbf{0.6405} & \\textbf{0.6142} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.6138} \\\\ \\hline\n 0.7 & Time & 5.90 & 0.04 & 19.99 & 43.37 & 4.46 \\\\\n & Accuracy & 0.3964 & 0.4463 & 0.4471 & \\textbf{0.5049} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.5051} \\\\\n & RMSE & 1.0710 & 0.9648 & 0.8935 & \\textbf{0.8104} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.8100} \\\\\n & MAD & 0.7756 & 0.6732 & 0.6335 & \\textbf{0.5488} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.5484} \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Synthetic data results (averaged by correlation parameter $s$)}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Discordance Minimization-based Imputation Algorithms for Missing Values in Rating Data", "authors": ["Young Woong Park", "Jinhak Kim", "Dan Zhu"], "url": "https://arxiv.org/abs/2311.04035v1", "attribution": "\"Discordance Minimization-based Imputation Algorithms for Missing Values in Rating Data\" by Young Woong Park, Jinhak Kim, and Dan Zhu, arXiv:2311.04035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18051v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|cccc|}\n \\hline\n $\\rho$& number of scenarios & obj & LB & UB & gap \\\\\n \\hline\n0.2\n&500&-10.19&-10.23&-10.19&0.43\\%%\\\\\n\\\\\n \\hline\n0.5\n&500&-10.57&-10.68&-10.56&1.09\\%%\\\\\n\\\\\n \\hline\n1.0\n&500&-11.13&-11.35&-11.12&2.06\\%%\\\\\n\\\\\n \\hline\n2.0\n&500&-12.12&-12.57&-12.10&3.89\\%%\\\\\n\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Objective values and 95\\% bounds with different sizes of uncertainty with triangle distribution under box demand uncertainty}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications", "authors": ["Jiaqi Lei", "Akhil Singla", "Sanjay Mehrotra"], "url": "https://arxiv.org/abs/2501.18051v2", "attribution": "\"A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications\" by Jiaqi Lei, Akhil Singla, and Sanjay Mehrotra, arXiv:2501.18051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10206v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\t\t\t\\hline\n\t\t\t& symmetric case & asymmetric case \\\\ \\hline \n\t\t\t$g$ low & . (no hegemon) & . (unipolar, $a$ dominates) \\\\ \\hline\n\t\t\t$g$ mid & . (unipolar, $a$ compromises)\n\t\t\t& . and .\n\t\t (bipolar. $a$ or $b$ compromises)\\\\ \\hline\n\t\t\t$g$ high & . (bipolar) \n\t\t\t& . (unipolar, $b$ dominates) \\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Power of Non-Superpowers", "authors": ["Tomoo Kikuchi", "Shuige Liu"], "url": "https://arxiv.org/abs/2209.10206v4", "attribution": "\"The Power of Non-Superpowers\" by Tomoo Kikuchi and Shuige Liu, arXiv:2209.10206v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.06864v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overall Performance Comparison}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c|c}\n\\hline\n\\textbf{Model} & \\textbf{\\hspace{1em} SR\\hspace{1em} } & \\textbf{\\hspace{1em} EV\\hspace{1em} } & \\textbf{ \\hspace{1em} XS-$R^2$ \\hspace{1em} } & \\textbf{\\hspace{1em} MSPE\\hspace{1em} } \\\\\n\\hline\n\\multicolumn{5}{c}{\\textit{Traditional Asset Pricing Models}} \\\\\n\\hline\nCAPM &0.49 & 0.05 & 0.00 & 2.91 \\\\\nFF3 & 0.52 & 0.06 & 0.00 & 2.37 \\\\\nFF5 & 0.50 & 0.09 & 0.02 & 2.13 \\\\\n\\hline\n\\multicolumn{5}{c}{\\textit{Modern Asset Pricing Methods}} \\\\\n\\hline\nLinear-SDF & 0.84 & 0.13 & 0.00 & 4.17 \\\\\nElasticnet-SDF & 1.03 & 0.12 & 0.00 & 3.16 \\\\\nIPCA-SDF & 1.56 & 0.11 & 0.00 & 1.57 \\\\\n\\hline\n\\multicolumn{5}{c}{\\textit{Deep Learning Methods}} \\\\\n\\hline\nFFN & 1.28 & 0.13 & 0.05 & 1.35 \\\\\nGAN-SDF & 0.65 & 0.14 & 0.11 & 0.87 \\\\\n\\hline\n\\multicolumn{5}{c}{\\textit{Text Integration Methods}} \\\\\n\\hline\nTF-IDF-SDF & 1.83 & 0.13 & 0.10 & 1.45 \\\\\nBERT-SDF & 1.81 & 0.12 & 0.07 & 1.43 \\\\\n\\hline\n\\multicolumn{5}{c}{\\textit{Ablation Studies}} \\\\\n\\hline\nNewsNet-SDF w/o macro & 1.92 & 0.14 & 0.09 & 1.56 \\\\\nNewsNet-SDF w/o news & 1.65 & 0.13 & 0.08 & 2.67 \\\\\n\\hline\n\\textbf{NewsNet-SDF} & \\textbf{2.80} & \\textbf{0.14} & \\textbf{0.11} & \\textbf{0.56} \\\\\n\\hline\n\\multicolumn{5}{p{0.85\\linewidth}}{\\small \\textit{Note:} For Sharpe Ratio, Explained Variation, and XS-$R^2$, higher values indicate better performance. For MSPE, lower values indicate better performance.} \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks", "authors": ["Shunyao Wang", "Ming Cheng", "Christina Dan Wang"], "url": "https://arxiv.org/abs/2505.06864v1", "attribution": "\"NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks\" by Shunyao Wang, Ming Cheng, and Christina Dan Wang, arXiv:2505.06864v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.20295v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Net Electric Charge Sectors in QED}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n\t\t\\hline\n\t\tSector $\\mathcal{H}_Q$ & Examples \\\\ \\hline\n\t\t$Q=0$ & $\\lvert 0 \\rangle$, $\\lvert e^-e^+\\rangle$, multi-photon states \\\\ \\hline\n\t\t$Q=-e$ & $\\lvert e^-\\rangle$, $\\lvert e^-e^-e^+\\rangle$ \\\\ \\hline\n\t\t$Q=+e$ & $\\lvert e^+\\rangle$, $\\lvert e^+e^+e^-\\rangle$ \\\\ \\hline\n\t\t$Q=-2e$ & $\\lvert e^-e^-\\rangle$ \\\\ \\hline\n\t\t$\\cdots$ & \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Symmetry Packaging II: A Group-Theoretic Framework for Packaging Under Finite, Compact, Higher-Form, and Hybrid Symmetries", "authors": ["Rongchao Ma"], "url": "https://arxiv.org/abs/2503.20295v3", "attribution": "\"Symmetry Packaging II: A Group-Theoretic Framework for Packaging Under Finite, Compact, Higher-Form, and Hybrid Symmetries\" by Rongchao Ma, arXiv:2503.20295v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00509v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Regret estimates for BFI and FMAB problems in deterministic case.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\toprule\n Function & Cost per iter. & Base optimizer & \\# iter for $R_B \\leq \\varepsilon$ & $R_O(T)$ \\\\\n \\midrule\n Convex $M$-Lipschitz &1 $\\nabla$, 1 proj. & PGD & $\\sum\\limits_{i=1}^K\\Bigl \\lceil\\frac{M_i^2R_i^2}{\\max(f_i^*-f^*-\\frac{\\varepsilon}{2},\\frac{\\varepsilon}{2})^2}\\Bigr \\rceil$& $O\\left(RM\\sqrt{KT}\\right)$ \\\\\n Convex $L$-smooth & 1 $\\nabla$ & AGD& $\\sum\\limits_{i=1}^K\\left\\lceil \\sqrt{\\frac{L_iR_i^2} {\\max(f_i^*-f^*-\\frac{\\varepsilon}{2}, \\frac{\\varepsilon}{2})}} \\; \\right\\rceil$ & $O\\left(K LR^2\\right)$ \\\\\n $\\mu$-convex $M$-Lipschitz & 1 $\\nabla$, 1 proj. & PGD & $\\sum\\limits_{i=1}^K\\Bigl \\lceil\\frac{M_i^2}{\\mu_i \\max(f_i^*-f^*-\\frac{\\varepsilon}{2}, \\frac{\\varepsilon}{2})}\\Bigr \\rceil$ & $O\\left(\\frac{M^2}{\\mu} K\\log{\\frac{T}{K}}\\right)$\\\\\n $\\mu$-convex $L$-smooth &1 $\\nabla$ & AGD & $\\sum\\limits_{i=1}^K\\Bigl \\lceil\\sqrt{\\frac{L_i}{\\mu_i}}\\log\\left(\\frac{\\mu_i R_i^2}{\\max(f_i^*-f^*-\\frac{\\varepsilon}{2}, \\frac{\\varepsilon}{2})}\\right)\\Bigr \\rceil$ & $O\\left(KR^2 \\exp\\left\\{ -\\frac{T}{K\\sqrt{\\kappa}}\\right\\}\\right)$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Functional multi-armed bandit and the best function identification problems", "authors": ["Yuriy Dorn", "Aleksandr Katrutsa", "Ilgam Latypov", "Anastasiia Soboleva"], "url": "https://arxiv.org/abs/2503.00509v1", "attribution": "\"Functional multi-armed bandit and the best function identification problems\" by Yuriy Dorn, Aleksandr Katrutsa, Ilgam Latypov, and Anastasiia Soboleva, arXiv:2503.00509v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.16834v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|r}\n Type & Method & BPC ($\\downarrow$) \\\\ \\hline\n Autoregressive Backbone& IAF/SCF & 1.88\\\\\n & AR Argmax Flow & 1.39\\\\\n & Discrete Flow & \\textbf{1.23}\\\\\n & Autoregressive & \\textbf{1.23}\\\\ \\hline\n Non-autoregressive & Mult. Diffusion & $\\le$ 1.72\\\\\n & MAC & $\\le$ 1.40\\\\\n & BFN & $\\le$ 1.41\\\\\n & D3PM Uniform & $\\le$ 1.61\\\\\n & D3PM Absorb & $\\le$ 1.45\\\\ \\hline\n Ours (NAR) & SEDD Uniform & $\\le$ 1.47\\\\\n & SEDD Absorb & $\\le$ \\textbf{1.39}\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{Bits Per Character on text8.} Our SEDD models achieve second-best overall result (best for non-autoregressive), only being beaten out by the autoregressive model and a discrete flow (which uses an autoregressive model as a backbone) by a small margin. SEDD also substantially improves upon prior the discrete diffusion model D3PM .}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution", "authors": ["Aaron Lou", "Chenlin Meng", "Stefano Ermon"], "url": "https://arxiv.org/abs/2310.16834v3", "attribution": "\"Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution\" by Aaron Lou, Chenlin Meng, and Stefano Ermon, arXiv:2310.16834v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.17244v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Chaotic Behavior Test Results for Target Variable (Sample Indices)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|}\n\\hline\n\\textbf{Index} & $\\lambda$ & $D_2$ & \\textbf{ApEn} & \\textbf{SampEn} & \\textbf{DFA $\\alpha$} & \\textbf{Spectral Entropy} \\\\\n\\hline\nCNXFMCG & 0.00497 & 2.106 & 1.533 & 1.573 & 0.517 & 6.908 \\\\\nXBANK & 0.00346 & 2.479 & 1.449 & 1.285 & 0.488 & 6.919 \\\\\nNIFTYQUALITY30 & 0.00067 & 2.209 & 1.490 & 1.524 & 0.515 & 6.923 \\\\\nDJUSRD & 0.00944 & 2.777 & 1.362 & 1.248 & 0.435 & 6.948 \\\\\nDJUSMC & 0.00586 & 2.662 & 1.355 & 1.219 & 0.424 & 6.945 \\\\\nNIFTY100LOWVOL30 & 0.00520 & 2.216 & 1.497 & 1.495 & 0.510 & 6.917 \\\\\nTRMI & 0.00082 & 2.704 & 1.352 & 1.128 & 0.482 & 6.899 \\\\\nTFBI & 0.01949 & 2.448 & 1.464 & 1.374 & 0.445 & 6.903 \\\\\nBSE.PR.OWER & 0.02258 & 2.327 & 1.459 & 1.372 & 0.469 & 6.921 \\\\\nDJUSTK & 0.00139 & 2.666 & 1.316 & 1.109 & 0.471 & 6.934 \\\\\nTBNI & 0.00378 & 2.758 & 1.328 & 1.171 & 0.509 & 6.920 \\\\\nDFMGI & 0.00046 & 2.630 & 1.393 & 1.312 & 0.460 & 6.908 \\\\\nNIFTY100ESGSECLDR & 0.00274 & 2.337 & 1.455 & 1.416 & 0.524 & 6.910 \\\\\nDJUSIP & 0.00205 & 2.586 & 1.369 & 1.193 & 0.464 & 6.918 \\\\\nDJUSDR & 0.00406 & 2.558 & 1.410 & 1.327 & 0.450 & 6.929 \\\\\nNIFTYMIDLIQ15 & 0.01189 & 2.393 & 1.452 & 1.409 & 0.433 & 6.905 \\\\\nNLIN & 0.00686 & 2.675 & 1.270 & 1.123 & 0.425 & 6.916 \\\\\nQERP & 0.00896 & 2.444 & 1.471 & 1.373 & 0.451 & 6.908 \\\\\nMXX & 0.00912 & 2.304 & 1.481 & 1.455 & 0.459 & 6.920 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data", "authors": ["Arif Pathan"], "url": "https://arxiv.org/abs/2506.17244v1", "attribution": "\"Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data\" by Arif Pathan, arXiv:2506.17244v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.13004v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ASR performance when training on either purely real or purely synthetic speech}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lr}\n \\toprule\n \\textbf{Training data} & \\textbf{WER} \\\\\n \\midrule\n 80h real & 11.4 \\\\\n 80h synth, unclustered, 50 voices & 36.2 \\\\\n 80h synth, clustered, 5 voices & 47.1 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Speech Synthesis as Augmentation for Low-Resource ASR", "authors": ["Deblin Bagchi", "Shannon Wotherspoon", "Zhuolin Jiang", "Prasanna Muthukumar"], "url": "https://arxiv.org/abs/2012.13004v1", "attribution": "\"Speech Synthesis as Augmentation for Low-Resource ASR\" by Deblin Bagchi, Shannon Wotherspoon, Zhuolin Jiang, and Prasanna Muthukumar, arXiv:2012.13004v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09574v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{rotating}\n\\usepackage{amsfonts}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation studies for the proposed method on seven datasets. The coarse refinement (CR), channel-wise attention (CA), spatial attention (SA), atrous spatial pyramid pooling (ASPP), fine-grained refinement (FR), feature aggregation (FA) and skip connection (SC) are removed from MSIRN.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|cccc|ccc|c}\n \\hline\n \\hline\n \\multicolumn{2}{c}{} & \\multicolumn{4}{|c|}{Coarse branch}&\\multicolumn{3}{c|}{Fine branch} & MSIRN \\\\\n \\cline{3-10}\n &Metric&w/o CR&w/o CA&w/o SA&w/o ASPP&w/o FR&w/o FA&w/o SC&Ours \\\\\n & &(a)&(b)&(c)&(d)&(e)&(f)&(g)& \\\\\n \\hline\n \\hline\n\\multirow{3}*{\\rotatebox{90}{\\emph{NJU2K}}} &$S_\\alpha$↑ &0.902 & 0.909 & 0.912 & 0.910 & 0.900 & \\textbf{0.913} & 0.909 & \\textbf{0.912} \\\\\n &$F_\\beta$↑ &0.886 & 0.889 & 0.894 & 0.884 & 0.876 & 0.884 & 0.901 & \\textbf{0.904} \\\\\n&$\\mathcal{M}$↓ &0.042 & 0.043 & 0.041 & 0.043 & 0.047 & 0.044 & 0.039 & \\textbf{0.038}\\\\\n\\hline\n\\multirow{3}*{\\rotatebox{90}{\\emph{LFSD}}} &$S_\\alpha$↑ &0.850 & 0.854 & 0.855 & 0.860 & 0.850 & 0.851 & \\textbf{0.862} & \\textbf{0.862} \\\\\n &$F_\\beta$↑ &0.816 & 0.826 & 0.835 & 0.827 & 0.816 & 0.822 & \\textbf{0.842} & 0.838 \\\\\n&$\\mathcal{M}$↓ &0.085 & 0.082 & 0.079 & 0.081 & 0.091 & 0.088 & \\textbf{0.074} & 0.077 \\\\\n\\hline\n\\multirow{3}*{\\rotatebox{90}{\\emph{STERE}}} &$S_\\alpha$↑ &0.903 & 0.906 & 0.903 & 0.897 & 0.897 & 0.903 & \\textbf{0.904} & \\textbf{0.904} \\\\\n &$F_\\beta$↑ &0.865 & 0.872 & 0.875 & 0.851 & 0.849 & 0.863 & 0.877 & \\textbf{0.892} \\\\\n&$\\mathcal{M}$↓ &0.049 & 0.045 & 0.044 & 0.053 & 0.054 & 0.050 & \\textbf{0.043} & 0.045 \\\\\n\\hline\n\\multirow{3}*{\\rotatebox{90}{\\emph{DES}}} &$S_\\alpha$↑ &0.929 & 0.930 & 0.920 & 0.929 & 0.917 & 0.925 & 0.926 & \\textbf{0.931} \\\\\n &$F_\\beta$↑ &0.896 & 0.896 & 0.896 & 0.882 & 0.873 & 0.887 & 0.899 & \\textbf{0.903} \\\\\n&$\\mathcal{M}$↓ &0.025 & \\textbf{0.023} & 0.025 & 0.026 & 0.029 & 0.027 & 0.024 & \\textbf{0.023}\\\\\n\\hline\n\\multirow{3}*{\\rotatebox{90}{\\emph{NLPR}}} &$S_\\alpha$↑ &0.929 & 0.923 & 0.930 & 0.923 & 0.917 & \\textbf{0.931} & 0.928 & \\textbf{0.931} \\\\\n &$F_\\beta$↑ &0.883 & 0.880 & 0.891 & 0.874 & 0.862 & 0.884 & 0.893 & \\textbf{0.894} \\\\\n&$\\mathcal{M}$↓ &0.027 & 0.028 & \\textbf{0.025} & 0.029 & 0.032 & 0.027 & \\textbf{0.025} & \\textbf{0.025}\\\\\n\\hline\n\\multirow{3}*{\\rotatebox{90}{\\emph{SSD}}} &$S_\\alpha$↑ &0.873 & 0.879 & 0.871 & 0.871 & 0.876 & 0.879 & 0.878 & \\textbf{0.880} \\\\\n &$F_\\beta$↑ &0.823 & 0.839 & 0.829 & 0.818 & 0.822 & 0.840 & 0.836 & \\textbf{0.843} \\\\\n&$\\mathcal{M}$↓ &0.055 & \\textbf{0.049} & 0.050 & 0.054 & 0.056 & 0.051 & 0.051 & 0.050 \\\\\n\\hline\n\\multirow{3}*{\\rotatebox{90}{\\emph{SIP}}} &$S_\\alpha$↑ &0.866 & 0.861 & 0.868 & 0.852 & 0.862 & 0.864 & 0.865 & \\textbf{0.879} \\\\\n &$F_\\beta$↑ &0.846 & 0.849 & 0.857 & 0.823 & 0.840 & 0.848 & 0.859 & \\textbf{0.867} \\\\\n&$\\mathcal{M}$↓ &0.066 & 0.066 & 0.061 & 0.074 & 0.069 & 0.066 & 0.062 & \\textbf{0.056}\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection", "authors": ["Ze-yu Liu", "Jian-wei Liu", "Xin Zuo", "Ming-fei Hu"], "url": "https://arxiv.org/abs/2201.09574v1", "attribution": "\"Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection\" by Ze-yu Liu, Jian-wei Liu, Xin Zuo, and Ming-fei Hu, arXiv:2201.09574v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18248v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Properties experimentally tested box model with mooring lines }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\textbf{Properties} & \\textbf{Value} &\\textbf{Units} \\\\\n \\midrule\n Box dimension ($L \\times W \\times H$) & $0.2 \\times 0.2 \\times 0.132$ & m\\\\\n Box mass (+ Connections) & $3.16$ & kg \\\\\n Centre of gravity ($x$,$y$,$z$) & $(0,0,-0.126)$ & m \\\\\n Box initial draft & $0.0786$ & m \\\\\n Box moment of inertia ($\\mathbf{I}_{xx}, \\mathbf{I}_{yy}, \\mathbf{I}_{zz}$) & $(0.015, 0.015, 0.021)$ & kg m$^{2}$ \\\\\n Mooring line diameter & $0.003656$ & m \\\\\n Mooring line length & $1.455$ & m\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A finite volume Simo-Reissner beam method for moored floating body dynamics", "authors": ["Amirhossein Taran", "Seevani Bali", "Zeljko Tukovic", "Vikram Pakrashi", "Philip Cardiff"], "url": "https://arxiv.org/abs/2504.18248v1", "attribution": "\"A finite volume Simo-Reissner beam method for moored floating body dynamics\" by Amirhossein Taran, Seevani Bali, Zeljko Tukovic, Vikram Pakrashi, and Philip Cardiff, arXiv:2504.18248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.04384v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{~ Cross Correlation Matrix}\n\\begin{tabular}{l|rrrrrrrrrrrrr}\n & NIM & RA & RBD & OC & LOGTA & LQR & MNGMT & IIP & DPZTG & DVRSTY & HHI & GDP & INF \\\\\n \\midrule\n \\midrule\n NIM & 1 & & & & & & & & & & & & \\\\\n RA & 0.302 & 1 & & & & & & & & & & & \\\\\n RBD & 0.053 & -0.068 & 1 & & & & & & & & & & \\\\\n OC & 0.307 & 0.483 & 0.054 & 1 & & & & & & & & & \\\\\n LOGTA & -0.127 & -0.474 & 0.023 & -0.362 & 1 & & & & & & & & \\\\\n LQR & -0.033 & 0.187 & -0.159 & 0.060 & -0.188 & 1 & & & & & & & \\\\\n MNGMT & -0.073 & -0.069 & 0.075 & 0.263 & -0.085 & 0.018 & 1 & & & & & & \\\\\n IIP & 0.660 & 0.148 & 0.077 & 0.300 & -0.116 & -0.108 & 0.072 & 1 & & & & & \\\\\n DPZTG & -0.095 & 0.128 & -0.026 & 0.106 & -0.071 & 0.092 & 0.011 & -0.077 & 1 & & & & \\\\\n DVRSTY & 0.009 & 0.032 & 0.066 & 0.124 & -0.089 & 0.124 & -0.146 & 0.064 & 0.077 & 1 & & & \\\\\n HHI & 0.116 & 0.117 & 0.387 & 0.236 & -0.338 & 0.034 & 0.001 & 0.039 & 0.020 & 0.092 & 1 & & \\\\\n GDP & -0.140 & -0.035 & -0.087 & -0.007 & -0.016 & -0.007 & 0.035 & -0.067 & 0.019 & 0.016 & 0.118 & 1 & \\\\\n INF & 0.227 & 0.082 & 0.470 & 0.198 & -0.263 & 0.078 & -0.041 & 0.104 & -0.010 & 0.081 & 0.661 & -0.246 & 1 \\\\\n \\bottomrule\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Determinants of Net Interest Margin in the Turkish Banking Sector: Does Bank Ownership Matter?", "authors": ["Fatih Kansoy"], "url": "https://arxiv.org/abs/2506.04384v2", "attribution": "\"The Determinants of Net Interest Margin in the Turkish Banking Sector: Does Bank Ownership Matter?\" by Fatih Kansoy, arXiv:2506.04384v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03218v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Basic CNN Model Accuracy}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\nS/N & Dataset & acc & pre & rec & f1 & sup \\\\\n\\hline\n\\hline\n1 & MNIST(Image) & 97\\% & 96\\% & 96\\% & 97\\% & 97\\%\\\\\n\\hline\n2 & SocoFing(Biometrics) & 82\\% & 82\\% & 82\\% & 83\\% & 82\\%\\\\\n\\hline\n3 & CTU-13(Botnet) & 92\\% & 93\\% & 93\\% & 93\\% & 93\\%\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning", "authors": ["Olakunle Ibitoye", "M. Omair Shafiq", "Ashraf Matrawy"], "url": "https://arxiv.org/abs/2101.03218v1", "attribution": "\"DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning\" by Olakunle Ibitoye, M. Omair Shafiq, and Ashraf Matrawy, arXiv:2101.03218v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10719v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Monthly pneumonia and influenza deaths time series optimal gamma.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n \\hline\nAhead & $\\gamma$\\_mape & $\\gamma$\\_smape\\\\ \n \\hline\n1.00 & 0.50 & 0.50 \\\\ \n2.00 & 0.22 & 0.22 \\\\ \n3.00 & 0.08 & 0.08 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Short-term prediction of Time Series based on bounding techniques", "authors": ["Pedro Cadahía", "Jose Manuel Bravo Caro"], "url": "https://arxiv.org/abs/2101.10719v1", "attribution": "\"Short-term prediction of Time Series based on bounding techniques\" by Pedro Cadahía and Jose Manuel Bravo Caro, arXiv:2101.10719v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.00213v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{siunitx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean performance in \\textbf{ratio} for player selection methods in CITR dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n \\toprule\n Threshold-based & Parameter & $\\text{traj}_{S}\\downarrow$ & $\\text{traj}_L\\downarrow$ & $\\text{Dist}_m\\uparrow$ & Consistency $\\uparrow$ & Player Num. & Runtime\\\\\n \\midrule\n \\midrule\n \\multirow{3}[0]{*}{\\textbf{PSN-Full}} & $m_{\\text{th}}=0.1$ & 1.222 & 0.996 & 0.936 & 0.994 & 5.523 & 0.193\\\\\n & $m_{\\text{th}}=0.3$ & 1.222 & 0.996 & 0.936 & 0.994 & 5.523 & 0.193\\\\\n & $m_{\\text{th}}=0.5$ & 1.222 & 0.996 & 0.938 & 0.994 & 5.523 & 0.193\\\\\n \\midrule\n \\multirow{3}[0]{*}{PSN-Partial} & $m_{\\text{th}}=0.1$ & 1.222 & 0.997 & 0.924 & \\textbf{0.996} & 4.633 & 0.112\\\\\n & $m_{\\text{th}}=0.3$ & 1.222 & 0.997 & 0.924 & \\textbf{0.996} & 4.633 & 0.112\\\\\n & $m_{\\text{th}}=0.5$ & 1.222 & 0.996 & 0.918 & \\textbf{0.996} & 4.633 & 0.112\\\\\n \\midrule\n \\multirow{3}[0]{*}{Distance} & $r_\\text{th}=\\SI{1.5}{\\meter}$ & 1.222 & 0.995 & 0.766 & \\textbf{0.996} & 1.175 & 0.002\\\\\n & $r_\\text{th}=\\SI{2.0}{\\meter}$ & 1.333 & 0.998 & 0.772 & \\textbf{0.996} & 1.360 & 0.004\\\\\n & $r_\\text{th}=\\SI{2.5}{\\meter}$ & 1.333 & 0.997 & 0.825 & 0.993 & 1.829 & 0.010\\\\\n \\toprule\n Ranking-based & Parameter & $\\text{traj}_{S}\\downarrow$ & $\\text{traj}_L\\downarrow$ & $\\text{Dist}_m\\uparrow$ & Consistency $\\uparrow$ & Player Num. \\\\\n \\midrule\n \\midrule\n \\multirow{2}[0]{*}{\\textbf{PSN-Full}} & $|U^i|=4$ & 1.222 & 0.996 & 0.922 & \\textbf{0.996} & 5.000 & 0.119\\\\\n & $|U^i|=6$ & 1.222 & 0.996 & 0.939 & 0.993 & 7.000 & 0.356\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\textbf{PSN-Partial}} & $|U^i|=4$ & 1.222 & 0.996 & 0.917 & 0.993 & 5.000 & 0.098\\\\\n & $|U^i|=6$ & 1.222 & 0.995 & 0.934 & 0.991 & 7.000 & 0.336\\\\\n \\midrule\n \\multirow{2}[0]{*}{Distance} & $|U^i|=4$ & \\textbf{1.111} & 0.998 & 0.926 & 0.991 & 5.000 & 0.109\\\\\n & $|U^i|=6$ & \\textbf{1.111} & 1.000 & \\textbf{0.969} & 0.989 & 7.000 & 0.349\\\\\n \\midrule\n \\multirow{2}[0]{*}{Cost Evolution} & $|U^i|=4$ & 1.222 & \\textbf{0.994} & 0.944 & 0.969 & 5.000 & 0.126\\\\\n & $|U^i|=6$ & \\textbf{1.111} & \\textbf{0.994} & 0.947 & 0.967 & 7.000 & 0.330\\\\\n \\midrule\n \\multirow{2}[0]{*}{Gradient} & \\multicolumn{6}{c}{\\multirow{2}[0]{*}{Not Applicable}} \\\\\n & \\multicolumn{6}{c}{} \\\\\n \\midrule\n \\multirow{2}[0]{*}{Hessian} & \\multicolumn{6}{c}{\\multirow{2}[0]{*}{Not Applicable}} \\\\\n & \\multicolumn{6}{c}{} \\\\\n \\midrule\n \\multirow{2}[0]{*}{BF} & $|U^i|=4$ & \\textbf{1.111} & 0.999 & 0.943 & 0.989 & 5.000 & 0.121\\\\\n & $|U^i|=6$ & \\textbf{1.111} & 0.998 & 0.964 & 0.989 & 7.000 & 0.344\\\\\n \\midrule\n \\multirow{2}[0]{*}{CBF} & \\multicolumn{6}{c}{\\multirow{2}[0]{*}{Not Applicable}} \\\\\n & \\multicolumn{6}{c}{} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "PSN Game: Game-theoretic Planning via a Player Selection Network", "authors": ["Tianyu Qiu", "Eric Ouano", "Fernando Palafox", "Christian Ellis", "David Fridovich-Keil"], "url": "https://arxiv.org/abs/2505.00213v1", "attribution": "\"PSN Game: Game-theoretic Planning via a Player Selection Network\" by Tianyu Qiu, Eric Ouano, Fernando Palafox, Christian Ellis, and David Fridovich-Keil, arXiv:2505.00213v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.06255v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Wealth values that show when to annuitize.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c}\\hline \n \\multirow{2}{*}{$\\theta$ values} & \\multicolumn{2}{c|}{ $\\bar c=1$} &\n \\multicolumn{2}{c|}{$\\bar c=5$} & \n \\multicolumn{2}{c}{$\\bar c=10$} \\\\ \\cline{2-7}\n &$\\eta=0.01$ &$\\eta=1.0 $ & $\\eta=0.01$ & $\\eta=1.0$ & $\\eta=0.01$ & $\\eta=1.0$ \\\\ \\hline\n 0 & $\\star$ & $\\star$ & $\\star$ & $7.77$ & $\\star$ & $14.47$ \\\\ \\hline\n 0.2 & $\\star$ & $\\star$ & $\\star$ & $8.47$ & $\\star$ & $15.53$ \\\\ \\hline\n 0.6 & $3.18$ & $2.82$ & $4.94$ & $12.35$ & $5.29$ & $21.17$ \\\\ \\hline \n \\multicolumn{7}{c}{$\\star$ means that the difference $\\Delta V$ is negative for all $w$} \\\\ \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Retirement spending problem under Habit Formation Model", "authors": ["S. Kirusheva", "H. Huang", "T. S. Salisbury"], "url": "https://arxiv.org/abs/2210.06255v1", "attribution": "\"Retirement spending problem under Habit Formation Model\" by S. Kirusheva, H. Huang, and T. S. Salisbury, arXiv:2210.06255v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.03413v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c||c|c||c|c|c|c|c||}\n \\hline\n & \\multicolumn{2}{c||}{\\textit{Constraint Violation}} & \\multicolumn{5}{c||}{\\textit{Objective Statistics}} \\\\\n \\hline\n $\\alpha$ & \\% Violated & Mean Violation & \\% Under & \\% Over & Avg-O & Avg-U & AR\\\\ \n \\hline\n 0-0.2 & 64.7 & 105.7 & 64.1 & 35.9 & 39.1 & 13.6 & 1.254 \\\\\n \\hline\n 0.2-0.4 & 66.1 & 8.77 & 56.8 & 43.2 & 3.26 & 4.49 & 1.0417 \\\\\n \\hline\n 0.4-0.6 & 95.42 & 9.9 & 14.92 & 85.1 & 3.17 & 1.36 & 1.029 \\\\ \n \\hline\n 0.6-0.8 & 99.84 & 10.4 & 8.6 & 91.4 & 1.97 & 0.55 & 1.0185 \\\\\n \\hline \n 0.8-1 & 100 & 7.55 & 3.17 & 96.83 & 0.66 & 0.11 & 1.0064 \\\\ \n \\hline\n All & 84.93 & 21.14 & 30 & 70 & 5.89 & 7.76 & 1.0703 \\\\\n \\hline\n \\end{tabular}\n\\caption{Baseline Neural Network Performance}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework", "authors": ["Mitchell Keegan", "Mahdi Abolghasemi"], "url": "https://arxiv.org/abs/2312.03413v1", "attribution": "\"Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework\" by Mitchell Keegan and Mahdi Abolghasemi, arXiv:2312.03413v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.01178v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|lc|cc|}\n\\hline\n\\multicolumn{2}{|l|}{\\multirow{2}{*}{}} & \\multicolumn{2}{c|}{\\textbf{Dataset}} \\\\ \\cline{3-4} \n\\multicolumn{2}{|l|}{} & DATASET-1 & DATASET-2 \\\\ \\hline\n\\multirow{2}{*}{\\vtop{\\hbox{\\strut \\textbf{Performance}}\\hbox{\\strut \\textbf{Scenario}}}} & \\textbf{Extracted} & 1218 & 6435 \\\\ \\cline{2-4} \n & \\textbf{Missing} & 22 & 1301 \\\\ \\hline\n\\multirow{2}{*}{\\vtop{\\hbox{\\strut \\textbf{Cost}}\\hbox{\\strut \\textbf{Evolution}}}} & \\textbf{Extracted} & 752 & 3925 \\\\ \\cline{2-4} \n & \\textbf{Missing} & 488 & 3811 \\\\ \\hline\n\\multirow{2}{*}{\\vtop{\\hbox{\\strut \\textbf{Cost}}\\hbox{\\strut \\textbf{Composition}}}} & \\textbf{Extracted} & 1153 & 6886 \\\\ \\cline{2-4} \n & \\textbf{Missing} & 87 & 850 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results of the Extractor for the different table types.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Information Extraction through AI techniques: The KIDs use case at CONSOB", "authors": ["Domenico Lembo", "Alessandra Limosani", "Francesca Medda", "Alessandra Monaco", "Federico Maria Scafoglieri"], "url": "https://arxiv.org/abs/2202.01178v1", "attribution": "\"Information Extraction through AI techniques: The KIDs use case at CONSOB\" by Domenico Lembo, Alessandra Limosani, Francesca Medda, Alessandra Monaco, and Federico Maria Scafoglieri, arXiv:2202.01178v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12464v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|r|r}\n\\toprule\nSignal & Number of Folds & Mean Importance \\\\\n\\midrule\nStoreCount & 10 & 0.4528 \\\\\nWrTmpCount & 10 & 0.2721 \\\\\nInsCount & 10 & 0.2178 \\\\\nSBExit & 10 & 0.0374 \\\\\nExitCount & 10 & 0.0111 \\\\\nSBEnter & 9 & 0.0093 \\\\\nInsAddrDiff & 7 & 0.0004 \\\\\nMaxInsAddr & 3 & 0.0002 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Using Dynamic Binary Instrumentation to Detect Failures in Robotics Software", "authors": ["Deborah S. Katz", "Christopher S. Timperley", "Claire Le Goues"], "url": "https://arxiv.org/abs/2201.12464v1", "attribution": "\"Using Dynamic Binary Instrumentation to Detect Failures in Robotics Software\" by Deborah S. Katz, Christopher S. Timperley, and Claire Le Goues, arXiv:2201.12464v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.09845v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation Results. This table presents simulation results for various trial and target sizes, comparing two methods (IPW and AIPW) under two assumptions (EPD and GPD) and across different proportions of well-represented groups.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|r|r|r|l|l|r|r|r|r|}\n\\hline\n\\textbf{Trial Size} & \\textbf{Target Size} & \\textbf{Proportion} & \\textbf{Method} & \\textbf{Assumption} & \\textbf{Bias} & \\textbf{MSE} & \\textbf{SD} & \\textbf{Coverage} \\\\\n\\hline\n200 & 1980 & 0.8 & IPW & EPD & 0.008 & 0.029 & 0.171 & 0.960 \\\\\n200 & 1980 & 0.8 & IPW & GPD & 0.017 & 0.061 & 0.248 & 0.960 \\\\\n500 & 4950 & 0.8 & IPW & EPD & -0.012 & 0.018 & 0.136 & 0.970 \\\\\n500 & 4950 & 0.8 & IPW & GPD & -0.013 & 0.039 & 0.198 & 0.980 \\\\\n1000 & 9990 & 0.8 & IPW & EPD & -0.004 & 0.013 & 0.113 & 0.930 \\\\\n1000 & 9990 & 0.8 & IPW & GPD & 0.001 & 0.031 & 0.176 & 0.930 \\\\\n200 & 1980 & 0.8 & AIPW & EPD & -0.005 & 0.008 & 0.089 & 0.950 \\\\\n200 & 1980 & 0.8 & AIPW & GPD & -0.009 & 0.016 & 0.126 & 0.950 \\\\\n500 & 4950 & 0.8 & AIPW & EPD & -0.003 & 0.004 & 0.063 & 0.960 \\\\\n500 & 4950 & 0.8 & AIPW & GPD & -0.007 & 0.010 & 0.098 & 0.970 \\\\\n1000 & 9990 & 0.8 & AIPW & EPD & -0.002 & 0.002 & 0.045 & 0.940 \\\\\n1000 & 9990 & 0.8 & AIPW & GPD & -0.005 & 0.005 & 0.071 & 0.940 \\\\\n200 & 1980 & 0.9 & IPW & EPD & 0.012 & 0.035 & 0.187 & 0.950 \\\\\n200 & 1980 & 0.9 & IPW & GPD & 0.020 & 0.073 & 0.270 & 0.950 \\\\\n500 & 4950 & 0.9 & IPW & EPD & -0.009 & 0.020 & 0.141 & 0.970 \\\\\n500 & 4950 & 0.9 & IPW & GPD & -0.010 & 0.042 & 0.205 & 0.980 \\\\\n1000 & 9990 & 0.9 & IPW & EPD & -0.002 & 0.014 & 0.119 & 0.920 \\\\\n1000 & 9990 & 0.9 & IPW & GPD & 0.000 & 0.032 & 0.179 & 0.920 \\\\\n200 & 1980 & 0.9 & AIPW & EPD & -0.004 & 0.009 & 0.094 & 0.950 \\\\\n200 & 1980 & 0.9 & AIPW & GPD & -0.008 & 0.017 & 0.130 & 0.950 \\\\\n500 & 4950 & 0.9 & AIPW & EPD & -0.003 & 0.005 & 0.066 & 0.960 \\\\\n500 & 4950 & 0.9 & AIPW & GPD & -0.006 & 0.011 & 0.103 & 0.970 \\\\\n1000 & 9990 & 0.9 & AIPW & EPD & -0.002 & 0.003 & 0.049 & 0.940 \\\\\n1000 & 9990 & 0.9 & AIPW & GPD & -0.005 & 0.006 & 0.074 & 0.940 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Addressing Positivity Violations in Extending Inference to a Target Population", "authors": ["Jun Lu", "Sanjib Basu"], "url": "https://arxiv.org/abs/2412.09845v2", "attribution": "\"Addressing Positivity Violations in Extending Inference to a Target Population\" by Jun Lu and Sanjib Basu, arXiv:2412.09845v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.17168v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Calibration of probabilistic arrival time forecasts for lead times $l=1$ to $l=8$ on control arm of real-world A/B test}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\t\t\t\\toprule\n\t\t\tProbability & $l=1$ & $l=2$ & $l=3$ & $l=4$ \\\\\n\t\t\t\\midrule\n\t\t\t0.0-0.1 & 0.175 ± 0.011 & 0.164 ± 0.006 & 0.166 ± 0.003 & 0.155 ± 0.001 \\\\\n\t\t\t0.1-0.2 & 0.216 ± 0.008 & 0.303 ± 0.006 & 0.302 ± 0.002 & 0.302 ± 0.002 \\\\\n\t\t\t0.2-0.3 & 0.483 ± 0.013 & 0.367 ± 0.005 & 0.417 ± 0.002 & 0.402 ± 0.002 \\\\\n\t\t\t0.3-0.4 & 0.548 ± 0.012 & 0.471 ± 0.004 & 0.510 ± 0.002 & 0.492 ± 0.002 \\\\\n\t\t\t0.4-0.5 & 0.712 ± 0.011 & 0.601 ± 0.003 & 0.581 ± 0.002 & 0.586 ± 0.002 \\\\\n\t\t\t0.5-0.6 & 0.642 ± 0.010 & 0.672 ± 0.002 & 0.669 ± 0.002 & 0.657 ± 0.002 \\\\\n\t\t\t0.6-0.7 & 0.733 ± 0.009 & 0.755 ± 0.002 & 0.720 ± 0.002 & 0.711 ± 0.002 \\\\\n\t\t\t0.7-0.8 & 0.841 ± 0.005 & 0.815 ± 0.001 & 0.801 ± 0.002 & 0.811 ± 0.002 \\\\\n\t\t\t0.8-0.9 & 0.897 ± 0.002 & 0.885 ± 0.001 & 0.870 ± 0.002 & 0.890 ± 0.002 \\\\\n\t\t\t0.9-1.0 & 0.988 ± 0.000 & 0.959 ± 0.000 & 0.950 ± 0.001 & 0.953 ± 0.001 \\\\\n\t\t\t\\cmidrule{2-5}\n\t\t\t & $l=5$ & $l=6$ & $l=7$ & $l=8$ \\\\\n\t\t\t\\cmidrule{2-5}\n\t\t\t0.0-0.1 & 0.138 ± 0.001 & 0.126 ± 0.001 & 0.110 ± 0.001 & 0.088 ± 0.000 \\\\\n\t\t\t0.1-0.2 & 0.281 ± 0.001 & 0.279 ± 0.001 & 0.264 ± 0.001 & 0.228 ± 0.001 \\\\\n\t\t\t0.2-0.3 & 0.394 ± 0.002 & 0.382 ± 0.002 & 0.377 ± 0.002 & 0.338 ± 0.002 \\\\\n\t\t\t0.3-0.4 & 0.498 ± 0.002 & 0.487 ± 0.003 & 0.467 ± 0.003 & 0.397 ± 0.002 \\\\\n\t\t\t0.4-0.5 & 0.566 ± 0.003 & 0.572 ± 0.003 & 0.580 ± 0.003 & 0.500 ± 0.003 \\\\\n\t\t\t0.5-0.6 & 0.649 ± 0.003 & 0.646 ± 0.003 & 0.637 ± 0.003 & 0.582 ± 0.004 \\\\\n\t\t\t0.6-0.7 & 0.716 ± 0.003 & 0.755 ± 0.003 & 0.720 ± 0.003 & 0.589 ± 0.009 \\\\\n\t\t\t0.7-0.8 & 0.823 ± 0.002 & 0.802 ± 0.002 & 0.763 ± 0.004 & -- \\\\\n\t\t\t0.8-0.9 & 0.887 ± 0.002 & 0.894 ± 0.002 & -- & -- \\\\\n\t\t\t0.9-1.0 & 0.935 ± 0.001 & 0.842 ± 0.006 & -- & -- \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Learning an Inventory Control Policy with General Inventory Arrival Dynamics", "authors": ["Sohrab Andaz", "Carson Eisenach", "Dhruv Madeka", "Kari Torkkola", "Randy Jia", "Dean Foster", "Sham Kakade"], "url": "https://arxiv.org/abs/2310.17168v2", "attribution": "\"Learning an Inventory Control Policy with General Inventory Arrival Dynamics\" by Sohrab Andaz, Carson Eisenach, Dhruv Madeka, Kari Torkkola, Randy Jia, Dean Foster, and Sham Kakade, arXiv:2310.17168v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.15103v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Two-Way Fixed Effects Model Estimates - Percentile\\_Price\\_Paid}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\tabularnewline \\midrule \\midrule\n Dependent Variable: & \\multicolumn{4}{c}{Percentile\\_Price\\_Paid}\\\\\n & Dry Dog Food & Dry Cat Food & Dog Hygiene & Cat Hygiene \\\\ \n Model: & (1) & (2) & (3) & (4)\\\\ \n \\midrule\n \\emph{Variables}\\\\\n $\\mathcal{I}\\{\\textrm{Adopter}_{i}\\} \\times \\mathcal{I}\\{ t \\geq T_{i}^{\\textrm{adopt}}\\}$ & -1.437$^{**}$ & -3.318$^{**}$ & -0.9569 & -4.104$^{***}$\\\\ \n & (0.4141) & (0.9727) & (0.6351) & (1.100)\\\\ \n \\midrule\n \\emph{Fixed-effects}\\\\\n Customer & Yes & Yes & Yes & Yes\\\\ \n YearMonth & Yes & Yes & Yes & Yes\\\\ \n \\midrule\n \\emph{Fit statistics}\\\\\n Observations & 99,233 & 27,623 & 40,183 & 42,596\\\\ \n R$^2$ & 0.86731 & 0.61190 & 0.50654 & 0.42914\\\\ \n Within R$^2$ & 0.00057 & 0.00135 & 0.00010 & 0.00126\\\\ \n \\midrule \\midrule\n \\multicolumn{5}{l}{\\emph{Clustered (Customer \\& YearMonth) standard-errors in parentheses}}\\\\\n \\multicolumn{5}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05}}\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "How does online shopping affect offline price sensitivity?", "authors": ["Shirsho Biswas", "Hema Yoganarasimhan", "Haonan Zhang"], "url": "https://arxiv.org/abs/2506.15103v1", "attribution": "\"How does online shopping affect offline price sensitivity?\" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06775v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mutual information between registered brain volumes and the brain atlas on ten test subjects using direct patient registration and inpainted volume registration.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c}\n\\hline\nMethods& Sub1 & Sub2 & Sub3 & Sub4 & Sub5 & Sub6 & Sub7 & Sub8 & Sub9 & Sub10 \\\\\n\\hline\\hline\nDirect registration\t&0.303&0.311&0.308&0.324&0.315&0.299&0.309&0.303&0.317&0.308\\\\\nInpainted registration &0.309&0.311&0.309&0.324&0.316&0.304&0.312&0.313&0.320&0.312\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology", "authors": ["Xiaofeng Liu", "Fangxu Xing", "Chao Yang", "C. -C. Jay Kuo", "Georges ElFakhri", "Jonghye Woo"], "url": "https://arxiv.org/abs/2101.06775v1", "attribution": "\"Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology\" by Xiaofeng Liu, Fangxu Xing, Chao Yang, C. -C. Jay Kuo, Georges ElFakhri, and Jonghye Woo, arXiv:2101.06775v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.15012v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\n\\textbf{Project Site - Capacity (MW)} & \\textbf{Payback period (Years)} \\\\ \\hline\nRI - 30 & 15.2 \\\\ \\hline\nMD - 252 & 11.4 \\\\ \\hline\nMA - 804 & 5.1 \\\\ \\hline\nNY - 888 & 6.6 \\\\ \\hline\nVA - 2640 & 13.6 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Economic payback period for the projects of interest}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Estimating the spatial economic and environmental impact of planned offshore wind energy in the USA using Environmentally Extended Multiregional Input-Output analysis", "authors": ["Apoorva Bademi", "Miriam Stevens", "Isha Sura", "Shweta Singh"], "url": "https://arxiv.org/abs/2508.15012v1", "attribution": "\"Estimating the spatial economic and environmental impact of planned offshore wind energy in the USA using Environmentally Extended Multiregional Input-Output analysis\" by Apoorva Bademi, Miriam Stevens, Isha Sura, and Shweta Singh, arXiv:2508.15012v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01045v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small The value and the infeasitility at the $k$th iteration.}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n Method& Complexity&\n val&\n infeas\\\\\n\\hline\nSG& $\\mathcal{O}(\\varepsilon^{-2})$ &$f_0(x^{(k)})$ & $\\max\\{\\overline{f}(x^{(k)}),0\\}$\\\\\n\\hline\nSingleDSG& $\\mathcal{O}(\\varepsilon^{-2})$ &$f_0(\\overline{x}^{(k)})$& $\\max\\{\\overline{f}(\\overline{x}^{(k)}),0\\}$\\\\\n\\hline\nMultiDSG& $\\mathcal{O}(\\varepsilon^{-2})$ &$f_0(\\overline{x}^{(k)})$ &$\\|F(\\overline{x}^{(k)})\\|_2+\\|A\\overline{x}^{(k)}-b\\|_2$\\\\\n\\hline\nPDS&$\\mathcal{O}(\\varepsilon^{-2r})\\,,\\,\\forall\\ r>1$& $f_0(x^{(k)})$& $\\|F({x}^{(k)})\\|_2+\\|A{x}^{(k)}-b\\|_2$ \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Comparing different subgradient methods for solving convex optimization problems with functional constraints", "authors": ["Thi Lan Dinh", "Ngoc Hoang Anh Mai"], "url": "https://arxiv.org/abs/2101.01045v2", "attribution": "\"Comparing different subgradient methods for solving convex optimization problems with functional constraints\" by Thi Lan Dinh and Ngoc Hoang Anh Mai, arXiv:2101.01045v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.04533v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\hline\\hline\n & \\multicolumn{2}{c}{Fraction of hits in cone} \\\\\n Network & 50\\% Prob. Working Point & 95\\% Prob. Working Point\\\\\n \\hline\n Spatial & 0.07 & 0.75 \\\\\n Cluster & 0.006 & 0.70 \\\\\n Combined & 0.001 & 0.39 \\\\\n Cluster ($n_{ch0} > 1$) & 0.003 & 0.09 \\\\\n Combined ($n_{ch0} > 1$) & 0.001 & 0.05 \\\\\n \\hline\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Efficiency and purity numbers at two possible working points, based on Figure~.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Beyond 4D Tracking: Using Cluster Shapes for Track Seeding", "authors": ["Patrick J. Fox", "Shangqing Huang", "Joshua Isaacson", "Xiangyang Ju", "Benjamin Nachman"], "url": "https://arxiv.org/abs/2012.04533v2", "attribution": "\"Beyond 4D Tracking: Using Cluster Shapes for Track Seeding\" by Patrick J. Fox, Shangqing Huang, Joshua Isaacson, Xiangyang Ju, and Benjamin Nachman, arXiv:2012.04533v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.07774v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{Annual discount rate} & \n$\\dfrac{\\sqrt{\\operatorname{cov}(R_{B,1}(0), R_{B,2}(0))}}{\\mathbb{E}[V_B(0)]}$ & \n$\\dfrac{\\sqrt{\\operatorname{cov}(R_{G,1}(0), R_{G,2}(0))}}{\\mathbb{E}[V_G(0)]}$ \\\\\n\\hline\n0.04 & 0.186 & 0.134 \\\\\n0.05 & 0.238 & 0.162 \\\\\n0.06 & 0.328 & 0.207 \\\\\n0.07 & 0.529 & 0.284 \\\\\n0.08 & 1.389 & 0.462 \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Modelling Prepayment and Default under Changing Credit Market Conditions for a Net Present Value Analysis", "authors": ["Quirini Lorenzo", "Vannucci Luigi", "Quirini Giovanni"], "url": "https://arxiv.org/abs/2508.07774v1", "attribution": "\"Modelling Prepayment and Default under Changing Credit Market Conditions for a Net Present Value Analysis\" by Quirini Lorenzo, Vannucci Luigi, and Quirini Giovanni, arXiv:2508.07774v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.08696v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|rr|rr|rr|rr|rr|rr|rr|rr|rr|}\n\\hline\n\\multirow{2}{*}{Model} &\n\\multicolumn{2}{c|}{Metals and Mining} &\n\\multicolumn{2}{c|}{Oil and Gas} &\n\\multicolumn{2}{c|}{Consumer Sector} &\n\\multicolumn{2}{c|}{Construction} &\n\\multicolumn{2}{c|}{Telecommunications} &\n\\multicolumn{2}{c|}{Transport} &\n\\multicolumn{2}{c|}{Finance} &\n\\multicolumn{2}{c|}{Chemical Industry} &\n\\multicolumn{2}{c|}{Power Engineering} \\\\ \\cline{2-19} \n &\n MTLR &\n TRMK &\n SNGS &\n SIBN &\n MGNT &\n LENT &\n PIKK &\n SMLT &\n MTSS &\n RTKMP &\n AFLT &\n FLOT &\n BSPB &\n SFIN &\n PHOR &\n KZOSP &\n HYDR &\n MRKC \\\\ \\hline\n\\multirow{2}{*}{LSTM} &\n \\textbf{56.364} &\n \\textbf{56.364} &\n 50.303 &\n \\textbf{58.182} &\n 46.667 &\n \\textbf{56.364} &\n 49.091 &\n \\textbf{53.939} &\n \\textbf{56.970} &\n \\textbf{55.152} &\n 55.152 &\n 47.273 &\n 46.061 &\n \\textbf{49.697} &\n 41.818 &\n \\textbf{57.576} &\n 59.394 &\n 40.000 \\\\\n &\n \\textbf{0.410} &\n \\textbf{0.362} &\n \\textbf{0.352} &\n \\textbf{0.341} &\n \\textbf{0.331} &\n \\textbf{0.371} &\n \\textbf{0.484} &\n \\textbf{0.328} &\n \\textbf{0.541} &\n \\textbf{0.246} &\n \\textbf{0.419} &\n \\textbf{0.258} &\n \\textbf{0.410} &\n \\textbf{0.447} &\n \\textbf{0.231} &\n \\textbf{0.458} &\n \\textbf{0.380} &\n \\textbf{0.768} \\\\ \\hline\n\\multirow{2}{*}{XGB} &\n 40.000 &\n 40.909 &\n 49.091 &\n 40.000 &\n 39.091 &\n 54.546 &\n 40.909 &\n 42.727 &\n 42.727 &\n 45.455 &\n 46.364 &\n 43.637 &\n 49.091 &\n 40.000 &\n 42.727 &\n 49.091 &\n 51.182 &\n 51.182 \\\\\n &\n 2.089 &\n 2.105 &\n 1.776 &\n 1.766 &\n 1.517 &\n 2.202 &\n 1.565 &\n 1.577 &\n 1.290 &\n 1.299 &\n 2.079 &\n 2.116 &\n 1.612 &\n 1.603 &\n 1.194 &\n 1.198 &\n 1.124 &\n 1.182 \\\\ \\hline\n\\multirow{2}{*}{KNN} &\n 42.273 &\n 38.182 &\n 48.182 &\n \\textbf{58.182} &\n 43.636 &\n 39.091 &\n 50.909 &\n 38.182 &\n 40.000 &\n 42.723 &\n \\textbf{57.273} &\n 38.182 &\n 50.909 &\n 30.909 &\n \\textbf{52.723} &\n 42.723 &\n \\textbf{60.000} &\n 49.091 \\\\\n &\n 2.050 &\n 2.167 &\n 1.775 &\n 1.746 &\n 1.493 &\n 2.178 &\n 1.563 &\n 1.552 &\n 1.306 &\n 1.303 &\n 2.017 &\n 2.124 &\n 1.695 &\n 1.647 &\n 1.149 &\n 1.237 &\n 1.130 &\n 1.225 \\\\ \\hline\n\\multirow{2}{*}{RF} &\n 50.909 &\n 47.273 &\n 50.000 &\n 46.364 &\n 49.091 &\n 52.723 &\n 50.000 &\n 46.364 &\n 45.455 &\n 42.727 &\n 52.727 &\n 42.727 &\n 50.909 &\n 39.091 &\n 48.182 &\n 49.091 &\n 48.182 &\n 54.545 \\\\\n &\n 2.020 &\n 2.154 &\n 1.735 &\n 1.788 &\n 1.519 &\n 2.145 &\n 1.558 &\n 1.539 &\n 1.520 &\n 1.335 &\n 2.062 &\n 2.104 &\n 1.598 &\n 1.743 &\n 1.168 &\n 1.210 &\n 1.214 &\n 1.214 \\\\ \\hline\n\\multirow{2}{*}{LinReg} &\n 50.000 &\n 49.091 &\n \\textbf{60.909} &\n 41.818 &\n 40.000 &\n 51.818 &\n 44.545 &\n 49.091 &\n 53.636 &\n 50.909 &\n 60.909 &\n 45.454 &\n \\textbf{54.545} &\n 48.182 &\n 50.000 &\n 46.364 &\n 45.455 &\n 50.000 \\\\\n &\n 2.029 &\n 2.114 &\n 1.744 &\n 1.839 &\n 1.709 &\n 2.220 &\n 1.637 &\n 1.536 &\n 1.419 &\n 1.355 &\n 1.976 &\n 2.074 &\n 1.602 &\n 1.960 &\n 1.227 &\n 1.217 &\n 1.151 &\n 1.224 \\\\ \\hline\n\\multirow{2}{*}{DT} &\n 42.727 &\n 52.727 &\n 52.727 &\n 51.818 &\n \\textbf{60.000} &\n 51.818 &\n \\textbf{51.818} &\n 41.818 &\n 50.000 &\n 48.182 &\n 51.818 &\n \\textbf{49.091} &\n 45.455 &\n 41.818 &\n 45.455 &\n 54.545 &\n 49.091 &\n \\textbf{55.455} \\\\\n &\n 2.679 &\n 2.308 &\n 1.857 &\n 1.813 &\n 1.672 &\n 2.589 &\n 1.592 &\n 1.683 &\n 1.395 &\n 1.411 &\n 2.194 &\n 2.294 &\n 1.829 &\n 1.959 &\n 1.218 &\n 1.581 &\n 1.355 &\n 1.403 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Returns vector forecasting metrics with only time-series in use. Accuracy (the upper row), MAPE (the lower row) in percentage}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market", "authors": ["Kasymkhan Khubiev", "Mikhail Semenov"], "url": "https://arxiv.org/abs/2503.08696v1", "attribution": "\"Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market\" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2503.08696v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2103.02017v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\textbf{Mass }& \\textbf{Dimensions }& \\textbf{Power Consumption } & \\textbf{Dipole Moment} \\\\\n\\hline\n$30$ g & $8$ cm x $8$ cm x $10$ cm& $300$ mW & $0.12$ A$\\text{m}^2$ \\\\ \n\\hline\n\\end{tabular}\n\\caption{Magnetorquers Main Properties}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "ADCS Preliminary Design For GNB", "authors": ["Alessio Bocci", "Giovanni Mingari Scarpello"], "url": "https://arxiv.org/abs/2103.02017v1", "attribution": "\"ADCS Preliminary Design For GNB\" by Alessio Bocci and Giovanni Mingari Scarpello, arXiv:2103.02017v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18896v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n Contamination $\\delta$ & $0$ & $0.5$ & $1$\\\\\n \\midrule\n Global shift &$16/1000$& $995/1000$ & $1000/1000$ \\\\\n Local shift of level $l = 10$ & - & $360/1000$ & $986/1000$ \\\\\n Local shift of level $l = 13$ & - &$769/1000$& $1000/1000$ \\\\\n Local shift of level $l = 15$ & - & $203/1000$ & $917/1000$ \\\\\n \\bottomrule \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Power of the performed statistical tests with confidence level $1-\\alpha = 0.95$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Calibration Bands for Mean Estimates within the Exponential Dispersion Family", "authors": ["Łukasz Delong", "Selim Gatti", "Mario V. Wüthrich"], "url": "https://arxiv.org/abs/2503.18896v1", "attribution": "\"Calibration Bands for Mean Estimates within the Exponential Dispersion Family\" by Łukasz Delong, Selim Gatti, and Mario V. Wüthrich, arXiv:2503.18896v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.08895v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\hline\n\\textbf{Package / Software} & \\textbf{Version} \\\\ \\hline\nUbuntu & 18.04 \\\\\nPython3 & 3.6.9 \\\\\nNumpy & 1.18.1 \\\\\nTensorflow & 2.1.0 \\\\\nKeras & 2.3.1 \\\\\nScikit-learn & 0.22.1 \\\\\nPandas & 0.25.3 \\\\\nAutograd & 1.3 \\\\\nMatplotlib & 3.1.1 \\\\\nPickle & (Available with Python3) \\\\\nArgparse & (Available with Python3) \\\\\nTime & (Available with Python3) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Software dependencies for running provided codes.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "ZORB: A Derivative-Free Backpropagation Algorithm for Neural Networks", "authors": ["Varun Ranganathan", "Alex Lewandowski"], "url": "https://arxiv.org/abs/2011.08895v1", "attribution": "\"ZORB: A Derivative-Free Backpropagation Algorithm for Neural Networks\" by Varun Ranganathan and Alex Lewandowski, arXiv:2011.08895v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14699v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\n \\hline \n Training data parameter & Value\\\\\n \\hline \n starting slice location $x_{0}$& 2.0 \\\\ \n slice distance $\\Delta x$ & 1.0\\\\ \n number of slices $m$ & 98 \\\\ \n number of $y$ points $n$ & 128 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{CNN model training data parameters.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Acceleration of RANS Solver Convergence via Initialization with Wake Extension Models", "authors": ["Kazuko W. Fuchi", "Eric M. Wolf", "Christopher R. Schrock", "Philip S. Beran"], "url": "https://arxiv.org/abs/2501.14699v1", "attribution": "\"Acceleration of RANS Solver Convergence via Initialization with Wake Extension Models\" by Kazuko W. Fuchi, Eric M. Wolf, Christopher R. Schrock, and Philip S. Beran, arXiv:2501.14699v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.10959v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of the data environment.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccccc}\n\\toprule\nSample $S$ & & Covariate $X$ & & Treatment $D$ & & Outcome $Y$ & & Remotely sensed $R$ \\\\\n\\hline \nExperimental & & \\checkmark & & \\checkmark & & Missing && \\checkmark \\\\\nObservational: Complete & & \\checkmark & & \\checkmark & & \\checkmark && \\checkmark \\\\\nObservational: Incomplete & & \\checkmark & & Missing or deterministic & & \\checkmark && \\checkmark \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Program Evaluation with Remotely Sensed Outcomes", "authors": ["Ashesh Rambachan", "Rahul Singh", "Davide Viviano"], "url": "https://arxiv.org/abs/2411.10959v2", "attribution": "\"Program Evaluation with Remotely Sensed Outcomes\" by Ashesh Rambachan, Rahul Singh, and Davide Viviano, arXiv:2411.10959v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.15439v1_tex_table24.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Effect sizes (Cohen's d) and post-hoc power tests for pairwise group comparisons}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccccc}\n \\hline\n \\hline\n \\multirow{2}{*}{\\textbf{Comparison groups}} & & & \\multicolumn{2}{c}{Frequency} & & \\multicolumn{2}{c}{Magnitude} & \\\\ \\cline{4-5} \\cline{7-8}\n & & & $d$ & $1-\\beta$ & & $d$ & $1-\\beta$ & \\\\\n \\hline\n Human & Machine & & 0.017 & 0.035 & & -0.120 & 0.069 & \\\\\n Human & Human Black Box & & 0.168 & 0.096 & & 0.673 & 0.577 & \\\\\n Machine & Machine Black Box & & 0.181 & 0.110 & & 1.503 & 0.999& \\\\\n Human Black Box & Machine Black Box & & -0.369 & 0.367 & & -0.751 & 0.662 & \\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty", "authors": ["Marius Protte", "Behnud Mir Djawadi"], "url": "https://arxiv.org/abs/2507.15439v1", "attribution": "\"Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty\" by Marius Protte and Behnud Mir Djawadi, arXiv:2507.15439v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14313v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc} \n\t\t\tCI & $n$ & Coverage & Mean Width\\\\\n\t\t\t\\hline\n\t\t\tNormal& $10^7$ & 82/100 & $0$ \\\\ \n\t\t\tPoisson & $10^7$ & 100/100& $1.476*10^{-6}$ \\\\ \n\t\t\tHeuristic & $10^7$ & 100/100& $1.476*10^{-6}$ \\\\ \n\t\t\tNormal& $10^8$ & 93/100 & 0 \\\\ \n\t\t\tPoisson & $10^8$ & 100/100& $1.476*10^{-7}$\\\\\n\t\t\tHeuristic & $10^8$ & 100/100& $1.476*10^{-7}$\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Results for Dynamic Uniform with $\\gamma=1.5$ and $r=3$ for large sample sizes $n$. These are based on $N=100$ replications.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Confidence Intervals Using Turing's Estimator: Simulations and Applications", "authors": ["Jie Chang", "Michael Grabchak", "Jialin Zhang"], "url": "https://arxiv.org/abs/2503.14313v1", "attribution": "\"Confidence Intervals Using Turing's Estimator: Simulations and Applications\" by Jie Chang, Michael Grabchak, and Jialin Zhang, arXiv:2503.14313v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.16624v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc} \n & \\multirow{2}{*}{NLL ($\\downarrow$)} & Sampling \\\\\n & & time ($\\downarrow$) \\\\\n \\hline\n & \\multicolumn{2}{c}{DW4} \\\\ \\hline\n $E(n)$-NF & 1.72 $\\pm$ 0.01 &\\textbf{0.024} ms \\\\ %\n OT-FM & \\textbf{1.70} $\\pm$ 0.02 &0.034 ms\\\\ %\n E-OT-FM & \\textbf{1.68} $\\pm$ 0.01 &0.033 ms \\\\ %\n $E(n)$-FFF (ours) & \\textbf{1.68} $\\pm$ 0.01&\\textbf{0.026} ms \\\\ %\n \\hline\n \n & \\multicolumn{2}{c}{LJ13} \\\\ \\hline\n $E(n)$-NF & -16.28 $\\pm$ 0.04 &0.27 ms \\\\ %\n OT-FM & -16.54 $\\pm$ 0.03 &0.77 ms \\\\ %\n E-OT-FM & -16.70 $\\pm$ 0.12 &0.72 ms \\\\ %\n $E(n)$-FFF (ours) & \\textbf{-17.09} $\\pm$ 0.16&\\textbf{0.11} ms \\\\ %\n \\hline\n \n & \\multicolumn{2}{c}{LJ55} \\\\ \\hline\n OT-FM & -88.45 $\\pm$ 0.04 & 40 ms \\\\ %\n E-OT-FM & \\textbf{-89.27} $\\pm$ 0.04 &40 ms \\\\ %\n $E(n)$-FFF (ours) & -88.72 $\\pm$ 0.16 &\\textbf{2.1} ms \\\\ %\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Free-form Flows: Make Any Architecture a Normalizing Flow", "authors": ["Felix Draxler", "Peter Sorrenson", "Lea Zimmermann", "Armand Rousselot", "Ullrich Köthe"], "url": "https://arxiv.org/abs/2310.16624v2", "attribution": "\"Free-form Flows: Make Any Architecture a Normalizing Flow\" by Felix Draxler, Peter Sorrenson, Lea Zimmermann, Armand Rousselot, and Ullrich Köthe, arXiv:2310.16624v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.10013v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n& AFEW & FPHA & NTU RGB+D\\ & HDM05\\\\ \n\\midrule\nGyrovector & $23.23$\\tiny$\\pm 0.98$& $61.33 $\\tiny$\\pm 4.74$ & $40.77$\\tiny$\\pm 3.10$ & $5.69 $\\tiny$\\pm 2.15$\\\\\nAff-Inv & $35.17$ \\tiny $\\pm 1.78$&$\\mathbf{66.53} $\\tiny$\\mathbf{\\pm 1.64}$ & $41.00$ \\tiny $\\pm 0.50$& $67.91$ \\tiny${\\pm 1.27}$\\\\\nLog-Euc&$\\mathbf{36.38}$\\tiny $\\mathbf{\\pm 1.29}$& $64.58 $\\tiny$\\pm 0.98$& $\\mathbf{42.99 }$\\tiny$\\mathbf{\\pm 0.23}$& $\\mathbf{69.80}$\\tiny$\\mathbf{ \\pm 1.51}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{We show that the Riemannian ResNet model with M\\\"obius addition struggles to reach the classification accuracies of our exponential map design. The difference is most pronounced on HDM05, where the gyrovector model struggles to learn meaningful representations.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Riemannian Residual Neural Networks", "authors": ["Isay Katsman", "Eric Ming Chen", "Sidhanth Holalkere", "Anna Asch", "Aaron Lou", "Ser-Nam Lim", "Christopher De Sa"], "url": "https://arxiv.org/abs/2310.10013v1", "attribution": "\"Riemannian Residual Neural Networks\" by Isay Katsman, Eric Ming Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser-Nam Lim, and Christopher De Sa, arXiv:2310.10013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19280v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The summary of performance test with Dirichlet condition with $\\theta=\\pi/2$. Severn scenarios (1,5,11-12,14,22,24) approach to $y_b$, eighteen scenarios (2-4,6-10,13,15-21,25) go to the other solution $y_s$, one scenario (23) diverges.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\t\tstatus & number & $\\max|y''-f|$ & $\\max|y_{opt}-y_{b}|$ & $\\max|y_{rk4}-y_{b}|$ \\\\ \\hline\\hline\n\t\t$\\to y_b$ & 17 & 1.0E-07 & 4.1E-10 & 8.1E-07 \\\\\n\t\t$\\to y_s$ & 7 & 1.1E-07 & 4.4E-01 & 2.2E-01 \\\\\n\t\t$diverge$ & 1 & diverge & diverge & diverge \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions", "authors": ["Xiaorong Zou"], "url": "https://arxiv.org/abs/2504.19280v1", "attribution": "\"Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions\" by Xiaorong Zou, arXiv:2504.19280v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.17126v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\n$n$ & $2,000$ & $5,000$ & $12,000$ \\\\ \\hline\nMean of the $\\hat{\\sigma}_\\theta^2$'s & $12.0$ & $11.7$ & 11.9 \\\\ \\hline\n90\\% Monte-Carlo CR of the $\\hat{\\sigma}_\\theta^2$'s & $(10.2, 13.7)$ & $(10.2, 13.5)$ & (10.3, 13.4) \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The bootstrap provides a good estimate of the true asymptotic variance $\\sigma_\\theta^2 = 11.455$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Estimation and Inference for the Average Treatment Effect in a Score-Explained Heterogeneous Treatment Effect Model", "authors": ["Kevin Christian Wibisono", "Debarghya Mukherjee", "Moulinath Banerjee", "Ya'acov Ritov"], "url": "https://arxiv.org/abs/2504.17126v1", "attribution": "\"Estimation and Inference for the Average Treatment Effect in a Score-Explained Heterogeneous Treatment Effect Model\" by Kevin Christian Wibisono, Debarghya Mukherjee, Moulinath Banerjee, and Ya'acov Ritov, arXiv:2504.17126v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06774v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\bf Shape Functions for Quadratic Line Elements}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\nlocal node & $\\{N\\}_m$ & $\\{\\Phi_i\\}_m$ $(i=x,y,z)$ \\\\\n\\hline\n$m = 1$ & $L_1(2L_1-1)$ & $\\Phi_{i1}$ \\\\\n$m = 2$ & $L_2(2L_2-1)$ & $\\Phi_{i2}$ \\\\\n$m = 3$ & $L_3=4L_1L_2$ & $\\Phi_{i3}$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Learning from pandemics: using extraordinary events can improve disease now-casting models", "authors": ["Sara Mesquita", "Cláudio Haupt Vieira", "Lília Perfeito", "Joana Gonçalves-Sá"], "url": "https://arxiv.org/abs/2101.06774v1", "attribution": "\"Learning from pandemics: using extraordinary events can improve disease now-casting models\" by Sara Mesquita, Cláudio Haupt Vieira, Lília Perfeito, and Joana Gonçalves-Sá, arXiv:2101.06774v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.00409v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccc}\n\t\t\t\t\t\\toprule\n\t\t\t\t\t& \\multicolumn{3}{c} {BoW} &\n\t\t\t\t\t\\multicolumn{3}{c}{Reduced Sample BoW} & \\multicolumn{3}{c}{tf-idf} \\\\ \n\t\t\t\t\t& (1) & (2) & (3) & (1) & (2) & (3) & (1) & (2) & (3) \\\\\n\t\t\t\t\t\\midrule\n\t\t\t\t\tsex cr. x placebo & 0.220 & & & 0.166 & & & -0.941 & & \\\\ \n\t\t\t\t\t& (0.584) & & & (0.675) & & & (0.831) & & \\\\ \n\t\t\t\t\tsex cr. x post & & 0.535 & & & 0.442 & & & 1.697** & \\\\ \n\t\t\t\t\t& & (0.424) & & & (0.438) & & & (0.821) & \\\\ \n\t\t\t\t\tsex cr. x post & & & 0.806 & & & 0.836* & & & 4.148* \\\\ \n\t\t\t\t\t& & & (0.534) & & & (0.505) & & & (2.254) \\\\ \n\t\t\t\t\tpost & X & X & X & X & X & X & X & X & X \\\\ \n\t\t\t\t\tcourt FE & X & X & X & X & X & X & X & X & X \\\\ \n\t\t\t\t\t\\# words & X & X & X & X & X & X & X & X & X \\\\ \n\t\t\t\t\t\\bottomrule\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach", "authors": ["Henrika Langen"], "url": "https://arxiv.org/abs/2209.00409v2", "attribution": "\"The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach\" by Henrika Langen, arXiv:2209.00409v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.17168v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Calibration of cumulative inventory received predicted $k$ weeks after submitting an order and actuals.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\t\t\t\\toprule\n\t\t\t & \\multicolumn{2}{c}{In Time Holdout} & \\multicolumn{2}{c}{Out of Time Holdout} \\\\\n\t\t\t\\cmidrule{2-5}\n\t\t\tWeeks After Order & Estimate & 95\\% CI & Estimate & 95\\% CI \\\\\n\t\t\t\\midrule\n\t\t\t1 & 1.0577 & [1.056, 1.060] & 1.0559 & [1.055, 1.057] \\\\\n\t\t\t2 & 1.1393 & [1.138, 1.141] & 1.1529 & [1.151, 1.154] \\\\\n\t\t\t3 & 1.108 & [1.107, 1.109] & 1.1311 & [1.130, 1.132] \\\\\n\t\t\t4 & 1.0866 & [1.086, 1.087] & 1.1147 & [1.114, 1.115] \\\\\n\t\t\t5 & 1.0789 & [1.078, 1.079] & 1.1094 & [1.109, 1.110] \\\\\n\t\t\t6 & 1.0745 & [1.074, 1.075] & 1.1021 & [1.102, 1.103] \\\\\n\t\t\t7 & 1.0694 & [1.069, 1.070] & 1.0995 & [1.099, 1.100] \\\\\n\t\t\t8 & 1.063 & [1.063, 1.063] & 1.0953 & [1.095, 1.096] \\\\\n\t\t\t9 & 1.0538 & [1.053, 1.054] & 1.0895 & [1.089, 1.090] \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Learning an Inventory Control Policy with General Inventory Arrival Dynamics", "authors": ["Sohrab Andaz", "Carson Eisenach", "Dhruv Madeka", "Kari Torkkola", "Randy Jia", "Dean Foster", "Sham Kakade"], "url": "https://arxiv.org/abs/2310.17168v2", "attribution": "\"Learning an Inventory Control Policy with General Inventory Arrival Dynamics\" by Sohrab Andaz, Carson Eisenach, Dhruv Madeka, Kari Torkkola, Randy Jia, Dean Foster, and Sham Kakade, arXiv:2310.17168v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.03710v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of model performances with specifications}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline \\hline\nModel & Model complexity & RMSE (s) & \\multicolumn{2}{c}{Computation time (s)}& Delta V \\\\\n($n$, $\\ell$, $G$)&$(\\mathcal{L}_{\\rm train},\\mathcal{L}_{\\rm val})\\times 10^{-3}$ & & average & worst case& average (km/s)\\\\\n\\hline\nTSG& - & - & 0.0871 & 0.8546 & 3.8741 \\\\\n\\hline\nCIKAN & 1,638,400 & \\multirow{2}{*}{0.9652} & \\multirow{2}{*}{0.0329} & \\multirow{2}{*}{0.5590} & \\multirow{2}{*}{3.4965}\\\\\n (128,5,17)& (1.3218, 1.2615) & & & & \\\\\n\\hline\nGRBF-CIKAN& 122,880 & \\multirow{2}{*}{0.8220} & \\multirow{2}{*}{0.0451} & \\multirow{2}{*}{1.0339 } & \\multirow{2}{*}{3.5489}\\\\\n(32,6,17)& (1.4758, 1.1540) & & & & \\\\\n\\hline\nRSWAF-CIKAN & 491,520 & \\multirow{2}{*}{1.2906} & \\multirow{2}{*}{0.0379} & \\multirow{2}{*}{0.5435} & \\multirow{2}{*}{3.4807}\\\\\n (64,6,17)& (1.5729, 1.2610) & & & & \\\\\n\\hline\nCINN-ReLU & 209,715,208 & \\multirow{2}{*}{0.7483} & \\multirow{2}{*}{0.0543} & \\multirow{2}{*}{1.3284} & \\multirow{2}{*}{3.6593}\\\\\n (2048,5,-)& (2.7907, 1.6458) & & & & \\\\\n\\hline\nCINN-GELU & 5,242,880 & \\multirow{2}{*}{1.1202} & \\multirow{2}{*}{0.0299} & \\multirow{2}{*}{0.5292} & \\multirow{2}{*}{3.4549}\\\\\n (1024,5,-)& (2.8983, 1.8538) & & & & \\\\\n\\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor", "authors": ["Taehyeun Kim", "Anouck Girard", "Ilya Kolmanovsky"], "url": "https://arxiv.org/abs/2412.03710v2", "attribution": "\"CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor\" by Taehyeun Kim, Anouck Girard, and Ilya Kolmanovsky, arXiv:2412.03710v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.21253v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Regression Results TV Demand}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n & \\multicolumn{4}{c}{Ln(audience)} \\\\\n \\cmidrule(lr){2-5}\n & (1) & (2) & (3) & (4) \\\\\n \\midrule\n Ln(suspense) && 0.042** & & 0.017 \\\\\n && (0.017) & & (0.020) \\\\\n Ln(surprise) && & 0.050*** & 0.040** \\\\\n && & (0.017) & (0.020) \\\\\n Average player rating & 4.358*** & 4.548*** & 4.615*** & 4.640*** \\\\\n & (1.423) & (1.434) & (1.429) & (1.434) \\\\\n Outcome uncertainty & -0.037 & -0.006 & -0.006 & 0.001 \\\\\n & (0.054) & (0.054) & (0.053) & (0.054) \\\\\n Derby match & 0.053* & 0.048 & 0.056* & 0.054* \\\\\n & (0.030) & (0.030) & (0.029) & (0.030) \\\\\n Match significance (championship) & 0.666*** & 0.646*** & 0.646*** & 0.642*** \\\\\n & (0.134) & (0.136) & (0.137) & (0.137) \\\\\n Match significance (European) & 0.193* & 0.187* & 0.196* & 0.193* \\\\\n & (0.111) & (0.111) & (0.111) & (0.111) \\\\\n Match significance (relegation) & 0.361*** & 0.365*** & 0.368*** & 0.368*** \\\\\n & (0.092) & (0.093) & (0.093) & (0.093) \\\\\n Christmas & 0.090** & 0.093*** & 0.101*** & 0.100*** \\\\\n & (0.035) & (0.036) & (0.035) & (0.035) \\\\\n Weekday & 0.015 & 0.014 & 0.016 & 0.015 \\\\\n & (0.020) & (0.020) & (0.020) & (0.020) \\\\\n BT & -0.790*** & -0.779*** & -0.783*** & -0.780*** \\\\\n & (0.049) & (0.051) & (0.049) & (0.050) \\\\\n BT × season ending: 2015 & 0.140** & 0.128** & 0.125** & 0.124** \\\\\n & (0.060) & (0.061) & (0.059) & (0.059) \\\\\n BT × season ending: 2016 & 0.277*** & 0.268*** & 0.270*** & 0.268*** \\\\\n & (0.064) & (0.064) & (0.064) & (0.064) \\\\\n BT × season ending: 2017 & 0.299*** & 0.288*** & 0.289*** & 0.290*** \\\\\n & (0.063) & (0.064) & (0.063) & (0.063) \\\\\n BT × season ending: 2018 & 0.391*** & 0.390*** & 0.398*** & 0.397*** \\\\\n & (0.066) & (0.067) & (0.066) & (0.066) \\\\\n BT × season ending: 2019 & 0.282*** & 0.275*** & 0.272*** & 0.271*** \\\\\n & (0.066) & (0.067) & (0.065) & (0.066) \\\\\n \\midrule\n Month FE & Yes & Yes & Yes & Yes \\\\\n Season FE & Yes & Yes & Yes & Yes \\\\\n Home team FE & Yes & Yes & Yes & Yes \\\\\n Away team FE & Yes & Yes & Yes & Yes \\\\\n Observations & 790 & 790 & 790 & 790 \\\\\n Adj-R\\textsuperscript{2} & 0.714 & 0.716 & 0.717 & 0.717 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Suspense and Surprise in European Football", "authors": ["Raphael Flepp", "Tim Pawlowski", "Travis Richardson"], "url": "https://arxiv.org/abs/2506.21253v1", "attribution": "\"Suspense and Surprise in European Football\" by Raphael Flepp, Tim Pawlowski, and Travis Richardson, arXiv:2506.21253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.11419v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overall generalization performance of the teacher model in $\\%$ for ATop-$k$ accuracy and ADBA score.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|c|c|c|c}\n\\hline\nMetric &W/o MHA & With MHA & Self-KD & Optimal \\\\\n\\hline\nTest loss & $1.141$ & $1.050$ & $\\bf 1.016$ & $0.8158$ \\\\\n\\hline\n ATop-$1$ & $40.77$ & $42.91$ & $\\bf 44.94$ & $50.20$ \\\\\n \\hline\n ATop-$3$ & $77.44$ & $79.97$ & $\\bf 81.45$ & $ 87.15$ \\\\\n \\hline\n ATop-$5$ & $92.31$ & $93.35$ & $\\bf 94.63$ & $96.81$ \\\\\n\\hline\n ADBA & $93.50$ & $94.47$ & $9\\bf 5.00$ & $96.63$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications", "authors": ["Mengyuan Ma", "Nhan Thanh Nguyen", "Nir Shlezinger", "Yonina C. Eldar", "A. Lee Swindlehurst", "Markku Juntti"], "url": "https://arxiv.org/abs/2509.11419v1", "attribution": "\"Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications\" by Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger, Yonina C. Eldar, A. Lee Swindlehurst, and Markku Juntti, arXiv:2509.11419v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.01756v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c|c||} \n \\hline\n \\textbf{Algorithm} & \\textbf{Unbounded} & \\textbf{Adaptive} & \\textbf{Regret} \\\\ \n \\hline\\hline\n & No & Yes & $\\widetilde O\\Big(\\sqrt{\\sum_{t=1}^T\\|\\ell_t\\|^2_{2}}\\Big) $\\\\ \n & Yes & No & $\\widetilde O\\Big(\\ell_{\\infty}\\sqrt{nT}\\Big)$\\\\ \n Non-Adaptive & Yes & No & $\\widetilde O\\Big(\\ell_{\\infty}\\sqrt{nT}+\\sqrt{n \\sum_{t=1}^T \\|\\ell_t\\|^2_2}\\Big)$\\\\\n Adaptive & Yes & Yes & $\\widetilde O\\Big(\\ell_{\\infty}\\sqrt{n\\sum_{t=1}^T \\|\\ell_t\\|_1}+\\sqrt{n \\sum_{t=1}^T \\|\\ell_t\\|^2_2}\\Big)$\\\\\n \\textbf{\\texttt{UMAB-G} Non-Adaptive} & Yes & No & $\\widetilde O\\Big( \\ell_\\infty^- \\sqrt{nT} +\\sqrt{n \\sum_{t=1}^T \\|\\ell_t\\|_\\infty^2}\\Big)$\\\\\n \\textbf{\\texttt{UMAB-G} Adaptive} & Yes & Yes & $\\widetilde O\\Big(\\ell_{\\infty}\\sqrt{n \\sum_{t=1}^T \\|\\ell_t\\|_\\infty}+\\sqrt{n \\sum_{t=1}^T \\|\\ell_t\\|_\\infty^2}\\Big)$\\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison between our results and previous works\\protect}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Improved Algorithms for Adversarial Bandits with Unbounded Losses", "authors": ["Mingyu Chen", "Xuezhou Zhang"], "url": "https://arxiv.org/abs/2310.01756v1", "attribution": "\"Improved Algorithms for Adversarial Bandits with Unbounded Losses\" by Mingyu Chen and Xuezhou Zhang, arXiv:2310.01756v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.13696v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|ll|ll|ll}\n\t\t\\hline \n\t\t& & \\multicolumn{2}{c}{$t$-fusion} & \\multicolumn{2}{c}{Laplace fusion} & \\multicolumn{2}{c}{$L_1$ fusion} \\\\\n\t\tSignal & $\\sigma$ & MSE & adj MSE & MSE & adj MSE & MSE & adj MSE \\\\\n\t\t\\hline \n\t\t& 0.1 & 0.002(0.001) & 0.001(0.000) & 0.232(0.008) & 0.081(0.016) & \n\t\t0.005(0.003) & 0.002(0.001) \\\\\n\t\teven & 0.3 & 0.037(0.017) & 0.013(0.006) & \n\t\t0.265(0.020) & 0.093(0.019) & 0.037(0.020) & 0.013(0.007) \\\\\n\t\t& 0.5 & 0.262(0.308) & 0.092(0.108) & 0.323(0.036) & 0.113(0.024) & 0.098(0.051) & 0.034(0.018) \\\\\n\t\t&&&&&&& \\\\\n\t\t& 0.1 & 0.002(0.001) & 0.001(0.001) & 0.274(0.008) & 0.113(0.017) & 0.006(0.003) & 0.002(0.001) \\\\\n\t\tuneven & 0.3 & 0.043(0.014) & 0.018(0.007) & 0.309(0.018) & 0.128(0.020) & 0.039(0.019) & 0.016(0.008) \\\\\n\t\t& 0.5 & 0.524(0.457) & 0.216(0.186) & 0.368(0.032) & 0.152(0.025) & 0.099(0.048) & 0.041(0.019) \\\\\n\t\t&&&&&&& \\\\\n\t\t& 0.1 & 0.007(0.006) & 0.003(0.003) & 0.337(0.006) & 0.156(0.019) & 0.009(0.002) & 0.004(0.001) \\\\\n\t\tv uneven & 0.3 & 0.192(0.240) & 0.088(0.110) & 0.365(0.015) & 0.169(0.022) & 0.063(0.023) & 0.029(0.010) \\\\\n\t\t& 0.5 & 0.989(0.111) & 0.459(0.075) & 0.411(0.025) & 0.191(0.028) & 0.139(0.059) & 0.064(0.027) \\\\\n\t\t\\hline \n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Linear Chain Graphs: MSE and adjusted MSE (their associated standard errors) for $t$-fusion, Laplace fusion, and $L_1$ fusion method, when the true signal is evenly spaced (``even''), unevenly spaced (``uneven''), and very unevenly spaced (``v uneven'').}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Graph signal denoising using $t$-shrinkage priors", "authors": ["Sayantan Banerjee", "Weining Shen"], "url": "https://arxiv.org/abs/2012.13696v1", "attribution": "\"Graph signal denoising using $t$-shrinkage priors\" by Sayantan Banerjee and Weining Shen, arXiv:2012.13696v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.16701v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Option Pricing Results: Microstructure-Enhanced vs Black-Scholes}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Parameter/Model} & \\textbf{Value} & \\textbf{Notes} \\\\\n\\midrule\n\\textbf{Option Specifications} & & \\\\\nUnderlying Asset & SPY & S\\&P 500 ETF \\\\\nCurrent Spot Price & \\$600.00 & At-the-money \\\\\nStrike Price & \\$600.00 & At-the-money \\\\\nTime to Expiration & 30 days & 0.0822 years \\\\\nRisk-free Rate & 5.0\\% & Annualized \\\\\nTree Time Steps & 10 & Computational constraint \\\\\nTime per Step & 3.0 days & $\\Delta t = 0.008219$ \\\\\n& & \\\\\n\\textbf{Pricing Results} & & \\\\\nInitial Price (Unscaled) & \\$0.38 & Time scaling error \\\\\nMicrostructure-Enhanced Price & \\$15.41 & After proper scaling \\\\\nBlack-Scholes Price & \\$17.87 & Traditional benchmark \\\\\nAbsolute Difference & \\$2.46 & 13.79\\% lower \\\\\nRelative Difference & -13.79\\% & Economically significant \\\\\n& & \\\\\n\\textbf{Implementation Details} & & \\\\\nScaling Factor Applied & $\\sqrt{807.8}$ & Minute to tree time \\\\\nHistorical Volatility & 24.3\\% & Annualized from data \\\\\nModel Implied Volatility & 21.0\\% & From scaled factors \\\\\nVolatility Difference & 3.2pp & Model efficiency gain \\\\\n& & \\\\\n\\textbf{Computational Performance} & & \\\\\nTree Nodes Generated & 2,047 & Exponential growth \\\\\nTree Construction Time & 87.31 sec & 10 steps only \\\\\nPricing Time & 0.004 sec & Per option \\\\\nState Mapping Method & Simplified & Closest p\\_RF match \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach", "authors": ["Akash Deep", "Chris Monico", "W. Brent Lindquist", "Svetlozar T. Rachev", "Frank J. Fabozzi"], "url": "https://arxiv.org/abs/2507.16701v1", "attribution": "\"Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach\" by Akash Deep, Chris Monico, W. Brent Lindquist, Svetlozar T. Rachev, and Frank J. Fabozzi, arXiv:2507.16701v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11547v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparisons of CorLoc on OD100 dataset (subset of Internet Images dataset). `-' indicates that the authors have not provided those results in the respective paper.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|lll|l}\n\\hline\\hline\nMethod & Airplane & Car & Horse & Mean \\\\ \\hline\\hline\nJoulin et al. & 32.93 & 66.29 & 54.84 & 51.35 \\\\ \nJoulin et al. & 57.32 & 64.04 & 52.69 & 58.02 \\\\ \nRubinstein et al. & 74.39 & 87.64 & 63.44 & 75.16 \\\\ \nTang et al. & 71.95 & 93.26& 64.52 & 76.58 \\\\ \nSCDA & 87.80 & 86.52 & 75.37 & 83.20 \\\\ \nCho et al. & 82.93 & 94.38 & 75.27 & 84.19 \\\\ \nVora et al. & 43.9 & 65.17 & 45.16 & 51.41 \\\\ \nVo et al. & - & - & - & 90.2 \\\\ \nDDT & \\textbf{91.46} & 95.51 & 77.42 & 88.13 \\\\ \nDDT+ & \\textbf{91.46} & 94.38 & 76.34 & 87.39 \\\\ \nOur ASOC & 90.24 & \\textbf{98.88} & \\textbf{86.02} & \\textbf{91.71} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "ASOC: Adaptive Self-aware Object Co-localization", "authors": ["Koteswar Rao Jerripothula", "Prerana Mukherjee"], "url": "https://arxiv.org/abs/2201.11547v1", "attribution": "\"ASOC: Adaptive Self-aware Object Co-localization\" by Koteswar Rao Jerripothula and Prerana Mukherjee, arXiv:2201.11547v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12682v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{This table represents the differences in proximity-weighted predictions using the training set. These predictions were compared with the random forest OOB predictions. RF-GAP predictions most reliably match the random forest predictions. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||cc||cc|cc|cc|cc|cr|}\n \\hline\nDataset & RF & & RF-GAP & & Original & & OOB & & PBK & & RFProxIH & \\\\ \n \\hline\nArrhythmia & 0.064 & (0.00) & \\textbf{0.006} & (0.00) & 0.078 & (0.00) & 0.066 & (0.00) & 0.074 & (0.00) & & (NA) \\\\ \n \\hline\nAuto-Mpg & 0.005 & (0.00) & \\textbf{0.001} & (0.00) & 0.021 & (0.00) & 0.012 & (0.00) & 0.017 & (0.00) & & (NA) \\\\ \n \\hline\nBalance Scale & 0.150 & (0.02) &\\textbf{ 0.010} & (0.01) & 0.077 & (0.01) & 0.069 & (0.01) & 0.079 & (0.01) & 0.080 & (0.01) \\\\ \n \\hline\nBanknote & 0.006 & (0.00) & \\textbf{0 }& (0.00) & 0.004 & (0.00) & 0.014 & (0.00) & 0.019 & (0.01) & 0.003 & (0.00) \\\\ \n \\hline\nBreast Cancer & 0.033 & (0.00) & \\textbf{0 }& (0.00) & 0.015 & (0.00) & 0.021 & (0.00) & 0.018 & (0.00) & 0.011 & (0.00) \\\\ \n \\hline\nCar & 0.043 & (0.01) & \\textbf{0.010} & (0.00) & 0.023 & (0.00) & 0.022 & (0.01) & 0.058 & (0.01) & & (NA) \\\\ \n \\hline\nDiabetes & 0.243 & (0.01) & \\textbf{0.002} & (0.00) & 0.151 & (0.01) & 0.081 & (0.02) & 0.129 & (0.01) & 0.151 & (0.01) \\\\ \n \\hline\nE. Coli & 0.140 & (0.02) & \\textbf{0.003} & (0.00) & 0.071 & (0.01) & 0.071 & (0.02) & 0.076 & (0.01) & 0.078 & (0.01) \\\\ \n \\hline\nGlass & 0.215 & (0.03) & \\textbf{0.003} & (0.00) & 0.137 & (0.02) & 0.119 & (0.02) & 0.123 & (0.03) & 0.141 & (0.03) \\\\ \n \\hline\nHeart Disease & 0.417 & (0.02) & \\textbf{0.008} & (0.00) & 0.301 & (0.02) & 0.221 & (0.01) & 0.270 & (0.02) & & (NA) \\\\ \n \\hline\nHill Valley & 0.419 & (0.01) & \\textbf{0.001} & (0.00) & 0.331 & (0.02) & 0.213 & (0.02) & 0.298 & (0.02) & 0.317 & (0.02) \\\\ \n \\hline\nIonosphere & 0.070 & (0.01) & \\textbf{0} & (0.00) & 0.030 & (0.00) & 0.054 & (0.01) & 0.030 & (0.00) & 0.029 & (0.00) \\\\ \n \\hline\nIris & 0.065 & (0.01) & \\textbf{0} & (0.00) & 0.038 & (0.02) & 0.010 & (0.01) & 0.034 & (0.01) & 0.044 & (0.01) \\\\ \n \\hline\nLiver & 0.296 & (0.02) & \\textbf{0.001} & (0.00) & 0.224 & (0.03) & 0.149 & (0.03) & 0.200 & (0.03) & & (NA) \\\\ \n \\hline\nLymphography & 0.160 & (0.02) & \\textbf{0.004} & (0.01) & 0.113 & (0.02) & 0.062 & (0.02) & 0.092 & (0.02) & 0.096 & (0.01) \\\\ \n \\hline\nOptical Digits & 0.020 & (0.00) & \\textbf{0} & (0.00) & 0.011 & (0.00) & 0.020 & (0.00) & 0.011 & (0.00) & 0.012 & (0.00) \\\\ \n \\hline\nParkinsons & 0.094 & (0.01) &\\textbf{ 0.003 }& (0.00) & 0.040 & (0.01) & 0.078 & (0.01) & 0.065 & (0.03) & 0.040 & (0.01) \\\\ \n \\hline\nRNAseq & 0.003 & (0.00) &\\textbf{ 0} & (0.00) & 0.003 & (0.00) & 0.005 & (0.00) & 0.002 & (0.00) & 0.003 & (0.00) \\\\ \n \\hline\nSeeds & 0.078 & (0.01) & \\textbf{0.001} & (0.00) & 0.045 & (0.01) & 0.027 & (0.01) & 0.043 & (0.01) & 0.053 & (0.01) \\\\ \n \\hline\nSonar & 0.184 & (0.02) & \\textbf{0.004 }& (0.01) & 0.163 & (0.03) & 0.104 & (0.03) & 0.156 & (0.03) & 0.171 & (0.03) \\\\ \n \\hline\nTic-Tac-Toe & 0.067 & (0.00) & \\textbf{0.004} & (0.00) & 0.047 & (0.00) & 0.032 & (0.01) & 0.024 & (0.00) & & (NA) \\\\ \n \\hline\nTitanic & 0.182 & (0.01) & \\textbf{0.011} & (0.01) & 0.076 & (0.01) & 0.070 & (0.01) & 0.074 & (0.01) & & (NA) \\\\ \n \\hline\nWaveform & 0.145 & (0.00) & \\textbf{0.002} & (0.00) & 0.104 & (0.00) & 0.038 & (0.01) & 0.099 & (0.00) & 0.117 & (0.00) \\\\ \n \\hline\nWine & 0.019 & (0.01) & \\textbf{0} & (0.00) & 0.019 & (0.01) & 0.006 & (0.00) & 0.018 & (0.01) & 0.019 & (0.01) \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Geometry- and Accuracy-Preserving Random Forest Proximities", "authors": ["Jake S. Rhodes", "Adele Cutler", "Kevin R. Moon"], "url": "https://arxiv.org/abs/2201.12682v2", "attribution": "\"Geometry- and Accuracy-Preserving Random Forest Proximities\" by Jake S. Rhodes, Adele Cutler, and Kevin R. Moon, arXiv:2201.12682v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.15365v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|} \n\t\t\\hline\n\t\tMethod & \\multicolumn{3}{c| }{VAM} & \\multicolumn{3}{c|}{FPT}\\\\\n\t\t\\hline\n\t\tTime & $E_{\\mathcal{M}_0}$ & $E_{\\mathcal{M}_1}$ & $E_{\\mathcal{M}_2}$ & $E_{\\mathcal{M}_0}$ & $E_{\\mathcal{M}_1}$ & $E_{\\mathcal{M}_2}$ \\\\\n\t\t\\hline \n\t\t0.3 & 2.57$\\times 10^{-11}$ & 1.45$\\times 10^{-11}$ & 1.21$\\times 10^{-2}$ & 2.57$\\times 10^{-11}$ & 1.45$\\times 10^{-11}$ & 1.01$\\times 10^{-2}$\\\\\n\t\t\\hline \n\t\t0.6 & 4.94$\\times 10^{-11}$ & 1.42$\\times 10^{-11}$ & 2.07$\\times 10^{-2}$& 4.94$\\times 10^{-11}$ & 1.42$\\times 10^{-11}$ & 2.53$\\times 10^{-2}$ \\\\\n\t\t\\hline \n\t\t0.9 & 4.63$\\times 10^{-11}$ & 6.63$\\times 10^{-11}$ & 3.22$\\times 10^{-2}$& 4.63$\\times 10^{-11}$ & 6.63$\\times 10^{-11}$ & 4.02$\\times 10^{-2}$ \\\\\n\t\t\\hline \n\t\t1.2 & 3.47$\\times 10^{-10}$ & 2.26$\\times 10^{-10}$ & 4.48$\\times 10^{-2}$& 3.47$\\times 10^{-10}$ & 2.26$\\times 10^{-10}$ & 5.71$\\times 10^{-2}$\\\\\n\t\t\\hline \n\t\t1.5 & 7.72$\\times 10^{-10}$ & 3.19$\\times 10^{-10}$ & 5.90$\\times 10^{-2}$& 7.72$\\times 10^{-10}$ & 3.19$\\times 10^{-10}$ & 7.67$\\times 10^{-2}$ \\\\\n\t\t\\hline \n\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Error analysis of different order moments for test case .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Convergence-rate and error analysis of sectional-volume average method for the collisional breakage equation with multi-dimensional modelling", "authors": ["Prakrati Kushwah", "Anupama Ghorai", "Jitraj Saha"], "url": "https://arxiv.org/abs/2504.15365v1", "attribution": "\"Convergence-rate and error analysis of sectional-volume average method for the collisional breakage equation with multi-dimensional modelling\" by Prakrati Kushwah, Anupama Ghorai, and Jitraj Saha, arXiv:2504.15365v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05036v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Left}: Results of mAP and calibration errors of probabilistic extensions of DETR, RetinaNet, and FasterRCNN. \\textbf{Right}: The number of output detection instances classified as true positives (TP), duplicates, localization errors, and false positives (FP).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccccccc}\n \n & & \\multicolumn{2}{c}{\\bf mAP(\\%) $\\uparrow$ } & \\multicolumn{2}{c}{\\bf MCE (Cls) $\\downarrow$ } & \\multicolumn{2}{c}{\\bf CE (Reg) $\\downarrow$ } & &\\multicolumn{4}{c}{\\bf Counts Per Partition (in-dist)}\\\\\n \\cmidrule(lr){3-4} \\cmidrule(lr){5-6} \\cmidrule(lr){7-8} \\cmidrule(lr){9-13}\n Detector& Loss & COCO & OpenIm& COCO& OpenIm& COCO& OpenIm & & TP & Dup. & Loc. Errors & FP\\\\\n \\midrule\n \\multirow{4}{*}{DETR} &NLL& 37.10 &35.16 &0.0735&\\textbf{0.0973}& 0.0512 &0.0331& & 24452&1103&11228&12604\\\\\n \\cmidrule(lr){2-8} \\cmidrule(lr){9-13}\n &DMM& 39.82 &38.88 &0.0737& 0.0996&0.0091&0.0171& &24929&1197&10374&10406\\\\\n \\cmidrule(lr){2-8} \\cmidrule(lr){9-13}\n &ES& \\textbf{39.96}& \\textbf{39.31} & \\textbf{0.0728} & 0.0993 & \\textbf{0.0068} & \\textbf{0.0167}& &24914&1216&10236&9761\\\\\n \\midrule\n \\multirow{4}{*}{RetinaNet}&NLL& 35.11&34.60 &0.0260&0.0385& 0.0242 &0.0176 & & 22733&2835&8992&6654\\\\\n \\cmidrule(lr){2-8} \\cmidrule(lr){9-13}\n &DMM& \\textbf{36.28} &34.84& \\textbf{0.0244}& 0.0372&0.0102& 0.0189& & 23096&2783&8902&6411\\\\\n \\cmidrule(lr){2-8} \\cmidrule(lr){9-13}\n &ES& 36.02& \\textbf{34.97}& 0.0245 & 0.0370 & \\textbf{0.0059} & \\textbf{0.0138} & &23096&2783&8902&6411\\\\\n \\midrule\n \\multirow{4}{*}{FasterRCNN}&NLL& 37.14&34.30 &\\textbf{0.0490}& \\textbf{0.0713}&\\textbf{0.0049}& 0.0133 & &23145&4841&7323&6989\\\\\n \\cmidrule(lr){2-8} \\cmidrule(lr){9-13}\n &DMM& 37.24 &34.42&0.0496&0.0854&0.0112&0.0229 & &23187&4628&7165&6905\\\\\n \\cmidrule(lr){2-8} \\cmidrule(lr){9-13}\n &ES& \\textbf{37.32}& \\textbf{34.75}&0.0495& 0.0849 & 0.0051 &\\textbf{0.0121}& &23123&4612&7208&6760\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors", "authors": ["Ali Harakeh", "Steven L. Waslander"], "url": "https://arxiv.org/abs/2101.05036v3", "attribution": "\"Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors\" by Ali Harakeh and Steven L. Waslander, arXiv:2101.05036v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.15718v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{xcolor}\n\\usepackage{booktabs}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean testing error (FZNS$|$Fused Lasso) of the outputs.}\n\\begin{tabular}{c|ccccccc}\n\t\t\\toprule\n\t\t($\\|\\widehat{B}x^*\\|_0, \\|x^*\\|_0$) & (2,1) & (3,2) & (3,3) &(3,4) & (3,5) & (3,6) & (3,8) \\\\\n \\midrule\n Mean testing error & {\\color{blue}8.35}$|$8.54 & {\\color{blue}7.34}$|$7.36 & 5.45$|${\\color{blue}5.15} & {\\color{blue}5.15}$|$5.74 & {\\color{blue}5.21}$|$6.32 & {\\color{blue}5.08}$|$5.27 & {\\color{blue}5.11}$|$5.70\\\\\n \\midrule\n ($\\|\\widehat{B}x^*\\|_0, \\|x^*\\|_0$) & (4,4) & (4,5)& (4,6)& (4,7)& (4,8) & (5,5) & (5,6) \\\\\n \\midrule\n Mean testing error & {\\color{blue}5.07}$|$5.52 & {\\color{blue}5.31}$|$5.86 & 5.49$|${\\color{blue}4.99} & 5.25$|${\\color{blue}4.97} & 5.33$|${\\color{blue}4.78} & {\\color{blue}5.10}$|$5.48 & 5.74$|${\\color{blue}5.38}\\\\\n \\midrule\n ($\\|\\widehat{B}x^*\\|_0, \\|x^*\\|_0$) & (5,7) & (5,8) & (6,6) & (6,7) & (6,8) & (7,7) & (7,8) \\\\\n \\midrule\n Mean testing error & {\\color{blue}5.46}$|$5.58 & 5.35$|${\\color{blue} 5.19} & {\\color{blue}4.41}$|$ 5.26 & 5.34$|${\\color{blue}4.95} & {\\color{blue}5.25}$|$5.34 & {\\color{blue}5.03}$|$5.22 & 5.24$|${\\color{blue}5.22}\\\\\n \\bottomrule\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An Inexact Projected Regularized Newton Method for Fused Zero-norms Regularization Problems", "authors": ["Yuqia Wu", "Shaohua Pan", "Xiaoqi Yang"], "url": "https://arxiv.org/abs/2312.15718v1", "attribution": "\"An Inexact Projected Regularized Newton Method for Fused Zero-norms Regularization Problems\" by Yuqia Wu, Shaohua Pan, and Xiaoqi Yang, arXiv:2312.15718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.02630v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Randomized variations used to identify agents’ choice sensitivities.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n Ind. Variables & Exogenous Variation \\\\\n \\midrule \n \\midrule\n Position & Randomly permute the position of the eight products \\\\\n Sponsored Tag & Randomly assign a {\\tt Sponsored} tag to $X$ listings, $X \\sim \\text{Unif}(\\{1,\\dots,4\\})$ \\\\\n Overall Pick Tag & Randomly assign a {\\tt Overall Pick} tag to a listing without {\\tt Sponspored} tag \\\\\n Scarcity Tag & Randomly assign a {\\tt Only X Remaining} tag to a listing without \\\\\n & a {\\tt Sponsored} or {\\tt Overall Pick} tag, $X \\sim \\text{Unif}(\\{1,\\dots,5\\})$ \\\\\n Price & Randomly perturb the original price $p_j$ of each product $j$, \\\\\n & $p_{j}^\\prime \\gets p_j \\cdot f_j$, where $f_j \\sim \\text{logNormal}(\\mu= 0, \\sigma = 0.3)$ \\\\\n Rating & Randomly perturb the original rating $r_j$ of each product $j$, \\\\ \n & $r_j^{\\prime} \\gets r_j + \\alpha_j \\cdot (5 - r_j)$, where $\\alpha_j \\sim \\text{Unif}([-0.8, 0.8])$ \\\\\n Number of reviews & Randomly perturb the original number of reviews $N_j$ of each product $j$, \\\\\n & $N_j^\\prime \\gets N_j \\cdot f_j$, where $f_j \\sim \\text{logNormal}(\\mu = 0, \\sigma = 1)$\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce", "authors": ["Amine Allouah", "Omar Besbes", "Josué D Figueroa", "Yash Kanoria", "Akshit Kumar"], "url": "https://arxiv.org/abs/2508.02630v1", "attribution": "\"What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce\" by Amine Allouah, Omar Besbes, Josué D Figueroa, Yash Kanoria, and Akshit Kumar, arXiv:2508.02630v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.01116v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\toprule\n\\textbf{Service Description} & \\multicolumn{2}{c}{\\textbf{Code}} \\\\\n\\midrule\n\\textbf{Panel A:} Infection Diagnoses & (ICD-9-CM) & (ICD-10-CM) \\\\\nAcute Respiratory Infections & 460-466 & J00-J06 \\\\\nPneumonia and Influenza\t& 480-488 & J09-J18\\\\\nOtitis media\t&381--382 & H65--H66\\\\\nStreptococcal sore throat and scarlet fever\t& 034 & A38, J02\\\\\nWhooping cough & 033 & A37 \\\\\nInfectious mononucleosis & 075 & B27 \\\\\nChickenpox & 052 & B01 \\\\\nUrinary Tract Infections &\t590, 595, 599 & N10--N12,N15,N16,N28,N30,N36,N39,R31\\\\\nFood poisoning (bacterial) & 005 &\tA05 \\\\\nOther Intestinal Infections\t& 008--009 & \tA04,A08,A09 \\\\\nNausea and Vomiting\t& 536.2, 787\t& R11,R12,R13,R14,R15,R19 \\\\ \nDyspepsia & \t536.8,536.9\t& K30--K31 \\\\\n\\midrule \n\\textbf{Panel B:} Injuries & (ICD-9-CM) & (ICD-10-CM) \\\\\nFracture of Upper Limb\t& 810-819\t& S12,S22,S32,S42,S52,S62 \\\\\nFracture of Lower Limb\t& 820-829\t& S72,S82,S92 \\\\ \nDislocation & \t830-839\t& S13,S23,S33,S43,S53,S63,S73,S83,S93 \\\\\nSprains and Strains & \t840-848 &\tCombined with above \\\\ \n\\midrule\n\\textbf{Panel C:} Place of Service Codes & (POS) & -- \\\\\nPhysician Office & 11, 72, 95 \\\\ \nUrgent Care Center & 17,20 \\\\ \nEmergency Department & 23 \\\\ \nHospital (including on-campus outpatient) & 21, 22, 28 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Identifying Injuries, Infections, and Places of Service}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Medical Bill Shock and Imperfect Moral Hazard", "authors": ["Alex Hoagland", "David M. Anderson", "Ed Zhu"], "url": "https://arxiv.org/abs/2211.01116v2", "attribution": "\"Medical Bill Shock and Imperfect Moral Hazard\" by Alex Hoagland, David M. Anderson, and Ed Zhu, arXiv:2211.01116v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.18611v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n{\\textbf{Metric}} & \\textbf{$\\boldsymbol{\\ell_{cl}}$} & \\textbf{$\\boldsymbol{\\ell_{bce}}$} & \\textbf{$\\boldsymbol{\\ell_{cl+bce}}$} & \\textbf{$\\boldsymbol{\\ell}$} \\\\\n\\hline\n\\textbf{SS} &0.39 &0.59 &0.62 &\\textbf{0.85} \\\\\n\\hline\n\\textbf{ARI} &0.52 &0.55 &0.59 &\\textbf{0.64} \\\\\n\\hline\n\\textbf{MI} &0.76 &0.84 &0.87 &\\textbf{0.94} \\\\\n\\hline\n\\textbf{ACC (\\%)} &70.16 &75.43 &76.04 &\\textbf{79.34} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of clustering metrics for training $g(\\cdot)$ using $\\ell_{cl}$, $\\ell_{bce}$, $\\ell_{cl+bce}$, and $l$, after clustering $z_i^u$ using K-means clustering}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Identification and Clustering of Unseen Ragas in Indian Art Music", "authors": ["Parampreet Singh", "Adwik Gupta", "Aakarsh Mishra", "Vipul Arora"], "url": "https://arxiv.org/abs/2411.18611v2", "attribution": "\"Identification and Clustering of Unseen Ragas in Indian Art Music\" by Parampreet Singh, Adwik Gupta, Aakarsh Mishra, and Vipul Arora, arXiv:2411.18611v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13085v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n \\hline\n \\textit{Parameter} & $t_0$ & $t_f$ & $\\kappa$ & $\\varrho$ & $\\sigma$ & $\\gamma$ & $\\delta $ & $\\varepsilon$ \\\\\n \\hline\n \\textit{Value} & 0 & 90 & 0.32 & 1 & 0.5 & 0.12 & 0.0294 & 0.0094 \\\\\n \\hline\n \\textit{Reference} & - & - & & & & & & \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Parameter values used for the numerical simulation and control of the CPDS .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Modified Patankar Semi-Lagrangian Scheme for the Optimal Control of Production-Destruction systems", "authors": ["Simone Cacace", "Alessio Oliviero", "Mario Pezzella"], "url": "https://arxiv.org/abs/2501.13085v1", "attribution": "\"Modified Patankar Semi-Lagrangian Scheme for the Optimal Control of Production-Destruction systems\" by Simone Cacace, Alessio Oliviero, and Mario Pezzella, arXiv:2501.13085v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.09028v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Characteristics of battery electric vehicle and hybrid electric vehicle}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c||} \n \\hline\n{\\textbf{EV Subsystem Type}} & {\\textbf{Battery EV}} & {\\textbf{Hybrid EV}} \\\\ \n \\hline\\hline\n Propulsion & Electric Motor & Electric Motor, ICE \\\\\n \\hline\n Energy Storage & Battery, Ultra Capacitor & Battery, Ultra Capacitor \\\\\n \\hline\n Infrastructure & Grid Charging & Gasoline Stations\\\\\n \\hline\n Characteristics & Zero Emissions, High Efficiency & Low Emissions, High Economy\\\\\n \\hline\n Major Issues & Charging Facilities & Battery Sizing \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Degradation-based Energy Management for Microgrids in the Presence of Energy Storage Elements", "authors": ["Satish Vedula"], "url": "https://arxiv.org/abs/2503.09028v2", "attribution": "\"Degradation-based Energy Management for Microgrids in the Presence of Energy Storage Elements\" by Satish Vedula, arXiv:2503.09028v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.13474v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|c|c|c|c|c}\nCondition & Width [m] & Discount $\\gamma$ & Action cost $\\nu$ \\\\\n\\hline\nValues &$[0.005, ... 0.04]$ & $[0.97,... 1.0]$ & $1e-5$ \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Model and cost parameters, where $M=2I$, $D=0.3I$, $\\sigma_\\tau=1e2$, and $h=0.02$ unless otherwise noted.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Planning Human-Robot Co-manipulation with Human Motor Control Objectives and Multi-component Reaching Strategies", "authors": ["Kevin Haninger", "Luka Peternel"], "url": "https://arxiv.org/abs/2412.13474v1", "attribution": "\"Planning Human-Robot Co-manipulation with Human Motor Control Objectives and Multi-component Reaching Strategies\" by Kevin Haninger and Luka Peternel, arXiv:2412.13474v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.01099v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n Description & Symbol & \\multicolumn{2}{c}{Value}\\\\\n & & $s=1$ & $s=2$\\\\\n \\midrule\n Investment horizon, quarters & $T$ & \\multicolumn{2}{c}{40}\\\\\n Macro transition probability & $p_{ss^\\prime}$ & 0.25 & 0.05 \\\\\n PE call rate, new commitments & $\\lambda_N(s)$ & 0.18 & 0.047 \\\\\n PE call rate, existing commitments & $\\lambda_K(s)$ & 0.050 & 0.078 \\\\\n PE distribution rate & $\\lambda_D(s)$ & 0.028 & 0.071 \\\\\n PE liquidation discount & $\\alpha(s)$ & 0.66 & 0.90 \\\\\n Risk-free rate & $\\log R_f(s)$ & 0.0028 & 0.0051 \\\\\n Stock expected return & $\\mu_S(s)$ & 0.0079 & 0.0238 \\\\\n Stock return volatility & $\\sigma_S(s)$ & 0.1493 & 0.0829 \\\\\n Stock-PE return correlation & $\\rho(s)$ & 0.9527 & 0.4575 \\\\\n PE return volatility & $\\sigma_P(s)$ & 0.0768 & 0.0424 \\\\\n PE expected return autocorrelation & $\\varrho_{P,1}+\\varrho_{P,2}$ & \\multicolumn{2}{c}{0.201} \\\\\n PE expected return state-dependance & $\\nu_P(s)$ & 0.0024 & 0.0317 \\\\\n Risk aversion & $\\gamma$ & \\multicolumn{2}{c}{2}\\\\\n PE adjustment costs & $\\gamma_N$ & \\multicolumn{2}{c}{0.1}\\\\\n PE adjustment costs & $\\overline{n}$ & \\multicolumn{2}{c}{0}\\\\\n Stock adjustment costs & $\\gamma_S$ & \\multicolumn{2}{c}{0.01}\\\\\n Risk budget threshold & $\\overline{\\theta}$ & \\multicolumn{2}{c}{1}\\\\\n Risk budget cost & $\\kappa$ & \\multicolumn{2}{c}{1}\\\\\n Risk weight, bonds & $\\theta_B$ & \\multicolumn{2}{c}{0}\\\\\n Risk weight, stocks & $\\theta_S$ & \\multicolumn{2}{c}{1.5}\\\\\n Risk weight, PE & $\\theta_P$ & \\multicolumn{2}{c}{1.5}\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{Parameters.} \\small We simulate the model at a quarterly frequency using the parameters in this table.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Dynamic Model of Private Asset Allocation", "authors": ["Hui Chen", "Giovanni Gambarotta", "Simon Scheidegger", "Yu Xu"], "url": "https://arxiv.org/abs/2503.01099v1", "attribution": "\"A Dynamic Model of Private Asset Allocation\" by Hui Chen, Giovanni Gambarotta, Simon Scheidegger, and Yu Xu, arXiv:2503.01099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.06675v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Determinants of major Roman roads (non-zero cells)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\hline\\hline\\\\\n& \\textit{(1)} & \\textit{(2)} & \\textit{(3)} & \\textit{(4)}\\\\\n\\textit{Dependent variable:} &&&& \\\\\n\\textit{Major Roman roads in km (log)}&&&& \\\\\n\\hline\\\\\n\\texttt{Ruggedness} (log)& -0.016 & -0.037 & -0.052 & -0.056 \\\\\n & (0.033) & (0.036) & (0.057) & (0.060) \\\\\n\\texttt{Elevation} (log)& 0.015 & 0.014 & 0.036 & 0.037 \\\\\n & (0.047) & (0.046) & (0.071) & (0.071) \\\\\n\\texttt{Slope} (log)& 0.056 & -0.005 & -0.226** & -0.207* \\\\\n & (0.117) & (0.132) & (0.109) & (0.121) \\\\\n\\texttt{Agriculture suitability pre 1500} (log) & 0.031***& 0.029 & 0.065***& 0.066***\\\\\n & (0.010) & (0.021) & (0.022) & (0.023) \\\\\n\\texttt{Forest} (dummy)& -0.069 & -0.077 & 0.030 & 0.037 \\\\\n & (0.063) & (0.071) & (0.057) & (0.067) \\\\\n\\texttt{Low vegetation} (dummy)& -0.035 & -0.040 & -0.015 & -0.030 \\\\\n & (0.046) & (0.072) & (0.063) & (0.081) \\\\\n\\texttt{Distance from sea} (log) & 0.031 & 0.055***& 0.089***& 0.101** \\\\\n & (0.021) & (0.020) & (0.032) & (0.039) \\\\\n\\texttt{Distance from nearest waterway} (log) & 0.015 & -0.005 & -0.020 & -0.003 \\\\\n & (0.025) & (0.023) & (0.039) & (0.033) \\\\\n\\texttt{Distance from nearest harbor} (log)& & -0.038 & & -0.103* \\\\\n & & (0.037) & & (0.054) \\\\\n\\texttt{Distance from Rome} (log)& & 0.018 & & -0.419** \\\\\n & & (0.046) & & (0.180) \\\\\n\\texttt{Pre-Roman amenities} (dummy)& & 0.500***& & 0.515***\\\\\n & & (0.091) & & (0.102) \\\\\n \\hline\\\\\n $ \\phi_{p}$\t&No&No&Yes&Yes\\\\ \n \\texttt{Smaller area} (dummy)&Yes&Yes&Yes&Yes\\\\ \n Observations& 1,359& 1,359& 1,359& 1,359\\\\\n Adjusted \\textit{R$^{2}$}& 0.682& 0.695& 0.682& 0.697\\\\\n\\hline \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "From the historical Roman road network to modern infrastructure in Italy", "authors": ["Luca De Benedictis", "Vania Licio", "Anna Pinna"], "url": "https://arxiv.org/abs/2208.06675v1", "attribution": "\"From the historical Roman road network to modern infrastructure in Italy\" by Luca De Benedictis, Vania Licio, and Anna Pinna, arXiv:2208.06675v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.13113v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Welch’s $t$-test results comparing agent profits under extreme $\\lambda$ settings.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nComparison & Mean Difference & $t$-Statistic & $p$-Value \\\\\n\\midrule\n$\\lambda = 0.1$ vs.\\ $\\lambda = 1.0$ & 5.1 & 7.34 & $<$ 0.01 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning", "authors": ["Stella C. Dong", "James R. Finlay"], "url": "https://arxiv.org/abs/2506.13113v1", "attribution": "\"Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning\" by Stella C. Dong and James R. Finlay, arXiv:2506.13113v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.08173v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\t\t\t\\hline\n\t\t\t\\textbf{Method} & \\textbf{Model Size} & \\textbf{Pre-training Time} & \\textbf{Fine-tuning Time} \\\\\n\t\t\t\\hline\n\t\t\tCD-TVD & 19.5& 36 & 5 \\\\ \n\t\t\tSSR-VFD & 51.4 & 0 & 4 \\\\ \n\t\t\tSRGAN & 20.9 & 0 & 3 \\\\\n\t\t\tPSRFlow & 16.6 & 0 & 4 \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "CD-TVD: Contrastive Diffusion for 3D Super-Resolution with Scarce High-Resolution Time-Varying Data", "authors": ["Chongke Bi", "Xin Gao", "Jiangkang Deng", "Guan Li", "Jun Han"], "url": "https://arxiv.org/abs/2508.08173v2", "attribution": "\"CD-TVD: Contrastive Diffusion for 3D Super-Resolution with Scarce High-Resolution Time-Varying Data\" by Chongke Bi, Xin Gao, Jiangkang Deng, Guan Li, and Jun Han, arXiv:2508.08173v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.17440v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{For the $L^2$ loss function, values of the objective function (and efficiencies), under each Gibbs expected utility, and for each design, including the maximum projection Latin hypercube design (LHD).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll} \\hline\n & \\multicolumn{3}{c}{Design} \\\\ \\cline{2-4}\n & NSE & SH & LHD \\\\\n & utility & utility & \\\\ \\hline\nNSE utility & -0.6950 & -0.7882 & -0.9191 \\\\\n& (100\\%) & (88\\%) & (75\\%) \\\\ \\hline\nSH utility & -12.21 & -4.299 & -7.932 \\\\\n& (45\\%) & (100\\%) & (70\\%) \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Gibbs optimal design of experiments", "authors": ["Antony M. Overstall", "Jacinta Holloway-Brown", "James M. McGree"], "url": "https://arxiv.org/abs/2310.17440v2", "attribution": "\"Gibbs optimal design of experiments\" by Antony M. Overstall, Jacinta Holloway-Brown, and James M. McGree, arXiv:2310.17440v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.17617v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\textbf{Model} & \\textbf{SSIM} & \\textbf{PSNR} & \\textbf{MSE} \\\\ \\hline\nAUNet(1500eps) & 0.9995 & 20.1592 & 0.0002 \\\\ \\hline\nAUNet+GANs(1500eps) & 0.9997 & 20.4588 & 0.0002 \\\\ \\hline\nMA3T-Fv1+GANs(20eps) & 0.9996 & 16.2408 & 0.0015 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Validation performance metrics for various frameworks tested for the inpainting task. Metrics include SSIM(closer to 1 is better), PSNR(higher is better), and MSE(smaller is better).}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "An Ensemble Approach for Brain Tumor Segmentation and Synthesis", "authors": ["Juampablo E. Heras Rivera", "Agamdeep S. Chopra", "Tianyi Ren", "Hitender Oswal", "Yutong Pan", "Zineb Sordo", "Sophie Walters", "William Henry", "Hooman Mohammadi", "Riley Olson", "Fargol Rezayaraghi", "Tyson Lam", "Akshay Jaikanth", "Pavan Kancharla", "Jacob Ruzevick", "Daniela Ushizima", "Mehmet Kurt"], "url": "https://arxiv.org/abs/2411.17617v1", "attribution": "\"An Ensemble Approach for Brain Tumor Segmentation and Synthesis\" by Juampablo E. Heras Rivera, Agamdeep S. Chopra, Tianyi Ren, Hitender Oswal, Yutong Pan, Zineb Sordo, Sophie Walters, William Henry, Hooman Mohammadi, Riley Olson, Fargol Rezayaraghi, Tyson Lam, Akshay Jaikanth, Pavan Kancharla, Jacob Ruzevick, Daniela Ushizima, and Mehmet Kurt, arXiv:2411.17617v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16929v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\textbf{Method} & {DiNO ViT-S/14} & {DiNO ViT-B/14} & {DiNO ViT-L/14} \\\\\n\\midrule\nBaseline & 55.51 & 63.03 & 65.72 \\\\\n\\hspace{12pt}+ KNNs & 56.43 & {64.26} & 65.70 \\\\\n\\hspace{12pt}+ 1-walks on KNN & \\textbf{58.09} & \\textbf{64.29} & \n{65.97} \\\\\n\\hspace{12pt}+ 2-walks on KNN & 57.84 & 64.27 & \\textbf{67.26} \\\\\n\\hspace{12pt}+ 3-walks on KNN & 57.82 & 64.15 & 67.02 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Ablation Study on Neighbor Propagation. Adding both KNNs and walks of length 1 or 2 on the KNN graph achieves the best performance. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "I-Con: A Unifying Framework for Representation Learning", "authors": ["Shaden Alshammari", "John Hershey", "Axel Feldmann", "William T. Freeman", "Mark Hamilton"], "url": "https://arxiv.org/abs/2504.16929v1", "attribution": "\"I-Con: A Unifying Framework for Representation Learning\" by Shaden Alshammari, John Hershey, Axel Feldmann, William T. Freeman, and Mark Hamilton, arXiv:2504.16929v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.03287v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{All Crypto Value-Weighted Portfolio Descriptive Statistics. Values are portfolio returns in percentage points and are at the weekly frequency. Crypto portfolios are formed on total TVL to market cap. There are 105 weekly observations. \\(^{***}\\), \\(^{**}\\), and \\(^{*}\\), denote statistical significance at the 1\\%, 5\\%, and 10\\% levels respectively.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\\hline\n & HML & Q1 & Q2 & Q3 & Q4\\\\\n\\hline\nmean & -0.14 & 1.78\\(^{**}\\) & 1.84 & 1.64\\(^{*}\\) & 1.64\\(^{*}\\)\\\\\nstd & 5.67 & 7.08 & 11.22 & 9.02 & 8.67\\\\\nmin & -14.18 & -14.86 & -22.82 & -24.21 & -18.29\\\\\n25\\% & -3.32 & -2.13 & -4.31 & -3.22 & -3.57\\\\\n50\\% & -0.71 & 0.04 & -0.49 & 0.51 & 0.49\\\\\n75\\% & 1.87 & 5.92 & 6.34 & 7.63 & 6.06\\\\\nmax & 19.75 & 26.98 & 44.31 & 31.87 & 28.22\\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns", "authors": ["Matt Brigida"], "url": "https://arxiv.org/abs/2506.03287v3", "attribution": "\"The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns\" by Matt Brigida, arXiv:2506.03287v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.07192v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|c|r|r|r|r|r|}\n\\hline\nNo & Data & N & $\\mu_2$ & $\\mu_4$ & $\\mu_6$ & Corr. \\\\\n\\hline\n0 & Theory & - & 1 & 2 & 5 & - \\\\\n1 & BTC/USD & 512 & 0.9945709163 & 2.0322554437 & 5.2461161423 & -0.39832 \\\\\n2 & XRP/USD & 256 & 0.992749619 & 2.033477488 & 5.272570986 & -0.014449 \\\\\n3 & ETH/USD & 128 & 0.986091805 & 1.992831612 & 5.088267897 & 0.0052927 \\\\\n4 & EUR/USD & 128 & 0.9919604654 & 2.0754426072 & 5.5220097433 & -0.22908 \\\\\n5 & USD/JPY & 128 & 0.9903583699 & 2.0592822343 & 5.4526831969 & -0.20065 \\\\\n6 & GBP/USD & 128 & 0.9892206415 & 2.0447484197 & 5.3697075654 & -0.16762 \\\\\n7 & USD/CHF & 128 & 0.99117701133 & 2.12874344704 & 5.86166248676 & -0.30042 \\\\\n8 & AUD/USD & 128 & 0.990287312 & 2.064446283 & 5.4880243294 & -0.17972 \\\\\n9 & USD/CAD & 128 & 0.9923546213 & 2.1736226772 & 6.114704425 & -0.35853 \\\\\n10 & NZD/USD & 128 & 0.989621843 & 2.060491384 & 5.462168462 & -0.224 \\\\\n11 & EUR/JPY & 128 & 0.99206671 & 2.063575112 & 5.460526944 & -0.20097 \\\\\n12 & EUR/CHF & 128 & 0.991928772 & 2.175657808 & 6.116291797 & -0.38479 \\\\\n13 & EUR/GBP & 128 & 0.992315126 & 2.125032234 & 5.800669878 & -0.34398 \\\\\n14 & USD/BRL & 128 & 0.986271542 & 1.987515102 & 5.060208482 & -0.063159 \\\\\n15 & USD/CNH & 128 & 0.991734947 & 2.107888089 & 5.716649647 & -0.33978 \\\\\n16 & USD/INR & 128 & 0.990322461 & 2.041044148 & 5.336929786 & -0.19754 \\\\\n17 & USD/ZAR & 128 & 0.99075651 & 2.051054249 & 5.38480614 & -0.14857 \\\\\n18 & USD/RUB & 128 & 0.986119801 & 2.044560147 & 5.414686924 & -0.21527 \\\\\n19 & WTI & 128 & 0.991015688 & 2.005806129 & 5.119663516 & 0.014502 \\\\\n20 & SOY & 128 & 0.98949516 & 1.97722104 & 4.96198781 & -0.035633 \\\\\n21 & CORN & 128 & 0.990315723 & 1.998988571 & 5.084888371 & 0.020687 \\\\\n22 & XAU & 128 & 0.992937334 & 2.0478933413 & 5.3498236085 & -0.14414 \\\\\n23 & VIX & 128 & 0.986618758 & 1.980342958 & 4.996256403 & 0.029936 \\\\\n24 & JGB & 64 & 0.9839351 & 1.9590719 & 4.8994693 & 0.0045459 \\\\\n25 & NKY225 & 64 & 0.9801519 & 1.96546533 & 4.96602964 & -0.010206 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deformation of semi-circle law for the correlated time series and Phase transition", "authors": ["Masato Hisakado", "Takuya Kaneko"], "url": "https://arxiv.org/abs/2508.07192v1", "attribution": "\"Deformation of semi-circle law for the correlated time series and Phase transition\" by Masato Hisakado and Takuya Kaneko, arXiv:2508.07192v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.19853v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n \\toprule\n Instance&$|\\mathcal{V}|$&$|A|$&Char. station\\\\\n &&&capacity\\\\\n \\midrule\n I1&60&2,190&1\\\\ \n I2&155&11,900&2\\\\ \n I3&248&29,499&3\\\\ \n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Chance-constrained battery management strategies for the electric bus scheduling problem", "authors": ["Léa Ricard", "Guy Desaulniers", "Andrea Lodi", "Louis-Martin Rousseau"], "url": "https://arxiv.org/abs/2503.19853v1", "attribution": "\"Chance-constrained battery management strategies for the electric bus scheduling problem\" by Léa Ricard, Guy Desaulniers, Andrea Lodi, and Louis-Martin Rousseau, arXiv:2503.19853v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.03861v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|l}\nSymbol & PRMS symbol & Quantity & Units & Description \\\\ \\hline \nRsw & swrad & per HRU & Langleys & shortwave radiation \\\\\nr & solf & per HRU & - & ratio of actual to potential daily solar radiation \\\\\nPn & ppt\\_net & per HRU & inches & total net precipitation \\\\\nPt & throughfall & per HRU & inches & precipitation throughfall after interception \\\\\nSc & AvailCanSt & per HRU & acre-inch & available storage in canopy \\\\\nETp & potet & per HRU & inches & potential evapotranspiration \\\\\nETpa & apet & per HRU & inches & available potential evapotranspiration \\\\\n$\\Delta$E & tcal & per HRU & Langleys & net snowpack energy balance \\\\\nI & cal & per HRU & Langleys & total incoming longwave \\\\\nQv & cecsub & per HRU & Langleys & convection and latent heat from condensation \\\\\nRn & swn & per HRU & Langleys & net shortwave radiation \\\\\nIp & lwp & per HRU & Langleys & perfect black-body emission \\\\\nTavg & temp & per HRU & $^{o}$F & average temperatue \\\\\nQc & qcond & per HRU & Langleys & conducted heat \\\\\nB & sub & per HRU & inches & sublimation \\\\\nPns & net\\_snow & per HRU & inches & snow portion of Pn \\\\\nD & pk\\_depth & per HRU & inches & snowdepth \\\\\nM & snowmelt & per HRU & inches & snowmelt \\\\\nfsa & snowcov\\_area & perHRU & . & fractional snow covered area \\\\\nPnr & net\\_rain & per HRU & inches & rain portion of Pn \\\\\nPsri & hru\\_sroffi & per HRU & inches & impervious surface runoff \\\\\nei & hru\\_impervevap & per HRU & inches & imprevious region evaporation \\\\ \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Model variables}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter", "authors": ["Sami A. Malek", "Alexandre M. Bayen", "Steven D. Glaser"], "url": "https://arxiv.org/abs/1912.03861v1", "attribution": "\"Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter\" by Sami A. Malek, Alexandre M. Bayen, and Steven D. Glaser, arXiv:1912.03861v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.06834v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Settings for server performance benchmark.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\toprule\n\\textbf{Parameter} & \\textbf{Minimum} & \\textbf{Maximum} & \\textbf{Steps} \\\\\n\\midrule\nSupply air temperature & 25\\textdegree{}C & 37\\textdegree{}C & 13\\\\\nRoom air flow rate ($\\text{m}^3/\\text{h}$) & 2,500 & 12,500 & 5 \\\\\nCPU utilization & 10\\% & 90\\% & 7 \\\\\nHDD throughput (MB/s) & 10 & 100 & 6 \\\\\nMemory block size (KB) & 8 & 256 & 6 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Reinforcement Learning for Tropical Air Free-Cooled Data Center Control", "authors": ["Duc Van Le", "Rongrong Wang", "Yingbo Liu", "Rui Tan", "Yew-Wah Wong", "Yonggang Wen"], "url": "https://arxiv.org/abs/2012.06834v1", "attribution": "\"Deep Reinforcement Learning for Tropical Air Free-Cooled Data Center Control\" by Duc Van Le, Rongrong Wang, Yingbo Liu, Rui Tan, Yew-Wah Wong, and Yonggang Wen, arXiv:2012.06834v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02844v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The amount of users, items, interactions and action repetition rate in each dataset.}\n\\begin{tabular}{ccccc}\n \\toprule\n Data & Users & Items & Interactions &Action Repetition \\\\\n \\midrule\n Reddit & 10000 & 1000 & 672447 &79\\% \\\\\n Wikipedia & 8227 & 1000 & 157474 & 61\\% \\\\\n LastFM & 1000 & 1000 & 1293103 & 8.6\\% \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Dynamic Graph Collaborative Filtering", "authors": ["Xiaohan Li", "Mengqi Zhang", "Shu Wu", "Zheng Liu", "Liang Wang", "Philip S. Yu"], "url": "https://arxiv.org/abs/2101.02844v1", "attribution": "\"Dynamic Graph Collaborative Filtering\" by Xiaohan Li, Mengqi Zhang, Shu Wu, Zheng Liu, Liang Wang, and Philip S. Yu, arXiv:2101.02844v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.19858v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance consistency across different architectures.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Architecture} & \\textbf{F1-score (\\%)} & \\textbf{AUC-ROC}\\\\\n\\midrule\nEfficientNet-B3~ & 94.68 & 0.9813 \\\\\nSwin Transformer~ & 93.34 & 0.9797 \\\\\nResNet-101~ & 91.07 & 0.9421 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization", "authors": ["Chia-Ming Lee", "Bo-Cheng Qiu", "Ting-Yao Chen", "Ming-Han Sun", "Fang-Ying Lin", "Jung-Tse Tsai", "I-An Tsai", "Yu-Fan Lin", "Chih-Chung Hsu"], "url": "https://arxiv.org/abs/2507.19858v1", "attribution": "\"Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization\" by Chia-Ming Lee, Bo-Cheng Qiu, Ting-Yao Chen, Ming-Han Sun, Fang-Ying Lin, Jung-Tse Tsai, I-An Tsai, Yu-Fan Lin, and Chih-Chung Hsu, arXiv:2507.19858v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16534v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Feature importance of DOFEN on red wine dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n ~ & \\textbf{1st} & \\textbf{2nd} & \\textbf{3rd} \\\\\n \\midrule\n Random Forest & alcohol (27.17\\%) & sulphates (15.44\\%) & volatile acidity (10.92\\%) \\\\\n XGBoost & alcohol (35.42\\%) & sulphates (15.44\\%) & volatile acidity (7.56\\%) \\\\\n LightGBM & alcohol (26.08\\%) & sulphates (15.75\\%) & volatile acidity (10.63\\%) \\\\\n CatBoost & sulphates (16.29\\%) & alcohol (15.67\\%) & volatile acidity (10.40\\%) \\\\\n GradientBoostingTree & alcohol (26.27\\%) & sulphates (16.24\\%) & volatile acidity (11.12\\%) \\\\\n Trompt & alcohol (11.83\\%) & sulphates (10.94\\%) & total sulfur dioxide (9.78\\%) \\\\\n DOFEN (ours) & alcohol (11.16\\%) & volatile acidity (10.77\\%) & sulphates (10.17\\%) \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DOFEN: Deep Oblivious Forest ENsemble", "authors": ["Kuan-Yu Chen", "Ping-Han Chiang", "Hsin-Rung Chou", "Chih-Sheng Chen", "Tien-Hao Chang"], "url": "https://arxiv.org/abs/2412.16534v2", "attribution": "\"DOFEN: Deep Oblivious Forest ENsemble\" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09577v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The properties of the considered multi-class classification data sets. The last column denotes the proportion of non-zero elements in the data table.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\n Data set & Instances & Features & Classes & Proportion of non-zero entries \\\\\n\\midrule\n chess & 3196 & 38 & 2 & 0.726558 \\\\\n biodeg-p2-discrete & 328 & 61 & 4 & 0.107357 \\\\\n optdigits & 5620 & 62 & 10 & 0.528117 \\\\\n madelon & 2000 & 500 & 2 & 0.999999 \\\\\n php88ZB4Q & 10299 & 561 & 6 & 0.999860 \\\\\npd\\_speech\\_features & 756 & 753 & 2 & 0.995294 \\\\\n dlbcl & 77 & 7070 & 2 & 0.997388 \\\\\n tumors C & 60 & 7129 & 2 & 0.995722 \\\\\n AP\\_Ovary\\_Kidney & 458 & 10935 & 2 & 1.000000 \\\\\n ohscal.wc & 11162 & 11465 & 10 & 0.005270 \\\\\n genes & 801 & 20531 & 5 & 0.857824 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings", "authors": ["Blaž Škrlj", "Sašo Džeroski", "Nada Lavrač", "Matej Petković"], "url": "https://arxiv.org/abs/2101.09577v1", "attribution": "\"ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings\" by Blaž Škrlj, Sašo Džeroski, Nada Lavrač, and Matej Petković, arXiv:2101.09577v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08279v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overall evaluation of the approximation methods: mean values of the quality criteria for all the centerlines distorted by spatial noise addition. The cells in gray correspond to the lowest error for each metric.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|ccccc|}\n \\hline\n & GNP & GNP-AIC & GP-AIC & SRP-AIC\\\\ \n \\hline\n$\\mathrm{RMSE_{spatial}}$ & 0.511 & 0.152 & 0.099 & \\cellcolor{gray!25}0.096 \\\\ \n$\\mathrm{RMSE_{radius}}$ & 0.008 & 0.009 & 0.018 & \\cellcolor{gray!25}0.007 \\\\ \n$\\mathrm{RMSEder_{spatial}}$ & 0.314 & 0.343 & \\cellcolor{gray!25}0.075 & 0.076 \\\\ \n$\\mathrm{RMSEder_{radius}}$ & 0.015 & 0.019 & 0.021 & \\cellcolor{gray!25}0.013 \\\\\n$\\mathrm{RMSEcurv}$ & 1.524 & 2.362 & \\cellcolor{gray!25}0.085 & 0.091 \\\\ \n$\\mathrm{L_{diff}}$ & 50.180 & 15.071 & 0.252 & \\cellcolor{gray!25}0.207 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Modeling and hexahedral meshing of cerebral arterial networks from centerlines", "authors": ["Méghane Decroocq", "Carole Frindel", "Pierre Rougé", "Makoto Ohta", "Guillaume Lavoué"], "url": "https://arxiv.org/abs/2201.08279v2", "attribution": "\"Modeling and hexahedral meshing of cerebral arterial networks from centerlines\" by Méghane Decroocq, Carole Frindel, Pierre Rougé, Makoto Ohta, and Guillaume Lavoué, arXiv:2201.08279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08678v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Proportion of crypto projects with the number of vulnerabilities in each group categorized according to code maintenance activities. Column `$\\geq x$' refers to the number (proportion) of projects having at least $x$ vulnerabilities.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n ID & 0& $\\geq 1$& $\\geq 2$& $\\geq 3$& $\\geq 4$ \\\\\\hline\n 1 & 43 (16.4\\%) & 219 (83.6\\%) & 135 (51.5\\%) & 93 (35.5\\%) & 17 (6.5\\%) \\\\\\hline\n 2 & 60 (23.4\\%) & 196 (76.6\\%) & 96 (37.5\\%) & 45 (17.6\\%) & 8 (3.1\\%) \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild", "authors": ["Jusop Choi", "Wonseok Choi", "William Aiken", "Hyoungshick Kim", "Jun Ho Huh", "Taesoo Kim", "Yongdae Kim", "Ross Anderson"], "url": "https://arxiv.org/abs/2201.08678v1", "attribution": "\"Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild\" by Jusop Choi, Wonseok Choi, William Aiken, Hyoungshick Kim, Jun Ho Huh, Taesoo Kim, Yongdae Kim, and Ross Anderson, arXiv:2201.08678v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16929v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\textbf{Method} & \\multicolumn{2}{c}{\\textbf{CIFAR10 (in distribution)}} & \\multicolumn{2}{c}{\\textbf{CIFAR100 (out of distribution)}} \\\\ \\cline{2-5}\n & Linear Probing & KNN & Linear Probing & KNN \\\\ \\midrule\n\\multicolumn{5}{c}{$q_\\phi$ is a Gaussian Distribution} \\\\ \\midrule\nSimCLR & 77.79 & 80.02 & 31.82 & 40.27 \\\\ \nDCL & 78.32 & 83.11 & 32.44 & 42.10 \\\\ \n\\textbf{Our Debiasing $\\alpha = 0.2$} & 79.50 & 84.07 & 32.53 & 43.19 \\\\ \n\\textbf{Our Debiasing $\\alpha = 0.4$} & 79.07 & 85.06 & 32.53 & \\textbf{43.29} \\\\ \n\\textbf{Our Debiasing $\\alpha = 0.6$} & 79.32 & 85.90 & 30.67 & 29.79 \\\\ \\midrule\n\\multicolumn{5}{c}{$q_\\phi$ is a Student's t-distribution} \\\\ \\midrule\nt-SimCLR & 90.97 & 88.14 & 38.96 & 30.75 \\\\\nDCL & Diverges & Diverges & Diverges & Diverges \\\\\n\\textbf{Our Debiasing $\\alpha = 0.2$} & 91.31 & 88.34 & 41.62 & 32.88 \\\\ \n\\textbf{Our Debiasing $\\alpha = 0.4$} & 92.70 & 88.50 & \\textbf{41.98} & 34.26 \\\\ \n\\textbf{Our Debiasing $\\alpha = 0.6$} & \\textbf{92.86} & \\textbf{88.92} & 38.92 & 32.51 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{{ Contrastive feature learning evaluation results for CIFAR10 and CIFAR100 datasets with various debasing $\\alpha$ factors. Adding some amount of debasing helps raising accuracy in both linear probing and KNN classification.}}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "I-Con: A Unifying Framework for Representation Learning", "authors": ["Shaden Alshammari", "John Hershey", "Axel Feldmann", "William T. Freeman", "Mark Hamilton"], "url": "https://arxiv.org/abs/2504.16929v1", "attribution": "\"I-Con: A Unifying Framework for Representation Learning\" by Shaden Alshammari, John Hershey, Axel Feldmann, William T. Freeman, and Mark Hamilton, arXiv:2504.16929v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.06219v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Data availability by SDG region}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\\toprule\nSDG region & Observations & Countries & Country-years\\\\\n\\midrule\nCentral and Southern Asia & 163 & 7 & 65\\\\\nEastern and South-Eastern Asia & 57 & 8 & 53\\\\\nLatin America and the Caribbean & 272 & 13 & 171\\\\\nNorth America, Europe, Australia and New Zealand & 460 & 30 & 368\\\\\nNorthern Africa and Western Asia & 43 & 8 & 42\\\\\nOceania (exc. Australia and New Zealand) & 1 & 1 & 1\\\\\nSub-Saharan Africa & 280 & 25 & 158\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Estimating the timing of stillbirths in countries worldwide using a Bayesian hierarchical penalized splines regression model", "authors": ["Michael Y. C. Chong", "Monica Alexander"], "url": "https://arxiv.org/abs/2212.06219v1", "attribution": "\"Estimating the timing of stillbirths in countries worldwide using a Bayesian hierarchical penalized splines regression model\" by Michael Y. C. Chong and Monica Alexander, arXiv:2212.06219v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.15943v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|ccc|ccc|ccc|ccc}\n\\hline\n\\multirow{2}{*}{$\\beta_k$} & \\multirow{2}{*}{$B_k$} & \\multicolumn{3}{c|}{$\\sigma^2=10^{-8}$} & \\multicolumn{3}{c|}{$\\sigma^2=10^{-4}$} & \\multicolumn{3}{c|}{$\\sigma^2=10^{-2}$} & \\multicolumn{3}{c}{$\\sigma^2=10^{-1}$} \n \\\\ \\cline{3-14} \n& & \\small Case 1 & \\small Case 2 & \\small Case 3 & \\small Case 1 & \\small Case 2 & \\small Case 3 & \\small Case 1 & \\small Case 2 & \\small Case 3 & \\small Case 1 & \\small Case 2 & \\small Case 3 \\\\ \\hline\n\\multirow{4}{*}{0.5} & Id & 90.3 & 0.1 & 9.6 & 91.3 & 0.2 & 8.5 & 95.0 & 0.1 & 4.9 & 54.7 & 0.9 & \\textbf{44.4} \\\\\n& SR1 & 93.8 & 0.1 & 6.1 & 92.7 & 0.1 & 7.2 & 94.6 & 0.1 & 5.7 & 56.2 & 1.1 & \\textbf{42.7} \\\\\n& EstH\t& 92.2 & 0.1 & 7.7 & 94.8 & 0.1 & 5.1 & 84.8 & 0.2 & 15.0 & 71.1 & 0.5 & \\textbf{28.4} \\\\\n& AveH & 92.5 & 0.1 & 7.4 & 94.1 & 0.1 & 5.8 & 88.2 & 0.2 & 11.6 & 64.2 & 0.4 & \\textbf{35.4} \\\\ \\hline\n\\multirow{4}{*}{1.0} & Id & 92.0 & 0.1 & 7.9 & 93.7 & 0.1 & 6.2 & 95.4 & 0.2 & 4.4 & 57.1 & 1.2 & \\textbf{41.7}\\\\\n& SR1 & 94.0 & 0.2 & 5.8 & 96.1 & 0.1 & 3.8 & 97.7 & 0.2 & 2.1 & 64.2 & 1.2 & \\textbf{34.6} \\\\\n& EstH & 92.4 & 0.1 & 7.5 & 93.8 & 0.1 & 6.1 & 87.5 & 0.4 & 12.1 & 72.8 & 0.5 & \\textbf{26.7} \\\\\n& AveH & 92.4 & 0.2 & 7.4 & 93.9 & 0.3 & 5.8 & 85.5 & 0.3 & 14.2 & 67.1 & 0.6 & \\textbf{32.3} \\\\ \\hline\n\\multirow{4}{*}{$k^{-0.6}$} & Id & 97.2 & 0.0 & 2.8 & 96.8 & 0.0 & 3.2 & 93.4 & 0.0 & 6.6 & 51.8 & 0.0 & \\textbf{48.2} \\\\\n& SR1 & 98.3 & 0.0 & 1.7 & 97.1 & 0.0 & 2.9 & 93.2 & 0.0 & 6.8 & 51.5 & 0.0 & \\textbf{48.5} \\\\\n& EstH\t& 97.9 & 0.0 & 2.1 & 95.8 & 0.0 & 4.2 & 86.6 & 0.0 & 13.4 & 69.1 & 0.0 & \\textbf{30.9} \\\\\n& AveH & 97.4 & 0.0 & 2.6 & 96.1 & 0.0 & 3.9 & 86.8 & 0.0 & 13.2 & 65.5 & 0.0 & \\textbf{34.8} \\\\ \\hline\n\\multirow{4}{*}{$k^{-0.8}$} & Id & 70.6 & 0.0 & \\textbf{29.4} & 68.1 & 0.0 & \\textbf{31.9} & 66.4 & 0.0 & \\textbf{33.6} & 45.8 & 0.0 & \\textbf{54.2} \\\\\n& SR1 & 56.1 & 0.0 & \\textbf{43.9} & 65.7 & 0.0 & \\textbf{34.3} & 66.6 & 0.0 & \\textbf{33.4} & 39.9 & 0.0 & \\textbf{60.1} \\\\\n& EstH & 67.5 & 0.0 & \\textbf{32.5} & 65.2 & 0.0 & \\textbf{34.8} & 62.0 & 0.0 & \\textbf{38.0} & 54.7 & 0.0 & \\textbf{45.3} \\\\\n& AveH & 67.9 & 0.0 & \\textbf{32.1} & 66.7 & 0.0 & \\textbf{33.3} & 65.9 & 0.0 & \\textbf{34.1} & 51.4 & 0.0 & \\textbf{48.6} \\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Proportions of the three cases in (\\%). We highlight the proportion of Case~3~if the value is higher than 25\\%.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems", "authors": ["Yuchen Fang", "Sen Na", "Michael W. Mahoney", "Mladen Kolar"], "url": "https://arxiv.org/abs/2211.15943v2", "attribution": "\"Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems\" by Yuchen Fang, Sen Na, Michael W. Mahoney, and Mladen Kolar, arXiv:2211.15943v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.20619v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\textbf{Nomenclature:}\\\\\n Sets:\\\\\n $h$ & Time periods (usually hours)\\\\\n $i$ & Bus of distribution grid\\\\\n $g$ & Generation units\\\\\n $pv(g)$ & Subset of photovoltaic renewable generation units\\\\\n $gi(g,i)$ & Generator g connected to node i\\\\\n \\\\\n Parameters:\\\\\n $FL$ & Feed-in power limitation; maximum feed-in depending on installed power (\\%)\\\\\n $D_{h,i}^P$ & Active power demand (MW)\\\\\n $\\overline{P}_g$ & Technical maximum power of unit (MW)\\\\\n $CF_{h,i,g}$ & Renewable profile per technology and node; Capacity factor (p.u.)\\\\\n $C^{Imp}$ & Cost of imports (€/MWh)\\\\\n $C^{Exp}$ & Cost of exports (€/MWh)\\\\\n $C^{PNS/QNS}$ & Cost of power non-served (€/MWh)/(€/MVArh)\\\\\n $C^{EPS/EQS}$ & Cost of excess power served (€/MWh)/(€/MVArh)\\\\\n $M$ & Big positive constant for activating or deactivating constraints\\\\\n \\\\\n Variables:\\\\\n $p_{h,g}$ & Active power generation of the unit (MW)\\\\\n $sp_{h,g}$ & Curtailment of renewable generation units (MW)\\\\\n $p_{h}^{Imp/Exp}$ & Active power import/export (MW)\\\\\n $q_{h}^{Imp/Exp}$ & Reactive power import/export (MVAr)\\\\\n $\\alpha_{h,i}$ & Allows curtailment \\{0,1\\}\\\\\n $scal$ & Scaling variable for all scalable PV systems (positive continuous)\\\\\n $pns_{h,i} / qns_{h,i}$ & Active / reactive power non-served (MW / MVAr)\\\\\n $eps_{h,i} / eqs_{h,i}$ & Excess active / reactive power served (MW / MVAr)\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Implementing Dynamic Power Feed-In Limitations of Photovoltaic Systems in Distribution Grids for Generation Expansion Planning", "authors": ["Alexander Konrad", "Robert Gaugl", "Christoph Maier", "Sonja Wogrin"], "url": "https://arxiv.org/abs/2503.20619v1", "attribution": "\"Implementing Dynamic Power Feed-In Limitations of Photovoltaic Systems in Distribution Grids for Generation Expansion Planning\" by Alexander Konrad, Robert Gaugl, Christoph Maier, and Sonja Wogrin, arXiv:2503.20619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.15726v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Our new Whisper Large-v2 based SOTA model, fine-tuned on long-form audio created with data generation method 'All' and supplemented with SRG (PL) and Common Voice 13 de, compared to the original Whisper Large-v2}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrr}\n \\toprule\n \\textbf{Test Dataset} & \\textbf{Model} & \\textbf{WER} & \\textbf{SubER} & \\textbf{BLEU} \\\\\n \\midrule\n \\multirow{2}{*}{SPC} & Our SOTA & \\textbf{20.98} & - & \\textbf{68.34} \\\\\n & Large-v2 & 28.21 & - & 58.08 \\\\\n \\midrule\n \\multirow{2}{*}{SDS-200}& Our SOTA & \\textbf{16.70} & - & \\textbf{72.69} \\\\\n & Large-v2 & 27.92 & - & 57.00 \\\\\n \\midrule\n \\multirow{2}{*}{STT4SG-350} & Our SOTA & \\textbf{12.11} & - & \\textbf{78.08} \\\\\n & Large-v2 & 22.41 & - & 64.13 \\\\\n \\midrule\n \\multirow{2}{*}{SRG (SW-TXT)} & Our SOTA & \\textbf{26.31} & \\textbf{29.76} & \\textbf{64.67} \\\\\n & Large-v2 & 28.42 & 30.63 & 63.61 \\\\\n \\midrule\n \\multirow{2}{*}{Dataset-A} & Our SOTA & \\textbf{34.50} & \\textbf{35.31} & \\textbf{51.40} \\\\\n & Large-v2 & 38.00 & 39.12 & 48.89 \\\\\n \\midrule\n \\multirow{2}{*}{CV13 de}& Our SOTA & \\textbf{6.42} & - & - \\\\\n & Large-v2 & 6.53 & - & - \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fine-tuning Whisper on Low-Resource Languages for Real-World Applications", "authors": ["Vincenzo Timmel", "Claudio Paonessa", "Reza Kakooee", "Manfred Vogel", "Daniel Perruchoud"], "url": "https://arxiv.org/abs/2412.15726v3", "attribution": "\"Fine-tuning Whisper on Low-Resource Languages for Real-World Applications\" by Vincenzo Timmel, Claudio Paonessa, Reza Kakooee, Manfred Vogel, and Daniel Perruchoud, arXiv:2412.15726v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07033v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\n\\multicolumn{3}{|c|}{Setting} & A &B & C & D \\\\ \\hline\n\\multicolumn{2}{|c|}{Algorithm} & Feat. Sel. & \\multicolumn{4}{c|}{Mean F1 score in 10 DS1 instances} \\\\ \\hline\n\\multicolumn{2}{|c|}{PLS} & ReliefF & \\textbf{0.653} & 0.349 & \\textbf{0.654} & \\textbf{0.400} \\\\ \\hline\n\\multicolumn{3}{|c|}{Undersampling} & 0.629 & \\textbf{0.378} & 0.653 & 0.339 \\\\ \\hline\n\\multicolumn{3}{|c|}{Oversampling} & 0.625 & 0.364 & 0.606 & 0.380 \\\\ \\hline \\hline\n\\multicolumn{3}{|c|}{} & \\multicolumn{4}{c|}{Mean F1 score in 10 DS2 instances} \\\\ \\hline\n\\multicolumn{2}{|c|}{KNN} & ReliefF & 0.634 & 0.363 & 0.638 & 0.352 \\\\ \\hline\n\\multicolumn{3}{|c|}{Undersampling} & \\textbf{0.658} & \\textbf{0.393} & 0.628 & 0.352 \\\\ \\hline\n\\multicolumn{3}{|c|}{Oversampling} & 0.640 & 0.381 & \\textbf{0.650} & \\textbf{0.357} \\\\ \\hline \\hline\n\\multicolumn{3}{|c|}{} & \\multicolumn{4}{c|}{Mean F1 score in 10 DS3 instances} \\\\ \\hline\n\\multicolumn{2}{|c|}{XGB} & ReliefF & \\textbf{0.653} & 0.349 & \\textbf{0.654} & \\textbf{0.400} \\\\ \\hline\n\\multicolumn{3}{|c|}{Undersampling} & 0.629 & \\textbf{0.378} & 0.635 & 0.339 \\\\ \\hline\n\\multicolumn{3}{|c|}{Oversampling} & 0.625 & 0.364 & 0.606 & 0.380 \\\\ \\hline \\hline\n\\multicolumn{3}{|c|}{} & \\multicolumn{4}{c|}{Mean F1 score in 10 DS4 instances} \\\\ \\hline\n\\multicolumn{2}{|c|}{KNN} & PCA & \\textbf{0.628} & \\textbf{0.368} & \\textbf{0.660} & 0.343 \\\\ \\hline\n\\multicolumn{3}{|c|}{Undersampling} & 0.589 & 0.362 & 0.588 & 0.346 \\\\ \\hline\n\\multicolumn{3}{|c|}{Oversampling} & 0.617 & 0.355 & 0.649 & \\textbf{0.364} \\\\ \\hline \\hline\n\\multicolumn{3}{|c|}{} & \\multicolumn{4}{c|}{Mean F1 score in 10 DS5 instances} \\\\ \\hline\n\\multicolumn{2}{|c|}{KNN} & ReliefF & 0.621 & 0.351 & \\textbf{0.617} & 0.340 \\\\ \\hline\n\\multicolumn{3}{|c|}{Undersampling} & \\textbf{0.639} & \\textbf{0.386} & 0.611 & 0.363 \\\\ \\hline\n\\multicolumn{3}{|c|}{Oversampling} & 0.625 & 0.383 & 0.611 & \\textbf{0.369} \\\\ \\hline \\hline\n\\multicolumn{3}{|c|}{} & \\multicolumn{4}{c|}{Mean F1 score in 10 DS6 instances} \\\\ \\hline\n\\multicolumn{2}{|c|}{PLS} & ReliefF & \\textbf{0.500} & 0.344 & \\textbf{0.501} & 0.233 \\\\ \\hline\n\\multicolumn{3}{|c|}{Undersampling} & 0.482 & \\textbf{0.367} & 0.435 & \\textbf{0.318} \\\\ \\hline\n\\multicolumn{3}{|c|}{Oversampling} & 0.489 & 0.163 & 0.497 & 0.211 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results for regression predictors after balancing the training dataset.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Analysis of key flavors of event-driven predictive maintenance using logs of phenomena described by Weibull distributions", "authors": ["Petros Petsinis", "Athanasios Naskos", "Anastasios Gounaris"], "url": "https://arxiv.org/abs/2101.07033v1", "attribution": "\"Analysis of key flavors of event-driven predictive maintenance using logs of phenomena described by Weibull distributions\" by Petros Petsinis, Athanasios Naskos, and Anastasios Gounaris, arXiv:2101.07033v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.05488v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparative results of image restoration tasks on ImageNet for lookup-table noise level correction. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c}\n\\hline\nImageNet & {4 x SR} & {Deblurring} & {Colorization} & {CS 25\\%} & {Inpainting} \\\\\nMethod & PSNR$\\uparrow$/SSIM$\\uparrow$/FID$\\downarrow$ & PSNR$\\uparrow$/SSIM$\\uparrow$/FID$\\downarrow$ & Cons$\\downarrow$/FID$\\downarrow$ & PSNR$\\uparrow$/SSIM$\\uparrow$/FID$\\downarrow$ & PSNR$\\uparrow$/SSIM$\\uparrow$/FID$\\downarrow$ \\\\ \\hline\nDDNM & 27.45 / 0.870/ 39.56 & 44.93 / 0.993 / 1.17 & 42.32 / 36.32 & 21.62 / 0.748 / 64.68 & 31.60 / 0.946 / 9.79 \\\\ \nDDNM-LT-NLC & 27.47 / 0.870/ 39.03 & 45.14 / 0.993 / 1.02 & 42.12 / 36.07 & 21.48 / 0.751 / 62.64 & 31.84 / 0.951 / 9.19 \\\\ \\hline\n\\textbf{DDNM-NLC} & 27.50 / 0.872 / 37.82 & 46.20 / 0.995 / 0.79 & 41.60 / 35.89 & 21.27 / 0.769 / 58.96 & 32.51 / 0.957 / 7.20 \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Enhancing Sample Generation of Diffusion Models using Noise Level Correction", "authors": ["Abulikemu Abuduweili", "Chenyang Yuan", "Changliu Liu", "Frank Permenter"], "url": "https://arxiv.org/abs/2412.05488v3", "attribution": "\"Enhancing Sample Generation of Diffusion Models using Noise Level Correction\" by Abulikemu Abuduweili, Chenyang Yuan, Changliu Liu, and Frank Permenter, arXiv:2412.05488v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12670v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\textbf{Name} & \\textbf{Average} & \\textbf{\\# Connected} & \\textbf{Density} & \\textbf{Average} \\\\\n & \\textbf{Degree} & \\textbf{Components} & & \\textbf{Clustering} \\\\\n & & & & \\textbf{Coefficient} \\\\\n \\hline\n {Cora} & 3.8980 & 78 & $1.4\\times10^{-3}$ & 0.2406 \\\\\n {CiteSeer} & 2.7363 & 438 & $8\\times10^{-4}$ & 0.1414 \\\\\n {PubMed} & 4.4960 & 1 & $2\\times10^{-4}$ & 0.0601 \\\\\n {DBLP} & 5.9682 & 589 & $3\\times10^{-4}$ & 0.1344 \\\\\n {ogbn-arxiv} & 13.6740 & 1 & $8\\times10^{-5}$ & 0.2261 \\\\\n {ogbn-products}& 50.5171 & 52,658 & $2\\times10^{-5}$ & x \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Dataset statistics}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "SMGRL: Scalable Multi-resolution Graph Representation Learning", "authors": ["Reza Namazi", "Elahe Ghalebi", "Sinead Williamson", "Hamidreza Mahyar"], "url": "https://arxiv.org/abs/2201.12670v3", "attribution": "\"SMGRL: Scalable Multi-resolution Graph Representation Learning\" by Reza Namazi, Elahe Ghalebi, Sinead Williamson, and Hamidreza Mahyar, arXiv:2201.12670v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.01937v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\t\t\\toprule \n\t\tVariant && Name && Spike distribution && Slab distribution \\\\ \n\t\t\\midrule \n\t\t1 && CSS && Independent Student's-$t$ && Independent Student's-$t$\\\\\n\t\t2 && DSS-i && Dirac-delta && Independent Student's-$t$ \\\\\n\t\t3 && DSS-g && Dirac-delta && Correlated Student's-$t$ \\\\ \n\t\t\\bottomrule \n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Variants of spike-and-slab priors considered in this study.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On spike-and-slab priors for Bayesian equation discovery of nonlinear dynamical systems via sparse linear regression", "authors": ["Rajdip Nayek", "Ramon Fuentes", "Keith Worden", "Elizabeth J. Cross"], "url": "https://arxiv.org/abs/2012.01937v2", "attribution": "\"On spike-and-slab priors for Bayesian equation discovery of nonlinear dynamical systems via sparse linear regression\" by Rajdip Nayek, Ramon Fuentes, Keith Worden, and Elizabeth J. Cross, arXiv:2012.01937v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12587v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Example 1B. A comparison of $L^2$-error by SAV/BDF2, R-SAV/BDF2, GSAV/BDF2 and R-GSAV/BDF2 schemes for Allen-Cahn equation at $T=200$ with different time step.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllll}\n\t\t\\hline\n\t\t & SAV & R-SAV & GSAV & R-GSAV \\\\\n\t\t\\hline\n\t1e-1 & 4.30E-04 & 2.72E-04 & 1.27E-03 & 2.65E-04 \\\\\n\t1e-2 & 4.53E-05 & 2.93E-06 & 1.47E-04 & 2.90E-06 \\\\\n\t1e-3 & 6.26E-07 & 2.96E-08 & 2.38E-06 & 2.94E-08 \\\\\n\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A generalized SAV approach with relaxation for dissipative systems", "authors": ["Yanrong Zhang", "Jie Shen"], "url": "https://arxiv.org/abs/2201.12587v1", "attribution": "\"A generalized SAV approach with relaxation for dissipative systems\" by Yanrong Zhang and Jie Shen, arXiv:2201.12587v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.18960v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|cccccc}\n\\toprule\n & & Mean & Variance & Kurtosis & Skewness \\\\\n\\midrule\n(a) &\\textbf{DAX} &$2.2(2.1) \\times 10^{-4}$&$2.2(3) \\times 10^{-4}$& 8.3(1.6) & -0.28(14) \\\\\n&\\textbf{Nikkei 225} &$0.9(1.8)\\times 10^{-4}$ &$2.2(3)\\times 10^{-4}$&8.6(2.3)& -0.3(1) \\\\\n&\\textbf{SSE} &$1.3(2.5)\\times 10^{-4}$ &$2.2(4)\\times 10^{-4}$ &$8.3(9)$ & $-0.35(21)$ \\\\\n&\\textbf{VIX} &$-0.6(2.7)\\times 10^{-4}$ & $4.8(5)\\times 10^{-3}$ &9(2) & 0.96(20) \\\\\n&\\textbf{Bitcoin} & $1.7(1.0) \\times 10^{-3}$ &$1.4(2)\\times 10^{-3}$& 16(10) & -0.80(75) \\\\\n&\\textbf{Ethereum} & $1.6(1.3)\\times 10^{-3}$ &$2.6(4)\\times 10^{-3}$&$12.5(5.6)$& $-0.57(63)$ \\\\\n\\midrule\n(b)&\\textbf{DAX} & $1.03(8)\\times 10^{-2}$ & $1.1(2)\\times 10^{-4} $&14(3) & 2.6(3) \\\\\n&\\textbf{Nikkei 225} & $1.05(7)\\times 10^{-2}$ & $1.1(2)\\times 10^{-4} $&17(6) & 2.7(7) \\\\\n&\\textbf{SSE} & $1.02(9)\\times 10^{-2}$ & $1.2(2)\\times 10^{-4} $&13(1) & 2.6(1) \\\\\n&\\textbf{VIX} & $4.8(2)\\times 10^{-2}$ & $2.2(3)\\times 10^{-3} $&20(6) & 2.8(4) \\\\\n&\\textbf{Bitcoin} & $2.4(3)\\times 10^{-2}$ & $7.9(1.9)\\times 10^{-4}$&33(24)& 3.4(1.2) \\\\\n&\\textbf{Ethereum} & $3.4(3)\\times 10^{-2}$ & $1.44(23)\\times 10^{-4}$&24(14)& $3.1(7)$ \\\\\n\\midrule\n(c)&\\textbf{DAX} &$-0.05(6.82)\\times 10^{-4}$&2.6(1)&4.36(18)& $2.7(3.2)\\times 10^{-2}$ \\\\\n&\\textbf{Nikkei 225} &$6.4(7.2)\\times 10^{-4}$&2.70(7)&4.6(1)& $-3.9(4.5)\\times 10^{-2}$ \\\\\n&\\textbf{SSE} & $-4.5(7.3)\\times 10^{-4}$ &2.95(11) &4.4(3) & $-0.6(2.6)\\times 10^{-2}$ \\\\\n&\\textbf{VIX} &$1.0(2.1)\\times 10^{-3}$ &$2.25(7)$ &3.9(1) & $2.6(2.7)\\times 10^{-2}$ \\\\\n&\\textbf{Bitcoin} & $0.07(1.86) \\times 10^{-3}$& 2.86(7) &4.2(3)& $3.1(4.0) \\times 10^{-2}$ \\\\\n&\\textbf{Ethereum} &$-0.6(1.7)\\times 10^{-3}$&2.6(1)&3.58(17)& $-1.2(5.8)\\times 10^{-2}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Impact of the COVID-19 pandemic on the financial market efficiency of price returns, absolute returns, and volatility increment: Evidence from stock and cryptocurrency markets", "authors": ["Tetsuya Takaishi"], "url": "https://arxiv.org/abs/2504.18960v1", "attribution": "\"Impact of the COVID-19 pandemic on the financial market efficiency of price returns, absolute returns, and volatility increment: Evidence from stock and cryptocurrency markets\" by Tetsuya Takaishi, arXiv:2504.18960v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.08096v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrr}\n\\toprule\nHyperparameter ($\\alpha$) & 0.0 & 0.1 & 0.25 & 0.5 & 0.75 & 0.9 & 1.0 \\\\\n\\midrule\n\\textit{Panel A: Calls} & & & & & & & \\\\\nCall (1--9 DTE) & 4.21\\% & 1.58\\% & 1.56\\% & 1.52\\% & 1.49\\% & 1.47\\% & 1.48\\% \\\\\nCall (10--30 DTE) & 4.65\\% & 0.68\\% & 0.66\\% & 0.62\\% & 0.59\\% & 0.57\\% & 0.57\\% \\\\\nCall (30+ DTE) & 6.13\\% & 1.04\\% & 0.96\\% & 0.88\\% & 0.83\\% & 0.79\\% & 0.74\\% \\\\\n\\midrule\n\\textit{Panel B: Puts} & & & & & & & \\\\\nPut (1--9 DTE) & 5.57\\% & 1.67\\% & 1.62\\% & 1.58\\% & 1.55\\% & 1.54\\% & 1.53\\% \\\\\nPut (10--30 DTE) & 5.06\\% & 0.82\\% & 0.75\\% & 0.71\\% & 0.67\\% & 0.66\\% & 0.65\\% \\\\\nPut (30+ DTE) & 5.06\\% & 0.75\\% & 0.72\\% & 0.68\\% & 0.65\\% & 0.62\\% & 0.58\\% \\\\\n\\midrule \n\\textit{Panel C: VIX term structure} & & & & & & & \\\\\n1--9 DTE & 1.17\\% & 1.18\\% & 1.21\\% & 1.27\\% & 1.37\\% & 1.53\\% & 3.98\\% \\\\\n10--30 DTE & 0.45\\% & 0.46\\% & 0.47\\% & 0.51\\% & 0.60\\% & 0.77\\% & 3.14\\% \\\\\n30+ DTE & 0.30\\% & 0.31\\% & 0.32\\% & 0.36\\% & 0.42\\% & 0.50\\% & 1.85\\% \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean absolute errors of in model implied volatilities for OTM options (panel A and B) and VIX term structure (panel C) across maturities. Maturities/option maturities used in replication given in round parentheses. }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Joint calibration of the volatility surface and variance term structure", "authors": ["Jiwook Yoo"], "url": "https://arxiv.org/abs/2509.08096v1", "attribution": "\"Joint calibration of the volatility surface and variance term structure\" by Jiwook Yoo, arXiv:2509.08096v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04898v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Detailed class mapping from ImageNet classes to CIFAR-10 classes. This mapping is used for the second transfer experiment in Section .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc} \\hline\n\\textbf{CIFAR-10 Class} & \\textbf{ImageNet Class} \\\\ \\hline\nAirplane & Airliner \\\\\nCar & Wagon \\\\\nBird & Humming Bird \\\\\nCat & Siamese Cat \\\\\nDeer & Ox \\\\\nDog & Golden Retriever \\\\\nFrog & Tailed Frog \\\\\nHorse & Zebra \\\\\nShip & Container Ship \\\\\nTruck & Trailer Truck \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Unlearnable Examples: Making Personal Data Unexploitable", "authors": ["Hanxun Huang", "Xingjun Ma", "Sarah Monazam Erfani", "James Bailey", "Yisen Wang"], "url": "https://arxiv.org/abs/2101.04898v2", "attribution": "\"Unlearnable Examples: Making Personal Data Unexploitable\" by Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, and Yisen Wang, arXiv:2101.04898v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.18421v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lrlr}\n \\toprule\n & & & PubMedQA \\\\\n Model & Params & Method & Test Accuracy \\\\\n \\midrule\n BioGPT (w/extra data) & 1.5B & fine-tune & 81.0 \\\\ \n Flan-PaLM & 540B & few-shot & 79.0 \\\\\n Galactica & 120B & zero-shot & 77.6 \\\\\n GPT-4 & - & zero-shot & 75.20 \\\\\n BioMedLM & 2.7B & fine-tune & \\textbf{74.4} \\\\\n DRAGON & 360M & fine-tune & 73.4 \\\\\n BioLinkBERT & 340M & fine-tune & 72.4 \\\\\n GPT-Neo 2.7B & 2.7B & fine-tune & 66.1 \\\\\n \\bottomrule\n \\end{tabular}\n\\caption{PubMedQA Performance of Various Systems}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text", "authors": ["Elliot Bolton", "Abhinav Venigalla", "Michihiro Yasunaga", "David Hall", "Betty Xiong", "Tony Lee", "Roxana Daneshjou", "Jonathan Frankle", "Percy Liang", "Michael Carbin", "Christopher D. Manning"], "url": "https://arxiv.org/abs/2403.18421v1", "attribution": "\"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text\" by Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall, Betty Xiong, Tony Lee, Roxana Daneshjou, Jonathan Frankle, Percy Liang, Michael Carbin, and Christopher D. Manning, arXiv:2403.18421v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.10852v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative Evaluation of the lip sync model. Directly using the pre-trained Wav2Lip model or fine-tuning it on noisy audios leads to poor results on noisy speech. Our student network leads to more clear audio-visual correspondence as denoted by LSE-C.}\n\\begin{tabular}{lccc}\n \\hline\n \\textbf{Model} & LSE-C$\\uparrow$ & LSE-D$\\downarrow$\\\\\n \\hline\n Wav2Lip trained on noisy data & 1.181 & 10.35\\\\\n Pre-trained Wav2Lip & 1.330 & \\textbf{8.933} \\\\\n \\textbf{Lip-sync student (ours)} & \\textbf{4.572} & 9.743\\\\\n \n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Visual Speech Enhancement Without A Real Visual Stream", "authors": ["Sindhu B Hegde", "K R Prajwal", "Rudrabha Mukhopadhyay", "Vinay Namboodiri", "C. V. Jawahar"], "url": "https://arxiv.org/abs/2012.10852v1", "attribution": "\"Visual Speech Enhancement Without A Real Visual Stream\" by Sindhu B Hegde, K R Prajwal, Rudrabha Mukhopadhyay, Vinay Namboodiri, and C. V. Jawahar, arXiv:2012.10852v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17533v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Summary of unification results}, among all validated formulas (left columns) and among the canonical forms (right columns). Formulas are harvested from 154 arXiv papers (), of which 151/154 (98\\%) had at least one formula proved connected by UMAPS and 84/154 (55\\%) have a formula residing in the same CMF.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Found relation & Same CMF & Found relation (canonical) & Same CMF (canonical) \\\\\n 381/407 (94\\%) & 188/407 (46\\%) & 136/153 (89\\%) & 81/153 (53\\%) \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "From Euler to AI: Unifying Formulas for Mathematical Constants", "authors": ["Tomer Raz", "Michael Shalyt", "Elyasheev Leibtag", "Rotem Kalisch", "Shachar Weinbaum", "Yaron Hadad", "Ido Kaminer"], "url": "https://arxiv.org/abs/2502.17533v2", "attribution": "\"From Euler to AI: Unifying Formulas for Mathematical Constants\" by Tomer Raz, Michael Shalyt, Elyasheev Leibtag, Rotem Kalisch, Shachar Weinbaum, Yaron Hadad, and Ido Kaminer, arXiv:2502.17533v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.06425v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Predicted vs. Empirical Sup-norms and KS Test $p$-values (DR vs. RS)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rcccccccc}\n\\hline\n & \\multicolumn{2}{c}{Linear} & & \\multicolumn{2}{c}{Exponential} & & \\multicolumn{2}{c}{Constant} \\\\ \\cline{2-3} \\cline{5-6} \\cline{8-9} \n & $c=0.5$ & $c=0.8$ & & $c=2.5$ & $c=4$ & & $c=0.4$ & $c=0.8$ \\\\\n & (small) & (large) & & (small) & (large) & & (small) & (large) \\\\ \\hline\n\\multicolumn{9}{l}{\\textit{Sup-norm} (\\emph{Direct Response vs. Reduced Strategy Methods})} \\\\\nPredicted & 0.128 & 0.251 & & 0.188 & 0.251 & & 0.072 & 0.460 \\\\\nEmpirical & 0.094 & 0.104 & & 0.052 & 0.052 & & 0.073 & 0.156 \\\\ \\hline\n\\multicolumn{9}{l}{\\textit{Kolmogorov-Smirnov Test $p$-value}} \\\\\n\\;\\;\\;\\;Predicted & 0.413 & \\phantom{$^\\dagger$}0.005$^\\dagger$ & & 0.068 & \\phantom{$^\\dagger$}0.005$^\\dagger$ & & 0.966 & \\phantom{$^\\dagger$}0.000$^\\dagger$\\\\\nEmpirical & 0.796 & 0.678 & & 1.000 & 1.000 & & 0.962 & 0.192 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Strategy Method Effects in Centipede Games: An Optimal Design Approach", "authors": ["Shiang-Hung Hu", "Po-Hsuan Lin", "Thomas R. Palfrey", "Joseph Tao-yi Wang", "Yu-Hsiang Wang"], "url": "https://arxiv.org/abs/2508.06425v1", "attribution": "\"Strategy Method Effects in Centipede Games: An Optimal Design Approach\" by Shiang-Hung Hu, Po-Hsuan Lin, Thomas R. Palfrey, Joseph Tao-yi Wang, and Yu-Hsiang Wang, arXiv:2508.06425v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.15031v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average EH Efficiency for MU ($K=3$) Scenario}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n Algorithm & TS & HERA \\\\ \\hline\nExhaustive search & 63.7\\% & 84.8\\% \\\\ \\hline\nProposed & 58.4\\% & 73.2\\% \\\\ \\hline\nTD3 & 47.2\\% & 68.4\\% \\\\ \\hline\nDDPG & 60.0\\% & 37.2\\% \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Energy-Efficient Irregular RIS-aided UAV-Assisted Optimization: A Deep Reinforcement Learning Approach", "authors": ["Mahmoud M. Salim", "Khaled M. Rabie", "Ali H. Muqaibel"], "url": "https://arxiv.org/abs/2504.15031v1", "attribution": "\"Energy-Efficient Irregular RIS-aided UAV-Assisted Optimization: A Deep Reinforcement Learning Approach\" by Mahmoud M. Salim, Khaled M. Rabie, and Ali H. Muqaibel, arXiv:2504.15031v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.11844v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\t \\toprule\n Indicator & Formula \\\\\n \\midrule\n A/D & $\\frac{H_t-C_{t-1}}{H_t-L_t}$\\\\\n CCI & $\\frac{M_t-SM_t}{0.015D_t}$\\\\\n LWR & $\\frac{H_n-C_t}{H_n-L_n}\\times 100$\\\\\n MACD & $MACD(n)_{t-1}+2/n+1\\times(DF_t-MACD(n)_{t-1})$\\\\\n MOM & $C_t-C_{t-n}$\\\\\n RSI & $100-\\frac{100}{1+(\\sum^{n-1}_{i=0}Up_{t-i}/n)/(\\sum^{n-1}_{i=0}Dw_{t-i}/n)}$ \\\\\n SMA & $\\frac{C_t+C_{t-1}+...+C_{t-n+1}}{n}$\\\\\n SD & $\\frac{\\sum^{n-1}_{i=0}K_{t-i}\\%}{n}$\\\\\n SK & $\\frac{C_t-LL_{t-n}}{HH_{t-n}-LL_{t-n}}\\times 100$\\\\\n WMA & $\\frac{n \\times C_t +(n-1) \\times C_{t-1}+...+C_{t-n+1}}{n+(n-1)+...+1}$\\\\\n \\bottomrule\n \\multicolumn{2}{l}{\\begin{minipage}{5.2in}\\baselineskip=12pt \\scriptsize $C_t$: closing price; $L_t$: lowest price; $H_t$: highest price at time $t$; $DF$: $EMA(12)_t-EMA(26)_t$; EMA: Exponential moving average; $EMA(k)_t$: $EMA(k)_{t-1}+\\alpha \\times (c_t-EMA(k)_{t-1})$; $\\alpha$: smoothing factor: $2/1+k$; $k$: time period of $k$ minute exponential moving average; $LL_t$ and $HH_t$: mean lowest low and highest high in the last $t$ minutes; $M_t:H_t+L_t+C_t/3$; $SM_t: \\sum^n_{i=1}M_{t-i+1})/n$; $D_t:(\\sum^n_{i=1}|M_{t-i+1}-SM_t|)/n; Up_t$: upward price change; $Dw_t$: downward price change at time $t$. \\\\\n \\end{minipage}\n } \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Technical indicators used and formulas as reported in .}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "ProteuS: A Generative Approach for Simulating Concept Drift in Financial Markets", "authors": ["Andrés L. Suárez-Cetrulo", "Alejandro Cervantes", "David Quintana"], "url": "https://arxiv.org/abs/2509.11844v1", "attribution": "\"ProteuS: A Generative Approach for Simulating Concept Drift in Financial Markets\" by Andrés L. Suárez-Cetrulo, Alejandro Cervantes, and David Quintana, arXiv:2509.11844v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23430v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The performance of DGSAM with 20 baseline algorithms on TerraIncognita}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc|ccc}\n\\hline\nAlgorithm & L100 & L38 & L43 & L46 & Avg & SD & (s/iter) \\\\ \\toprule\nARM\\(^\\dag\\) & {49.3\\scriptsize$\\pm$0.7} & {38.3\\scriptsize$\\pm$2.4} & {55.8\\scriptsize$\\pm$0.8} & {38.7\\scriptsize$\\pm$1.3} & 45.5 & 7.4 & 0.11 \\\\\nMTL\\(^\\dag\\) & {49.3\\scriptsize$\\pm$1.2} & {39.6\\scriptsize$\\pm$6.3} & {55.6\\scriptsize$\\pm$1.1} & {37.8\\scriptsize$\\pm$0.8} & 45.6 & 7.3 & 0.12 \\\\\nCDANN\\(^\\dag\\) & {47.0\\scriptsize$\\pm$1.9} & {41.3\\scriptsize$\\pm$4.8} & {54.9\\scriptsize$\\pm$1.7} & {39.8\\scriptsize$\\pm$2.3} & 45.8 & 5.9 & 0.11 \\\\\nERM\\(^\\dag\\) & {49.8\\scriptsize$\\pm$4.4} & {42.1\\scriptsize$\\pm$1.4} & {56.9\\scriptsize$\\pm$1.8} & {35.7\\scriptsize$\\pm$3.9} & 46.1 & 8.0 & 0.11 \\\\\nVREx\\(^\\dag\\) & {48.2\\scriptsize$\\pm$4.3} & {41.7\\scriptsize$\\pm$1.3} & {56.8\\scriptsize$\\pm$0.8} & {38.7\\scriptsize$\\pm$3.1} & 46.4 & 6.9 & 0.11 \\\\\nRSC\\(^\\dag\\) & {50.2\\scriptsize$\\pm$2.2} & {39.2\\scriptsize$\\pm$1.4} & {56.3\\scriptsize$\\pm$1.4} & {40.8\\scriptsize$\\pm$0.6} & 46.6 & 7.0 & 0.13 \\\\\nDANN\\(^\\dag\\) & {51.1\\scriptsize$\\pm$3.5} & {40.6\\scriptsize$\\pm$0.6} & {57.4\\scriptsize$\\pm$0.5} & {37.7\\scriptsize$\\pm$1.8} & 46.7 & 7.9 & 0.11 \\\\\nIRM\\(^\\dag\\) & {54.6\\scriptsize$\\pm$1.3} & {39.8\\scriptsize$\\pm$1.9} & {56.2\\scriptsize$\\pm$1.8} & {39.6\\scriptsize$\\pm$0.8} & 47.6 & 7.9 & 0.12 \\\\\nCORAL\\(^\\dag\\) & {51.6\\scriptsize$\\pm$2.4} & {42.2\\scriptsize$\\pm$1.0} & {57.0\\scriptsize$\\pm$1.0} & {39.8\\scriptsize$\\pm$2.9} & 47.7 & 7.0 & 0.12 \\\\\nMLDG\\(^\\dag\\) & {54.2\\scriptsize$\\pm$3.0} & {44.3\\scriptsize$\\pm$1.1} & {55.6\\scriptsize$\\pm$0.3} & {36.9\\scriptsize$\\pm$2.2} & 47.8 & 7.6 & 0.13 \\\\\nERM\\(^\\dag\\) & {54.3\\scriptsize$\\pm$0.4} & {42.5\\scriptsize$\\pm$0.7} & {55.6\\scriptsize$\\pm$0.3} & {38.8\\scriptsize$\\pm$2.5} & 47.8 & 7.3 & 0.11 \\\\\nSagNet\\(^\\dag\\) & {53.0\\scriptsize$\\pm$2.9} & {43.0\\scriptsize$\\pm$2.5} & {57.9\\scriptsize$\\pm$0.6} & {40.4\\scriptsize$\\pm$1.3} & 48.6 & 7.1 & 0.32 \\\\\nSWAD & {55.4\\scriptsize$\\pm$0.0} & {44.9\\scriptsize$\\pm$1.1} & {59.7\\scriptsize$\\pm$0.4} & {39.9\\scriptsize$\\pm$0.2} & 50.0 & 7.9 & 0.11 \\\\ \\midrule\nSAM\\(^\\dag\\) & {46.3\\scriptsize$\\pm$1.0} & {38.4\\scriptsize$\\pm$2.4} & {54.0\\scriptsize$\\pm$1.0} & {34.5\\scriptsize$\\pm$0.8} & 43.3 & 7.5 & 0.22 \\\\\nLookbehind-SAM & {44.6\\scriptsize$\\pm$0.8} & {41.1\\scriptsize$\\pm$1.4} & {57.4\\scriptsize$\\pm$1.2} & {34.9\\scriptsize$\\pm$0.6} & 44.5 & 8.2 & 0.50 \\\\\nGAM\\(^\\ddag\\) & {42.2\\scriptsize$\\pm$2.6} & {42.9\\scriptsize$\\pm$1.7} & {60.2\\scriptsize$\\pm$1.8} & {35.5\\scriptsize$\\pm$0.7} & 45.2 & 9.1 & 0.43 \\\\\nFAD & {44.3\\scriptsize$\\pm$2.2} & {43.5\\scriptsize$\\pm$1.7} & {60.9\\scriptsize$\\pm$2.0} & {34.1\\scriptsize$\\pm$0.5} & 45.7 & 9.6 & 0.38 \\\\\nDISAM & {46.2\\scriptsize$\\pm$2.9} & {41.6\\scriptsize$\\pm$0.1} & {58.0\\scriptsize$\\pm$0.5} & {40.5\\scriptsize$\\pm$2.2} & 46.6 & 6.9 & 0.33 \\\\\nGSAM\\(^\\dag\\) & {50.8\\scriptsize$\\pm$0.1} & {39.3\\scriptsize$\\pm$0.2} & {59.6\\scriptsize$\\pm$0.0} & {38.2\\scriptsize$\\pm$0.8} & 47.0 & 8.8 & 0.22 \\\\\nSAGM & {54.8\\scriptsize$\\pm$1.3} & {41.4\\scriptsize$\\pm$0.8} & {57.7\\scriptsize$\\pm$0.6} & {41.3\\scriptsize$\\pm$0.4} & 48.8 & 7.5 & 0.22 \\\\\nDGSAM (Ours) & {53.8\\scriptsize$\\pm$0.6} & {45.0\\scriptsize$\\pm$0.7} & {59.1\\scriptsize$\\pm$0.4} & {41.8\\scriptsize$\\pm$1.0} & 49.9 & 6.9 & 0.17 \\\\ \nDGSAM + SWAD & {55.6\\scriptsize$\\pm$1.2} & {45.9\\scriptsize$\\pm$0.5} & {59.6\\scriptsize$\\pm$0.5} & {43.1\\scriptsize$\\pm$0.9} & 51.1 & 6.8 & 0.17 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization", "authors": ["Youngjun Song", "Youngsik Hwang", "Jonghun Lee", "Heechang Lee", "Dong-Young Lim"], "url": "https://arxiv.org/abs/2503.23430v2", "attribution": "\"DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization\" by Youngjun Song, Youngsik Hwang, Jonghun Lee, Heechang Lee, and Dong-Young Lim, arXiv:2503.23430v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.10169v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sample size by exchange (returns)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrr}\n \\toprule\n Index & Total & 2000 & 2005 & 2010 & 2015 & 2020 \\\\\n \\midrule\n AEX & 5892 & 0 & 290 & 300 & 278 & 283 \\\\\n AS5 & 47252 & 0 & 2127 & 2340 & 2164 & 2361 \\\\\n CAC & 9946 & 0 & 475 & 474 & 474 & 480 \\\\\n DAX & 6531 & 0 & 360 & 360 & 360 & 351 \\\\\n HSC & 5946 & 0 & 0 & 281 & 435 & 585 \\\\\n HSI & 8949 & 0 & 0 & 522 & 582 & 597 \\\\\n IBE & 8663 & 0 & 398 & 419 & 410 & 419 \\\\\n IND & 5377 & 0 & 0 & 360 & 360 & 348 \\\\\n KOS & 36595 & 0 & 0 & 2381 & 2345 & 2369 \\\\\n MID & 120017 & 4585 & 4773 & 4770 & 4583 & 4750 \\\\\n NDX & 21624 & 0 & 1171 & 1200 & 1234 & 1197 \\\\\n NIF & 6794 & 0 & 0 & 52 & 599 & 600 \\\\\n NKY & 60783 & 2642 & 2674 & 2698 & 2676 & 2698 \\\\\n OMX & 7565 & 0 & 348 & 360 & 360 & 360 \\\\\n RAY & 830673 & 29792 & 32936 & 33833 & 31915 & 33247 \\\\\n SMI & 3715 & 0 & 0 & 233 & 240 & 228 \\\\\n SPT & 10200 & 0 & 0 & 707 & 708 & 720 \\\\\n SX5 & 9744 & 0 & 0 & 600 & 600 & 587 \\\\\n TOP & 5229 & 0 & 0 & 5 & 504 & 452 \\\\\n TPX & 518734 & 16490 & 19074 & 19783 & 21722 & 25588 \\\\\n TWY & 13663 & 0 & 0 & 355 & 1168 & 1028 \\\\\n UKX & 29775 & 1086 & 1180 & 1196 & 1180 & 1187 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Expectations Formation with Fat-tailed Processes: Evidence from Sales Forecasts", "authors": ["Eugene Larsen-Hallock", "Adam Rej", "David Thesmar"], "url": "https://arxiv.org/abs/2210.10169v1", "attribution": "\"Expectations Formation with Fat-tailed Processes: Evidence from Sales Forecasts\" by Eugene Larsen-Hallock, Adam Rej, and David Thesmar, arXiv:2210.10169v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2501.00509v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|lllll}\n & Full & Ul & Co & Mu & Nn \\\\ \\hline\nSyn & 41.4 & 22.2 & 8.8 & 10.4 & - \\\\\nMG & 17.3 & 3.1 & 5.6 & 3.4 & 5.2 \\\\\nSS & 259.7 & 55.5 & 104.9 & 95.4 & 3.9 \\\\\nAB & 36.6 & 10.3 & 2.5 & 14.6 & 9.2 \\\\\nCCh & 25 & - & 25.0 & - & - \\\\\nBBhÁ & 17.9 & - & 17.9 & - & - \\\\ \\hline\n\\textbf{Total} & \\textbf{397.9} & \\textbf{91.1} & \\textbf{164.7} & \\textbf{123.8} & \\textbf{18.3} \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Duration breakdown in hours of ASR training set by speaker variety.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fotheidil: an Automatic Transcription System for the Irish Language", "authors": ["Liam Lonergan", "Ibon Saratxaga", "John Sloan", "Oscar Maharog", "Mengjie Qian", "Neasa Ní Chiaráin", "Christer Gobl", "Ailbhe Ní Chasaide"], "url": "https://arxiv.org/abs/2501.00509v1", "attribution": "\"Fotheidil: an Automatic Transcription System for the Irish Language\" by Liam Lonergan, Ibon Saratxaga, John Sloan, Oscar Maharog, Mengjie Qian, Neasa Ní Chiaráin, Christer Gobl, and Ailbhe Ní Chasaide, arXiv:2501.00509v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11538v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of codebooks and vocabulary size per codebook, with the corresponding results on SUPERB ASR and IC tasks.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n\\mbox{No.} codebooks&\\mbox{Vocab.} size&ASR WER(\\%)&IC Acc(\\%)\\\\\n\\midrule\n\\multirow{3}{*}{16}&1024&14.83&91.83\\\\\n&2048&14.51&91.09\\\\\n&8192&14.62&92.25\\\\\n\\midrule\n1&2048&17.89&90.43\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond", "authors": ["Muhammad Huzaifah", "Geyu Lin", "Tianchi Liu", "Hardik B. Sailor", "Kye Min Tan", "Tarun K. Vangani", "Qiongqiong Wang", "Jeremy H. M. Wong", "Jinyang Wu", "Nancy F. Chen", "Ai Ti Aw"], "url": "https://arxiv.org/abs/2412.11538v3", "attribution": "\"MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond\" by Muhammad Huzaifah, Geyu Lin, Tianchi Liu, Hardik B. Sailor, Kye Min Tan, Tarun K. Vangani, Qiongqiong Wang, Jeremy H. M. Wong, Jinyang Wu, Nancy F. Chen, and Ai Ti Aw, arXiv:2412.11538v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.21174v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|lll}\n\\# & $\\tau_{large}$ &$\\tau_{mix}$ & $\\tau_{small}$ \\\\\\hline\n1 & 2 & 2 & 1 \\\\\n2 & 2 & 2 & 2 \\\\\n3 & 2 & 1 & 1 \\\\\n4 &3 & 4 & 2 \\\\\n5 & 3 & 4 & 2 \\\\\n6 & 3 & 3 & 2 \\\\\n7 & 3 & 5 & 2 \\\\\n8 & 4 & 5 & 2\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Number of outer fGMRES iterations for the new scheme (generalized case) with $M$-orthogonalization}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust iterative methods for linear systems with saddle point structure", "authors": ["Murat Manguoğlu", "Volker Mehrmann"], "url": "https://arxiv.org/abs/2502.21174v1", "attribution": "\"Robust iterative methods for linear systems with saddle point structure\" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.04878v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|} \\hline\n$z$ & $a$ & supersingular primes \\\\ \\hline\n$\\left[19, 19, 19, 1\\right]$ & $\\left(1, 17, 1\\right),\\left(1, 16, 2\\right)$, & $p \\equiv QNR \\bmod{19}$ \\\\\n& $\\left(1, 15, 3\\right), \\left(1, 11, 7\\right)$ & $p \\equiv QNR \\bmod{19}$ \\\\ \\hline\n$\\left[20, 20, 10, 1\\right]$ & $\\left(1, 17, 2\\right)$ & $p \\equiv 3, 7, 19 \\bmod{20}$ \\\\ \\hline\n & $\\left(1, 1, 18\\right)$ & $p \\equiv 19 \\bmod{20}$ \\\\ \\hline\n$\\left[21, 21, 7, 1\\right]$ & $\\left(1, 17, 3\\right), \\left(1, 11, 9\\right)$ & $p \\equiv 5, 10, 17, 19, 20 \\bmod{21}$ \\\\ \n & $\\left(1, 8, 12\\right)$ & $p \\equiv QNR \\bmod{7}$ \\\\ \\hline\n$\\left[24, 8, 6, 1\\right]$ & $\\left(1, 3, 20\\right)$ & $p \\equiv 23 \\bmod{24}$ \\\\ \\hline\n$\\left[24, 24, 4, 1\\right]$ & $\\left(1, 17, 6\\right)$ & $p \\equiv 7, 23 \\bmod{24}$ \\\\ \\hline\n & $\\left(1, 5, 18\\right)$ & $p \\equiv 19, 23 \\bmod{24}$ \\\\ \\hline\n$\\left[28, 7, 4, 1\\right]$ & $\\left(1, 20, 7\\right)$ & $p \\equiv 3, 19, 27 \\bmod{28}$ \\\\ \\hline\n$\\left[27, 27, 3, 1\\right]$ & $\\left(1, 17, 9\\right)$ & $p \\equiv 2 \\bmod{3}$ \\\\ \\hline\n$\\left[30, 10, 3, 1\\right]$ & $\\left(1, 9, 20\\right)$ & $p \\equiv 29 \\bmod{30}$ \\\\ \\hline\n$\\left[36, 36, 2, 1\\right]$ & $\\left(1, 17, 18\\right)$ & $p \\equiv 3 \\bmod{4}$ \\\\ \\hline\n$\\left[38, 19, 2, 1\\right]$ & $\\left(1, 18, 19\\right)$ & $p \\equiv QNR \\bmod{38}$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Cyclic genus $9$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Supersingular curves via the Shimura--Taniyama method", "authors": ["Jeremy Booher", "Rachel Pries"], "url": "https://arxiv.org/abs/2503.04878v2", "attribution": "\"Supersingular curves via the Shimura--Taniyama method\" by Jeremy Booher and Rachel Pries, arXiv:2503.04878v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1910.11960v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\t\\multirow{2}{1.5cm}{\\centering Model}&\n\t\t\t\\multirow{2}{1.5cm}{\\centering Real Images}&\n\t\t\t\\multirow{2}{1.5cm}{\\centering PGAN}& \\multicolumn{3}{c|}{ APGAN} \\\\ \n\t\t\t\\cline{4-6}& & & Stage 32 & Stage 64 & Stage 128 \\\\ \\hline\n\t\t\tGAN-train & 78.2& 67.7 & 63.3 & \\textbf{70.1} & 64.7 \\\\ \\hline\n\t\t\tGAN-test & - &60.8 & 56.0 & \\textbf{62.2} & 58.1 \\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{ Comparison of APGAN and PGAN. The SNL block is added to different stages of the generator and discriminator. All models have been presented with over 6M images, and the best GAN-train and GAN-test are reported.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network", "authors": ["Ibrahim Saad Ali", "Mamdouh Farouk Mohamed", "Yousef Bassyouni Mahdy"], "url": "https://arxiv.org/abs/1910.11960v1", "attribution": "\"Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network\" by Ibrahim Saad Ali, Mamdouh Farouk Mohamed, and Yousef Bassyouni Mahdy, arXiv:1910.11960v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11106v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Experiment} & \\textbf{SSIM} & \\textbf{FID} & \\textbf{PSNR} \\\\\n\\midrule\nbaseline & 0.8015 & 268.491 & 19.746 \\\\\n$\\lambda=0$ & 0.699 & 302.929 & 14.086 \\\\\n$\\lambda=0.05$ & \\textbf{0.909} & 278.253 & \\textbf{20.867} \\\\\n$\\lambda=0.25$ & 0.9002 & 280.730 & 20.8184 \\\\\n$\\lambda=0.45$ & 0.8863 & 278.305 & 20.2810 \\\\\n$\\lambda=0.65$ & 0.8651 & 263.039 & 19.862 \\\\\n$\\lambda=0.85$ & 0.8474 & \\textbf{246.999} & 19.833 \\\\\n$\\lambda=0.95$ & 0.8121 & 255.261 & 19.220 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion", "authors": ["Bing Xiong", "Yue Peng", "RanRan Zhang", "Fuqiang Chen", "JiaYe He", "Wenjian Qin"], "url": "https://arxiv.org/abs/2412.11106v1", "attribution": "\"Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion\" by Bing Xiong, Yue Peng, RanRan Zhang, Fuqiang Chen, JiaYe He, and Wenjian Qin, arXiv:2412.11106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13148v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc} \\hline\n $\\lambda_F$ & $\\lambda_G$ & $\\pi_F$ & $\\pi_G$ & Theoretical bounds & Estimated bounds \\\\\\hline\n 2 & 2 & 0.2 & 0.2 & $[-0.090, 0.434]$ & $[-0.090, 0.403]$ \\\\\n & & 0.2 & 0.8 & $[-0.359, 0.421]$ & $[-0.356, 0.418]$ \\\\\n & & 0.8 & 0.8 & $[-0.915, 0.971]$ & $[-0.915, 0.957]$ \\\\\\hline\n 2 & 8 & 0.2 & 0.2 & $[-0.104, 0.434]$ & $[-0.107, 0.425]$ \\\\\n & & 0.2 & 0.8 & $[-0.415, 0.431]$ & $[-0.406, 0.426]$ \\\\\n & & 0.8 & 0.8 & $[-0.956, 0.969]$ & $[-0.953, 0.968]$ \\\\\\hline\n 8 & 8 & 0.2 & 0.2 & $[-0.120, 0.488]$ & $[-0.123, 0.461]$ \\\\\n & & 0.2 & 0.8 & $[-0.480, 0.484]$ & $[-0.468, 0.482]$ \\\\\n & & 0.8 & 0.8 & $[-0.982, 0.992]$ & $[-0.980, 0.989]$ \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Estimation of the bounds for zero-inflated Poisson margins based on 1000 runs and $N=150$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Spearman's rho for bivariate zero-inflated data", "authors": ["Jasper Arends", "Elisa Perrone"], "url": "https://arxiv.org/abs/2503.13148v1", "attribution": "\"Spearman's rho for bivariate zero-inflated data\" by Jasper Arends and Elisa Perrone, arXiv:2503.13148v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12856v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Error analysis for population dynamic example using constrained NLS method}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\nvariable&bias&MAPE&MAE&RMSE&R$^2$\\\\ \\hline\n$x_1$&0.0769&0.0760&0.1139&0.1741&0.9830\\\\\n$x_2$&0.0537&0.0525&0.0764&0.1003&0.9535\\\\\n$f_1(\\cdot)$&-0.0727&0.6252&0.5815&0.8566&0.9010\\\\\n$f_2(\\cdot)$&0.0064&1.7939&0.2709&0.3492&0.7928\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods", "authors": ["S. Syafiie", "Aries Subiantoro", "Vivi Andasari", "Fernando Tadeo"], "url": "https://arxiv.org/abs/2501.12856v1", "attribution": "\"Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods\" by S. Syafiie, Aries Subiantoro, Vivi Andasari, and Fernando Tadeo, arXiv:2501.12856v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.17111v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\hline\nminute & $1$ & $2$ & $\\ldots$ & $T_1-1$ & $T_1$ & $T_1+1$ \\\\\n\\hline\nfight & $p_{21}$ & $p_{22}A_1$ & $\\ldots$ & $p_{2(T_1-1)}\\prod_{k=1}^{T_1-2}A_k$ & $p_{2T_1}\\prod_{k=1}^{T_1-1}A_k$ & $0$ \\\\\nhide & $1-\\frac{m}{N-e}$ & $\\prod_{k=1}^2A_k$ & $\\ldots$ & $\\prod_{k=1}^{T_1-1}A_k$ & $0$ & $0$ \\\\\nrun & $p_r$ & $p_rA_1$ & $\\ldots$ & $p_r\\prod_{k=1}^{T_1-2}A_k$ & $p_r\\prod_{k=1}^{T_1-1}A_k$ & $0$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Survival probabilities of a civilian in an open arena when $T_1\\leq T_2$, where $A_k=\\left[1-\\frac{m}{(N-e)-(k-1)(e+m)}\\right]$ and the $i$-th cell of the ``fight'' (respectively ``run'') row is the survival probability of the civilian when he hides for the first $(i-1)$ minutes and starts to fight back (respectively ``run'') at the beginning of the $i$-th minute..}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Analytic models for active shooter incidents with civilian resistance", "authors": ["Liang Hong"], "url": "https://arxiv.org/abs/2310.17111v2", "attribution": "\"Analytic models for active shooter incidents with civilian resistance\" by Liang Hong, arXiv:2310.17111v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.12530v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistical Functionals of Normalized $L$ for Non-Uniform Accident Occurrence with $\\rho^\\Phi=0.5$ and $\\zeta^{2a}$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrrrrrrrrr} \\toprule \n& \\multicolumn{6}{c}{Binomial Model} & \\quad\\quad & \\multicolumn{6}{c}{Poisson Model} \\\\ \\midrule \n& \\multicolumn{3}{c}{Gamma} & \\multicolumn{3}{c}{Log-Normal} & & \\multicolumn{3}{c}{Gamma} & \\multicolumn{3}{c}{Log-Normal} \\\\ \\midrule \n& $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ & $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ & & $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ & $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ \\\\ \\toprule \n&\\multicolumn{13}{c}{\\textbf{Non-Uniform Accident Occurrence}} \\\\ \n$\\mathbb{E}(L)$ & 93.5 & 93.1 & 93.5 & 93.7 & 93.4 & 93.8 & & 93.6 & 93.8 & 93.8 & 93.6 & 93.6 & 93.6 \\\\ \n$\\mathrm{Var}(L)$& 562.5 & 870.7 & 2199.0 & 569.1 & 867.6 & 2158.7 & & 559.7 & 879.1 & 2183.6 & 559.8 & 873.1 & 2177.8 \\\\ \n$\\varsigma_L$ & 0.333 & 0.561 & 0.993 & 0.377 & 0.660 & 2.152 & & 0.299 & 0.522 & 1.045 & 0.417 & 0.711 & 2.155 \\\\ \n$\\mathrm{VaR}_{0.9}(L)$ & 125.1 & 131.5 & 156.1 & 125.2 & 132.9 & 149.7 & & 124.5 & 133.0 & 155.5 & 124.3 & 132.0 & 148.7 \\\\ \n$\\mathrm{ES}_{0.9}(L)$ & 138.2 & 150.4 & 191.6 & 138.8 & 151.6 & 194.4 & & 137.7 & 151.4 & 191.6 & 138.5 & 152.0 & 193.6 \\\\ \n$\\mathrm{VaR}_{0.95}(L)$ & 134.9 & 146.5 & 182.0 & 135.6 & 146.3 & 177.6 & & 134.7 & 146.8 & 182.5 & 134.5 & 145.9 & 174.4 \\\\ \n$\\mathrm{ES}_{0.95}(L)$ & 146.3 & 162.8 & 215.0 & 147.6 & 164.3 & 227.5 & & 146.1 & 163.8 & 215.8 & 148.0 & 165.7 & 226.9 \\\\ \n$\\mathrm{VaR}_{0.99}(L)$ & 153.3 & 171.3 & 237.0 & 154.6 & 174.4 & 250.6 & & 153.6 & 174.6 & 238.7 & 156.8 & 176.4 & 256.7 \\\\ \n$\\mathrm{ES}_{0.99}(L)$ & 163.3 & 189.0 & 266.1 & 166.5 & 191.7 & 321.4 & & 163.4 & 188.8 & 269.3 & 168.6 & 196.4 & 325.4 \\\\ \n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Microscopic Traffic Models, Accidents, and Insurance Losses", "authors": ["Sojung Kim", "Marcel Kleiber", "Stefan Weber"], "url": "https://arxiv.org/abs/2208.12530v2", "attribution": "\"Microscopic Traffic Models, Accidents, and Insurance Losses\" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.09843v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n\\toprule \nAlgorithm & Median Abs. Error\\tabularnewline\n\\midrule\n\\midrule \nS-CFM & $\\boldsymbol{0.5829_{5.0\\text{e-}2}}$\\tabularnewline\nCausalNF-NSF & $0.6018_{2.0\\text{e-}2}$\\tabularnewline\nCausalNF-MAF & $0.6025_{3.0\\text{e-}2}$\\tabularnewline\nCAREFL & $\\boldsymbol{0.5983_{3.0\\text{e-}2}}$\\tabularnewline\nANM & $0.6746_{4.8\\text{e-}8}$\\tabularnewline\nLinear SCM & $0.6045_{0.0}$\\tabularnewline\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Learning Structural Causal Models from Ordering: Identifiable Flow Models", "authors": ["Minh Khoa Le", "Kien Do", "Truyen Tran"], "url": "https://arxiv.org/abs/2412.09843v1", "attribution": "\"Learning Structural Causal Models from Ordering: Identifiable Flow Models\" by Minh Khoa Le, Kien Do, and Truyen Tran, arXiv:2412.09843v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.16654v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Gini coefficient and HHI values with different quotas}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\bottomrule\n Quota (\\%) & Gini Coefficient & HHI \\\\\n \\toprule\n 50 & 0.7830 & 0.0712 \\\\\n 51 & 0.7827 & 0.0706 \\\\\n 52 & 0.7818 & 0.0690 \\\\\n 53 & 0.7804 & 0.0665 \\\\\n 54 & 0.7786 & 0.0633 \\\\\n 55 & 0.7765 & 0.0599 \\\\\n 56 & 0.7741 & 0.0563 \\\\\n 57 & 0.7715 & 0.0528 \\\\\n 58 & 0.7688 & 0.0495 \\\\\n 59 & 0.7660 & \\textbf{\\textcolor{blue}{0.0465}} \\\\\n 60 & \\textbf{\\textcolor{blue}{0.7632}} & 0.0438 \\\\\n 61 & 0.7604 & 0.0414 \\\\\n 62 & 0.7576 & 0.0393 \\\\\n 63 & 0.7548 & 0.0375 \\\\\n 64 & 0.7519 & 0.0359 \\\\\n 65 & 0.7491 & 0.0345 \\\\\n 66 & 0.7462 & 0.0332 \\\\\n 67 & 0.7433 & 0.0321 \\\\\n 68 & 0.7402 & 0.0310 \\\\\n 69 & 0.7371 & 0.0300 \\\\\n 70 & 0.7338 & 0.0291 \\\\\n \\bottomrule\n Current & Gini Coefficient & \n HHI \\\\\n \\toprule\n Weights & 0.7644 & 0.0476 \\\\\n \\toprule\n \\toprule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Optimising the decision threshold in a weighted voting system: The case of the IMF's Board of Governors", "authors": ["Dóra Gréta Petróczy"], "url": "https://arxiv.org/abs/2505.16654v1", "attribution": "\"Optimising the decision threshold in a weighted voting system: The case of the IMF's Board of Governors\" by Dóra Gréta Petróczy, arXiv:2505.16654v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.00145v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Generator specs for our $3$-unit grid UC instance. The data are taken from Ref.~.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\\toprule\n & $p_{j, \\min}$ & $p_{j, \\max}$ & $c_j$ & $b_j$ & $a_j$ \\\\\nUnit & (MW) & (MW) & & & \\\\\n\\midrule\n0 & 100 & 600 & 500 & 10 & 0.002 \\\\\n1 & 100 & 400 & 300 & 8 & 0.0025 \\\\\n2 & 50 & 200 & 100 & 6 & 0.005 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A New Hybrid Quantum-Classical Algorithm for Solving the Unit Commitment Problem", "authors": ["Willie Aboumrad", "Phani R V Marthi", "Suman Debnath", "Martin Roetteler", "Evgeny Epifanovsky"], "url": "https://arxiv.org/abs/2505.00145v1", "attribution": "\"A New Hybrid Quantum-Classical Algorithm for Solving the Unit Commitment Problem\" by Willie Aboumrad, Phani R V Marthi, Suman Debnath, Martin Roetteler, and Evgeny Epifanovsky, arXiv:2505.00145v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.03083v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The architecture of the multi-layer perceptron.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n \t\t\\cline{1-5}\n \t\t\\textbf{Layer} & Input & Hidden 1 & Hidden 2 & Output & \\\\ \\cline{1-5}\n \t\t\\textbf{Units} & 1 & 8 & 12 & 3 & \\\\ \\cline{1-5}\n \t\t\\textbf{Activation} & \\texttt{softplus} & \\texttt{softplus} & \\texttt{softplus} & \\texttt{softplus} & \\\\ \\cline{1-5}\n \t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Quantifying the value of information transfer in population-based SHM", "authors": ["Aidan J. Hughes", "Jack Poole", "Nikolaos Dervilis", "Paul Gardner", "Keith Worden"], "url": "https://arxiv.org/abs/2311.03083v1", "attribution": "\"Quantifying the value of information transfer in population-based SHM\" by Aidan J. Hughes, Jack Poole, Nikolaos Dervilis, Paul Gardner, and Keith Worden, arXiv:2311.03083v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.15103v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Two-Way Fixed Effects Model Estimates -- ln(Price\\_Paid)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\tabularnewline \\midrule \\midrule\n Dependent Variable: & \\multicolumn{4}{c}{ln(Price\\_Paid)}\\\\\n & Dry Dog Food & Dry Cat Food & Dog Hygiene & Cat Hygiene \\\\ \n Model: & (1) & (2) & (3) & (4)\\\\ \n \\midrule\n \\emph{Variables}\\\\\n $\\mathcal{I}\\{\\textrm{Adopter}_{i}\\} \\times \\mathcal{I}\\{ t \\geq T_{i}^{\\textrm{adopt}}\\}$ & -0.0102$^{*}$ & -0.0106$^{**}$ & -0.0046 & -0.0160$^{*}$\\\\ \n & (0.0040) & (0.0038) & (0.0030) & (0.0063)\\\\ \n \\midrule\n \\emph{Fixed-effects}\\\\\n Customer & Yes & Yes & Yes & Yes\\\\ \n YearMonth & Yes & Yes & Yes & Yes\\\\ \n \\midrule\n \\emph{Fit statistics}\\\\\n Observations & 99,233 & 27,623 & 40,183 & 42,596\\\\ \n R$^2$ & 0.89566 & 0.70460 & 0.62699 & 0.66927\\\\ \n Within R$^2$ & 0.00030 & 0.00065 & 0.00012 & 0.00074\\\\ \n \\midrule \\midrule\n \\multicolumn{5}{l}{\\emph{Clustered (Customer \\& YearMonth) standard-errors in parentheses}}\\\\\n \\multicolumn{5}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05}}\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "How does online shopping affect offline price sensitivity?", "authors": ["Shirsho Biswas", "Hema Yoganarasimhan", "Haonan Zhang"], "url": "https://arxiv.org/abs/2506.15103v1", "attribution": "\"How does online shopping affect offline price sensitivity?\" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.16187v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll|ccc}\nFire & Mean & Covariance & RMSE & CA & CA-High \\\\\\hline\nCombined & LR & Independent & 223 & 29.0 & 8.26 \\\\\n & LR & Spatial & 100 & 64.5 & 76.54 \\\\\n & SL & Independent & 108 & 62.4 & 59.06\\\\\n & SL & Spatial & 97 & 65.7 & 62.98\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Combined-fire cross-validation root mean squared prediction error (RMSE), classification accuracy (CA) percent for fire severity level (high enhanced growth (dNBR from -500 - -251), low enhanced regrowth (-250 - -101), unburned (-100 - 99), low (100 - 269), moderate-low (270 - 439), moderate-high (440 - 659), and high (660 - 1300)) and CA for the high categories for Linear Regression (LR) and Super Learner (SL) models with independent and spatial error structure. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modelling wildland fire burn severity in California using a spatial Super Learner approach", "authors": ["Nicholas Simafranca", "Bryant Willoughby", "Erin O'Neil", "Sophie Farr", "Brian J Reich", "Naomi Giertych", "Margaret Johnson", "Madeleine Pascolini-Campbell"], "url": "https://arxiv.org/abs/2311.16187v1", "attribution": "\"Modelling wildland fire burn severity in California using a spatial Super Learner approach\" by Nicholas Simafranca, Bryant Willoughby, Erin O'Neil, Sophie Farr, Brian J Reich, Naomi Giertych, Margaret Johnson, and Madeleine Pascolini-Campbell, arXiv:2311.16187v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16257v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\texttt{nxcorr} values for the different reconstructions presented.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n & \\texttt{nxcorr} intrinsic source & \\texttt{nxcorr} blurred screen & \\texttt{nxcorr} non-blurred screen\\\\\n \\midrule\n Figure~ & 0.99 (top row); 0.99 (bottom row) & 0.55 (top row); 0.57 (bottom row) & 0.73 (top row); 0.75 (bottom row) \\\\\n Figure~ & 0.99 (top row); 0.99 (bottom row) & 0.57 (top row); 0.45 (bottom row) & 0.75 (top row); 0.55 (bottom row)\\\\\n Figure~ & 0.97 & 0.81 & 0.88 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Multiobjective optimization for scattering mitigation and scattering screen reconstruction in VLBI observations of the Galactic Center", "authors": ["Alejandro Mus", "Teresa Toscano", "Hendrik Müller", "Guang-Yao Zhao", "Andrei Lobanov", "Ciriaco Goddi"], "url": "https://arxiv.org/abs/2504.16257v2", "attribution": "\"Multiobjective optimization for scattering mitigation and scattering screen reconstruction in VLBI observations of the Galactic Center\" by Alejandro Mus, Teresa Toscano, Hendrik Müller, Guang-Yao Zhao, Andrei Lobanov, and Ciriaco Goddi, arXiv:2504.16257v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.11606v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Robustness specifications on the effect of school closures on test scores at national level}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\t\t\t\t\t\n\t\t\t\t\t\\hline\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t& Math (ln) & Italian (ln) & Math (Rasch) & Italian (Rasch) & Math (std. - PSM) & Italian (std. - PSM) \\\\ \n\t\t\t\t\t\n\t\t\t\t\t& (1) & (2) & (3) & (4) & (5) & (6) \\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\t\n\t\t\t\t\t\\textbf{School closures} & -0.011*** & -0.003*** & -1.569*** & -0.434*** & -0.017*** & -0.006*** \\\\\n\t\t\t\t\t(Treated=1 X t=1) \t& (0.000) & (0.000) & (0.041) & (0.042) & (0.001) & (0.001) \\\\ \\hline\n\t\t\t\t\t\n\t\t\t\t\t\\textbf{Other controls} & Yes & Yes & Yes & Yes & Yes & Yes \\\\\n\t\t\t\t\t\\textbf{Student FEs} & Yes & Yes & Yes & Yes & Yes & Yes \\\\\n\t\t\t\t\t\\textbf{Relative time dummies} & Yes & Yes & Yes & Yes & Yes & Yes \\\\ \n\t\t\t\t\t\\textbf{Grade dummies}& Yes & Yes & Yes & Yes & Yes & Yes \\\\ \\hline \n\t\t\t\t\t\n\t\t\t\t\t\\textbf{Observations} & 4,613,714 & 4,613,714 & 4,613,714 & 4,613,714 & 4,613,714 & 4,613,714 \\\\\n\t\t\t\t\tR-squared & 0.395 & 0.274 & 0.413 & 0.280 & 0.379 & 0.261 \\\\\n\t\t\t\t\t\\textbf{Number of students} & 2,306,857 & 2,306,857 & 2,306,857 & 2,306,857 & 2,306,857 & 2,306,857 \\\\ \\hline\n\t\t\t\t\t\n\t\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Will the last be the first? School closures and educational outcomes", "authors": ["Michele Battisti", "Giuseppe Maggio"], "url": "https://arxiv.org/abs/2208.11606v1", "attribution": "\"Will the last be the first? School closures and educational outcomes\" by Michele Battisti and Giuseppe Maggio, arXiv:2208.11606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.14503v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Computational results for the instances introduced in.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrrrrrrrrrrrrrrr}\n\\toprule\n\\multicolumn{3}{c}{Instances} & MILP & LB-MST & LB-MI & \\multicolumn{2}{c}{HAD} & \\multicolumn{2}{c}{Mega} & \\multicolumn{2}{c}{HAD+} & \\multicolumn{2}{c}{BC} & \\multicolumn{3}{c}{BC} & \\multicolumn{5}{c}{CP-SAT} \\\\\n\\multicolumn{3}{c}{} & [5] & [5] & [5] & \\multicolumn{2}{c}{[2]} & \\multicolumn{2}{c}{[4]} & \\multicolumn{2}{c}{[2]} & \\multicolumn{2}{c}{[3]} & \\multicolumn{3}{c}{[1]} & \\multicolumn{3}{c}{} &\\multicolumn{2}{c}{Dev \\%} \\\\\nn & m & p & UB & LB & LB & LB & Sec & UB & Sec & UB & Sec & LB & UB & LB & UB & Sec & LB & UB & Sec & LB & UB \\\\\n\\cmidrule(lr){1-3}\\cmidrule(lr){4-4}\\cmidrule(lr){5-5}\\cmidrule(lr){6-6}\\cmidrule(lr){7-8}\\cmidrule(lr){9-10}\\cmidrule(lr){11-12}\\cmidrule(lr){13-14}\\cmidrule(lr){15-17}\\cmidrule(lr){18-22}\n50 & 200 & 199 & 708 & 701.1 & 702.8 & 705.5 & 0.3 & 708 & 0.7 & 726 & 0.4 & 708.0 & 708 & 708.0 & 708 & 0.2 & 708 & 708 & 5.1 & 0.0 & 0.0 \\\\\n50 & 200 & 398 & 770 & 739.8 & 757.8 & 761.0 & 0.3 & 770 & 0.7 & 780 & 0.5 & 770.0 & 770 & 770.0 & 770 & 0.5 & 770 & 770 & 9.2 & 0.0 & 0.0 \\\\\n50 & 200 & 597 & 917 & 782.7 & 807.8 & 867.7 & 0.4 & 917 & 0.6 & 1176 & 0.6 & 917.0 & 917 & 917.0 & 917 & 1.8 & 917 & 917 & 46.0 & 0.0 & 0.0 \\\\\n50 & 200 & 995 & 1324 & 835.7 & 877.5 & 961.9 & 0.4 & 1365.4 & 0.6 & 1587 & 0.8 & 1324.0 & 1324 & 1324.0 & 1324 & 7.4 & 1324 & 1324 & 199.6 & 0.0 & 0.0 \\\\\n50 & 200 & 3903 & 1636 & 775.1 & 877.5 & 1042.8 & 1.2 & 1636 & 0.5 & 1653 & 2.2 & - & - & - & - & - & 1636 & 1636 & 0.1 & 0.0 & 0.0 \\\\\n50 & 200 & 4877 & 2043 & 698.7 & 887.5 & 1116.3 & 1.6 & 2043 & 0.5 & 2043 & 2.5 & - & - & - & - & - & 2043 & 2043 & 0.2 & 0.0 & 0.0 \\\\\n50 & 200 & 5864 & 2338 & 626.9 & 1030.3 & 2338.0 & 0.8 & 2338 & 0.6 & 2338 & 0.8 & - & - & - & - & - & 2338 & 2338 & 0.1 & 0.0 & 0.0 \\\\\n100 & 300 & 448 & 4041 & 3893.5 & 3991.2 & 4036.6 & 0.9 & 4099.2 & 0.6 & 4099 & 1.2 & 4041.0 & 4041 & 4041.0 & 4041 & 4.6 & 4041 & 4041 & 232.0 & 0.0 & 0.0 \\\\\n100 & 300 & 897 & 5658 & 4508.2 & 4624.2 & 4982.0 & 1.4 & - & 1.9 & - & 2.1 & 5658.0 & 5658 & 5658.0 & 5658 & 178.5 & 5658 & 5658 & 2758.6 & 0.0 & 0.0 \\\\\n100 & 300 & 1344 & - & - & 4681.3 & - & - & - & 2.9 & - & - & 6621.2 & - & 6635.4 & - & 5010.0 & \\emph{Infeas} & \\emph{Infeas} & 104.6 & \\textbf{-100.0} & \\textbf{-100.0} \\\\\n100 & 300 & 8609 & 7434 & 4043.4 & 5754.9 & 7434.0 & 0.6 & 7434 & 1.9 & 7434 & 0.6 & - & - & - & - & - & 7434 & 7434 & 0.1 & 0.0 & 0.0 \\\\\n100 & 300 & 10686 & 7968 & 3970.5 & 6192.3 & 7968.0 & 0.6 & 7968 & 1.8 & 7968 & 0.5 & - & - & - & - & - & 7968 & 7968 & 0.2 & 0.0 & 0.0 \\\\\n100 & 300 & 12761 & 8166 & 3936.3 & 6758.6 & 8166.0 & 0.5 & 8166 & 1.9 & 8166 & 0.5 & - & - & - & - & - & 8166 & 8166 & 0.1 & 0.0 & 0.0 \\\\\n100 & 500 & 1247 & 4275 & 4124.5 & 4165.7 & 4268.6 & 2.3 & 4291.2 & 5.3 & 4301 & 3.0 & 4275.0 & 4275 & 4275.0 & 4275 & 11.5 & 4275 & 4275 & 86.6 & 0.0 & 0.0 \\\\\n100 & 500 & 2495 & 5997 & 4701.9 & 4805.4 & 5238.2 & 2.5 & 6325 & 5.0 & 8214 & 5.5 & 5951.4 & 6006 & 5997.0 & 5997 & 1239.4 & 5676 & 6005 & 5010.0 & 5.4 & 0.1 \\\\\n100 & 500 & 3741 & 7665 & 4743.8 & 4871.3 & 5418.8 & 2.6 & 7788 & 3.2 & - & 6.8 & 6510.8 & 9440 & 6707.8 & 8049 & 5010.1 & 6267 & 7787 & 5010.0 & 6.6 & \\textbf{-0.013} \\\\\n100 & 500 & 6237 & - & - & 4969.0 & - & - & - & 5.0 & - & - & 7568.7 & - & 7729.3 & - & 5010.0 & 7259 & - & 5010.0 & 6.1 & 0.0 \\\\\n100 & 500 & 12474 & - & - & 5194.7 & - & - & - & 6.9 & - & - & 9816.9 & - & 10560.2 & - & 5010.0 & \\emph{Infeas} & \\emph{Infeas} & 15.1 & \\textbf{-100.0} & \\textbf{-100.0} \\\\\n100 & 500 & 24740 & 12652 & 4289.9 & 5104.9 & 5565.2 & 9.0 & 12652 & 3.4 & 12681 & 20.8 & - & - & - & - & - & 12652 & 12652 & 0.1 & 0.0 & 0.0 \\\\\n100 & 500 & 30886 & 11232 & 3972.7 & 5078.8 & 5672.7 & 15.5 & 11232 & 3.6 & 11287 & 26.4 & - & - & - & - & - & 11232 & 11232 & 0.1 & 0.0 & 0.0 \\\\\n100 & 500 & 36827 & 11481 & 3918.1 & 5710.8 & 11481.0 & 22.9 & 11481 & 3.6 & 11481 & 24.4 & - & - & - & - & - & 11481 & 11481 & 0.2 & 0.0 & 0.0 \\\\\n200 & 400 & 13660 & 17728 & 14085.8 & 17245.9 & 17728.0 & 1.3 & 17728 & 7.7 & 17728 & 1.4 & - & - & - & - & - & 17728 & 17728 & 0.1 & 0.0 & 0.0 \\\\\n200 & 400 & 17089 & 18617 & 14067.3 & 18048.2 & 18617.0 & 1.5 & 18617 & 7.7 & 18617 & 1.4 & - & - & - & - & - & 18617 & 18617 & 0.1 & 0.0 & 0.0 \\\\\n200 & 400 & 20470 & 19140 & 13998.7 & 18646.2 & 19140.0 & 1.4 & 19140 & 7.6 & 19140 & 1.4 & - & - & - & - & - & 19140 & 19140 & 0.1 & 0.0 & 0.0 \\\\\n200 & 600 & 1797 & 15029 & 1111.6 & 11425.8 & 12451.6 & 4.9 & - & 12.6 & - & 8.6 & 13072.9 & 14707 & 13171.2 & 14086 & 5010.0 & 12841 & 14405 & 5010.0 & 2.5 & 2.3 \\\\\n200 & 600 & 3594 & & - & 12487.0 & & - & - & 24.0 & - & - & 17532.7 & - & 17595.0 & - & 5010.0 & 0 & 0 & 158.9 & \\textbf{-100.0} & \\textbf{-100.0} \\\\\n200 & 600 & 5391 & & - & 12873.2 & & - & - & 31.6 & - & - & \\emph{Infeas} & \\emph{Infeas} & \\emph{Infeas} & \\emph{Infeas} & 16.4 & \\emph{Infeas} & \\emph{Infeas} & 0.0 & 0.0 & 0.0 \\\\\n200 & 600 & 34504 & 20716 & 9466.1 & 15393.1 & 20716.0 & 3.0 & 20716 & 11.6 & 20716 & 3.0 & - & - & - & - & - & 20716 & 20716 & 0.1 & 0.0 & 0.0 \\\\\n200 & 600 & 42860 & 18025 & 9100.6 & 13971.5 & 18025.0 & 2.1 & 18025 & 12.0 & 18025 & 2.2 & - & - & - & - & - & 18025 & 18025 & 0.2 & 0.0 & 0.0 \\\\\n200 & 600 & 50984 & 20864 & 8734.5 & 16708.1 & 20864.0 & 1.9 & 20864 & 12.5 & 20864 & 2.0 & - & - & - & - & - & 20864 & 20864 & 0.1 & 0.0 & 0.0 \\\\\n200 & 800 & 3196 & 22110 & 17428.8 & 17922.6 & 19685.1 & 8.6 & 22350.8 & 22.2 & - & 18.9 & 20744.2 & 21852 & 20941.5 & 21553 & 5010.1 & 20030 & 22722 & 5010.0 & 4.4 & 5.4 \\\\\n200 & 800 & 6392 & - & - & 19705.7 & - & & - & 28.4 & - & - & 26361.3 & - & 26526.7 & - & 5010.1 & 24259 & - & 5010.0 & 8.5 & 0.0 \\\\\n200 & 800 & 9588 & - & - & 20684.8 & - & & - & 38.3 & - & - & 29443.6 & - & 30634.2 & - & 5010.0 & 28351 & - & 5010.0 & 7.5 & 0.0 \\\\\n200 & 800 & 15980 & - & - & 20226.9 & - & & - & 48.0 & - & - & 33345.1 & - & 36900.2 & - & 5010.0 & 0 & 0 & 35.4 & \\textbf{-100.0} & \\textbf{-100.0} \\\\\n200 & 800 & 62625 & 39895 & 16806.5 & 23792.3 & 39895.0 & 43.3 & 39895 & 16.7 & 39895 & 45.8 & - & - & - & - & - & 39895 & 39895 & 0.2 & 0.0 & 0.0 \\\\\n200 & 800 & 78387 & 37671 & 15803.1 & 22174.2 & 37671.0 & 7.7 & 37671 & 15.9 & 37671 & 8.2 & - & - & - & - & - & 37671 & 37671 & 0.2 & 0.0 & 0.0 \\\\\n200 & 800 & 93978 & 38798 & 15470.1 & 24907.0 & 38798.0 & 4.5 & 38798 & 17.3 & 38798 & 4.7 & - & - & - & - & - & 38798 & 38798 & 0.1 & 0.0 & 0.0 \\\\\n300 & 600 & 31000 & 43721 & 34154.2 & 42720.6 & 43721.0 & 3.0 & 43721 & 19.2 & 43721 & 3.4 & - & - & - & - & - & 43721 & 43721 & 0.1 & 0.0 & 0.0 \\\\\n300 & 600 & 38216 & 44267 & 33320.1 & 43486.7 & 44267.0 & 3.1 & 44267 & 22.3 & 44267 & 3.4 & - & - & - & - & - & 44267 & 44267 & 0.2 & 0.0 & 0.0 \\\\\n300 & 600 & 45310 & 43071 & 32072.3 & 42149.0 & 43071.0 & 3.0 & 43071 & 24.6 & 43071 & 3.1 & - & - & - & - & - & 43071 & 43071 & 0.3 & 0.0 & 0.0 \\\\\n300 & 800 & 3196 & - & - & 30190.1 & - & - & - & 62.8 & - & - & \\emph{Infeas} & \\emph{Infeas} & \\emph{Infeas} & \\emph{Infeas} & 2911.1 & \\emph{Infeas} & \\emph{Infeas} & 3.3 & 0.0 & 0.0 \\\\\n300 & 800 & 59600 & 43125 & 24384.3 & 36629.6 & 43125.0 & 3.3 & 43125 & 32.7 & 43125 & 3.1 & - & - & - & - & - & 43125 & 43125 & 0.1 & 0.0 & 0.0 \\\\\n300 & 800 & 74500 & 42292 & 22913.2 & 38069.3 & 42292.0 & 3.2 & 42292 & 35.6 & 42292 & 3.2 & - & - & - & - & - & 42292 & 42292 & 0.3 & 0.0 & 0.0 \\\\\n300 & 800 & 89300 & 44114 & 21624.6 & 38843.0 & 44114.0 & 3.4 & 44114 & 35.4 & 44114 & 3.2 & - & - & - & - & - & 44114 & 44114 & 0.1 & 0.0 & 0.0 \\\\\n300 & 1000 & 4995 & - & - & 40732.7 & - & - & - & 79.3 & - & - & 51451.3 & - & 51398.4 & - & 5010.0 & 48180 & - & 5010.0 & 6.4 & 0.0 \\\\\n300 & 1000 & 9990 & - & - & 42902.5 & - & - & - & 119.1 & - & - & 60907.8 & - & 61878.9 & - & 5010.0 & \\emph{Infeas} & \\emph{Infeas} & 181.3 & \\textbf{-100.0} & \\textbf{-100.0} \\\\\n300 & 1000 & 14985 & - & - & 44639.1 & - & - & - & 141.3 & - & - & \\emph{Infeas} & \\emph{Infeas} & \\emph{Infeas} & \\emph{Infeas} & 1820.0 & \\emph{Infeas} & \\emph{Infeas} & 9.7 & 0.0 & 0.0 \\\\\n300 & 1000 & 96590 & 71562 & 36544.7 & 56048.3 & 71562.0 & 9.2 & 71562 & 41.5 & 71562 & 9.5 & - & - & - & - & - & 71562 & 71562 & 0.1 & 0.0 & 0.0 \\\\\n300 & 1000 & 120500 & 76345 & 34380.8 & 58780.1 & 76345.0 & 6.6 & 76345 & 33.4 & 76345 & 6.7 & - & - & - & - & - & 76345 & 76345 & 0.2 & 0.0 & 0.0 \\\\\n300 & 1000 & 144090 & 78880 & 33481.2 & 60810.8 & 78880.0 & 5.2 & 78880 & 36.9 & 78880 & 5.4 & - & - & - & - & - & 78880 & 78880 & 0.1 & 0.0 & 0.0 \\\\ \\hline\n \\multicolumn{4}{l}{Averages} & & & & & & & & & & & & & & & & & -9.1 & -9.8 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On Solving the Minimum Spanning Tree Problem with Conflicting Edge Pairs", "authors": ["Roberto Montemanni", "Derek H. Smith"], "url": "https://arxiv.org/abs/2504.14503v1", "attribution": "\"On Solving the Minimum Spanning Tree Problem with Conflicting Edge Pairs\" by Roberto Montemanni and Derek H. Smith, arXiv:2504.14503v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.06926v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccccc}\n \\toprule\n & $a_\\gamma$ & $b_\\gamma$ & $a_\\lambda$ &$b_\\lambda$ & $a_1$ & $b_1$ & $a_2$ & $b_2$ & $\\mu$ & $\\Sigma$\\\\ \n \\midrule\n vague & 0.2 & 0.1 & 2.001 & 1 & 2.001 & 1 & 2.001 & 1 & $(0,\\ldots,0)^\\top$ & $100 \\mathrm{I}_{p}$\\\\\n regularized & 1 & 1 & 2.1 & 1.1 & 2.1 & 1.1 & 2.1 & 1.1 & $(0,\\ldots,0)^\\top$ & $10 \\mathrm{I}_{p}$\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Parameters of the prior distribution.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian inference and cure rate modeling for event history data", "authors": ["Panagiotis Papastamoulis", "Fotios Milienos"], "url": "https://arxiv.org/abs/2310.06926v2", "attribution": "\"Bayesian inference and cure rate modeling for event history data\" by Panagiotis Papastamoulis and Fotios Milienos, arXiv:2310.06926v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.16270v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n$\\mathfrak{g}$ & $\\mathbf{R}$ & $A_{\\mathbf{R}}$ & $B_{\\mathbf{R}}$ & $C_{\\mathbf{R}}$\\\\ \\hline\n$\\mathfrak{su}(N),$ & $\\mathbf{Adj}$ &$2N$&$0$&$N+6$\\\\ \\cline{2-5}\n$N=2,3$ & $\\mathbf{F}$ &$1$&$0$&$\\frac{1}{2}$\\\\ \\hline\n$\\mathfrak{su}(N),$ & $\\mathbf{Adj}$ &$2N$&$2N$&$6$\\\\ \\cline{2-5}\n$N\\geq 4$ & $\\mathbf{F}$ &$1$&$1$&$0$\\\\ \\hline\n\\multirow{2}{*}{$\\mathfrak{g}_2$} & $\\mathbf{Adj}$ &$8$&$0$&$10$\\\\ \\cline{2-5}\n & $\\mathbf{7}$ &$2$&$0$&$1$\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Group theory coefficients.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Refined BPS numbers on compact Calabi-Yau threefolds from Wilson loops", "authors": ["Min-xin Huang", "Sheldon Katz", "Albrecht Klemm", "Xin Wang"], "url": "https://arxiv.org/abs/2503.16270v2", "attribution": "\"Refined BPS numbers on compact Calabi-Yau threefolds from Wilson loops\" by Min-xin Huang, Sheldon Katz, Albrecht Klemm, and Xin Wang, arXiv:2503.16270v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10404v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|}\n\t\t\\cline{2-6}\n\t\t & \n\t\t\\textbf{Centralized} & \n\t\t\\multicolumn{4}{c|}{\\textbf{Independent \n\t\t\t\tplanning}} \n\t\t\\\\ \\cline{2-6} \n\t\t& \\textbf{4 UAS} & \\textbf{UAS 1} & \\textbf{UAS 2} & \n\t\t\\textbf{UAS 3} & \\textbf{UAS 4} \\\\ \\cline{2-6} \n\t\t\\hline\\hline\n\t\t\\textbf{Mean (s)} & \n\t\t81.918 & 15.596 & \n\t\t17.444 & \n\t\t17.553 & 16.128 \\\\ \\hline\n\t\t\\textbf{SD (s)} & \n\t\t27.215 & 6.257 & \n\t\t7.404 & \n\t\t6.912 & 6.764 \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme", "authors": ["Alëna Rodionova", "Yash Vardhan Pant", "Connor Kurtz", "Kuk Jang", "Houssam Abbas", "Rahul Mangharam"], "url": "https://arxiv.org/abs/2101.10404v1", "attribution": "\"Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme\" by Alëna Rodionova, Yash Vardhan Pant, Connor Kurtz, Kuk Jang, Houssam Abbas, and Rahul Mangharam, arXiv:2101.10404v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.13696v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|ll|ll|llll}\n\t\t\\hline \n\t\t& & \\multicolumn{2}{c}{$t$-fusion} & \\multicolumn{2}{c}{Laplace fusion} & \\multicolumn{2}{c}{L1 fusion} & & \\\\\n\t\tSignal & $\\sigma$ & MSE & se.MSE & MSE & se.MSE & MSE & se.MSE & & \\\\\n\t\t\\hline \n\t\t& 0.1 & 0.001805 & 0.001038 & 0.231678 & 0.007697 & 0.005109 & 0.002515 & & \\\\\n\t\teven & 0.3 & 0.037377 & 0.016703 & 0.26522 & 0.019801 & 0.03757 & 0.01968 & & \\\\\n\t\t& 0.5 & 0.262432 & 0.308113 & 0.322831 & 0.036163 & 0.097532 & 0.051369 & & \\\\\n\t\t&&&&&&&&&\\\\\n\t\t& 0.1 & 0.002014 & 0.001204 & 0.273741 & 0.00784 & 0.005525 & 0.00261 & & \\\\\n\t\tuneven & 0.3 & 0.042672 & 0.014474 & 0.308941 & 0.018109 & 0.038874 & 0.019228 & & \\\\\n\t\t& 0.5 & 0.524007 & 0.45675 & 0.368195 & 0.032283 & 0.099418 & 0.047619 & & \\\\\n\t\t&&&&&&&&&\\\\\n\t\t& 0.1 & 0.00667 & 0.006476 & 0.336768 & 0.00613 & 0.008828 & 0.001995 & & \\\\\n\t\tvery uneven & 0.3 & 0.192092 & 0.239714 & 0.364801 & 0.014689 & 0.063152 & 0.022884 & & \\\\\n\t\t& 0.5 & 0.988966 & 0.111079 & 0.410559 & 0.025365 & 0.139124 & 0.058646 & & \\\\\n\t\t\\hline \n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Linear Chain Graphs: Comparison of performance wrt MSE for $t$-fusion and Laplace fusion methods along with $L_1$ fusion method.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Graph signal denoising using $t$-shrinkage priors", "authors": ["Sayantan Banerjee", "Weining Shen"], "url": "https://arxiv.org/abs/2012.13696v1", "attribution": "\"Graph signal denoising using $t$-shrinkage priors\" by Sayantan Banerjee and Weining Shen, arXiv:2012.13696v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.05836v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\\hline\n{Parameter} & {$n$} & {\\thead{Average\\\\ Bias}} & {MSE} & {{\\thead{Average Asymptotic\\\\ Confidence Interval}}} & {{\\thead{Average Bootstrap\\\\ Confidence Interval}}} \\\\ \\hline\n$\\lambda_1$ & 50 & 0.0005 & 5.0713e-07 & (0.0288, 0.0312) & (0.0287, 0.0314) \\\\\n$\\lambda_2$ & 50 & 0.0015 & 3.6271e-06 & (0.0766, 0.0835) & (0.0764, 0.0838)\\\\\np & 50 & 0.0514 & 0.0042 & (0.5751, 0.8259) & (0.5707, 0.8208)\\\\\n$\\mu$ & 50 & 0.9429 & 1.3962 & (67.2930, 72.7170) & (67.7036, 72.3167)\\\\ \\hline\n$\\lambda_1$ & 100 & 0.0004 & 2.5451e-07 & (0.0292, 0.0309) & (0.0290, 0.0310)\\\\\n$\\lambda_2$ & 100 & 0.0011 & 1.7817e-06 & (0.0775, 0.0825) & (0.0775, 0.0827)\\\\\np & 100 & 0.0370 & 0.0021 & (0.6101,0.7888) & (0.6092, 0.7875)\\\\\n$\\mu$ & 100 & 0.6670 & 0.6988 & (68.3382, 71.6597) & (68.3776, 71.6330)\\\\ \\hline\n$\\lambda_1$ & 150 & 0.0003 & 1.6912e-07 & (0.0293, 0.0307) & (0.0292, 0.0308)\\\\\n$\\lambda_2$ & 150 & 0.0009 & 1.1934e-06 & (0.0780, 0.0820) & (0.0779, 0.0822)\\\\\np & 150 & 0.0296 & 0.0014 & (0.6265, 0.7727) & (0.6259, 0.7714)\\\\\n$\\mu$ & 150 & 0.5448 & 0.4664 & (68.6443, 71.3563) & (68.6727, 71.3374)\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modeling time to failure using a temporal sequence of events", "authors": ["Sandip K Pal", "Arnab Koley", "Pritam Ranjan", "Debasis Kundu"], "url": "https://arxiv.org/abs/2412.05836v1", "attribution": "\"Modeling time to failure using a temporal sequence of events\" by Sandip K Pal, Arnab Koley, Pritam Ranjan, and Debasis Kundu, arXiv:2412.05836v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12899v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance Evaluation of the SHAP-enabled Lighter Model using 5-fold Repeated Cross Validation}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n \\thead{Performance \\\\Metric} & \\thead{Baseline ML \\\\Model using \\\\all features} & \\thead{Lighter ML\\\\ Model using \\\\Top 5 features} & \\thead{Net\\\\Gain}\\\\\n \\hline\n RMSE & $3.542 \\pm 0.036$ & $3.661 \\pm 0.038$ & $-3.35\\%$\\\\\n \\hline\n $R^2$ & $0.854 \\pm 0.003$ & $0.844 \\pm 0.003$ & $-1.17\\%$\\\\\n \\hline\n Training Time & $1.896 \\pm 0.090$ & $0.681 \\pm 0.049$ & $\\mathbf{+64.07\\%}$\\\\\n \\hline\n Prediction Time & $0.077 \\pm 0.005$ & $0.023 \\pm 0.002$ & $\\mathbf{+70.13\\%}$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Interpretable AI-based Large-scale 3D Pathloss Prediction Model for enabling Emerging Self-Driving Networks", "authors": ["Usama Masood", "Hasan Farooq", "Ali Imran", "Adnan Abu-Dayya"], "url": "https://arxiv.org/abs/2201.12899v1", "attribution": "\"Interpretable AI-based Large-scale 3D Pathloss Prediction Model for enabling Emerging Self-Driving Networks\" by Usama Masood, Hasan Farooq, Ali Imran, and Adnan Abu-Dayya, arXiv:2201.12899v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.12910v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of detection methods on Folktables data.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nMethod & Power & FDP & Mean detection time \\\\ \\midrule\nQuantile detector & 0.48 & 0.019 & 3727 \\\\\nMean detector & 0.01 & 0.19 & 4945 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Sequential Harmful Shift Detection Without Labels", "authors": ["Salim I. Amoukou", "Tom Bewley", "Saumitra Mishra", "Freddy Lecue", "Daniele Magazzeni", "Manuela Veloso"], "url": "https://arxiv.org/abs/2412.12910v1", "attribution": "\"Sequential Harmful Shift Detection Without Labels\" by Salim I. Amoukou, Tom Bewley, Saumitra Mishra, Freddy Lecue, Daniele Magazzeni, and Manuela Veloso, arXiv:2412.12910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.01045v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance on the BraTs’20 Testing Data.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\n \\hline\n Metric (mean) & ET & WT & TC \\\\\n \\hline\n Dice & 0.78507 & 0.88595 & 0.84273 \\\\\n Sensitivity & 0.81308 & 0.91690 & 0.85934 \\\\\n Specificity & 0.99967 & 0.99905 & 0.99964 \\\\\n Hausdorff (95\\%) & 20.36071 & 6.66665 & 19.54915 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution", "authors": ["Theophraste Henry", "Alexandre Carre", "Marvin Lerousseau", "Theo Estienne", "Charlotte Robert", "Nikos Paragios", "Eric Deutsch"], "url": "https://arxiv.org/abs/2011.01045v2", "attribution": "\"Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution\" by Theophraste Henry, Alexandre Carre, Marvin Lerousseau, Theo Estienne, Charlotte Robert, Nikos Paragios, and Eric Deutsch, arXiv:2011.01045v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2102.00816v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison between VRoC and baselines on the rumor stance classification task. }\n\\begin{tabular}{lcccccc}\n\t\t\t\\toprule\n\t\t\t& & & Support & Deny & Comment & Query\\\\\n\t\t\t& Accuracy & Macro-F1 & F1 & F1 & F1 & F1 \\\\\\hline\n\t\t\tMT-US* & - & $0.400$ & $0.355$ & $0.116$ & $\\textbf{0.776}$ & $0.337$ \\\\\n\t\t\tMT-ES* & - & $0.430$ & $0.314$ & $0.158$ & $0.739$ & $\\textbf{0.531}$ \\\\\n\t\t\tVAE-LSTM & $0.464$ & $0.461$ & $0.447$ & $0.395$ & $0.588$ & $0.416$ \\\\\n\t\t\tVRoC & $\\textbf{0.533}$ & $\\textbf{0.522}$ & $\\textbf{0.452}$ & $\\textbf{0.415}$ & $0.712$ & $0.511$\\\\\\hline\n\t\t\tTreeCRF* (L) & - & $0.440$ & $0.462$ & $0.088$ & $\\textbf{0.773}$ & $0.435$ \\\\\n\t\t\tLinearCRF* (L) & - & $0.433$ & $0.454$ & $0.105$ & $0.767$ & $\\textbf{0.495}$ \\\\\n\t\t\tVAE-LSTM (L) & $0.467$ & $0.459$ & $\\textbf{0.463}$ & $0.423$ & $0.567$ & $0.384$ \\\\\n\t\t\tVRoC (L) & $\\textbf{0.480}$ & $\\textbf{0.473}$ & $0.452$ & $\\textbf{0.429}$ & $0.596$ & $0.416$\\\\\n \\bottomrule\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text", "authors": ["Mingxi Cheng", "Shahin Nazarian", "Paul Bogdan"], "url": "https://arxiv.org/abs/2102.00816v1", "attribution": "\"VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text\" by Mingxi Cheng, Shahin Nazarian, and Paul Bogdan, arXiv:2102.00816v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.14552v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Main parameters of real data. Values in parentheses represent the standard deviation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cl}\n \\toprule\n \\textbf{Parameter} & \\textbf{Value(s)} \\\\\n \\midrule\n fps & 10-20 \\\\\n Duration & 10 seconds \\\\\n Frames per sweep & 100-300 \\\\\n Plane & sagittal / transversal \\\\\n \\midrule\n Age & 38.33 (8.48) \\\\\n Height [m] & 1.65 (0.04)\\\\\n Weight [kg] & 64.53 (5.32)\\\\\n Ethnicity & White/European\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound", "authors": ["Max Krähenmann", "Sergio Tascon-Morales", "Fabian Laumer", "Julia E. Vogt", "Ece Ozkan"], "url": "https://arxiv.org/abs/2508.14552v1", "attribution": "\"From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound\" by Max Krähenmann, Sergio Tascon-Morales, Fabian Laumer, Julia E. Vogt, and Ece Ozkan, arXiv:2508.14552v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.00503v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{15Hz token rate for ASR experiment.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Method} & \\textbf{WER} & \\textbf{CER} \\\\\n\\midrule\nboundary + max pooling (w/o CALE) & 6.4 & 3.4 \\\\\nboundary + average pooling (w/o CALE) & 6.8 & 3.6 \\\\\nboundary + attention pooling (w/o CALE) & 6.1 & 3.3 \\\\\n\\textbf{Ours (with CALE)} & \\textbf{5.6} & \\textbf{2.9} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Entropy-based Coarse and Compressed Semantic Speech Representation Learning", "authors": ["Jialong Zuo", "Guangyan Zhang", "Minghui Fang", "Shengpeng Ji", "Xiaoqi Jiao", "Jingyu Li", "Yiwen Guo", "Zhou Zhao"], "url": "https://arxiv.org/abs/2509.00503v1", "attribution": "\"Entropy-based Coarse and Compressed Semantic Speech Representation Learning\" by Jialong Zuo, Guangyan Zhang, Minghui Fang, Shengpeng Ji, Xiaoqi Jiao, Jingyu Li, Yiwen Guo, and Zhou Zhao, arXiv:2509.00503v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12406v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\hline\nAttacker & Guesswork & ReId AUC\\\\ \n\\hline\nSAU & $8476 $ $(1971, 20225)$ & $0.50$ $(0.49, 0.52)$ \\\\\nViT & $8411 $ $(5219, 12033)$ & $0.50$ $(0.49, 0.51)$ \\\\\nResnet-18 & $10070$ $(9871, 10300)$ & $0.50$ $(0.47, 0.53)$ \t \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Syfer: Neural Obfuscation for Private Data Release", "authors": ["Adam Yala", "Victor Quach", "Homa Esfahanizadeh", "Rafael G. L. D'Oliveira", "Ken R. Duffy", "Muriel Médard", "Tommi S. Jaakkola", "Regina Barzilay"], "url": "https://arxiv.org/abs/2201.12406v1", "attribution": "\"Syfer: Neural Obfuscation for Private Data Release\" by Adam Yala, Victor Quach, Homa Esfahanizadeh, Rafael G. L. D'Oliveira, Ken R. Duffy, Muriel Médard, Tommi S. Jaakkola, and Regina Barzilay, arXiv:2201.12406v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.00101v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\hline\\hline\n& \\multicolumn{6}{c}{\\hspace{3ex} \\textbf{State}}\\\\\n\\cline{2-7}\n& $S_{0}$ & $S_{1}$ & $S_{2}$ & $S_{3}$ & $S_{4}$ & $S_{5+}$\\\\\n\\hline\nabs.freq & 64,253& 76,977& 58,342& 27,476& 15,462& 53,520\\\\\n \\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Number of observations in each state to model the time process.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "mCube: Multinomial Micro-level reserving Model", "authors": ["Emmanuel Jordy Menvouta", "Jolien Ponnet", "Robin Van Oirbeek", "Tim Verdonck"], "url": "https://arxiv.org/abs/2212.00101v1", "attribution": "\"mCube: Multinomial Micro-level reserving Model\" by Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek, and Tim Verdonck, arXiv:2212.00101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.16844v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small REP results over the year.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rllrrrrrrrrr} \\toprule\n \\multicolumn{3}{c}{\\textbf{ }}\n &\n \\textbf{Electrolyzer}&\n \\textbf{REP} &\n \\textbf{Average}\n \\\\ \n &\n &\n &\n \\textbf{average load} &\n \\textbf{profit} &\n \\textbf{LMP}$^{\\rm a}$\\\\\n \\multicolumn{3}{c}{\\textbf{Case}} &\n [MW] &\n [M \\$] &\n [\\$/MWh]\\\\\n \\toprule\n\\multirow{4}{*}{100 MW} & \\multirow{2}{*}{$\\$1.5$/kg} & bidder & 91.44 & 28.88 & 17.81 \\\\\n & & fixed & 91.44 & 28.14 & 17.75\\\\\n & \\multirow{2}{*}{$\\$6$/kg} & bidder & 100.00 & 98.12 & 17.83\\\\\n & & fixed & 100.00 & 98.12 & 17.83\\\\ \\midrule\n\\multirow{4}{*}{500 MW} & \\multirow{2}{*}{$\\$1.5$/kg} & bidder & 421.15 & 59.73 & 21.55 \\\\\n & & fixed & 421.15 & 47.36 & 24.30\\\\\n & \\multirow{2}{*}{$\\$6$/kg} & bidder & 499.12 & 362.15 & 31.77\\\\\n & & fixed & 499.01 & 352.36 & 33.92\\\\ \\midrule\n\\multirow{4}{*}{1000 MW} & \\multirow{2}{*}{$\\$1.5$/kg} & bidder & 629.77 & 67.76 & 24.45\\\\\n & & fixed & 617.38 & -730.61 & 179.60\\\\\n & \\multirow{2}{*}{$\\$6$/kg} & bidder & 729.34 & 320.83 & 81.54\\\\\n & & fixed & 672.3 & -563.48 & 241.06 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Electricity Market Bidding for Renewable Electrolyzer Plants: An Opportunity Cost Approach", "authors": ["Andrea Gloppen Johnsen", "Lesia Mitridati", "Jalal Kazempour", "Line Roald"], "url": "https://arxiv.org/abs/2501.16844v3", "attribution": "\"Electricity Market Bidding for Renewable Electrolyzer Plants: An Opportunity Cost Approach\" by Andrea Gloppen Johnsen, Lesia Mitridati, Jalal Kazempour, and Line Roald, arXiv:2501.16844v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13040v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc|cc}\n N & $L^2$-err in $h_h$ & rate \n & $L^2$-err in $u_h$ & rate\n& $L^2$-err in $m_h$ & rate\\\\\n\\hline\n 25& 1.420e-03 & -- & 1.670e-03 & -- & 1.379e-02 &-- \\\\\n 50& 1.567e-04 & 3.18 & 2.083e-04 & 3.00 & 1.683e-03 & 3.03 \\\\\n100& 1.917e-05 & 3.03 & 2.820e-05 & 2.89 & 2.371e-04 & 2.83 \\\\\n200& 2.363e-06 & 3.02 & 3.880e-06 & 2.86 & 3.403e-05 & 2.80 \\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A high-order velocity-based discontinuous Galerkin scheme for the shallow water equations: local conservation, entropy stability, well-balanced property, and positivity preservation", "authors": ["Guosheng Fu"], "url": "https://arxiv.org/abs/2201.13040v1", "attribution": "\"A high-order velocity-based discontinuous Galerkin scheme for the shallow water equations: local conservation, entropy stability, well-balanced property, and positivity preservation\" by Guosheng Fu, arXiv:2201.13040v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.05935v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Voltages for points $A$ and $B$ for the 9-bus test system in~.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline \n & Point A & Point B\\tabularnewline\n\\hline \n\\hline \n1 & 0.9025 + 0.0000i & 0.9058 + 0.0000i\\tabularnewline\n\\hline \n2 & 0.9042 - 0.1541i & 0.9220 + 0.0789i\\tabularnewline\n\\hline \n3 & 0.9063 - 0.1819i & 0.9082 + 0.2334i\\tabularnewline\n\\hline \n4 & 0.9052 - 0.0927i & 0.9089 - 0.0063i\\tabularnewline\n\\hline \n5 & 0.8961 - 0.1606i & 0.9103 + 0.0105i\\tabularnewline\n\\hline \n6 & 0.9082 - 0.1888i & 0.9297 + 0.1444i\\tabularnewline\n\\hline \n7 & 0.8934 - 0.2080i & 0.9246 + 0.0647i\\tabularnewline\n\\hline \n8 & 0.9034 - 0.1787i & 0.9275 + 0.0545i\\tabularnewline\n\\hline \n9 & 0.8848 - 0.1644i & 0.8995 - 0.0302i\\tabularnewline\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Certifying the Nonexistence of Feasible Path Between Power System Operating Points", "authors": ["Mohammad Rasoul Narimani", "Katherine R. Davis", "Daniel K. Molzahn"], "url": "https://arxiv.org/abs/2509.05935v1", "attribution": "\"Certifying the Nonexistence of Feasible Path Between Power System Operating Points\" by Mohammad Rasoul Narimani, Katherine R. Davis, and Daniel K. Molzahn, arXiv:2509.05935v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.06714v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{avg performance of PATH-STAB}\n\\begin{tabular}{c|cccc}\\toprule\ntime & $\\frac{\\text{best primal value}}{\\text{best known dual bound}}$ & $\\frac{\\text{best primal value}}{\\text{best dual bound of Gurobi}}$ \\\\\n\\midrule\n60s & 1.6\\% & 17.2\\% \\\\\n1200s & 1.6\\% & 12.1\\% \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Sensitivity analysis for mixed binary quadratic programming", "authors": ["Diego Cifuentes", "Santanu S. Dey", "Jingye Xu"], "url": "https://arxiv.org/abs/2312.06714v2", "attribution": "\"Sensitivity analysis for mixed binary quadratic programming\" by Diego Cifuentes, Santanu S. Dey, and Jingye Xu, arXiv:2312.06714v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16933v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of various PFD circuits}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|}\n\\hline\n & \\textbf{This Work} & & & & & & \\\\\n\\hline\n\\textbf{Process Technology (nm)} & \\textbf{28} & 180 & 28 & 28 & 28 & 28 & 65 \\\\\n\\hline\n\\textbf{Dead Zone (ps)} & \\textbf{40} & 0 & 0 & 0 & 70 & 45 & -- \\\\\n\\hline\n\\textbf{Power Consumption (uW)} & \\textbf{4.41} & 201.6* & 11.04** & 15.44** & 2.92** & 21.9** & 422.4* \\\\\n\\hline\n\\textbf{Transistor Count} & \\textbf{20} & 8 & 22 & 16 & 54 & 22 & 14 \\\\\n\\hline\n\\textbf{Max Frequency (GHz)} & \\textbf{3} & 4 & 2.9 & 3.3 & 0.8 & 1 & 2.2 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "TSPC-PFD: TSPC-Based Low-Power High-Resolution CMOS Phase Frequency Detector", "authors": ["Dhandeep Challagundla", "Venkata Krishna Vamsi Sundarapu", "Ignatius Bezzam", "Riadul Islam"], "url": "https://arxiv.org/abs/2508.16933v1", "attribution": "\"TSPC-PFD: TSPC-Based Low-Power High-Resolution CMOS Phase Frequency Detector\" by Dhandeep Challagundla, Venkata Krishna Vamsi Sundarapu, Ignatius Bezzam, and Riadul Islam, arXiv:2508.16933v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.15282v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|cc|cc|cc}\n \\toprule\n \n&\n \n&\n\\multicolumn{2}{c}{Off policy} \n& \n\\multicolumn{2}{c}{On policy} \n&\n\\multicolumn{2}{c}{Train time}\n\\\\\n\\cmidrule(l){3-4}\\cmidrule(l){5-6}\\cmidrule{7-8} \nTarget Date & Metric &Cf& Ss & Cf & Ss & Cf & Ss \\\\\n\\midrule\n& Demand Error& & & &\\\\\n21-11-2022 & & \\textbf{61.48} & 62.46 & 60.00 & \\textbf{58.41} & 2.55 & 1.63\\\\\n23-11-2020 & &\\textbf{65.94} & 68.67 &\\textbf{62.50} & 64.24 & 1.17 & 0.71\\\\\n25-11-2019 & &\\textbf{63.61} & 67.52 &61.85 &\\textbf{61.60} & 0.83 & 0.52\\\\\n\\midrule\n25-04-2022 & && & 46.55 & \\textbf{46.28} & 1.96 & 1.27\\\\\n06-06-2022 & && & \\textbf{47.84} & 50.26 & 2.11 & 1.36\\\\\n10-10-2022 & && &\\textbf{51.97} & 52.66 & 2.44 & 1.61\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Causal Forecasting for Pricing", "authors": ["Douglas Schultz", "Johannes Stephan", "Julian Sieber", "Trudie Yeh", "Manuel Kunz", "Patrick Doupe", "Tim Januschowski"], "url": "https://arxiv.org/abs/2312.15282v3", "attribution": "\"Causal Forecasting for Pricing\" by Douglas Schultz, Johannes Stephan, Julian Sieber, Trudie Yeh, Manuel Kunz, Patrick Doupe, and Tim Januschowski, arXiv:2312.15282v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2102.00509v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c||c|c|c}\n Metric~ & FAQ & FAKE & GEN \\\\ \\hline\\hline\n Precision \t& 0.44 & 0.22 & 0.58 \\\\\n Recall & 0.61 & 0.08 & 0.64 \\\\\n F1 score & 0.51 & 0.12 & 0.61 \\\\\n Accuracy & 0.67 & 0.602 & 0.68 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance metrics of the DNN classification.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "TruthBot: An Automated Conversational Tool for Intent Learning, Curated Information Presenting, and Fake News Alerting", "authors": ["Ankur Gupta", "Yash Varun", "Prarthana Das", "Nithya Muttineni", "Parth Srivastava", "Hamim Zafar", "Tanmoy Chakraborty", "Swaprava Nath"], "url": "https://arxiv.org/abs/2102.00509v1", "attribution": "\"TruthBot: An Automated Conversational Tool for Intent Learning, Curated Information Presenting, and Fake News Alerting\" by Ankur Gupta, Yash Varun, Prarthana Das, Nithya Muttineni, Parth Srivastava, Hamim Zafar, Tanmoy Chakraborty, and Swaprava Nath, arXiv:2102.00509v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.10858v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Contract trading results of unrestricted and mandatory contracting with different $\\beta$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|ccccc}\n\\hline\n\\hline\n& & $\\beta=0.2$ & $\\beta=0.4$ & $\\beta=0.6$ & $\\beta=0.8$ \\\\\n\\hline\n\\multirow{18}{*}{Unrestricted}&Trade Volume (GW) \\\\\n&\\quad Baseload \\\\ \n&\\qquad Future & 27.9 & 28.8 & 30.0 & 31.2 \\\\\n&\\qquad Options & 2.5 & 2.6 & 3.4 & 4.1 \\\\ \n&\\qquad Unit & 0 & 0 & 0 & 0 \\\\\n&\\quad Peaker \\\\ \n&\\qquad Future & 0 & 0 & 0 & 0 \\\\ \n&\\qquad Options & 120.0 & 119.4 & 119.5 & 121.4 \\\\ \n&\\qquad Unit & 0 & 0 & 0 & 0 \\\\\n&\\quad Variable \\\\\n&\\qquad Future & 0 & 0 & 0 & 0 \\\\\n&\\qquad Options & 0 & 0 & 0 & 0 \\\\ \n&\\qquad Unit & 159.0 & 157.3 & 155.4 & 154.8 \\\\\n&&&&\\\\\n&Contract risk premium (\\$/MW-yr) \\\\\n&\\qquad Future & 16470 & 16113 & 16032 & 13481 \\\\\n&\\qquad Options & 6747 & 6738 & 6261 & 3961 \\\\ \n&\\qquad Unit & 3455 & 3315 & 3451 & 3185 \\\\\n\\hline\n\\multirow{18}{*}{Mandatory}&Trade Volume (GW) \\\\\n&\\quad Baseload \\\\ \n&\\qquad Future & 52.5 & 45.7 & 41.6 & 33.7 \\\\\n&\\qquad Options & 52.5 & 45.7 & 41.6 & 32.8 \\\\ \n&\\qquad Unit & 0 & 0 & 0 & 0 \\\\\n&\\quad Peaker \\\\ \n&\\qquad Future & 0 & 0 & 0.08 & 0.3 \\\\\n&\\qquad Options & 80.0 & 84.3 & 86.9 & 93.4 \\\\ \n&\\qquad Unit & 0 & 0 & 0 & 0 \\\\\n&\\quad Variable \\\\\n&\\qquad Future & 0 & 0 & 0 & 59.6 \\\\\n&\\qquad Options & 16.0 & 18.5 & 20.1 & 22.4 \\\\ \n&\\qquad Unit & 106.8 & 122.7 & 131.6 & 0.0002 \\\\\n&&&&\\\\\n&Contract risk premium (\\$/MW-yr) \\\\\n&\\qquad Future & 5638 & 5839 & 6646 & 5524 \\\\\n&\\qquad Options & 17366 & 4597 & 10108 & 10870 \\\\ \n&\\qquad Unit & 693 & 773 & 1171 & 1125 \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Beyond capacity: contractual form in electricity reliability obligations", "authors": ["Han Shu", "Jacob Mays"], "url": "https://arxiv.org/abs/2210.10858v1", "attribution": "\"Beyond capacity: contractual form in electricity reliability obligations\" by Han Shu and Jacob Mays, arXiv:2210.10858v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09827v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of GMRES-IR and RGMRES-IR refinement steps with the number of GMRES iterations for each refinement step for prolate matrices with various $\\alpha$ values, using precisions ($u_f,u,u_r$) = (half,single,double) and ($m,k$) = (16,5).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|}\n\t\t\\hline\n\t\t$\\alpha$ & GMRES-IR (16) & RGMRES-IR (16,5) \\\\ \\hline\n\t\t0.475 & 12 (6,6) & 8 (6,2) \\\\\n\t\t0.47 & 16 (8,8) & 10 (8,2) \\\\\n\t\t0.467 & 19 (9,10) & 11 (9,2) \\\\\n\t\t0.455 & 50 (15,25,10) & 19 (15,4) \\\\%44 (15,16,13) & 19 (15,4) \\\\\n\t\t0.45 & 89 (14,43,32) & - \\\\%61 (14,16,16,15) & - \\\\\n\t\t0.4468 & - & - \\\\\n\t\t0.44 & - & - \\\\\n\t\t0.434 & - & - \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Mixed Precision GMRES-based Iterative Refinement with Recycling", "authors": ["Eda Oktay", "Erin Carson"], "url": "https://arxiv.org/abs/2201.09827v2", "attribution": "\"Mixed Precision GMRES-based Iterative Refinement with Recycling\" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13382v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n \\toprule\n $n$ & $p$ & $|E|$ & $\\gamma_2$ & $ \\gamma$ & \\text{Lower} & $K$ &\\text{Upper } & $K^{**}$ \\\\\n \\midrule\n 500 & 0.9 & 112373 & 428.18 & 471.34 & 498.11 & 498.11& 498.11&497.84\\\\\n 500 & 0.7 & 87303 & 313.29& 385.94& 498.43& 498.43& 498.43 & 497.62 \\\\\n 500 & 0.5 & 62670 & 215.95& 292.17& 498.99& 498.99& 498.99 & 498.07 \\\\\n 500 & 0.05 & 6226 & 12.48& 44.32& 518.51& 518.54& 518.56 & 532.59 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Kemeny's constant and bounds for $G(500,p)$ with $p=0.9, 0.7, 0.5, 0.05$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Spectral Approach to Kemeny's Constant", "authors": ["Aida Abiad", "Ángeles Carmona", "Andrés M. Encinas", "Maria José Jiménez", "Álvaro Samperio"], "url": "https://arxiv.org/abs/2503.13382v1", "attribution": "\"A Spectral Approach to Kemeny's Constant\" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07006v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Samples, average NET2 lesion counts, and percentage of patients with NET2 lesion activity in the \\textit{training set} at 1 and 2 years post-treatment. w048 values are measured relative to pre-treatment; w096 values are relative to w048.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n \\textbf{Treatment} & \\textbf{Samples} & \\multicolumn{2}{c}{\\textbf{Avg. NET2 Count}} & \\multicolumn{2}{c}{\\textbf{NET2 Activity (\\%)}} \\\\\n \\cmidrule(lr){3-4} \\cmidrule(lr){5-6}\n & & \\textbf{w048} & \\textbf{w096} & \\textbf{w048} & \\textbf{w096} \\\\\n \\midrule\n Placebo & 267 & 4.60 & 3.33 & 63.7 & 58.1 \\\\\n Laquinimod (NE) & 184 & 5.51 & 4.49 & 64.7 & 57.1 \\\\\n Avonex (LE) & 214 & 4.37 & 4.37 & 54.7 & 51.9 \\\\\n Interferon Beta-1a (MiE) & 418 & 0.63 & 1.59 & 24.9 & 33.7 \\\\\n Dimethyl Fumarate (MoE) & 153 & 0.60 & 1.03 & 19.6 & 26.1 \\\\\n Ocrelizumab (HE) & 466 & 0.07 & 0.07 & 3.20 & 1.50 \\\\\n \\midrule\n \\textbf{Total} & 1702 & & & & \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments", "authors": ["Gian Mario Favero", "Ge Ya Luo", "Nima Fathi", "Justin Szeto", "Douglas L. Arnold", "Brennan Nichyporuk", "Chris Pal", "Tal Arbel"], "url": "https://arxiv.org/abs/2508.07006v1", "attribution": "\"Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments\" by Gian Mario Favero, Ge Ya Luo, Nima Fathi, Justin Szeto, Douglas L. Arnold, Brennan Nichyporuk, Chris Pal, and Tal Arbel, arXiv:2508.07006v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.17149v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{F1 Score (IN \\%) for N-d-p-K combinations on PE-ZTWCC+SDC features with TDNN Classifier}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\n\\textbf{SDC Combinations} & \\textbf{Repetitions} & \\textbf{Filled Pause} & \\textbf{Prolongation} \\\\\n\\hline\n13-\\textbf{1}-3-7 & 80.89 & 81.28 & 79.87 \\\\\n13-\\textbf{2}-3-7 & 81.34 & 81.89 & \\textbf{82.78} \\\\\n13-\\textbf{3}-3-7 & 79.12 & 80.12 & 79.23 \\\\\n\\hline\n13-2-3-\\textbf{5} & 82.56 & \\textbf{85.45} & 80.17\\\\\n13-2-3-\\textbf{6} & \\textbf{86.98} & 84.67 & 80.34 \\\\\n13-2-3-\\textbf{7} & 81.34 & 81.89 & \\textbf{82.78} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Typical vs. Atypical Disfluency Classification: Introducing the IIITH-TISA Corpus and Temporal Context-Based Feature Representations", "authors": ["Priyanka Kommagouni", "Vamshiraghusimha Narasinga", "Purva Barche", "Sai Akarsh C", "Anil Vuppala"], "url": "https://arxiv.org/abs/2411.17149v1", "attribution": "\"Typical vs. Atypical Disfluency Classification: Introducing the IIITH-TISA Corpus and Temporal Context-Based Feature Representations\" by Priyanka Kommagouni, Vamshiraghusimha Narasinga, Purva Barche, Sai Akarsh C, and Anil Vuppala, arXiv:2411.17149v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.21305v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results for Constant Axial Strain Patch Test}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nStrain Component & Computed Average & Expected Value & Absolute Error \\\\\n\\midrule\n\\( \\varepsilon_r \\) (Radial strain) & 0.000000 & 0.000000 & 0.000000 \\\\\n\\( \\varepsilon_z \\) (Axial strain) & 0.010000 & 0.010000 & 0.000000 \\\\\n\\( \\varepsilon_\\theta \\) (Hoop strain) & 0.000000 & 0.000000 & \\(7.20\\times10^{-8}\\) \\\\\n\\( \\gamma_{rz} \\) (Shear strain) & 0.000000 & 0.000000 & \\(2.17\\times10^{-19}\\) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Virtual Element Method Applied to Two Dimensional Axisymmetric Elastic Problems", "authors": ["Paulo Akira F. Enabe", "Rodrigo Provasi"], "url": "https://arxiv.org/abs/2504.21305v2", "attribution": "\"Virtual Element Method Applied to Two Dimensional Axisymmetric Elastic Problems\" by Paulo Akira F. Enabe and Rodrigo Provasi, arXiv:2504.21305v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.16364v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc} \\toprule[1pt]\n CRSP & $\\mu^s$ & $\\sigma^s$ & $\\lambda^s$ & $u^s$ &\n $\\eta_1^s$ & $\\eta_2^s$ & $\\rho_{sb}$ \\\\ \\midrule\n & 0.087323 & 0.147716& 0.316326 & 0.225806 & 4.3591 & 5.53370 & 0.095933\\\\\n \\midrule[1pt]\n30-day T-bill & $\\mu^b$ & $\\sigma^b$ & $\\lambda^b$ & $u^b$ &\n $\\eta_1^b$ & $\\eta_2^b$ & $\\rho_{sb}$ \\\\ \\midrule\n & 0.0032 & 0.0140& 0.3878 & 0.3947 & 61.5350 & 53.4043 &0.095933\n\\\\\n\\bottomrule[1pt]\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Risk Measures for DC Pension Plan Decumulation", "authors": ["Peter A. Forsyth", "Yuying Li"], "url": "https://arxiv.org/abs/2502.16364v1", "attribution": "\"Risk Measures for DC Pension Plan Decumulation\" by Peter A. Forsyth and Yuying Li, arXiv:2502.16364v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.14292v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc}\n\\Xhline{2\\arrayrulewidth} \\hline\n \\multirow{2}{*}{} & \\multicolumn{2}{c}{Fed-EF-SGD} & \\multicolumn{2}{c}{Fed-EF-AMS} \\\\ %\\cline{2-5}\n & $\\eta$ & $\\eta_l$ & $\\eta$ & $\\eta_l$ \\\\ \\hline\nMNIST & $10$ & $10^{-3}$ & $10^{-3}$ & $10^{-2}$ \\\\\nFMNIST & $1$ & $10^{-1}$ & $10^{-2}$ & $10^{-1}$ \\\\\nCIFAR-10 & 1 & $10^{-1}$ & $10^{-3}$ & $10^{-2}$ \\\\ \\hline\n\\Xhline{2\\arrayrulewidth}\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Optimal global ($\\eta$) and local ($\\eta_l$) learning rate combinations to attain highest test accuracy.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression", "authors": ["Xiaoyun Li", "Ping Li"], "url": "https://arxiv.org/abs/2211.14292v1", "attribution": "\"Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression\" by Xiaoyun Li and Ping Li, arXiv:2211.14292v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.11765v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CNN Model Parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n & \\textbf{Basic CNN} & \\multicolumn{3}{c}{\\textbf{Deep CNN}} \\\\ \\cline{2-5} \n\\textbf{Parameter} & \\textbf{Block 1} & \\textbf{Block 1} & \\textbf{Block 2} & \\textbf{Block 3} \\\\ \\hline\nConvolutional 1D layer & & & & \\\\\nFilter Size & 64 & 32 & 64 & 128 \\\\\nKernel Size & 2 & 3 & 2 & 1 \\\\\nPadding & same & same & same & same \\\\\nDense Layer: Relu Activation & 265 & - & - & - \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis", "authors": ["Tanja Aue", "Adam Jatowt", "Michael Färber"], "url": "https://arxiv.org/abs/2212.11765v1", "attribution": "\"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis\" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2311.15985v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation Parameters}\n\\begin{tabular}{ll|ll}\n \\hline\n Parameter & Value & Parameter & Value \\\\\n \\hline\n Carrier frequency ($f_c$) & 2.4 GHz & Maximum latency ($\\tau^\\mathrm{max}$) & 5 ms \\\\\n Bandwidth (W) & 5 MHz & Channel noise power & -11.5 dBm \\\\\n DT required error variances $(\\xi^2_{pos}, \\xi^2_{vel})$ & (0.01, 0.001) & Max. distance ($d^{\\max}$) & 20 m \\\\\n Max. connection ($C$) & 10 & Leaning rate (both actor and critic) & $1e^{-3}$ \\\\ \n Rician factor ($G$) & 15 dB & Outage probability factor $(\\varepsilon)$& $1e^{-5}$\\\\\n System parameter ($\\Gamma$) & 1 & Data packet size $(D)$& $1024$ bits\\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Value-Based Reinforcement Learning for Digital Twins in Cloud Computing", "authors": ["Van-Phuc Bui", "Shashi Raj Pandey", "Pedro M. de Sant Ana", "Petar Popovski"], "url": "https://arxiv.org/abs/2311.15985v1", "attribution": "\"Value-Based Reinforcement Learning for Digital Twins in Cloud Computing\" by Van-Phuc Bui, Shashi Raj Pandey, Pedro M. de Sant Ana, and Petar Popovski, arXiv:2311.15985v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.16384v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small Mean and standard error (in brackets) from $50$ replicates of parameter estimates in the Jansen-Rit neural mass model (), under scenario \\textbf{JR-3}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\hline \n Parameter & True value & {Local Gaussian contrast} & {New contrast} \n \\\\\n \\hline \n \\\\\n $C$ & 135.0 & 134.80 (0.0062) & 134.80 (0.0062) \\\\ \n $\\mu$ & 220.0 & 220.84 (0.6167) & 220.84 (0.6176) \\\\ \n $\\sigma_2 $ & 2000.0 & \n 1843.49 (4.3520) & 1989.00 (4.750) \n \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Parameter Estimation with Increased Precision for Elliptic and Hypo-elliptic Diffusions", "authors": ["Yuga Iguchi", "Alexandros Beskos", "Matthew M. Graham"], "url": "https://arxiv.org/abs/2211.16384v4", "attribution": "\"Parameter Estimation with Increased Precision for Elliptic and Hypo-elliptic Diffusions\" by Yuga Iguchi, Alexandros Beskos, and Matthew M. Graham, arXiv:2211.16384v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.00180v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The mean of the MESEs for each nonparametric estimator fitted to the simulated piecewise constant regression function data.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|r|rrr|rrr|rrr|}\n \\hline\n & & $n=400$ & & & $n=800$ & & & $n=1600$ & \\\\ \n \\hline\n$\\sigma$ & LC & ALC & ALCT & LC & ALC & ALCT & LC & ALC & ALCT \\\\ \n 0.1 & 0.00363 & 0.00179 & 0.00012 & 0.00336 & 0.00183 & 6e-05 & 0.00981 & 0.00019 & 3e-05 \\\\ \n 0.5 & 0.079 & 0.02119 & 0.003 & 0.05924 & 0.00868 & 0.00147 & 0.04283 & 0.00467 & 0.00084 \\\\ \n 1 & 0.1855 & 0.06835 & 0.01181 & 0.13443 & 0.04382 & 0.00579 & 0.10027 & 0.02887 & 0.00331 \\\\ \n 2 & 0.43075 & 0.24942 & 0.04743 & 0.29903 & 0.16317 & 0.02315 & 0.20681 & 0.10365 & 0.01318 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Anisotropic local constant smoothing for change-point regression function estimation", "authors": ["John R. J. Thompson", "W. John Braun"], "url": "https://arxiv.org/abs/2012.00180v2", "attribution": "\"Anisotropic local constant smoothing for change-point regression function estimation\" by John R. J. Thompson and W. John Braun, arXiv:2012.00180v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2310.13103v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Detection Results of the Audio-only Network.}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\n\\textbf {Test-Set type} & \\textbf {Class} & \\textbf {Precision} & \\textbf {Recall} & \\textbf {F1-Score} & \\textbf {Accuracy} \\\\\n\\hline \\hline\n\\multirow{2}{*}{Testset-I} & Real & 0.71 & 1.00 & 0.83 & \\multirow{2}{*}{0.80} \\\\\n\\cline{2-5}\n& Fake & 1.00 & 0.60 & 0.75 & \\\\ \n\\hline\n\\multirow{2}{*}{Testset-II} & Real & 0.76 & 1.00 & 0.86 & \\multirow{2}{*}{0.84} \\\\\n\\cline{2-5}\n& Fake & 1.00 & 0.69 & 0.81 & \\\\ \n\\hline\n\\multirow{2}{*}{faceswap} & Real & 0.50 & 1.00 & 0.67 & \\multirow{2}{*}{0.50} \\\\\n\\cline{2-5}\n& Fake & 0.00 & 0.00 & 0.00 & \\\\ \n\\hline\n\\multirow{2}{*}{faceswap-wav2lip} & Real & 1.00 & 1.00 & 1.00 & \\multirow{2}{*}{1.00} \\\\\n\\cline{2-5}\n& Fake & 1.00 & 1.00 & 1.00 & \\\\ \n\\hline\n\\multirow{2}{*}{fsgan} & Real & 0.50 & 1.00 & 0.67 & \\multirow{2}{*}{0.50} \\\\\n\\cline{2-5}\n& Fake & 0.00 & 0.00 & 0.00 & \\\\ \n\\hline\n\\multirow{2}{*}{fsgan-wav2lip} & Real & 1.00 & 1.00 & 1.00 & \\multirow{2}{*}{1.00} \\\\\n\\cline{2-5}\n& Fake & 1.00 & 1.00 & 1.00 & \\\\ \n\\hline\n\\multirow{2}{*}{RTVC} & Real & 0.97 & 1.00 & 0.99 & \\multirow{2}{*}{0.99} \\\\\n\\cline{2-5}\n& Fake & 1.00 & 0.97 & 0.99 & \\\\ \n\\hline\n\\multirow{2}{*}{wav2lip} & Real & 0.69 & 1.00 & 0.82 & \\multirow{2}{*}{0.78} \\\\\n\\cline{2-5}\n& Fake & 1.00 & 0.56 & 0.72 & \\\\ \n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection", "authors": ["Ammarah Hashmi", "Sahibzada Adil Shahzad", "Chia-Wen Lin", "Yu Tsao", "Hsin-Min Wang"], "url": "https://arxiv.org/abs/2310.13103v2", "attribution": "\"AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection\" by Ammarah Hashmi, Sahibzada Adil Shahzad, Chia-Wen Lin, Yu Tsao, and Hsin-Min Wang, arXiv:2310.13103v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.09697v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Worst-case $p$-values in sensitivity analyses for the 1,474 matched KLoSA pairs}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccccccccc}\n\\hline \\hline\n\\multicolumn{2}{l}{} & \\multicolumn{7}{c}{$H_0(\\tau)$} & & \\multicolumn{3}{c}{$H_0$} \\\\ \n\\cline{3-9} \\cline{11-13}\nBias & & $\\tau=2$ & $\\tau=4$ & $\\tau=6$ & $\\tau=8$ & $\\tau=10$ & $\\tau=12$ & $\\tau=14$ & & $M$ & $PPW$ & $M + PPW$\\\\\n\\hline\n$\\Gamma=1$ & & 0.001&\t0.000&\t0.000&\t0.000&\t0.005&\t0.013&\t0.037&&\t0.000&\t0.003 & 0.000 \\\\ \n$\\Gamma=1.1$ & & 0.003&\t0.000&\t0.002&\t0.000&\t0.053&\t0.128&\t0.273&&\t0.002&\t0.039 & 0.002 \\\\ \n$\\Gamma=1.15$ & & 0.005&\t0.000&\t0.004&\t0.002&\t0.120&\t0.262&\t0.480&&\t0.009&\t0.091 & 0.009 \\\\ \n$\\Gamma=1.2$ & & 0.007&\t0.001&\t0.010&\t0.005&\t0.227&\t0.438&\t0.684&&\t0.030&\t0.179 & 0.031 \\\\ \n$\\Gamma=1.25$ & & 0.011&\t0.001&\t0.020&\t0.012&\t0.366&\t0.620&\t0.838&&\t0.082&\t0.302 & 0.084 \\\\ \n$\\Gamma=1.3$ & & 0.016&\t0.002&\t0.036&\t0.027&\t0.518&\t0.773&\t0.930&&\t0.179&\t0.445 & 0.182 \\\\ \n$\\Gamma=1.35$ & & 0.021&\t0.004&\t0.061&\t0.054&\t0.663&\t0.880&\t0.974&&\t0.321&\t0.591 & 0.324 \\\\ \n$\\Gamma=1.4$ & & 0.029&\t0.007&\t0.096&\t0.094&\t0.783&\t0.943&\t0.992&&\t0.490&\t0.720 & 0.493 \\\\ \n$\\Gamma=1.45$ & & 0.038&\t0.012&\t0.142&\t0.152&\t0.871&\t0.976&\t0.998&&\t0.656&\t0.823 & 0.658 \\\\ \n$\\Gamma=1.5$ & & 0.049 &\t0.019&\t0.199&\t0.226&\t0.929&\t0.991&\t0.999&&\t0.792&\t0.895 & 0.794 \\\\ \n$\\Gamma=1.55$ & & 0.061&\t0.028&\t0.264&\t0.314&\t0.964&\t0.997&\t1.000&&\t0.888&\t0.943 & 0.888 \\\\ \n$\\Gamma=1.6$ & & 0.075&\t0.041&\t0.337&\t0.411&\t0.983&\t0.999&\t1.000&& 0.945&\t0.970 & 0.946 \\\\ \n$\\Gamma=1.65$ & & 0.092&\t0.057&\t0.414&\t0.510&\t0.992&\t1.000&\t1.000&& 0.976&\t0.986 & 0.976 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Randomization-Based Method for Evaluating Time-Varying Treatment Effects", "authors": ["Sangjin Lee", "Kwonsang Lee"], "url": "https://arxiv.org/abs/2412.09697v3", "attribution": "\"A Randomization-Based Method for Evaluating Time-Varying Treatment Effects\" by Sangjin Lee and Kwonsang Lee, arXiv:2412.09697v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08442v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n \\toprule\n Abbrevation & Definition \\\\\n \\midrule\n AdaRound & Adaptive Rounding \\\\\n AIMET & AI Model Efficiency Toolkit \\\\\n API & Application Programming Interface \\\\\n BNF & Batch Normalization Folding \\\\\n CLE & Cross-layer Equalization\\\\\n DFQ & Data-free Quantization\\\\\n DLC & Deep Learning Container\\\\\n PTQ & Post-training Quantization \\\\\n QAT & Quantization-aware Training\\\\\n QuantSim & Quantization Simulation \\\\\n SDK & Software Development Kit\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Abbreviations}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)", "authors": ["Sangeetha Siddegowda", "Marios Fournarakis", "Markus Nagel", "Tijmen Blankevoort", "Chirag Patel", "Abhijit Khobare"], "url": "https://arxiv.org/abs/2201.08442v1", "attribution": "\"Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)\" by Sangeetha Siddegowda, Marios Fournarakis, Markus Nagel, Tijmen Blankevoort, Chirag Patel, and Abhijit Khobare, arXiv:2201.08442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09873v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{List of representative efficient transformer-based models. The data of the Table is from .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n\t\t\t\t\\hline\n\t\t\t\tModels & Compress Type & $\\#$Layer &Params & Speed Up \\\\\t\t\n\t\t\t\t\\hline\n\t\t\t\tBERT$_{BASE}$ & Baseline & 12 & 110M & $\\times$1\\\\\n\t\t\t\t\\hline\n\t\t\t\tALBERT & Decomposition & 12 & 12M & $\\times$5.6 \\\\\n\t\t\t\t\\hline\n\t\t\t\tBERT-& Architecture & \\multirow{2}*{6} & \\multirow{2}*{66M} & \\multirow{2}*{$\\times$1.94}\\\\\n\t\t\t\tof-Theseus & design & & & \\\\\n\t\t\t\t\\hline\n\t\t\t\tQ-BERT & \\multirow{2}*{Quantization} & 12 & \\multirow{2}*{-} & \\multirow{2}*{-} \\\\\n\t\t\t\tQ8BERT & &12 & & \\\\\n\t\t\t\t\\hline\n\t\t\t\tTinyBERT & \\multirow{5}*{Distillation} & 4 & 14.5M & $\\times$9.4 \\\\\n\t\t\t\tDistilBERT~ & & 6 & 6.6m & $\\times$1.63 \\\\\n\t\t\t\tBERT-PKD~ & & 3$\\sim$6 & 45.7$\\sim$67M & $\\times$3.73$\\sim$1.64 \\\\\n\t\t\t\tMobileBERT~ & &24 &25.3M & $\\times$4.0 \\\\\n\t\t\t\tPD~ & & 6 &67.5M &$\\times$2.0\\\\\n\t\t\t\t\\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Transformers in Medical Imaging: A Survey", "authors": ["Fahad Shamshad", "Salman Khan", "Syed Waqas Zamir", "Muhammad Haris Khan", "Munawar Hayat", "Fahad Shahbaz Khan", "Huazhu Fu"], "url": "https://arxiv.org/abs/2201.09873v1", "attribution": "\"Transformers in Medical Imaging: A Survey\" by Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, and Huazhu Fu, arXiv:2201.09873v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.06299v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n Census Tract & Population & SVI \\\\\n \\hline\n 1.01 & 5015 & 0.7236 \\\\\n 1.02 & 5696 & 0.7282 \\\\\n 2.03 & 1901 & 0.2533 \\\\\n 2.04 & 4430 & 0.7931 \\\\\n 2.05 & 6001 & 0.2650 \\\\\n 2.06 & 4725 & 0.0761 \\\\\n 2.07 & 6231 & 0.7299 \\\\\n 3.01 & 3575 & 0.1122 \\\\\n 3.02 & 5679 & 0.8001 \\\\\n 4.01 & 3577 & 0.8111 \\\\\n 4.02 & 6561 & 0.7432\n \\end{tabular}\n\\end{adjustbox}\n\\caption{A table of SVI for Harrisonburg, VA by census tract.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modeling Supply and Demand in Public Transportation Systems", "authors": ["Miranda Bihler", "Hala Nelson", "Erin Okey", "Noe Reyes Rivas", "John Webb", "Anna White"], "url": "https://arxiv.org/abs/2309.06299v2", "attribution": "\"Modeling Supply and Demand in Public Transportation Systems\" by Miranda Bihler, Hala Nelson, Erin Okey, Noe Reyes Rivas, John Webb, and Anna White, arXiv:2309.06299v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.12530v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n $\\gamma$& white & lightgray & darkgray & black \\\\ \\hline\n 0.01 &[0,\\, 0.011)&[0.011,\\, 0.05)&[0.05,\\, 150)&$[150,\\, \\infty)$ \\\\ \\hline\n 0.1 &[0,\\, 0.061)&[0.061,\\, 0.22)&[0.22,\\, 150)&$[150,\\, \\infty)$ \\\\ \\hline\n 1 \t &[0,\\, 0.125)&[0.125,\\, 0.7)&[0.7,\\, 150)&$[150,\\, \\infty)$ \\\\ \\hline\n 5 &[0,\\, 1.25)&[1.25,\\, 4.0)&[4.0,\\, 150)&$[150,\\, \\infty)$ \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Time intervals during which tipping was observed for each of the different gray shadings in Figure , as in Table~.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The effect of timescale separation on the tipping window for chaotically forced systems", "authors": ["Raphael Römer", "Peter Ashwin"], "url": "https://arxiv.org/abs/2504.12530v1", "attribution": "\"The effect of timescale separation on the tipping window for chaotically forced systems\" by Raphael Römer and Peter Ashwin, arXiv:2504.12530v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.05946v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\hline\n & NSI & Poverty Rate & Med HH Income & Emp-Pop Ratio \\\\\n \\hline\n ATT Estimate & 0.198(0.067) & -0.020(0.009) & 0.081(0.031)& 0.016(0.007)\\\\\n p-value & 0.00 & 0.03 & 0.01 & 0.02\\\\\n Baseline Mean & 0 & 0.37 & \\$27k & 0.39\\\\\n Pre-trends robust p & 0.23 & 0.29 & 0.26 & 0.36 \\\\\n Pre-trends robust 95\\% CI & (-0.09,0.36) & (-0.06, 0.02) & (-0.02, 0.11) & (-0.013, 0.037)\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{ATT Estimates in Primary Event Studies}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Place-Based Policies for Neighborhood Improvement: Evidence from Promise Zones", "authors": ["Adamson Bryant"], "url": "https://arxiv.org/abs/2503.05946v1", "attribution": "\"Place-Based Policies for Neighborhood Improvement: Evidence from Promise Zones\" by Adamson Bryant, arXiv:2503.05946v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.00472v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model Performance Across 5 Folds (ISIC-2017)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n\\textbf{Model} & \\textbf{Fold1} & \\textbf{Fold2} & \\textbf{Fold3} & \\textbf{Fold4} & \\textbf{Fold5} \\\\\n\\midrule\nXception & 0.9515 & 0.9190 & 0.9265 & 0.9665 & 0.9080 \\\\\nXception+Wavelet & 0.9510 & 0.9175 & 0.9585 & 0.9630 & 0.9625 \\\\\nXception+Wavelet+IGWO & 0.9665 & 0.9612 & 0.9625 & 0.9775 & 0.9625 \\\\\nXception+Wavelet+Fox & 0.9762 & 0.9676 & 0.9622 & 0.9776 & 0.9623 \\\\\nXception+Wavelet+MGTO & 0.9776 & 0.9653 & 0.9665 & 0.9767 & 0.9646 \\\\\nInception & 0.9311 & 0.9361 & 0.9791 & 0.9125 & 0.9515 \\\\\nInception+Wavelet & 0.9355 & 0.9460 & 0.9700 & 0.9105 & 0.9510 \\\\\nInception+Wavelet+IGWO & 0.9510 & 0.9690 & 0.9680 & 0.9590 & 0.9505 \\\\\nInception+Wavelet+Fox & 0.9725 & 0.9795 & 0.9700 & 0.9680 & 0.9690 \\\\\nInception+Wavelet+MGTO & 0.9771 & 0.9715 & 0.9700 & 0.9701 & 0.9791 \\\\\nDenseNet & 0.9590 & 0.9505 & 0.9523 & 0.9414 & 0.9570 \\\\\nDenseNet+Wavelet & 0.9653 & 0.9555 & 0.9543 & 0.9423 & 0.9523 \\\\\nDenseNet+Wavelet+IGWO & 0.9691 & 0.9523 & 0.9512 & 0.9643 & 0.9575 \\\\\nDenseNet+Wavelet+Fox & 0.9545 & 0.9674 & 0.9620 & 0.9605 & 0.9523 \\\\\nDenseNet+Wavelet+MGTO & 0.9700 & 0.9650 & 0.9690 & 0.9689 & 0.9723 \\\\\nMobileNet & 0.9300 & 0.9235 & 0.9610 & 0.9435 & 0.9585 \\\\\nMobileNet+Wavelet & 0.9412 & 0.9333 & 0.9620 & 0.9423 & 0.9553 \\\\\nMobileNet+Wavelet+IGWO & 0.9498 & 0.9498 & 0.9672 & 0.9443 & 0.9524 \\\\\nMobileNet+Wavelet+Fox & 0.9585 & 0.9590 & 0.9590 & 0.9620 & 0.9680 \\\\\nMobileNet+Wavelet+MGTO & 0.9680 & 0.9680 & 0.9675 & 0.9788 & 0.9755 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers", "authors": ["Ramin Mousa", "Saeed Chamani", "Mohammad Morsali", "Mohammad Kazzazi", "Parsa Hatami", "Soroush Sarabi"], "url": "https://arxiv.org/abs/2412.00472v1", "attribution": "\"Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers\" by Ramin Mousa, Saeed Chamani, Mohammad Morsali, Mohammad Kazzazi, Parsa Hatami, and Soroush Sarabi, arXiv:2412.00472v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07450v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of NER systems for Mutation NER using single-labeled data for training.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|llll|}\n \\hline\n \\textbf{System} & \\textbf{Precision} & \\textbf{Recall}&\\textbf{F-score (Std Dev)}\\\\ \\hline\nBioBERT& 73.4 & 63.3 & 67.9 (2.8)\\\\\nBilstm-crf&71.4&64.1&67.6\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Single versus Multiple Annotation for Named Entity Recognition of Mutations", "authors": ["David Martinez Iraola", "Antonio Jimeno Yepes"], "url": "https://arxiv.org/abs/2101.07450v1", "attribution": "\"Single versus Multiple Annotation for Named Entity Recognition of Mutations\" by David Martinez Iraola and Antonio Jimeno Yepes, arXiv:2101.07450v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2105.10402v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Total $LR$ for 5 bus system in different strategies}\n\\begin{tabular}{ccccc}\n\\hline\nStrategy & $c_1$ & $c_2$ & $c_3$ & $c_4$ \\\\ \\hline\nLR (MW) & 17.427 & 8.926 & 10.396 & 3.255 \\\\ \\hline\n$\\bar{\\alpha}^{LR}_{i=1,max}$ & 0&0&0& 0\\\\ \\hline \n$\\bar{\\alpha}^{LR}_{i=2,max}$ & 0.70&0.57&0.62& 0.33\\\\ \\hline \n$\\bar{\\alpha}^{LR}_{i=3,max}$ & 0.70&0.58&0.61& 0.32\\\\ \\hline \n$\\bar{\\alpha}^{LR}_{i=4,max}$ & 0.80&0.55&0.60& 0.35\\\\ \\hline \n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Controllable Transmission Networks under Demand Uncertainty with Modular FACTS", "authors": ["Alireza Soroudi"], "url": "https://arxiv.org/abs/2105.10402v1", "attribution": "\"Controllable Transmission Networks under Demand Uncertainty with Modular FACTS\" by Alireza Soroudi, arXiv:2105.10402v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.11822v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\toprule\n\\textbf{Cluster} & 1 & 2 & 3 & 4 & 5 \\\\ \\midrule\n2 & 2.187589$\\cdot 10^{-4}$ & \\\\\n3 & 3.039844$\\cdot 10^{-11}$ & 1.41$\\cdot 10^{-3}$ & \\\\\n4 & 1.070993$\\cdot 10^{-10}$ & \\textcolor{blue}{0.300540} & 2.98464$\\cdot 10^{-4}$ &\\\\\n5 & 3.038979$\\cdot 10^{-16}$ & \\textcolor{blue}{0.093018} & 6.015797$\\cdot 10^{-5}$ & \\textcolor{blue}{0.105446} & \\\\\n6 & 1.729616$\\cdot 10^{-6}$ & 0.010612 & 9.290826$\\cdot 10^{-9}$ & 2.105$\\cdot 10^{-3}$ & 5.624624$\\cdot 10^{-5}$ \\\\\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{$p$-values computed under model retrieved after testing on the protein data presented in Section~. The hierarchical clustering algorithm was set to find six clusters using average linkage. In blue, adjusted $p$-values for which the null is not rejected at level $\\alpha=0.05$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Post-clustering Inference under Dependence", "authors": ["Javier González-Delgado", "Mathis Deronzier", "Juan Cortés", "Pierre Neuvial"], "url": "https://arxiv.org/abs/2310.11822v2", "attribution": "\"Post-clustering Inference under Dependence\" by Javier González-Delgado, Mathis Deronzier, Juan Cortés, and Pierre Neuvial, arXiv:2310.11822v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12519v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n \\hline\n \\hline\n Methods & MOS\\\\\n \\hline\n \\hline\n Ground truth &4.45$\\pm$ 0.07\\\\\n WaveNet &4.3$\\pm$ 0.130 \\\\\n WaveGlow &3.95$\\pm$ 0.161\\\\\n DiffWave &4.325$\\pm$ 0.123\\\\\n WaveGrad &4.1$\\pm$ 0.158\\\\\n \\hline\n \\hline\n It\\^oWave & 4.35$\\pm$ 0.115 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "ItôWave: Itô Stochastic Differential Equation Is All You Need For Wave Generation", "authors": ["Shoule Wu", "Ziqiang Shi"], "url": "https://arxiv.org/abs/2201.12519v2", "attribution": "\"ItôWave: Itô Stochastic Differential Equation Is All You Need For Wave Generation\" by Shoule Wu and Ziqiang Shi, arXiv:2201.12519v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.17026v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n $N$ & Singularities of $T$ Series& $\\rho_{T}$\\\\\n \\hline\n $10$ & $3.136416979332549\\pm 4.716647383373361i$ & $5.664262882964409$\\\\\n \\hline\n $18$ & $3.041750097587512\\pm 4.457486116243105i$ & $5.396427163658719$\\\\\n \\hline\n $32$ & $2.908760467592041\\pm 4.316980448108888i$ & $5.205497828947840$\\\\\n \\hline\n $56$ & $2.841660240593447\\pm 4.233364593343863i$ & $5.098667345801946$\\\\\n \\hline\n $100$ & $2.793295077275252\\pm 4.181035559841080i$ & $5.028275623052675$\\\\\n \\hline\n $178$ & $2.765280611466495\\pm 4.150005924993069i$ & $4.986915483305295$\\\\\n \\hline\n $214$ & $2.758820421556256\\pm 4.142540491442002i$ & $4.977120838560420$\\\\\n \\hline\n $316$ & $2.749120742536354\\pm 4.131048556840228i$ & $4.962179665833893$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{This is a table of approximate singularities found from the zeroes of the polynomial of the reciprocal of the direct time series for the Bubonic plague example.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An Exact SIR Series Solution and an Exploration of the Related Parameter Space", "authors": ["Daniel P Hobbs"], "url": "https://arxiv.org/abs/2504.17026v1", "attribution": "\"An Exact SIR Series Solution and an Exploration of the Related Parameter Space\" by Daniel P Hobbs, arXiv:2504.17026v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13444v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Median $l_2$ Distortion of Targeted Soft-Label Attacks under the BD$(\\theta, \\sigma)$ defense. $\\sigma = 0.1$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|ccc}\n\t\n\t\t\\toprule\n\t\t Dataset & Attacks & No defense & $\\theta_1$ = 0.5 & $\\theta_2$ = 0.7 \\\\\n\t\t \t\\midrule\n\t & AZ & 0.0473 & 0.0478 & 0.0478 \\\\\n MNIST & GenAttack & 4.8086 & - & -\\\\\n & SimBA & 0.0957 & 0.0694 & 0.0823 \\\\\n\t\t \\midrule\n\t & AZ & 0.0672 & 0.0672 & 0.0672 \\\\\n\t CIFAR10 & GenAttack & 1.3874 & - & - \\\\\n\t & SimBA & 0.0323 & 0.0236 & 0.0218\\\\\n\t \\midrule \\midrule\n\t\t Dataset & Attacks & No Defense & $\\theta_1$ = 0.1 & $\\theta_2$ = 0.3 \\\\\n\t\t \\midrule\n\t & AZ & 0.1566 & - & - \\\\\n\tIMAGENET & GenAttack & 0.0377 & - & - \\\\\n\t & SimBA & 0.0163 & 0.0092 & 0.0075\\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Boundary Defense Against Black-box Adversarial Attacks", "authors": ["Manjushree B. Aithal", "Xiaohua Li"], "url": "https://arxiv.org/abs/2201.13444v1", "attribution": "\"Boundary Defense Against Black-box Adversarial Attacks\" by Manjushree B. Aithal and Xiaohua Li, arXiv:2201.13444v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.08761v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Channels - Kharif season}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\t\t\\midrule\n\t\t\\midrule\n\t\t & (1) & (2) & (3) & (4) \\\\\n\t\t\t\t& Sev./Extr.\t& Sev./Extr. \t& Sev./Extr. \t& Sev./Extr.\t \\\\ \n\t& Depression \t& Depression \t& Depression \t& Depression \\\\\n\t\t \n\t\t & Urban & Urban & Rural & Rural \\\\ \\hline\n & & & & \\\\\n\\# Days \\textgreater 27°C & -1.134 & & -1.272** & \\\\\n& (1.056) & & (0.511) & \\\\\n\\# Days \\textgreater 27°C * Kharif & 1.538 & & 1.992*** & \\\\\n& (0.992) & & (0.482) & \\\\\nHeatwave ($\\geq$2 days $\\geq$27°) & & -2.331 & & -4.621** \\\\\n& & (3.232) & & (2.017) \\\\\nHeatwave ($\\geq$2 days $\\geq$27°) * Kharif & & 2.969 & & 8.083*** \\\\\n& & (3.359) & & (2.045) \\\\\n& & & & \\\\\nObservations & 6,751 & 6,751 & 20,535 & 20,535 \\\\\nR-squared & 0.107 & 0.104 & 0.067 & 0.064 \\\\\n\\# of PSUs & 89 & 89 & 282 & 282 \\\\\nMean of dependent variable & 6.76\\% & 6.76\\% & 9.03\\% & 9.03\\%\\\\\n\t\t\\midrule\n\t\t\\midrule\n\t\t\\multicolumn{5}{p{14cm}}{\\textit{Notes:} All regressions include weather controls, individual controls, year-month fixed-effects, state fixed-effects and climate zone fixed effects. The temperature bin regressions (Columns (1) and (3)) include all bins and its interactions with the Kharif dummy, but on the top bin is shown in the table. The Kharif dummy is absorbed by the year-month fixed effects. Robust standard errors are clustered at the PSU level and presented in parentheses. *** p\\textless{}0.01, ** p\\textless{}0.05, * p\\textless{}0.1.}\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Beyond the heat: The mental health toll of temperature and humidity in India", "authors": ["Manuela Fritz"], "url": "https://arxiv.org/abs/2503.08761v1", "attribution": "\"Beyond the heat: The mental health toll of temperature and humidity in India\" by Manuela Fritz, arXiv:2503.08761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2112.15499v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc|c|c|c}\n\\toprule\n Market & Solver & State & Return (5, 95) percentile & Volatility (5, 95) percentile & Sharpe (5, 95) percentile \\\\\n\\midrule\n NASDAQ & & $\\frac{1}{n}$ Naive & (-112,180) & (14.0,84.0) & (-4.7,5.9) \\\\\n \\midrule\n NASDAQ & SLS & Full & (-126,131) & (14.0,85.0) & (-4.4,6.5) \\\\\n NASDAQ & SLS & Sparse & (-133,127) & (13.0,53.0) & (-5.1,6.5) \\\\\n NASDAQ & SLS & Sparse 0 & (-124,120) & (12.0,83.0) & (-4.4,7.5) \\\\\n NASDAQ & SLS & Sparse 1 & (-198,78) & (15.0,50.0) & (-6.1,4.5) \\\\\n \\midrule\n NASDAQ & CLA & Full & (-147,127) & (13.0,84.0) & (-4.7,5.4) \\\\\n NASDAQ & CLA & Sparse & (-149,135) & (13.0,53.0) & (-4.6,6.4) \\\\\n NASDAQ & CLA & Sparse 0 & (-101,144) & (12.0,46.0) & (-2.8,7.9) \\\\\n NASDAQ & CLA & Sparse 1 & (-187,88) & (15.0,62.0) & (-5.4,4.1) \\\\\n \n \\midrule\n FTSE & & $\\frac{1}{n}$ Naive & (-77,111) & (11.0,26.0) & (-4.7,8.1) \\\\\n \\midrule\n FTSE & SLS & Full & (-58,94) & (9.0,26.0) & (-4.5,11.4) \\\\\n FTSE & SLS & Sparse & (-59,95) & (10.0,25.0) & (-4.9,10.6) \\\\\n FTSE & SLS & Sparse 0 & (-39,96) & (9.0,17.0) & (-3.4,11.4) \\\\\n FTSE & SLS & Sparse 1 & (-72,75) & (9.0,22.0) & (-5.4,7.5) \\\\\n \\midrule\n FTSE & CLA & Full & (-82,84) & (10.0,25.0) & (-5.6,10.3) \\\\\n FTSE & CLA & Sparse & (-62,81) & (10.0,20.0) & (-6.2,10.1) \\\\\n FTSE & CLA & Sparse 0 & (-59,79) & (9.0,22.0) & (-4.3,11.7) \\\\\n FTSE & CLA & Sparse 1 & (-100,80) & (10.0,23.0) & (-6.0,9.0) \\\\\n \\midrule\n HS300 & & $\\frac{1}{n}$ Naive & (-102,236) & (11.0,44.0) & (-4.4,9.3) \\\\\n \\midrule\n HS300 & SLS & Full & (-133,246) & (15.0,42.0) & (-5.1,10.7) \\\\\n HS300 & SLS & Sparse & (-125,234) & (14.0,42.0) & (-5.0,10.3) \\\\\n HS300 & SLS & Sparse 0 & (-101,218) & (13.0,40.0) & (-2.8,11.3) \\\\\n HS300 & SLS & Sparse 1 & (-142,202) & (13.0,46.0) & (-5.2,7.9) \\\\\n \\midrule\n HS300 & CLA & Full & (-133,246) & (15.0,42.0) & (-5.1,10.7) \\\\\n HS300 & CLA & Sparse & (-125,234) & (14.0,42.0) & (-5.0,10.3) \\\\\n HS300 & CLA & Sparse 0 & (-62,247) & (12.0,41.0) & (-2.8,10.2) \\\\\n HS300 & CLA & Sparse 1 & (-131,187) & (14.0,48.0) & (-5.0,8.2) \\\\\n \n\\bottomrule\n \\\\\n \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Dynamic Portfolio Optimization with Inverse Covariance Clustering", "authors": ["Yuanrong Wang", "Tomaso Aste"], "url": "https://arxiv.org/abs/2112.15499v2", "attribution": "\"Dynamic Portfolio Optimization with Inverse Covariance Clustering\" by Yuanrong Wang and Tomaso Aste, arXiv:2112.15499v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.08774v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Information about the benchmark datasets from DS1 to DS8.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll}\n\\toprule\nDataset & \\# Species & \\# Sites & Reference \\\\ \n\\midrule\nDS1 & 27 & 1949 & \\\\\nDS2 & 29 & 2520 & \\\\\nDS3 & 36 & 1812 & \\\\\nDS4 & 41 & 1137 & \\\\\nDS5 & 50 & 378 & \\\\\nDS6 & 50 & 1133 & \\\\\nDS7 & 59 & 1824 & \\\\\nDS8 & 64 & 1008 & \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "PhyloGFN: Phylogenetic inference with generative flow networks", "authors": ["Mingyang Zhou", "Zichao Yan", "Elliot Layne", "Nikolay Malkin", "Dinghuai Zhang", "Moksh Jain", "Mathieu Blanchette", "Yoshua Bengio"], "url": "https://arxiv.org/abs/2310.08774v2", "attribution": "\"PhyloGFN: Phylogenetic inference with generative flow networks\" by Mingyang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, and Yoshua Bengio, arXiv:2310.08774v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2112.11968v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccc}\n\t\t\t\t\t\t\\hline\\hline\n\t\t\t\t\t\t&\\multicolumn{3}{c}{\\textbf{Variance Gamma}}\t& \\multicolumn{3}{c}{\\textbf{Heston}} & \\multicolumn{3}{c}{\\textbf{Heston-Nandi}}\\\\\t\t\n\t\t\t\t\t\tN\t& 500 & 10000 & 100000 & 500 & 10000 & 100000 & 500 & 10000 & 100000 \\\\\n\t\t\t\t\t\t\\hline\\hline \\\\\n\t\t\t\t\t\tStrike & \\multicolumn{9}{c}{\\textbf{Gumbel} $\\theta = 2.9$}\\\\\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\t0\t & 5.907 & 5.997 & 6.002 & 6.148 & 6.247 & 6.253 & 6.181 & 6.205 & 6.208\\\\\n\t\t\t\t\t\t2.5\t& 3.780 & 3.872 &\t3.877\t& 4.188 & 4.291 &\t4.297\t& 4.132 & 4.167 &\t4.169\\\\\n\t\t\t\t\t\t5\t & 2.061 & 2.154 &\t2.159\t& 2.607 & 2.711 &\t2.717\t& 2.428 & 2.473 &\t2.475\\\\\n\t\t\t\t\t\t7.5\t& 0.900 & 1.000 &\t1.005\t& 1.455 & 1.564 &\t1.570\t& 1.197 & 1.251 &\t1.254\\\\\n\t\t\t\t\t\t10\t& 0.277 & 0.391 &\t0.396\t& 0.709 & 0.823 &\t0.830\t& 0.530 & 0.537 &\t0.542\\\\\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\tsec.\t& 0.083 & 0.093 &\t0.163 & 0.142 & 0.173 &\t0.401 & 0.281 & 0.472 &\t0.552\\\\\n\t\t\t\t\t\t\\hline\\hline\\\\\n\t\t\t\t\t\tStrike & \t\\multicolumn{9}{c}{\\textbf{Clayton} $\\theta = 6.57$}\\\\\t\t\t\t\t\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\t0\t & 5.914 & 5.944 &\t5.947 & 5.949 & 5.989 &\t5.992 & 5.977 & 5.979 &\t5.980\\\\\n\t\t\t\t\t\t2.5\t& 3.616 & 3.645 &\t3.647\t& 3.838 & 3.880 &\t3.883\t& 3.820 & 3.823 &\t3.823\\\\\n\t\t\t\t\t\t5\t & 1.812 & 1.843 &\t1.846\t& 2.408 & 2.452 &\t2.456\t& 2.181 & 2.185 &\t2.186\\\\\n\t\t\t\t\t\t7,5\t& 0.921 & 0.955 &\t0.958\t& 1.546 & 1.594 &\t1.597\t& 1.175 & 1.184 &\t1.185\\\\\n\t\t\t\t\t\t10\t& 0.492 & 0.531 &\t0.534 & 1.012 & 1.064 &\t1.068 & 0.625 & 0.638 &\t0.639\\\\\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\tsec.\t& 0.086 & 0.154 &\t0.261\t& 0.122 & 0.145 &\t0.261\t& 0.172 & 0.173 &\t0.253\\\\\t\t\t\t\t\n\t\t\t\t\t\t\\hline\\hline\\\\\n\t\t\t\t\t\tStrike & \t\\multicolumn{9}{c}{\\textbf{Frank} $\\theta = 25.28$}\\\\\t\t\t\t\t\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\t0\t & 5.787 & 5.820 &\t5.823 & 5.826 & 5.890 &\t5.895 & 5.977 & 5.979 &\t5.980\\\\\n\t\t\t\t\t\t2.5\t& 3.409 & 3.444 &\t3.447\t& 3.586 & 3.654 &\t3.659\t& 3.820 & 3.823 &\t3.823\\\\\n\t\t\t\t\t\t5\t & 1.411 & 1.448 &\t1.451\t& 1.861 & 1.933 &\t1.938 & 2.181 & 2.185 &\t2.186\\\\\n\t\t\t\t\t\t7.5\t& 0.443 & 0.483 &\t0.486 & 0.910 & 0.989 &\t0.994\t& 1.175 & 1.184 &\t1.185\\\\\n\t\t\t\t\t\t10\t& 0.155 & 0.201 &\t0.204\t& 0.465 & 0.553 &\t0.559\t& 0.625 & 0.638 &\t0.639\\\\\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\tsec\t& 0.083 & 0.090 &\t0.093\t& 0.139 & 0.128 &\t0.206 & 0.174 & 0.173 &\t0.253\\\\\n\t\t\t\t\t\t\\hline\\hline\\\\\n\t\t\t\t\t\tStrike & \t\\multicolumn{9}{c}{\\textbf{Plackett} $\\theta = 50.52$}\\\\\t\t\t\t\t\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\t0\t & 5.966 & 5.997 &\t5.999 & 6.097 & 6.157 &\t6.161 & 6.153 & 6.149 &\t6.149\\\\\n\t\t\t\t\t\t2.5\t& 3.729 & 3.763 &\t3.765\t& 4.021 & 4.086 &\t4.090\t& 4.020 & 4.018 &\t4.019\\\\\n\t\t\t\t\t\t5\t & 1.908 & 1.945 &\t1.948 & 2.416 & 2.487 &\t2.492\t& 2.263 & 2.266 &\t2.266\\\\\n\t\t\t\t\t\t7.5\t& 0.862 & 0.903 &\t0.906\t& 1.387 & 1.466 &\t1.471\t& 1.102 & 1.110 &\t1.111\\\\\n\t\t\t\t\t\t10\t& 0.400 & 0.447 &\t0.450\t& 0.795 & 0.881 &\t0.886\t& 0.517 & 0.531 &\t0.533\\\\\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\tsec.\t& 0.082 & 0.090 &\t0.148 & 0.139 & 0.128 &\t0.206 & 0.174 & 0.201 &\t0.235\\\\\n\t\t\t\t\t\t\\hline\\hline\n\t\t\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "An Efficient Unified Approach for Spread Option Pricing in a Copula Market Model", "authors": ["Edoardo Berton", "Lorenzo Mercuri"], "url": "https://arxiv.org/abs/2112.11968v2", "attribution": "\"An Efficient Unified Approach for Spread Option Pricing in a Copula Market Model\" by Edoardo Berton and Lorenzo Mercuri, arXiv:2112.11968v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2312.09585v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ARMSE comparison of positions of different segments for scenario \\textbf{R1}~(units: m)}\n\\begin{tabular}{c|cccc} \n\t\t\\hline \\hline % 表格的横线\n\t\t\\textbf{Segment} & \\textbf{VBACIF}& \\textbf{VBAKF-CM}& \\textbf{SSVBAKF}& \\textbf{CVIAKF}\\\\ \\hline\t\n \n A : [0, 13) &419.99 & \\textbf{\\textcolor{blue}{341.10}} &361.40 & 346.06\n \\\\ \n B : [13, 34) &186.07 & \\textbf{\\textcolor{blue}{156.18}} &177.24 & 159.45\n \\\\ \n C : [34, 65) &237.36 & 238.56 &234.03 & \\textbf{\\textcolor{blue}{226.87}}\n \\\\ \n D : [65, 81) &943.45 & 1077.90 & \\textbf{\\textcolor{blue}{835.06}} & 840.71\n \\\\ \n E : [81, 100) &\\textbf{\\textcolor{blue}{877.71}} & 908.06 &904.38 & 893.37\n \\\\ \n F : [100, 121] &1269.20 & 1046.80 & 930.95&\\textbf{\\textcolor{blue}{878.05}}\n \\\\ \\hline\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Joint State Estimation and Noise Identification Based on Variational Optimization", "authors": ["Hua Lan", "Shijie Zhao", "Jinjie Hu", "Zengfu Wang", "Jing Fu"], "url": "https://arxiv.org/abs/2312.09585v1", "attribution": "\"Joint State Estimation and Noise Identification Based on Variational Optimization\" by Hua Lan, Shijie Zhao, Jinjie Hu, Zengfu Wang, and Jing Fu, arXiv:2312.09585v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11980v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Accuracy (in \\%) of logistic regression using SGD with a decreasing learning rate. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllccc}\n\\hline\n Method & Dataset & Model & \\!\\!\\!\\!Epochs\\!\\!\\!\\! & $\\epsilon$ & Acc. \\\\ \n \\hline\n DP-SGLD\\!\\! & CIFAR10 & Resnet18 & 30 & 1.0 & 70.1 \\\\\n DP-SGD & CIFAR10 & Resnet18 & 30 & 1.0 & 68.1 \\\\\n No DP & CIFAR10 & Resnet18 & 30 & - & 70.2 \\\\\n\\hline\n DP-SGLD\\!\\! & CIFAR10 & Alexnet & 30 & 1.0 & 57.3 \\\\\n DP-SGD & CIFAR10 & Alexnet & 30 & 1.0 & 56.4 \\\\\n No DP & CIFAR10 & Alexnet & 30 & - & 57.6 \\\\\n\\hline\nDP-SGLD\\!\\! & Pneumonia\\!\\!\\!\\! & Resnet18 & 50 & 1.0 & 58.8 \\\\\nDP-SGD & Pneumonia\\!\\!\\!\\! & Resnet18 & 50 & 1.0 & 58.8 \\\\\nNo DP & Pneumonia\\!\\!\\!\\! & Resnet18 & 50 & - & 59.3 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Differential Privacy Guarantees for Stochastic Gradient Langevin Dynamics", "authors": ["Théo Ryffel", "Francis Bach", "David Pointcheval"], "url": "https://arxiv.org/abs/2201.11980v2", "attribution": "\"Differential Privacy Guarantees for Stochastic Gradient Langevin Dynamics\" by Théo Ryffel, Francis Bach, and David Pointcheval, arXiv:2201.11980v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07757v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of different methods on the SMD dataset. \\(\\uparrow\\) and \\(\\downarrow\\) indicate whether higher or lower values are better.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|ccc}\n \\toprule\n \\multicolumn{2}{c|}{\\multirow{2}{*}{\\textbf{Model \\& Inputs}}} & \\multicolumn{3}{c}{\\textbf{SMD dataset}} \\\\\n \\cmidrule{3-5}\n & & MAE \\(\\downarrow\\) & STD \\(\\downarrow\\) & Recall \\(\\uparrow\\) \\\\\n \\midrule\n \\textbf{Score-informed Methods} & & & & \\\\\n DiffVel & multi. & 19.7 & 13.1 & 53.0\\% \\\\\n FiLM Conv & multi. & 15.1 & 12.3 & 85.8\\% \\\\\n FiLM U-Net & multi. & \\textbf{9.9} & 7.8 & \\textbf{89.7\\%} \\\\\n Score-HPT (ours) & & & & \\\\\n audio + onset & multi. & 13.0 & 9.0 & 70.3\\% \\\\\n audio + onset \\& frame$_{\\text{ex}}$ & multi. & 13.5 & 8.8 & 72.6\\% \\\\\n \\midrule\n \\textbf{AMT Systems} & & & & \\\\\n HPT & audio & 13.9 & 9.4 & 68.7\\% \\\\\n hFT-Transformer & audio & \\textbf{9.9} & \\textbf{7.3} & 78.0\\% \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Score-Informed BiLSTM Correction for Refining MIDI Velocity in Automatic Piano Transcription", "authors": ["Zhanhong He", "Roberto Togneri", "Defeng", "Huang"], "url": "https://arxiv.org/abs/2508.07757v1", "attribution": "\"Score-Informed BiLSTM Correction for Refining MIDI Velocity in Automatic Piano Transcription\" by Zhanhong He, Roberto Togneri, Defeng, and Huang, arXiv:2508.07757v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00133v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n Category & Example \\\\\n \\toprule\n \\textbf{\\em As valid as gold} \\\\\n \\midrule\n \\multirow{4}{*}{Semantically the same}\n & Q: When did israel split into israel and judah \\\\\n & Gold: about 930 BCE / Prediction: around 930 BCE \\\\\n & Q: What movie is bring it on the musical based off of \\\\\n & Gold: Bring It On / Prediction: 2000 film of the same name \\\\\n \\midrule\n \\multirow{2}{*}{Open-ended question}\n & Q: City belonging to mid west of united states \\\\\n & Gold: Des Moines / Prediction: kansas city\\\\\n \\midrule\n \\multirow{2}{*}{Ambiguous entity/event references}\n & Q: Gold woman in guardians of the galaxy 2 \\\\\n & Gold: Ayesha / Prediction: Elizabeth Debicki \\\\\n \\midrule\n \\multirow{4}{*}{Answer has different granularity}\n & Q: When did the musical cats open on broadway \\\\\n & Gold: 1982, 7 October 1982 / Prediction: october 1982 \\\\\n & Q: Where did the industrial revolution in the united states begin \\\\\n & Gold: in Pawtucket / Prediction: rhode island \\\\\n \\midrule\n \\multirow{2}{*}{Gold is incorrect}\n & Q: When did universal studios become a theme park \\\\\n & Gold: 1965 / Prediction: july 15 1964, 1964 \\\\\n \\toprule\n \\textbf{\\em Valid but not the best} \\\\\n \\midrule\n \\multirow{2}{*}{Ambiguous answer type}\n & Q: When did naruto find out about jiraiya's death \\\\\n & Gold: ``Somber News'' / Prediction: february 23 2013 \\\\\n \\midrule\n \\multirow{2}{*}{Answer is time-dependent}\n & Q: Who goes to the big 12 championship game \\\\\n & Gold: Oklahoma Sooners / Prediction: tcu horned frogs \\\\\n \\toprule\n \\textbf{\\em Less plausible} \\\\\n \\midrule\n \\multirow{2}{*}{Conflicting info in Wikipedia}\n & Q: Based on population what is the largest military base in the \\\\\n & united states / Gold: Fort Bragg / Prediction: fort hood \\\\\n \\midrule\n \\multirow{2}{*}{Plausible only in certain condition}\n & Q: Who appointed the chief justice of high court \\\\\n & Gold: President / Prediction: president of india \\\\\n \\midrule\n \\multirow{2}{*}{Mismatch with question intent}\n & Q: While the united states dealt with the depression it was also facing \\\\\n & Gold: Racial tensions / Prediction: great depression, economic downturns \\\\\n \\midrule\n \\multirow{2}{*}{Incorrect}\n & Q: When were banded iron formations formed on the sea floor \\\\\n & Gold: Precambrian / Prediction: some 185 billion years ago \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned", "authors": ["Sewon Min", "Jordan Boyd-Graber", "Chris Alberti", "Danqi Chen", "Eunsol Choi", "Michael Collins", "Kelvin Guu", "Hannaneh Hajishirzi", "Kenton Lee", "Jennimaria Palomaki", "Colin Raffel", "Adam Roberts", "Tom Kwiatkowski", "Patrick Lewis", "Yuxiang Wu", "Heinrich Küttler", "Linqing Liu", "Pasquale Minervini", "Pontus Stenetorp", "Sebastian Riedel", "Sohee Yang", "Minjoon Seo", "Gautier Izacard", "Fabio Petroni", "Lucas Hosseini", "Nicola De Cao", "Edouard Grave", "Ikuya Yamada", "Sonse Shimaoka", "Masatoshi Suzuki", "Shumpei Miyawaki", "Shun Sato", "Ryo Takahashi", "Jun Suzuki", "Martin Fajcik", "Martin Docekal", "Karel Ondrej", "Pavel Smrz", "Hao Cheng", "Yelong Shen", "Xiaodong Liu", "Pengcheng He", "Weizhu Chen", "Jianfeng Gao", "Barlas Oguz", "Xilun Chen", "Vladimir Karpukhin", "Stan Peshterliev", "Dmytro Okhonko", "Michael Schlichtkrull", "Sonal Gupta", "Yashar Mehdad", "Wen-tau Yih"], "url": "https://arxiv.org/abs/2101.00133v2", "attribution": "\"NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned\" by Sewon Min, Jordan Boyd-Graber, Chris Alberti, Danqi Chen, Eunsol Choi, Michael Collins, Kelvin Guu, Hannaneh Hajishirzi, Kenton Lee, Jennimaria Palomaki, Colin Raffel, Adam Roberts, Tom Kwiatkowski, Patrick Lewis, Yuxiang Wu, Heinrich Küttler, Linqing Liu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel, Sohee Yang, Minjoon Seo, Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Edouard Grave, Ikuya Yamada, Sonse Shimaoka, Masatoshi Suzuki, Shumpei Miyawaki, Shun Sato, Ryo Takahashi, Jun Suzuki, Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz, Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, and Wen-tau Yih, arXiv:2101.00133v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.03974v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|ccc|cc}\nUnit & X & Z & Y & $U_X$ & $U_Z$ & $U_Y$ & $Y(0)$ & $Y(1)$ \\\\ \\hline\n1 & 0 & 0 & 1 & 0 & 0 & 1 & 1 & 2 \\\\\n2 & 0 & 0 & 2 & 0 & 0 & 2 & 2 & 3 \\\\\n3 & 1 & 0 & 1 & 1 & 0 & 0 & 1 & 2 \\\\\n4 & 1 & 0 & 2 & 1 & 0 & 1 & 2 & 3\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Observed data ($X, Z, Y$), inferred exogenous variables ($U_X, U_Z, U_Y$), and predicted potential outcomes ($Y(0), Y(1)$) corresponding to the SCM in Equation~.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Counterfactuals for the Future", "authors": ["Lucius E. J. Bynum", "Joshua R. Loftus", "Julia Stoyanovich"], "url": "https://arxiv.org/abs/2212.03974v1", "attribution": "\"Counterfactuals for the Future\" by Lucius E. J. Bynum, Joshua R. Loftus, and Julia Stoyanovich, arXiv:2212.03974v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17854v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c||c|c||c|c||c|c}\n \\multicolumn{8}{c}{\\textbf{ODE Terms:}}\\\\\nEqn.~ & \\texttt{NDS.} & Eqn.~ & \\texttt{NDS.} & Eqn.~ & \\texttt{NDS.}\\\\\n\\hline\n$A_0$ & \\texttt{A0} & $B_{1}$ & \\texttt{B1} &$B_{2}$&\\texttt{B2}&&\\\\ \n$C_{1}$ & \\texttt{C1} & $D_{11}$ &\\texttt{D11}&$D_{12}$&\\texttt{D12}&&\\\\ \n$C_{2}$ & \\texttt{C2} & $D_{21}$ &\\texttt{D21}&$D_{22}$&\\texttt{D22}&& \\\\ \\hline \\\\\n \\multicolumn{8}{c}{ \\textbf{Discrete Delay Terms:}}\\\\\nEqn.~ & \\texttt{NDS.} & Eqn.~ & \\texttt{NDS.} & Eqn.~ & \\texttt{NDS.}& Eqn.~ & \\texttt{NDS.}\\\\\n\\hline\n$A_i$ & \\texttt{Ai\\{i\\}} & $B_{1i}$ & \\texttt{B1i\\{i\\}} &$B_{2i}$&\\texttt{B2i\\{i\\}}&$E_i$& \\texttt{Ei\\{i\\}}\\\\ \n$C_{1i}$ & \\texttt{C1i\\{i\\}} & $D_{11i}$ &\\texttt{D11i\\{i\\}}&$D_{12i}$&\\texttt{D12i\\{i\\}}&$E_{1i}$& \\texttt{E1i\\{i\\}}\\\\ \n$C_{2i}$ & \\texttt{C2i\\{i\\}} & $D_{21i}$ &\\texttt{D21i\\{i\\}}&$D_{22i}$&\\texttt{D22i\\{i\\}}&$E_{2i}$& \\texttt{E2i\\{i\\}}\\\\\n\\hline \\\\ \\multicolumn{8}{c}{\\textbf{Distributed Delay Terms: May be functions of \\texttt{pvar s}}}\\\\\nEqn.~ & \\texttt{NDS.} & Eqn.~ & \\texttt{NDS.} & Eqn.~ & \\texttt{NDS.}& Eqn.~ & \\texttt{NDS.}\\\\\n\\hline\n$A_{di} $ & \\texttt{Adi\\{i\\}} & $B_{1di}$ & \\texttt{B1di\\{i\\}} &$B_{2di}$&\\texttt{B2di\\{i\\}}&$E_{di}$& \\texttt{Edi\\{i\\}}\\\\ \n$C_{1di} $& \\texttt{C1di\\{i\\}} &$ D_{11di} $&\\texttt{D11di\\{i\\}}&$D_{12di}$&\\texttt{D12di\\{i\\}}&$E_{1di}$& \\texttt{E1di\\{i\\}}\\\\ \n$C_{2di}$ & \\texttt{C2di\\{i\\}} & $D_{21di} $&\\texttt{D21di\\{i\\}}&$D_{22di}$&\\texttt{D22di\\{i\\}}&$E_{2di}$& \\texttt{E2di\\{i\\}}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Equivalent names of Matlab elements of the \\texttt{NDS} structure terms for terms in Eqn.~. For example, to set term \\texttt{XX} to \\texttt{YY}, we use \\texttt{NDS.XX=YY}. In addition, the delay $\\tau_i$ is specified using the vector element \\texttt{NDS.tau(i)} so that if $\\tau_1=1, \\tau_2=2, \\tau_3=3$, then \\texttt{NDS.tau=[1 2 3]}. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "PIETOOLS 2024: User Manual", "authors": ["Sachin Shivakumar", "Declan Jagt", "Danilo Braghini", "Amritam Das", "Yulia Peet", "Matthew Peet"], "url": "https://arxiv.org/abs/2501.17854v1", "attribution": "\"PIETOOLS 2024: User Manual\" by Sachin Shivakumar, Declan Jagt, Danilo Braghini, Amritam Das, Yulia Peet, and Matthew Peet, arXiv:2501.17854v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cccc|}\n\\hline\nLabel & B & OBA & LUCE & Updating \\\\\n\\hline\n$x$ & X & & & \\\\\n$\\alpha$ & & X & X & \\\\\n$L$ & X & & & X \\\\\n$U$ & X & & & X \\\\\n$M$ & & X & X & \\\\\n$D$ & & X & X & \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.10263v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllllll}\n\\toprule\n & SDCD & SDCD-GPU & DCDI & DCDFG & GIES & DAGMA & NOTEARS & NOBEARS & SCORE & sortnregress \\\\\nd & & & & & & & & & & \\\\\n\\midrule\n10 & 14.7 ${\\scriptstyle \\pm 5.5}$ & NA & 24.3 ${\\scriptstyle \\pm 3.9}$ & 24.6 ${\\scriptstyle \\pm 6.0}$ & 27.8 ${\\scriptstyle \\pm 3.9}$ & 25.3 ${\\scriptstyle \\pm 6.2}$ & 35.3 ${\\scriptstyle \\pm 1.9}$ & 33.5 ${\\scriptstyle \\pm 3.0}$ & 14.4 ${\\scriptstyle \\pm 4.0}$ & 28.6 ${\\scriptstyle \\pm 4.7}$ \\\\\n20 & 35.7 ${\\scriptstyle \\pm 6.2}$ & NA & 31.7 ${\\scriptstyle \\pm 6.5}$ & 108.0 ${\\scriptstyle \\pm 14.3}$ & 123.4 ${\\scriptstyle \\pm 12.3}$ & 62.0 ${\\scriptstyle \\pm 12.0}$ & 75.4 ${\\scriptstyle \\pm 4.0}$ & 74.2 ${\\scriptstyle \\pm 3.0}$ & 118.6 ${\\scriptstyle \\pm 8.5}$ & 81.6 ${\\scriptstyle \\pm 6.3}$ \\\\\n30 & 53.8 ${\\scriptstyle \\pm 11.9}$ & NA & 55.5 ${\\scriptstyle \\pm 10.7}$ & 258.8 ${\\scriptstyle \\pm 32.6}$ & NA & 89.4 ${\\scriptstyle \\pm 10.9}$ & 113.1 ${\\scriptstyle \\pm 4.8}$ & 113.7 ${\\scriptstyle \\pm 3.3}$ & 275.9 ${\\scriptstyle \\pm 21.0}$ & 134.6 ${\\scriptstyle \\pm 8.4}$ \\\\\n40 & 64.0 ${\\scriptstyle \\pm 13.7}$ & NA & 102.4 ${\\scriptstyle \\pm 21.6}$ & 426.6 ${\\scriptstyle \\pm 73.7}$ & NA & 115.3 ${\\scriptstyle \\pm 13.4}$ & 147.9 ${\\scriptstyle \\pm 6.0}$ & 151.1 ${\\scriptstyle \\pm 3.4}$ & 454.3 ${\\scriptstyle \\pm 52.8}$ & 172.9 ${\\scriptstyle \\pm 12.2}$ \\\\\n50 & 69.9 ${\\scriptstyle \\pm 12.3}$ & 68.3 ${\\scriptstyle \\pm 13.3}$ & NA & 660.8 ${\\scriptstyle \\pm 126.1}$ & NA & NA & 183.4 ${\\scriptstyle \\pm 7.4}$ & 192.0 ${\\scriptstyle \\pm 3.5}$ & 619.4 ${\\scriptstyle \\pm 59.7}$ & 216.6 ${\\scriptstyle \\pm 12.4}$ \\\\\n100 & 92.7 ${\\scriptstyle \\pm 9.1}$ & 89.7 ${\\scriptstyle \\pm 11.0}$ & NA & 1807.3 ${\\scriptstyle \\pm 788.2}$ & NA & NA & 327.3 ${\\scriptstyle \\pm 7.5}$ & 389.0 ${\\scriptstyle \\pm 3.6}$ & NA & 421.3 ${\\scriptstyle \\pm 12.0}$ \\\\\n200 & 225.3 ${\\scriptstyle \\pm 13.7}$ & 228.0 ${\\scriptstyle \\pm 18.3}$ & NA & 5657.3 ${\\scriptstyle \\pm 2982.6}$ & NA & NA & 619.0 ${\\scriptstyle \\pm 4.2}$ & 770.0 ${\\scriptstyle \\pm 7.8}$ & NA & 824.0 ${\\scriptstyle \\pm 19.0}$ \\\\\n300 & 350.0 ${\\scriptstyle \\pm 12.5}$ & 360.0 ${\\scriptstyle \\pm nan}$ & NA & 7284.7 ${\\scriptstyle \\pm 5072.3}$ & NA & NA & NA & 1149.0 ${\\scriptstyle \\pm 14.0}$ & NA & 1190.7 ${\\scriptstyle \\pm 26.3}$ \\\\\n400 & 466.3 ${\\scriptstyle \\pm 62.4}$ & 471.7 ${\\scriptstyle \\pm 68.0}$ & NA & 3779.7 ${\\scriptstyle \\pm 507.3}$ & NA & NA & NA & 1534.7 ${\\scriptstyle \\pm 3.1}$ & NA & 1585.0 ${\\scriptstyle \\pm 59.3}$ \\\\\n500 & 621.7 ${\\scriptstyle \\pm 10.7}$ & 621.0 ${\\scriptstyle \\pm 10.5}$ & NA & 7252.7 ${\\scriptstyle \\pm 3284.6}$ & NA & NA & NA & 1915.7 ${\\scriptstyle \\pm 18.8}$ & NA & 1974.3 ${\\scriptstyle \\pm 34.6}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Detailed results of SHD means and standard deviations from . SDCD-GPU was only run for $d\\ge 50$. All other \\texttt{NA} values correspond to failed runs.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Stable Differentiable Causal Discovery", "authors": ["Achille Nazaret", "Justin Hong", "Elham Azizi", "David Blei"], "url": "https://arxiv.org/abs/2311.10263v2", "attribution": "\"Stable Differentiable Causal Discovery\" by Achille Nazaret, Justin Hong, Elham Azizi, and David Blei, arXiv:2311.10263v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11556v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l||l|l|l|}\\hline\n& \\multicolumn{6}{c|}{\\textbf{Temperature ($\\tau$) > 1.0}} \\\\ \\hline\n & 10 & 100 & 1000 & 10 & 100 & 1000 \\\\ \\hline\n\\textbf{Src+ Ext} & \\multicolumn{3}{|c||}{Prec$@$1} & \\multicolumn{3}{|c|}{Recall$@$3} \\\\ \\hline\n Yahoo! + TREC & 0.46 &0.38 & 0.38& 0.73&0.644 & 0.64 \\\\ \n Yahoo! + WHO& 0.37& 0.38& 0.36& 0.64& 0.65&0.64 \\\\\n Infobot + TREC & 0.44 & 0.59& 0.39& 0.72& 0.81& 0.75 \\\\\n Infobot + WHO & 0.65 & 0.41& 0.44 & 0.85 & 0.76 & 0.79\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Variation in P$@$1 and R$@$3 across different temperature values.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Powering COVID-19 community Q&A with Curated Side Information", "authors": ["Manisha Verma", "Kapil Thadani", "Shaunak Mishra"], "url": "https://arxiv.org/abs/2101.11556v1", "attribution": "\"Powering COVID-19 community Q&A with Curated Side Information\" by Manisha Verma, Kapil Thadani, and Shaunak Mishra, arXiv:2101.11556v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.06243v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c}\n \\Xhline{2\\arrayrulewidth}\n Encoder & Decoder & \\multicolumn{4}{c}{\\textbf{GF2} Dataset (Reduced-Resolution)} \\\\\n \\hline\n FFA & HQFE & SAM$\\downarrow$ & ERGAS$\\downarrow$ & SCC$\\uparrow$ & Q4$\\uparrow$\\\\\n \\hline\n & & 0.654 $\\pm$ 0.112 & 0.661 $\\pm$ 0.076 & 0.992 $\\pm$ 0.001 & 0.986 $\\pm$ 0.007 \\\\\n \\checkmark & & $0.654 \\pm 0.112$ & $0.636 \\pm 0.077$ & $0.993 \\pm 0.002$ & $0.986 \\pm 0.007$ \\\\\n & \\checkmark & $0.617 \\pm 0.117$ & $0.556 \\pm 0.104$ & $0.993 \\pm 0.002$ & $0.987 \\pm 0.007$ \\\\\n \\checkmark & \\checkmark & {\\color{red}{\\textbf{0.603 $\\pm$ 0.102}}} & {\\color{red}{\\textbf{0.537 $\\pm$ 0.077}}} & {\\color{red}{\\textbf{0.994 $\\pm$ 0.001}}} & {\\color{red}{\\textbf{0.988 $\\pm$ 0.006}}} \\\\\n \\Xhline{2\\arrayrulewidth}\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of Results with and without FFA and HQFE Blocks in FSA-T. Best values are highlighted in {\\color{red}{\\textbf{red}}}.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening", "authors": ["Sungpyo Kim", "Jeonghyeok Do", "Jaehyup Lee", "Munchurl Kim"], "url": "https://arxiv.org/abs/2412.06243v1", "attribution": "\"U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening\" by Sungpyo Kim, Jeonghyeok Do, Jaehyup Lee, and Munchurl Kim, arXiv:2412.06243v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.09028v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Rated values and simulation parameters for 4-zone SPS model }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n\\hline \\hline\n\\textbf{Parameter/Simulation Description} & \\textbf{Parameter Value} \\\\\n \\hline \\hline\nRated Power PGM-1 & 29 MW \\\\\nRated Power PGM-2 & 29 MW \\\\\nDesired Operating Power PGMs ($\\mathbf{p}_{g_i}^r$) & 15 MW \\\\\nRated Power PCM-1 & $\\pm$ 10.64 MW \\\\\nRated Power PCM-2 & $\\pm$ 10.64 MW \\\\\nRated Bus Voltage ($v_{bus}$) & 12 kV \\\\\nCapacity of PCMs ($Q_T$) & 20 AHr \\\\\n\\hline\nPGMs weight ($\\beta_i$) & 1 \\\\\nPCM-1 weight ($\\gamma_1$) & 1 \\\\\nGradient Step ($\\alpha$) & 0.1 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Degradation-based Energy Management for Microgrids in the Presence of Energy Storage Elements", "authors": ["Satish Vedula"], "url": "https://arxiv.org/abs/2503.09028v2", "attribution": "\"Degradation-based Energy Management for Microgrids in the Presence of Energy Storage Elements\" by Satish Vedula, arXiv:2503.09028v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06390v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textit{GridTracer} performance with the \"conventional\" data handling scheme in Fig. (a) and the \"in-domain\" scheme in the in Fig. (b), cross-domain training}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l||c|c|c|c}\n \\hline\n & Azrizona & Kansas & New Zealand & Average \\\\ \\hline\\hline\nData handling & \\multicolumn{4}{c}{Tower detection (DmaP)} \\\\ \\hline\n(a) & \\textbf{0.73} & \\textbf{0.54} & \\textbf{0.55} & \\textbf{0.61} \\\\\n(b) & 0.68 & 0.53 & 0.51 & 0.57 \\\\ \\hline\\hline\n & \\multicolumn{4}{c}{Line segmentation (IoU)} \\\\ \\hline\n(a) & \\textbf{54.43} & \\textbf{40.04} & 45.62 & \\textbf{46.70} \\\\\n(b) & 52.97 & 39.42 & \\textbf{46.50} & 46.30 \\\\ \\hline\\hline\n & \\multicolumn{4}{c}{Graph inference (F1)} \\\\ \\hline\n(a) & 0.68 & \\textbf{0.59} & \\textbf{0.61} & \\textbf{0.63} \\\\\n(b) & \\textbf{0.70} & 0.56 & 0.48 & 0.58 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery", "authors": ["Bohao Huang", "Jichen Yang", "Artem Streltsov", "Kyle Bradbury", "Leslie M. Collins", "Jordan Malof"], "url": "https://arxiv.org/abs/2101.06390v1", "attribution": "\"GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery\" by Bohao Huang, Jichen Yang, Artem Streltsov, Kyle Bradbury, Leslie M. Collins, and Jordan Malof, arXiv:2101.06390v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.18727v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Example of RIS codebook index assignment for $K=16$ codewords, where each codeword consists of $N=2$ phases, each quantized to $\\{0^\\circ, 90^\\circ, 180^\\circ, 270^\\circ\\}$ ($b=2$ bits).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\nGray Code & Codebook Index & Codeword\\\\\n\\hline\n0000 & 6 & $[90^\\circ,\\,180^\\circ]$ \\\\\n0001 & 2 & $[0^\\circ,\\,180^\\circ]$ \\\\\n0011 & 7 & $[90^\\circ,\\,270^\\circ]$ \\\\\n0010 & 3 & $[0^\\circ,\\,270^\\circ]$ \\\\\n0110 & 14 & $[270^\\circ,\\,180^\\circ]$ \\\\\n0111 & 10 & $[180^\\circ,\\,180^\\circ]$ \\\\\n0101 & 15 & $[270^\\circ,\\,270^\\circ]$ \\\\\n0100 & 11 & $[180^\\circ,\\,270^\\circ]$ \\\\\n1100 & 4 & $[90^\\circ,\\,0^\\circ]$ \\\\\n1101 & 0 & $[0^\\circ,\\,0^\\circ]$ \\\\\n1111 & 5 & $[90^\\circ,\\,90^\\circ]$ \\\\\n1110 & 1 & $[0^\\circ,\\,90^\\circ]$ \\\\\n1010 & 12 & $[270^\\circ,\\,0^\\circ]$ \\\\\n1011 & 8 & $[180^\\circ,\\,0^\\circ]$ \\\\\n1001 & 13 & $[270^\\circ,\\,90^\\circ]$ \\\\\n1000 & 9 & $[180^\\circ,\\,90^\\circ]$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "RIS Codebook Index Assignment under Imperfect Control Links Using TSP-Inspired Optimization", "authors": ["Liangshun Wu", "Wen Chen", "Qingqing Wu", "Xudong Bai", "Kunlun Wang"], "url": "https://arxiv.org/abs/2507.18727v1", "attribution": "\"RIS Codebook Index Assignment under Imperfect Control Links Using TSP-Inspired Optimization\" by Liangshun Wu, Wen Chen, Qingqing Wu, Xudong Bai, and Kunlun Wang, arXiv:2507.18727v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.02239v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|r|rrrrr|}\n \\hline\n & & & Storey & & estimated\\\\\n SNR & Bonf & Simes & Simes & Fisher & LRT \\\\ \n \\hline\n 1.00 & 410.52 & 384.66 & 327.55 & {\\bf 274.36} & 277.58 \\\\ \n 1.50 & 217.69 & 187.36 & 142.98 & {\\bf 107.85} & 113.88 \\\\ \n 2.00 & 81.63 & 65.52 & 49.12 & {\\bf 37.40} & 37.76 \\\\ \n 2.50 & 23.51 & 17.98 & 15.08 & 14.60 & {\\bf 11.91} \\\\ \n 3.00 & 6.42 & 5.35 & 5.18 & 7.78 & {\\bf 4.35} \\\\ \n 3.50 & 2.46 & 2.24 & 2.27 & 5.20 & {\\bf 2.02} \\\\ \n 4.00 & 1.39 & 1.34 & 1.37 & 4.38 & {\\bf 1.28} \\\\ \n 4.50 & 1.07 & 1.06 & 1.08 & 4.03 & {\\bf 1.03} \\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Average batch prediction set size at each SNR for different combining methods (columns). In bold, the combining method that produces the narrowest prediction region. $\\alpha = 0.1$ and $2000$ replications. For a single data generation, the average running time on a standard PC was less than 0.05 seconds for all methods but the estimated LRT, which has an average running time of 5.7 seconds. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Powerful batch conformal prediction for classification", "authors": ["Ulysse Gazin", "Ruth Heller", "Etienne Roquain", "Aldo Solari"], "url": "https://arxiv.org/abs/2411.02239v2", "attribution": "\"Powerful batch conformal prediction for classification\" by Ulysse Gazin, Ruth Heller, Etienne Roquain, and Aldo Solari, arXiv:2411.02239v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2410.24145v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Variables in the wind direction dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n\\hline\nVariable & Unit \\\\\n\\hline\nWind direction & rad \\\\\nCosine of wind direction in the previous hour & dimensionless \\\\ \nSine of wind direction in the previous hour & dimensionless \\\\ \nTotal precipitation in the previous hour & mm \\\\ \nAtmospheric pressure in the previous hour & mB \\\\\nAir temperature (dry bulb) in the previous hour\t& $^\\circ\\mathrm{C}$ \\\\\nDew point temperature in the previous hour & $^\\circ\\mathrm{C}$ \\\\\nRelative humidity in the previous hour & \\% \\\\\nWind gust in the previous hour & m/s \\\\\nWind speed in the previous hour\t& m/s \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Projected random forests and conformal prediction of circular data", "authors": ["Paulo C. Marques F.", "Rinaldo Artes", "Helton Graziadei"], "url": "https://arxiv.org/abs/2410.24145v2", "attribution": "\"Projected random forests and conformal prediction of circular data\" by Paulo C. Marques F., Rinaldo Artes, and Helton Graziadei, arXiv:2410.24145v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.03496v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Error in full $H^2$ norm in , $k=1$, $p=2$ except (last row $p=5$)}\n\\begin{tabular}{|c||c|c|c|c|c||l|}\n \\hline\n $\\ell \\;\\backslash\\; \\alpha^2$ & $10^6$ & $10^{3}$ & $10^{0}$ & $10^{-3}$ & $10^{-6}$ & DoF \\\\ \\hline \\hline\n $3$ & 7.84e-02 & 7.85e-02 & 7.86e-02 & 7.86e-02 & 7.86e-02 & 100\\\\ \\hline\n $4$ & 3.91e-02 & 3.91e-02 & 3.91e-02 & 3.91e-02 & 3.91e-02 & 324\\\\ \\hline\n $5$ & 1.95e-02 & 1.95e-02 & 1.95e-02 & 1.95e-02 & 1.95e-02 & 1156\\\\ \\hline\n $6$ & 9.76e-03 & 9.76e-03 & 9.76e-03 & 9.76e-03 & 9.76e-03 & 4356\\\\ \\hline\n $6 \\; (5)$ & 3.60e-09 & 8.22e-09 & 1.06e-07 & 1.10e-06 & 1.39e-05 & 4761\\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Variational Formulations of the Strong Formulation -- Forward and Inverse Modeling using Isogeometric Analysis and Physics-Informed Networks", "authors": ["Kent-Andre Mardal", "Jarle Sogn", "Marius Zeinhofer"], "url": "https://arxiv.org/abs/2312.03496v1", "attribution": "\"Variational Formulations of the Strong Formulation -- Forward and Inverse Modeling using Isogeometric Analysis and Physics-Informed Networks\" by Kent-Andre Mardal, Jarle Sogn, and Marius Zeinhofer, arXiv:2312.03496v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.15274v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccrr}\n\\toprule\n & & & & &\\\\\nSite & AgeGroup & Gender & Diagnosis & Axis 1 & Depression \\\\\n\\midrule\nSite 1 & adult & F & 0.0 & 35 & 35\\\\ % FSU\\_WAVE1\n & & & 1.0 & 59 & 59\\\\\n & & M & 0.0 & 11 & 11\\\\\n & & & 1.0 & 12 & 12\\\\\nSite 2 & child & F & 0.0 & 72 & 95 \\\\ % SDSU\\_D2018\n & & & 1.0 & 31 & 8 \\\\\n & & M & 0.0 & 101 & 119 \\\\\n & & & 1.0 & 19 & 1 \\\\\nSite 3 & child & F & 0.0 & 82 & 128\\\\ % fsu\\_d2018b50\n & & & 1.0 & 57 & 11\\\\\n & & M & 0.0 & 98 & 150\\\\\n & & & 1.0 & 54 & 2\\\\\nSite 4 & child & F & 0.0 & 41 & 58\\\\ % fsu\\_d2018w8\n & & & 1.0 & 21 & 4\\\\\n & & M & 0.0 & 45 & 63\\\\\n & & & 1.0 & 20 & 2\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Participant label distribution}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Representation learning for improved interpretability and classification accuracy of clinical factors from EEG", "authors": ["Garrett Honke", "Irina Higgins", "Nina Thigpen", "Vladimir Miskovic", "Katie Link", "Sunny Duan", "Pramod Gupta", "Julia Klawohn", "Greg Hajcak"], "url": "https://arxiv.org/abs/2010.15274v3", "attribution": "\"Representation learning for improved interpretability and classification accuracy of clinical factors from EEG\" by Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Sunny Duan, Pramod Gupta, Julia Klawohn, and Greg Hajcak, arXiv:2010.15274v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09574v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{rotating}\n\\usepackage{amsfonts}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative comparisons between our proposed MSIRN and other 15 methods: including traditional methods and deep learning based methods. Extensive experiments are conducted on 7 datasets using S-measure ($S_\\alpha$), max F-measure (max $F_\\beta$), mean F-measure (mean $F_\\beta$), adaptive F-measure (adaptive $F_\\beta$), max E-measure (max $E_\\xi$), adaptive E-measure (adaptive $E_\\xi$) and mean absolute error (MAE). ↑ and ↓ denote that the higher or lower is better.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc|cccc}\n\\hline\n\\hline\n & &ACSD&LBE&DCMC&MDSF&SE&DF&AFNet&CTMF&MMCI\\\\\n &Metric&ICIP14&CVPR16&SPL16&TIP17&ICME16&TIP17&arXiv19&TOC18&PR19\\\\\n & &[52]&[19]&[53]&[54]&[55]&[64]&[56]&[57]&[9]\\\\\n\\hline\n\\hline\n\\multirow{8}*{\\rotatebox{90}{\\emph{NJU2K} \n}} &$S_\\alpha$↑ & 0.699 & 0.695 & 0.686 & 0.748 & 0.664 & 0.763 & 0.772 & 0.849 & 0.858 \\\\ \n &max $F_\\beta$↑ & 0.711 & 0.748 & 0.715 & 0.775 & 0.748 & 0.804 & 0.775 & 0.845 & 0.852 \\\\\n &mean $F_\\beta$↑ & 0.512 & 0.606 & 0.556 & 0.628 & 0.583 & 0.65 & 0.764 & 0.779 & 0.793 \\\\\n &adp $F_\\beta$↑ & 0.696 & 0.74 & 0.717 & 0.757 & 0.734 & 0.784 & 0.768 & 0.788 & 0.812 \\\\\n &max $E_\\xi$↑ & 0.803 & 0.803 & 0.799 & 0.838 & 0.813 & 0.864 & 0.853 & 0.913 & 0.915 \\\\\n &mean $E_\\xi$↑ & 0.593 & 0.655 & 0.619 & 0.677 & 0.624 & 0.696 & 0.826 & 0.846 & 0.851 \\\\\n &adp $E_\\xi$↑ & 0.786 & 0.791 & 0.791 & 0.812 & 0.772 & 0.835 & 0.846 & 0.864 & 0.878 \\\\\n &$\\mathcal{M}$↓ & 0.202 & 0.153 & 0.172 & 0.157 & 0.169 & 0.141 & 0.100 & 0.085 & 0.079 \\\\ \n\\hline\n\\multirow{8}*{\\rotatebox{90}{\\emph{LFSD} \n}} &$S_\\alpha$↑&0.862 & 0.736 & 0.753 & 0.700 & 0.698 & 0.791 & 0.738 & 0.826 & 0.787 \\\\\n &max $F_\\beta$↑ &0.863 & 0.726 & 0.817 & 0.783 & 0.791 & 0.817 & 0.744 & 0.791 & 0.771 \\\\\n &mean $F_\\beta$↑ &0.838 & 0.611 & 0.655 & 0.521 & 0.640 & 0.679 & 0.735 & 0.756 & 0.722 \\\\\n &adp $F_\\beta$↑ &0.851 & 0.708 & 0.816 & 0.799 & 0.778 & 0.806 & 0.742 & 0.782 & 0.779 \\\\\n &max $E_\\xi$↑ &0.898 & 0.804 & 0.856 & 0.826 & 0.840 & 0.865 & 0.815 & 0.865 & 0.839 \\\\\n &mean $E_\\xi$↑ &0.873 & 0.670 & 0.683 & 0.588 & 0.653 & 0.726 & 0.796 & 0.810 & 0.775 \\\\\n &adp $E_\\xi$↑ &0.885 & 0.770 & 0.842 & 0.817 & 0.784 & 0.844 & 0.810 & 0.851 & 0.840 \\\\\n &$\\mathcal{M}$↓ &0.077 & 0.208 & 0.155 & 0.190 & 0.167 & 0.138 & 0.133 & 0.119 & 0.132\\\\ \n\\hline\n\\multirow{8}*{\\rotatebox{90}{\\emph{STERE} \n}} &$S_\\alpha$↑&0.692 & 0.660 & 0.731 & 0.728 & 0.708 & 0.757 & 0.825 & 0.848 & 0.873 \\\\\n &max $F_\\beta$↑ &0.669 & 0.633 & 0.740 & 0.719 & 0.755 & 0.757 & 0.823 & 0.831 & 0.863 \\\\\n &mean $F_\\beta$↑ &0.478 & 0.501 & 0.590 & 0.527 & 0.610 & 0.617 & 0.806 & 0.758 & 0.813 \\\\\n &adp $F_\\beta$↑ &0.661 & 0.595 & 0.742 & 0.744 & 0.748 & 0.742 & 0.807 & 0.771 & 0.829 \\\\\n &max $E_\\xi$↑ &0.806 & 0.787 & 0.819 & 0.809 & 0.846 & 0.847 & 0.887 & 0.912 & 0.927 \\\\\n &mean $E_\\xi$↑ &0.592 & 0.601 & 0.655 & 0.614 & 0.665 & 0.691 & 0.872 & 0.841 & 0.873 \\\\\n &adp $E_\\xi$↑ &0.793 & 0.749 & 0.831 & 0.830 & 0.825 & 0.838 & 0.886 & 0.864 & 0.901 \\\\\n &$\\mathcal{M}$↓ &0.200 & 0.250 & 0.148 & 0.176 & 0.143 & 0.141 & 0.075 & 0.086 & 0.068\\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection", "authors": ["Ze-yu Liu", "Jian-wei Liu", "Xin Zuo", "Ming-fei Hu"], "url": "https://arxiv.org/abs/2201.09574v1", "attribution": "\"Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection\" by Ze-yu Liu, Jian-wei Liu, Xin Zuo, and Ming-fei Hu, arXiv:2201.09574v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17626v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n \\toprule\n $n$ & QR & RQ & $Q(A^TA)^{1/2}$ & $(AA^T)^{1/2}Q$ \\\\\n \\midrule\n 10 & 1 & 12 & 1 & 3\\\\\n 100 & 1 & - & 1 & 3\\\\\n 1000& 1 & - & 2* & 5\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Preconditioned normal equations for solving discretised partial differential equations", "authors": ["Lorenzo Lazzarino", "Yuji Nakatsukasa", "Umberto Zerbinati"], "url": "https://arxiv.org/abs/2502.17626v2", "attribution": "\"Preconditioned normal equations for solving discretised partial differential equations\" by Lorenzo Lazzarino, Yuji Nakatsukasa, and Umberto Zerbinati, arXiv:2502.17626v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.04349v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulations results by varying basket dimension.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\toprule\nDim &\tMC Price & BSDE Price\t& Err.\\% & Time$^*$ \\\\\n\\midrule\\midrule\n5\t&\t0.903\t&\t0.908\t&\t0.59\\%\t&\t1984\t\\\\\n10\t&\t1.614\t&\t1.612\t&\t0.15\\%\t&\t1861\t\\\\\n25\t&\t3.618\t&\t3.612\t&\t0.17\\%\t&\t2105\t\\\\\n50\t&\t6.858\t&\t6.902\t&\t0.64\\%\t&\t2450\t\\\\\n75\t&\t10.138\t&\t10.093\t&\t0.44\\%\t&\t2840\t\\\\\n100\t&\t13.607\t&\t13.479\t&\t0.95\\%\t&\t4438\t\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A deep solver for BSDEs with jumps", "authors": ["Kristoffer Andersson", "Alessandro Gnoatto", "Marco Patacca", "Athena Picarelli"], "url": "https://arxiv.org/abs/2211.04349v3", "attribution": "\"A deep solver for BSDEs with jumps\" by Kristoffer Andersson, Alessandro Gnoatto, Marco Patacca, and Athena Picarelli, arXiv:2211.04349v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.16928v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The ablation study of AMamba with different modules and feature fusion.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\hline\nTMamba & SMamba & Concate & FEM & $\\overline{\\text{APE}}(m)$& $\\overline{\\text{Acc}}(\\%)$\\\\\n\\hline\n$\\checkmark$ & & & & {0.89} & 97.4 & \\\\\n&$\\checkmark$ & & & 1.16 & 95.2 & \\\\\n$\\checkmark$& $\\checkmark$ & $\\checkmark$ & & 1.12 & 96.8 & \\\\\n$\\checkmark$& $\\checkmark$ & & $\\checkmark$ & \\textbf{0.86} & \\textbf{97.9} & \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "AV-DTEC: Self-Supervised Audio-Visual Fusion for Drone Trajectory Estimation and Classification", "authors": ["Zhenyuan Xiao", "Yizhuo Yang", "Guili Xu", "Xianglong Zeng", "Shenghai Yuan"], "url": "https://arxiv.org/abs/2412.16928v1", "attribution": "\"AV-DTEC: Self-Supervised Audio-Visual Fusion for Drone Trajectory Estimation and Classification\" by Zhenyuan Xiao, Yizhuo Yang, Guili Xu, Xianglong Zeng, and Shenghai Yuan, arXiv:2412.16928v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.06845v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Computational Performance for Data Processing Steps}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nProcessing Step & Average Time (min) & Memory Usage (GB) & Processor Utilization (\\%) \\\\\n\\midrule\nData importation & 1.2 & 2.8 & 15 \\\\\nNoise reduction & 2.7 & 4.5 & 45 \\\\\nSurface trimming & 4.1 & 5.2 & 30 \\\\\nPre-positioning & 3.5 & 6.8 & 65 \\\\\nSequential merging & 18.6 & 18.4 & 85 \\\\\nFinal alignment & 7.2 & 12.6 & 70 \\\\\nDeviation mapping & 14.9 & 22.3 & 90 \\\\\nFeature extraction & 9.3 & 15.7 & 75 \\\\\n\\textbf{Total} & \\textbf{61.5} & \\textbf{22.3 (peak)} & \\textbf{90 (peak)} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Hybrid Machine Learning Framework for Predicting Geometric Deviations from 3D Surface Metrology", "authors": ["Hamidreza Samadi", "Md Manjurul Ahsan", "Shivakumar Raman"], "url": "https://arxiv.org/abs/2508.06845v1", "attribution": "\"Hybrid Machine Learning Framework for Predicting Geometric Deviations from 3D Surface Metrology\" by Hamidreza Samadi, Md Manjurul Ahsan, and Shivakumar Raman, arXiv:2508.06845v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.03168v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lcccccccc} \n \\hline\n $\\nu $&[6,2,2]&[6,2,1]& [6,1,1]&[5,2,2]& [5,2,1]&[4,4,2]&[4,4,1] &\n\\\\\n\\hline\n$P_{[3,1,1],[3,1,1]}^\\nu$& $\\frac{8}{190}$& $\\frac{8}{190}$ & $\\frac{2}{190}$ & $\\frac{8}{190}$ & $\\frac{8}{190}$ & $\\frac{16}{190}$ & $\\frac{8}{190}$ &\\\\\n\\hline \n &&&&&&&\\\\\n\\hline \n $\\nu $&[4,3,2]&[4,3,1]& [4,2,2]&[4,2,1]& [3,3,2]&[3,2,2]&[3,2,1] & otherwise \n\\\\\n\\hline\n$P_{[3,1,1],[3,1,1]}^\\nu$& $\\frac{48}{190}$& $\\frac{24}{190}$ & $\\frac{8}{190}$ & $\\frac{8}{190}$ & $\\frac{36}{190}$ & $\\frac{4}{190}$ & $\\frac{4}{190}$ & 0\\\\\n\\hline \n \\end{tabular}\n\\caption{Results of aggregation $[3,1,1]+[3,1,1]$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Lattice aggregations of boxes and symmetric functions", "authors": ["Natasha Rozhkovskaya"], "url": "https://arxiv.org/abs/2312.03168v1", "attribution": "\"Lattice aggregations of boxes and symmetric functions\" by Natasha Rozhkovskaya, arXiv:2312.03168v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.11346v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Running time for Algorithm 2 and the algorithm in with $e_t =1$ and $\\delta = 1$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\t\t\\hline\n\t\t& \\textbf{Algorithm 2} & Algorithm in \\\\\n\t\t\\hline\n\t\t$r=0.1$ & {19.81s} & 15.80s\\\\\n\t\t$r=0.3$ & {28.74s} & 15.83s\\\\\n\t\t$r=0.5$ & {41.42s} & 15.98s\\\\\n\t\t$r=0.8$ & {110.63s} & 16.11s\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MIMO Radar Waveform-Filter Design for Extended Target Detection from a View of Games", "authors": ["Zhou Xu", "Chongyi Fan", "Xiaotao Huang"], "url": "https://arxiv.org/abs/2011.11346v1", "attribution": "\"MIMO Radar Waveform-Filter Design for Extended Target Detection from a View of Games\" by Zhou Xu, Chongyi Fan, and Xiaotao Huang, arXiv:2011.11346v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.11927v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n\\textbf{Gender \\textbackslash Label} & \\textit{none} & \\textit{ideation} & \\textit{attempt} \\\\ \\midrule\nHusband & 31 & 22 & 9 \\\\\nWife & 34 & 15 & 13 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Suicidal risk demographics of 62 couples in our data}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Automatic prediction of suicidal risk in military couples using multimodal interaction cues from couples conversations", "authors": ["Sandeep Nallan Chakravarthula", "Md Nasir", "Shao-Yen Tseng", "Haoqi Li", "Tae Jin Park", "Brian Baucom", "Craig J. Bryan", "Shrikanth Narayanan", "Panayiotis Georgiou"], "url": "https://arxiv.org/abs/1911.11927v1", "attribution": "\"Automatic prediction of suicidal risk in military couples using multimodal interaction cues from couples conversations\" by Sandeep Nallan Chakravarthula, Md Nasir, Shao-Yen Tseng, Haoqi Li, Tae Jin Park, Brian Baucom, Craig J. Bryan, Shrikanth Narayanan, and Panayiotis Georgiou, arXiv:1911.11927v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.16951v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Running Time Summary (Numerical Features: Standardized).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\n{Models} & Total Fits & Tuning Time & Average Tuning & Fitting Time (Best Model)\\\\\n \\midrule\nLightGBM & 2000 & 159.92 & 0.08 & 0.19 \\\\\nLinear Regression & 150 & 3.32 & 0.02 & 0.07 \\\\\nMLP & 40 & 1948.63 & 48.72 & 116.05\\\\\nRandom Forest & 1200 & 1934.51 & 1.61 & 64.58 \\\\\nXGBoost & 5760 & 2429.88 & 0.42 & 4.30 \\\\\n\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Beyond Tides and Time: Machine Learning Triumph in Water Quality", "authors": ["Yinpu Li", "Siqi Mao", "Yaping Yuan", "Ziren Wang", "Yixin Kang", "Yuanxin Yao"], "url": "https://arxiv.org/abs/2309.16951v2", "attribution": "\"Beyond Tides and Time: Machine Learning Triumph in Water Quality\" by Yinpu Li, Siqi Mao, Yaping Yuan, Ziren Wang, Yixin Kang, and Yuanxin Yao, arXiv:2309.16951v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.09436v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Nodes on the designed ISIG BNs with assumed states}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\hline\nnode & description & states \\\\ \n \\hline\n SC & signal controller & N,L,M,H,C \\\\ \n RSE & roadside equipment & L,M,H \\\\ \n TC & traffic composition & UP,EP,UV,CR,EM,FR,TR \\\\ \n UP & unequipped pedestrians & N,L,M,H,C \\\\ \n EP & equipped pedestrians & N,L,M,H,C \\\\ \n UV & unequipped vehicles & N,L,M,H,C \\\\ \n CR & regular equipped vehicles & N,L,M,H,C \\\\ \n EM & emergency vehicles & N,L,M,H,C \\\\ \n FR & freight trucks & N,L,M,H,C \\\\ \n TR & transit vehicles & N,L,M,H,C \\\\ \n ITS & other ITS devices & L,M,H \\\\ \n TMC & traffic management center & L,M,H \\\\ \n S & sensor & True,False \\\\ \n DD & detection by driver & L,M,H \\\\ \n DP & detection by pedestrian & L,M,H \\\\ \n DS & detection by system (vehs) & L,M,H \\\\ \n PDS & detection by system (pedes) & L,M,H \\\\ \n RDS & detection by system (RSE) & L,M,H \\\\ \n SDS & detection by system (SA) & L,M,H \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks", "authors": ["Gurcan Comert", "Mashrur Chowdhury", "David M. Nicol"], "url": "https://arxiv.org/abs/2011.09436v1", "attribution": "\"Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks\" by Gurcan Comert, Mashrur Chowdhury, and David M. Nicol, arXiv:2011.09436v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Forward star representation of the network in Figure~. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rc|c|crc|c|c|c|c|}\n\t\tNode & \n\t\t & \n\t\tPoint & \n\t\t & \n\t\tLink & \n\t\t & \n\t\ttail & \n\t\thead & \n\t\tcost & \n\t\tcapacity \\\\\n\t\t\\cline{1-1}\n\t\t\\cline{3-3}\n\t\t\\cline{5-5}\n\t\t\\cline{7-10}\n\t\t1 & & 1 & & 1 & & 1 & 3 & $\\cdots$ & $\\cdots$ \\\\ \n\t\t\\cline{3-3}\n\t\t\\cline{7-10}\n\t\t2 & & 3 & & 2 & & 1 & 2 & $\\cdots$ & $\\cdots$ \\\\ \n\t\t\\cline{3-3}\n\t\t\\cline{7-10}\n\t\t3 & & 5 & & 3 & & 2 & 3 & $\\cdots$ & $\\cdots$ \\\\ \n\t\t\\cline{3-3}\n\t\t\\cline{7-10}\n\t\t4 & & 6 & & 4 & & 2 & 4 & $\\cdots$ & $\\cdots$ \\\\ \n\t\t\\cline{3-3}\n\t\t\\cline{7-10}\n\t\t5 & & 6 & & 5 & & 3 & 4 & $\\cdots$ & $\\cdots$ \\\\ \n\t\t\\cline{3-3}\n\t\t\\cline{7-10}\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.08561v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Continuous versus continuous summary statistics of false positives for each approximation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|c|}\n\\hline\n&& \\multicolumn{7}{c|}{Numerical summary of observed proportion of false positives}\\\\\n&& \\multicolumn{7}{c|}{}\\\\\nApproximation & $level$ & minimum & $Q(0.25)$ & median & average & $Q(0.75)$ & maximum &IQR\\\\\n\\hline\n$\\chi^2$ estimated d.f. & 0.05 & 0.011 & 0.030 & 0.041 & 0.040 & 0.047 & 0.073& 0.018 \\\\\n$\\chi^2$ simple d.f. & & 0.023 & 0.047 & 0.062 & 0.060 & 0.070 & 0.103 & 0.023 \\\\\n$\\Gamma(\\alpha, \\theta)$& &0.028 & 0.058 & 0.072 & 0.072 & 0.087 & 0.128 & 0.029\\\\\n\\hline\n$\\chi^2$ estimated d.f. & 0.01 & 0.000 & 0.005 & 0.008 & 0.007 & 0.009 & 0.017& 0.004\\\\\n$\\chi^2$ simple d.f. & & 0.002 & 0.008 & 0.012 & 0.012 & 0.015 & 0.025 & 0.007 \\\\ \n$\\Gamma(\\alpha, \\theta)$& &0.003 & 0.012 & 0.017 & 0.017 & 0.021 & 0.037 & 0.009 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Recursive random binning to detect and display pairwise dependence", "authors": ["Chris Salahub", "Wayne Oldford"], "url": "https://arxiv.org/abs/2311.08561v2", "attribution": "\"Recursive random binning to detect and display pairwise dependence\" by Chris Salahub and Wayne Oldford, arXiv:2311.08561v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.09176v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n$p(x)$ & $X$ & $\\Pr(X=x)$ & $\\Pr(X=x,Z_x=1)$ & $\\Pr(X=x,Z_x=2)$ & $\\lambda^{01}(x)$\n & $\\lambda^{02}(x)$ & $\\lambda(x)$\\\\\n \\toprule\n 0.66&1&0.04&0.026&0.014&0.026&0.014&0.04\\\\\n 0.20&2&0.06&0.012&0.048&0.013&0.050&0.06\\\\\n 0.45&3&0.10&0.045&0.055&0.050&0.061&0.11\\\\\n 0.87&4&0.14&0.122&0.018&0.152&0.023&0.18\\\\\n 0.20&5&0.09&0.018&0.072&0.027&0.109&0.14\\\\\n 0.81&6&0.06&0.049&0.011&0.085&0.020&0.11\\\\\n 0.05&7&0.14&0.007&0.133&0.014&0.261&0.27\\\\\n 0.78&8&0.18&0.140&0.040&0.379&0.107&0.49\\\\\n 0.25&9&0.07&0.018&0.053&0.092&0.276&0.37\\\\\n 0.42&10&0.12&0.050&0.070&0.420&0.580&1.00\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "On the Convergence of Credit Risk in Current Consumer Automobile Loans", "authors": ["Jackson P. Lautier", "Vladimir Pozdnyakov", "Jun Yan"], "url": "https://arxiv.org/abs/2211.09176v3", "attribution": "\"On the Convergence of Credit Risk in Current Consumer Automobile Loans\" by Jackson P. Lautier, Vladimir Pozdnyakov, and Jun Yan, arXiv:2211.09176v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2312.02826v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters of the CAT architecture used in this study.}\n\\begin{tabular}{lllc}\n \\toprule\n Module & Layers & Parameters & Notation \\\\\n \\midrule\n \\multirow{8}*{1D-CNN Backbone} & Conv1D, BatchNorm, ReLU & in=1, out=16, kernel=15 & \\multirow{10}*{$\\mathbf{W}_{\\text{E}}$} \\\\\n & Conv1D, BatchNorm, ReLU & in=16, out=32, kernel=3 \\\\\n & MaxPool1D & kernel=2, stride=2 \\\\\n & Conv1D, BatchNorm, ReLU & in=32, out=64, kernel=3 \\\\\n & Conv1D, BatchNorm, ReLU & in=64, out=128, kernel=3 \\\\\n & AdaptiveMaxPool1D & out=128$\\times$4 \\\\\n & FC, ReLU & in=128$\\times$4, out=256 \\\\\n & Dropout & p=0.5 \\\\\n \\cmidrule{1-3}\n \\multirow{2}*{Bottleneck} & FC, ReLU & in=256, out=256 \\\\\n & Dropout & p=0.5 \\\\\n \\midrule\n Classification head & FC, Softmax & in=256, out=13 & $\\mathbf{W}_{\\text{C}}$ \\\\\n \\midrule\n \\multirow{4}*{Domain Classifier} & FC, ReLU & in=256, out=1024 & \\multirow{4}*{$\\mathbf{W}_{\\text{DC}}$}\\\\\n & Dropout & p=0.5 \\\\\n & FC, ReLU & in=1024, out=1024 & \\\\\n & Dropout & p=0.5 \\\\\n & FC, Sigmoid & in=1024, out=1 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis", "authors": ["Florent Forest", "Olga Fink"], "url": "https://arxiv.org/abs/2312.02826v2", "attribution": "\"Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis\" by Florent Forest and Olga Fink, arXiv:2312.02826v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08068v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\nPosition & Before & After \\\\ \\hline\np. 25 & & Add abbreviations \\\\ \\hline\n & & \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{List of edits in the reviewed manuscript}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Power Allocation Algorithms for Massive MIMO Systems with Multi-Antenna Users", "authors": ["Evgeny Bobrov", "Boris Chinyaev", "Viktor Kuznetsov", "Dmitrii Minenkov", "Daniil Yudakov"], "url": "https://arxiv.org/abs/2201.08068v4", "attribution": "\"Power Allocation Algorithms for Massive MIMO Systems with Multi-Antenna Users\" by Evgeny Bobrov, Boris Chinyaev, Viktor Kuznetsov, Dmitrii Minenkov, and Daniil Yudakov, arXiv:2201.08068v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.02199v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n \\hline\n Spectral Bands & mAP \\\\\n \\hline\n Red & 61.99 \\\\\n Red, Green & 65.53 \\\\\n Red, Green, Blue & 66.73\\\\\n Red, Red-Edge 1, SWIR1 & 62.43 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{USatlas validation set performance comparison of USatMAE using different subsets of Sentinel-2 spectral bands.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery", "authors": ["Jeremy Irvin", "Lucas Tao", "Joanne Zhou", "Yuntao Ma", "Langston Nashold", "Benjamin Liu", "Andrew Y. Ng"], "url": "https://arxiv.org/abs/2312.02199v1", "attribution": "\"USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery\" by Jeremy Irvin, Lucas Tao, Joanne Zhou, Yuntao Ma, Langston Nashold, Benjamin Liu, and Andrew Y. Ng, arXiv:2312.02199v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.19200v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc} \\toprule[1pt]\nRebalancing & E[$R_T$] & Median[$R_T$] & $R_T: 5^{th}$ & $R_T: 95^{th}$ & ES( 5\\% )& $\\Omega(1)$ &$Prob[R_T > 1]$\\\\\n interval & & &percentile & percentile & & & \\\\\n \\hline\n & \\multicolumn{7}{c}{$\\alpha^\\ell = 0.30$} \\\\\n \\hline\nYearly & 0.9831 (.0004)& 0.9769 & 0.9026 & 1.0838 & 0.8671 & 0.4690 & 0.3139 \\\\\n\\hline\n & \\multicolumn{7}{c}{$\\alpha^\\ell = 0.45$} \\\\\n\\hline\nYearly & 1.1561 (.002) & 1.1228 & 0.7712 & 1.6502 & 0.6952 & 5.0141 & 0.7012 \\\\\nQuarterly & 1.1504 (.001) & 1.1313 & 0.7983 & 1.5646 & 0.7239 & 5.6502 & 0.7299 \\\\\nMonthly & 1.1494 (.001) & 1.1330 & 0.8033 & 1.5487 & 0.7308 & 5.8412 & 0.7367 \\\\\n\\bottomrule[1pt]\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Making Leveraged Exchange-Traded Funds Work for your Portfolio", "authors": ["Peter Forsyth", "Pieter van Staden", "Yuying Li"], "url": "https://arxiv.org/abs/2506.19200v1", "attribution": "\"Making Leveraged Exchange-Traded Funds Work for your Portfolio\" by Peter Forsyth, Pieter van Staden, and Yuying Li, arXiv:2506.19200v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.00070v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Abnormality classification performance on patches of size $64\\times64$. Metrics computed on fully-sampled images, (A) 4$\\times$ and (B) $8\\times$ undersampled images, which were reconstructed using the various methods studied in this paper.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c}\n\\hline\\hline\n\\multicolumn{6}{c}{AF=4$\\times$}\\\\\nInput & Sn & Sp & F1 score & Kappa & AUROC\\\\\n\\hline\nFully-sampled & 78.43 & 80.47 & 79.31 & 58.89 & 87.93\\\\\n\\hline\nU-Net & 69.87 & 83.12& 74.90 & 52.62 & 86.51 \\\\\nFNAF U-Net & 70.65 & 83.32 & 75.49 & 53.93 & 86.68\\\\\nB-Box U-Net & 73.08 & 82.73 & 76.85 & 55.78 & 87.00 \\\\\n\\hline\nI-RIM & 81.54 & 76.45 & 79.60 & 58.00 & 86.58 \\\\\nFNAF I-RIM & 81.44 & 76.45 & 79.54 & 57.90 & 86.63 \\\\\nB-Box I-RIM & 78.91 & 78.31 & 78.61 & 57.22 & 86.49 \\\\\n\\hline\\hline\n \\\\\n\\multicolumn{6}{c}{(A)} \\\\\n\\\\\n\\hline\\hline\n\\multicolumn{6}{c}{AF=8$\\times$}\\\\\nInput & Sn & Sp & F1 score & Kappa & AUROC\\\\\n\\hline\nFully-sampled & 78.43 & 80.47 & 79.31 & 58.89 & 87.93\\\\\n\\hline\nU-Net & 60.06 & 88.62 & 70.11 & 48.61 & 86.10\\\\\nFNAF U-Net & 62.00 & 88.32 & 71.44 & 50.26 & 86.36 \\\\\nB-Box U-Net &67.93 & 84.79 & 74.24 & 52.68 & 86.85 \\\\ \n\\hline\nI-RIM & 75.61 & 78.61 & 76.84 & 54.21 & 85.21 \\\\\nFNAF I-RIM & 74.44 & 78.70 & 76.14 & 53.13 & 85.31 \\\\\nB-Box I-RIM & 72.01 & 81.45 & 75.65 & 53.44 & 85.27 \\\\\n\\hline\\hline\n \\\\\n\\multicolumn{6}{c}{(B)} \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Adversarial Robust Training of Deep Learning MRI Reconstruction Models", "authors": ["Francesco Calivá", "Kaiyang Cheng", "Rutwik Shah", "Valentina Pedoia"], "url": "https://arxiv.org/abs/2011.00070v3", "attribution": "\"Adversarial Robust Training of Deep Learning MRI Reconstruction Models\" by Francesco Calivá, Kaiyang Cheng, Rutwik Shah, and Valentina Pedoia, arXiv:2011.00070v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.18500v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\\hline\n\\textbf{Parameter} & \\textbf{Symbol} & \\textbf{Value} \\\\ % inserts table %heading\n\\hline\nNumber of subcarriers&$N$&512\\\\\nMaximum number of frames &$\\bar{N}$&100\\\\\nModulations order &$\\bar{m}$&[1, 2, 4, 6]\\\\\nCarrier frequency &$f_c$&60 GHz \\\\\nWavelength&$\\omega$&$5\\times10^{-3}$ m \\\\\nBandwidth&$B_{\\text{w}}$&2.16 GHz\\\\\nMaximum range&$d_{max}$& 50 m\\\\\nMaximum velocity&$v_{max}$& 50 m/s\\\\\nNoise level&$N_0$& -174 dBm/Hz\\\\\nAntenna gain &$g^a$& 1.5\\\\\nTransmit power &$\\rho_i$& 15 dBm\\\\\nInterference &$\\mathbf{I}$&[24 , 24.5, 37, 37.5]~dB\\\\\nPath loss exponent &$\\mathbf{\\delta}$&[2.10, 1.22, 0.453, 0.240]\\\\\natmospheric attenuation &$\\mathbf{\\beta}$&[75.1, 94.6, 126, 135]\\\\\nBlocker type &$\\mathbf{B}$&['LOS', '1V','2V','3V']\\\\\nTransmission slot time &$s$&0.002\\\\\nPilote transmission time &$T_p$&0.01*s\\\\\nsector-level beamwidth &$\\psi$& $45^{\\circ}$\\\\\nhalf-power beamwidth &$\\varphi$& $10^{\\circ}$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Joint Adaptive OFDM and Reinforcement Learning Design for Autonomous Vehicles: Leveraging Age of Updates", "authors": ["Mamady Delamou", "Ahmed Naeem", "Huseyin Arslan", "El Mehdi Amhoud"], "url": "https://arxiv.org/abs/2412.18500v1", "attribution": "\"Joint Adaptive OFDM and Reinforcement Learning Design for Autonomous Vehicles: Leveraging Age of Updates\" by Mamady Delamou, Ahmed Naeem, Huseyin Arslan, and El Mehdi Amhoud, arXiv:2412.18500v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.11891v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The Training statistics of the 5 homophilic, heterophilic and real-world benchmark datasets used in this work for the baseline dDGM, MLP and GCN models.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc} \\hline\n & Training Epochs & Learning Rate & Weight Decay \\\\ \\hline\n \\textbf{Cora} & 1,500 & $1\\times10^{-2}$& $1\\times10^{-4}$\\\\\n \\textbf{CiteSeer} & 1,500 & $1\\times10^{-2}$& $1\\times10^{-4}$\\\\\n \\textbf{Squirrel} &1,000 &$1\\times10^{-2}$ & $1\\times10^{-3}$\\\\\n\\textbf{Chameleon} & 1,000& $1\\times10^{-2}$& $1\\times10^{-3}$\\\\\n \\textbf{TadPole} & 800& $1\\times10^{-3}$& $2\\times10^{-4}$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference", "authors": ["Yuan Lu", "Haitz Sáez de Ocáriz Borde", "Pietro Liò"], "url": "https://arxiv.org/abs/2311.11891v1", "attribution": "\"AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference\" by Yuan Lu, Haitz Sáez de Ocáriz Borde, and Pietro Liò, arXiv:2311.11891v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10937v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The attack success rate (\\textbf{higher} indicates better attacks) of targeted attacks under different defenses. We pick $\\epsilon_{\\infty}=0.18$, $m=100$, $\\gamma=0.25$ and select DGCNN as victim model. Other settings are the same as Sec 4.1. Our AOF outperforms other SOTA attacks under IF-Defense and reaches the second best position under SRS, SOR and DUP defenses. The ConvNet-Opt, ONet-Remesh and ONet-Opt are three variants of IF-Defense.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccr}\n\\toprule\nDefenses & 3D-Adv & kNN & AdvPC & AOF \\\\\n\\midrule\nNo defense & \\bf99.76 & 75.85 & 76.56 & 77.17 \\\\\nSRS & 3.04 & \\bf13.98 & 6.88 & 7.09 \\\\\nSOR & 1.54 & \\bf22.00 & 8.54 & 14.05 \\\\\nDUP-Net & 1.38 & \\bf13.25 &7.04 & 11.38 \\\\\nConvNet-Opt & 0.57 & 2.84 & 1.09 & \\bf4.66 \\\\\nONet-Remesh & 0.85 & 1.62 & 1.05 & \\bf1.98 \\\\\nONet-Opt & 0.65 & 1.94 & 1.01 & \\bf2.91 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Boosting 3D Adversarial Attacks with Attacking On Frequency", "authors": ["Binbin Liu", "Jinlai Zhang", "Lyujie Chen", "Jihong Zhu"], "url": "https://arxiv.org/abs/2201.10937v1", "attribution": "\"Boosting 3D Adversarial Attacks with Attacking On Frequency\" by Binbin Liu, Jinlai Zhang, Lyujie Chen, and Jihong Zhu, arXiv:2201.10937v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18248v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fairleads and anchors coordinates as reported in }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\textbf{Fairlead/Anchor} & \\textbf{Global Coordinates (m)} \\\\\n \\midrule\n Fairlead 1 & $(-0.1,0.1,0.0736)$ \\\\\n Fairlead 2 & $(-0.1,-0.1,-0.0736)$ \\\\ \n Fairlead 3 & $(0.1,0.1,-0.0736)$ \\\\\n Fairlead 4 & $(0.1,-0.1,-0.0736)$ \\\\\n Anchor 1 & $(-1.385,0.423,-0.5)$ \\\\\n Anchor 2 & $(-1.385,-0.423,-0.5)$ \\\\\n Anchor 3 & $(1.385,0.423,-0.5)$ \\\\\n Anchor 4 & $(1.385,-0.423,-0.5)$ \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A finite volume Simo-Reissner beam method for moored floating body dynamics", "authors": ["Amirhossein Taran", "Seevani Bali", "Zeljko Tukovic", "Vikram Pakrashi", "Philip Cardiff"], "url": "https://arxiv.org/abs/2504.18248v1", "attribution": "\"A finite volume Simo-Reissner beam method for moored floating body dynamics\" by Amirhossein Taran, Seevani Bali, Zeljko Tukovic, Vikram Pakrashi, and Philip Cardiff, arXiv:2504.18248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.03021v2_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Linear-regression transportability, synthetic ($\\delta = 0.1$)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\textbf{Model} & \\textbf{Min} & \\textbf{Median} & \\textbf{Mean} & \\textbf{Max}\\\\\n\\midrule\nS-Linear & $1.47\\times10^{-13}$ & $1.47\\times10^{-10}$ & $1.28\\times10^{-8}$ & $2.17\\times10^{-7}$ \\\\\nT-Linear & $1.54\\times10^{-11}$ & $1.10\\times10^{-8}$ & $4.57\\times10^{-7}$ & $9.10\\times10^{-6}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Testing Generalizability in Causal Inference", "authors": ["Daniel de Vassimon Manela", "Linying Yang", "Robin J. Evans"], "url": "https://arxiv.org/abs/2411.03021v2", "attribution": "\"Testing Generalizability in Causal Inference\" by Daniel de Vassimon Manela, Linying Yang, and Robin J. Evans, arXiv:2411.03021v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.08743v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CV of Window Topic Models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\hline\nUmass & NMF & LDA & NMF & LDA \\\\\nYear & \\multicolumn{2}{c}{K=6} & \\multicolumn{2}{c}{K=18} \\\\ \\hline\n2004 & -2.565 & -1.334 & -5.216 & -1.300 \\\\\n2005 & -1.869 & -0.609 & -3.928 & -1.092 \\\\\n2006 & -2.042 & -0.626 & -3.255 & -1.043 \\\\\n2007 & -1.762 & -0.744 & -3.224 & -1.23 \\\\\n2008 & -1.688 & -0.572 & -4.271 & -0.606 \\\\\n2009 & -1.299 & -0.449 & -2.761 & -0.833 \\\\\n2010 & -0.815 & -0.438 & -3.788 & -0.793 \\\\\n2011 & -3.008 & -0.572 & -3.540 & -0.719 \\\\\n2012 & -1.413 & -0.579 & -2.465 & -1.073 \\\\\n2013 & -1.297 & -0.514 & -2.196 & -0.621 \\\\\n2014 & -1.589 & -0.465 & -3.195 & -0.873 \\\\\n2015 & -0.950 & -0.376 & -4.786 & -0.600 \\\\\n2016 & -1.010 & -0.432 & -2.859 & -0.714 \\\\\n2017 & -1.063 & -0.372 & -2.560 & -0.669 \\\\\n2018 & -0.764 & -0.344 & -2.826 & -0.634 \\\\\n2019 & -1.242 & -0.322 & -5.582 & -0.536 \\\\\n2020 & -1.301 & -0.269 & -2.172 & -0.626 \\\\\n2021 & -0.788 & -0.288 & -2.088 & -0.832 \\\\\n2022 & -0.588 & -0.310 & -1.790 & -0.392 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Dynamic Topic Analysis in Academic Journals using Convex Non-negative Matrix Factorization Method", "authors": ["Yang Yang", "Tong Zhang", "Jian Wu", "Lijie Su"], "url": "https://arxiv.org/abs/2504.08743v1", "attribution": "\"Dynamic Topic Analysis in Academic Journals using Convex Non-negative Matrix Factorization Method\" by Yang Yang, Tong Zhang, Jian Wu, and Lijie Su, arXiv:2504.08743v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.02465v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n \\toprule\n Dataset & \\textit{normal} & \\textit{abnormal}\\\\\n \\midrule\n UCR & 83.0 & 46.8~(\\textbf{-36.2}) \\\\\n NASA-SMAP& 72.9 & 48.5~(\\textbf{-24.4})\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers", "authors": ["Jiaxin Zhuang", "Leon Yan", "Zhenwei Zhang", "Ruiqi Wang", "Jiawei Zhang", "Yuantao Gu"], "url": "https://arxiv.org/abs/2411.02465v1", "attribution": "\"See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers\" by Jiaxin Zhuang, Leon Yan, Zhenwei Zhang, Ruiqi Wang, Jiawei Zhang, and Yuantao Gu, arXiv:2411.02465v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.01758v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\t\t\\hline\n\t\tTraining Size & $\\tau$ & QKNN & QRF\\\\\n\t\t\\hline\n\t\t1000 & 0.5 & \\textbf{0.1553} (0.0032) & 0.1679 (0.0026)\\\\\n\t\t5000 & 0.5 & \\textbf{0.1485} (0.0016) & 0.1646 (0.0016) \\\\\n\t\t10000 & 0.5 & \\textbf{0.1479} (0.0017) & 0.1623 (0.0019)\\\\\n\t\t\\hline\n\t\t\\\\\n\t\t\\hline\n\t\tTraining Size & $\\tau$& QKNN & QRF \\\\\n\t\t\\hline\n\t\t1000 & 0.9 & \\textbf{0.8405} (0.0141) & 0.8315 (0.0086) \\\\\n\t\t5000 & 0.9& \\textbf{0.8569} (0.0076) & 0.8334 (0.0040)\\\\\n\t\t10000 & 0.9 & \\textbf{0.8585} (0.0061) & 0.8337 (0.0041)\\\\\n\t\t1000 & 0.95& \\textbf{0.9163} (0.0127) & 0.8887 (0.0084)\\\\\n\t\t5000 & 0.95& \\textbf{0.9216} (0.0099) & 0.8891 (0.0040)\\\\\n\t\t10000 & 0.95 & \\textbf{0.9274} (0.0097) & 0.8902 (0.0042) \\\\\n\t\t\\hline \n\t\t\\\\\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Average test set prediction error (across the 100 test sets) on California housing data. For median, we report the mean squared errors; for other quantiles ($\\tau \\in \\{0.9,0.95\\}$), we report the averaged proportion of the true data located in the predicted confidence interval. Standard Monte Carlo errors are recorded in parentheses. The number of nearest neighbors, $K$ is chosen as 5 for quantile $K$-NN fused lasso.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Non-parametric Quantile Regression via the K-NN Fused Lasso", "authors": ["Steven Siwei Ye", "Oscar Hernan Madrid Padilla"], "url": "https://arxiv.org/abs/2012.01758v4", "attribution": "\"Non-parametric Quantile Regression via the K-NN Fused Lasso\" by Steven Siwei Ye and Oscar Hernan Madrid Padilla, arXiv:2012.01758v4, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.00851v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MCS test for the future 1-step volatility forecasting model of the SSE}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllllllllll}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{2}{c}{$\\mathrm{QLIKE}_{\\alpha_1}$} & \\multicolumn{2}{c}{$\\mathrm{MSE}_{\\alpha_1}$} & \\multicolumn{2}{c}{$\\mathrm{QLIKE}_{\\alpha_2}$} & \\multicolumn{2}{c}{$\\mathrm{MSE}_{\\alpha_2}$} & \\multicolumn{2}{c}{$\\mathrm{QLIKE}_{\\alpha_3}$} & \\multicolumn{2}{c}{$\\mathrm{MSE}_{\\alpha_3}$} \\\\\n\\cmidrule(l){2-3} \\cmidrule(l){4-5}\\cmidrule(l){6-7}\\cmidrule(l){8-9}\\cmidrule(l){10-11}\\cmidrule(l){12-13}\n& $T_{max}$ & $T_{R}$ & $T_{max}$ & $T_{R}$ & $T_{max}$ & $T_{R}$ & $T_{max}$ & $T_{R}$ & $T_{max}$ & $T_{R}$ & $T_{max}$ & $T_{R}$ \\\\\n\\midrule\nGARCH & $0.000$ & $0.000$ & $0.019$ & $0.045$ & $0.034$ & $0.029$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ \\\\\nEGARCH & $0.000$ & $0.000$ & $0.099$ & $0.013$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ \\\\\nFGARCH & $0.000$ & $0.000$ & $0.964$ & $0.029$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ \\\\\nTGARCH & $0.000$ & $0.000$ & $0.013$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ \\\\\nHAR-RV & $0.000$ & $0.000$ & $0.235$ & $0.120$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ \\\\\nHAR-CJ & $0.000$ & $0.000$ & $0.183$ & $0.525$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ \\\\\nHAR-RS & $1.000$ & $0.000$ & $1.000$ & $0.465$ & $1.000$ & $0.000$ & $1.000$ & $0.324$ & $1.000$ & $0.000$ & $0.944$ & $0.319$ \\\\\nHAR-REX & $0.075$ & $0.525$ & $1.000$ & $0.574$ & $0.000$ & $0.000$ & $1.000$ & $0.423$ & $0.000$ & $0.000$ & $0.958$ & $0.412$ \\\\\nHAR-REQ & $0.391$ & $0.679$ & $1.000$ & $1.000$ & $1.000$ & $1.000$ & $0.243$ & $0.000$ & $0.000$ & $0.000$ & $1.000$ & $1.000$ \\\\\nHAR-PD-RV & $0.000$ & $0.000$ & $1.000$ & $0.485$ & $0.000$ & $0.000$ & $0.102$ & $0.099$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ \\\\\nHAR-PD-CJ & $0.712$ & $0.705$ & $1.000$ & $1.000$ & $0.000$ & $1.000$ & $0.802$ & $1.000$ & $1.000$ & $0.827$ & $0.499$ & $1.000$ \\\\\nHAR-PD-RS & $0.984$ & $0.005$ & $1.000$ & $1.000$ & $0.470$ & $0.248$ & $1.000$ & $1.000$ & $1.000$ & $1.000$ & $1.000$ & $1.000$ \\\\\nHAR-PD-REX & $1.000$ & $0.994$ & $1.000$ & $\\mathbf{1.000}$ & $0.661$ & $0.436$ & $\\mathbf{1.000}$ & $\\mathbf{1.000}$ & $0.000$ & $0.000$ & $\\mathbf{1.000}$ & $\\mathbf{1.000}$ \\\\\nHAR-PD-REQ & $1.000$ & $1.000$ & $1.000$ & $1.000$ & $1.000$ & $1.000$ & $1.000$ & $1.000$ & $0.000$ & $0.000$ & $1.000$ & $1.000$ \\\\\nLASSO-HAR-PD-CJ & $0.657$ & $0.679$ & $1.000$ & $1.000$ & $0.274$ & $1.000$ & $0.971$ & $0.440$ & $1.000$ & $1.000$ & $0.998$ & $0.977$ \\\\\nLASSO-HAR-PD-RS & $\\mathbf{1.000}$ & $\\mathbf{1.000}$ & $\\mathbf{1.000}$ & $1.000$ & $\\mathbf{1.000}$ & $\\mathbf{1.000}$ & $1.000$ & $1.000$ & $\\mathbf{1.000}$ & $\\mathbf{1.000}$ & $0.997$ & $1.000$ \\\\\nLASSO-HAR-PD-REX & $0.993$ & $0.705$ & $1.000$ & $1.000$ & $0.306$ & $0.243$ & $1.000$ & $1.000$ & $0.474$ & $1.000$ & $1.000$ & $1.000$ \\\\\nLASSO-HAR-PD-REQ & $0.037$ & $0.001$ & $1.000$ & $1.000$ & $0.000$ & $0.000$ & $1.000$ & $1.000$ & $0.000$ & $0.000$ & $1.000$ & $1.000$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Forecasting realized volatility in the stock market: a path-dependent perspective", "authors": ["Xiangdong Liu", "Sicheng Fu", "Shaopeng Hong"], "url": "https://arxiv.org/abs/2503.00851v1", "attribution": "\"Forecasting realized volatility in the stock market: a path-dependent perspective\" by Xiangdong Liu, Sicheng Fu, and Shaopeng Hong, arXiv:2503.00851v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2501.04018v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{List of the 5 employed CMIP6 models, the modelling groups providing them}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\nModel & Modelling centre (or group)\\\\\n\\hline\nGFDL-ESM4 & NOAA Geophysical Fluid Dynamics Laboratory\\\\\nIPSL-CM6A-LR & Institut Pierre-Simon Laplace\\\\\nMPI-ESM1-2-HR & Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology)\\\\\nMRI-ESM2-0 & Meteorological Research Institute NESM3\\\\\nUKESM1-0-LL & Met Office Hadley Centre\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "MERCURY: A fast and versatile multi-resolution based global emulator of compound climate hazards", "authors": ["Shruti Nath", "Julie Carreau", "Kai Kornhuber", "Peter Pfleiderer", "Carl-Friedrich Schleussner", "Philippe Naveau"], "url": "https://arxiv.org/abs/2501.04018v1", "attribution": "\"MERCURY: A fast and versatile multi-resolution based global emulator of compound climate hazards\" by Shruti Nath, Julie Carreau, Kai Kornhuber, Peter Pfleiderer, Carl-Friedrich Schleussner, and Philippe Naveau, arXiv:2501.04018v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.17848v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{In-Context Learning with LLMs for MRC}\n\\begin{tabular}{ccccccc}\n \\toprule\n \\multirow{2}{*}{Method} & \\multirow{2}{*}{Parameter}&\\multicolumn{5}{c}{Test} \\\\\n \\cline{3-7}\n & & EM & Recall & Precision & F1 & Contains \\\\\n \\midrule\n Mistral & 7B & 0.067 & 34.80 & 6.110 & 9.311 & 22.28 \\\\\n Qwen & 14B & 0.185 & 15.81 &6.86 & 8.533 & 7.862 \\\\\n Mixtral & 8x7B & 0.078 & 33.23 & 7.228 & 10.39 & 14.00 \\\\\n LlaMA2 & 70B & 0.0 & 16.80 & 2.233 & 16.80 & 7.11 \\\\\n PPLX & 70B & 0.014 & 69.52 & 13.06 & 20.17 & \\textbf{40.23} \\\\\n GPT3.5 & 175B & 0.164 & 32.16 & \\textbf{24.30} & \\textbf{32.16} & 32.161\\\\\n Falcon & 180B &\\textbf{0.613} & \\textbf{46.94} & 15.57 & 20.93 & 24.79 \\\\\n \n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "ArabicaQA: A Comprehensive Dataset for Arabic Question Answering", "authors": ["Abdelrahman Abdallah", "Mahmoud Kasem", "Mahmoud Abdalla", "Mohamed Mahmoud", "Mohamed Elkasaby", "Yasser Elbendary", "Adam Jatowt"], "url": "https://arxiv.org/abs/2403.17848v1", "attribution": "\"ArabicaQA: A Comprehensive Dataset for Arabic Question Answering\" by Abdelrahman Abdallah, Mahmoud Kasem, Mahmoud Abdalla, Mohamed Mahmoud, Mohamed Elkasaby, Yasser Elbendary, and Adam Jatowt, arXiv:2403.17848v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.00821v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline \n$L(\\epsilon)$ & \\\\\n&$\\left\\lceil \\frac{1}{2}+\\frac{\\log(\\widetilde{c}_{\\infty}^{1/\\alpha}\\mathbf{h})}{\\log(M)}+\\sqrt{\\left(\\frac{1}{2}+\\frac{\\log(\\widetilde{c}_{\\infty}^{1/\\alpha}\\mathbf{h})}{\\log(M)}\\right)^{2}+ 2\\frac{\\log(A/\\epsilon)}{\\alpha\\log(M)}}\\right\\rceil$\\\\ \n& \\\\\\hline\n$h(\\epsilon)$ & $\\mathbf{h}$ \\\\ \\hline\n$\\mu(\\epsilon)$ &\\\\\n&$\\mu_{1}=q^{*}(1+\\lambda h^{\\frac{\\beta}{2}})$\\\\\n&$\\mu_{l}=q^{*}\\lambda h^{\\frac{\\beta}{2}} \\underline{C}_{M,\\beta}|\\widetilde{W}_l(L,M)|M^{-\\frac{(1-\\beta)}{2}(l-1)}, l=2,\\dots ,L$,~$\\sum\\limits_{1\\leq l \\leq L}\\mu_{l}=1$\\\\ \n& \\\\ \\hline\n$N(\\epsilon)$ & \\\\\n&$\\left(1+\\frac{1}{2 \\alpha L}\\right)\\frac{\\text{Var}\\left(P(X^{0}_{T})\\right)\\left(1+ \\lambda h^{\\beta/2}+\\lambda h^{\\beta/2}\\overline{C}_{M,\\beta}\\sum\\limits_{l=2}^{L}|\\widetilde{W}_l(L,M)|M^{\\frac{1-\\beta}{2}(j-1)} \\right)}{\\epsilon^{2}q^{*}}$ \\\\ \n&\\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Optimal parameters for the ML2R estimator }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Multilevel Richardson-Romberg and Importance Sampling in Derivative Pricing", "authors": ["Devang Sinha", "Siddhartha P. Chakrabarty"], "url": "https://arxiv.org/abs/2209.00821v1", "attribution": "\"Multilevel Richardson-Romberg and Importance Sampling in Derivative Pricing\" by Devang Sinha and Siddhartha P. Chakrabarty, arXiv:2209.00821v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.17246v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{estimated bases of the central mean space. * denotes estimates after omitting influential observations.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\hline\n & FOLS & FOLS* & sa-OLS & sa-OLS* \\\\ \\hline\n\\% Non-Hispanic Blacks & 0.0612 & 0.1860 & 0.1728 & 0.3102 \\\\\n\\% Hispanics & -0.2771 & -0.0339 & -0.3974 & -0.3431 \\\\\n\\% Adults 65+ & -0.7254 & -0.5820 & -0.8304 & -0.8659 \\\\\n\\% No high school diploma & 0.3848 & -0.0789 & 0.3949 & -0.1724 \\\\\n\\% Living below poverty line & 0.2294 & -0.5378 & 0.0284 & -0.1615 \\\\\n\\% Unemployed & -0.0225 & 0.3190 & 0.0455 & 0.2875 \\\\\n\\% Renter-occupied homes & -0.8263 & 0.6574 & -0.6710 & 0.0731 \\\\\n\\% On public assistance & 0.0359 & -0.1459 & 0.0969 & 0.1479 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Detecting influential observations in single-index Fréchet regression", "authors": ["Abdul-Nasah Soale"], "url": "https://arxiv.org/abs/2311.17246v3", "attribution": "\"Detecting influential observations in single-index Fréchet regression\" by Abdul-Nasah Soale, arXiv:2311.17246v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.09287v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{clll}\n\\toprule\n\\multicolumn{4}{c}{Total People Affected Time Series} \\\\ \\midrule\nMean & Std. Dev. & Skewness & Kurtosis \\\\\n223,664.919 & 561,471.353 & 4.102 & 18.495 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Total number of people affected includes deaths, injuries, those requiring assistance, and individuals displaced from their homes due to natural disasters over the period spanning from 1900 to 2023. Source }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modeling Volatility of Disaster-Affected Populations: A Non-Homogeneous Geometric-Skew Brownian Motion Approach", "authors": ["Giacomo Ascione", "Michele Bufalo", "Giuseppe Orlando"], "url": "https://arxiv.org/abs/2309.09287v1", "attribution": "\"Modeling Volatility of Disaster-Affected Populations: A Non-Homogeneous Geometric-Skew Brownian Motion Approach\" by Giacomo Ascione, Michele Bufalo, and Giuseppe Orlando, arXiv:2309.09287v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.12266v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Averaged AD test p-values over rolling windows for selected assets from India (IN), the United States (USA), and Hong Kong (HK). Ticker symbols are shown alongside their corresponding p-values.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcl}\n\\hline\n\\textbf{IN} & \\textbf{USA} & \\textbf{HK} \\\\\n\\hline\nREL - 0.98 & MSFT - 0.99 & 0005.HK - 0.98 \\\\\nTCS - 0.93 & GOOGL - 0.99 & 0700.HK - 0.97 \\\\\nINFY - 0.97 & NVDA - 0.99 & 0001.HK - 0.99 \\\\\nHDFC - 0.999 & AMZN - 0.95 & 2318.HK - 0.71 \\\\\nICICI - 0.95 & META - 0.73 & 1299.HK - 0.98 \\\\\nHINDUNILVR - 0.88 & TSLA - 0.92 & 0388.HK - 0.91 \\\\\nITC - 0.97 & AAPL - 0.998 & 1398.HK - 0.90 \\\\\nKOTAK - 0.99 & HD - 0.96 & 2388.HK - 0.93 \\\\\nLT - 0.99 & ADBE - 0.79 & 0002.HK - 0.97 \\\\\nSBIN - 0.91 & NFLX - 0.95 & 2888.HK - 0.99 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Semiparametric Dynamic Copula Models for Portfolio Optimization", "authors": ["Savita Pareek", "Sujit K. Ghosh"], "url": "https://arxiv.org/abs/2504.12266v1", "attribution": "\"Semiparametric Dynamic Copula Models for Portfolio Optimization\" by Savita Pareek and Sujit K. Ghosh, arXiv:2504.12266v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.13116v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Contingency Table for McNemar's Test Between Two Methods}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n & \\textbf{ChatGPT4 Correct} & \\textbf{ChatGPT4 Incorrect} \\\\\n\\midrule\n\\textbf{ChatGPT3.5 Correct} & 31 & 4 \\\\\n\\textbf{ChatGPT3.5 Incorrect} & 22 & 8 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Equity in the Use of ChatGPT for the Classroom: A Comparison of the Accuracy and Precision of ChatGPT 3.5 vs. ChatGPT4 with Respect to Statistics and Data Science Exams", "authors": ["Monnie McGee", "Bivin Sadler"], "url": "https://arxiv.org/abs/2412.13116v1", "attribution": "\"Equity in the Use of ChatGPT for the Classroom: A Comparison of the Accuracy and Precision of ChatGPT 3.5 vs. ChatGPT4 with Respect to Statistics and Data Science Exams\" by Monnie McGee and Bivin Sadler, arXiv:2412.13116v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18051v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}\n \\hline\nUser &1 & 2 &3 & 4 &5 & 6 &7 & 8 &9 & 10 \\\\\n\\hline\nCPU&3&2.9&3.1&2.8&3.2&2.7&3.3&2.6&3.4&2.8\\\\ \n\\hline\nGPU& 30&29&31&28&32&27&33&26&34&28\\\\\n\\hline\nCPUMem& 6&5.9&6.1&5.8&6.2&5.7&6.3&5.6&6.4&5.8\\\\ \n\\hline\nGPUMem & 60&59&61&58&62&57&63&56&64&58\\\\ \n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Baseline and Nominal Resource}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications", "authors": ["Jiaqi Lei", "Akhil Singla", "Sanjay Mehrotra"], "url": "https://arxiv.org/abs/2501.18051v2", "attribution": "\"A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications\" by Jiaqi Lei, Akhil Singla, and Sanjay Mehrotra, arXiv:2501.18051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10066v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n Licensing & \\multirow{2}{*}{\\#} & Percentage \\\\\n properties & & of all entries\\\\\n \\midrule\n Missing & 71 & 37\\% \\\\\n Open license & 56 & 29\\%\\\\\n Copyright & 30 & 16\\%\\\\\n Non-commercial use & 18 & 9\\%\\\\\n Public domain & 18 & 9\\%\\\\\n Research use & 10 & 5\\%\\\\\n Multiple licenses & 7 & 4\\%\\\\\n Do not distribute & 2 & 1\\%\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Distribution of licensing properties.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources", "authors": ["Angelina McMillan-Major", "Zaid Alyafeai", "Stella Biderman", "Kimbo Chen", "Francesco De Toni", "Gérard Dupont", "Hady Elsahar", "Chris Emezue", "Alham Fikri Aji", "Suzana Ilić", "Nurulaqilla Khamis", "Colin Leong", "Maraim Masoud", "Aitor Soroa", "Pedro Ortiz Suarez", "Zeerak Talat", "Daniel van Strien", "Yacine Jernite"], "url": "https://arxiv.org/abs/2201.10066v1", "attribution": "\"Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources\" by Angelina McMillan-Major, Zaid Alyafeai, Stella Biderman, Kimbo Chen, Francesco De Toni, Gérard Dupont, Hady Elsahar, Chris Emezue, Alham Fikri Aji, Suzana Ilić, Nurulaqilla Khamis, Colin Leong, Maraim Masoud, Aitor Soroa, Pedro Ortiz Suarez, Zeerak Talat, Daniel van Strien, and Yacine Jernite, arXiv:2201.10066v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10707v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c}\n \\hline\n \\bf Method & $F_{0.5}$ \\\\\n \\hline\n \\bf Rule(Unimorph)$^\\star$ & 60.87 \\\\\n \\bf Rule(Aspell)$^\\star$ & 63.49 \\\\\n \\bf Rule(Combine)$^\\star$ & 62.55 \\\\\n \\hline\n \\bf WikiEdits$^\\star$ & 58.00 \\\\\n \\bf Rule + WikiEdits$^\\star$ & \\bf 66.66 \\\\\n \\hline\n \\bf Back-translate & 61.37 \\\\\n \\bf Round-trip translation & 62.91 \\\\\n \\hline\n \\bf Ours & \\bf 69.17 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{$F_{0.5}$ scores of different data construction approaches on the German dataset. For the approaches with $\\star$, their results are from .}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model", "authors": ["Xin Sun", "Tao Ge", "Shuming Ma", "Jingjing Li", "Furu Wei", "Houfeng Wang"], "url": "https://arxiv.org/abs/2201.10707v1", "attribution": "\"A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model\" by Xin Sun, Tao Ge, Shuming Ma, Jingjing Li, Furu Wei, and Houfeng Wang, arXiv:2201.10707v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.19721v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of estimated and actual MFDs}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cclll}\n\\toprule\nMethod & Network Types & \\% LD & RMSE (veh/h) & $R^2$ \\\\ \\midrule\n\\multirow{12}{*}{Statistical} & \\multirow{4}{*}{Non-hierarchy} & 30 & 39.9 & 0.98 \\\\\n & & 20 & 133.5 &0.79 \\\\\n & & 10 & 137.6 & 0.78 \\\\\n & & 5 & 175.5 & 0.64 \\\\ \\cmidrule(l){2-5} \n & \\multirow{4}{*}{Three-hierarchy} & 30 & \\textbf{35.9} & 0.98 \\\\\n & & 20 & \\textbf{36.5} & 0.98 \\\\\n & & 10 & \\textbf{45.3} & 0.97 \\\\\n & & 5 & \\textbf{48.9} & 0.97 \\\\ \\cmidrule(l){2-5} \n & \\multirow{4}{*}{Two-hierarchy} & 30 & 37.1 & 0.98 \\\\\n & & 20 & 37.3 & 0.98 \\\\\n & & 10 & 45.7 & 0.97 \\\\\n & & 5 & 51.8 & 0.96\\\\ \\midrule\n\\multirow{3}{*}{Variogram} & \\multirow{3}{*}{Non-hierarchy} & 30 & 51.5 & 0.97 \\\\\n & & 20 & 54.6 & 0.96 \\\\\n & & 10 & 55.2 & 0.96 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Scaling Methods To Estimate Macroscopic Fundamental Diagrams in Urban Networks with Sparse Sensor Coverage", "authors": ["Nandan Maiti", "Manon Seppecher", "Ludovic Leclercq"], "url": "https://arxiv.org/abs/2411.19721v2", "attribution": "\"Scaling Methods To Estimate Macroscopic Fundamental Diagrams in Urban Networks with Sparse Sensor Coverage\" by Nandan Maiti, Manon Seppecher, and Ludovic Leclercq, arXiv:2411.19721v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.19379v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Accuracy table of the 6th order scheme, isotropic media}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n\\hline\n$N$& $N=10$ & \\multicolumn{2}{c|}{$N=20$} & \\multicolumn{2}{c|}{$N=40$} & \\multicolumn{2}{c|}{$N=80$} \\\\ \n \\hline\n $\\omega^2$ & $|\\omega^2-\\omega_h^2|$ & $|\\omega^2-\\omega_h^2|$ & ord & $|\\omega^2-\\omega_h^2|$ & ord & $|\\omega^2-\\omega_h^2|$ & ord\\\\ \n \\hline\n0.25 & 4.00e-15 & 1.11e-15 & \\ & 2.22e-16 & \\ & 4.44e-16 & \\ \\\\ \n\\hline\n0.25 & 8.44e-15 & 1.57e-14 & \\ & 1.11e-16 & \\ & 1.67e-16 & \\ \\\\ \n\\hline\n0.25 & 1.00e-04 & 1.65e-06 & 5.93 & 2.61e-08 & 5.98 & 4.09e-10 & 6.00\\\\ \n\\hline\n0.25 & 1.00e-04 & 1.65e-06 & 5.93 & 2.61e-08 & 5.98 & 4.09e-10 & 6.00\\\\ \n\\hline\n1.25 & 8.18e-05 & 1.33e-06 & 5.95 & 2.09e-08 & 5.99 & 3.27e-10 & 6.00\\\\ \n\\hline\n1.25 & 8.18e-05 & 1.33e-06 & 5.95 & 2.09e-08 & 5.99 & 3.27e-10 & 6.00\\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Kernel compensation method for Maxwell eigenproblem with mimetic finite difference discretization", "authors": ["Chenhao Jin", "Yinhua Xia", "Yan Xu"], "url": "https://arxiv.org/abs/2503.19379v1", "attribution": "\"Kernel compensation method for Maxwell eigenproblem with mimetic finite difference discretization\" by Chenhao Jin, Yinhua Xia, and Yan Xu, arXiv:2503.19379v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.02630v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fail rate of different models on instruction-following tasks (standard errors in parentheses).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n & Budget & Color & Brand \\\\\n & Constrained & Based & Based \\\\\n \\midrule\n \\midrule\n Claude Sonnet 3.5 & $4.0\\%$ ($0.5\\%$) & $13.5\\%$ ($1.4\\%$) & \\textcolor{gray}{$0.0\\%$}\\\\\n Claude Sonnet 3.7 & \\textcolor{gray}{$0.0\\%$} & $4.4\\%$ ($0.5\\%$) & \\textcolor{gray}{$0.0\\%$} \\\\\n Claude Sonnet 4.0 & \\textcolor{gray}{$0.0\\%$} & $3.8\\%$ ($0.5\\%$) & \\textcolor{gray}{$0.0\\%$} \\\\\n GPT\\textendash4o & \\textcolor{gray}{$0.0\\%$} & \\textcolor{gray}{$0.0\\%$} & \\textcolor{gray}{$0.0\\%$}\\\\\n GPT\\textendash4.1 & \\textcolor{gray}{$0.0\\%$} & \\textcolor{gray}{$0.0\\%$} & \\textcolor{gray}{$0.0\\%$} \\\\\n Gemini 2.0 Flash & \\textcolor{gray}{$0.0\\%$} & \\textcolor{gray}{$0.0\\%$} & \\textcolor{gray}{$0.0\\%$} \\\\\n Gemini 2.5 Flash & \\textcolor{gray}{$0.0\\%$} & \\textcolor{gray}{$0.0\\%$} & \\textcolor{gray}{$0.0\\%$} \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce", "authors": ["Amine Allouah", "Omar Besbes", "Josué D Figueroa", "Yash Kanoria", "Akshit Kumar"], "url": "https://arxiv.org/abs/2508.02630v1", "attribution": "\"What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce\" by Amine Allouah, Omar Besbes, Josué D Figueroa, Yash Kanoria, and Akshit Kumar, arXiv:2508.02630v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00932v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of our results with two weakly-supervised methods (ASMO and WSS using image-tag supervision) and a fully-supervised method SOSD additionally using subitizing information in terms of S-measure (larger is better).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\t\\hline\n\t\tMethods & ASMO & WSS & SOSD & Ours \\\\\\hline\n\t\tECSSD & 0.827 & 0.829 & 0.837 & \\textbf{0.860} \\\\\\hline\n\t\tDUT & 0.736 & 0.803 & 0.816 & \\textbf{0.832} \\\\\\hline\n\t\tPascal-S & 0.702 & 0.815 & 0.742 & \\textbf{0.854} \\\\\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Weakly-Supervised Saliency Detection via Salient Object Subitizing", "authors": ["Xiaoyang Zheng", "Xin Tan", "Jie Zhou", "Lizhuang Ma", "Rynson W. H. Lau"], "url": "https://arxiv.org/abs/2101.00932v1", "attribution": "\"Weakly-Supervised Saliency Detection via Salient Object Subitizing\" by Xiaoyang Zheng, Xin Tan, Jie Zhou, Lizhuang Ma, and Rynson W. H. Lau, arXiv:2101.00932v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.08222v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Bootstrap Summary Statistics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n & Mean & Median & SD & Max & Min & Effective Replications \\\\ \n \\midrule\n$\\sigma^I$ & 0.484 & 0.482 & 0.268 & 0.985 & 0.011 & 287 \\\\ \n $\\sigma^M$ & 0.685 & 0.732 & 0.232 & 0.984 & 0.017 & 457 \\\\ \n $\\sigma^O$ & 0.727 & 0.725 & 0.066 & 0.983 & 0.540 & 997 \\\\ \n$\\alpha_K$ & 0.380 & 0.284 & 0.307 & 0.896 & 0.014 & 781 \\\\ \n $\\alpha_L$ & 0.053 & 0.062 & 0.026 & 0.085 & 0.009 & 781 \\\\ \n $\\alpha_M$ & 0.567 & 0.655 & 0.281 & 0.902 & 0.095 & 781 \\\\ \n $\\tau$ & 1.708 & 0.434 & 2.402 & 9.455 & 0.015 & 781 \\\\ \n $\\sigma_H$ & 0.689 & 0.697 & 0.121 & 1.013 & 0.326 & 1000 \\\\ \n $\\rho_H$ & 0.884 & 0.897 & 0.053 & 0.990 & 0.680 & 1000 \\\\ \n $\\beta_{H,1}$ & -0.029 & -0.029 & 0.015 & 0.017 & -0.070 & 1000 \\\\ \n $\\beta_{H,2}$ & -0.052 & -0.044 & 0.095 & 0.194 & -0.398 & 1000 \\\\ \n $\\rho_L$ & 0.908 & 0.909 & 0.015 & 0.965 & 0.827 & 1000 \\\\ \n $\\beta_{L,1}$ & 0.013 & 0.013 & 0.011 & 0.074 & -0.036 & 1000 \\\\ \n $\\beta_{L,2}$ & -0.065 & -0.065 & 0.063 & 0.212 & -0.303 & 1000 \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Do Temporary Workers Face Higher Wage Markdowns? Evidence from India's Automotive Sector", "authors": ["Davide Luparello"], "url": "https://arxiv.org/abs/2507.08222v1", "attribution": "\"Do Temporary Workers Face Higher Wage Markdowns? Evidence from India's Automotive Sector\" by Davide Luparello, arXiv:2507.08222v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.06982v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Performance comparison on LIDC dataset. Number of parameters, computation complexity for forward propagation, computation time per training epoch (t) and area under the receiver operating characteristics (AUROC) curve (higher is better) averaged over 5-fold cross validation for all methods are reported. All three tensor network models use $\\beta=5$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccrc}\n \\toprule\nModels & \\# Param. ($M$) & Complexity &time (s) & AUROC\\\\\n \\midrule\n {MLTN (ours)} &0.42 &$\n\t \\left ( \\frac{\\log N}{\\log k^{2\\cdot L}} + {L-1} \\right ) \\cdot k^2 \\cdot d \\cdot \\beta^2$&2.8& $0.88 \\pm 0.01$ \\\\\n \n\t {LoTeNet } &0.49&$ \\left ( \\frac{\\log N}{\\log k^{2\\cdot L}} + \\sum_{l=1}^{L-1}\\frac{N}{k^{2 \\cdot l}} \\right ) \\cdot k^2 \\cdot d \\cdot \\beta^2 $ & 18.5 & $0.87 \\pm 0.01$ \\\\\n {Tenet-X} & 0.82 &$\\mathcal{O}\\left( N \\cdot d \\cdot \\beta^2\\right)$ &30.2& $0.82 \\pm 0.01$\\\\\n {MLP (L=4)} &0.52 & $\\mathcal{O}\\left(N \\cdot L\\right)$ &3.2& $0.86 \\pm 0.01$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Multi-layered tensor networks for image classification", "authors": ["Raghavendra Selvan", "Silas Ørting", "Erik B Dam"], "url": "https://arxiv.org/abs/2011.06982v2", "attribution": "\"Multi-layered tensor networks for image classification\" by Raghavendra Selvan, Silas Ørting, and Erik B Dam, arXiv:2011.06982v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10066v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\multirow{2.5}{*}{Location} & \\multicolumn{4}{c}{Languages}\\\\\n \\cmidrule(lr){2-5}\n & En. & Fr. & Sp. & Port. \\\\\n \\midrule\n Africa* & 6 & 4 & 0 & 1 \\\\\n Americas$^\\dag$ & 0 & 1 & 2 & 1 \\\\\n Asia & 10 & 0 & 0 & 1 \\\\\n Europe & 13 & 13 & 11 & 5 \\\\\n Latin America and the Carib. & 3 & 0 & 15 & 2 \\\\\n North America & 13 & 1 & 2 & 1 \\\\\n Oceania & 5 & 0 & 0 & 0 \\\\\n World-wide & 16 & 11 & 10 & 11 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Distribution of entries in English, French, Spanish and Portuguese across continents.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources", "authors": ["Angelina McMillan-Major", "Zaid Alyafeai", "Stella Biderman", "Kimbo Chen", "Francesco De Toni", "Gérard Dupont", "Hady Elsahar", "Chris Emezue", "Alham Fikri Aji", "Suzana Ilić", "Nurulaqilla Khamis", "Colin Leong", "Maraim Masoud", "Aitor Soroa", "Pedro Ortiz Suarez", "Zeerak Talat", "Daniel van Strien", "Yacine Jernite"], "url": "https://arxiv.org/abs/2201.10066v1", "attribution": "\"Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources\" by Angelina McMillan-Major, Zaid Alyafeai, Stella Biderman, Kimbo Chen, Francesco De Toni, Gérard Dupont, Hady Elsahar, Chris Emezue, Alham Fikri Aji, Suzana Ilić, Nurulaqilla Khamis, Colin Leong, Maraim Masoud, Aitor Soroa, Pedro Ortiz Suarez, Zeerak Talat, Daniel van Strien, and Yacine Jernite, arXiv:2201.10066v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.19923v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\toprule\n\\textbf{Assumptions} & \\textbf{Ours} & \\textbf{Proxy Domain Adaptation} \\\\ \\midrule\nTask & Out-of-distribution & Domain Adaptation \\\\\nObserved variables & $X$, $Y$ and ($U$ or $S$) & $X$, $Y$, $U$ and $S$ \\\\\nNumber of labeled sources & 1 & multiple ($\\geq n_z$) \\\\\nConditional independence & $S \\perp X,Y \\mid Z$ (or $U \\perp X,Y \\mid Z$) & $U \\perp X,Y,S \\mid Z$ and $S \\perp X \\mid Z$ \\\\\nCausality between $X$ and $Y$ & $X \\leftrightarrow Y$ & $X \\rightarrow Y$\\\\\nFull rank & $P(S \\mid Z)$ (or $P(U \\mid Z)$) must be full rank & $P(S \\mid Z)$ must be full rank and $P(Z \\mid U,X)$ must be full rank for X \\\\\nDiscrete unobserved confounder & Yes & Yes \\\\\nInformative variable & No & Yes \\\\\nExistence of high-likelihood sample & Yes & No \\\\\nProxy available at test time & No & Yes \\\\\n$ns \\geq n_z$ & Yes & Yes \\\\\n$n_z$ known & No & No \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of Assumptions between Ours and }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Scalable Out-of-distribution Robustness in the Presence of Unobserved Confounders", "authors": ["Parjanya Prashant", "Seyedeh Baharan Khatami", "Bruno Ribeiro", "Babak Salimi"], "url": "https://arxiv.org/abs/2411.19923v2", "attribution": "\"Scalable Out-of-distribution Robustness in the Presence of Unobserved Confounders\" by Parjanya Prashant, Seyedeh Baharan Khatami, Bruno Ribeiro, and Babak Salimi, arXiv:2411.19923v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09827v2_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of GMRES-IR and RGMRES-IR refinement steps with the number of GMRES iterations for each refinement step for random dense matrices having geometrically distributed singular values (mode 3) with various condition numbers, using precisions ($u_f,u,u_r$) = (half, double, quad) and ($m,k$) = (100,30). For $10^{13}$, GMRES-IR did 3954 (102, 105, 98, 98, 88, 81, 85, 84, 81, 84, 83, 84, 89, 90, 87, 88, 89, 91, 83, 83, 87, 88, 91, 84, 89, 88, 83, 82, 89, 92, 88, 97, 83, 84, 90, 83, 84, 83, 90, 83, 94, 83, 82, 85, 99) iterations.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|cc|}\n\t\t\\hline\n\t\t$\\kappa_\\infty(A)$ & $\\kappa_2(A)$ & GMRES-IR (100) & RGMRES-IR (100,30) \\\\ \\hline\n\t\t$9 \\cdot 10^4$ & $10^4$ & 33 (16,17) & 33 (16,17) \\\\\n\t\t$8 \\cdot 10^5$ & $10^5$ & 85 (41,44) & 71 (41,15,15) \\\\\n\t\t$7 \\cdot 10^6$ & $10^6$ & 134 (66,68) & 85 (66,19) \\\\\n\t\t$7 \\cdot 10^7$ & $10^7$ & 167 (83,84) & 113 (83,30) \\\\\n\t\t$7 \\cdot 10^8$ & $10^8$ & 193 (96,97) & 138 (96,42) \\\\\n\t\t$6 \\cdot 10^9$ & $10^9$ & 200 (100,100) & 151 (100,51) \\\\\n\t\t$6 \\cdot 10^{10}$ & $10^{10}$ & 200 (100,100) & 158 (100,58) \\\\\n\t\t$6 \\cdot 10^{11}$ & $10^{11}$ & 200 (100,100) & 165 (100,65) \\\\\n\t\t$6 \\cdot 10^{12}$ & $10^{12}$ & 200 (100,100) & 170 (100,70) \\\\\n\t\t$5 \\cdot 10^{13}$ & $10^{13}$ & 3954* & 241 (171,70) \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Mixed Precision GMRES-based Iterative Refinement with Recycling", "authors": ["Eda Oktay", "Erin Carson"], "url": "https://arxiv.org/abs/2201.09827v2", "attribution": "\"Mixed Precision GMRES-based Iterative Refinement with Recycling\" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06551v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccc} \\hline\n & \\multicolumn{3}{c}{G--N} & \\multicolumn{3}{c}{LP} & \\multicolumn{3}{c}{MCT}\\\\ %first row\n Dataset & \\multicolumn{2}{c}{Metric} &\\#DC &\\multicolumn{2}{c}{Metric} &\\#DC &\\multicolumn{2}{c}{Metric} &\\#DC \\\\ %second row (inner)\n & Q & NMI & & Q & NMI & & Q & NMI & \\\\%third row (most inner) \n \\hline\n G-pTie &.908& .794 & 308 &.77& .602 & 1319 & .915 & .791 & 263 \\\\\n ego-Twitter &.334& .197 & 1431 &.215& .131 & 2131 &.307 & .230 & 1131 \\\\\n ego-Facebook &.522 & .590 & 1037 &.421 & .304 & 1780 &.503 & .372 & 1845 \\\\\n P-pTie & .473 & .311 & 1107 & .360 & .267 & 2071 &.601 & .472 & 985 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A multilevel clustering technique for community detection", "authors": ["Isa Inuwa-Dutse", "Mark Liptrott", "Yannis Korkontzelos"], "url": "https://arxiv.org/abs/2101.06551v1", "attribution": "\"A multilevel clustering technique for community detection\" by Isa Inuwa-Dutse, Mark Liptrott, and Yannis Korkontzelos, arXiv:2101.06551v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2301.08136v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n \\textbf{Pole} & \\textbf{E.T.A.} & $\\overline{\\mathbf{t}_i}$ & $\\underline{\\mathbf{t}_i}$ & $\\Delta{\\mathbf{t}_i}$ \\tabularnewline\n \\hline\n \\textbf{D01T03} & $1.59156$ & $5.798013421$ & $1.42593988$ & $0.037881366$ \\tabularnewline\n \\hline\n \\textbf{D05T06} & $2.60198$ & $11.26453202$ & $1.911225784$ & $0.073851342$ \\tabularnewline\n \\hline\n \\textbf{D07T08} & $1.95377$ & $8.258599566$ & $1.644376487$ & $0.046777$ \\tabularnewline\n \\hline\n \\textbf{D09} & $2.83355$ & $10.953322577$ & $1.88359841$ & $0.104738752$ \\tabularnewline\n \\hline\n \\textbf{D10T12} & $1.20384$ & $2.703975775$ & $1.151269114$ & $0.033857578$ \\tabularnewline\n \\hline\n \\textbf{D13T15} & $1.18072$ & $2.66411798$ & $1.147730769$ & $0.02175515$ \\tabularnewline\n \\hline\n \\textbf{D16} & $1.74648$ & $7.84390081$ & $1.60756193$ & $0.022275581$ \\tabularnewline\n \\hline\n \\textbf{D17T18} & $2.01144$ & $8.951063463$ & $1.705849426$ & $0.042178267$ \\tabularnewline\n \\hline\n \\textbf{D19} & $1.75013$ & $6.820058155$ & $1.5166711$ & $0.044020717$ \\tabularnewline\n \\hline\n \\textbf{D20T21} & $1.18653$ & $2.571492976$ & $1.139508057$ & $0.032836898$ \\tabularnewline\n \\hline\n \\textbf{D22} & $1.55758$ & $5.52228095$ & $1.401461946$ & $0.0378852$ \\tabularnewline\n \\hline\n \\textbf{D23} & $2.04427$ & $10.824915992$ & $1.872199216$ & $0.019219952$ \\tabularnewline\n \\hline\n \\textbf{D24} & $1.98661$ & $9.384341624$ & $1.744313355$ & $0.031714103$ \\tabularnewline\n \\hline\n \\textbf{D25} & $1.20291$ & $2.745771947$ & $1.154979536$ & $0.03012993$ \\tabularnewline\n \\hline\n \\textbf{D26} & $1.23853$ & $3.136547068$ & $1.189670291$ & $0.025096457$ \\tabularnewline\n \\hline\n \\textbf{D27} & $1.06766$ & $1.638851502$ & $1.056713541$ & $0.018803891$ \\tabularnewline\n \\hline\n \\textbf{D28} & $1.08302$ & $1.646084891$ & $1.05735568$ & $0.043592741$ \\tabularnewline\n \\hline\n \\textbf{D29} & $1.04159$ & $1.353595176$ & $1.031390135$ & $0.031656443$ \\tabularnewline\n \\hline\n \\textbf{D30} & $1.05987$ & $1.56508697$ & $1.050165153$ & $0.018847225$ \\tabularnewline\n \\hline\n \\textbf{D31T33} & $1.17029$ & $2.436422272$ & $1.127517261$ & $0.03267826$ \\tabularnewline\n \\hline\n \\textbf{D35T39} & $1.7901$ & $7.09122291$ & $1.540743539$ & $0.044925212$ \\tabularnewline\n \\hline\n \\textbf{D41T43} & $1.01321$ & $1.107244285$ & $1.009520527$ & $0.037754102$ \\tabularnewline\n \\hline\n \\textbf{D45T47} & $1.58799$ & $5.739340748$ & $1.42073126$ & $0.038729767$ \\tabularnewline\n \\hline\n \\textbf{D49T53} & $1.38415$ & $4.00360591$ & $1.26664276$ & $0.042933439$ \\tabularnewline\n \\hline\n \\textbf{D55T56} & $1.38708$ & $4.263278233$ & $1.289694967$ & $0.032750061$ \\tabularnewline\n \\hline\n \\textbf{D58T60} & $1.15296$ & $2.359375424$ & $1.120677488$ & $0.02606165$ \\tabularnewline\n \\hline\n \\textbf{D61} & $1.07444$ & $1.660539484$ & $1.058638875$ & $0.02625205$ \\tabularnewline\n \\hline\n \\textbf{D62T63} & $1.09222$ & $1.78142887$ & $1.069370735$ & $0.032089044$ \\tabularnewline\n \\hline\n \\textbf{D64T66} & $1.94122$ & $7.789725066$ & $1.60275252$ & $0.054706478$ \\tabularnewline\n \\hline\n \\textbf{D68} & $1.17236$ & $2.43074642$ & $1.127013392$ & $0.034782127$ \\tabularnewline\n \\hline\n \\textbf{D69T82} & $1.52432$ & $5.228638776$ & $1.375394093$ & $0.038649481$ \\tabularnewline\n \\hline\n \\textbf{D84} & $1.03745$ & $1.31337571$ & $1.027819683$ & $0.03372479$ \\tabularnewline\n \\hline\n \\textbf{D85} & $1.03546$ & $1.315536489$ & $1.028011504$ & $0.025905559$ \\tabularnewline\n \\hline\n \\textbf{86T88} & $1.18021$ & $2.531030273$ & $1.135916012$ & $0.031749362$ \\tabularnewline\n \\hline\n \\textbf{D90T96} & $1.27396$ & $3.286460329$ & $1.202978723$ & $0.034068588$ \\tabularnewline\n \\hline\n \\textbf{D97T98} & $1$ & $1$ & $1$ & $\\_$ \\tabularnewline\n \\hline\n \n \\end{tabular}\n\\caption{ }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Input-Output Analysis: New Results From Markov Chain Theory", "authors": ["Nizar Riane", "Claire David"], "url": "https://arxiv.org/abs/2301.08136v2", "attribution": "\"Input-Output Analysis: New Results From Markov Chain Theory\" by Nizar Riane and Claire David, arXiv:2301.08136v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.19929v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\nIteration \\( k \\) & Step Size \\( \\alpha_k \\) & Noise \\( \\xi_k \\) & Update \\( x_k \\) & Function Value \\( f(x_k) \\) \\\\ \\hline\n0 & - & - & 10 & 50 \\\\ \\hline\n1 & 1 & 0.5 & -0.5 & 0.125 \\\\ \\hline\n2 & 0.574 & -0.2 & 0.4 & 0.08 \\\\ \\hline\n3 & 0.494 & 0.1 & 0.154 & 0.012 \\\\ \\hline\n4 & 0.445 & -0.3 & 0.13 & 0.00845 \\\\ \\hline\n5 & 0.398 & 0.2 & -0.053 & 0.0014 \\\\ \\hline\n6 & 0.364 & -0.1 & -0.069 & 0.00238 \\\\ \\hline\n7 & 0.334 & 0.15 & -0.036 & 0.000648 \\\\ \\hline\n8 & 0.308 & -0.05 & -0.049 & 0.0012 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Stochastic Steepest Descent Iterations}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Generalized Steepest Descent Methods on Riemannian Manifolds and Hilbert Spaces: Convergence Analysis and Stochastic Extensions", "authors": ["Rashid A.", "Amal A Samad"], "url": "https://arxiv.org/abs/2502.19929v1", "attribution": "\"Generalized Steepest Descent Methods on Riemannian Manifolds and Hilbert Spaces: Convergence Analysis and Stochastic Extensions\" by Rashid A. and Amal A Samad, arXiv:2502.19929v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13148v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllllllll} \\hline\n $\\pi_F$ & $\\pi_G$ & $\\alpha$ & True $\\rho_S$ & & $\\hat \\rho_G$ & & & $\\hat \\rho_M$ & & & $\\hat \\rho_A$ & \\\\\\cline{6-7} \\cline{9-10} \\cline{12-13}\n & & & & & Mean & MSE & & Mean & MSE & & Mean & MSE \\\\\\hline\n 0.2 & 0.2 & 0.2 & 0.10 & & 0.20 & 1.97 & & 0.10 & 0.24 & & 0.10 & \\textbf{0.23} \\\\\n & & 0.5 & 0.24 & & 0.49 & 7.14 & & 0.24 & 0.33 & & 0.24 & \\textbf{0.32} \\\\\n & & 0.8 & 0.39 & & 0.80 & 17.2 & & 0.39 & 0.38 & & 0.39 & \\textbf{0.36} \\\\\\hline\n 0.2 & 0.8 & 0.2 & 0.10 & & 0.14 & 0.98 & & 0.10 & \\textbf{0.40} & & 0.10 & \\textbf{0.40} \\\\\n & & 0.5 & 0.24 & & 0.35 & 1.83 & & 0.24 & 0.42 & & 0.24 & \\textbf{0.41} \\\\\n & & 0.8 & 0.39 & & 0.56 & 3.35 & & 0.39 & 0.41 & & 0.39 & \\textbf{0.40} \\\\\\hline\n 0.8 & 0.8 & 0.2 & 0.20 & & 0.20 & 0.71 & & 0.20 & 0.71 & & 0.20 & \\textbf{0.70} \\\\\n & & 0.5 & 0.50 & & 0.50 & 0.60 & & 0.50 & 0.60 & & 0.49 & \\textbf{0.59} \\\\\n & & 0.8 & 0.79 & & 0.80 & 0.29 & & 0.80 & \\textbf{0.28} & & 0.79 & \\textbf{0.28} \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Simulation results for zero-inflated continuous data with exponential margins, based on $N = 150$ samples and 1000 repetitions. the lowest MSE is highlighted in bold.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Spearman's rho for bivariate zero-inflated data", "authors": ["Jasper Arends", "Elisa Perrone"], "url": "https://arxiv.org/abs/2503.13148v1", "attribution": "\"Spearman's rho for bivariate zero-inflated data\" by Jasper Arends and Elisa Perrone, arXiv:2503.13148v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10066v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc|lc}\n \\toprule\n \\multirow{1}{*}{Custodian location} & \\multirow{1}{*}{\\#} & \\multirow{1}{*}{Custodian Location} & \\multirow{1}{*}{\\#}\\\\\n \\midrule\n Spain & 27 & France & 9 \\\\\n USA & 22 & South Africa & 6 \\\\\n Vietnam & 14 & UK & 5 \\\\\n Indonesia & 14 & Australia & 4 \\\\\n India & 11 & Germany & 4 \\\\\n Colombia & 10 & China & 3 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Top 12 most frequent custodian locations.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources", "authors": ["Angelina McMillan-Major", "Zaid Alyafeai", "Stella Biderman", "Kimbo Chen", "Francesco De Toni", "Gérard Dupont", "Hady Elsahar", "Chris Emezue", "Alham Fikri Aji", "Suzana Ilić", "Nurulaqilla Khamis", "Colin Leong", "Maraim Masoud", "Aitor Soroa", "Pedro Ortiz Suarez", "Zeerak Talat", "Daniel van Strien", "Yacine Jernite"], "url": "https://arxiv.org/abs/2201.10066v1", "attribution": "\"Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources\" by Angelina McMillan-Major, Zaid Alyafeai, Stella Biderman, Kimbo Chen, Francesco De Toni, Gérard Dupont, Hady Elsahar, Chris Emezue, Alham Fikri Aji, Suzana Ilić, Nurulaqilla Khamis, Colin Leong, Maraim Masoud, Aitor Soroa, Pedro Ortiz Suarez, Zeerak Talat, Daniel van Strien, and Yacine Jernite, arXiv:2201.10066v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.04375v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average width and coverage of $90\\%$-confidence intervals for PATE. Constant treatment effect with $n=10^3$.}\n\\begin{tabular}{cccccccc}\n\\toprule\n\\multirow{2}{*}{Non-private} \n& \\multicolumn{7}{c}{0.901}\\\\\n& \\multicolumn{7}{c}{0.002 $\\pm$ 3.24e-05}\n\\\\ \\midrule\n &\n $\\varepsilon = 0.1$ &\n $\\varepsilon = 0.4$ &\n $\\varepsilon = 0.7$ &\n $\\varepsilon = 1.0$ &\n $\\varepsilon = 1.3$ &\n $\\varepsilon = 1.6$ &\n $\\varepsilon = 1.9$ \\\\ \\midrule\n\\multirow{2}{*}{Central Gaussian} \n& 0.905\n& 0.895\n& 0.899\n& 0.902\n& 0.904\n& 0.899\n& 0.899\n\\\\\n& 0.771 $\\pm$ 1.24e-07\n& 0.199 $\\pm$ 4.85e-07\n& 0.118 $\\pm$ 8.20e-07\n& 0.084 $\\pm$ 1.16e-06\n& 0.066 $\\pm$ 1.47e-06\n& 0.055 $\\pm$ 1.78e-06\n& 0.047 $\\pm$ 2.07e-06\n \\\\\\midrule\n \\multirow{2}{*}{PBM (m=256)} \n& 0.902\n& 0.900\n& 0.900\n& 0.903\n& 0.906\n& 0.900\n& 0.903 \n \\\\\n& 0.772 $\\pm$ 1.25e-07\n& 0.200 $\\pm$ 4.84e-07\n& 0.119 $\\pm$ 8.15e-07\n& 0.085 $\\pm$ 1.15e-06\n& 0.067 $\\pm$ 1.43e-06\n& 0.056 $\\pm$ 1.72e-06\n& 0.048 $\\pm$ 2.02e-06\n \\\\\\midrule\n \n \\multirow{2}{*}{PBM (m=1024)} \n& 0.900\n& 0.897\n& 0.902\n& 0.900\n& 0.904\n& 0.898\n& 0.896\n \\\\\n& 0.772 $\\pm$ 1.26e-07\n& 0.199 $\\pm$ 4.85e-07\n& 0.118 $\\pm$ 8.28e-07\n& 0.085 $\\pm$ 1.17e-06\n& 0.066 $\\pm$ 1.46e-06\n& 0.055 $\\pm$ 1.77e-06\n& 0.047 $\\pm$ 2.05e-06\n\\\\\\midrule\n \\multirow{2}{*}{PBM (m=2048)} \n& 0.897\n& 0.902\n& 0.901\n& 0.901\n& 0.899\n& 0.902\n& 0.898\n\\\\\n& 0.772 $\\pm$ 1.24e-07\n& 0.199 $\\pm$ 4.85e-07\n& 0.118 $\\pm$ 8.19e-07\n& 0.084 $\\pm$ 1.16e-06\n& 0.066 $\\pm$ 1.47e-06\n& 0.055 $\\pm$ 1.77e-06\n& 0.047 $\\pm$ 2.06e-06\n\\\\\n \\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Federated Experiment Design under Distributed Differential Privacy", "authors": ["Wei-Ning Chen", "Graham Cormode", "Akash Bharadwaj", "Peter Romov", "Ayfer Özgür"], "url": "https://arxiv.org/abs/2311.04375v1", "attribution": "\"Federated Experiment Design under Distributed Differential Privacy\" by Wei-Ning Chen, Graham Cormode, Akash Bharadwaj, Peter Romov, and Ayfer Özgür, arXiv:2311.04375v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.22779v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{{\\small Scheduling actions of storage units}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\hline\n \\toprule \n Action $c$ & $-2$ & $-1$ & 0 & 1 & 2 \\\\ \\hline\n Storage discharging power/MW & $-2$ & $-1$ & 0 & $+1$ & $+2$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games", "authors": ["Junkai Hu", "Li Xia"], "url": "https://arxiv.org/abs/2503.22779v2", "attribution": "\"Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games\" by Junkai Hu and Li Xia, arXiv:2503.22779v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.05862v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lcccccccc}\n\\hline\n & \\multicolumn{4}{c}{\\textbf{Human Advisor}} & \\multicolumn{4}{c}{\\textbf{LLM-advisor}} \\\\\n\\cline{2-5} \\cline{6-9}\n & \\textbf{All} & \\multicolumn{3}{c}{\\textbf{Narrative}} & \\textbf{All} & \\multicolumn{3}{c}{\\textbf{Narrative}} \\\\\n & & 1 & 2 & 3 & & 1 & 2 & 3 \\\\\n\\hline\n\\textbf{PC} & 0.85 & 0.78 & 0.89 & 0.89 & 0.69 & 0.80 & 0.83 & 0.41 \\\\\n\\textbf{MCR} & 0.89 & 0.67 & 1.00 & 1.00 & 0.76 & 0.90 & 0.90 & 0.44 \\\\\n\\hline\n\\end{tabular}\n\\caption{Comparison of Preference Coverage (PC) and Majority Capture Rate (MCR) between Human and LLM-advisors.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Are Generative AI Agents Effective Personalized Financial Advisors?", "authors": ["Takehiro Takayanagi", "Kiyoshi Izumi", "Javier Sanz-Cruzado", "Richard McCreadie", "Iadh Ounis"], "url": "https://arxiv.org/abs/2504.05862v2", "attribution": "\"Are Generative AI Agents Effective Personalized Financial Advisors?\" by Takehiro Takayanagi, Kiyoshi Izumi, Javier Sanz-Cruzado, Richard McCreadie, and Iadh Ounis, arXiv:2504.05862v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2312.12151v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of per-organ mean F1 scores on the OCELOT test set for tissue segmentation. The tissue segmentation model is denoted as TSM. The highest score for each organ is marked in \\textbf{bold}.}\n\\begin{tabular}{lccccccc}\n \\midrule\n & all & kidney & endometrium & bladder & prostate & stomach & head-neck \\\\\n & n=130 & n=41 & n=25 & n=26 & n=16 & n=12 & n=10 \\\\\n \\midrule\n TSM & .8927 & .9241 & .9062 & .8358 & .8119 & .9014 & \\textbf{.8863} \\\\\n TSM + TTA & \\textbf{.8951} & \\textbf{.9282} & \\textbf{.9088} & \\textbf{.8383} & \\textbf{.8178} & \\textbf{.9035} & .8774 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SoftCTM: Cell detection by soft instance segmentation and consideration of cell-tissue interaction", "authors": ["Lydia A. Schoenpflug", "Viktor H. Koelzer"], "url": "https://arxiv.org/abs/2312.12151v1", "attribution": "\"SoftCTM: Cell detection by soft instance segmentation and consideration of cell-tissue interaction\" by Lydia A. Schoenpflug and Viktor H. Koelzer, arXiv:2312.12151v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11165v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|cc|cc|}\n\\toprule\n & & \\multicolumn{2}{|c|}{\\textbf{LW}} & \\multicolumn{2}{|c|}{\\textbf{CS-LW}} \\\\\n\\midrule\n \\textbf{BN} & \\textbf{N} & \\textbf{MAE $\\pm$ Std.} & \\textbf{Time} & \\textbf{MAE $\\pm$ Std.} & \\textbf{Time} \\\\\n\\midrule\n\\midrule\n\\multirow{4}{*}{\\textit{Alarm}} & 100 & 0.2105 $\\pm$ 0.1372\t& 0.09 & \t\\textbf{0.0721} $\\pm$\t\\textbf{0.0983} & \\textbf{0.06}\\\\ \\cline{2-6}\n & 1000 & 0.0766 $\\pm$ 0.0608 &\t0.86 & \\textbf{0.0240}\t$\\pm$ \\textbf{0.0182}\t& \\textbf{0.53}\n \\\\\n \\cline{2-6}\n & 10000 & 0.0282 $\\pm$\t0.0181 & 8.64 & \\textbf{0.0091} $\\pm$ \\textbf{0.0069}\t& \\textbf{5.53} \\\\ \\cline{2-6}\n & 100000 & 0.0086 $\\pm$ 0.0067 & 89.93\t& \\textbf{0.0034} $\\pm$ \\textbf{0.0027} & \\textbf{57.64}\\\\\n\\midrule\n\\midrule\n\\multirow{4}{*}{\\textit{Andes}} & 100 & 0.0821 $\\pm$ 0.0477 & 1.07 &\t\\textbf{0.0619} $\\pm$ \\textbf{0.0453} & \\textbf{0.22} \\\\ \\cline{2-6}\n & 1000 & 0.0257 $\\pm$ 0.0184 & 10.62 & \\textbf{0.0163} $\\pm$ \\textbf{0.0139} & \\textbf{2.20} \\\\ \\cline{2-6}\n & 10000 & 0.0087 $\\pm$\t0.0069 & 106.55 & \\textbf{0.0058} $\\pm$ \\textbf{0.0042} &\t\\textbf{22.62}\\\\ \\cline{2-6} \n & 100000 & 0.0025 $\\pm$ \\textbf{0.0015} & 1074.93 & \\textbf{0.0020} $\\pm$ 0.0016 & \\textbf{233.72}\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The mean absolute error (MAE), the standard deviation of the error (Std.), and the average execution time (in seconds) versus the number of samples (N). For each case, LW and CS-LW were executed 30 times.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs", "authors": ["Nitesh Kumar", "Ondrej Kuzelka", "Luc De Raedt"], "url": "https://arxiv.org/abs/2201.11165v2", "attribution": "\"First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs\" by Nitesh Kumar, Ondrej Kuzelka, and Luc De Raedt, arXiv:2201.11165v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.14373v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Efficiency of our method compared against two-stage pretraining methods.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\\toprule\n\\textbf{Method} &\\textbf{\\# of Data} &\\textbf{Total \\# of Data} &\\textbf{Time (min.)} &\\textbf{Total Time (min.)} \\\\\\midrule\n1st Stage &2,513,435 &\\multirow{2}{*}{2,913,435} &$\\sim$1258.50 &\\multirow{2}{*}{$\\sim$1727.75} \\\\\n2nd Stage &400,000 & &$\\sim$469.25 & \\\\\n\\midrule\n\\textbf{ECG-Byte} A &1,000,000 &\\multirow{2}{*}{1,400,000} & $\\sim$152.47&\\multirow{2}{*}{$\\sim$\\textbf{572.79}} \\\\\nend-to-end A &400,000 & &$\\sim$420.32 & \\\\\n\\midrule\n\\textbf{ECG-Byte} B &2,500,000 &\\multirow{2}{*}{2,900,000} &$\\sim$304.27 &\\multirow{2}{*}{$\\sim$\\textbf{724.59}} \\\\\nend-to-end B &400,000 & &$\\sim$420.32 & \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling", "authors": ["William Han", "Chaojing Duan", "Michael A. Rosenberg", "Emerson Liu", "Ding Zhao"], "url": "https://arxiv.org/abs/2412.14373v3", "attribution": "\"ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling\" by William Han, Chaojing Duan, Michael A. Rosenberg, Emerson Liu, and Ding Zhao, arXiv:2412.14373v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02322v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative evaluation of the results in Fig. for AT , MSTV , HLO , and our method TGV. For each result, we list mean angular difference $\\theta$ (in degrees), vertex-based mesh-to-mesh error $E_v \\ (\\times 10^{-2})$ and the execution time (in seconds).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n \\toprule\n \\footnotesize Mesh &\n \\multicolumn{2}{c}{\\footnotesize AT}&\n \\multicolumn{2}{c}{\\footnotesize MSTV}&\n \\multicolumn{2}{c}{\\footnotesize HLO} &\n \\multicolumn{2}{c}{\\footnotesize TGV}\\\\\n \\midrule\n \\footnotesize Child& \\multicolumn{2}{c|}{7.61,\\,0.62;\\,3.80} & \\multicolumn{2}{c|}{7.22,\\,0.55;\\,7.06} & \\multicolumn{2}{c|}{7.93,\\,0.69;\\,1.31} & \\multicolumn{2}{c}{\\textbf{6.29},\\,\\textbf{0.48};\\,25.9}\\\\\n \\footnotesize Joint& \\multicolumn{2}{c|}{1.89,\\,0.79;\\,2.84} & \\multicolumn{2}{c|}{1.99,\\,0.81;\\,2.75} &\n \\multicolumn{2}{c|}{6.06,\\,2.15;\\,0.34} & \\multicolumn{2}{c}{\\textbf{1.45},\\,\\textbf{0.70};\\,12.4}\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Mesh Total Generalized Variation for Denoising", "authors": ["Zheng Liu", "YanLei Li", "Weina Wang", "Ligang Liu", "Renjie Chen"], "url": "https://arxiv.org/abs/2101.02322v2", "attribution": "\"Mesh Total Generalized Variation for Denoising\" by Zheng Liu, YanLei Li, Weina Wang, Ligang Liu, and Renjie Chen, arXiv:2101.02322v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.09607v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classification results for three binary tasks: H vs. BD, H vs. BPD and BD vs. BDP, using logistic regression. Results shown are macro-averaged $\\mathrm{F_1}$ and AUROC scores across all interviews. $p$-value used for feature selection: `*'$<$ 0.005, or else $<$ 0.001 is used.}\n\\begin{tabular}{c|cccccc}\n\\hline\n & \\multicolumn{2}{c}{\\bf H/BD} & \\multicolumn{2}{c}{\\bf H/BPD} & \\multicolumn{2}{c}{\\bf BD/BPD} \\\\\n\\bf Subject & $\\mathbf{F_1}$ & \\bf AUC & $\\mathbf{F_1}$ & \\bf AUC & $\\mathbf{F_1}$ & \\bf AUC \\\\ \\hline\nParticipant & \\textbf{0.738} & \\textbf{0.738} & \\textbf{0.827} & \\textbf{0.841} & \\textbf{0.710} & \\textbf{0.716}\\\\\\\nInterviewer & 0.581 & 0.583 & 0.102* & 0.100* & 0.552* & 0.556*\\\\\nBoth & 0.477 & 0.488 & 0.512* & 0.515* & 0.683 & 0.686\\\\\n\\hline \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Modelling Paralinguistic Properties in Conversational Speech to Detect Bipolar Disorder and Borderline Personality Disorder", "authors": ["Bo Wang", "Yue Wu", "Nemanja Vaci", "Maria Liakata", "Terry Lyons", "Kate E A Saunders"], "url": "https://arxiv.org/abs/2102.09607v1", "attribution": "\"Modelling Paralinguistic Properties in Conversational Speech to Detect Bipolar Disorder and Borderline Personality Disorder\" by Bo Wang, Yue Wu, Nemanja Vaci, Maria Liakata, Terry Lyons, and Kate E A Saunders, arXiv:2102.09607v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.08696v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n\\hline\nSector & Company (ticker)\\\\\\hline\nMetal and Mining & Mechel (MLTR), TMK-Group (TRMK) \\\\ \\hline\nOil and Gas & Surgutneftegas (SNGS), Gaspromneft (SIBN) \\\\ \\hline\nConsumer sector & Magnit (MGNT), Lenta (LENT) \\\\\\hline\nConstruction & PIK (PIKK), Samolet (SMLT)\\\\\\hline\nTelecommunications & MTS (MTSS), Rostelecom (RTKMP)\\\\\\hline\nTransport & AEROFLOT (AFLT), Sovcomflot (FLOT) \\\\\\hline\nFinance & Bank Saint-Petersburg (BSPB), SFI (SFIN) \\\\\\hline\nChemical Industry & Phosagro (PHOR), Kazanorgsintez (KZOSP) \\\\\\hline\nPower Engineering & Rushydro (HYDR), Rosseti Center (MRKC) \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Economic sectors and companies (tickers) included into the dataset}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market", "authors": ["Kasymkhan Khubiev", "Mikhail Semenov"], "url": "https://arxiv.org/abs/2503.08696v1", "attribution": "\"Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market\" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2503.08696v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.11311v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study of $\\beta$ in Softplus with respect to integral calibration error $\\overline{\\text{ECE}}$ and FID. The classifier is the official Pytorch ResNet checkpoint. The diffusion model is from . We generate 10,000 ImageNet 128x128 samples with 250 DDPM steps for evaluation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n & ReLU & SoftPlus ($\\beta$=8) & SoftPlus ($\\beta$=5) & SoftPlus ($\\beta$=4) & SoftPlus ($\\beta$=3) \\\\\n\\midrule\n$\\overline{\\text{ECE}}$ & 0.34 & 0.31 & 0.26 & 0.21 & 0.07 \\\\\nFID & 7.17 & 6.99 & 6.89 & 6.73 & 6.61 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Elucidating The Design Space of Classifier-Guided Diffusion Generation", "authors": ["Jiajun Ma", "Tianyang Hu", "Wenjia Wang", "Jiacheng Sun"], "url": "https://arxiv.org/abs/2310.11311v1", "attribution": "\"Elucidating The Design Space of Classifier-Guided Diffusion Generation\" by Jiajun Ma, Tianyang Hu, Wenjia Wang, and Jiacheng Sun, arXiv:2310.11311v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.08582v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparing different multi-step loss configuration on real-world (50).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\n & 1-step & 2-step & 3x1-step & 3-step & ours \\\\\n\\hline\nPSNR & 24.52 & 24.71 & 23.70 & \\underline{24.85} & \\textbf{25.18} \\\\\nSSIM & 0.904 & 0.904 & 0.901 & \\underline{0.906} & \\textbf{0.909} \\\\\nLPIPS & 0.133 & 0.123 & 0.132 & \\underline{0.120} & \\textbf{0.118} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Utilizing Multi-step Loss for Single Image Reflection Removal", "authors": ["Abdelrahman Elnenaey", "Marwan Torki"], "url": "https://arxiv.org/abs/2412.08582v2", "attribution": "\"Utilizing Multi-step Loss for Single Image Reflection Removal\" by Abdelrahman Elnenaey and Marwan Torki, arXiv:2412.08582v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.08683v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics number of people using antidepressants pill in Taiwan.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|c|c|c|c|}\n \\hline\n \\textbf{Country} & \\textbf{2018} & \\textbf{2017} & \\textbf{2016} & \\textbf{2015} & \\textbf{2014} \\\\\n \\hline\n Taiwan & 1,397,197 & 1,330,204 & 1,273,561 & 1,121,659 & 1,194,395 \\\\\n \\hline\n Under 30 & 162,402 & 143,024 & 127,508 & 118,186 & 115,740 \\\\\n \\hline\n \\textbf{Ratio} & \\textbf{11.62\\%} & \\textbf{10.57\\%} & \\textbf{10.01\\%} & \\textbf{9.75\\%} & \\textbf{9.69\\%} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Emotional Vietnamese Speech-Based Depression Diagnosis Using Dynamic Attention Mechanism", "authors": ["Quang-Anh N. D.", "Manh-Hung Ha", "Thai Kim Dinh", "Minh-Duc Pham", "Ninh Nguyen Van"], "url": "https://arxiv.org/abs/2412.08683v1", "attribution": "\"Emotional Vietnamese Speech-Based Depression Diagnosis Using Dynamic Attention Mechanism\" by Quang-Anh N. D., Manh-Hung Ha, Thai Kim Dinh, Minh-Duc Pham, and Ninh Nguyen Van, arXiv:2412.08683v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.17077v4_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{cccccc}\n\\hline\n\\multicolumn{6}{c}{Errors and convergence rates after $T/h$ iterations for $d=50$} \\\\ \\hline\n$h$\\textbackslash $\\phi$ & $\\phi_{1}(x)$ & $\\exp(-\\| x\\|)$ & $\\phi_{2}(x)$ & $\\arctan(\\| x\\|)$ & Iterations \\\\ \\hline\n$2^{-5}$ & 3.03e-01 & 1.66e-02 & 3.85e-01 & 2.48e-02 &\n192 \\\\ %\\hline\n$2^{-6}$ & 1.50e-01 & 7.04e-03 & 1.88e-01 & 1.04e-02 \n & 384 \\\\ %\\hline\n$2^{-7}$ & 7.23e-02 & 3.33e-03 & 9.06e-02 & 4.91e-03\n& 768 \\\\ %\\hline\n$2^{-8}$ & 3.50e-02 & 1.61e-03 & 4.38e-02 & 2.37e-03 \n& 1536\\\\ %\\hline\n$2^{-9}$ & 1.97e-02 & 7.69e-04 & 2.47e-02 & 1.13e-03 \n& 3072\\\\ \\hline\n\\text{Order} & 1.00 & 1.01 & 1.00 & 1.11\n& \\ \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Projected Langevin Monte Carlo algorithms in non-convex and super-linear setting", "authors": ["Chenxu Pang", "Xiaojie Wang", "Yue Wu"], "url": "https://arxiv.org/abs/2312.17077v4", "attribution": "\"Projected Langevin Monte Carlo algorithms in non-convex and super-linear setting\" by Chenxu Pang, Xiaojie Wang, and Yue Wu, arXiv:2312.17077v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.05916v2_tex_table16.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Other Outcome Variables: Full Specifications}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrrr}\n \\toprule\n & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) \\\\\n VARIABLES & & ln(labor income) & & & Work Hours & & & Housework & \\\\\n \\midrule\n & & & & & & & & & \\\\\n l = -3 & -0.025 & -0.028 & 0.022 & -143.255** & -95.776* & -142.031** & 0.097 & 0.103 & 0.215 \\\\\n & (0.064) & (0.064) & (0.084) & (61.976) & (49.798) & (62.915) & (1.166) & (1.165) & (0.852) \\\\\n l = -2 & 0.012 & -0.015 & 0.010 & -49.621 & -40.767 & -61.308 & 0.207 & 0.198 & 0.589 \\\\\n & (0.058) & (0.054) & (0.072) & (45.335) & (39.197) & (44.295) & (0.855) & (0.854) & (1.099) \\\\\n l = 0 & 0.024 & 0.022 & 0.020 & -48.726 & -44.768 & -79.193** & -0.474 & -0.465 & -0.358 \\\\\n & (0.047) & (0.045) & (0.059) & (37.386) & (29.462) & (35.584) & (0.687) & (0.687) & (0.869) \\\\\n l = 1 & 0.033 & 0.033 & -0.016 & 7.637 & -6.626 & 14.454 & 0.338 & 0.343 & 0.702 \\\\\n & (0.051) & (0.049) & (0.069) & (47.454) & (38.648) & (48.455) & (0.850) & (0.850) & (1.091) \\\\\n l = 2 & -0.041 & -0.050 & -0.030 & 9.686 & 22.483 & -8.922 & 0.684 & 0.675 & 0.571 \\\\\n & (0.056) & (0.054) & (0.070) & (49.447) & (42.005) & (45.956) & (0.789) & (0.789) & (0.951) \\\\\n l = 3 & 0.040 & 0.020 & 0.017 & -25.722 & -33.754 & -34.628 & 1.915** & 1.907** & 0.864 \\\\\n & (0.047) & (0.046) & (0.058) & (42.804) & (35.337) & (43.067) & (0.840) & (0.840) & (0.995) \\\\\n l = 4 & -0.010 & -0.006 & 0.001 & -4.272 & 0.737 & 35.705 & -0.273 & -0.273 & -0.187 \\\\\n & (0.010) & (0.006) & (0.025) & (12.965) & (11.317) & (21.896) & (0.221) & (0.222) & (0.447) \\\\\n Education (years) & 0.011 & -0.001 & 0.017 & 11.356 & 4.299 & 21.308 & 0.262 & 0.254 & -0.045 \\\\\n & (0.018) & (0.018) & (0.025) & (16.560) & (12.835) & (16.588) & (0.252) & (0.252) & (0.310) \\\\\n ln(Childcare expense) & 0.022*** & 0.019*** & 0.029*** & 18.282*** & 5.213* & 7.842** & -0.102* & -0.101* & -0.123* \\\\\n & (0.004) & (0.004) & (0.006) & (3.410) & (2.941) & (3.664) & (0.055) & (0.055) & (0.069) \\\\\n Age of Youngest Child & 0.015*** & 0.016*** & 0.021*** & 15.529*** & 5.710** & 7.171** & 0.004 & 0.005 & -0.034 \\\\\n & (0.003) & (0.003) & (0.004) & (3.035) & (2.467) & (3.079) & (0.042) & (0.042) & (0.061) \\\\\n Number of Children & 0.007 & 0.005 & 0.002 & -40.220*** & -32.125*** & -37.848*** & 0.997*** & 0.994*** & 0.784*** \\\\\n & (0.014) & (0.013) & (0.018) & (13.015) & (10.820) & (14.214) & (0.210) & (0.210) & (0.273) \\\\\n Black & -0.130 & -0.147 & -0.152 & -208.539 & -104.521 & -190.547 & 1.560 & 1.548 & -7.324** \\\\\n & (0.181) & (0.175) & (0.286) & (171.393) & (162.896) & (138.508) & (2.407) & (2.397) & (3.577) \\\\\n Latino/a & -0.239*** & -0.200*** & -0.297*** & -4.353 & 86.311 & 139.296* & 2.451 & 2.453 & -0.413 \\\\\n & (0.077) & (0.074) & (0.095) & (70.890) & (55.386) & (74.649) & (1.776) & (1.774) & (1.828) \\\\\n Work Hours & 0.001*** & 0.001*** & 0.001*** & & & & -0.002*** & -0.002*** & -0.001*** \\\\\n & (0.000) & (0.000) & (0.000) & & & & (0.000) & (0.000) & (0.000) \\\\\n ln(family income) & & 0.602*** & & & & 40.827 & & 0.192 & -0.555 \\\\\n & & (0.040) & & & & (64.075) & & (0.388) & (0.702) \\\\\n Housework Hours & -0.004*** & -0.003*** & -0.005*** & -7.222*** & -3.842*** & -3.520*** & & & \\\\\n & (0.001) & (0.001) & (0.002) & (1.005) & (0.865) & (1.101) & & & \\\\\n Catholic & -0.168 & -0.203 & & -454.816* & -270.834* & & 0.185 & 0.168 & \\\\\n & (0.246) & (0.256) & & (235.947) & (154.587) & & (3.810) & (3.821) & \\\\\n Jewish & -0.079 & 0.759*** & & -1,073.106*** & -758.387*** & & -7.022*** & -6.757*** & \\\\\n & (0.163) & (0.169) & & (144.539) & (116.780) & & (2.246) & (2.342) & \\\\\n Protestant & 0.135 & 0.379** & & 0.521 & -50.113 & & -1.542 & -1.459 & \\\\\n & (0.151) & (0.152) & & (139.890) & (111.234) & & (2.179) & (2.200) & \\\\\n Other Non-Christian & -0.129 & 0.195 & & 194.775 & 191.504 & & -24.495* & -24.398* & \\\\\n & (0.159) & (0.169) & & (179.901) & (145.063) & & (14.696) & (14.734) & \\\\\n Greek/Russian Orthodox & 0.100 & 0.252 & & 456.075** & 297.235* & & 27.384*** & 27.442*** & \\\\\n & (0.210) & (0.203) & & (189.377) & (164.771) & & (2.957) & (2.961) & \\\\\n ln(male labor income) & & & 0.087* & & & -74.467*** & & & 0.550 \\\\\n & & & (0.051) & & & (23.228) & & & (0.370) \\\\\n ln(female lab income) & & & & & 374.927*** & 328.198*** & -0.813*** & -0.868*** & -0.959** \\\\\n & & & & & (25.098) & (35.253) & (0.290) & (0.318) & (0.406) \\\\\n & & & & & & & & & \\\\\n \\midrule\n Constant & 7.914*** & 1.817*** & 6.641*** & 1,800.938*** & -1,644.613*** & -2,533.389*** & 31.826*** & 30.429*** & 36.298** \\\\\n & (0.432) & (0.541) & (0.953) & (519.097) & (495.367) & (926.240) & (9.442) & (9.861) & (17.142) \\\\\n & & & & & & & & & \\\\\n Observations & 6,876 & 6,872 & 4,495 & 6,876 & 6,876 & 4,493 & 6,876 & 6,872 & 4,493 \\\\\n R-squared & 0.748 & 0.789 & 0.762 & 0.551 & 0.670 & 0.704 & 0.524 & 0.524 & 0.560 \\\\\n \\midrule\n Robust standard errors in parentheses & & & & & & & & & \\\\\n *** p<0.01, ** p<0.05, * p<0.1 & & & & & & & & & \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Workplace Breastfeeding Legislation and Female Labor Force Participation in the United States", "authors": ["Julia Hatamyar"], "url": "https://arxiv.org/abs/2209.05916v2", "attribution": "\"Workplace Breastfeeding Legislation and Female Labor Force Participation in the United States\" by Julia Hatamyar, arXiv:2209.05916v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18787v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n $\\Dot{}$ & time derivative \\\\\n $*$ & indicates optimality \\\\\n $\\hat{}$ & denotes model prediction\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Sample-Efficient Reinforcement Learning of Koopman eNMPC", "authors": ["Daniel Mayfrank", "Mehmet Velioglu", "Alexander Mitsos", "Manuel Dahmen"], "url": "https://arxiv.org/abs/2503.18787v2", "attribution": "\"Sample-Efficient Reinforcement Learning of Koopman eNMPC\" by Daniel Mayfrank, Mehmet Velioglu, Alexander Mitsos, and Manuel Dahmen, arXiv:2503.18787v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.09712v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The correlation coefficients and the correlation test results between the complexity and the disagreement metrics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crlrl}\\hline\n \\multirow{2}{*}{Complexity metric} &\\multicolumn{2}{c}{obscurity} &\\multicolumn{2}{c}{discrepancy}\\\\\\cline{2-5}\n & $r$ & $p$-value & $r$ & $p$-value \\\\\\hline\n T2 & -0.019 & 0.9332 & -0.070 & 0.7614\\\\ \n T3 & -0.109 & 0.6377 & -0.172 & 0.4546\\\\\n T4 & -0.215 & 0.3485 & -0.292 & 0.1986\\\\\\hline\n \\cellcolor{yellow}L1 & \\cellcolor{yellow}0.767 & \\cellcolor{yellow}0.0001*** & \\cellcolor{yellow}0.560 & \\cellcolor{yellow}0.0082**\\\\\n \\cellcolor{yellow}L2 & \\cellcolor{yellow}0.754 & \\cellcolor{yellow}0.0001*** & \\cellcolor{yellow}0.577 & \\cellcolor{yellow}0.0061**\\\\\n \\cellcolor{yellow}L3 & \\cellcolor{yellow}0.740 & \\cellcolor{yellow}0.0001*** & \\cellcolor{yellow}0.568 & \\cellcolor{yellow}0.0071**\\\\\\hline\n F1 & -0.420 & 0.0587 & -0.334 & 0.1383\\\\\n F1v & -0.081 & 0.7241 & 0.195 & 0.3957\\\\\n F2 & 0.094 & 0.6823 & 0.039 & 0.8644\\\\\n F3 & 0.136 & 0.5542 & 0.013 & 0.9532\\\\\n F4 & 0.302 & 0.1820 & 0.220 & 0.3360\\\\\\hline\n N1 & 0.619 & 0.0056** & 0.447 & 0.0546\\\\\n N2 & -0.019 & 0.9397 & 0.143 & 0.5591\\\\\n N3 & 0.542 & 0.0180* & 0.407 & 0.0835\\\\\n \\cellcolor{yellow}N4 & \\cellcolor{yellow}0.512 & \\cellcolor{yellow}0.0266* & \\cellcolor{yellow}0.564 & \\cellcolor{yellow}0.0118*\\\\\n T1 & -0.147 & 0.5458 & -0.227 & 0.3494\\\\\n LSC & 0.438 & 0.0618 & 0.369 & 0.1195\\\\\\hline\n \\multicolumn{5}{r}{* $p$-value $<0.05$, ** $p$-value $<0.01$, *** $p$-value $<0.001$}\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective", "authors": ["Mustafa Cavus", "Przemyslaw Biecek"], "url": "https://arxiv.org/abs/2412.09712v1", "attribution": "\"Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective\" by Mustafa Cavus and Przemyslaw Biecek, arXiv:2412.09712v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.13992v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n \\toprule\n & \\multicolumn{2}{c}{Spearman's $\\rho$} & \\multicolumn{2}{c}{Kendall's $\\tau$}\\\\ \n LLM Judge & MMAU & MMAR & MMAU & MMAR \\\\\n \\midrule\n Qwen 2.5 7B & \\textbf{0.733} & \\textbf{0.746} & \\textbf{0.629} & \\textbf{0.652} \\\\\n Llama 3.1 8B & 0.692 & 0.664 & 0.585 & 0.574 \\\\\n Prometheus 2 7B & 0.542 & 0.606 & 0.448 & 0.514 \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Correlation between human and LLM-as-a-Judge for evaluating open-ended responses for answer correctness.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence", "authors": ["Sonal Kumar", "Šimon Sedláček", "Vaibhavi Lokegaonkar", "Fernando López", "Wenyi Yu", "Nishit Anand", "Hyeonggon Ryu", "Lichang Chen", "Maxim Plička", "Miroslav Hlaváček", "William Fineas Ellingwood", "Sathvik Udupa", "Siyuan Hou", "Allison Ferner", "Sara Barahona", "Cecilia Bolaños", "Satish Rahi", "Laura Herrera-Alarcón", "Satvik Dixit", "Siddhi Patil", "Soham Deshmukh", "Lasha Koroshinadze", "Yao Liu", "Leibny Paola Garcia Perera", "Eleni Zanou", "Themos Stafylakis", "Joon Son Chung", "David Harwath", "Chao Zhang", "Dinesh Manocha", "Alicia Lozano-Diez", "Santosh Kesiraju", "Sreyan Ghosh", "Ramani Duraiswami"], "url": "https://arxiv.org/abs/2508.13992v1", "attribution": "\"MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence\" by Sonal Kumar, Šimon Sedláček, Vaibhavi Lokegaonkar, Fernando López, Wenyi Yu, Nishit Anand, Hyeonggon Ryu, Lichang Chen, Maxim Plička, Miroslav Hlaváček, William Fineas Ellingwood, Sathvik Udupa, Siyuan Hou, Allison Ferner, Sara Barahona, Cecilia Bolaños, Satish Rahi, Laura Herrera-Alarcón, Satvik Dixit, Siddhi Patil, Soham Deshmukh, Lasha Koroshinadze, Yao Liu, Leibny Paola Garcia Perera, Eleni Zanou, Themos Stafylakis, Joon Son Chung, David Harwath, Chao Zhang, Dinesh Manocha, Alicia Lozano-Diez, Santosh Kesiraju, Sreyan Ghosh, and Ramani Duraiswami, arXiv:2508.13992v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.11765v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overview of the Dataset per Market Capitalization}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrr}\n & \\textbf{LargeCap} & \\textbf{MidCap} & \\textbf{SmallCap} & \\textbf{None} \\\\ \\hline\n\\textbf{\\# of companies} & 537 & 818 & 1502 & 28 \\\\ \\hline\n\\textbf{\\# of articles per company} & & & & \\\\\n\\textit{\\textbf{average}} & 2189 & 1445 & 822 & 309 \\\\\n\\textit{\\textbf{min}} & 5 & 5 & 5 & 13 \\\\\n\\textit{\\textbf{max}} & 2005 & 1789 & 1984 & 1122 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis", "authors": ["Tanja Aue", "Adam Jatowt", "Michael Färber"], "url": "https://arxiv.org/abs/2212.11765v1", "attribution": "\"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis\" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10787v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{soul}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc}\n\\toprule\n\\cline{4-5}\n & & & \\multicolumn{2}{c}{\\textbf{VMI (ours)}} \\\\ \\cline{4-5}\n & General MI & Generative MI & \\multicolumn{2}{c}{\\textbf{DCGAN}} \\\\ \\cline{4-5}\n & & & \\textbf{Gaussian} & \\textbf{Flow} \\\\\\cline{4-5}\n \\midrule\n\\textbf{Accuracy} & 0.00{\\tiny$\\pm$ 0.00 } & 0.92{\\tiny$\\pm$ 0.02 } & 0.93{\\tiny$\\pm$ 0.06 } & \\textbf{0.95}{\\tiny$\\pm$ 0.02 } \\\\\n\\textbf{Precision} & 0.00{\\tiny$\\pm$ 0.00 } & \\ul{0.25}{\\tiny$\\pm$ 0.14 } & \\ul{0.26}{\\tiny$\\pm$ 0.13 } & \\textbf{ 0.35}{\\tiny$\\pm$ 0.15 } \\\\\n\\textbf{Density} & 0.00{\\tiny$\\pm$ 0.00 } & \\ul{ 0.09}{\\tiny$\\pm$ 0.07 } & \\ul{ 0.11}{\\tiny$\\pm$ 0.06 } & {\\bf 0.14}{\\tiny$\\pm$ 0.09 } \\\\\n\\midrule\n\\textbf{Recall} & 0.00{\\tiny$\\pm$ 0.00 } & 0.39{\\tiny$\\pm$ 0.12 } & {\\bf 0.54}{\\tiny$\\pm$ 0.12 } & 0.25{\\tiny$\\pm$ 0.10 } \\\\\n\\textbf{Coverage} & 0.00{\\tiny$\\pm$ 0.00 } & \\ul{ 0.20}{\\tiny$\\pm$ 0.12 } & \\ul{ 0.17}{\\tiny$\\pm$ 0.08 } & {\\bf 0.24}{\\tiny$\\pm$ 0.12 } \\\\\n\\textbf{Diversity} & 0.00{\\tiny$\\pm$ 0.00 } & \\ul{ 0.29}{\\tiny$\\pm$ 0.17 } & {\\bf 0.36}{\\tiny$\\pm$ 0.15 } & \\ul{ 0.24}{\\tiny$\\pm$ 0.16 } \\\\\n\\midrule\n\\textbf{FID} & 376.7 & 88.91 & 82.52 & \\textbf{77.73 } \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{MNIST: comparing baseline and our attacks.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Variational Model Inversion Attacks", "authors": ["Kuan-Chieh Wang", "Yan Fu", "Ke Li", "Ashish Khisti", "Richard Zemel", "Alireza Makhzani"], "url": "https://arxiv.org/abs/2201.10787v1", "attribution": "\"Variational Model Inversion Attacks\" by Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, and Alireza Makhzani, arXiv:2201.10787v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2301.00790v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|l|l|}\n\\hline\nModel & Average & & Mean & Volatility & Max Draw & Sharpe & Calmar \\\\ \\hline\n\\multirow{4}{*}{LightGBM-dart} & \\multirow{2}{*}{Over Models} & Mean & 0.0255 & 0.0259 & 0.1500 & 0.9832 & 0.1704 \\\\ \\cline{3-8} \n & & S.D. & 0.0002 & 0.0001 & 0.0081 & 0.0094 & 0.0102 \\\\ \\cline{2-8} \n & \\multirow{2}{*}{Over Predictions} & Mean & 0.0276 & 0.0282 & 0.1605 & 0.9765 & 0.1721 \\\\ \\cline{3-8} \n & & S.D. & 0.0002 & 0.0001 & 0.0087 & 0.0089 & 0.0104 \\\\ \\hline\n\\multirow{4}{*}{LightGBM-gbdt} & \\multirow{2}{*}{Over Models} & Mean & 0.0253 & 0.0308 & 0.2233 & 0.8196 & 0.1134 \\\\ \\cline{3-8} \n & & S.D. & 0.0001 & 0.0002 & 0.0080 & 0.0062 & 0.0044 \\\\ \\cline{2-8} \n & \\multirow{2}{*}{Over Predictions} & Mean & 0.0260 & 0.0319 & 0.2306 & 0.8161 & 0.1131 \\\\ \\cline{3-8} \n & & S.D. & 0.0001 & 0.0002 & 0.0083 & 0.0061 & 0.0044 \\\\ \\hline\n\\multirow{4}{*}{MLP} & \\multirow{2}{*}{Over Models} & Mean & 0.0231 & 0.0259 & 0.1490 & 0.8922 & 0.1550 \\\\ \\cline{3-8} \n & & S.D. & 0.0003 & 0.0003 & 0.0055 & 0.0095 & 0.0065 \\\\ \\cline{2-8} \n & \\multirow{2}{*}{Over Predictions} & Mean & 0.0254 & 0.0284 & 0.1610 & 0.8930 & 0.1576 \\\\ \\cline{3-8} \n & & S.D. & 0.0002 & 0.0004 & 0.0062 & 0.0078 & 0.0050 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Online learning techniques for prediction of temporal tabular datasets with regime changes", "authors": ["Thomas Wong", "Mauricio Barahona"], "url": "https://arxiv.org/abs/2301.00790v4", "attribution": "\"Online learning techniques for prediction of temporal tabular datasets with regime changes\" by Thomas Wong and Mauricio Barahona, arXiv:2301.00790v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.04349v3_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{{Results for different values of $\\epsilon$. Reference value for $Y_0$ computed with the FFT (with $\\epsilon=0$) is given by $0.1375$.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n$\\epsilon$ &\t$\\widetilde{Y}_0^\\Theta\\ (Y_0=1375)$\t& Relative error & Time (s) \\\\\n\\midrule\\midrule\n0.005\t&\t0.295\t&\t115\\%\t&\t1698\t\t\\\\\n0.001\t&\t0.149\t&\t8\\%\t&\t1778\t\t\t\\\\\n0.0005\t&\t0.155\t&\t13\\%\t&\t1932\t\t\t\\\\\n0.0001\t&\t0.134\t&\t2.4\\%\t&\t2329\t\t\t\\\\\n0.00005\t&\t0.138\t&\t0.7\\%\t&\t3526\t\t\t\\\\\n0.00001\t &\t0.137\t&\t0.4\\%\t&\t9332\t\t\t\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A deep solver for BSDEs with jumps", "authors": ["Kristoffer Andersson", "Alessandro Gnoatto", "Marco Patacca", "Athena Picarelli"], "url": "https://arxiv.org/abs/2211.04349v3", "attribution": "\"A deep solver for BSDEs with jumps\" by Kristoffer Andersson, Alessandro Gnoatto, Marco Patacca, and Athena Picarelli, arXiv:2211.04349v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.19625v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary Statistics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n \\toprule\n \\textbf{Variable} & \\textbf{N} & \\textbf{Mean} & \\textbf{Std. Dev.} & \\textbf{Min} & \\textbf{P25} & \\textbf{P50} & \\textbf{P75} & \\textbf{Max} \\\\\n \\midrule\n \\multicolumn{9}{l}{\\textit{Panel A: Survey skills sample}} \\\\\n AI Teaching Impact & 90 & 0.05 & 1.13 & -2.00 & -1.00 & 0.00 & 1.00 & 2.00 \\\\\n AI Workplace Skill Disruption Index & 90 & -0.42 & 2.34 & -4.54 & -2.55 & -0.06 & 1.51 & 3.36 \\\\\n Open Skill & 88 & 0.53 & 0.50 & 0.00 & 0.00 & 1.00 & 1.00 & 1.00 \\\\\n \\midrule\n \\multicolumn{9}{l}{\\textit{Panel B: Full sample with imputed data}} \\\\\n AI Teaching Impact (imputed) & 194 & 0.12 & 0.83 & -2.00 & -0.44 & 0.21 & 0.60 & 2.00 \\\\\n AI Workplace Skill Disruption Index & 194 & 0.00 & 2.26 & -4.54 & -1.77 & 0.43 & 1.71 & 4.45 \\\\\n Open Skill & 147 & 0.60 & 0.49 & 0.00 & 0.00 & 1.00 & 1.00 & 1.00 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Training for Obsolescence? The AI-Driven Education Trap", "authors": ["Andrew J. Peterson"], "url": "https://arxiv.org/abs/2508.19625v1", "attribution": "\"Training for Obsolescence? The AI-Driven Education Trap\" by Andrew J. Peterson, arXiv:2508.19625v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10896v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Corpus specification}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\n Term & Value \\\\ \\hline\n \\# of speakers & 39 \\\\\n \\# of books & 24 \\\\\n \\# of audiobooks & 74 \\\\\n \\# of book-authors & 7 \\\\\n Duration [hour] & 31.5 \\\\\n Sampling rate [Hz] & 44.1 \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "J-MAC: Japanese multi-speaker audiobook corpus for speech synthesis", "authors": ["Shinnosuke Takamichi", "Wataru Nakata", "Naoko Tanji", "Hiroshi Saruwatari"], "url": "https://arxiv.org/abs/2201.10896v1", "attribution": "\"J-MAC: Japanese multi-speaker audiobook corpus for speech synthesis\" by Shinnosuke Takamichi, Wataru Nakata, Naoko Tanji, and Hiroshi Saruwatari, arXiv:2201.10896v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.16115v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{$L^1$-errors at time $t=0.05$ s for the flow rate $Q$ and the section area $A$ under grid refinement in case of smooth initial data and Neumann boundary conditions.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|cc|cc}\n &&\\multicolumn{2}{c|}{first order scheme} & \\multicolumn{2}{c}{second order scheme} \\\\\n Quantity & num.\\ cells & $L^1$-error & EOC & $L^1$-error & EOC \\\\\n \\hline\n $Q$ & 50 & 1.931 & & 15.86 & \\\\\n & 100 & 1.161 & 0.734 & 5.818 & 1.447\\\\\n & 200 & 0.633 & 0.874 & 1.795 & 1.696 \\\\\n & 400 & 0.318 & 0.996 & 0.500 & 1.844\\\\\n & 800 & 0.147 & 1.113 & 0.136 & 1.881\\\\\n & 1600 & 0.064 & 1.209 & 0.034 & 1.980\\\\\n \\hline\n $A$ & 50 & 4.278e-3 & & 2.943e-2 & \\\\ \n & 100 & 2.624e-3 & 0.705 & 1.082e-2 & 1.444\\\\\n & 200 & 1.407e-3 & 0.900 & 3.297e-3 & 1.714\\\\\n & 400 & 6.975e-4 & 1.012 & 9.129e-4 & 1.853\\\\\n & 800 & 3.210e-4 & 1.119 & 2.469-4 & 1.886\\\\\n & 1600 & 1.384e-4 & 1.214 & 6.247e-5 & 1.983\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Lax-Friedrichs method in one-dimensional hemodynamics", "authors": ["Anika Beckers", "Niklas Kolbe"], "url": "https://arxiv.org/abs/2501.16115v1", "attribution": "\"The Lax-Friedrichs method in one-dimensional hemodynamics\" by Anika Beckers and Niklas Kolbe, arXiv:2501.16115v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.05871v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\n\\textbf{RN-AdS$_4$ black brane} & \\textbf{Plane-Poiseulle flow}\\\\\n\\hline \nradial direction, $z$ & direction perpendicular to plates, $y$\\\\\n$U(1)$ symmetry & translations in $x$\\\\\nchemical potential deformation, $\\mu$ & applied pressure gradient, $\\partial_x P$\\\\\nradial electric field, $E_z = 2 \\mu$ & mean flow in $x$-direction, $U = 1-y^2$\\\\\nnon-normal Hamiltonian, $\\mathcal{H}$ & non-normal Hamiltonian, $O_\\text{OS}$ \\\\\ncharged scalar $\\psi$ QNMs (fig. ) & no-slip stream function perturbations $\\Phi$ (fig. )\\\\\nsuperradiance (spontaneously broken $U(1)$) & turbulence (spontaneously broken $\\partial_x$)\\\\\n$\\psi$ charge, $q$ & $\\Phi$ wave number, $\\alpha$\\\\\nnet energy loss through $\\mathcal{H}^+$ & net energy loss via kinematic viscosity $\\nu$ \n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Summary of the coarse analogy between transient superradiance phenomena discussed in this work, and transient effects in perturbations to plane-Poiseulle flow.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Non-modal effects in black hole perturbation theory: Transient Superradiance", "authors": ["Javier Carballo", "Christiana Pantelidou", "Benjamin Withers"], "url": "https://arxiv.org/abs/2503.05871v2", "attribution": "\"Non-modal effects in black hole perturbation theory: Transient Superradiance\" by Javier Carballo, Christiana Pantelidou, and Benjamin Withers, arXiv:2503.05871v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.04349v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Algorithm results for a basket call option example of dimension $100$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrr}\n\\toprule\nStep\t&\tLoss\t\t& $Y_0$\t\t& Time$^*$ \\\\\n\\midrule\\midrule\n0\t&\t274.500\t&\t-0.068\t&\t57\t\\\\\n100\t&\t40.219\t&\t1.590\t&\t242\t\\\\\n1000\t&\t31.378\t&\t12.241\t&\t444\t\\\\\n2000\t&\t23.488\t&\t12.820\t&\t648\t\\\\\n3000\t&\t35.340\t&\t11.852\t&\t854\t\\\\\n4000\t&\t25.627\t&\t12.367\t&\t1065\t\\\\\n5000\t&\t21.274\t&\t12.493\t&\t1270\t\\\\\n8000\t&\t29.688\t&\t13.299\t&\t1900\t\\\\\n10000\t&\t20.917\t&\t13.028\t&\t2319\t\\\\\n15000\t&\t18.102\t&\t13.510\t&\t3372\t\\\\\n20000\t&\t17.951\t&\t13.479\t&\t4438\t\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A deep solver for BSDEs with jumps", "authors": ["Kristoffer Andersson", "Alessandro Gnoatto", "Marco Patacca", "Athena Picarelli"], "url": "https://arxiv.org/abs/2211.04349v3", "attribution": "\"A deep solver for BSDEs with jumps\" by Kristoffer Andersson, Alessandro Gnoatto, Marco Patacca, and Athena Picarelli, arXiv:2211.04349v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.00789v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n & $\\sigma$ $(s^{-1})\\times 10^{6}$ & $\\theta$ $(^\\circ)$ & $\\zeta (s^{-1})\\times 10^{6}$ \\\\ \\cline{1-4}\n Strain-only Simulation \\\\ \\cline{1-4}\nSimulated & $1.17$ & $6.68$ & N/A \\\\ \nBootstrap & $1.32 \\pm 0.365$ & $6.29 \\pm 2.47$ & N/A \\\\ \\cline{1-4}\nStrain-dominated Simulation \\\\ \\cline{1-4}\nSimulated & $1.22$ & $6.78$ & $1.61$ \\\\ \nBootstrap & $1.55 \\pm 0.459$ & $8.08 \\pm 3.43$ & $1.94 \\pm 0.572$\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Observed standard errors from simulation, and average bootstrap standard error estimates from equation~ (where $B=100$), over 100 repeated simulations, for both the strain-only and strain-dominated simulations of Figure~ over 1 day. We also provide the standard deviation of bootstrap standard error estimates across the 100 simulations, as indicated after the $\\pm$ symbol.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories", "authors": ["Sarah Oscroft", "Adam M. Sykulski", "Jeffrey J. Early"], "url": "https://arxiv.org/abs/2012.00789v2", "attribution": "\"Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories\" by Sarah Oscroft, Adam M. Sykulski, and Jeffrey J. Early, arXiv:2012.00789v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.05630v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of the original datasets used in . All attributes are numeric except for InternetAds which has 1555 binary attributes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllll}\n\\toprule\nDataset & $n$ & Outliers & Attributes & Dataset & $n$ & Outliers & Attributes \\\\ \\midrule\nAnnthyroid & 7200 & 534 & 21 & InternetAds & 3264 & 454 & 1555* \\\\\nArrhythmia & 450 & 206 & 259 & PageBlocks & 5473 & 560 & 10 \\\\\nCardiotocography & 2126 & 471 & 21 & Parkinson & 195 & 147 & 22 \\\\\nHeartDisease & 270 & 120 & 13 & Pima & 768 & 268 & 8 \\\\\nHepatitis & 80 & 13 & 19 & SpamBase & 4601 & 1813 & 57 \\\\\nStamps & 340 & 31 & 9 & Wilt & 4839 & 261 & 5 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Boundary Peeling: Outlier Detection Method Using One-Class Peeling", "authors": ["Sheikh Arafat", "Na Sun", "Maria L. Weese", "Waldyn G. Martinez"], "url": "https://arxiv.org/abs/2309.05630v2", "attribution": "\"Boundary Peeling: Outlier Detection Method Using One-Class Peeling\" by Sheikh Arafat, Na Sun, Maria L. Weese, and Waldyn G. Martinez, arXiv:2309.05630v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11844v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Decision Confidence for Asia network}\n\\begin{tabular}{llllll}\n & States & Minimum & Maximum & Mean & Standard Deviation \\\\ \\hline\n \\multirow{2}{*}{No evidence} & False & 100.00\\% & 100.00\\% & 100.00\\% & 0.00\\% \\\\\n & True & 0.00\\% & 0.00\\% & 0.00\\% & 0.00\\% \\\\ \\hline\n \\multirow{2}{*}{Evidence} & False & 0.00\\% & 100.00\\% & 16.12\\% & 36.77\\% \\\\\n & True & 0.00\\% & 100.00\\% & 83.88\\% & 36.77\\%\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Taxonomy of Explainable Bayesian Networks", "authors": ["Iena Petronella Derks", "Alta de Waal"], "url": "https://arxiv.org/abs/2101.11844v1", "attribution": "\"A Taxonomy of Explainable Bayesian Networks\" by Iena Petronella Derks and Alta de Waal, arXiv:2101.11844v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.11971v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of clustering performance of k-means on original continuous data, binary SBM, multiplex SBM and multi-DirSBM, evaluated on synthetic data with varying settings: number of nodes $n$, numbers of clusters $K$, numbers of layers $S$, network densities (high or low) and cluster overlap (yes and no). The results are presented as mean adjusted Rand index values, with standard deviation in parentheses, computed across 20 synthetic data sets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc||cccc}\n \\multicolumn{5}{c||}{Scenario settings} & \\multicolumn{3}{c}{Model} \\\\\n \\hline \\hline\n $n$ & $K$ & $S$ & Density & Overlap & k-means & SBM & Multiplex SBM & multi-DirSBM \\\\\n \\hline\n 50 & 2 & 2 & High & No & 0.949 (0.227) & 1 (0) & 1 (0) & 1 (0) \\\\\n 50 & 2 & 4 & Low & Yes & 0.937 (0.226) & 0.384 (0.358) & 0.905 (0.295) & 1 (0) \\\\\n 50 & 3 & 2 & High & Yes & 0.629 (0.201) & 0.627 (0.217) & 0.979 (0.0408) & 1 (0) \\\\\n 50 & 3 & 4 & Low & No & 0.671 (0.207) & 0.0739 (0.130) & 0.922 (0.191) & 1 (0) \\\\\n 100 & 3 & 2 & High & No & 0.785 (0.248) & 1 (0) & 1 (0) & 1 (0) \\\\\n 100 & 3 & 4 & High & Yes & 0.789 (0.265) & 1 (0) & 1 (0) & 1 (0) \\\\\n 100 & 5 & 2 & High & No & 0.755 (0.151) & 1 (0) & 1 (0) & 0.993 (0.0323) \\\\\n 100 & 5 & 4 & Low & Yes & 0.696 (0.198) & 0.0510 (0.0745) & 0.977 (0.0697) & 0.987 (0.0562) \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Multiplex Dirichlet stochastic block model for clustering multidimensional compositional networks", "authors": ["Iuliia Promskaia", "Adrian O'Hagan", "Michael Fop"], "url": "https://arxiv.org/abs/2412.11971v1", "attribution": "\"Multiplex Dirichlet stochastic block model for clustering multidimensional compositional networks\" by Iuliia Promskaia, Adrian O'Hagan, and Michael Fop, arXiv:2412.11971v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.15103v1_tex_table18.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sample Sizes for the Counterfactual Study - By Category}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\n & Dry Dog Food & Dog Hygiene & Cat Hygiene & Total \\\\\n\\midrule\nAdopters & 765 & 838 & 450 & 2425\\\\ \nNon-Adopters & 2879 & 1400 & 1165 & 6314 \\\\ \\midrule\nTotal & 3644 & 2238 & 1615 & 8739 \\\\ \\midrule\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "How does online shopping affect offline price sensitivity?", "authors": ["Shirsho Biswas", "Hema Yoganarasimhan", "Haonan Zhang"], "url": "https://arxiv.org/abs/2506.15103v1", "attribution": "\"How does online shopping affect offline price sensitivity?\" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07450v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of sentences ranked by confidence for the different NER classifiers. Total sentences in training and development data: 1331, out of which 207 (15.6\\%) contain discrepancies with adjudicated ground truth.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|llllll|}\n \\hline\n \\textbf{NER} & \\textbf{Threshold} & \\textbf{Sentences} & \\textbf{Precision}& \\textbf{Recall}&\\textbf{F-score}\\\\\n&&\\textbf{to check}&&&\\\\\n\\hline\nBaseline&All&1331 (100\\%)&15.6&\\textbf{100.0}&26.9\\\\\n\\hline\n &Top-100&7.5\\%&\\textbf{34.0}&16.4&22.1\\\\\n Bilstm-crf&Top-200&15.0\\%&32.0&30.9&31.4\\\\\n &Top-500&37.6\\%&28.2&68.1&\\textbf{39.9}\\\\\n\\hline\n&Top-100&7.5\\%& 32.0 & 15.5 & 20.8\\\\\nBioBERT&Top-200&15\\%& 27.5 & 26.6 & 27.0\\\\\n&Top-500&37.6\\%& 22.4 & 54.1 & 31.7\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Single versus Multiple Annotation for Named Entity Recognition of Mutations", "authors": ["David Martinez Iraola", "Antonio Jimeno Yepes"], "url": "https://arxiv.org/abs/2101.07450v1", "attribution": "\"Single versus Multiple Annotation for Named Entity Recognition of Mutations\" by David Martinez Iraola and Antonio Jimeno Yepes, arXiv:2101.07450v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2302.14603v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Technical Change Estimates}\n\\begin{tabular}{lcccc|rrrr}\n\\toprule\nCost & \\multicolumn{4}{c}{\\textit{Point Estimates}} \t& \\multicolumn{4}{c}{\\textit{Inference Categories, \\%}} \\\\ \nQuantiles ($\\tau$)& Mean & 1st Qu. & Median & 3rd Qu. & $\\mathbf{=0}$ & $\\ne0$ & $\\mathbf{>0}$ & $\\le0$ \\\\\n\\midrule\n$\\mathcal{Q}(0.10)$ & --0.010 & --0.026 & --0.008 & 0.006 & \\bf 63.06 & 36.94 & \\bf 9.69 & 90.31\\\\\n\t\t& (--0.022, 0.004) & (--0.039, --0.010) & (--0.020, 0.006) & (--0.007, 0.021) & & & & \\\\\n$\\mathcal{Q}(0.25)$ & --0.005 & --0.019 & --0.003 & 0.011 & \\bf 61.23 & 38.77 & \\bf 17.52 & 82.48 \\\\\n\t\t& (--0.015, 0.007) & (--0.031, --0.007) & (--0.013, 0.009) & (0.000, 0.025) & & & & \\\\\n$\\mathcal{Q}(0.50)$ & 0.005 & --0.008 & 0.007 & 0.021 & \\bf 53.71 & 46.29 & \\bf 36.91 & 63.09 \\\\\n\t\t& (--0.004, 0.015) & (--0.018, 0.001) & (--0.002, 0.018) & (0.011, 0.037) & & & & \\\\\n$\\mathcal{Q}(0.75)$ & 0.015 & 0.002 & 0.017 & 0.031 & \\bf 47.22 & 52.78 & \\bf 54.33 & 45.67 \\\\\n\t\t& (0.006, 0.026) & (--0.008, 0.013) & (0.008, 0.030) & (0.019, 0.049) & & & & \\\\\n$\\mathcal{Q}(0.90)$ & 0.021 & 0.007 & 0.022 & 0.037 & \\bf 42.91 & 57.09 & \\bf 61.50 & 38.50 \\\\\n\t\t& (0.008, 0.032) & (--0.005, 0.019) & (0.010, 0.035) & (0.022, 0.056) & & & & \\\\\n\t\\midrule\n\\multicolumn{9}{p{15.5cm}}{\\scriptsize The left panel summarizes point estimates of $TC_t(\\tau)$ with the corresponding two-sided 95\\% bias-corrected confidence intervals in parentheses. Each bank-year is classified as exhibiting technical progress [$TC_t(\\tau)>0$] vs.~non-progress [$TC_t(\\tau)\\le0$] and technical stasis [$TC_t(\\tau)=0$] vs.~non-stasis [$TC_t(\\tau)\\ne0$] using the corresponding one- and two-sided 95\\% bias-corrected confidence bounds, respectively. The right panel reports sample shares for each category and for its corresponding negating alternative. Percentage points sum up to a hundred within binary groups only. } \\\\\n\\bottomrule[1pt]\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Off-Balance Sheet Activities and Scope Economies in U.S. Banking", "authors": ["Jingfang Zhang", "Emir Malikov"], "url": "https://arxiv.org/abs/2302.14603v1", "attribution": "\"Off-Balance Sheet Activities and Scope Economies in U.S. Banking\" by Jingfang Zhang and Emir Malikov, arXiv:2302.14603v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.14399v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n Regimen / c & 0 & 0.5 & 1 & 1.5 & 2 \\\\ \n \\hline\n T1 (n = 33) vs C1 (n = 8) & 40 & 40 & 40 & 40 & 31 \\\\ \n T2 (n = 32) vs C2 (n = 8) & 27 & 17 & 3 & 1 & 0 \\\\ \n T3 (n = 24) vs C3 (n = 8) & 15 & 4 & 3 & 0 & 0 \\\\ \n T4 (n = 23) vs C4 (n = 8) & 29 & 29 & 24 & 23 & 17 \\\\ \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Pointwise $95\\%$ lower confidence limits of $N(c)$ for four vaccine regimens, with $c=0, 0.5, 1, 1.5$ and $2$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "The role of randomization inference in unraveling individual treatment effects in early phase vaccine trials", "authors": ["Zhe Chen", "Xinran Li", "Bo Zhang"], "url": "https://arxiv.org/abs/2310.14399v2", "attribution": "\"The role of randomization inference in unraveling individual treatment effects in early phase vaccine trials\" by Zhe Chen, Xinran Li, and Bo Zhang, arXiv:2310.14399v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.00629v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\t\t\\textbf{Month} & \\textbf{Dead people} & \\textbf{Population of Florence} \\\\ \\hline\n\t\tMay\t& 600\t& 59400 \\\\\n\t\tJune &\t700 &\t58700 \\\\\n\t\tJuly &\t2700 &\t56000 \\\\\n\t\tAugust\t& 5000\t& 51000 \\\\\n\t\tSeptember &\t2000\t& 49000 \\\\\n\t\tOctober\t& 600\t& 48400 \\\\\n\t\tNovember\t& 200\t& 48200 \\\\\n\t\tDecember\t& 100 &\t48100 \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Temporal distribution of plague victims and population in Florence during the year 1417 }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "The k-statistics approach to epidemiology", "authors": ["Giorgio Kaniadakis", "Mauro M. Baldi", "Thomas S. Deisboeck", "Giulia Grisolia", "Dionissios T. Hristopulos", "Antonio M. Scarfone", "Amelia Sparavigna", "Tatsuaki Wada", "Umberto Lucia"], "url": "https://arxiv.org/abs/2012.00629v1", "attribution": "\"The k-statistics approach to epidemiology\" by Giorgio Kaniadakis, Mauro M. Baldi, Thomas S. Deisboeck, Giulia Grisolia, Dionissios T. Hristopulos, Antonio M. Scarfone, Amelia Sparavigna, Tatsuaki Wada, and Umberto Lucia, arXiv:2012.00629v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2501.00535v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\n\\hline\n\\multirow{2}{*}{\\textbf{Time Slice}} & \\multicolumn{3}{c|}{\\textbf{STM Method}} & \\multicolumn{3}{c|}{\\textbf{LDA Method}} & \\multicolumn{3}{c|}{\\textbf{Tensor LDA Method}} \\\\ \\cline{2-10}\n & \\textbf{[,1]} & \\textbf{[,2]} & \\textbf{[,3]} & \\textbf{[,1]} & \\textbf{[,2]} & \\textbf{[,3]} & \\textbf{[,1]} & \\textbf{[,2]} & \\textbf{[,3]} \\\\ \\hline\n\\multicolumn{10}{|c|}{\\textbf{Slice 1 (Statistics)}} \\\\ \\hline\nTheoretical Group & 0.36 & 0.34 & 0.27 & 0.00 & 0.26 & 0.25 & 0.00 & 0.00 & 0.00 \\\\ \\hline\nApplication Group & 0.21 & 0.18 & 0.19 & 0.25 & 0.25 & 0.25 & 0.00 & 0.00 & 0.00 \\\\ \\hline\n\\textbf{Total} & \\textbf{0.57} & \\textbf{0.52} & \\textbf{0.46} & \\textbf{0.25} & \\textbf{0.51} & \\textbf{0.50} & \\textbf{0.00} & \\textbf{0.00} & \\textbf{0.00} \\\\ \\hline\n\\multicolumn{10}{|c|}{\\textbf{Slice 2 (Math)}} \\\\ \\hline\nTheoretical Group & 0.52 & 0.54 & 0.52 & 0.00 & 0.23 & 0.25 & 0.00 & 0.00 & 0.00 \\\\ \\hline\nApplication Group & 0.00 & 0.00 & 0.00 & 0.26 & 0.25 & 0.25 & 0.00 & 0.00 & 0.00 \\\\ \\hline\n\\textbf{Total} & \\textbf{0.52} & \\textbf{0.54} & \\textbf{0.52} & \\textbf{0.26} & \\textbf{0.49} & \\textbf{0.50} & \\textbf{0.00} & \\textbf{0.00} & \\textbf{0.00} \\\\ \\hline\n\\multicolumn{10}{|c|}{\\textbf{Slice 3 (Computer Science)}} \\\\ \\hline\nTheoretical Group & 0.00 & 0.00 & 0.00 & 0.00 & 0.25 & 0.26 & 0.00 & 0.00 & 0.00 \\\\ \\hline\nApplication Group & 0.45 & 0.50 & 0.49 & 0.25 & 0.25 & 0.25 & 1.00 & 1.00 & 1.00 \\\\ \\hline\n\\textbf{Total} & \\textbf{0.45} & \\textbf{0.50} & \\textbf{0.49} & \\textbf{0.25} & \\textbf{0.50} & \\textbf{0.51} & \\textbf{1.00} & \\textbf{1.00} & \\textbf{1.00} \\\\ \\hline\n\\multicolumn{10}{|c|}{\\textbf{Slice 4 (Physics)}} \\\\ \\hline\nTheoretical Group & 0.12 & 0.13 & 0.21 & 0.00 & 0.25 & 0.24 & 1.00 & 1.00 & 1.00 \\\\ \\hline\nApplication Group & 0.35 & 0.32 & 0.33 & 0.25 & 0.25 & 0.25 & 0.00 & 0.00 & 0.00 \\\\ \\hline\n\\textbf{Total} & \\textbf{0.47} & \\textbf{0.45} & \\textbf{0.54} & \\textbf{0.25} & \\textbf{0.50} & \\textbf{0.50} & \\textbf{1.00} & \\textbf{1.00} & \\textbf{1.00} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of Core-Tensor Data (Rounded to 2 Decimal Places) for STM, LDA, and Tensor LDA Methods}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Tensor Topic Modeling Via HOSVD", "authors": ["Yating Liu", "Claire Donnat"], "url": "https://arxiv.org/abs/2501.00535v1", "attribution": "\"Tensor Topic Modeling Via HOSVD\" by Yating Liu and Claire Donnat, arXiv:2501.00535v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.15365v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{$L^1$ error and EOC for test case for VAM.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|cc|cc|}\n\t\t\\hline\n\t\t& \\multicolumn{2}{c|}{Nonuniform grids} & \\multicolumn{2}{c|}{Uniform grids} & \\multicolumn{2}{c|}{Locally-uniform grids} & \\multicolumn{2}{c|}{ Random grids} \\\\\n\t\t\\hline\n\t\tGrids & $L^1$ error & EOC & $L^1$ error & EOC & $L^1$ error & EOC & $L^1$ error & EOC \\\\\n\t\t\\hline\n\t\t30 & 0.97$\\times 10^{-1}$ & 0 & 3.56$\\times 10^{-2}$ & 0 & 8.25$\\times 10^{-1}$ & 0 & 3.59$\\times 10^{-1}$ & 0 \\\\\n\t\t60 & 4.55$\\times 10^{-1}$ & 1.16 & 1.71$\\times 10^{-2}$ & 1.05 & 2.31$\\times 10^{-1}$ & 1.18 & 2.28$\\times 10^{-1}$ & 0.57\\\\\n\t\t120 & 2.03$\\times 10^{-1}$ & 1.11& 8.36$\\times 10^{-3}$ & 1.03 & 1.07$\\times 10^{-1}$ & 1.10 & 1.53$\\times 10^{-1}$ & 0.65\\\\\n\t\t240 & 9.34$\\times 10^{-2}$ & 1.09 & 4.13$\\times 10^{-3}$ & 1.01 & 4.91$\\times 10^{-2}$ & 1.11 & 9.35$\\times 10^{-2}$ & 0.72 \\\\\n\t\t480 & 4.45$\\times 10^{-2}$ & 1.06 & 2.05$\\times 10^{-3}$ & 1.01 & 2.31$\\times 10^{-2} $ & 1.08 & 5.81$\\times 10^{-2}$ & 0.87\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Convergence-rate and error analysis of sectional-volume average method for the collisional breakage equation with multi-dimensional modelling", "authors": ["Prakrati Kushwah", "Anupama Ghorai", "Jitraj Saha"], "url": "https://arxiv.org/abs/2504.15365v1", "attribution": "\"Convergence-rate and error analysis of sectional-volume average method for the collisional breakage equation with multi-dimensional modelling\" by Prakrati Kushwah, Anupama Ghorai, and Jitraj Saha, arXiv:2504.15365v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16001v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|} \n \\hline\n$ M$ & 1 & 2 & 3 \\\\ \n \\hline\n $(r_1)_M$ by () & $-1.28324$ & $-1.33149$ & $-1.32556$ \\\\\n \\hline\n $\\eta=\\left|\\frac{(r_1)_{M}-(r_1)_{ex}}{(r_1)_{ex}}\\right|$ & 0.0291 & 0.0073 & 0.0028\n\\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Approximation of Invariant Solutions to the Nonlinear Filtration Equation by Modified Pade Approximants", "authors": ["Sergii Skurativskyi", "Sergiy Mykulyak", "Inna Skurativska"], "url": "https://arxiv.org/abs/2504.16001v1", "attribution": "\"Approximation of Invariant Solutions to the Nonlinear Filtration Equation by Modified Pade Approximants\" by Sergii Skurativskyi, Sergiy Mykulyak, and Inna Skurativska, arXiv:2504.16001v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13301v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n \\textbf{Index} & \\textbf{Test 1 Eigenvalues} & \\textbf{Test 2 Eigenvalues} \\\\ \\hline\n $\\lambda_1$ & \\(0.999999135\\) & \\(1.00000083\\) \\\\ \\hline\n $\\lambda_2$ & \\(0.993172638\\) & \\(0.99359029\\) \\\\ \\hline\n $\\lambda_3$ & \\(0.892089359\\) & \\(0.86772991\\) \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Triple-well system: First three eigenvalues of the Koopman generator estimated using SDMD.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Data-Driven Framework for Koopman Semigroup Estimation in Stochastic Dynamical Systems", "authors": ["Yuanchao Xu", "Kaidi Shao", "Isao Ishikawa", "Yuka Hashimoto", "Nikos Logothetis", "Zhongwei Shen"], "url": "https://arxiv.org/abs/2501.13301v4", "attribution": "\"A Data-Driven Framework for Koopman Semigroup Estimation in Stochastic Dynamical Systems\" by Yuanchao Xu, Kaidi Shao, Isao Ishikawa, Yuka Hashimoto, Nikos Logothetis, and Zhongwei Shen, arXiv:2501.13301v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.17026v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n $N$ & Singularities of Shifted Exponential Series& $\\rho_{y}$ \\\\\n \\hline\n $400$ & $1.048507030362304\\pm 0.016261862764783i$ & $1.048633129793141$ \\\\\n \\hline\n $500$ & $1.048551119968439\\pm 0.013042852480336i$ & $1.048632236385994$\\\\\n \\hline\n $600$ & $1.048575315780594\\pm 0.010887631559347i$ & $1.048631838819204$ \\\\\n \\hline\n $700$ & $1.048590010475455\\pm 0.009343675576619i$ & $1.048631639014480$\\\\\n \\hline\n $800$ & $1.048599783127986\\pm 0.008184349853659i$ & $1.048631722178280$ \\\\\n \\hline\n $900$ & $1.048615701318654\\pm 0.007362892510257i$ & $1.048641550406110$ \\\\\n \\hline\n $1000$ & $1.037911784988708\\pm 0.120142550117864i$ & $1.044842143946764$ \\\\\n \\hline\n $1100$ & $1.035149423509403\\pm 0.108874252051280i$ & $1.040859227634350$ \\\\\n \\hline\n $1200$ & $1.032233792801933\\pm 0.104922871742221i$ & $1.037552606867189$ \\\\\n \\hline\n $1300$ & $1.029749762322831\\pm 0.101594444326236i$ & $1.034749246978167$ \\\\\n \\hline\n $1400$ & $1.028040214339160\\pm 0.094141154453040i$ & $1.032341629142338$ \\\\\n \\hline\n $1500$ & $1.026134314996485\\pm 0.092001883451537i$ & $1.030250444781236$ \\\\\n \\hline\n $1600$ & $1.026031112794913\\pm 0.069980225083115i$ & $1.028414836690842$ \\\\\n \\hline\n $1700$ & $1.023578688961388\\pm 0.080932643408311i$ & $1.026773307631715$ \\\\\n \\hline\n $1800$ & $1.021301957127695\\pm 0.090615227338533i$ & $1.025314004126772$ \\\\\n \\hline\n $1900$ & $1.020290836269810\\pm 0.085701266977750i$ & $1.023883830196444$ \\\\\n \\hline\n $2000$ & $1.011907849593462\\pm 0.148133486304518i$ & $1.022693026197791$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{This is a table of approximate singularities found from the zeroes of the polynomial of the reciprocal of the $y$ series for the Bubonic plague example with $N$ varying from $N\\boldsymbol{=} 400$ to $N\\boldsymbol{=} 2000$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An Exact SIR Series Solution and an Exploration of the Related Parameter Space", "authors": ["Daniel P Hobbs"], "url": "https://arxiv.org/abs/2504.17026v1", "attribution": "\"An Exact SIR Series Solution and an Exploration of the Related Parameter Space\" by Daniel P Hobbs, arXiv:2504.17026v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.04541v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n\\hline\n Years & Absolute price change in per cent \\\\\\hline\n 2021--2022 & 432.41\\\\\\hline\n 2022--2023 & 45.20\\\\\\hline\n 2023--2024 & 1.06\\\\\\hline\n 2024-2025 & 90.00\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Coins median annual percentage price change.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Finance-Grounded Optimization For Algorithmic Trading", "authors": ["Kasymkhan Khubiev", "Mikhail Semenov", "Irina Podlipnova"], "url": "https://arxiv.org/abs/2509.04541v1", "attribution": "\"Finance-Grounded Optimization For Algorithmic Trading\" by Kasymkhan Khubiev, Mikhail Semenov, and Irina Podlipnova, arXiv:2509.04541v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2001.02757v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{DCI Formats in NR for PDSCH scheduling}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c||} \n\t\t\\hline\n\t\tFormat & Usage \\\\\n\t\t\\hline\\hline\n\t\tFormat 1$$0 & used for the scheduling of PDSCH in one DL cell \\\\ \n\t\tFormat 1$$1 & used for the scheduling of PDSCH in one cell\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "On the Performance of PDCCH in LTE and 5G New Radio", "authors": ["Hongzhi Chen", "De Mi", "Manuel Fuentes", "Eduardo Garro", "Jose Luis Carcel", "Belkacem Mouhouche", "Pei Xiao", "Rahim Tafazolli"], "url": "https://arxiv.org/abs/2001.02757v1", "attribution": "\"On the Performance of PDCCH in LTE and 5G New Radio\" by Hongzhi Chen, De Mi, Manuel Fuentes, Eduardo Garro, Jose Luis Carcel, Belkacem Mouhouche, Pei Xiao, and Rahim Tafazolli, arXiv:2001.02757v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10090v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|}\n \\hline\n \\bfseries Static Metric & \\bfseries Correlation Coefficient \\\\ \n \\hline\n Number of Bytecode Instructions (NBI) &\t-0.61652431 \\\\ \n \\hline\n Response for a Class (RFC)&\t-0.603928734 \\\\ \\hline\n Lines of Code (LOC)&\t-0.58334397 \\\\ \\hline\n Weighted Methods per Class (WMC)&\t-0.56027992 \\\\ \\hline\n Number of Method Calls (NMC)&\t-0.55492337 \\\\ \\hline\n Number of Method Calls External (NMCE)&\t-0.512557727 \\\\ \\hline\n Lines of Comment (LOCCOM)&\t-0.50032704 \\\\ \\hline\n Cohesion Among Methods in class (CAM)&\t0.521969871 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\fontsize{10pt}{11pt}\\selectfont{\\itshape{The Spearman correlation of static metrics with the mutation score}}}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Investigating Software Testability and Test cases Effectiveness", "authors": ["Mamdouh Alenezi"], "url": "https://arxiv.org/abs/2201.10090v1", "attribution": "\"Investigating Software Testability and Test cases Effectiveness\" by Mamdouh Alenezi, arXiv:2201.10090v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10643v12_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimation performance by benchmark algorithms under the default scenario}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n & \\multicolumn{3}{c}{\\textbf{Algorithms}} \\\\\n\\hline\n\\textbf{Metrics} & \\textbf{Binary} & \\textbf{TCS} & \\textbf{SNN} \\\\\n\\hline\n\\textbf{AUROC} & \\textbf{0.96 (0.816,1.106)} & 0.82 (0.729,0.914) & 0.85 (0.725,0.983) \\\\\n\\textbf{Concordance} & 0.76 (0.730,0.799) & \\textbf{0.90 (0.852,0.947)} & 0.86 (0.798,0.913) \\\\\n\\hline\n\\textbf{Bias (IPW)} & 0.65 (0.625,0.675) & \\textbf{-} & 0.15 (0.133,0.167) \\\\\n\\textbf{Bias (TMLE)} & 0.63 (0.576,0.684) & \\textbf{-} & 0.14 (0.129,0.151) \\\\\n\\textbf{Bias (ATE)} & - & \\textbf{0.10 (0.061,0.136)} & - \\\\\n\\textbf{Bias (ITE)} & 0.75 (0.706,0.794) & \\textbf{0.10 (0.064,0.137)} & 0.43 (0.401,0.459)\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Causal inference for observational longitudinal studies using deep survival models", "authors": ["Jie Zhu", "Blanca Gallego"], "url": "https://arxiv.org/abs/2101.10643v12", "attribution": "\"Causal inference for observational longitudinal studies using deep survival models\" by Jie Zhu and Blanca Gallego, arXiv:2101.10643v12, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.11427v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\nModel & Metric & $t=0$ & $t=1$ & $t=2$ & $t=3$ \\\\\n\\hline\n\\midrule\n\\multirow{2}{*}{FlowKac} & $D_{\\text{KL}}$ & 1.60e-2 & \\textbf{8.90e-3} & \\textbf{5.20e-3} & \\textbf{3.19e-2} \\\\\n& $L^2$ & 6.07e-2 & \\textbf{4.70e-2} & \\textbf{4.74e-2} & \\textbf{1.37e-1} \\\\\n\\midrule\n\\multirow{2}{*}{PINN} & $D_{\\text{KL}}$ & \\textbf{1.00e-4} & 4.94e-2 & 2.94e-1 & 1.68 \\\\\n& $L^2$ & \\textbf{5.70e-3} & 2.10e-1 & 5.18e-1 & 1.11 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Performance comparison of FlowKac and PINN models on the 2d Ornstein-Uhlenbeck process using KL divergence ($D_{\\text{KL}}$) and $L^2$ error metrics.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "FlowKac: An Efficient Neural Fokker-Planck solver using Temporal Normalizing Flows and the Feynman-Kac Formula", "authors": ["Naoufal El Bekri", "Lucas Drumetz", "Franck Vermet"], "url": "https://arxiv.org/abs/2503.11427v2", "attribution": "\"FlowKac: An Efficient Neural Fokker-Planck solver using Temporal Normalizing Flows and the Feynman-Kac Formula\" by Naoufal El Bekri, Lucas Drumetz, and Franck Vermet, arXiv:2503.11427v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07434v5_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n \\toprule[1pt]\n Axial Attention& + SE & + Our Channelization \\\\\n \\midrule[0.5pt]\n\t\t\\midrule[0.5pt]\n 50.27($\\pm$0.2) & 50.37($\\pm$0.2)& 51.06($\\pm$0.2) \\\\\n \\bottomrule[1pt]\n \\end{tabular}\n\\end{adjustbox}\n\\caption{ Result comparison between axial attention, axial attention + SE and channelized axial attention. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation", "authors": ["Ye Huang", "Di Kang", "Wenjing Jia", "Xiangjian He", "Liu Liu"], "url": "https://arxiv.org/abs/2101.07434v5", "attribution": "\"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation\" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.04809v5_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccclccccccccc}\n\\cmidrule{1-10} \\cmidrule{12-21}\n\\multicolumn{10}{c}{Stage 1 Regression} & & \\multicolumn{10}{c}{Stage 2 Regression} \\\\ \\cmidrule{1-10} \\cmidrule{12-21} \n & \\multicolumn{3}{c}{Supervised} & & \\multicolumn{3}{c}{Semi-Supervised} & & & & \\multicolumn{4}{c}{Supervised} & & \\multicolumn{3}{c}{Semi-Supervised} & & \\\\ \\cmidrule{2-4} \\cmidrule{6-8} \\cmidrule{12-15} \\cmidrule{17-19}\nParameter & Estimate & SE & P-val & & Estimate & SE & P-val & & RE & & Parameter & Estimate & SE & P-val & & Estimate & SE & P-val & & RE \\\\ \\cmidrule{1-4} \\cmidrule{6-8} \\cmidrule{10-10} \\cmidrule{12-15} \\cmidrule{17-19} \\cmidrule{21-21} \nIntercept & \\textbf{0.424} & \\textbf{0.082} & \\textbf{0.00} & & \\textbf{0.518} & \\textbf{0.028} & \\textbf{0.00} & & 2.937 & & $Y_1$ & \\textbf{0.37} & \\textbf{0.11} & \\textbf{0.00} & & \\textbf{0.55} & \\textbf{0.05} & \\textbf{0.00} & & 2.08 \\\\ \n Female & -0.237 & 0.167 & 0.16 & & \\textbf{-0.184} & \\textbf{0.067} & \\textbf{0.007} & & 2.514 & & Intercept & 0.08 & 0.06 & 0.17 & & 0.04 & 0.02 & 0.14 & & 2.40 \\\\ \n Age & 0.155 & 0.088 & 0.081 & & \\textbf{0.18} & \\textbf{0.034} & \\textbf{0.00} & & 2.588 & & Female & -0.01 & 0.10 & 0.92 & & -0.00 & 0.05 & 0.98 & & 2.21 \\\\ \n Charlson Score & 0.006 & 0.072 & 0.929 & & -0.047 & 0.026 & 0.075 & & 2.776 & & Age & 0.05 & 0.06 & 0.35 & & \\textbf{0.07} & \\textbf{0.02} & \\textbf{0.00} & & 2.33 \\\\ \n Prior anti-TNF & -0.038 & 0.06 & 0.524 & &\\textbf{ -0.085} & \\textbf{0.019} & \\textbf{0.00} & & 3.177 & & Charlson Score & 0.04 & 0.04 & 0.33 & & \\textbf{0.06} & \\textbf{0.02} & \\textbf{0.01} & & 2.06 \\\\ \n Perianal & \\textbf{0.138} & \\textbf{0.06} & \\textbf{0.022} & & \\textbf{0.179} & \\textbf{0.022} & \\textbf{0.00} & & 2.688 & & Prior anti-TNF & -0.05 & 0.05 & 0.29 & & \\textbf{-0.09} & \\textbf{0.02} & \\textbf{0.00} & & 2.39 \\\\ \n Bleeding & 0.049 & 0.08 & 0.54 & & 0.058 & 0.03 & 0.055 & & 2.675 & & Perianal & -0.01 & 0.04 & 0.80 & & \\textbf{-0.03} & \\textbf{0.02} & \\textbf{0.06} & & 2.31 \\\\ \n A1 & 0.163 & 0.488 & 0.739 & & 0.148 & 0.206 & 0.473 & & 2.374 & & Bleeding & -0.04 & 0.05 & 0.49 & & -0.03 & 0.03 & 0.29 & & 2.14 \\\\ \n Female$\\times A_1$ & 0.168 & 0.696 & 0.81 & & -0.042 & 0.287 & 0.886 & & 2.424 & & A1 & 0.11 & 0.25 & 0.67 & & 0.03 & 0.10 & 0.74 & & 2.60 \\\\ \n Age$\\times A_1$ & -0.177 & 0.264 & 0.503 & & \\textbf{-0.278} & \\textbf{0.109} & \\textbf{0.013} & & 2.418 & & Abscess$_2$ & 0.06 & 0.04 & 0.16 & & \\textbf{0.05} & \\textbf{0.01} & \\textbf{0.00} & & 2.68 \\\\ \n Charlson Score$\\times A_1$ & 0.136 & 0.391 & 0.728 & & 0.195 & 0.178 & 0.276 & & 2.194 & & Fistula$_2$ & 0.02 & 0.05 & 0.67 & & 0.01 & 0.02 & 0.62 & & 2.33 \\\\ \n Perianal$\\times A_1$ & -0.113 & 0.226 & 0.618 & & -0.019 & 0.08 & 0.808 & & 2.838 & & Female$\\times A_1$ & 0.13 & 0.38 & 0.74 & & 0.17 & 0.16 & 0.30 & & 2.37 \\\\ \n Bleeding$\\times A_1$ & 0.262 & 0.364 & 0.474 & & 0.127 & 0.161 & 0.431 & & 2.267 & & Age$\\times A_1$ & -0.02 & 0.12 & 0.88 & & -0.09 & 0.06 & 0.17 & & 1.94 \\\\ \n & & & & & & & & & & & Charlson Score$\\times A_1$ & -0.02 & 0.16 & 0.89 & & 0.04 & 0.07 & 0.55 & & 2.19 \\\\ \n & & & & & & & & & & & Perianal$\\times A_1$ & -0.14 & 0.09 & 0.15 & & \\textbf{-0.17} & \\textbf{0.04} & \\textbf{0.00} & & 2.34 \\\\ \n & & & & & & & & & & & Bleeding$\\times A_1$ & 0.13 & 0.20 & 0.51 & & 0.03 & 0.09 & 0.76 & & 2.17 \\\\ \n & & & & & & & & & & & A2 & 0.07 & 0.17 & 0.69 & & \\textbf{0.22} & \\textbf{0.07} & \\textbf{0.00} & & 2.55 \\\\ \n & & & & & & & & & & & Female$\\times A_2$ & -0.39 & 0.28 & 0.16 & & \\textbf{-0.51} & \\textbf{0.11} & \\textbf{0.00} & & 2.53 \\\\ \n & & & & & & & & & & & Age$\\times A_2$ & 0.09 & 0.10 & 0.40 & & \\textbf{0.15} & \\textbf{0.04} & \\textbf{0.00} & & 2.27 \\\\ \n & & & & & & & & & & & Charlson Score$\\times A_2$ & 0.01 & 0.07 & 0.84 & & -0.03 & 0.03 & 0.42 & & 2.08 \\\\ \n & & & & & & & & & & & Perianal$\\times A_2$ & \\textbf{0.20} & \\textbf{0.09} &\\textbf{ 0.04} & & \\textbf{0.23} & \\textbf{0.04} &\\textbf{ 0.00} & & 2.23 \\\\ \n & & & & & & & & & & & Bleeding$\\times A_2$ & 0.03 & 0.08 & 0.77 & & 0.02 & 0.04 & 0.49 & & 2.34 \\\\ \n & & & & & & & & & & & Abscess$_2\\times A_2$ & -0.13 & 0.07 & 0.06 & & \\textbf{-0.09} & \\textbf{0.03} & \\textbf{0.00} & & 2.31 \\\\ \n & & & & & & & & & & & Fistula$_2\\times A_2$ & -0.04 & 0.06 & 0.56 & & -0.03 & 0.03 & 0.36 & & 2.17 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results of Inflammatory Bowel Disease data set, for first and second stage regressions. Fully supervised $Q$-learning is shown on the left and semi-supervised is shown on the right. Last columns in the panels show relative efficiency (RE) defined as the ratio of standard errors of the semi-supervised vs. supervised method, RE greater than one favors semi-supervised. Significant coefficients at the 0.05 level are in bold.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Semi-Supervised Off Policy Reinforcement Learning", "authors": ["Aaron Sonabend-W", "Nilanjana Laha", "Ashwin N. Ananthakrishnan", "Tianxi Cai", "Rajarshi Mukherjee"], "url": "https://arxiv.org/abs/2012.04809v5", "attribution": "\"Semi-Supervised Off Policy Reinforcement Learning\" by Aaron Sonabend-W, Nilanjana Laha, Ashwin N. Ananthakrishnan, Tianxi Cai, and Rajarshi Mukherjee, arXiv:2012.04809v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11469v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{STT Performance Test Results with VOTE400}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\\hline\nRegion(\\texttt{G}) & Gender & Acc. \\texttt{M} (\\%) & Acc. \\texttt{C} (\\%) \\\\ \\hline \\hline\nSE & M & 90 & 90 \\\\ \\hline\nSE & F & 90 & 80 \\\\ \\hline\nGW & M & 80 & 90 \\\\ \\hline\nGW & F & 90 & 80 \\\\ \\hline\nDG & M & 70 & 80 \\\\ \\hline\nDG & F & 90 & 80 \\\\ \\hline\nGN & M & 90 & 80 \\\\ \\hline\nGN & F & 80 & 80 \\\\ \\hline\nJN & M & 70 & 50 \\\\ \\hline\nJN & F & 80 & 60 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "VOTE400(Voide Of The Elderly 400 Hours): A Speech Dataset to Study Voice Interface for Elderly-Care", "authors": ["Minsu Jang", "Sangwon Seo", "Dohyung Kim", "Jaeyeon Lee", "Jaehong Kim", "Jun-Hwan Ahn"], "url": "https://arxiv.org/abs/2101.11469v1", "attribution": "\"VOTE400(Voide Of The Elderly 400 Hours): A Speech Dataset to Study Voice Interface for Elderly-Care\" by Minsu Jang, Sangwon Seo, Dohyung Kim, Jaeyeon Lee, Jaehong Kim, and Jun-Hwan Ahn, arXiv:2101.11469v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.08321v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rccc}\n\t\t\\toprule\n\t\t& MOTA & IDF1 & HOTA \\\\\n\t\t\\midrule\n\t\tFRCNN detector & 0.016 & 0.017 & 0.119 \\\\\n\t\tTracktor++v2 tracker & 0.918 & 0.774 & 0.789 \\\\\n\t\tBoT\\_SORT tracker & 1 & 1 & 0.921 \\\\\n\t\tGMPHDOGM17 tracker & 1 & 1 & 0.942 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "TGOSPA Metric Parameters Selection and Evaluation for Visual Multi-object Tracking", "authors": ["Jan Krejčí", "Oliver Kost", "Ondřej Straka", "Yuxuan Xia", "Lennart Svensson", "Ángel F. García-Fernández"], "url": "https://arxiv.org/abs/2412.08321v3", "attribution": "\"TGOSPA Metric Parameters Selection and Evaluation for Visual Multi-object Tracking\" by Jan Krejčí, Oliver Kost, Ondřej Straka, Yuxuan Xia, Lennart Svensson, and Ángel F. García-Fernández, arXiv:2412.08321v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.19696v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Slopes for $2^{nd}$ to $7^{th}$ rows according to Algorithm 1 and $k$-means clustering, with data from the last $10^4$ elements and $k$ is the maximum in the Silhouette curve}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrr}\n\t\t\\hline\n\tslopes\t& $2^{nd}$ row & $3^{th}$ row & $4^{th}$ row & $5^{th}$ row & $6^{th}$ row & $7^{th}$ row \\\\ \n\t\t\\hline\n\t\t1 & 1 & 0.6180338 & 0.6157357 & 0.4897040 & 0.3786866 & 0.4033740 \\\\ \n\t\t2 & & 1.6180343 & 1.5376674 & 0.9215673 & 0.9111673 & 0.8630458 \\\\ \n\t\t3 & & & 1.9331150 & 1.4116338 & 1.3786555 & 1.3255402 \\\\ \n\t\t4 & & & & 1.9215719 & 1.8712844 & 1.8630575 \\\\ \n\t\t5 & & & & 1.9823043 & 1.9096087 & 1.8959612 \\\\ \n\t\t6 & & & & & 2.2667333 & 2.1209285 \\\\ \n\t\t7 & & & & & 2.4143427 & 2.1577673 \\\\ \n\t\t8 & & & & & 2.4750619 & 2.2913921 \\\\ \n\t\t9 & & & & & 2.4750933 & 2.2914530 \\\\ \n\t\t10 & & & & & & 2.2949202 \\\\ \n\t\t11 & & & & & & 2.3556537 \\\\ \n\t\t12 & & & & & & 2.7247096 \\\\ \n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Fibonacci-like partitions and their associated piecewise-defined permutations", "authors": ["Jon Asier Bárcena-Petisco", "Luis Martínez", "María Merino", "Juan Manuel Montoya", "Antonio Vera-López"], "url": "https://arxiv.org/abs/2503.19696v1", "attribution": "\"Fibonacci-like partitions and their associated piecewise-defined permutations\" by Jon Asier Bárcena-Petisco, Luis Martínez, María Merino, Juan Manuel Montoya, and Antonio Vera-López, arXiv:2503.19696v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15554v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Reagent prices in € mol$^{-1}$ (for reactants and reagents) or € L$^{-1}$ (for solvents) used in this study, as obtained from \\textit{Sigma-Aldrich} and \\textit{ABCR} on July 15, 2024.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n \\toprule\n Reagent & Price \\\\ \\midrule \\midrule\n $\\alpha$-Methylbenzylamine & 31 € mol$^{-1}$ \\\\\n Benzyl bromide & 58 € mol$^{-1}$ \\\\\n {[1,1$'$-Bis(di-tert-butylphosphino)ferrocen]}dichlorpalladium(II) (Pd(dtbpf)Cl$_2$) & 131,700 € mol$^{-1}$ \\\\\n Chloroform & 104 € L$^{-1}$ \\\\\n 2,4-Difluoronitrobenzene & 164 € mol$^{-1}$ \\\\\n Ethanol & 55 € L$^{-1}$ \\\\\n Morpholine & 21 € mol$^{-1}$ \\\\\n Potassium phosphate & 20 € mol$^{-1}$ \\\\\n 4-Trifluoromethylphenylboronic acid pinacol ester (CF$_3$-Ph-Bpin) & 940 € mol$^{-1}$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives", "authors": ["Mohammad Haddadnia", "Leonie Grashoff", "Felix Strieth-Kalthoff"], "url": "https://arxiv.org/abs/2501.15554v1", "attribution": "\"BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives\" by Mohammad Haddadnia, Leonie Grashoff, and Felix Strieth-Kalthoff, arXiv:2501.15554v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.10896v4_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll} \\hline \n Scaled variables & Unit\\\\ \\hline\n $r_\\pm=r^*_\\pm/R_\\pm$ & dimensionless \\\\\n $x=x^*/L$ & dimensionless\\\\\n $c_\\pm=c^*_\\pm/c_{\\pm,\\mathrm{max}}$ & dimensionless \\\\\n $c_\\mathrm{e}=c_\\mathrm{e}^*/c_{\\mathrm{e},0}$ & dimensionless \\\\$N_\\mathrm{e}=N_\\mathrm{e}^*/(c_{\\mathrm{e},0}L)$ & 1/s \\\\\n $j_\\pm=j^*_\\pm/(R_\\pm c_{\\pm,\\mathrm{max}})$ & 1/s\\\\\n $i_{0,\\pm}=i^*_{0,\\pm}/(F R_\\pm c_{\\pm,\\mathrm{max}})$ & 1/s\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Scaling of the variables of the full model. The variables of the full models are denoted with an asterisk in superscript and the new variables without. Other variables are kept the same as in the full dimensional model.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Physics-based battery model parametrisation from impedance data", "authors": ["Noël Hallemans", "Nicola E. Courtier", "Colin P. Please", "Brady Planden", "Rishit Dhoot", "Robert Timms", "S. Jon chapman", "David Howey", "Stephen R. Duncan"], "url": "https://arxiv.org/abs/2412.10896v4", "attribution": "\"Physics-based battery model parametrisation from impedance data\" by Noël Hallemans, Nicola E. Courtier, Colin P. Please, Brady Planden, Rishit Dhoot, Robert Timms, S. Jon chapman, David Howey, and Stephen R. Duncan, arXiv:2412.10896v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.02584v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\\hline\n Setting & $a_j = a_k$ & $a_j \\neq a_k$ \\\\ \\hline\n $\\nu_j = \\nu_k = 1/2$ & Laplace & asymmetric Laplace \\\\ %\\hline\n $\\nu_j = \\nu_k$ & Bessel function & Bessel function \\\\ %\\hline\n $\\nu_j \\neq \\nu_k$ & gamma difference & gamma difference \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Multivariate Matérn Models -- A Spectral Approach", "authors": ["Drew Yarger", "Stilian Stoev", "Tailen Hsing"], "url": "https://arxiv.org/abs/2309.02584v2", "attribution": "\"Multivariate Matérn Models -- A Spectral Approach\" by Drew Yarger, Stilian Stoev, and Tailen Hsing, arXiv:2309.02584v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.13663v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Methods used for comparison. Eq. refers to the equation in the paper describing the model; Tier classifies the model with respect tiers; EF specifies the source of the emission factor.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\t\t\t\\toprule\n\t\t\t& Method 1 & Method 2 & Method 3 & Method 4 & Method 5 & Method 6 \\\\ \\hline\n\t\t\tEq. & () & () & () & () & () & ()\\\\\n\t\t\tTier & 1 & 2 & 1 & 1 & 2 & 3 \\\\\n\t\t\tEF & IPCC & ISPRA & IPCC & IPCC & ISPRA & IPSRA\\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Accounting carbon emissions from electricity generation: a review and comparison of emission factor-based methods", "authors": ["Marina Bertolini", "Pierdomenico Duttilo", "Francesco Lisi"], "url": "https://arxiv.org/abs/2411.13663v1", "attribution": "\"Accounting carbon emissions from electricity generation: a review and comparison of emission factor-based methods\" by Marina Bertolini, Pierdomenico Duttilo, and Francesco Lisi, arXiv:2411.13663v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.17911v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dice scores for WM, CSF, and GM segmentations in masked areas on the HCP dataset, evaluated using FSL FAST and FastSurfer.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccc}\n\\toprule\n\\textbf{Tools} & \\textbf{Models} & \\textbf{WM} & \\textbf{CSF} & \\textbf{GM} \\\\\n\\midrule\n\\multirow{5}{*}{\\textbf{FAST}} \n& Autoencoder 3D & $0.755\\pm0.066$ & $0.444\\pm0.079$ & $0.656\\pm0.055$ \\\\\n & Pix2pix 3D & $0.787\\pm0.053$ & $0.538\\pm0.060$ & $0.689\\pm0.053$ \\\\\n & DDPM 3D & $0.776\\pm0.066$ & \n $0.475\\pm0.070$ & $0.656\\pm0.046$ \\\\\n & Make-A-Volume & $0.780\\pm0.053$ & $0.537\\pm0.066$ & $0.692\\pm0.043$ \\\\\n & Ours & \\textbf{0.827$\\pm$0.047} & \\textbf{0.627$\\pm$0.059} & \\textbf{0.742$\\pm$0.038} \\\\\n\\midrule\n\\multirow{5}{*}{\\textbf{FastSurfer}} \n& Autoencoder 3D & ${0.742\\pm0.069}$ & ${0.412\\pm0.178}$ & ${0.531\\pm0.096}$ \\\\\n & Pix2pix 3D & ${0.773\\pm0.056}$ & ${0.617\\pm0.169}$ & ${0.587\\pm0.094}$ \\\\\n \n & DDPM 3D & ${0.769\\pm0.064}$ & ${0.640\\pm0.140}$ & ${0.653\\pm0.066}$ \\\\\n & Make-A-Volume & ${0.773\\pm0.061}$ & ${0.676\\pm0.136}$ & ${0.672\\pm0.055}$ \\\\\n & Ours & \\textbf{0.829$\\pm$0.051} & \\textbf{0.780$\\pm$0.103} & \\textbf{0.737$\\pm$0.055} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Hierarchical Diffusion Framework for Pseudo-Healthy Brain MRI Inpainting with Enhanced 3D Consistency", "authors": ["Dou Hoon Kwark", "Shirui Luo", "Xiyue Zhu", "Yudu Li", "Zhi-Pei Liang", "Volodymyr Kindratenko"], "url": "https://arxiv.org/abs/2507.17911v1", "attribution": "\"Hierarchical Diffusion Framework for Pseudo-Healthy Brain MRI Inpainting with Enhanced 3D Consistency\" by Dou Hoon Kwark, Shirui Luo, Xiyue Zhu, Yudu Li, Zhi-Pei Liang, and Volodymyr Kindratenko, arXiv:2507.17911v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.15435v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllll}\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multicolumn{7}{c}{$\\Delta\\ln(s_{it})$} \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t& (1) & (2) & (3) & (4) & (5) &(6)\\\\\n\t\t\t\n\t\t\t& IV & IV & IV & IV & MG & LS\\\\\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t$\\Delta\\ln(y_{it})$ & 2.50*** &2.43***& 2.47*** & 2.12*** & 1.82*** & 1.52***\\\\\n\t\t\t\n\t\t\t& (0.81) & (0.82) & (0.83) & (0.71) & (0.20) & (0.15)\\\\\n\t\t\t\n\t\t\t\n\t\t\t$\\Delta\\ln(\\lambda_{it})$ & &-0.21**& -0.22** & -1.23* & -0.98** & -0.26***\\\\\n\t\t\t\n\t\t\t& & (0.09) & (0.10) & (0.68) & (0.42) & (0.10)\\\\\n\t\t\t\n\t\t\t\n\t\t\t$\\Delta\\ln(s_{it-1})$ & & & 0.19*** & 0.20*** & & \\\\\n\t\t\t\n\t\t\t& & & (0.07) & (0.07) & &\\\\\n\t\t\t\n\t\t\t\n\t\t\tKleibergen-Paap & 19.60 & 19.40 & 9.96& 3.70 & . & . \\\\\n\t\t\t\n\t\t\tF-stat & & & & & & \\\\\n\t\t\t\n\t\t\tCragg-Donald F-stat & 283.67 & 274.86 & 136.37& 14.04 & . & . \\\\\n\t\t\t\n\t\t\tEndogenous & $\\Delta\\ln(y_{it})$ & $\\Delta\\ln(y_{it})$ & $\\Delta\\ln(y_{it})$, $\\Delta\\ln(s_{it-1})$ & $\\Delta\\ln(y_{it})$, $\\Delta\\ln(\\lambda_{it})$, &. &.\\\\\n\t\t\t\n\t\t\tRegressors & & & & $\\Delta\\ln(s_{it-1})$& &\\\\\n\t\t\t\n\t\t\t\n\t\t\tInstruments & $OPS_{it}$ & $OPS_{it}$ & $OPS_{it}$, $\\ln(s_{it-2})$ & $OPS_{it}$, $\\ln(\\lambda_{it-1})$, &. &.\\\\\n\t\t\t\n\t\t\t& & & &$\\ln(s_{it-2})$ & &\\\\\n\t\t\t\n\t\t\tCountry FE & Yes & Yes & Yes&Yes & Yes & Yes \\\\\n\t\t\t\n\t\t\t\n\t\t\tYear FE & Yes & Yes & Yes & Yes& Yes & Yes \\\\\n\t\t\t\n\t\t\tObservations & 3781 & 3781 & 3781 &3781&3781 & 3781 \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Transitional Dynamics of the Saving Rate and Economic Growth", "authors": ["Markus Brueckner", "Tomoo Kikuchi", "George Vachadze"], "url": "https://arxiv.org/abs/2012.15435v3", "attribution": "\"Transitional Dynamics of the Saving Rate and Economic Growth\" by Markus Brueckner, Tomoo Kikuchi, and George Vachadze, arXiv:2012.15435v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10647v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative results on validation leaderboard}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\hline\n & & \\multicolumn{2}{c}{Dice$\\uparrow$ (\\%)} & \\multicolumn{2}{c}{ASSD$\\downarrow$ (mm)} \\\\ \\cline{3-6} \n\\# & Method & VS & Cochlea & VS & Cochlea \\\\ \\hline\n1 & nnU-Net (2D) & 72.90±22.77 & 68.14±12.62 & 1.32±2.83 & 1.27±3.65 \\\\\n2 & nnU-Net (3D) & 71.77±27.29 & 79.95±4.12 & 2.71±5.09 & 0.20±0.07 \\\\\n3 & U-Net (2.5D) & 74.81±22.14 & 80.39±3.18 & 1.37±2.63 & 0.22±0.05 \\\\\n4 & Mean teacher & 80.81±9.09 & 80.50±6.36 & 0.63±0.30 & 0.20±0.06 \\\\\n5 & \\#4 + self-training & 80.73±5.21 & 81.16±4.25 & 0.69±0.35 & 0.19±0.04 \\\\\n6 & \\#4 + fine-tuning & 81.66±10.5 & 81.24±3.52 & 0.61±0.37 & 0.20±0.06 \\\\\n7 & CUTSeg & 81.00±7.50 & 81.45±3.53 & 0.66±0.29 & 0.19±0.06 \\\\\n8 & \\#4 $\\circ$ \\#6 & 82.08±4.77 & 81.46±3.51 & 0.58±0.31 & 0.19±0.06 \\\\\n9 & \\#4 $\\circ$ \\#6 $\\circ$ \\#7 & \\textbf{83.02±7.72} & \\textbf{82.20±3.10} & \\textbf{0.57±0.27} & \\textbf{0.18±0.05} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion", "authors": ["Han Liu", "Yubo Fan", "Can Cui", "Dingjie Su", "Andrew McNeil", "Benoit M. Dawant"], "url": "https://arxiv.org/abs/2201.10647v1", "attribution": "\"Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion\" by Han Liu, Yubo Fan, Can Cui, Dingjie Su, Andrew McNeil, and Benoit M. Dawant, arXiv:2201.10647v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.09997v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small{Average PMSE across data sets for several simulation settings for BART and EB-coBART in the rigid tree setting ($\\alpha=0.1,$ $\\beta=4,$ $k=1$) and the flexible tree setting ($\\alpha=0.95,$ $\\beta=2,$ $k=2$). Also included are four competitors: ridge regression, random forest, and co-data learners CoRF and ecpc.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n & $N=100$ & $N=200$\\tabularnewline\n\\hline \n\\hline \nFlexible BART & 836.4 & 787.5\\tabularnewline\nFlexible EB-coBART & 744.5 & 688.6\\tabularnewline\nRigid BART & 883.4 & 863.0\\tabularnewline\nRigid EB-coBART & 847.8 & 822.2\\tabularnewline\nDART & 836 & 786\\tabularnewline\nRidge & 733.5 & 550.2\\tabularnewline\nEcpc & 493.4 & 241.3\\tabularnewline\nRandom forest & 857.5 & 826.6\\tabularnewline\nCoRF & 792.4 & 749.9\\tabularnewline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Co-data Learning for Bayesian Additive Regression Trees", "authors": ["Jeroen M. Goedhart", "Thomas Klausch", "Jurriaan Janssen", "Mark A. van de Wiel"], "url": "https://arxiv.org/abs/2311.09997v2", "attribution": "\"Co-data Learning for Bayesian Additive Regression Trees\" by Jeroen M. Goedhart, Thomas Klausch, Jurriaan Janssen, and Mark A. van de Wiel, arXiv:2311.09997v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05672v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimation performance of the quantile coefficient function at $\\tau = 0.5$ for \\textbf{Scenario }, based on 500 simulation runs.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\\hline\t\n&Method & $\\beta_0$ & $\\beta_1$ &$\\beta_0$ & $\\beta_1$\\\\\n\\hline\n && \\multicolumn{2}{c}{$N_1 = 500$}&\\multicolumn{2}{c}{$N_1 = 1000$}\\\\\n\\cline{3-6} \n\\multirow{3}{*}{Relative MSE}&HCQRF &1.9e-04&0.001&1.5e-04&6.1e-05\\\\\n&CQRF&9.6e-05&0.018&7.1e-05&0.008\\\\\n&HCQRF-c &1.7e-04&0.000&1.4e-04&4.9e-05\\\\\n&grf-c &5.0e-05&0.028&3.4e-05&0.025\\\\\n\\cline{2-6}\n\\multirow{3}{*}{Relative MAE}&HCQRF &0.011&0.011&0.010&0.006\\\\\n&CQRF&0.008&0.067&0.007&0.033\\\\\n&HCQRF-c &0.011&0.007&0.009&0.006\\\\\n&grf-c & 0.006&0.097&0.005&0.089\\\\\n\\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Hybrid Censored Quantile Regression Forest to Assess the Heterogeneous Effects", "authors": ["Huichen Zhu", "Yifei Sun", "Ying Wei"], "url": "https://arxiv.org/abs/2212.05672v1", "attribution": "\"Hybrid Censored Quantile Regression Forest to Assess the Heterogeneous Effects\" by Huichen Zhu, Yifei Sun, and Ying Wei, arXiv:2212.05672v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.11921v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Emperical average cost of the SUDO-DRL algorithm and the Benchmarks}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c}\n\t\t\\hline \n\t\t\\thead{System \\\\ Scale \\\\ $(N,M)$} & \\thead{Para.} & DDPG & \\thead{SE-\\\\DDPG\\\\ } & \\thead{MRII- \\\\DDPG\\\\ } & PPO & \\thead{\\textbf{SUDO-DRL}} \\\\\n\t\t\\hline\n\t\t$(10, 5)$ & 1 & 89.26 & 77.14 & 84.00 & 119.63 & \\textbf{85.52} \\\\\n\t\t& 2 & 98.87 & 87.30 & 90.28 & 123.01 & \\textbf{95.82} \\\\\n\t\t& 3 & $-$ & 106.60 & 119.55 & 195.37 & \\textbf{121.31} \\\\\n\t\t& 4 & 83.12 & 78.21 & 80.88 & 120.93 & \\textbf{80.68} \\\\\n\t\t\\hline\n\t\t \n\t\t$(20, 10)$ & 5 & $-$ & 357.14 & 369.14 & 569.96 & \\textbf{370.63} \\\\\n & 6 & $-$ & $-$ & 445.87 & 584.37 & \\textbf{426.29} \\\\\n\t\t & 7 & $-$ & 407.20 & 441.73 & 731.94 & \\textbf{417.90} \\\\\n\t\t & 8 & $-$ & 290.72 & 307.94 & 376.16 & \\textbf{308.91} \\\\\n\t\t \\hline\n\t\t$(30, 15)$ & 9 & $-$ & $-$ & $-$ & 805.45 & \\textbf{519.78} \\\\\n\t\t& 10 & $-$ & $-$ & $-$ & 739.97 & \\textbf{575.34} \\\\\n\t\t& 11 & $-$ & $-$ & $-$ & 900.71 & \\textbf{518.03} \\\\\n\t\t& 12 & $-$ & $-$ & $-$ & 901.42 & \\textbf{551.97} \\\\\n\t\t\\hline\n\t\t$(40, 20)$ & 13 & $-$ & $-$ & $-$ & 1057.05 & \\textbf{719.45} \\\\\n\t\t& 14 & $-$ & $-$ & $-$ & 971.35 & \\textbf{689.81} \\\\\n\t\t& 15 & $-$ & $-$ & $-$ & 1291.54 & \\textbf{994.80} \\\\\n\t\t& 16 & $-$ & $-$ & $-$ & 1012.20 & \\textbf{771.91} \\\\\n\t\t\\hline \n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Goal-oriented Transmission Scheduling: Structure-guided DRL with a Unified Dual On-policy and Off-policy Approach", "authors": ["Jiazheng Chen", "Wanchun Liu"], "url": "https://arxiv.org/abs/2501.11921v1", "attribution": "\"Goal-oriented Transmission Scheduling: Structure-guided DRL with a Unified Dual On-policy and Off-policy Approach\" by Jiazheng Chen and Wanchun Liu, arXiv:2501.11921v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.18600v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison across different training objectives}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{clccc} % p{3cm} for \"Model\" and \"Objective\" columns\n \\toprule\n \\textbf{Model} & \\textbf{Objective} & \\textbf{Return (\\%)} & \\textbf{SR} & \\textbf{IC (\\%)} \\\\\n \\midrule\n \\multirow{4}[2]{*}{LSTM} & CLF & 19.31 $\\pm$ 0.80 & 1.9757 $\\pm$ 0.1888 & 2.77 $\\pm$ 0.32 \\\\\n & IC & 36.03 $\\pm$ 3.99 & 1.8642 $\\pm$ 0.1930 & 3.62 $\\pm$ 0.09 \\\\\n & MSE & 8.97 $\\pm$ 10.41 & 0.4484 $\\pm$ 0.5537 & 1.78 $\\pm$ 1.20 \\\\\n & Ranking & -1.11 $\\pm$ 2.74 & -0.0978 $\\pm$ 0.2347 & -0.07 $\\pm$ 0.09 \\\\\n \\midrule\n \\multirow{4}[2]{*}{RGCN} & CLF & 19.53 $\\pm$ 1.48 & 2.1073 $\\pm$ 0.1532 & 2.66 $\\pm$ 0.11 \\\\\n & IC & 44.97 $\\pm$ 3.23 & 2.1990 $\\pm$ 0.1855 & 3.97 $\\pm$ 0.30 \\\\\n & MSE & 9.13 $\\pm$ 13.99 & 0.5141 $\\pm$ 0.9210 & 1.47 $\\pm$ 1.18 \\\\\n & Ranking & -3.75 $\\pm$ 6.26 & -0.2929 $\\pm$ 0.5210 & 0.05 $\\pm$ 0.03 \\\\\n \\midrule\n \\multirow{4}[2]{*}{DTML} & CLF & 14.10 $\\pm$ 1.96 & 1.5255 $\\pm$ 0.0703 & 2.55 $\\pm$ 0.10 \\\\\n & IC & 38.56 $\\pm$ 11.29 & 1.8796 $\\pm$ 0.6417 & 3.24 $\\pm$ 0.55 \\\\\n & MSE & -1.98 $\\pm$ 1.57 & -0.1616 $\\pm$ 0.1272 & 0.22 $\\pm$ 0.37 \\\\\n & Ranking & -2.21 $\\pm$ 3.59 & -0.1793 $\\pm$ 0.2833 & -0.07 $\\pm$ 0.07 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "QuantBench: Benchmarking AI Methods for Quantitative Investment", "authors": ["Saizhuo Wang", "Hao Kong", "Jiadong Guo", "Fengrui Hua", "Yiyan Qi", "Wanyun Zhou", "Jiahao Zheng", "Xinyu Wang", "Lionel M. Ni", "Jian Guo"], "url": "https://arxiv.org/abs/2504.18600v1", "attribution": "\"QuantBench: Benchmarking AI Methods for Quantitative Investment\" by Saizhuo Wang, Hao Kong, Jiadong Guo, Fengrui Hua, Yiyan Qi, Wanyun Zhou, Jiahao Zheng, Xinyu Wang, Lionel M. Ni, and Jian Guo, arXiv:2504.18600v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08246v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Frequency of Special Characters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccl}\n \\toprule\n Non-English or Math&Frequency&Comments\\\\\n \\midrule\n \\O & 1 in 1,000& For Swedish names\\\\\n $\\pi$ & 1 in 5& Common in math\\\\\n \\$ & 4 in 5 & Used in business\\\\\n $\\Psi^2_1$ & 1 in 40,000& Unexplained usage\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "\"Project smells\" -- Experiences in Analysing the Software Quality of ML Projects with mllint", "authors": ["Bart van Oort", "Luís Cruz", "Babak Loni", "Arie van Deursen"], "url": "https://arxiv.org/abs/2201.08246v1", "attribution": "\"\"Project smells\" -- Experiences in Analysing the Software Quality of ML Projects with mllint\" by Bart van Oort, Luís Cruz, Babak Loni, and Arie van Deursen, arXiv:2201.08246v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.12530v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc} \\toprule\n\\multicolumn{3}{c}{$\\rho^\\Phi$}\\\\ \\midrule\n \\quad\\quad\\quad 0.1\\quad\\quad\\quad &\\quad\\quad\\quad 0.5\\quad\\quad\\quad & \\quad\\quad\\quad 0.9 \\quad\\quad\\quad \\\\ \\toprule \n\\quad\\quad\\quad 0.407 \\quad\\quad\\quad & \\quad\\quad\\quad 2.035 \\quad\\quad\\quad & \\quad\\quad\\quad 3.663 \\quad\\quad\\quad\\\\ \n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Microscopic Traffic Models, Accidents, and Insurance Losses", "authors": ["Sojung Kim", "Marcel Kleiber", "Stefan Weber"], "url": "https://arxiv.org/abs/2208.12530v2", "attribution": "\"Microscopic Traffic Models, Accidents, and Insurance Losses\" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.20289v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{RMSE of Covariance Estimate between Neighborhood Lasso and MMHC}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline \n\\textbf{$\\Sigma$-Structure} &\\textbf{(n,p)} &\\textbf{Neighborhood Lasso RMSE} &\\textbf{MMHC RMSE}\\\\\n\\hline \n\\textbf{Toeplitz} & (100,100) & 0.122 & 0.130 \\\\ \n & (100,100) & 0.056 & 0.060 \\\\\n & (500,100) & 0.112 & 0.109 \\\\\n & (500,1000) & 0.055 & 0.053 \\\\\n\\textbf{Equal} & (100,100) & 0.115 & 0.108\\\\\n & (100,1000) & 0.039 & 0.039 \\\\\n & (500,100) & 0.108 & 0.106 \\\\\n & (500,1000) & 0.041 & 0.041 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Causal Discovery on Dependent Binary Data", "authors": ["Alex Chen", "Qing Zhou"], "url": "https://arxiv.org/abs/2412.20289v1", "attribution": "\"Causal Discovery on Dependent Binary Data\" by Alex Chen and Qing Zhou, arXiv:2412.20289v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13099v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c||c|c|c|}\n\\hline\n\\textbf{Tested}&\\multicolumn{6}{|c|}{\\textbf{\\phantom{joker} \\; Mean Values \\, \\& \\, Standard Deviations}}\\\\%&\\multicolumn{6}{|c|}{\\textbf{Standard deviations}} \\\\\n\\cline{2-7} \n\\textbf{ Algorithms } & \\textbf{\\text{ CPU(Sec) }}& \\textbf{\\text{ nIter }}& \\textbf{\\text{ nFun }}& \\textbf{\\text{ CPU(Sec) }}& \\textbf{\\text{ nIter }}& \\textbf{\\text{ nFun }}\\\\\n\\hline\n\\tt{FISTA}& ${\\mathrm{10.9}}$& ${\\mathrm{2527.2}}$ & ${\\mathrm{2528.2 }}$& ${\\mathrm{30.1}}$& ${\\mathrm{2141.6}}$ & ${\\mathrm{2141.6}}$\\\\\n\\hline\n\\tt{FPC-BB}& ${\\mathrm{2.3}}$& ${\\mathrm{592.7}}$ & ${\\mathrm{1129.7}}$& ${\\mathrm{5.7}}$& ${\\mathrm{471.6}}$ & ${\\mathrm{971.1}}$\\\\\n\\hline\n\\tt{SpaRSA}& ${\\mathrm{2.5}}$& ${\\mathrm{659.9}}$ & ${\\mathrm{659.9}}$& ${\\mathrm{6.4}}$& ${\\mathrm{527.3}}$ & ${\\mathrm{527.3}}$\\\\\n\\hline\n\\tt{TwIST}& ${\\mathrm{1.3}}$& ${\\mathrm{387.0}}$ & \n${\\mathrm{443.4}}$& ${\\mathrm{2.9}}$& \n${\\mathrm{324.2 }}$ & ${\\mathrm{352.5}}$\\\\\n\\hline\n\\tt{ISGA}& \n${\\mathrm{0.9}}$& \n${\\mathrm{209.7}}$ & \n${\\mathrm{291.5}}$& ${\\mathrm{1.6}}$& ${\\mathrm{37.3}}$ & ${\\mathrm{36.1}}$ \\\\\n\\hline\n\\tt{smISGA}& %\\cellcolor{gray!20}\n${\\mathrm{ 0.9}}$& \n${\\mathrm{216.6}}$ & \n${\\mathrm{492.4}}$& ${\\mathrm{1.8}}$& ${\\mathrm{67.7}}$ & ${\\mathrm{138.8}}$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Semi-Monotone Goldstein Line Search Strategy with Application in Sparse Recovery", "authors": ["Shima Shabani", "Michael Breuß"], "url": "https://arxiv.org/abs/2503.13099v1", "attribution": "\"Semi-Monotone Goldstein Line Search Strategy with Application in Sparse Recovery\" by Shima Shabani and Michael Breuß, arXiv:2503.13099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07542v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance percentage of two methods on the VML-MOC test set.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n&Mean pixel IU&Mean line IU\\\\\n\\midrule\nSingle-oriented Gaussian~&37.88&12.89\\\\ \\\\\nMulti-oriented Gaussian&80.96&60.99\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "VML-MOC: Segmenting a multiply oriented and curved handwritten text lines dataset", "authors": ["Berat Kurar Barakat", "Rafi Cohen", "Irina Rabaev", "Jihad El-Sana"], "url": "https://arxiv.org/abs/2101.07542v1", "attribution": "\"VML-MOC: Segmenting a multiply oriented and curved handwritten text lines dataset\" by Berat Kurar Barakat, Rafi Cohen, Irina Rabaev, and Jihad El-Sana, arXiv:2101.07542v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03988v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{F1-scores for different methods of vectorization on the TDT data split.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n Vectorization & Train F1-score & DEV F1-score & Test F1-score \\\\ \\hline\n distilBERT-tokenizer & \\textbf{0.9933} & \\textbf{0.9807} & \\textbf{0.9708} \\\\ \\hline\n neural stacking & 0.9645 & 0.9377 & 0.9461 \\\\ \\hline\n linear stacking & 0.9695 & 0.9445 & 0.9425 \\\\ \\hline\n tax2vec & 0.9895 & 0.9415 & 0.9407 \\\\ \\hline\n linear stacking\\_probs& 0.9710 & 0.9380 & 0.9390 \\\\ \\hline\n LSA& 0.9658 & 0.9302 & 0.9281 \\\\ \\hline\n roberta-large-nli-stsb-mean-tokens & 0.9623 & 0.9184 & 0.9142 \\\\ \\hline\n xlm-r-large-en-ko-nli-ststb &0.9376 & 0.9226 & 0.9124 \\\\ \\hline\n distilbert-base-nli-mean-tokens & 0.9365 & 0.9124 & 0.9113 \\\\ \\hline\n \n tax2vec(kg) & 0.8830 & 0.8842 & 0.8892 \\\\ \\hline\n Hand crafted features & 0.7861 & 0.7903 & 0.7805 \\\\ \\hline\n \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Identification of COVID-19 related Fake News via Neural Stacking", "authors": ["Boshko Koloski", "Timen Stepišnik Perdih", "Senja Pollak", "Blaž Škrlj"], "url": "https://arxiv.org/abs/2101.03988v2", "attribution": "\"Identification of COVID-19 related Fake News via Neural Stacking\" by Boshko Koloski, Timen Stepišnik Perdih, Senja Pollak, and Blaž Škrlj, arXiv:2101.03988v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.09436v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Nodes on the designed BNs with assumed metrics for CACC}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n \\hline\n Node & Description & Confidentiality & Integrity & Availability \\\\ \n \\hline\n RSE & connected vehicle roadside equipment & M & M & M \\\\ \n ITS & ITS roadway equipment & M & H & M \\\\ \n TMC & traffic management center & M & H & M \\\\ \n OBE & vehicle OBE & L & M & M \\\\ \n BV & basic vehicle & L & M & L \\\\ \n OV & other vehicle OBEs & L & H & M \\\\ \n DII & driver input information & M & H & H \\\\ \n DI & driver input & M & H & H \\\\ \n VCC & vehicle cluster coordination & L & H & M \\\\ \n VVV & vehicle path prediction & NA & H & M \\\\ \n VP & vehicle profile & L & M & M \\\\ \n DRV & driver update information & L & M & M \\\\ \n VCA & vehicle control automation & M & H & H \\\\ \n DR & driver updates & NA & M & M \\\\ \n BSC & basic safety communication & L & M & M \\\\ \n HVS & host vehicle status & L & M & H \\\\ \n DI & driver input & M & H & H \\\\ \n VED & vehicle environmental data & L & M & M \\\\ \n VLM & vehicle location and motion & L & H & M \\\\ \n VCW & vehicle control warning & L & H & H \\\\ \n VCP & vehicle cooperative cruise control & L & H & M \\\\ \n BSM & basic safety message & L & L & H \\\\ \n DD & detection by driver & L & M & H \\\\ \n DS & detection by system & L & M & H \\\\ \n RDD & roadside detection by system driver & L & M & H \\\\ \n RDS & roadside detection by system & L & M & H \\\\\n SA & certificate (security) authority & L & M & H \\\\ \n WSA & wrong safety warning and signal phasing & L & M & H \\\\ \n CB & sending channel busy & L & M & H \\\\ \n DCC & distribution congestion conrol mechanism & L & M & H \\\\ \n LC & location tracking & L & M & H \\\\ \n RDS & detection by system & L & M & H \\\\ \n LTC & fake long term certificate & L & M & H \\\\ \n CRL & fake certificate revocation list & L & M & H \\\\ \n PC & pseudonym certificate & L & M & H \\\\ \n DB & database map poisining & L & M & H \\\\ \n MP & map poisining (Web) & L & M & H \\\\ \n PC & pseudonym certificate & L & M & H \\\\ \n DOSR & denial of service (RSE) & L & M & H \\\\ \n SD & device shutdown & L & M & H \\\\ \n BLK & block pseudonym change & L & M & H \\\\ \n RB & remote reboot & L & M & H \\\\ \n DOS & denial of service (vehs,pedes) & L & M & H \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks", "authors": ["Gurcan Comert", "Mashrur Chowdhury", "David M. Nicol"], "url": "https://arxiv.org/abs/2011.09436v1", "attribution": "\"Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks\" by Gurcan Comert, Mashrur Chowdhury, and David M. Nicol, arXiv:2011.09436v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.13544v5_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overview of Experiment Configurations}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\toprule\n \\textbf{Category} & \\textbf{Variation} & \\textbf{Abbreviation} \\\\\n \\midrule\n \\multirow{3}{*}{Performance} \n & Cumulative Return & CR \\\\\n & Sharpe Ratio & SH \\\\\n & Sortino Ratio & SO \\\\\n \\midrule\n \\multirow{3}{*}{Benchmark} \n & Equal-weighted & EW \\\\\n & Value-weighted & VW \\\\\n & Portfolio Optimization & mSSRM \\\\\n \\midrule\n \\multirow{2}{*}{Objective}\n & Maximum Sharpe Ratio & MSH \\\\\n & Maximum Sortino Ratio & MSO \\\\\n \\midrule\n \\multirow{3}{*}{Model}\n & Mamba & M \\\\\n & LSTM & L \\\\\n & Transformer & T \\\\\n \\midrule\n \\multirow{3}{*}{Method}\n & Prediction-Focused Learning & PFL \\\\\n & End-to-End Learning & E2E \\\\\n & Decision by Supervised Learning & DSL (Ours) \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio Optimization", "authors": ["Juhyeong Kim", "Sungyoon Choi", "Youngbin Lee", "Yejin Kim", "Yongmin Choi", "Yongjae Lee"], "url": "https://arxiv.org/abs/2503.13544v5", "attribution": "\"Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio Optimization\" by Juhyeong Kim, Sungyoon Choi, Youngbin Lee, Yejin Kim, Yongmin Choi, and Yongjae Lee, arXiv:2503.13544v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table35.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|r|r|r}\n \\hline\n \\multicolumn{9}{c}{Implicit multi-stage optimization model with adjustable carry-overs} \\\\\n \\hline\n \\multicolumn{9}{c}{Instances including component demands} \\\\\n \\hline\n BOM & $|\\mathcal{I}|$ & $|\\mathcal{H}|$ & Distribution & Util. & $|\\Omega|$ & $|\\Phi|$ & Runtime (s) & Gap (\\%) \\\\\n \\hline\n \\multirow{10}{*}{General} &\n \\multirow{10}{*}{10} &\n \\multirow{10}{*}{7} &\n \\multirow{10}{*}{Non stationary} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & 19.1 & 0.00\\\\\n & & & & & 3 & 2,187 & 3,330.8 & 0.00\\\\\n & & & & & 4 & 16,384 & 10,800.0 & 20.27 \\\\\n & & & & & 5 & 78,125 & Memory & - \\\\\n & & & & & 6 & 279,936 & - & - \\\\\\cline{5-9}\n & & & &\\multirow{5}{*}{90\\%} \n & 2 & 128 & 72.6 & 0.00\\\\\n & & & & & 3 & 2,187 & 10,800.0 & 0.39\\\\\n & & & & & 4 & 16,384 & 10,800.0 & 96.69 \\\\\n & & & & & 5 & 78,125 & Memory & - \\\\\n & & & & & 6 & 279,936 & - & - \\\\\n \\hline\n \\multirow{10}{*}{General} &\n \\multirow{10}{*}{10} &\n \\multirow{10}{*}{7} &\n \\multirow{10}{*}{Uniform} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & 37.0 & 0.00 \\\\\n & & & & & 3 & 2,187 & 1,053.9 & 0.00 \\\\\n & & & & & 4 & 16,384 & 10,800.0 & 3.97 \\\\\n & & & & & 5 & 78,125 & 10,800.0 & 37.09 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\\cline{5-9}\n & & & & \\multirow{5}{*}{90\\%}\n & 2 & 128 & 65.1 & 0.00 \\\\\n & & & & & 3 & 2,187 & 5,364.0 & 0.00 \\\\\n & & & & & 4 & 16,384 & 10,800.0 & 95.46 \\\\\n & & & & & 5 & 78,125 & 10,800.0 & 97.92 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\n \\hline\n \\multirow{10}{*}{General} &\n \\multirow{10}{*}{10} &\n \\multirow{10}{*}{7} &\n \\multirow{10}{*}{Lumpy} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & 27.0 & 0.00 \\\\\n & & & & & 3 & 2,187 & 3,275.6 & 0.00 \\\\\n & & & & & 4 & 16,384 & 10,800.0 & 37.91 \\\\\n & & & & & 5 & 78,125 & Memory & - \\\\\n & & & & & 6 & 279,936 & - & - \\\\\\cline{5-9}\n & & & & \\multirow{5}{*}{90\\%} \n & 2 & 128 & 1681.1 & 0.00 \\\\\n & & & & & 3 & 2,187 & 10,800.0 & 2.74 \\\\\n & & & & & 4 & 16,384 & 10,800.0 & 64.66 \\\\\n & & & & & 5 & 78,125 & 10,800.0 & 83.23 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\n \\hline\n \\multirow{10}{*}{General} &\n \\multirow{10}{*}{10} &\n \\multirow{10}{*}{7} &\n \\multirow{10}{*}{Slow moving} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & 14.3 & 0.00 \\\\\n & & & & & 3 & 2,187 & 4,985.2 & 0.00 \\\\\n & & & & & 4 & 16,384 & 10,800.0 & 2.26 \\\\\n & & & & & 5 & 78,125 & 10,800.0 & 86.32 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\\cline{5-9}\n & & & & \\multirow{5}{*}{90\\%}\n & 2 & 128 & 326.2 & 0.00 \\\\\n & & & & & 3 & 2,187 & 10,800.0 & 10.61 \\\\\n & & & & & 4 & 16,384 & 10,800.0 & 83.49 \\\\\n & & & & & 5 & 78,125 & Memory & - \\\\\n & & & & & 6 & 279,936 & - & - \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Runtimes and gaps for the implicit multi-stage optimization model on the instances including component demands for different distributions and numbers of scenarios per branch with a time limit of 3 hours (10,800 s)}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.06442v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Validity for the point-wise convolution of GMDC on CVIU dataset. Note that the SubEC is not involved in these variants. The proposed GMDC uses grouped kernels denoted as $[K_1, K_2, \\dots, K_N]$. The best performance is highlighted in bold.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc|cccc}\n\\toprule\nModel & \\multicolumn{4}{c|}{w/o Point-wise Conv} & \\multicolumn{4}{c}{with Point-wise Conv} \\\\\n\\midrule\nGMDC Groups & SRCC $\\uparrow$ & KRCC $\\uparrow$ & PLCC $\\uparrow$& RMSE $\\downarrow$& SRCC $\\uparrow$& KRCC $\\uparrow$& PLCC $\\uparrow$& RMSE $\\downarrow$\\\\\n\\midrule\n$[3,7]$ & 0.945 & 0.800 & 0.951 & 0.884 & 0.974 & 0.866 & 0.977 & 0.605 \\\\\n$[3,5,7]$ & 0.967 & 0.850 & 0.968 & 0.713 & 0.972 & 0.857 & 0.972 & 0.642 \\\\\n$[3,5,7,9]$ & 0.973 & 0.864 & 0.976 & \\textbf{0.601} & \\textbf{0.975} & \\textbf{0.868} & \\textbf{0.978} & 0.632\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment", "authors": ["Yixiao Li", "Xiaoyuan Yang", "Guanghui Yue", "Jun Fu", "Qiuping Jiang", "Xu Jia", "Paul L. Rosin", "Hantao Liu", "Wei Zhou"], "url": "https://arxiv.org/abs/2509.06442v1", "attribution": "\"Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment\" by Yixiao Li, Xiaoyuan Yang, Guanghui Yue, Jun Fu, Qiuping Jiang, Xu Jia, Paul L. Rosin, Hantao Liu, and Wei Zhou, arXiv:2509.06442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.09307v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|ccc|}\n\\hline\n \\textbf{Kernel} & \\textbf{Number of points tested} & \\textbf{Test error} \\\\ \n \\hline\nEpanechnikov & 468 & $6.41025\\%$\\\\\nCosine & 468 & $7.2649\\%$\\\\\nUniform & 468 & $8.5470\\%$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Calculated EPE for Epanechnikov, cosine and uniform kernel run on infant 5}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Computational Challenges in Non-parametric Prediction of Bradycardia in Preterm Infants", "authors": ["Sinjini Mitra"], "url": "https://arxiv.org/abs/2011.09307v1", "attribution": "\"Computational Challenges in Non-parametric Prediction of Bradycardia in Preterm Infants\" by Sinjini Mitra, arXiv:2011.09307v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.07058v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n\\hline\n\\textbf{Conditional Variable} & \\textbf{Probability} \\\\ \\hline\n$X_1=0$ & $0.45$ \\\\ \\hline\n$X_1=1$ & $0.55$ \\\\ \\hline\n$X_2=0 | X_1 = 0$ & $0.55$ \\\\ \\hline\n$X_2=1 | X_1 = 0$ & $0.45$ \\\\ \\hline\n$X_2=0 | X_1 = 1$ & $0.45$ \\\\ \\hline\n$X_2=1 | X_1 = 1$ & $0.55$ \\\\ \\hline\n$W_1=0 | X_1 = 0$ & $0.46$ \\\\ \\hline\n$W_1=1 | X_1 = 0$ & $0.54$ \\\\ \\hline\n$W_1=0 | X_1 = 1$ & $0.54$ \\\\ \\hline\n$W_1=1 | X_1 = 1$ & $0.46$ \\\\ \\hline\n$W_2=0 | X_2 = 0$ & $0.52$ \\\\ \\hline\n$W_2=1 | X_2 = 0$ & $0.48$ \\\\ \\hline\n$W_2=0 | X_2 = 1$ & $0.48$ \\\\ \\hline\n$W_2=1 | X_2 = 1$ & $0.52$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Conditional Probability Distributions}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Budgeted and Non-budgeted Causal Bandits", "authors": ["Vineet Nair", "Vishakha Patil", "Gaurav Sinha"], "url": "https://arxiv.org/abs/2012.07058v1", "attribution": "\"Budgeted and Non-budgeted Causal Bandits\" by Vineet Nair, Vishakha Patil, and Gaurav Sinha, arXiv:2012.07058v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04188v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Selected indicators for the different methods}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c||c|c||c|c|c|c}\n\\thead{Approximation \\\\ formula} & \\thead{Arrival \\\\ process} & \\thead{Service \\\\ process} & \\thead{Mean \\\\ number of \\\\ iterations} & \\thead{Mean total \\\\ computation \\\\ time} & \\thead{Maximum \\\\ capacity \\\\ $\\hat{n}_{max}$} & \\thead{Traffic share \\\\ at max. \\\\ capacity} \\\\ \\hline \\hline\nNone & M & M & $9$ & $2.22$ s & $11.70$ & $0.5$ \\\\ \\hline\nNone & PH & M & $9$ & $17.04$ s & $12.97$ & $0.5$ \\\\ \\hline\nNone & M & PH & $8.44$ & $78.86$ s & $14.53$ & $0.5$ \\\\ \\hline\nNone & PH & PH & $9$ & $1432.20$ s & $16.90$ & $0.1$ \\\\ \\hline \\hline\nKingman & M & M & $8.89$ & $2.35$ s & $16.80$ & $0.5$ \\\\ \\hline\nKingman & PH & M & $8$ & $15.76$ s & $15.91$ & $0.5$ \\\\ \\hline\nKingman & M & PH & $7.78$ & $74.65$ s & $15.55$ & $0.5$ \\\\ \\hline \\hline\nHertel & M & M & $9.33$ & $2.31$ s & $17.29$ & $0.5$ \\\\ \\hline\nHertel & PH & M & $8$ & $15.70$ s & $15.91$ & $0.5$ \\\\ \\hline\nHertel & M & PH & $9.22$ & $87.33$ s & $18.17$ & $0.5$ \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Utilizing phase-type distributions for queueing-based railway junction performance determination", "authors": ["Tamme Emunds", "Nils Nießen"], "url": "https://arxiv.org/abs/2412.04188v1", "attribution": "\"Utilizing phase-type distributions for queueing-based railway junction performance determination\" by Tamme Emunds and Nils Nießen, arXiv:2412.04188v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.13174v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n \\toprule\n Name & Meaning \\\\\n \\midrule\n adjclose & Adjusted closing price \\\\\n amount & Total trading amount \\\\\n change & Price change compared to previous close \\\\\n close & Closing price of the trading day \\\\\n factor & Adjustment factor for price normalization \\\\\n high & Highest price during the trading day \\\\\n low & Lowest price during the trading day \\\\\n open & Opening price of the trading day \\\\\n volume & Number of shares traded \\\\\n vwap & Volume-weighted average price \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Feature names and their corresponding meanings.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining", "authors": ["Hongjun Ding", "Binqi Chen", "Jinsheng Huang", "Taian Guo", "Zhengyang Mao", "Guoyi Shao", "Lutong Zou", "Luchen Liu", "Ming Zhang"], "url": "https://arxiv.org/abs/2508.13174v1", "attribution": "\"AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining\" by Hongjun Ding, Binqi Chen, Jinsheng Huang, Taian Guo, Zhengyang Mao, Guoyi Shao, Lutong Zou, Luchen Liu, and Ming Zhang, arXiv:2508.13174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.17260v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Predicted plane error $e$ vs evaluation score}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\\hline\n$e = |p - t|$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\\\\n\\hline\n\\textbf{Score} & 1.00 & 0.74 & 0.50 & 0.32 & 0.18 & 0.10 & 0.05 & 0.02 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "MiceBoneChallenge: Micro-CT public dataset and six solutions for automatic growth plate detection in micro-CT mice bone scans", "authors": ["Nikolay Burlutskiy", "Marija Kekic", "Jordi de la Torre", "Philipp Plewa", "Mehdi Boroumand", "Julia Jurkowska", "Borjan Venovski", "Maria Chiara Biagi", "Yeman Brhane Hagos", "Roksana Malinowska-Traczyk", "Yibo Wang", "Jacek Zalewski", "Paula Sawczuk", "Karlo Pintarić", "Fariba Yousefi", "Leif Hultin"], "url": "https://arxiv.org/abs/2411.17260v1", "attribution": "\"MiceBoneChallenge: Micro-CT public dataset and six solutions for automatic growth plate detection in micro-CT mice bone scans\" by Nikolay Burlutskiy, Marija Kekic, Jordi de la Torre, Philipp Plewa, Mehdi Boroumand, Julia Jurkowska, Borjan Venovski, Maria Chiara Biagi, Yeman Brhane Hagos, Roksana Malinowska-Traczyk, Yibo Wang, Jacek Zalewski, Paula Sawczuk, Karlo Pintarić, Fariba Yousefi, and Leif Hultin, arXiv:2411.17260v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.20923v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{diagbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|l|l|}\n\\hline\n\\diagbox{$w$}{ASE$\\sim$}& $\\nu_0$ & $\\nu_2$ & $\\nu_4$ &$\\omega_1$ & $\\omega_3$ & $B_0$ & $B_2$ \\\\ \\hline\n $0.95$& $1e-7$ &$1e-7$ &$5e-11$ & $2e-8$ & $7e-11$ & $3e-12$ & $7e-12$\\\\ \\hline\n$0.89$ & $1e-7$ & $2e-7$ & $3e-11$& $4e-8$ & $4e-11$ & $2e-12$ & $1e-9$ \\\\ \\hline\n $0.79$& $3e-7$ & $2e-7$ & $2e-11$& $5e-8$ & $2e-11$ & $1e-12$ & $1e-9$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ASE for the multipolar coefficients and three different stars. We see how the magnitudes can vary depending on the field frequency.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Spinning boson stars in nonlinear sigma models and Universal Relations", "authors": ["Christoph Adam", "Jorge Castelo Mourelle", "Alberto García Martín-Caro", "Andrzej Wereszczynski"], "url": "https://arxiv.org/abs/2502.20923v3", "attribution": "\"Spinning boson stars in nonlinear sigma models and Universal Relations\" by Christoph Adam, Jorge Castelo Mourelle, Alberto García Martín-Caro, and Andrzej Wereszczynski, arXiv:2502.20923v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11165v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|} \n \\hline\n $\\mathtt{has\\_loan(C,L)}$ & $\\mathtt{status(L)}$ & $\\mathtt{credit\\_score(C)}$ \\\\\n \\hline\n \\hline\n $\\mathtt{true}$ & $\\mathtt{appr}$ & $\\mathtt{gaussian(700, 10.9)}$ \\\\\n $\\mathtt{true}$ & $\\mathtt{decl}$ & $\\mathtt{gaussian(600, 20.5)}$ \\\\\n $\\mathtt{false}$ & $\\mathtt{appr}$ & $\\mathtt{gaussian(650, 15.4)}$ \\\\\n $\\mathtt{false}$ & $\\mathtt{decl}$ & $\\mathtt{gaussian(650, 15.4)}$ \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs", "authors": ["Nitesh Kumar", "Ondrej Kuzelka", "Luc De Raedt"], "url": "https://arxiv.org/abs/2201.11165v2", "attribution": "\"First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs\" by Nitesh Kumar, Ondrej Kuzelka, and Luc De Raedt, arXiv:2201.11165v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11002v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Information on video and audio files that must accompany a manuscript submission.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Item & Video & Audio \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} File name & Video1, video2... & Audio1, audio2... \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Number of files & 0-10 & 0-10 \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Size of each file & 5 MB & 5 MB \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} File types accepted & .mpeg, .mov (Quicktime), .wmv (Windows Media Player) & .wav, .mp3 \\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Multi-rater Comparative Study of Automatic Target Localization Methods for Epilepsy Deep Brain Stimulation Procedures", "authors": ["Han Liu", "Kathryn L. Holloway", "Dario J. Englot", "Benoit M. Dawant"], "url": "https://arxiv.org/abs/2201.11002v1", "attribution": "\"A Multi-rater Comparative Study of Automatic Target Localization Methods for Epilepsy Deep Brain Stimulation Procedures\" by Han Liu, Kathryn L. Holloway, Dario J. Englot, and Benoit M. Dawant, arXiv:2201.11002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.16701v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Distribution of State-Dependent Factors Across Market States}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n\\textbf{Parameter} & \\textbf{Mean} & \\textbf{Std Dev} & \\textbf{Min} & \\textbf{Max} & \\textbf{Range} \\\\\n\\midrule\n\\textbf{Movement Factors} & & & & & \\\\\nUp Factor $u(s)$ & 1.000676 & 0.000261 & 1.000410 & 1.001577 & 0.001167 \\\\\nDown Factor $d(s)$ & 0.999335 & 0.000220 & 0.998683 & 0.999576 & 0.000893 \\\\\n& & & & & \\\\\n\\textbf{Probabilities} & & & & & \\\\\nPhysical $p_{RF}(s)$ & 0.498 & 0.277 & 0.059 & 0.939 & 0.881 \\\\\nRisk-Neutral $p_{MMM}(s)$ & 0.499 & 0.054 & 0.419 & 0.572 & 0.153 \\\\\nAbsolute Difference & 0.217 & 0.106 & 0.063 & 0.369 & 0.306 \\\\\n& & & & & \\\\\n\\textbf{Calibration Metrics} & & & & & \\\\\nImplied Volatility & 0.000551 & 0.000353 & 0.000322 & 0.001405 & 0.001083 \\\\\nKL Divergence & 0.177 & 0.142 & 0.001 & 0.560 & 0.559 \\\\\nSample Size & 465 & 219 & 43 & 689 & 646 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach", "authors": ["Akash Deep", "Chris Monico", "W. Brent Lindquist", "Svetlozar T. Rachev", "Frank J. Fabozzi"], "url": "https://arxiv.org/abs/2507.16701v1", "attribution": "\"Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach\" by Akash Deep, Chris Monico, W. Brent Lindquist, Svetlozar T. Rachev, and Frank J. Fabozzi, arXiv:2507.16701v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12637v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|llll|}\n\\hline\n & \\# Annotations & A1 & A2 & A3 \\\\ \\hline\nA1 & 10,685 & - & 0.492 & 0.600 \\\\\nA2 & 3,051 & 0.492 & \\textbf{-} & 0.500 \\\\\nA3 & 9,847 & 0.600 & 0.500 & - \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Number of Annotations and Pairwise Cohen's Kappa scores $\\kappa_{\\text{cohen}}$ between annotators.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "CD2CR: Co-reference Resolution Across Documents and Domains", "authors": ["James Ravenscroft", "Arie Cattan", "Amanda Clare", "Ido Dagan", "Maria Liakata"], "url": "https://arxiv.org/abs/2101.12637v1", "attribution": "\"CD2CR: Co-reference Resolution Across Documents and Domains\" by James Ravenscroft, Arie Cattan, Amanda Clare, Ido Dagan, and Maria Liakata, arXiv:2101.12637v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.03757v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mechanism Decomposition by CEO Gender}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n & \\multicolumn{2}{c}{Male CEOs} & \\multicolumn{2}{c}{Female CEOs} & \\\\\n & Effect & \\% of Total & Effect & \\% of Total & Difference \\\\\n\\midrule\nCost Reduction & 0.009*** & 39\\% & 0.012** & 36\\% & -0.003 \\\\\n & (0.003) & & (0.005) & & (0.006) \\\\\nRevenue Enhancement & 0.008** & 35\\% & 0.015*** & 45\\% & 0.007* \\\\\n & (0.003) & & (0.005) & & (0.004) \\\\\nInnovation Output & 0.006** & 26\\% & 0.006* & 18\\% & 0.000 \\\\\n & (0.003) & & (0.003) & & (0.004) \\\\\n\\midrule\nTotal Effect & 0.023*** & 100\\% & 0.033*** & 100\\% & 0.010* \\\\\n & (0.008) & & (0.012) & & (0.006) \\\\\n\\midrule\nObservations & 2,535 & & 200 & & 2,735 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises", "authors": ["Tatsuru Kikuchi"], "url": "https://arxiv.org/abs/2508.03757v1", "attribution": "\"AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises\" by Tatsuru Kikuchi, arXiv:2508.03757v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.06049v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c}\nDistrict & Governor & Treasurer & Sec. of State\\\\\n\\hline\nCO 1 & 0.755025 & 0.732848 & 0.733744\\\\\nCO 2 & 0.644199 & 0.608735 & 0.621864\\\\\nCO 3 & 0.480772 & 0.464260 & 0.471826\\\\\nCO 4 & 0.397891 & 0.394212 & 0.398713\\\\\nCO 5 & 0.407602 & 0.392769 & 0.384713\\\\\nCO 6 & 0.561725 & 0.547696 & 0.550642\\\\\nCO 7 & 0.597754 & 0.586725 & 0.588472\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Democratic vote shares for enacted districts in each of the three 2018 focus races.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Colorado in Context: Congressional Redistricting and Competing Fairness Criteria in Colorado", "authors": ["Jeanne Clelland", "Haley Colgate", "Daryl DeFord", "Beth Malmskog", "Flavia Sancier-Barbosa"], "url": "https://arxiv.org/abs/2011.06049v2", "attribution": "\"Colorado in Context: Congressional Redistricting and Competing Fairness Criteria in Colorado\" by Jeanne Clelland, Haley Colgate, Daryl DeFord, Beth Malmskog, and Flavia Sancier-Barbosa, arXiv:2011.06049v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.00578v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|}\n\\hline\nEquation \\\\\n\\hline\n$x \\cdot y = a \\oplus b$ \\\\\n$x\\cdot y = \\overline{a} \\oplus b$ \\\\\n$x\\cdot \\overline{y} = a \\oplus b$ \\\\\n$x \\cdot \\overline{y} = \\overline{a} \\oplus b$ \\\\\n$\\overline{x} \\cdot y = a \\oplus b$ \\\\\n$\\overline{x} \\cdot y = \\overline{a} \\oplus b$ \\\\\n$\\overline{x} \\cdot \\overline{y} = a \\oplus b$ \\\\\n$\\overline{x} \\cdot \\overline{y} = \\overline{a} \\oplus b$ \\\\\n$x + y = a \\oplus b$ \\\\\n$x + y = \\overline{a} \\oplus b$ \\\\\n$x + \\overline{y} = a \\oplus b$ \\\\\n$x + \\overline{y} = \\overline{a} \\oplus b$ \\\\\n$\\overline{x} + y = a \\oplus b$ \\\\\n$\\overline{x} + y = \\overline{a} \\oplus b$ \\\\\n$\\overline{x} + \\overline{y} = a \\oplus b$ \\\\\n$\\overline{x} + \\overline{y} = \\overline{a} \\oplus b$ \\\\\n\\hline\n \\end{tabular}\n\\caption{List of all equations leading to a winning probability of $|\\cos(\\frac{\\pi}{8})|^2$, for a classical maximum at $\\frac{3}{4}$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Two new non-equivalent three-qubit CHSH games", "authors": ["Hamza Jaffali", "Frédéric Holweck"], "url": "https://arxiv.org/abs/2312.00578v1", "attribution": "\"Two new non-equivalent three-qubit CHSH games\" by Hamza Jaffali and Frédéric Holweck, arXiv:2312.00578v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.01255v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n\\hline\n\\textbf{Real} & \\textbf{StyleGAN} & \\textbf{LDM} & \\textbf{Accuracy} & \\textbf{F1 Score} & \\textbf{Precision} & \\textbf{Recall} & \\textbf{MCC} \\\\\n\\hline\n0 & 0 & 500 & 84.8 ± 5.04 & 0.831 ± 0.070 & 0.872 ± 0.030 & 0.848 ± 0.050 & 0.821 ± 0.057 \\\\\\hline\n0 & 500 & 0 & 44.26 ± 7.94 & 0.327 ± 0.123 & 0.395 ± 0.215 & 0.442 ± 0.079 & 0.349 ± 0.087 \\\\\\hline\n0 & 250 & 250 & 88.13 ± 2.20 & 0.881 ± 0.021 & 0.886 ± 0.017 & 0.881 ± 0.022 & 0.852 ± 0.026 \\\\\\hline\n0 & 500 & 500 & 86.2 ± 3.41 & 0.862 ± 0.042 & 0.874 ± 0.033 & 0.865 ± 0.039 & 0.832 ± 0.042 \\\\\\hline\n0 & 1000 & 1000 & 91.13 ± 2.31 & 0.911 ± 0.022 & 0.916 ± 0.019 & 0.911 ± 0.023 & 0.892 ± 0.026 \\\\\\hline\n0 & 2000 & 2000 & 92.16 ± 1.15 & 0.928 ± 0.012 & 0.931 ± 0.011 & 0.928 ± 0.012 & 0.912 ± 0.013 \\\\\\hline\n0 & 3000 & 3000 & 90 ± 2.20 & 0.899 ± 0.021 & 0.902 ± 0.017 & 0.899 ± 0.021 & 0.876 ± 0.027 \\\\\\hline\n0 & 4000 & 4000 & 92 ± 1.31 & 0.919 ± 0.013 & 0.922 ± 0.014 & 0.919 ± 0.013 & 0.901 ± 0.015 \\\\\\hline\n0 & 5000 & 5000 & 92.8 ± 1.5 & 0.932 ± 0.017 & 0.935 ± 0.008 & 0.935 ± 0.020 & 0.916 ± 0.020 \\\\\\hline\n1000 & 0 & 0 & 94.46 ± 0.64 & 0.944 ± 0.006 & 0.944 ± 0.013 & 0.944 ± 0.006 & 0.934 ± 0.007 \\\\\\hline\n1000 & 500 & 500 & 96.46 ± 0.41 & 0.961 ± 0.003 & 0.964 ± 0.006 & 0.962 ± 0.003 & 0.956 ± 0.005 \\\\\\hline\n1000 & 1000 & 1000 & 95.66 ± 0.23 & 0.956 ± 0.002 & 0.954 ± 0.003 & 0.954 ± 0.002 & 0.943 ± 0.003 \\\\\\hline\n1000 & 2000 & 2000 & 96.33 ± 0.75 & 0.962 ± 0.006 & 0.962 ± 0.004 & 0.962 ± 0.006 & 0.952 ± 0.006 \\\\\\hline\n1000 & 3000 & 3000 & 96 ± 1.5 & 0.957 ± 0.016 & 0.957 ± 0.016 & 0.957 ± 0.016 & 0.947 ± 0.019 \\\\\\hline\n1000 & 4000 & 4000 & 96.26 ± 1.1 & 0.961 ± 0.009 & 0.962 ± 0.008 & 0.961 ± 0.009 & 0.951 ± 0.009 \\\\\\hline\n1000 & 5000 & 5000 & 96.96 ± 0.3 & 0.964 ± 0.004 & 0.964 ± 0.005 & 0.964 ± 0.005 & 0.955 ± 0.006 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean performance metrics (± standard deviation) of VGG16 models on the test dataset (consisting of real images), when fine-tuned using various combinations of real and synthetic images.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Merging synthetic and real embryo data for advanced AI predictions", "authors": ["Oriana Presacan", "Alexandru Dorobantiu", "Vajira Thambawita", "Michael A. Riegler", "Mette H. Stensen", "Mario Iliceto", "Alexandru C. Aldea", "Akriti Sharma"], "url": "https://arxiv.org/abs/2412.01255v2", "attribution": "\"Merging synthetic and real embryo data for advanced AI predictions\" by Oriana Presacan, Alexandru Dorobantiu, Vajira Thambawita, Michael A. Riegler, Mette H. Stensen, Mario Iliceto, Alexandru C. Aldea, and Akriti Sharma, arXiv:2412.01255v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.11108v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n $\\alpha = 0.1$ & \\textbf{Binseg changes} & \\textbf{Relevant changes} \\\\\n \\hline \n $\\xi_n = 4.25$ & $[260, 526, 1112, 1137, 1163, 1185]$ & 526, 1112, 1137\\\\\n $\\xi_n = 5.5$ & $[526, 1112, 1137, 1163, 1185]$ & 526, 1112, 1137\\\\ \n $\\xi_n = 6.1465$ & $[526, 1112, 1137, 1163, 1185]$ & 526, 1112, 1137\\\\\n $\\xi_n = 7$ & $[526]$ & 526\\\\\n \\hline \\hline \n $\\alpha = 0.05$ & \\textbf{Binseg changes} &\\textbf{ Relevant changes} \\\\\n \\hline \n $\\xi_n = 4.25$ & $[260, 526, 1112, 1137, 1163, 1185]$ & 1112\\\\\n $\\xi_n = 5.5$ & $[526, 1112, 1137, 1163, 1185]$ & 526, 1112, 1137\\\\\n $\\xi_n = 6.1465$ & $[526, 1112, 1137, 1163, 1185]$ & 526, 1112, 1137\\\\\n $\\xi_n = 7$ & $[526]$ & 526\\\\\n \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Multiple change point detection in functional data with applications to biomechanical fatigue data", "authors": ["Patrick Bastian", "Rupsa Basu", "Holger Dette"], "url": "https://arxiv.org/abs/2312.11108v3", "attribution": "\"Multiple change point detection in functional data with applications to biomechanical fatigue data\" by Patrick Bastian, Rupsa Basu, and Holger Dette, arXiv:2312.11108v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.10458v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{soul}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\t\\multirow{2}{*}{Case} & $G_{\\text{PD}}$ & $G_{T}$ & $P_{\\text{cyc}}$ & Cycles & ExPB &SS & Waste \\\\ \n\t\t\t\t & Euro & Euro & & &(years) & (\\%) & (kWh) \\\\\n\t\t\t\\hline\n\t\t\tLoad + PV & - & - & - & - & - &32.71 & 2.72 \\\\\n\t\t\t\\hline\n\t\t\t\\multicolumn{8}{|c|}{\\textbf{1 kWh} Battery} \\\\\n\t\t\t\\hline\n \\hl{\\textbf{0.25C-0.25C}} & 0 & 7.57 &\\textbf{0.1715 }& 27.26 & \\textbf{4.68} &32.73 & 0.01 \\\\\n { 1C-1C} \t\t& 0 & 8.13 & {\\textbf{0.0197}}& 41.76 & 7.18 & 32.53 & 0 \\\\\n 2C-2C \t\t& 0 & 8.14 & -0.0318 & 42.13 & 9.21 & 32.52 & 0 \\\\\n\t\t\t\\hline\n\t\t\t\\multicolumn{8}{|c|}{2 kWh Battery}\\\\\n\t\t\t\\hline\n\t\t\t0.25C-0.25C & 0 & 9.74 & \\textbf{0.0791}& 26.28 & 7.27 & 32.09 & 0 \\\\\n 1C-1C \t\t& 0 & 10.07 & \\textbf{0.0065} & 27.74 & 11.59 & 32.04 & 0 \\\\\n 2C-2C \t\t& 0 & 10.07 & -0.0436 & 27.76 & 14.90 & 32.04 & 0 \\\\\n\t\t\t\\hline\n\t\t\t\\multicolumn{8}{|c|}{5 kWh Battery} \\\\\n\t\t\t\\hline\n 0.25C-0.25C & 0 & 13.92 & 0.0189 & 22.25& 12.72 & 30.66 & 0 \\\\\n 1C-1C \t\t& 0 & 14.02 & -0.05 & 22.43 & 20.80 & 30.64 & 0 \\\\\n 2C-2C \t\t& 0 & 14.02 & -0.1 & 22.43 & 26.75 & 30.64 & 0 \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sizing and Profitability of Energy Storage for Prosumers in Madeira, Portugal", "authors": ["Md Umar Hashmi", "Jonathan Cavaleiro", "Lucas Pereira", "Ana Bušić"], "url": "https://arxiv.org/abs/1911.10458v1", "attribution": "\"Sizing and Profitability of Energy Storage for Prosumers in Madeira, Portugal\" by Md Umar Hashmi, Jonathan Cavaleiro, Lucas Pereira, and Ana Bušić, arXiv:1911.10458v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.13777v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccccc}\n \\toprule\n \\textbf{Event ID} & \\textbf{Timestamp (sec)} & $p^{(1)}_a$ & $v^{(1)}_a$ & $p^{(1)}_b$ & $v^{(1)}_b$ & $\\cdots$ & $p^{(10)}_a$ & $v^{(10)}_a$ & $p^{(10)}_b$ & $v^{(10)}_b$ \\\\ \n \\midrule\n \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & $\\cdots$ & \\vdots & \\vdots & \\vdots & \\vdots\\\\\n 1311 & 34713.685155243 & \\$11.86 & 12000 & \\$11.85 & 8800 & $\\cdots$ & \\$11.95 & 500 & \\$11.73 & 5500 \\\\\n 1312 & 34714.133632201 & \\$11.86 & 9484 & \\$11.85 & 8800 & $\\cdots$ & \\$11.95 & 500 & \\$11.73 & 5500 \\\\\n \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & $\\cdots$ & \\vdots & \\vdots & \\vdots & \\vdots\\\\\n \\midrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Example order book update from a LOBSTER order book file. This example corresponds to the order book dynamics shown in Figure .}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective", "authors": ["Lorenzo Lucchese", "Mikko Pakkanen", "Almut Veraart"], "url": "https://arxiv.org/abs/2211.13777v3", "attribution": "\"The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective\" by Lorenzo Lucchese, Mikko Pakkanen, and Almut Veraart, arXiv:2211.13777v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.21724v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllllll|}\n \\hline\n\\textbf{Approach} & \\textbf{Accuracy} & \\textbf{Recall} & \\textbf{Specificity} & \\textbf{$F_1$} \\\\ \\hline\n \\textbf{Cho et al.} & 0.6746 & & & \\\\\n \\textbf{Kumar et al.} & 0.6724 & & &\\\\\n \\textbf{Sadiq et al.} & 0.8502 & & & \\\\\n \\textbf{Sadiq et al.} & 0.8769 & 0.8762 & 0.8775 & 0.8770\\\\\n\t\\cellcolor{green!30}\\textbf{Ours} & \\cellcolor{green!30}0.995 & \\cellcolor{green!30}0.9986 & \\cellcolor{green!30}0.9983 & \\cellcolor{green!30}0.9984\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Table summarizing the findings from four studies that analyzed the same dataset. Our results, which outperform the others, are highlighted in green.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Single Subject Machine Learning Based Classification of Motor Imagery EEGs", "authors": ["Dario Sanalitro", "Marco Finocchiaro", "Pasquale Memmolo", "Emanuela Cutuli", "Maide Bucolo"], "url": "https://arxiv.org/abs/2508.21724v1", "attribution": "\"A Single Subject Machine Learning Based Classification of Motor Imagery EEGs\" by Dario Sanalitro, Marco Finocchiaro, Pasquale Memmolo, Emanuela Cutuli, and Maide Bucolo, arXiv:2508.21724v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08954v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{change detection results of different methods on the Bern dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccc}\n\\hline\\hline\nMethods & FP & FN & OE & PCC (\\%) & KC(\\%) & F1(\\%)\\\\ \\hline\nPCAKM & \\textbf{95} & 258 & 353 & 99.61 & 83.36 & 83.56\\\\\nNR-ELM & 104 & 228 & 332 & 99.63 & 84.63 & 84.81\\\\\nGaborPCANet & 131 & 193 & 324 & 99.64 & 85.41 & 85.59\\\\\nLR-CNN & 97 & 216 & 313 & 99.65 & 85.54 & 85.71\\\\\nMLFN & 97 & 225 & 322 & 99.64 & 85.06 & 85.24\\\\\nDBN & 145 & \\textbf{159} & 304 & 99.66 & \\textbf{86.59} & \\textbf{86.76}\\\\\nDCNet & 146 & 161 & 307 & 99.66 & 86.45 & 86.62\\\\\nGKSNet & 111 & 190 & \\textbf{301} & \\textbf{99.67} & 86.34 & 86.51 \\\\\n\\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network", "authors": ["Junjie Wang", "Feng Gao", "Junyu Dong", "Shan Zhang", "Qian Du"], "url": "https://arxiv.org/abs/2201.08954v2", "attribution": "\"Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network\" by Junjie Wang, Feng Gao, Junyu Dong, Shan Zhang, and Qian Du, arXiv:2201.08954v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.20720v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Optimal threshold values after optimization.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\hline\n $N$ &$\\mathbf{\\lambda_{opt}}$ &$\\mathbf{\\alpha(\\lambda_{opt})}$ &$\\mathbf{BTR(\\lambda_{opt})}$\\\\ \n \\hline\n \\textbf{256} & 0.72 & 0.81 & 0.17 \\\\ \n \\textbf{512} & 0.75 & 0.81 & 0.18 \\\\ \n \\textbf{1024} & 0.76 & 0.78 & 0.18 \\\\ \n \\textbf{2048} & 0.78 & 0.76 & 0.2 \\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Semantic Communications via Features Identification", "authors": ["Federico Francesco Luigi Mariani", "Michele Zhu", "Maurizio Magarini"], "url": "https://arxiv.org/abs/2503.20720v1", "attribution": "\"Semantic Communications via Features Identification\" by Federico Francesco Luigi Mariani, Michele Zhu, and Maurizio Magarini, arXiv:2503.20720v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.14098v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\t\t\t\\hline\n \\multirow{2}{*}{Algorithm} & \\multicolumn{2}{c}{\\textsf{Pendulum-v0}} & \\multicolumn{2}{c}{\\textsf{BipedalWalkerHardcore-v3}} \\\\\n \n\t\t\t & Mean & Variance & Mean & Variance \\\\\n \\hline\n TD3 & -122 & 6903 & 234 & 9348 \\\\\n VARAC & -126 & \\textbf{4826} & 221 & \\textbf{6090} \\\\\n\t\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{The mean and variance of the policy learned by TD3 and VARAC algorithms under two gym environments.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy", "authors": ["Han Zhong", "Xun Deng", "Ethan X. Fang", "Zhuoran Yang", "Zhaoran Wang", "Runze Li"], "url": "https://arxiv.org/abs/2012.14098v2", "attribution": "\"Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy\" by Han Zhong, Xun Deng, Ethan X. Fang, Zhuoran Yang, Zhaoran Wang, and Runze Li, arXiv:2012.14098v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09717v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study: the AUC scores of the proposed method and its variants on \\textbf{Dataset-2}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|}\n \\hline\n \\footnotesize Method & \\footnotesize AUC Score \\\\\n \\hline\n \\footnotesize Autoencoder & \\footnotesize {0.675} \\\\\n \\hline\n \\footnotesize LithoNet (OC-SVM) & \\footnotesize {0.720} \\\\\n \\hline\n \\footnotesize LithoNet (MC-SVM) & \\footnotesize {0.744} \\\\\n \\hline\n \\footnotesize Glocal (AE + LithoNet) & \\footnotesize {0.846} \\\\\n \\hline\n \\footnotesize SA-LithoNet (OC-SVM) & \\footnotesize {0.857} \\\\\n \\hline\n \\footnotesize SA-LithoNet (MC-SVM) & \\footnotesize {0.864} \\\\\n \\hline\n \\footnotesize \\textbf{SA-Glocal (AE + SA-LithoNet)} & \\footnotesize \\textbf{0.932} \\\\ \n \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning", "authors": ["Hao-Chiang Shao", "Hsing-Lei Ping", "Kuo-shiuan Chen", "Weng-Tai Su", "Chia-Wen Lin", "Shao-Yun Fang", "Pin-Yian Tsai", "Yan-Hsiu Liu"], "url": "https://arxiv.org/abs/2201.09717v1", "attribution": "\"Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning\" by Hao-Chiang Shao, Hsing-Lei Ping, Kuo-shiuan Chen, Weng-Tai Su, Chia-Wen Lin, Shao-Yun Fang, Pin-Yian Tsai, and Yan-Hsiu Liu, arXiv:2201.09717v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2310.09622v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model calibration for stock and sentiment index}\n\\begin{tabular}{|cccccc|}\\hline\n \\hline \n \\textbf{Parameters} & \\multicolumn{5}{c|}{\\textbf{Stocks}}\\\\\n & \\multicolumn{2}{c}{\\textbf{TSLA}} && \\textbf{NVDA} & \\textbf{NFLX} \\\\ \n & C245-Equity & C250-Equity && C435-Equity & C370-Equity\\\\\n&&&&&\\\\\n \\hline \\hline\n \n$\\mu_p$ & 0.0002054 & 0.0002054 && 0.0011744& -0.000762\\\\\n$\\mu_d$ & -0.000359 & -0.000359 && 0.004902& -0.000977\\\\\n$k$ & 0.009956 & 0.009956 && 0.06638& -0.112378\\\\\n$\\sigma_p$ & 0.0994 & 0.0994 && 0.01156& 0.06705\\\\\n$\\sigma_d$ & 0.555 & 0.555 && 0.427 & 0.433\\\\\n$\\lambda$ & 5.50 & 5.50 && 7.06& 2.00\\\\\\hline\n \\hline\n$T$ in year & 0.625 & 0.625 && 0.75 & 0.9167\\\\\nCurrent price $\\$$ & 190.90 & 190.90 && 269.97 & 338.21\\\\\\hline\nMarket option value & 19.31 & 17.52 && 6.95 & 49.65\\\\\nModel option value & 19.93 & 17.63 && 6.68 & 50.47\\\\\\hline\\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Neural Network for valuing Bitcoin options under jump-diffusion and market sentiment model", "authors": ["Edson Pindza", "Jules Clement Mba", "Sutene Mwambi", "Nneka Umeorah"], "url": "https://arxiv.org/abs/2310.09622v1", "attribution": "\"Neural Network for valuing Bitcoin options under jump-diffusion and market sentiment model\" by Edson Pindza, Jules Clement Mba, Sutene Mwambi, and Nneka Umeorah, arXiv:2310.09622v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.11079v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Regression results: beliefs}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\hline\n \t&\t(1)\t&\t(2)\t&\t(3)\t&\t(4)\t\\\\\n\t&\tBeliefs \t&\tBeliefs \t&\tBeliefs \t&\tBeliefs \t\\\\\n\t&\t(cont. PGG) \t& (cont. PGG) \t& (cont. PGG) \t&\t(cont. PGG) \t\\\\\n\\hline\t\n$AR$ & -0.439 & -0.436 & -0.380 & -0.376 \\\\\n & (0.349) & (0.343) & (0.343) & (0.343) \\\\\n$RA$ & -0.207 & -0.095 & -0.099 & -0.102 \\\\\n & (0.357) & (0.353) & (0.352) & (0.351) \\\\\n$AA$ & -0.218 & -0.111 & -0.095 & -0.104 \\\\\n & (0.351) & (0.347) & (0.349) & (0.349) \\\\\n$age$ & & -0.003 & -0.003 & -0.003 \\\\\n & & (0.009) & (0.009) & (0.009) \\\\\n$female$ & & -0.057 & -0.139 & -0.166 \\\\\n & & (0.253) & (0.253) & (0.255) \\\\\n$education$ & & -0.298*** & -0.287*** & -0.286*** \\\\\n & & (0.097) & (0.097) & (0.097) \\\\\n$altruism$ & & 0.406** & 0.429*** & 0.423*** \\\\\n & & (0.164) & (0.163) & (0.164) \\\\\n$envious$ & & -0.007 & -0.007 & -0.009 \\\\\n & & (0.093) & (0.093) & (0.093) \\\\\n$idiology (right)$ & & 0.001 & 0.003 & 0.004 \\\\\n & & (0.058) & (0.058) & (0.058) \\\\\n$gravity (warming)$ & & 0.210** & 0.204** & 0.203** \\\\\n & & (0.083) & (0.083) & (0.083) \\\\\n$number actions$ & & -0.174*** & -0.184*** & -0.184*** \\\\\n & & (0.064) & (0.064) & (0.064) \\\\\n$social transfer$ & & 0.507 & 0.515 & 0.499 \\\\\n & & (0.326) & (0.329) & (0.331) \\\\\n$CRT$ & & -0.631*** & -0.633*** & -0.636*** \\\\\n & & (0.136) & (0.137) & (0.136) \\\\\n$unemployed$ & & 0.669 & 0.641 & 0.657 \\\\\n & & (0.436) & (0.438) & (0.439) \\\\\n$risk$ $aversion$ & & & -1.001** & -1.220*** \\\\\n & & & (0.425) & (0.472) \\\\\n$ambiguity$ $aversion$ & & & & -0.323 \\\\\n & & & & (0.313) \\\\\nConstant & 9.187*** & 9.480*** & 9.571*** & 9.614*** \\\\\n & (0.251) & (1.033) & (1.029) & (1.029) \\\\\n & & & & \\\\\nObservations & 1,500 & 1,427 & 1,421 & 1,421 \\\\\nR-squared & 0.001 & 0.058 & 0.061 & 0.062 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The effect of ambiguity in strategic environments: an experiment", "authors": ["Pablo Brañas-Garza", "Antonio Cabrales", "María Paz Espinosa", "Diego Jorrat"], "url": "https://arxiv.org/abs/2209.11079v1", "attribution": "\"The effect of ambiguity in strategic environments: an experiment\" by Pablo Brañas-Garza, Antonio Cabrales, María Paz Espinosa, and Diego Jorrat, arXiv:2209.11079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.10706v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\toprule\n Within-regime Markov order $k_{i,g}$ ($i=1,\\dots,4,g=1,2,3$) & 0 & 1 & 2 & 3 & 4 \\\\\n \\midrule\n AIC of models with two regimes & 3897 & 3789 & 3768 & 3753 & 3759 \\\\\n AIC of models with three regimes & 3812 & 3692 & 3679 & 3676 & 3689 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The AIC values of models fitted by multi-stage approach, without external business cycle information. Markov order of 0 in the table corresponds to the hidden Markov model.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Margin-closed regime-switching multivariate time series models", "authors": ["Lin Zhang", "Harry Joe", "Natalia Nolde"], "url": "https://arxiv.org/abs/2312.10706v1", "attribution": "\"Margin-closed regime-switching multivariate time series models\" by Lin Zhang, Harry Joe, and Natalia Nolde, arXiv:2312.10706v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.02758v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Physical parameters of the DFIM~.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n\tParameter & Value & Parameter & Value \\\\\n\t\\hline\n\t$L_1$ & $0.02645$ & $R_1$ [$\\Omega$]& $0.036$ \\\\\n\t$L_2$ & $0.0264$& $R_2$ [$\\Omega$]& $0.038$ \\\\\n\t$L_m$ & $0.0257$& $\\omega_0$ [rad/s]& $2\\pi70.8$\\\\\n\t$p$ & $3$ & $\\omega_r$ [rad/s]& $2\\pi62$ \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration", "authors": ["Guido Carnevale", "Nicola Mimmo", "Giuseppe Notarstefano"], "url": "https://arxiv.org/abs/2412.02758v3", "attribution": "\"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration\" by Guido Carnevale, Nicola Mimmo, and Giuseppe Notarstefano, arXiv:2412.02758v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2401.00568v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{$\\text{RMST}_{\\text{diff}}$ for LUME-LUNG Treatment delay}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n \\hline\nModel & $\\text{RMST}_{\\text{diff}}$ & WAIC \\\\\n \\hline\n Change-point & 2.72 & 3933.05 \\\\\n Log-Normal & 1.83 & 3935.17 \\\\\n Generalized-Gamma & 1.95 & 3936.63 \\\\\n Royston Parmar (PH) & 2.63 & 3937.92 \\\\\n Royston Parmar (non-PH) & 2.69 & 3939.61 \\\\\n Log-Logistic & 2.09 & 3945.81 \\\\\n Gamma & 2.56 & 3958.73 \\\\\n Weibull & 2.60 & 3969.97 \\\\\n Gompertz & 2.53 & 3995.38 \\\\\n Exponential & 2.51 & 4001.31 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Extrapolation of Relative Treatment Effects using Change-point Survival Models", "authors": ["Philip Cooney", "Arthur White"], "url": "https://arxiv.org/abs/2401.00568v1", "attribution": "\"Extrapolation of Relative Treatment Effects using Change-point Survival Models\" by Philip Cooney and Arthur White, arXiv:2401.00568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.07477v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model Performance Based on DM Test}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccccccccccccc}\n\\hline\n&(1)&(2)&(3)&(4)&(5)&(6)&(7)&(8)&(9)&(10)&(11)&(12)&(13)&(14)&(15)&(16)&(17)&(18)&(19)&(20)\\\\\n& OLS & LASSO & Ridge & ENet & PCR & PLS & GLM \n& XGB(+H) & LGBM(+H) & RF & NN1 & NN2 & NN3 & NN4 & NN5\n& $Ens_{avg}$ & $Ens_{op}$ & $Ens_{wp}$ & $Ens_{\\theta=0.9}$ & $Ens_{\\theta=1}$ \\\\ \n\\hline\nOLS & 0 & 2.31** & -0.04 & 2.86*** & 2.44** & 4.24*** & 6.82*** & 4.67** & 4.69*** & 0.52 & 3.70*** & 2.85*** & 3.30*** & 3.29*** & 4.12*** & 7.39*** & 5.40*** & 8.58*** & 6.17*** & 7.18*** \\\\ \nLASSO & -2.31** & 0 & -2.32** & 2.56** & 0.29 & 4.07*** & 5.47*** & 3.82*** & 3.82*** & -0.24 & 2.13** & 1.71* & 2.20** & 2.30** & 2.99*** & 5.72*** & 4.20*** & 7.25*** & 4.49*** & 5.60*** \\\\ \nRidge & 0.04 & 2.32** & 0 & 2.87*** & 2.45** & 4.26*** & 6.82*** & 4.67*** & 4.70*** & 0.52 & 3.71*** & 2.85*** & 3.30*** & 3.29*** & 4.12*** & 7.41*** & 5.41*** & 8.59*** & 6.19*** & 7.20*** \\\\ \nENet & -2.86*** & -2.56** & -2.87*** & 0 & -0.79 & 3.20*** & 5.40*** & 3.73*** & 3.72*** & -0.37 & 1.94* & 1.57 & 2.08** & 2.17** & 2.87*** & 5.60*** & 4.09*** & 7.24*** & 4.32*** & 5.46*** \\\\ \nPCR & -2.44** & -0.29 & -2.45** & 0.79 & 0 & 4.26*** & 5.67*** & 3.92*** & 3.93*** & -0.29 & 2.23** & 1.76* & 2.29** & 2.37** & 3.07*** & 6.18*** & 4.31*** & 7.49*** & 4.72*** & 6.00*** \\\\ \nPLS & -4.24*** & -4.07*** & -4.26*** & -3.20*** & -4.26*** & 0 & 5.13*** & 3.50*** & 3.48*** & -0.72 & 1.41 & 1.15 & 1.74* & 1.81* & 2.60*** & 5.22*** & 3.85*** & 7.18*** & 3.86*** & 5.09*** \\\\ \nGLM & -6.82*** & -5.47*** & -6.82*** & -5.40*** & -5.67*** & -5.13*** & 0 & -0.04 & -0.44 & -7.15*** & -5.60*** & -5.57*** & -4.02*** & -3.56*** & -2.70*** & -3.99*** & -0.92 & 6.87*** & -4.42*** & -3.67*** \\\\ \nXGB(+H) & -4.67*** & -3.82*** & -4.67*** & -3.73*** & -3.92*** & -3.50*** & 0.04 & 0 & -0.70 & -8.46*** & -3.66*** & -3.90*** & -2.97*** & -2.79*** & -2.14** & -2.11** & -0.83 &\t7.37***\t& -2.71***&\t-2.02** \\\\\nLGBM(+H) & -4.69*** & -3.82*** & -4.70*** & -3.72*** & -3.93*** & -3.48*** & 0.44 & 0.70 & 0 & -8.66*** & -3.57*** & -3.90*** & -2.91*** & -2.60*** & -1.90* & -1.96** & -0.47 & 7.83*** & -2.60***& -1.86* \\\\\nRF & -0.52 & 0.24 & -0.52 & 0.37 & 0.29 & 0.72 & 7.15*** & 8.46*** & 8.66*** & 0 & 1.73* & 2.12** & 2.70*** & 2.51** & 3.33*** & 4.49*** & 6.20***& 10.10***& 3.62*** & 4.79*** \\\\\nNN1 & -3.70*** &\t-2.13**\t& -3.71*** & -1.94*\t& -2.23** & -1.41 & 5.60*** & 3.66*** & 3.57*** & -1.73* & 0 & 0.22 & 1.22 & 1.20 & 2.58*** & 4.97*** & 4.15***& 8.57*** & 2.83*** &\t5.00*** \\\\\nNN2 & -2.85*** & -1.71* & -2.85*** & -1.57 & -1.76* & -1.15 & 5.57*** & 3.90*** \t& 3.90*** & -2.12** & -0.22 & 0 & 1.27 & 0.84 & 2.28** & 3.30*** & 4.17***&\t8.96*** & 1.77*\t & 3.56*** \\\\\nNN3\t& -3.30*** & -2.20** & -3.30*** & -2.08** & -2.29** & -1.74* & 4.02*** & 2.97*** & 2.91*** & -2.70*** & -1.22 & -1.27 & 0 & -0.07 & 1.26 & 1.63 & 2.83***&\t8.70*** & 0.56 &\t1.79* \\\\\nNN4\t& -3.29*** & -2.30** & -3.29*** & -2.17** & -2.37** & -1.81* & 3.56*** &\t2.79*** & 2.60*** & -2.51** & -1.20 & -0.84 & 0.07 & 0 & 1.13 & 1.64 & 2.76***& 7.86*** & 0.67 & 1.83* \\\\\nNN5\t& -4.12*** & -2.99*** & -4.12*** & -2.87***\t& -3.07*** & -2.60*** & 2.70*** & 2.14** & 1.90* & -3.33*** & -2.58*** & -2.28** & -1.26 & -1.13 & 0 & 0.26 & 1.86* &\t8.84*** & -0.60 &\t0.42 \\\\\n$Ens_{avg}$ & -7.39*** & -5.72*** & -7.41*** & -5.60*** & -6.18*** & -5.22*** & 3.99*** & -2.11** & -1.96** & -4.49*** & -4.97*** & -3.30*** & -1.63 & -1.64 & 0.26 & 0 & 1.88* & 7.58*** & -2.32** & 0.94 \\\\ \n$Ens_{op}$ & -5.40*** & -4.20*** & -5.41*** & -4.09*** & -4.31*** & -3.85*** & 0.92 & -0.83 & -0.47 & -6.20*** & -4.15*** & -4.17*** & -2.83*** & -2.76*** & 1.86* & 1.88* & 0 & 7.79*** & -2.87*** & -1.92* \\\\ \n$Ens_{wp}$ & -8.58*** & -7.25*** & -8.59*** & -7.24*** & -7.49*** & -7.18*** & -6.87*** & -7.37*** & -7.83*** & -10.10*** & -8.57*** & -8.96*** & -8.70*** & -7.86*** & -8.84*** & -7.58*** & -7.79*** & 0 & -7.72*** & -7.68*** \\\\ \n$Ens_{\\theta=0.9}$ & -6.17*** & -4.49*** & -6.19*** & -4.32*** & -4.72*** & -3.86*** & 4.42*** & 2.71*** & 2.60*** & -3.62*** & -2.83*** & -1.77* & -0.56 & -0.67 & 0.60 & 2.32** & 2.87*** & 7.72*** & 0 & 2.85*** \\\\ \n$Ens_{\\theta=1}$ & -7.18*** & -5.60*** & -7.20*** & -5.46*** & -6.00*** & -5.09*** &3.67*** & 2.02** & 1.86* & -4.79*** &\t-5.00*** &\t-3.56*** & -1.79* &\t-1.83* & -0.42 & -0.94 & 1.92* &\t7.68*** & -2.85*** & 0 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Electricity Market Predictability: Virtues of Machine Learning and Links to the Macroeconomy", "authors": ["Jinbo Cai", "Wenze Li", "Wenjie Wang"], "url": "https://arxiv.org/abs/2507.07477v1", "attribution": "\"Electricity Market Predictability: Virtues of Machine Learning and Links to the Macroeconomy\" by Jinbo Cai, Wenze Li, and Wenjie Wang, arXiv:2507.07477v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.06675v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Determinants of constructing major Roman roads (all cells)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n\\hline\\hline\\\\\n & \\textit{(1)} & \\textit{(2)} & \\textit{(3)} & \\textit{(4)} & \\textit{(5)} & \\textit{(6)} & \\textit{(7)}\\\\\n \\textit{Dependent variable:}&&&&&&&\\\\\n\\textit{Major Roman roads in km (log)}&Total&Total&Total&Mountain&Hill&Plain&Total \\\\\n\\hline\\\\\n\\texttt{Ruggedness} (log)& -0.043** & -0.045** & -0.041** & 0.084 & -0.047 & -0.063* & -0.045** \\\\\n & (0.019) & (0.018) & (0.019) & (0.082) & (0.094) & (0.037) & (0.018) \\\\\n\\texttt{Elevation} (log)& -0.019 & -0.036* & -0.105***& -0.515***& -0.243***& 0.006 & -0.092***\\\\\n & (0.030) & (0.019) & (0.010) & (0.084) & (0.054) & (0.039) & (0.012) \\\\\n\\texttt{Slope} (log)& 0.077* & 0.040 & 0.096** & -1.355* & 0.156 & 0.080* & 0.078** \\\\\n & (0.042) & (0.030) & (0.041) & (0.732) & (0.151) & (0.041) & (0.037) \\\\\n\\texttt{Agriculture suitability pre 1500} (log) & 0.018** & 0.003 & 0.041***& 0.019** & 0.117***& 0.016 & 0.031***\\\\\n & (0.007) & (0.013) & (0.008) & (0.008) & (0.032) & (0.023) & (0.006) \\\\\n\\texttt{Forest} (dummy)& 0.027 & 0.037 & 0.067 & 0.250***& 0.114 & -0.042 & 0.078 \\\\\n & (0.064) & (0.033) & (0.071) & (0.031) & (0.177) & (0.049) & (0.069) \\\\\n\\texttt{Low vegetation} (dummy)& -0.086* & -0.102***& -0.073** & 0.095***& 0.012 & -0.075***& -0.063* \\\\\n & (0.050) & (0.038) & (0.034) & (0.035) & (0.094) & (0.016) & (0.034) \\\\\n\\texttt{Distance from sea} (log) & -0.033 & 0.016 & 0.029 & 0.096 & -0.110 & 0.052* & 0.036* \\\\\n & (0.034) & (0.027) & (0.025) & (0.098) & (0.106) & (0.028) & (0.022) \\\\\n\\texttt{Distance from nearest waterway} (log) & 0.019 & -0.013 & -0.023 & -0.004 & -0.044 & -0.016 & -0.016 \\\\\n & (0.018) & (0.017) & (0.023) & (0.036) & (0.033) & (0.042) & (0.020) \\\\\n\\texttt{Distance from nearest harbor} (log)& & -0.105** & & & & & -0.114***\\\\\n & & (0.043) & & & & & (0.040) \\\\\n\\texttt{Distance from Rome} (log)& & 0.049 & & & & & -0.435** \\\\\n & & (0.109) & & & & & (0.190) \\\\\n\\texttt{Pre-Roman amenities} (dummy)& & 0.684***& & & & & 0.638***\\\\\n & & (0.078) & & & & & (0.076) \\\\\n\\hline\\\\\n $ \\phi_{p}$\t&No&No&Yes&Yes&Yes&Yes&Yes \\\\ \n \\texttt{Smaller area} (dummy) &Yes&Yes&Yes&Yes&Yes&Yes&Yes \\\\ \nObservations & 5,095& 5,095& 5,095& 1,371& 1,304& 2,420& 5,095\\\\\nAdjusted \\textit{R$^{2}$}& 0.201& 0.267& 0.269& 0.196& 0.253& 0.336& 0.324\\\\\n\\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "From the historical Roman road network to modern infrastructure in Italy", "authors": ["Luca De Benedictis", "Vania Licio", "Anna Pinna"], "url": "https://arxiv.org/abs/2208.06675v1", "attribution": "\"From the historical Roman road network to modern infrastructure in Italy\" by Luca De Benedictis, Vania Licio, and Anna Pinna, arXiv:2208.06675v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14363v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n \\hline\n $a$ & $b$ & $\\varepsilon$ & $c$ & $d$ & $K$ & $\\tau_1$ & $\\tau_2$ \\\\ \\hline\n $0.0428$ & $[1.2,1.5]$ & $0.1$ & $0.16$ & $0.0522$ & $0.0435$ & $[0,500]$ & ${[0,1000]}$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Parameter values set { as given in~, and a wide range for delay parameters $\\tau_{1,2}$}.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transitions to Intermittent Chaos in Quorum Sensing Dynamics", "authors": ["A. Flores--Pérez", "M. A. González--Olvera", "V. F. Breña--Medina"], "url": "https://arxiv.org/abs/2503.14363v3", "attribution": "\"Transitions to Intermittent Chaos in Quorum Sensing Dynamics\" by A. Flores--Pérez, M. A. González--Olvera, and V. F. Breña--Medina, arXiv:2503.14363v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.06718v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average F1 Scores with Different Values of $\\beta$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\t\t\t\\toprule\n\t\t\t $\\beta$ & 0.01 & 0.05 & 0.1 & 0.6 & 1 \\\\\n\t\t\t\\midrule\t\t\n\t\t\tF1 score & 0.903 & 0.925 & 0.926 & 0.907 & 0.705 \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Power System Event Identification based on Deep Neural Network with Information Loading", "authors": ["Jie Shi", "Brandon Foggo", "Nanpeng Yu"], "url": "https://arxiv.org/abs/2011.06718v2", "attribution": "\"Power System Event Identification based on Deep Neural Network with Information Loading\" by Jie Shi, Brandon Foggo, and Nanpeng Yu, arXiv:2011.06718v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2101.10266v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\n\\multirow{2}{*}{\\textbf{Location}} & \\multicolumn{2}{c|}{\\textbf{VAR (\\%)}} \\\\ \\cline{2-3} \n & \\textit{\\textbf{MRE}} & \\textit{\\textbf{MAE}} \\\\ \\hline\nNew York & 11.28, 95\\% CI {[}10.9, 11.6{]} & 0.15 \\\\ \\hline\nCalifornia & 13.48, 95\\% CI {[}13.4, 13.5{]} & 0.23 \\\\ \\hline\nFlorida & 17.49, 95\\% CI {[}17.5, 17.5{]} & 0.38 \\\\ \\hline\nNew Jersey & 17.93, 95\\% CI {[}17.9, 18{]} & 0.26 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The errors of forecasting the outbreak of COVID-19 (\\% of people tested and positive) for the next 30 days using VAR model.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms", "authors": ["Rohan Sukumaran", "Parth Patwa", "T V Sethuraman", "Sheshank Shankar", "Rishank Kanaparti", "Joseph Bae", "Yash Mathur", "Abhishek Singh", "Ayush Chopra", "Myungsun Kang", "Priya Ramaswamy", "Ramesh Raskar"], "url": "https://arxiv.org/abs/2101.10266v2", "attribution": "\"COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms\" by Rohan Sukumaran, Parth Patwa, T V Sethuraman, Sheshank Shankar, Rishank Kanaparti, Joseph Bae, Yash Mathur, Abhishek Singh, Ayush Chopra, Myungsun Kang, Priya Ramaswamy, and Ramesh Raskar, arXiv:2101.10266v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10175v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Location Accuracy}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\toprule\n Model & Backbone & $\\rm AP_{50:95}$ & $\\rm AP_{50}$ & $\\rm AP_{75}$ & $\\rm Recall$\\\\\n \\midrule\n RFMask(4) & ResNet-18 & 0.586 & 0.966 & 0.678 & 0.662 \\\\\n RFMask(4) & ResNet-34 & 0.590 & 0.966 & 0.689 & 0.665 \\\\\n RFMask(4) & ResNet-50 & 0.581 & 0.966 & 0.671 & 0.656 \\\\\n RFMask(12) & ResNet-18 & 0.621 & 0.967 & 0.783 & 0.691 \\\\\n RFMask(12) & ResNet-34 & 0.631 & 0.967 & 0.817 & 0.699 \\\\\n RFMask(12) & ResNet-50 & \\textbf{0.632} & \\textbf{0.967} & \\textbf{0.824} & \\textbf{0.701} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signals", "authors": ["Zhi Wu", "Dongheng Zhang", "Chunyang Xie", "Cong Yu", "Jinbo Chen", "Yang Hu", "Yan Chen"], "url": "https://arxiv.org/abs/2201.10175v1", "attribution": "\"RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signals\" by Zhi Wu, Dongheng Zhang, Chunyang Xie, Cong Yu, Jinbo Chen, Yang Hu, and Yan Chen, arXiv:2201.10175v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.15370v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\nParameter & Default Value \\\\\n\\midrule\nReplay Buffer Size & 1000000 \\\\\nMinibatch Size & 256 \\\\\nDiscount factor $(\\gamma)$ & 0.99 \\\\\nLearning Rate & $3 \\times 10^{-4}$ \\\\\nUpdate Actor Every & 1 \\\\\nUpdate Critic Every & 1 \\\\\nUpdate Critic Target Every & 1 \\\\\nLearning Starts & 100 \\\\\nTarget Smoothing Coefficient $(\\tau)$ & 0.005 \\\\\nTarget Entropy & $\\vert \\mathcal{A} \\vert$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Default parameters for SAC implementation.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers", "authors": ["Gautham Vasan", "Mohamed Elsayed", "Alireza Azimi", "Jiamin He", "Fahim Shariar", "Colin Bellinger", "Martha White", "A. Rupam Mahmood"], "url": "https://arxiv.org/abs/2411.15370v2", "attribution": "\"Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers\" by Gautham Vasan, Mohamed Elsayed, Alireza Azimi, Jiamin He, Fahim Shariar, Colin Bellinger, Martha White, and A. Rupam Mahmood, arXiv:2411.15370v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2302.00761v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lrrrrrr}\n\\hline\\hline\nVariable&\\multicolumn{2}{c}{Zero-leverage}&\\multicolumn{2}{c}{Positive leverage}&\\multicolumn{2}{c}{T-test}\\tabularnewline\n\\hline\\hline\n&Mean&Median&Mean&Median&Diff in means&T-stat\\tabularnewline\n\\hline\nSize&$ 4.594$&$4.589$&$ 6.072$&$ 6.147$&$-1.478$&$-120.190$\\tabularnewline\nAge&$11.562$&$9.000$&$16.118$&$11.000$&$-4.557$&$ -45.524$\\tabularnewline\nProfitability&$-0.013$&$0.023$&$ 0.049$&$ 0.062$&$-0.062$&$ -46.259$\\tabularnewline\nTangibility&$ 0.095$&$0.067$&$ 0.260$&$ 0.216$&$-0.165$&$-161.476$\\tabularnewline\nCash&$ 0.421$&$0.395$&$ 0.087$&$ 0.051$&$ 0.334$&$ 168.966$\\tabularnewline\nR\\&D&$ 0.138$&$0.110$&$ 0.059$&$ 0.029$&$ 0.079$&$ 69.297$\\tabularnewline\nMB&$ 2.103$&$1.675$&$ 1.401$&$ 1.241$&$ 0.701$&$ 58.718$\\tabularnewline\nP/S&$ 3.500$&$2.630$&$ 0.978$&$ 0.835$&$ 2.522$&$ 98.716$\\tabularnewline\nROA&$-0.029$&$0.022$&$ 0.003$&$ 0.022$&$-0.032$&$ -24.032$\\tabularnewline\nROE&$-0.032$&$0.032$&$ 0.022$&$ 0.072$&$-0.054$&$ -27.356$\\tabularnewline\nAsset growth&$ 0.069$&$0.040$&$ 0.058$&$ 0.026$&$ 0.012$&$ 6.208$\\tabularnewline\nDividends&$ 0.009$&$0.000$&$ 0.008$&$ 0.000$&$ 0.001$&$ 5.165$\\tabularnewline\nCapex&$ 0.028$&$0.021$&$ 0.045$&$ 0.035$&$-0.017$&$ -68.447$\\tabularnewline\nCash Flow&$-0.011$&$0.055$&$ 0.043$&$ 0.068$&$-0.054$&$ -31.865$\\tabularnewline\nNet debt issuance&$-0.004$&$0.000$&$ 0.026$&$ 0.005$&$-0.030$&$ -72.829$\\tabularnewline\nEquity issuance&$ 0.042$&$0.003$&$ 0.004$&$ 0.000$&$ 0.037$&$ 37.451$\\tabularnewline\nProfitability SD&$ 0.145$&$0.118$&$ 0.074$&$ 0.054$&$ 0.071$&$ 73.737$\\tabularnewline\nCF SD&$ 0.160$&$0.125$&$ 0.086$&$ 0.060$&$ 0.074$&$ 59.824$\\tabularnewline\nTobin`s Q&$ 3.595$&$3.174$&$ 2.132$&$ 2.012$&$ 1.463$&$ 119.016$\\tabularnewline\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Zero-Leverage Puzzle", "authors": ["Mykola Pinchuk"], "url": "https://arxiv.org/abs/2302.00761v1", "attribution": "\"Zero-Leverage Puzzle\" by Mykola Pinchuk, arXiv:2302.00761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2007.07793v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{UAV physics model parameters }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c||} \n \\hline\n Parameter & Value \\\\ \n \\hline\n Timestep & 0.01 sec \\\\\n Motor lag & 0.05 sec \\\\\n Mass $m$ & 1.5 kg \\\\\n Arm length $l$ & 0.13 m \\\\\n Tilt angle range $[\\theta_{min}, \\theta_{max}]$ & $[-\\frac{\\pi}{3}, \\frac{\\pi}{3}]$ rad \\\\\n Motor thrust range $[F_{min}, F_{max}]$ & $[0, 15.0]$ N \\\\ %[-0.5ex]\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors", "authors": ["Aditya M. Deshpande", "Rumit Kumar", "Ali A. Minai", "Manish Kumar"], "url": "https://arxiv.org/abs/2007.07793v1", "attribution": "\"Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors\" by Aditya M. Deshpande, Rumit Kumar, Ali A. Minai, and Manish Kumar, arXiv:2007.07793v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11279v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Baseline results (PSNR) for $\\times2$ SR. We train RCAN on DF2K with the original protocol (1st row) and our large-batch training strategies (2nd and 3rd rows). The {\\setlength{\\fboxsep}{2pt}\\colorbox{green!25}{highlighted}} setting matches the original strategy and reduces the training time by $77\\%$, which is used as the baseline for further improvements.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\nBS, $\\eta$ & Time & Set5 & Set14 & B100 & Urban100 \n\\\\\n\\midrule\n16, ~~0.0001 & 7 Days & 38.35 & 34.33 & 32.48 & 33.59 \\\\\n256, 0.0016 & 1.6 Days & 38.34 & 34.35 & 32.45 & 33.52 \\\\\n\\rowcolor{green!25}\n256, 0.0032 & 1.6 Days & 38.35 & 34.42 & 32.46 & 33.61 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Revisiting RCAN: Improved Training for Image Super-Resolution", "authors": ["Zudi Lin", "Prateek Garg", "Atmadeep Banerjee", "Salma Abdel Magid", "Deqing Sun", "Yulun Zhang", "Luc Van Gool", "Donglai Wei", "Hanspeter Pfister"], "url": "https://arxiv.org/abs/2201.11279v1", "attribution": "\"Revisiting RCAN: Improved Training for Image Super-Resolution\" by Zudi Lin, Prateek Garg, Atmadeep Banerjee, Salma Abdel Magid, Deqing Sun, Yulun Zhang, Luc Van Gool, Donglai Wei, and Hanspeter Pfister, arXiv:2201.11279v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10002v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The position optimization of the 2 holes case. The position is represented by the coordinate of the hole center.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\toprule\nHole No. & Position before optimization & Position after optimization \\\\\n\\midrule\n1th hole & (64, 64) & (54, 54) \\\\\n2th hole & (64, 64) & (54, 74)\t\t\t\\\\\n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Heat Conduction Plate Layout Optimization using Physics-driven Convolutional Neural Networks", "authors": ["Hao Ma", "Yang Sun", "Mario Chiarelli"], "url": "https://arxiv.org/abs/2201.10002v1", "attribution": "\"Heat Conduction Plate Layout Optimization using Physics-driven Convolutional Neural Networks\" by Hao Ma, Yang Sun, and Mario Chiarelli, arXiv:2201.10002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12693v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{AD Test Statistics and Critical Values}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc}\n \\toprule\n \\textbf{Parameters}& \\textbf{SU 1} \\\\\n \\midrule\n AD Test Statistic & 46.89 \\\\\n Critical Value at 15\\% &0.57 \\\\\n Critical Value at 10\\% &0.65 \\\\\n Critical Value at 5\\% &0.78 \\\\\n Critical Value at 2.5\\% &0.90 \\\\\n Critical Value at 1\\% &1.08 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Hardware-in-the-Loop Evaluation of Goodness of Fit (GoF) Testing for Dynamic Spectrum Sharing", "authors": ["Mir Lodro", "Simon Armour", "Mark A. Beach"], "url": "https://arxiv.org/abs/2501.12693v1", "attribution": "\"Hardware-in-the-Loop Evaluation of Goodness of Fit (GoF) Testing for Dynamic Spectrum Sharing\" by Mir Lodro, Simon Armour, and Mark A. Beach, arXiv:2501.12693v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18790v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Monte Carlo estimates of MSCS coverage probability and size (in parentheses) for significance level $\\alpha = \\{0.01, 0.05, 0.1\\}$, based on $\\delta_n(\\hat{k}, k) = p_{k} - p_{\\hat k}$. The row $\\hat{k} = k_0$ shows Monte Carlo estimates of $\\mathbb{P}(\\hat k=k_0)$. Calculations are based on $B = 500$ Monte Carlo samples of size $n$. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n \\multirow{2}{*}{} & \\multirow{2}{*}{} & & Density 1 & Density 2& Density 3 & Density 4 \\\\\n $n$ & $\\alpha$ & & $k_0 = 2$ & $k_0 = 3$ &$k_0 = 3$ & $k_0 = 3$ \\\\\n \\midrule\n \\multirow{6}{*}{100} \n & \\multirow{2}{*}{0.10} & Coverage (\\%) & 98.0 (4.9) & 66.8 (4.4) & 65.4 (3.8) & 89.6 (5.6) \\\\\n & & $\\hat{k} = k_0$ (\\%) & 55.0 & 3.0 & 2.2 & 24.8 \\\\\n & \\multirow{2}{*}{0.05} & Coverage (\\%) & 98.8 (5.9) & 74.2 (5.8) & 66.0 (4.3) & 90.4 (7.6) \\\\\n & & $\\hat{k} = k_0$ (\\%) & 52.4 & 3.4 & 3.2 & 20.2 \\\\\n & \\multirow{2}{*}{0.01} & Coverage (\\%) & 99.8 (8.6) & 82.8 (8.5) & 80.4 (7.0) & 92.2 (9.5) \\\\\n & & $\\hat{k} = k_0$ (\\%) & 53.6 & 2.2 & 3.0 & 26.0 \\\\\n \\midrule\n \\multirow{6}{*}{250} \n & \\multirow{2}{*}{0.10} & Coverage (\\%) & 99.8 (2.9) & 74.0 (4.2) & 76.8 (4.3) & 95.8 (3.5) \\\\\n & & $\\hat{k} = k_0$ (\\%) & 94.2 & 4.6 & 5.8 & 73.8 \\\\\n & \\multirow{2}{*}{0.05} & Coverage (\\%) & 100 (3.8) & 79.4 (5.8) & 80.2 (5.4) & 95.6 (4.7) \\\\\n & & $\\hat{k} = k_0$ (\\%) & 93.6 & 4.0 & 3.8 & 69.6 \\\\\n & \\multirow{2}{*}{0.01} & Coverage (\\%) & 100 (6.3) & 87.4 (8.2) & 89.0 (9.0) & 95.0 (7.8) \\\\\n & & $\\hat{k} = k_0$ (\\%) & 92.4 & 5.0 & 4.2 & 68.0 \\\\\n \\midrule\n \\multirow{6}{*}{1000} \n & \\multirow{2}{*}{0.10} & Coverage (\\%) & 99.8 (1.6) & 99.4 (5.5) & 97.4 (5.2) & 96.4 (1.5) \\\\\n & & $\\hat{k} = k_0$ (\\%) & 99.8 & 34.2 & 36.4 & 96.4 \\\\\n & \\multirow{2}{*}{0.05} & Coverage (\\%) & 99.8 (2.1) & 99.4 (7.0) & 98.6 (6.5) & 98.8 (1.8) \\\\\n & & $\\hat{k} = k_0$ (\\%) & 99.8 & 28.8 & 36.4 & 98.4 \\\\\n & \\multirow{2}{*}{0.01} & Coverage (\\%) & 100 (3.2) & 100 (9.2) & 98.6 (9.2) & 99.2 (2.5) \\\\\n & & $\\hat{k} = k_0$ (\\%) & 99.6 & 32.6 & 33.2 & 99.0 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Confidence set for mixture order selection", "authors": ["Alessandro Casa", "Davide Ferrari"], "url": "https://arxiv.org/abs/2503.18790v2", "attribution": "\"Confidence set for mixture order selection\" by Alessandro Casa and Davide Ferrari, arXiv:2503.18790v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|r|r|r}\n \\hline\n \\multicolumn{5}{c}{End item demand only - no adjustments} \\\\\n \\hline \n Utilization & $\\lambda$ & Converged (\\%) & Runtime (s) & $\\Delta$ (\\%)\\\\\n \\hline\n \\multirow{4}{*}{50\\%} \n & 0.1 & 8.3 & 2662.9 & 0.00 \\\\\n & 1 & 29.1 & 3992.3 & 0.47 \\\\\n & 10 & 91.6 & 225.1 & 3.41 \\\\\n & 100 & 100.0 & 232.4 & 5.35\\\\\n \\hline\n \\multirow{4}{*}{90\\%} \n & 0.1 & 20.8 & 2041.1 & 0.00 \\\\\n & 1 & 25.0 & 1138.7 & 0.83\\\\\n & 10 & 75.0 & 401.7 & 3.24\\\\\n & 100 & 79.2 & 636.5 & 4.46 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Averaging consensus calculation: Evaluation of the $\\lambda$-multiple for the penalty parameter $\\rho$ in terms of total costs and runtime for end item demands only on a scenario tree with $|\\Omega| = 2$ and therefore $|\\Phi| = 128$. Reporting percentage of converged PH runs and respective runtimes and cost deltas to optimal costs over the converged runs.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.00070v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Abnormality classification performance on patches of size $32\\times32$. Metrics computed on fully-sampled images, (A) 4$\\times$ and (B) $8\\times$ undersampled images, which were reconstructed using the various methods studied in this paper. The reported metrics are Sensitivity (Sn), Specificity (Sp), F1 score, Kappa and Area Under the ROC curve (AUROC).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c}\n\\hline\\hline\n\\multicolumn{6}{c}{AF=4$\\times$}\\\\\nInput & Sn & Sp & F1 score & Kappa & AUROC\\\\\n\\hline\nFully-sampled & 86.12 & 65.90 & 78.25& 52.04& 86.24\\\\\n\\hline\nU-Net & 89.74 & 62.93 & 79.17 & 52.69& 86.24\\\\\nFNAF U-Net & 90.23 & 63.51 & 79.64 & 53.76 & 86.37\\\\\nB-Box U-Net & 91.13 & 62.11 & 79.63 & 53.27 & 86.71\\\\\n\\hline\nI-RIM & 94.50 & 52.31 & 78.09 & 46.84 & 85.70\\\\\nFNAF I-RIM & 94.09 & 54.20 & 78.49 & 48.32 & 85.79\\\\\nB-Box I-RIM & 91.38 & 55.77 & 77.62 & 47.17 & 84.89\\\\\n\\hline\\hline\n \\\\\n\\multicolumn{6}{c}{(A)} \\\\\n\\\\\n\\hline\\hline\n\\multicolumn{6}{c}{AF=8$\\times$}\\\\\nInput & Sn & Sp & F1 score & Kappa & AUROC\\\\\n\\hline\nFully-sampled & 86.12 & 65.90 & 78.25& 52.04& 86.24\\\\\n\\hline\nU-Net & 85.30 & 68.95 & 78.89 & 54.26 & 86.04\\\\\nFNAF U-Net & 85.71 & 69.19 & 79.21 & 54.92 & 86.28\\\\\nB-Box U-Net & 87.11 & 68.78 & 79.83 & 55.91 & 87.04\\\\\n\\hline\nI-RIM & 92.28 & 55.93 & 78.14 & 48.24 & 85.43\\\\\nFNAF I-RIM & 91.71 & 56.67 & 78.08 & 48.41 & 85.34\\\\\nB-Box I-RIM & 87.03 & 59.06 & 76.40 & 46.11 & 84.20 \\\\\n\\hline\\hline\n \\\\\n\\multicolumn{6}{c}{(B)} \\\\\n\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Adversarial Robust Training of Deep Learning MRI Reconstruction Models", "authors": ["Francesco Calivá", "Kaiyang Cheng", "Rutwik Shah", "Valentina Pedoia"], "url": "https://arxiv.org/abs/2011.00070v3", "attribution": "\"Adversarial Robust Training of Deep Learning MRI Reconstruction Models\" by Francesco Calivá, Kaiyang Cheng, Rutwik Shah, and Valentina Pedoia, arXiv:2011.00070v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.00711v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of Number of Trainable Parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n \\toprule\n ~ & Number of Parameters \\\\ \n \\midrule\n Vanilla Transformer & 1.68M \\\\ \n BNT & 11.2M \\\\ \n BolT & 1.68M \\\\ \n QuantumTSTransformer & 22K \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Resting-state fMRI Analysis using Quantum Time-series Transformer", "authors": ["Junghoon Justin Park", "Jungwoo Seo", "Sangyoon Bae", "Samuel Yen-Chi Chen", "Huan-Hsin Tseng", "Jiook Cha", "Shinjae Yoo"], "url": "https://arxiv.org/abs/2509.00711v1", "attribution": "\"Resting-state fMRI Analysis using Quantum Time-series Transformer\" by Junghoon Justin Park, Jungwoo Seo, Sangyoon Bae, Samuel Yen-Chi Chen, Huan-Hsin Tseng, Jiook Cha, and Shinjae Yoo, arXiv:2509.00711v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16908v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Frequency of Special Characters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccl}\n \\toprule\n Non-English or Math&Frequency&Comments\\\\\n \\midrule\n \\O & 1 in 1,000& For Swedish names\\\\\n $\\pi$ & 1 in 5& Common in math\\\\\n \\$ & 4 in 5 & Used in business\\\\\n $\\Psi^2_1$ & 1 in 40,000& Unexplained usage\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Localization using Angle-of-Arrival Triangulation", "authors": ["Amod K. Agrawal"], "url": "https://arxiv.org/abs/2508.16908v1", "attribution": "\"Localization using Angle-of-Arrival Triangulation\" by Amod K. Agrawal, arXiv:2508.16908v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.09083v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Validation results}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rcccccc}\n\t\t\t & \\multicolumn{3}{c}{\\textbf{Three-class}} & \\multicolumn{3}{c}{\\textbf{Five-class}} \\\\\n\t\t\t & \\% (Pts) & \\% (Pts) & \\% & \\% (Pts) & \\% (Pts) & \\% \\\\\n\t\t\tLevel & Acc/Mean Err. (Llama) & Acc/Mean Err. (Phi) & Baseline & Acc/Mean Err. (Llama) & Acc/Mean Err. (Phi) & Baseline \\\\\n\t\t\tParagraph & 65 (0.06) & 54 (0.14) & 38 & 58 (0.19) & 43.4 (0.18) & 31 \\\\\n\t\t\tSentence & 67.4 (0.06) & 53 (0.08) & 37.3 & 51 (0.16) & 39 (0.15) & 34 \n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Ornithologist: Towards Trustworthy \"Reasoning\" about Central Bank Communications", "authors": ["Dominic Zaun Eu Jones"], "url": "https://arxiv.org/abs/2505.09083v1", "attribution": "\"Ornithologist: Towards Trustworthy \"Reasoning\" about Central Bank Communications\" by Dominic Zaun Eu Jones, arXiv:2505.09083v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.08623v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results of DAC 2013 i-vector dataset without domain adaptation}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\t\t\\toprule\n\t\t\\textbf{Systems\\#} & \\textbf{PLDA} & \\textbf{EER (\\%)} & \\textbf{DCF10} & \\textbf{DCF08} \\\\\n\t\t\\hline\\hline\n\t\t1 & SRE & 2.33 & 0.402 & 0.235\\\\\n\t\t2 & SWB & 5.65 & 0.632 & 0.427\\\\\n\t\t3 & SRE-1phn & 9.35 & 0.724 & 0.520\\\\\n\t\t4 & SWB & 5.66 & 0.633 & 0.427\\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Adversarial Training for Multi-domain Speaker Recognition", "authors": ["Qing Wang", "Wei Rao", "Pengcheng Guo", "Lei Xie"], "url": "https://arxiv.org/abs/2011.08623v1", "attribution": "\"Adversarial Training for Multi-domain Speaker Recognition\" by Qing Wang, Wei Rao, Pengcheng Guo, and Lei Xie, arXiv:2011.08623v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.06378v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results From Industry Classification Task Using $k$-Fold Cross Validation With $k=4$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\toprule\n \\textbf{Model} & \\textbf{Precision} & \\textbf{Recall} & \\textbf{F1} & \\textbf{Accuracy} \\\\\n \\midrule\n Returns Embedding & 0.74 & 0.72 & 0.72 & 72\\% \\\\\n News Embedding & 0.89 & 0.85 & 0.84 & 85\\% \\\\\n Multimodal Embedding & \\textbf{0.91} & \\textbf{0.90} & \\textbf{0.90} & \\textbf{90\\% } \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets", "authors": ["Rian Dolphin", "Barry Smyth", "Ruihai Dong"], "url": "https://arxiv.org/abs/2211.06378v1", "attribution": "\"A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets\" by Rian Dolphin, Barry Smyth, and Ruihai Dong, arXiv:2211.06378v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2401.02962v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The Area Under the ROC Curve for The Vessel Segmentation in Pathological Images of STARE Dataset}\n\\begin{tabular}{|l|c|}\n \\hline\n \\textbf{Method} & \\textbf{Accuracy} \\\\\n \\hline\n \\multicolumn{2}{|c|}{Unsupervised Methods} \\\\\n \\hline\n Lam et al. & 0.9707 \\\\\n \\hline\n Ricci and Perfetti (Implemented in & 0.9343 \\\\\n \\hline\n Jiang and Mojon & 0.8906 \\\\\n \\hline\n Method 1 & 0.9446\\\\\n \\hline\n Method 2 & 0.9432\\\\\n \\hline\n \\multicolumn{2}{|c|}{Supervised Methods} \\\\\n \\hline\n Soares et al. (DRIVE) & 0.9455\\\\\n \\hline\n Soares et al. (STARE) & 0.9571\\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Automated Localization of Blood Vessels in Retinal Images", "authors": ["Vahid Mohammadi Safarzadeh"], "url": "https://arxiv.org/abs/2401.02962v1", "attribution": "\"Automated Localization of Blood Vessels in Retinal Images\" by Vahid Mohammadi Safarzadeh, arXiv:2401.02962v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.05411v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Test case 1 -- inputs.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\t\t\\toprule\n\t\t\\textbf{Metering period} & \\textbf{{\\color{red}User1}} & \\textbf{{\\color{red}User2}} & \\textbf{{\\color{green}User3}} & \\textbf{{\\color{orange}User4}} \\\\\n\t\t\\midrule\n\t\t\\midrule\n\t\t\\multicolumn{5}{c}{Consumption} \\\\\n\t\t\\midrule\n\t\t2017-03-01 00:00 & 0.17 & 0.21 & -0.50 & 0.08 \\\\\n\t\t2017-03-01 00:15 & 0.21 & 0.23 & -0.30 & -0.02 \\\\\n\t\t\\midrule\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Allocation of locally generated electricity in renewable energy communities", "authors": ["Miguel Manuel de Villena", "Samy Aittahar", "Sebastien Mathieu", "Ioannis Boukas", "Eric Vermeulen", "Damien Ernst"], "url": "https://arxiv.org/abs/2009.05411v2", "attribution": "\"Allocation of locally generated electricity in renewable energy communities\" by Miguel Manuel de Villena, Samy Aittahar, Sebastien Mathieu, Ioannis Boukas, Eric Vermeulen, and Damien Ernst, arXiv:2009.05411v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.05922v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Economic Significance: Holding Period Sensitivity Analysis}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrrrr}\n\\toprule\n\\textbf{Holding Period} & \\textbf{Strategy} & \\textbf{Total Net P\\&L} & \\textbf{Sharpe Ratio (Ann.)} & \\textbf{Profit Factor} & \\textbf{Max Drawdown} & \\textbf{Max Drawdown (\\%)} \\\\\n\\midrule\n\\multirow{2}{*}{5 Days (Baseline)} & Fixed-Size & \\$31,247.50 & 0.38 & 1.22 & (\\$52,682.50) & 55.66\\% \\\\\n& Pyramiding & \\$797,222.50 & 1.62 & 2.77 & (\\$176,712.50) & \\textbf{186.71\\%} \\\\\n\\midrule\n\\multirow{2}{*}{7 Days} & Fixed-Size & \\$112,385.00 & 1.23 & 1.93 & (\\$39,287.50) & 41.00\\% \\\\\n& Pyramiding & \\$1,180,760.00 & 2.00 & 3.95 & (\\$135,325.00) & \\textbf{141.21\\%} \\\\\n\\midrule\n\\multirow{2}{*}{10 Days} & Fixed-Size & \\$200,985.00 & 2.01 & 3.00 & (\\$25,000.00) & 10.76\\% \\\\\n& Pyramiding & \\$1,404,622.50 & 2.18 & 4.42 & (\\$239,230.00) & 15.74\\% \\\\\n\\midrule\n\\multirow{2}{*}{12 Days} & Fixed-Size & \\$235,210.00 & 2.03 & 3.34 & (\\$56,052.50) & 18.37\\% \\\\\n& Pyramiding & \\$1,165,810.00 & 1.21 & 2.50 & (\\$694,205.00) & 40.40\\% \\\\\n\\midrule\n\\multirow{2}{*}{20 Days} & Fixed-Size & \\$217,385.00 & 1.23 & 1.95 & (\\$229,240.00) & 57.59\\% \\\\\n& Pyramiding & \\$735,522.50 & 0.63 & 1.45 & (\\$1,634,867.50) & \\textbf{80.06\\%} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation", "authors": ["Peilin Rao", "Randall R. Rojas"], "url": "https://arxiv.org/abs/2509.05922v1", "attribution": "\"Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation\" by Peilin Rao and Randall R. Rojas, arXiv:2509.05922v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2308.12680v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of the LastFM, Delicious, MovieLens and Yahoo! datasets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n \\toprule\n & LastFM & Delicious & MovieLens & Yahoo! \\\\ \\hline\nnumber of users & $1,892$ & $1,867$ & $2,113$ & $290$ \\\\ \nnumber of items & $17,632$ & $69,226$ & $10,197$ & $20$ \\\\\nnumber of logs & $186,480$ & $437,594$ & $47,957$ & $128,960$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity Constraints", "authors": ["Hanchi Huang", "Li Shen", "Deheng Ye", "Wei Liu"], "url": "https://arxiv.org/abs/2308.12680v1", "attribution": "\"Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity Constraints\" by Hanchi Huang, Li Shen, Deheng Ye, and Wei Liu, arXiv:2308.12680v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.08102v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|r|r|r|r|r|r|r|}\n \\toprule\n \\% Supplied & mean & std & min & 25\\% & 50\\% & 75\\% & max \\\\\n \\midrule\n Solar ($n=2$) \\& Wind ($n=2$) & 0.6801 & 0.0002 & 0.6794 & 0.6799 & 0.6801 & 0.6802 & 0.6808 \\\\\n Solar ($n=2$) \\& Wind ($n=4$) & 0.6800 & 0.0003 & 0.6788 & 0.6797 & 0.6800 & 0.6802 & 0.6811 \\\\\n Solar ($n=4$) \\& Wind ($n=2$) & 0.6792 & 0.0003 & 0.6783 & 0.6790 & 0.6792 & 0.6794 & 0.6801 \\\\\n Solar ($n=4$) \\& Wind ($n=4$) & 0.6791 & 0.0004 & 0.6777 & 0.6788 & 0.6791 & 0.6793 & 0.6803 \\\\\n \\bottomrule\n \\end{tabular}\n\\caption{Summary statistics for the percent supplied using the weights $w_s = 45$ and $w_w = 22$ for the sash size $n$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Resampling Methods that Generate Time Series Data to Enable Sensitivity and Model Analysis in Energy Modeling", "authors": ["Kelly Wang", "Steven O. Kimbrough"], "url": "https://arxiv.org/abs/2502.08102v1", "attribution": "\"Resampling Methods that Generate Time Series Data to Enable Sensitivity and Model Analysis in Energy Modeling\" by Kelly Wang and Steven O. Kimbrough, arXiv:2502.08102v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.12151v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters of the four-area test system}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|llll|}\n\t\t\\hline\n\t\t\\multicolumn{4}{|c|}{\\bf Main parameters of the VSC-HVDC link (per-unit values)}\t\t\t\t\\\\\n\t\t\\hline\n\t\t\\multicolumn{2}{|l}{Converter-side inductors: $L = 0.05$}\t\t\t\n\t\t&\\multicolumn{2}{l|}{\\textit{LCL} capacitors:\t$C_{\\rm F} = 0.05$}\t\t\t\t\t\t\t\t\\\\\n\t\t\\multicolumn{2}{|l}{Grid-side inductors: $L_{\\rm g} = 0.05$}\t\t\t\n\t\t&\\multicolumn{2}{l|}{Grid-side resistors: $R_{\\rm g} = 0.01$}\t\t\t\t\t\t\t\t\t\\\\\n\t\t\\multicolumn{2}{|l}{DC-side capacitors: $C_{\\rm dc} = 0.06$}\t\t\t\n\t\t&\\multicolumn{2}{l|}{DC-link resistors: $R_{\\rm dc} = 0.015$}\t\t\t\t\t\t\t\t\t\\\\\n\t\t\\multicolumn{4}{|l|}{PI gains of the PLL: $103.1({\\rm{rad/s}}),5311.5({\\rm{rad/s}})$}\t\t\t\\\\\n\t\t\\multicolumn{4}{|l|}{PI gains of the current control loop: $0.3({\\rm{p.u.}}),10({\\rm{p.u.}})$}\t\\\\\n\t\t\\multicolumn{4}{|l|}{PI gains of the voltage control loop: $4({\\rm{p.u.}}),40({\\rm{p.u.}})$}\t\\\\\n\t\t\\multicolumn{4}{|l|}{PI gains of the power control loop: $0.2({\\rm{p.u.}}),2({\\rm{p.u.}})$}\t\t\\\\\n\t\t\\multicolumn{4}{|l|}{PI gains of the dc voltage control loop: $5({\\rm{p.u.}}),50({\\rm{p.u.}})$}\t\\\\\n\t\t\\hline\n\t\t\\multicolumn{4}{|c|}{\\bf Main parameters of the SGs (per-unit values)}\t\t\t\t\t\t\t\\\\\n\t\t\\hline\n\t\t$X_d = 2.065$\t\t&\t$X_q = 1.974$\t\t&\t$X'_d = 0.4879$\t\t\t&\t$X'_q = 1.19$\t\t\\\\\n\t\t$X''_d = 0.35$\t\t&\t$X''_q = 0.35$\t\t&\t$T'_{d0} = 6.56$\t\t&\t$T'_{q0} = 1.5$\t\t\\\\\n\t\t$T''_{d0} = 0.05$\t&\t$T''_{q0} = 0.035$\t&\t$J_{\\rm SG} = 8.658$\t&\t$R_a = 0.0025$\t\t\\\\\n\t\t\\hline\n\t\t\\multicolumn{4}{|c|}{Fast exciters (IEEET1 Model)}\t\t\t\t\t\t\t\t\t\t\t\t\\\\\n\t\t\\hline\n\t\t$K_A = 50$\t\t\t&\t$T_A = 0.05$\t\t&\t$K_F = 0.0057$\t\t\t&\t$T_F = 0.5$\t\t\t\\\\\n\t\t$T_R = 0.1$\t\t\t&\t\t\t\t\t\t&\t\t\t\t\t\t\t&\t\t\t\t\t\t\\\\\n\t\t\\hline\n\t\t\\multicolumn{4}{|c|}{Steam Turbine and Governor (IEEEG1 Model)}\t\t\t\t\t\t\t\t\t\\\\\n\t\t\\hline\n\t\t$T_1 = 0.5$\t\t\t&\t$T_2 = 1$\t\t\t&\t$T_3 = 0.6$\t\t\t\t&\t$T_4 = 0.6$\t\t\t\\\\\n\t\t$T_5 = 0.5$\t\t\t&\t$T_6 = 0.8$\t\t\t&\t$T_7 = 1$\t\t\t\t&\t$K = 5$\t\t\t\t\\\\\n\t\t$K_1 = 0.3$\t\t\t&\t$K_2 = 0$\t\t\t&\t$K_3 = 0.25$\t\t\t&\t$K_4 = 0$\t\t\t\\\\\n\t\t$K_5 = 0.3$\t\t\t&\t$K_6 = 0$\t\t\t&\t$K_7 = 0.15$\t\t\t&\t$K_8 = 0$\t\t\t\\\\\n\t\t\\hline\n\t\t\\multicolumn{4}{|c|}{\\bf Impedance of lines and power consumption of loads (per-unit values)}\t\\\\\n\t\t\\hline\n\t\t\\multicolumn{2}{|l}{Line 1-5 \\& 11-15: $0.005+j0.05$}\t\t\t\n\t\t&\\multicolumn{2}{l|}{Line 2-5 \\& 12-15: $0.02+j0.2$}\t\t\t\t\t\t\t\t\t\t\t\\\\\n\t\t\\multicolumn{2}{|l}{Line 5-6 \\& 15-16: $0.002+j0.02$}\t\t\t\n\t\t&\\multicolumn{2}{l|}{Line 6-10 \\& 12-20: $0.004+j0.04$}\t\t\t\t\t\t\t\t\t\t\t\\\\\n\t\t\\multicolumn{2}{|l}{Line 6-7 \\& 16-17: $0.01+j0.2$}\t\t\t\n\t\t&\\multicolumn{2}{l|}{Line 7-8 \\& 17-18: $0.014+j0.28$}\t\t\t\t\t\t\t\t\t\t\t\\\\\n\t\t\\multicolumn{2}{|l}{Line 8-9 \\& 18-19: $0.004+j0.08$}\t\t\t\n\t\t&\\multicolumn{2}{l|}{Line 8-18: $0.012+j0.12$}\t\t\t\t\t\t\t\t\t\t\t\t\t\\\\\n\t\t\\multicolumn{2}{|l}{Line 9-3 \\& 19-13: $0.05+j0.02$}\t\t\t\n\t\t&\\multicolumn{2}{l|}{Line 9-4 \\& 19-14: $0.05+j0.15$}\t\t\t\t\t\t\t\t\t\t\t\\\\\n\t\t\\multicolumn{2}{|l}{$P_{\\rm Load1} = 0.9493$ (IM: 0.5)}\t\t\t\n\t\t&\\multicolumn{2}{l|}{$P_{\\rm Load2} = 1.3$ (IM: 1.2)}\t\t\t\t\t\t\t\t\t\t\t\\\\\n\t\t\\multicolumn{2}{|l}{$P_{\\rm Load3} = 0.7$ (IM: 0.2)}\t\t\t\n\t\t&\\multicolumn{2}{l|}{$P_{\\rm Load4} = 1.7$ (IM: 1.4)}\t\t\t\t\t\t\t\t\t\t\t\\\\\n\t\t\\multicolumn{2}{|l}{$C_1$ \\& $C_3$: $0.25$}\t\t\t\n\t\t&\\multicolumn{2}{l|}{$C_2$ \\& $C_4$: $0.15$}\t\t\t\t\t\t\t\t\t\t\t\t\t\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Decentralized Data-Enabled Predictive Control for Power System Oscillation Damping", "authors": ["Linbin Huang", "Jeremy Coulson", "John Lygeros", "Florian Dörfler"], "url": "https://arxiv.org/abs/1911.12151v4", "attribution": "\"Decentralized Data-Enabled Predictive Control for Power System Oscillation Damping\" by Linbin Huang, Jeremy Coulson, John Lygeros, and Florian Dörfler, arXiv:1911.12151v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.05313v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Computed average MSPE, RMSPE, MAPE, $R^2$, and $R^2_{pred}$ values of the LQ and PLS methods for the North Dakota weather data. The subscripts $_{\\text{main}}$, $_{\\text{full}}$, and $_{\\text{selected}}$ respectively correspond to the case where the model is estimated using the main effect, full, and selected models. The values given in brackets are the estimated standard errors for the calculated performance metrics.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllrr} \n\\toprule\n{Method} & {MSPE} & {RMSPE} & {MAPE} & {$R^2$} & {$R^2_{pred}$} \\\\\n\\midrule\nLQ$_{\\text{main}}$ \t\t\t& 1.445 (0.080)\t& 0.094\t(0.003)\t& 0.069\t(0.002)\t& 0.988 (9.8 $\\times 10^{-4}$)\t& 0.986 (7.8 $\\times 10^{-4}$) \\\\\nLQ$_{\\text{full}}$\t\t\t& 1.422 (0.080)\t& 0.093\t(0.003)\t& 0.068\t(0.002)\t& 0.989 (8.1 $\\times 10^{-4}$)\t& 0.986 (7.5 $\\times 10^{-4}$) \\\\\nLQ$_{\\text{selected}}$\t\t& 1.389 (0.103)\t& 0.092\t(0.004)\t& 0.067\t(0.002)\t& 0.990 (8.6 $\\times 10^{-4}$)\t& 0.987 (9.7 $\\times 10^{-4}$) \\\\\nPLS$_{\\text{main}}$ \t\t& 1.343 (0.078)\t& 0.090\t(0.003)\t& 0.066\t(0.002)\t& 0.989 (1.1 $\\times 10^{-3}$)\t& 0.987 (7.6 $\\times 10^{-4}$) \\\\\nPLS$_{\\text{full}}$\t\t\t& 1.352 (0.078)\t& 0.090\t(0.003)\t& 0.066\t(0.002)\t& 0.989 (1.1 $\\times 10^{-3}$)\t& 0.987 (7.8 $\\times 10^{-4}$) \\\\\nPLS$_{\\text{selected}}$\t\t& 1.300 (0.093)\t& 0.087\t(0.003)\t& 0.064\t(0.002)\t& 0.990 (9.0 $\\times 10^{-4}$)\t& 0.989 (8.9 $\\times 10^{-4}$) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A partial least squares approach for function-on-function interaction regression", "authors": ["Ufuk Beyaztas", "Han Lin Shang"], "url": "https://arxiv.org/abs/2012.05313v1", "attribution": "\"A partial least squares approach for function-on-function interaction regression\" by Ufuk Beyaztas and Han Lin Shang, arXiv:2012.05313v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.18385v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n \\toprule \\multirow{2}{*}{Dataset} & \\multirow{2}{*}{Method} & \\multicolumn{2}{c}{50 Clients} & \\multicolumn{2}{c}{100 Clients} & \\multicolumn{2}{c}{200 Clients} \\\\\n \\cmidrule(lr){3-4} \\cmidrule(lr){5-6} \\cmidrule(lr){7-8}\n & & PM & GM & PM & GM & PM & GM \\\\\n \\midrule \\multirow{5}{*}{CIFAR10} & Local & $56.9 \\pm 0.1$ & - & $52.1 \\pm 0.1$ & - & $46.6 \\pm 0.1$ & - \\\\\n & pFedME~ & $72.3 \\pm 0.1$ & $56.6 \\pm 1.0$ & $71.4 \\pm 0.2$ & $6 0 . 1 \\pm 0.3$ & $68.5 \\pm 0.2$ & $58.7 \\pm 0.2$ \\\\\n & pFedBayes~ & $71.4 \\pm 0.3$ & $52.0 \\pm 1.0$ & $68.5 \\pm 0.3$ & $53.2 \\pm 0.7$ & $64.6 \\pm 0.2$ & $51.4 \\pm 0.3$ \\\\\n & pFedVEM~ & $7 3 . 2 \\pm 0 . 2$ & $56.0 \\pm 0.4$ & \\underline{$7 1 . 9 \\pm 0 . 1$ }& $6 0 . 1 \\pm 0 . 2$ & \\underline{$7 0 . 1 \\pm 0 . 3$ }& $59.4 \\pm 0.3$ \\\\\n & \\textbf{FedIvon@mean} & \\underline{$74.4 \\pm 0.3$} & \\underline{$67.1 \\pm 1.0$} & $71.7 \\pm 0.3$ & \\underline{$68.4 \\pm 0.2$} & $69.7 \\pm 0.7$ & \\underline{$68.2 \\pm 0.3$} \\\\\n & \\textbf{FedIvon} & $\\mathbf{75.5 \\pm 0.4}$ & $\\mathbf{67.8 \\pm 1.6}$ & $\\mathbf{72.6 \\pm 0.2}$ & $\\mathbf{69.2 \\pm 0.2}$ & $\\mathbf{70.8 \\pm 0.4}$ & $\\mathbf{68.7 \\pm 0.3}$ \\\\\n \\midrule \\multirow{5}{*}{CIFAR100} & Local & $34.3 \\pm 0.2$ & - & $27.6 \\pm 0.3$ & - & $22.2 \\pm 0.2$ & - \\\\\n & pFedME~ & $52.5 \\pm 0.5$ & $47.9 \\pm 0.5$ & $47.6 \\pm 0.5$ & $45.1 \\pm 0.3$ & $41.6 \\pm 1.8$ & $41.5 \\pm 1.6$ \\\\\n & pFedBayes~ & $49.6 \\pm 0.3$ & $42.5 \\pm 0.5$ & $46.5 \\pm 0.2$ & $41.3 \\pm 0.3$ & $40.1 \\pm 0.3$ & $37.4 \\pm 0.3$ \\\\\n & pFedVEM~ & $6 1 . 0 \\pm 0 . 4$ & $5 2 . 8 \\pm 0 . 4$ & $5 6 . 2 \\pm 0 . 4$ & $5 2 . 3 \\pm 0 . 4$ & $5 1 . 1 \\pm 0 . 6$ & $49.2 \\pm 0.5$ \\\\\n & \\textbf{FedIvon@mean} & $\\underline{65.4 \\pm 0.7}$ & $\\underline{63.2 \\pm 0.5}$ & $\\underline{63.2 \\pm 0.5}$ & $\\underline{62.1 \\pm 0.5}$ & $\\underline{56.1 \\pm 0.6}$ & $\\underline{55.5 \\pm 0.6}$\\\\\n & \\textbf{FedIvon} & $\\mathbf{66.7 \\pm 0.8}$ & $\\mathbf{63.8 \\pm 0.7}$ & $\\mathbf{63.5 \\pm 0.6}$ & $\\mathbf{62.4 \\pm 0.6}$ & $\\mathbf{56.5 \\pm 0.5}$ & $\\mathbf{55.7 \\pm 0.7}$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of Personalized FL Methods}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization", "authors": ["Shivam Pal", "Aishwarya Gupta", "Saqib Sarwar", "Piyush Rai"], "url": "https://arxiv.org/abs/2411.18385v1", "attribution": "\"Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization\" by Shivam Pal, Aishwarya Gupta, Saqib Sarwar, and Piyush Rai, arXiv:2411.18385v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.07752v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cllll} %\\hline \nEstimator & T/C-PS Models & Estimate & Boot SE & 95$\\%$ Boot CI \\\\ \n\\\\ %\\hline\n \\multirow{8}{*}{AIPW} & Cox/Cox-logit & 0.27 & 0.07 & (0.13, 0.40) \\\\\n& Cox/Cox-GBM & 0.25 & 0.07 & (0.11, 0.38) \\\\\n& Cox/RSF-logit & 0.27 & 0.07 & (0.13, 0.40) \\\\\n& Cox/RSF-GBM & 0.25 & 0.07 & (0.12, 0.39) \\\\\n& RSF/Cox-logit & 0.23 & 0.07 & (0.09, 0.36) \\\\\n& RSF/Cox-GBM & 0.22 & 0.07 & (0.07, 0.36) \\\\\n& RSF/RSF-logit & 0.23 & 0.07 & (0.09, 0.37) \\\\\n& RSF/RSF-GBM & 0.22 & 0.07 & (0.08, 0.37) \\\\\n\\\\ %\\hline \n\\multirow{4}{*}{IPW} & ~~~~~~~Cox-logit & 0.27 & 0.07 & (0.14, 0.40) \\\\\n& ~~~~~~~Cox-GBM & 0.25 & 0.06 & (0.13, 0.38) \\\\\n& ~~~~~~~RSF-logit & 0.27 & 0.07 & (0.14, 0.40) \\\\\n& ~~~~~~~RSF-GBM & 0.25 & 0.06 & (0.14, 0.37) \\\\\n\\\\ %\\hline \nNaive Cox & & 0.30 & 0.07 & (0.17, 0.44) \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Doubly Robust Estimation under Possibly Misspecified Marginal Structural Cox Model", "authors": ["Jiyu Luo", "Denise Rava", "Jelena Bradic", "Ronghui Xu"], "url": "https://arxiv.org/abs/2311.07752v1", "attribution": "\"Doubly Robust Estimation under Possibly Misspecified Marginal Structural Cox Model\" by Jiyu Luo, Denise Rava, Jelena Bradic, and Ronghui Xu, arXiv:2311.07752v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.04209v5_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\n\\hline\n & \\textbf{Hybrid QNN} & \\textbf{Classical NN} & \\textbf{Photonic QNN} \\\\ \\hline\n\\textbf{Features used} & 9 & 9 & 3 \\\\ \\hline\n\\textbf{Epochs} & 25 & 25 & 25 \\\\ \\hline\n\\textbf{Learning Rate} & 0.08 & 0.08 & 0.08 \\\\ \\hline\n\\textbf{Batch Size} & 5 & 5 & 5 \\\\ \\hline\n\\textbf{Shots} & 1024 & NA & 1024 \\\\ \\hline\n\\textbf{NN layers} & 3 & 3 & 3 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Quantum classical hybrid neural networks for continuous variable prediction", "authors": ["Prateek Jain", "Alberto Garcia Garcia"], "url": "https://arxiv.org/abs/2212.04209v5", "attribution": "\"Quantum classical hybrid neural networks for continuous variable prediction\" by Prateek Jain and Alberto Garcia Garcia, arXiv:2212.04209v5, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.07795v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{rotating}\n\\usepackage{multirow}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The performance of the optimised 2D U-Net for MRI prostate segmentation on four datasets using Dice Similarity Coefficient (DSC).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \n \\multicolumn{6}{c}{Training Datasets}\\\\\n &Dataset & Promise 12 & Prostate X & Decathlon & NIC ISBI \\\\ \\hline\\hline\n \\multirow{5}{*}{\\rotatebox[origin=c]{90}{Testing Datasets}}\n &Promise12 & \\textit{0.9069} & 0.8869 & 0.9239 &0.828 \\\\\n &Prostate X & 0.5891 & \\textit{0.9054} & 0.9107 & 0.8011 \\\\\n &Decathlon&0.6055 & 0.6479 & \\textit{0.9148} & 0.7395 \\\\\n &NIC ISBI & 0.6931 & 0.7463 & 0.7628 & \\textit{0.8909} \\\\\n &Combined&\\textbf{0.9171} & \\textbf{0.9061} & \\textbf{0.9427} & \\textbf{0.8960} \\\\\n \n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep learning in magnetic resonance prostate segmentation: A review and a new perspective", "authors": ["David Gillespie", "Connah Kendrick", "Ian Boon", "Cheng Boon", "Tim Rattay", "Moi Hoon Yap"], "url": "https://arxiv.org/abs/2011.07795v1", "attribution": "\"Deep learning in magnetic resonance prostate segmentation: A review and a new perspective\" by David Gillespie, Connah Kendrick, Ian Boon, Cheng Boon, Tim Rattay, and Moi Hoon Yap, arXiv:2011.07795v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.14111v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule \n$C$ & 0.05 & 0.1 & 0.2 & 0.5 \\\\\n\\midrule\nARE [$^\\circ$] & 0.871 & 0.379 & 0.449 & 0.747 \\\\\nEquiv. PhE [$\\cdot 10^5$] & 1.200 & 1.130 & 1.123 & 1.171 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Event-based Photometric Bundle Adjustment", "authors": ["Shuang Guo", "Guillermo Gallego"], "url": "https://arxiv.org/abs/2412.14111v2", "attribution": "\"Event-based Photometric Bundle Adjustment\" by Shuang Guo and Guillermo Gallego, arXiv:2412.14111v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.22282v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\n\\textbf{$k$} & \\textbf{$I_0(k)$} \\\\ \\hline\n$95 $ &$ 376.9346295103556$ \\\\ \\hline\n$96 $ & $304.9319294669602$ \\\\ \\hline\n$97 $ & $232.03812003788872 $\\\\ \\hline\n$98 $ & $158.01739738115333$ \\\\ \\hline\n$99$ & $82.0954389422525$ \\\\ \\hline\n$100 $ & $0.198425367071128 $\\\\ \\hline\n$100.001 $ & $0.19913824561039337$ \\\\ \\hline\n$100.002 $ & $0.19929381260883816$ \\\\ \\hline\n$100.003 $ & $0.18570061448085348$ \\\\ \\hline\n$100.004 $ & $0$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Implied volatility as a function of the strike when $T = 10^{-5}$.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Short-time behavior of the At-The-Money implied volatility for the jump-diffusion stochastic volatility Bachelier model", "authors": ["Elisa Alòs", "Òscar Burés", "Josep Vives"], "url": "https://arxiv.org/abs/2503.22282v1", "attribution": "\"Short-time behavior of the At-The-Money implied volatility for the jump-diffusion stochastic volatility Bachelier model\" by Elisa Alòs, Òscar Burés, and Josep Vives, arXiv:2503.22282v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11480v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\\hline\nSensor & Description & Resolution & Spatial Coverage \\\\ \\hline\nRadar & Hybrid surface rainfall & 500 m & 1,000 km $\\times$ 1,000 km \\\\ \nIR 10.5 $\\mu m$ & Infrared radiation & 2 km &1,000 km $\\times$ 1,000 km \\\\ \nWV 6.3 $\\mu m$ & Water vapor channels (upper-level) & 2 km & 1,000 km $\\times$ 1,000 km \\\\ \nWV 7.3 $\\mu m$ & Water vapor channels (lower-level) & 2 km & 1,000 km $\\times$ 1,000 km \\\\ \\hline\nDEM & Digital elevation model & 2 km& 1,000 km $\\times$ 1,000 km \\\\\n\\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Description of sensor types in our Sat2Rdr dataset}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Data-driven Precipitation Nowcasting Using Satellite Imagery", "authors": ["Young-Jae Park", "Doyi Kim", "Minseok Seo", "Hae-Gon Jeon", "Yeji Choi"], "url": "https://arxiv.org/abs/2412.11480v1", "attribution": "\"Data-driven Precipitation Nowcasting Using Satellite Imagery\" by Young-Jae Park, Doyi Kim, Minseok Seo, Hae-Gon Jeon, and Yeji Choi, arXiv:2412.11480v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08926v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of w/o self-attention for the GCN on two datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\hline\n \\textbf{Method} & \\textbf{DHG-14} & \\textbf{DHG-28}& \\textbf{FPHA}\n \\\\\n \\hline\n GCN & 90.83\\% & 87.74\\%& 83.48\\% \\\\\n SAGCN & 93.33\\% & 91.54\\%& 87.83\\% \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Two-stream Neural Network for Pose-based Hand Gesture Recognition", "authors": ["Chuankun Li", "Shuai Li", "Yanbo Gao", "Xiang Zhang", "Wanqing Li"], "url": "https://arxiv.org/abs/2101.08926v1", "attribution": "\"A Two-stream Neural Network for Pose-based Hand Gesture Recognition\" by Chuankun Li, Shuai Li, Yanbo Gao, Xiang Zhang, and Wanqing Li, arXiv:2101.08926v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.05177v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{RNN(Bidirectional LSTM) architecture} % title name of the table\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccrrrrrrr} % creating 10 columns\n\\hline\\hline % inserting double-line\n Layer &Type&Output Shape & Number of Parameters \n\\\\\n\\hline % inserts single-line\n 1 & Input& (None,300) & - \\\\\n2 & Bidirectional(LSTM)&(None,300,64) & 8960 \\\\\n3 & Bidirectional(LSTM)&(None,64) & 25088 \\\\\n4 & Dropout&(None,64) & 0 \\\\\n5 & $dense_1$(ReLu)&(None,64) & 4160 \\\\\n6 & $dense_2$(Sigmoid)&(None,64) & 130 \\\\\n\\hline % inserts single-line\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network", "authors": ["Prateek Singh", "Ambalika Sharma", "Shreesha Maiya"], "url": "https://arxiv.org/abs/2202.05177v1", "attribution": "\"Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network\" by Prateek Singh, Ambalika Sharma, and Shreesha Maiya, arXiv:2202.05177v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16534v2_tex_table40.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameter search space of Trompt.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\toprule\n\\textbf{Hyperparameter} & \\textbf{Distribution} \\\\ \\midrule\nhidden\\_dimension & $[18,128]$\\\\\nfeature\\_importances\\_type & $[\\text{concat},\\text{add}]$\\\\\nfeature\\_importances\\_dense & $[\\text{true},\\text{false}]$\\\\\nfeature\\_importances\\_residual\\_connection & $[\\text{true},\\text{false}]$\\\\\nfeature\\_importances\\_sharing\\_dense & $[\\text{true},\\text{false}]$\\\\\nfeature\\_embeddings\\_residual\\_connection & $[\\text{true},\\text{false}]$\\\\\nminimal\\_batch\\_ratio & $[0.1,0.01]$\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DOFEN: Deep Oblivious Forest ENsemble", "authors": ["Kuan-Yu Chen", "Ping-Han Chiang", "Hsin-Rung Chou", "Chih-Sheng Chen", "Tien-Hao Chang"], "url": "https://arxiv.org/abs/2412.16534v2", "attribution": "\"DOFEN: Deep Oblivious Forest ENsemble\" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.04003v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Block Builder Market Shares}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n\\hline\nBuilder & MEV Value (ETH) & Block Count \\\\\n\\hline \\hline\nbeaverbuild.org & 111907.12 & 1,390,451 \\\\\nTitan Builder & 77,989.67 & 1,070,749 \\\\\nrsync-builder.xyz & 31,392.580577 & 271,284 \\\\\nNotMEV & 0.000000 & 265,409 \\\\\nUltrasound Money & 2,439.68 & 3,7530 \\\\\nFlashbots & 5,186.19 & 34,316 \\\\\njetbldr.xyz & 916.93 & 23,614 \\\\\n@penguinbuild.org & 695.03 & 9,457 \\\\\nBuilder+ www.btcs.com/builder & 1,106.77 & 7,742 \\\\\nf1b.io & 591.957 & 7,050 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering", "authors": ["Bruce Mizrach", "Nathaniel Yoshida"], "url": "https://arxiv.org/abs/2508.04003v1", "attribution": "\"The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering\" by Bruce Mizrach and Nathaniel Yoshida, arXiv:2508.04003v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.17556v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results of an ablation experiment in which TAFM-Net was trained on each one of the ISIC datasets and tested on the other two. Numbers are means of a 3-fold cross-validation. Best score per metric and per training dataset in \\textbf{bold}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccc}\n\\toprule\n\\multirow{2}[4]{*}{\\textbf{Training}} & \\multirow{2}[4]{*}{\\textbf{Testing}} & \\multicolumn{5}{c}{\\textbf{Performance (\\%)}} \\\\\n\\cmidrule{3-7} & & $\\mathbf{A}$ & $\\mathbf{Sn}$ & $\\mathbf{Sp}$ & $\\mathbf{J}$ & $\\mathbf{D}$ \\\\\n\\toprule\n& ISIC 2016 & \\textbf{98.44} & \\textbf{96.65} & \\textbf{97.66} & \\textbf{93.64} & \\textbf{96.66} \\\\\nISIC 2016 & ISIC 2017 & 94.95 & 87.95 & 93.14 & 84.88 & 88.17 \\\\\n& ISIC 2018 & 95.87 & 91.39 & 95.92 & 89.88 & 93.53 \\\\\n\\midrule\n& ISIC 2016 & 96.44 & 94.65 & 94.66 & 90.64 & 94.66 \\\\\nISIC 2017 & ISIC 2017 & \\textbf{96.95} & \\textbf{92.95} & \\textbf{95.14} & \\textbf{86.88} & \\textbf{92.17} \\\\\n& ISIC 2018 & 94.87 & 93.39 & 94.92 & 88.88 & 94.53 \\\\\n\\midrule\n& ISIC 2016 & 95.44 & 92.65 & 93.66 & 90.64 & 91.66 \\\\\nISIC 2018 & ISIC 2017 & 91.95 & 89.95 & 93.14 & 83.88 & 90.17 \\\\\n& ISIC 2018 & \\textbf{97.87} & \\textbf{96.39} & \\textbf{97.92} & \\textbf{92.88} & \\textbf{96.53} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "TAFM-Net: A Novel Approach to Skin Lesion Segmentation Using Transformer Attention and Focal Modulation", "authors": ["Tariq M Khan", "Dawn Lin", "Shahzaib Iqbal", "Erik Meijering"], "url": "https://arxiv.org/abs/2411.17556v1", "attribution": "\"TAFM-Net: A Novel Approach to Skin Lesion Segmentation Using Transformer Attention and Focal Modulation\" by Tariq M Khan, Dawn Lin, Shahzaib Iqbal, and Erik Meijering, arXiv:2411.17556v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09954v3_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small{Statistics of the ensemble-based uncertainty quantification results obtained for transient subsurface pressure data in a binary channelized domain. The True and Mean models are those depicted in Figure . The mean model is obtained from the deep ensemble models \\#1 to \\#5. RMSE$_{\\rm data}$ denotes the RMSE in data space, ${\\rm l}_1$ refers to the ${\\rm l}_1$-norm and SSIM to the structural similarity index. The ${\\rm l}_1$ is calculated in terms of $\\log_{10}K_s$ (-) while the SSIM is computed in the rescaled $\\left[0,1\\right]$ domain.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}%\n\t\t\t\\hline\n\t\t\tTrue model & RMSE$_{\\rm data}$ (m) & ${\\rm l}_1$ (m) & SSIM (-)\\\\\n\t\t\t\\hline\n\t\t\t(a) True & 0.010 & 0 & 1 \\\\\n\t\t\t(b) Mean & 0.577 & 2492 & 0.67\\\\\n\t\t\t(d) Model \\#1 & 0.590 & 2594 & 0.67 \\\\\n\t\t\t(e) Model \\#2 & 0.095 & 2908 & 0.66 \\\\\n\t\t\t(f) Model \\#3 & 0.267 & 2254 & 0.71 \\\\\n\t\t\t(g) Model \\#4 & 0.523 & 2912 & 0.64 \\\\\n\t\t\t(h) Model \\#5 & 0.523 & 1782 & 0.73 \\\\\n\t\t\t\n\t\t\t(i) True & 0.010 & 0 & 1 \\\\\n\t\t\t(j) Mean & 0.449 & 2321 & 0.62\\\\\n\t\t\t(l) Model \\#1 & 0.216 & 2246 & 0.65 \\\\\n\t\t\t(m) Model \\#2 & 0.426 & 2558 & 0.63 \\\\\n\t\t\t(n) Model \\#3 & 0.183 & 2486 & 0.63 \\\\\n\t\t\t(o) Model \\#4 & 0.187 & 1988 & 0.67 \\\\\n\t\t\t(p) Model \\#5 & 0.233 & 2356 & 0.63 \\\\\n\t\t\t\n\t\t\t(q) True & 0.010 & 0 & 1 \\\\\n\t\t\t(r) Mean & 0.377 & 3046 & 0.60\\\\\n\t\t\t(t) Model \\#1 & 0.416 & 3334 & 0.60 \\\\\n\t\t\t(u) Model \\#2 & 0.252 & 3422 & 0.58 \\\\\n\t\t\t(v) Model \\#3 & 0.271 & 3226 & 0.59 \\\\\n\t\t\t(w) Model \\#4 & 0.049 & 2520 & 0.64 \\\\\n\t\t\t(x) Model \\#5 & 0.397 & 2728 & 0.63 \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Approaching geoscientific inverse problems with vector-to-image domain transfer networks", "authors": ["Eric Laloy", "Niklas Linde", "Diederik Jacques"], "url": "https://arxiv.org/abs/1912.09954v3", "attribution": "\"Approaching geoscientific inverse problems with vector-to-image domain transfer networks\" by Eric Laloy, Niklas Linde, and Diederik Jacques, arXiv:1912.09954v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.06045v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc} \\hline\n \\textbf{Congested} & \\textbf{Link}& \\multicolumn{2}{c}{ \\textbf{PG}} &\n\\multicolumn{2}{c}{ \\textbf{PIG}} \\\\\n \\textbf{link}& \\textbf{type}\n &\n$E(V / C)$& $P(V / C > 0.95)$& $E(V / C)$&\n$P(V / C > 0.95)$ \\\\\n\\hline\n 16841& Small regional road& 1.384& 1& 1.383&\n1 \\\\\n 17493& Local road& 1.152& 1& 1.151&\n1 \\\\\n 22149& Local road& 0.935& 0.046& 0.934&\n0.022 \\\\\n 28980& Highway& 2.114& 1& 2.114&\n1 \\\\\n29060& Local road& 0.935& 0.046& 0.934&\n0.022 \\\\\n 83662& Local road& 0.941& 0.236& 0.941&\n0.208 \\\\\n 83928& Highway& 1.260& 1& 1.259&\n1 \\\\\n 84514& Main regional road& 1.144& 1& 1.144&\n1 \\\\\n 92846& Local road& 0.941& 0.236& 0.941&\n0.208 \\\\\n 92849& Local road& 1.098& 1& 1.097&\n1 \\\\\n106252& Local road& 1.024& 1& 1.024&\n1 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures", "authors": ["Konstantinos Perrakis", "Dimitris Karlis", "Mario Cools", "Davy Janssens"], "url": "https://arxiv.org/abs/2011.06045v1", "attribution": "\"Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures\" by Konstantinos Perrakis, Dimitris Karlis, Mario Cools, and Davy Janssens, arXiv:2011.06045v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.09123v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the mesh refinement history}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n \\hline \\multicolumn{5}{c}{DC}\\\\\n\t\t\\hline \\textbf{MR} & \\textbf{Mesh} &\\textbf{Max. AL } & \\textbf{Max. CV} & \\textbf{NLP}\\\\\n \\textbf{Iteration} & \\textbf{Size} & \\textbf{Error} & \\textbf{Error} & \\textbf{Time}\\\\\n\t\t\\hline 1 & 100 & 3.6e-01 & 24.1 & 1.5 s\\\\\n\t\t\\hline 2 & 250 & 3.0e-01 & 24.1 & 3.9 s\\\\\n\t\t\\hline 3 & 1171 & 9.3e-03 & 24.1 & 26.7 s\\\\\n \\hline 4 & 2336 & 5.1e-03 & 24.1 & 44.9 s\\\\\n \\hline 5 & 2555 & 1.5e-04 & 24.1 & 26.7 s\\\\\n \\hline 6 & 2586 & 6.7e-05 & 24.1 & 41.5 s\\\\\n \\hline 7 & 2624 & 6.7e-05 & 24.1 & 42.1 s\\\\\n \\hline 8 & 2649 & 6.7e-05 & 24.1 & 44.4 s\\\\\n\t\t\\hline\n \\hline \\multicolumn{5}{c}{IRR-DC}\\\\\n\t\t\\hline \\textbf{MR} & \\textbf{Mesh} &\\textbf{Max. AL} & \\textbf{Max. CV} & \\textbf{NLP}\\\\\n \\textbf{Iteration} & \\textbf{Size} & \\textbf{Error} & \\textbf{Error} & \\textbf{Time}\\\\\n\t\t\\hline 1 & 100 & 2.6e-03 & 3.6e-02 & 2.7 s\\\\\n\t\t\\hline 2 & 138 & 5.6e-05 & 3.3e-03 & 4.5 s\\\\\n\t\t\\hline 3 & 139 & 5.6e-05 & 6.4e-04 & 7.1 s\\\\\n \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Reliable Solution to Dynamic Optimization Problems using Integrated Residual Regularized Direct Collocation", "authors": ["Yuanbo Nie", "Eric C. Kerrigan"], "url": "https://arxiv.org/abs/2503.09123v1", "attribution": "\"Reliable Solution to Dynamic Optimization Problems using Integrated Residual Regularized Direct Collocation\" by Yuanbo Nie and Eric C. Kerrigan, arXiv:2503.09123v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table25.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|r|c|c|r|r}\n \\hline\n \\multicolumn{8}{c}{PH algorithm using all scenario paths} \\\\\n \\hline\n \\multicolumn{8}{c}{Instances including component demands} \\\\\n \\hline\n Distribution & Util. & $|\\Omega|$ & $|\\Phi|$ & Converged & Gap (\\%) & Runtime (s) & Iterations \\\\\n \\hline\n \\multirow{10}{*}{Non stationary} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & Yes & 0.0 & 29.1 & 4\\\\\n & & 3 & 2,187 & Yes & 0.0 & 909.3 & 7\\\\\n & & 4 & 16,384 & No & 0.0 & 10,800.0 & 13\\\\\n & & 5 & 78,125 & & & &\\\\\n & & 6 & 279,936 & & & &\\\\\\cline{2-8}\n &\\multirow{5}{*}{90\\%} \n & 2 & 128 & Yes & 0.0 & 30.5 & 3\\\\\n & & 3 & 2,187 & Yes & 0.0 & 542.5 & 4\\\\\n & & 4 & 16,384 & Yes & 0.0 & 3056.8 & 3\\\\\n & & 5 & 78,125 & & & &\\\\\n & & 6 & 279,936 & & & &\\\\\n \\hline\n \\multirow{10}{*}{Uniform} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & Yes & 0.0 & 28.7 & 4\\\\\n & & 3 & 2,187 & Yes & 0.0 & 245.0 & 2 \\\\\n & & 4 & 16,384 & Yes & 0.0 & 1886.1 & 2\\\\\n & & 5 & 78,125 & & & & \\\\\n & & 6 & 279,936 & & & & \\\\\\cline{2-8}\n & \\multirow{5}{*}{90\\%}\n & 2 & 128 & Yes & 0.0 & 52.6 & 6\\\\\n & & 3 & 2,187 & Yes & 0.0 & 370.7 & 3 \\\\\n & & 4 & 16,384 & Yes & 0.0 & 3084.3 & 3 \\\\\n & & 5 & 78,125 & & & & \\\\\n & & 6 & 279,936 & & & & \\\\\n \\hline\n \\multirow{10}{*}{Lumpy} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & Yes & 0.9 & 258.9 & 24 \\\\\n & & 3 & 2,187 & Yes & 3.9 & 8462.9 & 55 \\\\\n & & 4 & 16,384 & Cancelled & & & \\\\\n & & 5 & 78,125 & & & & \\\\\n & & 6 & 279,936 & & & & \\\\\\cline{2-8}\n & \\multirow{5}{*}{90\\%} \n & 2 & 128 & Yes & 2.7 & 2125.5 & 37 \\\\\n & & 3 & 2,187 & No & 2.7 & 10,800.0 & 9 \\\\\n & & 4 & 16,384 & Cancelled & - & - & - \\\\\n & & 5 & 78,125 & & & & \\\\\n & & 6 & 279,936 & & & & \\\\\n \\hline\n \\multirow{10}{*}{Slow moving} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & No & 0.0 & 857.0 & 100\\\\\n & & 3 & 2,187 & No & 0.0 & 10,800.0& 90 \\\\\n & & 4 & 16,384 & No & Infeasible & 10,800.0& 13 \\\\\n & & 5 & 78,125 & & & & \\\\\n & & 6 & 279,936 & & & & \\\\\\cline{2-8}\n & \\multirow{5}{*}{90\\%}\n & 2 & 128 & No & 0.1 & 1728.1 & 100 \\\\\n & & 3 & 2,187 & No & 1.0 & 10,800.0 & 35 \\\\\n & & 4 & 16,384 & Cancelled & & & \\\\\n & & 5 & 78,125 & & & & \\\\\n & & 6 & 279,936 & & & & \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Runtimes and gaps for the progressive hedging algorithm on the instances including component demands for different distributions and numbers of scenarios per branch with a time limit of 3 hours (10,800 s) and maximum of 100 iterations}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.14431v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\hline\n\\hline\n\\textbf{Sector} & \\textbf{ADF Statistic} & \\textbf{ADF p-value} & \\textbf{PP Statistic} & \\textbf{PP p-value} \\\\\n\\hline\n\\textbf{Pre-crash period} & & & \\\\\n\\hline\nConsumer Discretionary & -12.47 & 0.00** & -12.34 & 0.00** \\\\\nConsumer Staples & -14.17 & 0.00** & -14.16 & 0.00** \\\\\nEnergy & -13.49 & 0.00** & -13.51 & 0.00** \\\\\nFinancials & -13.21 & 0.00** & -13.15 & 0.00** \\\\\nHealthcare & -14.09 & 0.00** & -14.02 & 0.00** \\\\\nIndustrials & -13.75 & 0.00** & -13.69 & 0.00** \\\\\nIT & -14.73 & 0.00** & -14.74 & 0.00** \\\\\nMaterials & -13.90 & 0.00** & -13.89 & 0.00** \\\\\nReal Estate & -13.67 & 0.00** & -13.63 & 0.00** \\\\\nUtilities & -14.32 & 0.00** & -14.31 & 0.00** \\\\\nCommunications Services & -13.58 & 0.00** & -13.52 & 0.00** \\\\\n\\hline\n\\textbf{Crash period} & & & \\\\\n\\hline\nConsumer Discretionary & -7.42 & 0.00** & -14.28 & 0.00** \\\\\nConsumer Staples & -5.56 & 0.00** & -13.52 & 0.00** \\\\\nEnergy & -10.39 & 0.00** & -14.25 & 0.00** \\\\\nFinancials & -6.28 & 0.00** & -13.45 & 0.00** \\\\\nHealthcare & -8.11 & 0.00** & -14.13 & 0.00** \\\\\nIndustrials & -7.34 & 0.00** & -13.91 & 0.00** \\\\\nIT & -5.38 & 0.00** & -12.23 & 0.00** \\\\\nMaterials & -6.95 & 0.00** & -13.67 & 0.00** \\\\\nReal Estate & -7.22 & 0.00** & -13.92 & 0.00** \\\\\nUtilities & -6.89 & 0.00** & -13.80 & 0.00** \\\\\nCommunications Services & -5.61 & 0.00** & -13.37 & 0.00** \\\\\n\\hline\n\\textbf{Post-crash period} & & & \\\\\n\\hline\nConsumer Discretionary & -9.12 & 0.00** & -14.18 & 0.00** \\\\\nConsumer Staples & -9.06 & 0.00** & -14.34 & 0.00** \\\\\nEnergy & -7.78 & 0.00** & -14.67 & 0.00** \\\\\nFinancials & -8.97 & 0.00** & -13.92 & 0.00** \\\\\nHealthcare & -10.23 & 0.00** & -14.07 & 0.00** \\\\\nIndustrials & -9.82 & 0.00** & -13.94 & 0.00** \\\\\nMaterials & -9.90 & 0.00** & -14.23 & 0.00** \\\\\nReal Estate & -10.12 & 0.00** & -14.16 & 0.00** \\\\\nUtilities & -11.00 & 0.00** & -14.19 & 0.00** \\\\\nCommunications Services & -10.53 & 0.00** & -14.07 & 0.00** \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective", "authors": ["Buddha Nath Sharma", "Anish Rai", "SR Luwang", "Md. Nurujjaman", "Sushovan Majhi"], "url": "https://arxiv.org/abs/2502.14431v1", "attribution": "\"Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective\" by Buddha Nath Sharma, Anish Rai, SR Luwang, Md. Nurujjaman, and Sushovan Majhi, arXiv:2502.14431v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.02400v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Lowest MLS value achieved for each of the ten cases and the corresponding registration configuration.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\nCase \\# & MLS (mm) & Regularizer ($\\mu$) & Grid Spacing (mm) \\\\ \\midrule\n1 & 1.15 & Diffusion ($10^{-2}$) & 30 \\\\\n2 & 1.28 & Diffusion ($10^{-3}$) & 10 \\\\\n3 & 1.52 & Linear Elastic ($10^{-8}$) & 10 \\\\ \n4 & 1.56 & Curvature ($10^{-1}$) & 20 \\\\\n5 & 1.80 & Curvature ($10^{-2}$) & 10 \\\\\n6 & 1.56 & Curvature ($10^{-1}$) & 10 \\\\\n7 & 1.74 & Curvature ($10^{-2}$) & 10 \\\\\n8 & 1.49 & Curvature ($10^{-2}$) & 10 \\\\\n9 & 1.50 & Third-order ($1$) & 10 \\\\\n10 & 1.42 & Curvature ($10^{-2}$) & 10 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Generalized Framework for Analytic Regularization of Uniform Cubic B-spline Displacement Fields", "authors": ["Keyur D. Shah", "James A. Shackleford", "Nagarajan Kandasamy", "Gregory C. Sharp"], "url": "https://arxiv.org/abs/2010.02400v3", "attribution": "\"A Generalized Framework for Analytic Regularization of Uniform Cubic B-spline Displacement Fields\" by Keyur D. Shah, James A. Shackleford, Nagarajan Kandasamy, and Gregory C. Sharp, arXiv:2010.02400v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.03154v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of the ASVspoof 2019 database.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|cc|cc}\n\t\t\\toprule\n\t\t\\multirow{3}{*}{Subset}&\\multicolumn{2}{c}{\\#Speaker}&\\multicolumn{4}{c}{\\#Utterances}\\\\ \n\t\t\\cmidrule(lr){2-3} \\cmidrule(lr){4-7} \n\t\t&\\multirow{2}{*}{M}&\\multirow{2}{*}{F}&\\multicolumn{2}{c}{LA}&\\multicolumn{2}{c}{PA}\\\\\n\t\t\\cmidrule(lr){4-5} \\cmidrule(lr){6-7}\n\t\t&&&Bona fide&Spoof&Bona fide&Spoof\\\\\n\t\t\\midrule\n\t\tTraining & 8 & 12 & 2580 & 22800 & 5400 & 48600 \\\\\n\t\tDev. & 8 & 12 & 2548 & 22296 & 5400 & 24300 \\\\\n\t\tEval. & 21 & 27 & 7355 & 63882 & 18090 & 116640 \\\\\n\t\t\\hline\n\t\tTotal & 37 & 51 & 12483& 108978& 28890 & 189540 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System", "authors": ["Yuanjun Zhao", "Roberto Togneri", "Victor Sreeram"], "url": "https://arxiv.org/abs/2012.03154v1", "attribution": "\"Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System\" by Yuanjun Zhao, Roberto Togneri, and Victor Sreeram, arXiv:2012.03154v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.18547v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The final classification architectures found for the GLUE datasets.}\n\\begin{tabular}{ccccccc}\n \\toprule\nmethod & sst2 & cola & mrpc & mnli & rte & qqp \\\\\n \\cmidrule(r){1-1} \\cmidrule(l){2-7} \npooling & mean & [CLS] & [CLS] & max & [CLS] & max \\\\\nnumber linear layers & 5 & 1 & 4 & 3 & 1 & 1 \\\\\nhidden dim linear layers & 50 & - & 74 & 117 & - & - \\\\\nnumber conv layers & 0 & 0 & 4 & 3 & 0 & 2 \\\\\nnumber heads conv & - & - & 107 & 9 & - & 159 \\\\\nkernel size & - & - & 7 & 11 & - & 7 \\\\\nskip connection & - & - & True & True & - & True \\\\\nnumber attention layers & 1 & 0 & 4 & 0 & 0 & 0 \\\\\nnumber attention heads & 4 & - & 16 & - & - & - \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Neural Architecture Search for Sentence Classification with BERT", "authors": ["Philip Kenneweg", "Sarah Schröder", "Barbara Hammer"], "url": "https://arxiv.org/abs/2403.18547v1", "attribution": "\"Neural Architecture Search for Sentence Classification with BERT\" by Philip Kenneweg, Sarah Schröder, and Barbara Hammer, arXiv:2403.18547v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2401.00292v1_tex_table21.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c|c|c|c|c|c}\n & \n\\multicolumn{ 3}{c}{$|S_U|$} & \n\\multicolumn{ 3}{c}{Time$\\,S_U (s)$}\\\\\nNo & \n$\\gamma=10$ & \n$\\gamma=30$ & \n$\\gamma=50$ & \n$\\gamma=10$ & \n$\\gamma=30$ & \n$\\gamma=50$ \\\\ \n\\hline\n1 & 11 & 31 & 52 & 439.96 (411.14) & 515.48 (402.94) & 665.55 (404.57)\\\\ \n2 & 10 & 27 & 44 & 482.23 (407.04) & 678.38 (402.56) & 765.19 (409.44)\\\\ \n3 & 8 & 20 & 32 & 443.50 (406.40) & 534.97 (410.38) & 667.23 (404.69)\\\\\n4 & 5 & 15 & 23 & 424.95 (403.06) & 465.13 (400.30) & 484.92 (402.56)\\\\ \n5 & 5 & 13 & 20 & 432.90 (400.51) & 485.67 (404.07) & 512.03 (403.40)\\\\ \n\\end{tabular}\n\\caption{Chute2, $|S_U|$, and Time$\\,S_U (s)$ for test problem Bi9.1 and $\\gamma \\in{\\{10,30,50\\}}$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems", "authors": ["Grzegorz Filcek", "Janusz Miroforidis"], "url": "https://arxiv.org/abs/2401.00292v1", "attribution": "\"A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems\" by Grzegorz Filcek and Janusz Miroforidis, arXiv:2401.00292v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11336v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Impact of the \\textit{T} values on accuracy }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c||c|c|c|c|c}\n Clauses & T & Training & Testing & Validation & Better Classification \\\\\n \\hline\n \\hline\n 30 & 2 & 83.5 \\% & 80.5 \\% & 83.8 \\% & \\checkmark\\\\\n 30 & 23 & 74.9 \\% & 71.1 \\% & 76.1 \\% & \\\\\n \\hline\n 450 & 2 & 89.7 \\% & 86.1 \\% & 84.9 \\% & \\\\\n 450 & 23 & 96.8 \\% & 92.5 \\% & 92.7 \\% & \\checkmark\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Low-Power Audio Keyword Spotting using Tsetlin Machines", "authors": ["Jie Lei", "Tousif Rahman", "Rishad Shafik", "Adrian Wheeldon", "Alex Yakovlev", "Ole-Christoffer Granmo", "Fahim Kawsar", "Akhil Mathur"], "url": "https://arxiv.org/abs/2101.11336v1", "attribution": "\"Low-Power Audio Keyword Spotting using Tsetlin Machines\" by Jie Lei, Tousif Rahman, Rishad Shafik, Adrian Wheeldon, Alex Yakovlev, Ole-Christoffer Granmo, Fahim Kawsar, and Akhil Mathur, arXiv:2101.11336v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09398v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classification accuracy ($\\%$) on Cora and CiteSeer, with $\\epsilon_x=1$. The performance are insignificant in various edge privacy budgets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c}\n\\toprule\n $\\epsilon_{a}$& 7.0 & 7.3 & 7.5& 7.7 & 7.9 \\\\ \n\\midrule\n Cora&$50.9\\pm3.8$ & $54.9\\pm3.7$ & $57.2\\pm2.5$ & $61.4\\pm2.6$& $63.7\\pm2.4$ \\\\\n CiteSeer&$35.0\\pm1.8$&$39.1\\pm1.3$&$42.3\\pm1.5$&$44.5\\pm1.2$&$45.7\\pm1.8$\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Towards Private Learning on Decentralized Graphs with Local Differential Privacy", "authors": ["Wanyu Lin", "Baochun Li", "Cong Wang"], "url": "https://arxiv.org/abs/2201.09398v1", "attribution": "\"Towards Private Learning on Decentralized Graphs with Local Differential Privacy\" by Wanyu Lin, Baochun Li, and Cong Wang, arXiv:2201.09398v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18259v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of lookback option prices between E-M method and INAR($\\infty$) implementations}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc|cccc}\n \\hline\n \\multirow{2}{*}{Strike} & \\multicolumn{4}{c|}{E-M Method} & \\multicolumn{4}{c}{INAR($\\infty$)} \\\\\n \\cline{2-9}\n & Call & 95\\% CI & Put & 95\\% CI & Call & 95\\% CI & Put & 95\\% CI \\\\\n \\hline\n 80 & 38.3583 & [38.3146, 38.4020] & 3.3696 & [3.3456, 3.3935] & 38.3021 & [38.2023, 38.4018] & 3.3969 & [3.3572, 3.4367] \\\\\n 90 & 28.3583 & [28.3146, 28.4020] & 8.3247 & [8.2974, 8.3520] & 28.3021 & [28.2023, 28.4018] & 8.3697 & [8.3092, 8.4302] \\\\\n 100 & 18.3583 & [18.3146, 18.4020] & 16.4693 & [16.4438, 16.4948] & 18.3021 & [18.2023, 18.4018] & 16.5321 & [16.4599, 16.6043] \\\\\n 110 & 10.4653 & [10.4193, 10.5113] & 26.4693 & [26.4438, 26.4948] & 10.4341 & [10.3457, 10.5225] & 26.5321 & [26.4599, 26.6043] \\\\\n 120 & 5.6092 & [5.5665, 5.6519] & 36.4693 & [36.4438, 36.4948] & 5.5953 & [5.5260, 5.6646] & 36.5321 & [36.4599, 36.6043] \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\\infty$) processes and applications", "authors": ["Yingli Wang", "Zhenyu Cui"], "url": "https://arxiv.org/abs/2503.18259v3", "attribution": "\"Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\\infty$) processes and applications\" by Yingli Wang and Zhenyu Cui, arXiv:2503.18259v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.00551v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Entropy and distribution of the flood model input variables}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccllc}\n\t\t\\toprule\n\t\t & Variable & Description & Distribution Function & Exponential Entropy $e^{H(X_i)}$ \\\\ \n\t\t\\cmidrule(lr){1-5} \n\t $x_1$ & $Q$ & Maximal annual flowrate [m$^3$/s]\t & Truncated Gumbel $\\mathcal{G}$(1013,558) on [500,3000] & 2051 \\\\\n\t $x_2$ & $K_s$ & Strickler coefficient [ - ]\t & Truncated Normal $\\mathcal{N}$(30,$8^2$) on [15, $\\infty$] & 30 \\\\ \n\t $x_3$ & $Z_{\\nu}$ & River downstream level [m]\t & Triangular $\\mathcal{T}$(49,50,51) & 1.65 \\\\ \n\t $x_3$ & $Z_m$ & River upstream level [m]\t & Triangular $\\mathcal{T}$(54,55,56) & 1.65 \\\\ \n\t $x_5$ & $D_d$ & Dyke height [m]\t & Uniform $\\mathcal{U}$[7,9] & 2 \\\\ \n\t $x_6$ & $C_b$ & Bank level [m]\t & Triangular $\\mathcal{T}$(55,55.5,56) & 0.825 \\\\\n\t $x_7$ & $L$ & Length of the river stretch [m]\t & Triangular $\\mathcal{T}$(4990,5000,5010) & 16.5\\\\ \n\t $x_8$ & $B$ & River width [m]\t & Triangular $\\mathcal{T}$(295,300,305) & 8.24 \\\\ \n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Derivative based global sensitivity analysis and its entropic link", "authors": ["Jiannan Yang"], "url": "https://arxiv.org/abs/2310.00551v3", "attribution": "\"Derivative based global sensitivity analysis and its entropic link\" by Jiannan Yang, arXiv:2310.00551v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.00180v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The mean of the MESEs for each nonparametric estimator fitted to the simulated continuous data with a jump in the regression function.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|r|rrr|rrr|rrr|}\n \\hline\n & & $n=400$ & & & $n=800$ & & & $n=1600$ & \\\\ \n \\hline\n$\\sigma$ & LC & ALC & ALCT & LC & ALC & ALCT & LC & ALC & ALCT \\\\ \n 0.1 & 0.01732 & 0.00319 & 0.00152 & 0.0106 & 0.00138 & 0.00087 & 0.00774 & 0.00104 & 0.00051 \\\\ \n 0.5 & 0.07338 & 0.04232 & 0.01516 & 0.05377 & 0.03069 & 0.00844 & 0.03689 & 0.02281 & 0.00503 \\\\ \n 1 & 0.15469 & 0.12098 & 0.04529 & 0.10683 & 0.07893 & 0.02545 & 0.07512 & 0.05144 & 0.01499 \\\\ \n 2 & 0.33026 & 0.28736 & 0.14387 & 0.2244 & 0.18895 & 0.07838 & 0.15711 & 0.12118 & 0.04594 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Anisotropic local constant smoothing for change-point regression function estimation", "authors": ["John R. J. Thompson", "W. John Braun"], "url": "https://arxiv.org/abs/2012.00180v2", "attribution": "\"Anisotropic local constant smoothing for change-point regression function estimation\" by John R. J. Thompson and W. John Braun, arXiv:2012.00180v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.02840v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc|cc|}\n & \\multicolumn{2}{c|}{01.01.2014-01.01.2024} & \\multicolumn{2}{c|}{01.01.2019-01.01.2024} & \\multicolumn{2}{c|}{01.01.2014-01.01.2019} \\\\ \\cline{2-7} \n & M & CM & M & CM & M & CM \\\\ \\hline\nB1 & 0.41 & \\textbf{0.76} & 0.27 & \\textbf{0.53} & 0.57 & \\textbf{1.02} \\\\ \\hline\nB2 & 1.03 & \\textbf{1.14} & 0.91 & \\textbf{1.15} & \\textbf{1.27} & 1.26 \\\\ \\hline\nB3 & 0.39 & \\textbf{0.61} & 0.33 & \\textbf{0.41} & 0.34 & \\textbf{0.94} \\\\ \\hline\nB4 & \\textbf{0.73} & \\textbf{0.73} & \\textbf{0.54} & 0.52 & 1.01 & \\textbf{1.12} \\\\ \\hline\nB5 & 0.42 & \\textbf{0.75} & 0.45 & \\textbf{0.73} & \\textbf{0.64} & 0.57 \\\\ \\hline\nB6 & \\textbf{0.76} & 0.62 & 0.44 & \\textbf{0.86} & \\textbf{1.12} & 0.94 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Statistical applications of the 20/60/20 rule in risk management and portfolio optimization", "authors": ["Kewin Pączek", "Damian Jelito", "Marcin Pitera", "Agnieszka Wyłomańska"], "url": "https://arxiv.org/abs/2504.02840v1", "attribution": "\"Statistical applications of the 20/60/20 rule in risk management and portfolio optimization\" by Kewin Pączek, Damian Jelito, Marcin Pitera, and Agnieszka Wyłomańska, arXiv:2504.02840v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08770v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c|c||}\n\\hline\n$U$ & $U_T$ & $MV$ & $MV_T$ \\\\\n\\hline\n0.1041 & 0.1064 & 0.1032 & 0.1018\\\\\n\\hline\n0.1040 & 0.1065 & 0.1021 & 0.1034\\\\\n\\hline\n0.1042 & 0.1067 & 0.1019 & 0.1018\\\\\n\\hline\n0.1038 & 0.1064 & 0.1019 & 0.1031\\\\\n\\hline\n0.1029 & 0.1062 & 0.1017 & 0.1033\\\\\n\\hline\n0.1044 & 0.1065 & 0.1018 & 0.1027\\\\\n\\hline\n0.1043 & 0.1068 & 0.1028 & 0.1036\\\\\n\\hline\n0.1048 & 0.1065 & 0.1024 & 0.1029\\\\\n\\hline\n0.1044 & 0.1064 & 0.1017 & 0.1039\\\\\n\\hline\n0.1056 & 0.1064 & 0.1038 & 0.1021\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{GAN quality-based metrics' values across various training datasets from the same problem instance.} The second and last columns display the values for the training set, defined as the $U$ and the $MV$ computed for the samples in $\\mathcal{D}_{\\text{Train}}$. We see that the GAN's $U$ is always less than the training threshold; however, this is not always true for $MV$, as the GAN has a lower $MV$ value only $70\\%$ of the time.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Generalization Metrics for Practical Quantum Advantage in Generative Models", "authors": ["Kaitlin Gili", "Marta Mauri", "Alejandro Perdomo-Ortiz"], "url": "https://arxiv.org/abs/2201.08770v3", "attribution": "\"Generalization Metrics for Practical Quantum Advantage in Generative Models\" by Kaitlin Gili, Marta Mauri, and Alejandro Perdomo-Ortiz, arXiv:2201.08770v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11187v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc} \n \\hline\n Method & Mean KPE (mm) & AUC (0-50 mm)\\\\ \n \\hline\n \\textit{DIREG3D}-Mono & 12.37 & 0.755\\\\ \n \\hline\n \\textit{DIREG3D}-Stereo & 11.39 & 0.774\\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results on the in-house synthetic dataset}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "DIREG3D: DIrectly REGress 3D Hands from Multiple Cameras", "authors": ["Ashar Ali", "Upal Mahbub", "Gokce Dane", "Gerhard Reitmayr"], "url": "https://arxiv.org/abs/2201.11187v1", "attribution": "\"DIREG3D: DIrectly REGress 3D Hands from Multiple Cameras\" by Ashar Ali, Upal Mahbub, Gokce Dane, and Gerhard Reitmayr, arXiv:2201.11187v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.15560v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc} % 只有两列\n \\toprule\n \\textbf{Step} & \\textbf{Predicted Values} \\\\\n \\midrule\n Max(Window 1, Window 2, Window 3) & [3.7487e-01, 1.0177e-05, 1.7565e-02, 2.0318e-02, 1.2092e-01, 1.7365e-04, 3.3524e-03, \\textbf{9.9258e-01}, 8.1936e-01, 1.2045e-02] \\\\\n \\cmidrule(lr){1-2} \n \\textbf{Final Prediction} & 7 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Max method}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings", "authors": ["Taketo Akama", "Zhuohao Zhang", "Pengcheng Li", "Kotaro Hongo", "Hiroaki Kitano", "Shun Minamikawa", "Natalia Polouliakh"], "url": "https://arxiv.org/abs/2412.15560v1", "attribution": "\"Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings\" by Taketo Akama, Zhuohao Zhang, Pengcheng Li, Kotaro Hongo, Hiroaki Kitano, Shun Minamikawa, and Natalia Polouliakh, arXiv:2412.15560v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table36.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|r|r|r}\n \\hline\n \\multicolumn{9}{c}{Implicit multi-stage optimization model with adjustable carry-overs} \\\\\n \\hline\n \\multicolumn{9}{c}{Instances with only end item demands} \\\\\n \\hline\n BOM & $|\\mathcal{I}|$ & $|\\mathcal{H}|$ & Distribution & Util. & $|\\Omega|$ & $|\\Phi|$ & Runtime (s) & Gap (\\%) \\\\\n \\hline\n \\multirow{10}{*}{General} &\n \\multirow{10}{*}{10} &\n \\multirow{10}{*}{7} &\n \\multirow{10}{*}{Non stationary} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & 8.4 & 0.00\\\\\n & & & & & 3 & 2,187 & 438.6 & 0.00\\\\\n & & & & & 4 & 16,384 & 10,800.0 & 1.18 \\\\\n & & & & & 5 & 78,125 & 10,800.0 & 20.4 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\\cline{5-9}\n & & & &\\multirow{5}{*}{90\\%} \n & 2 & 128 & 59.6 & 0.00 \\\\\n & & & & & 3 & 2,187 & 1,811.7 & 0.00 \\\\\n & & & & & 4 & 16,384 & 10,800.0 & 4.05 \\\\\n & & & & & 5 & 78,125 & Memory & - \\\\\n & & & & & 6 & 279,936 & - & - \\\\\n \\hline\n \\multirow{10}{*}{General} &\n \\multirow{10}{*}{10} &\n \\multirow{10}{*}{7} &\n \\multirow{10}{*}{Uniform} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & 8.6 & 0.00 \\\\\n & & & & & 3 & 2,187 & 286.5 & 0.00 \\\\\n & & & & & 4 & 16,384 & 5,421.7 & 0.00 \\\\\n & & & & & 5 & 78,125 & 10,800.0 & 23.66 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\\cline{5-9}\n & & & & \\multirow{5}{*}{90\\%}\n & 2 & 128 & 27.3 & 0.00 \\\\\n & & & & & 3 & 2,187 & 653.3 & 0.00 \\\\\n & & & & & 4 & 16,384 & 10,800.0 & 0.15 \\\\\n & & & & & 5 & 78,125 & Memory & - \\\\\n & & & & & 6 & 279,936 & - & - \\\\\n \\hline\n \\multirow{10}{*}{General} &\n \\multirow{10}{*}{10} &\n \\multirow{10}{*}{7} &\n \\multirow{10}{*}{Lumpy} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & 28.8 & 0.00 \\\\\n & & & & & 3 & 2,187 & 803.4 & 0.00 \\\\\n & & & & & 4 & 16,384 & 10,800.0 & 0.08 \\\\\n & & & & & 5 & 78,125 & 10,800.0 & 89.47 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\\cline{5-9}\n & & & & \\multirow{5}{*}{90\\%} \n & 2 & 128 & 86.5 & 0.00 \\\\\n & & & & & 3 & 2,187 & 10,800.0 & 0.21 \\\\\n & & & & & 4 & 16,384 & 10,800.0 & 16.07 \\\\\n & & & & & 5 & 78,125 & Memory & - \\\\\n & & & & & 6 & 279,936 & - & - \\\\\n \\hline\n \\multirow{10}{*}{General} &\n \\multirow{10}{*}{10} &\n \\multirow{10}{*}{7} &\n \\multirow{10}{*}{Slow moving} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & 19.1 & 0.00 \\\\\n & & & & & 3 & 2,187 & 2,050.5 & 0.00 \\\\\n & & & & & 4 & 16,384 & 10,800.0 & 0.57 \\\\\n & & & & & 5 & 78,125 & 10,800.0 & 86.01 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\\cline{5-9}\n & & & & \\multirow{5}{*}{90\\%}\n & 2 & 128 & 114.3 & 0.00 \\\\\n & & & & & 3 & 2,187 & 9,489.1 & 0.00 \\\\\n & & & & & 4 & 16,384 & 10,800.0 & 9.18 \\\\\n & & & & & 5 & 78,125 & Memory & - \\\\\n & & & & & 6 & 279,936 & - & - \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Runtimes and gaps for the implicit multi-stage optimization model on the instances including only end item demands for different distributions and numbers of scenarios per branch with a time limit of 3 hours (10,800 s)}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13229v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{One-way Chi-square test for hours }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll} \\toprule\n & All Type & Rear-end & Sideswipe \\\\ \\midrule\nStatstic & 5350.39& 5028.855 & 691.545 \\\\\nP-value & 0.0& 0.0 & 2.679e-131 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study", "authors": ["Xiwen Chen", "Hao Wang", "Abolfazl Razi", "Brendan Russo", "Jason Pacheco", "John Roberts", "Jeffrey Wishart", "Larry Head", "Alonso Granados Baca"], "url": "https://arxiv.org/abs/2201.13229v2", "attribution": "\"Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study\" by Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head, and Alonso Granados Baca, arXiv:2201.13229v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.01255v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\toprule\n\\textbf{Model} & \\textbf{2-cell} & \\textbf{4-cell} & \\textbf{8-cell} & \\textbf{blastocyst} & \\textbf{morula} \\\\ \\midrule\n\\textbf{LDM} & 24 & 28 & 31 & 10 & 14 \\\\ \\midrule\n\\textbf{StyleGAN} & 24 & 24 & 34 & 41 & 36 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{FID scores for the synthetic data generated by the best generative models trained for the different classes.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Merging synthetic and real embryo data for advanced AI predictions", "authors": ["Oriana Presacan", "Alexandru Dorobantiu", "Vajira Thambawita", "Michael A. Riegler", "Mette H. Stensen", "Mario Iliceto", "Alexandru C. Aldea", "Akriti Sharma"], "url": "https://arxiv.org/abs/2412.01255v2", "attribution": "\"Merging synthetic and real embryo data for advanced AI predictions\" by Oriana Presacan, Alexandru Dorobantiu, Vajira Thambawita, Michael A. Riegler, Mette H. Stensen, Mario Iliceto, Alexandru C. Aldea, and Akriti Sharma, arXiv:2412.01255v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00719v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrr}\n\\toprule\nModel & Training Accuracy & Test Accuracy & LogLik & AIC & BIC & AUC & Deviance & Parameters\\\\\n\\midrule\nLIWC\\_LM\\_Stress & 0.78 & 0.80 & -366.95 & 787.89 & 914.38 & 0.87 & 733.89 & 26\\\\\nLIWC\\_Stress & 0.77 & 0.79 & -374.89 & 797.78 & 910.21 & 0.86 & 749.78 & 23\\\\\nLM\\_Stress & 0.74 & 0.80 & -410.58 & 837.17 & 874.64 & 0.87 & 821.17 & 7\\\\\nStress\\_Diction & 0.72 & 0.79 & -428.09 & 866.18 & 889.61 & 0.86 & 856.18 & 4\\\\\nLIWC & 0.69 & 0.63 & -470.61 & 981.22 & 1074.92 & 0.72 & 941.22 & 19\\\\\n\\addlinespace\nLM & 0.68 & 0.74 & -489.88 & 987.75 & 1006.49 & 0.78 & 979.75 & 3\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Bankruptcy prediction using disclosure text features", "authors": ["Sridhar Ravula"], "url": "https://arxiv.org/abs/2101.00719v1", "attribution": "\"Bankruptcy prediction using disclosure text features\" by Sridhar Ravula, arXiv:2101.00719v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.14373v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study on varying number of merges $\\texttt{num\\_merges}$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\bfseries $\\texttt{num\\_merges}$ & \\bfseries BLEU-4 & \\bfseries Rouge-L & \\bfseries Meteor & \\bfseries BertScore F1 \\\\\n \\midrule\n 500 & 13.61 ± 0.53 & 46.50 ± 0.28 & 28.49 ± 0.49 & 92.33 ± 0.02 \\\\\n 1750 & 14.50 ± 0.25 & 46.74 ± 0.48 & 30.03 ± 0.25 & \\textbf{92.55} ± 0.01 \\\\\n 2500 & \\textbf{15.10} ± 0.39 & 46.37 ± 0.28 & \\textbf{30.12} ± 0.23 & 92.53 ± 0.05 \\\\\n 3500 & 13.93 ± 0.21 & \\textbf{47.08} ± 0.56 & 29.17 ± 0.31 & 92.53 ± 0.07 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling", "authors": ["William Han", "Chaojing Duan", "Michael A. Rosenberg", "Emerson Liu", "Ding Zhao"], "url": "https://arxiv.org/abs/2412.14373v3", "attribution": "\"ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling\" by William Han, Chaojing Duan, Michael A. Rosenberg, Emerson Liu, and Ding Zhao, arXiv:2412.14373v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.06601v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Measurement noise values as a fraction of trajectory range.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n Measurement noise value & 1e-6 & 1e-5 & 5e-5 & 1e-4 & 1e-3\\\\\n \\hline\n $2\\sigma$ to range percent ratio & 0.25 & 0.78 & 1.75 & 2.47 & 7.81 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A switching Kalman filter approach to online mitigation and correction of sensor corruption for inertial navigation", "authors": ["Artem Mustaev", "Nicholas Galioto", "Matt Boler", "John D. Jakeman", "Cosmin Safta", "Alex Gorodetsky"], "url": "https://arxiv.org/abs/2412.06601v2", "attribution": "\"A switching Kalman filter approach to online mitigation and correction of sensor corruption for inertial navigation\" by Artem Mustaev, Nicholas Galioto, Matt Boler, John D. Jakeman, Cosmin Safta, and Alex Gorodetsky, arXiv:2412.06601v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.17026v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n $N$ & Singularities of Shifted Exponential Series& $\\rho_{y}$ \\\\\n \\hline\n $10$ & $1.056842793206091\\pm 0.485710037224205i$ & $1.163112604098155$ \\\\\n \\hline\n $18$ & $1.036143795072208\\pm 0.283362835680417i$ & $1.074192003652739$\\\\\n \\hline\n $32$ & $1.037765731959409\\pm 0.175282863377030i$ & $1.052464629630326$ \\\\\n \\hline\n $56$ & $1.043755632992874\\pm 0.107370586044249i$ & $1.049263677133561$\\\\\n \\hline\n $100$ & $1.046872197250276\\pm 0.062602875686176i$ & $1.048742350351029$ \\\\\n \\hline\n $178$ & $1.048034587685202\\pm 0.035965767735149i$ & $1.048651530983133$ \\\\\n \\hline\n $214$ & $1.048212100828620\\pm 0.030060161212960i$ & $1.048643038224017$ \\\\\n \\hline\n $316$ & $1.048434308295043\\pm 0.020514745692475i$ & $1.048634995411145$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{This is a table of approximate singularities found from the zeroes of the polynomial of the reciprocal of the $y$ series for the Bubonic plague example.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An Exact SIR Series Solution and an Exploration of the Related Parameter Space", "authors": ["Daniel P Hobbs"], "url": "https://arxiv.org/abs/2504.17026v1", "attribution": "\"An Exact SIR Series Solution and an Exploration of the Related Parameter Space\" by Daniel P Hobbs, arXiv:2504.17026v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.20362v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n & Symbol & Description & Value \\\\\\hline\n & $K$ & controller gain & 0.2 \\\\\n Controller & $c$ & controller frequency [rad/s] & 2\\\\\n parameters & $a$ & pertubation amplitude & 0.2\\\\\n & $\\omega$ & pertubation frequency [rad/s] & 8\\\\\n \n \\hline\n & $\\theta^{*}$ & optimizer static map & 8 \\\\\n System & $y^{*}$ & optimal value static map & 64 \\\\\n parameters & $H$ & Hessian & -1 \\\\\n & $D(\\theta(t))$ & state-dependent delay & $D(\\theta(t))=\\frac{1}{2}\\sin(5\\theta(t))^2$ \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Slow and fast dynamics in measure functional differential equations with state-dependent delays through averaging principles and applications to extremum seeking", "authors": ["Jaqueline G. Mesquita", "Tiago Roux Oliveira", "Henrique C. dos Reis"], "url": "https://arxiv.org/abs/2412.20362v1", "attribution": "\"Slow and fast dynamics in measure functional differential equations with state-dependent delays through averaging principles and applications to extremum seeking\" by Jaqueline G. Mesquita, Tiago Roux Oliveira, and Henrique C. dos Reis, arXiv:2412.20362v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.06968v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\n\\textbf{Network type} & \\textbf{Real multiplications} \\\\\n\\hline\nMLP & $N_N N_I$ \\\\\n\\hline\n1D-CNN & $N_F N_I N_K N_O$ \\\\\n\\hline\nRNN & $N_S N_H (N_I + N_H)$ \\\\\n\\hline\nLSTM & $N_S N_H (4N_I + 4N_H + 3)$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities", "authors": ["Murat Temiz", "Yongwei Zhang", "Yanwei Fu", "Chi Zhang", "Chenfeng Meng", "Orhan Kaplan", "Christos Masouros"], "url": "https://arxiv.org/abs/2509.06968v1", "attribution": "\"Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities\" by Murat Temiz, Yongwei Zhang, Yanwei Fu, Chi Zhang, Chenfeng Meng, Orhan Kaplan, and Christos Masouros, arXiv:2509.06968v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13448v4_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\toprule\n Trait & ICC [95\\% CI] & \\textit{p}-value \\\\\n \\midrule\n ``warm'' & $0.68$ $[0.64$, $0.71]$ & $< 0.001$ \\\\\n ``well-intentioned'' & $0.77$ $[0.66$, $0.73]$ & $< 0.001$ \\\\\n ``competent'' & $0.57$ $[0.52$, $0.61]$ & $< 0.001$ \\\\\n ``intelligent'' & $0.56$ $[0.51$, $0.60]$ & $< 0.001$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Participants' evaluations of their co-players were highly consistent, as assessed by intraclass correlation coefficient (ICC)~. ICC ranges from 0 to 1, with higher values indicating greater consistency.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Warmth and competence in human-agent cooperation", "authors": ["Kevin R. McKee", "Xuechunzi Bai", "Susan T. Fiske"], "url": "https://arxiv.org/abs/2201.13448v4", "attribution": "\"Warmth and competence in human-agent cooperation\" by Kevin R. McKee, Xuechunzi Bai, and Susan T. Fiske, arXiv:2201.13448v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table26.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|r|c|c|r|r}\n \\hline\n \\multicolumn{8}{c}{PH algorithm using all scenario paths} \\\\\n \\hline\n \\multicolumn{8}{c}{Instances with only end item demands} \\\\\n \\hline\n Distribution & Util. & $|\\Omega|$ & $|\\Phi|$ & Converged & Gap (\\%) & Runtime (s) & Iterations \\\\\n \\hline\n \\multirow{10}{*}{Non stationary} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & Yes & 0.0 & 29.2 & 4\\\\\n & & 3 & 2,187 & Yes & 0.0 & 477.9 & 4\\\\\n & & 4 & 16,384 & Yes & 0.0 & 3554.3 & 4\\\\\n & & 5 & 78,125 & & & &\\\\\n & & 6 & 279,936 & & & &\\\\\\cline{2-8}\n &\\multirow{5}{*}{90\\%} \n & 2 & 128 & Yes & 0.0 & 35.8 & 5\\\\\n & & 3 & 2,187 & Yes & 1.1 & 552.4 & 5\\\\\n & & 4 & 16,384 & Yes & 0.0 & 4143.0 & 5\\\\\n & & 5 & 78,125 & & & &\\\\\n & & 6 & 279,936 & & & &\\\\\n \\hline\n \\multirow{10}{*}{Uniform} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & Yes & 0.0 & 14.4 & 2\\\\\n & & 3 & 2,187 & Yes & 0.0 & 242.8 & 2 \\\\\n & & 4 & 16,384 & Yes & 0.0 & 2637.6 & 3 \\\\\n & & 5 & 78,125 & & & & \\\\\n & & 6 & 279,936 & & & & \\\\\\cline{2-8}\n & \\multirow{5}{*}{90\\%}\n & 2 & 128 & Yes & 0.0 & 29.4 & 4\\\\\n & & 3 & 2,187 & Yes & 0.0 & 468.5 & 4 \\\\\n & & 4 & 16,384 & Yes & 0.0 & 3441.5 & 4 \\\\\n & & 5 & 78,125 & & & & \\\\\n & & 6 & 279,936 & & & & \\\\\n \\hline\n \\multirow{10}{*}{Lumpy} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & Yes & 1.6 & 342.8 & 44 \\\\\n & & 3 & 2,187 & No & Infeasible & 10,800.0 & 87 \\\\\n & & 4 & 16,384 & No& 3.2 & 10,800.0& 8 \\\\\n & & 5 & 78,125 & & & & \\\\\n & & 6 & 279,936 & & & & \\\\\\cline{2-8}\n & \\multirow{5}{*}{90\\%} \n & 2 & 128 & Yes & 1.3 & 445.4 & 26 \\\\\n & & 3 & 2,187 & No & 1.3 & 10,800.0 & 33\\\\\n & & 4 & 16,384 & No & Infeasible & 10,800.0 & 5\\\\\n & & 5 & 78,125 & & & & \\\\\n & & 6 & 279,936 & & & & \\\\\n \\hline\n \\multirow{10}{*}{Slow moving} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & No & 0.6 & 772.4 & 100\\\\\n & & 3 & 2,187 & No & 1.1 & 10,800.0& 95 \\\\\n & & 4 & 16,384 & No & 1.3 & 10,800.0 & 14 \\\\\n & & 5 & 78,125 & & & & \\\\\n & & 6 & 279,936 & & & & \\\\\\cline{2-8}\n & \\multirow{5}{*}{90\\%}\n & 2 & 128 & No & 1.9 & 1288.3 & 100 \\\\\n & & 3 & 2,187 & No & 2.4 & 10,800.0 & 67 \\\\\n & & 4 & 16,384 & Cancelled & & & \\\\\n & & 5 & 78,125 & & & & \\\\\n & & 6 & 279,936 & & & & \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Runtimes and gaps for the progressive hedging algorithm on the instances including component demands for different distributions and numbers of scenarios per branch with a time limit of 3 hours (10,800 s) and maximum of 100 iterations}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.08718v1_tex_table19.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hasbrouck's Information Share}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{Market} & & \\\\ \\hline\nCentralized Market & 0.986 & 0.981 \\\\ \\hline\nDecentralized Market & 0.019 & 0.014 \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Price Discovery in Cryptocurrency Markets", "authors": ["Juan Plazuelo Pascual", "Carlos Tardon Rubio", "Juan Toro Cebada", "Angel Hernando Veciana"], "url": "https://arxiv.org/abs/2506.08718v1", "attribution": "\"Price Discovery in Cryptocurrency Markets\" by Juan Plazuelo Pascual, Carlos Tardon Rubio, Juan Toro Cebada, and Angel Hernando Veciana, arXiv:2506.08718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.04686v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classification accuracy (\\%) on Digit-Five dataset~ with different federated DA methods. Baseline results repeated from . ``$\\to$ mt'' means ``mm,sv,sy,up$\\to$mt.'' Settings (\\uppercase\\expandafter{\\romannumeral1}), (\\uppercase\\expandafter{\\romannumeral2}), and (\\uppercase\\expandafter{\\romannumeral3}) as in . }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\toprule\n Methods & $\\to$mt & $\\to$mm & $\\to$up & $\\to$sv & $\\to$sy & Avg \\\\ \\midrule\n Source Only & 75.4 & 49.6 & 75.5 & 22.7 & 44.3 & 53.5 \\\\\n f-DANN[4]~ & 86.1 & 59.5 & 89.7 & 44.3 & 53.4 & 66.6 \\\\\n f-DAN[3]~ & 86.4 & 57.5 & \\underline{90.8} & \\underline{45.3} & \\underline{58.4} & \\underline{67.7} \\\\\n FADA~ & 91.4 & 62.5 & \\textbf{91.7} & \\textbf{50.5} & \\textbf{71.8} & \\textbf{73.6} \\\\ \\midrule \n FedRF-TCA (\\uppercase\\expandafter{\\romannumeral1}) & 97.3 & \\underline{64.6} & 89.2 & 41.5 & 43.9 & $67.3$ \\\\\n FedRF-TCA (\\uppercase\\expandafter{\\romannumeral2}) & \\textbf{97.5} & \\textbf{65.3} & 89.2 & 41.9 & 43.8 & $67.5$ \\\\ \n FedRF-TCA (\\uppercase\\expandafter{\\romannumeral3}) & \\underline{97.4} & 64.3 & 89.5 & 41.9 & 44.4 & $67.5$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Robust and Communication-Efficient Federated Domain Adaptation via Random Features", "authors": ["Zhanbo Feng", "Yuanjie Wang", "Jie Li", "Fan Yang", "Jiong Lou", "Tiebin Mi", "Robert. C. Qiu", "Zhenyu Liao"], "url": "https://arxiv.org/abs/2311.04686v2", "attribution": "\"Robust and Communication-Efficient Federated Domain Adaptation via Random Features\" by Zhanbo Feng, Yuanjie Wang, Jie Li, Fan Yang, Jiong Lou, Tiebin Mi, Robert. C. Qiu, and Zhenyu Liao, arXiv:2311.04686v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.24078v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\hline\n \\textbf{Variable} & \\textbf{Estimate} & \\textbf{Std. Error} & \\textbf{t-value} & \\textbf{p-value} \\\\\n \\hline\n (Intercept) & 5.0390 & 0.0083 & 604.473 & $<$2e-16*** \\\\\n Gender (Female) & -0.0297 & 0.0054 & -5.523 & 3.61e-08*** \\\\\n Titles: Assistant Professor $\\times$ Working Years & -0.0083 & 0.0007 & -12.075 & $<$2e-16*** \\\\\n Titles: Associate Professor $\\times$ Working Years & -0.0024 & 0.0004 & -5.651 & 1.74e-08*** \\\\\n Titles: Professor $\\times$ Working Years & 0.0030 & 0.0003 & 9.215 & $<$2e-16*** \\\\\n bachelor/master $\\times$ Dept. AH $\\times$ log$_{10}$(i10-index) & -0.0873 & 0.0135 & -6.461 & 1.20e-10*** \\\\\n DRU(H) $\\times$ Dept. AH $\\times$ log$_{10}$(i10-index) & -0.0828 & 0.0086 & -9.671 & $<$2e-16*** \\\\\n DU/VA $\\times$ Dept. AH $\\times$ log$_{10}$(i10-index) & -0.0559 & 0.0109 & -5.155 & 2.69e-07*** \\\\\n bachelor/master $\\times$ Dept. B $\\times$ log$_{10}$(i10-index) & 0.0179 & 0.0210 & 0.852 & 0.3942 \\\\\n DRU(H) $\\times$ Dept. B $\\times$ log$_{10}$(i10-index) & 0.0938 & 0.0113 & 8.314 & $<$2e-16*** \\\\\n DU/VA $\\times$ Dept. B $\\times$ log$_{10}$(i10-index) & 0.1610 & 0.0121 & 13.323 & $<$2e-16*** \\\\\n bachelor/master $\\times$ Dept. MHS $\\times$ log$_{10}$(i10-index) & -0.0182 & 0.0282 & -0.646 & 0.5185 \\\\\n DRU(H) $\\times$ Dept. MHS $\\times$ log$_{10}$(i10-index) & 0.0646 & 0.0083 & 7.833 & 6.52e-15*** \\\\\n DU/VA $\\times$ Dept. MHS $\\times$ log$_{10}$(i10-index) & 0.1479 & 0.0056 & 26.450 & $<$2e-16*** \\\\\n bachelor/master $\\times$ Dept. NS $\\times$ log$_{10}$(i10-index) & -0.0764 & 0.0175 & -4.359 & 1.35e-05*** \\\\\n DRU(H) $\\times$ Dept. NS $\\times$ log$_{10}$(i10-index) & -0.0482 & 0.0089 & -5.430 & 6.08e-08*** \\\\\n DU/VA $\\times$ Dept. NS $\\times$ log$_{10}$(i10-index) & 0.0356 & 0.0061 & 5.855 & 5.28e-09*** \\\\\n bachelor/master $\\times$ Dept. SS $\\times$ log$_{10}$(i10-index) & -0.0758 & 0.0172 & -4.398 & 1.13e-05*** \\\\\n DRU(H) $\\times$ Dept. SS $\\times$ log$_{10}$(i10-index) & -0.0565 & 0.0087 & -6.462 & 1.20e-10*** \\\\\n DU/VA $\\times$ Dept. SS $\\times$ log$_{10}$(i10-index) & 0.0429 & 0.0076 & 5.636 & 1.89e-08*** \\\\\n bachelor/master $\\times$ Dept. TE $\\times$ log$_{10}$(i10-index) & -0.0509 & 0.0246 & -2.072 & 0.0384* \\\\\n DRU(H) $\\times$ Dept. TE $\\times$ log$_{10}$(i10-index) & -0.0769 & 0.0095 & -0.008 & 0.9935 \\\\\n DU/VA $\\times$ Dept. TE $\\times$ log$_{10}$(i10-index) & 0.0397 & 0.0061 & 6.520 & 8.19e-11*** \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Three-way Interaction Model: Gender, Academic Context, and Productivity (log$_{10}$(i10-index))}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Estimation of Gender Wage Gap in the University of North Carolina System", "authors": ["Zihan Zhang", "Jan Hannig"], "url": "https://arxiv.org/abs/2505.24078v1", "attribution": "\"Estimation of Gender Wage Gap in the University of North Carolina System\" by Zihan Zhang and Jan Hannig, arXiv:2505.24078v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2305.00545v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{llrrrrr}\n \\hline\nempty & name & avg & sd & min & max & N \\\\ \n \\hline\n & Age & 41.79 & 10.95 & 19.00 & 99.00 & 4871 \\\\ \n & Female & 0.46 & 0.50 & 0.00 & 1.00 & 4871 \\\\ \n & Naturalization application & 0.07 & 0.25 & 0.00 & 1.00 & 4871 \\\\ \n & Years in Switzerland & 14.88 & 7.44 & 11.00 & 67.00 & 4871 \\\\ \n & Years in Zurich & 9.08 & 4.06 & 3.00 & 20.00 & 4871 \\\\ \n & Americas \\& Caribbean & 0.05 & 0.22 & 0.00 & 1.00 & 4871 \\\\ \n & Asia & 0.06 & 0.25 & 0.00 & 1.00 & 4871 \\\\ \n & Central-East Europe & 0.04 & 0.19 & 0.00 & 1.00 & 4871 \\\\ \n & Germany and Austria & 0.37 & 0.48 & 0.00 & 1.00 & 4871 \\\\ \n & Italy & 0.10 & 0.30 & 0.00 & 1.00 & 4871 \\\\ \n & Middle Easy and Northern Africa & 0.01 & 0.12 & 0.00 & 1.00 & 4871 \\\\ \n & South-East Europe & 0.12 & 0.32 & 0.00 & 1.00 & 4871 \\\\ \n & Spain and Portugal & 0.12 & 0.33 & 0.00 & 1.00 & 4871 \\\\ \n & Stateless & 0.00 & 0.04 & 0.00 & 1.00 & 4871 \\\\ \n & Sub-Saharian Africa & 0.02 & 0.12 & 0.00 & 1.00 & 4871 \\\\ \n & Western Europe & 0.11 & 0.32 & 0.00 & 1.00 & 4871 \\\\ \n \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Optimal multi-action treatment allocation: A two-phase field experiment to boost immigrant naturalization", "authors": ["Achim Ahrens", "Alessandra Stampi-Bombelli", "Selina Kurer", "Dominik Hangartner"], "url": "https://arxiv.org/abs/2305.00545v3", "attribution": "\"Optimal multi-action treatment allocation: A two-phase field experiment to boost immigrant naturalization\" by Achim Ahrens, Alessandra Stampi-Bombelli, Selina Kurer, and Dominik Hangartner, arXiv:2305.00545v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.08774v2_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\n&\\multicolumn{2}{c }{No random}&\\multicolumn{2}{c}{30\\% random}&\\multicolumn{2}{c}{50\\% random} \\\\\\cmidrule(lr){2-3}\\cmidrule(lr){4-5}\\cmidrule(lr){6-7}\n{Dataset}&PhyloGFN&VBPI-GNN&PhyloGFN&VBPI-GNN&PhyloGFN&VBPI-GNN\\\\\n\\midrule\nDS1 &\\bf 0.994 & 0.955 &\\bf 0.961 & 0.589 &\\bf 0.955 & 0.512 \\\\\nDS2 & 0.930 &\\bf 0.952 &\\bf 0.948 & 0.580 &\\bf 0.919 & 0.538 \\\\\nDS3 & 0.917 &\\bf 0.968 &\\bf 0.963 & 0.543 &\\bf 0.950 & 0.499 \\\\\nDS4 & 0.942 &\\bf 0.960 &\\bf 0.945 & 0.770 &\\bf 0.966 & 0.76 \\\\\nDS5 &\\bf 0.969 & 0.965 &\\bf 0.937 & 0.786 &\\bf 0.939 & 0.773 \\\\\nDS6 & \\bf 0.993 & 0.887 &\\bf 0.973 & 0.816 &\\bf 0.934 & 0.702 \\\\\nDS7 & 0.624 &\\bf 0.955 &\\bf 0.787 & 0.682 &\\bf 0.764 & 0.678 \\\\\nDS8 &\\bf 0.978 & 0.955 &\\bf 0.913 & 0.604 &\\bf 0.901 & 0.463 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "PhyloGFN: Phylogenetic inference with generative flow networks", "authors": ["Mingyang Zhou", "Zichao Yan", "Elliot Layne", "Nikolay Malkin", "Dinghuai Zhang", "Moksh Jain", "Mathieu Blanchette", "Yoshua Bengio"], "url": "https://arxiv.org/abs/2310.08774v2", "attribution": "\"PhyloGFN: Phylogenetic inference with generative flow networks\" by Mingyang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, and Yoshua Bengio, arXiv:2310.08774v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.18047v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Design of the penalty mechanism.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n\\hline\n\\toprule[1.3pt]\n\\textbf{Parameter} & \\textbf{Value} \\\\ \n\\midrule\nContract Capacity ($C_{contract}$) & 700~kW \\\\ \nBasic Capacity Rate ($R_{base}$) & \\$15/kW/month \\\\\n\\midrule[1.3pt]\n\\textbf{Exceed Electricity Amount} & \\textbf{Penalty Charge} \\\\ \\hline\nUp to 10\\% over $C_{contract}$ & \\( 2\\cdot R_{base} \\) \\\\ \nMore than 10\\% over $C_{contract}$ & \\( 3\\cdot R_{base} \\) \\\\ \n\\bottomrule[1.3pt]\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Uncertainty-Aware Critic Augmentation for Hierarchical Multi-Agent EV Charging Control", "authors": ["Lo Pang-Yun Ting", "Ali Şenol", "Huan-Yang Wang", "Hsu-Chao Lai", "Kun-Ta Chuang", "Huan Liu"], "url": "https://arxiv.org/abs/2412.18047v4", "attribution": "\"Uncertainty-Aware Critic Augmentation for Hierarchical Multi-Agent EV Charging Control\" by Lo Pang-Yun Ting, Ali Şenol, Huan-Yang Wang, Hsu-Chao Lai, Kun-Ta Chuang, and Huan Liu, arXiv:2412.18047v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18896v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n $s$ & $2000$ & $500$ & $200$ & $50$\\\\\n \\midrule\n Time (s) & $119.81$ & $25.16$ & $9.86$ & $2.72$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Time (seconds) required to construct the calibration bands on the mean of independent normal responses $(Y_i)_{i=1}^{n}$ using the set $\\mathcal{J}^{nbh}_{s}$ for various sizes $s$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Calibration Bands for Mean Estimates within the Exponential Dispersion Family", "authors": ["Łukasz Delong", "Selim Gatti", "Mario V. Wüthrich"], "url": "https://arxiv.org/abs/2503.18896v1", "attribution": "\"Calibration Bands for Mean Estimates within the Exponential Dispersion Family\" by Łukasz Delong, Selim Gatti, and Mario V. Wüthrich, arXiv:2503.18896v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2312.02250v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{llll}\n \\toprule\n Regression & Predictors & Coefficients & P-value \\\\\n \\midrule\n $\\beta_1$ $\\sim$ Category & Cancer & -0.185 & 0.419 \\\\\n & Immune & -0.650 & 0.0172* \\\\\n \\bottomrule\n \\end{tabular}\n\\caption{Regression results between $\\beta_1$ and the three features}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Valuing Post-Revenue Biopharmaceutical Assets with Pfizer's Current Portfolio as a Case Study", "authors": ["Yongzhuo Chen", "Yixuan Liang", "Yiran Liu", "Brian Hobbs", "Michael Kane"], "url": "https://arxiv.org/abs/2312.02250v1", "attribution": "\"Valuing Post-Revenue Biopharmaceutical Assets with Pfizer's Current Portfolio as a Case Study\" by Yongzhuo Chen, Yixuan Liang, Yiran Liu, Brian Hobbs, and Michael Kane, arXiv:2312.02250v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17106v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n $n$ & Error $\\|\\cdot\\|_1$ & Order $\\|\\cdot\\|_1$ & Error\n $\\|\\cdot\\|_{\\infty}$ & Order $\\|\\cdot\\|_{\\infty}$ \\\\\n \\hline\n 40 & 8.73E$-6$ & $-$ & 2.44E$-5$ & $-$ \\\\\n \\hline\n 80 & 2.70E$-7$ & 5.01 & 7.35E$-7$ & 5.05 \\\\\n \\hline\n 160 & 8.45E$-9$ & 5.00 & 2.31E$-8$ & 4.99 \\\\\n \\hline\n 320 & 2.64E$-10$ & 5.00 & 6.95E$-10$ & 5.06 \\\\\n \\hline\n 640 & 8.26E$-12$ & 5.00 & 2.13E$-11$ & 5.03 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Error table for problem , $t=1$, IW.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17106v1", "attribution": "\"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.01791v4_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n & \\multicolumn{4}{c}{Log rents} \\\\ \\cmidrule(lr){2-5} \n & \\multicolumn{2}{c}{Levels} & \\multicolumn{2}{c}{First Differences} \\\\ \\cmidrule(lr){2-3}\\cmidrule(lr){4-5} \n & \\shortstack{Baseline\\\\(1)} \n & \\shortstack{Arellano\\\\Bond (2)} \n & \\shortstack{Baseline\\\\(3)} \n & \\shortstack{Arellano\\\\Bond (4)} \\\\ \\midrule\n Residence MW & -0.0432 & -0.0055 & -0.0219 & -0.0221 \\\\\n & (0.1751) & (0.0298) & (0.0175) & (0.0234) \\\\\n Workplace MW & 0.0376 & 0.0065 & 0.0685 & 0.0702 \\\\\n & (0.2033) & (0.0346) & (0.0288) & (0.0390) \\\\\n Lagged log rents & & 0.8421 & & 0.3299 \\\\\n & & (0.0179) & & (0.0177) \\\\ \\midrule\n Economic controls & Yes & Yes & Yes & Yes \\\\\n P-value equality & 0.8264 & 0.8481 & 0.0514 & 0.1378 \\\\\n Observations & 80,340 & 80,321 & 80,241 & 80,217 \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "From Workplace to Residence: The Spillover Effects of Minimum Wage Policies on Local Housing Markets", "authors": ["Gabriele Borg", "Diego Gentile Passaro", "Santiago Hermo"], "url": "https://arxiv.org/abs/2208.01791v4", "attribution": "\"From Workplace to Residence: The Spillover Effects of Minimum Wage Policies on Local Housing Markets\" by Gabriele Borg, Diego Gentile Passaro, and Santiago Hermo, arXiv:2208.01791v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.00595v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Kolmogorov-Smirnov scores for the distribution of $p$-values for all stations on training and test sets for six models.}%\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}%\n\\toprule\n& Poisson & MEP & SEP & SMEP & SpMEP & GB-MEP \\\\\n\\midrule\nTraining set & $0.2186$ & $0.1610$ & $0.0751$ & $0.0326$ & $0.0734$ & $0.0246$ \\\\\nTest set & $0.2217$ & $0.1584$ & $0.0752$ & $0.0350$ & $0.0738$ & $0.0264$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Graph-based mutually exciting point processes for modelling event times in docked bike-sharing systems", "authors": ["Francesco Sanna Passino", "Yining Che", "Carlos Cardoso Correia Perello"], "url": "https://arxiv.org/abs/2311.00595v3", "attribution": "\"Graph-based mutually exciting point processes for modelling event times in docked bike-sharing systems\" by Francesco Sanna Passino, Yining Che, and Carlos Cardoso Correia Perello, arXiv:2311.00595v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08527v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation studies}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n \\hline\n CA-Module & AE-Module & center loss & accuracy(\\%) \\\\\n\\hline\n\\hline\n $\\checkmark$ & & & $86.8$ \\\\\n & $\\checkmark$ & & $86.5$ \\\\\n $\\checkmark$ & & $\\checkmark$ & $87.9$ \\\\\n & $\\checkmark$ & $\\checkmark$ & $87.5$ \\\\\n $\\checkmark$ & $\\checkmark$ & $\\checkmark$ & $88.3$ \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Progressive Co-Attention Network for Fine-grained Visual Classification", "authors": ["Tian Zhang", "Dongliang Chang", "Zhanyu Ma", "Jun Guo"], "url": "https://arxiv.org/abs/2101.08527v2", "attribution": "\"Progressive Co-Attention Network for Fine-grained Visual Classification\" by Tian Zhang, Dongliang Chang, Zhanyu Ma, and Jun Guo, arXiv:2101.08527v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04213v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Training time of deep learning-based methods (h)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\toprule\n\\multicolumn{2}{c}{Methods} & GRU & LSTM & CNN & FNN & Ours \\\\ \\midrule\n\\multirow{3}{*}{Batch Sizes} & 1 & 5.53 & 5.69 & 3.97 & 4.15 & 10.52 \\\\\n & 4 & 3.81 & 3.99 & 3.11 & 3.67 & 9.21 \\\\\n & 8 & 3.67 & 3.74 & 2.87 & 3.25 & 8.35 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals", "authors": ["Shuhao Ma", "Jie Zhang", "Chaoyang Shi", "Pei Di", "Ian D. Robertson", "Zhi-Qiang Zhang"], "url": "https://arxiv.org/abs/2412.04213v1", "attribution": "\"Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals\" by Shuhao Ma, Jie Zhang, Chaoyang Shi, Pei Di, Ian D. Robertson, and Zhi-Qiang Zhang, arXiv:2412.04213v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.12298v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|cc}\n & $\\lambda_s>\\lambda_u$ & $\\lambda_s<\\lambda_u$\\\\\n \\hline\n$\\rho<0$ & Type I (stable) & Type II (mixed) \\\\\n$\\rho>0$ & Type III (mixed) & Type IV (unstable)\n \\end{tabular}\n\\caption{Cases of the planar non-central SNICeroclinic loop bifurcation where $\\rho$ is the non-central eigenvalue of the saddle-node and $-\\lambda_s$, $\\lambda_u$ are the eigenvalues of the saddle. In this paper we concentrate on the stable case Type I and the unstable case Type IV which corresponds to time-reversal of Type I.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Novel Route to Oscillations via non-central SNICeroclinic Bifurcation: unfolding the separatrix loop between a saddle-node and a saddle", "authors": ["Kateryna Nechyporenko", "Peter Ashwin", "Krasimira Tsaneva-Atanasova"], "url": "https://arxiv.org/abs/2412.12298v1", "attribution": "\"A Novel Route to Oscillations via non-central SNICeroclinic Bifurcation: unfolding the separatrix loop between a saddle-node and a saddle\" by Kateryna Nechyporenko, Peter Ashwin, and Krasimira Tsaneva-Atanasova, arXiv:2412.12298v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.03222v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\n \\hline\n Policy & Vehicle & Contract & & \\multicolumn{6}{|c|}{Scope Variables} \\\\\n $i$ & $j$ & $t$ & $n_{i,j,t}$ & $n_{i,j,t-1}$ & $n_{i,j,t-2}$ & $n_{i,j,t-3}$ & $n_{i,\\bullet,t-1}$ & $n_{i,\\bullet,t-2}$ & $n_{i,\\bullet,t-3}$ \\\\ \n \\hline\n 1 & 1 & 1 & 0 & . & . & . & . & . & . \\\\ \n 1 & 1 & 2 & 2 & 0 & . & 0 & 0 & . & . \\\\ \n 1 & 1 & 3 & 1 & 2 & 0 & 2 & 4 & 0 & . \\\\ \n 1 & 1 & 4 & 0 & 1 & 2 & 0 & 1 & 4 & 0 \\\\ \n 1 & 2 & 2 & 2 & . & . & 0 & 0 & . & . \\\\ \n 1 & 2 & 3 & 0 & 2 & . & . & 4 & 0 & . \\\\ \n 2 & 1 & 1 & 0 & . & . & . & . & . & . \\\\\n 3 & 1 & 1 & 0 & . & . & . & . & . & . \\\\\n 3 & 2 & 1 & 1 & 0 & . & . & 1 & 0 & . \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Illustration of scope variables}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bonus-Malus scale premiums for tweedie's compound poisson models", "authors": ["Jean-Philippe Boucher", "Raïssa Coulibaly"], "url": "https://arxiv.org/abs/2311.03222v1", "attribution": "\"Bonus-Malus scale premiums for tweedie's compound poisson models\" by Jean-Philippe Boucher and Raïssa Coulibaly, arXiv:2311.03222v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.00697v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Generalized Hurst Exponents for Nifty 50 P/E Ratio}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\nOrder $q$ & Generalized Hurst Exponent $H(q)$ \\\\\n\\hline\n1 & 0.5573 \\\\\n2 & 0.5351 \\\\\n3 & 0.4899 \\\\\n4 & 0.4300 \\\\\n5 & 0.3768 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Multi Scale Analysis of Nifty 50 Return Characteristics Valuation Dynamics and Market Complexity 1990 to 2024", "authors": ["Chandradew Sharma"], "url": "https://arxiv.org/abs/2509.00697v1", "attribution": "\"Multi Scale Analysis of Nifty 50 Return Characteristics Valuation Dynamics and Market Complexity 1990 to 2024\" by Chandradew Sharma, arXiv:2509.00697v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.16684v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Empirical power (in percent) with $\\alpha= 0.05$ and $m = n = 50$ under the difference pattern (i) full difference. }\n\\begin{tabular}{cccccccccccccc}\n \\toprule\n $d$ & MATES & CM & GET & BD & GED & RF & MT & GPK & RISE & MMD & xMMD & aMMD & mMMD \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (I)} \\\\\n 200 & \\textbf{90.9} & 4.5 & 6.3 & 5.2 & 9.3 & 6.6 & 5.5 & 4.1 & 6.1 & 4.6 & 5.5 & 5.5 & 1.3 \\\\\n 500 & \\textbf{85.2} & 3.9 & 4.4 & 5.6 & 7.1 & 6.1 & 4.5 & 4.8 & 4.5 & 6.2 & 6.0 & 6.1 & 1.3 \\\\\n 1000 & \\textbf{85.8} & 3.3 & 4.7 & 5.5 & 6.8 & 6.6 & 5.7 & 4.8 & 4.3 & 5.2 & 6.3 & 5.7 & 0.9 \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (II)} \\\\\n 200 & \\textbf{81.2} & 3.4 & 5.2 & 3.6 & 9.8 & 5.7 & 5.2 & 4.1 & 5.2 & 5.5 & 5.2 & 6.0 & 2.1 \\\\\n 500 & \\textbf{71.3} & 2.9 & 5.2 & 5.6 & 8.0 & 5.4 & 5.4 & 4.5 & 4.1 & 4.9 & 6.3 & 6.1 & 1.5 \\\\\n 1000 & \\textbf{82.4} & 2.9 & 5.3 & 5.4 & 7.0 & 4.8 & 5.3 & 4.8 & 5.7 & 4.5 & 5.4 & 5.1 & 1.6 \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (III)} \\\\\n 200 & \\textbf{89.5} & 2.6 & 4.7 & 4.6 & 10.4 & 6.8 & 5.5 & 3.9 & 5.1 & 4.4 & 5.7 & 5.3 & 1.8 \\\\\n 500 & \\textbf{82.5} & 3.0 & 5.4 & 4.5 & 8.9 & 7.1 & 4.5 & 4.1 & 6.3 & 5.8 & 5.7 & 5.7 & 2.0 \\\\\n 1000 & \\textbf{82.7} & 3.2 & 4.8 & 5.2 & 5.4 & 5.7 & 5.5 & 4.1 & 4.4 & 4.6 & 6.3 & 4.1 & 1.2 \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (IV)} \\\\\n 200 & \\textbf{74.3} & 4.6 & 6.0 & 6.0 & 14.0 & 7.6 & 5.4 & 4.6 & 6.2 & 4.6 & 5.9 & 6.6 & 1.2 \\\\\n 500 & \\textbf{89.9} & 3.8 & 5.6 & 6.6 & 12.6 & 7.5 & 3.9 & 6.0 & 5.7 & 5.1 & 5.1 & 6.8 & 1.5 \\\\\n 1000 & \\textbf{84.7} & 3.8 & 4.9 & 4.8 & 10.3 & 6.9 & 3.9 & 4.8 & 5.1 & 5.0 & 6.1 & 6.1 & 2.4 \\\\\n \\midrule\n \\multicolumn{14}{c}{Setting (V)} \\\\\n 200 & \\textbf{77.9} & 5.1 & 10.0 & 6.2 & 13.4 & 7.5 & 5.0 & 4.3 & 9.6 & 5.4 & 6.4 & 7.4 & 1.3 \\\\\n 500 & \\textbf{88.3} & 4.2 & 7.6 & 5.1 & 11.7 & 5.7 & 4.5 & 4.3 & 8.6 & 4.6 & 5.1 & 6.4 & 0.9 \\\\\n 1000 & \\textbf{87.8} & 3.9 & 6.1 & 4.3 & 9.8 & 5.2 & 4.5 & 3.5 & 6.4 & 5.5 & 6.9 & 6.5 & 0.9 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "MATES: Multi-view Aggregated Two-Sample Test", "authors": ["Zexi Cai", "Wenbo Fei", "Doudou Zhou"], "url": "https://arxiv.org/abs/2412.16684v1", "attribution": "\"MATES: Multi-view Aggregated Two-Sample Test\" by Zexi Cai, Wenbo Fei, and Doudou Zhou, arXiv:2412.16684v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.06611v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary Statistics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccc}\n \\hline\n \\hline\n Variable & All students & & \\multicolumn{2}{c}{By poverty} & & \\multicolumn{2}{c}{By Academic Achievement} & & \n \\multicolumn{2}{c}{By Social Centrality} \\\\\n \\cline{4-5} \\cline{7-8} \\cline{10-11}\n & & & Poor students & Non-poor students & & Lower-achieving & Higher-achieving & & Less central & More central \\\\\n & (1) & & (2) & (3) & & (4) & (5) & & (6) & (7) \\\\\n \\hline\n\\textit{Demographics} \\\\\nMale (\\%)&0.43&&0.44&0.43&&0.43&0.43&&0.41&0.40\\\\\nRural (\\%)&0.25&&0.43&0.14&&0.29&0.21&&0.29&0.23\\\\\nPoor (\\%)&0.41&&1.00&0.00&&0.46&0.36&&0.47&0.39\\\\\n\\\\\n\\textit{Network variables} \\\\\n Connections&14.15&&14.19&14.95&&13.93&14.37&&11.51&14.97\\\\\n&6.85&&6.23&6.59&&6.90&6.78&&5.55&5.99\\\\\nConnections with poor students&5.54&&6.91&4.89&&5.63&5.45&&4.86&6.08\\\\\n&(4.11)&&(4.42)&(3.54)&&(4.17)&(4.05)&&(3.81)&(4.48)\\\\\nConnections with lower-achieving students&7.03&&7.26&7.29&&7.54&6.53&&5.76&7.34\\\\\n&(4.25)&&(3.99)&(4.23)&&(4.57)&(3.84)&&(3.31)&(3.57)\\\\\nConnections with less central students&9.58&&9.43&10.22&&9.50&9.66&&5.76&5.96\\\\\n&(7.23)&&(6.73)&(7.39)&&(7.21)&(7.25)&&(3.24)&(3.34)\\\\\n\\\\\nN&5,467&&2,148&3,063&&2,731&2,736&&1,696&1,687\\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Proximity, Similarity, and Friendship Formation: Theory and Evidence", "authors": ["A. Arda Gitmez", "Román Andrés Zárate"], "url": "https://arxiv.org/abs/2210.06611v1", "attribution": "\"Proximity, Similarity, and Friendship Formation: Theory and Evidence\" by A. Arda Gitmez and Román Andrés Zárate, arXiv:2210.06611v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00932v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The performance of unsupervised results (BSCA ) processed by our refinement module.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n Dataset & Metric & BSCA & BSCA w/ ref. & ours w/ ref. \\\\\\hline\n \\multirow{2}{*}{ECSSD} & $F_{\\beta}\\uparrow$ & 0.705 & 0.756 & \\textbf{0.858} \\\\\n \\cline{2-5}\n ~ & MAE$\\downarrow$ & 0.183 & 0.140 & \\textbf{0.108} \\\\\\hline\n \\multirow{2}{*}{DUT-O} & $F_{\\beta}\\uparrow$ & 0.500 & 0.618 & \\textbf{0.778} \\\\\n \\cline{2-5}\n ~ & MAE$\\downarrow$ & 0.196 & 0.134 & \\textbf{0.083} \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Weakly-Supervised Saliency Detection via Salient Object Subitizing", "authors": ["Xiaoyang Zheng", "Xin Tan", "Jie Zhou", "Lizhuang Ma", "Rynson W. H. Lau"], "url": "https://arxiv.org/abs/2101.00932v1", "attribution": "\"Weakly-Supervised Saliency Detection via Salient Object Subitizing\" by Xiaoyang Zheng, Xin Tan, Jie Zhou, Lizhuang Ma, and Rynson W. H. Lau, arXiv:2101.00932v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.15370v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\toprule\nParameter & AVG & SAC \\\\\n\\midrule\nReplay Buffer Size & 1 & 1 \\\\\n\\midrule\nMinibatch Size & 1 & 1 \\\\\n\\midrule\nDiscount factor $(\\gamma)$ & 0.95 & 0.99 \\\\\n\\midrule\nActor Learning Rate & $3 \\times 10^{-4}$ & $3 \\times 10^{-4}$ \\\\\n\\midrule\nCritic Learning Rate & $0.00087$ & $3 \\times 10^{-4}$ \\\\\n\\midrule\nUpdate Actor Every & 1 & 1 \\\\\n\\midrule\nUpdate Critic Every & 1 & 1 \\\\\n\\midrule\nUpdate Critic Target Every & N/A & 1 \\\\\n\\midrule\nTarget Smoothing Coefficient $(\\tau)$ & N/A & 0.005 \\\\\n\\midrule\nTarget Entropy & N/A & $-\\vert \\mathcal{A} \\vert$ \\\\\n\\midrule\nEntropy coefficient ($\\eta$) & 0.05 & Learnable parameter \\\\\n\\midrule\nOptimizer & Adam & Adam \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Default parameters for Robot Tasks.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers", "authors": ["Gautham Vasan", "Mohamed Elsayed", "Alireza Azimi", "Jiamin He", "Fahim Shariar", "Colin Bellinger", "Martha White", "A. Rupam Mahmood"], "url": "https://arxiv.org/abs/2411.15370v2", "attribution": "\"Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers\" by Gautham Vasan, Mohamed Elsayed, Alireza Azimi, Jiamin He, Fahim Shariar, Colin Bellinger, Martha White, and A. Rupam Mahmood, arXiv:2411.15370v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.01116v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\n\\toprule\n& Full Specification & Repeated Services \\\\\n\\midrule\nPost Service & 0.218*** & 0.268*** \\\\\n& (0.0032) & (0.0058) \\\\\nPost Bill & -0.109*** & -0.083*** \\\\\n& (0.0030) & (0.0056) \\\\\n\\midrule\n2$^\\text{nd}$ Service * Post Service & & -0.176*** \\\\\n& &(0.0070) \\\\ \n2$^\\text{nd}$ Service * Post Bill & & 0.118*** \\\\\n& &(0.0071) \\\\ \n\\midrule\n$\\overline{\\text{spend}_{it}}$ & \\$120.49 &\\$135.78 \\\\\nObservations & 61,860,735 & 29,344,385 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Reduced-Form Evidence of Learning}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Medical Bill Shock and Imperfect Moral Hazard", "authors": ["Alex Hoagland", "David M. Anderson", "Ed Zhu"], "url": "https://arxiv.org/abs/2211.01116v2", "attribution": "\"Medical Bill Shock and Imperfect Moral Hazard\" by Alex Hoagland, David M. Anderson, and Ed Zhu, arXiv:2211.01116v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.15012v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\n\\textbf{Project Site - Capacity (MW)} & \\textbf{Payback period (Months)} \\\\ \\hline\nRI - 30 & 6 \\\\ \\hline\nMD - 252 & 3 \\\\ \\hline\nMA - 804 & 2 \\\\ \\hline\nNY - 888 & 3 \\\\ \\hline\nVA - 2640 & 2 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Carbon payback period for the projects of interest}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Estimating the spatial economic and environmental impact of planned offshore wind energy in the USA using Environmentally Extended Multiregional Input-Output analysis", "authors": ["Apoorva Bademi", "Miriam Stevens", "Isha Sura", "Shweta Singh"], "url": "https://arxiv.org/abs/2508.15012v1", "attribution": "\"Estimating the spatial economic and environmental impact of planned offshore wind energy in the USA using Environmentally Extended Multiregional Input-Output analysis\" by Apoorva Bademi, Miriam Stevens, Isha Sura, and Shweta Singh, arXiv:2508.15012v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06840v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hardware overview of experimental system.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\hline\n\\multicolumn{2}{c}{\\textbf{DGX-2 node}} \\\\ \\hline\nGPU & 16 NVIDIA Tesla V100 Tensor Core GPUs \\\\\nGPU Memory & 32GB HBM2 on each GPU \\\\\nCPU & 2 Intel Xeon Platinum 8168 Processors \\\\\nCPU Memory & 1.5TB 2666MHz DDR4 \\\\\nCPU cache & L1, L2, and L3 are 32K, 1M, and 33M, respectively\\\\\nPCIe & bidirectional 32 GBps PCIe\n \\\\ \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "ZeRO-Offload: Democratizing Billion-Scale Model Training", "authors": ["Jie Ren", "Samyam Rajbhandari", "Reza Yazdani Aminabadi", "Olatunji Ruwase", "Shuangyan Yang", "Minjia Zhang", "Dong Li", "Yuxiong He"], "url": "https://arxiv.org/abs/2101.06840v1", "attribution": "\"ZeRO-Offload: Democratizing Billion-Scale Model Training\" by Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, and Yuxiong He, arXiv:2101.06840v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05239v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\\toprule\nDistribution & \\multicolumn{2}{c}{Cosine} & \\multicolumn{2}{c}{Uniform} & \\multicolumn{2}{c}{Banana}& \\multicolumn{2}{c}{Funnel}\\\\\nModel & KDSM & RFFSM & KDSM & RFFSM & KDSM & RFFSM & KDSM & RFFSM \\\\\n\\midrule\nF$_{train}$& \\textbf{2.197} & 5.331 & \\textbf{1.365} & 1.785 & 0.301 & \\textbf{0.28} & 0.34 & \\textbf{0.288} \\\\\nF$_{test}$ & \\textbf{1.858} & 5.102 & \\textbf{1.584} & 1.901 & \\textbf{0.291} & 0.319 & 0.339 & \\textbf{0.307} \\\\\nLL$_{train}$ & -5.53 & -5.008 & -3.66 & -3.649 & -3.529 & -3.528 & -2.867 & -2.846 \\\\\nLL$_{p~train}$ & -3.528 & -3.528 & -3.584 & -3.584 & -2.83 & -2.83 & -2.868 & -2.868 \\\\\nLL$_{test}$ & -5.648 & -5.056 & -3.689 & -3.692 & -3.659 & -3.697 & -2.821 & -2.783 \\\\\nLL$_{p~test}$ & -3.503 & -3.503 & -3.584 & -3.584 & -2.894 & -2.894 & -2.796 & -2.796 \\\\\nFSSD & -0.128 & 0.059 & 0.212 & 0.189 & -0.085 & -0.045 & 0.058 & -0.041 \\\\\np-value & \\textbf{0.425} & 0.308 & 0.093 & \\textbf{0.131} & \\textbf{0.604} & 0.452 & 0.299 & \\textbf{0.392} \\\\\nW$_1$ & \\textbf{0.251} & 0.372 & 0.06 & \\textbf{0.055} & \\textbf{0.047} & 0.052 & \\textbf{0.06} & 0.084 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results of score-matching algorithms, $100$ features and $1000$ sample size for cosine, uniform, banana and funnel distributions.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Denoising Score Matching with Random Fourier Features", "authors": ["Tsimboy Olga", "Yermek Kapushev", "Evgeny Burnaev", "Ivan Oseledets"], "url": "https://arxiv.org/abs/2101.05239v1", "attribution": "\"Denoising Score Matching with Random Fourier Features\" by Tsimboy Olga, Yermek Kapushev, Evgeny Burnaev, and Ivan Oseledets, arXiv:2101.05239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.15378v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Median identification confidence and improvement}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|r|r}\n Method & ID Confidence & Improvement\\\\\n \\hline\n PCA & 71.2\\% & 1.15\\\\\n KNN & 79.0\\% & 1.25\\\\\n KMeans & 49.9\\% & 1.05\\\\\n \\textbf{KNS} & 91.4\\% & 1.65\\\\\n \\textit{Annulus} & 87.6\\% & 1.38\\\\\n Global & 38.2\\% & \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Improved Background Estimation for Gas Plume Identification in Hyperspectral Images", "authors": ["Scout Jarman", "Zigfried Hampel-Arias", "Adra Carr", "Kevin R. Moon"], "url": "https://arxiv.org/abs/2411.15378v1", "attribution": "\"Improved Background Estimation for Gas Plume Identification in Hyperspectral Images\" by Scout Jarman, Zigfried Hampel-Arias, Adra Carr, and Kevin R. Moon, arXiv:2411.15378v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.00308v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MDP3 Transition Function}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c||c}\\hline\n State & Action & Next State \\\\\\hline\n 0 & 0 & 0 \\\\\n 0 & 1 & 0 \\\\\n 1 & 0 & 0 \\\\\n 1 & 1 & 1 \\\\\n 2 & 0 & 2 \\\\\n 2 & 1 & 3 \\\\\n 3 & 0 & 3 \\\\\n 3 & 1 & 3 \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings", "authors": ["Karine Karine", "Susan A. Murphy", "Benjamin M. Marlin"], "url": "https://arxiv.org/abs/2412.00308v1", "attribution": "\"BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings\" by Karine Karine, Susan A. Murphy, and Benjamin M. Marlin, arXiv:2412.00308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02047v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results of the ablation study of the proposed egocentric hand gesture recognition and fingertip detection algorithm}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\n\\hline\n\\multirow{2}{*}{Method} & \\multicolumn{8}{c|}{Gesture} & \\multirow{2}{*}{\\shortstack{Overall\\\\Error\\\\(px)}} \\\\ \\cline{2-9}\n & SingleOne & SingleTwo & SingleThree & SingleFour & SingleFive & SingleSix & SingleSeven & SingleEight & \\\\ \\hline\n\\multirow{3}{*}{\\shortstack{(1)~Method with \\\\ an Averaging Layer \\\\ Included in the CNN \\\\Architecture}} & \\multirow{3}{*}{4.64 $\\pm$ 3.17} & \\multirow{3}{*}{3.94 $\\pm$ 1.86} & \\multirow{3}{*}{3.61 $\\pm$ 1.86} & \\multirow{3}{*}{3.85 $\\pm$ 1.95} & \\multirow{3}{*}{3.63 $\\pm$ 1.53} & \\multirow{3}{*}{3.51 $\\pm$ 1.5} & \\multirow{3}{*}{3.65 $\\pm$ 1.46} & \\multirow{3}{*}{5.8 $\\pm$ 3.77} & \\multirow{3}{*}{4.08 $\\pm$ 2.14} \\\\\n & & & & & & & & & \\\\\n & & & & & & & & & \\\\ \\hline\n\\multirow{3}{*}{\\shortstack{(2)~Method with \\\\Randomly Sampled \\\\Output from the \\\\Ensemble}} & \\multirow{3}{*}{5.21 $\\pm$ 3.43} & \\multirow{3}{*}{4.57 $\\pm$ 2.3} & \\multirow{3}{*}{4.24 $\\pm$ 2.23} & \\multirow{3}{*}{4.28 $\\pm$ 1.98} & \\multirow{3}{*}{4.17 $\\pm$ 1.87} & \\multirow{3}{*}{3.98 $\\pm$ 1.78} & \\multirow{3}{*}{4.23 $\\pm$ 1.68} & \\multirow{3}{*}{6.41 $\\pm$ 3.56} & \\multirow{3}{*}{4.63 $\\pm$ 2.35} \\\\\n & & & & & & & & & \\\\\n & & & & & & & & & \\\\ \\hline\n\\multirow{3}{*}{\\shortstack{(3)~Method with \\\\Direct Regression \\\\of Fingertip \\\\Coordinates}} & \\multirow{3}{*}{5.97 $\\pm$ 3.75} & \\multirow{3}{*}{6.23 $\\pm$ 2.21} & \\multirow{3}{*}{6.01 $\\pm$ 2.49} & \\multirow{3}{*}{5.94 $\\pm$ 2.11} & \\multirow{3}{*}{5.12 $\\pm$ 1.66} & \\multirow{3}{*}{5.45 $\\pm$ 2.32} & \\multirow{3}{*}{4.88 $\\pm$ 2.25} & \\multirow{3}{*}{6.88 $\\pm$ 3.29} & \\multirow{3}{*}{5.81 $\\pm$ 2.51} \\\\\n & & & & & & & & & \\\\\n & & & & & & & & & \\\\ \\hline\n \n\\multirow{2}{*}{\\shortstack{Proposed Method}} & \\multirow{2}{*}{4.51 $\\pm$ 3.14} & \\multirow{2}{*}{3.89 $\\pm$ 1.91} & \\multirow{2}{*}{3.62 $\\pm$ 1.80} & \\multirow{2}{*}{3.79 $\\pm$ 1.89} & \\multirow{2}{*}{3.63 $\\pm$ 1.46} & \\multirow{2}{*}{3.4 $\\pm$ 1.48} & \\multirow{2}{*}{3.64 $\\pm$ 1.51} & \\multirow{2}{*}{5.68 $\\pm$ 3.51} & \\multirow{2}{*}{\\textbf{4.02 $\\mathbf{\\pm}$ 2.09}} \\\\\n & & & & & & & & & \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection", "authors": ["Mohammad Mahmudul Alam", "Mohammad Tariqul Islam", "S. M. Mahbubur Rahman"], "url": "https://arxiv.org/abs/2101.02047v3", "attribution": "\"Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection\" by Mohammad Mahmudul Alam, Mohammad Tariqul Islam, and S. M. Mahbubur Rahman, arXiv:2101.02047v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00561v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc|ccc|ccc|ccc}\n\\multirow{2}{*}{Instance} & \\multirow{2}{*}{} & & \\multicolumn{3}{c|}{\\textbf{Single-Level Ref.}} & \\multicolumn{3}{c|}{\\textbf{Co-EA}} & \\multicolumn{3}{c}{\\textbf{Warm-Started Single-Level Ref. }} \\\\\n & & & Profit & Time (sec) & Opt. Gap (\\%) & Profit & Avg. Time (sec) & Improv. (\\%) & Profit & Time (sec) & B-K Gap (\\%) \\\\ \\hline\n\\multirow{10}{*}{CAB} & 10-3-A & & 4.79 & $>$21600 & 102.89 & 7.57 & 357.8 & 34.04 & 7.84 & $>$10800 & 3.33 \\\\\n & 10-3-B & & 6.90 & $>$21600 & 40.74 & 7.27 & 417.2 & 5.15 & 7.53 & $>$10800 & 3.36 \\\\ \n & 10-3-C & & 4.37 & $>$21600 & 121.80 & 6.00 & 611.12 & 27.15 & 6.29 & $>$10800 & 1.50 \\\\\n & 10-3-D & & 6.92 & $>$21600 & 40.60 & 7.78 & 509.19 & 11.04 & 7.80 & $>$10800 & 0.28 \\\\\n & 10-3-E & & 4.38 & $>$21600 & 122.09 & 7.49 & 466.66 & 41.59 & 7.70 & $>$10800 & 2.61 \\\\\\cline{2-12} \n & 10-5-A & & 7.97 & $>$21600 & 46.24 & 9.12 & 714.14 & 12.67 & 9.37 & $>$10800 & 2.61 \\\\\n & 10-5-B & & 5.75 & $>$21600 & 120.53 & 8.95 & 757.12 & 35.76 & 9.22 & $>$10800 & 2.90 \\\\\n & 10-5-C & & 8.00 & $>$21600 & 45.52 & 9.03 & 746.48 & 11.34 & 9.50 & $>$10800 & 4.98 \\\\\n & 10-5-D & & 6.24 & $>$21600 & 86.86 & 9.53 & 742.49 & 34.51 & 9.72 & $>$10800 & 1.90 \\\\\n & 10-5-E & & 6.83 & $>$21600 & 70.49 & 9.26 & 735.09 & 26.22 & 9.44 & $>$10800 & 1.86 \\\\\\hline\n\\multirow{10}{*}{AP} & 10-3-A & & 3.41 & $>$21600 & 263.01 & 6.39 & 490.07 & 46.63 & 6.66 & $>$10800 & 4.05 \\\\\n & 10-3-B & & 4.74 & $>$21600 & 133.41 & 6.45 & 522.14 & 26.51 & 6.67 & $>$10800 & 3.29 \\\\ \n & 10-3-C & & 4.72 & $>$21600 & 136.95 & 6.37 & 505.12 & 25.90 & 6.45 & $>$10800 & 1.24 \\\\\n & 10-3-D & & 3.96 & $>$21600 & 216.76 & 6.38 & 490.86 & 37.93 & 6.46 & $>$10800 & 1.23 \\\\\n & 10-3-E & & 5.85 & $>$21600 & 91.70 & 6.72 & 595.57 & 12.94 & 7.24 & $>$10800 & 7.18 \\\\\\cline{2-12} \n & 10-5-A & & 6.60 & $>$21600 & 239.54 & 10.93 & 654.23 & 39.61 & 11.25 & $>$10800 & 2.84 \\\\\n & 10-5-B & & 8.26 & $>$21600 & 166.37 & 10.80 & 542.47 & 23.51 & 10.91 & $>$10800 & 1.00 \\\\\n & 10-5-C & & 6.91 & $>$21600 & 226.55 & 10.89 & 601.89 & 36.54 & 11.36 & $>$10800 & 4.13 \\\\\n & 10-5-D & & 7.67 & $>$21600 & 186.91 & 10.88 & 612.39 & 29.50 & 11.34 & $>$10800 & 4.05 \\\\\n & 10-5-E & & 8.55 & $>$21600 & 142.55 & 10.86 & 697.12 & 21.27 & 11.32 & $>$10800 & 4.06 \\\\\\hline\n\\multirow{10}{*}{TR} & 10-3-A & & 105.13 & $>$21600 & 138.05 & 152.97 & 738.99 & 31.27 & 153.12 & $>$10800 & 0.09 \\\\\n & 10-3-B & & 90.65 & $>$21600 & 259.48 & 153.11 & 686.01 & 40.79 & 153.73 & $>$10800 & 0.40 \\\\ \n & 10-3-C & & 126.12 & $>$21600 & 88.15 & 152.83 & 598.81 & 17.47 & 152.93 & $>$10800 & 0.06 \\\\\n & 10-3-D & & 117.16 & $>$21600 & 107.89 & 153.08 & 611.76 & 23.46 & 153.19 & $>$10800 & 0.07 \\\\\n & 10-3-E & & 125.54 & $>$21600 & 105.01 & 152.65 & 666.67 & 17.75 & 152.93 & $>$10800 & 0.18 \\\\\\cline{2-12} \n & 10-5-A & & 151.40 & $>$21600 & 206.17 & 258.07 & 634.14 & 41.33 & 258.18 & $>$10800 & 0.04 \\\\\n & 10-5-B & & 171.80 & $>$21600 & 167.10 & 258.14 & 616.71 & 33.44 & 258.73 & $>$10800 & 0.22 \\\\\n & 10-5-C & & 172.30 & $>$21600 & 150.14 & 257.92 & 643.39 & 33.19 & 258.11 & $>$10800 & 0.07 \\\\\n & 10-5-D & & 174.51 & $>$21600 & 161.12 & 258.12 & 619.04 & 32.39 & 258.12 & $>$10800 & 0.00 \\\\\n & 10-5-E & & 147.77 & $>$21600 & 205.54 & 257.77 & 755.87 & 42.67 & 257.84 & $>$10800 & 0.02 \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Showing the benefits of the proposed Co-EA.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology", "authors": ["Víctor Blanco", "José-Fernando Camacho-Vallejo", "Carlos Corpus"], "url": "https://arxiv.org/abs/2503.00561v1", "attribution": "\"A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology\" by Víctor Blanco, José-Fernando Camacho-Vallejo, and Carlos Corpus, arXiv:2503.00561v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.09862v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c|c|c||} \n\\hline\n$\\tilde\\kappa$ & 0.1 & \\textbf{0.3} & 0.5 & 1\\\\\n\\hline\nrate & -0.257 & \\textbf{-0.408} & -0.456 & -0.433\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Convergence rate for the proposed estimator for difference choice of hyperparameter $\\tilde\\kappa$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Wasserstein-based Minimax Estimation of Dependence in Multivariate Regularly Varying Extremes", "authors": ["Xuhui Zhang", "Jose Blanchet", "Youssef Marzouk", "Viet Anh Nguyen", "Sven Wang"], "url": "https://arxiv.org/abs/2312.09862v2", "attribution": "\"Wasserstein-based Minimax Estimation of Dependence in Multivariate Regularly Varying Extremes\" by Xuhui Zhang, Jose Blanchet, Youssef Marzouk, Viet Anh Nguyen, and Sven Wang, arXiv:2312.09862v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08261v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Optimal rates and corresponding values of $\\ell_D$ and $s$, for $m = 1000$ and $\\epsilon = 10^{-2}$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|cccc|cc|} \n \\hline & & & & & & & \\\\\n $q$ & $k'$ & $R'$ & $n'$ & $\\ell_D$ & $s$ & $\\tilde{\\ell}_D$ & $\\tilde{s}$ \\\\\n \\hline\n $2^{16}$ & 194 & 0.647 & 300 & 84.740 & 226 & 92.112 & 232\\\\\n $2^{32}$ & 137 & 0.591 & 232 & 48.128 & 136 & 54.912 & 128\\\\\n $2^{64}$ & 97 & 0.545 & 194 & 31.984 & 78 & 32.480 & 76\\\\\n $2^{128}$ & 69 & 0.548 & 126 & 21.504 & 56 & 22.704 & 51\\\\\n $2^{256}$ & 49 & 0.430 & 114 & 16.256 & 35 & 16.768 & 41\\\\\n $2^{512}$ & 35 & 0.402 & 87 & 13.344 & 27 & 15.424 & 38\\\\\n $2^{1024}$ & 25 & 0.321 & 78 & 11.680 & 19 & 15.040 & 37\\\\\n $2^{2048}$ & 18 & 0.295 & 61 & 11.072 & 16 & 17.984 & 35\\\\\n $2^{4096}$ & 13 & 0.224 & 58 & 12.160 & 12 & 23.840 & 33\\\\\n $2^{8192}$ & 9 & 0.155 & 58 & 14.656 & 10 & 36.672 & 30\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Optimization of a Reed-Solomon code-based protocol against blockchain data availability attacks", "authors": ["Paolo Santini", "Giulia Rafaiani", "Massimo Battaglioni", "Franco Chiaraluce", "Marco Baldi"], "url": "https://arxiv.org/abs/2201.08261v1", "attribution": "\"Optimization of a Reed-Solomon code-based protocol against blockchain data availability attacks\" by Paolo Santini, Giulia Rafaiani, Massimo Battaglioni, Franco Chiaraluce, and Marco Baldi, arXiv:2201.08261v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18792v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Assessment of the quality of the uncertainty estimates provided by the models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n & {GP} & {MoNNE} & {BMNN (VI)} \\\\\n\\midrule\nDTLZ2 & $0.622 (\\pm 0.01)$ & $0.924 (\\pm 0.014)$ & $0.855 (\\pm 0.019)$ \\\\\nVLMOP3 & $0.625 (\\pm 0.01)$ & $0.915 (\\pm 0.022)$ & $0.731 (\\pm 0.027)$ \\\\\nVehicleSafety & $0.63 (\\pm 0.016)$ & $0.863 (\\pm 0.045)$ & $0.848 (\\pm 0.04)$ \\\\\nOSY & $0.64 (\\pm 0.015)$ & $0.878 (\\pm 0.022)$ & $0.886 (\\pm 0.021)$ \\\\\nCarCabDesign & $0.658 (\\pm 0.008)$ & $0.951 (\\pm 0.08)$ & $0.911 (\\pm 0.02)$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble", "authors": ["Hanyang Wang", "Juergen Branke", "Matthias Poloczek"], "url": "https://arxiv.org/abs/2501.18792v1", "attribution": "\"Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble\" by Hanyang Wang, Juergen Branke, and Matthias Poloczek, arXiv:2501.18792v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.08430v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fitted average probabilities of observing a payment}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\\toprule\n Probability & $j=2$ & $j = 3$ & $j = 4$\\\\\n\\midrule\n $\\pi_{j,\\text{AB}}$ & 0.2367 & 0.2194 & 0.1267\\\\\n $\\pi_{j,\\text{BI}}$ & 0.1426 & 0.1220 & 0.0695\\\\\n $\\pi_{j,\\text{VD}}$ & 0.6391 & 0.1030 & 0.0311\\\\\n $\\pi_{j,\\text{LoU}}$ & 0.4228 & 0.0368 & 0.0087 \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Individual Claims Reserving using Activation Patterns", "authors": ["Marie Michaelides", "Mathieu Pigeon", "Hélène Cossette"], "url": "https://arxiv.org/abs/2208.08430v3", "attribution": "\"Individual Claims Reserving using Activation Patterns\" by Marie Michaelides, Mathieu Pigeon, and Hélène Cossette, arXiv:2208.08430v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.05646v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\n\\textbf{Date} \\& \\textbf{Return} \\& \\textbf{VaR (HS)} \\& \\textbf{VaR (GARCH-N)} \\& \\textbf{VaR (FHS)} \\\\\n\\midrule\n2005-11-01 \\& 0.002992 \\& -0.011347 \\& -0.000167 \\& -0.000155 \\\\\n2005-11-02 \\& 0.010403 \\& -0.011347 \\& -0.000148 \\& -0.000138 \\\\\n2005-11-03 \\& 0.004262 \\& -0.011347 \\& -0.000152 \\& -0.000143 \\\\\n2005-11-04 \\& -0.001309 \\& -0.011347 \\& -0.000137 \\& -0.000129 \\\\\n2005-11-07 \\& 0.000982 \\& -0.011347 \\& -0.000121 \\& -0.000115 \\\\\n2005-11-08 \\& 0.000000 \\& -0.011347 \\& -0.000107 \\& -0.000102 \\\\\n2005-11-09 \\& 0.001308 \\& -0.011347 \\& -0.000097 \\& -0.000091 \\\\\n2005-11-10 \\& 0.007732 \\& -0.011347 \\& -0.000087 \\& -0.000082 \\\\\n2005-11-11 \\& 0.003400 \\& -0.011347 \\& -0.000090 \\& -0.000086 \\\\\n2005-11-14 \\& -0.000566 \\& -0.011347 \\& -0.000084 \\& -0.000080 \\\\\n\\midrule\n2025-04-07 \\& -0.001783 \\& -0.017955 \\& -0.001355 \\& -0.001697 \\\\\n2025-04-08 \\& -0.015787 \\& -0.017955 \\& -0.001163 \\& -0.001450 \\\\\n2025-04-09 \\& 0.099863 \\& -0.017955 \\& -0.001017 \\& -0.001304 \\\\\n2025-04-10 \\& -0.044808 \\& -0.017955 \\& -0.002919 \\& -0.003728 \\\\\n2025-04-11 \\& 0.017686 \\& -0.018844 \\& -0.002906 \\& -0.003702 \\\\\n2025-04-14 \\& 0.009655 \\& -0.018844 \\& -0.002525 \\& -0.003235 \\\\\n2025-04-15 \\& -0.002805 \\& -0.018844 \\& -0.002209 \\& -0.002781 \\\\\n2025-04-16 \\& -0.022479 \\& -0.018844 \\& -0.001876 \\& -0.002373 \\\\\n2025-04-17 \\& 0.001426 \\& -0.019825 \\& -0.001682 \\& -0.002156 \\\\\n2025-04-21 \\& -0.024091 \\& -0.019825 \\& -0.001453 \\& -0.001839 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{First and Last 10 One-Day VaR$_{0.05}$ Estimates Using HS, GARCH+Normal, and FHS}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Comparative Evaluation of VaR Models: Historical Simulation, GARCH-Based Monte Carlo, and Filtered Historical Simulation", "authors": ["Xin Tian"], "url": "https://arxiv.org/abs/2505.05646v1", "attribution": "\"Comparative Evaluation of VaR Models: Historical Simulation, GARCH-Based Monte Carlo, and Filtered Historical Simulation\" by Xin Tian, arXiv:2505.05646v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.20537v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\n \\toprule\n Hyperparameters & $(a_{\\tau}, b_{\\tau}) = (0.1, 0.1)$ & $(a_{\\sigma}, b_{\\sigma}) = (0.1, 0.1)$ & $(a_{\\gamma}, b_{\\gamma}) = (0.1, 0.1)$ & $s = 0.01$ & $R = 30$ & $S = 15$ & $M = 15$ & $\\beta = 0.1$\\\\ \\hline\n TPR & 0.79(0.02) & 0.80(0.02) & 0.78(0.03) & 0.75(0.03) & 0.79(0.02) & 0.80(0.01) & 0.81(0.02) & 0.82(0.02) \\\\\n FDR & 0.16(0.03) & 0.18(0.03) & 0.18(0.05) & 0.20(0.04) & 0.23(0.02) & 0.19(0.03) & 0.15(0.03) & 0.21(0.02) \\\\\n MCC & 0.76(0.04) & 0.81(0.04) & 0.83(0.04) & 0.82(0.03) & 0.81(0.01) & 0.84(0.04) & 0.83(0.01) & 0.84(0.03)\\\\\n \\midrule\n Hyperparameters & $(a_{\\tau}, b_{\\tau}) = (0.1, 1)$ & $(a_{\\sigma}, b_{\\sigma}) = (0.01, 0.01)$ & $(a_{\\gamma}, b_{\\gamma}) = (0.1, 1)$ & $s = 0.03$ & $R = 20$ & $S = 10$ & $M = 20$ & $\\beta = 2$\\\\\n \\hline\n TPR & 0.80(0.02) & 0.79(0.03) & 0.76(0.02) & 0.75(0.03) & 0.78(0.03) & 0.79(0.02) & 0.82(0.04) & 0.81(0.03)\\\\\n FDR & 0.17(0.03) & 0.18(0.04) & 0.15(0.03) & 0.19(0.04) & 0.22(0.03) & 0.21(0.03) & 0.15(0.03) & 0.23(0.03) \\\\\n MCC & 0.77(0.02) & 0.80(0.02) & 0.82(0.02) & 0.82(0.03) & 0.80(0.02) & 0.83(0.03) & 0.83(0.03) & 0.85(0.05)\\\\\n \\midrule\n Hyperparameters & $(a_{\\tau}, b_{\\tau}) = (5, 1)$ & $(a_{\\sigma}, b_{\\sigma}) = (0.1, 1)$ & $(a_{\\gamma}, b_{\\gamma}) = (5, 1)$ & $s = 0.05$ & $R = 25$ & $S = 20$ & $M = 30$ & $\\beta = 5$\\\\\n \\hline\n TPR & 0.76(0.04) & 0.82(0.05) & 0.79(0.03) & 0.76(0.04) & 0.78(0.03) & 0.80(0.03) & 0.81(0.02) & 0.79(0.04)\\\\\n FDR & 0.17(0.05) & 0.19(0.04) & 0.19(0.02) & 0.20(0.03) & 0.23(0.03) & 0.22(0.04) & 0.16(0.03) & 0.21(0.03)\\\\\n MCC & 0.78(0.02 )& 0.83(0.03) & 0.83(0.05) & 0.81(0.03) & 0.81(0.01) & 0.84(0.01) & 0.82(0.02) & 0.83(0.03)\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data", "authors": ["Saptarshi Roy", "Raymond K. W. Wong", "Yang Ni"], "url": "https://arxiv.org/abs/2310.20537v1", "attribution": "\"Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data\" by Saptarshi Roy, Raymond K. W. Wong, and Yang Ni, arXiv:2310.20537v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.19206v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c||c|c|c|c|}\n \\hline\n \\rowcolor[HTML]{C0C0C0}\n \\multicolumn{4}{|c||}{LHS Parameter Samples} & Target Variable \\\\ \\hline\n $p_{1}$ & $p_{2}$ & $\\cdots$ & $p_{K}$ & $v$ \\\\ \\hline\n $p_{1}^{(1)}$ & $p_{2}^{(1)}$ & $\\cdots$ & $p_{K}^{(1)}$ & $v^{(1)}$ \\\\ \n $p_{1}^{(2)}$ & $p_{2}^{(2)}$ & $\\cdots$ & $p_{K}^{(2)}$ & $v^{(2)}$ \\\\ \n $\\vdots$ & $\\vdots$ & $\\ddots$ & $\\vdots$ & $\\vdots$ \\\\ \n $p_{1}^{(\\mathcal{S})}$ & $p_{2}^{(\\mathcal{S})}$ & $\\cdots$ & $p_{K}^{(\\mathcal{S})}$ & $v^{(\\mathcal{S})}$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Age Group Sensitivity Analysis of Epidemic Models: Investigating the Impact of Contact Matrix Structure", "authors": ["Zsolt Vizi", "Evans Kiptoo Korir", "Norbert Bogya", "Csaba Rosztóczy", "Géza Makay", "Péter Boldog"], "url": "https://arxiv.org/abs/2502.19206v2", "attribution": "\"Age Group Sensitivity Analysis of Epidemic Models: Investigating the Impact of Contact Matrix Structure\" by Zsolt Vizi, Evans Kiptoo Korir, Norbert Bogya, Csaba Rosztóczy, Géza Makay, and Péter Boldog, arXiv:2502.19206v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11702v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc|ccc}\n\t\t\tData set & \\# inst. & \\# features & \\# categorical & sensitive & unrelated 1 & unrelated 2 \\\\ \\hline\n\t\t\tCOMPAS & $ 6172 $ & $ 7 $ & $ 4 $ & \\textit{race} & \\textit{random1} & \\textit{random2} \\\\\n\t\t\tGerman & $ 1000 $ & $ 25 $ & $ 15 $ & \\textit{gender} & \\textit{pctOfIncome} & /\\\\\n\t\t\tCC & $ 1994 $ & $ 100 $ & $ 0 $ & \\textit{racePctWhite} & \\textit{random1} & \\textit{random2}\\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Basic information and sensitive features in the the used data sets. The target variable is not included in the number of features and is binary for all data sets. The \\textit{pctOfIncome} full name is \\textit{loanRateAsPercentOfIncome}.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Better sampling in explanation methods can prevent dieselgate-like deception", "authors": ["Domen Vreš", "Marko Robnik Šikonja"], "url": "https://arxiv.org/abs/2101.11702v1", "attribution": "\"Better sampling in explanation methods can prevent dieselgate-like deception\" by Domen Vreš and Marko Robnik Šikonja, arXiv:2101.11702v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2312.15233v2_tex_table18.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{F1 scores of all tested methods on NoisyCXR dataset}\n\\begin{tabular}{|c|c|c|}\n\t\t\\hline\n\t\tNoise & Clean & Noisy \\\\ \\hline\n\t\tbase & 79.36±0.3 & 62.45±0.53 \\\\ \\hline\n\t\to2u & 80.47±0.4 & 64.84±0.86 \\\\ \\hline\n\t\tmixup & 78.75±0.24 & 65.03±0.73 \\\\ \\hline\n\t\tco & 80.55±0.24 & 65.38±1.18 \\\\ \\hline\n\t\tcdr & 80.02±0.94 & 64.64±0.57 \\\\ \\hline\n\t\tself & 79.14±0.91 & 57.61±0.71 \\\\ \\hline\n\t\tmulti & 79.84±0.42 & 58.89±0.26 \\\\ \\hline\n\t\tlnlsr & 79.01±0.19 & 62.73±1.58 \\\\ \\hline\n\t\tours & 80.80±0.33 & 69.53±0.48 \\\\ \\hline\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sample selection with noise rate estimation in noise learning of medical image analysis", "authors": ["Maolin Li", "Giacomo Tarroni"], "url": "https://arxiv.org/abs/2312.15233v2", "attribution": "\"Sample selection with noise rate estimation in noise learning of medical image analysis\" by Maolin Li and Giacomo Tarroni, arXiv:2312.15233v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11711v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Full list of Codes for Model Interpretability study. Each row lists a code, the rate each code occurs for each verification technique, and the category we place the code in.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrl}\n \\toprule\n Code & BMC & BMC+K & SymEx & CEGAR & Category \\\\ \\midrule\n Assert Call & 0.010 & 0.018 & 0.036 & 0.009 & Error Reachability \\\\\n Assert Condition & 0.115 & 0.182 & 0.155 & 0.118 & Assertion Satisfiability \\\\\n Assert Function & 0.019 & 0.000 & 0.009 & 0.000 & Error Reachability \\\\\n Assert Return & 0.010 & 0.009 & 0.036 & 0.009 & Error Reachability \\\\\n Assume Condition & 0.019 & 0.045 & 0.000 & 0.027 & Assertion Satisfiability \\\\\n Branch Condition & 0.019 & 0.027 & 0.100 & 0.064 & Branching (No Loops) \\\\\n Call Of Indirectly Dependent (1) Function & 0.000 & 0.000 & 0.000 & 0.009 & Error Reachability \\\\\n Call Of Indirectly Dependent (2) Function & 0.000 & 0.009 & 0.000 & 0.000 & Error Reachability \\\\\n Conditional Statement & 0.010 & 0.036 & 0.009 & 0.018 & Branching (No Loops) \\\\\n Directly Dependent Function & 0.000 & 0.009 & 0.018 & 0.018 & Error Reachability \\\\\n Directly Dependent Function Call & 0.000 & 0.009 & 0.000 & 0.000 & Error Reachability \\\\\n Directly Dependent Function Return & 0.096 & 0.000 & 0.100 & 0.082 & Error Reachability \\\\\n Dead Code & 0.019 & 0.018 & 0.009 & 0.018 & Miscellaneous \\\\\n Decl Of Directly Dependent Variable & 0.058 & 0.018 & 0.009 & 0.045 & Assertion Satisfiability \\\\\n Decl Of Indirectly Dependent (1) Variable & 0.019 & 0.009 & 0.009 & 0.009 & Assertion Satisfiability \\\\\n Decl Of Indirectly Dependent (2) Variable & 0.010 & 0.000 & 0.009 & 0.000 & Assertion Satisfiability \\\\\n Decl Of Loop Condition Variable (Large Bound) & 0.010 & 0.000 & 0.000 & 0.000 & Loop (Large Bound) \\\\\n Error Call & 0.019 & 0.055 & 0.045 & 0.027 & Error Reachability \\\\\n Error Function & 0.019 & 0.000 & 0.000 & 0.000 & Error Reachability \\\\\n Error Return & 0.000 & 0.000 & 0.027 & 0.000 & Error Reachability \\\\\n Indirectly Dependent (1) Function & 0.000 & 0.000 & 0.000 & 0.009 & Error Reachability \\\\\n Indirectly Dependent (1) Function Return & 0.010 & 0.000 & 0.000 & 0.009 & Error Reachability \\\\\n Indirectly Dependent (1) Variable Update & 0.000 & 0.000 & 0.000 & 0.027 & Assertion Satisfiability \\\\\n Input Function & 0.000 & 0.000 & 0.018 & 0.018 & Miscellaneous \\\\\n Loop Body (Small Bound) & 0.000 & 0.018 & 0.000 & 0.000 & Loop (Small Bound) \\\\\n Loop Condition (Large Bound) & 0.010 & 0.027 & 0.000 & 0.045 & Loop (Large Bound) \\\\\n Loop Condition (Nondet Bound) & 0.010 & 0.009 & 0.000 & 0.009 & Loop (Large Bound) \\\\\n Loop Condition (Small Bound) & 0.038 & 0.027 & 0.027 & 0.000 & Loop (Small Bound) \\\\\n Loop Header (Large Bound) & 0.010 & 0.009 & 0.000 & 0.000 & Loop (Large Bound) \\\\\n Loop Header (Small Bound) & 0.010 & 0.000 & 0.009 & 0.000 & Loop (Small Bound) \\\\\n Recursion Call (Nondet Bound) & 0.000 & 0.000 & 0.000 & 0.018 & Loop (Large Bound) \\\\\n Recursion Call (Small Bound) & 0.000 & 0.000 & 0.027 & 0.009 & Loop (Small Bound) \\\\\n Recursion Condition (Nondet Bound) & 0.000 & 0.000 & 0.000 & 0.009 & Loop (Large Bound) \\\\\n Recursion Condition (Small Bound) & 0.000 & 0.000 & 0.018 & 0.009 & Loop (Small Bound) \\\\\n Recursion Function (Small Bound) & 0.000 & 0.000 & 0.000 & 0.009 & Loop (Small Bound) \\\\\n Recursion Function Decl (Nondet Bound) & 0.000 & 0.000 & 0.000 & 0.009 & Loop (Large Bound) \\\\\n Recursion Function Return (Small Bound) & 0.000 & 0.000 & 0.018 & 0.000 & Loop (Small Bound) \\\\\n Return Indirectly Dependent (1) Function & 0.010 & 0.000 & 0.000 & 0.000 & Error Reachability \\\\\n Return Of Recursion Function (Nondet Bound) & 0.000 & 0.000 & 0.000 & 0.027 & Loop (Large Bound) \\\\\n Type Def & 0.010 & 0.000 & 0.000 & 0.009 & Miscellaneous \\\\\n Type Of Directly Dependent Variable & 0.010 & 0.000 & 0.000 & 0.000 & Assertion Satisfiability \\\\\n Type Of Indirectly Dependent (1) Variable & 0.000 & 0.000 & 0.000 & 0.009 & Assertion Satisfiability \\\\\n Update Loop Condition Variable (Infinite Loop) & 0.000 & 0.000 & 0.064 & 0.009 & Loop (Large Bound) \\\\\n Update Loop Condition Variable (Nondet Bound) & 0.019 & 0.000 & 0.000 & 0.009 & Loop (Large Bound) \\\\\n Update Of Directly Dependent Variable & 0.087 & 0.182 & 0.136 & 0.091 & Assertion Satisfiability \\\\\n Update Of Indirectly Dependent (1) Variable & 0.135 & 0.064 & 0.045 & 0.018 & Assertion Satisfiability \\\\\n Update Of Indirectly Dependent (2) Variable & 0.048 & 0.045 & 0.009 & 0.000 & Assertion Satisfiability \\\\\n Update Of Indirectly Dependent (3) Variable & 0.000 & 0.027 & 0.000 & 0.000 & Assertion Satisfiability \\\\\n Update Of Loop Condition Variable (Infinite Loop) & 0.000 & 0.000 & 0.000 & 0.009 & Loop (Large Bound) \\\\\n Update Of Loop Condition Variable (Large Bound) & 0.019 & 0.064 & 0.000 & 0.009 & Loop (Large Bound) \\\\\n Update Of Loop Condition Variable (Nondet Bound) & 0.000 & 0.055 & 0.000 & 0.009 & Loop (Large Bound) \\\\\n Update Of Loop Condition Variable (Small Bound) & 0.038 & 0.009 & 0.000 & 0.000 & Loop (Small Bound) \\\\\n Update Of Recursion Condition Variable (Nondet Bound) & 0.000 & 0.000 & 0.000 & 0.055 & Loop (Large Bound) \\\\\n Update Of Recursion Condition Variable (Small Bound) & 0.000 & 0.000 & 0.055 & 0.036 & Loop (Small Bound) \\\\\n Use Of Directly Dependent Variable & 0.010 & 0.009 & 0.000 & 0.064 & Assertion Satisfiability \\\\\n Use Of Indirectly Dependent (1) Variable & 0.029 & 0.000 & 0.000 & 0.000 & Assertion Satisfiability \\\\\n Use Of Loop Condition Variable (Large Bound) & 0.000 & 0.009 & 0.000 & 0.000 & Loop (Large Bound) \\\\\n Use Of Loop Condition Variable (Small Bound) & 0.010 & 0.000 & 0.000 & 0.000 & Loop (Small Bound) \\\\\n Use Of Loop Condition Variable (Small Bound) & 0.038 & 0.000 & 0.000 & 0.000 & Loop (Small Bound) \\\\\n Use Of Recursion Condition Variable (Nondet Bound) & 0.000 & 0.000 & 0.000 & 0.009 & Loop (Large Bound) \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Algorithm Selection for Software Verification using Graph Neural Networks", "authors": ["Will Leeson", "Matthew B Dwyer"], "url": "https://arxiv.org/abs/2201.11711v3", "attribution": "\"Algorithm Selection for Software Verification using Graph Neural Networks\" by Will Leeson and Matthew B Dwyer, arXiv:2201.11711v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.03697v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\nMethod & Setting 1 & Setting 2 \\\\\n\\hline\nCORAL & 0.744 & 0.709 \\\\\nGlobal & 0.745 & 0.718 \\\\\nGroup-Aware Global & 0.745 & 0.674 \\\\\nCluster Global & 0.759 & 0.745 \\\\\nLabel Shift & 0.695 & 0.702 \\\\\nOur Method & 0.706 & 0.751 \\\\\n\\hline\nTrue Clustering & 0.696 & 0.791 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Average AUC calculated on the held-out test set for Amazon datasets in settings 1 and 2.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Leveraging Structure for Improved Classification of Grouped Biased Data", "authors": ["Daniel Zeiberg", "Shantanu Jain", "Predrag Radivojac"], "url": "https://arxiv.org/abs/2212.03697v1", "attribution": "\"Leveraging Structure for Improved Classification of Grouped Biased Data\" by Daniel Zeiberg, Shantanu Jain, and Predrag Radivojac, arXiv:2212.03697v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09449v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of the datasets showing the no. of sentences with corresponding sentiment polarities of constituent aspect terms.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n\\hline\n\\multirow{2}{*}{\\textbf{Dataset}} & \\multicolumn{4}{c|}{\\textbf{Train}} & \\multicolumn{4}{c|}{\\textbf{Test}} \\\\ \n\\cline{2-9} & \\textbf{Positive} & \\textbf{Negative} & \\textbf{Neutral} & \\textbf{Total} & \\textbf{Positive} & \\textbf{Negative} & \\textbf{Neutral} & \\textbf{Total} \\\\ \n\\hline\n\\textbf{Laptop} & 994 & 870 & 464 & 2328 & 341 & 128 & 169 & 638 \\\\\n\\hline\n\\textbf{Restaurant} & 2164 & 807 & 637 & 3608 & 728 & 196 & 196 & 1120 \\\\ \n\\hline\n\\textbf{Men's T-shirt} & 1122 & 699 & 50 & 1871 & 270 & 186 & 16 & 472 \\\\ \n\\hline\n\\textbf{Television} & 2540 & 919 & 287 & 3746 & 618 & 257 & 67 & 942 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild", "authors": ["Rajdeep Mukherjee", "Shreyas Shetty", "Subrata Chattopadhyay", "Subhadeep Maji", "Samik Datta", "Pawan Goyal"], "url": "https://arxiv.org/abs/2101.09449v1", "attribution": "\"Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild\" by Rajdeep Mukherjee, Shreyas Shetty, Subrata Chattopadhyay, Subhadeep Maji, Samik Datta, and Pawan Goyal, arXiv:2101.09449v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.13174v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{marvosym}\n\\usepackage{pifont}\n\\usepackage{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Method & Metrics & Code \\\\\n \\midrule\n GP & \\textbf{AR} & {\\color{red}\\ding{55}} \\\\\n AutoAlpha & IC, \\textbf{AR}, \\textbf{SR} & {\\color{red}\\ding{55}} \\\\\n AlphaEvolve & IC, \\textbf{SR} &{\\color{red}\\ding{55}} \\\\\n AlphaGen & IC, RankIC, \\textbf{AR} & {\\color{teal}\\ding{51}} \\\\\n RiskMiner & IC, ICIR, RankIC & {\\color{red}\\ding{55}}\\\\\n QFR & IC, RankIC & {\\color{red}\\ding{55}}\\\\\n $\\text{Alpha}^2$ & IC & {\\color{red}\\ding{55}}\\\\\n AlphaForge & IC & {\\color{teal}\\ding{51}} \\\\\n AlphaQCM & IC & {\\color{teal}\\ding{51}} \\\\\n FAMA & RankIC, RankICIR & {\\color{red}\\ding{55}} \\\\\n AlphaAgent & IC, ICIR, \\textbf{AR}, IR, \\textbf{MaxDD} & {\\color{red}\\ding{55}} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Summary of current alpha mining models. Metrics in \\textbf{bold} are based on backtesting.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining", "authors": ["Hongjun Ding", "Binqi Chen", "Jinsheng Huang", "Taian Guo", "Zhengyang Mao", "Guoyi Shao", "Lutong Zou", "Luchen Liu", "Ming Zhang"], "url": "https://arxiv.org/abs/2508.13174v1", "attribution": "\"AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining\" by Hongjun Ding, Binqi Chen, Jinsheng Huang, Taian Guo, Zhengyang Mao, Guoyi Shao, Lutong Zou, Luchen Liu, and Ming Zhang, arXiv:2508.13174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13382v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\toprule\n $n$ & $p$ & $\\varepsilon$ & $K'''$ & $K''$ & $K'$ & $E$ \\\\\n \\midrule\n 600 & 0.5 & 0.5 & 600.82 & 603.43 & 603.43 & 63061 \\\\\n 600 & 0.5 & 1.0 & 606.36 & 612.47 & 612.47 & 25315 \\\\\n 600 & 0.5 & 1.5 &616.06 & 632.53 & 632.53 & 12506\\\\\n 600 & 0.5 & 2.0 & 630.14 & 656.30 & 656.30 & 7336 \\\\\n \\midrule\n 500 & 0.5 & 0.5 & 500.64 &502.34& 502.34 & 46316 \\\\\n 500 & 0.5 & 1.0 &505.60 & 510.52 & 510.52& 19733 \\\\\n 500 & 0.5 & 1.5 & 514.31& 526.91 & 526.91 & 9939 \\\\\n 500 & 0.5 & 2.0 & 526.96 & 551.39& 551.39 & 5837 \\\\\n \\midrule\n 250 & 0.5 & 0.5 & 249.9400 & 251.1000 & 251.10 & 13770 \\\\\n 250 & 0.5 & 1.0 & 252.7700 & 258.5300 & 258.53 & 7493 \\\\\n 250 & 0.5 & 1.5 & 258.4700 &266.1700 & 266.17 & 4083 \\\\\n 250 & 0.5 & 2.0 & 265.4900 & 279.3700 & 279.37 & 2469 \\\\\n \\midrule\n 600 & 0.05 & 0.5 & 620.25 & 622.11 & 622.11 & 8896 \\\\\n 600 & 0.05 & 1.0 & 626.20 & 632.63 & 632.63 & 8253 \\\\\n 600 & 0.05& 1.5 & 635.89 & 661.40 &661.40 & 6525 \\\\\n 600 & 0.05& 2.0 & 651.49 &692.91 & 692.91 &4834 \\\\\n \\midrule\n 500 & 0.05& 0.5 & 520.22 & 522.90 & 522.90 & 6218 \\\\\n 500 & 0.05& 1.0 & 525.16 & 533.60 & 533.60 & 5858 \\\\\n 500 & 0.05& 1.5 & 534.76 &549.89 & 549.89 &4797 \\\\\n 500 & 0.05& 2.0 & 548.47 &587.21 & 587.21 &3683 \\\\\n \\midrule\n 250 & 0.05& 0.5 & 270.53 & 271.35 & 271.35 & 1598 \\\\\n 250 & 0.05& 1.0 & 273.95 & 277.07 & 277.07 & 1571 \\\\\n 250 & 0.05& 1.5 & 279.57 & 291.23 & 291.22 & 1416 \\\\\n 250 & 0.05& 2.0 & 289.00 &306.80 & 306.79 &1202\\\\\n \\bottomrule\n \\hspace{.2cm}\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Sparse approximations for different values of $\\varepsilon$ on $G(n,p)$, $p=0.5,0.05$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Spectral Approach to Kemeny's Constant", "authors": ["Aida Abiad", "Ángeles Carmona", "Andrés M. Encinas", "Maria José Jiménez", "Álvaro Samperio"], "url": "https://arxiv.org/abs/2503.13382v1", "attribution": "\"A Spectral Approach to Kemeny's Constant\" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.00180v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The sample standard deviation of the MESES for each nonparametric estimator fitted to the simulated continuous data with a jump in the regression function.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|r|rrr|rrr|rrr|}\n \\hline\n & & $n=400$ & & & $n=800$ & & & $n=1600$ & \\\\ \n \\hline\n$\\sigma$ & LC & ALC & ALCT & LC & ALC & ALCT & LC & ALC & ALCT \\\\ \n 0.1 & 0.00109 & 0.00043 & 0.00030 & 0.00123 & 0.00033 & 0.00015 & 0.00071 & 0.00118 & 0.00008 \\\\ \n 0.5 & 0.00735 & 0.01848 & 0.005 & 0.00494 & 0.00995 & 0.00222 & 0.00281 & 0.00597 & 0.00155 \\\\ \n 1 & 0.03002 & 0.03732 & 0.01904 & 0.01328 & 0.01753 & 0.00843 & 0.00816 & 0.00956 & 0.00515 \\\\ \n 2 & 0.08068 & 0.08431 & 0.07131 & 0.03541 & 0.04431 & 0.02913 & 0.02699 & 0.02902 & 0.01662 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Anisotropic local constant smoothing for change-point regression function estimation", "authors": ["John R. J. Thompson", "W. John Braun"], "url": "https://arxiv.org/abs/2012.00180v2", "attribution": "\"Anisotropic local constant smoothing for change-point regression function estimation\" by John R. J. Thompson and W. John Braun, arXiv:2012.00180v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.11266v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{cccccc}\n\\multicolumn{6}{c}{Inferred (MAP estimate)}\\\\\n\\hline\n$\\mu_\\ell$ [mPa.s] & $\\alpha$ & $\\beta$ & $x_c$ [cm] & $c$ [cm] & $d_{s_o}$ [cm]\\\\\n4.2 $\\pm$ 0.15 & 0.722 $\\pm$ 0.193 & 19.2 $\\pm$ 12.6 & 3.25 $\\pm$ 2.87 & 7.32 $\\pm$ 2.44 & 0.159 $\\pm$ 0.0881\\\\\n\\multicolumn{6}{c}{Prior}\\\\\n\\hline\n$\\mu_\\ell$ [mPa.s] & $\\alpha$ & $\\beta$ & $x_c$ [cm] & $c$ [cm] & $d_{s_o}$ [cm]\\\\\n4.2 $\\pm$ 0.15 & 2 $\\pm$ 6 & 10 $\\pm$ 15 & 0 $\\pm$ 30 & 8.5 $\\pm$ 30 & 0 $\\pm$ 15\n\\end{tabular}\n\\caption{Compound mixing length parameters.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Bayesian inference of mean velocity fields and turbulence models from flow MRI", "authors": ["A. Kontogiannis", "P. Nair", "M. Loecher", "D. B. Ennis", "A. Marsden", "M. P. Juniper"], "url": "https://arxiv.org/abs/2412.11266v1", "attribution": "\"Bayesian inference of mean velocity fields and turbulence models from flow MRI\" by A. Kontogiannis, P. Nair, M. Loecher, D. B. Ennis, A. Marsden, and M. P. Juniper, arXiv:2412.11266v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.07192v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccccccccccccccccccccccc}\n\\hline\n No.& 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 & 16 & 17 & 18 & 19 & 20 & 21 & 22 & 23 & 24 & 25 \\\\\n\\hline\n$\\mu_4$ & $\\times$ & & & & & & $\\times$ & & $\\times$ & & & $\\times$ & $\\times$ & & & & & & & & & & & & \\\\\n$\\mu_6$ & $\\times$ & $\\times$ & & $\\times$ & & & $\\times$ & & $\\times$ & & & $\\times$ & $\\times$ & & $\\times$ & & & & & & & & & & \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Test of normal i.i.d. for $\\mu_4$ and $\\mu_6$. The rejection of the null hypothesis is $\\times$. }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deformation of semi-circle law for the correlated time series and Phase transition", "authors": ["Masato Hisakado", "Takuya Kaneko"], "url": "https://arxiv.org/abs/2508.07192v1", "attribution": "\"Deformation of semi-circle law for the correlated time series and Phase transition\" by Masato Hisakado and Takuya Kaneko, arXiv:2508.07192v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.20923v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t Coeffs & $A_0= 1.47795615$ & $B= 2.14941469$ & $\\sim$& $\\sim$\\\\ \\hline\n $A_1^0=0.34739609$\t& $A_1^1=-0.16420366$ & $A_1^2= -0.23371246$ & $A_1^3= 0.2392852$ & $A_1^4= -0.04898879$ \\\\ \\hline\n\t$A_2^0= -0.16645427$ & $A_2^1= -0.11136905$ & $A_2^2= -0.0908392$& $A_2^3= 0.20073476 $& $A_2^4= -0.04924137 $\\\\ \\hline\n\t$A_3^0= -0.08061319$ & $A_3^1= 0.06648312 $& $A_3^2= -0.12334708$& $A_3^3=0.02485992$& $A_3^4=-0.01105319$\\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Coefficients that fit the universal $\\eta-\\chi-\\xi$ surface for $n=1$ BSs, corresponding to the set of data and fitting surface shown in . }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Spinning boson stars in nonlinear sigma models and Universal Relations", "authors": ["Christoph Adam", "Jorge Castelo Mourelle", "Alberto García Martín-Caro", "Andrzej Wereszczynski"], "url": "https://arxiv.org/abs/2502.20923v3", "attribution": "\"Spinning boson stars in nonlinear sigma models and Universal Relations\" by Christoph Adam, Jorge Castelo Mourelle, Alberto García Martín-Caro, and Andrzej Wereszczynski, arXiv:2502.20923v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.06931v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccrccccc}\n & & & Empirical & \\multicolumn{4}{c}{Power of the Test $\\hat{D}_n$}\\\\\n$s_0$ & $n$ & ${p}/{n}$ & Size & Isolated & Nested & 1-diff & 2-diff \\\\ \n4 & 300 & 0$\\cdot $5 & 0$\\cdot$022 & 1$\\cdot $000 & 0.034 & 0$\\cdot $348 & 0$\\cdot $468 \\\\\n & & 1 & 0$\\cdot$034 & 1$\\cdot $000 & 0.038 & 0$\\cdot $226 & 0$\\cdot $336 \\\\\n & & 2 & 0$\\cdot$024 & 1$\\cdot $000 & 0$\\cdot$024 & 0$\\cdot $170 & 0$\\cdot $248\\\\\n & 1000 & 0$\\cdot $5 & 0$\\cdot$034 & 1$\\cdot $000 & 0$\\cdot $274 & 0$\\cdot $998 & 1$\\cdot $000 \\\\\n & & 1 & 0$\\cdot$046 & 1$\\cdot $000 & 0$\\cdot $210 & 0$\\cdot $994 & 1$\\cdot $000 \\\\\n & & 2 & 0$\\cdot$038 & 1$\\cdot $000 & 0$\\cdot $204 & 0$\\cdot $988 & 1$\\cdot $000 \\\\\n6 & 300 & 0$\\cdot $5 & 0$\\cdot$032 & 1$\\cdot $000 &0$\\cdot$034& 0$\\cdot $320 &0$\\cdot $474\\\\ \n & & 1 & 0$\\cdot$030 & 1$\\cdot $000 & 0$\\cdot$028& 0$\\cdot $248& 0$\\cdot $328\\\\ \n & & 2 & 0$\\cdot$024 & 1$\\cdot $000 & 0$\\cdot$024 &0$\\cdot $164& 0$\\cdot $224\\\\ \n & 1000 & 0$\\cdot $5 & 0$\\cdot$046 & 1$\\cdot $000 & 0$\\cdot $130 & 0$\\cdot $992 & 1$\\cdot $000 \\\\\n & & 1 & 0$\\cdot$032 & 1$\\cdot $000 & 0$\\cdot $106 & 0$\\cdot $994 & 1$\\cdot $000 \\\\\n & & 2 & 0$\\cdot$024 & 1$\\cdot $000 & 0$\\cdot$084 & 0$\\cdot $994 & 1$\\cdot $000 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Testing the Graph of a Gaussian Graphical Model", "authors": ["Thien-Minh Le", "Ping-Shou Zhong", "Chenlei Leng"], "url": "https://arxiv.org/abs/2212.06931v1", "attribution": "\"Testing the Graph of a Gaussian Graphical Model\" by Thien-Minh Le, Ping-Shou Zhong, and Chenlei Leng, arXiv:2212.06931v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18392v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Stationary points in the Hubble normalization.}%\n\\begin{tabular}{cccccc}\n\\hline\\hline\n\\textbf{Point} & $\\mathbf{\\ }\\left( \\mathbf{x,y,\\eta }\\right) $ & \\textbf{%\nExistence} & $w_{eff}$ & \\textbf{Acceleration} & \\textbf{Attractor} \\\\ \\hline\n$P_{0}$ & $(0,0,0)$ & Always & Undefined & False & False \\\\ \n$P_{1}^{-}$ & $(0,0,-1)$ & Always & $-\\frac{1}{3}$ & False & True \\\\ \n$P_{1}^{+}$ & $(0,0,1)$ & Always & $-\\frac{1}{3}$ & False & False \\\\ \n$P_{2}^{-}$ & $\\left(-\\frac{1}{6\\beta },0,-1\\right)$ & $\\beta \\neq 0$ & $-\\frac{5}{9}$\n& True & False \\\\ \n$P_{2}^{+}$ & $\\left(\\frac{1}{6\\beta },0,1\\right)$ & $\\beta \\neq 0$ & $-\\frac{5}{9}$ & \nTrue & False \\\\ \n$P_{3}^{+}$ & $\\left(3\\sqrt{\\frac{2}{15-20\\sqrt{30}f_{0}}},0,-\\sqrt{\\frac{3}{3-4%\n\\sqrt{30}f_{0}}}\\right)$ & $f_{0}<\\frac{\\sqrt{\\frac{3}{10}}}{4}$ & $-1$ & de Sitter\n& False \\\\ \n$P_{3}^{-}$ & $\\left(-3\\sqrt{\\frac{2}{15-20\\sqrt{30}f_{0}}},0,\\sqrt{\\frac{3}{3-4%\n\\sqrt{30}f_{0}}}\\right)$ & $f_{0}<\\frac{\\sqrt{\\frac{3}{10}}}{4}$ & $-1$ & de Sitter\n& True in regions \\\\ \n$P_{4}^{-}$ & $\\left(-3\\sqrt{\\frac{2}{15+20\\sqrt{30}f_{0}}},0,\\sqrt{\\frac{3}{3+4%\n\\sqrt{30}f_{0}}}\\right)$ & $f_{0}>-\\frac{\\sqrt{\\frac{3}{10}}}{4}$ & $-1$ & de\nSitter & False \\\\ \n$P_{4}^{+}$ & $\\left(-3\\sqrt{\\frac{2}{15+20\\sqrt{30}f_{0}}},0,\\sqrt{\\frac{3}{3+4%\n\\sqrt{30}f_{0}}}\\right)$ & $f_{0}>-\\frac{\\sqrt{\\frac{3}{10}}}{4}$ & $-1$ & de\nSitter & True in regions \\\\ \n$P_{5}^{-}$ & $\\left(-\\frac{3\\sqrt{-3+8\\beta ^{2}}}{2\\beta \\sqrt{-3+8\\beta\n(8f_{0}+\\beta )}},0,-\\frac{\\sqrt{-3+8\\beta ^{2}}}{\\sqrt{-3+8\\beta\n(8f_{0}+\\beta )}}\\right)$ & some regions & $-1$ & de Sitter & False \\\\ \n$P_{5}^{+}$ & $\\left(\\frac{3\\sqrt{-3+8\\beta ^{2}}}{2\\beta \\sqrt{-3+8\\beta\n(8f_{0}+\\beta )}},0,\\frac{\\sqrt{-3+8\\beta ^{2}}}{\\sqrt{-3+8\\beta\n(8f_{0}+\\beta )}}\\right)$ & some regions & $-1$ & de Sitter & True in regions \\\\ \n$P_{6}^{-}$ & $\\left(0,\\sqrt{\\frac{3\\lambda }{-8f_{0}+\\lambda }},-\\sqrt{\\frac{%\n\\lambda }{-8f_{0}+\\lambda }}\\right)$ & some regions & $-1$ & de Sitter & False\n\\\\ \n$P_{6}^{+}$ & $(0,\\sqrt{\\frac{3\\lambda }{-8f_{0}+\\lambda }},\\sqrt{\\frac{%\n\\lambda }{-8f_{0}+\\lambda }})$ & some regions & $-1$ & de Sitter & True in\nregions \\\\ \\hline\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Avoiding Big Rip Singularities in Phantom Scalar Field theory with Gauss-Bonnet term", "authors": ["Giannis Papagiannopoulos", "Genly Leon", "Andronikos Paliathanasis"], "url": "https://arxiv.org/abs/2501.18392v2", "attribution": "\"Avoiding Big Rip Singularities in Phantom Scalar Field theory with Gauss-Bonnet term\" by Giannis Papagiannopoulos, Genly Leon, and Andronikos Paliathanasis, arXiv:2501.18392v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.02630v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fail rate of different models on price-based rationality tests (std. errors in parentheses).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n & Price reduced & Price reduced & Price reduced & Random & Random \\\\\n & for one listing & for one listing & for one listing & prices & prices \\\\ \n & ($1\\%$ discount) & ($5\\%$ discount ) & ($10\\%$ discount) & (low var.) & (high var.) \\\\\n \\midrule\n \\midrule\n Claude 3.5 & $63.7\\%$ $(1.7\\%)$ & $36.8\\%$ $(1.4\\%)$ & $32.5\\%$ $(1.0\\%)$ & $8.3\\%$ $(0.4\\%)$ & $5.0\\%$ $(0.3\\%)$\\\\\n Claude 3.7 & $21.0\\%$ $(0.9\\%)$ & $6.5\\%$ $(0.7\\%)$ & $1.2\\%$ $(0.2\\%)$ & $8.3\\%$ $(0.4\\%)$ & $6.0\\%$ $(0.3\\%)$\\\\\n Claude 4.0 & $0.5\\%$ $(0.1\\%)$ & $1.8\\%$ $(0.2\\%)$ & \\textcolor{gray}{$0.0\\%$} & $8.3\\%$ $(0.3\\%)$ & $4.3\\%$ $(0.2\\%)$ \\\\\n GPT\\textendash4o & $25.8\\%$ $(1.0\\%)$ & $5.6\\%$ $(0.4\\%)$ & $6.8\\%$ $(0.3\\%)$ & $17.4\\%$ $(0.9\\%)$& $3.6\\%$ $(0.2\\%)$ \\\\\n GPT\\textendash4.1 & $9.3\\%$ $(0.6\\%)$ & $1.3\\%$ $(0.1\\%)$ & $1.2\\%$ $(0.2\\%)$ & $12.6\\%$ $(0.8\\%)$ & $0.8\\%$ $(0.1\\%)$ \\\\\n Gemini 2.0 & $2.8\\%$ $(0.2\\%)$ & $0.5\\%$ $(0.1\\%)$ & $1.2\\%$ $(0.2\\%)$ & $1.0\\%$ $(0.1\\%)$ & $6.5\\%$ $(0.3\\%)$ \\\\\n Gemini 2.5 & $1.0\\%$ $(0.1\\%)$ & \\textcolor{gray}{$0.0\\%$} & \\textcolor{gray}{$0.0\\%$} & $0.8\\%$ $(0.1\\%)$ & \\textcolor{gray}{$0.0\\%$} \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce", "authors": ["Amine Allouah", "Omar Besbes", "Josué D Figueroa", "Yash Kanoria", "Akshit Kumar"], "url": "https://arxiv.org/abs/2508.02630v1", "attribution": "\"What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce\" by Amine Allouah, Omar Besbes, Josué D Figueroa, Yash Kanoria, and Akshit Kumar, arXiv:2508.02630v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16355v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c|c}\n Nature & Regime & Color & $t$ & $n$ & Baseline & $m$ & Compression\\\\\n \\hline\\hline\n Bound & Large $t$ & RGB & 154918 & $28 \\times 28 \\times 3$ & 18816 & 1194 & 6.346 \\\\\n & & & 796466 & $64 \\times 64 \\times 3$ & 98304 & 6495 & 6.607 \\\\ \n & & & 3165795 & $128 \\times 128 \\times 3$ & 393216 & 26403 & 6.715\\\\\n & & & 9668908 & $224 \\times 224 \\times 3$ & 1204224 & 81428 & 6.762\\\\\n \\hline\n Bound & Large $t$ & Gray- & 52711 & $28 \\times 28$ & 6272 & 378 & 6.027 \\\\\n & & scale & 267937 & $64 \\times 64$ & 32768 & 2115 & 6.454 \\\\\n & & & 1060162 & $128 \\times 128$ & 131072 & 8697 & 6.635 \\\\\n & & & 3231539 & $224 \\times 224$ & 401408 & 26957 & 6.716\\\\\n \\hline\\hline\n Bound & Small $t$ & RGB & 235 & $28 \\times 28 \\times 3$ & 18816 & 882 & 4.688 \\\\\n & & & 1228 & $64 \\times 64 \\times 3$ & 98304 & 5890 & 5.992 \\\\\n & & & 4915 & $128 \\times 128 \\times 3$ & 393216 & 23741 & 6.038 \\\\\n & & & 15052 & $224 \\times 224 \\times 3$ & 1204224 & 72754 & 6.042\\\\\n \\hline\n Bound & Small $t$ & Gray- & 78 & $28 \\times 28$ & 6272 & 883 & 14.078 \\\\\n & & scale & 409 & $64 \\times 64$ & 32768 & 1828 & 5.579 \\\\\n & & & 1638 & $128 \\times 128$ & 131072 & 7881 & 6.013 \\\\\n & & & 5017 & $224 \\times 224$ & 401408 & 24235 & 6.037 \\\\\n \\hline\\hline\n Our & Small $t$ & RGB & 235 & $28 \\times 28 \\times 3$ & 18816 & 2631 & 13.983 \\\\\n Scheme & & & 1228 & $64 \\times 64 \\times 3$ & 98304 & 16682 & 16.970\\\\\n & & & 4915 & $128 \\times 128 \\times 3$ & 393216 & 76600 & 19.480\\\\\n & & & 15052 & $224 \\times 224 \\times 3$ & 1204224 & 258889 & 21.498\\\\\n \\hline \n Our & Small $t$ & Gray- & 78 & $28 \\times 28$ & 6272 & 749 & 11.942 \\\\\n Scheme & & scale & 409 & $64 \\times 64$ & 32768 & 4908 & 14.978 \\\\\n & & & 1638 & $128 \\times 128$ & 131072 & 22932 & 17.496 \\\\\n & & & 5017 & $224 \\times 224$ & 401408 & 78338 & 19.516\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Lower bounds on compression achievable through a PPH with small $t \\approx 0.1 n$ and large $t \\approx 0.25qn$, and the actual compression through our scheme. The column labeled ``Compression'' is the percentage compression with respect to the baseline $n \\log_2 q$. Here $q = 256$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Property-Preserving Hashing for $\\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks", "authors": ["Hassan Asghar", "Chenhan Zhang", "Dali Kaafar"], "url": "https://arxiv.org/abs/2504.16355v1", "attribution": "\"Property-Preserving Hashing for $\\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks\" by Hassan Asghar, Chenhan Zhang, and Dali Kaafar, arXiv:2504.16355v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.01504v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Detection cost function (DCF) on development and evaluation sets of FS-2 speech activity detection challenge for raw and postprocessed classifications comparing two and thee-layer MLP trained with or without semi-supervised (A11) data.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|rr|c}\n\\toprule\n\\multirow{2}{*}{N\\textsuperscript{\\underline{o}}} & \\multirow{2}{*}{Model size} & \\multirow{2}{*}{Train data} & \\multicolumn{2}{c|}{DCF Dev, \\%} & DCF Eval, \\% \\\\\n& & & Raw & Post. & Post. \\\\\n\\midrule\n1 & 2 x 256 & FS-2 & 1.68 & 1.25 & 1.87 \\\\\n2 & 2 x 256 & FS-2 + A11 & 3.03 & 1.23 & 2.03 \\\\\n3 & 3 x 400 & FS-2 + A11 & 2.01 & 0.92 & 1.56 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "\"This is Houston. Say again, please\". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)", "authors": ["Arseniy Gorin", "Daniil Kulko", "Steven Grima", "Alex Glasman"], "url": "https://arxiv.org/abs/2008.01504v1", "attribution": "\"\"This is Houston. Say again, please\". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)\" by Arseniy Gorin, Daniil Kulko, Steven Grima, and Alex Glasman, arXiv:2008.01504v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.05781v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{arydshln}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|c|c|c|c|c|c|}\n \\hline\n \\multicolumn{2}{|c|}{} & \\multicolumn{2}{|c|}{$\\nu_{\\pi}=1$} & \\multicolumn{2}{|c|}{$\\nu_{\\pi}=3$} & \\multicolumn{2}{|c|}{$\\nu_{\\pi}=10$}\\\\\n \\cline{3-8}\n & & High $d$ & High $\\kappa_{\\pi}$ & High $d$ & High $\\kappa_{\\pi}$ & High $d$ & High $\\kappa_{\\pi}$ \\\\\n \\hline\n \\multirow{3}{4em}{$\\nu=1$} & Alg.~ & $2.25\\cdot 10^{-2}$ & $3.84 \\cdot 10^{-3}$ & $2.61 \\cdot 10^{-1}$ & $2.15 \\cdot 10^{-1}$ & $6.81 \\cdot 10^{-1}$ & $4.65 \\cdot 10^{-1}$ \\\\\n \\cdashline{3-8}\n & Alg.~ & $\\mathbf{3.01 \\cdot 10^{-1}}$ & $4.00 \\cdot 10^{-1}$ & $\\mathbf{3.80 \\cdot 10^{-1}}$ & $5.45 \\cdot 10^{-1}$ & $\\mathbf{7.30 \\cdot 10^{-1}}$ & $7.48 \\cdot 10^{-1}$ \\\\\n & Alg.~ & $1.13 \\cdot 10^{0}$ & $\\mathbf{6.22 \\cdot 10^{-2}}$ & $1.25 \\cdot 10^{0}$ & $\\mathbf{2.29 \\cdot 10^{-1}}$ & $1.64 \\cdot 10^{0}$ & $\\mathbf{4.72 \\cdot 10^{-1}}$ \\\\\n \\hline\n \\multirow{3}{4em}{$\\nu=3$} & Alg.~ & $7.02\\cdot 10^{-1}$ & $7.78 \\cdot 10^{-1}$ & $1.49 \\cdot 10^{-3}$ & $4.50 \\cdot 10^{-4}$ & $1.99 \\cdot 10^{-1}$ & $1.03 \\cdot 10^{-1}$ \\\\\n \\cdashline{3-8}\n & Alg.~ & $\\mathbf{1.08 \\cdot 10^{0}}$ & $1.83 \\cdot 10^{0}$ & $\\mathbf{1.46 \\cdot 10^{-1}}$ & $4.18 \\cdot 10^{-1}$ & $\\mathbf{2.49 \\cdot 10^{-1}}$ & $4.76 \\cdot 10^{-1}$ \\\\\n & Alg.~ & $2.08 \\cdot 10^{0}$ & $\\mathbf{1.02 \\cdot 10^{0}}$ & $1.03 \\cdot 10^{0}$ & $\\mathbf{1.78 \\cdot 10^{-2}}$ & $1.15 \\cdot 10^{0}$ & $\\mathbf{1.10 \\cdot 10^{-1}}$ \\\\\n \\hline\n \\multirow{3}{4em}{$\\nu=10$} & Alg.~ & $5.83 \\cdot 10^{0}$ & $\\times$ & $4.69 \\cdot 10^{-1}$ & $2.41 \\cdot 10^{-1}$ & $7.08 \\cdot 10^{-4}$ & $2.24 \\cdot 10^{-4}$ \\\\\n \\cdashline{3-3}\\cdashline{5-8}\n & Alg.~ & $\\mathbf{7.20 \\cdot 10^{0}}$ & $\\times$ & $\\mathbf{6.68 \\cdot 10^{-1}}$ & $1.07 \\cdot 10^{0}$ & $\\mathbf{5.50 \\cdot 10^{-2}}$ & $5.25 \\cdot 10^{-1}$ \\\\\n & Alg.~ & $9.36 \\cdot 10^{0}$ & $\\times$ & $1.71 \\cdot 10^{0}$ & $\\mathbf{2.74 \\cdot 10^{-1}}$ & $9.67 \\cdot 10^{-1}$ & $\\mathbf{8.63\\cdot 10^{-3}}$ \\\\\n \\hline\n \\multirow{3}{4em}{$\\nu=+\\infty$} & Alg.~ & $\\times$ & $\\times$ & $4.91 \\cdot 10^{1}$ & $1.67 \\cdot 10^{1}$ & $4.06 \\cdot 10^{1}$ & $1.48 \\cdot 10^{1}$ \\\\\n \\cdashline{5-8}\n & Alg.~ & $\\times$ & $\\times$ & $\\mathbf{5.01 \\cdot 10^{1}}$ & $1.96 \\cdot 10^{1}$ & $\\mathbf{4.07 \\cdot 10^{1}}$ & $1.56 \\cdot 10^{1}$ \\\\\n & Alg.~ & $\\times$ & $\\times$ & $5.74 \\cdot 10^{1}$ & $\\mathbf{1.69 \\cdot 10^{1}}$ & $4.18 \\cdot 10^{1}$ & $\\mathbf{1.48 \\cdot 10^{1}}$ \\\\\n \\hline\n \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Median of the Rényi divergence $RD_{\\alpha}(\\pi, q_{\\mu_K, \\Sigma_K})$ over $100$ runs of $K = 10^3$ iteration. \"High $d$\" corresponds to $d=20, \\kappa_{\\pi} = 10$ and \"High $\\kappa_{\\pi}$\" to $d=5, \\kappa_{\\pi} = 10^3$. The symbol $\\times$ denotes situations when Equation is not satisfied. For each target and each approximating family $\\mathcal{T}_{\\nu}^d$, we highlighted in bold font the algorithm achieving the lowest value between Algorithm and . The values obtained with the idealized Algorithm are indicated as a reference.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On variational inference and maximum likelihood estimation with the λ-exponential family", "authors": ["Thomas Guilmeau", "Emilie Chouzenoux", "Víctor Elvira"], "url": "https://arxiv.org/abs/2310.05781v2", "attribution": "\"On variational inference and maximum likelihood estimation with the λ-exponential family\" by Thomas Guilmeau, Emilie Chouzenoux, and Víctor Elvira, arXiv:2310.05781v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.17149v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{F1 Scores (in\\%) for different features with TDNN Classifier}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\n\\textbf{Features} & \\textbf{Repetition} & \\textbf{Filled-pause} & \\textbf{Prolongation} \\\\\n\\hline\nMFCC & 77.34 & 73.32 & 72.24 \\\\\nPLP Features & 80.12 & 80.36 & 78.74 \\\\\nZTWCC & 81.22 & 80.85 & 79.20 \\\\\nPE-SFCC& 76.09 & 73.33 & 74.21 \\\\\nPE-ZTWCC& \\textbf{83.22} & \\textbf{81.23} & \\textbf{81.15} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Typical vs. Atypical Disfluency Classification: Introducing the IIITH-TISA Corpus and Temporal Context-Based Feature Representations", "authors": ["Priyanka Kommagouni", "Vamshiraghusimha Narasinga", "Purva Barche", "Sai Akarsh C", "Anil Vuppala"], "url": "https://arxiv.org/abs/2411.17149v1", "attribution": "\"Typical vs. Atypical Disfluency Classification: Introducing the IIITH-TISA Corpus and Temporal Context-Based Feature Representations\" by Priyanka Kommagouni, Vamshiraghusimha Narasinga, Purva Barche, Sai Akarsh C, and Anil Vuppala, arXiv:2411.17149v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13444v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{For each \\( L \\geq 6 \\), the minimum value of \\( P \\), denoted as \\( P_{\\text{min}} \\), for which Theorem holds with \\( P < P_0 = 2^{L+1} \\).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccc}\n \\( L \\) & 6 & 8 & 10 & 12 & 14 & 16 \\\\ \\hline\\hline\n \\( P_0 \\) & 128 & 512 & 2048 & 8192 & 32768 & 131072 \\\\ \\hline\n \\( P_{\\text{min}} \\) & 49 & 138 & 281 & 355 & 609 & 821 \\\\ \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Explicit Construction of Quantum Quasi-Cyclic Low-Density Parity-Check Codes with Column Weight 2 and Girth 12", "authors": ["Daiki Komoto", "Kenta Kasai"], "url": "https://arxiv.org/abs/2501.13444v2", "attribution": "\"Explicit Construction of Quantum Quasi-Cyclic Low-Density Parity-Check Codes with Column Weight 2 and Girth 12\" by Daiki Komoto and Kenta Kasai, arXiv:2501.13444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.05543v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|}\n \\hline\n \\textbf{Model} & \\textbf{Weights}\\\\\n \\hline\n \\textbf{DenseNet201} & 0.22 \\\\\n \\textbf{Xception} & 0.29\\\\\n \\textbf{InceptionResNet} & 0.18\\\\\n \\textbf{ResNet152V2} & 0.17\\\\\n \\textbf{MobileNetV2} & 0.15\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Weighted Average Ensemble Model Weights}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images", "authors": ["Sagar Kora Venu"], "url": "https://arxiv.org/abs/2011.05543v1", "attribution": "\"An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images\" by Sagar Kora Venu, arXiv:2011.05543v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.08302v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Block counts according to the swap counts and arbitrage swap counts in the block}\n\\begin{tabular}{ccccccccccc}\n \\toprule\n & \\multicolumn{5}{c}{\\textbf{Arbitrage swaps count}} && \\multicolumn{4}{c}{\\textbf{Swaps count}} \\\\\\cmidrule{2-6}\\cmidrule{8-11} & \\textbf{0} & \\textbf{1} & \\textbf{2} & \\textbf{$\\ge$ 3} &\\textbf{Total}&& \\textbf{0} & \\textbf{1} & \\textbf{2} & \\textbf{$\\ge$ 3}\\\\\n \\midrule\n \\textbf{Arbitrage block count} &3,575 & 4,114& 584 & 104 &8,377&&2,376 &4,165&1,403&433\\\\\n \\textbf{Non-arbitrage block count} &241,265 & --& -- & -- &241,265&&168,286 &57,619&12,780&2,580\\\\\n \\midrule\n \\textbf{Arbitrage blocks proportion} &1.460$\\%$&--&--&--&3.356$\\%$&&1.392$\\%$\t& 6.741$\\%$\t & 9.892$\\%$\t & 14.371$\\%$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Arbitrage on Decentralized Exchanges", "authors": ["Xue Dong He", "Chen Yang", "Yutian Zhou"], "url": "https://arxiv.org/abs/2507.08302v1", "attribution": "\"Arbitrage on Decentralized Exchanges\" by Xue Dong He, Chen Yang, and Yutian Zhou, arXiv:2507.08302v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06551v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll} \\hline\n \\textbf{Notation} & \\textbf{Description}\\\\ \\hline\n $\\mathcal{D}$ & network data \\\\\n $\\mathcal{V}$ and $\\mathcal{E}$ & sets of nodes and edges, respectively\\\\\n $m_{v_i}$ & denotes the network of a user $v_i$\\\\\n $fr_{v_i}$ and $fl_{v_i}$ & denote sets of friends and followers of user $v_i$\\\\\n $\\kappa \\in m_{v_i} $ & set of reciprocal ties of user $v_i$ \\\\\n $\\mathcal{A}_f$ & set of all possible attributes of a node\\\\\n $\\mathcal{X}_{f}$ & set of features inducing reciprocity\\\\\n $a \\succ b$ & a binary relation between $a$ and $b$ \\\\\n $\\exp{x}$ or $e^x$ & base of a natural logarithm, $ln$, $\\exp{(lnx)}=x$; $e \\approx 2.71828$ \\\\\n $d_i, p_i, q_i, u_i$ & respective $ith$ component of vectors, $\\mathbf{D, P,Q,U}$ \\\\\n $\\tau$ & a predefined threshold for comparison, e.g. $\\tau \\geq 0.5$ \\\\\n $\\mathcal{S}_r$ and $\\mathcal{T}_r$ & sets of structurally-related and textually-related nodes, respectively \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A multilevel clustering technique for community detection", "authors": ["Isa Inuwa-Dutse", "Mark Liptrott", "Yannis Korkontzelos"], "url": "https://arxiv.org/abs/2101.06551v1", "attribution": "\"A multilevel clustering technique for community detection\" by Isa Inuwa-Dutse, Mark Liptrott, and Yannis Korkontzelos, arXiv:2101.06551v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.18611v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{Metric} & \\textbf{$\\boldsymbol{O_{NCD}=0.09}$} & \\textbf{$\\boldsymbol{O_{NCD}=0.18}$} \\\\\n\\hline\n\\textbf{SS} &0.85 &0.50 \\\\\n\\hline\n\\textbf{ARI} &0.64 &0.44 \\\\\n\\hline\n\\textbf{MI} &0.94 &0.83 \\\\\n\\hline\n\\textbf{ACC (\\%)} &79.34 &55.68 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Clustering Comparison for Different Levels of Openness Eq:~ ($O_{NCD}$ )}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Identification and Clustering of Unseen Ragas in Indian Art Music", "authors": ["Parampreet Singh", "Adwik Gupta", "Aakarsh Mishra", "Vipul Arora"], "url": "https://arxiv.org/abs/2411.18611v2", "attribution": "\"Identification and Clustering of Unseen Ragas in Indian Art Music\" by Parampreet Singh, Adwik Gupta, Aakarsh Mishra, and Vipul Arora, arXiv:2411.18611v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2401.00587v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Table II: Compared Methods}\n\\begin{tabular}{lcccc}\n\\toprule\nSegmentation Method & Whole & Core & Enh. & Mean \\\\\n\\midrule\nU-Net & 84.72 & 86.4 & 77.42 & 82.85 \\\\\nROI & 88.65 & 89.11 & 82 & 86.83 \\\\\nROI+SC & 88.92 & \\textbf{89.15} & 84.53 & 87.53 \\\\\nROI+SC+I-Norm & \\textbf{89.13} & 88.97 & \\textbf{87.47} & \\textbf{88.52} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction", "authors": ["Zachary Schwehr", "Sriman Achanta"], "url": "https://arxiv.org/abs/2401.00587v2", "attribution": "\"Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction\" by Zachary Schwehr and Sriman Achanta, arXiv:2401.00587v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.09833v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc|cc}\n\t\t\t\\hline\t\t\t\n\t\t\tn & $I_R(2)$ & $I_{\\Sigma}(2)$ & $I_R(3)$ & $I_{\\Sigma}(3)$\\\\\n\t\t\t\\hline\n\t\t\t10 & 36.31 & 59.94 & 40.70 & 55.07\\\\\n\t\t\t20 & 90.52 & 87.20 & 80.18 & 83.71\\\\\n\t\t\t50 & 92.34 & 96.67 & 82.94 & 94.46\\\\\n\t\t\t100 & 98.73 & 99.16 & 86.88 & 96.38\\\\\n\t\t\t\\hline \n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian semiparametric modelling of covariance matrices for multivariate longitudinal data", "authors": ["Georgios Papageorgiou"], "url": "https://arxiv.org/abs/2012.09833v1", "attribution": "\"Bayesian semiparametric modelling of covariance matrices for multivariate longitudinal data\" by Georgios Papageorgiou, arXiv:2012.09833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10771v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|ccc|}\n \\hline\n Models & Volatility MAPE (\\%) & Call Price MAPE (\\%) \\\\ \n \\hline\\hline\n PINN & 16.4823 & 13.5132 \\\\\n \\hline\n ConvLSTM & 8.4060 & 6.0774 \\\\\n \\hline\n SA-ConvLSTM & 8.4218 & 6.1265 \\\\\n \\hline\n ConvTF & 5.7891 & 4.5944 \\\\\n \\hline\n PI-ConvTF & 4.9174 & 3.8457 \\\\\n \\hline\n VAR & 8.8043 & 8.5821 \\\\\n \\hline\n ARIMA & 28.2453 & 32.5921 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Average MAPE values across test dates for all models trained with outlier data. Call price MAPE values are determined after excluding outlier values below the $20^{th}$ percentile.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Physics-Informed Convolutional Transformer for Predicting Volatility Surface", "authors": ["Soohan Kim", "Seok-Bae Yun", "Hyeong-Ohk Bae", "Muhyun Lee", "Youngjoon Hong"], "url": "https://arxiv.org/abs/2209.10771v3", "attribution": "\"Physics-Informed Convolutional Transformer for Predicting Volatility Surface\" by Soohan Kim, Seok-Bae Yun, Hyeong-Ohk Bae, Muhyun Lee, and Youngjoon Hong, arXiv:2209.10771v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18896v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n $l$ & $2000$ & $500$ & $200$ & $50$\\\\\n \\midrule\n Time (s) & $122.66$ & $4.31$ & $1.15$ & $0.63$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Time (seconds) required to construct the calibration bands on the mean of binned independent normal responses $(Y_i)_{i=1}^{n}$ for different amounts of bins.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Calibration Bands for Mean Estimates within the Exponential Dispersion Family", "authors": ["Łukasz Delong", "Selim Gatti", "Mario V. Wüthrich"], "url": "https://arxiv.org/abs/2503.18896v1", "attribution": "\"Calibration Bands for Mean Estimates within the Exponential Dispersion Family\" by Łukasz Delong, Selim Gatti, and Mario V. Wüthrich, arXiv:2503.18896v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.02813v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rcccccrr}\n \\hline\n $n_\\infty$ & $m_p$ & $\\Delta r$ & $\\beta$ & $n_{\\rm t}$ & $n_{\\rm t}/n_{\\infty}$ \\\\\n \\hline\n \\hline\n 1600 & $ 6.25\\cdot10^{-4}$ & 0.085498797 & 273.59615 & 4130 & 2.58 \\\\\n 3200 & $ 3.125\\cdot10^{-4}$ & 0.067860440 & 434.30682 & 6806 & 2.13 \\\\\n 6400 & $ 1.5625\\cdot10^{-4}$ & 0.053860867 & 689.41911 & 12688 & 1.98 \\\\\n 12800 & $ 7.8125\\cdot10^{-5}$ & 0.042749399 & 1094.3846 & 25154 & 1.97 \\\\\n 25600 & $3.90625\\cdot10^{-5}$ & 0.033930220 & 1737.2273 & 43138 & 1.69 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An optimal-transport finite-particle method for driven mass diffusion", "authors": ["Anna Pandolfi", "Ignacio Romero", "Michael Ortiz"], "url": "https://arxiv.org/abs/2503.02813v1", "attribution": "\"An optimal-transport finite-particle method for driven mass diffusion\" by Anna Pandolfi, Ignacio Romero, and Michael Ortiz, arXiv:2503.02813v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06635v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc|ccc}\n \\toprule\n &\\multicolumn{3}{c|}{Aircraft} &\\multicolumn{3}{c}{Cars} \\\\%& \\multicolumn{3}{c|}{Dogs}\\\\\n {Base CNN} & {\\#9} & {\\#27} & {\\#36} & {\\#9} & {\\#27} & {\\#36} \\\\% & {9} & {27} & {36}\\\\\n \\toprule\n ResNet-50 &85.9 &94.9 &91.2 &92.9 &94.9 & 91.9 \\\\% &90.5 &95.8 &92.1 \\\\\n Xception &87.8 &94.1 &90.0 &93.9 &95.7 &92.6 \\\\% &95.3 &96.1 &95.2\\\\\n NASNet-M &92.7 &93.8 &90.3 &92.4 &93.7 &90.9 \\\\% &91.7 &96.0 &93.3\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Accuracy (\\%) of our model with a varying number of integral regions. More results in the supplementary in the end.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification", "authors": ["Ardhendu Behera", "Zachary Wharton", "Pradeep Hewage", "Asish Bera"], "url": "https://arxiv.org/abs/2101.06635v1", "attribution": "\"Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification\" by Ardhendu Behera, Zachary Wharton, Pradeep Hewage, and Asish Bera, arXiv:2101.06635v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10952v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\hline\n Model & $F1_{micro}$ & $A$ & $D$ & $N$ \\\\\n \\hline\n {LR$_{ArgF}$} & 53.5 & 22.4 & 57.2 & 56.3 \\\\\n {LR$_{ArgF+SarcF}$} & \\textbf{56.4}$^{\\alpha{^{*}}}$ &\t31.0 & {58.4} & {58.9}\\\\\n\\hline\n{LSTM$_{Attn}$} & 51.8 & 28.0 & 49.4 & 59.2\\\\\n{LSTM$_{MT}$} & 53.1 & 30.0 & 53.2 & 56.5 \\\\\n{LSTM$_{{MT}_{uncert}}$} & \\textbf{54.6} $^{\\alpha{^{*}}}$ & 33.1 & 54.5 & 58.5 \\\\\n \\hline\n {BERT$_{orig}$} & 62.2 &\t41.8 &\t63.3 & 64.4\\\\\n{BERT$_{MT}$} & 63.2 & {44.5} & {64.1} & 65.4 \\\\\n{BERT$_{{MT}_{uncert}}$} & \\textbf{65.3}$^{\\alpha{^{*}}}$ & {44.6} & {66.2} & {67.5} \\\\\n\\hline\n{BERT$_{ALT}$} & 63.4 & 40.1 & 62.2 & {66.9} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results for argumentative relation detection ($F1_{micro}$ and F1 scores/category) on the $test$ set of $IAC_{orig}$. $^{\\alpha{^{*}}}$ depict significance on $p\\le 0.05$ (measured via Mcnemar's test) against the corresponding individual model (e.g., LR$_{ArgF}$, LSTM$_{Attn}$, BERT$_{orig}$, respectively). Highest scores per group of models are in \\textbf{bold}.}%, showing the benefits of joint modelling.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "\"Laughing at you or with you\": The Role of Sarcasm in Shaping the Disagreement Space", "authors": ["Debanjan Ghosh", "Ritvik Shrivastava", "Smaranda Muresan"], "url": "https://arxiv.org/abs/2101.10952v1", "attribution": "\"\"Laughing at you or with you\": The Role of Sarcasm in Shaping the Disagreement Space\" by Debanjan Ghosh, Ritvik Shrivastava, and Smaranda Muresan, arXiv:2101.10952v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01857v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc}\nTask & Flare (500K) & RAD (500K) & Flare (1M) & RAD (1M)\\\\ \\hline\nQuadruped Walk & $\\mathbf{296}\\pm139$ & $206\\pm112$ & $\\mathbf{488}\\pm221$ & $322\\pm229$ \\\\\nPendulum Swingup & $\\mathbf{242}\\pm152$ & $79\\pm73$ & $\\mathbf{809}\\pm31$ & $520\\pm321$\\\\ \nHopper Hop & $\\mathbf{90}\\pm55$ & $40\\pm41$ & $\\mathbf{217}\\pm59$ & $211\\pm27$ \\\\\nFinger Turn hard & $\\mathbf{282}\\pm67$ & $137\\pm98$ & $\\mathbf{661}\\pm315$ & $249\\pm98$ \\\\\nWalker Run & $426\\pm33$ & $\\mathbf{547}\\pm48$ & $556\\pm93$ & $\\mathbf{628}\\pm39$ \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Reinforcement Learning with Latent Flow", "authors": ["Wenling Shang", "Xiaofei Wang", "Aravind Srinivas", "Aravind Rajeswaran", "Yang Gao", "Pieter Abbeel", "Michael Laskin"], "url": "https://arxiv.org/abs/2101.01857v1", "attribution": "\"Reinforcement Learning with Latent Flow\" by Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, and Michael Laskin, arXiv:2101.01857v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.12919v2_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Visibility on arXiv during the review period, CMU, MIT, and Cornell} at ICLR 2020. Logistic regression predicting paper acceptance as a function of mean reviewer score and an indicator demarcating if the paper was on arXiv up to one week after the submission date. The accuracy of the classifier was 94\\% on a hold-out set containing 30\\% of 2020 papers.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc} \n\\bf Variable &\\bf Coefficient &\\bf Std. Error &\\bf Z-score &\\bf p-value\n\\\\ \\hline \\\\\n\\texttt{mean reviewer score} & 2.7144 & 0.372 & 7.295 & 0.000\\\\% 1.985 3.444\n\\texttt{visible on arXiv?} & 1.8285 & 0.551 & 3.317 & 0.001\\\\% 0.748 2.909\n\\texttt{constant} & -14.4988 & 1.967 & -7.372 & 0.000\\\\% -18.353 -10.644\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Analyzing the Machine Learning Conference Review Process", "authors": ["David Tran", "Alex Valtchanov", "Keshav Ganapathy", "Raymond Feng", "Eric Slud", "Micah Goldblum", "Tom Goldstein"], "url": "https://arxiv.org/abs/2011.12919v2", "attribution": "\"Analyzing the Machine Learning Conference Review Process\" by David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, and Tom Goldstein, arXiv:2011.12919v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.19853v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n Notation & Definition\\\\\n \\midrule\n $\\mathcal{V}$ & Set of timetabled trips\\\\\n $\\mathcal{D}$ & Set of depots\\\\\n $\\mathcal{H}$ & Set of charging stations\\\\\n $\\mathcal{H}^C$ & Set of charging nodes\\\\\n $\\mathcal{H}^W$ & Set of waiting nodes\\\\\n $\\mathcal{R}$ & Set of time intervals\\\\\n $\\mathcal{S}$ & Set of all feasible vehicle schedules\\\\\n $\\mathcal{S}^d$ & Set of all feasible vehicle schedules hosted in depot $d$\\\\\n $y_s$ & Binary variable equal to 1 if schedule $s$ is selected\\\\\n $a_{is}$ & Binary parameter equal to 1 if schedule $s$ covers trip $i$\\\\\n $w_s^{hr}$ & Binary parameter equal to 1 if schedule $s$ includes a charging activity at station $h$ in time interval $r$\\\\ \n $\\tau$ & Minimum layover time \\\\\n $\\rho$ & Duration of each time interval\\\\\n $\\sigma^{low}$ & Lower bound on the recommended SoC range\\\\\n $\\sigma^{up}$ & Upper bound on the recommended SoC range\\\\\n $\\sigma^{init}$ & Initial SoC at the beginning of the planning period\\\\\n $\\sigma^{min}$ & Minimum authorized SoC (e.g., 0\\%)\\\\\n $\\sigma^{max}$ & Maximum authorized SoC (e.g., 100\\%)\\\\\n $b_d$ & Number of available EBs at depot $d$\\\\\n $g_h$ & Number of available chargers at charging station $h$\\\\\n $\\epsilon$ & Chance constraint threshold\\\\\n $e_i(\\mu)$ & PMF with finite supports of the energy consumption of trip $i$ (relative to the total battery capacity)\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Overview of the notation of the E-VSP with battery degradation and stochastic energy consumption}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Chance-constrained battery management strategies for the electric bus scheduling problem", "authors": ["Léa Ricard", "Guy Desaulniers", "Andrea Lodi", "Louis-Martin Rousseau"], "url": "https://arxiv.org/abs/2503.19853v1", "attribution": "\"Chance-constrained battery management strategies for the electric bus scheduling problem\" by Léa Ricard, Guy Desaulniers, Andrea Lodi, and Louis-Martin Rousseau, arXiv:2503.19853v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08996v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|lll}\n\\hline\nModel & LASA & PSNR~(dB) $\\uparrow$ & SSIM $\\uparrow$ \\\\\\hline\nBase& - & 15.485 & 0.569 \\\\\nBase+SC&-& 28.702 & 0.784 \\\\\nBase+SC+GRL&-& 28.907&0.785 \\\\\nBase+SC+GRL+LRL&-& 28.673 & 0.784 \\\\\\hline\nBase+SC+GRL& $\\surd$ & 30.075 & \\textbf{0.794} \\\\\nBase+SC+GRL+LRL& $\\surd$ &\\textbf{30.170}&0.793 \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Ablation study on the design of Linear Array Network.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Linear Array Network for Low-light Image Enhancement", "authors": ["Keqi Wang", "Ziteng Cui", "Jieru Jia", "Hao Xu", "Ge Wu", "Yin Zhuang", "Lu Chen", "Zhiguo Hu", "Yuhua Qian"], "url": "https://arxiv.org/abs/2201.08996v2", "attribution": "\"Linear Array Network for Low-light Image Enhancement\" by Keqi Wang, Ziteng Cui, Jieru Jia, Hao Xu, Ge Wu, Yin Zhuang, Lu Chen, Zhiguo Hu, and Yuhua Qian, arXiv:2201.08996v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.21174v1_tex_table16.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|lll|lll|lll}\n\\# &\\multicolumn{3}{c}{$\\tau_{large}$} &\\multicolumn{3}{c}{$\\tau_{mix}$} & \\multicolumn{3}{c}{$\\tau_{small}$} \\\\\n & fgmres & lsqr & mrs & fgmres & lsqr & mrs & fgmres & lsqr & mrs \\\\ \\hline\n1 & 671.0 & 67.3 & 6.3 & 753.3 & 86.8 & 7.0 & 6.0 &107.3 & 7.0 \\\\\n2 & 987.0 & 113.1 & 1.3 & $\\ddagger$& 152.1 & 1.9 & 4.0 & 197.0 & 1.9 \\\\\n3 & 1.0 & 11.3 & 1.0 & 1.0 & 11.8 & 1.0 & 1.0 & 12.0 & 1.0 \\\\\n4 & 1.0 & 45.4 & 1.0 & 1.0 & 92.3 & 1.0 & 1.0 & 94.5 & 1.0 \\\\\n5 & 1.0 & 130.1 &1.0 &1.0 & 392.0 & 1.0 & 1.0 & 500.5 & 1.0 \\\\\n6 &1.0 & 216.5 & 1.0 &1.0 & 500.5 &1.0 & 1.0 & 500.5 & 1.0 \\\\\n7 & 8.0 & 87.1 & 8.0 & 7.0 & 95.4 & 10.6 & 7.0& 88.0 & 9.7 \\\\\n8 & 7.3 & 70.1 & 7.3 &7.0 & 155.7 & 7.5 & 6.2 & 373.9 & 8.3 \\\\\n9 & 2.0 & 178.6 & 7.4 & 2.0& 527.0 &1.0 & 2.0 & 611.0 & 1.0\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Average number of inner fGMRES, LSQR and MRS iterations for the new scheme (general case)}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust iterative methods for linear systems with saddle point structure", "authors": ["Murat Manguoğlu", "Volker Mehrmann"], "url": "https://arxiv.org/abs/2502.21174v1", "attribution": "\"Robust iterative methods for linear systems with saddle point structure\" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.03591v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Optimal cost estimates obtained as a ratio of shadow prices from four equivalent formulations of the original problem () -- () where alternative equation formulations (17.a) and (18.a) were optionally replacing the original formulations () and (). \\\\ }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccr}\n\\toprule\n\t\\textbf{Equations formulation} & ~~~ & \\textbf{Cost estimate $-\\xi_1/\\lambda_1$} \\\\\n\\midrule\n\t(17), (18) & & 0.794 \\\\ % file: apples.lst.95.ok.bak\n\t(17), (18.a) & & 0.767 \\\\ % file: apples-3rd-formulation.lst.95.ok.bak\n\t(17.a), (18) & & 1.027 \\\\ % file: apples-4th-formulation.lst.95.ok.bak\n\t(17.a), (18.a) & & 1.000 \\\\ % file: apples-2nd-formulation.lst.95.ok.bak\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Shadow prices and optimal cost in economic applications", "authors": ["Nikolay Khabarov", "Alexey Smirnov", "Michael Obersteiner"], "url": "https://arxiv.org/abs/2211.03591v2", "attribution": "\"Shadow prices and optimal cost in economic applications\" by Nikolay Khabarov, Alexey Smirnov, and Michael Obersteiner, arXiv:2211.03591v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.19371v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc}\n \\hline\n \\hline\n & MusicFM & MERT\\\\\n \\hline\n FT & 180M & 51M\\\\\n Probing & 0 & 0\\\\\n \\hline\n Adapter & 657696 & 322752\\\\\n \\hline\n Prompt & 65536 & 49152\\\\\n Prefix & 9649152 & 7385088\\\\\n \\hline\n BitFit & 116736 & 50688\\\\\n SSF & 190464 & 82944\\\\\n LoRA & 405504 & 165888\\\\\n \\hline\n \\hline\n \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of trainable parameters of different systems.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Parameter-Efficient Transfer Learning for Music Foundation Models", "authors": ["Yiwei Ding", "Alexander Lerch"], "url": "https://arxiv.org/abs/2411.19371v1", "attribution": "\"Parameter-Efficient Transfer Learning for Music Foundation Models\" by Yiwei Ding and Alexander Lerch, arXiv:2411.19371v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.13521v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model's forecasting performance (MAPE, bps) for BTC, ETH with different horizons and target values, 4 and 8 LOB feathers}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|rr|rr|rr|rr|rr|rr|}\n \\hline\n \\multicolumn{13}{|c|}{BTC}%USDT}\n \\\\\\hline\n \\multirow{2}{*}{\\centering{Model}} & \n \\multicolumn{2}{c|}{0.2s} &\n \\multicolumn{2}{c|}{1s} &\n \\multicolumn{2}{c|}{5s} &\n \\multicolumn{2}{c|}{10s} &\n \\multicolumn{2}{c|}{30s} &\n \\multicolumn{2}{c|}{60s} \\\\\n \\cline{2-13}\n & delta & returns % 0.2s\n & delta & returns % 1s\n & delta & returns % 5s\n & delta & returns % 10s\n & delta & returns % 30s\n & delta & returns % 60s\n \\\\\\hline\n Single 4F\n & 0.4048 & 1.7114 % 0.2s\n & 1.0032 & 1.9755 % 1s\n & 2.5337 & 3.4717 % 5s\n & 3.5883 & 4.4318 % 10s\n & 7.7604 & 8.0355 % 30s\n & 11.7311 & 11.7080 % 60s\n \\\\\\hline\n Mixed 4F\n & 0.4051 & 0.7939 % 0.2s\n & 1.004 & 1.4607 % 1s\n & \\textbf{2.5331} & 2.8572 % 5s\n & \\textbf{3.5875} & \\textbf{3.7164} % 10s\n & 7.7598 & 7.6370 % 30s\n & 11.7295 & 11.5760 % 60s\n \\\\\\hline\n One Shot 4F\n & 0.4052 & 1.2113 % 0.2s\n & 1.0036 & 1.3535 % 1s\n & \\textbf{2.5331} & 2.8329 % 5s\n & 3.5881 & 3.9512 % 10s\n & \\textbf{7.7584} & 7.8507 % 30s\n & \\textbf{11.7292} & 11.4855 % 60s\n \\\\\\hline\n Single 8F\n & 0.3704 & 3.7822 % 0.2s\n & 0.9156 & 2.3593 % 1s\n & 2.5390 & 4.0339 % 5s\n & 3.7001 & 4.6769 % 10s\n & 7.8176 & 8.4597 % 30s\n & 11.7454 & 12.6412 % 60s\n \\\\\\hline\n Mixed 8F\n & \\textbf{0.3691} & 1.5147 % 0.2s\n & \\textbf{0.9151} & 1.3259 % 1s\n & 2.5390 & 2.8935 % 5s\n & 3.6995 & 3.9481 % 10s\n & 7.8164 & \\textbf{7.6074} % 30s\n & 11.7447 & 11.7647 % 60s\n \\\\\\hline\n One Shot 8F\n & 0.3723 & \\textbf{0.4901} % 0.2s\n & 0.9207 & \\textbf{1.0849} % 1s\n & 2.5390 & \\textbf{2.6448} % 5s\n & 3.7020 & 3.7716 % 10s\n & 7.8208 & 7.9061 % 30s\n & 11.748 & \\textbf{11.4266} % 60s\n \\\\\\hline\n \\multicolumn{13}{|c|}{ETH}%USDT}\n \\\\\\hline\n \\multirow{2}{*}{\\centering{Model}} & \n \\multicolumn{2}{c|}{0.2s} &\n \\multicolumn{2}{c|}{1s} &\n \\multicolumn{2}{c|}{5s} &\n \\multicolumn{2}{c|}{10s} &\n \\multicolumn{2}{c|}{30s} &\n \\multicolumn{2}{c|}{60s} \\\\\n \\cline{2-13}\n & delta & returns % 0.2s\n & delta & returns % 1s\n & delta & returns % 5s\n & delta & returns % 10s\n & delta & returns % 30s\n & delta & returns % 60s\n \\\\\\hline\n Single 4F\n & 0.8237 & 4.1361 % 0.2s\n & \\textbf{1.5045} & 4.6105 % 1s\n & 3.5275 & 5.6284 % 5s\n & 5.2810 & 6.5229 % 10s\n & \\textbf{10.5570} & 11.0921 % 30s\n & \\textbf{14.1681} & 14.5003 % 60s\n \\\\\\hline\n Mixed 4F\n & 0.8223 & 2.5423 % 0.2s\n & 1.5048 & 2.9301 % 1s\n & \\textbf{3.5184} & 5.1383 % 5s\n & \\textbf{5.2632} & 6.4410 % 10s\n & 10.5649 & 11.1592 % 30s\n & 14.1869 & 14.7516 % 60s\n \\\\\\hline\n One Shot 4F\n & 0.8136 & 1.1673 % 0.2s\n & 1.4964 & \\textbf{1.7318} % 1s \n & 3.5372 & 4.1682 % 5s\n & 5.3003 & \\textbf{5.6292} % 10s\n & 10.6494 & 10.5734 % 30s\n & 14.3053 & \\textbf{13.3077} % 60s\n \\\\\\hline\n Single 8F\n & 0.7764 & 3.9465 % 0.2s\n & 1.5478 & 6.4999 % 1s\n & 3.6996 & 8.1708 % 5s\n & 5.7442 & 8.7679 % 10s\n & 10.6389 & 12.9193 % 30s\n & 14.3498 & 15.1891 % 60s\n \\\\\\hline\n Mixed 8F\n & 0.7728 & 2.9434 % 0.2s\n & 1.5435 & 3.0960 % 1s\n & 3.7018 & 5.3449 % 5s\n & 5.7571 & 7.3277 % 10s\n & 10.6575 & 12.2598 % 30s\n & 14.3861 & 15.4388 % 60s\n \\\\\\hline\n One Shot 8F\n & \\textbf{0.7666} & \\textbf{0.8241} % 0.2s\n & 1.5430 & 1.8555 % 1s \n & 3.7874 & \\textbf{3.8724} % 5s\n & 5.8725 & 5.7762 % 10s\n & 10.8632 & \\textbf{10.5564} % 30s\n & 14.6696 & 13.8308 % 60s\n \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deep Learning Models Meet Financial Data Modalities", "authors": ["Kasymkhan Khubiev", "Mikhail Semenov"], "url": "https://arxiv.org/abs/2504.13521v2", "attribution": "\"Deep Learning Models Meet Financial Data Modalities\" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2504.13521v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17103v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n $n$ & Error $\\|\\cdot\\|_1$ & Order $\\|\\cdot\\|_1$ & Error\n $\\|\\cdot\\|_{\\infty}$ & Order $\\|\\cdot\\|_{\\infty}$ \\\\\n \\hline\n 40 & 8.73E$-6$ & $-$ & 2.44E$-5$ & $-$ \\\\\n \\hline\n 80 & 2.70E$-7$ & 5.01 & 7.35E$-7$ & 5.05 \\\\\n \\hline\n 160 & 8.45E$-9$ & 5.00 & 2.31E$-8$ & 4.99 \\\\\n \\hline\n 320 & 2.64E$-10$ & 5.00 & 6.95E$-10$ & 5.06 \\\\\n \\hline\n 640 & 8.26E$-12$ & 5.00 & 2.13E$-11$ & 5.03 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Lagrange extrapolation (without filter).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17103v1", "attribution": "\"High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.12543v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation Parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc} % 去掉了竖线\n\t\t\t\\toprule % 使用 top rule\n\t\t\t\\textbf{Parameter} & \\textbf{Value} \\\\\n\t\t\t\\midrule % 使用 mid rule\n\t\t\tNumber of MEC servers $M$ & 5 \\\\\n\t\t\tPlateau factors of MZipf $q_m$ & [100, 200, 90, 40, 80] \\\\\n\t\t\tZipf factors of MZipf $k_m$ & [0.60, 0.60, 0.75, 0.90, 0.90] \\\\\n\t\t\tData size of contents $\\eta_c$ & 1$\\sim$8 GB \\\\\n\t\t\tDownloading payment of contents $\\phi_c$ & 0.05$\\sim$0.5 HKD \\\\\n\t\t\tNumber of DNN layers & 6 \\\\\n\t\t\tNumber of neurons per hidden layer & 128 \\\\\n\t\t\tLearning rate $\\xi$ & 0.002 \\\\\n\t\t\tUpdate coefficient $\\tau$ & 0.005 \\\\\n\t\t\tDiscounted factor $\\gamma$ & 0.99 \\\\\n\t\t\t\\bottomrule % 使用 bottom rule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Personalized Federated Deep Reinforcement Learning for Heterogeneous Edge Content Caching Networks", "authors": ["Zhen Li", "Tan Li", "Hai Liu", "Tse-Tin Chan"], "url": "https://arxiv.org/abs/2412.12543v1", "attribution": "\"Personalized Federated Deep Reinforcement Learning for Heterogeneous Edge Content Caching Networks\" by Zhen Li, Tan Li, Hai Liu, and Tse-Tin Chan, arXiv:2412.12543v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.00101v1_tex_table30.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\\hline\n& \\multicolumn{5}{c}{\\hspace{3ex} \\textbf{State}}\\\\\n\\cline{2-6}\n & $S_{1}$ & $S_{2}$ & $S_{3}$ & $S_{4}$ & $S_{5+}$\\\\\n\\hline\n &586.20 & -4045.03& -657.02 & 0.00 & 0.00 \\\\\n & 1,247.06 & -1,169.81& 1,179.08 & 965.04 & 611.74 \\\\\n& 2,584.57& 1,669.23& 3,615.08 & 3,576.90 & 2,596.18 \\\\\n & 7,955.07& 3,805.08& 4,775.55 & 5,070.10 & 3,501.23 \\\\\n \\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Splitting points for the previous payment (deltPay) in each data set.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "mCube: Multinomial Micro-level reserving Model", "authors": ["Emmanuel Jordy Menvouta", "Jolien Ponnet", "Robin Van Oirbeek", "Tim Verdonck"], "url": "https://arxiv.org/abs/2212.00101v1", "attribution": "\"mCube: Multinomial Micro-level reserving Model\" by Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek, and Tim Verdonck, arXiv:2212.00101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.02884v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|ccccccc|}\n\\hline\n$p$ & $0.01$ & $0.1$ & $0.3$ & $0.5$ & $0.6$ & $0.8$ & $0.9$ \\\\\n\\hline\n$1/\\lambda$ &$10^{139}$ &$6\\times10^{11}$ & $558.46$ &$11.99$ &$4.9$ &$1.73$ &$1.26$\\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Last passage percolation and limit theorems in Barak-Erdős directed random graphs and related models", "authors": ["Sergey Foss", "Takis Konstantopoulos", "Bastien Mallein", "Sanjay Ramassamy"], "url": "https://arxiv.org/abs/2312.02884v2", "attribution": "\"Last passage percolation and limit theorems in Barak-Erdős directed random graphs and related models\" by Sergey Foss, Takis Konstantopoulos, Bastien Mallein, and Sanjay Ramassamy, arXiv:2312.02884v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.02273v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Test SROCC and Pairwise Ranking Accuracy (Pairwise) for lecture ranking models (task 2) with standard error (higher values are better).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\hline\nModel & \\multicolumn{2}{c}{GBM} & \\multicolumn{2}{c}{RF} \\\\\nFeature Set & SROCC & Pairwise & SROCC & Pairwise \\\\ \\hline\nContent-based & \\textbf{.6241$\\pm$.0291} & \\textbf{.7221$\\pm$.0102} & .6190$\\pm$.0237 & .7202$\\pm$.0086 \\\\ \n+ Wiki-based & .6245$\\pm$.0339 & .7224$\\pm$.0115 & \\textbf{.6251$\\pm$.0322} & \\textbf{.7225$\\pm$.0123} \\\\ \n+ Video-specific & \\textbf{.6761$\\pm$.0434} & \\textbf{.7446$\\pm$.0183} & .6758$\\pm$.0458 & .7446$\\pm$.0197 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement", "authors": ["Sahan Bulathwela", "Maria Perez-Ortiz", "Emine Yilmaz", "John Shawe-Taylor"], "url": "https://arxiv.org/abs/2011.02273v1", "attribution": "\"VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement\" by Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, and John Shawe-Taylor, arXiv:2011.02273v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.09336v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Top \\& Bottom 5 UK Geographies by Growth in Overall Card Spending \\\\ (November 2020, Year-on-Year \\% Changes)}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|cc} \\hline\n\t\tTop 5 & \\% Change & Bottom 5 & \\% Change \\\\\n\t\t\\hline\n\t\tOuter London - South & 16.8\\% & Southern Scotland & 3.9\\% \\\\\n\t\tOuter London - East & 15.8\\% & Cumbria & 3.9\\% \\\\\n\t\tGloucestershire, Wiltshire & 14.6\\% & Derbyshire \\& Nottinghamshire & 4.3\\% \\\\\n\t\tBerkshire, Bucks \\& Oxfordshire & 13.5\\% & West Wales & 5.2\\% \\\\\n\t\tHampshire \\& Isle of Wight & 12.1\\% & Lancashire & 5.3\\% \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Levelling Down and the COVID-19 Lockdowns: Uneven Regional Recovery in UK Consumer Spending", "authors": ["John Gathergood", "Fabian Gunzinger", "Benedict Guttman-Kenney", "Edika Quispe-Torreblanca", "Neil Stewart"], "url": "https://arxiv.org/abs/2012.09336v2", "attribution": "\"Levelling Down and the COVID-19 Lockdowns: Uneven Regional Recovery in UK Consumer Spending\" by John Gathergood, Fabian Gunzinger, Benedict Guttman-Kenney, Edika Quispe-Torreblanca, and Neil Stewart, arXiv:2012.09336v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.19217v3_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of DeepMaxent performance according to different weight decay values for L2 regularisation. The criteria are the average AUC per region and the average AUC for all regions. The best average AUC for each column is highlighted in bold, while the second-best averaged AUC is underlined.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllccccc}\n \\toprule\n weight decay value & \\multicolumn{6}{c}{regions} \\\\\n \\cmidrule(r){2-7}\n & AWT & CAN & NSW & NZ & SA & SWI & avg \\\\\n \\\\ \n 0 & 0.713 & 0.719 & {0.750} & 0.744 & 0.804 & 0.843 & 0.762 \\\\\n \\midrule\n 1e-6 & 0.713 & 0.719 & 0.750 & 0.744 & \\textbf{0.804} & 0.843 & 0.762\\\\\n 3e-6 & 0.713 & 0.719 & 0.750 & 0.744 & \\textbf{0.804} & 0.843 & 0.762\\\\\n 1e-5 & 0.713 & 0.719 & 0.750 & 0.744 & \\textbf{0.804} & 0.844 & 0.762\\\\\n 3e-5 & 0.713 & 0.721 & 0.750 & 0.746 & \\textbf{0.804} & 0.845 & 0.763\\\\\n 1e-4 & 0.713 & 0.726 & 0.751 & \\underline{0.750} & \\textbf{0.804}& \\underline{0.848} & \\underline{0.765}\\\\\n 3e-4 & 0.714 & \\underline{0.732} & \\underline{0.752} & \\textbf{0.754} & \\underline{0.803} & \\textbf{0.850} & \\textbf{0.767}\\\\\n 1e-3 & \\underline{0.718} & \\textbf{0.734} & \\textbf{0.753} & 0.745 & 0.799 & 0.843 & \\underline{0.765}\\\\\n 3e-3 & \\textbf{0.722} & \\underline{0.732} & 0.749 & 0.723 & 0.780 & 0.833 & 0.757\\\\\n 1e-2 & 0.716 & 0.730 & 0.740 & 0.707 & 0.754 & 0.805 & {0.742}\\\\\n 3e-2 & 0.691 & 0.705 & 0.715 & 0.662 & 0.737 & 0.780 & {0.715}\\\\\n 1e-1 & 0.642 & 0.661 & 0.670 & 0.585 & 0.669 & 0.718 & 0.657\\\\\n 3e-1 & 0.592 & 0.636 & 0.615 & 0.560 & 0.604 & 0.644 & 0.609\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Applying the maximum entropy principle to neural networks enhances multi-species distribution models", "authors": ["Maxime Ryckewaert", "Diego Marcos", "Christophe Botella", "Maximilien Servajean", "Pierre Bonnet", "Alexis Joly"], "url": "https://arxiv.org/abs/2412.19217v3", "attribution": "\"Applying the maximum entropy principle to neural networks enhances multi-species distribution models\" by Maxime Ryckewaert, Diego Marcos, Christophe Botella, Maximilien Servajean, Pierre Bonnet, and Alexis Joly, arXiv:2412.19217v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.10585v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\\\Sample size (2011-14)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n & Black & Non-Black & Total \\\\\n \\midrule\n \\multicolumn{4}{c}{A. FairFace} \\\\\n No.\\ of players & 178 & 69 & 247 \\\\\n \\quad Mean L* value & 47.45 & 64.92 & 52.33 \\\\\n \\quad \\quad (SD) & (10.59) & (7.62) & (12.59) \\\\\n \\quad \\quad Min–max & 20.52–71.26 & 41.85–79.4813 & 20.52–79.4813 \\\\\n \\addlinespace\n No.\\ of referees & 17 & 24 & 41 \\\\\n \\quad Mean L* value & 43.01 & 57.88 & 51.71 \\\\\n \\quad \\quad (SD) & (9.40) & (7.47) & (11.07) \\\\\n \\quad \\quad Min–max & 30.06–60.75 & 43.21–72.92 & 30.06–72.92 \\\\\n \\addlinespace\n No.\\ of games & 816 & 813 & 816 \\\\\n No.\\ of player–games & 12,377 & 3,332 & 15,709 \\\\\n No.\\ of player–minutes & 265,311 & 64,516 & 329,827 \\\\\n \\multicolumn{4}{c}{B. Human raters} \\\\\n No.\\ of players & 178 & 69 & 247 \\\\\n \\quad Mean L* value & 47.50 & 64.79 & 52.33 \\\\\n \\quad \\quad (SD) & (10.64) & (7.77) & (12.59) \\\\\n \\quad \\quad Min–max & 20.52–71.26 & 41.85–79.4813 & 20.52–79.4813 \\\\\n \\addlinespace\n No.\\ of referees & 20 & 21 & 41 \\\\\n \\quad Mean L* value & 44.94 & 58.17 & 51.71 \\\\\n \\quad \\quad (SD) & (10.06) & (7.69) & (11.07) \\\\\n \\quad \\quad Min–max & 30.06–63.32 & 43.21–72.92 & 30.06–72.92 \\\\\n \\addlinespace\n No.\\ of games & 816 & 813 & 816 \\\\\n No.\\ of player–games & 12,267 & 3,442 & 15,709 \\\\\n No.\\ of player–minutes & 262,005 & 67,822 & 329,827 \\\\\n \\multicolumn{4}{c}{C. Referee crew compositions} \\\\\n No.\\ of unique crews & & & 666 \\\\\n \\quad Mean abs.\\ L* diff. & & & 7.87 \\\\\n \\quad \\quad (SD) & & & (3.91) \\\\\n \\quad \\quad Min–max & & & 0.414–25.06 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Racial bias, colorism, and overcorrection", "authors": ["Kenneth Colombe", "Alex Krumer", "Rosa Lavelle-Hill", "Tim Pawlowski"], "url": "https://arxiv.org/abs/2508.10585v1", "attribution": "\"Racial bias, colorism, and overcorrection\" by Kenneth Colombe, Alex Krumer, Rosa Lavelle-Hill, and Tim Pawlowski, arXiv:2508.10585v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15444v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Known classification of weighing matrices of order $n$ and weight $9$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n\\noalign{\\hrule height1pt}\n$n$ & $N(n,9)$ & References\\\\\n\\hline\n$1,2,\\ldots,9,11$ & $0$ & \\\\\n$10$ & $9$ & \\\\\n$12$ & $4$ & \\\\\n$13$ &$8$ & \\\\\n$14$ & $7$ & \\\\\n$15$ & $37$& \\\\\n$16$ & $704$ & \\\\\n$17$ & $2360$ & \\\\\n$18$ &$11891$ & \\\\\n\\noalign{\\hrule height1pt}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unbiased weighing matrices of weight $9$", "authors": ["Makoto Araya", "Masaaki Harada", "Hadi Kharaghani", "Sho Suda", "Wei-Hsuan Yu"], "url": "https://arxiv.org/abs/2501.15444v2", "attribution": "\"Unbiased weighing matrices of weight $9$\" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10070v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\\toprule\nDataset & \\#R & \\#E & Train & Test & Val. \\\\ \\midrule\nFB15K237\t& 237\t& 14,541 &\t272,115\t& 17,535 &\t20,466 \\\\\nWN18RR\t& 11\t& 40,943 &86,835& 3,134&3,034\\\\\nFB13\t& 13\t& 75,043 &\t316,232\t& 23,733 &\t5,908 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Statistics of the datasets}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding", "authors": ["Danushka Bollegala", "Huda Hakami", "Yuichi Yoshida", "Ken-ichi Kawarabayashi"], "url": "https://arxiv.org/abs/2101.10070v1", "attribution": "\"RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding\" by Danushka Bollegala, Huda Hakami, Yuichi Yoshida, and Ken-ichi Kawarabayashi, arXiv:2101.10070v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2312.02943v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sensitivity analysis for expected purchasing time}\n\\begin{tabular}{cccccccccc}\n\t\t\\toprule %添加表格头部粗线\n\t\t Parameter\t& base case & $\\gamma=0.6$ & $\\gamma=0.9$ & $m=0.026$ & \n\t\t $m=0.028$ & $\\rho=0.04$ &$\\rho=0.05$ \\\\ \n\t\t \\midrule\n\t$\\mathbb{E}[\\eta(z;b)]$\t&1.87 &3.58 &0.03& 1.56 &1.08&0.1 & 0.5 \\\\\n\t\\midrule\n\tParameter\t&$B=1000$&$B=1500$ &$r=0.02$& $r=0.03$ &$y=200$&$y=250$ & 0.5 \\\\\n\t\\midrule\n\t$\\mathbb{E}[\\eta(z;b)]$\t&0.22 &0.01 &0.03& 0.8 &0.02&0.1 & 0.5 \\\\\t\t\t\\bottomrule\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Striking the Balance: Life Insurance Timing and Asset Allocation in Financial Planning", "authors": ["An Chen", "Giorgio Ferrari", "Shihao Zhu"], "url": "https://arxiv.org/abs/2312.02943v1", "attribution": "\"Striking the Balance: Life Insurance Timing and Asset Allocation in Financial Planning\" by An Chen, Giorgio Ferrari, and Shihao Zhu, arXiv:2312.02943v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.18996v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{A quantitative comparison of the SOTA SR models pre-trained on the ImageNet dataset ($\\times 2$) and fine-tuned on the AID dataset ($\\times 2$, and $\\times 4$) in terms of FID, PSNR, SSIM, SAM, SRE, NIQE, and AG. The tests are conducted on the dev split test data. The best values are highlighted }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccc}\n\\toprule\n\\multicolumn{10}{c}{ImageNet} \\\\\\midrule\nCategory & Method & Scale & FID $\\downarrow$ & PSNR$\\uparrow$ & SSIM$\\uparrow$ & SAM $\\downarrow$ & SRE$\\downarrow$ & NIQE $\\downarrow$ & AG $\\uparrow$ \\\\\n\\multirow{2}{*}{GAN} & LE-GAN & \\multirow{9}{*}{$\\times 2$} & 48.39 & 48.27 & 0.85 & \\textbf{5.61} & \\textbf{5.16} & 12.82 & 4.51 \\\\\n & ESRGAN-DP & & 53.26 & 48.02 & 0.84 & 6.97 & 8.35 & 12.24 & 4.81 \\\\\n\\multirow{2}{*}{VAE} & RefVAE & & 47.15 & 45.61 & 0.8 & 7.66 & 9.04 & 13.27 & 4.98 \\\\\n & LSR-SR & & 47.44 & 45.49 & 0.84 & 7.84 & 8.28 & 12.84 & 5.12 \\\\\n\\multirow{5}{*}{Diffusion} & SR3 & & 40.52 & 50.47 & \\textbf{0.95} & 6.55 & 7.32 & 10.48 & 4.82 \\\\\n & IRSDE & & 45.03 & 48.56 & 0.85 & 6.62 & 7.94 & 12.42 & 5.36 \\\\\n & EDiffSR & & \\textbf{40.41} & 49.85 & 0.88 & 6.68 & 7.17 & 11.04 & 5.62 \\\\\n & LWTDM & & 43.98 & 46.3 & 0.77 & 7.8 & 9.07 & 14.27 & 4.33 \\\\\n & Proposed & & 40.45 & \\textbf{52.97} & \\textbf{0.95} & 5.79 & 5.65 & \\textbf{9.86} & \\textbf{5.85} \\\\\n\\midrule\n\\multicolumn{10}{c}{AID} \\\\\n\\midrule\nCategory & Method & Scale & FID $\\downarrow$ & PSNR$\\uparrow$ & SSIM$\\uparrow$ & SAM $\\downarrow$ & SRE$\\downarrow$ & NIQE $\\downarrow$ & AG $\\uparrow$ \\\\\n\\multirow{2}{*}{GAN} & LE-GAN & \\multirow{9}{*}{$\\times 2$} & 53.13 & 39.8 & 0.71 & 8.09 & \\textbf{7.68} & 14.3 & 3.9 \\\\\n & ESRGAN-DP & & 55.2 & 39.62 & 0.7 & 9.66 & 10.71 & 16.08 & 3.91 \\\\\n\\multirow{2}{*}{VAE} & RefVAE & & 59.76 & 37.7 & 0.67 & 10.39 & 11.46 & 17.01 & 3.73 \\\\\n & LSR-SR & & 60.21 & 37.22 & 0.7 & 10.58 & 11.37 & 17.06 & 3.65 \\\\\n\\multirow{5}{*}{Diffusion} & SR3 & & 51.43 & 40.8 & 0.67 & 10.07 & 10.31 & 13.2 & 3.98 \\\\\n & IRSDE & & 53.8 & 40.41 & 0.71 & 9.35 & 10.46 & 15.7 & 4.06 \\\\\n & EDiffSR & & 51.49 & \\textbf{41.49} & 0.73 & 9.11 & 9.99 & 14.98 & 4.07 \\\\\n & LWTDM & & 55.06 & 39.56 & 0.64 & 10.72 & 12.03 & 18.04 & 3.65 \\\\\n & Proposed & & \\textbf{50.9} & 41.48 & \\textbf{0.75} & \\textbf{8.07} & 7.71 & \\textbf{12.03} & \\textbf{4.24} \\\\\n\\midrule\n\\multirow{2}{*}{GAN} & LE-GAN & \\multirow{9}{*}{$\\times 4$} & 56.19 & 35.14 & 0.7 & 10.14 & 9.12 & 16.32 & 3.46 \\\\\n & ESRGAN-DP & & 61.05 & 35.64 & 0.63 & 11.31 & 12.75 & 18.67 & 2.82 \\\\\n\\multirow{2}{*}{VAE} & RefVAE & & 66.04 & 33.82 & 0.6 & 12.01 & 13.24 & 19.26 & 3.35 \\\\\n & LSR-SR & & 66.25 & 32.97 & 0.63 & 11.79 & 13.12 & 19.04 & 3.17 \\\\\n\\multirow{5}{*}{Diffusion} & SR3 & & 66.02 & 35.85 & 0.6 & 11.51 & 11.84 & 14.86 & 3.4 \\\\\n & IRSDE & & 59.58 & 35.75 & 0.64 & 11.22 & 11.82 & 17.36 & 2.98 \\\\\n & EDiffSR & & 56.67 & 37.17 & 0.66 & 10.06 & 11.05 & 16.8 & 3.55 \\\\\n & LWTDM & & 67.34 & 32.23 & 0.58 & 12.63 & 14.11 & 20.11 & 2.52 \\\\\n & Proposed & & \\textbf{53.18} & \\textbf{38.64} & \\textbf{0.71} & \\textbf{8.97} & \\textbf{8.47} & \\textbf{13.75} & \\textbf{4.14}\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images", "authors": ["Yue Shi", "Liangxiu Han", "Darren Dancy", "Lianghao Han"], "url": "https://arxiv.org/abs/2412.18996v1", "attribution": "\"WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images\" by Yue Shi, Liangxiu Han, Darren Dancy, and Lianghao Han, arXiv:2412.18996v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.05595v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Account Balance Scenario Test}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|}\n\\hline\n& \\textbf{Quantile-Attention LSTM} & \\textbf{FutureQuant Transformer} \\\\ \\hline\nInitial funds in account: & \\$1,000,000 & \\$1,000,000 \\\\ \\hline\nAccount balance after one month: & \\$1,122,540 & \\$1,143,160 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Trading Under Uncertainty: A Distribution-Based Strategy for Futures Markets Using FutureQuant Transformer", "authors": ["Wenhao Guo", "Yuda Wang", "Zeqiao Huang", "Changjiang Zhang", "Shumin ma"], "url": "https://arxiv.org/abs/2505.05595v1", "attribution": "\"Trading Under Uncertainty: A Distribution-Based Strategy for Futures Markets Using FutureQuant Transformer\" by Wenhao Guo, Yuda Wang, Zeqiao Huang, Changjiang Zhang, and Shumin ma, arXiv:2505.05595v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.05332v5_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small Definition of MPC baselines.}\n\\begin{tabular}{lll}\n \\toprule\n \\textbf{Name} & \\textbf{Formulation} & \\textbf{Solver} \\\\\n \\midrule\n MPC($N$) & Problem~, translated to QP via~-. & OSQP~ \\\\\n MPC-T($N$) & \\begin{minipage}{8cm}Problem~ with terminal cost $\\rho\\| x_N \\|^2$ added to the objective, translated to QP similarly to~-. Weight $\\rho$ grid-searched over $\\{ 1, 10, 100\\}$, picking the best-performing one.\\end{minipage} & OSQP~ \\\\\n Tube & \\begin{minipage}{8cm} Tube MPC implemented by open-sourced code of , simplified for linear systems. Horizon $N=16$ and terminal weight $\\rho = 10$. Tube size grid-searched over $\\{ 0.05, 0.1, 0.2, 0.3 \\}$, since using the actual upper bound on disturbance magnitude may result in infeasibility.\\end{minipage} & Mosek~ \\\\\n Scenario & \\begin{minipage}{8cm}Scenario-based MPC implemented by the do-mpc toolbox~. Horizon $N = 16$ and terminal weight $\\rho = 10$. Three scenarios are generated for each of $A, B, w$. \\end{minipage} & IPOPT~ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "MPC-Inspired Reinforcement Learning for Verifiable Model-Free Control", "authors": ["Yiwen Lu", "Zishuo Li", "Yihan Zhou", "Na Li", "Yilin Mo"], "url": "https://arxiv.org/abs/2312.05332v5", "attribution": "\"MPC-Inspired Reinforcement Learning for Verifiable Model-Free Control\" by Yiwen Lu, Zishuo Li, Yihan Zhou, Na Li, and Yilin Mo, arXiv:2312.05332v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08018v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{TL nominal parameters.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|}\n\\hline \nParameter & Zero Sequence & Positive Sequence\\\\ \\hline \nR ($\\Omega$/$(km)$) & $0.3864$ & $0.01273$\\\\ \nL ($mH$/$(km)$) & $4.1264$ & $0.9337$ \\\\ \nC ($\\mu$F/$(km)$) & $7.751\\times 10^{-3}$ & $12.74\\times 10^{-3}$ \\\\ \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Transfer Learning for Fault Diagnosis of Transmission Lines", "authors": ["Fatemeh Mohammadi Shakiba", "Milad Shojaee", "S. Mohsen Azizi", "Mengchu Zhou"], "url": "https://arxiv.org/abs/2201.08018v1", "attribution": "\"Transfer Learning for Fault Diagnosis of Transmission Lines\" by Fatemeh Mohammadi Shakiba, Milad Shojaee, S. Mohsen Azizi, and Mengchu Zhou, arXiv:2201.08018v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.15103v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|l|}\n\t\t\\hline\n\t\tKnot id & $\\sigma$ & $s$ & $d(Kh, \\mathbb{Q})$ & $Z_2$ diagonals & $Z_4$ diagonals & w \\\\ \\hline\n\t\t$15n_{041127}$ & 0 & 2 &\\{1, 3, 5\\} & \\{1, 3, -1 \\} & & 4 \n\t\t\\\\ \\hline\n\t\t$16n_{0196771}$ & 6 & 10 & \\{9, 11, 7 & \\{9, 5, 7 \\} & \\{7\\} & 4 \n\t\t\\\\ \\hline\n\t\t$\\mathbf{16n_{0229042}}$ & 0 & -2 & \\{-5, -3, -1\\} & \\{-5, -3, -1 \\} & & 3 %& 3 \n\t\t\\\\ \\hline\n\t\t$16n_{0245346}$ & -2 & 0 & \\{1, 3, -1 \\} & \\{1, -3, -1 \\} & & \\{4\\} %& 3 \n\t\t\\\\ \\hline\n\t\t$\\mathbf{17nh_{0000038}}$ & 0 & -2 & \\{-5, -3, -1 \\} & \\{-5, -3, -1\\} & & 3\n\t\t\\\\ \\hline\n\t\t$17nh_{0000075}$ & 8 & 12 & \\{9, 11, 13 \\} & \\{9, 11, 7 \\} & 9 & \\{4 \\}\n\t\t\\\\ \\hline\n\t\t$\\mathbf{17nh_{0000124}}$ & 2 & 0 & \\{1, -3, -1] & \\{1, -3, -1 ] & & 3 \n\t\t\\\\ \\hline\n\t\t$\\mathbf{17nh_{0000158}}$ & -8 & -12 & \\{-13, -11, -9] &\\{ -13, -11, -9 ] & \\{-11\\} & 3 \n\t\t\\\\ \\hline\n\t\t$17nh_{0000173}$ & 0 & 2 & \\{1, 3, 5 \\} & \\{1, 3, -1 \\} & & 4\n\t\t\\\\ \\hline\n\t\t$17nh_{0000192}$ & 0 & 2 & \\{1, 3, 5 \\} & \\{1, 3, -1\\} & & 4 %& 3 \n\t\t\\\\ \\hline\n\t\t$\\mathbf{17nh_{0000273}}$ & 0 & -2 & \\{-5, -3, -1\\} & \\{-5, -3, -1\\} & & 3 %& 3 \n\t\t\\\\ \\hline\n\t\t$17nh_{0000281}$ & -2 & 0 & \\{1, 3, -1 \\} & \\{1, -3, -1 \\} & & 4 %& 3\n\t\t\\\\ \\hline\n\t\t$17nh_{0000283}$ & 6 & 10 &\\{ 9, 11, 7\\} & \\{9, 5, 7 \\} & \\{7\\} & 4 %& 3\n\t\t\\\\ \\hline\n\t\t$17nh_{0000297}$ & 0 & 2 & \\{1, 3, 5 \\} & \\{1, 3, -1 \\} & & 4 %& 3 \n\t\t\\\\ \\hline\n\t\t$17nh_{0000410}$ & 0 & 2 & \\{1, 3, 5\\} & 1, 3, -1 \\} & & 4 %& 3 \n\t\t\\\\ \\hline\n\t\t$\\mathbf{17nh_{0000433}}$ & 0 & -2 & \\{-5, -3, -1 \\} & \\{-5, -3, -1\\} & & 3 %& 4 \n\t\t\\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Knots with at most 17 crossings whose signature does not fall in the span of the diagonals for the free part of Khovanov homology yet they are on adjacent diagonals. Bold font denotes knots with the same property for integral Khovanov homology.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Data Driven Perspectives on Knot Theory", "authors": ["Pawel Dlotko", "Davide Gurnari", "Radmila Sazdanovic"], "url": "https://arxiv.org/abs/2503.15103v1", "attribution": "\"Data Driven Perspectives on Knot Theory\" by Pawel Dlotko, Davide Gurnari, and Radmila Sazdanovic, arXiv:2503.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.09239v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\hline\n\\hline\ndate & n & climate change & covid & price & taxes & inequality \\\\ \n \\hline\n 2005-01-01 & 5 & 0 & 0 & 3 & 1 & 0 \\\\ \n 2005-02-01 & 2 & 0 & 0 & 1 & 0 & 0 \\\\ \n 2005-03-01 & 6 & 0 & 0 & 6 & 1 & 0 \\\\ \n 2005-04-01 & 7 & 0 & 0 & 6 & 2 & 0 \\\\ \n 2005-05-01 & 10 & 0 & 0 & 9 & 5 & 0 \\\\ \n 2005-06-01 & 12 & 0 & 0 & 12 & 2 & 1 \\\\ \n 2005-07-01 & 5 & 0 & 0 & 4 & 1 & 0 \\\\ \n 2005-08-01 & 2 & 0 & 0 & 1 & 0 & 0 \\\\ \n 2005-09-01 & 6 & 0 & 0 & 5 & 3 & 0 \\\\ \n 2005-10-01 & 12 & 0 & 0 & 9 & 2 & 0 \\\\ \n 2005-11-01 & 11 & 0 & 0 & 9 & 3 & 0 \\\\ \n 2005-12-01 & 3 & 0 & 0 & 3 & 0 & 0 \\\\ \n 2006-01-01 & 3 & 0 & 0 & 2 & 0 & 0 \\\\ \n 2006-02-01 & 8 & 0 & 0 & 6 & 4 & 0 \\\\ \n 2006-03-01 & 9 & 0 & 0 & 7 & 1 & 0 \\\\ \n 2006-04-01 & 6 & 0 & 0 & 6 & 1 & 0 \\\\ \n 2006-05-01 & 13 & 0 & 0 & 12 & 4 & 0 \\\\ \n 2006-06-01 & 12 & 0 & 0 & 11 & 2 & 0 \\\\ \n 2006-07-01 & 6 & 0 & 0 & 4 & 1 & 0 \\\\ \n 2006-09-01 & 10 & 0 & 0 & 8 & 4 & 0 \\\\ \n 2006-10-01 & 11 & 0 & 0 & 10 & 4 & 0 \\\\ \n 2006-11-01 & 12 & 0 & 0 & 11 & 1 & 0 \\\\ \n 2006-12-01 & 6 & 0 & 0 & 6 & 2 & 0 \\\\ \n 2007-01-01 & 8 & 0 & 0 & 7 & 1 & 1 \\\\ \n 2007-02-01 & 6 & 0 & 0 & 4 & 0 & 0 \\\\ \n 2007-03-01 & 8 & 1 & 0 & 6 & 2 & 0 \\\\ \n 2007-04-01 & 6 & 0 & 0 & 6 & 1 & 0 \\\\ \n 2007-05-01 & 12 & 0 & 0 & 9 & 0 & 2 \\\\ \n 2007-06-01 & 15 & 0 & 0 & 14 & 3 & 0 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\ ECB Speeches. 1997-2022}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Interactions of Social Norms about Climate Change: Science, Institutions and Economics", "authors": ["Antonio Cabrales", "Manu García", "David Ramos Muñoz", "Angel Sánchez"], "url": "https://arxiv.org/abs/2208.09239v1", "attribution": "\"The Interactions of Social Norms about Climate Change: Science, Institutions and Economics\" by Antonio Cabrales, Manu García, David Ramos Muñoz, and Angel Sánchez, arXiv:2208.09239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.06415v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Proportion of cases in which 95\\% confidence intervals contain 1 for $\\theta$ parameter, for $\\sigma^2=5$ in the scenario 2. \\\\ $\\theta_0$ means $\\theta$ when $x=0$ and $\\theta_1$ means $\\theta$ when $x=1$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|ccccccccccc}\n\\hline\n & & \\multicolumn{11}{c}{$n$}\\\\\n\\hline\nParameter & Model & 100 & 200 & 400 & 800 & 1000 & 2000 & 5000 & 8000 & 10000 & 12000 & 15000 \\\\\n\\hline\n\\multirow{2}{*}{$\\theta_0$} & DD model & 0.001\t& 0.002\t& 0 &\t0 &\t0 &\t0 &\t0 &\t0 &\t0 &\t0\t& 0 \\\\\n & DD-PVF model & 0.089\t& 0.042 &\t0.019 &\t0.028 &\t0.033 &\t0.026 &\t0.032 &\t0.036\t& 0.039 &\t0.041 &\t0.039\n \\\\\n\\hline\n\\multirow{2}{*}{$\\theta_1$} & DD model & 0.56\t& 0.68 & \t0.766 & \t0.83 & \t0.793 & \t0.547 & \t0.055 & \t0.001 & \t0\t& 0\t& 0\n \\\\\n & DD-PVF model & 0.73\t& 0.797 &\t0.862 &\t0.927 &\t0.935 &\t0.954\t& 0.969 &\t0.963 &\t0.965 &\t0.967 &\t0.962\n \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Long-Term Dagum-PVF Frailty Regression Model: Application in Health Studies", "authors": ["Agatha Rodrigues", "Patrick Borges"], "url": "https://arxiv.org/abs/2311.06415v1", "attribution": "\"Long-Term Dagum-PVF Frailty Regression Model: Application in Health Studies\" by Agatha Rodrigues and Patrick Borges, arXiv:2311.06415v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.23432v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Accuracies of average human raters and the best LLM for 24 skills in BIG-bench Lite; information from Figure 4 of . ``NA'' represents that the accuracy is unclear from the figure.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{Skill} & \\textbf{Accruacy of average human rater} & \\textbf{Accuracy of the best LLM} \\\\ \\hline\nauto\\_debugging & 0.15 & NA \\\\ \\hline\nbbq\\_lite\\_json & 0.73 & 0.73 \\\\ \\hline\ncode\\_line\\_description & 0.6 & 0.46 \\\\ \\hline\nconceptual\\_combinations & 0.83 & 0.48 \\\\ \\hline\nconlang\\_translation & NA & 0.5 \\\\ \\hline\nemoji\\_movie & 0.94 & 0.9 \\\\ \\hline\nformal\\_fallacies & 0.55 & 0.53 \\\\ \\hline\nhindu\\_knowledge & NA & 0.75 \\\\ \\hline\nknown\\_unknowns & 0.8 & 0.68 \\\\ \\hline\nlanguage\\_identification & NA & 0.36 \\\\ \\hline\nlinguistics\\_puzzles & NA & NA \\\\ \\hline\nlogic\\_grid\\_puzzle & 0.4 & 0.36 \\\\ \\hline\nlogical\\_deduction & 0.4 & 0.36 \\\\ \\hline\nmisconceptions\\_russian & NA & 0.68 \\\\ \\hline\nnovel\\_concepts & 0.65 & 0.57 \\\\ \\hline\noperators & 0.46 & 0.36 \\\\ \\hline\nparsinlu\\_reading\\_comprehension & NA & 0 \\\\ \\hline\nplay\\_dialog\\_same\\_or\\_different & NA & 0.63 \\\\ \\hline\nrepeat\\_copy\\_logic & 0.39 & 0.12 \\\\ \\hline\nstrange\\_stories & 0.8 & 0.63 \\\\ \\hline\nstrategyqa & 0.62 & 0.6 \\\\ \\hline\nsymbol\\_interpretation & 0.38 & 0.25 \\\\ \\hline\nvitamin\\_fact\\_verification & 0.63 & 0.57 \\\\ \\hline\nwinowhy & NA & 0.59 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Mathematical Framework for AI-Human Integration in Work", "authors": ["L. Elisa Celis", "Lingxiao Huang", "Nisheeth K. Vishnoi"], "url": "https://arxiv.org/abs/2505.23432v2", "attribution": "\"A Mathematical Framework for AI-Human Integration in Work\" by L. Elisa Celis, Lingxiao Huang, and Nisheeth K. Vishnoi, arXiv:2505.23432v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.14139v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The mean test accuracy (\\%) on 5-way Omniglot classification across 3 different runs. The 95\\% confidence intervals are displayed as $\\pm x$, and calculated over all runs and meta-test tasks (600 per run). The plain LSTM is outperformed by MAML. All methods (except LSTM) used a fully-connected feed-forward classifier. The best performances are dislpayed in bold font.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n \\toprule\n Technique & parameters & 1-shot & 5-shot \\\\\n \\midrule\n MAML & $247\\,621$ & 84.1 $\\pm$ 0.90 & \\textbf{93.5} $\\pm$ 0.30 \\\\\n ProtoNet & $247\\,621$ & 83.6 $\\pm$ 0.88 & 93.4 $\\pm$ 0.29 \\\\\n \\midrule\n LSTM & $13\\,530\\,097$ & 72.6 $\\pm$ 0.90 & 84.8 $\\pm$ 0.50 \\\\\n OP-LSTM (ours) & $249\\,167$ &\\textbf{84.3} $\\pm$ 0.90 & 91.8 $\\pm$ 0.30 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Are LSTMs Good Few-Shot Learners?", "authors": ["Mike Huisman", "Thomas M. Moerland", "Aske Plaat", "Jan N. van Rijn"], "url": "https://arxiv.org/abs/2310.14139v1", "attribution": "\"Are LSTMs Good Few-Shot Learners?\" by Mike Huisman, Thomas M. Moerland, Aske Plaat, and Jan N. van Rijn, arXiv:2310.14139v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10952v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\nModel & Precision & Recall & F1 \\\\\n\\hline\n{LSTM$_{Attn}$} & 52.9 &\t52.8&\t 52.9 \\\\\n{LSTM$_{MT}$} & 59.5 &\t59.3&\t\\textbf{59.4} \\\\\n\\hline\n \n{BERT$_{orig}$} & 57.4 & 57.4 & 57.4\\\\\n{BERT$_{MT}$} & 61.8 & 61.7 & \\textbf{61.8} \\\\\n{BERT$_{{MT}_{uncert}}$} & 64.1 & 63.5 & \\textbf{64.0} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Evaluations of sarcasm detection on the $test$ set of $IAC_{orig}$.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "\"Laughing at you or with you\": The Role of Sarcasm in Shaping the Disagreement Space", "authors": ["Debanjan Ghosh", "Ritvik Shrivastava", "Smaranda Muresan"], "url": "https://arxiv.org/abs/2101.10952v1", "attribution": "\"\"Laughing at you or with you\": The Role of Sarcasm in Shaping the Disagreement Space\" by Debanjan Ghosh, Ritvik Shrivastava, and Smaranda Muresan, arXiv:2101.10952v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00746v5_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Data statistics of synthetic traffic dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcrrrr}\n \\toprule\n \\multirow{3}{*}{\\textbf{Dataset}}\n & \\multirow{3}{*}{\\textbf{Total vehicles}}\n & \\multicolumn{4}{c}{\\textbf{Arrival rate (vehicles/300s)}} \\\\\n \\cmidrule(lr){3-6} \n & & \\textbf{Mean} & \\textbf{Std} & \\textbf{Max} & \\textbf{Min} \\\\\n \\midrule\n {mixed$_{l}$} & 2570 & 214.17 & 198.41 & 600 & 60 \\\\\n {mixed$_{h}$} & 4770 & 397.50 & 420.75 & 1200 & 60 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control", "authors": ["Liwen Zhu", "Peixi Peng", "Zongqing Lu", "Xiangqian Wang", "Yonghong Tian"], "url": "https://arxiv.org/abs/2101.00746v5", "attribution": "\"MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control\" by Liwen Zhu, Peixi Peng, Zongqing Lu, Xiangqian Wang, and Yonghong Tian, arXiv:2101.00746v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06395v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c}\n \\hline\n Training data & miniImageNet 5way1shot \\\\\n \\hline\n Support set only & $56.37 \\pm 0.68$\\\\\n Support set + 1 feature from the nearest class & $62.39 \\pm 0.49$\\\\\n Support set + 5 features from the nearest class & $59.73 \\pm 0.42$\\\\\n Support set + 10 features from the nearest class & $58.93 \\pm 0.49$\\\\\n Support set + 100 features from the nearest class & $57.33 \\pm 0.48$\\\\\n Support set + 100 features sampled from calibrated distribution & $\\textbf{68.53} \\pm \\textbf{0.32}$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The comparison with nearest class feature augmentation.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Free Lunch for Few-shot Learning: Distribution Calibration", "authors": ["Shuo Yang", "Lu Liu", "Min Xu"], "url": "https://arxiv.org/abs/2101.06395v3", "attribution": "\"Free Lunch for Few-shot Learning: Distribution Calibration\" by Shuo Yang, Lu Liu, and Min Xu, arXiv:2101.06395v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.18667v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The main producers of USVs}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc} \n \\toprule\n\t Company & Country \\\\\n\t \\midrule\n\t Kongsberg Maritime & Norway \\\\ \n\t Teledyne Technologies & USA \\\\ \n\t ECA Group & France \\\\ \n Elbit Systems Ltd. & USA \\\\ \n Textron Systems & USA \\\\ \n ASV Global & UK \\\\ \n Atlas Elektronik & UK \\\\ \n Rafael Advanced Defense Systems & Israel \\\\ \n L3 Harries Technologies & USA \\\\ \n Saab & Sweden \\\\ \n Fugro & Netherlands \\\\\n \\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "State-of-the-Art Underwater Vehicles and Technologies Enabling Smart Ocean: Survey and Classifications", "authors": ["Jiajie Xu", "Xabier Irigoien", "Mohamed-Slim Alouini"], "url": "https://arxiv.org/abs/2412.18667v1", "attribution": "\"State-of-the-Art Underwater Vehicles and Technologies Enabling Smart Ocean: Survey and Classifications\" by Jiajie Xu, Xabier Irigoien, and Mohamed-Slim Alouini, arXiv:2412.18667v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11803v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccc}\n\\toprule\n\\multirow{2}{*}{codename} &\n \\multirow{2}{*}{100\\%} &\n \\multirow{2}{*}{75\\%} &\n \\multirow{2}{*}{50\\%} &\n \\multirow{2}{*}{PARAs} &\n \\multirow{2}{*}{FLOPs} &\n \\multirow{2}{*}{$\\Gamma_{min}$} &\n \\multirow{2}{*}{\\%PARA} &\n \\multirow{2}{*}{\\%FLOPS} &\n IID &\n \\multicolumn{2}{c}{Non-IID} \\\\ \\cmidrule(l){10-12} \n & & & & & & & & & Accuracy & Global & Local \\\\ \\midrule\n1111111111 & 10 & & & 159010 & 158800 & 10 & 1.00 & 1.00 & 98.13 & 95.31 & 95.33 \\\\\n1111114444 & 6 & 4 & & 143110 & 142920 & 6 & 0.90 & 0.90 & 97.97 & 93.6 & 93.82 \\\\\n1111144447 & 5 & 4 & 1 & 135160 & 134980 & 5 & 0.85 & 0.85 & 97.395 & 91.92 & 92.18 \\\\\n1111223344 & 4 & 6 & & 135160 & 134980 & 8 & 0.85 & 0.85 & 97.865 & 91.9 & 92.42 \\\\\n1111234444 & 4 & 6 & & 135160 & 134980 & 6 & 0.85 & 0.85 & 97.86 & 92.99 & 92.93 \\\\\n1111234567 & 4 & 3 & 3 & 123235 & 123070 & 7 & 0.77 & 0.775 & 96.645 & 83.82 & 83.61 \\\\\n1111444444 & 4 & 6 & & 135160 & 134980 & 4 & 0.85 & 0.85 & 97.53 & 91.8 & 92.07 \\\\\n1111444477 & 4 & 4 & 2 & 127210 & 127040 & 4 & 0.80 & 0.80 & 96.775 & 84.91 & 85.02 \\\\\n1111556677 & 4 & & 6 & 111310 & 111160 & 6 & 0.70 & 0.70 & 96.575 & 69.11 & 69.63 \\\\\n1114556677 & 3 & 1 & 6 & 107335 & 107190 & 5 & 0.67 & 0.675 & 95.345 & 77.53 & 77.7 \\\\\n1234556677 & 1 & 3 & 6 & 99385 & 99250 & 5 & 0.62 & 0.625 & 95.475 & 72.8 & 72.4 \\\\\n1455666777 & 1 & 1 & 8 & 91435 & 91310 & 3 & 0.57 & 0.575 & 94.41 & 61.96 & 62.49 \\\\\n2233445677 & 0 & 6 & 4 & 103360 & 103220 & 5 & 0.65 & 0.65 & 96.375 & 60.23 & 61.01 \\\\\n1444777777 & 1 & 3 & 6 & 99385 & 99250 & 5 & 0.62 & 0.625 & 95.23 & 60.54 & 61.85 \\\\ \\midrule\n & & & & & & & & & & & \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results For Neuron Pruning on MNIST}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning", "authors": ["Hanhan Zhou", "Tian Lan", "Guru Venkataramani", "Wenbo Ding"], "url": "https://arxiv.org/abs/2201.11803v2", "attribution": "\"On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning\" by Hanhan Zhou, Tian Lan, Guru Venkataramani, and Wenbo Ding, arXiv:2201.11803v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09391v2_tex_table22.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n & Success \\% & Distance \\% \\\\\n \\hline\n Without Setup Policies & 1.9 & 36.3 \\\\\n With Setup Policies & \\textbf{71.2} & \\textbf{80.2} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Success rate and percentage of the total terrain length travelled from 1000 episodes of all 5 terrain types, randomly shuffled each episode.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Learning Setup Policies: Reliable Transition Between Locomotion Behaviours", "authors": ["Brendan Tidd", "Nicolas Hudson", "Akansel Cosgun", "Jurgen Leitner"], "url": "https://arxiv.org/abs/2101.09391v2", "attribution": "\"Learning Setup Policies: Reliable Transition Between Locomotion Behaviours\" by Brendan Tidd, Nicolas Hudson, Akansel Cosgun, and Jurgen Leitner, arXiv:2101.09391v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07558v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{DNSMOS scores on the Interspeech 2020 DNS Challenge blind test set. Discriminative models are shown with a white background; generative models are highlighted with a gray background. UniFlow variants correspond to different generative objectives.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n\\toprule\n\\textbf{Model} & \\multicolumn{3}{c}{\\textbf{With Reverb}} & \\multicolumn{3}{c}{\\textbf{No Reverb}} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7}\n& SIG & BAK & OVRL & SIG & BAK & OVRL \\\\\n\\midrule\nNoisy & 1.76 & 1.50 & 1.39 & 3.39 & 2.62 & 2.48 \\\\\n\\midrule\nConv-TasNet & 2.42 & 2.71 & 2.01 & 3.09 & 3.34 & 3.00 \\\\\nDEMUCS & 2.86 & 3.90 & 2.55 & 3.58 & 4.15 & 3.35 \\\\\n\\rowcolor{gray!20} SELM & 3.16 & 3.58 & 2.70 & 3.51 & 4.10 & 3.26 \\\\\n\\rowcolor{gray!20} AnyEnhance & 3.50 & 4.04 & 3.20 & 3.64 & 4.18 & 3.42 \\\\\n\\rowcolor{gray!20} LLaSE-G1 & 3.59 & 4.10 & 3.33 & 3.66 & 4.17 & 3.42 \\\\\n\\rowcolor{gray!20} FlowSE & \\textbf{3.61} & 4.11 & \\textbf{3.34} & 3.69 & 4.20 & 3.45 \\\\\n\\midrule\n\\rowcolor{gray!20} UniFlow$_\\text{DDPM}$ & 3.59 & \\textbf{4.12} & 3.32 & \\textbf{3.72} & \\textbf{4.21} & \\textbf{3.48} \\\\\n\\rowcolor{gray!20} UniFlow$_\\text{FM}$ & 3.54 & 4.07 & 3.28 & 3.68 & 4.17 & 3.45 \\\\\n\\rowcolor{gray!20} UniFlow$_\\text{MF}$ & 3.50 & 4.02 & 3.24 & 3.64 & 4.22 & 3.43 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling", "authors": ["Ziqian Wang", "Zikai Liu", "Yike Zhu", "Xingchen Li", "Boyi Kang", "Jixun Yao", "Xianjun Xia", "Chuanzeng Huang", "Lei Xie"], "url": "https://arxiv.org/abs/2508.07558v1", "attribution": "\"UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling\" by Ziqian Wang, Zikai Liu, Yike Zhu, Xingchen Li, Boyi Kang, Jixun Yao, Xianjun Xia, Chuanzeng Huang, and Lei Xie, arXiv:2508.07558v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10771v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|cccc}\n \\hline\n \\multirow{2}{*}{Moneyness} & \\multirow{2}{*}{Type} &\n \\multicolumn{4}{c}{Days to Expiration} \\\\\n & & $< 60$ & $60 - 180$ & $\\geq 180$ & Total \\\\\n \\hline\n \\multirow{3}{*}{$< 0.94$} & Number of contracts & $154371$ & $54295$ & $34298$ & $242964$ \\\\\n & Average Price & $(372.64)$ & $(503.72)$ & $(659.00)$ & $(442.36)$ \\\\\n & Average Bid-Ask Spread & $\\{4.51\\}$ & $\\{3.92\\}$ & $\\{8.90\\}$ & $\\{5.00\\}$ \\\\\n \\hline\n \\multirow{3}{*}{0.94-0.97} & Number of contracts & 175157 & 33151 & 11821 & 220129 \\\\\n & Average Price & (125.37) & (168.22) & (234.01) & (137.66) \\\\\n & Average Bid-Ask Spread & \\{2.87\\} & \\{2.04\\} & \\{5.61\\} & \\{2.90\\} \\\\\n \\hline\n \\multirow{3}{*}{0.97-1.00} & Number of contracts & 414233 & 74950 & 26835 & 516018 \\\\\n & Average Price & (59.87) & (105.71) & (178.32) & (72.68) \\\\\n & Average Bid-Ask Spread & \\{1.42\\} & \\{1.60\\} & \\{5.12\\} & \\{1.64\\} \\\\\n \\hline\n \\multirow{3}{*}{1.00-1.03} & Number of contracts & 525676 & 104193 & 30941 & 660810 \\\\\n & Average Price & (18.77) & (62.76) & (143.23) & (31.54) \\\\\n & Average Bid-Ask Spread & \\{0.54\\} & \\{1.26\\} & \\{4.64\\} & \\{0.85\\} \\\\\n \\hline\n \\multirow{3}{*}{1.03-1.06} & Number of contracts & 371810 & 96451 & 24757 & 493018 \\\\\n & Average Price & (6.43) & (30.57) & (103.07) & (16.01) \\\\\n & Average Bid-Ask Spread & \\{0.38\\} & \\{1.00\\} & \\{4.11\\} & \\{0.69\\} \\\\\n \\hline\n \\multirow{3}{*}{$\\geq 1.06$} & Number of contracts & 319262 & 169002 & 118009 & 606273 \\\\\n & Average Price & (3.51) & (12.23) & (37.12) & (12.48) \\\\\n & Average Bid-Ask Spread & \\{0.41\\} & \\{0.88\\} & \\{3.22\\} & \\{1.09\\} \\\\\n \\hline\n \\multirow{3}{*}{Total} & Number of contracts & 1960509 & 532042 & 246661 & 2739212 \\\\\n & Average Price & (60.02) & (98.49) & (168.32) & (77.24) \\\\\n & Average Bid-Ask Spread & \\{1.20\\} & \\{1.46\\} & \\{4.60\\} & \\{1.55\\} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Descriptive (summary) statistics for call option data. The total statistic is computed through the summation of the number of contracts and by taking a weighted average with respect to the number of contracts for the average price and average bid-ask spread. It is noteworthy that our raw data did not contain option price observations outside of the arbitrage boundaries.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Physics-Informed Convolutional Transformer for Predicting Volatility Surface", "authors": ["Soohan Kim", "Seok-Bae Yun", "Hyeong-Ohk Bae", "Muhyun Lee", "Youngjoon Hong"], "url": "https://arxiv.org/abs/2209.10771v3", "attribution": "\"Physics-Informed Convolutional Transformer for Predicting Volatility Surface\" by Soohan Kim, Seok-Bae Yun, Hyeong-Ohk Bae, Muhyun Lee, and Youngjoon Hong, arXiv:2209.10771v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2309.01565v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{S\\&P 500 Volatility Forecasting: Diebold-Mariano Test with HAR as the Base Model}\n\\begin{tabular}{lrrrr}\n\\toprule\n Model & MSE Loss & MSE p-value & MAD Loss & MAD p-value \\\\\n\\midrule\n $\\sigma$-Cell & 0.018927 & 4.610e-04 & 2.902154 & 5.639e-04 \\\\\n $\\sigma$-Cell-N & 0.017707 & 1.192e-03 & 2.775868 & 3.550e-03 \\\\\n $\\sigma$-Cell-NTV & 0.017764 & 1.693e-03 & 2.856639 & 1.175e-03 \\\\\n $\\sigma$-Cell-RL & 0.018542 & 1.203e-04 & 2.868097 & 1.916e-03 \\\\\n$\\sigma$-Cell-RLTV & 0.013536 & 2.247e-01 & 2.494049 & 2.589e-01 \\\\\n GARCH(1,1) & 0.015927 & 3.345e-03 & 2.625849 & 1.337e-02 \\\\\n EGARCH & 0.016999 & 2.934e-04 & 2.806154 & 6.765e-05 \\\\\n TARCH & 0.015689 & 4.127e-02 & 2.581056 & 6.787e-02 \\\\\n GJR-GARCH & 0.014825 & 1.968e-02 & 2.493900 & 1.927e-01 \\\\\n HAR & 0.011489 & - & 2.331576 & - \\\\\n SV & 6.781546 & 2.444e-34 & 68.241853 & 3.198e-63 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Introducing the $σ$-Cell: Unifying GARCH, Stochastic Fluctuations and Evolving Mechanisms in RNN-based Volatility Forecasting", "authors": ["German Rodikov", "Nino Antulov-Fantulin"], "url": "https://arxiv.org/abs/2309.01565v1", "attribution": "\"Introducing the $σ$-Cell: Unifying GARCH, Stochastic Fluctuations and Evolving Mechanisms in RNN-based Volatility Forecasting\" by German Rodikov and Nino Antulov-Fantulin, arXiv:2309.01565v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11899v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n \\hline\n Controller & Parameters System & Parameters Specification \\\\ \\hline\n P (no additional FCR-Control) & $D_i$ & $D, H$ \\\\ \\hline\n PI & $D_i, K_i$ & $D, K, H$ \\\\ \\hline\n PLI & $D_i, G_i, T_i$ & $D, G, T, H$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Summary of the tuneable parameters for the system and the specification.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Probabilistic Behavioral Aggregation: A Case Study on the Nordic Power Grid", "authors": ["Anna Büttner", "Frank Hellmann"], "url": "https://arxiv.org/abs/2412.11899v1", "attribution": "\"Probabilistic Behavioral Aggregation: A Case Study on the Nordic Power Grid\" by Anna Büttner and Frank Hellmann, arXiv:2412.11899v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.17375v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{r|c|c|c|}\n & \\multicolumn{3}{c|}{\\textbf{Fifty-year mobility type}} \\\\\n & $20 \\to 50$ & $50 \\to 80$ & $20 \\to 80$ \\\\\n \\hline \\hline \n Curved $R(p)$ with randomness & 3.54\\% & 1.92\\% & 0.005\\% \\\\\n Flat $R(p)$ with randomness & 16.05\\% & 9.78\\% & 1.13\\%\n \\end{tabular}\n\\caption{Mobility results for the given models. The 50-year $(P_1 \\to P_2)$-Mobility is the probability that a member of the $P_1$-percentile at time zero has an income level above the $P_2$-percentile at a final time $T$. Results are from using the parameter choices listed in .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Inequality and Mobility in a Minimal Model for Evolving Income Distributions", "authors": ["Scott A. McKinley", "Gary A. Hoover"], "url": "https://arxiv.org/abs/2503.17375v1", "attribution": "\"Inequality and Mobility in a Minimal Model for Evolving Income Distributions\" by Scott A. McKinley and Gary A. Hoover, arXiv:2503.17375v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.03797v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|cc|cc|cc|cc|cc|cc}\n \\hline\n & &\\multicolumn{6}{c|}{CS } &\\multicolumn{6}{c}{DS } \\\\ \\hline\n & &\\multicolumn{2}{c|}{Total}&\\multicolumn{2}{c|}{L-Markers}&\\multicolumn{2}{c|}{Fixed covariates } &\\multicolumn{2}{c|}{Total}&\\multicolumn{2}{c|}{L-Markers }&\\multicolumn{2}{c}{Fixed covariates } \\\\ \\hline\n & & Est. & SD. & Est. & SD. & Est. & SD. & Est. & SD. & Est. & SD. & Est. & SD. \\\\ \n \\hline\n \\multicolumn{14}{c}{ $\\rho=0.7,~\\sigma^2=0.5,~\\alpha^*=0.5,~\\gamma^*=0.5$} \\\\\n \\hline\nBF & TPR & 0.787 & 0.115 & 0.704 & 0.174 & 0.871 & 0.117 & 0.820 & 0.099 & 0.760 & 0.158 & 0.879 & 0.100 \\\\ \n& FPR & 0.028 & 0.023 & 0.049 & 0.060 & 0.021 & 0.025 & 0.053 & 0.029 & 0.097 & 0.088 & 0.040 & 0.032 \\\\ \n& MCC & 0.797 & 0.104 & 0.697 & 0.179 & 0.862 & 0.093 & 0.771 & 0.095 & 0.683 & 0.188 & 0.822 & 0.091 \\\\ \n \\hline\nlBFDR & TPR & 0.516 & 0.103 & 0.371 & 0.159 & 0.660 & 0.130 & 0.584 & 0.117 & 0.450 & 0.189 & 0.719 & 0.129 \\\\ \n& FPR & 0.001 & 0.005 & 0.001 & 0.011 & 0.001 & 0.005 & 0.005 & 0.010 & 0.012 & 0.030 & 0.003 & 0.008 \\\\ \n& MCC & 0.664 & 0.076 & 0.494 & 0.157 & 0.780 & 0.088 & 0.704 & 0.087 & 0.540 & 0.179 & 0.813 & 0.089 \\\\ \n\\hline\n \\multicolumn{14}{c}{$\\rho=0.1,~\\sigma^2=0.5,~\\alpha^*=0.5,~\\gamma^*=0.5$} \\\\\n\\hline\nBF & TPR & 0.941 & 0.054 & 1.000 & 0.000 & 0.881 & 0.109 & 0.947 & 0.054 & 1.000 & 0.000 & 0.894 & 0.108 \\\\ \n& FPR & 0.024 & 0.021 & 0.024 & 0.044 & 0.024 & 0.024 & 0.037 & 0.030 & 0.040 & 0.056 & 0.036 & 0.034 \\\\ \n& MCC & 0.913 & 0.051 & 0.973 & 0.050 & 0.860 & 0.090 & 0.892 & 0.069 & 0.954 & 0.063 & 0.840 & 0.109 \\\\ \n\\hline\nlBFDR & TPR & 0.844 & 0.064 & 1.000 & 0.000 & 0.688 & 0.127 & 0.858 & 0.056 & 1.000 & 0.000 & 0.717 & 0.112 \\\\ \n& FPR & 0.001 & 0.004 & 0.001 & 0.011 & 0.001 & 0.005 & 0.003 & 0.007 & 0.006 & 0.021 & 0.002 & 0.008 \\\\ \n& MCC & 0.895 & 0.042 & 0.998 & 0.013 & 0.800 & 0.083 & 0.900 & 0.042 & 0.994 & 0.024 & 0.814 & 0.078 \\\\ \n\\hline\n \\multicolumn{14}{c}{$\\rho=0.1,~\\sigma^2=1,~\\alpha^*=0.5,~\\gamma^*=0.5$} \\\\\n\\hline\nBF & TPR & 0.929 & 0.058 & 1.000 & 0.000 & 0.858 & 0.116 & 0.939 & 0.057 & 1.000 & 0.000 & 0.877 & 0.114 \\\\ \n& FPR & 0.025 & 0.022 & 0.028 & 0.043 & 0.024 & 0.027 & 0.041 & 0.029 & 0.062 & 0.067 & 0.034 & 0.035 \\\\ \n& MCC & 0.903 & 0.054 & 0.967 & 0.050 & 0.848 & 0.095 & 0.880 & 0.066 & 0.930 & 0.073 & 0.834 & 0.113 \\\\ \n\\hline\nlBFDR & TPR & 0.828 & 0.051 & 0.995 & 0.025 & 0.660 & 0.104 & 0.853 & 0.053 & 0.998 & 0.018 & 0.708 & 0.112 \\\\ \n& FPR & 0.003 & 0.009 & 0.005 & 0.020 & 0.003 & 0.011 & 0.002 & 0.006 & 0.005 & 0.020 & 0.001 & 0.005 \\\\ \n& MCC & 0.879 & 0.038 & 0.990 & 0.030 & 0.775 & 0.079 & 0.899 & 0.037 & 0.992 & 0.027 & 0.813 & 0.074 \\\\ \n\\hline\n\\multicolumn{14}{c}{$\\rho=0.1,~\\sigma^2=0.5,~\\alpha^*=1,~\\gamma^*=1$} \\\\\\hline\nBF & TPR & 0.999 & 0.009 & 1.000 & 0.000 & 0.998 & 0.018 & 0.999 & 0.009 & 1.000 & 0.000 & 0.998 & 0.018 \\\\ \n& FPR & 0.035 & 0.026 & 0.037 & 0.051 & 0.034 & 0.031 & 0.060 & 0.039 & 0.057 & 0.070 & 0.061 & 0.043 \\\\ \n& MCC & 0.933 & 0.048 & 0.958 & 0.058 & 0.914 & 0.075 & 0.890 & 0.064 & 0.936 & 0.077 & 0.856 & 0.088 \\\\ \n\\hline\nlBFDR & TPR & 0.989 & 0.030 & 1.000 & 0.000 & 0.978 & 0.060 & 0.988 & 0.025 & 1.000 & 0.000 & 0.975 & 0.051 \\\\ \n& FPR & 0.003 & 0.008 & 0.002 & 0.012 & 0.003 & 0.008 & 0.007 & 0.012 & 0.008 & 0.025 & 0.007 & 0.013 \\\\ \n& MCC & 0.987 & 0.026 & 0.998 & 0.014 & 0.978 & 0.047 & 0.977 & 0.032 & 0.990 & 0.029 & 0.965 & 0.053 \\\\ \n\\hline\n\\multicolumn{14}{c}{$\\rho=0.1,~\\sigma^2=1,~\\alpha^*=1,~\\gamma^*=1$} \\\\\\hline\nBF & TPR & 0.996 & 0.016 & 1.000 & 0.000 & 0.992 & 0.032 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\\\ \n& FPR & 0.038 & 0.026 & 0.053 & 0.056 & 0.034 & 0.027 & 0.062 & 0.025 & 0.081 & 0.056 & 0.057 & 0.029 \\\\ \n& MCC & 0.924 & 0.045 & 0.939 & 0.063 & 0.908 & 0.064 & 0.885 & 0.041 & 0.908 & 0.060 & 0.862 & 0.062 \\\\ \n \\hline\nlBFDR & TPR & 0.979 & 0.038 & 1.000 & 0.000 & 0.958 & 0.076 & 0.981 & 0.029 & 1.000 & 0.000 & 0.963 & 0.058 \\\\ \n& FPR & 0.003 & 0.007 & 0.006 & 0.021 & 0.002 & 0.006 & 0.010 & 0.011 & 0.019 & 0.036 & 0.007 & 0.011 \\\\ \n& MCC & 0.981 & 0.028 & 0.994 & 0.025 & 0.970 & 0.051 & 0.968 & 0.027 & 0.977 & 0.042 & 0.958 & 0.046 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Two-stage Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers and Competing Risk Outcomes", "authors": ["Taban Baghfalaki", "Reza Hashemi", "Christophe Tzourio", "Catherine Helmer", "Helene Jacqmin-Gadda"], "url": "https://arxiv.org/abs/2412.03797v1", "attribution": "\"A Two-stage Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers and Competing Risk Outcomes\" by Taban Baghfalaki, Reza Hashemi, Christophe Tzourio, Catherine Helmer, and Helene Jacqmin-Gadda, arXiv:2412.03797v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2304.00544v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Targeted Moments, Excess Mobility Model}\n\\begin{tabular}{lcclcc}\n\\toprule\n\\toprule\nMoment & Model & Data & Moment & Model & Data \\\\ \\cmidrule(lr){1-3} \\cmidrule(lr){4-6}\nAgg. output per worker mean & 1.005 & 1.000 & Rel. separation rate young/prime-aged & 2.134 & 1.994 \\\\\nAgg. output per worker persistence, $\\rho_{outpw}$ & 0.761 & 0.753 & Rel. separation rate recent hire/all & 5.230 & 4.944\\\\\nAgg. output per worker st. dev., $\\sigma_{outpw}$ & 0.0094 & 0.0094 & Prob (unemp. within 3 yr for empl.) & 0.148 & 0.122 \\\\\nMean unemployment & 0.0355 & 0.0355 & Empirical elasticity matching function & 0.522 & 0.500 \\\\\nAverage u. duration movers/stayers & 1.211 & 1.139 & 5-year OLS return to occ. tenure & 0.150 & 0.154 \\\\\nRepeat mobility: occ. stay after stay & 0.600 & 0.634 & 10-year OLS return to occ. tenure & 0.230 & 0.232 \\\\\nOcc. mobility young/prime-aged & 1.162 & 1.163 \\\\\nOcc. mobility-duration profiles & \\multicolumn{2}{c}{Fig 5a,b,c} & U. survival profiles & \\multicolumn{2}{c}{Fig 5d,e} \\\\%\\hline\n\\bottomrule\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Unemployment and Endogenous Reallocation over the Business Cycle", "authors": ["Carlos Carrillo-Tudela", "Ludo Visschers"], "url": "https://arxiv.org/abs/2304.00544v1", "attribution": "\"Unemployment and Endogenous Reallocation over the Business Cycle\" by Carlos Carrillo-Tudela and Ludo Visschers, arXiv:2304.00544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.04003v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Early Positioning of Sandwich Attacks}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\hline\nCategory & Mempool Position Prob.\t& Block Position Prob.\t& T-statistic\t& P-value \\\\\n\\hline \\hline\nFront Run\t& 0.4995\t& 0.0569\t& 16.9385\t& (0.0000) \\\\\nSandwich Middle\t& 0.4952\t& 0.0576\t& 7.8495\t & (0.0000) \\\\\nBack Run\t& 0.4979\t& 0.0749\t& 16.0773\t& (0.0000) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering", "authors": ["Bruce Mizrach", "Nathaniel Yoshida"], "url": "https://arxiv.org/abs/2508.04003v1", "attribution": "\"The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering\" by Bruce Mizrach and Nathaniel Yoshida, arXiv:2508.04003v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.16273v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\n\\hline\n Motion & Precision & Recall & F1 \\\\\n\\hline\nTurn left & 0.9757 & 0.9331 & 0.9539 \\\\\n\\hline\nTurn right & 0.9483 & 0.9670 & 0.9576 \\\\\n\\hline\nPick up object & 0.9861 & 0.9658 & 0.9758 \\\\\n\\hline\nBackwards & 0.9723 & 0.9565 & 0.9643 \\\\\n\\hline\nForwards & 0.9398 & 0.9969 & 0.9675 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{CNN performance metrics.}}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals", "authors": ["Silas Ruhrberg Estévez", "Josée Mallah", "Dominika Kazieczko", "Chenyu Tang", "Luigi G. Occhipinti"], "url": "https://arxiv.org/abs/2411.16273v1", "attribution": "\"Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals\" by Silas Ruhrberg Estévez, Josée Mallah, Dominika Kazieczko, Chenyu Tang, and Luigi G. Occhipinti, arXiv:2411.16273v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.13495v1_tex_table21.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average results of the models with the focal-R-MSE loss with $\\beta=2$ and $\\theta=1$ for two, three, and six lead months SSTA and MHW forecasts.}\n\\begin{tabular}{llllll}\n\\textbf{Across All Locations} & \\textbf{MSE↓} & \\textbf{CSI↑} & \\textbf{CSI 80↑} & \\textbf{Training Time↓} & \\textbf{PUR↓} \\\\ \\hline\nAverage (two lead months) & \\textbf{0.5240} & 0.0 & 0.2202 & 26.128 & 0\\% \\\\\nAverage (three lead months) & \\textbf{0.6484} & 0.0 & 0.0151 & 26.3625 & 48.3\\% \\\\\nAverage (six lead months) & \\textbf{0.7209} & 0.0 & 0.0 & 26.1644 & 99.2\\%\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models", "authors": ["Ding Ning", "Varvara Vetrova", "Sébastien Delaux", "Rachael Tappenden", "Karin R. Bryan", "Yun Sing Koh"], "url": "https://arxiv.org/abs/2502.13495v1", "attribution": "\"A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models\" by Ding Ning, Varvara Vetrova, Sébastien Delaux, Rachael Tappenden, Karin R. Bryan, and Yun Sing Koh, arXiv:2502.13495v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.13143v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Weighted-F1 scores of the four models in the final ensemble evaluated on the local validation set. 'Efficient Net' abbreviate as 'EffNet' for readability.}\n\\begin{tabular}{|l|l|l|}\n\\hline\n\\textbf{Model} & \\textbf{Accuracy} & \\textbf{Weighted-F1 score} \\\\\n\\hline\nEffNet-B3 (\\textit{Baseline}) & 0.95970 & 0.95911 \\\\\nEffNet-B4 (\\textit{Baseline}) & 0.96147 & 0.96104 \\\\\nEffNet-B3 VAE (mixup $\\alpha$=0.5) & 0.96767 & 0.96731 \\\\\nEffNet-B4 VAE (mixup $\\alpha$=0.5) & 0.96988 & 0.96909 \\\\\nEffNet-B3 VAE (mixup $\\alpha$=1.0) & 0.96944 & 0.96953 \\\\\nEffNet-B4 VAE (mixup $\\alpha$=1.0) & 0.96767 & 0.96738 \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Robust Pollen Imagery Classification with Generative Modeling and Mixup Training", "authors": ["Jaideep Murkute"], "url": "https://arxiv.org/abs/2102.13143v1", "attribution": "\"Robust Pollen Imagery Classification with Generative Modeling and Mixup Training\" by Jaideep Murkute, arXiv:2102.13143v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.21174v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|lll|lllll}\n&\\multicolumn{3}{c}{New Scheme} &\\multicolumn{5}{c}{ILUTP($\\tau$)} \\\\\n\\# & $\\tau_{large}$ & $\\tau_{mix}$ & $\\tau_{small}$ & $10^{-5}$ & $10^{-4}$ & $10^{-3}$ &$10^{-2}$ & $10^{-1}$ \\\\ \\hline\n1 & 3 & 2 & 2& 3 &$\\dagger$ &$\\dagger$ &$\\dagger$ &$\\dagger$ \\\\\n2 & 2 & 2 & 2 & 2 & $\\dagger$ &$\\dagger$ &$\\dagger$ &$\\dagger$ \\\\\n3 & 2 & 1 & 1 & 2 & 4 & 6 &12 &13 \\\\\n4 & 2 & 2 & 2&$1^\\star$ &$2^\\star$ & 3 & 6 & $32^\\star$ \\\\\n5 & 2 & 3 & 2 & $1^\\star$ &$1^\\star$ & 3 &7 &$44^\\star$ \\\\\n6 & 3 & 3 & 1 & $\\dagger$ & 2 & 3 & 7 &$\\ddagger$ \\\\\n7 & 3 & 3 & 2 &$\\dagger$ & $\\dagger$ & $\\ddagger$ & $9^\\star$ &$\\ddagger$ \\\\\n8 & 4 & 3 & 2 & $\\dagger$ & $\\dagger$ & $\\ddagger$ & 10 &$\\dagger$ \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Number of outer fGMRES/GMRES iterations for the generalized case}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust iterative methods for linear systems with saddle point structure", "authors": ["Murat Manguoğlu", "Volker Mehrmann"], "url": "https://arxiv.org/abs/2502.21174v1", "attribution": "\"Robust iterative methods for linear systems with saddle point structure\" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06986v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Training and test RMSE for the random forest and gam fits to the bike data. The gam has better test set performance than the random forest.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrr}\n \\hline\n & train & test \\\\ \n \\hline\nRF & 438.1 & 748.1 \\\\ \n GAM & 573.1 & 670.2 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Interactive slice visualization for exploring machine learning models", "authors": ["Catherine B. Hurley", "Mark O'Connell", "Katarina Domijan"], "url": "https://arxiv.org/abs/2101.06986v2", "attribution": "\"Interactive slice visualization for exploring machine learning models\" by Catherine B. Hurley, Mark O'Connell, and Katarina Domijan, arXiv:2101.06986v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.08173v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average PSNR, LPIPS, and CD values with a scaling factor of 4. The best ones are highlighted in bold.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\t\t\\hline\n\t\t\\textbf{Dataset} & \\textbf{Method} & \\textbf{PSNR} $\\uparrow$ & \\textbf{LPIPS} $\\downarrow$ & \\textbf{CD} $\\downarrow$ \\\\\n\t\t\\hline\n\t\t\\multirow{5}{*}{Tangaroa} \n\t\t& TI & 36.6797 & 0.1439 & 2.1923 \\\\\n\t\t& SRGAN & 39.8904 & 0.1115 & 2.1292 \\\\\n\t\t& SSR-VFD & 38.9527 & 0.1445 & 2.5566 \\\\\n\t\t& PSRFlow & 41.7391 & 0.0970 & 1.9757 \\\\\n\t\t& \\textbf{CD-TVD} & \\textbf{45.1457} & \\textbf{0.0425} & \\textbf{1.9320} \\\\\n\t\t\\hline\n\t\t\\multirow{5}{*}{Half Cylinder} \n\t\t& TI & 40.0438 & 0.0411 & 1.6275 \\\\\n\t\t& SRGAN & 41.8104 & 0.0340 & 1.7277 \\\\\n\t\t& SSR-VFD & 43.3012 & 0.0660 & 1.5289 \\\\\n\t\t& PSRFlow & 43.3228 & 0.0514 & 1.6310 \\\\\n\t\t& \\textbf{CD-TVD} & \\textbf{46.4046} & \\textbf{0.0246} & \\textbf{1.1318} \\\\\n\t\t\\hline\n\t\t\\multirow{5}{*}{Shock Vortex} \n\t\t& TI & 29.4103 & 0.0305 & 1.2840 \\\\\n\t\t& SRGAN & 32.2311 & 0.0102 & 2.0138 \\\\\n\t\t& SSR-VFD & 31.8848 & 0.0126 & 1.4101 \\\\\n\t\t& PSRFlow & 37.4574 &0.0089 & 1.1755 \\\\\n\t\t& \\textbf{CD-TVD} & \\textbf{43.8938} & \\textbf{0.0016} & \\textbf{1.0405} \\\\\n\t\t\\hline\n\t\t\\multirow{5}{*}{Hurricane} \n\t\t& TI & 38.6966 & 0.0802 & 3.1279 \\\\\n\t\t& SRGAN & 43.6950 & 0.0696 & 2.2285 \\\\\n\t\t& SSR-VFD & 44.8119 & 0.0584 & 2.6773 \\\\\n\t\t& PSRFlow & 45.6459 & 0.0632 & 1.7936 \\\\\n\t\t& \\textbf{CD-TVD} & \\textbf{48.0591} & \\textbf{0.0494} & \\textbf{1.4345} \\\\\n\t\t\\hline\n\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "CD-TVD: Contrastive Diffusion for 3D Super-Resolution with Scarce High-Resolution Time-Varying Data", "authors": ["Chongke Bi", "Xin Gao", "Jiangkang Deng", "Guan Li", "Jun Han"], "url": "https://arxiv.org/abs/2508.08173v2", "attribution": "\"CD-TVD: Contrastive Diffusion for 3D Super-Resolution with Scarce High-Resolution Time-Varying Data\" by Chongke Bi, Xin Gao, Jiangkang Deng, Guan Li, and Jun Han, arXiv:2508.08173v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.01100v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Objective results in CoVoC Challenge 2024}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n \\textbf{System} & \\textbf{CER(\\%)} & \\textbf{SECS} \\\\\n \\midrule\n Proposed & 10.29 & 0.797 \\\\\n \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "The Codec Language Model-based Zero-Shot Spontaneous Style TTS System for CoVoC Challenge 2024", "authors": ["Shuoyi Zhou", "Yixuan Zhou", "Weiqin Li", "Jun Chen", "Runchuan Ye", "Weihao Wu", "Zijian Lin", "Shun Lei", "Zhiyong Wu"], "url": "https://arxiv.org/abs/2412.01100v2", "attribution": "\"The Codec Language Model-based Zero-Shot Spontaneous Style TTS System for CoVoC Challenge 2024\" by Shuoyi Zhou, Yixuan Zhou, Weiqin Li, Jun Chen, Runchuan Ye, Weihao Wu, Zijian Lin, Shun Lei, and Zhiyong Wu, arXiv:2412.01100v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07282v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c}\n\\toprule\nTeam Name & Valence \\\\\n\\hline\nbiic & $\\textbf{0.6953}^{\\star}$ \\\\\nVoinosis & 0.6941 \\\\\nSRPOL & 0.6932 \\\\\nSRPOL & 0.6906 \\\\\nbiic & 0.6880 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Top-5 rank in emotional attributes (valence) among the \\textit{Test3} testing set.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Lessons Learnt: Revisit Key Training Strategies for Effective Speech Emotion Recognition in the Wild", "authors": ["Jing-Tong Tzeng", "Bo-Hao Su", "Ya-Tse Wu", "Hsing-Hang Chou", "Chi-Chun Lee"], "url": "https://arxiv.org/abs/2508.07282v1", "attribution": "\"Lessons Learnt: Revisit Key Training Strategies for Effective Speech Emotion Recognition in the Wild\" by Jing-Tong Tzeng, Bo-Hao Su, Ya-Tse Wu, Hsing-Hang Chou, and Chi-Chun Lee, arXiv:2508.07282v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.03115v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{\\textsc{RELand}} performance using \\textbf{blockV} validation on Bolívar department.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|cccc}\n\\toprule\nMunicipality & Imbalance\\% & ROC (↑) & PR (↑) & Height (↓) & rHeight (↓) \\\\ \\midrule\nCartagena de Indias & 0.04 & 0.94 & 0.00 & 0.06 & 140.00 \\\\\nCórdoba & 0.08 & 0.95 & 0.27 & 0.10 & 116.50 \\\\\nSan Juan Nepomuceno & 0.04 & 0.94 & 0.00 & 0.06 & 151.00 \\\\\nSanta Rosa del Sur & 0.04 & 0.92 & 0.00 & 0.30 & 731.00 \\\\\nZambrano & 1.94 & 0.92 & 0.24 & 1.84 & 95.21 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization", "authors": ["Mateo Dulce Rubio", "Siqi Zeng", "Qi Wang", "Didier Alvarado", "Francisco Moreno", "Hoda Heidari", "Fei Fang"], "url": "https://arxiv.org/abs/2311.03115v1", "attribution": "\"RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization\" by Mateo Dulce Rubio, Siqi Zeng, Qi Wang, Didier Alvarado, Francisco Moreno, Hoda Heidari, and Fei Fang, arXiv:2311.03115v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.00162v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\toprule\n & \\multicolumn{5}{c}{Equivalent HF function evaluations $N$} \\\\\n \\cmidrule(l){2-6}\n Method & $N=100$ & $N=1000$ & $N=10000$ & $N=20000$ & $N=30000$ \\\\ \n \\midrule\n GD & $2.48 \\pm 0.00$ & $2.48 \\pm 0.00$ & $0.62 \\pm 0.00$ & $0.43 \\pm 0.00$ & $0.34 \\pm 0.00$ \\\\ %\\hline\n CD & $\\mathbf{1.48 \\pm 0.00}$ & $1.48 \\pm 0.00$ & $0.70 \\pm 0.00$ & $0.49 \\pm 0.00$ & $0.40 \\pm 0.00$ \\\\ %\\hline\n FS-SSD & $2.47 \\pm 0.00$ & $2.45 \\pm 0.00$ & $2.26 \\pm 0.00$ & $2.08 \\pm 0.00$ & $1.92 \\pm 0.00$ \\\\ %\\hline\n SPSA & $1.95 \\pm 0.05$ & $\\mathbf{0.61 \\pm 0.04}$ & $0.19 \\pm 0.00$ & $0.14 \\pm 0.00$ & $0.12 \\pm 0.00$ \\\\ %\\hline\n GS & $2.47 \\pm 0.00$ & $2.46 \\pm 0.00$ & $2.36 \\pm 0.00$ & $2.25 \\pm 0.00$ & $2.15 \\pm 0.00$ \\\\ %\\hline\n HF-SSD & $2.07 \\pm 0.15$ & $0.77 \\pm 0.06$ & $0.22 \\pm 0.02$ & $0.17 \\pm 0.02$ & $0.14 \\pm 0.02$ \\\\ %\\hline\n BF-SSD & $2.00 \\pm 0.05$ & $0.66 \\pm 0.03$ & $\\mathbf{0.17 \\pm 0.00}$ & $\\mathbf{0.11 \\pm 0.00}$ & $\\mathbf{0.09 \\pm 0.00}$ \\\\ %\\hline\n VR-SSD & $2.47 \\pm 0.00$ & $2.10 \\pm 0.02$ & $0.63 \\pm 0.01$ & $0.43 \\pm 0.00$ & $0.35 \\pm 0.00$ \\\\ %\\hline\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance values (mean $\\pm$ std over 10 runs) showing the objective function for different optimization methods at various HF function evaluations $N$ with $\\ell=20$ and $c=0.99$. The minimum values in each \\emph{column} are highlighted in bold.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search", "authors": ["Nuojin Cheng", "Alireza Doostan", "Stephen Becker"], "url": "https://arxiv.org/abs/2505.00162v1", "attribution": "\"Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search\" by Nuojin Cheng, Alireza Doostan, and Stephen Becker, arXiv:2505.00162v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.12016v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Application Model Runtime}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n\\toprule\nSubgroup & Sample Size & Runtime$^a$\\\\\n\\midrule\nHispanic & 11959 & 6.28\\\\\nNon-Hispanic White & 46019 & 23.67\\\\\nNon-Hispanic Black & 5635 & 3.69\\\\\nNon-Hispanic Asian and Pacific Islander & 5521 & 3.72\\\\\nNon-Hispanic Other & 1886 & 1.26\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Estimating Heterogeneous Exposure Effects in the Case-Crossover Design using BART", "authors": ["Jacob Englert", "Stefanie Ebelt", "Howard Chang"], "url": "https://arxiv.org/abs/2311.12016v2", "attribution": "\"Estimating Heterogeneous Exposure Effects in the Case-Crossover Design using BART\" by Jacob Englert, Stefanie Ebelt, and Howard Chang, arXiv:2311.12016v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01532v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classification}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccc}\n\\bf METHOD & \\multicolumn{2}{c}{\\bf Cora} & \\multicolumn{2}{c}{\\bf Citeseer} & \\multicolumn{2}{c}{\\bf Pubmed} & \\multicolumn{2}{c}{\\bf Reddit} & \\multicolumn{2}{c}{\\bf Amazon2M} \\\\\n\\cline{2-11} \n& \\bf Test & \\bf Cold-Start & \\bf Test & \\bf Cold-Start & \\bf Test & \\bf Cold-Start & \\bf Test & \\bf Cold-Start & \\bf Test & \\bf Cold-Start \\\\\n\\hline\nSpectral-GCN & 87.94 ± 0.85 & NS & 77.92 ± 0.61 & NS & 86.20 ± 0.41 & NS & OOM & NS & OOM & NS \\\\\nNAGphoremer & 89.55 ± 0.48 & NS & 76.32 ± 0.52 & NS & 88.30 ± 0.29 & NS & 93.75 ± 0.03 & NS & 77.43 ± 0.24 & NS \\\\\n\\hline\nG-SAGE & 83.92 ± 1.25 & 66.02 ± 1.18 & 71.78 ± 2.67 & 51.46 ± 1.30 & 82.16 ± 1.92 & 69.87 ± 1.10 & 94.32 ± 0.00 & 85.63 ± 0.66 & OOM & OOM \\\\\nC-BREW & 84.66 ± 0.00 & 69.62 ± 0.00 & 71.18 ± 0.00 & 53.17 ± 0.00 & 86.81 ± 0.00 & 72.33 ± 0.00 & OOM & OOM & OOM & OOM \\\\\nGraph-MLP & - & 65.00 & - & 53.82 & - & 71.22 & - & - & - & - \\\\\n\\hline\nSPARC-GCN & 84.46 ± 1.51 & 73.88 ± 6.27 & 69.44 ± 4.23 & 63.66 ± 0.00 & 86.46 ± 3.77 & 82.78 ± 2.05 & 93.04 ± 0.87 & 91.46 ± 0.92 & 73.83 ± 0.20 & 72.33 ± 0.57 \\\\\nSPARCphormer & 79.41 ± 1.65 & 68.49 ± 0.89 & 73.80 ± 0.43 & 66.35 ± 0.92 & 85.12 ± 0.46 & 84.66 ± 0.25 & 94.67 ± 0.76 & 90.70 ± 0.43 & 74.58 ± 0.19 & 74.58 ± 0.19 \\\\\nSAMBA & 81.48 ± 1.12 & 69.33 ± 3.96 & 71.99 ± 0.71 & 70.10 ± 2.75 & 86.40 ± 0.90 & 82.61 ± 0.65 & 93.39 ± 0.23 & 90.83 ± 0.26 & 72.72 ± 0.33 & 72.02 ± 1.34 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "SPARC: Spectral Architectures Tackling the Cold-Start Problem in Graph Learning", "authors": ["Yahel Jacobs", "Reut Dayan", "Uri Shaham"], "url": "https://arxiv.org/abs/2411.01532v2", "attribution": "\"SPARC: Spectral Architectures Tackling the Cold-Start Problem in Graph Learning\" by Yahel Jacobs, Reut Dayan, and Uri Shaham, arXiv:2411.01532v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.16531v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of participants' domain, expertise and interview length}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllc}\n\t\t\\toprule\n\t\t\\textbf{Code} & \\textbf{Perspective} & \\textbf{Role} & \\textbf{Sector} & \\textbf{Interview length} \\\\\n\t\t & & & & (minutes) \\\\\n\t\t\\midrule\n\t\tP1 & Practitioner & Customer manager & Software vendor & 61 \\\\\n\t\tP2 & Practitioner & Data Scientist & Retail & 55 \\\\\n\t\tP3 & Academic & Professor & Computer Science & 55 \\\\\n\t\tP4 & Academic & Research Fellow & Population and Public health & 53 \\\\\n\t\tP5 & Practitioner & Emerging Technologies lead & Goverment & 60 \\\\\n\t\tP6 & Academic & Professor & Business & 54 \\\\\n\t\tP7 & Practitioner & Marketing manager & Software vendor & 64 \\\\\n\t\tP8 & Practitioner & Project Manager & Public health & 62 \\\\\n\t\tP9 & Practitioner & Senior analyst & public health supply chain & 47 \\\\\n\t\tP10 & Practitioner & Data Scientist & Retail & 57 \\\\\n\t\tP11 & Practitioner & CEO & Supply chain software & 57 \\\\ \n\t\tP12 & Practitioner & Director & Business & 44 \\\\\n\t\tP13 & Practitioner & Analyst & Global public health & 58 \\\\\n\t\tP14 & Practitioner & CEO & Software company & 40 \\\\\n\t\tP15 & Practitioner & Demand planner & Pharmaceutical & 65 \\\\\n\t\tP16 & Practitioner & Scientific lead & Humanitarian & 54 \\\\\n\t\tP17 & Practitioner & Director for data science and service innovation & Software & 57 \\\\\n\t\tP18 & Practitioner & Product manager & Technology & 56 \\\\\n\t\tP19 & Academic & Professor & Business & 50 \\\\\n\t\tP20 & Practitioner & Data Scientist & Retail & 55 \\\\\n\t\tP21 & Academic & Professor & Infectious disease & 43 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Good intentions, unintended consequences: exploring forecasting harms", "authors": ["Bahman Rostami-Tabar", "Travis Greene", "Galit Shmueli", "Rob J. Hyndman"], "url": "https://arxiv.org/abs/2411.16531v3", "attribution": "\"Good intentions, unintended consequences: exploring forecasting harms\" by Bahman Rostami-Tabar, Travis Greene, Galit Shmueli, and Rob J. Hyndman, arXiv:2411.16531v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04335v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Method signatures from Panorama's aidl file}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|} \n \\hline\n Generic APIs & Bluetooth, Wifi-Direct \\& Cloudlet APIs \\\\\n \\hline \n \\texttt{List<Device> discover\\_nw(service\\_name)} & \\texttt{List<Device> get\\_bt\\_devices} \\\\ \n \\texttt{Device get\\_local(service\\_name)} & \\texttt{create\\_piconet} \\\\ \n \\texttt{Plan perform\\_optimization(task)} & \\texttt{start\\_wd\\_discovery} \\\\ \n \\texttt{Result execute\\_optimization} & \\texttt{List<Device> get\\_wd\\_devices} \\\\ \n \\texttt{Result process\\_local(task)} & \\texttt{ connect\\_wd(device\\_name)} \\\\\n & \\texttt{connect\\_to\\_cloudlet} \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Panorama: A Framework to Support Collaborative Context Monitoring on Co-Located Mobile Devices", "authors": ["Khaled Alanezi", "Xinyang Zhou", "Lijun Chen", "Shivakant Mishra"], "url": "https://arxiv.org/abs/2101.04335v1", "attribution": "\"Panorama: A Framework to Support Collaborative Context Monitoring on Co-Located Mobile Devices\" by Khaled Alanezi, Xinyang Zhou, Lijun Chen, and Shivakant Mishra, arXiv:2101.04335v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10389v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary Statistics of the Beirut Meta-data.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n\\hline\n & \\textbf{Number of apartments} & \\textbf{Mean DSM} & \\textbf{Mean building height} & \\textbf{Area} & \\textbf{Perimeter} & \\textbf{Era} & \\textbf{Built year} & \\textbf{Floors} \\\\ \\hline\n\\textbf{Count} & 535 & 1023 & 1023 & 1257 & 1257 & 711 & 789 & 870 \\\\ \\hline\n\\textbf{Number of nulls} & 831 & 343 & 343 & 109 & 109 & 655 & 577 & 496 \\\\ \\hline\n\\textbf{Mean} & 6.72 & 41.24 & 18.47 & 568.03 & 86.95 & 2.74 & 1954.41 & 4.17 \\\\ \\hline\n\\textbf{Std} & 6.95 & 18.39 & 10.88 & 1484.34 & 76.44 & 0.97 & 36.93 & 2.63 \\\\ \\hline\n\\textbf{Min} & 1 & 3.78 & 1.66 & 17.58 & 17.81 & 1 & 1219 & 1 \\\\ \\hline\n\\textbf{Max} & 60 & 113.70 & 95.65 & 16574.86 & 1062.72 & 5 & 2021 & 26 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge", "authors": ["Ali Ismail", "Mariette Awad"], "url": "https://arxiv.org/abs/2201.10389v1", "attribution": "\"BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge\" by Ali Ismail and Mariette Awad, arXiv:2201.10389v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.17388v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Conditional video generation FVD results on HyperKvasir with different pathological findings, where lower values are better. Bold denotes best performance. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccccc|c}\n\\toprule\n{Method} & {Bar.} & {Cancer} & {Eso.} & {Ecta.} & {Perf.} & {Polyps} & {Ulcer} & {Varices} & {{Avg.}} \\\\\n\\midrule\n SimDA & 3479.1 & 5065.1 & 2041.4 & 3643.6 & 1641.7 & 3656.2 & 3688.0 & 3919.3 & 3391.8 \\\\\n VDM & 1758.8 & 4635.1 & 1366.9 & 2057.6 & 897.0 & 2348.3 & 2172.6 & 1766.5 & 2125.4 \\\\\n VidGPT & 1433.1 & 2965.7 & 955.7 & 1649.4 & 636.3 & 1705.1 & 1616.1 & 1427.7 & 1548.6 \\\\\n \\midrule\n \\textbf{EndoGen} & 402.1\t&908.0\t&286.3\t&628.1\t&300.6\t&423.2\t&496.6\t&612.9&\\textbf{507.2}\n\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "EndoGen: Conditional Autoregressive Endoscopic Video Generation", "authors": ["Xinyu Liu", "Hengyu Liu", "Cheng Wang", "Tianming Liu", "Yixuan Yuan"], "url": "https://arxiv.org/abs/2507.17388v1", "attribution": "\"EndoGen: Conditional Autoregressive Endoscopic Video Generation\" by Xinyu Liu, Hengyu Liu, Cheng Wang, Tianming Liu, and Yixuan Yuan, arXiv:2507.17388v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.11480v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllll}\n\\cline{3-8}\n & & $\\boldsymbol{S_1}$ & $\\boldsymbol{S_2}$ & $\\boldsymbol{S_3}$ & $\\boldsymbol{S_4}$ & $\\boldsymbol{S_5}$ & $\\boldsymbol{S_6}$ \\\\ \\hline\n\\multicolumn{2}{l}{\\textbf{Scenario 1}} & \\textbf{0.08} & \\textbf{0.11} & \\textbf{0.15} & \\textbf{0.3} & \\textbf{0.44} & \\textbf{0.52} \\\\ \\cline{1-8} \n\\multirow{4}{*}{3+3} & Mean sample size & 3.6 & 3.5 & 3.5 & \\textbf{3} & 1.6 & 0.4 \\\\ \\cline{2-8} \n & Logistic-DRtox & 8.6 & 5.9 & 19 & \\textbf{42.2} & 19.6 & 4.7 \\\\\n & Hierarchical-DRtox & 7.5 & 7.6 & 19.1 & \\textbf{43.8} & 18.6 & 3.4 \\\\\n & 3+3 & 13.9 & 16.1 & 32.2 & \\textbf{27.6} & 8.6 & 1.6 \\\\ \\cline{1-8} \n\\multirow{4}{*}{CRM} & Mean sample size & 4.2 & 3.7 & 5.6 & \\textbf{8.8} & 5.6 & 2.1 \\\\ \\cline{2-8} \n & Logistic-DRtox & 0 & 1.2 & 15.5 & \\textbf{64.6} & 15.5 & 3.2 \\\\\n & Hierarchical-DRtox & 0 & 0.8 & 12.8 & \\textbf{64.3} & 19.4 & 2.7 \\\\\n & Logistic CRM & 0 & 1.4 & 15.1 & \\textbf{50.4} & 27.1 & 6 \\\\ \\cline{1-8}\n\\multicolumn{2}{l}{\\textbf{Scenario 2}} & \\textbf{0.15} & \\textbf{0.3} & \\textbf{0.44} & \\textbf{0.52} & \\textbf{0.69} & \\textbf{0.83} \\\\ \\cline{1-8} \n\\multirow{4}{*}{3+3} & Mean sample size &4 & \\textbf{3.6} & 1.8 & 0.5 & 0.1 & 0\\\\ \\cline{2-8} \n & Logistic-DRtox & 27.2 & \\textbf{42.5} & 24.7 & 5.2 & 0.4 & 0 \\\\\n & Hierarchical-DRtox & 29.3 & \\textbf{41.2} & 24.3 & 4.8 & 0.4 & 0 \\\\\n & 3+3 & 57.3 & \\textbf{31} & 9.8 & 1.7 & 0.2 & 0 \\\\ \\cline{1-8} \n\\multirow{4}{*}{CRM} & Mean sample size & 8.7 & \\textbf{11.1} & 7.5 & 2.3 & 0.3 & 0 \\\\ \\cline{2-8} \n & Logistic-DRtox & 14.8 & \\textbf{65.9} & 17.4 & 1.7 & 0.2 & 0 \\\\\n & Hierarchical-DRtox & 12.3 & \\textbf{66.2} & 18.9 & 2.6 & 0 & 0 \\\\\n & Logistic CRM & 12.5 & \\textbf{56} & 26.7 & 4.7 & 0.1 & 0 \\\\ \\cline{1-8}\n\\multicolumn{2}{l}{\\textbf{Scenario 3}} & \\textbf{0.07} & \\textbf{0.11} & \\textbf{0.2} & \\textbf{0.3} & \\textbf{0.42} & \\textbf{0.56} \\\\ \\cline{1-8} \n\\multirow{4}{*}{3+3} & Mean sample size & 3.6 & 3.6 & 3.7 & \\textbf{2.7} & 1.4 & 0.4 \\\\ \\cline{2-8} \n & Logistic-DRtox & 7.8 & 6.4 & 25.2 & \\textbf{34.1} & 21.6 & 4.9 \\\\\n & Hierarchical-DRtox & 5.9 & 7.9 & 27.3 & \\textbf{35.8} & 20.6 & 2.5\\\\\n & 3+3 & 13.1 & 24.4 & 29.5 & \\textbf{24} & 7.7 & 1.3 \\\\ \\cline{1-8} \n\\multirow{4}{*}{CRM} & Mean sample size & 4 & 4 & 6.4 & \\textbf{8} & 5.2 & 2.3 \\\\ \\cline{2-8} \n & Logistic-DRtox & 0.1 & 1.4 & 19.6 & \\textbf{52} & 25.1 & 1.8 \\\\\n & Hierarchical-DRtox & 0.1 & 0.8 & 17.7 & \\textbf{54.4} & 25.9 & 1.1 \\\\\n & Logistic CRM & 0.1 & 2.3 & 20.3 & \\textbf{44.5} & 26.4 & 6.4 \\\\ \\cline{1-8}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian dose-regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics", "authors": ["Emma Gerard", "Sarah Zohar", "Hoai-Thu Thai", "Christelle Lorenzato", "Marie-Karelle Riviere", "Moreno Ursino"], "url": "https://arxiv.org/abs/2011.11480v1", "attribution": "\"Bayesian dose-regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics\" by Emma Gerard, Sarah Zohar, Hoai-Thu Thai, Christelle Lorenzato, Marie-Karelle Riviere, and Moreno Ursino, arXiv:2011.11480v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.02021v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimates of the NB model for IUD with all covariates}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\hline\nIUD-NB & Estimate({ $\\hat{\\boldsymbol\\beta}_2$}) & StdDev & $p$-value \\\\\n\\hline\nIntercept & -29.1300 & 193400 & 0.9999 \\\\\nSmoke & -0.7540 & 0.4080 & 0.0646 \\\\\nAge=2 & 2.4580 & 0.4043 & 0.0000 \\\\\nAge=3 & 2.6110 & 0.6959 & 0.0001 \\\\\nHC=1 & -0.3450 & 0.2736 & 0.2073 \\\\\nHC=2 & -0.2972 & 0.4763 & 0.5326 \\\\\nAFS=2 & -0.7378 & 0.4221 & 0.0804 \\\\\nAFS=3 & -1.3060 & 0.4552 & 0.0041 \\\\\nNSP=2 & 0.1421 & 0.2681 & 0.5959 \\\\\nNSP=3 & -0.8216 & 0.5913 & 0.1647 \\\\\nNSP=4 & -0.5206 & 1.145 & 0.6493 \\\\\nNP=1 & 26.64 & 1.934 & 0.9999 \\\\\nNP=2 & 26.90 & 193400 & 0.9999 \\\\\nDispersion & 0.3630 & & \\\\\n\\hline\n \\multicolumn{2}{l}{AIC = 592.11} & \\multicolumn{2}{ l }{-2log-Lik.= 564.11} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modeling Count Data via Copulas", "authors": ["Hadi Safari-Katesari", "S. Yaser Samadi", "Samira Zaroudi"], "url": "https://arxiv.org/abs/2012.02021v1", "attribution": "\"Modeling Count Data via Copulas\" by Hadi Safari-Katesari, S. Yaser Samadi, and Samira Zaroudi, arXiv:2012.02021v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12251v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|cccc}\n \\hline\n \n Experts & Solar Panels & $T_1$ &$T_2$&$T_3$&$T_4$ \\\\ \\hline\n & $P_1$ &EH&MH&EH & M\\\\\n & $P_2$ &VH &M&VH&M\\\\\n $E_1$ & $P_3$ &MH&L&ML&ML\\\\\n & $P_4$ &VH&MH&H&M\\\\\n & $P_5$ &H&MH&VH&MH\\\\ \\hline\n & $P_1$ &VH&H&VH & MH\\\\\n & $P_2$ &MH&M&H&MH\\\\\n $E_2$ & $P_3$ &M&M&M&ML\\\\\n & $P_4$ &VH&MH&VH&M\\\\\n & $P_5$ &VH&H&VH&M\n \\\\\\hline\n & $P_1$ &EH&VH&H & ML\\\\\n & $P_2$ &H&ML&MH&M\\\\\n $E_3$ & $P_3$ &ML&VL&ML&VL\\\\\n & $P_4$ &H&H&MH&ML\\\\\n & $P_5$ &H&VH&H&H\n \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{ Group decision matrix with linguistic term}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology", "authors": ["Mahmut Can Bozyiğit", "Mehmet Ünver"], "url": "https://arxiv.org/abs/2501.12251v1", "attribution": "\"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology\" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17103v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n $n$ & Error $\\|\\cdot\\|_1$ & Order $\\|\\cdot\\|_1$ & Error\n $\\|\\cdot\\|_{\\infty}$ & Order $\\|\\cdot\\|_{\\infty}$ & $\\%$\n Success \\\\\n \\hline\n 40 & 2.60E$-5$ & $-$ & 1.72E$-4$ & $-$ & 99.73 $\\%$ \\\\\n \\hline\n 80 & 3.25E$-7$ & 6.32 & 1.60E$-6$ & 6.75 & 99.92 $\\%$ \\\\\n \\hline\n 160 & 8.45E$-9$ & 5.27 & 2.31E$-8$ & 6.11 & 99.97 $\\%$ \\\\\n \\hline\n 320 & 2.64E$-10$ & 5.00 & 6.95E$-10$ & 5.06 & 99.99 $\\%$ \\\\\n \\hline\n 640 & 8.26E$-12$ & 5.00 & 2.13E$-11$ & 5.03 & 99.99 $\\%$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Filter without $D(x_i)$ terms, $\\delta=0.2,$ $\\delta'=0.1$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17103v1", "attribution": "\"High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.13388v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model performance summary of AUC for \\textit{TalkingPoint} and \\textit{ALZConnected}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n \\textbf{Model} & \\textbf{\\textit{TalkingPoint}} & \\textbf{\\textit{ALZConnected}} \\\\\n \\midrule\n XGBoost & 0.850 $_{\\pm0.026}$ & 0.867 $_{\\pm0.014}$ \\\\\n Gradient Boosting & 0.847 $_{\\pm0.027}$ & 0.867 $_{\\pm0.021}$ \\\\\n Random Forest & 0.842 $_{\\pm0.025}$ & 0.858 $_{\\pm0.014}$ \\\\\n Extra Trees & 0.837 $_{\\pm0.024}$ & 0.851 $_{\\pm0.021}$ \\\\\n AdaBoost & 0.830 $_{\\pm0.021}$ & 0.848 $_{\\pm0.016}$ \\\\\n Decision Tree & 0.793 $_{\\pm0.026}$ & 0.795 $_{\\pm0.023}$ \\\\\n LR & 0.593 $_{\\pm0.018}$ & 0.601 $_{\\pm0.012}$ \\\\\n SVC & 0.552 $_{\\pm0.017}$ & 0.567 $_{\\pm0.011}$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Catalysts of Conversation: Examining Interaction Dynamics Between Topic Initiators and Commentors in Alzheimer's Disease Online Communities", "authors": ["Congning Ni", "Qingxia Chen", "Lijun Song", "Patricia Commiskey", "Qingyuan Song", "Bradley A. Malin", "Zhijun Yin"], "url": "https://arxiv.org/abs/2412.13388v1", "attribution": "\"Catalysts of Conversation: Examining Interaction Dynamics Between Topic Initiators and Commentors in Alzheimer's Disease Online Communities\" by Congning Ni, Qingxia Chen, Lijun Song, Patricia Commiskey, Qingyuan Song, Bradley A. Malin, and Zhijun Yin, arXiv:2412.13388v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.05765v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Parameter estimates (Est.), standard deviation (SD.), and $95\\%$ credible intervals for the association parameters and the regression coefficients of the explanatory variables for dementia and death. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc|cccc}\n \\hline\n &\\multicolumn{4}{c|}{Dementia } &\\multicolumn{4}{c}{Death} \\\\ \\hline\nBiomarker & Est. & SD. & $2.5\\%$ & $97.5\\%$ & Est. & SD. & $2.5\\%$ & $97.5\\%$ \\\\ \n \\hline\nSBP & -0.129 & 0.089 & -0.310 & 0.043 & -2.616 & 0.128 & -2.870 & -2.371 \\\\ \nDBP & 0.222 & 0.086 & 0.053 & 0.394 & 0.398 & 0.077 & 0.252 & 0.551 \\\\ \nBMI & -0.059 & 0.022 & -0.102 & -0.019 & 0.033 & 0.017 & -0.000 & 0.066 \\\\ \nCESDT & -0.005 & 0.062 & -0.129 & 0.1160 & -0.269 & 0.065 & -0.399 & -0.142 \\\\ \nBENTON & -1.198 & 0.145 & -1.479 & -0.888 & -1.466 & 0.092 & -1.647 & -1.292 \\\\ \nISA & -0.442 & 0.085 & -0.616 & -0.269 & -0.254 & 0.076 & -0.408 & -0.117 \\\\ \nTOTMED & 0.119 & 0.062 & 0.002 & 0.246 & 0.359 & 0.052 & 0.254 & 0.459 \\\\ \nTMTA & 0.431 & 0.082 & 0.264 & 0.582 & 0.031 & 0.069 & -0.111 & 0.164 \\\\ \nTMTB & -0.803 & 0.139 & -1.068 & -0.548 & -0.257 & 0.083 & -0.426 & -0.101 \\\\ \nIADL & 0.377 & 0.074 & 0.229 & 0.522 & 0.174 & 0.051 & 0.070 & 0.272 \\\\ \nWMV & -0.069 & 0.038 & -0.143 & 0.008 & 0.090 & 0.034 & 0.026 & 0.159 \\\\ \nGMW & -0.009 & 0.029 & -0.068 & 0.044 & 0.017 & 0.027 & -0.037 & 0.069 \\\\ \nTIV & 0.065 & 0.018 & 0.030 & 0.099 & -0.008 & 0.016 & -0.038 & 0.022 \\\\ \nRHIPP & 0.002 & 0.031 & -0.062 & 0.064 & -0.083 & 0.024 & -0.130 & -0.037 \\\\ \nLHIPP & -0.039 & 0.031 & -0.102 & 0.022 & 0.067 & 0.024 & 0.019 & 0.115 \\\\ \nPeri & 0.019 & 0.013 & -0.007 & 0.042 & -0.036 & 0.016 & -0.066 & -0.006 \\\\ \nDeep & 0.066 & 0.043 & -0.017 & 0.147 & 0.059 & 0.041 & -0.021 & 0.139 \\\\ \\hline\nAPOE4 & 0.845 & 0.199 & 0.459 & 1.244 & 0.239 & 0.200 & -0.157 & 0.616 \\\\ \nSex & 0.419 & 0.250 & -0.071 & 0.909 & -1.799 & 0.220 & -2.230 & -1.352 \\\\ \nDiabetes & 0.221 & 0.262 & -0.313 & 0.710 & 1.235 & 0.213 & 0.829 & 1.658 \\\\ \nEduc & 0.767 & 0.212 & 0.346 & 1.184 & 0.098 & 0.172 & -0.249 & 0.435 \\\\ \nAge & 0.198 & 0.259 & -0.315 & 0.696 & 0.475 & 0.242 & -0.019 & 0.946 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Two-stage Joint Modeling Approach for Multiple Longitudinal Markers and Time-to-event Data", "authors": ["Taban Baghfalaki", "Reza Hashemi", "Catherine Helmer", "Helene Jacqmin-Gadda"], "url": "https://arxiv.org/abs/2412.05765v1", "attribution": "\"A Two-stage Joint Modeling Approach for Multiple Longitudinal Markers and Time-to-event Data\" by Taban Baghfalaki, Reza Hashemi, Catherine Helmer, and Helene Jacqmin-Gadda, arXiv:2412.05765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.13023v3_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of trade count for 19 labeled searchers.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n\\textbf{Searcher} & \\textbf{Total} & \\textbf{Inventory Adj.} & \\textbf{Arbitrage} & \\textbf{Profitable} & \\textbf{Unprofitable} \\\\\n & \\textbf{Trades} & \\textbf{Trades} & \\textbf{Trades} & \\textbf{Trades} & \\textbf{Trades} \\\\\n & [\\#] & [\\#] & [\\#] & [\\#] & [\\#]\\\\\n\\midrule\n\\midrule\nWintermute & 1,916,577 & 153,671 & 1,762,906 & 1,297,128 & 465,778 \\\\\nSCP & 2,188,986 & 142,086 & 2,046,900 & 1,579,632 & 467,268 \\\\\nKayle & 1,525,397 & 90,970 & 1,434,427 & 1,131,090 & 303,337 \\\\\nGalio & 257,377 & 15,858 & 241,519 & 196,208 & 45,311 \\\\\nShen & 707,491 & 12,658 & 694,833 & 649,458 & 45,375 \\\\\nTaric & 179,277 & 4,053 & 175,224 & 156,569 & 18,655 \\\\\nLucian & 528,422 & 4,502 & 523,920 & 498,731 & 25,189 \\\\\nRiven & 23,699 & 2,536 & 21,163 & 14,586 & 6,577 \\\\\nThresh & 31,622 & 5,904 & 25,718 & 20,061 & 5,657 \\\\\nAhri & 12,161 & 879 & 11,282 & 7,611 & 3,671 \\\\\nDarius & 12,415 & 700 & 11,715 & 9,312 & 2,403 \\\\\nKarma & 58,085 & 1,730 & 56,355 & 55,883 & 472 \\\\\nSenna & 55,910 & 5,523 & 50,387 & 44,399 & 5,988 \\\\\nMaokai & 27,976 & 649 & 27,327 & 26,345 & 982 \\\\\nZed & 96,954 & 2,649 & 94,305 & 86,205 & 8,100 \\\\\nGraves & 12,173 & 5,750 & 6,423 & 925 & 5,498 \\\\\nCaitlyn & 1,559 & 247 & 1,312 & 1,184 & 128 \\\\\nAkali & 9,803 & 55 & 9,748 & 9,595 & 153 \\\\\nPoppy & 8,131 & 35 & 8,096 & 7,865 & 231 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest", "authors": ["Fei Wu", "Danning Sui", "Thomas Thiery", "Mallesh Pai"], "url": "https://arxiv.org/abs/2507.13023v3", "attribution": "\"Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest\" by Fei Wu, Danning Sui, Thomas Thiery, and Mallesh Pai, arXiv:2507.13023v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13301v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n \\textbf{Index} & \\textbf{Test 1 Eigenvalues} & \\textbf{Test 2 Eigenvalues} \\\\ \\hline\n $\\lambda_1$ & \\(-5.75892737 \\times 10^{-5}\\) & \\(-4.93801632 \\times 10^{-5}\\) \\\\ \\hline\n $\\lambda_2$ & \\(-1.03753149\\) & \\(-1.15851652\\) \\\\ \\hline\n $\\lambda_3$ & \\(-2.23424145\\) & \\(-2.61595495\\) \\\\ \\hline\n $\\lambda_4$ & \\(-3.88774292\\) & \\(-3.59187336\\) \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{OU process: First four eigenvalues of the Koopman generator estimated using SDMD.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Data-Driven Framework for Koopman Semigroup Estimation in Stochastic Dynamical Systems", "authors": ["Yuanchao Xu", "Kaidi Shao", "Isao Ishikawa", "Yuka Hashimoto", "Nikos Logothetis", "Zhongwei Shen"], "url": "https://arxiv.org/abs/2501.13301v4", "attribution": "\"A Data-Driven Framework for Koopman Semigroup Estimation in Stochastic Dynamical Systems\" by Yuanchao Xu, Kaidi Shao, Isao Ishikawa, Yuka Hashimoto, Nikos Logothetis, and Zhongwei Shen, arXiv:2501.13301v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.18386v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Data description and sources}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\hline \\hline\n\\textbf{Variable} & \\textbf{Description} & \\textbf{Source} \\\\ \\hline\n\\textit{Aggregate Analysis:} \\\\\nGPBII and NGPBII & Green and Non-Green Patent Based Innovation index & Patents view + \\\\\nTFP & Total Factor Productivity & \\\\\nOutput & U.S. Real Gross Domestic Product & FRED \\\\\nConsumption & U.S. Real Consumption & FRED \\\\\nInvestment & U.S. Real Investment & FRED \\\\\nHours & Non-farm Business Sector: Hours Worked for All Workers & FRED \\\\\nConsumer Price Index & Consumer Price Index for All Urban Consumers: All Items & FRED \\\\\nFederal Funds Rate & Effective federal funds rate & FRED \\\\\nConsumer Confidence & Consumer Confidence Index & Michigan Survey of Consumers \\\\\nStock Price Index & Standard and Poor’s 500 Composite Stock Price Index & Robert Shiller’s website\\\\\nCPI Core & U.S. CPI for all urban consumers: all items less food\nand energy & FRED \\\\\nCPI Energy & U.S. CPI for all urban consumers: energy & FRED \\\\\nCPI Durables & U.S. CPI for all urban consumers: durables & FRED \\\\\nCPI Non-durables & U.S. CPI for all urban consumers: non-durables & FRED \\\\\nCPI Services & U.S. CPI for all urban consumers: services & FRED \\\\\nOil and Gas stock price & Average oil and gas sectoral stock price index & Datastream \\\\\nElectricity Stock Price & Average electricity sectoral stock price index & Datastream \\\\\nAutomobiles Stock Price & Average automobiles sectoral stock price index & Datastream \\\\\nMining Stock Price & Average mining sectoral stock price index & Datastream \\\\\nRetail Stock Price & Average retail sectoral stock price index & Datastream \\\\\nTravel and Leisure Stock Price & Average travel and leisure sectoral stock price index & Datastream \\\\\n\\hline \\hline\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Becoming Green: Decomposing the Macroeconomic Effects of Green Technology News Shocks", "authors": ["Oscar Jaulin", "Andrey Ramos"], "url": "https://arxiv.org/abs/2507.18386v1", "attribution": "\"Becoming Green: Decomposing the Macroeconomic Effects of Green Technology News Shocks\" by Oscar Jaulin and Andrey Ramos, arXiv:2507.18386v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02875v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ambiguous terms statistics for all gold standard datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\t\t\t\\hline\n\t\t\t&Noun\t&Verb\t&Adjective\t&Adverb\t\\\\\n\t\t\t\\hline\n\t\t\t\\# of terms\t&4300\t&1652&\t955\t&346\\\\\n\t\t\t\\# of ambiguous\t&3442&\t1555&\t732\t&208\\\\\n\t\t\tAverage granularity&5.7\t&11.0&\t4.7\t&4.4\\\\\n\t\t\tMax \\#senses&\t33&\t59&\t21\t&13\\\\\n\t\t\tMode\t&\t2&\t4&\t2&\t2\\\\\n\t\t\tMedian\t&\t5&\t7&\t4&\t3\\\\\t\t\t\t\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph", "authors": ["Mohannad AlMousa", "Rachid Benlamri", "Richard Khoury"], "url": "https://arxiv.org/abs/2101.02875v1", "attribution": "\"A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph\" by Mohannad AlMousa, Rachid Benlamri, and Richard Khoury, arXiv:2101.02875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12251v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|cccc}\n \\hline\n \n Engineers & Solar Panels & $T_1$ &$T_2$&$T_3$&$T_4$ \\\\ \\hline\n & $P_1$ &$\\langle (0.9,0.1);0.9\\rangle$&$\\langle (0.4,0.6);0.6\\rangle$&$\\langle (0.9,0.1);0.9\\rangle$ & $\\langle (0.5,0.5);0.5\\rangle$\\\\\n & $P_2$ &$\\langle (0.8,0.2);0.8\\rangle$ &$\\langle (0.5,0.5);0.5\\rangle$&$\\langle (0.8,0.2);0.8\\rangle$&$\\langle (0.5,0.5);0.5\\rangle$\\\\\n $E_1$ & $P_3$ &$\\langle (0.6,0.4);0.6\\rangle$&$\\langle (0.7,0.3);0.3\\rangle$&$\\langle (0.4,0.6);0.4\\rangle$&$\\langle (0.6,0.4);0.4\\rangle$\\\\\n & $P_4$ &$\\langle (0.8,0.2);0.8\\rangle$&$\\langle (0.4,0.6);0.6\\rangle$&$\\langle (0.7,0.3);0.7\\rangle$&$\\langle (0.5,0.5);0.5\\rangle$\\\\\n & $P_5$ &$\\langle (0.7,0.3);0.7\\rangle$&$\\langle (0.4,0.6);0.6\\rangle$&$\\langle (0.8,0.2);0.8\\rangle$&$\\langle (0.4,0.6);0.6\\rangle$\\\\ \\hline\n & $P_1$ &$\\langle (0.8,0.2);0.8\\rangle$&$\\langle (0.3,0.7);0.7\\rangle$&$\\langle (0.8,0.2);0.8\\rangle$ & $\\langle (0.4,0.6);0.6\\rangle$\\\\\n & $P_2$ &$\\langle (0.6,0.4);0.6\\rangle$&$\\langle (0.5,0.5);0.5\\rangle$&$\\langle (0.7,0.3);0.7\\rangle$&$\\langle (0.4,0.6);0.6\\rangle$\\\\\n $E_2$ & $P_3$ &$\\langle (0.5,0.5);0.5\\rangle$&$\\langle (0.5,0.5);0.5\\rangle$&$\\langle (0.5,0.5);0.5\\rangle$&$\\langle (0.6,0.4);0.4\\rangle$\\\\\n & $P_4$ &$\\langle (0.8,0.2);0.8\\rangle$&$\\langle (0.4,0.6);0.6\\rangle$&$\\langle (0.8,0.2);0.8\\rangle$&$\\langle (0.5,0.5);0.5\\rangle$\\\\\n & $P_5$ &$\\langle (0.8,0.2);0.8\\rangle$&$\\langle (0.3,0.7);0.7\\rangle$&$\\langle (0.8,0.2);0.8\\rangle$&$\\langle (0.5,0.5);0.5\\rangle$\n \\\\\\hline\n & $P_1$ &$\\langle (0.9,0.1);0.9\\rangle$&$\\langle (0.2,0.8);0.8\\rangle$&$\\langle (0.7,0.3);0.7\\rangle$ & $\\langle (0.6,0.4);0.4\\rangle$\\\\\n & $P_2$ &$\\langle (0.7,0.3);0.7\\rangle$&$\\langle (0.6,0.4);0.4\\rangle$&$\\langle (0.6,0.4);0.6\\rangle$&$\\langle (0.5,0.5);0.5\\rangle$\\\\\n $E_3$ & $P_3$ &$\\langle (0.4,0.6);0.4\\rangle$&$\\langle (0.8,0.2);0.2\\rangle$&$\\langle (0.4,0.6);0.4\\rangle$&$\\langle (0.8,0.2);0.2\\rangle$\\\\\n & $P_4$ &$\\langle (0.7,0.3);0.7\\rangle$&$\\langle (0.3,0.7);0.7\\rangle$&$\\langle (0.6,0.4);0.6\\rangle$&$\\langle (0.6,0.4);0.4\\rangle$\\\\\n & $P_5$ &$\\langle (0.7,0.3);0.7\\rangle$&$\\langle (0.2,0.8);0.8\\rangle$&$\\langle (0.7,0.3);0.7\\rangle$&$\\langle (0.3,0.7);0.7\\rangle$\n \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Disc intuitionistic fuzzy group normalized decision matrix}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology", "authors": ["Mahmut Can Bozyiğit", "Mehmet Ünver"], "url": "https://arxiv.org/abs/2501.12251v1", "attribution": "\"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology\" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05875v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline \n\\hline\n\\textbf{\\#$H$ - Heads} & \\textbf{Precision} & \\textbf{Recall} & \\textbf{F1}\\\\\n\\hline\n\\hline\n1 Head & 74.9 & 74.5 & 74.4 \\\\\n4 Heads & 76.9 & 76.8 & 76.8 \\\\\n8 Heads & 77.4 & 77.2 & 77.2 \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Ablation study with varying number of Heads \\#$H$ and fixed Layers \\textbf{\\#$L$ = 3} on the Sarcasm Corpus V2 Dialogues dataset .}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media", "authors": ["Ramya Akula", "Ivan Garibay"], "url": "https://arxiv.org/abs/2101.05875v1", "attribution": "\"Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media\" by Ramya Akula and Ivan Garibay, arXiv:2101.05875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01295v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Runtime and hyperparameters for fitting 25 different runs of each model, with a datasize of $15,\\!000$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc} % creating eight columns\n \\toprule %inserting double-line\n Model & Total Runtime & RQS Knots & Flow Layers & Learning Rate & NN Width & NN Depth \\\\\n \\toprule % inserts single-line\n $M_{1}$ & 45.4 mins & 8 & 5 & 5e-3 & 50 & 4\\\\ \n $M_{2}$ & 44.1 mins & 8 & 5 & 5e-3 & 50 & 4\\\\\n $M_{3}$ & 66.3 mins & 8 & 5 & 5e-3 & 50 & 4\\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Marginal Causal Flows for Validation and Inference", "authors": ["Daniel de Vassimon Manela", "Laura Battaglia", "Robin J. Evans"], "url": "https://arxiv.org/abs/2411.01295v2", "attribution": "\"Marginal Causal Flows for Validation and Inference\" by Daniel de Vassimon Manela, Laura Battaglia, and Robin J. Evans, arXiv:2411.01295v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16176v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Datasets used in HRTF individualization experiments}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc} \\toprule\n \\multirow{2}{*}{Dataset} & \\# Source & Source & \\multicolumn{2}{c}{\\# Subjects} \\\\\n & pos. $B$ & dist. $r$ (m) & Train & Test \\\\ \\midrule\n CIPIC & 1250 & 1.00 & 30 & 5 \\\\\n HUTUBS & 440 & 1.47 & 85 & 6 \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Head-Related Transfer Function Individualization Using Anthropometric Features and Spatially Independent Latent Representation", "authors": ["Ryan Niu", "Shoichi Koyama", "Tomohiko Nakamura"], "url": "https://arxiv.org/abs/2508.16176v1", "attribution": "\"Head-Related Transfer Function Individualization Using Anthropometric Features and Spatially Independent Latent Representation\" by Ryan Niu, Shoichi Koyama, and Tomohiko Nakamura, arXiv:2508.16176v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.06442v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The comparison between PBAN and spatial/channel attention on the CVIU database, with the best performance values highlighted in bold. The “spatial atten\" and “channel atten\" pool the channel and spatial dimensions to 1, respectively. The Flops is calculated with input tensor shape $3\\times32\\times32$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|cccc}\n\\toprule\nModel & Params/M & Flops/G & Speed/ms & SRCC $\\uparrow$ & KRCC $\\uparrow$ & PLCC $\\uparrow$ & RMSE $\\downarrow$ \\\\\n\\midrule \nChannel Atten & 2.222 & 0.181 & 11.284 & 0.963 & 0.836 & 0.964 & 0.716 \\\\\nSpatial Atten & 2.222 & 0.181 & 11.099 & 0.913 & 0.751 & 0.917 & 1.062 \\\\\nPBAN & 2.222 & 0.181 & 11.292 & \\textbf{0.978} & \\textbf{0.872} & \\textbf{0.981} & \\textbf{0.397} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment", "authors": ["Yixiao Li", "Xiaoyuan Yang", "Guanghui Yue", "Jun Fu", "Qiuping Jiang", "Xu Jia", "Paul L. Rosin", "Hantao Liu", "Wei Zhou"], "url": "https://arxiv.org/abs/2509.06442v1", "attribution": "\"Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment\" by Yixiao Li, Xiaoyuan Yang, Guanghui Yue, Jun Fu, Qiuping Jiang, Xu Jia, Paul L. Rosin, Hantao Liu, and Wei Zhou, arXiv:2509.06442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13372v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\hline\n Dataset & \\# Samples & \\# Features & \\# Categorical & \\# Classes \\\\\n \\hline\n statlog & 6,435 & 36 & 0 & 6 \\\\\n spambase & 4,601 & 57 & 0 & 2 \\\\\n electrical & 10,000 & 13 & 0 & 2 \\\\\n occupancy~ & 20,560 & 5 & 0 & 2 \\\\\n gas~ & 13,910 & 128 & 0 & 6 \\\\\n drybean~ & 13,611 & 16 & 0 & 7 \\\\\n \\hline\n energy~ & 19,735 & 27 & 0 & - \\\\\n bike~ & 17,379 & 10 & 5 & - \\\\\n metro & 48,204 & 6 & 1 & - \\\\\n sgemm~ & 241,600 & 14 & 0 & - \\\\\n ovctt & 68,784 & 20 & 2 & - \\\\\n californiahousing & 20,640 & 8 & 0 & - \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Main characteristics of the datasets used in experiments, including number of samples, number of features, number of categorical features and number of classes.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Robust supervised learning with coordinate gradient descent", "authors": ["Stéphane Gaïffas", "Ibrahim Merad"], "url": "https://arxiv.org/abs/2201.13372v1", "attribution": "\"Robust supervised learning with coordinate gradient descent\" by Stéphane Gaïffas and Ibrahim Merad, arXiv:2201.13372v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23369v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|} \n \\hline\n $\\bar{L}$ & $\\bar{R}$ & $\\nu$ & $\\bar{p}$ \\\\\n \\hline\n 10 & 3 or 10 & 0.3 & 1 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Geometric and material parameters.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Asymptotically accurate and geometric locking-free finite element implementation of a refined shell theory", "authors": ["Khanh Chau Le", "Hoang-Giang Bui"], "url": "https://arxiv.org/abs/2503.23369v4", "attribution": "\"Asymptotically accurate and geometric locking-free finite element implementation of a refined shell theory\" by Khanh Chau Le and Hoang-Giang Bui, arXiv:2503.23369v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.14542v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean (SD) bias when sample size is 3500 and covariates are imputed for subjects missing $\\leq2$ covariates.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrr}\n\\hline\n& \\multicolumn{2}{c}{Original} & \\multicolumn{2}{c}{Bootstrap} & \\multicolumn{2}{c}{.632} & \\multicolumn{2}{c}{.632+}\\\\\n\\hline\nScenario & BI & CC & BI & CC & BI & CC & BI & CC \\\\\n\\hline\n\\multicolumn{9}{l}{AUC} \\\\\n\\hline\nA & 0.000 (0.001) & 0.002 (0.004) & 0.000 (0.001) & 0.003 (0.004) & 0.000 (0.001) & 0.003 (0.004) & 0.000 (0.001) & 0.003 (0.004) \\\\ \n B & 0.001 (0.001) & 0.005 (0.007) & 0.001 (0.001) & 0.006 (0.007) & 0.001 (0.001) & 0.006 (0.007) & 0.001 (0.001) & 0.006 (0.007) \\\\ \n C & 0.001 (0.002) & 0.004 (0.020) & 0.001 (0.002) & 0.013 (0.021) & 0.001 (0.002) & 0.013 (0.021) & 0.001 (0.002) & 0.013 (0.021) \\\\ \n D & 0.001 (0.002) & 0.002 (0.006) & 0.001 (0.002) & 0.003 (0.006) & 0.001 (0.002) & 0.003 (0.006) & 0.001 (0.002) & 0.003 (0.006) \\\\ \n E & 0.005 (0.004) & 0.001 (0.013) & 0.005 (0.004) & 0.004 (0.013) & 0.005 (0.004) & 0.004 (0.013) & 0.005 (0.004) & 0.004 (0.013) \\\\ \n F & 0.008 (0.006) & -0.002 (0.025) & 0.009 (0.006) & 0.011 (0.026) & 0.009 (0.006) & 0.011 (0.026) & 0.009 (0.006) & 0.011 (0.026) \\\\ \n G & 0.002 (0.003) & 0.004 (0.009) & 0.003 (0.003) & 0.005 (0.009) & 0.003 (0.003) & 0.005 (0.009) & 0.003 (0.003) & 0.005 (0.009) \\\\ \n H & 0.006 (0.006) & 0.004 (0.021) & 0.007 (0.006) & 0.014 (0.022) & 0.007 (0.006) & 0.014 (0.022) & 0.007 (0.006) & 0.015 (0.022) \\\\ \n I & 0.010 (0.013) & & 0.013 (0.013) & & 0.013 (0.013) & & 0.013 (0.013) & \\\\ \n\\hline\n\\multicolumn{9}{l}{Brier} \\\\\n\\hline\nA & -0.000 (0.000) & 0.002 (0.001) & -0.000 (0.000) & 0.002 (0.001) & -0.000 (0.000) & 0.002 (0.001) & -0.000 (0.000) & 0.002 (0.001) \\\\ \n B & -0.000 (0.000) & 0.005 (0.002) & -0.000 (0.000) & 0.005 (0.002) & -0.000 (0.000) & 0.005 (0.002) & -0.000 (0.000) & 0.005 (0.002) \\\\ \n C & -0.000 (0.000) & 0.017 (0.005) & -0.000 (0.000) & 0.016 (0.005) & -0.000 (0.000) & 0.016 (0.005) & -0.000 (0.000) & 0.016 (0.005) \\\\ \n D & 0.000 (0.000) & 0.002 (0.002) & 0.000 (0.000) & 0.002 (0.002) & 0.000 (0.000) & 0.002 (0.002) & 0.000 (0.000) & 0.002 (0.002) \\\\ \n E & -0.000 (0.001) & 0.002 (0.003) & -0.000 (0.001) & 0.002 (0.003) & -0.000 (0.001) & 0.002 (0.003) & -0.000 (0.001) & 0.002 (0.003) \\\\ \n F & 0.001 (0.001) & 0.005 (0.007) & 0.001 (0.001) & 0.003 (0.007) & 0.001 (0.001) & 0.003 (0.007) & 0.001 (0.001) & 0.003 (0.007) \\\\ \n G & 0.000 (0.001) & 0.004 (0.002) & 0.000 (0.001) & 0.004 (0.002) & 0.000 (0.001) & 0.004 (0.002) & 0.000 (0.001) & 0.004 (0.002) \\\\ \n H & 0.001 (0.001) & 0.011 (0.005) & 0.001 (0.001) & 0.010 (0.005) & 0.001 (0.001) & 0.010 (0.005) & 0.001 (0.001) & 0.010 (0.005) \\\\ \n I & 0.005 (0.003) & & 0.005 (0.003) & & 0.005 (0.003) & & 0.005 (0.003) & \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Combining missing data imputation and internal validation in clinical risk prediction models", "authors": ["Junhui Mi", "Rahul D. Tendulkar", "Sarah M. C. Sittenfeld", "Sujata Patil", "Emily C. Zabor"], "url": "https://arxiv.org/abs/2411.14542v1", "attribution": "\"Combining missing data imputation and internal validation in clinical risk prediction models\" by Junhui Mi, Rahul D. Tendulkar, Sarah M. C. Sittenfeld, Sujata Patil, and Emily C. Zabor, arXiv:2411.14542v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.13457v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{AWOISV Parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n \\textbf{Parameter} & \\textbf{Symbol} & \\textbf{Unit} & \\textbf{Value} \\\\\n \\midrule\n Vehicle Mass & $m$ & kg & 10,000 \\\\\n Vehicle Inertia & $I_Z$ & $\\text{kg} \\cdot \\text{m}^2$ & 34,823 \\\\\n Wheel Steering Range & $[\\delta_{\\min}, \\delta_{\\max}]$ & rad & $[-\\pi/2, \\pi/2]$ \\\\\n Tire Cornering Stiffness & $C_\\alpha$ & N/rad & 40,000 \\\\\n Friction Coefficient & $\\mu$ & - & 0.85 \\\\\n Tire Rolling Radius & $R_{eff}$ & m & 0.35 \\\\\n Axle-to-CG X Distance & $X_{A1} \\sim X_{A4}$ & m & \\parbox{2cm}{$[3.55, 1.75,$\\\\ $-1.75, -3.55]$} \\\\\n Wheel Track & $M$ & m & 2.72 \\\\\n Height of CG & $H_{CG}$ & m & 1.21 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Modeling and Control of AWOISV: A Filtered Tube-Based MPC Approach for Simultaneous Tracking of Lateral Position and Heading Angle", "authors": ["Xu Yang", "Jun Ni", "Hengyang Feng", "Feiyu Wang", "Tiezhen Wang"], "url": "https://arxiv.org/abs/2508.13457v1", "attribution": "\"Modeling and Control of AWOISV: A Filtered Tube-Based MPC Approach for Simultaneous Tracking of Lateral Position and Heading Angle\" by Xu Yang, Jun Ni, Hengyang Feng, Feiyu Wang, and Tiezhen Wang, arXiv:2508.13457v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.11640v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n Layout & Type & $\\epsilon(1)$ & $\\vert \\mathcal{V} \\vert$ & $\\vert \\mathcal{E} \\vert$ & $d_{avg}$\\\\\n \\hline\n Campus A & City Block & 0.197 & 87 & 232 & 2.667 \\\\\n Campus B & University & 0.178 & 116 & 336 & 2.897\\\\\n Campus C & University & 0.192 & 113 & 288 & 2.549\\\\\n Campus D & University & 0.171 & 97 & 266 & 2.742\\\\\n Campus E & Military Base & 0.189 & 36 & 80 & 2.222\\\\\n Campus F & University & 0.185 & 50 & 128 & 2.56\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Attributes of the Campus Environments.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "$ε$-Optimal Multi-Agent Patrol using Recurrent Strategy", "authors": ["Deepak Mallya", "Arpita Sinha", "Leena Vachhani"], "url": "https://arxiv.org/abs/2509.11640v1", "attribution": "\"$ε$-Optimal Multi-Agent Patrol using Recurrent Strategy\" by Deepak Mallya, Arpita Sinha, and Leena Vachhani, arXiv:2509.11640v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.15876v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Panel D: Pre-pandemic transition (Jan 2016–Dec 2020).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrr}\n \\toprule\n Metric & LTT & MKT & STT+LTT & STT & MKT+STT+LTT & MKT+STT & SGCTAT \\\\\n \\midrule\n Cumulative Return (\\%) & 26.7 & 9.3 & 34.7 & 25.1 & 40.3 & 27.8 & 2.3 \\\\\n Annual Return (\\%) & 4.8 & 1.8 & 6.1 & 4.6 & 7.0 & 5.0 & 0.5 \\\\\n Volatility (\\%) & 9.2 & 11.3 & 8.5 & 8.1 & 9.3 & 9.1 & 11.0 \\\\\n Sharpe Ratio & 0.40 & 0.05 & 0.58 & 0.42 & 0.62 & 0.42 & -0.06 \\\\\n Max Drawdown (\\%) & 12.2 & 20.3 & 8.3 & 8.4 & 9.9 & 10.8 & 22.4 \\\\\n Return/Max-DD & 0.40 & 0.09 & 0.74 & 0.54 & 0.70 & 0.47 & 0.02 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach", "authors": ["Eric Benhamou", "Jean-Jacques Ohana", "Alban Etienne", "Béatrice Guez", "Ethan Setrouk", "Thomas Jacquot"], "url": "https://arxiv.org/abs/2507.15876v1", "attribution": "\"Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach\" by Eric Benhamou, Jean-Jacques Ohana, Alban Etienne, Béatrice Guez, Ethan Setrouk, and Thomas Jacquot, arXiv:2507.15876v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.09028v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{HEV system model design and references}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c||} \n \\hline\n {\\textbf{System Modelling}} & {\\textbf{List of References}} \\\\ \n \\hline\\hline\n Drive-line Dynamics & ,, \\\\\n \\hline\n Internal Combustion Engine (ICE) & , \\\\\n \\hline\n Electric Machine & , \\\\\n \\hline\n Energy Storage Elements & ,, \\\\\n \\hline\n Battery Life Estimation Models & ,, \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Degradation-based Energy Management for Microgrids in the Presence of Energy Storage Elements", "authors": ["Satish Vedula"], "url": "https://arxiv.org/abs/2503.09028v2", "attribution": "\"Degradation-based Energy Management for Microgrids in the Presence of Energy Storage Elements\" by Satish Vedula, arXiv:2503.09028v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01513v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Surface Hausdorff distance(mm) on CT and MRI.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|l|l|l|}\n\\hline\n\\multirow{2}{*}{Method} & \\multicolumn{4}{c|}{CT} &\\multicolumn{4}{c|}{MRI} \\\\\n\\cline{2-9}\n ~ & LVM & LAC& LVC & AA & LVM & LAC& LVC & AA \\\\\n\\hline\nSingle& 5.00 & 10.63 & 5.00 & 13.38 & 8.50 & 15.03 & 4.47 & 6.00\\\\\nUMMKD & 5.83 & 14.40 & 6.01 & 13.97 & 10.05 & 8.94 & 4.47 & 3.61 \\\\\nJoint & 5.39 & 14.32 & 4.24 & 13.30& 6.40 & 40.17 & 9.84 & 4.47 \\\\\nMSBN & 5.00 & 10.63 & 4.24 & 13.93 & 4.12 & 9.06 & 4.47 & 3.00 \\\\\nAffinity & 6.00 & 10.63 & 4.89 & 15.13 & 10.05 & 12.08 & 5.00 & 67.09\\\\\nOurs & 5.39 & \\textbf{10.30} & \\textbf{4.24} & 15.80 & \\textbf{4.12} & 9.49 & \\textbf{4.12} & \\textbf{2.83} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation", "authors": ["Jingkun Chen", "Wenqi Li", "Hongwei Li", "Jianguo Zhang"], "url": "https://arxiv.org/abs/2101.01513v1", "attribution": "\"Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation\" by Jingkun Chen, Wenqi Li, Hongwei Li, and Jianguo Zhang, arXiv:2101.01513v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.08696v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n \\hline\n Ticker & Keywords\\\\\\hline\n MTLR & mechel, mining, ore, raw materials, energy, ferroalloys, coal \\\\\\hline\n SNGS & gas, geological exploration, oil, Surgutneftegas, petroleum products, electricity, drilling \\\\\\hline\n SMLT & rent, development, developer, real estate, construction, Moscow region, residential areas\\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Keywords by companies extracted from their descriptions}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market", "authors": ["Kasymkhan Khubiev", "Mikhail Semenov"], "url": "https://arxiv.org/abs/2503.08696v1", "attribution": "\"Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market\" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2503.08696v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.07170v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Manufactured solution: convergence analysis for the test case presented in~. Numerical results obtained with the ROD-DG reconstruction on the conformal linear meshes described in~. One can notice that the high order precision is achieved for all polynomials.}%\\\\}\n\\begin{tabular}{ccccccccc}\n \\hline%\\\\\n &\\multicolumn{2}{c}{$\\rho$} &\\multicolumn{2}{c}{$\\rho u$} &\\multicolumn{2}{c}{$\\rho v$} &\\multicolumn{2}{c}{$\\rho E$}\\\\\n \\cline{2-9}\n Grid level & $L_2$ & $\\tilde{n}$ & $L_2$ & $\\tilde{n}$ & $L_2$ & $\\tilde{n}$ & $L_2$ & $\\tilde{n}$ \\\\\\hline\n &\\multicolumn{8}{c}{ROD-DG-$\\mathcal{P}_1$}\\\\\n 0 & 9.0353E-4 & -- & 9.3786E-4 & -- & 9.3503E-4 & -- & 2.8893E-3 & -- \\\\\n 1 & 2.0990E-4 & 2.11 & 2.0792E-4 & 2.17 & 2.0892E-4 & 2.16 & 6.6542E-4 & 2.12 \\\\\n 2 & 5.0528E-5 & 2.05 & 4.9278E-5 & 2.08 & 4.9736E-5 & 2.07 & 1.6082E-4 & 2.05 \\\\\n 3 & 1.2438E-5 & 2.02 & 1.2032E-5 & 2.03 & 1.2173E-5 & 2.03 & 3.9724E-5 & 2.02 \\\\\n &\\multicolumn{8}{c}{ROD-DG-$\\mathcal{P}_2$}\\\\\n 0 & 5.8327E-5 & -- & 4.0652E-5 & -- & 4.5945E-5 & -- & 1.7771E-4 & -- \\\\\n 1 & 9.5321E-6 & 2.61 & 5.1266E-6 & 2.99 & 6.2677E-6& 2.87 & 2.8402E-5 & 2.65 \\\\\n 2 & 1.4695E-6 & 2.70 & 6.9200E-7 & 2.89 & 8.6795E-7 & 2.85 & 4.3499E-6 & 2.71 \\\\\n 3 & 2.0870E-7 & 2.82 & 9.7264E-8 & 2.83 & 1.1487E-7 & 2.92 & 6.1764E-7 & 2.82 \\\\\n &\\multicolumn{8}{c}{ROD-DG-$\\mathcal{P}_3$}\\\\\n 0 & 1.0030E-6 & -- & 1.3555E-6 & -- & 1.4008E-6 & -- & 3.4352E-6 & -- \\\\\n 1 & 4.7755E-8 & 4.39 & 5.4425E-8 & 4.64 & 5.5114E-8 & 4.67 & 1.5203E-7 & 4.50 \\\\\n 2 & 2.5871E-9 & 4.21 & 2.7218E-9 & 4.32 & 2.7433E-9 & 4.33 & 8.1616E-9 & 4.22 \\\\\n 3 & 1.5035E-10& 4.10 & 1.5323E-10& 4.15 & 1.5474E-10& 4.15 & 4.7689E-10 & 4.10 \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Very high order treatment of embedded curved boundaries in compressible flows: ADER discontinuous Galerkin with a space-time Reconstruction for Off-site data", "authors": ["Mirco Ciallella", "Stephane Clain", "Elena Gaburro", "Mario Ricchiuto"], "url": "https://arxiv.org/abs/2312.07170v1", "attribution": "\"Very high order treatment of embedded curved boundaries in compressible flows: ADER discontinuous Galerkin with a space-time Reconstruction for Off-site data\" by Mirco Ciallella, Stephane Clain, Elena Gaburro, and Mario Ricchiuto, arXiv:2312.07170v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2308.15688v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lccc|ccc}\n\t\t\t\\hline\n\t\t\t& \\multicolumn{3}{c}{Standard error} & \\multicolumn{3}{c}{Coverage probability}\\\\\n\t\t\t\\cline{2-4} \\cline{5-7}\n\t\t\tTotal sample size: & 30 & 60 & 90 & 30 & 60 & 90 \\\\\n\t\t\tRandomization ratio: & 1:1 (2:1) & 1:1 (2:1) & 1:1 (2:1) & 1:1 (2:1) & 1:1 (2:1) & 1:1 (2:1) \\\\\\hline\n\t\t\tMethod & \\multicolumn{6}{c}{Scenario 1: Moderate treatment effect} \\\\\\hline\n\t\t\tProposed (model) & 0.115 (0.198) & 0.074 (0.078) & 0.060 (0.063) & 0.910 (0.884) & 0.929 (0.922) & 0.936 (0.935) \\\\ \n\t\t\tProposed (const) & 3.923 (336.276) & 0.087 (0.209) & 0.066 (0.074) & 0.953 (0.952) & 0.959 (0.962) & 0.956 (0.962) \\\\ \n\t\t\tProposed (HC0) & 0.107 (0.105) & 0.074 (0.075) & 0.060 (0.062) & 0.883 (0.842) & 0.921 (0.903) & 0.932 (0.925) \\\\ \n\t\t\tProposed (HC1) & 0.113 (0.111) & 0.076 (0.077) & 0.062 (0.063) & 0.898 (0.861) & 0.929 (0.911) & 0.938 (0.931) \\\\ \n\t\t\tProposed (HC2) & 0.123 (0.122) & 0.080 (0.082) & 0.063 (0.066) & 0.923 (0.888) & 0.942 (0.927) & 0.946 (0.940) \\\\ \n\t\t\tProposed (HC3) & 0.146 (0.148) & 0.087 (0.091) & 0.066 (0.070) & 0.954 (0.925) & 0.960 (0.947) & 0.957 (0.953) \\\\ \n\t\t\tProposed (HC4) & 0.196 (158.729) & 0.096 (0.106) & 0.069 (0.075) & 0.968 (0.940) & 0.974 (0.966) & 0.966 (0.967) \\\\ \n\t\t\tProposed (HC4m) & 0.160 (0.194) & 0.090 (0.095) & 0.068 (0.072) & 0.963 (0.936) & 0.967 (0.955) & 0.962 (0.959) \\\\ \n\t\t\tProposed (HC5) & 0.134 (9.418) & 0.084 (0.089) & 0.064 (0.069) & 0.940 (0.906) & 0.953 (0.944) & 0.952 (0.951) \\\\ \n\t\t\t\\hline\n\t\t\tMethod & \\multicolumn{6}{c}{Scenario 2: Large treatment effect} \\\\\\hline\n\t\t\tProposed (model) & 0.114 (0.174) & 0.067 (0.068) & 0.052 (0.053) & 0.919 (0.916) & 0.930 (0.916) & 0.935 (0.930) \\\\ \n\t\t\tProposed (const) & 35.804 (249.386) & 0.089 (0.872) & 0.060 (0.093) & 0.939 (0.950) & 0.962 (0.960) & 0.962 (0.966) \\\\ \n\t\t\tProposed (HC0) & 0.101 (0.096) & 0.065 (0.064) & 0.052 (0.052) & 0.890 (0.874) & 0.917 (0.889) & 0.929 (0.913) \\\\ \n\t\t\tProposed (HC1) & 0.106 (0.100) & 0.067 (0.065) & 0.053 (0.053) & 0.901 (0.888) & 0.925 (0.897) & 0.934 (0.918) \\\\ \n\t\t\tProposed (HC2) & 0.115 (0.109) & 0.072 (0.070) & 0.055 (0.056) & 0.922 (0.911) & 0.942 (0.916) & 0.945 (0.932) \\\\ \n\t\t\tProposed (HC3) & 0.134 (0.132) & 0.080 (0.079) & 0.059 (0.061) & 0.947 (0.940) & 0.961 (0.939) & 0.960 (0.949) \\\\ \n\t\t\tProposed (HC4) & 0.190 (396.900) & 0.099 (0.104) & 0.067 (0.072) & 0.955 (0.949) & 0.979 (0.959) & 0.977 (0.969) \\\\ \n\t\t\tProposed (HC4m) & 0.146 (0.219) & 0.085 (0.085) & 0.062 (0.064) & 0.953 (0.948) & 0.969 (0.947) & 0.967 (0.956) \\\\ \n\t\t\tProposed (HC5) & 0.127 (9.598) & 0.082 (0.084) & 0.061 (0.065) & 0.938 (0.928) & 0.962 (0.941) & 0.962 (0.956) \\\\ \\hline \\end{tabular}\n\\caption{Standard error and coverage probability of the 95\\% confidence interval under 1:1 (2:1) stratified randomization and correct model specification for the total sample size of 30, 60, and 90.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Covariate adjustment and estimation of difference in proportions in randomized clinical trials", "authors": ["Jialuo Liu", "Dong Xi"], "url": "https://arxiv.org/abs/2308.15688v1", "attribution": "\"Covariate adjustment and estimation of difference in proportions in randomized clinical trials\" by Jialuo Liu and Dong Xi, arXiv:2308.15688v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23340v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n & \\multicolumn{2}{c|}{\\textbf{Greedy}} & \\multicolumn{2}{c|}{\\textbf{Distorted Greedy}} \\\\\n\\hline\n$m$ & Subset $S_m$ & $\\mathbb{I}(P^{(S_m)})$ & Subset $S_m$ & $\\mathbb{I}(P^{(S_m)})$ \\\\\n\\hline\n2 & $\\{4,\\,10\\}$ & 0.00757 & $\\{1,\\,7\\}$ & 0.00757 \\\\\n3 & $\\{4,\\,7,\\,10\\}$ & 0.02350 & $\\{1,\\,6,\\,10\\}$ & 0.02398 \\\\\n4 & $\\{2,\\,4,\\,7,\\,10\\}$ & 0.04889 & $\\{1,\\,5,\\,7,\\,10\\}$ & 0.04961 \\\\\n5 & $\\{2,\\,4,\\,6,\\,7,\\,10\\}$ & 0.08592 & $\\{1,\\,3,\\,5,\\,7,\\,10\\}$ & 0.08591 \\\\\n6 & $\\{2,\\,4,\\,6,\\,7,\\,8,\\,10\\}$ & 0.13555 & $\\{1,\\,3,\\,5,\\,7,\\,8,\\,10\\}$ & 0.13533 \\\\\n7 & $\\{2,\\,3,\\,4,\\,6,\\,7,\\,8,\\,10\\}$ & 0.19989 & $\\{1,\\,3,\\,4,\\,5,\\,7,\\,8,\\,10\\}$ & 0.20017 \\\\\n8 & $\\{2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,10\\}$ & 0.28356 & $\\{1,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,10\\}$ & 0.28399 \\\\\n9 & $\\{2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,9,\\,10\\}$ & 0.39102 & $\\{1,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,9,\\,10\\}$ & 0.39191 \\\\\n10 & $\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,9,\\,10\\}$ & 0.53813 & $\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,9,\\,10\\}$ & 0.53813 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of the greedy algorithm and the distorted greedy algorithm (C-W model).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Information-theoretic subset selection of multivariate Markov chains via submodular optimization", "authors": ["Zheyuan Lai", "Michael C. H. Choi"], "url": "https://arxiv.org/abs/2503.23340v1", "attribution": "\"Information-theoretic subset selection of multivariate Markov chains via submodular optimization\" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11187v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|}\n\\hline\nMethod & Frobnability \\\\\n\\hline\\hline\nTheirs & Frumpy \\\\\nYours & Frobbly \\\\\nOurs & Makes one's heart Frob\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results. Ours is better.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "DIREG3D: DIrectly REGress 3D Hands from Multiple Cameras", "authors": ["Ashar Ali", "Upal Mahbub", "Gokce Dane", "Gerhard Reitmayr"], "url": "https://arxiv.org/abs/2201.11187v1", "attribution": "\"DIREG3D: DIrectly REGress 3D Hands from Multiple Cameras\" by Ashar Ali, Upal Mahbub, Gokce Dane, and Gerhard Reitmayr, arXiv:2201.11187v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17106v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n $n$ & Error $\\|\\cdot\\|_1$ & Order $\\|\\cdot\\|_1$ & Error\n $\\|\\cdot\\|_{\\infty}$ & Order $\\|\\cdot\\|_{\\infty}$ \\\\\n \\hline\n 40 & 1.88E$-3$ & $-$ & 3.65E$-2$ & $-$ \\\\\n \\hline\n 80 & 6.62E$-5$ & 4.82 & 3.94E$-3$ & 3.21 \\\\\n \\hline\n 160 & 1.72E$-7$ & 8.52 & 9.45E$-6$ & 8.70 \\\\\n \\hline\n 320 & 2.50E$-9$ & 6.10 & 9.45E$-6$ & 5.37 \\\\\n \\hline\n 640 & 4.49E$-11$ & 5.80 & 6.73E$-9$ & 5.08 \\\\\n \\hline\n 1280 & 9.61E$-13$ & 5.55 & 1.92E$-10$ & 5.13 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Error table for Burgers equation, $t=0.3$. WLS-UW.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17106v1", "attribution": "\"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00160v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrr} \\toprule\nInstance & Pilots & Airports & Flights & Pairings & Long pairings (\\%) & Flight time (min) \\\\ \\midrule\nI5 & 239 & 34 & 5743 & 1222 & 64.5 & 1037333 \\\\\nI7 & 322 & 54 & 7765 & 1473 & 57.0 & 1289546 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Description of the instances available}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Accelerated windowing for the crew rostering problem with machine learning", "authors": ["Philippe Racette", "Frédéric Quesnel", "Andrea Lodi", "François Soumis"], "url": "https://arxiv.org/abs/2503.00160v1", "attribution": "\"Accelerated windowing for the crew rostering problem with machine learning\" by Philippe Racette, Frédéric Quesnel, Andrea Lodi, and François Soumis, arXiv:2503.00160v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12599v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The mean square error of feature maps with different compression ratios using pixel-level and feature-level consistency as the loss function.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n\t\t\\toprule[1pt]\n\t\tcompression ratio (BPP) & 0.125 bpp & 0.25 bpp & 0.5 bpp \\\\\n\t\t\\hline\n\t\tpixel-level consistency & 2.7865 & 1.6392 & 1.4698 \\\\\n\t\t\\hline\n\t\tfeature-level consistency & 3.7512 & 2.0676 & 1.8129 \\\\\n\t\t\\toprule[1pt]\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Semantic-assisted image compression", "authors": ["Qizheng Sun", "Caili Guo", "Yang Yang", "Jiujiu Chen", "Xijun Xue"], "url": "https://arxiv.org/abs/2201.12599v1", "attribution": "\"Semantic-assisted image compression\" by Qizheng Sun, Caili Guo, Yang Yang, Jiujiu Chen, and Xijun Xue, arXiv:2201.12599v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.16451v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of V-LSPM and MCMC-LSPM for different truncation levels $p$, assessed through accuracy, $F_1$ score, Hamming distance, and posterior predictive network density and transitivity statistics. Standard deviations are given in brackets. The observed network density for the simulated networks is 0.21 (0.02) while the observed network transitivity is 0.50 (0.02). }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|cc|cc|cc|}\n\\hline\n{ } & \\multicolumn{2}{c|}{{ Accuracy}} & \\multicolumn{2}{c|}{{ $F_1$ score}} & \\multicolumn{2}{c|}{{ Hamming distance}} & \\multicolumn{2}{c|}{{ Network density}} & \\multicolumn{2}{c|}{{ Network transitivity}} \\\\ \\cline{2-11} \n\\multirow{-2}{*}{{ $p$}} & { VI} & { MCMC} & { VI} & { MCMC} & { VI} & { MCMC} & { VI} & { MCMC} & { VI} & { MCMC} \\\\ \\hline\n{ 2} & { 0.63 (0.04)} & { 0.85 (0.03)} & { 0.44 (0.03)} & { 0.63 (0.08)} & { 0.37 (0.04)} & { 0.16 (0.03)} & { 0.45 (0.05)} & { 0.21 (0.02)} & { 0.56 (0.04)} & { 0.46 (0.04)} \\\\ \\hline\n{ 4} & { 0.68 (0.03)} & { 0.89 (0.02)} & { 0.52 (0.02)} & { 0.75 (0.04)} & { 0.33 (0.03)} & { 0.11 (0.02)} & { 0.46 (0.05)} & { 0.21 (0.02)} & { 0.60 (0.05)} & { 0.50 (0.02)} \\\\ \\hline\n{ 10} & { 0.63 (0.02)} & { 0.89 (0.02)} & { 0.51 (0.02)} & { 0.74 (0.04)} & { 0.38 (0.03)} & { 0.11 (0.02)} & { 0.56 (0.04)} & { 0.21 (0.02)} & { 0.72 (0.03)} & { 0.50 (0.02)} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Variational Inference for the Latent Shrinkage Position Model", "authors": ["Xian Yao Gwee", "Isobel Claire Gormley", "Michael Fop"], "url": "https://arxiv.org/abs/2311.16451v2", "attribution": "\"Variational Inference for the Latent Shrinkage Position Model\" by Xian Yao Gwee, Isobel Claire Gormley, and Michael Fop, arXiv:2311.16451v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12677v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{EfficientDet-D0 Angle experts on VisDrone validation set}\n\\begin{tabular}{c|cccc}\n\t\t\t\t\n\t\t\t\t& B & A & mAP$_{50}$ & mAP$_{50}^\\text{avg}$\\\\\n\t\t\t\t\\hline\n\t\t\t\tEfficientDet-D0 & 21.5 & 24.9 & 26.3 & 23.2 \\\\\n\t\t\t\t\n\t\t\t\t\n\t\t\t\tAngle@backbone & \\bf 22.1 & \\bf 26.2 & \\bf 27.6 & \\bf 24.2 \t\t\t\t\n\t\t\t\t\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Diminishing Domain Bias by Leveraging Domain Labels in Object Detection on UAVs", "authors": ["Benjamin Kiefer", "Martin Messmer", "Andreas Zell"], "url": "https://arxiv.org/abs/2101.12677v2", "attribution": "\"Diminishing Domain Bias by Leveraging Domain Labels in Object Detection on UAVs\" by Benjamin Kiefer, Martin Messmer, and Andreas Zell, arXiv:2101.12677v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02419v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The Truth Table of Radix-4 Code and Proposed M-RD4 Code}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n \\multicolumn{4}{|c|}{Binary Bits} & Radix-4 Bit & M-RD4 Bit \\\\\n \\hline\n $t_{i+3}$&$t_{i+2}$ & $t_{i+1}$ & $t_{i}$ & & $z_j$ \\\\\n \\hline\n 0/1&0&0&0 &0 &0 \\\\\n \\hline\n 0/1&0&0&1 &1 &1 \\\\\n \\hline\n 0/1&0&1&0 &1 &1 \\\\\n \\hline\n 0&\\multirow{2}{*}{0}&\\multirow{2}{*}{1}&\\multirow{2}{*}{1} &\\multirow{2}{*}{2} &2\\\\\n \\cline{1-1}\n \\cline{6-6}\n 1& & & & & -2\\\\\n \\hline\n 0&\\multirow{2}{*}{1}&\\multirow{2}{*}{0}&\\multirow{2}{*}{0} &\\multirow{2}{*}{-2} &2\\\\\n \\cline{1-1}\n \\cline{6-6}\n 1& & & & & -2\\\\\n \\hline\n 0/1&1&0&1 &-1 &-1 \\\\\n \\hline\n 0/1&1&1&0 &-1 &-1 \\\\\n \\hline\n 0/1&1&1&1 &0 &0 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Low Power In-Memory Multiplication andAccumulation Array with Modified Radix-4 Inputand Canonical Signed Digit Weights", "authors": ["Rui Xiao", "Kejie Huang", "Yewei Zhang", "Haibin Shen"], "url": "https://arxiv.org/abs/2101.02419v1", "attribution": "\"A Low Power In-Memory Multiplication andAccumulation Array with Modified Radix-4 Inputand Canonical Signed Digit Weights\" by Rui Xiao, Kejie Huang, Yewei Zhang, and Haibin Shen, arXiv:2101.02419v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13096v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n \\toprule\n \\multirow{2}{*}{Model} & \\multirow{2}{*}{Dense} & \\multicolumn{2}{c|}{2:4} \\\\\n & & AP & \\textbf{+gAP} \\\\\n \\midrule\n YOLOv5n & 46.20 & 13.40 & \\textbf{37.10} \\\\\n YOLOv5s & 56.40 & 40.70 & \\textbf{51.60} \\\\\n YOLOv5m & 64.20 & 54.00 & \\textbf{61.20} \\\\\n YOLOv5l & 67.40 & 59.80 & \\textbf{65.40} \\\\\n \\midrule\n BERT SQuAD & 88.54 & 84.75 & 87.41 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "SPDY: Accurate Pruning with Speedup Guarantees", "authors": ["Elias Frantar", "Dan Alistarh"], "url": "https://arxiv.org/abs/2201.13096v2", "attribution": "\"SPDY: Accurate Pruning with Speedup Guarantees\" by Elias Frantar and Dan Alistarh, arXiv:2201.13096v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17276v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|l}\n Characteristic polynomial of $\\phi(T)$ & Order of $\\phi(T)$\\\\\\hline\n $ x^7 + x^6 - 3x^5 - 3x^4 + 3x^3 + 3x^2 - x - 1$& $2$\\\\\n $x^7 - x^6 - 2x^4 + 2x^3 + x - 1$& $3$\\\\\n $x^7 + x^6 - x - 1$& $6$\\\\\n $x^7-1$ & $7$\\\\\n $ x^7 + (1/2)(\\pm \\sqrt{13}-1)x^6 + (1/2)(3\\mp \\sqrt{13})x^5 + (1/2)(\\pm\\sqrt{13}-5)x^4$ & $13$\\\\\n $- (1/2)(\\pm \\sqrt{13}-5)x^3 - (1/2)(3\\mp \\sqrt{13})x^2 - (1/2)(\\pm \\sqrt{13}-1)x - 1$ & \n \\end{tabular}\n\\caption{Characteristic polynomials for $L_2(13)$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The moduli space of representations of the modular group into $G_2$", "authors": ["Angelica Babei", "Andrew Fiori", "Cameron Franc"], "url": "https://arxiv.org/abs/2501.17276v3", "attribution": "\"The moduli space of representations of the modular group into $G_2$\" by Angelica Babei, Andrew Fiori, and Cameron Franc, arXiv:2501.17276v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.09862v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{||c|c|c|c|c||} \n\\hline\n$\\kappa$ & 0.01 & 0.1 & 1 & 2\\\\\n\\hline\nrate & -0.352 & -0.470 & -0.456 & -0.446\\\\\n\\hline\n\\end{tabular}\n\\caption{Convergence rate for difference choices of hyperparameter $\\kappa$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Wasserstein-based Minimax Estimation of Dependence in Multivariate Regularly Varying Extremes", "authors": ["Xuhui Zhang", "Jose Blanchet", "Youssef Marzouk", "Viet Anh Nguyen", "Sven Wang"], "url": "https://arxiv.org/abs/2312.09862v2", "attribution": "\"Wasserstein-based Minimax Estimation of Dependence in Multivariate Regularly Varying Extremes\" by Xuhui Zhang, Jose Blanchet, Youssef Marzouk, Viet Anh Nguyen, and Sven Wang, arXiv:2312.09862v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.18423v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{SemRoDe's robustness to white-box attacks on BERT-MR}\n\\begin{tabular}{lccc}\n\\hline\n\\textbf{Approach} & \\textbf{CA (↑)} & \\textbf{AUA (↑)} & \\textbf{ASR (↓)} \\\\\n\\hline\nBaseline & 86.6 & 55.6 & 35.8 \\\\\nTextFooler + AT & 85.8 & 58.6 & 31.7 \\\\\nTextFooler + MMD & 86.0 & 67.6 & 21.4 \\\\ \n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks", "authors": ["Brian Formento", "Wenjie Feng", "Chuan Sheng Foo", "Luu Anh Tuan", "See-Kiong Ng"], "url": "https://arxiv.org/abs/2403.18423v1", "attribution": "\"SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks\" by Brian Formento, Wenjie Feng, Chuan Sheng Foo, Luu Anh Tuan, and See-Kiong Ng, arXiv:2403.18423v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.19625v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Rating-Level OLS with Clustered Standard Errors}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n& \\textbf{(1)} & \\textbf{(2)} \\\\\n& \\multicolumn{2}{c}{\\textit{Dependent Variable: AI Teaching Impact}} \\\\\n\\midrule\nAI Workplace Skill Disruption & 0.231*** & 0.231*** \\\\\n& (0.045) & (0.055) \\\\\n\\addlinespace\nSkill Type FE & Yes & Yes \\\\\nLanguage FE & Yes & Yes \\\\\n\\addlinespace\nIntercept & -0.042 & -0.042 \\\\\n& (0.252) & (0.188) \\\\\n\\midrule\nObservations & 152 & 152 \\\\\nAdj. $R^2$ & 0.302 & 0.302 \\\\\n\\addlinespace\nClustering & Respondent & Respondent \\& Skill \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Training for Obsolescence? The AI-Driven Education Trap", "authors": ["Andrew J. Peterson"], "url": "https://arxiv.org/abs/2508.19625v1", "attribution": "\"Training for Obsolescence? The AI-Driven Education Trap\" by Andrew J. Peterson, arXiv:2508.19625v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2306.12434v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|l|l|l|l|l|}\n \\hline\n \\textbf{ETF Basket} & \\textbf{Sharpe Ratio} & \\textbf{Type} \\\\ \\hline\n india, tw, can, israel, uk, spore, aus & 3.904 & Mixed \\\\ \\hline\n india, tw, can, israel, uk, spore, aus & 3.733 & Mixed \\\\ \\hline\n israel, tw, japan, spore, sk, india & 3.424 & Mixed \\\\ \\hline\n spore, india, usa, aus, israel, uk & 3.354 & Mixed \\\\ \\hline\n uk, india, sk, spore, china, eu & 3.311 & Mixed \\\\ \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Using Internal Bar Strength as a Key Indicator for Trading Country ETFs", "authors": ["Aditya Pandey", "Kunal Joshi"], "url": "https://arxiv.org/abs/2306.12434v1", "attribution": "\"Using Internal Bar Strength as a Key Indicator for Trading Country ETFs\" by Aditya Pandey and Kunal Joshi, arXiv:2306.12434v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.19033v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\\toprule\n & NN-RR & NN-VN & MAVE & SIR & SAVE \\\\ \\midrule\nmean & 0.602 & 0.774 & 0.743 & 1.324 & 0.772 \\\\ \nstd & 0.043 & 0.116 & 0.159 & 0.190 & 0.161 \\\\ \n$\\bar{d}$ & 19.6 & --- & 19.25 & 19.6 & 20.6 \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The results of testing errors on 20 replicates across different methods. We report the average and standard deviation of testing errors, along with averaged optimal $d$ determined through cross validation. NN-RR, NN-VN, MAVE, SIR, SAVE represent the neural network-based method with rank regularization, vanilla neural network regression, MAVE-based regression, SIR-based regression, and SAVE-based regression, respectively.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Neural Networks Perform Sufficient Dimension Reduction", "authors": ["Shuntuo Xu", "Zhou Yu"], "url": "https://arxiv.org/abs/2412.19033v1", "attribution": "\"Neural Networks Perform Sufficient Dimension Reduction\" by Shuntuo Xu and Zhou Yu, arXiv:2412.19033v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.03287v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Level 1 Crypto TVL Portfolio Descriptive Statistics. Values are portfolio returns in percentage points and are at the weekly frequency. TVL is total TVL. There are 105 weekly observations. \\(^{***}\\), \\(^{**}\\), and \\(^{*}\\), denote statistical significance at the 1\\%, 5\\%, and 10\\% levels respectively.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\\hline\n & HML & Q1 & Q2 & Q3 & Q4\\\\\n\\hline\nmean & -0.27 & 1.59 & 2.36\\(^{**}\\) & 1.70\\(^{**}\\) & 1.32\\(^{*}\\)\\\\\nstd & 7.79 & 12.19 & 12.49 & 8.99 & 7.90\\\\\nmin & -40.92 & -23.27 & -26.04 & -25.07 & -17.73\\\\\n25\\% & -2.90 & -5.13 & -4.35 & -2.30 & -3.29\\\\\n50\\% & 0.85 & -0.59 & 0.73 & 0.81 & 0.27\\\\\n75\\% & 3.63 & 6.62 & 7.60 & 7.77 & 5.98\\\\\nmax & 15.72 & 58.49 & 47.24 & 29.04 & 28.99\\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns", "authors": ["Matt Brigida"], "url": "https://arxiv.org/abs/2506.03287v3", "attribution": "\"The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns\" by Matt Brigida, arXiv:2506.03287v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.16975v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{UCI tabular datasets experiment, . Hyperparameter search space.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l||c||c|}\n\t\t\t\\hline\n\t\t\tHyperparameters & PCP-Map & COT-Flow\\\\\n\t\t\t\\hline\\hline\n\t\t\tBatch size & \\{32, 64\\} & \\{32, 64\\}\\\\\n\t\t\tLearning rate & \\{0.01, 0.005, 0.001\\} & \\{0.01, 0.005, 0.001\\} \\\\\n\t\t\tFeature Layer width, $w$ & \\{32, 64, 128, 256, 512\\} & \\{32, 64, 128, 256, 512\\}\\\\\n Context Layer width, $u$ & \\{$\\frac{w}{2^i}| \\frac{w}{2^i} > m, i=0,1,...$\\}$\\cup$\\{$m$\\} & \\\\\n\t\t\tNumber of layers, $K$ & \\{2, 3, 4, 5, 6\\} & \\{2\\} \\\\\n\t\t\tNumber of time steps, $n_t$ & & \\{8, 16\\}\\\\\n\t\t\t$[\\log(\\alpha_1), \\log(\\alpha_2)]$ & & [$\\mathcal{U}$(-1, 3), $\\mathcal{U}$(-1, 3)]\\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference", "authors": ["Zheyu Oliver Wang", "Ricardo Baptista", "Youssef Marzouk", "Lars Ruthotto", "Deepanshu Verma"], "url": "https://arxiv.org/abs/2310.16975v3", "attribution": "\"Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference\" by Zheyu Oliver Wang, Ricardo Baptista, Youssef Marzouk, Lars Ruthotto, and Deepanshu Verma, arXiv:2310.16975v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.06293v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison of the base and finetuned models across three retrieval metrics.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n & Base (Snowflake Arctic Embed-Medium) & Finetuned \\\\\n\\midrule\nMRR@10 & 38\\% & 84\\% \\\\\nMAP@100 & 39\\% & 84\\% \\\\\nNDCG@10 & 43\\% & 86\\% \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Generative AI Enhanced Financial Risk Management Information Retrieval", "authors": ["Amin Haeri", "Jonathan Vitrano", "Mahdi Ghelichi"], "url": "https://arxiv.org/abs/2504.06293v2", "attribution": "\"Generative AI Enhanced Financial Risk Management Information Retrieval\" by Amin Haeri, Jonathan Vitrano, and Mahdi Ghelichi, arXiv:2504.06293v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05544v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Summary of different approaches}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c||c|c|c|c|c|c|c}\n \\toprule\n Method & Co-distillation & Diversity & I.B. & Merging & Others & Branch/Net & Consistently better than Ind. \\\\\n \\midrule\n DML & Pred. pairwise & & & & & Net &\\\\\n CL-ILR & Pred. & & & & & Branch &\\\\\n ONE & Preds & & & Gate & & Branch&\\\\\n FFL & Pred. & & & Feat. Fus. & & Both&\\\\\n OKDDip & Pred. asymetric & & & & & Both & $\\approx$ \\\\\n KDCL & Pred. & Data Augmentation & & Weights on val & & Net &\\\\\n PCL & Pred. & Data Augmentation & & Feat. Fus. & Mean teacher & Branch & $\\approx$ \\\\\n AFD & Features & & & & & Net & $\\approx$ \\\\\n \\midrule\n GAL & & Gradients & & & & Net &\\\\\n GPMR & & Gradients & & & Grads. Magnitude & Net &\\\\\n ADP & & Non maximum pred. & & & Entropy Pred. & Both & $\\checkmark$\\\\\n DIBS & & JSD Features & VIB & & Custom sampling & Both & $?$ \\\\\n \\midrule\n \\midrule\n IB & & & VIB & & & Both & $\\checkmark$ \\\\\n CEB & & & VCEB & & & Both & $\\checkmark$\\\\\n \\midrule\n IBR (Ours ) & & R & VIB & & & Both & $\\checkmark$\\\\\n CEBR (Ours ) & & R & VCEB & & & Both & $\\checkmark$\\\\\n \\midrule\n DICE (Ours ) & & CR & VCEB & & & Both & $\\checkmark$\\\\%\n \\bottomrule%\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation", "authors": ["Alexandre Rame", "Matthieu Cord"], "url": "https://arxiv.org/abs/2101.05544v1", "attribution": "\"DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation\" by Alexandre Rame and Matthieu Cord, arXiv:2101.05544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.06550v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Differences Between With/Without Engagement Datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n& All Tweets until 03/2020 & Tweets with Retweets Data & Difference & p-value\\\\\n\\midrule\nFemale&0.275 (0.000)&0.276 (0.000)&0.001&0.196\\\\\n&&&&\\\\\nAge&57.711 (0.007)&58.665 (0.010)&0.955&0.000\\\\\n&&&&\\\\\nBlack&0.076 (0.000)&0.074 (0.000)&-0.002&0.000\\\\\n&&&&\\\\\nBachelor&0.329 (0.000)&0.336 (0.000)&0.007&0.000\\\\\n&&&&\\\\\nMaster or Higher&0.626 (0.000)&0.617 (0.000)&-0.009&0.000\\\\\n&&&&\\\\\nRepublican&0.431 (0.000)&0.450 (0.000)&0.019&0.000\\\\\n&&&&\\\\\n|Nominate Score|&0.434 (0.000)&0.429 (0.000)&-0.005&0.000\\\\\n&&&&\\\\\nObservations&2,322,957 &1,178,861 &&\\\\\n&&&&\\\\\n& All Tweets until 03/2020 & Tweets with Retweets Data & Difference & p-value\\\\\n\\midrule\nShare of Blame Tweets&0.144 (0.000)&0.148 (0.000)&0.004&0.000\\\\\n&&&&\\\\\nShare of Merit Tweets&0.168 (0.000)&0.206 (0.000)&0.038&0.000\\\\\n&&&&\\\\\nShare of None Tweets&0.687 (0.000)&0.646 (0.000)&-0.042&0.000\\\\\n&&&&\\\\\nShare of Tweets about Economy&0.150 (0.000)&0.151 (0.000)&0.001&0.064\\\\\n&&&&\\\\\nShare of Tweets about Environment&0.051 (0.000)&0.054 (0.000)&0.004&0.000\\\\\n&&&&\\\\\nShare of Tweets about Gender&0.070 (0.000)&0.064 (0.000)&-0.006&0.000\\\\\n&&&&\\\\\nShare of Tweets about Gun Control&0.032 (0.000)&0.032 (0.000)&-0.000&0.326\\\\\n&&&&\\\\\nShare of Tweets about Healthcare&0.118 (0.000)&0.122 (0.000)&0.003&0.000\\\\\n&&&&\\\\\nShare of Tweets about Immigration&0.055 (0.000)&0.059 (0.000)&0.003&0.000\\\\\n&&&&\\\\\nShare of Tweets about Police&0.027 (0.000)&0.027 (0.000)&0.000&0.042\\\\\n&&&&\\\\\nShare of Tweets about Racial Relations&0.064 (0.000)&0.059 (0.000)&-0.005&0.000\\\\\n&&&&\\\\\nShare of Tweets about Other Topics&0.433 (0.000)&0.432 (0.000)&-0.001&0.184\\\\\n&&&&\\\\\nObservations&2,322,957 &1,178,861 &&\\\\\n&&&&\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Blameocracy: Causal Attribution in Political Communication", "authors": ["Francesco Bilotta", "Alberto Binetti", "Giacomo Manferdini"], "url": "https://arxiv.org/abs/2504.06550v2", "attribution": "\"Blameocracy: Causal Attribution in Political Communication\" by Francesco Bilotta, Alberto Binetti, and Giacomo Manferdini, arXiv:2504.06550v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.09661v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c||c|c|}\n\t\t\t\\hline\n\t\t\t$m$ & $k$ & Talbot-Gaver-Stehfest & Euler-Gaver-Stehfest \\\\ \\hline \\hline\n\t\t\t30 & 15 & $0.016420$ & $0.016413$ \\\\ \\hline\n\t\t\t30 & 18& $0.016420$ & $0.016413$\\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Joint density $p(1,2,2)$.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Geometric Brownian motion with affine drift and its time-integral", "authors": ["Runhuan Feng", "Pingping Jiang", "Hans Volkmer"], "url": "https://arxiv.org/abs/2012.09661v1", "attribution": "\"Geometric Brownian motion with affine drift and its time-integral\" by Runhuan Feng, Pingping Jiang, and Hans Volkmer, arXiv:2012.09661v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19280v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The four testing boundary conditions }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrrl}\n\t Case\t& $d_{11}$ & $d_{12}$ & $d_{13}$ & $d_{14}$ & $d_{21}$ & $d_{22}$ & $d_{23}$ & $d_{24}$ & condition on \\\\ \\hline\\hline\n\t $Neumann$ & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & $v_s,u_s$ \\\\\n\t\t$Dirichlet$ & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & $v_s, v_e$ \\\\\n\t\t$Mix$ & 1 & 1 & 0 & 0 & 0 & 0 & 1 & 1 & $v_s+u_s, v_e+u_e$ \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions", "authors": ["Xiaorong Zou"], "url": "https://arxiv.org/abs/2504.19280v1", "attribution": "\"Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions\" by Xiaorong Zou, arXiv:2504.19280v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.17244v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Wilcoxon Signed-Rank Test Results (CMG vs. Other Models)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|}\n\\hline\n\\textbf{Comparison} & \\textbf{p-value} \\\\\n\\hline\nCMG vs. BiLSTM & 0.00399 \\\\\nCMG vs. Gaussian Naive Bayes & 0.00012 \\\\\nCMG vs. GRU & 0.02520 \\\\\nCMG vs. Logistic Regression & 0.00106 \\\\\nCMG vs. LSTM & 0.01004 \\\\\nCMG vs. Multi Logistic Reg. & $2.93 \\times 10^{-7}$ \\\\\nCMG vs. Random Forest & 0.00027 \\\\\nCMG vs. XGBoost & 0.00160 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data", "authors": ["Arif Pathan"], "url": "https://arxiv.org/abs/2506.17244v1", "attribution": "\"Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data\" by Arif Pathan, arXiv:2506.17244v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.16016v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\n$(m,n)$ & $p$ & \\multicolumn{5}{|c|}{$q$} \\\\\n\\hline\n& & CC & $\\deg2$ & $\\deg 3$ & $\\deg 2+2$ & $\\deg 4$ \\\\\n\\hline\n$(1,25)$ & $3$ & & & & & $7$ \\\\\n\\hline\n$(1,26)$ & $7$ & & $3$ & $3$ & $3$ & $3$ \\\\\n\\hline\n$(1,28)$ & $5$ & $ 1 \\pmod 4$ & $13$ & $13$ & $3$ & $3$ \\\\\n\\hline\n$(1,32)$ & $3$ & & & $5$ & & $5$ \\\\\n\\hline\n$(1,34)$ & $3$ & & & & & $5$ \\\\\n\\hline\n$(1,36)$ & $5$ & $ 1 \\pmod 4$ & &$13$ & & $7$ \\\\\n\\hline\n$(1,42)$ & $11$ & & $5$ & $5$ & $5$ & $5$ \\\\\n\\hline\n$(2,20)$ & $7$ & & $3$ & $3$ & $3$ & $3$ \\\\\n\\hline\n$(2,24)$ & $5$ & $ 1 \\pmod 4$ & & $17$ & & $7,11$ \\\\\n\\hline\n\\end{tabular}\n\\caption{}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Classification of torsion of elliptic curves over quartic fields", "authors": ["Maarten Derickx", "Filip Najman"], "url": "https://arxiv.org/abs/2412.16016v2", "attribution": "\"Classification of torsion of elliptic curves over quartic fields\" by Maarten Derickx and Filip Najman, arXiv:2412.16016v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10253v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The effect of interleaving self-supervision and multi-agent game-play. The game setup has two tasks: Sender Predicting Rotation as per Table~, while using various augmentations (original and SimCLR same or individual).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n \\toprule\n Feature Extractor & Comm. & Top-5 & \\#Target & Target & Rot.\\\\\n & rate & comm. & class & class & acc.\\\\\n & & rate & in top-5 & avg. rank & \\\\\n \\midrule\n \\multicolumn{6}{l}{\\textbf{Sender \\& Receiver images augmented with the original transforms:}}\\\\\n Learned end-end & 0.72 & 0.98 & 2.05 & 42.89 & 0.83\\\\\n Pretrained SS end-end & 0.84 & 0.99 & 2.19 & 40.19 & 0.79\\\\\n Pretrained SS \\& fixed & 0.80 & 0.99 & 2.23 & 39.72 & 0.7\\\\\n \\midrule\n \\multicolumn{6}{l}{\\textbf{Sender \\& Receiver images augmented with SimCLR transforms:}}\\\\\n Learned end-end & 0.53 & 0.92 & 2.22 & 37.16 & 0.80\\\\% this hasnowd\n Pretrained SS end-end & 0.49 & 0.89 & 2.18 & 38.74 & 0.79\\\\\n Pretrained SS \\& fixed & 0.42 & 0.85 & 2.14 & 39.57 & 0.78\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "The emergence of visual semantics through communication games", "authors": ["Daniela Mihai", "Jonathon Hare"], "url": "https://arxiv.org/abs/2101.10253v1", "attribution": "\"The emergence of visual semantics through communication games\" by Daniela Mihai and Jonathon Hare, arXiv:2101.10253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2301.00248v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{This table presents the hyper-parameter configuration used for building hidden Markov models using the hmmlearn package. The default value is used for hyper-parameters not listed. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\toprule\n\\textbf{Hyper-parameter} & \\textbf{Values} \\\\\n\\midrule\nn\\_components & 4 \\\\\nn\\_iter & 100 \\\\\nrandom\\_state & 42 \\\\\nemissions & Gaussian \\\\\nalgorithm & Viterbi \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Nowcasting Stock Implied Volatility with Twitter", "authors": ["Thomas Dierckx", "Jesse Davis", "Wim Schoutens"], "url": "https://arxiv.org/abs/2301.00248v1", "attribution": "\"Nowcasting Stock Implied Volatility with Twitter\" by Thomas Dierckx, Jesse Davis, and Wim Schoutens, arXiv:2301.00248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.10270v5_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cr|cc}\n\\hline\n & \\multicolumn{2}{c|}{Hodge Scattering + SVM} & \\multicolumn{2}{c|}{UGT} & \\multicolumn{2}{c}{GFN} \\\\ \\hline\nGraph & Accuracy & \\# Param & Accuracy & \\# Param & Accuracy & \\# Param \\\\ \\hline\nCollab & 80.38 & 256 & 77.84 & 866,746 & 81.50 & 68,754 \\\\\nDD & 72.71 & 256 & 80.23 & 76,928 & 79.37 & 68,754 \\\\\nIMDB-B & 73.10 & 256 & 77.04 & 55,508 & 73.40 & 68,754 \\\\\nIMDB-M & 49.67 & 256 & 53.60 & 48,698 & 51.80 & 68,818 \\\\\nMUTAG & 85.78 & 256 & 80.23 & 4,178 & 85.83 & 68,818 \\\\\nPROTEINS & 75.35 & 256 & 78.53 & 1,878 & 76.46 & 68,818 \\\\\nPTC & 68.28 & 256 & 69.63 & 12,038 & 66.60 & 68,818 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Multiscale Hodge Scattering Networks for Data Analysis", "authors": ["Naoki Saito", "Stefan C. Schonsheck", "Eugene Shvarts"], "url": "https://arxiv.org/abs/2311.10270v5", "attribution": "\"Multiscale Hodge Scattering Networks for Data Analysis\" by Naoki Saito, Stefan C. Schonsheck, and Eugene Shvarts, arXiv:2311.10270v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.12900v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Truth table of NAND.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc} \n \\hline\n \\multicolumn{2}{c}{input}&\\multicolumn{2}{c}{output}&\\multicolumn{2}{c}{buffer}\\\\ \\hline\n f & b & f&b & f&b\\\\ \\hline\n 0 & 0 & -&1&-&discharge\\\\\n 0 & 1 & -&1&-&-\\\\\n 1 & 1 & -&0&store&store\\\\\n 1 & 0 & -&1&store&discharge\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Electric Power Processing Using Logic Operation and Error Correction", "authors": ["Shota Inagaki", "Shiu Mochiyama", "Takashi Hikihara"], "url": "https://arxiv.org/abs/2010.12900v1", "attribution": "\"Electric Power Processing Using Logic Operation and Error Correction\" by Shota Inagaki, Shiu Mochiyama, and Takashi Hikihara, arXiv:2010.12900v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.01330v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n \\hline\n Resolution & Batch size & $m$ & $H$ & $K$ & $p_{d}$ & $s$ & $\\lambda$ \\\\ \\hline\n $64\\times64$ & 50 & 2 & \\multirow{3}{*}{256} & \\multirow{3}{*}{512} & 15 $mm$ & \\multirow{3}{*}{0.95} & \\multirow{3}{*}{(638 $nm$, 520 $nm$, 450 $nm$)} \\\\ \\cline{1-3} \\cline{6-6}\n $128\\times128$ & 20 & 3 & & & 21.5 $mm$ & & \\\\ \\cline{1-3} \\cline{6-6}\n $256\\times256$ & 5 & 4 & & & 25 $mm$ & & \\\\ \\hline\n \\end{tabular}\n\\caption{Holo-VQVAE configurations for various input resolutions.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Holo-VQVAE: VQ-VAE for phase-only holograms", "authors": ["Joohyun Park", "Hyeongyeop Kang"], "url": "https://arxiv.org/abs/2404.01330v1", "attribution": "\"Holo-VQVAE: VQ-VAE for phase-only holograms\" by Joohyun Park and Hyeongyeop Kang, arXiv:2404.01330v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08533v5_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison on Market1501 dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\t\t\\hline\n\t\t\\multirow{2}{*}{Methods} & \\multicolumn{2}{c}{Market1501} \\\\ \\cline{2-3} \n\t\t& Rank-1(\\%) & mAP(\\%) \\\\ \\hline\n\t\tIANet & 94.4 & 83.1 \\\\ \n\t\tDGNet & 94.8 & 86.0 \\\\ \n\t\tSCAL & 95.8 & 89.3 \\\\ \n\t\tCircle Loss & 96.1 & 87.4 \\\\ \n\t\tSB & 94.5 & 85.9 \\\\ \\hline\n\t\tSB + RK & 95.4 & 94.2 \\\\ \n\t\tSB + GGT(ours) & \\textbf{94.6} & 85.7 \\\\ \n\t\tSB + GGT+ RK(ours) & \\textbf{96.2} & \\textbf{94.7} \\\\ \n\t\tSB + LGT(ours) & \\textbf{95.1} & \\textbf{87.2} \\\\ \n\t\tSB + LGT + RK(ours) & \\textbf{95.9} & \\textbf{94.4} \\\\ \\hline\n\t\tFastReID & 96. 3 & 90.3 \\\\ \n\t\tFastReID + RK & 96.8 & 95.3 \\\\ \n\t\tFastReID + GGT(ours) & \\textbf{96.5} & \\textbf{91.2} \\\\ \n\t\tFastReID + GGT + RK(ours) & \\textbf{96.9} & \\textbf{95.6} \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method", "authors": ["Yunpeng Gong", "Liqing Huang", "Lifei Chen"], "url": "https://arxiv.org/abs/2101.08533v5", "attribution": "\"Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method\" by Yunpeng Gong, Liqing Huang, and Lifei Chen, arXiv:2101.08533v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.15496v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of running times for GCN, GAT-jmlr and GAT*.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc}\n\\toprule\n Method & GCN & GAT-jmlr & GAT* \\\\ \\hline \n Runtime (/s) & 8.63 & 10.03 & 8.93 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models", "authors": ["Zhongtian Ma", "Qiaosheng Zhang", "Bocheng Zhou", "Yexin Zhang", "Shuyue Hu", "Zhen Wang"], "url": "https://arxiv.org/abs/2412.15496v3", "attribution": "\"Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models\" by Zhongtian Ma, Qiaosheng Zhang, Bocheng Zhou, Yexin Zhang, Shuyue Hu, and Zhen Wang, arXiv:2412.15496v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17973v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Size and Power of the Cross-Fit Tests for testing $H_0:\\delta^{(j)}=0,j=1,2$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccccccc}\n \\toprule\n $n$ & Size & \\multicolumn{14}{c}{Power (values of $h$ below)} \\\\\n \\cmidrule{3-16}\n & & 0.105 & 0.211 & 0.316 & 0.421 & 0.526 & 0.632 & 0.737 & 0.842 & 0.947 & 1.053 & 1.158 & 1.263 & 1.368 & 1.474 \\\\\n \\hline\n 50 & 0 & 0.000 & 0.005 & 0.030 & 0.100 & 0.222 & 0.354 & 0.493 & 0.612 & 0.732 & 0.834 & 0.894 & 0.940 & 0.972 & 0.982 \\\\\n 100 & 0 & 0.000 & 0.011 & 0.070 & 0.194 & 0.375 & 0.499 & 0.631 & 0.748 & 0.864 & 0.926 & 0.963 & 0.978 & 0.989 & 0.998 \\\\\n 200 & 0 & 0.000 & 0.056 & 0.252 & 0.504 & 0.665 & 0.790 & 0.867 & 0.929 & 0.965 & 0.981 & 0.987 & 0.991 & 0.994 & 0.997 \\\\\n 300 & 0 & 0.006 & 0.158 & 0.558 & 0.809 & 0.912 & 0.954 & 0.973 & 0.989 & 0.998 & 0.996 & 0.996 & 0.998 & 0.997 & 0.997 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Universal Inference for Incomplete Discrete Choice Models", "authors": ["Hiroaki Kaido", "Yi Zhang"], "url": "https://arxiv.org/abs/2501.17973v1", "attribution": "\"Universal Inference for Incomplete Discrete Choice Models\" by Hiroaki Kaido and Yi Zhang, arXiv:2501.17973v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.03284v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrcc}\n\\hline\\hline\n & $d$ & MAP & MR1\\\\\n\\hline\\hline\n2DFTM~ & 50 & 0.275 & 155 \\\\\nDmax~ & $5.5\\,\\text{k}$ & 0.322 & 132 \\\\\nSiMPle~ & $2.2\\,\\text{k}$ & 0.332 & 142\\\\\nQmax~ & $5.5\\,\\text{k}$ & 0.365 & 113 \\\\\nQmax*~ & $5.5\\,\\text{k}$ & 0.373 & 104 \\\\\nEarlyFusion~ & $8.5\\,\\text{k}$ & 0.426 & 116 \\\\\nLateFusion~ & $5.5\\,\\text{k}$ & 0.454 & 177 \\\\\nMOVE~ & $4\\,\\text{k}$ & 0.495 & 42 \\\\ % 0.489 & 43 \\\\ \nMOVE~ & $16\\,\\text{k}$ & 0.507 & 40 \\\\ % \\textbf{0.506} & \\textbf{42} \\\\ \n\\textbf{Re-MOVE} & \\textbf{256} & \\textbf{0.524} & \\textbf{38} \\\\ \n\\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison with existing VI systems using Da-TACOS (taken from~). When not explicit, embedding sizes $d$ are estimated for a song duration of 3.5\\,min (see text). Results for the proposed methodology are highlighted in bold.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Less is more: Faster and better music version identification with embedding distillation", "authors": ["Furkan Yesiler", "Joan Serrà", "Emilia Gómez"], "url": "https://arxiv.org/abs/2010.03284v1", "attribution": "\"Less is more: Faster and better music version identification with embedding distillation\" by Furkan Yesiler, Joan Serrà, and Emilia Gómez, arXiv:2010.03284v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.15785v3_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Market Structure Effects}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc|ccc|ccc}\n \\toprule\n & \\multicolumn{3}{c}{DL-NW} & \\multicolumn{3}{c}{UA-CO} & \\multicolumn{3}{c}{AA-US} \\\\\n \\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10}\n & Price & Pas. & Seats & Price & Pas. & Seats & Price & Pas. & Seats \\\\\n \\midrule\n \\multicolumn{10}{l}{\\textit{Panel A:}} \\\\\n $\\Delta$HHI & 0.102*** & -0.082 & -0.422*** & 0.021** & -0.097 & 0.008 & 0.067*** & -0.290*** & -0.472*** \\\\\n & (0.012) & (0.069) & (0.047) & (0.010) & (0.063) & (0.044) & (0.014) & (0.086) & (0.060) \\\\\n \n \\multicolumn{10}{l}{\\textit{Panel B:}} \\\\\n 2 $\\to$ 1 & 0.059*** & 0.039 & -0.323*** & 0.032*** & -0.300** & -0.275*** & 0.029*** & 0.056 & -0.170*** \\\\\n & (0.005) & (0.149) & (0.011) & (0.005) & (0.143) & (0.010) & (0.004) & (0.122) & (0.008) \\\\\n 1 $\\to$ 2 & -0.080*** & 0.092 & 0.289*** & -0.077*** & 0.090 & 0.325*** & -0.030*** & 0.151 & 0.274*** \\\\\n & (0.005) & (0.143) & (0.010) & (0.006) & (0.168) & (0.012) & (0.005) & (0.154) & (0.011) \\\\\n 3 $\\to$ 2 & 0.015** & 0.073 & -0.145*** & 0.017*** & 0.288* & -0.163*** & 0.007 & 0.061 & -0.134*** \\\\\n & (0.006) & (0.170) & (0.012) & (0.006) & (0.163) & (0.011) & (0.005) & (0.149) & (0.010) \\\\\n 2 $\\to$ 3 & -0.057*** & -0.230 & 0.168*** & -0.052*** & -0.144 & 0.202*** & -0.042*** & 0.102 & 0.168*** \\\\\n & (0.006) & (0.168) & (0.012) & (0.007) & (0.178) & (0.012) & (0.005) & (0.163) & (0.011) \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Bridging Retrospective and Prospective Merger Analyses: The Case of US Airline Mergers", "authors": ["Gaurab Aryal", "Anirban Chattopadhyaya", "Federico Ciliberto"], "url": "https://arxiv.org/abs/2503.15785v3", "attribution": "\"Bridging Retrospective and Prospective Merger Analyses: The Case of US Airline Mergers\" by Gaurab Aryal, Anirban Chattopadhyaya, and Federico Ciliberto, arXiv:2503.15785v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07988v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{PCK@0.1 score for keypoint localization with different percentage of labeled images. We report mean and standard deviation from three runs for different randomly sampled labeled subsets. Pseudo-labeled (PL) baseline is not evaluated for 100\\% of labeled data because there is no unlabeled data to generate pseudo-labels for.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll} \n & \\multicolumn{5}{c}{Percentage of labeled images} \\\\ \nMethod & 5\\% & 10\\% & 20\\% & 50\\% & 100\\% \\\\ \n\\toprule \n\\multicolumn{6}{c}{\\textbf{Dataset 1: MPII } } \\\\\nHRNet &66.22$\\pm$1.60 &69.18$\\pm$1.03 &71.83$\\pm$0.87 &75.73$\\pm$0.35 &81.11$\\pm$0.15 \\\\ \nPL & 62.44$\\pm$1.75 & 64.78$\\pm$1.44 & 69.35$\\pm$1.11 & 77.43$\\pm$0.48 & - \\\\ \nELT &68.27$\\pm$0.64 &71.03$\\pm$0.46 &72.37$\\pm$0.58 &77.75$\\pm$0.31 &81.01$\\pm$0.15 \\\\ \nGen &71.59$\\pm$1.12 &72.63$\\pm$0.62 &74.95$\\pm$0.32 &79.86$\\pm$0.19 &80.92$\\pm$0.32 \\\\ \n\\textbf{Ours}&\\textbf{74.15}$\\pm$\\textbf{0.83} &\\textbf{76.56}$\\pm$\\textbf{0.48} &\\textbf{78.46}$\\pm$\\textbf{0.36} &\\textbf{80.75}$\\pm$\\textbf{0.32} &\\textbf{82.12}$\\pm$\\textbf{0.14} \\\\\n\\toprule \n\\multicolumn{6}{c}{\\textbf{Dataset 2: LSP } } \\\\\nHRNet &40.19$\\pm$1.46 &45.17$\\pm$1.15 &55.22$\\pm$1.41 &62.61$\\pm$1.25 &72.12$\\pm$0.30 \\\\ \nPL & 37.36$\\pm$1.89 & 42.05$\\pm$1.68 & 48.86$\\pm$1.23 & 64.45$\\pm$0.96 & - \\\\\nELT &41.77$\\pm$1.56 &47.22$\\pm$0.91 &57.34$\\pm$0.94 &66.81$\\pm$0.62 &72.22$\\pm$0.13 \\\\ \nGen &61.01$\\pm$1.41 &67.75$\\pm$1.00 &68.80$\\pm$0.91 &69.70$\\pm$0.77 &72.25$\\pm$0.55 \\\\ \n\\textbf{Ours}&\\textbf{66.98}$\\pm$\\textbf{0.94} &\\textbf{69.56}$\\pm$\\textbf{0.66} &\\textbf{71.85}$\\pm$\\textbf{0.33} &\\textbf{72.59}$\\pm$\\textbf{0.56} &\\textbf{74.29}$\\pm$\\textbf{0.21} \\\\\n\\toprule \n\\multicolumn{6}{c}{\\textbf{Dataset 3: CUB-200-2011 } } \\\\\nHRNet &85.77$\\pm$0.38 &88.62$\\pm$0.14 &90.18$\\pm$0.22 &92.60$\\pm$0.28 &93.62$\\pm$0.13 \\\\ \nPL & 86.31$\\pm$0.45 & 89.51$\\pm$0.32 & 90.88$\\pm$0.28 & 92.78$\\pm$0.27 & - \\\\\nELT &86.54$\\pm$0.34 &89.48$\\pm$0.25 &90.86$\\pm$0.13 &92.26$\\pm$0.06 &93.77$\\pm$0.18 \\\\ \nGen &88.37$\\pm$0.40 &90.38$\\pm$0.22 &91.31$\\pm$0.21 &92.79$\\pm$0.14 &93.62$\\pm$0.25 \\\\ \n\\textbf{Ours}&\\textbf{91.11}$\\pm$\\textbf{0.33} &\\textbf{91.47}$\\pm$\\textbf{0.36} &\\textbf{92.36}$\\pm$\\textbf{0.30} &\\textbf{92.80}$\\pm$\\textbf{0.24} &\\textbf{93.81}$\\pm$\\textbf{0.13} \\\\\n\\toprule \n\\multicolumn{6}{c}{\\textbf{Dataset 4: ATRW } } \\\\\nHRNet &69.22$\\pm$0.87 &77.55$\\pm$0.84 &86.41$\\pm$0.45 &92.17$\\pm$0.18 &94.44$\\pm$0.10 \\\\ \nPL & 67.97$\\pm$1.07 & 75.26$\\pm$0.74 & 84.69$\\pm$0.57 & 92.15$\\pm$0.24 & - \\\\\nELT &74.53$\\pm$1.24 &80.35$\\pm$0.96 &87.98$\\pm$0.47 &92.80$\\pm$0.21 &94.75$\\pm$0.14 \\\\ \nGen &89.54$\\pm$0.57 &90.48$\\pm$0.49 &91.16$\\pm$0.13 &92.27$\\pm$0.24 &94.80$\\pm$0.13 \\\\ \n\\textbf{Ours}&\\textbf{92.57}$\\pm$\\textbf{0.64} &\\textbf{94.29}$\\pm$\\textbf{0.66} &\\textbf{94.49}$\\pm$\\textbf{0.36} &\\textbf{94.63}$\\pm$\\textbf{0.18} &\\textbf{95.31}$\\pm$\\textbf{0.12} \\\\ \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Semi-supervised Keypoint Localization", "authors": ["Olga Moskvyak", "Frederic Maire", "Feras Dayoub", "Mahsa Baktashmotlagh"], "url": "https://arxiv.org/abs/2101.07988v1", "attribution": "\"Semi-supervised Keypoint Localization\" by Olga Moskvyak, Frederic Maire, Feras Dayoub, and Mahsa Baktashmotlagh, arXiv:2101.07988v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.12910v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of Power and FDP metrics for Detectron and SHSD across different OOD sizes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\toprule\n\\textbf{OOD Size} & \\textbf{Power Detectron} & \\textbf{FDP Detectron} & \\textbf{Power SHSD} & \\textbf{FDP SHSD} \\\\ \\midrule\n100 & N/A & 1.00 & N/A & 0.00 \\\\\n1000 & N/A & 1.00 & N/A & 0.00 \\\\\n2000 & 0.96 & 0.61 & 0.40 & 0.02 \\\\\n3000 & 0.98 & 0.60 & 0.63 & 0.02 \\\\\n3500 & 0.98 & 0.60 & 0.67 & 0.02 \\\\\n8593 & 0.98 & 0.60 & 0.74 & 0.04 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Sequential Harmful Shift Detection Without Labels", "authors": ["Salim I. Amoukou", "Tom Bewley", "Saumitra Mishra", "Freddy Lecue", "Daniele Magazzeni", "Manuela Veloso"], "url": "https://arxiv.org/abs/2412.12910v1", "attribution": "\"Sequential Harmful Shift Detection Without Labels\" by Salim I. Amoukou, Tom Bewley, Saumitra Mishra, Freddy Lecue, Daniele Magazzeni, and Manuela Veloso, arXiv:2412.12910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.14709v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean and standard deviation (across samples) results of the proposed DDSP vocoder for speech enhancement on a low-resource and causal CRN model.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n Metrics & CRN & (\\textbf{Ours}) CRN + DDSP \\\\\n \\midrule\n \\multicolumn{3}{c}{Small version ($\\approx$300K parameters)} \\\\\n \\midrule\n PESQ ($\\uparrow$) & 1.83 \\textcolor{gray}{$\\pm$ 0.29} & \\textbf{1.88} \\textcolor{gray}{$\\pm$ 0.29} \\\\\n STOI ($\\uparrow$) & 71.95 \\textcolor{gray}{$\\pm$ 8.50} & \\textbf{72.96} \\textcolor{gray}{$\\pm$ 8.42} \\\\\n DNSMOS {\\scriptsize (OVRL)} ($\\uparrow$) & 2.79 \\textcolor{gray}{$\\pm$ 0.23} & \\textbf{3.15} \\textcolor{gray}{$\\pm$ 0.25} \\\\\n MCD ($\\downarrow$) & 6.91 \\textcolor{gray}{$\\pm$ 1.27} & \\textbf{5.92} \\textcolor{gray}{$\\pm$ 0.95} \\\\\n SSIM ($\\uparrow$) & 0.81 \\textcolor{gray}{$\\pm$ 0.05} & \\textbf{0.83} \\textcolor{gray}{$\\pm$ 0.04}\\\\\n \\midrule\n \\multicolumn{3}{c}{Large version ($\\approx$600K parameters)} \\\\\n \\midrule\n PESQ ($\\uparrow$) & \\textbf{1.85} \\textcolor{gray}{$\\pm$ 0.30} & 1.81 \\textcolor{gray}{$\\pm$ 0.28} \\\\\n STOI ($\\uparrow$) & \\textbf{72.75} \\textcolor{gray}{$\\pm$ 8.59} & 72.36 \\textcolor{gray}{$\\pm$ 8.26} \\\\\n DNSMOS {\\scriptsize (OVRL)} ($\\uparrow$) & 2.77 \\textcolor{gray}{$\\pm$ 0.22} & \\textbf{3.12} \\textcolor{gray}{$\\pm$ 0.23} \\\\\n MCD ($\\downarrow$) & 6.86 \\textcolor{gray}{$\\pm$ 1.28} & \\textbf{6.09} \\textcolor{gray}{$\\pm$ 1.03}\\\\\n SSIM ($\\uparrow$) & 0.81 \\textcolor{gray}{$\\pm$ 0.05} & \\textbf{0.82} \\textcolor{gray}{$\\pm$ 0.04}\\\\\n \\midrule[\\heavyrulewidth]\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Improving Resource-Efficient Speech Enhancement via Neural Differentiable DSP Vocoder Refinement", "authors": ["Heitor R. Guimarães", "Ke Tan", "Juan Azcarreta", "Jesus Alvarez", "Prabhav Agrawal", "Ashutosh Pandey", "Buye Xu"], "url": "https://arxiv.org/abs/2508.14709v1", "attribution": "\"Improving Resource-Efficient Speech Enhancement via Neural Differentiable DSP Vocoder Refinement\" by Heitor R. Guimarães, Ke Tan, Juan Azcarreta, Jesus Alvarez, Prabhav Agrawal, Ashutosh Pandey, and Buye Xu, arXiv:2508.14709v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.01412v3_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{F1 Scores across IT data.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\toprule\nModel & Graph type & F1 Reg1 & F1 Reg 2 \\\\ \\midrule\nPCMCI+ &W& 12.1 & \\textbf{29.6} \\\\\nDYNOTEARS &W& 18.2 & \\textbf{28.5} \\\\ \nRhino &W& 28.6 & 25.8 \\\\ \nCASTOR &W& 18.2 & \\textbf{28.5} \\\\ \nCASTOR non-lin &W& \\textbf{40.0} & 24.5 \\\\ \\midrule\nCD-NOD & S& \\multicolumn{2}{c}{23.5}\\\\\nCASTOR non-lin & S& \\multicolumn{2}{c}{\\textbf{36.8}}\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Causal Temporal Regime Structure Learning", "authors": ["Abdellah Rahmani", "Pascal Frossard"], "url": "https://arxiv.org/abs/2311.01412v3", "attribution": "\"Causal Temporal Regime Structure Learning\" by Abdellah Rahmani and Pascal Frossard, arXiv:2311.01412v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.17168v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Calibration of probabilistic arrival time forecasts for lead times $l=1$ to $l=8$ on an out-of-time holdout set}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\t\t\t\\toprule\n\t\t\tProbability & $l=1$ & $l=2$ & $l=3$ & $l=4$ \\\\\n\t\t\t\\midrule\n\t\t\t0.0-0.1 & 0.072 ± 0.002 & 0.059 ± 0.001 & 0.065 ± 0.000 & 0.056 ± 0.000 \\\\\n\t\t\t0.1-0.2 & 0.163 ± 0.003 & 0.159 ± 0.001 & 0.154 ± 0.001 & 0.152 ± 0.001 \\\\\n\t\t\t0.2-0.3 & 0.285 ± 0.004 & 0.262 ± 0.001 & 0.250 ± 0.001 & 0.245 ± 0.001 \\\\\n\t\t\t0.3-0.4 & 0.409 ± 0.004 & 0.357 ± 0.001 & 0.346 ± 0.001 & 0.335 ± 0.001 \\\\\n\t\t\t0.4-0.5 & 0.485 ± 0.004 & 0.468 ± 0.001 & 0.438 ± 0.001 & 0.423 ± 0.001 \\\\\n\t\t\t0.5-0.6 & 0.612 ± 0.004 & 0.564 ± 0.001 & 0.540 ± 0.001 & 0.498 ± 0.002 \\\\\n\t\t\t0.6-0.7 & 0.693 ± 0.003 & 0.645 ± 0.001 & 0.644 ± 0.001 & 0.596 ± 0.002 \\\\\n\t\t\t0.7-0.8 & 0.793 ± 0.002 & 0.756 ± 0.001 & 0.723 ± 0.001 & 0.680 ± 0.003 \\\\\n\t\t\t0.8-0.9 & 0.887 ± 0.001 & 0.843 ± 0.001 & 0.815 ± 0.002 & 0.809 ± 0.003 \\\\\n\t\t\t0.9-1.0 & 0.983 ± 0.000 & 0.932 ± 0.001 & 0.922 ± 0.001 & 0.907 ± 0.002 \\\\\n\t\t\t\\cmidrule{2-5}\n\t\t\t & $l=5$ & $l=6$ & $l=7$ & $l=8$ \\\\\n\t\t\t\\cmidrule{2-5}\n\t\t\t0.0-0.1 & 0.050 ± 0.000 & 0.046 ± 0.000 & 0.040 ± 0.000 & 0.038 ± 0.000 \\\\\n\t\t\t0.1-0.2 & 0.144 ± 0.001 & 0.136 ± 0.001 & 0.126 ± 0.001 & 0.134 ± 0.001 \\\\\n\t\t\t0.2-0.3 & 0.230 ± 0.001 & 0.218 ± 0.001 & 0.213 ± 0.001 & 0.203 ± 0.001 \\\\\n\t\t\t0.3-0.4 & 0.321 ± 0.001 & 0.302 ± 0.001 & 0.281 ± 0.002 & 0.254 ± 0.002 \\\\\n\t\t\t0.4-0.5 & 0.398 ± 0.002 & 0.364 ± 0.002 & 0.344 ± 0.003 & 0.351 ± 0.003 \\\\\n\t\t\t0.5-0.6 & 0.461 ± 0.002 & 0.461 ± 0.003 & 0.428 ± 0.003 & 0.516 ± 0.005 \\\\\n\t\t\t0.6-0.7 & 0.559 ± 0.003 & 0.550 ± 0.003 & 0.548 ± 0.005 & 0.662 ± 0.013 \\\\\n\t\t\t0.7-0.8 & 0.710 ± 0.003 & 0.666 ± 0.004 & 0.612 ± 0.010 & 0.739 ± 0.023 \\\\\n\t\t\t0.8-0.9 & 0.781 ± 0.003 & 0.724 ± 0.005 & 0.893 ± 0.004 & 1.000 ± 0.000 \\\\\n\t\t\t0.9-1.0 & 0.863 ± 0.004 & 0.963 ± 0.001 & 1.000 ± 0.000 & 1.000 ± 0.000 \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Learning an Inventory Control Policy with General Inventory Arrival Dynamics", "authors": ["Sohrab Andaz", "Carson Eisenach", "Dhruv Madeka", "Kari Torkkola", "Randy Jia", "Dean Foster", "Sham Kakade"], "url": "https://arxiv.org/abs/2310.17168v2", "attribution": "\"Learning an Inventory Control Policy with General Inventory Arrival Dynamics\" by Sohrab Andaz, Carson Eisenach, Dhruv Madeka, Kari Torkkola, Randy Jia, Dean Foster, and Sham Kakade, arXiv:2310.17168v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.04092v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|r|r|r|}\n\\hline\nAcc. Factor & Spiral & Gaussian\\\\\n\\hline\n$R1$ & \\multicolumn{2}{c|}{$0.0001$} \\\\\n$R8$ & $0.1$ & $0.01$\\\\\n$R16$ & $1.0$ & $0.01$\\\\\n$R32$ & $10.0$ & $1.0$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Regularization values $\\lambda$ used for the reconstruction with BART.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Parameter estimation in fluid flow models from undersampled frequency space data", "authors": ["Miriam Löcke", "Pim van Ooij", "Cristóbal Bertoglio"], "url": "https://arxiv.org/abs/2503.04092v1", "attribution": "\"Parameter estimation in fluid flow models from undersampled frequency space data\" by Miriam Löcke, Pim van Ooij, and Cristóbal Bertoglio, arXiv:2503.04092v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.21174v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|lll|lllll}\n&\\multicolumn{3}{c}{New Scheme} &\\multicolumn{5}{c}{ILUTP($\\tau$)} \\\\\n\\# & $\\tau_{large}$ & $\\tau_{mix}$ & $\\tau_{small}$ & $10^{-5}$ & $10^{-4}$ & $10^{-3}$ &$10^{-2}$ & $10^{-1}$ \\\\ \\hline\n1 & 17 & $\\ddagger$ & 2& $\\dagger$ & $\\dagger$ &$\\dagger$ &$\\dagger$ &$\\dagger$ \\\\\n2 & $\\ddagger$ & $\\ddagger$ & 2&$1^{\\star}$ & $\\dagger$ &$\\dagger$ &$\\dagger$ &$\\dagger$ \\\\\n3 & 718 & $\\ddagger$ & 2& $1^{\\star}$ & $\\dagger$ &$\\dagger$ &$\\dagger$ &$\\dagger$ \\\\\n4 & 14 & 23 & 2& 2 & 3 & 5 &$\\dagger$ &$\\dagger$ \\\\\n5 & 99 & $\\ddagger$ & 4 & 3 & $6^{\\star}$ & $30^{\\star}$ &$\\dagger$ &$\\dagger$ \\\\\n6 & $\\ddagger$ & $\\ddagger$ & 12 & 4 & 7 & $\\ddagger$ &$\\dagger$ &$\\dagger$ \\\\\n7 & 3 & 6 & 2 & 3 & 4 & 6 & 9 &$\\dagger$ \\\\\n8 & $\\ddagger$ & $\\ddagger$ & 1 & $3^{\\star}$& $3^{\\star}$ & $7^{\\star}$ &$\\dagger$ &$\\dagger$ \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Number of outer fGMRES/GMRES iterations for the symmetric case}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust iterative methods for linear systems with saddle point structure", "authors": ["Murat Manguoğlu", "Volker Mehrmann"], "url": "https://arxiv.org/abs/2502.21174v1", "attribution": "\"Robust iterative methods for linear systems with saddle point structure\" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.12771v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\n\\textbf{Method} & \\textbf{Train Accuracy} & \\textbf{Test Accuracy} \\\\ \\hline\nMLP & 70.37\\% & 50.69\\% \\\\ \nCNN & 61.68\\% & 50.29\\% \\\\ \nResNet & 95.67\\% & 51.71\\% \\\\ \nRNN & 61.47\\% & 50.76\\% \\\\ \nGRU & 53.98\\% & 50.92\\% \\\\ \nLSTM & 80.32\\% & 50.21\\% \\\\ \nAutoencoder & 50.10\\% & 50.50\\% \\\\ \nt-CNN & 58.75\\% & 50.71\\% \\\\ \nm-CNN & 50.32\\% & 50.00\\% \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Average results of the complementary experiment using cryptocurrency price time series. The average test accuracy in all machine learning models are approximate 50\\%.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks", "authors": ["Yu Zhang", "Zelin Wu", "Claudio Tessone"], "url": "https://arxiv.org/abs/2504.12771v1", "attribution": "\"Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks\" by Yu Zhang, Zelin Wu, and Claudio Tessone, arXiv:2504.12771v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.17735v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation results for conversion model}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n System & WER ($\\% \\downarrow$) & $\\Delta\\text{PPG}$ ($\\downarrow$) & FD ($\\downarrow$) & F0 corr. ($\\uparrow$) \\\\\n \\midrule\n Source & 15.86 & 0.51 & 67.29 & 0.74 \\\\\n \\midrule\n Ours & {\\bf 16.25} & {\\bf 0.30} & 35.24 & {\\bf 0.77} \\\\\n \\ - accent embeddings & 16.82 & 0.31 & 35.75 & 0.76 \\\\\n \\quad - phone supervision & 19.55 & 0.33 & 34.97 & 0.77 \\\\\n \\ - extra data & 17.13 & 0.31 & 39.75 & 0.77 \\\\\n \\ - pre-training & 27.59 & 0.40 & {\\bf 30.83} & 0.76 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data", "authors": ["Qibing Bai", "Sho Inoue", "Shuai Wang", "Zhongjie Jiang", "Yannan Wang", "Haizhou Li"], "url": "https://arxiv.org/abs/2507.17735v1", "attribution": "\"Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data\" by Qibing Bai, Sho Inoue, Shuai Wang, Zhongjie Jiang, Yannan Wang, and Haizhou Li, arXiv:2507.17735v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.02796v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{U.S.: Parameter Estimates and Persistence for DCC, Scalar BEKK, and LSTM-BEKK Models (4 Assets).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccc}\n \\toprule\n Portfolio Size & Model & $a$ & $b$ & $a + b$ \\\\\n \\midrule\n \\multirow{3}{*}{4} & DCC & 0.042 & 0.871 & 0.913 \\\\\n & Scalar BEKK & 0.033 & 0.952 & 0.984 \\\\\n & LSTM-BEKK & 0.038 & 0.930 & {0.968} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deep Learning Enhanced Multivariate GARCH", "authors": ["Haoyuan Wang", "Chen Liu", "Minh-Ngoc Tran", "Chao Wang"], "url": "https://arxiv.org/abs/2506.02796v1", "attribution": "\"Deep Learning Enhanced Multivariate GARCH\" by Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, and Chao Wang, arXiv:2506.02796v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05626v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n \\cline{1-7}\n \\textbf{embeding method} &\n \\textbf{Classifier} &\n \\textbf{Accuracy} & \n \\textbf{AUC Score} &\n \\textbf{Precision} &\n \\textbf{Recall} &\n \\textbf{F1 Score} \\\\ \n\\cline{1-7}\n \\multirow{3}{*}{Without pre-trained word embedding} & CNN & 85.0\\% & 50 \\% & 0 & 0 & 0 \\\\\n & RNN & 84.3\\% & 50 \\% & 0 & 0 & 0\\\\ \n & CRNN & 85.3\\% & 50 \\% & 0 & 0 & 0\\\\\n \\cline{1-7}\n \\multirow{3}{*}{\\textsc{FastText}} & CNN & 70\\% & 68.6 \\% & 0.75 & 0.43 & 0.54 \\\\\n & RNN & 83.6\\% & 64.3 \\% & 1 & 0.28 & 0.44\\\\ \n & CRNN & 85.0\\% & 50 \\% & 0 & 0 & 0\\\\\n \\cline{1-7}\n \\multirow{3}{*}{word2vec} & CNN & 85.7\\% & 57.1 \\% & 1 & 0.14 & 0.25 \\\\\n & RNN & 82.9\\% & 57.1 \\% & 1 & 0.14 & 0.25\\\\ \n & CRNN & 85.3\\% & 64.3 \\% &1 & 0.29 & 0.44\\\\\n \\cline{1-7}\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Deep learning Classifier Overall Performance }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter", "authors": ["Sarah Alqurashi", "Btool Hamoui", "Abdulaziz Alashaikh", "Ahmad Alhindi", "Eisa Alanazi"], "url": "https://arxiv.org/abs/2101.05626v1", "attribution": "\"Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter\" by Sarah Alqurashi, Btool Hamoui, Abdulaziz Alashaikh, Ahmad Alhindi, and Eisa Alanazi, arXiv:2101.05626v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10545v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{We observe the impact of incorporating sequence of strategies on conversation outcome prediction in terms of Macro-F1 and Weighted-F1 score. For P4G, we observe strategies of the persuader (ER), persuadee (EE) and both. For CB, we observe strategies of the buyer (BU), seller (SE) and both.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcclcc}\n\\toprule\n \\multicolumn{3}{c}{Persuasion (P4G)} & \\multicolumn{3}{c}{Negotiation (CB)} \\\\\nUser & Macro-F1 & W-F1 & User & Macro-F1 & W-F1\\\\\n\\midrule\nER & 0.588& 0.620 & BU & \\textbf{0.618} &\\textbf{0.640}\\\\\nEE & 0.618& 0.640 & SE & 0.462& 0.508\\\\\nBoth &\\textbf{ 0.646}& \\textbf{0.671} & Both & 0.605& 0.626\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversations", "authors": ["Ritam Dutt", "Sayan Sinha", "Rishabh Joshi", "Surya Shekhar Chakraborty", "Meredith Riggs", "Xinru Yan", "Haogang Bao", "Carolyn Penstein Rosé"], "url": "https://arxiv.org/abs/2101.10545v1", "attribution": "\"RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversations\" by Ritam Dutt, Sayan Sinha, Rishabh Joshi, Surya Shekhar Chakraborty, Meredith Riggs, Xinru Yan, Haogang Bao, and Carolyn Penstein Rosé, arXiv:2101.10545v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.13687v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Constant parameters for the $\\rm UO_{2}$ creep model }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\nParameter & $A_{1}$ & $A_{2}$ & $A_{3}$ & $A_{4}$ & $A_{6}$ & $A_{7}$ \\\\\nValue & 0.3919 & $1.3100 \\times 10^{-19}$ & -87.7 & $2.0391\\times 10^{-25}$ & -90.5 & $3.7226 \\times 10^{-35}$\\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology", "authors": ["Kazuma Kobayashi", "Dinesh Kumar", "Syed Bahauddin Alam"], "url": "https://arxiv.org/abs/2211.13687v3", "attribution": "\"AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology\" by Kazuma Kobayashi, Dinesh Kumar, and Syed Bahauddin Alam, arXiv:2211.13687v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.03052v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance analysis of the Point-GR Classification Model Vs Total Number of Points.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{Total Points} & \\textbf{Overall Accuracy} & \\textbf{Mean Accuracy} \\\\ \n\\hline 2048 & 89.87 & 86.02 \\\\\n\\hline 1024 & 92.7 & 89.09 \\\\\n\\hline 921 & 92.26 & 89.92 \\\\\n\\hline 819 & 92.42 & 89.3 \\\\\n\\hline 716 & 91.65 & 87.68 \\\\\n\\hline 614 & 90.11 & 85.95 \\\\\n\\hline 512 & 88.08 & 83.5 \\\\\n\\hline 204 & 47.97 & 40.16 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation", "authors": ["Md Meraz", "Md Afzal Ansari", "Mohammed Javed", "Pavan Chakraborty"], "url": "https://arxiv.org/abs/2412.03052v1", "attribution": "\"Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation\" by Md Meraz, Md Afzal Ansari, Mohammed Javed, and Pavan Chakraborty, arXiv:2412.03052v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08445v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\n\\hline\nApproaches & Weights & Relative Performance \\\\ \\hline\nRule I & [0.15, 0.25, 0.25, 0.35] & 0.758 \\\\\\hline\nRule II & [0.1, 0.2, 0.3, 0.4] & 0.279\\\\ \\hline\nRule III & [0.1, 0.2, 0.2, 0.5]& 0.076\\\\\\hline\nRule IV & [0.05, 0.2, 0.35, 0.45] &0.120\\\\\\hline\nProposed method & Learned &1.152 \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Performance comparison to heuristic rules}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning", "authors": ["Yuan Tian", "Minghao Han", "Chetan Kulkarni", "Olga Fink"], "url": "https://arxiv.org/abs/2201.08445v1", "attribution": "\"A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning\" by Yuan Tian, Minghao Han, Chetan Kulkarni, and Olga Fink, arXiv:2201.08445v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19113v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{booktabs}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lrrrr}\n\\toprule\n &\n \\textbf{HILT} &\n \\cellcolor[HTML]{FFFFFF}\\textbf{Synthesized} &\n \\cellcolor[HTML]{FFFFFF}\\textbf{HILT} &\n \\cellcolor[HTML]{FFFFFF}\\textbf{Synthesized} \\\\ \\midrule\n\\textbf{Hallucination Type} &\n \\multicolumn{2}{c}{\\textbf{\\# Positive Pairs}} &\n \\multicolumn{2}{c}{\\cellcolor[HTML]{FFFFFF}\\textbf{\\# Negative Pairs}} \\\\\n\\textbf{Imaginary Figure} & 120800 & 507360 & 14800 & 62160 \\\\\n\\textbf{Place} & 116770 & 513788 & 13050 & 56115 \\\\\n\\textbf{Bothersome Number} & 68570 & 281137 & 7275 & 40740 \\\\\n\\textbf{Temporal Issue} & 57860 & 271942 & 6600 & 29700 \\\\\n\\textbf{Total} & \\multicolumn{2}{c}{1938227} & \\multicolumn{2}{c}{230440} \\\\ \\bottomrule\n\\end{tabular}\n\\caption{$\\mathcal{FACTOID}$ dataset statistics.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "FACTOID: FACtual enTailment fOr hallucInation Detection", "authors": ["Vipula Rawte", "S. M Towhidul Islam Tonmoy", "Krishnav Rajbangshi", "Shravani Nag", "Aman Chadha", "Amit P. Sheth", "Amitava Das"], "url": "https://arxiv.org/abs/2403.19113v1", "attribution": "\"FACTOID: FACtual enTailment fOr hallucInation Detection\" by Vipula Rawte, S. M Towhidul Islam Tonmoy, Krishnav Rajbangshi, Shravani Nag, Aman Chadha, Amit P. Sheth, and Amitava Das, arXiv:2403.19113v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13025v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{rotating}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classification Accuracy(\\%) under Netattack over 5 random seeds}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc|cccc}\n\\hline\n\\multirow{2}{*}{} & \\multirow{2}{*}{\\textbf{Model}} & \\multirow{2}{*}{\\textbf{Clean}} & \\multicolumn{4}{c}{\\textbf{Evasive}} & \\multicolumn{4}{c}{\\textbf{Poisoning}} \\\\ \\cline{4-11}\n & & \\textbf{} & \\textbf{1} & \\textbf{2} & \\textbf{3} & \\textbf{4} & \\textbf{1} & \\textbf{2} & \\textbf{3} & \\textbf{4} \\\\ \\hline\n\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Cora}}} & \\textbf{GCN} & 80.0\\scriptsize{$\\pm$7.87} & 51.5\\scriptsize{$\\pm$4.87} & 38.0\\scriptsize{$\\pm$6.22} & 31.0\\scriptsize{$\\pm$2.24} & 26.0\\scriptsize{$\\pm$3.79} & 47.5\\scriptsize{$\\pm$7.07} & 39.5\\scriptsize{$\\pm$2.74} & 30.0\\scriptsize{$\\pm$5.00} & 26.5\\scriptsize{$\\pm$3.79} \\\\\n & \\textbf{RGCN} & 80.0\\scriptsize{$\\pm$4.67} & 49.5\\scriptsize{$\\pm$6.47} & 36.0\\scriptsize{$\\pm$5.18} & 30.5\\scriptsize{$\\pm$3.25} & 25.5\\scriptsize{$\\pm$2.09} & 46.5\\scriptsize{$\\pm$5.75} & 35.5\\scriptsize{$\\pm$3.70} & 29.0\\scriptsize{$\\pm$3.79} & 25.5\\scriptsize{$\\pm$2.73} \\\\\n & \\textbf{GAT} & 77.8\\scriptsize{$\\pm$3.97} & 48.0\\scriptsize{$\\pm$8.73} & 39.5\\scriptsize{$\\pm$5.70} & 36.5\\scriptsize{$\\pm$5.48} & 32.5\\scriptsize{$\\pm$5.30} & 50.5\\scriptsize{$\\pm$5.70} & 38.0\\scriptsize{$\\pm$5.97} & 33.5\\scriptsize{$\\pm$2.85} & 26.0\\scriptsize{$\\pm$3.79} \\\\\n & \\textbf{DGI} & 82.5\\scriptsize{$\\pm$4.33} & 62.0\\scriptsize{$\\pm$4.81} & 46.0\\scriptsize{$\\pm$3.79} & 34.0\\scriptsize{$\\pm$5.18} & {27.5}\\scriptsize{$\\pm$3.06} & 62.5\\scriptsize{$\\pm$3.54} & 43.5\\scriptsize{$\\pm$3.79} & {31.5}\\scriptsize{$\\pm$6.75} & 26.5\\scriptsize{$\\pm$4.18} \\\\\n & \\textbf{GCL} & 64.4\\scriptsize{$\\pm$4.27} & 53.8\\scriptsize{$\\pm$5.20} & 38.8\\scriptsize{$\\pm$8.54} & 25.6\\scriptsize{$\\pm$3.15} & 18.8\\scriptsize{$\\pm$3.23} & 41.9\\scriptsize{$\\pm$6.25} & 33.1\\scriptsize{$\\pm$8.00} & 28.8\\scriptsize{$\\pm$7.77} & 23.1\\scriptsize{$\\pm$5.54} \\\\\n & \\textbf{GACL} & 82.0\\scriptsize{$\\pm$3.26} & \\textbf{67.5}\\scriptsize{$\\pm$5.00} & \\textbf{46.0}\\scriptsize{$\\pm$5.76} & \\textbf{35.5}\\scriptsize{$\\pm$4.81} & \\textbf{29.0}\\scriptsize{$\\pm$6.75} & \\textbf{64.5}\\scriptsize{$\\pm$5.70} & \\textbf{44.0}\\scriptsize{$\\pm$7.42} & \\textbf{33.5}\\scriptsize{$\\pm$6.02} & \\textbf{27.5}\\scriptsize{$\\pm$5.30} \\\\ \\hline\n\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Citeseer}}} & \\textbf{GCN} & 71.8\\scriptsize{$\\pm$6.94} & 42.5\\scriptsize{$\\pm$7.07} & 27.5\\scriptsize{$\\pm$6.37} & 18.0\\scriptsize{$\\pm$3.26} & 15.0\\scriptsize{$\\pm$2.50} & 29.0\\scriptsize{$\\pm$7.20} & 20.5\\scriptsize{$\\pm$1.12} & 17.5\\scriptsize{$\\pm$1.77} & 13.0\\scriptsize{$\\pm$2.09} \\\\\n & \\textbf{RGCN} & 73.5\\scriptsize{$\\pm$8.40} & 41.5\\scriptsize{$\\pm$7.42} & 24.5\\scriptsize{$\\pm$6.47} & 18.5\\scriptsize{$\\pm$6.52} & 13.0\\scriptsize{$\\pm$1.11} & 31.0\\scriptsize{$\\pm$5.48} & 19.5\\scriptsize{$\\pm$2.09} & 13.5\\scriptsize{$\\pm$2.85} & 5.00\\scriptsize{$\\pm$1.77} \\\\\n & \\textbf{GAT} & 72.3\\scriptsize{$\\pm$8.38} & {49.0}\\scriptsize{$\\pm$9.12} & 33.0\\scriptsize{$\\pm$5.97} & 22.0\\scriptsize{$\\pm$4.81} & 18.0\\scriptsize{$\\pm$3.26} & 38.0\\scriptsize{$\\pm$5.12} & 23.5\\scriptsize{$\\pm$4.87} & 16.5\\scriptsize{$\\pm$4.54} & 12.0\\scriptsize{$\\pm$2.09} \\\\\n & \\textbf{DGI} & 78.5\\scriptsize{$\\pm$5.76} & 64.0\\scriptsize{$\\pm$4.18} & 49.5\\scriptsize{$\\pm$4.47} & 36.5\\scriptsize{$\\pm$5.18} & 30.5\\scriptsize{$\\pm$5.97} & 57.5\\scriptsize{$\\pm$4.68} & 40.0\\scriptsize{$\\pm$7.70} & 31.0\\scriptsize{$\\pm$2.24} & {25.5}\\scriptsize{$\\pm$5.70} \\\\\n & \\textbf{GCL} & 70.0\\scriptsize{$\\pm$7.36} & 59.4\\scriptsize{$\\pm$6.57} & 47.5\\scriptsize{$\\pm$5.40} & 36.3\\scriptsize{$\\pm$6.29} & 32.5\\scriptsize{$\\pm$4.08} & 50.6\\scriptsize{$\\pm$5.54} & 38.3\\scriptsize{$\\pm$6.77} & 38.1\\scriptsize{$\\pm$4.27} & 26.9\\scriptsize{$\\pm$8.00}\\\\\n & \\textbf{GACL} & 77.5\\scriptsize{$\\pm$3.06} & \\textbf{66.0}\\scriptsize{$\\pm$3.79} & \\textbf{53.0}\\scriptsize{$\\pm$5.70} & \\textbf{46.0}\\scriptsize{$\\pm$4.87} & \\textbf{37.0}\\scriptsize{$\\pm$1.12} & \\textbf{63.5}\\scriptsize{$\\pm$4.18} & \\textbf{41.0}\\scriptsize{$\\pm$3.79} & \\textbf{40.0}\\scriptsize{$\\pm$9.19} & \\textbf{30.5}\\scriptsize{$\\pm$5.97} \\\\ \\hline\n\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Pubmed}}} & \\textbf{GCN} & 82.6\\scriptsize{$\\pm$6.98} & 39.5\\scriptsize{$\\pm$4.81} & 32.0\\scriptsize{$\\pm$4.81} & 31.0\\scriptsize{$\\pm$5.76} & 31.0\\scriptsize{$\\pm$5.76} & 36.0\\scriptsize{$\\pm$4.18} & 32.5\\scriptsize{$\\pm$6.37} & 31.0\\scriptsize{$\\pm$5.76} & 28.5\\scriptsize{$\\pm$5.18} \\\\\n & \\textbf{RGCN} & 79.0\\scriptsize{$\\pm$5.18} & 39.5\\scriptsize{$\\pm$5.70} & 33.0\\scriptsize{$\\pm$4.80} & 31.5\\scriptsize{$\\pm$4.18} & 30.0\\scriptsize{$\\pm$5.00} & 38.5\\scriptsize{$\\pm$4.18} & 31.5\\scriptsize{$\\pm$2.85} & 29.5\\scriptsize{$\\pm$3.70} & 27.0\\scriptsize{$\\pm$3.70} \\\\\n & \\textbf{GAT} & 78.6\\scriptsize{$\\pm$6.70} & 41.0\\scriptsize{$\\pm$8.40} & 33.5\\scriptsize{$\\pm$4.18} & 30.5\\scriptsize{$\\pm$4.47} & 31.0\\scriptsize{$\\pm$4.18} & 39.5\\scriptsize{$\\pm$3.26} & 31.0\\scriptsize{$\\pm$4.18} & 30.0\\scriptsize{$\\pm$3.06} & 35.5\\scriptsize{$\\pm$5.97} \\\\\n & \\textbf{DGI} & 79.0\\scriptsize{$\\pm$7.20} & 40.5\\scriptsize{$\\pm$5.86} & 31.0\\scriptsize{$\\pm$4.54} & 29.5\\scriptsize{$\\pm$3.71} & 28.0\\scriptsize{$\\pm$2.74} & 40.0\\scriptsize{$\\pm$4.81} & 31.0\\scriptsize{$\\pm$3.79} & 28.5\\scriptsize{$\\pm$4.18} & 28.0\\scriptsize{$\\pm$4.68} \\\\\n & \\textbf{GCL} & 67.5\\scriptsize{$\\pm$7.07} & \\textbf{45.3}\\scriptsize{$\\pm$1.77} & \\textbf{35.8}\\scriptsize{$\\pm$6.29} & 28.3\\scriptsize{$\\pm$5.10} & 28.1\\scriptsize{$\\pm$4.26} & 40.25\\scriptsize{$\\pm$1.77} & 33.25\\scriptsize{$\\pm$6.61} & \\textbf{30.3}\\scriptsize{$\\pm$1.44} & 19.4\\scriptsize{$\\pm$3.75}\\\\\n & \\textbf{GACL} & 83.0\\scriptsize{$\\pm$5.42} & 43.0\\scriptsize{$\\pm$5.42} & 34.0\\scriptsize{$\\pm$5.18} & \\textbf{30.0}\\scriptsize{$\\pm$3.06} & \\textbf{28.5}\\scriptsize{$\\pm$4.18} & \\textbf{41.0}\\scriptsize{$\\pm$3.79} & \\textbf{34.0}\\scriptsize{$\\pm$2.85} & 29.5\\scriptsize{$\\pm$3.71} & \\textbf{28.5}\\scriptsize{$\\pm$4.18} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Learning Robust Representation through Graph Adversarial Contrastive Learning", "authors": ["Jiayan Guo", "Shangyang Li", "Yue Zhao", "Yan Zhang"], "url": "https://arxiv.org/abs/2201.13025v1", "attribution": "\"Learning Robust Representation through Graph Adversarial Contrastive Learning\" by Jiayan Guo, Shangyang Li, Yue Zhao, and Yan Zhang, arXiv:2201.13025v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.13005v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{marvosym}\n\\usepackage{pifont}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll|cccccc}\n\t\t\t\\toprule\n\t\t\t\\multicolumn{3}{c}{overlap attention} & \\multicolumn{3}{c}{\\textit{3DMatch}} & \\multicolumn{3}{c}{\\textit{3DLoMatch}} \\\\\n \\cline{4-9}\n \\textit{ov.} & \\textit{$\\times$ov.} & \\textit{cond.} & FMR & IR & RR & FMR & IR & RR \\\\\n \\hline\n & & & \\underline{96.4} & 39.6 & 82.6 & 72.2 & 14.5 & 38.9\\\\\n \\ding{51} & & & 94.6 & 38.3 & 84.1 & 67.1 & 14.3 & 42.8 \\\\\n \\ding{51} & \\ding{51} & & \\underline{96.4} & 50.8 & 87.7 & \\underline{73.8} & 20.9 & 56.5 \\\\\n \\ding{51} & & \\ding{51} & 95.7 & \\underline{52.1} & \\underline{88.0} & 72.5 & \\underline{21.2} & \\underline{57.5} \\\\\n \\ding{51} & \\ding{51} & \\ding{51} & \\textbf{96.7} & \\textbf{58.0} & \\textbf{89.0} & \\textbf{78.6} & \\textbf{26.7}& \\textbf{59.8} \\\\\n\t\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Ablation of the network architecture. \\textit{ov.} denotes upsampling the overlap scores; \\textit{cond.} denotes conditioning the bottleneck features on the respective other point cloud; \\textit{$\\times$ov.} denotes upsampling the cross overlap scores.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "PREDATOR: Registration of 3D Point Clouds with Low Overlap", "authors": ["Shengyu Huang", "Zan Gojcic", "Mikhail Usvyatsov", "Andreas Wieser", "Konrad Schindler"], "url": "https://arxiv.org/abs/2011.13005v3", "attribution": "\"PREDATOR: Registration of 3D Point Clouds with Low Overlap\" by Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov, Andreas Wieser, and Konrad Schindler, arXiv:2011.13005v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09103v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The number of solutions for the ladder $N(2n)$ with $2n$ players for the case when the payoff parameters obey $x>y>x/2$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline\n $2n$ & $N_{ladd}(2n)$ \\\\ \\hline\n 4 & 2 \\\\\n 6 & 3 \\\\\n 8 & 4 \\\\\n 10 & 7 \\\\\n 12 & 10 \\\\\n 14 & 17 \\\\\n 16 & 26 \\\\\n 18 & 43 \\\\\n 20 & 68 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Nash Equilibria in certain two-choice multi-player games played on the ladder graph", "authors": ["Victoria Sánchez Muñoz", "Michael Mc Gettrick"], "url": "https://arxiv.org/abs/2101.09103v1", "attribution": "\"Nash Equilibria in certain two-choice multi-player games played on the ladder graph\" by Victoria Sánchez Muñoz and Michael Mc Gettrick, arXiv:2101.09103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.14267v4_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Employment, earnings, and wealth (transposed)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll}\n\\toprule\n\\midrule\n & \\textbf{$75\\%-25\\%$} & \\textbf{$\\max-\\min$} & \\textbf{std} & \\textbf{$20\\%$} & \\textbf{$40\\%$} & \\textbf{$60\\%$} & \\textbf{$80\\%$} & \\textbf{Avg.}\\\\\n\\midrule\n\\multicolumn{9}{l}{\\textbf{Panel A: Annual employment growth}}\\\\\n\\cmidrule{2-9}\nLower-tail CIQ& 0.46 ** & 0.33 & 0.27 & -0.41 * & -0.61 *** & 0.13 & 0.25 & -0.21\\\\\nCentral CIQ& 0.18 & 0.30 & 0.34 & 0.13 & 0.64 *** & 0.35 & 0.49 ** & 0.38 *\\\\\nUpper-tail CIQ & -0.38 * & -0.01 & -0.18 & 0.56 *** & 0.64 *** & 0.25 & 0.05 & 0.49 **\\\\\nCIV & -0.10 & -0.01 & -0.09 & -0.27 & -0.25 & -0.52 *** & -0.47 ** & -0.51 ***\\\\\n\\midrule\n\\multicolumn{9}{l}{\\textbf{Panel B: Annual earnings growth}}\\\\\n\\cmidrule{2-9}\nLower-tail CIQ & -0.19 & -0.42 * & -0.37 & -0.38 * & -0.46 ** & -0.50 *** & -0.56 *** & -0.50 ***\\\\\nCentral CIQ & -0.11 & 0.28 & -0.04 & 0.41 * & 0.45 ** & 0.46 ** & 0.40 * & 0.41 *\\\\\nUpper-tail CIQ & 0.12 & 0.49 ** & 0.20 & 0.45 ** & 0.57 *** & 0.62 *** & 0.59 *** & 0.49 **\\\\\nCIV & 0.02 & -0.0 & -0.15 & -0.48 ** & -0.54 *** & -0.54 *** & -0.47 ** & -0.51 ***\\\\\n\\midrule\n\\multicolumn{9}{l}{\\textbf{Panel C: Monthly house‐price growth (nine areas, seasonally adjusted)}}\\\\\n\\cmidrule{2-9}\nLower-tail CIQ & -0.03 & 0.06 & 0.04 & 0.13 *** & 0.06 & 0.08 & 0.13 *** & 0.11 **\\\\\nCentral CIQ & -0.02 & 0.09 & 0.07 & 0.05 & 0.05 & 0.00 & 0.04 & 0.05\\\\\nUpper-tail CIQ & -0.02 & 0.01 & 0.01 & -0.01 & 0.00 & -0.02 & -0.00 & 0.00\\\\\nCIV & 0.03 & -0.04 & -0.02 & -0.12 ** & -0.12 ** & -0.06 & -0.12 ** & -0.13 ***\\\\\n\\midrule\n\\multicolumn{9}{l}{\\textbf{Panel D: Quarterly house-price growth (100 largest MSAs, seasonally adjusted)}}\\\\\n\\cmidrule{2-9}\nLower-tail CIQ & 0.04 & 0.15 & 0.13 & 0.16 * & 0.21 ** & 0.12 & 0.19 ** & 0.18 *\\\\\nCentral CIQ & -0.06 & 0.10 & 0.14 & 0.06 & 0.05 & 0.01 & 0.01 & 0.03\\\\\nUpper-tail CIQ & -0.04 & -0.07 & -0.07 & 0.00 & 0.05 & -0.03 & 0.01 & 0.05\\\\\nCIV & -0.08 & 0.12 & -0.01 & -0.11 & -0.10 & -0.08 & -0.14 & -0.12\\\\\n\\midrule\n\\multicolumn{9}{l}{\\textbf{Panel E: Sales growth}}\\\\\n\\cmidrule{2-9}\nLower-tail CIQ & -0.26 ** & 0.02 & -0.15 & 0.13 & 0.06 & -0.08 & -0.12 & 0.05\\\\\nCentral CIQ & -0.02 & 0.05 & 0.04 & -0.00 & -0.02 & -0.04 & -0.01 & -0.04\\\\\nUpper-tail CIQ & -0.24 ** & -0.10 & -0.26 ** & -0.03 & -0.11 & -0.25 ** & -0.36 *** & -0.20 *\\\\\nCIV & 0.28 *** & -0.23 ** & -0.03 & -0.43 *** & -0.45 *** & -0.39 *** & -0.31 *** & -0.41 ***\\\\\n\\midrule\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Beyond Volatility: Common Factors in Idiosyncratic Quantile Risks", "authors": ["Jozef Barunik", "Matej Nevrla"], "url": "https://arxiv.org/abs/2208.14267v4", "attribution": "\"Beyond Volatility: Common Factors in Idiosyncratic Quantile Risks\" by Jozef Barunik and Matej Nevrla, arXiv:2208.14267v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16534v2_tex_table35.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameter search space of XGBoost.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\toprule\n\\textbf{Hyperparameter} & \\textbf{Distribution} \\\\ \\midrule\nmax\\_depth & $\\text{uniform\\_int}[1,11]$\\\\\nnum\\_estimators & $1000$\\\\\nmin\\_child\\_weight & $\\text{log\\_uniform\\_int}[1,1\\mathrm{e}2]$\\\\\nsubsample & $\\text{unifrom}[0.5,1]$\\\\\nlearning\\_rate & $\\text{log\\_unifrom}[1\\mathrm{e}{-5},0.7]$\\\\\ncol\\_sample\\_by\\_level & $\\text{uniform}[0.5,1]$\\\\\ncol\\_sample\\_by\\_tree & $\\text{uniform}[0.5,1]$\\\\\ngamma & $\\text{log\\_uniform}[1\\mathrm{e}{-8},7]$\\\\\nlambda & $\\text{log\\_uniform}[1,4]$\\\\\nalpha & $\\text{log\\_uniform}[1\\mathrm{e}{-8},1\\mathrm{e}2]$\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DOFEN: Deep Oblivious Forest ENsemble", "authors": ["Kuan-Yu Chen", "Ping-Han Chiang", "Hsin-Rung Chou", "Chih-Sheng Chen", "Tien-Hao Chang"], "url": "https://arxiv.org/abs/2412.16534v2", "attribution": "\"DOFEN: Deep Oblivious Forest ENsemble\" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.17111v2_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\\hline\nminute & $1$ & $2$ & $3$ & $4$ & $5$ & $6$ & $\\ldots$\\\\\n\\hline\nfight (armed) & $0.300$ & $0.276$ & $0.249$ & $0.213$ & $0.162$ & $0.162$ &$\\ldots$ \\\\\nfight (unarmed) & $0.050$ & $0.046$ & $0.041$ & $0.035$ & $0.027$ & $0.027$ & $\\ldots$ & \\\\\nhide & $0.919$ & $0.824$ & $0.706$ & $0.543$ & $0.217$ & $0.217$& $\\ldots$\\\\\nrun & $0.100$ & $0.092$ & $0.083$ & $0.071$ & $0.054$ & $0.054$ & $\\ldots$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Survival probabilities of a civilian of focus in an open arena when $N=210$, $m=15$, $e=25$, $p_r=0.1$, $p_1=0.05$, $p_2=0.3$, and $T_2=4\\leq T_1$ when there is only one possibly armed civilian.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Analytic models for active shooter incidents with civilian resistance", "authors": ["Liang Hong"], "url": "https://arxiv.org/abs/2310.17111v2", "attribution": "\"Analytic models for active shooter incidents with civilian resistance\" by Liang Hong, arXiv:2310.17111v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.04124v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c||c|c|c|c|c|c|c|c|c|c|c|c}\n$n$&4 &5&6 &7&8&9&10&11&12&13&14\\\\ \\hline\n$\\left\\lfloor\\frac{2n}{5}\\right\\rfloor$&1 &2&2 &2&3&3&4&4&4&5&5\\\\ \\hline\n$\\gamma_h(C_n) $ &2&2&2&3&4&3&4&4&4&5&6\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{$\\gamma_h(C_n)$ for small $n$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Tight upper bounds on the hop domination number of triangle-free graphs", "authors": ["Shinya Fujita", "Boram Park"], "url": "https://arxiv.org/abs/2503.04124v1", "attribution": "\"Tight upper bounds on the hop domination number of triangle-free graphs\" by Shinya Fujita and Boram Park, arXiv:2503.04124v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.08109v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|r|r|r}\\hline\n\t\tLevel& Taxon& MW &$R^2$ & \\textbf{CAT} \\\\\n \t\t&& $p$-value&difference& $p$-value\\\\\n\t\t\\hline\n \tPhylum &Bacteroidetes &$\\mathbf{<0.01}$ &0.0502\t&$0.072$\\\\\n\t\tPhylum &Firmicutes&$\\mathbf{<0.01}$&0.0337\t&$\\mathbf{0.035}$\\\\\n\t\tClass &Bacteroidia&$\\mathbf{<0.01}$\t&0.0503&$0.072$\\\\\n \tClass &Clostridia&$\\mathbf{<0.01}$&0.0304\t&$\\mathbf{0.050}$\\\\\n \tClass &Mollicutes&$\\mathbf{0.01}$&0.0001\t& $0.084$\\\\\n\t\tOrder &Bacteroidales&$\\mathbf{<0.01}$&0.0503\t&$0.072$\\\\\n\t\tOrder &Clostridiales&$\\mathbf{<0.01}$&0.0303\t&$\\mathbf{0.050}$\\\\\n \tFamily &Micrococcaceae&$\\mathbf{0.01}$&$<0.0001$\t&$0.071$\\\\\n\t\tFamily & Ruminococcaceae&$\\mathbf{0.03}$ & 0.0365 &$\\mathbf{<0.001}$\\\\\n \tGenus &\\textit{Faecalibacterium}&$\\mathbf{0.01}$&0.0151\t&$\\mathbf{0.005}$\\\\\n Genus &\\textit{Gardnerella}&$\\mathbf{0.03}$&\t$<0.0001$&$0.879$ \\\\\n Genus &\\textit{Peptoniphilus} &0.12&$<0.0001$&$0.148$ \\\\\n Genus &\\textit{Phascolarctobacterium}&$\\mathbf{0.01}$&0.0010&$\\mathbf{0.029}$\\\\\n Genus &\\textit{Rothia}\t&$\\mathbf{0.01}$ & $<0.0001$&$0.071$\\\\\n Genus &\\textit{Ruminococcus}&$\\mathbf{0.03}$ & 0.0096\t&$\\mathbf{<0.001}$\\\\\n Species &\\textit{B.\\ stercoris}&$\\mathbf{0.03}$\t&$<0.0001$&$0.866$\\\\\n\t\tSpecies &\\textit{F.\\ prausnitzii}&$\\mathbf{0.01}$\t&0.0151&$\\mathbf{0.005}$\\\\\n\t\tSpecies &\\textit{M.\\ hungatei}&0.18\t&$<0.0001$&$\\mathbf{0.038}$\\\\\n\t\tSpecies &\\textit{R.\\ bromii}&0.08\t&0.0043&$\\mathbf{0.002}$\\\\\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Level in the taxonomic tree, taxon, Mann-Whitney $p$-value, $R^2$ difference before and after removing candidate taxon, and $p$-value from \\textbf{CAT} when applied to features identified by LEfSe in .}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "CAT: a conditional association test for microbiome data using a leave-out approach", "authors": ["Yushu Shi", "Liangliang Zhang", "Kim-Anh Do", "Robert R. Jenq", "Christine B. Peterson"], "url": "https://arxiv.org/abs/2309.08109v1", "attribution": "\"CAT: a conditional association test for microbiome data using a leave-out approach\" by Yushu Shi, Liangliang Zhang, Kim-Anh Do, Robert R. Jenq, and Christine B. Peterson, arXiv:2309.08109v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.18417v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Total number of articles obtained per source before and after NER filtering, with percentage reduction shown in brackets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\hline\n\\textbf{News Source} & \\textbf{No. of articles pre processing} & \\textbf{No. of articles post NER filtering} \\\\ \\hline\nMarketWatch & 309,187 & 236,214 (23.6\\%)\\\\\nReuters & 38,141 & 35,741 (6.3\\%)\\\\\nThe Motley Fool & 205,270 & 147,413 (28.2\\%) \\\\\n\\hline\n\\textbf{Total} & \\textbf{552,598} & \\textbf{419,368} (\\textbf{24.1\\%}) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMs", "authors": ["Giorgos Iacovides", "Wuyang Zhou", "Danilo Mandic"], "url": "https://arxiv.org/abs/2507.18417v1", "attribution": "\"FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMs\" by Giorgos Iacovides, Wuyang Zhou, and Danilo Mandic, arXiv:2507.18417v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2501.18070v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\text{Beta} & Moderate Censoring & Higher Censoring \\\\\n\\hline\n$\\beta_{T_k}$ & $(-5.5, -0.2, -0.5, -0.025, -0.02, 0.1, -0.08, 0.05)^T$ & $(-6, -0.2, -0.5, -0.025, -0.02, 0.1, -0.08, 0.05)^T$\\\\\n$\\beta_{U_k}$ & $(-1.5, -0.2, -0.5, -0.025, -0.02, 0.1, -0.08, 0.05)^T$ & $(-1.5, -0.2, -0.5, -0.025, -0.02, 0.1, -0.08, 0.05)^T$ \\\\\n$\\beta_{C_k}$ & $(-10.5, -0.2, -0.5, -0.025, -0.02, 0.1, -0.08, 0.05)^T$ & $(-6, -0.2, -0.5, -0.025, -0.02, 0.1, -0.08, 0.05)^T$ \\\\\n\\hline\n\\end{tabular}\n\\caption{Beta coefficients for $\\tilde{K} = 10$ with N = 300 samples and $\\tau = 1000$}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An optimal dynamic treatment regime estimator for indefinite-horizon survival outcomes", "authors": ["Jane She", "Matthew Egberg", "Michael R. Kosorok"], "url": "https://arxiv.org/abs/2501.18070v1", "attribution": "\"An optimal dynamic treatment regime estimator for indefinite-horizon survival outcomes\" by Jane She, Matthew Egberg, and Michael R. Kosorok, arXiv:2501.18070v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.08488v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l|l|l|l}\n\\hline\n\\textbf{Regions} & \\textbf{1} & \\textbf{2} & \\textbf{3} & \\textbf{4} & \n\\textbf{5} & \\textbf{6} \\\\ \n\\hline \\hline\n\\textbf{OLr} & 23.4750 & 18.4683 & 16.9887 & 21.2629 & 14.9777 & 23.1817 \\\\ \n\\hline\n\\textbf{ORr} & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \\hline\n\\textbf{OLrf} & 24.5746 & 20.3071 & 18.5780 & 9.7478 & 18.0010 & 13.9349 \\\\ \n\\hline\n\\textbf{ORrf} & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \\hline\n\\textbf{OLrm} & 22.5746 & 16.7094 & 15.4745 & 17.1072 & 24.5693 & 19.3186 \\\\ \n\\hline\n\\textbf{ORrm} & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \n\\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Outliers in Mathematics grades by region and year}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Overview description of the Gambian GABECE Educational Data and associated algorithms and unsupervized learning process", "authors": ["Ousmane Saine", "Soumaila Dembélé", "Gane Samb Lo", "Mohamed Cheikh Haidara"], "url": "https://arxiv.org/abs/2012.08488v1", "attribution": "\"Overview description of the Gambian GABECE Educational Data and associated algorithms and unsupervized learning process\" by Ousmane Saine, Soumaila Dembélé, Gane Samb Lo, and Mohamed Cheikh Haidara, arXiv:2012.08488v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.17490v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Baseline and Remediation Performance Metrics for Models (Simple Prompt)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccccc}\n\\hline\n\\textbf{Model} & \\multicolumn{2}{c}{\\textbf{Baseline}} & & \\multicolumn{3}{c}{\\textbf{Remediation}} \\\\ \n\\cline{3-4} \\cline{6-8}\n& & \\textbf{\\hspace{1em}MAE\\hspace{1em}} & \\textbf{\\hspace{1em}Freq\\hspace{1em}} & & \\textbf{\\hspace{1em}MAE\\hspace{1em}} & \\textbf{\\hspace{1em}Freq\\hspace{1em}} & \\boldmath{$\\hat{\\alpha}^*$} \\\\ \n\\hline\n\\multicolumn{8}{c}{Direct Race Indicator} \\\\ \n\\hline\n\\texttt{Command R} & & 110.5 & 130 & & 100.4 & 122 & 0.2 \\\\ \n\\texttt{Command R+} & & 27.8 & 97 & & 21.3 & 94 & 0.2 \\\\ \n\\texttt{Llama 3.1 (8b)} & & 58.9 & 63 & & 37.6 & 35 & 0.16 \\\\ \n\\texttt{Llama 3.1 (70b)} & & 12.7 & 14 & & 5.2 & 7 & -0.04 \\\\ \n\\texttt{Mistral v0.3} & & 29.2 & 100 & & 16.8 & 45 & -0.04 \\\\ \n\\hline\n\\multicolumn{8}{c}{Indirect Race Indicator} \\\\\n\\hline\n\\texttt{Command R} & & 20.1 & 130 & & 15.2 & 101 & -0.2 \\\\ \n\\texttt{Command R+} & & 27.1 & 74 & & 21.6 & 71 & -0.1 \\\\ \n\\texttt{Llama 3.1 (8b)} & & 42.1 & 42 & & 26.4 & 26 & 0.08 \\\\ \n\\texttt{Llama 3.1 (70b)} & & 6.1 & 12 & & 3.6 & 7 & 0.02 \\\\ \n\\texttt{Mistral v0.3} & & 29.3 & 58 & & 16.1 & 72 & 0.04 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Social Group Bias in AI Finance", "authors": ["Thomas R. Cook", "Sophia Kazinnik"], "url": "https://arxiv.org/abs/2506.17490v1", "attribution": "\"Social Group Bias in AI Finance\" by Thomas R. Cook and Sophia Kazinnik, arXiv:2506.17490v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09784v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results of experiments performed on $TPN$\\textit{.}}\\centering%\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\nExamples & Tools & TINA & ROMEO & ORIS & \\multicolumn{2}{|c|}{ITPNAnalyzer}\n\\\\ \\hline\n& & DBM & DBM & DBM & DBM & Tdis \\\\ \\hline\n& Classes & 2 & 2 & 2 & 2 & 2 \\\\ \\cline{2-7}\\cline{5-7}\nProc 1 & Edges & 2 & 2 & $NA$ & 2 & 2 \\\\ \\cline{2-7}\\cline{5-7}\n& Times (ms) & 0 & $NA$ & 0 & 0 & 0 \\\\ \\hline\n& Classes & 186 & 186 & 188 & 186 & 186 \\\\ \\cline{2-7}\\cline{5-7}\nProc 1 2 & Edges & 262 & 262 & $NA$ & 262 & 262 \\\\ \\cline{2-7}\\cline{5-7}\n& Times (ms) & 0 & $NA$ & 16 & 0 & 0 \\\\ \\hline\n& Classes & 958 & 958 & 1.038 & 958 & 958 \\\\ \\cline{2-7}\\cline{5-7}\nProc 1 2 3 & Edges & 1506 & 1506 & $NA$ & 1506 & 1506 \\\\ \n\\cline{2-7}\\cline{5-7}\n& Times (ms) & 16 & $NA$ & 391 & 5 & 30 \\\\ \\hline\n& Classes & 5.219 & 5.219 & 6.029 & 5.219 & 5.219 \\\\ \\cline{2-7}\\cline{5-7}\nProc 1 2 3 4 & Edges & 8.580 & 8.580 & $NA$ & 8.580 & 8.580 \\\\ \n\\cline{2-7}\\cline{5-7}\n& Times (ms) & 140 & $NA$ & 2.719 & 38 & 80 \\\\ \\hline\n& Classes & 42.909 & 42.909 & 52.452 & 42.909 & 42.909 \\\\ \n\\cline{2-7}\\cline{5-7}\nProc 1 2 3 4 5 & Edges & 73.842 & 73.842 & $NA$ & 73.842 & 73.842 \\\\ \n\\cline{2-7}\\cline{5-7}\n& Times (ms) & 6.739 & $NA$ & 30.000 & 670 & 1.310 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Time distance based computation of the state space of preemptive real time systems", "authors": ["Abdelkrim Abdelli"], "url": "https://arxiv.org/abs/2201.09784v1", "attribution": "\"Time distance based computation of the state space of preemptive real time systems\" by Abdelkrim Abdelli, arXiv:2201.09784v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.00213v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{siunitx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean performance in \\textbf{ratio} for player selection methods in 4-Player scenarios.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n \\toprule\n Threshold-based & Parameter & $\\text{traj}_{S}\\downarrow$ & $\\text{traj}_L\\downarrow$ & $\\text{Dist}_m\\uparrow$ & Consistency $\\uparrow$ & Player Num. & Runtime\\\\\n \\midrule\n \\midrule\n \\multirow{3}[0]{*}{\\textbf{PSN-Full}} & $m_{\\text{th}}=0.1$ & 0.938 & 0.987 & 0.971 & 0.976 & 2.072 & 0.291\\\\\n & $m_{\\text{th}}=0.3$ & 0.813 & 0.972 & 0.973 & 0.981 & 1.754 & 0.201\\\\\n & $m_{\\text{th}}=0.5$ & 0.781 & 0.970 & 0.968 & 0.984 & 1.619 & 0.164\\\\\n \\midrule\n \\multirow{3}[0]{*}{\\textbf{PSN-Partial}} & $m_{\\text{th}}=0.1$ & 0.875 & 0.985 & 0.976 & 0.978 & 2.319 & 0.357\\\\\n & $m_{\\text{th}}=0.3$ & 0.781 & 0.979 & 0.974 & 0.981 & 1.977 & 0.271\\\\\n & $m_{\\text{th}}=0.5$ & \\textbf{0.750} & 0.971 & 0.973 & 0.983 & 1.806 & 0.233\\\\\n \\midrule\n \\multirow{3}[0]{*}{\\texttt{Distance}} & $r_\\text{th}=\\SI{1.5}{\\meter}$ & 1.031 & 0.967 & 0.961 & 0.987 & 1.287 & 0.054\\\\\n & $r_\\text{th}=\\SI{2.0}{\\meter}$ & 1.969 & 1.021 & 0.984 & 0.978 & 1.951 & 0.202\\\\\n & $r_\\text{th}=\\SI{2.5}{\\meter}$ & 1.219 & 1.011 & 0.984 & 0.987 & 2.506 & 0.368\\\\\n \\toprule\n Ranking-based & Parameter & $\\text{traj}_{S}\\downarrow$ & $\\text{traj}_L\\downarrow$ & $\\text{Dist}_m\\uparrow$ & Consistency $\\uparrow$ & Player Num. \\\\\n \\midrule\n \\midrule\n \\multirow{2}[0]{*}{\\textbf{PSN-Full}} & $|U^i|=1$ & 1.094 & 0.990 & 0.978 & 0.984 & 2.000 & 0.134\\\\\n & $|U^i|=2$ & 0.969 & 0.984 & 0.989 & \\textbf{0.989} & 3.000 & 0.441\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\textbf{PSN-Partial}} & $|U^i|=1$ & 1.000 & \\textbf{0.966} & 0.977 & 0.982 & 2.000 & 0.113\\\\\n & $|U^i|=2$ & 1.188 & 0.987 & 0.989 & 0.983 & 3.000 & 0.416\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\texttt{kNNs}} & $|U^i|=1$ & 1.438 & 1.021 & 0.993 & 0.985 & 2.000 & 0.128\\\\\n & $|U^i|=2$ & 1.281 & 0.999 & 0.994 & 0.978 & 3.000 & 0.428\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\texttt{Cost Evolution}} & $|U^i|=1$ & 1.813 & 0.976 & 0.997 & 0.931 & 2.000 & 0.120\\\\\n & $|U^i|=2$ & 2.188 & 0.975 & 0.999 & 0.904 & 3.000 & 0.426\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\texttt{Gradient}} & $|U^i|=1$ & 1.781 & 1.012 & 0.987 & 0.934 & 2.000 & 0.117\\\\\n & $|U^i|=2$ & 1.594 & 0.997 & 0.994 & 0.940 & 3.000 & 0.407\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\texttt{Hessian}} & $|U^i|=1$ & 1.500 & 1.014 & 0.995 & 0.972 & 2.000 & 0.134\\\\\n & $|U^i|=2$ & 1.281 & 1.000 & 0.995 & 0.976 & 3.000 & 0.419\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\texttt{BF}} & $|U^i|=1$ & 1.406 & 1.017 & 0.992 & 0.976 & 2.000 & 0.115\\\\\n & $|U^i|=2$ & 1.250 & 0.996 & 0.993 & 0.976 & 3.000 & 0.413\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\texttt{CBF}} & $|U^i|=1$ & 1.344 & 1.004 & \\textbf{1.000} & 0.960 & 2.000 & 0.121\\\\\n & $|U^i|=2$ & 1.281 & 0.993 & 0.998 & 0.958 & 3.000 & 0.429\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "PSN Game: Game-theoretic Planning via a Player Selection Network", "authors": ["Tianyu Qiu", "Eric Ouano", "Fernando Palafox", "Christian Ellis", "David Fridovich-Keil"], "url": "https://arxiv.org/abs/2505.00213v1", "attribution": "\"PSN Game: Game-theoretic Planning via a Player Selection Network\" by Tianyu Qiu, Eric Ouano, Fernando Palafox, Christian Ellis, and David Fridovich-Keil, arXiv:2505.00213v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05378v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model error for the population dynamics example. The first three rows show the error of the N-CTMC model as we increase the number of trajectories in the training set. For the largest training set, we also show GLM model errors. N-CTMC training is halted when $\\Delta\\text{loss} < 10^{-10}$. W denotes that the error metric is weighted. For the GLM model, training is halted when $\\| \\nabla \\theta \\| < 0.01$.} %table updated\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l|l|l}\nTrajectories & MAE & W-MAE & MSE & W-MSE & Epochs \\\\ \\hline\n100 & 0.304 & $2.45 \\cdot 10^{-1}$ & 0.244 & $1.53 \\cdot 10^{-1}$ & 12,900 \\\\ \n1000 & 0.157 & $1.01 \\cdot 10^{-1}$ & 0.093 & $2.84 \\cdot 10^{-2}$ & 33,500 \\\\ \n10000 & 0.099 & $3.82 \\cdot 10^{-2}$ & 0.051 & $5.13 \\cdot 10^{-3}$ & 89,800 \\\\ \\hline\n10000 (GLM) & 1.028 & $7.06 \\cdot 10^{-1}$ & 3.208 & $1.39 \\cdot 10^{ 0}$ & 2,300 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Neural Continuous-Time Markov Models", "authors": ["Majerle Reeves", "Harish S. Bhat"], "url": "https://arxiv.org/abs/2212.05378v1", "attribution": "\"Neural Continuous-Time Markov Models\" by Majerle Reeves and Harish S. Bhat, arXiv:2212.05378v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.02813v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rcccccrr}\n \\hline\n $n_\\infty$ & $m_p$ & $\\Delta x$ & $\\beta$ & $n_{\\rm t}$ & $n_{\\rm t}/n_{\\infty}$ \\\\\n \\hline\n \\hline\n 400 & 2.5 & 0.10607847 & 177.73604 & 908 & 2.27 \\\\\n 800 & 1.25 & 0.084194539 & 282.13837 & 1593 & 1.99125 \\\\\n 1600 & 0.625 & 0.066825250 & 447.86674 & 2879 & 1.799375 \\\\\n 3200 & 0.3125 & 0.053039236 & 710.94414 & 5154 & 1.610625 \\\\\n 6400 & 0.15625 & 0.042097269 & 1128.5535 & 9635 & 1.5054688 \\\\\n 12800 & 0.078125 & 0.033412625 & 1791.4670 & 17579 & 1.3733594 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An optimal-transport finite-particle method for driven mass diffusion", "authors": ["Anna Pandolfi", "Ignacio Romero", "Michael Ortiz"], "url": "https://arxiv.org/abs/2503.02813v1", "attribution": "\"An optimal-transport finite-particle method for driven mass diffusion\" by Anna Pandolfi, Ignacio Romero, and Michael Ortiz, arXiv:2503.02813v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.15397v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc|ccc}\n & Euler-IMEX & & & IMEX(2,2;1) & \\\\\n \\#Dofs & \\(\\text{err}_\\infty(1)\\) & rate & \\#Dofs & \\(\\text{err}_\\infty(0.5)\\) & rate \\\\ \\hline\n 129 & 4.32 E-01 & -- & 33 & 3.40 E-01 & -- \\\\\n 257 & 2.95 E-01 & 0.55 & 65 & 1.15 E-01 & 1.60 \\\\\n 513 & 1.79 E-01 & 0.72 & 129 & 3.15 E-02 & 1.89 \\\\\n 1025 & 1.00 E-01 & 0.84 & 257 & 7.82 E-03 & 2.02 \\\\\n 2049 & 5.31 E-02 & 0.91 & 513 & 1.97 E-03 & 1.99 \\\\\n 4079 & 2.73 E-02 & 0.96 & 1025 & 4.90 E-04 & 2.01 \\\\ \n 8193 & 1.39 E-02 & 0.97 & 2049 & 1.22 E-04 & 2.00 \\\\ \\hline\n & IMEX(3,3;1)& & & IMEX(5,4;1) & \\\\\n \\#Dofs & \\(\\text{err}_\\infty(0.5)\\) & rate & \\#Dofs & \\(\\text{err}_\\infty(0.5)\\) & rate \\\\ \\hline\n 33 & 1.12 E-01 & -- & 49 & 5.46 E-02 & -- \\\\\n 65 & 3.00 E-02 & 1.95 & 97 & 1.43 E-02 & 1.96 \\\\\n 129 & 3.88 E-03 & 2.98 & 193 & 1.83 E-03 & 2.99 \\\\\n 257 & 4.38 E-04 & 3.16 & 385 & 2.01 E-04 & 3.20 \\\\\n 513 & 3.52 E-05 & 3.65 & 769 & 3.98 E-05 & 2.34 \\\\\n 1025 & 3.03 E-06 & 3.55 & 1537 & 3.77 E-06 & 3.41 \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Convergence tables for the KdV equation on \\(D = (-10,10)\\). Top left: Euler-IMEX time stepping with CFL = 0.5. Top Right: IMEX(2,2;1) time stepping with CFL = 0.05. Bottom Left: IMEX(3,3;1) time stepping wtih CFL = 0.25. Bottom Right: IMEX(5,4;1) time stepping with CFL = 0.25.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A High Order IMEX Method for Generalized Korteweg de-Vries Equations", "authors": ["Seth Gerberding"], "url": "https://arxiv.org/abs/2503.15397v1", "attribution": "\"A High Order IMEX Method for Generalized Korteweg de-Vries Equations\" by Seth Gerberding, arXiv:2503.15397v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2309.00875v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{xcolor}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|ccccccc}\n & PV & ProbI & PredI & RI & PI & S\\&P & \\textcolor{blue}{ETF} \\\\ \\hline\n $R$ & 6.45\\% & 7.21\\% & 7.22\\% & -0.00\\% & -0.01\\% & 16.43 \\% & \\textcolor{blue}{-25.46\\%} \\\\\n $SR$ & 0.6195 & 0.7115 & 0.6852 & 0.0363 & -0.0299 & 0.8868 & \\textcolor{blue}{-0.6299}\n \\end{tabular}\n\\caption{Annualized performances with $c = 20$bps over the test sample of Section including the Brent, the Dubai and the WTI futures contract.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A hidden Markov model for statistical arbitrage in international crude oil futures markets", "authors": ["Viviana Fanelli", "Claudio Fontana", "Francesco Rotondi"], "url": "https://arxiv.org/abs/2309.00875v2", "attribution": "\"A hidden Markov model for statistical arbitrage in international crude oil futures markets\" by Viviana Fanelli, Claudio Fontana, and Francesco Rotondi, arXiv:2309.00875v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.03490v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n \\textsf{ Poisson brackets} & $\\begin{matrix}\n \\textsf{ No. of polynomials without} \\\\\n \\textsf{ using the grading method }\n \\end{matrix}$ & $\\begin{matrix}\n \\textsf{Maximum No. of polynomials after applying} \\\\\n \\textsf{ the grading method} \n \\end{matrix}$ & $\\Delta$ \\\\\n \\hline \n $\\{\\textbf{q}_2,\\textbf{q}_2\\}$ & $ 31 $ & $8 $ & $23$ \\\\\n \\hline \n $\\{\\textbf{q}_2,\\textbf{q}_3\\}$ & $102 $ & $39 $ & $63$ \\\\\n \\hline \n $\\{\\textbf{q}_2,\\textbf{q}_4\\}$ & $261$ & $129 $ & $133 $ \\\\\n \\hline \n $\\{\\textbf{q}_3,\\textbf{q}_4\\}$ & $478 $ & $236 $ & $242 $ \\\\\n \\hline \n $\\{\\textbf{q}_4,\\textbf{q}_4\\}$ & $ 990 $ & $492 $ & $498$ \\\\\n \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of the number of polynomials }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the construction of polynomial Poisson algebras: a novel grading approach", "authors": ["Rutwig Campoamor-Stursberg", "Danilo Latini", "Ian Marquette", "Junze Zhang", "Yao-Zhong Zhang"], "url": "https://arxiv.org/abs/2503.03490v1", "attribution": "\"On the construction of polynomial Poisson algebras: a novel grading approach\" by Rutwig Campoamor-Stursberg, Danilo Latini, Ian Marquette, Junze Zhang, and Yao-Zhong Zhang, arXiv:2503.03490v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.14860v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrr}\n\\hline\\noalign{\\smallskip}\n$\\alpha\\, \\backslash \\,\\beta$ & 0.8 & 0.825 & 0.85 & 0.875 & 0.9 & 0.925 & 0.95 & 0.975\\\\\n\\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\n1.1 &0.0775&0.0679&0.0590&0.0508&0.0438&0.0380&0.0340&0.0318 \\\\\n1.2 &0.0889&0.0817&0.0753&0.0700&0.0659&0.0629&0.0609&0.0596 \\\\\n1.3\t\t &0.1062&0.1066&0.0962&0.0927&0.0899&0.0878&0.0864&0.0854 \\\\\n1.4 &0.1253&0.1211&0.1117&0.1150&0.1129&0.1112&0.1101&0.1093 \\\\\n1.5 &0.1446&0.1413&0.1385&0.1363&0.1346&0.1333&0.1324&0.1318\\\\\n\\hline\\noalign{\\smallskip}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Self-sustained price bubbles driven by Bitcoin innovations and adaptive behavior", "authors": ["Misha Perepelitsa", "Ilya Timofeyev"], "url": "https://arxiv.org/abs/2012.14860v1", "attribution": "\"Self-sustained price bubbles driven by Bitcoin innovations and adaptive behavior\" by Misha Perepelitsa and Ilya Timofeyev, arXiv:2012.14860v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11866v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n Model & AUC & ECE \\\\\n \\midrule\n Baseline & 84.7 $\\pm$ 0.8 & 8.9 $\\pm$ 1.4\\\\\n Vanilla Label Smoothing & 85.6 $\\pm$ 0.6 & 3.2 $\\pm$ 0.6\\\\\n Agreement-Aware (Linear) & 86.1 $\\pm$ 0.9 & 5.3 $\\pm$ 0.7\\\\\n Agreement-Aware (Piecewise) & \\textbf{86.4} $\\pm$ 0.4 & 2.9 $\\pm$ 0.4\\\\\n Agreement-Aware (Nonlinear) & 86.3 $\\pm$ 0.7 & \\textbf{2.8} $\\pm$ 0.6\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Agreement-aware label smoothing improves performance (higher AUC) and model calibration (lower ECE). Means and standard deviations are reported across 10 seeds.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Calibrating Histopathology Image Classifiers using Label Smoothing", "authors": ["Jerry Wei", "Lorenzo Torresani", "Jason Wei", "Saeed Hassanpour"], "url": "https://arxiv.org/abs/2201.11866v1", "attribution": "\"Calibrating Histopathology Image Classifiers using Label Smoothing\" by Jerry Wei, Lorenzo Torresani, Jason Wei, and Saeed Hassanpour, arXiv:2201.11866v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.13222v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\t\t\t\\hline\\hline\n\t\t\tSeries & $p$ values \\\\\n\t\t\t\\hline\n\t\t\tFRM&$0.28$\\\\\n\t\t\tVIX &0.01\\\\\n\t\t\tDFRM&$0.01$\\\\\n\t\t\t\\hline\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{$p$ values of ADF test for stationarity}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "An AI approach to measuring financial risk", "authors": ["Lining Yu", "Wolfgang Karl Härdle", "Lukas Borke", "Thijs Benschop"], "url": "https://arxiv.org/abs/2009.13222v1", "attribution": "\"An AI approach to measuring financial risk\" by Lining Yu, Wolfgang Karl Härdle, Lukas Borke, and Thijs Benschop, arXiv:2009.13222v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01001v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Name of hyperparameters and their correspoding domain for the computer vision model.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\nHyperparameter & Domain\\\\\n\\midrule\nNumber of base filters & \\{4, 8, 16, 32, 64\\}\\\\\nDropout rate for convolutional blocks & {}[0.1, 0.6]\\\\\nBatch normalization for convolutional blocks & \\{false, true\\}\\\\\nType of global pooling & \\{max, average\\}\\\\\nIgnore additional inputs & \\{false, true\\}\\\\\n\\addlinespace\nNumber of units for the fully-connected layer & \\{128, 256, 512, 1024, 2048\\}\\\\\nBatch normalization for the fully-connected layer & \\{false, true\\}\\\\\nDropout rate for the fully-connected layer & {}[0.1, 0.6]\\\\\nLearning rate & {}[$10^{-8}$, $10^{-1}$]\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Automated Assessment of Residual Plots with Computer Vision Models", "authors": ["Weihao Li", "Dianne Cook", "Emi Tanaka", "Susan VanderPlas", "Klaus Ackermann"], "url": "https://arxiv.org/abs/2411.01001v1", "attribution": "\"Automated Assessment of Residual Plots with Computer Vision Models\" by Weihao Li, Dianne Cook, Emi Tanaka, Susan VanderPlas, and Klaus Ackermann, arXiv:2411.01001v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03563v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Best scores for SameGame}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrrrrrrrrr}\nProblem & NMCS & SP-MCTS & ~~~~~NRPA & SRNPA(4) & js-games.de \\\\ \n & & & & & \\\\\n 1 & 3,121 & 2,919 & 3,179 & 3,203 & 3,413 \\\\\n 2 & 3,813 & 3,797 & 3,985 & 3,987 & 4,023 \\\\\n 3 & 3,085 & 3,243 & 3,635 & 3,757 & 4,019 \\\\\n 4 & 3,697 & 3,687 & 3,913 & 4,001 & 4,215 \\\\\n 5 & 4,055 & 4,067 & 4,309 & 4,287 & 4,379 \\\\\n 6 & 4,459 & 4,269 & 4,809 & 4,799 & 4,869 \\\\\n 7 & 2,949 & 2,949 & 2,651 & 2,151 & 3,435 \\\\\n 8 & 3,999 & 4,043 & 3,879 & 4,079 & 4,771 \\\\\n 9 & 4,695 & 4,769 & 4,807 & 4,821 & 5,041 \\\\\n 10 & 3,223 & 3,245 & 2,831 & 3,333 & 3,937 \\\\\n 11 & 3,147 & 3,259 & 3,317 & 3,531 & 3,783 \\\\\n 12 & 3,201 & 3,245 & 3,315 & 3,355 & 3,921 \\\\\n 13 & 3,197 & 3,211 & 3,399 & 3,379 & 3,821 \\\\\n 14 & 2,799 & 2,937 & 3,097 & 3,121 & 3,263 \\\\\n 15 & 3,677 & 3,343 & 3,559 & 3,783 & 4,161 \\\\\n 16 & 4,979 & 5,117 & 5,025 & 5,377 & 5,517 \\\\\n 17 & 4,919 & 4,959 & 5,043 & 5,049 & 5,227 \\\\\n 18 & 5,201 & 5,151 & 5,407 & 5,491 & 5,503 \\\\\n 19 & 4,883 & 4,803 & 5,065 & 5,325 & 5,343 \\\\\n 20 & 4,835 & 4,999 & 4,805 & 5,203 & 5,217 \\\\\n & & & & & \\\\\nTotal & 77,934 & 78,012 & 80,030 & 82,032 & 87,858 \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Stabilized Nested Rollout Policy Adaptation", "authors": ["Tristan Cazenave", "Jean-Baptiste Sevestre", "Matthieu Toulemont"], "url": "https://arxiv.org/abs/2101.03563v1", "attribution": "\"Stabilized Nested Rollout Policy Adaptation\" by Tristan Cazenave, Jean-Baptiste Sevestre, and Matthieu Toulemont, arXiv:2101.03563v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.00162v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccc}%{|l|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|p{1.5cm}|}\n \\toprule\n & \\multicolumn{3}{c}{$c=0.8$} & \\multicolumn{3}{c}{$c=0.9$} & \\multicolumn{3}{c}{$c=0.99$} \\\\\n \\cmidrule(r){2-4} \\cmidrule(lr){5-7} \\cmidrule(l){8-10} \n Method & $\\ell=5$ & $\\ell=10$ & $\\ell=20$\n & $\\ell=5$ & $\\ell=10$ & $\\ell=20$\n & $\\ell=5$ & $\\ell=10$ & $\\ell=20$ \\\\\n \\midrule\n GD & 0.9026 & 0.9026 & 0.9026 & 0.9026 & 0.9026 & 0.9026 & 0.9026 & 0.9026 & \\textbf{0.9026} \\\\\n CD & 0.8984 & 0.8984 & 0.8984 & 0.8984 & 0.8984 & 0.8984 & 0.8984 & 0.8984 & \\textbf{0.8984} \\\\\n FS-SSD & 2.4495 & 2.4194 & \\textbf{2.3611} & 2.4495 & 2.4196 & 2.3619 & 2.4497 & 2.4194 & 2.3622 \\\\\n SPSA & 0.7713 & 0.7756 & 0.7502 & 0.6245 & \\textbf{0.4623} & 0.5549 & 0.6046 & 0.6721 & 0.7006 \\\\ \n GS & 2.4598 & 2.4442 & \\textbf{2.4129} & 2.4597 & 2.4447 & 2.4144 & 2.4596 & 2.4445 & 2.4150 \\\\ \n HF-SSD & 0.3620 & 0.2511 & \\textbf{0.2194} & 0.8667 & 0.5109 & 0.3337 & 3.4582 & 1.7191 & 1.0331 \\\\ \n BF-SSD & 0.3177 & 0.2947 & 0.2932 & 0.2686 & 0.2526 & 0.2497 & 0.2316 & 0.2104 & \\textbf{0.1984} \\\\ \n VR-SSD & 0.9885 & 0.9374 & 0.9178 & 0.9881 & 0.9464 & 0.9154 & 0.9925 & 0.9395 & \\textbf{0.9121} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance values for different optimization methods across various $c$ and $\\ell$ combinations at $N=5,\\!000$. The minimum value in each row is highlighted in bold.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search", "authors": ["Nuojin Cheng", "Alireza Doostan", "Stephen Becker"], "url": "https://arxiv.org/abs/2505.00162v1", "attribution": "\"Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search\" by Nuojin Cheng, Alireza Doostan, and Stephen Becker, arXiv:2505.00162v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.11576v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n \\textbf{Variable} & \\textbf{Symbol} & \\textbf{Asian Call} & \\textbf{Down-Out Call} \\\\\n \\hline\n Maturity & $T$ & $0.25y$ & $0.25y$\\\\\n \\hline\n Spot Price & $S_0$ & $100$ & $100$\\\\\n \\hline\nTime steps & $D$ & 32 & 32\\\\\n \\hline \nInterest rate & $r$ & $3\\%$ & $3\\%$\\\\\n \\hline \nVolatility & $\\sigma$ & $30\\%$ & $30\\%$ \\\\\n \\hline \nStrike & $K$ & 100 & 100 \\\\\n \\hline \nBarrier & $B$ & - & 90/80/70\\\\\n \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Pricing parameters for the financial instruments under analysis.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Effective dimensionality reduction for Greeks computation using Randomized QMC", "authors": ["Luca Albieri", "Sergei Kucherenko", "Stefano Scoleri", "Marco Bianchetti"], "url": "https://arxiv.org/abs/2504.11576v1", "attribution": "\"Effective dimensionality reduction for Greeks computation using Randomized QMC\" by Luca Albieri, Sergei Kucherenko, Stefano Scoleri, and Marco Bianchetti, arXiv:2504.11576v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.15403v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n \\textbf{Model} & \\multicolumn{3}{c}{\\textbf{Average RMSE}} & \\multicolumn{3}{c}{\\textbf{Training Time (Seconds)}} \\\\\n \\cmidrule(lr){2-4} \\cmidrule(lr){5-7}\n & \\textbf{3 Features} & \\textbf{4 Features} & \\textbf{5 Features} & \\textbf{3 Features} & \\textbf{4 Features} & \\textbf{5 Features} \\\\\n \\midrule\n LSTM & 0.00781 & 0.00670 & 0.00649 & 3790.59 & 3281.62 & 2735.92 \\\\\n RNN & 0.00772 & 0.00671 & 0.00659 & 3699.21 & 3139.98 & 2232.31 \\\\\n BiLSTM & 0.00775 & 0.00715 & 0.00669 & 4146.64 & 3675.25 & 3471.38 \\\\\n GRU & 0.00781 & 0.00687 & 0.00669 & 3806.17 & 3319.52 & 2854.31 \\\\\n CustomQNN & 0.07603 & 0.05528 & 0.06120 & 120765.63 & 155362.05 & 139337.06 \\\\\n HybridQNN1 & 0.02605 & 0.02161 & 0.01740 & 121020.84 & 155336.05 & 227781.18 \\\\\n HybridQNN2 & 0.02312 & 0.01959 & 0.01920 & 69841.69 & 92337.84 & 118833.32 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction", "authors": ["Prashant Kumar Choudhary", "Nouhaila Innan", "Muhammad Shafique", "Rajeev Singh"], "url": "https://arxiv.org/abs/2503.15403v1", "attribution": "\"HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction\" by Prashant Kumar Choudhary, Nouhaila Innan, Muhammad Shafique, and Rajeev Singh, arXiv:2503.15403v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.08096v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll}\n\\toprule\n Parameter: & $V_0$ & $\\kappa$ & $\\theta$ & $\\sigma_v$ & $\\rho$ & $\\lambda$ & $\\mu_J$ & $\\sigma_J$ \\\\\n\\midrule\n Initial value $(\\Theta_0)$ & 0.0576 & 2.03 & 0.04 & 0.38 & -0.7 & 0.59 & -0.05 & 0.07 \\\\\n\\hline Uniform distribution lower bound (a) & 0.01 & 0.01 & 1 & 0.1 & -1 & 0 & -0.1 & 0.0\\\\\n Uniform distribution upper bound (b) & 0.1 & 0.1 & 5 & 0.5 & 1 & 5 & 0.1 & 0.1 \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Initial parameters $\\Theta_0$ used by in their simulation study of the Bates model. Parameters are based on the analysis by of calibrated Bates models fitted to S\\&P 500 Index options. Lower and upper bounds of uniform noise in equation . }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Joint calibration of the volatility surface and variance term structure", "authors": ["Jiwook Yoo"], "url": "https://arxiv.org/abs/2509.08096v1", "attribution": "\"Joint calibration of the volatility surface and variance term structure\" by Jiwook Yoo, arXiv:2509.08096v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.11606v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary statistics for the Sicily sample by relative time}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc|cccccc}\n\t\t\t\t\\hline\n\t\t\t\t& \\multicolumn{6}{c}{\\textbf{Relative time=0}} & \\multicolumn{6}{c}{\\textbf{Relative time=1}} \\\\ \\hline\n\t\t\t\t& \\multicolumn{2}{c}{\\textbf{Full Sample}} & \\multicolumn{2}{c}{\\textbf{Treated}} & \\multicolumn{2}{c}{\\textbf{Control}} & \\multicolumn{2}{c}{\\textbf{Full Sample}} & \\multicolumn{2}{c}{\\textbf{Treated}} & \\multicolumn{2}{c}{\\textbf{Control}} \\\\ \n\t\t\t\t Variables & Mean & SD & Mean & SD & Mean & SD & Mean & SD & Mean & SD & Mean & SD \\\\ \\hline\n\t\t\t\tScore in Math test & 216.40 & 42.81 & 211.76 & 37.36 & 221.33 & 47.44 & 189.39 & 38.75 & 185.46 & 40.47 & 193.57 & 36.38 \\\\ \n\t\t\t\tScore in Italian test & 209.93 & 42.25 & 204.86 & 38.44 & 215.33 & 45.33 & 192.42 & 38.46 & 190.48 & 39.20 & 194.49 & 37.55 \\\\ \n\t\t\t\tDays of school closure & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 72.85 & 77.30 & 141.38 & 43.67 & 0.00 & 0.00 \\\\ \n\t\t\t\tPeers' score in Math test & 221.66 & 35.24 & 217.13 & 28.79 & 226.49 & 40.44 & 193.17 & 30.38 & 189.11 & 31.72 & 197.48 & 28.25 \\\\ \n\t\t\t\tPeers' score in Italian test & 216.59 & 32.22 & 212.50 & 28.40 & 220.94 & 35.32 & 196.55 & 29.36 & 195.44 & 31.97 & 197.73 & 26.26 \\\\ \n\t\t\t\tParents' year of education & 9.69 & 5.99 & 9.99 & 5.93 & 9.37 & 6.04 & 9.69 & 5.99 & 9.99 & 5.93 & 9.37 & 6.04 \\\\\n\t\t\t\tStudent repeating the year (1=yes) & 0.01 & 0.08 & 0.01 & 0.09 & 0.00 & 0.06 & 0.01 & 0.12 & 0.01 & 0.12 & 0.02 & 0.12 \\\\\n\t\t\t\tParents are unemployed or blue-collars (1=yes) & 0.19 & 0.39 & 0.20 & 0.40 & 0.19 & 0.39 & 0.19 & 0.39 & 0.20 & 0.40 & 0.19 & 0.39 \\\\ \\hline\n\t\t\t\tForeigner parents & 0.03 & 0.18 & 0.04 & 0.19 & 0.03 & 0.16 & 0.03 & 0.18 & 0.04 & 0.19 & 0.03 & 0.16 \\\\ \n\t\t\t\tClass size & 19.30 & 2.93 & 19.37 & 2.91 & 19.23 & 2.95 & 20.27 & 2.84 & 20.36 & 2.81 & 20.17 & 2.86 \\\\ \n\t\t\t\tMunicipality's population & 260,887 & 41,5004 & 257,873 & 410,850 & 264,091 & 419,352 & 285,908 & 425,487 & 282,295 & 421,231 & 289,747 & 429,933 \\\\ \n\t\t\t\tNumber of classes in the school & 13.99 & 7.36 & 14.18 & 7.44 & 13.80 & 7.27 & 21.56 & 16.15 & 21.82 & 16.14 & 21.28 & 16.17 \\\\\n\t\t\t\tCovid-19 cases per inhabitant & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.02 & 0.03 & 0.04 & 0.03 & 0.00 & 0.00 \\\\\n\t\t\t\tMunicipality's variance of Covid-19 cases over time & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 20,270 & 25,952 & 39,340 & 23599 & 0.00 & 0.00 \\\\ \\hline\n\t\t\t\tObservations & \\multicolumn{2}{c}{167,183} & \\multicolumn{2}{c}{86,140} & \\multicolumn{2}{c}{167,183} & \\multicolumn{2}{c}{86,140} & \\multicolumn{2}{c}{167,183} & \\multicolumn{2}{c}{86,140} \\\\ \\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Will the last be the first? School closures and educational outcomes", "authors": ["Michele Battisti", "Giuseppe Maggio"], "url": "https://arxiv.org/abs/2208.11606v1", "attribution": "\"Will the last be the first? School closures and educational outcomes\" by Michele Battisti and Giuseppe Maggio, arXiv:2208.11606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11864v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\nModel & Number of Parameters & Model &F1 \\\\\n\\hline\n & $1.1\\times10^7$ & CNN & 86.9\\% \\\\ \\hline\n & $8.7\\times10^7$ & CNN & 86.7\\% \\\\ \\hline\n & $2.5\\times 10^7$ & CNN & 85.7\\% \\\\ \\hline\n & 1,115 & CNN & 87.9\\% \\\\ \\hline\n \\textbf{Ours} & \\textbf{24} & RF & \\textbf{90.1\\%} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison with related works for the C-NMC data. The best approaches for the smallest number of parameters and highest F1 score are boldened.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features", "authors": ["William Franz Lamberti"], "url": "https://arxiv.org/abs/2201.11864v1", "attribution": "\"Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features\" by William Franz Lamberti, arXiv:2201.11864v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.03699v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\nComponent & Level $P_1$ & Slope $P_2$ & Curvature $P_3$ \\\\\n\\hline\nSkewness of $Z_k$ & 0.12 & -0.8 & -0.14 \\\\\n\\hline\nSkewness of $Z_k/V$ & 0.24 & -0.37 & 0.08 \\\\\n\\hline\nKurtosis of $Z_k$ & 3.59 & 6.49 & 4.62 \\\\\n\\hline\nKurtosis of $Z_k/V$ & 3.92 & 4.01 & 4.58 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Skewness and kurtosis for autoregression innovations from~. We see that division by $V$ makes $Z_2$ (innovations for slope) but not $Z_1$ and $Z_3$ (innovations for level and intercept) closer to Gaussian. Recall that skewness and kurtosis for the Gaussian law are 0 and 3.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Zero-Coupon Treasury Rates and Returns using the Volatility Index", "authors": ["Jihyun Park", "Andrey Sarantsev"], "url": "https://arxiv.org/abs/2411.03699v4", "attribution": "\"Zero-Coupon Treasury Rates and Returns using the Volatility Index\" by Jihyun Park and Andrey Sarantsev, arXiv:2411.03699v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.19039v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model Parameters for Reproducibility}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n \\hline\n \\textbf{Parameter} & \\textbf{Value} \\\\\n \\hline\n Notional value ($V$) & 18\\$ \\\\\n Time step ($dt$) & 1 year \\\\\n Number of time steps ($T$) & 3 \\\\\n Annual volatility ($\\sigma$) & $0.2382$ \\\\\n Annual average log-return ($\\mu$) & $0.1274$\\\\\n Put barrier ($b$) & 0.7 \\\\\n Put strike return ($K$)& 1 \\\\\n Risk-free rate ($r$) & $4\\%$\\\\\n First binary option strike ($k_1$) & 1.1 \\\\\n Second binary option strike ($k_2$) & 1.1 \\\\\n First binary option payoff ($f_1$)& $2$ \\\\\n Second binary option payoff ($f_2$)& $5$ \\\\\n Standard Gaussian truncation ($s_{min}$) & $3$\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Autocallable Options Pricing with Integration-Based Exponential Amplitude Loading", "authors": ["Francesca Cibrario", "Ron Cohen", "Emanuele Dri", "Christian Mattia", "Or Samimi Golan", "Tamuz Danzig", "Giacomo Ranieri", "Hanan Rosemarin", "Davide Corbelletto", "Amir Naveh", "Bartolomeo Montrucchio"], "url": "https://arxiv.org/abs/2507.19039v1", "attribution": "\"Autocallable Options Pricing with Integration-Based Exponential Amplitude Loading\" by Francesca Cibrario, Ron Cohen, Emanuele Dri, Christian Mattia, Or Samimi Golan, Tamuz Danzig, Giacomo Ranieri, Hanan Rosemarin, Davide Corbelletto, Amir Naveh, and Bartolomeo Montrucchio, arXiv:2507.19039v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.15096v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Means of measures grouped by placement and labeling methods. Bold p-values indicate when the difference of mean measures between placement methods is significant.\\newline DR = placement by dimensionality reduction.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n \\toprule\n \\textbf{Measure}& \\textbf{Label}& \\multicolumn{2}{c}{\\textbf{Placement}} & \\textbf{p-value}\\\\\n \\cmidrule(r){3-4}\n &&\\textbf{DR} &\\textbf{Random} & \\\\\n \\midrule\n Time (s) &&&&\\\\\n & Baseline & 12.3 & 12.7 & 0.54\\\\\n & Shape & 10.7 & 14.3 & \\textbf{0.0008}\\\\\n & Color & 11.6 & 13.7 & 0.051\\\\\n & Texture & 11.6 & 15.7 &\\textbf{0.0005}\\\\\n Hovered&&&&\\\\\n samples& Baseline & 14.9 & 17.9 & \\textbf{0.04}\\\\\n & Shape & 8.7 & 12.5 & \\textbf{0.004}\\\\\n & Color & 13.8 & 15.7 & 0.16\\\\\n & Texture & 13.5 & 15.3 &0.30\\\\\n Distance &&&&\\\\\n (pixels)& Baseline & 2807 & 3094 & 0.12\\\\\n & Shape & 2313 & 3123 & \\textbf{0.0005}\\\\\n & Color & 2765 & 3588 & \\textbf{0.002}\\\\\n & Texture & 2681 & 3403 & \\textbf{0.009}\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A proposal and evaluation of new timbre visualisation methods for audio sample browsers", "authors": ["Etienne Richan", "Jean Rouat"], "url": "https://arxiv.org/abs/2011.15096v1", "attribution": "\"A proposal and evaluation of new timbre visualisation methods for audio sample browsers\" by Etienne Richan and Jean Rouat, arXiv:2011.15096v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.19294v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of BSS Data Across Six Cities}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrl}\n \\toprule\n City & \\multicolumn{2}{c}{\\# of Rentals/Returns} & \\multicolumn{2}{c}{\\# of Stations Used} & Data Collection \\\\\n & Weekdays & Weekends & Weekdays & Weekends & Period \\\\\n \\midrule\n New York & 5036538 & 1759118 & 2664 & 2594 & 2023-10-13 to 11-11 \\\\\n London & 1210884 & 373178 & 803 & 802 & 2023-09-28 to 10-27 \\\\\n Tokyo & 1563721 & 464659 & 1300 & 777 & 2023-11-01 to 11-30 \\\\\n Boston & 528142 & 193625 & 468 & 468 & 2023-10-15 to 11-13 \\\\\n Chicago & 464355 & 147714 & 1151 & 989 & 2023-11-01 to 11-30 \\\\\n Washington D.C. & 549643 & 252463 & 750 & 747 & 2023-10-15 to 11-13 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Spatio-Temporal Differences in Bike Sharing Usage: A Tale of Six Cities", "authors": ["Shu-ichi Kinoshita", "Yuya Bando", "Hiroki Sayama"], "url": "https://arxiv.org/abs/2412.19294v1", "attribution": "\"Spatio-Temporal Differences in Bike Sharing Usage: A Tale of Six Cities\" by Shu-ichi Kinoshita, Yuya Bando, and Hiroki Sayama, arXiv:2412.19294v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.12595v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Percentage of rejections to the null hypothesis of Bayesian projection posterior procure (BP), linear regression procedure (LR), and piecewise linear fitting (PL) when the true regression functions are not coordinatewise increasing.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ccc|ccc|ccc|}\n\t\t\t\\hline\n\t\t\t& \\multicolumn{3}{c|}{$n=100$} & \\multicolumn{3}{c|}{$n=200$} & \\multicolumn{3}{c|}{$n=500$}\\\\\n\t\t\t& BP & LR & PL & BP & LR & PL & BP & LR & PL\\\\\n\t\t\t\\hline\n\t\t\t$f_7$ & 64.5 & 8.5 & 73 & 93.5 & 10 & 99.5 & 100 & 10.5& 100\\\\\n\t\t\t$f_8$ & 100 & 84.5&83 & 100 & 98.5 & 100 & 100& 100 & 100\\\\\n\t\t\t$f_9$ & 35.5& 7.6& 28.5 & 96& 94.5 & 66.5 & 100 & 100 & 100\\\\\n\t\t\t$f_{10}$ & 100 & 100 & 100 & 100& 100 & 100& 100 & 100 & 100\\\\\n\t\t\t$f_{11}$ & 100 & 100 & 98.5 & 100 & 100 & 100& 100 & 100 & 100\\\\\n\t\t\t$f_{12}$ & 35 & 0& 55&89.5 & 0 &94.5& 100 & 0&100\\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Posterior Contraction and Testing for Multivariate Isotonic Regression", "authors": ["Kang Wang", "Subhashis Ghosal"], "url": "https://arxiv.org/abs/2211.12595v1", "attribution": "\"Posterior Contraction and Testing for Multivariate Isotonic Regression\" by Kang Wang and Subhashis Ghosal, arXiv:2211.12595v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14009v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean values of the estimated parameter values with 95$\\%$ confidence intervals for model .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\hline\nParameter & Mean Value & 95$\\%$ CI\\\\ \n\\hline\n$\\sigma_e$&0.0788 & (0.055, 0.082) \\\\\n$\\sigma_h$& 1.36$e^{-5}$&(1.08$e^{-5}$, 1.7$e^{-5}$)\\\\\n$\\omega$&0.87&(0.83, 0.89)\\\\\n$\\delta$&4&(3.2, 5.5)\\\\\n$\\phi_1$&0.091&(0.078, 0.098)\\\\\n$a$&14&(10, 20)\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study", "authors": ["Adrita Ghosh", "Parthasakha Das", "Tanujit Chakraborty", "Pritha Das", "Dibakar Ghosh"], "url": "https://arxiv.org/abs/2503.14009v1", "attribution": "\"Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study\" by Adrita Ghosh, Parthasakha Das, Tanujit Chakraborty, Pritha Das, and Dibakar Ghosh, arXiv:2503.14009v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.03420v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ResNet architecture.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\t\t\\toprule\n\t\t\\multicolumn{4}{c}{Generator $G(z)$} \\\\\n\t\t\\midrule\n\t\tOperation &Kernel size& Resample& Output Dims\\\\\n\t\t\\midrule\n\t\tNoise & N/A & N/A & $128$ \\\\\n\t\tLinear & N/A & N/A & $128\\times4\\times4$ \\\\\n\t\tResidual block & $[3\\times3]\\times2$ & Up & $128\\times8\\times8$ \\\\\n\t\tResidual block & $[3\\times3]\\times2$ & Up & $128\\times16\\times16$ \\\\\n\t\tResidual block & $[3\\times3]\\times2$ & Up & $128\\times32\\times32$ \\\\\n\t\tConv, tanh & $3\\times3$ & N/A & $3\\times32\\times32$ \\\\\n\t\t\\bottomrule\n\t\t\\toprule\n\t\t\\multicolumn{4}{c}{Critic $D(x)$} \\\\\n\t\t\\midrule\n\t\tOperation &Kernel size& Resample& Output Dims\\\\\n\t\t\\midrule\n\t\tResidual block & $[3\\times3]\\times2$ & Down & $128\\times16\\times16$ \\\\\n\t\tResidual block & $[3\\times3]\\times2$ & Down & $128\\times8\\times8$ \\\\\n\t\tResidual block & $[3\\times3]\\times2$ & N/A & $128\\times8\\times8$ \\\\\n\t\tResidual block & $[3\\times3]\\times2$ & N/A & $128\\times8\\times8$ \\\\\n\t\tReLU, mean pool & N/A & N/A & $128$ \\\\\n\t\tLinear & N/A & N/A & $1$ \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Towards Generalized Implementation of Wasserstein Distance in GANs", "authors": ["Minkai Xu", "Zhiming Zhou", "Guansong Lu", "Jian Tang", "Weinan Zhang", "Yong Yu"], "url": "https://arxiv.org/abs/2012.03420v2", "attribution": "\"Towards Generalized Implementation of Wasserstein Distance in GANs\" by Minkai Xu, Zhiming Zhou, Guansong Lu, Jian Tang, Weinan Zhang, and Yong Yu, arXiv:2012.03420v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.10263v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{rotating}\n\\usepackage{multirow}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Normalized confusion matrix}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\cline{3-6} \n\\multirow{4}{*}{\\rotatebox{90}{True label}} & Trees & 0.54 & 0.18 & 0.28 & 0 \\\\\n & Grass & 0.32 & 0.65 & 0.14 & 0 \\\\\n & Impervious & 0.17 & 0.09 & 0.73 & 0 \\\\\n & Water & 0.01 & 0 & 0 & 0.99 \\\\ \\cline{3-6} \n\\multicolumn{2}{c}{\\multirow{2}{*}{}} & Trees\\hspace{1mm} & Grass\\hspace{1mm} & Impervious\\hspace{0mm} & Water\\hspace{1mm} \\\\\n\\multicolumn{2}{c}{} & \\multicolumn{4}{c}{Predicted label} \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees", "authors": ["Juan Carrillo", "Daniel Garijo", "Mark Crowley", "Rober Carrillo", "Yolanda Gil", "Katherine Borda"], "url": "https://arxiv.org/abs/2009.10263v1", "attribution": "\"Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees\" by Juan Carrillo, Daniel Garijo, Mark Crowley, Rober Carrillo, Yolanda Gil, and Katherine Borda, arXiv:2009.10263v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.19379v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Accuracy table of the 8th order scheme, isotropic media}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n\\hline\n$N$& $N=10$ & \\multicolumn{2}{c|}{$N=20$} & \\multicolumn{2}{c|}{$N=40$} & \\multicolumn{2}{c|}{$N=80$} \\\\ \n \\hline\n $\\omega^2$ & $|\\omega^2-\\omega_h^2|$ & $|\\omega^2-\\omega_h^2|$ & ord & $|\\omega^2-\\omega_h^2|$ & ord & $|\\omega^2-\\omega_h^2|$ & ord\\\\ \n \\hline\n0.25 & 5.11e-15 & 6.66e-16 & \\ & 1.11e-16 & \\ & 2.22e-16 & \\ \\\\ \n\\hline\n0.25 & 1.14e-14 & 1.09e-14 & \\ & 2.78e-17 & \\ & 5.55e-16 & \\ \\\\ \n\\hline\n0.25 & 9.16e-06 & 3.85e-08 & 7.89 & 1.53e-10 & 7.97 & 6.00e-13 & 7.99\\\\ \n\\hline\n0.25 & 9.16e-06 & 3.85e-08 & 7.89 & 1.53e-10 & 7.97 & 6.01e-13 & 7.99\\\\ \n\\hline\n1.25 & 5.35e-06 & 2.21e-08 & 7.92 & 8.75e-11 & 7.98 & 3.12e-13 & 8.13\\\\ \n\\hline\n1.25 & 5.35e-06 & 2.21e-08 & 7.92 & 8.75e-11 & 7.98 & 3.04e-13 & 8.17\\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Kernel compensation method for Maxwell eigenproblem with mimetic finite difference discretization", "authors": ["Chenhao Jin", "Yinhua Xia", "Yan Xu"], "url": "https://arxiv.org/abs/2503.19379v1", "attribution": "\"Kernel compensation method for Maxwell eigenproblem with mimetic finite difference discretization\" by Chenhao Jin, Yinhua Xia, and Yan Xu, arXiv:2503.19379v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.00503v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ASR performance at 15 Hz token rate under different entropy-based segmentation criteria: M1 global threshold, M2 relative change, M3 combined.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\nMethod & WER (\\%) & CER (\\%) \\\\\n\\midrule\nM1 & \\textbf{5.8} & \\textbf{3.1} \\\\\nM2 & 6.3 & 3.4 \\\\\nM3 & 5.9 & 3.2 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Entropy-based Coarse and Compressed Semantic Speech Representation Learning", "authors": ["Jialong Zuo", "Guangyan Zhang", "Minghui Fang", "Shengpeng Ji", "Xiaoqi Jiao", "Jingyu Li", "Yiwen Guo", "Zhou Zhao"], "url": "https://arxiv.org/abs/2509.00503v1", "attribution": "\"Entropy-based Coarse and Compressed Semantic Speech Representation Learning\" by Jialong Zuo, Guangyan Zhang, Minghui Fang, Shengpeng Ji, Xiaoqi Jiao, Jingyu Li, Yiwen Guo, and Zhou Zhao, arXiv:2509.00503v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.15274v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccrr}\n\\toprule\n & & & & &\\\\\nSite & AgeGroup & Gender & Diagnosis & Axis 1 & Depression \\\\\n\\midrule\nSite 1 & adult & F & 0.0 & 35 & 35\\\\ % FSU\\_WAVE1\n & & & 1.0 & 59 & 59\\\\\n & & M & 0.0 & 11 & 11\\\\\n & & & 1.0 & 12 & 12\\\\\nSite 2 & child & F & 0.0 & 72 & 95 \\\\ % SDSU\\_D2018\n & & & 1.0 & 31 & 8 \\\\\n & & M & 0.0 & 101 & 119 \\\\\n & & & 1.0 & 19 & 1 \\\\\nSite 3 & child & F & 0.0 & 82 & 128\\\\ % fsu\\_d2018b50\n & & & 1.0 & 57 & 11\\\\\n & & M & 0.0 & 98 & 150\\\\\n & & & 1.0 & 54 & 2\\\\\nSite 4 & child & F & 0.0 & 41 & 58\\\\ % fsu\\_d2018w8\n & & & 1.0 & 21 & 4\\\\\n & & M & 0.0 & 45 & 63\\\\\n & & & 1.0 & 20 & 2\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Participant label distribution}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Representation learning for improved interpretability and classification accuracy of clinical factors from EEG", "authors": ["Garrett Honke", "Irina Higgins", "Nina Thigpen", "Vladimir Miskovic", "Katie Link", "Sunny Duan", "Pramod Gupta", "Julia Klawohn", "Greg Hajcak"], "url": "https://arxiv.org/abs/2010.15274v3", "attribution": "\"Representation learning for improved interpretability and classification accuracy of clinical factors from EEG\" by Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Sunny Duan, Pramod Gupta, Julia Klawohn, and Greg Hajcak, arXiv:2010.15274v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.10112v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Coherent communication gain under Channel Model 2 with $D=10000$ and $r=100$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|r|r|r|r|r|r|r}\n$(N,M)$ & RB & RT & BT & SF & IO & ES & $G_{\\text{UB}}$ \\\\ \\hline \\hline\n$(3,10)$ & 29.40 & 38.36 & 49.47 & 50.51 & \\bf{50.63} & 50.63 & 90 \\\\ \\hline\n$(10,10)$ & 101.38 & 228.66 & 295.63 & 295.63 & \\bf{321.06} & - & 1000\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Coherent Communications in Self-Organizing Networks with Distributed Beamforming", "authors": ["Yi Shi", "Yalin E. Sagduyu"], "url": "https://arxiv.org/abs/1912.10112v1", "attribution": "\"Coherent Communications in Self-Organizing Networks with Distributed Beamforming\" by Yi Shi and Yalin E. Sagduyu, arXiv:1912.10112v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14913v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|cc|} \\hline \n & \\multicolumn{2}{|c|}{$V_{h}$}& \\multicolumn{2}{|c|}{$V_{h}^{+}$}& \\multicolumn{2}{|c|}{$V_{h}^{*}$}\\\\ \\hline \n n& $L^{2}$error & order & $L^{2}$error & order & $L^{2}$error & order \\\\ \\hline \n 10& 3.924e-04& - \n&1.398e-06& - \n&1.429e-06& - \n\\\\ \\hline \n 20& 4.940e-05&2.990\n&2.625e-07&2.413&2.624e-07&2.445\n\\\\ \\hline \n 40& 6.186e-06&2.997\n&3.565e-08&2.880&3.542e-08&2.889\n\\\\ \\hline \n 80& 7.736e-07&2.999\n&4.556e-09&2.968&4.521e-09&2.970\n\\\\ \\hline \n 160& 9.671e-08&3.000\n&5.727e-10&2.992\n&5.682e-10&2.992\n\\\\ \\hline\n 320& 1.209e-08&3.000&7.168e-11&2.998&7.113e-11&2.998\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{$L^{2}$error and order using $P^{2}$ Lagrange element, Poisson equation in 1d.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A PINN-enriched finite element method for linear elliptic problems", "authors": ["Xiao Chen", "Yixin Luo", "Jingrun Chen"], "url": "https://arxiv.org/abs/2503.14913v1", "attribution": "\"A PINN-enriched finite element method for linear elliptic problems\" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2311.14482v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Correlation coefficient of each annotator with the 10 click simulated user}\n\\begin{tabular}{|l|l|}\n \\hline\n \\textbf{Annotator} & \\textbf{Correlation Coefficient} \\\\ \\hline\n Annotator 1 & 0.9251 \\\\ \\hline\n Annotator 2 & 0.7357 \\\\ \\hline\n Annotator 3 & 0.7819 \\\\ \\hline\n Annotator 4 & 0.4040 \\\\ \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images", "authors": ["Matthias Hadlich", "Zdravko Marinov", "Moon Kim", "Enrico Nasca", "Jens Kleesiek", "Rainer Stiefelhagen"], "url": "https://arxiv.org/abs/2311.14482v1", "attribution": "\"Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images\" by Matthias Hadlich, Zdravko Marinov, Moon Kim, Enrico Nasca, Jens Kleesiek, and Rainer Stiefelhagen, arXiv:2311.14482v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00146v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance Comparison: Bandit Algorithms}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n \\multirow{2}{*}{\\textbf{Model}} & \\multicolumn{3}{c}{\\textbf{Accuracy, \\%}} \\\\\n & \\textbf{Train} & \\textbf{Valid} & \\textbf{Test} \\\\\n \\midrule\n No Bandit & 80.2 & 77.3 & 77.3 \\\\\n TS & 81.1 & 80.3 & 79.3 \\\\\n UCB & 81.6 & 80.8 & 80.2 \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations", "authors": ["Aleksey A. Kocherzhenko", "Nirmal Sobha Kartha", "Tengfei Li", "Hsin-Yi", "Shih", "Marco Mandic", "Mike Fuller", "Arshak Navruzyan"], "url": "https://arxiv.org/abs/2202.00146v1", "attribution": "\"Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations\" by Aleksey A. Kocherzhenko, Nirmal Sobha Kartha, Tengfei Li, Hsin-Yi, Shih, Marco Mandic, Mike Fuller, and Arshak Navruzyan, arXiv:2202.00146v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23211v2_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of the model for Scenario II with $T=500$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccccc}\n\\hline\nCoefficient & Truth & AB ($\\lfloor T\\hat{\\tau} \\rfloor$) & AB ($\\lfloor T\\tilde{\\tau} \\rfloor$) & RMSE ($\\lfloor T\\hat{\\tau} \\rfloor$) & RMSE ($\\lfloor T\\tilde{\\tau} \\rfloor$) & 90$\\%$ CP \n($\\lfloor T\\tilde{\\tau} \\rfloor$) & 95$\\%$ CP ($\\lfloor T\\tilde{\\tau} \\rfloor$) & 99$\\%$ CP ($\\lfloor T\\tilde{\\tau} \\rfloor$) \\\\\\hline\\hline\n& $\\lfloor T/3 \\rfloor$ & 6.510 & 6.350 & 10.074 & 9.976 & 0.940 & 0.970 & 1.000 \\\\\n$\\theta=-0.9$ & $\\lfloor T/2 \\rfloor$ & 7.920 & 7.830 & 12.504 & 12.349 & 0.940 & 0.950 & 0.970 \\\\\n$\\phi=-0.1$ & $\\lfloor 2T/3 \\rfloor$ & 7.120 & 6.210 & 11.889 & 10.449 & 0.950 & 0.960 & 0.990 \\\\\n& $\\lfloor 4T/5 \\rfloor$ & 6.780 & 6.200 & 13.324 & 12.636 & 0.920 & 0.940 & 0.950 \\\\\n\\hline\\hline\n& $\\lfloor T/3 \\rfloor$ & 9.630 & 9.050 & 14.999 & 13.873 & 0.930 & 0.980 & 0.990 \\\\\n$\\theta=-0.9$ & $\\lfloor T/2 \\rfloor$ & 11.660 & 10.350 & 17.311 & 14.855 & 0.930 & 0.940 & 0.980 \\\\\n$\\phi=-0.3$ & $\\lfloor 2T/3 \\rfloor$ & 9.850 & 9.490 & 15.989 & 15.391 & 0.950 & 0.960 & 0.990 \\\\\n& $\\lfloor 4T/5 \\rfloor$ & 11.120 & 9.580 & 20.316 & 16.468 & 0.890 & 0.920 & 0.950 \\\\\n\\hline\\hline\n& $\\lfloor T/3 \\rfloor$ & 16.540 & 16.190 & 36.083 & 34.738 & 0.900 & 0.950 & 0.980 \\\\\n$\\theta=-0.9$ & $\\lfloor T/2 \\rfloor$ & 15.070 & 13.900 & 29.896 & 28.574 & 0.920 & 0.970 & 0.990 \\\\\n$\\phi=-0.5$ & $\\lfloor 2T/3 \\rfloor$ & 12.720 & 11.460 & 21.085 & 17.281 & 0.910 & 0.940 & 0.970 \\\\\n& $\\lfloor 4T/5 \\rfloor$ & 14.370 & 12.440 & 25.380 & 21.172 & 0.870 & 0.900 & 0.940 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models", "authors": ["Sepideh Mosaferi", "Abolfazl Safikhani", "Peiliang Bai"], "url": "https://arxiv.org/abs/2503.23211v2", "attribution": "\"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models\" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18394v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{QKD system parameters.}\n\\begin{tabular}{cccc}\n\\hline\n$p_{pl}$ & $\\alpha_{sift}$ & $\\eta_{pd}$ & $\\alpha_{err}$ \\\\ \\hline\n0.5 & 0.2 & 0.3 & 0.2 \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Quantum-Key Distribution using Decoy Pulses to Combat Photon-Number Splitting by Eavesdropper: An Event-by-Event Impairment Enumeration Approach for Performance Evaluation and Design", "authors": ["Debasish Datta"], "url": "https://arxiv.org/abs/2501.18394v1", "attribution": "\"Quantum-Key Distribution using Decoy Pulses to Combat Photon-Number Splitting by Eavesdropper: An Event-by-Event Impairment Enumeration Approach for Performance Evaluation and Design\" by Debasish Datta, arXiv:2501.18394v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.09993v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The table communicates the numbers of reconstructed ticks per contract, numbers of positive labels, as well as total numbers of entries per contract. 'Reb.' corresponds to the rebound - the required number of ticks for the positive labelling. Rebound columns show numbers of positively labelled entries.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|r|rrr|r}\n Contract & No. Ticks &Reb. 7 & Reb. 11 & Reb. 15 & Total \\\\\n \\hline\n ESH2017 & 1271810 & 896 & 804 & 703 & 4911 \\\\\n ESM2017 & 1407792 & 951 & 881 & 756 & 4181 \\\\\n ESU2017 & 1243120 & 858 & 812 & 693 & 3446 \\\\\n ESZ2017 & 1137427 & 689 & 640 & 518 & 12317 \\\\\n ESH2018 & 2946336 & 2014 & 1983 & 1953 & 11537 \\\\\n ESM2018 & 2919757 & 1965 & 1936 & 1868 & 6534 \\\\\n ESU2018 & 1825417 & 1271 & 1226 & 1095 & 16331 \\\\\n ESZ2018 & 3633969 & 2677 & 2620 & 2565 & 11718 \\\\\n ESH2019 & 3066530 & 1990 & 1949 & 1883 & 9743 \\\\\n ESM2019 & 2591000 & 1711 & 1692 & 1630 & 10389 \\\\\n ESU2019 & 2537197 & 1761 & 1735 & 1704 & 6191 \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components", "authors": ["Artur Sokolovsky", "Luca Arnaboldi"], "url": "https://arxiv.org/abs/2009.09993v4", "attribution": "\"A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components\" by Artur Sokolovsky and Luca Arnaboldi, arXiv:2009.09993v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.12538v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ccc} \n\t\t\\toprule\n\t\tGraph 1 & Graph 2 \\\\\n\t\t\\midrule\n\t\t$\\begin{bmatrix}\n\t\t\t1 & -1 & 0 & 1 & 0 & 0\\\\\n\t\t\t0 & 1 & -1 & 0 & 1 & 0\\\\\n\t\t\t-1 & 0 & 1 & 0 & 0 & 1\\\\\n\t\t\\end{bmatrix}$ & $\\begin{bmatrix}\n\t\t\t1 & 1 & 0 & 0 & 0 & 0\\\\\n\t\t\t0 & -1 & 1 & 0 & 1 & 1\\\\\n\t\t\t0 & 0 & 0 & 1 & -1 & 0\\\\\n\t\t\\end{bmatrix}$ \\\\\n\t\t\\bottomrule\t\n\t\\end{tabular}\n\\caption{Choices of $\\eta$ for the two possible genus $3$ smoothed graphs.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Constructions of superabundant tropical curves in higher genus", "authors": ["Sae Koyama"], "url": "https://arxiv.org/abs/2312.12538v3", "attribution": "\"Constructions of superabundant tropical curves in higher genus\" by Sae Koyama, arXiv:2312.12538v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08927v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|ccc|}\n\\hline\n$n_0$ & $T_{\\mathrm{hor}}$ & $T$\\\\\n\\hline\n\\hline\n$0.25$ & $750$ days & $20.25$\\\\\n\\hline\n$0.50$ & $1000$ days & $27.00$\\\\\n\\hline\n$0.75$ & $1500$ days & $40.50$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\footnotesize Values of $T_{\\mathrm{hor}}$ used for the optimal control problems.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Ensemble optimal control for managing drug resistance in cancer therapies", "authors": ["Alessandro Scagliotti", "Federico Scagliotti", "Laura Deborah Locati", "Federico Sottotetti"], "url": "https://arxiv.org/abs/2503.08927v2", "attribution": "\"Ensemble optimal control for managing drug resistance in cancer therapies\" by Alessandro Scagliotti, Federico Scagliotti, Laura Deborah Locati, and Federico Sottotetti, arXiv:2503.08927v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.03475v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Computational time for each method}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr} \\hline\n\\multirow{2}{*}{Situation} & \\multirow{2}{*}{$\\Delta$} & \\multirow{2}{*}{MAF} & \\multicolumn{2}{c}{Sum of computational time (s)} \\\\ \\cline{4-5}\n & & & \\phantom{00}pMMCM\\phantom{00} & MMCM \\\\ \\hline\noverall null hypothesis & 0.00 & 0.33 & 298.25 & 0.92 \\\\\n(i) additive & 0.25 & 0.12 & 94.77 & 0.90 \\\\\n(ii) dominant & 1.00 & 0.50 & 16.78 & 0.92 \\\\\n(iii) recessive & 0.50 & 0.25 & 71.85 & 0.90 \\\\\n(iv) valley & 0.25 & 0.33 & 254.31 & 0.92 \\\\ \\hline\n\\multicolumn{5}{l}{\\scriptsize \\shortstack[l]{\\\\Abbreviations: MAF, minor allele frequency; pMMCM, permuted modified maximum contrast method;\\\\ MMCM, modified maximum contrast method.}}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A modified maximum contrast method for unequal sample sizes in pharmacogenomic studies", "authors": ["Kengo Nagashima", "Yasunori Sato", "Chikuma Hamada"], "url": "https://arxiv.org/abs/2012.03475v1", "attribution": "\"A modified maximum contrast method for unequal sample sizes in pharmacogenomic studies\" by Kengo Nagashima, Yasunori Sato, and Chikuma Hamada, arXiv:2012.03475v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.08732v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Composition of SISAR database}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\toprule\n & Animals\t& Buildings\t & Humans\t & Sports & Plants & Scenery \\\\ \\midrule\nSource Images & 20&20&20&20\t&11 &9 \\\\\nHR Images & 1680&1680&1680&1680\t&924 &756 \\\\ \\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning-Based Quality Assessment for Image Super-Resolution", "authors": ["Tiesong Zhao", "Yuting Lin", "Yiwen Xu", "Weiling Chen", "Zhou Wang"], "url": "https://arxiv.org/abs/2012.08732v1", "attribution": "\"Learning-Based Quality Assessment for Image Super-Resolution\" by Tiesong Zhao, Yuting Lin, Yiwen Xu, Weiling Chen, and Zhou Wang, arXiv:2012.08732v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.01048v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccccccc|c} \t\n & \\multicolumn{11}{c}{Uncertainty-Weighted Portfolios}\\\\\n \\hline\n&\t$D1$ &$D2$&$D3$&$D4$&$D5$&\t$D6$ &$D7$&$D8$&$D9$&$D10$&\t$All$\\\\\n\\hline\nE-GPR ($\\gamma$-exp) & 6.39 & 1.90 & 1.92 & 4.56 & 6.89 & 10.54 & 14.10 & 17.62 & 22.83 & 31.85 & 12.80 \\\\\nE-GPR (affine) & -0.21 & -0.46 & 0.35 & 0.67 & 0.61 & 3.02 & 1.23 & 2.66 & 6.72 & -0.45 & 1.49 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{In this table, we report the out-of-sample predictive performance, measured by $R^2_{pool}$ in percentage points, across the sorted portfolios obtained using uncertainty-weighted strategy. We compare the performance of our ensemble GPR model with $\\gamma$-exponential kernel to ensemble GPR model with affine kernel. The first ten columns ($D1$ to $D10$) report $R^2_{pool}$ for each decile while the last column (All) reports $R^2_{pool}$ calculated over the grand panel of all deciles.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Empirical Asset Pricing via Ensemble Gaussian Process Regression", "authors": ["Damir Filipović", "Puneet Pasricha"], "url": "https://arxiv.org/abs/2212.01048v2", "attribution": "\"Empirical Asset Pricing via Ensemble Gaussian Process Regression\" by Damir Filipović and Puneet Pasricha, arXiv:2212.01048v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.12379v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|cccc}\n\\multicolumn{3}{l}{bad controls in adjustment} & \\multicolumn{3}{c}{$\\epsilon_{pehe}$} \\\\\n\\hline\n&TARnet & {\\textsc \\textbf{Mod1}} & {\\textsc\\textbf{Mod2} } & {\\textsc \\textbf{Mod3}} & {\\textsc \\textbf{Mod4}}\\\\\n\\hline\n\\textbf{low} &Adjust All &$.35\\pm .12$ & $.17\\pm .02$ & $.30 \\pm .04$ & $.24 \\pm .04$\\\\\n& NICE &$.08 \\pm .04$ & $.05\\pm .01$ & $.07 \\pm .02$ & $.07 \\pm .01$\\\\\n\\hline\n\\textbf{med}& Adjust All &$.26 \\pm .02$ & $.27\\pm .05$ & $.39 \\pm .07$ & $.13\\pm .01$\\\\\n& NICE &$.04\\pm .01$ & $.05\\pm .02$ & $.06 \\pm .01$ & $.04 \\pm .01$\\\\\n\\hline\n\\textbf{high} & Adjust All&$.31 \\pm .04$ & $.23\\pm .06$ & $.31 \\pm .07$ & $.12\\pm .01$\\\\\n& NICE &$.07\\pm .02$ & $.07 \\pm .03$ & $.14\\pm .07$ & $.06\\pm .03$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Invariant Representation Learning for Treatment Effect Estimation", "authors": ["Claudia Shi", "Victor Veitch", "David Blei"], "url": "https://arxiv.org/abs/2011.12379v2", "attribution": "\"Invariant Representation Learning for Treatment Effect Estimation\" by Claudia Shi, Victor Veitch, and David Blei, arXiv:2011.12379v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.18600v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Loss comparison}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|cccc}\n \\toprule\n \\multirow{2}[2]{*}{Model} & \\multirow{2}[2]{*}{Training Loss} & \\multicolumn{4}{c}{SP500} \\\\\n & & IC & ICIR & ARR & Sharpe \\\\\n \\midrule\n \\multirow{3}[2]{*}{LSTM} & IC loss & 0.14 & 1.37 & 0.11 & 0.43 \\\\\n & MSE Loss & 0.12 & 1.20 & 0.17 & 0.61 \\\\\n & Pairwise ranking loss & 0.05 & 0.87 & 0.09 & 0.34 \\\\\n \\midrule\n \\multirow{3}[2]{*}{RSR} & IC loss & 0.09 & 1.21 & 0.12 & 0.51 \\\\\n & MSE Loss & 0.06 & 1.11 & 0.14 & 0.76 \\\\\n & Pairwise ranking loss & 0.03 & 0.47 & 0.02 & 0.19 \\\\\n \\midrule\n \\multirow{3}[2]{*}{Mixer} & IC loss & 0.09 & 1.21 & 0.14 & 0.64 \\\\\n & MSE Loss & 0.11 & 1.23 & 0.17 & 0.70 \\\\\n & Pairwise ranking loss & 0.06 & 0.93 & 0.11 & 0.59 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "QuantBench: Benchmarking AI Methods for Quantitative Investment", "authors": ["Saizhuo Wang", "Hao Kong", "Jiadong Guo", "Fengrui Hua", "Yiyan Qi", "Wanyun Zhou", "Jiahao Zheng", "Xinyu Wang", "Lionel M. Ni", "Jian Guo"], "url": "https://arxiv.org/abs/2504.18600v1", "attribution": "\"QuantBench: Benchmarking AI Methods for Quantitative Investment\" by Saizhuo Wang, Hao Kong, Jiadong Guo, Fengrui Hua, Yiyan Qi, Wanyun Zhou, Jiahao Zheng, Xinyu Wang, Lionel M. Ni, and Jian Guo, arXiv:2504.18600v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.03312v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Topics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\\hline\nTopic & \\hspace{1em}Frequency\\hspace{1em} & \\hspace{1em}Percentage\\hspace{1em} \\\\\\hline\nArtificial intelligence & 103 & 12.61 \\\\\nUS politics & 50 & 6.12 \\\\\nMacroeconomics & 31 & 3.79 \\\\\nIsrael and the Palestinian territories & 30 & 3.67 \\\\\nYouTube and its streamers & 27 & 3.3 \\\\\nVideo games & 24 & 2.94 \\\\\nManifold Markets & 22 & 2.69 \\\\\nThe Russo-Ukrainian War & 21 & 2.57 \\\\\nFootball & 19 & 2.33 \\\\\nAmerican football & 17 & 2.08 \\\\\nLegal rulings & 16 & 1.96 \\\\\nTelevision & 14 & 1.71 \\\\\nBasketball & 12 & 1.47 \\\\\nX (formerly Twitter) & 11 & 1.35 \\\\\nTaylor Swift & 10 & 1.22 \\\\\nUK politics & 10 & 1.22 \\\\\nCryptocurrency & 9 & 1.1 \\\\\nElon Musk & 9 & 1.1 \\\\\nThe 2024 Paris Olympics & 9 & 1.1 \\\\\nThe Oscars & 9 & 1.1 \\\\\nRussian politics & 9 & 1.1 \\\\\nApple Inc. & 8 & 0.98 \\\\\nScience & 8 & 0.98 \\\\\nNuclear energy and weapons & 7 & 0.86 \\\\\nSatellites and space & 7 & 0.86 \\\\\nAmazon.com, Inc. & 6 & 0.73 \\\\\nCanadian politics & 6 & 0.73 \\\\\nEffective altruism & 6 & 0.73 \\\\\nTesla, Inc. & 6 & 0.73 \\\\\nOther topics & 203 & 36.87 \\\\\n\\hline\nTotal & 719 & 100 \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "How manipulable are prediction markets?", "authors": ["Itzhak Rasooly", "Roberto Rozzi"], "url": "https://arxiv.org/abs/2503.03312v1", "attribution": "\"How manipulable are prediction markets?\" by Itzhak Rasooly and Roberto Rozzi, arXiv:2503.03312v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.14908v6_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Effects of Level 2 Subsidies on Level 2 Stations \\\\ and Sales Measures: Staggered Difference-in-Differences}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\toprule\n & Log(Lvl2) & Log(Tesla Destination Stations) & Log(PHEV Sales) & Log(BEV Sales)\\\\\n\\midrule\nBaseline & 0.025 (0.012) ** & -0.009 (0.019) & 0.018 (0.009) ** & 0.028 (0.009) ***\\\\\nBaseline + Controls & 0.028 (0.009) *** & 0.003 (0.020) & 0.022 (0.009) ** & 0.024 (0.009) **\\\\\nHigh Pop & 0.022 (0.010) ** & 0.009 (0.025) & 0.010 (0.009) & 0.009 (0.007)\\\\\nDisadvantaged & 0.016 (0.012) & 0.014 (0.029) & 0.011 (0.009) & -0.003 (0.008)\\\\\nUrban & 0.021 (0.011) * & 0.002 (0.025) & 0.008 (0.008) & 0.010 (0.007)\\\\\n\\addlinespace\nHigh GDP & 0.021 (0.011) * & 0.003 (0.026) & 0.010 (0.009) & 0.011 (0.007)\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Powering Up a Slow Charging Market: How Do Government Subsidies Affect Charging Station Supply?", "authors": ["Zunian Luo"], "url": "https://arxiv.org/abs/2210.14908v6", "attribution": "\"Powering Up a Slow Charging Market: How Do Government Subsidies Affect Charging Station Supply?\" by Zunian Luo, arXiv:2210.14908v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18130v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Same setup as Table~, but using \\textbf{geometric} annealing.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n& \\multicolumn{5}{c}{sample size $N$}\\\\\n& 100 & 300 & 1000 & 3000 & 10000 \\\\\n\\midrule\nSBTM (ours) & \\textbf{0.058} & \\textbf{0.032} & 0.037 & \\textbf{0.025} & 0.031 \\\\\nSDE & 0.060 & 0.032 & \\textbf{0.029} & 0.029 & \\textbf{0.028} \\\\\nSVGD & 0.47 & 0.69 & 0.99 & 1.1 & 1.0 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Score-Based Deterministic Density Sampling", "authors": ["Vasily Ilin", "Peter Sushko", "Jingwei Hu"], "url": "https://arxiv.org/abs/2504.18130v2", "attribution": "\"Score-Based Deterministic Density Sampling\" by Vasily Ilin, Peter Sushko, and Jingwei Hu, arXiv:2504.18130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.04092v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Cafeteer noise; STOI(\\%) and PESQ comparison}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccc}\n & \\multicolumn{2}{c}{SNR = -5} & \\multicolumn{2}{c}{SNR = 0} & \\multicolumn{2}{c}{SNR = 5} \\\\\n & STOI & PESQ & STOI & PESQ & STOI & PESQ \\\\\n \\hline\nNoisy & 50.5 & 1.54 & 68.6 & 1.87 & 83.0 & 2.22\\\\\n2D-RFCNN & 52.3 & 1.58 & 70.1 & 1.93 & 84.5 & 2.31 \\\\\nLSTM & 53.9 & 1.65 & 71.3 & 2.00 & 84.7 & 2.37\\\\\nNo weight & 54.1 & 1.75 & 72.7 & \\textbf{2.11} & 86.0 & 2.47 \\\\\nTemporal & 54.6 & 1.72 & 72.8 & 2.08 & 85.8 & 2.44 \\\\\nLocal & \\textbf{55.3} & 1.74 & \\textbf{73.5} & 2.10 & \\textbf{86.4} & 2.46 \\\\\nFreq.-wise & 54.2 & \\textbf{1.76} & 72.6 & \\textbf{2.11} & 86.1 & \\textbf{2.49} \\\\\nFreq.-wise (MSE) & 54.6 & 1.74 & 73.1 & 2.10 & 86.1 & 2.47\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Frequency Gating: Improved Convolutional Neural Networks for Speech Enhancement in the Time-Frequency Domain", "authors": ["Koen Oostermeijer", "Qing Wang", "Jun Du"], "url": "https://arxiv.org/abs/2011.04092v1", "attribution": "\"Frequency Gating: Improved Convolutional Neural Networks for Speech Enhancement in the Time-Frequency Domain\" by Koen Oostermeijer, Qing Wang, and Jun Du, arXiv:2011.04092v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09574v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Nomenclature}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n \\toprule\n \\multicolumn{1}{p{2.585em}}{Symbol} & Description \\\\\n \\midrule\n$f_i^{rgb}$ & The i-th level RGB feature \\\\\n$f_i^d $ & The i-th level depth feature \\\\\n$e_i $ & The i-th output feature of coarse refinement branch \\\\\n$R_c^i$ & The i-th coarse refinement block \\\\\n$S_i^c $ & The i-th output saliency map of coarse refinement branch \\\\\n${\\cal F}_i $\n & The i-th level encoding feature map of fine-grained refinement branch \\\\\n${\\cal O}_i $\n & The i-th level decoded feature map of fine-grained refinement branch \\\\\n$G$\n & The ground truth saliency map \\\\\n$S^f $\n & The predicted saliency map of fine-grained refinement branch \\\\\n$U\\left( \\cdot \\right)$\n & The upsample operation \\\\\n$ \\otimes $\n & Element-wise multiplication broadcasted along feature planes \\\\\n$Cat\\left( \\cdot \\right)$ \n & Channel-wise concatenation operation \\\\\n$GP\\left( \\cdot \\right)$\n & Global average pooling function \\\\\n$\\mathcal{L}\\left( \\cdot \\right)$\n & The binary cross-entropy loss \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection", "authors": ["Ze-yu Liu", "Jian-wei Liu", "Xin Zuo", "Ming-fei Hu"], "url": "https://arxiv.org/abs/2201.09574v1", "attribution": "\"Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection\" by Ze-yu Liu, Jian-wei Liu, Xin Zuo, and Ming-fei Hu, arXiv:2201.09574v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.18593v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dataset = \\textbf{COMPAS} [feature dim$=11$, \\#(train data)=4,316]}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccccccc}\n\\toprule\n\\multirow{2}{*}{$k$} & \\multirow{2}{*}{Method} & \\multirow{2}{*}{\\%Var($\\uparrow$)} & \\multirow{2}{*}{MMD$^2$($\\downarrow$)} & \\%Acc($\\uparrow$) & $\\Delta_{\\rm DP}$($\\downarrow$) & \\%Acc($\\uparrow$) & $\\Delta_{\\rm DP}$($\\downarrow$) & \\%Acc($\\uparrow$) & $\\Delta_{\\rm DP}$($\\downarrow$) \\\\\n& & & & \\multicolumn{2}{c}{kernel SVM} & \\multicolumn{2}{c}{linear SVM} & \\multicolumn{2}{c}{MLP} \\\\\n\\midrule\n\\multirow{18}{*}{2} & PCA & 2.09$_{(0.11)}$ & 0.057$_{(0.006)}$ & 64.84$_{(0.78)}$ & 0.28$_{(0.03)}$ & 58.91$_{(0.52)}$ & 0.2$_{(0.03)}$ & 64.16$_{(1.01)}$ & 0.3$_{(0.03)}$ \\\\\n& (0.1) & \\multicolumn{8}{c}{Memory Out} \\\\\n& (0.0) & \\multicolumn{8}{c}{Memory Out} \\\\\n& (1e-3) & 1.8$_{(0.1)}$ & 0.003$_{(0.002)}$ & 62.41$_{(1.09)}$ & 0.03$_{(0.02)}$ & 57.57$_{(1.18)}$ & 0.04$_{(0.03)}$ & 60.79$_{(1.59)}$ & 0.03$_{(0.01)}$ \\\\\n& (1e-6) & 1.78$_{(0.11)}$ & 0.003$_{(0.002)}$ & 62.65$_{(1.54)}$ & 0.03$_{(0.03)}$ & 57.56$_{(2.06)}$ & 0.04$_{(0.03)}$ & 61.09$_{(1.53)}$ & 0.03$_{(0.01)}$ \\\\\n& (mean) & 1.97$_{(0.1)}$ & 0.008$_{(0.003)}$ & 61.94$_{(0.59)}$ & 0.07$_{(0.02)}$ & 56.03$_{(1.09)}$ & 0.05$_{(0.04)}$ & 60.79$_{(0.8)}$ & 0.07$_{(0.04)}$ \\\\\n& (0.85) & 1.78$_{(0.13)}$ & 0.005$_{(0.002)}$ & 60.64$_{(1.08)}$ & 0.08$_{(0.05)}$ & 56.16$_{(1.21)}$ & 0.01$_{(0.02)}$ & 58.8$_{(1.74)}$ & 0.07$_{(0.04)}$ \\\\\n& (0.5) & 1.62$_{(0.09)}$ & 0.004$_{(0.001)}$ & 59.67$_{(0.89)}$ & 0.1$_{(0.03)}$ & 54.81$_{(1.04)}$ & 0.0$_{(0.0)}$ & 57.11$_{(1.35)}$ & 0.08$_{(0.05)}$ \\\\\n& (kernel) & N/A & N/A & 57.8$_{(1.82)}$ & 0.08$_{(0.06)}$ & 54.74$_{(1.21)}$ & 0.02$_{(0.04)}$ & 55.91$_{(1.27)}$ & 0.02$_{(0.03)}$ \\\\\n& & 0.62$_{(0.18)}$ & 0.0$_{(0.0)}$ & 56.35$_{(0.71)}$ & 0.01$_{(0.01)}$ & 54.81$_{(1.04)}$ & 0.0$_{(0.0)}$ & 55.38$_{(0.66)}$ & 0.01$_{(0.01)}$ \\\\\n& & 0.49$_{(0.03)}$ & 0.002$_{(0.002)}$ & 57.54$_{(0.74)}$ & 0.03$_{(0.03)}$ & 54.81$_{(1.04)}$ & 0.0$_{(0.0)}$ & 56.42$_{(1.29)}$ & 0.03$_{(0.03)}$ \\\\\n& & N/A & N/A & 64.19$_{(1.07)}$ & 0.15$_{(0.03)}$ & 59.15$_{(0.59)}$ & 0.13$_{(0.03)}$ & 64.4$_{(1.32)}$ & 0.15$_{(0.03)}$ \\\\\n\\cmidrule{2-10}\n& \\textbf{Ours} (offline, mean) & 1.97$_{(0.1)}$ & 0.008$_{(0.003)}$ & 61.94$_{(0.59)}$ & 0.07$_{(0.02)}$ & 56.03$_{(1.09)}$ & 0.05$_{(0.04)}$ & 60.61$_{(1.5)}$ & 0.06$_{(0.05)}$ \\\\\n& \\textbf{Ours} (FNPM, mean) & 1.97$_{(0.1)}$ & 0.008$_{(0.003)}$ & 61.94$_{(0.59)}$ & 0.07$_{(0.02)}$ & 56.03$_{(1.09)}$ & 0.05$_{(0.04)}$ & 60.5$_{(1.48)}$ & 0.07$_{(0.05)}$ \\\\\n& \\textbf{Ours} (offline, $m$=2) & 1.92$_{(0.11)}$ & 0.006$_{(0.002)}$ & 61.22$_{(0.85)}$ & 0.11$_{(0.04)}$ & 55.43$_{(0.9)}$ & 0.04$_{(0.04)}$ & 59.53$_{(1.35)}$ & 0.12$_{(0.06)}$ \\\\\n& \\textbf{Ours} (FNPM, $m$=2) & 1.92$_{(0.11)}$ & 0.006$_{(0.002)}$ & 61.08$_{(0.9)}$ & 0.09$_{(0.04)}$ & 55.51$_{(1.16)}$ & 0.03$_{(0.04)}$ & 59.56$_{(1.48)}$ & 0.12$_{(0.07)}$ \\\\\n& \\textbf{Ours} (offline, $m$=5) & 1.89$_{(0.1)}$ & 0.006$_{(0.002)}$ & 61.71$_{(0.78)}$ & 0.1$_{(0.04)}$ & 55.49$_{(0.79)}$ & 0.03$_{(0.04)}$ & 59.74$_{(2.0)}$ & 0.14$_{(0.06)}$ \\\\\n& \\textbf{Ours} (FNPM, $m$=5) & 1.89$_{(0.1)}$ & 0.006$_{(0.002)}$ & 61.67$_{(0.76)}$ & 0.11$_{(0.05)}$ & 55.55$_{(0.9)}$ & 0.03$_{(0.04)}$ & 59.63$_{(1.84)}$ & 0.13$_{(0.07)}$ \\\\\n\\midrule\n\\multirow{18}{*}{10} & PCA & 5.85$_{(0.36)}$ & 0.089$_{(0.002)}$ & 82.78$_{(0.43)}$ & 0.15$_{(0.04)}$ & 65.39$_{(1.15)}$ & 0.16$_{(0.05)}$ & 70.01$_{(0.93)}$ & 0.2$_{(0.04)}$ \\\\\n& (0.1) & \\multicolumn{8}{c}{Memory Out} \\\\\n& (0.0) & \\multicolumn{8}{c}{Memory Out} \\\\\n& (1e-3) & 5.36$_{(0.31)}$ & 0.002$_{(0.001)}$ & 80.76$_{(0.46)}$ & 0.1$_{(0.03)}$ & 63.89$_{(1.15)}$ & 0.04$_{(0.03)}$ & 68.97$_{(1.46)}$ & 0.03$_{(0.02)}$ \\\\\n& (1e-6) & 4.83$_{(0.45)}$ & 0.002$_{(0.001)}$ & 78.93$_{(0.84)}$ & 0.09$_{(0.03)}$ & 61.68$_{(1.54)}$ & 0.04$_{(0.03)}$ & 67.23$_{(1.66)}$ & 0.04$_{(0.02)}$ \\\\\n& (mean) & 5.67$_{(0.37)}$ & 0.004$_{(0.001)}$ & 80.87$_{(0.54)}$ & 0.09$_{(0.03)}$ & 64.76$_{(1.04)}$ & 0.02$_{(0.02)}$ & 69.83$_{(0.83)}$ & 0.04$_{(0.03)}$ \\\\\n& (0.85) & 5.41$_{(0.33)}$ & 0.004$_{(0.001)}$ & 80.64$_{(0.82)}$ & 0.09$_{(0.04)}$ & 63.63$_{(1.19)}$ & 0.02$_{(0.01)}$ & 68.69$_{(1.28)}$ & 0.05$_{(0.04)}$ \\\\\n& (0.5) & 5.29$_{(0.31)}$ & 0.003$_{(0.001)}$ & 79.22$_{(0.44)}$ & 0.1$_{(0.03)}$ & 61.62$_{(0.63)}$ & 0.02$_{(0.02)}$ & 68.63$_{(0.92)}$ & 0.07$_{(0.04)}$ \\\\\n& (kernel) & N/A & N/A & 65.96$_{(1.12)}$ & 0.26$_{(0.07)}$ & 64.35$_{(0.8)}$ & 0.05$_{(0.04)}$ & 64.93$_{(1.49)}$ & 0.04$_{(0.03)}$ \\\\\n& & 2.64$_{(0.47)}$ & 0.001$_{(0.0)}$ & 64.17$_{(1.46)}$ & 0.04$_{(0.02)}$ & 54.85$_{(1.03)}$ & 0.0$_{(0.0)}$ & 62.56$_{(1.79)}$ & 0.04$_{(0.03)}$ \\\\\n& & 2.45$_{(0.06)}$ & 0.001$_{(0.0)}$ & 71.75$_{(0.69)}$ & 0.07$_{(0.04)}$ & 55.26$_{(1.25)}$ & 0.0$_{(0.0)}$ & 66.66$_{(1.08)}$ & 0.06$_{(0.03)}$ \\\\\n& & N/A & N/A & 69.75$_{(1.0)}$ & 0.17$_{(0.02)}$ & 65.54$_{(0.72)}$ & 0.14$_{(0.03)}$ & 69.97$_{(0.81)}$ & 0.16$_{(0.03)}$ \\\\\n\\cmidrule{2-10}\n& \\textbf{Ours} (offline, mean) & 5.67$_{(0.37)}$ & 0.004$_{(0.001)}$ & 80.87$_{(0.54)}$ & 0.09$_{(0.03)}$ & 64.78$_{(1.04)}$ & 0.02$_{(0.02)}$ & 69.16$_{(0.5)}$ & 0.04$_{(0.01)}$ \\\\\n& \\textbf{Ours} (FNPM, mean) & 5.68$_{(0.37)}$ & 0.004$_{(0.001)}$ & 80.87$_{(0.49)}$ & 0.09$_{(0.03)}$ & 64.84$_{(1.01)}$ & 0.02$_{(0.02)}$ & 68.86$_{(0.82)}$ & 0.04$_{(0.03)}$ \\\\\n& \\textbf{Ours} (offline, $m$=2) & 5.57$_{(0.35)}$ & 0.004$_{(0.001)}$ & 80.93$_{(0.63)}$ & 0.09$_{(0.03)}$ & 64.72$_{(0.93)}$ & 0.02$_{(0.02)}$ & 69.25$_{(1.27)}$ & 0.04$_{(0.04)}$ \\\\\n& \\textbf{Ours} (FNPM, $m$=2) & 5.58$_{(0.35)}$ & 0.004$_{(0.001)}$ & 80.82$_{(0.48)}$ & 0.09$_{(0.03)}$ & 64.65$_{(1.1)}$ & 0.02$_{(0.02)}$ & 68.97$_{(1.31)}$ & 0.04$_{(0.02)}$ \\\\\n& \\textbf{Ours} (offline, $m$=5) & 5.55$_{(0.35)}$ & 0.004$_{(0.001)}$ & 81.03$_{(0.61)}$ & 0.09$_{(0.03)}$ & 64.47$_{(1.01)}$ & 0.02$_{(0.02)}$ & 68.92$_{(1.05)}$ & 0.03$_{(0.02)}$ \\\\\n& \\textbf{Ours} (FNPM, $m$=5) & 5.55$_{(0.34)}$ & 0.004$_{(0.001)}$ & 80.9$_{(0.49)}$ & 0.09$_{(0.03)}$ & 64.54$_{(1.23)}$ & 0.02$_{(0.02)}$ & 69.05$_{(1.09)}$ & 0.04$_{(0.03)}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint", "authors": ["Junghyun Lee", "Hanseul Cho", "Se-Young Yun", "Chulhee Yun"], "url": "https://arxiv.org/abs/2310.18593v1", "attribution": "\"Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint\" by Junghyun Lee, Hanseul Cho, Se-Young Yun, and Chulhee Yun, arXiv:2310.18593v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01006v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Accuracy on UEA multivariate time-series classification datasets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n \\toprule\n Dataset & Shapelet & TimesNet & T-Rep & TST & MOMENT & VQShape(score) & VQShape(embed.)\\\\\n & (Sup.) & (Sup.) & & (LP) & (LP) & (LP) & (LP) \\\\\n \\midrule\n ArticularyWordRecognition & 0.993 & 0.973 & 0.968 & 0.977 & 0.990 & 0.978 $\\pm$ 0.003 & 0.981 $\\pm$ 0.002\\\\\n AtrialFibrillation & 0.440 & 0.333 & 0.354 & 0.067 & 0.200 & 0.387 $\\pm$ 0.050 & 0.360 $\\pm$ 0.033\\\\\n BasicMotions & 1.000 & 0.975 & 1.000 & 0.975 & 1.000 & 0.955 $\\pm$ 0.019 & 0.870 $\\pm$ 0.010\\\\\n CharacterTrajectories & 0.985 & 0.987 & 0.989 & N/A & N/A & 0.953 $\\pm$ 0.002 & 0.952 $\\pm$ 0.001 \\\\\n Cricket & 0.986 & 0.903 & 0.958 & 0.986 & 1.000 & 0.958 $\\pm$ 0.011 & 0.961 $\\pm$ 0.010\\\\\n DuckDuckGeese & 0.416 & 0.580 & 0.457 & 0.480 & 0.600 & 0.284 $\\pm$ 0.032 & 0.284 $\\pm$ 0.034 \\\\\n ERing & 0.961 & 0.927 & 0.943 & 0.874 & 0.959 & 0.922 $\\pm$ 0.011 & 0.955 $\\pm$ 0.004\\\\\n EigenWorms & 0.553 & N/A & 0.884 & 0.748 & 0.809 & 0.536 $\\pm$ 0.016 & 0.525 $\\pm$ 0.006\\\\\n Epilepsy & 0.991 & 0.877 & 0.970 & 0.949 & 0.993 & 0.870 $\\pm$ 0.014 & 0.691 $\\pm$ 0.013\\\\\n \\textbf{EthanolConcentration} & 0.316 & 0.285 & 0.333 & 0.262 & 0.357 & 0.296 $\\pm$ 0.004 & 0.268 $\\pm$ 0.005\\\\\n \\textbf{FaceDetection} & 0.638 & 0.677 & 0.581 & 0.534 & 0.633 & 0.633 $\\pm$ 0.002 & 0.648 $\\pm$ 0.002\\\\\n FingerMovements & 0.600 & 0.530 & 0.495 & 0.560 & 0.490 & 0.534 $\\pm$ 0.026 & 0.586 $\\pm$ 0.015\\\\\n HandMovementDirection & 0.465 & 0.595 & 0.536 & 0.243 & 0.324 & 0.403 $\\pm$ 0.031 & 0.492 $\\pm$ 0.033\\\\\n \\textbf{Handwriting} & 0.376 & 0.311 & 0.414 & 0.225 & 0.308 & 0.165 $\\pm$ 0.003 & 0.207 $\\pm$ 0.002\\\\\n \\textbf{Heartbeat} & 0.740 & 0.732 & 0.725 & 0.746 & 0.722 & 0.729 $\\pm$ 0.005 & 0.722 $\\pm$ 0.000\\\\\n InsectWingbeat & 0.511 & 0.644 & 0.328 & 0.105 & 0.246 & & \\\\\n \\textbf{JapaneseVowels} & 0.958 & 0.954 & 0.962 & 0.978 & 0.716 & 0.745 $\\pm$ 0.005 & 0.737 $\\pm$ 0.005\\\\\n LSST & 0.624 & 0.382 & 0.526 & 0.408 & 0.411 & 0.568 $\\pm$ 0.003 & 0.537 $\\pm$ 0.002\\\\\n Libras & 0.880 & 0.761 & 0.829 & 0.656 & 0.850 & 0.772 $\\pm$ 0.007 & 0.732 $\\pm$ 0.004 \\\\\n MotorImagery & 0.624 & 0.610 & 0.495 & 0.500 & 0.500 & 0.622 $\\pm$ 0.018 & 0.622 $\\pm$ 0.015\\\\\n NATOPS & 0.882 & 0.833 & 0.804 & 0.850 & 0.828 & 0.763 $\\pm$ 0.024 & 0.768 $\\pm$ 0.012 \\\\\n \\textbf{PEMS-SF} & 0.885 & 0.844 & 0.800 & 0.740 & 0.896 & 0.741 $\\pm$ 0.020 & 0.809 $\\pm$ 0.026\\\\\n PenDigits & 0.976 & 0.984 & 0.971 & 0.560 & 0.972 & 0.952 $\\pm$ 0.001 & 0.957 $\\pm$ 0.001\\\\\n PhonemeSpectra & 0.259 & 0.146 & 0.232 & 0.085 & 0.233 & 0.102 $\\pm$ 0.002 & 0.078 $\\pm$ 0.001\\\\\n RacketSports & 0.907 & 0.855 & 0.883 & 0.809 & 0.796 & 0.861 $\\pm$ 0.010 & 0.837 $\\pm$ 0.008\\\\\n \\textbf{SelfRegulationSCP1} & 0.861 & 0.908 & 0.819 & 0.754 & 0.840 & 0.790 $\\pm$ 0.009 & 0.835 $\\pm$ 0.004\\\\\n \\textbf{SelfRegulationSCP2} & 0.532 & 0.539 & 0.591 & 0.550 & 0.478 & 0.591 $\\pm$ 0.012 & 0.561 $\\pm$ 0.009\\\\\n \\textbf{SpokenArabicDigits} & 0.992 & 0.988 & 0.994 & 0.923 & 0.981 & 0.967 $\\pm$ 0.001 & 0.965 $\\pm$ 0.001\\\\\n StandWalkJump & 0.560 & 0.533 & 0.441 & 0.267 & 0.400 & 0.533 $\\pm$ 0.042 & 0.573 $\\pm$ 0.033\\\\\n \\textbf{UWaveGestureLibrary} & 0.903 & 0.863 & 0.885 & 0.575 & 0.909 & 0.893 $\\pm$ 0.008 & 0.868 $\\pm$ 0.002\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification", "authors": ["Yunshi Wen", "Tengfei Ma", "Tsui-Wei Weng", "Lam M. Nguyen", "Anak Agung Julius"], "url": "https://arxiv.org/abs/2411.01006v3", "attribution": "\"Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification\" by Yunshi Wen, Tengfei Ma, Tsui-Wei Weng, Lam M. Nguyen, and Anak Agung Julius, arXiv:2411.01006v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01677v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{rotating}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|ccccc}\n\\toprule\n\\multicolumn{7}{c}{\\textbf{Cross-camera generalization (PackNet)}} \\\\\n\\toprule\n& \\textbf{Dataset} & \nAbs.Rel$\\downarrow$ &\nRMSE$\\downarrow$ &\nRMSE$_{log}$$\\downarrow$ &\nSILog$\\downarrow$ & \n$\\delta_{1.05}$$\\uparrow$ \\\\\n\\toprule\n\\parbox[t]{2mm}{\\multirow{4}{*}{\\rotatebox[origin=c]{90}{\\textit{Left}}}}\n& \\textit{Mug}\n& 0.032 & 0.015 & 0.051 & 4.944 & 0.820 \\\\\n& \\textit{Wine Glass}\n& 0.034 & 0.015 & 0.053 & 5.177 & 0.804 \\\\\n& \\textit{Fingers}\n& 0.047 & 0.020 & 0.068 & 6.392 & 0.628 \\\\\n& \\textit{Box}\n& 0.052 & 0.020 & 0.072 & 6.442 & 0.606 \\\\\n\\midrule\n\\parbox[t]{2mm}{\\multirow{4}{*}{\\rotatebox[origin=c]{90}{\\textit{Right}}}}\n& \\textit{Mug}\n& 0.031 & 0.012 & 0.041 & 3.871 & 0.803 \\\\\n& \\textit{Wine Glass}\n& 0.046 & 0.020 & 0.058 & 5.230 & 0.595 \\\\\n& \\textit{Fingers}\n& 0.062 & 0.026 & 0.077 & 4.484 & 0.389 \\\\\n& \\textit{Box}\n& 0.058 & 0.023 & 0.071 & 6.180 & 0.459 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Dataset transfer results using PackNet . Independent models were trained using input-output pairs from each of the available cameras, and evaluated on the remaining one.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Monocular Depth Estimation for Soft Visuotactile Sensors", "authors": ["Rares Ambrus", "Vitor Guizilini", "Naveen Kuppuswamy", "Andrew Beaulieu", "Adrien Gaidon", "Alex Alspach"], "url": "https://arxiv.org/abs/2101.01677v1", "attribution": "\"Monocular Depth Estimation for Soft Visuotactile Sensors\" by Rares Ambrus, Vitor Guizilini, Naveen Kuppuswamy, Andrew Beaulieu, Adrien Gaidon, and Alex Alspach, arXiv:2101.01677v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08168v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of iterations for various methods; setting as in Table~.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|ccccc}\n& & \\multicolumn{5}{c}{$\\delta$} \\\\ \nMethod & Stopping & $10^{-5}$ & $10^{-4}$ & $10^{-3}$ & $10^{-2}$ & $10^{-1}$ \\\\ \\hline\nNesterov & $k_{opt}$ & 371 & 163 & 65 & 26 & 15 \\\\\nLandweber & $k_{opt}$ & 11000 & 2193 & 512 & 145 & 36 \\\\\n$\\nu$-Method & $k_{opt}$ & 190 & 82 & 33 & 22 & 9 \\\\\nCGNE & $k_{opt}$ & 10 & 6 & 4 & 3 & 2 \\\\\nNesterov & Discrepancy & 260 & 111 & 39 & 13 & 1 \\\\ \nLandweber & Discrepancy & 5106 & 1080 & 220 & 37 & 1 \\\\\n$\\nu$-Method & Discrepancy & 190 & 96 & 33 & 10 & 1 \\\\\nCGNE & Discrepancy & 8 & 5 & 4 & 2 & 1 \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Optimal-order convergence of Nesterov acceleration for linear ill-posed problems", "authors": ["Stefan Kindermann"], "url": "https://arxiv.org/abs/2101.08168v1", "attribution": "\"Optimal-order convergence of Nesterov acceleration for linear ill-posed problems\" by Stefan Kindermann, arXiv:2101.08168v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06390v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Tower detection performance for \\textit{GridTracer} and human annotators using the $DmAP$ metric (higher better). Parenthesis indicates the subset of testing data over which each method was scored. We train \\textit{GridTracer} using the \"conventional\" data handling scheme in Fig. (a)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l||c|c|c|c}\n \\hline \\hline\n & Arizona & Kansas & New Zealand & Average \\\\\n \\hline \\hline\n \\textit{GridTracer} (100\\%) & 0.73 & 0.54 & 0.55 & 0.61 \\\\\n \\hline \\hline\n Human (20\\%) & 0.92 & 0.88 & 0.79 & 0.86 \\\\\n \\hline\n \\textit{GridTracer} (20\\% ) & 0.72 & 0.55 & 0.49 & 0.59 \\\\\n \\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery", "authors": ["Bohao Huang", "Jichen Yang", "Artem Streltsov", "Kyle Bradbury", "Leslie M. Collins", "Jordan Malof"], "url": "https://arxiv.org/abs/2101.06390v1", "attribution": "\"GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery\" by Bohao Huang, Jichen Yang, Artem Streltsov, Kyle Bradbury, Leslie M. Collins, and Jordan Malof, arXiv:2101.06390v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table38.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcr}%% Table column specifiers\n 1 & 2 & 3 \\\\ %% A tabular row ends with \\\\\n 4 & 5 & 6 \\\\\n 7 & 8 & 9 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Table Caption}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.06442v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The performance for cross-dataset evaluation, where the best and suboptimal performance values are in \\textcolor{red}{Red} and \\textcolor{blue}{Blue}, respectively.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|ccc}\n \\toprule\n Train & \\multicolumn{3}{c|}{CVIU} & \\multicolumn{3}{c}{QADS} \\\\\n \\midrule\n Test & \\multicolumn{3}{c|}{QADS} & \\multicolumn{3}{c}{CVIU} \\\\\n \\midrule\n Model & SRCC $\\uparrow$ & KRCC $\\uparrow$ & PLCC $\\uparrow$ & SRCC $\\uparrow$ & KRCC $\\uparrow$ & PLCC $\\uparrow$ \\\\\n \\midrule\n DeepSRQ & 0.723 &\\makebox[1em][c]{\\textemdash} & 0.749 & 0.648 &\\makebox[1em][c]{\\textemdash} &\\makebox[1em][c]{\\textemdash} \\\\\n HLSRIQA & \\textcolor{blue}{0.786} &\\makebox[1em][c]{\\textemdash} & \\textcolor{blue}{0.773} & $\\;$\\textcolor{red}{0.785 } &\\makebox[1em][c]{\\textemdash} & \\textcolor{blue}{0.776} \\\\\n EK-SR-IQA & 0.645 &\\makebox[1em][c]{\\textemdash} &\\makebox[1em][c]{\\textemdash} & 0.682 &\\makebox[1em][c]{\\textemdash} &\\makebox[1em][c]{\\textemdash} \\\\\n JCSAN & 0.702 & 0.530 & 0.710 & 0.631 & 0.463 & 0.729 \\\\\n TADSRNet & 0.739 & 0.547 & 0.736 & 0.657 & 0.489 & 0.729 \\\\\n PBAN & \\textcolor[rgb]{ 1, 0, 0}{$\\;$0.890 } & $\\;$\\textcolor{red}{0.732 } & $\\;$\\textcolor{red}{0.892 } & \\textcolor{blue}{0.730} & $\\;$\\textcolor{red}{0.550 } & $\\;$\\textcolor{red}{0.794 } \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment", "authors": ["Yixiao Li", "Xiaoyuan Yang", "Guanghui Yue", "Jun Fu", "Qiuping Jiang", "Xu Jia", "Paul L. Rosin", "Hantao Liu", "Wei Zhou"], "url": "https://arxiv.org/abs/2509.06442v1", "attribution": "\"Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment\" by Yixiao Li, Xiaoyuan Yang, Guanghui Yue, Jun Fu, Qiuping Jiang, Xu Jia, Paul L. Rosin, Hantao Liu, and Wei Zhou, arXiv:2509.06442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.06808v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{$P(\\text{reversal}|\\text{condition})$, assuming $OR_{xy}>1$, $OR_{wy}>1$, and conditional on Table .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\n&&Strong Simpson&Adjusted Risk Difference&Weak Simpson\\\\\n\\hline\nCornfield&& \t\t\t$.0544\\pm .0005$ \t&$.2064\\pm .0009$ \t\t&$.9586\\pm .0004$\\\\\nRisk Ratio&&\t\t\t$.0543\\pm .0005$ \t&$.2096\\pm .0009$\t\t&$.9400\\pm .0005$\\\\\nRisk Difference&&\t\t$.0506\\pm .0005$ \t&$.1890\\pm .0008$\t\t&$.9415\\pm .0005$\\\\\nPearson Correlation&& \t$.0801\\pm .0005$ \t&$.2572\\pm .0008$ \t\t&$1\\pm 0$ \\\\\nMixed &&\t\t\t\t$.0716\\pm .0005$ \t&$.2380\\pm .0008$ \t\t&$.9872\\pm .0002$\\\\\nOdds Ratio&&\t\t\t$.0430\\pm .0003$\t&$.1659\\pm .0005$\t\t&$.9089\\pm .0004$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Odds are the sign is right", "authors": ["Brian Knaeble", "Julian Chan"], "url": "https://arxiv.org/abs/2310.06808v1", "attribution": "\"Odds are the sign is right\" by Brian Knaeble and Julian Chan, arXiv:2310.06808v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17626v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n \\toprule\n $\\nu$ & $32\\times 32$ & $64\\times 64$ & $128\\times 128$\\\\\n \\midrule\n $1\\cdot 10^{-2}$ & 4 & 5 & 8 \\\\\n $5\\cdot 10^{-3}$ & 4 & 5 & 7\\\\\n $2.5\\cdot 10^{-3}$ & 5 & 5 & 7 \\\\ \n $1.25\\cdot 10^{-3}$ & 7 & 7 & 7 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Preconditioned normal equations for solving discretised partial differential equations", "authors": ["Lorenzo Lazzarino", "Yuji Nakatsukasa", "Umberto Zerbinati"], "url": "https://arxiv.org/abs/2502.17626v2", "attribution": "\"Preconditioned normal equations for solving discretised partial differential equations\" by Lorenzo Lazzarino, Yuji Nakatsukasa, and Umberto Zerbinati, arXiv:2502.17626v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12347v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Table of State and Events: Machine-1 First Task Robot}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|} \n \\hline\n States & Events \\\\ \n \\hline\n Robot Idle:0 & First goal started:1 \\\\ \n Walking:1 & First goal reached:3, Robot failed:0\\\\ \n Docking:2 & Docking finished:5, Robot failed:0\\\\ \n Fail:3 & Error flag:17 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "The Analysis of Discrete-Event System in Autonomous Package Delivery using Legged Robot and Conveyor Belt", "authors": ["Garen Haddeler"], "url": "https://arxiv.org/abs/2101.12347v1", "attribution": "\"The Analysis of Discrete-Event System in Autonomous Package Delivery using Legged Robot and Conveyor Belt\" by Garen Haddeler, arXiv:2101.12347v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.20517v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc||ccc}\n & LSTM(2, 80) & LSTM(3, 80) & Simple Avg & LSTM(2, 80) & LSTM(3, 80) & Simple Avg \\\\ \\hline\nMAE & \\textbf{0.548} & 0.759 & 0.624 & \\textbf{0.725} & 0.754 & 0.825 \\\\\nMSE & \\textbf{0.682} & 0.911 & 0.757 & \\textbf{0.952} & 0.992 & 1.062 \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of 2019 prediction results for Site 0 (left) and Site 1 (right), comparing LSTM Models and the simple averaging model. For LSTM(x, y) in the header, x is the number of layers of LSTM, and y is the number of epochs.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Multi-Objective Capacity-Constrained Optimization of Corn Planting Scheduling", "authors": ["Mingshi Cui", "Kunting Qi", "Byran Smucker", "Durai Sundarmoorthi"], "url": "https://arxiv.org/abs/2503.20517v1", "attribution": "\"A Multi-Objective Capacity-Constrained Optimization of Corn Planting Scheduling\" by Mingshi Cui, Kunting Qi, Byran Smucker, and Durai Sundarmoorthi, arXiv:2503.20517v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01723v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Abbreviations for model-related terms}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\t\\hline\n\t\t\\textbf{Abbreviation} & \\textbf{Full name} & \\textbf{Location} \\\\\n\t\\hline\n\t\tbcMLM & Bias-corrected multilevel model & Section~ \\\\\n\t\\hline\n\t\tbcRegFE & Bias-corrected regularized fixed effects model & Section~ \\\\\n \\hline \n\t\tCRSE & Cluster-robust standard error & Section~ \\\\\t\n\t\\hline \n\t\tFE & Fixed effects model & Section~ \\\\\n\t\\hline\n\t\tGroup-FE & Group fixed effects model & Section~ \\\\\n\t\\hline\n\t\tGLM & Generalized linear model & Section~ \\\\\n\t\\hline\n\t\tMLE & Maximum likelihood estimation & Section~ \\\\\n\t\\hline \n\t\tMLM & Multilevel model & Section~ \\\\\n\t\\hline \n\t\tRegFE & Regularized fixed effects model & Section~ \\\\\n\t\\hline\n\t\tRI & Random intercepts model & Section~ \\\\\n\t\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Comparing multilevel and fixed effect approaches in the generalized linear model setting", "authors": ["He Bai", "Asa Ferguson", "Leonard Wainstein", "Jonathan Wells"], "url": "https://arxiv.org/abs/2411.01723v2", "attribution": "\"Comparing multilevel and fixed effect approaches in the generalized linear model setting\" by He Bai, Asa Ferguson, Leonard Wainstein, and Jonathan Wells, arXiv:2411.01723v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.11150v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Country List}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c}\n Brazil \\\\\n Chile \\\\\n Indonesia \\\\\n Mexico \\\\\n Peru \\\\\n South Africa \\\\\n Turkey \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity", "authors": ["Santiago Camara", "Sebastian Ramirez Venegas"], "url": "https://arxiv.org/abs/2209.11150v1", "attribution": "\"The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity\" by Santiago Camara and Sebastian Ramirez Venegas, arXiv:2209.11150v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.09430v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l||c|c|c|}\n\\hline\n$M$ & \n$\\langle \\overline{K^{}}_{\\rm Bayes}\\rangle$ & \n$\\langle\\overline{K^{}}_{1}\\rangle$ & \n$\\langle\\overline{K^{}}_{1+2}\\rangle$ \\\\ \\hline \n55 & 0.233 & 1.756 & 1.685 \\\\ \n45 & 0.276 & 1.700 & 1.643 \\\\\n35 & 0.338 & 1.723 & 1.680 \\\\\n25 & 0.428 & 1.753 & 1.717 \\\\\n15 & 0.606 & 1.770 & 2.164 \\\\\n11 & 0.730 & 1.762 & 4.946 \\\\\n9 & 0.818 & 1.774 & 11.63 \\\\\n7 & 0.916 & 1.848 & 32.24 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Maximum Entropy competes with Maximum Likelihood", "authors": ["A. E. Allahverdyan", "N. H. Martirosyan"], "url": "https://arxiv.org/abs/2012.09430v1", "attribution": "\"Maximum Entropy competes with Maximum Likelihood\" by A. E. Allahverdyan and N. H. Martirosyan, arXiv:2012.09430v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.05236v4_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Kernel-level design (\\textbf{CIFAR-10}) on VGG16-opt.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Network & MFLOPs & Param. & Acc.(\\%)\\\\\n \\midrule\n \\midrule\n Baseline & 370 & 9.64M & 92.95\\\\\n \\midrule\n DW+PW & 50 & 1.11M & 92.12\\\\\n \\midrule\n DW+GPW-g2 & 30 & 0.67M & 92.35\\\\ \n \\midrule\n DW+GPW-g4 & 20 & 0.36M & 88.05\\\\\n \\midrule\n DW+GPW-g8 & 10 & 0.20M & 86.41\\\\\n \\midrule\n \\textbf{DW+RPW-g2-o33\\%} & \\textbf{30} & \\textbf{0.66M} & \\textbf{92.52}\\\\\n \\midrule\n \\textbf{DW+RPW-g2-o50\\%}& \\textbf{30} & \\textbf{0.66M} &\n \\textbf{92.70}\\\\ \n \\midrule\n \\textbf{DW+RPW-g4-o33\\%} & \\textbf{20} & \\textbf{0.36M} & \\textbf{91.61}\\\\\n \\midrule\n \\textbf{DW+RPW-g4-o50\\%} & \\textbf{20} & \\textbf{0.36M} &\n \\textbf{91.59}\\\\\n \\midrule\n \\textbf{DW+RPW-g8-o33\\%} & \\textbf{10} & \\textbf{0.20M} & \\textbf{89.86}\\\\ \n \\midrule\n \\textbf{DW+RPW-g8-o50\\%} & \\textbf{10} & \\textbf{0.20M} & \\textbf{90.19}\\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks", "authors": ["Yuke Wang", "Boyuan Feng", "Xueqiao Peng", "Yufei Ding"], "url": "https://arxiv.org/abs/2009.05236v4", "attribution": "\"An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks\" by Yuke Wang, Boyuan Feng, Xueqiao Peng, and Yufei Ding, arXiv:2009.05236v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2103.08727v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model Wind Power Accuracy.}\n\\begin{tabular}{lccc}\n \\hline \n & Training & Validation & Test \\\\ \n \\hline\n Single Linear & 0.8800 & 0.8469 & 0.8454 \\\\ \n Single ResNet & 0.9025 & 0.8426 & 0.8433 \\\\ \n Stacked Linear & 0.8360 & 0.8103 & 0.8189 \\\\ \n Stacked ResNet & \\textbf{0.9037} & \\textbf{0.8668} & \\textbf{0.8713} \\\\ \n \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Estimating Solar and Wind Power Production using Computer Vision Deep Learning Techniques on Weather Maps", "authors": ["Sebastian Bosma", "Negar Nazari"], "url": "https://arxiv.org/abs/2103.08727v2", "attribution": "\"Estimating Solar and Wind Power Production using Computer Vision Deep Learning Techniques on Weather Maps\" by Sebastian Bosma and Negar Nazari, arXiv:2103.08727v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.00973v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results for different quality levels of JPEG 2000.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\toprule\n JPEG 2000 & 100 & 250 & 500 & 750 & 900 \\\\\n \\midrule\n Ours & $35.29$ & $4.16$ & $00.31$ & $ 00.04 $ & $00.03$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Towards Robust Data Hiding Against (JPEG) Compression: A Pseudo-Differentiable Deep Learning Approach", "authors": ["Chaoning Zhang", "Adil Karjauv", "Philipp Benz", "In So Kweon"], "url": "https://arxiv.org/abs/2101.00973v1", "attribution": "\"Towards Robust Data Hiding Against (JPEG) Compression: A Pseudo-Differentiable Deep Learning Approach\" by Chaoning Zhang, Adil Karjauv, Philipp Benz, and In So Kweon, arXiv:2101.00973v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14641v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ccccccc}\n \\toprule\n &\\multicolumn{3}{c}{Fashion-MNIST (200 labels)} & \\multicolumn{3}{c}{Fashion-MNIST (400 labels)} \\\\\n \\cmidrule(r){2-7}\n $\\lambda$ & $\\sigma=0.05$ & $\\sigma=0.1$ & $\\sigma=0.5$ & $\\sigma=0.05$ & $\\sigma=0.1$ & $\\sigma=0.5$ \\\\\n \\midrule\n $0.1$ & $75.39\\pm1.12$ & $74.96\\pm0.96$ & $75.24\\pm1.20$ & $75.39\\pm1.12$ & $74.96\\pm0.96$ & $75.24\\pm1.20$ \\\\\n $0.05$ & $76.35\\pm1.09$ & $76.25\\pm1.30$&$76.81\\pm0.88$ & $76.35\\pm1.09$ & $76.25\\pm1.30$&$76.81\\pm0.88$\\\\\n $0.025$ & $76.42\\pm0.85$ & $76.77\\pm1.29$ & $76.84\\pm1.23$ & $76.42\\pm0.85$ & $76.77\\pm1.29$ & $76.84\\pm1.23$ \\\\\n \\bottomrule\n \\end{tabular}\n\\caption{Ablation study on Fashion-MNIST dataset with 200/400 labels. Test-set classification accuracy ($\\%$) is shown averaged over 10 runs.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Towards Scalable Topological Regularizers", "authors": ["Hiu-Tung Wong", "Darrick Lee", "Hong Yan"], "url": "https://arxiv.org/abs/2501.14641v2", "attribution": "\"Towards Scalable Topological Regularizers\" by Hiu-Tung Wong, Darrick Lee, and Hong Yan, arXiv:2501.14641v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16534v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{OpenML Task ID mappings for \\textbf{regression} datasets with \\textbf{heterogeneous features}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\toprule\n\\textbf{OpenML ID} & \\textbf{Dataset} \\\\ \\midrule\n361093 & analcatdata\\_supreme \\\\\n361094 & visualizing\\_soil \\\\\n361095 & black\\_friday \\\\\n361096 & diamonds \\\\\n361097 & Mercedes\\_Benz\\_Greener\\_Manufacturing \\\\\n361098 & Brazilian\\_houses \\\\\n361099 & Bike\\_Sharing\\_Demand \\\\\n361101 & nyc-taxi-green-dec-2016 \\\\ \n361102 & house\\_sales \\\\\n361103 & particulate-matter-ukair-2017 \\\\\n361104 & SGEMM\\_GPU\\_kernel\\_performance \\\\\n361287 & topo\\_2\\_1 \\\\\n361288 & abalone \\\\\n361289 & seattlecrime6 \\\\\n361291 & delays\\_zurich\\_transport \\\\\n361292 & Allstate\\_Claims\\_Severity \\\\\n361293 & Airlines\\_DepDelay\\_1M \\\\\n361294 & medical\\_charges \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DOFEN: Deep Oblivious Forest ENsemble", "authors": ["Kuan-Yu Chen", "Ping-Han Chiang", "Hsin-Rung Chou", "Chih-Sheng Chen", "Tien-Hao Chang"], "url": "https://arxiv.org/abs/2412.16534v2", "attribution": "\"DOFEN: Deep Oblivious Forest ENsemble\" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.11088v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Individual performance comparison for dog-trot $n=\\{3, 10, 30, 50, 75, 100\\}$. Figures are mean normalised scores $\\pm$ one standard error, with 0 and 100 representing random and expert policies, respectively. At the bottom of the table we also provide totals, split by data quality and the entire set. We see across all datasets that DecQN-IQL/OneStep perform best, followed by DecQN-CQL and DecQN-BCQ. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\nEnvironment -dataset & BC & DecQN & DecQN-BCQ & DecQN-CQL & DecQN-IQL & DecQN-OneStep \\\\\n\\midrule\nDogTrot ($n=3$) & & & & & & \\\\\n-expert & 98 $\\pm$ 0.7 & 0.1 $\\pm$ 0.1 & 94.4 $\\pm$ 0.5 & 99.5 $\\pm$ 0.7 & 98.9 $\\pm$ 2.2 & 101.2 $\\pm$ 1.6 \\\\\n-medium-expert & 62 $\\pm$ 3.7 & 0 $\\pm$ 0.3 & 74.9 $\\pm$ 3.5 & 84.8 $\\pm$ 3.7 & 89.3 $\\pm$ 1.4 & 93.9 $\\pm$ 1.4 \\\\\n-medium & 43.8 $\\pm$ 0.5 & 0.1 $\\pm$ 0.1 & 49.1 $\\pm$ 0.6 & 46.5 $\\pm$ 0.5 & 52 $\\pm$ 0.3 & 50.2 $\\pm$ 0.2 \\\\\n-random-medium-expert & 37.2 $\\pm$ 3.6 & 0.1 $\\pm$ 0.2 & 0.1 $\\pm$ 0.1 & 43.4 $\\pm$ 0.5 & 44.1 $\\pm$ 1.2 & 44.9 $\\pm$ 0.7 \\\\\n\\midrule\nDogTrot ($n=10$) & & & & & & \\\\\n-expert & 97.3 $\\pm$ 1.7 & -0.3 $\\pm$ 0 & 96.8 $\\pm$ 1.8 & 99.2 $\\pm$ 1.3 & 106.5 $\\pm$ 1.1 & 113.4 $\\pm$ 0.9 \\\\\n-medium-expert & 58.1 $\\pm$ 5.5 & 0.5 $\\pm$ 0.1 & 82.3 $\\pm$ 4.6 & 83.4 $\\pm$ 2.1 & 105.1 $\\pm$ 2.1 & 109.8 $\\pm$ 1.2 \\\\\n-medium & 34.7 $\\pm$ 0.4 & 0.5 $\\pm$ 0.2 & 36.9 $\\pm$ 0.3 & 39.1 $\\pm$ 0.6 & 41 $\\pm$ 0.8 & 47.2 $\\pm$ 0.4 \\\\\n-random-medium-expert & 33.6 $\\pm$ 4.4 & 0.1 $\\pm$ 0.1 & 6.1 $\\pm$ 2.8 & 33.4 $\\pm$ 1.5 & 52.1 $\\pm$ 2.3 & 45.4 $\\pm$ 4.1 \\\\\n\\midrule\nDogTrot ($n=30$) & & & & & & \\\\\n-expert & 99.7 $\\pm$ 0.4 & -0.3 $\\pm$ 0 & 100.3 $\\pm$ 2.3 & 99.8 $\\pm$ 0.8 & 102.8 $\\pm$ 0.4 & 106.4 $\\pm$ 0.8 \\\\\n-medium-expert & 70.3 $\\pm$ 4.9 & 0.6 $\\pm$ 0.2 & 74.1 $\\pm$ 8 & 90.7 $\\pm$ 3.7 & 98.3 $\\pm$ 1.6 & 100.7 $\\pm$ 3 \\\\\n-medium & 33.1 $\\pm$ 0.2 & 0.7 $\\pm$ 0.2 & 33.6 $\\pm$ 0.4 & 33.4 $\\pm$ 0.4 & 39.9 $\\pm$ 0.1 & 40.5 $\\pm$ 0.5 \\\\\n-random-medium-expert & 22.4 $\\pm$ 0.9 & 0.2 $\\pm$ 0.1 & 5.2 $\\pm$ 2.2 & 22.9 $\\pm$ 2.5 & 31.6 $\\pm$ 1.3 & 30.4 $\\pm$ 1.8 \\\\\n\\midrule\nDogTrot ($n=50$) & & & & & & \\\\\n-expert & 98.2 $\\pm$ 0.5 & 0.5 $\\pm$ 0.1 & 98.6 $\\pm$ 0.6 & 97.7 $\\pm$ 0.6 & 99.2 $\\pm$ 0.9 & 100.4 $\\pm$ 1.4 \\\\\n-medium-expert & 67.7 $\\pm$ 5 & -0.3 $\\pm$ 0 & 68.7 $\\pm$ 4.2 & 88.9 $\\pm$ 2.1 & 95 $\\pm$ 2.1 & 97.1 $\\pm$ 1.4 \\\\\n-medium & 34.9 $\\pm$ 0.4 & -0.3 $\\pm$ 0 & 36.5 $\\pm$ 0.7 & 36.7 $\\pm$ 0.2 & 45.1 $\\pm$ 0.6 & 43.1 $\\pm$ 0.7 \\\\\n-random-medium-expert & 21 $\\pm$ 2 & 0.7 $\\pm$ 0.1 & 9.3 $\\pm$ 1.5 & 27.6 $\\pm$ 1.3 & 27.9 $\\pm$ 2.4 & 36.4 $\\pm$ 1.8 \\\\\n\\midrule\nDogTrot ($n=75$) & & & & & & \\\\\n-expert & 98.4 $\\pm$ 0.8 & 0.6 $\\pm$ 0.2 & 99.6 $\\pm$ 1.1 & 98 $\\pm$ 1 & 101.4 $\\pm$ 1.4 & 102.9 $\\pm$ 0.9 \\\\\n-medium-expert & 75.3 $\\pm$ 2.6 & 0 $\\pm$ 0 & 74.9 $\\pm$ 16.7 & 85 $\\pm$ 2 & 89.9 $\\pm$ 0.7 & 97.9 $\\pm$ 2.5 \\\\\n-medium & 41.4 $\\pm$ 0.2 & 0.5 $\\pm$ 0.1 & 41.3 $\\pm$ 0.8 & 42.5 $\\pm$ 0.1 & 48.5 $\\pm$ 0.4 & 47.8 $\\pm$ 0.5 \\\\\n-random-medium-expert & 27.7 $\\pm$ 0.7 & 0.7 $\\pm$ 0.2 & 0 $\\pm$ 0.1 & 33.4 $\\pm$ 2 & 45.3 $\\pm$ 2.9 & 43.6 $\\pm$ 2.6 \\\\\n\\midrule\nDogTrot ($n=100$) & & & & & & \\\\\n-expert & 96.4 $\\pm$ 1.1 & 0.1 $\\pm$ 0 & 95 $\\pm$ 1.8 & 100.4 $\\pm$ 0.7 & 103.2 $\\pm$ 0.4 & 105.5 $\\pm$ 0.8 \\\\\n-medium-expert & 70.9 $\\pm$ 6.5 & 0.4 $\\pm$ 0.1 & 74.6 $\\pm$ 5.8 & 74.4 $\\pm$ 5.1 & 93.9 $\\pm$ 1 & 101.5 $\\pm$ 2.5 \\\\\n-medium & 34.7 $\\pm$ 0.8 & 0.1 $\\pm$ 0.1 & 34.5 $\\pm$ 0.5 & 34.8 $\\pm$ 0.7 & 40.9 $\\pm$ 0.2 & 41.5 $\\pm$ 0.5 \\\\\n-random-medium-expert & 18.9 $\\pm$ 2.7 & 0.3 $\\pm$ 0.1 & 0 $\\pm$ 0 & 12.7 $\\pm$ 2.4 & 24 $\\pm$ 2.8 & 26.2 $\\pm$ 1 \\\\\n\\midrule\nSum & & & & & & \\\\\n-expert & 588 $\\pm$ 5.2 & 0.7 $\\pm$ 0.4 & 584.7 $\\pm$ 14 & 594.6 $\\pm$ 5.1 & 612 $\\pm$ 6.4 & 629.8 $\\pm$ 6.4 \\\\\n-medium-expert & 404.3 $\\pm$ 28.2 & 1.2 $\\pm$ 0.7 & 449.5 $\\pm$ 64.9 & 507.2 $\\pm$ 18.7 & 571.5 $\\pm$ 8.9 & 600.9 $\\pm$ 12 \\\\\n-medium & 222.6 $\\pm$ 2.5 & 1.6 $\\pm$ 0.7 & 231.9 $\\pm$ 5.8 & 233 $\\pm$ 2.5 & 267.4 $\\pm$ 2.4 & 270.3 $\\pm$ 2.8 \\\\\n-random-medium-expert & 160.8 $\\pm$ 14.3 & 2.1 $\\pm$ 0.8 & 30.2 $\\pm$ 17.6 & 173.4 $\\pm$ 10.2 & 225 $\\pm$ 12.9 & 226.9 $\\pm$ 12 \\\\\n-all & 1375.7 $\\pm$ 50.2 & 5.6 $\\pm$ 2.6 & 1296.3 $\\pm$ 102.3& 1508.2 $\\pm$ 36.5 & 1675.9 $\\pm$ 30.6 & 1727.9 $\\pm$ 33.2 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces", "authors": ["Alex Beeson", "David Ireland", "Giovanni Montana"], "url": "https://arxiv.org/abs/2411.11088v1", "attribution": "\"An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces\" by Alex Beeson, David Ireland, and Giovanni Montana, arXiv:2411.11088v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.14361v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ccc}\n \\hline\\hline\n Methods & $\\|\\nabla f\\|$ & Time\\\\\n \\hline\n dim = 200\\\\\n ASK&\t1.8745e-09&\t6.8912e+00 \\\\\n GD&\t4.2493e-03&\t4.0453e+01 \\\\\n NAG&\t1.4150e-05&\t9.6620e+00 \\\\\n HB&\t1.5566e-03&\t4.8435e+01 \\\\\n \\hline\n dim = 400\\\\\n ASK&\t5.6230e-11&\t9.6911e+01 \\\\\n GD&\t1.5588e-02&\t1.4123e+02 \\\\\n NAG&\t2.3108e-05&\t4.5453e+01 \\\\\n HB&\t8.7132e-06&\t1.3917e+02 \\\\\n \\hline\n dim = 600\\\\\n ASK&\t2.9439e-09&\t4.5376e+02 \\\\\n GD&\t1.3730e-03&\t2.9747e+02 \\\\\n NAG&\t1.1905e-06&\t1.7545e+02 \\\\\n HB&\t5.4395e-05&\t3.6776e+02 \\\\\n \\hline\\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Gradient-Based Optimization Method Using the Koopman Operator", "authors": ["Mengqi Hu", "Bian Li", "Yi-An Ma", "Yifei Lou", "Xiu Yang"], "url": "https://arxiv.org/abs/2312.14361v1", "attribution": "\"A Gradient-Based Optimization Method Using the Koopman Operator\" by Mengqi Hu, Bian Li, Yi-An Ma, Yifei Lou, and Xiu Yang, arXiv:2312.14361v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.09961v8_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results of relative error for different methods, $d$, $n$ and $m_{mult}$: Mean (Std)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccc}\n \\toprule\n \\multirow{2}{*}{d} & \\multirow{2}{*}{scenario} & \\multicolumn{5}{c}{\\textcolor{blue}{$n$: 2000, $m_{mult}$: 1}} \\\\ \\cmidrule(lr){3-7}\n & & Dense NN & GAM & KNN-FL & RKHS & Trend Filtering \\\\ \\midrule \n \\multirow{6}{*}{2} & 1 & \\textbf{0.0294 (0.005)} & \\textit{\\textbf{0.2883 (0.004)}} & \\textit{\\textbf{0.0521 (0.004)}} & 1.2793 (0.047) & 0.2888 (0.006) \\\\\n & 2 & \\textbf{0.0091 (0.001)} & \\textit{\\textbf{0.0323 (0.002)}} & 0.0396 (0.003) & 0.1610 (0.020) & \\textit{\\textbf{0.0231 (0.002)}} \\\\\n & 3 & \\textbf{0.0052 (0.000)} & 0.3578 (0.002) & \\textit{\\textbf{0.0270 (0.003)}} & 0.4162 (0.003) & 0.3571 (0.002) \\\\\n & 4 & \\textbf{0.0004 (0.000)} & \\textit{\\textbf{0.0010 (0.000)}} & 0.0045 (0.000) & 0.2255 (0.001) & 0.0011 (0.000) \\\\\n & 5 & \\textbf{0.0338 (0.008)} & 0.4051 (0.013) & \\textit{\\textbf{0.0957 (0.013)}} & 0.9558 (0.043) & 0.4108 (0.018) \\\\\n & 6 & \\textbf{0.1626 (0.012)} & \\textit{\\textbf{0.3921 (0.026)}} & 0.4097 (0.027) & 1.2836 (0.170) & \\textit{\\textbf{0.2851 (0.019)}} \\\\\n \\midrule\n\\multirow{6}{*}{5} & 1 & \\textbf{0.2704 (0.011)} & \\textit{\\textbf{0.6361 (0.041)}} & 0.8273 (0.028) & 1.3507 (0.157) & \\textit{\\textbf{0.5161 (0.084)}} \\\\\n & 2 & \\textbf{0.1286 (0.010)} & \\textit{\\textbf{0.1746 (0.011)}} & 0.6055 (0.097) & 1.3393 (0.363) & 0.5241 (0.034) \\\\\n & 3 & \\textbf{0.0129 (0.001)} & 0.7955 (0.007) & \\textit{\\textbf{0.1993 (0.018)}} & 0.8052 (0.006) & 0.4704 (0.031) \\\\\n & 4 & \\textbf{0.0002 (0.000)} & 0.0016 (0.000) & 0.0051 (0.000) & 0.2000 (0.001) & \\textit{\\textbf{0.0015 (0.000)}} \\\\\n & 5 & \\textbf{0.1967 (0.015)} & \\textit{\\textbf{0.4542 (0.030)}} & 0.7335 (0.040) & 1.1670 (0.150) & 0.4642 (0.079) \\\\\n & 6 & \\textit{\\textbf{0.0939 (0.007)}} & \\textbf{0.0805 (0.005)} & 0.4274 (0.082) & 0.5648 (0.027) & 0.2699 (0.018) \\\\\n \\midrule\n\\multirow{6}{*}{7} & 1 & \\textbf{0.7807 (0.051)} & \\textit{\\textbf{0.8679 (0.057)}} & 1.3575 (0.089) & 1.9966 (0.130) & 1.2397 (0.081) \\\\\n & 2 & \\textbf{0.4197 (0.027)} & \\textit{\\textbf{1.0704 (0.070)}} & 1.6646 (0.109) & 3.8451 (0.251) & 1.4231 (0.093) \\\\\n & 3 & \\textbf{0.0146 (0.002)} & 0.8871 (0.002) & 0.4714 (0.002) & 0.8939 (0.002) & \\textit{\\textbf{0.2543 (0.038)}} \\\\\n & 4 & \\textbf{0.0001 (0.000)} & 0.0011 (0.000) & 0.0048 (0.000) & 0.1678 (0.002) & \\textit{\\textbf{0.0010 (0.000)}} \\\\\n & 5 & \\textbf{0.1771 (0.015)} & \\textit{\\textbf{0.6277 (0.041)}} & 0.9096 (0.059) & 1.5109 (0.099) & 0.7869 (0.051) \\\\\n & 6 & \\textbf{0.3764 (0.025)} & \\textit{\\textbf{0.5455 (0.036)}} & \\textit{\\textbf{0.6446 (0.042)}} & 1.8978 (0.124) & 0.5641 (0.037) \\\\\n \\midrule\n\\multirow{6}{*}{10} & 1 & \\textbf{0.7360 (0.048)} & 1.3649 (0.089) & \\textit{\\textbf{1.1902 (0.025)}} & 8.0562 (1.239) & 1.5662 (0.102) \\\\\n & 2 & \\textbf{0.2949 (0.019)} & \\textit{\\textbf{0.7665 (0.050)}} & 0.8280 (0.054) & 21.0288 (1.928) & 1.0935 (0.071) \\\\\n & 3 & \\textbf{0.0330 (0.002)} & 0.9460 (0.001) & 0.7523 (0.005) & 0.9497 (0.001) & \\textit{\\textbf{0.1360 (0.023)}} \\\\\n & 4 & \\textbf{0.0001 (0.000)} & 0.0007 (0.000) & 0.0054 (0.000) & 0.1290 (0.001) & \\textit{\\textbf{0.0006 (0.000)}} \\\\\n & 5 & \\textbf{0.1383 (0.017)} & \\textit{\\textbf{0.5327 (0.035)}} & \\textit{\\textbf{1.3057 (0.088)}} & 2.6521 (0.500) & 0.6275 (0.041) \\\\\n & 6 & \\textbf{0.1737 (0.011)} & \\textit{\\textbf{0.9733 (0.063)}} & \\textit{\\textbf{1.0557 (0.191)}} & 7.0305 (0.769) & 1.1130 (0.073) \\\\ \n \n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Dense ReLU Neural Networks for Temporal-spatial Model", "authors": ["Carlos Misael Madrid Padilla", "Zhi Zhang", "Xiaokai Luo", "Daren Wang", "Oscar Hernan Madrid Padilla"], "url": "https://arxiv.org/abs/2411.09961v8", "attribution": "\"Dense ReLU Neural Networks for Temporal-spatial Model\" by Carlos Misael Madrid Padilla, Zhi Zhang, Xiaokai Luo, Daren Wang, and Oscar Hernan Madrid Padilla, arXiv:2411.09961v8, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.21379v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Distribution of $\\hat{N} - N$ over 100 simulated data sequences from the structures (MV\\_Gauss), (MV\\_Gauss2), (MD1), (MD2), and (MD3). The average $d_H$ and computation time are also given}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|l|l|l|}\n\\cline{1-9}\n & & \\multicolumn{5}{|c|}{} & & \\\\\n & & \\multicolumn{5}{|c|}{$\\hat{N} - N$} & & \\\\\nMethod & Model & $\\leq -2$ & $-1$ & $0$ & $1$ &$\\geq 2$ & $d_H$ & Time (s)\\\\\n\\hline\n{\\textbf{NPID}} & & 0 & 1 & {\\textbf{87}} & 9 & 3 & 0.102 & 0.79\\\\\nCPMKS500 & & 0 & 0 & 19 & 24 & 57 & 0.278 & 0.03\\\\\nCPMCvM500 & & 0 & 0 & 21 & 26 & 53 & 0.277 & 0.02\\\\\nCPMKS1000 & & 0 & 1 & 37 & 27 & 35 & 0.223 & 0.03\\\\\nCPMCvM1000 & & 0 & 0 & 47 & 24 & 29 & 0.194 & 0.03\\\\\nECP & (MV\\_Gauss) & 0 & 78 & 20 & 2 & 0 & 0.631 & 3.84\\\\\nNMCD & & 0 & 0 & 33 & 18 & 49 & 0.197 & 0.03\\\\\nNBS & & 87 & 3 & 4 & 4 & 2 & 1.574 & 0.40\\\\\nNWBS & & 48 & 20 & 19 & 9 & 4 & 1.181 & 10.86\\\\\n\\hline\n\\hline\n{\\textbf{NPID}} & & 0 & 3 & {\\textbf{85}} & 7 & 5 & 0.171 & 2.32\\\\\nCPMKS500 & & 0 & 0 & 5 & 7 & 88 & 0.424 & 0.05\\\\\nCPMCvM500 & & 0 & 2 & 12 & 19 & 67 & 0.407 & 0.05\\\\\nCPMKS1000 & & 0 & 1 & 16 & 28 & 55 & 0.374 & 0.07\\\\\nCPMCvM1000 & & 1 & 3 & 31 & 21 & 44 & 0.391 & 0.06\\\\\nECP & (MV\\_Gauss2) & 71 & 23 & 5 & 0 & 1 & 0.736 & 13.60\\\\\nNMCD & & 0 & 0 & 26 & 18 & 56 & 0.266 & 0.07\\\\\nNBS & & 87 & 5 & 4 & 1 & 3 & 2.934 & 0.81\\\\\nNWBS & & 88 & 2 & 4 & 2 & 4 & 1.513 & 21.55\\\\\n\\hline\n\\hline\n{\\textbf{NPID}} & & 0 & 3 & {\\textbf{97}} & 0 & 0 & 0.070 & 0.84\\\\\nECP & (MD1) & 17 & 52 & 25 & 6 & 0 & 0.919 & 5.46\\\\\nNMCD & & 0 & 0 & 29 & 36 & 35 & 0.312 & 0.07\\\\\nNBS & & 0 & 51 & 41 & 7 & 1 & 0.569 & 0.65\\\\\nNWBS & & 2 & 60 & 31 & 3 & 4 & 0.684 & 17.45\\\\\n\\hline\n\\hline\n{\\textbf{NPID}} & & 0 & 0 & {\\textbf{98}} & 2 & 0 & 0.069 & 0.52\\\\\nCPMKS500 & & 0 & 0 & 23 & 36 & 51 & 0.347 & 0.01\\\\\nCPMCvM500 & & 0 & 0 & 34 & 29 & 37 & 0.301 & 0.01\\\\\n{\\textbf{ECP}} & (MD2) & 0 & 0 & {\\textbf{93}} & 6 & 1 & 0.080 & 3.17\\\\\nNMCD & & 0 & 0 & 52 & 24 & 24 & 0.150 & 0.02\\\\\nNBS & & 16 & 4 & 62 & 4 & 14 & 0.486 & 0.30\\\\\nNWBS & & 9 & 12 & 65 & 6 & 8 & 0.377 & 8.11\\\\\n\\hline\n\\hline\n{\\textbf{NPID}} & & 0 & 11 & {\\textbf{86}} & 3 & 0 & 0.173 & 2.20\\\\\nCPMKS500 & & 0 & 0 & 8 & 14 & 78 & 0.418 & 0.07\\\\\nCPMCvM500 & & 0 & 0 & 13 & 12 & 75 & 0.367 & 0.05\\\\\nCPMKS1000 & & 0 & 1 & 25 & 22 & 52 & 0.323 & 0.08\\\\\nCPMCvM1000 & & 0 & 0 & 31 & 22 & 47 & 0.288 & 0.06\\\\\nECP & (MD3) & 0 & 34 & 63 & 2 & 1 & 0.334 & 11.56\\\\\n{\\textbf{NMCD}} & & 0 & 0 & {\\textbf{77}} & 19 & 4 & 0.092 & 0.09\\\\\nNBS & & 0 & 53 & 32 & 11 & 4 & 0.555 & 0.82\\\\\nNWBS & & 0 & 69 & 22 & 2 & 7 & 0.624 & 21.51\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Non-parametric multiple change-point detection", "authors": ["Andreas Anastasiou", "Piotr Fryzlewicz"], "url": "https://arxiv.org/abs/2504.21379v1", "attribution": "\"Non-parametric multiple change-point detection\" by Andreas Anastasiou and Piotr Fryzlewicz, arXiv:2504.21379v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17106v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n Resolution & Error & Order \\\\\n \\hline\n $h_0$ & 5.72E$-$2 & $-$ \\\\\n \\hline\n $h_1$ & 1.18E$-$2 & $1.48$ \\\\\n \\hline\n $h_2$ & 7.40E$-$3 & $1.32$ \\\\\n \\hline\n $h_3$ & 3.24E$-$3 & $1.19$ \\\\\n \\hline\n $h_4$ & 1.51E$-$3 & $1.10$ \\\\\n \\hline\n $h_5$ & 7.26E$-$4 & $1.06$ \\\\\n \\hline\n $h_6$ & 3.56E$-$4 & $1.03$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Simple weights (), example 1.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17106v1", "attribution": "\"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05795v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Computational load (in hours) considering MNIST dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l|l|l|l|l|l|l|l|l|l|}\n\\cline{2-13}\n & \\multicolumn{4}{c|}{1L} & \\multicolumn{4}{c|}{2L} & \\multicolumn{4}{c|}{3L} \\\\ \\cline{2-13} \n\t\t\t\t\t\t & \\multicolumn{2}{c|}{DBN} & \\multicolumn{2}{c|}{DBM} & \\multicolumn{2}{c|}{DBN} & \\multicolumn{2}{c|}{DBM} & \\multicolumn{2}{c|}{DBN} & \\multicolumn{2}{c|}{DBM} \\\\ \\cline{2-13} \n & CD & PCD & CD & PCD & CD & PCD & CD & PCD & CD & PCD & CD & PCD \\\\ \\hline\n IHS & 0.35 & \\textbf{0.25} & \\textbf{0.45} & \\textbf{0.46} & 0.60 & 0.52 & 0.57 & \\textbf{0.55} & 0.54 & 0.56 & \\textbf{0.82} & \\textbf{0.53} \\\\ \\hline\n AIWPSO & 2.21 & 2.28 & 2.64 & 2.41 & 3.39 & 2.68 & 3.89 & 3.62 & 4.31 & 4.73 & 5.67 & 4.28 \\\\ \\hline\n CS & \\textbf{0.30} & 0.45 & 0.53 & 0.56 & \\textbf{0.49} & \\textbf{0.45} & \\textbf{0.44} & 0.80 & \\textbf{0.47} & \\textbf{0.29} & 0.84 & 0.97 \\\\ \\hline\n FA & 0.75 & 1.49 & 1.81 & 1.06 & 1.37 & 1.30 & 1.95 & 2.41 & 2.23 & 2.22 & 2.52 & 1.29 \\\\ \\hline\n BSA & 1.28 & 1.31 & 0.98 & 1.21 & 1.12 & 0.71 & 2.67 & 1.61 & 1.48 & 1.43 & 2.65 & 3.74 \\\\ \\hline\n JADE & 1.00 & 1.63 & 0.79 & 0.88 & 1.93 & 1.76 & 2.12 & 1.81 & 1.34 & 1.69 & 3.17 & 2.34 \\\\ \\hline\n CoBiDE & 1.25 & 1.29 & 1.11 & 1.11 & 1.50 & 1.67 & 2.13 & 2.22 & 2.29 & 1.60 & 2.92 & 2.26 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines", "authors": ["Leandro Aparecido Passos", "João Paulo Papa"], "url": "https://arxiv.org/abs/2101.05795v1", "attribution": "\"A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines\" by Leandro Aparecido Passos and João Paulo Papa, arXiv:2101.05795v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12027v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Initial Prefetcher Options -} We show the regular and irregular prefetcher options we used at each cache level to construct our set of 300 PSCs. We reduce the number of PSCs down to 5 PSCs before training.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l}\n\\hline\n\\hline\n\\textbf{L1I\\$} & \\textbf{L1D\\$} & \\textbf{L2\\$} & \\textbf{LLC} \\\\\n\\hline\nNo Prefetcher & No Prefetcher & No Prefetcher & No Prefetcher \\\\\n\\hline\nNext-line & Next-line & Next-line & Next-line \\\\\n\\hline\nFNL+MMA & IPCP & IPCP & \\\\\n\\hline\nDJOLT & MLOP & SPP & \\\\\n\\hline\nEIP & Bingo & KPCP & \\\\\n\\hline\n & & Ip-Stride & \\\\\n\\hline \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Puppeteer: A Random Forest-based Manager for Hardware Prefetchers across the Memory Hierarchy", "authors": ["Furkan Eris", "Marcia S. Louis", "Kubra Eris", "Jose L. Abellan", "Ajay Joshi"], "url": "https://arxiv.org/abs/2201.12027v1", "attribution": "\"Puppeteer: A Random Forest-based Manager for Hardware Prefetchers across the Memory Hierarchy\" by Furkan Eris, Marcia S. Louis, Kubra Eris, Jose L. Abellan, and Ajay Joshi, arXiv:2201.12027v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10364v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Computational setup of the test cases}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|} \n\t\t\\hline\n\t\t&1D M1 & 1D M2 & 2D M1 \\\\\n\t\t\\hline\n\t\t$T$ & $(0,0.7]$ & $(0,0.7]$ & $(0,10]$ \\\\\t\\hline\n\t\tTime steps & $8750$ & $8750$ & $33333$\\\\ \t\\hline\n\t\t$\\mathbf X$ & $[0,1]$ & $[0,1]$& $[-1.5,1.5]\\times[-1.5,1.5]$ \\\\\t\\hline\n\t\tGrid cells $n_x$&$5000$ & $5000$ & $2000^2$ \\\\\t\\hline\n\t\tQuadrature & Gauss-Legendre & Gauss-Legendre & Tensorized Gauss-Legendre\\\\\t\\hline\n\t\t$\\mathbf{V}$ & $[-1,1]$ & $[-1,1]$ & $[-1,1]\\times[0,2\\pi)$ \\\\ \t\\hline\n\t\tQuadrature points & $28$ & $28$ & $400$\\\\\t\\hline\n\t\tBasis & Monomial & Monomial & Monomial\\\\ \t\\hline\n\t\tCFL number & $0.4$ & $0.4$ & $0.4$ \\\\ \t\\hline\n\t\t$\\sigma$ & $1.0$ & $1.0$ & $0.0$\\\\ \t\\hline\n\t\t$\\tau$ & $0.5$ & $0.5$ & $0.0$\\\\ \t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Neural network-based, structure-preserving entropy closures for the Boltzmann moment system", "authors": ["Steffen Schotthöfer", "Tianbai Xiao", "Martin Frank", "Cory D. Hauck"], "url": "https://arxiv.org/abs/2201.10364v1", "attribution": "\"Neural network-based, structure-preserving entropy closures for the Boltzmann moment system\" by Steffen Schotthöfer, Tianbai Xiao, Martin Frank, and Cory D. Hauck, arXiv:2201.10364v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14913v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|cc|} \\hline \n & \\multicolumn{2}{|c|}{$V_{h}$}& \\multicolumn{2}{|c|}{$V_{h}^{+}$}& \\multicolumn{2}{|c|}{$V_{h}^{*}$}\\\\ \\hline \n n& $L^{2}$error & order & $L^{2}$error & order & $L^{2}$error & order \\\\ \\hline \n 10& 1.189e-02& -\n&7.823e-06& - &7.811e-06& -\n\\\\ \\hline \n 20& 2.983e-03&1.995\n&2.157e-06&1.859&2.175e-06&1.844\n\\\\ \\hline \n 40& 7.463e-04&1.999\n&5.866e-07&1.878&5.912e-07&1.880\n\\\\ \\hline \n 80& 1.866e-04&2.000\n&1.501e-07&1.966&1.513e-07&1.967\n\\\\ \\hline \n 160& 4.666e-05&2.000\n&3.776e-08&1.991&3.804e-08&1.991\n\\\\ \\hline\n 320& 1.166e-05&2.000&9.455e-09&1.998&9.523e-09&1.998\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{$L^{2}$error and order using $P^{1}$ Lagrange element, Poisson equation in 1d.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A PINN-enriched finite element method for linear elliptic problems", "authors": ["Xiao Chen", "Yixin Luo", "Jingrun Chen"], "url": "https://arxiv.org/abs/2503.14913v1", "attribution": "\"A PINN-enriched finite element method for linear elliptic problems\" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00932v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The performance of our framework trained with image tags and subitizing, respectively. The better ones are marked in \\textbf{bold}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n Dataset & Metric & w/ image-tag & w/ subitizing \\\\\\hline\n \\multirow{2}{*}{ECSSD} & $F_{\\beta}\\uparrow$ & 0.825 & \\textbf{0.858} \\\\\n \\cline{2-4}\n ~ & MAE$\\downarrow$ & 0.110 & \\textbf{0.108} \\\\\\hline\n \\multirow{2}{*}{DUT-OMRON} & $F_{\\beta}\\uparrow$ & 0.745 & \\textbf{0.778} \\\\\n \\cline{2-4}\n ~ & MAE$\\downarrow$ & 0.103 & \\textbf{0.083} \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Weakly-Supervised Saliency Detection via Salient Object Subitizing", "authors": ["Xiaoyang Zheng", "Xin Tan", "Jie Zhou", "Lizhuang Ma", "Rynson W. H. Lau"], "url": "https://arxiv.org/abs/2101.00932v1", "attribution": "\"Weakly-Supervised Saliency Detection via Salient Object Subitizing\" by Xiaoyang Zheng, Xin Tan, Jie Zhou, Lizhuang Ma, and Rynson W. H. Lau, arXiv:2101.00932v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.14991v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|} \\hline \n Parameters& Skinner box& Navigation\\\\ \\hline \n $N_\\text{agents}$& 100&30\\\\ \\hline \n $N_\\text{clones}$& 2&3\\\\ \\hline \n $N_\\text{episodes}$& 4 000& 40 000\\\\ \\hline \n Length episodes& \\multicolumn{2}{|c|}{80}\\\\ \\hline \n Forgetting rate $\\gamma$& \\multicolumn{2}{|c|}{0.001}\\\\ \\hline\n Reward scale R& \\multicolumn{2}{|c|}{3}\\\\ \\hline \n scaling parameter $\\zeta_\\text{wandering}$& \\{0\\} & \\{-3, -1, 0, 1, 3\\}\\\\ \\hline \n scaling parameter $\\zeta_\\text{task}$& -1& -3\\\\ \\hline \n p*& \\multicolumn{2}{|c|}{0.99}\\\\ \\hline\n $N_\\text{pref}$& \\multicolumn{2}{|c|}{1}\\\\\\hline\n Prediction horizon $T_h$& 2 & 3\\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Parameters used during the training and the testing of the agents}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Free Energy Projective Simulation (FEPS): Active inference with interpretability", "authors": ["Joséphine Pazem", "Marius Krumm", "Alexander Q. Vining", "Lukas J. Fiderer", "Hans J. Briegel"], "url": "https://arxiv.org/abs/2411.14991v1", "attribution": "\"Free Energy Projective Simulation (FEPS): Active inference with interpretability\" by Joséphine Pazem, Marius Krumm, Alexander Q. Vining, Lukas J. Fiderer, and Hans J. Briegel, arXiv:2411.14991v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2007.13024v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{PESQ comparisons of single-channel deep speech enhancement models on the Edinburgh noisy speech database. The average PESQ score for unprocessed noisy speech is $1.97$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\n \\hline\n \\hline\n Model \t\t& Parameters \\# \t& PESQ\t \\\\\n \\hline\n \\hline\n DNN\t\t & 5.5M\t\t & 2.82\t\t\\\\ %2.84\t\\\\\n \\hline\n CNN \t\t & 9.1M \t & 3.04 \t\\\\%3.11\t \\\\\n \\hline\n DNN-TT\t &\t0.55M\t\t & 2.81\t\t\\\\%2.83\t\\\\\t\n \\hline\n CNN-TT \t & 0.73M\t \t& 3.02\t\t\\\\%3.08\t\\\\\t\n CNN-TT \t &\t2.9M\t \t& 3.09\t\t\\\\\t%3.12\n CNN-TT \t & 5.1M\t\t & 3.13\t\t\\\\\t%3.15\n \\hline\n CNN-Tucker-3 & 8.9M & 2.89\t\t \\\\\n \\hline\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement", "authors": ["Jun Qi", "Hu Hu", "Yannan Wang", "Chao-Han Huck Yang", "Sabato Marco Siniscalchi", "Chin-Hui Lee"], "url": "https://arxiv.org/abs/2007.13024v2", "attribution": "\"Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement\" by Jun Qi, Hu Hu, Yannan Wang, Chao-Han Huck Yang, Sabato Marco Siniscalchi, and Chin-Hui Lee, arXiv:2007.13024v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08640v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Inter-comparison results for different combinations A to G of PTP-WR devices. Combinations A-D were measured in a laboratory at room temperature: 22.5\\textcelsius{} ($\\pm$2.5\\textcelsius{}) while combinations E-G were measured in a climate chamber at 23.5\\textcelsius{} ($\\pm$0.1\\textcelsius{}).}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccc}\n\t\t\\cline{2-6}\n\t\t & {} & PPS residual & n & $\\sigma$ & figure \\\\\n\t\t & {grandmaster $\\rightarrow$ follower} & (ps) & & (ps) & \\\\\n\t\t\\hline\n\t\t & {SPEC7\\textsubscript{Sn01\\_Nikhef}$\\rightarrow$SPEC7\\textsubscript{Sn07\\_Nikhef}} & 43 & 80 & 3.6 & \\\\\n\t\tA & {SPEC7\\textsubscript{Sn07\\_Nikhef}$\\rightarrow$SPEC7\\textsubscript{Sn01\\_Nikhef}} & -64 & 77 & 3.4 & \\\\\n\t\t\\cline{2-6}\n\t\t & {$\\mu$, $\\Delta$ (ps)} & 54, 21 & & & \\\\\n\t\t\\hline\n\t\t\\hline\n\t\t & {SPEC7\\textsubscript{Sn07\\_Nikhef}$\\rightarrow$SPEC7\\textsubscript{Sn10\\_NPL}} & 536 & 6 & 19.1 & \\\\\n\t\tB & {SPEC7\\textsubscript{Sn10\\_NPL}$\\rightarrow$SPEC7\\textsubscript{Sn07\\_Nikhef}} & -524 & 8 & 16.4 &\\\\\n\t\t\\cline{2-6}\n\t\t & {$\\mu$, $\\Delta$ (ps)} & 530, 12 & & &\\\\\n\t\t\\hline\n\t\t\\hline\n\t\t & SPEC7\\textsubscript{Sn07\\_Nikhef}$\\rightarrow$SPEC7\\textsubscript{Sn11\\_NPL} & 491 & 6 & 23.5 & \\\\\n\t\tC & {SPEC7\\textsubscript{Sn11\\_NPL}$\\rightarrow$SPEC7\\textsubscript{Sn07\\_Nikhef}} & -464 & 6 & 29.0 & \\\\\n\t\t\\cline{2-6}\n\t\t & {$\\mu$, $\\Delta$ (ps)} & 478, 27 & & & \\\\\n\t\t\\hline\n\t\t\\hline\n\t\t & SPEC7\\textsubscript{Sn10\\_NPL}$\\rightarrow$SPEC7\\textsubscript{Sn11\\_NPL} & -37 & 6 & 15.4 & \\\\\n\t\tD & {SPEC7\\textsubscript{Sn11\\_NPL}$\\rightarrow$SPEC7\\textsubscript{Sn10\\_NPL}} & 13 & 6 & 21.4 & \\\\\n\t\t\\cline{2-6}\n\t\t & {$\\mu$, $\\Delta$ (ps)} & 25, 24 & & & \\\\\n\t\t\\hline\n\t\t\\hline\n\t\t & {SPEC\\textsubscript{Nikhef}$\\rightarrow$SPEC\\textsubscript{VTT}} & 96 & 80 & 37.6 & \\\\\n\t\tE & {SPEC\\textsubscript{VTT}$\\rightarrow$SPEC\\textsubscript{Nikhef}} & -119 & 80 & 38.4 & \\\\\n\t\t\\cline{2-6}\n\t\t & {$\\mu$, $\\Delta$ (ps)} & 107, 23 & & & \\\\\n\t\t\\hline\n\t\t\\hline\n\t\t & {SPEC7\\textsubscript{Sn01\\_Nikhef}$\\rightarrow$SPEC\\textsubscript{Nikhef}} & 162 & 80 & 32.2 & \\\\\n\t\tF & {SPEC\\textsubscript{Nikhef}$\\rightarrow$SPEC7\\textsubscript{Sn01\\_Nikhef}} & -112 & 80 & 29.4 & \\\\\n\t\t\\cline{2-6}\n\t\t & {$\\mu$, $\\Delta$ (ps)} & 137, 50 & & & \\\\\n\t\t\\hline\n\t\t\\hline\n\t\t & SPEC7\\textsubscript{Sn01\\_Nikhef}$\\rightarrow$SPEC\\textsubscript{VTT} & 241 & 77 & 24.0 & \\\\\n\t\tG & {SPEC\\textsubscript{VTT}$\\rightarrow$SPEC7\\textsubscript{Sn01\\_Nikhef}} & -208 & 78 & 19.1 & \\\\\n\t\t\\cline{2-6}\n\t\t & {$\\mu$, $\\Delta$ (ps)} & 224, 33 & & & \\\\\n\t\t\\hline\n\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "First electrical White Rabbit absolute calibration inter-comparison", "authors": ["P. P. M. Jansweijer", "N. A. D. Boukadida", "K. Hanhijärvi", "A. Wallin", "B. Eglin", "E. Laier English"], "url": "https://arxiv.org/abs/2201.08640v1", "attribution": "\"First electrical White Rabbit absolute calibration inter-comparison\" by P. P. M. Jansweijer, N. A. D. Boukadida, K. Hanhijärvi, A. Wallin, B. Eglin, and E. Laier English, arXiv:2201.08640v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.08848v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The initial values and observation noise of the concentrations (states $x_i$)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccc} \\hline\n\t\t& $x_1$&$x_2$&$x_3$&$x_4$&$x_5$&$x_6$\\\\ \\hline %head \n initial\\ values& 66& 0.054& 0.019& 59& 0.09& 0.012 \\\\\n noise STD &0.005& $5\\times 10^{-5}$& $2\\times 10^{-5}$& 0.035& 0.0005& $5\\times 10^{-6}$ \\\\ \\midrule \n &$x_7$&$x_8$&$x_9$&$x_{10}$&$x_{11}$& \\\\ \\hline\n initial\\ values& 65& 26& 175& 161& 2.18& \\\\\n noise STD & 0.05& 0.02& 0.03& 0.003& 0.002& \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Ensemble Kalman filter based Sequential Monte Carlo Sampler for sequential Bayesian inference", "authors": ["Jiangqi Wu", "Linjie Wen", "Peter L Green", "Jinglai Li", "Simon Maskell"], "url": "https://arxiv.org/abs/2012.08848v2", "attribution": "\"Ensemble Kalman filter based Sequential Monte Carlo Sampler for sequential Bayesian inference\" by Jiangqi Wu, Linjie Wen, Peter L Green, Jinglai Li, and Simon Maskell, arXiv:2012.08848v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.04974v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c|c||} \n \\hline\n Model & $R^2$ with $\\mathbf{AUROC}_{t+1}$ & $R^2$ without $\\mathbf{AUROC}_{t+1}$ & $p$-value of difference \\\\ \n \\hline\\hline\n Linear & $\\mathbf{0.360^*}$ & 0.107 & 0.00 \\\\ \n \\hline\n Tree & $\\mathbf{0.357^*}$ &\t0.310 & 0.043 \\\\\n \\hline\n MLP & $\\mathbf{0.380^*}$ &\t0.317 & 0.003 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results of $\\text{log}$-$\\text{RV}$ forecasting at the 1-hour frequency}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Understanding stock market instability via graph auto-encoders", "authors": ["Dragos Gorduza", "Xiaowen Dong", "Stefan Zohren"], "url": "https://arxiv.org/abs/2212.04974v1", "attribution": "\"Understanding stock market instability via graph auto-encoders\" by Dragos Gorduza, Xiaowen Dong, and Stefan Zohren, arXiv:2212.04974v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.17072v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\toprule\nRatio & Formula & Global Range \\\\ \\midrule\nMarket Share & $\\frac{\\text{Premiums}}{\\text{Total Market Premiums}} \\times 100$ & 5\\% - 30\\% \\\\ \nClaims Paid Ratio & $\\frac{\\text{Claims Paid}}{\\text{Gross Premium Income}} \\times 100$ & 50\\% - 80\\% \\\\ \nLoss Ratio & $\\frac{\\text{Claims Incurred}}{\\text{Gross Premium Income}} \\times 100$ & 60\\% - 75\\% \\\\ \nUnderwriting Profit Ratio & $\\frac{\\text{Underwriting Profit}}{\\text{Gross Premium Income}} \\times 100$ & 5\\% - 15\\% \\\\ \nExpense Ratio & $\\frac{\\text{Expenses}}{\\text{Gross Premium Income}} \\times 100$ & 10\\% - 25\\% \\\\ \nCombined Ratio & $\\text{Loss Ratio} + \\text{Expense Ratio}$ & 90\\% - 105\\% \\\\ \nClaims Payout Ratio & $\\frac{\\text{Claims Paid}}{\\text{Claims Incurred}} \\times 100$ & 60\\% - 90\\% \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\footnotesize Key Financial Ratios used to evaluate insurance providers. The global range represents typical industry standards for each ratio, indicating the desired financial performance for insurance companies.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Decoding Financial Health in Kenyas' Medical Insurance Sector: A Data-Driven Cluster Analysis", "authors": ["Evans Kiptoo Korir", "Zsolt Vizi"], "url": "https://arxiv.org/abs/2502.17072v2", "attribution": "\"Decoding Financial Health in Kenyas' Medical Insurance Sector: A Data-Driven Cluster Analysis\" by Evans Kiptoo Korir and Zsolt Vizi, arXiv:2502.17072v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2102.00287v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|r|r|r|r|r|r|} \\hline\n& \\multicolumn{3}{c|}{FR} & \\multicolumn{3}{c|}{ES} \\\\\\cline{2-7} \n& B1 & B2 & B3 & B1 & B2 & B3 \\\\\\hline\nORIG & \\bf{79.80} & 6.59 & \\underline{13.61} & \\bf{77.80} & 6.83 & \\underline{15.36} \\\\\\hline\nPB-SMT & 81.78 & 6.48 & 11.74 & 79.77 & 6.86 & 13.36 \\\\\\hline\nLSTM & 82.95 & 6.18 & 10.88 & 80.34 & 6.84 & 12.81 \\\\\\hline\nTRANS & 82.01 & 6.24 & 11.75 & 82.35 & 6.99 & 10.67 \\\\\\hline\\hline\n& \\multicolumn{3}{c|}{EN$_{FR}$} & \\multicolumn{3}{c|}{EN$_{ES}$}\\\\\\cline{2-7}\n& B1 & B2 & B3 & B1 & B2 & B3 \\\\\\hline\nORIG & \\textbf{80.83} & 7.10 & \\underline{12.07} & \\bf{80.81} & 7.11 & \\underline{12.08}\\\\\\hline\nPB-SMT & 82.06 & 7.04 & 10.90 & 82.25 & 7.01 & 10.74\\\\\\hline\nLSTM & 83.23 & 6.93 & 9.81 & 83.29 & 6.93 & 9.78 \\\\\\hline\nTRANS & 82.25 & 7.05 & 10.70 & 82.35 & 6.99 & 10.67\\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Lexical Frequency Profile (French, Spanish, English (EN$_{FR}$ and EN$_{ES}$) with 3 bands (B1: 0-1000, B2: 1001-2000, B3: 2001-end) for the original data and the output of the MT systems.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation", "authors": ["Eva Vanmassenhove", "Dimitar Shterionov", "Matthew Gwilliam"], "url": "https://arxiv.org/abs/2102.00287v1", "attribution": "\"Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation\" by Eva Vanmassenhove, Dimitar Shterionov, and Matthew Gwilliam, arXiv:2102.00287v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09294v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\emph{People's Daily} vs. Wikipedia }\n\\begin{tabular}{lrrrrrr}\n\\toprule\n & \\multicolumn{2}{c}{Naive Bayes} & \\multicolumn{2}{c}{SVM} & \\multicolumn{2}{c}{TextCNN} \\\\\n\\cmidrule(l{3pt}r{3pt}){2-3} \\cmidrule(l{3pt}r{3pt}){4-5} \\cmidrule(l{3pt}r{3pt}){6-7}\n & estimate & p-value & estimate & p-value & estimate & p-value\\\\\n\\midrule\nFreedom & -0.22 & 0.00 & -0.08 & 0.00 & -0.12 & 0.00\\\\\nDemocracy & -0.14 & 0.00 & -0.06 & 0.02 & -0.07 & 0.00\\\\\nElection & -0.13 & 0.00 & -0.01 & 0.62 & -0.04 & 0.12\\\\\nCollective Action & -0.19 & 0.00 & -0.05 & 0.05 & -0.06 & 0.00\\\\\nNegative Figures & 0.01 & 0.78 & 0.01 & 0.72 & -0.05 & 0.01\\\\\n\\addlinespace\nSocial Control & 0.05 & 0.00 & 0.01 & 0.66 & 0.01 & 0.63\\\\\nSurveillance & -0.04 & 0.11 & -0.02 & 0.34 & -0.03 & 0.22\\\\\nCCP & 0.07 & 0.00 & 0.00 & 0.82 & 0.02 & 0.24\\\\\nHistorical Events & -0.01 & 0.77 & 0.02 & 0.29 & -0.01 & 0.44\\\\\nPositive Figures & 0.13 & 0.00 & 0.04 & 0.00 & 0.06 & 0.00\\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Censorship of Online Encyclopedias: Implications for NLP Models", "authors": ["Eddie Yang", "Margaret E. Roberts"], "url": "https://arxiv.org/abs/2101.09294v1", "attribution": "\"Censorship of Online Encyclopedias: Implications for NLP Models\" by Eddie Yang and Margaret E. Roberts, arXiv:2101.09294v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2010.13892v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Class distribution in training and test dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\nDataset & \\#Non-bankrupt & \\#Bankrupt\\tabularnewline\n\\hline\n\\hline\nTraining & 5487 & 113\\tabularnewline\n\\hline\nTest & 2105 & 47\\tabularnewline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction", "authors": ["Amir Mukeri", "Habibullah Shaikh", "D. P. Gaikwad"], "url": "https://arxiv.org/abs/2010.13892v2", "attribution": "\"Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction\" by Amir Mukeri, Habibullah Shaikh, and D. P. Gaikwad, arXiv:2010.13892v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.14720v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc}\n\\toprule\nPreprocessing method & BCE loss & Binary accuracy (\\%) \\\\\n\\midrule\nNo preprocessing & $0.1900 \\pm 0.0036$ & $0.9168 \\pm 0.0017$ \\\\\n$z$-score & $0.1871 \\pm 0.0011$ & $0.9176 \\pm 0.0007$ \\\\\n$z$-score + YJ & $0.1789 \\pm 0.0009$ & $0.9211 \\pm 0.0006$ \\\\\nWinsorize + $z$-score & $0.1876 \\pm 0.0010$ & $0.9172 \\pm 0.0006$ \\\\\nWinsorize + $z$-score + YJ & $0.1788 \\pm 0.0009$ & $0.9211 \\pm 0.0006$ \\\\\n\\underline{CDF inversion} & $\\underline{0.1627 \\pm 0.0009}$ & $\\underline{0.9289 \\pm 0.0006}$ \\\\\nBIN & $0.2191 \\pm 0.0010$ & $0.9036 \\pm 0.0006$ \\\\\nDAIN & $0.2153 \\pm 0.0015$ & $0.9048 \\pm 0.0008$ \\\\\nEDAIN (local-ware) & $0.2099 \\pm 0.0010$ & $0.9071 \\pm 0.0006$ \\\\\n\\textbf{EDAIN (global-aware)} & $\\mathbf{0.1636 \\pm 0.0009}$ & $\\mathbf{0.9283 \\pm 0.0005}$ \\\\\nEDAIN-KL & $0.1760 \\pm 0.0009$ & $0.9224 \\pm 0.0006$ \\\\\n\\underline{KDIT} ($\\alpha=0.1$) & $\\underline{0.1532 \\pm 0.0011}$ & $\\underline{0.9329 \\pm 0.0006}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks", "authors": ["Marcus A. K. September", "Francesco Sanna Passino", "Leonie Goldmann", "Anton Hinel"], "url": "https://arxiv.org/abs/2310.14720v2", "attribution": "\"Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks\" by Marcus A. K. September, Francesco Sanna Passino, Leonie Goldmann, and Anton Hinel, arXiv:2310.14720v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.01116v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc|ccc}\n\\toprule\n& \\multicolumn{2}{c}{Spending/Person} & & \\multicolumn{2}{c}{Conditional Differences}\\\\\\cmidrule{2-3}\\cmidrule{5-6}\n& Average & Median & \\% Diff $> 0$ & Average & Median \\\\\n\\midrule\n\\multicolumn{2}{l}{\\textbf{Panel A: Non-Learning Model}} \\\\ \nObserved Data & \\$2,001 & \\$1,123 & -- & -- & -- \\\\\nFull Information ($\\beta$ = 1) & \\$1,899 & \\$1,066 & 11.9\\% & \\$856 & \\$486 \\\\\nDeductible Resets Quarterly & \\$3,499 & \\$2,040 & 12.7\\% & \\$751 & \\$424 \\\\\nDeductible Resets Monthly & \\$3,745 & \\$2,272 & 8.5\\% & \\$657 & \\$361 \\\\ \n\\midrule\n\\multicolumn{2}{l}{\\textbf{Panel B: Learning Model}} \\\\ \nObserved Data & \\$2,119 & \\$1,181 & -- & -- & -- \\\\\nFull Information ($\\beta$ = 1) & \\$1,899 & \\$1,068 & 21.0\\% & \\$1,051 & \\$575 \\\\\nRe-centered Priors ($\\mu_{\\beta,0}=1$) & \\$1,896 & \\$1,065 & 21.7\\% & \\$1,031 &\\$562 \\\\\nDeductible Resets Quarterly & \\$3,662 & \\$2,135 & 29.9\\% & \\$863 & \\$477 \\\\\nDeductible Resets Monthly & \\$3,851 & \\$2,335 & 22.7\\% & \\$714 & \\$404 \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Comparison of Predicted Spending Across Counterfactual States of the World}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Medical Bill Shock and Imperfect Moral Hazard", "authors": ["Alex Hoagland", "David M. Anderson", "Ed Zhu"], "url": "https://arxiv.org/abs/2211.01116v2", "attribution": "\"Medical Bill Shock and Imperfect Moral Hazard\" by Alex Hoagland, David M. Anderson, and Ed Zhu, arXiv:2211.01116v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12273v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{clccc}\n\\hline\nType & Variable Name & Abbrev. & ECMWF ID & Levels \\\\\n\\hline\n & Land-sea mask & LSM & 172 \\\\\nStatic & Orography & OROG & 228002\\\\\n & Soil Type & SLT & 43\\\\\n\\hline\n & 2 metre temperature & T2m & 167 \\\\\nSingle & 10 metre U wind component & U10 & 165 \\\\\n & 10 metre V wind component & V10 & 166 \\\\\n\\hline\n & Geopotential & Z & 129 & 50, 250, 500, 600, 700, 850, 925 \\\\\n & U wind component & U & 131 & 50, 250, 500, 600, 700, 850, 925 \\\\\nAtmospheric & V wind component & V & 132 & 50, 250, 500, 600, 700, 850, 925 \\\\\n & Temperature & T & 130 & 50, 250, 500, 600, 700, 850, 925 \\\\\n & Specific humidity & Q & 133 & 50, 250, 500, 600, 700, 850, 925 \\\\\n & Relative humidity & R & 157 & 50, 250, 500, 600, 700, 850, 925 \\\\\n\\hline\n\\end{tabular}\n\\caption{Variables considered for global weather prediction model.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data", "authors": ["Siddharth Rout", "Eldad Haber", "Stéphane Gaudreault"], "url": "https://arxiv.org/abs/2503.12273v1", "attribution": "\"Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data\" by Siddharth Rout, Eldad Haber, and Stéphane Gaudreault, arXiv:2503.12273v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05995v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of re-scaled trajectory \\protect\\\\length with ground truth}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllll}\n\t\t\t\\toprule\n\t\t\tSeq & GT (m)&Ours (m) & RLE (\\%)\\\\\n\t\t\t\\midrule\n\t\t\t00 & 3645.213 & 3724.419 & 2.173 \\\\\n\t\t\t02 & 5071.151 &5067.233 &0.757 \\\\\n\t\t\t03 &547.774 &560.888 &0.558 \\\\\n\t\t\t04 &391.861 &393.645 &0.455 \\\\\n\t\t\t05 &2175.159 &2205.576 &1.398 \\\\\n\t\t\t06 &1235.982 &1232.876 &0.251 \\\\\n\t\t\t07 &709.065 &694.697 &0.767 \\\\\n\t\t\t08 &3137.398 &3222.795 &2.722 \\\\\n\t\t\t09 &1738.601 &1705.051 &1.929 \\\\\n\t\t\t10 &911.399 &919.518 &0.890 \\\\\n\t\t\t\\bottomrule \t\t\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry", "authors": ["Rui Tian", "Yunzhou Zhang", "Delong Zhu", "Shiwen Liang", "Sonya Coleman", "Dermot Kerr"], "url": "https://arxiv.org/abs/2101.05995v2", "attribution": "\"Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry\" by Rui Tian, Yunzhou Zhang, Delong Zhu, Shiwen Liang, Sonya Coleman, and Dermot Kerr, arXiv:2101.05995v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2305.01642v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{||cccc||} \n \\hline\n Method & Total Return(\\%) & Vol & Sharpe \\\\ \n \\hline\n Market capitalization of holding & 108.47 & 25.53 & 0.326 \\\\ \n \\hline\n Numbers of holding & 110.37 & 24.56 & 0.343 \\\\\n \\hline\n Weighted sum of holding & 112.01 & 25.10 & 0.338 \\\\\n\\hline\n\\end{tabular}\n\\caption{Performance of different weight method from 2013/12/31 to 2023/03/01, data from uqer.datayes.com}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Construct sparse portfolio with mutual fund's favourite stocks in China A share market", "authors": ["Ke Zhang"], "url": "https://arxiv.org/abs/2305.01642v1", "attribution": "\"Construct sparse portfolio with mutual fund's favourite stocks in China A share market\" by Ke Zhang, arXiv:2305.01642v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.16500v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n & \\textbf{Passage WER} & \\textbf{Recall@5} & \\textbf{Recall@10} & \\textbf{Recall@100} \\\\\n\\midrule\n\\multicolumn{5}{c}{\\textbf{SpokenSQuAD}} \\\\ \\midrule\nGT Text Baseline & 0\\% & 0.9707 & 0.9871 & 0.9985 \\\\ \nLow WER cascaded & $\\sim$20\\% & 0.9525 & 0.9745 & 0.9974 \\\\\nHigh WER cascaded & $\\sim$35\\% & 0.8768 & 0.9271 & 0.9926 \\\\\nSpeech Retriever & N/A & \\textbf{0.9702} & \\textbf{0.9869} & \\textbf{ 0.9986} \\\\ \\midrule\n\\multicolumn{5}{c}{\\textbf{VoxPopuli}} \\\\ \\midrule\nGT Text Baseline & 0\\% & 0.9942 & 0.9971 & 1.0 \\\\ \nLow WER cascaded & $\\sim$17\\% & 0.9826 & 0.9855 & 0.9961 \\\\\nHigh WER cascaded & $\\sim$45\\% & 0.7106 & 0.7493 & 0.8858 \\\\\nSpeech Retriever & N/A & \\textbf{0.9952} & \\textbf{0.9981} & \\textbf{0.9990} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Speech Retrieval-Augmented Generation without Automatic Speech Recognition", "authors": ["Do June Min", "Karel Mundnich", "Andy Lapastora", "Erfan Soltanmohammadi", "Srikanth Ronanki", "Kyu Han"], "url": "https://arxiv.org/abs/2412.16500v3", "attribution": "\"Speech Retrieval-Augmented Generation without Automatic Speech Recognition\" by Do June Min, Karel Mundnich, Andy Lapastora, Erfan Soltanmohammadi, Srikanth Ronanki, and Kyu Han, arXiv:2412.16500v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2307.03935v1_tex_table21.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|c|c|c|}\n\\hline\nMarket & tickSize & indexPrice & Min Bps \\\\\n\\hline\nCELO-USD & 0.0010 & 0.5306 & 0.188 \\\\\nLINK-USD & 0.0010 & 6.6375 & 0.015 \\\\\nDOGE-USD & 0.0001 & 0.0725 & 0.138 \\\\\n1INCH-USD & 0.0010 & 0.4171 & 0.240 \\\\\nXMR-USD & 0.1000 & 153.8950 & 0.065 \\\\\nFIL-USD & 0.0100 & 4.5030 & 0.222 \\\\\nETH-USD & 0.1000 & 1826.0380 & 0.005 \\\\\nAAVE-USD & 0.0100 & 63.6962 & 0.016 \\\\\nATOM-USD & 0.0010 & 10.9800 & 0.009 \\\\\nMKR-USD & 1.0000 & 637.6600 & 0.157 \\\\\nEOS-USD & 0.0010 & 0.8899 & 0.112 \\\\\nCOMP-USD & 0.1000 & 35.2400 & 0.284 \\\\\nALGO-USD & 0.0001 & 0.1660 & 0.060 \\\\\nXTZ-USD & 0.0010 & 0.8963 & 0.112 \\\\\nUNI-USD & 0.0010 & 5.1756 & 0.019 \\\\\nADA-USD & 0.0010 & 0.3700 & 0.270 \\\\\nZRX-USD & 0.0010 & 0.2232 & 0.448 \\\\\nYFI-USD & 1.0000 & 6975.0200 & 0.014 \\\\\nMATIC-USD & 0.0001 & 0.8688 & 0.012 \\\\\nETC-USD & 0.0100 & 18.3297 & 0.055 \\\\\nAVAX-USD & 0.0100 & 15.2459 & 0.066 \\\\\nLTC-USD & 0.1000 & 86.5360 & 0.116 \\\\\nENJ-USD & 0.0010 & 0.3414 & 0.293 \\\\\nDOT-USD & 0.0100 & 5.3837 & 0.186 \\\\\nSNX-USD & 0.0010 & 2.1035 & 0.048 \\\\\nRUNE-USD & 0.0010 & 1.1918 & 0.084 \\\\\nXLM-USD & 0.0001 & 0.0884 & 0.113 \\\\\nBCH-USD & 0.1000 & 118.6310 & 0.084 \\\\\nTRX-USD & 0.0001 & 0.0704 & 0.142 \\\\\nBTC-USD & 1.0000 & 27369.0000 & 0.004 \\\\\nUMA-USD & 0.0100 & 2.3360 & 0.428 \\\\\nNEAR-USD & 0.0010 & 1.6745 & 0.060 \\\\\nZEC-USD & 0.1000 & 33.2750 & 0.301 \\\\\nSOL-USD & 0.0010 & 21.3291 & 0.005 \\\\\nSUSHI-USD & 0.0010 & 0.8999 & 0.111 \\\\\nICP-USD & 0.0100 & 5.3205 & 0.188 \\\\\nCRV-USD & 0.0001 & 0.8171 & 0.012 \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "dYdX: Liquidity Providers' Incentive Programme Review", "authors": ["Colin Chan"], "url": "https://arxiv.org/abs/2307.03935v1", "attribution": "\"dYdX: Liquidity Providers' Incentive Programme Review\" by Colin Chan, arXiv:2307.03935v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.19858v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study showing individual component contributions, evaluated with EfficientNet-B3 and Swin Transformer.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\toprule\n\\textbf{SSFL++} & \\textbf{KDS} & \\textbf{F1-score (\\%)} & \\textbf{AUC-ROC} & \\textbf{Improvement} \\\\\n\\midrule\n& & 70.73 & 0.7804 & -- \\\\\n\\checkmark & & 80.49 & 0.8957 & +9.76\\% \\\\\n\\checkmark & \\checkmark & 94.68 & 0.9813 & +23.95\\% \\\\\n\\midrule\n& & 68.53 & 0.7654 & -- \\\\\n\\checkmark & & 78.82 & 0.8672 & +10.82\\% \\\\\n\\checkmark & \\checkmark & 93.34 & 0.9797 & +24.81\\% \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization", "authors": ["Chia-Ming Lee", "Bo-Cheng Qiu", "Ting-Yao Chen", "Ming-Han Sun", "Fang-Ying Lin", "Jung-Tse Tsai", "I-An Tsai", "Yu-Fan Lin", "Chih-Chung Hsu"], "url": "https://arxiv.org/abs/2507.19858v1", "attribution": "\"Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization\" by Chia-Ming Lee, Bo-Cheng Qiu, Ting-Yao Chen, Ming-Han Sun, Fang-Ying Lin, Jung-Tse Tsai, I-An Tsai, Yu-Fan Lin, and Chih-Chung Hsu, arXiv:2507.19858v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.16139v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n \\hline\n \\textbf{Parameter} & \\textbf{Description} & \\textbf{Units} &\\textbf{NMC (SANYO cell)} &\\textbf{LFP (A123 cell)}\\\\ \\hline\n $n_{\\textrm{SEI}}$ & Number of e$^-$ transferred in SEI side reaction & - &\\multicolumn{2}{c}{2.0}\\\\ \n $\\lambda$ & Constant $\\lambda = \\frac{c_s \\sqrt{D_s}}{c_p \\sqrt{D_p}}$ & - &\\multicolumn{2}{c}{5.51$\\times 10^{-5}$}\\\\ \n $OCV_s$& OCV of the side reaction & V &\\multicolumn{2}{c}{0.4}\\\\ \n $\\varepsilon_{\\textrm{AM}}$ & Active material volume fraction & - &\\multicolumn{2}{c}{0.552}\\\\ \n $R_s$ & Particle radius & m &7.5$\\times 10^{-6}$&5$\\times 10^{-6}$\\\\ \n $a_s$ & Specific surface area of the anode & m$^{-1}$ &\\multicolumn{2}{c}{\\( \\frac{3 \\cdot \\varepsilon_{\\textrm{AM}}}{R_s} \\)}\\\\ \n $A_n$ & Active surface area of the anode & m$^2$ &0.105 \n &0.18\\\\ \n $L_n$ & Thickness of anode & m &50$\\times 10^{-6}$&34$\\times 10^{-6}$\\\\ \n $i_0$ & Exchange current of the intercalation current & A/m$^2$ &\\multicolumn{2}{c}{1.5}\\\\ \n $k_{\\textrm{SEI}}$ & Kinetic rate & 1/$\\sqrt{\\text{s}}$ &\\multicolumn{2}{c}{66.85}\\\\ \n $E_{\\textrm{SEI}}$ & Activation energy & J/mol &\\multicolumn{2}{c}{39146.0}\\\\ \n $\\delta_{SEI,0}$& Initial value of the SEI layer thickness & m &\\multicolumn{2}{c}{2.0 $\\times 10^{-9}$}\\\\ \n $M_{\\textrm{SEI}}$ & Molecular weight of the SEI layer & kg/mol &\\multicolumn{2}{c}{0.162}\\\\ \n $\\rho_{\\textrm{SEI}}$ & Density of the SEI layer & kg/m$^3$ &\\multicolumn{2}{c}{1690.0}\\\\ \n $z_{100\\%}$& Full electrode stoichiometry & - &0.9 \n &0.81\\\\ \n $z_{0\\%}$& Empty electrode stoichiometry & - &0 \n &0.0176\\\\ \n $k_{\\textrm{AM}}$ & Kinetic rate & 1/Ah &\\multicolumn{2}{c}{0.0137}\\\\ \n $E_{\\textrm{AM}}$ & Activation energy & J/mol &\\multicolumn{2}{c}{39500.0}\\\\ \n $\\beta$ & Tuning parameter & - &\\multicolumn{2}{c}{1.7}\\\\ \n $t^{+}_0$ & Transport/transference number & - &0.363 \n &0.36\\\\ \n $E_{\\kappa}$ & Activation energy for $\\kappa$ & J/mol &\\multicolumn{2}{c}{34700.0}\\\\ \n $E_{D_e}$& Activation energy for $D_e$ & J/mol &\\multicolumn{2}{c}{34700.0}\\\\ \n $\\kappa_{\\textrm{ref}}$& Reference ionic conductivity for $\\kappa$ at reference temperature & S/m &\\multicolumn{2}{c}{0.174}\\\\ \n $D_{e,\\textrm{ref}}$& Reference value for $D_e$ at reference temperature & m$^2$/s &\\multicolumn{2}{c}{7.5$\\times 10^{-11}$}\\\\ \n $brug$ & Bruggeman coefficient & - &\\multicolumn{2}{c}{3/2}\\\\ \n $c_{e,\\textrm{avg}}$& Average volume concentration of Li in the electrolyte & mol/m$^3$ &1000&1200\\\\ \n $c_{e,\\textrm{max}}$& Maximum volume concentration of Li in the electrolyte & mol/m$^3$ &1000&1200\\\\ \n $\\sigma_n$ & Electronic conductivity & S/m &100&215\\\\\n $\\varepsilon_{s}$ & Volume fraction of solid electrolyte & - &0.59 &0.58\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Parameter values, descriptions, and units for PBROMs.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Aging-aware Energy Management for Residential Multi-Carrier Energy Systems", "authors": ["Darío Slaifstein", "Gautham Ram Chandra Mouli", "Laura Ramirez-Elizondo", "Pavol Bauer"], "url": "https://arxiv.org/abs/2503.16139v3", "attribution": "\"Aging-aware Energy Management for Residential Multi-Carrier Energy Systems\" by Darío Slaifstein, Gautham Ram Chandra Mouli, Laura Ramirez-Elizondo, and Pavol Bauer, arXiv:2503.16139v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.20993v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Correlation Matrix}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|c|}\n\\hline\n & GDP\\_Growth & UnEmpl\\_Rate & TAX & CPI & EPU\\_Index & Gini\\_Index & FDI & HDI \\\\ \\hline\nGDP\\_Growth & 1.0000 & & & & & & & \\\\ \\hline\nUnEmpl\\_Rate & -0.2129 & 1.0000 & & & & & & \\\\ \\hline\nTAX & -0.1583 & 0.0952 & 1.0000 & & & & & \\\\ \\hline\nCPI & 0.2279 & -0.0584 & -0.2219 & 1.0000 & & & & \\\\ \\hline\nEPU\\_Index & -0.1778 & -0.0151 & -0.1057 & -0.0616 & 1.0000 & & & \\\\ \\hline\nGini\\_Index & 0.1088 & 0.1151 & -0.5218 & 0.4252 & -0.0167 & 1.0000 & & \\\\ \\hline\nFDI & 0.0824 & -0.0290 & 0.0881 & -0.0375 & -0.0460 & -0.0846 & 1.0000 & \\\\ \\hline\nHDI & -0.3461 & -0.0392 & 0.3049 & -0.4273 & 0.1602 & -0.5099 & -0.0517 & 1.0000 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "GDP-GFCF Dynamics Across Global Economies: A Comparative Study of Panel Regressions and Random Forest", "authors": ["Alina Landowska", "Robert A. Kłopotek", "Dariusz Filip", "Konrad Raczkowski"], "url": "https://arxiv.org/abs/2504.20993v1", "attribution": "\"GDP-GFCF Dynamics Across Global Economies: A Comparative Study of Panel Regressions and Random Forest\" by Alina Landowska, Robert A. Kłopotek, Dariusz Filip, and Konrad Raczkowski, arXiv:2504.20993v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.17999v5_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n $j$ & Original & No bootstraps \\\\\n \\hline\n 0 & 0.214 & 0.217 \\\\\n 1 & 0.430 & 0.435 \\\\\n 2 & 0.703 & 0.715 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{RMSEs of estimated $(1-p_{j,n})$-quantiles where $p_{j,n}=1/(10^jn)$, for $j=0,1,2$, for the Gaussian case.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Automated threshold selection and associated inference uncertainty for univariate extremes", "authors": ["Conor Murphy", "Jonathan A. Tawn", "Zak Varty"], "url": "https://arxiv.org/abs/2310.17999v5", "attribution": "\"Automated threshold selection and associated inference uncertainty for univariate extremes\" by Conor Murphy, Jonathan A. Tawn, and Zak Varty, arXiv:2310.17999v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.03909v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n$n=m$ & lrAA iterations &iterations reported in & final rank\\\\\n\\hline\n21 &109 (7) &1083 & 13 (4) \\\\\n61 &287 (8) &8967 & 18 (7) \\\\\n101 &443 (13) &23849 & 20 (7)\\\\\n221 & 675 (11) &107388 & 26 (8) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Iterations needed and final rank for the lrAA method for solving the elliptic Monge-Amp\\`ere equation for an exact solution $u(x,y) = \\frac{2 \\sqrt{2}}{3}(x^2+y^2)^{\\frac{3}{4}}$. The numbers corresponds to a fixed tolerance $10^{-10}$ or a local truncation error guided tolerance $0.01h_x$ (in parenthesis).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "lrAA: Low-Rank Anderson Acceleration", "authors": ["Daniel Appelo", "Yingda Cheng"], "url": "https://arxiv.org/abs/2503.03909v2", "attribution": "\"lrAA: Low-Rank Anderson Acceleration\" by Daniel Appelo and Yingda Cheng, arXiv:2503.03909v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07563v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{WER (\\%) results on CHiME-8 MMCSG eval dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|cccc}\n \\hline\n Latency range & \\ System & All & Wearer & Partner & Latency(s) \\\\ \\hline\n \\multirow{3}{*}{0s$\\ \\sim\\ $0.15s} & baseline~ & 22.10 & 17.80 & 26.30 & 0.138 \\\\\n & NPU-TEA~ & 16.80 & 12.20 & 21.30 & 0.142 \\\\\n & \\textbf{Ours} & \\textbf{15.20} & \\textbf{10.50} & \\textbf{19.90} & \\textbf{0.127} \\\\ \\hline\n \\multirow{3}{*}{0.15s$\\ \\sim\\ $0.35s} & baseline~ & 18.90 & 15.00 & 22.90 & \\textbf{0.333} \\\\\n & NPU-TEA~ & 15.00 & 10.80 & 19.20 & 0.337 \\\\\n & \\textbf{Ours} & \\textbf{13.65} & \\textbf{9.30} & \\textbf{18.00} & 0.343 \\\\ \\hline\n \\multirow{3}{*}{0.35s$\\ \\sim\\ $1s} & baseline~ & 17.90 & 14.10 & 21.70 & 0.616 \\\\\n & FOSAFER~ & 14.00 & 9.60 & 18.40 & 0.686 \\\\\n & \\textbf{Ours} & \\textbf{13.05} & \\textbf{8.90} & \\textbf{17.20} & \\textbf{0.587} \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Exploring Efficient Directional and Distance Cues for Regional Speech Separation", "authors": ["Yiheng Jiang", "Haoxu Wang", "Yafeng Chen", "Gang Qiao", "Biao Tian"], "url": "https://arxiv.org/abs/2508.07563v1", "attribution": "\"Exploring Efficient Directional and Distance Cues for Regional Speech Separation\" by Yiheng Jiang, Haoxu Wang, Yafeng Chen, Gang Qiao, and Biao Tian, arXiv:2508.07563v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.16078v5_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|}\n\t\t\\hline \n\t\t\\textbf{Corruption Types} & \\textbf{\\# Sp} & \\textbf{\\# Ut} & \\textbf{\\# WI} & \\textbf{Vo}\\\\ \\hline\n\t\t\n\t\t\\textbf{\\textsc{RandomFrame and}} & 4,004 & 31,982 & 356,940 & 17,545\\\\\n\t\t\\textbf{\\textsc{Extreme Sparsity}} & & & &\\\\\n\t\t\\hline \n\t\t\\textbf{\\textsc{Visemic}} & 2,883 & 6,152 & 338,207 & 16,663\\\\ \\hline\n\t\t\n\t\\textbf{\\textsc{InterWord}} & 3,008 & 10,421 & 141,850 & 12,621 \\\\ \\hline\n\t\\textbf{\\textsc{IntraWord}} & 2,756 & 10,360 & 141,296 & 11,824\\\\ \\hline\n\t\\textbf{\\textsc{Prefix and Suffix}} & 4,004 & 31,982 & 356,940 & 17,545\\\\ \\hline \n\t\\textbf{\\textsc{POS Tagged Corruptions}} & 4,004 & 31,982 & 356,940 & 17,545\\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "LIFI: Towards Linguistically Informed Frame Interpolation", "authors": ["Aradhya Neeraj Mathur", "Devansh Batra", "Yaman Kumar", "Rajiv Ratn Shah", "Roger Zimmermann"], "url": "https://arxiv.org/abs/2010.16078v5", "attribution": "\"LIFI: Towards Linguistically Informed Frame Interpolation\" by Aradhya Neeraj Mathur, Devansh Batra, Yaman Kumar, Rajiv Ratn Shah, and Roger Zimmermann, arXiv:2010.16078v5, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10453v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Leaderboard for the validation phase and the final test phase at deadline.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|cc}\n \\toprule\n Track 1 & Validation phase & Track 1 & Test phase\\\\%\\multicolumn{4}{c}{Track 1}\\\\\n \\midrule\n ZLI & 8.404499999999793 & ZLI & 11.320000000002786 \\\\\n Convexers & 7.8099999999988885 & Margaridinhas & 11.320000000002786\\\\\n Margaridinhas & 7.533834999999982 & Convexers & 11.320000000002786\n \\\\\n Topline & 3.540000000000663 & Topline & 4.300000000000301\\\\\n VK & 0.5300000000000102\\\\\n \\midrule\n Track 2 & Validation phase & Track 2 & Test phase\\\\\n \\midrule\n Ratel & 10.69104 & RISEup & 10.77341\\\\\n ML for TSP & 9.82955 & Ratel & 10.58859\\\\\n RISEup & 9.16567 & ML for TSP & 10.39341\\\\\n VK & -6.69772 & & \\\\\n UniBw & -13.14861 & & \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems", "authors": ["Laurens Bliek", "Paulo da Costa", "Reza Refaei Afshar", "Yingqian Zhang", "Tom Catshoek", "Daniël Vos", "Sicco Verwer", "Fynn Schmitt-Ulms", "André Hottung", "Tapan Shah", "Meinolf Sellmann", "Kevin Tierney", "Carl Perreault-Lafleur", "Caroline Leboeuf", "Federico Bobbio", "Justine Pepin", "Warley Almeida Silva", "Ricardo Gama", "Hugo L. Fernandes", "Martin Zaefferer", "Manuel López-Ibáñez", "Ekhine Irurozki"], "url": "https://arxiv.org/abs/2201.10453v1", "attribution": "\"The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems\" by Laurens Bliek, Paulo da Costa, Reza Refaei Afshar, Yingqian Zhang, Tom Catshoek, Daniël Vos, Sicco Verwer, Fynn Schmitt-Ulms, André Hottung, Tapan Shah, Meinolf Sellmann, Kevin Tierney, Carl Perreault-Lafleur, Caroline Leboeuf, Federico Bobbio, Justine Pepin, Warley Almeida Silva, Ricardo Gama, Hugo L. Fernandes, Martin Zaefferer, Manuel López-Ibáñez, and Ekhine Irurozki, arXiv:2201.10453v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.20923v3_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t Coeffs & $A_0= 1.58343163$ & $B= 2.40214426$ & $\\sim$& $\\sim$\\\\ \\hline\n $A_1^0= 3.74934079e-1$\t& $A_1^1= -2.31572443e-1$ & $A_1^2= 5.07754830e-2$ & $A_1^3= -4.43298655e-3$ & $A_1^4= 1.20824431e-4$ \\\\ \\hline\n\t$A_2^0= 1.98238162e-1$ & $A_2^1=-2.57798417e-1$ & $A_2^2= 6.90393237e-2 $& $A_2^3= -6.33530647e-3 $& $A_2^4= 1.75569086e-4 $\\\\ \\hline\n\t$A_3^0= -9.45481987e-2$ & $A_3^1= 2.58285085e-2 $& $A_3^2= 8.41948922e-2$& $A_3^3= -2.35882788e-3$& $A_3^4= 1.78556670$\\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Coefficients that fit the universal $\\eta-\\chi-\\xi$ surface for $n=1$ BSs in the extreme region, corresponding to the set of data and fitting surface shown in .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Spinning boson stars in nonlinear sigma models and Universal Relations", "authors": ["Christoph Adam", "Jorge Castelo Mourelle", "Alberto García Martín-Caro", "Andrzej Wereszczynski"], "url": "https://arxiv.org/abs/2502.20923v3", "attribution": "\"Spinning boson stars in nonlinear sigma models and Universal Relations\" by Christoph Adam, Jorge Castelo Mourelle, Alberto García Martín-Caro, and Andrzej Wereszczynski, arXiv:2502.20923v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07434v5_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c} \n\t\t\\toprule\n\t\tAttention Base & Eval OS & Channelized & mIOU (\\%) \\\\\n\t\t\\midrule[0.5pt]\n\t\t\\midrule[0.5pt]\n\t\t\\multirow{2}{*}{Axial Attention }& 16 & & 50.27 \\\\\n\t\t& 16 \t & \\checkmark & 51.06 \\\\\n\t\t\\midrule\n\t\t\\multirow{2}{*}{ Self Attention} & 16 \t & & 50.42 \\\\\n\t\t& 16 \t & \\checkmark & 51.09 \\\\\n\t\t\\bottomrule[0.5pt]\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Ablation study of applying our Channelized Attention on self-attention with ResNet-101. \\textbf{Eval OS}: Output strides~ during evaluation.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation", "authors": ["Ye Huang", "Di Kang", "Wenjing Jia", "Xiangjian He", "Liu Liu"], "url": "https://arxiv.org/abs/2101.07434v5", "attribution": "\"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation\" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2401.00866v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c|c|c}%\n$e$ & Eigenvalues of $f_{e}(G)$ & \\# positive eigenvalues & \\# negative\neigenvalues\\\\\\hline\n$00$ & $(1,1,1)$ & 3 & 0\\\\\n$01$ & $(-4,-4,8)$ & 1 & 2\\\\\n$10$ & $(4,4,16)$ & 3 & 0\\\\\n$11$ & $(-16,-16,128)$ & 1 & 2\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Conditions for eigenvalue configurations of two real symmetric matrices", "authors": ["Hoon Hong", "Daniel Profili", "J. Rafael Sendra"], "url": "https://arxiv.org/abs/2401.00866v3", "attribution": "\"Conditions for eigenvalue configurations of two real symmetric matrices\" by Hoon Hong, Daniel Profili, and J. Rafael Sendra, arXiv:2401.00866v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.02873v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c}\n \\hline\n $N$ & $M$ & Upper bound & Upper bound & Upper bound for & $\\kappa(I+W)$ \\\\\n & & of $\\|X^{-1}\\|_{2}$ & of $\\|Y\\|_2$ & $\\kappa(I+W)$& \\\\\n & & & & from Theorem &\\\\\n \\hline \\hline \n 25 & 800 & $1935.87\\times10^{-6}$ & 9215.83 & 18.84 & 15.93\\\\\n 50 & 3200 & $4840.86\\times 10^{-7}$ & 38415.83 & 19.60 & 17.42\\\\\n 100 & 12800 & $1210.29\\times 10^{-7}$ & 156815.83 & 19.98 & 18.30\\\\\n 200 & 51200 & $3025.75\\times 10^{-8}$ & 633615.83 & 20.17 &18.84\\\\\n 400 & 204800 & $7564.41\\times 10^{-9}$ & 2547215.83 & 20.27 & 19.17\\\\\n 800 & 819200 & $1891.10\\times10^{-9}$ & 10214415.83 & 20.32 & 19.39\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Upper bound for $\\kappa(I+W)$, i.e. condition number of $(I+W)$, with parameters $a(z)=z+1$, $b(z)=(z+1)^2$, $T=1,$ $z_l=0$, and $z_r=1$. The condition numbers given in the last column are computed using MATLAB.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A novel difference equation approach for the stability and robustness of compact schemes for variable coefficient PDEs", "authors": ["Anindya Goswami", "Kuldip Singh Patel", "Pradeep Kumar Sahu"], "url": "https://arxiv.org/abs/2209.02873v3", "attribution": "\"A novel difference equation approach for the stability and robustness of compact schemes for variable coefficient PDEs\" by Anindya Goswami, Kuldip Singh Patel, and Pradeep Kumar Sahu, arXiv:2209.02873v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.01169v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n & Size & Modality \\\\\n \\hline\n LAION-Aesthetics-3M & 2M* & T,I \\\\ \n CC12M & 12M & T,I\\\\ \n COYO-700M(Subset) & 5M & T,I \\\\ \n LAION-COCO & 7M & T,I \\\\ \n SoundNet & 2M & T,A,I$\\dag$ \\\\ \n VGGSound & 0.2M & T,A,I$\\dag$ \\\\ \n T2I-2M & 2M & T,I \\\\ \n AudioSet & 2M & T,A \\\\ \n AudioCaps & 46K & T,A \\\\ \n WavCaps & 0.4M & T,A \\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{List of all datasets used in training. }*Some image URLs are no longer accessible. $\\dag$ We generate synthetic captions using BLIP. }\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows", "authors": ["Shufan Li", "Konstantinos Kallidromitis", "Akash Gokul", "Zichun Liao", "Yusuke Kato", "Kazuki Kozuka", "Aditya Grover"], "url": "https://arxiv.org/abs/2412.01169v2", "attribution": "\"OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows\" by Shufan Li, Konstantinos Kallidromitis, Akash Gokul, Zichun Liao, Yusuke Kato, Kazuki Kozuka, and Aditya Grover, arXiv:2412.01169v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.04686v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classification accuracy ($\\%$) of different kernels of RF-TCA (with $N = 500$) on Office-31 dataset with DECAF6 features.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n \\toprule\n Kernel & A$\\rightarrow$D & A$\\rightarrow$W & D$\\rightarrow$A & D$\\rightarrow$W & W$\\rightarrow$A & W$\\rightarrow$D & Avg \\\\ \\midrule\n Laplace & \\textbf{59.64} & 50.44 & \\textbf{35.78} & 87.80 & \\textbf{34.26} & \\textbf{93.98} & \\textbf{60.32} \\\\\n Gauss & 55.62 & \\textbf{51.06} & 29.42 & \\textbf{87.92} & 32.58 & 93.57 & 58.36 \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Robust and Communication-Efficient Federated Domain Adaptation via Random Features", "authors": ["Zhanbo Feng", "Yuanjie Wang", "Jie Li", "Fan Yang", "Jiong Lou", "Tiebin Mi", "Robert. C. Qiu", "Zhenyu Liao"], "url": "https://arxiv.org/abs/2311.04686v2", "attribution": "\"Robust and Communication-Efficient Federated Domain Adaptation via Random Features\" by Zhanbo Feng, Yuanjie Wang, Jie Li, Fan Yang, Jiong Lou, Tiebin Mi, Robert. C. Qiu, and Zhenyu Liao, arXiv:2311.04686v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16188v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n \\textbf{Model} & \\textbf{UA (\\%)} & \\textbf{WA (\\%)} & \\textbf{F1 (\\%)} \\\\\n \\midrule\n \\textit{Emotion Recognition} \\\\\n \\quad \\textsc{Speech-only} & 62.8 & 61.3 & 61.3 \\\\\n \\quad \\textsc{AVLM} & \\textbf{67.2} & \\textbf{67.1} & \\textbf{66.2} \\\\\n \\midrule\n \\textit{Speech Generation} \\\\\n \\quad \\textsc{Speech-only} & 43.79 & 38.85 & 38.39 \\\\\n \\quad \\textsc{AVLM} & \\textbf{46.03} & \\textbf{42.99} & \\textbf{42.49} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance comparison of AVLM and Speech-only model in terms of unweighted accuracy (UA), weighted accuracy (WA), and macro F1-score (F1).}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Seeing is Believing: Emotion-Aware Audio-Visual Language Modeling for Expressive Speech Generation", "authors": ["Weiting Tan", "Jiachen Lian", "Hirofumi Inaguma", "Paden Tomasello", "Philipp Koehn", "Xutai Ma"], "url": "https://arxiv.org/abs/2508.16188v2", "attribution": "\"Seeing is Believing: Emotion-Aware Audio-Visual Language Modeling for Expressive Speech Generation\" by Weiting Tan, Jiachen Lian, Hirofumi Inaguma, Paden Tomasello, Philipp Koehn, and Xutai Ma, arXiv:2508.16188v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08301v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{List of semantic attributes used in method}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccl}\n \\toprule\n Category&Attribute: Class\\\\\n \\midrule\n Top & high neck: ruffle semi-high, turtle,...\\\\\n&collar: rib collar, puritan collar,...\\\\\n&lapel: notched, shawl, collarless,...\\\\\n&neckline: V, square, round,...\\\\\n&sleeves length: sleeveless, cap, short,...\\\\\n&body length: high waist, long, regular,...\\\\\n \\midrule\n Bottom & skirt length: short, knee, midi, ankle,...\\\\\n&trousers length: short, mid, 3/4, cropped,...\\\\\n \\midrule\n Shoes & heel height: flat, 1 in-7/4 in, under 1 in,...\\\\\n&boots height: ankle, knee-high, mid-calf,...\\\\\n&closure: lace-up, slip-on, zipper,...\\\\\n&toe style: round, pointed, peep, open,...\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Aesthetics, Personalization and Recommendation: A survey on Deep Learning in Fashion", "authors": ["Wei Gong", "Laila Khalid"], "url": "https://arxiv.org/abs/2101.08301v1", "attribution": "\"Aesthetics, Personalization and Recommendation: A survey on Deep Learning in Fashion\" by Wei Gong and Laila Khalid, arXiv:2101.08301v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.05946v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\hline\n & \\textbf{Designee Tracts} & \\textbf{Finalist Tracts} & \\textbf{Nearby Tracts} \\\\\n \\hline\n Neighborhood Status Index & 0.16 (0.83) & -0.20 (0.94) & 1.01 (1.04) \\\\\n Med HH Income (\\$1000) & 28.8 (7.65) & 27.0 (9.73) & 41.0 (17.3) \\\\\n Poverty Rate (\\%) & 0.34 (0.12) & 0.37 (0.13) & 0.24 (0.14) \\\\\n Emp-Pop Ratio (\\%) & 0.40 (0.09) & 0.35 (0.08) & 0.46 (0.11) \\\\\n HS Dropout Rate (\\%) & 0.11 (0.13) & 0.13 (0.14) & 0.09 (0.13) \\\\\n \\% Single Parent HH & 0.35 (0.13) & 0.36 (0.11) & 0.25 (0.13) \\\\\n \\% Nonwhite & 0.85 (0.20) & 0.85 (0.20) & 0.67 (0.31) \\\\\n \\% College Degree & 0.13 (0.11) & 0.10 (0.08) & 0.25 (0.21) \\\\\n Unique Tracts & 326 & 409 & 514 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Summary Statistics by Tract Type}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Place-Based Policies for Neighborhood Improvement: Evidence from Promise Zones", "authors": ["Adamson Bryant"], "url": "https://arxiv.org/abs/2503.05946v1", "attribution": "\"Place-Based Policies for Neighborhood Improvement: Evidence from Promise Zones\" by Adamson Bryant, arXiv:2503.05946v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11211v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|} \\hline \n\\textbf{Operation}\t\t&\t\\textbf{Current Draw} \t\\\\ \\hline\nCPU and Idle Radio\t\t&\t8 mA \t\t\t\t\\\\ \nPacket Reception\t\t\t&\t7.03 mA \t\t\t\\\\\nPacket Transmission\t\t&\t10.4 mA \t\t\t\\\\\nEEPROM Read\t\t\t\t&\t6.2 mA \t\t\t\\\\\nEEPROM Write\t\t\t&\t18.4 mA \t\t\t\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Energy required by common operations}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Harvest: A Reliable and Energy Efficient Bulk Data Collection Service for Large Scale Wireless Sensor Networks", "authors": ["Vinayak Naik", "Anish Arora"], "url": "https://arxiv.org/abs/2101.11211v1", "attribution": "\"Harvest: A Reliable and Energy Efficient Bulk Data Collection Service for Large Scale Wireless Sensor Networks\" by Vinayak Naik and Anish Arora, arXiv:2101.11211v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.05089v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c||c|c|c|c|c|c|}\n \\hline\n $p^c_{1,1}$ & 25 & $p^c_{2,1}$ & 20 & $p^c_{3,1}$ & 25 & $q^c_{1,1}$ & 3 & $q^c_{2,1}$ & 5 & $q^c_{3,1}$ & 7 \\\\ \\hline\n $p^c_{1,2}$ & 21 & $p^c_{2,2}$ & 17 & $p^c_{3,2}$ & 18 & $q^c_{1,2}$ & 4 & $q^c_{2,2}$ & 9 & $q^c_{3,2}$ & 4 \\\\ \\hline\n $p^c_{1,3}$ & 13 & $p^c_{2,3}$ & 11 & $p^c_{3,3}$ & 14 & $q^c_{1,3}$ & 5 & $q^c_{2,3}$ & 10 & $q^c_{3,3}$ & 4 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Parameters of the inverse demand functions considered in the example.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Solidarity in natural gas storage: A potential allocation mechanism of stored quantities among several players during times of crisis", "authors": ["Dávid Csercsik", "Anne Neumann"], "url": "https://arxiv.org/abs/2209.05089v2", "attribution": "\"Solidarity in natural gas storage: A potential allocation mechanism of stored quantities among several players during times of crisis\" by Dávid Csercsik and Anne Neumann, arXiv:2209.05089v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.19911v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Baseline Spatial Concentration Patterns by Industry (2019)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n\\toprule\n\\multirow{2}{*}{Industry} & Location & Gini & Herfindahl- & Primary & \\multicolumn{2}{c}{Employment Share} & AI Adoption \\\\\n& Quotient & Coefficient & Hirschman Index & Ward & Central & Peripheral & Rate (\\%) \\\\\n\\midrule\nInformation \\& Communications & 3.42 & 0.68 & 0.31 & Shibuya & 67.4 & 8.2 & 34.7 \\\\\nFinance \\& Insurance & 2.87 & 0.72 & 0.28 & Chiyoda & 71.8 & 6.1 & 28.9 \\\\\nProfessional Services & 2.34 & 0.61 & 0.22 & Minato & 58.3 & 12.4 & 22.1 \\\\\nManufacturing & 0.78 & 0.45 & 0.15 & Ota & 23.7 & 31.2 & 8.3 \\\\\nRetail Trade & 1.12 & 0.32 & 0.08 & Shinjuku & 28.1 & 25.6 & 5.7 \\\\\nHealthcare & 0.95 & 0.28 & 0.06 & Setagaya & 22.4 & 28.9 & 7.2 \\\\\n\\midrule\n\\textbf{Average} & \\textbf{1.91} & \\textbf{0.51} & \\textbf{0.18} & -- & \\textbf{45.3} & \\textbf{18.7} & \\textbf{17.8} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "AI-Driven Spatial Distribution Dynamics: A Comprehensive Theoretical and Empirical Framework for Analyzing Productivity Agglomeration Effects in Japan's Aging Society", "authors": ["Tatsuru Kikuchi"], "url": "https://arxiv.org/abs/2507.19911v2", "attribution": "\"AI-Driven Spatial Distribution Dynamics: A Comprehensive Theoretical and Empirical Framework for Analyzing Productivity Agglomeration Effects in Japan's Aging Society\" by Tatsuru Kikuchi, arXiv:2507.19911v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.03247v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\hline\n & \\textbf{Duartion(hrs)} & \\textbf{\\#Speakers} \\\\ \\hline \\hline\n\\textbf{NPTEL-tr} & 358.0 & 273 \\\\ %\\hline\n\\textbf{NPTEL-dt} & 9.9 & 273 \\\\ %\\hline\n\\textbf{NPTEL-et} & 9.8 & 273 \\\\ %\\hline\n\\textbf{NPTEL-Unseen\\_1} & 2.0 & 50 \\\\ %\\hline\n\\textbf{NPTEL-Unseen\\_2} & 2.0 & 50 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{NPTEL data set statistics}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Investigation of Speaker-adaptation methods in Transformer based ASR", "authors": ["Vishwas M. Shetty", "Metilda Sagaya Mary N J", "S. Umesh"], "url": "https://arxiv.org/abs/2008.03247v2", "attribution": "\"Investigation of Speaker-adaptation methods in Transformer based ASR\" by Vishwas M. Shetty, Metilda Sagaya Mary N J, and S. Umesh, arXiv:2008.03247v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2103.05448v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n & \\multicolumn{3}{c|}{Execution time (seconds)} \\\\ \\hline\nStack name & Median filter (once) & Median filter (twice) & Median filter (three-times) \\\\ \\hline\nG\\_stack & 0.4111 & 0.6983 & 0.9364 \\\\ \\hline\nS\\_stack & 0.4107 & 0.6931 & 0.9160 \\\\ \\hline\n\\end{tabular}\n\\caption{Execution time for the median filtering on the $G$ and $S$ 3D-volume stack using the MATLAB tool . }\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)", "authors": ["Varun Mannam", "Yide Zhang", "Xiaotong Yuan", "Takashi Hato", "Pierre C. Dagher", "Evan L. Nichols", "Cody J. Smith", "Kenneth W. Dunn", "Scott Howard"], "url": "https://arxiv.org/abs/2103.05448v1", "attribution": "\"Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)\" by Varun Mannam, Yide Zhang, Xiaotong Yuan, Takashi Hato, Pierre C. Dagher, Evan L. Nichols, Cody J. Smith, Kenneth W. Dunn, and Scott Howard, arXiv:2103.05448v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.13337v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of Stacked vs.\\ Phased Training Strategies on the MRC Dataset Using the LLaMA 3B Model. Cells highlighted in green indicate better scores, and blue indicates higher sample efficiency (fewer samples used).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Benchmark}} & \\multicolumn{3}{c}{\\textbf{Score}} & \\multicolumn{2}{c}{\\textbf{Samples}} \\\\ \\cmidrule(lr){2-4} \\cmidrule(lr){5-6}\n & \\textbf{LLaMA Base} & \\textbf{Stacked} & \\textbf{Phased} & \\textbf{Stacked} & \\textbf{Phased} \\\\ \\midrule \n\\textbf{Leaderboard (MATH Lvl 5)} & 0.02 & \\cellcolor{green!30}0.04 & 0.03 & \\cellcolor{blue!20}9,980,259 & 18,455,850 \\\\\n\\textbf{Leaderboard (MuSR)} & 0.05 & \\cellcolor{green!30}0.08 & \\cellcolor{green!30}0.08 & \\cellcolor{blue!20}16,966,128 & 18,206,922 \\\\\n\\textbf{ARC} & \\cellcolor{green!30}0.78 & 0.75 & 0.71 & \\cellcolor{blue!20}2,745,290 & 14,964,367 \\\\\n\\textbf{GSM8K} & 0.27 & \\cellcolor{green!30}0.69 & 0.67 & \\cellcolor{blue!20}12,225,143 & 14,964,367 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs", "authors": ["Aldo Pareja", "Nikhil Shivakumar Nayak", "Hao Wang", "Krishnateja Killamsetty", "Shivchander Sudalairaj", "Wenlong Zhao", "Seungwook Han", "Abhishek Bhandwaldar", "Guangxuan Xu", "Kai Xu", "Ligong Han", "Luke Inglis", "Akash Srivastava"], "url": "https://arxiv.org/abs/2412.13337v1", "attribution": "\"Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs\" by Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang, Krishnateja Killamsetty, Shivchander Sudalairaj, Wenlong Zhao, Seungwook Han, Abhishek Bhandwaldar, Guangxuan Xu, Kai Xu, Ligong Han, Luke Inglis, and Akash Srivastava, arXiv:2412.13337v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.00921v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overhead from byte-based AC termination.}\n\\begin{tabular}{|c|c|c|c|c|} \\hline\n\\bf Bidirectional &\\bf Reversed &\\bf Share &\\bf Avrg. extra bits \\\\\n\\bf byte packing &\\bf bits &\\bf ratio &\\bf per bitstream $\\bar{T}$ \\\\ \\hline \\hline\n No & --- & --- & 4.56 \\\\ \\hline\n Yes & No & 45\\% & 2.77 \\\\ \\hline\n Yes & Yes & 69\\% & 1.78 \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Bitstream Organization for Parallel Entropy Coding on Neural Network-based Video Codecs", "authors": ["Amir Said", "Hoang Le", "Farzad Farhadzadeh"], "url": "https://arxiv.org/abs/2312.00921v1", "attribution": "\"Bitstream Organization for Parallel Entropy Coding on Neural Network-based Video Codecs\" by Amir Said, Hoang Le, and Farzad Farhadzadeh, arXiv:2312.00921v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2310.04907v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary Statistics of Pooled Distributions (\\%/day).}\n\\begin{tabular}{cccccccc}\n \\hline \\\\\n & Min & Mean & Median & StDev & Skew & Kurt & Max\\\\\n \\hline \\hline \\\\\n Cryptocurrencies & -86.33 & 0.04 & -0.09 & 8.56 & 0.58 & 13.27 & 88.87\\\\\n S\\&P 500 Companies & -17.99 & 0.05 & 0.09 & 2.11 & -0.14 & 8.41 & 17.98\\\\ \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "An Information Theory Approach to the Stock and Cryptocurrency Market: A Statistical Equilibrium Perspective", "authors": ["Emanuele Citera", "Francesco De Pretis"], "url": "https://arxiv.org/abs/2310.04907v1", "attribution": "\"An Information Theory Approach to the Stock and Cryptocurrency Market: A Statistical Equilibrium Perspective\" by Emanuele Citera and Francesco De Pretis, arXiv:2310.04907v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09698v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Effect of Top-$k$ Size (in Eqn. () on MSCOCO Image Captioning Dataset. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crrrrrr}\n\t\t\t\\toprule\n\t\t\ttop-$k$ & B1 & B4 & M & R & S & C \\\\\n\t\t\t\\midrule\n\t\t\t\\midrule\n\t\t\t1 & 80.1 &\t\\bf37.4 &\t28.0 &\t57.9 &\t21.7 &\t123.7 \\\\\n\t\t\t2 & \\bf80.3 &\t 37.3 &\t\\bf28.1 \t& \\bf58.0 \t&\\bf21.8 &\t\\bf124.0 \\\\\n\t\t\t5 & 80.1 &\t37.3 &\t28.0 &\t58.0 &\t21.7 &\t123.7 \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Fast Sequence Generation with Multi-Agent Reinforcement Learning", "authors": ["Longteng Guo", "Jing Liu", "Xinxin Zhu", "Hanqing Lu"], "url": "https://arxiv.org/abs/2101.09698v1", "attribution": "\"Fast Sequence Generation with Multi-Agent Reinforcement Learning\" by Longteng Guo, Jing Liu, Xinxin Zhu, and Hanqing Lu, arXiv:2101.09698v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08580v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of literal-entity alignment methods}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc|ccc}\n \\toprule\n \\multirow{2}{*}{Methods} & \\multicolumn{3}{c|}{Inside KG} & \\multicolumn{3}{c}{Outside KG} \\\\\n \\cmidrule{2-4} \\cmidrule{5-7}\n & P$\\,\\uparrow$ & R$\\,\\uparrow$ & F1$\\,\\uparrow$ & P$\\,\\uparrow$ & R$\\,\\uparrow$ & F1$\\,\\uparrow$ \\\\\n \\midrule\n OWE & 0.358 & 0.428 & 0.390 & 0.314 & 0.384 & 0.346 \\\\\n ConMask & 0.376 & 0.457 & 0.413 & 0.373 & 0.367 & 0.370 \\\\\n \\midrule\n Knowledge Vault & 0.386 & 0.471 & 0.424 & 0.381 & 0.369 & 0.375 \\\\\n OKELE & 0.436 & 0.491 & 0.462 & 0.434 & 0.452 & 0.443 \\\\\n \\midrule\n TKGC & \\textbf{0.537} & \\textbf{0.492} & \\textbf{0.514} & \\textbf{0.461} & \\textbf{0.483} & \\textbf{0.471} \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data", "authors": ["Jiacheng Huang", "Yao Zhao", "Wei Hu", "Zhen Ning", "Qijin Chen", "Xiaoxia Qiu", "Chengfu Huo", "Weijun Ren"], "url": "https://arxiv.org/abs/2201.08580v1", "attribution": "\"Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data\" by Jiacheng Huang, Yao Zhao, Wei Hu, Zhen Ning, Qijin Chen, Xiaoxia Qiu, Chengfu Huo, and Weijun Ren, arXiv:2201.08580v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.05428v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|} \n \\hline\n& AUROC & AUPR & DSC\\\\ \n \\hline\n VAE & 0.668 & 0.704 & 0.110 $\\pm$ 0.021\\\\ \n \\hline\n Context-encoding VAE & 0.766 & 0.640 & \\textbf{0.112 $\\pm$ 0.021 } \\\\\n \\hline\n \\textbf{VAE with Pretraining (ours) }& 0.673 & 0.772 & \\textbf{0.112 $\\pm$ 0.021 }\\\\\n \\hline\n \\textbf{Multi-task Framework (ours)} & \\textbf{0.822} & \\textbf{0.868} & 0.086 $\\pm$ 0.024\\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison with state-of-the-art anomaly detection methods.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Self-Supervised Out-of-Distribution Detection in Brain CT Scans", "authors": ["Abinav Ravi Venkatakrishnan", "Seong Tae Kim", "Rami Eisawy", "Franz Pfister", "Nassir Navab"], "url": "https://arxiv.org/abs/2011.05428v1", "attribution": "\"Self-Supervised Out-of-Distribution Detection in Brain CT Scans\" by Abinav Ravi Venkatakrishnan, Seong Tae Kim, Rami Eisawy, Franz Pfister, and Nassir Navab, arXiv:2011.05428v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.11860v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n& $q_0=0.1$ & $q_0=0.01$ & $q_0=0.001$ \\\\ \\cline{2-4} \n{Speckled Source} &8.710&8.742&9.055\\\\\\hline\\hline\n{Box Car Filter} &13.226&16.321&16.976\\\\\\hline\n{Frost Filter } &12.923&13.900&14.226\\\\\\hline\n{Total Variation } &9.339&10.895&11.483\\\\\\hline\n{Lee Filter / Enhanced } &10.505 / 13.865&18.402 / 19.703&22.303 / 22.577\\\\\\hline\n{Kuan Filter } / {Enhanced } &11.618 / 14.041&19.650 / 20.375& 23.017 / 23.244\\\\\\hline\n{BD-QMAP$_b$ (b=2 / b=3)} &14.786 / \\textbf{15.478}& 17.909 / 21.613& 19.530 / 24.363 \\\\\\hline\n{BD-QMAP (b=2 / b=3)} &14.696 / 15.072&20.961 / \\textbf{22.518}&27.052 / \\textbf{30.831} \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Bayesian Despeckling of Structured Sources", "authors": ["Ali Zafari", "Shirin Jalali"], "url": "https://arxiv.org/abs/2501.11860v2", "attribution": "\"Bayesian Despeckling of Structured Sources\" by Ali Zafari and Shirin Jalali, arXiv:2501.11860v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.15007v2_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll}\nmeta-estimator & outcome\\_model & mean\\_pehe & ate\\_rmse & ate\\_abs\\_bias & ate\\_std\\_error \\\\\nstratified\\_standardization & kNN & 3065.36 & 1683.85 & 406.78 & 1633.97 \\\\\nstratified\\_standardization & Standardized\\_SVM\\_rbf & 4312.85 & 2703.05 & 2438.89 & 1165.47 \\\\\nstratified\\_standardization & SVM\\_rbf & 4416.33 & 2674.25 & 2320.72 & 1328.86 \\\\\nstratified\\_standardization & KernelRidge & 4584.82 & 3965.28 & 258.97 & 3956.81 \\\\\nstratified\\_standardization & Lasso & 4614.18 & 4123.09 & 2797.95 & 3028.43 \\\\\nstratified\\_standardization & ElasticNet & 4767.89 & 4240.31 & 2716.97 & 3255.51 \\\\\nstratified\\_standardization & Ridge & 4825.75 & 4287.98 & 2706.11 & 3326.22 \\\\\nstratified\\_standardization & Standardized\\_SVM\\_sigmoid & 5333.67 & 2787.82 & 2398.72 & 1420.60 \\\\\nstratified\\_standardization & LinearRegression & 5379.93 & 3694.49 & 2855.89 & 2343.74 \\\\\nstandardization & LinearRegression\\_degree2 & 5961.98 & 3814.48 & 3122.71 & 2190.65 \\\\\nstandardization & LinearRegression\\_interact & 6094.42 & 3877.79 & 3157.29 & 2251.40 \\\\\nstandardization & Standardized\\_SVM\\_rbf & 6375.94 & 2298.04 & 2098.06 & 937.60 \\\\\nstratified\\_standardization & Standardized\\_LinearSVM & 8109.11 & 6060.79 & 5676.84 & 2122.89 \\\\\nstratified\\_standardization & DecisionTree & 8570.48 & 2894.01 & 1429.13 & 2516.52 \\\\\nstratified\\_standardization & LinearSVM & 8962.51 & 6703.79 & 2077.53 & 6373.75 \\\\\nstratified\\_standardization & SVM\\_sigmoid & 10744.12 & 1374.50 & 435.80 & 1303.58 \\\\\nstandardization & Standardized\\_SVM\\_sigmoid & 15968.21 & 10574.70 & 10567.02 & 402.89 \\\\\nstandardization & kNN & 18065.93 & 13125.92 & 13125.92 & 1.29 \\\\\nstandardization & DecisionTree & 18066.16 & 13124.53 & 13124.53 & 0.00 \\\\\nstandardization & ElasticNet & 18066.16 & 13124.53 & 13124.53 & 0.00 \\\\\nstandardization & Lasso & 18066.16 & 13124.53 & 13124.53 & 0.00 \\\\\nstandardization & LinearSVM & 18066.16 & 13124.53 & 13124.53 & 0.00 \\\\\nstandardization & SVM\\_rbf & 18066.16 & 13124.53 & 13124.53 & 0.00 \\\\\nstandardization & SVM\\_sigmoid & 18066.16 & 13124.53 & 13124.53 & 0.00 \\\\\nstandardization & Ridge & 18066.73 & 13125.30 & 13125.30 & 1.18 \\\\\nstandardization & KernelRidge & 18120.34 & 13198.56 & 13198.06 & 113.96 \\\\\nstandardization & LinearRegression & 18818.59 & 14189.19 & 14097.20 & 1613.08 \\\\\nstandardization & Standardized\\_LinearSVM & 19110.22 & 14556.98 & 14486.66 & 1429.18 \\\\\nstratified\\_standardization & LinearRegression\\_interact & 105866.18 & 53972.80 & 37749.80 & 38574.81 \\\\\nstratified\\_standardization & LinearRegression\\_degree2 & 201053.48 & 103618.34 & 93842.31 & 43936.11 \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "RealCause: Realistic Causal Inference Benchmarking", "authors": ["Brady Neal", "Chin-Wei Huang", "Sunand Raghupathi"], "url": "https://arxiv.org/abs/2011.15007v2", "attribution": "\"RealCause: Realistic Causal Inference Benchmarking\" by Brady Neal, Chin-Wei Huang, and Sunand Raghupathi, arXiv:2011.15007v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.18352v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study investigating the impact of the velocity loss and fine-tuning on studio-quality audio during heterogeneous KD.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n\\toprule\n& PBM accuracy $\\uparrow$& Jitter\\\\\n\\midrule\n$S_0$& 92.4& 0.0412\\\\\n\\midrule\n w/o velocity loss& 93.8&0.0536\\\\\n w/o fine-tuning& 86.2&0.0446\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Tiny is not small enough: High-quality, low-resource facial animation models through hybrid knowledge distillation", "authors": ["Zhen Han", "Mattias Teye", "Derek Yadgaroff", "Judith Bütepage"], "url": "https://arxiv.org/abs/2507.18352v1", "attribution": "\"Tiny is not small enough: High-quality, low-resource facial animation models through hybrid knowledge distillation\" by Zhen Han, Mattias Teye, Derek Yadgaroff, and Judith Bütepage, arXiv:2507.18352v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.07391v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Annotator agreement.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\n & Raw & Five Bins \\\\\n\\hline\nSpearman's $\\rho$ & 0.556 & 0.547\\\\\n\\hline\nWeighted Cohen's $\\kappa$ & 0.553 & 0.542 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Pardon the Interruption: An Analysis of Gender and Turn-Taking in U.S. Supreme Court Oral Arguments", "authors": ["Haley Lepp", "Gina-Anne Levow"], "url": "https://arxiv.org/abs/2009.07391v1", "attribution": "\"Pardon the Interruption: An Analysis of Gender and Turn-Taking in U.S. Supreme Court Oral Arguments\" by Haley Lepp and Gina-Anne Levow, arXiv:2009.07391v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2509.14198v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\textbf{Model} & \\textbf{N. Params} & \\textbf{Optimizer}& \\textbf{Time (ms/it)} & \\textbf{Rel. $L^2$ Error} \\\\\n \\midrule\n Baseline& 2011&SSBroyden & 26.1 & $1.67 \\times 10^{-7}$ \\\\\n $v RBA:\\Phi(r)=r^2$ &2011&SSBroyden & 28.2&$1.74 \\times 10^{-8}$ \\\\\n $v RBA:\\Phi(r)=e^r$ & 2011&SSBroyden & 27.6 & $\\mathbf{1.51 \\times 10^{-8}}$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance of the vRBA method for the Burgers equation. The vRBA strategies, using either a quadratic ($\\Phi(r)=r^2$) or exponential ($\\Phi(r)=e^r$) potential, are benchmarked against a baseline model with uniform sampling. All models are trained with the second-order SSBroyden optimizer. The results demonstrate that the vRBA methods improve the final relative $L^2$ error by an order of magnitude over the baseline, underscoring that an advanced adaptive sampling strategy is critical for achieving high accuracy.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning", "authors": ["Juan Diego Toscano", "Daniel T. Chen", "Vivek Oommen", "George Em Karniadakis"], "url": "https://arxiv.org/abs/2509.14198v1", "attribution": "\"A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning\" by Juan Diego Toscano, Daniel T. Chen, Vivek Oommen, and George Em Karniadakis, arXiv:2509.14198v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2311.16409v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Neural Network Description}\n\\begin{tabular}{|l|l|l|l|}\n\\hline\n\\textbf{Layers} & \\textbf{Layer type} & \\textbf{Size} & \\textbf{Activation function} \\\\ \\hline\nInput layer & - & (22) & - \\\\ \\hline\nHidden layer 1 & Fully connected & (24) & Leaky ReLU \\\\ \\hline\nHidden layer 2 & Fully connected & (16) & Leaky ReLU \\\\ \\hline\nOutput layer & - & (5) & - \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Deep Q-Learning based, Base-Station Connectivity-Aware, Decentralized Pheromone Mobility Model for Autonomous UAV Networks", "authors": ["Shreyas Devaraju", "Alexander Ihler", "Sunil Kumar"], "url": "https://arxiv.org/abs/2311.16409v1", "attribution": "\"A Deep Q-Learning based, Base-Station Connectivity-Aware, Decentralized Pheromone Mobility Model for Autonomous UAV Networks\" by Shreyas Devaraju, Alexander Ihler, and Sunil Kumar, arXiv:2311.16409v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.09598v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Coefficient on average variability of ancestral climatic conditions - Robustness by method of variable construction }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccc}\\hline\\hline\n \\multicolumn{6}{c}{Dependent variable: Self reported measure of attention to environment}\\\\\n \\hline\n \\textbf{Lifespan of generation:} & & \\underline{20 years} & \\underline{30 years} & \\underline{40 years} & \\underline{50 years} \\\\\n & & & & & \\\\\n \\multirow{4}{*}{Dev. from 10-90} & Avg. & -14.742*** & -13.926*** & -10.453*** & -5.884*** \\\\\n & & (4.739) & (2.433) & (2.067) & (1.770) \\\\\n & Avg. sq. & 316.651*** & 235.773*** & 143.257*** & 64.708*** \\\\\n & & (103.216) & (40.585) & (25.865) & (18.135) \\\\\n \\multirow{4}{*}{Dev. from 20-80} & Avg. & -9.877*** & -5.064*** & -5.618*** & -4.423*** \\\\\n & & (2.003) & (1.770) & (1.159) & (1.256) \\\\\n & Avg. sq. & 111.767*** & 44.420*** & 44.969*** & 31.565*** \\\\\n & & (21.346) & (16.391) & (8.496) & (8.888) \\\\\n \\multirow{4}{*}{Dev. from 30-70} & Avg. & -7.082*** & -2.055+ & -3.529*** & -3.402*** \\\\\n & & (1.602) & (1.399) & (1.095) & (1.132) \\\\\n & Avg. sq. & 57.857*** & 12.856 & 23.773*** & 19.033*** \\\\\n & & (12.367) & (9.672) & (6.999) & (6.150) \\\\\n \\multirow{4}{*}{Dev. (sq.) from 20-80} & Avg. & -9.064* & -13.880*** & -7.059** & -5.105** \\\\\n & & (4.686) & (4.641) & (2.750) & (2.144) \\\\\n & Avg. sq. & 1076.265*** & 1016.342*** & 400.162*** & 238.084*** \\\\\n & & (377.298) & (342.086) & (127.251) & (74.105) \\\\\n \\multirow{4}{*}{Standard Deviation} & Avg. & -4.131*** & -1.675* & -1.529* & -1.707** \\\\\n & & (0.993) & (0.993) & (0.922) & (0.799) \\\\\n & Avg. sq. & 21.710*** & 6.709+ & 6.903+ & 6.353** \\\\\n & & (5.015) & (4.347) & (4.316) & (3.039) \\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Ancestral origins of attention to environmental issues", "authors": ["César Barilla", "Palaash Bhargava"], "url": "https://arxiv.org/abs/2509.09598v1", "attribution": "\"Ancestral origins of attention to environmental issues\" by César Barilla and Palaash Bhargava, arXiv:2509.09598v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.10263v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{rotating}\n\\usepackage{multirow}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Confusion matrix, without normalization}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\cline{3-6} \n\\multirow{4}{*}{\\rotatebox{90}{True label}} & Trees & 108 & 37 & 55 & 0 \\\\\n & Grass & 42 & 131 & 27 & 0 \\\\\n & Impervious & 35 & 17 & 147 & 1 \\\\\n & Water & 1 & 0 & 0 & 199 \\\\ \\cline{3-6} \n\\multicolumn{2}{c}{\\multirow{2}{*}{}} & Trees\\hspace{1mm} & Grass\\hspace{1mm} & Impervious\\hspace{0mm} & Water\\hspace{1mm} \\\\\n\\multicolumn{2}{c}{} & \\multicolumn{4}{c}{Predicted label} \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees", "authors": ["Juan Carrillo", "Daniel Garijo", "Mark Crowley", "Rober Carrillo", "Yolanda Gil", "Katherine Borda"], "url": "https://arxiv.org/abs/2009.10263v1", "attribution": "\"Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees\" by Juan Carrillo, Daniel Garijo, Mark Crowley, Rober Carrillo, Yolanda Gil, and Katherine Borda, arXiv:2009.10263v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23340v1_tex_table19.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|}\n\\hline\n & \\multicolumn{2}{c|}{\\textbf{Bernoulli-Laplace level model}} & \\multicolumn{2}{c|}{\\textbf{Curie-Weiss model}} \\\\\n\\hline\n\\(m\\) & Subset $S_l$ & $D(P^{(W \\cup S_l)} \\| P^{(W)} \\otimes P^{(S_l)})$ & Subset $S_l$ & $D(P^{(W \\cup S_l)} \\| P^{(W)} \\otimes P^{(S_l)})$ \\\\\n\\hline\n1 & \\(\\{10\\}\\) & 0.14671 & \\(\\{4\\}\\) & 0.02751 \\\\\n2 & \\(\\{9,10\\}\\) & 0.26354 & \\(\\{4,10\\}\\) & 0.05651 \\\\\n3 & \\(\\{8,9,10\\}\\) & 0.37787 & \\(\\{4,5,10\\}\\) & 0.08919 \\\\\n4 & \\(\\{7,8,9,10\\}\\) & 0.49198 & \\(\\{4,5,9,10\\}\\) & 0.12616 \\\\\n5 & \\(\\{6,7,8,9,10\\}\\) & 0.61908 & \\(\\{4,5,6,9,10\\}\\) & 0.17028 \\\\\n6 & \\(\\{5,6,7,8,9,10\\}\\) & 0.79889 & \\(\\{4,5,6,8,9,10\\}\\) & 0.22527 \\\\\n7 & \\(\\{4,5,6,7,8,9,10\\}\\) & 1.06993 & \\(\\{4,5,6,7,8,9,10\\}\\) & 0.30491 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Performance evaluation of the batch greedy algorithm.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Information-theoretic subset selection of multivariate Markov chains via submodular optimization", "authors": ["Zheyuan Lai", "Michael C. H. Choi"], "url": "https://arxiv.org/abs/2503.23340v1", "attribution": "\"Information-theoretic subset selection of multivariate Markov chains via submodular optimization\" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18051v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cccc|}\n \\hline\n $\\Delta$& obj & LB & UB & gap \\\\\n \\hline\n0.05&-240.11&-250.27&-234.51&6.56\\%\\\\\n\\hline\n0.1&-254.90&-267.46&-252.10&6.02\\%\\\\\n\\hline\n0.2&-269.14&-279.31&-267.25&4.48\\%\\\\\n\\hline\n0.5&-307.21&-308.34&-304.10&1.38\\%\\\\\n\\hline\n0.8&-335.96&-337.72&-334.56&0.94\\%\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Objective value for the proposed model and 95\\% bounds with different sizes of the ambiguity set.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications", "authors": ["Jiaqi Lei", "Akhil Singla", "Sanjay Mehrotra"], "url": "https://arxiv.org/abs/2501.18051v2", "attribution": "\"A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications\" by Jiaqi Lei, Akhil Singla, and Sanjay Mehrotra, arXiv:2501.18051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.08718v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Top 5 Arbitrage Potential Cases (Gas Fees Considered)}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{Timestamp} & \\textbf{Price (Centralized)} & \\textbf{Price (Defi)} & \\textbf{Gas Fee (USD)} & \\textbf{Arbitrage Potential (USD)} \\\\\n\\hline\n19:36:08 & 3503.95 & 3538.99 & 34.92 & 0.11 \\\\\n19:36:05 & 3505.42 & 3538.99 & 34.92 & -1.36 \\\\\n19:36:10 & 3506.40 & 3538.99 & 34.92 & -2.34 \\\\\n19:36:06 & 3507.64 & 3538.99 & 34.92 & -3.58 \\\\\n19:36:07 & 3508.80 & 3538.99 & 34.92 & -4.74 \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Price Discovery in Cryptocurrency Markets", "authors": ["Juan Plazuelo Pascual", "Carlos Tardon Rubio", "Juan Toro Cebada", "Angel Hernando Veciana"], "url": "https://arxiv.org/abs/2506.08718v1", "attribution": "\"Price Discovery in Cryptocurrency Markets\" by Juan Plazuelo Pascual, Carlos Tardon Rubio, Juan Toro Cebada, and Angel Hernando Veciana, arXiv:2506.08718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.18352v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|ccc}\n \\midrule\n \\multirow{2}{*}{HuBERT CNN Encoder}\n & strides & \\multicolumn{3}{c}{5, 2, 2, 2, 2, 2, 2} \\\\\n & kernel width & \\multicolumn{3}{c}{10, 3, 3, 3, 3, 2, 2} \\\\\n ~ & channel & \\multicolumn{3}{c}{512} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Tiny is not small enough: High-quality, low-resource facial animation models through hybrid knowledge distillation", "authors": ["Zhen Han", "Mattias Teye", "Derek Yadgaroff", "Judith Bütepage"], "url": "https://arxiv.org/abs/2507.18352v1", "attribution": "\"Tiny is not small enough: High-quality, low-resource facial animation models through hybrid knowledge distillation\" by Zhen Han, Mattias Teye, Derek Yadgaroff, and Judith Bütepage, arXiv:2507.18352v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06390v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of the tower detection component using the $DmAP$ metric (higher better) of \\textit{GridTracer} with the \"conventional\" data handling scheme in Fig. (a)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l||c|c|c|c}\n \\hline \n Metric & Arizona & Kansas & New Zealand & Average \\\\\n \\hline \\hline\n $DmAP$ & 0.73 & 0.54 & 0.55 & 0.63 \\\\\n \\hline\n $mAP_{0.75}$ & 0.09 & 0.02 & 0.08 & 0.06 \\\\\n $mAP_{0.5}$ & 0.60 & 0.44 & 0.51 & 0.52 \\\\\n \\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery", "authors": ["Bohao Huang", "Jichen Yang", "Artem Streltsov", "Kyle Bradbury", "Leslie M. Collins", "Jordan Malof"], "url": "https://arxiv.org/abs/2101.06390v1", "attribution": "\"GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery\" by Bohao Huang, Jichen Yang, Artem Streltsov, Kyle Bradbury, Leslie M. Collins, and Jordan Malof, arXiv:2101.06390v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.04538v9_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model results for the discounted sales example.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|} \\hline \n\t\t& \\textit{lme4} & PLS & RPLS \\\\ \\hline\n\t\t$ \\beta_0 $ & $0.106$ & $0.164$ & $0.164$ \\\\ \\hline \n\t $ \\beta_1 $ & $-0.319$ & $0.259$ & $0.270$ \\\\ \\hline\n\t S.D. of random intercept & $0.671$ & $0.095$ & $0.094$ \\\\ \\hline\n\t S.D. of random slope & $0.361$ & $0.075$ & $0.156$ \\\\ \\hline\n\t S.D. of residuals & $1.388$ & $1.445$ & $1.447$ \\\\ \\hline \\hline\n\tMarginal $R^2$ & $0.000$ & $0.000$ & $0.000$ \\\\ \\hline\n\tConditional $ R^2 $ & $0.190$ & $0.488$ & $0.606$ \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects", "authors": ["Hao Chen", "Lanshan Han", "Alvin Lim"], "url": "https://arxiv.org/abs/2011.04538v9", "attribution": "\"Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects\" by Hao Chen, Lanshan Han, and Alvin Lim, arXiv:2011.04538v9, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.11561v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Users' tendency to engage with more novel content between Group I vs. III}\n\\begin{tabular}{ccccc}\n\\toprule \n & \\multicolumn{4}{c}{Engagement (Group I vs Group III)}\n \\\\\n \\cmidrule(r){2-5} \n & (1) & (2) & (3) & (4) \\\\\n\\midrule % In-table horizontal line\n$\\text{Algorithm}$ $\\times$ NR & $0.073^{***}$ & $0.093^{***}$ & & \\\\\n& (0.009) & (0.007) & & \\\\\nNR & $-0.632^{***}$ & $-0.689^{***}$ & & \\\\\n& (0.007) & (0.005) & & \\\\\n$\\text{Algorithm}$ $\\times$ Diversity & & & $0.237^{***}$ & $0.293^{***}$ \\\\\n& & & (0.030) & (0.025) \\\\\nDiversity & & & $-1.391^{***}$ & $-1.513^{***}$ \\\\\n& & & (0.022) & (0.018) \\\\\n$\\text{Algorithm}$ & $0.072^{***}$ & $0.077^{***}$ & $0.086^{***}$ & $0.098^{***}$ \\\\\n& (0.008) & (0.007) & (0.007) & (0.006) \\\\\nConstant & $-1.669^{***}$ & $-2.296^{***}$ & $-1.803^{***}$ & $-2.671^{***}$ \\\\\n& (0.006) & (0.030) & (0.005) & (0.030) \\\\\nControls & & Yes & & Yes \\\\\n\\midrule % In-table horizontal line\nObservations & 64,755,564 & 64,755,564 & 64,755,564 & 64,755,564 \\\\\nPseudo $R^{2}$ & 0.011 & 0.055 & 0.004 & 0.047 \\\\\n\\bottomrule \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Algorithms vs. Peers: Shaping Engagement with Novel Content", "authors": ["Shan Huang", "Yi Ji", "Leyu Lin"], "url": "https://arxiv.org/abs/2503.11561v2", "attribution": "\"Algorithms vs. Peers: Shaping Engagement with Novel Content\" by Shan Huang, Yi Ji, and Leyu Lin, arXiv:2503.11561v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10937v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Classification Accuracy} (smaller indicates better results) of different models on targeted adversarial samples. Here, we pick $\\epsilon_{\\infty}=0.18$, $m=100$, $\\gamma=0.25$ and select DGCNN as victim model. Other settings are the same as Sec 4.1. In terms of misclassification rate, our AOF outperforms the other baselines when transferred to different networks.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccr}\n\\toprule\nModel & PointNet & PointNet++ & DGCNN & PointConv \\\\\n\\midrule\n3D-Adv & 84.12 & 44.37 & \\bf0.04 & 52.84 \\\\\nkNN & 71.72 & 49.07 & 8.06 & 47.08 \\\\\nAdvPC & 60.04 & 20.81 & 3.77 & 20.00 \\\\\nAOF & \\bf53.60 & \\bf13.56 & 3.85 & \\bf13.16 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Boosting 3D Adversarial Attacks with Attacking On Frequency", "authors": ["Binbin Liu", "Jinlai Zhang", "Lyujie Chen", "Jihong Zhu"], "url": "https://arxiv.org/abs/2201.10937v1", "attribution": "\"Boosting 3D Adversarial Attacks with Attacking On Frequency\" by Binbin Liu, Jinlai Zhang, Lyujie Chen, and Jihong Zhu, arXiv:2201.10937v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01833v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance on generic recognition benchmarks with varying imbalance factors (IF), with and without prior class distribution. These benchmarks come with both novel class ratio and label ratio of 50\\%.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccc}\n \\toprule\n \\textbf{Dataset} & \\textbf{Prior} & \\multicolumn{3}{c}{\\textbf{Uniform (IF=1)}} & \\multicolumn{3}{c}{\\textbf{IF=10}} & \\multicolumn{3}{c}{\\textbf{IF=20}}\\\\\n \\cmidrule(lr){3-5} \\cmidrule(l){6-8} \\cmidrule(lr){9-11} \n & & Seen & Novel & All & Seen & Novel & All & Seen & Novel & All \\\\\n \\midrule\n \\multirow{2}{7em}{\\textbf{CIFAR-10}} & w/ & 96.5 & 97.1 & 96.8 & 93.7 & 72.1 & 82.5 & 92.9 & 70.1 & 80.9 \\\\\n & w/o & 96.9 & 90.9 & 93.9 & 95.8 & 66.5 & 80.3 & 95.3 & 64.2 & 78.8 \\\\\n \\multirow{2}{7em}{\\textbf{CIFAR-100}} & w/ & 80.1 & 63.9 & 71.9 & 76.8 & 42.0 & 57.3 & 76.1 & 35.2 & 51.9 \\\\\n & w/o & 82.5 & 57.9 & 69.2 & 74.6 & 39.7 & 54.1 & 73.9 & 33.9 & 49.2 \\\\\n \\multirow{2}{7em}{\\textbf{Tiny-ImageNet}} & w/ & 68.8 & 42.4 & 55.0 & 61.7 & 25.1 & 41.6 & 62.4 & 21.7 & 38.3 \\\\\n & w/o & 69.6 & 40.6 & 54.8 & 61.0 & 24.9 & 40.1 & 61.3 & 20.3 & 36.9 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning", "authors": ["Shengjie Niu", "Lifan Lin", "Jian Huang", "Chao Wang"], "url": "https://arxiv.org/abs/2411.01833v1", "attribution": "\"OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning\" by Shengjie Niu, Lifan Lin, Jian Huang, and Chao Wang, arXiv:2411.01833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.04372v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lccccccl}\nLabel & Model & Accuracy & Precision & Recall & AUC-ROC & Yes/No & Obs. \\\\ \\hline\n\\multirow{6}{*}{Pet} & TF-IDF+XGB & 0.83 $\\pm$ 0.02 & 0.81 $\\pm$ 0.03 & 0.97 $\\pm$ 0.03 & 0.79 $\\pm$ 0.07 & 23/77 & \\\\ \\cline{2-8} \n & TF-IDF+XGB & 0.80 $\\pm$ 0.02 & 0.74 $\\pm$ 0.03 & 0.96 $\\pm$ 0.04 & 0.95 $\\pm$ 0.05 & 27/73 & $^{1}$ \\\\ \\cline{2-8} \n & TF-IDF+XGB & 0.84 $\\pm$ 0.03 & 0.84 $\\pm$ 0.03 & 0.96 $\\pm$ 0.04 & 0.95 $\\pm$ 0.05 & 36/64 & $^{2}$ \\\\ \\cline{2-8} \n & TF-IDF+XGB & 0.81 $\\pm$ 0.02 & 0.84 $\\pm$ 0.03 & 0.90 $\\pm$ 0.03 & 0.85 $\\pm$ 0.05 & 41/59 & $^{3}$ \\\\ \\cline{2-8} \n & TF-IDF+XGB & 0.92 $\\pm$ 0.01 & 0.96 $\\pm$ 0.01 & 0.96 $\\pm$ 0.01 & 0.94 $\\pm$ 0.06 & 70/130 & $^{245}$ \\\\ \\cline{2-8} \n & DeBERTaV3 & 0.66 $\\pm$ 0.02 & 0.66 $\\pm$ 0.02 & 0.97 $\\pm$ 0.03 & 0.44 $\\pm$ 0.06 & 23/77 & \\\\ \\cline{2-8} \n & TF-IDF+XGB+Rand & 0.84 $\\pm$ 0.02 & 0.83 $\\pm$ 0.02 & 0.97 $\\pm$ 0.03 & 0.90 $\\pm$ 0.10 & 18/82 & $^{7}$ \\\\ \\hline\n\\multirow{3}{*}{Drinkable} & TF-IDF+XGB & 0.98 $\\pm$ 0.02 & 0.98 $\\pm$ 0.02 & 0.98 $\\pm$ 0.02 & 0.50 $\\pm$ 0.50 & 19/81 & \\\\ \\cline{2-8} \n & TF-IDF+XGB & 0.85 $\\pm$ 0.01 & 0.86 $\\pm$ 0.02 & 0.92 $\\pm$ 0.02 & 0.82 $\\pm$ 0.04 & 70/130 & $^{245}$ \\\\ \\cline{2-8} \n & DeBERTaV3 & 0.74 $\\pm$ 0.02 & 0.74 $\\pm$ 0.02 & 0.97 $\\pm$ 0.03 & 0.42 $\\pm$ 0.08 & 32/67 & \\\\ \\hline\n\\multirow{2}{*}{Low quality} & TF-IDF+XGB & 0.74 $\\pm$ 0.02 & 0.78 $\\pm$ 0.03 & 0.91 $\\pm$ 0.03 & 0.50 $\\pm$ 0.10 & 20/80 & \\\\ \\cline{2-8} \n & TF-IDF+XGB & 0.77 $\\pm$ 0.01 & 0.76 $\\pm$ 0.01 & 0.98 $\\pm$ 0.02 & 0.79 $\\pm$ 0.04 & 46/154 & $^{245}$ \\\\ \\hline \n\\multirow{4}{*}{Damaged} & TF-IDF+XGB & -- & -- & -- & 0.50 $\\pm$ 0.50 & 2/98 & \\\\ \\cline{2-8} \n & TF-IDF+XGB & 0.96 $\\pm$ 0.01 & 0.96 $\\pm$ 0.01 & 0.99 $\\pm$ 0.01 & 0.75 $\\pm$ 0.25 & 5/195 & $^{4}$ \\\\ \\cline{2-8} \n & TF-IDF+XGB & 0.82 $\\pm$ 0.04 & 0.81 $\\pm$ 0.04 & 0.95 $\\pm$ 0.05 & 0.88 $\\pm$ 0.12 & 30/70 & $^{6}$ \\\\ \\cline{2-8} \n & TF-IDF+XGB & 0.91 $\\pm$ 0.01 & 0.91 $\\pm$ 0.01 & 0.99 $\\pm$ 0.01 & 0.75 $\\pm$ 0.08 & 39/161 & $^{245}$ \\\\ \\cline{2-8} \n & TF-IDF+XGB+Rand & -- & -- & -- & 0.50 $\\pm$ 0.50 & 1/199 & $^{47}$ \\\\ \\hline \n\\end{tabular}\n\\caption{Final performance with different classification models for the proposed labels after 100 manual labels using our framework. \\\\ {\\small $^1$Done with $k$ splitted 50/50 on high and low uncertainty. \\\\ $^2$Done with $k$ splitted 30/70 on high and low uncertainty. \\\\ $^3$Done with $k$ splitted 70/30 on high and low uncertainty. \\\\ $^4$Done with 200 manual labelings. \\\\ $^5$Started with 20 pre-labelled texts.\\\\ $^6$Started with 40 pre-labelled texts.\\\\ $^7$ Random selection of entries no active learning.}}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Mining Unstructured Medical Texts With Conformal Active Learning", "authors": ["Juliano Genari", "Guilherme Tegoni Goedert"], "url": "https://arxiv.org/abs/2502.04372v1", "attribution": "\"Mining Unstructured Medical Texts With Conformal Active Learning\" by Juliano Genari and Guilherme Tegoni Goedert, arXiv:2502.04372v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10372v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}\n\\hline \n\\multirow{4}{*}{Algorithm} & \\multicolumn{2}{|c|}{Reference-Sim} & \\multicolumn{8}{|c|}{Catfish} \\\\ \\cline{2-11} \n & \\multicolumn{2}{|c|}{Human} & \\multicolumn{2}{|c|}{Zebrafish} & \\multicolumn{2}{|c|}{Mouse} & \\multicolumn{2}{|c|}{Human} & \\multicolumn{2}{|c|}{Human (salmon)} \\\\ %& \\multicolumn{2}{|c|}{All} \\\\ \n& \\multicolumn{2}{|c|}{25.6MB} & \\multicolumn{2}{|c|}{122MB} & \\multicolumn{2}{|c|}{137MB} & \\multicolumn{2}{|c|}{157MB} & \\multicolumn{2}{|c|}{2.5GB} \\\\ %& \\multicolumn{2}{|c|}{2.9GB} \\\\ \n\\cline{2-11} \n & Time & Mem & Time & Mem & Time & Mem & Time & Mem & Time & Mem \\\\ % & Time & Mem \\\\ \n \\hline\nUnitigs & 0.68s & 3.58MB & 13.82s & 3.51MB & 15.62s & 3.53MB & 18.22s & 3.54MB & 303.72s & 3.66MB \\\\\nExtUnitigs & 0.99s & 3.63MB & 18.31s & 3.52MB & 20.87s & 3.57MB & 23.64s & 3.56MB & 404.50s & 3.68MB \\\\\nSafe\\&Comp & 2.56s & 4.47MB & 20.17s & 3.56MB & 25.76s & 3.66MB & 28.59s & 3.54MB & 667.27s & 3.84MB \\\\\nGreedy & 7.71s & 4.88MB & 108.30s & 6.00MB & 127.38s & 6.29MB & 148.46s & 6.34MB & 2684.30s & 8.47MB \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Time and Memory requirements of the different algorithms for the evaluated datasets.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Safety and Completeness in Flow Decompositions for RNA Assembly", "authors": ["Shahbaz Khan", "Milla Kortelainen", "Manuel Cáceres", "Lucia Williams", "Alexandru I. Tomescu"], "url": "https://arxiv.org/abs/2201.10372v1", "attribution": "\"Safety and Completeness in Flow Decompositions for RNA Assembly\" by Shahbaz Khan, Milla Kortelainen, Manuel Cáceres, Lucia Williams, and Alexandru I. Tomescu, arXiv:2201.10372v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.03490v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|c|}\n\\hline\n\\([\\cdot, \\cdot]\\) & \\( H_1 \\) & \\( H_2 \\) & \\( E_{12} \\) & \\( E_{21} \\) & \\( E_{23} \\) & \\( E_{32} \\) & \\( E_{13} \\) & \\( E_{31} \\) \\\\\n\\hline\n\\( H_1 \\) & \\( 0 \\) & \\( 0 \\) & \\( 2E_{12} \\) & \\( -2E_{21} \\) & \\( -E_{23} \\) & \\( E_{32} \\) & \\( E_{13} \\) & \\( -E_{31} \\) \\\\\n\\hline\n\\( H_2 \\) & \\( 0 \\) & \\( 0 \\) & \\( E_{12} \\) & \\( -E_{21} \\) & \\( 2E_{23} \\) & \\( -2E_{32} \\) & \\( E_{13} \\) & \\( -E_{31} \\) \\\\\n\\hline\n\\( E_{12} \\) & \\( -2E_{12} \\) & \\( -E_{12} \\) & \\( 0 \\) & \\( H_1 \\) & \\( E_{13} \\) & \\( 0 \\) & \\( 0 \\) & \\( 0 \\) \\\\\n\\hline\n\\( E_{21} \\) & \\( 2E_{21} \\) & \\( E_{21} \\) & \\( -H_1 \\) & \\( 0 \\) & \\( 0 \\) & \\( 0 \\) & \\( 0 \\) & \\( 0 \\) \\\\\n\\hline\n\\( E_{23} \\) & \\( E_{23} \\) & \\( -2E_{23} \\) & \\( -E_{13} \\) & \\( 0 \\) & \\( 0 \\) & \\( H_2 \\) & \\( 0 \\) & \\( 0 \\) \\\\\n\\hline\n\\( E_{32} \\) & \\( -E_{32} \\) & \\( 2E_{32} \\) & \\( 0 \\) & \\( 0 \\) & \\( -H_2 \\) & \\( 0 \\) & \\( 0 \\) & \\( 0 \\) \\\\\n\\hline\n\\( E_{13} \\) & \\( -E_{13} \\) & \\( -E_{13} \\) & \\( 0 \\) & \\( 0 \\) & \\( 0 \\) & \\( 0 \\) & \\( 0 \\) & \\( H_1 + H_2 \\) \\\\\n\\hline\n\\( E_{31} \\) & \\( E_{31} \\) & \\( E_{31} \\) & \\( 0 \\) & \\( 0 \\) & \\( 0 \\) & \\( 0 \\) & \\( - (H_1 + H_2) \\) & \\( 0 \\) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Commutator Table of \\( \\mathfrak{sl}(3, \\mathbb{C}) \\)}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the construction of polynomial Poisson algebras: a novel grading approach", "authors": ["Rutwig Campoamor-Stursberg", "Danilo Latini", "Ian Marquette", "Junze Zhang", "Yao-Zhong Zhang"], "url": "https://arxiv.org/abs/2503.03490v1", "attribution": "\"On the construction of polynomial Poisson algebras: a novel grading approach\" by Rutwig Campoamor-Stursberg, Danilo Latini, Ian Marquette, Junze Zhang, and Yao-Zhong Zhang, arXiv:2503.03490v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.19235v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|c}\n\t\t\t\\hline\n\t\t\tMethod \\rule[-1ex]{0pt}{3.5ex} & Relative ${\\mathbb L}_2$ errors ($x, y, z$) \\\\\n\t\t\t\\hline\n\t\t\tCausal training~ \\rule[-1ex]{0pt}{3.5ex} & $1.14\\times 10^{-2}$, $1.66\\times 10^{-2}$, $7.04\\times 10^{-3}$ \\\\\n\t\t\t{\\bf PIDD initialization} & $\\bf{2.0\\times 10^{-4}}$, $\\bf{6.7\\times 10^{-4}}$, $\\bf{1.3\\times 10^{-4}}$\\\\\t\n\t\t\t{\\bf NbN training} & \n\t\t\t$\\bf{9.8\\times 10^{-4}}$, $\\bf{1.4\\times 10^{-3}}$, $\\bf{6.0\\times 10^{-4}}$ \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\caption{Lorentz system: Relative ${\\mathbb L}_2$ errors obtained by different approaches}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Are Two Hidden Layers Still Enough for the Physics-Informed Neural Networks?", "authors": ["Vasiliy A. Es'kin", "Alexey O. Malkhanov", "Mikhail E. Smorkalov"], "url": "https://arxiv.org/abs/2412.19235v1", "attribution": "\"Are Two Hidden Layers Still Enough for the Physics-Informed Neural Networks?\" by Vasiliy A. Es'kin, Alexey O. Malkhanov, and Mikhail E. Smorkalov, arXiv:2412.19235v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.02630v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fail rate of different models on rating-based rationality tests (std. {errors} in parentheses).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n & Rating of one listing & Random ratings & Random ratings \\\\ \n & increased by $0.1$ & (low variance) & (high variance) \\\\\n \\midrule\n \\midrule\n Claude Sonnet 3.5 & $57.3\\%$ $(1.5\\%)$ & $16.3\\%$ $(0.8\\%)$ & $2.7\\%$ $(0.2\\%)$ \\\\\n Claude Sonnet 3.7 & $6.7\\%$ $(0.5\\%)$ & \\textcolor{gray}{$0.0\\%$} & \\textcolor{gray}{$0.0\\%$} \\\\\n Claude Sonnet 4.0 & $28.7\\%$ $(1.2\\%)$ & $9.4\\%$ $(0.6\\%)$ & $4.7\\%$ $(0.3\\%)$ \\\\\n GPT\\textendash4o & $71.7\\%$ $(0.9\\%)$ & $16.0\\%$ $(0.5\\%)$ & $7.3\\%$ $(0.4\\%)$ \\\\\n GPT\\textendash4.1 & $15.1\\%$ $(0.6\\%)$ & $11.7\\%$ $(0.5\\%)$ & $6.0\\%$ $(0.4\\%)$ \\\\\n Gemini 2.0 Flash & \\textcolor{gray}{$0.0\\%$} & \\textcolor{gray}{$0.0\\%$} & $0.3\\%$ $(0.1\\%)$ \\\\\n Gemini 2.5 Flash & \\textcolor{gray}{$0.0\\%$} & \\textcolor{gray}{$0.0\\%$} & \\textcolor{gray}{$0.0\\%$} \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce", "authors": ["Amine Allouah", "Omar Besbes", "Josué D Figueroa", "Yash Kanoria", "Akshit Kumar"], "url": "https://arxiv.org/abs/2508.02630v1", "attribution": "\"What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce\" by Amine Allouah, Omar Besbes, Josué D Figueroa, Yash Kanoria, and Akshit Kumar, arXiv:2508.02630v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11316v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\emph{Effect of pre-training.} Mean accuracy (\\%) on CLEVR-CoGenT validation condition B and CLOSURE with two different feature extractors, which are an object detector pre-trained on Visual Genome (VG) and a ResNet-101 (without pre-training on VG).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c|c|c|c}\n\\toprule\n\\multirow{2}{*}{Methods} & \\multicolumn{3}{c|}{CLEVR-CoGenT} & \\multicolumn{3}{c}{CLOSURE} \\\\ \\cmidrule{2-7}\n& w/o VG & w/ VG & $\\Delta$ & w/o VG & w/ VG & $\\Delta$ \\\\ \\midrule %\\hline\nTransformer & 78.9 & 87.5 & 8.6 & 57.4 & 62.1 & 4.7 \\\\ %\\hline\nTransformer w/ PR & \\textbf{81.7} & \\textbf{88.7} & 6.9 & 64.5 & 64.5 & 0.005 \\\\ %\\hline \nTMN-Stack (ours) & 80.6 & 86.9 & 6.3 & 90.9 & 92.7 & 1.7 \\\\ %\\hline\nTMN-Tree (ours) & 80.1 & 86.4 & 6.4 & \\textbf{95.4} & \\textbf{96.4} & 1.0 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Transformer Module Networks for Systematic Generalization in Visual Question Answering", "authors": ["Moyuru Yamada", "Vanessa D'Amario", "Kentaro Takemoto", "Xavier Boix", "Tomotake Sasaki"], "url": "https://arxiv.org/abs/2201.11316v2", "attribution": "\"Transformer Module Networks for Systematic Generalization in Visual Question Answering\" by Moyuru Yamada, Vanessa D'Amario, Kentaro Takemoto, Xavier Boix, and Tomotake Sasaki, arXiv:2201.11316v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.08302v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Properties of the Equilibrium Strategy $(\\alpha^*,\\Phi^*,D_A^*)$}\n\\begin{tabular}{ccc}\n \\toprule\n& Case (i) & Case (ii)\\\\\n\\midrule\n\\textbf{Gas fee density $\\phi^*(g)$} & \\multicolumn{2}{c}{Strictly decreasing in $g\\in[\\hat g_L,g_H]$}\\\\ \\midrule\n\\textbf{Trading amount given gas fee $D_A^*(g)$} & \\multicolumn{2}{c}{Strictly increasing in $g\\in[\\hat g_L,g_H]$ and $D_A^*(g_H)=\\hat D_A$}\\\\\\midrule\n\\textbf{Effect of base gas fee $\\hat g_L$} & $\\hat g_L\\le \\hat g_H- L_B\\hat z$ & $\\hat g_L> \\hat g_H- L_B\\hat z$\\\\\\midrule\nTrading probability $\\alpha^*$ & $=1$ & $<1$ and strictly decreasing in $\\hat g_L$\\\\\nAbitrageur's expected profit $\\Pi^*$ & $>0$ and strictly decreasing in $\\hat g_L$ & $=0$\\\\\nGas fee paid $\\tilde{g}^*$ & Increasing in $\\hat g_L$ & decreasing in $\\hat g_L$\\\\\nRelative trading amount $\\tilde{D}^*/y_A$ & Independent of $\\hat g_L$ & decreasing in $\\hat g_L$\\\\ \\midrule\n\\textbf{Effect of pool liquidity $L_B$} & $L_B\\ge \\hat g_L/((1-O^{-1/2})^2-\\hat z)$ & $L_B< \\hat g_L/((1-O^{-1/2})^2-\\hat z)$\\\\\\midrule\nTrading probability $\\alpha^*$ & $=1$ & $<1$ and strictly increasing in $L_B$\\\\\nAbitrageur's expected profit $\\Pi^*$ & $>0$ and strictly increasing in $L_B$ & $=0$\\\\\nGas fee paid $\\tilde g^*$ & \\multicolumn{2}{c}{Increasing in $L_B$}\\\\\nRelative trading amount $\\tilde D^*/y_A$ & Independent of $L_B$ & Increasing in $L_B$\\\\ \\midrule\n\\textbf{Effect of arbitrage opportunity $O$} & $O\\ge \\bar{O}$ & $O<\\bar{O}$\\\\\\midrule\nTrading probability $\\alpha^*$ & $=1$ & $<1$ and strictly increasing in $O$\\\\\nAbitrageur's expected profit $\\Pi^*$ & $>0$ and strictly increasing in $O$ & $=0$\\\\\nGas fee paid $\\tilde g^*$ & \\multicolumn{2}{c}{Increasing in $O$}\\\\\nRelative trading amount $\\tilde D^*/y_A$ & \\multicolumn{2}{c}{Increasing in $O$}\\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Arbitrage on Decentralized Exchanges", "authors": ["Xue Dong He", "Chen Yang", "Yutian Zhou"], "url": "https://arxiv.org/abs/2507.08302v1", "attribution": "\"Arbitrage on Decentralized Exchanges\" by Xue Dong He, Chen Yang, and Yutian Zhou, arXiv:2507.08302v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.00140v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\toprule\n& Insurance & Credit \\\\\n\\midrule\nFirm A & 0.947 & 0.823\\\\\nFirm B & 0.947 & 0.885 \\\\\nFirm C & - & 0.793 \\\\\nFirm D & - & 0.902 \\\\\nFirm E & - & 0.763 \\\\\nGlobal & 0.895 & 0.791 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Local and global fairness values under a fairness-maximizing SP tax policy.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Balancing Profit and Fairness in Risk-Based Pricing Markets", "authors": ["Jesse Thibodeau", "Hadi Nekoei", "Afaf Taïk", "Janarthanan Rajendran", "Golnoosh Farnadi"], "url": "https://arxiv.org/abs/2506.00140v2", "attribution": "\"Balancing Profit and Fairness in Risk-Based Pricing Markets\" by Jesse Thibodeau, Hadi Nekoei, Afaf Taïk, Janarthanan Rajendran, and Golnoosh Farnadi, arXiv:2506.00140v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.00308v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MDP1 Reward Function}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c||c}\\hline\n State & Action & Reward \\\\\\hline\n 0 & 0 & 0 \\\\\n 0 & 1 & 10 \\\\\n 1 & 0 & 10 \\\\\n 1 & 1 & 0 \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings", "authors": ["Karine Karine", "Susan A. Murphy", "Benjamin M. Marlin"], "url": "https://arxiv.org/abs/2412.00308v1", "attribution": "\"BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings\" by Karine Karine, Susan A. Murphy, and Benjamin M. Marlin, arXiv:2412.00308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.05584v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimation Results of weighted citation on train data set with $50-50$ split via linear models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc} \n\\\\ \\hline\n\\hline \\\\\n\\textbf{Variable} & \\textbf{LMC} & \\textbf{LMI} & \\textbf{LMR} \\\\ \n\\hline \\\\\nClinical & 0.066$^{***}$ & 0.366$^{***}$ & \\\\ \nResearch & 0.288$^{***}$ & 0.177$^{***}$ & \\\\ \nH Score & $-$0.454$^{***}$ & $-$0.522$^{***}$ & $-$0.779$^{***}$ \\\\ \nA Score & $-$0.303$^{***}$ & $-$0.330$^{***}$ & $-$0.454$^{***}$ \\\\ \nC Score & 0.256$^{***}$ & 0.198$^{***}$ & 0.106$^{***}$ \\\\ \nTitle & $-$0.040$^{***}$ & $-$0.009$^{***}$ & $-$0.066$^{***}$ \\\\ \nTriangle ACH & 0.229$^{***}$ & 0.206$^{***}$ & \\\\ \nTriangle C & 0.021$^{***}$ & $-$0.001 & \\\\ \nTriangle H & 0.039$^{***}$ & 0.014$^{***}$ & \\\\ \nTriangle Outside & 0.128$^{***}$ & 0.100$^{***}$ & \\\\ \nReferences & 0.714$^{***}$ & 0.754$^{***}$ & 0.722$^{***}$ \\\\ \nAge & $-$0.511$^{***}$ & $-$0.548$^{***}$ & $-$0.480$^{***}$ \\\\ \nMeSH & 0.134$^{***}$ & 0.131$^{***}$ & 0.227$^{***}$ \\\\ \nAccess & 0.080$^{***}$ & 0.125$^{***}$ & \\\\ \nLanguage 2 & $-$0.838$^{***}$ & $-$0.870$^{***}$ & \\\\ \nLanguage 3 & $-$1.366$^{***}$ & $-$1.393$^{***}$ & \\\\ \nLength & 0.062$^{***}$ & 0.117$^{***}$ & 0.116$^{***}$ \\\\\nConstant & 1.935$^{***}$ & 2.030$^{***}$ & 1.759$^{***}$ \\\\ \n\\hline \nObservations & 6377785 & 6377785 & 6377785 \\\\ \nResidual Std. Error & 1.277 & 1.290 & 1.408 \\\\ \nF Statistic & 45,353.000$^{***}$ & 122,358.400$^{***}$ & 348,205.200$^{***}$ \\\\ \n\\hline\n\\hline\n\\textit{Note:} & \\multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Mitigating Consequences of Prestige in Citations of Publications", "authors": ["Michael Balzer", "Adhen Benlahlou"], "url": "https://arxiv.org/abs/2411.05584v2", "attribution": "\"Mitigating Consequences of Prestige in Citations of Publications\" by Michael Balzer and Adhen Benlahlou, arXiv:2411.05584v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.06718v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{F1 Scores for Different Event Classes}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\t\t\t\\toprule\n\t\t\t & \\multirow{2}{*}{\\textit{Non-event}} & \\multirow{2}{*}{\\textit{Line-event}} & \\textit{Generator} & \\textit{Oscillation} \\\\ \n\t\t\t & & &\\textit{event} & \\textit{event}\\\\\n\t\t\t\\midrule\t\t\n\t\t\t\\textit{Baseline} & 0.885 & 0.966 & 0.895 & 0.911 \\\\\n\t\t\t\\midrule\t\n\t\t\t\\textit{Baseline+info} & 0.884 & 0.965 & 0.908 & 0.894 \\\\\n\t\t\t\\midrule\n\t\t\t\\textit{Baseline+GSP} & 0.928 & 0.976 & 0.904 & 0.944 \\\\\n\t\t\t\\midrule\t\t\n\t\t\t\\textit{Baseline+GSP+info} & 0.950 & 0.970 & 0.931 & 0.972 \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Power System Event Identification based on Deep Neural Network with Information Loading", "authors": ["Jie Shi", "Brandon Foggo", "Nanpeng Yu"], "url": "https://arxiv.org/abs/2011.06718v2", "attribution": "\"Power System Event Identification based on Deep Neural Network with Information Loading\" by Jie Shi, Brandon Foggo, and Nanpeng Yu, arXiv:2011.06718v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.04339v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fraction of sales and volume (in USD) controlled by the top 10\\% sellers (columns 1 and 2), and by the top 10\\% buyers (columns 3 and 4).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n & \\multicolumn{2}{c}{\\textbf{Top 10\\% sellers}} & \\multicolumn{2}{c}{\\textbf{Top 10\\% buyers}} \\\\\n & \\textit{Sales} & \\textit{Volume} & \\textit{Sales} & \\textit{Volume} \\\\\nSuperRare & 67\\% & 79\\% & 67\\% & 88\\% \\\\\nAsync Art & 38\\% & 84\\% & 27\\% & 93\\% \\\\\nKnownOrigin & 61\\% & 86\\% & 37\\% & 93\\% \\\\\nFoundation & 37\\% & 80\\% & 30\\% & 85\\% \\\\\nMakersplace & 78\\% & 80\\% & 55\\% & 76\\% \\\\\nZora & 33\\% & 60\\% & 22\\% & 68\\% \\\\\n\\textbf{Overall} & \\textbf{71\\%} & \\textbf{86\\%} & \\textbf{64\\%} & \\textbf{92\\%} \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Seller-buyer networks in NFT art are driven by preferential ties", "authors": ["Giovanni Colavizza"], "url": "https://arxiv.org/abs/2210.04339v2", "attribution": "\"Seller-buyer networks in NFT art are driven by preferential ties\" by Giovanni Colavizza, arXiv:2210.04339v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11932v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Complexity of PGD-VAE and comparison models ($n$ is the size of the basic unit, $m$ is the size of the global pattern)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline Model & Number of generation steps & Time complexity & Space complexity\\\\\n \\hline\n VGAE & $O(1)$ & $O((mn)^2)$ & $O((nm)^2)$\\\\\n \\hline\n GraphRNN & $O((nm)^2)$ & $O((mn)^2)$ & $O((nm)^2)$\\\\\n \\hline\n GRAN & $O(nm)$ & $O((mn)^2)$ & $O((nm)^2)$\\\\\n \\hline\n PGD-VAE & $O(1)$ & $O(m^2+n^2+mn)$ & $O(max\\{n^2, m^2\\})$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Deep Generative Model for Periodic Graphs", "authors": ["Shiyu Wang", "Xiaojie Guo", "Liang Zhao"], "url": "https://arxiv.org/abs/2201.11932v4", "attribution": "\"Deep Generative Model for Periodic Graphs\" by Shiyu Wang, Xiaojie Guo, and Liang Zhao, arXiv:2201.11932v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.01559v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Outline of the Computational Study.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rccc}\n \\toprule\n & \\multicolumn{3}{c}{Uncertainty Set} \\\\\\cmidrule{2-4}\n & Budgeted & Continuous Knapsack & Binary Knapsack \\\\\\midrule\n Shortest Path & Sect.~ & --- & Sect.~ \\\\\n Knapsack & Sect.~ & Sect.~ & Sect.~ \\\\\n Portfolio Selection & Sect.~ & --- & --- \\\\\\toprule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solving Decision-Dependent Robust Problems as Bilevel Optimization Problems", "authors": ["Henri Lefebvre", "Martin Schmidt", "Simon Stevens", "Johannes Thürauf"], "url": "https://arxiv.org/abs/2503.01559v2", "attribution": "\"Solving Decision-Dependent Robust Problems as Bilevel Optimization Problems\" by Henri Lefebvre, Martin Schmidt, Simon Stevens, and Johannes Thürauf, arXiv:2503.01559v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.00999v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccc}\nfrequency & 3904 (97.6\\%) & 34 (0.85\\%) &1 (0.02\\%) &1 (0.02\\%) &1 (0.02\\%) &59 (1.48\\%) \\\\\n\\hline\n $I_\\delta(x,\\widehat x)$ & 0 & 1 & 2 & 3 & 4 & $\\geq 5$ \n\\end{tabular}\n\\end{adjustbox}\n\\caption{The frequencies of occurrency of dissimilarity index values. The total sample size is 4000, and $I_\\delta(x,\\widehat x)=0$ means perfect identification of the atoms, while $I_\\delta(x,\\widehat x) = k>0$ indicates that $k$ atoms were either not identified or an incorrect identification occurred.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian sparsity and class sparsity priors for dictionary learning and coding", "authors": ["Alberto Bocchinfuso", "Daniela Calvetti", "Erkki Somersalo"], "url": "https://arxiv.org/abs/2309.00999v1", "attribution": "\"Bayesian sparsity and class sparsity priors for dictionary learning and coding\" by Alberto Bocchinfuso, Daniela Calvetti, and Erkki Somersalo, arXiv:2309.00999v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.01735v5_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Coverage rates of the 95\\% bootstrap confidence intervals when employing the WQ-EE method in Simulation 1}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\hline\n &$m$ & $\\psi_{10}$ & $\\psi_{11}$ & $\\psi_{20}$ & $\\psi_{2A}$ & $\\psi_{22}$ \\\\\n \\hline\n Sample size 500 & 473 & 0.947 & 0.937 & 0.951 & 0.941 & 0.946 \\\\\n Sample size 2000 & 1956 & 0.947 & 0.945 & 0.953 & 0.950 & 0.945 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Weighted Q-learning for optimal dynamic treatment regimes with nonignorable missing covariates", "authors": ["Jian Sun", "Bo Fu", "Li Su"], "url": "https://arxiv.org/abs/2312.01735v5", "attribution": "\"Weighted Q-learning for optimal dynamic treatment regimes with nonignorable missing covariates\" by Jian Sun, Bo Fu, and Li Su, arXiv:2312.01735v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.11484v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\t\t\\toprule\n\t\t{Problem name} & {box} \t\t& $\\ell_2$\t\\\\\n\t\t\\midrule \n\t\t10teams & 870\t & 870\t\\\\ \n\t\tapp2-1 & 870 & 870\t\\\\ \n\t\tapp2-2 & 870 & 870\t\\\\ \n\t\texp-1-500-5-5 & 870 & 870\t\\\\ \n\t\tflugpl & 870 & 870\t\\\\ \n\t\tg200x740 & 870 & 870\t\\\\ \n\t\tgen-ip054 & 870 & 870\t\\\\ \n\t\tgr4x6 & 870 & 870\t\\\\ \n\t\tiis-glass-cov & 870 & 870\t\\\\ \n\t\tk16x240b & 870 & 870\t\\\\ \n\t\tmod010 & 870 & 870\t\\\\ \n\t\tmtest4ma & 870 & 870\t\\\\ \n\t\tneos-860300 & 870 & 870\t\\\\ \n\t\tneos5 & 870 & 870\t\\\\ \n\t\tp0201 & 870 & 870\t\\\\ \n\t\tr50x360 & 870 & 870\t\\\\ \n\t\tran12x21 & 870 & 870\t\\\\ \n\t\tran13x13 & 870 & 870\t\\\\ \n\t\tseymour1 & 870 & 870\t\\\\ \n\t\tsp98ir & 870 & 870\t\\\\ \n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{This table shows the number of instances solved for each problem at round \\(0\\). Each problem needs to solve at most 10 seeds \\(\\times\\) 3 s$\\_$increase \\(\\times\\) 29 scenario reductions = 870 instances.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Scenario Reduction for Distributionally Robust Optimization", "authors": ["Kevin-Martin Aigner", "Sebastian Denzler", "Frauke Liers", "Sebastian Pokutta", "Kartikey Sharma"], "url": "https://arxiv.org/abs/2503.11484v2", "attribution": "\"Scenario Reduction for Distributionally Robust Optimization\" by Kevin-Martin Aigner, Sebastian Denzler, Frauke Liers, Sebastian Pokutta, and Kartikey Sharma, arXiv:2503.11484v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12251v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrrrr}\n\\hline\n\\textbf{Region} & \\textbf{Year}& \\textbf{Hydropower} & \\textbf{Wind E.} & \\textbf{Solar E.} & \\textbf{Bioenergy} & \\textbf{Geothermal E.} \\\\\n\\hline\n & 2023 & 1 406 863 & 1 017 390 & 1 418 016 & 148 840 & 15 026 \\\\\nWorld & 2022 & 1 394 824 & 902 883 & 1 070 851 & 144 286 & 14 653 \\\\\n & 2021 & 1 362 416 & 824 321 & 870 643 & 138 338 & 14 432 \\\\ \\hline\n & 2023 & 40 280 & 8 654 & 13 438 & 1 899 & 991 \\\\\nAfrica & 2022 & 39 275 & 7 745 & 12 646 & 1 916 & 956 \\\\\n & 2021 & 37 529 & 6 909 & 11 582 & 1 917 & 870 \\\\ \\hline\n & 2023 & 630 030 & 508 599 & 839 329 & 62 068 & 5 011 \\\\\nAsia & 2022 & 620 391 & 426 482 & 601 390 & 58 729 & 4 761 \\\\\n & 2021 & 594 325 & 384 742 & 488 907 & 54 608 & 4 732 \\\\ \\hline\n & 2023 & 225 728 & 255 615 & 288 644 & 42 825 & 1 637 \\\\\nEurope & 2022 & 225 804 & 240 278 & 233 906 & 42 458 & 1 637 \\\\\n & 2021 & 224 212 & 221 517 & 190 928 & 41 601 & 1 633 \\\\ \\hline\n & 2023 & 199 492 & 172 328 & 156 000 & 14 576 & 3 678 \\\\\nN. America & 2022 & 199 581 & 164 389 & 129 156 & 14 684 & 3 647 \\\\\n & 2021 & 198 861 & 154 210 & 109 579 & 14 906 & 3 595 \\\\ \\hline\n & 2023 & 180 962 & 39 852 & 49 392 & 20 348 & 83 \\\\\nS. America & 2022 & 180 018 & 33 580 & 34 698 & 19 552 & 51 \\\\\n & 2021 & 178 482 & 29 737 & 21 259 & 18 622 & 40 \\\\ \\hline\n &2023 & 15 542 & 14 045 & 33 417 & 1 106 & 1 100 \\\\\nOceania &2022 & 14 749 & 13 035 & 29 566 & 1 104 & 1 100 \\\\\n &2021 & 14 509 & 11 522 & 24 895 & 1 104 & 1 093 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{CAP (MW) by energy source and region between 2021 and 2023 according to IRENA }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology", "authors": ["Mahmut Can Bozyiğit", "Mehmet Ünver"], "url": "https://arxiv.org/abs/2501.12251v1", "attribution": "\"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology\" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.14227v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Network architecture used for all experiments. \\textit{K}, \\textit{C}\\textsubscript{in}, and \\textit{C}\\textsubscript{out} represent the kernel size, number of input channels, and number of output channels, respectively. A stride of 3 was used for all convolutional layers. $E$ represents the dimension of the final representation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\toprule\nLayer Number &Layer Components&Kernel Dimension\\\\\n\\midrule\n\t\\multirow{5}{*}{1}&Conv 1D & 7 x 1 x 4 (\\textit{K} x \\textit{C}\\textsubscript{in} x \\textit{C}\\textsubscript{out})\\\\\n\t\t\t\t\t\t\t\t\t\t& BatchNorm &\\\\\n\t\t\t\t\t\t\t\t\t\t& ReLU& \\\\\n\t\t\t\t\t\t\t\t\t\t& MaxPool(2)& \\\\\n\t\t\t\t\t\t\t\t\t\t& Dropout(0.1) &\\\\\n\t\\midrule\n\t\\multirow{5}{*}{2}&Conv 1D & 7 x 4 x 16\\\\\n\t\t\t\t\t\t\t\t\t\t& BatchNorm& \\\\\n\t\t\t\t\t\t\t\t\t\t& ReLU &\\\\\n\t\t\t\t\t\t\t\t\t\t& MaxPool(2) &\\\\\n\t\t\t\t\t\t\t\t\t\t& Dropout(0.1)& \\\\\n\t\\midrule\n\t\\multirow{5}{*}{3}&Conv 1D & 7 x 16 x 32 \\\\\n\t\t\t\t\t\t\t\t\t\t& BatchNorm &\\\\\n\t\t\t\t\t\t\t\t\t\t& ReLU &\\\\\n\t\t\t\t\t\t\t\t\t\t& MaxPool(2) &\\\\\n\t\t\t\t\t\t\t\t\t\t& Dropout(0.1) &\\\\\n\t\\midrule\n\t\\multirow{2}{*}{4}&Linear&320 x $E$ \\\\\n\t\t\t\t\t\t\t\t\t\t& ReLU &\\\\\n\t\\midrule\n\t\\multirow{1}{*}{5}&Linear &$E$ x C (classes) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "PCPs: Patient Cardiac Prototypes", "authors": ["Dani Kiyasseh", "Tingting Zhu", "David A. Clifton"], "url": "https://arxiv.org/abs/2011.14227v1", "attribution": "\"PCPs: Patient Cardiac Prototypes\" by Dani Kiyasseh, Tingting Zhu, and David A. Clifton, arXiv:2011.14227v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11899v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\n \\hline\n & P & PI & PLI \\\\\n \\hline\n Baseline $o_{base}$ & 3.425 $\\pm$ 0.643 & 3.688 $\\pm$ 0.664 & 3.653 $\\pm$ 0.686 \\\\\n \\hline\n Initial $d^{\\rho}_{init}$ & 2.138 $\\pm$ 0.39 & 1.08 $\\pm$ 0.194 & 2.272 $\\pm$ 0.41 \\\\\n \\hline\n Tuned $d^{\\rho}_{end}$ & 0.028 $\\pm$ 0.006 & 0.067 $\\pm$ 0.016 & 0.043 $\\pm$ 0.009 \\\\\n \\hline\n Resampled $d^{\\rho}_{init}$ & 0.025 $\\pm$ 0.009 & 0.068 $\\pm$ 0.025 & 0.042 $\\pm$ 0.01 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Behavioral distance $d^{\\rho}$ between system and specification at the different steps in the tuning pipeline. The error is given by the standard deviation.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Probabilistic Behavioral Aggregation: A Case Study on the Nordic Power Grid", "authors": ["Anna Büttner", "Frank Hellmann"], "url": "https://arxiv.org/abs/2412.11899v1", "attribution": "\"Probabilistic Behavioral Aggregation: A Case Study on the Nordic Power Grid\" by Anna Büttner and Frank Hellmann, arXiv:2412.11899v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10760v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc|cc}\n\t\t\t\\hline\n\t\t\t{\\multirow{2}*{Algorithms}} & \n\t\t\t\\multicolumn{2}{c|}{Low} & \n\t\t\t\\multicolumn{2}{c}{High} \\\\\n\t\t\t\\cline{2-5}\n\t\t\t& PSNR & SSIM & PSNR & SSIM \\\\ \n\t\t\t\\hline\n\t\t\tdirect &\n\t\t\t35.45 & 0.9518 & 32.71 & 0.9200 \\\\\n\t\t\tfixed averaging weights &\n\t\t\t35.50 & 0.9449 & 32.60 & 0.9058 \\\\\n\t\t\trigid sampling grid $3\\times3\\times3$ &\n\t\t\t36.02 & 0.9555 & 33.33 & 0.9256 \\\\\n\t\t\trigid sampling grid $5\\times5\\times5$ &\n\t\t\t36.37 & 0.9592 & 33.73 & 0.9320 \\\\\n\t\t\tw/o concatenating offset features~~ &36.10&0.9590&33.46&0.9348 \\\\\n\t\t\tw/o regularization term & \n\t\t\t36.16 & 0.9601 & 33.48 & 0.9341 \\\\ \n\t\t\tour full model $3\\times3\\times3$ & \n\t\t\t\\bf 36.91 & 0.9622 & 34.23 & 0.9366 \\\\\n\t\t\tour full model $5\\times5\\times5$ &\n\t\t\t36.88 & \\bf 0.9631 & \\bf 34.25 & \\bf 0.9379\\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising", "authors": ["Xiangyu Xu", "Muchen Li", "Wenxiu Sun", "Ming-Hsuan Yang"], "url": "https://arxiv.org/abs/2101.10760v1", "attribution": "\"Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising\" by Xiangyu Xu, Muchen Li, Wenxiu Sun, and Ming-Hsuan Yang, arXiv:2101.10760v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.08430v3_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison between the activation patterns model and the independence model (in M~CAD)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\\toprule\n\\multirow{2}{*}{Coverage} & \\multirow{2}{*}{True reserve} & \\multicolumn{2}{c}{Activation patterns} & \\multicolumn{2}{c}{Independence} \\\\\n\\cline{3-6}\n & & Mean & $\\text{VaR}_{0.95}$ & Mean & $\\text{VaR}_{0.95}$ \\\\\n\\midrule\nAccident Benefits & $>$158.90 & 149.61 & 170.46 & 151.72 & 158.23 \\\\\nBodily Injury & $>$288.17 & 283.27 & 295.02 & 283.03 & 292.95 \\\\\nVehicle Damage & $>$73.63 & 70.77 & 76.43 & 68.99 & 78.59 \\\\\nLoss of Use & $>$4.20 & 4.17 & 4.24 & 4.06 & 4.13 \\\\\n\\midrule\nGlobal reserve & $>$524.91 & 504.82 & 546.15 & 507.8 & 533.90 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Individual Claims Reserving using Activation Patterns", "authors": ["Marie Michaelides", "Mathieu Pigeon", "Hélène Cossette"], "url": "https://arxiv.org/abs/2208.08430v3", "attribution": "\"Individual Claims Reserving using Activation Patterns\" by Marie Michaelides, Mathieu Pigeon, and Hélène Cossette, arXiv:2208.08430v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.16207v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\toprule\n \\textbf{Metric} & \\textbf{MAE} & \\textbf{MSE} & \\textbf{SAMPE} & \\textbf{ACD} \\\\ \n \\midrule\n \\textbf{Score Value} & 0.02 & 0.002 & 3.51 & 0.006 \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study", "authors": ["Ainaz Jamshidi", "Muhammad Arif", "Sabir Ali Kalhoro", "Alexander Gelbukh"], "url": "https://arxiv.org/abs/2412.16207v1", "attribution": "\"Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study\" by Ainaz Jamshidi, Muhammad Arif, Sabir Ali Kalhoro, and Alexander Gelbukh, arXiv:2412.16207v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.00562v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline\n Arm $k$ & $\\pi_k$ \\\\\n $1$ & $0.2$\\\\ \n $2$& $0.3$\\\\\n $3$& $0.87$\\\\\n $4$ & $0.15$ \\\\\n $5$ & $0.79$ \\\\\n $6$ & $0.12$\\\\\n $7$& $0.85$\\\\\n $8$ & $0.1$\\\\\n $9$ & $0.83$\\\\\n $10$ & $0.75$ \\\\\n $11$ & $0.14$\\\\\n $12$ & $0.9$\\\\\n $13$ & $0.2$\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Environment $2$}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Discrete Choice Multi-Armed Bandits", "authors": ["Emerson Melo", "David Müller"], "url": "https://arxiv.org/abs/2310.00562v1", "attribution": "\"Discrete Choice Multi-Armed Bandits\" by Emerson Melo and David Müller, arXiv:2310.00562v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.03154v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of the enrollment partition of the ASVspoof 2019 database.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|cc|cc}\n\t\t\\toprule\n\t\t\\multirow{3}{*}{Subset}&\\multicolumn{2}{c}{\\#Speaker}&\\multicolumn{4}{c}{\\#Utterances}\\\\ \n\t\t\\cmidrule(lr){2-3} \\cmidrule(lr){4-7} \n\t\t&\\multirow{2}{*}{M}&\\multirow{2}{*}{F}&\\multicolumn{2}{c}{LA}&\\multicolumn{2}{c}{PA}\\\\\n\t\t\\cmidrule(lr){4-5} \\cmidrule(lr){6-7}\n\t\t&&&M&F&M&F\\\\\n\t\t\\midrule\n\t\tEnrollment\\_Dev. & 4 & 6 & 76 & 66 & 2052 & 1782 \\\\\n\t\tEnrollment\\_Eval. & 21 & 27 & 399 & 297 & 10773 & 8019 \\\\\n\t\t\\hline\n\t\tTotal & 25 & 33 & 475 & 363 & 12825 & 9801 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System", "authors": ["Yuanjun Zhao", "Roberto Togneri", "Victor Sreeram"], "url": "https://arxiv.org/abs/2012.03154v1", "attribution": "\"Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System\" by Yuanjun Zhao, Roberto Togneri, and Victor Sreeram, arXiv:2012.03154v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03988v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Final submissions F1-score results.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n submission name & model & F1-score\\\\ \\hline\n btb\\_e8\\_4 & neural stacking & \\textbf{0.9720} \\\\ \\hline\n btb\\_e8\\_3 & LSA & 0.9416 \\\\ \\hline\n btb\\_e8\\_1 & tax2vec & 0.9219 \\\\ \\hline\n btb\\_e8\\_2 & linear stacking & 0.8464 \\\\ \\hline\n btb\\_e8\\_5 & distilbert & 0.5059 \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Identification of COVID-19 related Fake News via Neural Stacking", "authors": ["Boshko Koloski", "Timen Stepišnik Perdih", "Senja Pollak", "Blaž Škrlj"], "url": "https://arxiv.org/abs/2101.03988v2", "attribution": "\"Identification of COVID-19 related Fake News via Neural Stacking\" by Boshko Koloski, Timen Stepišnik Perdih, Senja Pollak, and Blaž Škrlj, arXiv:2101.03988v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.09372v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Case2 Result.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|llll}\n \\hline\n Policy & Hyperparameters & Mean Regret & Standard Error & Max & Min \\\\ \\hline\n ACIDP & L=2, n=1 & 157.3 & 170.2 & 619.4 & 33.4 \\\\ \\\n ~ & L=4, n=1 & 167.5 & 91.5 & 406.7 & 91.0 \\\\ \n ~ & L=2, n=2 & 152.7 & 56.0 & 235.8 & 78.2 \\\\ \n ACIDP-$\\theta$ & L=2, n=1 & 135.0 & 107.2 & 379.0 & 50.9 \\\\ \n EG & $\\epsilon$=0.05 & 546.7 & 305.1 & 1224.3 & 219.3 \\\\ \n ~ & $\\epsilon$=0.1 & 759.0 & 389.2 & 1379.8 & 381.4 \\\\ \n ~ & $\\epsilon$=0.15 & 929.9 & 311.4 & 1484.0 & 565.7 \\\\ \n TS & ~ & 766.7 & 226.5 & 1164.1 & 479.3 \\\\ \n UCBPI & ~ & 900.7 & 15.9 & 922.4 & 872.0 \\\\ \n UCB-Tuned & ~ & 1138.7 & 193.7 & 1248.1 & 741.2 \\\\ \n UCB & c=1 & 1299.4 & 354.6 & 1752.8 & 778.8 \\\\ \n ~ & c=2 & 1221.9 & 375.2 & 1894.8 & 576.3 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing", "authors": ["Po-Yi Liu", "Chi-Hua Wang", "Henghsiu Tsai"], "url": "https://arxiv.org/abs/2208.09372v3", "attribution": "\"Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing\" by Po-Yi Liu, Chi-Hua Wang, and Henghsiu Tsai, arXiv:2208.09372v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10760v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\t\t\t\\toprule\n\t\t\tNoise parameter & (2.5e-3, 1e-2) & (6.4e-3, 2e-2) & (1e-2, 5e-2)\\\\\n\t\t\t\\midrule\n\t\t\tAverage receptive field &6.8326& 7.294 & 7.7152 \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising", "authors": ["Xiangyu Xu", "Muchen Li", "Wenxiu Sun", "Ming-Hsuan Yang"], "url": "https://arxiv.org/abs/2101.10760v1", "attribution": "\"Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising\" by Xiangyu Xu, Muchen Li, Wenxiu Sun, and Ming-Hsuan Yang, arXiv:2101.10760v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.07408v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance Metrics by Event Label (2-Day Horizon)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrr}\n\\toprule\nLabel & Samples & MeanRet & StdRet & IC & Sharpe & p-value & Sig \\\\\n\\midrule\nSocial Media Backlash & 46 & 0.0036 & 0.0208 & 0.3979 & 0.1708 & 0.2527 & \\\\\nNegative Press & 42 & 0.0007 & 0.0214 & 0.0205 & 0.0308 & 0.8430 & \\\\\nBrand Boycott & 23 & -0.0018 & 0.0151 & 0.6781 & -0.1165 & 0.5821 & \\\\\nViral Marketing Campaign & 17 & -0.0047 & 0.0192 & -0.3636 & -0.2475 & 0.3227 & \\\\\nSpeculation/Rumor & 130 & -0.0058 & 0.0217 & -0.0586 & -0.2668 & 0.0028 & ** \\\\\nRetail Investor Buzz & 92 & -0.0049 & 0.0176 & 0.0097 & -0.2770 & 0.0093 & ** \\\\\nGeopolitical Tension & 25 & -0.0176 & 0.0274 & -0.1254 & -0.6426 & 0.0037 & ** \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading", "authors": ["Yueyi Wang", "Qiyao Wei"], "url": "https://arxiv.org/abs/2508.07408v1", "attribution": "\"Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading\" by Yueyi Wang and Qiyao Wei, arXiv:2508.07408v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.04941v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\n\\textbf{Variable} & \\textbf{Update rule} \\\\\n\\midrule\n\\textbf{Labor} \n& \\(\\dot{L}(t)= g_L\\,L(t)\\) \\quad (Perfect reallocation) \\\\\n& \\(\\dot{L}_i(t)= g_L\\,L_i(t)\\) \\quad (No reallocation) \\\\\n\\textbf{Capital stock} \n& \\(\\dot{K}(t) = I_k(t)-\\delta_K\\,K(t)\\) \\\\\n\\textbf{Compute stock} \n& \\(\\dot{Q}(t) = I_q(t)\\,H(t)-\\delta_Q\\,Q(t)\\) \\\\\n\\textbf{Size largest training run} \n& \\(\\dot{C}_{T}(t)=D(t)\\) \\\\\n\\textbf{Hardware efficiency} \n& \\(\\frac{1}{H(t)} \\frac{dH(t)}{dt} = \\theta_H [H(t)]^{-\\phi_H} [I_H^{RD}(t)]^{\\lambda_H}\\) \\\\\n\\textbf{Software efficiency} \n& \\(\\frac{1}{S(t)} \\frac{dS(t)}{dt} = \\theta_S [S(t)]^{-\\phi_S} [I_S^{RD}(t)]^{\\lambda_S}\\) \\\\\n\\textbf{Total effective compute} \n& \\(\\dot{C}(t)=C(t)\\,\\frac{\\dot{S}(t)}{S(t)}-\\delta_Q\\,C(t) + I_q(t)\\,H(t)\\,S(t)\\) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\small Summary of update rules for state variables in the dynamic model. The labor update rule depends on whether perfect reallocation (where labor moves freely across tasks) or no reallocation (where workers remain in their initial task) is assumed. Laws of motion for Total effective compute apply to the regime when heating bottlenecks are sufficiently distant; otherwise, those need to be incorporated (see ). Constraints on hardware and software efficiency are omitted for clarity (see ).}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "GATE: An Integrated Assessment Model for AI Automation", "authors": ["Ege Erdil", "Andrei Potlogea", "Tamay Besiroglu", "Edu Roldan", "Anson Ho", "Jaime Sevilla", "Matthew Barnett", "Matej Vrzla", "Robert Sandler"], "url": "https://arxiv.org/abs/2503.04941v2", "attribution": "\"GATE: An Integrated Assessment Model for AI Automation\" by Ege Erdil, Andrei Potlogea, Tamay Besiroglu, Edu Roldan, Anson Ho, Jaime Sevilla, Matthew Barnett, Matej Vrzla, and Robert Sandler, arXiv:2503.04941v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table29.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|r|r|r}\n \\hline\n \\multicolumn{9}{c}{Implicit multi-stage optimization model with fixed carry-overs} \\\\\n \\hline\n \\multicolumn{9}{c}{Instances including component demands} \\\\\n \\hline\n BOM & $|\\mathcal{I}|$ & $|\\mathcal{H}|$ & Distribution & Util. & $|\\Omega|$ & $|\\Phi|$ & Runtime (s) & Gap (\\%) \\\\\n \\hline\n \\multirow{10}{*}{General} &\n \\multirow{10}{*}{10} &\n \\multirow{10}{*}{7} &\n \\multirow{10}{*}{Non stationary} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & 0.7 & 0.00\\\\\n & & & & & 3 & 2,187 & 6.6 & 0.00\\\\\n & & & & & 4 & 16,384 & 79.4 & 0.00 \\\\\n & & & & & 5 & 78,125 & 964.6 & 0.00 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\\cline{5-9}\n & & & &\\multirow{5}{*}{90\\%} \n & 2 & 128 & 1.7 & 0.00\\\\\n & & & & & 3 & 2,187 & 12.8 & 0.00\\\\\n & & & & & 4 & 16,384 & 95.9 & 0.00 \\\\\n & & & & & 5 & 78,125 & 1,200.9 & 0.00 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\n \\hline\n \\multirow{10}{*}{General} &\n \\multirow{10}{*}{10} &\n \\multirow{10}{*}{7} &\n \\multirow{10}{*}{Uniform} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & 1.0 & 0.00 \\\\\n & & & & & 3 & 2,187 & 6.9 & 0.00 \\\\\n & & & & & 4 & 16,384 & 159.2 & 0.00 \\\\\n & & & & & 5 & 78,125 & 3,347.5 & 0.00 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\\cline{5-9}\n & & & & \\multirow{5}{*}{90\\%}\n & 2 & 128 & 1.5 & 0.00 \\\\\n & & & & & 3 & 2,187 & 8.1 & 0.00 \\\\\n & & & & & 4 & 16,384 & 178.8 & 0.00 \\\\\n & & & & & 5 & 78,125 & 3,518.5 & 0.00 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\n \\hline\n \\multirow{10}{*}{General} &\n \\multirow{10}{*}{10} &\n \\multirow{10}{*}{7} &\n \\multirow{10}{*}{Lumpy} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & 0.7 & 0.00 \\\\\n & & & & & 3 & 2,187 & 6.2 & 0.00 \\\\\n & & & & & 4 & 16,384 & 208.4 & 0.00 \\\\\n & & & & & 5 & 78,125 & 2,293.4 & 0.00 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\\cline{5-9}\n & & & & \\multirow{5}{*}{90\\%} \n & 2 & 128 & 9.8 & 0.00 \\\\\n & & & & & 3 & 2,187 & 17.5 & 0.00 \\\\\n & & & & & 4 & 16,384 & 223.5 & 0.00 \\\\\n & & & & & 5 & 78,125 & 2,714.2 & 0.00 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\n \\hline\n \\multirow{10}{*}{General} &\n \\multirow{10}{*}{10} &\n \\multirow{10}{*}{7} &\n \\multirow{10}{*}{Slow moving} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & 9.4 & 0.00 \\\\\n & & & & & 3 & 2,187 & 366.9 & 0.00 \\\\\n & & & & & 4 & 16,384 & 10,800.0 & 0.37 \\\\\n & & & & & 5 & 78,125 & - & - \\\\\n & & & & & 6 & 279,936 & - & - \\\\\\cline{5-9}\n & & & & \\multirow{5}{*}{90\\%}\n & 2 & 128 & 94.1 & 0.00 \\\\\n & & & & & 3 & 2,187 & 3,195.5 & 0.00 \\\\\n & & & & & 4 & 16,384 & 10,800.0 & 72.45 \\\\\n & & & & & 5 & 78,125 & 10,800.0 & 84.81 \\\\\n & & & & & 6 & 279,936 & - & - \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Runtimes and gaps for the implicit multi-stage optimization model on the instances including component demands for different distributions and numbers of scenarios per branch with a time limit of 3 hours (10,800 s)}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.08568v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Trajectory Generation And Open-Loop Control Results}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{Trajectory} & \\textbf{Average Tip Error } & \\textbf{Real-Time Speedup Factor} \\\\\n\\hline\nA & $7.6332 \\times 10^{-5}$ m & 22.2x \\\\\n\\hline\nB & $6.0634 \\times 10^{-5}$ m & 24.3x \\\\\n\\hline\nC & $4.0483 \\times 10^{-5}$ m & 23.8x \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness", "authors": ["Akua Dickson", "Juan C. Pacheco Garcia", "Ran Jing", "Meredith L. Anderson", "Andrew P. Sabelhaus"], "url": "https://arxiv.org/abs/2412.08568v1", "attribution": "\"Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness\" by Akua Dickson, Juan C. Pacheco Garcia, Ran Jing, Meredith L. Anderson, and Andrew P. Sabelhaus, arXiv:2412.08568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.12502v3_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{SIR millseconds elapsed for different image sizes. 128x128 and 256x256 are averaged over 1000 samples each. 1024x1024 is averaged over 500 samples.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\\toprule\nModel & 128x128 & 256x256 & 1024x1024 \\\\\n\\midrule\nNP & $0.1\\pm0.0$ & $0.1\\pm0.0$ & $0.1\\pm0.0$ \\\\\nCNP & $0.1\\pm0.0$ & $0.1\\pm0.0$ & $0.1\\pm0.0$ \\\\\nANP & $0.1\\pm0.0$ & OOM & OOM \\\\\nCANP & $0.1\\pm0.0$ & OOM & OOM \\\\\nConvCNP & $1.1\\pm0.0$ & OOM & OOM \\\\\nTNP-D & $0.8\\pm0.0$ & OOM & OOM \\\\\nTNP-KR: PERF & $0.3\\pm0.0$ & $0.3\\pm0.0$ & $1.2\\pm0.0$ \\\\\nTNP-KR: DKA & $0.3\\pm0.0$ & $0.3\\pm0.0$ & $1.2\\pm0.0$ \\\\\nTNP-KR: SA & $1.3\\pm0.0$ & $163.6\\pm0.1$ & $49505.6\\pm1.2$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Transformer Neural Processes - Kernel Regression", "authors": ["Daniel Jenson", "Jhonathan Navott", "Mengyan Zhang", "Makkunda Sharma", "Elizaveta Semenova", "Seth Flaxman"], "url": "https://arxiv.org/abs/2411.12502v3", "attribution": "\"Transformer Neural Processes - Kernel Regression\" by Daniel Jenson, Jhonathan Navott, Mengyan Zhang, Makkunda Sharma, Elizaveta Semenova, and Seth Flaxman, arXiv:2411.12502v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.02757v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison of classification accuracy. Numbers are in percentage.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n & \\textbf{German} & \\textbf{Recidivism} & \\textbf{Credit} \\\\\n\\midrule\n \\textbf{SAGE} & 67.3 $\\pm$ 2.14 & 89.8 $\\pm$ 0.66 & 75.9 $\\pm$ 2.18 \\\\\n \\textbf{E-SAGE} & \\textbf{71.0 $\\pm$ 1.27} & \\textbf{89.9 $\\pm$ 0.90} & \\textbf{73.4 $\\pm$ 0.50} \\\\\n \\textbf{GCN} & 59.6 $\\pm$ 3.64 & 90.5 $\\pm$ 0.73 & 65.8 $\\pm$ 0.29 \\\\\n \\textbf{E-GCN} & \\textbf{58.2 $\\pm$ 1.82} & \\textbf{89.6 $\\pm$ 0.74} & \\textbf{65.2 $\\pm$ 0.99} \\\\\n \\textbf{JK} & 63.3 $\\pm$ 4.11 & 91.9 $\\pm$ 0.54 & 76.6 $\\pm$ 0.69 \\\\\n \\textbf{E-JK} & \\textbf{62.3 $\\pm$ 4.07} & \\textbf{89.3 $\\pm$ 0.33} & \\textbf{77.7 $\\pm$ 0.27} \\\\\n \\textbf{APPNP} & 69.9 $\\pm$ 2.17 & 95.3 $\\pm$ 0.78 & 74.4 $\\pm$ 3.05 \\\\\n \\textbf{E-APPNP} & \\textbf{69.4 $\\pm$ 0.83} & \\textbf{95.9 $\\pm$ 0.02} & \\textbf{74.6 $\\pm$ 0.32} \\\\\n \\textbf{GCNII} & 60.9 $\\pm$ 1.00 & 90.4 $\\pm$ 0.95 & 77.7 $\\pm$ 0.22 \\\\\n \\textbf{E-GCNII} & \\textbf{60.4 $\\pm$ 4.45} & \\textbf{88.8 $\\pm$ 0.24} & \\textbf{77.6 $\\pm$ 0.02} \\\\\n\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Certified Defense on the Fairness of Graph Neural Networks", "authors": ["Yushun Dong", "Binchi Zhang", "Hanghang Tong", "Jundong Li"], "url": "https://arxiv.org/abs/2311.02757v2", "attribution": "\"Certified Defense on the Fairness of Graph Neural Networks\" by Yushun Dong, Binchi Zhang, Hanghang Tong, and Jundong Li, arXiv:2311.02757v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.06775v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mutual information between registered brain volumes and the brain atlas on ten test subjects using direct patient registration and inpainted volume registration.}\n\\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c}\n\\hline\nMethods& Sub1 & Sub2 & Sub3 & Sub4 & Sub5 & Sub6 & Sub7 & Sub8 & Sub9 & Sub10 \\\\\n\\hline\\hline\nDirect registration\t&0.303&0.311&0.308&0.324&0.315&0.299&0.309&0.303&0.317&0.308\\\\\nInpainted registration &0.309&0.311&0.309&0.324&0.316&0.304&0.312&0.313&0.320&0.312\\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology", "authors": ["Xiaofeng Liu", "Fangxu Xing", "Chao Yang", "C. -C. Jay Kuo", "Georges ElFakhri", "Jonghye Woo"], "url": "https://arxiv.org/abs/2101.06775v1", "attribution": "\"Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology\" by Xiaofeng Liu, Fangxu Xing, Chao Yang, C. -C. Jay Kuo, Georges ElFakhri, and Jonghye Woo, arXiv:2101.06775v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12856v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Initial and convergent parameters and iteration - CPU time of activator-inhibitor example}\\footnotesize\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\nInitial guess&$\\begin{bmatrix}\n1\\\\2\\\\1\\\\2\n\\end{bmatrix}$&$\\begin{bmatrix}\n10\\\\0\\\\3\\\\0.1\n\\end{bmatrix}$&$\\begin{bmatrix}\n0\\\\0\\\\-10\\\\0\n\\end{bmatrix}$&$\\begin{bmatrix}\n-1\\\\1\\\\-10\\\\9\n\\end{bmatrix}$&$\\begin{bmatrix}\n-10\\\\11\\\\12\\\\13\n\\end{bmatrix}$\\\\\n\\hline\n\\multirow{3}{*}{NR}&6-iter &9-iter&9-iter&7-iter&11-iter\\\\\n&0.0089 s&0.0155 s&0.0098 s&0.0091 s&0.0115 s\\\\\n&\\multicolumn{5}{|c|}{$\\begin{bmatrix}\n2.0117& 3.02825& 0.0948& 0.3737\n\\end{bmatrix}^\\top$}\\\\\n\\hline\n\\multirow{3}{*}{GD}&1210-iter &3897-iter&799-iter&462-iter&3865-iter\\\\\n&0.6407 s&1.9940 s&0.4111 s &2.0562 s&0.2417 s\\\\\n& \\multicolumn{5}{|c|}{$\\begin{bmatrix}\n2.0117& 3.02825& 0.0948& 0.3737\n\\end{bmatrix}^\\top$}\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods", "authors": ["S. Syafiie", "Aries Subiantoro", "Vivi Andasari", "Fernando Tadeo"], "url": "https://arxiv.org/abs/2501.12856v1", "attribution": "\"Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods\" by S. Syafiie, Aries Subiantoro, Vivi Andasari, and Fernando Tadeo, arXiv:2501.12856v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.10601v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average log-likelihood}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n & & RIO & BARC & RRLN & ABFLN \\\\\n & & (2.5\\%) & (2.3\\%) & (2.5\\%) & (2.1\\%) \\\\\n Model & dof & & & & \\\\ \\hline\n 1 exp + 1 wbl & 4 & -8.357 & -8.294 & -9.510 & -10.074 \\\\\n 0 exp + 2 wbl & 5 & -8.349 & -8.288 & -9.492 & -10.060 \\\\\n 3 exp + 0 wbl & 5 & -8.422 & -8.394 & -9.792 & -10.323 \\\\\n 2 exp + 1 wbl & 6 & -8.350 & -8.290 & -9.494 & -10.061 \\\\\n 1 exp + 2 wbl & 7 & -8.348 & -8.288 & -9.489 & -10.057 \\\\\n 4 exp + 0 wbl & 7 & -8.360 & -8.300 & -9.511 & -10.081 \\\\\n 0 exp + 3 wbl & 8 & -8.348 & -8.288 & -9.489 & -10.054\\\\\n 3 exp + 1 wbl & 8 & -8.349 & -8.289 & -9.491 & -10.055 \\\\ \n 5 exp + 0 wbl & 9 & -8.351 & -8.291 & -9.497 & -10.063 \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Censored EM algorithm for Weibull mixtures: application to arrival times of market orders", "authors": ["Markus Kreer", "Ayse Kizilersu", "Anthony W. Thomas"], "url": "https://arxiv.org/abs/2012.10601v1", "attribution": "\"Censored EM algorithm for Weibull mixtures: application to arrival times of market orders\" by Markus Kreer, Ayse Kizilersu, and Anthony W. Thomas, arXiv:2012.10601v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.14767v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Descriptive network measures by layer and for the aggregate network.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllllll}\n\\toprule\n & & & & & & \\multicolumn{2}{l}{Degree Distribution} &&&\\\\\n \\cmidrule(lr){7-11}\nLayer & Nodes & Ties & Clustering & Components & \\% giant comp. & Pctl. 5 & Mean & Median & Pctl. 95 & SD \\\\ \\hline\nFamily & 166,393 & 199,781 & 0.76 & 41235 & 0.01 & 1 & 2.40 & 2 & 4 & 1.07 \\\\\nHousehold & 164,013 & 273,344 & 1 & 41627 & 0.05 & 1 & 3.33 & 3 & 5 & 2.07 \\\\\nSchool & 58,079 & 1,386,609 & 1 & 1566 & 0.27 & 16 & 47.75 & 45 & 91 & 24.08 \\\\\nWork & 1,446,817 & 58,860,777 & 0.70 & 13022 & 0.93 & 3 & 81.37 & 100 & 164 & 54.90 \\\\ \\hline\nAggregate & 1,570,812 & 60,538,563 & 0.70 & 1 & 100 & 3 & 77.08 & 81 & 163 & 55.43 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Predicting COVID-19 Infections Using Multi-layer Centrality Measures in Population-scale Networks", "authors": ["Christine Hedde-von Westernhagen", "Javier Garcia-Bernardo", "Ayoub Bagheri"], "url": "https://arxiv.org/abs/2310.14767v2", "attribution": "\"Predicting COVID-19 Infections Using Multi-layer Centrality Measures in Population-scale Networks\" by Christine Hedde-von Westernhagen, Javier Garcia-Bernardo, and Ayoub Bagheri, arXiv:2310.14767v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.01221v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n $\\delta$ & $\\gamma$ & $\\bar{\\eta}$(k\\$) & $U(h)$ & $\\rho$ & $T$ \\\\\n \\hline\n $1$ & $0.05$ & $10$ & $\\sqrt h$ & $0.2$ & $1$ \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Parameters of the optimization problem.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A stochastic Gordon-Loeb model for optimal cybersecurity investment under clustered attacks", "authors": ["Giorgia Callegaro", "Claudio Fontana", "Caroline Hillairet", "Beatrice Ongarato"], "url": "https://arxiv.org/abs/2505.01221v1", "attribution": "\"A stochastic Gordon-Loeb model for optimal cybersecurity investment under clustered attacks\" by Giorgia Callegaro, Claudio Fontana, Caroline Hillairet, and Beatrice Ongarato, arXiv:2505.01221v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2306.10053v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|c|c|c}\n\\toprule\nCollection & Users & Items & Interactions \\\\\n\\hline\n\\midrule\nBAYC & 1,230 & 6,726 & 13,737 \\\\\nCool Cats & 1,357 & 6,824 & 14,890 \\\\\nDoodles & 804 & 4,771 & 7,250 \\\\\nMeebits & 1,184 & 6,693 & 21,104 \\\\\n\\hline\nTotal & 4,575 & 25,014 & 56,981\n\\end{tabular}\n\\caption{The number of users, items and interactions for each collection.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "NFTs to MARS: Multi-Attention Recommender System for NFTs", "authors": ["Seonmi Kim", "Youngbin Lee", "Yejin Kim", "Joohwan Hong", "Yongjae Lee"], "url": "https://arxiv.org/abs/2306.10053v1", "attribution": "\"NFTs to MARS: Multi-Attention Recommender System for NFTs\" by Seonmi Kim, Youngbin Lee, Yejin Kim, Joohwan Hong, and Yongjae Lee, arXiv:2306.10053v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.00270v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{multi-terminal hybrid AC/DC test cases.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccccc}\n\\hline\nTest case & \\# AC & \\# DC & \\#AC/DC & \\# AC & \\# DC \\\\\n & \\ buses & buses & conv & branches & branches \\\\\n\\hline\n5-buses~ & 5 & 3 & 3 & 7 & 3 \\\\\n39-buses~ & 39 & 10 & 10 & 46 & 12 \\\\\n67-buses~ & 67 & 9 & 9 & 102 & 11 \\\\\n588-buses~ & 588 & 7 & 7 & 686 & 8 \\\\\n3120-buses~ & 3120 & 5 & 5 & 3693 & 5 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Optimal Transmission Switching and Busbar Splitting in Hybrid AC/DC Grids", "authors": ["Giacomo Bastianel", "Marta Vanin", "Dirk Van Hertem", "Hakan Ergun"], "url": "https://arxiv.org/abs/2412.00270v1", "attribution": "\"Optimal Transmission Switching and Busbar Splitting in Hybrid AC/DC Grids\" by Giacomo Bastianel, Marta Vanin, Dirk Van Hertem, and Hakan Ergun, arXiv:2412.00270v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.10263v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\nd & SDCD-GPU \\\\\n\\midrule\n1000 & 1438.7 ${\\scriptstyle \\pm 59.2}$ \\\\\n2000 & 3356.7 ${\\scriptstyle \\pm 70.0}$ \\\\\n3000 & 5172.5 ${\\scriptstyle \\pm 89.8}$ \\\\\n4000 & 7567.0 ${\\scriptstyle \\pm 343.7}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Detailed results of SHD means and standard deviations from .}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Stable Differentiable Causal Discovery", "authors": ["Achille Nazaret", "Justin Hong", "Elham Azizi", "David Blei"], "url": "https://arxiv.org/abs/2311.10263v2", "attribution": "\"Stable Differentiable Causal Discovery\" by Achille Nazaret, Justin Hong, Elham Azizi, and David Blei, arXiv:2311.10263v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.11874v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The asymptotic variance of the ATT estimator when weights are unknown ($\\Sigma$) and known ($\\Sigma^*$), and the ratio (Unknown / Known) of the asymptotic standard deviations (SD Ratio).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrr}\n \\hline\n Scenario & $\\Sigma$ & $\\Sigma^*$ & SD Ratio \\\\\n \\hline\n(i) & 3.90 & 2.26 & 1.31 \\\\ \n (ii) & 1.36 & 4.33 & 0.56 \\\\ \n (iii) & 4.37 & 3.59 & 1.10 \\\\ \n (iv) & 11.28 & 24.50 & 0.68 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On variance of the treatment effect in the treated using inverse probability weighting", "authors": ["Sarah A. Reifeis", "Michael G. Hudgens"], "url": "https://arxiv.org/abs/2011.11874v1", "attribution": "\"On variance of the treatment effect in the treated using inverse probability weighting\" by Sarah A. Reifeis and Michael G. Hudgens, arXiv:2011.11874v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2310.15964v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Effects of the minimum wage introduction and its raises on regional employment \\textbf{With ``GDP per capita x quarter'' as additional control}}\n\\begin{tabular}{lccc}\n \\toprule\n \\toprule\n & (1) & (2) & (3) \\\\\n VARIABLES & Dependent & Employment & Marginal \\\\\n & employment & subject to SSC & employment \\\\\n \\midrule\n & & & \\\\\n Treatment 2014 & -0.00295** & -0.000136 & -0.0112* \\\\\n & (0.00144) & (0.00152) & (0.00589) \\\\\n Placebo & 0.000601 & 0.00142 & -0.00125 \\\\\n & (0.00111) & (0.00107) & (0.00245) \\\\\n Treatment 2014 x (Time $>$ 2016) & -0.00250 & 0.00151 & -0.0171*** \\\\\n & (0.00251) & (0.00256) & (0.00499) \\\\\n Treatment 2018 x (Time $>$ 2018) & -0.00773*** & -0.00627*** & -0.0181*** \\\\\n & (0.00236) & (0.00240) & (0.00545) \\\\\n Treatment 2018 x (Time $>$ 2019) & -0.000188 & 0.000870 & -0.00580*** \\\\\n & (0.00113) & (0.00115) & (0.00216) \\\\\n Treatment 2018 x (Time $>$ 2020) & 0.000748 & 0.000969 & -0.000631 \\\\\n & (0.000966) & (0.00102) & (0.00246) \\\\\n Treatment 2018 x (Time $>$ 2021) & -0.000849 & -0.000882 & -0.000636 \\\\\n & (0.000654) & (0.000688) & (0.00189) \\\\\n \\midrule\n Observations & 9,509 & 9,509 & 9,509 \\\\\n R$^{2}$ (within) & 0.605 & 0.588 & 0.500 \\\\\n \\midrule\n Labour market region FE & X & X & X \\\\\n Quarter FE & X & X & X \\\\\n Controls & X & X & X \\\\\n \\bottomrule\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Long-Term Employment Effects of the Minimum Wage in Germany: New Data and Estimators", "authors": ["Marco Caliendo", "Nico Pestel", "Rebecca Olthaus"], "url": "https://arxiv.org/abs/2310.15964v1", "attribution": "\"Long-Term Employment Effects of the Minimum Wage in Germany: New Data and Estimators\" by Marco Caliendo, Nico Pestel, and Rebecca Olthaus, arXiv:2310.15964v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.14642v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c} \n& $T_1$ & $T^C_1$ & $T_2$ & $T^C_2$\\\\\n \\hline\n Fidelity & 0.51 & 0.45 & 0.57 & 0.90\\\\ \n Diversity & 1.0 & 1.0 & 1.0 & 0.99\\\\\n TopF1 & 0.68 & 0.62 & 0.72 & 0.94\\\\ \n \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{TopP\\&R scores for VQalAttent}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "VQalAttent: a Transparent Speech Generation Pipeline based on Transformer-learned VQ-VAE Latent Space", "authors": ["Armani Rodriguez", "Silvija Kokalj-Filipovic"], "url": "https://arxiv.org/abs/2411.14642v1", "attribution": "\"VQalAttent: a Transparent Speech Generation Pipeline based on Transformer-learned VQ-VAE Latent Space\" by Armani Rodriguez and Silvija Kokalj-Filipovic, arXiv:2411.14642v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12246v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results for the $AAUC_{5\\%}$ measure on the synthetic and real data.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l||c|c|c||c|c|c}\n \\multirow{2}{*}{category} & \\multirow{2}{*}{algorithm name} & \\multicolumn{3}{c||}{synthetic data} & \\multicolumn{3}{c}{real data} \\\\\n && none & $\\mathcal{S}_{\\leq1}$ & $\\mathcal{S}_{\\leq2}$ & none & $\\mathcal{S}_{\\leq1}$ & $\\mathcal{S}_{\\leq2}$ \\\\\n\\hline\n\\multirow[c]{5}{24mm}{non-specific syndromic surveillance}\n&WSARE 2.0 & -- & 3.028 & 2.963 & -- & 0.661 & 0.590 \\\\\n&WSARE 2.5 & -- & 1.099 & 1.321 & -- & 0.917 & 0.867 \\\\\n&WSARE 3.0 & -- & 0.803 & 0.899 & -- & 0.882 & 0.847 \\\\\n&DMSS & 2.430 & -- & -- & 0.953 & -- & -- \\\\\n&Eigenevent & 4.993 & -- & -- & 0.878 & -- & -- \\\\\n\\hline \n\\multirow{7}{24mm}{anomaly detectors}\n&one-class SVM & -- & 1.043 & 1.262 & -- & 0.468 & 0.495 \\\\\n&local outlier factor & -- & 2.000 & 2.260 & -- & 0.642 & 0.610 \\\\\n&Gaussian mixture model & -- & 1.117 & 3.547 & -- & 0.444 & 0.791\\\\\n&isolation forest & -- & 4.576 & 4.948 & -- & 0.873 & 0.835 \\\\\n&COPOD & -- & 5.216 & 5.032 & -- & 0.816 & 0.800 \\\\\n&autoencoder & -- & 1.521 & 1.643 & -- & 0.550 & 0.576 \\\\\n&GAAL & -- & 7.024 & 6.766 & -- & 0.792 & 0.866 \\\\\n\\hline \n\\multirow{3}{24mm}{global benchmarks}\n&control chart & 5.086 & -- & -- & 0.891 & -- & -- \\\\\n&moving average & 7.012 & -- & -- & 0.910 & -- & -- \\\\\n&linear regression & 3.279 & -- & -- & 0.819 & -- & -- \\\\\n\\hline\n\\multirow{3}{24mm}{syndrome-based benchmarks}\n&Gaussian & -- & 0.806 & 0.941 & -- & 0.328 & 0.267 \\\\\n&Poisson & -- & 1.294 & 1.347 & -- & 0.598 & 0.486 \\\\\n&negative binomial & -- & 0.895 & 0.958 & -- & 0.299 & 0.216 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Revisiting Non-Specific Syndromic Surveillance", "authors": ["Moritz Kulessa", "Eneldo Loza Mencía", "Johannes Fürnkranz"], "url": "https://arxiv.org/abs/2101.12246v1", "attribution": "\"Revisiting Non-Specific Syndromic Surveillance\" by Moritz Kulessa, Eneldo Loza Mencía, and Johannes Fürnkranz, arXiv:2101.12246v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.17126v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\hline\n$n$ & 4-fold CV d.f. & MSE of $\\hat{\\alpha}(X)$ & 4-fold CV d.f. & MSE of $\\hat{\\alpha}(X, \\hat{\\eta})$ \\\\ \\hline\n$5,000$ & 3 & 0.16 & 3 & 0.22 \\\\ \\hline\n$10,000$ & 3 & 0.04 & 3 & 0.08 \\\\ \\hline\n$20,000$ & 3 & 0.04 & 3 & 0.05 \\\\ \\hline\n$50,000$ & 3 & 0.01 & 3 & 0.03 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The MSEs of $\\hat{\\alpha}(X)$ and $\\hat{\\alpha}(X, \\hat{\\eta})$ tend to decrease as the sample size $n$ increases.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Estimation and Inference for the Average Treatment Effect in a Score-Explained Heterogeneous Treatment Effect Model", "authors": ["Kevin Christian Wibisono", "Debarghya Mukherjee", "Moulinath Banerjee", "Ya'acov Ritov"], "url": "https://arxiv.org/abs/2504.17126v1", "attribution": "\"Estimation and Inference for the Average Treatment Effect in a Score-Explained Heterogeneous Treatment Effect Model\" by Kevin Christian Wibisono, Debarghya Mukherjee, Moulinath Banerjee, and Ya'acov Ritov, arXiv:2504.17126v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17798v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n $x$ & $I_{\\infty}(x)$ & $\\frac{dI_{\\infty}(x)}{dx}$ \\\\ \\hline \\hline\n $x_{\\rm max} = 0.051143193209885154$& $I_{\\rm max} = 0.015417976156715866$ & 0 \\\\\n $0.05351939825528394$ & $0.015414622120595075$ & $-0.0028148833020992196$ \\\\\n $0.05589560330068273$ & $0.015404637416021267$ & $-0.005580857722584676$ \\\\\n $\\vdots$ & $\\vdots$ & $\\vdots$ \\\\\n $0.2151013413424015$ & $0.006197891516995346$ & $-0.06709282841464406$ \\\\\n $0.21747754638780026$ & $0.00603983091028578$ & $-0.06593178042834522$ \\\\\n $\\vdots$ & $\\vdots$ & $\\vdots$\\\\\n $0.2816350826135675$ & $0.0034130953188769644$ & $-0.006683882435937927$\\\\\n $0.2840112876589663$ & $0.003401116673691529$ & $-0.0033842365907062744$ \\\\\n $x_{\\rm min} = 0.2863874927043651$& $I_{\\rm min} = 0.003397079040195275$ & $0$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{We have $\\frac{dI_\\infty}{dx}<0$ for $I_\\infty \\in (I_{\\max}, I_{\\min})$. Parameters are set according to~.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Fractional order induced bifurcations in Caputo-type denatured Morris-Lecar neurons", "authors": ["Indranil Ghosh", "Hammed Olawale Fatoyinbo"], "url": "https://arxiv.org/abs/2502.17798v1", "attribution": "\"Fractional order induced bifurcations in Caputo-type denatured Morris-Lecar neurons\" by Indranil Ghosh and Hammed Olawale Fatoyinbo, arXiv:2502.17798v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.14635v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{pifont}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Potential choices of \\( \\mathcal{I}(\\cdot) \\) and their properties. En, CoEn and RlEn represent entropy, conditional entropy and relative entropy, respectively.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\hline\n & Mean & Variance & En & CoEn & RlEn \\\\ \\hline\n Transformation invariant & \\ding{56} & \\ding{56} & \\ding{56} & \\ding{56} & \\ding{52} \\\\\n Background noise-free & \\ding{52} & \\ding{56} & \\ding{56} & \\ding{56} & \\ding{52} \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modelling Loss of Complexity in Intermittent Time Series and its Application", "authors": ["Jie Li", "Jian Zhang", "Samantha L. Winter", "Mark Burnley"], "url": "https://arxiv.org/abs/2411.14635v2", "attribution": "\"Modelling Loss of Complexity in Intermittent Time Series and its Application\" by Jie Li, Jian Zhang, Samantha L. Winter, and Mark Burnley, arXiv:2411.14635v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05831v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Different estimation approaches applied to simulated count time series from INGARCH$(1,1)$-CMEM with Poi-counting series, like in Table~, but by assuming a Bin-counting series (model misspecification): Mean, simulated standard error (SSE), and mean approximated standard error (ASE) for different sample sizes~$n$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrcrrrrcrrrr}\n\\toprule\n$n$ & & Param. & PQ & NQ & EQ & 2W & Param. & PQ & NQ & EQ & 2W \\\\\n\\midrule\n300 & Mean & $a_0=2.8$ & 2.997 & 2.922 & 2.915 & 2.936 & $a_0=2.8$ & 2.965 & 2.936 & 2.932 & 2.937 \\\\\n & SSE & & 0.722 & 0.686 & 0.688 & 0.696 & & 0.681 & 0.668 & 0.671 & 0.669 \\\\\n & ASE & & 0.790 & 0.688 & 0.685 & 0.689 & & 0.727 & 0.673 & 0.671 & 0.672 \\\\\n600 & Mean & & 2.904 & 2.858 & 2.854 & 2.865 & & 2.889 & 2.875 & 2.874 & 2.875 \\\\\n & SSE & & 0.529 & 0.492 & 0.493 & 0.498 & & 0.488 & 0.474 & 0.476 & 0.475 \\\\\n & ASE & & 0.544 & 0.477 & 0.475 & 0.476 & & 0.503 & 0.468 & 0.467 & 0.467 \\\\\n1000 & Mean & & 2.871 & 2.839 & 2.837 & 2.841 & & 2.855 & 2.844 & 2.843 & 2.843 \\\\\n & SSE & & 0.406 & 0.375 & 0.376 & 0.379 & & 0.380 & 0.366 & 0.367 & 0.366 \\\\\n & ASE & & 0.419 & 0.367 & 0.366 & 0.367 & & 0.387 & 0.360 & 0.359 & 0.359 \\\\\n\\midrule\n300 & Mean & $a_1=0.4$ & 0.384 & 0.393 & 0.394 & 0.393 & $a_1=0.4$ & 0.392 & 0.395 & 0.396 & 0.396 \\\\\n & SSE & & 0.081 & 0.077 & 0.077 & 0.077 & & 0.062 & 0.060 & 0.060 & 0.060 \\\\\n & ASE & & 0.085 & 0.075 & 0.075 & 0.075 & & 0.064 & 0.059 & 0.059 & 0.059 \\\\\n600 & Mean & & 0.392 & 0.397 & 0.397 & 0.397 & & 0.397 & 0.398 & 0.399 & 0.398 \\\\\n & SSE & & 0.057 & 0.053 & 0.053 & 0.053 & & 0.044 & 0.042 & 0.043 & 0.042 \\\\\n & ASE & & 0.061 & 0.053 & 0.053 & 0.053 & & 0.045 & 0.042 & 0.042 & 0.042 \\\\\n1000 & Mean & & 0.395 & 0.399 & 0.399 & 0.399 & & 0.398 & 0.399 & 0.399 & 0.399 \\\\\n & SSE & & 0.045 & 0.041 & 0.041 & 0.041 & & 0.034 & 0.033 & 0.033 & 0.033 \\\\\n & ASE & & 0.048 & 0.042 & 0.041 & 0.041 & & 0.035 & 0.033 & 0.033 & 0.033 \\\\\n\\midrule\n300 & Mean & $b_1=0.2$ & 0.180 & 0.184 & 0.185 & 0.182 & $b_1=0.2$ & 0.182 & 0.183 & 0.184 & 0.183 \\\\\n & SSE & & 0.121 & 0.116 & 0.117 & 0.118 & & 0.112 & 0.110 & 0.111 & 0.110 \\\\\n & ASE & & 0.130 & 0.116 & 0.115 & 0.116 & & 0.120 & 0.112 & 0.112 & 0.112 \\\\\n600 & Mean & & 0.189 & 0.192 & 0.193 & 0.191 & & 0.190 & 0.190 & 0.190 & 0.190 \\\\\n & SSE & & 0.089 & 0.083 & 0.084 & 0.084 & & 0.080 & 0.079 & 0.079 & 0.079 \\\\\n & ASE & & 0.090 & 0.081 & 0.080 & 0.080 & & 0.083 & 0.078 & 0.078 & 0.078 \\\\\n1000 & Mean & & 0.192 & 0.194 & 0.195 & 0.194 & & 0.194 & 0.194 & 0.194 & 0.194 \\\\\n & SSE & & 0.068 & 0.064 & 0.064 & 0.064 & & 0.063 & 0.061 & 0.062 & 0.061 \\\\\n & ASE & & 0.069 & 0.062 & 0.062 & 0.062 & & 0.064 & 0.060 & 0.060 & 0.060 \\\\\n\\midrule\n300 & Mean & $\\sigma^2=1$ & 1.174 & 1.171 & 1.171 & 1.173 & $\\sigma^2=0.4$ & 0.556 & 0.556 & 0.556 & 0.556 \\\\\n & SSE & & 0.124 & 0.124 & 0.124 & 0.125 & & 0.058 & 0.058 & 0.058 & 0.058 \\\\\n & ASE & & 0.128 & 0.127 & 0.127 & 0.128 & & 0.046 & 0.046 & 0.046 & 0.046 \\\\\n600 & Mean & & 1.173 & 1.172 & 1.172 & 1.172 & & 0.557 & 0.557 & 0.557 & 0.557 \\\\\n & SSE & & 0.087 & 0.087 & 0.087 & 0.087 & & 0.040 & 0.040 & 0.040 & 0.040 \\\\\n & ASE & & 0.092 & 0.091 & 0.091 & 0.091 & & 0.033 & 0.033 & 0.033 & 0.033 \\\\\n1000 & Mean & & 1.172 & 1.171 & 1.171 & 1.171 & & 0.557 & 0.557 & 0.557 & 0.557 \\\\\n & SSE & & 0.067 & 0.067 & 0.067 & 0.067 & & 0.031 & 0.031 & 0.031 & 0.031 \\\\\n & ASE & & 0.071 & 0.071 & 0.071 & 0.071 & & 0.025 & 0.025 & 0.025 & 0.025 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Conditional-mean Multiplicative Operator Models for Count Time Series", "authors": ["Christian H. Weiß", "Fukang Zhu"], "url": "https://arxiv.org/abs/2212.05831v2", "attribution": "\"Conditional-mean Multiplicative Operator Models for Count Time Series\" by Christian H. Weiß and Fukang Zhu, arXiv:2212.05831v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.02011v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Component-wise Stress Impact During the 2008 Global Financial Crisis}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Latent Factor} & $\\Delta$VaR & $\\Delta$ES & $\\Delta$Drawdown \\\\\n\\midrule\nZ1 & $-0.0066$ & $-0.0061$ & $+0.2367$ \\\\\nZ2 & $+0.0012$ & $+0.0011$ & $-0.3211$ \\\\\nZ3 & $+0.0014$ & $+0.0012$ & $-0.3899$ \\\\\nZ4 & $-0.0063$ & $-0.0062$ & $+0.2251$ \\\\\nZ5 & $+0.0017$ & $+0.0016$ & $-0.4353$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios", "authors": ["Vidya Sagar G", "Shifat Ali", "Siddhartha P. Chakrabarty"], "url": "https://arxiv.org/abs/2507.02011v1", "attribution": "\"Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios\" by Vidya Sagar G, Shifat Ali, and Siddhartha P. Chakrabarty, arXiv:2507.02011v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06919v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Symbols and definitions.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\t\t\\hline\n\t\t\\textbf{Symbol} & \\textbf{Definition} \\\\\n\t\t\\hline\n\t\t$G_t = (V_t, E_t)$ & the snapshot $G_t$ with node set $V_t$ and edge set $E_t$ \\\\\n\t\t\\hline\n\t\t$N$ & the final timestamp \\\\\n\t\t\\hline\n\t\t$\\textbf{W}_t$, $\\textbf{H}_t$ & the similarity matrices for $G_t$ \\\\\n\t\t\\hline\n\t\t$\\textbf{U}$ & the global representation matrix \\\\\n\t\t\\hline\n\t\t$\\textbf{V}_t$ & the temporary representation matrix for $G_t$ \\\\\n\t\t\\hline\n\t\t$m$ & the dimension of node representation \\\\\n\t\t\\hline\n\t\t$\\theta$ & decay weight\\\\\n\t\t\\hline\n\t\t$\\lambda$ & smoothness weight\\\\\n\t\t\\hline\n\t\t$\\gamma$ & control the importance of constraint terms \\\\\n\t\t\\hline\n\t\t$k$ & the steps of random walk \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Link Prediction and Unlink Prediction on Dynamic Networks", "authors": ["Christina Muro", "Boyu Li", "Kun He"], "url": "https://arxiv.org/abs/2101.06919v2", "attribution": "\"Link Prediction and Unlink Prediction on Dynamic Networks\" by Christina Muro, Boyu Li, and Kun He, arXiv:2101.06919v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.05824v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|c|c|c}\n & PCA & PB & FPC \\\\ \n \\hline\n$\\hat\\Lambda_{\\text{inf}}$ & 107.26 & 12306.19 & 51776.87 \\\\ \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Teststatistic for the independent noise test on the residuals of the spatial Canadian Weather Data.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Preprocessing noisy functional data: a multivariate perspective", "authors": ["Siegfried Hörmann", "Fatima Jammoul"], "url": "https://arxiv.org/abs/2012.05824v2", "attribution": "\"Preprocessing noisy functional data: a multivariate perspective\" by Siegfried Hörmann and Fatima Jammoul, arXiv:2012.05824v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.09239v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\hline\n\\hline\ndate & n & climate change & covid & price & taxes & inequality \\\\ \n \\hline\n 2000-01-01 & 5 & 0 & 0 & 5 & 1 & 0 \\\\ \n 2000-02-01 & 4 & 0 & 0 & 3 & 0 & 0 \\\\ \n 2000-03-01 & 5 & 0 & 0 & 5 & 1 & 0 \\\\ \n 2000-04-01 & 4 & 0 & 0 & 3 & 0 & 0 \\\\ \n 2000-05-01 & 7 & 0 & 0 & 6 & 2 & 0 \\\\ \n 2000-06-01 & 7 & 0 & 0 & 6 & 1 & 0 \\\\ \n 2000-07-01 & 1 & 0 & 0 & 1 & 0 & 0 \\\\ \n 2000-08-01 & 1 & 0 & 0 & 1 & 0 & 0 \\\\ \n 2000-09-01 & 15 & 1 & 0 & 15 & 2 & 0 \\\\ \n 2000-10-01 & 6 & 0 & 0 & 6 & 0 & 0 \\\\ \n 2000-11-01 & 10 & 0 & 0 & 10 & 1 & 0 \\\\ \n 2000-12-01 & 5 & 0 & 0 & 4 & 1 & 0 \\\\ \n 2001-01-01 & 5 & 0 & 0 & 5 & 0 & 0 \\\\ \n 2001-02-01 & 6 & 0 & 0 & 6 & 1 & 0 \\\\ \n 2001-03-01 & 3 & 0 & 0 & 2 & 1 & 1 \\\\ \n 2001-04-01 & 2 & 0 & 0 & 1 & 0 & 0 \\\\ \n 2001-05-01 & 8 & 0 & 0 & 7 & 3 & 0 \\\\ \n 2001-06-01 & 6 & 0 & 0 & 6 & 0 & 0 \\\\ \n 2001-07-01 & 1 & 0 & 0 & 1 & 0 & 0 \\\\ \n 2001-08-01 & 4 & 0 & 0 & 2 & 0 & 0 \\\\ \n 2001-09-01 & 8 & 0 & 0 & 8 & 0 & 0 \\\\ \n 2001-10-01 & 8 & 0 & 0 & 8 & 0 & 0 \\\\ \n 2001-11-01 & 9 & 0 & 0 & 8 & 0 & 0 \\\\ \n 2001-12-01 & 5 & 0 & 0 & 5 & 1 & 0 \\\\ \n 2002-01-01 & 4 & 0 & 0 & 4 & 1 & 0 \\\\ \n 2002-02-01 & 7 & 0 & 0 & 7 & 2 & 0 \\\\ \n 2002-03-01 & 9 & 0 & 0 & 8 & 1 & 0 \\\\ \n 2002-04-01 & 7 & 0 & 0 & 6 & 0 & 0 \\\\ \n 2002-05-01 & 7 & 0 & 0 & 6 & 1 & 0 \\\\ \n 2002-06-01 & 4 & 0 & 0 & 2 & 0 & 0 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\ ECB Speeches. 1997-2022}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Interactions of Social Norms about Climate Change: Science, Institutions and Economics", "authors": ["Antonio Cabrales", "Manu García", "David Ramos Muñoz", "Angel Sánchez"], "url": "https://arxiv.org/abs/2208.09239v1", "attribution": "\"The Interactions of Social Norms about Climate Change: Science, Institutions and Economics\" by Antonio Cabrales, Manu García, David Ramos Muñoz, and Angel Sánchez, arXiv:2208.09239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.00145v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Generator specs for our $26$-unit grid UC instance. The data are taken from Ref.~.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\\toprule\n & $p_{j, \\min}$ & $p_{j, \\max}$ & $c_j$ & $b_j$ & $a_j$ \\\\\nUnit & (MW) & (MW) & & & \\\\\n\\midrule\n0 & 2.4 & 12 & 24.3891 & 25.55 & 0.02533 \\\\\n1 & 2.4 & 12 & 24.411 & 25.68 & 0.02649 \\\\\n2 & 2.4 & 12 & 24.6382 & 25.8 & 0.02801 \\\\\n3 & 2.4 & 12 & 24.7605 & 25.93 & 0.02842 \\\\\n4 & 2.4 & 12 & 24.8882 & 26.06 & 0.02855 \\\\\n5 & 4 & 20 & 117.755 & 37.55 & 0.01199 \\\\\n6 & 4 & 20 & 118.108 & 37.66 & 0.01261 \\\\\n7 & 4 & 20 & 118.458 & 37.78 & 0.01359 \\\\\n8 & 4 & 20 & 118.821 & 37.89 & 0.01433 \\\\\n9 & 15.2 & 76 & 81.1364 & 13.33 & 0.00876 \\\\\n10 & 15.2 & 76 & 81.298 & 13.36 & 0.00895 \\\\\n11 & 15.2 & 76 & 81.4641 & 13.38 & 0.0091 \\\\\n12 & 15.2 & 76 & 81.6259 & 13.41 & 0.00932 \\\\\n13 & 25 & 100 & 217.895 & 18 & 0.00623 \\\\\n14 & 25 & 100 & 218.335 & 18.1 & 0.00612 \\\\\n15 & 25 & 100 & 218.775 & 18.2 & 0.00598 \\\\\n16 & 54.25 & 155 & 142.735 & 10.69 & 0.00463 \\\\\n17 & 54.25 & 155 & 143.029 & 10.72 & 0.00473 \\\\\n18 & 54.25 & 155 & 143.318 & 10.74 & 0.00481 \\\\\n19 & 54.25 & 155 & 143.597 & 10.76 & 0.00487 \\\\\n20 & 68.95 & 197 & 259.131 & 23 & 0.00259 \\\\\n21 & 68.95 & 197 & 259.649 & 23.1 & 0.0026 \\\\\n22 & 68.95 & 197 & 260.176 & 23.2 & 0.00263 \\\\\n23 & 140 & 350 & 177.058 & 10.86 & 0.00153 \\\\\n24 & 100 & 400 & 310.002 & 7.49 & 0.00194 \\\\\n25 & 100 & 400 & 311.91 & 7.5 & 0.00195 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A New Hybrid Quantum-Classical Algorithm for Solving the Unit Commitment Problem", "authors": ["Willie Aboumrad", "Phani R V Marthi", "Suman Debnath", "Martin Roetteler", "Evgeny Epifanovsky"], "url": "https://arxiv.org/abs/2505.00145v1", "attribution": "\"A New Hybrid Quantum-Classical Algorithm for Solving the Unit Commitment Problem\" by Willie Aboumrad, Phani R V Marthi, Suman Debnath, Martin Roetteler, and Evgeny Epifanovsky, arXiv:2505.00145v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.12285v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n \\ & \\multicolumn{2}{c}{ \\textbf{Original data}} & \\multicolumn{2}{c}{ \\textbf{After removing outliers}} \\\\\n \\textbf{Independent variables} & \\textbf{Kurtosis} & \\textbf{Skewness} & \\textbf{Kurtosis} & \\textbf{Skewness} \\\\\ncf\\_total\\_seconds & 130.29 & 8.97 & 44.65 & 5.21 \\\\\ncf\\_total\\_days & 108.01 & 9.98 & 101.01 & 9.76 \\\\\ngivaways\\_totals & 168.89 & 12.31 & 212.32 & 14.16 \\\\\npt\\_totals & 238.28 & 14.61 & 212.39 & 14.15 \\\\\nfm\\_total & 190.58 & 13.07 & 212.32 & 14.16 \\\\\nsch\\_total & 169.63 & 11.56 & 1.14 & 1.22 \\\\\nad\\_total & 535.18 & 22.62 & 314.46 & 17.23 \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Effects of Just-in-time Delivery on Social Engagement: A Cluster Analysis", "authors": ["Moisés Ramírez", "Raziel Ruíz", "Nathan Klarer"], "url": "https://arxiv.org/abs/2212.12285v1", "attribution": "\"The Effects of Just-in-time Delivery on Social Engagement: A Cluster Analysis\" by Moisés Ramírez, Raziel Ruíz, and Nathan Klarer, arXiv:2212.12285v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00561v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc|}\n\\hline\nInstances & Potential Sites & Customers & $p$ & $\\#$ Instances \\\\ \\hline\n\\multirow{4}{*}{AP} & 10 & 10 & 3, 5 & 10 \\\\\n & 15 & 15 & 3, 5, 7 & 15 \\\\\n & 20 & 20 & 3, 5, 7 & 15 \\\\\n & 25 & 25 & 3, 5, 7 & 15 \\\\ \\hline\n\\multirow{4}{*}{CAB} & 10 & 10 & 3, 5 & 10 \\\\\n & 15 & 15 & 3, 5, 7 & 15 \\\\\n & 20 & 20 & 3, 5, 7 & 15 \\\\\n & 25 & 25 & 3, 5, 7 & 15 \\\\ \\hline\n\\multirow{4}{*}{TR} & 10 & 10 & 3, 5 & 10 \\\\\n & 15 & 15 & 3, 5, 7 & 15 \\\\\n & 20 & 20 & 3, 5, 7 & 15 \\\\\n & 25 & 25 & 3, 5, 7 & 15 \\\\ \\hline\n & & & Total & 165 \\\\ \\cline{4-5} \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Summary of the tested instances.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology", "authors": ["Víctor Blanco", "José-Fernando Camacho-Vallejo", "Carlos Corpus"], "url": "https://arxiv.org/abs/2503.00561v1", "attribution": "\"A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology\" by Víctor Blanco, José-Fernando Camacho-Vallejo, and Carlos Corpus, arXiv:2503.00561v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.17563v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results of Multi-turn Memory}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\hline\n\\textbf{Model} & \\textbf{Accuracy (\\%) $\\uparrow$} \\\\\n\\hline\nGLM-4-Voice & 80.00 \\\\\nMiniCPM-o 2.6 & 86.67 \\\\\nBaichuan-Omni-1.5 & 78.67 \\\\\nSpeechGPT-2.0-preview & 20.00 \\\\\nFreeze-Omni & 62.67 \\\\\nQwen2.5-Omni & 88.67 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "BoSS: Beyond-Semantic Speech", "authors": ["Qing Wang", "Zehan Li", "Hang Lv", "Hongjie Chen", "Yaodong Song", "Jian Kang", "Jie Lian", "Jie Li", "Yongxiang Li", "Zhongjiang He", "Xuelong Li"], "url": "https://arxiv.org/abs/2507.17563v1", "attribution": "\"BoSS: Beyond-Semantic Speech\" by Qing Wang, Zehan Li, Hang Lv, Hongjie Chen, Yaodong Song, Jian Kang, Jie Lian, Jie Li, Yongxiang Li, Zhongjiang He, and Xuelong Li, arXiv:2507.17563v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.19929v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\toprule\n Iteration $k$ & $x_k$ (Normal) & $x_k$ (Adaptive) & $\\alpha_k$ (Normal) & $\\alpha_k$ (Adaptive) & $\\beta_k$ (Adaptive) \\\\\n \\midrule\n 0 & (0.000, 0.000) & (0.000, 0.000) & 0.25 & 0.38 & 0.00 \\\\\n 1 & (0.250, 0.500) & (0.380, 0.760) & 0.25 & 0.42 & 0.10 \\\\\n 2 & (0.375, 0.625) & (0.634, 1.012) & 0.25 & 0.45 & 0.18 \\\\\n 3 & (0.484, 0.707) & (0.801, 1.187) & 0.25 & 0.48 & 0.24 \\\\\n 4 & (0.573, 0.766) & (0.907, 1.298) & 0.25 & 0.50 & 0.30 \\\\\n 5 & (0.647, 0.810) & (0.973, 1.370) & 0.25 & 0.52 & 0.35 \\\\\n 6 & (0.709, 0.843) & (1.013, 1.417) & 0.25 & 0.53 & 0.40 \\\\\n 7 & (0.762, 0.870) & (1.037, 1.448) & 0.25 & 0.54 & 0.44 \\\\\n 8 & (0.807, 0.892) & (1.051, 1.468) & 0.25 & 0.55 & 0.47 \\\\\n 9 & (0.846, 0.910) & (1.058, 1.480) & 0.25 & 0.56 & 0.50 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of Normal and Adaptive Steepest Descent with Momentum}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Generalized Steepest Descent Methods on Riemannian Manifolds and Hilbert Spaces: Convergence Analysis and Stochastic Extensions", "authors": ["Rashid A.", "Amal A Samad"], "url": "https://arxiv.org/abs/2502.19929v1", "attribution": "\"Generalized Steepest Descent Methods on Riemannian Manifolds and Hilbert Spaces: Convergence Analysis and Stochastic Extensions\" by Rashid A. and Amal A Samad, arXiv:2502.19929v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.14139v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average cross-domain meta-test accuracy scores over 5 runs using a Conv-4 backbone. Techniques trained on tasks from one data set and were evaluated on tasks from another data set. The 95\\% confidence intervals, computed over the mean performances of the 3 different random seeds, are displayed as $\\pm$ x. The ``-\" indicates that the method did not finish within 2 days of running time. The best performances are dislpayed in bold font.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\n& \\multicolumn{2}{c}{MIN $\\rightarrow$ CUB} & \\multicolumn{2}{c}{CUB $\\rightarrow$ MIN} \\\\\n\\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n & 1-shot & 5-shot & 1-shot & 5-shot \\\\ \n\\midrule\nMAML & 37.9 $\\pm$ 2.22 & 53.6 $\\pm$ 0.67 & 31.1 $\\pm$ 1.19 & 45.8 $\\pm$ 2.06 \\\\\nWarp-MAML & 42.0 $\\pm$ 0.85 & 56.9 $\\pm$ 4.16 & 31.1 $\\pm$ 1.59 & 41.3 $\\pm$ 1.37 \\\\\nSAP & 41.5 $\\pm$ 3.72 & 58.0 $\\pm$ 1.79 & 33.3 $\\pm$ 2.33 & 47.1 $\\pm$ 1.28 \\\\\nProtoNet & 39.7 $\\pm$ 4.11 & 56.0 $\\pm$ 4.89 & 31.7 $\\pm$ 0.20 & 45.3 $\\pm$ 1.84 \\\\\n\\midrule\nLSTM & 20.1 $\\pm$ 0.77 & 20.0 $\\pm$ 0.40 & - & -\\\\\nOP-LSTM (ours) & \\textbf{42.3} $\\pm$ 1.90 & \\textbf{58.5} $\\pm$ 1.49 & \\textbf{35.8} $\\pm$ 2.98 & \\textbf{49.0} $\\pm$ 0.80 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Are LSTMs Good Few-Shot Learners?", "authors": ["Mike Huisman", "Thomas M. Moerland", "Aske Plaat", "Jan N. van Rijn"], "url": "https://arxiv.org/abs/2310.14139v1", "attribution": "\"Are LSTMs Good Few-Shot Learners?\" by Mike Huisman, Thomas M. Moerland, Aske Plaat, and Jan N. van Rijn, arXiv:2310.14139v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14565v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Daily returns and descriptive statistics for selected portfolios}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\toprule\n \\textbf{Date/Metric} & \\textbf{asset 1} & \\textbf{asset 2} & \\textbf{asset 3} & \\textbf{asset 4} & \\textbf{asset 5} \\\\\n \\midrule\n \\multicolumn{6}{l}{\\textit{Daily Returns}} \\\\\n 2024-07-01 & $-1.01$ & $-0.72$ & $1.10$ & $-1.80$ & $-0.65$ \\\\\n 2024-07-02 & $-2.50$ & $-0.22$ & $-0.86$ & $-0.09$ & $0.64$ \\\\\n 2024-07-03 & $-0.38$ & $0.27$ & $-0.21$ & $0.41$ & $0.41$ \\\\\n 2024-07-05 & $-1.11$ & $0.15$ & $-0.33$ & $2.10$ & $-1.61$ \\\\\n 2024-07-08 & $0.44$ & $-0.22$ & $-0.64$ & $0.59$ & $0.33$ \\\\\n 2024-07-09 & $0.05$ & $-1.49$ & $1.52$ & $-2.59$ & $-0.26$ \\\\\n 2024-07-10 & $-0.34$ & $0.79$ & $1.31$ & $2.75$ & $1.93$ \\\\\n 2024-07-11 & $4.00$ & $1.60$ & $2.53$ & $0.73$ & $3.72$ \\\\\n 2024-07-12 & $1.76$ & $1.04$ & $-0.06$ & $0.41$ & $2.17$ \\\\\n 2024-07-15 & $1.53$ & $0.02$ & $-3.42$ & $-2.50$ & $-0.63$ \\\\\n 2024-07-16 & $2.77$ & $1.28$ & $2.59$ & $0.40$ & $1.83$ \\\\\n 2024-07-17 & $0.71$ & $0.69$ & $-1.44$ & $1.12$ & $0.94$ \\\\\n 2024-07-18 & $-2.24$ & $-1.24$ & $-1.74$ & $-1.72$ & $-1.78$ \\\\\n 2024-07-19 & $0.08$ & $-1.51$ & $-0.23$ & $-2.67$ & $-1.81$ \\\\\n 2024-07-22 & $0.35$ & $0.29$ & $-0.54$ & $-0.42$ & $-0.25$ \\\\\n 2024-07-23 & $2.55$ & $-0.09$ & $0.08$ & $-0.39$ & $1.14$ \\\\\n 2024-07-24 & $-2.21$ & $2.88$ & $-1.61$ & $-2.51$ & $-0.41$ \\\\\n 2024-07-25 & $1.67$ & $-0.21$ & $1.05$ & $0.00$ & $2.21$ \\\\\n 2024-07-26 & $0.17$ & $1.26$ & $2.29$ & $0.93$ & $2.13$ \\\\\n 2024-07-29 & $-0.97$ & $-0.79$ & $-0.48$ & $-0.48$ & $-1.13$ \\\\\n 2024-07-30 & $-0.11$ & $0.79$ & $0.98$ & $-1.65$ & $0.07$ \\\\\n 2024-07-31 & $0.24$ & $1.89$ & $0.72$ & $-1.14$ & $-1.23$ \\\\\n \\midrule\n \\multicolumn{6}{l}{\\textit{Descriptive Statistics}} \\\\\n Mean & $0.25$ & $0.29$ & $0.12$ & $-0.39$ & $0.35$ \\\\\n Median & $0.12$ & $0.21$ & $-0.13$ & $-0.24$ & $0.20$ \\\\\n Std Dev & $1.66$ & $1.11$ & $1.50$ & $1.53$ & $1.51$ \\\\\n Variance & $2.74$ & $1.24$ & $2.25$ & $2.35$ & $2.29$ \\\\\n Skewness & $0.34$ & $0.29$ & $-0.22$ & $0.09$ & $0.37$ \\\\\n Kurtosis & $-0.22$ & $-0.23$ & $-0.16$ & $-0.75$ & $-0.68$ \\\\\n Minimum & $-2.50$ & $-1.51$ & $-3.42$ & $-2.67$ & $-1.81$ \\\\\n Maximum & $4.00$ & $2.88$ & $2.59$ & $2.75$ & $3.72$ \\\\\n Percentile 25 & $-0.82$ & $-0.22$ & $-0.61$ & $-1.70$ & $-0.64$ \\\\\n Percentile 75 & $1.32$ & $0.97$ & $1.08$ & $0.54$ & $1.65$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Higher-Order Stochastic Dominance Constraints in Optimization", "authors": ["Rajmadan Lakshmanan", "Alois Pichler", "Miloš Kopa"], "url": "https://arxiv.org/abs/2501.14565v3", "attribution": "\"Higher-Order Stochastic Dominance Constraints in Optimization\" by Rajmadan Lakshmanan, Alois Pichler, and Miloš Kopa, arXiv:2501.14565v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10707v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lr} \n\\hline\nConfigurations\t &\tValues\t\t\t\t\\\\\n\\hline\n\\multicolumn{2}{c}{\\bf Pretraining} \\\\\n\\hline\nModel Architecture \t & Transformer (base) \\\\\nDevices & 8 Nvidia V100 GPU \\\\\nMax tokens per GPU\t\t& 5120\t\t\t\t\t\\\\\nUpdate Frequency & 8 \\\\\nOptimizer \t\t\t\t& Adam \t\t\t\t\t\\\\\n\t\t\t\t\t\t& ($\\beta_1$=0.9, $\\beta_2$=0.98, $\\epsilon$=$1\\times10^{-8}$)\t\\\\\nLearning rate \t\t\t& [$5\\times10^{-4}$, $7\\times10^{-4}$]\t\t\t\\\\\nLearning rate scheduler & polynomial decay \\\\\nWarmup & 8000 \\\\\nweight decay & 0.0 \\\\\nLoss Function \t\t\t& label smoothed cross entropy \\\\\n\t\t\t\t\t\t& (label-smoothing=0.1) \\\\\nDropout\t\t\t\t\t& [0.1, 0.2]\t\\\\\nSource Dropout & 0.2 \\\\\nTarget Dropout & 0.1 \\\\\nEdit-weighted MLE & 3 \\\\\n\\hline\n\\multicolumn{2}{c}{\\bf Fine-tuning} \\\\\n\\hline\nDevices & 1 Nvidia V100 GPU \\\\\nUpdate Frequency & [2, 4] \\\\\nLearning rate \t\t\t& [$3\\times10^{-4}$, $5\\times10^{-4}$, $7\\times10^{-4}$]\t\t\t\\\\\nDropout\t\t\t\t\t& [0.1, 0.2, 0.3]\t\\\\\nWarmup & 2000 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Hyper-parameters values of pretraining and fine-tuning on Falko-Merlin (German) and RULEC-GEC (Russian).}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model", "authors": ["Xin Sun", "Tao Ge", "Shuming Ma", "Jingjing Li", "Furu Wei", "Houfeng Wang"], "url": "https://arxiv.org/abs/2201.10707v1", "attribution": "\"A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model\" by Xin Sun, Tao Ge, Shuming Ma, Jingjing Li, Furu Wei, and Houfeng Wang, arXiv:2201.10707v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.04227v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fr\\'{e}chet inception distance (FID) as a quantitative measurement of the generated image's similarity compared to the real images for each of the three GAN architectures. WGAN-GP-SN showed the highest similarity to the real data in terms of FID.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n\t\t\\toprule\n\t\tGAN architecture & FID \\\\\n\t\t\\midrule\n\t\tDCGAN & 105.96 \\\\\n\t\tWGAN-GP & 52.53 \\\\\n\t\tWGAN-GP-SN & \\textbf{38.05} \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networks", "authors": ["Tabea Kossen", "Pooja Subramaniam", "Vince I. Madai", "Anja Hennemuth", "Kristian Hildebrand", "Adam Hilbert", "Jan Sobesky", "Michelle Livne", "Ivana Galinovic", "Ahmed A. Khalil", "Jochen B. Fiebach", "Dietmar Frey"], "url": "https://arxiv.org/abs/2009.04227v3", "attribution": "\"Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networks\" by Tabea Kossen, Pooja Subramaniam, Vince I. Madai, Anja Hennemuth, Kristian Hildebrand, Adam Hilbert, Jan Sobesky, Michelle Livne, Ivana Galinovic, Ahmed A. Khalil, Jochen B. Fiebach, and Dietmar Frey, arXiv:2009.04227v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.10957v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of real-world graph datasets used in the experiments.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n \\hline\n Dataset & Type & Task & \\#Nodes & \\#Edges \\\\ \\hline\n ogbn-mag & Heterogeneous & Classify 349 venues for each paper & 1,939,743 & 21,111,007 \\\\\n ogbn-arxiv & Homogeneous & Classify 40 primary categories for arXiv papers & 169,343 & 1,166,243 \\\\\n ogbn-papers100m & Homogeneous & Classify 172 subject areas for arXiv papers & 111,059,956 & 1,615,685,872 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs", "authors": ["Sejun Park", "Joo Young Park", "Hyunwoo Park"], "url": "https://arxiv.org/abs/2411.10957v1", "attribution": "\"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs\" by Sejun Park, Joo Young Park, and Hyunwoo Park, arXiv:2411.10957v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2010.15622v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|cccccccc||} \n \\hline\n env & $\\pi_{size}$ & $Q_{size}$ & $\\alpha_{\\pi}$ & $\\alpha_{Q}$ & $O_{\\pi}$ & $O_{Q}$ & $I_G$ & $I_V$ \\\\\n \\hline \\hline\n CartPole & 32 & [64,64] & .00125 & .005 & RMS & RMS & 3 & 3 \\\\\n LunarLander & 64 & [64,64] & .0025 & .005 & RMS & RMS & 5 & 3 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Low-Variance Policy Gradient Estimation with World Models", "authors": ["Michal Nauman", "Floris Den Hengst"], "url": "https://arxiv.org/abs/2010.15622v1", "attribution": "\"Low-Variance Policy Gradient Estimation with World Models\" by Michal Nauman and Floris Den Hengst, arXiv:2010.15622v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09170v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\toprule\n\\textbf{Model} & \\textbf{Dim.} & $\\mathbf{D}_w$ & $\\mathbf{D}_a$ & ${}^I_w\\mathbf{R}$ & ${}^I_a\\mathbf{R}$ & $\\mathbf{T}_g$ \\\\ \\midrule\n\\textit{imu0} & 0 & - & - & - & - & - \\\\ \\midrule\n\\textit{imu1} & 15 & $\\mathbf{D}_{w6}$ & $\\mathbf{D}_{a6}$ & ${}^I_w\\mathbf{R}$ & - & - \\\\\n\\textit{imu2} & 15 & $\\mathbf{D}_{w6}$ & $\\mathbf{D}_{a6}$ & - & ${}^I_a\\mathbf{R}$ & - \\\\\n\\textit{imu3} & 15 & $\\mathbf{D}_{w9}$ & $\\mathbf{D}_{a6}$ & - & - & - \\\\\n\\textit{imu4} & 15 & $\\mathbf{D}_{w6}$ & $\\mathbf{D}_{a9}$ & - & - & - \\\\ \\midrule\n\\textit{imu5} & 18 & $\\mathbf{D}_{w6}$ & $\\mathbf{D}_{a6}$ & ${}^I_w\\mathbf{R}$ & ${}^I_a\\mathbf{R}$ & - \\\\\n\\textit{imu6} & 24 & $\\mathbf{D}'_{w6}$ & $\\mathbf{D}'_{a6}$ & ${}^I_w\\mathbf{R}$ & - & $\\mathbf{T}_{g9}$ \\\\ \\midrule\n\\textit{imu11} & 21 & $\\mathbf{D}_{w6}$ & $\\mathbf{D}_{a6}$ & ${}^I_w\\mathbf{R}$ & - & $\\mathbf{T}_{g6}$ \\\\\n\\textit{imu12} & 21 & $\\mathbf{D}_{w6}$ & $\\mathbf{D}_{a6}$ & - & ${}^I_a\\mathbf{R}$ & $\\mathbf{T}_{g6}$ \\\\\n\\textit{imu13} & 21 & $\\mathbf{D}_{w9}$ & $\\mathbf{D}_{a6}$ & - & - & $\\mathbf{T}_{g6}$ \\\\\n\\textit{imu14} & 21 & $\\mathbf{D}_{w6}$ & $\\mathbf{D}_{a9}$ & - & - & $\\mathbf{T}_{g6}$ \\\\ \\midrule\n\\textit{imu21} & 24 & $\\mathbf{D}_{w6}$ & $\\mathbf{D}_{a6}$ & ${}^I_w\\mathbf{R}$ & - & $\\mathbf{T}_{g9}$ \\\\\n\\textit{imu22} & 24 & $\\mathbf{D}_{w6}$ & $\\mathbf{D}_{a6}$ & - & ${}^I_a\\mathbf{R}$ & $\\mathbf{T}_{g9}$ \\\\\n\\textit{imu23} & 24 & $\\mathbf{D}_{w9}$ & $\\mathbf{D}_{a6}$ & - & - & $\\mathbf{T}_{g9}$ \\\\\n\\textit{imu24} & 24 & $\\mathbf{D}_{w6}$ & $\\mathbf{D}_{a9}$ & - & - & $\\mathbf{T}_{g9}$ \\\\ \\midrule\n\\textit{imu31} & 9 & - & $\\mathbf{D}_{a9}$ & - & - & - \\\\\n\\textit{imu32} & 9 & $\\mathbf{D}_{w9}$ & - & - & - & - \\\\\n\\textit{imu33} & 6 & - & - & - & - & $\\mathbf{T}_{g6}$ \\\\\n\\textit{imu34} & 9 & - & - & - & - & $\\mathbf{T}_{g9}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Online Self-Calibration for Visual-Inertial Navigation Systems: Models, Analysis and Degeneracy", "authors": ["Yulin Yang", "Patrick Geneva", "Xingxing Zuo", "Guoquan Huang"], "url": "https://arxiv.org/abs/2201.09170v3", "attribution": "\"Online Self-Calibration for Visual-Inertial Navigation Systems: Models, Analysis and Degeneracy\" by Yulin Yang, Patrick Geneva, Xingxing Zuo, and Guoquan Huang, arXiv:2201.09170v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.01846v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrr}\n\\toprule\n{} & maize & pork & poultry & eggs & maize g. oil & beer & alc. beverages & sweeteners \\\\\n\\midrule\nAfrica N & 17.1 & 2.5 & 4.9 & 4.9 & 1.2 & 0.2 & 0.7 & 5.5 \\\\\nAfrica S & 1.9 & 1.0 & 0.9 & 0.4 & 1.6 & 1.3 & 0.3 & 0.3 \\\\\nAmerica S & 1.7 & 0.8 & 0.9 & 1.4 & 2.2 & 0.9 & 0.6 & 0.4 \\\\\nAmerica N & 0.1 & 0.1 & 0.0 & 0.0 & 0.1 & 0.4 & 0.4 & 0.0 \\\\\nAsia C & 2.2 & 0.7 & 2.5 & 0.3 & 1.8 & 0.3 & 0.7 & 11.4 \\\\\nAsia E & 1.0 & 0.3 & 0.5 & 0.4 & 1.6 & 0.8 & 0.3 & 1.2 \\\\\nAsia S & 5.9 & 0.1 & 5.6 & 2.3 & 6.5 & 0.0 & 0.0 & 0.1 \\\\\nAsia SE & 2.6 & 0.6 & 0.8 & 0.8 & 1.9 & 0.4 & 0.2 & 0.2 \\\\\nAsia W & 22.2 & 5.1 & 3.9 & 5.5 & 10.8 & 2.1 & 2.4 & 1.1 \\\\\nAustralia & 9.3 & 0.6 & 0.3 & 0.3 & 0.7 & 0.7 & 1.7 & 0.2 \\\\\nEurope E & 5.4 & 1.7 & 1.4 & 1.1 & 5.8 & 1.9 & 1.4 & 1.3 \\\\\nEurope N & 39.1 & 2.1 & 2.6 & 2.6 & 6.8 & 0.7 & 3.6 & 2.3 \\\\\nEurpe S & 30.1 & 11.4 & 15.8 & 15.8 & 23.0 & 4.5 & 0.7 & 10.0 \\\\\nEurope W & 15.7 & 3.4 & 3.9 & 4.4 & 4.8 & 4.6 & 1.8 & 3.2 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Relative loss in \\% of different food products in different world regions after a shock to the maize production of Ukraine. These numbers correspond to left half of each cell in the upper part of fig. 5 in the main text.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability", "authors": ["Moritz Laber", "Peter Klimek", "Martin Bruckner", "Liuhuaying Yang", "Stefan Thurner"], "url": "https://arxiv.org/abs/2210.01846v3", "attribution": "\"Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability\" by Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang, and Stefan Thurner, arXiv:2210.01846v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.14130v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n(n,m)&$ (2000,10)$& $(5000,20)$ & $(10000,50)$\\\\ \\hline\nBenchmark & 35.85s (6.48s) & 1292.2s (84.9s) & 10000s (1362s) \\\\\n Random & 4.97s (1.32s) & 26.7s (0.8s) & 61.4s (12.8s)\\\\\nSpatial & 5.25s (2.49s) & 25.8s (1.7s) & 58.6s (12.1s)\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\scriptsize Hierarchical Bayes rescaling of the Mat\\'ern Gaussian process prior. Average run time for computing the posterior. Benchmark: Non-distributed method. Method 1: Random partitioning, Method 2: Spatial partitioning.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Adaptation using spatially distributed Gaussian Processes", "authors": ["Botond Szabo", "Amine Hadji", "Aad van der Vaart"], "url": "https://arxiv.org/abs/2312.14130v2", "attribution": "\"Adaptation using spatially distributed Gaussian Processes\" by Botond Szabo, Amine Hadji, and Aad van der Vaart, arXiv:2312.14130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04758v4_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n \\toprule\n \\textbf{Measure} & \\textbf{FT} & \\textbf{ST} & \\textbf{\\% improvement} \\\\\n \\hline\n True Positives & 155 & 165 & 6.5 \\% \\\\\n False Positive & 159 & 64 & \\textbf{59.7 \\%}\\\\\n False Negative & 162 & 168 & -3.7 \\% \\\\\n True Negative & 5,940 & 6,035 & 1.5 \\% \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of error categories in percentage between the fine-tuned model (FT) and the model combining fine-tuned+self-trained (ST) model, based on the dialectal part of the dev set of the NER task. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling", "authors": ["Muhammad Khalifa", "Muhammad Abdul-Mageed", "Khaled Shaalan"], "url": "https://arxiv.org/abs/2101.04758v4", "attribution": "\"Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling\" by Muhammad Khalifa, Muhammad Abdul-Mageed, and Khaled Shaalan, arXiv:2101.04758v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.09542v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\n \\hline\n $k$ & $n=3$ & $n=4$ & $n=5$ \\\\ \n \\hline\n $1$ & $1$ & $1$ & $1$ \\\\ \n $2$ & $2$ & $4$ & $6$ \\\\ \n $3$ & $2$ & $10$ & $37$ \\\\ \n $4$ & $2$ & $41$ & $715$ \\\\ \n $5$ & $1$ & $103$ & $13710$ \\\\ \n $6$ & $1$ & $309$ & $256751$ \\\\ \n $7$ & & $691$ & $4140666$ \\\\ \n $8$ & & $1458$ & $58402198$ \\\\ \n $9$ & & $2448$ & $726296995$ \\\\ \n $10$ & & $3703$ & $8060937770$ \\\\ \n $11$ & & $4587$ & $80604620206$ \\\\ \n $12$ & & $5050$ & $732149722382$ \\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Number of non-equivalent subsets of size $k$ in $S_n$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A note on Erdős matrices and Marcus\\unicode{x2013}Ree inequality", "authors": ["Aman Kushwaha", "Raghavendra Tripathi"], "url": "https://arxiv.org/abs/2503.09542v3", "attribution": "\"A note on Erdős matrices and Marcus\\unicode{x2013}Ree inequality\" by Aman Kushwaha and Raghavendra Tripathi, arXiv:2503.09542v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2307.07657v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c} \n\\hline\n & Multilayer Perceptron (MLP) \\\\ \n\\hline\nLayers & $2, 3$ \\\\\nNodes per layer & $50, 100, 150, 200, 250, 500$ \\\\\nActivation function & ReLU \\\\\nLoss function & MSE \\\\\nLearning rate & $10^{-5}$ \\\\\nBatch size & 64 \\\\\n\\hline\n\\end{tabular}\n\\caption{Overview of MLP configurations.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Machine learning for option pricing: an empirical investigation of network architectures", "authors": ["Laurens Van Mieghem", "Antonis Papapantoleon", "Jonas Papazoglou-Hennig"], "url": "https://arxiv.org/abs/2307.07657v1", "attribution": "\"Machine learning for option pricing: an empirical investigation of network architectures\" by Laurens Van Mieghem, Antonis Papapantoleon, and Jonas Papazoglou-Hennig, arXiv:2307.07657v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.08312v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|} \n \\hline\n \\textbf{Hyper-parameters} & \\textbf{Values} \\\\\n \\hline\n Dropout rate & $0.35$ \\\\ \n \\hline\n Batch Normalization & Quaternion Batch Normalization \\\\\n \\hline\n Kernel Initializer & Glorot \\\\\n \\hline\n Optimizer & Adam \\\\\n \\hline\n Loss Function & Binary Cross-entropy \\\\\n \\hline\n Epochs & $20$ \\\\\n \\hline\n Batch Size & $128$ \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Hyper-parameters}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "QUARC: Quaternion Multi-Modal Fusion Architecture For Hate Speech Classification", "authors": ["Deepak Kumar", "Nalin Kumar", "Subhankar Mishra"], "url": "https://arxiv.org/abs/2012.08312v1", "attribution": "\"QUARC: Quaternion Multi-Modal Fusion Architecture For Hate Speech Classification\" by Deepak Kumar, Nalin Kumar, and Subhankar Mishra, arXiv:2012.08312v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12682v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The regression slopes of each proximity type corresponding to the points in Figure~. RF-GAP does not exhibit bias towards the training data as they have a slope close to one, while the larger slope of the other proximity definitions indicates they are overfitting the training data.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|}\n \\hline\n Type & Slope\\\\\n \\hline\n RF-GAP & 1.040\\\\\n Original & 2.434\\\\\n OOB & 1.543\\\\\n PBK & 2.287\\\\\n RFProxIH & 2.147\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Geometry- and Accuracy-Preserving Random Forest Proximities", "authors": ["Jake S. Rhodes", "Adele Cutler", "Kevin R. Moon"], "url": "https://arxiv.org/abs/2201.12682v2", "attribution": "\"Geometry- and Accuracy-Preserving Random Forest Proximities\" by Jake S. Rhodes, Adele Cutler, and Kevin R. Moon, arXiv:2201.12682v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.21306v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\toprule\n scenario & increase & decrease \\\\ \\midrule\n real time zonal & 156.7 & 31.8 \\\\\n time invariant zonal & 157.2 & 30.7 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Total annual generation increase and decrease after the prosumer decision stage (TWh).}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Solar prosumage under different pricing regimes: Interactions with the transmission grid", "authors": ["Dana Kirchem", "Mario Kendziorski", "Enno Wiebrow", "Wolf-Peter Schill", "Claudia Kemfert", "Christian von Hirschhausen"], "url": "https://arxiv.org/abs/2502.21306v1", "attribution": "\"Solar prosumage under different pricing regimes: Interactions with the transmission grid\" by Dana Kirchem, Mario Kendziorski, Enno Wiebrow, Wolf-Peter Schill, Claudia Kemfert, and Christian von Hirschhausen, arXiv:2502.21306v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.15014v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{{Statistics for Cartpole environment.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c|c|c||}\n \\hline\n Algorithm & Return average & Variance average & Return median & Total running time(s)\\\\ \n \\hline\\hline\n PFedDQN-Rep & 143 &43 &154 &466\\\\ \n \\hline\n DQN & 135 &54 &127 &3840\\\\ \n \\hline\n FedDQN & 101 &67 &88 &387\\\\ \n \\hline\n FedQ-K & 112 &34 &107 &490\\\\ \n \\hline\n LFRL & 117 &47 &99 &434\\\\ \n \\hline\n PerDQNAvg & 127 &48 &131 &520\\\\ \n \\hline\n FedAsynQ-ImAvg & 119 &51 &117 &501\\\\ \n \\hline\n \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations", "authors": ["Guojun Xiong", "Shufan Wang", "Daniel Jiang", "Jian Li"], "url": "https://arxiv.org/abs/2411.15014v2", "attribution": "\"On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations\" by Guojun Xiong, Shufan Wang, Daniel Jiang, and Jian Li, arXiv:2411.15014v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06986v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrr}\n \\hline\n & random & kmeans & kmed \\\\ \n \\hline\nDecathlon & 8.2 (1.8) & 23.3 (3) & 22.4 (3.7) \\\\ \n Ames & 16.0 (4.3) & 33.6 (8.4) & 25.4 (7.6) \\\\ \n Normal & 1.0 (1.0) & 1.8 (0.2) & 1.8 (1.1)\\\\ \n Uniform & 1.1 (1.0) & 0.9 (0.1) & 1.3 (1.0)\\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Interactive slice visualization for exploring machine learning models", "authors": ["Catherine B. Hurley", "Mark O'Connell", "Katarina Domijan"], "url": "https://arxiv.org/abs/2101.06986v2", "attribution": "\"Interactive slice visualization for exploring machine learning models\" by Catherine B. Hurley, Mark O'Connell, and Katarina Domijan, arXiv:2101.06986v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01115v3_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Violation factor of our rounding algorithm with different $k$ ($\\delta = 0.2$)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccccccc}\n\\toprule\n\\textbf{dataset} & k=2 & 4 & 6 & 8 & 10 & 12 & 14 & 16 & 18 & 20 & 25 & 30 \\\\ \\hline\nMoons & 0 & 0.20 & 0.40 & 0.40 & 0.40 & 0.40 & 0.60 & 0.60 & 0.40 & 0.40 & 0.60 & 0.80 \\\\ \\hline\nHypercube & 0 & 0.56 & 0.69 & 0.88 & 0.90 & 1.125 & 0.80 & 0.91 & 0.90 & 0.97 & 0.90 & 0.90 \\\\ \\hline\nComplex & 0.92 & 1.02 & 0.92 & 0.768 & 0.96 & 1.01 & 0.95 & 0.79 & 0.88 & 0.90 & 1.04 & 1.01 \\\\ \\hline\nCluto & 0.85 & 0.90 & 0.90 & 0.88 & 0.83 & 1.024 & 0.85 & 0.86 & 0.90 & 0.88 & 0.96 & 1.00 \\\\ \\hline\nBiodeg & 0 & 0.50 & 0.56 & 0.39 & 0.51 & 0.69 & 0.19 & 0.57 & 0.56 & 0.64 & 0.75 & 0.65 \\\\ \\hline\nBreastcancer & 0 & 0.26 & 0.42 & 0.26 & 0.69 & 0.39 & 0.29 & 0.67 & 0.80 & 0.81 & 0.85 & 0.68 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems", "authors": ["Shihong Song", "Guanlin Mo", "Qingyuan Yang", "Hu Ding"], "url": "https://arxiv.org/abs/2411.01115v3", "attribution": "\"Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems\" by Shihong Song, Guanlin Mo, Qingyuan Yang, and Hu Ding, arXiv:2411.01115v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.15586v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n \\hline\n \\textbf{Stock Ticker} & \\textbf{Weight(\\%)} \\\\\n \\hline\n JPM & 252.71 \\\\\n MMM & 230.60 \\\\\n KO & 193.30 \\\\\n CAT & 185.56 \\\\\n MSFT & 156.86 \\\\\n GS\t& -121.70 \\\\\n AAPL & -130.76 \\\\\n PG\t& -142.19 \\\\\n AXP\t& -252.97 \\\\\n HON\t& -481.37 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Top five long and short positions in the optimal eigen-portfolio based on absolute weight magnitude, which sums up to 100\\%}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Eigen Portfolios: From Single Component Models to Ensemble Approaches", "authors": ["ZhengXiang Zhou", "Yuqi Luan"], "url": "https://arxiv.org/abs/2508.15586v1", "attribution": "\"Eigen Portfolios: From Single Component Models to Ensemble Approaches\" by ZhengXiang Zhou and Yuqi Luan, arXiv:2508.15586v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07434v5_tex_table21.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c}\n\t\t\\toprule[1pt]\n\t\t Methods & mIOU (\\%) \\\\\n\t\t\\midrule[0.5pt]\n\t\t\\midrule[0.5pt]\n\t\tHRNetV2 + OCR ~ & 40.5\\\\\n\t\tDRAN & 41.2 \\\\\n\t\tHRNetV2 + OCR + RMI & 45.2 \\\\\n\t\t\\midrule[0.5pt]\n\t\t\\textbf{Our CAA (EfficientNet-B7)} & \\textbf{45.4}\\\\\n\t\t\\bottomrule[1pt]\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Result comparison with the state-of-the-art approaches on the COCO-Stuff-10K test set for multi-scale prediction. Note that, the listed methods were not trained under the same settings, or using same backbone. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation", "authors": ["Ye Huang", "Di Kang", "Wenjing Jia", "Xiangjian He", "Liu Liu"], "url": "https://arxiv.org/abs/2101.07434v5", "attribution": "\"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation\" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.12110v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MDP Elements for Bank Agents in EconGym}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n& \\textbf{Non-Profit Platform} & \\textbf{Commercial Bank} \\\\\n\\midrule\n\\textbf{Observation} \n& --- & \\( o_t^{\\text{bank}} = \\{ \\iota_t, \\phi_t, A_{t-1}, K_{t-1}, B_{t-1} \\} \\) \\\\\n& --- & Interest benchmark, reserve ratio, deposits, firm loans, government bonds \\\\\n\\midrule\n\\textbf{Action} \n& Static allocation rule & \\(a_t^{\\text{bank}} = \\{ r^d_t, r^l_t \\} \\) \\\\\n& No control over rates & Deposit and lending rate decisions \\\\\n\\midrule\n\\textbf{Reward} \n& --- & \\( a_t^{\\text{bank}} = r^l_t (K_{t+1} + B_{t+1}) - r^d_t A_{t+1} \\) \\\\\n& Passive agent & Interest margin under reserve constraint \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "EconGym: A Scalable AI Testbed with Diverse Economic Tasks", "authors": ["Qirui Mi", "Qipeng Yang", "Zijun Fan", "Wentian Fan", "Heyang Ma", "Chengdong Ma", "Siyu Xia", "Bo An", "Jun Wang", "Haifeng Zhang"], "url": "https://arxiv.org/abs/2506.12110v1", "attribution": "\"EconGym: A Scalable AI Testbed with Diverse Economic Tasks\" by Qirui Mi, Qipeng Yang, Zijun Fan, Wentian Fan, Heyang Ma, Chengdong Ma, Siyu Xia, Bo An, Jun Wang, and Haifeng Zhang, arXiv:2506.12110v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.13655v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Most Active Block Proposers Since the Merge}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrl}\n\\hline\n Fee receiver address & Blocks vldtd. & No. Trans. & Validator name \\\\\n\\hline \\hline\n0xdafea492d9c6733ae3d56b7ed1adb60692c98bc5 & 45,688 & 8,809,237 & Flashbots \\\\\n0x388c818ca8b9251b393131c08a736a67ccb19297 & 36,753 & 5,536,598 & Lido Execution Layer \\\\\n0x4675c7e5baafbffbca748158becba61ef3b0a263 & 32,827 & 4,265,292 & Coinbase \\\\\n0xf2f5c73fa04406b1995e397b55c24ab1f3ea726c & 10,891 & 2,176,154 & bloXroute: Max Profit \\\\\n0xebec795c9c8bbd61ffc14a6662944748f299cacf & 10,540 & 1,383,861 & Contract1 \\\\\n0xe688b84b23f322a994a53dbf8e15fa82cdb71127 & 9,197 & 1,221,402 & Address1 \\\\\n0x690b9a9e9aa1c9db991c7721a92d351db4fac990 & 7,903 & 1,694,011 & builder0x69 \\\\\n0x199d5ed7f45f4ee35960cf22eade2076e95b253f & 5,045 & 1,057,766 & bloXroute: Regulated \\\\\n0x6d2e03b7effeae98bd302a9f836d0d6ab0002766 & 4,793 & 587,538 & Address2 \\\\\n0xb646d87963da1fb9d192ddba775f24f33e857128 & 3,577 & 766,706 & MEV Builder \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "An Event Study of the Ethereum Transition to Proof-of-Stake", "authors": ["Elie Kapengut", "Bruce Mizrach"], "url": "https://arxiv.org/abs/2210.13655v2", "attribution": "\"An Event Study of the Ethereum Transition to Proof-of-Stake\" by Elie Kapengut and Bruce Mizrach, arXiv:2210.13655v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.21130v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\n \\hline\n \\textbf{class} & \\textbf{class size} & \\textbf{graph category} & \\textbf{category size} \\\\ \\hline\\hline\n \\multirow{4}{*}{biological} & \\multirow{4}{*}{1219} & bio & 24 \\\\ \\cline{3-4} \n & & eco & 6 \\\\ \\cline{3-4} \n & & protein & 1178 \\\\ \\cline{3-4} \n & & bn & 11 \\\\ \\hline\\hline\n \\multirow{6}{*}{infrastructure} & \\multirow{6}{*}{29} & inf & 4 \\\\ \\cline{3-4} \n & & massive & 0 \\\\ \\cline{3-4} \n & & power & 8 \\\\ \\cline{3-4} \n & & road & 3 \\\\ \\cline{3-4} \n & & tech & 5 \\\\ \\cline{3-4} \n & & web & 9 \\\\ \\hline\\hline\n \\multirow{13}{*}{social} & \\multirow{13}{*}{23} & ca & 7 \\\\ \\cline{3-4} \n & & cit & 1 \\\\ \\cline{3-4} \n & & dynamic & 43 \\\\ \\cline{3-4} \n & & econ & 12 \\\\ \\cline{3-4} \n & & email & 6 \\\\ \\cline{3-4} \n & & ia & 17 \\\\ \\cline{3-4} \n & & proximity & 6 \\\\ \\cline{3-4} \n & & rec & 2 \\\\ \\cline{3-4} \n & & retweet\\_graphs & 28 \\\\ \\cline{3-4} \n & & rt & 31 \\\\ \\cline{3-4} \n & & soc & 21 \\\\ \\cline{3-4} \n & & socfb & 27 \\\\ \\cline{3-4} \n & & tscc & 33 \\\\ \\hline\\hline\n \\multirow{8}{*}{miscellaneous} & \\multirow{8}{*}{1820} & dimacs & 62 \\\\ \\cline{3-4} \n & & dimacs10 & 17 \\\\ \\cline{3-4} \n & & graph500 & 0 \\\\ \\cline{3-4} \n & & heter & 0 \\\\ \\cline{3-4} \n & & labeled & 47 \\\\ \\cline{3-4} \n & & misc & 1555 \\\\ \\cline{3-4} \n & & rand & 139 \\\\ \\cline{3-4} \n & & sc & 0 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Classification of graphs into four classes, composed from all 31 categories in the Network Repository. The counts reflect the 500\\,kB size limit of the compressed graph archives, which accounts for some categories being empty.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Numerical Performance of the Implicitly Restarted Arnoldi Method in OFP8, Bfloat16, Posit, and Takum Arithmetics", "authors": ["Laslo Hunhold", "James Quinlan", "Stefan Wesner"], "url": "https://arxiv.org/abs/2504.21130v1", "attribution": "\"Numerical Performance of the Implicitly Restarted Arnoldi Method in OFP8, Bfloat16, Posit, and Takum Arithmetics\" by Laslo Hunhold, James Quinlan, and Stefan Wesner, arXiv:2504.21130v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.11614v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c|c|c} \n & 000 & 001 & 010 & 011 & 100 & 101 & 110 & 111 \\\\ \n \\hline\n X=0 & & & & & 1 & & &\\\\ \n X=1 & 1 & & & & & & &\\\\\n \\hline\n \\hline\n X=0 & & & & & & 1 & &\\\\ \n X=1 & & 1 & & & & & &\\\\\n \\hline\n \\hline\n X=0 & & & & & & & 1 &\\\\ \n X=1 & & & 1 & & & & &\\\\\n \\hline\n \\hline\n X=0 & & & & & & & & 1\\\\ \n X=1 & & & & 1 & & & &\\\\\n \\hline\n \\hline\n X=0 & & & & & & & 1 &\\\\ \n X=1 & 1 & & & & & & &\\\\\n \\hline\n \\hline\n X=0 & & & & & & & & 1\\\\ \n X=1 & & 1 & & & & & &\\\\\n \\hline\n \\hline\n X=0 & & & & & & & & 1\\\\ \n X=1 & 1 & & & & & & &\\\\\n \\hline\n \\hline\n X=0 & & & & & & & 1 & \\\\ \n X=1 & & 1 & & & & & &\\\\\n \\hline\n \\hline\n X=0 & & & & & 1 & & &\\\\ \n X=1 & & & 1 & & & & &\\\\\n \\hline\n \\hline\n X=0 & & & & & & 1 & & \\\\ \n X=1 & & & & 1 & & & &\\\\\n \\hline\n \\hline\n X=0 & & & & & 1 & & & \\\\ \n X=1 & & & & 1& & & &\\\\\n \\hline\n \\hline\n X=0 & & & & & & 1 & & \\\\ \n X=1 & & & 1 & & & & &\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Conditional probabilities of the extremal points in Case 4.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On the physics of nested Markov models: a generalized probabilistic theory perspective", "authors": ["Xingjian Zhang", "Yuhao Wang"], "url": "https://arxiv.org/abs/2411.11614v1", "attribution": "\"On the physics of nested Markov models: a generalized probabilistic theory perspective\" by Xingjian Zhang and Yuhao Wang, arXiv:2411.11614v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.10258v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study : different way of feature fusion in GCM on PASCAL VOC 2007. $\\checkmark$ indicates that the component is used.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\t\t\\hline\\noalign{\\smallskip}\n\t\tmultiplication & addition & multiplication + addition & mAP \\\\\n\t\t\\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\n\t\t& & & 46 \\\\\n\t\t$\\checkmark$ & & & 47.8 \\\\\n\t\t& $\\checkmark$ & & 47.0 \\\\\n\t\t& & $\\checkmark$ & \\textbf{48.1} \\\\\n\t\t\\noalign{\\smallskip}\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Cascade Attentive Dropout for Weakly Supervised Object Detection", "authors": ["Wenlong Gao", "Ying Chen", "Yong Peng"], "url": "https://arxiv.org/abs/2011.10258v1", "attribution": "\"Cascade Attentive Dropout for Weakly Supervised Object Detection\" by Wenlong Gao, Ying Chen, and Yong Peng, arXiv:2011.10258v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2501.06534v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The empirical comparison of the estimated causal graph for the synthetic data in the dynamic LSEM setup for scenario S1F1,when the number of nodes varies. The number in parenthesis denotes the standard deviation. }\n\\begin{tabular}{ccccccc}\n\\toprule\nMethods & Metric & p=5 & p=8 & p=10 & & \\\\\n\\midrule\n\\multirow{4}{*}{DAGGNN} & FDR & 0.83(0.01) &0.31(0.08) & 0.80(0.01) & & \\\\\n& TPR & 0.82(0.04) &0.34(0.04) & 0.90(0.03) & & \\\\\n & SHD & 3.36(0.16) &3.32(0.70) & 3.38(0.15) & & \\\\\n& MSE & 0.65(0.04) & 0.48(0.04) & 2.69(0.26) & & \\\\\n\\midrule\n\\multirow{4}{*}{ANOCE} & FDR & 0.54(0.03) & 0.62(0.02) & 0.60(0.04) & & \\\\\n& TPR & \\textbf{1.00(0.00)} & \\textbf{1.00(0.00)} & \\textbf{1.00(0.00)} & & \\\\\n & SHD & 1.83(0.13) & 3.07(0.24) & 4.23(0.41) & & \\\\\n& MSE & 0.06(0.02), & \\textbf{0.04 (0.01)} & \\textbf{0.00 (0.00)} & & \\\\\n\\midrule\n\\multirow{4}{*}{NOTEARS} & FDR & \\textbf{0.00(0.00)} & \\textbf{0.00(0.00)} & \\textbf{0.00(0.00)} & & \\\\\n& TPR & \\textbf{1.00(0.00)} & \\textbf{1.00(0.00)} & \\textbf{1.00(0.00)}) & & \\\\\n & SHD & \\textbf{0.00(0.00)} & \\textbf{0.00(0.00)} & \\textbf{0.00(0.00)} & & \\\\\n& MSE & 0.37(0.00), & 0.37 (0.00) & 0.37(0.00) & & \\\\\n\\midrule\n\\multirow{4}{*}{DYNOTEARS} & FDR & \\textbf{0.00(0.00)} & \\textbf{0.00(0.00)} & 0.70(0.00) & & \\\\\n& TPR & \\textbf{1.00(0.00)} & \\textbf{1.00(0.00)} & 0.30(0.00) & & \\\\\n & SHD & \\textbf{0.00(0.00)} & \\textbf{0.00(0.00)} & 1.40(0.00) & & \\\\\n& MSE & 0.66(0.00), & 0.66(0.00) & 0.66(0.01) & & \\\\\n\\midrule\n\\multirow{4}{*}{Proposed} & FDR & \\textbf{0.00 (0.00)} & \\textbf{0.00 (0.00)}& \\textbf{0.01 (0.00)} & & \\\\\n& TPR & \\textbf{1.00(0.00)} & \\textbf{0.99 (0.01)}& \\textbf{0.99 (0.01)} & & \\\\\n& SHD & \\textbf{0.01(0.01)}& \\textbf{0.02(0.01)}& \\textbf{0.04(0.02)} & & \\\\\n& MSE & \\textbf{0.00(0.00)} & \\textbf{0.04(0.01)}& {0.10 (0.03)} & & \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Dynamic Causal Structure Discovery and Causal Effect Estimation", "authors": ["Jianian Wang", "Rui Song"], "url": "https://arxiv.org/abs/2501.06534v1", "attribution": "\"Dynamic Causal Structure Discovery and Causal Effect Estimation\" by Jianian Wang and Rui Song, arXiv:2501.06534v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10250v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llr}\n\\toprule\n\\bf Corpus\t\t\t\t\t\t& \\bf Type of Instances\t& \\bf Instances\t\\\\\n\\midrule\nClaimRev\\textsubscript{{\\tiny{BASE}}}\t& \\bf Total claim pairs\t& \\bf 210\\,222\t\\\\\n & Claim Clarification\t& 63 729\t\\\\\n & Typo/Grammar Correction\t& 59 690\t\t\\\\\n & Corrected/Added Links & 17 882\t\t\\\\\n & Changed Meaning of Claim & 1 178\t\t\\\\\n & Misc\t& 10 464\t\t\\\\\n & None\t& 57 279\t\t\\\\\n\\midrule\nClaimRev\\textsubscript{{\\tiny{EXT}}}\t\t& \\bf Total claim pairs\t& \\bf 377\\,659\t\\\\\n & Revision distance 1\t\t\t\t& 77 217\\\\\n & Revision distance 2\t\t\t\t& 27 819\\\\\n & Revision distance 3\t\t\t& 10 753\\\\\n & Revision distance 4\t\t\t\t& 4 460\t \\\\\n & Revision distance 5\t\t\t\t& 2 055 \\\\\n & Revision distance 6+\t\t\t\t& 2 008 \\\\\n \\midrule\t\nBoth Corpora\t\t\t\t\t& \\bf Claim revision chains\t\t\t& \\bf 124\\,312\t\\\\\t\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Statistics of the two provided corpus versions. ClaimRev\\textsubscript{{\\tiny{BASE}}}: Number of claim pairs in total and of each revision type. ClaimRev\\textsubscript{{\\tiny{EXT}}}: Number of claim pairs in total and of each revision distance. The bottom line shows the number of unique revision chains in the corpora.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale", "authors": ["Gabriella Skitalinskaya", "Jonas Klaff", "Henning Wachsmuth"], "url": "https://arxiv.org/abs/2101.10250v1", "attribution": "\"Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale\" by Gabriella Skitalinskaya, Jonas Klaff, and Henning Wachsmuth, arXiv:2101.10250v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2112.11968v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccccccccc}\n\t\t\t\\hline\\hline \n\t\t\t& $\\omega_1$ & $\\alpha_1$ & $\\beta_1$ & $\\gamma_1$ & $\\omega_2$ & $\\alpha_2$ & $\\beta_2$ & $\\gamma_2$ & $\\theta$\\\\\n\t\t\t\\hline\n\t\t\t$I$ & 0.028 & 0.063 & 0.025 & 0.037 & 0.033 & 0.034 & 0.030 & 0.029 & 0.054\\\\\n\t\t\t$\\hat{A}$ & 0.024 & 0.059 & 0.024 & 0.036 & 0.028 & 0.030 & 0.026 & 0.024 & 0.049\\\\\n\t\t\t\\hline\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{ Relative mean square error using the identity matrix $I$ and matrix $\\hat{A}$ reported in Table .}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "An Efficient Unified Approach for Spread Option Pricing in a Copula Market Model", "authors": ["Edoardo Berton", "Lorenzo Mercuri"], "url": "https://arxiv.org/abs/2112.11968v2", "attribution": "\"An Efficient Unified Approach for Spread Option Pricing in a Copula Market Model\" by Edoardo Berton and Lorenzo Mercuri, arXiv:2112.11968v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09656v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{BLEU scores of generated explanations.}\n\\begin{tabular}{|cc|ccc|}\n \\hline\n \n Dataset & Model & BLEU-1 & BLEU-2 & BLEU-4 \\\\\n \\hline\n & NARRE & 20.46 & 5.72 & 2.12 \\\\\n & NRT & 26.25 & 8.84 & 2.97 \\\\\n Yelp & SAER (topk) & 27.43 & 9.53 & 3.18 \\\\\n & SAER (reg + topk) & 28.69 & 10.29 & 3.37 \\\\\n & SAER & \\textbf{28.88} & \\textbf{10.44} & \\textbf{3.44} \\\\\n \\hline\n & NARRE & 29.78 & 9.47 & 3.27 \\\\\n & NRT & 42.16 & 17.54 & 5.63 \\\\\n Ratebeer & SAER (topk) & 43.92 & 19.60 & 6.56 \\\\\n & SAER (reg + topk) & 45.69 & 21.09 & 7.02 \\\\\n & SAER & \\textbf{46.01} & \\textbf{21.60} & \\textbf{7.32} \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Explanation as a Defense of Recommendation", "authors": ["Aobo Yang", "Nan Wang", "Hongbo Deng", "Hongning Wang"], "url": "https://arxiv.org/abs/2101.09656v1", "attribution": "\"Explanation as a Defense of Recommendation\" by Aobo Yang, Nan Wang, Hongbo Deng, and Hongning Wang, arXiv:2101.09656v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.15031v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average EH Efficiency for SU Scenario}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n Algorithm & TS & HERA \\\\ \\hline\nExhaustive search & 64.6\\% & 84.6\\% \\\\ \\hline\nProposed & 57.5\\% & 81.5\\% \\\\ \\hline\nTD3 & 43.9\\% & 74.3\\% \\\\ \\hline\nDDPG & 54.9\\% & 40.4\\% \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Energy-Efficient Irregular RIS-aided UAV-Assisted Optimization: A Deep Reinforcement Learning Approach", "authors": ["Mahmoud M. Salim", "Khaled M. Rabie", "Ali H. Muqaibel"], "url": "https://arxiv.org/abs/2504.15031v1", "attribution": "\"Energy-Efficient Irregular RIS-aided UAV-Assisted Optimization: A Deep Reinforcement Learning Approach\" by Mahmoud M. Salim, Khaled M. Rabie, and Ali H. Muqaibel, arXiv:2504.15031v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.17478v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll} \\hline\n Layer (type) & Output Shape & Param \\# \\\\ \\hline \n spectrogram (Conv2D) & (None, 511, 619, 32) & 320 \\\\ \n max\\_pooling2d (MaxPooling2D) & (None, 255, 309, 32) & 0 \\\\ \n conv2d (Conv2D) & (None, 253, 307, 64) & 18496 \\\\ \n max\\_pooling2d (MaxPooling2D)& (None, 126, 153, 64) & 0 \\\\ \n flatten (Flatten) & (None, 1233792) & 0 \\\\ \n dense (Dense) & (None, 128) & 157925504 \\\\ \n {\\textit{[odour]}} {subject} (Dense) & (None, {\\textit{[13]}} 11) & {\\textit{[1677]}} 1419 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Signal Transformation for Effective Multi-Channel Signal Processing", "authors": ["Sunil Kumar Kopparapu"], "url": "https://arxiv.org/abs/2412.17478v1", "attribution": "\"Signal Transformation for Effective Multi-Channel Signal Processing\" by Sunil Kumar Kopparapu, arXiv:2412.17478v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.08561v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Continuous versus continuous summary statistics of $p$-values computed by each approximation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|c|}\n\\hline\n&& \\multicolumn{7}{c|}{Numerical summary of observed $p$-values}\\\\\n&& \\multicolumn{7}{c|}{}\\\\\nApproximation & $p$ & minimum & $Q(0.25)$ & median & average & $Q(0.75)$ & maximum &IQR\\\\\n\\hline\n$\\chi^2$ estimated d.f. & 0.05 & 0.034 & 0.053 & 0.060 & 0.065 & 0.077 & 0.122 & 0.024 \\\\\n$\\chi^2$ simple d.f. & & 0.022 & 0.035 &0.041 & 0.044 & 0.053 & 0.086 & 0.018\\\\\n$\\Gamma(\\alpha, \\theta)$& &0.014 & 0.027 & 0.034 & 0.037 & 0.042 & 0.083 & 0.015 \\\\\n\\hline\n$\\chi^2$ estimated d.f. & 0.01 & 0.006 & 0.011 & 0.013 &0.016 & 0.019 & 0.045 & 0.008\\\\\n$\\chi^2$ simple d.f. & & 0.003 & 0.007 & 0.008 & 0.010 & 0.012 & 0.029 & 0.005 \\\\ \n$\\Gamma(\\alpha, \\theta)$& &0.002 & 0.004 & 0.006 & 0.007 & 0.008 & 0.024& 0.004\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Recursive random binning to detect and display pairwise dependence", "authors": ["Chris Salahub", "Wayne Oldford"], "url": "https://arxiv.org/abs/2311.08561v2", "attribution": "\"Recursive random binning to detect and display pairwise dependence\" by Chris Salahub and Wayne Oldford, arXiv:2311.08561v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10896v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of open-sourced audiobook corpora. Duration in ``hours.''}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc}\n Lang. & Corpus & Duration & \\# of speakers & Professional & Parallel \\\\ \\hline\n En & Blizzard2018~ & 6.5 & 1 & Yes & No\\\\\n En & Blizzard2013~ & 300 & 1 & Yes & No \\\\\n En & LibriTTS~ & 585 & 2,456 & No & Yes \\\\\n Fr & SynPaFlex~ & 87 & 1 & No & No \\\\ \n Multi & TUNDRA~ & 60 & 14 & No & No \\\\\n Ja & J-KAC~ & 9 & 1 & Yes & No \\\\\n Ja & \\textbf{J-MAC (ours)} & 32 & 39 & \\textbf{Yes} & \\textbf{Yes} \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "J-MAC: Japanese multi-speaker audiobook corpus for speech synthesis", "authors": ["Shinnosuke Takamichi", "Wataru Nakata", "Naoko Tanji", "Hiroshi Saruwatari"], "url": "https://arxiv.org/abs/2201.10896v1", "attribution": "\"J-MAC: Japanese multi-speaker audiobook corpus for speech synthesis\" by Shinnosuke Takamichi, Wataru Nakata, Naoko Tanji, and Hiroshi Saruwatari, arXiv:2201.10896v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12251v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline\n & $\\mbox{\\textit{D-IFWGO}}_q$ \\\\ \\hline\n $P_1$ & $\\langle (0.59,0.41);0.73 \\rangle$\\\\\n $P_2$ & $\\langle (0.61,0.39);0.61 \\rangle$\\\\\n $P_3$ & $\\langle (0.56,0.44);0.42 \\rangle$\\\\\n $P_4$ & $\\langle (0.59,0.41);0.66 \\rangle$\\\\\n $P_5$ & $\\langle (0.55,0.45);0.70 \\rangle$\\\\ \\hline\n $P_*$ & $\\langle (0.61,0.39);0.73 \\rangle$\\\\\n $P_\\neg$ & $\\langle (0.55,0.45);0.73 \\rangle$\\\\ \\hline\n \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Aggregation results with the displaced ideal and anti-ideal ratings}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology", "authors": ["Mahmut Can Bozyiğit", "Mehmet Ünver"], "url": "https://arxiv.org/abs/2501.12251v1", "attribution": "\"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology\" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.08732v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average Performance in Cross-Database Comparison}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nMethods \t\t& PLCC \t\t&SRCC\t\t\t&KRCC \t\\\\ \\midrule\nDIIVIVE \t \t& 0.4639 \t\t& 0.4566\t\t&0.3324 \\\\\nBRISQUE \t\t& 0.5301 \t\t& 0.5305\t\t&0.3965 \\\\\nCNN-IQA \t\t& 0.3994 \t& 0.4040\t\t&0.2900 \\\\\nHOSA \t \t& 0.5964\t\t\t& 0.5819\t\t&0.4443 \t\\\\\nLPC-SI\t\t\t& 0.6538 \t\t\t& 0.5458 \t\t&0.4277\t\t\\\\\nMLV \t\t\t& 0.5631 \t& 0.4248\t\t&0.3326 \\\\\nGPC \t\t\t& 0.6312 \t& 0.5965\t\t&0.4794\t \\\\\nSPARISH \t\t& 0.6575 \t& {\\color[HTML]{3166FF} \\textbf{0.6609}}\t\t&{\\color[HTML]{3166FF} \\textbf{0.5110}} \\\\\nSynthetic-MaxPol\t& 0.4873 \t& 0.3656\t\t&0.2824 \\\\\nHVS-MaxPol \t\t& 0.5788 \t& 0.5464\t\t&0.4157 \\\\\nFQPath \t\t\t\t& 0.5310 \t& 0.5209\t\t&0.3979 \\\\\nFocusLiteNN \t\t& {\\color[HTML]{3166FF} \\textbf{0.6651}} \t& 0.5640\t\t&0.4424 \\\\ \nHYQM \t\t\t& 0.3625 \t& 0.2599\t\t&0.2008 \\\\ \\midrule\nNSS-SR \t\t\t& 0.4293 & 0.3478\t\t&0.2558 \\\\\nLNQM \t\t\t& 0.6633 \t& 0.6413\t\t&0.4606\t\t \\\\ \nBSRIQA \t\t\t& 0.6587 \t\t& 0.5639 &0.3987 \t\\\\\nDISQ \t \t\t&{\\color[HTML]{CB0000} \\textbf{0.7615}}\t& {\\color[HTML]{CB0000} \\textbf{0.7643}} \t&{\\color[HTML]{CB0000} \\textbf{0.5784}}\\\\ \\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning-Based Quality Assessment for Image Super-Resolution", "authors": ["Tiesong Zhao", "Yuting Lin", "Yiwen Xu", "Weiling Chen", "Zhou Wang"], "url": "https://arxiv.org/abs/2012.08732v1", "attribution": "\"Learning-Based Quality Assessment for Image Super-Resolution\" by Tiesong Zhao, Yuting Lin, Yiwen Xu, Weiling Chen, and Zhou Wang, arXiv:2012.08732v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09827v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of SGMRES-IR and RSGMRES-IR refinement steps with the number of GMRES iterations for each refinement step for precisions ($u_f,u,u_r$) = (single, double, quad) and ($m,k$) = (80,18). For $\\kappa_2(A)=10^{15}$, RSGMRES-IR required 2093 (139, 60, 59, 60, 59, 59, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 48, 46, 46, 51, 60, 48, 48, 48, 51, 48, 48, 48, 58, 48, 48, 48, 48, 48, 48, 61) iterations.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|cc|}\n\t\t\\hline\n\t\t$\\kappa_\\infty(A)$ & $\\kappa_2(A)$ & SGMRES-IR (80) & RSGMRES-IR (80,18) \\\\ \\hline\n\t\t$6 \\cdot 10^9$ & $10^9$ & 64 (19,23,22) & 34 (19,8,7) \\\\\n\t\t$6 \\cdot 10^{10}$ & $10^{10}$ & 120 (39,40,41) & 65 (39,13,13) \\\\\n\t\t$6 \\cdot 10^{11}$ & $10^{11}$ & 160 (52,54,54) & 94 (52,21,21) \t\\\\\n\t\t$6 \\cdot 10^{12}$ & $10^{12}$ & 196 (65,65,66) & 163 (65,32,32,34) \\\\\n\t\t$5 \\cdot 10^{13}$ & $10^{13}$ & 301 (75,75,75,76) & 199 (75,41,41,42) \\\\\n\t\t$5 \\cdot 10^{14}$ & $10^{14}$ & - & 493 (131,51,51,52,52,52,52,52) \\\\\n\t\t$5 \\cdot 10^{15}$ & $10^{15}$ & - & 2093* \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Mixed Precision GMRES-based Iterative Refinement with Recycling", "authors": ["Eda Oktay", "Erin Carson"], "url": "https://arxiv.org/abs/2201.09827v2", "attribution": "\"Mixed Precision GMRES-based Iterative Refinement with Recycling\" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2312.15233v2_tex_table11.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{F1 scores of all tested methods on PathMNIST dataset under symmetric noise rate 0.2}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\t\t\\hline\n\t\tNoise rate & 0 & 0.1 & 0.2 & 0.3 & 0.4 \\\\ \\hline\n\t\tBaseline & 88.45±2.32 & 85.77±0.55 & 78.56±1.42 & 70.59±0.2 & 63.9±1.73 \\\\ \\hline\n\t\tO2U & 90.8±0.41 & 90.28±0.67 & 88.99±0.88 & 86.91±0.4 & 80.57±0.83 \\\\ \\hline\n\t\tMixUp & 87.7±0.78 & 85.62±0.89 & 83.13±1.49 & 77.5±1.82 & 68.51±1.03 \\\\ \\hline\n\t\tCoteacing+ & 89.24±1.32 & 90.2±0.26 & 88.71±0.19 & 84.54±1.11 & 84.3±0.68 \\\\ \\hline\n\t\tCDR & 90.29±0.32 & 86.92±0.87 & 83.62±0.07 & 77.16±2.45 & 67.0±0.71 \\\\ \\hline\n\t\tSelf-adaptive & 89.99±1.56 & 87.59±0.98 & 83.89±0.52 & 78.74±0.68 & 69.19±1.18 \\\\ \\hline\n\t\tMulticlass & 89.32±1.13 & 85.12±1.11 & 80.61±0.51 & 71.84±0.77 & 62.21±0.41 \\\\ \\hline\n\t\tLNL\\_SR & 87.98±1.49 & 87.8±1.42 & 86.07±0.95 & 86.88±0.58 & 86.33±1.03 \\\\ \\hline\n\t\tours & 89.95±1.38 & 88.24±0.88 & 88.2±1.83 & 88.68±0.86 & 87.42±0.46 \\\\ \\hline\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sample selection with noise rate estimation in noise learning of medical image analysis", "authors": ["Maolin Li", "Giacomo Tarroni"], "url": "https://arxiv.org/abs/2312.15233v2", "attribution": "\"Sample selection with noise rate estimation in noise learning of medical image analysis\" by Maolin Li and Giacomo Tarroni, arXiv:2312.15233v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.12121v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Granger causality test results for lag $1$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\t\n\\hline\n\\hline\nnumber of lags (no zero) 1\\\\\n\\hline\nssr based F test: & F=23.1736 &p=0.0000 & df\\_denom=825 & df\\_num=1\\\\\nssr based chi2 test: & chi2=23.2579 & p=0.0000 & df=1 &\\\\\nlikelihood ratio test: &chi2=22.9372 &p=0.0000 & df=1&\\\\\nparameter F test: &F=23.1736 &p=0.0000 & df\\_denom=825 & df\\_num=1\\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies", "authors": ["Xinwen Ni", "Wolfgang Karl Härdle", "Taojun Xie"], "url": "https://arxiv.org/abs/2009.12121v3", "attribution": "\"A Machine Learning Based Regulatory Risk Index for Cryptocurrencies\" by Xinwen Ni, Wolfgang Karl Härdle, and Taojun Xie, arXiv:2009.12121v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.05878v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{{\\color{black}A toy data example with numerical values having ten observations (eight class 0 examples and two class 1 examples).}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}\n\\hline Feature 1 & 2.0 & 2.2 & 2.4 & 2.6 & 2.8 & 1.8 & 1.9 & 2.3 & 4.0 & 4.1 \\\\ \\hline\n Feature 2 & 2.0 & 2.2 & 2.4 & 2.6 & 2.8 & 1.8 & 2.1 & 2.1 & 4.0 & 4.1 \\\\ \\hline\n Class & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Skew-Probabilistic Neural Networks for Learning from Imbalanced Data", "authors": ["Shraddha M. Naik", "Tanujit Chakraborty", "Madhurima Panja", "Abdenour Hadid", "Bibhas Chakraborty"], "url": "https://arxiv.org/abs/2312.05878v2", "attribution": "\"Skew-Probabilistic Neural Networks for Learning from Imbalanced Data\" by Shraddha M. Naik, Tanujit Chakraborty, Madhurima Panja, Abdenour Hadid, and Bibhas Chakraborty, arXiv:2312.05878v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02017v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Precision, Recall, and F-measures for different systems. MV\\_6 is the majority vote of all, and MV\\_3 is majority vote of GS, MS, and CH.}\n\\begin{tabular}{|c||c|c|c|c|c|c||c|c|}\n\\hline\nSystems & NSP & CH & STS & LSS & GS & MS & MV\\_6 & MV\\_3 \\\\ \\hline\nPrecision & 0.12 & 0.42 & 0.32 & 0.21 & \\textbf{0.50} & 0.37 & 0.39 & \\textbf{0.59} \\\\ \\hline\nRecall & 0.13 & 0.29 & 0.44 & 0.32 & 0.49 & \\textbf{0.76} & \\textbf{0.71} & 0.69 \\\\ \\hline\nF-measure & 0.12 & 0.34 & 0.37 & 0.15 & \\textbf{0.50} & \\textbf{0.50} & 0.49 & \\textbf{0.65} \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "COVID-19: Comparative Analysis of Methods for Identifying Articles Related to Therapeutics and Vaccines without Using Labeled Data", "authors": ["Mihir Parmar", "Ashwin Karthik Ambalavanan", "Hong Guan", "Rishab Banerjee", "Jitesh Pabla", "Murthy Devarakonda"], "url": "https://arxiv.org/abs/2101.02017v1", "attribution": "\"COVID-19: Comparative Analysis of Methods for Identifying Articles Related to Therapeutics and Vaccines without Using Labeled Data\" by Mihir Parmar, Ashwin Karthik Ambalavanan, Hong Guan, Rishab Banerjee, Jitesh Pabla, and Murthy Devarakonda, arXiv:2101.02017v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.18727v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Bit error rate $q$ versus BSC SNR (dB)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\hline\n{BSC SNR (dB)} & {Bit Error Rate $q$} \\\\\n\\hline\n0 & 0.07865 \\\\\n1 & 0.05628 \\\\\n2 & 0.03751 \\\\\n3 & 0.02288 \\\\\n4 & 0.01250 \\\\\n5 & 0.00595 \\\\\n6 & 0.00239 \\\\\n8 & $1.91 \\times 10^{-4}$\\\\\n12 & $9.01 \\times 10^{-9}$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "RIS Codebook Index Assignment under Imperfect Control Links Using TSP-Inspired Optimization", "authors": ["Liangshun Wu", "Wen Chen", "Qingqing Wu", "Xudong Bai", "Kunlun Wang"], "url": "https://arxiv.org/abs/2507.18727v1", "attribution": "\"RIS Codebook Index Assignment under Imperfect Control Links Using TSP-Inspired Optimization\" by Liangshun Wu, Wen Chen, Qingqing Wu, Xudong Bai, and Kunlun Wang, arXiv:2507.18727v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.12771v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\hline\n\\textbf{Features extracted from} & \\textbf{LR} & \\textbf{RF} & \\textbf{SVM} & \\textbf{KNN} & \\textbf{GB} \\\\ \\hline\nPrice Time Series (P)& 65.81\\% & 94.37\\% & 66.86\\% & 84.59\\% & 91.63\\%\\\\ \nReturn Rate Time Series (R) & 70.02\\% & 86.04\\% & 61.44\\% & 72.67\\% & 84.58\\%\\\\ \nNormalized Price Time Series (NP)& 65.68\\% & 78.40\\% & 65.33\\% & 63.92\\% & 74.20\\%\\\\ \nNormalized Return Rate Time Series (NR) & 69.46\\% & 80.96\\% & 68.09\\% & 70.86\\% & 79.16\\%\\\\ \nP+R & 64.81\\% & 96.05\\% & 66.36\\% & 87.92\\% & 93.47\\%\\\\\nNP+NR & 70.12\\% & 84.88\\% & 71.21\\% & 71.42\\% & 82.88\\% \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks", "authors": ["Yu Zhang", "Zelin Wu", "Claudio Tessone"], "url": "https://arxiv.org/abs/2504.12771v1", "attribution": "\"Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks\" by Yu Zhang, Zelin Wu, and Claudio Tessone, arXiv:2504.12771v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.09336v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Aggregate Card Spending in England by Dec 2020 Tiers \\\\ (Year-on-Year \\% Changes)}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc} \\hline\n\t\t\\textbf{Tier 2} & \\multicolumn{4}{c}{Month}\t\t\\\\\n\t\t\\hline\n\t\t& April & July & October & November \\\\\n\t\tOverall & -28.1\\% & -9.1\\% & +15.6\\% & +15.1\\% \\\\\n\t\tOffline & -45.3\\% & -22.6\\% & -3.2\\% & -12.4\\% \\\\\n\t\tOnline & +3.6\\% & +17.2\\% & + 47.9\\% & + 60.3\\% \\\\\n\t\t\\hline \\\\\n\t\t \\\\\n\t\t\\hline\n\t\t\\textbf{Tier 3} & \\multicolumn{4}{c}{Month}\t\t\\\\\n\t\t\\hline\n\t\t& April & July & October & November \\\\\n\t\tOverall & -27.9\\% & -10.1\\% & +8.5\\% & +9.3\\% \\\\\n\t\tOffline & -44.5\\% & -22.4\\% & -4.8\\% & -14.9\\% \\\\\n\t\tOnline & +2.3\\% & +9.8\\% & + 31.9\\% & + 49.4\\% \\\\\n\t\t\\hline\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Levelling Down and the COVID-19 Lockdowns: Uneven Regional Recovery in UK Consumer Spending", "authors": ["John Gathergood", "Fabian Gunzinger", "Benedict Guttman-Kenney", "Edika Quispe-Torreblanca", "Neil Stewart"], "url": "https://arxiv.org/abs/2012.09336v2", "attribution": "\"Levelling Down and the COVID-19 Lockdowns: Uneven Regional Recovery in UK Consumer Spending\" by John Gathergood, Fabian Gunzinger, Benedict Guttman-Kenney, Edika Quispe-Torreblanca, and Neil Stewart, arXiv:2012.09336v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11852v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Confusion matrix for the decision tree algorithm on the no-chin dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|ccc|}\n \\cline{3-5}\n \\multicolumn{2}{c|}{\\multirow{2}{*}{Diagnosis}} & \\multicolumn{3}{|c|}{Actual} \\\\\n \\cline{3-5}\n \\multicolumn{2}{c|}{} & P & C & H \\\\\n \\hline\n & P & 81.4\\,\\% & 35.0\\,\\% & 13.3\\,\\% \\\\\n & C & 11.8\\,\\% & 47.5\\,\\% & 21.7\\,\\% \\\\\n \\multirow{-3}{*}{Predicted} & H & 6.7\\,\\% & 17.5\\,\\% & 65.0\\,\\% \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features", "authors": ["C. V. Vletter", "H. L. Burger", "H. Alers", "N. Sourlos", "Z. Al-Ars"], "url": "https://arxiv.org/abs/2201.11852v1", "attribution": "\"Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features\" by C. V. Vletter, H. L. Burger, H. Alers, N. Sourlos, and Z. Al-Ars, arXiv:2201.11852v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.17011v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\textbf{Method} & \\textbf{Expert 1} & \\textbf{Expert 2} & \\textbf{Expert 3} & \\textbf{Average}$\\uparrow$ \\\\\n\\midrule\nRL & 4.5 & 5.0 & 4.5 & \\textbf{4.67} \\\\\nGAN & 4.0 & 4.0 & 4.5 & 4.17 \\\\\nActual & 3.5 & 3.5 & 4.0 & 3.67 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Expert Evaluation Scores for Different Methods. Best score denoted by \\textbf{bold}.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation", "authors": ["Jaskaran Singh Walia", "Aarush Sinha", "Srinitish Srinivasan", "Srihari Unnikrishnan"], "url": "https://arxiv.org/abs/2502.17011v1", "attribution": "\"Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation\" by Jaskaran Singh Walia, Aarush Sinha, Srinitish Srinivasan, and Srihari Unnikrishnan, arXiv:2502.17011v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09394v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n \\hline\n Region & (1) & (2) &ARIMA \\\\\n \\hline\n Matara& 11.38& \\bf{11.36} & 11.92\\\\\n Anuradhapura&14.70&\\bf{12.81}&14.70\\\\\n Polonnaruwa & 8.65&\\bf{8.04}&8.65\\\\\n Kurunegala & 14.70 &\\bf{10.08} & 10.35\\\\\n Hambantota & 32.76&\\bf{32.12} &34.63 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{RMSE prediction errors of different models}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics", "authors": ["Zhijian Li", "Jack Xin", "Guofa Zhou"], "url": "https://arxiv.org/abs/2201.09394v1", "attribution": "\"An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics\" by Zhijian Li, Jack Xin, and Guofa Zhou, arXiv:2201.09394v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.18430v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\hline\nPulsar & Dataset & Min Entropy (bits per bit) \\\\\n\\hline\nPSR J0030+0451 & EPTA & 0.974\\\\\nPSR J1918-0642 & EPTA & 0.826\\\\\nPSR J2124-3358 & EPTA & 0.801\\\\\nPSR J1843-1113 & EPTA & 0.911\\\\\nPSR J2322+2057 & EPTA & 0.699\\\\\nPSR J1832-0836 & NANOGrav & 0.679\\\\\nPSR J2302+4442 & NANOGrav & 0.909\\\\\nPSR J0030+0451 & NANOGrav & 0.882\\\\\nPSR J1918-0642 & NANOGrav & 0.739\\\\\nPSR J1012+5307 & NANOGrav & 0.798\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Empirical results validating non-trivial (significantly larger than $0$) min-entropy for $10$ pulsars, $5$ from NANOGrav and $5$ from EPTA. Note that the maximum possible min-entropy is $1$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Random Number Generation from Pulsars", "authors": ["Hayder Tirmazi"], "url": "https://arxiv.org/abs/2502.18430v2", "attribution": "\"Random Number Generation from Pulsars\" by Hayder Tirmazi, arXiv:2502.18430v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08910v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrlrl}\n \\toprule\n dataset & $\\sigma$ & $\\alpha$ & $\\rho_{SR}$ & $\\beta$ & N & $\\sigma_b$ & readout & $\\rho_A$ & normalization \\\\\n \\midrule\n CL63 & 0.007 & 0.28 & 0.40 & 6.630623e-03 & 1512.0 & 1.44 & quadratic & 0.98 & Scheme 1 \\\\\n CL63 & 1.566 & 1.00 & 0.01 & 2.201375e-08 & 2000.0 & 0.69 & biased & 0.98 & Scheme 2 \\\\\n L63 & 0.002 & 0.17 & 1.16 & 3.530047e-03 & 1833.0 & 2.36 & biased & 0.98 & Scheme 1 \\\\\n L63 & 5.985 & 0.60 & 0.80 & 8.493901e-08 & 2000.0 & 1.71 & quadratic & 0.98 & Scheme 2 \\\\\n L9610d & 0.005 & 0.40 & 0.75 & 2.292198e-01 & 1550.0 & 0.44 & biased & 0.98 & Scheme 1 \\\\\n L9610d & 0.116 & 0.72 & 0.21 & 7.640822e-09 & 2000.0 & 1.57 & biased & 0.98 & Scheme 2 \\\\\n L96-5D & 0.004 & 0.87 & 0.14 & 1.972360e-08 & 1739.0 & 3.12 & biased & 0.98 & Scheme 1 \\\\\n L96-5D & 1.017 & 0.70 & 0.58 & 6.332524e-09 & 2000.0 & 1.73 & biased & 0.98 & Scheme 2 \\\\\n colpitts & 0.527 & 0.19 & 0.44 & 1.780703e-03 & 1897.0 & 1.19 & biased & 0.98 & Scheme 1 \\\\\n colpitts & 10.184 & 1.00 & 1.20 & 1.000000e-08 & 2000.0 & 2.00 & linear & 0.98 & Scheme 2 \\\\\n rossler & 0.712 & 0.67 & 0.68 & 1.533086e-02 & 1595.0 & 4.30 & quadratic & 0.98 & Scheme 1 \\\\\n rossler & 2.204 & 0.47 & 0.50 & 2.101845e-09 & 2000.0 & 1.23 & quadratic & 0.98 & Scheme 2 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Fig. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics", "authors": ["Jason A. Platt", "Stephen G. Penny", "Timothy A. Smith", "Tse-Chun Chen", "Henry D. I. Abarbanel"], "url": "https://arxiv.org/abs/2201.08910v1", "attribution": "\"A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics\" by Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen, and Henry D. I. Abarbanel, arXiv:2201.08910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.07312v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classification of vacuum Friedmann cosmologies in General Relativity . The quantity \\(q\\) is defined as \\(q = \\sqrt{|\\Lambda|/3}\\).}\n\\begin{tabular}{c|c|c|c}\n & \\(\\Lambda < 0\\) & \\(\\Lambda = 0\\) & \\(\\Lambda > 0\\)\\\\\n\\hline\n\\(\\kappa = 1\\) & no solution & no solution & de Sitter\\\\\n & & & \\(a = \\cosh(q t) / q\\)\\\\\n\\hline\n\\(\\kappa = 0\\) & no solution & Minkowski & de Sitter\\\\\n & & \\(a = 1\\) & \\(a = \\exp(q t)\\)\\\\\n\\hline\n\\(\\kappa = -1\\) & anti de Sitter & Minkowski & de Sitter\\\\\n & \\(a = \\sin(q t) / q\\) & \\(a = t\\) & \\(a = \\sinh(q t) / q\\)\\\\\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Inflationary scenarios in an effective polynomial affine model of gravity", "authors": ["Oscar Castillo-Felisola", "Bastian Grez", "Jose Perdiguero", "Aureliano Skirzewski"], "url": "https://arxiv.org/abs/2312.07312v1", "attribution": "\"Inflationary scenarios in an effective polynomial affine model of gravity\" by Oscar Castillo-Felisola, Bastian Grez, Jose Perdiguero, and Aureliano Skirzewski, arXiv:2312.07312v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.11557v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllll}\n \\hline\n \\hline \\\\\n & \\multicolumn{7}{c}{Sub-sample 1: 1978:01 -- 1983:09} \\\\\n \\cline{2-8} \\\\\n & Model 1 & Model 2 & Model 3 & Model 4 & Model 5 & Model 6 & Model 7\\\\\n \\cline{2-8} \\\\\n Constant & 4.45*** & 4.48*** & 4.43*** & 4.56*** & 4.47*** & 6.08*** & 4.04*** \\\\\n News: inflation($\\phi^{\\pi}$) & 1.03*** & 0.98*** & 0.94*** & 0.83*** & 0.83*** & 0.87*** & 0.67*** \\\\\n News: monetary policy($\\phi^r$) & -- & 0.49*** & 0.47*** & 0.37*** & 0.35*** & 0.40*** & 0.22*** \\\\\n Log income & -- & -- & 0.07 & -- & -- & -- & 0.21*** \\\\\n Age & -- & -- & -0.03*** & -- & -- & -- &-0.03*** \\\\\n Female & -- & -- & 0.26*** & -- & -- & -- & 0.06 \\\\\n Education & -- & -- & 0.15*** & -- & -- & -- & 0.19*** \\\\\n Perception: government policy & -- & -- & -- & -1.08*** & -- & -- &-0.73*** \\\\\n Expectation: interest rate & -- & -- & -- & -- & 0.82*** & -- & 0.72*** \\\\\n Expectation: unemployment rate & -- & -- & -- & -- & 0.82*** & -- & 0.48*** \\\\\n Consumer sentiment & -- & -- & -- & -- & -- & -0.02*** &-0.01*** \\\\\n \\hline \\\\\n Adjusted-$R^2$ & 0.623 & 0.624 & 0.628 & 0.629& 0.633& 0.628& 0.642 \\\\\n \\hline \\\\\n &\\multicolumn{7}{c}{Sub-sample 2: 1983:10 -- 2016:02} \\\\\n \\cline{2-8} \\\\\n Constant & 3.21*** & 3.19*** & 9.04*** & 3.08*** & 2.92*** & 4.92*** & 8.87*** \\\\\n News: inflation($\\phi^{\\pi}$) & 0.64*** & 0.62*** & 0.61*** & 0.52*** & 0.50*** & 0.43*** & 0.39*** \\\\\n News: monetary policy($\\phi^r$) & -- & 0.35*** & 0.32*** & 0.26*** & 0.18*** & 0.17*** & 0.08** \\\\\n Log income & -- & -- & -0.45*** & -- & -- & -- &-0.35*** \\\\\n Age & -- & -- & -0.01*** & -- & -- & -- &-0.01*** \\\\\n Female & -- & -- & 0.66*** & -- & -- & -- & 0.54*** \\\\\n Education & -- & -- & -0.14*** & -- & -- & -- &-0.11*** \\\\\n Perception: government policy & -- & -- & -- & -0.75*** & -- & -- &-0.33*** \\\\\n Expectation: interest rate & -- & -- & -- & -- & 0.47*** & -- & 0.40*** \\\\\n Expectation: unemployment rate & -- & -- & -- & -- & 0.73*** & -- & 0.35*** \\\\\n Consumer sentiment & -- & -- & -- & -- & -- & -0.02*** &-0.01*** \\\\\n \\hline \\\\\n Adjusted-$R^2$ & 0.520 & 0.521 & 0.532 & 0.529& 0.532& 0.535& 0.549 \\\\\n \\hline \\\\\n \\multicolumn{8}{l}{Notes: 1. The sample size is 43,599 between January 1978 and September 1983 and 165,178 between October} \\\\\n \\multicolumn{8}{l}{\\qquad \\qquad \\; 1983 and February 2016.} \\\\\n \\multicolumn{8}{l}{\\qquad \\; \\; 2. *, **, *** represent significance at 10\\%, 5\\% and 1\\% levels of significance.} \\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Regression results for inflation expectations}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A note on the impact of news on US household inflation expectations", "authors": ["Ben Zhe Wang", "Jeffrey Sheen", "Stefan Trück", "Shih-Kang Chao", "Wolfgang Karl Härdle"], "url": "https://arxiv.org/abs/2009.11557v1", "attribution": "\"A note on the impact of news on US household inflation expectations\" by Ben Zhe Wang, Jeffrey Sheen, Stefan Trück, Shih-Kang Chao, and Wolfgang Karl Härdle, arXiv:2009.11557v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.16026v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\nParameter & Value & Description \\\\ \\hline\n$Lm$ & 344 & number of points in longitude direction \\\\ \\hline\n$Mm$ & 228 & number of points in latitude direction \\\\ \\hline\n$N$ & 10 & number of vertical (sigma) levels \\\\ \\hline\n$h_\\textrm{max}$ & 146 & maximum depth of the domain (metres) \\\\ \\hline\n$h_\\textrm{min}$ & 0.29 & minimum depth of the domain (metres) \\\\ \\hline\n$\\theta_s$ & 5.0 & sigma coordinate surface stretching factor \\\\ \\hline\n$\\theta_b$ & 0.4 & sigma coordinate bottom stretching factor \\\\ \\hline\n$\\Delta_t$ & 10 & baroclinic time-step (seconds) \\\\ \\hline\n$\\Delta_{tt}$ & 7 & barotropic time-step (seconds) \\\\ \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Numerical values for the parameters associated with the setup of the ocean model.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Dynamical systems for remote validation of very high-resolution ocean models", "authors": ["G. Garcia-Sanchez", "A. M. Mancho", "A. G. Ramos", "J. Coca", "J. A. Jimenez-Madrid"], "url": "https://arxiv.org/abs/2501.16026v1", "attribution": "\"Dynamical systems for remote validation of very high-resolution ocean models\" by G. Garcia-Sanchez, A. M. Mancho, A. G. Ramos, J. Coca, and J. A. Jimenez-Madrid, arXiv:2501.16026v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05875v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\hline \n \\hline\n \\textbf{Source} & \\textbf{Train} & \\textbf{Test} & \\textbf{Total} \\\\ \n \\hline\n \\hline\n Twitter, 2013 & 1,368 & 588 & 1,956 \\\\\n Dialogues, 2016 & 3754 & 938 & 4,692 \\\\\n Twitter, 2017 & 51,189 & 3,742 & 54,931 \\\\\n Reddit, 2018 & 154,702 & 64,666 & 219,368 \\\\\n Headlines, 2019 & 22895 & 5724 & 28,619 \\\\\n \\hline\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media", "authors": ["Ramya Akula", "Ivan Garibay"], "url": "https://arxiv.org/abs/2101.05875v1", "attribution": "\"Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media\" by Ramya Akula and Ivan Garibay, arXiv:2101.05875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.14137v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results achieved on RTE dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\thead{Method} & \\thead{Time-steps} \n & \\multicolumn{2}{c|}{\\thead{Avg.MAPE \\%}}\n \\\\ \\cline{3-4} &&\n\t\\thead{DeepDeFF}\n\t&\n\t\\thead{Basic}\n\t\\\\\n\\hline\nBGRU & 2 & 0.84& 1.06\\\\\n\\hline\n\\bfseries{GRU} & \\bfseries{2} & \\bfseries{0.81} & 1.09 \\\\\n\\hline\n\\bfseries{BLSTM} & \\bfseries{2} & 1.63 & \\bfseries{1.04} \\\\\n\\hline\nLSTM & 2 & 1.17 & 1.19 \\\\\n\\hline\nBRNN & 2 & 1.26 & 1.13 \\\\\n\\hline\nRNN & 2 & 1.39 & 1.14 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small Datasets", "authors": ["Abdul Wahab", "Muhammad Anas Tahir", "Naveed Iqbal", "Faisal Shafait", "Syed Muhammad Raza Kazmi"], "url": "https://arxiv.org/abs/2011.14137v1", "attribution": "\"Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small Datasets\" by Abdul Wahab, Muhammad Anas Tahir, Naveed Iqbal, Faisal Shafait, and Syed Muhammad Raza Kazmi, arXiv:2011.14137v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.13719v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The fuzziness of parameters of fist convolutional layer in LeNet.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\t\t\\toprule\n\t\t& MNIST & CIFAR10 \\\\ \\midrule\n\t\tStandard model & 0.4417 & 0.2842 \\\\\n\t\tAdv. model & 0.2611 & 0.1594 \\\\\n\t\tMin-Max model & \\textbf{0.0053} & \\textbf{0.0016} \\\\ \\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks", "authors": ["Haojing Shen", "Sihong Chen", "Ran Wang"], "url": "https://arxiv.org/abs/2011.13719v1", "attribution": "\"A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks\" by Haojing Shen, Sihong Chen, and Ran Wang, arXiv:2011.13719v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.08925v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Emotion distribution in the IEMOCAP dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc|c}\n\\toprule\nSplit & Happy & Sad & Neutral & Angry & Total \\\\\n\\midrule\nTrain + Val & 1262 & 828 & 1319 & 872 & 4290 \\\\\nTest & 442 & 245 & 384 & 170 & 1241 \\\\\n\\midrule\nTotal & 1704 & 1073 & 1703 & 1042 & 5531 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "LPGNet: A Lightweight Network with Parallel Attention and Gated Fusion for Multimodal Emotion Recognition", "authors": ["Zhining He", "Yang Xiao"], "url": "https://arxiv.org/abs/2508.08925v1", "attribution": "\"LPGNet: A Lightweight Network with Parallel Attention and Gated Fusion for Multimodal Emotion Recognition\" by Zhining He and Yang Xiao, arXiv:2508.08925v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.15257v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|rrr|rrr|}\n\t\t\t\n\t\t\t\\hline\n\t\t\tmodel & & $M_1$ & & & $M_2$ & \\\\\n\t\t\t\\hline\n\t\t\tquantile & q05 & q50 & q95 & q05 & q50 & q95\\\\\n\t\t\t\\hline\n\t\t\t$\\gamma_0$, (Intercept) & -4.233 & -4.171 & -4.111 & -4.621 & -4.542 & -4.465\\\\\n\t\t\t$\\gamma_1$, (Intercept) & -4.054 & -3.938 & -3.83 & -3.667 & -3.525 & -3.391\\\\\n\t\t\t$\\lambda$, (Intercept) & -1.51 & -1.492 & -1.474 & -1.853 & -1.786 & -1.718\\\\\n\t\t\t$\\lambda$, $L$ & & & & -0.16 & -0.134 & -0.108\\\\\n\t\t\t$\\lambda$, $A(1)$ & & & & -0.133 & 0.088 & 0.315\\\\\n\t\t\t$\\lambda$, $A(0.8)$ & & & & 2.027 & 2.249 & 2.467\\\\\n\t\t\t$\\lambda$, $I(A(1)=0)$ & & & & 0.102 & 0.191 & 0.279\\\\\n\t\t\t$\\lambda$, $I(A(1)=0):L$ & & & & -0.173 & -0.137 & -0.101\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Coefficients for Models 1 and 2. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Career Modeling with Missing Data and Traces", "authors": ["Théo Voldoire", "Robin J. Ryder", "Ryan Lahfa"], "url": "https://arxiv.org/abs/2311.15257v2", "attribution": "\"Career Modeling with Missing Data and Traces\" by Théo Voldoire, Robin J. Ryder, and Ryan Lahfa, arXiv:2311.15257v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.01983v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{GTS Parameter Estimation for Bitcoin}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n\\toprule\n\\textbf{Model} & \\textbf{Parameter} & \\textbf{Estimate} & \\textbf{Std Err} & \\textbf{$Pr(Z >|z|)$} \\\\ \\toprule\n\\multirow{10}{*}{\\textbf{GTS}} & \\multirow{1}{*}{\\textbf{$\\mu$}} &\\multirow{1}{*}{-0.121571} & \\multirow{1}{*}{(0.375)} & \\multirow{1}{*}{$7.5*10^{-01}$} \\\\\n & \\multirow{1}{*}{\\textbf{$\\beta_{+}$}} & \\multirow{1}{*}{0.315548} & \\multirow{1}{*}{(0.136)} & \\multirow{1}{*}{$2.0*10^{-02}$} \\\\\n &\\multirow{1}{*}{ \\textbf{$\\beta_{-}$}} & \\multirow{1}{*}{0.406563} & \\multirow{1}{*}{(0.117)} & \\multirow{1}{*}{$4.9*10^{-04}$} \\\\\n & \\multirow{1}{*}{\\textbf{$\\alpha_{+}$}} & \\multirow{1}{*}{0.747714} & \\multirow{1}{*}{(0.047)} & \\multirow{1}{*}{$6.2*10^{-56}$} \\\\\n & \\multirow{1}{*}{\\textbf{$\\alpha_{-}$}} & \\multirow{1}{*}{0.544565} & \\multirow{1}{*}{(0.037)} & \\multirow{1}{*}{$4.8*10^{-48}$} \\\\\n &\\multirow{1}{*}{\\textbf{$\\lambda_{+}$}} & \\multirow{1}{*}{0.246530} & \\multirow{1}{*}{(0.036)} & \\multirow{1}{*}{$4.9*10^{-12}$} \\\\\n & \\multirow{1}{*}{\\textbf{$\\lambda_{-}$}} & \\multirow{1}{*}{0.174772} & \\multirow{1}{*}{(0.026)} & \\multirow{1}{*}{$2.2*10^{-11}$} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Comparing Bitcoin and Ethereum tail behavior via Q-Q analysis of cryptocurrency returns", "authors": ["A. H. Nzokem"], "url": "https://arxiv.org/abs/2507.01983v1", "attribution": "\"Comparing Bitcoin and Ethereum tail behavior via Q-Q analysis of cryptocurrency returns\" by A. H. Nzokem, arXiv:2507.01983v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.12619v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\large Asymmetric treatment on all US counties}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\tabularnewline\\midrule\\midrule\n Dependent Variable: & \\multicolumn{3}{c}{$\\Delta$ Unemployment Rate}\\\\\n Model: & (1) & (2) & (3)\\\\\n \\midrule \\emph{Variables} & & & \\\\\n $\\Delta$ Active Mines\\textsubscript{t} & -0.0193 & -0.0752$^{**}$ & \\\\\n & (0.0201) & (0.0235) & \\\\\n $\\Delta$ Active Mines\\textsubscript{t-1} & 0.0221 & -0.0301 & \\\\\n & (0.0285) & (0.0219) & \\\\\n $\\Delta$ Active Mines\\textsubscript{t-2} & 0.0090 & 0.0546$^{***}$ & \\\\\n & (0.0236) & (0.0116) & \\\\\n $\\Delta$ (log) Real GDPPC\\textsubscript{t} & -0.9724$^{***}$ & -0.9724$^{***}$ & -0.9724$^{***}$\\\\\n & (0.2138) & (0.2138) & (0.2138)\\\\\n $\\Delta$ Active Mines\\textsubscript{t} $\\times$ $\\Delta_{negative_{t}}$ & -0.0559 & & -0.0752$^{**}$\\\\\n & (0.0370) & & (0.0235)\\\\\n $\\Delta$ Active Mines\\textsubscript{t-1} $\\times$ $\\Delta_{negative_{t-1}}$ & -0.0521 & & -0.0301\\\\\n & (0.0422) & & (0.0219)\\\\\n $\\Delta$ Active Mines\\textsubscript{t-2} $\\times$ $\\Delta_{negative_{t-2}}$ & 0.0457 & & 0.0546$^{***}$\\\\\n & (0.0277) & & (0.0116)\\\\\n $\\Delta$ Active Mines\\textsubscript{t} $\\times$ $\\Delta_{positive_{t}}$ & & 0.0559 & -0.0193\\\\\n & & (0.0370) & (0.0201)\\\\\n $\\Delta$ Active Mines\\textsubscript{t-1} $\\times$ $\\Delta_{positive_{t-1}}$ & & 0.0521 & 0.0221\\\\\n & & (0.0422) & (0.0285)\\\\\n $\\Delta$ Active Mines\\textsubscript{t-2} $\\times$ $\\Delta_{positive_{t-2}}$ & & -0.0457 & 0.0090\\\\\n & & (0.0277) & (0.0236)\\\\\n \\midrule \\emph{Fixed-effects} & & & \\\\\n County FIPS Code & Yes & Yes & Yes\\\\\n Year & Yes & Yes & Yes\\\\\n \\midrule \\emph{Fit statistics} & & & \\\\\n Observations & 55,295 & 55,295 & 55,295\\\\\n R$^2$ & 0.61355 & 0.61355 & 0.61355\\\\\n Within R$^2$ & 0.01513 & 0.01513 & 0.01513\\\\\n \\midrule\\midrule\\multicolumn{4}{l}{\\emph{Clustered (County FIPS Code \\& Year) standard-errors in parentheses}}\\\\\n \\multicolumn{4}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'", "authors": ["Ebba Mark", "Ryan Rafaty", "Moritz Schwarz"], "url": "https://arxiv.org/abs/2211.12619v1", "attribution": "\"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'\" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.19208v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n\\hline\nRegret at Period & 50 & 60 & 70 & 80 & 90 & 100 & 110 & 120\\\\ \\hline\nOne-Time MILP & 36.25 & 55.31 & 60.56 & 60.61 & 60.79 & 60.97 & 61.03 & 61.12 \\\\ \\hline\nOne-Time LP & 36.25 & 55.31 & 61.95 & 64.99 & 67.78 & 71.07 & 74.31 & 77.16 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of Regrets Without Cost Structure Assumption (Exploration Period is of Length $60$).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Learning While Repositioning in On-Demand Vehicle Sharing Networks", "authors": ["Hansheng Jiang", "Chunlin Sun", "Zuo-Jun Max Shen", "Shunan Jiang"], "url": "https://arxiv.org/abs/2501.19208v1", "attribution": "\"Learning While Repositioning in On-Demand Vehicle Sharing Networks\" by Hansheng Jiang, Chunlin Sun, Zuo-Jun Max Shen, and Shunan Jiang, arXiv:2501.19208v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.16844v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small REP-focused results under nodal, zonal, and copper-plate modeling. Results are reported for a 1000 MW electrolyzer, market bidding REP, under the lower and higher hydrogen price. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrr} \\toprule\n& \n& \\textbf{Electrolyzer} \n& \\textbf{REP} \n& \\textbf{Average} \\\\\n& \n& \\textbf{average load} \n& \\textbf{profit}\n& \\textbf{LMP}\\\\\n\\multicolumn{2}{c}{\\textbf{Case}} \n& { [}MW{]} \n& {[}M $\\$${]} \n& { [}\\$/MWh{]} \\\\ \\toprule\n\\multirow{3}{*}{$\\$$1.5/kg} & Copper & 843.53 & 80.92 & 22.05 \\\\ \n & Zonal & 829.25 & 79.80 & 22.26 \\\\\n & Nodal & 629.77 & 67.76 & 24.45 \\\\ \\midrule\n\\multirow{3}{*}{$\\$$6/kg} & Copper & 1000.00 & 769.76 & 22.39 \\\\\n & Zonal & 1000.00 & 766.06 & 22.85 \\\\\n & Nodal & 729.34 & 320.83 & 81.55 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Electricity Market Bidding for Renewable Electrolyzer Plants: An Opportunity Cost Approach", "authors": ["Andrea Gloppen Johnsen", "Lesia Mitridati", "Jalal Kazempour", "Line Roald"], "url": "https://arxiv.org/abs/2501.16844v3", "attribution": "\"Electricity Market Bidding for Renewable Electrolyzer Plants: An Opportunity Cost Approach\" by Andrea Gloppen Johnsen, Lesia Mitridati, Jalal Kazempour, and Line Roald, arXiv:2501.16844v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05831v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Different estimation approaches applied to simulated count time series from INGARCH$(1,1)$-CMEM with Poi-counting series: Mean, simulated standard error (SSE), and mean approximated standard error (ASE) for different sample sizes~$n$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrcrrrrcrrrr}\n\\toprule\n$n$ & & Param. & PQ & NQ & EQ & 2W & Param. & PQ & NQ & EQ & 2W \\\\\n\\midrule\n300 & Mean & $a_0=1$ & 1.452 & 1.435 & 1.446 & 1.474 & $a_0=1$ & 1.474 & 1.527 & 1.544 & 1.481 \\\\\n & SSE & & 0.738 & 0.695 & 0.699 & 0.857 & & 0.640 & 0.632 & 0.638 & 0.660 \\\\\n & ASE & & 0.612 & 0.541 & 0.544 & 0.556 & & 0.549 & 0.535 & 0.541 & 0.523 \\\\\n600 & Mean & & 1.208 & 1.206 & 1.217 & 1.211 & & 1.225 & 1.268 & 1.283 & 1.238 \\\\\n & SSE & & 0.388 & 0.361 & 0.367 & 0.416 & & 0.355 & 0.356 & 0.364 & 0.350 \\\\\n & ASE & & 0.382 & 0.333 & 0.335 & 0.334 & & 0.342 & 0.330 & 0.334 & 0.325 \\\\\n1000 & Mean & & 1.127 & 1.126 & 1.135 & 1.123 & & 1.130 & 1.163 & 1.175 & 1.145 \\\\\n & SSE & & 0.279 & 0.257 & 0.263 & 0.264 & & 0.256 & 0.255 & 0.261 & 0.249 \\\\\n & ASE & & 0.283 & 0.244 & 0.246 & 0.244 & & 0.252 & 0.241 & 0.244 & 0.239 \\\\\n\\midrule\n300 & Mean & $a_1=0.25$ & 0.246 & 0.257 & 0.258 & 0.255 & $a_1=0.25$ & 0.256 & 0.263 & 0.264 & 0.261 \\\\\n & SSE & & 0.061 & 0.058 & 0.058 & 0.061 & & 0.051 & 0.049 & 0.049 & 0.050 \\\\\n & ASE & & 0.067 & 0.059 & 0.059 & 0.059 & & 0.053 & 0.051 & 0.051 & 0.050 \\\\\n600 & Mean & & 0.248 & 0.255 & 0.256 & 0.255 & & 0.254 & 0.260 & 0.261 & 0.258 \\\\\n & SSE & & 0.044 & 0.040 & 0.040 & 0.041 & & 0.036 & 0.034 & 0.034 & 0.034 \\\\\n & ASE & & 0.048 & 0.041 & 0.041 & 0.041 & & 0.037 & 0.035 & 0.036 & 0.035 \\\\\n1000 & Mean & & 0.248 & 0.253 & 0.254 & 0.253 & & 0.252 & 0.256 & 0.257 & 0.255 \\\\\n & SSE & & 0.035 & 0.031 & 0.031 & 0.031 & & 0.027 & 0.026 & 0.026 & 0.026 \\\\\n & ASE & & 0.037 & 0.032 & 0.032 & 0.032 & & 0.029 & 0.027 & 0.027 & 0.027 \\\\\n\\midrule\n300 & Mean & $b_1=0.65$ & 0.600 & 0.593 & 0.591 & 0.590 & $b_1=0.65$ & 0.594 & 0.582 & 0.580 & 0.589 \\\\\n & SSE & & 0.114 & 0.104 & 0.105 & 0.123 & & 0.090 & 0.085 & 0.086 & 0.091 \\\\\n & ASE & & 0.102 & 0.091 & 0.092 & 0.093 & & 0.087 & 0.084 & 0.085 & 0.083 \\\\\n600 & Mean & & 0.628 & 0.622 & 0.620 & 0.622 & & 0.622 & 0.613 & 0.611 & 0.618 \\\\\n & SSE & & 0.066 & 0.059 & 0.059 & 0.067 & & 0.055 & 0.053 & 0.054 & 0.053 \\\\\n & ASE & & 0.067 & 0.059 & 0.059 & 0.059 & & 0.057 & 0.055 & 0.055 & 0.054 \\\\\n1000 & Mean & & 0.637 & 0.633 & 0.631 & 0.633 & & 0.634 & 0.627 & 0.625 & 0.630 \\\\\n & SSE & & 0.049 & 0.044 & 0.044 & 0.045 & & 0.042 & 0.040 & 0.041 & 0.040 \\\\\n & ASE & & 0.051 & 0.044 & 0.044 & 0.044 & & 0.043 & 0.041 & 0.042 & 0.041 \\\\\n\\midrule\n300 & Mean & $\\sigma^2=1$ & 1.039 & 1.020 & 1.018 & 1.051 & $\\sigma^2=0.4$ & 0.414 & 0.408 & 0.407 & 0.415 \\\\\n & SSE & & 0.155 & 0.125 & 0.123 & 0.258 & & 0.057 & 0.050 & 0.050 & 0.072 \\\\\n & ASE & & 0.143 & 0.131 & 0.130 & 0.153 & & 0.048 & 0.044 & 0.044 & 0.050 \\\\\n600 & Mean & & 1.026 & 1.015 & 1.013 & 1.025 & & 0.410 & 0.406 & 0.405 & 0.409 \\\\\n & SSE & & 0.111 & 0.090 & 0.088 & 0.147 & & 0.039 & 0.035 & 0.035 & 0.041 \\\\\n & ASE & & 0.100 & 0.094 & 0.093 & 0.101 & & 0.034 & 0.032 & 0.031 & 0.034 \\\\\n1000 & Mean & & 1.017 & 1.011 & 1.010 & 1.014 & & 0.407 & 0.405 & 0.404 & 0.406 \\\\\n & SSE & & 0.081 & 0.070 & 0.068 & 0.090 & & 0.029 & 0.027 & 0.027 & 0.029 \\\\\n & ASE & & 0.076 & 0.072 & 0.072 & 0.075 & & 0.026 & 0.024 & 0.024 & 0.025 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Conditional-mean Multiplicative Operator Models for Count Time Series", "authors": ["Christian H. Weiß", "Fukang Zhu"], "url": "https://arxiv.org/abs/2212.05831v2", "attribution": "\"Conditional-mean Multiplicative Operator Models for Count Time Series\" by Christian H. Weiß and Fukang Zhu, arXiv:2212.05831v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07033v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|} \\hline\nCase& \\multicolumn{2}{l|}{Description} \\\\ \n\\hline\\hline\nTrue Positive (TP)& \\multicolumn{2}{p{5.5cm}|}{\\raggedright Set to 1 if at least one failure prediction made during the Correct Prediction Period; otherwise, it is set to 0.}\\\\\n\\hline\nFalse Negative (FN) & \\multicolumn{2}{p{5.5cm}|}{\\raggedright Set to1 if no failure prediction made during the Correct Prediction Period; otherwise, it is set to 0.}\\\\\n\\hline\nFalse Positive (FP) & \\multicolumn{2}{p{5.5cm}|}{\\raggedright The amount of failure predictions during the Early Prediction Period.}\\\\\n\\hline\nTrue Negative (TN)& \\multicolumn{2}{p{5.5cm}|}{\\raggedright The amount of non-failure predictions during the Early Prediction Period.}\\\\\n\\hline\nIgnored& \\multicolumn{2}{p{5.5cm}|}{\\raggedright All failure predictions during the Repair Period.}\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Description of prediction cases within an episode.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Analysis of key flavors of event-driven predictive maintenance using logs of phenomena described by Weibull distributions", "authors": ["Petros Petsinis", "Athanasios Naskos", "Anastasios Gounaris"], "url": "https://arxiv.org/abs/2101.07033v1", "attribution": "\"Analysis of key flavors of event-driven predictive maintenance using logs of phenomena described by Weibull distributions\" by Petros Petsinis, Athanasios Naskos, and Anastasios Gounaris, arXiv:2101.07033v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.11991v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average bias regarding $\\theta_1$ and $\\theta_2$ depending on the odds ratio (OR) for the treatment effect on survival (rows) and the method of analysis (columns 3--9).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|r|rrr|rrr}\n \\hline\nEstimand & OR & CCA & SACE all cov & SACE no hc & SACE no ga & MI all cov & MI no hc & MI no ga \\\\ \n \\hline\n$\\theta_1$ & 2 & $-$0.4920 & 0.0185 & 0.0047 & $-$0.2079 & $-$0.0053 & $-$0.0234 & $-$0.2396 \\\\ \n $\\theta_1$ & 0 & $-$0.0003 & $-$0.0042 & $-$0.0023 & $-$0.0003 & 0.0094 & 0.0084 & $-$0.0020 \\\\ \n $\\theta_1$ & 0.5 & 0.6512 & 0.0069 & 0.0268 & 0.2970 & 0.0163 & 0.0324 & 0.3326 \\\\ \n \\hline\n$\\hat{\\theta}_2$ & 2 & $-$0.5226 & $-$0.0121 & $-$0.0259 & $-$0.2385 & $-$0.0359 & $-$0.0540 & $-$0.2702 \\\\ \n $\\hat{\\theta}_2$ & 0 & 0.0153 & 0.0113 & 0.0133 & 0.0153 & 0.0250 & 0.0239 & 0.0135 \\\\ \n $\\hat{\\theta}_2$ & 0.5 & 0.6359 & $-$0.0084 & 0.0115 & 0.2817 & 0.0010 & 0.0171 & 0.3173 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Outcomes truncated by death in RCTs: a simulation study on the survivor average causal effect", "authors": ["Stefanie von Felten", "Chiara Vanetta", "Christoph M. Rüegger", "Sven Wellmann", "Leonhard Held"], "url": "https://arxiv.org/abs/2312.11991v3", "attribution": "\"Outcomes truncated by death in RCTs: a simulation study on the survivor average causal effect\" by Stefanie von Felten, Chiara Vanetta, Christoph M. Rüegger, Sven Wellmann, and Leonhard Held, arXiv:2312.11991v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2301.01843v2_tex_table18.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{DiD Analysis -- Violence Spillovers: Extensive Margin, Events in Over 60\\% of Years}\n\\begin{tabular}{l|cccccccc}\n\\toprule\n& Gov. & FARC & ELN & Crim. Bands & FARC Diss. & Paramilitaries & Other & Total \\tabularnewline\n{}&{(1)}&{(2)}&{(3)}&{(4)}&{(5)}&{(6)}&{(7)}&{(8)} \\tabularnewline\n\\midrule \n\\midrule Ceasefire \\( \\times \\) FARC&--0.154&--0.162***&0.003&0.003&0.031**&0.029&--0.147***&--0.397* \\tabularnewline\n&(0.094)&(0.035)&(0.033)&(0.031)&(0.012)&(0.075)&(0.045)&(0.202) \\tabularnewline\n&&&&&&&& \\tabularnewline\nMean Sample (Pre)&0.551&0.263&0.036&0.114&0.000&0.230&0.124&1.319 \\tabularnewline\nMean Sample (Post)&0.373&0.113&0.109&0.114&0.043&0.264&0.088&1.104 \\tabularnewline\nObservations&2,827&2,827&2,827&2,827&2,827&2,827&2,827&2,827 \\tabularnewline\nR Squared&0.361&0.373&0.251&0.209&0.178&0.284&0.264&0.411 \\tabularnewline\n\\bottomrule \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Peace Dividends: The Economic Effects of Colombia's Peace Agreement", "authors": ["Miguel Fajardo-Steinhäuser"], "url": "https://arxiv.org/abs/2301.01843v2", "attribution": "\"Peace Dividends: The Economic Effects of Colombia's Peace Agreement\" by Miguel Fajardo-Steinhäuser, arXiv:2301.01843v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.07952v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|}\n\\hline\n\\textbf{Notation} & \\textbf{Description} \\\\ \\hline\n$y_{ct}^s(d)$ & Potential GDP level (in logs) at time $t+s$ for country $c$ transitioning to either democracy ($d=1$) or nondemocracy ($d=0$) at time $t$ \\\\ \\hline\n$\\Delta y_{ct}^s(d)$ & Potential change in (log) GDP per capita from time $t-1$ to time $t+s$ for a country with a change in political regime $d \\in \\{0,1\\}$ \\\\ \\hline\n$D_{ct}$ & Indicator variable for whether country $c$ is a democracy at time $t$ \\\\ \\hline\n$\\beta^s$ & Causal effect of a transition to democracy at time $t$ on GDP $s$ periods thereafter for democratizing countries \\\\ \\hline\n$\\perp$ & Independence symbol \\\\ \\hline\n$X_{ct}$ & Vector of covariates including year fixed effects and four lags of GDP \\\\ \\hline\n$\\pi^*$ & Coefficient vector for the linear regression model of GDP changes \\\\ \\hline\n$\\hat{\\beta}^s$ & Estimated effect of democracy on GDP $s$ years after a transition \\\\ \\hline\n$\\hat{\\mathbb{E}}[\\cdot \\mid S]$ & Sample average of $\\cdot$ for all observations in set $S$ \\\\ \\hline\n$\\hat{\\pi}^*$ & Ordinary least squares (OLS) estimate of $\\pi^*$ \\\\ \\hline\n$P(D_{ct} = 1 \\mid X_{ct}, D_{ct-1} = 0)$ & Probability of transitioning to democracy conditional on $X_{ct}$, $D_{ct-1} = 0$ estimated using a probit regression \\\\ \\hline\n$\\Phi(\\cdot)$ & Cumulative distribution function of the standard normal distribution \\\\ \\hline\n$\\hat{P}_{ct}$ & Estimated propensity score \\\\ \\hline\n$\\hat{w}_{ct}$ & Weighting scheme for observations determined by the estimated propensity scores \\\\ \\hline\n$\\tilde{\\pi}$ & Estimate for the counterfactual model \\\\ \\hline\n$\\hat{\\beta}^*$ & Doubly robust estimator of the effect of democracy on GDP \\\\ \\hline\n$\\tilde{u}_{tt}$ & Reweighting term in the doubly robust estimator \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Notation and Description for Sequential Unconfounding Type (Long Term Matching Type)}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Matching $\\leq$ Hybrid $\\leq$ Difference in Differences", "authors": ["Yechan Park", "Yuya Sasaki"], "url": "https://arxiv.org/abs/2411.07952v2", "attribution": "\"Matching $\\leq$ Hybrid $\\leq$ Difference in Differences\" by Yechan Park and Yuya Sasaki, arXiv:2411.07952v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.12687v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\\hline\n Elastic fully-functional & $\\Delta= 0$ & $\\Delta = 0.04$ & $\\Delta = 0.08$ & $\\Delta = 0.12$ & $\\Delta = 0.16$ \\\\ \\hline \\hline\n $n=15$ & $0.21$ & $0.15$ & $0.04$ & $0.05$ & $0.05$ \\\\ \\hline\n $n=30$ & $0.21$ & $0.12$ & $0.05$ & $0.04$ & $0.04$ \\\\ \\hline\n $n=50$ & $0.19$ & $0.11$ & $0.04$ & $0.04$ & $0.01$ \\\\ \\hline\n $n=75$ & $0.21$ & $0.04$ & $0.05$ & $0.04$ & $0.00$ \\\\ \\hline\\hline\n Cross-sectional fully-functional & $\\Delta= 0$ & $\\Delta = 0.04$ & $\\Delta = 0.08$ & $\\Delta = 0.12$ & $\\Delta = 0.16$ \\\\ \\hline \\hline\n $n=15$ & $0.22$ & $0.15$ & $0.07$ & $0.07$ & $0.03$ \\\\ \\hline\n $n=30$ & $0.22$ & $0.12$ & $0.06$ & $0.04$ & $0.02$ \\\\ \\hline\n $n=50$ & $0.22$ & $0.11$ & $0.05$ & $0.04$ & $0.01$ \\\\ \\hline\n $n=75$ & $0.22$ & $0.05$ & $0.05$ & $0.04$ & $0.01$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Sample-size-normalized RMSE of estimated change time for the phase change. (Top) Using the elastic fully-functional test statistic (Bottom) Using the cross-sectional fully-functional test statistic of .}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Elastic Functional Changepoint Detection of Climate Impacts from Localized Sources", "authors": ["J. Derek Tucker", "Drew Yarger"], "url": "https://arxiv.org/abs/2211.12687v2", "attribution": "\"Elastic Functional Changepoint Detection of Climate Impacts from Localized Sources\" by J. Derek Tucker and Drew Yarger, arXiv:2211.12687v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10404v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c}\n\t\t\\textbf{} &\n\t\t\\multirow{2}{*}{Centralized planning } &\n\t\t\\multicolumn{2}{c}{Decentralized planning with CA} \\\\ \\cline{3-4} \n\t\t&\n\t\t&\n\t\tIndependent planning &\n\t\t{CA with LNF} \\\\ \\hline\n\t\tComput. time (mean$\\pm$ std)(ms)\n\t\t&345.8$\\pm$ 87.2\n\t\t& 138.6$\\pm$ 62.4 & \n\t\t9.97 $\\pm$ 0.4\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Reach-avoid mission. Computation times (mean and standard deviation) comparison between centralized planning following~ and decentralized planning (independent planning with LNF) over 100 runs of the scenario.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme", "authors": ["Alëna Rodionova", "Yash Vardhan Pant", "Connor Kurtz", "Kuk Jang", "Houssam Abbas", "Rahul Mangharam"], "url": "https://arxiv.org/abs/2101.10404v1", "attribution": "\"Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme\" by Alëna Rodionova, Yash Vardhan Pant, Connor Kurtz, Kuk Jang, Houssam Abbas, and Rahul Mangharam, arXiv:2101.10404v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07434v5_tex_table16.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c|c}\n\t\t\\toprule\n\t\tBackbone \t\t\t\t \t\t\t\t\t\t\t\t\t\t& OS \t&AA & C & mIOU (\\%) \\\\\n\t\t\\midrule[0.5pt]\n\t\t\\midrule[0.5pt]\n\t\tResNet-50\n\t\t& 16 \t&\\checkmark\t\t & \t& 49.73 \\\\\n\t\t~& 16 \t \t&\\checkmark\t\t & \\checkmark \t& \\textbf{50.23} \\\\\n\t\t\\midrule\n\t\tXception65\n\t\t& 16 \t&\\checkmark\t\t & \t& 52.42 \\\\\n\t\t~& 16 \t \t&\\checkmark\t\t & \\checkmark \t& \\textbf{52.65} \\\\\n\t\t\\midrule\n\t\tEfficientNetB7\n\t\t& 16 \t&\\checkmark\t\t & \t& 57.24 \\\\\n\t\t~& 16 \t \t&\\checkmark\t\t & \\checkmark \t& \\textbf{57.93} \\\\\n\t\t& 8 \t \t&\\checkmark\t\t & \\checkmark \t& \\textbf{58.40} \\\\ \n\t\t\n\t\t\\bottomrule[0.5pt]\n\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation", "authors": ["Ye Huang", "Di Kang", "Wenjing Jia", "Xiangjian He", "Liu Liu"], "url": "https://arxiv.org/abs/2101.07434v5", "attribution": "\"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation\" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08640v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Electrical absolute calibrated devices}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\t\t\\hline\n\t\t \\textbf{Device name} & \\textbf{abbreviation} & \\textbf{reference} \\\\\n\t\t\\hline\n Simple PCIe FMC carrier & SPEC & ~ \\\\\n Simple PCIe FMC carrier 7 & SPEC7 & ~ \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "First electrical White Rabbit absolute calibration inter-comparison", "authors": ["P. P. M. Jansweijer", "N. A. D. Boukadida", "K. Hanhijärvi", "A. Wallin", "B. Eglin", "E. Laier English"], "url": "https://arxiv.org/abs/2201.08640v1", "attribution": "\"First electrical White Rabbit absolute calibration inter-comparison\" by P. P. M. Jansweijer, N. A. D. Boukadida, K. Hanhijärvi, A. Wallin, B. Eglin, and E. Laier English, arXiv:2201.08640v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10231v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n \\toprule\n \\textbf{Release Cycle} & \\textbf{4.2.0} & \\textbf{4.4.0} \\\\\n \\midrule\n Total Tickets & 273 & 393 \\\\\n Resolved Tickets & 252 & 346 \\\\\n Percent Resolved & 92.3\\% & 88.0\\% \\\\\n Resolved by Release & 205 & 330 \\\\\n Percent Resolved by Release & 75.1\\% & 84.0\\% \\\\\n Release Duration & 412 & 352 \\\\\n Tickets per Day & 0.66 & 1.12 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Statistics on performance related JIRA tickets over the previous two release cycles.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Creating a Virtuous Cycle in Performance Testing at MongoDB", "authors": ["David Daly"], "url": "https://arxiv.org/abs/2101.10231v2", "attribution": "\"Creating a Virtuous Cycle in Performance Testing at MongoDB\" by David Daly, arXiv:2101.10231v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.18996v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{A quantitative comparison of model performance in terms of PSNR and SRE metrics when different SR backbone models are integrated into the WaveDiffUS SDE solver for the UR tasks (scaling from $\\times 4$ to $\\times 128$) using the independent datasets DOTA, DIOR, and DDISR. Outputs marked with an asterisk (*) indicate evaluations are performed with pseudo ground truth images, where the UR image resolution exceeds the ground truth resolution.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\toprule\n & \\multicolumn{6}{c}{DOTA} \\\\\\midrule\n \\multirow{2}{*}{Method} & \\multicolumn{6}{c}{Scale} \\\\\n & $\\times 4$ & $\\times 8$ &$\\times 16$ &$\\times 32$* &$\\times 64$* & $\\times 128$* \\\\\\midrule\n LE-GAN & 19.15/11.41 & 17.11/12.88 & 15.91/13.32 & 11.53/15.42 & 9.21/18.89 & 7.91/23.33 \\\\\n LE-GAN-WaveDiffUR & 19.74/11.39 & 19.18/11.12 & 18.21/12.86 & 16.74/13.40 & 12.58/13.63 & 11.21/15.87 \\\\\n RefVAE & 22.14/14.22 & 22.01/15.98 & 20.34/16.23 & 16.34/18.23 & 12.01/23.99 & 10.34/30.24 \\\\\n RefVAE-WaveDiffUR & 22.38/13.13 & 22.82/14.25 & 21.38/14.14 & 19.38/15.14 & 18.32/16.26 & 17.38/19.15 \\\\\n EDiffSR & 20.15/16.31 & 18.92/17.31 & 15.15/18.32 & 10.15/22.32 & 8.92/27.32 & 7.15/38.33 \\\\\n EDiffSR-WaveDiffUR & 20.02/16.51 & 19.29/17.02 & 18.02/17.52 & 17.25/18.21 & 15.99/18.35 & 12.02/20.53 \\\\\n CSP-WaveDiffUR(one-step) & 28.21/7.82 & 28.18/7.88 & 26.21/9.83 & 21.21/12.83 & 19.18/17.89 & 18.21/19.84 \\\\\n CSP-WaveDiffUR & 28.32/7.22 & 28.31/7.37 & 27.32/8.03 & 25.52/9.23 & 24.31/11.38 & 24.28/13.24 \\\\\\midrule\n & \\multicolumn{6}{c}{DIOR} \\\\\\midrule\n \\multirow{2}{*}{Method} & \\multicolumn{6}{c}{Scale} \\\\\n &$\\times 4$ & $\\times 8$ &$\\times 16$* &$\\times 32$* &$\\times 64$* & $\\times 128$* \\\\\\midrule\n LE-GAN & 19.41/12.69 & 19.19/13.18 & 18.31/14.82 & 14.35/17.34 & 10.21/19.99 & 6.92/27.53 \\\\\n LE-GAN-WaveDiffUR & 19.98/12.46 & 19.33/13.08 & 18.42/14.66 & 17.21/16.89 & 15.99/17.72 & 14.25/20.55 \\\\\n RefVAE & 18.35/15.92 & 17.32/16.18 & 15.52/16.74 & 12.21/19.98 & 8.81/25.93 & 5.89/28.83 \\\\\n RefVAE-WaveDiffUR & 19.01/15.28 & 18.99/15.82 & 17.59/16.11 & 14.49/17.52 & 13.21/19.14 & 10.15/21.71 \\\\\n EDiffSR & 18.78/13.51 & 17.98/14.23 & 14.88/15.66 & 12.62/20.22 & 9.21/24.41 & 5.32/26.93 \\\\\n EDiffSR-WaveDiffUR & 18.11/12.82 & 17.23/13.31 & 16.32/14.83 & 15.12/16.11 & 13.11/18.21 & 10.31/19.68 \\\\\n CSP-WaveDiffUR(one-step) & 24.63/12.71 & 23.21/13.81 & 21.92/15.32 & 18.84/19.32 & 14.22/22.12 & 10.36/24.96 \\\\\n CSP-WaveDiffUR & 25.12/8.99 & 24.92/9.87 & 22.31/9.72 & 21.03/10.21 & 18.79/12.42 & 16.32/14.85 \\\\\\midrule\n & \\multicolumn{6}{c}{DDISR} \\\\\\midrule\n \\multirow{2}{*}{Method} & \\multicolumn{6}{c}{Scale} \\\\\n & $\\times 4$ & $\\times 8$ &$\\times 16$ &$\\times 32$ &$\\times 64$ & $\\times 128$ \\\\\\midrule\nLE-GAN & 24.14/16.89 & 21.91/18.87 & 19.19/21.82 & 16.59/24.34 & 12.21/28.99 & 8.21/32.31 \\\\\nLE-GAN-WaveDiffUR & 24.38/15.08 & 23.21/16.16 & 23.09/16.98 & 19.99/19.72 & 16.55/22.55 & 14.32/26.37 \\\\\nRefVAE & 28.12/18.18 & 25.52/19.74 & 22.11/21.81 & 18.81/25.93 & 12.89/28.23 & 9.82/36.20 \\\\\nRefVAE-WaveDiffUR & 28.29/17.21 & 27.59/18.91 & 26.39/19.12 & 22.99/20.41 & 19.15/23.71 & 16.15/27.71 \\\\\nEDiffSR & 28.98/23.13 & 27.88/26.66 & 24.92/29.12 & 19.21/34.19 & 13.28/39.93 & 12.08/38.82 \\\\\nEDiffSR-WaveDiffUR & 29.23/21.11 & 28.32/22.83 & 27.17/23.11 & 25.91/24.29 & 21.18/25.68 & 18.11/28.85 \\\\\nCSP-WaveDiffUR(one-step) & 31.21/12.19 & 30.92/13.39 & 28.84/16.32 & 24.22/18.12 & 20.36/21.62 & 16.13/28.21 \\\\\nCSP-WaveDiffUR & 34.92/8.27 & 34.19/8.75 & 32.33/9.26 & 30.79/11.95 & 28.32/14.85 & 23.48/16.85 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images", "authors": ["Yue Shi", "Liangxiu Han", "Darren Dancy", "Lianghao Han"], "url": "https://arxiv.org/abs/2412.18996v1", "attribution": "\"WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images\" by Yue Shi, Liangxiu Han, Darren Dancy, and Lianghao Han, arXiv:2412.18996v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13299v6_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n\\hline\nMethod & Recovery\\% \\\\ \\hline\nDenseCPD & 50.7 \\\\ \\hline\nProDCoNN & {\\color[HTML]{000000} 40.7} \\\\ \\hline\nSBROF & 39.2 \\\\ \\hline\nSPIN2 & 33.6 \\\\ \\hline\nWang's model & 33.0 \\\\ \\hline\nProteinSolver & 30.8 \\\\ \\hline\nSPIN & 30.3 \\\\ \\hline\nRosetta & 30.3 \\\\ \\hline\nGVP-GNN & 44.9 \\\\ \\hline\nDWNN & \\textbf{51.5} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{We test some variants of attack location search methods on AG-news dataset.POS means only using pos\\_tags to find the locations to be attacked and PR is the search method we use.Result shows that PR is more efficient than others.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning", "authors": ["Jiahan Li", "Shitong Luo", "Congyue Deng", "Chaoran Cheng", "Jiaqi Guan", "Leonidas Guibas", "Jian Peng", "Jianzhu Ma"], "url": "https://arxiv.org/abs/2201.13299v6", "attribution": "\"Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning\" by Jiahan Li, Shitong Luo, Congyue Deng, Chaoran Cheng, Jiaqi Guan, Leonidas Guibas, Jian Peng, and Jianzhu Ma, arXiv:2201.13299v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.03112v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|r|r|r|r|r|r|r|r|r}\n\\hline\nblack box & MBT & $impr$ & $alpha$ & n leaves & n leaves min & n leaves max & $R^2_{train}$ & sd $R^2_{train}$ & $R^2_{test}$ & sd $R^2_{test}$\\\\\n\\hline\nlm & SLIM & 0.15 & & 2.06 & 2 & 3 & 0.9650 & 0.0043 & 0.9631 & 0.0046\\\\\nlm & SLIM & 0.10 & & 12.11 & 5 & 16 & 0.9965 & 0.0052 & 0.9958 & 0.0060\\\\\nlm & SLIM & 0.05 & & 15.70 & 14 & 16 & 0.9995 & 0.0001 & 0.9993 & 0.0001\\\\\nlm & GUIDE & 0.15 & & 2.07 & 2 & 3 & 0.9651 & 0.0044 & 0.9632 & 0.0049\\\\\nlm & GUIDE & 0.10 & & 12.03 & 5 & 16 & 0.9965 & 0.0051 & 0.9957 & 0.0060\\\\\nlm & GUIDE & 0.05 & & 15.75 & 14 & 16 & 0.9995 & 0.0001 & 0.9993 & 0.0001\\\\\nlm & MOB & & 0.001 & 15.78 & 14 & 16 & 0.9994 & 0.0001 & 0.9993 & 0.0001\\\\\nlm & MOB & & 0.010 & 15.78 & 14 & 16 & 0.9994 & 0.0001 & 0.9993 & 0.0001\\\\\nlm & MOB & & 0.050 & 15.78 & 14 & 16 & 0.9994 & 0.0001 & 0.9993 & 0.0001\\\\\nlm & CTree & & 0.001 & 15.22 & 13 & 17 & 0.9993 & 0.0001 & 0.9992 & 0.0001\\\\\nlm & CTree & & 0.010 & 15.22 & 13 & 17 & 0.9993 & 0.0001 & 0.9992 & 0.0001\\\\\nlm & CTree & & 0.050 & 15.22 & 13 & 17 & 0.9993 & 0.0001 & 0.9992 & 0.0001\\\\\n\\hline\nlm & & & & & & & 0.9902 & 0.0006 & 0.9901 & 0.0008\\\\\n\\hline\nXGBoost & SLIM & 0.15 & & 2.31 & 2 & 6 & 0.9665 & 0.0069 & 0.9629 & 0.0079\\\\\nXGBoost & SLIM & 0.10 & & 7.33 & 2 & 14 & 0.9850 & 0.0060 & 0.9814 & 0.0062\\\\\nXGBoost & SLIM & 0.05 & & 14.30 & 8 & 17 & 0.9948 & 0.0010 & 0.9909 & 0.0017\\\\\nXGBoost & GUIDE & 0.15 & & 2.26 & 2 & 5 & 0.9664 & 0.0067 & 0.9628 & 0.0077\\\\\nXGBoost & GUIDE & 0.10 & & 6.92 & 2 & 14 & 0.9847 & 0.0061 & 0.9811 & 0.0062\\\\\nXGBoost & GUIDE & 0.05 & & 14.15 & 8 & 17 & 0.9945 & 0.0010 & 0.9906 & 0.0017\\\\\nXGBoost & MOB & & 0.001 & 10.89 & 8 & 13 & 0.9944 & 0.0005 & 0.9904 & 0.0011\\\\\nXGBoost & MOB & & 0.010 & 11.96 & 9 & 15 & 0.9946 & 0.0005 & 0.9906 & 0.0011\\\\\nXGBoost & MOB & & 0.050 & 12.86 & 11 & 15 & 0.9948 & 0.0005 & 0.9908 & 0.0011\\\\\nXGBoost & CTree & & 0.001 & 12.09 & 9 & 15 & 0.9940 & 0.0006 & 0.9900 & 0.0012\\\\\nXGBoost & CTree & & 0.010 & 13.21 & 10 & 15 & 0.9943 & 0.0006 & 0.9902 & 0.0013\\\\\nXGBoost & CTree & & 0.050 & 14.09 & 11 & 17 & 0.9944 & 0.0006 & 0.9904 & 0.0012\\\\\n\\hline\nXGBoost & & & & & & & 0.9858 & 0.0008 & 0.9768 & 0.0018\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean simulation results on 100 simulation runs as surrogate models on scenario linear smooth with sample size $n = 1500$ for different values of $impr$ and $alpha$}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation", "authors": ["Julia Herbinger", "Susanne Dandl", "Fiona K. Ewald", "Sofia Loibl", "Giuseppe Casalicchio"], "url": "https://arxiv.org/abs/2310.03112v1", "attribution": "\"Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation\" by Julia Herbinger, Susanne Dandl, Fiona K. Ewald, Sofia Loibl, and Giuseppe Casalicchio, arXiv:2310.03112v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.00700v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Baseline comparison of the MMSE score regression. Our test results are corresponding to the regression ensemble.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccc}\n \\toprule\n & Model & RMSE\\\\\n \\hline\n LOSO & Luz et al. & 4.38\\\\\n & Ensemble (\\textit{ours}) & \\textbf{0.82}\\\\\n \\hline\n TEST & Luz et al. & 5.20\\\\\n & Ensemble (\\textit{ours}) & \\textbf{4.60}\\\\\n \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity", "authors": ["Utkarsh Sarawgi", "Wazeer Zulfikar", "Nouran Soliman", "Pattie Maes"], "url": "https://arxiv.org/abs/2009.00700v1", "attribution": "\"Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity\" by Utkarsh Sarawgi, Wazeer Zulfikar, Nouran Soliman, and Pattie Maes, arXiv:2009.00700v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10182v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|} \n\\hline\n & Criminal & Non-Criminal \\\\ \n\\hline\nTrain dataset & 8336 & 8336 \\\\ \n\\hline\nTest dataset & 2000 & 2000 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Train-test data information.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Pre-Trained Language Transformers are Universal Image Classifiers", "authors": ["Rahul Goel", "Modar Sulaiman", "Kimia Noorbakhsh", "Mahdi Sharifi", "Rajesh Sharma", "Pooyan Jamshidi", "Kallol Roy"], "url": "https://arxiv.org/abs/2201.10182v1", "attribution": "\"Pre-Trained Language Transformers are Universal Image Classifiers\" by Rahul Goel, Modar Sulaiman, Kimia Noorbakhsh, Mahdi Sharifi, Rajesh Sharma, Pooyan Jamshidi, and Kallol Roy, arXiv:2201.10182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12733v5_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small Certified robust accuracy of TPC on CurveNet}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcc}\n \\toprule\n \\multirow{2}{*}[-2pt]{Transformation} & \\multirow{2}{*}[-2pt]{Attack radius} & \\multicolumn{2}{c}{Certified Accuracy ($\\%$)} \\\\ \\cline{3-4}\\\\\n & & PointNet& CurveNet \\\\\n \\midrule\n Z-rotation & 180$^\\circ$ & 81.3& \\textbf{85.4}\\\\\n Z-shear & 0.2 & 77.7 & \\textbf{87.8}\\\\\n Z-twist & 180$^\\circ$ & 64.3 & \\textbf{86.2}\\\\\n Z-taper & 0.2 & 76.5 & \\textbf{88.6}\\\\\n Linear & 0.2 & 59.9 & \\textbf{77.7} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "TPC: Transformation-Specific Smoothing for Point Cloud Models", "authors": ["Wenda Chu", "Linyi Li", "Bo Li"], "url": "https://arxiv.org/abs/2201.12733v5", "attribution": "\"TPC: Transformation-Specific Smoothing for Point Cloud Models\" by Wenda Chu, Linyi Li, and Bo Li, arXiv:2201.12733v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.16106v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{the values of $x_{\\pi,i}^*$ and $x_{\\mu,i}^*, i=1,2$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n \\hline\n $\\!\\!\\!$& $\\!\\!\\!\\!\\!\\beta_{i,1}\\geq \\overline{\\pi}\\!\\!$& $\\!\\!1\\leq \\beta_{i,1}\\leq \\overline{\\pi}\\!\\!$ &$\\!\\!\\beta_{i,1}\\leq1\\leq\\beta_{i,2}\\!\\!$ & $\\!\\!0\\leq\\beta_{i,2}\\leq1\\!\\!$\n & $\\!\\!\\beta_{i,2}\\leq 0\\leq \\beta_{i,3}\\!\\!$& $\\!\\!\\underline{\\pi}\\leq\\beta_{i,3}\\leq 0\\!\\!$& $\\!\\!\\beta_{i,3}\\leq \\underline{\\pi} $\\\\\n \\hline\n $\\!\\!\\!x_{\\pi,i}^*$ &$\\;\\;\\overline{\\pi}$ &$\\beta_{i,1}$ & $1$ & $\\beta_{i,2}$ & $0$ & $\\beta_{i,3}$& $\\underline{\\pi}$\\\\\n \\hline\n $\\!\\!\\!x_{\\mu,i}^*$ &$\\;\\;\\underline{\\mu}_i$ &$\\underline{\\mu}_i$ & $\\underline{\\mu}_i$ & $\\underline{\\mu}_i$ & $r$ & $\\overline{\\mu}_i$& $\\overline{\\mu}_i$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Pricing Model for Data Assets in Investment-Consumption Framework with Ambiguity", "authors": ["Xiaoshan Chen", "Chen Yang", "Zhou Yang"], "url": "https://arxiv.org/abs/2505.16106v1", "attribution": "\"Pricing Model for Data Assets in Investment-Consumption Framework with Ambiguity\" by Xiaoshan Chen, Chen Yang, and Zhou Yang, arXiv:2505.16106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00596v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|c|c|}\n\\hline\n & \\textbf{Recall} & \\textbf{Precision} \\\\ \\hline\nSCL & 0.37 & 0.96\\\\ \\hline\nApp. No. & 0.73 & 0.88 \\\\ \\hline\nOur Method & 0.89 & 0.86 \\\\ \\hline\n\\end{tabular}\n\\caption{Results of Manual Quality Assessment of sampled 120 cases against various approaches, for citation extraction and mapping.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "ECtHR-PCR: A Dataset for Precedent Understanding and Prior Case Retrieval in the European Court of Human Rights", "authors": ["T. Y. S. S Santosh", "Rashid Gustav Haddad", "Matthias Grabmair"], "url": "https://arxiv.org/abs/2404.00596v1", "attribution": "\"ECtHR-PCR: A Dataset for Precedent Understanding and Prior Case Retrieval in the European Court of Human Rights\" by T. Y. S. S Santosh, Rashid Gustav Haddad, and Matthias Grabmair, arXiv:2404.00596v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12741v6_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Paragraph mAP@IoU[.50:.95] score and F1$_{var}$ score comparisons. All models are tested on the PubLayNet development set. Numbers for mAP in the first 2 rows are from~.}\n\\begin{tabular}{lccc}\n\\noalign{\\smallskip}\\noalign{\\smallskip}\n\\hline \\hline\\noalign{\\smallskip}\nModel & Training Set & mAP & F1$_{var}$ \\\\\n\\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\nF-RCNN~ & PubLayNet training & 0.910 & - \\\\\nM-RCNN~ & PubLayNet training & 0.916 & - \\\\\nF-RCNN-Q & PubLayNet training & 0.914 & 0.945 \\\\\nTesseract~ & - & 0.571 & 0.707 \\\\\nOCR + Heuristic & - & 0.302 & 0.364 \\\\\nOCR + GCNs & Augmented web synthetic & 0.748 & 0.867 \\\\\nOCR + GCNs & PubLayNet training & 0.842 & 0.959 \\\\\nOCR + Ground Truth \\hspace{-2mm} & - & 0.892 & 0.997 \\\\\n\\noalign{\\smallskip}\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Post-OCR Paragraph Recognition by Graph Convolutional Networks", "authors": ["Renshen Wang", "Yasuhisa Fujii", "Ashok C. Popat"], "url": "https://arxiv.org/abs/2101.12741v6", "attribution": "\"Post-OCR Paragraph Recognition by Graph Convolutional Networks\" by Renshen Wang, Yasuhisa Fujii, and Ashok C. Popat, arXiv:2101.12741v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.17839v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Analysis of model performance against noise. Here, we evaluate the perceptual performance and downstream task performance on the Cityscapes dataset with channel PER = 10\\%. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c|c|}\n\\hline\n\\textbf{Steps} & \\textbf{FID}$\\downarrow$ & LPIPS$\\downarrow$ & Depth$\\downarrow$ \\\\\n\\hline\n28/32& 30.55 & 0.1560 & 5.99 \\\\\n\\hline\n50/62& 30.05 & 0.1524&6.08 \\\\\n\\hline\n94/100& 28.42& 0.1470& 5.97\\\\\n\\hline\n194/200& \\textbf{27.23} & \\textbf{0.1438} & \\textbf{5.88}\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency", "authors": ["Achintha Wijesinghe", "Suchinthaka Wanninayaka", "Weiwei Wang", "Yu-Chieh Chao", "Songyang Zhang", "Zhi Ding"], "url": "https://arxiv.org/abs/2412.17839v1", "attribution": "\"LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency\" by Achintha Wijesinghe, Suchinthaka Wanninayaka, Weiwei Wang, Yu-Chieh Chao, Songyang Zhang, and Zhi Ding, arXiv:2412.17839v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19428v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll} \n \\hline\n\tCurrent paper\t\t& \t& \t\\\\\n \\hline\n\t$\\alpha t$\t\t\t& $r\\mu$\t\t\t& $\\alpha$ \t\t\t\\\\\n\t$\\alpha s$\t\t\t& $r\\mu(1 - s_{\\rm HJR})$\t\t& $\\alpha(1 - r)$ \t\t\\\\\n\t$\\dfrac{\\beta(s)}{\\beta(t)} = \\dfrac{e^{\\alpha s} - 1}{e^{\\alpha t} - 1}$ & $\\dfrac{e^{r\\mu(1 - s_{\\rm HJR})} - 1}{e^{r\\mu} - 1}$\n\t\t\t\t\t\t\t& $q_r = \\dfrac{e^{-\\alpha r} - e^{-\\alpha}}{1 - e^{-\\alpha}}$\t\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Feller diffusion conditioned on a single ancestral founder", "authors": ["Conrad J. Burden", "Robert C. Griffiths"], "url": "https://arxiv.org/abs/2504.19428v2", "attribution": "\"The Feller diffusion conditioned on a single ancestral founder\" by Conrad J. Burden and Robert C. Griffiths, arXiv:2504.19428v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2301.00790v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|r|r|r|r||r|r|}\n\\hline\nModel & & Mean & Volatility & Max Draw & Sharpe & Calmar \\\\ \\hline \n\\multirow{2}{*}{\nLightGBM-dart without FE} & Mean & 0.0254 & 0.0266 & 0.1567 & 0.9593 & 0.1639 \\\\ \\cline{2-7} \n & S.D. & 0.0006 & 0.0007 & 0.0158 & 0.0365 & 0.0175 \\\\ \\hline\n\\multirow{2}{*}{LightGBM-gbdt without FE} & Mean & 0.0253 & 0.0312 & 0.2338 & 0.8104 & 0.1100 \\\\ \\cline{2-7} \n& S.D. & 0.0006 & 0.0006 & 0.0296 & 0.0278 & 0.0153 \\\\ \\hline\n\\multirow{2}{*}{\nMLP without FE} & Mean & 0.0233 & 0.0271 & 0.1643 & 0.8600 & 0.1446 \\\\ \\cline{2-7} \n& S.D. & 0.0009 & 0.0011 & 0.0248 & 0.0365 & 0.0219 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Variability of the performance of ML models in the test period (2015-05-15 to 2022-09-23). The mean and standard deviation of each portfolio metrics are calculated over models with 10 different random seeds for each method }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Online learning techniques for prediction of temporal tabular datasets with regime changes", "authors": ["Thomas Wong", "Mauricio Barahona"], "url": "https://arxiv.org/abs/2301.00790v4", "attribution": "\"Online learning techniques for prediction of temporal tabular datasets with regime changes\" by Thomas Wong and Mauricio Barahona, arXiv:2301.00790v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.21174v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|lll|lll|lll}\n\\# &\\multicolumn{3}{c}{$\\tau_{large}$} &\\multicolumn{3}{c}{$\\tau_{mix}$} & \\multicolumn{3}{c}{$\\tau_{small}$} \\\\\n & fgmres & lsqr & mrs & fgmres & lsqr & mrs & fgmres & lsqr & mrs \\\\ \\hline\n1 &3.0 & 1.5 & 2.9 & 4.0 & 1.5 & 4.0 & 2.0 & 1.5 & 4.0 \\\\\n2 &2.0 & 1.0 & 5.0 & 2.0 & 1.0 & 7.0 & 2.0& 1.0 & 7.0\\\\\n3 & 3.0 & 1.0 & 2.0 & 4.0 & 1.0 & 3.3 & 2.0 & 1.0 & 3.5\\\\\n4 & 2.5 & 16.8 & 17.8 & 3.0 & 24.3 & 27.5 & 2.0& 25.3&27.5\\\\\n5 & 2.0 & 16.5 & 31.8 & 3.0 & 23.7 & 48.0 & 2.0 & 24.8& 47.8\\\\\n6 & 3.7 & 17.3 & 101.4 & 5.0 & 23.3 & 164.5& 3.0 & 23.5 & 151.7\\\\\n7 & 9.0 & 17.5 & 230.3 & 10.0 & 23.8 & 374.0&7.0 & 23.8 & 360.7\\\\\n8 &52.5 & 18.3 & 493.9 & 81.3 & 22.8 & 791.9& 132.5& 27.8 & 809.6\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Average number of inner fGMRES, LSQR and MRS iterations for the new scheme (generalized case)}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust iterative methods for linear systems with saddle point structure", "authors": ["Murat Manguoğlu", "Volker Mehrmann"], "url": "https://arxiv.org/abs/2502.21174v1", "attribution": "\"Robust iterative methods for linear systems with saddle point structure\" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.16139v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccccc|}\n\\cline{2-9}\n & \\multicolumn{8}{c|}{Satellite image (2) data set} \\\\ \\cline{2-9} \n & \\multicolumn{2}{c|}{PCA} & \\multicolumn{2}{c|}{robPCA} & \\multicolumn{2}{c|}{ICA} & \\multicolumn{2}{c|}{ACA} \\\\ \\cline{2-9} \n & PC1 & PC2 & robPC1 & robPC2 & IC1 & IC2 & AC1 & AC2 \\\\ \\hline\nVar1 & 19 (3\\%) & 17 (5\\%) & 24 (7\\%) & 25 (6\\%) & 19 (4\\%) & 13 (4\\%) & 18 (8\\%) & 12 (9\\%) \\\\\nVar2 & 15 (3\\%) & 13 (5\\%) & 35 (7\\%) & 13 (6\\%) & 23 (4\\%) & 17 (4\\%) & 2 (8\\%) & 10 (8\\%) \\\\\nVar3 & 18 (3\\%) & 29 (5\\%) & 15 (6\\%) & 18 (6\\%) & 31 (4\\%) & 5 (4\\%) & 26 (6\\%) & 8 (7\\%) \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Contributions of three most important (for each component) variables for PCA, robPCA, ICA, and ACA for Satellite image (2) data set.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Abnormal component analysis", "authors": ["Romain Valla", "Pavlo Mozharovskyi", "Florence d'Alché-Buc"], "url": "https://arxiv.org/abs/2312.16139v2", "attribution": "\"Abnormal component analysis\" by Romain Valla, Pavlo Mozharovskyi, and Florence d'Alché-Buc, arXiv:2312.16139v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2308.00921v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lrrrrr}\n\\toprule\n\\multirow{2}{*}{\\textbf{Classifier}} & \\multicolumn{4}{r}{\\textbf{By-Class Balanced Accuracy}} & \\multirow{2}{*}{\\textbf{Average}} \\\\\n & \\textit{PV} & \\textit{DB} & \\textit{FE} & \\textit{ITE} & \\\\ \\midrule\nDecision tree & 0.8373 & 0.7827 & 0.6369 & 0.6357 & 0.7231 \\\\\nRandom forest & 0.8506 & 0.7976 & 0.7151 & 0.7193 & 0.7707 \\\\\nGradient boosted trees & 0.8534 & 0.7993 & 0.7270 & 0.6991 & 0.7697 \\\\\nLinear discriminant analysis & 0.7005 & 0.6777 & 0.5244 & 0.5746 & 0.6193 \\\\\nMultinomial logistic regression & 0.7054 & 0.6817 & 0.4744 & 0.4864 & 0.5870 \\\\\nMulti-layer perceptron & 0.7515 & 0.7138 & 0.8571 & 0.4864 & 0.7022 \\\\\nStack (Trees) & 0.8592 & 0.8021 & 0.7823 & \\textbf{0.7799} & \\textbf{0.8059} \\\\\nStack (Linear classifiers excluded) & \\textbf{0.8622} & 0.8030 & \\textbf{0.7865} & 0.7698 & 0.8054 \\\\\nStack (All) & 0.8616 & \\textbf{0.8032} & 0.7760 & 0.7789 & 0.8049 \\\\ \\bottomrule\n\\end{tabular}\n\\caption{Comparison of classification models.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Incident-Specific Cyber Insurance", "authors": ["Wing Fung Chong", "Daniel Linders", "Zhiyu Quan", "Linfeng Zhang"], "url": "https://arxiv.org/abs/2308.00921v1", "attribution": "\"Incident-Specific Cyber Insurance\" by Wing Fung Chong, Daniel Linders, Zhiyu Quan, and Linfeng Zhang, arXiv:2308.00921v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.04200v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c|c|c|c|c|c|c|c}\n$j$ & 1 & 2-3 & 4-5 & 6 & 7-8 & 9 & 10-11 & 12-13 \\\\ \\hline\n$nklm$ & 0010 & 10(1-2)0 & 20(1-2)0 & 0011 & 10(1-2)1 & 0110 & 30(1-2)0 & 20(1-2)1 \\\\\n$\\lambda_j^{(0)}$ & 0 & 3.39 & 9.33 & 9.87 & 13.26 & 14.68 & 17.65 & 19.20 \\\\\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Spectral properties of the Bloch-Torrey operator in three dimensions", "authors": ["Denis S. Grebenkov"], "url": "https://arxiv.org/abs/2312.04200v2", "attribution": "\"Spectral properties of the Bloch-Torrey operator in three dimensions\" by Denis S. Grebenkov, arXiv:2312.04200v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10253v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The effect of different weights in the feature extractor CNN. Measures are averaged across 7 runs of the game for each model on the CIFAR-10 validation set. Communication rate values in brackets are standard deviations across games, which show the sensitivity to different model initialisations and training runs. The message length standard deviation is measured across each game and averaged across the 7 runs, and show how much variance there is in transmitted message length.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n \\toprule\n Feature extractor & Comm. & Message & Top-5 & \\#Target & Target \\\\\n & rate & length & comm. & class & class \\\\\n & & & rate & in top-5 & avg. rank \\\\ \n \\midrule\n Pretrained \\& fixed &0.90 ($\\pm$0.02) & 4.93 ($\\pm$0.34) & 1 & 1.86 & 46.25\\\\\n \n Random \\& frozen & 0.93 ($\\pm$0.03) &4.90 ($\\pm$0.39) & 1 & 1.69 &51.65\\\\\n \n Learned end-end & 0.94 ($\\pm$0.02) &4.90 ($\\pm$0.39) & 1 & 1.5 &57.14\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "The emergence of visual semantics through communication games", "authors": ["Daniela Mihai", "Jonathon Hare"], "url": "https://arxiv.org/abs/2101.10253v1", "attribution": "\"The emergence of visual semantics through communication games\" by Daniela Mihai and Jonathon Hare, arXiv:2101.10253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.09983v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\toprule \n & Hyperparameter & Value \\\\\n \\midrule\n DQN HP & Optimizer & Adam~ \\\\\n & Critic architecture & MLP \\\\\n & Critic learning rate & 1{e}-3 \\\\\n & Critic hidden layers & 1 \\\\\n & Critic hidden dim & 24 \\\\\n & Critic activation function & \n ReLU \\\\\n & Mini-batch size & 1024 \\\\\n & Number of gradient steps & 50K \\\\\n & Discount factor & 0.99 \\\\\n & Target update rate & 1 \\\\\n & Target update period & 8 \\\\\n & Loss Function & Huber Bellman Error \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Hyperparameters for the DQN algorithm used in Section~.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping", "authors": ["Lauren H. Cooke", "Harvey Klyne", "Edwin Zhang", "Cassidy Laidlaw", "Milind Tambe", "Finale Doshi-Velez"], "url": "https://arxiv.org/abs/2312.09983v2", "attribution": "\"Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping\" by Lauren H. Cooke, Harvey Klyne, Edwin Zhang, Cassidy Laidlaw, Milind Tambe, and Finale Doshi-Velez, arXiv:2312.09983v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04758v4_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\toprule\n\\bf Tag & \\bf Description & \\bf Tag & \\bf Description \\\\ \n\\hline\nADV & adverb & ADJ & adjective \\\\\nCONJ & conjunction & DET & determiner \\\\\nNOUN & noun & NSUFF & noun suffix \\\\\nNUM & number & PART & particle \\\\\nPUNC & punctuation & PRON & pronoun \\\\\nPREP & preposition & V & verb \\\\\nABBREV & abbreviation & VSUFF & verb suffix \\\\\nFOREIGN & non-Arabic & FUT\\_PART & future particle \\\\\nPROG\\_PART & progressive particle & EMOT & Emoticon/Emoji\\\\\nMENTION & twitter mention & HASH & Hashtag \\\\\nURL & URL & ~~~ -- & ~~~ --\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ The POS tag set in~.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling", "authors": ["Muhammad Khalifa", "Muhammad Abdul-Mageed", "Khaled Shaalan"], "url": "https://arxiv.org/abs/2101.04758v4", "attribution": "\"Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling\" by Muhammad Khalifa, Muhammad Abdul-Mageed, and Khaled Shaalan, arXiv:2101.04758v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.00977v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation Parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline\n \\textbf{Parameter} & \\textbf{Value} \\\\\n \\hline\n \\hline\n \\textit{Cell radius} & $250$ \\textit{m} \\\\\n \\hline\n \\textit{Max D2D separation} & $20$ \\textit{m} \\\\\n \\hline\n \\textit{Max UE transmission power:} $P_{UE-max}$ & $25$ \\textit{dBm} \\\\\n \\hline\n \\textit{Total system bandwidth} & $5$ \\textit{MHz} \\\\\n \\hline\n \\textit{Carrier frequency} & $2$ \\textit{GHz} \\\\\n \\hline\n \\textit{Shadowing standard deviation:} $\\sigma$ & $8$ \\textit{dB}\\\\\n \\hline\n \\textit{Noise power spectral density:} $N_0$ & $-174$ \\textit{dBm/Hz}\\\\\n \\hline\n \\textit{Antenna separation at UE} & $0.3$ \\textit{m}\\\\\n \\hline\n \\textit{Path loss exponent} & $3$ \\\\\n \\hline\n \\textit{SI cancellation factor:} $\\zeta$ & $80$ \\textit{dB}\\\\\n \\hline\n \\textit{$R_{min-j}$ for all data files } & $5$ \\textit{Mbps}\\\\\n \\hline\n \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Optimal Power Allocation in Uplink NOMA with Simultaneous Cache-Enabled D2D Communications", "authors": ["Aditya Powari", "Daniel K. C. So"], "url": "https://arxiv.org/abs/2412.00977v1", "attribution": "\"Optimal Power Allocation in Uplink NOMA with Simultaneous Cache-Enabled D2D Communications\" by Aditya Powari and Daniel K. C. So, arXiv:2412.00977v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11932v4_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Implementation details of PGD-VAE. GIN represents the layer of Graph Isomorphism Network; ReLU represents the Rectified Linear Unit activation function; FC is the fully connected layer; Sigmoid is the sigmoid activation function.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n \\hline Layer & Local-pattern encoder&Global-pattern encoder&Local decoder & Neighborhood decoder & Global decoder & Assembler\\\\\\cline{2-7}\n \\hline\n Input & A & A & $z_l$ & $z_l$ & $z_g$ & $A^{(l)}$, $A^{(g)}$, $A^{(n)}$\\\\ \n \\hline\n Layer1 & GIN+ReLU & GIN+ReLU & FC+ReLU & FC+ReLU & FC+ReLU & -\\\\\n \\hline\n Layer2 & GIN+ReLU & GIN+ReLU & FC+ReLU & FC+ReLU & FC+ReLU & - \\\\\n \\hline\n Layer3 & GIN+ReLU & GIN+ReLU & Sigmoid & Sigmoid & Sigmoid & -\\\\\n \\hline\n Layer4 & Node clustering & Sum pooling &- &- & Replace diagonal with zero & - \\\\\n \\hline\n Layer5 & Concatenate representative node embedding & - &- &- & - & -\\\\\n \\hline\n Output & $z_l$ & $z_g$ & $A^{(l)}$ & $A^{(n)}$ & $A^{(g)}$ & A \\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Deep Generative Model for Periodic Graphs", "authors": ["Shiyu Wang", "Xiaojie Guo", "Liang Zhao"], "url": "https://arxiv.org/abs/2201.11932v4", "attribution": "\"Deep Generative Model for Periodic Graphs\" by Shiyu Wang, Xiaojie Guo, and Liang Zhao, arXiv:2201.11932v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.00101v1_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrr}\n \\hline\n & mCube & Bettonville & Crevecoeur \\\\ \n \\hline\nmean\\_CRPS & $\\mathbf{11,148.82}$ & 14,166.51 & 15,405.99 \\\\ \n median\\_CRPS & 4,077.85 & $\\mathbf{2,377.34}$ & 4,251.77 \\\\ \n PICP\\_99 & $\\mathbf{0.60}$ & 0.37 & 0.12 \\\\ \n IS\\_99 & {101,642.41} & $\\mathbf{385,729.23}$ & 71,808.31 \\\\ \n PICP\\_95 & $\\mathbf{0.57}$ & 0.29 & 0.11 \\\\ \n IS\\_95 & {66,168.42} & $\\mathbf{66,284.85}$ & 61,955.80 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{CRPS, IS, PICP for the RBNS claims of the 3 competing micro-reserving methods on the subset of Allianz bodily injury claims.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "mCube: Multinomial Micro-level reserving Model", "authors": ["Emmanuel Jordy Menvouta", "Jolien Ponnet", "Robin Van Oirbeek", "Tim Verdonck"], "url": "https://arxiv.org/abs/2212.00101v1", "attribution": "\"mCube: Multinomial Micro-level reserving Model\" by Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek, and Tim Verdonck, arXiv:2212.00101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11701v3_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{4-MNIST-Bags model results. The mean performance was calculated over ten repeat trainings of each model, and the standard error of the mean is given.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\t\t\\toprule\n\t\tModel & Train Accuracy & Val Accuracy & Test Accuracy \\\\\n\t\t\\midrule\n\t\tMI-Net & \\textbf{0.995 $\\pm$ 0.001} & 0.972 $\\pm$ 0.002 & 0.971 $\\pm$ 0.002 \\\\\n\t\tmi-Net & 0.993 $\\pm$ 0.001 & \\textbf{0.973 $\\pm$ 0.002} & \\textbf{0.974 $\\pm$ 0.002} \\\\\n\t\tMI-Attn & 0.991 $\\pm$ 0.002 & 0.967 $\\pm$ 0.003 & 0.967 $\\pm$ 0.003 \\\\\n\t\tMI-GNN & 0.984 $\\pm$ 0.002 & 0.968 $\\pm$ 0.002 & 0.966 $\\pm$ 0.002 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Model Agnostic Interpretability for Multiple Instance Learning", "authors": ["Joseph Early", "Christine Evers", "Sarvapali Ramchurn"], "url": "https://arxiv.org/abs/2201.11701v3", "attribution": "\"Model Agnostic Interpretability for Multiple Instance Learning\" by Joseph Early, Christine Evers, and Sarvapali Ramchurn, arXiv:2201.11701v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10020v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Difference in motivation pre- to post-selection.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|llll|}\n\t\t\\multicolumn{4}{c}{Least Squares Means} \\\\ \\hline\n\t\tCondition & MAB Assign. & $\\Delta$ Motivation & Std. Err. \\\\ \\hline\n\t\tControl & Arm-Downward & 0.0071 & 0.1268 \\\\\n\t\tControl & Arm-Mixed & -0.1189 & 0.1315 \\\\\n\t\tControl & Arm-Upward & -0.1117 & 0.1258 \\\\\n\t\tExperimental & Arm-Downward & 0.1013 & 0.1267 \\\\\n\t\tExperimental & Arm-Mixed & 0.3099 & 0.2216 \\\\\n\t\tExperimental & Arm-Upward & 0.0395 & 0.1134 \\\\ \\hline \n\t\t\\multicolumn{4}{c}{Overall Means} \\\\\n\t\t\\hline\n\t\tControl & - & 0.0194 & 0.0201 \\\\\n\t\tExperimental & - & 0.1456 & 0.0400 \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity", "authors": ["Jichen Zhu", "Diane H. Dallal", "Robert C. Gray", "Jennifer Villareale", "Santiago Ontañón", "Evan M. Forman", "Danielle Arigo"], "url": "https://arxiv.org/abs/2101.10020v2", "attribution": "\"Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity\" by Jichen Zhu, Diane H. Dallal, Robert C. Gray, Jennifer Villareale, Santiago Ontañón, Evan M. Forman, and Danielle Arigo, arXiv:2101.10020v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.01012v4_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|} \n\\hline Parameter & Values \\\\ \n\\hline minibatch size & 5,10,20,50,100,200 \\\\ \n\\hline components & 50 \\\\ \n\\hline epochs & 100 \\\\ \n\\hline lr & 0.01, 0.001, 0.0001, 0.00001 \\\\ \n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning", "authors": ["James Chapman", "Lennie Wells", "Ana Lawry Aguila"], "url": "https://arxiv.org/abs/2310.01012v4", "attribution": "\"Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning\" by James Chapman, Lennie Wells, and Ana Lawry Aguila, arXiv:2310.01012v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00507v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Linear evaluation accuracy (\\%) of contrastive learning representations learned by the objectives with/without regularizations on the mutual information between encoder and projector features.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccc}\n \\toprule \n Dataset & Framework & Baseline & Bottleneck Regularizer & Gains \\\\ \\midrule\n \\multirow{2}{*}{CIFAR-10} & SimCLR & 87.47 & 87.73 & +0.26 \\\\\n & Barlow Twins & 89.00 & 89.32 & +0.32 \\\\ \\midrule\n \\multirow{2}{*}{CIFAR-100} & SimCLR & 58.12 & 59.38 & +1.26 \\\\\n & Barlow Twins & 64.16 & 66.31 & +2.15 \\\\ \\midrule\n \\multirow{2}{*}{ImageNet-100} & SimCLR & 67.36 & 68.42 & +1.06 \\\\\n & Barlow Twins & 69.96 & 71.3 & +1.34 \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Projection Head is Secretly an Information Bottleneck", "authors": ["Zhuo Ouyang", "Kaiwen Hu", "Qi Zhang", "Yifei Wang", "Yisen Wang"], "url": "https://arxiv.org/abs/2503.00507v2", "attribution": "\"Projection Head is Secretly an Information Bottleneck\" by Zhuo Ouyang, Kaiwen Hu, Qi Zhang, Yifei Wang, and Yisen Wang, arXiv:2503.00507v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2303.14263v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{xcolor}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Robustness Check: Alternative Model Specification}\n\\begin{tabular}{lc}\n \\midrule\n Dependent Variable: & (1) \\\\\n NumOfPurchases & \\\\\n \\midrule\n Recommended & 0.401** \\\\\n & (0.061) \\\\\n Fund Type Dummies & Yes \\\\\n Month Dummies & Yes \\\\\n Day of Week Dummies & Yes \\\\\n Control Variables & Yes \\\\\n Number of Observations & 21,834 \\\\\n \\textcolor[rgb]{ .133, .133, .133}{Pseudo $R^2$} & 0.302 \\\\\n \\midrule\n \\multicolumn{2}{p{26em}}{\\scriptsize Notes: {***} $p<0.01$, {**} $p<0.05$, {*} $p<0.1$. NumberOfPurchases is the dependent variable in Column (1). Column (1) displays the result using a Poisson regression model. Control variables include management fees, purchase fees, custodial fees, asset size, dummy for valuable fund brand, fund past returns, fund risk level, and fund tenure. Robust standard errors are in parentheses.} \\\\\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Effect of Product Recommendations on Online Investor Behaviors", "authors": ["Ruiqi Rich Zhu", "Cheng He", "Yu Jeffrey Hu"], "url": "https://arxiv.org/abs/2303.14263v2", "attribution": "\"The Effect of Product Recommendations on Online Investor Behaviors\" by Ruiqi Rich Zhu, Cheng He, and Yu Jeffrey Hu, arXiv:2303.14263v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.22061v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|c|c|}\n \\hline\n & $\\hat{g}_{1}$ & $\\hat{g}_{2}$ & $\\hat{g}_{3}$ & $\\hat{g}_{4}$ & $\\hat{g}_{5}$ & $\\hat{g}_{6}$ & $\\hat{g}_{7}$ & $\\hat{g}_{8}$ & $\\hat{g}_{9}$ & $\\hat{g}_{10}$ & $\\hat{g}_{11}$\\\\\n \\hline\n$\\hat{g}_{1}$ & $0$ & $0$ & $0$ & $0$ & $-2\\hat{g}_{3}$ & $-2\\hat{g}_{1}$ & $0$ & $-4\\hat{g}_{6}-2\\hat{g}_{11}$ & $0$ & $-2\\hat{g}_{4}$ & $0$ \\\\\n\\hline\n$\\hat{g}_{2}$ & * & $0$ & $0$ & $-2\\hat{g}_{3}$ & $0$ & $0$ & $-2\\hat{g}_{2}$ & $0$ & $-4\\hat{g}_{7}-2\\hat{g}_{11}$ & $-2\\hat{g}_{5}$ & $0$ \\\\\n\\hline\n$\\hat{g}_{3}$ & * & * & $0$ & $-\\hat{g}_{1}$ & $-\\hat{g}_{2}$ & $-\\hat{g}_{3}$ & $-\\hat{g}_{3}$ & $-2\\hat{g}_{5}$ & $-2\\hat{g}_{4}$ & $-\\hat{g}_{6}-\\hat{g}_{7}-\\hat{g}_{11}$ & $0$ \\\\\n\\hline\n$\\hat{g}_{4}$ & * & * & * & $0$ & $\\hat{g}_{6}-\\hat{g}_{7}$ & $-\\hat{g}_{4}$ & $\\hat{g}_{4}$ & $-2\\hat{g}_{10}$ & $0$ & $-\\hat{g}_{9}$ & $0$ \\\\\n\\hline\n$\\hat{g}_{5}$ & * & * & * & * & $0$ & $\\hat{g}_{5}$ & $-\\hat{g}_{5}$ & $0$ & $-2\\hat{g}_{10}$ & $-\\hat{g}_{8}$ & $0$ \\\\\n\\hline\n$\\hat{g}_{6}$ & * & * & * & * & * & $0$ & $0$ & $-2\\hat{g}_{8}$ & $0$ & $-\\hat{g}_{10}$ & $0$ \\\\\n\\hline\n$\\hat{g}_{7}$ & * & * & * & * & * & * & $0$ & $0$ & $-2\\hat{g}_{9}$ & $-\\hat{g}_{10}$ & $0$ \\\\\n\\hline\n$\\hat{g}_{8}$ & * & * & * & * & * & * & * & $0$ & $0$ & $0$ & $0$ \\\\\n\\hline\n$\\hat{g}_{9}$ & * & * & * & * & * & * & * & * & $0$ & $0$ & $0$ \\\\\n\\hline\n$\\hat{g}_{10}$ & * & * & * & * & * & * & * & * & * & $0$ & $0$ \\\\\n\\hline\n$\\hat{g}_{11}$ & * & * & * & * & * & * & * & * & * & * & $0$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Structure table for a system of two coupled time-dependent harmonic oscillators. The symbol $*$ is used here to indicate that such entries are known once this table is anti-symmetric.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "$\\texttt{Symdyn}$: an automated algebraic solution for high-order quantum systems", "authors": ["D. Martínez-Tibaduiza", "Vladimir Vargas-Calderón", "J. G. Dueñas", "J. Flórez-Jiménez", "A. Z. Khoury"], "url": "https://arxiv.org/abs/2503.22061v2", "attribution": "\"$\\texttt{Symdyn}$: an automated algebraic solution for high-order quantum systems\" by D. Martínez-Tibaduiza, Vladimir Vargas-Calderón, J. G. Dueñas, J. Flórez-Jiménez, and A. Z. Khoury, arXiv:2503.22061v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2304.00544v1_tex_table21.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Occupational mobility and unemployment duration}\n\\begin{tabular}{lccccc}\n \\hline\n\t&Health prof, \t& Account, archit, legal\t& Managers, officials, &\tClerical \t& Retail and sales \\\\\n\t&School/college teachers &\ttech., others &proprietors\t&workers\t&workers \\\\\n\t\\hline\t\t\t\t\t\t\t\t\t\nBreak &\t-0.0002 &\t0.0040\t&-0.0014 &\t0.0034\t&0.0044\t \\\\\n\t&{\\footnotesize{(0.0027)}}\t&{\\footnotesize{(0.0039)}}\t&{\\footnotesize{(0.0043)}}\t&{\\footnotesize{(0.0033)}}\t&{\\footnotesize{( 0.0030)}}\t \\\\\nConstant &\t0.0458$^{***}$\t& 0.0774$^{***}$\t&0.1459$^{***}$\t&0.0966$^{***}$\t&0.0510$^{***}$\t\\\\\n\t&{\\footnotesize{(0.0040)}}\t&{\\footnotesize{(0.0100)}}\t&{\\footnotesize{(0.0064)}}\t&{\\footnotesize{(0.0134)}}&{\\footnotesize{(0.0121)}}\t\\\\\nTime trend &\tX\t&X\t&X\t&X\t&X \\\\\n\\hline\n$R^{2}$\t& 0.8482\t&0.9619\t&0.8552\t&0.8294\t&0.8793 \\\\\n\\hline\n\\hline\n\t& Foremen\t& Craftsmen\t &Operatives \t& Laborer\t& Other service \\\\\n\t&\tworkers\t&kindred workers\t&kindred workers\t&workers\t& workers \\\\\n\t\\hline\t\t\t\t\t\t\t\t\t\nBreak &\t-0.0004 \t&0.0012\t&0.0031\t&-0.0085$^{***}$ &\t-0.0031 \\\\\n\t&{\\footnotesize{(0.0023)}}\t&{\\footnotesize{(0.0029)}}\t&{\\footnotesize{(0.0044)}}\t&{\\footnotesize{(0.0017)}}\t& {\\footnotesize{(0.0023)}}\\\\\nConstant &\t0.0179$^{***}$&\t0.0494$^{***}$&\t0.1717$^{***}$\t&0.0524$^{***}$\t& 0.0907$^{***}$\\\\\n\t&{\\footnotesize{(0.0034)}}\t& {\\footnotesize{(0.0043)}} &\t{\\footnotesize{(0.0066)}}\t&{\\footnotesize{(0.0015)}}\t&{\\footnotesize{(0.0027)}} \\\\\nTime trend &\tX\t&X\t&X\t&X\t&X \\\\\n\\hline\n$R^{2}$\t& 0.6108\t&0.7911\t&0.9110&\t0.8437\t&0.4639 \\\\\n\\hline\n\\hline\n\\multicolumn{6}{l}{{\\footnotesize{Levels of significance: $^{*} p<0.1$, $^{**} p<0.05$, $^{***} p<0.01$}}}\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Unemployment and Endogenous Reallocation over the Business Cycle", "authors": ["Carlos Carrillo-Tudela", "Ludo Visschers"], "url": "https://arxiv.org/abs/2304.00544v1", "attribution": "\"Unemployment and Endogenous Reallocation over the Business Cycle\" by Carlos Carrillo-Tudela and Ludo Visschers, arXiv:2304.00544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.05866v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{(Scenario III) Averages of the 5 smallest of SP/KS-GSP/EO/KS-GEOs whose MAEs are less than the thresholds. Those scores are selected in the Pareto solutions appearing in Figure~. Standard deviations are in the parentheses next to the averages.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc}\n\\toprule\n& \\multicolumn{4}{c}{\\textbf{Community and Crime} (MAE $\\leq$ 0.12)} \\\\\n\\hline\nModel & SP ($\\downarrow$) & KS-GSP ($\\downarrow$) & EO ($\\downarrow$) & KS-GEO ($\\downarrow$) \\\\\n\\hline\n\\hline\nSBP & 0.225 (0.007) & 0.141 (0.004) & 0.083 (0.003) & 0.060 (0.002) \\\\\nCON & - & - & 0.094 (0.004) & 0.062 (0.002) \\\\\nHGR & 0.247 (0.002) & 0.155 (0.001) & 0.082 (0.004) & 0.069 (0.002) \\\\\n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes", "authors": ["Jinwon Sohn", "Qifan Song", "Guang Lin"], "url": "https://arxiv.org/abs/2311.05866v2", "attribution": "\"Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes\" by Jinwon Sohn, Qifan Song, and Guang Lin, arXiv:2311.05866v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04243v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c||cc|cc}\n & \\multicolumn{2}{c|}{\\textbf{ViT-H}} & \\multicolumn{2}{c}{\\textbf{ViT-B}} \\\\\n\\toprule\n$R$ & $\\tau$ & $\\rho$ & $\\tau$ & $\\rho$ \\\\\n\\midrule\n$1$ & $-0.82$ & $-0.96$ & $-0.79$ & $-0.94$ \\\\\n$3$ & $-0.80$ & $-0.95$ & $-0.76$ & $-0.94$ \\\\\n$5$ & $-0.77$ & $-0.93$ & $-0.75$ & $-0.93$ \\\\\n$7$ & $-0.74$ & $-0.92$ & $-0.73$ & $-0.91$ \\\\\n$9$ & $-0.72$ & $-0.91$ & $-0.70$ & $-0.90$ \\\\\n$11$ & $-0.71$ & $-0.90$ & $-0.70$ & $-0.89$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Rank correlation (Kendall $\\tau$ and Spearman $\\rho$) between SAM prediction IoU and object tree-likeness as measured by \\textbf{CPR} with different hyperparameter values $R$ on the \\textbf{synthetic dataset}, to supplement Table .}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects", "authors": ["Yixin Zhang", "Nicholas Konz", "Kevin Kramer", "Maciej A. Mazurowski"], "url": "https://arxiv.org/abs/2412.04243v2", "attribution": "\"Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects\" by Yixin Zhang, Nicholas Konz, Kevin Kramer, and Maciej A. Mazurowski, arXiv:2412.04243v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.11418v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc} \\hline\n $p_k$ & $0< r_0 < 1/2$ & $1/2< r_0 < 1$ \\\\ \\hline\n $p_3$ & $-2r_0^3+3r_0^2$ & $-2r_0^3+3r_0^2$ \\\\\n $p_2$ & $ - \\frac{4}{3}r_0^3+r_0^2$ & $\\frac{4}{3}r_0^3 - \\frac{1}{3}({3}r_0-1)^2$ \\\\\n $p_1$ & $\\frac{14}{3}r_0^3-6r_0^2+2r_0$ & $\\frac{2}{3}(1-r_0)^{3}$ \\\\\n $p_0$ & $(1-2r_0)^{3}$ & $0$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Individual graph probabilities for the 3 node hard RGG in $[0,1]$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Entropy of Random Geometric Graphs in High and Low Dimensions", "authors": ["Oliver Baker", "Carl P. Dettmann"], "url": "https://arxiv.org/abs/2503.11418v3", "attribution": "\"Entropy of Random Geometric Graphs in High and Low Dimensions\" by Oliver Baker and Carl P. Dettmann, arXiv:2503.11418v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04727v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\t\t\t\\toprule\n\t\t\t & 0~10 & 0~15 & 0~20 & 0 & 10 & 15 \\\\\n\t\t\t\\midrule\n\t\t\t\\midrule\n\t\t\t\\multirow{1}{*}{SIDD} & 39.31/0.9570 & 39.12/0.9562 & 29.73/0.7780 & 29.99/0.7689 & 38.77/0.9530 & 37.73/0.93521 \\\\\n\t\t\t\\midrule\n\t\t\t\\multirow{1}{*}{OOD avg} & 39.78/0.9823 & 39.83/0.9815 & 27.71/0.7426 & 29.90/0.8201 & 39.57/0.9770 & 14.32/0.2985 \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning to Translate Noise for Robust Image Denoising", "authors": ["Inju Ha", "Donghun Ryou", "Seonguk Seo", "Bohyung Han"], "url": "https://arxiv.org/abs/2412.04727v1", "attribution": "\"Learning to Translate Noise for Robust Image Denoising\" by Inju Ha, Donghun Ryou, Seonguk Seo, and Bohyung Han, arXiv:2412.04727v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.03355v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation results for type I error rate; no random censoring was assumed.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crrrrrrr}\n\\toprule\nDistribution & Sample size & $S_0(t)$ & identity & log & log-log & logit & arcsine \\\\ \\midrule\nExponential & 25 & 0.10 & 0.010 & \\textbf{0.098} & 0.033 & 0.033 & 0.033 \\\\\n & & 0.30 & 0.044 & \\textbf{0.098} & 0.044 & 0.044 & 0.044 \\\\\n & & 0.50 & 0.054 & \\textbf{0.115} & 0.054 & 0.054 & 0.054 \\\\\n & & 0.70 & \\textbf{0.091} & \\textbf{0.091} & 0.033 & 0.033 & \\textbf{0.091} \\\\\n & & 0.90 & \\textbf{0.072} & \\textbf{0.072} & \\textbf{0.072} & \\textbf{0.072} & \\textbf{0.072} \\\\\n & 50 & 0.10 & 0.024 & 0.058 & 0.058 & 0.058 & 0.058 \\\\\n & & 0.30 & 0.047 & \\textbf{0.084} & 0.047 & 0.047 & 0.047 \\\\\n & & 0.50 & 0.059 & 0.059 & 0.032 & 0.059 & 0.059 \\\\\n & & 0.70 & \\textbf{0.078} & \\textbf{0.078} & 0.040 & 0.040 & 0.040 \\\\\n & & 0.90 & \\textbf{0.112} & \\textbf{0.112} & 0.033 & 0.033 & \\textbf{0.112} \\\\\n & 100 & 0.10 & 0.021 & \\textbf{0.072} & 0.040 & \\textbf{0.072} & 0.040 \\\\\n & & 0.30 & 0.053 & 0.053 & 0.053 & 0.053 & 0.053 \\\\\n & & 0.50 & 0.044 & 0.066 & 0.044 & 0.044 & 0.044 \\\\\n & & 0.70 & \\textbf{0.075} & \\textbf{0.075} & 0.048 & 0.048 & 0.048 \\\\\n & & 0.90 & \\textbf{0.117} & \\textbf{0.117} & 0.024 & 0.024 & 0.057 \\\\ \\midrule\n$\\mathrm{Weibull}(\\lambda, k=0.5)$ & 25 & 0.10 & 0.009 & \\textbf{0.098} & 0.033 & 0.033 & 0.033 \\\\\n & & 0.30 & 0.044 & \\textbf{0.098} & 0.044 & 0.044 & 0.044 \\\\\n & & 0.50 & 0.054 & \\textbf{0.115} & 0.054 & 0.054 & 0.054 \\\\\n & & 0.70 & \\textbf{0.090} & \\textbf{0.090} & 0.033 & 0.033 & \\textbf{0.090} \\\\\n & & 0.90 & \\textbf{0.072} & \\textbf{0.072} & \\textbf{0.072} & \\textbf{0.072} & \\textbf{0.072} \\\\ \n & 50 & 0.10 & 0.024 & 0.058 & 0.058 & 0.058 & 0.058 \\\\\n & & 0.30 & 0.048 & \\textbf{0.085} & 0.048 & 0.048 & 0.048 \\\\\n & & 0.50 & 0.060 & 0.060 & 0.032 & 0.060 & 0.060 \\\\\n & & 0.70 & \\textbf{0.078} & \\textbf{0.078} & 0.040 & 0.040 & 0.040 \\\\\n & & 0.90 & \\textbf{0.112} & \\textbf{0.112} & 0.034 & 0.034 & \\textbf{0.112} \\\\\n & 100 & 0.10 & 0.020 & \\textbf{0.072} & 0.040 & \\textbf{0.072} & 0.040 \\\\\n & & 0.30 & 0.053 & 0.053 & 0.053 & 0.053 & 0.053 \\\\\n & & 0.50 & 0.044 & 0.066 & 0.044 & 0.044 & 0.044 \\\\\n & & 0.70 & \\textbf{0.075} & \\textbf{0.075} & 0.048 & 0.048 & 0.048 \\\\\n & & 0.90 & \\textbf{0.117} & \\textbf{0.117} & 0.024 & 0.024 & 0.058 \\\\ \\midrule\n$\\mathrm{Weibull}(\\lambda, k=2)$ & 25 & 0.10 & 0.010 & \\textbf{0.098} & 0.033 & 0.033 & 0.033 \\\\\n & & 0.30 & 0.044 & \\textbf{0.098} & 0.044 & 0.044 & 0.044 \\\\\n & & 0.50 & 0.054 & \\textbf{0.115} & 0.054 & 0.054 & 0.054 \\\\\n & & 0.70 & \\textbf{0.091} & \\textbf{0.091} & 0.033 & 0.033 & \\textbf{0.091} \\\\\n & & 0.90 & \\textbf{0.072} & \\textbf{0.072} & \\textbf{0.072} & \\textbf{0.072} & \\textbf{0.072} \\\\\n & 50 & 0.10 & 0.024 & 0.058 & 0.058 & 0.058 & 0.058 \\\\\n & & 0.30 & 0.048 & \\textbf{0.085} & 0.048 & 0.048 & 0.048 \\\\\n & & 0.50 & 0.060 & 0.060 & 0.033 & 0.060 & 0.060 \\\\\n & & 0.70 & \\textbf{0.079} & \\textbf{0.079} & 0.040 & 0.040 & 0.040 \\\\\n & & 0.90 & \\textbf{0.112} & \\textbf{0.112} & 0.034 & 0.034 & \\textbf{0.112} \\\\\n & 100 & 0.10 & 0.020 & \\textbf{0.072} & 0.040 & \\textbf{0.072} & 0.040 \\\\\n & & 0.30 & 0.053 & 0.053 & 0.053 & 0.053 & 0.053 \\\\\n & & 0.50 & 0.044 & 0.067 & 0.044 & 0.044 & 0.044 \\\\\n & & 0.70 & \\textbf{0.076} & \\textbf{0.076} & 0.048 & 0.048 & 0.048 \\\\\n & & 0.90 & \\textbf{0.118} & \\textbf{0.118} & 0.024 & 0.024 & 0.058 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Sample size calculations for single-arm survival studies using transformations of the Kaplan-Meier estimator", "authors": ["Kengo Nagashima", "Hisashi Noma", "Yasunori Sato", "Masahiko Gosho"], "url": "https://arxiv.org/abs/2012.03355v2", "attribution": "\"Sample size calculations for single-arm survival studies using transformations of the Kaplan-Meier estimator\" by Kengo Nagashima, Hisashi Noma, Yasunori Sato, and Masahiko Gosho, arXiv:2012.03355v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08949v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Exploring different variations of DFM. The best results are highlighted in \\textcolor{red}{red}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n\\toprule\n\\toprule\nVariation & A ($\\uparrow$) & R ($\\downarrow$) & EAO ($\\uparrow$) \\\\\n\\toprule\nTir-\\textgreater{}Rgb \\& Rgb-\\textgreater{}Tir & 0.6408 & 0.2938& 0.4063 \\\\\n\\midrule\nRgb-\\textgreater{}Tir & {\\color[HTML]{FE0000} 0.6475} & 0.2767& 0.4058\\\\\n\\midrule\nTir-\\textgreater{}Rgb(DFM) & 0.6426 & {\\color[HTML]{FE0000} 0.2647} & {\\color[HTML]{FE0000} 0.4164} \\\\\n\\bottomrule\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Temporal Aggregation for Adaptive RGBT Tracking", "authors": ["Zhangyong Tang", "Tianyang Xu", "Xiao-Jun Wu"], "url": "https://arxiv.org/abs/2201.08949v2", "attribution": "\"Temporal Aggregation for Adaptive RGBT Tracking\" by Zhangyong Tang, Tianyang Xu, and Xiao-Jun Wu, arXiv:2201.08949v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.12471v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary Statistics of overall sample aged 16-65 years}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n& STEM degree & Non-STEM degree \\\\\n\\midrule\nEmployment Participation & 0.510 & 0.331 \\\\\nMonthly wage (INR) & 19725.58 & 8387.115 \\\\\nAvg. age (years) & 31.62 & 35.53 \\\\\nFormal education (years) & 16.31 & 15.56 \\\\\n\\midrule\n\\multicolumn{3}{l}{\\textbf{Gender (\\%)}} \\\\\n\\midrule\nMale (\\%) & 69.68 & 55.57 \\\\\nFemale (\\%) & 30.32 & 44.43 \\\\\n\\midrule\n\\multicolumn{3}{l}{\\textbf{Marital Status (\\%)}} \\\\\n\\midrule\nUnmarried (\\%) & 49.25 & 34.33 \\\\\nCurrently married (\\%) & 49.88 & 63.44 \\\\\nWidow & 0.49 & 1.68 \\\\\nDivorced & 0.37 & 0.55 \\\\\n\\midrule\n\\multicolumn{3}{l}{\\textbf{Religion (\\%)}} \\\\\n\\midrule\nHinduism & 82.71 & 78.12 \\\\\nIslam & 8.54 & 9.63 \\\\\nChristianity & 5.52 & 8.03 \\\\\nOthers & 3.23 & 4.21 \\\\\n\\midrule\n\\multicolumn{3}{l}{\\textbf{Social Group (\\%)}} \\\\\n\\midrule\nMinority (Low caste) & 14.02 & 22.21 \\\\\nMajority (High caste) & 85.98 & 77.88 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Do STEM graduates fare better at times of Crises? Evidence from COVID 19 pandemic in India", "authors": ["Jheelum Sarkar"], "url": "https://arxiv.org/abs/2508.12471v1", "attribution": "\"Do STEM graduates fare better at times of Crises? Evidence from COVID 19 pandemic in India\" by Jheelum Sarkar, arXiv:2508.12471v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.22936v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Win Rates of Models Across Companies}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\\toprule\n\\textbf{Company} & \\textbf{GPT} & \\textbf{Perplexity} & \\textbf{Claude} & \\textbf{Gemini} & \\textbf{DeepSeek} \\\\\n\\midrule\nAmazon & 58.60\\% & 37.20\\% & 25.10\\% & 21.80\\% & 19.20\\% \\\\\nApple & 59.10\\% & 38.90\\% & 24.20\\% & 22.30\\% & 19.60\\% \\\\\nGoogle & 64.10\\% & 40.10\\% & 26.90\\% & 20.90\\% & 16.30\\% \\\\\nMeta & 61.00\\% & 36.30\\% & 22.30\\% & 18.40\\% & 16.80\\% \\\\\nMicrosoft & 66.40\\% & 41.20\\% & 30.50\\% & 18.60\\% & 15.90\\% \\\\\nNvidia & 62.50\\% & 38.10\\% & 23.90\\% & 18.90\\% & 14.70\\% \\\\\nTesla & 62.30\\% & 36.60\\% & 23.20\\% & 20.50\\% & 19.10\\% \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis", "authors": ["Md Talha Mohsin"], "url": "https://arxiv.org/abs/2507.22936v1", "attribution": "\"Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis\" by Md Talha Mohsin, arXiv:2507.22936v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.02675v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc}\n & $W_1 = 0$ & $W_2 = 1$ \\\\\n\\hline\n$X = 0$ & 0.01 & 0.5 \\\\\n$X = 1$ & 0.5 & 0.01\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Does Regression Produce Representative Causal Rankings?", "authors": ["Apoorva Lal"], "url": "https://arxiv.org/abs/2411.02675v1", "attribution": "\"Does Regression Produce Representative Causal Rankings?\" by Apoorva Lal, arXiv:2411.02675v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.13495v1_tex_table27.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{llllll}\n\\textbf{Location} & \\textbf{MSE↓} & \\textbf{CSI↑} & \\textbf{CSI 80↑} & \\textbf{Training Time↓} & \\textbf{PUR↓} \\\\ \\hline\nBOP & \\textbf{0.2997} & 0.0 & 0.1818 & 22.3693 & \\\\\nBP & \\textbf{0.5798} & 0.0 & 0.1324 & 22.2763 & \\\\\nCI & \\textbf{0.5285} & 0.0 & 0.1406 & 22.5064 & \\\\\nCR & \\textbf{0.3740} & 0.0 & 0.1600 & 22.3860 & \\\\\nCS & \\textbf{0.2327} & 0.0 & 0.3426 & 22.4155 & \\\\\nF & \\textbf{0.6498} & 0.0 & 0.3084 & 22.3326 & 0\\% \\\\\nHG & \\textbf{0.4216} & 0.0 & 0.2258 & 22.2642 & \\\\\nOP & \\textbf{0.5004} & 0.0 & \\textbf{0.2564} & 22.6545 & \\\\\nR & \\textbf{0.4687} & 0.0 & 0.2049 & 23.5724 & \\\\\nSI & \\textbf{0.4346} & 0.0 & 0.2600 & 22.6813 & \\\\\nT & \\textbf{0.8610} & 0.0 & 0.2426 & 22.2724 & \\\\\nW & \\textbf{0.9448} & 0.0 & 0.1805 & 22.3836 & \\\\ \\hline\nAverage & \\textbf{0.5246} & 0.0 & 0.2197 & 22.5095 & 0\\%\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models", "authors": ["Ding Ning", "Varvara Vetrova", "Sébastien Delaux", "Rachael Tappenden", "Karin R. Bryan", "Yun Sing Koh"], "url": "https://arxiv.org/abs/2502.13495v1", "attribution": "\"A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models\" by Ding Ning, Varvara Vetrova, Sébastien Delaux, Rachael Tappenden, Karin R. Bryan, and Yun Sing Koh, arXiv:2502.13495v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.03312v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Heterogeneity by total volume}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\hline\n & (1) & (2) & (3) & (4) \\\\ \\hline\nVariable & \\hspace{0.9em}Full sample\\hspace{0.9em} & \\hspace{0.9em}25\\% split\\hspace{0.9em} & \\hspace{0.9em}50\\% split\\hspace{0.9em} & \\hspace{0.9em}75\\% split\\hspace{0.9em} \\\\\nYes & 0.0748*** & 0.0706*** & 0.0692*** & 0.0539*** \\\\\n & {[}0.00446{]} & {[}0.00485{]} & {[}0.00623{]} & {[}0.0106{]} \\\\\nControl & 0.0313*** & 0.0284*** & 0.0374*** & 0.0195* \\\\\n & {[}0.00484{]} & {[}0.00575{]} & {[}0.00661{]} & {[}0.0112{]} \\\\\n$p_{-1, i}$ & 1.000*** & 0.999*** & 1.012*** & 0.998*** \\\\\n & {[}0.00781{]} & {[}0.0103{]} & {[}0.0124{]} & {[}0.0245{]} \\\\\nConstant & -0.0371*** & -0.0359*** & -0.0479*** & -0.0315** \\\\\n & {[}0.00547{]} & {[}0.00604{]} & {[}0.00699{]} & {[}0.0130{]} \\\\ \\hline\n$n$ & 817 & 613 & 410 & 205 \\\\\n$R^2$ & 0.933 & 0.926 & 0.918 & 0.858 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "How manipulable are prediction markets?", "authors": ["Itzhak Rasooly", "Roberto Rozzi"], "url": "https://arxiv.org/abs/2503.03312v1", "attribution": "\"How manipulable are prediction markets?\" by Itzhak Rasooly and Roberto Rozzi, arXiv:2503.03312v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.13608v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lr}\n\\hline\n\\textbf{Statistic} & \\textbf{Value} \\\\\n\\hline\nCount & 3652.00 \\\\\nMean & 23.80 \\\\\nStandard Deviation (std) & 11.51 \\\\\nMinimum (min) & 0.25 \\\\\n25th Percentile & 13.43 \\\\\nMedian (50\\%) & 28.75 \\\\\n75th Percentile & 33.75 \\\\\nMaximum (max) & 40.00 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Descriptive Statistics for production - ``joao\" Use case 2 `}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Integrating Dynamic Correlation Shifts and Weighted Benchmarking in Extreme Value Analysis", "authors": ["Dimitrios P. Panagoulias", "Elissaios Sarmas", "Vangelis Marinakis", "Maria Virvou", "George A. Tsihrintzis"], "url": "https://arxiv.org/abs/2411.13608v2", "attribution": "\"Integrating Dynamic Correlation Shifts and Weighted Benchmarking in Extreme Value Analysis\" by Dimitrios P. Panagoulias, Elissaios Sarmas, Vangelis Marinakis, Maria Virvou, and George A. Tsihrintzis, arXiv:2411.13608v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.16043v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Combined path loss characteristics for the DeepMIMO scenarios; provided .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n\\hline\n\\textbf{Scenario} & \\textbf{Max (dB)} & \\textbf{Min (dB)} & \\textbf{Mean \\(\\pm\\) Std (dB)} \\\\\n\\hline\n\\emph{O1} & \\(151.41\\) & \\(58.93\\) & \\(86.51 \\pm 8.88\\) \\\\\n\\emph{B5} & \\(133.03\\) & \\(60.61\\) & \\(93.02 \\pm 13.77\\) \\\\\n\\emph{I3} & \\(79.25\\) & \\(45.18\\) & \\(53.89 \\pm 3.21\\) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Downlink MIMO Channel Estimation from Bits: Recoverability and Algorithm", "authors": ["Rajesh Shrestha", "Mingjie Shao", "Mingyi Hong", "Wing-Kin Ma", "Xiao Fu"], "url": "https://arxiv.org/abs/2411.16043v2", "attribution": "\"Downlink MIMO Channel Estimation from Bits: Recoverability and Algorithm\" by Rajesh Shrestha, Mingjie Shao, Mingyi Hong, Wing-Kin Ma, and Xiao Fu, arXiv:2411.16043v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.13113v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of treaty generation features in the simulated reinsurance environment.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\n\\textbf{Feature} & \\textbf{Description} \\\\\n\\midrule\nExposure Size & Total insured value subject to reinsurance \\\\\nLoss Distribution & Empirical distribution calibrated from historical claims \\\\\nLine of Business & Categorical indicator (e.g., property, casualty) \\\\\nAttachment Point & Threshold at which reinsurance coverage begins \\\\\nCoverage Limit & Maximum payout by the reinsurer \\\\\nRetention Level & Portion of risk retained by the primary insurer \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning", "authors": ["Stella C. Dong", "James R. Finlay"], "url": "https://arxiv.org/abs/2506.13113v1", "attribution": "\"Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning\" by Stella C. Dong and James R. Finlay, arXiv:2506.13113v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.03247v2_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\n & \\textbf{Baseline} & \\textbf{x\\_add} & \\textbf{s\\_add} & \\textbf{x\\_cat} & \\textbf{s\\_cat} \\\\ \\hline \\hline\n\\textbf{dev\\_clean} & 6.8 & 6.3 & 6.4 & 6.2 & 6.7 \\\\ %\\hline\n\\textbf{test\\_clean} & 6.8 & 6.1 & 6.2 & 6.3 & 6.5 \\\\ \\hline %\\hline\n\\textbf{dev\\_other} & 13.8 & 14.8 & 14.1 & 15.1 & 15.5 \\\\ %\\hline\n\\textbf{test\\_other} & 14 & 14.5 & 14.3 & 15.3 & 15.6 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Librispeech: Average error rate (\\%WER)}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Investigation of Speaker-adaptation methods in Transformer based ASR", "authors": ["Vishwas M. Shetty", "Metilda Sagaya Mary N J", "S. Umesh"], "url": "https://arxiv.org/abs/2008.03247v2", "attribution": "\"Investigation of Speaker-adaptation methods in Transformer based ASR\" by Vishwas M. Shetty, Metilda Sagaya Mary N J, and S. Umesh, arXiv:2008.03247v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.15568v1_tex_table18.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Consistency of Predictions and Voting Behavior}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\hline\n \\hline\n & \\multirow{2}{*}{Test} & \\multirow{2}{*}{Lottery} & \\multirow{2}{*}{Total} \\\\\n Prediction & & & \\\\\n \\hline\n Own $>$ Group & 152 & 22 & 174 \\\\\n Own $\\leq$ Group & 40 & 58 & 98 \\\\\n \\hline\n Total & 192 & 80 & 272 \\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)", "authors": ["Marius Protte"], "url": "https://arxiv.org/abs/2507.15568v1", "attribution": "\"Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)\" by Marius Protte, arXiv:2507.15568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.07945v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n \\toprule\n Flow Extractor & Dataset & EPE & zEPE & Acc 01 & Acc 005 \\\\ \\midrule\n Segmenter & Single ShapeNet & 0.3786 & 0.9455 & 2.98\\% & 0.43\\% \\\\\n Segmenter & Multi ShapeNet & 0.8444 & 0.9595 & 0.34\\% & 0.03\\% \\\\\n Segmenter & FlyingThings3D & 0.7559 & 0.9953 & 4.82\\% & 0.98\\% \\\\ \\midrule\n FlowNet3D & Single ShapeNet & 0.3911 & 0.9768 & 2.76\\% & 0.3\\% \\\\\n FlowNet3D & Multi ShapeNet & 0.8242 & 0.9337 & 0.44\\% & 0.03\\% \\\\\n FlowNet3D & FlyingThings3D & 0.7538 & 0.9925 & 5.1\\% & 1.3\\% \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Flow Extractors trained with nearest neighbor loss and cycle consistency loss. }\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation", "authors": ["Victor Zuanazzi"], "url": "https://arxiv.org/abs/2011.07945v1", "attribution": "\"Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation\" by Victor Zuanazzi, arXiv:2011.07945v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.01012v4_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\hline\n Method & CIFAR-10 Top-1 & CIFAR-10 Top-5 & CIFAR-100 Top-1 & CIFAR-100 Top-5 \\\\\n \\hline\n Barlow twins & \\textbf{92.1} & 99.73 & \\textbf{71.38} & \\textbf{92.32} \\\\\n VICReg & 91.68 & 99.66 & 68.56 & 90.76 \\\\\n \\textbf{SSL-EY} & 91.43 & \\textbf{99.75} & 67.52 & 90.17 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance comparison of SSL methods on CIFAR-10 and CIFAR-100.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning", "authors": ["James Chapman", "Lennie Wells", "Ana Lawry Aguila"], "url": "https://arxiv.org/abs/2310.01012v4", "attribution": "\"Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning\" by James Chapman, Lennie Wells, and Ana Lawry Aguila, arXiv:2310.01012v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.17099v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation Framework Parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n\\toprule\n\\textbf{Parameter} & \\textbf{Value} \\\\\n\\midrule\nTotal simulation period & 30 days \\\\\nTraditional operations sample & 5,000 trips \\\\\nWeather-aware AI sample & 5,000 trips \\\\\nWeather prediction accuracy & 87\\% (3-hour forecast) \\\\\nBase revenue per minute & ¥52.3 \\\\\nWorking hours per day & 10 hours \\\\\nWorking days per year & 300 days \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity", "authors": ["Tatsuru Kikuchi"], "url": "https://arxiv.org/abs/2507.17099v1", "attribution": "\"Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity\" by Tatsuru Kikuchi, arXiv:2507.17099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.09083v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Relationship with market expectations}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rcccccccc}\n\t\t\t\\textbf{(Tenor)} & \\multicolumn{2}{c}{\\textbf{1-month}} & \\multicolumn{2}{c}{\\textbf{3-month}} & \\multicolumn{2}{c}{\\textbf{6-month}} & \\multicolumn{2}{c}{\\textbf{12-month}} \\\\ \\hline\n\t\t\t & \\textbf{Baseline} & \\textbf{Comms} & \\textbf{Baseline} & \\textbf{Comms} & \\textbf{Baseline} & \\textbf{Comms} & \\textbf{Baseline} & \\textbf{Comms} \\\\ \\hline\n\t\t\tCPI F'cast & \\textbf{0.08} & -0.08 & \\textbf{0.14} & \\textbf{-0.15} & \\textbf{0.2} & -0.16 & 0.13 & -0.11 \\\\\n\t\t\tActivity F'cast & -0.06 & -0.05 & -0.09 & -0.08 & -0.1 & -0.09 & -0.06 & -0.02 \\\\\n\t\t\tUnemp F'cast & \\textbf{-0.01} & -0.01 & \\textbf{-0.02} & -0.01 & \\textbf{-0.02} & -0.01 & \\textbf{-0.02} & 0.0 \\\\\n\t\t\t$HDS_{mins,t-1}$ & & \\textbf{0.36} & & \\textbf{0.67} & & \\textbf{1.0} & & \\textbf{1.27} \\\\\n\t\t\t$HDS_{mins,t-2}$ & & \\textbf{0.21} & & \\textbf{0.39} & & \\textbf{0.54} & & \\textbf{0.41} \\\\\n\t\t\t$HDS_{statement,t-1}$ & & \\textbf{0.11} & & \\textbf{0.17} & & 0.17 & & 0.08 \\\\\n\t\t\t$HDS_{statement,t-2}$ & & \\textbf{-0.14} & & \\textbf{-0.21} & & \\textbf{-0.35} & & \\textbf{-0.49} \\\\ \\hline\n\t\t\tAIC & 66.58 & 7.96 & 271.09 & 184.34 & 395.31 & 302.67 & 457.6 & 385.32 \n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Ornithologist: Towards Trustworthy \"Reasoning\" about Central Bank Communications", "authors": ["Dominic Zaun Eu Jones"], "url": "https://arxiv.org/abs/2505.09083v1", "attribution": "\"Ornithologist: Towards Trustworthy \"Reasoning\" about Central Bank Communications\" by Dominic Zaun Eu Jones, arXiv:2505.09083v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.06547v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{General case studies results in optimized KF-PLS, compared to the regular PLS. The performance was improved in all cases. *The number of iterations until convergence. $\\uparrow$ indicates if the metric is optimal if maximized, whereas $\\downarrow$ indicates that indicator is to be minimized. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c|c}\n \\textbf{Case} & \\multicolumn{3}{c|}{\\textbf{Original PLS}} & \\multicolumn{4}{c}{\\textbf{Kernel-Flows Optimized (KF-PLS)}} \\\\\n & $Q^2_{test} \\uparrow $ & NRMSE $\\downarrow$ & Accuracy $\\uparrow$ & $Q^2_{test} \\uparrow $ & NRMSE $\\downarrow$ & Accuracy $\\uparrow$ & Iterations*\\\\\n \\hline\n 1 & 0.85 & 5.2 & - & 0.99 & 1.21 & - & 213 \\\\\n 2 & - & - & 0.7 & - & - & 1 & 70 \\\\\n 3 & 0.9 & 0.82 & - & 0.96 & 0.53 & - & 30 \\\\\n 4 & 0.94 & 2.36 & - & 0.97 & 1.68 & - & 65\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "KF-PLS: Optimizing Kernel Partial Least-Squares (K-PLS) with Kernel Flows", "authors": ["Zina-Sabrina Duma", "Jouni Susiluoto", "Otto Lamminpää", "Tuomas Sihvonen", "Satu-Pia Reinikainen", "Heikki Haario"], "url": "https://arxiv.org/abs/2312.06547v1", "attribution": "\"KF-PLS: Optimizing Kernel Partial Least-Squares (K-PLS) with Kernel Flows\" by Zina-Sabrina Duma, Jouni Susiluoto, Otto Lamminpää, Tuomas Sihvonen, Satu-Pia Reinikainen, and Heikki Haario, arXiv:2312.06547v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11944v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study results. \\texttt{P2P} refers to the point-to-plane method from Open3D, \\texttt{DR} refers to Doppler residuals, \\texttt{DOR} indicates the usage of dynamic outlier rejection, and \\texttt{DICP} refers to Doppler ICP that uses point-to-plane method, Doppler residuals, and outlier rejection.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrrrrrrr}\n \\toprule\n Sequence &\n Method &\n \\shortstack{RPE\\\\Trans (m)} & \\shortstack{RPE\\\\Rot (deg)} & \\shortstack{Path\\\\Error (m)} & \\shortstack{\\# Iters\\\\(mean)} \\\\\n \\midrule\n\\multirow{4}{*}{\\shortstack{Robin\\\\Williams\\\\Tunnel}}\n& P2P\n& {1.9343} %\n& {0.1533} %\n& {219.10} %\n& {20.3} \\\\ %\n& P2P+DOR\n& {2.6035} %\n& {0.6699} %\n& {298.04} %\n& \\textbf{6.0} \\\\ %\n& P2P+DR\n& {0.0758} %\n& {0.1416} %\n& {0.05} %\n& {9.9} \\\\ %\n& DICP\n& \\textbf{0.0758} %\n& \\textbf{0.1415} %\n& \\textbf{0.05} %\n& {9.8} \\\\ %\n\\midrule\n\\multirow{4}{*}{\\shortstack{Brisbane\\\\Lagoon\\\\Freeway}}\n& P2P\n& {0.3270} %\n& {0.0915} %\n& {45.27} %\n& {10.6} \\\\ %\n& P2P+DOR\n& {0.1148} %\n& {0.0883} %\n& {9.67} %\n& {9.9} \\\\ %\n& P2P+DR\n& \\textbf{0.1112} %\n& {0.0869} %\n& \\textbf{4.38} %\n& {10.4} \\\\ %\n& DICP\n& {0.1301} %\n& \\textbf{0.0869} %\n& {7.20} %\n& \\textbf{9.7} \\\\ %\n\\midrule\n\\multirow{4}{*}{\\shortstack{San\\\\Francisco\\\\City}}\n& P2P\n& {0.0323} %\n& \\textbf{0.0479} %\n& {23.78} %\n& \\textbf{5.9} \\\\ %\n& P2P+DOR\n& {0.0322} %\n& {0.0481} %\n& \\textbf{4.55} %\n& {7.1} \\\\ %\n& P2P+DR\n& \\textbf{0.0316} %\n& {0.0482} %\n& {10.18} %\n& {7.1} \\\\ %\n& DICP\n& {0.0317} %\n& {0.0482} %\n& {8.43} %\n& {6.0} \\\\ %\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "DICP: Doppler Iterative Closest Point Algorithm", "authors": ["Bruno Hexsel", "Heethesh Vhavle", "Yi Chen"], "url": "https://arxiv.org/abs/2201.11944v2", "attribution": "\"DICP: Doppler Iterative Closest Point Algorithm\" by Bruno Hexsel, Heethesh Vhavle, and Yi Chen, arXiv:2201.11944v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.15435v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multicolumn{4}{c}{$\\Delta\\ln(\\text{Gross Corporate saving rate}_{it})$} \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t& (1) & (2) & (3) \\\\\n\t\t\t\n\t\t\t\n\t\t\t$\\Delta\\ln(y_{it}) $ & -0.621*** & -0.021 & -0.029 \\\\\n\t\t\t\n\t\t\t& (0.166)&(0.195) & (0.079)\\\\\n\t\t\t\n\t\t\t\n\t\t\t$\\Delta\\ln(y_{it})*y_i$ & & -0.030*** & -0.028*** \\\\\n\t\t\t\n\t\t\t& & (0.005)& (0.004)\\\\\n\t\t\t\n\t\t\t$\\Delta\\ln(y_{it})*\\lambda_{i}$ & & & -0.025** \\\\\n\t\t\t\n\t\t\t& & &(0.010)\\\\\n\t\t\t\n\t\t\t$\\Delta\\ln(\\lambda_{it})$ & & & -0.015 \\\\\n\t\t\t\n\t\t\t& & &(0.009)\\\\\n\t\t\t\n\t\t\t\n\t\t\tCountry FE & Yes & Yes & Yes \\\\\n\t\t\t\n\t\t\tYear FE & Yes & Yes & Yes \\\\\n\t\t\t\n\t\t\tObservations & 887 & 887 & 706 \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Transitional Dynamics of the Saving Rate and Economic Growth", "authors": ["Markus Brueckner", "Tomoo Kikuchi", "George Vachadze"], "url": "https://arxiv.org/abs/2012.15435v3", "attribution": "\"Transitional Dynamics of the Saving Rate and Economic Growth\" by Markus Brueckner, Tomoo Kikuchi, and George Vachadze, arXiv:2012.15435v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.17643v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results of average \\textit{NPCR} and \\textit{UACI} values.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\toprule\n\\multicolumn{3}{c|}{$NPCR$} & \\multicolumn{3}{c}{$UACI$} \\\\ \\midrule\nRed & Green & Blue & Red & Green & Blue \\\\ \\midrule\n$99.5911\\%$ & $99.6277\\%$ & $99.5209\\%$ & $33.3946$ & $33.4467\\%$ & $33.4536\\%$ \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A robust image encryption scheme based on new 4-D hyperchaotic system and elliptic curve", "authors": ["Yehia Lalili", "Toufik Bouden", "Morad Grimes", "Abderrazek Lachouri"], "url": "https://arxiv.org/abs/2411.17643v1", "attribution": "\"A robust image encryption scheme based on new 4-D hyperchaotic system and elliptic curve\" by Yehia Lalili, Toufik Bouden, Morad Grimes, and Abderrazek Lachouri, arXiv:2411.17643v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2311.14482v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Correlation coefficient of each annotator with the 10 click simulated user}\n\\begin{tabular}{|l|l|l|l|l|}\n \\hline\n \\textbf{Annotator 1} & \\textbf{Annotator 2} & \\textbf{Annotator 3} & \\textbf{Annotator 4} \\\\ \\hline\n 0.9251 & 0.7357 & 0.7819 & 0.4040 \\\\ \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images", "authors": ["Matthias Hadlich", "Zdravko Marinov", "Moon Kim", "Enrico Nasca", "Jens Kleesiek", "Rainer Stiefelhagen"], "url": "https://arxiv.org/abs/2311.14482v1", "attribution": "\"Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images\" by Matthias Hadlich, Zdravko Marinov, Moon Kim, Enrico Nasca, Jens Kleesiek, and Rainer Stiefelhagen, arXiv:2311.14482v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11189v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Detection accuracy on the UCAS-AOD dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n \\Xhline{1pt}\n Method & Backbone & car & airplane &mAP(07) \\\\\n \\Xhline{1pt}\n YOLOv3 & Darknet53 &74.63 &89.52 &82.08 \\\\\n RetinaNet & Resnet101 &84.64 &90.51 &87.57 \\\\\n FR-O & Resnet101 &86.87 &89.86 &88.36 \\\\\n ROI-trans & Resnet101 &87.99 &89.90 &88.95 \\\\\n FPN-CSL & Resnet101 &88.09 &90.38 &89.23 \\\\\n R$^3$Det-DCL & Resnet101 &88.15 &90.57 &89.36 \\\\\n DAL & Resnet101 &\\textcolor{red}{89.25} &90.49 &\\textcolor{blue}{89.87} \\\\\n \\hline\n CHPDet(ours) & DLA34 &88.58 &\\textcolor{blue}{90.64} &89.61 \\\\\n CHPDet(ours) & Hourglass104 &\\textcolor{blue}{89.18} &\\textcolor{red}{90.81} &\\textcolor{red}{90.00} \\\\\n \\Xhline{1pt}\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Arbitrary-Oriented Ship Detection through Center-Head Point Extraction", "authors": ["Feng Zhang", "Xueying Wang", "Shilin Zhou", "Yingqian Wang", "Yi Hou"], "url": "https://arxiv.org/abs/2101.11189v3", "attribution": "\"Arbitrary-Oriented Ship Detection through Center-Head Point Extraction\" by Feng Zhang, Xueying Wang, Shilin Zhou, Yingqian Wang, and Yi Hou, arXiv:2101.11189v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.18727v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr} \n \\toprule\n LLMs & Acc & F1 & Prec & Recall \\\\\n \\midrule\n LLAMA-2-7B & 47.1\\% & 54.3\\% & 48.9\\% & 61.1\\% \\\\\n Mistral-7B & 62.9\\% & 66.7\\% & 61.9\\% & \\cellcolor{cyan!20}\\textbf{72.2\\%} \\\\\n GPT-3.5 & \\cellcolor{cyan!20}\\textbf{68.6\\%} & \\cellcolor{cyan!20}\\textbf{68.6\\%} & \\cellcolor{cyan!20}\\textbf{70.6\\%} & 66.7\\% \\\\\n GPT-3.5 (temp=1) & 52.4\\% & 46.4\\% & 55.9\\% & 39.8\\% \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance comparison of different LLMs for predicting the interestingness of flight log benchmarks.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SAFLITE: Fuzzing Autonomous Systems via Large Language Models", "authors": ["Taohong Zhu", "Adrians Skapars", "Fardeen Mackenzie", "Declan Kehoe", "William Newton", "Suzanne Embury", "Youcheng Sun"], "url": "https://arxiv.org/abs/2412.18727v1", "attribution": "\"SAFLITE: Fuzzing Autonomous Systems via Large Language Models\" by Taohong Zhu, Adrians Skapars, Fardeen Mackenzie, Declan Kehoe, William Newton, Suzanne Embury, and Youcheng Sun, arXiv:2412.18727v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07973v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n \\textbf{Parameter} & \\textbf{Values} \\\\\n \\midrule\n Pattern & 28 patterns \\\\\n Tempo & 60 BPM, 80 BPM, 100 BPM \\\\\n Movement & little, normal, large \\\\\n Volume & quiet, medium, loud \\\\\n Technique & finger, pick \\\\\n Chords & major and minor chord progressions \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Results on microphone audio.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Joint Transcription of Acoustic Guitar Strumming Directions and Chords", "authors": ["Sebastian Murgul", "Johannes Schimper", "Michael Heizmann"], "url": "https://arxiv.org/abs/2508.07973v1", "attribution": "\"Joint Transcription of Acoustic Guitar Strumming Directions and Chords\" by Sebastian Murgul, Johannes Schimper, and Michael Heizmann, arXiv:2508.07973v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.09862v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{||c|c|c|c|c||} \n\\hline\n$\\tilde\\kappa$ & 0.1 & \\textbf{0.3} & 0.5 & 1\\\\\n\\hline\nrate & -0.257 & \\textbf{-0.408} & -0.456 & -0.433\\\\\n\\hline\n\\end{tabular}\n\\caption{Convergence rate for the proposed estimator for difference choice of hyperparameter $\\tilde\\kappa$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Wasserstein-based Minimax Estimation of Dependence in Multivariate Regularly Varying Extremes", "authors": ["Xuhui Zhang", "Jose Blanchet", "Youssef Marzouk", "Viet Anh Nguyen", "Sven Wang"], "url": "https://arxiv.org/abs/2312.09862v2", "attribution": "\"Wasserstein-based Minimax Estimation of Dependence in Multivariate Regularly Varying Extremes\" by Xuhui Zhang, Jose Blanchet, Youssef Marzouk, Viet Anh Nguyen, and Sven Wang, arXiv:2312.09862v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13833v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcr}\nLeft&Centered&Right\\\\\n\\hline\n1 & 2 & 3\\\\\n10 & 20 & 30\\\\\n100 & 200 & 300\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the Reasoning Capacity of AI Models and How to Quantify It", "authors": ["Santosh Kumar Radha", "Oktay Goktas"], "url": "https://arxiv.org/abs/2501.13833v1", "attribution": "\"On the Reasoning Capacity of AI Models and How to Quantify It\" by Santosh Kumar Radha and Oktay Goktas, arXiv:2501.13833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.08294v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Limit values of vector $\\Psi(x)=(\\psi_{\\alpha}(x),\\psi_{\\beta}(x))$} \n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc} \\hline\n\t\t$x\\to 0$\t& $q >1$ & \t\t$0<q<1$ \\\\ \\hline \n\t\t$\t\\alpha\\geq1$\t\t& \t$(+\\infty,-\\infty)$ & $(0,0)$ \\\\\n\t\t$\t0<\\alpha<1$\t& $(0,0)$ & $(+\\infty,-\\infty)$\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Maximum $\\log_q$ Likelihood Estimation for Parameters of Weibull Distribution and Properties: Monte Carlo Simulation", "authors": ["Mehmet Niyazi Çankaya", "Roberto Vila"], "url": "https://arxiv.org/abs/2012.08294v1", "attribution": "\"Maximum $\\log_q$ Likelihood Estimation for Parameters of Weibull Distribution and Properties: Monte Carlo Simulation\" by Mehmet Niyazi Çankaya and Roberto Vila, arXiv:2012.08294v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11112v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\t\t\t\\toprule\n\t\t\t& \\textbf{Test P} & \\textbf{Test R} & \\textbf{F1}\\\\\n\t\t\t\\hline\n\t\t\t\\textbf{Sequential fine-tune with RW} & \\textbf{73.1} & 76.2 & \\textbf{74.6} \\\\\n\t\t\tZero-transfer & 67.6 & \\textbf{77.4} & 72.1 \\\\\n\t\t\tSequential fine-tune with CE & 71.2 & 74.3 & 72.7 \\\\\n\t\t\tSkip fine-tune on English & 71.0 & 73.6 & 72.3 \\\\\n\t\t\tFine-tune on En/De mixed (CE) & \\textbf{73.1} & 75.9 & 74.5 \\\\\n\t\t\tFine-tune on En/De mixed (RW) & 65.5 & 42.6 & 51.6 \\\\\n\t\t\t\\toprule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Ablation study results evaluated on CoNLL2002 German NER data. All experiments used the mBERT-base model. RW denotes the re-weighted loss we proposed in the paper; CE denotes the regular cross entropy loss.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignment", "authors": ["Bing Li", "Yujie He", "Wenjin Xu"], "url": "https://arxiv.org/abs/2101.11112v1", "attribution": "\"Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignment\" by Bing Li, Yujie He, and Wenjin Xu, arXiv:2101.11112v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.10706v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{arydshln}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n \\toprule\n \\multirow{3}{*}{\\begin{minipage}{0.7in}Parameter notations\\end{minipage}} &\n \\multicolumn{3}{c}{Regime 1 ($g=1$)} & &\n \\multicolumn{3}{c}{Regime 2 ($g=2$)} \\\\\\cmidrule{2-4}\\cmidrule{6-8}\n & \\multirow{2}{*}{\\begin{minipage}{0.5in}True \\\\values\\end{minipage}} & \\multicolumn{2}{c}{Estimates} & & \\multirow{2}{*}{\\begin{minipage}{0.5in}True \\\\values\\end{minipage}} & \\multicolumn{2}{c}{Estimates} \\\\\\cmidrule{3-4}\\cmidrule{7-8}\n & & $k_{i,g}=1$ & $k_{i,g}=2$ & & & $k_{i,g}=1$ & $k_{i,g}=2$ \\\\\\midrule\n \\multirow{2}{*}{$\\alpha_{1,1,g}$} & \\multirow{2}{*}{$0.3$} & $0.30$ & $0.30$ & & \\multirow{2}{*}{$0.1$} & $0.09$ & $0.09$ \\\\\n & & $(0.05)$ & $(0.05)$ & & & $(0.04)$ & $(0.06)$ \\\\\\cdashline{1-8}\n \\multirow{2}{*}{$\\alpha_{1,2,g}$} & \\multirow{2}{*}{$0$} & --- & $-0.01$ & & \\multirow{2}{*}{$0$} & --- & $0.00$ \\\\\n & & --- & $(0.04)$ & & & --- & $(0.04)$ \\\\\\midrule\n \\multirow{2}{*}{$\\alpha_{2,1,g}$} & \\multirow{2}{*}{$0.3$} & $0.29$ & $0.29$ & & \\multirow{2}{*}{$0.1$} & $0.10$ & $0.10$ \\\\\n & & $(0.06)$ & $(0.06)$ & & & $(0.03)$ & $(0.07)$ \\\\\\cdashline{1-8}\n \\multirow{2}{*}{$\\alpha_{2,2,g}$} & \\multirow{2}{*}{$0$} & --- & $-0.01$ & & \\multirow{2}{*}{$0$} & --- & $0.00$ \\\\\n & & --- & $(0.03)$ & & & --- & $(0.04)$ \\\\\\midrule\n \\multirow{2}{*}{$\\alpha_{3,1,g}$} & \\multirow{2}{*}{$0.5$} & $0.49$ & $0.49$ & & \\multirow{2}{*}{$0.1$} & $0.09$ & $0.09$ \\\\\n & & $(0.05)$ & $(0.05)$ & & & $(0.04)$ & $(0.06)$ \\\\\\cdashline{1-8}\n \\multirow{2}{*}{$\\alpha_{3,2,g}$} & \\multirow{2}{*}{$0$} & --- & $0.00$ & & \\multirow{2}{*}{$0$} & --- & $0.00$ \\\\\n & & --- & $(0.04)$ & & & --- & $(0.04)$ \\\\\\midrule\n \\multirow{2}{*}{$\\alpha_{4,1,g}$} & \\multirow{2}{*}{$0.5$} & $0.49$ & $0.49$ & & \\multirow{2}{*}{$0.1$} & $0.09$ & $0.09$ \\\\\n & & $(0.04)$ & $(0.04)$ & & & $(0.04)$ & $(0.06)$ \\\\\\cdashline{1-8}\n \\multirow{2}{*}{$\\alpha_{4,2,g}$} & \\multirow{2}{*}{$0$} & --- & $0.00$ & & \\multirow{2}{*}{$0$} & --- & $0.01$ \\\\\n & & --- & $(0.04)$ & & & --- & $(0.04)$ \\\\\\midrule\n \\multirow{2}{*}{$\\rho_{1,2,g}$} & \\multirow{2}{*}{$0.3$} & $0.30$ & $0.30$ & & \\multirow{2}{*}{$0.1$} & $0.10$ & $0.10$ \\\\\n & & $(0.05)$ & $(0.05)$ & & & $(0.04)$ & $(0.04)$ \\\\\\cdashline{1-8}\n \\multirow{2}{*}{$\\rho_{1,3,g}$} & \\multirow{2}{*}{$0.2$} & $0.19$ & $0.19$ & & \\multirow{2}{*}{$0.4$} & $0.40$ & $0.39$ \\\\\n & & $(0.06)$ & $(0.06)$ & & & $(0.03)$ & $(0.03)$ \\\\\\cdashline{1-8}\n \\multirow{2}{*}{$\\rho_{1,4,g}$} & \\multirow{2}{*}{$0.2$} & $0.18$ & $0.18$ & & \\multirow{2}{*}{$0.1$} & $0.10$ & $0.10$ \\\\\n & & $(0.06)$ & $(0.06)$ & & & $(0.04)$ & $(0.04)$ \\\\\\cdashline{1-8}\n \\multirow{2}{*}{$\\rho_{2,3,g}$} & \\multirow{2}{*}{$0.3$} & $0.30$ & $0.30$ & & \\multirow{2}{*}{$0.2$} & $0.20$ & $0.20$ \\\\\n & & $(0.06)$ & $(0.06)$ & & & $(0.04)$ & $(0.04)$ \\\\\\cdashline{1-8}\n \\multirow{2}{*}{$\\rho_{2,4,g}$} & \\multirow{2}{*}{$0.2$} & $0.20$ & $0.20$ & & \\multirow{2}{*}{$0.1$} & $0.10$ & $0.10$ \\\\\n & & $(0.06)$ & $(0.06)$ & & & $(0.04)$ & $(0.04)$ \\\\\\cdashline{1-8}\n \\multirow{2}{*}{$\\rho_{3,4,g}$} & \\multirow{2}{*}{$0.8$} & $0.79$ & $0.79$ & & \\multirow{2}{*}{$-0.8$}& $-0.80$ & $-0.80$ \\\\\n & & $(0.02)$ & $(0.02)$ & & & $(0.01)$ & $(0.01)$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Margin-closed regime-switching multivariate time series models", "authors": ["Lin Zhang", "Harry Joe", "Natalia Nolde"], "url": "https://arxiv.org/abs/2312.10706v1", "attribution": "\"Margin-closed regime-switching multivariate time series models\" by Lin Zhang, Harry Joe, and Natalia Nolde, arXiv:2312.10706v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2509.14198v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\textbf{Problem}&\\textbf{Sampling} & \\textbf{N. Params} & \\textbf{Time (ms/it)} & \\textbf{Rel. $L^2$ Error} \\\\\n \\midrule\n BGD (DeepONet)&Baseline & 101100 & 220 & $7.41 \\times 10^{-2}$ \\\\\n &$vRBA:\\Phi(x)=r^2$ & 101100 & 230 & $6.24 \\times 10^{-3}$ \\\\\n &$vRBA:\\Phi(x)=e^r$ & 101100& 230 & $\\mathbf{4.97 \\times 10^{-3}}$ \\\\\n \\midrule\n NS (FNO)&Baseline & 1622849 & 445 & $5.13 \\times 10^{-2}$ \\\\\n &$vRBA:\\Phi(x)=r^2$ & 1622849 & 459 & $2.37 \\times 10^{-2}$ \\\\\n &$vRBA:\\Phi(x)=e^r$ & 1622849 & 460 & $\\mathbf{2.25\\times 10^{-2}}$ \\\\\n \\midrule\n WE (TC-UNet)&Baseline & 2432001 & 810 & $3.69 \\times 10^{-2}$ \\\\\n &$vRBA:\\Phi(x)=r^2$ & 2432001 & 866 & $\\mathbf{1.00 \\times 10^{-2}}$ \\\\\n &$vRBA:\\Phi(x)=e^r$ & 2432001 & 867 & $1.05\\times 10^{-2}$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance of the vRBA framework on three operator learning benchmarks: bubble growth dynamics (BGD) solved with a DeepONet, the Navier-Stokes (NS) equations for Kolmogorov flow with an FNO, and the wave equation (WE) with a TC-UNet. In all cases, the vRBA method significantly outperforms the baseline model, which uses uniform sampling. The performance gain is most pronounced for the DeepONet architecture, where vRBA reduces the relative $L^2$ error by an order of magnitude, with significant improvements also observed for the FNO and TC-UNet models. For these operator learning tasks, all models were trained with the Adam optimizer, and the vRBA method was applied using a hybrid strategy of importance weighting in the spatial domain and importance sampling over the function space.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning", "authors": ["Juan Diego Toscano", "Daniel T. Chen", "Vivek Oommen", "George Em Karniadakis"], "url": "https://arxiv.org/abs/2509.14198v1", "attribution": "\"A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning\" by Juan Diego Toscano, Daniel T. Chen, Vivek Oommen, and George Em Karniadakis, arXiv:2509.14198v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.09114v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fault parameters used for testing.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\hline\nParameters & Symbol & Value & Unit \\\\ \\hline\nTrajectory setpoint $\\theta_a$ & $s$ & $\\begin{bmatrix}\n4 + 2 \\sin\\left(\\frac{1}{2}t\\right) \\\\\n1.5 + 2 \\sin\\left(\\frac{1}{2}t\\right)\n\\end{bmatrix}$ & rad \\\\\nNoise amplitude & $\\nu$ & $\\mathcal{U}(-1,1)\\cdot10^{-6}$ & - \\\\ \\hline\n\\multicolumn{4}{l}{Fault Parameters Broken Belt} \\\\ \\hline\nTime of fault occurrence & - & $27, 31.5, 36, \\ldots, 58.5$ & $s$ \\\\ \\hline\n\\multicolumn{4}{l}{Fault Parameters Tilt} \\\\ \\hline\nUpper arm tilt & $\\alpha$ & $1.8, 4.5$ & deg \\\\\nLower arm tilt & $\\beta$ & $1.8, 4.5$ & deg \\\\\nEnd-effector tilt & $\\gamma$ & $1.8, 4.5$ & deg \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Hybrid Model-Data Fault Diagnosis for Wafer Handler Robots: Tilt and Broken Belt Cases", "authors": ["Tim van Esch", "Farhad Ghanipoor", "Carlos Murguia", "Nathan van de Wouw"], "url": "https://arxiv.org/abs/2412.09114v1", "attribution": "\"Hybrid Model-Data Fault Diagnosis for Wafer Handler Robots: Tilt and Broken Belt Cases\" by Tim van Esch, Farhad Ghanipoor, Carlos Murguia, and Nathan van de Wouw, arXiv:2412.09114v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.05675v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc|cc}\n \\toprule\n Datasets & \\multicolumn{2}{c|}{M$^3$PC-M} & \\multicolumn{2}{c|}{M$^3$PC-Q} & \\multicolumn{2}{c}{M$^3$PC-online} \\\\ \n & w/o & w & w/o & w & w/o & w \\\\ \n \\midrule\n hopper-m & 84.3$_{\\pm7.3}$ & 70.7$_{\\pm6.2}$ & 81.6$_{\\pm3.5}$ & 73.6$_{\\pm5.6}$ & 94.9$_{\\pm11.7}$ & 93.9$_{\\pm15.8}$ \\\\\n halfcheetah-m & 43.8$_{\\pm0.6}$ & 43.9$_{\\pm3.9}$ & 50.0$_{\\pm0.3}$ & 51.2$_{\\pm0.7}$ & 71.5$_{\\pm3.6}$ & 69.3$_{\\pm2.1}$ \\\\\n walker2d-m & 79.9$_{\\pm1.4}$ & 80.9$_{\\pm2.5}$ & 80.7$_{\\pm7.2}$ & 86.4$_{\\pm2.6}$ & 68.3$_{\\pm25.0}$ & 91.9$_{\\pm7.8}$ \\\\\n hopper-mr & 75.1$_{\\pm11.3}$ & 80.4$_{\\pm5.2}$ & 76.8$_{\\pm27.2}$ & 78.3$_{\\pm16.2}$ & 88.7$_{\\pm26.9}$ & 103.5$_{\\pm6.0}$ \\\\\n walker2d-mr & 78.5$_{\\pm16.0}$ & 78.2$_{\\pm10.2}$ & 94.0$_{\\pm0.8}$ & 92.2$_{\\pm2.4}$ & 108.1$_{\\pm3.5}$ & 105.2$_{\\pm1.0}$ \\\\\n halfcheetah-mr & 40.0$_{\\pm1.0}$ & 41.8$_{\\pm0.5}$ & 48.0$_{\\pm0.8}$ & 48.2$_{\\pm0.4}$ & 70.2$_{\\pm2.8}$ & 67.0$_{\\pm7.1}$ \\\\\n \\midrule\n Average & 66.9 & 66.0 & 71.8 & 71.6 & 83.6 & 88.5 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{Ablation Study on Entropy Constraint. }Comparison of M$^3$PC-M, M$^3$PC-Q, and online results w or w/o entropy constraint across D4RL datasets.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "M$^3$PC: Test-time Model Predictive Control for Pretrained Masked Trajectory Model", "authors": ["Kehan Wen", "Yutong Hu", "Yao Mu", "Lei Ke"], "url": "https://arxiv.org/abs/2412.05675v2", "attribution": "\"M$^3$PC: Test-time Model Predictive Control for Pretrained Masked Trajectory Model\" by Kehan Wen, Yutong Hu, Yao Mu, and Lei Ke, arXiv:2412.05675v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2302.14602v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimates of the Productivity Effects: Endogeneity of Lagged FDI}\n\\begin{tabular}{lc|ccc}\n\t\t\t\\toprule\n\t\t\t& Exogenous\t& IPW & IV-External & IV-Lewbel \\\\ \n\t\t\t\\midrule\n\t\t\t\\multicolumn{5}{c}{\\textbf{---Median Estimates---}} \\\\\n\t\t\t$SP$\t& 0.327 & 0.477 & 0.466 & 0.414 \\\\\n\t\t\t& (0.211, 0.399) & (0.413, 0.512) & (0.411, 0.585) & (0.276, 0.515) \\\\\n\t\t\t$DL$\t& 0.138 & 0.128 & 0.171 & 0.191 \\\\\n\t\t\t& (0.118, 0.164) & (0.105, 0.158) & (0.138, 0.241) & (0.053, 0.341) \\\\\n\t\t\t$TIL$\t& 0.037 & 0.055 & 0.067 & 0.066 \\\\\n\t\t\t& (0.022, 0.050) & (0.044, 0.069) & (0.056, 0.100) & (0.019, 0.126) \\\\\n\t\t\t\\midrule\n\t\t\t\\multicolumn{5}{c}{\\textbf{---Statistically $>0$ (\\% Obs.)---}} \\\\\n\t\t\t$SP$\t& 83.84 & 98.55 & 97.17 & 79.56 \\\\\n\t\t\t$DL$\t& 89.13 & 91.40 & 78.48 & 82.30 \\\\\n\t\t\t$TIL$\t& 86.71 & 98.99 & 97.64 & 83.48 \\\\\n\t\t\t\\midrule\n\t\t\t\\multicolumn{5}{p{11cm}}{\\footnotesize {\\sc Notes:} Reported are the results for the productivity process in under baseline specification. The two-sided 95\\% bootstrap percentile confidence intervals for the median point estimates are in parentheses. Statistical positiveness is at the 5\\% significance level, using one-sided bootstrap confidence intervals. The ``Exogenous'' column corresponds to our proposed estimation procedure under the structural assumption of weak exogeneity of $G_{i,t-1}$, with the second-stage eq.~ estimated via least squares. The three models on the right allow for the violation of $\\mathbb{E}[\\zeta_{it}+\\eta_{it}|G_{i,t-1}]=0$ and address the potential endogeneity of lagged FDI in via (i) inverse probability weighting [IPW], (ii) instrumentation using external IVs including coastal province dummy, province-level openness measure and their interactions with predetermined $L_{i,t-1}$ [IV-External], (iii) instrumentation with the heteroskedasticity-based internal IV \\`a la [IV-Lewbel]. } \\\\\n\t\t\t\\bottomrule[1pt]\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "On the Estimation of Cross-Firm Productivity Spillovers with an Application to FDI", "authors": ["Emir Malikov", "Shunan Zhao"], "url": "https://arxiv.org/abs/2302.14602v1", "attribution": "\"On the Estimation of Cross-Firm Productivity Spillovers with an Application to FDI\" by Emir Malikov and Shunan Zhao, arXiv:2302.14602v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.17508v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{{Local scores for the orientation of a single directed or bidirected edge, {\\color{black}see Appendix~D}.}} %\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n {\\footnotesize Edge} & {\\footnotesize Information} & {\\footnotesize Symmetrized fNML complexity (Markov equivalent)} \\medskip\\\\\n{\\footnotesize $\\!\\!\\!X\\to Y$} & {\\footnotesize $\\!\\!-I(X;Y|\\mathbf{Pa'}_{\\!_{Y\\!\\setminus\\! X}})$} & {\\footnotesize $\\!\\!\\!{1\\over 2N}\\Big(\\sum_j^{q_{_{x\\!\\setminus\\! y}}\\!r_y}\\log \\mathcal{C}_{n_j}^{r_x}-\\sum_j^{q_{_{x\\!\\setminus\\! y}}}\\log \\mathcal{C}_{n_j}^{r_x}+\\sum_j^{q_{_{y\\!\\setminus\\! x}}\\!r_x}\\log \\mathcal{C}_{n_j}^{r_y}-\\sum_j^{q_{_{y\\!\\setminus\\! x}}}\\log \\mathcal{C}_{n_j}^{r_y}\\Big)$}\\medskip \\\\\n{\\footnotesize $\\!\\!\\!X\\leftarrow Y$} & {\\footnotesize $\\!\\!-I(X;Y|\\mathbf{Pa'}_{\\!_{X\\!\\setminus\\! Y}})$} & {\\footnotesize $\\!\\!\\!{1\\over 2N}\\Big(\\sum_j^{q_{_{x\\!\\setminus\\! y}}\\!r_y}\\log \\mathcal{C}_{n_j}^{r_x}-\\sum_j^{q_{_{x\\!\\setminus\\! y}}}\\log \\mathcal{C}_{n_j}^{r_x}+\\sum_j^{q_{_{y\\!\\setminus\\! x}}\\!r_x}\\log \\mathcal{C}_{n_j}^{r_y}-\\sum_j^{q_{_{y\\!\\setminus\\! x}}}\\log \\mathcal{C}_{n_j}^{r_y}\\Big)$} \\medskip \\\\\n{\\footnotesize $\\!\\!\\!X\\leftrightarrow Y$} & {\\footnotesize $\\!\\!-I(X;Y|\\mathbf{Pa'}_{\\!_{XY}})$} & {\\footnotesize $\\!\\!\\!{1\\over 2N}\\Big(\\sum_j^{q_{_{xy}}r_y}\\log \\mathcal{C}_{n_j}^{r_x}-\\sum_j^{q_{_{xy}}}\\log \\mathcal{C}_{n_j}^{r_x}\\,+\\sum_j^{q_{_{yx}}r_x}\\log \\mathcal{C}_{n_j}^{r_y}-\\sum_j^{q_{_{yx}}}\\log \\mathcal{C}_{n_j}^{r_y}\\Big)$}\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An efficient search-and-score algorithm for ancestral graphs using multivariate information scores", "authors": ["Nikita Lagrange", "Herve Isambert"], "url": "https://arxiv.org/abs/2412.17508v1", "attribution": "\"An efficient search-and-score algorithm for ancestral graphs using multivariate information scores\" by Nikita Lagrange and Herve Isambert, arXiv:2412.17508v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.11150v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Heterogeneous Response of Investment to $\\epsilon^{X}_t$ \\\\ \\footnotesize Bank Leverage \\& Alt. Specification}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n & \\multicolumn{4}{c}{Firm investment - $\\Delta \\log k_{i,t}$ } \\\\\n & (1) & (2) & (3) & (4) \\\\ \\hline \\hline\n & & & & \\\\\n$\\left(l_{i,t-1} - \\mathbb{E}_{i} \\left[ l_{t}\\right] \\right) \\epsilon^{X}_t$ & -0.0959 & -0.106 & -0.0958 & -0.0619 \\\\\n & (0.0728) & (0.0753) & (0.0859) & (0.0874) \\\\\n & & & & \\\\\nFirm FE & yes & yes & yes & yes \\\\\nSector-Time FE & yes & yes & yes & yes \\\\\nControl for Lagged Inv. & no & yes & yes & yes \\\\\nFirm-Controls & no & no & yes & yes \\\\\nAggregate-Controls & no & no & no & yes \\\\\n & & & & \\\\\nObservations & 11,042 & 10,937 & 10,349 & 10,346 \\\\ \\hline\n$R^{2}$ & 0.043 & 0.046 & 0.055 & 0.064 \\\\ \\hline \\hline\n\\multicolumn{5}{c}{ Clustered standard errors in parentheses} \\\\\n\\multicolumn{5}{c}{ *** p$<$0.01, ** p$<$0.05, * p$<$0.1} \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity", "authors": ["Santiago Camara", "Sebastian Ramirez Venegas"], "url": "https://arxiv.org/abs/2209.11150v1", "attribution": "\"The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity\" by Santiago Camara and Sebastian Ramirez Venegas, arXiv:2209.11150v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01001v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of the comparison of decisions made by computer vision model with decisions made by conventional tests and visual tests conducted by human.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\\toprule\nViolations & \\#Samples & \\#Agreements & Agreement rate\\\\\n\\midrule\n\\multicolumn{4}{l}{\\textbf{Compared with conventional tests}}\\\\\n\\hspace{1em}heteroskedasticity & 540 & 464 & 0.8593\\\\\n\\hspace{1em}non-linearity & 576 & 459 & 0.7969\\\\\n\\multicolumn{4}{l}{\\textbf{Compared with visual tests conducted by human}}\\\\\n\\hspace{1em}heteroskedasticity & 540 & 367 & 0.6796\\\\\n\\hspace{1em}non-linearity & 576 & 385 & 0.6684\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Automated Assessment of Residual Plots with Computer Vision Models", "authors": ["Weihao Li", "Dianne Cook", "Emi Tanaka", "Susan VanderPlas", "Klaus Ackermann"], "url": "https://arxiv.org/abs/2411.01001v1", "attribution": "\"Automated Assessment of Residual Plots with Computer Vision Models\" by Weihao Li, Dianne Cook, Emi Tanaka, Susan VanderPlas, and Klaus Ackermann, arXiv:2411.01001v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.13426v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\\toprule\n\\textbf{Market} & \\textbf{Share ($k=1$)} & \\textbf{Share ($k=2$)} \\\\\n\\midrule\nControl Group & {0.14\\%} & {44.84\\%} \\\\\nPartial market test ($k=1$, 30\\%) & {67.02\\%} & {8.69\\%} \\\\\nTotal market test (30\\%) & {2.96\\%} & {81.30\\%} \\\\\n\\bottomrule\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Demand patterns representing scenario 1}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Second-degree Price Discrimination: Theoretical Analysis, Experiment Design, and Empirical Estimation", "authors": ["Soheil Ghili", "K. Sudhir", "Nitish Jain", "Ankur Garg"], "url": "https://arxiv.org/abs/2507.13426v1", "attribution": "\"Second-degree Price Discrimination: Theoretical Analysis, Experiment Design, and Empirical Estimation\" by Soheil Ghili, K. Sudhir, Nitish Jain, and Ankur Garg, arXiv:2507.13426v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2308.16438v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Test outcomes for Models 1--4 in Section .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc} \n \\toprule \n {\\bf Model A} & {\\bf Model B} & {\\bf S-W statistic} & {\\bf In favor}\\\\\n \\midrule\n1 & 2 & 0.987 & $-$\\\\\n1 & 3 & 0.791 & $-$\\\\\n1 & 4 & 0.680 & $-$\\\\\n2 & 3 & -0.706 & $-$\\\\\n2 & 4 & -0.706 & $-$\\\\\n3 & 4 & 0.727 & $-$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Model Selection for Ordinary Differential Equations: a Statistical Testing Approach", "authors": ["Itai Dattner", "Shota Gugushvili", "Oleksandr Laskorunskyi"], "url": "https://arxiv.org/abs/2308.16438v1", "attribution": "\"Model Selection for Ordinary Differential Equations: a Statistical Testing Approach\" by Itai Dattner, Shota Gugushvili, and Oleksandr Laskorunskyi, arXiv:2308.16438v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2304.06484v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|l|l|l|l|l|}\n\\hline\ncollection & month created & mean & median & mean (trim 2.5\\%) & total supply \\\\ \\hline\navariksagauniverse & 2021-09 & 0.15 & 0.10 & 0.11 & 8888 \\\\ \\hline\navastar & 2020-02 & 0.45 & 0.20 & 0.27 & 25458 \\\\ \\hline\nbcsnft & 2021-09 & 0.09 & 0.08 & 0.08 & 7777 \\\\ \\hline\nbored-mummy-baby-waking-up & 2021-08 & 0.13 & 0.10 & 0.11 & 3888 \\\\ \\hline\ncollectvox & 2021-07 & 1.85 & 0.60 & 1.14 & 8888 \\\\ \\hline\ncovidpunksnft & 2021-07 & 0.20 & 0.08 & 0.11 & 10000 \\\\ \\hline\ncrypto-hodlers-nft & 2021-07 & 0.14 & 0.07 & 0.08 & 10000 \\\\ \\hline\ncryptomutts-official & 2021-09 & 0.12 & 0.06 & 0.08 & 10000 \\\\ \\hline\ncryptopunks & 2017-06 & 78.41 & 33.00 & 45.88 & 10000 \\\\ \\hline\ncurrencypunks & 2021-09 & 0.04 & 0.03 & 0.03 & 10000 \\\\ \\hline\ncyphercity & 2021-07 & 0.07 & 0.03 & 0.04 & 8886 \\\\ \\hline\ndivineanarchy & 2021-11 & 0.32 & 0.29 & 0.29 & 10011 \\\\ \\hline\ndoobits & 2021-07 & 0.02 & 0.02 & 0.02 & 10024 \\\\ \\hline\ndynamic-duelers & 2021-08 & 0.06 & 0.06 & 0.06 & 983 \\\\ \\hline\ndystopunks & 2021-06 & 2.39 & 1.10 & 1.87 & 2077 \\\\ \\hline\nevaverse & 2021-07 & 0.28 & 0.22 & 0.25 & 10000 \\\\ \\hline\nexpansionpunks & 2021-08 & 0.30 & 0.17 & 0.23 & 10000 \\\\ \\hline\nfluf-world & 2021-08 & 3.05 & 1.33 & 2.06 & 10000 \\\\ \\hline\nfourierpunks & 2021-06 & 0.13 & 0.10 & 0.11 & 420 \\\\ \\hline\nguardians-of-the-metaverse & 2021-09 & 0.16 & 0.14 & 0.15 & 10000 \\\\ \\hline\nhashmasks & 2021-01 & 43.34 & 1.31 & 1.47 & 16384 \\\\ \\hline\nhd--punks & 2021-06 & 0.10 & 0.04 & 0.05 & 10000 \\\\ \\hline\nheavencomputer & 2021-08 & 0.27 & 0.17 & 0.25 & 7777 \\\\ \\hline\ninfluence-crew & 2021-09 & 0.24 & 0.10 & 0.15 & 7562 \\\\ \\hline\nlucha-libre-knockout & 2021-08 & 0.05 & 0.02 & 0.02 & 10000 \\\\ \\hline\nmetahero-generative & 2021-09 & 5.69 & 5.25 & 5.44 & 6458 \\\\ \\hline\nmisfit-university-official & 2021-06 & 0.05 & 0.02 & 0.03 & 10000 \\\\ \\hline\nmutant-punks-nft & 2021-10 & 0.05 & 0.04 & 0.04 & 10000 \\\\ \\hline\nneo-tokyo-identities & 2021-10 & 14.75 & 11.00 & 13.67 & 2021 \\\\ \\hline\nnfh & 2021-09 & 0.17 & 0.12 & 0.14 & 8888 \\\\ \\hline\nobitsofficial & 2021-09 & 0.23 & 0.16 & 0.18 & 7132 \\\\ \\hline\noctohedz & 2021-08 & 2.05 & 1.48 & 1.83 & 888 \\\\ \\hline\noctohedz-reloaded & 2021-10 & 0.10 & 0.07 & 0.09 & 8001 \\\\ \\hline\npixls-official & 2021-03 & 0.25 & 0.15 & 0.20 & 5468 \\\\ \\hline\nrole-for-metaverse & 2021-09 & 0.15 & 0.13 & 0.14 & 9800 \\\\ \\hline\nrug-wtf & 2021-07 & 0.06 & 0.03 & 0.05 & 10000 \\\\ \\hline\nskvllpvnkz-hideout & 2021-09 & 0.11 & 0.08 & 0.09 & 10000 \\\\ \\hline\nspunks-nft & 2021-06 & 0.21 & 0.09 & 0.12 & 10000 \\\\ \\hline\nsrsc & 2021-06 & 240.37 & 0.07 & 0.07 & 8888 \\\\ \\hline\nstoned-ape-saturn-club & 2021-09 & 0.08 & 0.05 & 0.07 & 6969 \\\\ \\hline\ntheantimasks & 2021-04 & 0.06 & 0.06 & 0.05 & 1551 \\\\ \\hline\nthelostglitches & 2021-08 & 0.18 & 0.09 & 0.13 & 9999 \\\\ \\hline\nwearetheoutkast & 2021-09 & 0.07 & 0.07 & 0.07 & 9996 \\\\ \\hline\nzunks & 2021-08 & 0.16 & 0.08 & 0.11 & 10000 \\\\ \\hline\n\\end{tabular}\n\\caption{Statistics for the 44 NFT collections analyzed.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Exploring Gender and Race Biases in the NFT Market", "authors": ["Howard Zhong", "Mark Hamilton"], "url": "https://arxiv.org/abs/2304.06484v1", "attribution": "\"Exploring Gender and Race Biases in the NFT Market\" by Howard Zhong and Mark Hamilton, arXiv:2304.06484v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15444v2_tex_table18.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mutually unbiased weighing matrices of order $21$ and weight $9$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l}\n\\noalign{\\hrule height1pt}\n$W_{21}$\\\\\n\\hline\n110100100210010010002\n000011002010002122100\n012002122020100020000\\\\\n100010101100000001111\n001200110100101002002\n010001020101201100200\\\\\n001110012021000001020\n000102200000021100112\n120002002000211002001\\\\\n100001220000101200120\n120000001201100120200\n100010000002220220202\\\\\n100000202100102010210\n001102021100002002020\n001121000022010020010\\\\\n011000000201020202011\n012010211020010002000\n110020010002020100021\\\\\n011202200010010021000\n100220000021202000102\n001210020222000110000\\\\\n\\hline\n$ A_{21,2}$\\\\\n\\hline\n000001021200002220210\n001020200002120021100\n102020100022001000011\\\\\n100001010020012120020\n100000022002202011002\n000120011100022010200\\\\\n000100221120011000002\n000100022001100101201\n001101000200000112110\\\\\n001122100010210020000\n100111010010001201000\n101200200101200000011\\\\\n010010100100020120012\n010001020112000002021\n011000000220221000220\\\\\n112002201210000100000\n110020002001100202002\n010112000020002200101\\\\\n011200101000110011000\n120000121001020000120\n121012000002100002200\\\\\n\\hline\n$ A_{21,3}$\\\\\n\\hline\n111000202100002000120\n000011010102000212200\n001100200201001210002\\\\\n010001110011100000110\n010002000212202200010\n102002000121010200010\\\\\n120001000201202002001\n121000100002001220100\n100000210222100100010\\\\\n010120002000001022201\n010200100220010002022\n000010221010110022000\\\\\n110200021000021010001\n001201022000010001210\n010111001020200021000\\\\\n100100120000122000202\n100020011010010001220\n102010002010201100002\\\\\n001020001100200102012\n001212010001020020200\n001112100000010110001\\\\\n\\noalign{\\hrule height1pt}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unbiased weighing matrices of weight $9$", "authors": ["Makoto Araya", "Masaaki Harada", "Hadi Kharaghani", "Sho Suda", "Wei-Hsuan Yu"], "url": "https://arxiv.org/abs/2501.15444v2", "attribution": "\"Unbiased weighing matrices of weight $9$\" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09997v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\textbf{Models} & \\textbf{\\textsc{per}} & \\textbf{\\textsc{loc}} & \\textbf{\\textsc{org}} & \\textbf{Overall} \\\\\n \\midrule\n \\texttt{SpaCy} & 0.64 & \\textbf{0.54} & \\textbf{0.16} & 0.95\\\\\n \\texttt{Stanza} & 0.69 & 0.4 & 0.11 & 0.94 \\\\\n \\texttt{Natasha} & \\textbf{0.77} & \\textbf{0.54} & 0.14 & \\textbf{0.96} \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The performance of off-the-shelf tools}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Razmecheno: Named Entity Recognition from Digital Archive of Diaries \"Prozhito\"", "authors": ["Timofey Atnashev", "Veronika Ganeeva", "Roman Kazakov", "Daria Matyash", "Michael Sonkin", "Ekaterina Voloshina", "Oleg Serikov", "Ekaterina Artemova"], "url": "https://arxiv.org/abs/2201.09997v1", "attribution": "\"Razmecheno: Named Entity Recognition from Digital Archive of Diaries \"Prozhito\"\" by Timofey Atnashev, Veronika Ganeeva, Roman Kazakov, Daria Matyash, Michael Sonkin, Ekaterina Voloshina, Oleg Serikov, and Ekaterina Artemova, arXiv:2201.09997v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.00851v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Goodness of fit of each index under different models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccccc}\n \\toprule\n & & \\multicolumn{2}{c}{SSE} & \\multicolumn{2}{c}{CSI 300} & \\multicolumn{2}{c}{SSE 50} & \\multicolumn{2}{c}{GEI} & \\multicolumn{2}{c}{SSE 50 ETF} & \\multicolumn{2}{c}{STAR 50} \\\\\n \\cmidrule(lr){3-4} \\cmidrule(lr){5-6} \\cmidrule(lr){7-8} \\cmidrule(lr){9-10} \\cmidrule(lr){11-12} \\cmidrule(lr){13-14}\n & & Train& Test & Train & Test & Train & Test & Train & Test & Train & Test & Train & Test \\\\\n \\midrule\n \\multirow{2}{*}{$M_1$} & $R^2$ & $0.495$ & $0.474$ & $0.455$ & $0.383$ & $0.486$ & $0.442$ & $0.456$ & $0.299$ & $0.175$ & $0.382$ & $0.248$ & $0.059$ \\\\\n & RMSE & $0.00033$ & $0.00007$ & $0.00035$ & $0.00011$ & $0.00036$ & $0.00009$ & $0.00041$ & $0.00015$ & $0.00084$ & $0.00009$ & $0.00016$ & $0.00016$ \\\\\n \\multirow{2}{*}{$M_2$} & $R^2$ & $0.521$ & $0.466$ & $0.503$ & $0.390$ & $0.530$ & $0.391$ & $0.538$ & $0.319$ & $0.206$ & $0.336$ & $0.255$ & $0.0687$ \\\\\n & RMSE & $0.00032$ & $0.00007$ & $0.00033$ & $0.00010$ & $0.00033$ & $0.00010$ & $0.00034$ & $0.00015$ & $0.00080$ & $0.00010$ & $0.00018$ & $0.00016$ \\\\\n \\multirow{2}{*}{$M_3$} & $R^2$ & $0.048$ & $0.044$ & $0.040$ & $0.018$ & $0.0432$ & $0.019$ & $0.0349$ & $0.002$ & $0.000$ & $-0.001$ & $0.0038$ & $0.0259$ \\\\\n & RMSE & $0.00042$ & $0.00009$ & $0.00105$ & $0.00141$ & $0.00046$ & $0.00012$ & $0.00051$ & $0.00018$ & $0.00096$ & $0.00125$ & $0.00018$ & $0.00019$ \\\\\n \\multirow{2}{*}{$M_4$} & $R^2$ & $0.279$ & $0.260$ & $0.255$ & $0.205$ & $0.245$ & $0.257$ & $0.018$ & $0.001$ & $0.0362$ & $0.279$ & $0.091$ & $0.012$ \\\\\n & RMSE & $0.00046$ & $0.00008$ & $0.00040$ & $0.00015$ & $0.00041$ & $0.00010$ & $0.00054$ & $0.00018$ & $0.00245$ & $0.00080$ & $0.00017$ & $0.00017$ \\\\\n \\multirow{2}{*}{$M_5$} & $R^2$ & $0.348$ & $0.200$ & $0.337$ & $0.241$ & $0.390$ & $0.196$ & $0.279$ & $0.103$ & $0.084$ & $0.239$ & $0.163$ & $0.038$ \\\\\n & RMSE & $0.00043$ & $0.00008$ & $0.00034$ & $0.00012$ & $0.00038$ & $0.00011$ & $0.00043$ & $0.00017$ & $0.00086$ & $0.00012$ & $0.00017$ & $0.00016$ \\\\\n \\bottomrule \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Forecasting realized volatility in the stock market: a path-dependent perspective", "authors": ["Xiangdong Liu", "Sicheng Fu", "Shaopeng Hong"], "url": "https://arxiv.org/abs/2503.00851v1", "attribution": "\"Forecasting realized volatility in the stock market: a path-dependent perspective\" by Xiangdong Liu, Sicheng Fu, and Shaopeng Hong, arXiv:2503.00851v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.09180v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Components and specifications for the mmW receiver}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\textbf{Component} & \\textbf{Gain} & \\textbf{NF} & \\textbf{OIP3} \\\\ \n & (dB) & (dB) & (dBm) \\\\ \\hline\nLNA ({MAAL-01111}) & 19 & 2.5 & 20 \\\\ \\hline\nDown converter ({HMC1065LP4E}) & 9 & 3 & 14 \\\\ \\hline\nLow-pass filter (LPF) ({LFCN-900+}) & $-1$ & 1 & N/A \\\\ \\hline\nIF amplifier ({RAM-8A+}) & [31.5,24] & 2.6 & 24.4 \\\\ \\hline\nVGA ({ADL5331}) & [$-15$,15] & 9 & 39 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Direct- Conversion Digital Beamforming Array Receiver with 800 MHz Channel Bandwidth at 28 GHz using Xilinx RF SoC", "authors": ["Sravan Pulipati", "Viduneth Ariyarathna", "Udara De Silva", "Najath Akram", "Elias Alwan", "Arjuna Madanayake", "Soumyajit Mandal", "Theodore S. Rappaport"], "url": "https://arxiv.org/abs/1911.09180v1", "attribution": "\"A Direct- Conversion Digital Beamforming Array Receiver with 800 MHz Channel Bandwidth at 28 GHz using Xilinx RF SoC\" by Sravan Pulipati, Viduneth Ariyarathna, Udara De Silva, Najath Akram, Elias Alwan, Arjuna Madanayake, Soumyajit Mandal, and Theodore S. Rappaport, arXiv:1911.09180v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.18643v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Coefficients of Linear Regression Analysis (****p<0.001, ***p<0.01, **p<0.05, *p<0.1)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\textbf{Predictors} & \\textbf{F-stat} & \\textbf{$R^2$} & \\textbf{RSE} \\\\ \\hline\nPanel & 1.861 & 0.02783 & 565.20 \\\\ \\hline\nDTA & 33.85 & 0.3425 & 464.80 \\\\ \\hline\nROE & 14.59 & 0.1834 & 518.00 \\\\ \\hline\nROA & 2.122 & 0.03027 & 594.00 \\\\ \\hline\nTATO & 67.9 & 0.5109 & 400 \\\\ \\hline\nCR & 5.272 & 0.7506 & 551.30 \\\\ \\hline\nTerm & 0.7566 & 0.01151 & 569.00 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Regression-Based Share Market Prediction Model for Bangladesh", "authors": ["Syeda Tasnim Fabiha", "Rubaiyat Jahan Mumu", "Farzana Aktar", "B M Mainul Hossain"], "url": "https://arxiv.org/abs/2507.18643v1", "attribution": "\"A Regression-Based Share Market Prediction Model for Bangladesh\" by Syeda Tasnim Fabiha, Rubaiyat Jahan Mumu, Farzana Aktar, and B M Mainul Hossain, arXiv:2507.18643v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18002v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{soul}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll|lllll|lllll|lllll}\n\\hline\n\\multicolumn{20}{c}{\\textbf{A Uniform}}\\\\\n\\hline\n\\multicolumn{5}{c|}{Shifted sinusoidal $d=2$}\n&\\multicolumn{5}{c|}{Shifted sinusoidal $d=5$}\n& \\multicolumn{5}{c|}{Rosenbrock $d=2$}\n& \\multicolumn{5}{c}{Rosenbrock $d=5$}\n\\\\\\hline\n$k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \\\\ \n\\hline\n 10 & 0.415 & 0.348 & 1.63 & 2.769 & \n 10 & {\\hl {0.177}} &{\\hl { 0.183}} & 2.811 & 3.411 & \n 10 & 0.867 & 0.867 & 185.542 & 232.845 & \n 10 & 0.721 & 0.721 & 642.486 & 840.008 \\\\ \n 18 & 0.173 & 0.174 & 0.998 & 2.229 & \n 15 & 0.544 & 0.517 & 2.67 & 2.961 & \n 110 & 0.012 & 0 & 1.256 & 29.326 & \n 114 & {\\hl {0.154}} & {\\hl {0.157}} & 198.088 & 791.735 \\\\ \n 26 & {\\hl {0.233}} & {\\hl {0.251}} & 0.998 & 1.993 & \n 32 & 0.165 & 0.148 & 2.594 & 3.347 & \n 212 & 0.019 & 0.129 & 1.256 & 41.415 & \n 214 & 0.071 & 0.068 & 139.612 & 763.788 \\\\ \n 35 & {\\hl {0.268}} & {\\hl {0.293}} & 0.998 & 1.772 &\n 38 & 0.21 & 0.185 & 2.594 & 3.287 & \n 312 & 0.059 & 0.042 & 1.256 & 37.179 & \n 319 & 0.004 & 0.004 & 33.084 & 718.587 \\\\ \n 45 & {\\hl {0.267}} & {\\hl {0.29}} & 0.998 & 1.772 & \n 47 & 0.212 & 0.182 & 2.594 & 3.284 & & & & & & & & & & \\\\ \n 55 & {\\hl {0.32}} & {\\hl {0.344}} & 0.998 & 1.63 & \n 57 & 0.209 & 0.181 & 2.594 & 3.287 & & & & & & & & & & \\\\ \n 65 & {\\hl {0.461}} & {\\hl {0.47}} & 0.998 & 1.332 & \n 67 & 0.08 & 0.068 & 1.92 & 3.284 & & & & & & & & & & \\\\ \n 85 & 0.054 & 0.053 & 0.095 & 1.233 &\n 120 & {\\hl {0.1}} & {\\hl {0.11}} & 1.92 & 3.181 & & & & & & & & & & \\\\ \n 129 & 0.004 & 0.004 & 0.008 & 1.165 & \n 142 & {\\hl {0.032}} & {\\hl {0.033}} & 1.505 & 3.06 & & & & & & & & & & \\\\ \n 141 & 0.004 & 0.004 & 0.008 & 1.162 & \n 159 & 0.005 & 0.004 & 1.168 & 2.978 & & & & & & & & & & \\\\ \n 161 & 0.004 & 0.005 & 0.008 & 1.108 & \n 217 & 0.005 & 0.005 & 1.168 & 2.958 & & & & & & & & & & \\\\ \n 180 & 0.008 & 0.008 & 0.008 & 0.909 & \n 258 & 0.008 & 0.008 & 1.168 & 2.738 & & & & & & & & & & \\\\ \n 200 & 0.011 & 0.011 & 0.008 & 0.829 & & & & & & & & & & & & & & & \\\\ \n 219 & 0.006 & 0.006 & 0.003 & 0.527 & & & & & & & & & & & & & & & \\\\ \n 265 & {\\hl {0.011}} & {\\hl {0.012}} & 0.003 & 0.254 & & & & & & & & & & & & & & & \\\\ \n & & & & & & & & & & & & & & & & & & & \\\\ \n \\hline\n\\multicolumn{20}{c}{\\textbf{B Gaussian Process}}\\\\ \n \\hline\n\\multicolumn{5}{c|}{Shifted sinusoidal $d=2$}\n&\\multicolumn{5}{c|}{Shifted sinusoidal $d=5$}\n& \\multicolumn{5}{c|}{Rosenbrock $d=2$}\n& \\multicolumn{5}{c}{Rosenbrock $d=5$}\n\\\\\\hline\n$k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \\\\ \n\\hline\n 10 & 0.415 & 0.348 & 1.63 & 2.769 & \n 10 &{\\hl {0.466}} & {\\hl {0.474}} & 3.286 & 3.5 & \n 10 & 0.867 & 0.867 & 185.542 & 232.845 & \n 10 & 0.721 & 0.721 & 642.486 & 840.008 \\\\ \n 64 & {\\hl {0.145}} & {\\hl {0.148}} & 1.002 & 2.769 & \n 20 & {\\hl {0.066}} & {\\hl {0.075}} & 2.531 & 3.489 & \n 111 & 0.705 & 0.361 & 158.721 & 645.963 & \n 114 & {\\hl {0.104}} & {\\hl {0.148}} & 358.04 & 2046.24 \\\\ \n 116 & {\\hl {0.161}} & {\\hl {0.181}} & 1.002 & 2.179 & \n 64 & 0.087 & 0.052 & 2.09 & 3.35 & \n 220 & 0.265 & 0.216 & 7.3 & 185.542 &\n 217 & 0.073 & 0.134 & 358.04 & 2404.12 \\\\ \n 170 & 0.165 & 0.165 & 1.002 & 2.251 & \n 67 & 0.091 & 0.037 & 2.09 & 3.358 & \n 327 & {\\hl {0.061}} & {\\hl {0.063}} & 0.542 & 122.652 & \n 321 & {\\hl {0.055}} & {\\hl {0.102}} & 358.04 & 2587.64 \\\\ \n 223 & 0.165 & 0.164 & 1.002 & 2.316 & \n 73 & {\\hl {0.019}} & {\\hl {0.02}} & 1.443 & 3.325 & & & & & & & & & & \\\\ \n 238 & {\\hl {0.058}} & {\\hl {0.059}} & 0.278 & 2.179 & \n 130 & {\\hl {0.022}} & {\\hl {0.023}} & 1.415 & 3.136 & & & & & & & & & & \\\\ \n 260 & {\\hl {0.069}} & {\\hl {0.074}} & 0.278 & 1.932 & \n 183 & {\\hl {0.022}} & {\\hl {0.023}} & 1.415 & 3.131 & & & & & & & & & & \\\\ \n & & & & & \n 246 & 0.012 & 0.011 & 1.321 & 3.047 & & & & & & & & & & \\\\ \n & & & & & \n 265 & 0.002 & 0.002 & 1.016 & 2.995 & & & & & & & & & & \\\\ \n & & & & & & & & & & & & & & & & & & & \\\\ \n \\hline\n\\multicolumn{20}{c}{\\textbf{C Quadratic Regression}}\\\\ \n \\hline\n\\multicolumn{5}{c|}{Shifted sinusoidal $d=2$}\n&\\multicolumn{5}{c|}{Shifted sinusoidal $d=5$}\n& \\multicolumn{5}{c|}{Rosenbrock $d=2$}\n& \\multicolumn{5}{c}{Rosenbrock $d=5$}\n\\\\\\hline\n$k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \\\\ \n\\hline\n 10 & 0.415 & 0.348 & 1.63 & 2.769 & \n 10 & {\\hl {0.177}} & {\\hl {0.183}} & 2.811 & 3.411 & \n 10 & 0.867 & 0.867 & 185.542 & 232.845 & \n 10 & 0.721 & 0.721 & 642.486 & 840.008 \\\\ \n 18 & {\\hl {0.173}} &{\\hl { 0.174}} & 0.998 & 2.229 & \n 15 & 0.544 & 0.517 & 2.67 & 2.961 &\n 110 & 0.038 & 0.001 & 1.067 & 5.379 & \n 114 & 0.66 & 0.66 & 16.327 & 20.32 \\\\ \n 26 & {\\hl {0.233}} &{\\hl { 0.251}} & 0.998 & 1.993 & \n 32 & 0.165 & 0.148 & 2.594 & 3.347 & \n 213 & 1 & 1 & 1.019 & 1.044 & \n 215 & {\\hl {0.114}} & {\\hl {0.115}} & 16.327 & 43.128 \\\\ \n 35 & {\\hl {0.268}} & {\\hl {0.293}} & 0.998 & 1.772 & \n 38 & 0.21 & 0.185 & 2.594 & 3.287 &\n 316 & 0 & 0 & 0 & 1.052 & \n 320 & 0.025 & 0.025 & 3.918 & 20.309 \\\\ \n 45 & {\\hl {0.267}} &{\\hl { 0.29}} & 0.998 & 1.772 &\n 47 & 0.212 & 0.182 & 2.594 & 3.284 & & & & & & & & & & \\\\ \n 55 & {\\hl {0.32}} & {\\hl {0.344}} & 0.998 & 1.63 &\n 57 & 0.209 & 0.181 & 2.594 & 3.287 & & & & & & & & & & \\\\ \n 65 & {\\hl {0.461}} & {\\hl {0.47}} & 0.998 & 1.332 & \n 67 & 0.08 & 0.068 & 1.92 & 3.284 & & & & & & & & & & \\\\ \n 85 & 0.054 & 0.053 & 0.095 & 1.233 & \n 120 & {\\hl {0.1}} & {\\hl {0.11}} & 1.92 & 3.181 & & & & & & & & & & \\\\ \n 129 & 0.004 & 0.004 & 0.008 & 1.165 &\n 142 & {\\hl {0.032}} &{\\hl { 0.033}} & 1.505 & 3.06 & & & & & & & & & & \\\\ \n 141 & 0.004 & 0.004 & 0.008 & 1.162 &\n 159 & 0.005 & 0.004 & 1.168 & 2.978 & & & & & & & & & & \\\\ \n 161 & {\\hl {0.004}} &{\\hl { 0.005}} & 0.008 & 1.108 &\n 217 & 0.005 & 0.005 & 1.168 & 2.958 & & & & & & & & & & \\\\ \n 180 & 0.008 & 0.008 & 0.008 & 0.909 & \n 258 & 0.008 & 0.008 & 1.168 & 2.738 & & & & & & & & & & \\\\ \n 200 & 0.011 & 0.011 & 0.008 & 0.829 & & & & & & & & & & & & & & & \\\\ \n 219 & 0.006 & 0.006 & 0.003 & 0.527 & & & & & & & & & & & & & & & \\\\ \n 265 & {\\hl {0.011}} & {\\hl {0.012}} & 0.003 & 0.254 & & & & & & & & & & & & & & & \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Branching Adaptive Surrogate Search Optimization (BASSO)", "authors": ["Pariyakorn Maneekul", "Zelda B. Zabinsky", "Giulia Pedrielli"], "url": "https://arxiv.org/abs/2504.18002v1", "attribution": "\"Branching Adaptive Surrogate Search Optimization (BASSO)\" by Pariyakorn Maneekul, Zelda B. Zabinsky, and Giulia Pedrielli, arXiv:2504.18002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.20298v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Credit scoring: performance of predictive models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccc}\\hline\n\\textbf{Model} & \\textbf{BA} & \\textbf{Acc.} & \\textbf{SP} & \\textbf{EO} & \\textbf{EOd} & \\textbf{PP} & \\textbf{PE} & \\textbf{TE} & \\textbf{ABROCA} \\\\ \\hline\n\\textbf{DT} & 0.9761 & 0.9924 & 0.0345 & 0.0032 & 0.0269 & 0.0003 & 0.0238 & 0.8333 & 0.0132 \\\\\n\\textbf{NB} & 0.8785 & 0.9585 & 0.0448 & 0.0149 & 0.1126 & 0.0003 & 0.0976 & 0.7148 & 0.0088 \\\\\n\\textbf{MLP} & 0.9923 & 0.9931 & 0.0299 & 0.0001 & 0.0124 & 0.0013 & 0.0122 & NaN & \\textbf{0.0005} \\\\\n\\textbf{kNN} & 0.8447 & 0.9581 & 0.0460 & 0.0236 & 0.0596 & 0.0082 & 0.0359 & 0.7267 & 0.0238 \\\\\\hdashline\n\\textbf{DIR-DT} & 0.9713 & 0.9908 & 0.0361 & 0.0009 & 0.0764 & 0.0043 & 0.0754 & 1.0286 & 0.0377 \\\\\n\\textbf{DIR-NB} & 0.9612 & 0.9292 & 0.0934 & 0.0702 & 0.0702 & \\textbf{0.0} & \\textbf{0.0} & NaN & 0.0064 \\\\\n\\textbf{DIR-MLP} & 0.9820 & 0.9924 & 0.0284 & 0.0001 & 0.0218 & 0.0029 & 0.0217 & -3.6667 & 0.0006 \\\\\n\\textbf{DIR-kNN} & 0.8378 & 0.9562 & 0.0503 & 0.0267 & 0.0868 & 0.0068 & 0.0601 & 0.8279 & 0.0222 \\\\\n\\textbf{LFR-DT} & 0.4674 & 0.7632 & 0.0143 & 0.0191 & 0.0360 & 0.0365 & 0.0169 & -0.7024 & 0.0057 \\\\\n\\textbf{LFR-NB} & 0.4683 & 0.8549 & -0.0611 & 0.0642 & 0.0642 & 0.0276 & \\textbf{0.0} & -1.0684 & 0.0235 \\\\\n\\textbf{LFR-MLP} & 0.5 & 0.9128 & \\textbf{0.0} & \\textbf{0.0} & \\textbf{0.0} & 0.0312 & \\textbf{0.0} & \\textbf{0.0} & 0.0644 \\\\\n\\textbf{LFR-kNN} & 0.4674 & 0.7632 & 0.0143 & 0.0191 & 0.0360 & 0.0365 & 0.0169 & -0.7024 & 0.0232 \\\\\\hdashline\n\\textbf{AdaFair} & \\textbf{0.9927} & \\textbf{0.9939} & 0.0319 & 0.0023 & 0.0146 & 0.0014 & 0.0123 & NaN & 0.0073 \\\\\n\\textbf{Agarwal's} & 0.9077 & 0.9649 & 0.0421 & 0.0157 & 0.0442 & 0.0038 & 0.0284 & 0.7200 & 0.0405 \\\\\\hdashline\n\\textbf{EOP-DT} & 0.9761 & 0.9924 & 0.0345 & 0.0032 & 0.0269 & 0.0030 & 0.0238 & 0.8333 & NaN \\\\\n\\textbf{EOP-NB} & 0.8598 & 0.9532 & 0.0273 & 0.0012 & 0.0498 & 0.0053 & 0.0486 & -0.2118 & NaN \\\\\n\\textbf{EOP-MLP} & 0.9901 & 0.9928 & 0.0293 & 0.0001 & 0.0185 & 0.0020 & 0.0184 & NaN & NaN \\\\\n\\textbf{EOP-kNN} & 0.8317 & 0.9524 & 0.0311 & 0.0126 & 0.0286 & 0.0129 & 0.0160 & 0.1192 & NaN \\\\\n\\textbf{CEP-DT} & 0.9761 & 0.9924 & 0.0345 & 0.0032 & 0.0270 & 0.0034 & 0.0238 & 0.8333 & NaN \\\\\n\\textbf{CEP-NB} & 0.8617 & 0.9566 & 0.0561 & 0.0182 & 0.2371 & 0.0081 & 0.2189 & 1.0442 & NaN \\\\\n\\textbf{CEP-MLP} & 0.9901 & 0.9928 & 0.0309 & 0.0001 & 0.0030 & 0.0002 & 0.0029 & -1.000 & NaN \\\\\n\\textbf{CEP-kNN} & 0.8229 & 0.9543 & 0.0563 & 0.0236 & 0.2111 & 0.0022 & 0.1875 & 0.7728 & NaN \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An experimental study on fairness-aware machine learning for credit scoring problem", "authors": ["Huyen Giang Thi Thu", "Thang Viet Doan", "Tai Le Quy"], "url": "https://arxiv.org/abs/2412.20298v1", "attribution": "\"An experimental study on fairness-aware machine learning for credit scoring problem\" by Huyen Giang Thi Thu, Thang Viet Doan, and Tai Le Quy, arXiv:2412.20298v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.17099v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Revenue Impact by AI Component}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n\\toprule\n\\textbf{AI Component} & \\textbf{Revenue Increase (\\%)} \\\\\n\\midrule\nWeather Prediction & +61.8 \\\\\nPositioning Optimization & +23.7 \\\\\nRoute Optimization & +12.4 \\\\\nDynamic Pricing Integration & +8.7 \\\\\nSystem Integration Effects & +0.7 \\\\\n\\midrule\n\\textbf{Total Weather-Aware AI Impact} & \\textbf{+107.3} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity", "authors": ["Tatsuru Kikuchi"], "url": "https://arxiv.org/abs/2507.17099v1", "attribution": "\"Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity\" by Tatsuru Kikuchi, arXiv:2507.17099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.04932v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|} \n\\hline \n&$f_{M1}$&$f_{M2}$\\\\\n\\hline\n$n$&0.8037121822811545&4.764615814550553\\\\\n\\hline\n$u_x$&-0.3403147128006618&0.05740548475117823\\\\\n\\hline\n$u_y$&0&0\\\\\n\\hline\n$u_z$&0&0\\\\\n\\hline\n$T$&0.1033754314349305&3.442950196134546\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{$n=\\pi^{3/2}$, $\\overline{\\mathbf{v}}=\\mathbf{0}$, and $T=3$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A low-rank, high-order implicit-explicit integrator for three-dimensional convection-diffusion equations", "authors": ["Joseph Nakao", "Gianluca Ceruti", "Lukas Einkemmer"], "url": "https://arxiv.org/abs/2503.04932v2", "attribution": "\"A low-rank, high-order implicit-explicit integrator for three-dimensional convection-diffusion equations\" by Joseph Nakao, Gianluca Ceruti, and Lukas Einkemmer, arXiv:2503.04932v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.10670v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n \\hline \n \\multicolumn{4}{|c|}{} & $(P_1)$ & $(P_2)$ \\\\\n \\hline\n \\hline\n $\\Delta_c$ & $N_c$ & $\\overline{d}_c$ & $Total \\ dose$ & $V(31)$ & $V(31)$ \\\\\n $day$ & & $mg/kg$ & $mg/kg$ & $mm^3$ & $mm^3$ \\\\\n \\hline $5$ & $6$ & $26.67$ & $160$ & $13378$ & $15413$\\\\\n \\hline $3$ & $10$ & $16$ & $160$ & $12956$ & $15238$\\\\\n \\hline $2$ & $14$ & $10.67$ & $149.33$ & $13258$ & $15414$\\\\\n \\hline $1$ & $28$ & $5.33$ & $149.33$ & $12968$ & $15290$\\\\\n \\hline $0.5$ & $55$ & $2.67$ & $146.67$ & $12962$ & $15297$\\\\\n \\hline $0.33$ & $82$ & $1.78$ & $145.78$ & $12971$ & $15304$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Results for the cytotoxic discrete treatment starting at $t_1 = 3$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A mathematical perspective on the paradox that chemotherapy sometimes works backwards", "authors": ["Luis A. Fernández", "Isabel Lasheras", "Cecilia Pola"], "url": "https://arxiv.org/abs/2503.10670v1", "attribution": "\"A mathematical perspective on the paradox that chemotherapy sometimes works backwards\" by Luis A. Fernández, Isabel Lasheras, and Cecilia Pola, arXiv:2503.10670v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18121v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}%{@{} l*{5}{S} @{}}\n \\toprule\n {$\\epsilon$} & {0.1} & {$10^{-1/2}$} & {1} & {$10^{1/2}$} & {10} \\\\ \n \\midrule\n Laplace & 1.828 & 2.095 & 2.269 & 2.311 & 1.973 \\\\ \n TuLap & 2.405 & 3.324 & 3.877 & 4.06 & 4.076 \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Variance ratio on population mean}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Optimal Survey Design for Private Mean Estimation", "authors": ["Yu-Wei Chen", "Raghu Pasupathy", "Jordan A. Awan"], "url": "https://arxiv.org/abs/2501.18121v1", "attribution": "\"Optimal Survey Design for Private Mean Estimation\" by Yu-Wei Chen, Raghu Pasupathy, and Jordan A. Awan, arXiv:2501.18121v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11881v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\hline\n Method & Input & Hidden layer 1 & Hidden layer 2 & Output\\\\\n\\hline\n\\hline\n LSTM & (6,1) & 32 & 32 & (1,4)\\\\\n \n BD-LSTM & (6,1) & 32 & 16 & (1,4) \\\\\n \n ED-LSTM & (6,1) & 32 & - & (1,4) \\\\\n \n\\hline &\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Respective LSTM model topologies for the univariate case.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Deep learning via LSTM models for COVID-19 infection forecasting in India", "authors": ["Rohitash Chandra", "Ayush Jain", "Divyanshu Singh Chauhan"], "url": "https://arxiv.org/abs/2101.11881v2", "attribution": "\"Deep learning via LSTM models for COVID-19 infection forecasting in India\" by Rohitash Chandra, Ayush Jain, and Divyanshu Singh Chauhan, arXiv:2101.11881v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00675v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\n\\textbf{Task} & \\textbf{Accuracy} \\\\ \\hline\nTrained on 2881 twits, & 49.5\\% \\\\ \nTested on all Alana dataset & \\\\ \\hline\nIncreasing the number of the trees & 70.2\\% \\\\ \nfrom 25 to 2000 & \\\\ \\hline\nCombining Vader dataset & \\\\ \n(600 sentiments words) &\\textbf{ 71.1\\%} \\\\ \nwith Alana dataset, 25 trees & \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Random Forest Model Experiments}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Sentiment Analysis for Open Domain Conversational Agent", "authors": ["Mohamad Alissa", "Issa Haddad", "Jonathan Meyer", "Jade Obeid", "Nicolas Wiecek", "Sukrit Wongariyakavee"], "url": "https://arxiv.org/abs/2101.00675v2", "attribution": "\"Sentiment Analysis for Open Domain Conversational Agent\" by Mohamad Alissa, Issa Haddad, Jonathan Meyer, Jade Obeid, Nicolas Wiecek, and Sukrit Wongariyakavee, arXiv:2101.00675v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14416v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\t\\small\\textbf{Case}& \t\\small\\textbf{Subcase}& \\small\\textbf{$\\boldsymbol{O_1}$}& \\small\\textbf{$\\boldsymbol{O_2}$} & \\small\\textbf{Global} \\\\\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{1.1}}\n\t\t\t&\n\t\t\t$b_1>0$\n\t\t\t&\n\t\t\t\\phantom{h} $L^1_2$ \\phantom{h}\n\t\t\t&\n\t\t\t\\phantom{h} $L^2_1$ \\phantom{h}\n\t\t\t&\n\t\t\tG1 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$b_1<0$\n\t\t\t&\n\t\t\t$L^1_4$\n\t\t\t&\n\t\t\t$L^2_3$\n\t\t\t&\n\t\t\tG2\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{1.2}}\n\t\t\t&\n\t\t\t$b_1>0$\n\t\t\t&\n\t\t\t$L^1_2$\n\t\t\t&\n\t\t\t$L^2_1$\n\t\t\t&\n\t\t\tG3, G4 or G5 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$b_1<0$\n\t\t\t&\n\t\t\t$L^1_4$\n\t\t\t&\n\t\t\t$L^2_3$\n\t\t\t&\n\t\t\tG6\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{1.3}}\n\t\t\t&\n\t\t\t$b_1>0$\n\t\t\t&\n\t\t\t$L^1_1$\n\t\t\t&\n\t\t\t$L^2_2$\n\t\t\t&\n\t\t\tG7, G8 or G9 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$b_1<0$\n\t\t\t&\n\t\t\t$L^1_3$\n\t\t\t&\n\t\t\t$L^2_4$\n\t\t\t&\n\t\t\tG10\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{1.4}}\n\t\t\t&\n\t\t\t$b_1>0$\n\t\t\t&\n\t\t\t$L^1_2$\n\t\t\t&\n\t\t\t$L^2_1$\n\t\t\t&\n\t\t\tG11 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$b_1<0$\n\t\t\t&\n\t\t\t$L^1_4$\n\t\t\t&\n\t\t\t$L^2_3$\n\t\t\t&\n\t\t\tG12\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{1.5}}\n\t\t\t&\n\t\t\t$b_1>0$\n\t\t\t&\n\t\t\t$L^1_1$\n\t\t\t&\n\t\t\t$L^2_2$\n\t\t\t&\n\t\t\tG13 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$b_1<0$\n\t\t\t&\n\t\t\t$L^1_3$\n\t\t\t&\n\t\t\t$L^2_4$\n\t\t\t&\n\t\t\tG14\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{2.1}}\n\t\t\t&\n\t\t\t$b_1>0$\n\t\t\t&\n\t\t\t$L^1_7$\n\t\t\t&\n\t\t\t$L^2_1$\n\t\t\t&\n\t\t\tG15 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$b_1<0$\n\t\t\t&\n\t\t\t$L^1_8$\n\t\t\t&\n\t\t\t$L^2_3$\n\t\t\t&\n\t\t\tG16\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{2.2}}\n\t\t\t&\n\t\t\t$b_1>0$\n\t\t\t&\n\t\t\t$L^1_7$\n\t\t\t&\n\t\t\t$L^2_1$\n\t\t\t&\n\t\t\tG17 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$b_1<0$\n\t\t\t&\n\t\t\t$L^1_8$\n\t\t\t&\n\t\t\t$L^2_3$\n\t\t\t&\n\t\t\tG18\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{2.3}}\n\t\t\t&\n\t\t\t$b_1>0$\n\t\t\t&\n\t\t\t$L^1_5$\n\t\t\t&\n\t\t\t$L^2_2$\n\t\t\t&\n\t\t\tG19 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$b_1<0$\n\t\t\t&\n\t\t\t$L^1_6$\n\t\t\t&\n\t\t\t$L^2_4$\n\t\t\t&\n\t\t\tG20\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{2.4}}\n\t\t\t&\n\t\t\t$b_1>0$\n\t\t\t&\n\t\t\t$L^1_7$\n\t\t\t&\n\t\t\t$L^2_1$\n\t\t\t&\n\t\t\tG21 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$b_1<0$\n\t\t\t&\n\t\t\t$L^1_8$\n\t\t\t&\n\t\t\t$L^2_3$\n\t\t\t&\n\t\t\tG22\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{3.1}}\n\t\t\t&\n\t\t\t$b_1>0$\n\t\t\t&\n\t\t\t$L^1_2$\n\t\t\t&\n\t\t\t$L^2_6$\n\t\t\t&\n\t\t\tG23 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$b_1<0$\n\t\t\t&\n\t\t\t$L^1_4$\n\t\t\t&\n\t\t\t$L^2_{8}$\n\t\t\t&\n\t\t\tG24\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{3.2}}\n\t\t\t&\n\t\t\t$b_1>0$\n\t\t\t&\n\t\t\t$L^1_1$\n\t\t\t&\n\t\t\t$L^2_5$\n\t\t\t&\n\t\t\tG25 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$b_1<0$\n\t\t\t&\n\t\t\t$L^1_3$\n\t\t\t&\n\t\t\t$L^2_7$\n\t\t\t&\n\t\t\tG26\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{3.3}}\n\t\t\t&\n\t\t\t$b_1>0$\n\t\t\t&\n\t\t\t$L^1_2$\n\t\t\t&\n\t\t\t$L^2_9$\n\t\t\t&\n\t\t\tG27 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$b_1<0$\n\t\t\t&\n\t\t\t$L^1_4$\n\t\t\t&\n\t\t\t$L^2_{10}$\n\t\t\t&\n\t\t\tG28\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{3.4}}\n\t\t\t&\n\t\t\t$b_1>0$\n\t\t\t&\n\t\t\t$L^1_1$\n\t\t\t&\n\t\t\t$L^2_5$\n\t\t\t&\n\t\t\tG29 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$b_1<0$\n\t\t\t&\n\t\t\t$L^1_3$\n\t\t\t&\n\t\t\t$L^2_7$\n\t\t\t&\n\t\t\tG30\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{4.1}}\n\t\t\t&\n\t\t\t$b_1>0$\n\t\t\t&\n\t\t\t$L^1_7$\n\t\t\t&\n\t\t\t$L^2_6$\n\t\t\t&\n\t\t\tG31\\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$b_1<0$\n\t\t\t&\n\t\t\t$L^1_8$\n\t\t\t&\n\t\t\t$L^2_{8}$\n\t\t\t&\n\t\t\tG32\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{4}{1cm}{\\centering{4.2}}\n\t\t\t&\n\t\t\t$b_1>0$, $c_0-b_0>0$\n\t\t\t&\n\t\t\t$L^1_5$\n\t\t\t&\n\t\t\t$L^2_5$\n\t\t\t&\n\t\t\tG33\\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{4}{*}\n\t\t\t&\n\t\t\t$b_1>0$, $c_0-b_0<0$\n\t\t\t&\n\t\t\t$L^1_7$\n\t\t\t&\n\t\t\t$L^2_9$\n\t\t\t&\n\t\t\tG34\\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{4}{*}\n\t\t\t&\n\t\t\t$b_1<0$, $c_0-b_0>0$\n\t\t\t&\n\t\t\t$L^1_6$\n\t\t\t&\n\t\t\t$L^2_7$\n\t\t\t&\n\t\t\tG35\\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{4}{*}\n\t\t\t&\n\t\t\t$b_1<0$, $c_0-b_0<0$\n\t\t\t&\n\t\t\t$L^1_8$\n\t\t\t&\n\t\t\t$L^2_{10}$\n\t\t\t&\n\t\t\tG36\\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Classification of the global phase portraits of systems .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Planar Kolmogorov systems with infinitely many singular points at infinity", "authors": ["Érika Diz-Pita", "Jaume Llibre", "M. Victoria Otero-Espinar"], "url": "https://arxiv.org/abs/2501.14416v1", "attribution": "\"Planar Kolmogorov systems with infinitely many singular points at infinity\" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.14416v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11311v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{SIMULATION SETUP PARAMETER}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\t\\textbf{Parameters} & \\textbf{Value}\\\\\n\t\t\t\\hline\n\t\t\tClassifier \n\t\t\tTraining examples & 1000\\\\\n\t\t\tWeight parameters & $\\alpha$=0.4;$\\beta$=0.6\\\\\n\t\t\tEpoch & 10\\\\\n\t\t\tIP & localhost\\\\\n\t\t\tShards & 8\\\\\n\t\t\tFL devices & 10 \\\\ \n\t\t\tPoW difficulty & 4\\\\\n\t\t\tPredefined parameters & $w_1$=$w_2$=$a$=0.5; te=60min; u=1\\\\\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Towards a Secure and Reliable Federated Learning using Blockchain", "authors": ["Hajar Moudoud", "Soumaya Cherkaoui", "Lyes Khoukhi"], "url": "https://arxiv.org/abs/2201.11311v1", "attribution": "\"Towards a Secure and Reliable Federated Learning using Blockchain\" by Hajar Moudoud, Soumaya Cherkaoui, and Lyes Khoukhi, arXiv:2201.11311v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.16378v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Descriptive statistics of cryptocurrency closing prices}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\\hline\n & BTC & ETH & ADA & BNB & XRP \\\\\n\\hline\ncount & 2001.00 & 2001.00 & 2001.00 & 2001.00 & 2001.00 \\\\\nmean & 44053.63 & 2096.51 & 0.65 & 352.68 & 0.80 \\\\\nstd & 28135.05 & 1097.27 & 0.55 & 212.10 & 0.70 \\\\\nmin & 4916.78 & 110.23 & 0.02 & 9.42 & 0.14 \\\\\n25\\% & 22201.44 & 1472.15 & 0.31 & 234.52 & 0.39 \\\\\n50\\% & 38184.86 & 1967.67 & 0.46 & 316.69 & 0.54 \\\\\n75\\% & 60863.56 & 2945.17 & 0.86 & 558.23 & 0.82 \\\\\nmax & 122184.23 & 4810.97 & 2.97 & 844.17 & 3.55 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies", "authors": ["Qizhao Chen"], "url": "https://arxiv.org/abs/2508.16378v1", "attribution": "\"Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies\" by Qizhao Chen, arXiv:2508.16378v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|ccccccccc|c}\n&\t$g_i^3$&\t4&\t2&\t0&\t6&\t4&\t2&\t8&\t6&\t4&\t\\\\\nIteration&\t$i$&\t$L_1$&\t$L_2$&\t$L_3$&\t$L_4$&\t$L_5$&\t$L_6$&\t$L_7$&\t$L_8$&\t$L_9$&\t$SEL$\\\\\n\\hline\n0&\t---&\t$\\infty$&\t$\\infty$&\t$\\infty$&\t$\\infty$&\t0&\t$\\infty$&\t$\\infty$&\t$\\infty$&\t$\\infty$&\t$\\{ 5 \\}$\\\\\n1&\t5&\t$\\infty$&\t4&\t$\\infty$&\t3&\t0&\t3&\t$\\infty$&\t2&\t$\\infty$&\t$\\{ 2, 4, 6, 8 \\}$\\\\\n2&\t6&\t$\\infty$&\t4&\t10&\t3&\t0&\t3&\t$\\infty$&\t2&\t11&\t$\\{ 2, 3, 4, 8, 9 \\}$\\\\\n3&\t2&\t6&\t4&\t9&\t3&\t0&\t3&\t$\\infty$&\t2&\t11&\t$\\{ 1, 3, 4, 8, 9 \\}$\\\\\n4&\t8&\t6&\t4&\t9&\t3&\t0&\t3&\t6&\t2&\t4&\t$\\{ 1, 3, 4, 7, 9 \\}$\\\\\n5&\t9&\t6&\t4&\t9&\t3&\t0&\t3&\t6&\t2&\t4&\t$\\{ 1, 3, 4, 7 \\}$\\\\\n6&\t3&\t6&\t4&\t9&\t3&\t0&\t3&\t6&\t2&\t4&\t$\\{ 1, 4, 7 \\}$\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16534v2_tex_table16.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The performance of \\textbf{large-sized classification} task (\\emph{numerical features only}).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n & \\textbf{361069} & \\textbf{361068} & \\textbf{361061} & \\textbf{361274} & \\textbf{Ranking} \\\\\n\\midrule\n\\multicolumn{6}{c}{Default} \\\\\n\\midrule\nDOFEN (ours) & \\underline{0.7306} & \\underline{0.9480} & 0.8222 & \\underline{0.8018} & $3.25 \\pm 5.22$ \\\\\nTrompt & 0.7213 & 0.9468 & \\underline{0.9004} & 0.7954 & $2.88 \\pm 4.35$ \\\\\nGRANDE & 0.7248 & 0.9425 & 0.8315 & 0.7979 & $5.00 \\pm 3.05$ \\\\\nFT-Transformer & 0.6960 & 0.9403 & 0.8983 & 0.7586 & $8.25 \\pm 4.16$ \\\\\nResNet & 0.6988 & 0.9409 & 0.8801 & 0.7358 & $8.50 \\pm 4.04$ \\\\\nMLP & nan & nan & nan & nan & $nan \\pm nan$ \\\\\nSAINT & 0.7181 & 0.9436 & 0.8694 & 0.7860 & $6.00 \\pm 1.30$ \\\\\nNODE & 0.7247 & 0.9461 & 0.8886 & 0.7946 & $3.75 \\pm 2.92$ \\\\\nCatBoost & 0.7261 & 0.9432 & 0.8377 & 0.7954 & $4.38 \\pm 2.77$ \\\\\nLightGBM & 0.7212 & 0.9371 & 0.8071 & 0.7870 & $8.00 \\pm 2.30$ \\\\\nXGBoost & 0.7164 & 0.9367 & 0.8361 & 0.7828 & $8.50 \\pm 2.61$ \\\\\nHistGradientBoostingTree & nan & nan & nan & nan & $nan \\pm nan$ \\\\\nGradientBoostingTree & 0.7103 & 0.9225 & 0.7698 & 0.7718 & $11.00 \\pm 4.58$ \\\\\nRandomForest & 0.7158 & 0.9308 & 0.8767 & 0.7797 & $8.50 \\pm 3.29$ \\\\\n\\midrule\n\\multicolumn{6}{c}{Searched} \\\\\n\\midrule\nDOFEN (ours) & \\underline{0.7355} & \\underline{0.9509} & 0.9033 & \\underline{0.8070} & $1.75 \\pm 4.76$ \\\\\nTrompt & 0.7286 & 0.9436 & \\underline{0.9127} & 0.7988 & $4.25 \\pm 3.63$ \\\\\nGRANDE & 0.7266 & 0.9455 & 0.8524 & 0.7981 & $6.25 \\pm 3.51$ \\\\\nFT-Transformer & 0.7294 & 0.9445 & 0.9060 & 0.7962 & $4.00 \\pm 3.11$ \\\\\nResNet & 0.7229 & 0.9444 & 0.8930 & 0.7832 & $10.00 \\pm 4.66$ \\\\\nMLP & nan & nan & nan & nan & $nan \\pm nan$ \\\\\nSAINT & 0.7259 & 0.9441 & 0.8954 & 0.7930 & $8.50 \\pm 1.92$ \\\\\nNODE & 0.7262 & 0.9475 & 0.8982 & 0.7946 & $5.50 \\pm 2.35$ \\\\\nCatBoost & 0.7300 & 0.9446 & 0.9021 & 0.7994 & $3.25 \\pm 3.70$ \\\\\nLightGBM & 0.7243 & 0.9435 & 0.8968 & 0.7939 & $9.25 \\pm 2.92$ \\\\\nXGBoost & 0.7280 & 0.9436 & 0.8942 & 0.7963 & $7.00 \\pm 2.17$ \\\\\nHistGradientBoostingTree & nan & nan & nan & nan & $nan \\pm nan$ \\\\\nGradientBoostingTree & 0.7249 & 0.9401 & 0.8976 & 0.7943 & $8.75 \\pm 2.59$ \\\\\nRandomForest & 0.7199 & 0.9353 & 0.9059 & 0.7885 & $9.50 \\pm 5.05$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DOFEN: Deep Oblivious Forest ENsemble", "authors": ["Kuan-Yu Chen", "Ping-Han Chiang", "Hsin-Rung Chou", "Chih-Sheng Chen", "Tien-Hao Chang"], "url": "https://arxiv.org/abs/2412.16534v2", "attribution": "\"DOFEN: Deep Oblivious Forest ENsemble\" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.05590v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccc}\n \\hline\\hline\n \n & Method & C2ST & MMD & MED-DIST & MEAN-ERR \\\\\n \\hline\n \n \\multirow{2}{*}{\\textit{\\shortstack{Bernoulli \\\\ GLM}}} & SNPE-C & 0.749 $\\pm$ 0.017 & 0.210 $\\pm$ 0.024 & \\textbf{11.454} $\\pm$ 0.255 & 0.188 $\\pm$ 0.117 \\\\\n & ASNPE & \\textbf{0.725} $\\pm$ 0.012 & \\textbf{0.146} $\\pm$ 0.057 & 11.993 $\\pm$ 0.172 & \\textbf{0.150} $\\pm$ 0.085 \\\\\n \\hline\n \n \\multirow{2}{*}{\\textit{\\shortstack{SLCP \\\\ distractors}}} & SNPE-C & 0.987 $\\pm$ 0.001 & 0.172 $\\pm$ 0.001 & 16.716 $\\pm$ 1.014 & \\textbf{0.899} $\\pm$ 0.065 \\\\\n & ASNPE & \\textbf{0.985} $\\pm$ 0.002 & \\textbf{0.148} $\\pm$ 0.022 & \\textbf{16.547} $\\pm$ 0.499 & 0.906 $\\pm$ 0.220 \\\\\n \\hline\n \n \\multirow{2}{*}{\\textit{\\shortstack{Gaussian \\\\ mixture}}} & SNPE-C & 0.773 $\\pm$ 0.009 & 0.167 $\\pm$ 0.006 & 1.051 $\\pm$ 0.037 & 0.532 $\\pm$ 0.074 \\\\\n & ASNPE & \\textbf{0.771} $\\pm$ 0.006 & \\textbf{0.150} $\\pm$ 0.025 & \\textbf{1.010} $\\pm$ 0.066 & \\textbf{0.440} $\\pm$ 0.164 \\\\\n \\hline\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Results comparing SNPE-C and ASNPE for various metrics on the \\textit{Bernoulli GLM}, \\textit{SLCP distractors}, and \\textit{Gaussian mixture} tasks from . Experimentation details, metrics, and associated plots can be found in Appendix~.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference", "authors": ["Sam Griesemer", "Defu Cao", "Zijun Cui", "Carolina Osorio", "Yan Liu"], "url": "https://arxiv.org/abs/2412.05590v1", "attribution": "\"Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference\" by Sam Griesemer, Defu Cao, Zijun Cui, Carolina Osorio, and Yan Liu, arXiv:2412.05590v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.04878v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|} \\hline\n$z$ & $a$ & supersingular primes \\\\ \\hline\n$\\left[15, 15, 15, 1\\right]$ & $\\left(1, 13, 1\\right)$ & $p \\equiv 14 \\bmod{15}$ \\\\ \\hline\n$\\left[16, 16, 8, 1\\right]$ & $\\left(1, 13, 2\\right), \\left(1, 1, 14\\right)$ & $p \\equiv 15 \\bmod{16}$ \\\\ \n & $\\left(1, 9, 6\\right)$ & $p \\equiv 7, 15 \\bmod{16}$ \\\\ \\hline\n$\\left[18, 9, 6, 1\\right]$ & $\\left(1, 14, 3\\right), \\left(1, 2, 15\\right)$ & $p \\equiv 2 \\bmod{3}$ \\\\ \\hline\n$\\left[20, 10, 4, 1\\right]$ & $\\left(1, 14, 5\\right)$ & $p \\equiv 19 \\bmod{20}$ \\\\ \\hline\n$\\left[21, 21, 3, 1\\right]$ & $\\left(1, 13, 7\\right)$ & $p \\equiv 2 \\bmod{3}$ \\\\ \\hline\n$\\left[24, 8, 3, 1\\right]$ & $\\left(1, 15, 8\\right)$ & $p \\equiv 23 \\bmod{24}$ \\\\ \\hline\n$\\left[28, 28, 2, 1\\right]$ & $\\left(1, 13, 14\\right)$ & $p \\equiv 3 \\bmod{4}$ \\\\ \\hline\n$\\left[30, 15, 2, 1\\right]$ & $\\left(1, 14, 15\\right)$ & $p \\equiv 29 \\bmod{30}$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Cyclic genus $7$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Supersingular curves via the Shimura--Taniyama method", "authors": ["Jeremy Booher", "Rachel Pries"], "url": "https://arxiv.org/abs/2503.04878v2", "attribution": "\"Supersingular curves via the Shimura--Taniyama method\" by Jeremy Booher and Rachel Pries, arXiv:2503.04878v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.10787v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results on the Radiology Model. (Sym-- Model using embeddings from the $S$-model. PT-- VGG16 uses pre-trained weights from the end-to-end model)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nModel & Accuracy & AUC \\\\ \n\\midrule\nVGG-16 & 0.83 & 0.96 \\\\\nVGG-16 + Sym & 0.86 & 0.97 \\\\\nVGG-16 + Sym (PT) & \\textbf{0.88} & \\textbf{0.99} \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays", "authors": ["Rishab Khincha", "Soundarya Krishnan", "Tirtharaj Dash", "Lovekesh Vig", "Ashwin Srinivasan"], "url": "https://arxiv.org/abs/2012.10787v2", "attribution": "\"Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays\" by Rishab Khincha, Soundarya Krishnan, Tirtharaj Dash, Lovekesh Vig, and Ashwin Srinivasan, arXiv:2012.10787v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.15041v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n\\textbf{Measure} & \\textbf{Expert 1} & \\textbf{Expert 2} & \\textbf{Expert 3} & \\textbf{Gold} & \\textbf{Model} \\\\\n\\midrule\nAverage & 2.22 & 2.36 & 2.29 & 2.91 & 2.32 \\\\\nStandard deviation & 1.95 & 2.12 & 2.04 & 2.35 & 1.15 \\\\\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Narrative counts per document for human experts and \\texttt{GPT-4o}.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Identifying economic narratives in large text corpora -- An integrated approach using Large Language Models", "authors": ["Tobias Schmidt", "Kai-Robin Lange", "Matthias Reccius", "Henrik Müller", "Michael Roos", "Carsten Jentsch"], "url": "https://arxiv.org/abs/2506.15041v1", "attribution": "\"Identifying economic narratives in large text corpora -- An integrated approach using Large Language Models\" by Tobias Schmidt, Kai-Robin Lange, Matthias Reccius, Henrik Müller, Michael Roos, and Carsten Jentsch, arXiv:2506.15041v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23211v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of coverage probabilities and average of confidence interval lengths for two methods. The results for the lengths are in the parentheses.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc} \n\\hline\nScenario I & Truth & method & $90\\%$ & $95\\%$ & $99\\%$ \\\\ \\hline\\hline\n$(\\theta=0.9, \\phi=-0.5)$ & $T/2$ & NSP & 0.370 & 0.370 & 0.329 \\\\\n& & & (279.130) & (275.920) & (191.210)\\\\\n&& our method & 0.920 & 0.960 & 0.970 \\\\\n& & & (50.220) & (60.420) & (85.850) \\\\\n\\hline\n$(\\theta=0.9, \\phi=0.5)$ & $T/2$ & NSP & 0.273 & 0.343 & 0.271 \\\\\n& & & (273.790) & (243.440) & (189.990) \\\\\n&& our method & 0.940 & 0.970 & 0.970 \\\\ \n& & & (50.860) & (61.200) & (86.750) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models", "authors": ["Sepideh Mosaferi", "Abolfazl Safikhani", "Peiliang Bai"], "url": "https://arxiv.org/abs/2503.23211v2", "attribution": "\"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models\" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.16187v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc}\n Variable & Source & Resolution\\\\\n \\hline\n Evapotranspiration (ET) & ECOSTRESS & 70m \\\\\n Evaportaive Stress Index (ESI) & ECOSTRESS & 70m \\\\\n Water Use Efficiency (WUE) & ECOSTRESS & 70m \\\\\n Land Surface Temperature (LST) & ECOSTRESS & 70m \\\\\n Normalized Differened Vegetation Index (NDVI) & Harmonized Landsat Sentinel &30m \\\\\n Leaf Area Index (LAI) & MODIS & 500m \\\\\n AM Soil Moisture (AM SM) & SMAP & 9km \\\\\n PM Soil Moisture (PM SM) & SMAP & 9km \\\\\n Daily Average Dew Point Temperature (TDMEAN) & PRISM & 4km \\\\\n Daily Average Air Temperature (TMEAN) & PRISM & 4km \\\\\n Vapor Pressure Deficit Maximum (VPDMAX) & PRISM & 4km\\\\\n Vapor Pressure Deficit Minimum (VPDMIN) & PRISM & 4km\\\\\n Elevation & Digital Elevation Model & 30m\\\\\n Aspect &Digital Elevation Model &30m\\\\\n Slope& Digital Elevation Model & 30m\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Variables used as covariates in the Super Learner model. With the exception of the final three variables, all variable were included as both their current state and temporal trend.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modelling wildland fire burn severity in California using a spatial Super Learner approach", "authors": ["Nicholas Simafranca", "Bryant Willoughby", "Erin O'Neil", "Sophie Farr", "Brian J Reich", "Naomi Giertych", "Margaret Johnson", "Madeleine Pascolini-Campbell"], "url": "https://arxiv.org/abs/2311.16187v1", "attribution": "\"Modelling wildland fire burn severity in California using a spatial Super Learner approach\" by Nicholas Simafranca, Bryant Willoughby, Erin O'Neil, Sophie Farr, Brian J Reich, Naomi Giertych, Margaret Johnson, and Madeleine Pascolini-Campbell, arXiv:2311.16187v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09954v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small{Statistics for the case of crosshole GPR data in a multi-Gaussian domain when considering the 1000 independent test models. The comparison is made between the predicted model (Predicted \\#1) and the closest training model in data space (Closest TR) within the training set of 20,000 examples using the RMSE in data space ( RMSE$_{\\rm data}$), the ${\\rm l}_1$-norm (${\\rm l}_1$) and the structural similarity index (SSIM). For each metric, the minimum (Min), median (Median) and maximum (Max) values are reported together with the 10$^{\\rm th}$, 25$^{\\rm th}$, 75$^{\\rm th}$ and 90$^{\\rm th}$ percentiles (P10, P25, P75, P90), respectively. The TR size variable signifies the number of training examples used to train \\textit{vec2pix}.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}%\n\t\t\t\\hline\n\t\t\tModel & TR size & Min & P10 & P25 & Median & P75 & P90 & Max\\\\\n\t\t\t\\hline\n\t\t\t& & & & & & & & \\\\\n\t\t\t\\multicolumn{9}{c}{RMSE$_{\\rm data}$ (ns)} \\\\\n\t\t\tClosest TR & 20,000 & 0.77 & 0.92 & 0.97 & 1.03 & 1.10 & 1.16 & 1.73\\\\\n\t\t\tPredicted & 20,000 & 0.50 & 0.55 & 0.56 & 0.58 & 0.61 & 0.63 & 0.92 \\\\\n\t\t\tPredicted & 10,000 & 0.53 & 0.59 & 0.61 & 0.64 & 0.68 & 0.73 & 1.12 \\\\\n\t\t\tPredicted & 5,000 & 0.56 & 0.63 & 0.67 & 0.81 & 1.08 & 1.39 & 2.24 \\\\\n\t\t\t\\multicolumn{9}{c}{${\\rm l}_1$ (m/ns)} \\\\\n\t\t\tClosest TR & 20,000 & 6.92 & 10.57 & 11.41 & 12.36 & 13.56 & 14.56 & 19.27 \\\\\n\t\t\tPredicted & 20,000 & 3.68 & 5.19 & 5.80 & 6.50 & 7.35 & 8.28 & 12.16 \\\\\n\t\t\tPredicted & 10,000 & 4.29 & 5.77 & 6.47 & 7.24 & 8.16 & 9.05 & 13.48 \\\\\n\t\t\tPredicted & 5,000 & 4.20 & 6.48 & 7.11 & 7.97 & 8.83 & 10.00 & 13.14 \\\\\n\t\t\t\\multicolumn{9}{c}{SSIM (-)}\\\\\n\t\t\tClosest TR & 20,000 & 0.66 & 0.75 & 0.78 & 0.80 & 0.83 & 0.84 & 0.91 \\\\\n\t\t\tPredicted & 20,000 & 0.87 & 0.91 & 0.92 & 0.93 & 0.94 & 0.95 & 0.96 \\\\\n\t\t\tPredicted & 10,000 & 0.84 & 0.89 & 0.91 & 0.92 & 0.93 & 0.94 & 0.96 \\\\\n\t\t\tPredicted & 5,000 & 0.84 & 0.88 & 0.89 & 0.91 & 0.92 & 0.93 & 0.95 \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Approaching geoscientific inverse problems with vector-to-image domain transfer networks", "authors": ["Eric Laloy", "Niklas Linde", "Diederik Jacques"], "url": "https://arxiv.org/abs/1912.09954v3", "attribution": "\"Approaching geoscientific inverse problems with vector-to-image domain transfer networks\" by Eric Laloy, Niklas Linde, and Diederik Jacques, arXiv:1912.09954v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.13719v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MNIST: The comparison of accuracy between standard models (Std., Std.+L1 and Std.+L2) and Min-Max model.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\t\t\\toprule\n\t\t& Std. & Std.+L1 & Std.+L2 & Min-Max \\\\ \\midrule\n\t\tNo attack & \\textbf{0.9861} & 0.9747 & 0.9576 & 0.9844 \\\\\n\t\tFGSM & 0.8152 & 0.7610 & 0.7631 & \\textbf{0.9475} \\\\\n\t\tPGD & 0.5928 & 0.6198 & 0.7130 & \\textbf{0.8369} \\\\\n\t\tCW & 0.5697 & 0.4961 & 0.5403 & \\textbf{0.6892} \\\\\n\t\tMIA & 0.6268 & 0.6190 & \\textbf{0.7181} & 0.6980 \\\\\n\t\tL2BIA & 0.9747 & 0.9659 & 0.9501 & \\textbf{0.9765} \\\\\n\t\tLinfBIA & 0.6046 & 0.6276 & 0.7126 & \\textbf{0.8599} \\\\ \\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks", "authors": ["Haojing Shen", "Sihong Chen", "Ran Wang"], "url": "https://arxiv.org/abs/2011.13719v1", "attribution": "\"A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks\" by Haojing Shen, Sihong Chen, and Ran Wang, arXiv:2011.13719v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.08293v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameter space used for PPO grid search optimization (108 total combinations)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\hline\n\\textbf{Hyperparameter} & \\textbf{Values} & \\textbf{Description} \\\\ \\hline\nlearning\\_rate & 0.0001, 0.0003, 0.001 & Adjusts the step size for model updates. \\\\\ngamma & 0.9, 0.99, 0.999 & Discount factor for future rewards. \\\\\nbatch\\_size & 64, 128 & Number of samples per training step. \\\\\nmax\\_grad\\_norm & 0.5, 0.9 & Clips gradients to stabilize training. \\\\\nent\\_coef & 0, 0.001, 0.01 & Controls the balance between exploration and exploitation. \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning", "authors": ["Alejandro Campoy-Nieves", "Antonio Manjavacas", "Javier Jiménez-Raboso", "Miguel Molina-Solana", "Juan Gómez-Romero"], "url": "https://arxiv.org/abs/2412.08293v1", "attribution": "\"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning\" by Alejandro Campoy-Nieves, Antonio Manjavacas, Javier Jiménez-Raboso, Miguel Molina-Solana, and Juan Gómez-Romero, arXiv:2412.08293v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.01251v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{List of Notations}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|} \\hline % 其中，|c|表示文本居中，文本两边有竖直表线。\n\t\t\t\\bf{Notations} & \\bf{Definition} \\\\ \\hline\t\t\n\t\t\t$N$ & Number of transmit antennas \\\\ \\hline \n\t\t\t$M$ & Number of reflection/refraction elements \\\\ \\hline \n\t\t\t$M_s$ & Number of sensing elements \\\\ \\hline \n\t\t\t$\\mathcal{D}$& Set of spaces \\\\ \\hline\n\t\t\t$\\mathcal{K}_d$& Set of users in space $d$\\\\ \\hline\n\t\t\t$\\mathcal{J}_d$ &Set of targets in space $d$ \\\\ \\hline\n\t\t\t$K_d$ & Number of users in space $d$\\\\ \\hline \n\t\t\t$J_d$ & Number of targets in space $d$\\\\ \\hline \n\t\t\t$L$ & Number of time indexes\\\\ \\hline\n\t\t\t$\\kappa$ & Rician factor\\\\ \\hline\n\t\t\t$\\lambda$ & Carrier wavelength \\\\\\hline\n\t\t\t$\\zeta$ & Antenna or element separation \\\\ \\hline\n\t\t\t$\\mathbf{H}$ & Channel between the BS and the MF-RIS \\\\ \\hline \n\t\t\t$\\mathbf{g}_{k}$ &Channel between the MF-RIS and the $k$-th user \\\\ \\hline \n\t\t\t$\\mathbf{G}_d$ & Multi-target response matrix in space $d$ \\\\ \\hline \n\t\t\t$\\sigma_k^2$ & Noise power for the $k$-th user\\\\ \\hline \n\t\t\t$\\sigma_r^2$ & Noise power for reflection/refraction elements \\\\ \\hline \n\t\t\t$\\sigma_s^2$ & Noise power for sensing elements \\\\ \\hline \n\t\t\t$\\gamma_k$ & Receiving SINR of the $k$-th user \\\\ \\hline\n\t\t\t$\\gamma_d$ & Echo SINR received by the MF-RIS in space $d$ \\\\ \\hline\n\t\t\t$\\gamma_{\\rm{th}}$ &QoS requirement of each user \\\\ \\hline\n\t\t\t$P^{\\rm{max}}_{\\rm{total}}$& Total power budget of the system \\\\ \\hline\n\t\t\t$P^{\\rm{max}}_{\\rm{b}}$ &Maximum transmit power of the BS\\\\ \\hline\n\t\t\t$P^{\\rm{max}}_{\\rm{r}}$& Power budget of the MF-RIS \\\\ \\hline\n\t\t\t$\\mathcal{R}_{\\rm{MF}}$ & Feasible set of the MF-RIS coefficients \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Multi-Functional RIS Integrated Sensing and Communications for 6G Networks", "authors": ["Dongsheng Han", "Peng Wang", "Wanli Ni", "Wen Wang", "Ailing Zheng", "Dusit Niyato", "Naofal Al-Dhahir"], "url": "https://arxiv.org/abs/2412.01251v3", "attribution": "\"Multi-Functional RIS Integrated Sensing and Communications for 6G Networks\" by Dongsheng Han, Peng Wang, Wanli Ni, Wen Wang, Ailing Zheng, Dusit Niyato, and Naofal Al-Dhahir, arXiv:2412.01251v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.19727v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Training time in hours}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\toprule\n & SVGP & DKLGP & TSDiff & RS$^3$GP & VRS$^3$GP\\\\\n \\midrule\n Solar & 1.90 & 2.41 & 2.75 & 0.27 & 0.45\\\\\n Electricity & 2.23 & 1.91 & 4.09 & 0.62 & 0.98 \\\\\n Traffic & - & - & 4.44 & 2.31 & 2.70 \\\\\n Exchange & 0.75 & 0.65 & 2.00 & 0.23 & 0.29 \\\\\n M4 & 1.68 & 1.94 & 1.96 & 0.86 & 1.02 \\\\\n UberTLC & 1.89 & 2.19 & 3.19 & 0.53 & 0.71 \\\\\n KDDCup & 2.03 & 1.55 & 2.10 & 0.65 & 0.93 \\\\\n Wikipedia & - & - & 6.17 & 3.52 & 6.10 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes", "authors": ["Csaba Tóth", "Masaki Adachi", "Michael A. Osborne", "Harald Oberhauser"], "url": "https://arxiv.org/abs/2412.19727v1", "attribution": "\"Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes\" by Csaba Tóth, Masaki Adachi, Michael A. Osborne, and Harald Oberhauser, arXiv:2412.19727v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.10684v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Different performance with different block and grid settings}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n \\hline\n Grid size / block size & run time(ms) on Tesla K40c & Tasks per thread \\\\ \\hline\n 32 / 32 & 3714.5 & $2^6$ \\\\ \\hline\n 128 / 128 & 335.5 & $2^2$ \\\\ \\hline\n 256 / 256 & 388.1 & $2^0$ \\\\ \\hline\n 512 / 512 & 474.9 & \\textless$2^0$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "GPU acceleration of a patient-specific airway image segmentation and its assessment", "authors": ["Yu-Wei Chang", "Tony W. H. Sheu"], "url": "https://arxiv.org/abs/2012.10684v1", "attribution": "\"GPU acceleration of a patient-specific airway image segmentation and its assessment\" by Yu-Wei Chang and Tony W. H. Sheu, arXiv:2012.10684v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.02796v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Aggregated Descriptive Statistics of Daily Log Returns (\\%) for the Top 250 U.K. Equities.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\hline\n Statistic & Minimum & Average & Maximum \\\\\n \\hline\n Mean Return (\\%) & -0.068 & 0.000 & 0.045 \\\\\n Standard Deviation (\\%) & 0.509 & 1.937 & 4.881 \\\\\n Minimum Return (\\%) & -83.974 & -18.412 & -4.292 \\\\\n Maximum Return (\\%) & 3.897 & 16.081 & 87.385 \\\\\n Skewness (log returns) & -4.899 & -0.380 & 3.856 \\\\\n Kurtosis (log returns) & 2.465 & 19.151 & 552.298 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deep Learning Enhanced Multivariate GARCH", "authors": ["Haoyuan Wang", "Chen Liu", "Minh-Ngoc Tran", "Chao Wang"], "url": "https://arxiv.org/abs/2506.02796v1", "attribution": "\"Deep Learning Enhanced Multivariate GARCH\" by Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, and Chao Wang, arXiv:2506.02796v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.02813v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rcccccrr}\n \\hline\n $n_{\\rm initial}$ & $m_p$ & $dx$ & $b$ & $dt$ & $\\beta$ & $n_{\\rm bl}$ & $n_{\\rm final}$ \\\\\n \\hline\n \\hline\n 1000 & 1. & 0.0721 & 0.190 & 0.005200 & 192.30 & 20 & 1347 \\\\\n 2000 & 0.5 & 0.0572 & 0.169 & 0.003275 & 305.26 & 25 & 2365 \\\\\n 4000 & 0.25 & 0.0454 & 0.150 & 0.002063 & 484.57 & 32 & 4240 \\\\\n 8000 & 0.125 & 0.0360 & 0.134 & 0.001300 & 769.20 & 40 & 7617 \\\\\n 16000 & 0.0625 & 0.0286 & 0.120 & 0.000819 & 1221.03 & 51 & 14005 \\\\\n 32000 & 0.03125 & 0.0227 & 0.107 & 0.000519 & 1938.26 & 64 & 25835 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An optimal-transport finite-particle method for driven mass diffusion", "authors": ["Anna Pandolfi", "Ignacio Romero", "Michael Ortiz"], "url": "https://arxiv.org/abs/2503.02813v1", "attribution": "\"An optimal-transport finite-particle method for driven mass diffusion\" by Anna Pandolfi, Ignacio Romero, and Michael Ortiz, arXiv:2503.02813v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11108v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c|c|c|c|c|c|c|c|}\n\\hline \nStimulus & \\textcolor{red}{$K$} & $K_{min}$& $K_{max}$ & \\textcolor{red}{$C$} & $C_{min}$ & $C_{max}$ & \\textcolor{red}{$\\mu_{P}$} & $\\sigma_{P}$ & $\\mu_{R}$ & $\\sigma_{R}$ \\\\\n\\hline \\hline\nNatural Movie & \\textcolor{red}{1} & 0 & 4 & \\textcolor{red}{6} & 2 & 6 & \\textcolor{red}{0.3} & 0.1 & 0.04 & 0.02 \\\\\n\\hline\nWhite Noise & \\textcolor{red}{2} & 0 & 4 & \\textcolor{red}{2} & 2 & 6 & \\textcolor{red}{0.55} & 0.05 & 0.04 & 0.02 \\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Hyper-parameters fitted to Off-Brisk Transient RGC responses to white noise and natural movie stimuli. Key differences highlighted in red.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A probabilistic latent variable model for detecting structure in binary data", "authors": ["Christopher Warner", "Kiersten Ruda", "Friedrich T. Sommer"], "url": "https://arxiv.org/abs/2201.11108v1", "attribution": "\"A probabilistic latent variable model for detecting structure in binary data\" by Christopher Warner, Kiersten Ruda, and Friedrich T. Sommer, arXiv:2201.11108v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.06451v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters of fitted Gamma distributions using Chave dataset .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Variable & Shape & Location & Scale \\\\ %\\hline \\\\\n Diameter [cm] & 0.68 & 5.00 & 30.18\\\\\n Height [m] & 1.92 & 1.18 & 7.75 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning", "authors": ["Yuanyuan Wang", "Qian Song", "Dawood Wasif", "Muhammad Shahzad", "Christoph Koller", "Jonathan Bamber", "Xiao Xiang Zhu"], "url": "https://arxiv.org/abs/2412.06451v1", "attribution": "\"How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning\" by Yuanyuan Wang, Qian Song, Dawood Wasif, Muhammad Shahzad, Christoph Koller, Jonathan Bamber, and Xiao Xiang Zhu, arXiv:2412.06451v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.05399v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The optimal RobustCRF's hyperparameters for the feature-based attacks.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\nHyperparameter & Cora & CiteSeer & PubMed & CS \\\\\n\\midrule\n$r$ & 0.1 & 0.9 & 0.3 &0.3 \\\\\n$\\sigma$ & 0.9 & 0.8 & 0.9 &0.5 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields", "authors": ["Yassine Abbahaddou", "Sofiane Ennadir", "Johannes F. Lutzeyer", "Fragkiskos D. Malliaros", "Michalis Vazirgiannis"], "url": "https://arxiv.org/abs/2411.05399v1", "attribution": "\"Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields\" by Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer, Fragkiskos D. Malliaros, and Michalis Vazirgiannis, arXiv:2411.05399v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.13961v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n\\toprule\n Parameter & Value \\\\\n\\midrule\nKite mass $m$ & 1 kg \\\\\nCharacteristic area $A$ & 10 m$^2$\\\\\nAir density $\\rho$ & 1.2 kg/m$^3$\\\\\nEngine drum mass $M$ & 10 kg\\\\\nEngine drum radius $R$ & 0.2 m\\\\\nWind speed $W^w$ (constant flow) & 10 m/s\\\\\nSpeed threshold $\\dot{r}_{\\mathrm{thr}}$($T \\rightarrow R$ phase) & 0.2 m/s\\\\\nDistance threshold ${r}_{\\mathrm{thr}}$ ($R \\rightarrow T$ phase) & 27 m\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Parameters for the AWE system under consideration.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Harvesting energy from turbulent winds with Reinforcement Learning", "authors": ["Lorenzo Basile", "Maria Grazia Berni", "Antonio Celani"], "url": "https://arxiv.org/abs/2412.13961v2", "attribution": "\"Harvesting energy from turbulent winds with Reinforcement Learning\" by Lorenzo Basile, Maria Grazia Berni, and Antonio Celani, arXiv:2412.13961v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03252v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Averaged non-binary dice coefficient and SSIM at epoch $210$. The models \\textbf{orig} and \\textbf{gen5} perform best.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\nmodel & non-binary dice & SSIM \\\\ \\hline\n\\textbf{orig} & \\textbf{0.2638} & 0.8409 \\\\\n\\textbf{gen5} & 0.2266 & \\textbf{0.8492} \\\\\n\\textbf{dis3} & 0.1566 & 0.8448 \\\\\n\\textbf{l11,gan100} & 0.1578 & 0.7926 \\\\\n\\textbf{l150,gan50} & 0.1484 & 0.7379 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Synthetic Glacier SAR Image Generation from Arbitrary Masks Using Pix2Pix Algorithm", "authors": ["Rosanna Dietrich-Sussner", "Amirabbas Davari", "Thorsten Seehaus", "Matthias Braun", "Vincent Christlein", "Andreas Maier", "Christian Riess"], "url": "https://arxiv.org/abs/2101.03252v2", "attribution": "\"Synthetic Glacier SAR Image Generation from Arbitrary Masks Using Pix2Pix Algorithm\" by Rosanna Dietrich-Sussner, Amirabbas Davari, Thorsten Seehaus, Matthias Braun, Vincent Christlein, Andreas Maier, and Christian Riess, arXiv:2101.03252v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00434v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n & \\textbf{Masc} & \\textbf{Fem} & \\textbf{Bias} & \\textbf{Overall} \\\\\n\\midrule\n\\midrule\nc2f + SpanBERT-Large & 90.5 & \\bf 86.3 & \\bf 0.95 & \\bf 88.4 \\\\\nc2f + Longformer-Base & 87.6 & 82.3 & 0.94 & 84.9 \\\\\nc2f + Longformer-Large & 90.1 & 85.4 & \\bf 0.95 & 87.8 \\\\\n\\midrule\ns2e + Longformer-Large & \\bf 90.6 & 85.8 & \\bf 0.95 & 88.3 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Performance on the test set of the GAP coreference dataset. The reported metrics are F1 scores.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Coreference Resolution without Span Representations", "authors": ["Yuval Kirstain", "Ori Ram", "Omer Levy"], "url": "https://arxiv.org/abs/2101.00434v2", "attribution": "\"Coreference Resolution without Span Representations\" by Yuval Kirstain, Ori Ram, and Omer Levy, arXiv:2101.00434v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02750v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|cccc|} \\hline\n & uni & bi & uni-VF & bi-VF \\\\ \\hline\nLine & 2.71 & 0.75 & 0 & 0 \\\\ \\hline\nPath & 3.33 & 1.14 & 0.28 & 0 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Average number of loss of contact with the surface during the line /path following tasks}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Assistive arm and hand manipulation: How does current research intersect with actual healthcare needs?", "authors": ["Laura Petrich", "Jun Jin", "Masood Dehghan", "Martin Jagersand"], "url": "https://arxiv.org/abs/2101.02750v1", "attribution": "\"Assistive arm and hand manipulation: How does current research intersect with actual healthcare needs?\" by Laura Petrich, Jun Jin, Masood Dehghan, and Martin Jagersand, arXiv:2101.02750v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.09412v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|}\n \\hline\n & \\textbf{Half NN -- Half MC} & \\textbf{Fully Neural} \\\\\n \\hline\n \\texttt{calibration time (avg)} & 12 m & 5 s \\\\\n \\texttt{device} & GPU & CPU \\\\\n \\texttt{\\# cores} & & 1 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Average calibration times under our approach (right) as opposed to the state of the art (left).}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Joint deep calibration of the 4-factor PDV model", "authors": ["Fabio Baschetti", "Giacomo Bormetti", "Pietro Rossi"], "url": "https://arxiv.org/abs/2507.09412v1", "attribution": "\"Joint deep calibration of the 4-factor PDV model\" by Fabio Baschetti, Giacomo Bormetti, and Pietro Rossi, arXiv:2507.09412v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.06442v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Validity for the point-wise convolution of GMDC on CVIU database. Note that the SubEC is not involved in these variants. The proposed GMDC uses grouped kernels denoted as $[K_1, K_2, \\dots, K_N]$. The best performance is highlighted in bold.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc|cccc}\n\\toprule\nModel & \\multicolumn{4}{c|}{w/o Point-wise Conv} & \\multicolumn{4}{c}{with Point-wise Conv} \\\\\n\\midrule\nGMDC Groups & SRCC $\\uparrow$ & KRCC $\\uparrow$ & PLCC $\\uparrow$& RMSE $\\downarrow$& SRCC $\\uparrow$& KRCC $\\uparrow$& PLCC $\\uparrow$& RMSE $\\downarrow$\\\\\n\\midrule\n$[3,7]$ & 0.945 & 0.800 & 0.951 & 0.884 & 0.974 & 0.866 & 0.977 & 0.605 \\\\\n$[3,5,7]$ & 0.967 & 0.850 & 0.968 & 0.713 & 0.972 & 0.857 & 0.972 & 0.642 \\\\\n$[3,5,7,9]$ & 0.973 & 0.864 & 0.976 & \\textbf{0.601} & \\textbf{0.975} & \\textbf{0.868} & \\textbf{0.978} & 0.632\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment", "authors": ["Yixiao Li", "Xiaoyuan Yang", "Guanghui Yue", "Jun Fu", "Qiuping Jiang", "Xu Jia", "Paul L. Rosin", "Hantao Liu", "Wei Zhou"], "url": "https://arxiv.org/abs/2509.06442v1", "attribution": "\"Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment\" by Yixiao Li, Xiaoyuan Yang, Guanghui Yue, Jun Fu, Qiuping Jiang, Xu Jia, Paul L. Rosin, Hantao Liu, and Wei Zhou, arXiv:2509.06442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17215v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccc} % centered columns (4 columns)\n\t\t\t\\hline\\hline %inserts double horizontal line\n\t\t \\multicolumn{4}{c}{\\bf{$\\phi=2,~\\gamma=3$}}& \\multicolumn{4}{c}{\\bf{$\\theta=1.3,~\\gamma=3$}} & \\multicolumn{4}{c}{$\\theta=2,~\\phi=3$ } \\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\textbf{$\\theta$} & $\\textbf{n}$ & \t \\textbf{SD}& \\textbf{AB}& \\textbf{$\\phi$} & \\textbf{ $n$} & \\textbf{SD} & \\textbf{AB} & \\textbf{$\\gamma$} &\\textbf{ $n$}& \\textbf{SD}& \\textbf{AB} \\\\\n\t\t\t &~&~&\\textbf{(MSE)} & ~& &&\\textbf{(MSE)}&~&~ & ~ &\\textbf{(MSE)} \\\\\n\t\t\t\\hline\\hline\n\\multirow{10}{1.9cm}\n~ &$100$ & $ 0.0245934$ & $0.0052743$& &$100$&$0.0324264$& $0.0079737$ & & $100$& 0.0974722 & $0.0179370$ \\\\\n~& ~& ~ & $( 0.0006327)$&~ & ~&~& $( 0.0011151)$&~ & &~ & $(0.0098226)$ \\\\\n$1.5$ & $300$ & $0.0130183$ & $0.0025353$& $1.2$ & $300$ & $0.0173983$ & $0.0044773$& $0.5$& $300$& $0.0570010$& $0.0019719$ \\\\\n~& ~& ~ & $(0.0001759)$&~ &&~ &$( 0.0003227)$&~ & &~ & $( 0.0032530)$ \\\\\n~ & $500$ & $0.0096063$ & $0.0015510$& &$500$& 0.0126885 & 0.0027339& & $500$& $0.0461926$& $0.0035639$ \\\\\n~& ~& ~ & $(0.0000947)$&~ & &~&$(0.0001685)$ & & &~ & $( 0.0021465)$ \\\\\n\\hline\n\\multirow{10}{1.9cm}\n~ & $100$ & $ 0.0217484$ & $0.0019649$& &$100$&$0.029511$& 0.0075343 & & $100$& $0.078738$& $0.0106371$ \\\\\n~& ~& ~ & $(0.0004769)$&~ & &~& (0.0009277)&~ & &~ & $(0.0063128)$ \\\\\n$2.5$ & $300$ & $0.0113239$ & $0.0013551$& $1.5$ & $300$ & 0.0157624 &0.0040916& $1.5$& $300$& $0.0442851$& $0.0017329$ \\\\\n~& ~& ~ & $( 0.0001301)$&~ & &~ &( 0.0002652)&~ & &~ & $(0.0019642)$ \\\\\n~ & $500$ & $0.0083987$ & $0.0006652$& &$500$&0.0114699 & 0.0023602& & $500$& $0.0353862$& $0.0024685$ \\\\\n~& ~& ~ & $( 0.0000710)$&~ & &~&(0.0001371) &~ & &~ & $( 0.0012583)$ \\\\\n\\hline\n\\multirow{10}{1.9cm}\n~ & $100$ & $0.0200045$ & $0.0006341$& &$100$& 0.0247303& 0.0056805 & & $100$& $0.0372384$& $0.0040772$ \\\\\n~& ~& ~ & $(0.0004006)$&~ & &~&(0.0006439)&~ & &~ & $(0.0014033)$ \\\\\n$4.0$ & $300$ & $0.0104665$ & $0.0010358$& $2.0$ & $300$ & 0.013035 & 0.0029709& 2.0 & $300$& $0.0204910$& $0.0008475$ \\\\\n~& ~& ~ & $(0.0001106)$&~ & &~ &(0.0001787)&~ & &~ & $( 0.0004206)$ \\\\\n~ & $500$ & $0.0077281$ & $0.0003415$& &$500$& 0.0095975 & 0.0017498& & $500$& $ 0.0161779$& $0.0010487$ \\\\\n~& ~& ~ & $( 0.0000598)$&~ & &~&(0.0000952) &~ & &~ & $(0.0002628)$ \\\\\n\\hline\n\\multirow{10}{1.9cm}\n~ & $100$ & $ 0.0198164$ & $0.0004607$& &$100$&0.0206426& 0.0038791 && $100$& $ 0.0240840$& $0.0020565$ \\\\\n~& ~& ~ & $(0.0003929)$&~ & &~&(0.0004412)&~ & &~ & $(0.0005843)$ \\\\\n$4.5$ & $300$ & $0.0103788$ & $0.0010481$& 3.0& $300 $& 0.0110333 & 0.0017643& $2.5$& $300$& $0.0129993$& $0.0005611$ \\\\\n~& ~& ~ & $(0.0001088)$&~ & &~ &(0.0001248)&~ & &~ & $( 0.0001693)$ \\\\\n~ & $500$ & $0.007664$ & $0.0003406$& &$500$& 0.0083454& 0.0011776& & 500& $ 0.0101264$& $0.0006012$ \\\\\n~& ~& ~ & $( 0.0000589)$&~ &&~& (0.0000710) &~ & &~ & $(0.0001029)$ \\\\\n\\hline\n\\multirow{10}{1.9cm}\n~ & $100$ & $ 0.0195212$ & $0.0003768$& &$100$& 0.0192975& 0.0034742 & & $100$& $0.0119660$& $0.0003506$ \\\\\n~& ~& ~ & $(0.0003812)$&~ & &~&(0.0003845)&~ & &~ & $( 0.0001433)$ \\\\\n$6.0$ & $300$ & $0.0103065$ & $0.0011128$& $4.0$ & $300$ & 0.0105864 & 0.0013553& $4.0$& $300$& $0.0062432$& $0.0002862$ \\\\\n~& ~& ~ & $(0.0001075)$&~ & &~ &(0.0001139)&~ &&~ & $(0.0000391)$ \\\\\n& $500$ & $ 0.007664$ & $0.0003406$& &$500$& 0.0081574 & 0.0010267& & $500$& $0.0046964$& $0.0002018$ \\\\\n~& ~& ~ & $( 0.0000589)$&~ & &~&(0.0000676) &~ & &~ & $( 0.0000221)$ \\\\\n\\hline \\hline\n\t\t \t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Multivariate Rényi inaccuracy measures based on copulas: properties and application", "authors": ["Shital Saha", "Suchandan Kayal"], "url": "https://arxiv.org/abs/2502.17215v1", "attribution": "\"Multivariate Rényi inaccuracy measures based on copulas: properties and application\" by Shital Saha and Suchandan Kayal, arXiv:2502.17215v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.16919v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Descriptive statistics of daily log returns across the six markets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllll}\n\\toprule\nMarket&Mean&Median&Min&Max&Std&Skewness&Kurtosis\\\\ \\midrule\nAORD&0.0155&0.0691&-7.261&4.533&0.946&-0.730&9.118\\\\\nDAX&0.0134&0.0763&-11.863&12.027&1.456&-0.165&9.637\\\\\nFTSE 100&0.0024&0.0343&-10.137&9.485&1.162&-0.308&10.471\\\\\nHang Seng&0.0048&0.0369&-13.582&13.407&1.449&-0.018&10.618\\\\\nNikkei&0.0054&0.0438&-12.111&13.235&1.481&-0.381&9.140\\\\\nS\\&P 500&0.0186&0.0636&-12.670&10.642&1.233&-0.405&13.829\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Combining a Large Pool of Forecasts of Value-at-Risk and Expected Shortfall", "authors": ["James W. Taylor", "Chao Wang"], "url": "https://arxiv.org/abs/2508.16919v1", "attribution": "\"Combining a Large Pool of Forecasts of Value-at-Risk and Expected Shortfall\" by James W. Taylor and Chao Wang, arXiv:2508.16919v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13822v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Error profile for computing .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|rr|rr} \n \\hline \n$G_i$ & % \\multicolumn{2}{c|}{$\\|\\b u-\\b u_h\\|_{0}$ $O(h^r)$} & \n $\\|\\b u-\\b u_h\\|_{0}$&$O(h^r)$ & $\\|\\nabla_w(\\b u-\\b u_h)\\|_0$ & $O(h^r)$ \\\\ \\hline \n & \\multicolumn{4}{c}{By the $P_1$ WG element on Figure grids} \\\\\n 6& 0.362E-03 & 2.5& 0.127E+00 & 1.5 \\\\\n 7& 0.766E-04 & 2.2& 0.561E-01 & 1.2 \\\\ \\hline \n & \\multicolumn{4}{c}{By the $P_1$ WG element on Figure grids} \\\\ \n 6& 0.308E-02 & 2.0& 0.470E+00 & 1.0 \\\\\n 7& 0.767E-03 & 2.0& 0.236E+00 & 1.0 \\\\\n \\hline \n & \\multicolumn{4}{c}{By the $P_2$ WG element on Figure grids} \\\\ \n 5& 0.267E-03 & 4.0& 0.775E-01 & 3.0 \\\\\n 6& 0.171E-04 & 4.0& 0.998E-02 & 3.0 \\\\\n \\hline \n & \\multicolumn{4}{c}{By the $P_2$ WG element on Figure grids} \\\\ \n 5& 0.714E-03 & 2.9& 0.931E-01 & 2.1 \\\\\n 6& 0.900E-04 & 3.0& 0.229E-01 & 2.0 \\\\\n \\hline \n & \\multicolumn{4}{c}{By the $P_3$ WG element on Figure grids} \\\\ \n 5& 0.176E-01 & 5.0& 0.797E+01 & 4.0 \\\\\n 6& 0.548E-03 & 5.0& 0.499E+00 & 4.0 \\\\\n \\hline \n & \\multicolumn{4}{c}{By the $P_3$ WG element on Figure grids} \\\\ \n 4& 0.797E-03 & 4.4& 0.633E-01 & 3.6 \\\\\n 5& 0.470E-04 & 4.1& 0.590E-02 & 3.4 \\\\\n \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An Auto-Stabilized Weak Galerkin Method for Elasticity Interface Problems on Nonconvex Meshes", "authors": ["Chunmei Wang", "Shangyou Zhang"], "url": "https://arxiv.org/abs/2501.13822v1", "attribution": "\"An Auto-Stabilized Weak Galerkin Method for Elasticity Interface Problems on Nonconvex Meshes\" by Chunmei Wang and Shangyou Zhang, arXiv:2501.13822v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.06425v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison Between the FR Order and the RF Order}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccc}\n\\hline\n & \\multicolumn{3}{c}{Direct Response Method} & & \\multicolumn{3}{c}{Reduced Strategy Method} & & \\multicolumn{3}{c}{Full Strategy Method} \\\\ \\cline{2-4} \\cline{6-8} \\cline{10-12} \nMean Terminal Nodes & FR Order & RF Order & $p$-values & & FR Order & RF Order & $p$-values & & FR Order & RF Order & $p$-values \\\\ \\hline\nPooled Data & 3.812 & 3.906 & 0.653 & & 4.056 & 3.948 & 0.543 & & 3.851 & 3.872 & 0.918 \\\\\n & (2.110) & (2.084) & & & (2.011) & (2.085) & & & (2.026) & (2.070) & \\\\ \\hline\nSmall Linear & 4.604 & 4.604 & 0.940 & & 4.917 & 4.896 & 0.946 & & 4.167 & 4.458 & 0.412 \\\\\n & (1.717) & (1.811) & & & (1.525) & (1.723) & & & (1.783) & (1.620) & \\\\\nLarge Linear & 5.271 & 5.521 & 0.618 & & 5.667 & 5.083 & 0.407 & & 5.562 & 5.417 & 0.918 \\\\\n & (1.655) & (1.414) & & & (1.296) & (2.009) & & & (1.353) & (1.706) & \\\\\nSmall Exponential & 5.292 & 5.438 & 0.933 & & 5.562 & 5.208 & 0.208 & & 5.417 & 5.271 & 0.578 \\\\\n & (1.485) & (1.039) & & & (1.059) & (1.443) & & & (1.115) & (1.220) & \\\\\nLarge Exponential & 4.604 & 4.688 & 0.760 & & 4.625 & 4.771 & 0.468 & & 4.479 & 4.542 & 0.657 \\\\\n & (1.350) & (1.277) & & & (1.130) & (1.212) & & & (1.354) & (1.485) & \\\\\nSmall Constant & 1.167 & 1.292 & 0.282 & & 1.396 & 1.417 & 0.852 & & 1.521 & 1.500 & 0.655 \\\\\n & (0.373) & (0.538) & & & (0.729) & (0.886) & & & (0.790) & (0.979) & \\\\\nLarge Constant & 1.938 & 1.896 & 0.727 & & 2.167 & 2.312 & 0.575 & & 1.958 & 2.042 & 0.814 \\\\\n & (0.876) & (0.918) & & & (0.874) & (1.102) & & & (1.020) & (1.190) & \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Strategy Method Effects in Centipede Games: An Optimal Design Approach", "authors": ["Shiang-Hung Hu", "Po-Hsuan Lin", "Thomas R. Palfrey", "Joseph Tao-yi Wang", "Yu-Hsiang Wang"], "url": "https://arxiv.org/abs/2508.06425v1", "attribution": "\"Strategy Method Effects in Centipede Games: An Optimal Design Approach\" by Shiang-Hung Hu, Po-Hsuan Lin, Thomas R. Palfrey, Joseph Tao-yi Wang, and Yu-Hsiang Wang, arXiv:2508.06425v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06223v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Pretraining on IsarStep for the MetaMathStep task.}\n\\begin{tabular}{lllllll}\n \\toprule\n Model & Top-1 Acc. & Top-10 Acc. \\\\ \n \\midrule\n No pretrain & 67.7 & 76.5 \\\\\n LIME \\texttt{Mix} &69.1 &77.9 \\\\\n Pretrain on IsarStep &67.0 &76.1 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning", "authors": ["Yuhuai Wu", "Markus Rabe", "Wenda Li", "Jimmy Ba", "Roger Grosse", "Christian Szegedy"], "url": "https://arxiv.org/abs/2101.06223v2", "attribution": "\"LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning\" by Yuhuai Wu, Markus Rabe, Wenda Li, Jimmy Ba, Roger Grosse, and Christian Szegedy, arXiv:2101.06223v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.03035v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcr|rrr}\n\t\tvariable & stat. & current & lag 1 & lag 2 & lag 3 \\\\\n\t\t\\hline\n\t\tMarkit NA credit index\n\t\t\t& correl. & \\color{blue}{\\textbf{0.380}} & \\textbf{0.323} & \\textbf{0.266} & \\textbf{0.209} \\\\\n\t\t\t& p-value & 0.000 & 0.000 & 0.000 & 0.000 \\\\\n\t\tBloomberg FRN index, OAS\n\t\t\t& correl. & \\color{blue}{\\textbf{0.714}} & \\textbf{0.512} & \\textbf{0.310} & \\textbf{0.107}\\\\\n\t\t\t& p-value & 0.000 & 0.000 & 0.000 & 0.000\\\\\n\t\tBloomberg FRN index\n\t\t\t& correl. & \\color{blue}{\\textbf{-0.623}} & \\textbf{-0.445} & \\textbf{-0.267} & \\textbf{-0.088}\\\\\n\t\t\t& p-value & 0.000 & 0.000 & 0.000 & 0.058 \\\\\n\t\tBloomberg US corporate index, OAS\n\t\t\t& correl. & \\color{blue}{\\textbf{0.670}} & \\textbf{0.498} & \\textbf{0.327} & \\textbf{0.155}\\\\\n\t\t\t& p-value & 0.000 & 0.000 & 0.000 & 0.001 \\\\\n\t\tBloomberg US corporate index\n\t\t\t& correl. & \\color{blue}{\\textbf{-0.276}} & \\textbf{-0.233} & \\textbf{-0.190} & \\textbf{-0.147}\\\\\n\t\t\t& p-value & 0.000 & 0.000 & 0.000 & 0.002 \\\\\n\t\tNational financial conditions index\n\t\t & correl. & \\textbf{0.595} & \\color{blue}{\\textbf{0.622}} & \\textbf{0.534} & \\textbf{0.368}\\\\\n\t\t & p-value & 0.000 & 0.000 & 0.000 & 0.000 \\\\\n\t\tCredit market distress index\n\t\t & correl. & \\color{blue}{\\textbf{0.309}} & \\textbf{0.173} & \\textbf{0.150} & \\textbf{-0.025} \\\\\n\t\t & p-value & 0.000 & 0.000 & 0.001 & 0.593 \\\\\n LIBOR-OIS spread\n\t\t & correl. & \\color{blue}{\\textbf{0.617}} & \\textbf{0.443} & \\textbf{0.269} & \\textbf{0.095} \\\\\n\t\t & p-value & 0.000 & 0.000 & 0.000 & 0.042 \\\\\n TED spread\n\t\t & correl. & \\color{blue}{\\textbf{0.611}} & \\textbf{0.449} & \\textbf{0.288} & \\textbf{0.126} \\\\\n\t\t & p-value & 0.000 & 0.000 & 0.000 & 0.007 \\\\\n\t\t\\hline\n\t\tSPDR financial sector ETF\n\t\t\t& correl. & \\color{blue}{\\textbf{-0.426}} & \\textbf{-0.344} & \\textbf{-0.262} & \\textbf{-0.180}\\\\\n\t\t\t& p-value & 0.000 & 0.000 & 0.000 & 0.000 \\\\\n\t\t\\hline\n\t\tEconomic policy uncertainty\n\t\t & correl. & \\color{blue}{\\textbf{0.244}} & \\textbf{0.184} & \\textbf{0.123} & 0.062 \\\\\n\t\t & p-val. & 0.000 & 0.000 & 0.009 & 0.184 \\\\\n\t\tVolatility index\n\t\t\t& correl. & \\textbf{0.103} & \\color{blue}{\\textbf{0.146}} & \\textbf{0.188} & \\textbf{0.230}\\\\\n\t\t\t& p-value & 0.026 & 0.002 & 0.005 & 0.000 \\\\\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Monthly correlations of AXI with selected financial indicators}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Case for AXI", "authors": ["Viktor Tsyrennikov"], "url": "https://arxiv.org/abs/2509.03035v1", "attribution": "\"A Case for AXI\" by Viktor Tsyrennikov, arXiv:2509.03035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.00530v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ccc}\n Model & Active Parameters & One-Step SPE \\\\ \n\\hline\nGNAR(1, [1]) & 2 & 10.286 \\\\\n VAR(1) & $140^2$ & 12.52 \\\\\n Res. VAR(1) & 3730 & 11.605 \\\\\n Sparse VAR(1) & 2785 & 12.986 \\\\\n AR(1) & $140$ & 82.571 \\\\\n GNAR(1, [1])* & 141 & 10.173 \n\\end{tabular}\n\\caption{One-step prediction error, $\\hat{\\boldsymbol{X}}_{445}$ is predicted using the previous 444 observations.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "New tools for network time series with an application to COVID-19 hospitalisations", "authors": ["Guy Nason", "Daniel Salnikov", "Mario Cortina-Borja"], "url": "https://arxiv.org/abs/2312.00530v1", "attribution": "\"New tools for network time series with an application to COVID-19 hospitalisations\" by Guy Nason, Daniel Salnikov, and Mario Cortina-Borja, arXiv:2312.00530v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08550v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Histogram-based Preprocessing Techniques.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\nReference & Year & Team & Details\\\\\n\\hline\n & 2010 & S Samsi & Histogram equalization\\\\\n & 2011 & H Kong & Histogram equalization\\\\\n & 2012 & M Oger & Adaptive equalization of histogram\\\\\n & 2012 & HC Akakin & Normalizing co-occurrence histograms\\\\\n & 2014 & E Michail & Histogram equalization\\\\\n & 2014 & K Dimitropoulos & Histogram equalization\\\\\n & 2016 & N Codella & Histogram equalization\\\\\n & 2017 & TAA Tosta & Histogram equalization\\\\\n & 2017 & TAA Tosta & Histogram equalization\\\\\n &2017 & BB Cheikh & Histogram stretching\\\\\n & 2018 & TAA Tosta & Histogram equalization\\\\\n & 2019 & UV Somaratne & Histogram equalization\\\\\n & 2020 & F Bianconi & Histogram equalization\\\\\n & 2021 & TA Azevedo Tosta & Histogram equalization\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review", "authors": ["Xiaoqi Li", "Haoyuan Chen", "Chen Li", "Md Mamunur Rahaman", "Xintong Li", "Jian Wu", "Xiaoyan Li", "Hongzan Sun", "Marcin Grzegorzek"], "url": "https://arxiv.org/abs/2201.08550v2", "attribution": "\"What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review\" by Xiaoqi Li, Haoyuan Chen, Chen Li, Md Mamunur Rahaman, Xintong Li, Jian Wu, Xiaoyan Li, Hongzan Sun, and Marcin Grzegorzek, arXiv:2201.08550v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23340v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n & \\multicolumn{2}{c|}{\\textbf{Greedy}}\n & \\multicolumn{2}{c|}{\\textbf{Distorted Greedy}} \\\\\n\\hline\n$m$\n & Subset $S_m$\n & $H\\bigl(P^{(S_m)}\\bigr)$\n & Subset $S_m$\n & $H\\bigl(P^{(S_m)}\\bigr)$ \\\\\n\\hline\n1 & $\\{10\\}$ & 0.46094 & $\\{10\\}$ & 0.46094 \\\\\n2 & $\\{3,\\,10\\}$ & 0.83616 & $\\{1,\\,10\\}$ & 0.83573 \\\\\n3 & $\\{1,\\,3,\\,10\\}$ & 1.17940 & $\\{1,\\,2,\\,5\\}$ & 1.18116 \\\\\n4 & $\\{1,\\,2,\\,3,\\,10\\}$ & 1.49461 & $\\{1,\\,2,\\,3,\\,5\\}$ & 1.50706 \\\\\n5 & $\\{1,\\,2,\\,3,\\,4,\\,10\\}$ & 1.77855 & $\\{1,\\,2,\\,3,\\,4,\\,5\\}$ & 1.80193 \\\\\n6 & $\\{1,\\,2,\\,3,\\,4,\\,5,\\,10\\}$ & 2.03516 & $\\{1,\\,2,\\,3,\\,4,\\,5,\\,6\\}$ & 2.06105 \\\\\n7 & $\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,10\\}$ & 2.25729 & $\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7\\}$ & 2.28328 \\\\\n8 & $\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,10\\}$ & 2.43498 & $\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8\\}$ & 2.45453 \\\\\n9 & $\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,10\\}$ & 2.51897 & $\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,10\\}$ & 2.51897 \\\\\n10 & $\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,10\\}$ & 2.51897 & $\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,10\\}$ & 2.51897 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of the greedy algorithm and the distorted greedy algorithm. Entropy rate of the full chain of the Bernoulli-Laplace level model is $H(P) = 1.96068$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Information-theoretic subset selection of multivariate Markov chains via submodular optimization", "authors": ["Zheyuan Lai", "Michael C. H. Choi"], "url": "https://arxiv.org/abs/2503.23340v1", "attribution": "\"Information-theoretic subset selection of multivariate Markov chains via submodular optimization\" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09391v2_tex_table25.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n & Gap & Hurdle & Stairs & Steps \\\\\n \\hline\n Without Setup Policies & 74 & \\textbf{41} & 64 & 142 \\\\\n With Setup Policies & \\textbf{44} & 51 & \\textbf{49} & \\textbf{90} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Number of failures by terrain type from 1000 episodes of 4 terrain types \\textbf{(without the jump terrain)}, randomly shuffled each episode.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Learning Setup Policies: Reliable Transition Between Locomotion Behaviours", "authors": ["Brendan Tidd", "Nicolas Hudson", "Akansel Cosgun", "Jurgen Leitner"], "url": "https://arxiv.org/abs/2101.09391v2", "attribution": "\"Learning Setup Policies: Reliable Transition Between Locomotion Behaviours\" by Brendan Tidd, Nicolas Hudson, Akansel Cosgun, and Jurgen Leitner, arXiv:2101.09391v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.00710v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n \\toprule\n \\textsc{Term} & \\textsc{Coefficient} & \\textsc{Std. Error} & \\textsc{t-value} \\\\\n \\midrule\n \\textsc{(intercept)} & 0.15 & 0.07 & 2.22 \\\\\n \\textsc{spatial} & 0.02 & 0.08 & 0.26 \\\\\n \\textsc{spatial+} & 0.00 & 0.08 & 0.06 \\\\\n \\textsc{gcnn} & 0.10 & 0.09 & 1.17 \\\\\n \\textsc{dapsm} & 0.78 & 0.19 & 4.13 \\\\\n \\textsc{gmerror} & 0.00 & 0.08 & 0.01 \\\\\n \\textsc{s2sls} & -0.01 & 0.08 & -0.08 \\\\\n \\textsc{ols:smoothness} & -0.15 & 0.10 & -1.51 \\\\\n \\textsc{spatial:smoothness} & -0.19 & 0.10 & -1.88 \\\\\n \\textsc{spatial+:smoothness} & -0.09 & 0.10 & -0.94 \\\\\n \\textsc{gcnn:smoothness} & -0.21 & 0.13 & -1.66 \\\\\n \\textsc{dapsm:smoothness} & -0.80 & 0.15 & -5.29 \\\\\n \\textsc{gmerror:smoothness} & -0.15 & 0.10 & -1.53 \\\\\n \\textsc{s2sls:smoothness} & -0.14 & 0.10 & -1.46 \\\\\n \\textsc{ols:confounding} & 0.61 & 0.34 & 1.77 \\\\\n \\textsc{spatial:confounding} & 0.98 & 0.34 & 2.86 \\\\\n \\textsc{spatial+:confounding} & 1.09 & 0.34 & 3.17 \\\\\n \\textsc{gcnn:confounding} & 0.73 & 0.37 & 1.96 \\\\\n \\textsc{dapsm:confounding} & -2.94 & 2.38 & -1.23 \\\\\n \\textsc{gmerror:confounding} & 0.61 & 0.34 & 1.78 \\\\\n \\textsc{s2sls:confounding} & 0.63 & 0.34 & 1.83 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Coefficients of a statistical linear model for the error in ERF estimation with random effects per dataset and environment and fixed effects by algorithm and environment complexity (smoothness and confounding).}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "SpaCE: The Spatial Confounding Environment", "authors": ["Mauricio Tec", "Ana Trisovic", "Michelle Audirac", "Sophie Woodward", "Jie Kate Hu", "Naeem Khoshnevis", "Francesca Dominici"], "url": "https://arxiv.org/abs/2312.00710v3", "attribution": "\"SpaCE: The Spatial Confounding Environment\" by Mauricio Tec, Ana Trisovic, Michelle Audirac, Sophie Woodward, Jie Kate Hu, Naeem Khoshnevis, and Francesca Dominici, arXiv:2312.00710v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.13570v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Levels Available in \\texttt{maritalstatus}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cl}\n\t\\toprule\n\t\\textbf{Final Level} & \\textbf{Description} \\\\\n\t\\midrule\n\t\\textit{married} & Married or in registered civil partnership \\\\\n\t\\textit{single} & Single \\\\\n\t\\textit{divorced} & Divorced \\\\\n\t\\textit{widowed} & Widowed \\\\\n\t\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study", "authors": ["Jakob Schwerter", "Andrés Romero", "Florian Dumpert", "Markus Pauly"], "url": "https://arxiv.org/abs/2412.13570v1", "attribution": "\"Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study\" by Jakob Schwerter, Andrés Romero, Florian Dumpert, and Markus Pauly, arXiv:2412.13570v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.01474v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of all first order methods in terms of time to reach a $5\\times 10^{-6}$ relative error. X represents failure of the method to reach such accuracy in the 15 minutes time budget. Geometric mean time to reach the relative error threshold is on the last row reported in italic.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|ccc|cc|c}\n \\toprule\n BPLM & BLM & SUBG-L & SUBG-EP & SUBG & DA & DOWG & FGM \\\\ \\midrule\n\t\\multicolumn{8}{c}{dataset : Belgian} \\\\ \\hline\n\t{899} & {X} & {\\textbf{776}} & {793} & {X} & {X} & {X} & {X} \\\\\n\t\\textbf{900} & X & \\textbf{900} & X & X & X & X & X \\\\\n\t559 & \\textbf{549} & 830 & 900 & X & X & X & X \\\\\n\t\\textbf{160} & 760 & 810 & 737 & X & X & X & X \\\\\n\t\\textbf{263} & 883 & 828 & 773 & X & X & X & X \\\\\n\t820 & 782 & 892 & \\textbf{493} & X & X & X & X \\\\\n\t\\textbf{614} & X & 868 & X & X & X & X & X \\\\\n\t\\textbf{302} & 656 & 891 & 760 & X & X & X & X \\\\ \n \\midrule \\textit{482} & X & \\textit{848} & X & X & X & X & X \\\\\n \\midrule \\multicolumn{8}{c}{dataset : Californian} \\\\ \\hline\n\t{258} & {74.6} & {179} & {\\textbf{20.9}} & {126} & {141} & {201} & {152} \\\\\n\t385 & 74.2 & 169 & \\textbf{21.1} & 117 & 317 & 219 & 142 \\\\\n\t153 & 74.4 & 170 & \\textbf{20.9} & 119 & 464 & 207 & 165 \\\\\n\t198 & 74.2 & 166 & \\textbf{20.9} & 128 & 459 & 212 & 221 \\\\\n\t738 & X & 479 & \\textbf{112} & 444 & X & X & 742 \\\\\n\t563 & X & 590 & \\textbf{140} & 289 & X & X & 641 \\\\\n\t561 & X & 516 & \\textbf{112} & 131 & X & X & 628 \\\\\n\t864 & X & 521 & 140 & \\textbf{130} & X & X & 600 \\\\\n\t185 & 122 & 64.3 & 39.7 & \\textbf{18.7} & 92.8 & 241 & 93.0 \\\\\n\t196 & 123 & 65.9 & 38.9 & \\textbf{25.8} & 149 & 258 & 97.0 \\\\\n\t161 & 122 & 49.7 & 39.6 & \\textbf{25.5} & 149 & 192 & 93.8 \\\\\n\t167 & 124 & 77.9 & 39.2 & \\textbf{19.1} & 152 & 194 & 93.9 \\\\\n\t534 & 438 & 493 & \\textbf{140} & 783 & X & 891 & 270 \\\\\n\t409 & 313 & 494 & 689 & X & X & X & \\textbf{271} \\\\\n\t452 & 311 & 479 & 676 & X & X & X & 385 \\\\\n\t482 & \\textbf{260} & 486 & 677 & X & X & 406 & 329 \\\\\n \\midrule \\textit{337} & X & \\textit{230} & \\textit{83} & X & X & X & \\textit{275} \\\\\n \\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Dual first-order methods for efficient computation of convex hull prices", "authors": ["Sofiane Tanji", "Yassine Kamri", "François Glineur", "Mehdi Madani"], "url": "https://arxiv.org/abs/2504.01474v1", "attribution": "\"Dual first-order methods for efficient computation of convex hull prices\" by Sofiane Tanji, Yassine Kamri, François Glineur, and Mehdi Madani, arXiv:2504.01474v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11189v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Detection accuracy on the HRSC2016 dataset, 07 means using the 2007 evaluation metric.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n \\Xhline{1pt}\n Method & Backbone & mAP(07) \\\\\n \\Xhline{1pt}\n R$^{2}$CNN & Resnet101 & 73.07 \\\\\n RRPN & Resnet101 & 79.08 \\\\\n R$^{2}$PN & VGG16 & 79.6 \\\\\n ROI-trans & Resnet101 & 86.20 \\\\\n Gliding Vertex & Resnet101 & 88.20 \\\\\n BBAVectors & Resnet101 & 88.6 \\\\\n R$^3$Det & Resnet101 & 89.26 \\\\\n FPN-CSL & Resnet101 & 89.62\\\\\n R$^3$Det-DCL & Resnet101 & 89.46\\\\\n DAL & Resnet101 & 89.77 \\\\\n R$^3$Det-GWD & Resnet101 & 89.85 \\\\\n RSDet & ResNet152 & 86.5 \\\\\n FR-Est & Resnet101 & 89.7 \\\\\n S$^{2}$A-Net & Resnet101 & 90.2 \\\\\n Oriented RepPoints& Resnet50 & 90.38 \\\\\n ReDet & ReResnet50 & 90.46 \\\\\n Oriented R-CNN & Resnet101 & \\textcolor{blue}{90.50} \\\\\n DARDet & Resnet50 & 90.37 \\\\\n \\hline\n CHPDet(ours) & DLA34 & 88.81 \\\\\n CHPDet(ours) & Hourglass104 &\\textcolor{red}{90.55}\\\\\n \\Xhline{1pt}\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Arbitrary-Oriented Ship Detection through Center-Head Point Extraction", "authors": ["Feng Zhang", "Xueying Wang", "Shilin Zhou", "Yingqian Wang", "Yi Hou"], "url": "https://arxiv.org/abs/2101.11189v3", "attribution": "\"Arbitrary-Oriented Ship Detection through Center-Head Point Extraction\" by Feng Zhang, Xueying Wang, Shilin Zhou, Yingqian Wang, and Yi Hou, arXiv:2101.11189v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07450v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of NER systems for Mutation with pre-adjudicated annotations over held-out test data (test1).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|lllll|}\n \\hline\n \\textbf{Training}&\\textbf{System} & \\textbf{Precision} & \\textbf{Recall}&\\textbf{F-score (Std Dev)}\\\\ \\hline\nPre-adjudicated&BioBERT & 78.1 & 63.6 & 70.1 (2.4)\\\\\n&bilstm-crf&72.2&70.7&71.4\\\\\n\\hline\nAdjudicated&BioBERT& 81.0 & 74.7 & 77.7 (2.4)\\\\\n&bilstm-crf&85.7&72.7&78.7\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Single versus Multiple Annotation for Named Entity Recognition of Mutations", "authors": ["David Martinez Iraola", "Antonio Jimeno Yepes"], "url": "https://arxiv.org/abs/2101.07450v1", "attribution": "\"Single versus Multiple Annotation for Named Entity Recognition of Mutations\" by David Martinez Iraola and Antonio Jimeno Yepes, arXiv:2101.07450v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06323v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Coverage Summary of Two Graph Construction Methods: almost full coverage after adopting ANN Neighbors}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc|cc}\n \\toprule\n & \\multicolumn{2}{c|}{Click Only}&\\multicolumn{2}{c}{ANN}\\\\\n \\cline{2-3}\\cline{4-5}\n &Q&K&Q&K\\\\\n \\midrule\n 1 Neighbor & 4\\% & 7\\% & 5\\% & 7\\%\\\\\n 2 Neighbors & 3\\% & 4\\% & 3\\% & 4\\%\\\\\n 3 Neighbors & 30\\% & 76\\% & 92\\% & 88\\%\\\\\n \\midrule\n \\textbf{Coverage} & \\textbf{37\\%} & \\textbf{87\\%} & \\textbf{100\\%} & \\textbf{99\\%} \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search", "authors": ["Jason Yue Zhu", "Yanling Cui", "Yuming Liu", "Hao Sun", "Xue Li", "Markus Pelger", "Tianqi Yang", "Liangjie Zhang", "Ruofei Zhang", "Huasha Zhao"], "url": "https://arxiv.org/abs/2101.06323v3", "attribution": "\"TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search\" by Jason Yue Zhu, Yanling Cui, Yuming Liu, Hao Sun, Xue Li, Markus Pelger, Tianqi Yang, Liangjie Zhang, Ruofei Zhang, and Huasha Zhao, arXiv:2101.06323v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.12619v1_tex_table20.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\large Impacts of Spatial Lag-Error (SARAR) models of overall county economy employment indicators}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\n & \\multicolumn{3}{c}{$\\Delta$ (log) Employed Persons} & \\multicolumn{3}{c}{$\\Delta$ (log)Unemployed Persons} \\\\\n\\cmidrule(l{3pt}r{3pt}){2-4} \\cmidrule(l{3pt}r{3pt}){5-7}\n & Direct & Indirect & Total & Direct & Indirect & Total\\\\\n\\midrule\n$\\Delta$ Active Mines\\textsubscript{t} & 0.001** & 0.002** & 0.002** & -0.003*** & -0.022*** & -0.026***\\\\\n~ & (0.000) & (0.001) & (0.001) & (0.001) & (0.004) & (0.005)\\\\\n\\addlinespace\n$\\Delta$ Active Mines\\textsubscript{t-1} & 0.001*** & 0.003*** & 0.004*** & 0.000 & -0.002 & -0.003\\\\\n~ & (0.000) & (0.000) & (0.001) & (0.001) & (0.004) & (0.004)\\\\\n\\addlinespace\n$\\Delta$ Active Mines\\textsubscript{t-2} & 0.000 & 0.000 & 0.001 & 0.003*** & 0.017*** & 0.019***\\\\\n~ & (0.000) & (0.000) & (0.001) & (0.001) & (0.004) & (0.004)\\\\\n\\addlinespace\n$\\Delta$ (log) Real GDP & 0.037*** & 0.082*** & 0.119*** & -0.058*** & -0.382*** & -0.44***\\\\\n~ & (0.001) & (0.004) & (0.005) & (0.004) & (0.027) & (0.031)\\\\\n\\addlinespace\n$\\Delta$ (log) Population & 0.414*** & 0.925*** & 1.339*** & 0.67*** & 4.399*** & 5.069***\\\\\n~ & (0.012) & (0.038) & (0.047) & (0.033) & (0.249) & (0.279)\\\\\n\\addlinespace\n\\midrule\n\\addlinespace\n\\midrule\n & \\multicolumn{3}{c}{$\\Delta$ (log)Labour Force} & \\multicolumn{3}{c}{$\\Delta$ (log)Population} \\\\\n\\cmidrule(l{3pt}r{3pt}){2-4} \\cmidrule(l{3pt}r{3pt}){5-7}\n & Direct & Indirect & Total & Direct & Indirect & Total\\\\\n\\midrule\n$\\Delta$ Active Mines\\textsubscript{t} & 0.000 & 0.001 & 0.001 & 0.000 & 0.000 & 0.000\\\\\n~ & (0.000) & (0.000) & 0.001 & (0.000) & (0.000) & (0.000)\\\\\n\\addlinespace\n$\\Delta$ Active Mines\\textsubscript{t-1} & 0.001*** & 0.002*** & 0.003*** & 0.000 & 0.000 & 0.000\\\\\n~ & (0.000) & (0.000) & (0.001) & (0.000) & (0.000) & (0.000)\\\\\n\\addlinespace\n$\\Delta$ Active Mines\\textsubscript{t-2} & 0.001** & 0.001** & 0.002** & 0.000* & 0.000* & 0.000*\\\\\n~ & (0.000) & (0.000) & (0.001) & (0.000) & (0.000) & (0.000)\\\\\n\\addlinespace\n$\\Delta$ (log) Real GDP & 0.030*** & 0.061*** & 0.091*** & 0.005*** & 0.012*** & 0.017***\\\\\n~ & (0.001) & (0.003) & (0.004) & (0.000) & (0.001) & (0.002)\\\\\n\\addlinespace\n$\\Delta$ (log) Population & 0.437*** & 0.890*** & 1.328*** & ~ & ~ & ~\\\\\n~ & (0.011) & (0.036) & (0.045) & ~ & ~ & ~\\\\\n\\addlinespace\n\\bottomrule\n\\addlinespace\n\\multicolumn{6}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'", "authors": ["Ebba Mark", "Ryan Rafaty", "Moritz Schwarz"], "url": "https://arxiv.org/abs/2211.12619v1", "attribution": "\"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'\" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.19947v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{White-box attack robust accuracy for ResNet-18 on CIFAR-100. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n\\hline\n Defense & Natural & $PGD-20$ &$CW$ & AA \\\\\n\\hline\n \\textit{Standard-AT} & 56.59\\tiny{ $\\pm$ 0.22}\n & 28.18\\tiny{ $\\pm$ 0.19} & 25.64\\tiny{ $\\pm$ 0.18} &24.07\\tiny{ $\\pm$ 0.15} \\\\\n\\textbf{AT - SDI \\tiny{(Ours)}} \n & \\textbf{57.96}\\tiny{ $\\pm$ 0.14}\n & \\textbf{30.78}\\tiny{ $\\pm$ 0.12}\n & \\textbf{29.37}\\tiny{ $\\pm$ 0.15}\n & \\textbf{26.38}\\tiny{ $\\pm$ 0.10} &\\\\\n\\hline\n \\textit{TRADES} & 56.96\\tiny{ $\\pm$ 0.16} & 29.21\\tiny{ $\\pm$ 0.12} & 25.57\\tiny{ $\\pm$ 0.08} &24.65\\tiny{ $\\pm$ 0.06} \\\\\n\\textbf{TRADES - SDI \\tiny{(Ours)}}& \\textbf{60.68}\\tiny{ $\\pm$ 0.11} & \\textbf{31.21}\\tiny{ $\\pm$ 0.04} & \\textbf{28.73}\\tiny{ $\\pm$ 0.04} &\\textbf{26.45}\\tiny{ $\\pm$ 0.04} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness", "authors": ["Olukorede Fakorede", "Modeste Atsague", "Jin Tian"], "url": "https://arxiv.org/abs/2412.19947v1", "attribution": "\"Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness\" by Olukorede Fakorede, Modeste Atsague, and Jin Tian, arXiv:2412.19947v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.09782v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rrrrrrrrrrrrrrrrrrr}\n \\hline\n\\hline\n & \\multicolumn{6}{c}{LCRIX}& \\multicolumn{6}{c}{LECRIX}& \\multicolumn{6}{c}{LEFCRIX}&\\\\& AIC & GC & GFC & SH & Cp & FPE & AIC & GC & GFC & SH & Cp & FPE & AIC & GC & GFC & SH & Cp & FPE & max\\\\ \\hline\n1 & 5 & 10 & 15 & 15 & 5 & 35 & 2 & 3 & 3 & 3 & 1 & 36 & 2 & 6 & 16 & 16 & 1 & 36 & 36 \\\\ \n 2 & 5 & 10 & 10 & 10 & 5 & 100 & 2 & 4 & 4 & 4 & 1 & 93 & 2 & 94 & 93 & 93 & 1 & 93 & 113 \\\\ \n 3 & 5 & 5 & 20 & 20 & 5 & 100 & 3 & 4 & 4 & 4 & 1 & 93 & 6 & 94 & 93 & 93 & 1 & 93 & 158 \\\\ \n 4 & 15 & 20 & 20 & 30 & 5 & 95 & 3 & 2 & 2 & 2 & 1 & 90 & 3 & 91 & 90 & 90 & 1 & 90 & 182 \\\\ \n 5 & 5 & 5 & 5 & 5 & 5 & 100 & 1 & 1 & 1 & 1 & 1 & 93 & 93 & 94 & 93 & 93 & 1 & 93 & 169 \\\\ \n 6 & 5 & 5 & 5 & 5 & 5 & 100 & 2 & 2 & 2 & 2 & 1 & 93 & 9 & 94 & 93 & 93 & 1 & 93 & 171 \\\\ \n 7 & 10 & 25 & 30 & 35 & 5 & 100 & 1 & 2 & 2 & 2 & 1 & 93 & 1 & 94 & 93 & 93 & 1 & 93 & 176 \\\\ \n 8 & 10 & 20 & 35 & 35 & 5 & 95 & 1 & 1 & 1 & 1 & 1 & 91 & 3 & 92 & 91 & 91 & 1 & 91 & 140 \\\\ \n 9 & 5 & 10 & 10 & 10 & 5 & 100 & 2 & 2 & 2 & 2 & 1 & 93 & 2 & 94 & 93 & 93 & 1 & 93 & 188 \\\\ \n 10 & 10 & 10 & 10 & 10 & 5 & 100 & 1 & 1 & 1 & 1 & 1 & 93 & 1 & 94 & 93 & 93 & 1 & 93 & 207 \\\\ \n 11 & 5 & 15 & 15 & 15 & 5 & 100 & 2 & 3 & 3 & 3 & 1 & 93 & 2 & 94 & 93 & 93 & 1 & 93 & 221 \\\\ \n \\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of AIC, GC, GFC, SH, Cp and the FPE method for the selection of the number of index constituents for the LCRIX, LECRIX and LEFCRIX in the 11 periods}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "CRIX an index for cryptocurrencies", "authors": ["Simon Trimborn", "Wolfgang Karl Härdle"], "url": "https://arxiv.org/abs/2009.09782v1", "attribution": "\"CRIX an index for cryptocurrencies\" by Simon Trimborn and Wolfgang Karl Härdle, arXiv:2009.09782v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.05401v5_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Formulas or descriptions of different stepsize schedules.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|l}\n\\hline\nName & Formula/Description \\\\ \\hline\nconstant & $\\alpha(t)=\\alpha_0$ \\\\\nlinear & $\\alpha(t)=\\frac{T-t}{T}(\\alpha_0-\\alpha_1)+\\alpha_1$ \\\\\nexponential & $\\alpha(t)=\\alpha_0\\cdot\\left(\\alpha_1/\\alpha_0\\right)^{t/T}$ \\\\\nstep & Remain the same stepsize within one ``step\", and decay between ``steps\". \\\\\ncyclical & Follow Eq.~1 in . \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Entropy-MCMC: Sampling from Flat Basins with Ease", "authors": ["Bolian Li", "Ruqi Zhang"], "url": "https://arxiv.org/abs/2310.05401v5", "attribution": "\"Entropy-MCMC: Sampling from Flat Basins with Ease\" by Bolian Li and Ruqi Zhang, arXiv:2310.05401v5, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.00503v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{24Hz token rate for voice conversion experiment.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Method} & \\textbf{Q-MOS} & \\textbf{S-MOS} & \\textbf{WER (\\%)} \\\\\n\\midrule\nboundary + max pooling (w/o CALE) & 4.02 & 3.88 & 7.1 \\\\\nboundary + average pooling (w/o CALE) & 3.97 & 3.85 & 7.4 \\\\\nboundary + attention pooling (w/o CALE) & 4.06 & 3.92 & 6.9 \\\\\n\\textbf{Ours (with CALE)} & \\textbf{4.10} & \\textbf{3.95} & \\textbf{6.7} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Entropy-based Coarse and Compressed Semantic Speech Representation Learning", "authors": ["Jialong Zuo", "Guangyan Zhang", "Minghui Fang", "Shengpeng Ji", "Xiaoqi Jiao", "Jingyu Li", "Yiwen Guo", "Zhou Zhao"], "url": "https://arxiv.org/abs/2509.00503v1", "attribution": "\"Entropy-based Coarse and Compressed Semantic Speech Representation Learning\" by Jialong Zuo, Guangyan Zhang, Minghui Fang, Shengpeng Ji, Xiaoqi Jiao, Jingyu Li, Yiwen Guo, and Zhou Zhao, arXiv:2509.00503v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.02552v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average runtime in seconds and number of cluster features after BETULA initialization for different algorithms and data generators with $N$\\,=\\,50\\,000 and $d$\\,=\\,5 dimensions.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|rr|rr}\n \\toprule\n \\multirow{2}{*}{Algorithm} & \\multirow{2}{*}{Input} & \\multicolumn{2}{c|}{Uniform} & \\multicolumn{2}{c}{Gaussian} \\\\\n & & Runtime & |CF| & Runtime & |CF| \\\\\n \\midrule\n Anderberg & full data & 142.96 & - & 147.05 & - \\\\\n Anderberg & CF centers & 12.99 & 17482.4 & 4.88 & 9496.7 \\\\\n Anderberg & CF linkage & 12.85 & 17482.4 & 4.71 & 9496.7 \\\\\n NN-Chain & full data & 49.64 & - & 49.44 & - \\\\\n NN-Chain & CF aggregation & 11.39 & 17482.4 & 4.36 & 9496.7 \\\\\n NN-Chain & CF linkage & 7.18 & 17482.4 & 3.15 & 9496.7 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Data Aggregation for Hierarchical Clustering", "authors": ["Erich Schubert", "Andreas Lang"], "url": "https://arxiv.org/abs/2309.02552v1", "attribution": "\"Data Aggregation for Hierarchical Clustering\" by Erich Schubert and Andreas Lang, arXiv:2309.02552v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2303.04581v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[table]{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|ccccccccccc|}\n \\hline\n & 0 & 0.1& 0.2& 0.3& 0.4& 0.5& 0.6& 0.7 & 0.8& 0.9&1\\cr\\cline{1-12}\n Iron ores & -0.374 & -1.651& -2.246& \\cellcolor{yellow}-3.139& \\cellcolor{yellow}-4.309& \\cellcolor{yellow}-6.040& \\cellcolor{yellow} -8.691& \\cellcolor{yellow} -11.774 & \\cellcolor{yellow} -18.417& \\cellcolor{yellow}-27.614& \\cellcolor{yellow}-78.621\\cr\n Rebar & -2.079 & -2.403& \\cellcolor{yellow}-3.049& \\cellcolor{yellow}-4.156& \\cellcolor{yellow}-5.632& \\cellcolor{yellow} -7.860& \\cellcolor{yellow} -11.267& \\cellcolor{yellow}-15.182 & \\cellcolor{yellow}-23.465& \\cellcolor{yellow}-34.486& \\cellcolor{yellow}-78.921\\cr\nHot rolled coils & -1.690 & -2.060& -2.771&\\cellcolor{yellow} -3.889& \\cellcolor{yellow} -5.361& \\cellcolor{yellow} -7.540& \\cellcolor{yellow}-10.852& \\cellcolor{yellow} -14.656 & \\cellcolor{yellow}-22.709& \\cellcolor{yellow}-33.473& \\cellcolor{yellow}-78.978\\cr\nStainless steel & -1.830 & -2.071& -2.638&\\cellcolor{yellow} -3.495& \\cellcolor{yellow}-4.699& \\cellcolor{yellow} -6.501& \\cellcolor{yellow}-9.281& \\cellcolor{yellow} -12.537 & \\cellcolor{yellow}-19.680& \\cellcolor{yellow}-29.717& \\cellcolor{yellow}-93.928\\cr\nFerro Silicon & -1.966 & -2.168& -2.672& \\cellcolor{yellow}-3.488& \\cellcolor{yellow}-4.623& \\cellcolor{yellow}-6.331& \\cellcolor{yellow}-8.979& \\cellcolor{yellow}-12.024 &\\cellcolor{yellow} -18.528& \\cellcolor{yellow}-27.166&\\cellcolor{yellow}-62.703\\cr\nManganese silicon & -2.240 & -2.558& \\cellcolor{yellow}-3.257& \\cellcolor{yellow}-4.305& \\cellcolor{yellow}-5.767& \\cellcolor{yellow}-7.911& \\cellcolor{yellow}-11.203& \\cellcolor{yellow}-14.944 & \\cellcolor{yellow}-22.643& \\cellcolor{yellow}-32.366&\\cellcolor{yellow}-62.569\\cr\nShanghai Copper & -2.354 & -2.592&\\cellcolor{yellow} -3.158& \\cellcolor{yellow}-4.099&\\cellcolor{yellow} -5.417& \\cellcolor{yellow}-7.428& \\cellcolor{yellow}-10.519& \\cellcolor{yellow}-14.148 &\\cellcolor{yellow} -21.988& \\cellcolor{yellow}-32.780&\\cellcolor{yellow}-89.515\\cr\nShanghai Aluminium & -1.744 & -1.939& -2.368& \\cellcolor{yellow}-3.073& \\cellcolor{yellow}-4.070& \\cellcolor{yellow}-5.584&\\cellcolor{yellow} -7.928& \\cellcolor{yellow}-10.714 & \\cellcolor{yellow}-16.764& \\cellcolor{yellow}-25.439&\\cellcolor{yellow}-90.908\\cr\nShanghai Zinc & -1.906 & -2.233& \\cellcolor{yellow}-2.919&\\cellcolor{yellow} -3.962& \\cellcolor{yellow}-5.374& \\cellcolor{yellow}-7.461& \\cellcolor{yellow}-10.657& \\cellcolor{yellow}-14.355 & \\cellcolor{yellow}-22.326& \\cellcolor{yellow}-33.259&\\cellcolor{yellow}-89.992\\cr\nShanghai Lead &\\cellcolor{yellow} -4.584 &\\cellcolor{yellow} -5.550& \\cellcolor{yellow}-7.381&\\cellcolor{yellow} -10.119&\\cellcolor{yellow} -13.667&\\cellcolor{yellow} -18.750&\\cellcolor{yellow} -26.154& \\cellcolor{yellow}-34.291 & \\cellcolor{yellow}-48.889&\\cellcolor{yellow} -64.613&\\cellcolor{yellow}-92.167\\cr\nShanghai Nickel & -1.487 & -1.759& -2.327&\\cellcolor{yellow} -3.196& \\cellcolor{yellow}-4.363& \\cellcolor{yellow}-6.101&\\cellcolor{yellow} -8.768&\\cellcolor{yellow} -11.897 &\\cellcolor{yellow} -18.720& \\cellcolor{yellow}-28.326&\\cellcolor{yellow}-93.281\\cr\nShanghai Tin & -1.564 & -1.586& -1.722& -2.055& -2.572&\\cellcolor{yellow} -3.415& \\cellcolor{yellow}-4.769& \\cellcolor{yellow}-6.387 &\\cellcolor{yellow} -10.017& \\cellcolor{yellow}-15.297&\\cellcolor{yellow}-86.878\\cr\nInternational Copper & -2.266 & -2.505& \\cellcolor{yellow}-3.070& \\cellcolor{yellow}-3.995& \\cellcolor{yellow}-5.297& \\cellcolor{yellow}-7.274&\\cellcolor{yellow} -10.315& \\cellcolor{yellow}-13.888 & \\cellcolor{yellow}-21.606& \\cellcolor{yellow}-32.258&\\cellcolor{yellow}-89.528\\cr\nShanghai Gold & -2.458 & \\cellcolor{yellow}-2.966& \\cellcolor{yellow}-4.002&\\cellcolor{yellow} -5.573&\\cellcolor{yellow} -7.638&\\cellcolor{yellow} -10.680&\\cellcolor{yellow} -15.31& \\cellcolor{yellow}-20.631 & \\cellcolor{yellow}-31.752& \\cellcolor{yellow}-46.377&\\cellcolor{yellow}-101.248\\cr\nBean One &\\cellcolor{yellow} -3.791 &\\cellcolor{yellow} -4.311&\\cellcolor{yellow} -5.351&\\cellcolor{yellow} -7.042& \\cellcolor{yellow}-9.362& \\cellcolor{yellow}-12.810& \\cellcolor{yellow}-18.013&\\cellcolor{yellow} -23.870 & \\cellcolor{yellow}-35.332&\\cellcolor{yellow} -48.882&\\cellcolor{yellow}-80.202\\cr\nBean Two & -2.107 & -2.314& -2.852& \\cellcolor{yellow}-3.708&\\cellcolor{yellow} -4.895& \\cellcolor{yellow}-6.694& \\cellcolor{yellow}-9.497& \\cellcolor{yellow}-12.740 &\\cellcolor{yellow} -19.811&\\cellcolor{yellow} -29.479&\\cellcolor{yellow}-77.838\\cr\nSoybean oil & -1.827 & -2.019& -2.511&\\cellcolor{yellow} -3.279& \\cellcolor{yellow}-4.351&\\cellcolor{yellow} -5.986& \\cellcolor{yellow}-8.480&\\cellcolor{yellow} -11.395 & \\cellcolor{yellow}-17.791& \\cellcolor{yellow}-26.665&\\cellcolor{yellow}-77.745\\cr\nSoybean meal & -1.937 & -2.203& -2.800&\\cellcolor{yellow} -3.760&\\cellcolor{yellow} -5.017& \\cellcolor{yellow}-6.932& \\cellcolor{yellow}-9.885& \\cellcolor{yellow}-13.310 & \\cellcolor{yellow}-20.677& \\cellcolor{yellow}-30.741&\\cellcolor{yellow}-79.534\\cr\nVegetable oil & -1.858 & -2.030& -2.430& \\cellcolor{yellow}-3.118&\\cellcolor{yellow} -4.092&\\cellcolor{yellow} -5.589& \\cellcolor{yellow}-7.922& \\cellcolor{yellow}-10.653 & \\cellcolor{yellow}-16.647&\\cellcolor{yellow} -25.071&\\cellcolor{yellow}-78.483\\cr\nVegetable meal & -2.092 & -2.345& \\cellcolor{yellow}-2.897& \\cellcolor{yellow}-3.826&\\cellcolor{yellow} -5.059& \\cellcolor{yellow}-6.958&\\cellcolor{yellow} -9.893& \\cellcolor{yellow}-13.292 & \\cellcolor{yellow}-20.630& \\cellcolor{yellow}-30.646&\\cellcolor{yellow}-78.871\\cr\nPalm oil & -1.623 & -1.771& -2.155& -2.791& \\cellcolor{yellow}-3.685&\\cellcolor{yellow} -5.056&\\cellcolor{yellow} -7.173&\\cellcolor{yellow} -9.644 &\\cellcolor{yellow} -15.128& \\cellcolor{yellow}-22.870&\\cellcolor{yellow}-77.838\\cr\nCorn & -1.765 & -2.046& -2.640&\\cellcolor{yellow} -3.570&\\cellcolor{yellow} -4.824& \\cellcolor{yellow}-6.695& \\cellcolor{yellow}-9.579&\\cellcolor{yellow} -12.925 & \\cellcolor{yellow}-20.128&\\cellcolor{yellow} -30.000&\\cellcolor{yellow}-80.550\\cr\nCorn Starch & -2.273 & -2.572&\\cellcolor{yellow} -3.234&\\cellcolor{yellow} -4.312& \\cellcolor{yellow}-5.765& \\cellcolor{yellow}-7.969&\\cellcolor{yellow} -11.349&\\cellcolor{yellow} -15.259 &\\cellcolor{yellow} -23.566& \\cellcolor{yellow}-34.661&\\cellcolor{yellow}-80.752\\cr\nEggs & -2.652 & \\cellcolor{yellow}-2.947&\\cellcolor{yellow} -3.682& \\cellcolor{yellow}-4.894& \\cellcolor{yellow}-6.521& \\cellcolor{yellow}-8.947& \\cellcolor{yellow}-12.566&\\cellcolor{yellow} -16.684 & \\cellcolor{yellow}-24.942& \\cellcolor{yellow}-34.955&\\cellcolor{yellow}-61.599\\cr\nNo.1 Cotton & -1.459 & -1.544& -1.820& -2.295&\\cellcolor{yellow} -2.991&\\cellcolor{yellow} -4.050& \\cellcolor{yellow}-5.728&\\cellcolor{yellow} -7.712 &\\cellcolor{yellow} -12.121&\\cellcolor{yellow} -18.468&\\cellcolor{yellow}-77.492\\cr\nCotton Yarn & -1.813 & -1.913& -2.278&\\cellcolor{yellow} -2.876&\\cellcolor{yellow} -3.755& \\cellcolor{yellow}-5.084& \\cellcolor{yellow}-7.199& \\cellcolor{yellow}-9.701 & \\cellcolor{yellow}-15.203& \\cellcolor{yellow}-23.017&\\cellcolor{yellow}-77.862\\cr\nWhite sugar & -2.850 & \\cellcolor{yellow}-3.024& \\cellcolor{yellow}-3.550& \\cellcolor{yellow}-4.526&\\cellcolor{yellow} -5.885& \\cellcolor{yellow}-8.001&\\cellcolor{yellow} -11.299&\\cellcolor{yellow} -15.096 & \\cellcolor{yellow}-23.256&\\cellcolor{yellow} -34.188&\\cellcolor{yellow}-79.838\\cr\nApple & -0.491 & -0.733& -1.177& -1.803& -2.579&\\cellcolor{yellow} -3.697& \\cellcolor{yellow}-5.378& \\cellcolor{yellow}-7.307 & \\cellcolor{yellow}-11.504& \\cellcolor{yellow}-17.424&\\cellcolor{yellow}-61.124\\cr\nRed dates & -1.229 & -1.393& -1.758& -2.353&\\cellcolor{yellow} -3.151& \\cellcolor{yellow}-4.355&\\cellcolor{yellow} -6.222& \\cellcolor{yellow}-8.409 &\\cellcolor{yellow} -13.178&\\cellcolor{yellow} -19.858&\\cellcolor{yellow}-62.742\\cr\nPeanuts & -1.395 & -1.646& -2.128&\\cellcolor{yellow} -2.870& \\cellcolor{yellow}-3.859&\\cellcolor{yellow} -5.344& \\cellcolor{yellow}-7.615&\\cellcolor{yellow} -10.254 &\\cellcolor{yellow} -15.930& \\cellcolor{yellow}-23.642&\\cellcolor{yellow}-60.799\\cr\nStalked rice & -0.942 & -1.222& -1.775& -2.597& \\cellcolor{yellow}-3.632&\\cellcolor{yellow} -5.161& \\cellcolor{yellow}-7.499& \\cellcolor{yellow}-10.210 &\\cellcolor{yellow} -16.114& \\cellcolor{yellow}-24.446&\\cellcolor{yellow}-81.155\\cr\nPig & -1.966 & -1.760& -1.702& -1.784& -2.242& \\cellcolor{yellow}-2.868& \\cellcolor{yellow}-3.975& \\cellcolor{yellow}-5.281 &\\cellcolor{yellow} -8.243&\\cellcolor{yellow} -12.613&\\cellcolor{yellow}-60.838\\cr\nFuel & -1.428 & -1.615& -2.019& -2.704& \\cellcolor{yellow}-3.618& \\cellcolor{yellow}-5.016& \\cellcolor{yellow}-7.184&\\cellcolor{yellow} -9.724 &\\cellcolor{yellow} -15.279& \\cellcolor{yellow}-23.158&\\cellcolor{yellow}-79.110\\cr\nLiquefied Petroleum Gas & -1.353 & -1.570& -2.036& -2.769&\\cellcolor{yellow} -3.750& \\cellcolor{yellow}-5.213& \\cellcolor{yellow}-7.478& \\cellcolor{yellow}-10.109 & \\cellcolor{yellow}-15.865& \\cellcolor{yellow}-23.974&\\cellcolor{yellow}-78.700\\cr\nEthylene glycol & -2.809 &\\cellcolor{yellow} -3.234&\\cellcolor{yellow} -4.156&\\cellcolor{yellow} -5.650& \\cellcolor{yellow}-7.603&\\cellcolor{yellow} -10.516& \\cellcolor{yellow}-14.920& \\cellcolor{yellow}-19.927 & \\cellcolor{yellow}-30.077&\\cellcolor{yellow} -42.728&\\cellcolor{yellow}-79.186\\cr\nPolyethylene & -2.615 &\\cellcolor{yellow} -3.069& \\cellcolor{yellow}-4.063&\\cellcolor{yellow} -5.545& \\cellcolor{yellow}-7.515& \\cellcolor{yellow}-10.414&\\cellcolor{yellow} -14.779& \\cellcolor{yellow}-19.733 & \\cellcolor{yellow}-29.808&\\cellcolor{yellow} -42.343&\\cellcolor{yellow}-78.727\\cr\nPTA & -1.728 & -1.870& -2.268&\\cellcolor{yellow} -2.961& \\cellcolor{yellow}-3.392& \\cellcolor{yellow}-5.391& \\cellcolor{yellow}-7.680&\\cellcolor{yellow} -10.369 & \\cellcolor{yellow}-16.236&\\cellcolor{yellow} -24.498&\\cellcolor{yellow}-79.117\\cr\nPolyvinyl chloride & -1.502 & -1.905& -2.792& \\cellcolor{yellow}-4.053&\\cellcolor{yellow} -5.670& \\cellcolor{yellow}-8.015& \\cellcolor{yellow}-11.542&\\cellcolor{yellow} -15.581 & \\cellcolor{yellow}-24.040& \\cellcolor{yellow}-35.189&\\cellcolor{yellow}-79.259\\cr\nPolypropylene &\\cellcolor{yellow} -3.028 & \\cellcolor{yellow}-3.635& \\cellcolor{yellow}-4.872& \\cellcolor{yellow}-6.724& \\cellcolor{yellow}-9.145&\\cellcolor{yellow} -12.659& \\cellcolor{yellow}-17.878& \\cellcolor{yellow}-23.733 &\\cellcolor{yellow} -35.080& \\cellcolor{yellow}-48.368&\\cellcolor{yellow}-78.921\\cr\nStyrene & -2.438 & -2.555& \\cellcolor{yellow}-3.124&\\cellcolor{yellow} -4.137&\\cellcolor{yellow} -5.491&\\cellcolor{yellow} -7.554& \\cellcolor{yellow}-10.763& \\cellcolor{yellow}-14.514 & \\cellcolor{yellow}-22.418& \\cellcolor{yellow}-33.132&\\cellcolor{yellow}-79.563\\cr\nPure soda & -2.377 & -2.384& -2.543&\\cellcolor{yellow} -2.966& \\cellcolor{yellow}-3.692& \\cellcolor{yellow}-4.866& \\cellcolor{yellow}-6.771& \\cellcolor{yellow}-9.041 & \\cellcolor{yellow}-14.088& \\cellcolor{yellow}-21.360&\\cellcolor{yellow}-77.330\\cr\nUrea & -2.068 & -2.300& -2.748&\\cellcolor{yellow} -3.541& \\cellcolor{yellow}-4.648&\\cellcolor{yellow} -6.345& \\cellcolor{yellow}-8.960& \\cellcolor{yellow}-11.991 & \\cellcolor{yellow}-18.450& \\cellcolor{yellow}-27.080&\\cellcolor{yellow}-62.846\\cr\nRubber & -2.720 & \\cellcolor{yellow}-3.325& \\cellcolor{yellow}-4.504& \\cellcolor{yellow}-6.262& \\cellcolor{yellow}-8.566& \\cellcolor{yellow}-11.913&\\cellcolor{yellow} -16.925& \\cellcolor{yellow}-22.584 & \\cellcolor{yellow}-33.727& \\cellcolor{yellow}-47.110&\\cellcolor{yellow}-80.432\\cr\nNo.20 rubber &\\cellcolor{yellow} -4.229 & \\cellcolor{yellow}-4.997& \\cellcolor{yellow}-6.617& \\cellcolor{yellow}-9.088& \\cellcolor{yellow}-12.283& \\cellcolor{yellow}-16.876& \\cellcolor{yellow}-22.508& \\cellcolor{yellow}-30.773 &\\cellcolor{yellow} -43.653& \\cellcolor{yellow}-57.311&\\cellcolor{yellow}-80.153\\cr\nPaper pulp & -1.998 & -2.178& -2.657&\\cellcolor{yellow} -3.442& \\cellcolor{yellow}-4.548& \\cellcolor{yellow}-6.236& \\cellcolor{yellow}-8.869& \\cellcolor{yellow}-11.921 & \\cellcolor{yellow}-18.590& \\cellcolor{yellow}-27.822&\\cellcolor{yellow}-78.333\\cr\nStaple Fiber & -2.565 &\\cellcolor{yellow} -2.876& \\cellcolor{yellow}-3.644& \\cellcolor{yellow}-4.885& \\cellcolor{yellow}-6.526& \\cellcolor{yellow}-8.997&\\cellcolor{yellow} -12.783& \\cellcolor{yellow}-17.152 &\\cellcolor{yellow} -26.248& \\cellcolor{yellow}-38.022&\\cellcolor{yellow}-78.730\\cr\nCoking coal & -1.676 & -1.840& -2.240& \\cellcolor{yellow}-2.919&\\cellcolor{yellow} -3.860&\\cellcolor{yellow} -5.297& \\cellcolor{yellow}-7.543& \\cellcolor{yellow}-10.172 & \\cellcolor{yellow}-15.957& \\cellcolor{yellow}-24.110&\\cellcolor{yellow}-78.567\\cr\nCoke & -1.866 & -2.188& -2.858& \\cellcolor{yellow}-3.906& \\cellcolor{yellow}-5.304&\\cellcolor{yellow} -7.382& \\cellcolor{yellow}-10.562&\\cellcolor{yellow} -14.237 &\\cellcolor{yellow} -22.057& \\cellcolor{yellow}-32.601&\\cellcolor{yellow}-79.257\\cr\nCSI & -1.706 & -2.020& -2.642&\\cellcolor{yellow} -3.608& \\cellcolor{yellow}-4.899&\\cellcolor{yellow} -6.806& \\cellcolor{yellow}-9.714&\\cellcolor{yellow} -13.073 & \\cellcolor{yellow}-20.109&\\cellcolor{yellow} -29.431&\\cellcolor{yellow}-66.374\\cr\nShanghai and Shenzhen & -1.142 & -1.420& -1.971& -2.791&\\cellcolor{yellow} -3.854& \\cellcolor{yellow}-5.415& \\cellcolor{yellow}-7.795& \\cellcolor{yellow}-10.552 &\\cellcolor{yellow} -16.467&\\cellcolor{yellow} -24.594&\\cellcolor{yellow}-67.359\\cr\nSSE & -1.239 & -1.511& -2.051& \\cellcolor{yellow}-2.865&\\cellcolor{yellow} -3.931&\\cellcolor{yellow} -5.503& \\cellcolor{yellow}-7.908&\\cellcolor{yellow} -10.693 &\\cellcolor{yellow} -16.678& \\cellcolor{yellow}-24.879&\\cellcolor{yellow}-67.436\\cr\nTen debts & -1.654 & -1.836& -2.302&\\cellcolor{yellow} -3.054&\\cellcolor{yellow} -4.101&\\cellcolor{yellow} -5.657& \\cellcolor{yellow}-8.075&\\cellcolor{yellow} -10.890 & \\cellcolor{yellow}-16.922&\\cellcolor{yellow} -25.262&\\cellcolor{yellow}-67.843\\cr\nFive debts & -1.687 & -1.858& -2.328& \\cellcolor{yellow}-3.080&\\cellcolor{yellow} -4.141& \\cellcolor{yellow}-5.704& \\cellcolor{yellow}-8.136&\\cellcolor{yellow} -10.976 & \\cellcolor{yellow}-17.042& \\cellcolor{yellow}-25.401&\\cellcolor{yellow}-67.324\\cr\nSecond Debt & -1.550 & -1.769& -2.293&\\cellcolor{yellow} -3.127& \\cellcolor{yellow}-4.248& \\cellcolor{yellow}-5.911& \\cellcolor{yellow}-8.479& \\cellcolor{yellow}-11.462 & \\cellcolor{yellow}-17.814&\\cellcolor{yellow} -26.502&\\cellcolor{yellow}-68.633\\cr\nShanghai Silver & -1.724 & -2.337&\\cellcolor{yellow} -3.494& \\cellcolor{yellow}-5.141& \\cellcolor{yellow}-7.226& \\cellcolor{yellow}-10.234& \\cellcolor{yellow}-14.766&\\cellcolor{yellow} -19.955 &\\cellcolor{yellow} -30.795& \\cellcolor{yellow}-45.103&\\cellcolor{yellow}-101.618\\cr\nCrude Oil & -1.398 & -1.521& -1.833& -2.389& \\cellcolor{yellow}-3.161& \\cellcolor{yellow}-4.350&\\cellcolor{yellow} -6.203& \\cellcolor{yellow}-8.392 & \\cellcolor{yellow}-13.252& \\cellcolor{yellow}-20.301&\\cellcolor{yellow}-100.094\\cr\nLow sulphur crude & -0.759 & -1.015& -1.490& -2.197& \\cellcolor{yellow}-3.083& \\cellcolor{yellow}-4.385&\\cellcolor{yellow} -6.367& \\cellcolor{yellow}-8.658 &\\cellcolor{yellow} -13.660& \\cellcolor{yellow}-20.799&\\cellcolor{yellow}-78.810\\cr\nPetroleum bitumen & -1.114 & -1.440& -2.068&\\cellcolor{yellow} -2.987& \\cellcolor{yellow}-4.164& \\cellcolor{yellow}-5.885& \\cellcolor{yellow}-8.497& \\cellcolor{yellow}-11.516 & \\cellcolor{yellow}-18.017& \\cellcolor{yellow}-27.019&\\cellcolor{yellow}-78.242\\cr\nMethanol & -2.150 & -2.532&\\cellcolor{yellow} -3.357& \\cellcolor{yellow}-4.626&\\cellcolor{yellow} -6.293& \\cellcolor{yellow}-8.753& \\cellcolor{yellow}-12.482& \\cellcolor{yellow}-16.762 & \\cellcolor{yellow}-25.667& \\cellcolor{yellow}-37.210&\\cellcolor{yellow}-78.386\\cr\nGlass & -0.802 & -1.117& -1.701& -2.545& \\cellcolor{yellow}-3.612&\\cellcolor{yellow} -5.167&\\cellcolor{yellow} -7.524& \\cellcolor{yellow}-10.256 &\\cellcolor{yellow} -16.172&\\cellcolor{yellow} -24.481&\\cellcolor{yellow}-79.247\\cr\\cline{1-12}\n \\end{tabular}\n\\caption{ Optimal FradDiff stationarity of contract indices for china futures trading varieties.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Application of supervised learning models in the Chinese futures market", "authors": ["Fuquan Tang"], "url": "https://arxiv.org/abs/2303.04581v1", "attribution": "\"Application of supervised learning models in the Chinese futures market\" by Fuquan Tang, arXiv:2303.04581v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.13777v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\toprule\n \\textbf{Event ID} & \\textbf{Timestamp (sec)} & \\textbf{Event Type} & \\textbf{Order ID} & \\textbf{Size (shares)} & \\textbf{Price} & \\textbf{Direction} \\\\ \n \\midrule\n \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots \\\\\n 1312 & 34714.133632201 & deletion & 206833312 & 2516 & \\$11.86 & sell \\\\\n \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots \\\\\n \\midrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Example message from a LOBSTER message file. This example corresponds to the order book dynamics shown in Figure .}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective", "authors": ["Lorenzo Lucchese", "Mikko Pakkanen", "Almut Veraart"], "url": "https://arxiv.org/abs/2211.13777v3", "attribution": "\"The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective\" by Lorenzo Lucchese, Mikko Pakkanen, and Almut Veraart, arXiv:2211.13777v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.07545v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{FD Filter results for the simulated data.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|ll|}\n\\cline{3-4}\n & & \\multicolumn{2}{c|}{FD filter} \\\\ \\cline{3-4} \n & & Healthy & Faulty \\\\ \\hline\n\\multirow{2}{*}{Ground truth} & Healthy & 2474 & 1 \\\\ \\cline{2-4} \n & Faulty & 92 & 1258 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fault Isolation for the Ink Deposition Process in High-End Industrial Printers", "authors": ["Casper van Peijpe", "Farhad Ghanipoor", "Youri de Loore", "Pim Hacking", "Nathan van de Wouw", "Peyman Mohajerin Esfahani"], "url": "https://arxiv.org/abs/2412.07545v1", "attribution": "\"Fault Isolation for the Ink Deposition Process in High-End Industrial Printers\" by Casper van Peijpe, Farhad Ghanipoor, Youri de Loore, Pim Hacking, Nathan van de Wouw, and Peyman Mohajerin Esfahani, arXiv:2412.07545v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2301.11079v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Audit exemption thresholds in Norway, Nordic EU Member States, and the UK. Obtained from the Federation of European Accountants.}\n\\begin{tabular}{lccc}\n\\toprule\nCountry &Total assets (€) &\toperating revenue (€) &\tNumber of employees\\\\\n\\midrule\nNorway&\t\t2,000,000&\t500,000 &\t10 \\\\\nSweden &\t150,000 &\t300,000&\t3\t\\\\\nDenmark&\t4,837,000&\t9,674,000&\t50\t\\\\\nFinland&\t100,000&\t200,000&\t3\t\\\\\nIceland&\t1,400,000&\t2,800,000&\t50\t\\\\\nUK&\t\t6,541,000&\t13,082,000 &\t50 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Adults in the room? The auditor and dividends in small firms: Evidence from a natural experiment", "authors": ["Hakim Lyngstadås", "Johannes Mauritzen"], "url": "https://arxiv.org/abs/2301.11079v1", "attribution": "\"Adults in the room? The auditor and dividends in small firms: Evidence from a natural experiment\" by Hakim Lyngstadås and Johannes Mauritzen, arXiv:2301.11079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.18854v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of Convergence Rates for Errors}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\t\t\t\t\\hline\n\t\t\t\t& Coupling Error & Modeling Error & Total Error \\\\\n\t\t\t\t\\hline\n\t\t\t\tB-QCE & $\\varepsilon^2$ & $\\varepsilon^2$ & $\\varepsilon^2$ \\\\\n\t\t\t\tB-QHOCE & $\\varepsilon^2$ & $\\varepsilon^4$ & $\\varepsilon^2$ \\\\\n\t\t\t\tB-QCF & - & $\\varepsilon^2$ & $\\varepsilon^2$ \\\\\n\t\t\t\tB-QHOCF & - & $\\varepsilon^4$ & $\\varepsilon^4$ \\\\\n\t\t\t\t\\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Formulation and Analysis of Blended Atomistic to Higher-Order Continuum Coupling Methods for Crystalline Defects", "authors": ["Junfeng Lu", "Hao Wang", "Yangshuai Wang"], "url": "https://arxiv.org/abs/2502.18854v1", "attribution": "\"Formulation and Analysis of Blended Atomistic to Higher-Order Continuum Coupling Methods for Crystalline Defects\" by Junfeng Lu, Hao Wang, and Yangshuai Wang, arXiv:2502.18854v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.18008v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experimental datasets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllll}\n\t\t\t\\hline\n\t\t\tDataset & Abbrevation & Type & Train & Test & Dimensions & Length & Classes \\\\ \\hline\n\t\t\tArticularyWordRecognition & AWR & Motion & 275 & 300 & 9 & 144 & 25 \\\\\n\t\t\tAtrialFibrillation & AF & ECG & 15 & 15 & 2 & 640 & 3 \\\\\n\t\t\tBasicMotions & BM & HAR & 40 & 40 & 6 & 100 & 4 \\\\\n\t\t\tCharacterTrajectories & CT & Motion & 1422 & 1436 & 3 & 182 & 20 \\\\\n\t\t\tCricket & CR & HAR & 108 & 72 & 6 & 1197 & 12 \\\\\n\t\t\tEthanolConcentration & EC & HAR & 261 & 263 & 3 & 1751 & 4 \\\\\n\t\t\tFaceDetection & FD & EEG/MEG & 5890 & 3524 & 144 & 62 & 2 \\\\\n\t\t\tHandMovementDirection & HMD & EEG/MEG & 160 & 74 & 10 & 400 & 4 \\\\\n\t\t\tHeartbeat & HB & AS & 204 & 205 & 61 & 405 & 2 \\\\\n\t\t\tJapaneseVowels & JV & AS & 270 & 370 & 12 & 29 & 9 \\\\\n\t\t\tLibras & LIB & HAR & 180 & 180 & 2 & 45 & 15 \\\\\n\t\t\tLSST & LSST & Other & 2459 & 2466 & 6 & 36 & 14 \\\\\n\t\t\tMotorImagery & MI & EEG/MEG & 278 & 100 & 64 & 3000 & 2 \\\\\n\t\t\tNATOPS & NA & HAR & 180 & 180 & 24 & 51 & 6 \\\\\n\t\t\tPEMS-SF & PEMS & Other & 267 & 173 & 963 & 144 & 7 \\\\\n\t\t\tPenDigits & PD & Motion & 7494 & 3498 & 2 & 8 & 10 \\\\\n\t\t\tSelfRegulationSCP1 & SR1 & EEG/MEG & 268 & 293 & 6 & 896 & 2 \\\\\n\t\t\tSelfRegulationSCP2 & SR2 & EEG/MEG & 200 & 180 & 7 & 1152 & 2 \\\\\n\t\t\tSpokenArabicDigits & SAD & AS & 6599 & 2199 & 13 & 93 & 10 \\\\\n\t\t\tStandWalkJump & SWJ & ECG & 12 & 15 & 4 & 2500 & 3 \\\\\n\t\t\tUWaveGestureLibrary & UW & HAR & 120 & 320 & 3 & 315 & 8 \\\\ \\hline \n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Causal and Local Correlations Based Network for Multivariate Time Series Classification", "authors": ["Mingsen Du", "Yanxuan Wei", "Xiangwei Zheng", "Cun Ji"], "url": "https://arxiv.org/abs/2411.18008v1", "attribution": "\"Causal and Local Correlations Based Network for Multivariate Time Series Classification\" by Mingsen Du, Yanxuan Wei, Xiangwei Zheng, and Cun Ji, arXiv:2411.18008v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.01026v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccccccccccccccccccccccc}\n$j$ & 0 & 1& 2& 3& 4& 5& 6& 7& 8& 9&10&11&12&13&14&15&16&17&18&19\\\\\n\\hline\n$h(j)$ & 0& 1& 1& 2& 3& 4& 4& 5& 5& 6& 7& 7& 8& 9&10&10&11&12&13&13\n\\end{tabular}\n\\end{adjustbox}\n\\caption{First few values of the $h$ sequence.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Narayana Morphism and Related Words", "authors": ["Jeffrey Shallit"], "url": "https://arxiv.org/abs/2503.01026v3", "attribution": "\"The Narayana Morphism and Related Words\" by Jeffrey Shallit, arXiv:2503.01026v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11787v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Rounding Marks for Power Law Distributions}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccc}\n$f$& \\multicolumn{11}{c}{$\\beta$} \\\\\n&$-\\infty$&-4&-3 &-2&-1&0&+1&+2&+3&+4&$+\\infty$\\\\\n\\hline\t% horizontal line to separate headings from data\n0&0.00&\t0.00\t&0.00\t&0.00&\t0.00&\t0.37&\t0.50\t&0.58\t&0.63\t&0.67&1.00\\\\\n1&1.00&\t1.36&\t1.39&\t1.41&\t1.44&\t1.47&\t1.50&\t1.53\t&1.55\t&1.58&2.00\\\\\n2&2.00&\t2.42&\t2.43&\t2.45&\t2.47&\t2.48&\t2.50&\t2.52&\t2.53&\t2.55&3.00\\\\\n3&3.00&3.44\t&3.45&\t3.46&\t3.48&\t3.49\t&3.50\t&3.51\t&3.52\t&3.54&4.00\\\\\n4&4.00&\t4.45&\t4.46\t&4.47&\t4.48\t&4.49\t&4.50\t&4.51\t&4.52&\t4.53&5.00\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A New Perspective on Impartial and Unbiased Apportionment", "authors": ["Ross Hyman", "Nicolaus Tideman"], "url": "https://arxiv.org/abs/2201.11787v2", "attribution": "\"A New Perspective on Impartial and Unbiased Apportionment\" by Ross Hyman and Nicolaus Tideman, arXiv:2201.11787v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.05558v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experiment results on the transformed layers in wav2vec 2.0.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n\t\t\\toprule\n\t\t\\multirow{2}[2]{*}{method} & \\multirow{2}[2]{*}{Shallow transformer} & \\multirow{2}[2]{*}{Deep transformer} & \\multirow{2}[2]{*}{ACC(\\%)} & \\multirow{2}[2]{*}{WF1(\\%)} \\\\\n\t\t& & & & \\\\\n\t\t\\midrule\n\t\tconcat & 12 & 0 & 66.67 & 66.78 \\\\\n\t\t\\midrule\n\t\t\\multirow{4}[2]{*}{Attention} & 11 & 1 & 68.61 & 68.55 \\\\\n\t\t& 10 & 2 & 68.54 & 68.32 \\\\\n\t\t& 9 & 3 & 70.53 & 70.6 \\\\\n\t\t& 8 & 4 & 69.29 & 69.06 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "WavFusion: Towards wav2vec 2.0 Multimodal Speech Emotion Recognition", "authors": ["Feng Li", "Jiusong Luo", "Wanjun Xia"], "url": "https://arxiv.org/abs/2412.05558v1", "attribution": "\"WavFusion: Towards wav2vec 2.0 Multimodal Speech Emotion Recognition\" by Feng Li, Jiusong Luo, and Wanjun Xia, arXiv:2412.05558v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.12502v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{1D GP Bayesian Optimization with Gaussian Process Upper Confidence Bound (UCB) criterion.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\\toprule\nModel & Matérn 3/2 & Periodic & RBF \\\\\n\\midrule\nNP & $0.134\\pm0.009$ & $0.123\\pm0.010$ & $0.077\\pm0.006$ \\\\\nCNP & $0.108\\pm0.008$ & $0.132\\pm0.010$ & $0.068\\pm0.006$ \\\\\nBNP & $0.126\\pm0.008$ & $0.133\\pm0.010$ & $0.067\\pm0.006$ \\\\\nANP & $0.022\\pm0.003$ & $0.047\\pm0.006$ & $0.009\\pm0.003$ \\\\\nCANP & $0.021\\pm0.003$ & $0.049\\pm0.005$ & $0.010\\pm0.002$ \\\\\nBANP & $0.025\\pm0.004$ & $0.046\\pm0.006$ & $0.012\\pm0.003$ \\\\\nConvCNP & $0.022\\pm0.003$ & $0.046\\pm0.005$ & $0.005\\pm0.002$ \\\\\nTNP-D & $0.016\\pm0.003$ & $0.045\\pm0.005$ & $0.006\\pm0.002$ \\\\\nTNP-KR: PERF & $0.015\\pm0.002$ & $0.045\\pm0.005$ & $0.006\\pm0.002$ \\\\\nTNP-KR: DKA & $0.021\\pm0.004$ & $0.048\\pm0.005$ & $0.002\\pm0.000$ \\\\\nTNP-KR: SA & $0.020\\pm0.003$ & $0.045\\pm0.006$ & $0.004\\pm0.001$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Transformer Neural Processes - Kernel Regression", "authors": ["Daniel Jenson", "Jhonathan Navott", "Mengyan Zhang", "Makkunda Sharma", "Elizaveta Semenova", "Seth Flaxman"], "url": "https://arxiv.org/abs/2411.12502v3", "attribution": "\"Transformer Neural Processes - Kernel Regression\" by Daniel Jenson, Jhonathan Navott, Mengyan Zhang, Makkunda Sharma, Elizaveta Semenova, and Seth Flaxman, arXiv:2411.12502v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.21253v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Descriptive Statistics of Suspense and Surprise in Women’s Matches}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccccccc}\n \\toprule\n League & Metric & N & Mean & Median & SD & Min & Max& BM & $<$ BM \\\\\n \\midrule\n \\multirow{2}{*}{Top 5 Leagues} \n & Suspense & 725 & 4.93 &5.45 & 2.47 & 0.16&9.08 &5.96& *** \\\\\n & Surprise & 725 &1.24 & 1.01 & 0.84 &0.10 & 4.96&1.17& \\\\\n \\midrule\n \\multirow{2}{*}{England} \n & Suspense & 132 & 4.94 & 5.27 & 2.36 & 0.27 & 8.33 &5.96&*** \\\\\n & Surprise & 132 & 1.33 & 1.06 & 0.91 & 0.16 & 4.70 &1.17& \\\\\n \\midrule\n \\multirow{2}{*}{Germany} \n & Suspense & 132 & 4.65 & 5.10 & 2.47 & 0.16 & 8.35 &5.96& ***\\\\\n & Surprise & 132 & 1.11 & 0.88 & 0.77 & 0.15 & 3.56 &1.17& \\\\\n \\midrule\n \\multirow{2}{*}{Spain} \n & Suspense & 240 & 5.03 & 5.63 & 2.43 & 0.26 & 9.08 &5.96&*** \\\\\n & Surprise & 240 & 1.21 & 1.01 & 0.77 & 0.12 & 4.03 &1.17& \\\\\n \\midrule\n \\multirow{2}{*}{Italy} \n & Suspense & 90 & 5.10& 5.80& 2.53 &0.27 & 8.35&5.96&*** \\\\\n & Surprise & 90 & 1.21 & 1.13 & 0.78& 0.10 &4.46 &1.17& \\\\\n \\midrule\n \\multirow{2}{*}{France} \n & Suspense & 131 & 4.88 & 5.48 & 2.58 & 0.36 & 8.58&5.96&*** \\\\\n & Surprise & 131 & 1.34 & 1.13 & 0.95 & 0.17 & 4.96&1.17& \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Suspense and Surprise in European Football", "authors": ["Raphael Flepp", "Tim Pawlowski", "Travis Richardson"], "url": "https://arxiv.org/abs/2506.21253v1", "attribution": "\"Suspense and Surprise in European Football\" by Raphael Flepp, Tim Pawlowski, and Travis Richardson, arXiv:2506.21253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10242v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CHARACTERISTICS OF THE LARGE SCALE DATASETS}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\t\t\\hline\n\t\t\\multirow{2}{*}{Dataset} & \\multicolumn{6}{c}{Characteristics}\\\\\n\t\t\\cline{2-7} & N & \tD & \tcovariance & \tross-validation & \tNumber of samples in class 1 & \tNumber of samples in lass 2\\\\\n\t\t\\hline\n\t\tSynth1 & \t15000 &\t200 &\t 250 & \t5 & \t7592 & \t7408\\\\\n\t\tSynth2 &\t15000 &\t200 & \t0 & \t5 & \t6619 & \t8381\\\\\n\t\tSynth3 & \t15000 & \t200 & \t250 & \t10 & \t8434 & \t6566\\\\\n\t\tSynth4 & \t15000 & \t200 & \t0 & \t10 & \t6619 & \t8381\\\\\n\t\tSynth5 & \t15000 & \t200 & \t250 & \t3 & \t7707 & \t7293\\\\\n\t\tSynth6 & \t15000 & \t200 & \t0 & \t3 & \t7796 & \t7204\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "GMM Discriminant Analysis with Noisy Label for Each Class", "authors": ["Jian-wei Liu", "Zheng-ping Ren", "Run-kun Lu", "Xiong-lin Luo"], "url": "https://arxiv.org/abs/2201.10242v1", "attribution": "\"GMM Discriminant Analysis with Noisy Label for Each Class\" by Jian-wei Liu, Zheng-ping Ren, Run-kun Lu, and Xiong-lin Luo, arXiv:2201.10242v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.10933v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc} \n \\toprule\n \\textbf{Variable} & \\textbf{Value} \\\\\n \\midrule\n Mean riskless rate & 1.03\\\\\n Mean deposits over assets & 0.65\\\\ \n \n Prob. of being in disaster, $\\zeta=\\underline{\\zeta}$ & 1.45\\%\\\\ \n \n Prob. of financial crisis & 0.50\\%\\\\ \n \\bottomrule \n \\end{tabular}\n\\end{adjustbox}\n\\caption{ Characteristics of the equilibrium.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Bailouts and Redistribution", "authors": ["Mikayel Sukiasyan"], "url": "https://arxiv.org/abs/2509.10933v1", "attribution": "\"Bailouts and Redistribution\" by Mikayel Sukiasyan, arXiv:2509.10933v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.04047v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Recovery of Structural Model Parameters Under Different Measurement Sizes}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrrrrrrrrrrrrrr}\n\\toprule\n & & \\multicolumn{5}{c}{Bias} & \\multicolumn{5}{c}{RMSE} & \\multicolumn{5}{c}{Coverage} \\\\\n\\cmidrule(lr){3-7} \\cmidrule(lr){8-12} \\cmidrule(lr){13-17}\n\\textit{Model} & $J$ & $\\tau_0$ & $\\tau_1$ & $\\omega$ & $\\beta$ & $\\gamma$ & $\\tau_0$ & $\\tau_1$ & $\\omega$ & $\\beta$ & $\\gamma$ & $\\tau_0$ & $\\tau_1$ & $\\omega$ & $\\beta$ & $\\gamma$ \\\\\n\\midrule\nRasch & 50 & 0.000 & -0.004 & 0.004 & -0.005 & 0.001 & 0.045 & 0.044 & 0.064 & 0.051 & 0.026 & 0.960 & 0.970 & 0.920 & 0.960 & 0.925 \\\\\n & 100 & 0.001 & -0.002 & -0.001 & -0.002 & -0.001 & 0.046 & 0.048 & 0.055 & 0.054 & 0.028 & 0.920 & 0.950 & 0.960 & 0.930 & 0.965 \\\\\n & 200 & -0.001 & -0.004 & 0.008 & 0.007 & -0.001 & 0.042 & 0.044 & 0.057 & 0.049 & 0.030 & 0.970 & 0.950 & 0.930 & 0.935 & 0.935 \\\\\n\\cmidrule{1-17}\n2PL & 50 & 0.010 & -0.015 & 0.023 & -0.002 & 0.000 & 0.047 & 0.059 & 0.071 & 0.057 & 0.028 & 0.970 & 0.930 & 0.940 & 0.970 & 0.960 \\\\\n & 100 & 0.008 & -0.002 & 0.001 & -0.009 & 0.003 & 0.046 & 0.051 & 0.060 & 0.065 & 0.028 & 0.970 & 0.970 & 0.960 & 0.955 & 0.925 \\\\\n & 200 & -0.002 & 0.001 & -0.001 & -0.007 & 0.000 & 0.043 & 0.055 & 0.065 & 0.054 & 0.027 & 0.980 & 0.910 & 0.950 & 0.935 & 0.945 \\\\\n\\cmidrule{1-17}\nGPCM & 50 & -0.004 & 0.009 & -0.014 & -0.018 & -0.003 & 0.051 & 0.056 & 0.065 & 0.055 & 0.028 & 0.930 & 0.930 & 0.910 & 0.970 & 0.935 \\\\\n & 100 & 0.005 & 0.002 & 0.003 & -0.015 & 0.005 & 0.052 & 0.044 & 0.051 & 0.048 & 0.028 & 0.910 & 0.940 & 0.990 & 0.960 & 0.955 \\\\\n & 200 & 0.002 & 0.002 & -0.003 & -0.015 & 0.004 & 0.049 & 0.052 & 0.064 & 0.048 & 0.030 & 0.950 & 0.920 & 0.910 & 0.960 & 0.945 \\\\\n\\cmidrule{1-17}\nGRM & 50 & 0.002 & 0.008 & -0.006 & -0.009 & 0.003 & 0.047 & 0.049 & 0.054 & 0.058 & 0.026 & 0.940 & 0.950 & 0.950 & 0.955 & 0.975 \\\\\n & 100 & 0.007 & 0.002 & -0.002 & -0.005 & 0.001 & 0.048 & 0.046 & 0.057 & 0.057 & 0.025 & 0.970 & 0.940 & 0.970 & 0.955 & 0.960 \\\\\n & 200 & -0.001 & 0.004 & -0.001 & -0.017 & 0.003 & 0.051 & 0.051 & 0.059 & 0.048 & 0.028 & 0.940 & 0.970 & 0.960 & 0.960 & 0.955 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fully Latent Principal Stratification With Measurement Models", "authors": ["Sooyong Lee", "Adam C Sales", "Hyeon-Ah Kang", "Tiffany A. Whittaker"], "url": "https://arxiv.org/abs/2309.04047v2", "attribution": "\"Fully Latent Principal Stratification With Measurement Models\" by Sooyong Lee, Adam C Sales, Hyeon-Ah Kang, and Tiffany A. Whittaker, arXiv:2309.04047v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09230v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Impact of Macroeconomic Inputs on Deposits}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\\toprule\n\\textbf{} & \\textbf{Reserves} & \\textbf{Total Loans} & \\textbf{Retail Loans} \\\\ \\hline\nRetail & -6\\% & 53\\% & -23\\% \\\\\nWholesale & 61\\% & 64\\% & -23\\% \\\\\nTotal & 54\\% & 116\\% & -45\\% \\\\ \n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits", "authors": ["Ni Zhan"], "url": "https://arxiv.org/abs/2101.09230v1", "attribution": "\"Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits\" by Ni Zhan, arXiv:2101.09230v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00400v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l}\n\\hline\n\\textbf{Dataset} & \\textbf{\\# contests} & \\textbf{avg. \\# participants / contest} \\\\ \\hline\nCodeforces & 1087 & 2999 \\\\ %\\hline\nTopCoder & 2023 & 403 \\\\ %\\hline\nReddit & 1000 & 20 \\\\\nSynthetic & 50 & 2500 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Summary of test datasets.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An Elo-like System for Massive Multiplayer Competitions", "authors": ["Aram Ebtekar", "Paul Liu"], "url": "https://arxiv.org/abs/2101.00400v1", "attribution": "\"An Elo-like System for Massive Multiplayer Competitions\" by Aram Ebtekar and Paul Liu, arXiv:2101.00400v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.03217v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n \\hline\n Criterion & 1 & 2 & 3 & 4 \\\\ \n \\hline\n AIC & 12781.58 & 10559.51 & \\textbf{10454.77} & 10457.10 \\\\ \n BIC & 12832.06 & 10631.62 & \\textbf{10548.51} & 10572.47 \\\\ \n DIC & 12781.36 & 10559.41 & \\textbf{10453.53} & 10459.58 \\\\ \n Neg log marginal likelihood & 6405.793 & 5299.607 & \\textbf{5246.078} & 5249.384 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Information criteria and marginal likelihoods for results from models with 1--4 transitions. Bold indicates the smallest value for each criterion.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Bayesian approach to deconvolution in well test analysis", "authors": ["Themistoklis Botsas", "Jonathan A. Cumming", "Ian H. Jermyn"], "url": "https://arxiv.org/abs/2012.03217v1", "attribution": "\"A Bayesian approach to deconvolution in well test analysis\" by Themistoklis Botsas, Jonathan A. Cumming, and Ian H. Jermyn, arXiv:2012.03217v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.10467v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n \\toprule\n \\textbf{Model} & \\textbf{60\\% labels} & \\textbf{80\\% labels} & \\textbf{100\\% labels} \\\\ \\midrule\n GAT & 37.90 $\\pm$ 0.41 & 39.22 $\\pm$ 0.71 & 33.75 $\\pm$ 3.12 \\\\\n \\textbf{+MGM} & \\textbf{40.80 $\\pm$ 3.83} & \\textbf{41.99 $\\pm$ 1.44} & \\textbf{43.63 $\\pm$ 2.80} \\\\ \n FiLM & 39.89 $\\pm$ 1.69 & 38.59 $\\pm$ 4.30 & 43.32 $\\pm$ 2.25 \\\\\n \\textbf{+MGM} & \\textbf{42.93 $\\pm$ 4.00} & \\textbf{38.62 $\\pm$ 2.73} & \\textbf{49.68 $\\pm$ 1.62} \\\\ \n FAGCN & 24.32 $\\pm$ 3.18 & 22.73 $\\pm$ 0.00 & 24.77 $\\pm$ 7.52 \\\\\n \\textbf{+MGM} & \\textbf{34.10 $\\pm$ 7.36} & \\textbf{39.89 $\\pm$ 4.04} & \\textbf{48.77 $\\pm$ 0.00} \\\\ \n GATv2 & 40.54 $\\pm$ 1.47 & 42.14 $\\pm$ 2.76 & 51.42 $\\pm$ 2.32 \\\\\n \\textbf{+MGM} & \\textbf{42.98 $\\pm$ 2.51} & \\textbf{44.35 $\\pm$ 2.14} & \\textbf{54.50 $\\pm$ 2.55} \\\\ \n \\bottomrule\n \n \\end{tabular}\n\\end{adjustbox}\n\\caption{The impact of different proportions of training labeled data on the performance (Macro-F1) of MGM for the Fact-2020 task.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media", "authors": ["Muhammad Arslan Manzoor", "Ruihong Zeng", "Dilshod Azizov", "Preslav Nakov", "Shangsong Liang"], "url": "https://arxiv.org/abs/2412.10467v1", "attribution": "\"MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media\" by Muhammad Arslan Manzoor, Ruihong Zeng, Dilshod Azizov, Preslav Nakov, and Shangsong Liang, arXiv:2412.10467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.14912v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{This table highlights the eigenvalues of the Jacobian and their multiplicities for the static phase wave state for different system sizes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc} \n $N$ value \\hspace{1 cm} & (Eigenvalue, Multiplicity)\\\\ \\hline\n \n 2 \\hspace{1 cm} &$(0, 4)$\\\\ \n \n 3 \\hspace{1 cm} &$(0, 4), (-\\dfrac{J}{4}, 2)$ \\\\\n \n 4 \\hspace{1 cm} &$(0, 6), (-\\dfrac{J}{2}, 2)$ \\\\\n \n $N\\ge 5$ \\hspace{1 cm} &$(0, N+1), (-\\dfrac{J}{2}, N-3), (-\\dfrac{J}{4}, 2)$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Dynamics of pulsating swarmalators on a ring", "authors": ["Samali Ghosh", "Kevin O'Keeffe", "Gourab Kumar Sar", "Dibakar Ghosh"], "url": "https://arxiv.org/abs/2504.14912v1", "attribution": "\"Dynamics of pulsating swarmalators on a ring\" by Samali Ghosh, Kevin O'Keeffe, Gourab Kumar Sar, and Dibakar Ghosh, arXiv:2504.14912v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.18593v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dataset = \\textbf{COMPAS} [feature dim$=11$, \\#(train data)=4,316]}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccccccc}\n\\toprule\n\\multirow{2}{*}{$k$} & \\multirow{2}{*}{Method} & \\multirow{2}{*}{\\%Var($\\uparrow$)} & \\multirow{2}{*}{MMD$^2$($\\downarrow$)} & \\%Acc($\\uparrow$) & $\\Delta_{\\rm DP}$($\\downarrow$) & \\%Acc($\\uparrow$) & $\\Delta_{\\rm DP}$($\\downarrow$) & \\%Acc($\\uparrow$) & $\\Delta_{\\rm DP}$($\\downarrow$) \\\\\n& & & & \\multicolumn{2}{c}{kernel SVM} & \\multicolumn{2}{c}{linear SVM} & \\multicolumn{2}{c}{MLP} \\\\\n\\midrule\n\\multirow{18}{*}{2} & PCA & 2.09$_{(0.11)}$ & 0.057$_{(0.006)}$ & 64.84$_{(0.78)}$ & 0.28$_{(0.03)}$ & 58.91$_{(0.52)}$ & 0.2$_{(0.03)}$ & 64.16$_{(1.01)}$ & 0.3$_{(0.03)}$ \\\\\n& Olfat \\& Aswani, 2019 (0.1) & \\multicolumn{8}{c}{Memory Out} \\\\\n& Olfat \\& Aswani, 2019 (0.0) & \\multicolumn{8}{c}{Memory Out} \\\\\n& Lee et al., 2022 (1e-3) & 1.8$_{(0.1)}$ & 0.003$_{(0.002)}$ & 62.41$_{(1.09)}$ & 0.03$_{(0.02)}$ & 57.57$_{(1.18)}$ & 0.04$_{(0.03)}$ & 60.79$_{(1.59)}$ & 0.03$_{(0.01)}$ \\\\\n& Lee et al., 2022 (1e-6) & 1.78$_{(0.11)}$ & 0.003$_{(0.002)}$ & 62.65$_{(1.54)}$ & 0.03$_{(0.03)}$ & 57.56$_{(2.06)}$ & 0.04$_{(0.03)}$ & 61.09$_{(1.53)}$ & 0.03$_{(0.01)}$ \\\\\n& Kleindessner et al., 2023 (mean) & 1.97$_{(0.1)}$ & 0.008$_{(0.003)}$ & 61.94$_{(0.59)}$ & 0.07$_{(0.02)}$ & 56.03$_{(1.09)}$ & 0.05$_{(0.04)}$ & 60.79$_{(0.8)}$ & 0.07$_{(0.04)}$ \\\\\n& Kleindessner et al., 2023 (0.85) & 1.78$_{(0.13)}$ & 0.005$_{(0.002)}$ & 60.64$_{(1.08)}$ & 0.08$_{(0.05)}$ & 56.16$_{(1.21)}$ & 0.01$_{(0.02)}$ & 58.8$_{(1.74)}$ & 0.07$_{(0.04)}$ \\\\\n& Kleindessner et al., 2023 (0.5) & 1.62$_{(0.09)}$ & 0.004$_{(0.001)}$ & 59.67$_{(0.89)}$ & 0.1$_{(0.03)}$ & 54.81$_{(1.04)}$ & 0.0$_{(0.0)}$ & 57.11$_{(1.35)}$ & 0.08$_{(0.05)}$ \\\\\n& Kleindessner et al., 2023 (kernel) & N/A & N/A & 57.8$_{(1.82)}$ & 0.08$_{(0.06)}$ & 54.74$_{(1.21)}$ & 0.02$_{(0.04)}$ & 55.91$_{(1.27)}$ & 0.02$_{(0.03)}$ \\\\\n& Ravfogel et al., 2020 & 0.62$_{(0.18)}$ & 0.0$_{(0.0)}$ & 56.35$_{(0.71)}$ & 0.01$_{(0.01)}$ & 54.81$_{(1.04)}$ & 0.0$_{(0.0)}$ & 55.38$_{(0.66)}$ & 0.01$_{(0.01)}$ \\\\\n& Ravfogel et al., 2022 & 0.49$_{(0.03)}$ & 0.002$_{(0.002)}$ & 57.54$_{(0.74)}$ & 0.03$_{(0.03)}$ & 54.81$_{(1.04)}$ & 0.0$_{(0.0)}$ & 56.42$_{(1.29)}$ & 0.03$_{(0.03)}$ \\\\\n& Samadi et al., 2018 & N/A & N/A & 64.19$_{(1.07)}$ & 0.15$_{(0.03)}$ & 59.15$_{(0.59)}$ & 0.13$_{(0.03)}$ & 64.4$_{(1.32)}$ & 0.15$_{(0.03)}$ \\\\\n\\cmidrule{2-10}\n& \\textbf{Ours} (offline, mean) & 1.97$_{(0.1)}$ & 0.008$_{(0.003)}$ & 61.94$_{(0.59)}$ & 0.07$_{(0.02)}$ & 56.03$_{(1.09)}$ & 0.05$_{(0.04)}$ & 60.61$_{(1.5)}$ & 0.06$_{(0.05)}$ \\\\\n& \\textbf{Ours} (FNPM, mean) & 1.97$_{(0.1)}$ & 0.008$_{(0.003)}$ & 61.94$_{(0.59)}$ & 0.07$_{(0.02)}$ & 56.03$_{(1.09)}$ & 0.05$_{(0.04)}$ & 60.5$_{(1.48)}$ & 0.07$_{(0.05)}$ \\\\\n& \\textbf{Ours} (offline, $m$=2) & 1.92$_{(0.11)}$ & 0.006$_{(0.002)}$ & 61.22$_{(0.85)}$ & 0.11$_{(0.04)}$ & 55.43$_{(0.9)}$ & 0.04$_{(0.04)}$ & 59.53$_{(1.35)}$ & 0.12$_{(0.06)}$ \\\\\n& \\textbf{Ours} (FNPM, $m$=2) & 1.92$_{(0.11)}$ & 0.006$_{(0.002)}$ & 61.08$_{(0.9)}$ & 0.09$_{(0.04)}$ & 55.51$_{(1.16)}$ & 0.03$_{(0.04)}$ & 59.56$_{(1.48)}$ & 0.12$_{(0.07)}$ \\\\\n& \\textbf{Ours} (offline, $m$=5) & 1.89$_{(0.1)}$ & 0.006$_{(0.002)}$ & 61.71$_{(0.78)}$ & 0.1$_{(0.04)}$ & 55.49$_{(0.79)}$ & 0.03$_{(0.04)}$ & 59.74$_{(2.0)}$ & 0.14$_{(0.06)}$ \\\\\n& \\textbf{Ours} (FNPM, $m$=5) & 1.89$_{(0.1)}$ & 0.006$_{(0.002)}$ & 61.67$_{(0.76)}$ & 0.11$_{(0.05)}$ & 55.55$_{(0.9)}$ & 0.03$_{(0.04)}$ & 59.63$_{(1.84)}$ & 0.13$_{(0.07)}$ \\\\\n\\midrule\n\\multirow{18}{*}{10} & PCA & 5.85$_{(0.36)}$ & 0.089$_{(0.002)}$ & 82.78$_{(0.43)}$ & 0.15$_{(0.04)}$ & 65.39$_{(1.15)}$ & 0.16$_{(0.05)}$ & 70.01$_{(0.93)}$ & 0.2$_{(0.04)}$ \\\\\n& Olfat \\& Aswani, 2019 (0.1) & \\multicolumn{8}{c}{Memory Out} \\\\\n& Olfat \\& Aswani, 2019 (0.0) & \\multicolumn{8}{c}{Memory Out} \\\\\n& Lee et al., 2022 (1e-3) & 5.36$_{(0.31)}$ & 0.002$_{(0.001)}$ & 80.76$_{(0.46)}$ & 0.1$_{(0.03)}$ & 63.89$_{(1.15)}$ & 0.04$_{(0.03)}$ & 68.97$_{(1.46)}$ & 0.03$_{(0.02)}$ \\\\\n& Lee et al., 2022 (1e-6) & 4.83$_{(0.45)}$ & 0.002$_{(0.001)}$ & 78.93$_{(0.84)}$ & 0.09$_{(0.03)}$ & 61.68$_{(1.54)}$ & 0.04$_{(0.03)}$ & 67.23$_{(1.66)}$ & 0.04$_{(0.02)}$ \\\\\n& Kleindessner et al., 2023 (mean) & 5.67$_{(0.37)}$ & 0.004$_{(0.001)}$ & 80.87$_{(0.54)}$ & 0.09$_{(0.03)}$ & 64.76$_{(1.04)}$ & 0.02$_{(0.02)}$ & 69.83$_{(0.83)}$ & 0.04$_{(0.03)}$ \\\\\n& Kleindessner et al., 2023 (0.85) & 5.41$_{(0.33)}$ & 0.004$_{(0.001)}$ & 80.64$_{(0.82)}$ & 0.09$_{(0.04)}$ & 63.63$_{(1.19)}$ & 0.02$_{(0.01)}$ & 68.69$_{(1.28)}$ & 0.05$_{(0.04)}$ \\\\\n& Kleindessner et al., 2023 (0.5) & 5.29$_{(0.31)}$ & 0.003$_{(0.001)}$ & 79.22$_{(0.44)}$ & 0.1$_{(0.03)}$ & 61.62$_{(0.63)}$ & 0.02$_{(0.02)}$ & 68.63$_{(0.92)}$ & 0.07$_{(0.04)}$ \\\\\n& Kleindessner et al., 2023 (kernel) & N/A & N/A & 65.96$_{(1.12)}$ & 0.26$_{(0.07)}$ & 64.35$_{(0.8)}$ & 0.05$_{(0.04)}$ & 64.93$_{(1.49)}$ & 0.04$_{(0.03)}$ \\\\\n& Ravfogel et al., 2020 & 2.64$_{(0.47)}$ & 0.001$_{(0.0)}$ & 64.17$_{(1.46)}$ & 0.04$_{(0.02)}$ & 54.85$_{(1.03)}$ & 0.0$_{(0.0)}$ & 62.56$_{(1.79)}$ & 0.04$_{(0.03)}$ \\\\\n& Ravfogel et al., 2022 & 2.45$_{(0.06)}$ & 0.001$_{(0.0)}$ & 71.75$_{(0.69)}$ & 0.07$_{(0.04)}$ & 55.26$_{(1.25)}$ & 0.0$_{(0.0)}$ & 66.66$_{(1.08)}$ & 0.06$_{(0.03)}$ \\\\\n& Samadi et al., 2018 & N/A & N/A & 69.75$_{(1.0)}$ & 0.17$_{(0.02)}$ & 65.54$_{(0.72)}$ & 0.14$_{(0.03)}$ & 69.97$_{(0.81)}$ & 0.16$_{(0.03)}$ \\\\\n\\cmidrule{2-10}\n& \\textbf{Ours} (offline, mean) & 5.67$_{(0.37)}$ & 0.004$_{(0.001)}$ & 80.87$_{(0.54)}$ & 0.09$_{(0.03)}$ & 64.78$_{(1.04)}$ & 0.02$_{(0.02)}$ & 69.16$_{(0.5)}$ & 0.04$_{(0.01)}$ \\\\\n& \\textbf{Ours} (FNPM, mean) & 5.68$_{(0.37)}$ & 0.004$_{(0.001)}$ & 80.87$_{(0.49)}$ & 0.09$_{(0.03)}$ & 64.84$_{(1.01)}$ & 0.02$_{(0.02)}$ & 68.86$_{(0.82)}$ & 0.04$_{(0.03)}$ \\\\\n& \\textbf{Ours} (offline, $m$=2) & 5.57$_{(0.35)}$ & 0.004$_{(0.001)}$ & 80.93$_{(0.63)}$ & 0.09$_{(0.03)}$ & 64.72$_{(0.93)}$ & 0.02$_{(0.02)}$ & 69.25$_{(1.27)}$ & 0.04$_{(0.04)}$ \\\\\n& \\textbf{Ours} (FNPM, $m$=2) & 5.58$_{(0.35)}$ & 0.004$_{(0.001)}$ & 80.82$_{(0.48)}$ & 0.09$_{(0.03)}$ & 64.65$_{(1.1)}$ & 0.02$_{(0.02)}$ & 68.97$_{(1.31)}$ & 0.04$_{(0.02)}$ \\\\\n& \\textbf{Ours} (offline, $m$=5) & 5.55$_{(0.35)}$ & 0.004$_{(0.001)}$ & 81.03$_{(0.61)}$ & 0.09$_{(0.03)}$ & 64.47$_{(1.01)}$ & 0.02$_{(0.02)}$ & 68.92$_{(1.05)}$ & 0.03$_{(0.02)}$ \\\\\n& \\textbf{Ours} (FNPM, $m$=5) & 5.55$_{(0.34)}$ & 0.004$_{(0.001)}$ & 80.9$_{(0.49)}$ & 0.09$_{(0.03)}$ & 64.54$_{(1.23)}$ & 0.02$_{(0.02)}$ & 69.05$_{(1.09)}$ & 0.04$_{(0.03)}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint", "authors": ["Junghyun Lee", "Hanseul Cho", "Se-Young Yun", "Chulhee Yun"], "url": "https://arxiv.org/abs/2310.18593v1", "attribution": "\"Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint\" by Junghyun Lee, Hanseul Cho, Se-Young Yun, and Chulhee Yun, arXiv:2310.18593v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10787v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{soul}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccc}\n\\toprule\n\\cline{4-7}\n\\textbf{} & & & \\multicolumn{4}{c}{\\textbf{VMI (ours)}} \\\\ \\cline{4-7}\n & General MI & Generative MI & \\multicolumn{2}{c|}{\\textbf{DCGAN}} & \\multicolumn{2}{c}{\\textbf{StyleGAN}} \\\\ \\cline{4-7}\n & & & \\textbf{Gaussian} & \\textbf{Flow} & \\textbf{Gaussian} & \\textbf{Flow} \\\\ \\cline{4-7}\n \\midrule\n\\textbf{Accuracy} & 0.23{\\tiny$\\pm$ 0.29 } & 0.28{\\tiny$\\pm$ 0.24 } & 0.36{\\tiny$\\pm$ 0.25 } & \\ul{ 0.42}{\\tiny$\\pm$ 0.28 } & \\ul{ 0.54}{\\tiny$\\pm$ 0.24 } & {\\bf 0.69}{\\tiny$\\pm$ 0.23 } \\\\\n\\textbf{Precision} & 0.00{\\tiny$\\pm$ 0.00 } & 0.15{\\tiny$\\pm$ 0.09 } & 0.20{\\tiny$\\pm$ 0.05 } & 0.08{\\tiny$\\pm$ 0.13 } & {\\bf 0.30}{\\tiny$\\pm$ 0.09 } & 0.15{\\tiny$\\pm$ 0.12 } \\\\\n\\textbf{Density} & 0.00{\\tiny$\\pm$ 0.00 } & 0.06{\\tiny$\\pm$ 0.03 } & 0.08{\\tiny$\\pm$ 0.03 } & 0.02{\\tiny$\\pm$ 0.03 } & {\\bf 0.18}{\\tiny$\\pm$ 0.06 } & 0.08{\\tiny$\\pm$ 0.06 } \\\\\n\\midrule\n\\textbf{Recall} & 0.00{\\tiny$\\pm$ 0.00 } & 0.04{\\tiny$\\pm$ 0.04 } & 0.07{\\tiny$\\pm$ 0.06 } & 0.00{\\tiny$\\pm$ 0.00 } & {\\bf 0.32}{\\tiny$\\pm$ 0.10 } & 0.05{\\tiny$\\pm$ 0.04 } \\\\\n\\textbf{Coverage} & 0.00{\\tiny$\\pm$ 0.00 } & 0.14{\\tiny$\\pm$ 0.07 } & 0.17{\\tiny$\\pm$ 0.04 } & 0.00{\\tiny$\\pm$ 0.01 } & {\\bf 0.43}{\\tiny$\\pm$ 0.09 } & 0.12{\\tiny$\\pm$ 0.08 } \\\\\n\\textbf{Diversity} & 0.00{\\tiny$\\pm$ 0.00 } & 0.09{\\tiny$\\pm$ 0.08 } & 0.12{\\tiny$\\pm$ 0.07 } & 0.00{\\tiny$\\pm$ 0.01 } & {\\bf 0.38}{\\tiny$\\pm$ 0.13 } & 0.09{\\tiny$\\pm$ 0.09 } \\\\\n\\midrule\n\\textbf{FID} & 499.54 & 142.66 & 104.23 & 265.14 & \\textbf{63.78 } & 123.17 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ChestX-ray8: comparing baseline and our attacks. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Variational Model Inversion Attacks", "authors": ["Kuan-Chieh Wang", "Yan Fu", "Ke Li", "Ashish Khisti", "Richard Zemel", "Alireza Makhzani"], "url": "https://arxiv.org/abs/2201.10787v1", "attribution": "\"Variational Model Inversion Attacks\" by Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, and Alireza Makhzani, arXiv:2201.10787v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.10140v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of corresponding notations and quantities.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n \\toprule\n Proof & Notation & Original Notation & Previous Works & Short Description \\\\\n \\midrule\n General & & & & \\\\\n \\midrule\n & $C_a$ & $n$ & VOO & Number of action samples \\\\\n & $C_s$ & $C$ & Sparse Sampling & Number of state samples \\\\\n & & & POWSS & Number of state/observation samples\\\\\n & $D$ & $H$ & VOO & Horizon of the problem \\\\\n & & & Sparse Sampling & \\\\\n & $d$ & $h$ & VOO & Given depth of the tree \\\\\n & $d$ & $h, n$ & Sparse Sampling & Given depth of the tree/estimator \\\\\n \\midrule\n VOWSS & & & & \\\\\n \\midrule\n & $\\mathcal{R}_{C_a}$ & $\\mathcal{R}_n$ & VOO & VOO agent regret bound\\\\\n & $\\epsilon$ & $\\frac{\\lambda}{1-\\gamma}$ & POWSS & POWSS concentration bound\\\\\n & $p$ & $\\delta$ & POWSS & POWSS bound tail probability \\\\\n \\midrule\n VOSS & & & & \\\\\n \\midrule\n & $\\mathcal{R}_{C_a}$ & $\\mathcal{R}_n$ & VOO & VOO agent regret bound\\\\\n & $\\epsilon$ & $\\lambda$ & Sparse Sampling & Chernoff concentration bound\\\\\n & $p$ & $e^{-\\lambda^2C/V_{\\max}^2}$ & Sparse Sampling & Chernoff bound tail probability \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Voronoi Progressive Widening: Efficient Online Solvers for Continuous State, Action, and Observation POMDPs", "authors": ["Michael H. Lim", "Claire J. Tomlin", "Zachary N. Sunberg"], "url": "https://arxiv.org/abs/2012.10140v3", "attribution": "\"Voronoi Progressive Widening: Efficient Online Solvers for Continuous State, Action, and Observation POMDPs\" by Michael H. Lim, Claire J. Tomlin, and Zachary N. Sunberg, arXiv:2012.10140v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09863v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Values of GA hyperparameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n \\cmidrule(r){1-2}\n parameter name & value \\\\\n \\midrule\n mutation rate & $10\\%$\\\\\n survivor rate range & $[0.0, 0.6]$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots", "authors": ["Jagdeep Singh Bhatia", "Holly Jackson", "Yunsheng Tian", "Jie Xu", "Wojciech Matusik"], "url": "https://arxiv.org/abs/2201.09863v1", "attribution": "\"Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots\" by Jagdeep Singh Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, and Wojciech Matusik, arXiv:2201.09863v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.09598v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ loss functions of DNN}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n\t\t\\hline\n\t Name & $\\mathcal{L}(\\boldsymbol{p},\\boldsymbol{r})$ \\\\ \\hline\n \tMSE & $||\\boldsymbol{p}-\\boldsymbol{r}||_2^2$ \\\\ \\hline\n\tCategorical cross-entropy & $-\\sum_jp_j\\log(r_j)$ \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks", "authors": ["Feibo Jiang", "Kezhi Wang", "Li Dong", "Cunhua Pan", "Wei Xu", "Kun Yang"], "url": "https://arxiv.org/abs/1911.09598v1", "attribution": "\"Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks\" by Feibo Jiang, Kezhi Wang, Li Dong, Cunhua Pan, Wei Xu, and Kun Yang, arXiv:1911.09598v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.09933v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small{Memory of {\\color{black}LU solver} and fast solver in Example . (total memory: 256GB) }}\n\\begin{tabular}{c|ccccccccc}\n\t\t\t\t\t\\hline\n\t\t\t\t\t$n$ & $2^7$ & $2^8$ &$2^9$ & $2^{10}$ & $2^{11}$ & $2^{12}$ &6000 \\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\t{\\color{black}mem(Fast Solver)} (MB)&1.5& 3.3&9.7&34 &131& 515 & 554\\\\\n\t\t\t\t\t{\\color{black}mem(LU Solver)} (MB) &-& 262 &1049& 4719&20709&88343& 204997 \\\\\n {\\color{black}mem(Fast Solver)/mem(LU Solver)} (\\%)&-&1.26&0.92&0.72&0.63&0.58&0.27\\\\\n \\hline\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Fast Maxwell Solvers Based on Exact Discrete Eigen-Decompositions I. Two-Dimensional Case", "authors": ["Lixiu Wang", "Lueling Jia", "Zijian Cao", "Huiyuan Li", "Zhimin Zhang"], "url": "https://arxiv.org/abs/2503.09933v1", "attribution": "\"Fast Maxwell Solvers Based on Exact Discrete Eigen-Decompositions I. Two-Dimensional Case\" by Lixiu Wang, Lueling Jia, Zijian Cao, Huiyuan Li, and Zhimin Zhang, arXiv:2503.09933v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.13122v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c}\n \\hline\n \\textbf{Metric} & \\textbf{Fd-SDR} & \\textbf{FOPG} & \\textbf{GWIRE} \\\\ \n \\hline\\hline\n Adjusted R-squared & 0.519 & 0.361 & 0.504 \\\\ \n Fréchet R-squared & 0.367 & 0.344 & 0.365 \\\\ \n Polynomial R-squared & 0.783 & 0.704 & 0.769 \\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance metrics for the bike rental data}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fréchet Sufficient Dimension Reduction for Metric Space-Valued Data via Distance Covariance", "authors": ["Hsin-Hsiung Huang", "Feng Yu", "Kang Li", "Teng Zhang"], "url": "https://arxiv.org/abs/2412.13122v1", "attribution": "\"Fréchet Sufficient Dimension Reduction for Metric Space-Valued Data via Distance Covariance\" by Hsin-Hsiung Huang, Feng Yu, Kang Li, and Teng Zhang, arXiv:2412.13122v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12176v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The choice of $\\lambda$ for SL and FB Phantoms.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c|c}\n\t\t\t\\hline\n\t\t\t\\multirow{7}{*}{SL} & Projection \t\t\t\t\t& Noise level & $\\lambda$ &\n\t\t\t\\multirow{7}{*}{FB} \t\t & Projection \t\t\t\t\t& Noise level & $\\lambda$ \\\\ \\cline{2-4} \\cline{6-8}\n\t\t\t& \\multirow{3}{*}{90$^{\\circ}$} & noiseless & 0.25 &\n\t\t\t& \\multirow{3}{*}{90$^{\\circ}$} & noiseless & 0.25 \\\\ \\cline{3-4} \\cline{7-8}\n\t\t\t& & 0.1\\% & 0.25 & & & 0.1\\% & 0.25 \\\\ \\cline{3-4} \\cline{7-8}\n\t\t\t& & 0.5\\% & 1.00 & & & 0.5\\% & 1.00 \\\\ \\cline{2-4} \\cline{6-8}\n\t\t\t& \\multirow{3}{*}{150$^{\\circ}$} & noiseless & 0.25 &\n\t\t\t& \\multirow{3}{*}{150$^{\\circ}$} & noiseless & 0.25 \\\\ \\cline{3-4} \\cline{7-8}\n\t\t\t& & 0.1\\% & 0.25 & & & 0.1\\% & 0.80 \\\\ \\cline{3-4} \\cline{7-8}\n\t\t\t& & 0.5\\% & 1.00 & & & 0.5\\% & 1.00 \\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Single-loop Proximal Subgradient Algorithm for A Class Structured Fractional Programs", "authors": ["Deren Han", "Min Tao", "Zihao Xia"], "url": "https://arxiv.org/abs/2503.12176v1", "attribution": "\"A Single-loop Proximal Subgradient Algorithm for A Class Structured Fractional Programs\" by Deren Han, Min Tao, and Zihao Xia, arXiv:2503.12176v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.13115v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Details On Battery Cell \\& Experiment Parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\nCell Parameter & Values & Experiment Parameter & Values\\\\\n\\hline\nQ & 5 $Ah$ & Parameter Uncertainty & 5\\% \\\\\nC & 4.3 $kF$ & Sampling Rate & 100 Hz \\\\\nR & 3.8 $m \\Omega$ & Measurement Noise & $\\pm$ 2 $m\\,V$ \\\\\n$R_c$ & 4 $m \\Omega$ & SC Resistance & 15 $\\Omega$ \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Koopman Mode-Based Detection of Internal Short Circuits in Lithium-ion Battery Pack", "authors": ["Sanchita Ghosh", "Soumyoraj Mallick", "Tanushree Roy"], "url": "https://arxiv.org/abs/2412.13115v1", "attribution": "\"Koopman Mode-Based Detection of Internal Short Circuits in Lithium-ion Battery Pack\" by Sanchita Ghosh, Soumyoraj Mallick, and Tanushree Roy, arXiv:2412.13115v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05795v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Computational load (in hours) considering CalTech 101 Silhouettes dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l|l|l|l|l|l|l|l|l|l|}\n\\cline{2-13}\n & \\multicolumn{4}{c|}{1L} & \\multicolumn{4}{c|}{2L} & \\multicolumn{4}{c|}{3L} \\\\ \\cline{2-13} \n\t\t\t\t\t\t & \\multicolumn{2}{c|}{DBN} & \\multicolumn{2}{c|}{DBM} & \\multicolumn{2}{c|}{DBN} & \\multicolumn{2}{c|}{DBM} & \\multicolumn{2}{c|}{DBN} & \\multicolumn{2}{c|}{DBM} \\\\ \\cline{2-13} \n & CD & PCD & CD & PCD & CD & PCD & CD & PCD & CD & PCD & CD & PCD \\\\ \\hline\n IHS & 1.64 & 1.47 & \\textbf{1.81} & \\textbf{1.62} & 1.28 & \\textbf{1.37} & \\textbf{1.84} & 2.26 & \\textbf{1.13} & \\textbf{1.06} & \\textbf{1.98} & 1.58 \\\\ \\hline\n AIWPSO & 8.87 & 9.44 & 10.54 & 11.50 & 9.41 & 7.79 & 12.30 & 12.34 & 11.17 & 7.95 & 13.50 & 13.82 \\\\ \\hline\n CS & \\textbf{1.55} & \\textbf{1.01} & 1.86 & 1.63 & \\textbf{0.93} & 1.76 & 2.45 & \\textbf{2.17} & 1.46 & 1.35 & 2.00 & \\textbf{0.80} \\\\ \\hline\n FA & 3.38 & 5.27 & 6.03 & 3.00 & 6.25 & 3.27 & 7.26 & 2.62 & 3.58 & 8.08 & 6.55 & 8.83 \\\\ \\hline\n BSA & 6.40 & 5.08 & 6.55 & 8.30 & 6.04 & 5.60 & 9.19 & 8.42 & 4.23 & 4.53 & 7.95 & 9.90 \\\\ \\hline\n JADE & 8.24 & 4.31 & 9.22 & 7.90 & 7.71 & 4.10 & 11.15 & 7.40 & 8.25 & 4.57 & 9.43 & 8.29 \\\\ \\hline\n CoBiDE & 5.64 & 5.28 & 7.48 & 7.02 & 5.64 & 5.36 & 7.52 & 7.61 & 4.47 & 5.38 & 6.63 & 8.70 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines", "authors": ["Leandro Aparecido Passos", "João Paulo Papa"], "url": "https://arxiv.org/abs/2101.05795v1", "attribution": "\"A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines\" by Leandro Aparecido Passos and João Paulo Papa, arXiv:2101.05795v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.01026v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccccccccccccccccccccccc}\n$j$ & 1& 2& 3& 4& 5& 6& 7& 8& 9&10&11&12&13&14&15&16&17&18\\\\\n\\hline\n$n'_j$ & 0&1&2&0&0&1&0&1&2&0&1&2&0&0&1&2&0&0&\\\\\n$p_0 (j)$ & 1& 4& 5& 7&10&13&14&17&18&20&23&24&26&29&32&33&35&38\\\\\n$p_1 (j)$ & 2& 6& 8&11&15&19&21&25&27&30&34&36&39&43&47&49&52&56\\\\\n$p_2 (j)$ & 3& 9&12&16&22&28&31&37&40&44&50&53&57&63&69&72&76&82\\\\\n$p_{02} (j)$ & 1& 3& 4& 5& 7& 9&10&12&13&14&16&17&18&20&22&23&24&26\n\\end{tabular}\n\\end{adjustbox}\n\\caption{First few values of $p_0$, $p_1$, $p_2$, and $p_{02}$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Narayana Morphism and Related Words", "authors": ["Jeffrey Shallit"], "url": "https://arxiv.org/abs/2503.01026v3", "attribution": "\"The Narayana Morphism and Related Words\" by Jeffrey Shallit, arXiv:2503.01026v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.20087v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|ccc|ccc|ccc|ccc}\n \\hline\n \\multirow{2}{3em}{$N$} & \\multirow{2}{2em}{Sc} & \\multirow{2}{6em}{Method} & \\multicolumn{3}{|c|}{True $\\Delta = 0.00$*} & \\multicolumn{3}{|c|}{True $\\Delta = 9.42$*} & \n \\multicolumn{3}{|c|}{True $\\Delta = 15.67$*} &\n \\multicolumn{3}{|c}{True $\\Delta = 19.52$*} \\\\\n & & & $\\hat{\\Delta}$* & SD & MSE* & $\\hat{\\Delta}$* & SD & MSE* & $\\hat{\\Delta}$* & SD & MSE* & $\\hat{\\Delta}$* & SD & MSE* \\\\\n \\hline\n \\multirow{15}{3em}{300} & \\multirow{5}{2em}{Sc 1} & Baseline & -0.37 & 0.06 & 0.35 & 5.69 & 0.06 & 0.50 & 12.60 & 0.06 & 0.50 & 17.73 & 0.06 & 0.38 \\\\\n & & PSCL1 & -0.24 & 0.02 & 0.05 & 6.18 & 0.03 & 0.20 & 12.77 & 0.03 & 0.16 & 18.25 & 0.03 & 0.11 \\\\\n & & PSCL2 & 3.15 & 0.04 & 0.26 & 9.57 & 0.05 & 0.22 & 16.13 & 0.04 & 0.19 & 21.57 & 0.04 & 0.23 \\\\\n & & PSCL3 & 0.41 & 0.03 & 0.11 & 6.90 & 0.04 & 0.20 & 13.48 & 0.03 & 0.17 & 19.02 & 0.03 & 0.12 \\\\\n & & PAM-HC & 0.29 & 0.03 & 0.11 & 6.65 & 0.04 & 0.23 & 13.14 & 0.04 & 0.22 & 18.52 & 0.04 & 0.16 \\\\\n \\cline{2-15}\n & \\multirow{5}{2em}{Sc 2} & Baseline & -0.34 & 0.06 & 0.37 & 5.85 & 0.06 & 0.49 & 12.72 & 0.06 & 0.48 & 18.04 & 0.06 & 0.39\\\\\n & & PSCL1 & -4.56 & 0.04 & 0.37 & 1.76 & 0.04 & 0.76 & 8.40 & 0.04 & 0.73 & 13.94 & 0.04 & 0.48 \\\\\n & & PSCL2 & -4.54 & 0.04 & 0.32 & 1.78 & 0.03 & 0.68 & 8.50 & 0.03 & 0.64 & 13.84 & 0.03 & 0.42 \\\\\n & & PSCL3 & -6.08 & 0.04 & 0.56 & 0.25 & 0.04 & 1.01 & 6.99 & 0.04 & 0.97 & 12.42 & 0.04 & 0.70 \\\\\n & & PAM-HC & -0.61 & 0.03 & 0.12 & 5.67 & 0.03 & 0.26 & 12.25 & 0.03 & 0.26 & 17.69 & 0.03 & 0.16 \\\\\n \\cline{2-15}\n & \\multirow{5}{2em}{Sc 3} & Baseline & -0.48 & 0.06 & 0.34 & 6.73 & 0.06 & 0.51 & 12.50 & 0.06 & 0.46 & 17.78 & 0.06 & 0.39 \\\\\n & & PSCL1 & -0.34 & 0.02 & 0.06 & 6.17 & 0.03 & 0.18 & 12.68 & 0.03 & 0.16 & 18.23 & 0.03 & 0.09 \\\\\n & & PSCL2 & -4.01 & 0.03 & 0.25 & 2.60 & 0.03 & 0.58 & 9.01 & 0.03 & 0.55 & 14.65 & 0.03 & 0.33 \\\\\n & & PSCL3 & -0.50 & 0.02 & 0.06 & 6.08 & 0.03 & 0.18 & 12.51 & 0.03 & 0.17 & 18.14 & 0.03 & 0.08 \\\\\n & & PAM-HC & -0.30 & 0.03 & 0.08 & 6.08 & 0.03 & 0.20 & 12.49 & 0.03 & 0.20 & 17.95 & 0.03 & 0.13 \\\\\n \\hline\n \\multirow{15}{3em}{450} & \\multirow{5}{2em}{Sc 1} & Baseline & -0.51 & 0.05 & 0.23 & 5.93 & 0.05 & 0.37 & 12.68 & 0.05 & 0.31 & 17.89 & 0.05 & 0.28 \\\\\n & & PSCL1 & -0.01 & 0.02 & 0.06 & 6.52 & 0.02 & 0.14 & 13.22 & 0.02 & 0.10 & 18.61 & 0.02 & 0.07 \\\\\n & & PSCL2 & 3.36 & 0.04 & 0.25 & 9.92 & 0.04 & 0.14 & 16.51 & 0.03 & 0.12 & 21.96 & 0.03 & 0.17 \\\\\n & & PSCL3 & 0.75 & 0.03 & 0.09 & 7.35 & 0.03 & 0.14 & 14.02 & 0.02 & 0.09 & 19.42 & 0.03 & 0.07 \\\\\n & & PAM-HC & 0.32 & 0.03 & 0.08 & 6.86 & 0.02 & 0.13 & 13.41 & 0.02 & 0.10 & 18.73 & 0.03 & 0.07 \\\\\n \\cline{2-15}\n & \\multirow{5}{2em}{Sc 2} & Baseline & -0.57 & 0.05 & 0.24 & 6.04 & 0.05 & 0.37 & 12.77 & 0.05 & 0.31 & 17.95 & 0.05 & 0.28\\\\\n & & PSCL1 & -4.33 & 0.04 & 0.31 & 2.26 & 0.03 & 0.62 & 9.06 & 0.03 & 0.56 & 14.23 & 0.04 & 0.41 \\\\\n & & PSCL2 & -4.30 & 0.03 & 0.28 & 2.42 & 0.03 & 0.59 & 9.17 & 0.03 & 0.53 & 14.36 & 0.03 & 0.35 \\\\\n & & PSCL3 & -6.20 & 0.04 & 0.52 & 0.49 & 0.04 & 0.92 & 7.36 & 0.04 & 0.85 & 12.39 & 0.04 & 0.64 \\\\\n & & PAM-HC & -0.22 & 0.03 & 0.08 & 6.40 & 0.03 & 0.17 & 13.08 & 0.03 & 0.14 & 18.18 & 0.03 & 0.10 \\\\\n \\cline{2-15}\n & \\multirow{5}{2em}{Sc 3} & Baseline & -0.40 & 0.05 & 0.26 & 5.70 & 0.05 & 0.41 & 12.58 & 0.05 & 0.35 & 17.85 & 0.05 & 0.31 \\\\\n & & PSCL1 & -0.23 & 0.02 & 0.06 & 6.18 & 0.02 & 0.15 & 12.87 & 0.02 & 0.12 & 18.32 & 0.02 & 0.06 \\\\\n & & PSCL2 & -3.73 & 0.03 & 0.23 & 2.65 & 0.03 & 0.54 & 9.33 & 0.02 & 0.47 & 14.82 & 0.03 & 0.29 \\\\\n & & PSCL3 & -0.43 & 0.02 & 0.05 & 5.96 & 0.02 & 0.18 & 12.64 & 0.02 & 0.14 & 18.06 & 0.02 & 0.08 \\\\\n & & PAM-HC & 0.16 & 0.03 & 0.10 & 6.49 & 0.03 & 0.20 & 13.18 & 0.02 & 0.15 & 18.49 & 0.03 & 0.11 \\\\\n \\hline\n \\multicolumn{10}{l}{* True $\\Delta$, $\\hat{\\Delta}$, and MSE values times 100.} \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Simulation results on binary outcome for PAM-HC compares to other methods for the three scenarios under various values of $\\Delta$. TE = Treatment Effect, SD = Standard deviation of treatment effect. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data", "authors": ["Dehua Bi", "Tianjian Zhou", "Wei Zhong", "Yuan Ji"], "url": "https://arxiv.org/abs/2310.20087v1", "attribution": "\"PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data\" by Dehua Bi, Tianjian Zhou, Wei Zhong, and Yuan Ji, arXiv:2310.20087v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2010.05398v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Basket 2020 Market Data}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|r|r|r|r|r|r|r|r|r|r|}\n \\hline\nDate & 01/01 & 02/01 & 03/01 & 04/01 & 05/01 & 06/01 & 07/01 & 08/01 & 09/01 \\\\\n \\hline\n APA & 27.10 & 24.80 & 4.16 & 13.02 & 10.77 & 13.48 & 15.32 & 14.8 & 11.1 \\\\\n \\hline\n AXP & 128.13 & 108.83 & 84.75 & 90.34 & 94.64 & 94.77 & 92.90 & 101.60 & 95.56 \\\\\n \\hline \n CAT & 128.29 & 122.19 & 114.13 & 114.46 & 119.24 & 125.56 & 131.89 & 142.31 & 144.38 \\\\\n \\hline\n COF & 98.61 & 87.20 & 50.01 & 64.23 & 67.49 & 62.50 & 63.70 & 68.92 & 68.13 \\\\\n \\hline\n FCX & 11.06 & 9.96 & 6.75 & 8.83 & 9.07 & 11.57 & 12.92 & 15.61 & 14.77 \\\\\n \\hline\n IBM & 138.55 & 125.46 & 108.05 & 122.30 & 121.65 & 119.21 & 121.35 & 121.72 & 118.83 \\\\\n \\hline\n MMM & 154.29 & 145.13 & 133.95 & 149.07 & 153.51 & 154.58 & 149.11 & 161.55 & 159.51 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Tight Bounds for a Class of Data-Driven Distributionally Robust Risk Measures", "authors": ["Derek Singh", "Shuzhong Zhang"], "url": "https://arxiv.org/abs/2010.05398v2", "attribution": "\"Tight Bounds for a Class of Data-Driven Distributionally Robust Risk Measures\" by Derek Singh and Shuzhong Zhang, arXiv:2010.05398v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23211v2_tex_table19.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Location of estimated change points from the video data set for the ``\\textit{first person walks out of the lobby} (so-called action 1)\" and ``\\textit{second person walks into the lobby} (so-called action 2)\" using PELT method. Note that the true value is 116 for action 1 and 174 for action 2.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\nAction & Pixel & Estimated change points based on \\textbf{PELT} method \\\\ \\hline\\hline\n1 & 700 & $\\{9, 12, 29, 31, 37, 48, 60, 62, 64, 70, 72, 77, 79, 83, 92, 104, 115, 117, 120, 158, 161 \\}$ \\\\\n& 702 & $\\{12, 18, 21, 24, 26, 54, 56, 58, 62, 64, 70, 72, 83, 87, 92, 96, 99,$ \\\\\n & & $103, 108, 110, 123, 125, 135, 138, 144, 147, 151, 168 \\}$ \\\\\n & 731 & $\\{4, 6, 8, 10, 14, 17, 43, 45, 53, 55, 57, 91, 94, 109, 114, 125, 127, 131, 141, 143, 161, 163 \\}$ \\\\\n& 762 & $\\{24, 26, 30, 32, 37, 41, 43, 48, 59, 62, 64, 80, 82, 97, 101, 106, 122, 128 \\}$ \\\\\n& 764 & $\\{4, 6, 12, 16, 22, 24, 34, 36, 53, 55, 61, 63, 72, 90, 96, 98, 104, 118, 127, 130, 132, 149, 155, 163 \\}$ \\\\\n\\hline\n2 & 48 & $\\{131, 134, 214, 216, 219, 221 \\}$ \\\\\n& 78 & $\\{129, 131, 157, 167, 169, 237, 239, 260, 262, 278 \\}$ \\\\\n& 110 & missing \\\\\n& 174 & $\\{119, 132, 134, 142, 144, 165, 167, 170, 172, 225, 227, 247, 253, 255, 287 \\}$ \\\\\n& 209 & $\\{129, 131, 149, 151, 159, 162, 231, 233 \\}$ \\\\\n& 241 & $\\{130, 132, 134, 137, 139, 167, 169, 184, 186, 209, 211, 216, 218,$ \\\\\n& & $224, 228, 231, 233, 240, 242, 244, 246, 275, 279, 287\\}$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models", "authors": ["Sepideh Mosaferi", "Abolfazl Safikhani", "Peiliang Bai"], "url": "https://arxiv.org/abs/2503.23211v2", "attribution": "\"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models\" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.04854v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc} \n\\toprule\nClass & \\multicolumn{2}{c}{$\\mathbb{E}(N(\\omega_{ix}) N(\\omega_{iy}))$} \\\\\n & i.i.d. & transient repelling \\\\\n\\midrule\nSame walk, $\\omega_{ix} = \\omega_{iy}$ & $m c^{\\omega_{ix}} + m(m - 1) c^{2{\\omega_{ix}}}$ & $mc^{\\omega_{ix}}$ \\\\ \nSubwalk, $\\omega_{ix} \\in \\omega_{iy}$ & $m c^{\\omega_{iy}} + m(m - 1) c^{\\omega_{ix} + \\omega_{iy}}$ &$mc^{\\omega_{iy}}$\\\\\nDifferent walks, diverge at $i$ & $m(m-1)c^{\\omega_{ix}+\\omega_{iy}}$ & $\\frac{d}{d-1} m(m-1)c^{\\omega_{ix}+\\omega_{iy}}$ \\\\\nDifferent walks, diverge at $d \\neq i$ & $m(m-1)c^{\\omega_{ix}+\\omega_{iy}}$ & $0$ \\\\\n$\\textrm{len}(\\omega_{ix}) = 0$ & $m^2 c^{\\omega_{iy}}$ & $m^2 c^{\\omega_{iy}}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Repelling Random Walks", "authors": ["Isaac Reid", "Eli Berger", "Krzysztof Choromanski", "Adrian Weller"], "url": "https://arxiv.org/abs/2310.04854v2", "attribution": "\"Repelling Random Walks\" by Isaac Reid, Eli Berger, Krzysztof Choromanski, and Adrian Weller, arXiv:2310.04854v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.11765v1_tex_table22.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Transformer Model Parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrlr}\n & \\multicolumn{2}{c}{\\textbf{CNN Part}} & & \\textbf{Transformer Part} \\\\ \\cline{2-3} \\cline{5-5} \n\\textbf{Parameter} & \\textbf{Block 1} & \\textbf{Block 2} & & \\textbf{All Blocks} \\\\ \\hline\nConvolutional 1D layer & & & & \\\\\nFilter Size & 64 & 128 & & \\\\\nKernel Size & 3 & 1 & & \\\\\nPadding & same & same & & \\\\ \\hline\nMulti-Head Attention Layer & & & & \\\\\nNumber of Heads & & & & 8 \\\\\nHead Size & & & & 200 \\\\\nDropout & & & & 0.2 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis", "authors": ["Tanja Aue", "Adam Jatowt", "Michael Färber"], "url": "https://arxiv.org/abs/2212.11765v1", "attribution": "\"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis\" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.10375v3_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Results with different numbers of context views on RealEstate10K}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n \\toprule\n & 2-view PSNR$\\uparrow$ & 3-view PSNR$\\uparrow$ \\\\\n \\midrule \n & 21.65 & 21.88 \\\\\n + GTA (Ours)& \\textbf{22.85} & \\textbf{23.22} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers", "authors": ["Takeru Miyato", "Bernhard Jaeger", "Max Welling", "Andreas Geiger"], "url": "https://arxiv.org/abs/2310.10375v3", "attribution": "\"GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers\" by Takeru Miyato, Bernhard Jaeger, Max Welling, and Andreas Geiger, arXiv:2310.10375v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.09055v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Localization and channel charting performance varying the supervision level for the static and dynamic DNTs.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n\\textbf{Scenario} & \\textbf{Sup.} (\\%) & \\textbf{CT} $\\uparrow$ & \\textbf{KS} $\\downarrow$ & \\textbf{TW} $\\uparrow$ & \\textbf{Loc. error}\\\\\n\\noalign{\\smallskip}\n\\hline\n\\noalign{\\smallskip}\n\\multirow{5}{*}{Static DNT} & 5 \\% & $0.92$ & $0.22$ & $0.92$ & $50.88$ m\\\\\\noalign{\\smallskip}\n & 10 \\% & $0.96$ & $0.15$ & $0.96$ & $31.39$ m\\\\\\noalign{\\smallskip}\n & 25 \\% & $0.98$ & $0.09$ & $0.98$ & $12.52$ m\\\\\\noalign{\\smallskip}\n & 35 \\% & $0.99$ & $0.07$ & $0.99$ & $8.21$ m\\\\\\noalign{\\smallskip}\n & 50 \\% & $1.00$ & $0.04$ & $1.00$ & $4.50$ m\\\\\n\\midrule\n\\multirow{5}{*}{Dynamic DNT} & 5 \\% & $0.91$ & $0.24$ & $0.91$ & $57.18$ m\\\\\\noalign{\\smallskip}\n & 10 \\% & $0.94$ & $0.18$ & $0.94$ & $36.54$ m\\\\\\noalign{\\smallskip}\n & 25 \\% & $0.97$ & $0.12$ & $0.97$ & $17.36$ m\\\\\\noalign{\\smallskip}\n & 35 \\% & $0.98$ & $0.10$ & $0.98$ & $12.28$ m\\\\\\noalign{\\smallskip}\n & 50 \\% & $0.99$ & $0.06$ & $0.99$ & $5.99$ m\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Chartwin: a Case Study on Channel Charting-aided Localization in Dynamic Digital Network Twins", "authors": ["Lorenzo Cazzella", "Francesco Linsalata", "Mahdi Maleki", "Damiano Badini", "Matteo Matteucci", "Umberto Spagnolini"], "url": "https://arxiv.org/abs/2508.09055v1", "attribution": "\"Chartwin: a Case Study on Channel Charting-aided Localization in Dynamic Digital Network Twins\" by Lorenzo Cazzella, Francesco Linsalata, Mahdi Maleki, Damiano Badini, Matteo Matteucci, and Umberto Spagnolini, arXiv:2508.09055v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.10889v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n $c$ & $0.05$ & $0.1$ & $0.15$ \\\\\n \\hline\n $e_C/(n^2\\sigma^2)$ & $0.8432+0.0078\\cdot n$ & $0.7322+0.0265\\cdot n$ & $0.6513+0.0512\\cdot n$ \\\\\n \\hline\n $e_N/(n^2\\sigma^2)$ & $0.8278$ & $0.6864$ & $0.5666$ \\\\\n \\hline\n \\hline\n $c$ & $0.2$ & $0.25$ & $0.3$ \\\\\n \\hline\n $e_C/(n^2\\sigma^2)$ & $0.5933+0.0785\\cdot n$ & $0.5531+0.1055\\cdot n$ & $0.5269+0.1304\\cdot n$ \\\\\n \\hline\n $e_N/(n^2\\sigma^2)$ & $0.4644$ & $0.3771$ & $0.3027$ \\\\\n \\hline\n \\hline\n $c$ & $0.35$ & $0.4$ & $0.45$ \\\\%& $0.5$ \\\\\n \\hline\n $e_C/(n^2\\sigma^2)$ & $0.5112+0.1512\\cdot n$ & $0.5033+0.1670\\cdot n$ & $0.5004+0.1768\\cdot n$\\\\%& $0.5+0.1801\\cdot n$ \\\\\n \\hline\n $e_N/(n^2\\sigma^2)$ & $0.2397$ & $0.1866$ & $0.1425$\\\\% & $0.1063$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{This table shows the error terms $e_C$ and $e_N$ of different choices of $c\\in (0,0.5)$ with $c=k/n$, normalized by dividing $n^2\\sigma^2$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms", "authors": ["Chengyuan Deng", "Jie Gao", "Kevin Lu", "Feng Luo", "Hongbin Sun", "Cheng Xin"], "url": "https://arxiv.org/abs/2411.10889v2", "attribution": "\"Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms\" by Chengyuan Deng, Jie Gao, Kevin Lu, Feng Luo, Hongbin Sun, and Cheng Xin, arXiv:2411.10889v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.15439v1_tex_table26.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Logit regression for likelihood of cheating - Coefficients}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccc} \n\\hline\n\\hline\n & & \\multicolumn{5}{c}{Dependent variable: Likelihood of cheating} \\\\ \\cmidrule(l){3-7} \n & & (1) & (2) & (3) & (4) & (5) \\\\ \n\\midrule\n\\multicolumn{2}{l}{Intercept} & -0.083 & -0.441 & -2.480 & -0.825 & -4.414\\\\\n & & (0.290) & (1.196) & (1.836) & (1.012) & (2.541) \\\\ \n\\multicolumn{2}{l}{Treatment} & & & & & \\\\ \n & \\textit{Machine} & 0.035 & 0.121 & -0.352 & 0.697 & 0.188 \\\\ \n & & (0.427) & (0.434) & (1.342) & (0.951) & (1.717) \\\\ \n & \\textit{Human Black Box} & -0.342 & -0.340 & -0.383 & -0.287 & -0.332 \\\\ \n & & (0.426) & (0.433) & (0.436) & (0.431) & (0.448) \\\\ \n & \\textit{Machine Black Box} & 0.402 & 0.468 & -0.087 & 1.030 & 0.550 \\\\ \n & & (0.439) & (0.493) & (1.451) & (0.970) & (1.850) \\\\ \n & & & & & & \\\\ \n \\multicolumn{2}{l}{Age} & & 0.047 & & & 0.027\\\\ \n & & & (0.054) & & & (0.054) \\\\ \n \\multicolumn{2}{l}{Female} & & -1.199 & & & -1.406 \\\\ \n & & & (0.336) & & & (0.439) \\\\ \n & & & & & & \\\\\n \\multicolumn{2}{l}{Field of Study} & & & & & \\\\ \n & \\textit{Cultural \\& social studies} & & 0.056 & & & 0.176 \\\\ \n & & & (0.372) & & & (0.410) \\\\ \n & \\textit{Natural science} & & -0.510 & & & -0.720 \\\\ \n & & & (0.592) & & & (0.122) \\\\ \n & & & & & & \\\\\n \\multicolumn{2}{l}{Risk} & & & 0.174 & & 0.195 \\\\ \n & & & & (0.077) & & (0.081) \\\\ \n \\multicolumn{2}{l}{Ethical sensitivity} & & & 0.106 & & 0.654 \\\\ \n & & & & (0.345) & & (0.385) \\\\ \n \\multicolumn{2}{l}{Closeness} & & & 0.029 & & -0.017 \\\\ \n & & & & (0.116) & & (0.123) \\\\ \n & & & & & & \\\\\n \\multicolumn{2}{l}{Verification by machine \\# ATI} & & & & & \\\\ \n & \\textit{$0$} & & & 0.242 & & -0.093 \\\\ \n & & & & (0.263) & & (0.314) \\\\ \n & \\textit{$1$} & & & 0.342 & & 0.087 \\\\ \n & & & & (0.240) & & (0.293) \\\\ \n & & & & & & \\\\ \n \\multicolumn{2}{l}{Verification by preferred entity} & & & & 0.180 & 0.421\\\\ \n & & & & & (0.325) & (0.357) \\\\ \n \\multicolumn{2}{l}{Verification by more error-prone entity} & & & & -0.412 & -0.256 \\\\ \n & & & & & (0.510) & (0.600) \\\\ \n \\multicolumn{2}{l}{Verification by higher discretion entity} & & & & 1.015 & 0.975 \\\\ \n & & & & & (0.839) & (0.914) \\\\ \n\\midrule\n \\multicolumn{2}{l}{Wald $\\chi^{2}(3)$} & 2.68 & 14.89 & 13.63 & 5.07 & 28.18 \\\\\n \\multicolumn{2}{l}{Pseudo $R^{2}$} & 0.012 & 0.075 & 0.053 & 0.535 & 0.120\\\\\n \\multicolumn{2}{l}{N} & 170 & 170 & 170 & 170 & 170 \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty", "authors": ["Marius Protte", "Behnud Mir Djawadi"], "url": "https://arxiv.org/abs/2507.15439v1", "attribution": "\"Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty\" by Marius Protte and Behnud Mir Djawadi, arXiv:2507.15439v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table30.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Cell transmission model example, on a link with three cells, $q_{max} \\Delta t = 10$, $\\bar{n} = 30$, and $\\delta = \\frac{2}{3}$. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|lc|cccccc|cc|}\n\\hline\n & & & &Cell 0 & &Cell 1 & &Cell 2 & & \\\\\n$t$ & $d(t)$ {\\footnotesize (*)} &$R(t)$ & $y(0,t)$ & $n(0,t)$ & $y(1,t)$ & $n(1,t)$ & $y(2,t)$ & $n(2,t)$ & $S(t)$ & $y(3,t)$ \\\\\n\\hline\n0 &10 &10 &10 &0 &0 &0 &0 &0 &0 &0 \\\\\n1 &10 &10 &10 &10 &10 &0 &0 &0 &0 &0 \\\\\n2 &10 &10 &10 &10 &10 &10 &10 &0 &0 &0 \\\\\n3 &10 &10 &10 &10 &10 &10 &10 &10 &10 &0 \\\\\n4 &10 &10 &10 &10 &10 &10 &6.7 &20 &10 &0 \\\\\n5 &9 &10 &9 &10 &10 &13.3 &2.2 &26.7 &10 &0 \\\\\n6 &8 &10 &8 &9 &5.9 &21.1 &0.7 &28.9 &10 &0 \\\\\n7 &7 &10 &7 &11.1 &2.5 &26.3 &0.2 &29.6 &10 &0 \\\\\n8 &6 &9.6 &6 &15.6 &1 &28.5 &0.1 &29.9 &10 &0 \\\\\n9 &5 &6.3 &5 &20.6 &0.4 &29.4 &0 &30 &10 &0 \\\\\n10 &4 &3.2 &3.2 &25.2 &0.1 &29.8 &0 &30 &10 &10 \\\\\n11 &3 (+0.8) &1.2 &1.2 &28.3 &0.1 &29.9 &6.7 &20 &10 &10 \\\\\n12 &2 (+2.6) &0.4 &0.4 &29.4 &4.5 &23.3 &8.9 &16.7 &10 &10 \\\\\n13 &1 (+4.2) &3.1 &3.1 &25.3 &7.4 &18.9 &9.6 &15.6 &10 &10 \\\\\n14 &0 (+2.1) &6 &2.1 &21.0 &8.9 &16.7 &9.9 &15.2 &10 &10 \\\\\n15 &0 &10 &0 &14.2 &9.5 &15.7 &10 &15.1 &10 &10 \\\\\n16 &0 &10 &0 &4.7 &4.7 &15.3 &10 &15 &10 &10 \\\\\n17 &0 &10 &0 &0 &0 &10 &10 &15 &10 &10 \\\\\n18 &0 &10 &0 &0 &0 &0 &0 &15 &10 &10 \\\\\n19 &0 &10 &0 &0 &0 &0 &0 &5 &5 &5 \\\\\n20 &0 &10 &0 &0 &0 &0 &0 &0 &0 &0 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.11403v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[table]{xcolor}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{cccccccc}\n \\hline\\hline\n Parameters & Formulation & Hessian & Platform & Build time & Solve time & Iterations & Time/iter. \\\\\n \\hline\n -- & No surrogate & Exact & CPU & 45 ms & 0.4 s & 41 & 9 ms \\\\\n \\hline\n 7k & Full-space & Exact & CPU & 0.1 s & 2 s & 468 & 4 ms \\\\\n 25k & Full-space & Exact & CPU & 0.3 s & 5 s & 642 & 8 ms \\\\\n 578k & Full-space & Exact & CPU & 0.3 s & 699 s & 755 & 0.9 s \\\\\n 7M & Full-space$^*$& Exact & CPU & -- & -- & -- & -- \\\\\n \\hline\n 7k & Reduced-space & Exact & CPU & 0.1 s & 7 s & 49 & 0.1 s \\\\\n 25k & Reduced-space & Exact & CPU & 1 s & 1125 s & 41 & 27 s \\\\\n 578k & Reduced-space$^\\dag$ & Exact & CPU & -- & -- & -- & -- \\\\\n 7M & Reduced-space$^\\ddag$ & Exact & CPU & -- & -- & -- & -- \\\\\n \\hline\n 7k & Gray-box & Exact & CPU & 0.1 s & 8 s & 41 & 0.2 s \\\\\n 25k & Gray-box & Exact & CPU & 0.1 s & 9 s & 42 & 0.2 s \\\\\n 578k & Gray-box & Exact & CPU & 0.1 s & 11 s & 42 & 0.3 s \\\\\n 7M & Gray-box & Exact & CPU & 0.1 s & 22 s & 42 & 0.5 s \\\\\n 68M & Gray-box & Exact & CPU & 0.1 s & 140 s & 42 & 3 s \\\\\n 592M & Gray-box & Exact & CPU & 0.6 s & 748 s & 42 & 18 s \\\\\n \\hline\n 7k & Gray-box & Exact & CPU+GPU & 0.1 s & 7 s & 41 & 0.2 s \\\\\n 25k & Gray-box & Exact & CPU+GPU & 0.1 s & 7 s & 42 & 0.2 s \\\\\n 578k & Gray-box & Exact & CPU+GPU & 0.1 s & 8 s & 42 & 0.2 s \\\\\n 7M & Gray-box & Exact & CPU+GPU & 0.1 s & 8 s & 42 & 0.2 s \\\\\n 68M & Gray-box & Exact & CPU+GPU & 0.2 s & 9 s & 42 & 0.2 s \\\\\n 592M & Gray-box & Exact & CPU+GPU & 0.7 s & 15 s & 42 & 0.3 s \\\\\n \\hline\n 7k & Gray-box & Approx. & CPU & 0.1 s & 0.3 s & 61 & 6 ms \\\\\n 25k & Gray-box & Approx. & CPU & 48 ms & 0.3 s & 57 & 6 ms \\\\\n 578k & Gray-box & Approx. & CPU & 0.1 s & 1 s & 66 & 15 ms \\\\\n 7M & Gray-box & Approx. & CPU & 0.1 s & 6 s & 57 & 0.1 s \\\\\n 68M & Gray-box & Approx. & CPU & 0.1 s & 7 s & 56 & 0.1 s \\\\\n 592M & Gray-box & Approx. & CPU & 0.9 s & 17 s & 56 & 0.3 s \\\\\n \\hline\n 7k & Gray-box & Approx. & CPU+GPU & 50 ms & 0.5 s & 63 & 7 ms \\\\\n 25k & Gray-box & Approx. & CPU+GPU & 48 ms & 0.4 s & 58 & 7 ms \\\\\n 578k & Gray-box & Approx. & CPU+GPU & 0.1 s & 0.5 s & 62 & 8 ms \\\\\n 7M & Gray-box & Approx. & CPU+GPU & 0.1 s & 0.5 s & 57 & 9 ms \\\\\n 68M & Gray-box & Approx. & CPU+GPU & 0.2 s & 1 s & 56 & 21 ms \\\\\n 592M & Gray-box & Approx. & CPU+GPU & 0.7 s & 1 s & 56 & 23 ms \\\\\n \\hline\\hline\n \\rowcolor{white}\\multicolumn{8}{l}{\\scriptsize $^*$ Fails with a segfault, possibly due to memory requirements of MA27}\\\\\n \\rowcolor{white}\\multicolumn{8}{l}{\\scriptsize $^\\dag$ Exceeds resource manager's memory limits}\\\\\n \\rowcolor{white}\\multicolumn{8}{l}{\\scriptsize $^\\ddag$ Exceeds ten-hour time limit}\\\\\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Formulations and scalability of neural network surrogates in nonlinear optimization problems", "authors": ["Robert B. Parker", "Oscar Dowson", "Nicole LoGiudice", "Manuel Garcia", "Russell Bent"], "url": "https://arxiv.org/abs/2412.11403v1", "attribution": "\"Formulations and scalability of neural network surrogates in nonlinear optimization problems\" by Robert B. Parker, Oscar Dowson, Nicole LoGiudice, Manuel Garcia, and Russell Bent, arXiv:2412.11403v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.04395v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative Results on Best Parameter}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n\t\t\\hline\n\t\tModel & F1 & Precision & Recall \\\\ \\hline\n\t\tSR & 0.4719 & 0.5137 & 0.4364 \\\\\n\t\tHBOS & 0.4969 & 0.3910 & 0.6817 \\\\\n\t\tS-H-ESD & 0.4711 & 0.3651 & 0.6638 \\\\ \n\t\tAuto-Selector & \\textbf{0.5714} & 0.4909 & 0.6837 \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Automated Model Selection for Time-Series Anomaly Detection", "authors": ["Yuanxiang Ying", "Juanyong Duan", "Chunlei Wang", "Yujing Wang", "Congrui Huang", "Bixiong Xu"], "url": "https://arxiv.org/abs/2009.04395v1", "attribution": "\"Automated Model Selection for Time-Series Anomaly Detection\" by Yuanxiang Ying, Juanyong Duan, Chunlei Wang, Yujing Wang, Congrui Huang, and Bixiong Xu, arXiv:2009.04395v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01863v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of UrbanSound8K by classes and salience.}\n\\begin{tabular}{llll}\nClass & Foreground & Background & Total\\\\\n\\hline\nAir Conditioner & 569 & 431 & 1000\\\\\nCar Horn & 153 & 276 & 429\\\\\nChildren Playing & 588 & 412 & 1000\\\\\nDog Bark & 645 & 355 & 1000\\\\\nDrilling & 902 & 98 & 1000\\\\\nEngine Idling & 916 & 84 & 1000\\\\\nGun Shot & 304 & 70 & 374\\\\\nJackhammer & 731 & 269 & 1000\\\\\nSiren & 269 & 660 & 929\\\\\nStreet Music & 625 & 375 & 1000\\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Environment Transfer for Distributed Systems", "authors": ["Chunheng Jiang", "Jae-wook Ahn", "Nirmit Desai"], "url": "https://arxiv.org/abs/2101.01863v1", "attribution": "\"Environment Transfer for Distributed Systems\" by Chunheng Jiang, Jae-wook Ahn, and Nirmit Desai, arXiv:2101.01863v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07664v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|l|}\n\\hline\nAbbreviation & Meaning \\\\ \\hline\nNTA & Not the Asshole \\\\ %\\hline\nYTA & You're the Asshole \\\\ %\\hline\nNAH & No assholes here \\\\ %\\hline\nESH & Everyone sucks here \\\\ \\hline\n\\end{tabular}\n\\caption{The four judgements that users can pass on the subreddit r/AmItheAsshole.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Analysis of Moral Judgement on Reddit", "authors": ["Nicholas Botzer", "Shawn Gu", "Tim Weninger"], "url": "https://arxiv.org/abs/2101.07664v1", "attribution": "\"Analysis of Moral Judgement on Reddit\" by Nicholas Botzer, Shawn Gu, and Tim Weninger, arXiv:2101.07664v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.20488v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lcr}\nLeft&Centered&Right\\\\\n\\hline\n1 & 2 & 3\\\\\n10 & 20 & 30\\\\\n100 & 200 & 300\\\\\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Scaling and shape of financial returns distributions modeled as conditionally independent random variables", "authors": ["Hernán Larralde", "Roberto Mota Navarro"], "url": "https://arxiv.org/abs/2504.20488v1", "attribution": "\"Scaling and shape of financial returns distributions modeled as conditionally independent random variables\" by Hernán Larralde and Roberto Mota Navarro, arXiv:2504.20488v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.15798v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Table Type Styles}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\textbf{Table}&\\multicolumn{3}{|c|}{\\textbf{Table Column Head}} \\\\\n\\cline{2-4} \n\\textbf{Head} & \\textbf{\\textit{Table column subhead}}& \\textbf{\\textit{Subhead}}& \\textbf{\\textit{Subhead}} \\\\\n\\hline\ncopy& More table copy$^{\\mathrm{a}}$& & \\\\\n\\hline\n\\multicolumn{4}{l}{$^{\\mathrm{a}}$Sample of a Table footnote.}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "M3-CVC: Controllable Video Compression with Multimodal Generative Models", "authors": ["Rui Wan", "Qi Zheng", "Yibo Fan"], "url": "https://arxiv.org/abs/2411.15798v2", "attribution": "\"M3-CVC: Controllable Video Compression with Multimodal Generative Models\" by Rui Wan, Qi Zheng, and Yibo Fan, arXiv:2411.15798v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.14532v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n\\hline\nTicker & Type & Sector & Company & Average Amount \\\\ \\hline\nSH601988 & Share & Bank & Bank of China Limited & 427,647,786 \\\\\nSH601601 & Share & Public Utility & China Pacific Insurance (Group) & 819,382,926 \\\\\nSH600028 & Share & Public Utility & China Petroleum \\& Chemical Corporation & 748,927,952\\\\\nSH600016 & Share & Bank & China Minsheng Banking Corporation &285,852,414 \\\\\nSH601186 & Share & Public Utility & China Railway Construction Corporation & 594,970,588\\\\\nSH601328 & Share & Bank & Bank of Communications Corporation & 484,445,915 \\\\\nSH601628 & Share & Public Utility & China Life Insurance Company Limited&368,179,861 \\\\\nSH601939 & Share & Bank & China Construction Bank Corporation &527,876,669 \\\\\n\\hline\nSH510300 & ETF & CSI 300 & Huatai-PineBridge CSI 300 ETF &1,960,687,059 \\\\\nSH510050 & ETF & CSI 50 & ChinaAMC China CSI 50 ETF &2,020,385,879 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Summary of the underlying portfolios in the China market, ten assets in total. The average amount (in the currency of RMB) is calculated in the period of the test set.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies", "authors": ["Jun Lu", "Joerg Osterrieder"], "url": "https://arxiv.org/abs/2209.14532v1", "attribution": "\"Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies\" by Jun Lu and Joerg Osterrieder, arXiv:2209.14532v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11653v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c|c||} \n \\hline\n Optimizer name & Experimented hyper-parameters\\\\ \n \\hline\n \\hline\n Common & weight decay = np.logspace(-5, 1, 11)\\\\\n \\hline \n Common & learning rate = np.logspace(-5, 3, 15)\\\\\n \\hline \n SGD & momentum = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.999]\\\\\n \\hline\n Adagrad & $\\cdot$\\\\\n \\hline \n Adadelta & rho = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.999]\\\\\n \\hline \n Adam & betas = [0.0001, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.999]\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Representations learnt by SGD and Adaptive learning rules: Conditions that vary sparsity and selectivity in neural networks", "authors": ["Jin Hyun Park"], "url": "https://arxiv.org/abs/2201.11653v3", "attribution": "\"Representations learnt by SGD and Adaptive learning rules: Conditions that vary sparsity and selectivity in neural networks\" by Jin Hyun Park, arXiv:2201.11653v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.18072v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experimental Results Using Multiple IMU Data from PAMAP2 \\\\ (Estimator: LR, Metric: Mean F1)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\hline\n Input Pattern & Target & Baseline & AAE~ & C-AAE (Proposed) \\\\\n \\hline \\hline\n Input 1 & Activity & 0.881 & \\textbf{0.898} & 0.811 \\\\\n & User & 0.449 & 0.226 & \\textbf{0.153} \\\\\n \\hline\n Input 2 & Activity & 0.832 & \\textbf{0.855} & 0.790 \\\\\n & User & 0.457 & \\textbf{0.152} & 0.168 \\\\\n \\hline\n Input 3 & Activity & 0.863 & \\textbf{0.880} & 0.779 \\\\\n & User & 0.502 & \\textbf{0.138} & 0.142 \\\\\n \\hline\n Input 4 & Activity & 0.889 & \\textbf{0.906} & 0.872 \\\\\n & User & 0.558 & 0.240 & \\textbf{0.193} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "C-AAE: Compressively Anonymizing Autoencoders for Privacy-Preserving Activity Recognition in Healthcare Sensor Streams", "authors": ["Ryusei Fujimoto", "Yugo Nakamura", "Yutaka Arakawa"], "url": "https://arxiv.org/abs/2507.18072v1", "attribution": "\"C-AAE: Compressively Anonymizing Autoencoders for Privacy-Preserving Activity Recognition in Healthcare Sensor Streams\" by Ryusei Fujimoto, Yugo Nakamura, and Yutaka Arakawa, arXiv:2507.18072v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.09920v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr} \\\\\n & \\multicolumn{2}{c}{\\textbf{Standardised differences}} & & \\multicolumn{2}{c}{\\textbf{Variance ratio}} \\\\ \\cline{2-3} \\cline{5-6} \n\\textbf{Confounder} & \\textbf{Raw} & \\textbf{Weighted} & & \\textbf{Raw} & \\textbf{Weighted} \\\\\n\\hline \\\\\n Gender & 0.0931272 & 0.0004124 & & 0.9771947 & 0.9999057 \\\\\n Age & -0.0613524 & -0.0038196 & & 0.8174922 & 0.7899075 \\\\\n Education & 0.0913642 & -0.0025822 & & 1.0147230 & 1.0250380 \\\\\n Race & -0.0022396 & 0.0023428 & & 1.0295870 & 1.0254230 \\\\\n Carcinoma & -0.1051837 & 0.0012232 & & 0.8386081 & 1.0226510 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Tutorial: Introduction to computational causal inference using reproducible Stata, R and Python code", "authors": ["Matthew J. Smith", "Camille Maringe", "Bernard Rachet", "Mohammad A. Mansournia", "Paul N. Zivich", "Stephen R. Cole", "Miguel Angel Luque-Fernandez"], "url": "https://arxiv.org/abs/2012.09920v2", "attribution": "\"Tutorial: Introduction to computational causal inference using reproducible Stata, R and Python code\" by Matthew J. Smith, Camille Maringe, Bernard Rachet, Mohammad A. Mansournia, Paul N. Zivich, Stephen R. Cole, and Miguel Angel Luque-Fernandez, arXiv:2012.09920v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.14080v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Some typical parameter values}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llc}\n\\hline\nParameter&\n\\multicolumn{2}{c}{$\\quad\\quad$ Value} \\\\\n\\hline\n\\\\\n$\\text{Electron Charge}~(q)\n$ &&\\quad\\quad$1.6\\times10^{-19}$~\\text{C} \\\\\n$\\text{Dark Current}~(I_\\text{DC})\n$ &&\\quad\\quad$5$~\\text{pA} \\\\\n$\\text{Channel Noise Factor}~(\\Gamma)\n$ &&\\quad\\quad$1.5$\\\\\n$\\text{Photodiode Responsively}~(R_\\text{p})\n$ &&\\quad\\quad$0.4$~\\text{mA/mW} \\\\\n$\\text{Equivalent Noise Bandwidth}~(B)\n$ &&\\quad\\quad$400$~\\text{MHz}\\\\\n$\\text{Open-Loop Voltage Gain}~(G)\n$ &&\\quad\\quad$10$\\\\\n$\\text{Boltzmann's Constant}~(\\kappa)\n$ &&\\quad\\quad$1.38\\times10^{-23}$\\\\\n$\\text{Absolute Temperature}~(T_\\text{e})\n$&&\\quad\\quad$300$~\\text{K}\\\\\n$\\text{Active Area}~(S)\n$&&\\quad\\quad$.2~\\text{cm}^2$\\\\\n$\\text{Transconductance}~(g_\\text{m})\n$ &&\\quad\\quad$30$~\\text{ms}\\\\\n$\\text{Optical Filter Bandwidth}~(\\breve{\\lambda})\n$&&\\quad\\quad$400\\;(380-780)$~\\text{nm} \\\\\n$\\text{Spectral Irradiance }~(p_\\text{BS})\n$ &&\\quad\\quad$5.8\\times10^{-6}$~\\text{W/cm$^2\\cdot$nm} \\\\\n$\\text{Photodiode Fixed Capacitance}~(\\eta)\n$ &&\\quad\\quad$112$~\\text{pF/cm$^2$} \\\\\n$\\text{Interference}~(P_{\\text{diff}})\n$&&\\quad\\quad0~\\text{W} \\\\\n\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Theoretical Accuracy Analysis of RSS-Based Range Estimation for Visible Light Communication", "authors": ["Changeez Amini", "Paeiz Azmi", "Seyed Sadra Kashef"], "url": "https://arxiv.org/abs/2011.14080v1", "attribution": "\"Theoretical Accuracy Analysis of RSS-Based Range Estimation for Visible Light Communication\" by Changeez Amini, Paeiz Azmi, and Seyed Sadra Kashef, arXiv:2011.14080v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.11692v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model Evaluation}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc} \nMetric & Value\\\\\\hline\nRoot Mean Squared Error & $3.06\\%$ \\\\\nMean Squared Error & $0.09\\%$ \\\\\nMean Absolute Error & $ 0.0214^\\circ$ ($ 3.7{ e^{-4}}$ radians) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Thermostatic control for demand response using distributed averaging and deep neural networks", "authors": ["Kshitij Singh", "Pratik K. Bajaria"], "url": "https://arxiv.org/abs/1912.11692v1", "attribution": "\"Thermostatic control for demand response using distributed averaging and deep neural networks\" by Kshitij Singh and Pratik K. Bajaria, arXiv:1912.11692v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2401.08622v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Joint probability table for joint probability mass function $p_{(X,Y)}$ of $X\\in \\Omega_X=\\{1,2,\\ldots, 6\\}$ and $Y\\in \\Omega_Y=\\{1,2,\\ldots,6\\}$ along with marginal PMFs $p_X(x)$ and $p_Y(y)$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\n \\hline\n $X \\backslash Y$ & 1 & 2 & 3 & 4 & 5 & 6 & $p_X(x)$ \\\\ \n \\hline\n 1 & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ \\\\ \n \\hline\n 2 & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ \\\\\n \\hline\n 3 & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ \\\\\n \\hline \n 4 & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ \\\\\n \\hline\n 5 & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ \\\\ \n \\hline \n 6 & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ \\\\ \n \\hline \n $p_Y(y)$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & $\\frac{1}{36}$ & 1 \\\\ \n \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Introduction to probability and statistics: a computational framework of randomness", "authors": ["Lakshman Mahto"], "url": "https://arxiv.org/abs/2401.08622v2", "attribution": "\"Introduction to probability and statistics: a computational framework of randomness\" by Lakshman Mahto, arXiv:2401.08622v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.17918v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Unique speaker IDs used in training and development sets compared to the full S\\&I corpus (TRAIN + DEV).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n Dataset version & Unique speaker IDs & \\% of corpus \\\\\n \\midrule \n Standard & 1684 & 77.2\\% \\\\ \n 4x & 1871 & 85.8\\% \\\\ \n OE & 1134 & 52.0\\% \\\\\n OE+OE$_{d}$ & 976 & 44.8\\% \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "One Whisper to Grade Them All", "authors": ["Nhan Phan", "Anusha Porwal", "Yaroslav Getman", "Ekaterina Voskoboinik", "Tamás Grósz", "Mikko Kurimo"], "url": "https://arxiv.org/abs/2507.17918v1", "attribution": "\"One Whisper to Grade Them All\" by Nhan Phan, Anusha Porwal, Yaroslav Getman, Ekaterina Voskoboinik, Tamás Grósz, and Mikko Kurimo, arXiv:2507.17918v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.04003v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average Marginal Effects for October 1, 2024}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n \\toprule\n & & Marginal Effects & \\\\\n Variable & Prob. In First Quartile & Gas Equivalent Cost & Cost in USD \\\\\n \\midrule\n \\midrule\n max fee per gas & 0.02\\% & 53 & \\$0.0031 \\\\\n to DEX & 16.87\\% & 899 & \\$0.0524 \\\\\n to MEV & 37.34\\% & 1,991 & \\$0.1161 \\\\\n from DEX & 30.97\\% & 1,651 & \\$0.0963 \\\\\n from MEV & -43.54\\% & 2,321 & \\$0.1353 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering", "authors": ["Bruce Mizrach", "Nathaniel Yoshida"], "url": "https://arxiv.org/abs/2508.04003v1", "attribution": "\"The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering\" by Bruce Mizrach and Nathaniel Yoshida, arXiv:2508.04003v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.20289v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulated random DAG F-1 scores of three different methods: Baseline, Average, and Consensus. The respective \\% increase over the baseline for the best performing method (in bold) is given. Each number refers to the average over 10 different simulations.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\hline\n \\textbf{$\\Sigma$-Structure} &\\textbf{(n,p)} &\\textbf{Baseline} &\\textbf{Average} &\\textbf{Consensus} &\\textbf{\\% Increase}\\\\\n \\hline\n \\textbf{Mixed} & (100,100) & 0.235 & 0.261 & \\textbf{0.271} & 15.3\\\\\n & (100,500) & 0.144 & 0.174 & \\textbf{0.181} & 25.7 \\\\\n & (100,1000) & 0.116 & 0.135 & \\textbf{0.143} & 23.3 \\\\\n & (500,100) & 0.570 & 0.690 & \\textbf{0.725} & 27.2 \\\\\n & (500,500) & 0.548 & 0.605 & \\textbf{0.658} & 23.5 \\\\\n & (500,1000) & 0.537 & 0.605 & \\textbf{0.677} & 22.5 \\\\\n\\textbf{Toeplitz} & (100,100) & 0.231 & 0.148 & \\textbf{0.260} & 12.6\\\\\n & (100,500) & 0.144 & \\textbf{0.255} & 0.175 & 21.5 \\\\\n & (100,1000) & 0.115 & \\textbf{0.160} & 0.140 & 21.7 \\\\\n & (500,100) & 0.630 & 0.664 & \\textbf{0.727} & 25.1 \\\\\n & (500,500) & 0.580 & 0.619 & \\textbf{0.696} & 22.1 \\\\\n & (500,1000) & 0.529 & 0.569 & \\textbf{0.602} & 20.6\\\\\n\\textbf{Equal} & (100,100) & 0.168 & 0.278 & \\textbf{0.283} & 28.6\\\\\n & (100,500) & 0.139 & 0.167 & \\textbf{0.182} & 33.8 \\\\\n & (100,1000) & 0.086 & 0.144 & \\textbf{0.153} & 33\\\\\n & (500,100) & 0.449 & 0.660 & \\textbf{0.712} & 24.9 \\\\\n & (500,500) & 0.372 & 0.550 & \\textbf{0.613} & 15 \\\\\n & (500,1000) & 0.327 & 0.564 & \\textbf{0.649} & 25.3 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Causal Discovery on Dependent Binary Data", "authors": ["Alex Chen", "Qing Zhou"], "url": "https://arxiv.org/abs/2412.20289v1", "attribution": "\"Causal Discovery on Dependent Binary Data\" by Alex Chen and Qing Zhou, arXiv:2412.20289v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.09865v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ranges of quantities satisfying $q\\ge 0$, $u'>0$, and $u'' < 0$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc}\n\\hline\n&& \\\\\n& \\quad \\quad $\\alpha \\ge 0$ & \\quad $\\alpha<0$ \\\\ \\hline\n&& \\\\\n$\\alpha \\sigma \\ge 0$ & \\quad \\quad $q \\in [0,\\infty)$ & \\quad $q \\in [0, - \\frac{\\beta}{\\alpha} )$ \\\\\n$\\alpha \\sigma<0$ & \\quad \\quad $q \\in [0, -\\frac{1}{\\alpha \\sigma} ) $ & \\quad $q \\in [0, -\\frac{1}{\\alpha \\sigma}) \\cap [0, -\\frac{\\beta}{\\alpha} )$$^\\dagger$ \\\\ \\hline\n\\multicolumn{3}{p{10.6cm}}{\\scriptsize{{\\it Notes}: $^\\dagger$ The CRRA case with $\\beta =\\frac{1}{\\sigma}$ requires $\\sigma>1$ for $\\beta \\in (0,1)$, which implies that either the top-left cell or the bottom-right cell is applicable. In the latter case, $q \\in [0, -\\frac{1}{\\alpha \\sigma}) \\cap [0, -\\frac{\\beta}{\\alpha} )$ must be replaced with $q \\in [0,\\infty)$ (see footnote~).}}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Linear fractional relative risk aversion", "authors": ["Kristian Behrens", "Yasusada Murata"], "url": "https://arxiv.org/abs/2509.09865v1", "attribution": "\"Linear fractional relative risk aversion\" by Kristian Behrens and Yasusada Murata, arXiv:2509.09865v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.06888v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Values of estimators of $\\sigma_1$ under entropy loss function $L_2(t)$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\t\t\t\\hline \\hline\n\t\t\t$(a_1,a_2)$ & $(b_1,b_2)$ & $\\delta_{01}^2$ & $\\delta_{Rmle}$ & $\\delta_{1S1}$ & $\\delta_{1S2}$ & $\\delta_{1S3}$ & $\\delta_{\\phi_1^{01}}$ & $\\delta_{\\phi_{1.5,2}}$ \\\\ \\hline \\hline\n\t\t\t(1,1) & (30,30) & 314.47 & 279.64 & 289.28 & 264.10 & 314.47 & 270.03 & 360.61 \\\\ \n\t\t\t(2,3) & (27,28) & 347.96 & 285.16 & 296.57 & 245.18 & 347.96 & 280.85 & 363.16 \\\\ \n\t\t\t(1,1) & (29,27) & 325.40 &290.56 & 301.32 & 255.71 & 325.40 & 284.56 & 376.36 \\\\ \n\t\t\t(4,2) & (30,30) & 313.94 & 262.44 & 272.16 & 251.30 & 313.94 & 253.02 & 333.57 \\\\ \\hline\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On the improved estimation of ordered parameters based on doubly type-II censored sample", "authors": ["Shrajal Bajpai", "Lakshmi Kanta Patra"], "url": "https://arxiv.org/abs/2411.06888v2", "attribution": "\"On the improved estimation of ordered parameters based on doubly type-II censored sample\" by Shrajal Bajpai and Lakshmi Kanta Patra, arXiv:2411.06888v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.16370v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|c|c|c|c|}\n\\hline\n\\textbf{Problem} & \\textbf{Equation Type} & \\textbf{PINN} & \\textbf{A-PINN} & \\textbf{SA-PINN} & \\textbf{RISN} \\\\\n\\hline\n1 & 1D Second-kind Linear Volterra Integral Equation & $9.53 \\times 10^{-3}$ & $2.59 \\times 10^{-4}$ & $2.23 \\times 10^{-3}$ & $8.46 \\times 10^{-6}$ \\\\\n2 & 1D Second-kind Nonlinear Volterra Integral Equation & $2.80 \\times 10^{-4}$ & $8.28 \\times 10^{-3}$ & - & $1.05 \\times 10^{-5}$ \\\\\n3 & 1D Second-kind Volterra-Fredholm Integral Equation & $1.95 \\times 10^{-4}$ & $1.91 \\times 10^{-3}$ & - & $7.05 \\times 10^{-6}$ \\\\\n4 & 1D Second-kind Volterra-Fredholm Integral Equation & $3.40 \\times 10^{-4}$ & $1.51 \\times 10^{-3}$ & - & $1.48 \\times 10^{-5}$ \\\\\n5 & 1D Linear Abel Integral Equation & $3.27 \\times 10^{-3}$ & - & - & $3.27 \\times 10^{-3}$ \\\\\n6 & 1D Nonlinear Abel Integral Equation & - & - & - & $1.70 \\times 10^{-3}$ \\\\\n7 & 2D Second-kind Fredholm Integral Equation & $2.85 \\times 10^{-3}$ & $3.20 \\times 10^{-2}$ & $1.30 \\times 10^{-2}$ & $1.31 \\times 10^{-4}$ \\\\\n8 & 2D Second-kind Volterra Integral Equation & $4.34 \\times 10^{-2}$ & - & - & $5.64 \\times 10^{-3}$ \\\\\n9 & System of Second-kind Fredholm Integral Equations & $\\{4.57, 5.93\\} \\times 10^{-3}$ & $\\{1.35, 1.07\\} \\times 10^{-3}$ & - & $\\{1.33, 1.24\\} \\times 10^{-3}$ \\\\\n10 & System of Second-kind Volterra Integral Equations & $\\{9.01, 1.98\\} \\times 10^{-5}$ & $8.67 \\times 10^{-4}, 1.03 \\times 10^{-3}$ & $\\{1.75, 4.19\\} \\times 10^{-3}$ & $3.49 \\times 10^{-5}, 1.18 \\times 10^{-4}$ \\\\\n11 & System of First-kind Volterra Integral Equations & $\\{4.70, 4.18\\} \\times 10^{-3}$ & - & - & $\\{2.56, 2.88\\} \\times 10^{-3}$ \\\\\n12 & Second-kind Fredholm Ordinary Integro-Differential Equation & $2.89 \\times 10^{-6}$ & $6.47 \\times 10^{-6}$ & $1.01 \\times 10^{-4}$ & $5.76 \\times 10^{-7}$ \\\\\n13 & Second-kind Fredholm Ordinary Integro-Differential Equation & - & $2.95 \\times 10^{-5}$ & $2.54 \\times 10^{-5}$ & $1.86 \\times 10^{-6}$ \\\\\n14 & First-kind Volterra Ordinary Integro-Differential Equation & $5.56 \\times 10^{-5}$ & $1.26 \\times 10^{-5}$ & - & $9.48 \\times 10^{-6}$ \\\\\n15 & First-kind Volterra Ordinary Integro-Differential Equation & - & $1.70 \\times 10^{-5}$ & - & $5.38 \\times 10^{-4}$ \\\\\n16 & Fredholm Partial Integro-Differential Equation & $3.00 \\times 10^{-3}$ & $3.27 \\times 10^{-1}$ & - & $2.51 \\times 10^{-4}$ \\\\\n17 & Fredholm Partial Integro-Differential Equation & $1.24 \\times 10^{-3}$ & $2.86 \\times 10^{-1}$ & - & $1.95 \\times 10^{-4}$ \\\\\n18 & Nonlinear Fredholm Partial Integro-Differential Equation & $1.89 \\times 10^{-3}$ & $5.26 \\times 10^{-1}$ & - & $1.05 \\times 10^{-4}$ \\\\\n19 & System of Volterra Integro-Differential Equations & $\\{1.94, 8.53\\} \\times 10^{-5}$ & $6.87 \\times 10^{-2}, 1.35 \\times 10^{-1}$ & $\\{2.09, 2.52\\} \\times 10^{-4}$ & $\\{4.89, 7.81\\} \\times 10^{-6}$ \\\\\n20 & Fractional Integro-Differential Equation & $5.50 \\times 10^{-3}$ & - & - & $3.64 \\times 10^{-3}$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of the MAE achieved by PINN, A-PINN, SA-PINN, and RISN across 20 different integral and integro-differential problems. The types of equations vary from one-dimensional Volterra and Fredholm equations to multidimensional systems and fractional integro-differential problems.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations", "authors": ["Mahdi Movahedian Moghaddam", "Kourosh Parand", "Saeed Reza Kheradpisheh"], "url": "https://arxiv.org/abs/2501.16370v2", "attribution": "\"Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations\" by Mahdi Movahedian Moghaddam, Kourosh Parand, and Saeed Reza Kheradpisheh, arXiv:2501.16370v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06220v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Routine Correlations}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccccccccccc}\n \\toprule\n & \\textbf{Practice} & \\textbf{Sleep} & \\textbf{Exercise} & \\textbf{MHC-SF} & \\textbf{PSS} & \\textbf{Health} & \\textbf{Days Training} \\\\\n \\midrule\n\\textbf{Practice} & 1\\\\\n \\textbf{Sleep} & -.1 & 1\\\\\n \\textbf{Exercise} & -.05 & -.08 & 1\\\\\n \\textbf{MHC-SF} & .19 & .01 & -.05 & 1\\\\\n \\textbf{PSS} & -.08 & .08 & -.01 & -.63*** & 1\\\\\n \\textbf{Health} & -.01 & .07 & .01 & .27 & -.24 & 1\\\\\n \\textbf{Days Training} & .55*** & -.06 & -.13 & .27 & -.07 & .22 & 1\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Player-AI Interaction: What Neural Network Games Reveal About AI as Play", "authors": ["Jichen Zhu", "Jennifer Villareale", "Nithesh Javvaji", "Sebastian Risi", "Mathias Löwe", "Rush Weigelt", "Casper Harteveld"], "url": "https://arxiv.org/abs/2101.06220v2", "attribution": "\"Player-AI Interaction: What Neural Network Games Reveal About AI as Play\" by Jichen Zhu, Jennifer Villareale, Nithesh Javvaji, Sebastian Risi, Mathias Löwe, Rush Weigelt, and Casper Harteveld, arXiv:2101.06220v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.13533v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Cash flow statement is a financial report that shows a company's cash inflows and outflows over a period of time, reflecting how the company generates and uses its cash through operating, investing, and financing activities}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lr}\n\\multicolumn{2}{c}{\\textbf{CASH FLOW STATEMENT}} \\\\\n\\hline\n\\hline\n\\textbf{Cash Flows from Operating Activities} & \\\\ \\cline{1-2}\nNet profit & \\underline{-1645141.43} \\\\ \nDepreciation & \\underline{45751.41} \\\\ \n(Increase) Decrease in Current Assets & \\\\ \n\\quad Accounts Receivable & \\underline{(2429482.13)} \\\\ \n\\quad Interest Receivable & \\underline{(2672.87)} \\\\ \n\\quad Inventory & \\underline{(5090000))} \\\\ \nTotal (Increase) Decrease in Current Assets & \\underline{(7522155)} \\\\ \\cline{1-2}\nIncrease (Decrease) in Current Liabilities &\\\\ \n\\quad Accounts Payable & \\underline{1590000} \\\\ \n\\quad Tax Payable & \\underline{271550.92} \\\\ \nTotal Increase (Decrease) in Current Liabilities & \\underline{1861550.92} \\\\ \n\\hline\n\\hline\n\\textbf{\\textit{Net Cash Flow From Operations}} & \\underline{-7259994.1} \\\\ \\cline{2-2}\n\\hline\n\\hline\n\\textbf{Cash Flows from Investing Activities} & \\\\ \\cline{1-2}\nPurchase of Fixed Assets & \\underline{305354.43} \\\\ \\cline{2-2}\n\\hline\n\\hline\n\\textbf{\\textit{Net Cash Flows from Investing Activities}} & \\underline{(305354.43)} \\\\ \\cline{1-2}\n\\hline\n\\hline\n\\cline{1-2}\n\\textbf{Beginning Cash and Cash Equivalents Balance} & \\underline{8000000} \\\\ \\cline{2-2}\n\\textbf{Ending Cash and Cash Equivalents Balance} & \\underline{434651.47} \\\\ \\cline{2-2}\n\\textbf{Net Increase} & \\underline{(7565348.53)} \\\\ \n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs", "authors": ["Junzhe Jiang", "Chang Yang", "Aixin Cui", "Sihan Jin", "Ruiyu Wang", "Bo Li", "Xiao Huang", "Dongning Sun", "Xinrun Wang"], "url": "https://arxiv.org/abs/2505.13533v1", "attribution": "\"FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs\" by Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Ruiyu Wang, Bo Li, Xiao Huang, Dongning Sun, and Xinrun Wang, arXiv:2505.13533v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.09414v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Notation}\n\\begin{tabular}{ll}\n\\hline \n\\multicolumn{2}{l}{\\textbf{Sets}} \\\\ \\hline\n$\\mathcal{N}$ & Set of nodes \\\\\n$\\mathcal{N}_f$ & Set of nodes to be sprayed \\\\\n$\\mathcal{K}$ & Set of tanker's trips \\\\\n\\hline\n\\multicolumn{2}{l}{\\textbf{Indices}} \\\\ \\hline\n$i,j$ & Node indices \\\\\n$k,r$ & Trip indeces \\\\ \\hline\n\\multicolumn{2}{l}{\\textbf{Parameters}} \\\\ \\hline\n$numSp$ & Number of sprayers \\\\\n$q_i$ & Quantity of fertilizer to be applied at node $i\\in\\mathcal{N}_f$ \\\\\n$s_i$ & Service time of node $i\\in\\mathcal{N}_f$ \\\\\n$tMax$ & Working hours/length of the planning horizon \\\\\n$t_{ij}$ & Travelling time between nodes $i$ and $j$ \\\\\n$Q_s$ & Capacity of the sprayer \\\\\n$Q_t$ & Capacity of the tanker \\\\\n$\\xi$ & Time needed to refill a sprayer \\\\\n$\\gamma$ & Time needed to refill the tanker at the depot \\\\\n$M$ & Big positive number \\\\ \\hline\n\\multicolumn{2}{l}{\\textbf{Decision variables}} \\\\ \\hline\n$x_{ij} $ & Binary variable that is equal to 1 if and only if sprayer travels along arc $(i,j)$ \\\\\n$g_{ij}^k$ & Binary variable that is equal to 1 if and only if the tanker travels arc $(i,j)$ in trip $k$ \\\\\n$\\delta_i$ & Binary variable that is equal to 1 if and only if there is a refill at node $i\\in\\mathcal{N}_f$ \\\\\n$z_{kr}$ & Binary variable that is equal to 1 if and only if trip $k\\in\\mathcal{K}$ proceeds trip $r\\in\\mathcal{K}$ \\\\\n$\\theta_i^k$ & Arrival time of the tender tanker at node $i\\in\\mathcal{N}_f$ on the $k^{th}$ trip\\\\\n$y_i$ & Time of arrival of a sprayer at node $i\\in\\mathcal{N}_f$ \\\\\n$a_i$ & Time of spraying service start time by a sprayer at node $i\\in\\mathcal{N}_f$ \\\\\n$w_i^k$ & Refilling start time of the tender tanker at node $i\\in\\mathcal{N}_f$ on the $k^{th}$ trip\\\\\n$h_i^k$ & Level of fertilizer of the tender tanker at arrival to node $i\\in\\mathcal{N}_f$ on the $k^{th}$ trip\\\\\n$l_i$ & Level of fertilizer of sprayer when arriving to node $i\\in\\mathcal{N}_f$ \\\\\n$m_i$ & Waiting time of a sprayer at node $i\\in\\mathcal{N}_f$ \\\\\n$v_i$ & Quantity of fertilizer supplied to a sprayer at node $i\\in\\mathcal{N}_f$ \\\\\n \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Adaptive Large Neighborhood Search Metaheuristic for Vehicle Routing Problem with Multiple Synchronization Constraints and Multiple Trips", "authors": ["Faisal Alkaabneh"], "url": "https://arxiv.org/abs/2312.09414v1", "attribution": "\"Adaptive Large Neighborhood Search Metaheuristic for Vehicle Routing Problem with Multiple Synchronization Constraints and Multiple Trips\" by Faisal Alkaabneh, arXiv:2312.09414v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.05009v4_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n \\hline\n $i$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 \\\\ \n \\hline\n $z^*_i$ & 0.000&2.000&-2.000&0.996&-0.996&2.805 &-2.806\\\\\n \\hline\n $m_i$& -0.000&2.000&-2.000&0.996&-0.995&2.805&-2.806\\\\\n \\hline\n $f''(z^*_i)$ & 41.478&41.478&41.478&41.468&41.462&15.273&15.539\\\\\n \\hline\n $f''_{\\theta_{i}} \\pm {V}^{-1}$ & 40.974&37.442&36.916&38.283&39.519&11.841&13.380\\\\\n \\hline\n $i$ & 6 & 7 & 8& 9 & 10 & 11 & 12 \\\\\n \\hline\n $z^*_i$ & -2.806 &1.500&-1.502&0.503&-0.501&2.416&-2.413\\\\\n \\hline\n $m_i$& -2.806 & 1.499&-1.503&0.502&-0.501&2.414&-2.413\\\\\n \\hline\n \n $f''(z^*_i)$ & 15.539 & -37.478&-37.473&-37.474&-37.478&-31.901&-31.688\\\\\n \\hline\n $f''_{\\theta_{i}} \\pm {V}_i^{-1}$ &15.380 & -36.796&-36.271&-37.515&-37.285&-31.521&-31.189\\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Consensus-based adaptive sampling and approximation for high-dimensional energy landscapes", "authors": ["Liyao Lyu", "Huan Lei"], "url": "https://arxiv.org/abs/2311.05009v4", "attribution": "\"Consensus-based adaptive sampling and approximation for high-dimensional energy landscapes\" by Liyao Lyu and Huan Lei, arXiv:2311.05009v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.11150v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Firms' Summary Statistics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n\t&\tMean\t&\tp10\t&\tp25 \t&\tMedian\t&\tp75 \t&\tp90\t&\tSD\t\\\\ \\hline \\hline\nInvestment Growth $\\log \\Delta k_{i,t} $\t&\t0.029\t&\t-0.051\t&\t-0.010\t&\t0.017\t&\t0.054\t&\t0.115\t&\t0.196\t\\\\\nInvestment Growth $\\log \\Delta k_{i,t+4} $\t&\t0.136\t&\t-0.093\t&\t0.004\t&\t0.090\t&\t0.221\t&\t0.412\t&\t0.406\t\\\\\n\\\\\nLeverage: Liab. - Assets\t&\t0.481\t&\t0.077\t&\t0.242\t&\t0.434\t&\t0.594\t&\t0.770\t&\t0.862\t\\\\\nLeverage: Short-Term Liab. - Assets\t&\t0.219\t&\t0.027\t&\t0.070\t&\t0.157\t&\t0.279\t&\t0.454\t&\t0.313\t\\\\\n\\\\\nCurrency-Mismatch & -0.025\t&\t-0.311\t&\t-0.020\t&\t0.000\t&\t0.015\t&\t0.181\t&\t0.242 \\\\ \\hline \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity", "authors": ["Santiago Camara", "Sebastian Ramirez Venegas"], "url": "https://arxiv.org/abs/2209.11150v1", "attribution": "\"The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity\" by Santiago Camara and Sebastian Ramirez Venegas, arXiv:2209.11150v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.17099v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Productivity Comparison: Traditional vs Weather-Aware AI Operations}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Metric} & \\textbf{Traditional} & \\textbf{Weather-AI} & \\textbf{Improvement} \\\\\n\\midrule\nRevenue per minute (¥) & 50.1 & 103.9 & +107.3\\% \\\\\nWait time (minutes) & 9.1 & 5.1 & -43.8\\% \\\\\nUtilization rate (\\%) & 48.1 & 78.4 & +63.0\\% \\\\\nDaily earnings (¥) & 15,493 & 53,326 & +244.2\\% \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity", "authors": ["Tatsuru Kikuchi"], "url": "https://arxiv.org/abs/2507.17099v1", "attribution": "\"Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity\" by Tatsuru Kikuchi, arXiv:2507.17099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.21358v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{cllrc}\n Symbol & Interpretation & True & Estimate & std.dev\n \\\\\n \\hline \n $\\lambda$ & Decay rate & 1 & 1.02 & 0.12 \\\\\n $\\mu$ & Stationary mean & 2 & 2.04 & 0.03 \\\\\n $\\sigma$ & Process noise intensity & 1 & 0.95 & 0.06 \\\\\n $s$ & Measurement noise standard deviation & 0.5 & - & - \\\\\n \\end{tabular}\n\\caption{Parameters for the linear example: True values, estimates, standard errors.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Inference in stochastic differential equations using the Laplace approximation: Demonstration and examples", "authors": ["Uffe Høgsbro Thygesen", "Kasper Kristensen"], "url": "https://arxiv.org/abs/2503.21358v2", "attribution": "\"Inference in stochastic differential equations using the Laplace approximation: Demonstration and examples\" by Uffe Høgsbro Thygesen and Kasper Kristensen, arXiv:2503.21358v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.14431v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrr}\n\\hline \\hline\n\\textbf{Metric} & \\textbf{Sector} & \\textbf{ADF Stat} & \\textbf{ADF p-val} & \\textbf{PP Stat} & \\textbf{PP p-val} \\\\\n\\hline\n\\textbf{Pre-crash period} & & & \\\\\n\\hline\n$WD_1$ & Commodity & -1.51 & 0.53 & -1.57 & 0.50 \\\\\n$WD_1$ & Stock & -1.57 & 0.50 & -1.75 & 0.40 \\\\\n$WD_2$ & Commodity & -1.28 & 0.64 & -1.59 & 0.49 \\\\\n$WD_2$ & Stock & -1.66 & 0.45 & -1.87 & 0.35 \\\\\n\\hline\n\\textbf{Crash period} & & & \\\\\n\\hline\n$WD_1$ & Commodity & -2.09 & 0.25 & -0.61 & 0.87 \\\\\n$WD_1$ & Stock & -1.89 & 0.34 & -1.35 & 0.60 \\\\\n$WD_2$ & Commodity & -1.54 & 0.51 & -0.53 & 0.89 \\\\\n$WD_2$ & Stock & -1.70 & 0.43 & -1.38 & 0.59 \\\\\n\\hline\n\\textbf{Post-crash period} & & & \\\\\n\\hline\n$WD_1$ & Commodity & -1.31 & 0.62 & -1.62 & 0.47 \\\\\n$WD_1$ & Stock & -2.91 & 0.04** & -1.85 & 0.36 \\\\\n$WD_2$ & Commodity & -2.73 & 0.07 & -1.61 & 0.48 \\\\\n$WD_2$ & Stock & -3.11 & 0.03** & -2.03 & 0.27 \\\\\n\\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective", "authors": ["Buddha Nath Sharma", "Anish Rai", "SR Luwang", "Md. Nurujjaman", "Sushovan Majhi"], "url": "https://arxiv.org/abs/2502.14431v1", "attribution": "\"Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective\" by Buddha Nath Sharma, Anish Rai, SR Luwang, Md. Nurujjaman, and Sushovan Majhi, arXiv:2502.14431v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05954v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Best Test Accuracies of models using MovieQA and Youtube2TextQA datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n\\hline\n\\textbf{Datasets} & \\multicolumn{4}{c|}{\\textit{\\textbf{YouTube2TextQA}}} & \\multicolumn{4}{c|}{\\textit{\\textbf{MovieQA}}} \\\\ \\hline\n\\textit{\\textbf{Model}} & r-ANL {[}5{]} & DLAN {[}9{]} & Fan et al. {[}13{]} & L-GCN {[}21{]} & DEMN {[}8{]} & MDAM {[}12{]} & PAMN {[}20{]} & AMN {[}23{]} \\\\ \\hline\n\\textit{\\textbf{Accuracy}} & 61.4 & 36.33 & 82.5 & 83.9 & 30 & 41.41 & 42.53 & 45.31 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Recent Advances in Video Question Answering: A Review of Datasets and Methods", "authors": ["Devshree Patel", "Ratnam Parikh", "Yesha Shastri"], "url": "https://arxiv.org/abs/2101.05954v2", "attribution": "\"Recent Advances in Video Question Answering: A Review of Datasets and Methods\" by Devshree Patel, Ratnam Parikh, and Yesha Shastri, arXiv:2101.05954v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09574v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{rotating}\n\\usepackage{amsfonts}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc|cccc}\n\\hline\n\\hline\n & &ACSD&LBE&DCMC&MDSF&SE&DF&AFNet&CTMF&MMCI\\\\\n &Metric&ICIP14&CVPR16&SPL16&TIP17&ICME16&TIP17&arXiv19&TOC18&PR19\\\\\n & &[52]&[19]&[53]&[54]&[55]&[64]&[56]&[57]&[9]\\\\\n\\hline\n\\hline\n\\multirow{8}*{\\rotatebox{90}{\\emph{DES} \n}} &$S_\\alpha$↑ &0.728 & 0.703 & 0.707 & 0.741 & 0.741 & 0.752 & 0.770 & 0.863 & 0.848 \\\\ \n &max $F_\\beta$↑ &0.756 & 0.788 & 0.666 & 0.746 & 0.741 & 0.766 & 0.728 & 0.844 & 0.822 \\\\\n &mean $F_\\beta$↑ &0.513 & 0.576 & 0.542 & 0.523 & 0.617 & 0.604 & 0.713 & 0.756 & 0.735 \\\\\n &adp $F_\\beta$↑ &0.717 & 0.796 & 0.702 & 0.744 & 0.726 & 0.753 & 0.730 & 0.778 & 0.762 \\\\\n &max $E_\\xi$↑ &0.850 & 0.890 & 0.773 & 0.851 & 0.856 & 0.870 & 0.881 & 0.932 & 0.928 \\\\\n &mean $E_\\xi$↑ &0.612 & 0.649 & 0.632 & 0.621 & 0.707 & 0.684 & 0.810 & 0.826 & 0.825 \\\\\n &adp $E_\\xi$↑ &0.855 & 0.911 & 0.849 & 0.869 & 0.852 & 0.877 & 0.874 & 0.911 & 0.904 \\\\\n &$\\mathcal{M}$↓ &0.169 & 0.208 & 0.111 & 0.122 & 0.090 & 0.093 & 0.068 & 0.055 & 0.065\\\\ \n\\hline\n\\multirow{8}*{\\rotatebox{90}{\\emph{NLPR} \n}} &$S_\\alpha$↑ &0.673 & 0.762 & 0.724 & 0.805 & 0.756 & 0.802 & 0.799 & 0.860 & 0.856 \\\\\n &max $F_\\beta$↑ &0.673 & 0.762 & 0.724 & 0.805 & 0.756 & 0.802 & 0.799 & 0.860 & 0.856 \\\\\n &mean $F_\\beta$↑ &0.429 & 0.626 & 0.543 & 0.649 & 0.624 & 0.664 & 0.755 & 0.740 & 0.737 \\\\\n &adp $F_\\beta$↑ &0.535 & 0.736 & 0.614 & 0.665 & 0.692 & 0.744 & 0.747 & 0.724 & 0.730 \\\\\n &max $E_\\xi$↑ &0.780 & 0.855 & 0.793 & 0.885 & 0.847 & 0.880 & 0.879 & 0.929 & 0.913 \\\\\n &mean $E_\\xi$↑ &0.578 & 0.719 & 0.684 & 0.745 & 0.742 & 0.775 & 0.851 & 0.840 & 0.841 \\\\\n &adp $E_\\xi$↑ &0.742 & 0.855 & 0.786 & 0.812 & 0.839 & 0.868 & 0.884 & 0.869 & 0.872 \\\\\n &$\\mathcal{M}$↓ &0.179 & 0.081 & 0.117 & 0.095 & 0.091 & 0.085 & 0.058 & 0.056 & 0.059\\\\ \n\\hline\n\\multirow{8}*{\\rotatebox{90}{\\emph{SSD} \n}} &$S_\\alpha$↑ &0.675 & 0.621 & 0.704 & 0.673 & 0.675 & 0.747 & 0.714 & 0.776 & 0.813 \\\\\n &max $F_\\beta$↑ &0.682 & 0.619 & 0.711 & 0.703 & 0.710 & 0.735 & 0.687 & 0.729 & 0.781 \\\\\n &mean $F_\\beta$↑ &0.469 & 0.489 & 0.572 & 0.470 & 0.564 & 0.624 & 0.672 & 0.689 & 0.721 \\\\\n &adp $F_\\beta$↑ &0.656 & 0.613 & 0.679 & 0.674 & 0.693 & 0.724 & 0.694 & 0.710 & 0.748 \\\\\n &max $E_\\xi$↑ &0.785 & 0.736 & 0.786 & 0.779 & 0.800 & 0.828 & 0.807 & 0.865 & 0.882 \\\\\n &mean $E_\\xi$↑ &0.566 & 0.574 & 0.646 & 0.576 & 0.631 & 0.690 & 0.762 & 0.796 & 0.796 \\\\\n &adp $E_\\xi$↑ &0.765 & 0.729 & 0.786 & 0.772 & 0.778 & 0.812 & 0.803 & 0.838 & 0.860 \\\\\n &$\\mathcal{M}$↓ &0.203 & 0.278 & 0.169 & 0.192 & 0.165 & 0.142 & 0.118 & 0.099 & 0.082\\\\ \n\\hline\n\\multirow{8}*{\\rotatebox{90}{\\emph{SIP} \n}} &$S_\\alpha$↑ &0.732 & 0.727 & 0.683 & 0.717 & 0.628 & 0.653 & 0.720 & 0.716 & 0.833 \\\\\n &max $F_\\beta$↑ &0.763 & 0.751 & 0.618 & 0.698 & 0.661 & 0.657 & 0.712 & 0.694 & 0.818 \\\\\n &mean $F_\\beta$↑ &0.542 & 0.571 & 0.499 & 0.568 & 0.515 & 0.464 & 0.702 & 0.608 & 0.771 \\\\\n &adp $F_\\beta$↑ &0.727 & 0.733 & 0.645 & 0.694 & 0.662 & 0.673 & 0.705 & 0.684 & 0.795 \\\\\n &max $E_\\xi$↑ &0.838 & 0.853 & 0.743 & 0.798 & 0.771 & 0.759 & 0.819 & 0.829 & 0.897 \\\\\n &mean $E_\\xi$↑ &0.614 & 0.651 & 0.598 & 0.645 & 0.592 & 0.565 & 0.793 & 0.705 & 0.845 \\\\\n &adp $E_\\xi$↑ &0.827 & 0.841 & 0.786 & 0.805 & 0.756 & 0.794 & 0.815 & 0.824 & 0.886 \\\\\n &$\\mathcal{M}$↓ &0.172 & 0.200 & 0.186 & 0.167 & 0.164 & 0.185 & 0.118 & 0.139 & 0.086\\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection", "authors": ["Ze-yu Liu", "Jian-wei Liu", "Xin Zuo", "Ming-fei Hu"], "url": "https://arxiv.org/abs/2201.09574v1", "attribution": "\"Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection\" by Ze-yu Liu, Jian-wei Liu, Xin Zuo, and Ming-fei Hu, arXiv:2201.09574v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.02109v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Analysis results given by ISPW lasso and ISPW likelihood function using MG data}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|}\\hline\nParameter & lasso & likelihood (LN) & likelihood (W) & likelihood (LL)\\\\ \\hline\nIntercept & 3.78 & 4.47 & 2.40 & 2.64\\\\\nTreatment group & - & 0.17 & 0.11 & 0.19\\\\\nAge & - & -0.01 & 0.02 & 0.00\\\\\nYears of diagnosis & 0.01 & 0.03 & - & -\\\\\nKarnofsky performance score & 0.19 & 0.30 & - & 0.70\\\\\nRace & -0.12 & -0.39 & - & -\\\\\nRadiation therapy & - & 0.48 & - & -\\\\\nSex & 0.23 & - & - & -\\\\\nNitro & -0.18 & -0.38 & - & -\\\\\nPath & 0.02 & 0.06 & 0.43 &0.28 \\\\\nGrade & -0.01 & -0.58 & - & -\\\\\nError & - & 0.42 & 0.83 & 0.38\\\\\\hdashline\nAIC & - & \\textcolor{red}{\\bf 5387.56} & 5804.40 &5582.18\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Variable Selection using Inverse Survival Probability Weighting", "authors": ["Masahiro Kojima"], "url": "https://arxiv.org/abs/2212.02109v1", "attribution": "\"Variable Selection using Inverse Survival Probability Weighting\" by Masahiro Kojima, arXiv:2212.02109v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.09248v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Observed vs Fitted values.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|ccc|ccc|ccc}\n\t\t\t\\hline\\noalign{\\smallskip}\n Branch & Deductible&\\multicolumn{3}{c}{Beta}&\\multicolumn{3}{c}{$p_0$}&\\multicolumn{3}{c}{$p_1$}\\\\\\cline{3-11}\n \t\n& &Exposure& Observed&Fitted&Exposure&Observed&Fitted&Exposure&Observed&Fitted\\\\\n\t\\hline\\noalign{\\smallskip}\nSurgery&Level 1& 366 &88.72\\%&88.08\\%& 47,845 &97.88\\%&97.88\\%& 47,845 &1.35\\%&1.35\\%\\\\\n&Level 2& 167 &79.66\\%&80.30\\%& 9,566 &98.24\\%&98.24\\%& 9,566 &0.01\\%&0.01\\%\\\\\n&Level 3& 867 &76.90\\%&76.37\\%& 6,379 &86.41\\%&86.41\\%& 6,379 &0.00\\%&0.00\\%\\\\\n\\hline\\noalign{\\smallskip}\nDiagnostic&Level 1& 14,358 &77.04\\%&76.58\\%& 44,245 &52.67\\%&52.67\\%& 44,245 &14.88\\%&14.88\\%\\\\\n&Level 2& 8,649 &70.08\\%&69.47\\%& 8,889 &2.70\\%&2.70\\%& 8,889 &0.00\\%&0.01\\%\\\\\n&Level 3& 4,298 &64.10\\%&64.12\\%& 5,860 &26.66\\%&26.66\\%& 5,860 &0.00\\%&0.00\\%\\\\\n\t\t\t\\hline\\noalign{\\smallskip}\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An application of Zero-One Inflated Beta regression models for predicting health insurance reimbursement", "authors": ["Fabio Baione", "Davide Biancalana", "Paolo De Angelis"], "url": "https://arxiv.org/abs/2011.09248v1", "attribution": "\"An application of Zero-One Inflated Beta regression models for predicting health insurance reimbursement\" by Fabio Baione, Davide Biancalana, and Paolo De Angelis, arXiv:2011.09248v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.17617v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ccc|ccc|ccc|ccc|}\n\\hline\n\\multirow{2}{*}{Model Architecture} & \\multicolumn{3}{c|}{Dice [\\%]} & \\multicolumn{3}{c|}{HD95 [mm]} & \\multicolumn{3}{c|}{LW Dice [\\%]} & \\multicolumn{3}{c|}{LW HD95 [mm]} \\\\\n\\cline{2-13}\n& WT & ET & TC & WT & ET & TC & WT & ET & TC & WT & ET & TC \\\\\n\\hline\nOptimized U-Net & 81.44 & 72.25& 75.81& 22.30& 48.47& 29.94& 68.50& 67.85& 71.27& 84.84 & 77.05& 51.79\\\\\nnnU-Net & 88.11 & 79.98 & 82.51 & 17.49 & 37.00 & 23.79 & 82.56 & 78.07 & 79.81 & 39.62 & 48.26 & 34.54 \\\\\nSwin-UNetR & 78.59 & 72.51 & 74.75 & 22.85 & 48.88 & 31.28 & 60.96 & 65.75 & 68.03 & 116.09 & 90.51 & 67.75 \\\\\nUMamba & 85.69 & 77.41 & 82.07 & 14.51 & 37.48 & 18.90 & 78.60 & 73.95 & 78.55 & 50.24 & 62.11 & 38.17 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Test performance metrics for the different training pipelines considered. Metrics include Dice, HD95, lesion-wise Dice (LW Dice), and lesion-wise HD95 for whole tumor (WT), tumor core (TC) and ET.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "An Ensemble Approach for Brain Tumor Segmentation and Synthesis", "authors": ["Juampablo E. Heras Rivera", "Agamdeep S. Chopra", "Tianyi Ren", "Hitender Oswal", "Yutong Pan", "Zineb Sordo", "Sophie Walters", "William Henry", "Hooman Mohammadi", "Riley Olson", "Fargol Rezayaraghi", "Tyson Lam", "Akshay Jaikanth", "Pavan Kancharla", "Jacob Ruzevick", "Daniela Ushizima", "Mehmet Kurt"], "url": "https://arxiv.org/abs/2411.17617v1", "attribution": "\"An Ensemble Approach for Brain Tumor Segmentation and Synthesis\" by Juampablo E. Heras Rivera, Agamdeep S. Chopra, Tianyi Ren, Hitender Oswal, Yutong Pan, Zineb Sordo, Sophie Walters, William Henry, Hooman Mohammadi, Riley Olson, Fargol Rezayaraghi, Tyson Lam, Akshay Jaikanth, Pavan Kancharla, Jacob Ruzevick, Daniela Ushizima, and Mehmet Kurt, arXiv:2411.17617v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table33.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cccc|}\n\\hline\nCell 1 & Cell 2 & Cell 3 & Cell 4 \\\\\n\\hline\n8 & 10 & 30 & 5 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.12815v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc} \\toprule\nTask & \\shortstack{Epoch \\ ber} & \\shortstack{Batch \\\\ size} & \\shortstack{Learning \\\\ rate} \\\\ \\midrule\nLoDoPaB-CT &70 &32 & 0.001 \\\\\nfastMRI &70 &8 & 0.001 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{Hyperparameters used for the U-Net.} }\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning Multiscale Convolutional Dictionaries for Image Reconstruction", "authors": ["Tianlin Liu", "Anadi Chaman", "David Belius", "Ivan Dokmanić"], "url": "https://arxiv.org/abs/2011.12815v3", "attribution": "\"Learning Multiscale Convolutional Dictionaries for Image Reconstruction\" by Tianlin Liu, Anadi Chaman, David Belius, and Ivan Dokmanić, arXiv:2011.12815v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05672v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimation performance of the quantile coefficient functions at $\\tau = 0.5$ for \\textbf{Scenario } with heterogeneous error term, heavy-tailed error term and covariate-dependent censoring time based on 500 simulation runs.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\\hline\\hline\n\\textbf{Scenario} &&Method & $\\beta_0$ & $\\beta_1$&$\\beta_2$ &$\\beta_0$ & $\\beta_1$&$\\beta_2$\\\\\n \\hline\n && &\\multicolumn{3}{c}{$N_1 = 500$}&\\multicolumn{3}{c}{$N_1 = 1000$}\\\\\n \\cline{4-9}\n \\multirow{3}{*}{a}&\\multirow{3}{*}{MSE}&HCQRF&0.771&1.052&0.678&0.458&0.658&0.430\\\\\n && HCQRF-c&0.638&0.752&0.541&0.391&0.471&0.347\\\\\n &&grf-c&1.588&1.268&0.706&1.009&0.846&0.496\\\\\n \\cline{3-9}\n &\\multirow{3}{*}{MAE}&HCQRF&0.663&0.795&0.627&0.518&0.635&0.493\\\\\n &&HCQRF-c& 0.612&0.677&0.559&0.482&0.541&0.443\\\\\n &&grf-c& 0.973&0.846&0.684&0.773&0.704&0.570\\\\\n \\hline\n \\multirow{3}{*}{b}&\\multirow{3}{*}{MSE}&HCQRF&1.370&1.487&1.008&0.906&0.965&0.691\\\\\n && HCQRF-c&1.162&1.070&0.825&0.779&0.705&0.563\\\\\n &&grf-c&1.921&1.639&0.863&1.230&1.080&0.622\\\\\n \\cline{3-9}\n &\\multirow{3}{*}{MAE}&HCQRF&0.896&0.948&0.772&0.732&0.771&0.630\\\\\n &&HCQRF-c&0.834&0.811&0.699&0.686&0.666&0.571\\\\\n &&grf-c& 1.080&0.965&0.749&0.863&0.796&0.634\\\\\n \\hline\n \\multirow{3}{*}{c}&\\multirow{3}{*}{MSE}&HCQRF&0.977&1.095&0.683&0.655&0.686&0.435\\\\\n&&HCQRF-c&0.829&0.804&0.561&0.578&0.509&0.359\\\\\n&&grf-c &2.324&1.435&0.736&1.622&0.971&0.520\\\\\n\\cline{3-9}\n&\\multirow{3}{*}{MAE}&HCQRF&0.773&0.811&0.617&0.653&0.643&0.483\\\\\n&&HCQRF-c&0.724&0.696&0.555&0.622&0.555&0.437\\\\\n&&grf-c&1.222&0.894&0.693&1.038&0.750&0.575\\\\\n \\hline\\hline\n \\multicolumn{9}{l}{\\footnotesize{MSE: mean squared error, MAE: mean absolute error}}\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Hybrid Censored Quantile Regression Forest to Assess the Heterogeneous Effects", "authors": ["Huichen Zhu", "Yifei Sun", "Ying Wei"], "url": "https://arxiv.org/abs/2212.05672v1", "attribution": "\"Hybrid Censored Quantile Regression Forest to Assess the Heterogeneous Effects\" by Huichen Zhu, Yifei Sun, and Ying Wei, arXiv:2212.05672v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.18123v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fixed model size $d=100$ and the number of sampling $n=100$, SHD results of causal discovery using Algorithm 1 for different $C_m$ values ($10$ runs).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c}\n \\toprule[1pt]\n $C_m$ & $1$ & $2$ & $4$ & $8$ & $16$ \\\\\n \\midrule\n SHD & $2941.0\\pm29.5$ & $2905.7 \\pm 50.8$ & $2900.6 \\pm 80.8$ & $2637.1 \\pm 200.4$ & $1512.4 \\pm 283.6$\\\\\n \\midrule[1pt]\n $C_m$ & $32$ & $64$ & $128$ & $256$ & $512$\\\\\n \\midrule\n SHD & $413.9 \\pm 93.4$ & $55.0 \\pm 16.0$ & $ 23.9 \\pm 4.6$ & $21.2 \\pm 5.0$ & $13.8 \\pm 1.8$ \\\\\n\\bottomrule[1pt]\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling", "authors": ["Zhenyu Zhu", "Francesco Locatello", "Volkan Cevher"], "url": "https://arxiv.org/abs/2310.18123v1", "attribution": "\"Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling\" by Zhenyu Zhu, Francesco Locatello, and Volkan Cevher, arXiv:2310.18123v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.04279v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Predictions of piecewise constant index with four layers, from $6$ transmission eigenvalues.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c||c||c|}\n\\hline\noriginal & \\multirow{2}{*}{kNN} & \\multirow{2}{*}{RF} & \\multirow{2}{*}{MLP} \\\\\n$(n_1,n_2,n_3,n_4)$ & & & \\\\\n \\hline\n \\hline\n $4,4,4,4$ & $4,4.25,4,3.75$ &$ 4.01,4.08,4.01,3.93$ & $3.98,4.06,4.02,4.05$ \\\\\n \\hline\n $5.5,3.1,3.1,3.1$ &$5.5,2.92,3,3.58$&$ 5.55,3.05,3.04,3.44$ & $5.62,3.07,3.07,3.34$\\\\\n \\hline\n $6.1,6.1,3.5,3.5$ & $6,6.08,3.75,3.5$ &$6.09,6.28,3.66,3.44 $ & $6.03,6.25,3.72,3.46$\\\\\n \\hline\n $4.6,7.9,8.2,5.5$ & $4.5, 7.75, 8.17, 5.92$& $4.23,7.67,8.12,6.06 $& $4.61,7.75,8.18,5.78$\\\\\n \\hline\n $2.5,7.3,3.7,6.5$ & $3,8.08,4,6.08$&$3.02,8.09,3.9,6.39$ & $3.11,8,3.91,6.59$ \\\\\n \\hline\n $6.8,7.3,5.6,5.3$ &$6.83,7.42,5.56,5.25$&$6.69,7.43,5.58,5.39$ & $6.74,7.38,5.65,5.31$\\\\\n \\hline\n $6.8,2.5,2,5$ & $6.5,2.5,2,6.67$ & $6.32,2.52,2,6.63$ & $7.11,2.38,1.89,7.11$\\\\\n \\hline\n $6.6,5,6,9$ & $6.58,4.92,6.08,9.25$&$ 6.66,4.92,6.1,9.21$ & $6.39,4.89,5.99,9.49$\\\\\n \\hline\n $7.7,7.5,8,2.8$ &$8.67,8.92,8.58,2.17$ & $ 7.96,7.23,8.04,3.7$& $7.79,7.64,8.17,3.17$ \\\\\n \\hline\n $6.1,8.4,4,4.8$ & $6,9.25,4.25,4.5$& $6.01,9.11,4.29,4.52$& $5.83,9.03,4.31,4.52$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Application of machine learning regression models to inverse eigenvalue problems", "authors": ["Nikolaos Pallikarakis", "Andreas Ntargaras"], "url": "https://arxiv.org/abs/2212.04279v1", "attribution": "\"Application of machine learning regression models to inverse eigenvalue problems\" by Nikolaos Pallikarakis and Andreas Ntargaras, arXiv:2212.04279v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12432v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Distribution of patients on the basis of the discharge tag and according to the actual list of the outcomes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c|c|c|c}\n & {\\sc White} & {\\sc Green} & {\\sc Yellow} & {\\sc Red} & \\\\ \\hline\n \\emph{O1} & 121 & 1025 & 23 & & 1169 \\\\\n \\emph{O2} & 39 & 496 & 21 & & 556 \\\\\n \\emph{O3} & & 29 & 182 & 14 & 225 \\\\\n \\emph{O4} & & & 4 & 4 & 8 \\\\\n \\emph{O5} & & 19 & 10 & & 29 \\\\\n \\emph{O6} & 3 & 17 & 2 & & 22 \\\\\n \\emph{O7} & 8 & 26 & 1 & & 35 \\\\\n \\emph{O8} & & & & 2 & 2 \\\\\n \\emph{O9} & & & & & - \\\\ \\hline\n & 171 & 1612 & 243 & 20 & 2046 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Effect of different patient peak arrivals on an Emergency Department via discrete event simulation", "authors": ["G. Fava", "T. Giovannelli", "M. Messedaglia", "M. Roma"], "url": "https://arxiv.org/abs/2101.12432v1", "attribution": "\"Effect of different patient peak arrivals on an Emergency Department via discrete event simulation\" by G. Fava, T. Giovannelli, M. Messedaglia, and M. Roma, arXiv:2101.12432v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.12530v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n $\\gamma$& white & lightgray & darkgray & black \\\\ \\hline\n 0.01 &[0,\\, 0.0175)&[0.0175,\\, 0.05)&[0.05,\\, 150)&$[150,\\, \\infty)$ \\\\ \\hline\n 0.1 &[0,\\, 0.12)&[0.12,\\, 0.5)&[0.5,\\, 150)&$[150,\\, \\infty)$ \\\\ \\hline\n 1 \t &[0,\\, 1.25)&[1.25,\\, 3.0)&[3.0,\\, 150)&$[150,\\, \\infty)$ \\\\ \\hline\n 10 &[0,\\, 12)&[12,\\, 20)&[20,\\, 150)&$[150,\\, \\infty)$ \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Time intervals used to determine tipping for each of the different gray shadings in Figure~; if a randomly chosen initial condition for $y$ results in tipping in the corresponding time interval then the shading is used, otherwise we used black to indicate the tipping has not occurred by time $150$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The effect of timescale separation on the tipping window for chaotically forced systems", "authors": ["Raphael Römer", "Peter Ashwin"], "url": "https://arxiv.org/abs/2504.12530v1", "attribution": "\"The effect of timescale separation on the tipping window for chaotically forced systems\" by Raphael Römer and Peter Ashwin, arXiv:2504.12530v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.21253v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Descriptive Statistics of Suspense and Surprise in Men's European Football Matches}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccccccc}\n \\toprule\n League & Metric & N & Mean & Median & SD & Min & Max & BM & $<$ BM \\\\\n \\midrule\n \\multirow{2}{*}{Top 5 Leagues} \n & Suspense & 25,389 & 5.85 & 6.53 & 2.14 & 0.08 & 9.27 & 6.03 & *** \\\\\n & Surprise & 25,389 & 1.41 & 1.18 & 0.80 & 0.12 & 5.93 & 1.17 & \\\\\n \\midrule\n \\multirow{2}{*}{Premier League} \n & Suspense & 5,320 & 5.74 & 6.40 & 2.17 & 0.08 & 9.19 & 6.03 & *** \\\\\n & Surprise & 5,320 & 1.38 & 1.13 & 0.81 & 0.14 & 5.28 & 1.17 & \\\\\n \\midrule\n \\multirow{2}{*}{Bundesliga} \n & Suspense & 4,284 & 5.80 & 6.39 & 2.18 & 0.25 & 9.03 & 6.03 & *** \\\\\n & Surprise & 4,284 & 1.43 & 1.26 & 0.80 & 0.15 & 5.93 & 1.17 & \\\\\n \\midrule\n \\multirow{2}{*}{La Liga} \n & Suspense & 5,320 & 5.88 & 6.61 & 2.21 & 0.16 & 9.27 & 6.03 & *** \\\\\n & Surprise & 5,320 & 1.41 & 1.15 & 0.81 & 0.12 & 5.76 & 1.17 & \\\\\n \\midrule\n \\multirow{2}{*}{Serie A} \n & Suspense & 5,320 & 5.85 & 6.52 & 2.11 & 0.23 & 9.09 & 6.03 & *** \\\\\n & Surprise & 5,320 & 1.42 & 1.21 & 0.81 & 0.14 & 5.42 & 1.17 & \\\\\n \\midrule\n \\multirow{2}{*}{Ligue 1} \n & Suspense & 5,145 & 5.99 & 6.63 & 2.02 & 0.17 & 9.19 & 6.03 & * \\\\\n & Surprise & 5,145 & 1.41 & 1.20 & 0.78 & 0.12 & 5.16 & 1.17 & \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Suspense and Surprise in European Football", "authors": ["Raphael Flepp", "Tim Pawlowski", "Travis Richardson"], "url": "https://arxiv.org/abs/2506.21253v1", "attribution": "\"Suspense and Surprise in European Football\" by Raphael Flepp, Tim Pawlowski, and Travis Richardson, arXiv:2506.21253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16246v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\hline\n $ n $ & $ \\mathrm{Re}(c_n) $ & $ \\mathrm{Im}(c_n) $ & $ |c_n| $ \\\\\n \\hline\n 0 & 0.84666501 & -0.00040490 & 0.84666511 \\\\ \n 1 & 0.00027459 & -0.42244138 & 0.42244147 \\\\ \n 2 & -0.15781570 & -0.00016986 & 0.15781579 \\\\ \n 3 & 0.00001754 & 0.04339763 & 0.04339763 \\\\ \n 4 & 0.00928797 & -0.00002342 & 0.00928800 \\\\ \n 5 & -0.00000055 & -0.00159853 & 0.00159853 \\\\ \n 6 & -0.00022354 & -0.00000190 & 0.00022355 \\\\ \n 7 & -0.00000017 & 0.00002544 & 0.00002545 \\\\ \n 8 & 0.00000247 & -0.00000011 & 0.00000247 \\\\ \n 9 & -0.00000001 & -0.00000023 & 0.00000023 \\\\ \n 10 & -0.00000002 & -0.00000000 & 0.00000002 \\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Numerical results of the projection coefficients $ c_n $ for $ f(z) = e^{iz} $.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Numerical Derivatives, Projection Coefficients, and Truncation Errors in Analytic Hilbert Space With Gaussian Measure", "authors": ["M. W. AlMasri"], "url": "https://arxiv.org/abs/2504.16246v2", "attribution": "\"Numerical Derivatives, Projection Coefficients, and Truncation Errors in Analytic Hilbert Space With Gaussian Measure\" by M. W. AlMasri, arXiv:2504.16246v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.08427v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Percentage of observed values for the variables in the model, categorized according to their role in the domain and stratified by cohort type (PBC, CBC).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{Variable}\n& \\textbf{Label}\n& \\textbf{Category}\n& \\textbf{PBC}\n& \\textbf{CBC} \\\\\n\\hline\\hline\nAge>35 years & \\texttt{age35} & Prognostic factor & 100\\% & 100\\%\\\\\nTumor Hystology & \\texttt{histology} & Prognostic factor & 100\\% & 100\\%\\\\\nTumor grading & \\texttt{grade} & Prognostic factor & 0\\% & 89\\%\\\\\nVascular invasion & \\texttt{vascular} & Prognostic factor & 0\\% & 57\\%\\\\\nKi67 positivity & \\texttt{ki67} & Prognostic factor & 0\\% & 92\\%\\\\\nTumor receptor status & \\texttt{receptors} & Prognostic factor & 0\\% & 94\\%\\\\\nTumor dimension & \\texttt{pT} & Prognostic factor & 0\\% & 65\\%\\\\\nLymph nodes involvement & \\texttt{pN} & Prognostic factor & 0\\% & 68\\%\\\\\nDeath in 5 years & \\texttt{death\\_in\\_5y} & Survival & 90\\% & 45\\%\\\\\nNeo-adjuvant chemotherapy & \\texttt{chemo\\_neo} & Treatment & 100\\% & 99\\%\\\\\nNeo-adjuvant radiotherapy & \\texttt{radio\\_neo} & Treatment & 100\\%& 99\\%\\\\\nNeo-adjuvant target therapy & \\texttt{target\\_neo} & Treatment & 100\\% & 99\\%\\\\\nNeo-adjuvant hormon therapy & \\texttt{hormons\\_neo} & Treatment & 100\\% & 99\\%\\\\\nSurgery type & \\texttt{surgery} & Treatment & 100\\% & 100\\%\\\\\nadjuvant chemotherapy & \\texttt{chemo\\_adju} & Treatment & 100\\% & 96\\%\\\\\nadjuvant radiotherapy & \\texttt{radio\\_adju} & Treatment & 100\\% & 96\\%\\\\\nadjuvant target therapy & \\texttt{target\\_adju} & Treatment & 100\\% & 96\\%\\\\\nadjuvant hormon therapy & \\texttt{hormons\\_adju} & Treatment & 100\\% & 96\\%\\\\\nDyslipidemia & \\texttt{dyslipidemia} & CVDs risk factor & 100\\% & 5\\%\\\\\nHypertension & \\texttt{hypertension} & CVDs risk factor & 100\\% & 3\\%\\\\\nType 2 Diabete Mellitus & \\texttt{t2db} & CVDs risk factor & 100\\% & 1\\%\\\\\nCardiotoxicity & \\texttt{cardiotoxicity} & CVDs risk factor & 100\\% & 0\\%\\\\\nIschemic heart disease & \\texttt{ischemic\\_heart\\_disease} & CVDs risk factor & 100\\% & 0\\%\\\\\nCardiovascular disease in 5 years & \\texttt{cvds} & Target variable & 100\\% & 0\\%\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Towards a Transportable Causal Network Model Based on Observational Healthcare Data", "authors": ["Alice Bernasconi", "Alessio Zanga", "Peter J. F. Lucas", "Marco Scutari", "Fabio Stella"], "url": "https://arxiv.org/abs/2311.08427v2", "attribution": "\"Towards a Transportable Causal Network Model Based on Observational Healthcare Data\" by Alice Bernasconi, Alessio Zanga, Peter J. F. Lucas, Marco Scutari, and Fabio Stella, arXiv:2311.08427v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00173v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccc}\n\\toprule\nMetric & \\multicolumn{4}{c}{Top-1 Accuracy (\\%)} & & \\multicolumn{4}{c}{Seen-Unseen AUC (\\%)} \\\\ \\cmidrule(lr){2-5} \\cmidrule(l){7-10} \nDataset & \\multicolumn{2}{c}{CUB} & \\multicolumn{2}{c}{NAB} & & \\multicolumn{2}{c}{CUB} & \\multicolumn{2}{c}{NAB} \\\\\nSplit-Mode & Easy & Hard & Easy & Hard & & Easy & Hard & Easy & Hard \\\\ \\midrule\n$K^u=100 (w/o)$& \\textbf{42.3} & 14.8 & 33.5 & \\textbf{9.8} & & \\textbf{38.6} & 12.4 & \\textbf{21.7} & 7.2\\\\\n$K^u=100 (w/)$ & 41.8 & \\textbf{15.2} & \\textbf{33.7} & 9.5 & & 38.5 & \\textbf{12.9} & 21.3 & \\textbf{7.3}\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparative results of adding categorization loss on hallucinated visual features of hallucinated text in a semantically guided way.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "CIZSL++: Creativity Inspired Generative Zero-Shot Learning", "authors": ["Mohamed Elhoseiny", "Kai Yi", "Mohamed Elfeki"], "url": "https://arxiv.org/abs/2101.00173v2", "attribution": "\"CIZSL++: Creativity Inspired Generative Zero-Shot Learning\" by Mohamed Elhoseiny, Kai Yi, and Mohamed Elfeki, arXiv:2101.00173v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.24078v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\hline\n \\textbf{Variable} & \\textbf{Estimate} & \\textbf{Std. Error} & \\textbf{t-value} & \\textbf{p-value} \\\\\n \\hline\n (Intercept) & 5.0194 & 0.0075 & 665.570 & $<$2e-16*** \\\\\n Gender (Female) & -0.0293 & 0.0040 & -7.346 & 2.26e-13*** \\\\\n Google Scholar ID (Yes) & 0.0029 & 0.0036 & 0.798 & 0.4251 \\\\\n Assistant Prof $\\times$ Working Years & -0.0071 & 0.0004 & -18.011 & $<$2e-16*** \\\\\n Associate Prof $\\times$ Working Years & -0.0017 & 0.0003 & -6.580 & 5.01e-11*** \\\\\n Professor $\\times$ Working Years & 0.0024 & 0.0002 & 12.311 & $<$2e-16*** \\\\\n bachelor/master $\\times$ AH $\\times$ log$_{10}$(i10-index) & -0.0971 & 0.0092 & -10.524 & $<$2e-16*** \\\\\n DRU(H) $\\times$ AH $\\times$ log$_{10}$(i10-index) & -0.0900 & 0.0068 & -13.257 & $<$2e-16*** \\\\\n DU/VA $\\times$ AH $\\times$ log$_{10}$(i10-index) & -0.0517 & 0.0075 & -6.864 & 7.27e-12*** \\\\\n bachelor/master $\\times$ B $\\times$ log$_{10}$(i10-index) & 0.0260 & 0.0144 & 1.805 & 0.0712 \\\\\n DRU(H) $\\times$ B $\\times$ log$_{10}$(i10-index) & 0.1082 & 0.0087 & 12.367 & $<$2e-16*** \\\\\n DU/VA $\\times$ B $\\times$ log$_{10}$(i10-index) & 0.1791 & 0.0101 & 17.786 & $<$2e-16*** \\\\\n bachelor/master $\\times$ MHS $\\times$ log$_{10}$(i10-index) & -0.0253 & 0.0117 & -2.159 & 0.0309* \\\\\n DRU(H) $\\times$ MHS $\\times$ log$_{10}$(i10-index) & 0.0971 & 0.0063 & 15.536 & $<$2e-16*** \\\\\n DU/VA $\\times$ MHS $\\times$ log$_{10}$(i10-index) & 0.1783 & 0.0046 & 38.502 & $<$2e-16*** \\\\\n bachelor/master $\\times$ NS $\\times$ log$_{10}$(i10-index) & -0.0757 & 0.0115 & -6.573 & 5.29e-11*** \\\\\n DRU(H) $\\times$ NS $\\times$ log$_{10}$(i10-index) & -0.0482 & 0.0071 & -6.810 & 1.06e-11*** \\\\\n DU/VA $\\times$ NS $\\times$ log$_{10}$(i10-index) & 0.0452 & 0.0053 & 8.582 & $<$2e-16*** \\\\\n bachelor/master $\\times$ SS $\\times$ log$_{10}$(i10-index) & -0.0750 & 0.0095 & -7.881 & 3.72e-15*** \\\\\n DRU(H) $\\times$ SS $\\times$ log$_{10}$(i10-index) & -0.0613 & 0.0068 & -9.072 & $<$2e-16*** \\\\\n DU/VA $\\times$ SS $\\times$ log$_{10}$(i10-index) & 0.0499 & 0.0061 & 8.154 & 4.12e-16*** \\\\\n bachelor/master $\\times$ TE $\\times$ log$_{10}$(i10-index) & -0.0623 & 0.0132 & -4.731 & 2.28e-06*** \\\\\n DRU(H) $\\times$ TE $\\times$ log$_{10}$(i10-index) & -0.0084 & 0.0075 & -1.119 & 0.2632 \\\\\n DU/VA $\\times$ TE $\\times$ log$_{10}$(i10-index) & 0.0486 & 0.0053 & 9.112 & $<$2e-16*** \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Three-Way Interaction Regression: Gender, Rank × Experience, and Productivity × Context Effects}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Estimation of Gender Wage Gap in the University of North Carolina System", "authors": ["Zihan Zhang", "Jan Hannig"], "url": "https://arxiv.org/abs/2505.24078v1", "attribution": "\"Estimation of Gender Wage Gap in the University of North Carolina System\" by Zihan Zhang and Jan Hannig, arXiv:2505.24078v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2010.05398v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Basket Constituents}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n Ticker & Name & Industry & Market Cap (bn) \\\\\n \\hline\n APA & Apache Corporation & Energy: Oil and Gas & 4.11 \\\\\n \\hline\n AXP & American Express Company & Credit Services & 76.80 \\\\\n \\hline\n CAT & Caterpillar Inc. & Farm Machinery & 78.60 \\\\\n \\hline\n COF & Capital One Financial Corp. & Credit Services & 31.10 \\\\\n \\hline\n FCX & Freeport-McMoRan Inc. & Copper & 22.35 \\\\\n \\hline\n IBM & 1nternational Business Machines Corp. & Technology & 105.17 \\\\\n \\hline\n MMM & 3M Company & Industrial Machinery & 92.41 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Tight Bounds for a Class of Data-Driven Distributionally Robust Risk Measures", "authors": ["Derek Singh", "Shuzhong Zhang"], "url": "https://arxiv.org/abs/2010.05398v2", "attribution": "\"Tight Bounds for a Class of Data-Driven Distributionally Robust Risk Measures\" by Derek Singh and Shuzhong Zhang, arXiv:2010.05398v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.06288v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{NLL($\\downarrow$) for zero-shot and fine-tuning with varying sample sizes. Pretraining on a context length of 32 yields the best zero-shot performance, while pretraining on a longer context length of 128 improves fine-tuning, especially with 10-100 samples.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rccccc}\n\\toprule\nModel & Zero-Shot & 1 Sample & 10 Samples & 100 Samples & 1000 Samples\\\\ \\midrule\nSmall-16 &\n-0.0449 &\n-0.1189 &\n-0.1523 &\n-0.1824 &\n-0.1870 \\\\\nSmall-32 & \\textbf{-0.0978} & -0.0842 & -0.1681 & -0.1873 & -0.1898 \\\\\nSmall-64 &\n-0.0808 &\n\\textbf{-0.1418} &\n-0.1684 &\n-0.1861 &\n-0.1853\n\\\\\nSmall-128 & -0.0842 & -0.1287 & \\textbf{-0.1825} & \\textbf{-0.1907} & \\textbf{-0.1918} \\\\\n\\midrule\nMedium-16 &\n-0.0483 &\n-0.0893 &\n-0.1542 &\n-0.1767 &\n-0.1897\\\\\nMedium-32 & \\textbf{-0.1330} & \\textbf{-0.1486} & -0.1673 & -0.1792 & -0.1847\\\\\nMedium-64 &\n-0.0793 &\n-0.1146 &\n\\textbf{-0.1612} &\n\\textbf{-0.1873} &\n-0.1875 \\\\\nMedium-128 & -0.1020 & -0.1113& -0.1711 & -0.1843 & \\textbf{-0.1899} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "DELPHYNE: A Pre-Trained Model for General and Financial Time Series", "authors": ["Xueying Ding", "Aakriti Mittal", "Achintya Gopal"], "url": "https://arxiv.org/abs/2506.06288v1", "attribution": "\"DELPHYNE: A Pre-Trained Model for General and Financial Time Series\" by Xueying Ding, Aakriti Mittal, and Achintya Gopal, arXiv:2506.06288v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.08484v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of Key Metrics}\n\\begin{tabular}{lrr}\n\\toprule\n Metric & Initial (Iter 0) & Iter 225 \\\\\n\\midrule\n Pri Res & 0.488 & 1.550000e-07 \\\\\n Dua Res & 15.300 & 9.030000e-08 \\\\\n Gap & 274.000 & 5.540000e-07 \\\\\nObjective & 153.000 & 1.240000e+01 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "ConvMesh: Reimagining Mesh Quality Through Convex Optimization", "authors": ["Alexander Valverde"], "url": "https://arxiv.org/abs/2412.08484v1", "attribution": "\"ConvMesh: Reimagining Mesh Quality Through Convex Optimization\" by Alexander Valverde, arXiv:2412.08484v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.05765v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ The estimated regression coefficients of the longitudinal and the time-to-death sub-models from the MMJM and TSJM on PBC2 data. Est: posterior mean, SD: standard deviation, 2.5$\\% $ CI: lower bound of credible interval and 97.5$\\% $ CI: upper bound of credible interval. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n \\hline\n &\\multicolumn{4}{c}{TSJM} &\\multicolumn{4}{c}{MMJM} \\\\ \\hline\n & Est. & SD & $2.5\\%$ CI & $97.5\\%$ CI & Est. & SD & $2.5\\%$ CI & $97.5\\%$ CI \\\\ \n \\hline\n &\\multicolumn{8}{c}{Longitudinal sub-model} \\\\ \\hline\n \\multicolumn{2}{c}{\\bf{Albumin}} &\\multicolumn{4}{c}{} \\\\ \n Intercept ($\\beta_{10}$) & 3.538 & 0.027 & 3.486 & 3.593 & 3.561 & 0.034 & 3.491 & 3.622 \\\\ \n Time ($\\beta_{11}$) & -0.102 & 0.012 & -0.124 & -0.079 & -0.106 & 0.013 & -0.134 & -0.080 \\\\ \n $\\sigma_1^2$ & 0.093 & 0.004 & 0.085 & 0.102 & 0.094 & 0.004 & 0.086 & 0.102 \\\\\n \\multicolumn{2}{c}{\\bf{log(Alkaline)}} &\\multicolumn{4}{c}{} \\\\ \n Intercept ($\\beta_{20}$) & 7.170 & 0.048 & 7.073 & 7.273 & 7.132 & 0.047 & 7.046 & 7.232 \\\\ \n Time ($\\beta_{21}$) & -0.051 & 0.011 & -0.072 & -0.030 & -0.047 & 0.012 & -0.073 & -0.026 \\\\ \n $\\sigma_2^2$ & 0.106 & 0.005 & 0.097 & 0.115 & 0.106 & 0.005 & 0.097 & 0.116 \\\\ \n \\multicolumn{2}{c}{\\bf{log(SGOT)}} &\\multicolumn{4}{c}{} \\\\ \n Intercept ($\\beta_{30}$) & 4.705 & 0.032 & 4.640 & 4.767 & 4.677 & 0.037 & 4.611 & 4.753 \\\\ \n Time ($\\beta_{31}$) & -0.002 & 0.011 & -0.024 & 0.019 & 0.007 & 0.011 & -0.013 & 0.029 \\\\ \n $\\sigma_3^2$ & 0.077 & 0.003 & 0.070 & 0.084 & 0.077 & 0.004 & 0.071 & 0.084 \\\\ \n \\multicolumn{2}{c}{\\bf{log(Platelets)}} &\\multicolumn{4}{c}{} \\\\ \n Intercept ($\\beta_{40}$) & 5.459 & 0.029 & 5.405 & 5.518 & 5.445 & 0.030 & 5.385 & 5.497 \\\\ \n Time ($\\beta_{41}$) & -0.070 & 0.010 & -0.091 & -0.052 & -0.074 & 0.011 & -0.096 & -0.054 \\\\ \n $\\sigma_4^2$ & 0.043 & 0.002 & 0.039 & 0.047 & 0.043 & 0.002 & 0.039 & 0.047 \\\\ \n \\multicolumn{2}{c}{\\bf{log(SerBilir)}} &\\multicolumn{4}{c}{} \\\\ \n Intercept ($\\beta_{50}$) & 0.483 & 0.058 & 0.372 & 0.589 & 0.444 & 0.087 & 0.285 & 0.598 \\\\ \nTime ($\\beta_{51}$) & 0.202 & 0.019 & 0.165 & 0.242 & 0.162 & 0.015 & 0.130 & 0.190 \\\\ \n $\\sigma_5^2$ & 0.115 & 0.006 & 0.105 & 0.126 & 0.112 & 0.005 & 0.102 & 0.123 \\\\ \\hline\n &\\multicolumn{8}{c}{Survival sub-model} \\\\ \\hline\n Drug ($\\gamma_1$) & -0.044 & 0.199 & -0.421 & 0.359 & -0.132 & 0.233 & -0.589 & 0.327 \\\\ \n Age ($\\gamma_2$) & 0.662 & 0.115 & 0.436 & 0.887 & 0.743 & 0.137 & 0.480 & 1.013 \\\\ \n Albumin ($\\alpha_1$) & -1.613 & 0.278 & -2.182 & -1.088 & -1.779 & 0.358 & -2.489 & -1.115 \\\\ \nlog(Alkaline) ($\\alpha_2$) & 0.156 & 0.193 & -0.206 & 0.589 & 0.213 & 0.285 & -0.412 & 0.771 \\\\ \n log(SGOT) ($\\alpha_3$) & 0.322 & 0.235 & -0.148 & 0.799 & 0.588 & 0.358 & -0.065 & 1.340 \\\\ \n log(Platelets) ($\\alpha_4$) & -0.371 & 0.210 & -0.804 & 0.035 & -0.604 & 0.286 & -1.153 & -0.094 \\\\ \n log(SerBilir) ($\\alpha_5$) & 1.216 & 0.128 & 0.957 & 1.462 & 1.038 & 0.172 & 0.691 & 1.365 \\\\ \n \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Two-stage Joint Modeling Approach for Multiple Longitudinal Markers and Time-to-event Data", "authors": ["Taban Baghfalaki", "Reza Hashemi", "Catherine Helmer", "Helene Jacqmin-Gadda"], "url": "https://arxiv.org/abs/2412.05765v1", "attribution": "\"A Two-stage Joint Modeling Approach for Multiple Longitudinal Markers and Time-to-event Data\" by Taban Baghfalaki, Reza Hashemi, Catherine Helmer, and Helene Jacqmin-Gadda, arXiv:2412.05765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.08793v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|l|l|l|l|}\n \\hline \n & $\\ell$ & $C\\circ \\Phi^*$ & $S\\circ \\Gamma$ & $B$ &$\\Xi$ \\\\\n \\hline \n PSRF & 1.032 &(1.000, 1.012)&(1.000, 1.042)&(1.001, 1.028)&(1.000, 1.021) \\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Potential scale reduction factors (PSRF) for log-likelihood ($\\ell$) and 2.5 and 97.5 elementwise PSRF quantile for model parameters. *Approximately 15\\% of factor switches cil are equal to zero with probability $< 1/10000$ \\textit{a posteriori}, so PSRF is undefined; the reported range reflects convergence diagnostic statistics for those $(i, l)$ which have nonzero posterior expected value.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Joint species distribution modeling of abundance data through latent variable barcodes", "authors": ["Braden Scherting", "Otso Ovaskainen", "David B. Dunson"], "url": "https://arxiv.org/abs/2412.08793v2", "attribution": "\"Joint species distribution modeling of abundance data through latent variable barcodes\" by Braden Scherting, Otso Ovaskainen, and David B. Dunson, arXiv:2412.08793v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12682v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The average ranks of the imputation scores across the various UCI datasets and various percentages of missing values. Each imputation experiment was repeated 100 times with different random initialization. RF-GAP generally produces the best imputations across all percentages of missing values. See Table~ for the full imputation results.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n& 5\\% & 10\\% & 25\\% & 50\\% & 75\\% \\\\ \n \\hline\nRF-GAP\t&\t1.10\t&\t1.20\t&\t1.15\t&\t1.35\t&\t1.65\t\\\\\nOriginal\t&\t2.55\t&\t2.70\t&\t2.65\t&\t2.60\t&\t2.35\t\\\\\nOOB\t&\t2.50\t&\t2.55\t&\t2.45\t&\t2.40\t&\t2.30\t\\\\\nRFProxIH\t&\t3.20\t&\t3.33\t&\t3.33\t&\t3.40\t&\t3.20\t\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Geometry- and Accuracy-Preserving Random Forest Proximities", "authors": ["Jake S. Rhodes", "Adele Cutler", "Kevin R. Moon"], "url": "https://arxiv.org/abs/2201.12682v2", "attribution": "\"Geometry- and Accuracy-Preserving Random Forest Proximities\" by Jake S. Rhodes, Adele Cutler, and Kevin R. Moon, arXiv:2201.12682v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.13222v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crrrrr}\n\t\t\t\\hline\\hline\n\t\t\tModel & Order VAR & PT (asymptotic)& PT (adjusted) & BG & ES\\\\\n\t\t\t\\hline\n\t\t\t\\multirow{2}{*}{FRM and VIX}& 3 &$< 2.2 \\times 10^{-16}$&$< 2.2 \\times 10^{-16}$& $1.1\\times10^{-07}$& $1.0\\times10^{-07}$\\\\\n\t\t\t\\cline{2-6}\n\t\t\t& 11 &$2.5 \\times 10^{-07}$&$2.0\\times 10^{-07}$& $1.6\\times10^{-01}$& $1.7\\times10^{-01}$\\\\\n\t\t\t\\cline{2-6}\n\t\t\t& 20 &$< 2.2 \\times 10^{-16}$&$< 2.2 \\times 10^{-16}$& $3.1\\times10^{-08}$& $4.1\\times10^{-08}$\\\\\n\t\t\t\\cline{2-6}\n\t\t\t\\hline\n\t\t\t\\multirow{4}{*}{DFRM and VIX}& 5 &$2.2 \\times 10^{-16}$&$2.2 \\times 10^{-16}$& $3.2\\times10^{-08}$& $ 3.1\\times10^{-08}$\\\\\n\t\t\t\\cline{2-6}\n\t\t\t& 8 &$6.7 \\times 10^{-12}$ &$4.9\\times 10^{-12}$ &$1.4\\times 10^{-06}$ &$1.5\\times 10^{-06}$\\\\\n\t\t\t\\cline{2-6}\n\t\t\t& 11 &$ 2.3 \\times 10^{-09}$ &$1.8\\times 10^{-09}$ &$1.5\\times 10^{-03}$ &$1.7\\times 10^{-03}$\\\\\n\t\t\t\\cline{2-6}\n\t\t\t& 19 &$1.7 \\times 10^{-03}$ &$1.6 \\times 10^{-03}$ &$5.5\\times 10^{-08}$ &$7.2\\times 10^{-08}$\\\\\n\t\t\t\\hline\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{$p$ values of model selection tests}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "An AI approach to measuring financial risk", "authors": ["Lining Yu", "Wolfgang Karl Härdle", "Lukas Borke", "Thijs Benschop"], "url": "https://arxiv.org/abs/2009.13222v1", "attribution": "\"An AI approach to measuring financial risk\" by Lining Yu, Wolfgang Karl Härdle, Lukas Borke, and Thijs Benschop, arXiv:2009.13222v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.08359v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of Booster-related electronic log (Elog)~ entries corresponding to spikes in Figures~ and~. Original Central Time times are given with values in parenthesis designating UTC. Here, ``RF'' denotes radiofrequency.}\n\\begin{tabular}{|c|l|}\n\\hline\nDate & Elog Entry Summary \\\\ \\hline\n6/8/2019 & GMPS in AC mode until 8:00 AM (13:00) on 6/08, then switched off until 6/17 \\\\ \\hline\n\\multirow{2}{*}{6/10/2019} & GMPS was locked/tagged out for outage, West Booster gallery RF off from \\\\\n & 8:30-10:00 (13:30-3:00) for work \\\\ \\hline\n6/17/2019 & GMPS turned back on and put in AC mode \\\\ \\hline\n6/22/2019 & High energy physics beam turned off at 8:00 PM (1:00 +1) (GMPS remained in AC mode) \\\\ \\hline\n6/24/2019 & DC studies from 8:00 AM - 6:00 PM (13:00 - 23:00), back to AC mode \\\\ \\hline\n6/26/2019 & Alternated between AC and DC mode, GMPS off for 30 min around 5:30 PM (22:30) \\\\ \\hline\n\\multirow{2}{*}{6/27/2019} & GMPS in AC mode all day, but removing certain study events caused bias to creep up, \\\\\n & eventually tripping the RF \\\\ \\hline\n6/28/2019 & Alternated between AC and DC mode \\\\ \\hline\n12/8/2019 & \\texttt{B\\_VIMIN} adjusted, GMPS in AC mode \\\\ \\hline\n12/12/2019 & AC mode, operators reset virtual machine environment locally \\\\ \\hline\n12/28 - 12/30/2019 & No beam from injector, GMPS in AC mode \\\\ \\hline\n12/31/2019 & Booster injection back, GMPS in AC mode \\\\ \\hline\n1/1/2020 & GMPS off for 15 min around 9:30 AM (14:30), in AC mode for rest of day \\\\ \\hline\n2/4/2020 & RF sparking in gallery due to reverting of RF capture settings, GMPS in AC mode \\\\ \\hline\n2/5/2020 & GMPS off from 6:00 AM - 3:30 PM (11:00 - 20:30), then in AC mode for rest of day \\\\ \\hline\n2/6/2020 & Lowered beam intensity to users, but GMPS was in AC mode all day \\\\ \\hline\n3/5/2020 & Beam tails were large, so turned \\texttt{B\\_VIMIN} down \\\\ \\hline\n\\multirow{2}{*}{3/10/2020} & GMPS in AC mode all day, \\texttt{B:ACMNPG} changed from 6.5 to 13.5, \\texttt{B\\_VIMIN} decreased \\\\\n & from 103.440 to 103.420 \\\\ \\hline\n\\multirow{2}{*}{3/11/2020} & GMPS in AC mode from 12:00 AM - 6:00 AM (5:00 - 11:00), off from 6:00 - 10:00 AM \\\\\n & (11:00 - 15:00), then alternated between AC and DC mode, \\texttt{B\\_VIMIN} adjusted from \\\\\n & 103.418 to 103.386 \\\\ \\hline\n3/13/2020 & GMPS off from 9:30 AM - 11:00 AM (14:30-16:00), back on and put in AC mode \\\\ \\hline\n3/20/2020 & Booster turned off on account of Covid-19 pandemic at 12:00 PM (17:00) \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "BOOSTR: A Dataset for Accelerator Control Systems", "authors": ["Diana Kafkes", "Jason St. John"], "url": "https://arxiv.org/abs/2101.08359v3", "attribution": "\"BOOSTR: A Dataset for Accelerator Control Systems\" by Diana Kafkes and Jason St. John, arXiv:2101.08359v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.01478v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of the train, validation and internal test splits.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|c|ccccc|cc}\n& & Cam16 & \\multicolumn{5}{c}{ Cam17 (5 Centers)}\\vline & \\multicolumn{2}{c}{ PanNuke (2 Folds) } \\\\\n& Label & & C. 0 & C. 1 & C. 2 & C. 3 & C. 4 & F. 1 & F. 2 \\\\\\hline\n\\multirow{2}{*}{ Train } & Neg. & 12954 & 31108 & 25137 & 38962 & 25698 & 0 & 1425 & 1490 \\\\\n& Pos. & 6036 & 8036 & 5998 & 2982 & 1496 & 0 & 2710 & 2255 \\\\\\hline\n\\multirow{2}{*}{ Val. } & Neg. & 0 & 325 & 0 & 495 & 0 & 0 & 0 & 0 \\\\\n& Pos. & 0 & 500 & 0 & 500 & 0 & 0 & 0 & 0 \\\\\\hline\n\\multirow{2}{*}{ Int. Test } & Neg. & 0 & 0 & 274 & 483 & 458 & 0 & 0 & 0 \\\\\n& Pos. & 0 & 500 & 999 & 0 & 0 & 0 & 0 & 0 \\\\%\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization", "authors": ["Mara Graziani", "Sebastian Otalora", "Stephane Marchand-Maillet", "Henning Muller", "Vincent Andrearczyk"], "url": "https://arxiv.org/abs/2008.01478v3", "attribution": "\"Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization\" by Mara Graziani, Sebastian Otalora, Stephane Marchand-Maillet, Henning Muller, and Vincent Andrearczyk, arXiv:2008.01478v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.18061v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n \\hline\n \\textbf{Mode} & \\textbf{Multiturn\\_memory-zh} \\\\ \\hline\n GLM-4-Voice & 80.00 \\\\\n MiniCPM-o-2.6 & 86.67 \\\\\n Baichuan-Omni-1.5 & 78.67 \\\\\n SpeechGPT-2.0-preview & 20.00 \\\\\n Freeze-Omni & 62.67 \\\\\n Qwen2.5-Omni & 88.67 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Model scores (\\%) on the Context Memory task.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "TELEVAL: A Dynamic Benchmark Designed for Spoken Language Models in Chinese Interactive Scenarios", "authors": ["Zehan Li", "Hongjie Chen", "Yuxin Zhang", "Jing Zhou", "Xuening Wang", "Hang Lv", "Mengjie Du", "Yaodong Song", "Jie Lian", "Jian Kang", "Jie Li", "Yongxiang Li", "Zhongjiang He", "Xuelong Li"], "url": "https://arxiv.org/abs/2507.18061v1", "attribution": "\"TELEVAL: A Dynamic Benchmark Designed for Spoken Language Models in Chinese Interactive Scenarios\" by Zehan Li, Hongjie Chen, Yuxin Zhang, Jing Zhou, Xuening Wang, Hang Lv, Mengjie Du, Yaodong Song, Jie Lian, Jian Kang, Jie Li, Yongxiang Li, Zhongjiang He, and Xuelong Li, arXiv:2507.18061v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14416v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ll|}\n\t\t\t\\hline\n\t\t\t\\textbf{Case} & \\textbf{Conditions} & \\textbf{Finite singular points} \\\\\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\t1& $ b_3 \\neq 0$, $b_2\\neq 0$. & $P_0$, $P_1$, $P_2$. \\\\\t\\hline\n\t\t\t2 & $ b_3 \\neq 0$, $b_2=0$, $b_0\\neq0$. & $P_0$, $P_1$.\\\\\n\t\t\t\\hline\n\t\t\t3 & $ b_3=0$, $c_0\\neq 0$, $b_2\\neq 0$. & $P_0$, $P_2$. \\\\\n\t\t\t\\hline\n\t\t\t4 & $ b_3=0$, $c_0\\neq 0$, $b_2=0$, $b_0\\neq 0$. & $P_0$. \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{The different cases for the finite singular points.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Planar Kolmogorov systems with infinitely many singular points at infinity", "authors": ["Érika Diz-Pita", "Jaume Llibre", "M. Victoria Otero-Espinar"], "url": "https://arxiv.org/abs/2501.14416v1", "attribution": "\"Planar Kolmogorov systems with infinitely many singular points at infinity\" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.14416v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.08297v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Measured Outputs of ACC system and Inputs to Neural Network Controller}\n\\begin{tabular}{|l||l|}\n\\hline\nDriver-set velocity & $v_{\\mathrm{set}}$ \\\\ \n \\hline\nTime gap & $t_{\\mathrm{gap}}$ \\\\ \n \\hline\nVelocity of the ego car & $v_e$ \\\\ \n \\hline\nRelative distance to the lead car & $d_{\\mathrm{rel}} = x_l - x_e$\\\\ \n \\hline\n Relative velocity to the lead car & $v_{\\mathrm{rel}} = v_l - v_e$\\\\ \n \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Run-Time Safety Monitoring of Neural-Network-Enabled Dynamical Systems", "authors": ["Weiming Xiang"], "url": "https://arxiv.org/abs/2101.08297v1", "attribution": "\"Run-Time Safety Monitoring of Neural-Network-Enabled Dynamical Systems\" by Weiming Xiang, arXiv:2101.08297v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.15384v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{makecell}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of the Statistical Test Results for level of significance $0.01$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccccc}\n\\toprule\n\\multirow[b]{2}{*}{\\thead{\\textbf{Clustering Algorithm}}}\n & \\multicolumn{2}{c}{\\thead{\\textbf{Sign Test}}}\n & \\multicolumn{2}{c}{\\thead{\\textbf{WSR Test}}}\n \\\\\n\\cmidrule(lr){2-3}\\cmidrule(lr){4-5} %\\cmidrule(lr){11-13}\n& \\thead{Statistic} & \\thead{\\textit{\\textbf{p}}-value} & \\thead{Statistic} & \\thead{\\textit{\\textbf{p}}-value} \n\\\\\n\\midrule\n\\addlinespace\n$k$-means ++ & 15 & 0.0002594 & 135 & 0.0000305 \\\\\nSparse $k$-means & 15 & 0.0002594 & 135 & 0.0000305 \\\\\n$k$-medians & 15 & 0.0002594 & 135 & 0.0000305 \\\\\nPartition Around Medoids & 15 & 0.0002594 & 134 & 0.0000458 \\\\\nRobust Continuous Clustering & 15 & 0.0002594 & 135 & 0.0000305 \\\\\nDP means & 15 & 0.0002594 & 133 & 0.0000763 \\\\\nKb MoM & 15 & 0.0002594 & 135 & 0.0000305 \\\\\nMoM Power $k$-means & 15 & 0.0002594 & 135 & 0.0000305 \\\\\nOWL $k$-means & 14 & 0.0020900 & 125 & 0.0008392\\\\\n\\addlinespace\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Dirichlet Process-based Robust Clustering using the Median-of-Means Estimator", "authors": ["Supratik Basu", "Jyotishka Ray Choudhury", "Debolina Paul", "Swagatam Das"], "url": "https://arxiv.org/abs/2311.15384v2", "attribution": "\"Dirichlet Process-based Robust Clustering using the Median-of-Means Estimator\" by Supratik Basu, Jyotishka Ray Choudhury, Debolina Paul, and Swagatam Das, arXiv:2311.15384v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.04711v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of Algorithm on Example }\n\\begin{tabular}{cccc} \n\\hline\nNumber of& Algorithm &Iterations &CPU time\\\\\ninitial points& &(Min, Max, Mean, Median, Mode, SD) &(Min, Max, Mean, Median, $\\lceil\\text{Mode}\\rceil$, SD) \\\\ \n\\hline \n$100$ & QNM&($1,~6,~5.7400,~6,~6,~0.4845$) & ($1.2551,~42.8398,~23.8484,~29.5973,~11,~11.9814$) \\\\ \n & SD &($1,~14,~13.9900,~14,14,0.1000$) & ($0.112,~4.2931,~3.2962,~3.2843,~3,~0.1097$) \\\\\n\\hline \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Quasi-Newton Method for Set Optimization Problems with Set-Valued Mapping Given by Finitely Many Vector-Valued Functions", "authors": ["Debdas Ghosh", "Anshika", "Jen-Chih Yao", "Xiaopeng Zhao"], "url": "https://arxiv.org/abs/2501.04711v1", "attribution": "\"Quasi-Newton Method for Set Optimization Problems with Set-Valued Mapping Given by Finitely Many Vector-Valued Functions\" by Debdas Ghosh, Anshika, Jen-Chih Yao, and Xiaopeng Zhao, arXiv:2501.04711v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.02011v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{GARCH(1,1) Model Coefficients for Selected Companies}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n\\hline\n\\textbf{Company} & $\\omega$ & $\\alpha$ & $\\beta$ \\\\\n\\hline\nBritannia & 0.2291 & 0.0824 & 0.8340 \\\\\nDabur & 0.1756 & 0.1535 & 0.8149 \\\\\nBPCL & 55.9961 & 0.0082 & 0.0000 \\\\\nAxis Bank & 0.0964 & 0.0768 & 0.9077 \\\\\nInfosys & 0.5283 & 0.1633 & 0.6948 \\\\\nCipla & 0.3283 & 0.0958 & 0.8022 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios", "authors": ["Vidya Sagar G", "Shifat Ali", "Siddhartha P. Chakrabarty"], "url": "https://arxiv.org/abs/2507.02011v1", "attribution": "\"Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios\" by Vidya Sagar G, Shifat Ali, and Siddhartha P. Chakrabarty, arXiv:2507.02011v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.14882v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc} % centered columns (4 columns)\n\t\t\\hline\\hline %inserts double horizontal lines\n\t\t$h$ & $L$ & $\\texttt{err}$ & \\texttt{MCerr} \\\\ % inserts table\n\t\t\\hline % inserts single horizontal line\n\t\t0.2 & $10^{5}$ & 0.00537 & 0.0003 \\\\\n\t\t0.1 & $10^{5}$ & 0.00162 & 0.0003 \\\\\n\t\t0.05 & $10^{7}$ & 0.000605 & 0.00003 \\\\\n\t\t0.025 & $10^7$ & 0.000258 & 0.00003 \\\\%[1ex] % [1ex] adds vertical space\n\t\t\\hline %inserts single line\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Sampling and estimation on manifolds using the Langevin diffusion", "authors": ["Karthik Bharath", "Alexander Lewis", "Akash Sharma", "Michael V Tretyakov"], "url": "https://arxiv.org/abs/2312.14882v3", "attribution": "\"Sampling and estimation on manifolds using the Langevin diffusion\" by Karthik Bharath, Alexander Lewis, Akash Sharma, and Michael V Tretyakov, arXiv:2312.14882v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.14635v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\toprule\n Subject & \\( N_{c} \\) & \\( CP \\) & \\(CP/ N_{c} \\) & \\( \\overline{ApEn}_{1}(std.) \\) & \\( \\overline{ApEn}_{2}(std.) \\) & \\( p \\)-value \\\\\\hline\n 1 & 70 & 68 & 97.14\\% & 0.0062(0.0023) & 0.0114(0.0050) & 0.215 \\\\\n 2 & 38 & 10 & 26.32\\% & 0.0134(0.0043) & 0.0037(0.0018) & 1.15e-04 \\\\\n 3 & 54 & 18 & 33.33\\% & 0.0181(0.0067) & 0.0040(0.0025) & 1.33e-07 \\\\\n 4 & 79 & -- & -- & -- & -- & -- \\\\\n 5 & 289 & 239 & 82.70\\% & 0.0139(0.0053) & 0.0073(0.0031) & 1.64e-22 \\\\\n 6 & 54 & 26 & 48.15\\% & 0.0144(0.0046) & 0.0040(0.0031) & 5.22e-12 \\\\\n 7 & 80 & 48 & 60.00\\% & 0.0129(0.0042) & 0.0059(0.0030) & 4.54e-13 \\\\\n 8 & 177 & 78 & 44.07\\% & 0.0068(0.0019) & 0.0047(0.0013) & 8.36e-13 \\\\\n 9 & 52 & 19 & 36.54\\% & 0.0231(0.0059) & 0.0046(0.0031) & 1.94e-11 \\\\\n 10 & 87 & 33 & 37.93\\% & 0.0098(0.0023) & 0.0063(0.0026) & 5.80e-09 \\\\\n 11 & 89 & 39 & 43.82\\% & 0.0123(0.0031) & 0.0047(0.0021) & 4.41e-19 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modelling Loss of Complexity in Intermittent Time Series and its Application", "authors": ["Jie Li", "Jian Zhang", "Samantha L. Winter", "Mark Burnley"], "url": "https://arxiv.org/abs/2411.14635v2", "attribution": "\"Modelling Loss of Complexity in Intermittent Time Series and its Application\" by Jie Li, Jian Zhang, Samantha L. Winter, and Mark Burnley, arXiv:2411.14635v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01677v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|c|c}\n\\toprule\n& \\textbf{Layer Description} & \\textbf{K} & \\textbf{Output Tensor Dim.} \\\\ \n\\toprule\n\\#0 & Input RGB image & & 3$\\times$H$\\times$W \\\\ \n\\midrule\n\\midrule\n\\multicolumn{4}{c}{\\textbf{ResidualBlock}} \\\\ \\hline\n& Conv2d + BatchNorm + ReLU & 3 & \\\\\n& Conv2d + BatchNorm & 3 & \\\\\n\\midrule\n\\multicolumn{4}{c}{\\textbf{Depth Encoder}} \\\\ \\hline\n\\#1 & Conv2d (S2) + BatchNorm + ReLU & 7 & 64$\\times$H/2$\\times$W/2 \\\\\n\\#2 & Conv2d + BatchNorm + ReLU & 3 & 64$\\times$H/2$\\times$W/2 \\\\\n\\#3 & ResidualBlock (\\#2) x2 & - & 64$\\times$H/2$\\times$W/2 \\\\\n\\#4 & Max. Pooling ($\\times$1/2) & 3 & 64$\\times$H/4$\\times$W/4 \\\\\n\\#5 & ResidualBlock (\\#3 + \\$2) x2 & - & 128$\\times$H/4$\\times$W/4 \\\\\n\\#6 & Max. Pooling ($\\times$1/2) & 3 & 128$\\times$H/8$\\times$W/8 \\\\\n\\#7 & ResidualBlock (\\#4 + \\#3) x2 & - & 256$\\times$H/8$\\times$W/8 \\\\\n\\#8 & Max. Pooling ($\\times$1/2) & 3 & 256$\\times$H/16$\\times$W/16 \\\\\n\\#9 & ResidualBlock (\\#5 + \\#4) x2 & - & 512$\\times$H/16$\\times$W/16 \\\\\n\\midrule\n\\multicolumn{4}{c}{\\textbf{Depth Decoder}} \\\\ \n\\midrule\n\\#10 & Conv2D + ELU (\\#9) & 3 & 128$\\times$H/16$\\times$W/16 \\\\\n\\#11 & Conv2D + Upsample (\\#10) & 3 & 128$\\times$H/8$\\times$W/8 \\\\\n\\textbf{\\#12} & Conv2D + Sigmoid & 3 & 1$\\times$H/8$\\times$W/8 \\\\\n\\#13 & Conv2D + ELU & 3 & 64$\\times$H/8$\\times$W/8 \\\\\n\\#14 & Conv2D + Upsample(\\#7 $\\oplus$ \\#13) & 3 & 64$\\times$H/4$\\times$W/4 \\\\\n\\textbf{\\#15} & Conv2D + Sigmoid & 3 & 1$\\times$H/8$\\times$W/8 \\\\\n\\#16 & Conv2D + ELU & 3 & 32$\\times$H/4$\\times$W/4 \\\\\n\\#17 & Conv2D + Upsample (\\#5 $\\oplus$ \\#16) & 3 & 32$\\times$H/2$\\times$W/2 \\\\\n\\textbf{\\#18} & Conv2D + Sigmoid & 3 & 1$\\times$H/8$\\times$W/8 \\\\\n\\#19 & Conv2D + ELU & 3 & 16$\\times$H/2$\\times$W/2 \\\\\n\\#20 & Conv2D + Upsample (\\#3 $\\oplus$ \\#19) & 3 & 16$\\times$H$\\times$W \\\\\n\\textbf{\\#21} & Conv2D + Sigmoid & 3 & 1$\\times$H$\\times$W \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{ResNet architecture.} Line numbers in bold indicate output inverse depth layer scales. \\emph{Upsample} is a nearest-neighbor interpolation operation that doubles the spatial dimensions of the input tensor. $\\oplus$ denotes feature concatenation for skip connections.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Monocular Depth Estimation for Soft Visuotactile Sensors", "authors": ["Rares Ambrus", "Vitor Guizilini", "Naveen Kuppuswamy", "Andrew Beaulieu", "Adrien Gaidon", "Alex Alspach"], "url": "https://arxiv.org/abs/2101.01677v1", "attribution": "\"Monocular Depth Estimation for Soft Visuotactile Sensors\" by Rares Ambrus, Vitor Guizilini, Naveen Kuppuswamy, Andrew Beaulieu, Adrien Gaidon, and Alex Alspach, arXiv:2101.01677v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.03164v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ll|ll|ll}\n & lag = 10 & p value & lag = 15 & p value & lag = 20 & p value \\\\\n \\hline\nConstvol & 3505.63 & \\cellcolor[HTML]{FD6864}0.00 & 4913.57 & \\cellcolor[HTML]{FD6864}0.00 & 6086.93 & \\cellcolor[HTML]{FD6864}0.00 \\\\\nP & 103.92 & \\cellcolor[HTML]{FD6864}0.00 & 113.66 & \\cellcolor[HTML]{FD6864}0.00 & 115.56 & \\cellcolor[HTML]{FD6864}0.00 \\\\\nGK & 31.32 & \\cellcolor[HTML]{FD6864}0.00 & 35.96 & \\cellcolor[HTML]{FD6864}0.00 & 37.12 & \\cellcolor[HTML]{FD6864}0.01 \\\\\nRS & 2.05 & \\cellcolor[HTML]{67FD9A}1.00 & 2.42 & \\cellcolor[HTML]{67FD9A}1.00 & 3.53 & \\cellcolor[HTML]{67FD9A}1.00 \\\\\nGKYZ & 32.26 & \\cellcolor[HTML]{FD6864}0.00 & 38.20 & \\cellcolor[HTML]{FD6864}0.00 & 39.21 & \\cellcolor[HTML]{FD6864}0.01 \\\\\nYZ & 21.71 & \\cellcolor[HTML]{FD6864}0.02 & 22.38 & \\cellcolor[HTML]{67FD9A}0.10 & 23.27 & \\cellcolor[HTML]{67FD9A}0.28 \\\\\nMA21days & 44.66 & \\cellcolor[HTML]{FD6864}0.00 & 60.92 & \\cellcolor[HTML]{FD6864}0.00 & 97.31 & \\cellcolor[HTML]{FD6864}0.00 \\\\\nMA63days & 415.37 & \\cellcolor[HTML]{FD6864}0.00 & 438.82 & \\cellcolor[HTML]{FD6864}0.00 & 446.16 & \\cellcolor[HTML]{FD6864}0.00 \\\\\nGarch & 21.50 & \\cellcolor[HTML]{FD6864}0.02 & 24.04 & \\cellcolor[HTML]{67FD9A}0.06 & 24.64 & \\cellcolor[HTML]{67FD9A}0.22 \\\\\nGJRGarch & 22.43 & \\cellcolor[HTML]{FD6864}0.01 & 25.78 & \\cellcolor[HTML]{FD6864}0.04 & 26.72 & \\cellcolor[HTML]{67FD9A}0.14 \\\\\neGarch & 25.97 & \\cellcolor[HTML]{FD6864}0.00 & 30.74 & \\cellcolor[HTML]{FD6864}0.01 & 33.39 & \\cellcolor[HTML]{FD6864}0.03 \\\\\nEWMA & 26.54 & \\cellcolor[HTML]{FD6864}0.00 & 30.04 & \\cellcolor[HTML]{FD6864}0.01 & 31.29 & \\cellcolor[HTML]{67FD9A}0.05\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Ljung - Box test with three different lags in the SPX asset for all the estimators. }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Estimation of Historical volatility and Allocation strategies using Variance Swaps", "authors": ["Lucio Fiorin"], "url": "https://arxiv.org/abs/2208.03164v1", "attribution": "\"Estimation of Historical volatility and Allocation strategies using Variance Swaps\" by Lucio Fiorin, arXiv:2208.03164v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.16139v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|cc|cc|cc|}\n\\hline\n\\multicolumn{2}{|c|}{MVN(A09)} & \\multicolumn{2}{c|}{MVN(hCN)} & \\multicolumn{2}{c|}{ELL($t(5)$)} & \\multicolumn{2}{c|}{EXP} \\\\ \\hline\nAC1 & AC2 & AC1 & AC2 & AC1 & AC2 & AC1 & AC2 \\\\ \\hline\n0.05 & -0.01 & 0.11 & -0.45 & -0.04 & -0.31 & -0.99 & 0.02 \\\\\n-0.39 & -0.59 & 0.38 & 0.25 & -0.44 & -0.33 & 0.11 & 0.2 \\\\\n-0.31 & 0.32 & 0.27 & -0.06 & -0.42 & -0.31 & -0.03 & -0.21 \\\\\n0.42 & 0.15 & 0.27 & 0.19 & 0.35 & -0.36 & -0.02 & 0.05 \\\\\n0.47 & -0.02 & 0.42 & 0.14 & 0.54 & -0.04 & -0.01 & -0.05 \\\\\n-0.12 & 0.61 & -0.15 & -0.11 & -0.19 & 0.12 & -0.01 & -0.24 \\\\\n-0.46 & 0.24 & -0.16 & 0.46 & -0.27 & -0.17 & 0.01 & 0.19 \\\\\n-0.34 & -0.08 & 0.48 & -0.54 & -0.17 & -0.22 & -0.01 & 0.9 \\\\\n0.02 & 0.16 & 0.42 & 0.4 & -0.24 & 0.62 & -0. & -0.06 \\\\\n0.12 & -0.26 & 0.27 & 0. & -0.15 & 0.3 & 0.01 & -0.06 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{First and second abnormal components for all settings.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Abnormal component analysis", "authors": ["Romain Valla", "Pavlo Mozharovskyi", "Florence d'Alché-Buc"], "url": "https://arxiv.org/abs/2312.16139v2", "attribution": "\"Abnormal component analysis\" by Romain Valla, Pavlo Mozharovskyi, and Florence d'Alché-Buc, arXiv:2312.16139v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.15733v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance metrics for the first three eigenvalues ($32 \\times 32$+dp+ma).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|} \\hline\n\t\t\t\tEigenvalue index & RMSE & $R^2$ & MAPE \\\\ \\hline\n\t\t\t\t1 & 2.2121 & 0.9988 & 1.15\\% \\\\\n\t\t\t\t2 & 6.7920 & 0.9963 & 1.22\\%\\\\\n\t\t\t\t3 & 9.2938 & 0.9850 & 1.78\\% \\\\ \\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Operator Inference for Elliptic Eigenvalue Problems", "authors": ["Haoqian Li", "Jiguang Sun", "Zhiwen Zhang"], "url": "https://arxiv.org/abs/2504.15733v1", "attribution": "\"Operator Inference for Elliptic Eigenvalue Problems\" by Haoqian Li, Jiguang Sun, and Zhiwen Zhang, arXiv:2504.15733v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.03617v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Optimality gap summary. Sample size $s$ takes values of 1$\\%$, 5$\\%$, and 10$\\%$ of $n$. Each value represents mean and sample standard deviation of 5 runs.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n& & \\multicolumn{3}{c}{Optimality Gap} \\\\\n\\cmidrule(r){3-5}\nDataset & $s$ & det & prob & unif \\\\\n\\midrule\nEthylene CO & 1$\\%$ & 4.66 & 1.01 $\\pm$ 0.11 & 5.47 $\\pm$ 0.65 \\\\\n& 5$\\%$ & 0.02 & 0.43 $\\pm$ 0.03 & 4.40 $\\pm$ 1.03 \\\\\n& 10$\\%$ & 5.67E-06 & 0.25 $\\pm$ 0.04 & 3.24 $\\pm$ 0.48 \\\\\nEthylene CH4 & 1$\\%$ & 1.59 & 1.48 $\\pm$ 0.09 & 3.53 $\\pm$ 0.42 \\\\\n& 5$\\%$ & 0.05 & 0.66 $\\pm$ 0.05 & 1.69 $\\pm$ 0.15 \\\\\n& 10$\\%$ & 0.01 & 0.37 $\\pm$ 0.08 & 1.35 $\\pm$ 0.11 \\\\\nSkin & 1$\\%$ & 0.75 & 0.07 $\\pm$ 0.02 & 0.25 $\\pm$ 0.13 \\\\\n& 5$\\%$ & 0.56 & 0.02 $\\pm$ 0.01 & 0.05 $\\pm$ 0.03 \\\\\n& 10$\\%$ & -3.55E-15 & 0.03 $\\pm$ 0.01 & 0.04 $\\pm$ 0.02\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Efficient Data-Driven Leverage Score Sampling Algorithm for the Minimum Volume Covering Ellipsoid Problem in Big Data", "authors": ["Elizabeth Harris", "Ali Eshragh", "Bishnu Lamichhane", "Jordan Shaw-Carmody", "Elizabeth Stojanovski"], "url": "https://arxiv.org/abs/2411.03617v1", "attribution": "\"Efficient Data-Driven Leverage Score Sampling Algorithm for the Minimum Volume Covering Ellipsoid Problem in Big Data\" by Elizabeth Harris, Ali Eshragh, Bishnu Lamichhane, Jordan Shaw-Carmody, and Elizabeth Stojanovski, arXiv:2411.03617v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00205v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lcc}\n\\toprule\nAgreement & Accuracy & \\% Remaining \\\\\n\\cmidrule(lr){1-1}\\cmidrule(lr){2-2}\\cmidrule(lr){3-3}\nNone & 81.1 & 100 \\\\\n8/10 & 89.7 & 73 \\\\\n10/10 & 97.2 & 47 \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{Sanity-check experiment on StrategyQA dev set. This compares the performance and remaining instances if we only evaluate instances where a certain number of predictions out of 10 samples agree. Experiments shown use Llama-70B-instruct and CoT.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Conceptual and Unbiased Reasoning in Language Models", "authors": ["Ben Zhou", "Hongming Zhang", "Sihao Chen", "Dian Yu", "Hongwei Wang", "Baolin Peng", "Dan Roth", "Dong Yu"], "url": "https://arxiv.org/abs/2404.00205v1", "attribution": "\"Conceptual and Unbiased Reasoning in Language Models\" by Ben Zhou, Hongming Zhang, Sihao Chen, Dian Yu, Hongwei Wang, Baolin Peng, Dan Roth, and Dong Yu, arXiv:2404.00205v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.05488v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameters used for the training}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|ccc}\n\\hline\n\\multicolumn{2}{c|}{Settings} & CIFAR-10 & ImageNet & Celeba-HQ \\\\ \\hline\n\\multirow{3}{*}{Hyperparameters} & Batch size & 128 & 64 & 64 \\\\\n & Learning rate & 0.0003 & 0.0003 & 0.0003 \\\\\n & \\# Iterations & 150 K & 300 K & 300 K \\\\ \\hline\n\\multirow{2}{*}{\\# Samples} & Training $r_\\theta(\\cdot)$ & 19.2 M & 19.2 M & 19.2 M \\\\\n & \\textit{Training $\\epsilon_\\theta(\\cdot)$} & $\\approx$ 200 M & $\\approx$ 2500 M & $\\approx$ 1000 M \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Enhancing Sample Generation of Diffusion Models using Noise Level Correction", "authors": ["Abulikemu Abuduweili", "Chenyang Yuan", "Changliu Liu", "Frank Permenter"], "url": "https://arxiv.org/abs/2412.05488v3", "attribution": "\"Enhancing Sample Generation of Diffusion Models using Noise Level Correction\" by Abulikemu Abuduweili, Chenyang Yuan, Changliu Liu, and Frank Permenter, arXiv:2412.05488v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.02273v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{14 types of lectures in the VLEngagement dataset and their abbriviation (Abbr.) and frequency (Freq).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrllr} \\hline\n Abbr.&Description&Freq.&Abbr.&Description&Freq.\\\\ \\hline\n \\texttt{vbp}&Best Paper&16&\\texttt{vdb}&Debate&30 \\\\\n \\texttt{vdm}&Demonstration&124&\\texttt{viv}&Interview&52 \\\\\n \\texttt{vid}&Introduction&15&\\texttt{vit}&Invited Talk&300 \\\\\n \\texttt{vkn}&Keynote&115&\\texttt{vl}&Lecture&2956 \\\\\n \\texttt{vop}&Opening&31&\\texttt{oth}&Other&15 \\\\\n \\texttt{vpa}&Panel&44&\\texttt{vps}&Poster&56 \\\\ \n \\texttt{vpr}&Promotional Video&23&\\texttt{vtt}&Tutorial&269 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement", "authors": ["Sahan Bulathwela", "Maria Perez-Ortiz", "Emine Yilmaz", "John Shawe-Taylor"], "url": "https://arxiv.org/abs/2011.02273v1", "attribution": "\"VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement\" by Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, and John Shawe-Taylor, arXiv:2011.02273v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.13021v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameters of the baseline machine learning models employed to predict soybean yield.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\hline\n \\textbf{Model} & \\textbf{Parameters} & \\textbf{Best Parameter} \\\\\n \\hline\n Random Forest & Number of estimators & 550 \\\\\n & Max. feature numbers & Sqrt \\\\\n & Max. depth & 55 \\\\\n & Min. samples split & 5 \\\\\n & Min. samples leaf & 1 \\\\\n & Bootstrap & FALSE \\\\\n \\hline\n XGBoost & Max. depth & 13 \\\\\n & Objective & [reg:squared error] \\\\\n & regularization alpha & 0.0001 \\\\\n & Min. child weight & 5 \\\\\n & Gamma & 0.05 \\\\\n & Learning rate & 0.09 \\\\\n & Booster & Gbtree \\\\\n & Subsample & 0.7 \\\\\n & Column sample by tree & 0.9 \\\\\n \\hline\n Lasso regression & alpha & 0.0001\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection", "authors": ["Zahra Khalilzadeh", "Motahareh Kashanian", "Saeed Khaki", "Lizhi Wang"], "url": "https://arxiv.org/abs/2309.13021v1", "attribution": "\"A Hybrid Deep Learning-based Approach for Optimal Genotype by Environment Selection\" by Zahra Khalilzadeh, Motahareh Kashanian, Saeed Khaki, and Lizhi Wang, arXiv:2309.13021v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.12379v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{NICE reduces estimation bias in the presence of bad controls. The table reports the average MAE and bootstrap standard deviation of SATT. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|cccc}\n\\multicolumn{3}{l}{Bad Controls in Adjustment Set} & \\multicolumn{3}{c}{$\\epsilon_{att}$} \\\\\n\\hline\n& & {\\textsc \\textbf{Mod1}} & {\\textsc\\textbf{Mod2} } & {\\textsc \\textbf{Mod3}} & {\\textsc \\textbf{Mod4}}\\\\\n\\hline\n\\textbf{low} &Adjust All &$.26\\pm .09$ & $.42\\pm .03$ & $.34 \\pm .08$ & $.46 \\pm .09$\\\\\n& NICE &$.09 \\pm .07$ & $.03\\pm .01$ & $.11 \\pm .04$ & $.08 \\pm .04$\\\\\n\\hline\n\\textbf{med}& Adjust All &$.38 \\pm .10$ & $.35\\pm .06$ & $.40 \\pm .17$ & $.3\\pm .09$\\\\\n& NICE &$.06\\pm .03$ & $.06 \\pm .03$ & $.06 \\pm .02$ & $.03 \\pm .03$\\\\\n\\hline\n\\textbf{high} & Adjust All&$.32 \\pm .14$ & $.38\\pm .09$ & $.42 \\pm .05$ & $.28\\pm .05$\\\\\n& NICE &$.05\\pm .03$ & $.11 \\pm .03$ & $.16\\pm .05$ & $.11 \\pm .05$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Invariant Representation Learning for Treatment Effect Estimation", "authors": ["Claudia Shi", "Victor Veitch", "David Blei"], "url": "https://arxiv.org/abs/2011.12379v2", "attribution": "\"Invariant Representation Learning for Treatment Effect Estimation\" by Claudia Shi, Victor Veitch, and David Blei, arXiv:2011.12379v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2303.16773v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|} \n\t\t\t\t\\hline\n\t\t\t\t\\textbf{Statistic} & \\textbf{Option price} & \\textbf{Stock price} \\\\\n\t\t\t\t\\hline\n\t\t\t\tMax & $0.106250$ & $498.5040$ \\\\\n\t\t\t\tMin & $9.50000$ & $496.4980$ \\\\ \n\t\t\t\tMean & $9.971304$ & $497.3208$ \\\\ \n\t\t\t\t\\hline\n\t\t\t\\end{tabular}\n\\caption{Option and the stock index prices.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The inverse Black-Scholes problem in Radon measures space revisited: towards a new measure of market uncertainty", "authors": ["Nizar Riane"], "url": "https://arxiv.org/abs/2303.16773v1", "attribution": "\"The inverse Black-Scholes problem in Radon measures space revisited: towards a new measure of market uncertainty\" by Nizar Riane, arXiv:2303.16773v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.15726v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overview of the datasets used for training, validation, and testing, including totals per split.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n \\textbf{Name (Variant)} & \\textbf{Split} & \\textbf{Hours} & \\textbf{\\# Speakers} \\\\\n \\midrule\n SDS-200 (Clean) & Train & 50 & 1,799 \\\\\n STT4SG-350 (All) & Train & 276 & 219 \\\\\n SPC (0.9 IOU) & Train & 176 & 194 \\\\\n SRG (PL) & Train & 406 & -- \\\\\n \\midrule\n Total & & 908 & $>$ 2,212 \\\\\n \\midrule\n SDS-200 (Clean) & Val & 5.2 & 288 \\\\\n STT4SG-350 (All) & Val & 21 & 219 \\\\\n SRG (PL) & Val & 20 & -- \\\\\n \\midrule\n Total & & 46.2 & $>$ 507 \\\\\n \\midrule\n SDS-200 (Clean) & Test & 5.2 & 281 \\\\\n STT4SG-350 (All) & Test & 34 & 56 \\\\\n SPC (0.9 IOU) & Test & 6 & 26 \\\\\n SRG (SWISS TXT) & Test & 20 & -- \\\\\n Dataset-A & Test & 0.22 & 2 \\\\\n \\midrule\n Total & & 65.42 & $>$ 365 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fine-tuning Whisper on Low-Resource Languages for Real-World Applications", "authors": ["Vincenzo Timmel", "Claudio Paonessa", "Reza Kakooee", "Manfred Vogel", "Daniel Perruchoud"], "url": "https://arxiv.org/abs/2412.15726v3", "attribution": "\"Fine-tuning Whisper on Low-Resource Languages for Real-World Applications\" by Vincenzo Timmel, Claudio Paonessa, Reza Kakooee, Manfred Vogel, and Daniel Perruchoud, arXiv:2412.15726v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12784v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ll|cc|c}\n\t\t\t\\multirow{2}{*}{Bottom pressure} & \\multirow{2}{*}{$n_x$} & \\multicolumn{2}{c|}{Numerical simulation} & {Linear stability} \\\\\n\t\t\t& & Top row & Eigenprofile & analysis \\\\\n\t\t\t\\hline\n\t\t\t2464 Pa (base) & 8 & 73 050 s & 33 250 s & 22 693 s \\\\ \\hline\n\t\t\t2958 Pa & 8 & 65 400 s & 26 150 s & 19 317 s \\\\\n\t\t\t3451 Pa & 18 & 38 500 s & 23 650 s & 20 899 s\\\\\n\t\t\t3944 Pa & 16 & 27 400 s & 6 750 s & 10 100 s \\\\ \\hline\n\t\t\\end{tabular}\n\\caption{Onset times for numerical simulations and linear stability analysis considering a specific number of waves. The numerical simulation considers two different types of perturbation; either perturbing only at the top grid cells, or using the eigenprofile from Figure .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Impact of saturation on evaporation-driven density instabilities in porous media: mathematical and numerical analysis", "authors": ["C. Bringedal", "S. Kiemle", "C. J. van Duijn", "R. Helmig"], "url": "https://arxiv.org/abs/2501.12784v1", "attribution": "\"Impact of saturation on evaporation-driven density instabilities in porous media: mathematical and numerical analysis\" by C. Bringedal, S. Kiemle, C. J. van Duijn, and R. Helmig, arXiv:2501.12784v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08125v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The mAP@20 results on image-to-text ($I \\to T$) and text-to-image ($T \\to I$) retrieval tasks performed for $B=64$ with different data augmentation setups.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c} \n \\hline\n Modality & Setup & $I \\to T$ & $T \\to I$ \\\\ \n \\hline\\hline\n \\multirow{4}{*}{Image} & NoAugmentation & 0.801 & 0.804 \\\\\n & AugmentedCenterCrop & \\textbf{0.836} & \\textbf{0.824} \\\\\n & AugmentedRandomCrop & {0.832} & {0.822} \\\\\n & RandomAugmentation & 0.827 & 0.815 \\\\\n \\hline\\hline\n \\multirow{3}{*}{Text} & RuleBased & {0.836} & {0.824} \\\\\n & Back-translation & {0.836} & \\textbf{0.829} \\\\\n & Back-translationChain & \\textbf{0.839} & {0.824} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing", "authors": ["Georgii Mikriukov", "Mahdyar Ravanbakhsh", "Begüm Demir"], "url": "https://arxiv.org/abs/2201.08125v1", "attribution": "\"Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing\" by Georgii Mikriukov, Mahdyar Ravanbakhsh, and Begüm Demir, arXiv:2201.08125v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.20238v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|}\n \\hline\n & ODE & Sinkhorn \\\\ \\hline\n Computed regularized primal value & 0.3807 & 0.3807\\\\ \\hline\n Optimal unregularized primal value & 0.3767 & 0.3767\\\\ \\hline\n Optimal unregularized primal value + Entropy & 0.4127 & 0.4127 \\\\ \\hline\n Iterations & 25 & 60 \\\\ \\hline\n CPU time (sec) & 8.71 & 7.40 \\\\ \\hline\n \\end{tabular}\n\\caption{Comparison of the performance between 4-th order Runge-Kutta ODE method and Sinkhorn algorithm for multi-period martingale transport problem.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An ordinary differential equation for entropic optimal transport and its linearly constrained variants", "authors": ["Joshua Zoen-Git Hiew", "Luca Nenna", "Brendan Pass"], "url": "https://arxiv.org/abs/2403.20238v1", "attribution": "\"An ordinary differential equation for entropic optimal transport and its linearly constrained variants\" by Joshua Zoen-Git Hiew, Luca Nenna, and Brendan Pass, arXiv:2403.20238v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02844v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experiments on three datasets compare DGCF with six baseline models based on Mean Reciprocal Rank (MRR) and Recall@10 (R@10). The bold and underlined numbers mean the best and second-best results on each dataset and metric, respectively. \"Improvement\" means the minimum improvement among all baselines.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{clccccccccc}\n \\toprule\n {Models} \n &\\multicolumn{2}{c}{LastFM} &\\multicolumn{2}{c}{Wikipedia} &\\multicolumn{2}{c}{Reddit} \\\\\n \\cmidrule(lr){2-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-7} \n & MRR & R@10 & MRR & R@10 & MRR & R@10 \\\\\n \\midrule \n LSTM & 0.081 & 0.127 & 0.332 & 0.459 & 0.367 & 0.573 \\\\\n Time-LSTM & 0.088 & 0.146 & 0.251 & 0.353 & 0.398 & 0.601 \\\\\n RRN & 0.093 & 0.199 & 0.530 & 0.628 & 0.605 & 0.751 \\\\\n CTDNE & 0.010 &0.010 &0.035 &0.056 &0.165 &0.257 \\\\\n DeepCoevolve & 0.021 & 0.042 & 0.515 & 0.563 & 0.243 & 0.305 \\\\\n Jodie & \\underline{0.239} & \\underline{0.387} & \\underline{0.746} & \\underline{0.821} & \\underline{0.724} & \\underline{0.851} \\\\\n DGCF & \\textbf{0.321} & \\textbf{0.456} & \\textbf{0.786} & \\textbf{0.852} & \\textbf{0.726} & \\textbf{0.856} \\\\\n \\midrule\n Improvement & 34.3\\% & 27.7\\% & 5.4\\% & 3.6\\% & 0.2\\% & 0.5\\% \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Dynamic Graph Collaborative Filtering", "authors": ["Xiaohan Li", "Mengqi Zhang", "Shu Wu", "Zheng Liu", "Liang Wang", "Philip S. Yu"], "url": "https://arxiv.org/abs/2101.02844v1", "attribution": "\"Dynamic Graph Collaborative Filtering\" by Xiaohan Li, Mengqi Zhang, Shu Wu, Zheng Liu, Liang Wang, and Philip S. Yu, arXiv:2101.02844v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.20993v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Descriptive Statistics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|}\n\\hline\n & Mean & Median & Minimum & Maximum & Std. Dev. & Skewness & Kurtosis \\\\ \\hline\nGFCF\\_Ratio & 0.2180 & 0.2164 & 0.0515 & 0.5427 & 0.0511 & 0.5737 & 3.2900 \\\\ \\hline\nGDP\\_Growth & 2.4981 & 2.4259 & -11.1673 & 24.4753 & 3.4266 & -0.0661 & 4.0179 \\\\ \\hline\nUnEmpl\\_Rate & 7.6046 & 6.2908 & 1.9000 & 29.8800 & 4.8563 & 2.1597 & 5.1403 \\\\ \\hline\nTAX & 19.3272 & 20.8857 & 7.7000 & 36.5003 & 6.4674 & -0.0468 & -0.9822 \\\\ \\hline\nCPI & 3.3177 & 2.3300 & -4.4800 & 72.3100 & 5.1021 & 7.3497 & 78.5700 \\\\ \\hline\nEPU\\_Index & 4.7990 & 4.7616 & 3.2281 & 6.4837 & 0.4901 & 0.4741 & 0.4930 \\\\ \\hline\nGini\\_Index & 0.3394 & 0.3286 & 0.2380 & 0.5870 & 0.0657 & 1.4441 & 2.2856 \\\\ \\hline\nFDI & 4.2858 & 2.4049 & -394.4716 & 234.2487 & 20.9781 & -7.3066 & 200.3755 \\\\ \\hline\nHDI & 0.8498 & 0.8880 & 0.4900 & 0.9670 & 0.0963 & -1.3701 & 1.2231 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "GDP-GFCF Dynamics Across Global Economies: A Comparative Study of Panel Regressions and Random Forest", "authors": ["Alina Landowska", "Robert A. Kłopotek", "Dariusz Filip", "Konrad Raczkowski"], "url": "https://arxiv.org/abs/2504.20993v1", "attribution": "\"GDP-GFCF Dynamics Across Global Economies: A Comparative Study of Panel Regressions and Random Forest\" by Alina Landowska, Robert A. Kłopotek, Dariusz Filip, and Konrad Raczkowski, arXiv:2504.20993v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.12619v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\large Regression Results: Impact of changes in active mines on unemployment rate accounting for county-level renewable energy investment}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\tabularnewline\\midrule\\midrule\n Dependent Variable: & \\multicolumn{2}{c}{$\\Delta$ Unemployment Rate}\\\\\n Model: & (1) & (2)\\\\\n \\midrule \\emph{Variables} & & \\\\\n $\\Delta$ Active Mines\\textsubscript{t} & -0.0640$^{**}$ & -0.0470$^{**}$\\\\\n & (0.0163) & (0.0144)\\\\\n $\\Delta$ Active Mines\\textsubscript{t-1} & -0.0015 & -0.0001\\\\\n & (0.0160) & (0.0113)\\\\\n $\\Delta$ Active Mines\\textsubscript{t-2} & 0.0434$^{**}$ & 0.0369$^{**}$\\\\\n & (0.0135) & (0.0111)\\\\\n \n $\\Delta$ Active Mines\\textsubscript{t} $\\times$ REE\\textsubscript{t} & 0.0606$^{.}$ & 0.0562$^{*}$\\\\\n & (0.0286) & (0.0262)\\\\\n $\\Delta$ Active Mines\\textsubscript{t-1} $\\times$ REE\\textsubscript{t-1} & 0.0150 & 0.0182\\\\\n & (0.0180) & (0.0149)\\\\\n $\\Delta$ Active Mines\\textsubscript{t-2} $\\times$ REE\\textsubscript{t-2} & -0.0427$^{*}$ & -0.0335\\\\\n & (0.0176) & (0.0272)\\\\\n REE $\\geq 0.1$\\% of GDP\\textsubscript{t} & -0.0614$^{*}$ & -0.0015\\\\\n & (0.0232) & (0.0408)\\\\\n REE $\\geq 0.1$\\% of GDP\\textsubscript{t-1} & -0.0227 & 0.0192\\\\\n & (0.0236) & (0.0494)\\\\\n REE $\\geq 0.1$\\% of GDP\\textsubscript{t-2} & -0.0034 & 0.0765\\\\\n & (0.0233) & (0.0661)\\\\\n $\\Delta$ (log) Real GDPPC\\textsubscript{t} & -1.009$^{**}$ & -1.617$^{***}$\\\\\n & (0.2519) & (0.3656)\\\\\n \\midrule \\emph{Fixed-effects} & & \\\\\n County FIPS Code & Yes & Yes\\\\\n Year & Yes & Yes\\\\\n \\midrule \\emph{Fit statistics} & & \\\\\n Observations & 49,152 & 4,016\\\\\n R$^2$ & 0.62396 & 0.66401\\\\\n Within R$^2$ & 0.01613 & 0.04872\\\\\n \\midrule\\midrule\\multicolumn{3}{l}{\\emph{Clustered (County FIPS Code \\& Year) standard-errors in parentheses}}\\\\\n \\multicolumn{3}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'", "authors": ["Ebba Mark", "Ryan Rafaty", "Moritz Schwarz"], "url": "https://arxiv.org/abs/2211.12619v1", "attribution": "\"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'\" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06733v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Traditional Text Mining (TM) vs. Software Development Process Mining (SDPM)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\t\\hline\\noalign{\\smallskip}\n\t\t\t\\textbf{} & \\textbf{TM} & \\textbf{SDPM}\\\\\n\t\\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\n\t\n\t\\multicolumn{3}{l}{\\cellcolor{gray!10}\\textbf{Inputs}}\\\\\n\t\\textbf{Corpus} & Documents/Articles & Development Work Sessions\\\\\n\t\\textbf{Document} & Mixture of Topics & Mixture of Behaviours\\\\\n\t\\textbf{Topic} & Frequent Words/Terms & Frequent Actions/Commands\\\\\n \\multicolumn{3}{l}{\\cellcolor{gray!10}\\textbf{Outputs}}\\\\\n\t\\textbf{Discovers} & Distinct Subjects/Topics & Development Patterns\\\\\n\t\\textbf{Usefulness} & Identify Social Trends & Optimize Resource Allocation\\\\\n\t\\textbf{} & Frame Research Interests & Detect Practices Deviations\\\\\n\t\\textbf{} & Sentiment Analysis & Forensic Project Analysis\\\\\n \\noalign{\\smallskip}\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Profiling Software Developers with Process Mining and N-Gram Language Models", "authors": ["João Caldeira", "Fernando Brito e Abreu", "Jorge Cardoso", "Ricardo Ribeiro", "Claudia Werner"], "url": "https://arxiv.org/abs/2101.06733v1", "attribution": "\"Profiling Software Developers with Process Mining and N-Gram Language Models\" by João Caldeira, Fernando Brito e Abreu, Jorge Cardoso, Ricardo Ribeiro, and Claudia Werner, arXiv:2101.06733v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.03112v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|r|r|r|r|r|r|r|r|r}\n\\hline\nblack box & MBT & $impr$ & $alpha$ & n leaves & n leaves min & n leaves max & $R^2_{train}$ & sd $R^2_{train}$ & $R^2_{test}$ & sd $R^2_{test}$\\\\\n\\hline\nstandalone & SLIM & 0.15 & & 2.04 & 2 & 3 & 0.9542 & 0.0034 & 0.9536 & 0.0036\\\\\nstandalone & SLIM & 0.10 & & 36.94 & 15 & 58 & 0.9895 & 0.0024 & 0.9880 & 0.0024\\\\\nstandalone & SLIM & 0.05 & & 55.84 & 50 & 61 & 0.9910 & 0.0002 & 0.9890 & 0.0004\\\\\nstandalone & GUIDE & 0.15 & & 2.03 & 2 & 3 & 0.9540 & 0.0030 & 0.9534 & 0.0031\\\\\nstandalone & GUIDE & 0.10 & & 36.23 & 14 & 52 & 0.9894 & 0.0023 & 0.9881 & 0.0024\\\\\nstandalone & GUIDE & 0.05 & & 56.35 & 47 & 62 & 0.9909 & 0.0002 & 0.9891 & 0.0003\\\\\nstandalone & MOB & & 0.001 & 17.15 & 16 & 21 & 0.9901 & 0.0002 & 0.9893 & 0.0004\\\\\nstandalone & MOB & & 0.010 & 19.14 & 16 & 22 & 0.9902 & 0.0002 & 0.9894 & 0.0004\\\\\nstandalone & MOB & & 0.050 & 21.71 & 18 & 26 & 0.9903 & 0.0002 & 0.9895 & 0.0004\\\\\nstandalone & CTree & & 0.001 & 19.19 & 17 & 22 & 0.9901 & 0.0002 & 0.9894 & 0.0004\\\\\nstandalone & CTree &0 & 0.010 & 21.58 & 17 & 25 & 0.9902 & 0.0002 & 0.9895 & 0.0004\\\\\nstandalone & CTree & & 0.050 & 24.56 & 21 & 30 & 0.9903 & 0.0002 & 0.9895 & 0.0003\\\\\n\\hline\nlm & SLIM & 0.15 & & 2.00 & 2 & 2 & 0.9628 & 0.0009 & 0.9625 & 0.0012\\\\\nlm & SLIM & 0.10 & & 52.19 & 43 & 60 & 0.9999 & 0.0001 & 0.9998 & 0.0001\\\\\nlm & SLIM & 0.05 & & 63.99 & 63 & 64 & 1.0000 & 0.0000 & 1.0000 & 0.0000\\\\\nlm & GUIDE & 0.15 & & 2.00 & 2 & 2 & 0.9628 & 0.0009 & 0.9625 & 0.0012\\\\\nlm & GUIDE & 0.10 & & 52.37 & 43 & 60 & 0.9999 & 0.0001 & 0.9998 & 0.0001\\\\\nlm & GUIDE & 0.05 & & 63.99 & 63 & 64 & 1.0000 & 0.0000 & 1.0000 & 0.0000\\\\\nlm & MOB & & 0.001 & 64.00 & 64 & 64 & 1.0000 & 0.0000 & 1.0000 & 0.0000\\\\\nlm & MOB & & 0.010 & 64.00 & 64 & 64 & 1.0000 & 0.0000 & 1.0000 & 0.0000\\\\\nlm & MOB & & 0.050 & 64.00 & 64 & 64 & 1.0000 & 0.0000 & 1.0000 & 0.0000\\\\\nlm & CTree & & 0.001 & 63.60 & 62 & 64 & 1.0000 & 0.0000 & 1.0000 & 0.0000\\\\\nlm & CTree & & 0.010 & 63.60 & 62 & 64 & 1.0000 & 0.0000 & 1.0000 & 0.0000\\\\\nlm & CTree &5 & 0.050 & 63.60 & 62 & 64 & 1.0000 & 0.0000 & 1.0000 & 0.0000\\\\\n\\hline\nlm & & & & & & & 0.9901 & 0.0002 & 0.9901 & 0.0003\\\\\n\\hline\nXGBoost & SLIM & 0.15 & & 2.22 & 2 & 7 & 0.9647 & 0.0063 & 0.9643 & 0.0063\\\\\nXGBoost & SLIM & 0.10 & & 19.23 & 3 & 40 & 0.9908 & 0.0062 & 0.9902 & 0.0062\\\\\nXGBoost & SLIM & 0.05 & & 49.05 & 34 & 56 & 0.9978 & 0.0003 & 0.9972 & 0.0003\\\\\nXGBoost & CTree & 0.15 & & 32.81 & 26 & 41 & 0.9972 & 0.0002 & 0.9965 & 0.0003\\\\\nXGBoost & CTree & 0.10 & & 36.36 & 29 & 43 & 0.9973 & 0.0002 & 0.9966 & 0.0003\\\\\nXGBoost & CTree & 0.05 & & 39.37 & 32 & 47 & 0.9974 & 0.0002 & 0.9967 & 0.0003\\\\\nXGBoost & MOB & & 0.001 & 38.44 & 29 & 47 & 0.9979 & 0.0002 & 0.9972 & 0.0003\\\\\nXGBoost & MOB & & 0.010 & 42.62 & 31 & 53 & 0.9980 & 0.0002 & 0.9973 & 0.0003\\\\\nXGBoost & MOB & & 0.050 & 46.02 & 39 & 55 & 0.9981 & 0.0002 & 0.9974 & 0.0003\\\\\nXGBoost & GUIDE & & 0.001 & 2.21 & 2 & 8 & 0.9645 & 0.0061 & 0.9641 & 0.0062\\\\\nXGBoost & GUIDE & & 0.010 & 19.27 & 3 & 38 & 0.9907 & 0.0062 & 0.9900 & 0.0062\\\\\nXGBoost & GUIDE & & 0.050 & 49.90 & 34 & 58 & 0.9977 & 0.0003 & 0.9970 & 0.0004\\\\\n\\hline\nXGBoost & & & & & & & 0.9877 & 0.0003 & 0.9852 & 0.0006\\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean simulation results on 100 simulation runs as stand alone and surrogate models on scenario linear smooth with sample size $n = 7500$ for different values of $impr$ and $alpha$}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation", "authors": ["Julia Herbinger", "Susanne Dandl", "Fiona K. Ewald", "Sofia Loibl", "Giuseppe Casalicchio"], "url": "https://arxiv.org/abs/2310.03112v1", "attribution": "\"Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation\" by Julia Herbinger, Susanne Dandl, Fiona K. Ewald, Sofia Loibl, and Giuseppe Casalicchio, arXiv:2310.03112v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.02921v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{Reference} & \\textbf{Year} & \\textbf{Authors} & \\textbf{Total\nCitations} & \\textbf{Taxonomy} \\\\ \\hline\\hline\n1 & 1948 & Shannon & 43150 & G0 \\\\ \\hline\n31 & 2000 & Richman, Moorman & 16606 & G2 \\\\ \\hline\n18 & 1988 & Tsallis & 14855 & G0 \\\\ \\hline\n22 & 1991 & Pincus & 13763 & G2 \\\\ \\hline\n148 & 2002 & Bandt, Pompe & 10260 & G1 \\\\ \\hline\n248 & 2000 & Schreiber & 9743 & G2 \\\\ \\hline\n226 & 1992 & Coifman, Wickerhause & 8003 & G1 \\\\ \\hline\n21 & 2005 & Costa, Goldberger, Pen & 5624 & G1 \\\\ \\hline\n101 & 1972 & De Luca, Termini & 5217 & G2 \\\\ \\hline\n178 & 1982 & Rao & 4027 & G1 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Most referenced publications by DOI as of this writting (December 2024). The total citations are based on Semantic Scholar, CrossRef and OpenCitations.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Applications of Entropy in Data Analysis and Machine Learning: A Review", "authors": ["Salomé A. Sepúveda Fontaine", "José M. Amigó"], "url": "https://arxiv.org/abs/2503.02921v1", "attribution": "\"Applications of Entropy in Data Analysis and Machine Learning: A Review\" by Salomé A. Sepúveda Fontaine and José M. Amigó, arXiv:2503.02921v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11320v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textsc{Final results upon cross-validation.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc} \n \\toprule[1.5pt]\n State & $P_+$ (\\%) & Se (\\%) & $F_1$ (\\%) \\\\\n \\midrule[1.5pt]\n S1 & 85.7 & 86.5 & 86.0 \\\\\n Sys & 90.0 & 90.0 & 90.0 \\\\\n S2 & 87.1 & 86.7 & 86.9 \\\\\n Dias & 94.9 & 94.6 & 94.8 \\\\\n \\midrule[1pt]\n Average & 89.3 & 89.5 & 89.4 \\\\\n \\midrule[1pt]\n ACC (\\%) & 91.34 & & \\\\\n \\bottomrule[1.5pt]\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Deep Recurrent Learning for Heart Sounds Segmentation based on Instantaneous Frequency Features", "authors": ["Alvaro Joaquín Gaona", "Pedro David Arini"], "url": "https://arxiv.org/abs/2201.11320v1", "attribution": "\"Deep Recurrent Learning for Heart Sounds Segmentation based on Instantaneous Frequency Features\" by Alvaro Joaquín Gaona and Pedro David Arini, arXiv:2201.11320v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.12266v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary Statistics of Selected US Stocks (April 2018 -- March 2025)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllccl}\n\\toprule\n\\textbf{Ticker} & \\textbf{Company Name} & \\textbf{Sector} & \\textbf{Mean Return (\\%)} & \\textbf{Mean SD (\\%)} & \\textbf{Skewness} \\\\\n\\midrule\nMSFT & Microsoft Corporation & Information Technology & 0.09 & 1.81 & -0.29 \\\\\nGOOGL & Alphabet Inc. (Class A) & Communication Services & 0.06 & 1.95 & -0.24 \\\\\nNVDA & NVIDIA Corporation & Information Technology & 0.17 & 3.29 & -0.24 \\\\\nAMZN & Amazon.com, Inc. & Consumer Discretionary & 0.06 & 2.16 & -0.14 \\\\\nMETA & Meta Platforms, Inc. & Communication Services & 0.07 & 2.67 & -1.34 \\\\\nTSLA & Tesla, Inc. & Consumer Discretionary & 0.16 & 4.06 & -0.07 \\\\\nAAPL & Apple Inc. & Information Technology & 0.10 & 1.93 & -0.23 \\\\\nHD & The Home Depot, Inc. & Consumer Discretionary & 0.05 & 1.70 & -1.43 \\\\\nADBE & Adobe Inc. & Information Technology & 0.03 & 2.33 & -0.83 \\\\\nNFLX & Netflix, Inc. & Communication Services & 0.07 & 2.83 & -2.18 \\\\\nBRK-B & Berkshire Hathaway Inc. & Financials & 0.06 & 1.30 & -0.21 \\\\\nV & Visa Inc. & Financials & 0.06 & 1.64 & -0.07 \\\\\nMA & Mastercard Incorporated & Financials & 0.07 & 1.84 & 0.03 \\\\\nJNJ & Johnson \\& Johnson & Health Care & 0.03 & 1.22 & -0.33 \\\\\nUNH & UnitedHealth Group & Health Care & 0.06 & 1.80 & -0.53 \\\\\nJPM & JPMorgan Chase \\& Co. & Financials & 0.06 & 1.86 & -0.02 \\\\\nPG & Procter \\& Gamble Company & Consumer Staples & 0.05 & 1.27 & -0.03 \\\\\nPEP & PepsiCo, Inc. & Consumer Staples & 0.03 & 1.31 & -0.57 \\\\\nXOM & Exxon Mobil Corporation & Energy & 0.05 & 1.95 & -0.16 \\\\\nCOST & Costco Wholesale Corporation & Consumer Staples & 0.10 & 1.45 & -0.53 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Semiparametric Dynamic Copula Models for Portfolio Optimization", "authors": ["Savita Pareek", "Sujit K. Ghosh"], "url": "https://arxiv.org/abs/2504.12266v1", "attribution": "\"Semiparametric Dynamic Copula Models for Portfolio Optimization\" by Savita Pareek and Sujit K. Ghosh, arXiv:2504.12266v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.04538v9_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model results for the sleep deprivation study.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n\t\\hline\n\t& \\textit{lme4} & PLS & PRLS \\\\ \\hline\n\tIntercept & 251.405 & 250.389 & 250.356 \\\\ \\hline\n\tDays & 10.467 & 10.789 & 10.795 \\\\ \\hline\n\tS.D. of random intercept & 25.051 & 25.555 & 26.407 \\\\ \\hline\n\tS.D. of random slope & 5.988 & 6.228 & 6.232 \\\\ \\hline\n\tS.D. of residuals & 25.565 & 20.140 & 19.509 \\\\ \\hline \\hline\n\tMarginal $R^2$ & 0.415 & 0.467 & 0.463 \\\\ \\hline\n\tConditional $R^2$ & 0.702 & 0.803 & 0.817 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects", "authors": ["Hao Chen", "Lanshan Han", "Alvin Lim"], "url": "https://arxiv.org/abs/2011.04538v9", "attribution": "\"Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects\" by Hao Chen, Lanshan Han, and Alvin Lim, arXiv:2011.04538v9, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14613v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccccc}\n \\toprule\n Problem & Dim & \\multicolumn{3}{c}{Away} & \\multicolumn{3}{c}{PCG} & \\multicolumn{3}{c}{BCG} & \\multicolumn{3}{c}{BPCG} \\tabularnewline\n \\cmidrule(lr){3-5}\n \\cmidrule(lr){6-8}\n \\cmidrule(lr){9-11}\n \\cmidrule(lr){12-14}\n & &\\thead{Time \\\\ (s)} & \\thead{Average \\\\ memory \\\\ (GB)} &\\thead{Dual gap} & \\thead{Time \\\\ (s)} & \\thead{Average \\\\ memory \\\\ (GB)} &\\thead{Dual gap} & \\thead{Time \\\\ (s)} & \\thead{Average \\\\ memory \\\\ (GB)} &\\thead{Dual gap} &\\thead{Time \\\\ (s)} & \\thead{Average \\\\ memory \\\\ (GB)}&\\thead{Dual gap} \\\\\\\\\n \\midrule\n Birkhoff & $n$ & & & & & & & & & & & & \\\\\n \\midrule\n & 10000 & 2.05e2 & 9.96e1 & 1.22e-7 & 1.86e2 & 1.20e2 & 1.00e-7 & 2.48e1 & 3.44e1 & 1.00e-7 & 3.14e2 & 1.45e2 & 1.00e-7 \\\\\n & 62500 & 2.24e3 & 3.46e2 & 1.16e-7 & 2.35e3 & 5.74e2 & 1.00e-7 & 2.33e2 & 1.79e2 & 1.26e-7 & 3.60e3* & 6.46e2 & 2.10e-7 \\\\\n & 160000 & 3.56e3 & 4.58e2 & 1.14e-7 & 3.60e3* & 7.13e2 & 1.41e-7 & 6.75e2 & 5.32e2 & 1.00e-7 & 3.60e3* & 5.21e2 & 3.55e-7 \\\\\n & 422500 & 3.60e3* & 3.23e2 & 2.40e-7 & 3.60e3* & 5.19e2 & 3.06e-7 & 2.23e3 & 1.13e3 & 1.00e-7 & 3.60e3* & 3.93e2 & 5.69e-7 \\\\\n & 640000 & 3.60e3* & 3.03e2 & 3.46e-7 & 3.60e3* & 5.01e2 & 3.77e-7 & 1.35e3 & 3.37e2 & 1.86e-7 & 3.60e3* & 3.71e2 & 6.50e-7 \\\\\n & 1000000 & 3.60e3* & 3.01e2 & 4.17e-7 & 3.60e3* & 4.99e2 & 5.11e-7 & 2.46e3 & 4.65e2 & 1.50e-7 & 3.60e3* & 3.51e2 & 7.32e-7 \\\\\n \\midrule\n Nuclear & $n$ & & & & & & & & & & & &\\\\\n \\midrule\n & 10000 & 3.60e3* & 2.89e1 & 2.04e-4 & 3.60e3* & 3.38e1 & 1.36e-3 & 3.60e3* & 1.35e4 & 1.64e-2 & 3.60e3* & 8.86e1 & 7.14e-7 \\\\\n & 62500 & 3.60e3* & 2.29 & 2.78 & 3.60e3* & 2.54 & 3.20 & 3.60e3* & 1.14e4 & 3.16e5 & 3.60e3* & 4.80 & 2.24e-4 \\\\\n & 160000 & 3.60e3* & 2.48 & 1.28e2 & 3.60e3* & 2.22 & 3.44e2 & 3.60e3* & 7.72e3 & 4.94e8 & 3.60e3* & 3.41 & 6.13e-1 \\\\\n & 302500 & 3.60e3* & 1.48 & 2.96e3 & 3.60e3* & 1.52 & 3.68e6 & 3.60e3* & 7.31e3 & 1.19e10 & 3.60e3* & 2.64 & 8.57e1 \\\\\n & 490000 & 3.60e3* & 1.47 & 1.46e6 & 3.60e3* & 1.52 & 3.43e8 & 3.60e3* & 5.77e3 & 8.21e10 & 3.60e3* & 2.64 & 5.93e5 \\\\\n & 722500 & 3.60e3* & 1.47 & 7.52e7 & 3.60e3* & 1.49 & 1.98e9 & 3.60e3* & 6.02e3 & 2.15e11 & 3.60e3* & 2.57 & 5.80e7 \\\\\n \\midrule\n Simplex & $m, n$ & & & & & & & & & & & & \\\\\n \\midrule\n & 5000, 2500 & 9.60 & 1.80 & 1.00e-7 & 5.56 & 1.32 & 1.00e-7 & 1.09 & 2.84e-1 & 1.07e-7 & 4.72 & 1.32 & 1.01e-7 \\\\\n & 5000, 6000 & 5.71e1 & 4.54 & 1.00e-7 & 3.46e1 & 3.47 & 1.00e-7 & 6.81 & 4.37e-1 & 1.00e-7 & 3.31e1 & 3.47 & 1.00e-7 \\\\\n & 7000, 3500 & 2.30e1 & 2.94 & 1.00e-7 & 1.55e1 & 2.21 & 1.00e-7 & 3.17 & 4.99e-1 & 1.00e-7 & 1.59e1 & 2.21 & 1.00e-7 \\\\\n & 7000, 8500 & 9.10e1 & 7.33 & 1.00e-7 & 5.82e1 & 5.48 & 1.00e-7 & 1.52e1 & 6.83e-1 & 1.14e-7 & 6.07e1 & 5.46 & 1.00e-7 \\\\\n & 10000, 5000 & 4.45e1 & 4.71 & 1.00e-7 & 3.39e1 & 3.55 & 1.00e-7 & 7.56 & 8.17e-1 & 1.12e-7 & 3.17e1 & 3.50 & 1.00e-7 \\\\\n & 10000, 12000 & 2.31e2 & 1.22e1 & 1.00e-7 & 1.36e2 & 9.38 & 1.00e-7 & 3.84e1 & 1.27 & 1.21e-7 & 1.52e2 & 9.40 & 1.00e-7 \\\\\n & 20000, 10000 & 1.79e2 & 1.29e1 & 1.00e-7 & 1.17e2 & 9.84 & 1.00e-7 & 3.51e1 & 2.47 & 1.13e-7 & 1.16e2 & 9.49 & 1.00e-7 \\\\\n & 20000, 24000 & 8.94e2 & 3.28e1 & 1.00e-7 & 5.79e2 & 2.52e1 & 1.00e-7 & 1.73e2 & 3.70 & 1.00e-7 & 6.71e2 & 2.48e1 & 1.00e-7 \\\\\n \\midrule\n Sparse & $n$ & & & & & & & & & & & & \\\\\n \\midrule\n & 10000 & 1.76e1 & 4.82e1 & 1.00e-7 & 3.33 & 1.10e1 & 1.00e-7 & 3.41 & 9.60 & 1.00e-7 & 6.64 & 2.07e1 & 1.02e-7 \\\\\n & 40000 & 6.82e1 & 2.00e2 & 1.16e-7 & 5.89e1 & 1.75e2 & 1.82e-7 & 1.18e2 & 3.22e2 & 1.27e-7 & 1.39e2 & 3.93e2 & 1.03e-7 \\\\\n & 80000 & 9.89e2 & 3.04e3 & 2.23e-6 & 2.31e2 & 7.43e2 & 2.25e-6 & 3.60e3* & 1.15e4 & 7.43e-7 & 1.36e3 & 4.15e3 & 8.99e-7 \\\\\n & 140000 & 2.31e2 & 7.56e2 & 1.43e-6 & 7.59e2 & 2.28e3 & 4.28e-6 & 3.60e3* & 1.12e4 & 1.11e-6 & 6.14e2 & 1.93e3 & 1.08e-6 \\\\\n & 200000 & 3.60e3* & 1.16e4 & 1.68e-5 & 3.60e3* & 1.09e4 & 5.02e-5 & 3.60e3* & 1.14e4 & 5.85e-6 & 3.60e3* & 1.11e4 & 4.76e-5 \\\\\n & 350000 & 3.60e3* & 9.81e3 & 2.88e-5 & 3.60e3* & 9.66e3 & 2.12e-3 & 3.60e3* & 9.84e3 & 1.07e-5 & 3.60e3* & 9.95e3 & 6.46e-4 \\\\\n & 500000 & 3.60e3* & 9.94e3 & 6.42e-3 & 3.60e3* & 9.36e3 & 2.99e-2 & 3.60e3* & 9.69e3 & 8.31e-4 & 3.60e3* & 1.01e4 & 3.22e-4 \\\\\n \\midrule\n Spectrahedron & $n$ & & & & & & & & & & & & \\\\\n \\midrule\n & 10000 & 1.74e-2 & 2.85e-3 & 1.17e-7 & 4.26e1 & 2.91e1 & 7.73e-7 & 1.91e-2 & 2.28e-2 & 1.19e-7 & 2.60e-2 & 3.51e-3 & 1.25e-7 \\\\\n & 62500 & 4.41e-2 & 1.08e-2 & 1.00e-7 & 4.31 & 1.16 & 1.56e-7 & 7.73e-2 & 4.24e-2 & 1.42e-7 & 3.09e-2 & 1.06e-2 & 1.00e-7 \\\\\n & 250000 & 2.17e1 & 2.27e-1 & 1.67e-7 & 3.60e3* & 2.12e1 & 5.99e-6 & 7.24 & 5.13 & 1.29e-7 & 1.12e1 & 1.87e-1 & 1.78e-7 \\\\\n & 490000 & 2.12e1 & 3.31e-1 & 1.24e-7 & 3.60e3* & 1.31e1 & 7.10e-6 & 1.70e1 & 9.83 & 1.09e-7 & 1.58e1 & 3.17e-1 & 1.17e-7 \\\\\n & 722500 & 3.12e3 & 2.50 & 4.45e-7 & 3.60e3* & 1.08e1 & 3.05e-5 & 2.07e2 & 9.30e2 & 1.29e-7 & 9.07e2 & 7.88 & 1.77e-7 \\\\\n & 1000000 & 3.60e3* & 2.71 & 6.59e-7 & 3.60e3* & 1.21e1 & 3.38e-5 & 3.43e2 & 7.71e2 & 1.45e-7 & 1.83e3 & 1.42e1 & 1.54e-7 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance comparison of the active set variants with \\texttt{ActiveSetQuadraticProductCaching}. Entries marked with * timed out.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Improved algorithms and novel applications of the FrankWolfe.jl library", "authors": ["Mathieu Besançon", "Sébastien Designolle", "Jannis Halbey", "Deborah Hendrych", "Dominik Kuzinowicz", "Sebastian Pokutta", "Hannah Troppens", "Daniel Viladrich Herrmannsdoerfer", "Elias Wirth"], "url": "https://arxiv.org/abs/2501.14613v2", "attribution": "\"Improved algorithms and novel applications of the FrankWolfe.jl library\" by Mathieu Besançon, Sébastien Designolle, Jannis Halbey, Deborah Hendrych, Dominik Kuzinowicz, Sebastian Pokutta, Hannah Troppens, Daniel Viladrich Herrmannsdoerfer, and Elias Wirth, arXiv:2501.14613v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.17417v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n & \\multicolumn{5}{c}{Stock Ticker} & \\\\ % Separate header row\n \\cmidrule(lr){2-6}\n Event-Type & AAPL & AMZN & GOOG & INTC & MSFT & Probability \\\\\n \\midrule\n $LB^+$ & $16,805$ & $6,611$ & $6,496$ & $739$ & $951$ & $0.01497$ \\\\\n $LS^-$ & $17,474$ & $6,993$ & $6,381$ & $869$ & $1,078$ & $0.01554$ \\\\\n $MB^+$ & $5,876$ & $1,871$ & $1,748$ & $725$ & $931$ & $0.00528$ \\\\\n $MS^-$ & $6,227$ & $2,368$ & $2,111$ & $695$ & $888$ & $0.00582$ \\\\\n $BC^-$ & $8,969$ & $5,013$ & $3,904$ & $95$ & $104$ & $0.00857$ \\\\\n $SC^+$ & $8,999$ & $4,701$ & $3,444$ & $94$ & $108$ & $0.00822$ \\\\\n $LB^0$ & $65,714$ & $56,792$ & $27,626$ & $162,421$ & $153,972$ & $0.22101$ \\\\\n $LS^0$ & $91,021$ & $61,588$ & $30,755$ & $140,761$ & $173,565$ & $0.23577$ \\\\\n $MB^0$ & $12,385$ & $3,644$ & $4,121$ & $17,939$ & $15,784$ & $0.02552$ \\\\\n $MS^0$ & $10,502$ & $3,535$ & $3,697$ & $13,123$ & $15,811$ & $0.02211$ \\\\\n $BC^0$ & $65,830$ & $56,413$ & $27,839$ & $153,903$ & $144,480$ & $0.21245$ \\\\\n $SC^0$ & $90,588$ & $60,248$ & $29,793$ & $132,675$ & $161,092$ & $0.22474$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The total number of occurrences of each LOB event type from up to ten LOB levels in the LOBSTER dataset on June 21st, 2012, along with the probability of each event occurring based on these 5 stocks.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Event-Based Limit Order Book Simulation under a Neural Hawkes Process: Application in Market-Making", "authors": ["Luca Lalor", "Anatoliy Swishchuk"], "url": "https://arxiv.org/abs/2502.17417v1", "attribution": "\"Event-Based Limit Order Book Simulation under a Neural Hawkes Process: Application in Market-Making\" by Luca Lalor and Anatoliy Swishchuk, arXiv:2502.17417v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.18352v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|lllll|}\n\\hline\n Layer type & Kernel & Stride & Outputs & Activation \\\\ \\hline\nWaveform & - & - & $1\\times 8192$& - \\\\\n Conv 1D + GN& 10 & 5 & $C\\times 1637$& gelu \\\\\nConv 1D ($\\times 4$)& 3 & 2 & $C\\times 101$& gelu \\\\\n Conv 1D ($\\times 2$)& 2 & 2 & $C\\times 25$& gelu \\\\\n LN + Linear& - & - & $C\\times 25$& - \\\\\n Conv 1D + residual & 64 & 1 & $C\\times 25$& gelu \\\\\n & & & &\\\\%\n LN + Conv 1D ($\\times 3$)& 3 & 2 & $C\\times 2$& gelu \\\\\n Conv 1D & 2 & 2 & $C$& gelu \\\\\n & & & &\\\\\n FC ($\\times 2$)& - & - & 150 & gelu \\\\\n FC & - & - & 78 &- \\\\\n Non-linearity & - & - & 78 &tanh \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Architecture details of our proposed real-time student networks. Here, GN and LN refer to group normalization and layer normalization, respectively. Our models differ in the number of channels $C$. The input size stays the same irrespective of the length of future context. }\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Tiny is not small enough: High-quality, low-resource facial animation models through hybrid knowledge distillation", "authors": ["Zhen Han", "Mattias Teye", "Derek Yadgaroff", "Judith Bütepage"], "url": "https://arxiv.org/abs/2507.18352v1", "attribution": "\"Tiny is not small enough: High-quality, low-resource facial animation models through hybrid knowledge distillation\" by Zhen Han, Mattias Teye, Derek Yadgaroff, and Judith Bütepage, arXiv:2507.18352v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.21174v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|lll}\n\\# & $\\tau_{large}$ &$\\tau_{mix}$ & $\\tau_{small}$ \\\\\\hline\n1 & 19 & $\\ddagger$ & 2 \\\\\n2 & $\\ddagger$ & $\\ddagger$ & $\\S$ \\\\\n3 & $\\ddagger$ & $\\ddagger$ & 2 \\\\\n4 & 12 & 20 & 2 \\\\\n5 & 570 & $\\ddagger$ & 4 \\\\\n6 & $\\ddagger$ & 58 & 12 \\\\\n7 & 3 & 6 & 2 \\\\\n8 & $\\ddagger$ & 1 & 1\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Number of outer fGMRES iterations for the new scheme (symmetric case) with $M$-orthogonalization}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust iterative methods for linear systems with saddle point structure", "authors": ["Murat Manguoğlu", "Volker Mehrmann"], "url": "https://arxiv.org/abs/2502.21174v1", "attribution": "\"Robust iterative methods for linear systems with saddle point structure\" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.15589v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Prediction Error for THz Channel Factors computed across 4 Channel Paths}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n Average Absolute Error & Mean & Standard Deviation\\\\\n \\hline\n AoD$~\\phi$~(deg) & 5.67 & 1.23\\\\\n AoD~$\\theta$~(deg) & 2.36 & 0.47\\\\\n AoA~$\\phi$~(deg) & 2.22 & 0.63\\\\\n AoA~$\\theta$~(deg) & 4.43 & 0.91\\\\\n Phase (deg) & 5.93 & 1.08\\\\\n ToA ($10^{-18} s$) & 3.80 & 0.67\\\\\n Pathloss ($10^{-19}$) & 5.70 & 0.35\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Learning for THz Channel Estimation and Beamforming Prediction via Sub-6GHz Channel", "authors": ["Sagnik Bhattacharya", "Abhishek K. Gupta"], "url": "https://arxiv.org/abs/2411.15589v1", "attribution": "\"Deep Learning for THz Channel Estimation and Beamforming Prediction via Sub-6GHz Channel\" by Sagnik Bhattacharya and Abhishek K. Gupta, arXiv:2411.15589v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10731v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters settings of GPOPS Version 4.1}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n Parameter & Setting \\\\\n \\midrule\n setup.autoscale & `on' \\\\\n limits(p).nodesPerInterval & 25 (p=1,...,7) \\\\\n limits(p).meshPoints & [-1,1](p=1,...,7) \\\\\n setup.mesh.iteration & 5 \\\\\n setup.derivatives & `finite-difference' \\\\\n setup.tolerances & 1e-1 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A fast-solved model for energy-efficient train control based on convex optimization", "authors": ["Minling Feng", "Kunpeng Wu", "Shaofeng Lu"], "url": "https://arxiv.org/abs/2201.10731v1", "attribution": "\"A fast-solved model for energy-efficient train control based on convex optimization\" by Minling Feng, Kunpeng Wu, and Shaofeng Lu, arXiv:2201.10731v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.11203v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{rotating}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the S-BERT similarity scores and the true positive rates (TPR) of the two watermarking schemes: green/red list and our proposed scheme for the Phi-3-mini-4k-instruct (3.8B). }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll|rr|rr|r}\n\\toprule\n\\multirow{3}{*}{\\rotatebox[origin=c]{90}{Data}} & \\multirow{3}{*}{k} & \\multirow{3}{*}{Metric} & & \\multicolumn{4}{c}{Green/red list} & \\multirow{2}{*}{Our method} \\\\\n&&&& \\multicolumn{2}{c}{$\\delta = 2$} & \\multicolumn{2}{c}{$\\delta = 1$} & \\\\\n&&&& $\\gamma = 0.25$ & $\\gamma = 0.5$ & $\\gamma = 0.25$ & $\\gamma = 0.5$ & $\\gamma = 0.5$\\\\\n\\midrule\n\\multirow{8}{*}{\\rotatebox[origin=c]{90}{FinQA}} & \\multirow{4}{*}{2} & \\multirow{2}{*}{S-BERT} & mean & 0.7887 & 0.8001 & 0.8269 & 0.8167 & 0.7963 \\\\\n & & & median & 0.8229 & 0.8404 & 0.8598 & 0.8472 & 0.8382 \\\\\n & & TPR & & 0.9700 & 0.9700 & 0.5900 & 0.6550 & 0.9750 \\\\\n & & TPR aug. & & 0.9150 & 0.8850 & 0.3250 & 0.3500 & 0.8700 \\\\\n\\cmidrule{3-9}\n & \\multirow{4}{*}{4} & \\multirow{2}{*}{S-BERT} & mean & 0.7957 & 0.7967 & 0.8305 & 0.8233 & 0.8023 \\\\\n & & & median & 0.8241 & 0.8277 & 0.8528 & 0.8494 & 0.8288 \\\\\n & & TPR & & 0.9650 & 0.9600 & 0.7850 & 0.7750 & 0.9500 \\\\\n & & TPR aug. & & 0.8750 & 0.8900 & 0.4450 & 0.5150 & 0.8300 \\\\\n\\cmidrule{2-9}\n\\multirow{8}{*}{\\rotatebox[origin=c]{90}{ELI5}} & \\multirow{4}{*}{2} & \\multirow{2}{*}{S-BERT} & mean & 0.7022 & 0.6962 & 0.7084 & 0.7178 & 0.7161 \\\\\n & & & median & 0.7175 & 0.7060 & 0.7264 & 0.7239 & 0.7290 \\\\\n & & TPR & & 1.0000 & 1.0000 & 0.7800 & 0.9100 & 0.9900 \\\\\n & & TPR aug. & & 0.9900 & 0.9850 & 0.4900 & 0.6500 & 0.9350 \\\\\n\\cmidrule{3-9}\n & \\multirow{4}{*}{4} & \\multirow{2}{*}{S-BERT} & mean & 0.7030 & 0.7161 & 0.7060 & 0.7084 & 0.7185 \\\\\n & & & median & 0.7076 & 0.7299 & 0.7214 & 0.7236 & 0.7344 \\\\\n & & TPR & & 1.0000 & 0.9950 & 0.8850 & 0.8850 & 1.0000 \\\\\n & & TPR aug. & & 0.9500 & 0.9850 & 0.5450 & 0.5750 & 0.9550 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Debiasing Watermarks for Large Language Models via Maximal Coupling", "authors": ["Yangxinyu Xie", "Xiang Li", "Tanwi Mallick", "Weijie J. Su", "Ruixun Zhang"], "url": "https://arxiv.org/abs/2411.11203v2", "attribution": "\"Debiasing Watermarks for Large Language Models via Maximal Coupling\" by Yangxinyu Xie, Xiang Li, Tanwi Mallick, Weijie J. Su, and Ruixun Zhang, arXiv:2411.11203v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16944v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccrr}\n \\toprule\n $n$ & \\textit{density} & \\textit{found} & \\textit{others} \\\\\n \\toprule\n 6 & 0.33 & 1 & 5 \\\\\n \\midrule\n 7 & 0.28 & 1 & 10 \\\\\n & 0.33 & 1 & 32 \\\\\n \\midrule\n 8 & 0.25 & 2 & 21 \\\\\n & 0.28 & 3 & 86 \\\\\n & 0.32 & 6 & 230 \\\\\n & 0.25 & 2 & 484 \\\\\n \\midrule\n 9 & 0.22 & 2 & 45 \\\\\n & 0.25 & 4 & 236 \\\\\n & 0.37 & 11 & 786 \\\\\n & 0.30 & 25 & 2,050 \\\\\n & 0.33 & 37 & 4,458 \\\\\n & 0.36 & 24 & 8,380 \\\\\n & 0.38 & 7 & 13,848 \\\\\n \\midrule\n 10 & 0.20 & 6 & 100 \\\\\n & 0.22 & 14 & 643 \\\\\n & 0.24 & 24 & 2,654 \\\\\n & 0.26 & 56 & 8,492 \\\\\n & 0.28 & 146 & 22,804 \\\\\n & 0.31 & 298 & 53,565 \\\\\n & 0.33 & 462 & 112,156 \\\\\n & 0.35 & 459 & 211,407 \\\\\n & 0.37 & 288 & 361,054 \\\\\n & 0.40 & 117 & 560,989 \\\\\n & 0.42 & 24 & 795,606 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\small Details about some results of the exhaustive search.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Burning some myths on privacy properties of social networks against active attacks", "authors": ["Serafino Cicerone", "Gabriele Di Stefano", "Sandi Klavžar", "Ismael G. Yero"], "url": "https://arxiv.org/abs/2504.16944v1", "attribution": "\"Burning some myths on privacy properties of social networks against active attacks\" by Serafino Cicerone, Gabriele Di Stefano, Sandi Klavžar, and Ismael G. Yero, arXiv:2504.16944v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2401.00292v1_tex_table27.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c|c|c|c|c|c}\n & \n\\multicolumn{ 3}{c}{$|S_U|$} & \n\\multicolumn{ 3}{c}{Time$\\,S_U (s)$}\\\\\nNo & \n$\\gamma=10$ & \n$\\gamma=30$ & \n$\\gamma=50$ & \n$\\gamma=10$ & \n$\\gamma=30$ & \n$\\gamma=50$ \\\\ \n\\hline\n1 & 8 & 20 & 31 & 519.63 (420.77) & 548.70 (400.11) & 654.78 (411.71)\\\\ \n2 & 12 & 32 & 55 & 412.71 (403.34) & 478.80 (409.01) & 521.99 (401.65)\\\\ \n3 & 12 & 32 & 52 & 444.12 (400.92) & 527.00 (404.25) & 605.74 (405.75)\\\\ \n4 & 9 & 22 & 36 & 425.02 (400.11) & 462.81 (405.86) & 515.93 (405.72)\\\\ \n5 & 5 & 12 & 20 & 325.93 (323.31) & 335.62 (324.23) & 360.17 (330.95)\\\\ \n\\end{tabular}\n\\caption{Chute2, $|S_U|$, and Time$\\,S_U (s)$ for test problem Three9.1 and $\\gamma \\in{\\{10,30,50\\}}$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems", "authors": ["Grzegorz Filcek", "Janusz Miroforidis"], "url": "https://arxiv.org/abs/2401.00292v1", "attribution": "\"A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems\" by Grzegorz Filcek and Janusz Miroforidis, arXiv:2401.00292v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23430v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The zeroth-order sharpness result at converged minima}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\\toprule\n & \\multicolumn{5}{c}{Individual domains} & \\multirow{2}{*}{Mean (Std)} & \\multirow{2}{*}{Total} & \\multirow{2}{*}{Unseen} \\\\\n & Clipart & Painting & Quickdraw & Real & Sketch & & & \\\\ \\midrule\nSAM & 1.63 & 6.22 & 7.86 & 4.89 & 3.38 & 4.79 (2.17) & 19.68 & 70.59 \\\\\nDGSAM & \\textbf{1.17} & \\textbf{2.78} & \\textbf{4.74} & \\textbf{4.39} & \\textbf{1.80} & \\textbf{2.98 (1.40)} & \\textbf{6.41} & \\textbf{42.46} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization", "authors": ["Youngjun Song", "Youngsik Hwang", "Jonghun Lee", "Heechang Lee", "Dong-Young Lim"], "url": "https://arxiv.org/abs/2503.23430v2", "attribution": "\"DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization\" by Youngjun Song, Youngsik Hwang, Jonghun Lee, Heechang Lee, and Dong-Young Lim, arXiv:2503.23430v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11315v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{NF-ToN-IoT-v2 multi-class classification results}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l|l|l|l|}\n\\cline{2-7}\n\\textbf{} & \\multicolumn{2}{c|}{\\textbf{ToN-IoT}} & \\multicolumn{2}{c|}{\\textbf{NF-ToN-IoT}} & \\multicolumn{2}{c|}{\\textbf{NF-ToN-IoT-v2}} \\\\ \\hline\n\\textbf{Class Name} & \\textbf{DR} & \\textbf{F1 Score} & \\textbf{DR} & \\textbf{F1 Score}& \\textbf{DR} & \\textbf{F1 Score} \\\\ \\hline\n{Benign} & 89.97\\% & 0.94 & 98.97\\% & 0.99 & 99.44\\% & 0.99 \\\\ \\hline\n{Backdoor} & 98.05\\% & 0.31 & 99.22\\% & 0.98 & 99.79\\% & 1.00 \\\\ \\hline\n{DDoS} & 96.90\\% & 0.98 & 63.22\\% & 0.72 & 98.76\\% & 0.99 \\\\ \\hline\n{DoS} & 53.89\\% & 0.57 & 95.91\\% & 0.48 & 89.41\\% & 0.91 \\\\ \\hline\n{Injection} & 96.67\\% & 0.96 & 41.47\\% & 0.51 & 90.14\\% & 0.91 \\\\ \\hline\n{MITM} & 66.25\\% & 0.16 & 52.81\\% & 0.38 & 37.45\\% & 0.45 \\\\ \\hline\n{Password} & 86.99\\% & 0.92 & 27.36\\% & 0.24 & 97.16\\% & 0.97 \\\\ \\hline\n{Ransomware} & 89.87\\% & 0.11 & 87.33\\% & 0.83 & 97.29\\% & 0.98 \\\\ \\hline\n{Scanning} & 75.05\\% & 0.85 & 31.30\\% & 0.08 & 99.67\\% & 1.00 \\\\ \\hline\n{XSS} & 98.83\\% & 0.99 & 24.49\\% & 0.19 & 96.83\\% & 0.96 \\\\ \\hline\n\\textbf{Weighted Average} & \\textbf{84.61\\%} & \\textbf{0.87} & \\textbf{56.34\\%} & \\textbf{0.60}& \\textbf{98.05\\%} & \\textbf{0.98} \\\\ \\hline\n\\textbf{Prediction Time (\\textmu s)} & \\multicolumn{2}{c|}{\\textbf{12.02}} & \\multicolumn{2}{c|}{\\textbf{21.21}} & \\multicolumn{2}{c|}{\\textbf{12.15}} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Towards a Standard Feature Set for Network Intrusion Detection System Datasets", "authors": ["Mohanad Sarhan", "Siamak Layeghy", "Marius Portmann"], "url": "https://arxiv.org/abs/2101.11315v2", "attribution": "\"Towards a Standard Feature Set for Network Intrusion Detection System Datasets\" by Mohanad Sarhan, Siamak Layeghy, and Marius Portmann, arXiv:2101.11315v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.08473v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{siunitx}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{DERs (\\%) of each number of speakers on the CALLHOME dataset with \\SI{10}{\\second} chunk size. \\textit{Both estimated the number of speakers and included the overlapping regions without using oracle SAD.}}\n\\begin{tabular}{lccc}\n \\toprule\n &\\multicolumn{3}{c}{Number of speakers} \\\\ \\cmidrule(l){2-4}\n Method & 2 & 3 & 4 \\\\ \\midrule\n BW-EDA-EEND & 11.8 & 18.3 & 26.0 \\\\ \n EEND-EDA w/ FLEX-STB & \\bf 10.0 & \\bf14.0 & \\bf 21.1 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers", "authors": ["Yawen Xue", "Shota Horiguchi", "Yusuke Fujita", "Yuki Takashima", "Shinji Watanabe", "Paola Garcia", "Kenji Nagamatsu"], "url": "https://arxiv.org/abs/2101.08473v2", "attribution": "\"Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers\" by Yawen Xue, Shota Horiguchi, Yusuke Fujita, Yuki Takashima, Shinji Watanabe, Paola Garcia, and Kenji Nagamatsu, arXiv:2101.08473v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11954v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|c|c|}\n\\hline\n\\bf Method & \\bf Accuracy & \\bf F1-score\\\\ \n\\hline\nSVM Model(tf-idf) & 0.939 & 0.938 \\\\ \\hline\nBagging Model(word2vec) & 0.910 & 0.910 \\\\ \nBoosting Model(word2vec) & 0.927 & 0.926 \\\\ \\hline\nroberta-base & 0.9864 & 0.9864 \\\\ \nelectra-base & 0.9827 & 0.9827 \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\caption{Results on the official test set for COVID-19 Fake news identification task for English language. The first section denotes the baseline models on tf-idf. Te second section denotes the baseline models on word2vec. The third section refers to the transformers models.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Identifying COVID-19 Fake News in Social Media", "authors": ["Tathagata Raha", "Vijayasaradhi Indurthi", "Aayush Upadhyaya", "Jeevesh Kataria", "Pramud Bommakanti", "Vikram Keswani", "Vasudeva Varma"], "url": "https://arxiv.org/abs/2101.11954v2", "attribution": "\"Identifying COVID-19 Fake News in Social Media\" by Tathagata Raha, Vijayasaradhi Indurthi, Aayush Upadhyaya, Jeevesh Kataria, Pramud Bommakanti, Vikram Keswani, and Vasudeva Varma, arXiv:2101.11954v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00443v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Evaluation results for the fine-tuned LongFormer model on the manually annotated Wikipedia dataset. A generic neural NER model was also used for comparison.} %\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc} \n\t\\toprule %\n\t & $R_{di+qi}$ & $\\textit{ER}_{di}$ & $\\textit{ER}_{qi}$ & $P_{di+qi}$ & $\\textit{WP}_{di+qi} $ \\\\\n\\midrule \n RoBERTa (fined-tuned on Ontonotes) &\t0.845 & 0.810 & 0.801 & 0.770 & 0.836\\\\\n \\midrule\nLongformer (fine-tuned on TAB) & 0.952 & 0.999 & 0.923 & 0.708 & 0.765\\\\ \n\t\\midrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization", "authors": ["Ildikó Pilán", "Pierre Lison", "Lilja Øvrelid", "Anthi Papadopoulou", "David Sánchez", "Montserrat Batet"], "url": "https://arxiv.org/abs/2202.00443v2", "attribution": "\"The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization\" by Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi Papadopoulou, David Sánchez, and Montserrat Batet, arXiv:2202.00443v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.21316v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\toprule\n $h=1$ & $h=2$ & $h=3$\\\\\n \\midrule\n $(\\,2\\,)$ & $(\\,2\\,\\,2\\,)$ & $(\\,2\\,\\,2\\,\\,2\\,)$\\\\\n $(\\,4\\,)$ & $(\\,2\\,\\,4\\,)$ & $(\\,2\\,\\,2\\,\\,4\\,)$\\\\\n $(\\,6\\,)$ & $(\\,2\\,\\,6\\,)$ & \\\\\n $(\\,8\\,)$ & $(\\,4\\,\\,4\\,)$ & \\\\\n $(\\,10\\,)$ & & \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Possible sub-signatures with $d=11$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Relative period realization of holomorphic differentials with prescribed invariants", "authors": ["Dawei Chen", "Gianluca Faraco"], "url": "https://arxiv.org/abs/2503.21316v1", "attribution": "\"Relative period realization of holomorphic differentials with prescribed invariants\" by Dawei Chen and Gianluca Faraco, arXiv:2503.21316v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.11119v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|ccc|c}\n \\hline\n Problem &\n \\multicolumn{3}{c|}{Max Poly. Degrees}&\n \\multicolumn{3}{c|}{Time (seconds)}&\n Successful\\\\\n \\cline{2-7}\n & Max & Avg & Median & Avg & Median & Total & runs \\\\\n \\hline\n \\multirow{2}{*}{\\texttt{left\\_strict}}\n & 8 & 5.8 & 6 & 0.041 & 0.036 & 3.557 & 87/100 \\\\\n & 14 & 7.16 & 6 & 0.092 & 0.057 & 9.185 & 100/100 \\\\\n \\hline\n \\multirow{2}{*}{\\texttt{right\\_strict}}\n & 8 & 5.775 & 6 & 0.041 & 0.036 & 3.287 & 81/100 \\\\\n & 8 & 6.88 & 8 & 0.064 & 0.058 & 6.411 & 100/100 \\\\\n \\hline\n \\multirow{2}{*}{\\texttt{lifted\\_prods}}\n & 8 & 5.775 & 6 & 0.007 & 0.007 & 0.548 & 74/100 \\\\\n & 8 & 6.88 & 6 & 0.064 & 0.057 & 6.387 & 100/100 \\\\\n \\hline\n \\multirow{2}{*}{\\texttt{arch\\_poly}}\n & 8 & 5.932 & 6 & 0.039 & 0.037 & 1.667 & 43/100 \\\\\n & 8 & 7.06 & 6 & 0.085 & 0.07 & 8.493 & 100/100 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Computing certificates of four kinds of strictly positive univariate polynomials}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Computing Certificates of Strictly Positive Polynomials in Archimedean Quadratic Modules", "authors": ["Weifeng Shang", "Chenqi Mou", "Jose Abel Castellanos Joo", "Deepak Kapur"], "url": "https://arxiv.org/abs/2503.11119v1", "attribution": "\"Computing Certificates of Strictly Positive Polynomials in Archimedean Quadratic Modules\" by Weifeng Shang, Chenqi Mou, Jose Abel Castellanos Joo, and Deepak Kapur, arXiv:2503.11119v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.10715v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{RMSE, MAPE, and Mean NQL by the seasonality modes for Prophet models.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n \\toprule\n Model & RMSE & MAPE & Mean NQL[0.1, 0.9] \\\\\n \\midrule\n Prophet w/ additive seasonality & \\bf 83.89$\\pm$1.98 & 0.461$\\pm$0.038 & \\bf 0.202$\\pm$0.009 \\\\\n Prophet w/ multiplicative seasonality & 97.02$\\pm$16.85 & \\bf 0.460$\\pm$0.044 & 0.222$\\pm$0.010 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting", "authors": ["Seungjae Jung", "Kyung-Min Kim", "Hanock Kwak", "Young-Jin Park"], "url": "https://arxiv.org/abs/2011.10715v1", "attribution": "\"A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting\" by Seungjae Jung, Kyung-Min Kim, Hanock Kwak, and Young-Jin Park, arXiv:2011.10715v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.13655v2_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fees in USD on Ethereum, Polygon and Solana}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n \\hline\n & Pre- & Post- \\\\ \\hline \\hline\n Ether Transactions & \\$1.9453 & \\$1.7031 \\\\\n Ethereum Tokens & \\$3.2387 & \\$2.6106\\\\ \n Polygon Matic Transactions & \\$0.0232 & \\$0.0242 \\\\ \n Polygon Tokens & \\$0.1333 & \\$0.1244 \\\\ \n Solana Tokens & \\$0.0011 & \\$0.0008\\\\ \n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "An Event Study of the Ethereum Transition to Proof-of-Stake", "authors": ["Elie Kapengut", "Bruce Mizrach"], "url": "https://arxiv.org/abs/2210.13655v2", "attribution": "\"An Event Study of the Ethereum Transition to Proof-of-Stake\" by Elie Kapengut and Bruce Mizrach, arXiv:2210.13655v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00470v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rr}\n \\toprule\n \\multirow{2}{*}{\\textbf{Language}} & \\multicolumn{2}{c}{\\textbf{Error rate level}} \\\\\n & \\textbf{Character} & \\textbf{Word} \\\\\n \\midrule\n Dutch & 0.286 & 0.536 \\\\\n English & 0.075 & 0.146 \\\\\n French & 0.064 & 0.193 \\\\\n German & 0.240 & 0.813 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{OCR error rates per language, averaged over documents.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An Assessment of the Impact of OCR Noise on Language Models", "authors": ["Konstantin Todorov", "Giovanni Colavizza"], "url": "https://arxiv.org/abs/2202.00470v1", "attribution": "\"An Assessment of the Impact of OCR Noise on Language Models\" by Konstantin Todorov and Giovanni Colavizza, arXiv:2202.00470v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.10787v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Conversion of Neural Radiology Model output to Symbolic Tree input}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\nMax Prob. & ASO & GGO \\\\ \n\\midrule\nASO & 1 & 0 \\\\ \nGGO & 0 & 1 \\\\ \nASO\\_GGO & 1 & 1 \\\\ \nNo\\_ASO\\_GGO & 0 & 0 \\\\ \nMissing\\_ASO\\_GGO & ? & ? \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays", "authors": ["Rishab Khincha", "Soundarya Krishnan", "Tirtharaj Dash", "Lovekesh Vig", "Ashwin Srinivasan"], "url": "https://arxiv.org/abs/2012.10787v2", "attribution": "\"Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays\" by Rishab Khincha, Soundarya Krishnan, Tirtharaj Dash, Lovekesh Vig, and Ashwin Srinivasan, arXiv:2012.10787v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2308.09480v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Monetary policy rules}\n\\begin{tabular}{lll}\n\\hline \\hline\n Nr.\\ & Name & Equation\\\\\\hline\\\\\n(1) & Taylor rule with smoothing & $\\tilde{i}_t = \\rho_i \\tilde{i}_{t-1} + (1-\\rho_i)\\left(\\phi_{\\pi}\\pi_t + \\phi_x \\widehat{x}_t \n\\right)$\\\\\\\\\n(2) & Taylor rule without smoothing & $\\tilde{i}_t = \\phi_{\\pi}\\pi_t $\\\\\\\\\n(3) & Optimal RE commitment policy & $\\pi_t + \\tfrac{\\Lambda}{\\kappa}\\left(\\widehat{x}_t - \\widehat{x}_{t-1}\\right) = 0$ \\\\\\\\\n(4) & Optimal RE discretionary policy & $\\pi_t + \\tfrac{\\Lambda}{\\kappa} \\widehat{x}_t = 0$ \\\\\\\\\n(5) & Strict inflation targeting & $\\pi_t = 0$ \\\\\\\\\n\\hline \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Inflation Attention Threshold and Inflation Surges", "authors": ["Oliver Pfäuti"], "url": "https://arxiv.org/abs/2308.09480v3", "attribution": "\"The Inflation Attention Threshold and Inflation Surges\" by Oliver Pfäuti, arXiv:2308.09480v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07450v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of sentences identified randomly. Total sentences in training data: 1331, out of which 207 (15.6\\%) contain discrepancies with adjudicated ground truth. Highest score per column in bold.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|lllll|}\n \\hline\n \\textbf{Threshold} & \\textbf{Sentences to check} & \\textbf{Precision}& \\textbf{Recall}&\\textbf{F-score}\\\\\n&&&&\\textbf{(Std Dev)}\\\\\n\\hline\n100&7.5\\% & \\textbf{16.0} & 8.6 & 11.2 (1.6)\\\\\n200& 15.0\\% & 14.0 & 15.5 & 14.7 (2.2)\\\\\n500& 37.6\\% & 15.9 & \\textbf{41.0} & \\textbf{22.8} (1.6)\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Single versus Multiple Annotation for Named Entity Recognition of Mutations", "authors": ["David Martinez Iraola", "Antonio Jimeno Yepes"], "url": "https://arxiv.org/abs/2101.07450v1", "attribution": "\"Single versus Multiple Annotation for Named Entity Recognition of Mutations\" by David Martinez Iraola and Antonio Jimeno Yepes, arXiv:2101.07450v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.17943v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean annotation time across all datasets with different prompt numbers.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{Time (secs)} & \\textbf{MRI Ovarian} & \\textbf{MRI Renal} & \\textbf{CT Lung} & \\textbf{US Breast} \\\\ \n\\hline\n\\textbf{Radiologist} & 237.0 ± 72.0 & 150.6 ± 68.4 & 286.8 ± 109.2 & 12.6 ± 5.4 \\\\\n\\hline\n\\textbf{RL Agent} & 21.8 ± 1.8 & 10.8 ± 9.2 & 29.0 ± 4.8 & 1.4 ± 1.2 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities", "authors": ["Yuli Wang", "Victoria Shi", "Wen-Chi Hsu", "Yuwei Dai", "Sophie Yao", "Zhusi Zhong", "Zishu Zhang", "Jing Wu", "Aaron Maxwell", "Scott Collins", "Zhicheng Jiao", "Harrison X. Bai"], "url": "https://arxiv.org/abs/2412.17943v2", "attribution": "\"Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities\" by Yuli Wang, Victoria Shi, Wen-Chi Hsu, Yuwei Dai, Sophie Yao, Zhusi Zhong, Zishu Zhang, Jing Wu, Aaron Maxwell, Scott Collins, Zhicheng Jiao, and Harrison X. Bai, arXiv:2412.17943v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18035v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\t\t\\hline\n\t\tParameter & Definition & Dimension \\\\ \n\t\t\\hline\n\t\tT & Time & time\\\\ \n\t\tN & Prey density & biomass \\\\\n\t\tP & Predator density & biomass \\\\\n\t\tA & Additional food & biomass \\\\\n\t\tr & Prey intrinsic growth rate & time$^{-1}$ \\\\\n\t\tK & Prey carrying capacity & biomass \\\\\n\t\tc & Rate of predation & time$^{-1}$ \\\\\n\t\ta & Half Saturation value of the predators & biomass \\\\\n\t\t$\\delta$ & Conversion efficiency & time$^{-1}$ \\\\\n\t\tm & death rate of predators in absence of prey & time$^{-1}$ \\\\\n\t\td & Predator Intra-specific competition & biomass$^{-1}$ time$^{-1}$ \\\\\n\t\t$\\alpha$ & quality of additional food & Dimensionless \\\\\n\t\t$\\xi$ & quantity of additional food & biomass$^{2}$ \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Description of variables and parameters present in the systems () and ().}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Dynamics and Control of Additional Food Provided Prey-Predator Systems exhibiting Holling Type-III Functional Response and Intra-specific Competition among Predators", "authors": ["D Bhanu Prakash", "D K K Vamsi"], "url": "https://arxiv.org/abs/2504.18035v1", "attribution": "\"Dynamics and Control of Additional Food Provided Prey-Predator Systems exhibiting Holling Type-III Functional Response and Intra-specific Competition among Predators\" by D Bhanu Prakash and D K K Vamsi, arXiv:2504.18035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.12118v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Shape of Data through the TAES Model}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{clccc}\n\\toprule\n\\textbf{Layer} & \\textbf{Type} & \\textbf{Input size} & \\textbf{Output Size} & \\textbf{Feature Num} \\\\ \\midrule\n1 & Transformer & batch size*360*1 & batch size*360 & 360 \\\\\n2 & Conv1 & batch size*360 & batch size*252 & 252 \\\\\n3 & Conv2 & batch size*252 & batch size*128 & 128 \\\\\n4 & Conv3 & batch size*128 & batch size*72 & 72 \\\\\n5 & Conv4 & batch size*72 & batch size*56 & 56 \\\\ \n6 & Conv5 & batch size*56 & batch size*32 & 32 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "High-Throughput Detection of Risk Factors to Sudden Cardiac Arrest in Youth Athletes: A Smartwatch-Based Screening Platform", "authors": ["Evan Xiang", "Thomas Wang", "Vivan Poddar"], "url": "https://arxiv.org/abs/2412.12118v1", "attribution": "\"High-Throughput Detection of Risk Factors to Sudden Cardiac Arrest in Youth Athletes: A Smartwatch-Based Screening Platform\" by Evan Xiang, Thomas Wang, and Vivan Poddar, arXiv:2412.12118v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06896v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Detailed information of the apps that were successfully attacked. App names are omitted for security.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\toprule\n Category & Downloads & App description \\\\\n \\midrule\nFinance & 100,000,000+ & payment app \\\\\nFinance & 10,000,000+ & personal finance app \\\\\nFinance & 10,000,000+ & financial service app \\\\\nPhotography & 5,000,000+ & camera with blur effects \\\\\nPhotography & 5,000,000+ & photo filter for sky \\\\\nEntertainment & 5,000,000+ & palm reader, fun photo editor \\\\\nFinance &1,000,000+ & credit card service \\\\\nEntertainment & 1,000,000+ & piloting app for drones \\\\\nPhotography & 1,000,000+ & photo \\& art word editor \\\\\nPhotography & 1,000,000+ & photo editing app \\\\\nFinance & 1,000,000+ & financial mobile service app \\\\\nPhotography & 1,000,000+ & photo beauty camera \\\\\nTools & 500,000+ & parental control app for child phone \\\\\nLifestyle & 500,000+ & photo editor \\\\\nBusiness & 100,000+ & document scanner \\\\\nProductivity & 100,000+ & document scanner and translator \\\\\nEducation & 100,000+ & helper for career fairs \\\\\nEntertainment & 100,000+ & AI guess drawing game \\\\\nArcade & 100,000+ & AI guess drawing game \\\\\nFinance & 100,000+ & bank app \\\\\nBusiness & 100,000+ & internal app access control tool \\\\\nLibraries\\&Demo & 50,000+ & face recognition demo \\\\\nEducation & 50,000+ & insect study app \\\\\nEntertainment & 10,000+ & camera frame classifier \\\\\nLibraries\\&Demo & 10,000+ & object detection demo \\\\\nEducation & 10,000+ & drawing teaching app \\\\\nMusic\\&Audio & 5,000+ & record scanner and detector \\\\\nHealth\\&Fitness & 5,000+ & skin cancer detection \\\\\nLibraries\\&Demo & 5,000+ & object detector demo \\\\\nLibraries\\&Demo & 5,000+ & camera frame classifier \\\\\nLibraries\\&Demo &1,000+ & demo app for a mobile AI SDK \\\\\nMedical & 1,000+ & confirm medication ingestion \\\\\nTools & 1,000+ & attendance checker \\\\\nLibraries\\&Demo & 1,000+ & machine learning kit demo \\\\\nLibraries\\&Demo & 1,000+ & image classifier demo \\\\\nLibraries\\&Demo & 1,000+ & flower image classifier \\\\\nAuto\\&Vehicles & 1,000+ & traffic sign detector \\\\\nTools & 1,000+ & sneaker classifier \\\\\nEducation & 500+ & object detection demo \\\\\nTools & 500+ & machine learning benchmark tool \\\\\nEducation & 500+ & hand-written digit recognition demo \\\\\nAuto\\&Vehicles & 500+ & car image classifier \\\\\nMedical & 500+ & screening app for diabetic retinopathy \\\\\nMedical & 100+ & histology classifier \\\\\nLibraries\\&Demo & 100+ & image classifier demo \\\\\nEducation & 100+ & accessibility tool for visually impaired \\\\\nEducation & 100+ & FTC game robot detection demo \\\\\nHealth\\&Fitness & 100+ & feces image classifier \\\\\nProductivity & 100+ & image classifier demo \\\\\nTools & 100+ & mobile image classification \\\\\nFinance & 100+ & tax rate retriever for goods \\\\\nTools & 100+ & cash recognizer for visually impaired \\\\\nTools & 50+ & camera frame classifier \\\\\nMedical & 10+ & diagnostics of dermatoscopic images \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection", "authors": ["Yuanchun Li", "Jiayi Hua", "Haoyu Wang", "Chunyang Chen", "Yunxin Liu"], "url": "https://arxiv.org/abs/2101.06896v1", "attribution": "\"DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection\" by Yuanchun Li, Jiayi Hua, Haoyu Wang, Chunyang Chen, and Yunxin Liu, arXiv:2101.06896v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.06305v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\t\t\\toprule\n\t\t& True value & Estimation $T=5000$ & Quadratic error \\\\ \\midrule\n\t\t$b(1)$& 6& 6.0027876 & 0.000008 \\\\ \\midrule\n\t\t$b(2)$& 3& 2.9989096 & 0.000001 \\\\ \\midrule\n\t\t$\\lambda$& 2& 2.4268411 & 0.18219 \\\\ \\midrule\n\t\t$\\delta$& 1& 0.6528876 & 0.12049 \\\\ \\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Estimation of $\\theta$ for $T = 5000$, $h=0.1$, $\\epsilon=0.03$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Quasi-likelihood-based EM algorithm for regime-switching SDE", "authors": ["Yuzhong Cheng", "Hiroki Masuda"], "url": "https://arxiv.org/abs/2412.06305v1", "attribution": "\"Quasi-likelihood-based EM algorithm for regime-switching SDE\" by Yuzhong Cheng and Hiroki Masuda, arXiv:2412.06305v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.13287v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\hline\n & \\textbf{Axial} & \\textbf{Coronal} & \\textbf{Sagittal} & \\textbf{Average} \\\\ \\hline\n\\textbf{PSNR (dB)} & 32.4796 & 31.8621 & 33.0792 & 32.4736 \\\\ \n\\textbf{SSIM} & 0.9664 & 0.9618 & 0.9746 & 0.9676 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{SSIM and PSNR metrics for each axis and overall average. The model converged after approximately \\textbf{25.9} minutes of training.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "InnerGS: Internal Scenes Rendering via Factorized 3D Gaussian Splatting", "authors": ["Shuxin Liang", "Yihan Xiao", "Wenlu Tang"], "url": "https://arxiv.org/abs/2508.13287v1", "attribution": "\"InnerGS: Internal Scenes Rendering via Factorized 3D Gaussian Splatting\" by Shuxin Liang, Yihan Xiao, and Wenlu Tang, arXiv:2508.13287v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06116v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of the HSI datasets used for experimental evaluation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l||c||c||c||c|c} \\hline \n --- & \\textbf{IP} & \\textbf{PU} & \\textbf{KSC} & \\textbf{UH} & \\textbf{UT}\\\\ \\hline \n \\textbf{Year} & 1992 & 2001 & 1996 & 2013 & \\\\\n \\textbf{Source} & AVIRIS & ROSIS-03 & AVIRIS & CASI & AISA \\\\\n \\textbf{Spatial} & $145\\times 145$ & $610 \\times 610$ & $512\\times 614$ & $340\\times 1905$ & $600 \\times 166$ \\\\\n \\textbf{Spectral} & 220 & 115 & 176 & 144 & 63 \\\\\n \\textbf{Wavelength} & $400-2500$ & $430-860$ & $400-2500$ & $0.35-1.05$ & $0.42-0.99$ \\\\\n \\textbf{Samples} & 21025 & 207400 & 314368 & 1329690 & 199200 \\\\\n \\textbf{Classes} & 16 & 9 & 13 & 15 & 6 \\\\\n \\textbf{Sensor} & Aerial & Aerial & Aerial & Aerial & Aerial \\\\\n \\textbf{Resolution} & $20~m$ & $1.3~m$ & $10~nm$ & $2.5~mpp$ & $1~mpp$\\\\ \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects", "authors": ["Muhammad Ahmad", "Sidrah Shabbir", "Swalpa Kumar Roy", "Danfeng Hong", "Xin Wu", "Jing Yao", "Adil Mehmood Khan", "Manuel Mazzara", "Salvatore Distefano", "Jocelyn Chanussot"], "url": "https://arxiv.org/abs/2101.06116v3", "attribution": "\"Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects\" by Muhammad Ahmad, Sidrah Shabbir, Swalpa Kumar Roy, Danfeng Hong, Xin Wu, Jing Yao, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, and Jocelyn Chanussot, arXiv:2101.06116v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03272v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|llll}\n\\cline{1-3}\n\\multirow{2}{*}{Method} & \\multicolumn{2}{c}{Model} & & \\\\ \\cline{2-3}\n & EfficientNet & Xception & & \\\\ \\cline{1-3}\nClean image & 97\\% & 93\\% & & \\\\\nPGD $L_{inf} (\\epsilon=0.3)$ & \\textbf{0\\%} & 5\\% & & \\\\\nPGD $L_2 (\\epsilon=0.3)$ & 60\\% & 63\\% & & \\\\\nFGSM $L_{inf}$ $(\\epsilon=0.3)$ & 13\\% & 5\\% & & \\\\ \nNoise(ours) & \\textbf{0\\%} & \\textbf{0\\%} & & \\\\\nLatent(ours) & \\textbf{0\\%} & \\textbf{0\\%} & & \\\\\nNoise and latent(ours) & \\textbf{0\\%} & \\textbf{0\\%} & & \\\\\\cline{1-3}\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Accuracy different models perform on our method and other adversarial attack method, PGD $L_2$, PGD $L_{inf}$ and FGSM attack. our method has the same ability to bypass the forensic detectors as norm-based adversarial attack, PGD $L_{inf}$ and has better adversarial strength than FGSM and PGD $L_2$ attack. PGD $L_2$ attack shows poor performance on both models because of the limited perturbation scale.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Exploring Adversarial Fake Images on Face Manifold", "authors": ["Dongze Li", "Wei Wang", "Hongxing Fan", "Jing Dong"], "url": "https://arxiv.org/abs/2101.03272v2", "attribution": "\"Exploring Adversarial Fake Images on Face Manifold\" by Dongze Li, Wei Wang, Hongxing Fan, and Jing Dong, arXiv:2101.03272v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01006v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistic and comparisons of the baselines and VQShape. The best case is marked with bold, the second best is marked with italic, and the third best is marked with underline. Some baselines fail are incompatible with some datasets which result in ``N/A''. For fair comparison, we report the statistics with and without ``N/A''. Complete results are presented in Table .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccccccc}\n\\toprule\n & \\multicolumn{2}{c}{Classical} & \\multicolumn{5}{c}{Supervised} & \\multicolumn{4}{c}{Unsupervised Representation} & \\multicolumn{3}{c}{Pre-trained}\\\\\n \\cmidrule(lr){2-3} \\cmidrule(lr){4-8} \\cmidrule(lr){9-12} \\cmidrule(lr){13-15}\n & DTW & STRF & DLinear & Autoformer & FEDformer & PatchTST & TimesNet & TS-TCC & TST & T-Rep & TS2Vec & MOMENT & UniTS & VQShape \\\\\n\\midrule\n\\textbf{Statistics with N/A} \\\\\nMean Accuracy & 0.648 & 0.660 & 0.635 & 0.570 & 0.612 & 0.669 & 0.710 & 0.682 & 0.630 & \\textit{0.719} & \\underline{0.712} & 0.686 & 0.629 & \\textbf{0.723} \\\\\nMedian Accuracy & 0.711 & 0.679 & 0.673 & 0.553 & 0.586 & 0.756 & 0.797 & 0.753 & 0.620 & \\underline{0.804} & \\textbf{0.812} & 0.759 & 0.684 & \\textit{0.810} \\\\\nMean Rank & 7.138 & 7.828 & 8.690 & 9.448 & 7.750 & 8.296 & \\textit{5.143} & 7.172 & 8.448 & \\underline{5.207} & \\textbf{4.897} & 5.929 & 9.828 & 5.621 \\\\\nMedian Rank & 7.0 & 8.0 & 9.0 & 10.0 & 8.0 & 8.0 & \\underline{5.0} & 8.0 & 9.0 & \\textit{4.0} & \\textbf{3.0} & 5.5 & 10.0 & \\underline{5.0} \\\\\nNum. Top-1 & 3 & 2 & 1 & 0 & 1 & 0 & 1 & 3 & 1 & 4 & \\textbf{6} & \\underline{5} & 0 & \\textbf{6} \\\\\nNum. Top-3 & 8 & 5 & 5 & 0 & 8 & 2 & 8 & 5 & 4 & \\textit{12} & \\textbf{16} & \\underline{11} & 0 & 9 \\\\\nNum. Win/Tie & 14 & 20 & 22 & 25 & 19 & 20 & 14 & 18 & 20 & 15 & 13 & 13 & 25 & - \\\\\nNum. Lose & 15 & 9 & 7 & 4 & 9 & 7 & 14 & 11 & 9 & 14 & 16 & 15 & 4 & - \\\\\nWilcoxon p-value & 0.206 & 0.023 & 0.002 & 0.000 & 0.051 & 0.000 & 0.898 & 0.156 & 0.022 & 0.536 & 0.576 & 0.733 & 0.000 & - \\\\\n\\midrule\n\\textbf{Statistics without N/A} \\\\\nMean Accuracy & 0.642 & 0.658 & 0.635 & 0.561 & 0.601 & 0.657 & 0.703 & 0.669 & 0.623 & \\textit{0.710} & \\underline{0.704} & 0.688 & 0.618 & \\textbf{0.720} \\\\\nMedian Accuracy & 0.714 & 0.712 & 0.690 & 0.552 & 0.585 & 0.739 & \\underline{0.797} & 0.752 & 0.638 & \\textit{0.802} & 0.748 & 0.759 & 0.679 & \\textbf{0.812} \\\\\nMean Rank & 7.231 & 7.692 & 8.923 & 9.462 & 7.731 & 8.346 & \\underline{5.308} & 7.538 & 8.462 & \\textit{5.192} & \\textbf{5.038} & 5.885 & 10.115 & 5.538 \\\\\nMedian Rank & 6.5 & 7.5 & 9.5 & 9.5 & 8.0 & 8.5 & 5.000 & 8.0 & 9.5 & \\textit{4.5} & \\textbf{3.0} & 5.5 & 10.5 & \\textit{4.5} \\\\\nNum. Top-1 & 3 & 2 & 1 & 0 & 1 & 0 & 1 & 3 & 1 & 3 & \\textbf{5} & \\textbf{5} & 0 & \\textbf{5} \\\\\nNum. Top-3 & 7 & 5 & 5 & 0 & 8 & 2 & 7 & 4 & 4 & \\textit{10} & \\textbf{14} & \\textit{10} & 0 & 8 \\\\\nNum. Win/Tie & 13 & 18 & 20 & 22 & 17 & 20 & 13 & 17 & 19 & 14 & 12 & 12 & 23 & - \\\\\nNum. Lose & 13 & 8 & 6 & 4 & 9 & 6 & 13 & 9 & 7 & 12 & 14 & 14 & 3 & - \\\\\nWilcoxon p-value & 0.187 & 0.036 & 0.001 & 0.001 & 0.111 & 0.001 & 0.803 & 0.094 & 0.036 & 0.653 & 0.696 & 1.000 & 0.000 & - \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification", "authors": ["Yunshi Wen", "Tengfei Ma", "Tsui-Wei Weng", "Lam M. Nguyen", "Anak Agung Julius"], "url": "https://arxiv.org/abs/2411.01006v3", "attribution": "\"Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification\" by Yunshi Wen, Tengfei Ma, Tsui-Wei Weng, Lam M. Nguyen, and Anak Agung Julius, arXiv:2411.01006v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05866v4_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Silhouette scores for the clustering based on XPER values and features }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Number of Clusters & XPER clustering & Feature clustering \\\\\n \\midrule\n 2 & 0.2037 & 0.2129 \\\\\n 3 & -0.0244 & 0.1846 \\\\\n 4 & -0.0608 & 0.1718 \\\\\n 5 & -0.1858 & 0.1609 \\\\\n 6 & -0.0624 & 0.1589 \\\\\n 7 & -0.0734 & 0.1595 \\\\\n 8 & -0.0596 & 0.1501 \\\\\n 9 & -0.1027 & 0.1514 \\\\\n 10 & -0.0665 & 0.1471 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring", "authors": ["Hué Sullivan", "Hurlin Christophe", "Pérignon Christophe", "Saurin Sébastien"], "url": "https://arxiv.org/abs/2212.05866v4", "attribution": "\"Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring\" by Hué Sullivan, Hurlin Christophe, Pérignon Christophe, and Saurin Sébastien, arXiv:2212.05866v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.03222v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ccc|c|}\n\t\t\\hline\n\t\t& Poisson & Gamma & CPG & Tweedie \\\\ \n\t\t\\hline \n\t\tSurcharge per Claim & 0.324 & 0.054 & 0.395 & 0.401 \\\\ \n\t\tClaims Free Discount & -0.089 & -0.026 & -0.113 & -0.106 \\\\ \n\t\tMin. \\& Max. Relativities & [0.626, 1.753] & [0.855, 1.000] & [0.535, 1.753] & [0.570, 1.568] \\\\ \n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Impacts of past claims for all BMS Models}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bonus-Malus scale premiums for tweedie's compound poisson models", "authors": ["Jean-Philippe Boucher", "Raïssa Coulibaly"], "url": "https://arxiv.org/abs/2311.03222v1", "attribution": "\"Bonus-Malus scale premiums for tweedie's compound poisson models\" by Jean-Philippe Boucher and Raïssa Coulibaly, arXiv:2311.03222v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.00546v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{xcolor}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textcolor{black}{Simulation parameters}}\n\\begin{tabular}{l|l}\n\\hline\n\\textbf{Parameters} & \\textbf{Values} \\\\\n\\hline\n\\hline\nCarrier frequency, $f_c$ & 2 GHz \\\\ \n\\hline\nBandwidth, $B$ & 10 MHz \\\\\n\\hline\nLength of coherence block, $\\tau_c$ & 100\\\\\n\\hline\nCoherence bandwidth, $B_c$ & 100 KHz\\\\\n\\hline\nCoherence time, $T_c$ & 1 ms\\\\\n\\hline\nPower of pilots, $p$ & 0.2 W\\\\\n\\hline\nMaximum transmitted power of symbols, $\\rho$ & 1 W\\\\\n\\hline\nSymbol duration, $T_s$ & 10 $\\mu s$\\\\\n\\hline\nOscillator constants, $c_i,\\forall i$ & $1\\times 10^{-18}$\\\\\n\\hline\nSpacings of RIS elements, $d_{\\text{RIS}}$ & $\\lambda/4$\\\\\n\\hline\nSpacings of AP elements, $d_{\\text{AP}}$ & $\\lambda/2$\\\\\n\\hline\nHeight of UEs & $1.5$ m\\\\\n\\hline\nHeight of RIS & $30$ m\\\\\n\\hline\nHeight of APs & $12.5$ m\\\\\n\\hline\nMax inertia weight, $\\omega_\\text{max}$ & $0.9$\\\\\n\\hline\nMin inertia weight, $\\omega_\\text{min}$ & $0.4$\\\\\n\\hline\nNumber of UEs in the reflection space, $K_R$ & $3$\\\\\n\\hline\nNumber of UEs in the reflection space, $K_T$ & $3$\\\\\n\\hline\nNumber of antennas with each AP, $L$ & $4$\\\\\n\\hline\nHorizontal width of each RIS element, $d_H$ & $\\lambda/4$\\\\\n\\hline\nVertical height of each RIS element, $d_V$ & $\\lambda/4$\\\\\n\\hline\nNumber of pilots, $\\tau_p$ & $3$\\\\\n\\hline\nTolerance parameter of bisection algorithm, $\\epsilon_\\text{bi}$ & $0.01$\\\\\n\\hline\nMax number of APSO iterations, $T_P$ & $100$\\\\\n\\hline\nNumber of APSO particles, $L_P$ & $10$\\\\\n\\hline\nRIS phase error parameter, $\\vartheta$ & $3$\\\\\n\\hline\nNumber of APs, $M$, & $16$\\\\\n\\hline\nNumber of RIS elements, $N$, & $16$\\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Performance Analysis and Optimization of STAR-RIS-Aided Cell-Free Massive MIMO Systems Relying on Imperfect Hardware", "authors": ["Zeping Sui", "Hien Quoc Ngo", "Michail Matthaiou", "Lajos Hanzo"], "url": "https://arxiv.org/abs/2501.00546v2", "attribution": "\"Performance Analysis and Optimization of STAR-RIS-Aided Cell-Free Massive MIMO Systems Relying on Imperfect Hardware\" by Zeping Sui, Hien Quoc Ngo, Michail Matthaiou, and Lajos Hanzo, arXiv:2501.00546v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.09106v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance Metrics for Community}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|}\n \\hline\n \\textbf{Simulation Type} & \\textbf{$TRM_{h}$} & \\textbf{$LRM_{cri}$} & \\textbf{$LRM_o$} & \\textbf{$LGR$} \\\\ \\hline\n Off-Grid BL (WACSC) & 0.31 & 0.19 & 0.1 & 1 \\\\ \\hline\n Off-Grid BL (WOACSC) & 0.54 & 0.27 & 0.14 & 1 \\\\ \\hline\n Off-Grid RB (WACSC) & 0.77 & 0.56 & 0.27 & 1 \\\\ \\hline\n Off-Grid RB (WOACSC) & 0.78 & 0.52 & 0.25 & 1 \\\\ \\hline\n On-Grid BL (WOACSC) & 1 & 1 & 1 & 0.76 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Smart Residential Community Simulator for Developing and Benchmarking Energy Management Systems", "authors": ["Ninad Gaikwad", "Anamika Dubey"], "url": "https://arxiv.org/abs/2508.09106v1", "attribution": "\"Smart Residential Community Simulator for Developing and Benchmarking Energy Management Systems\" by Ninad Gaikwad and Anamika Dubey, arXiv:2508.09106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08239v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The two domains we experiment with LaMDA for domain grounding}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\toprule\n Name & Domain & Role \\\\\n \\midrule\n Everest & Education & It teaches facts about Mount Everest, while pretending to be Mount Everest itself. \\\\\n Music & Recommendation & It is a music recommendation agent. \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "LaMDA: Language Models for Dialog Applications", "authors": ["Romal Thoppilan", "Daniel De Freitas", "Jamie Hall", "Noam Shazeer", "Apoorv Kulshreshtha", "Heng-Tze Cheng", "Alicia Jin", "Taylor Bos", "Leslie Baker", "Yu Du", "YaGuang Li", "Hongrae Lee", "Huaixiu Steven Zheng", "Amin Ghafouri", "Marcelo Menegali", "Yanping Huang", "Maxim Krikun", "Dmitry Lepikhin", "James Qin", "Dehao Chen", "Yuanzhong Xu", "Zhifeng Chen", "Adam Roberts", "Maarten Bosma", "Vincent Zhao", "Yanqi Zhou", "Chung-Ching Chang", "Igor Krivokon", "Will Rusch", "Marc Pickett", "Pranesh Srinivasan", "Laichee Man", "Kathleen Meier-Hellstern", "Meredith Ringel Morris", "Tulsee Doshi", "Renelito Delos Santos", "Toju Duke", "Johnny Soraker", "Ben Zevenbergen", "Vinodkumar Prabhakaran", "Mark Diaz", "Ben Hutchinson", "Kristen Olson", "Alejandra Molina", "Erin Hoffman-John", "Josh Lee", "Lora Aroyo", "Ravi Rajakumar", "Alena Butryna", "Matthew Lamm", "Viktoriya Kuzmina", "Joe Fenton", "Aaron Cohen", "Rachel Bernstein", "Ray Kurzweil", "Blaise Aguera-Arcas", "Claire Cui", "Marian Croak", "Ed Chi", "Quoc Le"], "url": "https://arxiv.org/abs/2201.08239v3", "attribution": "\"LaMDA: Language Models for Dialog Applications\" by Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Vincent Zhao, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Pranesh Srinivasan, Laichee Man, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, and Quoc Le, arXiv:2201.08239v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.00238v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{An Example of a Table}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|}\n\\hline\nOne & Two\\\\\n\\hline\nThree & Four\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Time-Reversal Control Synthesis for Steering the State of Stochastic Systems", "authors": ["Yuhang Mei", "Amirhossein Taghvaei", "Ali Pakniyat"], "url": "https://arxiv.org/abs/2504.00238v2", "attribution": "\"A Time-Reversal Control Synthesis for Steering the State of Stochastic Systems\" by Yuhang Mei, Amirhossein Taghvaei, and Ali Pakniyat, arXiv:2504.00238v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.05595v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of Models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{Model} & \\textbf{PICP} & \\textbf{CWC} \\\\ \\hline\nquantile Gradient Boosting Regression & 0.144 & 24.923 \\\\ \\hline\nquantile linear regression & 0.541 & 14.034 \\\\ \\hline\nquantile LSTM & 0.549 & 14.085 \\\\ \\hline\nquantile TCN & 1.0 & 20.609 \\\\ \\hline\nquantile-attention-LSTM & 0.998 & 7.246 \\\\ \\hline\n\\textbf{FuturQuant Transformer} & \\textbf{0.962} & \\textbf{3.175} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Trading Under Uncertainty: A Distribution-Based Strategy for Futures Markets Using FutureQuant Transformer", "authors": ["Wenhao Guo", "Yuda Wang", "Zeqiao Huang", "Changjiang Zhang", "Shumin ma"], "url": "https://arxiv.org/abs/2505.05595v1", "attribution": "\"Trading Under Uncertainty: A Distribution-Based Strategy for Futures Markets Using FutureQuant Transformer\" by Wenhao Guo, Yuda Wang, Zeqiao Huang, Changjiang Zhang, and Shumin ma, arXiv:2505.05595v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06580v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Errors on Estimated Traffic Density and Model Parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\toprule\n \\toprule\n $m$ & \n $\\hat{\\rho}(t,x;\\theta^*)$ & \n $\\delta^*$(\\%) & \n $p^*$(\\%) &\n $\\sigma^*$(\\%) &\n $\\rho^*_{max}$(\\%) &\n $\\epsilon^*$(\\%)\\\\\n \\midrule\n 2 & 1.2040$\\times 10^{0}$ & 58.19 & 153.72 & $>$1000 & $>$1000 & 99.98\\\\ \n 3 & 7.550$\\times 10^{-1}$ & 54.15 & 124.52 & $>$1000 & $>$1000 & 99.95\\\\\n 4 & 1.004$\\times 10^{-1}$ & 59.07 & 72.63 & 381.31 & 14.60 & 6.72\\\\\n \\hdashline\n 5 & 3.186$\\times 10^{-2}$ & 2.75 & 4.03 & 6.97 & 0.29 & 3.00\\\\\n 6 & 1.125$\\times 10^{-2}$ & 0.69 & 2.49 & 2.26 & 0.49 & 7.56\\\\\n 8 & 7.619$\\times 10^{-3}$ & 1.03 & 2.43 & 3.60 & 0.30 & 7.85\\\\\n 12 & 5.975$\\times 10^{-3}$ & 1.83 & 1.65 & 1.24 & 0.82 & 5.70\\\\\n \\bottomrule\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Physics-Informed Deep Learning for Traffic State Estimation", "authors": ["Rongye Shi", "Zhaobin Mo", "Kuang Huang", "Xuan Di", "Qiang Du"], "url": "https://arxiv.org/abs/2101.06580v1", "attribution": "\"Physics-Informed Deep Learning for Traffic State Estimation\" by Rongye Shi, Zhaobin Mo, Kuang Huang, Xuan Di, and Qiang Du, arXiv:2101.06580v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.05584v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimated coefficients of boosted linear models (GB-LM) and boosted negative binomial regression (GB-NB) on respective train data sets. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll} \n\\\\ \\hline\n\\hline \\\\\n\\textbf{Variable} & \\textbf{GB-LM} & \\textbf{GB-NB} \\\\ \n\\hline \\\\\nClinical & 0.128 & 0.154 \\\\ \nResearch & 0.113 & 0.180 \\\\ \nH Score & -0.303 & \\\\ \nA Score & -0.230 & -0.078 \\\\ \nC Score & 0.321 & 0.128 \\\\ \nTitle & -0.036 & -0.057 \\\\ \nTriangle ACH & 0.192 & 0.103 \\\\ \nTriangle C & -0.056 & -0.039 \\\\ \nTriangle H & 0.039 & -0.032 \\\\ \nTriangle Outside & 0.159 & 0.066 \\\\ \nReferences & 0.707 & 0.566\\\\ \nAge & -0.504 & -0.243 \\\\ \nMeSH & 0.128 & 0.112 \\\\ \nAccess & 0.065 & 0.256 \\\\ \nLanguage 2 & -0.807 & -0.568 \\\\ \nLanguage 3 & -1.369 & -1.885 \\\\ \nLength & 0.063 & 0.143 \\\\ \n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Mitigating Consequences of Prestige in Citations of Publications", "authors": ["Michael Balzer", "Adhen Benlahlou"], "url": "https://arxiv.org/abs/2411.05584v2", "attribution": "\"Mitigating Consequences of Prestige in Citations of Publications\" by Michael Balzer and Adhen Benlahlou, arXiv:2411.05584v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.09475v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Counts of images in each dataset with a positive label for the pathology listed.}\n\\begin{tabular}{lrrrrrr}\n\\toprule\nDataset & Atelectasis & Cardiomegaly & Effusion & Lung Opacity & Mass & Pneumothorax \\\\\n\\midrule\nNIH & 5728 & 1563 & 6589 & 0 & 3567 & 3407 \\\\\nPC & 3981 & 8420 & 3342 & 0 & 806 & 223 \\\\\nRSNA & 0 & 0 & 0 & 1348 & 0 & 0 \\\\\nSIIM & 0 & 0 & 0 & 0 & 0 & 3576 \\\\\nMIMIC\\_CH & 10076 & 9831 & 12064 & 13825 & 0 & 2350 \\\\\nCheX & 3195 & 2909 & 8078 & 9736 & 0 & 1802 \\\\\nGoogle & 0 & 0 & 0 & 221 & 0 & 46 \\\\\nOpenI & 271 & 185 & 120 & 327 & 6 & 14 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays", "authors": ["Joseph Paul Cohen", "Rupert Brooks", "Sovann En", "Evan Zucker", "Anuj Pareek", "Matthew P. Lungren", "Akshay Chaudhari"], "url": "https://arxiv.org/abs/2102.09475v2", "attribution": "\"Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays\" by Joseph Paul Cohen, Rupert Brooks, Sovann En, Evan Zucker, Anuj Pareek, Matthew P. Lungren, and Akshay Chaudhari, arXiv:2102.09475v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.07770v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ccccc|}\n\\hline \n\\multirow{2}{*}{\\textbf{Political position}} & \\multicolumn{5}{c|}{\\textbf{Share in...}}\\tabularnewline\n & \\textbf{2004} & \\textbf{2008} & \\textbf{2012} & \\textbf{2016} & \\textbf{2020}\\tabularnewline\n\\hline \n\\textbf{0 (Left)} & 1.169\\% & 1.641\\% & 1.790\\% & 2.734\\% & 4.469\\%\\tabularnewline\n\\textbf{1} & 2.682\\% & 2.478\\% & 1.769\\% & 2.623\\% & 2.784\\%\\tabularnewline\n\\textbf{2} & 4.478\\% & 3.880\\% & 4.359\\% & 4.845\\% & 5.825\\%\\tabularnewline\n\\textbf{3} & 6.211\\% & 5.126\\% & 5.405\\% & 6.007\\% & 7.018\\%\\tabularnewline\n\\textbf{4} & 7.316\\% & 6.516\\% & 6.802\\% & 6.717\\% & 7.728\\%\\tabularnewline\n\\textbf{5} & 28.280\\% & 30.580\\% & 33.277\\% & 29.168\\% & 25.292\\%\\tabularnewline\n\\textbf{6} & 9.868\\% & 11.313\\% & 10.281\\% & 10.594\\% & 7.998\\%\\tabularnewline\n\\textbf{7} & 14.086\\% & 12.148\\% & 11.607\\% & 9.796\\% & 10.074\\%\\tabularnewline\n\\textbf{8} & 12.395\\% & 10.739\\% & 9.689\\% & 11.612\\% & 10.935\\%\\tabularnewline\n\\textbf{9} & 5.558\\% & 6.731\\% & 5.207\\% & 6.815\\% & 5.366\\%\\tabularnewline\n\\textbf{10 (Right)} & 7.956\\% & 8.849\\% & 9.815\\% & 9.090\\% & 12.511\\%\\tabularnewline\n\\hline \n\\textbf{Average position} & 5.875 & 5.925 & 5.858 & 5.803 & 5.723\\tabularnewline\n\\textbf{Variance of positions} & 5.336 & 5.426 & 5.397 & 6.108 & 7.376\\tabularnewline\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Shares of respondents by self-reported position on a 0-10 left-right axis, by year. Source: ANES dataset, electionstudies.org/data-tools/anes-continuity-guide/\\#left-right. Answers are weighted using the weights provided by ANES. Respondents who did not know how to answer or refused to answer are omitted.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Flexible Measure of Voter Polarization", "authors": ["Boris Ginzburg"], "url": "https://arxiv.org/abs/2507.07770v1", "attribution": "\"A Flexible Measure of Voter Polarization\" by Boris Ginzburg, arXiv:2507.07770v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.21174v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|rrrll}\n\\#& Matrix Name &n & m & nnz & $\\lambda_{min}(A)$ & $\\lambda_{max}(A)$\\\\\\hline\n1 & reorientation\\_1 & 396 & 281 &7,326 & $-1.7\\times10^6$ & $1.0\\times 10^9$\\\\\n2 & reorientation\\_4 &1,596 & 1,121 &33,630 & -43.2 & $3.8\\times10^8$ \\\\\n3 & reorientation\\_8 &1,826 & 1,282 &37,894 & -43.3 & $5.9\\times10^8$ \\\\\n4 & dynamicSoaringProblem\\_1 &363 & 284 &5,367 & $7.1\\times10^{-7}$ & 237.1 \\\\\n5 & dynamicSoaringProblem\\_4 &1,794 & 1,397 & 36,516 & $1.1\\times10^{-8}$ & $4.2\\times 10^5$ \\\\\n6 & goddardRocketProblem\\_2 &434 & 433 & 9,058 & $-1.1\\times 10^3$ & $1.1\\times 10^3$ \\\\\n7 & tumorAntiAngiogenesis\\_1 &123 & 82 &2,748 & -25.3 & 65.2\\\\\n8 & tumorAntiAngiogenesis\\_8 & 294 & 196 &4,776 & -99.6 & $7.3\\times 10^{11}$\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Symmetric case: test problems}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust iterative methods for linear systems with saddle point structure", "authors": ["Murat Manguoğlu", "Volker Mehrmann"], "url": "https://arxiv.org/abs/2502.21174v1", "attribution": "\"Robust iterative methods for linear systems with saddle point structure\" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.08518v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Denoised PSNR values per noise level.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll}\n\\toprule\n$\\sigma$ & $\\eta$ & Attention U-Net & DnCNN & HFormer & REDCNN & ResUNet++ & Sharp U-Net & SharpXR \\\\\n\\midrule\n5 & 300 & 36.367 & 36.166 & 33.151 & 37.218 & 33.777 & 37.760 & 37.819 \\\\\n10 & 200 & 35.092 & 34.915 & 32.295 & 35.856 & 32.696 & 36.475 & 36.583 \\\\\n15 & 150 & 33.981 & 33.812 & 31.476 & 34.736 & 31.826 & 35.445 & 35.569 \\\\\n20 & 100 & 32.897 & 32.726 & 30.648 & 33.698 & 30.803 & 34.491 & 34.641 \\\\\n25 & 50 & 31.517 & 31.163 & 29.535 & 32.385 & 29.357 & 33.332 & 33.514 \\\\\n30 & 100 & 31.428 & 31.125 & 29.522 & 32.405 & 29.378 & 33.343 & 33.534 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SharpXR: Structure-Aware Denoising for Pediatric Chest X-Rays", "authors": ["Ilerioluwakiiye Abolade", "Emmanuel Idoko", "Solomon Odelola", "Promise Omoigui", "Adetola Adebanwo", "Aondana Iorumbur", "Udunna Anazodo", "Alessandro Crimi", "Raymond Confidence"], "url": "https://arxiv.org/abs/2508.08518v1", "attribution": "\"SharpXR: Structure-Aware Denoising for Pediatric Chest X-Rays\" by Ilerioluwakiiye Abolade, Emmanuel Idoko, Solomon Odelola, Promise Omoigui, Adetola Adebanwo, Aondana Iorumbur, Udunna Anazodo, Alessandro Crimi, and Raymond Confidence, arXiv:2508.08518v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07434v5_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\n\t\t\\toprule[1pt]\n\t\t Methods & Backbone & mIOU (\\%) \\\\\n\t\t\\midrule[0.5pt]\n\t\t\\midrule[0.5pt]\n\t\tENCNet~ & ResNet-101 & 51.7 \\\\\n\t\tANNet~ & ResNet-101 & 52.8 \\\\\n\t\tEMANet~ & ResNet-101 & 53.1 \\\\\n\t\tSPYGR~ & ResNet-101 & 52.8 \\\\\n\t\tCPN~ & ResNet-101 & 53.9 \\\\\n\t\tCFNet~ & ResNet-101 & 54.0 \\\\\n\t\t\\midrule[0.5pt]\n\t\tDANet~ & ResNet-101 & 52.6\\\\\n\t\t\\midrule[0.5pt]\n\t\tOur CAA (OS = 16) & ResNet-101 & 53.7 \\\\\n\t\t\\textbf{Our CAA (OS =\\enspace8)} & ResNet-101 & \\textbf{55.0} \\\\\n\t\t\\bottomrule[1pt]\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparisons with other state-of-the-art approaches on the PASCAL Context test set. For a fair comparison, all compared methods used ResNet-101 and naive upsampling.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation", "authors": ["Ye Huang", "Di Kang", "Wenjing Jia", "Xiangjian He", "Liu Liu"], "url": "https://arxiv.org/abs/2101.07434v5", "attribution": "\"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation\" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1910.10836v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{siunitx}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Reflection coefficients $R_s$ and $R_p$ (see Equation ), and percentage of reflection that remains unpolarized, for minimum, average and maximum refractive index found in oil paintings, when the scanner is configured at the average Brewster's angle ($\\theta$=\\SI{56.3}{\\degree}), at the left image boundary ($A$), center ($B$) and right image boundary ($C$).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllllll} \n \\cmidrule{2-10}\n & \\multicolumn{3}{c}{$n_2$=1.47} & \\multicolumn{3}{c}{$n_2$=1.495 (average)} & \\multicolumn{3}{c}{$n_2$=1.52}\\\\\n \\cmidrule(r){2-4}\\cmidrule(r){5-7}\\cmidrule(r){8-10}\n & $A$ & $B$ & $C$ & $A$ & $B$ & $C$ & $A$ & $B$ & $C$\\\\\n\\midrule\n$R_\\mathrm{p}$ coefficient &0.0037 &\\num{2.08e-5} &0.0047 &0.0044 &0 \t &0.0042 &0.0052 &\\num{2.18e-5} &0.0037 \\\\\n$R_\\mathrm{s}$ coefficient &0.098 &0.138 \t\t &0.181\t &0.1045 &0.1456 &0.1899 &0.111 &0.153 \t\t&0.199 \\\\\n$R_\\mathrm{p}$/($R_\\mathrm{p}$+$R_\\mathrm{s}$) error (\\%)\t&3.66 &0.015 &2.18 &4.06 &0 &2.18 &4.47 &0.014 &1.84\\\\\n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Gloss, Color and Topography Scanning for Reproducing a Painting's Appearance using 3D printing", "authors": ["Willemijn Elkhuizen", "Tessa Essers", "Yu Song", "Jo Geraedts", "Clemens Weijkamp", "Joris Dik", "Sylvia Pont"], "url": "https://arxiv.org/abs/1910.10836v1", "attribution": "\"Gloss, Color and Topography Scanning for Reproducing a Painting's Appearance using 3D printing\" by Willemijn Elkhuizen, Tessa Essers, Yu Song, Jo Geraedts, Clemens Weijkamp, Joris Dik, and Sylvia Pont, arXiv:1910.10836v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.05333v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Charging service features}\n\\begin{tabular}{|l|c|}\n \\hline\n \\hline\n \\textbf{Feature Name} & \\textbf{Range of Values}\\\\\n \\hline\n \\hline\n Charging requests weighted rolling average & Float\\\\\n \\hline\n Province-wide number of available public EVCSs & Integer\\\\\n \\hline\n Province-wide number of registered EVs & Integer\\\\\n \\hline\n Region-specific number of available public EVCSs & Integer\\\\\n \\hline\n Region-specific number of registered EVs & Integer\\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Data-Driven Framework for Improving Public EV Charging Infrastructure: Modeling and Forecasting", "authors": ["Nassr Al-Dahabreh", "Mohammad Ali Sayed", "Khaled Sarieddine", "Mohamed Elhattab", "Maurice Khabbaz", "Ribal Atallah", "Chadi Assi"], "url": "https://arxiv.org/abs/2312.05333v1", "attribution": "\"A Data-Driven Framework for Improving Public EV Charging Infrastructure: Modeling and Forecasting\" by Nassr Al-Dahabreh, Mohammad Ali Sayed, Khaled Sarieddine, Mohamed Elhattab, Maurice Khabbaz, Ribal Atallah, and Chadi Assi, arXiv:2312.05333v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.21249v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Computational complexity of different methods.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\nMethod & FLOPs & Params \\\\ \\hline\nD-NTSCC & 69.07 G & 70.19 M \\\\ \nNTSCC & 28.35 G & 44.04 M \\\\ \nD-DJSCC & 60.79 G & 13.35 M \\\\ \nDHF-JSCC & 163.68 G & 25.37 M \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Distributed Nonlinear Transform Source-Channel Coding for Wireless Correlated Image Transmission", "authors": ["Yufei Bo", "Meixia Tao"], "url": "https://arxiv.org/abs/2503.21249v1", "attribution": "\"Distributed Nonlinear Transform Source-Channel Coding for Wireless Correlated Image Transmission\" by Yufei Bo and Meixia Tao, arXiv:2503.21249v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00143v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||ccc||ccc||} \n \\hline\n Parameter & Value & Pearson & Parameter & Value & Pearson \\\\\n Changed & & Correlation & Changed & & Correlation\\\\ \n \\hline\\hline\n Head Count ($H$) & 2 & 0.684 & Depth ($L$) & 4 & 0.700\\\\ \n \\hline\n & 4 & 0.696 & & 4 & 0.702\\\\ \n \\hline\n & 4 & 0.731 & & 4 & 0.723\\\\ \n \\hline\n & 4 & 0.707 & & 8 & 0.746\\\\ \n \\hline\n & 16 & 0.751 & & 16 & 0.771\\\\ \n \\hline\n & 32 & 0.765 & & 16 & 0.792\\\\ \n \\hline\n & 32 & 0.757 & MLP Dim ($d_{f f}$) & 32 & 0.706\\\\ \n \\hline\n & 32 & 0.767 & & 64 & 0.696\\\\ \n \\hline\n & 64 & 0.742 & & 64 & 0.715\\\\ \n \\hline\n Embedding Dim ($d_t$) & 32 & 0.641 & & 128 & 0.713\\\\ \n \\hline\n & 64 & 0.684 & & 256 & 0.725\\\\ \n \\hline\n & 256 & 0.706 & & 256 & 0.731\\\\ \n \\hline\n & 256 & 0.703 & Residual Block Size & 0 & 0.713 \\\\ \n \\hline\n Patch Size ($d_1 = d_2 = d_3$)& 8 & 0.696 & & 0 & 0.717 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Selected results of hyperparameter tuning trials on the dipeptide dataset. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runs", "authors": ["Tom Pan", "Evan Dramko", "Mitchell D. Miller", "George N. Phillips", "Anastasios Kyrillidis"], "url": "https://arxiv.org/abs/2503.00143v1", "attribution": "\"RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runs\" by Tom Pan, Evan Dramko, Mitchell D. Miller, George N. Phillips, and Anastasios Kyrillidis, arXiv:2503.00143v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.13645v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c||c|c|c|}\n \\hline\n & \\multicolumn{3}{c||}{\\multirow{2}{*}{\\shortstack{In-memory \\\\ Compute Model}}} & \\multicolumn{2}{c|}{\\multirow{2}{*}{\\shortstack{Analog Core\\\\ Precision}}} & \\multirow{2}{*}{\\shortstack{ADC \\\\ Precision}} \\tabularnewline\n & \\multicolumn{3}{c||}{} & \\multicolumn{2}{c|}{} & \\tabularnewline\n \\cline{2-7}\n & QS & IS & QR & $B_x$ & $B_w$ & $B_\\text{ADC}$ \\tabularnewline\n \\hline\n \\hline \n Kang \\emph{et al.} & \\checkmark & & \\checkmark & 8 & 8 & 8\\tabularnewline\n \n \\hline\n Biswas \\emph{et al.} & & & \\checkmark & 8 & 1& 7 \\tabularnewline\n \n \\hline \n Zhang \\emph{et al.} & \\checkmark & & & 5 & 1 & 1\\tabularnewline\n \n \\hline \n Valavi \\emph{et al.} & & & \\checkmark & 1 & 1& 1 \\tabularnewline\n \n \\hline \n Khwa \\emph{et al.} & &\\checkmark & & 1 & 1 & 1\\tabularnewline\n \n \\hline \n Jiang \\emph{et al.} & & \\checkmark & & 1 & 1& 3.46 \\tabularnewline\n \n \\hline \n Si \\emph{et al.} & \\checkmark & & \\checkmark & 2 & 5 & 5\\tabularnewline\n \n \\hline \n Jia \\emph{et al.} & & & \\checkmark & 1 & 1& 8 \\tabularnewline\n \n \\hline \n Okumura \\emph{et al.} & & \\checkmark& & 1 & T & 8\\tabularnewline\n \n \\hline \n Kim \\emph{et al.} & &\\checkmark & &1 & 1& 1\\tabularnewline\n \n \\hline \n Guo \\emph{et al.} & \\checkmark & & & 1 & 1 & 3\\tabularnewline\n \n \\hline \n Yue \\emph{et al.} & \\checkmark & & \\checkmark & 2 & 5 & 5\\tabularnewline\n \n \\hline \n Su \\emph{et al.} & \\checkmark & & & 2 & 1 & 5\\tabularnewline\n \n \\hline \n Dong \\emph{et al.} & \\checkmark & & \\checkmark & 4 & 4 & 4\\tabularnewline\n \n \\hline \n Si \\emph{et al.} & \\checkmark & & & 2 & 2 & 5\\tabularnewline\n \n \\hline \n Jiang \\emph{et al.} & & & \\checkmark & 1 & 1 & 5\\tabularnewline\n \n \\hline \n Jaiswal \\emph{et al.} & & \\checkmark & & 4 & 4 & 4\\tabularnewline\n \n \\hline \n Ali \\emph{et al.} &\\checkmark & & \\checkmark & 4 & 4 & 4 \\tabularnewline\n \n \\hline \n Si \\emph{et al.} & \\checkmark & & & 1 & 1 & 1 \\tabularnewline\n \n \\hline \n Liu \\emph{et al.} & & \\checkmark & & A & 1 & 1 \\tabularnewline\n \n \\hline \n Zhang \\emph{et al.} & & \\checkmark & & 8 & 8 & 8 \\tabularnewline\n \n \\hline \n Gong \\emph{et al.} & \\checkmark & & & 2 & 3 & 8 \\tabularnewline\n \n \\hline \n Agrawal \\emph{et al.} & & & \\checkmark & 1 & 1 & 5 \\tabularnewline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fundamental Limits on Energy-Delay-Accuracy of In-memory Architectures in Inference Applications", "authors": ["Sujan Kumar Gonugondla", "Charbel Sakr", "Hassan Dbouk", "Naresh R. Shanbhag"], "url": "https://arxiv.org/abs/2012.13645v1", "attribution": "\"Fundamental Limits on Energy-Delay-Accuracy of In-memory Architectures in Inference Applications\" by Sujan Kumar Gonugondla, Charbel Sakr, Hassan Dbouk, and Naresh R. Shanbhag, arXiv:2012.13645v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.06425v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The Predicted Sup-norm of the Selected Centipede Games}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\\hline\n & \\multicolumn{2}{c}{Linear} & & \\multicolumn{2}{c}{Exponential} & & \\multicolumn{2}{c}{Constant} \\\\ \\cline{2-3} \\cline{5-6} \\cline{8-9} \n & $c=0.5$ & $c=0.8$ & & $c=2.5$ & $c=4$ & & $c=0.4$ & $c=0.8$ \\\\\n & (small) & (large) & & (small) & (large) & & (small) & (large) \\\\ \\hline\n\\multirow{2}{*}{Sup-norm} & \\multirow{2}{*}{0.128} & \\multirow{2}{*}{\\textbf{0.251$^\\dagger$}} & \\multirow{2}{*}{} & \\multirow{2}{*}{0.188} & \\multirow{2}{*}{\\textbf{0.251$^\\dagger$}} & \\multirow{2}{*}{} & \\multirow{2}{*}{0.072} & \\multirow{2}{*}{\\textbf{0.460$^\\dagger$}} \\\\\n & & & & & & & & \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Strategy Method Effects in Centipede Games: An Optimal Design Approach", "authors": ["Shiang-Hung Hu", "Po-Hsuan Lin", "Thomas R. Palfrey", "Joseph Tao-yi Wang", "Yu-Hsiang Wang"], "url": "https://arxiv.org/abs/2508.06425v1", "attribution": "\"Strategy Method Effects in Centipede Games: An Optimal Design Approach\" by Shiang-Hung Hu, Po-Hsuan Lin, Thomas R. Palfrey, Joseph Tao-yi Wang, and Yu-Hsiang Wang, arXiv:2508.06425v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2401.00194v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Theoretical probabilities for unique recovery of $\\mathbf{s}$ \\\\ in Scenario $2$.}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\n \\hline\n N&5&6&7&8&10&11&14&16 \\\\ \\hline\n Prob.&0.47&0.14&0.49&0.04&0.22&0.50&0.24&0.03\\\\ \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the Identifiability from Modulo Measurements under DFT Sensing Matrix", "authors": ["Qi Zhang", "Jiang Zhu", "Fengzhong Qu", "Zheng Zhu", "De Wen Soh"], "url": "https://arxiv.org/abs/2401.00194v2", "attribution": "\"On the Identifiability from Modulo Measurements under DFT Sensing Matrix\" by Qi Zhang, Jiang Zhu, Fengzhong Qu, Zheng Zhu, and De Wen Soh, arXiv:2401.00194v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.09436v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Propagated $P(SC|RSE)$, $P(SC|S)$, and $P(RSE|RDS)$ with and without sensor $p$=$100\\%$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrrrrrr}\n \\hline\n & \\multicolumn{6}{c}{SC} & \\multicolumn{4}{c}{RSE} \\\\\n& & none & low & med & high & cri& & low & med & high \\\\ \n \\hline\n RSE & low & 0.929 & 0.0698 & 0.0003 & 0.0004 & 0.0002& RDS& 0.468 & 0.162 & 0.370 \\\\ \n & med & 0.900 & 0.0669 & 0.0176 & 0.0140 & 0.0011 & & 0.915 & 0.045 & 0.040 \\\\ \n & high & 0.897 & 0.066 & 0.0013 & 0.0206 & 0.0143 & & 0.994 & 0.003 & 0.003 \\\\ \n \\hline\n S& true & 0.929 & 0.069 & 0.0003& 0.0004 & 0.0003 &&&&\\\\ \n & false & 0.729 & 0.266 & 0.0012 & 0.0016 & 0.0011 &&&&\\\\\n \\hline \nRSE & low & 0.735 & 0.261 & 0.0012 & 0.0015 & 0.0010& RDS& 0.444 & 0.163 & 0.393 \\\\ \n & med & 0.671 & 0.238 & 0.0468 & 0.0394 & 0.0042 & & 0.913 & 0.046 & 0.041 \\\\ \n & high & 0.662 & 0.235 & 0.0047 & 0.0593 & 0.0385 & & 0.995 & 0.003 & 0.002 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks", "authors": ["Gurcan Comert", "Mashrur Chowdhury", "David M. Nicol"], "url": "https://arxiv.org/abs/2011.09436v1", "attribution": "\"Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks\" by Gurcan Comert, Mashrur Chowdhury, and David M. Nicol, arXiv:2011.09436v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.11418v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n \\hline\n Geometry & $\\eta=1$ & $\\eta=2$ & $\\eta=3$ & $\\eta=4$ & $\\eta=5$ & $\\eta=6$ & Hard \\\\\n \\hline\n $[0,1]$ & 2.994 & 2.945 & 2.888 & 2.849 & 2.826 & 2.812 & \\textbf{2.771} \\\\\n $\\mathbb{T}^1$ & 2.998 & 2.982 & 2.929 & 2.893 & 2.870 & 2.854 & \\textbf{2.812} \\\\\n $[0,1]^2$ & 2.999 & 2.992 & 2.978 & 2.960 & 2.949 & 2.940 & 2.907 \\\\\n $\\mathbb{T}^2$ & 2.999 & 2.998 & 2.994 & 2.988 & 2.983 & 2.978 & 2.962 \\\\\n $[0,1]^3$ & 2.999 & 2.998 & 2.991 & 2.983 & 2.976 & 2.969 & 2.942 \\\\\n $\\mathbb{T}^3$ & 2.999 & 2.999 & 2.999 & 2.996 & 2.994 & 2.991 & 2.975 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Maximum entropy for $n=3$ in different geometries and connection functions with uniform node distributions. Values in bold are calculated analytically.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Entropy of Random Geometric Graphs in High and Low Dimensions", "authors": ["Oliver Baker", "Carl P. Dettmann"], "url": "https://arxiv.org/abs/2503.11418v3", "attribution": "\"Entropy of Random Geometric Graphs in High and Low Dimensions\" by Oliver Baker and Carl P. Dettmann, arXiv:2503.11418v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10937v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{In this table, we evaluate the performance of targeted attacks under different defenses using \\textbf{1 - accuracy} (higher indicates better attacks)). We pick $\\epsilon_{\\infty}=0.18$, $m=100$, $\\gamma=0.25$ and select DGCNN as victim model. Other settings are the same as Sec 4.1. Our AOF consistently and significantly outperforms other SOTA attacks. The ConvNet-Opt, ONet-Remesh and ONet-Opt are three variants of IF-Defense.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccr}\n\\toprule\nDefenses & 3D-Adv & kNN & AdvPC & AOF \\\\\n\\midrule\nNo defense & \\bf99.76 & 91.94 & 96.23 & 96.15 \\\\\nSRS & 54.42 & 71.11 & 88.02 & \\bf91.26 \\\\\nSOR & 27.92 & 54.78 & 75.47 & \\bf85.63 \\\\\nDUP-Net & 29.58 & 51.66 &71.90 & \\bf83.16 \\\\\nConvNet-Opt & 21.76 & 22.85 & 45.59 & \\bf61.38 \\\\\nONet-Remesh & 30.27 & 30.19 & 49.51 & \\bf58.46 \\\\\nONet-Opt & 24.51 & 25.24 & 41.34 & \\bf51.34 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Boosting 3D Adversarial Attacks with Attacking On Frequency", "authors": ["Binbin Liu", "Jinlai Zhang", "Lyujie Chen", "Jihong Zhu"], "url": "https://arxiv.org/abs/2201.10937v1", "attribution": "\"Boosting 3D Adversarial Attacks with Attacking On Frequency\" by Binbin Liu, Jinlai Zhang, Lyujie Chen, and Jihong Zhu, arXiv:2201.10937v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.11871v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The $(n-1)$th order approximation $|\\vec{H}_a|$ for angular transients $\\theta$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|l|}\n \\hline\n\\thead{n} & \\thead{Associated mean curvature $f:=|\\vec{H}_a|$}& \\thead{Difference $\\mathfrak{T}(\\widetilde{f})-\\mathfrak{T}(e^{\\mathrm{i}\\theta})$} \\\\\n \\hline\n$10$ & $\\frac{1}{9}\\left(\\frac{1-\\cos\\theta}{\\sin\\theta}\\right)^{10}\\sin^{11}\\left(\\frac{\\pi-\\theta}{2}\\right)$ & $\\mathfrak{T}(\\widetilde{f})-\\mathfrak{T}(e^{\\mathrm{i}\\theta})=-\\frac{1}{9216}\\theta^{10}+O(\\theta^{11})$ \\\\\n \\hline\n$50$ & $\\frac{1}{49}\\left(\\frac{1-\\cos\\theta}{\\sin\\theta}\\right)^{50}\\sin^{51}\\left(\\frac{\\pi-\\theta}{2}\\right)$ & $\\mathfrak{T}(\\widetilde{f})-\\mathfrak{T}(e^{\\mathrm{i}\\theta})=c_{50}\\theta^{50}+\\mathcal{O}(\\theta^{51})$\\\\\n \\hline\n$100$ & $\\frac{1}{99}\\left(\\frac{1-\\cos\\theta}{\\sin\\theta}\\right)^{100}\\sin^{101}\\left(\\frac{\\pi-\\theta}{2}\\right)$ & $\\mathfrak{T}(\\widetilde{f})-\\mathfrak{T}(e^{\\mathrm{i}\\theta})=c_{100}\\theta^{100}+\\mathcal{O}(\\theta^{101})$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Associated Discrete Laplacian in $\\mathbb{R}^3$ and Mean Curvature with Higher order Approximations", "authors": ["Wei-Hung Liao"], "url": "https://arxiv.org/abs/2501.11871v1", "attribution": "\"The Associated Discrete Laplacian in $\\mathbb{R}^3$ and Mean Curvature with Higher order Approximations\" by Wei-Hung Liao, arXiv:2501.11871v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.03330v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrr}\n \\hline\n & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\\\ \n \\hline\n(Intercept) & 0.0100 & 0.0022 & 4.57 & 0.0000 \\\\ \n M & -0.0000 & 0.0031 & -0.01 & 0.9917 \\\\ \n R & -0.0000 & 0.0031 & -0.00 & 0.9999 \\\\ \n G & -0.0001 & 0.0031 & -0.04 & 0.9659 \\\\ \n MR & -0.0001 & 0.0031 & -0.02 & 0.9809 \\\\ \n MG & -0.0001 & 0.0031 & -0.02 & 0.9806 \\\\ \n L & 0.7016 & 0.0031 & 226.58 & 0.0000 \\\\ \n LsNL & 0.7284 & 0.0031 & 235.22 & 0.0000 \\\\ \n LNL & 0.8049 & 0.0031 & 259.91 & 0.0000 \\\\ \n NL & 0.1040 & 0.0031 & 33.59 & 0.0000 \\\\ \n M:L & -0.6887 & 0.0044 & -157.25 & 0.0000 \\\\ \n R:L & -0.6979 & 0.0044 & -159.37 & 0.0000 \\\\ \n G:L & -0.7016 & 0.0044 & -160.22 & 0.0000 \\\\ \n MR:L & -0.7016 & 0.0044 & -160.20 & 0.0000 \\\\ \n MG:L & -0.7016 & 0.0044 & -160.22 & 0.0000 \\\\ \n M:LsNL & -0.7131 & 0.0044 & -162.84 & 0.0000 \\\\ \n R:LsNL & -0.6918 & 0.0044 & -157.98 & 0.0000 \\\\ \n G:LsNL & -0.6970 & 0.0044 & -159.16 & 0.0000 \\\\ \n MR:LsNL & -0.7257 & 0.0044 & -165.71 & 0.0000 \\\\ \n MG:LsNL & -0.7259 & 0.0044 & -165.76 & 0.0000 \\\\ \n M:LNL & -0.7842 & 0.0044 & -179.07 & 0.0000 \\\\ \n R:LNL & -0.6965 & 0.0044 & -159.04 & 0.0000 \\\\ \n G:LNL & -0.7060 & 0.0044 & -161.20 & 0.0000 \\\\ \n MR:LNL & -0.7970 & 0.0044 & -181.98 & 0.0000 \\\\ \n MG:LNL & -0.7972 & 0.0044 & -182.03 & 0.0000 \\\\ \n M:NL & -0.0966 & 0.0044 & -22.05 & 0.0000 \\\\ \n R:NL & 0.0005 & 0.0044 & 0.11 & 0.9161 \\\\ \n G:NL & -0.0051 & 0.0044 & -1.17 & 0.2431 \\\\ \n MR:NL & -0.0962 & 0.0044 & -21.96 & 0.0000 \\\\ \n MG:NL & -0.0963 & 0.0044 & -21.99 & 0.0000 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{OLS output for squared error regressed on design $\\times$ response model in the simulation of Section~ for $n = 100$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Better Experimental Design by Hybridizing Binary Matching with Imbalance Optimization", "authors": ["Abba M. Krieger", "David Azriel", "Adam Kapelner"], "url": "https://arxiv.org/abs/2012.03330v2", "attribution": "\"Better Experimental Design by Hybridizing Binary Matching with Imbalance Optimization\" by Abba M. Krieger, David Azriel, and Adam Kapelner, arXiv:2012.03330v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.21280v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Node Weights Assigned to Political Positions}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|}\n \\hline\n \\textbf{Position} & \\textbf{Weight} \\\\\n \\hline\n Councilor & 2 \\\\\n Provincial Board Member & 2 \\\\\n Vice Mayor & 3 \\\\\n Vice Governor & 3 \\\\\n Mayor & 5 \\\\\n Member, House of Representatives & 5 \\\\\n Governor & 5 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Families that Stay Together: A Network Analysis of Dynastic Power in Philippine Politics", "authors": ["Rafael Acuna", "Aldie Alejandro", "Robert Leung"], "url": "https://arxiv.org/abs/2505.21280v1", "attribution": "\"The Families that Stay Together: A Network Analysis of Dynastic Power in Philippine Politics\" by Rafael Acuna, Aldie Alejandro, and Robert Leung, arXiv:2505.21280v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.02796v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Paired $t$-test Results on Test NLL Differences Across 500 Portfolios.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Comparison} & \\textbf{Mean NLL Difference} & \\textbf{$t$-statistic} & \\textbf{$p$-value} \\\\\n\\midrule\n\\multicolumn{4}{l}{\\textit{U.S. Market}} \\\\\nLSTM-BEKK $-$ DCC & -1.518 & -15.326 & \\makebox[1.5cm][r]{<0.000\\textsuperscript{***}} \\\\\nLSTM-BEKK $-$ Scalar BEKK & -0.247 & -2.587 & \\makebox[1.5cm][r]{0.009\\textsuperscript{***}} \\\\\n\\midrule\n\\multicolumn{4}{l}{\\textit{U.K. Market}} \\\\\nLSTM-BEKK $-$ DCC & -0.430 & -2.468 & \\makebox[1.25cm][r]{0.014\\textsuperscript{**}} \\\\\nLSTM-BEKK $-$ Scalar BEKK & -0.259 & -1.676 & \\makebox[0.95cm][r]{0.094\\textsuperscript{*}} \\\\\n\\midrule \n\\multicolumn{4}{l}{\\textit{Japan Market}} \\\\\nLSTM-BEKK $-$ DCC & -0.422 & -3.856 & \\makebox[1.55cm][r]{<0.001\\textsuperscript{***}} \\\\\nLSTM-BEKK $-$ Scalar BEKK & -0.383 & -3.498 & \\makebox[1.55cm][r]{0.001\\textsuperscript{***}} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deep Learning Enhanced Multivariate GARCH", "authors": ["Haoyuan Wang", "Chen Liu", "Minh-Ngoc Tran", "Chao Wang"], "url": "https://arxiv.org/abs/2506.02796v1", "attribution": "\"Deep Learning Enhanced Multivariate GARCH\" by Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, and Chao Wang, arXiv:2506.02796v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.03761v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Computational results of the adaptive partition-based level decomposition approach (``PILD-ODA''), the sequential sampling procedure by (``Sequential-BP-L''), and Algorithm with the stopping criterion and sample size schedule proposed in (``Adaptive-seq-BP-L (100)'') on our test instances DEAK and DEAK-H.}%\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllllllll}\n\\hline\nIns & $N$ & & \\multicolumn{2}{c}{PILD-ODA} & & \\multicolumn{3}{c}{Sequential-BP-L($100$)} & & \\multicolumn{3}{c}{Adaptive-seq-BP-L($100$)} \\\\\n \\cline{4-5} \\cline{7-9} \\cline{11-13}\n & & & Time & M & & Time & $M(L,n_L)$ & CI (cov.) & & Time & $M(L,n_L)$ & CI (cov.) \\\\\n \\cline{4-13}\n\\multirow{3}{*}{40x20} & 50K & & 53.4 & 19 & & 5.4 & 14(5,1070) & (0.1,97)& & 1.5 & 20(5,1094) & (0.1,97)\\\\\n & 100K & & 101.8 & 18 & & 5.1 & 13(5,1032) & (0.1,99) & & 1.3 & 19(5,1014) & (0.1,97)\\\\\n\\hline\n\\multirow{3}{*}{40x40} & 50K & & 74.6 & 12 & & 4.3 & 19(3,584) & (0.0,83) & & 1.2 & 12(3,630) & (0.1,80)\\\\\n & 100K & & 134.1 & 12 & & 5.6 & 20(3,660) & (0.1,90) & & 1.3 & 13(3,676) & (0.1,82) \\\\\n\\hline\n\\multirow{3}{*}{40x60} & 50K & & 206.2 & 19 & & 4.3 & 20(2,374) & (0.1,96) & & 1.7 & 21(2,396) & (0.1,100)\\\\\n & 100K & & 413.1 & 20 & & 4.1 & 20(2,360) & (0.1,99) & & 1.6 & 21(2,366) & (0.1,100) \\\\\n\\hline\n\\multirow{3}{*}{60x20} & 50K & & 114.4 & 56 & & 86.1 & 41(13,2540) & (0.1,100) & & 18.5 & 64(13,2596) & (0.1,100)\\\\\n & 100K & & 252.2 & 60 & & 87.8 & 42(13,2584) & (0.1,100) & & 19.1 & 64(13,2636) & (0.1,100)\\\\\n\\hline\n\\multirow{3}{*}{60x40} & 50K & & 502.0 & 65 & & 23.2 & 32(4,824) & (0.1,100) & & 12.3 & 70(4,834) & (0.1,100)\\\\\n & 100K & & 929.4 & 67 & & 25.1 & 33(4,864) & (0.1,100) & & 13.5 & 70(4,876) & (0.1,100)\\\\\n\\hline\n\\multirow{3}{*}{60x60} & 50K & & 333.8 & 24 & & 5.9 & 22(2,414) & (0.1,100) & & 2.2 & 25(2,424) & (0.1,100)\\\\\n & 100K & & 622.3 & 24 & & 6.5 & 22(2,436) & (0.1,100) & & 2.3 & 25(2,436) & (0.1,100)\\\\\n\\hline\n\\hline\n\\multirow{1}{*}{40x20} & 50K & & 63.9 & 17 & & 18.6 & 27(9,1776) & (0.1,96) & & 4.4 & 23(8,1698) & (0.1,98)\\\\\nHigh & 100K & & 139.2 & 18 & & 18.2 & 27(9,1772) & (0.1,96) & & 5.3 & 24(9,1854) & (0.1,95) \\\\\n\\hline\n\\multirow{1}{*}{40x40} & 50K & & 58.9 & 9 & & 4.5 & 17(3,580) & (0.0,83) & & 1.3 & 10(3,640) & (0.0,70)\\\\\nHigh & 100K & & 117.0 & 9 & & 4.0 & 17(3,556) & (0.1,88) & & 1.3 & 10(3,646) & (0.1,80)\\\\\n\\hline\n\\multirow{1}{*}{40x60} & 50K & & 711.5 & 25 & & 60.8 & 42(6,1140) & (0.1,99) & & 22.4 & 29(6,1132) & (0.1,93)\\\\\nHigh & 100K & & 1520.0 & 24 & & 55.5 & 41(6,1102) & (0.1,100) & & 20.8 & 29(6,1130) & (0.1,93) \\\\\n\\hline\n\\multirow{1}{*}{60x20} & 50K & & 162.6 & 46 & & 139.4 & 53(16,3194) & (0.1,99) & & 78.9 & 52(16,3280) & (0.1,100)\\\\\nHigh & 100K & & 263.2 & 43 & & 132.9 & 54(16,3160) & (0.1,100) & & 73.5 & 52(16,3230) & (0.1,100) \\\\\n\\hline\n\\multirow{1}{*}{60x40} & 50K & & 432.8 & 31 & & 112.6 & 55(9,1824) & (0.1,99) & & 127.4 & 42(10,1920) & (0.1,98)\\\\\nHigh & 100K & & 958.5 &32 & & 124.0 & 56(9,1834) & (0.1,99) & & 122.4 & 42(10,1940) & (0.1,98) \\\\\n\\hline\n\\multirow{1}{*}{60x60} & 50K & & 673.5 & 23 & & 96.6 & 48(6,1290) & (0.1,100) & & 38.2 & 31(6,1282) & (0.1,90) \\\\\nHigh & 100K & & 1591.9 & 25 & & 107.2 & 49(7,1316) & (0.1,96) & & 42.3 & 31(7,1362) & (0.1,89)\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Adaptive Sequential SAA for Solving Two-stage Stochastic Linear Programs", "authors": ["Raghu Pasupathy", "Yongjia Song"], "url": "https://arxiv.org/abs/2012.03761v1", "attribution": "\"Adaptive Sequential SAA for Solving Two-stage Stochastic Linear Programs\" by Raghu Pasupathy and Yongjia Song, arXiv:2012.03761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.06346v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Segmentation Performance}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n \\toprule\n \\cmidrule(r){1-1}\n & IoU & Dice & Accuracy \\\\\n \\midrule\n Reduced-ResUnet & \\(0.96\\) & \\(0.97\\) & \\(0.96\\)\\\\\n Unet & \\(0.86\\) & \\(0.88\\) & \\(0.87\\) \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Distant Domain Transfer Learning for Medical Imaging", "authors": ["Shuteng Niu", "Meryl Liu", "Yongxin Liu", "Jian Wang", "Houbing Song"], "url": "https://arxiv.org/abs/2012.06346v1", "attribution": "\"Distant Domain Transfer Learning for Medical Imaging\" by Shuteng Niu, Meryl Liu, Yongxin Liu, Jian Wang, and Houbing Song, arXiv:2012.06346v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.18611v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average detection delays for SKF on \\textbf{misspecified} configurations.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|cc|c|}\n\\hline\n\\multicolumn{2}{|c|}{Configuration} & \\multicolumn{2}{c|}{SKF} & BOCPD \\\\ \\hline\nTrue & Missepcified & M1 & M2 & \\\\ \\hline\nMat{\\'e}rn kernel & Exponential kernel & 5.20 & \\textbf{3.90} & 12.81 \\\\ \\hline\n$\\sigma^2 = 1$ & $\\sigma^2 = 4$ & 2.89 & \\textbf{2.71} & 12.81 \\\\ \\hline\n$\\gamma = 10$ & $\\gamma = 5$ & 3.85 & \\textbf{3.17} & 12.81 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Sequential Kalman filter for fast online changepoint detection in longitudinal health records", "authors": ["Hanmo Li", "Yuedong Wang", "Mengyang Gu"], "url": "https://arxiv.org/abs/2310.18611v3", "attribution": "\"Sequential Kalman filter for fast online changepoint detection in longitudinal health records\" by Hanmo Li, Yuedong Wang, and Mengyang Gu, arXiv:2310.18611v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05978v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the models.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\t\t\t\\hline\n\t\t\t\tModel & RMSE & MAE\\\\ \t\t \\hline\n\t\t\t\tGPR & 75.92 & 45.32\\\\\n\t\t\t\tBSTS\\_short & 3010.72 & 2533.07\\\\\n\t\t\t\tBSTS\\_long & 257.482 & 196.14\\\\ \t\n\t\t\t\tGBM & 78.37 & 36.26 \\\\\n\t\t\t\tAQR & 92.83 & 37.49 \\\\\n\t\t\t\tTwo-layer DGPR & 360.74 & 256.55 \\\\\t\t\\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Robust modelling framework for short-term forecasting of global horizontal irradiance", "authors": ["Edina Chandiwana", "Caston Sigauke", "Alphonce Bere"], "url": "https://arxiv.org/abs/2212.05978v1", "attribution": "\"Robust modelling framework for short-term forecasting of global horizontal irradiance\" by Edina Chandiwana, Caston Sigauke, and Alphonce Bere, arXiv:2212.05978v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.15568v1_tex_table20.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Logit regression of Vote for Test Over Ratio of Performance Predictions}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\hline\n \\hline\n & & pseudo-$R^{2}$ & & $\\chi^{2}$ & & Prob $ > \\chi^{2}$ \\\\\\cline{3-7}\n & & 0.2209 & & 37.61 & & 0.0000\\\\\n \\midrule\n Vote for Test & & Coefficient & & $z$ & & $P>z$ \\\\\n \\hline\n Better & & 5.0265 & & 6.13 & & 0.000 \\\\\n Constant & & -4.3622 & & -5.23 & & 0.000 \\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)", "authors": ["Marius Protte"], "url": "https://arxiv.org/abs/2507.15568v1", "attribution": "\"Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)\" by Marius Protte, arXiv:2507.15568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05593v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textit{\\#Docs} is the number of documents. \\textit{Docs Year} is the documents' publication time. \\textit{\\#Annotations} is the number of annotations (Number of \\textit{non-NIL} annotations). \\textit{Annot. Year} is the reference KB time period where the annotations were taken from.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|}\t\n \\hline\n \\textbf{Dataset} & \\textbf{\\#Docs} & \\textbf{Docs Year} & \\textbf{\\#Annotations} & \\textbf{Annot. Year} \\\\ \\hline\n\t\tACE04\t~ \t\t\t\t\t\t \t\t & 57 \t & 2000 \t & 257 \t\t & 2010 \\\\ \\hline\n\t\tAIDA/CONLL\t~\t\t\t\t\t\t & 231 \t & 1996 & 4.485 \t\t & 2010 \\\\ \\hline\n\t\tAQUAINT50\t~ \t\t\t\t\t\t\t & 50 & 1998-2000 & 727 \t \t\t & 2007 \\\\ \\hline\n\t\tIITB\t~\t\t\t\t\t\t\t\t & 107 & 2008 \t & 12.099 \t & 2008 \\\\ \\hline\n\t\tMSNBC\t~ \t\t\t\t\t\t \t\t & 20 & 2007 & 747\t\t & 2006 \\\\ \\hline\t\t\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "On the Temporality of Priors in Entity Linking", "authors": ["Renato Stoffalette Joao"], "url": "https://arxiv.org/abs/2101.05593v1", "attribution": "\"On the Temporality of Priors in Entity Linking\" by Renato Stoffalette Joao, arXiv:2101.05593v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.13082v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Details of BRATS $2018$, $2019$ and $2020$ datasets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n\\hline\nDataset&{Type}&Patients&\nHGG&\nLGG\\\\\n\\hline\nBRATS 2018 &Training &285&210&75\\\\\n& Validation &66&&\\\\\nBRATS 2019 &Training &335&259&76\\\\\n& Validation &125&&\\\\\nBRATS 2020 &Training &369&293&76\\\\\n& Validation &125&&\\\\\n& Testing &166&&\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Context Aware 3D UNet for Brain Tumor Segmentation", "authors": ["Parvez Ahmad", "Saqib Qamar", "Linlin Shen", "Adnan Saeed"], "url": "https://arxiv.org/abs/2010.13082v2", "attribution": "\"Context Aware 3D UNet for Brain Tumor Segmentation\" by Parvez Ahmad, Saqib Qamar, Linlin Shen, and Adnan Saeed, arXiv:2010.13082v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2103.01528v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Notations used in the TDTL MIP formulation. \\strut }\n\\begin{tabular}{ll} \n\t\t\\hline \n\t\t\\multicolumn{2}{l}{\\textbf{Sets}}\\\\\n\t\t\\hline\n $ \\mathcal{N}$ & Set of nodes of graph $G = (\\mathcal{N}, \\mathcal{A})$. \\\\\n $\\mathcal{A}$ & Set of directed links in $G = (\\mathcal{N}, \\mathcal{A})$.\\\\\n\t\t$o/e$ & The origin/ending node of the mission, $o,e \\in \\mathcal{N}$.\\\\\n\t\t$\\delta^+(i)$ & Nodes that can be reached from $i$, $i \\in \\mathcal{N}$.\\\\\n\t\t$\\delta^-(i)$ & Nodes that can reach to node $i$, $i \\in \\mathcal{N}$.\\\\\t\t\n\t\t\\hline \n\t\t\\multicolumn{2}{l}{\\textbf{Parameters}} \\\\\n\t\t\\hline\n\t\tQ & Battery capacity expressed in time units. \\\\\n\t\t$t_{ij}^T/$ & Truck travel time at link $(i,j) \\in \\mathcal{A}$.\\\\\n\t\t$t_{ij}^D$ & Drone travel time at link $(i,j) \\in \\mathcal{A}$.\\\\\n\t\tM & A big enough constant.\\\\\n\t\t\\hline \n\t \\multicolumn{2}{l}{\\textbf{Variables}} \\\\\n\t\t\\hline\n\t $u_{ij}$ & If $u_{ij} = 1$, link $(i,j)$ is traversed by the truck. \\\\\n\t $v_{ij}$ & If $v_{ij} = 1$, link $(i,j)$ is traversed by the drone.\\\\\n $s_i$ & The earliest departure time from node $i\\in \\mathcal{N}$.\\\\\n $b_i^-$ & Drone battery level when if drone is just coming to the node $i \\in \\mathcal{N}$.\\\\\n $b_i^+$ & Drone battery level when the drone is just departing from node $i \\in \\mathcal{N}$.\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Nested Vehicle Routing Problem: Optimizing Drone-Truck Surveillance Operations", "authors": ["Fanruiqi Zeng", "Zaiwei Chen", "John-Paul Clarke", "David Goldsman"], "url": "https://arxiv.org/abs/2103.01528v2", "attribution": "\"Nested Vehicle Routing Problem: Optimizing Drone-Truck Surveillance Operations\" by Fanruiqi Zeng, Zaiwei Chen, John-Paul Clarke, and David Goldsman, arXiv:2103.01528v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06319v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Vulnerability Detection datasets, from the Devign paper. VFCs = Vulnerability-Fixing Commits.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n \\toprule\n \\textbf{Project} & \\textbf{Total commits} & \\textbf{VFCs} & \\textbf{Non-VFCs} \\\\\n \\midrule\n qemu & 11,910 & 4,932 & 6,978 \\\\\n ffmpeg & 13,962 & 5,962 & 8,000 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Old but Gold: Reconsidering the value of feedforward learners for software analytics", "authors": ["Rahul Yedida", "Xueqi Yang", "Tim Menzies"], "url": "https://arxiv.org/abs/2101.06319v2", "attribution": "\"Old but Gold: Reconsidering the value of feedforward learners for software analytics\" by Rahul Yedida, Xueqi Yang, and Tim Menzies, arXiv:2101.06319v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2401.09436v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Benchmark Functions for Global Optimization}\n\\begin{tabular}{|c|c|}\n\t\t\t\\hline\n\t\t\t\\textbf{Name} & \\textbf{Formula} \\\\\n\t\t\t\\hline\n\t\t\tRastrigin & $f(x) = An + \\sum_{i=1}^n \\left(x_i^2 - A \\cos(2 \\pi x_i)\\right), \\; A = 10$ \\\\\n\t\t\t\\hline\n\t\t\tAckley & $\\begin{aligned}\n\t\t\t\tf(x, y) = &-20 \\exp\\left(-0.2 \\sqrt{0.5(x^2 + y^2)}\\right) \\\\\n\t\t\t\t& - \\exp\\left(0.5\\left(\\cos(2\\pi x) + \\cos(2\\pi y)\\right)\\right) + e + 20\n\t\t\t\\end{aligned}$ \\\\\n\t\t\t\\hline\n\t\t\tSphere & $f(x) = \\sum_{i=1}^n x_i^2$ \\\\\n\t\t\t\\hline\n\t\t\tRosenbrock & $f(x) = \\sum_{i=1}^{n-1} \\left[100(x_{i+1} - x_i^2)^2 + (1 - x_i)^2\\right]$ \\\\\n\t\t\t\\hline\n\t\t\tBeale & $f(x, y) = (1.5 - x + xy)^2 + (2.25 - x + xy^2)^2 + (2.625 - x + xy^3)^2$ \\\\\n\t\t\t\\hline\n\t\t\tBooth & $f(x, y) = (x + 2y - 7)^2 + (2x + y - 5)^2$ \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Uncomputability of Global Optima for Nonconvex Functions in the Oracle Model", "authors": ["K Lakshmanan"], "url": "https://arxiv.org/abs/2401.09436v2", "attribution": "\"Uncomputability of Global Optima for Nonconvex Functions in the Oracle Model\" by K Lakshmanan, arXiv:2401.09436v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.11765v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Rel-Noise Ratios grouped by Market Capitalization}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{clll}\n & \\textbf{2018} & \\textbf{2019} & \\textbf{2020} \\\\ \\hline\n\\textit{\\textbf{small cap}} & 0.44 & 0.39 & 0.27 \\\\\n\\textit{\\textbf{mid cap}} & 0.44 & 0.40 & 0.29 \\\\\n\\textit{\\textbf{large cap}} & 0.46 & 0.41 & 0.29 \\\\\n\\textit{\\textbf{none}} & 0.34 & 0.24 & 0.19 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis", "authors": ["Tanja Aue", "Adam Jatowt", "Michael Färber"], "url": "https://arxiv.org/abs/2212.11765v1", "attribution": "\"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis\" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.13460v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|ccc}\n\\hline\n\\multirow{2}{*}{Model} & \\multicolumn{3}{c|}{Dice \\small{[median (min, max)]}} & \\multicolumn{3}{c}{95\\% HD mm \\small{[median (min, max)]}} \\\\\n\\cline{2-7}\n & TC & ET & WT & TC & ET & WT \\\\\n\\hline\nBLM & 88.2 (0.0, 98.4) & 86.8 (0.0, 100) & 93.0 (0.0, 97.0) & 2.8 (1.0, 375.1) & 2.5 (1.0, 375.1) & 2.4 (1.0, 375.1) \\\\\n\\hline\nCIM & 89.3 (0.0, 98.4) & 86.2 (0.0, 100) & 93.0 (0.0, 97.2) & 2.8 (1.0, 375.1) & 2.1 (1.0, 375.1) & 2.5 (1.0, 375.1) \\\\\n\\hline\nCIP & 89.2 (0.0, 98.4) & 88.4 (0.0, 100) & 92.9 (0.0, 97.3) & 2.9 (1.0, 375.1) & 2.5 (1.0, 375.1) & 2.5 (1.0, 15.6) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?", "authors": ["Iulian Emil Tampu", "Neda Haj-Hosseini", "Anders Eklund"], "url": "https://arxiv.org/abs/2010.13460v3", "attribution": "\"Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?\" by Iulian Emil Tampu, Neda Haj-Hosseini, and Anders Eklund, arXiv:2010.13460v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.07178v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Self-supervised SV results on the VoxCeleb1 test set using proposed contrastive methods.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc}\n \\toprule\n Model & CDS EER (\\%) & DCF \\\\\n \\hline\n Disent~ & 22.09 & N/A \\\\\n CDDL~ & 17.52 & N/A \\\\\n GCL~ & 15.26 & N/A \\\\\n i-vector~ & 15.28 & 0.63 (p=0.05) \\\\\n AP~ & 9.56 & 0.51 (p=0.05) \\\\\n AP+AAT~ & 8.65 & 0.45 (p=0.05) \\\\\n \\hline\n SimCLR & 18.14 & 0.81 \\\\\n MoCo & 15.11 & 0.80 \\\\\n MoCo + SpecAug & 15.50 & 0.81 \\\\\n MoCo + WavAug & \\bf{8.63} & \\bf{0.64} \\\\\n MoCo + WavAug (ProtoNCE) & \\bf{8.23} & \\bf{0.59} \\\\\n \\bottomrule\n \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning", "authors": ["Wei Xia", "Chunlei Zhang", "Chao Weng", "Meng Yu", "Dong Yu"], "url": "https://arxiv.org/abs/2012.07178v2", "attribution": "\"Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning\" by Wei Xia, Chunlei Zhang, Chao Weng, Meng Yu, and Dong Yu, arXiv:2012.07178v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16534v2_tex_table39.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameter search space of NODE.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\toprule\n\\textbf{Hyperparameter} & \\textbf{Distribution} \\\\ \\midrule\nnum\\_layers & $[2,4,8]$\\\\\ntotal\\_tree\\_count & $[1024,2048]$\\\\\ntree\\_depth & $[6,8]$\\\\\ntree\\_output\\_dimension & $[2,3](regression),[\\text{num\\_classes}](classification)$\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DOFEN: Deep Oblivious Forest ENsemble", "authors": ["Kuan-Yu Chen", "Ping-Han Chiang", "Hsin-Rung Chou", "Chih-Sheng Chen", "Tien-Hao Chang"], "url": "https://arxiv.org/abs/2412.16534v2", "attribution": "\"DOFEN: Deep Oblivious Forest ENsemble\" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.05068v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Training settings for each example.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\toprule {Loss} & Example & Example &\nExample &\nExample \\\\\n\\midrule\nGradient descent method & AdamW & AdamW & AdamW & AdamW \\\\\nLearning rate & 0.001 & 0.0005 & 0.0005 & 0.001\\\\\nWeight decay & 0.01 & 0 & 0.02 & 0.02\\\\\nNo. of epochs & 500 & 400 & 2000 & 400\\\\\nNo. of training trajectories & 200 & 400 & 200 & 300 \\\\\nHidden layers & 1 & 3 & 3 & 2\\\\\nActivation function & ReLU & ReLU & ReLU & ReLU\\\\\nNeurons in each layer & 40 & 50 &\n10 & 50 \\\\\ntime step $\\Delta t$ & 0.1 & 0.1 & 0.05 & 0.1 \\\\\nNumber of timesteps $N_T$ &81 & 21 & 41 & 21 \\\\\nInitialization for biases & 2 & 0.01\n& $\\mathcal{N}(0, 0.03^2)$ & 0.01 \\\\ \nInitialization for weights & $\\mathcal{N}(0, 0.01^2)$ & $\\mathcal{N}(0, 0.01^2)$ & $\\mathcal{N}(0, 0.03^2)$ & $\\mathcal{N}(0, 0.01^2)$\n \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems", "authors": ["Mingtao Xia", "Qijing Shen", "Philip Maini", "Eamonn Gaffney", "Alex Mogilner"], "url": "https://arxiv.org/abs/2503.05068v1", "attribution": "\"A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems\" by Mingtao Xia, Qijing Shen, Philip Maini, Eamonn Gaffney, and Alex Mogilner, arXiv:2503.05068v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17706v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Notations of various achievable regions, where ``cr'' means ``common randomness''}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c|} % Define 5 columns\n \\hline\n &DP joint coding & RDP & DP separate coding \\\\ \\hline\\hline\nstrong-sense, w/ cr & $\\mathcal{J}$ & $\\mathcal{G}$ & $\\mathcal{J}'$ \\\\ \\hline\nweak-sense, w/ cr & $\\mathcal{J}_w$ & $\\mathcal{G}_w$ & $\\mathcal{J}'_w$ \\\\ \\hline\nstrong-sense, w/o cr & $\\hat{\\mathcal{J}}$ & $\\hat{\\mathcal{G}}$ & $\\hat{\\mathcal{J}}'$ \\\\ \\hline\nweak-sense, w/o cr & $\\hat{\\mathcal{J}}_w$ & $\\hat{\\mathcal{G}}_w$ & $\\hat{\\mathcal{J}}'_w$\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Source-Channel Separation Theorems for Distortion Perception Coding", "authors": ["Chao Tian", "Jun Chen", "Krishna Narayanan"], "url": "https://arxiv.org/abs/2501.17706v2", "attribution": "\"Source-Channel Separation Theorems for Distortion Perception Coding\" by Chao Tian, Jun Chen, and Krishna Narayanan, arXiv:2501.17706v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.15012v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l}\n\\cline{1-3}\n\\textbf{Cost category} & \\textbf{NAICS description} & \\textbf{NAICS} & \\\\ \\cline{1-3}\nArray System & Communication and energy wire and cable manufacturing & 33592 & \\\\ \\cline{1-3}\nExport System & Communication and energy wire and cable manufacturing & 33592 & \\\\ \\cline{1-3}\nOffshore Substation & Substation and switching station, power transmission line, construction & 237130 & \\\\ \\cline{1-3}\nScour Protection & Other heavy and civil engineering construction & 237990 & \\\\ \\cline{1-3}\nSubstructure & Wind Turbine Manufaturing & 333611 & \\\\ \\cline{1-3}\nArray System Installation & Cable Laying & 237130 & \\\\ \\cline{1-3}\nExport System Installation & Cable Laying & 237130 & \\\\ \\cline{1-3}\nOffshore Substation Installation & Substation and switching station, power transmission line, construction & 237130 & \\\\ \\cline{1-3}\nScour Protection Installation & Other heavy and civil engineering construction & 237990 & \\\\ \\cline{1-3}\nSubstructure Installation & Structural Steel and Precast Concrete Contractors & 238120 & \\\\ \\cline{1-3}\nTurbine Installation & Heavy and civil engineering construction & 237 & \\\\ \\cline{1-3}\nTurbines & Wind Turbine Manufaturing & 333 & \\\\ \\cline{1-3}\nInsurance & Insurance agencies and brokerages & 524210 & \\\\ \\cline{1-3}\nFinancing & Credit Intermediation and related activities & 522 & \\\\ \\cline{1-3}\nContingency & Emergency and other relief services & 624230 & \\\\ \\cline{1-3}\nCommissioning & Building commissioning services & 541350 & \\\\ \\cline{1-3}\nDecommissioning & Site preparation contractors & 238910 & \\\\ \\cline{1-3}\nSite Auction & Real Estate & 531 & \\\\ \\cline{1-3}\nSite Assessment & Site preparation contractors & 238910 & \\\\ \\cline{1-3}\nConstruction Plan & Construction of buildings & 236 & \\\\ \\cline{1-3}\nInstallation Plan & Professional, scientific and technical services & 541 & \\\\ \\cline{1-3}\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mapping costs from ORBIT cost categories to NAICS Codes}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Estimating the spatial economic and environmental impact of planned offshore wind energy in the USA using Environmentally Extended Multiregional Input-Output analysis", "authors": ["Apoorva Bademi", "Miriam Stevens", "Isha Sura", "Shweta Singh"], "url": "https://arxiv.org/abs/2508.15012v1", "attribution": "\"Estimating the spatial economic and environmental impact of planned offshore wind energy in the USA using Environmentally Extended Multiregional Input-Output analysis\" by Apoorva Bademi, Miriam Stevens, Isha Sura, and Shweta Singh, arXiv:2508.15012v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.08696v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|r|r|r|r|r|r|r|}\n \\hline\n\\textbf{Ticker} & \\textbf{Mean} & \\textbf{Std} & \\textbf{Min} & \\textbf{Max} & \\textbf{Q25} & \\textbf{Q50} & \\textbf{Q75} \\\\\\hline\nMTLR & 191.8245 & 72.5652 & 81.2800 & 332.8800 & 123.8500 & 187.6700 & 251.6400 \\\\\\hline\nTRMK & 153.1245 & 64.9362 & 55.8200 & 271.0000 & 87.1400 & 166.4200 & 218.7800 \\\\\\hline\nSNGS & 27.0104 & 4.0119 & 17.3500 & 36.9600 & 23.7750 & 27.3300 & 30.0250 \\\\\\hline\nSIBN & 601.5097 & 163.9205 & 335.5500 & 934.2500 & 452.0500 & 582.6500 & 748.9000 \\\\\\hline\nMGNT & 5691.6429 & 1161.7684 & 4040.0000 & 8444.0000 & 4665.0000 & 5495.0000 & 6375.0000 \\\\\\hline\nLENT & 814.3870 & 154.9502 & 650.0000 & 1263.0000 & 716.5000 & 749.0000 & 843.5000 \\\\\\hline\nPIKK & 732.6617 & 94.8650 & 518.0000 & 955.5000 & 656.7000 & 732.9000 & 811.5000 \\\\\\hline\nSMLT & 3120.8996 & 594.1018 & 1926.5000 & 4145.5000 & 2572.0000 & 3045.0000 & 3713.0000 \\\\\\hline\nMTSS & 264.5382 & 32.0791 & 183.0000 & 346.9500 & 239.0000 & 266.2500 & 289.7500 \\\\\\hline\nRTKMP & 68.1797 & 9.2753 & 52.2500 & 92.1000 & 60.4500 & 68.0000 & 74.7000 \\\\\\hline\nAFLT & 38.1316 & 10.3131 & 22.4400 & 64.4000 & 27.9700 & 38.8800 & 44.1200 \\\\\\hline\nFLOT & 88.0111 & 39.5834 & 29.9200 & 149.3000 & 42.1000 & 97.2000 & 124.1800 \\\\\\hline\nBSPB & 211.1501 & 101.2533 & 67.5700 & 387.6800 & 100.8400 & 210.9900 & 295.3400 \\\\\\hline\nSFIN & 762.9939 & 428.5679 & 425.8000 & 1975.0000 & 497.4000 & 518.0000 & 992.0000 \\\\\\hline\nPHOR & 6774.6040 & 618.1977 & 4997.0000 & 8153.0000 & 6416.0000 & 6763.0000 & 7278.0000 \\\\\\hline\nKZOSP & 25.8603 & 5.2029 & 15.3500 & 40.5700 & 21.9400 & 27.0700 & 29.8500 \\\\\\hline\nHYDR & 0.7697 & 0.0810 & 0.5178 & 1.0278 & 0.7318 & 0.7721 & 0.8210 \\\\\\hline\nMRKS & 0.5247 & 0.2382 & 0.2025 & 1.0745 & 0.2735 & 0.5550 & 0.7475 \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Descriptive characteristics for company shares}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market", "authors": ["Kasymkhan Khubiev", "Mikhail Semenov"], "url": "https://arxiv.org/abs/2503.08696v1", "attribution": "\"Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market\" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2503.08696v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.06703v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{An Example of a Table}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|}\n\\hline\nOne & Two\\\\\n\\hline\nThree & Four\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Source Separation & Automatic Transcription for Music", "authors": ["Bradford Derby", "Lucas Dunker", "Samarth Galchar", "Shashank Jarmale", "Akash Setti"], "url": "https://arxiv.org/abs/2412.06703v1", "attribution": "\"Source Separation & Automatic Transcription for Music\" by Bradford Derby, Lucas Dunker, Samarth Galchar, Shashank Jarmale, and Akash Setti, arXiv:2412.06703v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.14431v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrr}\n\\hline\n\\hline\n\\textbf{Metric} & \\textbf{Sector} & \\textbf{ADF Stat} & \\textbf{ADF p-val} & \\textbf{PP Stat} & \\textbf{PP p-val} \\\\\n\\hline\n\\textbf{Pr-crash period} & & & \\\\\n\\hline\n$WD_1$ & Commodity & -15.42 & 0.00** & -15.52 & 0.00** \\\\\n$WD_1$ & Stock & -11.10 & 0.00** & -11.99 & 0.00** \\\\\n$WD_2$ & Commodity & -15.34 & 0.00** & -15.49 & 0.00** \\\\\n$WD_2$ & Stock & -5.96 & 0.00** & -12.94 & 0.00** \\\\\n\\hline\n\\textbf{Crash period} & & & \\\\\n\\hline\n$WD_1$ & Commodity & -3.64 & 0.01** & -12.64 & 0.00** \\\\\n$WD_1$ & Stock & -3.06 & 0.03** & -9.58 & 0.00** \\\\\n$WD_2$ & Commodity & -2.40 & 0.14 & -14.02 & 0.00** \\\\\n$WD_2$ & Stock & 4.29 & 0.00** & -11.57 & 0.00** \\\\\n\\hline\n\\textbf{Post-crash period} & & & \\\\\n\\hline\n$WD_1$ & Commodity & -7.00 & 0.00** & -13.64 & 0.00** \\\\\n$WD_1$ & Stock & 6.31 & 0.00** & -12.66 & 0.00** \\\\\n$WD_2$ & Commodity & -13.50 & 0.00** & -13.66 & 0.00** \\\\\n$WD_2$ & Stock & 12.62 & 0.00** & -13.38 & 0.00** \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective", "authors": ["Buddha Nath Sharma", "Anish Rai", "SR Luwang", "Md. Nurujjaman", "Sushovan Majhi"], "url": "https://arxiv.org/abs/2502.14431v1", "attribution": "\"Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective\" by Buddha Nath Sharma, Anish Rai, SR Luwang, Md. Nurujjaman, and Sushovan Majhi, arXiv:2502.14431v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09398v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistical Description of Used Datasets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c}\n\\toprule\nDataset &\\#Nodes & \\#Edges& \\#Classes & \\#Features & Avg.~Degree\\\\\n\\midrule\nCora &$2,708$ & $5,278$ &$7$& $1,433$ & $3.90$\\\\\nCiteseer &$2,110$ & $3,668$ &$6$& $3,703$ & $2.74$\\\\\nLASTFM &$7,083$ & $25,814$ &$10$& $7,842$ & $7.29$\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Towards Private Learning on Decentralized Graphs with Local Differential Privacy", "authors": ["Wanyu Lin", "Baochun Li", "Cong Wang"], "url": "https://arxiv.org/abs/2201.09398v1", "attribution": "\"Towards Private Learning on Decentralized Graphs with Local Differential Privacy\" by Wanyu Lin, Baochun Li, and Cong Wang, arXiv:2201.09398v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02419v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Accuracy estimate of different RRAM-based schemes}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n Network & Structure & Top-1 Error Rate \\\\\n \\hline\n \\multirow{5}{*}{LeNet on MNIST}& Software Based & 0.90 \\%\\\\\n \\cline{2-3}\n &MBRAI&0.97 \\%\\\\\n \\cline{2-3} \n &MBHS-mCNN&2.44 \\%\\\\\n \\cline{2-3}\n &RPN\\&BLM&0.90 \\%\\\\\n \\cline{2-3}\n &Proposed&\\textbf{ 0.91 \\%}\\\\\n \\hline\n \\multirow{4}{*}{AlexNet on ILSVRC12}& Software Based & 42.70 \\%\\\\\n \\cline{2-3}\n &MBRAI&44.16 \\%\\\\\n \\cline{2-3}\n &RPN\\&BLM&43.60 \\%\\\\\n \\cline{2-3}\n &Proposed&\\textbf{43.10 \\%}\\\\\n \\hline\n \\multirow{2}{*}{ResNet34 on ILSVRC12}& Software Based & 26.70 \\%\\\\\n \\cline{2-3}\n &Proposed&\\textbf{27.80 \\%}\\\\\n \\hline\n \\multirow{2}{*}{VGG16 on ILSVRC12}& Software Based & 28.40 \\%\\\\\n \\cline{2-3}\n &Proposed&\\textbf{29.30 \\%}\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Low Power In-Memory Multiplication andAccumulation Array with Modified Radix-4 Inputand Canonical Signed Digit Weights", "authors": ["Rui Xiao", "Kejie Huang", "Yewei Zhang", "Haibin Shen"], "url": "https://arxiv.org/abs/2101.02419v1", "attribution": "\"A Low Power In-Memory Multiplication andAccumulation Array with Modified Radix-4 Inputand Canonical Signed Digit Weights\" by Rui Xiao, Kejie Huang, Yewei Zhang, and Haibin Shen, arXiv:2101.02419v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.05782v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Demand estimation results}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n \\toprule\n & \\multicolumn{2}{c}{Logit} & & \\multicolumn{2}{c}{Nested Logit} & & \\multicolumn{2}{c}{CENL} \\\\\n\\cmidrule{2-3}\\cmidrule{5-6}\\cmidrule{8-9} Utility parameter & (1) & (2) & & (3) & (4) & & (5) & (6) \\\\\n \\hline\n & & & & & & & & \\\\\n $\\alpha$ (price) & -1.774*** & -1.772*** & & -0.578*** & -0.578*** & & -0.362*** & -0.361*** \\\\\n & (0.061) & (0.084) & & (0.074) & (0.073) & & (0.036) & (0.036) \\\\\n $\\sigma$ ($\\ln s_{j|g}$) & & & & 0.819*** & 0.819*** & & 0.703*** & 0.704*** \\\\\n & & & & (0.051) & (0.050) & & (0.044) & (0.044) \\\\\n $\\beta_1$ (imgscore) & 1.412*** & 1.412*** & & 0.177** & 0.177** & & 0.372*** & 0.371*** \\\\\n & (0.064) & (0.057) & & (0.083) & (0.082) & & (0.070) & (0.070) \\\\\n $\\beta_2$ (cumadv) & 1.212*** & 1.212*** & & 0.283*** & 0.283*** & & 0.409*** & 0.408*** \\\\\n & (0.061) & (0.058) & & (0.067) & (0.067) & & (0.061) & (0.061) \\\\\n \\midrule\n Fixed effects & & & & & & & & \\\\\n \\hspace{0.1in}Product & Yes & Yes & & Yes & Yes & & Yes & Yes \\\\\n \\hspace{0.1in}Time & Yes & - & & Yes & - & & Yes & - \\\\\n \\hspace{0.1in}Region & Yes & - & & Yes & - & & Yes & - \\\\\n \\hspace{0.1in}Market & - & Yes & & - & Yes & & - & Yes \\\\\n \\hspace{0.1in}Cold $\\times$ Month & Yes & Yes & & Yes & Yes & & Yes & Yes \\\\\n \\midrule\n Observations & 18,960 & 18,960 & & 18,960 & 18,960 & & 18,960 & 18,960 \\\\\n SW F statistic & & & & & & & & \\\\\n \\hspace{0.1in}price & 51941.53 & 76448.36 & & 426.77 & 430.11 & & 2090.15 & 2113.38 \\\\\n \\hspace{0.1in}$\\ln s_{j|g}$ & - & - & & 350.45 & 352.42 & & 402.69 & 404.96 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Branding through responsibility: the advertising impact of CSR activities in the Korean instant noodles market", "authors": ["Youngjin Hong", "In Kyung Kim", "Kyoo il Kim"], "url": "https://arxiv.org/abs/2507.05782v1", "attribution": "\"Branding through responsibility: the advertising impact of CSR activities in the Korean instant noodles market\" by Youngjin Hong, In Kyung Kim, and Kyoo il Kim, arXiv:2507.05782v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12498v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n & $\\|\\Pi_{\\mathcal{I}}(\\pmb{y})\\|_F$ & $\\|\\Pi_{\\mathcal{N}}(\\pmb{y})\\|_F$ & $\\|\\pmb{J}\\pmb{J}^T\\pmb{y}\\|_F/\\|\\pmb{J}\\pmb{J}^T\\|_F$ \\\\\n \\hline\n Pretrained for 1000 epochs& 10.063 & 29.979 & 3.184 \\\\\n \\hline\n Pretrained for 100 epochs & 10.036 & 29.988 & 3.175 \\\\\n \\hline\n Randomly initialized & 10.014 & 29.995 & 3.055 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Alignment between the Jacobian matrix and the clean labels.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Investigating Why Contrastive Learning Benefits Robustness Against Label Noise", "authors": ["Yihao Xue", "Kyle Whitecross", "Baharan Mirzasoleiman"], "url": "https://arxiv.org/abs/2201.12498v4", "attribution": "\"Investigating Why Contrastive Learning Benefits Robustness Against Label Noise\" by Yihao Xue, Kyle Whitecross, and Baharan Mirzasoleiman, arXiv:2201.12498v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.00530v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n Model & Active Parameters & One-Step SPE \\\\ \n\\hline\nGNAR(1, [1]) & 2 & 7.86 \\\\\n VAR(1) & $140^2$ & 12.804 \\\\\n Res. VAR(1) & 3712 & 11.504 \\\\\n Sparse VAR(1) & 2992 & 11.533 \\\\\n AR(1) & $140$ & 78.748 \\\\\n GNAR(1, [1])* & 141 & 7.833 \n\\end{tabular}\n\\end{adjustbox}\n\\caption{One-step prediction error, $\\hat{\\boldsymbol{X}}_{444}$ is predicted using the previous 443 observations.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "New tools for network time series with an application to COVID-19 hospitalisations", "authors": ["Guy Nason", "Daniel Salnikov", "Mario Cortina-Borja"], "url": "https://arxiv.org/abs/2312.00530v1", "attribution": "\"New tools for network time series with an application to COVID-19 hospitalisations\" by Guy Nason, Daniel Salnikov, and Mario Cortina-Borja, arXiv:2312.00530v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17779v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n 0.0/0.0 & .406/.373 & .363/.343 & .316/.303 & {\\bf .351/.378} \\\\ \\hline\n{\\bf .386/.409} & 0.0/0.0 & .430/.414 & .358/.337 & .376/.365 \\\\ \\hline\n{\\bf .343/.372} & .434/.421 & 0.0/0.0 & .375/.344 & .317/.295 \\\\ \\hline\n{\\bf .314/.333} & .367/.363 & {\\bf .384/.395} & 0.0/0.0 & .327/.316 \\\\ \\hline\n.349/.345 & .376/.372 & {\\bf .306/.309} & .328/.305 & 0.0/0.0 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "FFT-based Alignment of 2d Closed Curves with Application to Elastic Shape Analysis", "authors": ["Günay Dogan", "Javier Bernal", "Charles Hagwood"], "url": "https://arxiv.org/abs/2501.17779v1", "attribution": "\"FFT-based Alignment of 2d Closed Curves with Application to Elastic Shape Analysis\" by Günay Dogan, Javier Bernal, and Charles Hagwood, arXiv:2501.17779v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.06234v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{First special class of circles with apparent symmetric lattice points on each boundary.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}\n\\hline\n$k$ & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\\\\n\\hline\n$f(k)$ & 4 & 16 & 32 & 60 & 88 & 124 & 172 & 216 & 276 & 332 \\\\\n\\hline\n$\\pi R_{f(k)}^2\\approx $ & 8 & 20 & 39 & 64 & 96 & 134 & 176 & 228 & 284 & 347 \\\\\n\\hline\n$k$ & 11 & 12 & 13 & 14 & 15 & 16 & 17 & 18 &19 &20\\\\\n\\hline\n$f(k)$ & 408 & 484 & 560 & 648 & 740 & 848 & 952 & 1060 &1184 & 1304\\\\\n\\hline\n$\\pi R_{f(k)}^2\\approx $ & 416& 492& 573& 661& 756& 856& 963& 1076 & 1195 &1321 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Largest Circle Enclosing $n$ Lattice Points", "authors": ["Jianqiang Zhao"], "url": "https://arxiv.org/abs/2505.06234v2", "attribution": "\"The Largest Circle Enclosing $n$ Lattice Points\" by Jianqiang Zhao, arXiv:2505.06234v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.03490v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n \\hline\n $[\\cdot,\\cdot]$ & $L_0$ & $L_{+1}$ & $L_{-1}$ & $J_0$ & $J_{+1}$ & $J_{-1}$ & $J_{+2}$ & $J_{-2}$ \\\\ \\hline\n $L_0$ & $0$ & $L_{+1}$ & $-L_{-1}$ & $0$ & $J_{+1}$ & $-J_{-1}$ & $2J_{+2}$ & $-2J_{-2}$ \\\\ \\hline\n $L_{+1}$ & $-L_{+1}$ & $0$ & $-L_0$ & $-\\frac{3\\sqrt{3}}{4} J_{+1}$ & $-2\\sqrt{2} J_{+2}$ & $-\\frac{4}{\\sqrt{3}} J_0$ & $0$ &$-\\frac{1}{\\sqrt{2}} J_{-1}$ \\\\ \\hline\n $L_{-1}$ & $L_{-1}$ & $L_0$ & $0$ & $\\frac{3\\sqrt{3}}{4} J_{-1}$ & $\\frac{4}{\\sqrt{3}}J_0$ & $2\\sqrt{2}J_{-2}$ &$\\frac{1}{\\sqrt{2}} J_{+1}$ & $0$ \\\\ \\hline\n $J_0$ & $0$ & $\\frac{3\\sqrt{3}}{4} J_{+1}$ & $-\\frac{3\\sqrt{3}}{4} J_{-1}$ & $0$ & $\\frac{3\\sqrt{3}}{2} L_{+1}$ & $-\\frac{3\\sqrt{3}}{2}L_{-1}$ & $0$ & $0$ \\\\ \\hline\n $J_{+1}$ & $-J_{+1}$ & $2\\sqrt{2} J_{+2}$ & $-\\frac{4}{\\sqrt{3}}J_0$ & $-\\frac{3\\sqrt{3}}{2} L_{+1}$ & $0$ & $-2L_0$ & $0$ & $\\sqrt{2}L_{-1}$ \\\\ \\hline\n $J_{-1}$ & $J_{-1}$& $\\frac{4}{\\sqrt{3}} J_0$ & $-2\\sqrt{2} J_{-2}$ & $\\frac{3\\sqrt{3}}{2} L_{-1}$ &$2L_0$ &$0$ & $- \\sqrt{2} L_{+1}$ & $0$ \\\\ \\hline\n $J_{+2}$ & $-2J_{+2}$ & $0$ &$ -\\frac{1}{\\sqrt{2}}J_{+1}$ &$0$ & $0$ & $ \\sqrt{2}L_{+1}$ & $0$ & $L_0$ \\\\ \\hline\n $J_{-2}$ & $2J_{-2}$ & $\\frac{1}{\\sqrt{2}}J_{-1}$ & $0$ & $0$ & $-\\sqrt{2} L_{-1}$ &$0$ & $-L_0$ & $0$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Commutator relations of $\\mathfrak{su}(3)$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the construction of polynomial Poisson algebras: a novel grading approach", "authors": ["Rutwig Campoamor-Stursberg", "Danilo Latini", "Ian Marquette", "Junze Zhang", "Yao-Zhong Zhang"], "url": "https://arxiv.org/abs/2503.03490v1", "attribution": "\"On the construction of polynomial Poisson algebras: a novel grading approach\" by Rutwig Campoamor-Stursberg, Danilo Latini, Ian Marquette, Junze Zhang, and Yao-Zhong Zhang, arXiv:2503.03490v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08910v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrrlr}\n\\toprule\n Name & $N_\\text{output}$ & $N_\\text{halo}$ & $\\sigma$ & $\\alpha$ & $\\rho_{SR}$ & $\\beta$ & $N$ & $\\sigma_b$ & readout & $\\rho_A$ \\\\\n\\midrule\n Local RC & 2 & 2 & 0.011 & 0.49 & 0.85 & 1.155972e-08 & 720 & 1.11 & linear & 0.98 \\\\\n Single RC & 40 & 0 & 0.001 & 1.00 & 0.07 & 1.758120e-07 & 6000 & 0.63 & linear & 0.98 \\\\\nModel 3 from & 2 & 4 & 0.052 & 0.41 & 0.34 & 3.611497e-06 & 6000 & 0.00 & linear & 0.99 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Fig. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics", "authors": ["Jason A. Platt", "Stephen G. Penny", "Timothy A. Smith", "Tse-Chun Chen", "Henry D. I. Abarbanel"], "url": "https://arxiv.org/abs/2201.08910v1", "attribution": "\"A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics\" by Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen, and Henry D. I. Abarbanel, arXiv:2201.08910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1910.11114v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Baseline WER (\\%) achieved on single-speaker or two-speaker mixtures before enhancement/separation. All results reported in this paper are with reverberated speech.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{$1$ speaker} & \\textbf{$1$ speaker + noise} & \\textbf{$2$ speakers + noise} \\\\ \\hline\n12.2 & 23.6 & 58.2 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Analyzing the impact of speaker localization errors on speech separation for automatic speech recognition", "authors": ["Sunit Sivasankaran", "Emmaneul Vincent", "Dominique Fohr"], "url": "https://arxiv.org/abs/1910.11114v1", "attribution": "\"Analyzing the impact of speaker localization errors on speech separation for automatic speech recognition\" by Sunit Sivasankaran, Emmaneul Vincent, and Dominique Fohr, arXiv:1910.11114v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.10265v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Tabacof attack}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ll|ll}\n & \\multicolumn{2}{c}{Base} & \\multicolumn{2}{c}{DVIB} \\\\\\hline \nModel & Acc@1 & \\#\\,Hits & Acc@1 & \\#\\,Hits\\\\\\hline\n\\cellcolor[HTML]{EFEFEF}Resnet-18 & \\cellcolor[HTML]{EFEFEF}61.6 & \\cellcolor[HTML]{EFEFEF}927 & \\cellcolor[HTML]{EFEFEF}52.26 & \\cellcolor[HTML]{EFEFEF}1802 \\\\\nResnet-50 & 76.57 & 879 & 72.66 & 1126 \\\\\n\\cellcolor[HTML]{EFEFEF}Resnet-101 & \\cellcolor[HTML]{EFEFEF}33.17 & \\cellcolor[HTML]{EFEFEF}448 &\\cellcolor[HTML]{EFEFEF}34.73 & \\cellcolor[HTML]{EFEFEF}1641 \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication", "authors": ["Alireza Furutanpey", "Pantelis A. Frangoudis", "Patrik Szabo", "Schahram Dustdar"], "url": "https://arxiv.org/abs/2412.10265v1", "attribution": "\"Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication\" by Alireza Furutanpey, Pantelis A. Frangoudis, Patrik Szabo, and Schahram Dustdar, arXiv:2412.10265v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16908v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Frequency of Special Characters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccl}\n \\toprule\n Non-English or Math&Frequency&Comments\\\\\n \\midrule\n \\O & 1 in 1,000& For Swedish names\\\\\n $\\pi$ & 1 in 5& Common in math\\\\\n \\$ & 4 in 5 & Used in business\\\\\n $\\Psi^2_1$ & 1 in 40,000& Unexplained usage\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Localization using Angle-of-Arrival Triangulation", "authors": ["Amod K. Agrawal"], "url": "https://arxiv.org/abs/2508.16908v1", "attribution": "\"Localization using Angle-of-Arrival Triangulation\" by Amod K. Agrawal, arXiv:2508.16908v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.02796v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Japan: Parameter Estimates and NLL for DCC, Scalar BEKK, and LSTM-BEKK Models.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccc}\n \\toprule\n \\textbf{Portfolio Size} & & NLL & $a$ & $b$ \\\\\n \\midrule\n \\multirow{3}{*}{100} & DCC & 164.067 & 0.009 & 0.697 \\\\\n & Scalar BEKK & 163.322 & 0.007 & 0.931 \\\\\n & LSTM-BEKK & \\textbf{162.731} & 0.002 & 0.998 \\\\\n \\midrule\n \\multirow{3}{*}{175} & DCC & 289.456 & 0.005 & 0.696 \\\\\n & Scalar BEKK & 289.320 & 0.006 & 0.945 \\\\\n & LSTM-BEKK & \\textbf{285.631} & 0.002 & 0.993 \\\\\n \\midrule\n \\multirow{3}{*}{250} & DCC & 423.885 & 0.004 & 0.698 \\\\\n & Scalar BEKK & 421.414 & 0.003 & 0.971 \\\\\n & LSTM-BEKK & \\textbf{417.788} & 0.002 & 0.997 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deep Learning Enhanced Multivariate GARCH", "authors": ["Haoyuan Wang", "Chen Liu", "Minh-Ngoc Tran", "Chao Wang"], "url": "https://arxiv.org/abs/2506.02796v1", "attribution": "\"Deep Learning Enhanced Multivariate GARCH\" by Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, and Chao Wang, arXiv:2506.02796v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.03162v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of processing time of different algorithms}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|} \n \\hline\nNA & Processing Time (ms) \\\\ \n \\hline\\hline\n Deep Q-Network & 210 \\\\ \n Exhaustive Search & 4117 \\\\\n K-Means & 387 \\\\\n Fixed & 0 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Q-Network Based Dynamic Movement Strategy in a UAV-Assisted Network", "authors": ["Xukai Zhong", "Yiming Huo", "Xiaodai Dong", "Zhonghua Liang"], "url": "https://arxiv.org/abs/2008.03162v1", "attribution": "\"Deep Q-Network Based Dynamic Movement Strategy in a UAV-Assisted Network\" by Xukai Zhong, Yiming Huo, Xiaodai Dong, and Zhonghua Liang, arXiv:2008.03162v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.02602v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{pifont}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The iteration number stats of phase \\ding{172} optimal cutting post-processing and phase \\ding{173} optimal nodal shape design for different models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllll}\n \\hline\n \\multirow{2}{*}{Model} & \\multirow{2}{*}{Degree} & \\multicolumn{3}{c}{Iteration number of \\ding{172}} & \\multicolumn{3}{c}{Iteration number of \\ding{173}} \\\\\n \\cline{3-8}\n & & Min & Max & Average & Min & Max & Average \\\\\n \\hline\n Screw & 5-10 & 5 & 27 & 14 & 4 & 65 & 16\\\\\n Boot & 5-10 & 6 & 26 & 13 & 4 & 69 & 18\\\\\n Bracket & 5-10 & 5 & 26 & 13 & 6 & 62 & 16\\\\\n Kitten & 9-15 & 5 & 30 & 17 & 6 & 78 & 23\\\\\n Hinge & 9-15 & 7 & 35 & 19 & 6 & 85 & 26\\\\\n Teddy & 9-15 & 6 & 35 & 17 & 5 & 71 & 24\\\\\n Gear & 12-20 & 7 & 43 & 21 & 7 & 87 & 31\\\\\n Impeller & 12-20 & 8 & 49 & 23 & 5 & 90 & 35\\\\\n Connector & 12-20 & 8 & 48 & 22 & 7 & 85 & 33\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Quasi-Optimal Shape Design Method for Lattice Structure Construction", "authors": ["Sifan Chen", "Yuan Kong", "Qiang Zou"], "url": "https://arxiv.org/abs/2502.02602v1", "attribution": "\"A Quasi-Optimal Shape Design Method for Lattice Structure Construction\" by Sifan Chen, Yuan Kong, and Qiang Zou, arXiv:2502.02602v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.06049v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rrr|ll}\nContest & D votes & R votes & Other votes & \\% D & \\% R\\\\ \\hline\nGovernor & 1,348,888 &1,080,801 & 95,373 &55.52 & 44.48\\\\\nSecretary of State & 1,313,716 & 1,113,927 & 64,992 & 54.11& 45.89\\\\\nState Treasurer & 1,292,281 & 1,111,641 & 70,475 & 53.76 & 46.24\\\\\nAttorney General & 1,285,464 & 1,124,757 & 81,733 & 55.64& 44.36\\\\\nU of CO Regent (at large) & 1,246,318 & 1,031,993 & 120,714 & 54.70 & 45.30 \n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\footnotesize Voting Data for Statewide Offices in Colorado, 2018}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Colorado in Context: Congressional Redistricting and Competing Fairness Criteria in Colorado", "authors": ["Jeanne Clelland", "Haley Colgate", "Daryl DeFord", "Beth Malmskog", "Flavia Sancier-Barbosa"], "url": "https://arxiv.org/abs/2011.06049v2", "attribution": "\"Colorado in Context: Congressional Redistricting and Competing Fairness Criteria in Colorado\" by Jeanne Clelland, Haley Colgate, Daryl DeFord, Beth Malmskog, and Flavia Sancier-Barbosa, arXiv:2011.06049v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.02155v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Distribution of global envelope $p$-values for 30 cross inhomogeneous $J$-functions}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccccc}\n $p$-value & 0.01 & 0.02 & 0.03 & 0.04 & $>$0.05 \\\\ \\hline\n frequency & 8 & 3 & 2 & 2 & 15\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Second order semi-parametric inference for multivariate log Gaussian Cox processes", "authors": ["Kristian Bjørn Hessellund", "Ganggang Xu", "Yongtao Guan", "Rasmus Waagepetersen"], "url": "https://arxiv.org/abs/2012.02155v2", "attribution": "\"Second order semi-parametric inference for multivariate log Gaussian Cox processes\" by Kristian Bjørn Hessellund, Ganggang Xu, Yongtao Guan, and Rasmus Waagepetersen, arXiv:2012.02155v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.03247v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\hline\n\\textbf{Duration (secs)} & \\textbf{NPTEL-dt} & \\textbf{NPTEL-et} & \\textbf{NPTEL-Unseen\\_1} & \\textbf{NPTEL-Unseen\\_2} \\\\ \\hline \\hline\n\\textbf{less\\_5} & 1317-25.0\\% & 1226-24.0\\% & 207-21.0\\% & 224-23.0\\% \\\\ %\\hline\n\\textbf{5\\_15} & 3713-72.0\\% & 3790-73.0\\% & 737-75.0\\% & 718-73.0\\% \\\\ %\\hline\n\\textbf{15\\_above} & 153-3.0\\% & 1683-3.0\\% & 38-4.0\\% & 40-4.0\\% \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Data split in terms of ``Number of Utterances'' based on utterance duration in NPTEL test sets}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Investigation of Speaker-adaptation methods in Transformer based ASR", "authors": ["Vishwas M. Shetty", "Metilda Sagaya Mary N J", "S. Umesh"], "url": "https://arxiv.org/abs/2008.03247v2", "attribution": "\"Investigation of Speaker-adaptation methods in Transformer based ASR\" by Vishwas M. Shetty, Metilda Sagaya Mary N J, and S. Umesh, arXiv:2008.03247v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.17099v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Weather-Performance Correlation Matrix}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\textbf{Weather Variable} & \\textbf{Revenue} & \\textbf{Wait Time} & \\textbf{Utilization} & \\textbf{Earnings} \\\\\n\\midrule\nRain Intensity & 0.575*** & 0.551*** & 0.428*** & 0.522*** \\\\\nTemperature Extreme & 0.442*** & 0.287*** & 0.356*** & 0.398*** \\\\\nLow Visibility & -0.384*** & -0.298*** & -0.267*** & -0.341*** \\\\\nHigh Wind Speed & 0.234*** & 0.167*** & 0.201*** & 0.198*** \\\\\n\\bottomrule\n\\multicolumn{5}{l}{\\footnotesize *** $p < 0.001$}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity", "authors": ["Tatsuru Kikuchi"], "url": "https://arxiv.org/abs/2507.17099v1", "attribution": "\"Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity\" by Tatsuru Kikuchi, arXiv:2507.17099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10127v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\hline\n $n=7500$ & Predicted: Default & Predicted: Not default \\\\ \\hline\nActual: Default & 397 & 1198 \\\\ \\hline\nActual: Not default & 168 & 5737 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Confusion matrix of the logistic regression for the Taiwan credit score dataset.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Interpretable Selective Learning in Credit Risk", "authors": ["Dangxing Chen", "Weicheng Ye", "Jiahui Ye"], "url": "https://arxiv.org/abs/2209.10127v1", "attribution": "\"Interpretable Selective Learning in Credit Risk\" by Dangxing Chen, Weicheng Ye, and Jiahui Ye, arXiv:2209.10127v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10082v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Separability matrix of the GGNAM for the bankruptcy dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\hline\n Feature & $x_{17}$ & $x_{19}$ & $x_{22}$ \\\\ \\hline\n $x_{17}$ & 0.913 & 0.908 & 0.881 \\\\ \\hline\n $x_{19}$ & & 0.913 & 0.910 \\\\ \\hline\n $x_{22}$ & & & 0.913 \\\\ \\hline\n \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance", "authors": ["Dangxing Chen", "Weicheng Ye"], "url": "https://arxiv.org/abs/2209.10082v2", "attribution": "\"Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance\" by Dangxing Chen and Weicheng Ye, arXiv:2209.10082v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.07827v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Intensity of price reversal}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\t\t\t& Competitive Agents & Market Makers \\\\\n\t\t\t\\hline\n\t\t\tInsider trader & $\\frac{2}{1 +\\sqrt{1 +\\frac{4}{\\rho_M^2}}-2s}$& $\\frac{2}{1 +\\sqrt{1 +\\frac{4}{\\rho_M^2}}-2s}$\\\\\n\t\t\t\\hline\n\t\t\tStrategic trader & $\\frac{\\rho_M^2}{4+\\rho_M^2(1-s)}$\t& $\\frac{\\rho_M^2}{4}$ \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Order routing and market quality: Who benefits from internalisation?", "authors": ["Umut Çetin", "Alaina Danilova"], "url": "https://arxiv.org/abs/2212.07827v1", "attribution": "\"Order routing and market quality: Who benefits from internalisation?\" by Umut Çetin and Alaina Danilova, arXiv:2212.07827v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table25.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\nNode $i$ & $L_i$ & $W_i$ & $X_i$ \\\\\n\\hline\n1 & 0 & 1 & 2368 \\\\\n2 & 2 & 2 & 1368 \\\\\n3 & 1 & 1 & 2000 \\\\\n4 & 3 & 2 & 2000 \\\\\n5 & 3 & $2 + e^{-1}$ & 2368 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.16139v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|cc|cc|cc|}\n\\hline\n\\multicolumn{2}{|c|}{MVN(A09)} & \\multicolumn{2}{c|}{MVN(hCN)} & \\multicolumn{2}{c|}{ELL($t(5)$)} & \\multicolumn{2}{c|}{EXP} \\\\ \\hline\nrobPC1 & robPC2 & robPC1 & robPC2 & robPC1 & robPC2 & robPC1 & robPC2 \\\\ \\hline\n-0.18 & 0.4 & -0.03 & 0.58 & -0.2 & 0.34 & -0. & 0. \\\\\n0.25 & -0.43 & -0.37 & 0.13 & 0.28 & -0.39 & -0. & -0.02 \\\\\n-0.29 & 0.38 & -0.38 & -0.18 & -0.35 & 0.34 & -0.01 & 0.03 \\\\\n0.33 & -0.29 & 0.19 & -0.04 & 0.35 & -0.28 & -0.02 & 0. \\\\\n-0.35 & 0.13 & 0.42 & -0.15 & -0.37 & 0.11 & -0.02 & -0. \\\\\n0.38 & 0.04 & 0.14 & 0.47 & 0.36 & 0.08 & -0.02 & -0.04 \\\\\n-0.38 & -0.23 & -0.65 & -0.08 & -0.37 & -0.25 & 0.02 & -0.04 \\\\\n0.36 & 0.36 & -0.05 & -0.32 & 0.32 & 0.36 & -0.02 & -0.11 \\\\\n-0.31 & -0.36 & 0.11 & 0.22 & -0.29 & -0.42 & -0.09 & 0.99 \\\\\n0.26 & 0.33 & -0.2 & 0.46 & 0.21 & 0.39 & 0.99 & 0.09 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{First and second robust principal components for all settings.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Abnormal component analysis", "authors": ["Romain Valla", "Pavlo Mozharovskyi", "Florence d'Alché-Buc"], "url": "https://arxiv.org/abs/2312.16139v2", "attribution": "\"Abnormal component analysis\" by Romain Valla, Pavlo Mozharovskyi, and Florence d'Alché-Buc, arXiv:2312.16139v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.22282v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\n\\textbf{$k$} & \\textbf{$I_0(k)$} \\\\ \\hline\n$95 $ &$ 376.9346295103556$ \\\\ \\hline\n$96 $ & $304.9319294669602$ \\\\ \\hline\n$97 $ & $232.03812003788872 $\\\\ \\hline\n$98 $ & $158.01739738115333$ \\\\ \\hline\n$99$ & $82.0954389422525$ \\\\ \\hline\n$100 $ & $0.198425367071128 $\\\\ \\hline\n$100.001 $ & $0.19913824561039337$ \\\\ \\hline\n$100.002 $ & $0.19929381260883816$ \\\\ \\hline\n$100.003 $ & $0.18570061448085348$ \\\\ \\hline\n$100.004 $ & $0$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Implied volatility as a function of the strike when $T = 10^{-5}$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Short-time behavior of the At-The-Money implied volatility for the jump-diffusion stochastic volatility Bachelier model", "authors": ["Elisa Alòs", "Òscar Burés", "Josep Vives"], "url": "https://arxiv.org/abs/2503.22282v1", "attribution": "\"Short-time behavior of the At-The-Money implied volatility for the jump-diffusion stochastic volatility Bachelier model\" by Elisa Alòs, Òscar Burés, and Josep Vives, arXiv:2503.22282v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.14270v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation Error of Complete Simulation}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\\toprule\nEstimates & \\multicolumn{6}{c}{Simulation RMSE}\\\\ \\cmidrule(lr){2-9}\n &\\multicolumn{2}{c}{CC} & \\multicolumn{2}{c}{DC} & \\multicolumn{2}{c}{Stoich Mis} & \\multicolumn{2}{c}{$\\varepsilon_s^-$ Mis}\\\\\\cmidrule(lr){2-9}\n & V+E & V & V+E & V & V+E & V & V+E & V \\\\ \\midrule\n $\\hat{V}_{t}$ & {7} & {7.7} & {5.2} & {5.4} & {3.2}& {3.6} & {7.2} & {7.6} \\\\\n $\\hat{c}_{ss}^-$ & {89} & {70} & {76} & {62} & {90} & {431} & {350} & {277} \\\\\n $\\hat{c}_{s,avg}^-$ & {89} & {97} & {76} & {104} & {90} & {419} & {314} & {234} \\\\\n $\\hat{c}_{ss}^+$ & {94} & {93} & {51} & {70} & {83} & {156} & {116} & {107} \\\\\n $\\hat{c}_{s,avg}^+$ & {139} & {143} & {135} & {152} & {75} & {140} & {167} & {154} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Improved Battery State Estimation Under Parameter Uncertainty Caused by Aging Using Expansion Measurements", "authors": ["Sravan Pannala", "Puneet Valecha", "Peyman Mohtat", "Jason B. Siegel", "Anna G. Stefanopoulou"], "url": "https://arxiv.org/abs/2009.14270v1", "attribution": "\"Improved Battery State Estimation Under Parameter Uncertainty Caused by Aging Using Expansion Measurements\" by Sravan Pannala, Puneet Valecha, Peyman Mohtat, Jason B. Siegel, and Anna G. Stefanopoulou, arXiv:2009.14270v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18896v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n Number of claims occurred for each policy& 0 & 1 & 2 & 3 & 4 & 5\\\\\n \\midrule\n Number of policies & $653,069$ & $23,571$ & $1298$ & $62$ & $5$ & $2$ \\\\\n Total exposure & $341,090$ & $16,315$ & $909$ & $42$ & $2$ & $1$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Number of policies and total exposure within the portfolio that is split with respect to the number of claims occurred for each policy.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Calibration Bands for Mean Estimates within the Exponential Dispersion Family", "authors": ["Łukasz Delong", "Selim Gatti", "Mario V. Wüthrich"], "url": "https://arxiv.org/abs/2503.18896v1", "attribution": "\"Calibration Bands for Mean Estimates within the Exponential Dispersion Family\" by Łukasz Delong, Selim Gatti, and Mario V. Wüthrich, arXiv:2503.18896v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11559v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Speeding-up all shortest paths computation}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\nDataset & Space gain & Speed-up \\\\ \\hline\nCA-ASTROPH & $45.82\\%$ & $1.496$ \\\\\nBLOG-CATALOG & $46.52\\%$ & $1.323$ \\\\\nCA-HEPTH & $31.08\\%$ & $1.065$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Neighborhood-preserving Graph Summarization", "authors": ["Abd Errahmane Kiouche", "Julien Baste", "Mohammed Haddad", "Hamida Seba"], "url": "https://arxiv.org/abs/2101.11559v1", "attribution": "\"A Neighborhood-preserving Graph Summarization\" by Abd Errahmane Kiouche, Julien Baste, Mohammed Haddad, and Hamida Seba, arXiv:2101.11559v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01001v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The test performance of three optimized models with different input sizes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\n & RMSE & $R^2$ & MAE & Huber loss\\\\\n\\midrule\n$32 \\times 32$ & 0.660 & 0.901 & 0.434 & 0.18\\\\\n$64 \\times 64$ & 0.674 & 0.897 & 0.438 & 0.19\\\\\n$128 \\times 128$ & 0.692 & 0.892 & 0.460 & 0.20\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Automated Assessment of Residual Plots with Computer Vision Models", "authors": ["Weihao Li", "Dianne Cook", "Emi Tanaka", "Susan VanderPlas", "Klaus Ackermann"], "url": "https://arxiv.org/abs/2411.01001v1", "attribution": "\"Automated Assessment of Residual Plots with Computer Vision Models\" by Weihao Li, Dianne Cook, Emi Tanaka, Susan VanderPlas, and Klaus Ackermann, arXiv:2411.01001v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.12447v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc} \n \\hline\n $n$ & 250 & 500 & 1000 & 2000 \\\\ \n \\hline\n Coverage & 0.95 & 0.95 & 0.96 & 0.96 \\\\ \n \\hline\n Bias & 0.42 & 0.27 & 0.18 & 0.13 \\\\ \n \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{ \\textbf{NHANES Data.} Bias $ = ||\\beta -\\hat{\\beta}||$ and coverage of the moment condition model based estimates of the regression parameters for varying sample sizes.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Constrained Reweighting of Distributions: an Optimal Transport Approach", "authors": ["Abhisek Chakraborty", "Anirban Bhattacharya", "Debdeep Pati"], "url": "https://arxiv.org/abs/2310.12447v2", "attribution": "\"Constrained Reweighting of Distributions: an Optimal Transport Approach\" by Abhisek Chakraborty, Anirban Bhattacharya, and Debdeep Pati, arXiv:2310.12447v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.09180v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dimensions for the Patch Antenna Design }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{Patches} & \\textbf{1st, 8th} & \\textbf{2nd, 7th} & \\textbf{3rd, 6th} & \\textbf{4th, 5th} \\\\\n\\hline\nWidth & 1.55 & 2.06 & 2.59 & 3.12 \\\\ \n\\hline\nLength & 2.81 & 2.77 & 2.74 & 2.72 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Direct- Conversion Digital Beamforming Array Receiver with 800 MHz Channel Bandwidth at 28 GHz using Xilinx RF SoC", "authors": ["Sravan Pulipati", "Viduneth Ariyarathna", "Udara De Silva", "Najath Akram", "Elias Alwan", "Arjuna Madanayake", "Soumyajit Mandal", "Theodore S. Rappaport"], "url": "https://arxiv.org/abs/1911.09180v1", "attribution": "\"A Direct- Conversion Digital Beamforming Array Receiver with 800 MHz Channel Bandwidth at 28 GHz using Xilinx RF SoC\" by Sravan Pulipati, Viduneth Ariyarathna, Udara De Silva, Najath Akram, Elias Alwan, Arjuna Madanayake, Soumyajit Mandal, and Theodore S. Rappaport, arXiv:1911.09180v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.09028v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\n& \\multicolumn{2}{c}{MIN $\\rightarrow$ CUB} & \\multicolumn{2}{c}{Tiered $\\rightarrow$ CUB} \\\\\n\\cline{2-3} \\cline{4-5}\n & 1-shot & 5-shot & 1-shot & 5-shot \\\\ \n\\midrule\nMAML & 37.3 $\\pm$ 0.3 & 54.7 $\\pm$ 0.3 & 38.1 $\\pm$ 0.3 & 55.1 $\\pm$ 0.3 \\\\\nT-Net & 38.0 $\\pm$ 0.3 & 55.6 $\\pm$ 0.3 & 37.5 $\\pm$ 0.3 & 54.8 $\\pm$ 0.3 \\\\\nMT-Net & 37.1 $\\pm$ 0.3 & 53.1 $\\pm$ 0.3 & 38.0 $\\pm$ 0.3 & 55.5 $\\pm$ 0.3 \\\\\nWarp-MAML & \\textbf{41.0} $\\pm$ 0.3 & 55.3 $\\pm$ 0.3 & 40.9 $\\pm$ 0.3 & 56.8 $\\pm$ 0.3 \\\\\n\\midrule\nSAP (ours) & 40.9 $\\pm$ 0.3 & \\textbf{55.8} $\\pm$ 0.3 & \\textbf{41.1} $\\pm$ 0.3 & \\textbf{60.7} $\\pm$ 0.3 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Average cross-domain meta-test accuracy scores over 5 runs a 32-channel Conv-4 backbone. Techniques trained on tasks from one data set were evaluated on tasks from another data set. The 95\\% confidence intervals are displayed as $\\pm$ x.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Subspace Adaptation Prior for Few-Shot Learning", "authors": ["Mike Huisman", "Aske Plaat", "Jan N. van Rijn"], "url": "https://arxiv.org/abs/2310.09028v1", "attribution": "\"Subspace Adaptation Prior for Few-Shot Learning\" by Mike Huisman, Aske Plaat, and Jan N. van Rijn, arXiv:2310.09028v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.17640v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameter settings.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll} \n \\toprule\n \\textbf{Parameters} & \\textbf{Value} \\\\\n \\midrule \n Number of antennas ($M$ ($M_x\\times M_y$)) & $16\\ (4\\times 4)$\\\\\n Number of subcarriers ($K$) & $8$\\\\\n BS carrier frequency and bandwidth & $60$ GHz, $50$ MHz\\\\\n Number of paths ($L$) & $25$\\\\\n Speed of light ($c$) & $3\\times 10^8 $m/s\\\\\n Image size ($W_I, H_I, C_I$) & $960, 540, 3$\\\\\n Feature sizes ($d_\\text{CSI}$, $d_\\text{Vision}$) & $64$\\\\\n Embedding size ($d_\\text{Embed}$) & $16$\\\\\n Batch size ($B$) & $32$\\\\\n Temperature parameter ($\\alpha$) & $0.07$\\\\\n Training epochs & $50$\\\\\n Initial learning rate & $0.001$\\\\\n Decay mode& $0.5$ per $10$ epochs\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Multimodal Radio and Vision Fusion for Robust Localization in Urban V2I Communications", "authors": ["Can Zheng", "Jiguang He", "Chung G. Kang", "Guofa Cai", "Henk Wymeersch"], "url": "https://arxiv.org/abs/2508.17640v1", "attribution": "\"Multimodal Radio and Vision Fusion for Robust Localization in Urban V2I Communications\" by Can Zheng, Jiguang He, Chung G. Kang, Guofa Cai, and Henk Wymeersch, arXiv:2508.17640v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05975v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison with different fusion strategies}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc}\n\\hline\n\\multirow{2}{*}{Fusion Strategy} & \\multicolumn{2}{c|}{-5 dB} & \\multicolumn{2}{c}{0 dB} \\\\ \\cline{2-5} \n & STOI (\\%) & PESQ & STOI (\\%) & PESQ \\\\ \\hline\nEarly Fusion & 75.23 & 2.24 & 81.41 & 2.66 \\\\ \\hline\nLate Fusion & 75.01 & 2.21 & 81.54 & 2.59 \\\\ \\hline\nIntermediate Fusion (VSE) & 76.68 & 2.28 & 83.54 & 2.71 \\\\ \\hline\nIntermediate Fusion (AV(SE)$^2$) & 79.31 & 2.37 & 84.88 & 2.79 \\\\ \\hline\nMulti-layer Feature Fusion & 80.47 & 2.39 & 85.02 & 2.83 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multi-layer Feature Fusion Convolution Network for Audio-visual Speech Enhancement", "authors": ["Xinmeng Xu", "Jianjun Hao"], "url": "https://arxiv.org/abs/2101.05975v3", "attribution": "\"Multi-layer Feature Fusion Convolution Network for Audio-visual Speech Enhancement\" by Xinmeng Xu and Jianjun Hao, arXiv:2101.05975v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1910.10655v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Evaluation of voice activity detection models, in terms of detection error rate (DetER \\%), false alarm (FA \\%), and missed detection (Miss \\%) rates. Results on the development set are reported using small font size. We report two variants: the first one is based on handcrafted features (MFCCs) and the other one is an end-to-end model processing the waveform directly.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|rr|rrrr|}\n \\hline\n & \\multicolumn{2}{c|}{DetER \\%} & \\multicolumn{2}{c|}{FA \\%} & \\multicolumn{2}{c|}{Miss \\%} \\\\\n \\hline\n Baseline & 11.2 & & 6.5 & & 4.7 & \\\\\n \\hline\n MFCC~ & 10.5 & {\\scriptsize{10.0}} & 6.8 & {\\scriptsize{5.4}} & 3.7 & {\\scriptsize{4.6}} \\\\\n Waveform & \\textbf{9.9} & {\\scriptsize{9.3}} & 5.7 & {\\scriptsize{3.7}} & 4.2 & {\\scriptsize{5.6}} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "End-to-end Domain-Adversarial Voice Activity Detection", "authors": ["Marvin Lavechin", "Marie-Philippe Gill", "Ruben Bousbib", "Hervé Bredin", "Leibny Paola Garcia-Perera"], "url": "https://arxiv.org/abs/1910.10655v2", "attribution": "\"End-to-end Domain-Adversarial Voice Activity Detection\" by Marvin Lavechin, Marie-Philippe Gill, Ruben Bousbib, Hervé Bredin, and Leibny Paola Garcia-Perera, arXiv:1910.10655v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.16605v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{r|r|r|r|}\n $N_f$ & $\\varepsilon_2^{(N_f)}$ & $\\varepsilon_4^{(N_f)}$ & $\\varepsilon_6^{(N_f)}$ \\\\\n \\hline \n10 (\\phantom{1}30) & 0.47427 & 9.22067 & 162.92010\\\\\n20 (\\phantom{1}60) & 0.03533 & 0.35907 & 5.32643\\\\\n40 (120) & 0.00405 & 0.00273 & 0.02023\\\\\n80 (240)& 0.00048 & 0.00030 & 0.00078\\\\\n160 (480)& 0.00006 & 0.00004 & 0.00009\\\\\n\\hline\n \\end{tabular}\n\\caption{Absolute error of the far-field with 64 equidistant incident and observation directions for the disk with radius $R=2$ and the physical parameters $a=3$ and $\\eta=1$ for varying number of faces (collocation nodes). The wave numbers are $k=2$, $k=4$, and $k=6$, respectively.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Two direct sampling methods for an anisotropic scatterer with a conductive boundary", "authors": ["Isaac Harris", "Victor Hughes", "Andreas Kleefeld"], "url": "https://arxiv.org/abs/2412.16605v2", "attribution": "\"Two direct sampling methods for an anisotropic scatterer with a conductive boundary\" by Isaac Harris, Victor Hughes, and Andreas Kleefeld, arXiv:2412.16605v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00676v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\textbf{Dataset} & \\textbf{DFT} & \\textbf{DWT} & \\textbf{DFT + DWT} \\\\ \\hline\nStarGAN & 78.81\\% & 79.34\\% & 97.67\\% \\\\ \\hline\nStyleGAN & 61.45\\% & 69.11\\% & 87.1\\% \\\\ \\hline\nSITD & 83.61\\% & 49.72\\% & 90.3\\% \\\\ \\hline\nBigGAN & 64.92\\% & 66.92\\% & 72.08\\% \\\\ \\hline\nStyleGAN2 & 68.02\\% & 59.54\\% & 91.83\\% \\\\ \\hline\nCycleGAN & 65.31\\% & 55.94\\% & 77.53\\% \\\\ \\hline\nWhichfaceisreal & 39.85\\% & 77.40\\% & 80.15\\% \\\\ \\hline\nGauGAN & 50.91\\% & 67.22\\% & 70.91\\% \\\\ \\hline\nDeepfake & 56.39\\% & 51.88\\% & 68.90\\% \\\\ \\hline\nCRN & 61.75\\% & 83.85\\% & 55.06\\% \\\\ \\hline\nIMLE & 47.82\\% & 79.90\\% & 55.06\\% \\\\ \\hline\nSAN & 69.18\\% & 46.80\\% & 49.32\\% \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Evaluation of DFT and DWT combination for fake image detection. Percentage accuracy is reported for the full image using only DFT, only DWT, and the combination DFT+DWT.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Fake Visual Content Detection Using Two-Stream Convolutional Neural Networks", "authors": ["Bilal Yousaf", "Muhammad Usama", "Waqas Sultani", "Arif Mahmood", "Junaid Qadir"], "url": "https://arxiv.org/abs/2101.00676v1", "attribution": "\"Fake Visual Content Detection Using Two-Stream Convolutional Neural Networks\" by Bilal Yousaf, Muhammad Usama, Waqas Sultani, Arif Mahmood, and Junaid Qadir, arXiv:2101.00676v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00719v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|r|r|l|r|r|r}\n\\hline\nPredictors & Coefficients & SE & pvalue & Lower CI & Upper CI & Odds Ratio\\\\\n\\hline\nIntercept & 2.75 & 3.34 & 0.411 & -3.92 & 9.16 & 15.68\\\\\n\\hline\nWPS & 0.08 & 0.02 & <0.001 & 0.04 & 0.12 & 1.08\\\\\n\\hline\nWC & 0.00 & 0.00 & 0.103 & 0.00 & 0.00 & 1.00\\\\\n\\hline\nSixltr & -0.07 & 0.05 & 0.202 & -0.17 & 0.04 & 0.93\\\\\n\\hline\nDic & -0.16 & 0.04 & <0.001 & -0.24 & -0.08 & 0.85\\\\\n\\hline\nfunction. & 0.60 & 0.11 & <0.001 & 0.39 & 0.81 & 1.82\\\\\n\\hline\naffect & -0.60 & 5.21 & 0.909 & -11.50 & 9.26 & 0.55\\\\\n\\hline\nsocial & 0.37 & 0.14 & 0.007 & 0.10 & 0.65 & 1.45\\\\\n\\hline\ncogproc & -0.34 & 0.15 & 0.021 & -0.64 & -0.06 & 0.71\\\\\n\\hline\npercept & 0.73 & 0.40 & 0.070 & -0.05 & 1.52 & 2.07\\\\\n\\hline\nbio & -0.05 & 0.23 & 0.829 & -0.49 & 0.39 & 0.95\\\\\n\\hline\ndrives & 0.30 & 0.15 & 0.049 & 0.00 & 0.60 & 1.35\\\\\n\\hline\nrelativ & -0.06 & 0.12 & 0.604 & -0.29 & 0.17 & 0.94\\\\\n\\hline\nAllPunc & -0.08 & 0.04 & 0.062 & -0.16 & 0.00 & 0.92\\\\\n\\hline\nfocuspast & 1.10 & 0.23 & <0.001 & 0.66 & 1.55 & 3.00\\\\\n\\hline\nfocuspresent & 0.06 & 0.21 & 0.773 & -0.35 & 0.48 & 1.06\\\\\n\\hline\nfocusfuture & 1.65 & 0.33 & <0.001 & 1.00 & 2.31 & 5.20\\\\\n\\hline\nanger & 0.68 & 2.18 & 0.754 & -3.57 & 5.05 & 1.98\\\\\n\\hline\nposemo & 1.15 & 5.21 & 0.825 & -8.70 & 12.05 & 3.17\\\\\n\\hline\nnegemo & 1.34 & 5.24 & 0.799 & -8.57 & 12.30 & 3.81\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Bankruptcy prediction using disclosure text features", "authors": ["Sridhar Ravula"], "url": "https://arxiv.org/abs/2101.00719v1", "attribution": "\"Bankruptcy prediction using disclosure text features\" by Sridhar Ravula, arXiv:2101.00719v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.16260v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\toprule\n & Bias due to imperfect fit & Bias due to overfitting\\\\\n \\midrule\n $\\hat{\\gamma}^{sep}$ & $O(1)$ & $O\\left(\\frac{1}{\\sqrt{T_0}}\\right)$\\\\\n $\\hat{\\gamma}^{cat}$ & $O(1)$ & $O\\left(\\frac{1}{\\sqrt{T_0K}}\\right)$\\\\\n $\\hat{\\gamma}^{avg}$ & $O\\left(\\frac{1}{\\sqrt{K}}\\right)$ & $O\\left(\\frac{1}{\\sqrt{T_0K}}\\right)$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Leading terms in high probability bounds on the bias due to imperfect fit and overfitting in Theorem~, with $N$ fixed.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Using Multiple Outcomes to Improve the Synthetic Control Method", "authors": ["Liyang Sun", "Eli Ben-Michael", "Avi Feller"], "url": "https://arxiv.org/abs/2311.16260v3", "attribution": "\"Using Multiple Outcomes to Improve the Synthetic Control Method\" by Liyang Sun, Eli Ben-Michael, and Avi Feller, arXiv:2311.16260v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11002v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Worldwide regulations on 5925 – 7125 MHz spectrum}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|}\n\\hline\nFrequency band {[}MHz{]} & 5925 – 6425 & 6425 – 7125 \\\\ \\hline\nTotal Bandwidth {[}MHz{]} & 500 & 700 \\\\ \\hline\n & Microwave links & Mobile \\\\ \\cline{2-3} \n\\multirow{-2}{*}{Licensed users} & \\multicolumn{2}{c|}{Fixed - Satellite} \\\\ \\hline\nUS & \\multicolumn{2}{c|}{14 dBm EIRP AFC} \\\\ \\hline\nSaudi Arabia & \\multicolumn{2}{c|}{Indoor/Outdoor AFC} \\\\ \\hline\nEurope & Indoor/Outdoor & Not available \\\\ \\hline\nJapan & 23 dBm EIRP for 20 MHz & Not available \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Communications over Unlicensed sub-8 GHz Spectrum: Opportunities and Challenges", "authors": ["Karim Saifullin", "Hussein Al-Shatri", "Mohamed-Slim Alouini"], "url": "https://arxiv.org/abs/2412.11002v1", "attribution": "\"Communications over Unlicensed sub-8 GHz Spectrum: Opportunities and Challenges\" by Karim Saifullin, Hussein Al-Shatri, and Mohamed-Slim Alouini, arXiv:2412.11002v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.03021v2_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{IHDP: MSE statistics over 50 trials}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\textbf{Model} & \\textbf{Min} & \\textbf{Max} & \\textbf{Min Rank} & \\textbf{Max Rank}\\\\\n \\midrule\n TARNet & 10.2 & 86.0 & 6 & 6 \\\\\n CausalForest & 0.03 & 6.19 & 1 & 5 \\\\\n S-BART & 0.03 & 6.72 & 1 & 4 \\\\\n T-BART & 0.02 & 6.25 & 1 & 3 \\\\\n S-engression & 0.10 & 10.16 & 2 & 5 \\\\\n T-engression & 0.05 & 6.45 & 1 & 5 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Testing Generalizability in Causal Inference", "authors": ["Daniel de Vassimon Manela", "Linying Yang", "Robin J. Evans"], "url": "https://arxiv.org/abs/2411.03021v2", "attribution": "\"Testing Generalizability in Causal Inference\" by Daniel de Vassimon Manela, Linying Yang, and Robin J. Evans, arXiv:2411.03021v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.18826v4_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n \\toprule\n Scenario & $\\delta$ (s) & Runtime (ms) \\\\\n \\midrule\n Paris & 0.1 & 13.65 \\\\\n & 0.2 & 0.01 \\\\\n New York & 0.1 & 92.50 \\\\\n Singapore & 0.1 & 33.33 \\\\\n & 0.2 & 23.01 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Booktabs table}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Geometry-Aware Normalizing Wasserstein Flows for Optimal Causal Inference", "authors": ["Kaiwen Hou"], "url": "https://arxiv.org/abs/2311.18826v4", "attribution": "\"Geometry-Aware Normalizing Wasserstein Flows for Optimal Causal Inference\" by Kaiwen Hou, arXiv:2311.18826v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.17879v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c||c|} \n \\hline\n &T & $T_0$ & $T_1$ &$T_2$&$T_J$ \\\\ \n \\hline\n \\hline\n $p$-value &0.08 & 0.052 & 0.011& 0.027&0.081 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Two-Sample Tests for Optimal Lifts, Manifold Stability and Reverse Labeling Reflection Shap", "authors": ["Do Tran Van", "Susovan Pal", "Benjamin Eltzner", "Stephan F. Huckemann"], "url": "https://arxiv.org/abs/2503.17879v1", "attribution": "\"Two-Sample Tests for Optimal Lifts, Manifold Stability and Reverse Labeling Reflection Shap\" by Do Tran Van, Susovan Pal, Benjamin Eltzner, and Stephan F. Huckemann, arXiv:2503.17879v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.05735v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the greedily selected $p^1,~p^2$ versus the optimal $p^1,~p^2$ for a $T=2$ rounds data mixing problem. On 2 out of 3 datasets, the greedily selected proportions match the optimal proportions.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n \\multirow{2}{*}{\\textbf{Solving}} & \\multicolumn{2}{c}{\\textbf{Arxiv/SE}} & \\multicolumn{2}{c}{\\textbf{GH/C4}} & \\multicolumn{2}{c}{\\textbf{Books/SE}} \\\\\n & $p_1^1, p_1^2$ & Avg test PPL & $p_1^1, p_1^2$ & Avg test PPL & $p_1^1, p_1^2$ & Avg test PPL \\\\\n \\midrule\n Greedy & $0.4, 0.4$ & $16.039$ & $0.6, 0.4$ & $36.525$ & $0.3, 0.6$ & $45.513$ \\\\\n Optimal & $0.4, 0.4$ & $16.039$ & $0.3, 0.6$ & $34.709$ & $0.3, 0.6$ & $45.513$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Aioli: A Unified Optimization Framework for Language Model Data Mixing", "authors": ["Mayee F. Chen", "Michael Y. Hu", "Nicholas Lourie", "Kyunghyun Cho", "Christopher Ré"], "url": "https://arxiv.org/abs/2411.05735v2", "attribution": "\"Aioli: A Unified Optimization Framework for Language Model Data Mixing\" by Mayee F. Chen, Michael Y. Hu, Nicholas Lourie, Kyunghyun Cho, and Christopher Ré, arXiv:2411.05735v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.21452v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n \\hline\n h & 1/8 & 1/16 & 1/32 & 1/64 & 1/128 & 1/256\\\\\n \\hline\n e & 7.99E-04 & 2.33E-04 & 6.80E-05 & 1.85E-05 & 4.73E-06 & 1.19E-06\\\\\n r & & 1.78 & 1.78 & 1.88 & 1.97 & 1.98\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{the magnitude of the error and order of convergence.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Numerical solution of locally loaded Volterra integral equations", "authors": ["Vladislav Byankin", "Aleksandr Tynda", "Denis Sidorov", "Aliona Dreglea"], "url": "https://arxiv.org/abs/2503.21452v1", "attribution": "\"Numerical solution of locally loaded Volterra integral equations\" by Vladislav Byankin, Aleksandr Tynda, Denis Sidorov, and Aliona Dreglea, arXiv:2503.21452v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.11910v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sample Table Title}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\textbf{PART} &\\textbf{DESCRIPTION} \\\\\n\\hline \\\\\nDendrite &Input terminal \\\\\nAxon &Output terminal \\\\\nSoma &Cell body (contains cell nucleus) \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks", "authors": ["Eduard Tulchinskii", "Daria Voronkova", "Ilya Trofimov", "Evgeny Burnaev", "Serguei Barannikov"], "url": "https://arxiv.org/abs/2503.11910v1", "attribution": "\"RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks\" by Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, and Serguei Barannikov, arXiv:2503.11910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18869v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\\hline\n s & $R_2(s)$ &Classic Bound & Our Bound \\\\\\hline\\hline\n $5$ & $R_2(5) \\leq 46$ & $m_2(5)\\leq 2,677$ & $m_2(5)\\leq 392$ \\\\\\hline\n $6$ & $R_2(6)\\leq 160$ & $m_2(6)\\leq 1,293,533$ & $m_2(6)\\leq 152,000$ \\\\\\hline\n $7$ & $R_2(7)\\leq 492$ & $m_2(7)\\leq 1,265,045,472$ & $m_2(7)\\leq 282,475,249$ \\\\\\hline\n $8$ & $R_2(8)\\leq 1,518$ & $m_2(8)\\leq 5,114,696,152,715$ & $m_2(8)\\leq 4,065,272,127,504$ \\\\\\hline\n $9$ & $R_2(9)\\leq 4,956$ & $m_2(9)\\leq 1.437\\times 10^{17}$ & $m_2(9)\\leq 6.947\\times 10^{17}$ \\\\\\hline\n $10$& $R_2(10)\\leq 16,064$ & $m_2(10)\\leq 1.788\\times 10^{22}$ & $m_2(10)\\leq 1.032 \\times 10^{24}$ \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Values of the bound given by , v.s. the classical probabilistic bound .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Bounds on the Critical Multiplicity of Ramsey Numbers with Many Colors", "authors": ["Bryce Christopherson", "Casia Steinhaus"], "url": "https://arxiv.org/abs/2501.18869v2", "attribution": "\"Bounds on the Critical Multiplicity of Ramsey Numbers with Many Colors\" by Bryce Christopherson and Casia Steinhaus, arXiv:2501.18869v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.02634v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation results to the Type I error}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\n\\hline\n$n$/$\\alpha$ & $\\alpha=$ 0.01 & $\\alpha=$ 0.05 & $\\alpha=$ 0.10 \\\\ \\hline\n$n=1000$ & 0.010 & 0.0490 & 0.1010 \\\\ \\hline\n$n=10000$ & 0.0140 & 0.0539 & 0.1071 \\\\ \\hline\n$n=100000$ & 0.0128 & 0.0566 & 0.1106 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Pointwise Data Depth for Univariate and Multivariate Functional Outlier Detection", "authors": ["Cristian F. Jimenez-Varon", "Fouzi Harrou", "Ying Sun"], "url": "https://arxiv.org/abs/2311.02634v1", "attribution": "\"Pointwise Data Depth for Univariate and Multivariate Functional Outlier Detection\" by Cristian F. Jimenez-Varon, Fouzi Harrou, and Ying Sun, arXiv:2311.02634v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.13170v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|} \n \\hline\n Task & 0 & 1 & 2 & 3 & 4 \\\\ \n \\hline\n Relaxation gap $\\delta$ (\\%) & 0.9 & 2.1 & 14.3 & 24.4 & 5.8 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Relaxation gap of the TFR for moving the robot arm to different configurations in minimum time.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A New Semidefinite Relaxation for Linear and Piecewise-Affine Optimal Control with Time Scaling", "authors": ["Lujie Yang", "Tobia Marcucci", "Pablo A. Parrilo", "Russ Tedrake"], "url": "https://arxiv.org/abs/2504.13170v1", "attribution": "\"A New Semidefinite Relaxation for Linear and Piecewise-Affine Optimal Control with Time Scaling\" by Lujie Yang, Tobia Marcucci, Pablo A. Parrilo, and Russ Tedrake, arXiv:2504.13170v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.11123v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Failure case for Wav2Vec2.0 XLSR-53. This highlights a limitation in transcribing Nigerian Pidgin speech, showing how numeric elements may be misinterpreted, thus affecting transcription accuracy.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\n\\textbf{Reference} & \\textbf{Prediction} \\\\\n\\midrule\ndem go wan kill chief femi fani kayode wife 27 & dem go wan kill chief femi fani kayode wife 217 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Towards End-to-End Training of Automatic Speech Recognition for Nigerian Pidgin", "authors": ["Amina Mardiyyah Rufai", "Afolabi Abeeb", "Esther Oduntan", "Tayo Arulogun", "Oluwabukola Adegboro", "Daniel Ajisafe"], "url": "https://arxiv.org/abs/2010.11123v2", "attribution": "\"Towards End-to-End Training of Automatic Speech Recognition for Nigerian Pidgin\" by Amina Mardiyyah Rufai, Afolabi Abeeb, Esther Oduntan, Tayo Arulogun, Oluwabukola Adegboro, and Daniel Ajisafe, arXiv:2010.11123v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.05648v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Data cleaning steps and remaining wind parks for validation in Brazil}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n \\hline\n & Applies to & Remaining \\\\&&wind parks \\\\ \n \\hline\n \\hline\n - total number of observed wind park time series \n & & 174 \\\\ \\hline\n 1. matching of ONS and ANEEL \n & & 72 \\\\ \n - keep only 100 matching score\t\n & & 70 \\\\ \\hline\n 2. data cleaning & &\\\\\n - remove constant parts of time series except 0 ($>$24h) \n & 50 & 70 \\\\ \n - remove constant parts of 0 generation ($>$180h)\n & 28 & 70\\\\\n - remove capacity factors $>$ 1\n & 59 & 70\\\\\n - remove short time series ($<$2y)\n & 17 & 53 \\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Towards a global dynamic wind atlas: A multi-country validation of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the Global Wind Atlas", "authors": ["Katharina Gruber", "Peter Regner", "Sebastian Wehrle", "Marianne Zeyringer", "Johannes Schmidt"], "url": "https://arxiv.org/abs/2012.05648v1", "attribution": "\"Towards a global dynamic wind atlas: A multi-country validation of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the Global Wind Atlas\" by Katharina Gruber, Peter Regner, Sebastian Wehrle, Marianne Zeyringer, and Johannes Schmidt, arXiv:2012.05648v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.01349v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccc}\n \\toprule\n \\multicolumn{7}{c}{\\textbf{MIMIC}} \\\\\n \\midrule\n \\multicolumn{7}{c}{Training set} \\\\\n \\midrule\n & All & White & Black & Asian & Male & Female \\\\\n Patients & 42,148 & 31,936 (75.77\\%) & 8,398 (19.93\\%) & 1,814 (4.30\\%) & 20,123 (47.74\\%) & 22,025 (52.26\\%) \\\\\n Scans & 181,342 & 140,445 (77.45\\%) & 33,906 (18,70\\%) & 6,991 (3.86\\%) & 97,361 (53.69\\%) & 83961 (46.21\\%) \\\\\n Age & $62.6 \\pm 16.6$ & $63.9 \\pm 16.3$ & $57.7 \\pm 16.7$ & $62.1 \\pm 17.8$ & $62.32 \\pm 15.8$ & $63.0 \\pm 17.5$\\\\ \n \\midrule\n \\multicolumn{7}{c}{Test set} \\\\\n \\midrule\n & All & White & Black & Asian & Male & Female \\\\\n Patients & 257 & 205 (79.77\\%) & 45 (17.51\\%) & 7 (2.72\\%) & 141 (54.86\\%) & 116 (45.14\\%) \\\\\n Scans & 3,041 & 2,235 (73.50\\%) & 676 (22.22\\%) & 130 (4.27\\%) & 1,658 (54.52\\%) & 1,383 (45.48\\%) \\\\\n Age & $65.8 \\pm 12.1$ & $66.2 \\pm 12.3$ & $64.1 \\pm 11.9$ & $67.4 \\pm 9.5$ & $66.0 \\pm 11.6$ & $65.4 \\pm 12.8$\\\\ \n \\toprule\n \\multicolumn{7}{c}{\\textbf{CheXpert}} \\\\\n \\midrule\n \\multicolumn{7}{c}{Training set} \\\\\n \\midrule\n & All & White & Black & Asian & Male & Female \\\\\n Patients & 25,730 & 20,034 (77.86\\%) & 1,751 (6.81\\%)) & 3,945 (15.33\\%) & 14,165 (55.05\\%) & 11,565 (44.95\\%) \\\\\n Scans & 76,205 & 59,238 (77.73\\%) & 5,596 (7.34\\%) & 11,371 (14.92\\%) & 44,774 (58.75\\%) & 31,431 (41.25\\%) \\\\\n Age & $63.1 \\pm 17.4$ & $64.3 \\pm 17.2$ & $55.7 \\pm 17.4$ & $61.6 \\pm 17.4$ & $62.5 \\pm 17.0$ & $63.8 \\pm 17.9$\\\\ \n \\midrule\n \\multicolumn{7}{c}{Test set} \\\\\n \\midrule\n & All & White & Black & Asian & Male & Female \\\\\n Patients & 12,866 & 9,956 (77.38\\%) & 879 (6.83\\%) & 2,031 (15.79\\%) & 7,091 (55.11\\%) & 5,775 (44.89\\%) \\\\\n Scans & 38,240 & 29,844 (78.04\\%) & 2746 (7.18\\%) & 5,650 (14.278\\%) & 22,265 (58.22\\%) & 15,975 (41.78\\%) \\\\\n Age & $63.3 \\pm 17.2$ & $64.2 \\pm 17.1$ & $57.4 \\pm 16.3$ & $61.1 \\pm 17.6$ & $62.8 \\pm 16.4$ & $63.9 \\pm 18.3$\\\\ \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Statistics of the utilized MIMIC and CheXpert subsets per split and subgroups.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Post-hoc Orthogonalization for Mitigation of Protected Feature Bias in CXR Embeddings", "authors": ["Tobias Weber", "Michael Ingrisch", "Bernd Bischl", "David Rügamer"], "url": "https://arxiv.org/abs/2311.01349v2", "attribution": "\"Post-hoc Orthogonalization for Mitigation of Protected Feature Bias in CXR Embeddings\" by Tobias Weber, Michael Ingrisch, Bernd Bischl, and David Rügamer, arXiv:2311.01349v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11780v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cl|}\n\\hline\n $h_r$ & number of honest PoW queries in slot $r$ \\\\\n $n_r$ & number of total PoW queries in slot $r$ \\\\\n $\\alpha_0$ & lower bound on the fraction of honest mining power \\\\\n $sl_{\\rm re}$ & recency parameter for PoW blocks \\\\\n $\\Delta$ & network delay in slots \\\\\n $\\kappa$ & security parameter; length of the hash function output \\\\\n $R \\in \\mathbb{N}$ & duration of an epoch in number of slots\\\\\n $(\\gamma, s)$ & restriction on the fluctuation of the number of \\\\\n & honest queries across slots (Definition~) \\\\\n $f^{(s)}$ & expected PoS mining rate in number of blocks per slot \\\\\n $f^{(w)}$ & expected PoW mining rate in number of blocks per slot \\\\\n $\\epsilon$ & quality of concentration of random variables \\\\\n $\\sigma$ & PoW fairness parameter \\\\\n & \\& advantage of honest parties (Assumption~.1 and .1) \\\\\n $\\delta$ & ``goodness'' parameter of an epoch/slot (Definition~) \\\\\n $\\lambda$ & typicality parameter of the execution \\\\\n $\\ell$ & minimum number of slots for concentration bounds\\\\\n $L$ & total number of epochs in the execution\\\\\n $\\omega$ & the weighing parameter \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The parameters used in our analysis.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Minotaur: Multi-Resource Blockchain Consensus", "authors": ["Matthias Fitzi", "Xuechao Wang", "Sreeram Kannan", "Aggelos Kiayias", "Nikos Leonardos", "Pramod Viswanath", "Gerui Wang"], "url": "https://arxiv.org/abs/2201.11780v2", "attribution": "\"Minotaur: Multi-Resource Blockchain Consensus\" by Matthias Fitzi, Xuechao Wang, Sreeram Kannan, Aggelos Kiayias, Nikos Leonardos, Pramod Viswanath, and Gerui Wang, arXiv:2201.11780v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.12615v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n\t\\hline\n\t\t&& Rich. & Rich. & Rich. & Rich. & & \\\\\n\t\t&& (default) & (optimal) & (adaptive) & (2o) & CG & BayesCG \\\\\\hline\n\t\t\\multirow{2}{*}{\\textsc{default}} \n& $\\textsc{mmd}_{\\mathcal{F}_k}^2$ & 1.90e-04 & -3.11e-05 & 9.76e-06 & 5.36e-05 & -2.80e-05 & \\textbf{1.14e-03} \\\\\n& $q$ & 0.34 & 0.52 & 0.45 & 0.43 & 0.49 & \\textbf{0.03} \\\\\\hline\n\t\t\\multirow{2}{*}{\\textsc{natural}} \n& $\\textsc{mmd}_{\\mathcal{F}_k}^2$ & -1.72e-04 & -2.71e-04 & -2.44e-04 & -3.20e-04 & -2.98e-04 & \\textbf{4.18e-03} \\\\\n& $q$ & 0.60 & 0.64 & 0.64 & 0.68 & 0.68 & \\textbf{0.00} \\\\\\hline\n\t\t\\multirow{2}{*}{\\textsc{opt}} \n& $\\textsc{mmd}_{\\mathcal{F}_k}^2$ & 3.59e-05 & 1.00e-05 & 4.30e-06 & -6.62e-06 & 3.57e-05 & \\textbf{6.57e-03} \\\\\n& $q$ & 0.48 & 0.47 & 0.48 & 0.49 & 0.47 & \\textbf{0.00} \\\\\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Probabilistic Iterative Methods for Linear Systems", "authors": ["Jon Cockayne", "Ilse C. F. Ipsen", "Chris J. Oates", "Tim W. Reid"], "url": "https://arxiv.org/abs/2012.12615v2", "attribution": "\"Probabilistic Iterative Methods for Linear Systems\" by Jon Cockayne, Ilse C. F. Ipsen, Chris J. Oates, and Tim W. Reid, arXiv:2012.12615v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00060v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l||c|c|}\n\\hline\n\\textbf{Model} & \\textbf{Valid AUC} & \\textbf{Test AUC} \\\\ \n\\hline\nMLP & 0.6507 & 0.6465 \\\\ \nGCN & 0.6087 & 0.6135 \\\\ \nGraphSAGE & 0.6344 & 0.6344 \\\\ \n\\hline\nAllSetTransformer & \\underline{0.6779} & 0.6829 \\\\ \nHNHN & 0.6775 & \\underline{0.6845} \\\\ \nHGNN & 0.6429 & 0.6450 \\\\ \nHCHA & 0.6289 & 0.6318 \\\\ \n\\hline\nTGN(Sum) & 0.7517 & 0.7640 \\\\ \nTGN(Mean) & 0.7569 & \\textbf{0.7747} \\\\ \nTGN(GCN) & \\textbf{0.7581} & 0.7716 \\\\ \nTGN(GAT) & 0.7569 & 0.7691 \\\\ \n\\hline\n\\textbf{\\textit{improv.}} & 11.83\\% & 13.18\\% \\\\ \n\\hline\n\\end{tabular}\n\\caption{AUC comparison between the models: the best-performing TGN model is highlighted in bold, and the top baseline model is underlined.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Temporal Graph Networks for Graph Anomaly Detection in Financial Networks", "authors": ["Yejin Kim", "Youngbin Lee", "Minyoung Choe", "Sungju Oh", "Yongjae Lee"], "url": "https://arxiv.org/abs/2404.00060v1", "attribution": "\"Temporal Graph Networks for Graph Anomaly Detection in Financial Networks\" by Yejin Kim, Youngbin Lee, Minyoung Choe, Sungju Oh, and Yongjae Lee, arXiv:2404.00060v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12599v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classification results on the STL dataset with 0.125 bpp using VGG16 as downstream AI task.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllll}\n\t\t\\toprule[1pt]\n\t\tMethod & SI & ACC & F1-score & PSNR & MSE & SSIM \\\\\n\t\t\\hline\n\t\toriginal & 672.02 & 88.94\\% & 0.8899 & - & 0 & 1 \\\\\n\t\tTDIC & 42.29 & 57.04\\% & 0.5733 & 23.55 & 0.0050 & 0.8042 \\\\\n\t\tAPIC & 89.67 & 71.04\\% & 0.7102 & 18.96 & 0.0137 & 0.6959 \\\\\n\t\t\\hline\n\t\tSAIC & \\textbf{91.65} & \\textbf{71.99\\%} & \\textbf{0.7186} & 18.94 & 0.0138 & 0.6939\\\\\n\t\t\\toprule[1pt]\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Semantic-assisted image compression", "authors": ["Qizheng Sun", "Caili Guo", "Yang Yang", "Jiujiu Chen", "Xijun Xue"], "url": "https://arxiv.org/abs/2201.12599v1", "attribution": "\"Semantic-assisted image compression\" by Qizheng Sun, Caili Guo, Yang Yang, Jiujiu Chen, and Xijun Xue, arXiv:2201.12599v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.15439v1_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Derivation of payoff-maximizing decision strategy}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{clcccccc}\n \\hline\n \\hline\n & & \\multicolumn{6}{c}{\\textbf{Report}}\\\\ \\cline{3-8}\n & & 1 & 2 & 3 & 4 & 5 & 6\\\\\n \\hline\n \\textbf{Number drawn} & & & & & & & \\\\\n \\hline\n \\multirow{7}{*}{\\textbf{1}} & P(audit) & 0.1 & 0.2 & 0.3 & 0.4 & 0.5 & 0.6 \\\\\n & P(punishment$|$audit) & 0 & 0.5 & 0.667 & 0.75 & 0.8 & 0.833\\\\\n & P(punishment) & 0 & 0.1 & 0.2 & 0.3 & 0.4 & 0.5\\\\\n & P(cheating successful) & 0 & 0.9 & 0.8 & 0.7 & 0.6 & 0.5\\\\\n & E(payoff) & 15 & 27.75 & 37.5 & 44.25 & 48 & \\textbf{48.75}\\\\\n & U(cheating) & & 12.75 & 22.5 & 29.25 & 33 & 33.75\\\\\n & U'(cheating) & & 12.75 & 9.75 & 6.75 & 3.75 & 0.75\\\\\n \\hline\n \\multirow{7}{*}{\\textbf{2}} & P(audit) & & 0.2 & 0.3 & 0.4 & 0.5 & 0.6 \\\\\n & P(punishment$|$audit) & & 0 & 0.333 & 0.5 & 0.6 & 0.667\\\\\n & P(punishment) & & 0 & 0.1 & 0.2 & 0.3 & 0.4 \\\\\n & P(cheating successful) & & & 0.9 & 0.8 & 0.7 & 0.6\\\\\n & E(payoff) & & & 42.00 & 51.00 & 57.00 & \\textbf{60.00}\\\\\n & U(cheating) & & & 12 & 21 & 27 & 30\\\\\n & U'(cheating) & & & 12 & 9 & 6 & 3\\\\\n \\hline\n \\multirow{7}{*}{\\textbf{3}} & P(audit) & & & 0.3 & 0.4 & 0.5 & 0.6 \\\\\n & P(punishment$|$audit) & & & & 0.25 & 0.4 & 0.5\\\\\n & P(punishment) & & & & 0.1 & 0.2 & 0.3 \\\\\n & P(cheating successful) & & & & 0.9 & 0.8 & 0.7\\\\\n & E(payoff) & & & & 56.25 & 64.50 & \\textbf{69.75}\\\\\n & U(cheating) & & & & 11.25 & 19.5 & 24.75\\\\\n & U'(cheating) & & & & 11.25 & 8.25 & 5.25\\\\\n \\hline\n \\multirow{7}{*}{\\textbf{4}} & P(audit) & & & & 0.4 & 0.5 & 0.6 \\\\\n & P(punishment$|$audit) & & & & 0 & 0.2 & 0.333 \\\\\n & P(punishment) & & & & 0 & 0.1 & 0.2\\\\\n & P(cheating successful) & & & & 0 & 0.9 & 0.8\\\\\n & E(payoff) & & & & 60 & 70.50 & \\textbf{78.00}\\\\\n & U(cheating) & & & & & 10.50 & 18\\\\\n & U'(cheating) & & & & & 10.50 & 7.50\\\\\n \\hline\n \\multirow{7}{*}{\\textbf{5}} & P(audit) & & & & & 0.5 & 0.6 \\\\\n & P(punishment$|$audit) & & & & & 0 & 0.167\\\\\n & P(punishment) & & & & & 0 & 0.1\\\\\n & P(cheating successful) & & & & & 0 & 0.9 \\\\\n & E(payoff) & & & & & 75 & \\textbf{84.75} \\\\\n & U(cheating) & & & & & & 9.75\\\\\n & U'(cheating) & & & & & & 9.75\\\\\n \\hline\n \\multirow{7}{*}{\\textbf{6}} & P(audit) & & & & & & 0.6 \\\\\n & P(punishment$|$audit) & & & & & & 0 \\\\\n & P(punishment) & & & & & & 0\\\\\n & P(cheating successful) & & & & & & 0\\\\\n & E(payoff) & & & & & & 90\\\\\n & U(cheating) & & & & & & \\\\\n & U'(cheating) & & & & & & \\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty", "authors": ["Marius Protte", "Behnud Mir Djawadi"], "url": "https://arxiv.org/abs/2507.15439v1", "attribution": "\"Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty\" by Marius Protte and Behnud Mir Djawadi, arXiv:2507.15439v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07006v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MSE of log NET2 count against an average baseline on the test set.} %128 sampling steps, CFG=1.5 (null image), 1x5x5 opening (t = 0.5).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\hline\n & \\textbf{Placebo}~$\\downarrow$ & \\textbf{NE}~$\\downarrow$ & \\textbf{LE}~$\\downarrow$ & \\textbf{MiE}~$\\downarrow$ & \\textbf{MoE}~$\\downarrow$ & \\textbf{HE}~$\\downarrow$ \\\\\n \\hline\n Proposed Method & \\textbf{1.538} & \\textbf{1.158} & \\textbf{1.093} & \\textbf{0.842} & \\textbf{0.531} & \\textbf{0.006} \\\\ \\hline\n Avg. Baseline & 1.775 & 1.620 & 1.780 & 0.991 & 0.697 & 0.010 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments", "authors": ["Gian Mario Favero", "Ge Ya Luo", "Nima Fathi", "Justin Szeto", "Douglas L. Arnold", "Brennan Nichyporuk", "Chris Pal", "Tal Arbel"], "url": "https://arxiv.org/abs/2508.07006v1", "attribution": "\"Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments\" by Gian Mario Favero, Ge Ya Luo, Nima Fathi, Justin Szeto, Douglas L. Arnold, Brennan Nichyporuk, Chris Pal, and Tal Arbel, arXiv:2508.07006v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.00101v1_tex_table28.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\hline\\hline\n&Obs Reserve & NoCalib &Calib & chain-ladder \\\\\n\\hline\nAmount& 2,698,159 &9,361,273 &2,723,594 &6,103,197\\\\\n Ratio& 1.00& 3.47& 1.01& 2.26\\\\\n \\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean of the BE distribution for the RBNS predictions of the multi-state model and the chain-ladder. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "mCube: Multinomial Micro-level reserving Model", "authors": ["Emmanuel Jordy Menvouta", "Jolien Ponnet", "Robin Van Oirbeek", "Tim Verdonck"], "url": "https://arxiv.org/abs/2212.00101v1", "attribution": "\"mCube: Multinomial Micro-level reserving Model\" by Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek, and Tim Verdonck, arXiv:2212.00101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.12530v2_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rcc} \\toprule \n& Uniform & Non-Uniform \\\\ \\toprule \n \\\\ \n$\\mathbf{\\rho^\\Phi=0.1:}$ & & \\\\ \n$\\xi^{1a}$ & 40.7 & 5.5 \\\\ \n$\\xi^{1b}$ & 40.7 & 5.5 \\\\ \n$\\xi^{2a}$ & 40.7 & 14.5 \\\\ \n$\\xi^{2b}$ & 40.7 & 14.4 \\\\ \n$\\xi^{3a}$ & 40.7 & 39.7\\\\ \n$\\xi^{3b}$ & 40.7 & 39.8\\\\ \n$\\mathbf{\\rho^\\Phi=0.5:}$ & & \\\\ \n$\\xi^{1a}$ & 203.5 & 24.6 \\\\ \n$\\xi^{1b}$ & 203.5 & 25.3 \\\\ \n$\\xi^{2a}$ & 203.5 & 72.1 \\\\ \n$\\xi^{2b}$ & 203.5 & 72.3 \\\\ \n$\\xi^{3a}$ & 203.5 & 193.3 \\\\ \n$\\xi^{3b}$ & 203.5 & 199.1 \\\\ \n$\\mathbf{\\rho^\\Phi=0.9:}$ & & \\\\ \n$\\xi^{1a}$ & 366.3 & 43.6 \\\\ \n$\\xi^{1b}$ & 366.3 & 43.7 \\\\ \n$\\xi^{2a}$ & 366.3 & 118.9 \\\\ \n$\\xi^{2b}$ & 366.3 & 125.9 \\\\ \n$\\xi^{3a}$ & 366.3 & 352.8 \\\\ \n$\\xi^{3b}$ & 366.3 & 351.0 \\\\ \n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Microscopic Traffic Models, Accidents, and Insurance Losses", "authors": ["Sojung Kim", "Marcel Kleiber", "Stefan Weber"], "url": "https://arxiv.org/abs/2208.12530v2", "attribution": "\"Microscopic Traffic Models, Accidents, and Insurance Losses\" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.17331v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|l|l|l|l|}\n\\hline\n\\textbf{Number of clusters} & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\\\ \\hline\n\\textbf{Silhouette score} & 0.4195 & 0.4319 & 0.4092 & 0.3757 & 0.3888 & 0.3940 & 0.3747 & 0.3965 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Table showing the Silhouette scores for clustering analysis of centermost cluster.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Topological Data Analysis Framework for Quantifying Necrosis in Glioblastomas", "authors": ["Francisco Tellez", "Enrique Torres-Giese"], "url": "https://arxiv.org/abs/2503.17331v1", "attribution": "\"A Topological Data Analysis Framework for Quantifying Necrosis in Glioblastomas\" by Francisco Tellez and Enrique Torres-Giese, arXiv:2503.17331v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.10270v5_tex_table19.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cr|cc}\n\\hline\n & \\multicolumn{2}{c|}{Hodge Scattering + SVM} & \\multicolumn{2}{c|}{UGT} & \\multicolumn{2}{c}{GFN} \\\\ \\hline\nGraph & Accuracy & \\# Param & Accuracy & \\# Param & Accuracy & \\# Param \\\\ \\hline\nCollab & 80.39 & 256 & 77.84 & 866,746 & 81.50 & 68,754 \\\\\nDD & 72.71 & 256 & 80.23 & 76,928 & 79.37 & 68,754 \\\\\nIMDB-B & 73.10 & 256 & 77.04 & 55,508 & 73.40 & 68,754 \\\\\nIMDB-M & 49.67 & 256 & 53.60 & 48,698 & 51.80 & 68,818 \\\\\nMUTAG & 85.78 & 256 & 80.23 & 4,178 & 85.83 & 68,818 \\\\\nPROTEINS & 75.35 & 256 & 78.53 & 1,878 & 76.46 & 68,818 \\\\\nPTC & 68.28 & 256 & 69.63 & 12,038 & 66.60 & 68,818 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Multiscale Hodge Scattering Networks for Data Analysis", "authors": ["Naoki Saito", "Stefan C. Schonsheck", "Eugene Shvarts"], "url": "https://arxiv.org/abs/2311.10270v5", "attribution": "\"Multiscale Hodge Scattering Networks for Data Analysis\" by Naoki Saito, Stefan C. Schonsheck, and Eugene Shvarts, arXiv:2311.10270v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.09982v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Classification accuracy determined from manually labeled experimental detections for clustering with handpicked features (K-means, GMM, hierarchical) and deep learning (DEC). }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\\hline\nMethod & Accuracy & Recall & Precision\\\\\n\\hline\nK-means ($K\\!=\\!2$) & {\\bf 0.66} & 0.42 & 0.47 \\\\\nGMM ($K\\!=\\!2$) & {\\bf 0.55} & 0.84 & 0.40 \\\\\nHierarchical ($K\\!=\\!2$) & {\\bf 0.63} & 0.23 & 0.38 \\\\\nGMM ($K\\!=\\!2$), latent features & {\\bf 0.78} & 0.33 & 0.90\\\\\nDEC ($P\\!=\\!15$, $K\\!=\\!2$) & {\\bf 0.66} & 0.75 & 0.48\\\\\n\\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep embedded clustering of coral reef bioacoustics", "authors": ["Emma Ozanich", "Aaron Thode", "Peter Gerstoft", "Lauren A. Freeman", "Simon Freeman"], "url": "https://arxiv.org/abs/2012.09982v2", "attribution": "\"Deep embedded clustering of coral reef bioacoustics\" by Emma Ozanich, Aaron Thode, Peter Gerstoft, Lauren A. Freeman, and Simon Freeman, arXiv:2012.09982v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.09771v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Measures of Model Fit: / Data}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc}\n \\hline \\hline \n\\multirow{2}{*}{Criterion} & \\multicolumn{2}{c}{\\# of components} \\\\ \\cline{2-3}\n & 1 & 2 \\\\ \n\\hline\nAIC & 50,964.90 & \\textbf{49,983.40} \\\\\nBIC & 51,112.52 & \\textbf{50,296.42} \\\\\nDIC & 50,917.04 & \\textbf{49,885.84} \\\\\nWAIC & 50,972.77 & \\textbf{49,992.57} \\\\\nLOO & 50,972.67 & \\textbf{49,992.82} \\\\\n \\hline \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian estimation of finite mixtures of Tobit models", "authors": ["Caio Waisman"], "url": "https://arxiv.org/abs/2411.09771v1", "attribution": "\"Bayesian estimation of finite mixtures of Tobit models\" by Caio Waisman, arXiv:2411.09771v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.06442v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The validity of the upsampling factor S in the Sub-Pixel Weight branch of SubEC. The best performance is highlighted in bold.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|cc|cc}\n \\toprule\n &\\multicolumn{3}{c|}{Model Efficiency} &\\multicolumn{2}{c|}{CVIU} &\\multicolumn{2}{c}{Waterloo} \\\\\n \\midrule\n Upscale Factor S & Params/M & Flops/G & Speed/ms & SRCC $\\uparrow$ & PLCC $\\uparrow$ & SRCC $\\uparrow$ & PLCC $\\uparrow$ \\\\\n \\midrule\n 2 & 2.220 & 0.179 & 11.010 & \\textbf{0.978} & \\textbf{0.981} & 0.965& 0.979 \\\\\n 3 & 2.223 & 0.183 & 11.174 & 0.977 & 0.980 & \\textbf{0.970} & 0.980\\\\\n 4 & 2.232 & 0.188 & 11.322 & 0.976 & 0.978 & 0.965 & 0.982\\\\\n 5 & 2.241 & 0.195 & 11.205 & 0.978 & 0.981 & 0.965 & 0.980\\\\\n 6 & 2.252 & 0.203 & 11.121 & 0.975 & 0.977 & 0.968 & \\textbf{0.983} \\\\\n 8 & 2.277 & 0.223 & 11.141 & 0.963 & 0.967 & 0.965 & 0.981\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment", "authors": ["Yixiao Li", "Xiaoyuan Yang", "Guanghui Yue", "Jun Fu", "Qiuping Jiang", "Xu Jia", "Paul L. Rosin", "Hantao Liu", "Wei Zhou"], "url": "https://arxiv.org/abs/2509.06442v1", "attribution": "\"Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment\" by Yixiao Li, Xiaoyuan Yang, Guanghui Yue, Jun Fu, Qiuping Jiang, Xu Jia, Paul L. Rosin, Hantao Liu, and Wei Zhou, arXiv:2509.06442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2306.10591v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Breast cancer data: performance of the optimization approach when selecting features for the logistic regression model with different dependency measures.}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\t\n$\\rho_{Feature}^X$ & $\\rho_{Target}^Y$ & Optimization value & AUC & ACC & Number of features \\\\\n\t\\hline\n\t$X=Correl$ & $Y=Correl$ &-0.91611 & 0.9806 & 0.9209 & 3\\\\\n\t\\hline\n\t$X=Correl$ & $Y=MI$ & -0.3300 & 0.9644 & 0.8910 & 1 \\\\\n\t\\hline\n\t$X=Correl$ & $Y=ROC$ & -1.7031 & 0.9822 & 0.9192 & 4 \\\\\n\t\\hline\n\t$X=Correl$ & $Y=ANOVA$ & -2913.6696 & 0.9839 & {\\bf 0.9315} &9 \\\\\n\t\\hline \n\t$X=MI$ & $Y=Correl$ & -1.438 & 0.9796 & 0.9262 & 5\\\\\n\t\\hline\n\t$X=MI$ & $Y=MI$ &-0.4139 & {\\bf 0.9859} & 0.9297 &3\\\\\n\t\\hline\n\t$X=MI$ & $Y=ROC$ & -2.6366 & 0.9828 & 0.9279 & 6\\\\\n\t\\hline\n\t$X=MI$ & $Y=ANOVA$ & -2914.5083 & 0.9839 & {\\bf 0.9315} & 9\\\\\n\t\\hline \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Quantum computer based Feature Selection in Machine Learning", "authors": ["Gerhard Hellstern", "Vanessa Dehn", "Martin Zaefferer"], "url": "https://arxiv.org/abs/2306.10591v1", "attribution": "\"Quantum computer based Feature Selection in Machine Learning\" by Gerhard Hellstern, Vanessa Dehn, and Martin Zaefferer, arXiv:2306.10591v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12834v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|l|l|l|l|}\n \\hline\n & $L=10^{9}$ & $L=10^{10}$ & $L=10^{11}$ \\\\ \\hline\n $D_{\\text{E}}^{\\text{(G)}}[s,d]$ & $4.6 \\times 10^{-5}$ & $1.7 \\times 10^{-5}$ & $7.5 \\times 10^{-6}$\\\\ \\hline\n $D_{\\text{CLE}}^{\\text{(G)}}[s,d]$ & $2.2 \\times 10^{-5}$ & $0.6 \\times 10^{-5}$ & $1.8 \\times 10^{-6}$\\\\ \\hline\n $D_{\\text{CFE}}^{\\text{(G)}}[s,d]$ & $2.4 \\times 10^{-5}$ & $1.1 \\times 10^{-5}$ & $5.7 \\times 10^{-6}$\\\\\n \\hline\n \\end{tabular}\n\\caption{The evaulation of $D_{\\text{E}}^{\\text{(G)}}[s,d]$, $D_{\\text{CLE}}^{\\text{(G)}}[s,d]$ and $D_{\\text{CFE}}^{\\text{(G)}}[s,d]$ for various computational limit $L$ values considereing $(128,64)$-random tree codes under BSC with $p=0.02$ where $s$ is optimized using the SBP algorithm and $d$ is in the form where $\\gamma=0.9992$. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Optimization of Random Tree Codes for Limited Computational Resources", "authors": ["B. Tan Bacinoglu"], "url": "https://arxiv.org/abs/2501.12834v1", "attribution": "\"The Optimization of Random Tree Codes for Limited Computational Resources\" by B. Tan Bacinoglu, arXiv:2501.12834v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.10782v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Median, Maximum and Minimum test loss of the optimizers for the Heat equation.}\n\\begin{tabular}{lrrr}\n\\toprule\n & Median & Minimum & Maximum \\\\\n\\midrule\nANaGRAM & \\textbf{6.82e-11} & \\textbf{1.90e-11} & \\textbf{2.50e-10} \\\\\nAdam & 2.37e-04 & 1.11e-04 & 1.41e-03 \\\\\nE-NGD & 2.18e-09 & 5.62e-10 & 1.35e-08 \\\\\nGD & 1.01e-02 & 4.70e-03 & 1.93e-02 \\\\\nL-BFGS & 6.84e-04 & 5.85e-05 & 3.34e-03 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning", "authors": ["Nilo Schwencke", "Cyril Furtlehner"], "url": "https://arxiv.org/abs/2412.10782v2", "attribution": "\"ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning\" by Nilo Schwencke and Cyril Furtlehner, arXiv:2412.10782v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.15195v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Vehicle data used for case studies}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\n\\textbf{Parameters} & \\textbf{Values} \\\\ \\hline\nVehicle mass $(M)$ & $2000$ kg \\\\ \\hline\nAir density $(\\rho_a)$ & $1.22$ kg/m$^3$ \\\\ \\hline\nAerodynamic drag coefficient $(C_d)$ & $0.3$ \\\\ \\hline\nFrontal Area $(A_f)$ & $1.6$ m$^2$ \\\\ \\hline\nRolling resistance coefficient $(f_r)$ & $0.01$ \\\\ \\hline\nWheel radius $(r_d)$ & $0.28$ m \\\\ \\hline\nWind speed $(V_w)$ & $0$ \\\\ \\hline\nVehicle mass factor $(\\delta)$ & $1.04$ \\\\ \\hline\nGear ratio of final drive/differential $(i_0)$ & $4.18$ \\\\ \\hline\nGear ratio of transmission $(i_g)$ & $1.3$ \\\\ \\hline\nSpeed ratio, maximum speed to base speed $(x)$ & $4$ \\\\ \\hline\nTraction motor efficiency $(\\eta_m)$ & $85\\%$ \\\\ \\hline\nInverter efficiency $(\\eta_i)$ & $95\\%$ \\\\ \\hline\nGear-box and final drive efficiency $(\\eta_t)$ & $90\\%$ \\\\ \\hline\nTotal travel distance $(S)$ & $5$ miles \\\\ \\hline\nMaximum time limit $(T_{max})$ & $420$ sec \\\\ \\hline\nMaximum acceleration $(a_{max})$ & $8$ mph/s \\\\ \\hline\nMaximum cruising speed $(V_{max})$ & $75$ mph \\\\ \\hline\nOverall regenerative efficiency & $50\\%$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A GA-based Approach to Eco-driving of Electric Vehicles Considering Regenerative Braking", "authors": ["Mukesh Gautam", "Narayan Bhusal", "Mohammed Benidris", "Poria Fajri"], "url": "https://arxiv.org/abs/2012.15195v1", "attribution": "\"A GA-based Approach to Eco-driving of Electric Vehicles Considering Regenerative Braking\" by Mukesh Gautam, Narayan Bhusal, Mohammed Benidris, and Poria Fajri, arXiv:2012.15195v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.11243v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n Model & {Sharpe} & {Return \\%} & {Std Dev \\%} & {Beta} & {Daily} & {Short} \\\\\n & {Ratio} & & {Return} & & {Turnover} & {Fraction} \\\\\n \\midrule\nLong-Conv & $3.64\\,\\pm\\,0.14$ & 12.8 & 3.51 & 0.033 & 0.97 & 0.48 \\\\\nS4 & $\\underline{3.94}\\,\\pm\\,0.29$ & 13.5 & 3.43 & 0.028 & 0.90 & 0.48 \\\\\nH3 & $3.46\\,\\pm\\,0.28$ & 10.6 & 3.08 & 0.026 & 1.12 & 0.48 \\\\\nTransformer & $3.65\\,\\pm\\,0.52$ & 12.9 & 3.62 & 0.035 & 0.83 & 0.47 \\\\\nHyena & $2.91\\,\\pm\\,0.33$ & 8.7 & 3.02 & 0.032 & 1.28 & 0.47 \\\\\nSet-Seq (Ours) & $\\textbf{4.82}\\,\\pm\\,0.12$ & 13.0 & 2.69 & 0.028 & 0.91 & 0.48 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Set-Sequence Model for Time Series", "authors": ["Elliot L. Epstein", "Apaar Sadhwani", "Kay Giesecke"], "url": "https://arxiv.org/abs/2505.11243v1", "attribution": "\"A Set-Sequence Model for Time Series\" by Elliot L. Epstein, Apaar Sadhwani, and Kay Giesecke, arXiv:2505.11243v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.11662v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of MindVL and comparison models on multimodal benchmarks. \"+\" indicates that there is a portion of data with unlabeled quantities. The results of the comparative models are referenced from~.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|ccccccc}\n \\toprule\n \\textbf{Model} & \\#Tokens & \\textbf{MME} & \\textbf{MMBench} & \\textbf{OCRBench} & \\textbf{DocVQA} & \\textbf{ChartQA} & \\textbf{InfoVQA} & \\textbf{Overall} \\\\\n \\midrule\n Qwen2.5-VL 7B~ & 4.1T+ & 83.8 & 82.6 & \\underline{86.4} & \\textbf{95.7} & \\textbf{87.3} & 82.6 & \\underline{86.4}\\\\\n Keye-VL 8B~& 4.1T+ & \\textbf{87.3} & \\textbf{85.8} & 82.3 & 87.0 & 72.5 & 63.0 & 79.9\\\\\n GLM-4.1V 9B~ & 2T+ & 79.1 & 76.3 & 85.3 & 93.3 & 70.0 & 80.3 & 80.7\\\\\n InternVL3.5 8B~ & 380B+ & \\underline{85.0} & 79.5 & 83.2 & 92.3 & 86.7 & 76.2 & 83.8\\\\\n \\midrule\n MindVL 8B & 447B & 84.1 & \\underline{84.3} & \\textbf{87.6} & \\underline{94.7} & \\underline{87.2} & \\underline{81.1} & \\textbf{86.5}\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs", "authors": ["Feilong Chen", "Yijiang Liu", "Yi Huang", "Hao Wang", "Miren Tian", "Ya-Qi Yu", "Minghui Liao", "Jihao Wu"], "url": "https://arxiv.org/abs/2509.11662v1", "attribution": "\"MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs\" by Feilong Chen, Yijiang Liu, Yi Huang, Hao Wang, Miren Tian, Ya-Qi Yu, Minghui Liao, and Jihao Wu, arXiv:2509.11662v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17533v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n LLM & Successful detections & Correct proofs \\\\\n \\midrule\n GPT-4o & 1/10 & 2/10 \\\\\n Gemini 2.5 Pro Preview & 8/10 & 5/10 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "From Euler to AI: Unifying Formulas for Mathematical Constants", "authors": ["Tomer Raz", "Michael Shalyt", "Elyasheev Leibtag", "Rotem Kalisch", "Shachar Weinbaum", "Yaron Hadad", "Ido Kaminer"], "url": "https://arxiv.org/abs/2502.17533v2", "attribution": "\"From Euler to AI: Unifying Formulas for Mathematical Constants\" by Tomer Raz, Michael Shalyt, Elyasheev Leibtag, Rotem Kalisch, Shachar Weinbaum, Yaron Hadad, and Ido Kaminer, arXiv:2502.17533v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07253v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\textbf{Name} &\n \\textbf{Type} &\n \\textbf{Channels} \\\\\n \\hline\\\\\n \\textbf{Mean} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Variance} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Skewness} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Kurtosis} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Interquartile range} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Min} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Max} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Hjorth complexity} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Hjorth mobility} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Petrosian fractal dimension} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Intermittency} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Voltage auc} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Spikiness} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Standard deviation} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Zero crossing} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Peak to peak} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Absolute area under signal} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Total signal energy} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Spike count} & $\\mathcal{T}$ & $\\bullet$ \\\\\n \\textbf{Coastline} & $\\mathcal{T}$, $\\delta,\\theta,\\alpha,\\beta,\\gamma$ & $\\bullet$ \\\\\n \\textbf{Power spectral density} & $\\delta,\\theta,\\alpha,\\beta,\\gamma$ & $\\bullet$ \\\\\n \\textbf{Power spectral centroid} & $\\delta,\\theta,\\alpha,\\beta,\\gamma$ & $\\bullet$ \\\\\n \\textbf{Signal monotony} & $\\delta,\\theta,\\alpha,\\beta,\\gamma$ & $\\bullet$ \\\\\n \\textbf{Signal to noise} & $\\delta,\\theta,\\alpha,\\beta,\\gamma$ & $\\bullet$ \\\\\n \\textbf{Energy percentage} & $\\delta,\\theta,\\alpha,\\beta,\\gamma$ & $\\bullet$ \\\\\n \\textbf{Discrete wavelet transform} & $\\delta,\\theta,\\alpha,\\beta,\\gamma$ & $\\bullet$ \\\\\n \\textbf{Cross correlation max coef} & $\\rho$ & $\\bullet\\bullet$ \\\\\n \\textbf{Coherence} & $\\rho$ & $\\bullet\\bullet$ \\\\\n \\textbf{Imaginary coherence} & $\\rho$ & $\\bullet\\bullet$ \\\\\n \\textbf{Phase slope index} & $\\rho$ & $\\bullet\\bullet$ \\\\\n \\textbf{Eccentricity} & $\\mathcal{G}$ & $\\bigodot$ \\\\\n \\textbf{Clustering coefficient} & $\\mathcal{G}$ & $\\bigodot$ \\\\\n \\textbf{Betweenness centrality} & $\\mathcal{G}$ & $\\bigodot$ \\\\\n \\textbf{Local efficiency} & $\\mathcal{G}$ & $\\bigodot$ \\\\\n \\textbf{Global efficiency} & $\\mathcal{G}$ & $\\bigodot$ \\\\\n \\textbf{Diameter} & $\\mathcal{G}$ & $\\bigodot$ \\\\\n \\textbf{Radius} & $\\mathcal{G}$ & $\\bigodot$ \\\\\n \\textbf{Characteristic path} & $\\mathcal{G}$ & $\\bigodot$ \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "PySeizure: A single machine learning classifier framework to detect seizures in diverse datasets", "authors": ["Bartlomiej Chybowski", "Shima Abdullateef", "Hollan Haule", "Alfredo Gonzalez-Sulser", "Javier Escudero"], "url": "https://arxiv.org/abs/2508.07253v1", "attribution": "\"PySeizure: A single machine learning classifier framework to detect seizures in diverse datasets\" by Bartlomiej Chybowski, Shima Abdullateef, Hollan Haule, Alfredo Gonzalez-Sulser, and Javier Escudero, arXiv:2508.07253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23729v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\toprule\n Relative $L_2$ error & IR-PINNs2 & Causal PINNs \\\\\n \\midrule\n $x$ & 3.5086e-03 & 1.139e-02 \\\\\n $y$ & 5.1053e-03 & 1.656e-02 \\\\\n $z$ & 2.1651e-03 & 7.038e-03 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\textit{Lorentz system:} Relative $L_2$ errors between the proposed method and causal PINNs.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Integral regularization PINNs for evolution equations", "authors": ["Xiaodong Feng", "Haojiong Shangguan", "Tao Tang", "Xiaoliang Wan"], "url": "https://arxiv.org/abs/2503.23729v1", "attribution": "\"Integral regularization PINNs for evolution equations\" by Xiaodong Feng, Haojiong Shangguan, Tao Tang, and Xiaoliang Wan, arXiv:2503.23729v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16232v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance and efficiency trade-offs on the VoxCeleb dataset. EERs\\% are reported for the VoxCeleb1-O/E/H test sets. Speedups measured on an AMD EPYC 7A53 CPU and an AMD MI250 GPU are reported relative to the unpruned model.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n\\toprule\n\\multirow{2}{*}{Model} & \\multirow{2}{*}{Sparsity} & \\multirow{2}{*}{\\#Param} & \\multicolumn{2}{c}{Speedup} & \\multicolumn{3}{c}{VoxCeleb} \\\\\n\\cmidrule(lr){4-5} \\cmidrule(lr){6-8}\n& & & CPU & GPU & O & E & H \\\\\n\\midrule\n\\multicolumn{2}{c}{ResNet50~} & 11.2 M & 1.1$\\times$ & 2.6$\\times$ & 0.80 & 0.88 & 1.52 \\\\\n\\midrule\n\\rowcolor{gray!15} \\multirow{4}{*}{WavLM Base+} & 0\\% & 95.6\\,M & - & - & 0.70 & 0.69 & 1.40 \\\\\n& 60\\% & 38.9 M & 2.2$\\times$ & 2.0$\\times$ & 0.70 & 0.78 & 1.50 \\\\\n& 70\\% & 29.5 M & 2.9$\\times$ & 2.6$\\times$ & 0.73 & 0.84 & 1.61 \\\\\n& 80\\% & 19.9 M & 3.8$\\times$ & 3.4$\\times$ & 0.92 & 1.02 & 1.91 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Hybrid Pruning: In-Situ Compression of Self-Supervised Speech Models for Speaker Verification and Anti-Spoofing", "authors": ["Junyi Peng", "Lin Zhang", "Jiangyu Han", "Oldřich Plchot", "Johan Rohdin", "Themos Stafylakis", "Shuai Wang", "Jan Černocký"], "url": "https://arxiv.org/abs/2508.16232v1", "attribution": "\"Hybrid Pruning: In-Situ Compression of Self-Supervised Speech Models for Speaker Verification and Anti-Spoofing\" by Junyi Peng, Lin Zhang, Jiangyu Han, Oldřich Plchot, Johan Rohdin, Themos Stafylakis, Shuai Wang, and Jan Černocký, arXiv:2508.16232v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.13704v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Denoising performance in PSNR (dB) for the `house' image for different algorithms with different optimized hyper-parameters. The \\textbf{top} category only requires the noisy image. The \\textbf{middle} requires additional information such as noise level (KSVD, WNNM, BM3D) or additional noisy images with matched noise level (n2v$^{\\dagger}$). The \\textbf{bottom} requires large clean datasets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n & $\\sigma$=15 & $\\sigma$=25 & $\\sigma$=50 \\\\\n \\midrule\n n2v$^{\\star}$ & 32.05 & 29.20 & 25.42\\\\\n \\small{MTMKL} & \\textbf{34.29} & 31.88 & 28.08 \\\\\n GSC & 33.78 & 32.01 & 28.35 \\\\\n S5C & 33.50 & 32.08 & 28.35 \\\\\n var-BSC & 33.50 & 32.32 & 28.91 \\\\\n TVAE & 34.27 $\\pm$ .02 & \\textbf{32.65 $\\pm$ .06} & \\textbf{29.98 \\small{$\\pm$ .05}} \\\\\n \\midrule\n n2v$^{\\dagger}$ & 33.91 & 32.10 & 28.94 \\\\\n KSVD & 34.32 & 32.15 & 27.95 \\\\\n WNNM & 35.13 & 33.22 & 30.33 \\\\\n BM3D & 34.94 & 32.86 & 29.37 \\\\\n \\midrule\n EPLL & 34.17 & 32.17 & 29.12 \\\\\n BDGAN & 34.57 & 33.28 & 30.61 \\\\\n DPDNN & \\textbf{35.40} & \\textbf{33.54} & \\textbf{31.04} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Direct Evolutionary Optimization of Variational Autoencoders With Binary Latents", "authors": ["Enrico Guiraud", "Jakob Drefs", "Jörg Lücke"], "url": "https://arxiv.org/abs/2011.13704v2", "attribution": "\"Direct Evolutionary Optimization of Variational Autoencoders With Binary Latents\" by Enrico Guiraud, Jakob Drefs, and Jörg Lücke, arXiv:2011.13704v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.17072v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\nData Metric & Description \\\\ \\midrule\nGross Premium Income & Total revenue generated from insurance premiums from policyholders. \\\\\nClaims Paid & Total amount disbursed to settle approved policyholder claims. \\\\\nClaims Incurred & Total expected cost of claims during the period. \\\\\nUnderwriting Profit & Profit derived from insurance operations less claims and expenses. \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\footnotesize Key Financial Data Metrics Used in the Study. These metrics provide insights into the financial performance and operational efficiency of insurance companies.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Decoding Financial Health in Kenyas' Medical Insurance Sector: A Data-Driven Cluster Analysis", "authors": ["Evans Kiptoo Korir", "Zsolt Vizi"], "url": "https://arxiv.org/abs/2502.17072v2", "attribution": "\"Decoding Financial Health in Kenyas' Medical Insurance Sector: A Data-Driven Cluster Analysis\" by Evans Kiptoo Korir and Zsolt Vizi, arXiv:2502.17072v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.04208v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrr}\n\\toprule\n\\toprule\nPortfolio & Distribution & AIC & BIC & KS statistic \\\\\n\\cmidrule{3-5} \\\\\n\\multirow{2}{*}{Sharpe}\n & Normal & -553.411 & -548.201 & 0.10450 \\\\\n & NIG & -590.036 & -579.615 & 0.04144 \\\\\n\\midrule\n\\multirow{2}{*}{CVaR}\n & Normal & -498.331 & -493.121 & 0.10883 \\\\\n & NIG & -514.163 & -503.742 & 0.05381 \\\\\n\\midrule\n\\multirow{2}{*}{DJIA}\n & Normal & -577.127 & -571.917 & 0.19146 \\\\\n & NIG & -660.546 & -650.125 & 0.05142 \\\\\n\\bottomrule\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Goodness‐of‐fit metrics by distribution and portfolio}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Behavioral Probability Weighting and Portfolio Optimization under Semi-Heavy Tails", "authors": ["Ayush Jha", "Abootaleb Shirvani", "Ali M. Jaffri", "Svetlozar T. Rachev", "Frank J. Fabozzi"], "url": "https://arxiv.org/abs/2507.04208v1", "attribution": "\"Behavioral Probability Weighting and Portfolio Optimization under Semi-Heavy Tails\" by Ayush Jha, Abootaleb Shirvani, Ali M. Jaffri, Svetlozar T. Rachev, and Frank J. Fabozzi, arXiv:2507.04208v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23340v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\(m\\) & \\textbf{Subset \\(S_{m,1}\\)} & \\textbf{Subset \\(S_{m,2}\\)} & \\textbf{Subset \\(S_{m,3}\\)} & $\\mathbb I \\left(\\otimes_{i=1}^3 P^{(S_{m, i})} \\right)$ \\\\\n\\hline\n4 & \\(\\{1\\}\\) & \\(\\{5,\\,7\\}\\) & \\(\\{8\\}\\) & 0.00778 \\\\\n5 & \\(\\{1,\\,4\\}\\) & \\(\\{5,\\,7\\}\\) & \\(\\{8\\}\\) & 0.01556 \\\\\n6 & \\(\\{1,\\,4\\}\\) & \\(\\{5,\\,7\\}\\) & \\(\\{8,\\,10\\}\\) & 0.02376 \\\\\n7 & \\(\\{1,\\,3,\\,4\\}\\) & \\(\\{5,\\,7\\}\\) & \\(\\{8,\\,10\\}\\) & 0.04172 \\\\\n8 & \\(\\{1,\\,3,\\,4\\}\\) & \\(\\{5,\\,6,\\,7\\}\\) & \\(\\{8,\\,10\\}\\) & 0.06029 \\\\\n9 & \\(\\{1,\\,3,\\,4\\}\\) & \\(\\{5,\\,6,\\,7\\}\\) & \\(\\{8,\\,9,\\,10\\}\\) & 0.07972 \\\\\n10 & \\(\\{1,\\,2,\\,3,\\,4\\}\\) & \\(\\{5,\\,6,\\,7\\}\\) & \\(\\{8,\\,9,\\,10\\}\\) & 0.10911 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Performance evaluation of the generalized distorted greedy algorithm (C-W model).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Information-theoretic subset selection of multivariate Markov chains via submodular optimization", "authors": ["Zheyuan Lai", "Michael C. H. Choi"], "url": "https://arxiv.org/abs/2503.23340v1", "attribution": "\"Information-theoretic subset selection of multivariate Markov chains via submodular optimization\" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.14110v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||cccc||} \n \\hline\n $\\nu$ & $\\Tilde{\\Delta}_{\\text{opt}}$ & $\\mathbb{E}\\{\\Tilde{\\varsigma}^{2}_{d}\\} = \\Tilde{b}-\\Tilde{a}^2$ & $\\Tilde{a}$ \\\\ \n \\hline\\hline\n 1 & 1.596 & 0.2313 & 0.6366 \\\\ \n \\hline\n 2 & 0.9957 & 0.10472 & 0.88115 \\\\\n \\hline\n 3 & 0.586 & 0.036037 & 0.96256 \\\\\n \\hline \n 4 & 0.3352 & 0.011409 & 0.98845 \\\\\n \\hline \n 5 & 0.1881 & 0.003482 & 0.996505 \\\\ \n \\hline\n 6 & 0.1041 & 0.0010389 & 0.99896 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Optimal Uniform Quantization Parameters}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Full-Duplex Cell-Free mMIMO Systems: Analysis and Decentralized Optimization", "authors": ["Soumyadeep Datta", "Dheeraj Naidu Amudala", "Ekant Sharma", "Rohit Budhiraja", "Shivendra S. Panwar"], "url": "https://arxiv.org/abs/2010.14110v4", "attribution": "\"Full-Duplex Cell-Free mMIMO Systems: Analysis and Decentralized Optimization\" by Soumyadeep Datta, Dheeraj Naidu Amudala, Ekant Sharma, Rohit Budhiraja, and Shivendra S. Panwar, arXiv:2010.14110v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.12508v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccccc}\n \\toprule\n Topic & \\multicolumn{3}{c}{Offline w/o RL} & \\multicolumn{3}{c}{Offline w/RL} & \\multicolumn{3}{c}{Streaming w/o RL} & \\multicolumn{3}{c}{Streaming w/ RL} \\\\\n & WER & acc & rec & WER & acc & rec & WER & acc & rec & WER & acc & rec \\\\\n \\midrule\n biology & 1.67 & 0.98 & 0.99 & 1.70 & 0.97 & 1.00 & 2.04& 0.98& 0.98 & 1.97& 0.99& 0.98 \\\\\n math & 0.86& 0.99& 0.99 & 0.86& 0.99& 0.99 & 1.29& 0.99& 0.99 & 1.01& 0.99& 1.00 \\\\\n religion & 3.20& 0.98& 0.98 & 2.87& 0.99& 0.99 & 3.71& 0.99& 0.98 & 3.35& 0.99& 0.97 \\\\\n food & 1.90 & 0.98& 0.99 & 1.55& 0.99& 1.00 & 2.01& 0.99& 0.99 & 1.47& 0.99& 0.99 \\\\\n name & 0.53& 1.00& 0.95 & 0.35& 1.00& 1.00 & 1.29& 1.00& 0.95 & 0.88& 1.00& 0.98 \\\\\n brand & 0.41& 1.00& 0.99 & 0.33& 1.00& 0.99 & 1.08& 0.99& 0.95 & 0.38& 1.00& 1.00 \\\\\n astronomy & 2.11& 1.00& 0.97 & 1.97& 0.99& 0.97 & 2.28& 0.98& 0.95 & 2.39& 1.00& 0.98 \\\\\n chemistry & 1.76& 0.99& 0.97 & 1.91& 0.99& 0.98 & 2.81& 0.98& 0.97 & 1.83& 0.99& 0.97 \\\\\n philosophy & 3.03& 0.99& 0.96 & 2.84& 0.99& 0.97 & 3.31& 0.99& 0.96 & 3.03& 0.99& 0.95 \\\\\n physics & 1.72& 0.99& 1.00 & 1.82& 0.98& 1.00 & 2.31& 0.99& 0.98 & 1.8& 0.99& 0.99 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{Hotword customization} comparison between the models w/ or w/o reinforcement learning.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "FunAudio-ASR Technical Report", "authors": ["Keyu An", "Yanni Chen", "Chong Deng", "Changfeng Gao", "Zhifu Gao", "Bo Gong", "Xiangang Li", "Yabin Li", "Xiang Lv", "Yunjie Ji", "Yiheng Jiang", "Bin Ma", "Haoneng Luo", "Chongjia Ni", "Zexu Pan", "Yiping Peng", "Zhendong Peng", "Peiyao Wang", "Hao Wang", "Wen Wang", "Wupeng Wang", "Biao Tian", "Zhentao Tan", "Nan Yang", "Bin Yuan", "Jieping Ye", "Jixing Yu", "Qinglin Zhang", "Kun Zou", "Han Zhao", "Shengkui Zhao", "Jingren Zhou"], "url": "https://arxiv.org/abs/2509.12508v1", "attribution": "\"FunAudio-ASR Technical Report\" by Keyu An, Yanni Chen, Chong Deng, Changfeng Gao, Zhifu Gao, Bo Gong, Xiangang Li, Yabin Li, Xiang Lv, Yunjie Ji, Yiheng Jiang, Bin Ma, Haoneng Luo, Chongjia Ni, Zexu Pan, Yiping Peng, Zhendong Peng, Peiyao Wang, Hao Wang, Wen Wang, Wupeng Wang, Biao Tian, Zhentao Tan, Nan Yang, Bin Yuan, Jieping Ye, Jixing Yu, Qinglin Zhang, Kun Zou, Han Zhao, Shengkui Zhao, and Jingren Zhou, arXiv:2509.12508v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10845v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of students for each class}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\hline\n & N° of Students \\\\ \\hline\nClass 1 & 15 \\\\\nClass 2 & 16 \\\\\nClass 3 & 12 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face Recognition", "authors": ["Angelo G. Menezes"], "url": "https://arxiv.org/abs/2101.10845v1", "attribution": "\"Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face Recognition\" by Angelo G. Menezes, arXiv:2101.10845v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.04207v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CIs and variance components for $m=5000$ under low traffic intensity. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|} \n\\hline\nLow Traffic & \\multicolumn{3}{c|}{$k=20$} & \\multicolumn{3}{c|}{$k=40$} \\\\ \\hline\n$m=5000$ & $n=10$ & $n=50$ & $n=100$ & $n=10$ & $n=50$ & $n=100$ \\\\\n \\hline\nCoverage of $\\mbox{CI}_0$ & 61.5\\% & 82.2\\% & 88.8\\% & 74\\% & 90\\% & 92.5\\%\\\\ \\hline\n$\\mbox{CI}_0$ Width (mean)& 0.623 & 0.658 & 0.718 & 0.749 & 0.778 & 0.788 \n\\\\ \\hline \n$\\mbox{CI}_0$ Width (SD) & 0.404 & 0.26 & 0.191 & 0.36 & 0.186 & 0.129\n\\\\ \\hline \nCoverage of $\\mbox{CI}_{+}$ & 87.8\\% & 95.3\\% & 95.5\\% & 96.7\\% & 96.1\\% & 96.1\\% \\\\ \\hline\n$\\mbox{CI}_{+}$ Width (mean) & 1.34 & 0.968 & 0.892 & 1.43 & 0.963 & 0.87\n\\\\ \\hline \n$\\mbox{CI}_{+}$ Width (SD) & 0.569 & 0.237 & 0.167 & 0.379 & 0.176 & 0.128\n\\\\ \\hline \\hline\n$\\widehat{\\sigma}_T$ & 0.343 & 0.25 & 0.23 & 0.366 & 0.248 & 0.224\n\\\\ \\hline \n$\\widehat{\\sigma}_I$ & 0.166 & 0.177 & 0.191 & 0.197 & 0.204 & 0.205\n\\\\ \\hline \n$\\widehat{\\sigma}_M$ & 0.29 & 0.166 & 0.121 & 0.297 & 0.132 & 0.086 \\\\\\hline\n$\\widehat{\\sigma}_I/\\widehat{\\sigma}_T$ & 0.484 & 0.708 & 0.83 & 0.538 & 0.823 & 0.915\n\\\\ \\hline\\hline\nLow Traffic & \\multicolumn{3}{c|}{$k=80$} & \\multicolumn{3}{c|}{$k=130$} \\\\ \\hline\n$m=5000$ & $n=10$ & $n=50$ & $n=100$ & $n=10$ & $n=50$ & $n=100$ \\\\\n \\hline\nCoverage of $\\mbox{CI}_0$ & 79.8\\% & 93.7\\% & 94.1\\% & 84.1\\% & 93.8\\% & 95.6\\%\\\\ \\hline\n$\\mbox{CI}_0$ Width (mean) & 0.829 & 0.809 & 0.798 & 0.845 & 0.798 & 0.797\n\\\\ \\hline \n $\\mbox{CI}_0$ Width (SD) & 0.33 & 0.131 & 0.108 & 0.3 & 0.129 & 0.105\n\\\\ \\hline \nCoverage of $\\mbox{CI}_{+}$ & 98.9\\% & 96.8\\% & 95.6\\% & 99\\% & 96.4\\% & 97.2\\% \\\\ \\hline\n$\\mbox{CI}_{+}$ Width (mean) & 1.46 & 0.915 & 0.854 & 1.45 & 0.893 & 0.844\n\\\\ \\hline \n$\\mbox{CI}_{+}$ Width (SD) & 0.279 & 0.139 & 0.102 & 0.235 & 0.134 & 0.091\n\\\\ \\hline\\hline \n$\\widehat{\\sigma}_T$ & 0.374 & 0.235 & 0.22 & 0.371 & 0.23 & 0.217\n\\\\ \\hline \n$\\widehat{\\sigma}_I$ & 0.216 & 0.211 & 0.207 & 0.219 & 0.207 & 0.206\n\\\\ \\hline \n$\\widehat{\\sigma}_M$ & 0.291 & 0.099 & 0.069 & 0.287 & 0.089 & 0.06\n\\\\ \\hline\n$\\widehat{\\sigma}_I/\\widehat{\\sigma}_T$ & 0.578 & 0.898 & 0.941 & 0.59 & 0.9 & 0.949\n\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Statistical Uncertainty Analysis for Stochastic Simulation", "authors": ["Wei Xie", "Barry L. Nelson", "Russell R. Barton"], "url": "https://arxiv.org/abs/2011.04207v1", "attribution": "\"Statistical Uncertainty Analysis for Stochastic Simulation\" by Wei Xie, Barry L. Nelson, and Russell R. Barton, arXiv:2011.04207v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.10670v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c||c|c||c|c|}\n \\hline \n \\multicolumn{3}{|c|}{} & \\multicolumn{2}{c|}{$(P_1)$} & \\multicolumn{2}{c|}{$(P_2)$} \\\\\n \\hline\n \\hline\n $\\Delta_a$ &$N_a$ & $\\overline{d}_a$ & $V(31)$ & $V(72)$ & $V(31)$ & $V(72)$ \\\\\n $day$ & & $mg/kg$ & $mm^3$& $mm^3$ & $mm^3$& $mm^3$ \\\\\n \\hline $7$ & $5$ & $6.4$ & $2600$ & $5726$ & $2698$ & $5764$ \\\\\n \\hline $4$ & $8$ & $4$ & $2492 $ & $5802$ & $2570$ & $ 5835$ \\\\\n \\hline $2$ & $15$ & $2.13$ & $2442$ & $5865$ & $2505$ & $5892$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Results for antiangiogenic discrete treatments starting at $t_1 = 0$ with the same total dose.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A mathematical perspective on the paradox that chemotherapy sometimes works backwards", "authors": ["Luis A. Fernández", "Isabel Lasheras", "Cecilia Pola"], "url": "https://arxiv.org/abs/2503.10670v1", "attribution": "\"A mathematical perspective on the paradox that chemotherapy sometimes works backwards\" by Luis A. Fernández, Isabel Lasheras, and Cecilia Pola, arXiv:2503.10670v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.14373v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study on using different LLMs.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\bfseries LLM & \\bfseries BLEU-4 & \\bfseries Rouge-L & \\bfseries Meteor & \\bfseries BertScore F1 \\\\\n \\midrule\n GPT2 XL 1.5B & 12.30 ± 0.19 & 41.33 ± 0.57 & 26.48 ± 0.33 & 92.00 ± 0.06 \\\\\n Gemma 2B & 13.78 ± 0.18 & 45.48 ± 0.55 & 28.32 ± 0.23 & 92.01 ± 0.02 \\\\\n OPT 1.3B & 12.26 ± 0.20 & 41.84 ± 0.52 & 26.21 ± 0.29 & 91.78 ± 0.04 \\\\\n Llama 3.2 1B & \\textbf{13.93} ± 0.21 & \\textbf{47.08} ± 0.56 & \\textbf{29.17} ± 0.31 & \\textbf{92.53} ± 0.07 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling", "authors": ["William Han", "Chaojing Duan", "Michael A. Rosenberg", "Emerson Liu", "Ding Zhao"], "url": "https://arxiv.org/abs/2412.14373v3", "attribution": "\"ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling\" by William Han, Chaojing Duan, Michael A. Rosenberg, Emerson Liu, and Ding Zhao, arXiv:2412.14373v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.04655v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Graph Properties of the used datasets and the corresponding learned hyperparameters in GAGCN w/ Count of walks.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rrr|rrrrrrr}\\toprule\nDataset & \\multicolumn{3}{c}{Graph Properties} & \\multicolumn{7}{c}{Hyperparameters} \\\\ \\hline\n & Avg. K-core & Avg. Count. Walks & homophily & $e_1$ & $e_2$ & $e_3$ & $m_1$ & $m_2$&$m_3$ & $a$ \\\\ \\hline\nPhysics & $ 7.71 $ & $ 449.22 $ & $ 0.931 $ & $ 0.95~(0.01) $ & $ -0.02~(0.02) $ & $ -0.02~(0.02) $ & $ 0.89~(0.02) $ & $ 0.96~(0.03) $ & $ -0.10~(0.01) $ & $ -0.09~(0.01)$ \\\\\nPhoto & $ 16.97 $ & $ 3204.098 $ & $ 0.827 $ & $ -0.06~(0.01) $ & $ -0.07~(0.00) $ & $ -0.07~(0.00) $ & $ -0.05~(0.01) $ & $ 0.87~(0.00) $ & $ -0.04~(0.02) $ & $ -0.09~(0.01)$ \\\\\nCora & $ 2.31 $ & $ 42.52 $ & $ 0.809 $ & $ 0.36~(0.02) $ & $ 0.03~(0.01) $ & $ 0.02~(0.01) $ & $ 0.68~(0.02) $ & $ 1.37~(0.09) $ & $ 0.63~(0.02) $ & $ 0.64~(0.01)$ \\\\\nCS & $ 4.94 $ & $ 162.75 $ & $ 0.808 $ & $ 0.45~(0.02) $ & $ 0.03~(0.01) $ & $ 0.02~(0.01) $ & $ 0.62~(0.03) $ & $ 1.23~(0.04) $ & $ 0.47~(0.02) $ & $ 0.52~(0.02)$ \\\\\nPubMed & $ 2.39 $ & $ 75.43 $ & $ 0.802 $ & $ 0.30~(0.02) $ & $ -0.15~(0.02) $ & $ -0.16~(0.02) $ & $ 0.60~(0.02) $ & $ 1.58~(0.03) $ & $ 0.56~(0.02) $ & $ 1.38~(0.04)$ \\\\\nComputers & $ 18.84 $ & $ 6221.39 $ & $ 0.777 $ & $ -0.05~(0.01) $ & $ -0.07~(0.00) $ & $ -0.07~(0.00) $ & $ -0.05~(0.02) $ & $ 0.86~(0.01) $ & $ -0.04~(0.03) $ & $ -0.10~(0.02)$ \\\\\nCiteSeer & $ 1.73 $ & $ 18.91 $ & $ 0.735 $ & $ 0.25~(0.01) $ & $ -0.13~(0.01) $ & $ -0.14~(0.01) $ & $ 0.67~(0.01) $ & $ 1.63~(0.01) $ & $ 0.62~(0.01) $ & $ 1.63~(0.01)$ \\\\\nogbn-arxiv & $ 7.13 $ & $ 4898.16 $ & $ 0.654 $ & $ 0.12~(0.00) $ & $ -0.08~(0.00) $ & $ -0.19~(0.00) $ & $ 0.32~(0.00) $ & $ 1.57~(0.02) $ & $ 0.32~(0.00) $ & $ 0.93~(0.01)$ \\\\\ndeezer-europe & $ 3.57 $ & $ 106.16 $ & $ 0.525 $ & $ 0.26~(0.03) $ & $ -1.04~(0.03) $ & $ -0.07~(0.03) $ & $ 0.58~(0.04) $ & $ 0.88~(0.06) $ & $ 0.60~(0.04) $ & $ 0.67~(0.06)$ \\\\\nPenn94 & $33.68$& $ 10662.08 $ & $ 0.470 $ & $ 0.30~(0.00) $ & $ -0.77~(0.03) $ & $ 0.06~(0.09) $ & $ 0.58~(0.00) $ & $ -0.46~(0.16) $ & $ 1.39~(0.01) $ & $ -0.31~(0.17)$ \\\\\nchameleon & $ 16.60 $ & $ 2913.48 $ & $ 0.231 $ & $ 0.32~(0.06) $ & $ -0.05~(0.01) $ & $ -0.05~(0.01) $ & $ -0.28~(0.08) $ & $ 0.89~(0.02) $ & $ -0.17~(0.03) $ & $ -0.14~(0.02)$ \\\\\nsquirrel & $ 41.55 $ & $ 31888.02 $ & $ 0.222 $ & $ 0.23~(0.11) $ & $ -0.71~(0.10) $ & $ 0.35~(0.10) $ & $ -0.46~(0.46) $ & $ 1.28~(0.22) $ & $ -0.32~(0.33) $ & $ -0.29~(0.48)$ \\\\\narxiv-year& $ 7.13 $ & $ 82.85 $ & $ 0.218 $ & $ 0.23~(0.01) $ & $ -0.28~(0.01) $ & $ -0.16~(0.01) $ & $ -0.26~(0.01) $ & $ 0.99~(0.03) $ & $ -0.01~(0.04) $ & $ 0.84~(0.03)$ \\\\\nWisconsin & $ 2.05 $ & $ 76.26 $ & $ 0.206 $ & $ 0.40~(0.01) $ & $ -0.79~(0.07) $ & $ 0.18~(0.05) $ & $ 0.61~(0.01) $ & $ -1.38~(0.08) $ & $ 1.48~(0.01) $ & $ -0.69~(0.06)$ \\\\\nCornell & $ 1.74 $ & $ 58.47 $ & $ 0.132 $ & $ 0.40~(0.01) $ & $ -0.21~(0.04) $ & $ -0.22~(0.04) $ & $ 0.59~(0.01) $ & $ -1.51~(0.06) $ & $ 1.47~(0.01) $ & $ -0.61~(0.05)$ \\\\\nTexas & $ 1.71 $ & $ 70.72 $ & $ 0.111 $ & $ 0.40~(0.01) $ & $ -0.72~(0.03) $ & $ 0.24~(0.03) $ & $ 0.58~(0.01) $ & $ -1.48~(0.05) $ & $ 1.47~(0.01) $ & $ -0.59~(0.03)\n $ \\\\ \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Centrality Graph Shift Operators for Graph Neural Networks", "authors": ["Yassine Abbahaddou", "Fragkiskos D. Malliaros", "Johannes F. Lutzeyer", "Michalis Vazirgiannis"], "url": "https://arxiv.org/abs/2411.04655v1", "attribution": "\"Centrality Graph Shift Operators for Graph Neural Networks\" by Yassine Abbahaddou, Fragkiskos D. Malliaros, Johannes F. Lutzeyer, and Michalis Vazirgiannis, arXiv:2411.04655v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09391v2_tex_table18.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n & Gap & Hurdle & Stairs & Steps \\\\\n \\hline\n Without Setup Policies & 74 & \\textbf{41} & 64 & 142 \\\\\n With Setup Policies & \\textbf{44} & 51 & \\textbf{49} & \\textbf{90} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Number of failures by terrain type from 1000 episodes of 4 terrain types \\textbf{(without the jump terrain)}, randomly shuffled each episode.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Learning Setup Policies: Reliable Transition Between Locomotion Behaviours", "authors": ["Brendan Tidd", "Nicolas Hudson", "Akansel Cosgun", "Jurgen Leitner"], "url": "https://arxiv.org/abs/2101.09391v2", "attribution": "\"Learning Setup Policies: Reliable Transition Between Locomotion Behaviours\" by Brendan Tidd, Nicolas Hudson, Akansel Cosgun, and Jurgen Leitner, arXiv:2101.09391v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.07393v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n\\cline{1-5}\n & Aurory & Autoglphs & Bored Ape Kennel Club & Bored Ape Yacht Club & \\\\ \\cline{1-5}\n12-month Realized Volatility & 291\\% & 1,084\\% & 420\\% & 340\\% & \\\\ \\cline{1-5}\n & & & & & \\\\ \\cline{1-5}\n & Cryptopunks & Degenerate Ape Academy & Meebits & Mutant Ape Yacht Club & \\\\ \\cline{1-5}\n12-month Realized Volatility & 1,140\\% & 464\\% & 1,013\\% & 896\\% & \\\\ \\cline{1-5}\n & & & & & \n\\end{tabular}\n\\end{adjustbox}\n\\caption{12 Month Realized Volatility of eight profile picture collections}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Non-fungible token transactions: data and challenges", "authors": ["Jason B. Cho", "Sven Serneels", "David S. Matteson"], "url": "https://arxiv.org/abs/2210.07393v1", "attribution": "\"Non-fungible token transactions: data and challenges\" by Jason B. Cho, Sven Serneels, and David S. Matteson, arXiv:2210.07393v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18248v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Wave gauges coordinates within the wave tank as reported in }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\textbf{WG number} & \\textbf{Global Coordinates (m)} \\\\\n \\midrule\n Wave gauge 1 & $(-2.74 , 0)$ \\\\\n Wave gauge 2 & $(-0.05 , 0.26)$ \\\\\n Wave gauge 3 & $(0.07 , -0.36)$ \\\\\n Wave gauge 4 & $(0.55 , 0)$ \\\\\n Wave gauge 5 & $(1.90 , 0)$ \\\\\n Wave gauge 6 & $(2.90 , 0)$\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A finite volume Simo-Reissner beam method for moored floating body dynamics", "authors": ["Amirhossein Taran", "Seevani Bali", "Zeljko Tukovic", "Vikram Pakrashi", "Philip Cardiff"], "url": "https://arxiv.org/abs/2504.18248v1", "attribution": "\"A finite volume Simo-Reissner beam method for moored floating body dynamics\" by Amirhossein Taran, Seevani Bali, Zeljko Tukovic, Vikram Pakrashi, and Philip Cardiff, arXiv:2504.18248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08279v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters used for the distortion of the ground truth centerlines}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cccccc|}\n\\hline\ndensity $(mm^{-1})$ & $2$ & $4$ & $10$ & $16$ & $20$ \\\\\n\\hline\n$\\sigma_{radius} (mm)$ & $0.01r$ & $0.05r$ & $0.1r$ & $0.3r$ & $0.5r$ \\\\\n\\hline\n$\\sigma_{spatial} (mm)$ & $0.01r$ & $0.05r$ & $0.1r$ & $0.3r$ & $0.5r$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Modeling and hexahedral meshing of cerebral arterial networks from centerlines", "authors": ["Méghane Decroocq", "Carole Frindel", "Pierre Rougé", "Makoto Ohta", "Guillaume Lavoué"], "url": "https://arxiv.org/abs/2201.08279v2", "attribution": "\"Modeling and hexahedral meshing of cerebral arterial networks from centerlines\" by Méghane Decroocq, Carole Frindel, Pierre Rougé, Makoto Ohta, and Guillaume Lavoué, arXiv:2201.08279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02419v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{PVT simulation on ENOB}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n \\hline\n \\multicolumn{2}{|c|}{Process}&\\multicolumn{2}{c|}{ff}&\\multicolumn{2}{c|}{ss}&tt \\\\\n \\hline\n \\multicolumn{2}{|c|}{Temprature ($^{\\circ}$C)}&-40&80&-40&80&27\\\\\n \\hline\n \\multirow{3}{*}{Voltage (V)}&1& & & & & 7.42\\\\\n \\cline{2-7}\n &0.9&7.41&7.28&7.29&7.21&\\\\\n \\cline{2-7}\n &1.1&7.11&7.16&7.25&7.11&\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Low Power In-Memory Multiplication andAccumulation Array with Modified Radix-4 Inputand Canonical Signed Digit Weights", "authors": ["Rui Xiao", "Kejie Huang", "Yewei Zhang", "Haibin Shen"], "url": "https://arxiv.org/abs/2101.02419v1", "attribution": "\"A Low Power In-Memory Multiplication andAccumulation Array with Modified Radix-4 Inputand Canonical Signed Digit Weights\" by Rui Xiao, Kejie Huang, Yewei Zhang, and Haibin Shen, arXiv:2101.02419v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|ccccccccc|c}\n&\t$g^3_i$&\t7&\t5&\t0&\t9&\t9&\t7&\t12&\t11&\t13&\t\t\\\\\nIteration&\t$i$&\t$L_1$&\t$L_2$&\t$L_3$&\t$L_4$&\t$L_5$&\t$L_6$&\t$L_7$&\t$L_8$&\t$L_9$&\t$SEL$\\\\\n\\hline\n0&\t---&\t$\\infty$&\t$\\infty$&\t$\\infty$&\t$\\infty$&\t0&\t$\\infty$&\t$\\infty$&\t$\\infty$&\t$\\infty$&\t$\\{ 5 \\}$\\\\\n1&\t5&\t$\\infty$&\t4&\t$\\infty$&\t3&\t0&\t3&\t$\\infty$&\t2&\t$\\infty$&\t$\\{ 2, 4, 6, 8 \\}$\\\\\n2&\t2&\t6&\t4&\t9&\t3&\t0&\t3&\t$\\infty$&\t2&\t$\\infty$&\t$\\{ 1, 3, 4, 6, 8 \\}$\\\\\n3&\t3&\t6&\t4&\t9&\t3&\t0&\t3&\t$\\infty$&\t2&\t$\\infty$&\t$\\{ 1, 4, 6, 8 \\}$\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.12266v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of Rolling Window (RW) Setup and Market Characteristics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\\toprule\n{Economy} & {Daily Returns} & {RW Size} & {No. of RW (Daily)} & {No. of RW (Weekly)} & {Avg Volatility (SD, \\%)} \\\\\n\\midrule\nUnited States & 1,758 & 250 & 1,508 & 301 & 2.01 \\\\\nIndia & 1,728 & 245 & 1,483 & 296 & 1.80 \\\\\nHong Kong & 1,719 & 244 & 1,475 & 295 & 1.72\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Semiparametric Dynamic Copula Models for Portfolio Optimization", "authors": ["Savita Pareek", "Sujit K. Ghosh"], "url": "https://arxiv.org/abs/2504.12266v1", "attribution": "\"Semiparametric Dynamic Copula Models for Portfolio Optimization\" by Savita Pareek and Sujit K. Ghosh, arXiv:2504.12266v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.20247v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Success rates of the CBO method on the non-convex function with heart-shaped constraint.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|r|r|r|r|}\n \\hline\n \\multirow{2}{*}{$K$} & \\multicolumn{4}{c|}{${N}$} \\\\\n \\cline{2-5}\n & \\textbf{10} & \\textbf{20} & \\textbf{50} & \\textbf{100} \\\\\n \\hline\n 5 & 0.29 & 0.54 & 0.90 & 0.95 \\\\\n 10 & 0.46 & 0.69 & 0.88 & 0.99 \\\\\n 20 & 0.54 & 0.81 & 0.95 & 1.00 \\\\\n 50 & 0.59 & 0.78 & 0.98 & 0.99 \\\\\n 100 & 0.57 & 0.81 & 0.97 & 1.00 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Well-posedness and approximation of reflected McKean-Vlasov SDEs with applications", "authors": ["P. D. Hinds", "A. Sharma", "M. V. Tretyakov"], "url": "https://arxiv.org/abs/2412.20247v1", "attribution": "\"Well-posedness and approximation of reflected McKean-Vlasov SDEs with applications\" by P. D. Hinds, A. Sharma, and M. V. Tretyakov, arXiv:2412.20247v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.13005v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n\t\t\t\\toprule\n & $n_p$ & $\\gamma$ & $V$ & $r_p$ & $r_s$ & $r_o$ & $r_m$\\\\\n \\hline\n \\emph{3DMatch} & 256 & 24 & 0.025 & 0.0375 & 0.1 & 0.0375 & 0.05\\\\\n \\emph{ModelNet} & 384 & 64 & 0.06 & 0.018 & 0.06 & 0.04 & 0.04 \\\\\n \\emph{odometryKITTI} & 512 & 48 & 0.3 & 0.21 & 0.75 & 0.45 & 0.3 \\\\\n\t\t\t\\bottomrule\n\t\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Hyper-parameters configurations for different datasets.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "PREDATOR: Registration of 3D Point Clouds with Low Overlap", "authors": ["Shengyu Huang", "Zan Gojcic", "Mikhail Usvyatsov", "Andreas Wieser", "Konrad Schindler"], "url": "https://arxiv.org/abs/2011.13005v3", "attribution": "\"PREDATOR: Registration of 3D Point Clouds with Low Overlap\" by Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov, Andreas Wieser, and Konrad Schindler, arXiv:2011.13005v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.11592v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Extended GEE Mean Parameter Estimates and Robust Standard Errors (BC0) for CARDIA Study}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\\hline\n\\textbf{} & \\multicolumn{8}{c}{\\textit{Exchangeable}} \\\\ \\cline{2-9}\n\\textbf{} & \\multicolumn{2}{c}{\\textit{Black Females}} & \\multicolumn{2}{c}{\\textit{Black Males}} & \\multicolumn{2}{c}{\\textit{White Females}} & \\multicolumn{2}{c}{\\textit{White Males}} \\\\\n & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 \\\\\n \\hline\nIntercept & -0.021 & 0.111 & 0.168 & 0.110 & 0.165 & 0.147 & 0.300 & 0.155 \\\\\nAge/10 & \\textbf{0.343} & 0.157 & \\textbf{0.654} & 0.177 & -0.245 & 0.187 & -0.099 & 0.197 \\\\\nAge-Square & -0.597 & 0.437 & 0.293 & 0.469 & -0.309 & 0.514 & -0.757 & 0.576 \\\\\nSome College & \\textbf{-0.680} & 0.117 & \\textbf{-0.949} & 0.125 & \\textbf{-0.593} & 0.161 & \\textbf{-0.960} & 0.169 \\\\\nCollege Degree & \\textbf{-1.744} & 0.171 & \\textbf{-1.795} & 0.177 & \\textbf{-1.864} & 0.161 & \\textbf{-1.994} & 0.168 \\\\\nYear(Yr/10) & \\textbf{0.463} & 0.214 & 0.385 & 0.241 & \\textbf{-0.707} & 0.265 & -0.004 & 0.248 \\\\\nYear-square & \\textbf{-0.934} & 0.373 & -0.334 & 0.415 & 0.510 & 0.474 & -0.388 & 0.433 \\\\\nYear-cubic & \\textbf{0.358} & 0.168 & 0.037 & 0.186 & -0.200 & 0.215 & 0.132 & 0.193 \\\\\n\\hline\n\\textbf{} & \\multicolumn{8}{c}{\\textit{Modified AR(1)}} \\\\ \\cline{2-9}\n\\textbf{} & \\multicolumn{2}{c}{\\textit{Black Females}} & \\multicolumn{2}{c}{\\textit{Black Males}} & \\multicolumn{2}{c}{\\textit{White Females}} & \\multicolumn{2}{c}{\\textit{White Males}} \\\\\n & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 \\\\\n \\hline\nIntercept & -0.014 & 0.111 & 0.164 & 0.110 & 0.146 & 0.147 & 0.294 & 0.156 \\\\\nAge/10 & \\textbf{0.318} & 0.157 & \\textbf{0.648} & 0.176 & -0.207 & 0.187 & -0.046 & 0.193 \\\\\nAge-Square & -0.597 & 0.431 & 0.314 & 0.468 & -0.185 & 0.506 & -0.641 & 0.577 \\\\\nSome College & \\textbf{-0.681} & 0.117 & \\textbf{-0.971} & 0.125 & \\textbf{-0.566} & 0.162 & \\textbf{-1.006} & 0.169 \\\\\nCollege Degree & \\textbf{-1.775} & 0.171 & \\textbf{-1.784} & 0.177 & \\textbf{-1.902} & 0.163 & \\textbf{-1.986} & 0.167 \\\\\nYear(Yr/10) & \\textbf{0.492} & 0.228 & 0.469 & 0.258 & \\textbf{-0.695} & 0.280 & -0.037 & 0.258 \\\\\nYear-square & \\textbf{-1.009} & 0.392 & -0.467 & 0.439 & 0.496 & 0.496 & -0.286 & 0.447 \\\\\nYear-cubic & \\textbf{0.390} & 0.174 & 0.094 & 0.194 & -0.202 & 0.222 & 0.071 & 0.197 \\\\\n\\hline\n & \\multicolumn{8}{c}{\\textit{5-Dependent}} \\\\ \\cline{2-9}\n & \\multicolumn{2}{c}{\\textit{Black Females}} & \\multicolumn{2}{c}{\\textit{Black Males}} & \\multicolumn{2}{c}{\\textit{White Females}} & \\multicolumn{2}{c}{\\textit{White Males}} \\\\\n\\textbf{} & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 \\\\\n\\hline\nIntercept & -0.020 & 0.110 & 0.171 & 0.109 & 0.157 & 0.146 & 0.295 & 0.155 \\\\\nAge/10 & \\textbf{0.332} & 0.156 & \\textbf{0.651} & 0.175 & -0.231 & 0.185 & -0.079 & 0.193 \\\\\nAge-Square & -0.597 & 0.432 & 0.310 & 0.464 & -0.260 & 0.505 & -0.702 & 0.570 \\\\\nSome College & \\textbf{-0.679} & 0.117 & \\textbf{-0.963} & 0.124 & \\textbf{-0.582} & 0.160 & \\textbf{-0.981} & 0.168 \\\\\nCollege Degree & \\textbf{-1.758} & 0.169 & \\textbf{-1.795} & 0.176 & \\textbf{-1.885} & 0.160 & \\textbf{-1.995} & 0.166 \\\\\nYear(Yr/10) & \\textbf{0.480} & 0.214 & 0.386 & 0.241 & \\textbf{-0.704} & 0.266 & -0.014 & 0.244 \\\\\nYear-square & \\textbf{-0.963} & 0.371 & -0.327 & 0.416 & 0.512 & 0.475 & -0.348 & 0.429 \\\\\nYear-cubic & \\textbf{0.369} & 0.166 & 0.035 & 0.186 & -0.204 & 0.215 & 0.107 & 0.191 \\\\\n\\hline\n & \\multicolumn{8}{c}{\\textit{Unequal Time}} \\\\ \\cline{2-9}\n & \\multicolumn{2}{c}{\\textit{Black Females}} & \\multicolumn{2}{c}{\\textit{Black Males}} & \\multicolumn{2}{c}{\\textit{White Females}} & \\multicolumn{2}{c}{\\textit{White Males}} \\\\\n\\textbf{} & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 \\\\\n\\hline\nIntercept & -0.020 & 0.110 & 0.167 & 0.110 & 0.152 & 0.146 & 0.290 & 0.154 \\\\\nAge/10 & \\textbf{0.332} & 0.156 & \\textbf{0.656} & 0.175 & -0.239 & 0.184 & -0.086 & 0.193 \\\\\nAge-Square & -0.597 & 0.432 & 0.325 & 0.465 & -0.257 & 0.503 & -0.688 & 0.569 \\\\\nSome College & \\textbf{-0.679} & 0.117 & \\textbf{-0.960} & 0.124 & \\textbf{-0.574} & 0.160 & \\textbf{-0.980} & 0.168 \\\\\nCollege Degree & \\textbf{-1.758} & 0.169 & \\textbf{-1.794} & 0.176 & \\textbf{-1.882} & 0.160 & \\textbf{-1.984} & 0.166 \\\\\nYear(Yr/10) & \\textbf{0.480} & 0.214 & 0.401 & 0.241 & \\textbf{-0.701} & 0.266 & -0.012 & 0.245 \\\\\nYear-square & \\textbf{-0.963} & 0.371 & -0.356 & 0.414 & 0.508 & 0.475 & -0.353 & 0.428 \\\\\nYear-cubic & \\textbf{0.369} & 0.166 & 0.046 & 0.185 & -0.205 & 0.215 & 0.107 & 0.191 \\\\\n\\hline\n\\multicolumn{9}{l}{*Bold estimates indicate p-value\\textless{}0.05.}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "GEECORR: A SAS macro for regression models of correlated binary responses and within-cluster correlation using generalized estimating equations", "authors": ["Tracie L. Shing", "John S. Preisser", "Richard C. Zink"], "url": "https://arxiv.org/abs/2011.11592v1", "attribution": "\"GEECORR: A SAS macro for regression models of correlated binary responses and within-cluster correlation using generalized estimating equations\" by Tracie L. Shing, John S. Preisser, and Richard C. Zink, arXiv:2011.11592v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.05003v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n\\hline\nFeatures & \\textbf{Next}(mAP) & Our Model(mAP) & Improve \\\\\n\\hline\\hline\nFull Features & 0.192 & \\textbf{0.213} & 10.9$\\%$ \\\\\nNo P-Objects & 0.198 & \\textbf{0.213} & 7.6$\\%$ \\\\\nNo P-Scene & 0.206 & \\textbf{0.211} & 2.4$\\%$ \\\\\nNo P-Keypoint & 0.190 & \\textbf{0.212} & 11.6$\\%$ \\\\\nNo P-Appearence & 0.154 & \\textbf{0.179} & 16.2$\\%$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison to baseline method \\textbf{Next} on the ActEV/VIRAT dataset}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SCR-Graph: Spatial-Causal Relationships based Graph Reasoning Network for Human Action Prediction", "authors": ["Bo Chen", "Decai Li", "Yuqing He", "Chunsheng Hua"], "url": "https://arxiv.org/abs/1912.05003v1", "attribution": "\"SCR-Graph: Spatial-Causal Relationships based Graph Reasoning Network for Human Action Prediction\" by Bo Chen, Decai Li, Yuqing He, and Chunsheng Hua, arXiv:1912.05003v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00990v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|c|c|c|c|c|}\n\\cline{3-7}\n& & \\multicolumn{5}{c|}{\\textit{\\textbf{Predicted class}}} \\\\\n\\cline{3-7} \n& & \\textbf{Snow} & \\textbf{Sunset} & \\textbf{Trees} & \\textbf{Rocks} & \\textbf{Night} \\\\ \n\\hline\n\\multirow{5}{*}{\\textit{\\textbf{Actual class}}} & \\textbf{Snow} & 99.5 & 0 & 0 & 0 & 0.5 \\\\ \\cline{2-7} \n & \\textbf{Sunset} & 0 & 99.5 & 0.5 & 0 & 0 \\\\ \n\\cline{2-7} \n & \\textbf{Trees} & 1 & 0.5 & 56.5 & 42 & 0 \\\\ \n\\cline{2-7} \n & \\textbf{Rocks} & 2 & 0.5 & 7 & 91 & 0 \\\\ \\cline{2-7} \n & \\textbf{Night} & 1 & 8 & 9 & 2.5 & 79.5\n\\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Guiding GANs: How to control non-conditional pre-trained GANs for conditional image generation", "authors": ["Manel Mateos", "Alejandro González", "Xavier Sevillano"], "url": "https://arxiv.org/abs/2101.00990v1", "attribution": "\"Guiding GANs: How to control non-conditional pre-trained GANs for conditional image generation\" by Manel Mateos, Alejandro González, and Xavier Sevillano, arXiv:2101.00990v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00644v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Thresholds of Staked ETH Required for Various Ethereum Attack Scenarios }\n\\begin{tabular}{|c|l|}\n\\hline\n\\textbf{Staked ETH} & \\textbf{Attack Type} \\\\ \\hline\n33\\% & Delay finality \\\\ \n34\\% & Cause double finality \\\\ \n51\\% & Censorship and control over blockchain's future \\\\ \n66\\% & Censorship and control over blockchain's future and past \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "SoK: Liquid Staking Tokens (LSTs) and Emerging Trends in Restaking", "authors": ["Krzysztof Gogol", "Yaron Velner", "Benjamin Kraner", "Claudio Tessone"], "url": "https://arxiv.org/abs/2404.00644v3", "attribution": "\"SoK: Liquid Staking Tokens (LSTs) and Emerging Trends in Restaking\" by Krzysztof Gogol, Yaron Velner, Benjamin Kraner, and Claudio Tessone, arXiv:2404.00644v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01383v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Factors and Levels for Blood Glucose Experiment}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cllcc} % Defines table with left and center columns\n\\toprule\n\\multicolumn{2}{c}{\\text{Factor}} & \\multicolumn{3}{c}{\\text{Level}} \\\\ \n\\cmidrule(lr){3-5}\n & & \\text{1} & \\text{2} & \\text{3} \\\\\n\\midrule\n\\text{A},& Wash & No & Yes & \\\\\n\\text{B},& Microvial volume (ml) & 2.0 & 2.5 & 3.0 \\\\\n\\text{C},& Caras H$_2$O level (ml) & 20 & 28 & 35 \\\\\n\\text{D},& Centrifuge rpm & 2,100 & 2,300 & 2,500 \\\\\n\\text{E},& Centrifuge time (min) & 1.75 & 3 & 4.5 \\\\\n\\text{F},& Sensitivity, absorption & (.10, 2.5) & (.25, 2) & (.50, 1.5) \\\\\n\\text{G},& Temperature ($^\\circ$C) & 25 & 30 & 37 \\\\\n\\text{H},& Dilution ratio & 1:51 & 1:101 & 1:151 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Automated Analysis of Experiments using Hierarchical Garrote", "authors": ["Wei-Yang Yu", "V. Roshan Joseph"], "url": "https://arxiv.org/abs/2411.01383v1", "attribution": "\"Automated Analysis of Experiments using Hierarchical Garrote\" by Wei-Yang Yu and V. Roshan Joseph, arXiv:2411.01383v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16944v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrr}\n \\toprule\n \\textit{property} & $p=0.25$ & $p=0.20$ & $p=0.15$ & $p=0.10$ \\\\\n \\midrule\n generated \t\t& 180,000,000 & 180,000,000 & 180,000,000 & 180,000,000 \\\\\n connected \t\t& 174,497,428 & 167,659,141 & 152,927,782 & 118,197,366 \\\\\n total found \t\t& 32,328 & 58,121 & 59,666 & 23,900 \\\\\n distinct found \t& 19,077 & 35,286 & 41,866 & 21,004 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\small General results about graphs generated with the $G(n,p)$ model.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Burning some myths on privacy properties of social networks against active attacks", "authors": ["Serafino Cicerone", "Gabriele Di Stefano", "Sandi Klavžar", "Ismael G. Yero"], "url": "https://arxiv.org/abs/2504.16944v1", "attribution": "\"Burning some myths on privacy properties of social networks against active attacks\" by Serafino Cicerone, Gabriele Di Stefano, Sandi Klavžar, and Ismael G. Yero, arXiv:2504.16944v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.17623v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Unit-task data for the batch process problem}%标题\n\\begin{tabular}{lccc}%四个c代表该表一共四列，内容全部居中\n \\toprule%第一道横线\n Unit & Capacity& Suitability&Mean processing time\\\\\n \\midrule%第二道横线 \n Heater 1 & 100 & Heating & 4.0 \\\\\n Heater 2 & 120 & Heating & 4.0 \\\\\n Heactor 1~~ & ~70~ & ~~~~Reaction 1, 2, 3~~~~& 4.0 \\\\\n Reactor 2 & 80 & Reaction 1, 2, 3 & 3.0 \\\\\n Reactor 3 & 70 & Reaction 1, 2, 3 & 4.0 \\\\\n Reactor 4 & 120 & Reaction 1, 2, 3 & 5.0 \\\\\n Still 1 & 200 & Separation & 5.0 \\\\\n Still 2 & 150 & Separation & 5.0 \\\\\n \\bottomrule%第三道横线\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Towards An Unsupervised Learning Scheme for Efficiently Solving Parameterized Mixed-Integer Programs", "authors": ["Shiyuan Qu", "Fenglian Dong", "Zhiwei Wei", "Chao Shang"], "url": "https://arxiv.org/abs/2412.17623v2", "attribution": "\"Towards An Unsupervised Learning Scheme for Efficiently Solving Parameterized Mixed-Integer Programs\" by Shiyuan Qu, Fenglian Dong, Zhiwei Wei, and Chao Shang, arXiv:2412.17623v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.00101v1_tex_table20.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n\\hline\\hline\nState & Min & Median & Mean & Max & IQR & Skewness & Kurtosis\\\\\n\\hline\n$S_{1}$ & 1.00 & 1.00 & 1.67 & 62.00 & 0 & 8.99 & 116.50 \\\\ \n $S_{2}$& 1.00 & 1.00 & 1.31 & 117.00 & 0 & 29.27 & 1,609.20 \\\\\n $S_{3}$ & 1.00 & 1.00 & 1.21 & 57.00 & 0 & 21.55 & 668.24 \\\\ \n $S_{4}$& 1.00 & 1.00 & 1.29 & 70.00 & 0 & 22.09 & 734.76 \\\\ \n $S_{5}$ & 1.00 & 1.00 & 1.21 & 16.00 & 0 & 8.21 & 96.75 \\\\ \n $S_{6}$& 1.00 & 1.00 & 1.33 & 28.00 & 0 & 8.79 & 101.93 \\\\ \n \\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Summary statistics for the reporting delay of claims in each state, with a time discretization of 30 days.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "mCube: Multinomial Micro-level reserving Model", "authors": ["Emmanuel Jordy Menvouta", "Jolien Ponnet", "Robin Van Oirbeek", "Tim Verdonck"], "url": "https://arxiv.org/abs/2212.00101v1", "attribution": "\"mCube: Multinomial Micro-level reserving Model\" by Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek, and Tim Verdonck, arXiv:2212.00101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2301.00248v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Example of expanding walk forward validation without where $t_{i}$ represents the feature vector of trading day $i$. In this example, a training window with an initial size $s=3$ is taken together with a testing window of size $k=1$. We therefore consistently use the feature vectors of past trading days to train a model (underlined) and subsequently test said model on trading day $t+k$ (bold). }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n \\toprule\n Iteration & Variable roles\\\\\n \\midrule\n 1 & $ \\underline{t_{1} \\hspace{1.5mm} t_{2} \\hspace{1.5mm} t_{3}} \\hspace{1.5mm} \\mathbf{ t_{4} } \\hspace{1.5mm} t_{5} \\hspace{1.5mm} \\dotsi \\hspace{1.5mm} t_{n} $ \\\\\n 2 & $ \\underline{ t_{1} \\hspace{1.5mm} t_{2} \\hspace{1.5mm} t_{3} \\hspace{1.5mm} t_{4}} \\hspace{1.5mm} \\mathbf{t_{5}} \\hspace{1.5mm} \\dotsi \\hspace{1.5mm} t_{n} $ \\\\\n \\vdots & \\vdots \\\\\n m & $ \\underline{ t_{1} \\hspace{1.5mm} \\dotsi \\hspace{1.5mm} t_{n-3} \\hspace{1.5mm} t_{n-2} \\hspace{1.5mm} t_{n-1}} \\hspace{1.5mm} \\mathbf{t_{n}} $ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Nowcasting Stock Implied Volatility with Twitter", "authors": ["Thomas Dierckx", "Jesse Davis", "Wim Schoutens"], "url": "https://arxiv.org/abs/2301.00248v1", "attribution": "\"Nowcasting Stock Implied Volatility with Twitter\" by Thomas Dierckx, Jesse Davis, and Wim Schoutens, arXiv:2301.00248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.01000v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\toprule\n \\textbf{Number of Cameras} & \\textbf{ADAE Study (Table )} & \\textbf{CoV} \\\\\n \\midrule \n Single smart camera trap & 5 & 1.59 \\\\\n Smart camera trap network & 7 & 7.33\\\\\n Single quadcopter & 3 & 3.01 \\\\\n \\bottomrule \\\\ \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Quantifying Burstiness of ADAE Studies}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Characterizing and Modeling AI-Driven Animal Ecology Studies at the Edge", "authors": ["Jenna Kline", "Austin O'Quinn", "Tanya Berger-Wolf", "Christopher Stewart"], "url": "https://arxiv.org/abs/2412.01000v1", "attribution": "\"Characterizing and Modeling AI-Driven Animal Ecology Studies at the Edge\" by Jenna Kline, Austin O'Quinn, Tanya Berger-Wolf, and Christopher Stewart, arXiv:2412.01000v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.11910v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Global structure preservation quality during dimensionality reduction of real-life datasets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n & \\multicolumn{4}{c}{Quality measure} \\\\\n \\cmidrule(r){2-5}\n Method & L. C. & W. D. $H_{0}$ & T. A. & RTD \\\\\n \\midrule\n \\multicolumn{5}{c}{\\textbf{F-MNIST into 16D space}} \\\\\n UMAP & 0.602 & 592.0 $\\pm$ 3.9 & 0.741 $\\pm$ 0.02 & 12.31 $\\pm$ 0.4\\\\\n AE & 0.879 & 320.5 $\\pm$ 1.9 & 0.850 $\\pm$ 0.00 & 5.52 $\\pm$ 0.2 \\\\\n TopoAE & 0.905 & 190.7 $\\pm$ 1.2 & 0.867 $\\pm$ 0.01 & 3.69 $\\pm$ 0.2 \\\\\n RTD & \\underline{0.960} &\\underline {181.2 $\\pm$ 0.8} & \\underline{0.907 $\\pm$ 0.00} & \\underline{3.01 $\\pm$ 0.1}\\\\\n RTD-L & {0.933} & \\underline{181.7 $\\pm$ 1.4} & \\underline{0.910 $\\pm$ 0.01} & {3.75 $\\pm$ 0.2}\\\\\n \\midrule\n \\multicolumn{5}{c}{\\textbf{COIL-20 into 16D space}} \\\\\n UMAP & 0.301 & 274.7 $\\pm$ 0.0 & 0.574 $\\pm$ 0.01 & 15.99 $\\pm$ 0.5\\\\\n AE & 0.834 & 183.6 $\\pm$ 0.0 & 0.809 $\\pm$ 0.01 & 8.35 $\\pm$ 0.2\\\\\n TopoAE & 0.910 & 148.0 $\\pm$ 0.0 & 0.822 $\\pm$ 0.02 & 6.90 $\\pm$ 0.2\\\\\n RTD & {0.944} & {88.9 $\\pm$ 0.0} & {0.892 $\\pm$ 0.01} & {5.78 $\\pm$ 0.1} \\\\\n RTD-L & \\underline{0.949} & \\underline{77.1 $\\pm$ 0.0} & \\underline{0.898 $\\pm$ 0.01} & \\underline{5.35 $\\pm$ 0.2}\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks", "authors": ["Eduard Tulchinskii", "Daria Voronkova", "Ilya Trofimov", "Evgeny Burnaev", "Serguei Barannikov"], "url": "https://arxiv.org/abs/2503.11910v1", "attribution": "\"RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks\" by Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, and Serguei Barannikov, arXiv:2503.11910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06391v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c||c|c|}\n\t\t\\hline\n\t\t\\multirow{2}{*}{}\n\t\tDataset & \\multicolumn{2}{c||}{MARS*}\n\t\t& \\multicolumn{2}{c|}{Duke-Raw-TKL} \\\\\n\t\t\\hline %\\cline{2-5}\n\t\t$\\epsilon$& Rank-1 & mAP\n\t\t& Rank-1 & mAP \\\\ \n\t\t\\hline \\hline\n\t\t0.9\n\t\t& 48.7 & 30.4 & 48.3 & 33.3 \\\\ \\hline\n\t\t0.8\n\t\t& 54.2 & 36.2 & 55.3 & 41.3 \\\\ \\hline\n\t\t0.7 \n\t\t& \\bf 54.5 & \\bf 37.2 &\\bf 56.1 &\\bf 41.5 \\\\ \\hline\n\t\t0.6\n\t\t& 50.7 & 34.1 & 47.7 & 35.2 \\\\\n\t\t\\hline \n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Unsupervised Noisy Tracklet Person Re-identification", "authors": ["Minxian Li", "Xiatian Zhu", "Shaogang Gong"], "url": "https://arxiv.org/abs/2101.06391v1", "attribution": "\"Unsupervised Noisy Tracklet Person Re-identification\" by Minxian Li, Xiatian Zhu, and Shaogang Gong, arXiv:2101.06391v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.05399v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Inference time for different values of the number of iterations/samples.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nNum Samples $L$ & 0 Iter & 1 Iter & 2 Iter \\\\\n\\midrule\n5 & $0.26 \\pm 0.52 $ & $1.99 \\pm 0.54 $& $16.40\\pm 2.04 $\\\\\n10 & $0.20 \\pm 0.41 $ & $3.24 \\pm 0.47 $& $58.28\\pm 1.15 $\\\\\n20 & $0.22 \\pm 0.44 $ & $5.86 \\pm 0.53 $& $224.86\\pm 0.71 $\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields", "authors": ["Yassine Abbahaddou", "Sofiane Ennadir", "Johannes F. Lutzeyer", "Fragkiskos D. Malliaros", "Michalis Vazirgiannis"], "url": "https://arxiv.org/abs/2411.05399v1", "attribution": "\"Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields\" by Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer, Fragkiskos D. Malliaros, and Michalis Vazirgiannis, arXiv:2411.05399v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.01053v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\it $p$-values for the Augmented Dickey-Fuller (ADF) test for a unit root and the Engle-Granger test for cointegration between $G_t$ and $E_t$ on the GCP data (1959--2022), see Section for details. First six rows are ADF tests and the last row is the Engle-Granger test. ``AR'': The null model is a unit root model while the alternative model is an AR model. ``ARD'': The null model is a unit root model while the alternative model is an AR model with drift. ``TS'': The null model is a unit root model with a constant (i.e. a drift in levels) while the alternative model is an AR model with a time trend. ``Engle-Granger'': The null hypothesis is that the error term $u_t$ in $G_t = \\alpha E_t + u_t$ has a unit root, i.e. that $G_t$ and $E_t$ are not cointegrated. The value ``$L$'' denotes the number of lags included in both the null and alternative models, see equations --.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc} \\hline%\\hline\n & $L=0$ & $L=1$ & $L=2$ & $L=3$ & $L=4$ & $L=5$ \\\\ \\hline \n$y_t = G_t$ (AR) & 0.2349 & 0.4867 &0.6597 & 0.7982 & 0.8403& 0.8470 \\\\\n$y_t = G_t$ (ARD) & 0.0039 & 0.0778 & 0.3105 & 0.4384 &0.4564 & 0.3895 \\\\\n$y_t = G_t$ (TS) & 0.0010 & 0.0010& 0.0035 & 0.0192& 0.0231 & 0.0056 \\\\\n$y_t = E_t$ (AR) & 0.9990 & 0.9990 & 0.9990 & 0.9990 & 0.9953 & 0.9984 \\\\\n$y_t = E_t$ (ARD) & 0.9488 & 0.9329 & 0.9038 & 0.8533 & 0.8201 & 0.8871 \\\\\n$y_t = E_t$ (TS) & 0.0933 & 0.2108 & 0.3108 & 0.3339 & 0.2250 & 0.4775 \\\\\nEngle-Granger & 0.0010 & 0.0010 & 0.0019 & 0.0135 & 0.0173 & 0.0038 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Regression-Based Approach to the CO2 Airborne Fraction: Enhancing Statistical Precision and Tackling Zero Emissions", "authors": ["Mikkel Bennedsen", "Eric Hillebrand", "Siem Jan Koopman"], "url": "https://arxiv.org/abs/2311.01053v4", "attribution": "\"A Regression-Based Approach to the CO2 Airborne Fraction: Enhancing Statistical Precision and Tackling Zero Emissions\" by Mikkel Bennedsen, Eric Hillebrand, and Siem Jan Koopman, arXiv:2311.01053v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.11836v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparisons between the marginal predictors of and the proposed joint predictors for the data presented in .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\\toprule\n\t\t\\multirow{3}{*}{Marginal predictors} & \\multirow{3}{*}{Joint predictors} & \\multirow{3}{*}{D-efficiency} & \\multirow{3}{*}{Trace-efficiency} & Overall \\\\\n\t\t&&&& efficiency \\\\\n\t\t&&&& gain\\\\\\midrule\n\t\t\n\t\t$\\hat{X}_{(6)} = 148.73$ & \t$\\tilde{X}_{(6)} = 148.58$ & \\multirow{2}{*}{0.9737} & \\multirow{2}{*}{0.9937} & \\multirow{2}{*}{0.0200}\\\\\n\t\t$\\hat{X}_{(7)} = 158.99$ & \t$\\tilde{X}_{(7)} = 158.86$ &&&\\\\\n\t\t&&&&\\\\\n\t\t$\\hat{X}_{(6)} = 148.73$ & \t$\\tilde{X}_{(6)} = 148.58$ & \\multirow{2}{*}{0.9758} & \\multirow{2}{*}{0.9954} & \\multirow{2}{*}{0.0196}\\\\\n\t\t$\\hat{X}_{(8)} = 170.36$ & \t$\\tilde{X}_{(8)} = 170.25$ &&&\\\\\n\t\t\n\t\t&&&&\\\\\n\t\t$\\hat{X}_{(6)} = 148.73$ & \t$\\tilde{X}_{(6)} = 148.58$ & \\multirow{2}{*}{0.9777} & \\multirow{2}{*}{0.9968} & \\multirow{2}{*}{0.0190}\\\\\n\t\t$\\hat{X}_{(9)} = 184.37$ & \t$\\tilde{X}_{(9)} = 184.29$ &&&\\\\\n\t\t\n\t\t&&&&\\\\\n\t\t$\\hat{X}_{(6)} = 148.73$ & \t$\\tilde{X}_{(6)} = 148.58$ & \\multirow{2}{*}{0.9798} & \\multirow{2}{*}{0.9980} & \\multirow{2}{*}{0.0182}\\\\\n\t\t$\\hat{X}_{(10)} = 206.19$ & \t$\\tilde{X}_{(10)} = 206.12$ &&&\\\\\n\t\t\n\t\t\n\t\t&&&&\\\\\n\t\t$\\hat{X}_{(7)} = 158.99$ & \t$\\tilde{X}_{(7)} = 158.86$ & \\multirow{2}{*}{0.9785} & \\multirow{2}{*}{0.9966} & \\multirow{2}{*}{0.0181}\\\\\n\t\t$\\hat{X}_{(8)} = 170.36$ & \t$\\tilde{X}_{(8)} = 170.25$ &&&\\\\\n\t\t\n\t\t&&&&\\\\\n\t\t$\\hat{X}_{(7)} = 158.99$ & \t$\\tilde{X}_{(7)} = 158.86$ & \\multirow{2}{*}{0.9806} & \\multirow{2}{*}{0.9976} & \\multirow{2}{*}{0.0170}\\\\\n\t\t$\\hat{X}_{(9)} = 184.37$ & \t$\\tilde{X}_{(9)} = 184.29$ &&&\\\\\t \n\t\t\n\t\t&&&&\\\\\n\t\t$\\hat{X}_{(7)} = 158.99$ & \t$\\tilde{X}_{(7)} = 158.86$ & \\multirow{2}{*}{0.9829} & \\multirow{2}{*}{0.9985} & \\multirow{2}{*}{0.0157}\\\\\n\t\t$\\hat{X}_{(10)} = 206.19$ & \t$\\tilde{X}_{(10)} = 206.12$ &&&\\\\\t \t \n\t\t\n\t\t\n\t\t&&&&\\\\\n\t\t$\\hat{X}_{(8)} = 170.35$ & \t$\\tilde{X}_{(8)} = 170.25$ & \\multirow{2}{*}{0.9830} & \\multirow{2}{*}{0.9983} & \\multirow{2}{*}{0.0152}\\\\\n\t\t$\\hat{X}_{(9)} = 184.37$ & \t$\\tilde{X}_{(9)} = 184.29$ &&&\\\\\t \t \t \n\t\t\n\t\t&&&&\\\\\n\t\t$\\hat{X}_{(8)} = 170.35$ & \t$\\tilde{X}_{(8)} = 170.25$ & \\multirow{2}{*}{0.9854} & \\multirow{2}{*}{0.9989} & \\multirow{2}{*}{0.0135}\\\\\n\t\t$\\hat{X}_{(10)} = 206.18$ & \t$\\tilde{X}_{(10)} = 206.12$ &&&\\\\\t \t\n\t\t\n\t\t&&&&\\\\\n\t\t$\\hat{X}_{(9)} = 184.37$ & \t$\\tilde{X}_{(9)} = 184.29$ & \\multirow{2}{*}{0.9877} & \\multirow{2}{*}{0.9992} & \\multirow{2}{*}{0.0116}\\\\\n\t\t$\\hat{X}_{(10)} = 206.18$ & \t$\\tilde{X}_{(10)} = 206.12$ &&&\\\\\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "D-optimal joint best linear unbiased prediction of order statistics", "authors": ["Narayanaswamy Balakrishnan", "Ritwik Bhattacharya"], "url": "https://arxiv.org/abs/2012.11836v1", "attribution": "\"D-optimal joint best linear unbiased prediction of order statistics\" by Narayanaswamy Balakrishnan and Ritwik Bhattacharya, arXiv:2012.11836v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.03638v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\footnotesize Artificial neural network and optimization details.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllllllll}\n\\hline\\hline\n\\multicolumn{6}{c}{\\multirow{1}{*}{ANN architecture}} & & \\multicolumn{6}{c}{\\multirow{1}{*}{ANN optimization}}\\\\\\cline{1-6}\\cline{8-13}\n & FxA & FxL & FxFlA & HidL & 3 & & E & 3000 & InitLR & $10^{-3}$ & Opt & \\texttt{Adam} \\\\\nInS & 7 & 6 & 9 & HidS & 200 & & B & $1024$ & DecR & 0.1 & LossFunc & MSE \\\\\nOutS & 21 & 21 & 14 & ActUn & \\texttt{ReLU} & & & & DecS & 1000 & & \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "On Pricing of Discrete Asian and Lookback Options under the Heston Model", "authors": ["Leonardo Perotti", "Lech A. Grzelak"], "url": "https://arxiv.org/abs/2211.03638v2", "attribution": "\"On Pricing of Discrete Asian and Lookback Options under the Heston Model\" by Leonardo Perotti and Lech A. Grzelak, arXiv:2211.03638v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13775v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline \n & Transition data $p_{ij}$ of $V$ \\\\\n \\hline\n $U_{i} \\cap D = U_{j} \\cap D = \\emptyset$ & $E_{ij}$\\\\\n \\hline\n $U_{i} \\cap D = \\emptyset, \\, U_{j} \\cap D \\neq \\emptyset$ & $E_{ij} \\mathrm{diag}\\{s_{j}^{\\alpha_{1}},\\cdots,s_{j}^{\\alpha_{r}}\\}$\\\\\n \\hline\n $U_{i} \\cap D \\neq \\emptyset, \\, U_{j} \\cap D = \\emptyset$ & $\\mathrm{diag}\\{s_{i}^{-\\alpha_{1}},\\cdots,s_{i}^{-\\alpha_{r}}\\}E_{ij}$\\\\\n \\hline\n $U_{i} \\cap U_{j} \\cap D \\neq \\emptyset$ & $\\mathrm{diag}\\{s_{i}^{-\\alpha_{1}},\\cdots,s_{i}^{-\\alpha_{r}}\\}E_{ij} \\mathrm{diag}\\{s_{j}^{\\alpha_{1}},\\cdots,s_{j}^{\\alpha_{r}}\\}$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The transition data for higher rank.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Constructing Parabolic Non-Abelian Hodge Correspondence in Positive Characteristic Using Parabolic Bases", "authors": ["Xiaojin Lin"], "url": "https://arxiv.org/abs/2501.13775v1", "attribution": "\"Constructing Parabolic Non-Abelian Hodge Correspondence in Positive Characteristic Using Parabolic Bases\" by Xiaojin Lin, arXiv:2501.13775v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.09436v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Propagated $P(OBE)$ and $P(OBE_{10})$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrr}\n \\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\n $OBE$ & 0.946 & 0.0538 & 0.0000215 & 0.00000332 & 0.00000299\\\\ \n $OBE_{10}$ & 0.942 & 0.0582 & 0.0000673 & 0.0000192 & 0.0000174\\\\ \n \\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks", "authors": ["Gurcan Comert", "Mashrur Chowdhury", "David M. Nicol"], "url": "https://arxiv.org/abs/2011.09436v1", "attribution": "\"Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks\" by Gurcan Comert, Mashrur Chowdhury, and David M. Nicol, arXiv:2011.09436v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.14000v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Linear Hypothesis Tests (Table )}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrr}\n \\hline\nSpecification & Hypothesis & Chisq & Pr($>$Chisq) \\\\ \n \\hline\n(1) & $\\hat{\\beta}_{Democrat}-\\hat{\\beta}_{Repuclican}=0$ & 0.04 & 0.84 \\\\ \n (2) & $\\hat{\\beta}_{Democrat}-\\hat{\\beta}_{Repuclican}=0$ & 3.04 & 0.08 \\\\ \n (3) & $\\hat{\\beta}_{Democrat}-\\hat{\\beta}_{Repuclican}=0$ & 0.13 & 0.72 \\\\ \n (4) & $\\hat{\\beta}_{Democrat}-\\hat{\\beta}_{Repuclican}=0$ & 0.26 & 0.61 \\\\ \n (1) & $\\hat{\\beta}_{Dem. city}-\\hat{\\beta}_{Rep. city}=0$ & 2.94 & 0.09 \\\\ \n (2) & $\\hat{\\beta}_{Dem. city}-\\hat{\\beta}_{Rep. city}=0$ & 7.51 & 0.01 \\\\ \n (3) & $\\hat{\\beta}_{Dem. city}-\\hat{\\beta}_{Rep. city}=0$ & 2.19 & 0.14 \\\\ \n (4) & $\\hat{\\beta}_{Dem. city}-\\hat{\\beta}_{Rep. city}=0$ & 0.10 & 0.76 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Personalization of Web Search During the 2020 US Elections", "authors": ["Ulrich Matter", "Roland Hodler", "Johannes Ladwig"], "url": "https://arxiv.org/abs/2209.14000v1", "attribution": "\"Personalization of Web Search During the 2020 US Elections\" by Ulrich Matter, Roland Hodler, and Johannes Ladwig, arXiv:2209.14000v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.19583v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Number of iterations to convergence on the ItP and ItC cases with a finer mesh. A value of $\\infty$ shows non-convergence due to divergence, while * indicates convergence but not to the required precision.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|r|r}\nMethod & Iterations (ItP) & Iterations (ItC) \\\\\\hline\nDogleg & 7* & >100\\\\\nSteihaug & >100 & >100 \\\\\nNewton & 6* & $\\infty$\\\\\nL-BFGS & >100 & >100\\\\\nADAM & >100 & >100 \\\\\nADAGRAD & >100 & >100\\\\\nPicard &$\\infty$ & $\\infty$ \\\\\nBroyden & 18* & 31\\\\\nBroyden (inv) & 15* & 30*\\\\\nBFGS & $\\infty$ & $\\infty$\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Rootfinding and Optimization Techniques for Solving Nonlinear Systems of Equations Arising from Cohesive Zone Models", "authors": ["Alberto Cattaneo", "Varun Shankar", "M. Keith Ballard"], "url": "https://arxiv.org/abs/2502.19583v1", "attribution": "\"Rootfinding and Optimization Techniques for Solving Nonlinear Systems of Equations Arising from Cohesive Zone Models\" by Alberto Cattaneo, Varun Shankar, and M. Keith Ballard, arXiv:2502.19583v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.00210v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimation results of non-zero elements in $\\boldsymbol{\\beta}$ in Scenario 1. Standard deviations are in parentheses.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|lllll}\n\\hline\nMethod & Bias($\\widehat{\\beta}_1$) & Bias($\\widehat{\\beta}_2$) & Bias($\\widehat{\\beta}_{p-2}$) & Bias($\\widehat{\\beta}_{p-1}$) & Bias($\\widehat{\\beta}_{p}$) \\\\\n\\hline\n\\multicolumn{6}{c}{$n=600,p=300$} \\\\\n\\hline\nBAR(AIC) & -0.01(0.15) & 0.005(0.14) & 0.01(0.13) & -0.03(0.14) & -0.01(0.14) \\\\\nBAR(BIC) & -0.24(0.15) & 0.23(0.15) & 0.26(0.13) & -0.27(0.24) & -0.19(0.22) \\\\\nLASSO & -0.49(0.11) & 0.48(0.11) & 0.49(0.10) & -0.40(0.10) & -0.32(0.11) \\\\\nALASSO & -0.23(0.16) & 0.22(0.16) & 0.23(0.15) & -0.22(0.16) & -0.14(0.15) \\\\\nOracle & 0.05(0.15) & -0.06(0.14) & -0.06(0.13) & 0.04(0.13) & 0.04(0.14) \\\\\n\\hline\n\\multicolumn{6}{c}{$n=800,p=300$} \\\\\n\\hline\nBAR(AIC) & -0.01(0.11) & 0.01(0.11) & 0.01(0.11) & -0.02(0.12) & -0.02(0.12) \\\\\nBAR(BIC) & -0.18(0.11) & 0.17(0.11) & 0.18(0.11) & -0.17(0.15) & -0.15(0.14) \\\\\nLASSO & -0.43(0.09) & 0.43(0.09) & 0.43(0.09) & -0.35(0.09) & -0.29(0.09) \\\\\nALASSO & -0.20(0.13) & 0.19(0.13) & 0.20(0.12) & -0.18(0.12) & -0.14(0.12) \\\\\nOracle & 0.04(0.12) & -0.04(0.12) & -0.04(0.11) & 0.03(0.12) & 0.02(0.11) \\\\\n\\hline\n\\multicolumn{6}{c}{$n=600,p=450$} \\\\\n\\hline\nBAR(AIC) & -0.01(0.15) & 0.004(0.16) & 0.01(0.14) & -0.03(0.14) & -0.01(0.13) \\\\\nBAR(BIC) & -0.23(0.15) & 0.23(0.17) & 0.27(0.17) & -0.29(0.25) & -0.19(0.20) \\\\\nLASSO & -0.50(0.11) & 0.49(0.12) & 0.50(0.10) & -0.41(0.10) & -0.33(0.09) \\\\\nALASSO & -0.22(0.15) & 0.22(0.16) & 0.22(0.14) & -0.21(0.15) & -0.14(0.13) \\\\\nOracle & 0.06(0.15) & -0.06(0.16) & -0.06(0.14) & 0.03(0.13) & 0.04(0.13) \\\\\n\\hline\n\\multicolumn{6}{c}{$n=800,p=450$} \\\\\n\\hline\nBAR(AIC) & -0.01(0.21) & 0.01(0.21) & -0.001(0.18) & -0.001(0.18) & -0.01(0.20) \\\\\nBAR(BIC) & -0.17(0.19) & 0.18(0.19) & 0.17(0.16) & -0.16(0.17) & -0.14(0.18) \\\\\nLASSO & -0.44(0.17) & 0.45(0.17) & 0.44(0.15) & -0.35(0.15) & -0.30(0.16) \\\\\nALASSO & -0.19(0.20) & 0.19(0.19) & 0.18(0.17) & -0.15(0.17) & -0.13(0.19) \\\\\nOracle & 0.04(0.12) & -0.04(0.13) & -0.05(0.13) & 0.04(0.12) & 0.02(0.11) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Broken Adaptive Ridge Method for Variable Selection in Generalized Partly Linear Models with Application to the Coronary Artery Disease Data", "authors": ["Christian Chan", "Xiaotian Dai", "Thierry Chekouo", "Quan Long", "Xuewen Lu"], "url": "https://arxiv.org/abs/2311.00210v1", "attribution": "\"Broken Adaptive Ridge Method for Variable Selection in Generalized Partly Linear Models with Application to the Coronary Artery Disease Data\" by Christian Chan, Xiaotian Dai, Thierry Chekouo, Quan Long, and Xuewen Lu, arXiv:2311.00210v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.04382v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline \n Ativos & BRKM5 & ITUB4 & KLBN4 & VALE3 \\tabularnewline\n\\hline \n\\hline \nBRKM5 & 1.00 & 0.37 & 0.28 & 0.33\\tabularnewline\n\\hline \nITUB4 & 0.37 & 1.00 & 0.18 & 0.36\\tabularnewline\n\\hline \nKLBN4 & 0.28 & 0.18 & 1.00 & 0.24\\tabularnewline\n\\hline \nVALE3 & 0.33 & 0.36 & 0.24 & 1.00\\tabularnewline\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Matriz de covariância dos preços de fechamento das ações da Braskem (BRKM5), Itaú (ITUB4), Vale (VALE3) e Klabin (KLBN4) referentes ao período entre 01 de janeiro de 2016 até 20 de setembro de 2021.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Quantum Finance: a tutorial on quantum computing applied to the financial market", "authors": ["Askery Canabarro", "Taysa M. Mendonça", "Ranieri Nery", "George Moreno", "Anton S. Albino", "Gleydson F. de Jesus", "Rafael Chaves"], "url": "https://arxiv.org/abs/2208.04382v2", "attribution": "\"Quantum Finance: a tutorial on quantum computing applied to the financial market\" by Askery Canabarro, Taysa M. Mendonça, Ranieri Nery, George Moreno, Anton S. Albino, Gleydson F. de Jesus, and Rafael Chaves, arXiv:2208.04382v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.14800v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{xcolor}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Spectral sequence converging to $\\bar{H}_\\bullet(\\Sigma_{d,n}),$ twisted by $\\mathbf{Q}(-v_{d,n}).$}\\centering\n\\begin{tabular}{c|cccccc}\n$-3$&$\\mathbf{Q}(-1)$&&&&&\\\\\n$-4$&&&&&&\\\\\n$-5$&$\\mathbf{Q}(-2)^2$&&&&&\\\\\n$-6$&$\\mathbf{Q}(-3)^2$&&&&&\\\\\n$-7$&&&&&&\\\\\n$-8$&$\\mathbf{Q}(-4)$&&&&&\\\\\n$-9$&$\\mathbf{Q}(-5)$&&&&&\\\\\n$-10$&&&&&&\\\\\n$-11$&&$\\mathbf{Q}(-6)$&&&&\\\\\n$-12$&&$\\mathbf{Q}(-7)$&&&&\\\\\n$-13$&&&$\\mathbf{Q}(-7)$&&&\\\\\n$-14$&&{$\\mathbf{Q}(-8)^2$}&{$\\mathbf{Q}(-8)$}&&&\\\\\n$-15$&&$\\mathbf{Q}(-9)^2$&&&&\\\\\n$-16$&&&$\\mathbf{Q}(-9)^3$&&&\\\\\n$-17$&& $\\mathbf{Q}(-10)$&$\\mathbf{Q}(-10)^3$&\\colorbox{red!25}{$\\mathbf{Q}(-10)$}&&\\\\\n$-18$&&$\\mathbf{Q}(-11)$&& \\colorbox{red!25}{$\\mathbf{Q}(-10)$}$+\\mathbf{Q}(-10)$&&\\\\\n$-19$&&&$\\mathbf{Q}(-11)^3$&$\\mathbf{Q}(-11)^2$&$\\mathbf{Q}(-11)$&\\\\\n$-20$&&&$\\mathbf{Q}(-12)^3$&$\\mathbf{Q}(-12)^3$&$\\mathbf{Q}(-12)$&\\\\\n$-21$&&&&\\colorbox{yellow!25}{$\\mathbf{Q}(-12)^4$}$+\\mathbf{Q}(-13)^2$&\\colorbox{yellow!25}{$\\mathbf{Q}(-12)^2$}&\\\\\n$-22$&&&$\\mathbf{Q}(-13)$&$\\mathbf{Q}(-13)^2+\\mathbf{Q}(-14)^4$&$\\mathbf{Q}(-13)^5$&\\\\\n$-23$&&&$\\mathbf{Q}(-14)$&$\\mathbf{Q}(-14)^3$&$\\mathbf{Q}(-13)^3+\\mathbf{Q}(-14)^4$&$\\dots$\\\\\n$-24$&&&&$\\mathbf{Q}(-14)^5+\\mathbf{Q}(-15)$&$\\mathbf{Q}(-14)^7+\\mathbf{Q}(-15)$&\\\\\n$-25$&&&&$\\mathbf{Q}(-15)^6$&$\\mathbf{Q}(-15)^8$&\\\\\n$-26$&&&&$\\mathbf{Q}(-16)$&$\\mathbf{Q}(-15)^6+\\mathbf{Q}(-16)^6$&\\\\\n$-27$&&&&$\\mathbf{Q}(-16)^2$&$\\mathbf{Q}(-16)^8+\\mathbf{Q}(-17)^2$&\\\\\n$-28$&&&&$\\mathbf{Q}(-17)^2$&$\\mathbf{Q}(-17)^5$&\\\\\n$-29$&&&&&$\\mathbf{Q}(-17)^3+\\mathbf{Q}(-18)^4$&\\\\\n$-30$&&&&&$\\mathbf{Q}(-18)^3+\\mathbf{Q}(-19)$&\\\\\n$-31$&&&&&$\\mathbf{Q}(-19)$&\\\\\n$-32$&&&&&$\\mathbf{Q}(-20)$&\\\\\n$\\dots$&&&&&&\\\\\n\\hline\n&$L=1,2$&$L=3$&$L=4$&$L=5$&$L=6$&$\\dots$\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Stable cohomology of moduli spaces of hyperelliptic curves on Hirzebruch surfaces", "authors": ["Jonas Bergström", "Angelina Zheng"], "url": "https://arxiv.org/abs/2312.14800v2", "attribution": "\"Stable cohomology of moduli spaces of hyperelliptic curves on Hirzebruch surfaces\" by Jonas Bergström and Angelina Zheng, arXiv:2312.14800v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.20002v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\hline\nParameter name & Description & Value & Unit \\\\\n\\hline\n\\texttt{NFIXORB} & Switch to fix orbital parameters & 1 & \\\\\n\\texttt{ECCEN} & Eccentricity & 1.67$\\times 10^{-2}$ & \\\\\n\\texttt{MVELP} & Longitude of perihelion & 102.9 & \\\\\n\\texttt{OBLIQ} & Obliquity & 23.44 & \\\\\n\\texttt{GSOL0} & Solar constant & 1367.0 & W\\,m$^{-2}$ \\\\\n\\texttt{CO2} & Atm. CO$_2$ concentration & is varied & ppm \\\\\n\\texttt{zstar} & & 2500 & m \\\\\n\\texttt{lambda} & & 4.5$\\times 10^{-3}$ & m$^{-1}$ \\\\\n\\texttt{astar} & & 0.8714$\\times 10^{-4}$ & \\\\\n\\texttt{arange} & & 0.2843$\\times 10^{-4}$ & \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{PlaSim-LSG model settings for selected namelist parameters.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Global stability of the Atlantic overturning circulation: Edge states, long transients and a boundary crisis under CO$_2$ forcing", "authors": ["Reyk Börner", "Oliver Mehling", "Jost von Hardenberg", "Valerio Lucarini"], "url": "https://arxiv.org/abs/2504.20002v2", "attribution": "\"Global stability of the Atlantic overturning circulation: Edge states, long transients and a boundary crisis under CO$_2$ forcing\" by Reyk Börner, Oliver Mehling, Jost von Hardenberg, and Valerio Lucarini, arXiv:2504.20002v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2306.10591v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Breast cancer data: performance of the logistic regression model for different feature selection approaches.}\n\\begin{tabular}{|c|c|c|c|}\n\t\\hline\n\t& AUROC & Accuracy & Number of features \\\\\n\t\\hline\n\tAll features & 0.9844 & 0.9297 & 10\\\\\n\t\\hline\n\tBest choice of features & {\\bf 0.9863} & 0.9297 & 5 \\\\\n\t\\hline\n\tSelection with RFE & 0.9839 & 0.9297 & 6 \\\\\n\t\\hline\n\tSelection with LASSO & 0.9845 & 0.9262 & 3\\\\\n\t\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Quantum computer based Feature Selection in Machine Learning", "authors": ["Gerhard Hellstern", "Vanessa Dehn", "Martin Zaefferer"], "url": "https://arxiv.org/abs/2306.10591v1", "attribution": "\"Quantum computer based Feature Selection in Machine Learning\" by Gerhard Hellstern, Vanessa Dehn, and Martin Zaefferer, arXiv:2306.10591v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.11256v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Forecasting accuracy under different dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n\\hline\n\t\t& \\multicolumn{2}{c|}{Wind Speed}\t\t\t& \\multicolumn{2}{c|}{Wind Direction} \\\\\n\\hline \nMethod & MAE & RMSE & MAE & RMSE \\\\\n\\hline\nRemote Sensing \t& $0.0403$ & $ 0.0573 $ \t\t& $0.0596$ & $ 0.1151 $ \\\\\n\\hline\nHour Ahead \t& $0.0719$ & $ 0.0972 $ & $0.1051$ & $ 0.1687 $ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Edge Intelligence Empowered UAVs for Automated Wind Farm Monitoring in Smart Grids", "authors": ["Hwei-Ming Chung", "Sabita Maharjan", "Yan Zhang", "Frank Eliassen", "Tingting Yuan"], "url": "https://arxiv.org/abs/2009.11256v1", "attribution": "\"Edge Intelligence Empowered UAVs for Automated Wind Farm Monitoring in Smart Grids\" by Hwei-Ming Chung, Sabita Maharjan, Yan Zhang, Frank Eliassen, and Tingting Yuan, arXiv:2009.11256v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.14479v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Selected variables on which natural regression splines are fit, along with the number of knots.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llc}\n \\toprule\n \\textbf{Variable} & \\textbf{MLR model} $kl$ \\textbf{Knots} & \\\\\n \\midrule\n \\texttt{BalanceToPrincipal} & P$l$ & 3 \\\\\n \\texttt{CreditLeverage} & P$l$ & 3 \\\\\n \\texttt{g0\\_Delinq\\_Num} & P$l$ & 5 \\\\\n \\multirow[t]{2}{*}{\\texttt{InterestRate\\_Margin} } & P$l$ & 3 \\\\\n & D$l$ & 3 \\\\\n \\texttt{Prepaid\\_Pc} & P$l$ & 4 \\\\\n \\texttt{Repo\\_rate} & P$l$ & 3 \\\\\n \\multirow[t]{2}{*}{\\texttt{RollEver\\_24}} & P$l$ & 5 \\\\\n & D$l$ & 6 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework", "authors": ["Arno Botha", "Tanja Verster", "Roland Breedt"], "url": "https://arxiv.org/abs/2502.14479v3", "attribution": "\"Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework\" by Arno Botha, Tanja Verster, and Roland Breedt, arXiv:2502.14479v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.09572v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{SNR and CNR of strain estimates from our unsupervised method and OVERWIND, and local NCC scores for the three image pair results from the \\textit{in vivo} dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\hline\n{} & \\multicolumn{3}{c}{Unsupervised} & \\multicolumn{3}{c}{OVERWIND} \\\\ %\\cline{2-7} \n & Case 1 & Case 2 & Case 3 & Case 1 & Case 2 & Case 3 \\\\ \\hline\nSNR & 0.14 & 0.07 & 0.1 & 0.12 & 0.35 & 0.23 \\\\ \nCNR & 6.22 & 5.2 & 4.5 & 4.9 & 3.15 & 2.23 \\\\ \nLNCC & 0.87 & 0.81 & 0.94 & 0.90 & 0.81 & 0.93 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation", "authors": ["Rémi Delaunay", "Yipeng Hu", "Tom Vercauteren"], "url": "https://arxiv.org/abs/2008.09572v1", "attribution": "\"An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation\" by Rémi Delaunay, Yipeng Hu, and Tom Vercauteren, arXiv:2008.09572v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.09175v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model 1 in scenario 1}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|rl|ccccc|cccc}\n\\hline\nMonte-Carlo sample & \\multicolumn{2}{c|}{Network} & $L$ & $p$ & $q$ & $\\sigma$ & $B_r$ & Validation Error & Test Error & Training Time (sec) & \\# of Parameters \\\\ \\hline\n & DNN&\\hspace{-1.54em}-best & 14 & 128 & & tanh & & 0.04912 & 0.03219 & 10.13 & 215681 \\\\ \n & DANN&\\hspace{-1.54em}-best & 3 & 1024 & 5 & & poly & 0.01568 & 0.01699 & 10.40 & 10524673 \\\\ \n & DANN&\\hspace{-1.54em}-small & 1 & 16 & 11 & & poly & 0.04245 & 0.04857 & 10.29 & 1249 \\\\ \n & HDANN1&\\hspace{-1.54em}-best & 5 & 1024 & 7 & ReLU & poly & 0.01375 & 0.01594 & 3.84 & 4243457 \\\\ \n{1st} & HDANN1&\\hspace{-1.54em}-small & 3 & 16 & 7 & ReLU & poly & 0.04384 & 0.04862 & 9.26 & 1249 \\\\ \n & HDANN2&\\hspace{-1.54em}-best & 7 & 256 & 9 & tanh & poly & 0.02946 & 0.01961 & 13.14 & 398849 \\\\ \n & HDANN2&\\hspace{-1.54em}-small & 5 & 64 & 9 & tanh & poly & 0.03890 & 0.02693 & 16.46 & 17665 \\\\ \n & HDANN3&\\hspace{-1.54em}-best & 9 & 256 & 11 & ReLU & poly & 0.01648 & 0.01751 & 9.65 & 546305 \\\\ \n & HDANN3&\\hspace{-1.54em}-small & 5 & 16 & 5 & ReLU & poly & 0.04639 & 0.04658 & 12.87 & 1665 \\\\ \\hline \n & DNN&\\hspace{-1.54em}-best & 16 & 128 & & tanh & & 0.03601 & 0.04036 & 16.61 & 248705 \\\\ \n & DANN&\\hspace{-1.54em}-best & 3 & 256 & 5 & & poly & 0.01563 & 0.01482 & 9.82 & 665089 \\\\ \n & DANN&\\hspace{-1.54em}-small & 9 & 4 & 11 & & poly & 0.02473 & 0.02340 & 110.74 & 1753 \\\\ \n & HDANN1&\\hspace{-1.54em}-best & 7 & 1024 & 5 & ReLU & poly & 0.01280 & 0.01269 & 5.67 & 6330369 \\\\ \n{2nd} & HDANN1&\\hspace{-1.54em}-small & 3 & 16 & 5 & ReLU & poly & 0.03287 & 0.03253 & 13.12 & 1057 \\\\ \n & HDANN2&\\hspace{-1.54em}-best & 9 & 256 & 5 & tanh & poly & 0.02912 & 0.02662 & 17.63 & 529409 \\\\ \n & HDANN2&\\hspace{-1.54em}-small & 9 & 256 & 5 & tanh & poly & 0.02912 & 0.02662 & 17.63 & 529409 \\\\ \n & HDANN3&\\hspace{-1.54em}-best & 9 & 256 & 5 & ReLU & poly & 0.01526 & 0.01437 & 8.36 & 535553 \\\\ \n & HDANN3&\\hspace{-1.54em}-small & 5 & 4 & 7 & ReLU & poly & 0.02893 & 0.03243 & 44.98 & 281 \\\\ \\hline \n & DNN&\\hspace{-1.54em}-best & 12 & 128 & & tanh & & 0.03655 & 0.03968 & 13.85 & 182657 \\\\ \n & DANN&\\hspace{-1.54em}-best & 3 & 256 & 11 & & poly & 0.01530 & 0.01723 & 13.32 & 1462273 \\\\ \n & DANN&\\hspace{-1.54em}-small & 1 & 16 & 11 & & poly & 0.03546 & 0.04420 & 17.96 & 1249 \\\\ \n & HDANN1&\\hspace{-1.54em}-best & 5 & 1024 & 9 & ReLU & poly & 0.01419 & 0.01524 & 5.36 & 4255745 \\\\ \n{3th} & HDANN1&\\hspace{-1.54em}-small & 3 & 16 & 7 & ReLU & poly & 0.03300 & 0.03943 & 10.67 & 1249 \\\\ \n & HDANN2&\\hspace{-1.54em}-best & 7 & 256 & 5 & tanh & poly & 0.02505 & 0.02811 & 15.29 & 397825 \\\\ \n & HDANN2&\\hspace{-1.54em}-small & 9 & 64 & 3 & tanh & cos & 0.03651 & 0.03975 & 15.10 & 33921 \\\\ \n & HDANN3&\\hspace{-1.54em}-best & 7 & 256 & 11 & ReLU & poly & 0.01555 & 0.01936 & 10.87 & 414721 \\\\ \n & HDANN3&\\hspace{-1.54em}-small & 5 & 16 & 5 & ReLU & poly & 0.03628 & 0.04354 & 17.56 & 1665 \\\\ \\hline \n & DNN&\\hspace{-1.54em}-best & 12 & 128 & & tanh & & 0.04206 & 0.03322 & 17.76 & 182657 \\\\ \n & DANN&\\hspace{-1.54em}-best & 5 & 1024 & 5 & & poly & 0.01798 & 0.01697 & 21.24 & 21012481 \\\\ \n & DANN&\\hspace{-1.54em}-small & 1 & 64 & 9 & & poly & 0.03951 & 0.04386 & 17.93 & 4097 \\\\ \n & HDANN1&\\hspace{-1.54em}-best & 7 & 1024 & 7 & ReLU & poly & 0.01461 & 0.01387 & 6.80 & 6342657 \\\\ \n{4th} & HDANN1&\\hspace{-1.54em}-small & 5 & 16 & 9 & ReLU & poly & 0.03809 & 0.03149 & 13.40 & 1985 \\\\ \n & HDANN2&\\hspace{-1.54em}-best & 5 & 256 & 11 & tanh & poly & 0.03115 & 0.02291 & 18.35 & 267777 \\\\ \n & HDANN2&\\hspace{-1.54em}-small & 5 & 256 & 5 & tanh & poly & 0.03815 & 0.02401 & 18.90 & 266241 \\\\ \n & HDANN3&\\hspace{-1.54em}-best & 7 & 1024 & 7 & ReLU & poly & 0.02388 & 0.02013 & 10.95 & 6348801 \\\\ \n & HDANN3&\\hspace{-1.54em}-small & 1 & 64 & 7 & sigmoid & poly & 0.04018 & 0.03666 & 18.00 & 7361 \\\\ \\hline \n & DNN&\\hspace{-1.54em}-best & 16 & 32 & & tanh & & 0.03117 & 0.04580 & 21.02 & 16097 \\\\ \n & DANN&\\hspace{-1.54em}-best & 3 & 1024 & 5 & & poly & 0.01481 & 0.02156 & 13.26 & 10524673 \\\\ \n & DANN&\\hspace{-1.54em}-small & 5 & 4 & 11 & & poly & 0.02274 & 0.06149 & 58.08 & 1033 \\\\ \n & HDANN1&\\hspace{-1.54em}-best & 3 & 1024 & 7 & ReLU & poly & 0.01323 & 0.01779 & 3.87 & 2144257 \\\\ \n{5th} & HDANN1&\\hspace{-1.54em}-small & 5 & 16 & 7 & ReLU & poly & 0.03088 & 0.05785 & 12.18 & 1793 \\\\ \n & HDANN2&\\hspace{-1.54em}-best & 5 & 1024 & 5 & ReLU & poly & 0.02621 & 0.03963 & 9.37 & 4210689 \\\\ \n & HDANN2&\\hspace{-1.54em}-small & 7 & 64 & 3 & tanh & poly & 0.03031 & 0.03333 & 19.76 & 25601 \\\\ \n & HDANN3&\\hspace{-1.54em}-best & 9 & 64 & 5 & ReLU & poly & 0.01324 & 0.03074 & 11.70 & 35585 \\\\ \n & HDANN3&\\hspace{-1.54em}-small & 5 & 4 & 11 & ReLU & poly & 0.02543 & 0.08000 & 40.19 & 393 \\\\ \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Hybrid deep additive neural networks", "authors": ["Gyu Min Kim", "Jeong Min Jeon"], "url": "https://arxiv.org/abs/2411.09175v2", "attribution": "\"Hybrid deep additive neural networks\" by Gyu Min Kim and Jeong Min Jeon, arXiv:2411.09175v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.15224v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of CHSZ and TUSZ datasets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n\t\t\t\t\\toprule\n\t\t\t\tDataset& ABSZ & FSZ & TNSZ & TCSZ \\\\\n\t\t\t\t\\midrule\n\t\t\t\tCHSZ & 81 & 87 & 15 & 16 \\\\\n\t\t\t\tTUSZ & 76 & 418 & 62 & 48 \\\\\n\t\t\t\t\\bottomrule\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification", "authors": ["Ruimin Peng", "Zhenbang Du", "Changming Zhao", "Jingwei Luo", "Wenzhong Liu", "Xinxing Chen", "Dongrui Wu"], "url": "https://arxiv.org/abs/2412.15224v1", "attribution": "\"Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification\" by Ruimin Peng, Zhenbang Du, Changming Zhao, Jingwei Luo, Wenzhong Liu, Xinxing Chen, and Dongrui Wu, arXiv:2412.15224v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09526v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters of the Plates Used in Experiments}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll} \\toprule\n \\textit{Plate Name} & \\textit{h, l, w (mm)} & \\textit{m (kg)} & $\\boldsymbol{r}_g$ & $\\mu_0$ & $\\mu_1$ \\\\ \\midrule\n Acrylic Board & 300, 300, 40 & 4.0 & Geom.Cent. & 0.5 & 0.4 \\\\\n Stainless Box & 300, 400, 150 & 6.0 & Geom.Cent. & 0.4 & 0.1 \\\\\n Plywood Board & 500, 400, 44 & 6.4 & Geom.Cent. & 0.6 & 0.3 \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Dual-arm Robot that Autonomously Lifts Up and Tumbles Heavy Plates Using Crane Pulley Blocks", "authors": ["Shogo Hayakawa", "Weiwei Wan", "Keisuke Koyama", "Kensuke Harada"], "url": "https://arxiv.org/abs/2101.09526v1", "attribution": "\"A Dual-arm Robot that Autonomously Lifts Up and Tumbles Heavy Plates Using Crane Pulley Blocks\" by Shogo Hayakawa, Weiwei Wan, Keisuke Koyama, and Kensuke Harada, arXiv:2101.09526v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10127v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experimental scale factor values of buyer compared with theoretical scale factor values for case-1}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c||}\\toprule\n \\textit{} & \\textit{} & \\textit{$\\alpha_{B1}$} \\\\ \\midrule\n Theoretical Equilibrium & & 0.666667 \\\\\n \\hline\n DDPG Empirical Equilibrium & mean & 0.577733 \\\\\n \\hline\n & std & 0.023132 \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multi-unit Double Auctions: Equilibrium Analysis and Bidding Strategy using DDPG in Smart-grids", "authors": ["Sanjay Chandlekar", "Easwar Subramanian", "Sanjay Bhat", "Praveen Paruchuri", "Sujit Gujar"], "url": "https://arxiv.org/abs/2201.10127v2", "attribution": "\"Multi-unit Double Auctions: Equilibrium Analysis and Bidding Strategy using DDPG in Smart-grids\" by Sanjay Chandlekar, Easwar Subramanian, Sanjay Bhat, Praveen Paruchuri, and Sujit Gujar, arXiv:2201.10127v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.03164v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\nSPX & Eurostoxx & Footsie & SPX - Eurostoxx & SPX - Footsie \\\\\n\\hline\n138.89 & 26.84 & 38.15 & 2.61 & 2.16 \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Half lives in days of $12$ months At The Money Volatilities and their spreads}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Estimation of Historical volatility and Allocation strategies using Variance Swaps", "authors": ["Lucio Fiorin"], "url": "https://arxiv.org/abs/2208.03164v1", "attribution": "\"Estimation of Historical volatility and Allocation strategies using Variance Swaps\" by Lucio Fiorin, arXiv:2208.03164v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11305v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The two-layer model. Relative Model Error and Relative Misfit Function of $W_2$ with the operators $\\mathcal{P}_{2}$ and $\\mathcal{P}_{3}$ in $20$, $40$ and $80$ iteration steps, respectively.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\t\\toprule\n\t\\multirow{2}{*}{Iteration Steps} & \\multicolumn{2}{c}{Relative Model Error} & \\multicolumn{2}{c}{Relative Misfit Function} \\\\ \n\t\\cline{2-5}\n\t & $W_2$ with $P_2$ & $W_2$ with $P_3$ & $W_2$ with $P_2$ & $W_2$ with $P_3$ \\\\\n \\midrule\t\n\t $20$ & $3.69 \\times 10^{-1}$ & $2.15 \\times 10^{-1}$ & $4.99 \\times 10^{-3}$ & $6.90 \\times 10^{-4}$ \\\\ \n\t $40$ & $2.23 \\times 10^{-1}$ & $1.04 \\times 10^{-1}$ & $3.61 \\times 10^{-4}$ & $9.41 \\times 10^{-5}$ \\\\ \n\t \n\t $80$ & $ 8.35 \\times 10^{-2}$ & $3.04 \\times 10^{-2}$ & $1.25 \\times 10^{-5}$ & $2.75 \\times 10^{-6}$ \\\\ \n\t \\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "The Quadratic Wasserstein Metric With Squaring Scaling For Seismic Velocity Inversion", "authors": ["Zhengyang Li", "Yijia Tang", "Jing Chen", "Hao Wu"], "url": "https://arxiv.org/abs/2201.11305v1", "attribution": "\"The Quadratic Wasserstein Metric With Squaring Scaling For Seismic Velocity Inversion\" by Zhengyang Li, Yijia Tang, Jing Chen, and Hao Wu, arXiv:2201.11305v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.16139v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll} \n \\hline\n & \\multicolumn{2}{c}{$C_{\\text{g}}$ [\\texteuro]} & \\multicolumn{2}{c}{$Q_{\\text{loss}}$ [mAh]}\\\\\n Planner& summer & winter & summer & winter \\\\\n \\hline\n \\textbf{BNoDeg} & 16.01& 77.33& 206.9& 249.2\\\\\n \\textbf{CEmpDeg} & 15.22& 77.45& 216.4& 236.7\\\\\n \\textbf{CPBDeg} & 20.28& 78.25& 206.6&234.3\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Planner comparison cost summary.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Aging-aware Energy Management for Residential Multi-Carrier Energy Systems", "authors": ["Darío Slaifstein", "Gautham Ram Chandra Mouli", "Laura Ramirez-Elizondo", "Pavol Bauer"], "url": "https://arxiv.org/abs/2503.16139v3", "attribution": "\"Aging-aware Energy Management for Residential Multi-Carrier Energy Systems\" by Darío Slaifstein, Gautham Ram Chandra Mouli, Laura Ramirez-Elizondo, and Pavol Bauer, arXiv:2503.16139v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.10491v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative comparison of SSSD-ECG and FlowECG performance metrics using 200 neural function evaluations}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n & MMD $\\downarrow$ & DTW $\\downarrow$ & Wasserstein $\\downarrow$ & SimScore $\\uparrow$\\\\\n \\midrule\n SSSD-ECG & 80.70 & \\textbf{73.49} & 1.03 & 0.30 \\\\\n FlowECG (ours) & \\textbf{21.09} & 83.96 & \\textbf{0.62} & \\textbf{0.37} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "FlowECG: Using Flow Matching to Create a More Efficient ECG Signal Generator", "authors": ["Vitalii Bondar", "Serhii Semenov", "Vira Babenko", "Dmytro Holovniak"], "url": "https://arxiv.org/abs/2509.10491v1", "attribution": "\"FlowECG: Using Flow Matching to Create a More Efficient ECG Signal Generator\" by Vitalii Bondar, Serhii Semenov, Vira Babenko, and Dmytro Holovniak, arXiv:2509.10491v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.17719v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Problem (), number of mesh-points required by the codes for increasing values of $T$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|r|r|r|}\n\\hline\n$T$ & {\\tt fhbvm} & {\\tt fhbvm2}\\\\\n\\hline\n$10$ & 95& 30\\\\\n$50$ & 483 & 70\\\\\n$100$ & 968 & 120 \\\\\n$500$ & 4850 &520 \\\\\n$1000$ & 9702& 1020\\\\\n$5000$ & 48521 & 5020\\\\\n$10000$ &97044 & 10020\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Analysis and implementation of collocation methods for fractional differential equations", "authors": ["Luigi Brugnano", "Gianmarco Gurioli", "Felice Iavernaro", "Mikk Vikerpuur"], "url": "https://arxiv.org/abs/2503.17719v1", "attribution": "\"Analysis and implementation of collocation methods for fractional differential equations\" by Luigi Brugnano, Gianmarco Gurioli, Felice Iavernaro, and Mikk Vikerpuur, arXiv:2503.17719v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.20726v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results for various $\\gamma$ values}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{Threshold $\\gamma$} & \n\\textbf {$| \\mathcal{\\zeta} |$} & \\textbf{Number of samples} &\n\\textbf{Mean} & \n\\textbf{Variance}\\\\\n\\hline\n2 dB & 9 & 20 & 1.060 & 0.561\\\\\n3 dB & 7 & 100 & 1.576 & 0.888\\\\\n5 dB & 3 & 20 & 2.306 & 3.419\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Beam Codebook Refinement for mmWave Devices with Random Orientations: Concept and Experimental Validation", "authors": ["Bora Bozkurt", "Ahmet Muaz Aktas", "Hasan Atalay Gunel", "Mohaned Chraiti", "Ali Gorcin", "Ibrahim Hokelek"], "url": "https://arxiv.org/abs/2412.20726v1", "attribution": "\"Beam Codebook Refinement for mmWave Devices with Random Orientations: Concept and Experimental Validation\" by Bora Bozkurt, Ahmet Muaz Aktas, Hasan Atalay Gunel, Mohaned Chraiti, Ali Gorcin, and Ibrahim Hokelek, arXiv:2412.20726v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00234v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lcc}\n \\toprule [0.2em]\n \\multirow{1}{*}{\\textbf{\\textsc{Model}}} %\n & \\textbf{Param.} & \\textbf{BLEU}\n \\\\ \\midrule[0.1em]\n \\textit{All-Shared} & 24M & 14.3 \\\\\n \\textit{All-Shared, $d_m= 768$} & 41M & 22.0 \\\\\n \t\\textit{All-Shared (Independent FFN)} & 27M & 22.4 \\\\\n \t\\textit{All-Shared (except last)} & 31M & 23.2 \\\\\n \t\\textit{Every 2 layers shared} & 38M & 27.2 \\\\\n \t\\textsc{Sandwich} & 38M & 27.3 \\\\\n \t\\textsc{Sandwich, $L=8$} & 38M & \\textbf{27.7} \\\\\n \t\\midrule[0.07em]\n \t & 65M & 27.3 \\\\\n \t\\textsc{Transformer-base} (Our reimpl.) & 61M & \\textbf{27.7} \\\\\n \\bottomrule[0.15em]\n \\end{tabular}\n\\caption{Experiments performed on WMT'14 EN-DE using different parameter sharing techniques.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers", "authors": ["Machel Reid", "Edison Marrese-Taylor", "Yutaka Matsuo"], "url": "https://arxiv.org/abs/2101.00234v3", "attribution": "\"Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers\" by Machel Reid, Edison Marrese-Taylor, and Yutaka Matsuo, arXiv:2101.00234v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.12619v1_tex_table23.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\large Coefficient estimates of 1-factor models of each county employment/economy indicator}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\emph{Variables} & $\\Delta$ Employed Persons & $\\Delta$ Unemployed Persons & $\\Delta$ Labour Force& $\\Delta$ Population \\\\\n~& (log)& (log)& (log)& (log)\\\\\n\\cmidrule(l{3pt}r{3pt}){1-1} \\cmidrule(l{3pt}r{3pt}){2-2} \\cmidrule(l{3pt}r{3pt}){3-3} \\cmidrule(l{3pt}r{3pt}){4-4} \\cmidrule(l{3pt}r{3pt}){5-5}\n$\\Delta$ Active Mines\\textsubscript{t} & 0.001*** & -0.006*** & 0.000. & 0.000\\\\\n~ & (0.000) & (0.001) & (0.000) & (0.000)\\\\\n$\\Delta$ Active Mines\\textsubscript{t-1} & 0.002*** & 0.000 & 0.002*** & 0.000\\\\\n~ & (0.000) & (0.001) & (0.000) & (0.000)\\\\\n$\\Delta$ Active Mines\\textsubscript{t-2} & 0.000 & 0.004*** & 0.001*** & 0.000\\\\\n~ & (0.000) & (0.001) & (0.000) & (0.000)\\\\\n$\\Delta$ (log) Real GDP & 0.049*** & -0.100*** & 0.038*** & 0.004***\\\\\n~ & (0.002) & (0.006) & (0.002) & (0.001)\\\\\n$\\Delta$(log) Population & 0.495*** & 0.775*** & 0.508*** & ~\\\\\n~ & (0.016) & (0.048) & (0.014) & ~\\\\\n\\bottomrule\n\\addlinespace\nR$^2$ & 0.246 & 0.323 & 0.217 & 0.501 \\\\\nAdditive Effects Type & two-ways & two-ways & two-ways & two-ways\\\\\n\\# Unobserved Factors Used & 1 & 1 & 1 & 1\\\\\n\\bottomrule\n\\multicolumn{4}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'", "authors": ["Ebba Mark", "Ryan Rafaty", "Moritz Schwarz"], "url": "https://arxiv.org/abs/2211.12619v1", "attribution": "\"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'\" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17106v1_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n $n$ & Error $\\|\\cdot\\|_1$ & Order $\\|\\cdot\\|_1$ & Error\n $\\|\\cdot\\|_{\\infty}$ & Order $\\|\\cdot\\|_{\\infty}$ \\\\\n \\hline\n 20 & 3.78E$-3$ & $-$ & 3.06E$-2$ & $-$ \\\\\n \\hline\n 40 & 8.04E$-5$ & 5.56 & 1.41E$-3$ & 4.44 \\\\\n \\hline\n 80 & 1.81E$-6$ & 5.48 & 2.39E$-5$ & 5.88 \\\\\n \\hline\n 160 & 6.81E$-8$ & 4.73 & 2.42E$-6$ & 3.31 \\\\\n \\hline\n 320 & 2.13E$-9$ & 5.00 & 9.96E$-8$ & 4.60 \\\\\n \\hline\n 640 & 6.58E$-11$ & 5.01 & 3.57E$-9$ & 4.80 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Error table for problem - , Complex domain, WLS-GAW.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17106v1", "attribution": "\"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.09066v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline \n \\textbackslash & $z_3=1$ &\n$z_3=2$ & $z_3=3$ & $ z_3=4 $ & $z_3=5 $ & $ z_3=6 $\\\\ \n\\hline \\hline\ncosts & 1 & 0.73 & 0.63 & 0.52 & 0.43 & 0.30 \\\\ \\hline\nshare & (8, 4, 88) & (11, 5, 84) & (13,5,82) & (15,6,79) & (17, 7, 75) & (29, 12, 59)\\\\ \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Optimal abatement cost and cost-sharing}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Climate uncertainty, financial frictions and constrained efficient carbon taxation", "authors": ["Felix Kübler"], "url": "https://arxiv.org/abs/2210.09066v1", "attribution": "\"Climate uncertainty, financial frictions and constrained efficient carbon taxation\" by Felix Kübler, arXiv:2210.09066v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18013v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cl}\n\\hline\nparameter/variables & biological meaning \\\\\n\\hline\n$v$ & membrane potential \\\\\n$\\omega$ & recovery (adaptation) variable \\\\\n$I$ & input current\\\\\n$\\mu$ & time course for membrane potential \\\\\n$\\gamma$ & depletion strength of recovery variable \\\\\n$a$ & threshold between electrical silence and\nelectrical firing \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A comprehensive numerical investigation of a coupled mathematical model of neuronal excitability", "authors": ["Burcu Gürbüz", "Aytül Gökçe", "Mahmut Modanlı"], "url": "https://arxiv.org/abs/2501.18013v1", "attribution": "\"A comprehensive numerical investigation of a coupled mathematical model of neuronal excitability\" by Burcu Gürbüz, Aytül Gökçe, and Mahmut Modanlı, arXiv:2501.18013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.11765v1_tex_table21.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CNN Model Parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n & \\textbf{Basic CNN} & \\multicolumn{3}{c}{\\textbf{Deep CNN}} \\\\ \\cline{2-5} \n\\textbf{Parameter} & \\textbf{Block 1} & \\textbf{Block 1} & \\textbf{Block 2} & \\textbf{Block 3} \\\\ \\hline\nConvolutional 1D layer & & & & \\\\\nFilter Size & 64 & 32 & 64 & 128 \\\\\nKernel Size & 2 & 3 & 2 & 1 \\\\\nPadding & same & same & same & same \\\\\nDense Layer: Relu Activation & 265 & - & - & - \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis", "authors": ["Tanja Aue", "Adam Jatowt", "Michael Färber"], "url": "https://arxiv.org/abs/2212.11765v1", "attribution": "\"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis\" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.10270v5_tex_table23.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccccc}\n\\hline\n\\textbf{} & \\multicolumn{3}{c|}{Diff+SVR} & \\multicolumn{3}{c|}{HGLET+SVR} & \\multicolumn{3}{c|}{GHWT+SVR} & \\multirow{2}{*}{SchNet} & \\multirow{2}{*}{PaiNN} & \\multirow{2}{*}{SO3Net I} & \\multirow{2}{*}{SO3Net II} \\\\ \\cline{1-10}\nFeature Type & Node & Edge & Both & Node & Edge & Both & Node & Edge & Both & & & & \\\\ \\hline\n\\multicolumn{14}{c}{Aspirin} \\\\ \\hline\nMAE & 4.856 & 3.132 & 3.267 & 4.884 & 3.135 & 3.285 & 4.928 & \\textbf{3.075} & 3.225 & 13.5 & 3.8 & 3.8 & \\textbf{2.6} \\\\\nRMSE & 6.181 & 4.144 & 4.314 & 6.215 & 4.129 & 4.407 & 6.213 & \\textbf{4.123} & 4.316 & 18.3 & 5.9 & 5.7 & \\textbf{3.8} \\\\\n\\# Parameters & 924 & 3784 & 4708 & 924 & 3784 & 4708 & 924 & 3784 & 4708 & $\\sim$ 432k & $\\sim$ 341k & $\\sim$ 283k & $\\sim$ 341k \\\\ \\hline\n\\multicolumn{14}{c}{Paracetamol} \\\\ \\hline\nMAE & 4.609 & 2.715 & 2.795 & 4.723 & 2.643 & 2.71 & 4.748 & \\textbf{2.624} & 2.699 & 8.4 & 2.1 & 2.2 & \\textbf{1.4} \\\\\nRMSE & 5.860 & 3.418 & 4.116 & 5.964 & 3.338 & 3.424 & 5.961 & \\textbf{3.299} & 3.408 & 11.2 & 2.9 & 3.0 & \\textbf{1.9} \\\\\n\\# Parameters & 924 & 3784 & 4444 & 924 & 3784 & 4444 & 924 & 3784 & 4444 & $\\sim$432k & $\\sim$ 341k & $\\sim$283k & $\\sim$341k \\\\ \\hline\n & & & & & & & & & & & & & \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of the performance of our MHSNs and the other state-of-the-art GNNs for nuclear energy prediction. We report the accuracy via MAE and RMSE as-well as the number of trainable parameters in each network}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Multiscale Hodge Scattering Networks for Data Analysis", "authors": ["Naoki Saito", "Stefan C. Schonsheck", "Eugene Shvarts"], "url": "https://arxiv.org/abs/2311.10270v5", "attribution": "\"Multiscale Hodge Scattering Networks for Data Analysis\" by Naoki Saito, Stefan C. Schonsheck, and Eugene Shvarts, arXiv:2311.10270v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.06407v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{SSIM scores for different SR loss functions (LF) across multiple datasets. The highest SSIM values for each dataset and the overall average are highlighted in bold, with second-best values underlined.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\textbf{Dataset / LF} & \\textbf{Baseline} & \\textbf{L1} & \\textbf{Combo} & \\textbf{Hybrid} \\\\ \n \\midrule\n FUSAR & 0.930 & 0.929 & \\underline{0.932} & \\textbf{0.934} \\\\ \n MSTAR & 0.730 & 0.758 & \\textbf{0.761} & \\underline{0.760} \\\\ \n HRSID & 0.800 & \\textbf{0.935} & \\underline{0.933} & \\underline{0.933} \\\\ \n \\midrule\n \\textbf{Average} & 0.820 & 0.874 & \\underline{0.875} & \\textbf{0.876} \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery", "authors": ["Ch Muhammad Awais", "Marco Reggiannini", "Davide Moroni", "Oktay Karakus"], "url": "https://arxiv.org/abs/2508.06407v1", "attribution": "\"A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery\" by Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, and Oktay Karakus, arXiv:2508.06407v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10426v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{rotating}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Feasibility response of hybrid precoders.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccccccccc}\n\t\t\\toprule\n\t\t\\multirow{3}{*}{\\rotatebox{90}{\\kern-2.25em Case}} & \n\t\t\\multirow{1}{*}{Parameters} & \n\t\t\\multirow{2}{*}{\\texttt{FALCON}} &\n\t\t\\multicolumn{15}{c}{\\texttt{WMMSE} (\\%)} \\\\ \n\t\t\\cmidrule{4-18} \n\t\t& configuration & & \\multicolumn{3}{c}{\\scalebox{.65}{$ P^{(0)}_\\mathrm{m} = 0.70 P_\\mathrm{tx} $}} & \\multicolumn{3}{c}{\\scalebox{.65}{$ P^{(0)}_\\mathrm{m} = 0.80 P_\\mathrm{tx} $}} & \\multicolumn{3}{c}{\\scalebox{.65}{$ P^{(0)}_\\mathrm{m} = 0.90 P_\\mathrm{tx} $}} & \\multicolumn{3}{c}{\\scalebox{.65}{$ P^{(0)}_\\mathrm{m} = 0.95 P_\\mathrm{tx} $}} & \\multicolumn{3}{c}{\\scalebox{.65}{$ P^{(0)}_\\mathrm{m} = 0.99 P_\\mathrm{tx} $}} \\\\\n\t\t\\cmidrule(lr){3-3} \n\t\t\\cmidrule(lr){4-6} \n\t\t\\cmidrule(lr){7-9} \n\t\t\\cmidrule(lr){10-12} \n\t\t\\cmidrule(lr){13-15} \n\t\t\\cmidrule(lr){16-18} \n\t\t& \\scalebox{.65}{$ { \\left[ N_\\mathrm{tx}, K, C^\\mathrm{th}_0 \\right]} $} & (\\%) & \\texttt{MRT} & \\texttt{ZF} & \\texttt{SLNR} & \\texttt{MRT} & \\texttt{ZF} & \\texttt{SLNR} & \\texttt{MRT} & \\texttt{ZF} & \\texttt{SLNR} & \\texttt{MRT} & \\texttt{ZF} & \\texttt{SLNR} & \\texttt{MRT} & \\texttt{ZF} & \\texttt{SLNR} \\\\\n\t\t\\midrule\n\t\t\n\t\tA & $ {\\scriptscriptstyle \\left[ 4, 2, 2.5 \\right]} $ & \\textbf{100} & 54 & 58 & 69 & 82 & 77 & 68 & 82 & 82 & 78 & 87 & 87 & 84 & \\textbf{\\underline{87}} & 87 & 87 \\\\\n\t\t\\midrule\n\t\t\n\t\tB & $ {\\scriptscriptstyle \\left[ 6, 3, 2.0 \\right]} $ & \\textbf{100} & 63 & 54 & 62 & 70 & 72 & 67 & 79 & 79 & 76 & 84 & 83 & 81 & \\textbf{\\underline{88}} & 87 & 85 \\\\\n\t\t\\midrule\t\n\t\t\n\t\tC & $ {\\scriptscriptstyle \\left[ 8, 4, 1.5 \\right]} $ & \\textbf{96} & 74 & 81 & 79 & 85 & 85 & 86 & 93 & 94 & 91 & 93 & 95 & 94 & \\textbf{\\underline{96}} & 96 & 96 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Sequential Parametric Optimization for Rate-Splitting Precoding in Non-Orthogonal Unicast and Multicast Transmissions", "authors": ["Luis F. Abanto-Leon", "Matthias Hollick", "Bruno Clerckx", "Gek Hong Sim"], "url": "https://arxiv.org/abs/2201.10426v1", "attribution": "\"Sequential Parametric Optimization for Rate-Splitting Precoding in Non-Orthogonal Unicast and Multicast Transmissions\" by Luis F. Abanto-Leon, Matthias Hollick, Bruno Clerckx, and Gek Hong Sim, arXiv:2201.10426v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.02687v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Test accuracy (\\%) of node classification benchmarks using DGI and MVGRL methods. We compare the performances of models trained with JSD loss (Contrast), loss part only involving negative pairs (NO Pos), loss only involving positive pairs (NO Neg), and no optimization objective (NO Training). Mean accuracy with standard derivation is reported after 10 runs. We conduct significance testing using Wilcoxon Signed Rank Test , comparing the contrastive loss with other loss types. The p-value is averaged across datasets. A value below 0.05 denotes significant accuracy difference (\\colorbox{red!10}{red}), while a value above 0.05 indicates insignificance (\\colorbox{green!10}{green}).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccccccc}\n \\toprule\n \\textbf{Method} & \\textbf{Loss} & Cora & CiteSeer & PubMed & Photo & Computers & Chameleon & Squirrel & \\textbf{Avg} &\\textbf{Avg p-value} \\\\\n \\midrule \n \\multirow{5}*{DGI} & Contrast & 83.38 $\\pm$ 2.67 & 72.07 $\\pm$ 2.37 & 84.77 $\\pm$ 0.71 & 88.10 $\\pm$ 1.81 & 83.35 $\\pm$ 0.71 & 39.56 $\\pm$ 2.86 & 34.55 $\\pm$ 0.88 & 69.40 & -\\\\\n \\cmidrule(r){2-11} \n ~ & NO Training & 69.78 $\\pm$ 3.39 & 55.15 $\\pm$ 2.09 & 79.56 $\\pm$ 1.35 & 69.08 $\\pm$ 3.30 & 56.03 $\\pm$ 1.97 & 31.44 $\\pm$ 1.70 & 24.57 $\\pm$ 1.22 & 55.09 & \\cellcolor{red!10}0.0020 \\\\\n \\cmidrule(r){2-11}\n ~ & NO Pos & 66.84 $\\pm$ 3.54 & 54.79 $\\pm$ 3.33 & 78.25 $\\pm$ 0.99 & 58.34 $\\pm$ 3.92 & 71.98 $\\pm$ 1.38 & 35.81 $\\pm$ 2.34\t& 26.99 $\\pm$ 0.20 & 56.14 & \\cellcolor{red!10}0.0020 \\\\\n \\cmidrule(r){2-11}\n ~ & NO Neg & 67.35 $\\pm$ 4.61 & 58.17 $\\pm$ 2.57 & 77.23 $\\pm$ 1.05 & 62.75 $\\pm$ 3.75 & 72.66 $\\pm$ 1.48 & 31.62 $\\pm$ 4.06 & 27.75 $\\pm$ 1.85 & 56.79 & \\cellcolor{red!10}0.0022 \\\\\n \\midrule\n \\multirow{5}*{MVGRL} & Contrast & 84.41 $\\pm$ 1.44 & 75.27 $\\pm$ 0.79 & 85.62 $\\pm$ 0.63 & 89.23 $\\pm$ 1.52 & 79.58 $\\pm$ 0.15 & 42.45 $\\pm$ 2.43 & 33.97 $\\pm$ 2.54 & 70.08 & - \\\\\n \\cmidrule(r){2-11}\n ~ & NO Training & 77.94 $\\pm$ 2.23 & 58.92 $\\pm$ 2.88 & 82.13 $\\pm$ 0.63 & 81.15 $\\pm$ 3.25 & 69.07 $\\pm$ 0.40 & 32.23 $\\pm$ 1.94 & 24.41 $\\pm$ 1.10 & 60.84 & \\cellcolor{red!10}0.0022\\\\\n \\cmidrule(r){2-11}\n ~ & NO Pos & 75.44 $\\pm$ 1.42 & 61.08 $\\pm$ 2.48 & 81.26 $\\pm$ 1.30 & 36.03 $\\pm$ 1.57 & 38.36 $\\pm$ 0.55 & 36.86 $\\pm$ 2.56 & 29.98 $\\pm$ 1.52 & 51.29 & \\cellcolor{red!10}0.0020 \\\\\n \\cmidrule(r){2-11}\n ~ & NO Neg & 54.93 $\\pm$ 4.67 & 35.03 $\\pm$ 5.20 & 56.26 $\\pm$ 1.91 & 36.47 $\\pm$ 2.37 & 38.36 $\\pm$ 0.56 & 29.34 $\\pm$ 2.04 & 28.06 $\\pm$ 1.66 & 39.78 & \\cellcolor{red!10}0.0020 \\\\\n \\bottomrule \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning", "authors": ["Xiaojun Guo", "Yifei Wang", "Zeming Wei", "Yisen Wang"], "url": "https://arxiv.org/abs/2311.02687v1", "attribution": "\"Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning\" by Xiaojun Guo, Yifei Wang, Zeming Wei, and Yisen Wang, arXiv:2311.02687v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.11459v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average European league ratings of top 5 teams}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|}\n \\hline\n \\# & league & rating & \\# & league & rating \\\\ \\hline\n 1 & England. First & 2118.8 & 50 & Lithuania. First & 1661.7 \\\\ \\hline\n 2 & Germany. First & 2069.9 & 51 & North Macedonia. First & 1651.8 \\\\ \\hline\n 3 & Italy. First & 2064.0 & 52 & Portugal. Second & 1646.9 \\\\ \\hline\n 4 & Spain. First & 2039.4 & 53 & Kosovo. First & 1642.1 \\\\ \\hline\n 5 & Portugal. First & 2000.7 & 54 & Luxembourg. First & 1640.6 \\\\ \\hline\n 6 & France. First & 1981.4 & 55 & Estonia. First & 1637.8 \\\\ \\hline\n 7 & Netherlands. First & 1965.8 & 56 & Moldova. First & 1636.9 \\\\ \\hline\n 8 & Belgium. First & 1923.3 & 57 & Malta. First & 1622.7 \\\\ \\hline\n 9 & Turkey. First & 1911.9 & 58 & Austria. Second & 1619.6 \\\\ \\hline\n 10 & Russia. First & 1906.5 & 59 & Denmark. Second & 1616.9 \\\\ \\hline\n 11 & Austria. First & 1899.3 & 60 & Faroe Islands. First & 1615.1 \\\\ \\hline\n 12 & Switzerland. First & 1893.4 & 61 & Albania. First & 1614.2 \\\\ \\hline\n 13 & Poland. First & 1890.7 & 62 & Gibraltar. First & 1612.3 \\\\ \\hline\n 14 & Sweden. First & 1883.9 & 63 & Greece. Second & 1611.2 \\\\ \\hline\n 15 & Ukraine. First & 1878.1 & 64 & Netherlands. Second & 1602.1 \\\\ \\hline\n 16 & Greece. First & 1868.8 & 65 & Sweden. Second & 1587.9 \\\\ \\hline\n 17 & Denmark. First & 1852.3 & 66 & Belgium. Second & 1581.6 \\\\ \\hline\n 18 & Czech Republic. First & 1832.4 & 67 & Romania. Second & 1577.1 \\\\ \\hline\n 19 & Scotland. First & 1826.2 & 68 & Montenegro. First & 1562.7 \\\\ \\hline\n 20 & Israel. First & 1817.1 & 69 & Hungary. Second & 1557.3 \\\\ \\hline\n 21 & Serbia. First & 1816.6 & 70 & Wales. First & 1556.8 \\\\ \\hline\n 22 & Bulgaria. First & 1809.7 & 71 & Israel. Second & 1553.7 \\\\ \\hline\n 23 & Norway. First & 1808.1 & 72 & Scotland. Second & 1532.3 \\\\ \\hline\n 24 & Romania. First & 1807.6 & 73 & Croatia. Second & 1525.0 \\\\ \\hline\n 25 & Croatia. First & 1807.2 & 74 & Ukraine. Second & 1519.7 \\\\ \\hline\n 26 & Cyprus. First & 1795.4 & 75 & Slovenia. Second & 1517.7 \\\\ \\hline\n 27 & Germany. Second & 1785.3 & 76 & Norway. Second & 1516.7 \\\\ \\hline\n 28 & Latvia. First & 1767.9 & 77 & Slovakia. Second & 1490.9 \\\\ \\hline\n 29 & Italy. Second & 1767.6 & 78 & Kazakhstan. Second & 1477.3 \\\\ \\hline\n 30 & England. Second & 1766.0 & 79 & Serbia. Second & 1473.3 \\\\ \\hline\n 31 & Spain. Second & 1763.3 & 80 & North Macedonia. Second & 1466.5 \\\\ \\hline\n 32 & Slovenia. First & 1749.0 & 81 & Bulgaria. Second & 1460.6 \\\\ \\hline\n 33 & Slovakia. First & 1748.5 & 82 & Georgia. Second & 1456.8 \\\\ \\hline\n 34 & Belarus. First & 1735.6 & 83 & Bosnia And Herzegovina. Second & 1456.6 \\\\ \\hline\n 35 & Armenia. First & 1732.3 & 84 & Czech Republic. Second & 1453.2 \\\\ \\hline\n 36 & Hungary. First & 1727.1 & 85 & Andorra. First & 1443.9 \\\\ \\hline\n 37 & Georgia. First & 1726.1 & 86 & Armenia. Second & 1443.3 \\\\ \\hline\n 38 & Bosnia And Herzegovina. First & 1724.8 & 87 & Cyprus. Second & 1436.2 \\\\ \\hline\n 39 & Finland. First & 1721.9 & 88 & Finland. Second & 1430.4 \\\\ \\hline\n 40 & France. Second & 1721.9 & 89 & Ireland. Second & 1427.2 \\\\ \\hline\n 41 & Northern Ireland. First & 1706.5 & 90 & San Marino. First & 1412.1 \\\\ \\hline\n 42 & Azerbaijan. First & 1704.0 & 91 & Belarus. Second & 1409.0 \\\\ \\hline\n 43 & Switzerland. Second & 1700.7 & 92 & Albania. Second & 1374.1 \\\\ \\hline\n 44 & Ireland. First & 1698.4 & 93 & Latvia. Second & 1365.0 \\\\ \\hline\n 45 & Turkey. Second & 1689.6 & 94 & Iceland. Second & 1362.8 \\\\ \\hline\n 46 & Russia. Second & 1684.8 & 95 & Malta. Second & 1327.4 \\\\ \\hline\n 47 & Kazakhstan. First & 1683.2 & 96 & Lithuania. Second & 1316.7 \\\\ \\hline\n 48 & Poland. Second & 1671.4 & 97 & Estonia. Second & 1304.2 \\\\ \\hline\n 49 & Iceland. First & 1669.8 & 98 & Northern Ireland. Second & 1276.8 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Ratings of European and South American Football Leagues Based on Glicko-2 with Modifications", "authors": ["Andrei Shelopugin", "Alexander Sirotkin"], "url": "https://arxiv.org/abs/2310.11459v1", "attribution": "\"Ratings of European and South American Football Leagues Based on Glicko-2 with Modifications\" by Andrei Shelopugin and Alexander Sirotkin, arXiv:2310.11459v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.04588v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|} \n \\hline\n $N$ & $X_a$ (cm) & \t$Y_a$ (cm) &\t$X_s$ (cm) &\t$Y_s$ (cm) \\\\\n \\hline \n\t1008 &\t50.2 &\t\t69.0 &\t83.0 &\t85.0 \\\\\n\t252 &\t26.6 &\t\t34.4 &\t45.0 &\t46.5 \\\\\n \t9 &\t\t15.6 &\t\t21.4 &\t25.9 &\t23.3 \\\\\n \t500 &\t50.8 &\t\t50.9 &\t78.4 &\t74.8 \\\\\n \t1026 &\t67.8 &\t\t48.9 &\t88.0 &\t86.8 \\\\\n \t27 &\t296.8 &\t\t14.6 &\t107.2&\t92.0 \\\\\n \t2000 &\t99.1 &\t\t68.6 &\t123.1 &\t131.0 \\\\\n \t1000 &\t50.8 &\t\t68.5 &\t112.0 &\t69.0 \\\\\n 1000 &\t99.3 &\t\t33.0 &\t132.4 &\t57.5 \\\\\n \\hline\n\\end{tabular}\n\\caption{Measurements of puzzle unassembled and assembled dimensions for 9 puzzles. We estimated the uncertainty in our measurement of $X_a$ and $Y_a$ as $0.2$ cm and the uncertainty in in $X_s$ and $Y_s$ as $0.5$ cm.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "How big a table do you need for your jigsaw puzzle?", "authors": ["Madeleine Bonsma-Fisher", "Kent Bonsma-Fisher"], "url": "https://arxiv.org/abs/2312.04588v1", "attribution": "\"How big a table do you need for your jigsaw puzzle?\" by Madeleine Bonsma-Fisher and Kent Bonsma-Fisher, arXiv:2312.04588v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.05421v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Run time and Hardware used for our method (SITD) and the state-of-the-art baseline Rhino.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Category\\textbackslash Method} & \\textbf{Rhino} & \\textbf{SITD (ours)} \\\\\n\\midrule\nRuntime & 18 mins 40 sec $\\pm$ 30 sec & 57.12 sec $\\pm$ 1.6 sec \\\\ \n\\midrule\nHardware & 1 NVIDIA A100 GPU + AMD EPYC 7402 24-Core CPU & 11th Gen Core i5-1140F CPU \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Diffusion Based Causal Representation Learning", "authors": ["Amir Mohammad Karimi Mamaghan", "Andrea Dittadi", "Stefan Bauer", "Karl Henrik Johansson", "Francesco Quinzan"], "url": "https://arxiv.org/abs/2311.05421v1", "attribution": "\"Diffusion Based Causal Representation Learning\" by Amir Mohammad Karimi Mamaghan, Andrea Dittadi, Stefan Bauer, Karl Henrik Johansson, and Francesco Quinzan, arXiv:2311.05421v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.06625v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Recovered viscosity constants for the MFG in with $20$ points of observations (both $m$ and $V$ are observed), and $M=400$ collocation points. $\\nu$ stands for the true viscosity and $\\nu^\\dagger$ represents the recovered viscosity. }\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n \\hline\n $\\nu$ & 1 & 0.5 & 0.1 & 0.05 & 0.01\\\\\n \\hline\n $\\nu^{\\dagger}$& 1.000804 & 0.497811 & 0.099312 & 0.050905 & 0.015023 \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes", "authors": ["Jinyan Guo", "Chenchen Mou", "Xianjin Yang", "Chao Zhou"], "url": "https://arxiv.org/abs/2312.06625v2", "attribution": "\"Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes\" by Jinyan Guo, Chenchen Mou, Xianjin Yang, and Chao Zhou, arXiv:2312.06625v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.18897v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\toprule\n \\textbf{Codebook Size} & $\\mathbf{1024}$ & $\\mathbf{2048}$ & $\\mathbf{4096}$ & $\\mathbf{8192}$& $\\mathbf{16384}$ \\\\\n \\midrule\n Classic VQ & $99$\\% & $95$\\% & $90$\\% & $56$\\% & $42$\\% \\\\\n Single SLM-VQ & $99$\\% & $98$\\% & $95$\\% & $92$\\% & $87$\\%\\\\\n SLM-VQ & $99\\%$ & $98$\\% & $98$\\% & $98$\\% & $94$\\% \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Codebook utilization of different quantizers under varying codebook sizes. SLM-VQ works well in all settings.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "HH-Codec: High Compression High-fidelity Discrete Neural Codec for Spoken Language Modeling", "authors": ["Rongkun Xue", "Yazhe Niu", "Shuai Hu", "Zixin Yin", "Yongqiang Yao", "Jing Yang"], "url": "https://arxiv.org/abs/2507.18897v1", "attribution": "\"HH-Codec: High Compression High-fidelity Discrete Neural Codec for Spoken Language Modeling\" by Rongkun Xue, Yazhe Niu, Shuai Hu, Zixin Yin, Yongqiang Yao, and Jing Yang, arXiv:2507.18897v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2010.08259v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Percentage variation of the average of volatility (5 days before and after the announcement date) with respect to the volatility of the announcement day for four volatility time series.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\t\t&France&Germany&Italy&Spain\\\\ \\hline\t\n\t\tbefore&-4,40&\t-5,92&\t-9,79&\t-8,56\\\\\n\t\tafter&-5,52&\t-5,39&\t-7,39&\t-5,02\\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Unconventional Policies Effects on Stock Market Volatility: A MAP Approach", "authors": ["Demetrio Lacava", "Giampiero M. Gallo", "Edoardo Otranto"], "url": "https://arxiv.org/abs/2010.08259v2", "attribution": "\"Unconventional Policies Effects on Stock Market Volatility: A MAP Approach\" by Demetrio Lacava, Giampiero M. Gallo, and Edoardo Otranto, arXiv:2010.08259v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.20258v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c|c|c|c}\n Theoretical $p_g$& Observed $p_g$& Perfect& EKF & $T_{avg}$\\\\\n \\hline\n 0.50 & 1.00 & x & & 0.17\\\\\n \\hline\n 0.50 & 1.00 & & x & 0.20\\\\\n \\end{tabular}\n\\caption{S-FxT-CLF results from the 2D nonlinear system goal-reaching study over 100 trials.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Risk-Aware Fixed-Time Stabilization of Stochastic Systems under Measurement Uncertainty", "authors": ["Mitchell Black", "Georgios Fainekos", "Bardh Hoxha", "Dimitra Panagou"], "url": "https://arxiv.org/abs/2403.20258v1", "attribution": "\"Risk-Aware Fixed-Time Stabilization of Stochastic Systems under Measurement Uncertainty\" by Mitchell Black, Georgios Fainekos, Bardh Hoxha, and Dimitra Panagou, arXiv:2403.20258v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23211v2_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of the model for Scenario II with $T=500$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccccc}\n\\hline\nCoefficient & Truth & AB ($\\lfloor T\\hat{\\tau} \\rfloor$) & AB ($\\lfloor T\\tilde{\\tau} \\rfloor$) & RMSE ($\\lfloor T\\hat{\\tau} \\rfloor$) & RMSE ($\\lfloor T\\tilde{\\tau} \\rfloor$) & 90$\\%$ CP \n($\\lfloor T\\tilde{\\tau} \\rfloor$) & 95$\\%$ CP ($\\lfloor T\\tilde{\\tau} \\rfloor$) & 99$\\%$ CP ($\\lfloor T\\tilde{\\tau} \\rfloor$) \\\\\\hline\\hline\n& $\\lfloor T/3 \\rfloor$ & 5.080 & 4.920 & 8.117 & 7.947 & 0.920 & 0.950 & 0.970 \\\\\n$\\theta=-0.9$ & $\\lfloor T/2 \\rfloor$ & 4.880 & 4.700 & 8.393 & 8.019 & 0.950 & 0.970 & 0.970 \\\\\n$\\phi=0.1$ & $\\lfloor 2T/3 \\rfloor$ & 4.140 & 3.950 & 6.841 & 6.701 & 0.950 & 0.970 & 0.990 \\\\\n& $\\lfloor 4T/5 \\rfloor$ & 3.680 & 3.030 & 7.139 & 5.696 & 0.970 & 0.980 & 0.990 \\\\\n\\hline\\hline\n& $\\lfloor T/3 \\rfloor$ & 4.040 & 3.510 & 6.589 & 5.709 & 0.880 & 0.910 & 0.960 \\\\\n$\\theta=-0.9$ & $\\lfloor T/2 \\rfloor$ & 3.780 & 3.620 & 6.585 & 6.153 & 0.920 & 0.940 & 0.960 \\\\\n$\\phi=0.3$ & $\\lfloor 2T/3 \\rfloor$ & 3.250 & 2.880 & 5.379 & 4.695 & 0.940 & 0.950 & 0.990 \\\\\n& $\\lfloor 4T/5 \\rfloor$ & 2.880 & 2.690 & 5.769 & 5.474 & 0.950 & 0.980 & 0.990 \\\\\n\\hline\\hline\n& $\\lfloor T/3 \\rfloor$ & 1.980 & 1.790 & 3.597 & 3.369 & 0.890 & 0.890 & 0.910 \\\\\n$\\theta=-0.9$ & $\\lfloor T/2 \\rfloor$ & 1.760 & 1.810 & 2.926 & 2.972 & 0.860 & 0.880 & 0.930 \\\\\n$\\phi=0.7$ & $\\lfloor 2T/3 \\rfloor$ & 2.160 & 1.630 & 3.734 & 2.648 & 0.900 & 0.900 & 0.940 \\\\\n& $\\lfloor 4T/5 \\rfloor$ & 1.510 & 1.500 & 2.696 & 2.702 & 0.920 & 0.950 & 0.970 \\\\\n\\hline\\hline\n& $\\lfloor T/3 \\rfloor$ & 1.030 & 1.010 & 1.775 & 1.752 & 0.930 & 0.930 & 0.940 \\\\\n$\\theta=-0.9$ & $\\lfloor T/2 \\rfloor$ & 0.990 & 0.940 & 1.565 & 1.562 & 0.940 & 0.950 & 0.960 \\\\\n$\\phi=0.9$ & $\\lfloor 2T/3 \\rfloor$ & 1.290 & 1,260 & 2.170 & 2.163 & 0.900 & 0.910 & 0.920 \\\\\n& $\\lfloor 4T/5 \\rfloor$ & 1.200 & 1.200 & 1.897 & 1.860 & 0.890 & 0.890 & 0.930 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models", "authors": ["Sepideh Mosaferi", "Abolfazl Safikhani", "Peiliang Bai"], "url": "https://arxiv.org/abs/2503.23211v2", "attribution": "\"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models\" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.17496v5_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccc}\n\t\t\\toprule & \\multicolumn{3}{c}{Proportion of short videos} & \n\t\t\\multicolumn{3}{c}{Stay durations} & \\multicolumn{3}{c}{Finishing rates} \\\\ \n\t\t\\midrule & Bias & STD & SE & Bias & STD & SE & Bias & STD & SE \\\\ \n\t\t\n\t\tWeighted & 0.005 & 0.007 & 0.001 & -0.010 & 0.021 & 0.009 & 0.001 & 0.002 & 0.001 \\\\\n\t\tData splitting & -0.008 & 0.015 & 0.001 & 0.018 & 0.040 & 0.009 & -0.003 & 0.004 & 0.001 \\\\\n\t\tData pooling & 0.039 & 0.002 & 0.001 & -0.124 & 0.009 & 0.009 & 0.014 & 0.001 & 0.001 \\\\\n\t\tSnapshot & 0.031 & 0.001 & 0.001 & -0.107 & 0.009 & 0.009 & 0.013 & 0.001 & 0.001 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach", "authors": ["Nian Si"], "url": "https://arxiv.org/abs/2310.17496v5", "attribution": "\"Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach\" by Nian Si, arXiv:2310.17496v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.13571v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Blitzes characteristics in Fortaleza-CE, 2012-13}\n\\begin{tabular}{lccc}\n\t\t\t\t\t\\\\\\hline \n\t\t\t\t\t\\hline \\\\ \n\t\t\t\t\t& Mean & Maximum & Minimum \\\\ %& Alagoas\n\t\t\t\t\t\\hline \\\\ \n\t\t\t\t\t\n\t\t\t\t\tDuration (hours) & 5.57 & 8 & 0.5 \\\\ %& \n\t\t\t\t\t\n\t\t\t\t\tNumber of policemen & 5.84 & 75 & 2 \\\\ %& \n\t\t\t\t\t\n\t\t\t\t\tPolice vehicles allocated & 1.8 & 21 & 0 \\\\ %& \n\t\t\t\t\t\\\\\\hline \n\t\t\t\t\t\\hline \\\\ \n\t\t\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Is Crime Displacement Inevitable? Evidence from Police Crackdowns in Fortaleza, Brazil", "authors": ["José Raimundo Carvalho", "Marcelino Guerra"], "url": "https://arxiv.org/abs/2503.13571v1", "attribution": "\"Is Crime Displacement Inevitable? Evidence from Police Crackdowns in Fortaleza, Brazil\" by José Raimundo Carvalho and Marcelino Guerra, arXiv:2503.13571v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05036v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{NLL, ES, and Spatial Quality ($\\mathcal{Q}_S$) results of the three predictive distributions from our toy example.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \n Case $\\#$& NLL $\\downarrow$ & ES $\\downarrow$ & $\\mathcal{Q}_S$ $\\uparrow$ ($\\%$) \\\\\n \\midrule\n1& 20.49 &23.10 & \\textbf{51.88} \\\\\n2 & 20.49 & 23.10 & 45.98 \\\\\n3 & \\textbf{19.33} & \\textbf{21.21} & 50.02 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors", "authors": ["Ali Harakeh", "Steven L. Waslander"], "url": "https://arxiv.org/abs/2101.05036v3", "attribution": "\"Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors\" by Ali Harakeh and Steven L. Waslander, arXiv:2101.05036v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14747v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ccccccc}\\hline\n Design &$\\mu_Y(z)$ &$\\mu_X(z)$ & $\\sigma_Y(Z)$ & $\\sigma_X(Z)$ & $U,V$ & $z_{\\ell}$ \\\\ \\hline\n 1a& $z$& $z$& $z^2$& $z^2$ & $N(0,1)$ & 0.5\\\\\n 1b& $1.05z$& $z$& $z^2$& $z^2$& $N(0,1)$& 0.5\\\\\n 1c& $z$& $z$& $z^2$& $z^2$& $N(0,1)$& 0.25, 0.75\\\\ \\hline\n 1d& $0.95z$& $z$& $z^2$& $z^2$& $N(0,1)$& 0.5\\\\ \\hline\n 2a& $z$ & $z^2+0.25$& $z^2$& $z^2$& $N(0,1)$& 0.5\\\\\n 2b& $1.05z$ & $0.5z + 0.25$& $z^2$& $z^2$& $N(0,1)$& 0.5\\\\\n 2c& $z$ & $z-(z-0.25)(z-0.75)$& $z^2$& $z^2$& $N(0,1)$& 0.25, 0.75\\\\ \\hline\n 2d& $z$ & $0.6z + 0.25$& $z^2$& $z^2$& $N(0,1)$& 0.5\\\\ \\hline\n 3a& $z$& $z$& $z^2$& $z^2$& $U[0,1]$& 0.5\\\\\n 3b& $z+0.1z^2$& $z$& $0.95z^2$& $z^2$& $U[0,1]$& 0.5\\\\\n 3c& $z$& $z$& $z^2$& $z^2$& $U[0,1]$& 0.25, 0.75\\\\ \\hline\n 3d& $z$& $z$& $0.90z^2$& $z^2$& $U[0,1]$& 0.5\\\\ \\hline\n 4a& 0& 0& $z^2$& $z^2$& C-logN& 0.5\\\\\n 4b& 0& 0& $1.05z^2$& $z^2$& C-logN & 0.5 \\\\\n 4c& 0& 0& $z^2$& $z^2$& C-logN& 0.25, 0.75\\\\ \\hline\n 4d& 0& 0& $0.90z^2$& $z^2$& C-logN & 0.5 \\\\ \\hline\n 5a& 0 & --& -- & -- & discrete & 0.5 \\\\\n 5b& 1 & --& -- & -- & discrete & 0.5 \\\\\n 5c& 0 & --& -- & -- & discrete& 0.25, 0.75\\\\ \\hline\n 5d& -1/2 & --& -- & -- & discrete & 0.5 \\\\ \\hline\n 6a& 0 & --& --& --& Binomial & 0.5 \\\\\n 6b& 1 & --& --& --& Binomial & 0.5 \\\\\n 6c& 0 & --& --& --& Binomial& 0.25, 0.75\\\\ \\hline\n 6d& -1 & --& --& --& Binomial & 0.5 \\\\ \\hline\n \\end{tabular}\n\\caption{Parameter values for each simulation design. Designs 1 to 4 are based on the location-scale model in , while Designs 5 and 6 are discrete designs as described in the main text. Cases ``a\" to ``c\" are under the null, while case ``d\" is under the alternative.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Testing Conditional Stochastic Dominance at Target Points", "authors": ["Federico A. Bugni", "Ivan A. Canay", "Deborah Kim"], "url": "https://arxiv.org/abs/2503.14747v2", "attribution": "\"Testing Conditional Stochastic Dominance at Target Points\" by Federico A. Bugni, Ivan A. Canay, and Deborah Kim, arXiv:2503.14747v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.10670v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n \\hline\n \\textbf{Controller} & \\textbf{$x_{error}$ (cm)} & \\textbf{$y_{error}$ (cm)} & \\textbf{$z_{error}$ (cm)} \\\\ \\hline\n HL Commander & 2.7112 & 3.0767 & 0.3595 \\\\ \\hline\n No Dynamics & 4.9282 & 5.8952 & 0.812 \\\\ \\hline\n Magnet Dynamics & 4.2868 & 5.7235 & 0.4739 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Controller average error comparison for figure-8, showing the least error from HL Commander, followed by LL Commander with magnet dynamics, and lastly LL Commander without magnet dynamics.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Magnisketch Drone Control", "authors": ["Ashley Kline", "Abirami Elangovan", "Dominique Escandon", "Scott Wade", "Aatish Gupta"], "url": "https://arxiv.org/abs/2412.10670v1", "attribution": "\"Magnisketch Drone Control\" by Ashley Kline, Abirami Elangovan, Dominique Escandon, Scott Wade, and Aatish Gupta, arXiv:2412.10670v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.18663v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n Beam & Solution Length & Solution Length \\\\\n Width & Nonbacktracking RW & Simple RW \\\\\n \\hline\n 2**10 & None & None \\\\\n \\hline\n 2**12 & 252 & None \\\\\n \\hline \n 2**14 & 202 & None \\\\\n \\hline \n 2**16 & 194 & None \\\\\n \\hline \n 2**18 & 194 & None \\\\\n \\hline \n 2**20 & 190 (Ideal) & None \\\\\n \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Without non-backtracking system cannot perform well on single epoch training. Effect of beam size is clear, but not so strong. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs", "authors": ["A. Chervov", "A. Soibelman", "S. Lytkin", "I. Kiselev", "S. Fironov", "A. Lukyanenko", "A. Dolgorukova", "A. Ogurtsov", "F. Petrov", "S. Krymskii", "M. Evseev", "L. Grunvald", "D. Gorodkov", "G. Antiufeev", "G. Verbii", "V. Zamkovoy", "L. Cheldieva", "I. Koltsov", "A. Sychev", "M. Obozov", "A. Eliseev", "S. Nikolenko", "N. Narynbaev", "R. Turtayev", "N. Rokotyan", "S. Kovalev", "A. Rozanov", "V. Nelin", "S. Ermilov", "L. Shishina", "D. Mamayeva", "A. Korolkova", "K. Khoruzhii", "A. Romanov"], "url": "https://arxiv.org/abs/2502.18663v1", "attribution": "\"CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs\" by A. Chervov, A. Soibelman, S. Lytkin, I. Kiselev, S. Fironov, A. Lukyanenko, A. Dolgorukova, A. Ogurtsov, F. Petrov, S. Krymskii, M. Evseev, L. Grunvald, D. Gorodkov, G. Antiufeev, G. Verbii, V. Zamkovoy, L. Cheldieva, I. Koltsov, A. Sychev, M. Obozov, A. Eliseev, S. Nikolenko, N. Narynbaev, R. Turtayev, N. Rokotyan, S. Kovalev, A. Rozanov, V. Nelin, S. Ermilov, L. Shishina, D. Mamayeva, A. Korolkova, K. Khoruzhii, and A. Romanov, arXiv:2502.18663v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.11891v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The Training statistics of the 5 homophilic, heterophilic and real-world benchmark datasets used in this work for AMES in this work.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc} \\hline\n & Training Epochs & Learning Rate & Weight Decay \\\\ \\hline\n \\textbf{Cora} & 1,500 & $5\\times10^{-3}$& $1\\times10^{-5}$\\\\\n \\textbf{CiteSeer} & 1,500 & $4\\times10^{-3}$& $1\\times10^{-5}$\\\\\n \\textbf{Squirrel} &1,000 &$1\\times10^{-2}$ & $1\\times10^{-5}$\\\\\n\\textbf{Chameleon} & 1,000& $1\\times10^{-2}$& $1\\times10^{-5}$\\\\\n \\textbf{TadPole} & 800& $1\\times10^{-3}$& $2\\times10^{-4}$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference", "authors": ["Yuan Lu", "Haitz Sáez de Ocáriz Borde", "Pietro Liò"], "url": "https://arxiv.org/abs/2311.11891v1", "attribution": "\"AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference\" by Yuan Lu, Haitz Sáez de Ocáriz Borde, and Pietro Liò, arXiv:2311.11891v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.00650v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean squared error between value of optimal DTR and estimated optimal DTR.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|l|l|l|l|l|l|l|l}\n & & \\multicolumn{2}{|c|}{IPW} & \\multicolumn{2}{|c|}{sIPW} & \\multicolumn{2}{|c|}{G-comp} &\\multicolumn{2}{|c|}{AIPWE} \\\\\n N & CV or $w$ & MSE & MC error & MSE & MC error & MSE & MC error & MSE & MC error\\\\ \\hline \n \\multicolumn{10}{c}{Simulation Setting 1} \\\\ \\hline\n \\multirow{3}{*}{200} & 0.75 & 0.0043 & 0.0002 & 0.0005 & 0.0000 & 0.0002 & 0.0000 & 0.0002 & 0.0000 \\\\ \n & 1.0 & 0.0057 & 0.0002 & 0.0008 & 0.0000 & 0.0004 & 0.0000 & 0.0004 & 0.0000 \\\\ \n & 1.25 & 0.0073 & 0.0003 & 0.0014 & 0.0001 & 0.0007 & 0.0000 & 0.0007 & 0.0000 \\\\ \n \\multirow{3}{*}{500} & 0.75 & 0.0017 & 0.0001 & 0.0002 & 0.0000 & 0.0001 & 0.0000 & 0.0001 & 0.0000 \\\\ \n & 1.00 & 0.0022 & 0.0001 & 0.0004 & 0.0000 & 0.0002 & 0.0000 & 0.0002 & 0.0000 \\\\ \n & 1.25 & 0.0032 & 0.0001 & 0.0006 & 0.0000 & 0.0003 & 0.0000 & 0.0003 & 0.0000 \\\\ \n \\multirow{3}{*}{1000} & 0.75 & 0.0009 & 0.0000 & 0.0001 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\\\ \n & 1.00 & 0.0011 & 0.0000 & 0.0002 & 0.0000 & 0.0001 & 0.0000 & 0.0001 & 0.0000 \\\\ \n & 1.25 & 0.0015 & 0.0001 & 0.0003 & 0.0000 & 0.0001 & 0.0000 & 0.0001 & 0.0000 \\\\ \\hline\n \\multicolumn{10}{c}{Simulation Setting 2} \\\\ \\hline \n \\multirow{3}{*}{200}& 0.75 & 0.0008 & 0.0000 & 0.0007 & 0.0000 & 0.0048 & 0.0002 & 0.0050 & 0.0002 \\\\ \n & 1.00 & 0.0029 & 0.0001 & 0.0028 & 0.0001 & 0.0722 & 0.0009 & 0.0029 & 0.0001 \\\\ \n & 1.25 & 0.0052 & 0.0002 & 0.0038 & 0.0002 & 0.0833 & 0.0009 & 0.0061 & 0.0002 \\\\ \n \\multirow{3}{*}{500} & 0.75 & 0.0004 & 0.0000 & 0.0004 & 0.0000 & 0.0024 & 0.0001 & 0.0025 & 0.0001 \\\\ \n & 1.00 & 0.0014 & 0.0001 & 0.0014 & 0.0001 & 0.0793 & 0.0006 & 0.0013 & 0.0001 \\\\ \n & 1.25 & 0.0029 & 0.0001 & 0.0016 & 0.0001 & 0.0835 & 0.0006 & 0.0042 & 0.0001 \\\\ \n \\multirow{3}{*}{1000} & 0.75 & 0.0018 & 0.0002 & 0.0004 & 0.0000 & 0.0016 & 0.0001 & 0.0016 & 0.0001 \\\\ \n & 1.00 & 0.0010 & 0.0001 & 0.0009 & 0.0001 & 0.0846 & 0.0005 & 0.0009 & 0.0001 \\\\ \n & 1.25 & 0.0014 & 0.0001 & 0.0010 & 0.0000 & 0.0855 & 0.0004 & 0.0040 & 0.0001 \\\\ \\hline\n \\multicolumn{10}{c}{Simulation Setting 3} \\\\ \\hline\n \\multirow{3}{*}{200} & 0.75 & 0.0045 & 0.0002 & 0.0047 & 0.0002 & 0.0254 & 0.0006 & 0.0047 & 0.0002 \\\\ \n & 1.00 & 0.0031 & 0.0002 & 0.0032 & 0.0002 & 0.0151 & 0.0004 & 0.0032 & 0.0002 \\\\ \n & 1.25 & 0.0040 & 0.0002 & 0.0042 & 0.0002 & 0.0118 & 0.0003 & 0.0045 & 0.0002 \\\\ \n \\multirow{3}{*}{500}& 0.75 & 0.0039 & 0.0001 & 0.0040 & 0.0001 & 0.0254 & 0.0005 & 0.0027 & 0.0001 \\\\ \n & 1.00 & 0.0013 & 0.0001 & 0.0013 & 0.0001 & 0.0176 & 0.0003 & 0.0013 & 0.0001 \\\\ \n & 1.25 & 0.0015 & 0.0001 & 0.0015 & 0.0001 & 0.0105 & 0.0003 & 0.0019 & 0.0001 \\\\ \n \\multirow{3}{*}{1000} & 0.75 & 0.0038 & 0.0001 & 0.0039 & 0.0001 & 0.0246 & 0.0004 & 0.0020 & 0.0001 \\\\ \n & 1.00 & 0.0008 & 0.0000 & 0.0007 & 0.0000 & 0.0193 & 0.0002 & 0.0007 & 0.0000 \\\\ \n & 1.25 & 0.0009 & 0.0000 & 0.0008 & 0.0000 & 0.0112 & 0.0002 & 0.0010 & 0.0001 \\\\ \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Dynamic treatment regime characterization via value function surrogate with an application to partial compliance", "authors": ["Nikki L. B. Freeman", "Sydney E. Browder", "Katharine L. McGinigle", "Michael R. Kosorok"], "url": "https://arxiv.org/abs/2212.00650v1", "attribution": "\"Dynamic treatment regime characterization via value function surrogate with an application to partial compliance\" by Nikki L. B. Freeman, Sydney E. Browder, Katharine L. McGinigle, and Michael R. Kosorok, arXiv:2212.00650v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.14499v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\nMethod & Metric & 50D & 100D & 150D & 200D \\\\ \\hline\\hline\n\\multirow{3}{*}{PINN } & Speed & 19.82it/s & 6.56it/s & 3.50it/s & N.A.\\\\ \\cline{2-6} \n & Memory & 6199MB & 21453MB & 44631MB & $>$80GB \\\\ \\cline{2-6} \n & Error & 2.34E-2$\\pm$1.91E-2 & 6.01E-3$\\pm$2.32E-3 & 3.11E-3$\\pm$1.79E-3 & N.A. \\\\ \\hline\\hline\n \\multirow{3}{*}{HTE ($V=16$)} & Speed & 438.79it/s\t&109.57it/s\t&54.86it/s&\t25.89it/s\n\\\\ \\cline{2-6} \n& Memory & 991MB\t&2085MB\t&3589MB&\t7225MB\n\\\\ \\cline{2-6} \n & Error & 2.36E-2$\\pm$1.97E-2 & 6.83E-3$\\pm$1.63E-3 & 3.91E-3$\\pm$1.47E-3 & 4.40E-3$\\pm$5.65E-3\\\\ \\hline\\hline\n\\multirow{3}{*}{HTE ($V=512$)} & Speed & 123.98it/s & 54.07it/s& 42.44it/s & 23.50it/s\\\\ \\cline{2-6} \n& Memory & 1861MB & 2089MB & 3593MB & 7229MB\\\\ \\cline{2-6} \n & Error & 2.34E-2$\\pm$1.96E-2 & 6.08E-3$\\pm$2.28E-3 & 3.39E-3$\\pm$1.99E-3 & 4.03E-3$\\pm$4.85E-3\\\\ \\hline\\hline\n \\multirow{3}{*}{HTE ($V=1024$)} & Speed &78.50it/s\t&34.72it/s&\t33.77it/s&\t20.36it/s\\\\ \\cline{2-6} \n& Memory & 2535MB&\t3037MB\t&3595MB&\t7231MB\n\\\\ \\cline{2-6} \n & Error & 2.34E-2$\\pm$1.96E-2 & 6.01E-3$\\pm$2.35E-3 & 3.13E-3$\\pm$1.82E-3 & 3.97E-3$\\pm$4.91E-3\\\\ \\hline\n\\end{tabular}\n\\caption{Computational results for the Biharmonic equation. In all-dimensional biharmonic PDEs, HTE consistently achieves results similar to those of full PINN while significantly outpacing full PINN in terms of speed and notably reducing memory consumption. In the case of 200 dimensions, where full PINN exceeds memory limits, HTE still converges rapidly.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks", "authors": ["Zheyuan Hu", "Zekun Shi", "George Em Karniadakis", "Kenji Kawaguchi"], "url": "https://arxiv.org/abs/2312.14499v2", "attribution": "\"Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks\" by Zheyuan Hu, Zekun Shi, George Em Karniadakis, and Kenji Kawaguchi, arXiv:2312.14499v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.14614v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Values of the calculated errors for pricing results for ETH, rounded to three significant figures.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc} \n\t\t\n\t\t\\hline \n\t\t& RMSE& MAE &MAPE&MSLE \\\\ \n\t\t\\hline \n\t\tBS & 64.4 &48.2 & 0.105 & 0.0413\\\\ \n Heston & 46.7 & 29.1 & 0.0682 & 0.0139 \\\\\n Kou &30.8 & 18.2 & 0.0412 & 0.00534\\\\\n MJD &25.9& 16.6 & 0.0423 & 0.00662 \\\\\n SVJ &39.1 &15.1 & 0.019 & 0.00284 \\\\\n\t\tVG & 48.1 & 29 & 0.0662 & 0.0128 \\\\\n \\hline\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Pricing options on the cryptocurrency futures contracts", "authors": ["Julia Kończal"], "url": "https://arxiv.org/abs/2506.14614v1", "attribution": "\"Pricing options on the cryptocurrency futures contracts\" by Julia Kończal, arXiv:2506.14614v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.01473v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Comparison of Performances (Mean$\\pm$Standard Deviation) on Multi-Class Semantic Segmentation Task of Dataset-3}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\n\\multirow{2}{*}{\\textbf{Network}} & \\multicolumn{5}{c|}{\\textbf{Dataset-3}} \\\\ \\cline{2-6} \n & \\textbf{Sen.(\\%)} & \\textbf{Spec.(\\%)} & \\textbf{Dice(\\%)} & \\textbf{IoU(\\%)} & \\textbf{p-Value} \\\\ \\hline\n\\textbf{Unet~} & 56.9$\\pm$0.19 & 68.6$\\pm$0.23 & 42.2$\\pm$0.35 & 37.7$\\pm$0.28 & - \\\\ \\hline\n\\textbf{Unet++~} & 57.3$\\pm$0.25 & 70.4$\\pm$0.31 & 44.8$\\pm$0.29 & 40.1$\\pm$0.33 & - \\\\ \\hline\n\\textbf{Attention-Unet-2D~} & 58.7$\\pm$0.22 & 71.8$\\pm$0.29 & 48.5+0.42 & 43.9$\\pm$0.25 & - \\\\ \\hline\n\\textbf{CPF-Net~} & 61.5$\\pm$0.28 & 73.1$\\pm$0.17 & 51.4$\\pm$0.38 & 47.7$\\pm$0.34 & - \\\\ \\hline\n\\textbf{Semi-Inf-Net~} & 64.9$\\pm$0.31 & 76.3$\\pm$0.27 & 50.9$\\pm$0.27 & 46.4$\\pm$0.26 & - \\\\ \\hline\n\\textbf{CovSegNet(ours)} & \\textbf{76.4$\\pm$0.18} & \\textbf{87.7$\\pm$0.16} & \\textbf{64.6$\\pm$0.21} & \\textbf{59.5$\\pm$0.29} & \\textbf{6e-5} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans", "authors": ["Tanvir Mahmud", "Md Awsafur Rahman", "Shaikh Anowarul Fattah", "Sun-Yuan Kung"], "url": "https://arxiv.org/abs/2012.01473v1", "attribution": "\"CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans\" by Tanvir Mahmud, Md Awsafur Rahman, Shaikh Anowarul Fattah, and Sun-Yuan Kung, arXiv:2012.01473v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12826v4_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison of MobileNet-V1 on ImageNet.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\nMethod & Sparsity & Params & FLOPs & Top-1 Acc. \\\\ \\midrule\nMobileNet-V1 & 0.00 & 4.12M & 569M & 71.95 \\\\\n\\midrule \nGMP & 74.11 & 1.09M & 163M & 67.70 \\\\\nSTR & 75.28 & 1.04M & 101M & 68.35 \\\\\nSTR & 79.07 & 0.88M & 81M & 66.52 \\\\\n\\rowcolor[gray]{0.9} OptG & 80.00 & 0.82M & 124M & \\textbf{70.27} \\\\\n\\midrule \nGMP & 89.03 & 0.46M & 82M & 61.80 \\\\\nSTR & 85.80 & 0.60M & 55M & 64.83 \\\\\nSTR & 89.01 & 0.46M & 42M & 62.10 \\\\\nSTR & 89.62 & 0.44M & 40M & 61.51 \\\\\n\\rowcolor[gray]{0.9} OptG & 90.00 & 0.41M & 80M & \\textbf{66.80} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "OptG: Optimizing Gradient-driven Criteria in Network Sparsity", "authors": ["Yuxin Zhang", "Mingbao Lin", "Mengzhao Chen", "Fei Chao", "Rongrong Ji"], "url": "https://arxiv.org/abs/2201.12826v4", "attribution": "\"OptG: Optimizing Gradient-driven Criteria in Network Sparsity\" by Yuxin Zhang, Mingbao Lin, Mengzhao Chen, Fei Chao, and Rongrong Ji, arXiv:2201.12826v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11272v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n \\hline\n \\textbf{Field} & \\textbf{Value}\\\\\n \\hline\n \\hline\n File format & PDF \\\\\n \\hline\n Page limit & 11 pages, {\\bf not }\\\\\n & {\\bf including references}\\\\\n \\hline\n Paper size & US Letter 8.5in $\\times$ 11in\\\\\n \\hline\n Top margin & 1in\\\\\n \\hline\n Bottom margin & 1in\\\\\n \\hline\n Left margin & 0.75in\\\\\n \\hline\n Right margin & 0.75in\\\\\n \\hline\n Body & 2-column, single-spaced\\\\\n \\hline\n Separation b/t columns & 0.25in\\\\\n \\hline\n Body font & 10pt\\\\\n \\hline\n Abstract font & 10pt, italicized\\\\\n \\hline\n Section heading font & 12pt, bold\\\\\n \\hline\n Subsection heading font & 10pt, bold\\\\\n \\hline\n Caption font & 9pt, bold\\\\\n \\hline\n References & 8pt, no page limit, list \\\\\n & all authors' names\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Formatting guidelines for submission. }\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "WhisperFlow: speech foundation models in real time", "authors": ["Rongxiang Wang", "Zhiming Xu", "Felix Xiaozhu Lin"], "url": "https://arxiv.org/abs/2412.11272v2", "attribution": "\"WhisperFlow: speech foundation models in real time\" by Rongxiang Wang, Zhiming Xu, and Felix Xiaozhu Lin, arXiv:2412.11272v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11343v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c|c|c}\n System (Spec.) & Approach & $|Q|$ & $|A|$ & $N$ & $N_\\text{cluster}$ & $e_{avg}$ & Abstr. Time & Synth. Time \\\\\n \\midrule\n Pendulum ($\\varphi_1$) & UMDP & $10^4$ & $5$ & $5\\times 10^3$ & $47$ & $0.552$ & $1.796$ & $3.643$ \\\\\n & UMDP & $10^4$ & $5$ & $10^4$ & $44$ & $0.007$ & $1.857$ & $6.679$ \\\\\n & UMDP & $10^4$ & $5$ & $10^5$ & $47$ & $0.003$ & $1.797$ & $2.515$ \\\\\n & & $4\\times10^4$ & $5$ & $10^{6}$ & $49$ & $0$ & $5.273$ & $61.167$ \\\\\n \\midrule\n Pendulum ($\\varphi_1$) & UMDP & $1.225\\times 10^5$ & $5$ & $5\\times10^4$ & $320$ & $0.136$ & $138.705$ & $187.653$ \\\\\n (Torque-Limited)& UMDP & $6.25\\times 10^4$ & $5$ & $10^5$ & $175$ & $0.082$ & $40.602$ & $70.773$ \\\\\n & UMDP & $4\\times 10^4$ & $5$ & $10^5$ & $172$ & $0.279$ & $25.751$ & $42.321$ \\\\\n & UMDP & $4\\times 10^4$ & $5$ & $10^6$ & $179$ & $0.058$ & $26.694$ & $39.667$ \\\\\n & UMDP & $4\\times 10^4$ & $5$ & $10^7$ & $191$ & $0.033$ & $27.852$ & $27.066$ \\\\\n & IMDP (Learn Support) & $4\\times 10^4$ & $5$ & $10^7$ & $191$ & $0.172$ & $14.474$ & $20.281$ \\\\\n \\midrule\n $3D$ Unicycle ($\\varphi_1$) & UMDP & $5.932\\times10^4$ & $10$ & $10^{4}$ & $235$ & $0.579$ & $34.689$ & $33.156$ \\\\\n & UMDP & $5.932\\times10^4$ & $10$ & $10^{5}$ & $325$ & $0.267$ & $45.610$ & $36.18$ \\\\\n & UMDP & $5.932\\times10^4$ & $10$ & $10^{6}$ & $358$ & $0.156$ & $52.733$ & $33.751$ \\\\\n & & $6.4\\times10^4$ & $10$ & $5\\times10^{8}$ & $8869$ & $0.447$ & $457.431$ & $43.342$\\\\\n \\midrule\n $3D$ Unicycle ($\\varphi_1$) & UMDP & $7.401\\times10^4$ & $10$ & $10^5$ & $269$ & $0.404$ & $48.057$ & $75.164$ \\\\\n (difficult) & UMDP & $5.932\\times10^4$ & $10$ & $10^6$ & $589$ & $0.312$ & $87.753$ & $56.221$ \\\\\n & UMDP & $7.401\\times10^4$ & $10$ & $10^6$ & $295$ & $0.249$ & $53.371$ & $69.565$ \\\\\n & IMDP (Learn Support) & $7.401\\times10^4$ & $10$ & $10^6$ & $295$ & $0.6923$ & $25.109$ & $38.680$ \\\\\n & Na\\\"ive IMDP & $5.932\\times10^4$ & $10$ & $10^7$ & $340$ & $0.870$ & $45.865$ & $3.68$ \\\\\n & UMDP & $5.932\\times10^4$ & $10$ & $10^7$ & $357$ & $0.199$ & $54.505$ & $47.365$ \\\\\n & UMDP & $7.401\\times10^4$ & $10$ & $10^7$ & $345$ & $0.1848$ & $62.510$ & $63.437$ \\\\\n \\midrule\n Multiplicative & UMDP & $3.6\\times10^{3}$ & $1$ & $4.652\\times10^{3}$ & $231$ & $0.316$ & $0.294$ & $0.302$ \\\\\n noise ($\\varphi_1$) & IMDP (Learn Support) & $3.6\\times10^{3}$ & $1$ & $4.371\\times10^{3}$ & $235$ & $0.437$ & $0.412$ & $0.086$ \\\\\n & UMDP & $3.6\\times10^{3}$ & $1$ & $4.68\\times10^{4}$ & $257$ & $0.251$ & $0.339$ & $0.034$ \\\\\n & UMDP & $9.6\\times10^{3}$ & $1$ & $4.66\\times10^{5}$ & $279$ & $0.223$ & $1.150$ & $0.146$ \\\\\n & IMDP & $9.6\\times10^{3}$ & $1$ & $4.66\\times10^{5}$ & $276$ & $0.307$ & $0.228$ & $0.034$ \\\\\n & & $10^{4}$ & $1$ & $4.66\\times10^{5}$ & $1066$ & $0.323$ & $13.149$ & $3.863$ \\\\\n \\midrule\n $2D$ Unicycle ($\\varphi_2$) & UMDP & $3.6\\times10^3$ & $8$& $10^{3}$ & $32$ & $0.288$ & $0.302$ & $3.304$\\\\\n & IMDP (Learn Support) & $3.6\\times10^3$ & $8$& $5\\times10^{3}$ & $31$ & $0.387$ & $0.291$ & $1.174$\\\\\n & UMDP & $3.6\\times10^3$ & $8$& $5\\times10^{3}$ & $34$ & $0.095$ & $0.318$ & $2.818$\\\\\n & UMDP & $3.6\\times10^3$ & $8$& $10^{4}$ & $37$ & $0.062$ & $0.341$ & $3.103$\\\\\n & UMDP & $3.6\\times10^3$ & $8$& $10^{5}$ & $41$ & $0.017$ & $0.375$ & $3.075$\\\\\n & UMDP & $3.6\\times10^3$ & $8$& $10^{6}$ & $43$ & $0.003$ & $0.390$ & $2.767$\\\\\n & UMDP & $3.6\\times10^3$ & $8$& $10^{7}$ & $45$ & $0.001$ & $0.408$ & $2.667$\\\\\n & & $3.6\\times10^3$ & $8$ & $10^{7}$ & $46$ & $0.03$ & $0.106$ & $3.043$\\\\\n \\midrule\n 4-Room Heating ($\\varphi_3$) & UMDP & $2.074\\times 10^4$ & $16$ & $5\\times 10^4$ & $620$ & $0.086$ & $188.506$ & $6.296$\\\\\n & UMDP & $2.074\\times 10^4$ & $16$ & $10^6$ & $818$ & $0.044$ & $282.854$ & $7.156$\\\\\n & IMDP (Learn Support) & $2.074\\times 10^4$ & $16$ & $10^6$ & $818$ & $0.069$ & $204.963$ & $3.296$\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Temporal Logic Control for Nonlinear Stochastic Systems Under Unknown Disturbances", "authors": ["Ibon Gracia", "Luca Laurenti", "Manuel Mazo", "Alessandro Abate", "Morteza Lahijanian"], "url": "https://arxiv.org/abs/2412.11343v3", "attribution": "\"Temporal Logic Control for Nonlinear Stochastic Systems Under Unknown Disturbances\" by Ibon Gracia, Luca Laurenti, Manuel Mazo, Alessandro Abate, and Morteza Lahijanian, arXiv:2412.11343v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11391v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n &\\textbf{Inter-Sentential} & \\textbf{Intra-Sentential} \\\\\n\\hline\nEnglish-Sanskrit & 2,356 & 2,338 \\\\\nSanskrit-English & 2366 & 851 \\\\\nEnglish-Bengali & 339 & 124 \\\\\nBengali-English & 339 & 0 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Code-Mixing type for our dataset}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages", "authors": ["Jivnesh Sandhan", "Ayush Daksh", "Om Adideva Paranjay", "Laxmidhar Behera", "Pawan Goyal"], "url": "https://arxiv.org/abs/2201.11391v2", "attribution": "\"Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages\" by Jivnesh Sandhan, Ayush Daksh, Om Adideva Paranjay, Laxmidhar Behera, and Pawan Goyal, arXiv:2201.11391v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13714v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cll|}\n\\hline\n\\textbf{Case} & \\textbf{Conditions} & \\textbf{Finite singular points} \\\\\n\\hline\n\\hline\n1& $c_3^2>4c_0c_2$, $c_1\\mu\\neq0$. & $P_0$, $P_1$, $P_2$, $P_4$. \\\\\t\\hline\n2 & $c_3^2>4c_0c_2$, $c_1\\mu=0$, $a_0\\neq0$. & $P_0$, $P_1$, $P_2$. \\\\\n\\hline\n3 & $c_3^2=4c_0c_2$, $c_1\\mu\\neq0$. & $P_0$, $P_3$, $P_4$. \\\\\n\\hline\n4 & $c_3^2=4c_0c_2$, $c_1\\mu=0$, $a_0\\neq0$. & $P_0$, $P_3$. \\\\\n\\hline\n5 & $c_3^2<4c_0c_2$, $c_1\\mu\\neq0$. & $P_0$, $P_4$. \\\\\n\\hline\n6 & $c_3^2<4c_0c_2$, $c_1\\mu=0$, $a_0\\neq0$. & $P_0$. \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The different cases for the finite singular points.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Phase portraits of a family of Kolmogorov systems depending on six parameters", "authors": ["Érika Diz-Pita", "Jaume Llibre", "M. Victoria Otero-Espinar"], "url": "https://arxiv.org/abs/2501.13714v1", "attribution": "\"Phase portraits of a family of Kolmogorov systems depending on six parameters\" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.13714v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.13337v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Effect of Learning Rate Sweep on Benchmark Scores. Cells highlighted in green indicate better scores.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Benchmark}} & \\multirow{2}{*}{\\textbf{Granite Base Pretrained}} & \\multicolumn{4}{c}{\\textbf{Learning Rates}} \\\\ \\cmidrule(lr){3-6}\n & & \\textbf{2e-5} & \\textbf{4e-5} & \\textbf{8e-5} & \\textbf{1e-4} \\\\ \\midrule\n\\textbf{MMLU} & 0.48 & \\cellcolor{green!30}0.52 & \\cellcolor{green!30}0.52 & \\cellcolor{green!30}0.52 & \\cellcolor{green!30}0.52 \\\\\n\\textbf{MTBench} & - & \\cellcolor{green!30}6.76 & 6.64 & 6.53 & 6.47 \\\\\n\\textbf{Leaderboard (BBH)} & 0.09 & \\cellcolor{green!30}0.10 & 0.09 & 0.09 & 0.08 \\\\\n\\textbf{Leaderboard (MuSR)} & 0.01 & \\cellcolor{green!30}0.08 & 0.07 & \\cellcolor{green!30}0.08 & 0.06 \\\\\n\\textbf{ARC} & 0.78 & 0.74 & \\cellcolor{green!30}0.75 & \\cellcolor{green!30}0.75 & 0.73 \\\\\n\\textbf{GSM8K} & 0.11 & \\cellcolor{green!30}0.38 & 0.36 & 0.37 & 0.30 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs", "authors": ["Aldo Pareja", "Nikhil Shivakumar Nayak", "Hao Wang", "Krishnateja Killamsetty", "Shivchander Sudalairaj", "Wenlong Zhao", "Seungwook Han", "Abhishek Bhandwaldar", "Guangxuan Xu", "Kai Xu", "Ligong Han", "Luke Inglis", "Akash Srivastava"], "url": "https://arxiv.org/abs/2412.13337v1", "attribution": "\"Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs\" by Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang, Krishnateja Killamsetty, Shivchander Sudalairaj, Wenlong Zhao, Seungwook Han, Abhishek Bhandwaldar, Guangxuan Xu, Kai Xu, Ligong Han, Luke Inglis, and Akash Srivastava, arXiv:2412.13337v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.12125v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experts classification accuracy.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c|}\n\\hline\nDataset & Average & Best expert & Voting\\\\\n\\hline\n\\hline\n3 classes & 60.88 & 62.67 & 62.00\\\\\n\\hline\n0 vs 0.1 & 88.17 & 90.50 & 89.00\\\\\n\\hline\n0 vs 1 & 89.34 & 93.00 & 90.50\\\\\n\\hline\n0.1 vs 1 & 50.17 & 52.00 & 51.00\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks", "authors": ["Aleksei Shpilman", "Dmitry Boikiy", "Marina Polyakova", "Daniel Kudenko", "Anton Burakov", "Elena Nadezhdina"], "url": "https://arxiv.org/abs/2012.12125v1", "attribution": "\"Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks\" by Aleksei Shpilman, Dmitry Boikiy, Marina Polyakova, Daniel Kudenko, Anton Burakov, and Elena Nadezhdina, arXiv:2012.12125v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.09361v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{xcolor}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\toprule\n & \\multicolumn{3}{c }{\\textbf{ MDID }}&\n \\multicolumn{3}{c }{\\textbf{ CSIQ }}\\\\% & \n\\cline{2-7}\n & \\textbf{PLCC} & \\textbf{SROCC} & \\textbf{KROCC}& \\textbf{PLCC} & \\textbf{SROCC} & \\textbf{KROCC} \\\\\n\\hline\nSSIM &\\colorbox{green!30}{0.581}&\\colorbox{green!30}{0.576}&\\colorbox{green!30}{0.411}&\\colorbox{green!30}{0.812}&\\colorbox{green!30}{0.812}&\\colorbox{green!30}{0.606} \\\\\nMS-SSIM &\\colorbox{green!30}{0.836}&\\colorbox{green!30}{0.841}&\\colorbox{green!30}{0.654}&\\colorbox{green!30}{0.913}&\\colorbox{green!30}{0.917}&\\colorbox{green!30}{0.743} \\\\\nMAD &\\colorbox{green!30}{0.742} &\\colorbox{green!30}{0.725} &\\colorbox{green!30}{0.533}&\\colorbox{green!30}{0.950}&\\colorbox{green!30}{0.947}&\\colorbox{green!30}{0.796} \\\\\nGSM &\\colorbox{green!30}{0.825} &\\colorbox{green!30}{0.827} &\\colorbox{green!30}{0.636}&\\colorbox{green!30}{0.906}&\\colorbox{green!30}{0.910}&\\colorbox{green!30}{0.729} \\\\\nHaarPSI &\\colorbox{green!30}{0.904}&\\colorbox{green!30}{0.903}&\\colorbox{green!30}{0.734}&\\colorbox{green!30}{0.946}&\\colorbox{green!30}{0.960}&\\colorbox{green!30}{0.823}\\\\\nMDSI &\\colorbox{green!30}{0.829}&\\colorbox{green!30}{0.836}&\\colorbox{green!30}{0.653}&\\colorbox{green!30}{0.953}&\\colorbox{green!30}{0.957}&\\colorbox{green!30}{0.812} \\\\\nCSV &\\colorbox{green!30}{0.879}&\\colorbox{green!30}{0.881}&\\colorbox{green!30}{0.700}&\\colorbox{green!30}{0.933}&\\colorbox{green!30}{0.933}&\\colorbox{green!30}{0.766}\\\\\nGMSD &\\colorbox{green!30}{0.864}&\\colorbox{green!30}{0.862}&\\colorbox{green!30}{0.680}&\\colorbox{green!30}{0.954}&\\colorbox{green!30}{0.957}&\\colorbox{green!30}{0.812}\\\\\nDSS &\\colorbox{green!30}{0.870}&\\colorbox{green!30}{0.866}&\\colorbox{green!30}{0.679}&\\colorbox{green!30}{0.953}&\\colorbox{green!30}{0.955}&\\colorbox{green!30}{0.811} \\\\\nVSI &\\colorbox{green!30}{0.855}&\\colorbox{green!30}{0.857}&\\colorbox{green!30}{0.671}&\\colorbox{green!30}{0.928}&\\colorbox{green!30}{0.942}&\\colorbox{green!30}{0.785}\\\\\nPerSIM &\\colorbox{green!30}{0.823}&\\colorbox{green!30}{0.820}&\\colorbox{green!30}{0.630}&\\colorbox{green!30}{0.924}&\\colorbox{green!30}{0.929}&\\colorbox{green!30}{0.768}\\\\\nBLeSS-SR-SIM &\\colorbox{green!30}{0.805}&\\colorbox{green!30}{0.815}&\\colorbox{green!30}{0.626}&\\colorbox{green!30}{0.892}&\\colorbox{green!30}{0.893}&\\colorbox{green!30}{0.718}\\\\\nBLeSS-FSIM &\\colorbox{green!30}{0.848}&\\colorbox{green!30}{0.847}&\\colorbox{green!30}{0.658}&\\colorbox{green!30}{0.882}&\\colorbox{green!30}{0.885}&\\colorbox{green!30}{0.701}\\\\\nBLeSS-FSIMc &\\colorbox{green!30}{0.878}&\\colorbox{green!30}{0.883}&\\colorbox{green!30}{0.702}&\\colorbox{green!30}{0.913}&\\colorbox{green!30}{0.917}&\\colorbox{green!30}{0.743}\\\\\nLCSIM1 &-&-&-&\\colorbox{green!30}{0.897}&\\colorbox{green!30}{0.949}&\\colorbox{green!30}{0.799}\\\\\nReSIFT &\\colorbox{green!30}{0.905}&\\colorbox{green!30}{0.895}&\\colorbox{green!30}{0.716}&\\colorbox{green!30}{0.884}&\\colorbox{green!30}{0.868}&\\colorbox{green!30}{0.695}\\\\\nIQ($\\mathcal{L}_T$) &\\colorbox{green!30}{0.867}&\\colorbox{green!30}{0.865}&\\colorbox{green!30}{0.708} & \\colorbox{green!30}{0.915} & \\colorbox{green!30}{0.912} & \\colorbox{green!30}{0.720} \\\\\nMS-UNIQUE &\\colorbox{green!30}{0.863}&\\colorbox{green!30}{0.871}&\\colorbox{green!30}{0.689}&\\colorbox{green!30}{0.918}&\\colorbox{green!30}{0.929}&\\colorbox{green!30}{0.759}\\\\\nSSIM CNN &\\colorbox{green!30}{0.904}&\\colorbox{green!30}{0.907}&\\colorbox{green!30}{0.732}&\\colorbox{green!30}{0.952}&\\colorbox{green!30}{0.946}&\\colorbox{green!30}{0.794}\\\\\nRVSIM &\\colorbox{green!30}{0.884}&\\colorbox{green!30}{0.884}&\\colorbox{green!30}{0.709}&\\colorbox{green!30}{0.923}&\\colorbox{green!30}{0.903}&\\colorbox{green!30}{0.728}\\\\\n2stepQA & \\colorbox{green!30}{0.753}& \\colorbox{green!30}{0.759}& \\colorbox{green!30}{0.562}&\\colorbox{green!30}{0.841}&\\colorbox{green!30}{0.849}&\\colorbox{green!30}{0.655}\\\\\nSUMMER &\\colorbox{green!30}{0.742}&\\colorbox{green!30}{0.734}&\\colorbox{green!30}{0.543}&\\colorbox{green!30}{0.826}&\\colorbox{green!30}{0.830}&\\colorbox{green!30}{0.658}\\\\\nCEQI &\\colorbox{green!30}{0.863}&\\colorbox{green!30}{0.864}&\\colorbox{green!30}{0.685}&\\colorbox{green!30}{0.956}&\\colorbox{green!30}{0.956}&\\colorbox{green!30}{0.814} \\\\\nCEQIc &\\colorbox{green!30}{0.864}&\\colorbox{green!30}{0.863}&\\colorbox{green!30}{0.684}&\\colorbox{green!30}{0.956}&\\colorbox{green!30}{0.955}&\\colorbox{green!30}{0.810}\\\\\nVCGS &\\colorbox{green!30}{0.867}&\\colorbox{green!30}{0.869}&\\colorbox{green!30}{0.687}&\\colorbox{green!30}{0.931}&\\colorbox{green!30}{0.944}&\\colorbox{green!30}{0.790}\\\\\nDISTS &\\colorbox{green!30}{0.862}&\\colorbox{green!30}{0.860}&\\colorbox{green!30}{0.669}&\\colorbox{green!30}{0.930}&\\colorbox{green!30}{0.930}&\\colorbox{green!30}{0.764}\\\\\n\\hline\nDeepFL-IQA &- &- &- &0.946 &0.930 &- \\\\\nBLINDER &- &- &- &0.968 &0.961 &- \\\\\nRankIQA &- &- &- &0.960 &0.947 &- \\\\\nBPSOM-MD &- &- &- &0.860 &0.904 &- \\\\\nNSSADNN &- &- &- &0.927 &0.893 &- \\\\\n\\midrule\n\\textit{ ActMapFeat}(ours)&\\textbf{0.930}&\\textbf{0.927}&\\textbf{0.769}&\\textbf{0.971}&\\textbf{0.970}&\\textbf{0.850}\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A combined full-reference image quality assessment approach based on convolutional activation maps", "authors": ["Domonkos Varga"], "url": "https://arxiv.org/abs/2010.09361v3", "attribution": "\"A combined full-reference image quality assessment approach based on convolutional activation maps\" by Domonkos Varga, arXiv:2010.09361v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01121v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of a Softmax network under adversarial (FGSM-based) training for the CIFAR-10 dataset. Block size is $U=2$, wherever applicable.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\t\t\t\t\\toprule \n\t\t\t\t\\addlinespace\n\t\t\t\tMethod & Benign & PGD & CW & BSA & RAND \\tabularnewline\n\t\t\t\t\\midrule \n\t\t\t\t\\addlinespace\n\t\t\t\tBaseline (ReLU) & .864 & .070 & .080 & .040 & .404 \\tabularnewline\n\t\t\t\tLWTA & .867 & .804 & .820 & \\textbf{.870} & \\textbf{.701} \\tabularnewline\n\t\t\t\tLWTA \\& IBP & \\textbf{.869} & \\textbf{.814} & \\textbf{.860} & \\textbf{.870} & .652\n\t\t\t\t\\tabularnewline\n\t\t\t\t\\midrule \n\t\t\t\tBaseline (ReLU) + FGSM & .780 & .200 & .380 & .340 & .185 \\tabularnewline\n\t\t\t\tLWTA + FGSM & .820 & .812 & .810 & \\textbf{.870} & .440 \\tabularnewline\n\t\t\t\tLWTA \\& IBP + FGSM & \\textbf{.830} & \\textbf{.825} & \\textbf{.870} & \\textbf{.870} & \\textbf{.790} \\tabularnewline\n\t\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Local Competition and Stochasticity for Adversarial Robustness in Deep Learning", "authors": ["Konstantinos P. Panousis", "Sotirios Chatzis", "Antonios Alexos", "Sergios Theodoridis"], "url": "https://arxiv.org/abs/2101.01121v2", "attribution": "\"Local Competition and Stochasticity for Adversarial Robustness in Deep Learning\" by Konstantinos P. Panousis, Sotirios Chatzis, Antonios Alexos, and Sergios Theodoridis, arXiv:2101.01121v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.14431v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\\hline\n\\hline\n\\textbf{Sector} & \\textbf{Unidirectional Cause} & \\textbf{Unidirectional Effect} & \\textbf{Bidirectional Causality} \\\\\n\\hline\n\\textbf{Pre-crash period} & & & \\\\\n\\hline\nConsumer Discretionary & 1 & 0 & 0 \\\\\nConsumer Staples & 1 & 1 & 1 \\\\\nEnergy & 0 & 3 & 0 \\\\\nFinancials & 2 & 0 & 0 \\\\\nHealthcare & 0 & 3 & 1 \\\\\nIndustrials & 2 & 1 & 0 \\\\\nIT & 1 & 1 & 0 \\\\\nMaterials & 0 & 2 & 1 \\\\\nReal Estate & 3 & 0 & 0 \\\\\nUtilities & 1 & 0 & 0 \\\\\nCommunications Services & 1 & 1 & 1 \\\\\n\\hline\n\\textbf{Crash period} & & & \\\\\n\\hline\nConsumer Discretionary & 6 & 1 & 3 \\\\\nConsumer Staples & 2 & 0 & 8 \\\\\nEnergy & 0 & 4 & 5 \\\\\nFinancials & 1 & 4 & 5 \\\\\nHealthcare & 5 & 2 & 1 \\\\\nIndustrials & 2 & 2 & 5 \\\\\nIT & 2 & 2 & 5 \\\\\nMaterials & 1 & 2 & 7 \\\\\nReal Estate & 3 & 1 & 5 \\\\\nUtilities & 0 & 3 & 6 \\\\\nCommunications Services & 2 & 3 & 2 \\\\\n\\hline\n\\textbf{Post-crash period} & & & \\\\\n\\hline\nConsumer Discretionary & 1 & 2 & 0 \\\\\nConsumer Staples & 0 & 2 & 1 \\\\\nEnergy & 3 & 0 & 1 \\\\\nFinancials & 1 & 4 & 1 \\\\\nHealthcare & 0 & 3 & 4 \\\\\nIndustrials & 1 & 3 & 3 \\\\\nIT & 2 & 2 & 1 \\\\\nMaterials & 4 & 0 & 2 \\\\\nReal Estate & 2 & 2 & 1 \\\\\nUtilities & 4 & 0 & 1 \\\\\nCommunications Services & 1 & 1 & 1 \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective", "authors": ["Buddha Nath Sharma", "Anish Rai", "SR Luwang", "Md. Nurujjaman", "Sushovan Majhi"], "url": "https://arxiv.org/abs/2502.14431v1", "attribution": "\"Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective\" by Buddha Nath Sharma, Anish Rai, SR Luwang, Md. Nurujjaman, and Sushovan Majhi, arXiv:2502.14431v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.10787v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results on the COVID-19 prediction}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nModel & Accuracy & F1 \\\\ \\midrule\nEnd-to-End & $\\textbf{0.985} \\pm \\textbf{0.007}$ & \\textbf{0.95} \\\\\nNS-RF & $0.973 \\pm 0.009$ & 0.915 \\\\\nNS-XGB & $0.973 \\pm 0.009$ & 0.930 \\\\\nNS-DTree & $0.973 \\pm 0.009$ & 0.915 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays", "authors": ["Rishab Khincha", "Soundarya Krishnan", "Tirtharaj Dash", "Lovekesh Vig", "Ashwin Srinivasan"], "url": "https://arxiv.org/abs/2012.10787v2", "attribution": "\"Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays\" by Rishab Khincha, Soundarya Krishnan, Tirtharaj Dash, Lovekesh Vig, and Ashwin Srinivasan, arXiv:2012.10787v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01842v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sample table title}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\toprule\n \\multicolumn{2}{c}{Part} \\\\\n \\cmidrule(r){1-2}\n Name & Description & Size ($\\mu$m) \\\\\n \\midrule\n Dendrite & Input terminal & $\\sim$100 \\\\\n Axon & Output terminal & $\\sim$10 \\\\\n Soma & Cell body & up to $10^6$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer", "authors": ["Jiawen Zhang", "Shun Zheng", "Xumeng Wen", "Xiaofang Zhou", "Jiang Bian", "Jia Li"], "url": "https://arxiv.org/abs/2411.01842v1", "attribution": "\"ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer\" by Jiawen Zhang, Shun Zheng, Xumeng Wen, Xiaofang Zhou, Jiang Bian, and Jia Li, arXiv:2411.01842v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.15274v3_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll|cc|cc|cc|cc|cc||cc}\n\\toprule\n\\multicolumn{3}{l|}{} & \\multicolumn{2}{c|}{Age} & \\multicolumn{2}{c|}{Gender} & \\multicolumn{2}{c|}{Site} & \\multicolumn{2}{c|}{Depression} & \\multicolumn{2}{c||}{Axis 1 } & \\multicolumn{2}{c}{Overall} \\\\\n& Train $\\downarrow$ & Test $\\rightarrow$ & ERP & SMPL & ERP & SMPL & ERP & SMPL & ERP & SMPL & ERP & SMPL & ERP & SMPL \\\\\n\\midrule\n\\multicolumn{3}{l|}{Chance} & \\multicolumn{2}{c|}{0.50} &\\multicolumn{2}{c|}{0.50} &\\multicolumn{2}{c|}{0.25} &\\multicolumn{2}{c|}{0.50} & \\multicolumn{2}{c||}{0.50} & \\multicolumn{2}{c}{0.45} \\\\\n\\midrule\nLR & ERP & \\multirow{2}{*}{$\\beta$-VAE} & 0.85 & 0.62 & 0.66 & 0.53 & 0.51 & 0.34 & 0.68 & 0.59 & 0.60 & 0.55 & 0.66 & \\textbf{0.53} \\\\\nLR & SMPL & & 0.76 & 0.65 & 0.58 & 0.53 & 0.40 & \\textbf{0.36} & 0.67 & 0.58 & 0.56 & 0.56 & 0.59 & \\textbf{0.54} \\\\\n\\midrule\nRF & ERP & \\multirow{2}{*}{$\\beta$-VAE} & 0.75 & \\textbf{0.56} & 0.75 & 0.56 & 0.45 & 0.29 & 0.47 & 0.47 & 0.66 & 0.54 & 0.62 & 0.48 \\\\\nRF & SMPL & & 0.78 & \\textbf{0.72} & 0.65 & 0.55 & 0.43 & 0.29 & 0.70 & 0.47 & 0.65 & \\textbf{0.66} & 0.64 & \\textbf{0.54} \\\\\n\\midrule\nLDA & ERP & \\multirow{2}{*}{$\\beta$-VAE} & 0.86 & 0.63 & 0.66 & 0.54 & 0.46 & 0.31 & 0.68 & \\textbf{0.63} & 0.58 & 0.54 & 0.65 & \\textbf{0.53} \\\\\nLDA & SMPL & & 0.82 & 0.61 & 0.63 & 0.54 & 0.50 & 0.31 & 0.69 & 0.52 & 0.56 & 0.53 & 0.64 & 0.50 \\\\\n\\midrule\nSVM & ERP & \\multirow{2}{*}{$\\beta$-VAE} & \\textbf{0.93} & 0.61 & \\textbf{0.78} & 0.54 & \\textbf{0.71} & 0.33 & \\textbf{0.83} & 0.59 & \\textbf{0.74} & 0.57 & \\textbf{0.80} & \\textbf{0.53} \\\\\nSVM & SMPL & & 0.88 & 0.64 & 0.71 & \\textbf{0.56} & 0.60 & 0.34 & 0.80 & 0.57 & 0.69 & 0.53 & 0.74 & \\textbf{0.53} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Balanced classification accuracy calculated as the average of precision and recall. ERP - averaged trajectories, SMPL - single sampled EEG trajectories. LPP - canonical late positive potential baseline representation. LR - L2 regularised logistic regression, RF - random forest, LDA - linear discriminant analysis, SVM - support vector machine. Models trained on single EEG trajectories (SMPL) are tested on different single EEG trajectories in the SMPL/SMPL train/test case. Average balanced classification accuracy across all conditions: LR: 0.58, RF: 0.57, LDA: 0.58, SVM: 0.65}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Representation learning for improved interpretability and classification accuracy of clinical factors from EEG", "authors": ["Garrett Honke", "Irina Higgins", "Nina Thigpen", "Vladimir Miskovic", "Katie Link", "Sunny Duan", "Pramod Gupta", "Julia Klawohn", "Greg Hajcak"], "url": "https://arxiv.org/abs/2010.15274v3", "attribution": "\"Representation learning for improved interpretability and classification accuracy of clinical factors from EEG\" by Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Sunny Duan, Pramod Gupta, Julia Klawohn, and Greg Hajcak, arXiv:2010.15274v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2401.05424v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The mean duration with the standard error (1.96 $\\times$ standard deviation) taken for a function execution of a single event of a single learner in the PEEKC dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\hline\n Function & TrueLearn Model & Execution Time (s) \\\\\n \\hline\n & Interest & 0.002016 $\\pm$ 0.00478\n \\\\\n \\texttt{fit()} & Novelty & 0.002232 $\\pm$ 0.00468 \\\\\n & INK & 0.005000 $\\pm$ 0.00712 \\\\\n \\hline\n & Interest & 0.000084 $\\pm$ 0.000020 \\\\\n \\texttt{predict\\_proba()} & Novelty & 0.000189 $\\pm$ 0.000018 \\\\\n & INK & 0.000330 $\\pm$ 0.000025 \\\\\n \\hline\n & Interest & 0.000093 $\\pm$ 0.000014 \\\\\n \\texttt{predict()} & Novelty & 0.000203 $\\pm$ 0.000027 \\\\\n & INK & 0.000335 $\\pm$ 0.000029 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Toolbox for Modelling Engagement with Educational Videos", "authors": ["Yuxiang Qiu", "Karim Djemili", "Denis Elezi", "Aaneel Shalman", "María Pérez-Ortiz", "Emine Yilmaz", "John Shawe-Taylor", "Sahan Bulathwela"], "url": "https://arxiv.org/abs/2401.05424v1", "attribution": "\"A Toolbox for Modelling Engagement with Educational Videos\" by Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor, and Sahan Bulathwela, arXiv:2401.05424v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08740v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sample table title}\n\\begin{tabular}{lll}\n \\toprule\n \\multicolumn{2}{c}{Part} \\\\\n \\cmidrule(r){1-2}\n Name & Description & Size ($\\mu$m) \\\\\n \\midrule\n Dendrite & Input terminal & $\\sim$100 \\\\\n Axon & Output terminal & $\\sim$10 \\\\\n Soma & Cell body & up to $10^6$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Model-based Policy Search for Partially Measurable Systems", "authors": ["Fabio Amadio", "Alberto Dalla Libera", "Ruggero Carli", "Daniel Nikovski", "Diego Romeres"], "url": "https://arxiv.org/abs/2101.08740v1", "attribution": "\"Model-based Policy Search for Partially Measurable Systems\" by Fabio Amadio, Alberto Dalla Libera, Ruggero Carli, Daniel Nikovski, and Diego Romeres, arXiv:2101.08740v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07434v5_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c} \n\t\t\\toprule[1pt]\n\t\tMethods&OS& MF &Aux& mIOU (\\%) & FLOPs\\\\ \n\t\t\\midrule[0.5pt]\n\t\t\\midrule[0.5pt]\n\t\tResNet& 16 & & - & - & 59.85G \\\\\n\t\t-101& 8 & & - & - & 190.70G \\\\\n\t\t\\midrule\n\t\tDANet& 8 & & & & +101.25G\\\\ \n\t\t & 8 & \\checkmark & \\checkmark &52.60 & - \\\\ \n\t\t\\midrule\n\t\tAxial & 16 & & & 50.27($\\pm$0.2)& +8.85G\\\\ \n\t\tAttention & 16 & \\checkmark & & 52.01($\\pm$0.2) & -\\\\ \n\t\t & 8 & & & 51.24($\\pm$0.2) & +34.33G\\\\ \n\t\t & 8 & \\checkmark & &52.51($\\pm$0.2) & - \\\\ \n\t\t\\midrule\n\t\tOur & 16 & & & 51.06($\\pm$0.2)& +8.85G\\\\\n\t\tCAA & 16 & \\checkmark & &53.09($\\pm$0.3) & - \\\\\n\t\t & 8 & & & 52.73($\\pm$0.1) & +34.33G\\\\ \n\t\t & 8 & \\checkmark & & 54.05($\\pm$0.1) & - \\\\ \n\t\t\\midrule\n\t\tOur & 16 & & \\checkmark & 51.80($\\pm$0.2)& +8.85G\\\\\n\t\t CAA & 16 &\\checkmark& \\checkmark & 53.52($\\pm$0.2)& - \\\\\n\t\t + & 8 & & \\checkmark& 53.48($\\pm$0.3) & +34.33G\\\\\n\t\t Aux loss & 8 & \\checkmark &\\checkmark &54.65($\\pm$0.4) & - \\\\\n\t\t\\bottomrule[1pt]\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation", "authors": ["Ye Huang", "Di Kang", "Wenjing Jia", "Xiangjian He", "Liu Liu"], "url": "https://arxiv.org/abs/2101.07434v5", "attribution": "\"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation\" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.17988v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Three groups of operators by stage of expertise}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr} \\toprule\n Group & Years of Experience & Number of Operators & Number of Entries\\\\ \\midrule\n (1) Novice operators & $\\leq 1$ & 54 & 390 \\\\\n (2) Intermediate operators & $1-4$ & 54 & 876 \\\\\n (3) Expert operators & $\\geq 4$ & 26 & 822 \\\\ \\midrule\n All operators & $0 - 14$ & 67 & 2088 \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Network Models of Expertise in the Complex Task of Operating Particle Accelerators", "authors": ["Roussel Rahman", "Jane Shtalenkova", "Aashwin Ananda Mishra", "Wan-Lin Hu"], "url": "https://arxiv.org/abs/2412.17988v1", "attribution": "\"Network Models of Expertise in the Complex Task of Operating Particle Accelerators\" by Roussel Rahman, Jane Shtalenkova, Aashwin Ananda Mishra, and Wan-Lin Hu, arXiv:2412.17988v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.11907v5_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\t\\multicolumn{6}{|c|}{Numerical values for $\\log_{10}\\det(\\Psi_{n})$ for several values of $n$}\\\\\n\t\t\t\\hline\n\t\t\t$n$ &\\hspace*{0.3cm}2\\hspace*{0.3cm} &\\hspace*{0.3cm}3\\hspace*{0.3cm} &\\hspace*{0.3cm}4\\hspace*{0.3cm} &\\hspace*{0.3cm}5\\hspace*{0.3cm} &\\hspace*{0.3cm}6\\hspace*{0.3cm} \\\\\n\t\t\t\\hline $\\log_{10}\\det(\\Psi_{n})$&\\hspace*{0.3cm}-4.97\\hspace*{0.3cm} &\\hspace*{0.3cm}-13.4\\hspace*{0.3cm} &\\hspace*{0.3cm}-33.9\\hspace*{0.3cm} &\\hspace*{0.3cm}-82.03\\hspace*{0.3cm} &\\hspace*{0.3cm}-192.81\\hspace*{0.3cm} \\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Numerical values for $\\log_{10}\\det(\\Psi_{n})$ for several values of $n$. Already for small values of $n$, the determinant of $\\Psi_{n}$ is extremely small, which makes the problem ill-posed from a numerical point of view.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Robust Faber--Schauder approximation based on discrete observations of an antiderivative", "authors": ["Xiyue Han", "Alexander Schied"], "url": "https://arxiv.org/abs/2211.11907v5", "attribution": "\"Robust Faber--Schauder approximation based on discrete observations of an antiderivative\" by Xiyue Han and Alexander Schied, arXiv:2211.11907v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.09028v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{An Example of a Table}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|}\n\\hline\nOne & Two\\\\\n\\hline\nThree & Four\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning and Current Prediction of PMSM Drive via Differential Neural Networks", "authors": ["Wenjie Mei", "Xiaorui Wang", "Yanrong Lu", "Ke Yu", "Shihua Li"], "url": "https://arxiv.org/abs/2412.09028v1", "attribution": "\"Learning and Current Prediction of PMSM Drive via Differential Neural Networks\" by Wenjie Mei, Xiaorui Wang, Yanrong Lu, Ke Yu, and Shihua Li, arXiv:2412.09028v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.13915v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The MSD, MAD, and ABWD for DJI stock index dataset. The best bootstrapping technique with the least MSD, MAD, and ABWD is marked in bold. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\t\t\t\\hline\n\t\t\t& Bootstrap Methods & MSD & MAD & ABWD\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{3}{*}{Open Price}& \n\t\t\t$\\quad$NOBB$\\quad$ & $423577.78$ & $521.95$ & $102842.9$ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $122350.40$ & $269.28$ & $70829.4$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{69056.29}$ & $\\textbf{199.93}$ & $\\textbf{62479.8}$ \\\\\n\t\t\t\n\t\t\t\\hline \\multirow{3}{*}{High Price} & $\\quad$NOBB$\\quad$ & $400258.80$ & $511.40$ & $102246.5$ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $142436.70$ & $294.90$ & $77182.3$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{99061.31}$ & $\\textbf{244.62}$ & $\\textbf{65184.8}$ \\\\\n\t\t\t\n\t\t\t\\hline\\multirow{3}{*}{Low Price}& \n\t\t\t$\\quad$NOBB$\\quad$ & $520594.69$ & $580.24$ & $108897.8 $ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $199595.09$ & $341.32$ & $87625.0$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{136009.59}$ & $\\textbf{301.05}$ & $\\textbf{67656.0}$ \\\\\n\t\t\t\n\t\t\t\\hline \\multirow{3}{*}{Closing Price} & $\\quad$NOBB$\\quad$ & $562463.66$ & $579.19$ & $104487.9$ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $283476.60$ & $431.77$ & $70167.3$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{222248.98}$ & $\\textbf{381.74}$ & $\\textbf{64935.7}$ \\\\\n\t\t\t\n\t\t\t\\hline \\multirow{3}{*}{Volume}\n\t\t\t& $\\quad$NOBB$\\quad$ & $8.56e+15\\quad$ & $6.63e+07\\quad$ & $7.99e+09\\quad$ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $6.62e+15\\quad$ & $5.58e+07\\quad$ & $7.47e+09\\quad$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{6.27e+15}\\quad$ & $\\textbf{5.52e+07}\\quad$ & $\\textbf{6.95e+09}\\quad$ \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network", "authors": ["Aryan Bhambu", "Arabin Kumar Dey"], "url": "https://arxiv.org/abs/2211.13915v1", "attribution": "\"Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network\" by Aryan Bhambu and Arabin Kumar Dey, arXiv:2211.13915v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19262v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative results of the baselines, the optimized version of the supervised CNN, and the proposed algorithm}\n\\begin{tabular}{cccc}\n\\hline\nMethod & MAE (mm) & MAE (mm) \\\\ \n & All Samples & NLOS \\\\ \\hline\nUncorrected UWB & 162 & 194 \\\\\nSupervised CNN & 124 & 153 \\\\\nOptimized Supervised CNN & 112 & 137 \\\\\n\\textbf{Proposed RL Algorithm} & 112 & 128 \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning", "authors": ["Dieter Coppens", "Ben Van Herbruggen", "Adnan Shahid", "Eli De Poorter"], "url": "https://arxiv.org/abs/2403.19262v2", "attribution": "\"Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning\" by Dieter Coppens, Ben Van Herbruggen, Adnan Shahid, and Eli De Poorter, arXiv:2403.19262v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13448v4_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n \\multirow{2}{*}{Parameter} & Agent & Tutorial & Co-play \\\\\n & training & episodes & episodes \\\\\n \\midrule\n Number of & \\multirow{2}{*}{$2$} & \\multirow{2}{*}{$1$} & \\multirow{2}{*}{$2$*} \\\\\n players ($n$) & & & \\\\\n Room width ($w$) & $\\sim\\mathcal{U}\\{10, 15\\}$ & $5$ & $11$ \\\\\n Room depth ($d$) & $\\sim\\mathcal{U}\\{10, 15\\}$ & $7$ & $11$ \\\\\n Coin spawn & \\multirow{2}{*}{$0.0005$} & \\multirow{2}{*}{$0.0015$} & \\multirow{2}{*}{$0.0005$} \\\\\n probability ($P$) & & & \\\\\n Episode length ($T$) & $500$ & $1500$** & $300$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Parameters for the Coins task in agent training and human-agent cooperation studies. *In Study 3, co-play episodes involve either ${n = 1}$ or ${n = 2}$ players, depending on whether the participant chose to play alone or with the co-player from the prior episode, respectively. **Tutorial episodes terminated after participants collected five coins or after ${T = 1500}$ steps (5 minutes).}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Warmth and competence in human-agent cooperation", "authors": ["Kevin R. McKee", "Xuechunzi Bai", "Susan T. Fiske"], "url": "https://arxiv.org/abs/2201.13448v4", "attribution": "\"Warmth and competence in human-agent cooperation\" by Kevin R. McKee, Xuechunzi Bai, and Susan T. Fiske, arXiv:2201.13448v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11843v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The results of MAP comparison on NUS-WIDE and MIRFlickr.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|}\n\\hline\nDatasets & Methods & I2T & T2I & Average \\\\\n\\hline\n\\multirow{6}{*}{NUS-WIDE} & CCA & 0.3099 & 0.3103 & 0.3101 \\\\\n & KCCA & 0.3088 & 0.3174 & 0.3096 \\\\\n & ml-CCA & 0.2787 & 0.2801 & 0.2794 \\\\\n & KDM & 0.3247 & 0.3118 & 0.3183 \\\\\n & CKD & 0.4149 & 0.4211 & 0.4180 \\\\\n & DS²L & \\textbf{0.4488} & \\textbf{0.4514} & \\textbf{0.4501} \\\\\n\\hline\n\\multirow{6}{*}{MIRFlickr} & CCA & 0.5466 & 0.5477 & 0.5472 \\\\\n & KCCA & 0.5521 & 0.5529 & 0.5525 \\\\\n & ml-CCA & 0.5309 & 0.5302 & 0.5306 \\\\\n & KDM & 0.5951 & 0.5823 & 0.5887 \\\\\n & CKD & 0.6103 & 0.5933 & 0.6018 \\\\\n & DS²L & \\textbf{0.6288} & \\textbf{0.6093} & \\textbf{0.6191} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Discriminative Supervised Subspace Learning for Cross-modal Retrieval", "authors": ["Haoming Zhang", "Xiao-Jun Wu", "Tianyang Xu", "Donglin Zhang"], "url": "https://arxiv.org/abs/2201.11843v1", "attribution": "\"Discriminative Supervised Subspace Learning for Cross-modal Retrieval\" by Haoming Zhang, Xiao-Jun Wu, Tianyang Xu, and Donglin Zhang, arXiv:2201.11843v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.10812v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance and time comparisons of all methods}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|}\n\\hline\nAlgorithm & Training & Test & Test \\\\\n& time (min) & sum-rate & time (msec) \\\\\n\\hline \\hline\nWMMSE~ & - & 82.94 & 16 \\\\ \nTr-WMMSE & - & 76.49 & 1.0 \\\\\nMLP~ & 0.5 & 78.17 & 3.2 \\\\\nREGNN~ & 15 & 57.92 & 2.5 \\\\\nIGCNet~ & 5 & 55.30 & 3.0 \\\\\nUWMMSE & 15 & \\textbf{83.21} & \\textbf{2.0} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation", "authors": ["Arindam Chowdhury", "Gunjan Verma", "Chirag Rao", "Ananthram Swami", "Santiago Segarra"], "url": "https://arxiv.org/abs/2009.10812v4", "attribution": "\"Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation\" by Arindam Chowdhury, Gunjan Verma, Chirag Rao, Ananthram Swami, and Santiago Segarra, arXiv:2009.10812v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.06657v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Despeckling performance measures}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rlrr|rr|rr}\n \\toprule\n & & \\multicolumn{2}{c|}{L = 3} & \\multicolumn{2}{c|}{L = 5} & \\multicolumn{2}{c}{L = 7} \\\\\n & & PSNR & S/MSE & PSNR & S/MSE & PSNR & S/MSE \\\\\n \\toprule\n Image-1 & Noisy & 22.363 & 4.785 & 24.484 & 6.995 & 25.920 & 8.474 \\\\\n & $L_1$ & 22.652 & 5.245 & 22.977 & 5.571 & 23.136 & 5.730 \\\\\n & $TV$ & \\textbf{25.461} & \\textbf{8.056} & 25.834 & 8.429 & 25.990 & 8.586 \\\\\n & Cauchy & 25.204 & 7.799 & \\textbf{26.089} & \\textbf{8.685} & \\textbf{26.569} & \\textbf{9.166} \\\\\n \\midrule\n Image-2 & Noisy & 25.126 & 4.792 & 27.274 & 6.987 & 28.712 & 8.460 \\\\\n & $L_1$ & 25.409 & 5.184 & 25.726 & 5.501 & 25.881 & 5.656 \\\\\n & $TV$ & \\textbf{28.106} & \\textbf{7.884} & 28.471 & 8.249 & 28.624 & 8.403 \\\\\n & Cauchy & 27.800 & 7.577 & \\textbf{28.646} & \\textbf{8.424} & \\textbf{29.106} & \\textbf{8.885} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface", "authors": ["Oktay Karakuş", "Igor Rizaev", "Alin Achim"], "url": "https://arxiv.org/abs/2012.06657v1", "attribution": "\"A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface\" by Oktay Karakuş, Igor Rizaev, and Alin Achim, arXiv:2012.06657v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.05224v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|c|c|c|c}\n \\hline\n & \\multicolumn{2}{c|}{Kinetics 400~}\n & \\multicolumn{2}{c}{Something V1~}\\\\ \n Models & Top-1 & Top-5 & Top-1 & Top-5 \\\\ \n \\hline \n TSN (R-50)~ & 70.6\\% & 89.2\\% & 20.5\\% & 47.5\\% \\\\\n 2-Stream I3D~ & 71.6\\% & 90.0\\% & 41.6\\% & 72.2\\% \\\\\n TSM (R-50)~ & 74.1\\% & 91.2\\% & 47.3\\% & 76.2\\% \\\\\n TPN (TSM)~ & 78.9\\% & 93.9\\% & 50.2\\% & 75.8\\% \\\\\n \\hline\n \\hline Skeleton-based\n & \\multicolumn{2}{c|}{Kinetics 400~}\n & \\multicolumn{2}{c}{NTU-RGB+D~}\\\\ \n Models & Top-1 & Top-5 & X-Sub & X-View \\\\ \n \\hline\n Deep LSTM~ & 16.4\\% & 35.3\\% & 62.9\\% & 70.3\\% \\\\\n ST-GCN~ & 30.7\\% & 52.8\\% & 81.5\\% & 88.3\\% \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "HAA500: Human-Centric Atomic Action Dataset with Curated Videos", "authors": ["Jihoon Chung", "Cheng-hsin Wuu", "Hsuan-ru Yang", "Yu-Wing Tai", "Chi-Keung Tang"], "url": "https://arxiv.org/abs/2009.05224v2", "attribution": "\"HAA500: Human-Centric Atomic Action Dataset with Curated Videos\" by Jihoon Chung, Cheng-hsin Wuu, Hsuan-ru Yang, Yu-Wing Tai, and Chi-Keung Tang, arXiv:2009.05224v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09863v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Values of Simulation hyperparameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n \\cmidrule(r){1-2}\n parameter name & value \\\\\n \\midrule\n point mass & $1.0$\\\\\n viscous drag & $0.1$ \\\\\n gravity & $110$\\\\\n \\cmidrule(r){1-2}\n contact stiffness coefficient & $2.1 \\cdot 10^7$\\\\\n collision penetration depth additive & $5 \\cdot 10^{-3}$\\\\\n coefficient of friction & $0.1$\\\\\n frictional penalty factor & $0.5$\\\\\n friction multiplier & $2.4 \\cdot 10^3$\\\\\n \\cmidrule(r){1-2} \n rigid main spring const & $\\frac{3 \\cdot 10^8}{3.5}$\\\\\n rigid structural spring const & $\\frac{3 \\cdot 10^8}{7}$\\\\\n soft main spring const & $\\frac{3 \\cdot 10^8}{5}$\\\\\n soft structural spring const & $\\frac{3 \\cdot 10^8}{10}$\\\\\n actuator main spring const & $\\frac{3 \\cdot 10^8}{6}$\\\\\n actuator structural spring const & $\\frac{3 \\cdot 10^8}{24}$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots", "authors": ["Jagdeep Singh Bhatia", "Holly Jackson", "Yunsheng Tian", "Jie Xu", "Wojciech Matusik"], "url": "https://arxiv.org/abs/2201.09863v1", "attribution": "\"Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots\" by Jagdeep Singh Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, and Wojciech Matusik, arXiv:2201.09863v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.15584v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n\\toprule\n\\textbf{Model} & \\textbf{\\#Param.} & \\textbf{FLD+} \\\\ \\midrule\nStable Diffusion v1.5~ & 0.9 B & $3.3277 \\pm 0.0005$\\\\ \nStable Diffusion XL~ & 3.5 B & $3.2167 \\pm 0.0002$ \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{When comparing the performance of text-to-image diffusion models, FLD+ effectively distinguishes the images generated by the larger Stable Diffusion XL model as higher quality than those produced by the smaller Stable Diffusion V1.5 model (mean $\\pm$ std error).}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "FLD+: Data-efficient Evaluation Metric for Generative Models", "authors": ["Pranav Jeevan", "Neeraj Nixon", "Amit Sethi"], "url": "https://arxiv.org/abs/2411.15584v1", "attribution": "\"FLD+: Data-efficient Evaluation Metric for Generative Models\" by Pranav Jeevan, Neeraj Nixon, and Amit Sethi, arXiv:2411.15584v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13822v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Error profile for computing on Figure meshes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|rr|rr} \n \\hline \n$G_i$ & % \\multicolumn{2}{c|}{$\\|\\b u-\\b u_h\\|_{0}$ $O(h^r)$} & \n $\\|\\b u-\\b u_h\\|_{0}$&$O(h^r)$ & $\\|\\nabla_w(\\b u-\\b u_h)\\|_0$ & $O(h^r)$ \\\\ \\hline \n & \\multicolumn{4}{c}{By the $P_1$ WG element} \\\\ \n 5& 0.791E-02 & 2.8& 0.121E+01 & 1.8 \\\\\n 6& 0.135E-02 & 2.6& 0.415E+00 & 1.5 \\\\\n 7& 0.283E-03 & 2.3& 0.176E+00 & 1.2 \\\\\n \\hline \n & \\multicolumn{4}{c}{By the $P_2$ WG element} \\\\ \n 4& 0.233E-01 & 4.2& 0.315E+01 & 2.9 \\\\\n 5& 0.143E-02 & 4.0& 0.401E+00 & 3.0 \\\\\n 6& 0.905E-04 & 4.0& 0.509E-01 & 3.0 \\\\ \n \\hline \n & \\multicolumn{4}{c}{By the $P_3$ WG element} \\\\ \n 4& 0.577E-02 & 5.1& 0.128E+01 & 4.0 \\\\\n 5& 0.179E-03 & 5.0& 0.805E-01 & 4.0 \\\\\n 6& 0.557E-05 & 5.0& 0.504E-02 & 4.0 \\\\ \n \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An Auto-Stabilized Weak Galerkin Method for Elasticity Interface Problems on Nonconvex Meshes", "authors": ["Chunmei Wang", "Shangyou Zhang"], "url": "https://arxiv.org/abs/2501.13822v1", "attribution": "\"An Auto-Stabilized Weak Galerkin Method for Elasticity Interface Problems on Nonconvex Meshes\" by Chunmei Wang and Shangyou Zhang, arXiv:2501.13822v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.13237v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative comparison between our model and the model by Ozcelik et al. in terms of reconstruction (after the full pipeline) performance on the test set, in various configurations, according to the type of architecture used for the fMRI-VDVAE latent variables regression and the number of the VDVAE latent variables used. These statistics were computed over subject \\#1 only.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|cccc|cccc}\n \\toprule\n \\multicolumn{2}{c}{\\textbf{Architecture}} & \\multicolumn{4}{c}{\\textbf{Low-level metrics}} & \\multicolumn{4}{c}{\\textbf{High-level metrics (eq. )}}\\\\ \n \\midrule\n & Latents & SSIM$\\uparrow$ & PixCorr$\\uparrow$ & AlexNet(2)$\\uparrow$ & AlexNet(5)$\\uparrow$ & InceptionV3$\\uparrow$ & CLIP$\\uparrow$ & EfficientNet-B$\\downarrow$ & SwAV-ResNet50$\\downarrow$ \\\\\n \\midrule\n Ridge & 15 & 0.358 & 0.286 & 0.935 & 0.962 & 0.881 & 0.925 & 0.772 & \\textbf{0.413} \\\\\n Ridge & 31 & 0.367 & \\textbf{0.305} & \\textbf{0.967} & \\textbf{0.974} & 0.878 & 0.925 & \\textbf{0.768} & 0.415 \\\\\n Ours (CLIP) & 15 & 0.357 & 0.230 & 0.870 & 0.922 & 0.860 & 0.916 & 0.778 & 0.437 \\\\\n Ours & 15 & 0.370 & 0.242 & 0.904 & 0.942 & 0.868 & 0.924 & 0.779 & 0.421 \\\\\n Ours & 31 & \\textbf{0.374} & 0.207 & 0.880 & 0.929 & \\textbf{0.890} & \\textbf{0.958} & \\textbf{0.768} & 0.416 \\\\\n \\midrule\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Optimized two-stage AI-based Neural Decoding for Enhanced Visual Stimulus Reconstruction from fMRI Data", "authors": ["Lorenzo Veronese", "Andrea Moglia", "Luca Mainardi", "Pietro Cerveri"], "url": "https://arxiv.org/abs/2412.13237v1", "attribution": "\"Optimized two-stage AI-based Neural Decoding for Enhanced Visual Stimulus Reconstruction from fMRI Data\" by Lorenzo Veronese, Andrea Moglia, Luca Mainardi, and Pietro Cerveri, arXiv:2412.13237v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.08222v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CES Share Parameters and Hicks-Neutral Productivity Innovation Standard Deviation Estimates}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccc}\n \\toprule\n & $\\alpha_L$ & $\\alpha_M$ & $\\alpha_K$ & $\\alpha_B$ & $\\alpha_H$ & $\\alpha_D$ & $\\alpha_C$ & $\\sigma_H$\\\\ \n \\midrule\n Estimate & 0.064 & 0.680 & 0.256 & 0.551 & 0.449 & 0.693 & 0.307 & 0.725\\\\ \n SE & (0.026) & (0.281) & (0.307) & (-) & (-) & (-) & (-) & (0.121)\\\\ \n \\bottomrule\n \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Do Temporary Workers Face Higher Wage Markdowns? Evidence from India's Automotive Sector", "authors": ["Davide Luparello"], "url": "https://arxiv.org/abs/2507.08222v1", "attribution": "\"Do Temporary Workers Face Higher Wage Markdowns? Evidence from India's Automotive Sector\" by Davide Luparello, arXiv:2507.08222v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.13629v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Writing Rules: Within Paper Comparison with Controls}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccc}\n\\hline\\hline\n &(1)&(2)&(3)&(4)&(5)&(6)&(7)&(8)&(9)&(10)&(11)\\\\ &Rule1a&Rule1b&Rule2&Rule4&Rule5&Rule7a&Rule7b&Rule8&Rule9&Rule10a&Rule10b\\\\\n\\hline\nVersion1 & -46.52{***}& -1.293{***}& 4.050{***}& 4.829{***}& 1.618{***}& -5.566{***}& 8.503{***}& -4.953{***}& -2.608{***}& -1.080{***}& -0.921{***}\\\\\n & (0.0690) & (0.00268) & (0.0276) & (0.0161) & (0.00348) & (0.0348) & (0.0540) & (0.0314) & (0.0227) & (0.0164) & (0.0125) \\\\\nVersion2 & -45.14{***}& -1.349{***}& 1.419{***}& 3.848{***}& 1.435{***}& -5.330{***}& 5.296{***}& -5.490{***}& -2.780{***}& -1.198{***}& -0.961{***}\\\\\n & (0.0701) & (0.00271) & (0.0274) & (0.0170) & (0.00351) & (0.0367) & (0.0542) & (0.0325) & (0.0232) & (0.0169) & (0.0127) \\\\\nVersion3 & -47.03{***}& -1.469{***}& 0.269{***}& 1.222{***}& 1.349{***}& -6.205{***}& 6.344{***}& -5.639{***}& -2.818{***}& -1.229{***}& -0.968{***}\\\\\n & (0.0714) & (0.00277) & (0.0282) & (0.0184) & (0.00359) & (0.0374) & (0.0543) & (0.0325) & (0.0232) & (0.0170) & (0.0127) \\\\\nVersion4 & -47.28{***}& -1.459{***}& 0.841{***}& 3.534{***}& 1.427{***}& -5.995{***}& 8.111{***}& -5.521{***}& -2.778{***}& -1.206{***}& -0.964{***}\\\\\n & (0.0711) & (0.00276) & (0.0285) & (0.0174) & (0.00357) & (0.0373) & (0.0547) & (0.0324) & (0.0231) & (0.0171) & (0.0127) \\\\\nVersion5 & -44.62{***}& -1.389{***}& 0.432{***}& 3.235{***}& 1.435{***}& -5.562{***}& 7.905{***}& -5.124{***}& -2.588{***}& -1.127{***}& -0.906{***}\\\\\n & (0.0701) & (0.00271) & (0.0275) & (0.0167) & (0.00346) & (0.0360) & (0.0529) & (0.0315) & (0.0224) & (0.0165) & (0.0124) \\\\\nVersion6 & -20.93{***}& -0.635{***}& -2.364{***}& 4.153{***}& 0.857{***}& -2.628{***}& 10.21{***}& -4.918{***}& -2.587{***}& -0.930{***}& -0.876{***}\\\\\n & (0.0563) & (0.00236) & (0.0274) & (0.0163) & (0.00357) & (0.0334) & (0.0548) & (0.0313) & (0.0224) & (0.0160) & (0.0122) \\\\\nAfricans & -0.0628 & 0.0274 & 0.631{**} & 0.288 & -0.149{***}& -1.592{***}& 0.100 & 0.0219 & -0.00632 & -0.303 & -0.291{**} \\\\\n & (0.705) & (0.0239) & (0.232) & (0.153) & (0.0443) & (0.465) & (0.375) & (0.297) & (0.156) & (0.209) & (0.0989) \\\\\nBritish & 5.153{***}& 0.125{***}& -0.692{***}& 0.186{*} & 0.00529 & 0.296 & 1.183{***}& 0.705{***}& 0.388{***}& -0.0691 & -0.0567 \\\\\n & (0.343) & (0.0115) & (0.109) & (0.0729) & (0.0208) & (0.223) & (0.178) & (0.142) & (0.0768) & (0.101) & (0.0535) \\\\\nEastAsian & -0.0569 & 0.194{***}& 2.127{***}& 1.627{***}& 0.775{***}& 3.205{***}& 3.043{***}& -1.586{***}& -0.165{*} & -0.756{***}& -0.0341 \\\\\n & (0.308) & (0.0103) & (0.0969) & (0.0668) & (0.0188) & (0.201) & (0.166) & (0.124) & (0.0666) & (0.0940) & (0.0498) \\\\\nEastEuropean & -7.721{***}& -0.233{***}& 1.173{***}& -0.219{*} & 0.0719{*} & -1.278{***}& -2.483{***}& -0.230 & 0.0538 & -0.624{***}& -0.193{**} \\\\\n & (0.483) & (0.0159) & (0.156) & (0.105) & (0.0298) & (0.305) & (0.235) & (0.194) & (0.108) & (0.134) & (0.0716) \\\\\nIndian & 4.479{***}& 0.136{***}& 0.265{*} & 0.436{***}& -0.169{***}& -3.426{***}& -0.102 & 0.123 & -0.275{***}& 0.0495 & 0.0512 \\\\\n & (0.396) & (0.0133) & (0.124) & (0.0837) & (0.0236) & (0.252) & (0.211) & (0.163) & (0.0810) & (0.127) & (0.0665) \\\\\nJewish & 0.358 & 0.0468{**} & 0.786{***}& 0.203 & 0.0147 & -0.773{*} & -0.189 & 0.860{***}& 0.356{**} & -0.440{**} & -0.178{*} \\\\\n & (0.541) & (0.0182) & (0.174) & (0.115) & (0.0328) & (0.349) & (0.273) & (0.226) & (0.123) & (0.155) & (0.0818) \\\\\nMuslim & 4.032{***}& 0.152{***}& 0.570{***}& 0.0356 & -0.296{***}& -1.205{***}& 2.644{***}& -0.0272 & -0.183 & -0.968{***}& -0.0872 \\\\\n & (0.491) & (0.0169) & (0.161) & (0.107) & (0.0305) & (0.335) & (0.276) & (0.208) & (0.104) & (0.143) & (0.0774) \\\\\nBiology & 18.44{***}& 1.540{***}& 5.899{***}& -2.265{***}& 0.879{***}& 6.212{***}& 20.76{***}& -0.0878 & -0.866{***}& 2.498{***}& 0.232{**} \\\\\n & (0.656) & (0.0255) & (0.229) & (0.179) & (0.0431) & (0.478) & (0.437) & (0.278) & (0.142) & (0.225) & (0.0800) \\\\\nComputer Science& 21.93{***}& 1.725{***}& 5.877{***}& 1.683{***}& 0.0889{***}& 14.78{***}& 19.27{***}& 1.612{***}& -1.119{***}& 2.137{***}& 0.885{***}\\\\\n & (0.217) & (0.00721) & (0.0712) & (0.0460) & (0.0136) & (0.143) & (0.102) & (0.0917) & (0.0515) & (0.0495) & (0.0273) \\\\\nEconomics & 6.332{***}& 0.992{***}& 5.455{***}& 3.120{***}& -0.708{***}& 4.613{***}& 11.36{***}& 0.509 & -0.622{***}& 0.408{**} & -0.0209 \\\\\n & (0.709) & (0.0275) & (0.283) & (0.185) & (0.0515) & (0.585) & (0.496) & (0.361) & (0.185) & (0.151) & (0.0667) \\\\\nElectrical Eng. & 23.85{***}& 1.799{***}& 5.104{***}& -2.250{***}& 0.171{***}& 10.91{***}& 18.72{***}& 0.0548 & -1.549{***}& 3.367{***}& 1.726{***}\\\\\n & (0.335) & (0.0127) & (0.124) & (0.0781) & (0.0233) & (0.277) & (0.226) & (0.157) & (0.0709) & (0.138) & (0.0836) \\\\\nFinance & 7.212{***}& 1.009{***}& 4.774{***}& 1.293{***}& -1.052{***}& 5.788{***}& 12.55{***}& -0.103 & -0.760{***}& 0.941{***}& 0.563{***}\\\\\n & (0.890) & (0.0337) & (0.346) & (0.211) & (0.0597) & (0.727) & (0.618) & (0.418) & (0.223) & (0.243) & (0.158) \\\\\nPhysics & 31.77{***}& 1.440{***}& -1.056{***}& -2.548{***}& 0.611{***}& 2.356{***}& 15.10{***}& -0.461{***}& -0.662{***}& 3.662{***}& 1.110{***}\\\\\n & (0.225) & (0.00708) & (0.0685) & (0.0470) & (0.0134) & (0.131) & (0.0976) & (0.0872) & (0.0524) & (0.0560) & (0.0299) \\\\\nStatistics & 21.03{***}& 1.673{***}& 5.981{***}& -0.260{**} & 0.423{***}& 13.87{***}& 11.33{***}& 1.250{***}& -1.463{***}& 1.776{***}& 0.140{**} \\\\\n & (0.406) & (0.0154) & (0.148) & (0.0922) & (0.0275) & (0.347) & (0.253) & (0.202) & (0.0838) & (0.130) & (0.0479) \\\\\nFemale & 0.597 & -0.0244 & -1.529{***}& 0.385{***}& -0.209{***}& -3.212{***}& 0.535{*} & 0.542{**} & 0.463{***}& 0.470{***}& -0.0145 \\\\\n & (0.419) & (0.0142) & (0.131) & (0.0916) & (0.0253) & (0.270) & (0.224) & (0.173) & (0.0949) & (0.129) & (0.0670) \\\\\nMale & -7.949{***}& -0.205{***}& 0.0322 & 1.423{***}& 0.145{***}& -1.233{***}& -2.416{***}& -0.500{***}& 0.0445 & -0.186{*} & -0.126{**} \\\\\n & (0.254) & (0.00847) & (0.0780) & (0.0546) & (0.0151) & (0.160) & (0.134) & (0.101) & (0.0546) & (0.0779) & (0.0415) \\\\\nNo. of Papers& 3.692{***}& 0.0828{***}& -0.306{***}& -0.000276 & -0.00893 & 0.612{***}& 0.820{***}& 0.268{***}& 0.00977 & 0.101{***}& 0.144{***}\\\\\n & (0.0970) & (0.00318) & (0.0288) & (0.0199) & (0.00589) & (0.0629) & (0.0541) & (0.0383) & (0.0194) & (0.0292) & (0.0192) \\\\\nYoE & -1.131{***}& -0.110{***}& -0.476{***}& 0.226{***}& 0.0190{**} & -0.994{***}& -2.188{***}& 0.0809{*} & 0.193{***}& -0.177{***}& -0.00452 \\\\\n & (0.103) & (0.00337) & (0.0314) & (0.0217) & (0.00611) & (0.0620) & (0.0505) & (0.0390) & (0.0215) & (0.0280) & (0.0160) \\\\\nBaseline & 167.6{***}& 5.449{***}& 22.56{***}& 70.54{***}& 14.40{***}& 21.68{***}& 1.002{***}& 12.92{***}& 5.078{***}& 1.980{***}& 1.273{***}\\\\\n & (0.348) & (0.0114) & (0.107) & (0.0728) & (0.0205) & (0.211) & (0.166) & (0.137) & (0.0790) & (0.0915) & (0.0495) \\\\\n\\hline\nR2 & 0.113 & 0.144 & 0.0371 & 0.0332 & 0.0315 & 0.0325 & 0.0465 & 0.00665 & 0.00613 & 0.00548 & 0.00339 \\\\\nN & 4379544 & 4379544 & 4379544 & 4379544 & 4379544 & 4379544 & 4379544 & 4379544 & 4379544 & 4379544 & 4379544 \\\\\n\\hline\\hline\n\\textbf{Date FE} & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\\\\n\\textbf{Obs.} & 4,379,544 & 4,379,544 & 4,379,544 & 4,379,544 & 4,379,544 & 4,379,544 & 4,379,544 & 4,379,544 & 4,379,544 & 4,379,544 & 4,379,544 \\\\\n\\textbf{R-squared} & 0.110 & 0.142 & 0.037 & 0.033 & 0.032 & 0.032 & 0.046 & 0.007 & 0.006 & 0.004 & 0.003 \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing", "authors": ["Cong William Lin", "Wu Zhu"], "url": "https://arxiv.org/abs/2504.13629v1", "attribution": "\"Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing\" by Cong William Lin and Wu Zhu, arXiv:2504.13629v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.03879v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n No. of elements & Proposed Method & Nurkanovic\\\\\n \\hline\n 10 & 26 & 470 \\\\\n 20 & 56 & 759 \\\\\n 50 & 65 & 750 \\\\\n 100 & 303 & 1162\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Number of iterations in IPOPT}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On Optimal Control of Hybrid Dynamical Systems using Complementarity Constraints", "authors": ["Saif R. Kazi", "Kexin Wang", "Lorenz T. Biegler"], "url": "https://arxiv.org/abs/2503.03879v2", "attribution": "\"On Optimal Control of Hybrid Dynamical Systems using Complementarity Constraints\" by Saif R. Kazi, Kexin Wang, and Lorenz T. Biegler, arXiv:2503.03879v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09290v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{No ground truth vs 30\\% ground truths vs approximate ground truths}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\n\\hline\n & no gt &30\\% gt & appro gt \\\\ \\hline\nMovieLens & 0.701&\\textbf{0.757} & 0.681 \\\\ \\hline\nAmazon & 0.820 & \\textbf{0.850} & 0.823 \\\\ \\hline\nEchonest & 0.934&\\textbf{0.951} & 0.868 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Reinforcement Routing on Proximity Graph for Efficient Recommendation", "authors": ["Chao Feng", "Defu Lian", "Xiting Wang", "Zheng liu", "Xing Xie", "Enhong Chen"], "url": "https://arxiv.org/abs/2201.09290v1", "attribution": "\"Reinforcement Routing on Proximity Graph for Efficient Recommendation\" by Chao Feng, Defu Lian, Xiting Wang, Zheng liu, Xing Xie, and Enhong Chen, arXiv:2201.09290v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.11745v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of simulation parameters.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|} \n\t\t\\hline\n\t\t\\multicolumn{5}{|c|}{\\textbf{Parameters for Fig.~~,~, ~, ~, ~\\&~}} \\\\ \n\t\t\\hline\n\t\t$\\eta_t= \\frac{1}{\\sqrt{T}}$ & $\\mu_t = \\frac{1}{\\sqrt{T}}$ & $\\nu_t = \\frac{1}{\\sqrt{T}}$ &\n\t\t$\\iota = \\frac{1}{\\sqrt{T}}$ & $ \\theta^t = T$ \\\\ \n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching", "authors": ["Krishnendu S. Tharakan", "Hayssam Dahrouj", "Nour Kouzayha", "Hesham ElSawy", "Tareq Y. Al-Naffouri"], "url": "https://arxiv.org/abs/2501.11745v2", "attribution": "\"Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching\" by Krishnendu S. Tharakan, Hayssam Dahrouj, Nour Kouzayha, Hesham ElSawy, and Tareq Y. Al-Naffouri, arXiv:2501.11745v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.05764v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n \\toprule\n Model & T & Accuracy & F$_1$(0) & F$_1$(1) & Support 0 & Support 1 & Run time \\\\\n \\midrule\n XGBoost & 1 & 0.5732 & 0.5757 & 0.5707 & 73\\,953 & 74\\,387 & 1 m 36 s \\\\\n XGBoost & 10 & 0.5949 & 0.5989 & 0.5909 & 73\\,951 & 74\\,387 & 7 m 04 s \\\\\n Logistic Reg. & 1 & 0.5551 & 0.5442 & 0.5656 & 73\\,953 & 74\\,387 & 1 m 11 s \\\\\n Logistic Reg. & 10 & 0.5716 & 0.5654 & 0.5777 & 73\\,951 & 74\\,387 & 9 m 13 s \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of model performance and runtimes (SG filter) across sequence lengths (Run time: time to train the model on 1 million data points on M2 chip MacBook}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layer", "authors": ["Haochuan Wang"], "url": "https://arxiv.org/abs/2506.05764v2", "attribution": "\"Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layer\" by Haochuan Wang, arXiv:2506.05764v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.04452v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\hline\n \\textbf{Method} & \\textbf{Training time [s]} & \\textbf{Execution time [s]} \\\\\n \\hline\n \\textbf{NB} & \\phantom{00}0.004 & 0.008 \\\\\n \\textbf{KNN} & \\phantom{00}0.004 & 0.156 \\\\\n \\textbf{Lexicon} & \\phantom{00}0.246 & 0.102 \\\\\n \\textbf{LR} & \\phantom{00}0.255 & 0.004 \\\\\n \\textbf{SVM} & \\phantom{00}0.485 & 0.136 \\\\\n \\textbf{RF} & \\phantom{00}0.897 & 0.160 \\\\\n \\textbf{CNN} & 119\\phantom{.000} & 0.200 \\\\\n \\textbf{FCNN} & 133\\phantom{.000} & 0.319 \\\\\n \\textbf{LSTM} & 185\\phantom{.000} & 1.050 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Short text classification with machine learning in the social sciences: The case of climate change on Twitter", "authors": ["Karina Shyrokykh", "Maksym Girnyk", "Lisa Dellmuth"], "url": "https://arxiv.org/abs/2310.04452v1", "attribution": "\"Short text classification with machine learning in the social sciences: The case of climate change on Twitter\" by Karina Shyrokykh, Maksym Girnyk, and Lisa Dellmuth, arXiv:2310.04452v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.13023v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Searcher profitability summary sorted by total volume.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\\toprule\n\\textbf{Searcher} & \\textbf{Total} & \\textbf{Total} & \\textbf{Total} & \\textbf{Total} & \\textbf{Median} & \\textbf{Median} & \\textbf{Median} \\\\\n& \\textbf{Volume}& \\textbf{Est. Rev.} & \\textbf{Builder Tips} & \\textbf{Est. PnL} & \\textbf{Trade Rev.} & \\textbf{Trade PnL} & \\textbf{Profit Margin} \\\\\n& [USD] & [USD] & [USD] & [USD] & [USD] & [USD] & \\\\\n\\midrule\n\\midrule\nWintermute & 74.8B & 71.4M & 47.1M & 24.3M & 11.36 & 1.65 & 20.9\\% \\\\\nSCP & 63.5B & 71.1M & 57.4M & 13.7M & 7.34 & 0.86 & 18.5\\% \\\\\nKayle & 41.0B & 28.3M & 16.0M & 12.3M & 6.05 & 1.15 & 27.9\\% \\\\\nGalio & 25.9B & 9.9M & 4.5M & 5.4M & 24.93 & 7.10 & 31.7\\% \\\\\nShen & 12.3B & 18.1M & 6.3M & 11.8M & 8.42 & 3.77 & 53.2\\% \\\\\nTaric & 5.0B & 4.4M & 1.8M & 2.6M & 22.92 & 9.28 & 43.3\\% \\\\\nLucian & 3.9B & 10.9M & 3.9M & 7.0M & 14.74 & 5.83 & 45.8\\% \\\\\nRiven & 3.1B & 3.1M & 1.3M & 1.9M & 31.42 & 8.16 & 42.5\\% \\\\\nThresh & 2.7B & 3.2M & 1.0M & 2.2M & 34.12 & 13.65 & 59.7\\% \\\\\nAhri & 1.7B & 541.4K & 399.0K & 162.4K & 24.08 & 4.30 & 22.8\\% \\\\\nDarius & 1.1B & 497.0K & 331.3K & 165.6K & 29.62 & 6.62 & 27.9\\% \\\\\nKarma & 674.8M & 3.0M & 517.5K & 2.5M & 22.82 & 15.02 & 71.2\\% \\\\\nSenna & 596.3M & 503.9K & 268.1K & 235.8K & 5.22 & 1.06 & 27.2\\% \\\\\nMaokai & 588.4M & 4.4M & 1.1M & 3.3M & 52.40 & 35.13 & 69.7\\% \\\\\nZed & 580.2M & 3.1M & 1.1M & 2.1M & 13.99 & 4.83 & 41.8\\% \\\\\nGraves & 229.3M & 173.4K & 353.1K & -179.7K & 8.77 & -19.34 & -272.1\\% \\\\\nCaitlyn & 96.1M & 253.7K & 67.6K & 186.1K & 69.02 & 39.62 & 68.8\\% \\\\\nAkali & 49.4M & 378.6K & 100.5K & 278.1K & 22.20 & 12.37 & 59.4\\% \\\\\nPoppy & 35.5M & 371.7K & 171.0K & 200.7K & 25.00 & 13.43 & 55.6\\% \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest", "authors": ["Fei Wu", "Danning Sui", "Thomas Thiery", "Mallesh Pai"], "url": "https://arxiv.org/abs/2507.13023v3", "attribution": "\"Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest\" by Fei Wu, Danning Sui, Thomas Thiery, and Mallesh Pai, arXiv:2507.13023v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.15435v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multicolumn{4}{c}{$\\Delta\\ln(\\text{New Businesses Registered}_{it})$} \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\t& (1) & (2) & (3) \\\\\n\t\t\t\n\t\t\t\n\t\t\t$\\Delta\\ln(y_{it}) $ & 1.994*** & 2.417*** & 2.605*** \\\\\n\t\t\t\n\t\t\t& (0.319)&(0.391) & (0.394)\\\\\n\t\t\t\n\t\t\t\n\t\t\t$\\Delta\\ln(y_{it})*y_i$ & & -0.024* & -0.035** \\\\\n\t\t\t\n\t\t\t& & (0.013)& (0.014)\\\\\n\t\t\t\n\t\t\t$\\Delta\\ln(\\lambda_{it})$ & & & 0.226** \\\\\n\t\t\t\n\t\t\t& & &(0.104)\\\\\n\t\t\t\n\t\t\t$\\Delta\\ln(\\lambda_{it})*y_i$ & & & -0.012** \\\\\n\t\t\t\n\t\t\t& & &(0.005)\\\\\n\t\t\t\n\t\t\t\n\t\t\tCountry FE & Yes & Yes & Yes \\\\\n\t\t\t\n\t\t\tYear FE & Yes & Yes & Yes \\\\\n\t\t\t\n\t\t\tObservations & 810 & 810 & 810 \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Transitional Dynamics of the Saving Rate and Economic Growth", "authors": ["Markus Brueckner", "Tomoo Kikuchi", "George Vachadze"], "url": "https://arxiv.org/abs/2012.15435v3", "attribution": "\"Transitional Dynamics of the Saving Rate and Economic Growth\" by Markus Brueckner, Tomoo Kikuchi, and George Vachadze, arXiv:2012.15435v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09391v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n & Success \\% & Distance \\% \\\\\n \\hline\n Without Setup Policies & 1.9 & 36.3 \\\\\n With Setup Policies & \\textbf{71.2} & \\textbf{80.2} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Success rate and percentage of the total terrain length travelled from 1000 episodes of all 5 terrain types, randomly shuffled each episode.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Learning Setup Policies: Reliable Transition Between Locomotion Behaviours", "authors": ["Brendan Tidd", "Nicolas Hudson", "Akansel Cosgun", "Jurgen Leitner"], "url": "https://arxiv.org/abs/2101.09391v2", "attribution": "\"Learning Setup Policies: Reliable Transition Between Locomotion Behaviours\" by Brendan Tidd, Nicolas Hudson, Akansel Cosgun, and Jurgen Leitner, arXiv:2101.09391v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.04773v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average perceived noise (on a scale of 0-10) in speech samples with and without acoustic nonce collected in the three environments.}\n\\begin{tabular}{lcccc}\n \\toprule[1.5pt]\n Location & \\parbox[t]{1.7cm}{w/o Nonce} & \\parbox[t]{1.7cm}{w/ Nonce} & t-test, $\\alpha=0.05$ & Statistically Significant \\\\\n \\midrule\n Conference Room & 3.8 & 4.8 & $t =4.2022, p =0.0001489$ & Yes\\\\\n Dining Hall & 5.7 & 6.1 & $t=1.4595, p =0.1524$ & No\\\\\n Gas Station (Car) & 5 & 5.6 & $t=3.0697, p=0.00389$ & Yes\\\\\n \\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Practical Speech Re-use Prevention in Voice-driven Services", "authors": ["Yangyong Zhang", "Maliheh Shirvanian", "Sunpreet S. Arora", "Jianwei Huang", "Guofei Gu"], "url": "https://arxiv.org/abs/2101.04773v1", "attribution": "\"Practical Speech Re-use Prevention in Voice-driven Services\" by Yangyong Zhang, Maliheh Shirvanian, Sunpreet S. Arora, Jianwei Huang, and Guofei Gu, arXiv:2101.04773v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12755v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{System comparison on DNS-2020 synthetic test set.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\t\t\t\\toprule\n\t\t\tModel & PESQ & STOI(\\%) & SI-SDR(dB) \\\\\n\t\t\t\\midrule\n\t\t\tNoisy & 1.582 & 91.5 & 9.071 \\\\\t\n\t\t\tDCRN & 2.615 & 95.7 & 17.275 \\\\\n\t\t\tDCCRN & 2.711 & 96.0 & 17.967 \\\\\n\t\t\t\\midrule\n\t\t\tCEM & 2.753 & 96.1 & 17.539 \\\\\n\t\t\t\\ +GHCM & 2.812 & 96.3 & 17.841 \\\\\n\t\t\t\\ \\ \\ \\ \\ +HM & \\textbf{2.883} & \\textbf{96.5} & \\textbf{18.144} \\\\\n\t\t\t\\bottomrule\n\t\t\t\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "HGCN: Harmonic gated compensation network for speech enhancement", "authors": ["Tianrui Wang", "Weibin Zhu", "Yingying Gao", "Junlan Feng", "Shilei Zhang"], "url": "https://arxiv.org/abs/2201.12755v2", "attribution": "\"HGCN: Harmonic gated compensation network for speech enhancement\" by Tianrui Wang, Weibin Zhu, Yingying Gao, Junlan Feng, and Shilei Zhang, arXiv:2201.12755v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09945v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lcc}\\hline\n & $\\mathcal{L}^2$-like norm & $\\mathcal{L^{\\infty}}$-like norm \\\\\\hline\n asymptotic expansion up to 1st-order & 1.9521 & 0.0154 \\\\\\hline\n asymptotic expansion up to 2nd-order & 0.4636 & 0.0037 \\\\\\hline\n asymptotic expansion up to 3rd-order & 0.2471 & 0.0019 \\\\\\hline\n asymptotic expansion up to 4th-order & 0.2113 & 0.0017 \\\\\\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Asymptotic Assessment of Distribution Voltage Profile Using a Nonlinear ODE Model", "authors": ["Haruki Tadano", "Yoshihiko Susuki", "Atsushi Ishigame"], "url": "https://arxiv.org/abs/2101.09945v1", "attribution": "\"Asymptotic Assessment of Distribution Voltage Profile Using a Nonlinear ODE Model\" by Haruki Tadano, Yoshihiko Susuki, and Atsushi Ishigame, arXiv:2101.09945v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2203.12460v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|}\n\\hline\n & (1) & (2) & (3) & (4) & (5) \\\\ \\hline\nVariables & Model 1 & Model 2 & Model 3 & Model 4 & Model 5 \\\\ \\hline\n & & & & & \\\\ \\hline\npositive & 0.11*** & 0.11*** & 0.11*** & 0.11*** & 0.11** \\\\ \\hline\n & (0.02) & (0.02) & (0.02) & (0.02) & (0.04) \\\\ \\hline\nnegative & -0.12 & -0.12 & -0.15 & -0.15 & -0.10 \\\\ \\hline\n & (0.11) & (0.11) & (0.11) & (0.11) & (0.17) \\\\ \\hline\nanxiety & 0.25 & 0.25 & 0.10 & 0.09 & -0.08 \\\\ \\hline\n & (0.19) & (0.19) & (0.20) & (0.21) & (0.33) \\\\ \\hline\nanger & 0.10 & 0.10 & 0.28 & 0.28 & 0.60 \\\\ \\hline\n & (0.21) & (0.21) & (0.21) & (0.21) & (0.33) \\\\ \\hline\nsad & -0.09 & -0.11 & -0.07 & -0.08 & -0.04 \\\\ \\hline\n & (0.16) & (0.16) & (0.17) & (0.17) & (0.27) \\\\ \\hline\ncertain & 0.07 & 0.07 & 0.06 & 0.06 & 0.09 \\\\ \\hline\n & (0.06) & (0.06) & (0.06) & (0.06) & (0.10) \\\\ \\hline\n & & & & & \\\\ \\hline\nMAR(2 m) & & YES & YES & YES & YES \\\\ \\hline\nIndustry & & & YES & YES & YES \\\\ \\hline\nYear & & & & YES & YES \\\\ \\hline\nMAR(5 d) & & & & & YES \\\\ \\hline\nN & 24,245 & 24,245 & 24,245 & 24,245 & 10,063 \\\\ \\hline\nBIC & 33616 & 33654 & 33710 & 33751 & 14153 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Logistic Regression of Value Based Label Function ($y_I(T^c_d)(3)$) on Analysts' Recommendations within 2 months ($MAR(2\\,m)$) and Sentiment of Earnings Calls Transcripts. Standard errors in parentheses. Significance levels marked with stars: *** p<0.001, ** p<0.01, * p<0.05}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An Exploratory Study of Stock Price Movements from Earnings Calls", "authors": ["Sourav Medya", "Mohammad Rasoolinejad", "Yang Yang", "Brian Uzzi"], "url": "https://arxiv.org/abs/2203.12460v1", "attribution": "\"An Exploratory Study of Stock Price Movements from Earnings Calls\" by Sourav Medya, Mohammad Rasoolinejad, Yang Yang, and Brian Uzzi, arXiv:2203.12460v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2010.10703v4_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\textbf{Period} & \\textbf{2.50\\%} & \\textbf{5.00\\%} & \\textbf{10.00\\%} \\\\ \\hline\n1397-1420 & 72.66\\% & 63.90\\% & 51.11\\% \\\\\n1420-1435 & 91.13\\% & 64.85\\% & 40.69\\% \\\\\n1435-1450 & 99.82\\% & 63.09\\% & 36.11\\% \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Cancellation of principal in banking: Four radical ideas emerge from deep examination of double entry bookkeeping in banking", "authors": ["Brian P. Hanley"], "url": "https://arxiv.org/abs/2010.10703v4", "attribution": "\"Cancellation of principal in banking: Four radical ideas emerge from deep examination of double entry bookkeeping in banking\" by Brian P. Hanley, arXiv:2010.10703v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13382v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\n\\multicolumn{7}{|c|}{$G(500,p)$} \\\\\n\\hline\n$p$ & $\\varepsilon$ & $|E|$& $K'''$ & $K$ & Relative Error & \\% $|E|$ Variation \\\\\n\\hline\n0.90 & 0.5 &62251 &499.72 & 498.11 & 0.003 & 61.464 \\\\\n0.90 & 1.0 & 21756&504.68 & 498.11 & 0.012 & 87.723 \\\\\n0.90 & 1.5 &10439 &513.23 & 498.11 & 0.028 & 94.267 \\\\\n0.90 & 2.0 &5993 &525.41 & 498.11 & 0.053 & 96.742 \\\\\n0.70 & 0.5 & 56042&500.01 & 498.43 & 0.003 & 55.437 \\\\\n0.70 & 1.0 & 21092&504.92 & 498.43 & 0.012 & 83.212 \\\\\n0.70 & 1.5 &10222 &513.62 & 498.43 & 0.029 & 91.863 \\\\\n0.70 & 2.0 &5950 &526.64 & 498.43 & 0.053 & 95.265 \\\\\n0.50 & 0.5 & 46316&500.64 & 499.00 & 0.003 & 48.431 \\\\\n0.50 & 1.0 &19733 &505.60 & 499.00 & 0.012 & 78.014 \\\\\n0.50 & 1.5 & 9939&514.31 & 499.00 & 0.028 & 88.923 \\\\\n0.50 & 2.0 & 5837&526.96 & 499.00 & 0.052 & 94.284 \\\\\n0.05 & 0.5 & 6218&520.22 & 518.51 & 0.003 & 0.876 \\\\\n0.05 & 1.0 & 5858&525.16 & 518.51 & 0.013 & 5.798 \\\\\n0.05 & 1.5 &4797 &534.76 & 518.51 & 0.028 & 22.743 \\\\\n0.05 & 2.0 & 3683&548.47 & 518.51 & 0.054 & 40.786 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Sparse approximations for different values of $\\epsilon$ on $G(500,p)$, $p=0.9, 0.7, 0.5,0.05$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Spectral Approach to Kemeny's Constant", "authors": ["Aida Abiad", "Ángeles Carmona", "Andrés M. Encinas", "Maria José Jiménez", "Álvaro Samperio"], "url": "https://arxiv.org/abs/2503.13382v1", "attribution": "\"A Spectral Approach to Kemeny's Constant\" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.19878v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrrrrr}\n \\hline\n $N$ & shape & $t_{\\text{mean}}$ & $t_{\\text{median}}$ & \\# of knots& $t_{\\text{mean}}^{lcd}$ & $t_{\\text{median}}^{lcd}$ & $L_1(\\hat{F}_{lc}$) & $L_1(\\hat{F}_{un}$) & $L_1(\\hat{F}_{lcd})$\\\\ \n \\hline\n 1000& 0.3 & 0.17 & 0.11 & 8.38 (1.38) & 0.08 & 0.08 & 1.25 & 1.88 & 6.71\\\\ \n & 1& 0.18 & 0.13 & 9.01 (1.62) & 13.8 & 13.4 & 1.56 & 2.49 & 1.20 \\\\ \n &2 & 0.14 & 0.12 & 8.77 (1.36) & 12.6 & 12.5 & 1.44 & 2.38 & 1.24\\\\ \n 10000 & 0.3 & 2.81 & 1.97 & 13.95 (1.76) &3.12& 3.09 & 0.45 & 0.80 & 6.69 \\\\ \n& 1 & 3.12 & 2.10 & 14.90 (2.01)& 83.6 & 40.3 & 0.57 & 1.08 & 0.90\\\\ \n &2 & 2.54 & 2.14 & 15.29 (1.87) & 1044 & 941 & 0.56 & 1.05 & 0.49\\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Computational Performance of the Proposed Algorithm for the Log-concave MLE. The empirical absolute bias $L_1(\\hat{F}_{lc})$, $L_1(\\hat{F}_{un})$ and $L_1(\\hat{F}_{lcd})$ are reported in the units of $10^2$. The underlying distribution of the event time is Weibull, with the shape parameter specified in the table and scale parameter equals $1$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Nonparametric Estimation for a Log-concave Distribution Function with Interval-censored Data", "authors": ["Chi Wing Chu", "Hok Kan Ling", "Chaoyu Yuan"], "url": "https://arxiv.org/abs/2411.19878v1", "attribution": "\"Nonparametric Estimation for a Log-concave Distribution Function with Interval-censored Data\" by Chi Wing Chu, Hok Kan Ling, and Chaoyu Yuan, arXiv:2411.19878v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.14930v1_tex_table33.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of results for AMG solving the variable coefficient Poisson equation}\n\\begin{tabular}{|c|c|c|c||c|c|c|}\n\t\t\\hline\n\t\t&\\multicolumn{3}{c||}{Prediction}&\\multicolumn{3}{c|}{Traversal} \\\\ \\hline\n\t\t$n$ & $\\theta$ & iter & cputime & $\\theta$ & iter & traversal cputime \\\\ \\hline\n\t\t136 & 0.1292 & 34 & 0.2656 & 0.1272 & 33 & 76.3940 \\\\\\hline\n\t\t168 & 0.1329 & 35 & 0.3437 & 0.1319 & 34 & 126.1280 \\\\\\hline\n\t\t200 & 0.1291 & 35 & 0.4375 & 0.1296 & 35 & 179.8060 \\\\ \\hline\n\t\t312 & 0.1282 & 49 & 1.1562 & 0.1290 & 48 & 514.1610 \\\\ \\hline\n\t\t328 & 0.1263 & 51 & 1.0937 & 0.1263 & 51 & 586.7200 \\\\ \\hline\n\t\t376 & 0.1264 & 56 & 2.5781 & 0.1281 & 51 & 853.1070 \\\\ \\hline\n\t\t392 & 0.1271 & 52 & 2.7500 & 0.1273 & 51 & 946.3540 \\\\ \\hline\n\t\t440 & 0.1258 & 55 & 3.0781 & 0.1266 & 52 & 1260.6170 \\\\ \\hline\n\t\t472 & 0.1252 & 54 & 3.3750 & 0.1232 & 54 & 1423.1620 \\\\ \\hline\n\t\t496 & 0.1264 & 53 & 2.9843 &0.1266 &53 &1804.5950 \\\\\\hline\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs", "authors": ["Juan Zhang", "Junyue Luo", "Fangfang Zhang", "Xiaoqiang Yue"], "url": "https://arxiv.org/abs/2504.14930v1", "attribution": "\"Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs\" by Juan Zhang, Junyue Luo, Fangfang Zhang, and Xiaoqiang Yue, arXiv:2504.14930v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17215v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc} % centered columns (4 columns)\n\t\t\t\\hline %inserts double horizontal lines\n\t\t Measures & ~~~~~~~~~~~~~~~~~Values \\\\\n\t\t\t\\hline\n\t\t\n\t\t$CCRI(\\textbf{Y},\\textbf{X})$& ~~~~~~~~~~~~~~~~~2.717615\\\\\n\t$CCRI(\\textbf{Z},\\textbf{X})$& ~~~~~~~~~~~~~~~~~4.328715\\\\\n\t$CCRI(\\textbf{W},\\textbf{X})$& ~~~~~~~~~~~~~~~~~2.767670\\\\\n\t\t\t\\hline\t \t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Multivariate Rényi inaccuracy measures based on copulas: properties and application", "authors": ["Shital Saha", "Suchandan Kayal"], "url": "https://arxiv.org/abs/2502.17215v1", "attribution": "\"Multivariate Rényi inaccuracy measures based on copulas: properties and application\" by Shital Saha and Suchandan Kayal, arXiv:2502.17215v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05652v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{hhline}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccccccccccccc}\n\\hhline{-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|}\n\\hhline{-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|}\n\\hhline{-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|}\n\\cellcolor[HTML]{EFEFEF}{} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{ABC}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{QABC}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{OABC}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{AIWPSO}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{QAIWPSO}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{OAIWPSO}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{BA}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{QBA}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{OBA}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{CS}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{QCS}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{OCS}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{FA}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{QFA}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{OFA}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{FPA}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{QFPA}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{OFPA}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{PSO}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{QPSO}} &\n\\cellcolor[HTML]{EFEFEF}{\\textbf{OPSO}}\n\\\\ \\hline\nArcene & 43.48s & 42.11s & 43.06s & 21.59s & 23.08s & 23.45s & 21.92s & 23.97s & 25.78s & \\textbf{7.81s} & 10.06s & 11.68s & 22.90s & 27.62s & 34.57s & 22.59s & 27.62s & 31.83s & 22.21s & 22.70s & 23.42s\n\\\\ \\hline\nBASEHOCK & 1114.33s & 1105.60s & 1108.30s & 563.38s & 563.89s & 566.88s & 568.81s & 571.82s & 571.79s & \\textbf{189.89s} & 203.89s & 203.53s & 545.73s & 543.84s & 547.47s & 564.81s & 565.72s & 567.54s & 556.54s & 558.63s & 561.71s\n\\\\ \\hline\nCOIL20 & 179.37s & 181.11s & 181.33s & 90.92s & 93.17s & 93.01s & 90.47s & 92.77s & 94.90s & \\textbf{30.19s} & 33.10s & 33.57s & 89.41s & 88.91s & 90.34s & 92.62s & 92.79s & 94.25s & 90.70s & 91.86s & 91.87s\n\\\\ \\hline\nDNA & 240.43s & 241.94s & 242.54s & 171.38s & 171.86s & 171.85s & 124.60s & 123.88s & 123.78s & \\textbf{45.01s} & 46.74s & 46.34s & 121.66s & 122.14s & 122.58s & 123.84s & 125.02s & 124.48s & 171.20s & 171.06s & 169.50s\n\\\\ \\hline\nIsolet & 135.52s & 138.87s & 138.42s & 69.96s & 70.40s & 70.60s & 69.60s & 71.11s & 71.01s & \\textbf{21.85s} & 25.73s & 26.05s & 68.44s & 68.02s & 69.73s & 70.96s & 71.09s & 70.74s & 68.78s & 69.86s & 69.86s\n\\\\ \\hline\nLung & 15.19s & 15.07s & 15.50s & 7.84s & 8.12s & 8.42s & 7.73s & 8.60s & 9.01s & \\textbf{2.76s} & 3.61s & 4.17s & 7.98s & 9.61s & 11.74s & 7.98s & 9.87s & 11.24s & 7.71s & 7.78s & 8.05s\n\\\\ \\hline\nMadelon & 704.37s & 698.42s & 698.56s & 354.86s & 356.56s & 357.16s & 358.11s & 359.54s & 361.50s & \\textbf{120.80s} & 127.19s & 128.23s & 343.79s & 341.46s & 343.17s & 355.31s & 353.68s & 354.26s & 351.40s & 352.89s & 352.85s\n\\\\ \\hline\nMPEG7-BAS & 38.05s & 35.59s & 37.25s & 25.01s & 25.71s & 25.25s & 18.78s & 18.46s & 18.67s & \\textbf{6.61s} & 7.29s & 7.39s & 18.96s & 19.36s & 19.17s & 19.51s & 18.89s & 19.64s & 25.40s & 25.16s & 25.06s\n\\\\ \\hline\nMPEG7-Fourier & 27.92s & 25.70s & 25.78s & 19.29s & 19.54s & 19.46s & 13.74s & 13.87s & 13.97s & \\textbf{4.68s} & 5.00s & 5.02s & 13.31s & 13.33s & 13.25s & 13.37s & 13.17s & 13.26s & 18.91s & 18.69s & 18.87s\n\\\\ \\hline\nMushrooms & 791.66s & 785.72s & 785.15s & 600.75s & 600.03s & 601.13s & 405.80s & 408.04s & 409.10s & \\textbf{143.20s} & 151.30s & 152.22s & 398.83s & 397.73s & 398.85s & 404.05s & 403.06s & 404.90s & 597.34s & 596.48s & 595.58s\n\\\\ \\hline\nNTL-Commercial & 272.72s & 271.35s & 270.80s & 139.44s & 138.38s & 138.36s & 139.15s & 138.73s & 138.41s & 50.04s & 49.76s & \\textbf{49.56s} & 132.70s & 133.49s & 133.30s & 138.46s & 137.85s & 138.23s & 138.58s & 137.60s & 137.83s\n\\\\ \\hline\nNTL-Industrial & 113.70s & 111.91s & 111.74s & 57.60s & 56.71s & 56.55s & 57.79s & 57.30s & 57.27s & 20.58s & 20.62s & \\textbf{20.55s} & 54.88s & 54.97s & 54.58s & 57.11s & 57.30s & 57.02s & 57.68s & 56.60s & 56.46s\n\\\\ \\hline\nORL & 13.07s & 13.05s & 13.08s & 6.73s & 6.77s & 6.93s & 6.38s & 6.96s & 7.20s & \\textbf{2.07s} & 2.62s & 2.82s & 6.67s & 7.56s & 8.63s & 6.87s & 7.19s & 7.67s & 6.64s & 6.79s & 6.80s\n\\\\ \\hline\nPCMAC & 751.25s & 742.09s & 744.23s & 377.87s & 381.31s & 380.74s & 382.81s & 382.50s & 386.86s & \\textbf{128.28s} & 136.87s & 137.31s & 365.66s & 366.69s & 370.00s & 380.84s & 382.02s & 381.16s & 374.06s & 376.45s & 377.26s\n\\\\ \\hline\nPhishing & 1119.79s & 1110.41s & 1110.58s & 943.66s & 941.99s & 939.85s & 576.86s & 578.64s & 578.91s & \\textbf{206.02s} & 218.22s & 218.06s & 569.57s & 567.55s & 567.86s & 574.09s & 572.32s & 573.75s & 936.62s & 933.18s & 932.34s\n\\\\ \\hline\nSegment & 72.41s & 72.66s & 72.15s & 36.87s & 36.99s & 36.63s & 37.23s & 37.26s & 37.11s & \\textbf{13.02s} & 13.43s & 13.53s & 35.18s & 35.53s & 35.46s & 36.98s & 37.02s & 36.75s & 37.25s & 36.33s & 36.58s\n\\\\ \\hline\nSonar & 0.51s & 0.53s & 0.53s & 0.40s & 0.41s & 0.41s & 0.27s & 0.28s & 0.29s & \\textbf{0.10s} & 0.11s & 0.13s & 0.28s & 0.34s & 0.41s & 0.28s & 0.31s & 0.33s & 0.40s & 0.40s & 0.41s\n\\\\ \\hline\nSplice & 37.29s & 34.84s & 34.96s & 30.49s & 30.29s & 29.83s & 18.24s & 18.19s & 18.50s & 6.94s & \\textbf{6.91s} & 6.99s & 17.79s & 17.92s & 17.83s & 18.33s & 18.41s & 18.75s & 31.50s & 30.83s & 29.60s\n\\\\ \\hline\nVehicle & 10.01s & 9.82s & 9.71s & 4.90s & 4.93s & 4.96s & 4.96s & 5.00s & 5.04s & \\textbf{1.77s} & 1.79s & 1.82s & 4.86s & 4.86s & 4.89s & 5.00s & 5.04s & 5.07s & 5.02s & 4.98s & 4.96s\n\\\\ \\hline\nWine & 0.44s & 0.44s & 0.44s & 0.22s & 0.23s & 0.23s & 0.22s & 0.23s & 0.23s & \\textbf{0.08s} & \\textbf{0.08s} & 0.09s & 0.22s & 0.23s & 0.25s & 0.23s & 0.23s & 0.24s & 0.22s & 0.22s & 0.22s\n\\\\\n\\hhline{-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|}\n\\hhline{-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|}\n\\hhline{-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|}\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Average computation time required by the optimization process considering all datasets.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Nature-Inspired Feature Selection Approach based on Hypercomplex Information", "authors": ["Gustavo H. de Rosa", "João Paulo Papa", "Xin-She Yang"], "url": "https://arxiv.org/abs/2101.05652v1", "attribution": "\"A Nature-Inspired Feature Selection Approach based on Hypercomplex Information\" by Gustavo H. de Rosa, João Paulo Papa, and Xin-She Yang, arXiv:2101.05652v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.08683v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Environment Resources Configuration.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n \\hline\n \\textbf{Component} & \\textbf{Details} \\\\\n \\hline\n CPU & Intel® Core™ i5-8400 \\\\\n \\hline\n GPU & NVIDIA RTX GeForce 2080Ti \\\\\n \\hline\n RAM & 32Gb \\\\\n \\hline\n Python Version & Python 3.11.9 \\\\\n \\hline\n Host Environment & Window 11 Pro 64 bits \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Emotional Vietnamese Speech-Based Depression Diagnosis Using Dynamic Attention Mechanism", "authors": ["Quang-Anh N. D.", "Manh-Hung Ha", "Thai Kim Dinh", "Minh-Duc Pham", "Ninh Nguyen Van"], "url": "https://arxiv.org/abs/2412.08683v1", "attribution": "\"Emotional Vietnamese Speech-Based Depression Diagnosis Using Dynamic Attention Mechanism\" by Quang-Anh N. D., Manh-Hung Ha, Thai Kim Dinh, Minh-Duc Pham, and Ninh Nguyen Van, arXiv:2412.08683v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.10334v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c|c}\n \\toprule\n \\multirow{2}{*}{Model} & \\multicolumn{2}{|c}{Valid}& \\multicolumn{2}{|c}{Test} \\\\\\cline{2-5}\n & CER & WER & CER & WER \\\\\n \\midrule\n HMM-DNN & 8.5 & 21.6 & 7.9 & 19.8 \\\\\n Transformer & 4.5 & 13.7 & 3.9 & 12.4 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The CER (\\%) and WER (\\%) performances of different ASR models built using KSC.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline", "authors": ["Yerbolat Khassanov", "Saida Mussakhojayeva", "Almas Mirzakhmetov", "Alen Adiyev", "Mukhamet Nurpeiissov", "Huseyin Atakan Varol"], "url": "https://arxiv.org/abs/2009.10334v2", "attribution": "\"A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline\" by Yerbolat Khassanov, Saida Mussakhojayeva, Almas Mirzakhmetov, Alen Adiyev, Mukhamet Nurpeiissov, and Huseyin Atakan Varol, arXiv:2009.10334v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.05108v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimated optimal treatment length strategy for PEG inssertion.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllllll}\n\\hline\n PS & Est & (Int.) & FVC & BMI Red. & $\\hat{R}(\\hat{\\eta}_{opt})$ & $\\hat{R}(1)$ & $\\hat{R}(0)$ & $\\hat{R}(F)$ & $\\hat{R}(B)$ \\\\ \\hline\n\\multirow{2}{*}{Logit} & IPW & 0.995 & -0.011 & -0.097 & 72.24(4.75) & 33.07(8.08) & 57.30(2.51) & 61.26(2.57) & 58.51(3.17)\\\\\n & BC-IPW & 0.996 & -0.014 & -0.084 & 71.95(4.14) & 33.07(8.08) & 57.30(2.51) & 60.84(2.57) & 58.52(3.17)\\\\ \\hline\n\\multirow{2}{*}{HAL} & IPW & 0.998 & -0.012 & -0.066 & 76.09(4.29) & 23.59(7.08) & 60.24(2.16) & 65.01(2.53) & 60.66(2.39) \\\\\n & BC-IPW & 0.998 & -0.012 & -0.054 & 75.05(4.25) & 23.59(7.08) & 60.24(2.16) & 64.70(2.53) & 60.66(2.39) \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Constructing optimal treatment length strategies to maximize quality-adjusted lifetimes", "authors": ["Hao Sun", "Ashkan Ertefaie", "Luke Duttweiler", "Brent A. Johnson"], "url": "https://arxiv.org/abs/2412.05108v1", "attribution": "\"Constructing optimal treatment length strategies to maximize quality-adjusted lifetimes\" by Hao Sun, Ashkan Ertefaie, Luke Duttweiler, and Brent A. Johnson, arXiv:2412.05108v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cccc|}\n\\hline\nEntry time & Node 1 & Node 2 & Node 3 & Node 4 \\\\\n\\hline\n6 & $\\infty$ & $\\infty$ & 6 & 6 \\\\\n5 & $\\infty$ & $\\infty$ & $\\infty$ & 5 \\\\\n4 & $\\infty$ & $\\infty$ & 4 & $\\infty$ \\\\\n3 & $\\infty$ & $\\infty$ & $\\infty$ & $\\infty$ \\\\\n2 & $\\infty$ & 1 & $\\infty$ & $\\infty$ \\\\\n1 & 0 & $\\infty$ & $\\infty$ & $\\infty$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19280v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\t\tstatus & number & $\\max(|y''-f|)$ & $\\max(|y_{opt}-y_{b}|)$ \\\\ \\hline\\hline\n\t\t$\\to y_b$ & 23 & 1.0E-07 & 4.1E-10 \\\\\n\t\t$\\to y_s$ & 0 & NA & NA \\\\ \n\t\t$diverge$ & 2 & diverge & diverge \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions", "authors": ["Xiaorong Zou"], "url": "https://arxiv.org/abs/2504.19280v1", "attribution": "\"Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions\" by Xiaorong Zou, arXiv:2504.19280v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16534v2_tex_table27.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average training time of different methods using default and optimal hyperparameter settings on 50 medium-sized datasets. Numbers are in Seconds, with lower values indicating faster training speed.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n \\textbf{Model Name} & \\textbf{Training Time (Default)} & \\textbf{Training Time (Optimal)} \\\\\n \\midrule\n DOFEN & 332.6998 +/- 125.1965 & 1143.7674 +/- 804.3809 \\\\\n Trompt & 552.3495 +/- 213.3278 & 535.1781 +/- 291.9933 \\\\\n FT-Transformer & 80.3425 +/- 57.2647 & 99.1068 +/- 79.2272 \\\\\n NODE & 95.0274 +/- 54.7463 & 427.8625 +/- 394.1191 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DOFEN: Deep Oblivious Forest ENsemble", "authors": ["Kuan-Yu Chen", "Ping-Han Chiang", "Hsin-Rung Chou", "Chih-Sheng Chen", "Tien-Hao Chang"], "url": "https://arxiv.org/abs/2412.16534v2", "attribution": "\"DOFEN: Deep Oblivious Forest ENsemble\" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.01118v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Example of binary BCH code $\\mathcal{C}_{(q,m,\\delta)}$ for $m\\geq 4$ and $\\delta\\in [2,q^{\\lfloor (2m-1)/3\\rfloor+1}]$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n\\hline\n$q$ & $m$ & $\\delta$ & $n$ & $k$ & $d_B$ & $d$ & Optimality \\\\ \\hline\n2\t&\t4\t&\t2 -- 3\t&\t15\t&\t11\t&\t3\t&3\t&Optimal\t\\\\ \\hline\n2\t&\t4\t&\t4 -- 5\t&\t15\t&\t7\t&\t5\t&5\t&Optimal\t\\\\ \\hline\n2\t&\t4\t&\t6 --7\t&\t15\t&\t5\t&\t7\t&7\t&Optimal\t\\\\ \\hline\n2\t&\t5\t&\t2 -- 3\t&\t31\t&\t26\t&\t3\t&3\t&Optimal\t\\\\ \\hline\n2\t&\t5\t&\t4 -- 5\t&\t31\t&\t21\t&\t5\t&\t5&Optimal\t\\\\ \\hline\n2\t&\t5\t&\t6 --7\t&\t31\t&\t16\t&\t7\t&\t7&Optimal\t\\\\ \\hline\n2\t&\t5\t&\t8 -- 11\t&\t31\t&\t11\t&\t11\t&\t11&Optimal\t\\\\ \\hline\n2\t&\t5\t&\t12 -- 15\t&\t31\t&\t6\t&\t15\t&\t15&Optimal\t\\\\ \\hline\n2\t&\t6\t&\t2 -- 3\t&\t63\t&\t57\t&\t3\t&\t3&Optimal\t\\\\ \\hline\n2\t&\t6\t&\t4 -- 5\t&\t63\t&\t51\t&\t5\t&\t5&Optimal\t\\\\ \\hline\n2\t&\t6\t&\t8 -- 9\t&\t63\t&\t39\t&\t9\t&\t9&Best Known\t\\\\ \\hline\n2\t&\t6\t&\t10 -- 11\t&\t63\t&\t36\t&\t11\t&\t11&Best Known\t\\\\ \\hline\n2\t&\t6\t&\t12 -- 13\t&\t63\t&\t30\t&\t13\t&13\t&Best Known\t\\\\ \\hline\n2\t&\t7\t&\t2 -- 3\t&\t127\t&\t120\t&\t3\t&\t3&Optimal\t\\\\ \\hline\n2\t&\t7\t&\t4 -- 5\t&\t127\t&\t113\t&\t5\t&\t5&Optimal\t\\\\ \\hline\n2\t&\t7\t&\t6 -- 7\t&\t127\t&\t106\t&\t7\t&\t7&Best Known\t\\\\ \\hline\n2\t&\t7\t&\t8 -- 9\t&\t127\t&\t99\t&\t9\t&\t9&Best Known\t\\\\ \\hline\n2\t&\t7\t&\t10 -- 11\t&\t127\t&\t92\t&\t11\t&\t11&Best Known\t\\\\ \\hline\n2\t&\t7\t&\t12 -- 13\t&\t127\t&\t85\t&\t13\t&\t13&Best Known\t\\\\ \\hline\n2\t&\t7\t&\t14 -- 15\t&\t127\t&\t78\t&\t15\t&15\t&Best Known\t\\\\ \\hline\n2\t&\t7\t&\t16 -- 19\t&\t127\t&\t71\t&\t19\t&\t19&Best Known\t\\\\ \\hline\n2\t&\t7\t&\t20 -- 21\t&\t127\t&\t64\t&\t21\t&\t21&Best Known\t\\\\ \\hline\n2\t&\t7\t&\t24 -- 27\t&\t127\t&\t50\t&\t27\t&27&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t2 -- 3\t&\t254\t&\t247\t&\t3\t&\t3&Optimal\t\\\\ \\hline\n2\t&\t8\t&\t5\t&\t254\t&\t239\t&\t5\t&\t5&Optimal\t\\\\ \\hline\n2\t&\t8\t&\t6 -- 7\t&\t254\t&\t231\t&\t7\t&\t7&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t8 -- 9\t&\t254\t&\t223\t&\t9\t&\t9&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t10 -- 11\t&\t254\t&\t215\t&\t11\t&11\t&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t12 -- 13\t&\t254\t&\t207\t&\t13\t&\t13&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t14 -- 15\t&\t254\t&\t199\t&\t15\t&\t15&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t16 -- 17\t&\t254\t&\t191\t&\t17\t&\t17&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t18 -- 19\t&\t254\t&\t187\t&\t19\t&\t19&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t20 -- 21\t&\t254\t&\t179\t&\t21\t&\t21&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t22 -- 23\t&\t254\t&\t171\t&\t23\t&\t23&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t24 -- 25\t&\t254\t&\t163\t&\t25\t&\t25&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t26 -- 27\t&\t254\t&\t155\t&\t27\t&\t27&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t28 -- 29\t&\t254\t&\t147\t&\t29\t&\t29&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t30 -- 31\t&\t254\t&\t139\t&\t31\t&31\t&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t32 -- 37\t&\t254\t&\t131\t&\t37\t&\t37&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t38 -- 39\t&\t254\t&\t123\t&\t39\t&\t39&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t40 -- 43\t&\t254\t&\t115\t&\t43\t&43\t&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t44 -- 45\t&\t254\t&\t107\t&\t45\t&\t45&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t46 -- 47\t&\t254\t&\t99\t&\t47\t&47\t&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t48 -- 51\t&\t254\t&\t91\t&\t51\t&\t51&Best Known\t\\\\ \\hline\n2\t&\t8\t&\t52 -- 53\t&\t254\t&\t87\t&\t53\t&\t53&Best Known\t\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The dimension and Bose distance of certain primitive BCH codes", "authors": ["Run Zheng", "Nung-Sing Sze", "Zejun Huang"], "url": "https://arxiv.org/abs/2503.01118v3", "attribution": "\"The dimension and Bose distance of certain primitive BCH codes\" by Run Zheng, Nung-Sing Sze, and Zejun Huang, arXiv:2503.01118v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.14388v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lll}\n \\toprule\n \\textbf{Parameters} & \\textbf{Value} & \\textbf{Explanation} \\\\\n \\midrule\n $C$ & $2$ & Clipping bound \\\\\n $\\delta^{\\ell}$ & $10^{-5}$ & Indistinguishability parameter \\\\\n $\\epsilon^{\\ell}$ & $[0.01,2]$ & Privacy budget \\\\\n $\\eta$ & $0.05$ & Step size of the gradient \\\\\n $m$ & $100$ & The number of clients \\\\\n $n$ & $60,000$ & Total number of training samples \\\\\n $T$ & $50$ & The number of epochs \\\\\n \\bottomrule\n \\end{tabular}\n\\caption{Experiment Setting for the Shuffled Personalized-SGD on the MNIST dataset.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility", "authors": ["E Chen", "Yang Cao", "Yifei Ge"], "url": "https://arxiv.org/abs/2312.14388v3", "attribution": "\"A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility\" by E Chen, Yang Cao, and Yifei Ge, arXiv:2312.14388v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.05301v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of possible dose-escalation paths in the standard 3+3 design, as a function of the number $D$ of prespecified doses.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrc}\n \\hline\n$D$ & $J$ & $\\log(J)$ & $\\Delta \\log(J)$ \\\\ \n \\hline\n1 & 10 & 2.30 & -- \\\\ \n 2 & 46 & 3.83 & 1.53 \\\\ \n 3 & 154 & 5.04 & 1.21 \\\\ \n 4 & 442 & 6.09 & 1.05 \\\\ \n 5 & 1162 & 7.06 & 0.97 \\\\ \n 6 & 2890 & 7.97 & 0.91 \\\\ \n 7 & 6922 & 8.84 & 0.87 \\\\ \n 8 & 16138 & 9.69 & 0.85 \\\\ \n 9 & 36874 & 10.52 & 0.83 \\\\ \n 10 & 82954 & 11.33 & 0.81 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "What Were They Thinking? Pharmacologic priors implicit in a choice of 3+3 dose-escalation design", "authors": ["David C. Norris"], "url": "https://arxiv.org/abs/2012.05301v2", "attribution": "\"What Were They Thinking? Pharmacologic priors implicit in a choice of 3+3 dose-escalation design\" by David C. Norris, arXiv:2012.05301v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.08696v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|r|r|r|}\n \\hline\n Model & Accuracy, \\% & MAPE, \\% \\\\\\hline\n LSTM-Qwen-Mean & 48.552 & \\textbf{0.256} \\\\\\hline\n LSTM-Qwen-Sum & 46.970 & 0.367 \\\\\\hline\n LSTM & \\textbf{52.020 }& 0.397 \\\\\\hline\n LSTM-RuBert-Mean & 49.798 & 0.437 \\\\\\hline\n LSTM-RuBert-Sum & 48.148 & 0.445 \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The Dual-Modality Approach forecast. Accuracy, MAPE in percentage.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market", "authors": ["Kasymkhan Khubiev", "Mikhail Semenov"], "url": "https://arxiv.org/abs/2503.08696v1", "attribution": "\"Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market\" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2503.08696v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.02098v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{SSIM and CW-SSIM scores on the test set.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l||c|c|c}\n & \\multicolumn{3}{c}{{SSIM / CW-SSIM}} \\\\\n\\cline{2-4}\nspeaker & ~~FC-DNN~~ & ~~~CNN~~~ & ~~LSTM~~ \\\\\n\\hline\\hline\nf1 & 0.77 / 0.94 & 0.75 / 0.94 & 0.80 / 0.95 \\\\\nf2 & 0.70 / 0.92 & 0.68 / 0.92 & 0.73 / 0.94 \\\\\nm1 & 0.80 / 0.92 & 0.77 / 0.91 & 0.81 / 0.92 \\\\\nm2 & 0.75 / 0.92 & 0.73 / 0.92 & 0.77 / 0.94 \\\\\n\\hline\naverage & 0.76 / 0.93 & 0.73 / 0.92 & 0.78 / 0.94 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Speaker dependent acoustic-to-articulatory inversion using real-time MRI of the vocal tract", "authors": ["Tamás Gábor Csapó"], "url": "https://arxiv.org/abs/2008.02098v1", "attribution": "\"Speaker dependent acoustic-to-articulatory inversion using real-time MRI of the vocal tract\" by Tamás Gábor Csapó, arXiv:2008.02098v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12389v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\centering The comparison of precision, recall and F1-score for our model and Double-net in percentage from three different views for VerSe 2020 dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\hline\n\\textbf{Plane} & \\textbf{Model}& \\textbf{Phase }& \\textbf{Precision(\\%)} &\\textbf{Recall(\\%)}&\\textbf{F1-score(\\%)} \\\\\\hline\n\\multirow{4}{*}{Sagittal } & \\multirow{2}{*}{DoubleU-Net } &Valid& 92.19 & 91.49 & 91.83 \\\\ & & Test & 90.23 & 90.14 & 90.18 \\\\\\cline{2-6}\n& \\multirow{2}{*}{\\textbf{DoubleU-Net ++} } &Valid& \\textbf{96.34} & \\textbf{95.94} & \\textbf{96.13} \\\\ & & Test & \\textbf{95.18} & \\textbf{94.08} & \\textbf{94.62} \\\\ \n\\hline\n\\multirow{4}{*}{Coronal } & \\multirow{2}{*}{DoubleU-Net } &Valid& 90.09 & 90.13 & 90.10 \\\\ & & Test & 89.51 & 89.04 & 89.27 \\\\\\cline{2-6}\n& \\multirow{2}{*}{\\textbf{DoubleU-Net ++ }} &Valid&\\textbf{95.12} &\\textbf{95.43} & \\textbf{95.27} \\\\ & & Test & \\textbf{94.02} & \\textbf{94.24} &\\textbf{94.12 } \\\\ \\hline\n\\multirow{4}{*}{Axial } & \\multirow{2}{*}{DoubleU-Net } &Valid& 92.37 & 92.51 & 92.43 \\\\ & & Test & 91.45 & 91.73 & 91.58 \\\\\\cline{2-6}\n& \\multirow{2}{*}{\\textbf{DoubleU-Net ++}} &Valid& \\textbf{94.69} & \\textbf{94.93} & \\textbf{94.80} \\\\ & & Test & \\textbf{94.01} & \\textbf{93.86}& \\textbf{93.93} \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "DoubleU-Net++: Architecture with Exploit Multiscale Features for Vertebrae Segmentation", "authors": ["Simindokht Jahangard", "Mahdi Bonyani", "Abbas Khosravi"], "url": "https://arxiv.org/abs/2201.12389v1", "attribution": "\"DoubleU-Net++: Architecture with Exploit Multiscale Features for Vertebrae Segmentation\" by Simindokht Jahangard, Mahdi Bonyani, and Abbas Khosravi, arXiv:2201.12389v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.12619v1_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\large Regression Results: Impact of renewable energy investments on detected unemployment rate recovery following a change in active mines}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\tabularnewline\\midrule\\midrule\n Dependent Variable: & \\multicolumn{4}{c}{$\\Delta$Unemployment Rate}\\\\\n Model: & (1) & (2) & (3) & (4)\\\\\n \\midrule \\emph{Variables} & & & & \\\\\n $\\Delta$Active Mines\\textsubscript{t-2} & 0.0402$^{*}$ & 0.0400$^{*}$ & 0.0433$^{*}$ & 0.0427$^{*}$\\\\\n & (0.0151) & (0.0161) & (0.0175) & (0.0178)\\\\\n $\\Delta$ Active Mines\\textsubscript{t-2} $\\times$ REE\\textsubscript{t-1} & & 0.0049 & & 0.0229\\\\\n & & (0.0249) & & (0.0300)\\\\\n $\\Delta$ Active Mines\\textsubscript{t-2} $\\times$ REE\\textsubscript{t-2} & & & -0.0440$^{.}$ & -0.0518$^{*}$\\\\\n & & & (0.0209) & (0.0204)\\\\\n $\\Delta$ (log) Real GDPPC\\textsubscript{t} & -0.9846$^{***}$ & -0.9986$^{***}$ & -1.021$^{**}$ & -1.021$^{**}$\\\\\n & (0.2138) & (0.2246) & (0.2519) & (0.2519)\\\\\n \\midrule \\emph{Fixed-effects} & & & & \\\\\n County FIPS Code & Yes & Yes & Yes & Yes\\\\\n Year & Yes & Yes & Yes & Yes\\\\\n \\midrule \\emph{Fit statistics} & & & & \\\\\n Observations & 55,295 & 52,224 & 49,152 & 49,152\\\\\n R$^2$ & 0.61267 & 0.61459 & 0.62292 & 0.62292\\\\\n Within R$^2$ & 0.01288 & 0.01308 & 0.01340 & 0.01341\\\\\n \\midrule\\midrule\\multicolumn{5}{l}{\\emph{Clustered (County FIPS Code \\& Year) standard-errors in parentheses}}\\\\\n \\multicolumn{5}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .:0.1}}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'", "authors": ["Ebba Mark", "Ryan Rafaty", "Moritz Schwarz"], "url": "https://arxiv.org/abs/2211.12619v1", "attribution": "\"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'\" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.12490v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Case description for example 5.1.2.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll}\n\t\\hline\n Case No. & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8\\\\ \n \\hline \n number of mixture components, $K$ & 1 & 2 & 3 & 5 & 10 & 20 & 100 & BIC\\\\ \n \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian improved cross entropy method with categorical mixture models", "authors": ["Jianpeng Chan", "Iason Papaioannou", "Daniel Straub"], "url": "https://arxiv.org/abs/2309.12490v1", "attribution": "\"Bayesian improved cross entropy method with categorical mixture models\" by Jianpeng Chan, Iason Papaioannou, and Daniel Straub, arXiv:2309.12490v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07434v5_tex_table22.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c}\n\t\t\\toprule[1pt]\n\t\t Methods & mIOU (\\%) \\\\\n\t\t\\midrule[0.5pt]\n\t\t\\midrule[0.5pt]\n\t\tResNet-50 + DeepLabV3+ ~ & 38.4\\\\\n\t\tHRNetV2 + OCR & 42.3 \\\\\n\t\tSETR~ & 45.8\\\\\n\t Segformer-B5~ & 46.7 \\\\\n\t\t\\midrule[0.5pt]\n\t\t\\textbf{Our CAA (EfficientNet-B5)} & \\textbf{47.30}\\\\\n\t\t\\bottomrule[1pt]\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Result comparison with the state-of-the-art approaches on the COCO-Stuff-164K test set for multi-scale prediction. Note that, the listed methods were not trained under same settings, or using same backbone. \\textit{Methods} other than CAA and Segformer are reproduced in Segformer paper. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation", "authors": ["Ye Huang", "Di Kang", "Wenjing Jia", "Xiangjian He", "Liu Liu"], "url": "https://arxiv.org/abs/2101.07434v5", "attribution": "\"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation\" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07357v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrrrrrrr}\n \\hline\n{\\footnotesize Dataset} & {\\footnotesize } & {\\footnotesize HIVAE} & {\\footnotesize Mean} & {\\footnotesize MICE} & {\\footnotesize MissForest} & {\\footnotesize MIWAE} & {\\footnotesize VAEAC} & {\\footnotesize NIMIWAE} & {\\footnotesize IMIWAE} \\\\\n \\hline\nbanknote & \\textit{ MCAR } & 1.62 & 2.00 & 1.63 & \\textcolor{red}{0.95} & 1.37 & 1.32 & 1.49 & 1.19 \\\\\n{\\footnotesize $n=1372$} & \\textit{ MAR } & 2.00 & 2.27 & 2.43 & 1.88 & 2.90 & \\textcolor{red}{1.76} & 1.89 & 1.96 \\\\\n & \\textit{ MNAR } & 3.39 & 3.92 & 3.78 & 3.18 & 3.10 & 3.46 & \\textcolor{red}{1.46} & 3.30 \\\\\n \\rowcolor[gray]{0.95}concrete & \\textit{ MCAR } & 47.54 & 51.28 & 29.97 & \\textcolor{red}{25.57} & 40.53 & 33.03 & 42.12 & 33.69 \\\\\n \\rowcolor[gray]{0.95}{\\footnotesize $n=1030$} & \\textit{ MAR } & 67.37 & 60.00 & \\textcolor{red}{44.77} & 53.19 & 66.35 & 61.09 & 55.82 & 57.56 \\\\\n \\rowcolor[gray]{0.95} & \\textit{ MNAR } & 59.48 & 95.85 & 68.24 & 79.87 & 76.79 & 70.12 & \\textcolor{red}{47.46} & 74.44 \\\\\n hepmass & \\textit{ MCAR } & 0.75 & 0.82 & 0.74 & NA & 0.80 & \\textcolor{red}{0.68} & 0.78 & 0.69 \\\\\n {\\footnotesize $n=525,123$} & \\textit{ MAR } & 0.76 & 0.84 & 0.75 & NA & 0.84 & 0.72 & 0.72 & \\textcolor{red}{0.71} \\\\\n & \\textit{ MNAR } & 1.41 & 1.54 & 1.40 & NA & 1.36 & 1.30 & \\textcolor{red}{0.99} & 1.36 \\\\\n \\rowcolor[gray]{0.95}power & \\textit{ MCAR } & 0.54 & 0.66 & 0.50 & NA & 0.59 & \\textcolor{red}{0.48} & 0.56 & 0.49 \\\\\n \\rowcolor[gray]{0.95}{\\footnotesize $n=1,000,000$} & \\textit{ MAR } & 0.61 & 0.75 & 0.56 & NA & 0.67 & \\textcolor{red}{0.54} & 0.78 & 0.57 \\\\\n \\rowcolor[gray]{0.95} & \\textit{ MNAR } & 0.75 & 1.14 & 0.84 & NA & 0.86 & 0.79 & \\textcolor{red}{0.73} & 0.79 \\\\\n red & \\textit{ MCAR } & 1.15 & 1.64 & 1.04 & \\textcolor{red}{0.86} & 1.10 & 1.10 & 1.08 & 0.98 \\\\\n {\\footnotesize $n=1599$} & \\textit{ MAR } & 1.23 & 1.67 & 1.16 & \\textcolor{red}{0.98} & 1.30 & 1.29 & 1.09 & 1.06 \\\\\n & \\textit{ MNAR } & 2.12 & 3.24 & 2.46 & 2.06 & 1.87 & 2.73 & \\textcolor{red}{0.90} & 1.74 \\\\\n \\rowcolor[gray]{0.95}white & \\textit{ MCAR } & 2.14 & 2.61 & 1.97 & \\textcolor{red}{1.60} & 2.18 & 1.93 & 2.16 & 1.88 \\\\\n \\rowcolor[gray]{0.95}{\\footnotesize $n=4898$} & \\textit{ MAR } & 2.28 & 2.63 & 1.99 & \\textcolor{red}{1.69} & 2.15 & 2.11 & 1.89 & 1.89 \\\\\n \\rowcolor[gray]{0.95} & \\textit{ MNAR } & 4.42 & 5.36 & 4.21 & 4.27 & 4.46 & 4.10 & \\textcolor{red}{3.47} & 4.70 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Average L1 distance between true and imputed missing values in various datasets, under different mechanisms of simulated missingness. Best imputation performance (lowest average L1) in each row is highlighted in red. Proportion of missing entries was fixed at 50\\% per feature, with 50\\% of the features containing missingness. We see that NIMIWAE consistently performs best in imputing MNAR missingness, while performance of the ``Ignorable'' IMIWAE model is comparable to other methods under MCAR and MAR. Although MissForest claims superiority in MCAR and some MAR cases in the smaller datasets, it was not scalable to larger datasets like hepmass and power.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders", "authors": ["David K. Lim", "Naim U. Rashid", "Junier B. Oliva", "Joseph G. Ibrahim"], "url": "https://arxiv.org/abs/2101.07357v3", "attribution": "\"Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders\" by David K. Lim, Naim U. Rashid, Junier B. Oliva, and Joseph G. Ibrahim, arXiv:2101.07357v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00963v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{RMSE geometric mean over 100 trials of $N$ random fictitious centers, with different convection velocities $\\vec{v}$ and perturbation radius $\\delta$ (shape parameter $\\varepsilon=2.5$).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c||c|c|c|c|} \n \\hline \n & \\multicolumn{4}{c||}{$\\vec{v}=(0,0)$} & \\multicolumn{4}{c|}{$\\vec{v}=(1,1)$} \\\\\n \\hline \n $N$ & $\\;\\;\\delta=0.1\\;\\;$ & $\\;\\delta=0.01\\;$ & $\\delta=0.001$ & $\\;\\;\\;\\delta=0\\;\\;\\;$ & $\\;\\;\\delta=0.1\\;\\;$ & $\\;\\delta=0.01\\;$ & $\\delta=0.001$ & $\\;\\;\\;\\delta=0\\;\\;\\;$\\\\\n \\hline \n $121$ & 2.9e-01 & 6.9e-02 & 6.8e-02 & 6.9e-02 & 3.4e-01 & 7.1e-02 & 7.3e-02 & 7.2e-02 \\\\\n \\hline\n $441$ & 4.7e-02 & 7.9e-03 & 1.8e-03 & 1.4e-03 & 6.2e-02 & 8.2e-03 & 1.7e-03 & 1.5e-03 \\\\\n \\hline\n $961$ & 1.4e-03 & 9.6e-04 & 4.5e-04 & 3.4e-05 & 1.7e-03 & 9.1e-04 & 4.5e-04 & 3.5e-05 \\\\\n \\hline\n $1681$ & 4.3e-05 & 9.8e-06 & 1.0e-05 & 7.5e-06 & 4.7e-05 & 1.2e-05 & 1.2e-05 & 6.3e-06 \\\\\n \\hline \n \n \\hline \n & \\multicolumn{4}{c||}{$\\vec{v}=(1,100)$} & \\multicolumn{4}{c|}{$\\vec{v}=(100,100)$} \\\\\n \\hline \n $N$ & $\\;\\;\\delta=0.1\\;\\;$ & $\\;\\delta=0.01\\;$ & $\\delta=0.001$ & $\\;\\;\\;\\delta=0\\;\\;\\;$ & $\\;\\;\\delta=0.1\\;\\;$ & $\\;\\delta=0.01\\;$ & $\\delta=0.001$ & $\\;\\;\\;\\delta=0\\;\\;\\;$\\\\\n \\hline \n $121$ & 6.1e+00 & 5.1e+00 & 3.1e+00 & 3.1e+00 & 1.6e-01 & 8.3e-02 & 4.7e-01 & 3.9e-01\\\\\n \\hline\n $441$ & 2.4e+00 & 6.5e-01 & 1.1e-01 & 6.5e-02 & 1.4e-02 & 5.9e-03 & 3.7e-03 & 4.2e-03\\\\\n \\hline\n $961$ & 7.7e-02 & 3.7e-02 & 2.4e-02 & 1.5e-03 & 8.5e-04 & 4.9e-04& 2.5e-04 & 1.2e-04 \\\\ \n \\hline\n $1681$ & 3.1e-03 & 8.1e-04 & 7.9e-04 & 2.1e-04 & 4.8e-05 & 2.3e-05 & 1.8e-05 & 1.9e-05 \\\\\n \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unisolvence of randomized MultiQuadric Kansa collocation for convection-diffusion with mixed boundary conditions", "authors": ["Maryam Mohammadi", "Alvise Sommariva", "Marco Vianello"], "url": "https://arxiv.org/abs/2503.00963v1", "attribution": "\"Unisolvence of randomized MultiQuadric Kansa collocation for convection-diffusion with mixed boundary conditions\" by Maryam Mohammadi, Alvise Sommariva, and Marco Vianello, arXiv:2503.00963v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.03797v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation results with differential effects between events: Mean and standard deviation of AUC and Brier score computed for landmark times $s=0,0.25,0.5,0.75$ and prediction window of $0.25$ for $\\rho=0.1,~\\sigma^2=0.5,~\\alpha^*=0.5,~\\gamma^*=0.5$. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|cc|cc|cccc|cccc}\n \\hline\n & &\\multicolumn{2}{c|}{Real } &\\multicolumn{2}{c|}{Oracle } &\\multicolumn{4}{c|}{CS } &\\multicolumn{4}{c}{DS } \\\\ \\hline\n & &\\multicolumn{2}{c|}{ } &\\multicolumn{2}{c|}{ } &\\multicolumn{2}{c}{BF } &\\multicolumn{2}{c}{lBFDR }&\\multicolumn{2}{c}{BF } &\\multicolumn{2}{c}{lBFDR } \\\\ \\hline\n & s & Est. & SD. & Est. & SD. & Est. & SD. & Est. & SD. & Est. & SD. & Est. & SD. \\\\ \n \\hline\n \\multicolumn{14}{c}{Single effects on cause 1} \\\\\n\\hline\nAUC & 0 & 0.781 & 0.029 & 0.747 & 0.026 & 0.742 & 0.019 & 0.740 & 0.020 & 0.742 & 0.017 & 0.737 & 0.020 \\\\ \n & 0.25 & 0.790 & 0.047 & 0.759 & 0.060 & 0.761 & 0.050 & 0.762 & 0.053 & 0.757 & 0.052 & 0.757 & 0.053 \\\\ \n & 0.5 & 0.786 & 0.063 & 0.766 & 0.057 & 0.769 & 0.054 & 0.772 & 0.058 & 0.769 & 0.059 & 0.771 & 0.066 \\\\ \n & 0.75 & 0.778 & 0.090 & 0.794 & 0.087 & 0.786 & 0.095 & 0.796 & 0.090 & 0.787 & 0.089 & 0.790 & 0.092 \\\\ \\hline\nBS & 0 & 0.147 & 0.011 & 0.153 & 0.010 & 0.157 & 0.008 & 0.153 & 0.010 & 0.157 & 0.008 & 0.154 & 0.009 \\\\ \n & 0.25 & 0.117 & 0.014 & 0.121 & 0.017 & 0.121 & 0.016 & 0.120 & 0.017 & 0.121 & 0.016 & 0.121 & 0.017 \\\\ \n & 0.5 & 0.075 & 0.019 & 0.077 & 0.019 & 0.077 & 0.017 & 0.077 & 0.019 & 0.077 & 0.018 & 0.076 & 0.019 \\\\ \n & 0.75 & 0.063 & 0.017 & 0.063 & 0.018 & 0.063 & 0.017 & 0.063 & 0.018 & 0.063 & 0.018 & 0.064 & 0.018 \\\\ \\hline\n \\multicolumn{14}{c}{Single effects on both causes} \\\\\n \\hline\nAUC & 0 & 0.733 & 0.027 & 0.729 & 0.026 & 0.723 & 0.021 & 0.708 & 0.026 & 0.723 & 0.020 & 0.709 & 0.025 \\\\ \n & 0.25 & 0.762 & 0.035 & 0.752 & 0.036 & 0.743 & 0.035 & 0.734 & 0.038 & 0.746 & 0.034 & 0.736 & 0.037 \\\\ \n & 0.5 & 0.756 & 0.073 & 0.743 & 0.082 & 0.736 & 0.079 & 0.725 & 0.071 & 0.733 & 0.079 & 0.736 & 0.077 \\\\ \n & 0.75 & 0.756 & 0.073 & 0.730 & 0.071 & 0.741 & 0.064 & 0.736 & 0.056 & 0.730 & 0.066 & 0.743 & 0.060 \\\\ \n\\hline\nBS & 0 & 0.149 & 0.011 & 0.151 & 0.011 & 0.156 & 0.011 & 0.156 & 0.010 & 0.156 & 0.011 & 0.156 & 0.010 \\\\ \n & 0.25 & 0.134 & 0.017 & 0.136 & 0.017 & 0.138 & 0.016 & 0.139 & 0.017 & 0.137 & 0.017 & 0.139 & 0.017 \\\\ \n & 0.5 & 0.124 & 0.017 & 0.124 & 0.018 & 0.127 & 0.018 & 0.128 & 0.017 & 0.128 & 0.018 & 0.126 & 0.017 \\\\ \n & 0.75 & 0.100 & 0.020 & 0.106 & 0.020 & 0.104 & 0.018 & 0.106 & 0.019 & 0.106 & 0.019 & 0.105 & 0.019 \\\\ \n\\hline\n \\multicolumn{14}{c}{Opposite effects on the two causes} \\\\\n \\hline\nAUC & 0 & 0.784 & 0.021 & 0.791 & 0.027 & 0.765 & 0.027 & 0.757 & 0.026 & 0.761 & 0.029 & 0.758 & 0.030 \\\\ \n& 0.25 & 0.762 & 0.042 & 0.762 & 0.039 & 0.753 & 0.048 & 0.745 & 0.048 & 0.745 & 0.039 & 0.745 & 0.040 \\\\ \n & 0.5 & 0.758 & 0.078 & 0.735 & 0.073 & 0.716 & 0.070 & 0.710 & 0.065 & 0.720 & 0.056 & 0.697 & 0.060 \\\\ \n & 0.75 & 0.732 & 0.112 & 0.691 & 0.105 & 0.710 & 0.115 & 0.693 & 0.137 & 0.706 & 0.114 & 0.706 & 0.132 \\\\ \n\\hline\nBS & 0 & 0.138 & 0.011 & 0.137 & 0.011 & 0.146 & 0.012 & 0.149 & 0.011 & 0.147 & 0.012 & 0.149 & 0.012 \\\\ \n& 0.25 & 0.136 & 0.017 & 0.137 & 0.019 & 0.140 & 0.021 & 0.143 & 0.022 & 0.143 & 0.020 & 0.143 & 0.022 \\\\ \n & 0.5 & 0.145 & 0.029 & 0.153 & 0.028 & 0.156 & 0.026 & 0.160 & 0.025 & 0.159 & 0.023 & 0.164 & 0.025 \\\\ \n & 0.75 & 0.145 & 0.034 & 0.150 & 0.038 & 0.148 & 0.034 & 0.154 & 0.032 & 0.158 & 0.039 & 0.157 & 0.033 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Two-stage Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers and Competing Risk Outcomes", "authors": ["Taban Baghfalaki", "Reza Hashemi", "Christophe Tzourio", "Catherine Helmer", "Helene Jacqmin-Gadda"], "url": "https://arxiv.org/abs/2412.03797v1", "attribution": "\"A Two-stage Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers and Competing Risk Outcomes\" by Taban Baghfalaki, Reza Hashemi, Christophe Tzourio, Catherine Helmer, and Helene Jacqmin-Gadda, arXiv:2412.03797v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2207.13319v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lr}\nMacro Factor & PC1 Loading \\\\\n\\hline\nReal disposable income growth & -0.229 \\\\\nReal GDP growth & -0.525 \\\\\nChange House Price Index & -0.467 \\\\\nCPI inflation rate & -0.079 \\\\\nChange unemployment & 0.529 \\\\\nChange Dow & -0.293 \\\\\nChange Treasury Spread & 0.287 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Loadings of first principal component on macro variables.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Should Bank Stress Tests Be Fair?", "authors": ["Paul Glasserman", "Mike Li"], "url": "https://arxiv.org/abs/2207.13319v2", "attribution": "\"Should Bank Stress Tests Be Fair?\" by Paul Glasserman and Mike Li, arXiv:2207.13319v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.17244v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Paired t-Test Results (CMG vs. Other Models)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|}\n\\hline\n\\textbf{Comparison} & \\textbf{p-value} \\\\\n\\hline\nCMG vs. BiLSTM & 0.0063 \\\\\nCMG vs. Gaussian Naive Bayes & 0.0001 \\\\\nCMG vs. GRU & 0.0153 \\\\\nCMG vs. Logistic Regression & 0.0006 \\\\\nCMG vs. LSTM & 0.0073 \\\\\nCMG vs. Multi Logistic Reg. & 0.0000 \\\\\nCMG vs. Random Forest & 0.0001 \\\\\nCMG vs. XGBoost & 0.0008 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data", "authors": ["Arif Pathan"], "url": "https://arxiv.org/abs/2506.17244v1", "attribution": "\"Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data\" by Arif Pathan, arXiv:2506.17244v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.08732v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{PLCC Performance Comparison with or without LR image as Reference}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\nMethods \t& SISAR\t& Waterloo-15 & CVIU-17 &QADS\\\\ \\midrule\n w/o \t\t&0.8610\t \t&0.6207\t \t&0.5024 \t&{\\color[HTML]{CB0000} \\textbf{0.8403}}\t\\\\\n w/\t\t&{\\color[HTML]{CB0000} \\textbf{0.9143}} \t&{\\color[HTML]{CB0000} \\textbf{0.8232}} \t&{\\color[HTML]{CB0000} \\textbf{0.6440}}\t\t&0.8173\t\\\\ \\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning-Based Quality Assessment for Image Super-Resolution", "authors": ["Tiesong Zhao", "Yuting Lin", "Yiwen Xu", "Weiling Chen", "Zhou Wang"], "url": "https://arxiv.org/abs/2012.08732v1", "attribution": "\"Learning-Based Quality Assessment for Image Super-Resolution\" by Tiesong Zhao, Yuting Lin, Yiwen Xu, Weiling Chen, and Zhou Wang, arXiv:2012.08732v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12295v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c|c|}\n \\hline\n $k$ & 1 & 2 & 3 & 4 \\\\ \\hline\n MSE & $5.0 \\times 10^{-13}$ & $2.5 \\times 10^{-11}$ & $2.5 \\times 10^{-11}$ & $3.1 \\times 10^{-10}$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{We can learn up to $4$ iterations of GD at once with our current training techniques. Model stability becomes a bottleneck with harder tasks.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Towards Learning High-Precision Least Squares Algorithms with Sequence Models", "authors": ["Jerry Liu", "Jessica Grogan", "Owen Dugan", "Ashish Rao", "Simran Arora", "Atri Rudra", "Christopher Ré"], "url": "https://arxiv.org/abs/2503.12295v1", "attribution": "\"Towards Learning High-Precision Least Squares Algorithms with Sequence Models\" by Jerry Liu, Jessica Grogan, Owen Dugan, Ashish Rao, Simran Arora, Atri Rudra, and Christopher Ré, arXiv:2503.12295v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.06261v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Prediction accuracy of multiple binary classification problems $\\left( N=64\\right) $.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\t\t\t& 0.987 & 0.99 & 0.996 & 0.991 & 0.988 & 0.987 & 0.987 & 0.991 \\\\\n\t\t\t& 0.993 & 0.989 & 0.985 & 0.99 & 0.995 & 0.99 & 0.987 & 0.989 \\\\\n\t\t\t& 0.995 & 0.991 & 0.997 & 1 & 0.995 & 0.989 & 0.998 & 0.993 \\\\\n\t\t\t& 0.997 & 1 & 0.987 & 0.991 & 0.989 & 0.988 & 0.988 & 0.987 \\\\\n\t\t\t& 0.99 & 0.989 & 0.994 & 0.995 & 0.987 & 0.987 & 0.98 & 0.99 \\\\\n\t\t\t& 0.99 & 0.987 & 0.989 & 0.988 & 0.992 & 0.991 & 0.99 & 0.994 \\\\\n\t\t\t& 0.99 & 0.994 & 0.99 & 0.989 & 0.994 & 0.996 & 0.988 & 0.991 \\\\\n\t\t\t& 0.982 & 0.993 & 0.994 & 0.977 & 0.99 & 0.99 & 0.992 & 0.994 \\\\\n\t \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Reconfigurable Intelligent Surface Based Hybrid Precoding for THz Communications", "authors": ["Yu Lu", "Mo Hao", "Richard MAcKenzie"], "url": "https://arxiv.org/abs/2012.06261v2", "attribution": "\"Reconfigurable Intelligent Surface Based Hybrid Precoding for THz Communications\" by Yu Lu, Mo Hao, and Richard MAcKenzie, arXiv:2012.06261v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.15616v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experimental results of our experimental setup using spoken English data}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrcc}\n\\toprule\nmodel & type & mean & max & sd & entropy \\\\ \n\\midrule\n\\multirow{2}{*}{wav2vec} & Large & \\textbf{-69.1} & \\textbf{68.2} & 64.0 & \\textbf{-69.9} \\\\\n& VQ & -60.1 & 58.4 & \\textbf{68.3} & -69.0 \\\\\n\\midrule[\\heavyrulewidth]\n\\multirow{5}{*}{\\parbox{2cm}{wav2vec2 \\\\ ASR}} & base 10m & 6.5 & 23.4 & 26.8 & -23.0 \\\\\n& base 100h & -23.8 & 45.5 & 46.3 & \\textbf{-39.2} \\\\\n& base 960h & \\textbf{-45.7} & \\textbf{50.6} & \\textbf{52.4} & -37.3 \\\\\n& large 960h & -9.9 & 39.9 & 40.3 & -25.7 \\\\\n& lv60k 960h & 32.4 & -1.6 & 45.8 & -37.4 \\\\\n& voxpop.\\ en & -39.0 & 32.1 & 42.7 & -43.6 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Uncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Prediction", "authors": ["Aditya Ravuri", "Erica Cooper", "Junichi Yamagishi"], "url": "https://arxiv.org/abs/2312.15616v1", "attribution": "\"Uncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Prediction\" by Aditya Ravuri, Erica Cooper, and Junichi Yamagishi, arXiv:2312.15616v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.14813v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cccc|}\n \\hline\n \\textbf{beta} & \\textbf{0.5} & \\textbf{1} & \\textbf{2} & \\textbf{5} \\\\ \\hline\n \\textbf{N=2} & 1.46\\% & 1.26\\% & 1.01\\% & 0.60\\% \\\\\n \\textbf{N=3} & 0.10\\% & 0.05\\% & 0.02\\% & 0.02\\% \\\\ \n \\textbf{N=5} & 0.01\\% & 0.01\\% & 0.01\\% & 0.01\\% \\\\ \n \\textbf{N=8} & 0.01\\% & 0.01\\% & 0.01\\% & 0.00\\% \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Relative absolute difference for different $n$ and $\\beta$ on the option price compared to $n=10$, with $Y_0=D, h_0=1, \\alpha=0.1, T_0=2$, strike $K=2$ and the time to maturity $\\vartheta=1$. Pure jump case.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Pricing Options on Forwards in Function-Valued Affine Stochastic Volatility Models", "authors": ["Jian He", "Sven Karbach", "Asma Khedher"], "url": "https://arxiv.org/abs/2508.14813v1", "attribution": "\"Pricing Options on Forwards in Function-Valued Affine Stochastic Volatility Models\" by Jian He, Sven Karbach, and Asma Khedher, arXiv:2508.14813v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.08696v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The Dual-Modality returns vector forecasting metrics. Accuracy (the upper row), MAPE (the lower row) in percentage.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|rr|rr|rr|rr|rr|rr|rr|rr|rr|}\n \\hline\n \\multirow{2}{*}{Model} & \n \\multicolumn{2}{c|}{Metals and Mining} &\n \\multicolumn{2}{c|}{Oil and Gas} &\n \\multicolumn{2}{c|}{Consumer Sector} &\n \\multicolumn{2}{c|}{Construction} &\n \\multicolumn{2}{c|}{Telecommunications} &\n \\multicolumn{2}{c|}{Transport} &\n \\multicolumn{2}{c|}{Finance} &\n \\multicolumn{2}{c|}{Chemical Industry} &\n \\multicolumn{2}{c|}{Power Engineering} \\\\\n \\cline{2-19}\n & MTLR & TRMK & SNGS & SIBN & MGNT & LENT & PIKK & SMLT & MTSS & RTKMP & AFLT & FLOT & BSPB & SFIN & PHOR & KZOSP & HYDR & MRKC\\\\\n \\hline\n \\multirow{2}{*}{vanilla LSTM}\n & \\textbf{56.364\\%} & \\textbf{56.364\\%} % MTLR | TRMK\n & 50.303\\% & \\textbf{58.182\\%} % SNGS | SIBN\n & 46.667\\% & \\textbf{56.364\\%} % MGNT | LENT\n & 49.091\\% & \\textbf{53.939\\%} % PIKK | SMLT\n & \\textbf{56.970\\%} & \\textbf{55.152\\%} % MTSS | RTKMP\n & \\textbf{55.152\\%} & 47.273\\% % AFLT | FLOT\n & 46.061\\% & 49.697\\% % BSPB | SFIN\n & 41.818\\% & \\textbf{57.576\\%} % PHOR | KZOSP\n & 59.394\\% & 40.000\\% % HYDR | MRKC\n \\\\\n & 0.410\\% & 0.362\\% % MTLR | TRMK\n & 0.352\\% & 0.341\\% % SNGS | SIBN\n & 0.331\\% & 0.371\\% % MGNT | LENT\n & 0.484\\% & 0.328\\% % PIKK | SMLT\n & 0.541\\% & 0.246\\% % MTSS | RTKMP\n & 0.419\\% & \\textbf{0.258\\%} % AFLT | FLOT\n & 0.410\\% & 0.447\\% % BSPB | SFIN\n & 0.231\\% & 0.458\\% % PHOR | KZOSP\n & 0.380\\% & 0.768\\% % HYDR | MRKC\n \\\\\\hline\n \\multirow{2}{*}{LSTM\\_RuBert\\_SUM}\n & 39.394\\% & 35.152\\% % MTLR | TRMK\n & 53.939\\% & \\textbf{58.182\\%} % SNGS | SIBN\n & \\textbf{53.333\\%} & 49.091\\% % MGNT | LENT\n & 50.303\\% & 38.788\\% % PIKK | SMLT\n & 53.939\\% & 49.697\\% % MTSS | RTKMP\n & 51.515\\% & 43.636\\% % AFLT | FLOT\n & 47.879\\% & 44.848\\% % BSPB | SFIN\n & 53.333\\% & 42.424\\% % PHOR | KZOSP\n & 58.788\\% & 42.424\\% % HYDR | MRKC\n \\\\\n & 0.409\\% & 0.392\\% % MTLR | TRMK\n & 0.865\\% & 0.265\\% % SNGS | SIBN\n & 0.417\\% & 0.400\\% % MGNT | LENT\n & 0.462\\% & \\textbf{0.200\\%} % PIKK | SMLT\n & 0.473\\% & 0.274\\% % MTSS | RTKMP\n & 0.641\\% & 0.532\\% % AFLT | FLOT\n & 0.406\\% & 0.445\\% % BSPB | SFIN\n & 0.264\\% & 0.492\\% % PHOR | KZOSP\n & 0.326\\% & 0.742\\% % HYDR | MRKC\n \\\\\\hline\n \\multirow{2}{*}{LSTM\\_RuBert\\_MEAN}\n & 38.788\\% & 42.424\\% % MTLR | TRMK\n & \\textbf{58.182\\%} & \\textbf{58.182\\%} % SNGS | SIBN\n & 47.879\\% & 50.909\\% % MGNT | LENT\n & \\textbf{57.576\\%} & 46.061\\% % PIKK | SMLT\n & 55.152\\% & 45.455\\% % MTSS | RTKMP\n & 50.303\\% & \\textbf{52.121\\%} % AFLT | FLOT\n & 50.909\\% & 47.273\\% % BSPB | SFIN\n & 55.152\\% & 41.212\\% % PHOR | KZOSP\n & 55.758\\% & \\textbf{43.030\\%} % HYDR | MRKC\n \\\\\n & 0.410\\% & \\textbf{0.192\\%} % MTLR | TRMK\n & 1.824\\% & 0.216\\% % SNGS | SIBN\n & 0.299\\% & 0.359\\% % MGNT | LENT\n & 0.436\\% & 0.270\\% % PIKK | SMLT\n & 0.368\\% & 0.271\\% % MTSS | RTKMP\n & 0.348\\% & 0.262\\% % AFLT | FLOT\n & 0.326\\% & 0.390\\% % BSPB | SFIN\n & 0.238\\% & 0.491\\% % PHOR | KZOSP\n & 0.321\\% & 0.839\\% % HYDR | MRKC\n \\\\\\hline\n \\multirow{2}{*}{LSTM\\_QWEN\\_SUM}\n & 45.455\\% & 36.364\\% % MTLR | TRMK\n & 44.848\\% & 39.394\\% % SNGS | SIBN\n & 46.061\\% & 53.333\\% % MGNT | LENT\n & 47.273\\% & 36.364\\% % PIKK | SMLT\n & 47.879\\% & 44.848\\% % MTSS | RTKMP\n & 45.455\\% & 43.636\\% % AFLT | FLOT\n & 47.879\\% & \\textbf{56.970\\%} % BSPB | SFIN\n & \\textbf{60.000\\%} & 48.485\\% % PHOR | KZOSP\n & 47.879\\% & 42.424\\% % HYDR | MRKC\n \\\\\n & 0.522\\% & 0.504\\% % MTLR | TRMK\n & 0.307\\% & 0.368\\% % SNGS | SIBN\n & 0.307\\% & 0.346\\% % MGNT | LENT\n & 0.529\\% & 0.311\\% % PIKK | SMLT\n & 0.316\\% & \\textbf{0.171\\%} % MTSS | RTKMP\n & 0.259\\% & 0.392\\% % AFLT | FLOT\n & 0.369\\% & \\textbf{0.195\\%} % BSPB | SFIN\n & 0.354\\% & 0.369\\% % PHOR | KZOSP\n & 0.292\\% & 0.660\\% % HYDR | MRKC\n \\\\\\hline\n \\multirow{2}{*}{LSTM\\_QWEN\\_MEAN}\n & 52.121\\% & 35.758\\% % MTLR | TRMK\n & 49.697\\% & 47.879\\% % SNGS | SIBN\n & 48.485\\% & 52.121\\% % MGNT | LENT\n & 53.333\\% & 43.030\\% % PIKK | SMLT\n & 45.455\\% & 44.242\\% % MTSS | RTKMP\n & 52.121\\% & 43.636\\% % AFLT | FLOT\n & \\textbf{52.121\\%} & \\textbf{56.970\\%} % BSPB | SFIN\n & 44.848\\% & 49.697\\% % PHOR | KZOSP\n & \\textbf{61.212\\%} & 41.818\\% % HYDR | MRKC\n \\\\\n & \\textbf{0.246}\\% & 0.419\\% % MTLR | TRMK\n & \\textbf{0.106\\%} & \\textbf{0.165\\%} % SNGS | SIBN\n & \\textbf{0.235\\%} & \\textbf{0.331\\%} % MGNT | LENT\n & \\textbf{0.322\\%} & 0.241\\% % PIKK | SMLT\n & \\textbf{0.193\\%} & 0.178\\% % MTSS | RTKMP\n & \\textbf{0.182\\%} & 0.345\\% % AFLT | FLOT\n & \\textbf{0.227\\%} & 0.272\\% % BSPB | SFIN\n & \\textbf{0.219\\%} & \\textbf{0.352\\%} % PHOR | KZOSP\n & \\textbf{0.178\\%} & \\textbf{0.543\\%} % HYDR | MRKC\n \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market", "authors": ["Kasymkhan Khubiev", "Mikhail Semenov"], "url": "https://arxiv.org/abs/2503.08696v1", "attribution": "\"Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market\" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2503.08696v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12856v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Error analysis for Lorenz's system example using NR method}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\nvariable&bias&MAPE&MAE&RMSE&R$^2$\\\\ \\hline\n$x_1$&0.0392&0.2008&1.1157&1.3487&0.9498\\\\\n$x_2$&0.1503&0.2391&1.3254&1.6295&0.9420\\\\\n$x_3$&0.5704&0.0727&1.4757&1.8549&0.9380\\\\\n$f_1(\\cdot)$&1.0479&5.1133&14.3894&17.8915&0.7762\\\\\n$f_2(\\cdot)$&0.7459&1.1711&13.4830&17.2733&0.9036\\\\\n$f_3(\\cdot)$&-0.3850&2.4390&16.7952&22.1846&0.8856\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods", "authors": ["S. Syafiie", "Aries Subiantoro", "Vivi Andasari", "Fernando Tadeo"], "url": "https://arxiv.org/abs/2501.12856v1", "attribution": "\"Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods\" by S. Syafiie, Aries Subiantoro, Vivi Andasari, and Fernando Tadeo, arXiv:2501.12856v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10866v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Detailed information of the restricted output network architecture}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n\t\t\t\\hline\n\t\t\tLayer number & Layer & output shape & number of parameter & activation function \\\\ \\hline\n\t\t\t1 & dense\\_1 (Dense) & (None, 24) & 600 & relu \\\\ \\hline\n\t\t\t2 & dropout\\_1 (Dropout) & (None, 24) & 0 & - \\\\ \\hline\n\t\t\t3 & dense\\_2 (Dense) & (None, 500) & 12500 & relu \\\\ \\hline\n\t\t\t4 & dropout\\_2 (Dropout) & (None, 500) & 0 & - \\\\ \\hline\n\t\t\t5 & dense\\_3 (Dense) & (None, 500) & 250500 & relu \\\\ \\hline\n\t\t\t6 & dropout\\_3 (Dropout) & (None, 500) & 0 & - \\\\ \\hline\n\t\t\t7 & dense\\_4 (Dense) & (None, 500) & 250500 & relu \\\\ \\hline\n\t\t\t8 & dropout\\_4 (Dropout) & (None, 500) & 0 & - \\\\ \\hline\n\t\t\t9 & dense\\_5 (Dense) & (None, 500) & 250500 & relu \\\\ \\hline\n\t\t\t10 & dense\\_6 (Dense) & (None, 48) & 24048 & sigmoid \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Deep neural network-based automatic metasurface design with a wide frequency range", "authors": ["Fardin Ghorbani", "Sina Beyraghi", "Javad Shabanpour", "Homayoon Oraizi", "Hossein Soleimani", "Mohammad Soleimani"], "url": "https://arxiv.org/abs/2101.10866v1", "attribution": "\"Deep neural network-based automatic metasurface design with a wide frequency range\" by Fardin Ghorbani, Sina Beyraghi, Javad Shabanpour, Homayoon Oraizi, Hossein Soleimani, and Mohammad Soleimani, arXiv:2101.10866v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.19294v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fitting Parameters for the Function in Equation~()}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrr}\n \\toprule\n City & \\multicolumn{2}{c}{$\\alpha$} & \\multicolumn{2}{c}{$\\beta$} & \\multicolumn{2}{c}{$\\gamma$} \\\\\n & Weekday & Weekend & Weekday & Weekend & Weekday & Weekend \\\\\n \\midrule\n New York & $0.129$ & $0.153$ & $5.6 \\times 10^{-4}$ & $2.5 \\times 10^{-4}$ & $1.16$ & $1.26$ \\\\\n London & $0.267$ & $0.289$ & $3.6 \\times 10^{-8}$ & $4.4 \\times 10^{-8}$ & $2.57$ & $2.54$ \\\\\n Tokyo & $0.314$ & $0.324$ & $2.2 \\times 10^{-6}$ & $2.1 \\times 10^{-5}$ & $2.06$ & $1.70$ \\\\\n Boston & $0.305$ & $0.379$ & $2.0 \\times 10^{-5} $ & $1.1 \\times 10^{-5}$ & $1.99$ & $2.08$ \\\\\n Chicago & $0.221$ & $0.224$ & $1.0 \\times 10^{-4}$ & $1.3 \\times 10^{-4}$ & $1.62$ & $1.57$ \\\\\n Washington D.C. & $0.230$ & $0.157$ & $3.7 \\times 10^{-5}$ & $2.0 \\times 10^{-3}$ & $1.82$ & $1.18$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Spatio-Temporal Differences in Bike Sharing Usage: A Tale of Six Cities", "authors": ["Shu-ichi Kinoshita", "Yuya Bando", "Hiroki Sayama"], "url": "https://arxiv.org/abs/2412.19294v1", "attribution": "\"Spatio-Temporal Differences in Bike Sharing Usage: A Tale of Six Cities\" by Shu-ichi Kinoshita, Yuya Bando, and Hiroki Sayama, arXiv:2412.19294v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.11619v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\nCombination & First & Second & Metric (PSNR/SSIM) \\\\ \\midrule\n1. & App. Flow & - & 32.55 / 0.891 \\\\\n2. & Intensity & - & 32.31 / 0.891 \\\\\n3. & App. Flow & App. Flow & 32.35 / 0.903 \\\\\n4. & Intensity & Intensity & 32.32 / 0.903 \\\\\n5. & Intensity & App. Flow & 33.01 / 0.904 \\\\\n6. & App. Flow & Intensity & \\textbf{33.04 / 0.904} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Quantitative evaluation of multi-stage (first and second) residual estimation on \\textit{Flower} dataset. Appearance flow and intensity denotes ${L}_{rf}(\\textbf{x},\\textbf{u})$ and ${L}_{ri}(\\textbf{x},\\textbf{u})$, respectively.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Joint Spatial and Angular Super-Resolution from a Single Image", "authors": ["Andre Ivan", "Williem", "In Kyu Park"], "url": "https://arxiv.org/abs/1911.11619v3", "attribution": "\"Joint Spatial and Angular Super-Resolution from a Single Image\" by Andre Ivan, Williem, and In Kyu Park, arXiv:1911.11619v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12432v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of physicians on duty in the weekdays (WD) and in the public holidays (PH).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\n & WD & PH \\\\ \\hline\n Morning (8.00 a.m. -- 2.00 p.m.) & 2 & 1 \\\\ %\\hline\n Afternoon (2.00 p.m. -- 9.00 p.m.) & 2 & 1 \\\\ %\\hline\n Night (9.00 p.m. -- 8.00 a.m.) & 1 & 1 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Effect of different patient peak arrivals on an Emergency Department via discrete event simulation", "authors": ["G. Fava", "T. Giovannelli", "M. Messedaglia", "M. Roma"], "url": "https://arxiv.org/abs/2101.12432v1", "attribution": "\"Effect of different patient peak arrivals on an Emergency Department via discrete event simulation\" by G. Fava, T. Giovannelli, M. Messedaglia, and M. Roma, arXiv:2101.12432v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23211v2_tex_table21.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Location of estimated change points from the video data set for the ``\\textit{first person walks out of the lobby} (so-called action 1)\" and ``\\textit{second person walks into the lobby} (so-called action 2)\" using DP-UNIVAR method. Note that the true value is 116 for action 1 and 174 for action 2.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\nAction & Pixel & Estimated change points based on \\textbf{DP-UNIVAR} method \\\\ \\hline\\hline\n1 & 700 & $\\{48, 91, 104, 114\\}$ \\\\\n& 702 & $\\{83, 90, 101, 108 \\}$ \\\\\n & 731 & $\\{93, 109 \\}$ \\\\\n& 762 & $\\{97, 106 \\}$ \\\\\n& 764 & $\\{104, 118 \\}$ \\\\\n\\hline\n2 & 48 & $\\{126, 144, 151, 182, 188, 207, 218, 224, 230, 260, 274 \\}$ \\\\\n& 78 & $\\{157, 178, 248, 278, 285, 287 \\}$ \\\\\n& 110 & $\\{133, 145, 152, 173, 176, 231, 234, 276, 283 \\}$ \\\\\n& 174 & $\\{121, 128, 157, 170, 227, 246, 262, 272, 284 \\}$ \\\\\n& 209 & $\\{138, 147, 158, 164, 203, 254, 259 \\}$ \\\\\n& 241 & $\\{142, 219, 260 \\}$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models", "authors": ["Sepideh Mosaferi", "Abolfazl Safikhani", "Peiliang Bai"], "url": "https://arxiv.org/abs/2503.23211v2", "attribution": "\"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models\" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.10458v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\tBattery &$\\eta_{\\text{fric}}$ & Profit & $P_{cyc}$ & SS & Waste & Cycles & ExPB \\\\ \n\t\t\tModel& & euros & & (\\%) & (kWh) & & yrs. \\\\\n\t\t\t\\hline\n\t\t\t\\multicolumn{8}{|c|}{For prosumer U2} \\\\\n\t\t\t\\hline\n \\textbf{1kWh,1C-1C} & 0.796 & 12.85 & 0.0962 & 32.45 & 25.70 & 47.38 & 4.54 \\\\\n \\textbf{1kWh,2C-2C} & 0.797 & 14.45 & 0.0796 & 32.53 & 24.99 & 47.44 & 5.19 \\\\\n\t\t\t\\hline\n\t\t\t\\multicolumn{8}{|c|}{For prosumer U8}\\\\\n\t\t\t\\hline\n \\textbf{1kWh,1C-1C} & 0.796 & 38.17 & 0.6805 & 29.33 & 26.82 & 44.62 & 1.53 \\\\\n \\textbf{1kWh,2C-2C} & 0.796 & 38.50 & 0.5829 & 29.37 & 25.16 & 47.65 & 1.95 \\\\\n\t\t\t\\hline\n\t\t\t\\textbf{2kWh,1C-1C} & 0.9398 &43.98 & 0.3143 & 29.67 & 13.71 & 52.29 & 1.61 \\\\\n\t\t\t\\textbf{2kWh,1C-1C} & 0.9397 & 43.47 & 0.3286 & 29.73 & 14.34 & 43.17 & 2.68\\\\\n \\textbf{2kWh,2C-2C} & 0.9397 & 47.85 & 0.3228 & 29.76 & 12.91 & 43.68 & 3.13 \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sizing and Profitability of Energy Storage for Prosumers in Madeira, Portugal", "authors": ["Md Umar Hashmi", "Jonathan Cavaleiro", "Lucas Pereira", "Ana Bušić"], "url": "https://arxiv.org/abs/1911.10458v1", "attribution": "\"Sizing and Profitability of Energy Storage for Prosumers in Madeira, Portugal\" by Md Umar Hashmi, Jonathan Cavaleiro, Lucas Pereira, and Ana Bušić, arXiv:1911.10458v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11701v3_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MILLI hyperparameters.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\t\t\t\\toprule\n\t\t\tDataset & Sample Size & $\\alpha$ & $\\beta$ & $\\mathop{\\mathbb{E}}[|z|]$ \\\\\n\t\t\t\\midrule\n\t\t\tSIVAL & 200 & 0.05 & -0.01 & 13 \\\\\n\t\t\t4-MNIST-Bags & 150 & 0.05 & 0.01 & 16 \\\\\n\t\t\tCRC & 1000 & 0.008 & -5.0 & 2 \\\\\n\t\t\tMUSK & 150 & 0.3 & -1.0 & 2 \\\\\n\t\t\tTEF & 150 & 0.3 & 0.01 & 3 \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Model Agnostic Interpretability for Multiple Instance Learning", "authors": ["Joseph Early", "Christine Evers", "Sarvapali Ramchurn"], "url": "https://arxiv.org/abs/2201.11701v3", "attribution": "\"Model Agnostic Interpretability for Multiple Instance Learning\" by Joseph Early, Christine Evers, and Sarvapali Ramchurn, arXiv:2201.11701v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.04251v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of Cutting-Plane Algorithm (Not Warm-Started)}\n\\begin{tabular}{lrrrrrrrr}\n\\toprule\n& \\multicolumn{5}{c}{Cutting-Plane Algorithm} & \\multicolumn{2}{c}{Primal bound} & \\\\\n\\cmidrule(l{0.5em}r{0.40em}){2-6} \\cmidrule(l{0.5em}r{0.40em}){7-8}\nCase & Objective & Time & Computed & Added & Rounds & Objective & Time & \\\\\n\\midrule\n9241pegase & 309221.81 & 378.82 & 135599 & 29875 & 23 & 315911.56 & 96.74 & \\\\\n9241pegase-api & 6924650.57 & 277.32 & 128316 & 30230 & 21 & 7068721.98 & 73.85 & \\\\\n9241pegase-sad & 6141202.28 & 386.51 & 113686 & 27273 & 21 & 6318468.57 & 33.92 & \\\\\n9591goc-api & 1346373.10 & 187.26 & 87812 & 22469 & 22 & 1570263.74 & 42.85 & \\\\\n9591goc-sad & 1055493.25 & 246.87 & 90153 & 20514 & 27 & 1167400.79 & 28.15 & \\\\\nACTIVSg10k & 2476851.62 & 132.16 & 60803 & 18183 & 19 & 2485898.75 & 76.71 & \\\\\n10000goc-api & 2502026.03 & 147.12 & 73084 & 19666 & 24 & 2678659.51 & 23.46 & \\\\\n10000goc-sad & 1387303.02 & 114.97 & 58984 & 18528 & 17 & 1490209.66 & 103.06 & \\\\\n10192epigrids-api & 1849488.30 & 152.87 & 97921 & 24882 & 22 & 1977687.11 & 117.15 & \\\\\n10192epigrids-sad & 1672819.53 & 185.02 & 95740 & 23726 & 23 & 1720194.13 & 23.74 & \\\\\n10480goc-api & 2708819.18 & 200.48 & 114967 & 29805 & 21 & 2863484.4 & 38.71 & \\\\\n10480goc-sad & 2287314.69 & 270.38 & 118122 & 28004 & 24 & 2314712.14 & 27.93 & \\\\\n13659pegase & 379084.55 & 841.83 & 176962 & 37297 & 22 & 386108.81 & 1184.15 & \\\\\n13659pegase-api & 9270988.77 & 326.57 & 147479 & 34390 & 19 & 9385711.45 & 44.43 & \\\\\n13659pegase-sad & 8868216.24 & 301.87 & 130682 & 32662 & 19 & 9042198.49 & 42.08 & \\\\\n19402goc-api & 2448812.41 & 440.67 & 213564 & 52388 & 22 & 2583627.35 & 87.33 & \\\\\n19402goc-sad & 1954047.79 & 488.33 & 218291 & 49749 & 25 & 1983807.59 & 64.01 & \\\\\n20758epigrids-api & 3042956.88 & 464.17 & 189436 & 46124 & 25 & 3126508.30 & 61.39 & \\\\\n20758epigrids-sad & 2612551.03 & 379.36 & 180790 & 44624 & 24 & 2638200.23 & 58.11 & \\\\\n24464goc-api & 2560407.12 & 471.14 & 226595 & 57162 & 22 & 2683961.9 & 533.03 & \\\\\n24464goc-sad & 2605128.51 & 506.39 & 222908 & 55242 & 23 & 2653957.66 & 73.87 & \\\\\nACTIVSg25k & 5993266.85 & 592.39 & 156285 & 43851 & 28 & 6017830.61 & 56.69 & \\\\\n30000goc-api & 1531110.84 & 464.16 & 142385 & 41840 & 24 & 1777930.63 & 134.71 & \\\\\n30000goc-sad & 1130733.51* & 147.74 & 76546 & 76546 & 6 & 1317280.55 & 565.05 & \\\\\nACTIVSg70k & 16326225.66 & 1065.76 & 350572 & 123431 & 13 & 16439499.83 & 240.55 & \\\\\n78484epigrids-api & 15877674.54 & 1007.99 & 556893 & 240576 & 10 & 16140427.68 & 1079.03 & \\\\\n78484epigrids-sad & 15175077.19 & 1062.55 & 501202 & 313587 & 8 & 15315885.86 & 343.45 & \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Accurate Linear Cutting-Plane Relaxations for ACOPF", "authors": ["Daniel Bienstock", "Matias Villagra"], "url": "https://arxiv.org/abs/2312.04251v3", "attribution": "\"Accurate Linear Cutting-Plane Relaxations for ACOPF\" by Daniel Bienstock and Matias Villagra, arXiv:2312.04251v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08436v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average over 1000 trials of the number of iterations required to obtain an optimal solution starting from a guess within +/- 80\\% of the known optimum}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc} \n \\cline{2-4}\n & SQP & LSQP & SLCP \\\\ \\hline \n Floudas & 32.79 & 16.29 (-50.32\\%) & 14.92 (-54.50\\%) \\\\ \n Kirschen-Ozturk & 91.18 & 21.06 (-76.90\\%) & 17.66 (-80.63\\%) \\\\ \n Hoburg-0 & - & 19.33 & 17.13 (-11.40\\%) \\\\ \n Hoburg-1 & - & 19.30 & 17.70 (-8.30\\%) \\\\ \n Hoburg-3 & - & 19.33 & 18.02 (-6.77\\%) \\\\ \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Method of Sequential Log-Convex Programming for Engineering Design", "authors": ["Cody Karcher", "Robert Haimes"], "url": "https://arxiv.org/abs/2201.08436v1", "attribution": "\"A Method of Sequential Log-Convex Programming for Engineering Design\" by Cody Karcher and Robert Haimes, arXiv:2201.08436v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.09442v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|c|c|c|}\n\\cline{2-4}\n & aa & aA & AA \\\\ \n\\cline{1-5} \nCase & $r_0$ & $r_1$ & $r_2$ & $r$ \\\\\n\\cline{1-5}\nControl & $s_0$ & $s_1$ & $s_2$ & $s$ \\\\ \n\\cline{1-5}\n& $n_0$ & $n_1$ & $n_2$ & $n$ \\\\\n\\cline{2-5}\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Nomenclature for Contingency Table with one SNP}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On the Use of the Kantorovich-Rubinstein Distance for Dimensionality Reduction", "authors": ["Gaël Giordano"], "url": "https://arxiv.org/abs/2309.09442v1", "attribution": "\"On the Use of the Kantorovich-Rubinstein Distance for Dimensionality Reduction\" by Gaël Giordano, arXiv:2309.09442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.08761v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary statistics - Dry bulb temperature}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\t\t\\midrule\n\t\t\\midrule \n & Mean & SD & Min & Max \\\\\n \\midrule\n Dry bulb temperature & 29.87& 4.52& 11& 43\\\\\n & & & & \\\\\n\\# Days \\textless 23.5°C& 3.87& 8.18& 0& 30\\\\\n\\# Days 23.5°C-25°C& 1.73& 3.43& 0& 24\\\\\n\\# Days 25°C-26.5°C& 2.20& 3.71& 0& 30\\\\\n\\# Days 26.5°C-28°C& 2.87& 3.87& 0& 28\\\\\n\\# Days 28°C-29.5°C& 3.74& 5.05& 0& 23\\\\\n\\# Days 29.5°C-31°C& 3.43& 4.43& 0& 25\\\\\n\\# Days 31°C-32.5°C& 3.11& 4.31& 0& 21\\\\\n\\# Days 32.5°C-34°C& 2.71& 3.77& 0& 19\\\\\n\\# Days 34°C-35.5°C& 2.62& 4.11& 0& 19\\\\\n\\# Days 35.5°C-37°C& 2.09& 3.79& 0& 19\\\\\n\\# Days $\\geq$ 37°C& 1.62& 4.12& 0& 28\\\\\n & & & & \\\\\nHeat wave ( days $\\geq$ 37°)& 0.16& 0.36& 0& 1\\\\\nHeat wave ($\\geq$ 3 days $\\geq$ 37°)& 0.14& 0.34& 0& 1\\\\\n\\# Consecutive days $\\geq$ 37°& 1.11& 3.10& 0& 26\\\\\n & & & & \\\\\n\\# Days \\textless 25.5°C& 6.28& 10.25& 0& 30\\\\\n\\# Days 25.5.5°C-27°C& 2.39& 3.77& 0& 30\\\\\n\\# Days 27°C-28.5°C& 3.15& 4.17& 0& 28\\\\\n\\# Days 28.5°C-30°C& 3.86& 5.18& 0& 26\\\\\n\\# Days 30°C-31.5°C& 3.19& 4.03& 0& 25\\\\\n\\# Days 31.5°C-33°C& 3.06& 4.09& 0& 20\\\\\n\\# Days 33°C-34.5°C& 2.67& 3.96& 0& 20\\\\\n\\# Days 34.5°C-36°C& 2.48& 4.01& 0& 19\\\\\n\\# Days 36°C-37.5°C& 1.82& 3.61& 0& 20\\\\\n\\# Days 37.5°C-39°C& 0.78& 2.07& 0& 15\\\\\n\\# Days $\\geq$ 39°C& 0.32& 1.55& 0& 20\\\\\n & & & & \\\\\nHeat wave ($\\geq$ 2 days $\\geq$ 39°)& 0.05& 0.21& 0& 1\\\\\nHeat wave ($\\geq$ 3 days $\\geq$ 39°)& 0.04& 0.19& 0& 1\\\\\n\\# Consecutive days $\\geq$ 39°& 0.23& 1.04& 0& 13\\\\\n\\hline\nObservations & 30300& & & \\\\\n\\hline\\hline\n\t\\multicolumn{5}{p{8cm}}{} \n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Beyond the heat: The mental health toll of temperature and humidity in India", "authors": ["Manuela Fritz"], "url": "https://arxiv.org/abs/2503.08761v1", "attribution": "\"Beyond the heat: The mental health toll of temperature and humidity in India\" by Manuela Fritz, arXiv:2503.08761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.06043v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\t\t\\hline\n\t\t& $k$-proto. & KAMILA & CPF \\\\\n\t\t\\hline\n\t\tCov Type& \t52603.612& \t283.262 & 17782.344\\\\\n\t\tPoker &\t74835.257\t& 298.656\t & \t30229.664\\\\\n\t\tKDD '99 DOS vs. NORM& -\t& \t416.293\t& \t137283.899\t \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{\t Time (seconds) for each method on large datasets.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Clustering of Big Data with Mixed Features", "authors": ["Joshua Tobin", "Mimi Zhang"], "url": "https://arxiv.org/abs/2011.06043v1", "attribution": "\"Clustering of Big Data with Mixed Features\" by Joshua Tobin and Mimi Zhang, arXiv:2011.06043v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.12281v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{cc}\n $e_2 = \\frac{1}{2}\\cdot[2]$, \\quad \\quad & $e_1 = [1] -\\frac{1}{2}\\cdot[2]$. \\\\\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An algebraic model for rational excisive functors", "authors": ["David Barnes", "Magdalena Kędziorek", "Niall Taggart"], "url": "https://arxiv.org/abs/2412.12281v1", "attribution": "\"An algebraic model for rational excisive functors\" by David Barnes, Magdalena Kędziorek, and Niall Taggart, arXiv:2412.12281v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.15395v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study on ConstraintMatch, results averaged over 5 folds with $n_c=10000$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n Model & \\multicolumn{3}{c}{Test Performance} \\\\\n & ACC & NMI & ARI \\\\\n \\midrule\n SCAN & 45.90 & 46.80 & 30.10 \\\\\n + Constrained & 52.45 & 46.57 & 33.79 \\\\ \n + Pseudo-Labeling & 55.38 & 53.49 & 39.98 \\\\ \n \\textbf{+ Pseudo-Constraining} & \\textbf{57.15} & \\textbf{54.37} & \\textbf{40.59}\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "ConstraintMatch for Semi-constrained Clustering", "authors": ["Jann Goschenhofer", "Bernd Bischl", "Zsolt Kira"], "url": "https://arxiv.org/abs/2311.15395v1", "attribution": "\"ConstraintMatch for Semi-constrained Clustering\" by Jann Goschenhofer, Bernd Bischl, and Zsolt Kira, arXiv:2311.15395v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.19351v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Improvements by adding each of the major design choice of ETTA (evaluated on MusicCaps)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Ablation} & \n\\textbf{FD$_{P}\\!\\downarrow$} & \n\\textbf{KL$_{S}\\!\\downarrow$} &\n\\textbf{CL$_{\\mathrm{M}}\\!\\uparrow$}\n\\\\\n\\hline\nStable Audio Open & 39.96 & 1.81 & 0.41 \\\\\n\\hline\n~+ AF-Synthetic & 26.22 & 1.57 & 0.43 \\\\\n~~+ ETTA-DiT & 20.48 & 1.38 & 0.45 \\\\\n~~~+ OT-CFM, $t\\sim\\mathcal{U}(0, 1)$ & 22.16 & 1.35 & 0.45 \\\\\n~~~~+ $t\\sim\\sigma(\\mathcal{N}(0, 1))$ & 21.59 & 1.41 & 0.45 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "ETTA: Elucidating the Design Space of Text-to-Audio Models", "authors": ["Sang-gil Lee", "Zhifeng Kong", "Arushi Goel", "Sungwon Kim", "Rafael Valle", "Bryan Catanzaro"], "url": "https://arxiv.org/abs/2412.19351v2", "attribution": "\"ETTA: Elucidating the Design Space of Text-to-Audio Models\" by Sang-gil Lee, Zhifeng Kong, Arushi Goel, Sungwon Kim, Rafael Valle, and Bryan Catanzaro, arXiv:2412.19351v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.08206v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Features selection in bipartite representations}\n\\begin{tabular}{cccc}\n\\toprule\n& name & description \\\\\n\\midrule\n\\multirow{7}{*}{Variable} & obj & normalized coefficient of variables in the objective function\\\\\n& v\\_coeff &average coefficient of the variable in all constraints\\\\\n& Nv\\_coeff & degree of variable node in the bipartite representation\\\\\n& max\\_coeff & maximum value among all coefficients of the variable\\\\\n& min\\_coeff &minimum value among all coefficients of the variable\\\\\n&int & binary representation to show if the variable is an\ninteger variable\\\\\n& cur\\_sol & value in the current solution\\\\\n\\midrule\n\\multirow{6}{*}{Constraint} & c\\_coeff & average of all coefficients in the constraint\\\\\n& Nc\\_coeff & degree of constraint nodes in the bipartite representation\\\\\n& rhs &right-hand-side value of the constraint\\\\\n& sense & the sense of the constraint\\\\\n& norm & $\\ell_2$-norm of the coefficient vector(e.g. the vector formed by all coefficients in the constraint.)\\\\\n& cos\\_sim & cosine similarity between the coefficient vectors of the constraint and objective.\\\\\n\\midrule\nEdge& coeff& coefficient of variables in constraints\\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Mixed-Integer Linear Optimization via Learning-Based Two-Layer Large Neighborhood Search", "authors": ["Wenbo Liu", "Akang Wang", "Wenguo Yang", "Qingjiang Shi"], "url": "https://arxiv.org/abs/2412.08206v1", "attribution": "\"Mixed-Integer Linear Optimization via Learning-Based Two-Layer Large Neighborhood Search\" by Wenbo Liu, Akang Wang, Wenguo Yang, and Qingjiang Shi, arXiv:2412.08206v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07174v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\protect MATLAB MCS: $\\mathcal{FOR}$ and $\\mathcal{SAIDI}$ Results}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|l|l|} \n \\hline \\thead{Run} & \\thead{$\\mathcal{FOR}_{PV}$} & \\thead{$\\mathcal{FOR}_{STEAM}$} & \\thead{$\\mathcal{SAIDI}$} \\\\ \n\\hline \\hline\n 1 & 88.55e-2 & 36.18e-3 & 5.8023 \\\\ \n\\hline \n 2 & 107.19e-2 & 40.03e-3 & 6.5045 \\\\ \n\\hline \n 3 & 93.52e-2 & 36.35e-3 & 6.0222 \\\\ \n\\hline \n 5 & 110.17e-2 & 43.03e-3 & 7.0495 \\\\ \n \\hline \n 4 & 95.24e-2 & 38.66e-3 & 6.3960 \\\\ \n\\hline \nAverage & 98.93e-2 & 38.85e-3 & 6.3549 \\\\ \n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Formal FT-based Cause-Consequence Reliability Analysis using Theorem Proving", "authors": ["Mohamed Abdelghany", "Sofiene Tahar"], "url": "https://arxiv.org/abs/2101.07174v1", "attribution": "\"Formal FT-based Cause-Consequence Reliability Analysis using Theorem Proving\" by Mohamed Abdelghany and Sofiene Tahar, arXiv:2101.07174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09541v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Ranges of dimensionless numbers.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n\\hline\\hline\nFroude number $Fr$ & $7$--$31$ \\\\ \nOhnesorge number $Oh$ & $6\\times10^{-3}$--$7\\times10^{-3}$ \\\\ \nReynolds number $Re$ & $7.7\\times10^{2}$--$3.0\\times10^{3}$ \\\\ \nStokes number $St$ & $4.6\\times10^{4}$--$1.8\\times10^{5}$ \\\\ \nWeber number $We$ & 31--472\\\\\n\\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Image features of a splashing drop on a solid surface extracted using a feedforward neural network", "authors": ["Jingzu Yee", "Akinori Yamanaka", "Yoshiyuki Tagawa"], "url": "https://arxiv.org/abs/2201.09541v1", "attribution": "\"Image features of a splashing drop on a solid surface extracted using a feedforward neural network\" by Jingzu Yee, Akinori Yamanaka, and Yoshiyuki Tagawa, arXiv:2201.09541v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.07210v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Scenarios for the Brown-Resnick model with parameters $ \\lambda, \\nu $.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\t\t\\hline\n\t\tScenario & 1 & 2 & 3 & 4 \\\\\n\t\t\\hline\n\t\t$ \\lambda\t$ & $ 0.5 $ & $ 0.5 $ & $ 1.5 $ & $ 1.5 $ \\\\\n\t\t$ \\nu\t\t$ & $ 0.5 $ & $ 1.5 $ & $ 0.5 $ & $ 1.5 $ \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Variational inference for max-stable processes", "authors": ["Patrik Andersson", "Alexander Engberg"], "url": "https://arxiv.org/abs/2212.07210v1", "attribution": "\"Variational inference for max-stable processes\" by Patrik Andersson and Alexander Engberg, arXiv:2212.07210v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08239v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Safety annotations data distribution}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n \\toprule\n Utterance label & Total collected \\\\\n \\midrule\n All & 48,348 \\\\\n Unsafe - Harm & 5,570 \\\\\n Unsafe - Bias & 2,560 \\\\\n Unsafe - Misinformation & 1,260 \\\\\n Safe & 41,810 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "LaMDA: Language Models for Dialog Applications", "authors": ["Romal Thoppilan", "Daniel De Freitas", "Jamie Hall", "Noam Shazeer", "Apoorv Kulshreshtha", "Heng-Tze Cheng", "Alicia Jin", "Taylor Bos", "Leslie Baker", "Yu Du", "YaGuang Li", "Hongrae Lee", "Huaixiu Steven Zheng", "Amin Ghafouri", "Marcelo Menegali", "Yanping Huang", "Maxim Krikun", "Dmitry Lepikhin", "James Qin", "Dehao Chen", "Yuanzhong Xu", "Zhifeng Chen", "Adam Roberts", "Maarten Bosma", "Vincent Zhao", "Yanqi Zhou", "Chung-Ching Chang", "Igor Krivokon", "Will Rusch", "Marc Pickett", "Pranesh Srinivasan", "Laichee Man", "Kathleen Meier-Hellstern", "Meredith Ringel Morris", "Tulsee Doshi", "Renelito Delos Santos", "Toju Duke", "Johnny Soraker", "Ben Zevenbergen", "Vinodkumar Prabhakaran", "Mark Diaz", "Ben Hutchinson", "Kristen Olson", "Alejandra Molina", "Erin Hoffman-John", "Josh Lee", "Lora Aroyo", "Ravi Rajakumar", "Alena Butryna", "Matthew Lamm", "Viktoriya Kuzmina", "Joe Fenton", "Aaron Cohen", "Rachel Bernstein", "Ray Kurzweil", "Blaise Aguera-Arcas", "Claire Cui", "Marian Croak", "Ed Chi", "Quoc Le"], "url": "https://arxiv.org/abs/2201.08239v3", "attribution": "\"LaMDA: Language Models for Dialog Applications\" by Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Vincent Zhao, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Pranesh Srinivasan, Laichee Man, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, and Quoc Le, arXiv:2201.08239v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.12687v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\\hline\n Elastic PCA & $\\Delta= 0$ & $\\Delta = 0.04$ & $\\Delta = 0.08$ & $\\Delta = 0.12$ & $\\Delta = 0.16$ \\\\ \\hline \\hline\n $n=15$ & $0.01$ & $0.07$ & $0.58$ & $0.77$ & $0.86$ \\\\ \\hline\n $n=30$ & $0.02$ & $0.22$ & $0.92$ & $0.99$ & $1.00$ \\\\ \\hline\n $n=50$ & $0.03$ & $0.25$ & $0.95$ & $1.00$ & $1.00$ \\\\ \\hline\n $n=75$ & $0.01$ & $0.39$ & $0.98$ & $1.00$ & $1.00$ \\\\ \\hline\\hline\n Cross-sectional PCA & $\\Delta= 0$ & $\\Delta = 0.04$ & $\\Delta = 0.08$ & $\\Delta = 0.12$ & $\\Delta = 0.16$ \\\\ \\hline \\hline\n $n=15$ & $0.01$ & $0.02$ & $0.03$ & $0.05$ & $0.07$ \\\\ \\hline\n $n=30$ & $0.02$ & $0.09$ & $0.17$ & $0.18$ & $0.30$ \\\\ \\hline\n $n=50$ & $0.02$ & $0.08$ & $0.13$ & $0.34$ & $0.36$ \\\\ \\hline\n $n=75$ & $0.05$ & $0.04$ & $0.24$ & $0.24$ & $0.40$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Proportion of simulations with estimated changepoint at the $\\alpha = 0.05$ level for the amplitude change. (Top) Using the elastic PCA test statistic (Bottom) Using the cross-sectional PCA test statistic of . }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Elastic Functional Changepoint Detection of Climate Impacts from Localized Sources", "authors": ["J. Derek Tucker", "Drew Yarger"], "url": "https://arxiv.org/abs/2211.12687v2", "attribution": "\"Elastic Functional Changepoint Detection of Climate Impacts from Localized Sources\" by J. Derek Tucker and Drew Yarger, arXiv:2211.12687v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.18663v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n Experiment & epochs: & epochs: & epochs: & epochs: \\\\\n & 30+200 & 30+0 & 100+0 & 0+230 \\\\\n \\hline\n 1 & 490 & None & 446 & None \\\\% AC\n 2 & 396 & 658 & 516 & None \\\\% AC\n 3 & 382 & None & None & None \\\\% AC\n 4 & 408 & 650 & 436 & None \\\\ % AC\n 5 & 428 & 706 & 1046 & None \\\\ % Firnov\n 6 & 396 & None & None & 508 \\\\ % Fironov\n 7 & 426 & 890 & 614 & None \\\\ % Fironov\n 8 & None& 598 & None & None \\\\ %Rustem\n 9 & 392 & 464 & 524 & None \\\\ % Shishina\n 10 & None& 626 & 452 & 434 \\\\ %Liuda \n Median & 402& 650 & 516 & 455.5 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs", "authors": ["A. Chervov", "A. Soibelman", "S. Lytkin", "I. Kiselev", "S. Fironov", "A. Lukyanenko", "A. Dolgorukova", "A. Ogurtsov", "F. Petrov", "S. Krymskii", "M. Evseev", "L. Grunvald", "D. Gorodkov", "G. Antiufeev", "G. Verbii", "V. Zamkovoy", "L. Cheldieva", "I. Koltsov", "A. Sychev", "M. Obozov", "A. Eliseev", "S. Nikolenko", "N. Narynbaev", "R. Turtayev", "N. Rokotyan", "S. Kovalev", "A. Rozanov", "V. Nelin", "S. Ermilov", "L. Shishina", "D. Mamayeva", "A. Korolkova", "K. Khoruzhii", "A. Romanov"], "url": "https://arxiv.org/abs/2502.18663v1", "attribution": "\"CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs\" by A. Chervov, A. Soibelman, S. Lytkin, I. Kiselev, S. Fironov, A. Lukyanenko, A. Dolgorukova, A. Ogurtsov, F. Petrov, S. Krymskii, M. Evseev, L. Grunvald, D. Gorodkov, G. Antiufeev, G. Verbii, V. Zamkovoy, L. Cheldieva, I. Koltsov, A. Sychev, M. Obozov, A. Eliseev, S. Nikolenko, N. Narynbaev, R. Turtayev, N. Rokotyan, S. Kovalev, A. Rozanov, V. Nelin, S. Ermilov, L. Shishina, D. Mamayeva, A. Korolkova, K. Khoruzhii, and A. Romanov, arXiv:2502.18663v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16929v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\textbf{Method} & \\multicolumn{2}{c}{\\textbf{STL-10 (in distribution)}} & \\multicolumn{2}{c}{\\textbf{Oxford-IIIT Pet (out of distribution)}} \\\\ \\cline{2-5}\n & Linear Probing & KNN & Logistic Regression & KNN \\\\ \\midrule\n \n SimCLR & 77.71 & 74.92 & 74.80 & 71.48 \\\\\n \n DCL & 78.32 & 75.03 & 74.41 & 70.22 \\\\\n\\midrule\n\\multicolumn{5}{c}{$q_\\phi$ is a Student's t-distribution} \\\\ \\midrule\nt-SimCLR & 85.11 & 83.05 & 83.40 & 81.41 \\\\\n\\textbf{Our Debiasing $\\alpha = 0.2$} & 85.94 & 83.15 & 84.11 & 81.15 \\\\ \n\\textbf{Our Debiasing $\\alpha = 0.4$} & 86.13 & \\textbf{84.14} & 84.07 & \\textbf{84.13} \\\\\n\\textbf{Our Debiasing $\\alpha = 0.6$} & \\textbf{87.18} & 83.58 & \\textbf{84.51} & 83.04 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "I-Con: A Unifying Framework for Representation Learning", "authors": ["Shaden Alshammari", "John Hershey", "Axel Feldmann", "William T. Freeman", "Mark Hamilton"], "url": "https://arxiv.org/abs/2504.16929v1", "attribution": "\"I-Con: A Unifying Framework for Representation Learning\" by Shaden Alshammari, John Hershey, Axel Feldmann, William T. Freeman, and Mark Hamilton, arXiv:2504.16929v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.11166v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc|ccccc}\n\\hline \n$T=1$& \\multicolumn{2}{c}{$p=2$} & \\multicolumn{2}{c|}{$p=3$} &~$T=3$ \n& \\multicolumn{2}{c}{$p=2$} & \\multicolumn{2}{c}{$p=3$} \\\\\n\\hline\n~$h$ & \\hspace{-0.1cm}$\\mu=10$ & \\hspace{-0.1cm}$\\mu=10^5$ & \\hspace{-0.1cm}$\\mu=10$ & \\hspace{-0.1cm}$\\mu=10^5$ &~$h$ & \\hspace{-0.1cm}$\\mu=10$ & \\hspace{-0.1cm}$\\mu=10^5$ & \\hspace{-0.1cm}$\\mu=10$ & \\hspace{-0.1cm}$\\mu=10^5$ \\\\\n\\hline\n 0.125 & 0.237 & 39.64 & 0.222 & 29.05 & 0.375 & 0.101 & 112.7 & 0.093 & 68.06\n \\\\ 0.063 & 0.206 & 10.39 & 0.200 & 10.33 & 0.188 & 0.075 & 26.57 & 0.072 & 20.10\n \\\\ 0.031 & 0.194 & 2.526 & 0.191 & 2.500 & 0.094 & 0.066 & 6.796 & 0.065 & 6.818\n \\\\ 0.016 & 0.188 & 0.730 & 0.187 & 0.729 & 0.047 & 0.063 & 1.645 & 0.063 & 1.650\n \\\\ 0.008 & 0.186 & 0.319 & 0.185 & 0.319 & 0.023 & 0.062 & 0.438 & 0.062 & 0.441\n \\\\ 0.004 & 0.185 & 0.219 & 0.185 & 0.219 & 0.012 & 0.062 & 0.153 & 0.062 & 0.154\n \\\\ 0.002 & 0.184 & 0.194 & 0.184 & 0.193 & 0.006 & 0.061 & 0.084 & 0.061 & 0.085\n \\\\ 0.001 & 0.184 & 0.187 & 0.184 & 0.187 & 0.003 & 0.061 & 0.067 & 0.061 & 0.067 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Discrete coercivity constant of~$a_\\mu(\\cdot,\\L_T \\cdot)$ in the~$H^1$ norm (see~) with maximal regularity splines of degree~$p=2,3$, by varying~$\\mu=10,10^5$,~$T=1,3$, and the mesh size~$h$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Intrinsic unconditional stability in space-time isogeometric approximation of the acoustic wave equation in second-order formulation", "authors": ["Matteo Ferrari", "Ilaria Perugia"], "url": "https://arxiv.org/abs/2503.11166v2", "attribution": "\"Intrinsic unconditional stability in space-time isogeometric approximation of the acoustic wave equation in second-order formulation\" by Matteo Ferrari and Ilaria Perugia, arXiv:2503.11166v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.05922v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Complete Robust Causal Estimates from the DML-PLR Model}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrr}\n\\toprule\n\\textbf{Treatment Variable} & \\textbf{Coeff. ($\\hat{\\theta}$)} & \\textbf{$p$-value} & \\textbf{bias\\_phi} & \\textbf{Adj. 95\\% CI Lower} & \\textbf{Adj. 95\\% CI Upper} & \\textbf{Benchmark $R^2_Y$} & \\textbf{Benchmark $R^2_D$} \\\\\n\\midrule\nffr\\_slope\\_trend\\_z\\_scaled\\_last & -0.0157 & 0.0000 & 0.0000 & -0.0216 & -0.0098 & 0.0520 & 0.0000 \\\\\nffr\\_slope\\_roc63\\_scaled\\_last & -0.0228 & 0.0000 & 0.0048 & -0.0368 & -0.0087 & 0.0518 & 0.0159 \\\\\ncredit\\_spread\\_scaled\\_std & -0.0524 & 0.0000 & 0.0000 & -0.0743 & -0.0306 & 0.0433 & 0.0000 \\\\\ncredit\\_spread\\_trend\\_z\\_scaled\\_std & -0.0522 & 0.0000 & 0.0000 & -0.0747 & -0.0297 & 0.0424 & 0.0000 \\\\\nfx\\_rv\\_6j\\_21d\\_trend\\_z\\_scaled\\_mean & -0.0143 & 0.0000 & 0.0000 & -0.0207 & -0.0078 & 0.0664 & 0.0000 \\\\\nffr\\_slope\\_roc63\\_scaled\\_mean & -0.0122 & 0.0001 & 0.0000 & -0.0181 & -0.0062 & 0.0520 & 0.0000 \\\\\nffr\\_slope\\_scaled\\_last & -0.0154 & 0.0001 & 0.0000 & -0.0230 & -0.0079 & 0.0518 & 0.0000 \\\\\namihud\\_illiquidity\\_trend\\_z\\_scaled\\_std & -0.0608 & 0.0001 & 0.0000 & -0.0917 & -0.0299 & 0.0569 & 0.0000 \\\\\nffr\\_slope\\_scaled\\_mean & -0.0145 & 0.0003 & 0.0000 & -0.0224 & -0.0066 & 0.0520 & 0.0000 \\\\\nfx\\_rv\\_6j\\_21d\\_trend\\_z\\_scaled\\_last & -0.0153 & 0.0005 & 0.0000 & -0.0238 & -0.0067 & 0.0656 & 0.0000 \\\\\nffr\\_slope\\_scaled\\_trend & -0.1436 & 0.0010 & 0.0000 & -0.2293 & -0.0579 & 0.0515 & 0.0000 \\\\\nfx\\_rv\\_6j\\_21d\\_scaled\\_mean & -0.0128 & 0.0025 & 0.0038 & -0.0248 & -0.0007 & 0.0656 & 0.0069 \\\\\npcr\\_oi\\_roc63\\_scaled\\_std & -0.0549 & 0.0057 & 0.0000 & -0.0938 & -0.0160 & 0.0932 & 0.0000 \\\\\nrisk\\_neutral\\_skewness\\_scaled\\_trend & -0.1346 & 0.0069 & 0.0000 & -0.2321 & -0.0370 & 0.0448 & 0.0000 \\\\\nrisk\\_neutral\\_skewness\\_trend\\_z\\_scaled\\_trend & -0.1533 & 0.0082 & 0.0000 & -0.2669 & -0.0397 & 0.0446 & 0.0000 \\\\\nflow\\_concentration\\_10d\\_scaled\\_std & 0.0636 & 0.0124 & 0.0000 & 0.0138 & 0.1134 & 0.0533 & 0.0000 \\\\\nfx\\_rv\\_6j\\_21d\\_scaled\\_last & -0.0096 & 0.0135 & 0.0000 & -0.0172 & -0.0020 & 0.0682 & 0.0000 \\\\\nffr\\_basis\\_roc63\\_scaled\\_mean & 0.0111 & 0.0177 & 0.0000 & 0.0019 & 0.0202 & 0.0520 & 0.0000 \\\\\nrisk\\_neutral\\_kurtosis\\_trend\\_z\\_scaled\\_mean & 0.0186 & 0.0196 & 0.0000 & 0.0030 & 0.0342 & 0.0448 & 0.0000 \\\\\nflow\\_concentration\\_10d\\_trend\\_z\\_scaled\\_mean & -0.0093 & 0.0202 & 0.0000 & -0.0172 & -0.0015 & 0.0523 & 0.0000 \\\\\nflow\\_concentration\\_10d\\_trend\\_z\\_scaled\\_std & 0.0404 & 0.0219 & 0.0000 & 0.0059 & 0.0750 & 0.0546 & 0.0000 \\\\\nflow\\_concentration\\_10d\\_roc63\\_scaled\\_std & 0.0515 & 0.0228 & 0.0000 & 0.0072 & 0.0958 & 0.0594 & 0.0000 \\\\\nffr\\_basis\\_roc63\\_scaled\\_last & 0.0081 & 0.0260 & 0.0000 & 0.0010 & 0.0152 & 0.0526 & 0.0000 \\\\\nffr\\_slope\\_trend\\_z\\_scaled\\_trend & -0.0834 & 0.0306 & 0.0000 & -0.1590 & -0.0078 & 0.0520 & 0.0000 \\\\\nrisk\\_neutral\\_kurtosis\\_scaled\\_mean & 0.0173 & 0.0306 & 0.0000 & 0.0016 & 0.0329 & 0.0448 & 0.0000 \\\\\nrisk\\_neutral\\_skewness\\_roc63\\_scaled\\_trend & -0.0946 & 0.0359 & 0.0000 & -0.1829 & -0.0062 & 0.0465 & 0.0000 \\\\\npcr\\_oi\\_trend\\_z\\_scaled\\_std & -0.0337 & 0.0420 & 0.0000 & -0.0662 & -0.0012 & 0.1126 & 0.0000 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation", "authors": ["Peilin Rao", "Randall R. Rojas"], "url": "https://arxiv.org/abs/2509.05922v1", "attribution": "\"Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation\" by Peilin Rao and Randall R. Rojas, arXiv:2509.05922v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2203.12460v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|}\n\\hline\n & (1) & (2) & (3) & (4) & (5) \\\\ \\hline\nVariables & Model 1 & Model 2 & Model 3 & Model 4 & Model 5 \\\\ \\hline\n & & & & & \\\\ \\hline\npositive & 0.19* & 0.19* & 0.21* & 0.11 & -0.07 \\\\ \\hline\n & (0.08) & (0.08) & (0.09) & (0.09) & (0.15) \\\\ \\hline\nnegative & -0.83* & -0.82* & -0.93* & -0.82 & 0.36 \\\\ \\hline\n & (0.40) & (0.40) & (0.41) & (0.42) & (0.73) \\\\ \\hline\nanxiety & 1.06 & 1.10 & 1.07 & 0.93 & -2.40 \\\\ \\hline\n & (0.72) & (0.73) & (0.77) & (0.78) & (1.40) \\\\ \\hline\nanger & 0.52 & 0.56 & 0.98 & 0.91 & 0.16 \\\\ \\hline\n & (0.71) & (0.71) & (0.73) & (0.74) & (1.44) \\\\ \\hline\nsad & -0.23 & -0.33 & -0.06 & -0.25 & -1.50 \\\\ \\hline\n & (0.59) & (0.59) & (0.62) & (0.63) & (1.10) \\\\ \\hline\ncertain & -0.02 & 0.01 & 0.04 & -0.01 & -0.27 \\\\ \\hline\n & (0.23) & (0.23) & (0.23) & (0.24) & (0.40) \\\\ \\hline\n & & & & & \\\\ \\hline\nMAR(3 m) & & YES & YES & YES & YES \\\\ \\hline\nIndustry & & & YES & YES & YES \\\\ \\hline\nYear & & & & YES & YES \\\\ \\hline\nMAR(5 d) & & & & & YES \\\\ \\hline\nN & 1,742 & 1,742 & 1,739 & 1,739 & 651 \\\\ \\hline\nBIC & 2426 & 2450 & 2484 & 2521 & 1047 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Logistic Regression of Value Based Label Function ($y_I(T^c_d)(5)$) on Analysts' Recommendations within 3 months ($MAR(3\\,m)$) and Sentiment of Earnings Calls Transcripts. Standard errors in parentheses. Significance levels marked with stars: *** p<0.001, ** p<0.01, * p<0.05}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An Exploratory Study of Stock Price Movements from Earnings Calls", "authors": ["Sourav Medya", "Mohammad Rasoolinejad", "Yang Yang", "Brian Uzzi"], "url": "https://arxiv.org/abs/2203.12460v1", "attribution": "\"An Exploratory Study of Stock Price Movements from Earnings Calls\" by Sourav Medya, Mohammad Rasoolinejad, Yang Yang, and Brian Uzzi, arXiv:2203.12460v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19833v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quaternionic Errors and Syndrome Measurements for the Five-Qubit Code in~}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccl}\n\\hline\n\\textbf{Error} & \\textbf{Syndrome} $(S_1, S_2, S_3, S_4)$ & \\textbf{Quaternionic Error} & \\textbf{Error Description for each q-qubit} \\\\\n\\hline\n$X_1$ & $(-1, -1, +1, +1)$ & $iX_1$ & Bit-flip with quaternionic phase on q-qubit 1 \\\\\n$Y_1$ & $(-1, -1, +1, +1)$ & $jY_1$ & Bit and phase-flip with quaternionic phase \\\\\n$Z_1$ & $(+1, -1, +1, +1)$ & $kZ_1$ & Phase-flip with quaternionic phase \\\\\n$X_2$ & $(-1, -1, -1, +1)$ & $iX_2$ & Bit-flip on q-qubit 2 \\\\\n$Y_2$ & $(-1, -1, -1, +1)$ & $jY_2$ & Y-error on q-qubit 2 \\\\\n$Z_2$ & $(+1, -1, -1, +1)$ & $kZ_2$ & Z-error on q-qubit 2 \\\\\n$X_3$ & $(-1, +1, -1, -1)$ & $iX_3$ & Bit-flip on q-qubit 3 \\\\\n$Y_3$ & $(-1, +1, -1, -1)$ & $jY_3$ & Y-error on q-qubit 3 \\\\\n$Z_3$ & $(+1, +1, -1, -1)$ & $kZ_3$ & Z-error on q-qubit 3 \\\\\n$X_4$ & $(-1, +1, +1, -1)$ & $iX_4$ & Bit-flip on q-qubit 4 \\\\\n$Y_4$ & $(-1, +1, +1, -1)$ & $jY_4$ & Y-error on q-qubit 4 \\\\\n$Z_4$ & $(+1, +1, +1, -1)$ & $kZ_4$ & Z-error on q-qubit 4 \\\\\n$X_5$ & $(+1, +1, +1, -1)$ & $iX_5$ & Bit-flip on q-qubit 5 \\\\\n$Y_5$ & $(+1, +1, +1, -1)$ & $jY_5$ & Y-error on q-qubit 5 \\\\\n$Z_5$ & $(+1, +1, +1, -1)$ & $kZ_5$ & Z-error on q-qubit 5 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Quantum Error Correction in Quaternionic Hilbert Spaces", "authors": ["Valentine Nyirahafashimana", "Nurisya Mohd Shah", "Umair Abdul Halim", "Mohamed Othman", "Sharifah Kartini Said Husain"], "url": "https://arxiv.org/abs/2504.19833v1", "attribution": "\"Quantum Error Correction in Quaternionic Hilbert Spaces\" by Valentine Nyirahafashimana, Nurisya Mohd Shah, Umair Abdul Halim, Mohamed Othman, and Sharifah Kartini Said Husain, arXiv:2504.19833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.09258v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|rrrr}\n \\hline\n$\\alpha$ & 0.05 & 0.01 & 0.005 & 0.001 \\\\ \\hline\n $\\textrm{Pr}(V\\geq 1)$ & \\bf{0.2296} & 0.0678 & 0.0360 & 0.0074 \\\\ \n $\\textrm{E}(V)$ & 4.0825 & 0.7680 & 0.3598 & 0.0527 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Observed false alarm probability for repeated testing on overlapping samples $20,\\dots,100$, based on 10,000 simulations.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Detection of data drift and outliers affecting machine learning model performance over time", "authors": ["Samuel Ackerman", "Eitan Farchi", "Orna Raz", "Marcel Zalmanovici", "Parijat Dube"], "url": "https://arxiv.org/abs/2012.09258v3", "attribution": "\"Detection of data drift and outliers affecting machine learning model performance over time\" by Samuel Ackerman, Eitan Farchi, Orna Raz, Marcel Zalmanovici, and Parijat Dube, arXiv:2012.09258v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.10392v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\n\t\t\t\\hline \\hline\n\t\t\tSentiment Variable & Description\\\\\\hline\n\t\t\t$I_{i,t}$ & \\ Indicator for whether there is an article.\\\\\n\t\t\t$Pos_{i,t}$ (BL) & \\ The average proportion of positive words using the BL lexicon.\\\\\n\t\t\t$Neg_{i,t}$ (BL) & \\ The average proportion of negative words using the BL lexicon.\\\\\n\t\t\t$Pos_{i,t}$ (LM)& \\ The average proportion of positive words using the LM lexicon.\\\\\n\t\t\t$Neg_{i,t}$ (LM)& \\ The average proportion of negative words using the LM lexicon.\\\\\n\t\t\t$Pos_{i,t}$ (MPQA)& \\ The average proportion of positive words using the MPQA lexicon.\\\\\n\t\t\t$Neg_{i,t}$ (MPQA)& \\ The average proportion of negative words using the MPQA lexicon.\\\\\\hline\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Sentiment variables for articles published on or after trading day $t$ and before trading day $t+1$.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Distillation of News Flow into Analysis of Stock Reactions", "authors": ["Junni L. Zhang", "Wolfgang Karl Härdle", "Cathy Y. Chen", "Elisabeth Bommes"], "url": "https://arxiv.org/abs/2009.10392v1", "attribution": "\"Distillation of News Flow into Analysis of Stock Reactions\" by Junni L. Zhang, Wolfgang Karl Härdle, Cathy Y. Chen, and Elisabeth Bommes, arXiv:2009.10392v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.14130v2_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n(n,m)&$ (1000,10)$& $(3000,20)$ & $(5000,20)$\\\\ \\hline\n BM& 0.91 & 1.00 & 1.00\\\\\n M1& 0.05 & 0.00 & 0.00 \\\\\n M2& 1.00 & 1.00 & 1.00\\\\\n M3& 1.00 & 1.00 & 1.00\\\\\n M4& 1.00 & 1.00 & 1.00\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\scriptsize Hierarchical Bayes rescaling of the GP with squared exponential covariance kernel. Proportion of runs when the $L_2$-credible ball contains $f_0$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Adaptation using spatially distributed Gaussian Processes", "authors": ["Botond Szabo", "Amine Hadji", "Aad van der Vaart"], "url": "https://arxiv.org/abs/2312.14130v2", "attribution": "\"Adaptation using spatially distributed Gaussian Processes\" by Botond Szabo, Amine Hadji, and Aad van der Vaart, arXiv:2312.14130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.05366v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of the analyzed marks data of Table }\n\\begin{tabular}{lrrrrrrr}\n\t\t\t\\hline\n\t\t\tData set &$n$ &max.&min. &median &mean&CS\\\\\\hline\n\t\t\t$X_1$\t& 570 & 20.00 & 0.00&11.35 &7.61 & $-0.0603$&\\\\\n\t\t\t$X_2$ & 570 & 20.00 &0.00& 5.00 &5.495 & 0.2972 \\\\\n\t\t\t$X_3$&570 &\t20.00 &0.00 &8.90&7.147& $-0.0310$ \\\\\t\t\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An effective estimation of multivariate density functions using extended-beta kernels with Bayesian adaptive bandwidths", "authors": ["Sobom M. Somé", "Célestin C. Kokonendji", "Francial G. B. Libengué Dobélé-Kpoka"], "url": "https://arxiv.org/abs/2502.05366v1", "attribution": "\"An effective estimation of multivariate density functions using extended-beta kernels with Bayesian adaptive bandwidths\" by Sobom M. Somé, Célestin C. Kokonendji, and Francial G. B. Libengué Dobélé-Kpoka, arXiv:2502.05366v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.09935v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model Architecture Comparison}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\nModel & Parameters & Layers & Key Innovation & Training Time (s/epoch) \\\\\n\\midrule\nSimple LSTM & 714,499 & 4 & Bidirectional processing & 1--2 \\\\\nAdvanced LSTM & 1,424,387 & 5 & Stacked BiLSTM & 2--3 \\\\\nCustom Attention & 3,143,783 & 8 & Attention mechanism & 3--4 \\\\\nTransformer & 3,917,187 & 7 & Self-attention & 18--19 \\\\\nCNN & 1,397,347 & 9 & Multi-scale convolutions & 1--2 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach", "authors": ["Sayem Hossen", "Monalisa Moon Joti", "Md. Golam Rashed"], "url": "https://arxiv.org/abs/2508.09935v1", "attribution": "\"Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach\" by Sayem Hossen, Monalisa Moon Joti, and Md. Golam Rashed, arXiv:2508.09935v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2303.01111v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Multinomial Logit Regression Summary}\n\\begin{tabular}{|l|c|c|c|}\n \\hline\n & AVG & Prediction = C1 & Prediction = C2 \\\\\n \\hline coef & & -22.8763 (0.0000) & 14.9750 (0.000)\\\\\n \\hline yield & 1.003 & 22.4499 (0.0000) & -16.0069 (0.000)\\\\\n \\hline LL & \\multicolumn{3}{r|}{-3151.802} \\\\\n \\hline LLR test: $\\chi^2$ & \\multicolumn{3}{r|}{214.596 (0.0000)} \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Stock Price Movement as an Image Classification Problem", "authors": ["Matej Steinbacher"], "url": "https://arxiv.org/abs/2303.01111v1", "attribution": "\"Predicting Stock Price Movement as an Image Classification Problem\" by Matej Steinbacher, arXiv:2303.01111v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10650v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ISBI 2017 dataset classification results}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\n\\textbf{Method} & \\textbf{AUC} & \\textbf{SE} & \\textbf{SP} & \\textbf{AC} & \\textbf{BAC} \\\\ \\hline\n\\multicolumn{6}{c}{Melanoma vs Rest} \\\\ \\hline\nISBI 2017 Winner & \\textbf{0.868} & \\textbf{0.735} & \\textbf{0.851} & \\textbf{0.828} & \\textbf{0.793} \\\\\n\\textbf{Proposed} & 0.806 & 0.695 & 0.780 & 0.765 & 0.737 \\\\ \\hline\n \\multicolumn{6}{c}{Seborrheic keratosis vs Rest} \\\\ \\hline\nISBI 2017 Winner & \\textbf{0.953} & \\textbf{0.978} & 0.773 & 0.803 & \\textbf{0.876} \\\\\n\\textbf{Proposed} & 0.883 & 0.795 & \\textbf{0.813} & \\textbf{0.810} & 0.804 \\\\ \\hline\n\\multicolumn{6}{c}{Average} \\\\ \\hline\nISBI 2017 Winner & \\textbf{0.911} & \\textbf{0.856} & \\textbf{0.812} & \\textbf{0.816} & \\textbf{0.834} \\\\\n\\textbf{Proposed} & 0.845 & 0.745 & 0.797 & 0.788 & 0.771 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data", "authors": ["José G. M. Esgario", "Renato A. Krohling"], "url": "https://arxiv.org/abs/2201.10650v1", "attribution": "\"Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data\" by José G. M. Esgario and Renato A. Krohling, arXiv:2201.10650v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13229v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The contingency Chi-square results for Year (2015-2019) Vs Type (Rear-end/Sideswipe).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll} \\toprule\n & Statistic & P-value \\\\\\midrule\nYear vs type & 5.605 & 0.231 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study", "authors": ["Xiwen Chen", "Hao Wang", "Abolfazl Razi", "Brendan Russo", "Jason Pacheco", "John Roberts", "Jeffrey Wishart", "Larry Head", "Alonso Granados Baca"], "url": "https://arxiv.org/abs/2201.13229v2", "attribution": "\"Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study\" by Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head, and Alonso Granados Baca, arXiv:2201.13229v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table20.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|r|r|r|r|r}\n \\hline\n \\multicolumn{2}{c}{}&\\multicolumn{2}{|c}{PH ($\\lambda=1$, avg)}&\\multicolumn{2}{|c|}{Partial model}& \\\\\n Util. & External demand & Runtime & Costs & Runtime & Costs & Gap (\\%)\\\\\n \\hline\n \\multirow{2}{*}{50\\%} \n & end item demand & 1168 & 35268.0 & 456 & 35268.0 & \\\\\n & incl. component demand & & & 1018 & 37176.7 & \\\\\n \\hline\n \\multirow{2}{*}{90\\%} \n & end item demand & 996 & 66433.9 & 6086 & 66433.9 & \\\\\n & incl. component demand & 3678 & 72255.0 & 10800 & 71939 (gap 2.2\\%) & \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of the partial implicit model considering 256 scenario paths sampled from the full scenario tree with $|\\Omega| = 3$ and therefore $|\\Phi|=2187$ to the PH algorithm with $\\rho = 1$ and averaging as consensus calculation considering the same 256 scenario paths. Solutions of the PH and partial model are evaluated on the full scenario tree and the respective costs are reported alongside runtime in seconds.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.11079v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Other interactions models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\hline\t\t\t\t\t\n\t& (1)\t&\t(2) \t\\\\\t\n\t& \tContributions PGG\t&\t Contributions PGG\t\\\\\n\\hline\t\t\t\t\t\n\t&\t\t&\t\t\\\\\nAR & 0.054 & 0.048 \\\\\n & (0.118) & (0.179) \\\\\nRA & 0.118 & -0.027 \\\\\n & (0.114) & (0.177) \\\\\nAA & 0.019 & 0.163 \\\\\n & (0.126) & (0.176) \\\\\nage & -0.005** & -0.005** \\\\\n & (0.002) & (0.002) \\\\\nfemale & 0.059 & 0.008 \\\\\n & (0.125) & (0.060) \\\\\nCRT & -0.137*** & -0.130** \\\\\n & (0.031) & (0.060) \\\\\nbeliefs & 0.170*** & 0.170*** \\\\\n & (0.007) & (0.007) \\\\\nrisk aversion & -0.255** & -0.254** \\\\\n & (0.102) & (0.102) \\\\\nAR*female & -0.085 & \\\\\n & (0.168) & \\\\\nRA*female & -0.173 & \\\\\n & (0.164) & \\\\\nAA*female & 0.085 & \\\\\n & (0.168) & \\\\\nAR*CRT & & -0.025 \\\\\n & & (0.085) \\\\\nRA*CRT & & 0.035 \\\\\n & & (0.084) \\\\\nAA*CRT & & -0.061 \\\\\n & & (0.084) \\\\\nConstant & 1.381*** & 1.416*** \\\\\n & (0.273) & (0.270) \\\\\n & & \\\\\nObservations & 1,421 & 1,421 \\\\\nR-squared & 0.391 & 0.390 \t\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The effect of ambiguity in strategic environments: an experiment", "authors": ["Pablo Brañas-Garza", "Antonio Cabrales", "María Paz Espinosa", "Diego Jorrat"], "url": "https://arxiv.org/abs/2209.11079v1", "attribution": "\"The effect of ambiguity in strategic environments: an experiment\" by Pablo Brañas-Garza, Antonio Cabrales, María Paz Espinosa, and Diego Jorrat, arXiv:2209.11079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01833v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model performance across benchmarks with varying novel class ratios.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccc}\n \\toprule\n \\textbf{Novel class ratio} & \\multicolumn{3}{c}{\\textbf{CIFAR10}} & \\multicolumn{3}{c}{\\textbf{CIFAR100}} & \\multicolumn{3}{c}{\\textbf{Tiny-ImageNet}} \\\\\n \\cmidrule(lr){2-4} \\cmidrule(l){5-7} \\cmidrule(l){8-10} \n & Seen & Novel & All & Seen & Novel & All & Seen & Novel & All \\\\\n \\midrule\n 50\\% & 96.5 & 97.1 & 96.8 & 80.1 & 63.9 & 71.9 & 68.8 & 42.4 & 55.0\\\\\n 60\\% & 96.5 & 92.1 & 93.9 & 80.3 & 60.4 & 68.1 & 68.5 & 41.1 & 51.7 \\\\\n 70\\% & 97.5 & 91.0 & 93.0 & 81.3 & 58.6 & 65.3 & 71.3 & 34.7 & 45.6 \\\\\n 80\\% & 98.4 & 92.9 & 94.0 & 78.9 & 58.0 & 61.8 & 69.5 & 32.9 & 40.0 \\\\\n 90\\% & 97.8 & 93.6 & 94.0 & 82.0 & 50.7 & 53.5 & 69.4 & 26.4 & 30.3 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning", "authors": ["Shengjie Niu", "Lifan Lin", "Jian Huang", "Chao Wang"], "url": "https://arxiv.org/abs/2411.01833v1", "attribution": "\"OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning\" by Shengjie Niu, Lifan Lin, Jian Huang, and Chao Wang, arXiv:2411.01833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15403v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\nLayer & Layer Type & kernel & num. parameters & num. activations \\\\\n\\hline\n1 & Convolutional & 3 $\\times$ 3 $\\times$ 1 $\\times$ $d$ & 480 & 37632 \\\\\n2 & Convolutional & 3 $\\times$ 3 $\\times$ $d$ $\\times$ $d$ & 20784 & 37632 \\\\\n3 & MaxPool & 2 $\\times$ 2 & - & - \\\\\n4 & Convolutional & 3 $\\times$ 3 $\\times$ $d$ $\\times$ 2$d$ & 41568 & 18816 \\\\\n5 & Convolutional & 3 $\\times$ 3 $\\times$ 2$d$ $\\times$ 2$d$ & 83040 & 18816 \\\\\n6 & MaxPool & 2 $\\times$ 2 & - & - \\\\\n7 & Convolutional & 3 $\\times$ 3 $\\times$ 2$d$ $\\times$ 4$d$ & 166080 & 9408 \\\\\n8 & Convolutional & 3 $\\times$ 3 $\\times$ 4$d$ $\\times$ 4$d$ & 331968 & 9408 \\\\\n9 & MaxPool & 2 $\\times$ 2 & - & - \\\\\n10 & Dense & 36$d$ $\\times$ 4$d$ & 331968 & 192 \\\\\n11 & Dense & 4$d$ $\\times$ 4$d$ & 37056 & 192 \\\\\n12 & Dense & 4$d$ $\\times$ 10 & 1930 & 10 \\\\\n\\hline\nTotal & & & 1014874 & 132106 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Number of parameters (weights) and activations (nodes) for each layer to be trained in a medium sized network evaluated in this work. In this specific example the depth of the input convolutional layer is $d=48$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Scaling of hardware-compatible perturbative training algorithms", "authors": ["Bakhrom G. Oripov", "Andrew Dienstfrey", "Adam N. McCaughan", "Sonia M. Buckley"], "url": "https://arxiv.org/abs/2501.15403v1", "attribution": "\"Scaling of hardware-compatible perturbative training algorithms\" by Bakhrom G. Oripov, Andrew Dienstfrey, Adam N. McCaughan, and Sonia M. Buckley, arXiv:2501.15403v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10707v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc}\n \\hline\n \\bf Method & $P$ & $R$ & $F_{0.5}$ \\\\\n \\hline\n \\bf Ours & \\bf 73.86 & 60.74 & \\bf 70.8 \\\\\n ~ - [MASK] replacement & 71.17 & 55.07 & 67.24 \\\\\n ~ - [MASK] insert & 72.52 & 59.00 & 69.34 \\\\\n ~ - post edit & 73.00 & \\bf 61.36 & 70.34 \\\\\n ~ - bilingual constraint & 71.17 & 55.89 & 67.48 \\\\\n ~ w/ an autoregressive translator & 66.99 & 55.44 & 64.31 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance of our approach with different schemes on the German dataset. - denotes removing the component or replacing it with the rule-based operation.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model", "authors": ["Xin Sun", "Tao Ge", "Shuming Ma", "Jingjing Li", "Furu Wei", "Houfeng Wang"], "url": "https://arxiv.org/abs/2201.10707v1", "attribution": "\"A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model\" by Xin Sun, Tao Ge, Shuming Ma, Jingjing Li, Furu Wei, and Houfeng Wang, arXiv:2201.10707v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12899v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Network Scenario Settings}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline\n \\thead{System Parameters} & \\thead{Values} \\\\\n \\hline\n Air Technology & 4G LTE \\\\\n \\hline\n Cellular Layout & 10 Macrocell sites \\\\\n \\hline\n Sectors & 3 sectors per eNB\\\\\n \\hline\n Simulation Location & Brussels, Belgium\\\\\n \\hline\n Simulation Area & 3.80 km$^2$\\\\\n \\hline\n User Distribution & Poisson Distribution\\\\\n \\hline\n Propagation Model & Aster (Ray Tracing)\\\\\n \\hline\n Path Loss Matrix Resolution & 10m\\\\\n \\hline\n Geographic Information & \\vtop{\\hbox{\\strut (\\textit{1-m Resolution GeoData})}\\hbox{\\strut Ground Heights (DTM) + }\\hbox{\\strut Building Heights (DHM) + }\\hbox{\\strut Land Use Map (DLU)}}\\\\\n \\hline\n Land Cover (Clutter) Types & 15 different classes\\\\\n \\hline\n eNB Transmit Power (max) & 43 dBm\\\\\n \\hline\n eNB Noise Figure & 5 dB\\\\\n \\hline\n eNB Antenna Height & Actual site heights\\\\\n \\hline\n eNB Antenna Model & \\vtop{\\hbox{\\strut Kathrein Directional Antenna}\\hbox{\\strut (Model 742 265)}}\\\\\n \\hline\n eNB Antenna Gain & 18.3 dBi\\\\\n \\hline\n \\vtop{\\hbox{\\strut eNB Antenna Horizontal}\\hbox{\\strut Half Power Beamwidth}} & 63$^o$\\\\\n \\hline\n \\vtop{\\hbox{\\strut eNB Antenna Vertical}\\hbox{\\strut Half Power Beamwidth}} & 4.7$^o$\\\\\n \\hline\n Frequency Band & 2110 FDD (E-UTRA Band 1)\\\\\n \\hline\n Channel Bandwidth (CBW) & 5 MHz\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Interpretable AI-based Large-scale 3D Pathloss Prediction Model for enabling Emerging Self-Driving Networks", "authors": ["Usama Masood", "Hasan Farooq", "Ali Imran", "Adnan Abu-Dayya"], "url": "https://arxiv.org/abs/2201.12899v1", "attribution": "\"Interpretable AI-based Large-scale 3D Pathloss Prediction Model for enabling Emerging Self-Driving Networks\" by Usama Masood, Hasan Farooq, Ali Imran, and Adnan Abu-Dayya, arXiv:2201.12899v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.03112v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|r|r|r|r|r|r|r|r|r}\n\\hline\nblack box & MBT & $impr$ & $alpha$ & n leaves & n leaves min & n leaves max & $R^2_{train}$ & sd $R^2_{train}$ & $R^2_{test}$ & sd $R^2_{test}$\\\\\n\\hline\nstandalone & SLIM & 0.15 & & 2.30 & 2 & 8 & 0.8637 & 0.0165 & 0.8626 & 0.0176\\\\\nstandalone & SLIM & 0.10 & & 38.96 & 21 & 54 & 0.9865 & 0.0029 & 0.9849 & 0.0030\\\\\nstandalone & SLIM & 0.05 & & 59.03 & 51 & 64 & 0.9904 & 0.0005 & 0.9884 & 0.0006\\\\\nstandalone & GUIDE & 0.15 & & 2.30 & 2 & 8 & 0.8637 & 0.0165 & 0.8626 & 0.0176\\\\\nstandalone & GUIDE & 0.10 & & 38.06 & 21 & 54 & 0.9866 & 0.0028 & 0.9851 & 0.0029\\\\\nstandalone & GUIDE & 0.05 & & 58.20 & 45 & 63 & 0.9903 & 0.0005 & 0.9885 & 0.0006\\\\\nstandalone & MOB & & 0.001 & 48.54 & 43 & 55 & 0.9888 & 0.0004 & 0.9861 & 0.0007\\\\\nstandalone & MOB & & 0.010 & 53.17 & 48 & 58 & 0.9891 & 0.0003 & 0.9864 & 0.0007\\\\\nstandalone & MOB & & 0.050 & 56.07 & 51 & 60 & 0.9893 & 0.0003 & 0.9866 & 0.0006\\\\\nstandalone & CTree & & 0.001 & 51.70 & 47 & 57 & 0.9887 & 0.0004 & 0.9858 & 0.0006\\\\\nstandalone & CTree & & 0.010 & 54.85 & 50 & 58 & 0.9889 & 0.0004 & 0.9860 & 0.0006\\\\\nstandalone & CTree & & 0.050 & 56.76 & 52 & 61 & 0.9890 & 0.0003 & 0.9860 & 0.0006\\\\\n\\hline\nlm & SLIM & 0.15 & & 2.17 & 2 & 16 & 0.8683 & 0.0094 & 0.8677 & 0.0103\\\\\nlm & SLIM & 0.10 & & 39.69 & 19 & 55 & 0.9956 & 0.0028 & 0.9953 & 0.0030\\\\\nlm & SLIM & 0.05 & & 57.51 & 46 & 64 & 0.9991 & 0.0005 & 0.9990 & 0.0006\\\\\nlm & GUIDE & 0.15 & & 2.17 & 2 & 16 & 0.8683 & 0.0094 & 0.8677 & 0.0103\\\\\nlm & GUIDE & 0.10 & & 40.32 & 19 & 55 & 0.9960 & 0.0026 & 0.9956 & 0.0028\\\\\nlm & GUIDE & 0.05 & & 57.32 & 46 & 64 & 0.9991 & 0.0005 & 0.9990 & 0.0006\\\\\nlm & MOB & & 0.001 & 63.11 & 60 & 64 & 0.9973 & 0.0002 & 0.9964 & 0.0003\\\\\nlm & MOB & & 0.010 & 63.11 & 60 & 64 & 0.9973 & 0.0002 & 0.9964 & 0.0003\\\\\nlm & MOB & & 0.050 & 63.11 & 60 & 64 & 0.9973 & 0.0002 & 0.9964 & 0.0003\\\\\nlm & CTree & & 0.001 & 62.72 & 59 & 64 & 0.9973 & 0.0002 & 0.9964 & 0.0003\\\\\nlm & CTree & & 0.010 & 62.72 & 59 & 64 & 0.9973 & 0.0002 & 0.9964 & 0.0003\\\\\nlm & CTree & & 0.050 & 62.72 & 59 & 64 & 0.9973 & 0.0002 & 0.9964 & 0.0003\\\\\n\\hline\nlm & & & & & & & 0.9901 & 0.0003 & 0.9902 & 0.0004\\\\\n\\hline\n\\hline\nXGBoost & SLIM & 0.15 & & 2.22 & 2 & 12 & 0.8693 & 0.0121 & 0.8706 & 0.0129\\\\\nXGBoost & SLIM & 0.10 & & 39.52 & 23 & 53 & 0.9931 & 0.0027 & 0.9914 & 0.0027\\\\\nXGBoost & SLIM & 0.05 & & 56.86 & 46 & 63 & 0.9964 & 0.0005 & 0.9948 & 0.0007\\\\\nXGBoost & GUIDE & 0.15 & & 2.15 & 2 & 9 & 0.8693 & 0.0119 & 0.8705 & 0.0127\\\\\nXGBoost & GUIDE & 0.10 & & 38.13 & 22 & 52 & 0.9928 & 0.0027 & 0.9912 & 0.0028\\\\\nXGBoost & GUIDE & 0.05 & & 56.72 & 46 & 63 & 0.9963 & 0.0006 & 0.9946 & 0.0007\\\\\nXGBoost & MOB & & 0.001 & 58.76 & 54 & 63 & 0.9961 & 0.0003 & 0.9942 & 0.0005\\\\\nXGBoost & MOB & & 0.010 & 59.47 & 55 & 64 & 0.9961 & 0.0003 & 0.9943 & 0.0005\\\\\nXGBoost & MOB & & 0.050 & 59.69 & 55 & 64 & 0.9961 & 0.0003 & 0.9943 & 0.0005\\\\\nXGBoost & CTree & & 0.001 & 58.51 & 53 & 62 & 0.9956 & 0.0003 & 0.9936 & 0.0005\\\\\nXGBoost & CTree & & 0.010 & 58.95 & 54 & 63 & 0.9956 & 0.0003 & 0.9936 & 0.0005\\\\\nXGBoost & CTree & & 0.050 & 59.09 & 54 & 63 & 0.9956 & 0.0003 & 0.9936 & 0.0005\\\\\n\\hline\nXGBoost & & & & & & & 0.9877 & 0.0007 & 0.9836 & 0.0011\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean simulation results on 100 simulation runs as stand alone and surrogate models on scenario linear mixed with sample size $n = 7500$ for different values of $impr$ and $alpha$}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation", "authors": ["Julia Herbinger", "Susanne Dandl", "Fiona K. Ewald", "Sofia Loibl", "Giuseppe Casalicchio"], "url": "https://arxiv.org/abs/2310.03112v1", "attribution": "\"Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation\" by Julia Herbinger, Susanne Dandl, Fiona K. Ewald, Sofia Loibl, and Giuseppe Casalicchio, arXiv:2310.03112v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.11311v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{EDM baseline and the off-the-shelf ResNet guided EDM sampling. Sampled for 256 SDE steps. Generating 50000 ImageNet 64x64 samples for evaluation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nImageNet 64x64 & Classifier & FID \\\\ \n\\midrule\nEDM baseline & - & 1.41 \\\\ \nEDM Res101 guided & Off-the-Shelf & \\bf{1.33} \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Elucidating The Design Space of Classifier-Guided Diffusion Generation", "authors": ["Jiajun Ma", "Tianyang Hu", "Wenjia Wang", "Jiacheng Sun"], "url": "https://arxiv.org/abs/2310.11311v1", "attribution": "\"Elucidating The Design Space of Classifier-Guided Diffusion Generation\" by Jiajun Ma, Tianyang Hu, Wenjia Wang, and Jiacheng Sun, arXiv:2310.11311v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08062v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of assembling time and solving time for the C0WG method and the SF-C0WG method.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|cc|cc}\n\\toprule[1.5pt]\n\\multirow{2}*{$k$}&\\multirow{2}*{level}&\\multicolumn{2}{c|}{C0WG method }&\\multicolumn{2}{c}{SF-C0WG method}\\\\\n\\cline{3-6}\n& & Assembling Time &Solving Time & Assembling Time &Solving Time \\\\\n\\hline\n \\multirow{5}*{0}\n &1&0.052233 & 0.001690&0.065710 & 0.003116\\\\\n &2&0.175486 & 0.007218&0.157510 & 0.006127\\\\\n &3&0.734831 & 0.039118&0.546378 & 0.030866\\\\\n &4&2.602549 & 0.171534&2.240196 & 0.133192\\\\\n &5&10.67890 & 0.874419&8.766250 & 0.628217\\\\\n \\hline\n \\multirow{5}*{1}\n &1&0.347160 & 0.027630&0.057160 & 0.016028\\\\\n &2&0.184210 & 0.020082&0.201938 & 0.017132\\\\\n &3&0.864096 & 0.072827&0.793079 & 0.061735\\\\\n &4&3.537430 & 0.458761&2.800155 & 0.305041\\\\\n &5&23.91767& 2.630822&12.14147 & 1.752295\\\\\n \\bottomrule[1.5pt]\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A stabilizer-free $C^0$ weak Galerkin method for the biharmonic equations", "authors": ["Peng Zhu", "Shenglan Xie", "Xiaoshen Wang"], "url": "https://arxiv.org/abs/2201.08062v1", "attribution": "\"A stabilizer-free $C^0$ weak Galerkin method for the biharmonic equations\" by Peng Zhu, Shenglan Xie, and Xiaoshen Wang, arXiv:2201.08062v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10005v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n& Go & Ruby & Python & Java & JS & PHP & Avg. \\\\\n\\Xhline{2.5\\arrayrulewidth} \\\\\n\\small{CodeBERT} & 69.3 & 70.6 & 84.0 & 86.8 & 74.8 & 70.6 & 76.0 \\\\\n\\small{GraphCodeBERT} & 84.1 & 73.2 & 87.9 & 75.7 & 71.1 & 72.5 & 77.4 \\\\\n\\Xhline{1\\arrayrulewidth} \\\\\n\\texttt{cpt-code} S & \\textbf{97.7} & \\textbf{86.3} & 99.8 & 94.0 & 86.0 & 96.7 & 93.4 \\\\\n\\texttt{cpt-code} M & 97.5 & 85.5 & \\textbf{99.9} & \\textbf{94.4} & \\textbf{86.5} & \\textbf{97.2} & \\textbf{93.5} \\\\\n\\Xhline{2.5\\arrayrulewidth}\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of $\\texttt{cpt-code}$ on code search across 6 programming languages with CodeBERT and GraphCodeBERT . The task requires finding the relevant code block among 1K candidates for a given natural language query. $\\texttt{cpt-code}$ performs substantially better than previous methods on all the languages.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Text and Code Embeddings by Contrastive Pre-Training", "authors": ["Arvind Neelakantan", "Tao Xu", "Raul Puri", "Alec Radford", "Jesse Michael Han", "Jerry Tworek", "Qiming Yuan", "Nikolas Tezak", "Jong Wook Kim", "Chris Hallacy", "Johannes Heidecke", "Pranav Shyam", "Boris Power", "Tyna Eloundou Nekoul", "Girish Sastry", "Gretchen Krueger", "David Schnurr", "Felipe Petroski Such", "Kenny Hsu", "Madeleine Thompson", "Tabarak Khan", "Toki Sherbakov", "Joanne Jang", "Peter Welinder", "Lilian Weng"], "url": "https://arxiv.org/abs/2201.10005v1", "attribution": "\"Text and Code Embeddings by Contrastive Pre-Training\" by Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, Johannes Heidecke, Pranav Shyam, Boris Power, Tyna Eloundou Nekoul, Girish Sastry, Gretchen Krueger, David Schnurr, Felipe Petroski Such, Kenny Hsu, Madeleine Thompson, Tabarak Khan, Toki Sherbakov, Joanne Jang, Peter Welinder, and Lilian Weng, arXiv:2201.10005v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.13657v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c||c|c|c|}\n\\hline\nCandidate & voter-1 & voter-2 & P-cN & N/P & $W_{b}^{c}$ \\\\\n \\hline \n\\hline\nA & 10 & -X & 10-cX & $X/10$ & $ 10 \\left( \\frac{10-cX}{10+bX} \\right) $ \\\\\n\\hline\nB & 0 & 10-X & 10-X & 0 & 10-X \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Negative votes to depolarize politics", "authors": ["Karthik H. Shankar"], "url": "https://arxiv.org/abs/2012.13657v1", "attribution": "\"Negative votes to depolarize politics\" by Karthik H. Shankar, arXiv:2012.13657v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13986v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{BEBs convergence orders}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n\\toprule\nBEB & Proposition & Proposition & First bound in Theorem & Second bound in Theorem \\\\\n \\midrule\nconvergence order & $1/K^{1/2}$ & $K^{1/2}/n^{1/4}$ & $1/ n^{1/4}$ & $1/n^{1/2}$\\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Stratified Permutational Berry--Esseen Bounds and Their Applications to Statistics", "authors": ["Pengfei Tian", "Fan Yang", "Peng Ding"], "url": "https://arxiv.org/abs/2503.13986v1", "attribution": "\"Stratified Permutational Berry--Esseen Bounds and Their Applications to Statistics\" by Pengfei Tian, Fan Yang, and Peng Ding, arXiv:2503.13986v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.05216v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|c|c|}\n\\hline\n\\textbf{Metric} & \\textbf{} & \\textbf{Test Dataset 1} & \\textbf{Test Dataset 2} \\\\ \\hline\n\\multirow{3}{*}{\\textbf{Classification}} & \\textbf{Accuracy} & 0.4898 & 0.8175 \\\\ \\cline{2-4}\n & \\textbf{Recall} & 0.4898 & 0.7448 \\\\ \\cline{2-4}\n & \\textbf{F1-score} & 0.8056 & 0.8213 \\\\ \\hline\n\\multirow{2}{*}{\\textbf{Detection}} & \\textbf{Avg. Precision} & 0.5000 & 0.8250 \\\\ \\cline{2-4}\n & \\textbf{IoU Score} & 0.1254 & 0.6315 \\\\ \\hline\n\\multirow{2}{*}{\\textbf{Segmentation}} & \\textbf{Dice-Coefficient} & 0.1767 & 0.6833 \\\\ \\cline{2-4}\n & \\textbf{IoU Score} & 0.2029 & 0.6128 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Performance Statistics}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "ColonNet: A Hybrid Of DenseNet121 And U-NET Model For Detection And Segmentation Of GI Bleeding", "authors": ["Ayushman Singh", "Sharad Prakash", "Aniket Das", "Nidhi Kushwaha"], "url": "https://arxiv.org/abs/2412.05216v1", "attribution": "\"ColonNet: A Hybrid Of DenseNet121 And U-NET Model For Detection And Segmentation Of GI Bleeding\" by Ayushman Singh, Sharad Prakash, Aniket Das, and Nidhi Kushwaha, arXiv:2412.05216v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.10044v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\toprule\n IC-based & Cumulative IC-based & Other\\\\\n \\midrule\n 30\\% & 70\\% & IC\\textunderscore diff\\textunderscore 90\\%\\\\\n 40\\% & 80\\% & IC\\textunderscore thp\\\\\n 50\\% & 90\\% & IC\\textunderscore thp*pos(mean)\\\\\n 70\\% & mean & \\\\\n 80\\% & median & \\\\\n 90\\% & std\\textunderscore dev & \\\\\n std\\textunderscore dev & var & \\\\\n var & kurtosis & \\\\\n & max & \\\\\n & range & \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Critical IC features}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Data-Driven Quantification of Battery Degradation Modes via Critical Features from Charging", "authors": ["Yuanhao Cheng", "Hanyu Bai", "Yichen Liang", "Xiaofan Cui", "Weiren Jiang", "Ziyou Song"], "url": "https://arxiv.org/abs/2412.10044v1", "attribution": "\"Data-Driven Quantification of Battery Degradation Modes via Critical Features from Charging\" by Yuanhao Cheng, Hanyu Bai, Yichen Liang, Xiaofan Cui, Weiren Jiang, and Ziyou Song, arXiv:2412.10044v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.14181v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Validation confidences and empirical safety for four in-distribution and ten out-of-distribution environments. The ID safety chance from \\texttt{PRISM} is $[0.5837, 1.0000]$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\toprule\nCase & Distance & Success rate & 99\\% success CI & $1-\\gamma$ %& $\\mathsf{mean}(1-\\gamma)$ \n\\\\\n\\midrule\nID & 0.00 & 1.000 & [0.9947, 1.0000] & 0.7782 %& 0.6864 \n\\\\\nID & 0.00 & 1.000 & [0.9947, 1.0000] & 0.7593 % & 0.6645 \n\\\\\nID & 0.00 & 1.000 & [0.9947, 1.0000] & 0.7093 % & 0.5976 \n\\\\\nID & 0.00 & 1.000 & [0.9947, 1.0000] & 0.6530 % & 0.6482 \n\\\\\nOOD & 2.45 & 0.505 & [0.4638, 0.5461] & 0.0000 %& 2.0e-5 \n\\\\\nOOD & 2.47 & 0.454 & [0.4132, 0.4952] & 0.0000 % & 0.0000 \n\\\\\nOOD & 2.50 & 0.405 & [0.3651, 0.4459] & 0.0000 %& 1.1e-6 \n\\\\\nOOD & 2.52 & 0.391 & [0.3514, 0.4317] & 0.0000 % & 3.2e-6 \n\\\\\nOOD & 2.55 & 0.289 & [0.2527, 0.3274] & 0.0000 %& 0.0000 \n\\\\\nOOD & 2.60 & 0.263 & [0.2278, 0.3004] & 0.0000 % & 0.0000 \n\\\\\nOOD & 2.70 & 0.176 & [0.1461, 0.2091] & 0.0000 %& 0.0000 \n\\\\\nOOD & 2.80 & 0.092 & [0.0700, 0.1180] & 0.0000 %& 0.0000 \n\\\\\nOOD & 2.90 & 0.061 & [0.0431, 0.0832] & 0.0000 % & 0.0000 \n\\\\\nOOD & 3.00 & 0.025 & [0.0141, 0.0407] & 0.0000 %& 0.0000 \n\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Towards Unified Probabilistic Verification and Validation of Vision-Based Autonomy", "authors": ["Jordan Peper", "Yan Miao", "Sayan Mitra", "Ivan Ruchkin"], "url": "https://arxiv.org/abs/2508.14181v1", "attribution": "\"Towards Unified Probabilistic Verification and Validation of Vision-Based Autonomy\" by Jordan Peper, Yan Miao, Sayan Mitra, and Ivan Ruchkin, arXiv:2508.14181v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.10670v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c||c|c|c|}\n \\hline\n \\multicolumn{6}{|c||}{Discrete treatment} & \\multicolumn{3}{c|}{Constant effect treatment} \\\\\n \\hline\n \\multicolumn{3}{|c|}{} & \\multicolumn{2}{c|}{$(P_1)$} & $(P_2)$ & & $(P_1)$ & $(P_2)$ \\\\\n \\hline\n \\hline\n $\\Delta_c$ & $N_c$ & $\\overline{d}_c$ & $T$ & $V(T)$ & $V(31)$ & $E_c$ & $V(31)$ & $V(31)$ \\\\\n $day$ & & $mg/kg$ & $day$ & $mm^3$ & $mm^3$ & & $mm^3$ & $mm^3$ \\\\\n \\hline $5$ & $6$ & $26.67$ & $1$ & $V_c$ & $15707$ & $1.37$ & $16429$ & $15613$ \\\\\n \\hline $3$ & $10$ & $16$ & $1$ & $V_c$ & $15455$ & $1.46$ & $16543$ & $15381$ \\\\\n \\hline $2$ & $15$ & $10.67$ & $1$ & $V_c$ & $15294$ & $1.51$ & $16630$ & $15235$ \\\\\n \\hline $1$ & $30$ & $5.33$ & $1$ & $V_c$ & $15094$ & $1.57$ & $16748$ & $15062$ \\\\\n \\hline $0.5$ & $60$ & $2.67$ & $1$ & $V_c$ & $14985$ & $1.6$ & $16822$ & $14963$ \\\\\n \\hline $0.33$ & $90$ & $1.78$ & $31$ & $17154$ & $14954$ & $1.61$ & $16850$ & $14928$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Results for the cytotoxic discrete treatment and constant drug effect starting at $t_1 = 0$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A mathematical perspective on the paradox that chemotherapy sometimes works backwards", "authors": ["Luis A. Fernández", "Isabel Lasheras", "Cecilia Pola"], "url": "https://arxiv.org/abs/2503.10670v1", "attribution": "\"A mathematical perspective on the paradox that chemotherapy sometimes works backwards\" by Luis A. Fernández, Isabel Lasheras, and Cecilia Pola, arXiv:2503.10670v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.10013v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n& AFEW & FPHA & NTU RGB+D\\ & HDM05\\\\ \n\\midrule\nAff-Inv w/o Nonlinearity & $35.17$\\tiny$\\pm 1.78$& $65.03$\\tiny$ \\pm 2.22$ & $41.27$\\tiny$ \\pm 0.22$ & $65.92 $\\tiny$\\pm 1.27$\\\\\nAff-Inv & $35.17$ \\tiny $\\pm 1.78$&$\\mathbf{66.53} $\\tiny$\\mathbf{\\pm 1.64}$ & $41.00$ \\tiny $\\pm 0.50$& $67.91$ \\tiny${\\pm 0.66}$\\\\\nLog-Euc w/o Nonlinearity & $36.38$\\tiny$\\pm 1.29$& $65.25 $\\tiny$\\pm 2.14$ & $42.87$\\tiny$\\pm 0.83$ & $68.73 $\\tiny$\\pm 1.75$\\\\\nLog-Euc&$\\mathbf{36.38}$\\tiny $\\mathbf{\\pm 1.29}$& $64.58 $\\tiny$\\pm 0.98$& $\\mathbf{42.99 }$\\tiny$\\mathbf{\\pm 0.23}$& $\\mathbf{69.80}$\\tiny$\\mathbf{ \\pm 1.51}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{We show that classification accuracy either improves or remains the same with the nonlinearity. For AFEW, we use one layer in the vector field, which is why the reported accuracies are the same.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Riemannian Residual Neural Networks", "authors": ["Isay Katsman", "Eric Ming Chen", "Sidhanth Holalkere", "Anna Asch", "Aaron Lou", "Ser-Nam Lim", "Christopher De Sa"], "url": "https://arxiv.org/abs/2310.10013v1", "attribution": "\"Riemannian Residual Neural Networks\" by Isay Katsman, Eric Ming Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser-Nam Lim, and Christopher De Sa, arXiv:2310.10013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.13161v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Generated Dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|} \\hline\nID& $t_i$ & $c_i$& $x_i$&$\\lambda_{it}$ & $\\lambda_{ic}$ &$y_i$ &$s_i$\n\\\\\n\\hline\\hline\n1&0.035 & 0.178 & ( 1.663, -0.045) & 26.565 & 5.154 & 1 & 1 \\\\\n2&0.192 & 0.271 & ( 1.019, 0.393) & 11.531 & 3.316 &1 &1 \\\\\n3&0.021& 0.090 & (1.158, 0.474) &16.557 & 3.956 &1 &1 \\\\\n4& 0.155& 0.301 & (0.173, 1.399) & 6.097 & 2.337 & 1 &1 \\\\ \n\\hline\n$\\vdots$& $\\vdots$& $\\vdots$&$\\vdots$ &$\\vdots$&$\\vdots$ &$\\vdots$ &$\\vdots$ \\\\ \n\\hline\n4123&0.200 & 0.037 & (0.259, 0.057) &1.782 & 1.329 &0 &0 \\\\\n4124& 0.281 & 0.764 & (-0.109, 1.088) & 2.508 & 1.520 &1 &0 \\\\\n4125& 0.552 & 0.247 & ( 0.684, -0.223) & 3.101 & 1.766 &0 &0 \\\\\n4126& 0.400 & 1.113 & ( 0.383, 0.489) & 3.578 & 1.850 &1 &0 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Positive-Unlabelled Survival Data Analysis", "authors": ["Tomoki Toyabe", "Yasuhiro Hasegawa", "Takahiro Hoshino"], "url": "https://arxiv.org/abs/2011.13161v1", "attribution": "\"Positive-Unlabelled Survival Data Analysis\" by Tomoki Toyabe, Yasuhiro Hasegawa, and Takahiro Hoshino, arXiv:2011.13161v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.10585v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n{\\bf Test} & {\\bf Mean} & {\\bf STD} & {\\bf Normalized Distance} & {\\bf Max\nDistance} \\\\\n\\hline\\hline\nBaseline $T=0$ & 14.02 & 1.47 & 0 & 0 \\\\\n\\hline\nSample $T=0$ & 14.02 & 1.47 & 2.27E-04 & 1.45E-03 \\\\\n\\hline\nCapitalization/Punctuation & 13.85 & 1.48 & 5.82E-02 & 5.12E-02\\\\ \n\\hline\nSpelling & 14.07 & 1.51 & 1.78E-02 & 5.28E-02\\\\\n\\hline\nGrammar + Spelling & 13.95 & 1.52 & 4.32E-02 & 1.36E-01\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ {\\bf Negligible Effect of Paraphrasing:} With GPT-3 set at zero temperature, repeated samples generate nearly identical responses (first two rows). Variability in the query obtained by changing punctuation and capitalization, or introducing spelling errors and grammatical errors, causes changes in the distributions but no change in the highest ranking response or in the median. Two different distances between sample distributions are chosen: the normalized distance (absolute difference between the means, normalized by the sum of the standard deviations) and the maximum absolute value of the difference between corresponding histogram bins. In all cases, the distances are insignificant to the outcome of my experiments.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Minimum Wage as an Anchor: Effects on Determinations of Fairness by Humans and AI", "authors": ["Dario G. Soatto"], "url": "https://arxiv.org/abs/2210.10585v3", "attribution": "\"The Minimum Wage as an Anchor: Effects on Determinations of Fairness by Humans and AI\" by Dario G. Soatto, arXiv:2210.10585v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15444v2_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mutually unbiased weighing matrices of order $19$ and weight $9$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l}\n\\noalign{\\hrule height1pt}\n$W_{19}$\\\\\n\\hline\n0100200020021001212\n1010010100222000022\n0102002002210012200\n1002000022001001121\\\\\n0101100200020100221\n0121020000000210122\n0100110111011001000\n0010022210200221000\\\\\n1000200201012111000\n0122011200002220000\n1002121010020010010\n0012000201101002022\\\\\n0110102020102000110\n1001010200201002110\n1011001002110200200\n1020002012100120002\\\\\n1220102021000200200\n1100220101000022001\n0000121020210120002\\\\\n\\hline\n$ A_{19,2}$\\\\\n\\hline\n1022102100001110000\n1100101002200202002\n0120000210210001201\n0101010021010100012\\\\\n1010100011000020111\n1201020000001021202\n0000001111110002220\n1110000002102101020\\\\\n1000200201220102020\n1000200200111210100\n1020212100002220000\n0001110001020211020\\\\\n1200001020002010211\n0112022021000200200\n0120001020121020001\n0101220110020010010\\\\\n0012211100201001000\n0001020120210000121\n0011012002001002201\\\\\n\\hline\n$ A_{19,3}$\\\\\n\\hline\n1001100110021000102\n0121202011110000000\n1000200200022201120\n1110000001202012010\\\\\n0001020120122100001\n1220011001000022001\n0100020200201120101\n0000001110212101020\\\\\n0011100021010221000\n0010200112000220011\n0110011000101010021\n0100112002002022020\\\\\n1212002000110100100\n0122010120000001110\n1102021000100020202\n0001201022010002102\\\\\n1001010202000101210\n1000202120201000220\n1020120002010210001\\\\\n\\hline\n$ A_{19,4}$\\\\\n\\hline\n1020001201200000212\n0111002201002000022\n0011101100201000220\n0100201102002201002\\\\\n1202100000000210122\n1012020010012000201\n0000001111022102100\n0001110002012012010\\\\\n1201200121110000000\n0012210020020012200\n1201202012220000000\n1000110020022021001\\\\\n0120002101200210001\n0001021200120210001\n0000122100120000212\n1110000000001111110\\\\\n0010020020200222110\n1100010010101222000\n1120020022000102020\\\\\n\\hline\n$ A_{19,5}$\\\\\n\\hline\n1022100211110000000\n0000121022102010002\n0120210000002100122\n0121020000001022012\\\\\n0100020200202202021\n0112020100011100020\n0100111220201001000\n0100110110020012200\\\\\n1000200202121000220\n0011102212000100100\n1011001001002120200\n1010012020010202002\\\\\n1000022001220011002\n1220100102200020020\n1101000100100201101\n0012001010020220102\\\\\n1000201012210010010\n0102002002002021210\n1002000020020102111\\\\\n\\hline\n$ A_{19,6}$\\\\\n\\hline\n1000110021010010101\n0010202000200011211\n1011001002200021100\n1120020020022200010\\\\\n1001102000200102202\n0121201200001110000\n1002001100020100221\n0102000110201002110\\\\\n0010020021101120010\n0100112002121001000\n0010020022001212020\n0111210100102002000\\\\\n0110101211000200200\n1200000212100002011\n0001022011020000121\n1200210001021200002\\\\\n1000020110110011002\n0012000200022110102\n1102202200010020020\\\\\n\\noalign{\\hrule height1pt}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unbiased weighing matrices of weight $9$", "authors": ["Makoto Araya", "Masaaki Harada", "Hadi Kharaghani", "Sho Suda", "Wei-Hsuan Yu"], "url": "https://arxiv.org/abs/2501.15444v2", "attribution": "\"Unbiased weighing matrices of weight $9$\" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.15439v1_tex_table22.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{OLS Regression for Magnitude of Cheating}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccc}\n\\hline\n\\hline\n & & \\multicolumn{5}{c}{Dependent variable: Magnitude of cheating} \\\\ \\cmidrule{3-7} \n & & (1) & (2) & (3) & (4) & (5) \\\\ \n\\midrule\n \\multicolumn{2}{l}{Intercept} & $2.261^{***}$ & $ 1.480^{*}$ & -1.069 & $2.218^{***}$ & -0.835\\\\ \n & & (0.253) & (0.675) & (1.068) & (0.616) & (1.564) \\\\\n & & & & & & \\\\\n \\multicolumn{2}{l}{Treatment} & & & & & \\\\ \n & \\textit{Machine} & 0.139 & 0.067 & $2.500^{**}$ & 0.245 & 1.823 \\\\ \n & &(0.353) & (0.371) & (0.795) & (0.615) & (0.924) \\\\ \n & \\textit{Human Black Box} & $0.798^{*}$ & 0.625 & 0.478 & $0.816^{*}$ & 0.486 \\\\ \n & & (0.375) & (0.364) & (0.364) & (0.364) & (0.352) \\\\ \n & \\textit{Machine Black Box} & $1.466^{***}$ & $1.640^{***}$ & $3.957^{***}$ & $1.600^{**}$ & $3.520^{***}$ \\\\ \n & & (0.287) & (0.252) & (0.814) & (0.580) & (0.944) \\\\ \n & & & & & & \\\\ \n \\multicolumn{2}{l}{Age} & & 0.054 & & & 0.041\\\\ \n & & & (0.030) & & & (0.032) \\\\ \n \\multicolumn{2}{l}{Female} & & -0.391 & & & -0.357 \\\\ \n & & & (0.218) & & & (0.221) \\\\ \n & & & & & & \\\\\n \\multicolumn{2}{l}{Field of Study} & & & & & \\\\ \n & \\textit{Cultural \\& social studies} & & $-0.530^{*}$ & & & -0.325 \\\\ \n & & & (0.251) & & & (0.244) \\\\ \n & \\textit{Natural science} & & $-0.939^{**}$ & & & -0.369 \\\\ \n & & & (0.303) & & & (0.372) \\\\ \n & & & & & & \\\\\n \\multicolumn{2}{l}{Risk} & & & $0.138^{*}$ & & $0.149^{*}$ \\\\ \n & & & & (0.066) & & (0.056) \\\\ \n \\multicolumn{2}{l}{Ethical sensitivity} & & & 0.151 & & 0.159 \\\\ \n & & & & (0.227) & & (0.229) \\\\ \n \\multicolumn{2}{l}{Closeness} & & & 0.080 & & 0.086 \\\\ \n & & & & (0.064) & & (0.071) \\\\ \n & & & & & & \\\\\n \\multicolumn{2}{l}{Verification by machine \\# ATI} & & & & & \\\\ \n & $0$ & & & $0.466^{*}$ & & 0.183 \\\\ \n & & & & (0.192) & & (0.208) \\\\ \n & $1$ & & & -0.225 & & $-0.334^{*}$ \\\\ \n & & & & (0.132) & & (0.147) \\\\ \n & & & & & & \\\\ \n \\multicolumn{2}{l}{Verification by preferred entity} & & & & -0.242 & -0.051\\\\ \n & & & & & (0.227) & (0.219) \\\\ \n \\multicolumn{2}{l}{Verification by more error-prone entity} & & & & $0.812^{*}$ & $0.806^{**}$\\\\ \n & & & & & (0.308) & (0.295) \\\\ \n \\multicolumn{2}{l}{Verification by higher discretion entity} & & & & -0.517 & -0.624 \\\\ \n & & & & & (0.535) & (0.560) \\\\ \n\\midrule\n \\multicolumn{2}{l}{F-test} & $13.31^{***}$ & $14.59^{***}$ & $9.64^{***}$ & $10.87^{***}$ & $10.53^{***}$ \\\\\n \\multicolumn{2}{l}{$R^{2}$} & 0.2600 & 0.3712 & 0.4342 & 0.3454 & 0.5527 \\\\\n \\multicolumn{2}{l}{Adj. $R^{2}$} & 0.2615 & 0.3117 & 0.3722 & 0.2931 & 0.4510\\\\\n \\multicolumn{2}{l}{N} & 82 & 82 & 82 & 82 & 82\\\\\n \\hline\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty", "authors": ["Marius Protte", "Behnud Mir Djawadi"], "url": "https://arxiv.org/abs/2507.15439v1", "attribution": "\"Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty\" by Marius Protte and Behnud Mir Djawadi, arXiv:2507.15439v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.14230v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of instances (number of patients) used during training. These represent sample sizes for all 12 leads.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n\\toprule\nDataset & Train & Validation & Test\\\\\n\\midrule\n\\multirow{1}{*}{Chapman}&76,614 (6,387)&25,524 (2,129)&25,558 (2,130)\\\\\n\\multirow{1}{*}{PTB-XL}& 22,670 (11,335) &3,284 (1,642)&3,304 (1,152)\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age", "authors": ["Dani Kiyasseh", "Tingting Zhu", "David A. Clifton"], "url": "https://arxiv.org/abs/2011.14230v2", "attribution": "\"CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age\" by Dani Kiyasseh, Tingting Zhu, and David A. Clifton, arXiv:2011.14230v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00434v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lr}\n\\toprule\nModel & Memory (GB) \\\\\n\\midrule\nc2f + SpanBERT-Large & 16.2 \\\\\nc2f + Longformer-Base & 12.0 \\\\\nc2f + Longformer-Large & 15.7 \\\\\n\\midrule\ns2e + Longformer-Large & ~~\\textbf{4.3} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Peak GPU memory usage during inference on OntoNotes, when processing one document at a time.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Coreference Resolution without Span Representations", "authors": ["Yuval Kirstain", "Ori Ram", "Omer Levy"], "url": "https://arxiv.org/abs/2101.00434v2", "attribution": "\"Coreference Resolution without Span Representations\" by Yuval Kirstain, Ori Ram, and Omer Levy, arXiv:2101.00434v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.08774v2_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Common hyperparameters for PhyloGFN-Bayesian and PhyloGFN-Parsimony.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n \\multicolumn{2}{c}{Transformer encoder} \\\\\n \\midrule\n hidden size & 128 \\\\\n number of layers & 6 \\\\\n number of heads & 4 \\\\\n learning rate (model) & 5e-5 \\\\\n learning rate (Z) & 5e-3 \\\\\n \\midrule\n \\multicolumn{2}{c}{tree MLP} \\\\\n \\midrule\n hidden size & 256 \\\\\n number of layers & 3 \\\\\n \\midrule\n \\multicolumn{2}{c}{edge MLP} \\\\\n \\midrule\n hidden size & 256 \\\\\n number of layers & 3 \\\\\n \\midrule\n \\multicolumn{2}{c}{Z MLP (in temperature-conditioned PhyloGFN)} \\\\\n \\midrule\n hidden size & 128 \\\\\n number of layers & 1 \\\\\n \\midrule\n \\multicolumn{2}{c}{temp MLP (in temperature-conditioned PhyloGFN)} \\\\\n \\midrule\n hidden size & 256 \\\\\n number of layers & 3 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "PhyloGFN: Phylogenetic inference with generative flow networks", "authors": ["Mingyang Zhou", "Zichao Yan", "Elliot Layne", "Nikolay Malkin", "Dinghuai Zhang", "Moksh Jain", "Mathieu Blanchette", "Yoshua Bengio"], "url": "https://arxiv.org/abs/2310.08774v2", "attribution": "\"PhyloGFN: Phylogenetic inference with generative flow networks\" by Mingyang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, and Yoshua Bengio, arXiv:2310.08774v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.17643v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Correlation coefficients of the plain and cipher image.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\nDirection & \\multicolumn{6}{c}{Correlation coefficients} \\\\ \\midrule\n & \\multicolumn{3}{c}{Plain image} & \\multicolumn{3}{c}{Cipher Image} \\\\ \\cmidrule(l){2-7} \n & Red & Green & Blue & Red & Green & Blue \\\\ \\midrule\nHorizontal & 09424 & 0.9559 & 0.9319 & 0.0034 & -0.0013 & -0.007 \\\\\nVertical & 0.9464 & 0.9617 & 0.9406 & 0.0032 & -0.0063 & -0.0002 \\\\\nDiagonal & 0.9094 & 0.9285 & 0.8981 & 0.0002 & -0.0006 & 0.0009 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A robust image encryption scheme based on new 4-D hyperchaotic system and elliptic curve", "authors": ["Yehia Lalili", "Toufik Bouden", "Morad Grimes", "Abderrazek Lachouri"], "url": "https://arxiv.org/abs/2411.17643v1", "attribution": "\"A robust image encryption scheme based on new 4-D hyperchaotic system and elliptic curve\" by Yehia Lalili, Toufik Bouden, Morad Grimes, and Abderrazek Lachouri, arXiv:2411.17643v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23729v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Relative $L_2$ error & PINNs & IR-PINNs1 & IR-PINNs2 \\\\\n \\midrule\n $u$ & 1.1278e-01 & 6.9356e-03 & 3.9945e-03 \\\\\n \\midrule\n Running time (hours) & 46.91 & 49.36 & 48.23 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\textit{Kuramoto-Sivashinsky equation:} Relative $L_2$ errors and running time of different methods.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Integral regularization PINNs for evolution equations", "authors": ["Xiaodong Feng", "Haojiong Shangguan", "Tao Tang", "Xiaoliang Wan"], "url": "https://arxiv.org/abs/2503.23729v1", "attribution": "\"Integral regularization PINNs for evolution equations\" by Xiaodong Feng, Haojiong Shangguan, Tao Tang, and Xiaoliang Wan, arXiv:2503.23729v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04369v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Borough} & \\textbf{EMS Stations} & \\textbf{Hospitals} & \\textbf{Total} \\\\ \\midrule\nBronx & 19 & 12 & 31 \\\\\nBrooklyn & 76 & 15 & 91 \\\\\nManhattan & 28 & 23 & 51 \\\\\nQueens & 15 & 10 & 25 \\\\\nStaten Island & 23 & 6 & 29 \\\\ \\midrule\n\\textbf{Total NYC} & \\textbf{161} & \\textbf{66} & \\textbf{227} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Number of EMS Stations and Hospitals by boroughs in NYC}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Intersection-Aware Assessment of EMS Accessibility in NYC: A Data-Driven Approach", "authors": ["Haoran Su", "Joseph Y. J. Chow"], "url": "https://arxiv.org/abs/2412.04369v1", "attribution": "\"Intersection-Aware Assessment of EMS Accessibility in NYC: A Data-Driven Approach\" by Haoran Su and Joseph Y. J. Chow, arXiv:2412.04369v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.12084v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of Bilateral Geopolitical Events by Era, 1950--2024}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\n& \\multicolumn{4}{c}{Event Counts} & \\multicolumn{2}{c}{Goldstein Score} \\\\\n\\cmidrule(lr){2-5} \\cmidrule(lr){6-7}\nPeriod & Cooperation & Conflict & Total & Conflict (\\%) & Mean & SD \\\\\n\\midrule\nCold War (1950--1990) & 206,857 & 58,770 & 265,627 & 22.1 & 2.33 & 5.04 \\\\\nGlobalization (1991--2009) & 220,840 & 37,958 & 258,798 & 14.7 & 3.92 & 4.11 \\\\\nFragmentation (2010--2024) & 251,060 & 58,000 & 309,060 & 18.8 & 3.70 & 3.68 \\\\\n\\midrule\nFull Period & 678,757 & 154,728 & 833,485 & 18.6 & 3.38 & 4.33 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Geopolitical Barriers to Globalization", "authors": ["Tianyu Fan", "Mai Wo", "Wei Xiang"], "url": "https://arxiv.org/abs/2509.12084v2", "attribution": "\"Geopolitical Barriers to Globalization\" by Tianyu Fan, Mai Wo, and Wei Xiang, arXiv:2509.12084v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05326v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Confusion Matrix for EoE Diagnosis}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c|}\n\\multicolumn{2}{c}{}&\\multicolumn{2}{c}{\\textbf{Predicted}}\\\\\n\\cline{3-4}\n\\multicolumn{2}{c|}{}& EoE & Normal \\\\\n\\cline{2-4}\n\\multirow{2}{*}{\\textbf{True Diagnosis}}& EoE & 44 & 0 \\\\\n\\cline{2-4}\n& Normal & 1 & 56 \\\\\n\\cline{2-4}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision", "authors": ["William Adorno", "Alexis Catalano", "Lubaina Ehsan", "Hans Vitzhum von Eckstaedt", "Barrett Barnes", "Emily McGowan", "Sana Syed", "Donald E. Brown"], "url": "https://arxiv.org/abs/2101.05326v1", "attribution": "\"Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision\" by William Adorno, Alexis Catalano, Lubaina Ehsan, Hans Vitzhum von Eckstaedt, Barrett Barnes, Emily McGowan, Sana Syed, and Donald E. Brown, arXiv:2101.05326v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06399v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc}\n\\hline \n\\toprule\n& \\multicolumn{4}{|c}{VQA v2.0 val} \\\\\n\\hline\n& {\\footnotesize All} & {\\footnotesize Yes/No} & {\\footnotesize Num} & {\\footnotesize Other} \\\\\n\\hline\n\\hline\nUpDn & 63.15 & 80.38 & 42.84 & 55.86 \\\\\nUpDn + caption & 63.85 & 81.10 & 43.63 & 55.90 \\\\\nUpDn + category & 63.51 & 81.62 & 42.17 & 55.38 \\\\\n\\midrule\nOurs w/o caption & \\bf 64.09 & 81.82 & 44.37 & 55.74 \\\\\nOurs w/ caption & \\underline{64.24} & 82.36 & 44.52 & 56.02 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Ablation study to investigate the effect of each component: caption, and answer category. ``Ours w/o caption\" indicates our final model in which only image and question pairs are needed at test time; while ``Ours w/ caption\" represents the model using caption during evaluation. The result of our best model are in bold; while the best performance with captions as inputs during testing is underlined for comparison.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Latent Variable Models for Visual Question Answering", "authors": ["Zixu Wang", "Yishu Miao", "Lucia Specia"], "url": "https://arxiv.org/abs/2101.06399v2", "attribution": "\"Latent Variable Models for Visual Question Answering\" by Zixu Wang, Yishu Miao, and Lucia Specia, arXiv:2101.06399v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.03248v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n \\hline\n & j=1& j=2 & j=3 \\\\\n \\hline \\hline\n $\\hat{\\mu}_{j,1}$ & -9.997 & 10.028 & -0.019 \\\\\n (CI) & (-10.057 -9.938) & (9.923 10.137) & (-0.274 0.236) \\\\\n $\\hat{\\mu}_{j,2}$ & 0.054 & 10.003 & 0.22 \\\\\n (CI) & (-0.084 0.188) & (9.895 10.113) & (-0.054 0.493) \\\\\n $\\hat{\\eta}_{j,1}$ & 0.01 & 0.994 & -1.977 \\\\\n (CI) & (-0.233 0.254) & (0.908 1.082) & (-2.07 -1.908) \\\\\n $\\hat{\\eta}_{j,2}$ & 0.015 & 0.962 & 0.005 \\\\\n (CI) & (-0.244 0.265) & (0.873 1.054) & (-0.049 0.056) \\\\\n $\\hat{\\tau}_{j}$ & 0.806 & 0.816 & 0.915 \\\\\n (CI) & (0.796 0.816) & (0.777 0.862) & (0.77 0.996) \\\\\n $\\hat{\\rho}_{j}$ & 0.205 & 0.511 & 0.824 \\\\\n (CI) & (0.197 0.211) & (0.488 0.537) & (0.791 0.84) \\\\\n $\\hat{\\boldsymbol{\\Sigma}}_{j,1,1}$ & 0.483 & 0.982 & 0.975 \\\\\n (CI) & (0.454 0.512) & (0.928 1.04) & (0.917 1.037) \\\\\n $\\hat{\\boldsymbol{\\Sigma}}_{j,1,2}$ & 0.006 & -0.244 & 0.264 \\\\\n (CI) & (-0.06 0.074) & (-0.275 -0.215) & (0.222 0.308) \\\\\n $\\hat{\\boldsymbol{\\Sigma}}_{j,2,2}$ & 4.962 & 0.495 & 0.976 \\\\\n (CI) & (4.679 5.259) & (0.468 0.524) & (0.917 1.039) \\\\\n $\\hat{{\\pi}}_{1,j}$ & 0.799 & 0.101 & 0.099 \\\\\n (CI) & (0.781 0.817) & (0.089 0.114) & (0.086 0.114) \\\\\n $\\hat{{\\pi}}_{2,j}$ & 0.09 & \\textbf{0.817} & 0.093 \\\\\n (CI) & (0.079 0.102) & (0.801 0.832) & (0.081 0.105) \\\\\n $\\hat{{\\pi}}_{3,j}$ & 0.107 & 0.092 & 0.801 \\\\\n (CI) & (0.094 0.121) & (0.08 0.105) & (0.783 0.818) \\\\\n $\\hat{\\beta}_{j}$ & 0.317 & 0.304 & 0.31 \\\\\n (CI) & (0.053 0.673) & (0.052 0.675) & (0.047 0.668) \\\\\n \\hline \\hline\n & $\\alpha$ & $\\kappa$ & $\\gamma$ \\\\\n \\hline\n $\\hat{}$ & 0.327 & 3.377 & 0.724 \\\\\n (CI) & (0.007 1.279) & (1.16 6.848) & (0.076 2.315) \\\\\n \\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Posterior means and 95\\% CIs of the parameters of the third simulated dataset for K=3. The parameter whose 95\\% CI does not contain the true value is shown in bold.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modeling animal movement with directional persistence and attractive points", "authors": ["Gianluca Mastrantonio"], "url": "https://arxiv.org/abs/2012.03248v2", "attribution": "\"Modeling animal movement with directional persistence and attractive points\" by Gianluca Mastrantonio, arXiv:2012.03248v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.14072v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc} \n& \\textbf{2006-2010} & \\textbf{2011-2015} & \\textbf{2016-2020}\\\\ \n\\hline \n \\textbf{Number of nodes} & 318 & 1005 & 1064 \\\\ \n \\textbf{Number of links} & 1152 & 3937& 3520\\\\ \n \\textbf{Volume of citations} & 7935 & 63411 & 63059\\\\ \n \\textbf{Density} & 0.01 & 0.004 & 0.003 \\\\\n\\textbf{Average in-degree (std)}& 3.6 (3.9) & 3.9 (6.2) & 3.3 (5.1)\\\\\n\\textbf{Average out-degree (std)} & 3.6 (13.7) & 3.9 (27.9) & 3.3 (26.7)\\\\\n\\textbf{Max in-degree} & 22 & 50&41\\\\\n\\textbf{Max out-degree}& 212 & 782 &783\\\\\n\\textbf{Average in-strength (std)}& 25 (78) & 63 (221) & 59 (254)\\\\\n\\textbf{Average out-strength (std)}& 25 (85) & 63 (1334) & 59 (1445)\\\\\n\\textbf{Max in-strength}& 719 & 3500 & 4982\\\\\n \\textbf{Max out-strength}& 2954 & 42138 & 46951\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Network binary properties}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Leveraging Knowledge Networks: Rethinking Technological Value Distribution in mRNA Vaccine Innovations", "authors": ["Rossana Mastrandrea", "Fabio Montobbio", "Gabriele Pellegrino", "Massimo Riccaboni", "Valerio Sterzi"], "url": "https://arxiv.org/abs/2503.14072v1", "attribution": "\"Leveraging Knowledge Networks: Rethinking Technological Value Distribution in mRNA Vaccine Innovations\" by Rossana Mastrandrea, Fabio Montobbio, Gabriele Pellegrino, Massimo Riccaboni, and Valerio Sterzi, arXiv:2503.14072v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.01310v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{llllllll}\n \\hline\\hline\n & \\multicolumn{1}{p{3em}}{\\textbf{Means Men}} & \\multicolumn{1}{p{3em}}{\\textbf{Means Women}} & \\multicolumn{1}{p{3em}}{\\textbf{Std Mean Diff}} & \\multicolumn{1}{p{3em}}{\\textbf{Var. Ratio}} & \\multicolumn{1}{p{3em}}{\\textbf{eCDF Mean}} & \\multicolumn{1}{p{3em}}{\\textbf{eCDF Max}} & \\multicolumn{1}{p{4em}}{\\textbf{Std. Pair Dist.}} \\\\\n \\hline\n Distance & 0.37 & 0.37 & 0.01 & 1.02 & 0.00 & 0.01 & 0.01 \\\\ \n Region Capital & 0.28 & 0.29 & -0.01 & & 0.00 & 0.00 & 0.86 \\\\ \n Region Central Jutland & 0.21 & 0.21 & 0.01 & & 0.00 & 0.00 & 0.81 \\\\ \n Region North Jutland & 0.13 & 0.12 & 0.01 & & 0.00 & 0.00 & 0.64 \\\\ \n Region South Denmark & 0.22 & 0.22 & -0.00 & & 0.00 & 0.00 & 0.82 \\\\ \n Region Zealand & 0.16 & 0.16 & -0.01 & & 0.00 & 0.00 & 0.72 \\\\ \n Married couple & 0.53 & 0.52 & 0.02 & & 0.01 & 0.01 & 0.88 \\\\ \n Multi-family households & 0.09 & 0.10 & -0.00 & & 0.00 & 0.00 & 0.58 \\\\ \n Other couples & 0.07 & 0.07 & 0.00 & & 0.00 & 0.00 & 0.12 \\\\ \n Single & 0.31 & 0.31 & -0.02 & & 0.01 & 0.01 & 0.70 \\\\ \n Income (DKK) & 176338 & 179615 & -0.03 & 0.98 & 0.01 & 0.04 & 0.59 \\\\ \n Age & 59.94 & 59.90 & 0.01 & 1.01 & 0.00 & 0.01 & 1.11 \\\\ \n Education in months & 138.11 & 137.75 & 0.01 & 0.97 & 0.04 & 0.14 & 0.70 \\\\ \n\\hline\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Gender Differences in Healthcare Utilisation -- Evidence from Unexpected Adverse Health Shocks", "authors": ["Nadja van 't Hoff", "Giovanni Mellace", "Seetha Menon"], "url": "https://arxiv.org/abs/2509.01310v1", "attribution": "\"Gender Differences in Healthcare Utilisation -- Evidence from Unexpected Adverse Health Shocks\" by Nadja van 't Hoff, Giovanni Mellace, and Seetha Menon, arXiv:2509.01310v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12682v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Extended imputation results. For each dataset, values were removed completely at random in the amounts of 5\\%, 10\\%, 25\\%, 50\\%, and 75\\%. The two numbers directly below each dataset designate the number of observations and number of variables, respectively, for the listed dataset. The average mean-squared errors (MSE) between the original and imputed values were recorded along with the standard deviations. RF-GAP generally outperforms the other definitions. Average rankings can be found in Table~.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|cc|cc|cc|cc|cr|}\n \\hline\nDataset & Proximity & 5\\% & & 10\\% & & 25\\% & & 50\\% & & 75\\% & \\\\ \n \\hline\nArrhythmia & RF-GAP & \\textbf{0.637} & (0.05) & \\textbf{0.913} & (0.06) & \\textbf{1.463} & (0.06) & \\textbf{2.142} & (0.06) & \\textbf{2.783} & (0.06) \\\\ \n 452 & Original & 0.665 & (0.05) & 0.950 & (0.06) & 1.512 & (0.06) & 2.188 & (0.06) & 2.792 & (0.05) \\\\ \n 279 & OOB & 0.674 & (0.05) & 0.964 & (0.06) & 1.532 & (0.06) & 2.205 & (0.06) & 2.766 & (0.05) \\\\ \n & RFProxIH & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \\\\ \n \\hline\nAuto-Mpg & RF-GAP & 0.230 & (0.05) & 0.338 & (0.06) & 0.587 & (0.06) & 0.941 & (0.06) & 1.484 & (0.20) \\\\ \n 392 & Original & \\textbf{0.226} & (0.05) & \\textbf{0.334} & (0.05) & \\textbf{0.584} & (0.06) & \\textbf{0.938} & (0.06) & \\textbf{1.400} & (0.10) \\\\ \n 8 & OOB & 0.232 & (0.05) & 0.342 & (0.06) & 0.595 & (0.06) & 0.955 & (0.06) & 1.425 & (0.10) \\\\ \n & RFProxIH & NA & NA & NA & NA & NA & NA & NA& NA & NA & NA \\\\ \n \\hline\nBalance Scale & RF-GAP & \\textbf{3.240} & (0.22) & \\textbf{4.581 }& (0.22) & \\textbf{7.288} & (0.23) & \\textbf{10.278} & (0.24) & \\textbf{12.553} & (0.26) \\\\ \n 645 & Original & 3.287 & (0.22) & 4.649 & (0.22) & 7.385 & (0.23) & 10.336 & (0.24) & 12.561 & (0.27) \\\\ \n 4 & OOB & 3.318 & (0.22) & 4.686 & (0.22) & 7.432 & (0.23) & 10.382 & (0.24) & 12.609 & (0.27) \\\\ \n & RFProxIH & 3.305 & (0.23) & 4.674 & (0.23) & 7.414 & (0.23) & 10.351 & (0.25) & 12.571 & (0.27) \\\\ \n \\hline\nBanknote & RF-GAP & \\textbf{1.372} & (0.12) & \\textbf{1.965} & (0.12) & \\textbf{3.211} & (0.13) & \\textbf{4.798} & (0.12) & \\textbf{ 6.176} & (0.12) \\\\ \n 1372 & Original & 1.427 & (0.12) & 2.048 & (0.12) & 3.361 & (0.12) & 4.986 & (0.11) & 6.313 & (0.12) \\\\ \n 5 & OOB & 1.443 & (0.12) & 2.065 & (0.12) & 3.379 & (0.12) & 4.994 & (0.12) & 6.319 & (0.12) \\\\ \n & RFProxIH & 1.420 & (0.12) & 2.042 & (0.12) & 3.357 & (0.12) & 4.985 & (0.12) & 6.314 & (0.12) \\\\ \n \\hline\nBreast Cancer & RF-GAP & \\textbf{2.816} & (0.20) & \\textbf{4.027} & (0.22) & \\textbf{6.479} & (0.20) & \\textbf{9.362} & (0.20) & \\textbf{11.737} & (0.23) \\\\ \n 699 & Original & 2.903 & (0.20) & 4.154 & (0.23) & 6.670 & (0.21) & 9.563 & (0.22) & 11.848 & (0.23) \\\\ \n 16 & OOB & 2.891 & (0.20) & 4.125 & (0.23) & 6.604 & (0.21) & 9.483 & (0.21) & 11.800 & (0.23) \\\\ \n & RFProxIH & 2.921 & (0.20) & 4.183 & (0.23) & 6.713 & (0.22) & 9.601 & (0.22) & 11.869 & (0.23) \\\\ \n \\hline\nDiabetes & RF-GAP & \\textbf{0.710 }& (0.20) & \\textbf{0.960} & (0.16) & \\textbf{1.591} & (0.17) & \\textbf{2.328} & (0.15) & \\textbf{2.932} & (0.14) \\\\ \n 678 & Original & 0.713 & (0.19) & 0.973 & (0.15) & 1.622 & (0.17) & 2.364 & (0.16) & 2.941 & (0.14) \\\\ \n 8 & OOB & 0.713 & (0.19) & 0.971 & (0.15) & 1.616 & (0.17) & 2.360 & (0.15) & 2.941 & (0.14) \\\\ \n & RFProxIH & 0.715 & (0.19) & 0.977 & (0.15) & 1.628 & (0.17) & 2.367 & (0.16) & 2.942 & (0.14) \\\\ \n \\hline\nE. Coli & RF-GAP & 0.862 & (0.14) & 1.235 & (0.12) & 1.991 & (0.11) & 2.873 & (0.13) & 3.673 & (0.14) \\\\ \n 336 & Original & 0.886 & (0.14) & 1.267 & (0.12) & 2.031 & (0.12) & 2.909 & (0.14) & 3.706 & (0.17) \\\\ \n 8 & OOB & 0.898 & (0.14) & 1.286 & (0.12) & 2.059 & (0.12) & 2.955 & (0.14) & 3.782 & (0.23) \\\\ \n & RFProxIH & \\textbf{0.831} & (0.23) & \\textbf{1.156} & (0.36) & \\textbf{1.774} & (0.67) & \\textbf{2.170} & (1.26) & \\textbf{2.146} & (1.80) \\\\ \n \\hline\nGlass & RF-GAP & \\textbf{0.064} & (0.02) & \\textbf{0.093} & (0.02) & \\textbf{0.161} & (0.02) & 0.251 & (0.12) & 0.479 & (0.49) \\\\ \n 214 & Original & 0.067 & (0.02) & 0.098 & (0.02) & 0.169 & (0.03) & 0.248 & (0.03) & 0.325 & (0.02) \\\\ \n 10 & OOB & 0.068 & (0.02) & 0.099 & (0.02) & 0.169 & (0.03) &\\textbf{ 0.246} & (0.03) & \\textbf{0.323} & (0.02) \\\\ \n & RFProxIH & 0.067 & (0.02) & 0.099 & (0.02) & 0.171 & (0.03) & 0.250 & (0.03) & 0.326 & (0.02) \\\\ \n \\hline\nHeart Disease & RF-GAP & \\textbf{0.314} & (0.07) & \\textbf{0.487} & (0.11) & \\textbf{0.757} & (0.09) & \\textbf{1.116} & (0.08) & 1.411 & (0.09) \\\\ \n 303 & Original & 0.317 & (0.07) & 0.491 & (0.11) & 0.764 & (0.09) & 1.121 & (0.08) & 1.405 & (0.08) \\\\ \n 13 & OOB & 0.316 & (0.07) & 0.489 & (0.11) & 0.761 & (0.09) & \\textbf{1.116} & (0.08) & \\textbf{1.399 }& (0.08) \\\\ \n & RFProxIH & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \\\\ \n \\hline\nHill Valley & RF-GAP & \\textbf{1.418} & (0.20) & \\textbf{4.093 }& (0.43) & \\textbf{9.971} & (0.65) & \\textbf{15.307} & (0.75) & \\textbf{19.586} & (0.88) \\\\ \n 606 & Original & 2.052 & (0.29) & 5.304 & (0.39) & 10.656 & (0.60) & 16.134 & (0.76) & 20.568 & (0.92) \\\\ \n 101 & OOB & 1.673 & (0.26) & 4.996 & (0.42) & 10.526 & (0.59) & 15.984 & (0.75) & 20.489 & (0.92) \\\\ \n & RFProxIH & 2.232 & (0.30) & 5.512 & (0.38) & 10.716 & (0.59) & 16.161 & (0.76) & 20.578 & (0.92) \\\\ \n \\hline\nIonosphere & RF-GAP & \\textbf{4.472} & (0.23) & \\textbf{6.522} & (0.23) & \\textbf{10.560} & (0.29) & \\textbf{15.636} & (0.34) & \\textbf{20.092} & (0.39) \\\\ \n 351 & Original & 4.782 & (0.22) & 6.991 & (0.23) & 11.291 & (0.27) & 16.406 & (0.30) & 20.501 & (0.35) \\\\ \n 34 & OOB & 4.745 & (0.22) & 6.933 & (0.23) & 11.184 & (0.27) & 16.299 & (0.31) & 20.446 & (0.35) \\\\ \n & RFProxIH & 4.800 & (0.22) & 7.021 & (0.23) & 11.344 & (0.27) & 16.449 & (0.30) & 20.519 & (0.35) \\\\ \n \\hline\nIris & RF-GAP & \\textbf{0.211} & (0.04) & \\textbf{0.300} & (0.04) & \\textbf{0.494} & (0.04) & \\textbf{0.716} & (0.04) & 0.935 & (0.05) \\\\ \n 150 & Original & 0.219 & (0.04) & 0.310 & (0.04) & 0.509 & (0.04) & 0.727 & (0.04) & \\textbf{0.934} & (0.05) \\\\ \n 4 & OOB & 0.217 & (0.04) & 0.307 & (0.04) & 0.503 & (0.04) & 0.722 & (0.04) & 0.937 & (0.05) \\\\ \n & RFProxIH & 0.221 & (0.04) & 0.312 & (0.05) & 0.512 & (0.04) & 0.730 & (0.04) & 0.936 & (0.05) \\\\ \n \\hline\nLiver & RF-GAP & \\textbf{0.315} & (0.16) & \\textbf{0.501} & (0.23) & \\textbf{0.828} & (0.24) & \\textbf{1.168} & (0.23) & \\textbf{1.491} & (0.20) \\\\ \n 345 & Original & 0.322 & (0.16) & 0.512 & (0.22) & 0.847 & (0.23) & 1.192 & (0.22) & 1.503 & (0.19) \\\\ \n 7 & OOB & 0.321 & (0.16) & 0.510 & (0.22) & 0.844 & (0.24) & 1.190 & (0.22) & 1.503 & (0.19) \\\\ \n & RFProxIH & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \\\\ \n \\hline\nLymphography & RF-GAP & \\textbf{1.046} & (0.14) &\\textbf{ 1.521} & (0.13) &\\textbf{ 2.480} & (0.13) & \\textbf{3.599} & (0.12) & 4.569 & (0.12) \\\\ \n 148 & Original & 1.065 & (0.15) & 1.551 & (0.14) & 2.529 & (0.14) & 3.650 & (0.13) & \\textbf{4.568} & (0.12) \\\\ \n 18 & OOB & 1.065 & (0.14) & 1.549 & (0.13) & 2.524 & (0.13) & 3.644 & (0.13) & \\textbf{4.568} & (0.13) \\\\ \n & RFProxIH & 1.076 & (0.15) & 1.567 & (0.14) & 2.551 & (0.14) & 3.670 & (0.13) & 4.579 & (0.12) \\\\ \n \\hline\nOptical Digits & RF-GAP & \\textbf{15.774} & (0.17) & \\textbf{22.572} & (0.17) & \\textbf{37.003} & (0.19) & \\textbf{55.369} & (0.21) & \\textbf{71.456} & (0.26) \\\\ \n 3823 & Original & 17.222 & (0.20) & 24.612 & (0.19) & 40.138 & (0.20) & 58.897 & (0.22) & 73.616 & (0.27) \\\\ \n 64 & OOB & 17.199 & (0.19) & 24.549 & (0.18) & 39.904 & (0.20) & 58.569 & (0.21) & 73.442 & (0.27) \\\\ \n & RFProxIH & 17.303 & (0.20) & 24.732 & (0.19) & 40.336 & (0.20) & 59.062 & (0.22) & 73.681 & (0.27) \\\\ \n \\hline\nParkinsons & RF-GAP & \\textbf{0.400} & (0.18) &\\textbf{ 0.60}0 & (0.20) &\\textbf{ 0.968} & (0.19) & \\textbf{1.471} & (0.16) & 1.841 & (0.15) \\\\ \n 197 & Original & 0.411 & (0.18) & 0.613 & (0.20) & 0.988 & (0.18) & 1.484 & (0.16) & 1.846 & (0.15) \\\\ \n 23 & OOB & 0.408 & (0.18) & 0.610 & (0.20) & 0.979 & (0.19) & 1.475 & (0.16) & \\textbf{1.836} & (0.15) \\\\ \n & RFProxIH & 0.412 & (0.18) & 0.616 & (0.20) & 0.994 & (0.18) & 1.489 & (0.16) & 1.851 & (0.15) \\\\ \n \\hline\nSeeds & RF-GAP & \\textbf{0.204} & (0.04) & \\textbf{0.283} & (0.04) &\\textbf{ 0.476} & (0.04) & \\textbf{0.703} & (0.04) & \\textbf{0.895} & (0.04) \\\\ \n 210 & Original & 0.223 & (0.05) & 0.308 & (0.04) & 0.506 & (0.04) & 0.730 & (0.04) & 0.911 & (0.04) \\\\ \n 7 & OOB & 0.218 & (0.05) & 0.300 & (0.04) & 0.494 & (0.04) & 0.715 & (0.04) & 0.897 & (0.04) \\\\ \n & RFProxIH & 0.226 & (0.05) & 0.313 & (0.04) & 0.512 & (0.04) & 0.737 & (0.04) & 0.917 & (0.04) \\\\ \n \\hline\nSonar & RF-GAP & \\textbf{3.044 }& (0.13) & \\textbf{4.551} & (0.13) &\\textbf{ 7.278 }& (0.14) & \\textbf{10.879} & (0.16) &\\textbf{ 13.923} & (0.18) \\\\ \n 208 & Original & 3.246 & (0.13) & 4.834 & (0.14) & 7.631 & (0.15) & 11.174 & (0.16) & 14.055 & (0.18) \\\\ \n 60 & OOB & 3.211 & (0.13) & 4.778 & (0.13) & 7.544 & (0.15) & 11.101 & (0.16) & 14.011 & (0.18) \\\\ \n & RFProxIH & 3.264 & (0.14) & 4.864 & (0.14) & 7.673 & (0.15) & 11.200 & (0.16) & 14.069 & (0.18) \\\\ \n \\hline\nTitanic & RF-GAP & \\textbf{0.409} & (0.22) & \\textbf{0.609} & (0.23) & \\textbf{1.067} & (0.26) & \\textbf{1.600} & (0.22) & \\textbf{1.991} & (0.16) \\\\ \n 712 & Original & 0.416 & (0.21) & 0.621 & (0.23) & 1.096 & (0.26) & 1.639 & (0.22) & 2.010 & (0.16) \\\\ \n 7 & OOB & 0.415 & (0.21) & 0.620 & (0.23) & 1.093 & (0.26) & 1.637 & (0.22) & 2.010 & (0.16) \\\\ \n & RFProxIH & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \\\\ \n\\hline\nWine & RF-GAP & \\textbf{0.236} & (0.08) & 0.345 & (0.07) & 0.579 & (0.07) & 0.825 & (0.07) & 1.044 & (0.08) \\\\ \n 178 & Original & \\textbf{0.236} & (0.08) & 0.344 & (0.08) & \\textbf{0.577} & (0.07) & \\textbf{0.822} & (0.07) & \\textbf{1.038} & (0.08) \\\\ \n 13 & OOB & 0.240 & (0.08) & 0.353 & (0.07) & 0.585 & (0.07) & 0.829 & (0.07) & 1.043 & (0.08) \\\\ \n & RFProxIH & 0.237 & (0.08) & \\textbf{0.343} & (0.08) &\\textbf{ 0.577} & (0.07) & 0.823 & (0.07) & 1.040 & (0.07) \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Geometry- and Accuracy-Preserving Random Forest Proximities", "authors": ["Jake S. Rhodes", "Adele Cutler", "Kevin R. Moon"], "url": "https://arxiv.org/abs/2201.12682v2", "attribution": "\"Geometry- and Accuracy-Preserving Random Forest Proximities\" by Jake S. Rhodes, Adele Cutler, and Kevin R. Moon, arXiv:2201.12682v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.10258v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study : different dimension of dropout in CADM on PASCAL VOC 2007. $\\checkmark$ indicates that the component is used.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\t\t\\hline\\noalign{\\smallskip}\n\t\tchannel-dropout & spatial-dropout & channel + spatial dropout & mAP \\\\\n\t\t\\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\n\t\t& & & 48.1 \\\\\n\t\t$\\checkmark$ & & & 47.6 \\\\\n\t\t& $\\checkmark$ & & 48.8 \\\\\n\t\t& & $\\checkmark$ & \\textbf{49.8} \\\\\n\t\t\\noalign{\\smallskip}\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Cascade Attentive Dropout for Weakly Supervised Object Detection", "authors": ["Wenlong Gao", "Ying Chen", "Yong Peng"], "url": "https://arxiv.org/abs/2011.10258v1", "attribution": "\"Cascade Attentive Dropout for Weakly Supervised Object Detection\" by Wenlong Gao, Ying Chen, and Yong Peng, arXiv:2011.10258v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.08769v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Demographics for the PND data set. FU: follow-up time }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc} \n\\toprule\n & AD & SCD & MCIc & MCInc \\\\\n\\cmidrule(lr){2-3}\n\\cmidrule(lr){4-5}\n\\emph{\\# participants} & 199 & 138 & 48 & 91 \\\\\n\\emph{male / female} & 94 / 105 & 92 / 46 & 33 / 15 & 56 / 35 \\\\\n\\emph{age (y; mean$\\pm$std)} & $73.1\\pm9.6$ & $63.2\\pm10.3$ & $70.4\\pm7.9$ & $68.8\\pm12.6$ \\\\ \n\\emph{FU (y; mean$\\pm$std)} & N.A. & N.A. & $2.7 \\pm 1.2$ & $2.2 \\pm 0.8$\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease", "authors": ["Esther E. Bron", "Stefan Klein", "Janne M. Papma", "Lize C. Jiskoot", "Vikram Venkatraghavan", "Jara Linders", "Pauline Aalten", "Peter Paul De Deyn", "Geert Jan Biessels", "Jurgen A. H. R. Claassen", "Huub A. M. Middelkoop", "Marion Smits", "Wiro J. Niessen", "John C. van Swieten", "Wiesje M. van der Flier", "Inez H. G. B. Ramakers", "Aad van der Lugt"], "url": "https://arxiv.org/abs/2012.08769v3", "attribution": "\"Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease\" by Esther E. Bron, Stefan Klein, Janne M. Papma, Lize C. Jiskoot, Vikram Venkatraghavan, Jara Linders, Pauline Aalten, Peter Paul De Deyn, Geert Jan Biessels, Jurgen A. H. R. Claassen, Huub A. M. Middelkoop, Marion Smits, Wiro J. Niessen, John C. van Swieten, Wiesje M. van der Flier, Inez H. G. B. Ramakers, and Aad van der Lugt, arXiv:2012.08769v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.01064v4_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc|c}\n \\toprule\n Ours-NFE & \\textbf{FID} $\\downarrow$ & \\textbf{FVD} $\\downarrow$ & \\textbf{E-FID} $\\downarrow$ & \\textbf{LSE-D} $\\downarrow$ & \\textbf{FPS} $\\uparrow$\\\\\n \\cline{2-6}\n \\hline\n Ours-2 & 21.785 & 178.831 & 1.542 & 7.559 & 45.22 \\\\\n Ours-5 & 21.440 & 164.463 & 1.331 & 7.155 & 44.74 \\\\\n Ours-10 \\textbf{(default)} & 21.100 & 162.052 & 1.229 & 7.290 & 41.37 \\\\\n Ours-20 & 21.158 & 164.392 & 1.293 & 7.343 & 38.20 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait", "authors": ["Taekyung Ki", "Dongchan Min", "Gyeongsu Chae"], "url": "https://arxiv.org/abs/2412.01064v4", "attribution": "\"FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait\" by Taekyung Ki, Dongchan Min, and Gyeongsu Chae, arXiv:2412.01064v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.10784v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison between our ASFDA, DKD+ASD, and other SOTA methods when adapting Med-VFMs to the Abdominal MRI domain. The performance was evaluated using Dice scores, and the results were reported as Mean$\\pm$SD. \\textbf{Bold} and \\underline{underline} represent the best and the second best results (Lower bound $0\\%$: source-training without ADA; Upper bound $100\\%$: fully supervised training).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c}\n\\toprule\n\\multirow{2}{*}{Methods} & Low Bound & \\multicolumn{3}{c|}{Query Budgets (Number of Iterations)} & Upper Bound\\\\\n\\cline{3-5}\n & $0\\%$ & 3-shot (r=1) & 5-shot (r=2) & 7-shot (r=3) & $100\\%$ \\\\\n\\midrule\nRAND & 2.01$\\pm$0.39 & 66.11$\\pm$15.50 & 69.77$\\pm$12.71 & 76.47$\\pm$11.32 & 92.95$\\pm$1.14 \\\\\nENPY & 2.01$\\pm$0.39 & 74.10$\\pm$12.77 & 79.52$\\pm$11.78 & 81.05$\\pm$10.12 & 92.95$\\pm$1.14 \\\\\nLCON & 2.01$\\pm$0.39 & 75.46$\\pm$12.19 & 80.32$\\pm$11.82 & 82.54$\\pm$9.94 & 92.95$\\pm$1.14 \\\\\nMMAR & 2.01$\\pm$0.39 & 76.70$\\pm$12.25 & 80.45$\\pm$11.20 & 82.76$\\pm$9.93 & 92.95$\\pm$1.14 \\\\\nCore-set & 2.01$\\pm$0.39 & 77.94$\\pm$11.15 & 81.62$\\pm$10.82 & 84.21$\\pm$9.45 & 92.95$\\pm$1.14 \\\\\nBADGE & 2.01$\\pm$0.39 & 77.19$\\pm$11.11 & 81.70$\\pm$9.47 & 84.35$\\pm$9.39 & 92.95$\\pm$1.14 \\\\\nSANN & 2.01$\\pm$0.39 & 77.48$\\pm$10.14 & 82.13$\\pm$8.86 & 85.34$\\pm$8.44 & 92.95$\\pm$1.14 \\\\\nUGTST & 2.01$\\pm$0.39 & 78.11$\\pm$9.51 & 82.21$\\pm$8.98 & 85.43$\\pm$8.30 & 92.95$\\pm$1.14 \\\\\nCUP & 2.01$\\pm$0.39 & 74.92$\\pm$12.84 & 80.22$\\pm$11.92 & 81.95$\\pm$10.61 & 92.95$\\pm$1.14 \\\\\n\\midrule\nDKD+ASD & 2.01$\\pm$0.39 & \\underline{81.40}$\\pm$9.41 & \\underline{85.34}$\\pm$8.91 & \\underline{87.95}$\\pm$8.16 & 92.95$\\pm$1.14 \\\\\nASFDA & 2.01$\\pm$0.39 & \\textbf{83.98}$\\pm$8.29 & \\textbf{87.63}$\\pm$7.47 & \\textbf{88.36}$\\pm$7.32 & 92.95$\\pm$1.14 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning", "authors": ["Jin Yang", "Daniel S. Marcus", "Aristeidis Sotiras"], "url": "https://arxiv.org/abs/2509.10784v1", "attribution": "\"Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning\" by Jin Yang, Daniel S. Marcus, and Aristeidis Sotiras, arXiv:2509.10784v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.14098v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Total Absolute Prediction Error on Source Energy (${S}_{e}$) by Flamelet Key}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n Flamelet Key & Single Model & Deep Ensemble \\\\\n \\hline\n 0.00024894 & 5.506834e+12 & \\textbf{1.908204e+12} \\\\\n 0.00046070 & 7.157733e+11 & \\textbf{ 6.082280e+11} \\\\\n 0.00059539 & 5.062102e+11 & \\textbf{ 4.530053e+11} \\\\\n 0.00065971 & 3.284762e+11 & \\textbf{ 2.980305e+11} \\\\\n 0.00291989 & \\textbf{ 1.514082e+11 }& 2.100692e+11 \\\\\n 0.00085258 & \\textbf{ 1.443497e+11} & 2.128944e+11 \\\\\n 0.00115982 & \\textbf{ 1.213801e+11} & 1.711747e+11 \\\\\n 0.00174825 & \\textbf{ 1.047254e+11} & 1.322080e+11 \\\\\n 0.00193711 & \\textbf{ 1.004693e+11} & 1.289064e+11 \\\\\n 0.00225936 & \\textbf{ 9.594018e+10} & 1.238106e+11 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds", "authors": ["Amol Salunkhe", "Georgios Georgalis", "Abani Patra", "Varun Chandola"], "url": "https://arxiv.org/abs/2211.14098v1", "attribution": "\"An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds\" by Amol Salunkhe, Georgios Georgalis, Abani Patra, and Varun Chandola, arXiv:2211.14098v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.19307v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\t\t\\toprule\n\t\t& \\textbf{Available Companies} & \\textbf{Average Intensity}&\\textbf{Standard Deviation}\\\\\n\t\t\\midrule\n\t\t\\rowcolor{gray!15}\n\t\tLow & 1417 & 0.032 & 0.062\\\\\n\t\tMedium & 4733 & 0.071 & 0.104 \\\\\n\t\t\\rowcolor{gray!15}\n\t\tHigh & 797 & 0.202 & 0.224 \\\\\n\t\tExtreme & 1476 & 0.345 & 0.338 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{\\raggedright Descriptive analysis of asset intensity clustered in Low, Medium, High, and Extreme Risk. We report the number of available companies, the average vulnerability score, and the standard deviation. Descriptive statistics are computed at the company level.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Climate Physical Risk Assessment in Asset Management", "authors": ["Michele Azzone", "Matteo Ghesini", "Davide Stocco", "Lorenzo Viola"], "url": "https://arxiv.org/abs/2504.19307v1", "attribution": "\"Climate Physical Risk Assessment in Asset Management\" by Michele Azzone, Matteo Ghesini, Davide Stocco, and Lorenzo Viola, arXiv:2504.19307v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04188v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters of the studied models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c||c|c|c|c|c|c|c}\n\\thead{Arrival \\\\ process} & \\thead{Service \\\\ process} & \\thead{Number \\\\ of phases \\\\ Arrival} & \\thead{Number \\\\ of phases \\\\ Service} & \\thead{$v_A$} & \\thead{$v_S$} & \\thead{Number \\\\ of waiting slots \\\\ $m$} & \\thead{Number \\\\ of states} & \\thead{Number \\\\ of transitions} \\\\ \\hline \\hline\nM & M & 1 & 1 & 1 & 1 & 5 & 10 368 & 63 688 \\\\ \\hline\nPh & M & 2 & 1 & 0.8 & 1 & 5 & 141 108 & 829 825 \\\\ \\hline\nM & Ph & 1 & 12 & 1 & 0.3 & 5 & 623 376 & 3 664 703 \\\\ \\hline\nPh & Ph & 2 & 12 & 0.8 & 0.3 & 5 & 8 192 448 & 46 447 056\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Utilizing phase-type distributions for queueing-based railway junction performance determination", "authors": ["Tamme Emunds", "Nils Nießen"], "url": "https://arxiv.org/abs/2412.04188v1", "attribution": "\"Utilizing phase-type distributions for queueing-based railway junction performance determination\" by Tamme Emunds and Nils Nießen, arXiv:2412.04188v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.11088v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{DQN-CQL vs DecQN-CQL - performance and computation comparison for cheetah-run task. Performance figures are mean normalised scores $\\pm$ one standard error, with 0 and 100 representing random and expert policies, respectively. Computation figures are training time and GPU usage. Actions figures are total number of actions requiring value estimation based on atomic/factorised representation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\toprule\nMethod & $n_i$ & Actions & Score & Training time (mins) & GPU usage (MB) \\\\\n\\midrule\nDQN-CQL & 3 & 729 & 79.7 $\\pm$ 6.0 & 19 & 266 \\\\\nDQN-CQL & 4 & 4096 & 30.2 $\\pm$ 1.7 & 30 & 412 \\\\\nDQN-CQL & 5 & 15625 & 32.2 $\\pm$ 1.4 & 85 & 958 \\\\\nDQN-CQL & 6 & 46656 & 32.3 $\\pm$ 4.0 & 227 & 2388 \\\\\nDecQN-CQL & 3 & 18 & 92.7 $\\pm$ 3.7 & 19 & 244 \\\\\nDecQN-CQL & 4 & 24 & 85.4 $\\pm$ 3.8 & 19 & 246 \\\\\nDecQN-CQL & 5 & 30 & 79.5 $\\pm$ 1.8 & 20 & 246 \\\\\nDecQN-CQL & 6 & 36 & 84.3 $\\pm$ 2.5 & 20 & 246 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces", "authors": ["Alex Beeson", "David Ireland", "Giovanni Montana"], "url": "https://arxiv.org/abs/2411.11088v1", "attribution": "\"An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces\" by Alex Beeson, David Ireland, and Giovanni Montana, arXiv:2411.11088v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14913v1_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n \\hline\n basis & \\multicolumn{4}{|c|}{$V_{h}$}& \\multicolumn{4}{|c|}{$V_{h}^{*}$}\\\\\n \\hline\n h &$L^{2}$error & order & $H^{1}$error & order & $L^{2}$error & order & $H^{1}$error & order \\\\\n \\hline\n $2^{-1}$ &1.190& - &8.123& - &3.440e-04& - &3.623e-03& - \\\\\n \\hline\n $2^{-2}$ &4.640e-01&1.359&5.038&0.689&1.193e-04&1.528&3.014e-03&0.265\\\\\n \\hline\n $2^{-3}$ &1.335e-01&1.798&2.687&0.907&9.495e-05&0.330&3.069e-03&-0.026\\\\\n \\hline\n $2^{-4}$ &3.466e-02&1.945&1.367&0.976&5.086e-05&0.900&2.257e-03&0.444\\\\\n \\hline\n $2^{-5}$ &8.749e-03&1.986&6.863e-01&0.994&1.754e-05&1.536&1.311e-03&0.783 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{error and order using $P^{1}$ element, Poisson equation in 3d.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A PINN-enriched finite element method for linear elliptic problems", "authors": ["Xiao Chen", "Yixin Luo", "Jingrun Chen"], "url": "https://arxiv.org/abs/2503.14913v1", "attribution": "\"A PINN-enriched finite element method for linear elliptic problems\" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.05163v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||l|cc|cc||} % centered columns (4 columns)\n\t\t\t\\hline\\hline %inserts double horizontal lines\n\t\t\tAlgorithms & bad data & bad data & data attack & data attack \\\\\n& (EPFL) & (Texas) & (EPFL) & (Texas) \\\\\t\t\t\\hline\\hline\n\t\t\tICA-GAN\t& 97\\%\t & 95\\% \t & 94\\% & 80\\%\t\\\\\n\t\t\t\\hline\n\t\t\tOC-SVM\t & 94\\% \t& 35\\%\t & 61\\% & 71\\% \\\\\n\t\t\t\\hline\n\t\t\tF-AnoGAN\t& 78\\% & 5\\% \t & 53\\% & 18\\% \\\\\n\t\t\t\\hline\n\t\t\tNRT\t & 50\\%\t\t& 88\\%\t & 5\\% & 5\\% \\\\\n\t\t\t\\hline\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{TPR values of different algorithms at FPR=0.05 constraint under bad data and data attack anomalies.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems", "authors": ["Kursat Rasim Mestav", "Xinyi Wang", "Lang Tong"], "url": "https://arxiv.org/abs/2012.05163v2", "attribution": "\"A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems\" by Kursat Rasim Mestav, Xinyi Wang, and Lang Tong, arXiv:2012.05163v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.06690v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\toprule\n & \\multicolumn{3}{c}{\\textbf{Center E (Unseen)}} \\\\\n \\cmidrule{2-5}\n \\textbf{Method} & \\textbf{MAE [HU]} & \\textbf{SSIM} & \\textbf{PSNR [dB]} & \\textbf{Time} \\\\\n \\midrule\n 2D+ & 104.0 (99.5-115.6) & 0.89 (0.86-0.89) & 26.49 (25.43-27.35) & 16h 00min \\\\ \n 2D Patches & 162.4 (155.3-173.0) & 0.84 (0.82-0.85) & 25.52 (24.65-26.12) & \\textbf{8h 39min} \\\\ \n Multi-2D & 111.7 (107.7-122.3) & 0.88 (0.85-0.89) & 26.33 (25.32-27.20) & 9h 35min \\\\\n \\midrule\n \\textbf{Random}\\\\\\textbf{Multi-2D (Our)} & \\textbf{102.0 (96.7-110.5)} & \\textbf{0.89 (0.86-0.89)} & \\textbf{26.58 (25.52-27.42)} & 12h 34min \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The median and interquartile range (IR) for the mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) obtained for each training paradigm. The total training time required to complete 25 rounds is also reported.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis", "authors": ["Ciro Benito Raggio", "Mathias Krohmer Zabaleta", "Nils Skupien", "Oliver Blanck", "Francesco Cicone", "Giuseppe Lucio Cascini", "Paolo Zaffino", "Lucia Migliorelli", "Maria Francesca Spadea"], "url": "https://arxiv.org/abs/2412.06690v2", "attribution": "\"FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis\" by Ciro Benito Raggio, Mathias Krohmer Zabaleta, Nils Skupien, Oliver Blanck, Francesco Cicone, Giuseppe Lucio Cascini, Paolo Zaffino, Lucia Migliorelli, and Maria Francesca Spadea, arXiv:2412.06690v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.04444v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{xcolor}\n\\usepackage{booktabs}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textcolor{black}{Average running times (seconds) of one realisation of estimators for the FHN model, from partial observations.}}\n\\begin{tabular}{cc} \n\\toprule \\\\\n $ \\hat{\\theta}_{2,n}$ & $\\hat{\\theta}_{3,n} $ \n \\\\ \\midrule\n \\\\ \n100.976 & 138.428 \n \\\\ \\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Parameter Inference for Hypo-Elliptic Diffusions under a Weak Design Condition", "authors": ["Yuga Iguchi", "Alexandros Beskos"], "url": "https://arxiv.org/abs/2312.04444v2", "attribution": "\"Parameter Inference for Hypo-Elliptic Diffusions under a Weak Design Condition\" by Yuga Iguchi and Alexandros Beskos, arXiv:2312.04444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.15058v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n \\toprule\n Context Size & Inference Time (ms) & GPU Memory \\\\\n \\midrule\n 1 & $25.28 \\pm 0.16$ & 28 MB \\\\\n 16 & $57.05 \\pm 0.20$ & 1.89 GB \\\\\n 32 & $86.57 \\pm 0.06$ & 3.64 GB\\\\\n 64 & $146.04 \\pm 0.16$ & 7.15 GB \\\\\n 128 & $267.42 \\pm 0.24$ & 12.16 GB \\\\\n 256 & $604.15 \\pm 0.36$ & 24.17 GB \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance", "authors": ["Hallee E. Wong", "Jose Javier Gonzalez Ortiz", "John Guttag", "Adrian V. Dalca"], "url": "https://arxiv.org/abs/2412.15058v2", "attribution": "\"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance\" by Hallee E. Wong, Jose Javier Gonzalez Ortiz, John Guttag, and Adrian V. Dalca, arXiv:2412.15058v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.11079v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Regression results for the difference between sale prices of ambiguity and uncertainty lotteries}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\hline\n & (1) & (2) & (3) \\\\\n & Cont. PGG & Cont. PGG & Cont. PGG \\\\\n \\hline\n & & & \\\\\n$AR$ & -0.188 & -0.025 & -0.009 \\\\\n & (0.178) & (0.159) & (0.158) \\\\\n$RA$ & 0.046 & 0.152 & 0.166 \\\\\n & (0.171) & (0.149) & (0.149) \\\\\n$AA$ & 0.183 & 0.205 & 0.226 \\\\\n & (0.189) & (0.169) & (0.168) \\\\\n$age$ & -0.011** & -0.011*** & -0.011*** \\\\\n & (0.005) & (0.004) & (0.004) \\\\\n$CRT$ & -0.257*** & -0.154*** & -0.144** \\\\\n & (0.068) & (0.056) & (0.056) \\\\\n$unemployed$ & 0.463** & 0.217 & 0.222 \\\\\n & (0.235) & (0.173) & (0.172) \\\\\n$ambiguity$ $aversion$ & -0.041 & -0.037 & 0.196 \\\\\n & (0.084) & (0.064) & (0.122) \\\\\n$beliefs$ & & 0.152*** & 0.152*** \\\\\n & & (0.015) & (0.015) \\\\\n$AR\\times ambiguity aversion$ & & & -0.216 \\\\\n & & & (0.152) \\\\\n$RA\\times ambiguity aversion$ & & & -0.298 \\\\\n & & & (0.190) \\\\\n$AA\\times ambiguity aversion$ & & & -0.437** \\\\\n & & & (0.184) \\\\\nConstant & 3.509*** & 1.943*** & 1.887*** \\\\\n & (0.552) & (0.542) & (0.546) \\\\\n & & & \\\\\nObservations & 456 & 456 & 456 \\\\\nR-squared & 0.088 & 0.324 & 0.332 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The effect of ambiguity in strategic environments: an experiment", "authors": ["Pablo Brañas-Garza", "Antonio Cabrales", "María Paz Espinosa", "Diego Jorrat"], "url": "https://arxiv.org/abs/2209.11079v1", "attribution": "\"The effect of ambiguity in strategic environments: an experiment\" by Pablo Brañas-Garza, Antonio Cabrales, María Paz Espinosa, and Diego Jorrat, arXiv:2209.11079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.02102v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Threshold values obtained for different genes. The genes are selected in the following order. The starting values for threshold are 1st(Q1), 2nd(Q2) \\& 3rd(Q3) quartile respectively. Cutoff1, cutoff2, cutoff3 \\& cutoff4 denotes the optimal cutoffs obtained for the genes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|llll|llll|llll|}\n\t\t\t\t\t\\hline\n\t\t\t\t\t\\multirow{2}{*}{gene order} & \\multicolumn{4}{c|}{Starting threshold value = Q1} & \\multicolumn{4}{c|}{Starting threshold value = Q2} & \\multicolumn{4}{c|}{Starting threshold value = Q3} \\\\\n\t\t\t\t\t\\cline{2-13}\n\t\t\t\t\t& cutoff1 & cutoff2 & cutoff3 & cutoff4 & cutoff1 & cutoff2 & cutoff3 & cutoff4 & cutoff1 & cutoff2 & cutoff3 & cutoff4 \\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\tER, ERBB2, HER2, PR & 11.655 & 11.077 & 11.046 & 12.559 & 11.655 & 11.077 & 10.913 & 12.559 & 12.750 & 11.077 & 11.046 & 8.511 \\\\\n\t\t\t\t\tER, ERBB2, PR, HER2 & 11.655 & 11.077 & 12.559 & 11.046 & 11.655 & 11.077 & 12.559 & 10.779 & 11.655 & 11.077 & 12.559 & 10.913 \\\\\n\t\t\t\t\tER, HER2, ERBB2, PR & 11.655 & 11.046 & 10.535 & 12.559 & 11.655 & 10.913 & 11.077 & 12.559 & 12.750 & 11.046 & 11.077 & 8.511 \\\\\n\t\t\t\t\tER, HER2, PR, ERBB2 & 11.655 & 11.046 & 12.559 & 11.077 & 11.655 & 10.913 & 12.559 & 11.077 & 12.750 & 11.046 & 8.511 & 11.077 \\\\\n\t\t\t\t\tER, PR, ERBB2, HER2 & 11.655 & 12.559 & 11.077 & 11.046 & 11.655 & 14.126 & 11.077 & 10.779 & 11.655 & 12.559 & 11.077 & 10.913 \\\\\n\t\t\t\t\tER, PR, HER2, ERBB2 & 11.655 & 14.126 & 10.779 & 11.077 & 11.655 & 14.126 & 10.779 & 11.077 & 11.655 & 8.511 & 10.713 & 11.077 \\\\\n\t\t\t\t\tERBB2, ER, HER2, PR & 11.077 & 11.655 & 11.046 & 12.559 & 11.077 & 11.655 & 10.913 & 12.559 & 11.077 & 12.750 & 11.046 & 8.511 \\\\\n\t\t\t\t\tERBB2, ER, PR, HER2 & 11.077 & 11.655 & 12.559 & 11.046 & 11.077 & 11.655 & 12.559 & 10.779 & 11.077 & 11.655 & 12.559 & 10.913 \\\\\n\t\t\t\t\tERBB2, HER2, ER, PR & 11.077 & 10.913 & 11.655 & 12.559 & 11.077 & 10.913 & 11.655 & 12.559 & 11.077 & 11.046 & 11.655 & 8.511 \\\\\n\t\t\t\t\tERBB2, HER2, PR, ER & 11.077 & 10.913 & 12.559 & 11.655 & 11.077 & 11.046 & 8.511 & 11.655 & 11.077 & 11.046 & 8.511 & 11.655 \\\\\n\t\t\t\t\tERBB2, PR, ER, HER2 & 11.077 & 12.559 & 11.655 & 11.046 & 11.077 & 14.126 & 11.655 & 10.779 & 11.077 & 12.559 & 11.655 & 10.913 \\\\\n\t\t\t\t\tERBB2, PR, HER2, ER & 11.077 & 12.559 & 10.913 & 11.655 & 11.077 & 12.559 & 10.913 & 11.655 & 11.077 & 12.559 & 10.913 & 11.655 \\\\\n\t\t\t\t\tHER2, ER, ERBB2, PR & 10.913 & 11.655 & 10.535 & 12.559 & 10.913 & 11.655 & 11.077 & 12.559 & 10.713 & 11.655 & 11.077 & 8.511 \\\\\n\t\t\t\t\tHER2, ER, PR, ERBB2 & 10.913 & 11.655 & 12.559 & 11.077 & 10.913 & 11.655 & 12.559 & 11.077 & 10.713 & 11.655 & 8.511 & 11.077 \\\\\n\t\t\t\t\tHER2, ERBB2, ER, PR & 10.913 & 11.077 & 11.655 & 12.559 & 10.913 & 11.077 & 11.655 & 12.559 & 10.713 & 11.077 & 11.655 & 8.511 \\\\\n\t\t\t\t\tHER2, ERBB2, PR, ER & 10.913 & 11.077 & 12.559 & 11.655 & 10.713 & 11.077 & 8.511 & 11.655 & 10.713 & 11.077 & 8.511 & 11.655 \\\\\n\t\t\t\t\tHER2, PR, ER, ERBB2 & 10.713 & 14.126 & 11.655 & 11.077 & 10.713 & 14.126 & 11.655 & 11.077 & 10.713 & 14.126 & 11.655 & 11.077 \\\\\n\t\t\t\t\tHER2, PR, ERBB2, ER & 10.713 & 14.126 & 11.077 & 11.655 & 10.713 & 14.126 & 11.077 & 11.655 & 10.713 & 14.126 & 11.077 & 11.655 \\\\\n\t\t\t\t\tPR, ER, ERBB2, HER2 & 12.559 & 11.655 & 11.077 & 11.046 & 14.126 & 11.655 & 11.077 & 10.779 & 12.559 & 11.655 & 11.077 & 10.913 \\\\\n\t\t\t\t\tPR, ER, HER2, ERBB2 & 14.126 & 11.655 & 10.779 & 11.077 & 14.126 & 11.655 & 10.779 & 11.077 & 14.126 & 11.655 & 10.713 & 11.077 \\\\\n\t\t\t\t\tPR, ERBB2, ER, HER2 & 12.559 & 11.077 & 11.655 & 11.046 & 14.126 & 11.077 & 11.655 & 10.779 & 12.559 & 11.077 & 11.655 & 10.913 \\\\\n\t\t\t\t\tPR, ERBB2, HER2, ER & 12.559 & 11.077 & 10.913 & 11.655 & 12.559 & 11.077 & 10.913 & 11.655 & 12.559 & 11.077 & 10.913 & 11.655 \\\\\n\t\t\t\t\tPR, HER2, ER, ERBB2 & 14.126 & 10.713 & 11.655 & 11.077 & 14.126 & 10.713 & 11.655 & 11.077 & 14.126 & 10.713 & 11.655 & 11.077 \\\\\n\t\t\t\t\tPR, HER2, ERBB2, ER & 14.126 & 10.713 & 11.077 & 11.655 & 14.126 & 10.713 & 11.077 & 11.655 & 14.126 & 10.713 & 11.077 & 11.655 \\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A modified risk detection approach of biomarkers by frailty effect on multiple time to event data", "authors": ["Atanu Bhattacharjee", "Gajendra K. Vishwakarma", "Souvik Banerjee"], "url": "https://arxiv.org/abs/2012.02102v2", "attribution": "\"A modified risk detection approach of biomarkers by frailty effect on multiple time to event data\" by Atanu Bhattacharjee, Gajendra K. Vishwakarma, and Souvik Banerjee, arXiv:2012.02102v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07434v5_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\n\t\t\\toprule[1pt]\n\t\t Methods & Backbone & mIOU (\\%) \\\\\n\t\t\\midrule[0.5pt]\n\t\t\\midrule[0.5pt]\n\t\tDSSPN~ & ResNet-101 & 38.9 \\\\\n\t\tSVCNet~ & ResNet-101 & 39.6 \\\\\n\t\tEMANet~ & ResNet-101 & 39.9 \\\\\n\t\tSPYGR~ & ResNet-101 & 39.9 \\\\\n\t\tOCR~ & ResNet-101 & 39.5 \\\\\n\t\t\\midrule[0.5pt]\n\t\tDANet~ & ResNet-101 & 39.7\\\\\n\t\t\\midrule[0.5pt]\n\t\t\\textbf{Our CAA} & ResNet-101 & \\textbf{41.2} \\\\\n\t\t\\bottomrule[0.5pt]\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparisons with other state-of-the-art approaches on the COCO-Stuff 10K test set. For a fair comparison, all compared methods use ResNet-101 and naive upsampling.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation", "authors": ["Ye Huang", "Di Kang", "Wenjing Jia", "Xiangjian He", "Liu Liu"], "url": "https://arxiv.org/abs/2101.07434v5", "attribution": "\"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation\" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|r|r|r}\n \\hline\n \\multicolumn{5}{c}{Including component demand - no adjustments} \\\\\n \\hline \n Utilization & $\\lambda$ & Converged (\\%) & Runtime (s) & $\\Delta$ (\\%)\\\\\n \\hline\n \\multirow{4}{*}{50\\%} \n & 0.1 & 0.00 & - & - \\\\\n & 1 & 29.2 & 7816.1 & 0.56 \\\\\n & 10 & 100.0 & 322.1 & 3.26 \\\\\n & 100 & 100.0 & 208.8 & 4.62 \\\\\n \\hline\n \\multirow{4}{*}{90\\%} \n & 0.1 & 0.00 & - & - \\\\\n & 1 & 8.3 & 4264.0 & 0.09 \\\\\n & 10 & 79.2 & 1390.2 & 2.10 \\\\\n & 100 & 66.6 & 1262.3 & 3.53 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Averaging consensus calculation: Evaluation of the $\\lambda$-multiple for the penalty parameter $\\rho$ in terms of total costs and runtime for the case including component demands on a scenario tree with $|\\Omega| = 2$ and therefore $|\\Phi| = 128$. Reporting percentage of converged PH runs and respective runtimes and cost deltas to optimal costs over the converged runs.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.15929v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Types of heat transfer}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\\toprule[1.25pt]\n \\textbf{Type} & \\textbf{Model} \\\\\n \\hline\n Advection & $\\dot{m}c_pT$ \\\\\n Diffusion & $\\frac{kA}{L}T$ \\\\ \n Convection & $hAT$ \\\\\n \\bottomrule[1.25pt]\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Nonlinear Model Predictive Control of a Hybrid Thermal Management System", "authors": ["Demetrius Gulewicz", "Uduak Inyang-Udoh", "Trevor Bird", "Neera Jain"], "url": "https://arxiv.org/abs/2411.15929v1", "attribution": "\"Nonlinear Model Predictive Control of a Hybrid Thermal Management System\" by Demetrius Gulewicz, Uduak Inyang-Udoh, Trevor Bird, and Neera Jain, arXiv:2411.15929v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23340v1_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|}\n\\hline\n & \\multicolumn{2}{c|}{\\textbf{Bernoulli-Laplace level model}} & \\multicolumn{2}{c|}{\\textbf{Curie-Weiss model}} \\\\\n\\hline\n\\(m\\) & \\textbf{Subset \\(S_m\\)} & $D(P^{(S_m)} \\| \\Pi^{(S_m)})$ & \\textbf{Subset \\(S_m\\)} & $D(P^{(S_m)} \\| \\Pi^{(S_m)})$ \\\\\n\\hline\n1 & \\(\\{10\\}\\) & 0.23219 & \\(\\{1\\}\\) & 0.39435 \\\\\n2 & \\(\\{1,\\,10\\}\\) & 0.57719 & \\(\\{1,\\,10\\}\\) & 0.79669 \\\\\n3 & \\(\\{1,\\,2,\\,10\\}\\) & 0.98552 & \\(\\{1,\\,2,\\,10\\}\\) & 1.20915 \\\\\n4 & \\(\\{1,\\,2,\\,3,\\,5\\}\\) & 1.45314 & \\(\\{1,\\,2,\\,9,\\,10\\}\\) & 1.63086 \\\\\n5 & \\(\\{1,\\,2,\\,3,\\,4,\\,5\\}\\) & 1.99871 & \\(\\{1,\\,2,\\,3,\\,9,\\,10\\}\\) & 2.06307 \\\\\n6 & \\(\\{1,\\,2,\\,3,\\,4,\\,5,\\,6\\}\\) & 2.63821 & \\(\\{1,\\,2,\\,3,\\,8,\\,9,\\,10\\}\\) & 2.50704 \\\\\n7 & \\(\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7\\}\\) & 3.39168 & \\(\\{1,\\,2,\\,3,\\,4,\\,8,\\,9,\\,10\\}\\) & 2.96498 \\\\\n8 & \\(\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8\\}\\) & 4.30094 & \\(\\{1,\\,2,\\,3,\\,4,\\,5,\\,8,\\,9,\\,10\\}\\) & 3.43971 \\\\\n9 & \\(\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,10\\}\\) & 5.46950 & \\(\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,8,\\,9,\\,10\\}\\) & 3.93647 \\\\\n10 & \\(\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,9,\\,10\\}\\) & 7.56130 & \\(\\{1,\\,2,\\,3,\\,4,\\,5,\\,6,\\,7,\\,8,\\,9,\\,10\\}\\) & 4.46975 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Performance evaluation of the distorted greedy algorithm.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Information-theoretic subset selection of multivariate Markov chains via submodular optimization", "authors": ["Zheyuan Lai", "Michael C. H. Choi"], "url": "https://arxiv.org/abs/2503.23340v1", "attribution": "\"Information-theoretic subset selection of multivariate Markov chains via submodular optimization\" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.09613v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of different baselines on the dataset without confounder. The numbers in ``( )'' indicate relative performance compared to ERM: \\textcolor{green}{green} for improvement, \\textcolor{red}{red} for inferiority.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccc}\n\t\t\t\\hline\n \n\t\t\t\n\t\t\t\n\t\t\tMethod & noise=0 & noise=1 & noise=2 \\\\\n\t\t\t\\hline\n\t\t\t\\text{ERM} & 48.33$\\pm$0.70 & 35.16$\\pm$1.42 & 24.91$\\pm$1.21 \\\\\n \t\t\t\\text{ASAP} & 48.94$\\pm$0.63 (\\textcolor{green}{+0.61}) & 33.52$\\pm$1.34 (\\textcolor{red}{-1.64}) & 26.35$\\pm$0.88 (\\textcolor{green}{+1.44}) \\\\\n \\hline\n \\hline\n\t\t\t\\text{DIR } & 46.80 $\\pm$0.92(\\textcolor{red}{-1.53}) & 36.37$\\pm$1.18(\\textcolor{green}{+1.21}) & 27.53$\\pm$1.02(\\textcolor{green}{+2.62}) \\\\\n\t\t\t\\text{CIGA } & 43.18$\\pm$1.24(\\textcolor{red}{-5.15}) & 26.42$\\pm$1.38(\\textcolor{red}{-8.74}) & 24.47$\\pm$1.29(\\textcolor{red}{-0.44}) \\\\\n\t\t\t\\text{RCGRL } & 52.72$\\pm$1.60(\\textcolor{green}{+4.39}) & 30.50$\\pm$0.52(\\textcolor{red}{-4.66}) & 26.44$\\pm$1.26(\\textcolor{green}{+1.53}) \\\\\n \t\t\t\\text{DISC } & 45.60$\\pm$0.79(\\textcolor{red}{-2.73}) & 38.35$\\pm$1.31(\\textcolor{green}{+3.19}) & 26.80$\\pm$0.98(\\textcolor{green}{+1.89}) \\\\\n\t\t\t\\hline \n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Rethinking Causal Relationships Learning in Graph Neural Networks", "authors": ["Hang Gao", "Chengyu Yao", "Jiangmeng Li", "Lingyu Si", "Yifan Jin", "Fengge Wu", "Changwen Zheng", "Huaping Liu"], "url": "https://arxiv.org/abs/2312.09613v1", "attribution": "\"Rethinking Causal Relationships Learning in Graph Neural Networks\" by Hang Gao, Chengyu Yao, Jiangmeng Li, Lingyu Si, Yifan Jin, Fengge Wu, Changwen Zheng, and Huaping Liu, arXiv:2312.09613v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.17077v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{cccccc}\n\\hline\n\\multicolumn{6}{c}{Errors and convergence rates after $T/h$ iterations for $d=6$} \\\\ \\hline\n$h$\\textbackslash $\\phi$ & $\\phi_{1}(x)$ & $\\exp(-\\| x\\|)$ & $\\phi_{2}(x)$ & $\\arctan(\\| x\\|)$ & Iterations \\\\ \\hline\n$2^{-5}$ & 3.13e-02 & 8.85e-04 & 3.83e-02 & 1.28e-03 &\n192 \\\\ %\\hline\n$2^{-6}$ & 1.33e-02 & 5.58e-04 & 1.68e-02 & 7.93e-04 \n & 384 \\\\ %\\hline\n$2^{-7}$ & 6.00e-03 & 2.17e-04 & 8.00e-03 & 3.10e-04 \n& 768 \\\\ %\\hline\n$2^{-8}$ & 3.00e-03 & 1.36e-04 & 4.17e-03 & 1.92e-04 \n& 1536\\\\ %\\hline\n$2^{-9}$ & 1.33e-03 & 7.35e-05 & 1.83e-03 & 1.03e-04 \n& 3072\\\\ \\hline\n\\text{Order} & 1.13 & 0.92 & 1.08 & \\ 0.93 \n& \\ \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Projected Langevin Monte Carlo algorithms in non-convex and super-linear setting", "authors": ["Chenxu Pang", "Xiaojie Wang", "Yue Wu"], "url": "https://arxiv.org/abs/2312.17077v4", "attribution": "\"Projected Langevin Monte Carlo algorithms in non-convex and super-linear setting\" by Chenxu Pang, Xiaojie Wang, and Yue Wu, arXiv:2312.17077v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.01128v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc|cc}\n\t\t\t\\hline\n\t\t\t& \\multicolumn{2}{c}{PL} & \\multicolumn{2}{c}{Pairwise} \\\\\n\t\t\t\\hline\n\t\t\tSNP & est. effect & adj. p-value & est. effect & adj. p-value \\\\ \n\t\t\t\\hline\n\t\t\trs11892031 & 0.052 (0.032) & 0.125 & 0.053 (0.027) & 0.059 \\\\ \n\t\t\trs1052133 & 0.007 (0.030) & 0.692 & 0.012 (0.025) & 0.342 \\\\ \n\t\t\trs10936599 & 0.028 (0.030) & 0.376 & 0.041 (0.026) & 0.115 \\\\ \n\t\t\trs710521 & 0.097 (0.031) & \\textbf{0.009} & 0.100 (0.026) & \\textbf{0.001} \\\\ \n\t\t\trs798766 & -0.024 (0.030) & 0.835 & 0.049 (0.025) & 0.063 \\\\ \n\t\t\trs401681 & 0.067 (0.030) & 0.059 & 0.077 (0.026) & \\textbf{0.007} \\\\ \n\t\t\trs884225 & -0.012 (0.031) & 0.823 & 0.028 (0.026) & 0.263 \\\\ \n\t\t\trs1057868 & -0.061 (0.029) & 0.992 & -0.028 (0.025) & 0.894 \\\\ \n\t\t\trs17149580 & -0.017 (0.030) & 0.823 & 0.015 (0.026) & 0.342 \\\\ \n\t\t\trs12666814 & -0.020 (0.030) & 0.727 & 0.013 (0.025) & 0.342 \\\\ \n\t\t\trs73223045 & -0.014 (0.030) & 0.823 & 0.016 (0.026) & 0.342 \\\\ \n\t\t\trs41515546 & -0.016 (0.030) & 0.823 & 0.015 (0.026) & 0.342 \\\\ \n\t\t\trs12673089 & -0.015 (0.030) & 0.823 & 0.016 (0.026) & 0.342 \\\\ \n\t\t\trs17149628 & -0.016 (0.030) & 0.823 & 0.016 (0.026) & 0.342 \\\\ \n\t\t\trs17149630 & -0.016 (0.030) & 0.823 & 0.016 (0.026) & 0.342 \\\\ \n\t\t\trs17149636 & -0.015 (0.030) & 0.823 & 0.016 (0.026) & 0.342 \\\\ \n\t\t\trs1495741 & 0.063 (0.031) & 0.081 & 0.073 (0.025) & \\textbf{0.007} \\\\ \n\t\t\trs9642880 & 0.089 (0.030) & \\textbf{0.011} & 0.092 (0.026) & \\textbf{0.001} \\\\ \n\t\t\trs2294008 & 0.056 (0.030) & 0.106 & 0.103 (0.026) & \\textbf{0.001} \\\\ \n\t\t\trs142492877 & 0.006 (0.031) & 0.692 & 0.014 (0.026) & 0.342 \\\\ \n\t\t\trs907611 & 0.023 (0.030) & 0.419 & 0.024 (0.025) & 0.303 \\\\\n\t\t\trs217727 & 0.022 (0.029) & 0.419 & -0.002 (0.025) & 0.569 \\\\ \n\t\t\trs9344 & -0.072 (0.030) & 0.992 & -0.041 (0.026) & 0.944 \\\\ \n\t\t\trs4907479 & 0.084 (0.029) & \\textbf{0.011} & 0.072 (0.025) & \\textbf{0.007} \\\\ \n\t\t\trs17674580 & 0.100 (0.029) & \\textbf{0.005} & 0.090 (0.025) & \\textbf{0.001} \\\\\n\t\trs1058396 & 0.054 (0.030) & 0.113 & 0.047 (0.025) & 0.073 \\\\ \n\t\t\trs8102137 & 0.104 (0.029) & \\textbf{0.005} & 0.081 (0.025) & \\textbf{0.003} \\\\ \n\t\t\trs62185668 & 0.050 (0.029) & 0.123 & 0.068 (0.025) & \\textbf{0.009} \\\\ \n\t\t\trs6104690 & 0.038 (0.030) & 0.227 & 0.025 (0.025) & 0.291 \\\\ \n\t\t\trs4813953 & 0.042 (0.030) & 0.193 & 0.073 (0.025) & \\textbf{0.007} \\\\ \n\t\t\trs1014971 & 0.078 (0.031) & \\textbf{0.028} & 0.067 (0.026) & \\textbf{0.016} \\\\ \n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Replicability analysis of 31 SNPs based on the UKB UBC data: estimated effects (standard errors), and BH-adjusted p-values for the PL and the proposed pairwise pseudolikelihood with $K_n=100$. SEs for the pairwise pseudolikelihood are based on Bootstrap 3. Significant effects at the 0.05 threshold are marked in bold. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Unlocking Retrospective Prevalent Information in EHRs -- a Pairwise Pseudolikelihood Approach", "authors": ["Nir Keret", "Malka Gorfine"], "url": "https://arxiv.org/abs/2309.01128v1", "attribution": "\"Unlocking Retrospective Prevalent Information in EHRs -- a Pairwise Pseudolikelihood Approach\" by Nir Keret and Malka Gorfine, arXiv:2309.01128v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.00530v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n Model & Active Parameters & One-Step SPE \\\\ \n\\hline\nGNAR(1, [1]) & 2 & 3.061 \\\\\n VAR(1) & $140^2$ & 7.196 \\\\\n Res. VAR(1) & 3759 & 5.888 \\\\\n Sparse VAR(1) & 3017 & 6.842 \\\\\n AR(1) & $140$ & 88.066 \\\\\n GNAR(1, [1])* & 141 & 3.089 \n\\end{tabular}\n\\end{adjustbox}\n\\caption{One-step prediction error, $\\hat{\\boldsymbol{X}}_{450}$ is predicted using the previous 449 observations.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "New tools for network time series with an application to COVID-19 hospitalisations", "authors": ["Guy Nason", "Daniel Salnikov", "Mario Cortina-Borja"], "url": "https://arxiv.org/abs/2312.00530v1", "attribution": "\"New tools for network time series with an application to COVID-19 hospitalisations\" by Guy Nason, Daniel Salnikov, and Mario Cortina-Borja, arXiv:2312.00530v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08436v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average over 1000 trials of the number of iterations required to obtain an optimal solution starting from a guess within +/- 50\\% of the known optimum}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc} \n \\cline{2-4}\n & SQP & LSQP & SLCP \\\\ \\hline \n Floudas & 30.17 & 14.30 (-52.60\\%) & 12.92 (-57.18\\%) \\\\ \n Kirschen-Ozturk & 72.55 & 18.89 (-73.96\\%) & 16.38 (-77.42\\%) \\\\ \n Hoburg-0 & - & 17.79 & 16.29 (-8.44\\%) \\\\ \n Hoburg-1 & - & 17.82 & 16.66 (-6.47\\%) \\\\ \n Hoburg-3 & - & 17.66 & 16.87 (-4.49\\%) \\\\ \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Method of Sequential Log-Convex Programming for Engineering Design", "authors": ["Cody Karcher", "Robert Haimes"], "url": "https://arxiv.org/abs/2201.08436v1", "attribution": "\"A Method of Sequential Log-Convex Programming for Engineering Design\" by Cody Karcher and Robert Haimes, arXiv:2201.08436v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02875v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{WordNet sense ranking based on SemCor frequencies}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llr}\n\t\t\t\t\\hline\n\t\t\t\t\\textbf{Sense} & \\textbf{Definition} & \\textbf{Frequency} \\\\\n\t\t\t\t\\hline \n\t\t\t\tplant-1 & Buildings for carrying on industrial labor & 338 \\\\\n\t\t\t\tplant-2 & A living organism lacking the power of locomotion & 207 \\\\\n\t\t\t\tplant-3 & Something planted secretly for discovery by another & 2\\\\\n\t\t\t\tplant-4 & An actor situated in the audience whose acting is & 0 \\\\\n\t\t\t\t& rehearsed but seems spontaneous to the audience &\\\\\n\t\t\t\t\\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph", "authors": ["Mohannad AlMousa", "Rachid Benlamri", "Richard Khoury"], "url": "https://arxiv.org/abs/2101.02875v1", "attribution": "\"A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph\" by Mohannad AlMousa, Rachid Benlamri, and Richard Khoury, arXiv:2101.02875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.12619v1_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\large Impacts of Spatial Lag Model}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\\toprule\n\\multicolumn{9}{c}{Outcome Variable: $\\Delta$Unemployment Rate}\\\\\n\\cmidrule{3-7}\n & \\multicolumn{2}{c}{$\\Delta$ Active Mines\\textsubscript{t}} & \\multicolumn{2}{c}{$\\Delta$ Active Mines\\textsubscript{t-1}} & \\multicolumn{2}{c}{$\\Delta$ Active Mines\\textsubscript{t-2}} & \\multicolumn{2}{c}{$\\Delta$ (log) Real GDPPC\\textsubscript{t}} \\\\\n\\cmidrule(l{3pt}r{3pt}){2-3} \\cmidrule(l{3pt}r{3pt}){4-5} \\cmidrule(l{3pt}r{3pt}){6-7} \\cmidrule(l{3pt}r{3pt}){8-9}\n & Estimate & Sim. SE & Estimate & Sim. SE & Estimate & Sim. SE & Estimate & Sim. SE\\\\\n\\midrule\nDirect & -0.036*** & 0.005 & -0.009. & 0.005 & 0.028*** & 0.005 & -0.651*** & 0.032\\\\\nIndirect & -0.082*** & 0.012 & -0.020. & 0.011 & 0.064*** & 0.010 & -1.490*** & 0.080\\\\\nTotal & -0.118*** & 0.017 & -0.029. & 0.016 & 0.091*** & 0.015 & -2.141*** & 0.111\\\\\n\\bottomrule\n\\multicolumn{9}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'", "authors": ["Ebba Mark", "Ryan Rafaty", "Moritz Schwarz"], "url": "https://arxiv.org/abs/2211.12619v1", "attribution": "\"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'\" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2102.00287v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\\hline\n Lang. pair & Train & Test & Dev \\\\\\hline\n EN-FR/FR-EN & 1,467,489 & 499,487 & 7,723\\\\%\\hline\n EN-ES/ES-EN & 1,472,203 & 459,633 & 5,734\\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Number of parallel sentences for the training, testing and development sets.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation", "authors": ["Eva Vanmassenhove", "Dimitar Shterionov", "Matthew Gwilliam"], "url": "https://arxiv.org/abs/2102.00287v1", "attribution": "\"Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation\" by Eva Vanmassenhove, Dimitar Shterionov, and Matthew Gwilliam, arXiv:2102.00287v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.01846v3_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l} \n\\toprule\nregion & country \\\\ \\hline\nAustralia & Australia \\\\\nAustralia & New Zealand \\\\\nMelanesia & Fiji \\\\\nMelanesia & New Caledonia \\\\\nMelanesia & Papua New Guinea \\\\\nMelanesia & Solomon Islands \\\\\nMelanesia & Vanuatu \\\\\nMicronesia & Kiribati \\\\\nPolynesia & French Polynesia \\\\\nPolynesia & Samoa \\\\\nnot classified & Belgium-Luxembourg \\\\\nnot classified & Czechoslovakia \\\\\nnot classified & Netherlands Antilles \\\\\nnot classified & RoW \\\\\nnot classified & Serbia and Montenegro \\\\\nnot classified & USSR \\\\\nnot classified & Yugoslav SFR \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Regions of Australia, New Zealand and Oceania and not classified}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability", "authors": ["Moritz Laber", "Peter Klimek", "Martin Bruckner", "Liuhuaying Yang", "Stefan Thurner"], "url": "https://arxiv.org/abs/2210.01846v3", "attribution": "\"Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability\" by Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang, and Stefan Thurner, arXiv:2210.01846v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08321v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average lap times on the six corners of different control methods. The minimum speed (in [km/h]) and maximum slip angle (in [$^{\\circ}$]) at each corner are also analyzed with our method. The success rates (SR) are also displayed.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccc|c}\nLap time [s] & \\#1 & \\#2 & \\#3 & \\#4 & \\#5 & \\#6 & SR \\\\\\hline\nNo Feedback & 7.8 & 9.4 & 12.2 & 8.7 & 7.0 & 11.9 & 1/10 \\\\\nLow Gain & 7.9 & 8.9 & 12.2 & 8.9 & 6.9 & 12.2 & 3/10 \\\\\nHigh Gain & 8.3 & 8.7 & 12.2 & 10.0 & 7.6 & 13.6 & 10/10 \\\\\n\\rowcolor{gray!50} TOAST & 7.7 & 7.8 & 11.8 & 9.5 & 8.3 & 9.4 & 10/10 \\\\ \\hline \\hline\n\\rowcolor{gray!50} Min. Speed & 33.2 & 24.4 & 22.6 & 33.5 & 26.7 & 26.7 & \\\\ \n\\rowcolor{gray!50} Max. Slip & 3.4 & 7.2 & 6.8 & 2.8 & 7.4 & 4.2 & \\\\ \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "TOAST: Trajectory Optimization and Simultaneous Tracking using Shared Neural Network Dynamics", "authors": ["Taekyung Kim", "Hojin Lee", "Seongil Hong", "Wonsuk Lee"], "url": "https://arxiv.org/abs/2201.08321v3", "attribution": "\"TOAST: Trajectory Optimization and Simultaneous Tracking using Shared Neural Network Dynamics\" by Taekyung Kim, Hojin Lee, Seongil Hong, and Wonsuk Lee, arXiv:2201.08321v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.08827v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ccccc|llll}\n\\hline\n$k_1 = k_2 = 5$& \\multicolumn{9}{c|}{$\\text{RMSE}$}\\\\ \\hline\n$p+q$ & $K = 1$ & $K = 1.25$ & $K = 1.5$ & $K = 1.75$ & $K = 2$& $K = 2.25$& $K = 2.5$& $K = 2.75$& $K = 3$\\\\ \\hline\n$100$& 0.2754 & 0.2606 & 0.2782 & 0.2631 & 0.2269 & 0.2255 & \\textbf{0.2230} & \\textbf{0.2065} & \\textbf{0.2143} \\\\\n$800$ \n& 0.1779 & 0.1534 & 0.1464 & 0.1427 & 0.1330 & \\textbf{0.1237} & 0.1261 & \\textbf{0.1167} & \\textbf{0.1224} \\\\\n$1500$\n& 0.2747 & 0.2600 & 0.2731 & 0.2546 & 0.2531 & 0.1978 & \\textbf{0.1976} & \\textbf{0.1660} & \\textbf{0.1763} \\\\\n$2500$& 0.1425 & 0.1385 & 0.1214 & 0.1123 & 0.1117 & \\textbf{0.1101} & \\textbf{0.1102} & \\textbf{0.1062} & 0.1153 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Sensitivity Analysis of $(K_1, K_2)$ in RMSE over 400 simulation replications $n = 1250$ and $\\sigma^2 = 0.5$ (The three lowest RMSE in each row have been boldfaced).}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Debiased Estimator for the Mediation Functional in Ultra-High-Dimensional Setting in the Presence of Interaction Effects", "authors": ["Shi Bo", "AmirEmad Ghassami", "Debarghya Mukherjee"], "url": "https://arxiv.org/abs/2412.08827v3", "attribution": "\"A Debiased Estimator for the Mediation Functional in Ultra-High-Dimensional Setting in the Presence of Interaction Effects\" by Shi Bo, AmirEmad Ghassami, and Debarghya Mukherjee, arXiv:2412.08827v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.01846v3_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\\toprule\n{} & snflw. seed & snflw. oil & snflw. cake \\\\\n\\midrule\nAfrica N & 1.8 & 48.2 & 42.9 \\\\\nAfrica S & 0.0 & 4.5 & 2.1 \\\\\nAmerica S & 0.0 & 1.1 & 1.2 \\\\\nAmerica N & 0.6 & 6.7 & 1.1 \\\\\nAsia C & 0.1 & 5.1 & 0.2 \\\\\nAsia E & 0.0 & 48.7 & 0.0 \\\\\nAsia S & 0.5 & 67.7 & 1.2 \\\\\nAsia SE & 0.0 & 7.1 & 0.4 \\\\\nAsia W & 5.2 & 27.0 & 18.8 \\\\\nAustralia & 0.0 & 8.5 & 2.5 \\\\\nEurope E & 0.2 & 2.3 & 15.9 \\\\\nEurope N & 1.8 & 38.2 & 39.1 \\\\\nEurpe S & 0.3 & 12.5 & 15.3 \\\\\nEurope W & 0.6 & 10.3 & 28.1 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Relative loss in \\% of different food products in different world regions after a shock to the sunflower seed production of Ukraine. We use the abbreviation snflw. for sunflower. These numbers correspond to the left half of each cell in the lower part of fig. 5 in the main text.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability", "authors": ["Moritz Laber", "Peter Klimek", "Martin Bruckner", "Liuhuaying Yang", "Stefan Thurner"], "url": "https://arxiv.org/abs/2210.01846v3", "attribution": "\"Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability\" by Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang, and Stefan Thurner, arXiv:2210.01846v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.05585v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\nMagnification & Benign & Malignant & Total\\\\\n\\hline\n40X\t\t&625\t\t&1370\t&1995\\\\\n100X\t&644\t\t&1437\t&2081\\\\\n200X\t&623\t\t&1390\t&2013\\\\\n400X\t&588\t\t&1232\t&1820\\\\\nTotal\t&2480\t &5429\t&7909\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Details about the whole dataset.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Performance Comparison of Balanced and Unbalanced Cancer Datasets using Pre-Trained Convolutional Neural Network", "authors": ["Ali Narin"], "url": "https://arxiv.org/abs/2012.05585v1", "attribution": "\"Performance Comparison of Balanced and Unbalanced Cancer Datasets using Pre-Trained Convolutional Neural Network\" by Ali Narin, arXiv:2012.05585v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2112.09783v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Effects on Listing Outcomes}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllll}\n\\toprule\n & Log Nights \n (3 Months) & Log Revenue \n (3 Months) & Log Platform Revenue \n (3 Months) & Log Nights \n (12 Months) & Log Revenue \n (12 Months) & Log Platform Revenue \n (12 Months)\\\\\n\\midrule\nTreatment & 0.002 & 0.006 & 0.004 & 0.002 & 0.003 & -1.267\\\\\n & (0.004) & (0.008) & (0.006) & (0.004) & (0.008) & (2.423)\\\\\n\\midrule\nNum.Obs. & 651947 & 651947 & 651947 & 651947 & 651947 & 651947\\\\\nR2 & 0.125 & 0.110 & 0.119 & 0.116 & 0.111 & 0.146\\\\\nControls for User and Listing Char. & Yes & Yes & Yes & Yes & Yes & Yes\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "More Reviews May Not Help: Evidence from Incentivized First Reviews on Airbnb", "authors": ["Andrey Fradkin", "David Holtz"], "url": "https://arxiv.org/abs/2112.09783v1", "attribution": "\"More Reviews May Not Help: Evidence from Incentivized First Reviews on Airbnb\" by Andrey Fradkin and David Holtz, arXiv:2112.09783v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.20889v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of filtered results for all instances (i.e., only feasible solutions)}\n\\begin{tabular}{llrrr}\n \\hline\\noalign{\\smallskip}\n Model & Cut & OptimalTimes & GapWithoutInf & \\#Inf \\\\\n \\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\n DD-JS & IIS & 350.0 & 0.5 & 1 \\\\\n DD-JS & No-Good & 345.5 & 0.6 & 0 \\\\\n DD-LJ & IIS & 430.7 & 0.7 & 1 \\\\\n DD-LJ & No-Good & 420.1 & 0.7 & 0 \\\\\n IP & IIS & 384.0 & 1.8 & 229 \\\\\n IP & No-Good & 468.3 & 1.2 & 2 \\\\\n \\noalign{\\smallskip}\\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Decision Diagram Approach for the Parallel Machine Scheduling Problem with Chance Constraints", "authors": ["Nicolás Casassus", "Margarita Castro", "Gustavo Angulo"], "url": "https://arxiv.org/abs/2504.20889v1", "attribution": "\"A Decision Diagram Approach for the Parallel Machine Scheduling Problem with Chance Constraints\" by Nicolás Casassus, Margarita Castro, and Gustavo Angulo, arXiv:2504.20889v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09972v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{OvA configuration for the dataset in~.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n \\textbf{Problem} & \\textbf{Classification Problem} & \\textbf{(\\%min,\\%maj)} & \\textbf{IR}\\\\ %\\midrule\n \\hline\n 1 & Artemisia vs. all & (3.00,97.00) & 32.33\\\\\n \\hline\n 2 & Bathsheba vs. all & (3.00,97.00) & 32.33\\\\\n\\hline\n3 & Danae vs. all & (12.00,88.00) & 7.33\\\\ \n\\hline\n4 &\nDoctor\\_Nicolaes vs. all & (3.00,97.00) & 32.33\\\\\n\\hline\n5 & HollyFamilly vs. all & (2.00,98.00) & 49.00\\\\\n\\hline\n6 & PortraitOfMariaTrip vs. all & (3.00,97.00) &\n32.33\\\\\n\\hline\n 7 & PortraitOfSaskia vs. all &\n(1.00,99.00) & 99.00\\\\\n\\hline\n 8 & RembrandtXXPortrai vs. all & (2.00,98.00) & 49.00\\\\\n \\hline\n 9 & SaskiaAsFlora vs. all & (3.00,97.00) & 32.33\\\\\n \\hline\n 10 & SelfportraitAsStPaul vs. all & (8.00,92.00) & 11.50\\\\\n \\hline\n 11 & TheJewishBride vs. all & (4.00,96.00) & 24.00\\\\\n \\hline\n 12 & TheNightWatch vs. all & (9.00,91.00) & 10.11\\\\\n \\hline\n 13 & TheProphetJeremiah vs\nall & (7.00,93.00) & 13.28\\\\\n\\hline\n 14 &\nTheReturnOfTheProdigalSon vs. all & (9.00,91.00) &\n10.11\\\\\n\\hline\n 15 & TheSyndicsoftheClothmakersGuild vs. all \n& (5.00,95.00) & 19.00\\\\\n \\hline\n 16 & Other vs. all &\n(26.00,74.00) & 2.84\\\\%\\bottomrule\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications", "authors": ["Mario Manzo"], "url": "https://arxiv.org/abs/1912.09972v1", "attribution": "\"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications\" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.13533v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Audit Basic Error Classification}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n \\toprule\n Task Name & Error Category \\\\\n \\midrule\n Transaction TYPE Record Error & Record Error \\\\ \n Transaction DATE Record Error & Record Error\\\\\n Transaction PAYMENT/RECEIPT\\_STATUS Record Error & Record Error \\\\\n Transaction PAYMENT\\_METHOD Record Error & Record Error\\\\\n Transaction QUANTITY Record Error & Record Error\\\\\n Transaction UNIT\\_PRICE Record Error & Record Error\\\\\n Transaction RECEIVE\\_METHOD Record Error & Record Error\\\\\n Transaction AMOUNT Calculation Error & Calculation Error\\\\\n Transaction TAX\\_AMOUNT Calculation Error & Calculation Error\\\\\n Transaction PROFIT Calculation Error & Calculation Error\\\\\n Transaction Without PREPARER Error & Transaction Approval Mismatch\\\\\n Transaction Without APPROVER Error& Transaction Approval Mismatch\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs", "authors": ["Junzhe Jiang", "Chang Yang", "Aixin Cui", "Sihan Jin", "Ruiyu Wang", "Bo Li", "Xiao Huang", "Dongning Sun", "Xinrun Wang"], "url": "https://arxiv.org/abs/2505.13533v1", "attribution": "\"FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs\" by Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Ruiyu Wang, Bo Li, Xiao Huang, Dongning Sun, and Xinrun Wang, arXiv:2505.13533v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.15103v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Within-Customer Analysis of Offline Price Elasticity - Main Results}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\tabularnewline \\midrule \\midrule\n Dependent Variable: & \\multicolumn{6}{c}{Offline Price Elasticity}\\\\\n & \\multicolumn{2}{c}{Dry Dog Food} & \\multicolumn{2}{c}{Dog Hygiene} & \\multicolumn{2}{c}{Cat Hygiene} \\\\ \n Model: & (1) & (2) & (3) & (4) & (5) & (6) \\\\ \n \\midrule\n \\emph{Variables}\\\\\n Constant & -2.935$^{***}$ & & -2.464$^{***}$ & & -2.125$^{***}$ & \\\\ \n & (0.2142) & & (0.2429) & & (0.1998) & \\\\ \n $\\mathcal{I}\\{t \\geq T_{i}^{\\textrm{adopt}}\\}$ & -0.3870$^{***}$ & -0.1173$^{*}$ & -1.916$^{***}$ & -0.7239$^{**}$ & -0.8860$^{***}$ & -0.3347$^{**}$\\\\ \n & (0.0759) & (0.0543) & (0.3563) & (0.2361) & (0.1727) & (0.1250)\\\\ \n \\midrule\n \\emph{Fixed-effects}\\\\\n Customer & & Yes & & Yes & & Yes\\\\ \n YearMonth & & Yes & & Yes & & Yes\\\\ \n \\midrule\n \\emph{Fit statistics}\\\\\n Observations & 29,416 & 29,416 & 28,375 & 28,375 & 15,081 & 15,081\\\\ \n R$^2$ & 0.31325 & 0.86657 & 0.34866 & 0.72137 & 0.37526 & 0.75980\\\\ \n Within R$^2$ & & 0.04011 & & 0.03352 & & 0.03960\\\\ \n \\midrule \\midrule\n \\multicolumn{5}{l}{\\emph{Bootstrapped standard-errors in parentheses via 500 replications}}\\\\\n \\multicolumn{7}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05}}\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "How does online shopping affect offline price sensitivity?", "authors": ["Shirsho Biswas", "Hema Yoganarasimhan", "Haonan Zhang"], "url": "https://arxiv.org/abs/2506.15103v1", "attribution": "\"How does online shopping affect offline price sensitivity?\" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16534v2_tex_table30.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Analysis of performance and efficiency across varied settings of $d$ on medium-sized datasets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n\\toprule\n& $d$ & \\textbf{2} & \\textbf{3} & \\textbf{4 (default)} & \\textbf{6} & \\textbf{8} \\\\ \\midrule\n\\multirow{2}{*}{Classification} & Performance (Accuracy) & 0.7402 & 0.7588 & \\underline{0.7602} & 0.7583 & 0.7545 \\\\\n& Parameters (M) & \\underline{0.0058} & 0.0064 & 0.0070 & 0.0087 & 0.0108 \\\\\n& FLOPs (M) & \\underline{0.1801} & 0.1807 & 0.1815 & 0.1834 & 0.1857 \\\\\n\\midrule\n\\multirow{2}{*}{Regression} & Performance (R2 score) & 0.5961 & 0.6699 & 0.6796 & 0.6111 & \\underline{0.6914} \\\\\n& Parameters (M) & \\underline{0.0047} & 0.0051 & 0.0056 & 0.0069 & 0.0087 \\\\\n& FLOPs (M) & \\underline{0.1786} & 0.1791 & 0.1797 & 0.1812 & 0.1831 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DOFEN: Deep Oblivious Forest ENsemble", "authors": ["Kuan-Yu Chen", "Ping-Han Chiang", "Hsin-Rung Chou", "Chih-Sheng Chen", "Tien-Hao Chang"], "url": "https://arxiv.org/abs/2412.16534v2", "attribution": "\"DOFEN: Deep Oblivious Forest ENsemble\" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00173v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccc}\n\\toprule\nMetric & \\multicolumn{4}{c}{Top-1 Accuracy (\\%)} & & \\multicolumn{4}{c}{Seen-Unseen AUC (\\%)} \\\\ \\cmidrule(lr){2-5} \\cmidrule(l){7-10} \nDataset & \\multicolumn{2}{c}{CUB} & \\multicolumn{2}{c}{NAB} & & \\multicolumn{2}{c}{CUB} & \\multicolumn{2}{c}{NAB} \\\\\nSplit-Mode & Easy & Hard & Easy & Hard & & Easy & Hard & Easy & Hard \\\\ \\midrule\nGAZSL~& 43.7 & 10.3 & 35.6 & 8.6 & & 35.4 & 8.7 & 20.4 & 5.8 \\\\ \\midrule\nGAZSL~ + \\textbf{CIZSL}\\\\ \\cmidrule{1-1}\nInterpolate & 41.87 & \\textbf{15.48} & 35.01 & \\textbf{9.61} & & 39.19 & 12.58 & \\textbf{23.59} & \\textbf{6.92}\\\\ \\hline\nNegative Extrapolate & 43.37 & 14.93 & \\textbf{35.32} & 9.28 & & 38.88 & 12.74 & 23.22 & 6.73\\\\ \\hline\nPositive Extrapolate & 43.50 & 14.84 & 35.08 & 9.49 & & 39.22 & 12.73 & 23.44& 6.70\\\\ \\hline\nNeg\\&Pos Extrapolate & \\textbf{43.85} & 15.35 & 35.30 & 9.47 & & 39.04 & \\textbf{12.95} & 23.44 & 6.81\\\\ \\hline\nInterpolate \\& Extrapolate & 42.89 & 14.97 & \\textbf{35.36} & 9.51 & & \\textbf{39.42} & 12.38 & 23.44 & 6.77\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Interpolate and extrapolate of hallucinated text. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "CIZSL++: Creativity Inspired Generative Zero-Shot Learning", "authors": ["Mohamed Elhoseiny", "Kai Yi", "Mohamed Elfeki"], "url": "https://arxiv.org/abs/2101.00173v2", "attribution": "\"CIZSL++: Creativity Inspired Generative Zero-Shot Learning\" by Mohamed Elhoseiny, Kai Yi, and Mohamed Elfeki, arXiv:2101.00173v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2312.13380v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c|c}\n \\hline\n Algorithm & Global Acc & Local Acc \\\\\n \\hline\n FedAvg & 20.77 & 66.64 \\\\\n \\hline\n FedProx & 18.70 & 75.60 \\\\\n \\hline\n FedPAQ & 18.56 & 76.57 \\\\\n \\hline\n Fed-SimCLR & 39.12 & 77.43 \\\\\n \\hline\n Fed-SimSiam & 36.44 & 75.21 \\\\\n \\hline\n \\textbf{Fed-QSSL} & \\textbf{62.26} & \\textbf{83.03} \\\\\n \\hline \\hline\n Fed-SimCLR (Full) & 40.76 & 80.56 \\\\\n \\hline\n \\textbf{Fed-QSSL} (Full) & \\textbf{72.26} & \\textbf{90.01} \\\\\n \\hline\n\\end{tabular}\n\\caption{Experiments on CIFAR-10 with model bitwidth configuration $50\\% 6$-bit and $50\\% 12$-bit.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity", "authors": ["Yiyue Chen", "Haris Vikalo", "Chianing Wang"], "url": "https://arxiv.org/abs/2312.13380v1", "attribution": "\"Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity\" by Yiyue Chen, Haris Vikalo, and Chianing Wang, arXiv:2312.13380v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.16015v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ccccc|ccc}\n\\hline\n$x$ & $\\alpha$ & $\\mu$ & $\\delta$ & $\\alpha\\omega$ & $N$ & $R_N$ & Bound \\\\\n\\hline\n\t1 & 5 & 1/4 & 1 & 6.25 & 42 & $8.8\\cdot 10^{-19}$ & $1.1\\cdot 10^{-18}$\\\\\n\t1/2 & 1/3 & 1/4 & 1/10 & 0.09 & 236 & $2.9\\cdot 10^{-17}$ & $2.9\\cdot 10^{-17}$\\\\\n 1/3 & 10 & 1/5 & 1/50 & 1.35 & 1,494 & $2.1\\cdot 10^{-16}$ & $2.2\\cdot 10^{-16}$\\\\\n 1 & 10 & 1/5 & 5 & 50.64 & 25 & $1.9\\cdot 10^{-29}$ & $4.0\\cdot 10^{-21}$\\\\\n 3 & 10 & 1/5 & 10 & 103.85 & 53 & $2.4\\cdot 10^{-36}$ & $1.4\\cdot 10^{-19}$\\\\\n 10 & 1/10 & 1/5 & 10 & 1 & 53 & $3.8\\cdot 10^{-18}$ & $3.9\\cdot 10^{-18}$\\\\\n\t\\hline\n\t\\end{tabular}\n\\caption{The remainder of the series expansion and bound , estimating $N$ using to achieve machine precision.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On the computation of the cumulative distribution function of the Normal Inverse Gaussian distribution", "authors": ["Guillermo Navas-Palencia"], "url": "https://arxiv.org/abs/2502.16015v1", "attribution": "\"On the computation of the cumulative distribution function of the Normal Inverse Gaussian distribution\" by Guillermo Navas-Palencia, arXiv:2502.16015v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02320v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Summary of notations.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline \n Notation& Meaning \\\\ \n\\hline \n $\\mathcal{G}(\\mathcal{V},\\mathcal{E})$& Graph (or Network), vertex set $\\mathcal{V}$, edge set $\\mathcal{E}$ \\\\ \n\\hline \n$\\mathcal{T}$& Tree \\\\ \n\\hline \n $\\mathcal{P}_{uv}$& A path connecting vertex $u$ to vertex $v$\\\\ \n\\hline \n$e_{uv}$(or $uv$)& An edge connecting vertices $u$ and $v$ \\\\ \n\\hline \n $d_{uv}$& Distance between vertices $u$ and $v$\\\\ \n\\hline \n$D$& Diameter of network \\\\ \n\\hline \n $d_{f}$& Fractal dimension of fractal network\\\\ \n\\hline \n$r$& Pearson correlation coefficient\\\\ \n\\hline \n$\\mathcal{N}_{k}$& The number of vertices with degree $k$\\\\ \n\\hline \n$\\Omega_{u}$& The neighboring set $\\Omega_{u}$ of vertex $u$ in graph\\\\ \n\\hline \n$A_{v}(t)$& Set of new vertices inserted on all the existing edges incident with vertex $v$ in $\\mathcal{T}_{t}$ \\\\ \n\\hline \n$B_{v}(t)$& Set of new vertices as leaves directly connected to vertex $v$ in tree $\\mathcal{T}_{t}$\\\\ \n\\hline \n$k_{v}(t)$& Degree of vertex $v$ in tree $\\mathcal{T}_{t}$\\\\ \n\\hline \n$P(k)$& Degree distribution of network\\\\ \n\\hline \n$P_{cum}(k)$& Cumulative degree distribution of network \\\\ \n\\hline \n$\\mathcal{H}_{u\\rightarrow v}$& Hitting time from vertex $u$ to $v$ for random walks on network\\\\ \n\\hline \n$\\langle\\mathcal{H}_{\\mathcal{G}}\\rangle$& Mean hitting time for random walks on network $\\mathcal{G}(\\mathcal{V},\\mathcal{E})$\\\\ \n\\hline \n$\\langle\\mathcal{W}_{\\mathcal{G}}\\rangle$& Average shortest path length of network $\\mathcal{G}(\\mathcal{V},\\mathcal{E})$\\\\ \n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Scale-free tree network with an ultra-large diameter", "authors": ["Fei Ma", "Ping Wang"], "url": "https://arxiv.org/abs/2101.02320v2", "attribution": "\"Scale-free tree network with an ultra-large diameter\" by Fei Ma and Ping Wang, arXiv:2101.02320v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.01116v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|cccc}\n\\toprule\n& Main & \\multicolumn{4}{c}{Alternative Specifications} \\\\\n\\midrule\nPost Service & 0.137*** & 0.365*** & 0.367*** & 0.366*** & 0.300*** \\\\\n& (0.0103) & (0.0102) & (0.0102) & (0.0102) & (0.105) \\\\\nPost Bill & -0.074*** & -0.066*** & -0.069*** & -0.067*** & -0.062*** \\\\\n& (0.0099) & (0.0101) & (0.0100) & (0.0100) & (0.0010) \\\\\n\\midrule\n$\\overline{\\text{spend}_{it}}$ & \\$93.82 & \\$93.82 & \\$93.82 & \\$93.82 & \\$93.82 \\\\\nHousehold FEs & X & X & X & X & X \\\\\nYear FEs & X & & X & X & X \\\\\nWeek of Year FEs & X & & & X & X \\\\\nProvider FEs & X & & & & X\\\\\nEvent Type FEs & X & & & & \\\\\nObservations & 126,747,805 & 126,747,805 & 126,747,805 & 126,747,805 & 126,747,805 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Robustness of Effects to Expanded Services: Injuries and Appendectomies}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Medical Bill Shock and Imperfect Moral Hazard", "authors": ["Alex Hoagland", "David M. Anderson", "Ed Zhu"], "url": "https://arxiv.org/abs/2211.01116v2", "attribution": "\"Medical Bill Shock and Imperfect Moral Hazard\" by Alex Hoagland, David M. Anderson, and Ed Zhu, arXiv:2211.01116v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05866v4_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Components of $\\phi_1$ in a three-feature model}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\t\t\t\t\\hline\n\t\t\t\t$S$ & $\\omega_{S}$ & $\\mathbb{E}_{y,x_1,\\mathbf{x}^{S}}\\mathbb{E}_{\\mathbf{x}^{\\overline{S}}}\\left(\n\t\t\t\tG\\left(y;\\mathbf{x};\\delta_0\\right)\\right)- \\mathbb{E}_{y,\\mathbf{x}^{S}}\\mathbb{E}_{x_1,\\mathbf{x}^{\\overline{S}}}\\left(\n\t\t\t\tG\\left(y;\\mathbf{x};\\delta_0\\right)\\right)$ \\\\\n\t\t\t\t\\hline\n\t\t\t\t$\\left\\{\\emptyset\\right\\}$ & $1/3$ &\n\t\t\t\t$\\mathbb{E}_{y,x_1}\\mathbb{E}_{x_2,x_3}\\left(\n\t\t\t\tG\\left(y;\\mathbf{x};\\delta_0\\right)\\right)-\\mathbb{E}_{y}\\mathbb{E}_{x_1,x_2,x_3}\\left(\n\t\t\t\tG\\left(y;\\mathbf{x};\\delta_0\\right)\\right)$\\\\\n\t\t\t\t$\\left\\{x_{2}\\right\\}$ & $1/6$ &\n\t\t\t\t$\\mathbb{E}_{y,x_1,x_2}\\mathbb{E}_{x_3}\\left(\n\t\t\t\tG\\left(y;\\mathbf{x};\\delta_0\\right)\\right)-\\mathbb{E}_{y,x_2}\\mathbb{E}_{x_1,x_3}\\left(\n\t\t\t\tG\\left(y;\\mathbf{x};\\delta_0\\right)\\right)$ \\\\\n\t\t\t\t$\\left\\{x_{3}\\right\\}$ & $1/6$ & $\\mathbb{E}_{y,x_1,x_3}\\mathbb{E}_{x_2}\\left(\n\t\t\t\tG\\left(y;\\mathbf{x};\\delta_0\\right)\\right)-\\mathbb{E}_{y,x_3}\\mathbb{E}_{x_1,x_2}\\left(\n\t\t\t\tG\\left(y;\\mathbf{x};\\delta_0\\right)\\right)$ \\\\\n\t\t\t\t$\\left\\{x_{2},x_{3}\\right\\}$ & $1/3$ & $\\mathbb{E}_{y,x_1,x_2,x_3}\\left(\n\t\t\t\tG\\left(y;\\mathbf{x};\\delta_0\\right)\\right)-\\mathbb{E}_{y,x_2,x_3}\\mathbb{E}_{x_1}\\left(\n\t\t\t\tG\\left(y;\\mathbf{x};\\delta_0\\right)\\right)$\\\\\n\t\t\t\t\\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring", "authors": ["Hué Sullivan", "Hurlin Christophe", "Pérignon Christophe", "Saurin Sébastien"], "url": "https://arxiv.org/abs/2212.05866v4", "attribution": "\"Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring\" by Hué Sullivan, Hurlin Christophe, Pérignon Christophe, and Saurin Sébastien, arXiv:2212.05866v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16490v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[table]{xcolor}\n\\usepackage{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\rowcolor[HTML]{b1457c} \\textcolor{white}{Algorithm} & \\textcolor{white}{type} & \\textcolor{white}{$\\epsilon$} & \\textcolor{white}{$T$} \\\\\n {Non-smooth} & - & 0.00 & $18.08 \\pm 0.85$ \\\\\n \\hline\n {Smooth} & - & 0.00 & $18.15 \\pm 0.75$ \\\\\n \\hline \\hline\n \\multirow{2}{*}{Non-smooth} & \\multirow{2}{*}{random} & 0.025& $18.22 \\pm 1.28$\\\\\n & & 0.050& $18.68 \\pm 1.97$\\\\\n \\hline\n \\multirow{2}{*}{Smooth} & \\multirow{2}{*}{random}& 0.025 & $18.17 \\pm 0.84$\\\\\n & & 0.050 & $18.33 \\pm 2.23$\\\\\n \\hline \\hline\n \\multirow{2}{*}{Non-smooth} & \\multirow{2}{*}{adv}& 0.025 & $19.33 \\pm 1.76$ \\\\\n & & 0.050& $19.76 \\pm 2.85$\\\\\n \\hline\n \\multirow{2}{*}{Smooth} & \\multirow{2}{*}{adv}& 0.025 & $18.51 \\pm 0.65$ \\\\\n & & 0.050 & $18.84 \\pm 1.12$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of smooth and non-smooth policies under with no (-), random, or adversarial (Adv) noise at noise level $\\epsilon$. $T$ is the time required to complete the task.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Multi-agent Robust and Optimal Policy Learning for Data Harvesting", "authors": ["Shili Wu", "Yancheng Zhu", "Aniruddha Datta", "Sean B. Andersson"], "url": "https://arxiv.org/abs/2508.16490v1", "attribution": "\"Multi-agent Robust and Optimal Policy Learning for Data Harvesting\" by Shili Wu, Yancheng Zhu, Aniruddha Datta, and Sean B. Andersson, arXiv:2508.16490v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.05906v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|} \n\t\\hline\n\t& Sample Mean & Sample Variance & Sharp Ratio \\\\\t\n\t\\hline\n\tMonthly Rebalancing, 5 years, $d = 8$ & 8.07335 & 1.47720 & 6.60138 \\\\ \n\t\\hline\n\tMonthly Rebalancing, 1 year, $d = 1.4$ & 1.40067 & 0.06056 & 5.48819 \\\\\n\t\\hline\n\tDaily Rebalancing, half year, $d = 1.2$& 1.19658 & 0.03186 & 6.42324 \\\\\n\t\\hline\n\tDaily Rebalancing, 1 year, $d = 1.4$ & 1.39368 & 0.04920 & 6.05735 \\\\\n\t\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\small Sample Mean, Sample Variance and Sample Sharp Ratio of terminal wealth in different investing \\\\ horizon and expected return}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application", "authors": ["Lucky Li"], "url": "https://arxiv.org/abs/2412.05906v2", "attribution": "\"Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application\" by Lucky Li, arXiv:2412.05906v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.13157v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Forecasting volatility: Short range forecast comparison of time-varying and time-constant model for startpoint at 500. TV and TC stands for time-varying and time-constant models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l||c|c||c|c||c|c||c|c||c|c||c|c||}\n\\hline\n& \\multicolumn{2}{c||}{ahead=25} & \\multicolumn{2}{c||}{ahead=50} & \\multicolumn{2}{c||}{ahead=75} & \\multicolumn{2}{c||}{ahead=100} & \\multicolumn{2}{c||}{ahead=150} & \\multicolumn{2}{c||}{ahead=200} \\\\\n $Data$ & TC & TV & TC & TV & TC & TV & TC & TV & TC & TV & TC & TV \\\\\n\\hline\\hline\nUSGBP & 0.101 & 0.07 & 0.085 & 0.057 & 0.078 & 0.053 & 0.075 & 0.052 & 0.07 & 0.048 & 0.067 & 0.046 \\\\\nUSCHF & 2.968 & 2.775 & 1.702 & 1.528 & 1.269 & 1.115 & 1.048 & 0.099 & 0.803 & 0.601 & 0.646 & 0.524 \\\\\nUSCAD & 0.029 & 0.026 & 0.024 & 0.024 & 0.022 & 0.023 & 0.02 & 0.021 & 0.017 & 0.018 & 0.015 & 0.017 \\\\\nEURGBP & 0.083 & 0.074 & 0.057 & 0.048 & 0.044 & 0.036 & 0.039 & 0.032 & 0.034 & 0.029 & 0.028 & 0.025 \\\\\nEURUSD & 0.052 & 0.033 & 0.044 & 0.028 & 0.041 & 0.027 & 0.042 & 0.028 & 0.038 & 0.032 & 0.036 & 0.038 \\\\\n\\hline \\hline\nSP Merval & 7.722 & 7.094 & 6.632 & 4.679 & 5.317 & 4.376 & 5.763 & 4.514 & 5.372 & 4.529 & 5.086 & 4.620 \\\\\n\\hline \nBSE & 3219 & 3038 & 2916 & 1901 & 2313 & 2040 & 2259 & 1321 & 2000 & 1106 & 1897 & 1038 \\\\ \n\\hline \nSP500 old & 5.475 & 5.346 & 8.752 & 7.511 & 4.265 & 4.048 & 3.339 & 2.344 & 2.092 & 1.079 & 1.768 & 0.939 \\\\\n\\hline\nSP500 & 0.319 & 0.042 & 0.379 & 0.372 & 0.249 & 0.324 & 0.304 & 0.306 & 0.277 & 0.280 & 0.269 & 0.274 \\\\\n\\hline\nDow Jones & 0.037 & 0.0365 & 0.316 & 0.277 & 0.223 & 0.231 & 0.215 & 0.200 & 0.185 & 0.183 & 0.169 & 0.176 \\\\\n\\hline\nFTSE & 0.470 & 0.461 & 0.456 & 0.283 & 0.322 & 0.279 & 0.346 & 0.206 & 0.336 & 0.240 & 0.319 & 0.278 \\\\\n\\hline \nNASDAQ & 0.424 &0.399 & 0.498 & 0.237 & 0.283 & 0.233 & 0.404 & 0.175 & 0.388 & 0.200 & 0.364 & 0.218 \\\\\n\\hline \nSmallcap & 0.448 & 0.292 & 1.035 & 0.189 & 0.398 & 0.157 & 0.981 & 0.143 & 0.979 & 0.144 & 0.970 & 0.143 \\\\ \n\\hline\nNYSE & 0.318 & 0.245 & 0.470 & 0.144 & 0.277 & 0.148 & 0.418 & 0.107 & 0.413 & 0.121 & 0.404 & 0.136 \\\\\n\\hline\nDAX & 0.903 & 0.800 & 1.142 & 0.559 & 0.639 & 0.552 & 0.992 & 0.501 & 0.961 & 0.580 & 0.920 & 0.697 \\\\\n\\hline \nApple & 3.736 & 3.535 & 1.853 & 2.100 & 2.069 & 1.816 & 1.380 & 1.787 & 1.305 & 1.818 & 1.280 & 1.749 \\\\\n\\hline \nMicrosoft & 2.729 & 3.388 & 1.600 & 1.999 & 1.156 & 1.898 & 0.981 & 1.335 & 0.766 & 1.084 & 0.664 & 0.973 \\\\\n\\hline AXP & 2.350 & 2.13 & 2.142 & 1.500 & 1.456 & 1.264 & 1.726 & 1.065 & 1.759 & 0.984 & 1.625 & 0.928 \\\\ \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Simultaneous inference for time-varying models", "authors": ["Sayar Karmakar", "Stefan Richter", "Wei Biao Wu"], "url": "https://arxiv.org/abs/2011.13157v2", "attribution": "\"Simultaneous inference for time-varying models\" by Sayar Karmakar, Stefan Richter, and Wei Biao Wu, arXiv:2011.13157v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.02465v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n \\toprule\n LMM & \\textit{Naive} & \\textit{+\\textbf{TAMA}} \\\\\n \\midrule\n GPT-4o & 41.8 & 80.2 (\\textbf{+38.4})\\\\\n GPT-4o-mini & 11.8 & 51.1 (\\textbf{+39.3})\\\\\n Gemini-1.5-pro & 25.4 & 87.8 (\\textbf{+62.4})\\\\\n Gemini-1.5-flash& 17.9 & 36.4 (\\textbf{+18.5})\\\\\n qwen-vl-max-0809& 61.7 & 80.5 (\\textbf{+18.8})\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers", "authors": ["Jiaxin Zhuang", "Leon Yan", "Zhenwei Zhang", "Ruiqi Wang", "Jiawei Zhang", "Yuantao Gu"], "url": "https://arxiv.org/abs/2411.02465v1", "attribution": "\"See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers\" by Jiaxin Zhuang, Leon Yan, Zhenwei Zhang, Ruiqi Wang, Jiawei Zhang, and Yuantao Gu, arXiv:2411.02465v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.14431v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fully parameterized local volatility function $\\sigma(t,s)/100$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c|c|c|c|c|c|c|} \\hline\n\\rule[0pt]{0pt}{5mm}\n\\tt\t day/s &\\tt \t0.0\t &\\tt\t 0.1 &\\tt 0.2 \t&\\tt 0.3\t&\\tt 0.4 &\\tt 0.5 &\\tt 0.6 &\\tt \\ $\\cdots$\\ \\ &\\tt 1.0 \\\\\n\\hline \\hline\n\\tt 0.0\t&\\tt\t4.300\t&\\tt\t4.300\t&\\tt 4.300\t&\\tt 4.300\t&\\tt 4.300\t&\\tt 7.746 &\\tt 4.300 &\\tt $\\cdots$ &\\tt 4.300 \\\\\n\\tt\t 1.0\t&\\tt\t4.303\t&\\tt\t4.376\t&\\tt 3.950\t&\\tt 3.190\t&\\tt 2.449\t&\\tt 5.150 &\\tt 2.449 &\\tt $\\cdots$ &\\tt 4.317 \\\\\n\\tt\t 2.5\t&\\tt\t4.299\t&\\tt\t4.292\t&\\tt 4.322\t&\\tt 3.947\t&\\tt 2.630\t&\\tt 0.828 &\\tt 2.630 &\\tt $\\cdots$ &\\tt 4.301 \\\\\n\\tt\t 4.0\t&\\tt\t4.299\t&\\tt\t4.207\t&\\tt 3.842\t&\\tt 3.535\t&\\tt 2.353\t&\\tt 0.218 &\\tt 2.353 &\\tt $\\cdots$ &\\tt 4.315 \\\\\n\\tt\t 9.0\t&\\tt\t4.309\t&\\tt\t4.308\t&\\tt 3.726\t&\\tt 2.681\t&\\tt 3.266\t&\\tt 3.054 &\\tt 3.266 &\\tt $\\cdots$ &\\tt 4.336 \\\\\n\\tt\t14.0\t&\\tt\t4.315\t&\\tt\t4.528\t&\\tt 4.234\t&\\tt 3.244\t&\\tt 3.833\t&\\tt 3.374 &\\tt 3.833 &\\tt $\\cdots$ &\\tt 4.363 \\\\\n\\tt\t17.5\t&\\tt\t4.309\t&\\tt\t4.515\t&\\tt 4.885\t&\\tt 4.396\t&\\tt 3.679\t&\\tt 3.775 &\\tt 3.679 &\\tt $\\cdots$ &\\tt 4.343 \\\\\n\\tt\t21.0\t&\\tt\t4.309\t&\\tt\t4.741\t&\\tt 5.978\t&\\tt 6.007\t&\\tt 4.991\t&\\tt 5.540 &\\tt 4.991 &\\tt $\\cdots$ &\\tt 4.356 \\\\\n\\tt\t25.5\t&\\tt\t4.301\t&\\tt\t4.610\t&\\tt 6.211\t&\\tt 6.686\t&\\tt 6.227\t&\\tt 5.987 &\\tt 6.227 &\\tt $\\cdots$ &\\tt 4.318 \\\\\n\\tt\t30.0\t&\\tt\t4.310\t&\\tt\t4.344\t&\\tt 4.954\t&\\tt 3.995\t&\\tt 2.646\t&\\tt 2.803 &\\tt 2.646 &\\tt $\\cdots$ &\\tt 4.334 \\\\\n\\tt\t59.0\t&\\tt\t4.324\t&\\tt\t4.892\t&\\tt 5.252\t&\\tt 4.943\t&\\tt 4.983\t&\\tt 5.132 &\\tt 5.382 &\\tt $\\cdots$ &\\tt 4.455 \\\\\n\\tt\t91.0\t&\\tt\t4.282\t&\\tt\t4.324\t&\\tt 5.675\t&\\tt 5.333\t&\\tt 4.329\t&\\tt 4.422 &\\tt 4.098 &\\tt $\\cdots$ &\\tt 4.338 \\\\\n\\tt\t179.0\t&\\tt\t4.290\t&\\tt\t4.204\t&\\tt 3.783\t&\\tt 3.902\t&\\tt 3.763\t&\\tt 3.124 &\\tt 4.156 &\\tt $\\cdots$ &\\tt 4.552 \\\\\n\\tt\t273.0\t&\\tt\t4.291\t&\\tt\t4.121\t&\\tt 4.214\t&\\tt 4.761\t&\\tt 4.628\t&\\tt 3.896 &\\tt 4.098 &\\tt $\\cdots$ &\\tt 4.462 \\\\\n\\tt\t365.0\t&\\tt\t4.293\t&\\tt\t4.240\t&\\tt 3.823\t&\\tt 4.021\t&\\tt 4.199\t&\\tt 3.393 &\\tt 4.214 &\\tt $\\cdots$ &\\tt 4.524 \\\\\n\\tt\t547.0\t&\\tt\t4.293\t&\\tt\t4.167\t&\\tt 4.125\t&\\tt 4.854\t&\\tt 4.751\t&\\tt 3.773 &\\tt 4.336 &\\tt $\\cdots$ &\\tt 4.446 \\\\\n\\tt\t730.0\t&\\tt\t4.298\t&\\tt\t4.233\t&\\tt 3.629\t&\\tt 3.830\t&\\tt 3.911\t&\\tt 3.255 &\\tt 4.485 &\\tt $\\cdots$ &\\tt 4.469 \\\\\n\\tt\t1094.0\t&\\tt\t4.298\t&\\tt\t4.254\t&\\tt 3.976\t&\\tt 5.202\t&\\tt 5.454\t&\\tt 3.303 &\\tt 4.859 &\\tt $\\cdots$ &\\tt 4.362 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Efficient and Accurate Calibration to FX Market Skew with Fully Parameterized Local Volatility Model", "authors": ["Dongli Wu", "Bufan Zhang", "Xiao Lin"], "url": "https://arxiv.org/abs/2211.14431v2", "attribution": "\"Efficient and Accurate Calibration to FX Market Skew with Fully Parameterized Local Volatility Model\" by Dongli Wu, Bufan Zhang, and Xiao Lin, arXiv:2211.14431v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.13765v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|cccc}\n & \\multicolumn{2}{c}{Construction} & \\multicolumn{2}{c}{Evaluation} \\\\\n & Tuning & Constants & Mean & Covariance \\\\\n \\hline\n Unstructured GP & $\\mathcal{O}(n^3)$ & $\\mathcal{O}(n^3)$ & $\\mathcal{O}(n)$ & $\\mathcal{O}(n^2)$ \\\\\n Structured GP & $\\mathcal{O}(n \\log n)$ & $\\mathcal{O}(n \\log n)$ & $\\mathcal{O}(n)$ & $\\mathcal{O}(n \\log n)$\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of construction and evaluation costs between the unstructured and structured Gaussian processes in terms of the number of samples $n$ used to fit the Gaussian process. Construction costs include tuning parameters and computing constants for evaluating the posterior mean and covariance.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Computationally Efficient and Error Aware Surrogate Construction for Numerical Solutions of Subsurface Flow Through Porous Media", "authors": ["Aleksei G. Sorokin", "Aleksandra Pachalieva", "Daniel O'Malley", "James M. Hyman", "Fred J. Hickernell", "Nicolas W. Hengartner"], "url": "https://arxiv.org/abs/2310.13765v1", "attribution": "\"Computationally Efficient and Error Aware Surrogate Construction for Numerical Solutions of Subsurface Flow Through Porous Media\" by Aleksei G. Sorokin, Aleksandra Pachalieva, Daniel O'Malley, James M. Hyman, Fred J. Hickernell, and Nicolas W. Hengartner, arXiv:2310.13765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17854v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c||c|c||c|c}\n \\multicolumn{6}{c}{\\textbf{ODE Terms:}}\\\\\nEqn.~ & \\texttt{DDE.} & Eqn.~ & \\texttt{DDE.} & Eqn.~ & \\texttt{DDE.}\\\\\n\\hline\n$A_0$ & \\texttt{A0} & $B_{1}$ & \\texttt{B1} &$B_{2}$&\\texttt{B2}\\\\ \n$C_{1}$ & \\texttt{C1} & $D_{11}$ &\\texttt{D11}&$D_{12}$&\\texttt{D12}\\\\ \n$C_{2}$ & \\texttt{C2} & $D_{21}$ &\\texttt{D21}&$D_{22}$&\\texttt{D22} \\\\ \\hline \\\\\n \\multicolumn{6}{c}{ \\textbf{Discrete Delay Terms:}}\\\\\nEqn.~ & \\texttt{DDE.} & Eqn.~ & \\texttt{DDE.} & Eqn.~ & \\texttt{DDE.}\\\\\n\\hline\n$A_i$ & \\texttt{Ai\\{i\\}} & $B_{1i}$ & \\texttt{B1i\\{i\\}} &$B_{2i}$&\\texttt{B2i\\{i\\}}\\\\ \n$C_{1i}$ & \\texttt{C1i\\{i\\}} & $D_{11i}$ &\\texttt{D11i\\{i\\}}&$D_{12i}$&\\texttt{D12i\\{i\\}}\\\\ \n$C_{2i}$ & \\texttt{C2i\\{i\\}} & $D_{21i}$ &\\texttt{D21i\\{i\\}}&$D_{22i}$&\\texttt{D22i\\{i\\}}\\\\\n\\hline \\\\ \\multicolumn{6}{c}{\\textbf{Distributed Delay Terms: May be functions of \\texttt{pvar s}}}\\\\\nEqn.~ & \\texttt{DDE.} & Eqn.~ & \\texttt{DDE.} & Eqn.~ & \\texttt{DDE.}\\\\\n\\hline\n$A_{di} $ & \\texttt{Adi\\{i\\}} & $B_{1di}$ & \\texttt{B1di\\{i\\}} &$B_{2di}$&\\texttt{B2di\\{i\\}}\\\\ \n$C_{1di} $& \\texttt{C1di\\{i\\}} &$ D_{11di} $&\\texttt{D11di\\{i\\}}&$D_{12di}$&\\texttt{D12di\\{i\\}}\\\\ \n$C_{2di}$ & \\texttt{C2di\\{i\\}} & $D_{21di} $&\\texttt{D21di\\{i\\}}&$D_{22di}$&\\texttt{D22di\\{i\\}}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Equivalent names of Matlab elements of the \\texttt{DDE} structure terms for terms in Eqn.~. For example, to set term \\texttt{XX} to \\texttt{YY}, we use \\texttt{DDE.XX=YY}. In addition, the delay $\\tau_i$ is specified using the vector element \\texttt{DDE.tau(i)} so that if $\\tau_1=1, \\tau_2=2, \\tau_3=3$, then \\texttt{DDE.tau=[1 2 3]}. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "PIETOOLS 2024: User Manual", "authors": ["Sachin Shivakumar", "Declan Jagt", "Danilo Braghini", "Amritam Das", "Yulia Peet", "Matthew Peet"], "url": "https://arxiv.org/abs/2501.17854v1", "attribution": "\"PIETOOLS 2024: User Manual\" by Sachin Shivakumar, Declan Jagt, Danilo Braghini, Amritam Das, Yulia Peet, and Matthew Peet, arXiv:2501.17854v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.11606v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Augmented dataset hyperparameters.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n\\toprule\n\\textbf{Hyperparameter} & \\textbf{mPCG Model} \\\\\n\\hline\n\\midrule\nLearning rate & 1e-5\\\\\nWeight decay & 6.1148e-05\\\\\nMomentum & 0.17562 \\\\\nGamma & 0.02444 \\\\\nStep size & 4\\\\\nBatch size & 32\\\\\nNumber of hidden layers & 3\\\\\nHidden layer size & 512\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Scaling to Multimodal and Multichannel Heart Sound Classification: Fine-Tuning Wav2Vec 2.0 with Synthetic and Augmented Biosignals", "authors": ["Milan Marocchi", "Matthew Fynn", "Kayapanda Mandana", "Yue Rong"], "url": "https://arxiv.org/abs/2509.11606v1", "attribution": "\"Scaling to Multimodal and Multichannel Heart Sound Classification: Fine-Tuning Wav2Vec 2.0 with Synthetic and Augmented Biosignals\" by Milan Marocchi, Matthew Fynn, Kayapanda Mandana, and Yue Rong, arXiv:2509.11606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.02476v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n\\hline \n\\multirow{2}{*}{Sampling Method} & \\multirow{2}{*}{Reweighting} & \\multicolumn{2}{c}{Ten Class}\\tabularnewline\n\\cline{3-5} \\cline{4-5} \\cline{5-5} \n & & 300&1000 & 2000\\tabularnewline\n\\hline \nRandom with $Y$ & Equal & 58.35 (0.39)&71.79 (0.54) &78.24 (0.41)\\tabularnewline\n\\hline \nUncertainty with $Y$(Clip) & Equal& 58.28 (0.73)&72.19 (0.36) &\t78.96 (0.24) \\tabularnewline\n\\hline \nUncertainty with y(Clip) & Inverse Uncertainty (Clip) &60.88 (0.39) & 73.24 (0.6) &\t79.49 (0.06) \\tabularnewline\n\\hline \nUncertainty without y(Clip) & Equal & 58.13 (1.02)& 72.69 (0.57) &78.94 (0.21)\t\\tabularnewline\n\\hline \nUncertainty without y(Clip) & Inverse Uncertainty (Clip) &58.88 (0.53) &72.21 (0.31) & 78.96 (0.28) \\tabularnewline\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{ This table illustrates the performance of different sampling methods on ten-class classification tasks for ResNet20 on CIFAR. 1000/2000/5000 means 1000/2000/5000 samples for each class. We run each method 5 times and report the mean and std for them. Here we use dropout to estimate the uncertainty. (Figure dropout VS 10 models, bootstrap)}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning", "authors": ["Yong Lin", "Chen Liu", "Chenlu Ye", "Qing Lian", "Yuan Yao", "Tong Zhang"], "url": "https://arxiv.org/abs/2309.02476v1", "attribution": "\"Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning\" by Yong Lin, Chen Liu, Chenlu Ye, Qing Lian, Yuan Yao, and Tong Zhang, arXiv:2309.02476v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.07404v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experiment Data}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\nExperiment & Task's Euclidean Distance (m) & Time (sec) & Exploit Nodes\\\\\n\\hline\n{Obstacle avoidance} \n& 7 & 22.2319 & 13000 \\\\ \n\\hline\n{Long Distance} \n& 45.44 & 98.6619 & 91000 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Cell A* for Navigation of Unmanned Aerial Vehicles in Partially-known Environments", "authors": ["Wenjian Hao", "Rongyao Wang", "Alexander Krolicki", "Yiqiang Han"], "url": "https://arxiv.org/abs/2009.07404v1", "attribution": "\"Cell A* for Navigation of Unmanned Aerial Vehicles in Partially-known Environments\" by Wenjian Hao, Rongyao Wang, Alexander Krolicki, and Yiqiang Han, arXiv:2009.07404v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.07616v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|cc|cc}\n\\toprule \n\\textbf{Model} & \\textbf{Linear} & \\multicolumn{2}{c|}{\\textbf{Larger noisy set}} & \\multicolumn{2}{c}{\\textbf{Small clean set}} \\\\\n\\midrule \n(weights in M) & - & random* & p-t & random & p-t \\\\\n\\midrule \nResNet-18 (11) & \\textbf{74.3} & 65.4 & \\textbf{78.2} & 56.5 & \\textbf{77.9} \\\\\nVGG-like (0.3) & 70.0 & 70.6 & \\textbf{72.8} & 61.1 & \\textbf{72.3} \\\\\nCRNN (1) & 64.4 & 72.0 & \\textbf{74.2} & 58.7 & \\textbf{69.1} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Unsupervised Contrastive Learning of Sound Event Representations", "authors": ["Eduardo Fonseca", "Diego Ortego", "Kevin McGuinness", "Noel E. O'Connor", "Xavier Serra"], "url": "https://arxiv.org/abs/2011.07616v1", "attribution": "\"Unsupervised Contrastive Learning of Sound Event Representations\" by Eduardo Fonseca, Diego Ortego, Kevin McGuinness, Noel E. O'Connor, and Xavier Serra, arXiv:2011.07616v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.03473v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{True regression parameters used for data generation}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\t\t\\toprule\n\t\t& $q_{1}$ & $q_{2}$ & $q_{3}$ & $q_{4}$ \\\\ \n\t\t\\midrule\n\t\t$p_{0}$ (Intercept) & 0.000 & 0.433 & 0.433 & 0.000 \\\\ \n\t\t$p_{1}$ ($T_{ji}$) & 0.000 & 0.000 & 0.000 & 0.000 \\\\ \n\t\t$p_{2}$ ($w_{ji}$) & 1.027 & 0.601 & 0.427 & 0.000 \\\\ \n\t\t$p_{3}$ ($w_{ji}T_{ji}$)& -2.055 & -1.201 & -0.854 & 0.000 \\\\ \n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian multilevel multivariate logistic regression for superiority decision-making under observable treatment heterogeneity", "authors": ["Xynthia Kavelaars", "Joris Mulder", "Maurits Kaptein"], "url": "https://arxiv.org/abs/2212.03473v4", "attribution": "\"Bayesian multilevel multivariate logistic regression for superiority decision-making under observable treatment heterogeneity\" by Xynthia Kavelaars, Joris Mulder, and Maurits Kaptein, arXiv:2212.03473v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10774v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{A summary of hyperparameters by datasets. Logistic denotes a logistic model and NN denotes a neural network one hidden layer.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccccc}\n \\toprule\n \\multirow{2}{*}{Dataset} & \\multirow{2}{*}{Seed data $n_{\\mathrm{s}}$} & \\multicolumn{4}{c}{ML predictor $f$} & \\\\ \\cmidrule{3-7}\n & & Model & \\# of hidden nodes & Epoch & Learning rate & Retrain period \\\\\n \\midrule\n \\texttt{Insurance} & 100 & Logistic & - & 10 & $5\\times 10^{-3}$ & 50 \\\\\n \\texttt{Adult} & 100 & Logistic & - & 10 & $10^{-2}$ & 50 \\\\\n \\texttt{Postures} & 200 & Logistic & - & 10 & $3\\times10^{-2}$ & 50 \\\\\n \\texttt{Skin-nonskin} & 50 & Logistic & - & 10 & $3\\times10^{-2}$ & 50 \\\\\n \\texttt{Fashion-MNIST} & 50 & NN & 400 & 30 & $10^{-4}$ & 150 \\\\\n \\texttt{MNIST} & 50 & NN & 400 & 30 & $10^{-4}$ & 150 \\\\\n \\texttt{CIFAR10} & 100 & NN & 400 & 30 & $10^{-4}$ & 150 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Competition over data: how does data purchase affect users?", "authors": ["Yongchan Kwon", "Antonio Ginart", "James Zou"], "url": "https://arxiv.org/abs/2201.10774v2", "attribution": "\"Competition over data: how does data purchase affect users?\" by Yongchan Kwon, Antonio Ginart, and James Zou, arXiv:2201.10774v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.07899v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{A comparison of prediction accuracy on MMWHS MR and CT test datasets from different methods trained with images from MMWHS training set. An asterisk $*$ indicates statistically significant accuracy differences, compared with Table , resulted from training on a smaller datset based on t-tests (p$<$0.05).}\n\\begin{tabular}{llllllllll|l}\n\\toprule\n & & & Epi & LA & LV & RA & RV & Ao & PA & WH \\\\\n\\midrule\n\\multirow{16}{*}{CT} & \\multirow{4}{*}{Dice ($\\uparrow$)} & Ours & \\textbf{0.880} & \\textbf{0.926} & \\textbf{0.931} & \\textbf{0.868} & \\textbf{0.885}* & \\textbf{0.945} & 0.786* & \\textbf{0.900}* \\\\\n & & 2DUNet & 0.877* & 0.916 & 0.926 & 0.855 & 0.876* & 0.916 & \\textbf{0.805} & 0.892* \\\\\n & & 3DUNet & 0.816* & 0.916 & 0.914 & 0.848 & 0.878 & 0.923 & 0.793 & 0.877 \\\\\n & & Voxel2Mesh & 0.501* & 0.748* & 0.669* & 0.717* & 0.698* & 0.555* & 0.491* & 0.656* \\\\\n\\cline{2-11}\n & \\multirow{4}{*}{Jaccard ($\\uparrow$)} & Ours & \\textbf{0.790} & \\textbf{0.863} & \\textbf{0.874} & \\textbf{0.773} & \\textbf{0.798}* & \\textbf{0.897} & 0.666* & \\textbf{0.819}* \\\\\n & & 2DUNet & 0.784* & 0.847 & 0.864 & 0.753 & 0.787* & 0.850 & \\textbf{0.692} & 0.807* \\\\\n & & 3DUNet & 0.696* & 0.848 & 0.844 & 0.741 & 0.787 & 0.860 & 0.670 & 0.782 \\\\\n & & Voxel2Mesh & 0.337* & 0.600* & 0.510* & 0.570* & 0.543* & 0.397* & 0.337* & 0.491* \\\\\n\\cline{2-11}\n & \\multirow{4}{*}{ASSD (mm) ($\\downarrow$)} & Ours & 1.357 & \\textbf{1.137} & 0.966 & 1.750 & \\textbf{ 1.320} & \\textbf{0.729}* & 2.020 & 1.333* \\\\\n & & 2DUNet & \\textbf{1.014}* & 1.141 & \\textbf{0.911} & \\textbf{1.702} & 1.433* & 0.808 & \\textbf{1.754} & \\textbf{1.240}* \\\\\n & & 3DUNet & 1.809* & 1.389 & 1.134 & 2.176 & 1.585 & 0.832 & 2.276 & 1.668 \\\\\n & & Voxel2Mesh & 3.412* & 3.147* & 4.973* & 3.638* & 4.300* & 4.326* & 5.857* & 4.287* \\\\\n\\cline{2-11}\n & \\multirow{4}{*}{HD (mm) ($\\downarrow$)} & Ours & 13.789 & 10.362 & 9.628 & \\textbf{14.467} & \\textbf{12.766} & 12.740* & \\textbf{ 25.362} & \\textbf{ 27.567} \\\\\n & & 2DUNet & \\textbf{13.582} & \\textbf{10.221} & \\textbf{6.700} & 14.788 & 16.608* & 11.410 & 28.128& 32.514 \\\\\n & & 3DUNet & 15.044 & 40.157* & 9.730 & 15.037 & 13.777 & \\textbf{10.821} & 27.467 & 48.731\\\\\n & & Voxel2Mesh & 15.526* & 13.683* & 22.146* & 16.834* & 18.390* & 19.419* & 35.322* & 37.065* \\\\\n\\cline{1-11}\n\\cline{2-11}\n\\multirow{16}{*}{MR} & \\multirow{4}{*}{Dice ($\\uparrow$)} & Ours & \\textbf{0.773} & 0.826* & \\textbf{0.913} & \\textbf{0.838}* & \\textbf{0.861} & 0.824* & 0.663* & \\textbf{0.846}* \\\\\n & & 2DUNet & 0.751 & \\textbf{0.831} & 0.880 & 0.815 & 0.852 & \\textbf{0.838}* & \\textbf{0.747} & 0.834 \\\\\n & & 3DUNet & 0.733 & 0.811 & 0.885 & 0.827* & 0.829 & 0.825 & 0.741 & 0.823 \\\\\n & & Voxel2Mesh & 0.282* & 0.498* & 0.515* & 0.599* & 0.539* & 0.241* & 0.300* & 0.483* \\\\\n\\cline{2-11}\n & \\multirow{4}{*}{Jaccard ($\\uparrow$)} & Ours & \\textbf{0.639} & 0.712* & \\textbf{0.842} & \\textbf{0.727}* & \\textbf{0.768} & 0.715* & 0.517* & \\textbf{0.737}* \\\\\n & & 2DUNet & 0.611* & \\textbf{0.720} & 0.793 & 0.702 & 0.753 & \\textbf{0.726}* & 0.608 & 0.719* \\\\\n & & 3DUNet & 0.588 & 0.695* & 0.803 & 0.718* & 0.727 & 0.718 & \\textbf{0.615} & 0.709 \\\\\n & & Voxel2Mesh & 0.170* & 0.339* & 0.367* & 0.442* & 0.388* & 0.144* & 0.187* & 0.327* \\\\\n\\cline{2-11}\n & \\multirow{4}{*}{ASSD (mm) ($\\downarrow$)} & Ours & \\textbf{2.385}& 2.166*& \\textbf{1.300} & \\textbf{2.358}* & 1.812 & 3.243 & 3.138 & 2.235* \\\\\n & & 2DUNet & 2.692* & \\textbf{1.688} & 1.603 & 3.151* & \\textbf{1.736} & \\textbf{2.920} & 2.281* & \\textbf{1.897} \\\\\n & & 3DUNet & 2.713 & 3.866 & 1.551 & 2.475 & 1.931 & 4.049 & \\textbf{2.259} & 2.120 \\\\\n & & Voxel2Mesh & 6.886* & 5.987* & 8.679* & 6.173* & 8.192* & 7.877* & 9.200* & 7.419* \\\\\n\\cline{2-11}\n & \\multirow{4}{*}{HD (mm) ($\\downarrow$)} & Ours & \\textbf{16.804} & 15.559* & \\textbf{12.197} & \\textbf{17.286}* & \\textbf{14.480} & 26.012 & \\textbf{19.927} & \\textbf{29.983} \\\\\n & & 2DUNet & 23.798 & \\textbf{14.887}* & 14.651 & 22.028* & 22.810* & \\textbf{24.237} & 22.883* & 39.724* \\\\\n & & 3DUNet & 20.136 & 32.978 & 13.643 & 23.735 & 22.351 & 31.900 & 21.363 & 43.475 \\\\\n & & Voxel2Mesh & 27.272* & 22.748* & 31.327* & 24.456* & 28.987* & 29.381 & 33.637* & 40.072* \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction", "authors": ["Fanwei Kong", "Nathan Wilson", "Shawn C. Shadden"], "url": "https://arxiv.org/abs/2102.07899v2", "attribution": "\"A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction\" by Fanwei Kong, Nathan Wilson, and Shawn C. Shadden, arXiv:2102.07899v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18455v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\nLevel & Pixel Erasure Rate & Rotation $^{\\circ}$ & Image Scale & Translations\\\\\n\\hline\nLight & $5 \\%$ & $[-5,5]$ & $[0.9,1.1]$ & $(0\\%,0\\%) $ \\\\\n\\hline\nMedium & $10 \\%$& $[-7.5,7.5]$ & $[0.8,1.2]$ & $(0\\%,0\\%) $ \\\\\n\\hline\nHeavy & $20 \\%$ & $[-10,10]$ & $[0.6,1.4]$ & $(20\\%,20\\%)$ \\\\\n\\hline\nUltimate & $40 \\%$& $[-20,20]$ & $[0.5,1.5]$ & $(40\\%,40\\%)$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior", "authors": ["Milad Sefidgaran", "Abdellatif Zaidi", "Piotr Krasnowski"], "url": "https://arxiv.org/abs/2504.18455v1", "attribution": "\"Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior\" by Milad Sefidgaran, Abdellatif Zaidi, and Piotr Krasnowski, arXiv:2504.18455v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.08430v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulated RBNS reserves (in M~CAD) using the activation patterns with development periods of 6 months (columns 3 and 4) and 1 year (columns 5 and 6).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrr}\n\\toprule\n\\multirow{2}{*}{Coverage} & \\multirow{2}{*}{True reserves} & \\multicolumn{2}{c}{Act. Patterns (6 mo.)} & \\multicolumn{2}{c}{Act. Patterns (1 y.)} \\\\\n\\cline{3-6}\n & & Mean & $\\text{VaR}_{0.95}$ & Mean & $\\text{VaR}_{0.95}$ \\\\\n\\toprule\nAccident Benefits & $>$158.90 & 146.61 & 170.46 & 162.15 & 196.64\\\\\nBodily Injury & $>$288.17 & 283.27 & 295.02 & 300.02 & 312.58 \\\\\nVehicle Damage & $>$73.63 & 70.77 & 76.43 & 70.80 & 72.95 \\\\\nLoss of Use & $>$4.20 & 4.17 & 4.24 & 3.41 & 4.01 \\\\\n\\midrule\nGlobal reserve & $>$524.91 & 504.82 & 546.15 & 536.86 & 574.58 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Individual Claims Reserving using Activation Patterns", "authors": ["Marie Michaelides", "Mathieu Pigeon", "Hélène Cossette"], "url": "https://arxiv.org/abs/2208.08430v3", "attribution": "\"Individual Claims Reserving using Activation Patterns\" by Marie Michaelides, Mathieu Pigeon, and Hélène Cossette, arXiv:2208.08430v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.08925v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Effect of distillation loss weights on performance.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc|cc}\n\\toprule\n$\\lambda_{task}$ & $\\lambda_{CE}$ & $\\lambda_{KL}$ & ACC (\\%) & F1 (\\%) \\\\\n\\midrule\n1.0 & 1.0 & 1.0 & 87.35 & 87.38 \\\\\n1.0 & 0.0 & 1.0 & 86.36 & 86.53 \\\\\n1.0 & 1.0 & 0.0 & 87.27 & 87.26 \\\\\n1.5 & 1.0 & 0.0 & 87.59 & 87.58 \\\\\n\\textbf{1.5} & \\textbf{1.0} & \\textbf{0.3} & \\textbf{87.99} & \\textbf{87.96} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "LPGNet: A Lightweight Network with Parallel Attention and Gated Fusion for Multimodal Emotion Recognition", "authors": ["Zhining He", "Yang Xiao"], "url": "https://arxiv.org/abs/2508.08925v1", "attribution": "\"LPGNet: A Lightweight Network with Parallel Attention and Gated Fusion for Multimodal Emotion Recognition\" by Zhining He and Yang Xiao, arXiv:2508.08925v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03788v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Postman headers }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n \\hline\n Key & Value \\\\ \\hline\n Content-Type & application/json \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Second Hand Price Prediction for Tesla Vehicles", "authors": ["Sayed Erfan Arefin"], "url": "https://arxiv.org/abs/2101.03788v1", "attribution": "\"Second Hand Price Prediction for Tesla Vehicles\" by Sayed Erfan Arefin, arXiv:2101.03788v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16908v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{(\\( \\ell_2 \\)) norm Localization Error (Meters)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccc}\n \\toprule\n Setup & Method & Mean & Median & 90th \\% \\\\\n \\midrule\n 2 Arrays (MLE) & GCC-PHAT & 3.62 & 1.74 & 8.48 \\\\\n & \\textbf{GCC+} & \\textbf{1.83} & \\textbf{1.25} & \\textbf{4.13} \\\\\n 3 Arrays (IRLS) & GCC-PHAT & 2.84 & 1.45 & 8.40 \\\\\n & \\textbf{GCC+} & \\textbf{1.58} & \\textbf{1.25} & \\textbf{3.32} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Localization using Angle-of-Arrival Triangulation", "authors": ["Amod K. Agrawal"], "url": "https://arxiv.org/abs/2508.16908v1", "attribution": "\"Localization using Angle-of-Arrival Triangulation\" by Amod K. Agrawal, arXiv:2508.16908v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.21709v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\n\\textbf{Years $y$} & 0 & 1 & 2 & 3 & 4 & 5\\\\ \n\\hline\n\\hline\n\\textbf{Milestone year $m$} & \\textcolor{blue}{0} & - & \\textcolor{blue}{2} & - & - & \\textcolor{blue}{5}\\\\ \n\\hline\n\\textbf{Milestone year weight $W_{m}^{I}$} & \\textcolor{blue}{2} & - & \\textcolor{blue}{3} & - & - & \\textcolor{blue}{1} \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Milestone years}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Discounting Approaches in Multi-Year Investment Modelling for Energy Systems", "authors": ["Ni Wang", "Diego A. Tejada-Arango"], "url": "https://arxiv.org/abs/2504.21709v1", "attribution": "\"Discounting Approaches in Multi-Year Investment Modelling for Energy Systems\" by Ni Wang and Diego A. Tejada-Arango, arXiv:2504.21709v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table32.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|r|c|c|r|r|r}\n \\hline\n \\multicolumn{9}{c}{PH algorithm using 128 scenario paths for the case of adjustable carry-overs (constant $\\rho$)} \\\\\n \\hline\n \\multicolumn{9}{c}{Instances with only end item demands} \\\\\n \\hline\n Distribution & Util. & $|\\Omega|$ & $|\\Phi|$ & Converged & Gap (\\%) & Resolve (s) & Runtime (s) & Iterations \\\\\n \\hline\n \\multirow{10}{*}{Non stationary} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & Yes & 0.0 & & 26.0 & 4\\\\\n & & 3 & 2,187 & Yes & 0.0 & & 27.3 & 4\\\\\n & & 4 & 16,384 & Yes & 0.0 & & 29.3 & 4\\\\\n & & 5 & 78,125 & Yes & 0.1 & & 24.2 & 2\\\\\n & & 6 & 279,936 & & & & &\\\\\\cline{2-9}\n &\\multirow{5}{*}{90\\%} \n & 2 & 128 & Yes & 0.0 & & 26.4 & 4\\\\\n & & 3 & 2,187 & Yes & 1.1 & & 49.7 & 7\\\\\n & & 4 & 16,384 & Yes & 0.0 & & 29.3 & 4\\\\\n & & 5 & 78,125 & Yes & - & &41.1 & 4\\\\\n & & 6 & 279,936 & & & & &\\\\\n \\hline\n \\multirow{10}{*}{Uniform} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & Yes & 0.0 & 0.2 & 11.9 & 2\\\\\n & & 3 & 2,187 & Yes & 0.0 & 3.7& 12.4 & 2 \\\\\n & & 4 & 16,384 & Yes & 0.0 & 36.3 & 21.4 & 3 \\\\\n & & 5 & 78,125 & Yes & -20.2* & 556.1 & 25.3 & 4\\\\\n & & 6 & 279,936 & & & & &\\\\\\cline{2-9}\n & \\multirow{5}{*}{90\\%}\n & 2 & 128 & Yes & 0.0 & 0.2 & 26.7 & 4\\\\\n & & 3 & 2,187 & Yes & 0.0 & 2.5 & 20.0 & 3 \\\\\n & & 4 & 16,384 & Yes & 0.0 & 26.6 & 28.8 & 4 \\\\\n & & 5 & 78,125 & Yes & -$\\infty$* & 900.7 & 41.9 & 4 \\\\\n & & 6 & 279,936 & & & & &\\\\\n \\hline\n \\multirow{10}{*}{Lumpy} &\n \\multirow{5}{*}{50\\%} \n & 2 & 128 & Yes & 3.0 & 0.1 & 157.5 & 17 \\\\\n & & 3 & 2,187 & Yes & 3.9 & 5.7 & 345.1 & 41 \\\\\n & & 4 & 16,384 & Yes & 12.5* & 134.8 & 579.6 & 69 \\\\\n & & 5 & 78,125 & Yes & -88.6* & 767.2 & 613.1 & 64\\\\\n & & 6 & 279,936 & & & & & \\\\\\cline{2-9}\n & \\multirow{5}{*}{90\\%} \n & 2 & 128 & Yes & 1.3 & 0.2 & 370.4 & 25 \\\\\n & & 3 & 2,187 & Yes & 2.7 & 3,525.2 & 544.1 & 44\\\\\n & & 4 & 16,384 & Yes & -12.4* & 49.0 & 452.6 & 38\\\\\n & & 5 & 78,125 & Yes & -$\\infty$* & 10,8000 & 559.4 & 54\\\\\n & & 6 & 279,936 & & & & & \\\\\n \\hline\n \\multirow{10}{*}{Slow moving} &\n \\multirow{5}{*}{50\\%}\n & 2 & 128 & No & Infeasible & & 741.4 & 100\\\\\n & & 3 & 2,187 & Yes & 2.7 & & 494.4 & 65 \\\\\n & & 4 & 16,384 & Yes & 2.7* & & 554.6 & 72 \\\\\n & & 5 & 78,125 & Yes & - & &643.7 & 76 \\\\\n & & 6 & 279,936 & & & & &\\\\\\cline{2-9}\n & \\multirow{5}{*}{90\\%}\n & 2 & 128 & Yes & 4.7 & & 603.4 & 73 \\\\\n & & 3 & 2,187 & Yes & 6.6 & & 738.6 & 95 \\\\\n & & 4 & 16,384 & Yes & 9.3* & & 531.5 & 64\\\\\n & & 5 & 78,125 & Yes & - & &781.5 & 86 \\\\\n & & 6 & 279,936 & & & & &\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Runtimes and gaps for the progressive hedging algorithm using 128 scenario paths on the instances including component demands for different distributions and numbers of scenarios per branch with a time limit of 3 hours (10,800 s) and maximum of 100 iterations}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.08761v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary statistics - Dry bulb temperature}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\t\t\\midrule\n\t\t\\midrule \n & Mean & SD & Min & Max \\\\\n \\midrule\n Dry bulb temperature & 29.87& 4.52& 11.42& 42.62\\\\\n & & & & \\\\\n\\# Days \\textless 23.5°C& 3.87& 8.18& 0& 30\\\\\n\\# Days 23.5°C-25°C& 1.73& 3.43& 0& 24\\\\\n\\# Days 25°C-26.5°C& 2.20& 3.71& 0& 30\\\\\n\\# Days 26.5°C-28°C& 2.87& 3.87& 0& 28\\\\\n\\# Days 28°C-29.5°C& 3.74& 5.05& 0& 23\\\\\n\\# Days 29.5°C-31°C& 3.43& 4.43& 0& 25\\\\\n\\# Days 31°C-32.5°C& 3.11& 4.31& 0& 21\\\\\n\\# Days 32.5°C-34°C& 2.71& 3.77& 0& 19\\\\\n\\# Days 34°C-35.5°C& 2.62& 4.11& 0& 19\\\\\n\\# Days 35.5°C-37°C& 2.09& 3.79& 0& 19\\\\\n\\# Days $\\geq$ 37°C& 1.62& 4.12& 0& 28\\\\\n & & & & \\\\\nHeat wave ( days $\\geq$ 37°)& 0.16& 0.36& 0& 1\\\\\nHeat wave ($\\geq$ 3 days $\\geq$ 37°)& 0.14& 0.34& 0& 1\\\\\n\\# Consecutive days $\\geq$ 37°& 1.11& 3.10& 0& 26\\\\\n & & & & \\\\\n\\# Days \\textless 25.5°C& 6.28& 10.25& 0& 30\\\\\n\\# Days 25.5.5°C-27°C& 2.39& 3.77& 0& 30\\\\\n\\# Days 27°C-28.5°C& 3.15& 4.17& 0& 28\\\\\n\\# Days 28.5°C-30°C& 3.86& 5.18& 0& 26\\\\\n\\# Days 30°C-31.5°C& 3.19& 4.03& 0& 25\\\\\n\\# Days 31.5°C-33°C& 3.06& 4.09& 0& 20\\\\\n\\# Days 33°C-34.5°C& 2.67& 3.96& 0& 20\\\\\n\\# Days 34.5°C-36°C& 2.48& 4.01& 0& 19\\\\\n\\# Days 36°C-37.5°C& 1.82& 3.61& 0& 20\\\\\n\\# Days 37.5°C-39°C& 0.78& 2.07& 0& 15\\\\\n\\# Days $\\geq$ 39°C& 0.32& 1.55& 0& 20\\\\\n & & & & \\\\\nHeat wave ($\\geq$ 2 days $\\geq$ 39°)& 0.05& 0.21& 0& 1\\\\\nHeat wave ($\\geq$ 3 days $\\geq$ 39°)& 0.04& 0.19& 0& 1\\\\\n\\# Consecutive days $\\geq$ 39°& 0.23& 1.04& 0& 13\\\\\n\\midrule\nObservations & 30,300& & & \\\\\n\\midrule\n\\midrule\n\t\\multicolumn{5}{p{8cm}}{} \n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Beyond the heat: The mental health toll of temperature and humidity in India", "authors": ["Manuela Fritz"], "url": "https://arxiv.org/abs/2503.08761v1", "attribution": "\"Beyond the heat: The mental health toll of temperature and humidity in India\" by Manuela Fritz, arXiv:2503.08761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.01164v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{PI sets for correlation.}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n \\hline\n & $\\textup{LL}_{\\textup{ridge}}$ & $\\textup{LL}_{\\textup{knn}}$ & $\\eta = 0$ & $\\eta = 10$ & $\\eta = 20$ & $\\eta = 30$ & $\\eta = 40$ & $\\eta = 50$ \\\\\n \\hline\n \\textup{upper bound} & 1.005 & 1.187 & 0.997 & 0.932 & 0.911 & 0.900 & 0.895 & 0.887\\\\\n \\hline\n \\textup{lower bound} & -0.707 & -1.036 & -0.997 & -0.646 & -0.623 & -0.607 & -0.597 & -0.592\\\\\n \\hline\n \\textup{PI interval length} & 1.712 & 2.223 & 1.994 & 1.578 & 1.534 & 1.507 & 1.492 & 1.479\\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Tightening Causal Bounds via Covariate-Aware Optimal Transport", "authors": ["Sirui Lin", "Zijun Gao", "Jose Blanchet", "Peter Glynn"], "url": "https://arxiv.org/abs/2502.01164v1", "attribution": "\"Tightening Causal Bounds via Covariate-Aware Optimal Transport\" by Sirui Lin, Zijun Gao, Jose Blanchet, and Peter Glynn, arXiv:2502.01164v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07363v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study on class token position and Mamba type. Mid-position tokens and bidirectional Mamba configurations (BiMamba-Bi-Bi) provide the best accuracy, highlighting the importance of token placement and directional flow.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\toprule\n \\textbf{Ablation Objective} & \\textbf{Type} & \\textbf{V1-12} \\\\\n \\midrule\n \\multirow{3}{*}{class token position} \n & Mid & \\cellcolor{blue!20}{\\textbf{98.01}} \\\\\n & Head & 97.75 \\\\\n & End & 97.92 \\\\\n \\midrule\n \\multirow{3}{*}{mamba type}\n & BiMamba-Bi-Bi & \\cellcolor{blue!20}{\\textbf{98.01}} \\\\\n & BiMamba-Fo-Bi & 97.69\\\\\n & Mamba-Fo-Fo & 78.29\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Keyword Mamba: Spoken Keyword Spotting with State Space Models", "authors": ["Hanyu Ding", "Wenlong Dong", "Qirong Mao"], "url": "https://arxiv.org/abs/2508.07363v1", "attribution": "\"Keyword Mamba: Spoken Keyword Spotting with State Space Models\" by Hanyu Ding, Wenlong Dong, and Qirong Mao, arXiv:2508.07363v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.04216v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ The IKLD between original and synthetic data, averaged over 250 Monte Carlo runs and standardised between 0 and 1. Higher values indicate similar datasets in terms of underlying distribution.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n \\textbf{Generator} & \\textbf{Debiased} & $n=50$ & $n=160$ & $n=500$ & $n=1600$ & $n=5000$ \\\\\n \\midrule\n \\textbf{CTGAN (SDV)} & \\textbf{no} & 0.849 & 0.924 & 0.939 & 0.963 & 0.980 \\\\\n \\textbf{CTGAN (SDV)} & \\textbf{yes} & 0.860 & 0.935 & 0.957 & 0.966 & 0.981 \\\\\n \\textbf{CTGAN (Synthcity)} & \\textbf{no} & 0.838 & 0.894 & 0.867 & 0.890 & 0.929 \\\\\n \\textbf{CTGAN (Synthcity)} & \\textbf{yes} & 0.849 & 0.906 & 0.885 & 0.897 & 0.933 \\\\\n \\textbf{TVAE (SDV)} & \\textbf{no} & 0.704 & 0.819 & 0.908 & 0.985 & 0.980 \\\\\n \\textbf{TVAE (SDV)} & \\textbf{yes} & 0.734 & 0.832 & 0.913 & 0.987 & 0.983 \\\\\n \\textbf{TVAE (Synthcity)} & \\textbf{no} & 0.791 & 0.830 & 0.887 & 0.929 & 0.977 \\\\\n \\textbf{TVAE (SDV)} & \\textbf{yes} & 0.813 & 0.860 & 0.913 & 0.943 & 0.980 \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Debiasing Synthetic Data Generated by Deep Generative Models", "authors": ["Alexander Decruyenaere", "Heidelinde Dehaene", "Paloma Rabaey", "Christiaan Polet", "Johan Decruyenaere", "Thomas Demeester", "Stijn Vansteelandt"], "url": "https://arxiv.org/abs/2411.04216v2", "attribution": "\"Debiasing Synthetic Data Generated by Deep Generative Models\" by Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet, Johan Decruyenaere, Thomas Demeester, and Stijn Vansteelandt, arXiv:2411.04216v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.14130v2_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n(n,m)&$ (1000,10)$& $(3000,20)$ & $(5000,20)$\\\\ \\hline\n BM& 0.94 & 1.00 & 1.00\\\\\n M1& 0.22 & 0.02 & 0.00 \\\\\n M2& 1.00 & 1.00 & 1.00\\\\\n M3& 1.00 & 1.00 & 0.05\\\\\n M4& 1.00 & 1.00 & 1.00\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\scriptsize Empirical Bayes (MMLE) squared exponential Gaussian process prior. Proportion of runs when the $L_2$-credible ball contains $f_0$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Adaptation using spatially distributed Gaussian Processes", "authors": ["Botond Szabo", "Amine Hadji", "Aad van der Vaart"], "url": "https://arxiv.org/abs/2312.14130v2", "attribution": "\"Adaptation using spatially distributed Gaussian Processes\" by Botond Szabo, Amine Hadji, and Aad van der Vaart, arXiv:2312.14130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12295v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\nConfig & Setting \\\\\n\\hline\n\\hline\nEmbedding size & 256 \\\\\nNumber of layers & 3 \\\\\nNumber of heads & 16 \\\\\nMLPs & False \\\\\nLayerNorms & False \\\\\n\\hline\nInput dim & 5 \\\\\nSequence length & 20 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Linear attention architecture details.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Towards Learning High-Precision Least Squares Algorithms with Sequence Models", "authors": ["Jerry Liu", "Jessica Grogan", "Owen Dugan", "Ashish Rao", "Simran Arora", "Atri Rudra", "Christopher Ré"], "url": "https://arxiv.org/abs/2503.12295v1", "attribution": "\"Towards Learning High-Precision Least Squares Algorithms with Sequence Models\" by Jerry Liu, Jessica Grogan, Owen Dugan, Ashish Rao, Simran Arora, Atri Rudra, and Christopher Ré, arXiv:2503.12295v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.13521v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|r|}\n\\hline\n\\textbf{Aggregation} & \\textbf{MAPE, \\%} \\\\\\hline\nstacked & 0.060703 \\\\\\hline\nmerged & 0.361174\\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Aggregation method average performance.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deep Learning Models Meet Financial Data Modalities", "authors": ["Kasymkhan Khubiev", "Mikhail Semenov"], "url": "https://arxiv.org/abs/2504.13521v2", "attribution": "\"Deep Learning Models Meet Financial Data Modalities\" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2504.13521v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.05013v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|c|ccc|c}\n Dataset & Pool & bps & \\multicolumn{3}{c|}{Time Range} & Spacing \\\\\n & & && Start & End & \\\\ \\hline\n \\multirow{2}{*}{ARB5} & \\multirow{2}{*}{ARB--USDC} & \\multirow{2}{*}{5}\n & Date--time: & Aug 23, 2023 & Nov 26, 2024 & 8.39 h \\\\\n & & \n & Block Number: &\\texttt{124163200} & \\texttt{278646400} & \\texttt{115200} \\\\ \\hline\n \\multirow{2}{*}{ETH5, ETH30} & \\multirow{2}{*}{ETH--USDC} & \\multirow{2}{*}{5, 30}\n & Date-time: & Aug 1, 2021 & Nov 26, 2024 & 8.26 h \\\\\n & &\n & Block Number: &\\texttt{12940529} & \\texttt{21274529} & \\texttt{2400} \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Full dataset ranges. Dates are rounded to calendar days (UTC). The Spacing column reports time spacing (hours) on the date row and block step on the block row. ETH5 and ETH30 share the same blocks and times. Spacing in hours is computed as the average hourly time between the clock times at the exact block-number intervals.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Dynamics of Liquidity Surfaces in Uniswap v3", "authors": ["Jimmy Risk", "Shen-Ning Tung", "Tai-Ho Wang"], "url": "https://arxiv.org/abs/2509.05013v1", "attribution": "\"Dynamics of Liquidity Surfaces in Uniswap v3\" by Jimmy Risk, Shen-Ning Tung, and Tai-Ho Wang, arXiv:2509.05013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.05035v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Clip version & CC & BSS & CSS \\\\\n \\midrule\n Inter (low) & $0.78$ & $0.38$ & $0.51$ \\\\ \n Inter (medium) & $0.85$ & $0.49$ & $0.67$ \\\\\n Inter (high) & $0.89$ & $0.52$ & $0.74$ \\\\\n Intra () & $0.90$ & $0.48$ & $0.72$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Semantic conservation comparisons for Clip-based compression schemes. Different parametrizations of our inter-item model are compared to their intra version .}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SMIC: Semantic Multi-Item Compression based on CLIP dictionary", "authors": ["Tom Bachard", "Thomas Maugey"], "url": "https://arxiv.org/abs/2412.05035v1", "attribution": "\"SMIC: Semantic Multi-Item Compression based on CLIP dictionary\" by Tom Bachard and Thomas Maugey, arXiv:2412.05035v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.00101v1_tex_table22.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\hline\\hline\n &Min & $q_{.25}$& Median & Mean & $q_{.75}$ & Max \\\\\n\\hline\n Relative differences&0.65& 0.86& 1.00& 1.03& 1.15& 1.54\n\\\\\n Absolute differences&-1,485,636& -532,913& 4,910& 20,462& 481,347& 1,459,625\\\\\n \\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Summary statistics for the relative and absolute differences between the predicted and observed reserves in the calibration set. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "mCube: Multinomial Micro-level reserving Model", "authors": ["Emmanuel Jordy Menvouta", "Jolien Ponnet", "Robin Van Oirbeek", "Tim Verdonck"], "url": "https://arxiv.org/abs/2212.00101v1", "attribution": "\"mCube: Multinomial Micro-level reserving Model\" by Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek, and Tim Verdonck, arXiv:2212.00101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2308.14705v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{ IND Generalization}: Top-1 accuracy, ECE and NLL averaged over in-distribution on test samples of the \\textbf{ImageNet} dataset over three random seeds. The best score for each metric is shown in \\textbf{bold}, and the second-best is \\underline{underlined}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c}\n \\toprule\n Method & Top-1 Acc (\\%) ($\\uparrow$) & ECE ($\\downarrow$) & \n NLL ($\\downarrow$) \\\\\n \\midrule\n Baseline & 73.8 $\\pm$ 0.3 & 0.013 $\\pm$ 0.015 & 1.05 $\\pm$ 0.01 \\\\\n SSL-Ensemble & \\textbf{75.1 $\\pm$ 0.1} & \\underline{0.014 $\\pm$ 0.000} & \\textbf{0.98 $\\pm$ 0.01} \\\\ \n Our method & \\underline{74.0 $\\pm$ 0.0} & \\textbf{0.010 $\\pm$ 0.000} & \\underline{1.03 $\\pm$ 0.01} \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning", "authors": ["Amirhossein Vahidi", "Lisa Wimmer", "Hüseyin Anil Gündüz", "Bernd Bischl", "Eyke Hüllermeier", "Mina Rezaei"], "url": "https://arxiv.org/abs/2308.14705v2", "attribution": "\"Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning\" by Amirhossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz, Bernd Bischl, Eyke Hüllermeier, and Mina Rezaei, arXiv:2308.14705v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.03328v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{diagbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n \\diagbox{~}{$r_{\\delta,t}$}{~~~~~~} & CT & DT \\\\\n \\hline\n non-Contractive & Theorem & Theorem \\\\\n \\hline\n Contractive & Theorem & Theorem \\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Safety Verification of Nonlinear Stochastic Systems via Probabilistic Tube", "authors": ["Zishun Liu", "Saber Jafarpour", "Yongxin Chen"], "url": "https://arxiv.org/abs/2503.03328v1", "attribution": "\"Safety Verification of Nonlinear Stochastic Systems via Probabilistic Tube\" by Zishun Liu, Saber Jafarpour, and Yongxin Chen, arXiv:2503.03328v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.11471v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\toprule\n & Point mass & Mean & $\\ell_{Z|_{\\{Z<1\\}}}$ & $\\ell_Z$ & AIC \\\\\n \\midrule\n Empirical density (Blue) & 0.034 & 0.339 & - & - & - \\\\\n MLE of the standard problem (Green) & 0.040 & 0.380 &26 907 &8 164 & -16 323 \\\\\n MLE of the extended problem (Red) & 0.034 & 0.362 &30 453 &11 783 & -23 558 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Sine logarithmic linked exposure example: results.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modeling lower-truncated and right-censored insurance claims with an extension of the MBBEFD class", "authors": ["Selim Gatti", "Mario V. Wüthrich"], "url": "https://arxiv.org/abs/2310.11471v2", "attribution": "\"Modeling lower-truncated and right-censored insurance claims with an extension of the MBBEFD class\" by Selim Gatti and Mario V. Wüthrich, arXiv:2310.11471v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2311.14482v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|c|c}\n \\toprule\n \\multirow{2}{*}{Click Generation Strategy (Training)} & \\multicolumn{2}{c}{Inference Strategy (Validation)} \\\\ \\cline{2-3}\n {} & Global & Local Patch-wise \\\\ \\hline\n Non-corrective (10 clicks) & $47.00\\%$ & $47.00\\%$ \\\\\n Corrective: $N_\\text{max}=10$ (DeepEdit ) & $84.90\\%$ & $82.02\\%$ \\\\\n Corrective: $N_\\text{max}=10$, $p=50\\%$ & $81.07\\%$ & $81.23\\%$ \\\\\n Corrective: $N_\\text{max}=10$, $\\text{Dice}_\\text{max}=90\\%$ & \\textbf{85.34}$\\%$ & \\textbf{85.50$\\%$} \\\\\n Corrective: $N_\\text{max}=10$, $p=50\\%$, $\\text{Dice}_\\text{max}=90\\%$ & $82.54\\%$ & $82.93\\%$ \\\\\n \\bottomrule\n \\end{tabular}\n\\caption{Results (Dice score) for \\textsc{SW-FastEdit} when using different click generation strategies during training and various inference strategies during validation. During validation, we always simulate exactly 10 clicks for each sample.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images", "authors": ["Matthias Hadlich", "Zdravko Marinov", "Moon Kim", "Enrico Nasca", "Jens Kleesiek", "Rainer Stiefelhagen"], "url": "https://arxiv.org/abs/2311.14482v1", "attribution": "\"Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images\" by Matthias Hadlich, Zdravko Marinov, Moon Kim, Enrico Nasca, Jens Kleesiek, and Rainer Stiefelhagen, arXiv:2311.14482v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12525v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The performance of different network components.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n\tComponents & NSS & CC & AUC \\\\ \\hline\n S-SPCNN & 0.6301 & 0.4058 & 0.5471 \\\\ \\hline\n S-CNN & 0.5819 & 0.3837 & 0.5173 \\\\ \\hline\n T-SPCNN & 0.782 & 0.4384 & 0.5762 \\\\ \\hline\n T-CNN & 0.617 & 0.4137 & 0.5335 \\\\ \\hline\n ST-SPCNN- & 0.9653 & 0.5587 & 0.6069 \\\\ \\hline\n ST-CNN & 0.8487 & 0.4634 & 0.5589 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback", "authors": ["Jie Li", "Ling Han", "Cong Zhang", "Qiyue Li", "Zhi Liu"], "url": "https://arxiv.org/abs/2201.12525v1", "attribution": "\"Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback\" by Jie Li, Ling Han, Cong Zhang, Qiyue Li, and Zhi Liu, arXiv:2201.12525v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00884v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|r|r|r|r|r|r|r|r|r|r|r}\n & Agr & Ast & Bio & Che & CS & ES & Eng & MS & Mat & Med & Overall \\\\ \\hline\n$CoNLL$ P & 65.5 & 57.5 & 53.4 & 60.8 & 82.4 & 62.1 & 60.7 & 53.1 & 53.5 & 66.2 & 62.0 \\\\\n$CoNLL$ R & 60.9 & 66.4 & 63.5 & 53.6 & 69.1 & 55.8 & 59.9 & 54.1 & 61.3 & 59.2 & 61.0 \\\\\n$CoNLL$ F1 & 62.8 & 61.1 & 57.5 & 56.3 & 74.9 & 57.5 & 59.8 & 52.1 & 55.7 & 62.1 & 61.4\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Coreference Resolution in Research Papers from Multiple Domains", "authors": ["Arthur Brack", "Daniel Uwe Müller", "Anett Hoppe", "Ralph Ewerth"], "url": "https://arxiv.org/abs/2101.00884v1", "attribution": "\"Coreference Resolution in Research Papers from Multiple Domains\" by Arthur Brack, Daniel Uwe Müller, Anett Hoppe, and Ralph Ewerth, arXiv:2101.00884v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00104v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameters of QMIX and VDN}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llc}\n \\toprule\n Method & Name & Value \\\\\n \\midrule\n QMIX \\& VDN & learning rate & $5 \\times 10^{-4}$\\\\\n & RMSprop $\\alpha$ & 0.99 \\\\\n & replay buffer size & 5000 episodes \\\\\n & target network update interval & 200 episodes \\\\\n & $\\gamma$ & 0.99 \\\\\n & double DQN target & True \\\\\n & initial $\\epsilon$ & 1 \\\\\n & final $\\epsilon$ & 0.05 \\\\\n & $\\epsilon$ anneal period & 50000 steps \\\\\n & $\\epsilon$ anneal rule & linear \\\\\n \\midrule\n QMIX & mixing network hidden layers & 1 \\\\\n & mixing network hidden layer units & 32 \\\\\n & mixing network non-linearity & ELU \\\\\n & hypernetwork hidden layers & 2 \\\\\n & hypernetwork hidden layer units & 64 \\\\\n & hypernetwork non-linearity & ReLU \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Generalization in Cooperative Multi-Agent Systems", "authors": ["Anuj Mahajan", "Mikayel Samvelyan", "Tarun Gupta", "Benjamin Ellis", "Mingfei Sun", "Tim Rocktäschel", "Shimon Whiteson"], "url": "https://arxiv.org/abs/2202.00104v2", "attribution": "\"Generalization in Cooperative Multi-Agent Systems\" by Anuj Mahajan, Mikayel Samvelyan, Tarun Gupta, Benjamin Ellis, Mingfei Sun, Tim Rocktäschel, and Shimon Whiteson, arXiv:2202.00104v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.14598v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{diagbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n\\toprule [1.0pt]\n\\hline\n\\diagbox{$i$ (trustor)}{$j$ (trustee)} & R & \\quad B\\\\\n\\midrule [0.5pt]\nT & ${1+(g\\omega-1)x, (1-\\omega)gx}$ & \\quad ${1-x, gx}$ \\\\\nN & ${1, 0}$ & \\quad ${1, 0}$ \\\\\n\\bottomrule[1.0pt]\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The payoff matrix in the trust game. Within each item, the first payoff is for the trustor (row player) and the second is for the trustee (column player).}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Decoding trust: A reinforcement learning perspective", "authors": ["Guozhong Zheng", "Jiqiang Zhang", "Jing Zhang", "Weiran Cai", "Li Chen"], "url": "https://arxiv.org/abs/2309.14598v2", "attribution": "\"Decoding trust: A reinforcement learning perspective\" by Guozhong Zheng, Jiqiang Zhang, Jing Zhang, Weiran Cai, and Li Chen, arXiv:2309.14598v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.07482v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n \\bfseries Dataset & \\bfseries Result\\\\\n \\midrule\n Data1 & 0.12345\\\\\n Data2 & 0.67890\\\\\n Data3 & 0.54321\\\\\n Data4 & 0.09876\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Towards Trainable Saliency Maps in Medical Imaging", "authors": ["Mehak Aggarwal", "Nishanth Arun", "Sharut Gupta", "Ashwin Vaswani", "Bryan Chen", "Matthew Li", "Ken Chang", "Jay Patel", "Katherine Hoebel", "Mishka Gidwani", "Jayashree Kalpathy-Cramer", "Praveer Singh"], "url": "https://arxiv.org/abs/2011.07482v1", "attribution": "\"Towards Trainable Saliency Maps in Medical Imaging\" by Mehak Aggarwal, Nishanth Arun, Sharut Gupta, Ashwin Vaswani, Bryan Chen, Matthew Li, Ken Chang, Jay Patel, Katherine Hoebel, Mishka Gidwani, Jayashree Kalpathy-Cramer, and Praveer Singh, arXiv:2011.07482v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17626v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n \\toprule\n $\\nu$ & P (GMRES) & $R^TR$ (CGNE) & $P^TP$ (CGNE) \\\\\n \\midrule\n $1 \\cdot 10^{-2}$ &1435& 2118 & 2117 \\\\\n $5\\cdot 10^{-3}$ & 699& 1073 & 1056 \\\\\n $1\\cdot 10^{-3}$ & 136& 218 & 217\\\\\n $5\\cdot 10^{-4}$ & 69 & 112 &113 \\\\\n $1\\cdot 10^{-4}$ & 16 & 27 &27 \\\\\n $5\\cdot 10^{-5}$ & 2 & 7 &7 \\\\\n $1\\cdot 10^{-5}$ & 5 & 8 &8 \\\\\n $5\\cdot 10^{-6}$ & 4 & 7 &7 \\\\\n $1\\cdot 10^{-6}$ & 3 & 4 &4 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Preconditioned normal equations for solving discretised partial differential equations", "authors": ["Lorenzo Lazzarino", "Yuji Nakatsukasa", "Umberto Zerbinati"], "url": "https://arxiv.org/abs/2502.17626v2", "attribution": "\"Preconditioned normal equations for solving discretised partial differential equations\" by Lorenzo Lazzarino, Yuji Nakatsukasa, and Umberto Zerbinati, arXiv:2502.17626v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06840v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Memory savings for offload strategies that minimizes communication volume compared to the baseline.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c}\n FWD-BWD&p16&g16&Update&Memory&Reduction \\\\\n \\hline\n gpu&gpu&gpu&gpu&16M&1x (baseline)\\\\\n gpu&gpu&cpu&gpu&14M&1.14x\\\\\n gpu&gpu&gpu&cpu&4M&4x\\\\\n gpu&gpu&cpu&cpu&4M&8x\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "ZeRO-Offload: Democratizing Billion-Scale Model Training", "authors": ["Jie Ren", "Samyam Rajbhandari", "Reza Yazdani Aminabadi", "Olatunji Ruwase", "Shuangyan Yang", "Minjia Zhang", "Dong Li", "Yuxiong He"], "url": "https://arxiv.org/abs/2101.06840v1", "attribution": "\"ZeRO-Offload: Democratizing Billion-Scale Model Training\" by Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, and Yuxiong He, arXiv:2101.06840v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.00745v5_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Treatment distribution}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\t\t\t\\hline\\hline\n\t\t\ttreatment & observations \\\\\n\t\t\t\\hline\n\t\t\t randomized out of JC & 1698 \\\\\n\t\t controls (no training) & 200 \\\\\n\t\t\tacademic training & 830 \\\\\n\t\t\t vocational training & 843 \\\\\n\t\t\t\\hline\t\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Double machine learning for sample selection models", "authors": ["Michela Bia", "Martin Huber", "Lukáš Lafférs"], "url": "https://arxiv.org/abs/2012.00745v5", "attribution": "\"Double machine learning for sample selection models\" by Michela Bia, Martin Huber, and Lukáš Lafférs, arXiv:2012.00745v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2102.00291v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Development set results with a ratio of the leading characters correctly recognized.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\t\t\\toprule\n\t\tModel & Ratio & CER & SER \\\\\n\t\t\\midrule\n\t\t\\multirow{3}{*}[-1pt]{Conv1d resnet 3} & 0 & 54.8 & 49.6 \\\\\n\t\t& 1/3 & 61.9 & 55.3\\\\\n\t\t& 1/2 & 57.3 & 51.4\\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Speech Recognition by Simply Fine-tuning BERT", "authors": ["Wen-Chin Huang", "Chia-Hua Wu", "Shang-Bao Luo", "Kuan-Yu Chen", "Hsin-Min Wang", "Tomoki Toda"], "url": "https://arxiv.org/abs/2102.00291v1", "attribution": "\"Speech Recognition by Simply Fine-tuning BERT\" by Wen-Chin Huang, Chia-Hua Wu, Shang-Bao Luo, Kuan-Yu Chen, Hsin-Min Wang, and Tomoki Toda, arXiv:2102.00291v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00095v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c}\n\\hline\n & \\textbf{Corruption Types} \\\\ \\hline\n\\textbf{Noise} & Gaussian Noise, Impulse noise, Shot noise \\\\\n\\textbf{Blur} & Motion blur, Zoom blur, Defocus blur, Glass blur \\\\\n\\textbf{Weather} & Snow, Frost, Fog, Brightness \\\\\n\\textbf{Digital} & Contrast, Jpeg compression, Pixelate, Elastic transform \\\\ \\hline\n\\end{tabular}\n\\caption{Detail of four corruption groups with 15 corruption types}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "GDA: Generalized Diffusion for Robust Test-time Adaptation", "authors": ["Yun-Yun Tsai", "Fu-Chen Chen", "Albert Y. C. Chen", "Junfeng Yang", "Che-Chun Su", "Min Sun", "Cheng-Hao Kuo"], "url": "https://arxiv.org/abs/2404.00095v2", "attribution": "\"GDA: Generalized Diffusion for Robust Test-time Adaptation\" by Yun-Yun Tsai, Fu-Chen Chen, Albert Y. C. Chen, Junfeng Yang, Che-Chun Su, Min Sun, and Cheng-Hao Kuo, arXiv:2404.00095v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.10221v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\nOld Scheme&New Scheme&Ratio\\\\\n\\hline\n139.85&121.54&1.151\\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\sf Example 3: The CPU times (in seconds) consumed by the studied schemes.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "New More Efficient A-WENO Schemes", "authors": ["Shaoshuai Chu", "Alexander Kurganov", "Ruixiao Xin"], "url": "https://arxiv.org/abs/2503.10221v2", "attribution": "\"New More Efficient A-WENO Schemes\" by Shaoshuai Chu, Alexander Kurganov, and Ruixiao Xin, arXiv:2503.10221v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.05764v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n Filter & CatBoost & DeepLOB & CNN+LSTM & CNN+XGB & XGBoost & Logistic Reg. \\\\\n \\midrule\n \\multicolumn{7}{c}{\\textbf{Next 100 ms, 5‐level LOB}} \\\\\n Raw & 0.3922 & 0.4080 & 0.3987 & 0.4029 & 0.3939 & 0.3777 \\\\\n Kalman & 0.3651 & 0.3838 & 0.3594 & 0.3609 & 0.3875 & 0.3732 \\\\\n Savitzky–Golay & 0.4135 & \\textbf{0.4281} & 0.4207 & 0.4179 & 0.4173 & 0.4101 \\\\\n \\midrule\n \\multicolumn{7}{c}{\\textbf{Next 500 ms, 40‐level LOB}} \\\\\n Raw & 0.4066 & 0.4286 & 0.4160 & 0.4159 & 0.4457 & 0.4356 \\\\\n Kalman & 0.3216 & 0.3044 & 0.3255 & 0.3211 & 0.4114 & 0.2993 \\\\\n Savitzky–Golay & 0.5189 & 0.5237 & 0.5252 & 0.5282 & 0.5393 & \\textbf{0.5434} \\\\\n \\midrule\n \\multicolumn{7}{c}{\\textbf{Next 1000 ms, 40‐level LOB}} \\\\\n Raw & 0.4046 & 0.4099 & 0.4142 & 0.4118 & 0.4357 & 0.4901 \\\\\n Kalman & 0.3191 & 0.3618 & 0.3603 & 0.3397 & 0.4084 & 0.3421 \\\\\n Savitzky–Golay & 0.4611 & 0.4762 & 0.4936 & 0.4604 & 0.4999 & \\textbf{0.5382} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Ternary Classification Across Horizons and Filters (\\(T=1\\))}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layer", "authors": ["Haochuan Wang"], "url": "https://arxiv.org/abs/2506.05764v2", "attribution": "\"Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layer\" by Haochuan Wang, arXiv:2506.05764v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00758v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{rotating}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\usepackage{graphicx}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{llrrr}\n\\toprule\n& & \\multicolumn{3}{c}{Token corruption [\\%]} \\\\\n\\cmidrule{3-5}\n& & $10$ & $15$ & $20$ \\\\\n\\midrule\n\\multirow{4}{*}{\\rotatebox[origin=c]{0}{BERT}}\n& Base & $.721$ & $.703$ & $.671$ \\\\\n& Jacobian & $.729$ & $.710$ & $.680$ \\\\\n& H\\\"{o}lder & $.734$ & $.717$ & $.684$ \\\\\n& \\cellcolor{lightgray} \\textsc{JacHess} & $\\mathbf{.751}$ & $\\mathbf{.742}$ & $\\mathbf{.729}$ \\\\\n\\midrule\n\\multirow{4}{*}{\\rotatebox[origin=c]{0}{OPT-125m}}\n& Base & $.681$ & $.654$ & $.613$ \\\\\n& Jacobian & $.694$ & $.667$ & $.642$ \\\\\n& H\\\"{o}lder & $.693$ & $.679$ & $.648$ \\\\\n& \\cellcolor{lightgray} \\textsc{JacHess} & $\\mathbf{.731}$ & $\\mathbf{.723}$ & $\\mathbf{.704}$ \\\\\n\\midrule\n\\multirow{4}{*}{\\rotatebox[origin=c]{0}{OPT-1.3b}}\n& Base & $.814$ & $.802$ & $.743$ \\\\\n& Jacobian & $.820$ & $.807$ & $.759$ \\\\\n& H\\\"{o}lder & $.823$ & $.814$ & $.770$ \\\\\n& \\cellcolor{lightgray} \\textsc{JacHess} & $\\mathbf{.840}$ & $\\mathbf{.831}$ & $\\mathbf{.802}$ \\\\\n\\midrule\n\\multirow{4}{*}{\\rotatebox[origin=c]{0}{OPT-6.7b*}}\n& Base & $.831$ & $.809$ & $.787$ \\\\\n& Jacobian & $.834$ & $.819$ & $.799$ \\\\\n& H\\\"{o}lder & $.834$ & $.817$ & $.796$ \\\\\n& \\cellcolor{lightgray} \\textsc{JacHess} & $\\mathbf{.851}$ & $\\mathbf{.839}$ & $\\mathbf{.813}$ \\\\\n\\midrule\n\\multirow{4}{*}{\\rotatebox[origin=c]{0}{LLaMA-2-7b*}}\n& Base & $.831$ & $.813$ & $.786$ \\\\\n& Jacobian & $.837$ & $.822$ & $.804$ \\\\\n& H\\\"{o}lder & $.832$ & $.811$ & $.796$ \\\\\n& \\cellcolor{lightgray} \\textsc{JacHess} & $\\mathbf{.869}$ & $\\mathbf{.853}$ & $\\mathbf{.829}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{Average predictive accuracy with token corruption. We adjust the percentage of token corruption to $10$\\%, $15$\\%, and $20$\\%. For each dataset, we conduct experiments five times using different seeds and report the average score on the GLUE benchmark. Best scores within the same model and token corruption setup are shown in \\textbf{bold}.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "From Robustness to Improved Generalization and Calibration in Pre-trained Language Models", "authors": ["Josip Jukić", "Jan Šnajder"], "url": "https://arxiv.org/abs/2404.00758v1", "attribution": "\"From Robustness to Improved Generalization and Calibration in Pre-trained Language Models\" by Josip Jukić and Jan Šnajder, arXiv:2404.00758v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.09794v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average prevalence rates (unit: \\%).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\tDataset $\\backslash$ Number of seeds & 1 & 2 & 3 & 4 & 5 \\\\ \\hline\n\t\t\tsmall-world & 1.26 & 2.51 & 3.75 & 4.95 & 6.13 \\\\ \\hline\n\t\t\temail-Eu-core & 2.63 & 5.15 & 7.42 & 9.62 & 11.68 \\\\ \\hline\n\t\t\tpolitical blogs & 1.91 & 3.77 & 5.56 & 7.21 & 8.78 \\\\ \\hline\n\t\t\tego-Facebook & 1.26 & 2.44 & 3.59 & 4.72 & 5.79 \\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Positively Correlated Samples Save Pooled Testing Costs", "authors": ["Yi-Jheng Lin", "Che-Hao Yu", "Tzu-Hsuan Liu", "Cheng-Shang Chang", "Wen-Tsuen Chen"], "url": "https://arxiv.org/abs/2011.09794v2", "attribution": "\"Positively Correlated Samples Save Pooled Testing Costs\" by Yi-Jheng Lin, Che-Hao Yu, Tzu-Hsuan Liu, Cheng-Shang Chang, and Wen-Tsuen Chen, arXiv:2011.09794v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.09782v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rrrrrrrrrrrrrrrrrrr}\n \\hline\n\\hline\n & \\multicolumn{6}{c}{CRIX}& \\multicolumn{6}{c}{ECRIX}& \\multicolumn{6}{c}{EFCRIX}&\\\\& AIC & GC & GFC & SH & Cp & FPE & AIC & GC & GFC & SH & Cp & FPE & AIC & GC & GFC & SH & Cp & FPE & max\\\\ \\hline\n1 & 5 & 10 & 10 & 10 & 10 & 35 & 2 & 2 & 2 & 3 & 1 & 36 & 2 & 7 & 30 & 30 & 1 & 36 & 36 \\\\ \n 2 & 10 & 15 & 15 & 15 & 5 & 100 & 3 & 3 & 3 & 3 & 1 & 93 & 3 & 94 & 93 & 93 & 1 & 93 & 113 \\\\ \n 3 & 5 & 10 & 35 & 35 & 5 & 100 & 5 & 5 & 5 & 5 & 1 & 93 & 5 & 94 & 93 & 93 & 1 & 93 & 158 \\\\ \n 4 & 10 & 10 & 10 & 40 & 5 & 95 & 3 & 3 & 3 & 3 & 1 & 90 & 3 & 91 & 90 & 90 & 1 & 90 & 182 \\\\ \n 5 & 10 & 20 & 20 & 20 & 5 & 100 & 2 & 4 & 4 & 4 & 1 & 93 & 12 & 94 & 93 & 93 & 1 & 93 & 169 \\\\ \n 6 & 10 & 10 & 20 & 20 & 5 & 100 & 2 & 2 & 2 & 2 & 1 & 93 & 2 & 94 & 93 & 93 & 1 & 93 & 171 \\\\ \n 7 & 5 & 20 & 20 & 20 & 5 & 100 & 1 & 1 & 1 & 1 & 1 & 93 & 16 & 94 & 93 & 93 & 1 & 93 & 176 \\\\ \n 8 & 15 & 20 & 20 & 20 & 5 & 95 & 3 & 4 & 4 & 4 & 1 & 91 & 3 & 92 & 91 & 91 & 1 & 91 & 140 \\\\ \n 9 & 15 & 5 & 5 & 5 & 5 & 100 & 3 & 3 & 3 & 3 & 1 & 93 & 3 & 94 & 93 & 93 & 1 & 93 & 188 \\\\ \n 10 & 15 & 15 & 25 & 25 & 5 & 100 & 3 & 5 & 5 & 5 & 1 & 93 & 3 & 94 & 93 & 93 & 1 & 93 & 207 \\\\ \n 11 & 10 & 35 & 45 & 45 & 5 & 100 & 2 & 2 & 2 & 2 & 1 & 93 & 4 & 94 & 93 & 93 & 1 & 93 & 221 \\\\ \n \\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of AIC, GC, GFC, SH, Cp and the FPE method for the selection of the number of index constituents for the CRIX, ECRIX and EFCRIX in the 11 periods}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "CRIX an index for cryptocurrencies", "authors": ["Simon Trimborn", "Wolfgang Karl Härdle"], "url": "https://arxiv.org/abs/2009.09782v1", "attribution": "\"CRIX an index for cryptocurrencies\" by Simon Trimborn and Wolfgang Karl Härdle, arXiv:2009.09782v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2207.12494v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary Statistics: Inflation Measures}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\hline \\hline\n & \\multicolumn{4}{c}{Inflation Measures} \\\\\n & \\multirow{2}{*}{Headline} & \\multirow{2}{*}{Core} & \\multirow{2}{*}{Median} & Trimmed \\\\\n & & & & Mean \\\\\n \\cline{2-5}\n & \\multicolumn{4}{c}{Full Sample (748 months)}\\\\\n \\cline{2-5}\n Mean & 3.27 & 3.21 & 3.33 & 2.96 \\\\\n Std. Dev. & 2.42 & 2.13 & 2.01 & 1.86 \\\\\n Coeff. Var. & 0.74 & 0.66 & 0.60 & 0.63 \\\\\n \\cline{2-5}\n & \\multicolumn{4}{c}{$\\pi<2.5\\%$ (373 months)}\\\\\n \\cline{2-5}\n Mean & 1.55 & 1.73 & 2.01 & 1.72 \\\\\n Std. Dev. & 0.67 & 0.53 & 0.95 & 0.70 \\\\\n Coeff. Var. & 0.43 & 0.31 & 0.47 & 0.41 \\\\\n \\cline{2-5}\n & \\multicolumn{4}{c}{$2.5\\%\\leq\\pi<5\\%$ (252 months)}\\\\\n \\cline{2-5}\n Mean & 3.61 & 3.51 & 3.55 & 3.17 \\\\\n Std. Dev. & 0.71 & 1.06 & 0.84 & 0.77 \\\\\n Coeff. Var. & 0.20 & 0.30 & 0.24 & 0.24 \\\\\n \\cline{2-5}\n & \\multicolumn{4}{c}{$5\\%\\leq\\pi$ (123 months)}\\\\\n \\cline{2-5}\n Mean & 7.76 & 7.09 & 6.85 & 6.31 \\\\\n Std. Dev. & 2.00 & 1.59 & 1.60 & 1.57 \\\\\n Coeff. Var. & 0.26 & 0.22 & 0.23 & 0.25 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Extending the Range of Robust PCE Inflation Measures", "authors": ["Sergio Ocampo", "Raphael Schoenle", "Dominic A. Smith"], "url": "https://arxiv.org/abs/2207.12494v3", "attribution": "\"Extending the Range of Robust PCE Inflation Measures\" by Sergio Ocampo, Raphael Schoenle, and Dominic A. Smith, arXiv:2207.12494v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.11969v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation results for mis-specified model (design 2) based on $500$ replications}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrr}\n\\hline\n Method & ATE & \\multicolumn{5}{c}{QTE} \\\\ \n & & 0.10 & 0.25 & 0.50 & 0.75 & 0.90 \\\\ \n \\hline\n \\multicolumn{7}{c}{Bias} \\\\\n \\hline\n IPS (exp) & 2.0073 & -4.0190 & -2.2247 & 0.7095 & 5.2301 & 10.4740 \\\\ \n IPS (ind) & 2.2844 & -3.0931 & -1.3918 & 1.1960 & 5.1096 & 9.4856 \\\\ \n IPS (proj) & 0.3018 & -2.6261 & -1.8409 & -0.4423 & 1.9471 & 4.2648 \\\\ \n CBPS (j) & 2.6558 & -3.2478 & -1.4447 & 1.4964 & 5.8841 & 10.5782 \\\\ \n DPS (j MQ) & 0.6931 & -1.4792 & -0.8639 & -0.0642 & 1.8188 & 4.1355 \\\\ \n DPS (j MD) & 0.2633 & -1.2441 & -0.8092 & -0.1933 & 1.1209 & 2.5604 \\\\ \n DPS (j D) & -0.4888 & -0.4936 & -0.6743 & -0.5461 & -0.2158 & -0.4357 \\\\ \n CBPS (o) & 2.9651 & -2.9019 & -1.1142 & 1.7327 & 6.0674 & 11.2099 \\\\ \n DPS (o MQ) & 0.6931 & -1.1227 & -0.6876 & -0.1783 & 1.1199 & 2.9258 \\\\ \n DPS (o MD) & 0.2839 & -0.7870 & -0.5274 & -0.0616 & 0.8369 & 1.8841 \\\\ \n DPS (o D) & 0.1041 & 0.0239 & -0.1466 & -0.0090 & 0.3576 & 0.3468 \\\\ \n \\hline\n \\multicolumn{7}{c}{Variance} \\\\\n \\hline\n IPS (exp) & 6.7101 & 13.8096 & 9.8939 & 8.0500 & 13.4849 & 38.8827 \\\\ \n IPS (ind) & 7.2561 & 15.4085 & 11.1044 & 8.8530 & 14.4145 & 34.6430 \\\\ \n IPS (proj) & 5.9519 & 14.6141 & 10.8652 & 7.9917 & 12.8501 & 31.4331 \\\\ \n CBPS (j) & 6.5495 & 13.2423 & 9.5448 & 8.4143 & 14.5832 & 35.3577 \\\\ \n DPS (j MQ) & 5.9922 & 11.2284 & 8.8770 & 8.5189 & 12.2348 & 24.3055 \\\\ \n DPS (j MD) & 6.5039 & 12.2954 & 9.9444 & 9.2286 & 12.8167 & 25.2888 \\\\ \n DPS (j D) & 5.7912 & 11.6075 & 9.7880 & 8.7243 & 12.1644 & 22.9389 \\\\ \n CBPS (o) & 7.6237 & 12.1805 & 9.4589 & 8.5988 & 15.6431 & 42.3916 \\\\ \n DPS (o MQ) & 5.9922 & 10.5597 & 8.4340 & 8.1760 & 11.6608 & 23.6440 \\\\ \n DPS (o MD) & 5.4491 & 10.7479 & 8.6797 & 8.0561 & 10.7197 & 19.8878 \\\\ \n DPS (o D) & 5.1791 & 10.5077 & 8.5301 & 7.9814 & 10.8403 & 20.0139 \\\\\n \\hline\n \\multicolumn{7}{c}{RMSE} \\\\\n \\hline\n IPS (exp) & 3.2771 & 5.4737 & 3.8527 & 2.9246 & 6.3905 & 12.1896 \\\\ \n IPS (ind) & 3.5319 & 4.9976 & 3.6113 & 3.2068 & 6.3657 & 11.1633 \\\\ \n IPS (proj) & 2.4582 & 4.6380 & 3.7755 & 2.8614 & 4.0794 & 7.0442 \\\\ \n CBPS (j) & 3.6882 & 4.8775 & 3.4106 & 3.2640 & 7.0147 & 12.1349 \\\\ \n DPS (j MQ) & 2.5441 & 3.6628 & 3.1021 & 2.9194 & 3.9424 & 6.4349 \\\\ \n DPS (j MD) & 2.5638 & 3.7207 & 3.2556 & 3.0440 & 3.7514 & 5.6431 \\\\ \n DPS (j D) & 2.4556 & 3.4426 & 3.2004 & 3.0037 & 3.4944 & 4.8092 \\\\ \n CBPS (o) & 4.0516 & 4.5389 & 3.2711 & 3.4060 & 7.2427 & 12.9635 \\\\ \n DPS (o MQ) & 2.5441 & 3.4380 & 2.9844 & 2.8649 & 3.5937 & 5.6749 \\\\ \n DPS (o MD) & 2.3515 & 3.3715 & 2.9930 & 2.8390 & 3.3794 & 4.8412 \\\\ \n DPS (o D) & 2.2782 & 3.2416 & 2.9243 & 2.8251 & 3.3118 & 4.4871 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Survey calibration for causal inference: a simple method to balance covariate distributions", "authors": ["Maciej Beręsewicz"], "url": "https://arxiv.org/abs/2310.11969v2", "attribution": "\"Survey calibration for causal inference: a simple method to balance covariate distributions\" by Maciej Beręsewicz, arXiv:2310.11969v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.08293v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Top 10 PPO agents based on the best mean reward obtained during on-training evaluations. Values rounded to 4 decimals.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllllll}\n\\toprule\n\\textbf{PPO} & \\textbf{Best} & \\textbf{Temp.} & \\textbf{Time} & \\textbf{Power} & \\textbf{Learn.} & \\textbf{Gam.} & \\textbf{Batch} & \\textbf{Max.} & \\textbf{Ent.} \\\\\n \\textbf{Model} & \\textbf{reward} & \\textbf{dev. (\\textcelsius)} & \\textbf{dev. (\\%)} & \\textbf{dem. (W)} & \\textbf{rate} & & \\textbf{size} & \\textbf{norm.} & \\textbf{coef.} \\\\\n\\midrule\n\\textbf{1st} & -0.4310 & 0.0009 & 0.9389 & 17221.1037 & 0.0003 & 0.9 & 128 & 0.9 & 0 \\\\\n\\textbf{2nd} & -0.4318 & 0.0059 & 5.4366 & 17152.8005 & 0.0003 & 0.9 & 64 & 0.5 & 0 \\\\\n\\textbf{3rd} & -0.4336 & 0.0003 & 0.4937 & 17338.2957 & 0.0003 & 0.9 & 128 & 0.9 & 0.01 \\\\\n\\textbf{4th} & -0.4342 & 0.0017 & 1.5097 & 17331.0688 & 0.0003 & 0.9 & 64 & 0.9 & 0.01 \\\\\n\\textbf{5th} & -0.4346 & 0.0013 & 1.4668 & 17355.1309 & 0.0003 & 0.9 & 64 & 0.5 & 0.01 \\\\\n\\textbf{6th} & -0.4347 & 0.0063 & 5.0884 & 17262.0097 & 0.0003 & 0.9 & 64 & 0.9 & 0 \\\\\n\\textbf{7th} & -0.4348 & 0.0006 & 0.3881 & 17380.3400 & 0.0003 & 0.9 & 128 & 0.9 & 0.001 \\\\\n\\textbf{8th} & -0.4348 & 0.0027 & 2.2716 & 17338.9685 & 0.0003 & 0.9 & 64 & 0.9 & 0.001 \\\\\n\\textbf{9th} & -0.4351 & 0.0011 & 0.6150 & 17379.9875 & 0.001 & 0.9 & 128 & 0.5 & 0.001 \\\\\n\\textbf{10th} & -0.4356 & 0.0035 & 2.3630 & 17352.6699 & 0.0003 & 0.9 & 64 & 0.5 & 0.001 \\\\ \\hline \\hline\n\\textbf{baseline} & -0.4377 & 0.0043 & 2.0134 & 17423.4776 & 0.0003 & 0.99 & 64 & 0.5 & 0 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning", "authors": ["Alejandro Campoy-Nieves", "Antonio Manjavacas", "Javier Jiménez-Raboso", "Miguel Molina-Solana", "Juan Gómez-Romero"], "url": "https://arxiv.org/abs/2412.08293v1", "attribution": "\"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning\" by Alejandro Campoy-Nieves, Antonio Manjavacas, Javier Jiménez-Raboso, Miguel Molina-Solana, and Juan Gómez-Romero, arXiv:2412.08293v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.02225v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Test accuracy (\\%) of on various neural networks trained on CIFAR10.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc}\n \\textbf{Model} & $\\l=0.0$ & $\\l=0.5$ & $\\l=1.0$ & $\\l=1/t$ & $\\l=1-1/t$ \\\\\n \\hline\n ResNet-18 & $96.13_{\\pm 0.05}$ & $96.16_{\\pm 0.03}$ & $\\boldsymbol{96.33}_{\\pm 0.03}$ & $96.20_{\\pm 0.05}$ & $96.22_{\\pm 0.09}$ \\\\\n WideResNet-28-10 & $\\boldsymbol{97.26}_{\\pm 0.44}$ & $96.98_{\\pm 0.08}$ & $97.05_{\\pm 0.05}$ & $96.73_{\\pm 0.04}$ & $96.63_{\\pm 0.35}$ \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Sharpness-Aware Minimization: General Analysis and Improved Rates", "authors": ["Dimitris Oikonomou", "Nicolas Loizou"], "url": "https://arxiv.org/abs/2503.02225v1", "attribution": "\"Sharpness-Aware Minimization: General Analysis and Improved Rates\" by Dimitris Oikonomou and Nicolas Loizou, arXiv:2503.02225v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09884v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results showing the speaker diarization performance using baseline (B) and proposed methods (M1 and M2) on \\textbf{development set} of third DIHARD challenge. M1: domain-dependent threshold, M2: domain-dependent threshold and domain-dependent parameter for PCA.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n \\multirow{2}{*}{Method} & \\multicolumn{2}{|c|}{Full} & \\multicolumn{2}{|c|}{Core}\\\\\n \\cline{2-5}\n & DER & JER & DER & JER \\\\\n \\hline\n B\t& 19.59\t&43.01\t&20.17&\t47.28\\\\\n M1\t& 17.97\t&40.33\t&18.73\t&44.77\\\\\n M2\t& 17.40&\t38.08&\t17.95&\t42.12\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Domain-Dependent Speaker Diarization for the Third DIHARD Challenge", "authors": ["A Kishore Kumar", "Shefali Waldekar", "Goutam Saha", "Md Sahidullah"], "url": "https://arxiv.org/abs/2101.09884v1", "attribution": "\"Domain-Dependent Speaker Diarization for the Third DIHARD Challenge\" by A Kishore Kumar, Shefali Waldekar, Goutam Saha, and Md Sahidullah, arXiv:2101.09884v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.20357v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\\toprule\n\\textbf{Scenario} & \\textbf{Survival Changes} & \\textbf{Health Changes} & \\textbf{Total Value} \\\\\n\\midrule\nCR0 & 274.68 & 916.92 & 1,191.59 \\\\\nCR1 & 986.78 & 3,628.88 & 4,615.66 \\\\\nCR2 & 1,216.19 & 5,515.61 & 6,731.80 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Willingness-to-pay at age 50 for changes in survival, health, and their combination over remaining lifespan (thousands of CHF).}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Economic impact of biomarker-based aging interventions on healthcare costs and individual value", "authors": ["Federico Felizzi"], "url": "https://arxiv.org/abs/2503.20357v1", "attribution": "\"Economic impact of biomarker-based aging interventions on healthcare costs and individual value\" by Federico Felizzi, arXiv:2503.20357v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.05978v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of the MAISI models and the WDM model for both the MS and ADNI dataset, across image quality, subject preservation, and text alignment accuracy.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc|c}\n \\hline\n & \\multicolumn{2}{c|}{\\textbf{Quality and Diversity}} \n & \\multicolumn{2}{c|}{\\textbf{Subject Preservation}} \n & \\textbf{Text Alignment} \\\\\n \\cline{2-6}\n \\textbf{Model} & FID $\\downarrow$ & MS-SSIM $\\downarrow$ \n & MS-SSIM $\\uparrow$ & PSNR (dB) $\\uparrow$ \n & Accuracy (\\%) $\\uparrow$ \\\\\n \\hline\n \\multicolumn{6}{c}{\\textbf{MS Dataset}} \\\\\n \\hline\n MAISI Linear & 0.1734 & \\textbf{0.8626} & 0.8614 & 21.18 & 92.64 \\\\\n MAISI Rflow & 0.1625 & 0.8715 & 0.9638 & 27.92 & 89.19 \\\\\n WDM & \\textbf{0.1622} & 0.8684 & \\textbf{0.9680} & \\textbf{28.47} & \\textbf{94.58} \\\\\n \\hline\n \\multicolumn{6}{c}{\\textbf{ADNI}} \\\\\n \\hline\n MAISI Linear & 0.1752 & 0.8362 & 0.8635 & 22.16 & 62.26 \\\\\n MAISI Rflow & 0.1690 & 0.8357 & 0.9864 &\\textbf{ 34.86} & \\textbf{72.01}\\\\\n WDM & \\textbf{0.1647} & \\textbf{0.7485} & \\textbf{0.9895} & 34.77 & 71.17\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Imagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidance", "authors": ["Mohamed Mohamed", "Brennan Nichyporuk", "Douglas L. Arnold", "Tal Arbel"], "url": "https://arxiv.org/abs/2509.05978v1", "attribution": "\"Imagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidance\" by Mohamed Mohamed, Brennan Nichyporuk, Douglas L. Arnold, and Tal Arbel, arXiv:2509.05978v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13178v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The performance (\\%) of different VOT models under BOBA on OTB100 dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc|cc}\n\\toprule\nAttack Mode$\\rightarrow$ & \\multicolumn{2}{c|}{No Attack} & \\multicolumn{2}{c|}{One-Shot} & \\multicolumn{2}{c}{Few-Shot} \\\\ \\hline\nModel$\\downarrow$, Metric$\\rightarrow$ & Pr-B & AUC-B & Pr-A & AUC-A & Pr-A & AUC-A \\\\ \\hline\nSiamFC & 72.70 & 53.78 & 11.44 & 9.51 & 9.37 & 7.64 \\\\\nSiamRPN++ & 76.89 & 54.85 & 35.71 & 21.02 & 23.79 & 15.84 \\\\\nSiamFC++ & 79.71 & 60.81 & 77.51 & 57.79 & 75.67 & 57.04 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Few-Shot Backdoor Attacks on Visual Object Tracking", "authors": ["Yiming Li", "Haoxiang Zhong", "Xingjun Ma", "Yong Jiang", "Shu-Tao Xia"], "url": "https://arxiv.org/abs/2201.13178v2", "attribution": "\"Few-Shot Backdoor Attacks on Visual Object Tracking\" by Yiming Li, Haoxiang Zhong, Xingjun Ma, Yong Jiang, and Shu-Tao Xia, arXiv:2201.13178v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.05935v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Voltages for points $A$ and $B$ for pglib 30-bus-ieee.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline \nBus & Point A & Point B \\tabularnewline\n\\hline \n\\hline \n1 & 1.0600 +0i & 1.0600 + 0i\\tabularnewline\n\\hline \n2 & 1.0405 - 0.0707i & 1.0350 - 0.0687i\\tabularnewline\n\\hline \n3 & 1.0077 - 0.1168i & 1.0081 - 0.1174i\\tabularnewline\n\\hline \n4 & 0.9935 - 0.1422i & 0.9940 - 0.1429i\\tabularnewline\n\\hline \n5 & 0.9816 - 0.2249i & 0.9805 - 0.2248i\\tabularnewline\n\\hline \n6 & 0.9841 - 0.1698i & 0.9875 - 0.1721i\\tabularnewline\n\\hline \n7 & 0.9739 - 0.2000i & 0.9755 - 0.2013i\\tabularnewline\n\\hline \n8 & 0.9760 - 0.1803i & 0.9857 - 0.1856i\\tabularnewline\n\\hline \n9 & 1.0088 - 0.2315i & 1.0088 - 0.2334i\\tabularnewline\n\\hline \n10 & 0.9973 - 0.2591i & 0.9957 - 0.2608i\\tabularnewline\n\\hline \n11 & 1.0332 - 0.2371i & 1.0327 - 0.2390i\\tabularnewline\n\\hline \n12 & 1.0149 - 0.2510i & 1.0074 - 0.2492i\\tabularnewline\n\\hline \n13 & 1.0290 - 0.2545i & 1.0133 - 0.2507i\\tabularnewline\n\\hline \n14 & 0.9959 - 0.2630i & 0.9891 - 0.2616i\\tabularnewline\n\\hline \n15 & 0.9907 - 0.2631i & 0.9847 - 0.2623i\\tabularnewline\n\\hline \n16 & 0.9988 - 0.2572i & 0.9937 - 0.2571i\\tabularnewline\n\\hline\n17 & 0.9918 - 0.2611i & 0.9891 - 0.2622i\\tabularnewline\n\\hline \n18 & 0.9778 - 0.2707i & 0.9733 - 0.2709i\\tabularnewline\n\\hline \n19 & 0.9742 - 0.2727i & 0.9705 - 0.2734i\\tabularnewline\n\\hline \n20 & 0.9789 - 0.2702i & 0.9758 - 0.2711i\\tabularnewline\n\\hline \n21 & 0.9832 - 0.2638i & 0.9814 - 0.2655i\\tabularnewline\n\\hline \n22 & 0.9838 - 0.2638i & 0.9820 - 0.2653i\\tabularnewline\n\\hline\n23 & 0.9781 - 0.2666i & 0.9737 - 0.2667i\\tabularnewline\n\\hline \n24 & 0.9710 - 0.2674i & 0.9690 - 0.2686i\\tabularnewline\n\\hline \n25 & 0.9692 - 0.2586i & 0.9695 - 0.2610i\\tabularnewline\n\\hline \n26 & 0.9499 - 0.2612i & 0.9503 - 0.2635i\\tabularnewline\n\\hline \n27 & 0.9775 - 0.2509i & 0.9794 - 0.2538i\\tabularnewline\n\\hline \n28 & 0.9773 - 0.1796i & 0.9818 - 0.1824i\\tabularnewline\n\\hline \n29 & 0.9524 - 0.2670i & 0.9543 - 0.2698i\\tabularnewline\n\\hline \n30 & 0.9368 - 0.2787i & 0.9388 - 0.2815i\\tabularnewline\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Certifying the Nonexistence of Feasible Path Between Power System Operating Points", "authors": ["Mohammad Rasoul Narimani", "Katherine R. Davis", "Daniel K. Molzahn"], "url": "https://arxiv.org/abs/2509.05935v1", "attribution": "\"Certifying the Nonexistence of Feasible Path Between Power System Operating Points\" by Mohammad Rasoul Narimani, Katherine R. Davis, and Daniel K. Molzahn, arXiv:2509.05935v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00752v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{rotating}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\usepackage{graphicx}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{llcccc}\n\\toprule\n\\multirow{7}*{\\rotatebox[origin=c]{90}{\\emph{Epsilon ($\\epsilon$ = 0.02)}}} & \\emph{Pseudo-Reference} & de-en & en-de & ru-en & en-ru \\\\\n\\cmidrule(lr){2-6}\n & Ancestral & 58.77 & 64.21 & \\textbf{47.92} & 71.19 \\\\\n & Beam & \\underline{57.24} & \\underline{63.49} & 46.56 & \\underline{68.26} \\\\\n & Epsilon ($\\epsilon$ = 0.02) & 59.07 & 65.32 & 46.56 & 71.85 \\\\\n & Epsilon ($\\epsilon$ = 0.02)$^*$ & 59.19 & 65.25 & 46.59 & 71.79 \\\\\n & Nucleus ($p$ = 0.6) & 57.82 & 64.31 & \\underline{45.42} & 71.08 \\\\\n & Nucleus ($p$ = 0.9) & \\textbf{59.65} & \\textbf{65.44} & 47.77 & \\textbf{72.33} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "On the True Distribution Approximation of Minimum Bayes-Risk Decoding", "authors": ["Atsumoto Ohashi", "Ukyo Honda", "Tetsuro Morimura", "Yuu Jinnai"], "url": "https://arxiv.org/abs/2404.00752v1", "attribution": "\"On the True Distribution Approximation of Minimum Bayes-Risk Decoding\" by Atsumoto Ohashi, Ukyo Honda, Tetsuro Morimura, and Yuu Jinnai, arXiv:2404.00752v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.03052v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Comparative evaluation of Classification network on varied values of Nearest Neighbors($k$=5,10,20...40) points on ModelNet-40 dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{$\\textbf{k}$} & \\textbf{Overall Accuracy} & \\textbf{Mean Accuracy}\\\\\n \\hline 5 & 29.53 & 24.79 \\\\\n \\hline 10 & 85.21 & 79.26 \\\\\n \\hline 15 & 91.73 & 88.28 \\\\\n \\hline 20 & 92.7 & 89.09 \\\\\n \\hline 30 & 91.12 & 87.23 \\\\\n \\hline 40 & 87.72 & 83.23 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation", "authors": ["Md Meraz", "Md Afzal Ansari", "Mohammed Javed", "Pavan Chakraborty"], "url": "https://arxiv.org/abs/2412.03052v1", "attribution": "\"Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation\" by Md Meraz, Md Afzal Ansari, Mohammed Javed, and Pavan Chakraborty, arXiv:2412.03052v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.12285v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\hline\n\\# & Column & Non-null count \\\\\n\\hline\n 0 & campaign & 907 non-null \\\\\n 1 & cf\\_total\\_days & 907 non-null \\\\\n 2 & cf\\_total\\_seconds & 907 non-null \\\\\n 3 & cf\\_type & 907 non-null \\\\\n 4 & giveaways\\_categories & 646 non-null \\\\\n 5 & givaways\\_totals & 646 non-null \\\\\n 6 & pt\\_categories & 907 non-null \\\\\n 7 & pt\\_totals & 907 non-null \\\\\n 8 & fm\\_categories & 729 non-null \\\\\n 9 & fm\\_total & 729 non-null \\\\\n 10 & sch\\_categories & 907 non-null \\\\\n 11 & sch\\_total & 907 non-null \\\\\n 12 & ad\\_categories & 591 non-null \\\\\n 13 & ad\\_total & 591 non-null \\\\\n 14 & schm\\_total & 907 non-null \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Effects of Just-in-time Delivery on Social Engagement: A Cluster Analysis", "authors": ["Moisés Ramírez", "Raziel Ruíz", "Nathan Klarer"], "url": "https://arxiv.org/abs/2212.12285v1", "attribution": "\"The Effects of Just-in-time Delivery on Social Engagement: A Cluster Analysis\" by Moisés Ramírez, Raziel Ruíz, and Nathan Klarer, arXiv:2212.12285v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09773v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n\\textbf{Model} & \\textbf{Hit} (\\%) & \\textbf{UnRel} (\\%) & \\textbf{F1} (\\%) \\\\ \\midrule\nMLP-AD & 9.62 & 83.37 & 18.75 \\\\\nMLP-ASD & 63.26 & 81.88 & 31.35 \\\\\nGMemNN & \\textbf{67.30} & \\textbf{81.05} & \\textbf{32.59} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The experimental results of the conversational evaluation. MLP-AD stands for the pretrained state-of-the-art MLP model for automatic diagnosis (AD) provided by the authors of . MLP-ASD stands for the MLP model for automatic symptom detection (ASD) in this work. Hit stands for average hit rate $R_h$, UnRel stands for average unrelated rate $R_u$.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks", "authors": ["Hongyin Luo", "Shang-Wen Li", "James Glass"], "url": "https://arxiv.org/abs/2101.09773v1", "attribution": "\"Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks\" by Hongyin Luo, Shang-Wen Li, and James Glass, arXiv:2101.09773v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14913v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|} \\hline \n dimensions& 1d& 2d& 3d\\\\ \\hline \n layers-MLP& \\{1,20,1\\} & \\{2,20,40,20,1\\} & \\{3,20,40,20,1\\} \\\\ \\hline \n parameters& 61 & 1741 & 1761 \\\\ \\hline \n activate function& tanh & tanh & tanh \\\\ \\hline \n lr& 2e-03&1e-03&3e-03 \\\\ \\hline \n loss function& $J_{r}(\\theta)$& $J_{r}(\\theta)$ or $J_{R}(\\theta)$&$J_{R}(\\theta)$ and $J_{r}(\\theta)$ \\\\ \\hline \n epochs& 10000 & 10000 & 15000+10000 \\\\ \\hline \n collection points& 1000 & 400 &1000 \\\\ \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Network structure and training parameters for different dimensions.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A PINN-enriched finite element method for linear elliptic problems", "authors": ["Xiao Chen", "Yixin Luo", "Jingrun Chen"], "url": "https://arxiv.org/abs/2503.14913v1", "attribution": "\"A PINN-enriched finite element method for linear elliptic problems\" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15444v2_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mutually unbiased weighing matrices of order $16$ and weight $9$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l}\n\\noalign{\\hrule height1pt}\n$W_{16,569}$\\\\\n\\hline\n1001010111202002\n0020110121101010\n0120202010010211\n1110000221201200\\\\\n2122000201022002\n2201010220012200\n1202001000022211\n2010012000220111\\\\\n0012112010100220\n2101001100020221\n0220122001200021\n0001122002020212\\\\\n0201200211121000\n1020212022020020\n0010222121102000\n1101100200102101\\\\\n\\hline\n$ A_{16,569,2}$\\\\\n\\hline\n0000000112221211\n0000001012212112\n0000100011211222\n0000010012121122\\\\\n0010000011122212\n0100000011222121\n0001000012112221\n1111111120000000\\\\\n1121222100000002\n1211212200200000\n1221121200020000\n1112122202000000\\\\\n1122211200000200\n1212221100000020\n1222112100002000\n1000000011111111\\\\\n\\hline\n$ A_{16,569,3}$\\\\\n\\hline\n0010222200020211\n0010220100011112\n0110101012101001\n0120122020011200\\\\\n0001211022201200\n0011112001021002\n0102200100120222\n1101200211010020\\\\\n1221000110100210\n1001020120020121\n1102010101200011\n1202001221101000\\\\\n1202002012201020\n1120000202020112\n1010012022112000\n1010121000202202\\\\\n\\hline\n$ A_{16,569,4}$\\\\\n\\hline\n0001112020202120\n0011002202101110\n0101120210200201\n0101200121011001\\\\\n0121210010122000\n0102222000202110\n0112010022020201\n1201220022002200\\\\\n1022002200110021\n1110000112010022\n1100120021120002\n1210002111020001\\\\\n1010011201012010\n1020012000201212\n1020101102000111\n1000201200221120\\\\\n\\hline\n$ A_{16,569,5}$\\\\\n\\hline\n0001111011020011\n0101120101012020\n0101200200202212\n0111210002000121\\\\\n0012002211210001\n0102221021020001\n0122010000112110\n1021002221001100\\\\\n1102110000220022\n1110002100101210\n1210000120202110\n1200210001102220\\\\\n1010021210100102\n1020022012022001\n1020201110211000\n1000101222010201\\\\\n\\hline\n$ A_{16,569,6}$\\\\\n\\hline\n0100212221002001\n0100022102110101\n0120201001101102\n0120102111020200\\\\\n0101100222021020\n0111010010211010\n0102101210012020\n1221010010000121\\\\\n1011021001100201\n1021100020012012\n1012110000120110\n1022020200201011\\\\\n1002010120011220\n1100201102222000\n1010022001200122\n1000202212100202\\\\\n\\hline\n$ A_{16,569,7}$\\\\\n\\hline\n0010210200111201\n0110102021010110\n0011001111001120\n0121011000212001\\\\\n0121020000121011\n0112001210220010\n0122202001201020\n1111002000022220\\\\\n1001020202211002\n1102001022100120\n1002120110010201\n1200220201002101\\\\\n1200110020221001\n1020110211100002\n1000201121000212\n1000212112000110\\\\\n\\hline\n$ A_{16,569,8}$\\\\\n\\hline\n0010120201021101\n0100021120201022\n0011220112120000\n0111100200110202\\\\\n0012001120110011\n0012102110200012\n0102200212011100\n1101002022200011\\\\\n1201011000001112\n1012200220022002\n1022100002121200\n1120021011002010\\\\\n1100012101100120\n1200120002012120\n1200222001011200\n1010011010200221\\\\\n\\noalign{\\hrule height1pt}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unbiased weighing matrices of weight $9$", "authors": ["Makoto Araya", "Masaaki Harada", "Hadi Kharaghani", "Sho Suda", "Wei-Hsuan Yu"], "url": "https://arxiv.org/abs/2501.15444v2", "attribution": "\"Unbiased weighing matrices of weight $9$\" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.13124v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Technology adoption and bribery across countries.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\hline\n \\hline\n & {Low-income} & {Middle-income} & {High-income} \\\\\n & {countries} & {countries} & {countries} \\\\\n \\hline \n {Share of modern firms (\\% of all firms) } & 36.8\\% & 55.4\\% & 75.6\\% \\\\\n {Bribe of traditional firms (\\% of annual sales)}& 1.96\\% & 0.99\\% & 1.01\\% \\\\ \n {Probability of bribe payment of traditional firms} & 28\\% & 15\\% & 13\\% \\\\ \n {Bribe of modern firms (\\% of annual sales)} & 2.60\\% & 1.11\\% & 0.60\\% \\\\\n {Probability of bribe payment of modern firms} & 34\\% & 16\\% & 11\\% \\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Heterogeneous Bribery, Technology Choice, and Capital Accumulation", "authors": ["Jafar M. Olimov", "Yi-Chan Tsai", "Hao-Yu Yang"], "url": "https://arxiv.org/abs/2507.13124v2", "attribution": "\"Heterogeneous Bribery, Technology Choice, and Capital Accumulation\" by Jafar M. Olimov, Yi-Chan Tsai, and Hao-Yu Yang, arXiv:2507.13124v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01100v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\hline \nCovariate & Levels & Available from target?\\\\\n\\hline\nAge group & 18-24, 25-34, 35-39, 40+ & Yes\\\\\nGender & Female, non-female & Yes \\\\\nParty& Democratic, Republican, Other/Unknown & Yes\\\\\nRace& White, Black, Latinx, other & No\\\\\nVoted in 2000 & 0, 1 & No\\\\\nVoted in 2002 & 0, 1 & No\\\\\nVoted in 2004 & 0, 1 & Available in some counties\\\\\nVoted in 2006 & 0, 1 & Available in some counties\\\\\nVoted in 2008 & 0, 1 & Available in some counties\\\\\nVoted in 2010 & 0, 1 & Yes\\\\\nVoted in 2012 & 0, 1 & Yes\\\\\nVoted in 2014 & 0, 1 & Yes\\\\\nVoted in 2016 & 0, 1 & Yes\\\\\nVoted in 2018 & 0, 1 & Yes\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Descriptions on covariates in pooled data.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Transfer Learning Between U.S. Presidential Elections: How Should We Learn From A 2020 Ad Campaign To Inform 2024 Ad Campaigns?", "authors": ["Xinran Miao", "Jiwei Zhao", "Hyunseung Kang"], "url": "https://arxiv.org/abs/2411.01100v2", "attribution": "\"Transfer Learning Between U.S. Presidential Elections: How Should We Learn From A 2020 Ad Campaign To Inform 2024 Ad Campaigns?\" by Xinran Miao, Jiwei Zhao, and Hyunseung Kang, arXiv:2411.01100v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.11079v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Balance across treatments}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\hline\n & $Mean_{RR}$ & $p(T_{AR}-T_{RR})$ & $p(T_{RA}-T_{RR})$ & $p(T_{AA}-T_{RR})$ \\\\\n\\hline\n$age$ & 43.197 & 0.432 & 0.683 & 0.025** \\\\\n$female$ & 0.526 & 0.794 & 0.683 & 0.684 \\\\\n$education$ & 2.961 & 0.642 & 0.723 & 0.819 \\\\\n$patience$ & 3.300 & 0.144 & 0.879 & 0.863 \\\\\n$ambiguity$ $aversion$ & 0.019 & 0.185 & 0.706 & 0.288 \\\\\n$risk$ $aversion$ & 0.041 & 0.513 & 0.980 & 0.434 \\\\\n$CRT$ & 1.531 & 0.300 & 0.267 & 0.288 \\\\\n$math$ $ability$ & 2.055 & 0.356 & 0.247 & 0.085* \\\\\n$altruism$ & 1.629 & 0.233 & 0.599 & 0.144 \\\\\n$envy$ & 2.113 & 0.730 & 0.312 & 0.430 \\\\\n$ideology$ $(right)$ & 4.939 & 0.657 & 0.994 & 0.979 \\\\\n$gravity$ $(warming)$ & 7.721 & 0.777 & 0.668 & 0.793 \\\\\n$number$ $actions$ & 4.464 & 0.731 & 0.166 & 0.032 \\\\\n$unemployed$ & 0.111 & 0.524 & 0.445 & 0.736 \\\\\n$social$ $transfer$ & 0.176 & 0.675 & 0.115 & 0.955 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The effect of ambiguity in strategic environments: an experiment", "authors": ["Pablo Brañas-Garza", "Antonio Cabrales", "María Paz Espinosa", "Diego Jorrat"], "url": "https://arxiv.org/abs/2209.11079v1", "attribution": "\"The effect of ambiguity in strategic environments: an experiment\" by Pablo Brañas-Garza, Antonio Cabrales, María Paz Espinosa, and Diego Jorrat, arXiv:2209.11079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2303.10806v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of Double Linear Policy with MA Indicator}\n\\begin{tabular}{ccccc}\n \\hline\n & $w_{{\\rm MA}_5}$ & $w_{{\\rm MA}_{10}}$ & $w_{{\\rm MA}_{20}}$ & $w_{{\\rm MA}_{30}}$\\\\\n \\hline\n \\hline\n {\\rm Gain-Loss} & 0.0061 & 0.0076 & 0.0117 & 0.0042\\\\\n \\hline\n Variance & 9.7e-06 & 1.1e-05 & 1.4e-05 & 2.2e-06\\\\\n \\hline\n Sharpe Ratio & 0.7097 & 0.8806 & 1.6343 & 0.8651\\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "On Robustness of Double Linear Policy with Time-Varying Weights", "authors": ["Xin-Yu Wang", "Chung-Han Hsieh"], "url": "https://arxiv.org/abs/2303.10806v1", "attribution": "\"On Robustness of Double Linear Policy with Time-Varying Weights\" by Xin-Yu Wang and Chung-Han Hsieh, arXiv:2303.10806v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2103.01146v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\n\\multirow{2}{*}{Classifier} & \\multicolumn{2}{c|}{Surface} & \\multicolumn{2}{c|}{Section} & \\multicolumn{2}{c|}{Mixed} \\\\ \\cline{2-7} \n & P & R & P & R & P & R \\\\ \\hline\nR. Forest & 0.87 & 0.82 & 0.82 & 0.82 & \\textbf{0.91} & \\textbf{0.91} \\\\ \\hline\nXGboost & 0.93 & 0.93 & 0.89 & 0.89 & \\textbf{0.96} & \\textbf{0.96} \\\\ \\hline\nAlexNet & 0.93 & 0.95 & 0.83 & 0.82 & 0.92 & 0.92 \\\\ \\hline\nVGG19 & 0.95 & 0.96 & 0.91 & 0.92 & 0.94 & 0.92 \\\\ \\hline\nInception & \\textbf{0.98} & \\textbf{0.97} & \\textbf{0.94} & \\textbf{0.96} & \\textbf{0.97} & \\textbf{0.98} \\\\ \\hline\n\\end{tabular}\n\\caption{Weighted average metrics comparison for section and surface patches, as well as mixed patches.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Assessing deep learning methods for the identification of kidney stones in endoscopic images", "authors": ["Francisco Lopez", "Andres Varela", "Oscar Hinojosa", "Mauricio Mendez", "Dinh-Hoan Trinh", "Jonathan ElBeze", "Jacques Hubert", "Vincent Estrade", "Miguel Gonzalez", "Gilberto Ochoa", "Christian Daul"], "url": "https://arxiv.org/abs/2103.01146v1", "attribution": "\"Assessing deep learning methods for the identification of kidney stones in endoscopic images\" by Francisco Lopez, Andres Varela, Oscar Hinojosa, Mauricio Mendez, Dinh-Hoan Trinh, Jonathan ElBeze, Jacques Hubert, Vincent Estrade, Miguel Gonzalez, Gilberto Ochoa, and Christian Daul, arXiv:2103.01146v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.01026v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccccccccccccccccccccccc}\n$j$ & 0 & 1& 2& 3& 4& 5& 6& 7& 8& 9&10&11&12&13&14&15&16&17&18&19\\\\\n\\hline\n{\\tt SP0}[j] & 0&2&1&0&1&0&0&2&1&0&0&2&1&0&2&1&0&1&0&0&\\\\\n{\\tt SP1}[j] & 1&0&0&2&1&0&2&1&0&1&0&0&2&1&0&1&0&0&2&1&\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Infinite words coding the right-special factors of ${\\bf n}$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Narayana Morphism and Related Words", "authors": ["Jeffrey Shallit"], "url": "https://arxiv.org/abs/2503.01026v3", "attribution": "\"The Narayana Morphism and Related Words\" by Jeffrey Shallit, arXiv:2503.01026v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11926v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccr}\n\\toprule\nData set & Naive & Flexible & Better? \\\\\n\\midrule\nBreast & 95.9$\\pm$ 0.2& 96.7$\\pm$ 0.2& $\\surd$ \\\\\nCleveland & 83.3$\\pm$ 0.6& 80.0$\\pm$ 0.6& $\\times$\\\\\nGlass2 & 61.9$\\pm$ 1.4& 83.8$\\pm$ 0.7& $\\surd$ \\\\\nCredit & 74.8$\\pm$ 0.5& 78.3$\\pm$ 0.6& \\\\\nHorse & 73.3$\\pm$ 0.9& 69.7$\\pm$ 1.0& $\\times$\\\\\nMeta & 67.1$\\pm$ 0.6& 76.5$\\pm$ 0.5& $\\surd$ \\\\\nPima & 75.1$\\pm$ 0.6& 73.9$\\pm$ 0.5& \\\\\nVehicle & 44.9$\\pm$ 0.6& 61.5$\\pm$ 0.4& $\\surd$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Simplifying deflation for non-convex optimization with applications in Bayesian inference and topology optimization", "authors": ["Mohamed Tarek", "Yijiang Huang"], "url": "https://arxiv.org/abs/2201.11926v1", "attribution": "\"Simplifying deflation for non-convex optimization with applications in Bayesian inference and topology optimization\" by Mohamed Tarek and Yijiang Huang, arXiv:2201.11926v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.20279v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Maximal subgroups of $\\mathrm{Sp}_4(q)$, for $q=2^e, e>1$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|ccc|}\n\\hline\nGroup & Order& Conditions\\\\\n\\hline\n$\\vphantom{E^{3^2}}E_q^3\\colon\\mathrm{GL}_2(q)$ & $q^3\\cdot (q^2+q)(q-1)^2$ & \\\\%\n$\\vphantom{E^{3^2}}E_q^3\\colon\\mathrm{GL}_2(q)$ & $q^3\\cdot (q^2+q)(q-1)^2$ & \\\\%\n$\\mathrm{Sp}_2(q)\\wr 2$ & $2q^2(q^2-1)^2$ & \\\\%\n$\\mathrm{Sp}_2(q^2)\\colon 2$ & $2q^2(q^4-1)$ & \\\\\n$\\mathrm{Sp}_4(q_0)$ & $q_0^4(q_0^2-1)(q_0^4-1)$ & $q=q_0^r, r$ is prime\\\\%\n$\\mathrm{SO}_4^+(q)$ & $2q^2(q^2-1)^2$ & \\\\\n$\\mathrm{SO}_4^-(q)$ & $2q^2(q^4-1)$ & \\\\\n$\\mathrm{Sz}(q)$ & $q^2(q^2+1)(q-1)$ & $e$ odd\\\\%\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Strong Gelfand Pairs of the Symplectic Group $\\boldsymbol{\\rm Sp}_4(q)$ where $q$ is even", "authors": ["Stephen P. Humphries", "Joseph E. Marrow"], "url": "https://arxiv.org/abs/2504.20279v2", "attribution": "\"Strong Gelfand Pairs of the Symplectic Group $\\boldsymbol{\\rm Sp}_4(q)$ where $q$ is even\" by Stephen P. Humphries and Joseph E. Marrow, arXiv:2504.20279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.07330v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Table summarizing the objective speech quality evaluation on the DNS $no\\_reverb$ ($with\\_reverb$) test set}\n\\begin{tabular}{l|l|l|l}\nMethod & PESQ & SI-SDR & STOI \\\\\n & (MOS) & (dB) & (\\%) \\\\ \n\\hline\nNoisy & 1.58 (1.82) & 9.1 (9.0) & 91.5 (86.6) \\\\\nDNS Baseline & 1.83 (1.52) & 12.5 (9.2) & 90.6 (82.1) \\\\\nGRU(TFE) & 2.27 (2.36) & 14.9 (13.2) & 94.2 (89.4) \\\\\nGRU(STME) & 2.59 (2.64) & 12.4 (12.0) & 94.2 (90.1) \\\\\nGRU(TFE+STME$^{R}$) & 2.57 (2.63) & \\textbf{15.9 (14.5)} & 95.2 (90.6) \\\\\nGRU(TFE+STME) & \\textbf{2.71 (2.75)} & \\textbf{15.9 (14.5)} & \\textbf{95.5 (91.2)} \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Modulation-Domain Loss for Neural-Network-based Real-time Speech Enhancement", "authors": ["Tyler Vuong", "Yangyang Xia", "Richard M. Stern"], "url": "https://arxiv.org/abs/2102.07330v1", "attribution": "\"A Modulation-Domain Loss for Neural-Network-based Real-time Speech Enhancement\" by Tyler Vuong, Yangyang Xia, and Richard M. Stern, arXiv:2102.07330v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.19379v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Accuracy of the 2nd order scheme, isotropic media}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n\\hline\n$N$& $N=10$ & \\multicolumn{2}{c|}{$N=20$} & \\multicolumn{2}{c|}{$N=40$} & \\multicolumn{2}{c|}{$N=80$} \\\\ \n \\hline\n $\\omega^2$ & $|\\omega^2-\\omega_h^2|$ & $|\\omega^2-\\omega_h^2|$ & ord & $|\\omega^2-\\omega_h^2|$ & ord & $|\\omega^2-\\omega_h^2|$ & ord\\\\ \n \\hline\n0.25 & 4.11e-15 & 7.22e-16 & \\ & 1.11e-16 & \\ & 1.11e-16 & \\ \\\\ \n\\hline\n0.25 & 1.00e-14 & 2.70e-14 & \\ & 2.25e-14 & \\ & 1.11e-16 & \\ \\\\ \n\\hline\n0.25 & 8.17e-03 & 2.05e-03 & 1.99 & 5.14e-04 & 2.00 & 1.28e-04 & 2.00\\\\ \n\\hline\n0.25 & 8.17e-03 & 2.05e-03 & 1.99 & 5.14e-04 & 2.00 & 1.28e-04 & 2.00\\\\ \n\\hline\n1.25 & 3.25e-02 & 8.20e-03 & 1.99 & 2.05e-03 & 2.00 & 5.14e-04 & 2.00\\\\ \n\\hline\n1.25 & 3.25e-02 & 8.20e-03 & 1.99 & 2.05e-03 & 2.00 & 5.14e-04 & 2.00\\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Kernel compensation method for Maxwell eigenproblem with mimetic finite difference discretization", "authors": ["Chenhao Jin", "Yinhua Xia", "Yan Xu"], "url": "https://arxiv.org/abs/2503.19379v1", "attribution": "\"Kernel compensation method for Maxwell eigenproblem with mimetic finite difference discretization\" by Chenhao Jin, Yinhua Xia, and Yan Xu, arXiv:2503.19379v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09113v4_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Wall-clock time of finite volume (FV) model and attention-GMUS to simulate a trajectory for three flow cases.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n \\hline\n Dataset & GMUS & Transformer & GT Model & Speed-up \\\\ \n \\hline\n Cylinder flow (for 400 time steps) & $2$sec & $0.4758$sec & $1688.59$sec & $\\boldsymbol{682}$ \\\\\n \\hline\n Sonic flow (for 40 time steps)& $0.2$sec & $0.047$sec & $25.026$sec & $\\boldsymbol{100}$ \\\\\n \\hline\n Vascular flow (for 250 time steps)& $2.63$sec & $0.31$sec & $2451$sec & $\\boldsymbol{800}$ \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Predicting Physics in Mesh-reduced Space with Temporal Attention", "authors": ["Xu Han", "Han Gao", "Tobias Pfaff", "Jian-Xun Wang", "Li-Ping Liu"], "url": "https://arxiv.org/abs/2201.09113v4", "attribution": "\"Predicting Physics in Mesh-reduced Space with Temporal Attention\" by Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, and Li-Ping Liu, arXiv:2201.09113v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18200v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc|cc}\n\\hline\n $G_i$ & $\\|Q_0 u- u_0\\|_0$ & $ h^r $ & $\\|\\nabla_w(Q_h u- u_h)\\|_0$ & $ h^r $ &\n $\\|E_w(Q_h u- u_h)\\|_0$ & $ h^r $ \\\\\n\\hline&\\multicolumn{6}{c}{ By the $P_2$/$P_2$/$P_{1}$ WG finite element .}\\\\\n\\hline \n 4& 0.152E-3 & 1.6& 0.165E-2 & 1.7& 0.199E+0 & 0.9 \\\\\n 5& 0.405E-4 & 1.9& 0.435E-3 & 1.9& 0.102E+0 & 1.0 \\\\\n 6& 0.102E-4 & 2.0& 0.109E-3 & 2.0& 0.513E-1 & 1.0 \\\\\n\\hline&\\multicolumn{6}{c}{ By the $P_3$/$P_3$/$P_{2}$ WG finite element .}\\\\\n\\hline \n 3& 0.229E-4 & 3.3& 0.800E-3 & 2.5& 0.586E-1 & 2.2 \\\\\n 4& 0.164E-5 & 3.8& 0.109E-3 & 2.9& 0.156E-1 & 1.9 \\\\\n 5& 0.107E-6 & 3.9& 0.138E-4 & 3.0& 0.400E-2 & 2.0 \\\\\n\\hline&\\multicolumn{6}{c}{ By the $P_4$/$P_4$/$P_{3}$ WG finite element .}\\\\\n\\hline \n 2& 0.133E-4 & 5.4& 0.957E-3 & 5.3& 0.801E-1 & 4.8 \\\\\n 3& 0.484E-6 & 4.8& 0.120E-3 & 3.0& 0.870E-2 & 3.2 \\\\\n 4& 0.145E-7 & 5.1& 0.792E-5 & 3.9& 0.118E-2 & 2.9 \\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Simple Weak Galerkin Finite Element Method for a Class of Fourth-Order Problems in Fluorescence Tomography", "authors": ["Chunmei Wang", "Shangyou Zhang"], "url": "https://arxiv.org/abs/2503.18200v1", "attribution": "\"A Simple Weak Galerkin Finite Element Method for a Class of Fourth-Order Problems in Fluorescence Tomography\" by Chunmei Wang and Shangyou Zhang, arXiv:2503.18200v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.02757v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The results under ($2^1$, $10^{0}$) in terms of node classification accuracy, AUC score, F1 score, $\\Delta_{SP}$, and $\\Delta_{EO}$ . All numerical numbers are in percentage.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\toprule\n\\textbf{($2^1$, $10^{0}$)} & \\textbf{Accuracy} & \\textbf{AUC} & \\textbf{F1 Score} & \\textbf{$\\Delta_{SP}$} & \\textbf{$\\Delta_{EO}$} \\\\\n\\midrule\n\\textbf{GCN} & 58.4\\% & 66.4\\% & 63.9\\% & 41.4\\% & 33.4\\% \\\\\n\\textbf{NIFTY} & 61.2\\% & 68.2\\% & 66.2\\% & 36.1\\% & 13.3\\% \\\\\n\\textbf{FairGNN} & 55.2\\% & 62.2\\% & 61.4\\% & 16.8\\% & 7.77\\% \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Certified Defense on the Fairness of Graph Neural Networks", "authors": ["Yushun Dong", "Binchi Zhang", "Hanghang Tong", "Jundong Li"], "url": "https://arxiv.org/abs/2311.02757v2", "attribution": "\"Certified Defense on the Fairness of Graph Neural Networks\" by Yushun Dong, Binchi Zhang, Hanghang Tong, and Jundong Li, arXiv:2311.02757v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.10334v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|c|c}\n \\toprule\n \\multicolumn{2}{l|}{Category} & Valid & Test \\\\\n \\midrule\n \\multirow{2}{*}{Gender (\\%)} & Female & 51.7 & 51.7 \\\\\n & Male & 48.3 & 48.3 \\\\\\hline\n \\multirow{4}{*}{Age (\\%)} & 18-27 & 37.9 & 34.5 \\\\\n & 28-37 & 34.5 & 31.0 \\\\\n & 38-47 & 10.4 & 13.8 \\\\\n & 48 and above & 17.2 & 20.7 \\\\\\hline\n \\multirow{5}{*}{Region (\\%)} & East & 13.8 & 13.8 \\\\\n & West & 20.7 & 17.2 \\\\\n & North & 13.8 & 20.7 \\\\\n & South & 37.9 & 41.4 \\\\\n & Center & 13.8 & 6.9 \\\\\\hline\n \\multirow{2}{*}{Device (\\%)} & Phone & 62.1 & 79.3 \\\\\n & Computer & 37.9 & 20.7 \\\\\\hline\n \\multirow{2}{*}{Headphone (\\%)} & Yes & 20.7 & 17.2 \\\\\n & No & 79.3 & 82.8 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The validation and test sets speaker details.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline", "authors": ["Yerbolat Khassanov", "Saida Mussakhojayeva", "Almas Mirzakhmetov", "Alen Adiyev", "Mukhamet Nurpeiissov", "Huseyin Atakan Varol"], "url": "https://arxiv.org/abs/2009.10334v2", "attribution": "\"A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline\" by Yerbolat Khassanov, Saida Mussakhojayeva, Almas Mirzakhmetov, Alen Adiyev, Mukhamet Nurpeiissov, and Huseyin Atakan Varol, arXiv:2009.10334v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.07016v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ccc|cccc|cccc}\n\t\t\\hline\n\t\t&&&\\multicolumn{4}{c|}{ISFLP1}&\\multicolumn{4}{c}{ISFLP2}\\\\ \n\t\t\\hline\n\t\t\n\t\tInstance&$n$&$\\mathbf{\\bar{x}}=(\\bar{x},\\bar{y})$&$C^*$&$iter$&$\\dfrac{|\\bar{F}-F_k|}{|\\bar{F}|}$&$CPU$&$C^*$&$iter$&$\\dfrac{|\\bar{F}-F_k|}{|\\bar{F}|}$&$CPU$\\\\\n\t\t&&&&&&(in sec)&&&&(in sec)\\\\\n\t\t\\hline\n\t\tdata &18&$(7,7)$&$22.8798$&$3$\n\t\t&$0.0080$&$34.9575$&$22.6452$&3&0.0088&$32.0751$\\\\\n\t\t& &$(-3,-5)$&$390.2082$&$4$&$0.0034$&$55.3343$\n\t\t&$360.2234$&$4$&$0.0037$&$46.2091$\\\\\n\t\t\\hline\n\t\tRuspini&75&$(50,50)$&2.77e+03&4&$0.0048$&$161.72$&2.65e+03&4&0.0074&121.22\\\\\n\t\t& &$(-80,-20)$&3.25e+04&5&0.0032&$220.70$&3.30e+04&5&0.0032&151.66\\\\\n\t\t& &$(-20,80)$&7.10e+03&5&0.0050&$230.35$&7.28e+03&5&0.0040&169.99\\\\\n\t\t\\hline\n\t\tBongartz&287&$(15,35)$&4.04e+03&12&0.0084&$3033.47$&4.08e+03&13&0.0071&1278.28\\\\\n\t\t& &$(20,29)$&1.17e+03&6&0.0089&748.78&1.27e+03&7&0.0077&623.36\\\\\n\t\t& &$(50,45)$&2.17e+04&36&0.0082&79334.95&2.18e+04&43&0.0079&32920.78\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Inverse single facility location problem in the plane with variable coordinates", "authors": ["Nazanin Tour-Savadkoohi", "Jafar Fathali"], "url": "https://arxiv.org/abs/2503.07016v1", "attribution": "\"Inverse single facility location problem in the plane with variable coordinates\" by Nazanin Tour-Savadkoohi and Jafar Fathali, arXiv:2503.07016v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.15771v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|} \n \\cline{2-4}\n & \\multicolumn{3}{c|}{\\textbf{Confidence $1-\\gamma$}} \\\\\n \\hline\n \\textbf{Method} & 0.5 & 0.8 & 0.9 \\\\\n \\hline\n RESET Ensemble & 82987 & 82729 & 82558 \\\\\n \\hline\n FDP-SD & 75977 & 75464 & 75254 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{The average number of discoveries at 1\\% FDP threshold at varying confidence parameters using the HEK293 data.} The average number of discoveries reported for RESET Ensemble and FDP-SD on the combined HEK293 spectrum files at different confidence values $1 - \\gamma = 0.5, 0.8, 0.9$. The averages are taken over 5 applications of each method, once for each of the 5 combined target-decoy databases.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A semi-supervised framework for diverse multiple hypothesis testing scenarios", "authors": ["Jack Freestone", "William Stafford Noble", "Uri Keich"], "url": "https://arxiv.org/abs/2411.15771v2", "attribution": "\"A semi-supervised framework for diverse multiple hypothesis testing scenarios\" by Jack Freestone, William Stafford Noble, and Uri Keich, arXiv:2411.15771v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.20517v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Criterion values for solutions $\\mathbf{p}^*$ (final single optimal solution), $\\mathbf{p}_{challenge}$ (solution submitted to the Challenge), $\\mathbf{p}_{initial}$ (initial solution given by the Challenge), under Scenario 1, Site 0}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n & \\textbf{$\\mathbf{p}^*$} & \\textbf{$\\mathbf{p}_{challenge}$} & \\textbf{$\\mathbf{p}_{initial}$} \\\\ \\hline\n\\textbf{Median Absolute Difference ($\\mathbf{f}_{11}$)} & 53 & 823 & 4{,}012 \\\\\n\\textbf{Max Absolute Difference ($\\mathbf{f}_{12}$)} & 2{,}537 & 4{,}320 & 19{,}222 \\\\\n\\textbf{\\# of Non-zero Harvest Week ($\\mathbf{f}_{13}$)} & 50 & 53 & 54 \\\\\n\\textbf{Total Amount of Wasted Product ($\\mathbf{f}_{14}$)} & 16{,}514 & 34{,}320 & 102{,}204 \\\\ \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Multi-Objective Capacity-Constrained Optimization of Corn Planting Scheduling", "authors": ["Mingshi Cui", "Kunting Qi", "Byran Smucker", "Durai Sundarmoorthi"], "url": "https://arxiv.org/abs/2503.20517v1", "attribution": "\"A Multi-Objective Capacity-Constrained Optimization of Corn Planting Scheduling\" by Mingshi Cui, Kunting Qi, Byran Smucker, and Durai Sundarmoorthi, arXiv:2503.20517v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.15586v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|r|r|r}\n \\textbf{PC Index} & \\textbf{Annualized Return} & \\textbf{Annualized Volatility} & \\textbf{Sharpe Ratio} \\\\\n \\hline\n 28 & 2.434612 & 1.576678 & 1.544140 \\\\\n 0 & 0.176736 & 0.142872 & 1.237029 \\\\\n 1 & 0.202403 & 0.400229 & 0.505717 \\\\\n 2 & 0.157184 & 0.452778 & 0.347156 \\\\\n 20 & 0.338361 & 1.080644 & 0.313110 \\\\\n 17 & 0.001927 & 0.869588 & 0.002216 \\\\\n 24 & -1.000000 & 21.857259 & -0.045751 \\\\\n 15 & -1.000000 & 14.337079 & -0.069749 \\\\\n 27 & -0.271841 & 2.452422 & -0.110846 \\\\\n 9 & -0.228341 & 1.346807 & -0.169543 \\\\\n 21 & -0.421951 & 2.404992 & -0.175448 \\\\\n 10 & -0.999998 & 5.298901 & -0.188718 \\\\\n 5 & -0.133552 & 0.691459 & -0.193146 \\\\\n 8 & -0.999412 & 4.495682 & -0.222305 \\\\\n 25 & -0.804200 & 2.987966 & -0.269146 \\\\\n 13 & -0.992816 & 3.578139 & -0.277467 \\\\\n 16 & -0.999438 & 3.376408 & -0.296006 \\\\\n 7 & -0.208852 & 0.675296 & -0.309275 \\\\\n 6 & -0.196401 & 0.583769 & -0.336437 \\\\\n 12 & -0.792039 & 1.651738 & -0.479519 \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Clear demonstration of Sharpe ratios, annualized returns, and volatilities for selected eigen-portfolios computed from the training set. Principal Component 28 achieves the highest Sharpe ratio of 1.54, corresponding to an annualized return of approximately 243\\%, suggesting strong in-sample performance.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Eigen Portfolios: From Single Component Models to Ensemble Approaches", "authors": ["ZhengXiang Zhou", "Yuqi Luan"], "url": "https://arxiv.org/abs/2508.15586v1", "attribution": "\"Eigen Portfolios: From Single Component Models to Ensemble Approaches\" by ZhengXiang Zhou and Yuqi Luan, arXiv:2508.15586v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.10684v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hard environment}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n \\hline\n Card & Cores & Peak single precision flops \\\\ \\hline\n Nvidia Tesla K40c & 2880 & 4.29Tflops \\\\ \\hline\n Nvidia Tesla K20c & 2496 & 3.52 Tflops \\\\ \\hline\n Intel Xeon E5-2630 & 6 & 0.134 Tflops \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "GPU acceleration of a patient-specific airway image segmentation and its assessment", "authors": ["Yu-Wei Chang", "Tony W. H. Sheu"], "url": "https://arxiv.org/abs/2012.10684v1", "attribution": "\"GPU acceleration of a patient-specific airway image segmentation and its assessment\" by Yu-Wei Chang and Tony W. H. Sheu, arXiv:2012.10684v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.09307v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cccc|}\n\\hline\nInfant & $F_{s}$ (Hz) & Base & Gain \\\\ \n \\hline\n1 & 250 & 16 & 800.6597\\\\\n2 & 500 & 16 & 1220.7707\\\\\n3 & 250 & 16 & 1140.7954\\\\\n4 & 500 & 16 & 834.3036\\\\\n5 & 500 & 16 & 800.6597\\\\\n6 & 500 & 16 & 800.6141\\\\\n7 & 500 & 16 & 1283.8528\\\\\n8 & 500 & 16 & 1420.7631\\\\\n9 & 500 & 16 & 800.6597\\\\\n10 & 500 & 16 & 800.4159\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Information obtained from the header file(.hea) for each infant from the database. }\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Computational Challenges in Non-parametric Prediction of Bradycardia in Preterm Infants", "authors": ["Sinjini Mitra"], "url": "https://arxiv.org/abs/2011.09307v1", "attribution": "\"Computational Challenges in Non-parametric Prediction of Bradycardia in Preterm Infants\" by Sinjini Mitra, arXiv:2011.09307v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.22334v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccll}\n $K$ & $\\gamma_K$ & $\\alpha_K$ & & $\\sigma^2_K$\\\\\n \\hline\n 1 &1 &2 & $=2$ & 0.420\\\\\n2& $\\frac{3}{2}$ &$\\frac{9}{5}$ &$=1.8$ & 0.242 \\\\\n3 &$\\frac{133}{72}$ &$\\frac{133}{78}$ &$\\approx1.7051$ & 0.135\\\\\n4 & $\\frac{2384}{1125}$ &$\\frac{9536}{5775}$ &$\\approx1.6513$ & 0.083\\\\\n5 &$\\frac{36469}{15552}$& $\\frac{182345}{112752}$& $\\approx1.6172$ & 0.056 \\\\\n6 & $\\frac{31796145419183}{12522149640000}$ &$\\frac{31796145419183}{19945995498000}$ & $\\approx1.5941$ \n& 0.040\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean and variance for small $K$. The variance is approximated using a Monte-Carlo method on with 1 million samples of $D$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Probabilistic analysis of optimal multi-pivot QuickSort", "authors": ["Cecilia Holmgren", "Jasper Ischebeck", "Daniel Krenn", "Florian Lesny", "Ralph Neininger"], "url": "https://arxiv.org/abs/2503.22334v1", "attribution": "\"Probabilistic analysis of optimal multi-pivot QuickSort\" by Cecilia Holmgren, Jasper Ischebeck, Daniel Krenn, Florian Lesny, and Ralph Neininger, arXiv:2503.22334v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.10988v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc}\n & Euler-Maruyama & Milstein & simplified order 2.0 weak Taylor\\\\\n \\hline\n weak & $1$ & $1$ & $2$\\\\\n strong & $1/2$ & $1$ & $1$\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Weak and strong convergence rates of given different discretization schemes}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A numerical Fourier cosine expansion method with higher order Taylor schemes for fully coupled FBSDEs", "authors": ["Balint Negyesi", "Cornelis W. Oosterlee"], "url": "https://arxiv.org/abs/2501.10988v1", "attribution": "\"A numerical Fourier cosine expansion method with higher order Taylor schemes for fully coupled FBSDEs\" by Balint Negyesi and Cornelis W. Oosterlee, arXiv:2501.10988v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03735v5_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The mean and standard deviation of the total reward achieved by different strategies under various configurations on unit rewards and failure costs.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|cc|cc|cc|cc|cc|}\n\\hline\n & & \\multicolumn{2}{c|}{Fixed Threshold} & \\multicolumn{2}{c|}{RL ignoring MR} & \\multicolumn{2}{c|}{Myopic Policy} & \\multicolumn{2}{c|}{RL with MR} & \\multicolumn{2}{c|}{Perfect Info MDP} \\\\ \\hline\nProtein Value & Failure Cost & $\\widehat{\\rho}^c(\\pi)$ & $\\widehat{\\mbox{SD}}^c(\\pi)$ & $\\widehat{\\rho}^c(\\pi)$ & $\\widehat{\\mbox{SD}}^c(\\pi)$ & $\\widehat{\\rho}^c(\\pi)$ & $\\widehat{\\mbox{SD}}^c(\\pi)$ & $\\widehat{\\rho}^c(\\pi)$ & $\\widehat{\\mbox{SD}}^c(\\pi)$ & $\\widehat{\\rho}^c(\\pi)$ & $\\widehat{\\mbox{SD}}^c(\\pi)$ \\\\ \\hline\n5 & 400 & 29.68 & 142.86 & 66.22 & 58.89 & 67.84 & 59.08 & 71.28 & 33.90 & 72.20 & 34.01 \\\\\n10 & 400 & 140.60 & 184.12 & 155.00 & 135.88 & 175.80 & 122.88 & 181.34 & 110.70 & 185.71 & 87.15 \\\\\n15 & 400 & 251.51 & 226.89 & 298.34 & 140.28 & 285.80 & 173.85 & 299.47 & 123.00 & 314.25 & 105.63 \\\\ \\hline\n5 & 880 & -13.52 & 279.42 & 61.42 & 102.12 & 60.88 & 102.09 & 71.28 & 33.90 & 71.80 & 33.87 \\\\\n10 & 880 & 97.40 & 318.19 & 143.47 & 196.39 & 168.21 & 166.31 & 171.63 & 126.68 & 177.23 & 125.86 \\\\\n15 & 880 & 208.31 & 358.48 & 268.61 & 227.41 & 273.80 & 228.98 & 281.01 & 194.74 & 295.97 & 152.93 \\\\ \\hline\n5 & 1000 & -24.32 & 313.76 & 60.22 & 113.53 & 59.49 & 113.49 & 71.28 & 33.90 & 71.33 & 34.23 \\\\\n10 & 1000 & 86.60 & 352.19 & 139.87 & 215.71 & 165.18 & 181.73 & 170.43 & 137.03 & 176.03 & 136.27 \\\\\n15 & 1000 & 197.51 & 392.07 & 246.12 & 246.75 & 270.20 & 247.66 & 271.32 & 209.92 & 294.77 & 162.50 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Biomanufacturing Harvest Optimization with Small Data", "authors": ["Bo Wang", "Wei Xie", "Tugce Martagan", "Alp Akcay", "Bram van Ravenstein"], "url": "https://arxiv.org/abs/2101.03735v5", "attribution": "\"Biomanufacturing Harvest Optimization with Small Data\" by Bo Wang, Wei Xie, Tugce Martagan, Alp Akcay, and Bram van Ravenstein, arXiv:2101.03735v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17996v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrr|rrrr} \n\\multicolumn{4}{c}{problem sizes} & \\multicolumn{3}{c}{timing (s)} & \n\\multirow{2}{*}{iterations} \\\\ \n$n$ & $q$ & $m$ & $nm$ & MOSEK & PDMCF (CPU) & PDMCF (GPU) \\\\\n\\hline\n$3000$ & $10$ & $34424$ & $1\\times 10^8$ & OOM & $106274$ & $139$ & $4140$ \\\\\n$5000$ & $10$ & $57338$ & $3\\times 10^8$ & OOM & $382400$ & $421$ & $3970$ \\\\\n$10000$ & $10$ & $114054$ & $1\\times 10^9$ & OOM & $1809517$ & $2078$ & $4380$ \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Runtime table for large size problems (JAX).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solving Large Multicommodity Network Flow Problems on GPUs", "authors": ["Fangzhao Zhang", "Stephen Boyd"], "url": "https://arxiv.org/abs/2501.17996v2", "attribution": "\"Solving Large Multicommodity Network Flow Problems on GPUs\" by Fangzhao Zhang and Stephen Boyd, arXiv:2501.17996v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08239v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Percentage of helpful and persona-consistent messages from each agent.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n & Helpful \\% & Role Consistent \\% \\\\\n \\midrule\n LaMDA Everest & 65 & 91 \\\\\n PT Everest & 18 & 85 \\\\\n LaMDA Music & 57 & 89 \\\\\n PT Music & 31 & 84 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "LaMDA: Language Models for Dialog Applications", "authors": ["Romal Thoppilan", "Daniel De Freitas", "Jamie Hall", "Noam Shazeer", "Apoorv Kulshreshtha", "Heng-Tze Cheng", "Alicia Jin", "Taylor Bos", "Leslie Baker", "Yu Du", "YaGuang Li", "Hongrae Lee", "Huaixiu Steven Zheng", "Amin Ghafouri", "Marcelo Menegali", "Yanping Huang", "Maxim Krikun", "Dmitry Lepikhin", "James Qin", "Dehao Chen", "Yuanzhong Xu", "Zhifeng Chen", "Adam Roberts", "Maarten Bosma", "Vincent Zhao", "Yanqi Zhou", "Chung-Ching Chang", "Igor Krivokon", "Will Rusch", "Marc Pickett", "Pranesh Srinivasan", "Laichee Man", "Kathleen Meier-Hellstern", "Meredith Ringel Morris", "Tulsee Doshi", "Renelito Delos Santos", "Toju Duke", "Johnny Soraker", "Ben Zevenbergen", "Vinodkumar Prabhakaran", "Mark Diaz", "Ben Hutchinson", "Kristen Olson", "Alejandra Molina", "Erin Hoffman-John", "Josh Lee", "Lora Aroyo", "Ravi Rajakumar", "Alena Butryna", "Matthew Lamm", "Viktoriya Kuzmina", "Joe Fenton", "Aaron Cohen", "Rachel Bernstein", "Ray Kurzweil", "Blaise Aguera-Arcas", "Claire Cui", "Marian Croak", "Ed Chi", "Quoc Le"], "url": "https://arxiv.org/abs/2201.08239v3", "attribution": "\"LaMDA: Language Models for Dialog Applications\" by Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Vincent Zhao, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Pranesh Srinivasan, Laichee Man, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, and Quoc Le, arXiv:2201.08239v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11653v3_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c|c||} \n \\hline\n Name & sorted? & epochs & batch size and hyper-parameters\\\\ \n \\hline\n \\hline\n baseline & No & 100 & $\\cdot$\\\\\n \\hline\n sorted & Yes & 100 & $\\cdot$\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Experimental details of baseline model vs sorted. $\\cdot$ indicates that the \\textit{batch size} = 50 and it uses the baseline hyper-parameters.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Representations learnt by SGD and Adaptive learning rules: Conditions that vary sparsity and selectivity in neural networks", "authors": ["Jin Hyun Park"], "url": "https://arxiv.org/abs/2201.11653v3", "attribution": "\"Representations learnt by SGD and Adaptive learning rules: Conditions that vary sparsity and selectivity in neural networks\" by Jin Hyun Park, arXiv:2201.11653v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.12530v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrrr} \\toprule \n& \\multicolumn{7}{c}{Binomial Model} \\\\ \\midrule \n& \\multicolumn{3}{c}{Gamma} && \\multicolumn{3}{c}{Log-Normal} \\\\ \\midrule \n& $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ && $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ \\\\ \\toprule \n \\\\ \n$\\mathbb{E}(L)$ & 1577.8 & 1571.5 & 1578.4 && 1581.7 & 1576.2 & 1582.7\\\\ \n$\\mathrm{Var}(L)$& 160179.5 & 247943.9 & 626190.6 && 162067.5 & 247069.8 & 614713.4 \\\\ \n$\\varsigma_L$ & 0.333 & 0.561 & 0.993 && 0.377 & 0.660 & 2.152 \\\\ \n$\\mathrm{VaR}_{0.9}(L)$ & 2111.9 & 2219.1 & 2634.2 && 2112.9 & 2242.4 & 2526.6 \\\\ \n$\\mathrm{ES}_{0.9}(L)$ & 2331.7 & 2538.8 & 3233.3 && 2342.4 & 2557.5 & 3280.3 \\\\ \n$\\mathrm{VaR}_{0.95}(L)$ & 2275.9 & 2471.3 & 3070.5 && 2288.4 & 2468.1 & 2996.2 \\\\ \n$\\mathrm{ES}_{0.95}(L)$ & 2468.9 & 2748.0 & 3628.9 && 2491.4 & 2772.5 & 3838.4 \\\\ \n$\\mathrm{VaR}_{0.99}(L)$ & 2587.3 & 2891.3 & 3999.6 && 2608.8 & 2943.8 & 4228.6 \\\\ \n$\\mathrm{ES}_{0.99}(L)$ & 2755.9 & 3188.6 & 4490.2 && 2809.0 & 3234.4 & 5424.2 \\\\ \n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Microscopic Traffic Models, Accidents, and Insurance Losses", "authors": ["Sojung Kim", "Marcel Kleiber", "Stefan Weber"], "url": "https://arxiv.org/abs/2208.12530v2", "attribution": "\"Microscopic Traffic Models, Accidents, and Insurance Losses\" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.03490v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n \\textsf{ Poisson brackets} & $\\begin{matrix}\n \\textsf{ No. of polynomials without} \\\\\n \\textsf{ using the grading method }\n \\end{matrix}$ & $\\begin{matrix}\n \\textsf{Maximum No. of polynomials after applying} \\\\\n \\textsf{ the grading method} \n \\end{matrix}$ \\\\\n \\hline \n $\\{\\textbf{q}_2,\\textbf{q}_2\\}$ & $ 12 $ & $2 $ \\\\\n \\hline \n $\\{\\textbf{q}_2,\\textbf{q}_3\\}$ & $24 $ & $6 $ \\\\\n \\hline \n $\\{\\textbf{q}_3,\\textbf{q}_3\\}$ & $42 $ & $12 $ \\\\\n \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of the number of polynomials }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the construction of polynomial Poisson algebras: a novel grading approach", "authors": ["Rutwig Campoamor-Stursberg", "Danilo Latini", "Ian Marquette", "Junze Zhang", "Yao-Zhong Zhang"], "url": "https://arxiv.org/abs/2503.03490v1", "attribution": "\"On the construction of polynomial Poisson algebras: a novel grading approach\" by Rutwig Campoamor-Stursberg, Danilo Latini, Ian Marquette, Junze Zhang, and Yao-Zhong Zhang, arXiv:2503.03490v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.07545v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{FD Filter results for the experimental data.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|ll|}\n\\cline{3-4}\n & & \\multicolumn{2}{c|}{FD filter} \\\\ \\cline{3-4} \n & & Healthy & Faulty \\\\ \\hline\n\\multirow{2}{*}{Ground truth} & Healthy & 48 & 0 \\\\ \\cline{2-4} \n & Faulty & 0 & 99 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fault Isolation for the Ink Deposition Process in High-End Industrial Printers", "authors": ["Casper van Peijpe", "Farhad Ghanipoor", "Youri de Loore", "Pim Hacking", "Nathan van de Wouw", "Peyman Mohajerin Esfahani"], "url": "https://arxiv.org/abs/2412.07545v1", "attribution": "\"Fault Isolation for the Ink Deposition Process in High-End Industrial Printers\" by Casper van Peijpe, Farhad Ghanipoor, Youri de Loore, Pim Hacking, Nathan van de Wouw, and Peyman Mohajerin Esfahani, arXiv:2412.07545v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.00288v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\nDimension & Method & $N$ & Epochs & Test error & Training Time (Seconds) \\\\\\hline\n\\multirow{2}{*}{$d=2$} & RF & 500 & 1000 & $2.22 \\times 10^{-3}$ & 42.29 \\\\\\cline{2-6}\n & PINN & - & 2000 & $8.98 \\times 10^{-3}$ & 51.46 \\\\\\hline \n\\multirow{2}{*}{$d=4$} & RF & 500 & 1000 & $1.12 \\times 10^{-3}$ & 51.24 \\\\\\cline{2-6}\n & PINN & - & 2000 & $9.58 \\times 10^{-4}$ & 55.49 \\\\\\hline \n \\multirow{2}{*}{$d=8$} & RF & 100 & 1500 & $1.81\\times 10^{-3}$ & 37.84 \\\\\\cline{2-6}\n & PINN & - & 2000 & $3.21 \\times 10^{-3}$ & 54.14 \\\\\\hline \n\\end{tabular}\n\\caption{Comparison between random feature model and PINN for the nonlinear Poisson PDEs. We report the number of random features, number of epochs, test error, and training time for each model.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solving Partial Differential Equations with Random Feature Models", "authors": ["Chunyang Liao"], "url": "https://arxiv.org/abs/2501.00288v1", "attribution": "\"Solving Partial Differential Equations with Random Feature Models\" by Chunyang Liao, arXiv:2501.00288v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.01780v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Frequency of Special Characters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccl}\n \\toprule\n Non-English or Math&Frequency&Comments\\\\\n \\midrule\n \\O & 1 in 1,000& For Swedish names\\\\\n $\\pi$ & 1 in 5& Common in math\\\\\n \\$ & 4 in 5 & Used in business\\\\\n $\\Psi^2_1$ & 1 in 40,000& Unexplained usage\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments", "authors": ["James Enouen", "Tianshu Sun", "Yan Liu"], "url": "https://arxiv.org/abs/2309.01780v1", "attribution": "\"Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments\" by James Enouen, Tianshu Sun, and Yan Liu, arXiv:2309.01780v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14913v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|cc|} \\hline \n & \\multicolumn{2}{|c|}{$V_{h}$}& \\multicolumn{2}{|c|}{$V_{h}^{+}$}& \\multicolumn{2}{|c|}{$V_{h}^{*}$}\\\\ \\hline \n n& $L^{2}$error & order & $L^{2}$error & order & $L^{2}$error & order \\\\ \\hline \n 10& 1.527e-05& - \n&3.657e-07& - \n&3.596e-07& - \n\\\\ \\hline \n 20& 9.554e-07&3.998\n&2.614e-08&3.806&2.500e-08&3.846\n\\\\ \\hline \n 40& 5.973e-08&4.000\n&1.755e-09&3.896&1.693e-09&3.885\n\\\\ \\hline \n 80& 3.733e-09&4.000\n&1.119e-10&3.971&1.084e-10&3.964\n\\\\ \\hline \n 160& 2.335e-10&3.999\n&7.031e-12&3.993&6.833e-12&3.988\n\\\\ \\hline\n 320& 3.909e-11&2.579&4.400e-13&3.998&3.220e-12&1.085\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{$L^{2}$error and order using $P^{3}$ Lagrange element, Poisson equation in 1d.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A PINN-enriched finite element method for linear elliptic problems", "authors": ["Xiao Chen", "Yixin Luo", "Jingrun Chen"], "url": "https://arxiv.org/abs/2503.14913v1", "attribution": "\"A PINN-enriched finite element method for linear elliptic problems\" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.10933v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc} \n \\toprule\n \\textbf{Parameter} & \\textbf{Meaning} & \\textbf{Value} \\\\\n \\midrule\n $\\beta$ & Discount factor & 0.97\\\\ \n $\\gamma$ & Risk aversion & 2.00\\\\ \n $\\nu$ & Inverse Frisch & 1.00\\\\ \n \n $\\psi$ & Labor scaling parameter & 1.00\\\\ \n $\\xi$ & Capital production parameter & 0.80\\\\ \n $A$ & Capital production parameter & 1.25\\\\ \n $\\alpha$ & Capital share & 0.36\\\\ \n $\\delta$ & Depreciation rate & 0.025\\\\ \n $\\rho_Z$ & Persistence of log productivity & 0.90\\\\ \n $\\sigma_\\varepsilon$ & Std dev of innovation & 0.08\\\\ \n \n $\\underline{Z}$ & Disaster log productivity & -0.15\\\\ \n $\\underline{\\zeta}$ & Capital quality in bad state & 0.95\\\\ \n $\\pi_{\\underline{\\zeta}}$ & Prob. of switching to $\\underline{\\zeta}$ & 0.5\\%\\\\ \n $\\pi_{\\overline{\\zeta}}$ & Prob. of switching to $\\underline{\\zeta}$ & 34\\%\\\\ \n $\\lambda$ & Planner's Pareto weight & 0.01\\\\ \n \\bottomrule \n \\end{tabular}\n\\end{adjustbox}\n\\caption{ Parameters of the model.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Bailouts and Redistribution", "authors": ["Mikayel Sukiasyan"], "url": "https://arxiv.org/abs/2509.10933v1", "attribution": "\"Bailouts and Redistribution\" by Mikayel Sukiasyan, arXiv:2509.10933v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08678v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Proportion of vulnerable crypto projects.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|r|}\n \\hline\n CVE ID & Type & CVSS & Number (\\%) \\\\\\hline\n CVE-2012-1909 & DoS & 5.0 & 0 (0.0\\%) \\\\\\hline\n CVE-2012-3789 & DoS & 5.0 & 0 (0.0\\%) \\\\\\hline\n CVE-2012-1910 & DoS, Execute code & 7.5 & 0 (0.0\\%) \\\\\\hline\n CVE-2012-2459 & DoS & 5.0 & 0 (0.0\\%) \\\\\\hline\n CVE-2014-0160 & Overflow, Gain information & 5.0 & 149 (29.2\\%) \\\\\\hline\n CVE-2013-4627 & DoS & 5.0 & 294 (57.7\\%) \\\\\\hline\n CVE-2013-4165 & Gain information & 4.3 & 43 (8.4\\%) \\\\\\hline\n CVE-2014-0224 & Gain information & 5.8 & 176 (34.5\\%) \\\\\\hline\n CVE-2018-12356 & Execute code & 7.5 & 0 (0.0\\%) \\\\\\hline\n CVE-2018-17144 & DoS & 5.0 & 27 (5.3\\%) \\\\\\hline\n CVE-2019-6250 & Execute code, Overflow & 9.0 & 113 (22.2\\%) \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild", "authors": ["Jusop Choi", "Wonseok Choi", "William Aiken", "Hyoungshick Kim", "Jun Ho Huh", "Taesoo Kim", "Yongdae Kim", "Ross Anderson"], "url": "https://arxiv.org/abs/2201.08678v1", "attribution": "\"Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild\" by Jusop Choi, Wonseok Choi, William Aiken, Hyoungshick Kim, Jun Ho Huh, Taesoo Kim, Yongdae Kim, and Ross Anderson, arXiv:2201.08678v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10981v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Segmentation accuracy of the SWTR-Unet depending on the number of used patient cases ($\\#_{pc}$) for training purposes. Stated are the average DSCs for liver and hepatic lesions.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\n$\\#_{pc}$ & $DSC_{liver}$ & $DSC_{lesion}$ \\\\\n\\hline\n25 & $0.78~\\pm~0.10$ & $0.60~\\pm~0.18$ \\\\\n30 & $0.86~\\pm~0.07$ & $0.72~\\pm~0.21$ \\\\\n35 & $0.92~\\pm~0.04$ & $0.76~\\pm~0.19$ \\\\\n40 & $0.98~\\pm~0.02$ & $0.80~\\pm~0.21$ \\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers", "authors": ["Georg Hille", "Shubham Agrawal", "Pavan Tummala", "Christian Wybranski", "Maciej Pech", "Alexey Surov", "Sylvia Saalfeld"], "url": "https://arxiv.org/abs/2201.10981v3", "attribution": "\"Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers\" by Georg Hille, Shubham Agrawal, Pavan Tummala, Christian Wybranski, Maciej Pech, Alexey Surov, and Sylvia Saalfeld, arXiv:2201.10981v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.02689v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{UAR values for predicting Arousal (A) and Valence (V) levels on the elderly sub-challenge. E2E systems combined ten DNNs to produce the predictions.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\\hline\n {\\bf System} & {\\bf Dev (A/V)} & {\\bf Test (A/V)} \\\\\n \\hline\n Baseline (linguistic) & 40.6/\\textbf{49.2} & 44.0/\\textbf{49.0} \\\\\n \\hline\n Baseline(acoustic) & 35.0/31.6 & \\textbf{50.4}/40.3\\\\\n E2E (single task) & 35.0/39.7 & ?\\\\\n E2E (multitask) & 39.5/39.7 & ?\\\\\n E2E (single task + upsampl.) & 39.8/41.5 & ?\\\\\n E2E (multitask+ upsampl.) & \\textbf{42.9}/42.4 & 38.0/39.5\\\\\n E2E (single task + prob. sampl.) & 35.6/39.6 & ?\\\\\n E2E (multitask+ prob. sampl.) & 40.0/\\textbf{45.5} & 45.8/34.8\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Aalto's End-to-End DNN systems for the INTERSPEECH 2020 Computational Paralinguistics Challenge", "authors": ["Tamás Grósz", "Mittul Singh", "Sudarsana Reddy Kadiri", "Hemant Kathania", "Mikko Kurimo"], "url": "https://arxiv.org/abs/2008.02689v1", "attribution": "\"Aalto's End-to-End DNN systems for the INTERSPEECH 2020 Computational Paralinguistics Challenge\" by Tamás Grósz, Mittul Singh, Sudarsana Reddy Kadiri, Hemant Kathania, and Mikko Kurimo, arXiv:2008.02689v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06560v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule \n & \\multicolumn{3}{c}{Clean} & \\multicolumn{3}{c}{Perturbed} \\\\\n \\cmidrule{2-4} \\cmidrule{5-7}\n Agents & 2 & 4 & 6 & 2 & 4 & 6 \\\\\n \\midrule \n Mean Pool & 82.09 & 89.25 & 92.43 & 0.90 & 12.93 & 41.77 \\\\\n GNN(Mean) & \\textbf{82.19} & \\textbf{89.93} & \\textbf{92.94} & 7.55 & 52.31 & 76.18 \\\\\n GNN(Median) & 82.11 & 87.12 & 90.75 & 12.8 & \\textbf{67.70} & \\textbf{86.30} \\\\\n GNN(Soft Med) & 82.19 & 89.67 & 92.49 & \\textbf{21.53} & 61.37 & 84.99 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Choice of fusion in V2VNet affects performance and robustness. We investigate using mean pooling and using a GNN with various aggregation methods.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Adversarial Attacks On Multi-Agent Communication", "authors": ["James Tu", "Tsunhsuan Wang", "Jingkang Wang", "Sivabalan Manivasagam", "Mengye Ren", "Raquel Urtasun"], "url": "https://arxiv.org/abs/2101.06560v2", "attribution": "\"Adversarial Attacks On Multi-Agent Communication\" by James Tu, Tsunhsuan Wang, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren, and Raquel Urtasun, arXiv:2101.06560v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.11662v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison Between NVIDIA CUDA and Ascend CANN.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\toprule\n \\textbf{Component} & \\textbf{CUDA} & \\textbf{CANN} \\\\\n \\midrule\n GE Graph Engine & TensorRT plugins \\& parser & Graph Engine \\\\\n Collective Communication Library & NV NCCL & HCCL \\\\\n Library/Template & NV CUTLASS & Ascend C High-Level API \\\\\n General Programming & NV CUDA-C & Ascend C Low-Level API \\\\\n Operator Acceleration Library & NV cuDNN & Ascend aclNN \\\\\n Runtime & NV Runtime & Ascend Runtime \\\\\n Driver & NV Driver & Ascend Driver \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs", "authors": ["Feilong Chen", "Yijiang Liu", "Yi Huang", "Hao Wang", "Miren Tian", "Ya-Qi Yu", "Minghui Liao", "Jihao Wu"], "url": "https://arxiv.org/abs/2509.11662v1", "attribution": "\"MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs\" by Feilong Chen, Yijiang Liu, Yi Huang, Hao Wang, Miren Tian, Ya-Qi Yu, Minghui Liao, and Jihao Wu, arXiv:2509.11662v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.07798v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n $n$ & Error $\\|\\cdot\\|_1$ & Order $\\|\\cdot\\|_1$ & Error\n $\\|\\cdot\\|_{\\infty}$ & Order $\\|\\cdot\\|_{\\infty}$ \\\\\n \\hline\n 40 & 2.63E$-5$ & $-$ & 2.97E$-4$ & $-$ \\\\\n \\hline\n 80 & 1.58E$-6$ & 4.06 & 2.01E$-5$ & 3.89 \\\\\n \\hline\n 160 & 6.66E$-8$ & 4.57 & 1.06E$-6$ & 4.24 \\\\\n \\hline\n 320 & 2.33E$-9$ & 4.84 & 4.08E$-8$ & 4.70 \\\\\n \\hline\n 640 & 7.60E$-11$ & 4.94 & 1.34E$-9$ & 4.93 \\\\\n \\hline\n 1280 & 2.35E$-12$ & 5.02 & 4.06E$-11$ & 5.04 \\\\\n \\hline\n \\end{tabular}\n\\caption{Error table for 2D Euler equation, $t=0.025$. WENO5-LWAF5.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order in Space and Time Schemes Through an Approximate Lax-Wendroff Procedure", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2502.07798v1", "attribution": "\"High Order in Space and Time Schemes Through an Approximate Lax-Wendroff Procedure\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2502.07798v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.15104v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative evaluation of EO-1 data experiments for different HSI reconstruction methods from CASSI and RGB measurements.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n \\toprule\n Method & TV & DeSCI & TV-RGB & DeSCI-RGB & DLTR & Fusion & PFusion \\\\\n \\midrule\n M-PSNR & 26.64 & 28.96 & 29.91 & 37.01 & 35.56 & 41.8 & \\textbf{43.34} \\\\\n M-SSIM & 0.839 & 0.948 & 0.931 & 0.977 & 0.963 & 0.983 & \\textbf{0.984} \\\\\n MSA & 4.929 & 4.253 & 3.864 & 1.6 & 1.78 & 1.03 & \\textbf{0.85} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fast Hyperspectral Image Recovery via Non-iterative Fusion of Dual-Camera Compressive Hyperspectral Imaging", "authors": ["Wei He", "Naoto Yokoya", "Xin Yuan"], "url": "https://arxiv.org/abs/2012.15104v1", "attribution": "\"Fast Hyperspectral Image Recovery via Non-iterative Fusion of Dual-Camera Compressive Hyperspectral Imaging\" by Wei He, Naoto Yokoya, and Xin Yuan, arXiv:2012.15104v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.06705v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantiles for sum of three consecutive terms}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\hline\n Quantile & Actual & Trans-Lin Reg-Var & Gaussian & Lin Reg-Var & Lin Reg-Var \\\\\n & & in $\\mathbb{R}^+$ & & in $\\mathbb{R}$ & in $\\mathbb{R}^+$ \\\\\\hline\n 0.95 & 8.28 & 8.45 & 7.93 & 7.45 & 8.92 \\\\\n 0.98 & 13.09 & 12.91 & 11.62 & 11.01 & 13.79 \\\\ \n 0.99 & 16.79 & 16.72 & 14.34 & 14.08 & 17.74 \\\\ \n 0.995 & 20.29 & 20.46 & 17.02 & 18.00 & 21.47 \\\\ \n 0.999 & 27.17 & 28.09 & 23.00 & 27.22 & 29.24 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Transformed-Linear Models for Time Series Extremes", "authors": ["Nehali Mhatre", "Daniel Cooley"], "url": "https://arxiv.org/abs/2012.06705v3", "attribution": "\"Transformed-Linear Models for Time Series Extremes\" by Nehali Mhatre and Daniel Cooley, arXiv:2012.06705v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2311.05743v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sample table title}\n\\begin{tabular}{lll}\n \\toprule\n \\multicolumn{2}{c}{Part} \\\\\n \\cmidrule(r){1-2}\n Name & Description & Size ($\\mu$m) \\\\\n \\midrule\n Dendrite & Input terminal & $\\sim$100 \\\\\n Axon & Output terminal & $\\sim$10 \\\\\n Soma & Cell body & up to $10^6$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models", "authors": ["Gang Hu"], "url": "https://arxiv.org/abs/2311.05743v1", "attribution": "\"Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models\" by Gang Hu, arXiv:2311.05743v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.19625v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mechanism Tests: OLS Regressions on `Open` Skill Classification}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n& \\textbf{(1)} & \\textbf{(2)} \\\\\n& \\textit{Dep. Var.:} & \\textit{Dep. Var.:} \\\\\n& AI Teaching Impact & AI Skill Disruption \\\\\n\\midrule\nOpen Skill & -0.837*** & -3.377*** \\\\\n& (0.232) & (0.436) \\\\\n\\addlinespace\nIntercept & 0.527*** & 1.623*** \\\\\n& (0.165) & (0.260) \\\\\n\\midrule\nObservations & 152 & 152 \\\\\nAdj. $R^2$ & 0.113 & 0.441 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Training for Obsolescence? The AI-Driven Education Trap", "authors": ["Andrew J. Peterson"], "url": "https://arxiv.org/abs/2508.19625v1", "attribution": "\"Training for Obsolescence? The AI-Driven Education Trap\" by Andrew J. Peterson, arXiv:2508.19625v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.02102v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameter estimates for Independent Frailty Model for Simulated Data}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\n\t\t\t\t\t\\hline\n\t\t\t\t\tparameter & Progression & & & Relapse & & & Death & & \\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\t& true & estimate & std. & true & estimate & std.& true & estimate & std \\\\\n\t\t\t\t\t& value & estimate & error & value & estimate & error & value & estimate & error\\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\tAge & -0.06 & -0.063 & 0.030 & -0.05 & -0.076 & 0.033 & -0.03 & -0.047 & 0.023\\\\\n\t\t\t\t\tt-stage &0.1 & 0.859 & 0.901 & 0.2 & -0.01 & 0.841 & 0.3 & 0.150 & 0.724 \\\\\n\t\t\t\t\tn-stage &0.5 & 0.192 & 0.916 & 0.2 & 0.632 & 0.837 & 0.3 & 0.282 & 0.723\\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\tFrailty & 0.25 & 7.7e-09 & p-value & 0.243 & 7.7e-09 & p-value & 0.238 & 7.5e-09 & p-value\\\\\n\t\t\t\t\tVariances & & &= 0.95 & & & = 0.99& & & = 0.96\\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A modified risk detection approach of biomarkers by frailty effect on multiple time to event data", "authors": ["Atanu Bhattacharjee", "Gajendra K. Vishwakarma", "Souvik Banerjee"], "url": "https://arxiv.org/abs/2012.02102v2", "attribution": "\"A modified risk detection approach of biomarkers by frailty effect on multiple time to event data\" by Atanu Bhattacharjee, Gajendra K. Vishwakarma, and Souvik Banerjee, arXiv:2012.02102v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12423v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Deep Neural Network Models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cll}\n\\toprule\n\\textbf{Domain/Model Class}&\\textbf{Model}&\\textbf{Reference} \\\\\n\\midrule\n & VGG16&\\\\\nComputer Vision (CV) & Inceptionv3&\\\\\n & ResNet50&\\\\\n\\midrule\nLanguage (NLP) & BERT&\\\\\n\\midrule\nGeometric & DimeNet&\\\\\n&SchNet&\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Benchmarking Resource Usage for Efficient Distributed Deep Learning", "authors": ["Nathan C. Frey", "Baolin Li", "Joseph McDonald", "Dan Zhao", "Michael Jones", "David Bestor", "Devesh Tiwari", "Vijay Gadepally", "Siddharth Samsi"], "url": "https://arxiv.org/abs/2201.12423v1", "attribution": "\"Benchmarking Resource Usage for Efficient Distributed Deep Learning\" by Nathan C. Frey, Baolin Li, Joseph McDonald, Dan Zhao, Michael Jones, David Bestor, Devesh Tiwari, Vijay Gadepally, and Siddharth Samsi, arXiv:2201.12423v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.20108v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\n\t\t\t\\toprule\n\t\t\t\\textbf{Feature} &\n\t\t\t\\textbf{Calculation} \\\\\n\t\t\t\\midrule\n\t\t\t$x_t^{o}$ &\n\t\t\t$x_t^{o}=S_t^o/S_t^c-1$ \\\\\n\t\t\t$x_t^{h}$ &\n\t\t\t$x_t^{h}=S_t^h/S_t^c-1$ \\\\\n\t\t\t$x_t^{l}$ &\n\t\t\t$x_t^{l}=S_t^l/S_t^c-1$ \\\\\n\t\t\t$x_t^{c}$ &\n\t\t\t$x_t^{c}=S_t^c/S_{t-1}^c-1$ \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Mitigating Distribution Shift in Stock Price Data via Return-Volatility Normalization for Accurate Prediction", "authors": ["Hyunwoo Lee", "Jihyeong Jeon", "Jaemin Hong", "U Kang"], "url": "https://arxiv.org/abs/2508.20108v2", "attribution": "\"Mitigating Distribution Shift in Stock Price Data via Return-Volatility Normalization for Accurate Prediction\" by Hyunwoo Lee, Jihyeong Jeon, Jaemin Hong, and U Kang, arXiv:2508.20108v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.11257v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Root Mean Squared Error from 200 Experiments}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\hline\n \\textbf{Method} & $n=1000$ & $n=3000$ & $n=5000$ & $n=7000$ & $n=9000$ & $n=11000$ \\\\\n \\hline\n Monte Carlo (MC) & 0.0096 & 0.0062 & 0.0048 & 0.0039 & 0.0035 & 0.0029 \\\\\n PEMC & 0.0055 & 0.0028 & 0.0024 & 0.0019 & 0.0018 & 0.0015 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate", "authors": ["Fengpei Li", "Haoxian Chen", "Jiahe Lin", "Arkin Gupta", "Xiaowei Tan", "Honglei Zhao", "Gang Xu", "Yuriy Nevmyvaka", "Agostino Capponi", "Henry Lam"], "url": "https://arxiv.org/abs/2412.11257v3", "attribution": "\"Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate\" by Fengpei Li, Haoxian Chen, Jiahe Lin, Arkin Gupta, Xiaowei Tan, Honglei Zhao, Gang Xu, Yuriy Nevmyvaka, Agostino Capponi, and Henry Lam, arXiv:2412.11257v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10408v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllllllll}\nTotal & White & Black & Asian & Native & Other & Two+ & Male & Female & Young & Middle & Old \\\\\n\\hline \\\\\n0.0004 & 0.016 & 0.065& 0.023 & 0.0367 & 0.020 & 0.033 & 0.016 & 0.008 & 0.040 & 0.045 & 0.030 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An Algorithmic Framework for Bias Bounties", "authors": ["Ira Globus-Harris", "Michael Kearns", "Aaron Roth"], "url": "https://arxiv.org/abs/2201.10408v4", "attribution": "\"An Algorithmic Framework for Bias Bounties\" by Ira Globus-Harris, Michael Kearns, and Aaron Roth, arXiv:2201.10408v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2010.13892v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Neural Network Confusion Matrix}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n & Predicted NO & Predicted YES\\tabularnewline\n\\hline\n\\hline\nTrue NO & 2087 & 18\\tabularnewline\n\\hline\nTrue YES & 39 & 8\\tabularnewline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction", "authors": ["Amir Mukeri", "Habibullah Shaikh", "D. P. Gaikwad"], "url": "https://arxiv.org/abs/2010.13892v2", "attribution": "\"Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction\" by Amir Mukeri, Habibullah Shaikh, and D. P. Gaikwad, arXiv:2010.13892v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.15035v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n \\hline\n \\multicolumn{5}{l}{Data 1: Player-level match performance (\\textit{N} = 357)}\\\\\n \\hline\n & Q1 & Median & Mean & Q3 \\\\\n Winning rate (\\%) & 31 &46 &43 &81 \\\\\n \\hline\n \\multicolumn{5}{l}{Data 2: Move decisions (\\textit{N} = 1,357,523)}\\\\\n \\hline\n & Q1 &Median &Mean&Q3 \\\\\n Move counts within a match &176&212&216&254\\\\\n The Human-AI Gap (percentage points) &0.39&1.68&3.49&4.51 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Summary Statistics}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo", "authors": ["Minkyu Shin", "Jin Kim", "Minkyung Kim"], "url": "https://arxiv.org/abs/2012.15035v3", "attribution": "\"Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo\" by Minkyu Shin, Jin Kim, and Minkyung Kim, arXiv:2012.15035v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00675v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{System name} & \\textbf{Acc} & \\textbf{Alana Dataset use} \\\\ \\hline\nRandom Forest & { \\textbf{ 69.1\\%}} & Training on 70\\% \\\\ \n & & Testing on 30\\% \\\\ \n & & with 25 trees \\\\ \\hline\nAFINN & 55.4\\% & Testing on all \\\\ \\hline\nNLTK Vader & 55.1\\% & Testing on all \\\\ \\hline\nStanford CoreNLP & 53.6\\% & Training on 70\\% \\\\ \n & & Testing on 30\\% \\\\ \\hline\nNaive Bayes & 49.8\\% & Testing on all \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Systems Tested and Accuracy}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Sentiment Analysis for Open Domain Conversational Agent", "authors": ["Mohamad Alissa", "Issa Haddad", "Jonathan Meyer", "Jade Obeid", "Nicolas Wiecek", "Sukrit Wongariyakavee"], "url": "https://arxiv.org/abs/2101.00675v2", "attribution": "\"Sentiment Analysis for Open Domain Conversational Agent\" by Mohamad Alissa, Issa Haddad, Jonathan Meyer, Jade Obeid, Nicolas Wiecek, and Sukrit Wongariyakavee, arXiv:2101.00675v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16730v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of each metric with different summary functions on RAMIE and AutoLaparo, whereby $r$ denotes Pearson and $\\tau$ denotes Kendall correlation. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n Metric & AutoLaparo ($r$) & AutoLaparo ($\\tau)$ & RAMIE ($r$) & RAMIE ($\\tau$) \\\\\n\\midrule\nLogME (mean) & 0.627 & 0.833 & 0.653 & 0.835 \\\\\nLogME (min) & 0.655 & 0.825 & 0.674 & 0.835 \\\\\nLogME (max) & 0.594 & 0.696 & 0.608 & 0.747 \\\\\n\\midrule\nHscore (mean) & 0.056 & -0.203 & -0.024 & 0.365 \\\\\nHscore (min) & 0.501 & 0.158 & 0.056 & 0.352 \\\\\nHscore (max) & -0.031 & -0.190 & 0.074 & -0.138 \\\\\n\\midrule\nTransRate (mean) & -0.252 & -0.195 & -0.095 & -0.181 \\\\\nTransRate (min) & -0.194 & -0.188 & -0.052 & -0.201 \\\\\nTransRate (max) & -0.208 & -0.203 & -0.095 & -0.187 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Analysis of Transferability Estimation Metrics for Surgical Phase Recognition", "authors": ["Prabhant Singh", "Yiping Li", "Yasmina Al Khalil"], "url": "https://arxiv.org/abs/2508.16730v1", "attribution": "\"Analysis of Transferability Estimation Metrics for Surgical Phase Recognition\" by Prabhant Singh, Yiping Li, and Yasmina Al Khalil, arXiv:2508.16730v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.17260v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Predicted plane error $e$ vs evaluation score}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\\hline\n$e = |p - t|$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\\\\n\\hline\n\\textbf{Score} & 1.00 & 0.74 & 0.50 & 0.32 & 0.18 & 0.10 & 0.05 & 0.02 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MiceBoneChallenge: Micro-CT public dataset and six solutions for automatic growth plate detection in micro-CT mice bone scans", "authors": ["Nikolay Burlutskiy", "Marija Kekic", "Jordi de la Torre", "Philipp Plewa", "Mehdi Boroumand", "Julia Jurkowska", "Borjan Venovski", "Maria Chiara Biagi", "Yeman Brhane Hagos", "Roksana Malinowska-Traczyk", "Yibo Wang", "Jacek Zalewski", "Paula Sawczuk", "Karlo Pintarić", "Fariba Yousefi", "Leif Hultin"], "url": "https://arxiv.org/abs/2411.17260v1", "attribution": "\"MiceBoneChallenge: Micro-CT public dataset and six solutions for automatic growth plate detection in micro-CT mice bone scans\" by Nikolay Burlutskiy, Marija Kekic, Jordi de la Torre, Philipp Plewa, Mehdi Boroumand, Julia Jurkowska, Borjan Venovski, Maria Chiara Biagi, Yeman Brhane Hagos, Roksana Malinowska-Traczyk, Yibo Wang, Jacek Zalewski, Paula Sawczuk, Karlo Pintarić, Fariba Yousefi, and Leif Hultin, arXiv:2411.17260v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13232v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of equilibria concerning $P_I=0$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n Scenario & Range of $R$ & Condition on $C_I$ & Equilibrium \\\\\n \\hline & & & \\\\\n 1 & $R > \\dfrac{c_w}{\\mu-\\lambda} $ & $C_I\\geq C_{I,0}^{1}(R)\\equiv \\rho^{n_e} \\left[ \\dfrac{c_w n_e}{\\mu}+\\dfrac{c_w}{\\mu-\\lambda}-R\\right]$ & $(0,1)$ \\\\\n 2 \\& 3 & $R \\in \\left(\\dfrac{c_w}{\\mu},\\dfrac{c_w}{\\mu-\\lambda}\\right]$ & $C_I\\geq C_{I,0}^{2,3}(R) \\equiv \\dfrac{n_ec_w}{\\mu}\\left(1-\\dfrac{c_w}{R\\mu} \\right)^{n_e}$ & $(0,\\tilde{P}_J)$\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Queues with inspection cost: To see or not to see?", "authors": ["Jake Clarkson", "Konstantin Avrachenkov", "Eitan Altman"], "url": "https://arxiv.org/abs/2503.13232v1", "attribution": "\"Queues with inspection cost: To see or not to see?\" by Jake Clarkson, Konstantin Avrachenkov, and Eitan Altman, arXiv:2503.13232v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16302v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sources for definitions of network classes in Figure .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l||}\n\\hline \nNetwork class & Source for definition \\\\ \\hline\nTime-consistent galled trees & , p.~3\\\\\nGalled trees & , p.~16 \\\\ \nGalled tree-child networks & , Definition 5 \\\\\nNormal networks & , p.~10\\\\\nTree-child networks & , p.~9\\\\\nGalled networks & , p.~16 \\\\\nReticulation-visible networks & , p.~11\\\\\nPhylogenetic networks & , p.~5 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Enumerative combinatorics of unlabeled and labeled time-consistent galled trees", "authors": ["Lily Agranat-Tamir", "Michael Fuchs", "Bernhard Gittenberger", "Noah A. Rosenberg"], "url": "https://arxiv.org/abs/2504.16302v1", "attribution": "\"Enumerative combinatorics of unlabeled and labeled time-consistent galled trees\" by Lily Agranat-Tamir, Michael Fuchs, Bernhard Gittenberger, and Noah A. Rosenberg, arXiv:2504.16302v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10524v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\hline\n\\textbf{ Lang/Model} &\\textbf{MUSE} & \\textbf{XLM} & \\textbf{XLM-R} \\\\\nCS & 87.0 & 86.6 & 94.4 \\\\ \nCS + EN & 88.1& 93.0 & 95.4 \\\\\n\\hline\nEN & 39.2 & 54.8 & 66.6 \\\\\nES & 69.9 & 78.3 & 88.1\\\\\nEN+ES & 88.2 & 87.8 & 91.2\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Full-training (top) and zero-shot (bottom) accuracy of XL models when using different monolingual corpora. ES is an internal dataset to showcase the effect of having a big Spanish corpus.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing", "authors": ["Arash Einolghozati", "Abhinav Arora", "Lorena Sainz-Maza Lecanda", "Anuj Kumar", "Sonal Gupta"], "url": "https://arxiv.org/abs/2101.10524v3", "attribution": "\"El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing\" by Arash Einolghozati, Abhinav Arora, Lorena Sainz-Maza Lecanda, Anuj Kumar, and Sonal Gupta, arXiv:2101.10524v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.11576v4_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Additional Results for \\textsc{Conjecturing} on Datasets from }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllll}\n & & Number of & & & & \\\\\nInstance & Equation & Invariants & Complexity & Noise & Time (s) & NRMSE \\\\\n\\hline\\hline\nI.6.2.a & $f=e^{-\\theta^2/2}/\\sqrt{2\\pi}$ & 2 & 9 & $10^{-2}$ & 16 & 0.154 \\\\\nI.6.2 & $f=e^{-\\theta^2/2\\sigma^2}/\\sqrt{2\\pi\\sigma^2}$ & 3 & 13 & $10^{-4}$ & 2992 & 0.614 \\\\\nI.6.2.b & $f=e^{-(\\theta-\\theta_1)^2/2\\sigma^2}/\\sqrt{2\\pi\\sigma^2}$ & 5 & 16 & $10^{-4}$ & 4792 & 1.188\\\\\nI.8.14 & $d=\\sqrt{(x_2-x_1)^2+(y_2-y_1)^2}$ & 4 & 10 & $10^{-4}$ & 544 & 1.323\\\\\nI.9.18 & $\\frac{Gm_1m_2}{(x_2-x_1)^2+(y_2-y_1)^2+(z_2-z_1)^2}$ & 9 & 17 & $10^{-5}$ & 5975 & 1.252 \\\\\nI.10.7 & $m=\\frac{m_0}{\\sqrt{1-\\frac{v^2}{c^2}}}$ & 3 & 9 & $10^{-4}$ & 14 & 0.034 \\\\\nI.11.19 & $A=x_1y_1+x_2y_2+x_3y_3$ & 6 & 11 & $10^{-3}$ & 184 & 0.633\\\\\nI.12.1 & $F=\\mu N_n$ & 2 & 3 & $10^{-3}$ & 12 & 0.002\\\\\nI.12.2 & $F=\\frac{q_1q_2}{4\\pi\\epsilon r^2}$ & 5 & 12 & $10^{-2}$ & 17 & 0.885\\\\\nI.12.4 & $E_f = \\frac{q_1}{4\\pi\\epsilon r^2}$ & 4 & 11 & $10^{-2}$ & 12 & 0.525 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Conjecturing-Based Discovery of Patterns in Data", "authors": ["J. P. Brooks", "D. J. Edwards", "C. E. Larson", "N. Van Cleemput"], "url": "https://arxiv.org/abs/2011.11576v4", "attribution": "\"Conjecturing-Based Discovery of Patterns in Data\" by J. P. Brooks, D. J. Edwards, C. E. Larson, and N. Van Cleemput, arXiv:2011.11576v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.11948v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison of Sphere-GAN: estimating saliency based on previous predictions and using previous ground-truth saliency at intervals of N Frames.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\# of Frames & CC ↑ & NSS ↑ & KL ↓ & AUC\\_JUDD ↑ \\\\ \\hline\n1 & \\textbf{0.9082} & \\textbf{6.6322} & \\textbf{0.3896} & \\textbf{0.9717} \\\\ \\hline\n2 & 0.8869 & 6.4312 & 6.4312 & 0.9685 \\\\ \\hline\n3 & 0.8632 & 6.2071 & 0.5447 & 0.9647 \\\\ \\hline\n4 & 0.8377 & 5.9764 & 0.6317 & 0.9605 \\\\ \\hline\n5 & 0.8121 & 5.7479 & 0.7191 & 0.9563 \\\\ \\hline\n6 & 0.7879 & 5.5397 & 0.8038 & 0.9519 \\\\ \\hline\n7 & 0.7631 & 5.3249 & 0.8864 & 0.9477 \\\\ \\hline\n8 & 0.7371 & 5.1114 & 0.9741 & 0.9430 \\\\ \\hline\n9 & 0.7143 & 4.9208 & 1.0840 & 0.9373 \\\\ \\hline\n10 & 0.6914 & 4.7191 & 1.1862 & 0.9321 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sphere-GAN: a GAN-based Approach for Saliency Estimation in 360° Videos", "authors": ["Mahmoud Z. A. Wahba", "Sara Baldoni", "Federica Battisti"], "url": "https://arxiv.org/abs/2509.11948v1", "attribution": "\"Sphere-GAN: a GAN-based Approach for Saliency Estimation in 360° Videos\" by Mahmoud Z. A. Wahba, Sara Baldoni, and Federica Battisti, arXiv:2509.11948v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.10585v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\n{\\bf Test} & {\\bf Time Stamp} & {\\bf Mean} & {\\bf STD} & {\\bf Max\nDistance} & {\\bf Bot Answer}\\\\\n\\hline\\hline\nBaseline $T=0$ & 6:13PM & 14.02 & 0 & 0 & 15 \\\\\n\\hline\nPronoun {\\bf He} & 6:20PM & {\\bf 14.64} & 2.03 & 5.47E-02 & {\\bf {\\color{blue} 15}} \\\\\n\\hline\nPronoun {\\bf She} & 6:21PM & {\\bf 13.74} & 1.47 & 7.37E-02 & {\\bf {\\color{red} 12}} \\\\ \n\\hline\nPronoun {\\bf She} & 6:42PM & 13.74 & 1.48 & 7.62E-02 & 12\\\\\n\\hline\nPronoun {\\bf He} & 6:45PM & {\\bf 13.39} & 1.43 & 5.46E-02 & {\\bf {\\color{blue} 12}} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ {\\bf Effect of Sensitive Attributes: Gender.} I test GPT-3 by changing the original pronoun ``They'' in the prompt, to ``He'' and ``She.'' Gendered pronouns changed the response, dropping from a consistent answer of \\$15 to \\$12 when using the pronoun ``she.'' However, in a later identical test (see the time stamps), the response to the pronoun ``he'' returned by GPT-3 changed to match ``she'' response despite identical prompt sequences. This suggests that additional post-processing steps are implemented in the DaVinci interface beyond sampling from the softmax probabilities. }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Minimum Wage as an Anchor: Effects on Determinations of Fairness by Humans and AI", "authors": ["Dario G. Soatto"], "url": "https://arxiv.org/abs/2210.10585v3", "attribution": "\"The Minimum Wage as an Anchor: Effects on Determinations of Fairness by Humans and AI\" by Dario G. Soatto, arXiv:2210.10585v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00443v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Evaluation results for the two baselines (generic neural NER model and Microsoft Presidio) on the development and test sections of the TAB corpus. We report both the standard, token-level recall $R_{di+qi}$ and precision $P_{di+qi}$ on all identifiers (micro-averaged over all annotators) as well as the three proposed evaluation metrics $\\textit{ER}_{di}$, $\\textit{ER}_{qi}$ and $\\textit{WP}_{di+qi}$ from Section .} %\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccc} \n\t\\toprule %\n\t System & Set & $R_{di+qi}$ & $\\textit{ER}_{di}$ & $\\textit{ER}_{qi}$ & $P_{di+qi}$ & $\\textit{WP}_{di+qi} $ \\\\\n\t \\midrule \nNeural NER (RoBERTa &\tDev & 0.910 & 0.970 & 0.874 & 0.447 & 0.531 \\\\ \nfine-tuned on Ontonotes v5) & Test & 0.906 & 0.940 & 0.874 & 0.441 & 0.515\\\\ \n\t\\midrule \n Presidio (default) & Dev & 0.696 & 0.452 & 0.739 & 0.771 & 0.795 \\\\ \n & Test & 0.707 & 0.460 & 0.758 & 0.761 & 0.790 \\\\ \n \\midrule \nPresidio (+ORG) & Dev & 0.767 & 0.465 & 0.779 & 0.549 & 0.622 \\\\ \n & Test & 0.782 & 0.463 & 0.802 & 0.542 & 0.609 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization", "authors": ["Ildikó Pilán", "Pierre Lison", "Lilja Øvrelid", "Anthi Papadopoulou", "David Sánchez", "Montserrat Batet"], "url": "https://arxiv.org/abs/2202.00443v2", "attribution": "\"The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization\" by Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi Papadopoulou, David Sánchez, and Montserrat Batet, arXiv:2202.00443v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.06345v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Intensities of limit order and cancellation arrivals for noise traders.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\t\t\t\t\t\\toprule \n\t\t\t\t\t$k$ (ticks) & $\\displaystyle \\lambda^{L,k}$ & $10 \\lambda^{C,k}$ \\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\t1 & 0.2842 & 0.8636 \\\\\n\t\t\t\t\t2 & 0.5255 & 0.4635 \\\\\n\t\t\t\t\t3 & 0.2971 &0.1487 \\\\\n\t\t\t\t\t4 & 0.2307 & 0.1096 \\\\\n\t\t\t\t\t5 & 0.0826 & 0.0402 \\\\\n\t\t\t\t\t6 & 0.0682 & 0.0341 \\\\\n\t\t\t\t\t7 & 0.0631 & 0.0311 \\\\\n\t\t\t\t\t8 & 0.0481 & 0.0237 \\\\\n\t\t\t\t\t9 & 0.0462 & 0.0233 \\\\\n\t\t\t\t\t10 & 0.0321 & 0.0178 \\\\\n\t\t\t\t\t11 & 0.0178 & 0.0127 \\\\\n\t\t\t\t\t12 & 0.0015 & 0.0012 \\\\ \n\t\t\t\t\t13 & 0.0001 & 0.0001 \\\\ \n\t\t\t\t\t14 & 0.0000 & 0.0000 \\\\ \n\t\t\t\t\t\\vdots & \\vdots & \\vdots \\\\ \n\t\t\t\t\t30 & 0.0000 & 0.0000 \\\\ \n\t\t\t\t\t\\hline \n\t\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Reinforcement Learning for Trade Execution with Market Impact", "authors": ["Patrick Cheridito", "Moritz Weiss"], "url": "https://arxiv.org/abs/2507.06345v1", "attribution": "\"Reinforcement Learning for Trade Execution with Market Impact\" by Patrick Cheridito and Moritz Weiss, arXiv:2507.06345v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.03247v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\n & \\textbf{less\\_5} & \\textbf{5\\_above} \\\\ \\hline \\hline %& \\textbf{15\\_above} \\\\ \\hline \\hline\n\\textbf{NPTEL-tr} & 23.0\\% & 77.0\\% \\\\%& 4.0\\% \\\\ %\\hline\n\\textbf{NPTEL-dt} & 25.0\\% & 75.0\\% \\\\%& 3.0\\% \\\\ %hline\n\\textbf{NPTEL-et} & 24.0\\% & 76.0\\% \\\\%& 3.0\\% \\\\ %hline\n\\textbf{NPTEL-Unseen\\_1} & 21.0\\% & 79.0\\% \\\\%& 4.0\\% \\\\ %\\hline\n\\textbf{NPTEL-Unseen\\_2} & 23.0\\% & 77.0\\% \\\\\\hline %& 4.0\\% \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Percentage wise data split based on utterance duration in NPTEL data set}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Investigation of Speaker-adaptation methods in Transformer based ASR", "authors": ["Vishwas M. Shetty", "Metilda Sagaya Mary N J", "S. Umesh"], "url": "https://arxiv.org/abs/2008.03247v2", "attribution": "\"Investigation of Speaker-adaptation methods in Transformer based ASR\" by Vishwas M. Shetty, Metilda Sagaya Mary N J, and S. Umesh, arXiv:2008.03247v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2311.15843v4_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{siunitx}\n\\usepackage{amsmath}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Technical data and characteristics of the selected EMLAs for implementation in the manipulator's joints}\n\\begin{tabular}{c||c||c||c}\n \\hline\n \\hline\n \\textbf{Term} & \\textbf{Lift EMLA} & \\textbf{\\textcolor{black}{Tilt EMLA}} & \\textbf{Tel. EMLA}\\\\\n \\hline\n \\hline\n \\multicolumn{4}{c}{\\textit{Linear Unit and Gearbox}}\\\\\n \\hline\n \\hline\n $F_{c0}$& \\SI{145.7}{\\kilo\\newton} & \\SI{120.6}{\\kilo\\newton}&\\SI{6.4}{\\kilo\\newton}\\\\\n \\hline\n $F_{c}$&\\SI{89.8}{\\kilo\\newton}&\\SI{68.6}{\\kilo\\newton}&\\SI{6.1}{\\kilo\\newton}\\\\\n \\hline\n $F_{p0}$&\\SI{261.1}{\\kilo\\newton}&\\SI{199.7}{\\kilo\\newton}&\\SI{17.1}{\\kilo\\newton}\\\\\n \\hline\n $F_{p}$&\\SI{147.4}{\\kilo\\newton}&\\SI{145.5}{\\kilo\\newton}&\\SI{17.1}{\\kilo\\newton}\\\\\n \\hline\n $a_{max}$&\\SI{3.4}{\\meter\\per\\second^2}&\\SI{2}{\\meter\\per\\second^2}&\\SI{6}{\\meter\\per\\second^2}\\\\\n \\hline\n $v_{max}$&\\SI{136}{\\milli\\meter\\per\\second}&\\SI{100}{\\milli\\meter\\per\\second}&\\SI{210}{\\milli\\meter\\per\\second}\\\\\n \\hline\n $M_{BS}$&\\SI{156.5}{\\kilo\\gram}&\\SI{83.6}{\\kilo\\gram}&\\SI{30.4}{\\kilo\\gram}\\\\\n \\hline\n $l$&\\SI{0.02}{\\meter}&\\SI{0.01}{\\meter}&\\SI{0.01}{\\meter}\\\\\n \\hline\n $d$&\\SI{0.075}{\\meter}&\\SI{0.06}{\\meter}&\\SI{0.04}{\\meter}\\\\\n \\hline\n $1/{\\rho}$&\\SI{7}{}&\\SI{5}{}&\\SI{1}{}\\\\\n \\hline\n \\hline\n \\multicolumn{4}{c}{\\textit{Electric Motor (PMSM)}}\\\\\n \\hline\n \\hline\n $J_m$&\\SI{0.016}{\\kilogram\\meter^2}&\\SI{0.0105}{\\kilogram\\meter^2}&\\SI{0.00085} {\\kilogram\\meter^2}\\\\\n \\hline\n $n_N$&\\SI{2850}{rpm}&\\SI{3000}{rpm}&\\SI{3000}{rpm}\\\\\n \\hline\n $\\tau_N$&\\SI{53}{\\newton\\cdot\\meter}&\\SI{29}{\\newton\\cdot\\meter}&\\SI{8}{\\newton\\cdot\\meter}\\\\\n \\hline\n $\\tau_{max}$&\\SI{190}{\\newton\\cdot\\meter}&\\SI{129}{\\newton\\cdot\\meter}&\\SI{32}{\\newton\\cdot\\meter}\\\\\n \\hline\n $i_N$&\\SI{29.5}{\\ampere} & \\SI{18.5}{\\ampere} & \\SI{7.6}{\\ampere}\\\\\n \\hline\n $u_N$&\\SI{315}{\\volt} & \\SI{300}{\\volt} & \\SI{600}{\\volt}\\\\\n \\hline\n $P_N$&\\SI{15.8}{\\kilo\\watt} & \\SI{9.1}{\\kilo\\watt} & \\SI{2.5}{\\kilo\\watt}\\\\\n \\hline\n $R_s$&\\SI{0.14}{\\ohm}&\\SI{0.16}{\\ohm}&\\SI{0.35}{\\ohm}\\\\\n \\hline\n $L_N$&\\SI{2.4}{\\milli\\henry}&\\SI{3.2}{\\milli\\henry}&\\SI{12}{\\milli\\henry}\\\\\n \\hline\n $\\Phi_{P\\!M}$&\\SI{0.15}{\\weber}&\\SI{0.13}{\\weber}&\\SI{0.12}{\\weber}\\\\\n \\hline\n $N_p$&\\SI{8}{}&\\SI{8}{}&\\SI{6}{}\\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Robustness-Guaranteed Observer-Based Control Strategy with Modularity for Cleantech EMLA-Driven Heavy-Duty Robotic Manipulator", "authors": ["Mehdi Heydari Shahna", "Mohammad Bahari", "Jouni Mattila"], "url": "https://arxiv.org/abs/2311.15843v4", "attribution": "\"Robustness-Guaranteed Observer-Based Control Strategy with Modularity for Cleantech EMLA-Driven Heavy-Duty Robotic Manipulator\" by Mehdi Heydari Shahna, Mohammad Bahari, and Jouni Mattila, arXiv:2311.15843v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.10195v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MOSFET device parameters and traditional scaling factors}\n\\begin{tabular}{l|l}\n\t\t\t\t\t\\hline\n\t\t\t\t\t \\textbf{Parameter} & \\textbf{Scaling factor}\\\\\n\\hline\nDevice dimension ~{\\ensuremath{(W, L, tox)}} & ~{\\ensuremath{1{/}K}}\\\\\n\\hline\nDoping concentration ~{\\ensuremath{Na}} & ~{\\ensuremath{K}}\\\\\n\\hline\nVoltage ~{\\ensuremath{V}} & ~{\\ensuremath{1{/}K}}\\\\\n\\hline \nCurrent ~{\\ensuremath{I}} & ~{\\ensuremath{1{/}K}}\\\\\n\\hline\nCapacitance~{\\ensuremath{\\epsilon{A{/}t}}} & ~{\\ensuremath{1{/}K}}\\\\\n\\hline\nDelay time {\\ensuremath{VC{/}I}} & ~{\\ensuremath{1{/}K}}\\\\\n\\hline\nPower dissipation ~{\\ensuremath{VI}} & ~{\\ensuremath{1{/}{K^2}}}\\\\\n\\hline\nPower density ~{\\ensuremath{VI{/}A}} & ~{\\ensuremath{1}}\\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "DeepScaleTool : A Tool for the Accurate Estimation of Technology Scaling in the Deep-Submicron Era", "authors": ["Satyabrata Sarangi", "Bevan Baas"], "url": "https://arxiv.org/abs/2102.10195v1", "attribution": "\"DeepScaleTool : A Tool for the Accurate Estimation of Technology Scaling in the Deep-Submicron Era\" by Satyabrata Sarangi and Bevan Baas, arXiv:2102.10195v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11316v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\emph{Supplemental results on GQA-SGL.} Mean and standard deviation of accuracy (\\%) for four question types in GQA-SGL across at least three repetitions. We use a pre-trained object detector as a feature extractor.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c|c}\n\\toprule\n\\multirow{2}{*}{Methods} & \\multicolumn{4}{c}{Four question types in GQA-SGL} \\\\ \\cmidrule{2-5}\n & verify & query & choose & logical \\\\ \\midrule %\\hline\nTransformer & 50.0 $\\pm$ 1.6 & 35.3 $\\pm$ 5.0 & 50.0 $\\pm$ 7.5 & 55.3 $\\pm$ 4.1 \\\\ %\\hline\nTransformer w/ PR & 51.6 $\\pm$ 7.7 & 45.6 $\\pm$ 5.6 & \\textbf{56.0} $\\pm$ 6.6 & 55.6 $\\pm$ 5.4\\\\ %\\hline\nTMN-Stack (ours) & 51.6 $\\pm$ 4.1 & \\textbf{49.6} $\\pm$ 5.0 & 44.8 $\\pm$ 3.7 & 56.4 $\\pm$ 7.1 \\\\ %\\hline\nTMN-Tree (ours) & \\textbf{56.0} $\\pm$ 7.3 & 48.0 $\\pm$ 5.5 & 47.2 $\\pm$ 3.2 & \\textbf{64.0} $\\pm$ 4.6 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Transformer Module Networks for Systematic Generalization in Visual Question Answering", "authors": ["Moyuru Yamada", "Vanessa D'Amario", "Kentaro Takemoto", "Xavier Boix", "Tomotake Sasaki"], "url": "https://arxiv.org/abs/2201.11316v2", "attribution": "\"Transformer Module Networks for Systematic Generalization in Visual Question Answering\" by Moyuru Yamada, Vanessa D'Amario, Kentaro Takemoto, Xavier Boix, and Tomotake Sasaki, arXiv:2201.11316v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.19806v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Retrieval performances of our model on the MUSDB18 dataset. All metrics are in percentages.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n \\toprule\n & \\multicolumn{3}{c}{Recall $\\uparrow$} & & \\multicolumn{2}{c}{Normalized Rank $\\downarrow$} \\\\\n Model &\n R@1 & R@5 & R@10 & & mean & median \\\\\n \\midrule\n CLAP conditioning (6a7099c4) &\n 33.5 & 63.0 & 74.5 & & 2.5 & 0.5 \\\\\n +contrastive pretraining (6484864f) &\n 32.2 & \\textbf{91.0} & \\textbf{96.2} & & \\textbf{0.6} & \\textbf{0.3} \\\\\n +FiLM conditioning (dfe9828d) &\n \\textbf{38.8} & 89.7 & 95.0 & & 0.7 & \\textbf{0.3} \\\\\n \\midrule\n Contrastive (3ff57432) &\n 2.2 & 72.5 & 86.0 & & 1.5 & 0.7 \\\\\n Stem-JEPA &\n 33.0 & 63.2 & 76.2 & & 2.0 & 0.5 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Zero-shot Musical Stem Retrieval with Joint-Embedding Predictive Architectures", "authors": ["Alain Riou", "Antonin Gagneré", "Gaëtan Hadjeres", "Stefan Lattner", "Geoffroy Peeters"], "url": "https://arxiv.org/abs/2411.19806v2", "attribution": "\"Zero-shot Musical Stem Retrieval with Joint-Embedding Predictive Architectures\" by Alain Riou, Antonin Gagneré, Gaëtan Hadjeres, Stefan Lattner, and Geoffroy Peeters, arXiv:2411.19806v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10148v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rr}\n \\hline\n & value \\\\ \n \\hline\nAccuracy & 0.75 \\\\ \n Kappa & 0.45 \\\\ \n Sensitivity (No burn accuracy) & 0.74 \\\\ \n Specificity (Burn accuracy) & 0.76 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Detecting Crop Burning in India using Satellite Data", "authors": ["Kendra Walker", "Ben Moscona", "Kelsey Jack", "Seema Jayachandran", "Namrata Kala", "Rohini Pande", "Jiani Xue", "Marshall Burke"], "url": "https://arxiv.org/abs/2209.10148v1", "attribution": "\"Detecting Crop Burning in India using Satellite Data\" by Kendra Walker, Ben Moscona, Kelsey Jack, Seema Jayachandran, Namrata Kala, Rohini Pande, Jiani Xue, and Marshall Burke, arXiv:2209.10148v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.01116v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc|cc}\n\\toprule\n& \\multicolumn{2}{c}{\\textbf{Regression Coefficients}} & \\multicolumn{2}{c}{\\textbf{Pre-Treatment Averages}} \\\\\n& Post Service & Post Bill &\\% $\\geq 0 $ & Conditional Mean \\\\\n\\midrule\n\\textbf{Hospital Care} \\\\\nEmergency Department & 0.108*** & -0.021 & 0.67\\% & \\$929.98 \\\\\n& (0.0129) & (0.0133) \\\\\nPreventable Hospitalizations & 0.371*** & -0.194* & 0.04\\% & \\$19,979.89 \\\\\n& (0.0829) & (0.0848) \\\\\n\\midrule\n\\textbf{Outpatient Care} \\\\\nBehavioral Health & -0.02 & 0.018 & 1.19\\% & \\$119.47 \\\\\n& (0.0132) & (0.0134) \\\\\nChiropractic Care & -0.005 & 0.027 & 1.86\\% & \\$133.39 \\\\\n& (0.0147) & (0.0151) \\\\\nEvaluation \\& Management & 1.440*** & -0.518*** &1.05\\% & \\$121.45\\\\\n& (0.0066) & (0.0062) \\\\\nImaging & 0.098*** & -0.037*** & 2.55\\% & \\$265.52 \\\\\n& (0.0108) & (0.0111) \\\\\nLab Services & 0.198*** & -0.178*** & 3.96\\% & \\$62.14 \\\\\n& (0.0113) & (0.0119) \\\\\nLow-Value Services & 0.084*** & 0.028** & 6.58\\% & \\$148.61 \\\\\n& (0.0094) & (0.0097) \\\\\nPreventive Care & 0.345*** & -0.249*** & 11.68\\% & \\$120.47 \\\\\n& (0.0036) & (0.0037) \\\\\nSpecialist Care & 0.550*** & -0.181*** & 0.57\\% & \\$114.70 \\\\\n& (0.0192) & (0.0198) \\\\\n\\midrule\n\\textbf{Prescriptions} & 0.018*** & -0.006 & 18.30\\% & \\$147.14 \\\\\n& (0.0047) & (0.0048) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Estimated Impact of Bill Arrival on Service-Specific Spending}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Medical Bill Shock and Imperfect Moral Hazard", "authors": ["Alex Hoagland", "David M. Anderson", "Ed Zhu"], "url": "https://arxiv.org/abs/2211.01116v2", "attribution": "\"Medical Bill Shock and Imperfect Moral Hazard\" by Alex Hoagland, David M. Anderson, and Ed Zhu, arXiv:2211.01116v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.01048v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rrrr|rrrr|rrrr}\n & \\multicolumn{4}{c}{EW} & \\multicolumn{4}{c}{VW} & \\multicolumn{4}{c}{PW} \\\\\n \\hline\n & Pred & Avg & Std & SR & Pred & Avg & Std & SR & Pred & Avg & Std & SR \\\\\n \\hline\n$D1$ & -0.19 & -0.09 & 0.25 & -0.36 & -0.17 & -0.04 & 0.24 & -0.17 & -0.32 & -0.16 & 0.29 & -0.55 \\\\\n$D2$ & -0.04 & 0.01 & 0.20 & 0.06 & -0.04 & 0.02 & 0.19 & 0.10 & -0.06 & 0.00 & 0.21 & 0.01 \\\\\n$D3$ & 0.01 & 0.04 & 0.18 & 0.20 & 0.01 & 0.05 & 0.17 & 0.28 & 0.00 & 0.03 & 0.19 & 0.19 \\\\\n$D4$ & 0.05 & 0.06 & 0.17 & 0.36 & 0.05 & 0.07 & 0.15 & 0.48 & 0.04 & 0.06 & 0.17 & 0.34 \\\\\n$D5$ & 0.08 & 0.08 & 0.16 & 0.46 & 0.08 & 0.07 & 0.15 & 0.47 & 0.07 & 0.07 & 0.17 & 0.45 \\\\\n$D6$ & 0.11 & 0.09 & 0.17 & 0.55 & 0.11 & 0.09 & 0.15 & 0.63 & 0.11 & 0.10 & 0.17 & 0.58 \\\\\n$D7$ & 0.14 & 0.11 & 0.17 & 0.66 & 0.14 & 0.10 & 0.15 & 0.68 & 0.14 & 0.11 & 0.17 & 0.66 \\\\\n$D8$ & 0.17 & 0.13 & 0.18 & 0.73 & 0.17 & 0.11 & 0.16 & 0.71 & 0.18 & 0.14 & 0.18 & 0.75 \\\\\n$D9$ & 0.23 & 0.18 & 0.21 & 0.88 & 0.23 & 0.14 & 0.18 & 0.81 & 0.24 & 0.20 & 0.22 & 0.91 \\\\\n$D10$ & 0.38 & 0.34 & 0.27 & 1.30 & 0.33 & 0.16 & 0.21 & 0.72 & 0.52 & 0.50 & 0.31 & 1.60 \\\\\n$LS$ & 0.51 & 0.38 & 0.16 & 2.36 & 0.44 & 0.14 & 0.18 & 0.80 & 0.79 & 0.60 & 0.22 & 2.73 \\\\\n \\hline\n & \\multicolumn{4}{c}{UW} & \\multicolumn{4}{c}{PUW1} & \\multicolumn{4}{c}{PUW10} \\\\\n \\hline\n & Pred & Avg & Std & SR & Pred & Avg & Std & SR & Pred & Avg & Std & SR \\\\\n \\hline\n$D1$ & -0.13 & -0.06 & 0.36 & -0.16 & -0.71 & -0.66 & 0.79 & -0.83 & -0.53 & -0.32 & 0.60 & -0.54 \\\\\n$D2$ & -0.03 & 0.09 & 0.27 & 0.32 & -0.08 & -0.08 & 0.34 & -0.23 & -0.07 & 0.02 & 0.30 & 0.08 \\\\\n$D3$ & 0.01 & 0.05 & 0.21 & 0.22 & -0.01 & 0.00 & 0.31 & 0.01 & -0.01 & 0.05 & 0.30 & 0.15 \\\\\n$D4$ & 0.05 & 0.01 & 0.21 & 0.03 & 0.03 & 0.04 & 0.21 & 0.18 & 0.04 & 0.05 & 0.23 & 0.23 \\\\\n$D5$ & 0.08 & 0.05 & 0.20 & 0.26 & 0.07 & 0.06 & 0.20 & 0.30 & 0.07 & 0.05 & 0.21 & 0.24 \\\\\n$D6$ & 0.11 & 0.13 & 0.23 & 0.57 & 0.12 & 0.05 & 0.21 & 0.26 & 0.12 & 0.09 & 0.23 & 0.37 \\\\\n$D7$ & 0.14 & 0.07 & 0.19 & 0.38 & 0.15 & 0.03 & 0.26 & 0.12 & 0.15 & 0.06 & 0.25 & 0.25 \\\\\n$D8$ & 0.17 & 0.11 & 0.24 & 0.47 & 0.20 & 0.17 & 0.23 & 0.76 & 0.19 & 0.15 & 0.20 & 0.72 \\\\\n$D9$ & 0.22 & 0.26 & 0.31 & 0.83 & 0.27 & 0.25 & 0.34 & 0.73 & 0.26 & 0.24 & 0.31 & 0.77 \\\\\n$D10$ & 0.30 & 0.16 & 0.31 & 0.52 & 0.96 & 1.12 & 1.27 & 0.89 & 0.73 & 0.36 & 0.71 & 0.51 \\\\\n$LS$ & 0.37 & 0.16 & 0.43 & 0.37 & 1.61 & 1.73 & 1.42 & 1.22 & 1.21 & 0.63 & 0.94 & 0.67 \\\\\n \\hline\n & \\multicolumn{4}{c}{PUW20} & \\multicolumn{4}{c}{PUW100} & \\multicolumn{4}{c}{PUW250} \\\\\n \\hline\n & Pred & Avg & Std & SR & Pred & Avg & Std & SR & Pred & Avg & Std & SR \\\\\n \\hline\n$D1$ & -0.39 & -0.18 & 0.55 & -0.32 & -0.17 & -0.17 & 0.41 & -0.42 & -0.14 & -0.09 & 0.37 & -0.23 \\\\\n$D2$ & -0.07 & -0.06 & 0.28 & -0.23 & -0.05 & 0.07 & 0.28 & 0.26 & -0.04 & 0.10 & 0.27 & 0.35 \\\\\n$D3$ & 0.00 & 0.01 & 0.22 & 0.04 & 0.01 & 0.08 & 0.21 & 0.35 & 0.01 & 0.07 & 0.20 & 0.33 \\\\\n$D4$ & 0.04 & 0.04 & 0.22 & 0.18 & 0.04 & 0.01 & 0.20 & 0.05 & 0.05 & -0.01 & 0.21 & -0.03 \\\\\n$D5$ & 0.07 & 0.06 & 0.20 & 0.31 & 0.08 & 0.07 & 0.20 & 0.34 & 0.08 & 0.06 & 0.20 & 0.30 \\\\\n$D6$ & 0.12 & 0.11 & 0.24 & 0.46 & 0.11 & 0.12 & 0.23 & 0.51 & 0.11 & 0.13 & 0.23 & 0.55 \\\\\n$D7$ & 0.15 & 0.04 & 0.25 & 0.17 & 0.14 & 0.05 & 0.20 & 0.27 & 0.14 & 0.05 & 0.19 & 0.29 \\\\\n$D8$ & 0.19 & 0.14 & 0.22 & 0.62 & 0.18 & 0.10 & 0.23 & 0.44 & 0.17 & 0.10 & 0.24 & 0.40 \\\\\n$D9$ & 0.25 & 0.30 & 0.32 & 0.92 & 0.23 & 0.26 & 0.30 & 0.85 & 0.22 & 0.26 & 0.31 & 0.84 \\\\\n$D10$ & 0.60 & 0.28 & 0.60 & 0.48 & 0.34 & 0.20 & 0.33 & 0.61 & 0.32 & 0.13 & 0.32 & 0.42 \\\\\n$LS$ & 0.94 & 0.41 & 0.81 & 0.51 & 0.45 & 0.32 & 0.47 & 0.68 & 0.40 & 0.17 & 0.44 & 0.38 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{In this table, we compare the economic performance of prediction sorted portfolios over the 30-year out-of-sample testing period for the ensemble GPR model with affine kernel. We compare the performance of the decile portfolios corresponding to EW, VW, PW, UW and PUW strategies. We report the performance of PUW portfolios for different values, $\\{1, 10 20, 100, 250\\}$ of $\\zeta$, the uncertainty-aversion parameter. We also compare the long-short portfolios. For each portfolio, we report the predicted monthly returns (``Pred\"), the average realized monthly returns (``Avg\"), their standard deviations (``Std\"), and Sharpe ratios (``SR\"). We calculate these measures using realized simple excess returns of the portfolios over the test sample. The values of ``Avg\", ``Std\" and ``SR\" for the S\\&P 500 are 0.054, 0.150 and 0.360 respectively. All measures are annualized.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Empirical Asset Pricing via Ensemble Gaussian Process Regression", "authors": ["Damir Filipović", "Puneet Pasricha"], "url": "https://arxiv.org/abs/2212.01048v2", "attribution": "\"Empirical Asset Pricing via Ensemble Gaussian Process Regression\" by Damir Filipović and Puneet Pasricha, arXiv:2212.01048v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05650v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n\\textbf{Method} & \\textbf{Scale factor} & \\textbf{Acc (\\%)} & \\textbf{Rel. Cont. (\\%)} \\\\\\midrule\n\\multirow{3}{*}{Dense} & 1$\\times$ & 66.24 & - \\\\\n & 2$\\times$ & 72.67 & - \\\\\n & 4$\\times$ & 76.6 & - \\\\\\midrule\n\\multirow{2}{*}{$f$-RepeatNet} & 2$\\times$ & 69.96 & 57.85 \\\\\n & 4$\\times$ & 72.41 & 59.56 \\\\\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Performance scores obtained on the task of classification of Imagenet samples obtained using ResNet-18, its RepeatNet variants as well as the corresponding dense scaled-up models. Here, `Rel. Cont.' denotes the relative contribution of RepeatNet and can be interpreted as the gain obtained by using RepeatNet normalized by the total gain observed by the corresponding dense architecture.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Rescaling CNN through Learnable Repetition of Network Parameters", "authors": ["Arnav Chavan", "Udbhav Bamba", "Rishabh Tiwari", "Deepak Gupta"], "url": "https://arxiv.org/abs/2101.05650v2", "attribution": "\"Rescaling CNN through Learnable Repetition of Network Parameters\" by Arnav Chavan, Udbhav Bamba, Rishabh Tiwari, and Deepak Gupta, arXiv:2101.05650v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03847v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||ccccc||} \n \\hline\n Index & Species & Name & $\\phi_i^0$ & Sc \\\\ \n \\hline\\hline\n 1 & IXa & Enzym IXa & 0.09 & 15949 \\\\ \n 2 & IX & Zymogen IX & 90 & 17762 \\\\\n 3 & VIIIa & Enzym VIIIa & 0.0007 & 25510 \\\\ \n 4 & VIII & Zymogen VIII & 0.7 & 32051 \\\\ \n 5 & Va & Enzym Va &0.02 & 26178 \\\\ \n 6 & V & Zymogen V & 20 & 17762 \\\\ \n 7 & Xa & Enzym Xa & 0.17 & 13568 \\\\ \n 8 & X & Zymogen X & 170 & 17762 \\\\ \n 9 & IIa & Thrombin & 1.4 & 15456 \\\\ \n 10 & II & Prothrombin & 1400 & 19194 \\\\ \n 11 & Ia & Fibrin & 7.0 & 40486 \\\\\n 12 & I & Fibrinogen & 7000 & 32256 \\\\ \n 13 & XIa & Enzym XIa & 0.03 & 20000 \\\\ \n 14 & XI & Zymogen XI & 30 & 25189 \\\\ \n 15 & ATIII & AntiThrombin-III & 2410 & 17953 \\\\ \n 16 & TFPI & Tissue factor pasthway inhibitor & 2.5 & 15873 \\\\ \n 17 & APC & Active Protein C & 0.06 & 18182 \\\\ \n 18 & PC & Protein C & 60 & 18382 \\\\ \n 19 & $\\alpha_1$ AT& $\\alpha_1$-AntiTrypsin & 45000 & 17182 \\\\ \n 20 & tPA & Tissue plasminogen activator & 0.08 & 18939 \\\\ \n 21 & PLA & Plasmin & 2.18 & 20284 \\\\ \n 22 & PLS & Plasminogen & 2180 & 20790 \\\\ \n 23 & $\\alpha_2$AP & $\\alpha_2$-AntiPlasmin & 105 & 19048 \\\\ \n 24 & Z & Tenase & 1.125$\\times10^{-4}$ & ... \\\\ \n 25 & W & Prothrombinase & 0.034 & ... \\\\ \n \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Name of species used in this problem with their initial concentrations and Schmidt numbers.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "On-the-fly Reduced Order Modeling of Passive and Reactive Species via Time-Dependent Manifolds", "authors": ["Donya Ramezanian", "Arash G. Nouri", "Hessam Babaee"], "url": "https://arxiv.org/abs/2101.03847v1", "attribution": "\"On-the-fly Reduced Order Modeling of Passive and Reactive Species via Time-Dependent Manifolds\" by Donya Ramezanian, Arash G. Nouri, and Hessam Babaee, arXiv:2101.03847v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.05978v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation of guidance scale (MAISI Rflow on MS).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n \\hline\n \\textbf{Guidance} & \\textbf{Text Alignment (\\%) $\\uparrow$} & \\textbf{Subject MS-SSIM $\\uparrow$} & \\textbf{PSNR (dB) $\\uparrow$} \\\\\n \\hline\n No CFG & 89.17 & 0.964 & 27.92 \\\\\n \\hline\n 0.5 & 70.08 & 0.985 & 32.30 \\\\\n 1 & 85.96 & 0.964 & 27.89 \\\\\n 2 & 99.39 & 0.926 & 24.39 \\\\\n 3 & 100 & 0.898 & 22.75 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Imagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidance", "authors": ["Mohamed Mohamed", "Brennan Nichyporuk", "Douglas L. Arnold", "Tal Arbel"], "url": "https://arxiv.org/abs/2509.05978v1", "attribution": "\"Imagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidance\" by Mohamed Mohamed, Brennan Nichyporuk, Douglas L. Arnold, and Tal Arbel, arXiv:2509.05978v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.03861v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sample table title}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\toprule\n \\multicolumn{2}{c}{Part} \\\\\n \\cmidrule(r){1-2}\n Name & Description & Size ($\\mu$m) \\\\\n \\midrule\n Dendrite & Input terminal & $\\sim$100 \\\\\n Axon & Output terminal & $\\sim$10 \\\\\n Soma & Cell body & up to $10^6$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter", "authors": ["Sami A. Malek", "Alexandre M. Bayen", "Steven D. Glaser"], "url": "https://arxiv.org/abs/1912.03861v1", "attribution": "\"Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter\" by Sami A. Malek, Alexandre M. Bayen, and Steven D. Glaser, arXiv:1912.03861v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.16643v5_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Top 20 Sponsors}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lclc}\n\\toprule\n\\multicolumn{2}{c}{Initialized} &\\multicolumn{2}{c}{Completed} \\\\\n\\cmidrule(lr){1-2} \\cmidrule(lr){3-4}\n Sponsor & \\#SPAC & Sponsor & \\#SPAC \\\\\n\\midrule\n The Gores Group & 13 & The Gores Group & 9 \\\\\n Cohen \\& Company & 11 & Chardan & 7 \\\\\n Social Capital & 10 & Social Capital & 6 \\\\\n Hennessy Capital Group & 8 & Cohen \\& Company & 6 \\\\\n Cantor Fitzgerald & 8 & TPG & 5 \\\\\n TPG & 8 & Cantor Fitzgerald & 5 \\\\\n Michael Klein & 8 & Riverstone Investment Group & 5 \\\\\n Jonathan J. Ledecky & 7 & Hennessy Capital Group & 5 \\\\\n Chardan & 7 & Harry L. You & 5 \\\\\n Hedosophia & 6 & Eagle Equity Partners & 5 \\\\\n Harry L. You & 6 & Michael Klein & 4 \\\\\nRiverstone Investment Group & 6 & Jonathan S. Huberman & 4 \\\\\n Fortress Investment Group & 6 & Hedosophia & 4 \\\\\n GigCapital Global & 6 & GigCapital Global & 4 \\\\\n Apollo Global Management & 6 & Chinh Chu & 4 \\\\\n Bill Foley & 6 & Niccolo de Masi & 4 \\\\\n Chinh Chu & 5 & Bill Foley & 4 \\\\\n Jaws Estates Capital & 5 & Apollo Global Management & 4 \\\\\n Craig-Hallum Capital Group & 5 & Union Acquisition Group & 3 \\\\\n Niccolo de Masi & 5 & Casdin Capital & 3 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Security Issuance, Institutional Investors and Quid Pro Quo", "authors": ["Gaurab Aryal", "Zhaohui Chen", "Yuchi Yao", "Chris Yung"], "url": "https://arxiv.org/abs/2211.16643v5", "attribution": "\"Security Issuance, Institutional Investors and Quid Pro Quo\" by Gaurab Aryal, Zhaohui Chen, Yuchi Yao, and Chris Yung, arXiv:2211.16643v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12251v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n \\hline\n Linguistic term & D-IFV \\\\ \\hline\n Extremely High (EH) & $\\langle ( 0.9,0.1);0.9\\rangle$ \\\\\n Very High (VH) & $\\langle ( 0.8,0.2);0.8\\rangle$ \\\\\n High (H) & $\\langle ( 0.7,0.3);0.7\\rangle$ \\\\\n Moderately High (MH) & $\\langle ( 0.6,0.4),0.6\\rangle$ \\\\\n Medium (M) & $\\langle ( 0.5,0.5);0.5\\rangle$ \\\\\n Moderately Low (ML) & $\\langle ( 0.4,0.6);0.4\\rangle$ \\\\\n Low (L) & $\\langle ( 0.3,0.7);0.3\\rangle$ \\\\\n Very Low (VL) & $\\langle ( 0.2,0.8);0.2\\rangle$ \\\\\n Extremely Low (EL) & $\\langle ( 0.1,0.9);0.1\\rangle$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Linguistic terms corresponding D-IFV}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology", "authors": ["Mahmut Can Bozyiğit", "Mehmet Ünver"], "url": "https://arxiv.org/abs/2501.12251v1", "attribution": "\"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology\" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12567v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Speech naturalness and Speaker similarity}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\toprule[1.5pt]\n Method & Speech naturalness$\\uparrow$ & Speaker similarity$\\uparrow$ \\\\\n \\midrule\n ground truth & 4.62$\\pm$0.14 & 4.75$\\pm$0.16 \\\\\n ppg-fast & 3.46$\\pm$0.19 & 3.69$\\pm$0.20 \\\\\n ppg-taco & 3.87$\\pm$0.21 & 3.91$\\pm$0.17 \\\\\n proposed & \\textbf{4.01$\\pm$0.20} & \\textbf{3.98$\\pm$0.19} \\\\\n \\bottomrule[1.5pt]\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "The HCCL-DKU system for fake audio generation task of the 2022 ICASSP ADD Challenge", "authors": ["Ziyi Chen", "Hua Hua", "Yuxiang Zhang", "Ming Li", "Pengyuan Zhang"], "url": "https://arxiv.org/abs/2201.12567v1", "attribution": "\"The HCCL-DKU system for fake audio generation task of the 2022 ICASSP ADD Challenge\" by Ziyi Chen, Hua Hua, Yuxiang Zhang, Ming Li, and Pengyuan Zhang, arXiv:2201.12567v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02074v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Variable parameter values.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n\t\t\\hline\n\t\t\\textbf{Publisher frequency}\t\t\t& 1, 10, \\dots, 90, 100 \\\\ \\hline\n\t\t\\textbf{Payload} & 128\\,B, 1\\,KB, 10\\,KB, 100\\,KB, 500\\,KB \\\\ \\hline\n\t\t\\textbf{Number of Nodes} & 3, 5, \\dots, 21, 23 \\\\ \\hline\n\t\t\\textbf{DDS Backend} & Connext, FastRTPS, CycloneDDS \\\\ \\hline\n\t\t\\textbf{Reliability} & reliable, best effort \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Latency Analysis of ROS2 Multi-Node Systems", "authors": ["Tobias Kronauer", "Joshwa Pohlmann", "Maximilian Matthe", "Till Smejkal", "Gerhard Fettweis"], "url": "https://arxiv.org/abs/2101.02074v3", "attribution": "\"Latency Analysis of ROS2 Multi-Node Systems\" by Tobias Kronauer, Joshwa Pohlmann, Maximilian Matthe, Till Smejkal, and Gerhard Fettweis, arXiv:2101.02074v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.19748v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\nMethod & Coverage probability & Mean length \\\\\n\\hline \nBayes with Jeffreys prior & 0.861 & 2.31 \\\\\nBayes with right Haar prior & 0.929 & 3.26 \\\\\n{\\tt t.test} with Welch df & 0.881 & 2.79 \\\\\nMarginal IM's inner approximation & 0.904 & 2.71 \\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Coverage probabilities and mean lengths for the 90\\% confidence intervals returned by the various methods. Standard errors for all of the coverage probabilities and mean lengths are roughly 0.003 and 0.01, respectively.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "No-prior Bayesian inference reIMagined: probabilistic approximations of inferential models", "authors": ["Ryan Martin"], "url": "https://arxiv.org/abs/2503.19748v2", "attribution": "\"No-prior Bayesian inference reIMagined: probabilistic approximations of inferential models\" by Ryan Martin, arXiv:2503.19748v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.05935v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Voltages for points $A$ and $B$ for the 3-bus test system in~.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline \n & Point A & Point B\\tabularnewline\n\\hline \n\\hline \n1 & 0.8962 - 0.0919i & 0.8846 - 0.1660i\\tabularnewline\n\\hline \n2 & 0.9040 + 0.0000i & 0.9996 + 0.0000i\\tabularnewline\n\\hline \n3 & 1.0282 + 0.0888i & 0.9278 - 0.0740i\\tabularnewline\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Certifying the Nonexistence of Feasible Path Between Power System Operating Points", "authors": ["Mohammad Rasoul Narimani", "Katherine R. Davis", "Daniel K. Molzahn"], "url": "https://arxiv.org/abs/2509.05935v1", "attribution": "\"Certifying the Nonexistence of Feasible Path Between Power System Operating Points\" by Mohammad Rasoul Narimani, Katherine R. Davis, and Daniel K. Molzahn, arXiv:2509.05935v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.09598v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Coefficients for the impact of average variability of ancestral climatic conditions on individual's self-reported attention to the environment }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\hline\n \\multicolumn{5}{c}{Dependent variable: Self reported measure of attention to environment}\\\\\n \\hline\n \\hline\n Avg. variability & -9.877***& -17.402** & -9.055***& -9.677***\\\\\n & (2.003) & (7.035) & (2.108) & (2.135) \\\\\n Avg. variability sq.& 111.767***& 276.862* & 105.537***& 109.681***\\\\\n & (21.346) & (145.560) & (21.700) & (21.829) \\\\\n Avg. variability cub.& & -1130.897 & & \\\\\n & & (951.849) & & \\\\\n Variability across generations& & & -0.166 & \\\\\n & & & (0.123) & \\\\\n Occupation category Controls & Y & Y & Y & Y \\\\\n Historical ethnic group characteristics & Y & Y & Y & Y \\\\\n Historical topographic characteristics & Y & Y & Y & Y \\\\\n Moments of full temp. distribution & N & N & N & Y \\\\\n Country-year Fixed effects & Y & Y & Y & Y \\\\\n \\hline\n Mean of dep var & 0.704 & 0.704 & 0.704 & 0.704 \\\\\n St. Dev. of dep var & 0.250 & 0.250 & 0.250 & 0.250 \\\\\n Min value Avg. Variability& 0.015 & 0.015 & 0.015 & 0.015 \\\\\n Max value Avg. Variability& 0.093 & 0.093 & 0.093 & 0.093 \\\\\n R-sq & 0.113 & 0.114 & 0.114 & 0.114 \\\\\n Adj. R-sq & 0.113 & 0.113 & 0.113 & 0.113 \\\\\n N & 138067 & 138067 & 138067 & 138067 \\\\\n \\hline\n \\multicolumn{5}{c}{ {*}{*}{*} p$<$0.01, {*}{*}p$<$0.05, {*} p$<$0.1, {+} p$<$0.15}\\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Ancestral origins of attention to environmental issues", "authors": ["César Barilla", "Palaash Bhargava"], "url": "https://arxiv.org/abs/2509.09598v1", "attribution": "\"Ancestral origins of attention to environmental issues\" by César Barilla and Palaash Bhargava, arXiv:2509.09598v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.07667v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n$M$ & $(x,y)$ & Expected Duration& Standard Deviation \\\\\n\\hline\n$2$ & $(1,1)$ & $1$ & $0$\\\\\\hline\n$4$ & $(2,2)$ & $4.470588235$ & $ 2.891342524$\\\\ \\hline\n$6$ & $(3,3)$ & $10.3030$ & $7.102295958$\\\\ \\hline\n$8$ & $(4,4)$ & $18.47746573 $ & $12.97689858$ \\\\ \\hline\n$10$ & $(5,5)$ & $28.99020033 $ & $20.52455308$\\\\ \\hline\n$12$ & $(6,6)$ & $ 41.84019550$ & $29.74741677$\\\\ \\hline\n$14$ & $(7,7)$ & $ 57.02707373$ & $40.64621816$\\\\ \\hline\n$16$ & $(8,8)$ & $ 74.55067222$ & $53.22126050$\\\\ \\hline\n$18$ & $(9,9)$ & $94.41091165 $ & $67.47268859$\\\\ \\hline\n$20$ & $(10,10)$ & $116.6077497 $ & $83.40057864$ \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A symbolic computational approach to the generalized gambler's ruin problem in one and two dimensions", "authors": ["Lucy Martinez"], "url": "https://arxiv.org/abs/2412.07667v1", "attribution": "\"A symbolic computational approach to the generalized gambler's ruin problem in one and two dimensions\" by Lucy Martinez, arXiv:2412.07667v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.06716v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters : Scenario 1 }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\t\t\t\t\\toprule \n\t\t\t\tParameter & Value\\\\\n\t\t\t\t\\midrule\n\t\t\t\t$\\Delta T$ & 2 seconds.\\\\\n\t\t\t\t$\\sigma^1_r$, $\\sigma^1_\\theta$, $\\sigma^1_\\phi$ & $100$ m, $2^\\circ$, $2^\\circ$ \\\\\n\t\t\t\t$\\sigma^2_r$, $\\sigma^2_\\theta$, $\\sigma^2_\\phi$ & $100$ m, $2^\\circ$, $2^\\circ$ \\\\\n\t\t\t\t$\\sigma^3_r$, $\\sigma^3_\\theta$, $\\sigma^3_\\phi$ & $100$ m, $1.5^\\circ$, $1.5^\\circ$ \\\\\n\t\t\t\t$\\tilde{q}_x$ & 0.5 $\\text{m}^2/\\text{sec}^3$\\\\\n\t\t\t\t$\\tilde{q}_y$ & 0.5 $\\text{m}^2/\\text{sec}^3$\\\\\n\t\t\t\t$\\tilde{q}_z$ & 0.001 $\\text{m}^2/\\text{sec}^3$\\\\\n\t\t\t\tSimulation time & 120 seconds.\\\\\n\t\t\t\t\\bottomrule\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "On Pooling-Based Track Fusion Strategies : Harmonic Mean Density", "authors": ["Nikhil Sharma", "Shovan Bhaumik", "Ratnasingham Tharmarasa", "Thiagalingam Kirubarajan"], "url": "https://arxiv.org/abs/2412.06716v1", "attribution": "\"On Pooling-Based Track Fusion Strategies : Harmonic Mean Density\" by Nikhil Sharma, Shovan Bhaumik, Ratnasingham Tharmarasa, and Thiagalingam Kirubarajan, arXiv:2412.06716v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16933v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Transistor sizing for the proposed PFD circuit}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline\n \\textbf{Transistors} & \\textbf{W/L Ratio} \\\\\n \\hline\n P1, P6 & 4 \\\\ \\hline\n P2, P7 & 4 \\\\ \\hline\n P3, P8 & 2 \\\\ \\hline\n P4, P9 & 4 \\\\ \\hline\n P5, P10 & 4 \\\\ \\hline\n N1, N6 & 2 \\\\ \\hline\n N2, N7 & 4 \\\\ \\hline\n N3, N8 & 4 \\\\ \\hline\n N4, N9 & 2 \\\\ \\hline\n N5, N10 & 2 \\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "TSPC-PFD: TSPC-Based Low-Power High-Resolution CMOS Phase Frequency Detector", "authors": ["Dhandeep Challagundla", "Venkata Krishna Vamsi Sundarapu", "Ignatius Bezzam", "Riadul Islam"], "url": "https://arxiv.org/abs/2508.16933v1", "attribution": "\"TSPC-PFD: TSPC-Based Low-Power High-Resolution CMOS Phase Frequency Detector\" by Dhandeep Challagundla, Venkata Krishna Vamsi Sundarapu, Ignatius Bezzam, and Riadul Islam, arXiv:2508.16933v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.12956v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance Objectives}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllccc}\n \\hline\n \\textbf{Requirement} & \\textbf{Achieved Value} & \\\\\n \\hline\n $|e_z|_{\\max,r}$ & 0.5 & [g] \\\\\nOvershoot & 20 & [\\%] \\\\\n$|\\eta|_{\\max}$ & 15 & [º] \\\\\n$\\eta_{noise,r}$ & 1 & [rad] \\\\\n$\\eta_{noise,t}$ & 0.2 & [rad] \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Reinforcement Learning for Robust Missile Autopilot Design", "authors": ["Bernardo Cortez"], "url": "https://arxiv.org/abs/2011.12956v2", "attribution": "\"Reinforcement Learning for Robust Missile Autopilot Design\" by Bernardo Cortez, arXiv:2011.12956v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10266v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of plans and planning time with the learned axioms expressed as a fraction of the values without the learned axioms. Reasoning with the learned axioms improves performance.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline\n \\textbf{Measures} & \\textbf{Ratio (with/without)}\\\\ \n \\hline\n Number of plans & 0.33\\\\\n \\hline\n Planning time & 0.89\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Combining Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning in Robotics", "authors": ["Mohan Sridharan", "Tiago Mota"], "url": "https://arxiv.org/abs/2201.10266v1", "attribution": "\"Combining Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning in Robotics\" by Mohan Sridharan and Tiago Mota, arXiv:2201.10266v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10090v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|}\n \\hline\n \\bfseries Classifier & \\bfseries Accuracy & \\bfseries Precision &\t\\bfseries Recall & \\bfseries F-Measure & \\bfseries AUC \\\\ \\hline\n Random Forrest & 0.877 & 0.879 & 0.877 & 0.877 & 0.942 \\\\ \\hline\n Multilayer Perceptron&\t0.830&\t0.830&\t0.830&\t0.830&\t0.895 \\\\ \\hline\n Decision Tree&\t0.825&\t0.827&\t0.826&\t0.826&\t0.870 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\fontsize{10pt}{11pt}\\selectfont{\\itshape{The classification results of the prediction model}}}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Investigating Software Testability and Test cases Effectiveness", "authors": ["Mamdouh Alenezi"], "url": "https://arxiv.org/abs/2201.10090v1", "attribution": "\"Investigating Software Testability and Test cases Effectiveness\" by Mamdouh Alenezi, arXiv:2201.10090v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18002v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{soul}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll|lllll|lllll|lllll}\n\\hline\n\\multicolumn{20}{c}{\\textbf{A Uniform}}\\\\\n\\hline\n\\multicolumn{5}{c|}{Shifted sinusoidal $d=2$}\n&\\multicolumn{5}{c|}{Shifted sinusoidal $d=5$}\n& \\multicolumn{5}{c|}{Rosenbrock $d=2$}\n& \\multicolumn{5}{c}{Rosenbrock $d=5$}\n\\\\\\hline\n$k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \\\\ \n\\hline\n 10 & 0.445 & 0.41 & 1.63 & 2.769 & \n 10 & {\\hl {0.155}} & {\\hl {0.169}} & 2.811 & 3.411 & \n 10 & 0.238 & 0.235 & 7.63 & 123.944 & \n 10 & {\\hl {0.093}} & {\\hl {0.096}} & 319.605 & 2467.76 \\\\ \n 15 & 0.351 & 0.32 & 1.259 & 2.442 & \n 15 & 0.556 & 0.551 & 2.67 & 2.961 & \n 55 & 0.242 & 0.241 & 6.874 & 104.011 & 18 & 0.067 & 0.067 & 143.092 & 854.155 \\\\ \n 25 & 0.08 & 0.075 & 0.63 & 2.15 & \n 32 & 0.204 & 0.176 & 2.594 & 3.347 & \n 56 & {\\hl {0.171}} & {\\hl {0.186}} & 6.874 & 104.011 & \n 25 & 0.061 & 0.061 & 143.092 & 943.599 \\\\ \n 48 & 0.086 & 0.066 & 0.229 & 1.319 & \n 38 & 0.275 & 0.225 & 2.594 & 3.287 & \n 62 & {\\hl {0.212}} & {\\hl {0.221}} & 6.44 & 100.838 & \n 35 & 0.069 & 0.069 & 143.092 & 854.155 \\\\ \n 56 & 0.084 & 0.064 & 0.229 & 1.319 & \n 47 & 0.254 & 0.22 & 2.594 & 3.284 & \n 68 & 0.231 & 0.225 & 4.275 & 57.553 & \n 45 & 0.063 & 0.063 & 143.092 & 937.713 \\\\ \n 66 & 0.073 & 0.061 & 0.172 & 1.259 &\n 57 & 0.24 & 0.213 & 2.594 & 3.287 & \n 84 & 0.022 & 0.02 & 0.196 & 62.788 & \n 55 & 0.027 & 0.027 & 88.036 & 854.155 \\\\ \n 77 & 0.045 & 0.034 & 0.053 & 1.028 & \n 67 & 0.157 & 0.077 & 1.92 & 3.284 & \n 91 & 0.027 & 0.023 & 0.196 & 57.553 &\n 106 & {\\hl {0.025}} &{\\hl {0.027}} & 88.036 & 830.225 \\\\ \n 96 & 0.009 & 0.007 & 0.008 & 0.829 & \n 110 & 0.086 & 0.041 & 1.51 & 3.038 &\n 111 & 0.051 & 0.047 & 0.196 & 18.663 & \n 158 & 0.029 & 0.028 & 88.036 & 873.822 \\\\ \n 156 & 0.021 & 0.02 & 0.008 & 0.353 & \n 121 & 0.106 & 0.039 & 1.51 & 3.078 &\n 123 & 0.059 & 0.053 & 0.07 & 16.368 & \n 201 & 0.008 & 0.008 & 45.281 & 827.083 \\\\ \n 188 & {\\hl{0.017}} & {\\hl{0.026}} & 0.008 & 0.267 & \n 139 & 0.112 & 0.048 & 1.51 & 2.98 & \n 187 & 0.022 & 0.02 & 0.07 & 15.322 & \n 209 & 0.009 & 0.009 & 45.281 & 740.015 \\\\ \n 202 & 0.009 & 0.014 & 0.004 & 0.248 &\n 158 & 0.124 & 0.067 & 1.51 & 2.811 & \n 247 & 0.011 & 0.01 & 0.003 & 15.322 &\n 221 & 0.01 & 0.01 & 45.281 & 645.793 \\\\ \n 233 & 0 & 0.002 & 0.001 & 0.196 & \n 177 & 0.038 & 0.015 & 1.221 & 2.61 & \n 257 & 0.019 & 0.016 & 0.003 & 11.401 & \n 241 & 0.004 & 0.004 & 28.19 & 554.874 \\\\ \n 253 & 0 & 0.003 & 0.001 & 0.172 &\n 218 & 0.017 & 0.013 & 1.105 & 2.171 & & & & & & \n 265 & 0.006 & 0.006 & 28.19 & 411.649 \\\\ \n & & & & & \n 235 & 0.017 & 0.003 & 0.673 & 2.053 & & & & & & & & & & \\\\ \n & & & & & \n 261 & 0.037 & 0.003 & 0.673 & 1.958 & & & & & & & & & & \\\\ \n & & & & & & & & & & & & & & & & & & & \\\\ \n\\hline\n\\multicolumn{20}{c}{\\textbf{B Gaussian Process}}\\\\ \n\\hline\n\\multicolumn{5}{c|}{Shifted sinusoidal $d=2$}\n&\\multicolumn{5}{c|}{Shifted sinusoidal $d=5$}\n& \\multicolumn{5}{c|}{Rosenbrock $d=2$}\n& \\multicolumn{5}{c}{Rosenbrock $d=5$}\n\\\\\\hline\n$k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \\\\ \n\\hline\n 10 & 0.445 & 0.41 & 1.63 & 2.769 & \n 10 & {\\hl {0.155}} & {\\hl {0.169}} & 2.811 & 3.411 & \n 10 & {\\hl {0.502 }}& {\\hl {0.503}} & 8.298 & 35.649 & \n 10 & {\\hl {0.399 }}& {\\hl {0.4}} & 427.597 & 863.028 \\\\ \n 64 & 0.17 & 0.14 & 1.002 & 2.636 & \n 15 & 0.548 & 0.534 & 2.489 & 2.811 &\n 15 & {\\hl {0.416}} & {\\hl {0.426}} & 8.298 & 35.649 & \n 62 & {\\hl {0.23}} & {\\hl {0.234}} & 427.597 & 1407.291 \\\\ \n 94 & 0.141 & 0.131 & 0.848 & 2.097 & \n 25 & 0.37 & 0.349 & 2.44 & 2.961 & \n 25 & {\\hl {0.176 }}& {\\hl {0.182}} & 3.834 & 35.649 &\n 118 & {\\hl {0.226}} &{\\hl { 0.23}} & 427.597 & 1407.291 \\\\ \n 102 & 0.18 & 0.157 & 0.848 & 1.933 & \n 78 & 0.101 & 0.06 & 2.068 & 3.411 & \n 53 & {\\hl {0.047}} & {\\hl {0.051}} & 1.22 & 72.152 & \n 173 & 0.216 & 0.216 & 427.597 & 1533.6 \\\\ \n 109 & 0.217 & 0.183 & 0.848 & 1.9 & \n 133 & 0.124 & 0.056 & 2.068 & 3.442 & \n 59 & 0.098 & 0.098 & 1.22 & 33.315 & \n 225 & 0.199 & 0.199 & 427.597 & 1695.92 \\\\ \n 119 & 0.27 & 0.239 & 0.848 & 1.615 & \n 188 & 0.094 & 0.049 & 2.068 & 3.463 & \n 68 & 0.066 & 0.065 & 0.416 & 15.14 & \n 254 &{\\hl {0.177 }}& {\\hl {0.186}} & 427.597 & 1761.241 \\\\ \n 130 & 0.355 & 0.286 & 0.572 & 1.331 & \n 239 & 0.091 & 0.041 & 2.068 & 3.506 & \n 123 & 0.068 & 0.064 & 0.416 & 29.507 & & & & & \\\\ \n 191 & 0.167 & 0.113 & 0.262 & 1.456 & \n 273 & 0.153 & 0.051 & 2.068 & 3.463 & \n 184 & 0.079 & 0.075 & 0.416 & 21.804 & & & & & \\\\ \n 259 & 0.125 & 0.058 & 0.105 & 1.361 & & & & & & \n 250 & 0.081 & 0.077 & 0.416 & 20 & & & & & \\\\ \n & & & & & & & & & & \n 266 & 0.053 & 0.052 & 0.243 & 11.429 & & & & & \\\\ \n & & & & & & & & & & & & & & & & & & & \\\\ \n\\hline\n\\multicolumn{20}{c}{\\textbf{C Quadratic Regression}}\\\\ \n\\hline\n\\multicolumn{5}{c|}{Shifted sinusoidal $d=2$}\n&\\multicolumn{5}{c|}{Shifted sinusoidal $d=5$}\n& \\multicolumn{5}{c|}{Rosenbrock $d=2$}\n& \\multicolumn{5}{c}{Rosenbrock $d=5$}\n\\\\\\hline\n$k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \n& $k$& LHS & RHS & $y$ & $z$ \\\\ \n\\hline\n 10 & 0.039 & 0.038 & 0.493 & 2.228 & \n 10 & {\\hl {0.155}} & {\\hl {0.169}} & 2.811 & 3.411 & \n 10 & 0.238 & 0.235 & 7.63 & 123.944 & \n 10 & {\\hl {0.093 }}&{\\hl { 0.096}} & 319.605 & 2467.76 \\\\ \n 60 & 0.235 & 0.235 & 0.493 & 0.913 & \n 15 & 0.556 & 0.551 & 2.67 & 2.961 & \n 39 & {\\hl {0.104}} & {\\hl {0.113}} & 4.086 & 100 & \n 18 & 0.067 & 0.067 & 143.092 & 854.155 \\\\ \n 110 & 0.301 & 0.3 & 0.493 & 0.904 & \n 32 & 0.204 & 0.176 & 2.594 & 3.347 & \n 40 & {\\hl {0.065}} &{\\hl { 0.071}} & 2.563 & 72.526 & \n 25 & 0.061 & 0.061 & 143.092 & 943.599 \\\\ \n 162 & 0.399 & 0.372 & 0.493 & 0.913 &\n 38 & 0.275 & 0.225 & 2.594 & 3.287 & \n 67 & {\\hl {0.114}} & {\\hl {0.116}} & 1.653 & 29.617 & \n 35 & 0.069 & 0.069 & 143.092 & 854.155 \\\\ \n 171 & 0.094 & 0.078 & 0.077 & 0.912 & \n 47 & 0.254 & 0.22 & 2.594 & 3.284 & \n 75 & {\\hl {0.062 }}&{\\hl { 0.065}} & 0.83 & 28.374 & \n 45 & 0.063 & 0.063 & 143.092 & 937.713 \\\\ \n 226 & 0.104 & 0.072 & 0.077 & 0.898 &\n 57 & 0.24 & 0.213 & 2.594 & 3.287 & \n 128 & {\\hl {0.065}} &{\\hl { 0.067}} & 0.83 & 27.782 & \n 55 & 0.027 & 0.027 & 88.036 & 854.155 \\\\ \n 251 & 0.075 & 0.068 & 0.077 & 0.895 & \n 67 & 0.157 & 0.077 & 1.92 & 3.284 & \n 173 & {\\hl {0.036}} & {\\hl {0.044}} & 0.643 & 16.368 & \n 106 & {\\hl {0.025}} & {\\hl {0.027 }}& 88.036 & 830.225 \\\\ \n & & & & & \n 125 & 0.181 & 0.139 & 1.92 & 2.982 & \n 182 & {\\hl {0.041}} &{\\hl { 0.047}} & 0.48 & 10.173 &\n 158 & 0.03 & 0.029 & 88.036 & 830.225 \\\\ \n & & & & & \n 135 & 0.073 & 0.036 & 1.452 & 2.981 & \n 244 & {\\hl {0.004}} &{\\hl { 0.005}} & 0.028 & 13.268 & \n 207 & 0.013 & 0.012 & 49.609 & 618.208 \\\\ \n & & & & & \n 152 & 0.079 & 0.036 & 1.452 & 2.961 & \n 266 & {\\hl {0.005}} & {\\hl {0.006}} & 0.028 & 9.159 & \n 216 & 0.01 & 0.01 & 41.59 & 567.647 \\\\ \n & & & & & \n 170 & 0.087 & 0.048 & 1.452 & 2.792 & & & & & & \n 262 & 0.013 & 0.013 & 41.59 & 449.238 \\\\ \n & & & & & \n 184 & 0.083 & 0.052 & 1.423 & 2.67 & & & & & & & & & & \\\\ \n & & & & & \n 221 & 0.032 & 0.015 & 1.192 & 2.496 & & & & & & & & & & \\\\ \n & & & & & \n 261 & 0.034 & 0.022 & 1.192 & 2.273 & & & & & & & & & & \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Branching Adaptive Surrogate Search Optimization (BASSO)", "authors": ["Pariyakorn Maneekul", "Zelda B. Zabinsky", "Giulia Pedrielli"], "url": "https://arxiv.org/abs/2504.18002v1", "attribution": "\"Branching Adaptive Surrogate Search Optimization (BASSO)\" by Pariyakorn Maneekul, Zelda B. Zabinsky, and Giulia Pedrielli, arXiv:2504.18002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.05238v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|}\n \\hline\n $\\mathfrak{g}$& $[e_1,e_4]$ & $[e_1,e_5]$ & $[e_2,e_3]$&$[e_2,e_4]$&$[e_2,e_5]$&$[e_3,e_4]$&$[e_3,e_5]$& $[e_4,e_5]$&Polyn. & quasi-rect. \\\\\\hline\n $\\mathfrak{s}_{5,21} $ & $0$ & $-2e_1$ &$e_1$&$0$&$-e_2-e_3$&$0$&$-e_3-e_4$&$-e_4$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,22} $ & $0$ & $-(a+1)e_1$ &$e_1$&$0$&$-e_2$&$0$&$-ae_3$&$-b e_4$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,23} $ & $0$ & $-(a+1)e_1$ &$e_1$&$0$&$-ae_2$&$0$&$-e_4-e_3$&$-e_4$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,24} $ & $0$ & $-2e_1$ &$e_1$&$0$&$-e_2-e_3$&$0$&$-e_3$&$-a e_4$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,25} $ & $0$ & $-2\\alpha e_1$ &$e_1$&$0$&$e_3-\\alpha e_2$&$0$&$-e_2-\\alpha e_3$&$-\\beta e_4$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,26} $ & $0$ & $-(a+1)e_1$ &$e_1$&$0$&$-e_2$&$0$&$-ae_3$&$-e_1-(a+1)e_4$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,27} $ & $0$ & $-2e_1$ &$e_1$&$0$&$-e_2-e_3$&$0$&$-e_3$&$-e_1-2e_4$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,28} $ & $0$ & $-2\\alpha e_1$ &$e_1$&$0$&$-\\alpha e_2-e_3$&$0$&$e_2-\\alpha e_3$&$-e_1-2\\alpha e_4$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,29} $ & $0$ & $-e_1$ &$e_1$&$0$&$0$&$0$&$-e_4-e_3$&$-e_4$&$P_{123}$& No\\\\\\hline \n $\\mathfrak{s}_{5,30} $ & $0$ & $-e_1$ &$e_1$&$0$&$-e_2$&$0$&$0$&$-ae_4$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,31} $ & $0$ & $-e_1$ &$e_1$&$0$&$-e_2$&$0$&$0$&$-e_1-e_4$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,32} $ &$0$ &$-e_1$ &$e_1$&$0$&$0$&$-e_3-e_4$&$-e_4-e_1$&$0$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,33} $ & $0$ & $0$ &$0$&$e_1$&$e_2$&$e_2$&$2e_3$&$-e_4$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,34} $ & $0$ & $-3e_1$ &$0$&$e_1$&$-2e_2$&$e_2$&$-e_3$&$-e_4-e_3$&$P_{124}$& No\\\\\\hline\n $\\mathfrak{s}_{5,35} $ & $0$ & $-(a+2)e_1$ &$0$&$e_1$&$-(a+1)e_2$&$e_2$&$-ae_3$&$-e_4$&$P_{124}$& No\\\\\\hline\n $\\mathfrak{s}_{5,36} $ & $0$ & $-2e_1$ &$0$&$e_1$&$-e_2$&$e_2$&$0$&$-e_4$&$P_{124}$& No\\\\\\hline\n $\\mathfrak{s}_{5,37} $ & $0$ & $-e_1$ &$0$&$e_1$&$-e_2$&$e_2$&$-e_3$&$0$&$P_{124}$& No\\\\\\hline\n $\\mathfrak{s}_{5,38} $ & $0$ & $-e_1$ &$0$&$e_1$&$-e_2$&$e_2$&$-\\epsilon e_1-e_3$&$0$&$P_{124}$& No\\\\\\hline\n $\\mathfrak{s}_{5,39} $ & $0$ & $0$ &$0$&$-e_2$&$0$&$0$&$-e_3$&$-e_1$&$P_{145}$& No\\\\\\hline\n $\\mathfrak{s}_{5,40} $ & $0$ & $0$ &$0$&$-e_2$&$e_3$&$-e_3$&$-e_2$&$-e_1$&$P_{145}$& No\\\\\\hline\n $\\mathfrak{s}_{5,41} $ &$-e_1$ & $0$ &$e_1$&$0$&$-e_2$&$-ae_3$&$-be_3$&$0$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,42} $ & $-ae_1$ & $-e_1$ &0&$-e_2$&$0$&$-e_3$&$-e_2$&$0$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,43} $ & $-\\alpha e_1$ & $-\\beta e_1$ &$0$&$-e_2$&$e_3$&$-e_3$&$-e_2$&$0$&$P_{235}$& No\\\\\\hline\n $\\mathfrak{s}_{5,44} $ & $-e_1$ & $0$ &$e_1$&$-e_2$&$-e_2$&$0$&$e_3$&$0$&$P_{123}$& No\\\\\\hline\n $\\mathfrak{s}_{5,45} $ & $-2e_1$ & $0$ &$e_1$&$-e_2$&$-e_3$&$-e_3$&$e_2$&$0$&$P_{123}$& No\\\\\\hline\n 2$\\mathfrak{n}_{1,1} $ & $e_5$ & $0$ &$2e_3$&$e_4$&$-e_5$&$0$&$e_4$&$0$&$P_{145}$& No\\\\\\hline\n \\end{tabular}\n\\caption{Continuation of the classification of quasi-rectifiable non-Abelian indecomposable five-dimensional Lie algebras. In this table, $[e_1,e_2]=[e_1,e_3]=0$ except for $2\\mathfrak{n}_{1,1}$, where $[e_1,e_2]=2e_1$ and $[e_1,e_3]=-e_2$. Note that $\\lambda\\in (-1,1).$ Only one coefficient of equation () is needed to prove that there is no quasi-rectifiable basis. The written polynomial coefficient is proportional to $\\lambda_1^2$ except for the cases $2\\mathfrak{n}_{1,1}$ and $\\mathfrak{s}_{5,43}$, whose coefficients are proportional to the polynomials $\\lambda_2^2+\\lambda_3^2$ and $\\lambda_5^2$ respectively.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Quasi-rectifiable Lie algebras for partial differential equations", "authors": ["A. M. Grundland", "J. de Lucas"], "url": "https://arxiv.org/abs/2312.05238v2", "attribution": "\"Quasi-rectifiable Lie algebras for partial differential equations\" by A. M. Grundland and J. de Lucas, arXiv:2312.05238v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05388v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lc}\n\\toprule\n\\textbf{Parameter} & \\textbf{Value} \\\\\n\\hline \nClipping Range & 0.08 \\\\\nDiscount Factor & 0.993 \\\\\nEntropy Coefficient & 0.003 \\\\\nGAE & 0.95 \\\\\nGradient Norm Clipping & 0.64 \\\\\nLearning Rate & .000343 \\\\\nNumber of Actors & 16\\\\\nTraining Epochs per Update & 2 \\\\\nValue Function Coefficient & 0.5 \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{Hyperparameters used for each global step of the Agent training. We reused these for selfplay too.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning", "authors": ["Paul Garnier", "Théophane Gregoir"], "url": "https://arxiv.org/abs/2101.05388v1", "attribution": "\"Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning\" by Paul Garnier and Théophane Gregoir, arXiv:2101.05388v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.07429v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|} \\hline\n\\multicolumn{3}{|c|}{Poverty data ($n=89,755$)} \\\\ \\hline\n & Main effects ($p=60$) & Interactions ($p=1,469$)\\\\\ngZellner ALA & 19.5 sec. & 5.2 min. \\\\\ngZellner LA & 10.1 days & 17.3 days \\\\\n\\hline\n\\multicolumn{3}{|c|}{Simulation under true AFT model ($J=100,p=300$)} \\\\ \\hline\n & $n=100$ & $n=500$\\\\\ngMOM ALA & 4.0 sec. & 7.5 sec. \\\\\ngZellner ALA & 9.6 sec. & 13.2 sec. \\\\\ngZellner LA & 188.9 sec.& 251.1 sec.\\\\\n\\hline\n\\multicolumn{3}{|c|}{Simulation under true PH model ($J=100,p=300$)} \\\\ \\hline\n & $n=100$ & $n=500$\\\\\ngMOM ALA & 2.0 sec. & 4.1 sec.\\\\\ngZellner ALA & 2.3 sec. & 10.7 sec.\\\\\ngZellner LA & 152.4 sec.& 81.1 sec.\\\\\n\\hline\n\\multicolumn{3}{|c|}{Colon cancer data ($n=260$, $p=175$)} \\\\ \\hline\n & Uniform $p(\\gamma)$ & BetaBin $p(\\gamma)$ \\\\\ngMOM ALA & 3.3min & 39.3 sec.\\\\\ngZellner ALA & 11.1min & 48.8 sec. \\\\\ngZellner LA & 55.0h & 15.1min \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Approximate Laplace approximations for scalable model selection", "authors": ["David Rossell", "Oriol Abril", "Anirban Bhattacharya"], "url": "https://arxiv.org/abs/2012.07429v2", "attribution": "\"Approximate Laplace approximations for scalable model selection\" by David Rossell, Oriol Abril, and Anirban Bhattacharya, arXiv:2012.07429v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13382v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|ccccc||cccc|}\n \\hline\n $G(600,p)$ & $p$ & $\\varepsilon$ & $|E|$ & $K'''$ & $p$ & $\\varepsilon$ & $|E|$ & $K'''$ \\\\\n \\hline\n & 0.9 & 0.5 & 81979 & 599.95 & 0.5 & 0.5 & 63061 & 600.82\\\\\n & 0.9 & 1.0 & 27430& 605.54 & 0.5 & 1.0 & 25315& 606.36 \\\\\n & 0.9 & 1.5 & 12957 & 615.09 & 0.5 & 1.5 & 12506&616.06\\\\\n & 0.9 & 2.0 & 7442 & 629.93 & 0.5 & 2.0 & 7336& 630.14 \\\\\n \\hline\\hline\n & 0.7 & 0.5 & 74207 & 600.25 & 0.05 & 0.5 &8896 & 620.25\\\\\n & 0.7 & 1.0 & 26638 & 605.90& 0.05 & 1.0 & 8253 & 626.20 \\\\\n & 0.7 & 1.5 & 12813& 615.52 & 0.05& 1.5 & 6525 & 635.89 \\\\\n & 0.7 & 2.0 & 7408 & 629.97 & 0.05& 2.0 &4834& 651.49 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Sparse approximations for different values of $\\varepsilon$ on $G(600,p)$, $p=0.9, 0.7, 0.5,0.05$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Spectral Approach to Kemeny's Constant", "authors": ["Aida Abiad", "Ángeles Carmona", "Andrés M. Encinas", "Maria José Jiménez", "Álvaro Samperio"], "url": "https://arxiv.org/abs/2503.13382v1", "attribution": "\"A Spectral Approach to Kemeny's Constant\" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.17891v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|}\n\\hline \nLabel & Sample Size \\tabularnewline\n\\hline \nA10 & 44 \\tabularnewline\n\\hline \nA11 & 92 \\tabularnewline\n\\hline \nA12 & 75 \\tabularnewline\n\\hline \nA20 & 135 \\tabularnewline\n\\hline \nA21 & 54 \\tabularnewline\n\\hline \nA30 & 127 \\tabularnewline\n\\hline \nA31 & 115 \\tabularnewline\n\\hline \nA40 & 18 \\tabularnewline\n\\hline \nA41 & 105 \\tabularnewline\n\\hline \nA50 & 89 \\tabularnewline\n\\hline \nA51 & 78 \\tabularnewline\n\\hline \nA60 & 72 \\tabularnewline\n\\hline \nA61 & 75 \\tabularnewline\n\\hline \nA70 & 96 \\tabularnewline\n\\hline \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Image-based Novel Fault Detection with Deep Learning Classifiers using Hierarchical Labels", "authors": ["Nurettin Sergin", "Jiayu Huang", "Tzyy-Shuh Chang", "Hao Yan"], "url": "https://arxiv.org/abs/2403.17891v1", "attribution": "\"Image-based Novel Fault Detection with Deep Learning Classifiers using Hierarchical Labels\" by Nurettin Sergin, Jiayu Huang, Tzyy-Shuh Chang, and Hao Yan, arXiv:2403.17891v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13382v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\n\\multicolumn{7}{|c|}{$G(600,p)$} \\\\\n\\hline\n $p$ & $\\epsilon$ & $|E|$ & $K'''$ & $K$ & Relative Error & \\% $|E|$ Variation \\\\\n\\hline\n0.90 & 0.5 & 81979& 599.95 & 598.11 & 0.003 & 49.299 \\\\\n0.90 & 1.0 &27430 &605.54 & 598.11 & 0.012 & 83.035 \\\\\n0.90 & 1.5 & 12957&615.09 & 598.11 & 0.028 & 91.987 \\\\\n0.90 & 2.0 & 7442 &629.93 & 598.11 & 0.053 & 95.397 \\\\\n0.70 & 0.5 & 74207&600.25 & 598.43 & 0.003 & 40.961 \\\\\n0.70 & 1.0 & 26638&605.90 & 598.43 & 0.012 & 78.813 \\\\\n0.70 & 1.5 &12813 &615.52 & 598.43 & 0.029 & 89.797 \\\\\n0.70 & 2.0 &7408 &629.97 & 598.43 & 0.053 & 94.106 \\\\\n0.50 & 0.5 & 63061&600.82 & 599.00 & 0.003 & 29.798 \\\\\n0.50 & 1.0 & 25315&606.36 & 599.00 & 0.012 & 71.812 \\\\\n0.50 & 1.5 & 12506&616.06 & 599.00 & 0.028 & 86.072 \\\\\n0.50 & 2.0 & 7336&630.14 & 599.00 & 0.052 & 91.830 \\\\\n0.05 & 0.5 &8896 &620.25 & 618.37 & 0.003 & 0.180 \\\\\n0.05 & 1.0 & 8253&626.20 & 618.37 & 0.013 & 7.404 \\\\\n0.05 & 1.5 & 6525&635.89 & 618.37 & 0.028 & 26.775 \\\\\n0.05 & 2.0 & 4834&651.49 & 618.37 & 0.054 & 45.740 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Sparse approximations for different values of $\\epsilon$ on $G(600,p)$, $p=0.9, 0.7, 0.5,0.05$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Spectral Approach to Kemeny's Constant", "authors": ["Aida Abiad", "Ángeles Carmona", "Andrés M. Encinas", "Maria José Jiménez", "Álvaro Samperio"], "url": "https://arxiv.org/abs/2503.13382v1", "attribution": "\"A Spectral Approach to Kemeny's Constant\" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.21452v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n \\hline\n $h$ & 1/512 & 1/1024 & 1/2048 & 1/4096 & 1/8192 & 1/16384\\\\\n \\hline\n $\\varepsilon_h$ & 8.31E-08 & 2.09E-08 & 5.23E-09 & 1.31E-09 & 3.27E-10 & 8.18E-11\\\\\n \\hline\n $r$ & 1.99 & 1.99 & 1.93 & 2.07 & 2.00 & 2.00\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{the magnitude of the error and order of convergence.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Numerical solution of locally loaded Volterra integral equations", "authors": ["Vladislav Byankin", "Aleksandr Tynda", "Denis Sidorov", "Aliona Dreglea"], "url": "https://arxiv.org/abs/2503.21452v1", "attribution": "\"Numerical solution of locally loaded Volterra integral equations\" by Vladislav Byankin, Aleksandr Tynda, Denis Sidorov, and Aliona Dreglea, arXiv:2503.21452v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11703v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Notations and definitions.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\t\t\\hline\n\t\t$X$ & Training sample set\\\\\n\t\t$Y$ & Set of training samples in a low-dimensional space\\\\\n\t\t$n$ & Number of training samples\\\\\n\t\t$D$ & Dimensionality of the samples in the original space\\\\\n\t\t$d$ & Dimensionality of embedding features\\\\\n\t\t$c_i$ & Labels of samples $x_i$\\\\\n\t\t$C$ & Number of classes\\\\\n\t\t$P$ & Projection matrix\\\\\n\t\t$CLG$& Contrastive learning graph\\\\\n\t\t$G^{pos}$ & Positive graph\\\\\n\t\t$G^{neg}$ & Negative graph\\\\\n\t\t$S^{pos}$ & Similarity matrix for positive pairs\\\\\n\t\t$S^{neg}$ & Dissimilarity matrix of negative pairs\\\\\n\t\t$\\sigma$ & Positive parameter\\\\\n\t\t$k$ & Number of neighbors\\\\\n\t\t$NK(x_j)$ &The $k$ nearest neighbors of $x_j$\\\\\n\t\t$NK^+_{(x_j)}$ &The $k$ nearest neighbors of $x_j$ in the $c_j$ th class\\\\\n\t\t$\\nabla L(P)$ & Gradient of $L(P)$ with respect to $P$\\\\\n\t\t$T$ & Number of iterations\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Unified Framework for Feature Extraction based on Contrastive Learning", "authors": ["Hongjie Zhang"], "url": "https://arxiv.org/abs/2101.11703v2", "attribution": "\"Unified Framework for Feature Extraction based on Contrastive Learning\" by Hongjie Zhang, arXiv:2101.11703v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.04382v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of results for the Bayes classifier applied to alternative datasets. Columns for Decision and Boundary are percentages.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|r|rrr|rr}\n \\hline\n & & \\multicolumn{3}{c|}{Decision} & \\multicolumn{2}{c}{Boundary} \\\\\nDataset & Count & Adeno & COVID & SARS & No & Yes \\\\\n \\hline\n ReadsOriginal & 5869 & 32.94 & 34.09 & 32.97 & 69.48 & 30.52 \\\\\n ReadsNew & 6000 & 32.43 & 33.82 & 33.75 & 68.7 & 31.3 \\\\\n ReadsMixed2 & 5969 & 33.86 & 33.59 & 32.55 & 69.69 & 30.31 \\\\\n ReadsMixed1 & 7869 & 48.1 & 26.2 & 25.7 & 74.1 & 25.9 \\\\\n ReadsEcoli & 6000 & 92.767 & 2.933 & 4.300 & 87.35 & 12.65 \\\\\n Random6K & 6000 & 90.8333 & 0.3833 & 8.7833 & 78.75 & 21.25 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Structure of Classifier Boundaries: Case Study for a Naive Bayes Classifier", "authors": ["Alan F. Karr", "Zac Bowen", "Adam A. Porter"], "url": "https://arxiv.org/abs/2212.04382v2", "attribution": "\"Structure of Classifier Boundaries: Case Study for a Naive Bayes Classifier\" by Alan F. Karr, Zac Bowen, and Adam A. Porter, arXiv:2212.04382v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.18675v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n Method & LSE-D $\\downarrow$ & PSNR $\\uparrow$ & SSIM $\\uparrow$ & LPIPS $\\downarrow$ \\\\\n \\toprule\n w/o alignment & 12.66 & 21.02 & 0.9104 & 0.1855 \\\\\n w/o audio finetune & 11.78 & 22.73 & 0.9355 & 0.1198 \\\\ \n w/o wrinkle features & 11.28 & 23.14 & 0.9311 & 0.1162 \\\\\n w/o color MLP \\& latent finetune & 11.32 & 23.96 & 0.9367 & 0.1133 \\\\\n \\bottomrule\n Ours (Full) & \\textbf{11.15} & \\textbf{24.97} & \\textbf{0.9470} & \\textbf{0.1101} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "GaussianSpeech: Audio-Driven Gaussian Avatars", "authors": ["Shivangi Aneja", "Artem Sevastopolsky", "Tobias Kirschstein", "Justus Thies", "Angela Dai", "Matthias Nießner"], "url": "https://arxiv.org/abs/2411.18675v1", "attribution": "\"GaussianSpeech: Audio-Driven Gaussian Avatars\" by Shivangi Aneja, Artem Sevastopolsky, Tobias Kirschstein, Justus Thies, Angela Dai, and Matthias Nießner, arXiv:2411.18675v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.12597v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Previously proposed lung segmentation methods and respective dice similarity coefficient}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\hline\nAuthors, citation & Dice similarity coef.\\\\\n\\hline\nLi et al., & 0.964 \\\\\nCandemir et al., & 0.967 \\\\\nShao et al., & 0.972 \\\\\nNovikov et al., & 0.974 \\\\\nYang et al., & 0.975 \\\\\nHwang et al., & 0.980 \\\\\nOurs & \\bfseries{0.988} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Potential Features of ICU Admission in X-ray Images of COVID-19 Patients", "authors": ["Douglas P. S. Gomes", "Anwaar Ulhaq", "Manoranjan Paul", "Michael J. Horry", "Subrata Chakraborty", "Manas Saha", "Tanmoy Debnath", "D. M. Motiur Rahaman"], "url": "https://arxiv.org/abs/2009.12597v2", "attribution": "\"Potential Features of ICU Admission in X-ray Images of COVID-19 Patients\" by Douglas P. S. Gomes, Anwaar Ulhaq, Manoranjan Paul, Michael J. Horry, Subrata Chakraborty, Manas Saha, Tanmoy Debnath, and D. M. Motiur Rahaman, arXiv:2009.12597v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.09854v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrrrrrrrrrrrr}\n\\toprule\n&& \\multicolumn{12}{c}{April 2021 Survey} \\\\\n\\cmidrule{3-16}\n&& \\multicolumn{4}{c}{\\textsc{20th Quantile}} \n&& \\multicolumn{4}{c}{\\textsc{50th Quantile}} \n&& \\multicolumn{4}{c}{\\textsc{80th Quantile}} \\\\\n\\cmidrule{3-6} \\cmidrule{8-11} \\cmidrule{13-16}\n&& $\\Delta$P(y=1) & $\\Delta$P(y=2) & $\\Delta$P(y=3) & $\\Delta$P(y=4)\n&& $\\Delta$P(y=1) & $\\Delta$P(y=2) & $\\Delta$P(y=3) & $\\Delta$P(y=4)\n&& $\\Delta$P(y=1) & $\\Delta$P(y=2) & $\\Delta$P(y=3) & $\\Delta$P(y=4) \\\\\n\\midrule\n\\textrm{EV Info} \n&& $ -0.0722$ & $-0.0421$ & $ 0.0527$ & $0.0615$ \n&& $ -0.0792$ & $-0.0374$ & $ 0.0172$ & $0.0995$ \n&& $ -0.0722$ & $-0.0453$ & $-0.0089$ & $0.1265$ \\\\\n\\textrm{Env Better} \n&& $ -0.1958$ & $-0.0191$ & $ 0.1135$ & $0.1014$ \n&& $ -0.1771$ & $-0.0730$ & $ 0.1122$ & $0.1379$ \n&& $ -0.1384$ & $-0.1127$ & $ 0.0789$ & $0.1722$ \\\\\n\\textrm{EV Owner} \n&& $ -0.1357$ & $-0.0896$ & $ 0.0633$ & $0.1620$ \n&& $ -0.1212$ & $-0.0757$ & $-0.0113$ & $0.2083$ \n&& $ -0.1053$ & $-0.0953$ & $-0.0265$ & $0.2270$ \\\\\n\\textrm{GACC (TM)} \n&& $ 0.2192$ & $0.0097$ & $ -0.1217$ & $-0.1072$ \n&& $ 0.1974$ & $0.0635$ & $ -0.1203$ & $-0.1406$ \n&& $ 0.1283$ & $0.0779$ & $ -0.0575$ & $-0.1487$ \\\\\n\\textrm{GACC (RA)} \n&& $ 0.1216$ & $0.0312$ & $ -0.0780$ & $-0.0747$ \n&& $ 0.1097$ & $0.0534$ & $ -0.0613$ & $-0.1018$ \n&& $ 0.0763$ & $0.0421$ & $ -0.0124$ & $-0.1059$ \\\\\n\\textrm{Age $< 50$} \n&& $ -0.0237$ & $-0.0106$ & $ 0.0156$ & $0.0187$ \n&& $ -0.0303$ & $-0.0134$ & $ 0.0095$ & $0.0343$ \n&& $ -0.0265$ & $-0.0164$ & $ 0.0003$ & $0.0427$ \\\\\n\\textrm{Metropolitan} \n&& $ -0.0483$ & $-0.0174$ & $ 0.0318$ & $0.0338$ \n&& $ -0.0565$ & $-0.0225$ & $ 0.0237$ & $0.0553$ \n&& $ -0.0345$ & $-0.0185$ & $ 0.0030$ & $0.0500$ \\\\\n\\textrm{Midwest} \n&& $ 0.0185$ & $ 0.0080$ & $ -0.0123$ & $-0.0141$ \n&& $ 0.0161$ & $ 0.0068$ & $ -0.0053$ & $-0.0177$ \n&& $ 0.0114$ & $ 0.0066$ & $ -0.0003$ & $-0.0177$ \\\\\n\\textrm{South} \n&& $ 0.0124$ & $0.0056$ & $ -0.0083$ & $-0.0098$ \n&& $ \\times$ & $\\times$ & $ \\times$ & $\\times$ \n&& $ \\times$ & $\\times$ & $ \\times$ & $\\times$ \\\\\n\\textrm{West} \n&& $ -0.0327$ & $-0.0168$ & $ 0.0220$ & $0.0275$ \n&& $ -0.0382$ & $-0.0176$ & $ 0.0092$ & $0.0467$ \n&& $ -0.0311$ & $-0.0200$ & $-0.0015$ & $0.0526$ \\\\\n\\textrm{Black} \n&& $ 0.0784$ & $ 0.0256$ & $ -0.0519$ & $-0.0520$ \n&& $ 0.0613$ & $ 0.0244$ & $ -0.0259$ & $-0.0598$ \n&& $ 0.0269$ & $ 0.0141$ & $ -0.0014$ & $-0.0396$ \\\\\n\\textrm{Democrat} \n&& $ -0.1242$ & $-0.0523$ & $ 0.0808$ & $0.0958$ \n&& $ -0.1081$ & $-0.0631$ & $ 0.0431$ & $0.1280$ \n&& $ -0.0828$ & $-0.0676$ & $ 0.0070$ & $0.1434$ \\\\\n\\midrule\n&& \\multicolumn{12}{c}{May 2022 Survey} \\\\\n\\cmidrule{3-16}\n\\textrm{EV Owner} \n&& $ -0.1782$ & $-0.0723$ & $ 0.0880$ & $0.1624$ \n&& $ -0.1573$ & $-0.0695$ & $ 0.0121$ & $0.2147$ \n&& $ -0.1465$ & $-0.1110$ & $-0.0071$ & $0.2646$ \\\\\n\\textrm{GACC (TM)} \n&& $ 0.3425$ & $-0.0568$ & $ -0.1515$ & $-0.1341$ \n&& $ 0.3304$ & $ 0.0226$ & $ -0.1778$ & $-0.1752$ \n&& $ 0.2471$ & $ 0.0963$ & $ -0.1459$ & $-0.1975$ \\\\\n\\textrm{GACC (RA)} \n&& $ 0.1541$ & $0.0026$ & $ -0.0790$ & $-0.0777$ \n&& $ 0.1527$ & $0.0497$ & $ -0.0852$ & $-0.1171$ \n&& $ 0.1269$ & $0.0392$ & $ -0.0308$ & $-0.1352$ \\\\\n\\textrm{Age $< 50$} \n&& $ -0.0362$ & $-0.0066$ & $ 0.0199$ & $0.0229$ \n&& $ -0.0390$ & $-0.0085$ & $ 0.0130$ & $0.0344$ \n&& $ -0.0358$ & $-0.0167$ & $ 0.0072$ & $0.0453$ \\\\\n\\textrm{Metropolitan} \n&& $ -0.0684$ & $-0.0054$ & $ 0.0360$ & $0.0378$ \n&& $ -0.0643$ & $-0.0109$ & $ 0.0258$ & $0.0493$ \n&& $ -0.0483$ & $-0.0165$ & $ 0.0114$ & $0.0535$ \\\\\n\\textrm{Black} \n&& $ 0.0642$ & $0.0060$ & $ -0.0342$ & $-0.0359$ \n&& $ \\times$ & $\\times$ & $ \\times$ & $\\times$ \n&& $ \\times$ & $\\times$ & $ \\times$ & $\\times$ \\\\\n\\textrm{Democrat} \n&& $ -0.1719$ & $-0.0295$ & $ 0.0927$ & $0.1087$ \n&& $ -0.1437$ & $-0.0574$ & $ 0.0601$ & $0.1410$ \n&& $ -0.0996$ & $-0.0659$ & $ 0.0217$ & $0.1438$ \\\\\n\\midrule\n&& \\multicolumn{12}{c}{May--June 2023 Survey} \\\\\n\\cmidrule{3-16}\n\\textrm{EV Infra} \n&& $ -0.1863$ & $-0.0428$ & $ 0.0956$ & $0.1335$ \n&& $ -0.1827$ & $-0.0603$ & $ 0.0616$ & $0.1813$ \n&& $ -0.1452$ & $-0.0730$ & $ 0.0187$ & $0.1995$ \\\\\n\\textrm{EV Owner} \n&& $ -0.1684$ & $-0.0350$ & $ 0.0727$ & $0.1307$ \n&& $ -0.1842$ & $-0.0542$ & $ 0.0459$ & $0.1925$ \n&& $ -0.1580$ & $-0.0817$ & $ 0.0183$ & $0.2215$ \\\\\n\\textrm{GACC (TM)} \n&& $ 0.3323$ & $-0.0635$ & $ -0.1405$ & $-0.1283$ \n&& $ 0.3103$ & $-0.0250$ & $ -0.1426$ & $-0.1427$ \n&& $ 0.2512$ & $ 0.0486$ & $ -0.1423$ & $-0.1575$ \\\\\n\\textrm{GACC (RA)} \n&& $ 0.1318$ & $-0.0065$ & $ -0.0592$ & $-0.0661$ \n&& $ 0.1238$ & $ 0.0132$ & $ -0.0570$ & $-0.0799$ \n&& $ 0.0975$ & $ 0.0183$ & $ -0.0329$ & $-0.0830$ \\\\\n\\textrm{Age $< 50$} \n&& $ -0.0296$ & $-0.0007$ & $ 0.0131$ & $0.0173$ \n&& $ -0.0363$ & $-0.0034$ & $ 0.0139$ & $0.0258$ \n&& $ -0.0349$ & $-0.0082$ & $ 0.0102$ & $0.0329$ \\\\\n\\textrm{Metropolitan} \n&& $ -0.0568$ & $ 0.0013$ & $ 0.0250$ & $0.0304$ \n&& $ -0.0511$ & $-0.0026$ & $ 0.0204$ & $0.0333$ \n&& $ -0.0408$ & $-0.0067$ & $ 0.0129$ & $0.0345$ \\\\\n\\textrm{Black} \n&& $ 0.0665$ & $-0.0010$ & $ -0.0296$ & $-0.0359$ \n&& $ 0.0462$ & $ 0.0029$ & $ -0.0182$ & $-0.0309$ \n&& $ 0.0239$ & $ 0.0039$ & $ -0.0069$ & $-0.0209$ \\\\\n\\textrm{Democrat} \n&& $ -0.1832$ & $-0.0084$ & $ 0.0851$ & $0.1066$ \n&& $ -0.1815$ & $-0.0384$ & $ 0.0798$ & $0.1401$ \n&& $ -0.1393$ & $-0.0660$ & $ 0.0491$ & $0.1562$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "To Buy an Electric Vehicle or Not? A Bayesian Analysis of Consumer Intent in the United States", "authors": ["Nafisa Lohawala", "Mohammad Arshad Rahman"], "url": "https://arxiv.org/abs/2504.09854v1", "attribution": "\"To Buy an Electric Vehicle or Not? A Bayesian Analysis of Consumer Intent in the United States\" by Nafisa Lohawala and Mohammad Arshad Rahman, arXiv:2504.09854v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.12285v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\hline\n Description & Independent variable \\\\\n\\hline\n Fulfillment method category & \\texttt{fm\\_categories} \\\\\n Give away method category & \\texttt{giveaways\\_categories} \\\\ \n Campaign flight total days & \\texttt{cf\\_total\\_days} \\\\ \n Allocated deliveries total & \\texttt{ad\\_total} \\\\ \n Price type category & \\texttt{pt\\_categories} \\\\ \n Social channel category & \\texttt{sch\\_categories} \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Effects of Just-in-time Delivery on Social Engagement: A Cluster Analysis", "authors": ["Moisés Ramírez", "Raziel Ruíz", "Nathan Klarer"], "url": "https://arxiv.org/abs/2212.12285v1", "attribution": "\"The Effects of Just-in-time Delivery on Social Engagement: A Cluster Analysis\" by Moisés Ramírez, Raziel Ruíz, and Nathan Klarer, arXiv:2212.12285v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.03818v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n \\hline\n\t\t\\hline\n\t\t\\multicolumn{3}{c}{Computational Approach} \\\\\n\t\t\\hline\n Robust Gradient Descent & Algorithm~ & {\\color{blue} slow} as parameter dimension goes large \\\\\n \\hline\n \\multirow{2}{*}{Empirical Risk-based Methods} & Algorithm~ & parameter dimension-free\\\\ \\cline{2-3}\n & Algorithm~ & easy-implementable with DL framework \\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Advantages and disadvantages of three algorithms discussed in the appendix.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Empirical Risk Minimization for Losses without Variance", "authors": ["Guanhua Fang", "Ping Li", "Gennady Samorodnitsky"], "url": "https://arxiv.org/abs/2309.03818v1", "attribution": "\"Empirical Risk Minimization for Losses without Variance\" by Guanhua Fang, Ping Li, and Gennady Samorodnitsky, arXiv:2309.03818v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01833v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average accuracy on the CIFAR-10/100 and ImageNet-100 with both novel class ratio and label ratio of 50\\%. We compare OwMatch with existing literature on OwSSL. Also compared with other related approaches of traditional SSL, OSSL, and NCD approaches following . Proper modifications are made to make these approaches compatible with OwSSL; the details are in Appendix~. The results are averaged over three independent runs. The baseline figures are sourced from the respective papers.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccc}\n \\toprule\n \\textbf{Method} & \\multicolumn{3}{c}{\\textbf{CIFAR-10}} & \\multicolumn{3}{c}{\\textbf{CIFAR-100}} & \\multicolumn{3}{c}{\\textbf{ImageNet-100}}\\\\\n \\cmidrule(lr){2-4} \\cmidrule(l){5-7} \\cmidrule(lr){8-10} \n & Seen & Novel & All & Seen & Novel & All & Seen & Novel & All \\\\\n \\midrule\n FixMatch & 71.5 & 50.4 & 49.5 & 39.6 & 23.5 & 20.3 & 65.8 & 36.7 & 34.9 \\\\\n DS\\textsuperscript{3}L & 77.6 & 45.3 & 40.2 & 55.1 & 23.7 & 24.0 & 71.2 & 32.5 & 30.8 \\\\\n CGDL & 72.3 & 44.6 & 39.7 & 49.3 & 22.5 & 23.5 & 67.3 & 33.8 & 31.9 \\\\\n DTC & 53.9 & 39.5 & 38.3 & 31.3 & 22.9 & 18.3 & 25.6 & 20.8 & 21.3 \\\\\n RankStats & 86.6 & 81.0 & 82.9 & 36.4 & 28.4 & 23.1 & 47.3 & 28.7 & 40.3 \\\\\n SimCLR & 58.3 & 63.4 & 51.7 & 28.6 & 21.1 & 22.3 & 39.5 & 35.7 & 36.9 \\\\\n UNO & 91.6 & 69.3 & 80.5 & 68.3 & 36.5 & 51.5 & - & - & - \\\\\n ORCA & 88.2 & 90.4 & 89.7 & 66.9 & 43.0 & 48.1 & 89.1 & 72.1 & 77.8 \\\\\n NACH & 89.5 & 92.2 & 91.3 & 68.7 & 47.0 & 52.1 & 91.0 & 75.5 & 79.6 \\\\\n OpenLDN & 95.7 & 95.1 & 95.4 & 73.5 & 46.8 & 60.1 & 89.6 & 68.6 & 79.1 \\\\\n TRSSL & \\textbf{96.8} & 92.8 & 94.8 & 80.0 & 49.3 & 64.7 & - & - & - \\\\\n OpenCon & 89.3 & 91.1 & 90.4 & 69.1 & 47.8 & 52.7 & 90.6 & 80.8 & 83.8 \\\\\n \\midrule\n OwMatch & 93.0 & 95.9 & 94.4 & 74.5 & 55.9 & 65.1 & \\textbf{91.7} & 72.0 & 81.8 \\\\\n OwMatch+ & 96.5 & \\textbf{97.1} & \\textbf{96.8} & \\textbf{80.1} & \\textbf{63.9} & \\textbf{71.9} & 91.5 & 79.6 & \\textbf{85.5} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning", "authors": ["Shengjie Niu", "Lifan Lin", "Jian Huang", "Chao Wang"], "url": "https://arxiv.org/abs/2411.01833v1", "attribution": "\"OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning\" by Shengjie Niu, Lifan Lin, Jian Huang, and Chao Wang, arXiv:2411.01833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13410v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Structured singular values, and peak frequencies, for different noise levels.}\n\\begin{tabular}{c|cc|cc}\n \\hline\n \\multirow{2}{*}{$\\sigma^2$} & \\multicolumn{2}{c|}{$\\tilde{\\mu}$} & \\multicolumn{2}{c}{$\\bar{\\mu}$} \\\\\n & Value & Frequency & Value & Frequency \\\\\n \\hline\n 0 & 3.4832 & 0.0000 & 3.4864 & 0.1137 \\\\\n $10^{-6}$ & 3.9034 & 0.0415 & 4.1362 & 0.0258 \\\\\n $10^{-5}$ & 4.7400 & 0.1005 & 6.0842 & 0.4876 \\\\\n $10^{-4}$ & 8.2426 & 2.9016 & 11.6509 & 0.3914 \\\\\n $10^{-3}$ & 22.4976 & 2.3989 & 33.6600 & 0.4115 \\\\\n 0.01 & 65.4947 & 1.6757 & 125.8755 & 2.7307 \\\\\n \\hline\n MUSSV & 3.4833 & 0.1181 & -- & -- \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Data-Driven Estimation of Structured Singular Values", "authors": ["Margarita A. Guerrero", "Braghadeesh Lakshminarayanan", "Cristian R. Rojas"], "url": "https://arxiv.org/abs/2503.13410v1", "attribution": "\"Data-Driven Estimation of Structured Singular Values\" by Margarita A. Guerrero, Braghadeesh Lakshminarayanan, and Cristian R. Rojas, arXiv:2503.13410v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.09239v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rlllllll}\n \\hline\n & date & n & climate & global & tax & taxes & inequality \\\\ \n & & & change & warming & & & \\\\ \n \\hline\n 61 & 2012 Q1 & 11 & 0 & 0 & 5 & 0 & 0 \\\\ \n 62 & 2012 Q2 & 33 & 0 & 0 & 13 & 5 & 0 \\\\ \n 63 & 2012 Q3 & 17 & 0 & 0 & 6 & 1 & 0 \\\\ \n 64 & 2012 Q4 & 30 & 0 & 0 & 17 & 4 & 1 \\\\ \n 65 & 2013 Q1 & 29 & 1 & 0 & 12 & 5 & 0 \\\\ \n 66 & 2013 Q2 & 42 & 0 & 0 & 22 & 3 & 3 \\\\ \n 67 & 2013 Q3 & 25 & 0 & 0 & 10 & 1 & 1 \\\\ \n 68 & 2013 Q4 & 37 & 0 & 0 & 20 & 4 & 3 \\\\ \n 69 & 2014 Q1 & 20 & 0 & 0 & 6 & 3 & 1 \\\\ \n 70 & 2014 Q2 & 31 & 0 & 0 & 14 & 3 & 1 \\\\ \n 71 & 2014 Q3 & 23 & 0 & 0 & 10 & 3 & 0 \\\\ \n 72 & 2014 Q4 & 30 & 0 & 0 & 9 & 4 & 1 \\\\ \n 73 & 2015 Q1 & 20 & 0 & 0 & 6 & 1 & 1 \\\\ \n 74 & 2015 Q2 & 25 & 0 & 0 & 12 & 3 & 3 \\\\ \n 75 & 2015 Q3 & 17 & 0 & 0 & 5 & 0 & 1 \\\\ \n 76 & 2015 Q4 & 42 & 1 & 0 & 12 & 3 & 1 \\\\ \n 77 & 2016 Q1 & 23 & 1 & 0 & 9 & 5 & 0 \\\\ \n 78 & 2016 Q2 & 32 & 1 & 0 & 12 & 1 & 3 \\\\ \n 79 & 2016 Q3 & 21 & 1 & 0 & 10 & 1 & 1 \\\\ \n 80 & 2016 Q4 & 34 & 0 & 0 & 19 & 2 & 4 \\\\ \n 81 & 2017 Q1 & 34 & 1 & 0 & 9 & 1 & 2 \\\\ \n 82 & 2017 Q2 & 45 & 2 & 0 & 10 & 3 & 1 \\\\ \n 83 & 2017 Q3 & 30 & 0 & 0 & 6 & 1 & 4 \\\\ \n 84 & 2017 Q4 & 39 & 0 & 0 & 10 & 2 & 4 \\\\ \n 85 & 2018 Q1 & 29 & 0 & 0 & 8 & 1 & 2 \\\\ \n 86 & 2018 Q2 & 28 & 1 & 0 & 13 & 8 & 4 \\\\ \n 87 & 2018 Q3 & 20 & 0 & 0 & 5 & 0 & 1 \\\\ \n 88 & 2018 Q4 & 36 & 5 & 1 & 11 & 1 & 2 \\\\ \n 89 & 2019 Q1 & 30 & 1 & 0 & 9 & 1 & 3 \\\\ \n 90 & 2019 Q2 & 26 & 4 & 0 & 8 & 1 & 2 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Interactions of Social Norms about Climate Change: Science, Institutions and Economics", "authors": ["Antonio Cabrales", "Manu García", "David Ramos Muñoz", "Angel Sánchez"], "url": "https://arxiv.org/abs/2208.09239v1", "attribution": "\"The Interactions of Social Norms about Climate Change: Science, Institutions and Economics\" by Antonio Cabrales, Manu García, David Ramos Muñoz, and Angel Sánchez, arXiv:2208.09239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.20978v2_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average (across $\\gamma$ and $\\beta$) optimal $\\alpha$ levels minimizing Monte Carlo standard errors (raw data), under different combinations of $\\nu$ and $\\xi$. \\\\}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccccc|}\n\\cline{2-8} & \\multicolumn{7}{c|}{$\\xi$} \\\\ \\hline\n$\\nu$ & -0.15 & -0.10 & -0.05 & 0 & 0.05 & 0.10 & 0.15 \\\\ \\hline\n2.1 & 0.1025 & 0.1200 & 0.1200 & 0.1275 & 0.1725 & 0.1450 & 0.1675 \\\\\n2.5 & 0.1000 & 0.1025 & 0.0925 & 0.0950 & 0.0975 & 0.1050 & 0.1175 \\\\\n5 & 0.0750 & 0.0800 & 0.0850 & 0.0850 & 0.0900 & 0.0825 & 0.0900 \\\\\n8 & 0.0700 & 0.0800 & 0.0800 & 0.0675 & 0.0800 & 0.0675 & 0.0650 \\\\\n10 & 0.0725 & 0.0500 & 0.0500 & 0.0500 & 0.0550 & 0.0525 & 0.0550 \\\\\n15 & 0.0500 & 0.0500 & 0.0500 & 0.0500 & 0.0550 & 0.0525 & 0.0525 \\\\\n20 & 0.0500 & 0.0500 & 0.0500 & 0.0500 & 0.0525 & 0.0500 & 0.0525 \\\\\n$\\infty$ & - & - & - & 0.0500 & - & - & - \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Using quantile time series and historical simulation to forecast financial risk multiple steps ahead", "authors": ["Richard Gerlach", "Antonio Naimoli", "Giuseppe Storti"], "url": "https://arxiv.org/abs/2502.20978v2", "attribution": "\"Using quantile time series and historical simulation to forecast financial risk multiple steps ahead\" by Richard Gerlach, Antonio Naimoli, and Giuseppe Storti, arXiv:2502.20978v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19577v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of users correctly re-identified after 1, 2, and 3 observations for several simulation runs.}\n\\begin{tabular}{cccc}\n \\toprule\n Simulation& 1 observation & 2 observations & 3 observations \\\\\n \\midrule\n Run 1 &29 users &28 users &42 users \\\\\n Run 2 &28 users &37 users &49 users \\\\\n Run 3 &29 users &47 users &63 users \\\\\n Run 4 &24 users &33 users &46 users \\\\\n Run 5 &30 users &39 users &52 users \\\\\n Run 6 &24 users &33 users &37 users \\\\\n Run 7 &31 users &38 users &49 users \\\\\n Run 8 &32 users &41 users &56 users \\\\\n Run 9 &29 users &27 users &48 users \\\\\n Run 10 &27 users &44 users &62 users \\\\ \\hline\n \\textbf{mean $\\pm$ std} & \\textbf{28.3 $\\pm$ 2.7} & \\textbf{36.7 $\\pm$ 6.5} & \\textbf{50.4 $\\pm$ 8.2} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Public and Reproducible Assessment of the Topics API on Real Data", "authors": ["Yohan Beugin", "Patrick McDaniel"], "url": "https://arxiv.org/abs/2403.19577v3", "attribution": "\"A Public and Reproducible Assessment of the Topics API on Real Data\" by Yohan Beugin and Patrick McDaniel, arXiv:2403.19577v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.15639v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small{Empirical probability of Type-I error of the different tests. } }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\t\t\t\t\\toprule \n\t\t\t\tDistribution\n\t\t\t\t& Multivariate Gaussian & Gaussian Copula & Cauchy \\\\\n\t\t\t\t\\midrule \n\t\t\t\tProposed & 0.038 & 0.042 & 0.042\\\\\n\t\t\t\t\n\t\t\t\tJdCov & 0.036 & 0.030& 0.046 \\\\\n\t\t\t\t\n\t\t\t\tMatteson-Tsay (MT) & 0.056 & 0.046& 0.056 \\\\\n\t\t\t\t\n\t\t\t\tdHSIC & 0.032 & 0.074& 0.050 \\\\\n\t\t\t\t\\bottomrule \n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Distribution-free joint independence testing and robust independent component analysis using optimal transport", "authors": ["Ziang Niu", "Bhaswar B. Bhattacharya"], "url": "https://arxiv.org/abs/2211.15639v2", "attribution": "\"Distribution-free joint independence testing and robust independent component analysis using optimal transport\" by Ziang Niu and Bhaswar B. Bhattacharya, arXiv:2211.15639v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.13657v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c||c|c|c|c|c|}\n\\hline\nCandidate & voter-1 & voter-2 &P & N & P-N & N/P & $W_{1}^{1}$ \\\\\n \\hline \n\\hline\nA & 10 & -5 & 10 & 5 & 5 & 0.5 & 3.33 \\\\\n\\hline\nB & 0 & 4 & 4 & 0 & 4 & 0 & 4 \\\\\n\\hline\nC & 0 & 1 & 1 & 0 & 1 & 0 & 1 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Negative votes to depolarize politics", "authors": ["Karthik H. Shankar"], "url": "https://arxiv.org/abs/2012.13657v1", "attribution": "\"Negative votes to depolarize politics\" by Karthik H. Shankar, arXiv:2012.13657v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04051v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc}\n \\hline\n Method & max-IoU $\\uparrow$ & min-CDR $\\downarrow$ & min-BDE $\\downarrow$ & mAP $\\uparrow$\\\\ \n \\hline\n SOD & 53.3\\% & 11.94\\% & 12.31\\% & 47.48\\% \\\\\n FP & 23.29\\% & 14.49\\% & 19.04\\% & 11.41\\% \\\\\n Image Cropping & 12.19\\% & 42.92\\%&36.25\\%& 10.42\\%\\\\\n Image Cropping & 17.84\\% & 32.82\\%&28.62\\%& 27.22\\%\\\\\n \\hline\n Rank-SS ({\\it H2V-142K})& 54.54\\% & 4.69\\% & 4.74\\%& 59.59\\%\\\\\n Rank-SS (ASR)& 60.26\\% & 4.52\\% & 4.22\\% & 67.16\\% \\\\\n Rank-SS (ASR-ft) & \\textbf{60.42\\%} & \\textbf{4.39\\%} & \\textbf{4.13\\%} & \\textbf{67.25\\%} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of the proposed {\\it Sub-Select} modules with state-of-the-art Salient Object Detection, Fixation Prediction and Image Cropping methods in the ASR dataset . }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Horizontal-to-Vertical Video Conversion", "authors": ["Tun Zhu", "Daoxin Zhang", "Yao Hu", "Tianran Wang", "Xiaolong Jiang", "Jianke Zhu", "Jiawei Li"], "url": "https://arxiv.org/abs/2101.04051v2", "attribution": "\"Horizontal-to-Vertical Video Conversion\" by Tun Zhu, Daoxin Zhang, Yao Hu, Tianran Wang, Xiaolong Jiang, Jianke Zhu, and Jiawei Li, arXiv:2101.04051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.07901v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lcccr}\n \\toprule\n \\textbf{Dataset} & \\textbf{Link Type} & \\textbf{$n_\\mathrm{r}$ (\\%)} & $h$ & \\textbf{$N$} \\\\\n \\midrule\n Mobile Phone & Phone Calls & 55.1 & $0.1$ & 2,173,030 \\\\\n Pokec & Friendship & 46.6 & $-0.2$ & 383,943 \\\\\n Last.fm & Friendship & 68.3 & $0.1$ & 188,672 \\\\\n Oklahoma FB & Friendship & 49.3 & $0.2$ & 16,245 \\\\\n CPH & Proximity, Calls, etc. & 78.1 & $0.0$ & 779 \\\\\n SocioPatterns & Proximity & 57.4 & $0.0$ & 115 \\\\\n \\bottomrule\n \\end{tabular}\n\\caption{Summary of datasets used in this study. The variable $n_\\mathrm{r}$ represents the relative size of the red (male) group. The Coleman index $h$ is for overall sex homophily.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Homophily Within and Across Groups", "authors": ["Abbas K. Rizi", "Riccardo Michielan", "Clara Stegehuis", "Mikko Kivelä"], "url": "https://arxiv.org/abs/2412.07901v2", "attribution": "\"Homophily Within and Across Groups\" by Abbas K. Rizi, Riccardo Michielan, Clara Stegehuis, and Mikko Kivelä, arXiv:2412.07901v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09893v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\n \\textbf{Acronym} & \\textbf{Long-form} & \\textbf{Rule} \\\\ \\hline \\hline\n AABM & Analyzing Avatar Boundary Matching & Character Match \\\\ \\hline\n ABBREX & Abbreviation Expander & Bounded Schwartz's \\\\ \\hline\n AD & acronym disambiguation & Character Match \\\\ \\hline\n AI & Acronym identification & Character Match \\\\ \\hline\n BADREX & Biomedical Abbreviations using & Bounded Schwartz's \\\\\n & Dynamic Regular Expressions & \\\\ \\hline\n BiLSTM & Bi - directional Long ShortTerm Memory & Bounded Schwartz's \\\\ \\hline\n DOG & Diverse acrOnym Glossary & Bounded Schwartz's \\\\ \\hline\n MAD & Massive Acronym Disambiguation & Capital Initials \\\\ \\hline\n MF & most frequent & Character Match \\\\ \\hline\n USMC & User - guided Social Media Crawling & Capital Initials\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The acronym glossary extracted from the text of this paper using MadDog.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "MadDog: A Web-based System for Acronym Identification and Disambiguation", "authors": ["Amir Pouran Ben Veyseh", "Franck Dernoncourt", "Walter Chang", "Thien Huu Nguyen"], "url": "https://arxiv.org/abs/2101.09893v1", "attribution": "\"MadDog: A Web-based System for Acronym Identification and Disambiguation\" by Amir Pouran Ben Veyseh, Franck Dernoncourt, Walter Chang, and Thien Huu Nguyen, arXiv:2101.09893v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16004v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c||c|c|}\n\\hline\n\\textbf{Dice Score} & \\textbf{Fold 1} & \\textbf{Fold 2} & \\textbf{Fold 3} & \\textbf{Fold 4} & \\textbf{Fold 5} & \\textbf{Mean} & \\textbf{STD} \\\\\n\\hline\nBest Preproc. & 0.2167 & \\textcolor{blue}{0.3184} & 0.2181 & 0.1521 & 0.1696 & 0.2150 & 0.0647 \\\\\n+ Vessel Segm. & 0.2257 & \\textcolor{blue}{0.3273} & 0.2129 & 0.2852 & 0.2495 & \\textbf{0.2601} & \\textbf{0.0466} \\\\\n\\hline\n\\(\\Delta\\) (\\%) & +4.2 & +2.8 & -2.4 & +87.5 & +47.1 & \\textbf{+21.0} & \\textbf{-27.9} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Validation Dice scores for the best preprocessing pipeline from Section versus the same model trained with the vessel segmentation replacing CTA, evaluated on the same 5-fold split. \\(\\Delta\\) gives the relative percentage change with respect to the best preprocessing pipeline from Section . The best fold for both experiments is highlighted in \\textcolor{blue}{blue}.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Clinically-Informed Preprocessing Improves Stroke Segmentation in Low-Resource Settings", "authors": ["Juampablo E. Heras Rivera", "Hitender Oswal", "Tianyi Ren", "Yutong Pan", "William Henry", "Caitlin M. Neher", "Mehmet Kurt"], "url": "https://arxiv.org/abs/2508.16004v1", "attribution": "\"Clinically-Informed Preprocessing Improves Stroke Segmentation in Low-Resource Settings\" by Juampablo E. Heras Rivera, Hitender Oswal, Tianyi Ren, Yutong Pan, William Henry, Caitlin M. Neher, and Mehmet Kurt, arXiv:2508.16004v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.04933v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|lccccccc|c|c|}\n\\hline\nj \\textbackslash r&0& 1&2&3&4&5&6&7&$\\chi $& HZ\\\\\n\\hline\n21& & &&& & & &\\fbox{1} & -1& numerator\\\\\n19& & &&& &\\fbox{1}& & $\\ast$ &-1 & \\\\\n\\hline\n17& & && & &1 &$\\underline{1}$ & &0 &\\\\\n15& & &&1& 1& & & &0& \\\\\n\\hline\n13& & && $\\ast$ & $\\textcircled{\\underline{1}}$& & & & 1&\\\\\n11& & &\\textcircled{1}& & & && &1& \\\\\n9& \\textcircled{1} & & & & & & && 1 & denominator \\\\\n7& \\textcircled{1} & & & & & & && 1& \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The HOMFLY-PT polynomial and HZ factorisation", "authors": ["Andreani Petrou", "Shinobu Hikami"], "url": "https://arxiv.org/abs/2412.04933v3", "attribution": "\"The HOMFLY-PT polynomial and HZ factorisation\" by Andreani Petrou and Shinobu Hikami, arXiv:2412.04933v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.18905v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of eight estimators for the fully marginal excursion effect under Scenario (2)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\toprule\nEstimator & Time Length & Bias & SE & SD & RMSE & CP\\\\\n\\midrule\n & 30 & \\textbf{-0.015} & 0.065 & 0.067 & 0.068 & 0.93\\\\\n & 100 & \\textbf{-0.015} & 0.036 & 0.036 & 0.039 & \\textbf{0.92}\\\\\n\\multirow{-3}{*}{ECE} & 150 & \\textbf{-0.015} & 0.029 & 0.030 & 0.034 & \\textbf{0.91}\\\\\n\\cmidrule{1-7}\n & 30 & \\textbf{-0.017} & 0.062 & 0.067 & 0.069 & \\textbf{0.92}\\\\\n & 100 & \\textbf{-0.015} & 0.034 & 0.036 & 0.039 & \\textbf{0.91}\\\\\n\\multirow{-3}{*}{ECE-NonP} & 150 & \\textbf{-0.016} & 0.028 & 0.030 & 0.034 & \\textbf{0.89}\\\\\n\\cmidrule{1-7}\n & 30 & \\textbf{-0.015} & 0.065 & 0.067 & 0.068 & 0.93\\\\\n & 100 & \\textbf{-0.015} & 0.036 & 0.036 & 0.039 & \\textbf{0.92}\\\\\n\\multirow{-3}{*}{G-estimator} & 150 & \\textbf{-0.015} & 0.029 & 0.030 & 0.034 & \\textbf{0.91}\\\\\n\\cmidrule{1-7}\n & 30 & \\textbf{-0.015} & 0.065 & 0.067 & 0.068 & 0.93\\\\\n & 100 & \\textbf{-0.015} & 0.036 & 0.036 & 0.039 & \\textbf{0.92}\\\\\n\\multirow{-3}{*}{EMEE} & 150 & \\textbf{-0.015} & 0.029 & 0.030 & 0.034 & \\textbf{0.91}\\\\\n\\cmidrule{1-7}\n & 30 & \\textbf{-0.017} & 0.067 & 0.067 & 0.069 & 0.94\\\\\n & 100 & \\textbf{-0.015} & 0.037 & 0.036 & 0.039 & 0.93\\\\\n\\multirow{-3}{*}{EMEE-NonP} & 150 & \\textbf{-0.016} & 0.030 & 0.030 & 0.034 & \\textbf{0.92}\\\\\n\\cmidrule{1-7}\n & 30 & -0.003 & 0.066 & 0.068 & 0.068 & 0.94\\\\\n & 100 & -0.002 & 0.036 & 0.037 & 0.037 & 0.94\\\\\n\\multirow{-3}{*}{DR-EMEE-NonP} & 150 & -0.002 & 0.030 & 0.031 & 0.031 & 0.95\\\\\n\\cmidrule{1-7}\n & 30 & \\textbf{-0.031} & 0.062 & 0.064 & 0.071 & \\textbf{0.92}\\\\\n & 100 & \\textbf{-0.031} & 0.034 & 0.034 & 0.046 & \\textbf{0.85}\\\\\n\\multirow{-3}{*}{GEE (ind)} & 150 & \\textbf{-0.032} & 0.028 & 0.029 & 0.043 & \\textbf{0.78}\\\\\n\\cmidrule{1-7}\n & 30 & \\textbf{-0.031} & 0.062 & 0.064 & 0.071 & \\textbf{0.92}\\\\\n & 100 & \\textbf{-0.031} & 0.034 & 0.034 & 0.046 & \\textbf{0.85}\\\\\n\\multirow{-3}{*}{GEE (exch)} & 150 & \\textbf{-0.032} & 0.028 & 0.029 & 0.043 & \\textbf{0.78}\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes", "authors": ["Xueqing Liu", "Tianchen Qian", "Lauren Bell", "Bibhas Chakraborty"], "url": "https://arxiv.org/abs/2310.18905v3", "attribution": "\"Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes\" by Xueqing Liu, Tianchen Qian, Lauren Bell, and Bibhas Chakraborty, arXiv:2310.18905v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.03247v2_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\n & \\textbf{Baseline} & \\textbf{x\\_add} & \\textbf{s\\_add} & \\textbf{x\\_cat} & \\textbf{s\\_cat} \\\\ \\hline \\hline\n\\textbf{dev\\_clean - less\\_5} & 7.2 & 7.2 & 7.5 & 7.2 & 7.7 \\\\ %\\hline\n\\textbf{test\\_clean - less\\_5} & 7.4 & 7.9 & 7.8 & 7.8 & 8.3 \\\\ \\hline %\\hline\n\\textbf{dev\\_other - less\\_5} & 14.2 & 16.2 & 15.5 & 16.1 & 16.4 \\\\ %\\hline\n\\textbf{test\\_other - less\\_5} & 15.5 & 17.9 & 17.5 & 18.3 & 18.7 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Librispeech: Average error rate (\\%WER) on utterances with duration lesser than five seconds }\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Investigation of Speaker-adaptation methods in Transformer based ASR", "authors": ["Vishwas M. Shetty", "Metilda Sagaya Mary N J", "S. Umesh"], "url": "https://arxiv.org/abs/2008.03247v2", "attribution": "\"Investigation of Speaker-adaptation methods in Transformer based ASR\" by Vishwas M. Shetty, Metilda Sagaya Mary N J, and S. Umesh, arXiv:2008.03247v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15444v2_tex_table19.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mutually unbiased weighing matrices of order $22$ and weight $9$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l}\n\\noalign{\\hrule height1pt}\n$W_{22}$\\\\\n\\hline\n0010000212201000022001\n0001000201022022120000\n1020102000000012002120\n0000011002000102110110\n1002000012122020200000\\\\\n1110000111200100000002\n0001100010012201000110\n1020002000000101100211\n1200221000000001100120\n0001200000222010210001\\\\\n0121021002000000002202\n0110202000100200112000\n0001110000021021010020\n1011000220110100200000\n0102121201000000010001\\\\\n0120210210010000001020\n0001020110101002001001\n0010100002002010101220\n1100011120000200020001\n0100022022200020001100\\\\\n1200000000210222010200\n1000000200021210001012\\\\\n\\hline\n$ A_{22,2}$\\\\\n\\hline\n1000010220000000011222\n0100210012200000102002\n1212000002020100100001\n1000221000002220020200\n0000101001210122100000\\\\\n0011000010002121210000\n1101100100021000000210\n1002100010110200012000\n1100000220200000202101\n0000110102002002220020\\\\\n0011010001002210100001\n1020000011000111020020\n0100001210120002001100\n1010002000010000021112\n0012211101001000200000\\\\\n0010022001020002002022\n0000001020102110002012\n0011021002011010000020\n1221200100000002010100\n0100202000110102000201\\\\\n0100000120100021100120\n0102020100202010011000\\\\\n\\hline\n$ A_{22,3}$\\\\\n\\hline\n0110100011100002100200\n1001020000002110010022\n0011001200210000010011\n0121200010021200010000\n0010210002101110002000\\\\\n0000012221220012000000\n0000011101200001102020\n1002202101010000010010\n0100120120001010000101\n1012020200020201002000\\\\\n1120110002002000002010\n0010000102220000101012\n1200001100020002200201\n0100220000200122022000\n0000001011000010220112\\\\\n1110010000000020201120\n0100200000002011021201\n1001000020011200020202\n1201002010000000120101\n0000102010201101200200\\\\\n1022001200001100101000\n0001000021120121000010\\\\\n\\hline\n$ A_{22,4}$\\\\\n\\hline\n0011210000011000021001\n1000001210000221011000\n0012101000020000220101\n1210001100010100012000\n0111000202002011002000\\\\\n0120001200010102200010\n0000001012021012100200\n0000120002210020020220\n1002200020200210200200\n0100001101100001020202\\\\\n0102000102010200100110\n0011100001200202000012\n1021100100002010001001\n0000010000102222002201\n1100010020220120100000\\\\\n1200020220100000120010\n1100022011001000002001\n0012002010002100001210\n0001220102020020200010\n0110020020100002011020\\\\\n1000200010002002020122\n1000112002101000200002\\\\\n\\hline\n$ A_{22,5}$\\\\\n\\hline\n1000202002002022100010\n1022020000201200010100\n0100100120100202002002\n1220010000022010200002\n1001102001200000100220\\\\\n1000001220120020000021\n0101200002200200220020\n1100001102000111100000\n0012022020020010021000\n0120000001000120021102\\\\\n0000121200202000022010\n0000011020201002001210\n0100012200021001002010\n0012010000200102002120\n0100020010020102210200\\\\\n0100200221012010010000\n1011000101000000200111\n0102110010002200001001\n1012000000010021200202\n1000000210111012020000\\\\\n0010201011020200100002\n0011100202000000011102\\\\\n\\noalign{\\hrule height1pt}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unbiased weighing matrices of weight $9$", "authors": ["Makoto Araya", "Masaaki Harada", "Hadi Kharaghani", "Sho Suda", "Wei-Hsuan Yu"], "url": "https://arxiv.org/abs/2501.15444v2", "attribution": "\"Unbiased weighing matrices of weight $9$\" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.03617v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Time summary for the two algorithms. Tests run on Gaussian datasets of dimension $d = 100$, with varying $n$. Each value represents mean and sample standard deviation (when multiple runs are computed).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n& & & & \\multicolumn{2}{c}{Time (s)} \\\\\n\\cmidrule(r){5-6}\n$d$ & $n$ & $\\delta$ & $\\#$ runs & Our algorithm & Fixed Point algorithm\\\\ \n\\midrule\n100 & $10\\,000$ & $10^{-2}$ & 100 & 0.0927 $\\pm$ 0.0402 & 0.7270 $\\pm$ 0.1886 \\\\\n& & $10^{-3}$ & 100 & 0.2578 $\\pm$ 0.1991 & 5.6216 $\\pm$ 1.5382 \\\\\n& & $10^{-4}$ & 100 & 0.3013 $\\pm$ 0.0833 & 18.6236 $\\pm$ 4.8307 \\\\\n& & $10^{-5}$ & 50 & 0.5162 $\\pm$ 0.1292 & 82.4097 $\\pm$ 26.7802 \\\\\n& $100\\,000$ & $10^{-3}$ & 20 & 1.9359 $\\pm$ 0.1371 & 160.1398 $\\pm$ 34.8062 \\\\\n& $1\\,000\\,000$ & $10^{-3}$ & 1 & 101.0625 & 4837.5313\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Efficient Data-Driven Leverage Score Sampling Algorithm for the Minimum Volume Covering Ellipsoid Problem in Big Data", "authors": ["Elizabeth Harris", "Ali Eshragh", "Bishnu Lamichhane", "Jordan Shaw-Carmody", "Elizabeth Stojanovski"], "url": "https://arxiv.org/abs/2411.03617v1", "attribution": "\"Efficient Data-Driven Leverage Score Sampling Algorithm for the Minimum Volume Covering Ellipsoid Problem in Big Data\" by Elizabeth Harris, Ali Eshragh, Bishnu Lamichhane, Jordan Shaw-Carmody, and Elizabeth Stojanovski, arXiv:2411.03617v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07612v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\nTechnique & Inference time & Inference time\\\\\n & With GPU & Without GPU\\\\\n\\hline\n2D & 70 & 1145\\\\\n3D & 17 & 229\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Inference time for 2D and 3D techniques in seconds}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans", "authors": ["Abhishek Shivdeo", "Rohit Lokwani", "Viraj Kulkarni", "Amit Kharat", "Aniruddha Pant"], "url": "https://arxiv.org/abs/2101.07612v1", "attribution": "\"Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans\" by Abhishek Shivdeo, Rohit Lokwani, Viraj Kulkarni, Amit Kharat, and Aniruddha Pant, arXiv:2101.07612v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16944v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crl}\n \\toprule\n $n$ & \\textit{found} & \\textit{connectivity} \\\\\n \\midrule\n 11 & 18 & 1: 11; 2: 7 \\\\\n 12 & 19 & 1: 8; 2: 11 \\\\\n 13 & 21 & 1: 13; 2: 8 \\\\\n 14 & 9 & 1: 4; 2: 5 \\\\\n 15 & 1 & 1: 1 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\small Details about the 1-metric antidimensional graphs found with the Barab\\'{a}si–Albert model.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Burning some myths on privacy properties of social networks against active attacks", "authors": ["Serafino Cicerone", "Gabriele Di Stefano", "Sandi Klavžar", "Ismael G. Yero"], "url": "https://arxiv.org/abs/2504.16944v1", "attribution": "\"Burning some myths on privacy properties of social networks against active attacks\" by Serafino Cicerone, Gabriele Di Stefano, Sandi Klavžar, and Ismael G. Yero, arXiv:2504.16944v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.10480v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccccc}\n\t\t\t\\toprule\n\t\t\tNumber of Robots\t&\t5\t&\t10\t&\t20\t&\t40 & 80 \\\\\n\t\t\t\\midrule\n\t\t\tAdditional training time (min) & $\\infty$ & 320 & 1132 & 1248 & 2945 \\\\ \n\t\t\t\\toprule\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{The additional training time used for a complete communication network to reach the same performance as Star-<5>, evaluated on an NVIDIA Tesla K80 GPU.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Distributed Map Classification using Local Observations", "authors": ["Guangyi Liu", "Arash Amini", "Martin Takáč", "Héctor Muñoz-Avila", "Nader Motee"], "url": "https://arxiv.org/abs/2012.10480v2", "attribution": "\"Distributed Map Classification using Local Observations\" by Guangyi Liu, Arash Amini, Martin Takáč, Héctor Muñoz-Avila, and Nader Motee, arXiv:2012.10480v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2302.00761v1_tex_table69.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lrrrrrr}\n\\hline\\hline\n&-3 years&-2 years&-1 year&0 year&+1 year&+2 years\\tabularnewline\n\\hline\nMB levering&$ 1.875$&$ 1.895$&$ 1.979$&$ 1.624$&$ 1.619$&$ 1.479$\\tabularnewline\nMB t-stat&$ $&$ 0.25$&$ 1.16$&$ -5.934$&$-0.11$&$-3.06$\\tabularnewline\nP/S levering&$ 3.335$&$ 3.958$&$ 5.284$&$ 2.918$&$ 2.418$&$ 2.040$\\tabularnewline\nP/S t-stat&$ $&$2.98$&$4.70$&$ -9.33$&$-4.13$&$ 3.54$\\tabularnewline\nTobin`s Q levering&$ 3.298$&$ 3.352$&$ 3.419$&$ 2.623$&$ 2.575$&$ 2.441$\\tabularnewline\nTobin`s Q t-stat&$ $&$0.63$&$0.86$&$ -12.53$&$-0.92$&$-2.57$\\tabularnewline\nEquity iss. levering&$ 0.118$&$ 0.144$&$ 0.152$&$ 0.072$&$ 0.080$&$ 0.052$\\tabularnewline\nEquity iss. unlevering&$ 0.061$&$ 0.062$&$ 0.107$&$ 0.215$&$ 0.055$&$ 0.035$\\tabularnewline\nEquity iss. t-stat&$ 4.895$&$ 7.127$&$ 3.622$&$-10.942$&$ 2.982$&$ 3.636$\\tabularnewline\nDebt iss. levering&$-0.006$&$-0.003$&$ 0.000$&$ 0.215$&$ 0.026$&$ 0.018$\\tabularnewline\nDebt iss. unlevering&$-0.013$&$-0.013$&$-0.026$&$ -0.158$&$ 0.000$&$ 0.013$\\tabularnewline\nDebt iss. t-stat&$ 1.460$&$ 2.217$&$ 6.264$&$ 42.549$&$ 7.050$&$ 1.044$\\tabularnewline\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Zero-Leverage Puzzle", "authors": ["Mykola Pinchuk"], "url": "https://arxiv.org/abs/2302.00761v1", "attribution": "\"Zero-Leverage Puzzle\" by Mykola Pinchuk, arXiv:2302.00761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.05824v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc|ccc}\n & cutoff & $m$ & 0.01 & 0.05 & 0.1 \\\\ \n \\hline\n & 0.05 & 3 & 0.090 & 0.187 & 0.270 \\\\ \n & 0.05 & 5 & 0.060 & 0.126 & 0.177 \\\\ \n & 0.05 & 10 & 0.044 & 0.106 & 0.157 \\\\ \n & & & & & \\\\ \n & 0.10 & 3 & 0.059 & 0.130 & 0.194 \\\\ \n & 0.10 & 5 & 0.045 & 0.102 & 0.160 \\\\ \n & 0.10 & 10 & 0.045 & 0.104 & 0.146 \\\\ \n & & & & & \\\\ \n & 0.20 & 3 & 0.051 & 0.112 & 0.167 \\\\ \n & 0.20 & 5 & 0.047 & 0.097 & 0.156 \\\\ \n & 0.20 & 10 & 0.046 & 0.081 & 0.125 \\\\ \n \\hline\n $u_{\\text{Norm}}$ & 0.10 & 3 & 0.013 & 0.047 & 0.102 \\\\ \n $u_{\\text{Exp}}$ & 0.10 & 3 & 0.014 & 0.054 & 0.097 \\\\ \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Simulation results for the empirical testsize.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Preprocessing noisy functional data: a multivariate perspective", "authors": ["Siegfried Hörmann", "Fatima Jammoul"], "url": "https://arxiv.org/abs/2012.05824v2", "attribution": "\"Preprocessing noisy functional data: a multivariate perspective\" by Siegfried Hörmann and Fatima Jammoul, arXiv:2012.05824v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10719v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrrrrrr}\n \\hline\nModel & Ahead & $\\gamma$ & tr\\_MAPE & te\\_MAPE & $\\gamma$ & tr\\_SMAPE & te\\_SMAPE \\\\ \n \\hline\nCP & 1.00 & \\textbf{0.01} & 6.75 & 5.09 & \\textbf{0.06} & 6.73 & 5.13 \\\\ \n LL1 & 1.00 & 13.01 & 6.66 & 4.94 & 13.01 & 6.62 & 5.03 \\\\ \n LL2 & 1.00 & 12.01 & 6.54 & 5.00 & 12.01 & 6.50 & 5.09 \\\\ \n LL3 & 1.00 & 16.01 & 6.72 & 4.86 & 16.01 & 6.68 & 4.94 \\\\ \n NW1 & 1.00 & 4.33 & 8.42 & 10.53 & 4.47 & 7.99 & 10.98 \\\\ \n NW2 & 1.00 & 4.03 & 8.28 & 10.40 & 4.03 & 7.87 & 10.88 \\\\ \n NW3 & 1.00 & 5.11 & 8.42 & 10.69 & 5.27 & 7.99 & 11.18 \\\\ \n CP & 2.00 & \\textbf{0.00} & 10.08 & 9.31 & \\textbf{0.02} & 10.07 & 8.88 \\\\ \n LL1 & 2.00 & 11.01 & 9.94 & 9.41 & 11.01 & 9.85 & 9.79 \\\\ \n LL2 & 2.00 & 11.01 & 9.82 & 9.29 & 12.01 & 9.66 & 8.83 \\\\ \n LL3 & 2.00 & 12.01 & 9.99 & 9.67 & 12.01 & 9.89 & 10.11 \\\\ \n NW1 & 2.00 & 4.76 & 9.45 & 12.29 & 4.77 & 8.91 & 12.87 \\\\ \n NW2 & 2.00 & 4.31 & 9.33 & 11.83 & 4.31 & 8.79 & 12.39 \\\\ \n NW3 & 2.00 & 5.49 & 9.46 & 12.41 & 5.66 & 8.92 & 13.04 \\\\ \n CP & 3.00 & \\textbf{0.02} & 11.18 & 12.49 & \\textbf{0.04} & 11.27 & 11.73 \\\\ \n LL1 & 3.00 & 12.01 & 11.24 & 11.29 & 12.01 & 10.92 & 11.84 \\\\ \n LL2 & 3.00 & 10.01 & 10.98 & 11.32 & 12.01 & 10.72 & 11.40 \\\\ \n LL3 & 3.00 & 14.01 & 11.36 & 11.20 & 16.01 & 11.00 & 11.58 \\\\ \n NW1 & 3.00 & 4.96 & 9.88 & 13.04 & 4.96 & 9.30 & 13.71 \\\\ \n NW2 & 3.00 & 4.35 & 9.58 & 12.76 & 4.35 & 9.03 & 13.43 \\\\ \n NW3 & 3.00 & 5.70 & 9.92 & 13.07 & 5.70 & 9.34 & 13.75 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Canadian Lynx time series results}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Short-term prediction of Time Series based on bounding techniques", "authors": ["Pedro Cadahía", "Jose Manuel Bravo Caro"], "url": "https://arxiv.org/abs/2101.10719v1", "attribution": "\"Short-term prediction of Time Series based on bounding techniques\" by Pedro Cadahía and Jose Manuel Bravo Caro, arXiv:2101.10719v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.13247v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{pifont}\n\\usepackage{adjustbox}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccccccc}\n\\hline\n\\multicolumn{2}{c}{\\multirow{2}{*}{denoiser}} & \\multicolumn{8}{c}{kernel (see )} & \\multirow{2}{*}{convergence} \\\\\n\\cline{3-10}\n && (a) & (b) & (c) & (d) & (e) & (f) & (g) & (h)\\\\\n\\hline\n\\multicolumn{2}{c}{Observation} & $23.36$ & $22.93$ & $23.43$ & $19.49$ & $23.84$ & $19.85$ & $20.75$ & $20.67$ \\\\\n\\multicolumn{2}{c}{RealSN } & $26.24$ & $26.25$ & $26.34$ & $25.89$ & $25.08$ & $25.84$ & $24.81$ & $23.92$ & \\checkmark \\\\\n\\multicolumn{2}{c}{$\\text{prox}_{\\mu_{\\ell_1}\\|\\Psi^\\dagger \\cdot\\|_1}$} & $29.44$ & $29.20$ & $29.31$ & $28.87$ & $30.90$ & $30.81$ & $29.40$ & $29.06$ & \\checkmark \\\\\n\\multicolumn{2}{c}{$\\text{prox}_{\\mu_\\text{TV} \\|\\cdot\\|_\\text{TV}}$} & $29.70$ & $29.35$ & $29.43$ & $29.15$ & $30.67$ & $30.62$ & $29.61$ & $29.23$ & \\checkmark \\\\\n\\multicolumn{2}{c}{DnCNN } & $29.82$ & $29.24$ & $29.26$ & $28.88$ & $30.84$ & $30.95$ & $29.54$ & $29.17$ & \\ding{55} \\\\\n\\multicolumn{2}{c}{BM3D } & $30.05$ & $29.53$ & $29.93$ & $29.10$ & $31.08$ & $30.78$ & $29.56$ & $29.41$ & \\ding{55}\\\\\n\\multicolumn{2}{c}{Proposed} & $\\mathbf{30.86}$ & $\\mathbf{30.33}$ & $\\mathbf{30.31}$ & $\\mathbf{30.14}$ & $\\mathbf{31.72}$ & $\\mathbf{31.69}$ & $\\mathbf{30.42}$ & $\\mathbf{30.09}$ & \\checkmark \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\small Average PSNR values obtained by different denoisers plugged in the PnP-FB algorithm , to solve the deblurring problem~ with kernels of (a)-(h) considering the BSD10 test set. The last row provides the average SSIM values for the observed blurred image $y$ in each experimental setting. Each algorithm is stopped after a fixed number of iterations equal to 1000. The best PSNR values are indicated in bold.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning Maximally Monotone Operators for Image Recovery", "authors": ["Jean-Christophe Pesquet", "Audrey Repetti", "Matthieu Terris", "Yves Wiaux"], "url": "https://arxiv.org/abs/2012.13247v2", "attribution": "\"Learning Maximally Monotone Operators for Image Recovery\" by Jean-Christophe Pesquet, Audrey Repetti, Matthieu Terris, and Yves Wiaux, arXiv:2012.13247v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16534v2_tex_table22.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The performance of datasets used in FT-Transformer paper, averaged within 5 seeds. As the tree-based models act as an upper bound of performance, we highlight the best performing \"deep learning model\" in \\textbf{\\underline{Bold with underline}}, and the \"deep learning model\" with second performance in \\textbf{Bold}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccc}\n\\toprule\n & \\textbf{CA ↓} & \\textbf{AD ↑} & \\textbf{HE ↑} & \\textbf{JA ↑} & \\textbf{HI ↑} & \\textbf{AL ↑} & \\textbf{EP ↑} & \\textbf{YE ↓} & \\textbf{CO ↑} & \\textbf{YA ↓} & \\textbf{MI ↓} \\\\\n\\midrule\n\\multicolumn{12}{c}{Default} \\\\\n\\midrule\nDOFEN (ours) & \\textbf{\\underline{0.4584}} & \\textbf{\\underline{0.8667}} & \\textbf{0.3858} & \\textbf{\\underline{0.7332}} & \\textbf{\\underline{0.7311}} & \\textbf{0.9567} & OOM & \\textbf{\\underline{8.7572}} & 0.9104 & \\textbf{\\underline{0.7470}} & \\textbf{\\underline{0.7438}} \\\\\nGRANDE & 0.481 & 0.859 & 0.350 & 0.723 & 0.721 & 0.785 & 0.870 & 8.943 & 0.792 & 0.779 & 0.754 \\\\\nTrompt & 0.4793 & \\textbf{0.8622} & 0.3665 & \\textbf{0.7287} & \\textbf{0.7299} & 0.9316 & 0.8941 & \\textbf{8.8427} & 0.9035 & \\textbf{0.7535} & \\textbf{0.7465} \\\\\nFT-Transformer & 0.4677 & 0.8580 & 0.3803 & 0.7237 & 0.7237 & 0.9522 & \\textbf{0.8960} & 8.9173 & \\textbf{0.9667} & 0.7560 & 0.7471 \\\\\nCatBoost & 0.4303 & 0.8730 & 0.3820 & 0.7207 & 0.7255 & 0.9464 & 0.8882 & 8.9140 & 0.9077 & 0.7510 & 0.7454 \\\\\nXGBoost & 0.4622 & 0.8741 & 0.3479 & 0.7110 & 0.7165 & 0.9242 & 0.8799 & 9.1922 & 0.9640 & 0.7607 & 0.7514 \\\\\n\\midrule\n\\multicolumn{12}{c}{Searched} \\\\\n\\midrule\nFT-Transformer & \\textbf{0.4597} & 0.8583 & \\textbf{\\underline{0.3916}} & \\textbf{\\underline{0.7332}} & 0.7292 & \\textbf{\\underline{0.9608}} & \\textbf{\\underline{0.8981}} & 8.8589 & \\textbf{\\underline{0.9695}} & N/A & 0.7462 \\\\\nCatBoost & 0.4317 & 0.8722 & 0.3846 & 0.7231 & 0.7243 & OOM & 0.8881 & 8.8765 & 0.9658 & 0.7429 & 0.7429 \\\\\nXGBoost & 0.4338 & 0.8722 & 0.3749 & 0.7218 & 0.7264 & OOM & 0.8835 & 8.953 & 0.9689 & 0.7357 & 0.7424 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DOFEN: Deep Oblivious Forest ENsemble", "authors": ["Kuan-Yu Chen", "Ping-Han Chiang", "Hsin-Rung Chou", "Chih-Sheng Chen", "Tien-Hao Chang"], "url": "https://arxiv.org/abs/2412.16534v2", "attribution": "\"DOFEN: Deep Oblivious Forest ENsemble\" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.09028v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{PCM degradation measure heuristics and references}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n\\hline \\hline\n\\textbf{Degradation Measure } & \\textbf{References} \\\\\n \\hline \\hline\nDepth of Discharge (DoD) & \\\\\nState of Charge (SoC) & \\\\\nCharge/Discharge Cycle & \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Degradation-based Energy Management for Microgrids in the Presence of Energy Storage Elements", "authors": ["Satish Vedula"], "url": "https://arxiv.org/abs/2503.09028v2", "attribution": "\"Degradation-based Energy Management for Microgrids in the Presence of Energy Storage Elements\" by Satish Vedula, arXiv:2503.09028v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.15913v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Ablation study} of Query Preservation Sweeping (default: $\\alpha$=0.9).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n& Content & Musical Instrument & Sound FX \\\\\n& Preservation ($\\uparrow$) & Style Fit ($\\uparrow$) & Style Fit ($\\uparrow$) \\\\\n\\midrule\n$\\gamma$ = 0 & 0.242 & 0.144 & 0.161 \\\\\n$\\gamma$ = 0.25 & 0.353 & 0.156 & 0.158 \\\\\n$\\gamma$ = 0.5 & 0.435 & 0.157 & 0.149 \\\\\n$\\gamma$ = 0.75 & 0.486 & \\textbf{0.157} & \\textbf{0.139} \\\\\n$\\gamma$ = 1 & \\textbf{0.519} & \\textbf{0.158} & 0.133 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Training-Free Approach for Music Style Transfer with Latent Diffusion Models", "authors": ["Heehwan Wang", "Joonwoo Kwon", "Sooyoung Kim", "Shinjae Yoo", "Yuewei Lin", "Jiook Cha"], "url": "https://arxiv.org/abs/2411.15913v2", "attribution": "\"A Training-Free Approach for Music Style Transfer with Latent Diffusion Models\" by Heehwan Wang, Joonwoo Kwon, Sooyoung Kim, Shinjae Yoo, Yuewei Lin, and Jiook Cha, arXiv:2411.15913v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.14912v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the emerging states in 1d swarmalators between the Kuramoto coupling and the Winfree coupling:}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc} \n State \\hspace{0.001 cm} & Kuramoto coupling \\hspace{0.01 cm} & Winfree coupling\\\\ \\hline \n \n Static $\\pi$ \\hspace{0.01 cm} & \\checkmark \\hspace{0.01 cm} & \\checkmark \\\\ \n \n Static async \\hspace{0.01 cm} &\\checkmark \\hspace{0.01 cm} &\\checkmark \\\\\n \n Static phase wave \\hspace{0.01 cm} &\\checkmark \\hspace{0.01 cm} &\\checkmark \\\\\n \n Active async \\hspace{0.01 cm} &\\checkmark \\hspace{0.01 cm} & $\\times$ \\\\\n \n $x$-(antiphase) sync\\hspace{0.01 cm} & $\\times$ \\hspace{0.01 cm} & \\checkmark \\\\\n \n $\\theta$-(antiphase) sync\\hspace{0.01 cm} & $\\times$ \\hspace{0.01 cm} & \\checkmark \\\\\n \n Intermediate mixed \\hspace{0.01 cm} &$\\times$ \\hspace{0.01 cm} & \\checkmark \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Dynamics of pulsating swarmalators on a ring", "authors": ["Samali Ghosh", "Kevin O'Keeffe", "Gourab Kumar Sar", "Dibakar Ghosh"], "url": "https://arxiv.org/abs/2504.14912v1", "attribution": "\"Dynamics of pulsating swarmalators on a ring\" by Samali Ghosh, Kevin O'Keeffe, Gourab Kumar Sar, and Dibakar Ghosh, arXiv:2504.14912v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.01926v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Feature extraction hyperparameters} optimised using the leave-$p$-out cross-validation as described in Section~ } % title name of the table\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\t\\textbf{Hyperparameter} & \\textbf{Description} & \\textbf{Range} \\\\\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\t\\multirow{2}{*}{MFCC ($\\mathcal{M}$)} & Number of lower-order & $13 \\times k_1$, where \\\\\n\t\t\t& MFCCs to keep & $k_1=1, 2, 3, 4, 5$ \\\\ \n\t\t\t\\hline\n\t\t\t\\multirow{2}{*}{Frame ($\\mathcal{F}$)} & Frame-size in which & $2^{k_2}$ where \\\\\n\t\t\t& audio is segmented & $k_2=8, \\cdots, 12$\\\\\n\t\t\t\\hline\n\t\t\t\\multirow{2}{*}{Seg ($\\mathcal{S}$)} & Number of frames & $10 \\times k_3$, where \\\\\n\t\t\t& extracted from the audio & $k_3=5, 7, 10, 12, 15$ \\\\\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings", "authors": ["Madhurananda Pahar", "Marisa Klopper", "Robin Warren", "Thomas Niesler"], "url": "https://arxiv.org/abs/2012.01926v2", "attribution": "\"COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings\" by Madhurananda Pahar, Marisa Klopper, Robin Warren, and Thomas Niesler, arXiv:2012.01926v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.17542v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ccc}\n \\hline\n \\textbf{Exploration parameter} & \\textbf{Value} \\\\\n \\hline\n Initial $\\epsilon / \\tau$/target rate & 1 \\\\\n Final $\\epsilon / \\tau$/target rate & 0.01 \\\\\n Decay period in agent steps & 250k \\\\\n \\hline\n VPD value of $k$ & 5 \\\\\n SimHash number of bits & 256 \\\\\n \\hline\n \\end{tabular}\n\\caption{Exploration parameters used across all exploration strategies.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts", "authors": ["Marius Captari", "Remo Sasso", "Matthia Sabatelli"], "url": "https://arxiv.org/abs/2403.17542v1", "attribution": "\"VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts\" by Marius Captari, Remo Sasso, and Matthia Sabatelli, arXiv:2403.17542v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.13977v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\n\\textbf{Symbol} & \\textbf{Description} \\\\ \n\\midrule\n$V(P,Q)$ & Total variation distance \\\\ \n$\\chi^2(P,Q)$ & $\\chi^2$ distance \\\\ \n$\\text{KL}(P,Q)$ & Kullback–Leibler divergence \\\\ \n$\\delta_{j}(B) = \n\\begin{cases}\n\t1 & \\text{if } j \\in B \\\\\n\t0 & \\text{otherwise}\n\\end{cases}$ & Dirac delta \\\\ \n$\\delta_{j,k} = \\delta_{j}(\\{k\\}) = \n\\begin{cases}\n\t1 & \\text{if } j = k \\\\\n\t0 & \\text{otherwise}\n\\end{cases}$ & Dirac delta \\\\ \n$B_{j,t}(p) = \\binom{t}{j} \\cdot p^j \\cdot (1-p)^{t-p}, \\; 0 \\leq p \\leq 1$ & $j$-th element of the $t$-order Bernstein basis \\\\ \n$b_{j,t}(\\pi) = E_{p \\sim \\pi}[B_{j,t}(p)]$ & Expected $j$-th fingerprint under $\\pi$ \\\\ \n$P_{\\pi}(A) = \\int \\text{Bin}(t,p)(A)\\ d\\pi(p) = \\sum_{j=1}^t b_j(\\pi) \\cdot \\delta_{j}(A)$ & Measure of set $A$ for binomial mixture \\\\ \n$m_l(\\pi) = E_{p\\sim\\pi}[p^l]$ & $l$-moment of distribution $\\pi$ \\\\ \n$V(\\pi) = V_{p\\sim\\pi}[p]$ & Variance of distribution $\\pi$ \\\\ \n$\\mu_p = p \\cdot (1-p)$ & Variance of $\\text{Ber}(p)$ \\\\ \n$B_t(f)(x) = \\sum_{j=0}^t f\\left(j/t\\right) \\cdot B_{j,t}(x)$ & Bernstein polynomial approximation of $f$ \\\\ \n$K_m(x,p,t) = \\sum_{v=0}^m (-1)^{m-v} \\binom{t-x}{m-v}\\binom{x}{v}p^{m-v}(1-p)^v$ & $m$-th Kravchuk polynomial \\\\ \n$\\tilde{K}_m(x,p,t) = \\binom{t}{m}^{-1} \\cdot K_m(x,p,t)$ & $m$-th normalized Kravchuk polynomial \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Testing Random Effects for Binomial Data", "authors": ["Lucas Kania", "Larry Wasserman", "Sivaraman Balakrishnan"], "url": "https://arxiv.org/abs/2504.13977v1", "attribution": "\"Testing Random Effects for Binomial Data\" by Lucas Kania, Larry Wasserman, and Sivaraman Balakrishnan, arXiv:2504.13977v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.01846v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrrr}\n\\toprule\n{} & maize & wheat & barley & sorghum & snflw. seed & snflw. oil & r.\\&.m seed & rape oil & maize g. oil \\\\\n\\midrule\nAfrica N & 17.1 & 24.7 & 6.8 & 0.2 & 1.8 & 48.3 & 5.0 & 9.6 & 1.2 \\\\\nAfrica S & 1.9 & 7.8 & 0.0 & 0.1 & 0.0 & 4.5 & 0.8 & 2.0 & 1.6 \\\\\nAmerica S & 1.7 & 0.1 & 0.0 & 0.2 & 0.0 & 1.1 & 2.0 & 2.4 & 2.2 \\\\\nAmerica N & 0.1 & 0.0 & 0.0 & 0.1 & 0.6 & 6.7 & 0.0 & 0.1 & 0.1 \\\\\nAsia C & 2.2 & 1.1 & 0.0 & 0.0 & 0.1 & 5.1 & 0.2 & 0.3 & 1.8 \\\\\nAsia E & 1.0 & 0.3 & 1.7 & 0.6 & 0.0 & 48.8 & 0.1 & 1.5 & 1.6 \\\\\nAsia S & 5.9 & 0.9 & 0.1 & 0.0 & 0.5 & 67.8 & 1.8 & 2.2 & 6.5 \\\\\nAsia SE & 2.6 & 4.4 & 0.1 & 0.2 & 0.0 & 7.1 & 2.4 & 6.4 & 1.9 \\\\\nAsia W & 22.2 & 2.6 & 10.4 & 1.9 & 5.2 & 27.0 & 14.9 & 15.8 & 10.8 \\\\\nAustralia & 9.3 & 0.2 & 0.2 & 0.0 & 0.0 & 8.5 & 0.0 & 1.8 & 0.7 \\\\\nEurope E & 5.4 & 0.9 & 0.5 & 1.5 & 0.2 & 2.3 & 6.8 & 9.0 & 5.8 \\\\\nEurope N & 39.1 & 0.3 & 0.1 & 1.5 & 1.8 & 38.2 & 4.2 & 6.1 & 6.8 \\\\\nEurpe S & 30.1 & 0.3 & 0.1 & 38.8 & 0.3 & 12.5 & 17.1 & 18.8 & 23.0 \\\\\nEurope W & 15.7 & 0.1 & 0.1 & 0.7 & 0.6 & 10.3 & 22.1 & 21.1 & 4.8 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Relative loss of different food products in different world regions after a shock to the complete agricultural production of Ukraine. We abbreviate sunflower as snflw. and use r.\\&m. seed to refer to rapeseed and mustardseed.These numbers correspond to fig.\\,4 in the main text.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability", "authors": ["Moritz Laber", "Peter Klimek", "Martin Bruckner", "Liuhuaying Yang", "Stefan Thurner"], "url": "https://arxiv.org/abs/2210.01846v3", "attribution": "\"Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability\" by Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang, and Stefan Thurner, arXiv:2210.01846v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.13446v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Sensitivity indices for the engineering model.}\n\\begin{tabular}{rrrrrrrrrr}\n\\hline\n & $H$ & $W$ & $E$ & $A_c$ & $I_c$ & $A_b$ & $I_b$ & $P$ & $q_0$ \\\\\n & (m) & (m) & (N/$m^{2}$) & (m$^{2}$) & (m$^{4}$) & (m$^{2}$) & (m$^{4}$) & (N) & (N/m) \\\\ \\hline\nFirst-order indices & 41 \\% & 16 \\% & 13 \\% & {\\color[HTML]{9B9B9B} 0 \\%} & 10 \\% & {\\color[HTML]{C0C0C0} 0 \\%} & {\\color[HTML]{C0C0C0} 0 \\%} & {\\color[HTML]{C0C0C0} 0 \\%} & 13 \\% \\\\ \\hline\n & & & & & & & & & \\\\\nSecond-order indices & & & & & & & & & \\\\ \\hline\n$H$ (m) & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} 1 \\%} & {\\color[HTML]{9B9B9B} 1 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 1 \\%} \\\\\n$W$ (m) & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} \\\\\n$E$ (N/m$^{2}$) & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} \\\\\n$A_c$ (m$^{2}$) & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} \\\\\n$I_c$ (m$^{4}$) & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} \\\\\n$A_b$ (m$^{2}$) & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} \\\\\n$I_b$ (m$^{4}$) & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} 0 \\%} & {\\color[HTML]{9B9B9B} 0 \\%} \\\\\n$P$ (N) & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} } & {\\color[HTML]{9B9B9B} 0 \\%} \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Simple binning algorithm and SimDec visualization for comprehensive sensitivity analysis of complex computational models", "authors": ["Mariia Kozlova", "Antti Ahola", "Pamphile T. Roy", "Julian Scott Yeomans"], "url": "https://arxiv.org/abs/2310.13446v2", "attribution": "\"Simple binning algorithm and SimDec visualization for comprehensive sensitivity analysis of complex computational models\" by Mariia Kozlova, Antti Ahola, Pamphile T. Roy, and Julian Scott Yeomans, arXiv:2310.13446v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02305v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Data variable definition and description}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n \\hline\n \\textbf{Name} & \\textbf{~~~~~~Description~~~~~~} \\\\ \\hline\n abnormal\\_ALP & Number of patients with abnormal alkaline phosphatase\t\\\\ \\hline\n abnormal\\_MPV & Number of patients with abnormal mean platelet volume\t\\\\ \\hline\n abnormal\\_hematocrit & Number of patients with abnormal hematocrit \\\\ \\hline\n abnormal\\_PO2 & Number of patients with abnormal partial pressure of oxygen \\\\ \\hline\n abnormal\\_creatinine & Number of patients with abnormal creatinine \\\\ \\hline\n abnormal\\_INR\t& Number of patients with abnormal international normalized ratio \\\\ \\hline\n abnormal\\_MCHb & Number of patients with abnormal mean corpuscular hemoglobin\t\\\\ \\hline\n abnormal\\_MCHb\\_conc & Number of patients with abnormal mean corpuscular hemoglobin concentration \\\\ \\hline\n abnormal\\_hb & Number of patients with abnormal hemoglobin \\\\ \\hline\n abnormal\\_mcv & Number of patients with abnormal mean corpuscular volume\t\\\\ \\hline\n abnormal\\_plt & Number of patients with abnormal platelet count\t\\\\ \\hline\n abnormal\\_redcellwidth & Number of patients with abnormal red cell distribution width\t\\\\ \\hline\n abnormal\\_wbc & Number of patients with abnormal white cell count\t\\\\ \\hline\n abnormal\\_ALC & Number of patients with abnormal absolute lymphocyte count \\\\ \\hline\n location\\_GeneralMedicine & Number of patients in general medicine \\\\ \\hline\n location\\_Hematology & Number of patients in hematology \\\\ \\hline\n location\\_IntensiveCare & Number of patients in intensive care \\\\ \\hline\n location\\_CardiovascularSurgery & Number of patients in cardiovascular surgery \\\\ \\hline\n location\\_Pediatric\t& Number of patients in pediatrics \\\\ \\hline\n Monday & Indicating the day of the week\t\\\\ \\hline\n Tuesday & Indicating the day of the week \\\\ \\hline\n Wednesday & Indicating the day of the week \\\\ \\hline\n Thursday & Indicating the day of the week \\\\ \\hline\n Friday & Indicating the day of the week\t\\\\ \\hline\n Saturday & Indicating the day of the week \\\\ \\hline\n Sunday & Indicating the day of the week\t\\\\ \\hline\n lastWeek\\_Usage & Number of units transfused in the previous week \\\\ \\hline\n yesterday\\_Usage & Number of platelet units transfused in the previous day \\\\ \\hline\n yesterday\\_ReceivedUnits & Number of units received by the hospital in the previous day \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models", "authors": ["Maryam Motamedi", "Jessica Dawson", "Na Li", "Douglas G. Down", "Nancy M. Heddle"], "url": "https://arxiv.org/abs/2101.02305v2", "attribution": "\"Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models\" by Maryam Motamedi, Jessica Dawson, Na Li, Douglas G. Down, and Nancy M. Heddle, arXiv:2101.02305v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18455v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The architecture of the convolutional encoder used in the experiments. The convolutional layers are parameterized respectively by the number of input channels, the number of output channels, and the filter size. The linear layers are defined by their input and output sizes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|cc|cc|}\n\\hline\n\\multicolumn{2}{|c|}{Encoder} & \\multicolumn{2}{c|}{Encoder cont'd} & \\multicolumn{2}{c|}{Encoder cont'd} \\\\ \\hline\nNumber & Layer & Number & Layer & Number & Layer \\\\ \\hline\n1 & Conv2D(3,8,5) & 6 & Conv2D(16,16,3) & 11 & LeakyReLU(0.1) \\\\ \\hline\n2 & Conv2D(3,8,5) & 7 & LeakyReLU(0.1) & 12 & Linear(256,128) \\\\ \\hline\n3 & LeakyReLU(0.1) & 8 & MaxPool(2,2) & \\multicolumn{2}{c|}{Decoder} \\\\ \\hline\n4 & MaxPool(2,2) & 9 & Flatten & 1 & Linear(64,10) \\\\ \\hline\n5 & Conv2D(8,16,3) & 10 & Linear(1024,256) & 2 & Softmax \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior", "authors": ["Milad Sefidgaran", "Abdellatif Zaidi", "Piotr Krasnowski"], "url": "https://arxiv.org/abs/2504.18455v1", "attribution": "\"Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior\" by Milad Sefidgaran, Abdellatif Zaidi, and Piotr Krasnowski, arXiv:2504.18455v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.15058v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n \\textbf{Augmentations} & $p$ & Parameters\n \\\\\n \\midrule\n \\cellcolor{gray!15} &\\cellcolor{gray!15} & \\cellcolor{gray!15} $\\text{degrees} \\in [-25, 25]$ \n \\\\\n \\cellcolor{gray!15} & \\cellcolor{gray!15} & \\cellcolor{gray!15} $\\text{translation} \\in [0, 0.2]$ \n \\\\\n \\multirow{-3}{*}{\\cellcolor{gray!15}Random Affine} & \\multirow{-3}{*}{\\cellcolor{gray!15} 0.8} & \\cellcolor{gray!15} $\\text{scale} \\in [0.9, 1.5]$\n \\\\\n \\cellcolor{white} & \\cellcolor{white} & \\cellcolor{white} $\\text{brightness}\\in[-0.1, 0.1]$\n \\\\\n \\multirow{-2}{*}{\\cellcolor{white}{Brightness Contrast}} & \\multirow{-2}{*}{\\cellcolor{white}{0.5}} & \\cellcolor{white} $\\text{contrast}\\in[0.5, 1.5]$ \n \\\\\n \\cellcolor{gray!15} & \\cellcolor{gray!15} & \\cellcolor{gray!15} $\\alpha\\in[1, 10]$\n \\\\\n \\multirow{-2}{*}{\\cellcolor{gray!15}Elastic Transform} & \\multirow{-2}{*}{\\cellcolor{gray!15} 0.8} & \\cellcolor{gray!15} $\\sigma\\in[8,15]$\n \\\\\n Sharpness & 0.5 & $\\text{sharpness} = 5$\n \\\\\n \\cellcolor{gray!15} & \\cellcolor{gray!15} & \\cellcolor{gray!15} $\\sigma\\in[0.1, 1.5]$ \n \\\\\n \\multirow{-2}{*}{\\cellcolor{gray!15}Gaussian Blur} & \\multirow{-2}{*}{\\cellcolor{gray!15} 0.5} & \\cellcolor{gray!15} $k=5$\n \\\\\n \\cellcolor{white} & \\cellcolor{white} & \\cellcolor{white} $\\mu\\in[0, 0.05]$\n \\\\\n \\multirow{-2}{*}{\\cellcolor{white}Gaussian Noise} & \\multirow{-2}{*}{\\cellcolor{white} 0.5} & \\cellcolor{white} $ \\sigma \\in [0, 0.05]$\n \\\\\n Horizontal Flip & 0.5 & None \n \\\\\n Vertical Flip & 0.5 & None \n \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{Data augmentations for generating synthetic tasks}. Given a set of $m+1$ copies of the same example, we randomly sampled data augmentations for each instance to increase the diversity of examples within the task. Each augmentation is sampled with probability $p$.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance", "authors": ["Hallee E. Wong", "Jose Javier Gonzalez Ortiz", "John Guttag", "Adrian V. Dalca"], "url": "https://arxiv.org/abs/2412.15058v2", "attribution": "\"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance\" by Hallee E. Wong, Jose Javier Gonzalez Ortiz, John Guttag, and Adrian V. Dalca, arXiv:2412.15058v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.01979v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{SMAPE and IEHI Rank by Industry with Spearman Correlation}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n Industry & SMAPE (\\%) & IEHI Rank \\\\\n \\midrule\n Financial Activities & 13.42 & 1 \\\\\n Education Services & 13.08 & 2 \\\\\n Professional Services & 16.94 & 3 \\\\\n Construction & 21.44 & 4 \\\\\n Transportation & 32.96 & 5 \\\\\n Leisure/Hospitality & 18.13 & 6 \\\\\n Other Services & 33.72 & 7 \\\\\n Information & 41.09 & 8 \\\\\n Manufacturing & 51.32 & 9 \\\\\n Natural Resources & 52.94 & 10 \\\\\n \\midrule\n \\multicolumn{3}{l}{\\textit{Spearman Rank Correlation}} \\\\\n \\cmidrule(lr){1-2}\n Spearman's $\\rho$ & 0.95 & \\\\\n $p$-value & 2.28e-05 & \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach", "authors": ["Adam Nelson-Archer", "Aleia Sen", "Meena Al Hasani", "Sofia Davila", "Jessica Le", "Omar Abbouchi"], "url": "https://arxiv.org/abs/2507.01979v1", "attribution": "\"Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach\" by Adam Nelson-Archer, Aleia Sen, Meena Al Hasani, Sofia Davila, Jessica Le, and Omar Abbouchi, arXiv:2507.01979v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.00101v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n\\hline\\hline\nState & Min & Median & Mean & Max & IQR & Skewness & Kurtosis\\\\\n\\hline\n$S_{0}$ & -18,365.69 & 1,149.30 & 2,808.84 & 343,463.26 & 2,360.00 & 22.35 & 679.28 \\\\ \n $S_{1}$ & -45,968.06 & 662.06 & 1,390.16& 198,923.65 & 1,747.04 & 10.19 & 256.32\\\\\n $S_{2}$& -58,154.22 & 808.90 & 2041.66 &165,782.32 & 1,936.61 & 8.89 & 131.32\\\\\n $S_{3}$& -53,078.64 & 924.56 & 2,549.96 & 273,638.48 & 2,291.40 & 8.67 & 154.79 \\\\\n $S_{4}$ & -33,892.65 & 1,013.95 & 2,550.49 & 97,253.20 & 2,622.99 & 5.19 & 57.49 \\\\\n $S_{5+}$ & -123,556.32 & 938.34 & 4,088.08 & 586,846.69 & 2,932.84 & 16.84 &472.04\\\\ \n \\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Summary statistics for the payment distribution in each state.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "mCube: Multinomial Micro-level reserving Model", "authors": ["Emmanuel Jordy Menvouta", "Jolien Ponnet", "Robin Van Oirbeek", "Tim Verdonck"], "url": "https://arxiv.org/abs/2212.00101v1", "attribution": "\"mCube: Multinomial Micro-level reserving Model\" by Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek, and Tim Verdonck, arXiv:2212.00101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.11162v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The table provides an overview of the graph benchmark datasets, including the number of nodes and links, as well as the input attributes and output label classes. For the experiments, we randomly sampled 20 node instances per class as the training set for each graph dataset, and 1,000 random nodes as the testing set.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\multirow{2}{*}{} & \\multicolumn{3}{c|}{\\textbf{Graph Datasets}} \\\\ \\cline{2-4}\n & \\textbf{Cora} & \\textbf{Citeseer} & \\textbf{Pubmed} \\\\ \\hline\\hline\nNode \\#\t& 2,708 & 3,327 & 19,717 \\\\ \\hline\nLink \\#\t& 10,556 & 9,104 & 88,648 \\\\ \\hline\nInput Dim.\t& 1,433 & 3,703 & 500 \\\\ \\hline\nOutput Dim.& 7 & 6 & 3 \\\\ \\hline\nTrain \\#\t& 140 & 120 & 60 \\\\ \\hline\nTest \\#\t& 1,000 & 1,000 & 1,000 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer", "authors": ["Jiawei Zhang"], "url": "https://arxiv.org/abs/2411.11162v1", "attribution": "\"RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer\" by Jiawei Zhang, arXiv:2411.11162v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.10509v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc} \\toprule\n CRSP & $\\mu^s$ & $\\sigma^s$ & $\\lambda^s$ & $u^s$ &\n $\\eta_1^s$ & $\\eta_2^s$ & $\\rho_{sb}$ \\\\ \\midrule\n & 0.08912 & 0.1460& 0.3263 & 0.2258 & 4.3625 & 5.5335 & 0.08420\\\\\n \\midrule\n \\midrule\n30-day T-bill & $\\mu^b$ & $\\sigma^b$ & $\\lambda^b$ & $u^b$ &\n $\\eta_1^b$ & $\\eta_2^b$ & $\\rho_{sb}$ \\\\ \\midrule\n & 0.0046 & 0.0130 & 0.5053 & 0.3958 & 65.801 & 57.793 & 0.08420\n\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Optimal performance of a tontine overlay subject to withdrawal constraints", "authors": ["Peter A. Forsyth", "Kenneth R. Vetzal", "G. Westmacott"], "url": "https://arxiv.org/abs/2211.10509v1", "attribution": "\"Optimal performance of a tontine overlay subject to withdrawal constraints\" by Peter A. Forsyth, Kenneth R. Vetzal, and G. Westmacott, arXiv:2211.10509v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12295v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\nConfig & Setting \\\\\n\\hline\n\\hline\nBatch size & 256 \\\\\nOptimizer & Adam \\\\\nLearning rate & $10^{-3}$ \\\\\nScheduler & StepLR \\\\\nTraining iterations & $10^6$ \\\\\nStep rate & $10^4$ \\\\\nDecay rate & 0.9 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Standard optimizer settings.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Towards Learning High-Precision Least Squares Algorithms with Sequence Models", "authors": ["Jerry Liu", "Jessica Grogan", "Owen Dugan", "Ashish Rao", "Simran Arora", "Atri Rudra", "Christopher Ré"], "url": "https://arxiv.org/abs/2503.12295v1", "attribution": "\"Towards Learning High-Precision Least Squares Algorithms with Sequence Models\" by Jerry Liu, Jessica Grogan, Owen Dugan, Ashish Rao, Simran Arora, Atri Rudra, and Christopher Ré, arXiv:2503.12295v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.19911v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Heterogeneous Treatment Effects by Industry AI Readiness and Location Characteristics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n\\multirow{2}{*}{Category} & Treatment & Standard & \\multirow{2}{*}{P-value} & N & Share of Total \\\\\n& Effect & Error & & Observations & Employment (\\%) \\\\\n\\midrule\n\\multicolumn{6}{l}{\\textbf{Panel A: By Industry AI Readiness}} \\\\\nHigh AI Readiness & 0.084** & 0.022 & 0.000 & 1,104 & 28.7 \\\\\n\\quad (IT, Finance, Professional) & & & & & \\\\\nMedium AI Readiness & 0.041* & 0.017 & 0.016 & 736 & 34.2 \\\\\n\\quad (Manufacturing, Healthcare) & & & & & \\\\\nLow AI Readiness & 0.012 & 0.015 & 0.427 & 1,472 & 37.1 \\\\\n\\quad (Retail, Hospitality, Transport) & & & & & \\\\\n\\midrule\n\\multicolumn{6}{l}{\\textbf{Panel B: By Infrastructure Quality}} \\\\\nHigh Infrastructure (Top Quartile) & 0.067** & 0.019 & 0.001 & 828 & 42.3 \\\\\nMedium Infrastructure (Middle 50\\%) & 0.038* & 0.016 & 0.018 & 1,656 & 41.8 \\\\\nLow Infrastructure (Bottom Quartile) & 0.019 & 0.018 & 0.292 & 828 & 15.9 \\\\\n\\midrule\n\\multicolumn{6}{l}{\\textbf{Panel C: By Human Capital Level}} \\\\\nHigh Human Capital (Top Tertile) & $0.059^{**}$ & 0.020 & 0.003 & 1,104 & 38.7 \\\\\nMedium Human Capital (Middle Tertile) & $0.043^{*}$ & 0.017 & 0.012 & 1,104 & 35.2 \\\\\nLow Human Capital (Bottom Tertile) & $0.030^\\dagger$ & 0.016 & 0.061 & 1,104 & 26.1 \\\\\n\\midrule\n\\multicolumn{6}{l}{\\textbf{Panel D: Statistical Tests}} \\\\\nF-test: Industry Groups Equal & \\multicolumn{3}{c}{F(2,156) = 8.47, p = 0.000} & & \\\\\nF-test: Infrastructure Groups Equal & \\multicolumn{3}{c}{F(2,156) = 3.92, p = 0.022} & & \\\\\nF-test: Human Capital Groups Equal & \\multicolumn{3}{c}{F(2,156) = 2.84, p = 0.062} & & \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "AI-Driven Spatial Distribution Dynamics: A Comprehensive Theoretical and Empirical Framework for Analyzing Productivity Agglomeration Effects in Japan's Aging Society", "authors": ["Tatsuru Kikuchi"], "url": "https://arxiv.org/abs/2507.19911v2", "attribution": "\"AI-Driven Spatial Distribution Dynamics: A Comprehensive Theoretical and Empirical Framework for Analyzing Productivity Agglomeration Effects in Japan's Aging Society\" by Tatsuru Kikuchi, arXiv:2507.19911v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05831v2_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrcrrrrcrrrr}\n\\toprule\n$n$ & & Param. & PQ & NQ & EQ & 2W & Param. & PQ & NQ & EQ & 2W \\\\\n\\midrule\n\\multicolumn{12}{l}{Simulations from Table~ vs.\\ Table~} \\\\\n\\midrule\n300 & lin & $\\sigma^2=1$ & 5.885 & 5.892 & 5.893 & 5.896 & $\\sigma^2=0.4$ & 4.040 & 4.041 & 4.042 & 4.041 \\\\\n & soft & & 6.127 & 6.132 & 6.134 & 6.135 & & 4.155 & 4.156 & 4.157 & 4.156 \\\\\n600 & lin & & 5.899 & 5.902 & 5.903 & 5.904 & & 4.052 & 4.053 & 4.054 & 4.053 \\\\\n & soft & & 6.151 & 6.153 & 6.153 & 6.153 & & 4.170 & 4.171 & 4.171 & 4.171 \\\\\n1000 & lin & & 5.904 & 5.906 & 5.906 & 5.906 & & 4.055 & 4.055 & 4.055 & 4.055 \\\\\n & soft & & 6.157 & 6.157 & 6.157 & 6.157 & & 4.176 & 4.176 & 4.176 & 4.176 \\\\\n\\midrule\n\\multicolumn{12}{l}{Simulations from Table~ vs.\\ Table~} \\\\\n\\midrule\n300 & lin & $\\sigma^2=1$ & 5.336 & 5.337 & 5.338 & 5.337 & $\\sigma^2=0.4$ & 3.050 & 3.047 & 3.048 & 3.047 \\\\\n & soft & & 5.561 & 5.559 & 5.560 & 5.563 & & 3.146 & 3.144 & 3.144 & 3.144 \\\\\n600 & lin & & 5.322 & 5.320 & 5.320 & 5.320 & & 3.029 & 3.027 & 3.027 & 3.027 \\\\\n & soft & & 5.546 & 5.540 & 5.541 & 5.542 & & 3.127 & 3.125 & 3.125 & 3.125 \\\\\n1000 & lin & & 5.309 & 5.306 & 5.307 & 5.307 & & 3.018 & 3.017 & 3.017 & 3.017 \\\\\n & soft & & 5.532 & 5.527 & 5.527 & 5.527 & & 3.115 & 3.114 & 3.114 & 3.114 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Conditional-mean Multiplicative Operator Models for Count Time Series", "authors": ["Christian H. Weiß", "Fukang Zhu"], "url": "https://arxiv.org/abs/2212.05831v2", "attribution": "\"Conditional-mean Multiplicative Operator Models for Count Time Series\" by Christian H. Weiß and Fukang Zhu, arXiv:2212.05831v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18130v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Same setup as Table~, but using \\textbf{dilation} annealing.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n& \\multicolumn{5}{c}{sample size $N$}\\\\\n& 100 & 300 & 1000 & 3000 & 10000 \\\\\n\\midrule\nSBTM (ours) & \\textbf{0.037} & 0.12 & 0.10 & 0.096 & 0.11 \\\\\nSDE & 0.062 & \\textbf{0.031} & \\textbf{0.080} & \\textbf{0.054} & \\textbf{0.061} \\\\\nSVGD & 0.48 & 0.32 & 1.00 & 1.10 & 0.99 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Score-Based Deterministic Density Sampling", "authors": ["Vasily Ilin", "Peter Sushko", "Jingwei Hu"], "url": "https://arxiv.org/abs/2504.18130v2", "attribution": "\"Score-Based Deterministic Density Sampling\" by Vasily Ilin, Peter Sushko, and Jingwei Hu, arXiv:2504.18130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.13657v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c||c||c|c|c|}\n\\hline\nCandidate & voter-1 & voter-2 & voter-3 & Borda & $W_{0}^{1}$ & $W_{1}^{1}$ & S \\\\\n \\hline \n\\hline\nA & 5 [1] & -5 [4] & 3 [2] & 3+0+2 & 3.0 &1.84 & 6\\\\\n\\hline\nB & 2 [2] & 0 [3] & 4 [1] & 2+1+3 & 6.0 & 6.0 & 14\\\\\n\\hline\nC & 1 [3] & 1 [2] & -2 [4] & 1+2+0 & 0.0 & 0.0 & 6 \\\\\n\\hline\nD & -1 [4] & 4 [1] & 1 [3] & 0+3+1 & 4.0 & 3.33 & 11\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Negative votes to depolarize politics", "authors": ["Karthik H. Shankar"], "url": "https://arxiv.org/abs/2012.13657v1", "attribution": "\"Negative votes to depolarize politics\" by Karthik H. Shankar, arXiv:2012.13657v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04611v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation results with large $p = 0.8$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccc} \n\t\t\t\\toprule\n\t\t\t&\n\t\t\t& \\multicolumn{5}{c}{Nelder-Mead Algorithm}\n\t\t\t& \\multicolumn{5}{c}{Metropolis-Hastings Algorithm}\n\t\t\t\\\\ \n\t\t\t\\cmidrule(lr){3-7} \\cmidrule(lr){8-12}\n\t\t\t\\multicolumn{2}{c}{Parameters} & $\\hat{\\alpha}$ & \n\t\t\t$\\hat{\\beta}$ & $\\hat{p}$ & $\\hat{\\delta}_{\\rm in}$ & \n\t\t\t$\\hat{\\delta}_{\\rm out}$ & $\\hat{\\alpha}$ & \n\t\t\t$\\hat{\\beta}$ & \n\t\t\t$\\hat{p}$ & $\\hat{\\delta}_{\\rm in}$ & \n\t\t\t$\\hat{\\delta}_{\\rm out}$ \\\\ \n\t\t\t\\midrule\n\t\t\t$\\alpha = 0.1$ & Est. & 0.0996 & 0.8004 & 0.7908 & \n\t\t\t1.2304 & 0.6345 & 0.0997 & 0.8002 & 0.8183 & 1.4513 & \n\t\t\t0.8331 \\\\\n\t\t\t$\\beta = 0.8$ & Bias(\\%) & 0.4068 & 0.0534 & 1.1653 & \n\t\t\t5.6584 & 10.3172 & 0.2669 & 0.0201 & 2.2357 & 10.4235 & \n\t\t\t15.9719 \\\\\n\t\t\t$p = 0.8$ & S.E. & 0.0003 & 0.0004 & 0.0010 & 0.0103 & \n\t\t\t0.0058 & 0.0003 & 0.0004 & 0.0079 & 0.0640 & 0.0567 \\\\\n\t\t\t\\midrule\n\t\t\t$\\alpha = 0.8$ & Est. & 0.8003 & 0.0999 & 0.7611 & \n\t\t\t1.1776 \n\t\t\t& 0.6251 & 0.8001 & 0.1000 & 0.8128 & 1.3351 & 0.8302 \n\t\t\t\\\\\n\t\t\t$\\beta = 0.1$ & Bias(\\%) & 0.0359 & 0.1087 & 5.1091 & \n\t\t\t10.3971 & 11.9915 & 0.0118 & 0.0231 & 1.5728 & 2.6264 & \n\t\t\t15.6826 \\\\\n\t\t\t$p = 0.8$ & S.E. & 0.0004 & 0.0003 & 0.0048 & 0.0154 & \n\t\t\t0.0138 & 0.0004 & 0.0003 & 0.0118 & 0.0361 & 0.0449 \\\\\n\t\t\t\\midrule\n\t\t\t$\\alpha = 0.45$ & Est. & 0.4497 & 0.1003 & 0.8153& \n\t\t\t1.3608 & 0.7339 & 0.4494 & 0.1005 & 0.8305 & 1.4193 & \n\t\t\t0.7714 \\\\\n\t\t\t$\\beta = 0.1$ & Bias(\\%) & 0.0763 & 0.3218 & 1.8762 & \n\t\t\t4.6677 & 4.6199 & 0.1397 & 0.4989 & 3.6774 & 8.4022 & \n\t\t\t9.2520 \\\\\n\t\t\t$p = 0.8$ & S.E. & 0.0005 & 0.0003 & 0.0049 & 0.0156 & \n\t\t\t0.0113 & 0.0005 & 0.0003 & 0.0098 & 0.0324 & 0.0236 \\\\\n\t\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Directed Hybrid Random Networks Mixing Preferential Attachment with Uniform Attachment Mechanisms", "authors": ["Tiandong Wang", "Panpan Zhang"], "url": "https://arxiv.org/abs/2101.04611v2", "attribution": "\"Directed Hybrid Random Networks Mixing Preferential Attachment with Uniform Attachment Mechanisms\" by Tiandong Wang and Panpan Zhang, arXiv:2101.04611v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03735v5_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The mean and standard deviation of the total rewards and harvesting decision epochs under the RL with MR policy starting with different prior information obtained from $J_0=3$ data points.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|lcccc|}\n\\hline\n$k$ & $\\widehat{\\rho}^c(\\pi)$ & $\\widehat{\\mbox{SD}}^c(\\pi)$ & Avg. $T$ & Std. dev. $T$ \\\\ \\hline\n$0.25$ & 122.60 & 221.07 & 5.45 & 1.05 \\\\\n$0.5$ & 147.57 & 166.15 & 5.45 & 0.77 \\\\ \n $1$ & 159.16 & 128.76 & 5.42 & 0.82 \\\\ \n$2$ & 127.22 & 69.10 & 4.69 & 0.90 \\\\ \n$4$ & 34.86 & 23.42 & 1.98 & 0.98 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Biomanufacturing Harvest Optimization with Small Data", "authors": ["Bo Wang", "Wei Xie", "Tugce Martagan", "Alp Akcay", "Bram van Ravenstein"], "url": "https://arxiv.org/abs/2101.03735v5", "attribution": "\"Biomanufacturing Harvest Optimization with Small Data\" by Bo Wang, Wei Xie, Tugce Martagan, Alp Akcay, and Bram van Ravenstein, arXiv:2101.03735v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09972v1_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n \\hline\n\\multicolumn{7}{|c|}{\\emph{\\textbf{AGF}}} \\\\\n\\hline\n\\textbf{Problem} &\n\\textbf{AKS} & \\textbf{C4.5} & \\textbf{RIPPER} & \\textbf{L2-L\nSVM} & \\textbf{L2 RLR} & \\textbf{RDR}\\\\\n\\hline\n1 & 0.9822 & 0.6967 & 0.5122 & 0.4232 & 0.4322 & 0.6121\\\\\n\\hline\n2 & 0.9143 & 0.5132 & 0.4323 & 0.4121 & 0.4212 & 0.5323\\\\\n\\hline\n3 & 0.9641 & 0.4121 & 0.4211 & 0.4213 & 0.3221 & 0.4323\\\\\n\\hline\n4 & 0.9454 & 0.4332 & 0.1888 & 0.4583 & 0.3810& 0.3810\\\\\n\\hline\n5 & 0.9554 & 0.3810 & 0.2575 & 0.5595 & 0.3162 & 0.6967\\\\\n\\hline\n6 & 0.9624 & 0.3001 & 0.1888 & 0.1312 & 0.3456 & 0.3121\\\\\n\\hline\n7 & 0.9344 & 0.3810 & 0.5566 & 0.4122 & 0.4455 & 0.2234\\\\\n\\hline\n8 & 0.9225 & 0.4333 & 0.1112 & 0.2575 & 0.1888& 0.1888\\\\\n\\hline\n9 & 0.9443 & 0.6322 & 0.1888 & 0.1888 & 0.6122 & 0.6641\\\\\n\\hline\n10 & 0.9653 & 0.1897 & 0.5234 & 0.6956 & 0.1888 & 0.1121\\\\%\\bottomrule\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications", "authors": ["Mario Manzo"], "url": "https://arxiv.org/abs/1912.09972v1", "attribution": "\"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications\" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.18492v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\\toprule\n \\textbf{Disturbance generator set}&&\\textbf{Average Daily Cost (€)} & \\textbf{$\\#$ of Constraint Violations} \\\\\\midrule\n Normal($[-1,1]\\times[-1,1]$) & DFMPC & 457.3 & 0 \\\\\n & NoMPC & 395.2 & 378 \\\\\n &\\textbf{CTMPC1} &\\textbf{421.5} & \\textbf{0} \\\\\n &CTMPC1.5 &435.4 & 0 \\\\\n &CTMPC2 &449.3 & 0 \\\\ \\hline\n Challenging($[-1,-0.5]\\times[0.5,1]$) & \\textbf{DFMPC} & \\textbf{705.2} & \\textbf{17} \\\\\n & NoMPC & 769.3 & 2214 \\\\\n &CTMPC1 &769.0 & 1769 \\\\\n &CTMPC1.5 &766.9 & 1723 \\\\\n &CTMPC2 &763.4 & 1614 \\\\ \\hline\n Extreme($\\{-1\\}\\times\\{1\\}$) & DFMPC & 755.3 & 2068 \\\\\n & NoMPC & 764.9 & 2298 \\\\\n &CTMPC1 &759.4 & 2240\\\\\n &CTMPC1.5 &756.5 & 2226 \\\\\n &CTMPC2 & 753.3 & 2230 \n \\\\\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Robust Predictive Control Method for Pump Scheduling in Water Distribution Networks", "authors": ["Mirhan Ürkmez", "Carsten Kallesøe", "Jan Dimon Bendtsen", "Eric C. Kerrigan", "John Leth"], "url": "https://arxiv.org/abs/2507.18492v1", "attribution": "\"A Robust Predictive Control Method for Pump Scheduling in Water Distribution Networks\" by Mirhan Ürkmez, Carsten Kallesøe, Jan Dimon Bendtsen, Eric C. Kerrigan, and John Leth, arXiv:2507.18492v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08239v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyper-parameters for pre-training 2B, 8B and 137B models. All models were trained with 256K tokens per batch.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n \\toprule\n Parameters & Layers & Units & Heads & pre-train steps & pre-train chips & pre-train time (days) & fine-tune chips & fine-tune time (hours) \\\\\n \\midrule\n 2B & 10 & 2560 & 40 & 501k & 64 & 1.5 & 16 & 3 \\\\\n 8B & 16 & 4096 & 64 & 521k & 64 & 23 & 16 & 6 \\\\\n 137B & 64 & 8192 & 128 & 3M & 1024 & 57.7 & 64 & 36 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "LaMDA: Language Models for Dialog Applications", "authors": ["Romal Thoppilan", "Daniel De Freitas", "Jamie Hall", "Noam Shazeer", "Apoorv Kulshreshtha", "Heng-Tze Cheng", "Alicia Jin", "Taylor Bos", "Leslie Baker", "Yu Du", "YaGuang Li", "Hongrae Lee", "Huaixiu Steven Zheng", "Amin Ghafouri", "Marcelo Menegali", "Yanping Huang", "Maxim Krikun", "Dmitry Lepikhin", "James Qin", "Dehao Chen", "Yuanzhong Xu", "Zhifeng Chen", "Adam Roberts", "Maarten Bosma", "Vincent Zhao", "Yanqi Zhou", "Chung-Ching Chang", "Igor Krivokon", "Will Rusch", "Marc Pickett", "Pranesh Srinivasan", "Laichee Man", "Kathleen Meier-Hellstern", "Meredith Ringel Morris", "Tulsee Doshi", "Renelito Delos Santos", "Toju Duke", "Johnny Soraker", "Ben Zevenbergen", "Vinodkumar Prabhakaran", "Mark Diaz", "Ben Hutchinson", "Kristen Olson", "Alejandra Molina", "Erin Hoffman-John", "Josh Lee", "Lora Aroyo", "Ravi Rajakumar", "Alena Butryna", "Matthew Lamm", "Viktoriya Kuzmina", "Joe Fenton", "Aaron Cohen", "Rachel Bernstein", "Ray Kurzweil", "Blaise Aguera-Arcas", "Claire Cui", "Marian Croak", "Ed Chi", "Quoc Le"], "url": "https://arxiv.org/abs/2201.08239v3", "attribution": "\"LaMDA: Language Models for Dialog Applications\" by Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Vincent Zhao, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Pranesh Srinivasan, Laichee Man, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, and Quoc Le, arXiv:2201.08239v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2401.00255v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sizes of two sample tests with Scenario (2)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n \\hline\n \\hline\n \\qquad distribution & Normal&$t_3$ \\\\\n \\hline\n \\qquad $p$ & 200\\ 400\\ 600&200\\ 400\\ 600\\\\\n \\hline\n$n=100$\\ \\ \\ MAX1\\qquad\\ & 0.032\\ 0.033\\ 0.045 &0.037\\ 0.037\\ 0.021\\\\\n \\qquad SUM1&0.063\\ 0.450\\ 0.055&0.061\\ 0.063\\ 0.048 \\\\\n \\qquad COM1&0.069\\ 0.050\\ 0.063&0.049\\ 0.054\\ 0.042 \\\\\n \\qquad MAX2&0.050\\ 0.055\\ 0.071&0.028\\ 0.042\\ 0.024\\\\\n \\qquad SUM2&0.059\\ 0.046\\ 0.056&0.001\\ 0.000\\ 0.000\\\\\n \\qquad COM2&0.044\\ 0.051\\ 0.072&0.013\\ 0.023\\ 0.010\\\\\n \\hline\n $n=200$\\ \\ \\ MAX1\\qquad\\ &0.049\\ 0.035\\ 0.050& 0.038\\ 0.034\\ 0.050\\\\\n \\qquad SUM1&0.062\\ 0.058\\ 0.071&0.060\\ 0.060\\ 0.069\\\\\n \\qquad COM1&0.076\\ 0.053\\ 0.067& 0.054\\ 0.048\\ 0.074\\\\\n \\qquad MAX2&0.060\\ 0.053\\ 0.065\\ & 0.028\\ 0.030\\ 0.038\\\\\n \\qquad SUM2&0.054\\ 0.048\\ 0.056\\ &0.001\\ 0.001\\ 0.001\\\\\n \\qquad COM2&0.057\\ 0.052\\ 0.055\\ & 0.010\\ 0.012\\ 0.014\n \\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Adaptive Rank-based Tests for High Dimensional Mean Problems", "authors": ["Yu Zhang", "Long Feng"], "url": "https://arxiv.org/abs/2401.00255v1", "attribution": "\"Adaptive Rank-based Tests for High Dimensional Mean Problems\" by Yu Zhang and Long Feng, arXiv:2401.00255v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.03617v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Time summary. Sample size $s$ takes values of 1$\\%$, 5$\\%$, and 10$\\%$ of $n$. Each value represents mean and sample standard deviation of 5 runs.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n& & \\multicolumn{3}{c}{Time (s)} \\\\\n\\cmidrule(r){3-5}\nDataset & $s$ & det & prob & unif \\\\\n\\midrule\nEthylene CO & 1$\\%$ & 6.00 $\\pm$ 0.12 & 5.42 $\\pm$ 0.74 & 4.01 $\\pm$ 0.39 \\\\\n& 5$\\%$ & 15.00 $\\pm$ 0.56 & 9.94 $\\pm$ 0.47 & 7.55 $\\pm$ 0.05 \\\\\n& 10$\\%$ & 15.71 $\\pm$ 0.14 & 14.32 $\\pm$ 1.26 & 11.66 $\\pm$ 0.95 \\\\\nEthylene CH4 & 1$\\%$ & 5.91 $\\pm$ 0.30 & 5.24 $\\pm$ 0.36 & 3.35 $\\pm$ 0.30 \\\\\n& 5$\\%$ & 13.56 $\\pm$ 0.58 & 10.00 $\\pm$ 1.10 & 7.95 $\\pm$ 1.08 \\\\\n& 10$\\%$ & 15.18 $\\pm$ 0.34 & 13.05 $\\pm$ 0.81 & 10.26 $\\pm$ 0.93 \\\\\nSkin & 1$\\%$ & 0.04 $\\pm$ 0.01 & 0.04 $\\pm$ 0.01 & 0.05 $\\pm$ 0.03 \\\\\n& 5$\\%$ & 0.15 $\\pm$ 0.03 & 0.13 $\\pm$ 0.01 & 0.10 $\\pm$ 0.02 \\\\\n& 10$\\%$ & 0.15 $\\pm$ 0.03 & 0.17 $\\pm$ 0.03 & 0.13 $\\pm$ 0.02\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Efficient Data-Driven Leverage Score Sampling Algorithm for the Minimum Volume Covering Ellipsoid Problem in Big Data", "authors": ["Elizabeth Harris", "Ali Eshragh", "Bishnu Lamichhane", "Jordan Shaw-Carmody", "Elizabeth Stojanovski"], "url": "https://arxiv.org/abs/2411.03617v1", "attribution": "\"Efficient Data-Driven Leverage Score Sampling Algorithm for the Minimum Volume Covering Ellipsoid Problem in Big Data\" by Elizabeth Harris, Ali Eshragh, Bishnu Lamichhane, Jordan Shaw-Carmody, and Elizabeth Stojanovski, arXiv:2411.03617v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.11800v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Causal graph learning comparison on different sparsity length $l_m$. The $SC_t$ is the computation time of the PCMCI skeleton structure learning.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|}\n\\hline\n\\textbf{$\\mathbf{l_m}$} & \\textbf{Compressed Data Size} & \\textbf{$\\mathbf{SC_t}$ (sec)}$\\downarrow$ & \\textbf{Link Pruning Adjustment} & \\textbf{APRC}$\\uparrow$ & \\textbf{SHD}$\\downarrow$ \\\\ \\hline\n\\multirow{2}{*}{10} & \\multirow{2}{*}{911} & \\multirow{2}{*}{18.969} & False & - & - \\\\ \\cline{4-6} \n & & & True & - & - \\\\ \\hline\n\\multirow{2}{*}{15} & \\multirow{2}{*}{1274} & \\multirow{2}{*}{21.625} & False & 0.741 & 29 \\\\ \\cline{4-6} \n & & & True & \\textbf{0.748} & \\textbf{12 } \\\\ \\hline\n\\multirow{2}{*}{20} & \\multirow{2}{*}{1629} & \\multirow{2}{*}{30.219} & False & \\textbf{0.731 } & 31 \\\\ \\cline{4-6} \n & & & True & 0.723 & \\textbf{14 } \\\\ \\hline\n\\multirow{2}{*}{25} & \\multirow{2}{*}{1974} & \\multirow{2}{*}{30.703} & False & 0.692 & 35 \\\\ \\cline{4-6} \n & & & True & \\textbf{0.748 } & \\textbf{12} \\\\ \\hline\n\\multirow{2}{*}{30} & \\multirow{2}{*}{2319} & \\multirow{2}{*}{34.625} & False & 0.711 & 38 \\\\ \\cline{4-6} \n & & & True & \\textbf{0.777} & \\textbf{11} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data", "authors": ["Mulugeta Weldezgina Asres", "Christian Walter Omlin", "The CMS-HCAL Collaboration"], "url": "https://arxiv.org/abs/2412.11800v1", "attribution": "\"Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data\" by Mulugeta Weldezgina Asres, Christian Walter Omlin, and The CMS-HCAL Collaboration, arXiv:2412.11800v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.11765v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Selected Training Parameters for the Classification Model}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llr}\n\\hline\n\\textbf{Parameter} & \\textbf{Definition} & \\textbf{Value} \\\\ \\hline\nLearning Rate - Step 1 & Learning rate to fine-tune classifcation layer & 10-3 \\\\\nLearning Rate - Step 2 & Learning rate to fine-tune whole model & 10-4 \\\\\nEarly Stopping Criteria & Training epochs without improvement before stopping & 1 \\\\\nLearning Rate Reduction Criteria & Training steps without improvement before reducing the learning rate by a factor of 0.1 & 5 \\\\ \\hline\nMaximum Sequence Length & Maximum input sequence lenght for DistilBERT & 512 \\\\\nBatch Size & Batch size used for training and validation & 32 \\\\\nLoss Funcion & \\multicolumn{2}{l}{Model optimization based on minimizing: Sparse categorical cross-entropy loss} \\\\\nEvaluation Metric & \\multicolumn{2}{l}{Model performance evaluation metric: Accuracy} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis", "authors": ["Tanja Aue", "Adam Jatowt", "Michael Färber"], "url": "https://arxiv.org/abs/2212.11765v1", "attribution": "\"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis\" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.13124v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Bribery in traditional and modern sectors in selected countries in Asia in 2012-2015.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n \\hline\n \\hline\n {Country}& {Year} & {Traditional firms'} & {Modern firms'} & {$\\frac{\\tau_0}{\\tau_1}$} & {Growth rate}\\\\\n & & {{bribe ($\\tau_0$)} } & {bribe ($\\tau_1$)} & & {in 2012-2015}\\\\\n \\hline\n{China} & {2012} & 5.2\\% & 2.9\\% & 1.8 & 7.5\\%\\\\\n{Pakistan} & {2013} & 6.1\\% & 5.0\\% & 1.2 & 4.2\\%\\\\\n{Philippines} & {2015} & 2.4\\% & 2.4\\% & 1.0 & 6.5\\%\\\\\n{Nepal} & {2013} & 5.3\\% & 6.9\\% & 0.8 & 4.3\\%\\\\\n{India} & {2014} & 2.4\\% & 4.2\\% & 0.6 & 6.5\\%\\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Heterogeneous Bribery, Technology Choice, and Capital Accumulation", "authors": ["Jafar M. Olimov", "Yi-Chan Tsai", "Hao-Yu Yang"], "url": "https://arxiv.org/abs/2507.13124v2", "attribution": "\"Heterogeneous Bribery, Technology Choice, and Capital Accumulation\" by Jafar M. Olimov, Yi-Chan Tsai, and Hao-Yu Yang, arXiv:2507.13124v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.11241v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\textbf{Acronym} & \\textbf{Inference Algorithm} & \\shortstack{\\textbf{Beat} \\\\ \\textbf{F-measure}} & \\shortstack{\\textbf{Beat} \\\\ \\textbf{AML$_t$}} & \\shortstack{\\textbf{Downbeat} \\\\ \\textbf{F-measure}} \\\\\n\\midrule\nBP-HMM & Viterbi Algorithm & 0.718 & 0.722 & 0.44 \\\\\nBP-AMPF & Auxiliary Mixed Particle Filter & 0.825 & 0.906 & 0.574 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Baseline meter tracking performance on $\\mathrm{CMR}_f$ using Bar Pointer model}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Revisiting Meter Tracking in Carnatic Music using Deep Learning Approaches", "authors": ["Satyajeet Prabhu"], "url": "https://arxiv.org/abs/2509.11241v1", "attribution": "\"Revisiting Meter Tracking in Carnatic Music using Deep Learning Approaches\" by Satyajeet Prabhu, arXiv:2509.11241v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.04183v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|} \n\\hline\n\\textbf{Data detection strategy} & \\textbf{Computational complexity} \\\\\n\\hline\nRML with NN search & $\\mathcal{O}(N_{\\mathrm{NN}}K \\max\\{{N_{\\mathrm{NN}},M}\\})$ \\\\\n\\hline\nJD & $\\mathcal{O}(L^K)$ \\\\\n\\hline\n$N$-JD & $\\mathcal{O}(N^K)$ \\\\\n\\hline\nH-SUD & $\\mathcal{O}(L)$ \\\\\n\\hline\nE-SUD & $\\mathcal{O}(L^K)$ \\\\\n\\hline\nGenie-aided data detection & $\\mathcal{O}(L)$ \\\\\n\\hline\n\\end{tabular}\n\\caption{Computational complexity.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Enhanced Uplink Data Detection for Massive MIMO with 1-Bit ADCs: Analysis and Joint Detection", "authors": ["Amin Radbord", "Italo Atzeni", "Antti Tolli"], "url": "https://arxiv.org/abs/2312.04183v2", "attribution": "\"Enhanced Uplink Data Detection for Massive MIMO with 1-Bit ADCs: Analysis and Joint Detection\" by Amin Radbord, Italo Atzeni, and Antti Tolli, arXiv:2312.04183v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.02635v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Noise Robustness Evaluation on GPRNet with two metrics.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\n\\hline\n& \\multicolumn{2}{|c|}{CD distance} & \\multicolumn{2}{|c|}{\\emph{L1} distance} \\\\ \n\\hline\n\\hline\nVariance \\& Noise density = 0.01 & \\multicolumn{2}{|c|}{6.350} & \\multicolumn{2}{|c|}{23.029} \\\\\nVariance \\& Noise density = 0.05 & \\multicolumn{2}{|c|}{6.715} & \\multicolumn{2}{|c|}{24.851} \\\\\nVariance \\& Noise density = 0.1 & \\multicolumn{2}{|c|}{7.374} & \\multicolumn{2}{|c|}{25.639} \\\\\nVariance \\& Noise density = 0.2 & \\multicolumn{2}{|c|}{7.765} & \\multicolumn{2}{|c|}{26.589} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "GPR-based Model Reconstruction System for Underground Utilities Using GPRNet", "authors": ["Jinglun Feng", "Liang Yang", "Ejup Hoxha", "Diar Sanakov", "Stanislav Sotnikov", "Jizhong Xiao"], "url": "https://arxiv.org/abs/2011.02635v3", "attribution": "\"GPR-based Model Reconstruction System for Underground Utilities Using GPRNet\" by Jinglun Feng, Liang Yang, Ejup Hoxha, Diar Sanakov, Stanislav Sotnikov, and Jizhong Xiao, arXiv:2011.02635v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.17331v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|l|l|l|l|}\n\\hline\n\\textbf{Number of clusters} & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\\\ \\hline\n\\textbf{Silhouette score} & 0.5233 & 0.5730 & 0.5134 & 0.4960 & 0.4916 & 0.4873 & 0.4758 & 0.5030 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The table shows the Silhouette scored for number of clusters $n=3,\\cdots ,10$, for the cluster analysis using median of primary indices.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Topological Data Analysis Framework for Quantifying Necrosis in Glioblastomas", "authors": ["Francisco Tellez", "Enrique Torres-Giese"], "url": "https://arxiv.org/abs/2503.17331v1", "attribution": "\"A Topological Data Analysis Framework for Quantifying Necrosis in Glioblastomas\" by Francisco Tellez and Enrique Torres-Giese, arXiv:2503.17331v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.19379v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Accuracy table of the 4th order scheme, isotropic media}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n\\hline\n$N$& $N=10$ & \\multicolumn{2}{c|}{$N=20$} & \\multicolumn{2}{c|}{$N=40$} & \\multicolumn{2}{c|}{$N=80$} \\\\ \n \\hline\n $\\omega^2$ & $|\\omega^2-\\omega_h^2|$ & $|\\omega^2-\\omega_h^2|$ & ord & $|\\omega^2-\\omega_h^2|$ & ord & $|\\omega^2-\\omega_h^2|$ & ord\\\\ \n \\hline\n0.25 & 3.77e-15 & 8.33e-16 & \\ & 5.55e-17 & \\ & 2.22e-16 & \\ \\\\ \n\\hline\n0.25 & 1.45e-14 & 2.56e-14 & \\ & 1.67e-16 & \\ & 5.55e-17 & \\ \\\\ \n\\hline\n0.25 & 1.05e-03 & 6.78e-05 & 3.96 & 4.27e-06 & 3.99 & 2.67e-07 & 4.00\\\\ \n\\hline\n0.25 & 1.05e-03 & 6.78e-05 & 3.96 & 4.27e-06 & 3.99 & 2.67e-07 & 4.00\\\\ \n\\hline\n1.25 & 1.43e-03 & 9.08e-05 & 3.97 & 5.70e-06 & 3.99 & 3.57e-07 & 4.00\\\\ \n\\hline\n1.25 & 1.43e-03 & 9.08e-05 & 3.97 & 5.70e-06 & 3.99 & 3.57e-07 & 4.00\\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Kernel compensation method for Maxwell eigenproblem with mimetic finite difference discretization", "authors": ["Chenhao Jin", "Yinhua Xia", "Yan Xu"], "url": "https://arxiv.org/abs/2503.19379v1", "attribution": "\"Kernel compensation method for Maxwell eigenproblem with mimetic finite difference discretization\" by Chenhao Jin, Yinhua Xia, and Yan Xu, arXiv:2503.19379v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.12976v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n & \\multicolumn{2}{c|}{\\textbf{Mean error}} \\\\\n \\hline\n \\textbf{Experiment} & \\textbf{CCMLE} & \\textbf{Classical estimation} \\\\\n \\hline\n (a) & 0.03 & 0.11 \\\\\n \\hline\n (b) & 0.18 & 0.41 \\\\\n \\hline\n (c) & 0.06 & 0.12 \\\\\n \\hline\n (d) & 0.1 & 0.16 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Mean errors for experiments described in Figure . The mean error in (d) only considers time steps from $t=21$ to $t=70$, as there are no errors before the change at $t=20$.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation", "authors": ["Melkior Ornik", "Ufuk Topcu"], "url": "https://arxiv.org/abs/1911.12976v2", "attribution": "\"Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation\" by Melkior Ornik and Ufuk Topcu, arXiv:1911.12976v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.15496v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MSE of the CART student as a function of to the percentage of randomly added data. The original training set $D$ contains 3,866 observations and 1\\% of the recently labeled dataset $D\"$ corresponds to 2,368 observations.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\toprule\n\\% added data & Resulting MSE \\\\ \\midrule\n0 & 24.73 \\\\\n2 & 13.66 \\\\\n4 & 10.31 \\\\\n\\textbf{5} & \\textbf{10.26} \\\\\n6 & 10.48 \\\\\n8 & 10.67 \\\\\n10 & 11.40 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Using Knowledge Distillation to improve interpretable models in a retail banking context", "authors": ["Maxime Biehler", "Mohamed Guermazi", "Célim Starck"], "url": "https://arxiv.org/abs/2209.15496v1", "attribution": "\"Using Knowledge Distillation to improve interpretable models in a retail banking context\" by Maxime Biehler, Mohamed Guermazi, and Célim Starck, arXiv:2209.15496v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09254v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Dependent variable: Log(structural diversity index)}. The regression models elucidate the correlations between the structural diversity index and network characteristics as illustrated in Figure : (i) the correlations with degree-heterogeneity, the Wiener index, and edge density are robust. (ii) The correlation with clustering fades when accounting for degree-heterogeneity, Wiener index, size and edge density. (iii) The correlation with size reverses when factoring in other network characteristics. All variables are standardized for a direct comparison between regression coefficients. See main text for interpretations of these results and Methods for technical details about the regressions.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\toprule\n \\textbf{Variable} & \\textbf{Model 1} & \\textbf{Model 2} & \\textbf{Model 3} & \\textbf{Model 4} & \\textbf{Model 5}\\\\ \n \\midrule\n Log(degree heterogeneity) & -0.67*** & & & & -0.56*** \\\\\n & (0.07) & & & & (0.06) \\\\\n Log(wiener index) & & 0.03 & & & 0.37*** \\\\\n & & (0.09) & & & (0.08) \\\\\n Log(edge density) & & & 0.65*** & & 1.38***\\\\\n & & & (0.07) & & (0.12) \\\\\n Log(clustering) & & & & 0.84*** & 0.02 \\\\\n & & & & (0.05) & (0.06) \\\\\n Log(size) & & & & & 0.70*** \\\\\n & & & & & (0.07) \\\\\n \\midrule\n $\\#$ observations & 119 & 119 & 119 & 119 & 119 \\\\ \n $R^{2}$ & 0.45 & 0.0 & 0.43 & 0.72 & 0.93 \\\\\n Adjusted $R^{2}$ & 0.44 & 0.0 & 0.42 & 0.71 & 0.92 \\\\\n \\bottomrule\n \n \\multicolumn{3}{l}{\\textsuperscript{***}$p<0.001$, \n \\textsuperscript{**}$p<0.01$, \\textsuperscript{*}$p<0.1$}\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "How networks shape diversity for better or worse", "authors": ["Andrea Musso", "Dirk Helbing"], "url": "https://arxiv.org/abs/2201.09254v4", "attribution": "\"How networks shape diversity for better or worse\" by Andrea Musso and Dirk Helbing, arXiv:2201.09254v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13444v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ASR (\\%) of Targeted Soft-Label Attacks. $\\sigma$ = 0.1.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|ccc}\n\t\n\t\t\\toprule\n\t\tDataset & Attacks %& & ASR (\\%) &\\\\ & \n\t\t& No defense & $\\theta_1$ = 0.5 & $\\theta_2$ = 0.7 \\\\\n\t\t\\midrule\n\t\t & AZ & 100 & 8 & 8 \\\\\n\t\t MNIST & GA & 100 & 0 & 0\\\\\n\t\t & SimBA & 97 & 3 & 0 \\\\\n\t\t \\midrule\n\t\t & AZ & 100 & 9 & 9 \\\\\n\t\t CIFAR10 & GA & 98.76 & 0 & 0 \\\\\n\t\t & SimBA & 97.14 & 23 & 15\\\\\n\t\t \\midrule \\midrule\n\t\t Dataset & Attacks %& &ASR (\\%) &\\\\\n\t\t & No Defense & $\\theta_1$ = 0.1 & $\\theta_2$ = 0.3 \\\\\n\t\t \\midrule\n\t\t & AZ & 100 & 0 & 0 \\\\\n\t\t IMAGENET & NES-QL & 100 & 69 & 8 \\\\\n\t\t & GA & 100 & 0 & 0\\\\\n\t\t & SimBA & 96.5 & 6 & 2 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Boundary Defense Against Black-box Adversarial Attacks", "authors": ["Manjushree B. Aithal", "Xiaohua Li"], "url": "https://arxiv.org/abs/2201.13444v1", "attribution": "\"Boundary Defense Against Black-box Adversarial Attacks\" by Manjushree B. Aithal and Xiaohua Li, arXiv:2201.13444v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.01116v2_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc|cc}\n\\toprule\n& \\multicolumn{2}{c}{\\textbf{Regression Coefficients}} & \\multicolumn{2}{c}{\\textbf{Pre-Treatment Averages}} \\\\\n& Post Service & Post Bill &\\% $\\geq 0 $ & Conditional Mean \\\\\n\\midrule\n\\textbf{Hospital Care} \\\\\nEmergency Department & 0.123*** & 0.017 & 0.67\\% & \\$929.98 \\\\\n& (0.0141) & (0.0147) \\\\\nPreventable Hospitalizations & 0.412*** & -0.170 & 0.04\\% & \\$19,979.89 \\\\\n& (0.0880) & (0.0879) \\\\\n\\midrule\n\\textbf{Outpatient Care} \\\\\nBehavioral Health & -0.032* & 0.029* & 1.19\\% & \\$119.47 \\\\\n& (0.0144) & (0.0142) \\\\\nChiropractic Care & -0.015 & 0.017 & 1.86\\% & \\$133.39 \\\\\n& (0.0160) & (0.0161) \\\\\nEvaluation \\& Management & 1.469*** & -0.272*** &1.05\\% & \\$121.45\\\\\n& (0.0076) & (0.0064) \\\\\nImaging & 0.102*** & 0.005 & 2.55\\% & \\$265.52 \\\\\n& (0.0119) & (0.0123) \\\\\nLab Services & 0.196*** & -0.147*** & 3.96\\% & \\$62.14 \\\\\n& (0.0123) & (0.0130) \\\\\nLow-Value Services & 0.066*** & 0.028** & 6.58\\% & \\$148.61 \\\\\n& (0.0094) & (0.0097) \\\\\nPreventive Care & 0.349*** & -0.211*** & 11.89\\% & \\$120.79 \\\\\n& (0.0039) & (0.0040) \\\\\nSpecialist Care & 0.546*** & -0.108*** & 0.57\\% & \\$114.70 \\\\\n& (0.0221) & (0.0226) \\\\\n\\midrule\n\\textbf{Prescriptions} & 0.013*** & 0.010 & 18.30\\% & \\$147.14 \\\\\n& (0.0049) & (0.0050) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Estimated Impact of Bill Arrival on Service-Specific Spending}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Medical Bill Shock and Imperfect Moral Hazard", "authors": ["Alex Hoagland", "David M. Anderson", "Ed Zhu"], "url": "https://arxiv.org/abs/2211.01116v2", "attribution": "\"Medical Bill Shock and Imperfect Moral Hazard\" by Alex Hoagland, David M. Anderson, and Ed Zhu, arXiv:2211.01116v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2301.01843v2_tex_table22.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{DiD Additional Economic Outcomes: Extensive Margin, Events in Over 60\\% of Years}\n\\begin{tabular}{lccccccc}\n\\toprule\n{}&{(1)}&{(2)}&{(3)}&{(4)}&{(5)}&{(6)}&{(7)} \\tabularnewline\n\\midrule \n\\textbf{Panel A. Robustness}&Nighttime Light&GDP per Capita&Agr. Production&Agr. Productivity&Urban Built Up&Human Dev. Index& \\tabularnewline\n\\midrule Ceasefire \\( \\times \\) FARC&--0.008&--0.346&0.222&0.301&--0.010***&--0.001& \\tabularnewline\n&(0.021)&(0.402)&(0.138)&(0.493)&(0.002)&(0.001)& \\tabularnewline\n&&&&&&& \\tabularnewline\nMean Dep. Var.&--0.314&6.364&1.693&6.361&0.025&0.716& \\tabularnewline\nObservations&2,827&2,822&2,827&2,827&2,827&2,056& \\tabularnewline\n&&&&&&& \\tabularnewline\n\\midrule \\textbf{Panel B. Agricultural Trade}&\\# Products Supplied&\\# Markets Supplied&\\# Deliveries&Avr. Price&Total Value&Total Quantity& \\tabularnewline\n\\midrule Ceasefire \\( \\times \\) FARC&--0.510&0.452*&--11.841&0.026&675.025&3.488& \\tabularnewline\n&(0.782)&(0.252)&(15.600)&(0.167)&(653.091)&(2.241)& \\tabularnewline\n&&&&&&& \\tabularnewline\nMean Dep. Var.&14.420&2.907&86.224&1.564&3887.102&6.771& \\tabularnewline\nObservations&1,799&1,799&1,799&1,605&1,799&1,799& \\tabularnewline\n&&&&&&& \\tabularnewline\n\\midrule \\textbf{Panel C. Tourism \\& Migration}&\\# Beds&Acc. Employees (pc)&Tourists (Total)&Tourists (pc)&Travel Expenses&Immigrated&Immigrated (20+) \\tabularnewline\n\\midrule Ceasefire \\( \\times \\) FARC&--3.882&1.643&1.853***&1.112***&--1.598&7.502&--12.977 \\tabularnewline\n&(31.143)&(1.413)&(0.497)&(0.372)&(33.771)&(203.868)&(139.428) \\tabularnewline\n&&&&&&& \\tabularnewline\nMean Dep. Var.&129.053&4.990&4.319&1.791&142.614&460.222&251.603 \\tabularnewline\nObservations&2,827&2,827&1,028&1,028&1,028&514&514 \\tabularnewline\n&&&&&&& \\tabularnewline\n\\midrule \\textbf{Panel D. Health \\& Education}&Birth Weight&Birth Size&Apgar Score (5 Min.)&Child Mortality&\\# Off. Schools&\\# Teachers&\\# Students \\tabularnewline\n\\midrule Ceasefire \\( \\times \\) FARC&2.447&--0.178&--0.027&--0.001&--0.075*&--0.337**&--8.836* \\tabularnewline\n&(5.833)&(0.113)&(0.021)&(0.007)&(0.044)&(0.158)&(5.192) \\tabularnewline\n&&&&&&& \\tabularnewline\nMean Dep. Var.&3193.500&50.772&9.588&0.050&3.212&11.345&231.496 \\tabularnewline\nObservations&2,827&2,827&2,827&2,827&2,827&2,827&2,827 \\tabularnewline\n&&&&&&& \\tabularnewline\n\\midrule \\textbf{Panel E. GEIH Survey Data}&Min. House Price&Asset Index&Weeks Job Search&Months Current Job&Months Emp. Past Year&Weekly Work Hours&Monthly Salary \\tabularnewline\n\\midrule Ceasefire \\( \\times \\) FARC&--3.316&0.025&--1.294&--3.173&--0.071&--0.360&0.218 \\tabularnewline\n&(4.624)&(0.025)&(1.080)&(2.451)&(0.057)&(0.469)&(0.215) \\tabularnewline\n&&&&&&& \\tabularnewline\nMean Dep. Var.&32.994&--0.521&17.194&81.379&11.076&46.138&7.561 \\tabularnewline\nObservations&112,268&295,070&50,642&382,977&382,977&382,974&158,039 \\tabularnewline\n\\bottomrule \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Peace Dividends: The Economic Effects of Colombia's Peace Agreement", "authors": ["Miguel Fajardo-Steinhäuser"], "url": "https://arxiv.org/abs/2301.01843v2", "attribution": "\"Peace Dividends: The Economic Effects of Colombia's Peace Agreement\" by Miguel Fajardo-Steinhäuser, arXiv:2301.01843v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.12636v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\hline\nSymbol & Meaning \\\\\n\\hline\n$k_{lev}$& Leverage Ratio.\\\\\n\\hline\n$K(x)$ & Internal Ratings Based (IRB) Capital Requirement for the portfolio $x$. \\\\\n\\hline\n$\\delta$ & Opportunity cost of the capital (equity). \\\\\n\\hline\n$X_{x}$ & Realizations for the loan portfolio $x$. \\\\\n\\hline\n$\\rho(x)$ & Risk measure for the portfolio $x$ (EL, UL etc.)\\\\\n\\hline\n$\\mu$ & Lower-bound on Expected Return \\\\\n\\hline\n$\\theta$ & Upper-bound on the risk. \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{List of symbols used in the model}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Does limited liability reduce leveraged risk?: The case of loan portfolio management", "authors": ["Deb Narayan Barik", "Siddhartha P. Chakrabarty"], "url": "https://arxiv.org/abs/2209.12636v1", "attribution": "\"Does limited liability reduce leveraged risk?: The case of loan portfolio management\" by Deb Narayan Barik and Siddhartha P. Chakrabarty, arXiv:2209.12636v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.21099v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sample rows from the pre-processed dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\toprule\n\\texttt{ID} & \\texttt{HY} & $Y_{i,t}$ & $v_{i,t}$ & $\\widehat{\\mu}_{i,t}$ \\\\ \\midrule\n2460002101 & 2019H1 & 1209.00 & 1 & 1750.443 \\\\\n2460002101 & 2019H2 & 13712.17 & 4 & 2011.496 \\\\\n2460002101 & 2020H1 & 0 & 0 & - \\\\\n2460002101 & 2020H2 & 0 & 0 & - \\\\\n$\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An Observation-Driven State-Space Model for Claims Size Modeling", "authors": ["Jae Youn Ahn", "Himchan Jeong", "Mario V. Wüthrich"], "url": "https://arxiv.org/abs/2412.21099v1", "attribution": "\"An Observation-Driven State-Space Model for Claims Size Modeling\" by Jae Youn Ahn, Himchan Jeong, and Mario V. Wüthrich, arXiv:2412.21099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05360v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|cc|ccc}\\toprule\n\t\t\\midrule\n\t\tBaselines & accuracy (\\%) & AUC & soft coverage (\\%) & hard coverage & ML agreement (\\%) \\\\\n\t\t\\midrule\n\t\tHuman only & 49.87 & - & 100.0 & -\\\\\n\t\t\\\\\n\t\tNo tuning of threshold \\\\\n\t\tMLonly & 64.12 $\\pm$ 1.06 & 0.70 $\\pm$ 0.01 & 0.00 $\\pm$ 0.00 & 0.00 $\\pm$ 0.00 & 45.13 $\\pm$ 4.69 \\\\\n\t\tunconstrained & 63.15 $\\pm$ 1.13 & 0.69 $\\pm$ 0.01 & 38.58 $\\pm$ 1.64 & 0.00 $\\pm$ 0.00 & 41.18 $\\pm$ 6.25 \\\\\n\t\tlogBarrier & 60.31 $\\pm$ 2.23 & 0.61 $\\pm$ 0.04 & 50.19 $\\pm$ 3.62 & 53.13 $\\pm$ 22.26 & 22.22 $\\pm$ 10.93 \\\\\n\t\tproj & 56.33 $\\pm$ 3.07 & 0.56 $\\pm$ 0.03 & 55.69 $\\pm$ 5.19 & 67.93 $\\pm$ 16.59 & 32.18 $\\pm$ 12.47 \\\\\n\t\t\\\\\n\t\tTuning based on AUC \\\\\n\t\tMLonly & 64.12 $\\pm$ 1.06 & 0.70 $\\pm$ 0.01 & 0.00 $\\pm$ 0.00 & 0.00 $\\pm$ 0.00 & - \\\\\n\t\tunconstrained & 63.15 $\\pm$ 1.13 & 0.69 $\\pm$ 0.01 & 38.58 $\\pm$ 1.64 & 0.00 $\\pm$ 0.00 & - \\\\\n\t\tlogBarrier & 62.84 $\\pm$ 1.08 & 0.68 $\\pm$ 0.01 & 50.19 $\\pm$ 3.62 & 9.48 $\\pm$ 7.18 & 34.52 $\\pm$ 10.68 \\\\\n\t\tproj & 62.95 $\\pm$ 0.97 & 0.69 $\\pm$ 0.01 & 55.69 $\\pm$ 5.19 & 1.12 $\\pm$ 1.52 & 41.80 $\\pm$ 8.82 \\\\\n\t\t\\\\\n\t\tTuning based on Accuracy \\\\\n\t\tMLonly & 63.86 $\\pm$ 1.42 & 0.70 $\\pm$ 0.01 & 0.00 $\\pm$ 0.00 & 0.00 $\\pm$ 0.00 & - \\\\\n\t\tunconstrained & 63.37 $\\pm$ 0.99 & 0.69 $\\pm$ 0.02 & 38.58 $\\pm$ 1.64 & 9.45 $\\pm$ 11.13 & 35.41 $\\pm$ 10.70 \\\\\n\t\tlogBarrier & 62.23 $\\pm$ 1.91 & 0.66 $\\pm$ 0.03 & 50.19 $\\pm$ 3.62 & 22.54 $\\pm$ 12.47 & 28.86 $\\pm$ 8.28 \\\\\n\t\tproj & 61.19 $\\pm$ 1.69 & 0.64 $\\pm$ 0.03 & 55.69 $\\pm$ 5.19 & 18.12 $\\pm$ 15.10 & 38.94 $\\pm$ 4.23 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{ \\textbf{Performance vs Coverage (Psychiatry)}: \\textbf{L1=0.1} Preferential MoE relies much more often on human expertise while preserving predictive performance. Predictive performance measured by Area-Under-the-operating-ROC-Curve (AUC) and percentage of correct predictions (accuracy) in the test set; Coverage refers to the percentage of predictions based on the human rules.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible", "authors": ["Melanie F. Pradier", "Javier Zazo", "Sonali Parbhoo", "Roy H. Perlis", "Maurizio Zazzi", "Finale Doshi-Velez"], "url": "https://arxiv.org/abs/2101.05360v1", "attribution": "\"Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible\" by Melanie F. Pradier, Javier Zazo, Sonali Parbhoo, Roy H. Perlis, Maurizio Zazzi, and Finale Doshi-Velez, arXiv:2101.05360v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.12110v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Economic problems and role type selection in EconGym.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\nEconomic Problems & Government Type & Individual Type & Firm Type & Bank Type \\\\\n\\midrule\n\\textbf{Pension Issues} &&&&\\\\\nQ1: How does delayed retirement affect the economy? & Pension & OLG & Perfect & Non-profit \\\\\nQ2: Do personal pensions improve security? & Pension & OLG & Perfect & Non-profit \\\\\nQ3: How does aging impact the macroeconomy? & Pension & OLG & Perfect & Non-profit \\\\\nQ4: How to close pension funding gaps? & Pension & OLG & Perfect & Non-profit \\\\\nQ5: How do pension systems vary across countries? & Pension & OLG & Perfect & Non-profit \\\\\n\\midrule\n\\textbf{Fiscal Policy Issues} &&&&\\\\\nQ6: Can consumption taxes boost growth and fairness? & Fiscal & Ramsey & Perfect & Non-profit \\\\\nQ7: How does inheritance tax affect wealth distribution? & Fiscal & OLG & Perfect & Non-profit \\\\\nQ8: Does universal basic income enhance equity? & Fiscal & OLG & Perfect & Non-profit \\\\\nQ9: How to design optimal tax policies? & Fiscal & Ramsey & Perfect & Non-profit \\\\\nQ10: How does wealth tax impact wealth concentration? & Fiscal & Ramsey & Perfect & Non-profit \\\\\n\\midrule\n\\textbf{Monetary Policy Issues} &&&&\\\\\nQ11: How effective are negative interest rates? & Central Bank & Ramsey & Perfect & Commercial \\\\\nQ12: How to control inflation via monetary policy? & Central Bank & Ramsey & Perfect & Commercial \\\\\nQ13: What are the long-term effects of quantitative easing? & Central Bank & Ramsey & Perfect & Commercial \\\\\nQ14: How to set optimal bank rate spreads? & Central Bank & Ramsey & Perfect & Commercial \\\\\nQ15: How to coordinate monetary and fiscal policies? & Central Bank + Fiscal & Ramsey & Perfect & Commercial \\\\\n\\midrule\n\\textbf{Market Competition Issues} &&&&\\\\\nQ16: How does technology drive long-term growth? & Fiscal & OLG & Perfect & Non-profit \\\\\nQ17: How do monopolies affect resources and welfare? & Fiscal & Ramsey & Monopoly & Non-profit \\\\\nQ18: What is algorithmic collusion in oligopolies? & Fiscal & Ramsey & Oligopoly & Non-profit \\\\\nQ19: How does product diversity affect welfare? & Fiscal & Ramsey & Monopolistic & Non-profit \\\\\n\\midrule\n\\textbf{Individual Decision Issues} &&&&\\\\\nQ20: Does “996” work culture improve utility and efficiency? & * & OLG & Perfect & Non-profit \\\\\nQ21: How does age affect consumption patterns? & * & OLG & Perfect & Commercial \\\\\nQ22: How does asset allocation affect wealth? & * & Ramsey & Perfect & Commercial \\\\\nQ23: How does work-life balance impact well-being? & * & OLG & Perfect & Non-profit \\\\\nQ24: How does over-leveraged consumption impact the economy? & * & OLG & Perfect & Commercial \\\\\nQ25: How do market structures shape consumer behavior? & * & Ramsey & All & Non-profit \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "EconGym: A Scalable AI Testbed with Diverse Economic Tasks", "authors": ["Qirui Mi", "Qipeng Yang", "Zijun Fan", "Wentian Fan", "Heyang Ma", "Chengdong Ma", "Siyu Xia", "Bo An", "Jun Wang", "Haifeng Zhang"], "url": "https://arxiv.org/abs/2506.12110v1", "attribution": "\"EconGym: A Scalable AI Testbed with Diverse Economic Tasks\" by Qirui Mi, Qipeng Yang, Zijun Fan, Wentian Fan, Heyang Ma, Chengdong Ma, Siyu Xia, Bo An, Jun Wang, and Haifeng Zhang, arXiv:2506.12110v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.01704v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccccc}\n\\toprule\n\\textbf{Gen} & \\textbf{1} & \\textbf{2} & \\textbf{3} & \\textbf{4} & \\textbf{5} & \\textbf{6} & \\textbf{7} & \\textbf{8} & \\textbf{9} & \\textbf{10} & \\textbf{11} & \\textbf{12} \\\\\n\\midrule\n$\\boldsymbol{\\alpha}$ & 1.33 & 1.25 & 1.22 & 0.61 & 2.7 & 1.83 & 0.61 & 1.79 & 1.16 & 0.65 & 0.67 & 1.08 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Dirichlet concentration parameters ($\\boldsymbol{\\alpha}$) for transfer share sampling to find Pareto-improving policies.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Using Machine Learning to Compute Constrained Optimal Carbon Tax Rules", "authors": ["Felix Kübler", "Simon Scheidegger", "Oliver Surbek"], "url": "https://arxiv.org/abs/2507.01704v1", "attribution": "\"Using Machine Learning to Compute Constrained Optimal Carbon Tax Rules\" by Felix Kübler, Simon Scheidegger, and Oliver Surbek, arXiv:2507.01704v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.04003v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average Marginal Effects for October 1, 2024}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n \\toprule\n & & Marginal Effects & \\\\\n Variable & Prob. In First Quartile & Gas Equivalent Cost & Cost in USD \\\\\n \\midrule\n \\midrule\n max fee per gas & 0.02\\% & 53 & \\$0.0032 \\\\\n to DEX & 17.61\\% & 979 & \\$0.0571 \\\\\n to MEV & 36.91\\% & 2,051 & \\$0.1196 \\\\\n from DEX & 31.18\\% & 1,733 & \\$0.1010 \\\\\n from MEV & -40.54\\% & 2,253 & \\$0.1314 \\\\\n front run & 82.33\\% & 4,575 & \\$0.2668 \\\\\n back run & 78.05\\% & 4,337 & \\$0.2529 \\\\\n \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering", "authors": ["Bruce Mizrach", "Nathaniel Yoshida"], "url": "https://arxiv.org/abs/2508.04003v1", "attribution": "\"The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering\" by Bruce Mizrach and Nathaniel Yoshida, arXiv:2508.04003v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11002v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Margins and print area specifications.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|} \n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Margin & A4 & Letter \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Top margin & 2.54 cm & 1.0 in. \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Bottom margin & 4.94 cm & 1.25 in. \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Left, right margin & 1.925 cm & .875 in. \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Printable area & 17.15 x 22.23 cm & 6.75 x 8.75 in. \\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Multi-rater Comparative Study of Automatic Target Localization Methods for Epilepsy Deep Brain Stimulation Procedures", "authors": ["Han Liu", "Kathryn L. Holloway", "Dario J. Englot", "Benoit M. Dawant"], "url": "https://arxiv.org/abs/2201.11002v1", "attribution": "\"A Multi-rater Comparative Study of Automatic Target Localization Methods for Epilepsy Deep Brain Stimulation Procedures\" by Han Liu, Kathryn L. Holloway, Dario J. Englot, and Benoit M. Dawant, arXiv:2201.11002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04243v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc||cc|cc}\n\\multicolumn{2}{c}{} & \\multicolumn{2}{c|}{\\textbf{ViT-H}} & \\multicolumn{2}{c}{\\textbf{ViT-B}} \\\\\n\\toprule\n$a$ & $b$ & $\\tau$ & $\\rho$ & $\\tau$ & $\\rho$ \\\\\n\\midrule\n$63$ & $3$ & $0.42$ & $0.60$ & $0.48$ & $0.66$ \\\\\n$63$ & $7$ & $0.45$ & $0.63$ & $0.49$ & $0.68$ \\\\\n$63$ & $15$ & $0.44$ & $0.64$ & $0.48$ & $0.68$ \\\\\n$63$ & $31$ & $0.45$ & $0.66$ & $0.49$ & $0.71$ \\\\\n$127$ & $3$ & $0.61$ & $0.80$ & $0.66$ & $0.84$ \\\\\n$127$ & $7$ & $0.63$ & $0.81$ & $0.67$ & $0.84$ \\\\\n$127$ & $15$ & $0.62$ & $0.81$ & $0.66$ & $0.84$ \\\\\n$127$ & $31$ & $0.60$ & $0.80$ & $0.64$ & $0.83$ \\\\\n$255$ & $3$ & $0.66$ & $0.84$ & $0.68$ & $0.85$ \\\\\n$255$ & $7$ & $0.66$ & $0.84$ & $0.68$ & $0.86$ \\\\\n$255$ & $15$ & $0.65$ & $0.84$ & $0.67$ & $0.85$ \\\\\n$255$ & $31$ & $0.59$ & $0.79$ & $0.60$ & $0.80$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Rank correlation (Kendall $\\tau$ and Spearman $\\rho$) between SAM prediction IoU and object tree-likeness as measured by \\textbf{DoGD} with different hyperparameter values $a,b$ on the \\textbf{synthetic dataset}, to supplement Table .}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects", "authors": ["Yixin Zhang", "Nicholas Konz", "Kevin Kramer", "Maciej A. Mazurowski"], "url": "https://arxiv.org/abs/2412.04243v2", "attribution": "\"Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects\" by Yixin Zhang, Nicholas Konz, Kevin Kramer, and Maciej A. Mazurowski, arXiv:2412.04243v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.06010v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cccc|}\n\\hline\n\\bf{coefficient} & \\bf{estimate} & \\bf{Student} $p$-\\bf{value} & \\bf{stderr} \\\\\n\\hline\n$\\alpha$ & 0.023893 & 51.6\\% & 0.037\\\\\n$\\beta$ & 0.008608 & 0.3\\% & 0.003\\\\\n$\\gamma$ & 0.190133 & 0.2\\% & 0.060\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Regression results for~. Coefficients $\\beta$ and $\\gamma$ are statistically significant. Innovations $U$ are IID but not Gaussian.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Time Series Model for Three Asset Classes used in Financial Simulator", "authors": ["Andrey Sarantsev", "Angel Piotrowski", "Ian Anderson"], "url": "https://arxiv.org/abs/2508.06010v1", "attribution": "\"A Time Series Model for Three Asset Classes used in Financial Simulator\" by Andrey Sarantsev, Angel Piotrowski, and Ian Anderson, arXiv:2508.06010v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12195v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\nParameter & Value \\\\ \\hline\n$\\eta$ & 0.0003 \\\\ \\hline\nMaxIters & 500 \\\\ \\hline\nFPIters & 10 \\\\ \\hline\nSQIters & 10 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Parameters used when performing the maximum likelihood estimation.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Measure Estimation in the Barycentric Coding Model", "authors": ["Matthew Werenski", "Ruijie Jiang", "Abiy Tasissa", "Shuchin Aeron", "James M. Murphy"], "url": "https://arxiv.org/abs/2201.12195v2", "attribution": "\"Measure Estimation in the Barycentric Coding Model\" by Matthew Werenski, Ruijie Jiang, Abiy Tasissa, Shuchin Aeron, and James M. Murphy, arXiv:2201.12195v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10511v5_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\nConsistency & (0.4,0.5] & (0.5,0.6] & (0.6,0.7] & (0.7,0.8] & (0.8,1] \\\\ \\hline\nRating & 1.4 & 1.24 & 1.20 & 1.16 & 1.04\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Average audit rating vs. average F1 consistency score on our Kinetics-GEBD Val set.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Generic Event Boundary Detection: A Benchmark for Event Segmentation", "authors": ["Mike Zheng Shou", "Stan Weixian Lei", "Weiyao Wang", "Deepti Ghadiyaram", "Matt Feiszli"], "url": "https://arxiv.org/abs/2101.10511v5", "attribution": "\"Generic Event Boundary Detection: A Benchmark for Event Segmentation\" by Mike Zheng Shou, Stan Weixian Lei, Weiyao Wang, Deepti Ghadiyaram, and Matt Feiszli, arXiv:2101.10511v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.09769v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\n\\hline\n\\multirow{2}{*}{Learning Method} & \\multirow{2}{*}{Feature Selection} & \\multicolumn{3}{c|}{Training Set Result} & \\multicolumn{3}{c|}{Testing Set Result} \\\\ \\cline{3-8} \n & & MAE & MSE & $R^2$ Score & MAE & MSE & $R^2$ Score \\\\ \\hline\n\\multirow{2}{*}{Linear Regression} & No & 0.082 & 0.010 & 0.981 & 0.528 & 1.378 & -0.787 \\\\ \\cline{2-8} \n & Yes & 0.086 & 0.011 & 0.978 & 0.463 & 1.163 & -0.508 \\\\ \\hline\n\\multirow{2}{*}{K-NN regression} & No & 0.119 & 0.127 & 0.752 & 0.180 & 0.120 & 0.844 \\\\ \\cline{2-8} \n & Yes & 0.119 & 0.127 & 0.752 & 0.180 & 0.120 & 0.844 \\\\ \\hline\n\\multirow{2}{*}{Kernel Ridge} & No & 0.119 & 0.027 & 0.948 & 0.235 & 0.111 & 0.856 \\\\ \\cline{2-8} \n & Yes & 0.121 & 0.028 & 0.945 & 0.231 & 0.107 & 0.861 \\\\ \\hline\n\\multirow{2}{*}{Random Forest} & No & 0.104 & 0.064 & 0.874 & 0.238 & 0.165 & 0.786 \\\\ \\cline{2-8} \n & Yes & 0.098 & 0.064 & 0.875 & 0.204 & 0.106 & 0.862 \\\\ \\hline\n\\multirow{2}{*}{Stacking} & No & 0.003 & 1e-4 & 0.999 & 0.168 & 0.071 & 0.908 \\\\ \\cline{2-8} \n & Yes & 0.004 & 1e-4 & 0.999 & 0.155 & 0.062 & 0.920 \\\\ \\hline\n\\end{tabular}\n\\caption{The Performance of Credit Spread Prediction Models}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Novel Methodology in Credit Spread Prediction Based on Ensemble Learning and Feature Selection", "authors": ["Yu Shao", "Jiawen Bai", "Yingze Hou", "Xia'an Zhou", "Zhanhao Pan"], "url": "https://arxiv.org/abs/2412.09769v1", "attribution": "\"A Novel Methodology in Credit Spread Prediction Based on Ensemble Learning and Feature Selection\" by Yu Shao, Jiawen Bai, Yingze Hou, Xia'an Zhou, and Zhanhao Pan, arXiv:2412.09769v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.02796v1_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Japan:Performance Comparison of GMV Portfolios: Quantile Loss and Joint Loss.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccc}\n \\toprule\n Portfolio Size & & QLoss$_{1\\%}$ & QLoss$_{5\\%}$ & JointLoss$_{1\\%}$ & JointLoss$_{5\\%}$ \\\\\n \\midrule\n \\multirow{3}{*}{100} & DCC & \\textbf{7.725} & \\textbf{31.698} & \\textbf{2.397} & \\textbf{1.676} \\\\\n & Scalar BEKK & 10.992 & 33.243 & 3.107 & 2.014 \\\\\n & LSTM-BEKK & 12.328 & 35.190 & 3.333 & 2.147 \\\\\n \\midrule\n \\multirow{3}{*}{175} & DCC & 9.019 & 30.624 & \\textbf{2.186} & 1.617 \\\\\n & Scalar BEKK & \\textbf{7.414} & 25.543 & 2.346 & \\textbf{1.544} \\\\\n & LSTM-BEKK & 7.865 & \\textbf{25.180} & 2.491 & 1.605 \\\\\n \\midrule\n \\multirow{3}{*}{250} & DCC & 9.935 & 30.143 & 2.131 & 1.608 \\\\\n & Scalar BEKK & \\textbf{6.485} & 25.712 & \\textbf{2.083} & \\textbf{1.466} \\\\\n & LSTM-BEKK & 7.341 & \\textbf{24.794} & 2.168 & 1.488 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deep Learning Enhanced Multivariate GARCH", "authors": ["Haoyuan Wang", "Chen Liu", "Minh-Ngoc Tran", "Chao Wang"], "url": "https://arxiv.org/abs/2506.02796v1", "attribution": "\"Deep Learning Enhanced Multivariate GARCH\" by Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, and Chao Wang, arXiv:2506.02796v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.13923v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ List of Variables Used in the Analysis.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\hline\nSymbol& Description \\\\\n \\hline\n $\\Omega$& similarity region \\\\\n $n$& URLLC modulation order\\\\\n $m$& eMBB modulation order\\\\\n $L$& BS downlink frequency resources \\\\\n $l$& average URLLC traffic \\\\\n $\\gamma_{\\rm e}$& eMBB Signal to Noise Ratio \\\\\n $\\gamma_{\\rm u}$& URLLC Signal to Noise Ratio \\\\\n $L_m$& eMBB frequency resources with modulation order $m$ \\\\\n $l_{n,m}$& {punctured} eMBB symbols of modulation order $m$ \\\\\n &by URLLC traffic of modulation order $n$\\\\\n $\\mathcal{L}_{n,m}$& effectively {punctured} eMBB symbols of modulation order $m$ \\\\ \n &by URLLC traffic of modulation order $n$\\\\\n $\\overline{\\mathcal{L}}_{n,m}$& non-effectively {punctured} eMBB symbols of modulation order $m$\\\\ \n &by URLLC traffic of modulation order $n$\\\\\n $p_m$& the probability of encoding eMBB with modulation order $m$ \\\\\n $P(.)$& eMBB traffic symbol SER \\\\\n $\\mathcal{P}(.)$& URLLC traffic symbol SER \\\\\n $\\zeta$& URLLC block size \\\\\n $U_{n,m}(.)$& average similar symbols\\\\\n ${K}$& similarity search space\\\\\n $s_u$& URLLC symbol \\\\\n $s_e$& eMBB symbol \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Downlink Puncturing Scheme for Simultaneous Transmission of URLLC and eMBB Traffic by Exploiting Data Similarity", "authors": ["Mohammed AL-Mekhlafi", "Mohaned Chraiti", "Mohamed Amine Arfaoui", "Chadi Assi", "Ali Ghrayeb", "Amira Alloum"], "url": "https://arxiv.org/abs/2012.13923v1", "attribution": "\"A Downlink Puncturing Scheme for Simultaneous Transmission of URLLC and eMBB Traffic by Exploiting Data Similarity\" by Mohammed AL-Mekhlafi, Mohaned Chraiti, Mohamed Amine Arfaoui, Chadi Assi, Ali Ghrayeb, and Amira Alloum, arXiv:2012.13923v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07973v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{siunitx}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n \\textbf{Method} & $\\text{F1}_\\text{any}$ & $\\text{P}_\\text{any}$ & $\\text{R}_\\text{any}$ \\\\\n \\midrule\n Spectral Flux & $\\SI{79.49}{\\percent}$ & $\\SI{78.53}{\\percent}$ & $\\SI{81.86}{\\percent}$ \\\\\n Super Flux & $\\SI{74.36}{\\percent}$ & $\\SI{77.04}{\\percent}$ & $\\SI{73.36}{\\percent}$ \\\\\n CD-ODF & $\\SI{79.32}{\\percent}$ & $\\SI{68.50}{\\percent}$ & $\\SI{98.15}{\\percent}$ \\\\\n \\midrule\n \\textbf{Ours} & \\textbf{$\\SI{97.60}{\\percent}$} & \\textbf{$\\SI{96.54}{\\percent}$} & \\textbf{$\\SI{98.73}{\\percent}$} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Strumming detection results on pickup audio.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Joint Transcription of Acoustic Guitar Strumming Directions and Chords", "authors": ["Sebastian Murgul", "Johannes Schimper", "Michael Heizmann"], "url": "https://arxiv.org/abs/2508.07973v1", "attribution": "\"Joint Transcription of Acoustic Guitar Strumming Directions and Chords\" by Sebastian Murgul, Johannes Schimper, and Michael Heizmann, arXiv:2508.07973v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2010.10703v4_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n & \\multicolumn{3}{l}{\\textbf{2.5\\% of capital retained/yr}} \\\\\n & \\textbf{Deposits =} & \\textbf{Deposits =} & \\textbf{Deposits =} \\\\\n\\textbf{Period} & \\textbf{capital} & \\textbf{3 x capital} & \\textbf{7$\\times$ capital} \\\\ \\hline\n1397-1420 & 45.62\\% & 27.19\\% & 12.35\\% \\\\\n1420-1435 & 53.04\\% & 31.61\\% & 14.35\\% \\\\\n1435-1450 & 55.72\\% & 33.21\\% & 15.08\\% \\\\\n & \\multicolumn{3}{l}{\\textbf{5\\% of capital retained/yr}} \\\\\n & \\textbf{Deposits =} & \\textbf{Deposits =} & \\textbf{Deposits =} \\\\\n\\textbf{Period} & \\textbf{capital} & \\textbf{3 x capital} & \\textbf{7$\\times$ capital} \\\\ \\hline\n1397-1420 & 46.25\\% & 27.37\\% & 12.42\\% \\\\\n1420-1435 & 53.50\\% & 31.66\\% & 14.37\\% \\\\\n1435-1450 & 55.72\\% & 32.98\\% & 14.96\\% \\\\\n & \\multicolumn{3}{l}{\\textbf{10\\% of capital retained/yr}} \\\\\n & \\textbf{Deposits =} & \\textbf{Deposits =} & \\textbf{Deposits =} \\\\\n\\textbf{Period} & \\textbf{capital} & \\textbf{3 x capital} & \\textbf{7$\\times$ capital} \\\\ \\hline\n1397-1420 & 39.68\\% & 23.14\\% & 10.48\\% \\\\\n1420-1435 & 36.29\\% & 21.17\\% & 9.59\\% \\\\\n1435-1450 & 33.79\\% & 19.71\\% & 8.93\\% \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Cancellation of principal in banking: Four radical ideas emerge from deep examination of double entry bookkeeping in banking", "authors": ["Brian P. Hanley"], "url": "https://arxiv.org/abs/2010.10703v4", "attribution": "\"Cancellation of principal in banking: Four radical ideas emerge from deep examination of double entry bookkeeping in banking\" by Brian P. Hanley, arXiv:2010.10703v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18259v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of barrier option prices between E-M method and INAR($\\infty$) implementations}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc|cccc}\n \\hline\n \\multirow{2}{*}{Strike} & \\multicolumn{4}{c|}{E-M Method} & \\multicolumn{4}{c}{INAR($\\infty$)} \\\\\n \\cline{2-9}\n & Up-in Call($B=110$) & 95\\% CI & Down-out Put($B=90$) & 95\\% CI & Up-in Call($B=110$) & 95\\% CI & Down-out Put($B=90$) & 95\\% CI \\\\\n \\hline\n 80 & 19.4249 & [19.3717, 19.4781] & 0.0000 & [0.0000, 0.0000] & 19.3532 & [19.2167, 19.4897] & 0.0000 & [0.0000, 0.0000] \\\\\n 90 & 13.8247 & [13.7708, 13.8787] & 0.0000 & [0.0000, 0.0000] & 13.7837 & [13.6696, 13.8978] & 0.0000 & [0.0000, 0.0000] \\\\\n 100 & 8.9994 & [8.9471, 9.0516] & 0.1539 & [0.1500, 0.1578] & 8.9860 & [8.8936, 9.0784] & 0.1555 & [0.1498, 0.1612] \\\\\n 110 & 5.3024 & [5.2570, 5.3477] & 0.9393 & [0.9262, 0.9525] & 5.2951 & [5.2233, 5.3669] & 0.9359 & [0.9167, 0.9551] \\\\\n 120 & 2.8982 & [2.8625, 2.9339] & 2.5395 & [2.5147, 2.5643] & 2.8911 & [2.8378, 2.9445] & 2.5302 & [2.4923, 2.5681] \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\\infty$) processes and applications", "authors": ["Yingli Wang", "Zhenyu Cui"], "url": "https://arxiv.org/abs/2503.18259v3", "attribution": "\"Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\\infty$) processes and applications\" by Yingli Wang and Zhenyu Cui, arXiv:2503.18259v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.15876v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Panel B: Full-sample performance (Jan 2010–Jun 2025).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrr}\n \\toprule\n Metric & LTT & MKT & STT+LTT & STT & MKT+STT+LTT & MKT+STT & SGCTAT \\\\\n \\midrule\n Cumulative Return (\\%) & 259.1 & 246.7 & 267.0 & 105.6 & 313.1 & 308.9 & 55.6 \\\\\n Annual Return (\\%) & 8.6 & 8.4 & 8.8 & 4.8 & 9.6 & 9.5 & 2.9 \\\\\n Volatility (\\%) & 10.3 & 11.4 & 9.6 & 9.3 & 10.3 & 10.1 & 11.0 \\\\\n Sharpe Ratio & 0.70 & 0.61 & 0.76 & 0.37 & 0.80 & 0.80 & 0.14 \\\\\n Max Drawdown (\\%) & 18.8 & 20.3 & 16.7 & 15.2 & 17.7 & 14.9 & 23.0 \\\\\n Return/Max-DD & 0.46 & 0.41 & 0.53 & 0.31 & 0.54 & 0.64 & 0.13 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach", "authors": ["Eric Benhamou", "Jean-Jacques Ohana", "Alban Etienne", "Béatrice Guez", "Ethan Setrouk", "Thomas Jacquot"], "url": "https://arxiv.org/abs/2507.15876v1", "attribution": "\"Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach\" by Eric Benhamou, Jean-Jacques Ohana, Alban Etienne, Béatrice Guez, Ethan Setrouk, and Thomas Jacquot, arXiv:2507.15876v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10374v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Material parameters (taken from table 4 in~).}%\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\\toprule\n & Mortar matrix & Aggregates\\\\ \\midrule\n\t Young's modulus & $E_{\\mathrm{m}} = 30\\,000\\,\\mathrm{MPa}$ & $E_{\\mathrm{a}}=60\\,000\\,\\mathrm{MPa}$ \\\\\n\t Poisson ratio & $\\nu_{\\mathrm{m}} = 0.2$ & $\\nu_{\\mathrm{a}}=0.2$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multiscale modeling of linear elastic heterogeneous structures via localized model order reduction", "authors": ["Philipp Diercks", "Karen Veroy", "Annika Robens-Radermacher", "Jörg F. Unger"], "url": "https://arxiv.org/abs/2201.10374v4", "attribution": "\"Multiscale modeling of linear elastic heterogeneous structures via localized model order reduction\" by Philipp Diercks, Karen Veroy, Annika Robens-Radermacher, and Jörg F. Unger, arXiv:2201.10374v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12491v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nMethod & Translations on x & Translations on y & Rotations \\\\ \\hline\nRandom & $25.57\\%_{\\pm0.028}$ & $25.54\\%_{\\pm0.028}$ & $13.49\\%_{\\pm0.009}$ \\\\ \nCapsNet & $83.78\\%_{\\pm0.006}$ & $79.82\\%_{\\pm0.009}$ & $88.01\\%_{\\pm0.006}$ \\\\ \nEfficient-CapsNet & $89.69\\%_{\\pm0.005}$ & $87.28\\%_{\\pm0.008}$ & $88.75\\%_{\\pm0.005}$ \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Average percentage of variance captured by the first component of PCA performed on the output capsule vectors of the different transformations applied to test set images.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Efficient-CapsNet: Capsule Network with Self-Attention Routing", "authors": ["Vittorio Mazzia", "Francesco Salvetti", "Marcello Chiaberge"], "url": "https://arxiv.org/abs/2101.12491v2", "attribution": "\"Efficient-CapsNet: Capsule Network with Self-Attention Routing\" by Vittorio Mazzia, Francesco Salvetti, and Marcello Chiaberge, arXiv:2101.12491v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cccc|cccc|}\n\\hline\n$t$ & $L_1^t$ & $L_2^t$ & $L_3^t$ & $L_4^t$ & $q_1^t$ & $q_2^t$ & $q_3^t$ & $q_4^t$ \\\\ \n\\hline \n20 & $0$ & $5$ & $5$ & $10$ & $1$ & $1:15$ & $2:20$ & $3:15$ \\\\ \n19 & $0$ & $5$ & $5$ & $10$ & $1$ & $1:14$ & $2:19$ & $3:14$ \\\\ \n18 & $0$ & $5$ & $5$ & $10$ & $1$ & $1:13$ & $2:18$ & $3:13$ \\\\ \n17 & $0$ & $5$ & $5$ & $10$ & $1$ & $1:12$ & $2:17$ & $3:12$ \\\\ \n16 & $0$ & $5$ & $5$ & $10$ & $1$ & $1:11$ & $2:16$ & $3:11$ \\\\ \n15 & $0$ & $5$ & $5$ & $10$ & $1$ & $1:10$ & $2:15$ & $3:10$ \\\\ \n14 & $0$ & $5$ & $5$ & $\\infty$ & $1$ & $1:9$ & $2:14$ & $-1$ \\\\ \n13 & $0$ & $5$ & $5$ & $12$ & $1$ & $1:8$ & $2:13$ & $2:6$ \\\\ \n12 & $0$ & $5$ & $5$ & $10$ & $1$ & $1:7$ & $2:12$ & $3:7$ \\\\ \n11 & $0$ & $5$ & $5$ & $11$ & $1$ & $1:6$ & $2:11$ & $2:5$ \\\\ \n10 & $0$ & $5$ & $5$ & $\\infty$ & $1$ & $1:5$ & $2:10$ & $-1$ \\\\ \n9 & $0$ & $5$ & $\\infty$ & $8$ & $1$ & $1:4$ & $-1$ & $3:4$ \\\\ \n8 & $0$ & $5$ & $\\infty$ & $\\infty$ & $1$ & $1:3$ & $-1$ & $-1$ \\\\ \n7 & $0$ & $5$ & $5$ & $\\infty$ & $1$ & $1:2$ & $1:2$ & $-1$ \\\\ \n6 & $0$ & $5$ & $\\infty$ & $6$ & $1$ & $1:1$ & $-1$ & $3:1$ \\\\ \n5 & $0$ & $5$ & $\\infty$ & $\\infty$ & $1$ & $1:0$ & $-1$ & $-1$ \\\\ \n4 & $0$ & $\\infty$ & $3$ & $\\infty$ & $1$ & $-1$ & $1:1$ & $-1$ \\\\ \n3 & $0$ & $\\infty$ & $\\infty$ & $\\infty$ & $1$ & $-1$ & $-1$ & $-1$ \\\\ \n2 & $0$ & $\\infty$ & $\\infty$ & $\\infty$ & $1$ & $-1$ & $-1$ & $-1$ \\\\ \n1 & $0$ & $\\infty$ & $1$ & $\\infty$ & $1$ & $-1$ & $1:0$ & $-1$ \\\\ \n0 & $0$ & $\\infty$ & $\\infty$ & $\\infty$ & $1$ & $-1$ & $-1$ & $-1$ \\\\ \n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08471v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Test collection statistics for the CLEF and HC4 newswire collections.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccccccc}\n\\toprule\n\\multirow{2}{*}{Collection} & HC4 & HC4 & CLEF & CLEF & CLEF & CLEF & CLEF\\\\\n & Chinese & Persian & French & German & Italian & Russian & Spanish \\\\ \\midrule\n\\#documents & 646K & 486K & 129k & 294k & 157k & 16k & 454k \\\\\n\\#passages & 3.6M & 3.1M & 0.7M & 1.6M & 0.8M & 0.1M & 2.7M \\\\\n\\#queries & 50 & 50 & 200 & 200 & 200 & 62 & 160 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models", "authors": ["Suraj Nair", "Eugene Yang", "Dawn Lawrie", "Kevin Duh", "Paul McNamee", "Kenton Murray", "James Mayfield", "Douglas W. Oard"], "url": "https://arxiv.org/abs/2201.08471v1", "attribution": "\"Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models\" by Suraj Nair, Eugene Yang, Dawn Lawrie, Kevin Duh, Paul McNamee, Kenton Murray, James Mayfield, and Douglas W. Oard, arXiv:2201.08471v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.04339v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average preferential tie scores: the fraction of sales and volume (in USD) from a seller to their preferential buyer (columns 1 and 2), and from a buyer to their preferential seller (columns 3 and 4).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n & \\multicolumn{2}{c}{\\textbf{Seller to buyer}} & \\multicolumn{2}{c}{\\textbf{Buyer to seller}} \\\\\n & \\textit{Sales} & \\textit{Volume} & \\textit{Sales} & \\textit{Volume} \\\\\nSuperRare & 79\\% & 83\\% & 80\\% & 85\\% \\\\\nAsync Art & 89\\% & 90\\% & 96\\% & 96\\% \\\\\nKnownOrigin & 89\\% & 91\\% & 90\\% & 93\\% \\\\\nFoundation & 94\\% & 95\\% & 94\\% & 96\\% \\\\\nMakersplace & 84\\% & 87\\% & 91\\% & 93\\% \\\\\nZora & 94\\% & 94\\% & 96\\% & 97\\% \\\\\n\\textbf{Overall} & \\textbf{82\\%} & \\textbf{86\\%} & \\textbf{86\\%} & \\textbf{89\\%} \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Seller-buyer networks in NFT art are driven by preferential ties", "authors": ["Giovanni Colavizza"], "url": "https://arxiv.org/abs/2210.04339v2", "attribution": "\"Seller-buyer networks in NFT art are driven by preferential ties\" by Giovanni Colavizza, arXiv:2210.04339v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.00851v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{$R^2_{\\mathrm{oos}}$(\\%) test results based on a 1,000-day predicting sample}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nModel & $R^2_{\\mathrm{oos}}(\\%)$ &{MSPE}-adjust & $p$-value\\\\\\midrule\nGARCH & $-0.172$ & $3.881 \\times 10^{-9}$ & $0.265$ \\\\ \nEGARCH & $-0.633 $ & $-2.571 \\times 10^{-9}$ & $0.852$ \\\\ \nFGARCH & $-0.625$ & $-8.033 \\times 10^{-9}$ & $0.868$ \\\\ \nTGARCH& $-0.164$ & $4.756 \\times 10^{-9}$ & $0.091$ \\\\ \nHAR-CJ & $0.187$ & $2.154 \\times 10^{-8}$ & $0.084$ \\\\ \nHAR-RS & $0.249$ & $3.104 \\times 10^{-8}$ & $0.100$ \\\\ \nHAR-REX & $0.187$ & $2.153 \\times 10^{-8}$ & $0.085$ \\\\ \nHAR-REQ & $0.187$ & $2.153 \\times 10^{-8}$ & $0.085$ \\\\ \nHAR-PD-RV & $0.255$ & $2.738 \\times 10^{-8}$ & $0.079$ \\\\ \nHAR-PD-CJ & $0.262$ & $2.761 \\times 10^{-8}$ & $0.071$ \\\\ \nHAR-PD-RS & $0.256$ & $3.437 \\times 10^{-8}$ & $0.105$ \\\\ \nHAR-PD-REX & \\textbf{$\\mathbf{0.263}$} & $2.636 \\times 10^{-8}$ & $0.062$ \\\\ \nHAR-PD-REQ & $0.259$ & $2.627 \\times 10^{-8}$ & $0.063$ \\\\ \nLASSO-HAR-PD-CJ & $0.183$ & $2.140 \\times 10^{-8}$ & $0.085$ \\\\ \nLASSO-HAR-PD-RS & $0.263$ & $2.632 \\times 10^{-8}$ & $0.061$ \\\\ \nLASSO-HAR-PD-REX & $0.256$ & $2.732 \\times 10^{-8}$ & $0.065$ \\\\ \nLASSO-HAR-PD-REQ& $0.291$ & $3.331 \\times 10^{-8}$ & $0.102$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Forecasting realized volatility in the stock market: a path-dependent perspective", "authors": ["Xiangdong Liu", "Sicheng Fu", "Shaopeng Hong"], "url": "https://arxiv.org/abs/2503.00851v1", "attribution": "\"Forecasting realized volatility in the stock market: a path-dependent perspective\" by Xiangdong Liu, Sicheng Fu, and Shaopeng Hong, arXiv:2503.00851v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2308.11838v6_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccccc}\n\t\t\t\\toprule \n\t\t\t & \\textbf{Method} &\\textbf{Acc} & \\textbf{ECE} & \\textbf{\\text{HCS}$_{1}$} & \\textbf{\\text{HCS}$_{2}$} & \\textbf{\\text{HCS}$_{3}$} & \\textbf{MMCE}\n & \\textbf{KSCE}\\\\\n\t\t\t\\midrule\n\t\t\t\\multirow{4}{*}{Accuracy} & Regularized Evolution~ & 93.91 & 4.20 & 94.84 & 95.16 & 95.32 & 3.71 & 4.18\\\\\n\t\t & \nLocal Search~ & 94.01 & 4.25 & 94.87 & 95.16 & 95.31 & 3.82 & 4.23\\\\ \n\t\t\t& Random Search~ & 93.52 & 4.15 & 94.67 & 95.06 & 95.26 & 3.69 & 4.14\\\\\n \t\t\t\\midrule\n\t\t\t\\multirow{4}{*}{$\\text{HCS}_1$} & Regularized Evolution~ & 93.94 & 4.17 & 94.88 & 95.19 & 95.35 & 3.74 & 4.15\\\\\t\n\t\t\t& Local Search~ & 93.98 & 4.09 & 94.94 & 95.26 & 95.42 & 3.67 & 4.06\\\\\n\t\t\t& Random Search~ & 93.59 & 4.21 & 94.68 & 95.05 & 95.23 & 3.73 & 4.16\\\\\n \\midrule\n\t\t\t\\multirow{4}{*}{$\\text{HCS}_2$} & Regularized Evolution~ & 93.80 & 4.14 & 94.82 & 95.17 & 95.34 & 3.71 & 4.11\\\\\t\n\t\t\t& Local Search~ & 93.73 & 4.05 & 94.83 & 95.20 & 95.39 & 3.62 & 4.01\\\\\n\t\t\t& Random Search~ & 93.06 & 4.12 & 94.45 & 94.92 & 95.16 & 3.58 & 4.08\\\\\n \t\t\t\\midrule\n\t\t\t\\multirow{4}{*}{$\\text{HCS}_3$} & Regularized Evolution~& 93.62 & 3.90 & 94.84 & 95.26 & 95.47 & 3.42 & 3.86\\\\\t\n\t\t\t& Local Search~& 93.57 & 3.91 & 94.81 & 95.24 & 95.45 & 3.42 & 3.87\\\\\n\t\t\t& Random Search~& 93.55 & 4.23 & 94.65 & 95.02 & 95.21 & 3.71 & 4.17\\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{\\textbf{NAS methods search on HCS and Accuracy}}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Benchmark Study on Calibration", "authors": ["Linwei Tao", "Younan Zhu", "Haolan Guo", "Minjing Dong", "Chang Xu"], "url": "https://arxiv.org/abs/2308.11838v6", "attribution": "\"A Benchmark Study on Calibration\" by Linwei Tao, Younan Zhu, Haolan Guo, Minjing Dong, and Chang Xu, arXiv:2308.11838v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14913v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|cc|} \\hline \n & \\multicolumn{2}{|c|}{$V_{h}$}& \\multicolumn{2}{|c|}{$V_{h}^{+}$}& \\multicolumn{2}{|c|}{$V_{h}^{*}$}\\\\ \\hline \n n& $L^{2}$error & order & $L^{2}$error & order & $L^{2}$error & order \\\\ \\hline \n 10& 2.544e-02& - \n&9.413e-05& - \n&9.569e-05& - \n\\\\ \\hline \n 20& 6.404e-03&1.990\n&3.421e-05&1.460&3.419e-05&1.485 \n\\\\ \\hline \n 40& 1.604e-03&1.998\n&9.254e-06&1.886&9.194e-06&1.895 \n\\\\ \\hline \n 80& 4.011e-04&1.999\n&2.363e-06&1.970&2.345e-06&1.971 \n\\\\ \\hline \n 160& 1.003e-04&2.000\n&5.939e-07&1.992&5.892e-07&1.993 \n\\\\ \\hline\n 320& 2.507e-05&2.000&1.487e-07&1.998 &1.475e-07&1.998 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{$H^1$error and order using $P^{2}$ Lagrange element, Poisson equation in 1d.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A PINN-enriched finite element method for linear elliptic problems", "authors": ["Xiao Chen", "Yixin Luo", "Jingrun Chen"], "url": "https://arxiv.org/abs/2503.14913v1", "attribution": "\"A PINN-enriched finite element method for linear elliptic problems\" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.11241v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc|cc}\n\\toprule\n\\textbf{Model} \n & \\multicolumn{2}{c|}{\\textbf{Beat}} \n & \\multicolumn{2}{c}{\\textbf{Downbeat}} \\\\\n\\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n & \\textbf{F-measure} & \\textbf{AMLt} \n & \\textbf{F-measure} & \\textbf{AMLt} \\\\\n\\midrule\n\\textit{Beat This!} & \\textbf{89.1 ± 0.3} & 89.8 ± 0.4 & \\textbf{78.3 ± 0.4} & 79.1 ± 0.6 \\\\\nTCN + DBN & 88.5 & \\textbf{93.1} & 67.2 & \\textbf{83.2} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of TCN and Beat This! performance on the GTZAN dataset.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Revisiting Meter Tracking in Carnatic Music using Deep Learning Approaches", "authors": ["Satyajeet Prabhu"], "url": "https://arxiv.org/abs/2509.11241v1", "attribution": "\"Revisiting Meter Tracking in Carnatic Music using Deep Learning Approaches\" by Satyajeet Prabhu, arXiv:2509.11241v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.11765v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overview of Asset4 ESG Scores}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrr}\n\\hline\n\\textbf{} & \\textbf{Count} & \\textbf{Average Score} \\\\ \\hline\n\\textbf{Companies} & 3,343 & \\\\\n\\textbf{Companies} & 3,343 & \\\\\n\\textbf{Market Capitalization} & 3,309 & \\\\ \\hline\n\\textbf{Scores 2018} & 2,842 & 35 \\\\\n\\textbf{Scores 2019} & 3,078 & 37 \\\\\n\\textbf{Scores 2020} & 3,075 & 38 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis", "authors": ["Tanja Aue", "Adam Jatowt", "Michael Färber"], "url": "https://arxiv.org/abs/2212.11765v1", "attribution": "\"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis\" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19820v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage[table]{xcolor}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Similarity measures for the different XAI methods using the XGB model and the \\textit{maximum} set of features.}\n\\begin{tabular}{ccccccc}\n\\toprule\n & MDI-XGB & MDA-XGB & SHAP-XGB & LIME-XGB & Guidelines & Experts \\\\\n\\cmidrule(lr){1-1}\\cmidrule(lr){2-5}\\cmidrule(lr){6-7}\nMDI-XGB & \\cellcolor[gray]\n {0.8}1.00 & 0.24 & 0.60 & 0.26 & 0.43 & 0.40 \\\\\nMDA-XGB & 0.24 & \\cellcolor[gray]\n {0.8}1.00 & 0.38 & 0.26 & 0.26 & 0.29 \\\\\nSHAP-XGB & 0.60 & 0.38 & \\cellcolor[gray]\n {0.8}1.00 & 0.35 & 0.40 & 0.40 \\\\\nLIME-XGB & 0.26 & 0.26 & 0.35 & \\cellcolor[gray]\n {0.8}1.00 & 0.19 & 0.18 \\\\\n\\midrule\nGuidelines & 0.43 & 0.26 & 0.40 & 0.19 & \\cellcolor[gray]\n {0.8}1.00 & 0.63 \\\\\nExperts & 0.40 & 0.29 & 0.40 & 0.18 & 0.63 & \\cellcolor[gray]\n {0.8}1.00 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach", "authors": ["José Bobes-Bascarán", "Eduardo Mosqueira-Rey", "Ángel Fernández-Leal", "Elena Hernández-Pereira", "David Alonso-Ríos", "Vicente Moret-Bonillo", "Israel Figueirido-Arnoso", "Yolanda Vidal-Ínsua"], "url": "https://arxiv.org/abs/2403.19820v1", "attribution": "\"Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach\" by José Bobes-Bascarán, Eduardo Mosqueira-Rey, Ángel Fernández-Leal, Elena Hernández-Pereira, David Alonso-Ríos, Vicente Moret-Bonillo, Israel Figueirido-Arnoso, and Yolanda Vidal-Ínsua, arXiv:2403.19820v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.08070v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n Type & Affine & Fine-scale& Difference\\\\\n \\hline\n $\\sigma=0.0$ & 0.76 & 0.83 & 0.070\\\\\n $\\sigma=0.1$ & 0.72 & 0.80 & 0.076\\\\\n $\\sigma=0.2$ & 0.62 & 0.71 & 0.086\\\\\n $\\sigma=0.3$ & 0.51 & 0.60 & 0.091\\\\\n $\\sigma=0.4$ & 0.41 & 0.50 & 0.085\\\\\n $\\sigma=0.5$ & 0.33 & 0.40 & 0.070\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Structurally Coherent Spatial Phase Estimate", "authors": ["Brian Knight", "Naoki Saito"], "url": "https://arxiv.org/abs/2412.08070v2", "attribution": "\"A Structurally Coherent Spatial Phase Estimate\" by Brian Knight and Naoki Saito, arXiv:2412.08070v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.03567v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\toprule\n\\textbf{Parameters} & \\textbf{Description} & \\textbf{Age group} & \\textbf{Value} &\\textbf{Ref.} \\\\ \\midrule\n$r$ & relative infectiousness of asymptomatic individuals & - & 50\\% & $\\dagger$ \\\\ \\midrule\n$k$ & proportion of pre-symptomatic transmission & - & 50\\% & \\\\ \\midrule\n$\\epsilon$ & incubation period & - & 5 days & \\\\ \\midrule\n$p_a$ & symptomatic probability & 0-9 & 0.181 & \\\\ \n & & 10-19 & 0.181 & \\\\\n & & 20-29 & 0.225 & \\\\\n & & 30-39 & 0.225 & \\\\\n & & 40-49 & 0.300 & \\\\\n & & 50-59 & 0.300 & \\\\\n & & 60-69 & 0.360 & \\\\\n & & 70-79 & 0.360 & \\\\\n & & $\\geq 80$ & 0.646 & \\\\ \\midrule\n$\\chi$ & susceptibility & 0-18 & 0.56 & \\\\\n& & $\\geq 19$ & 1.00 & \\\\ \\midrule\n$\\gamma$ & pre-symptomatic period & - & 2 days & \\\\ \\midrule\n$\\mu$ & time to isolation & - & 2.5 days & \\\\ \\midrule\n$\\delta$ & days from isolation to death & - & 12.5 & \\\\ \\midrule\nIFR & infection fatality ratio & 0-9 & 0.00161\\% & \\ddag \\\\ \n & & 10-19 & 0.00695\\% & \\\\\n & & 20-29 & 0.0309\\% & \\\\\n & & 30-39 & 0.0844\\% & \\\\\n & & 40-49 & 0.161\\% & \\\\\n & & 50-59 & 0.595\\% & \\\\\n & & 60-69 & 1.93\\% & \\\\\n & & 70-79 & 4.28\\% & \\\\\n & & $\\geq 80$ & 7.80\\% & \\\\ \\midrule\n$T_n$ & Notification of death & - & 7 days & \\\\ \\midrule\n$\\theta$ & outdoor transmissibility & - & 0.05 & \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Baseline set of parameters. $\\dagger$: assumed ;$*$: calibrated to the generation time $T_g$; $\\ddag$ Only applied to symptomatic individuals. As such, a correction factor of 1/p is applied to all age groups.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The unequal effects of the health-economy tradeoff during the COVID-19 pandemic", "authors": ["Marco Pangallo", "Alberto Aleta", "R. Maria del Rio Chanona", "Anton Pichler", "David Martín-Corral", "Matteo Chinazzi", "François Lafond", "Marco Ajelli", "Esteban Moro", "Yamir Moreno", "Alessandro Vespignani", "J. Doyne Farmer"], "url": "https://arxiv.org/abs/2212.03567v1", "attribution": "\"The unequal effects of the health-economy tradeoff during the COVID-19 pandemic\" by Marco Pangallo, Alberto Aleta, R. Maria del Rio Chanona, Anton Pichler, David Martín-Corral, Matteo Chinazzi, François Lafond, Marco Ajelli, Esteban Moro, Yamir Moreno, Alessandro Vespignani, and J. Doyne Farmer, arXiv:2212.03567v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.17440v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr} \\hline\n Number of runs, $n$ & 10 & 20 & 30 & 40 & 50 \\\\ \\hline\n $\\hat{U}_G(\\bar{X}_B)/\\hat{U}_G(\\bar{X}_G)$ & 0.037 & 0.061 & 0.131 & 0.159 & 0.175 \\\\\n $\\hat{U}_B(\\bar{X}_G)/\\hat{U}_B(\\bar{X}_B)$ & 0.270 & 0.510 & 0.600 & 0.650 & 0.680 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Estimated expected utility efficiency of the Gibbs and Bayesian optimal designs under the alternative utility.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Gibbs optimal design of experiments", "authors": ["Antony M. Overstall", "Jacinta Holloway-Brown", "James M. McGree"], "url": "https://arxiv.org/abs/2310.17440v2", "attribution": "\"Gibbs optimal design of experiments\" by Antony M. Overstall, Jacinta Holloway-Brown, and James M. McGree, arXiv:2310.17440v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.00087v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lllllllll}\\toprule{} & \n \\multicolumn{4}{l}{Data Generation Process 1}\n &\n \\multicolumn{4}{l}{Data Generation Process 2}\\\\\n &\\multicolumn{2}{l}{Proposed Method} & \\multicolumn{2}{l}{Oracle Method} & \n \\multicolumn{2}{l}{Proposed Method} & \\multicolumn{2}{l}{Oracle Method} \\\\\n State & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 \\\\\n N & & & & & & & & \\\\\\midrule\n 40 & 0.49(0.020) & 0.51(0.013) & 0.89(0.011) & 0.89(0.009) & 0.53(0.014) & 0.61(0.006) & -- & -- \\\\ 80 & 0.55(0.065) & 0.62(0.133) & 0.91(0.011) & 0.91(0.014) & 0.65(0.030) & 0.82(0.009) & 0.79(0.014) & 0.92(0.004) \\\\ 120 & 0.67(0.090) & 0.52(0.025) & 0.91(0.011) & 0.91(0.014) & 0.87(0.015) & 0.95(0.002) & 0.94(0.009) & 1.00(0.001) \\\\ 160 & 0.92(0.018) & 0.87(0.021) & 0.93(0.009) & 0.97(0.014) & 0.92(0.019) & 0.98(0.001) & 0.98(0.002) & 1.00(0.000) \\\\ 200 & 0.95(0.018) & 0.86(0.027) & 0.94(0.020) & 0.97(0.004) & 0.96(0.006) & 0.98(0.001) & 1.00(0.003) & 1.00(0.000) \n \\end{tabular}\n\\caption{The AUC of the simulated task in Figure~. Each value in the cell is the average AUC over $10$ independent runs and the value inside the parenthesis is the standard deviation. The oracle method is assumed that the latent state is known. In this case, $X(t)$ is unknown and we get the estimates, $\\hat{X}(t)$, by using the wavelet smoothing method discussed in Section~. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High-Dimensional Markov-switching Ordinary Differential Processes", "authors": ["Katherine Tsai", "Mladen Kolar", "Sanmi Koyejo"], "url": "https://arxiv.org/abs/2501.00087v1", "attribution": "\"High-Dimensional Markov-switching Ordinary Differential Processes\" by Katherine Tsai, Mladen Kolar, and Sanmi Koyejo, arXiv:2501.00087v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.19308v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experimental results on dev set with Whisper large v3 Turbo as encoder, EuroLLM 1.7B Instruct as LLM tuned with different LoRA parameters.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\hline\n\\textbf{Experimental Setting} & \\textbf{Dev Set (WER)} \\\\ \\hline\nLoRA ($\\alpha = 8, r = 8$) & 21.14\\% \\\\\nLoRA ($\\alpha = 8, r = 16$) & 17.80\\% \\\\\nLoRA ($\\alpha = 16, r = 16$) & 20.04\\% \\\\\nLoRA ($\\alpha = 32, r = 16$) & 15.88\\% \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "The Eloquence team submission for task 1 of MLC-SLM challenge", "authors": ["Lorenzo Concina", "Jordi Luque", "Alessio Brutti", "Marco Matassoni", "Yuchen Zhang"], "url": "https://arxiv.org/abs/2507.19308v1", "attribution": "\"The Eloquence team submission for task 1 of MLC-SLM challenge\" by Lorenzo Concina, Jordi Luque, Alessio Brutti, Marco Matassoni, and Yuchen Zhang, arXiv:2507.19308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12251v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|}\n \\hline\n \n Solar Panels & $SD^q$ &$SD^p$ \\\\ \\hline\n $P_1$ &$\\langle (0.619,0.380);0.717\\rangle$&$\\langle (0.619,0.380);0.741\\rangle$\\\\\n $P_2$ &$\\langle (0.601,0.399);0.596\\rangle$&$\\langle (0.601,0.399);0.620\\rangle$\\\\\n $P_3$ &$\\langle (0.553,0.447);0.420\\rangle$&$\\langle (0.553,0.447);0.429\\rangle$\\\\\n $P_4$ &$\\langle (0.593,0.407);0.651\\rangle$&$\\langle (0.593,0.407);0.668\\rangle$\\\\\n $P_5$ &$\\langle (0.553,0.447);0.695\\rangle$&$\\langle (0.553,0.447);0.702\\rangle$\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The results of significance degrees $SD_i$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology", "authors": ["Mahmut Can Bozyiğit", "Mehmet Ünver"], "url": "https://arxiv.org/abs/2501.12251v1", "attribution": "\"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology\" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.16019v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average false positive (standard error) for variable selection}\n\\begin{tabular}{llccccccccccc}\n\t\t\t\\hline\n\t\t\t$\\epsilon$ & $N$ & \\multicolumn{3}{c}{p=20} && \\multicolumn{3}{c}{p=50} && \\multicolumn{3}{c}{p=100} \\\\\n\t\t\t\\cline{3-5} \\cline{7-9} \\cline{11-13} \n\t\t\t& & gQ-Lasso & rq-Lasso & GCQR && gQ-Lasso & rq-Lasso & GCQR && gQ-Lasso & rq-Lasso & GCQR\\\\\n\t\t\t\\hline\n\t\t\tN(0,1) & 200 & 8.730 & 6.754 & 1.306 && 13.360 & 10.552 & 2.364 && 15.840 & 14.048 & 3.308 \\\\ \n\t\t\t& & \\textit{(3.440)} & \\textit{(2.067)} & \\textit{(1.049)} && \\textit{(5.885)} & \\textit{(3.598)} & \\textit{(1.318)} && \\textit{(8.147)} & \\textit{(5.677)} & \\textit{(2.058)} \\\\ \n\t\t\t& 500 & 8.860 & 6.148 & 0.330 && 12.630 & 9.402 & 0.408 && 17.110 & 13.052 & 0.520 \\\\ \n\t\t\t& & \\textit{(3.490)} & \\textit{(1.784)} & \\textit{(0.438)} && \\textit{(5.863)} & \\textit{(3.502)} & \\textit{(0.549)} && \\textit{(9.056)} & \\textit{(4.888)} & \\textit{(0.613)} \\\\ \n\t\t\t& 1000 & 8.930 & 5.958 & 0.034 && 13.100 & 9.442 & 0.018 && 18.210 & 12.798 & 0.030 \\\\ \n\t\t\t& & \\textit{(3.400)} & \\textit{(1.986)} & \\textit{(0.095)} && \\textit{(5.342)} & \\textit{(3.327)} & \\textit{(0.064)} && \\textit{(9.524)} & \\textit{(3.662)} & \\textit{(0.096)} \\\\ \n\t\t\t\\hline\n\t\t\t$T_2$ & 200 & 8.650 & 6.568 & 2.390 && 11.940 & 9.942 & 4.406 && 15.120 & 13.804 & 5.930 \\\\ \n\t\t\t& & \\textit{(3.304)} & \\textit{(2.095)} & \\textit{(1.460)} && \\textit{(5.750)} & \\textit{(3.027)} & \\textit{(2.155)} && \\textit{(8.086)} & \\textit{(5.098)} & \\textit{(2.795)} \\\\ \n\t\t\t& 500 & 9.180 & 6.362 & 1.016 && 13.780 & 9.824 & 1.598 && 16.050 & 12.268 & 1.940 \\\\ \n\t\t\t& & \\textit{(3.705)} & \\textit{(1.955)} & \\textit{(0.716)} && \\textit{(6.458)} & \\textit{(3.346)} & \\textit{(1.119)} && \\textit{(8.281)} & \\textit{(4.348)} & \\textit{(1.390)} \\\\ \n\t\t\t& 1000 & 9.220 & 6.132 & 0.214 && 14.020 & 9.834 & 0.396 && 16.470 & 11.928 & 0.422 \\\\ \n\t\t\t& & \\textit{(3.386)} & \\textit{(1.722)} & \\textit{(0.310)} && \\textit{(6.627)} & \\textit{(3.377)} & \\textit{(0.514)} && \\textit{(8.241)} & \\textit{(4.027)} & \\textit{(0.479)} \\\\ \n\t\t\t\\hline\n\t\t heter. & 200 & 5.590 & 4.224 & 3.078 && 6.700 & 5.450 & 5.562 && 6.400 & 4.834 & 6.882 \\\\ \n\t\t\t& & \\textit{(3.838)} & \\textit{(2.419)} & \\textit{(1.776)} && \\textit{(5.608)} & \\textit{(3.458)} & \\textit{(2.912)} && \\textit{(5.735)} & \\textit{(3.373)} & \\textit{(4.093)} \\\\ \n\t\t\t& 500 & 7.980 & 5.964 & 3.226 && 10.480 & 7.906 & 5.424 && 13.370 & 9.272 & 8.996 \\\\ \n\t\t\t& & \\textit{(3.687)} & \\textit{(2.058)} & \\textit{(1.771)} && \\textit{(5.654)} & \\textit{(3.360)} & \\textit{(2.392)} && \\textit{(7.396)} & \\textit{(4.275)} & \\textit{(3.858)} \\\\ \n\t\t\t& 1000 & 8.620 & 5.764 & 2.358 && 11.640 & 8.752 & 4.770 && 14.490 & 11.600 & 8.062 \\\\ \n\t\t\t& & \\textit{(3.487)} & \\textit{(1.939)} & \\textit{(1.445)} && \\textit{(6.028)} & \\textit{(3.083)} & \\textit{(2.433)} && \\textit{(6.722)} & \\textit{(4.098)} & \\textit{(3.533)} \\\\ \n\t\t\t\\hline\n\t\t\tasym. & 200 & 7.170 & 6.230 & 3.030 && 11.080 & 10.056 & 5.104 && 13.790 & 13.446 & 8.360 \\\\ \n\t\t\t&& \\textit{(3.414)} & \\textit{(2.060)} & \\textit{(1.734)} && \\textit{(6.147)} & \\textit{(3.845)} & \\textit{(2.897)} && \\textit{(7.263)} & \\textit{(5.854)} & \\textit{(4.208)} \\\\ \n\t\t\t&500 & 8.260 & 6.352 & 1.872 && 12.870 & 10.338 & 2.914 && 15.400 & 13.212 & 4.304 \\\\ \n\t\t\t&& \\textit{(3.529)} & \\textit{(1.964)} & \\textit{(1.393)} && \\textit{(7.894)} & \\textit{(4.061)} & \\textit{(1.885)} && \\textit{(8.962)} & \\textit{(4.803)} & \\textit{(2.669)} \\\\ \n\t\t\t&1000 & 8.030 & 6.038 & 0.786 && 12.360 & 9.734 & 1.032 && 14.610 & 11.612 & 1.310 \\\\ \n\t\t\t&& \\textit{(3.597)} & \\textit{(1.912)} & \\textit{(0.747)} && \\textit{(6.994)} & \\textit{(3.750)} & \\textit{(0.908)} && \\textit{(7.256)} & \\textit{(4.176)} & \\textit{(0.939)} \\\\ \n\t\t\t\\hline\n\t\t\theter. + asym. & 200 & 4.250 & 3.314 & 2.416 && 4.860 & 4.006 & 4.800 && 3.580 & 3.496 & 5.269 \\\\ \n\t\t\t&& \\textit{(3.514)} & \\textit{(1.909)} & \\textit{(1.529)} && \\textit{(5.472)} & \\textit{(2.953)} & \\textit{(3.154)} && \\textit{(5.101)} & \\textit{(2.915)} & \\textit{(3.631)} \\\\ \n\t\t\t& 500 & 5.850 & 3.782 & 2.698 && 8.500 & 6.206 & 4.538 && 8.680 & 6.384 & 6.474 \\\\ \n\t\t\t&& \\textit{(3.380)} & \\textit{(1.692)} & \\textit{(1.670)} && \\textit{(6.414)} & \\textit{(3.516)} & \\textit{(2.797)} && \\textit{(6.903)} & \\textit{(4.122)} & \\textit{(4.044)} \\\\ \n\t\t\t& 1000 & 7.170 & 4.508 & 2.294 && 9.940 & 6.772 & 4.220 && 11.830 & 7.384 & 5.274 \\\\ \n\t\t\t&& \\textit{(3.447)} & \\textit{(1.911)} & \\textit{(1.311)} && \\textit{(5.517)} & \\textit{(2.703)} & \\textit{(2.203)} && \\textit{(6.784)} & \\textit{(3.594)} & \\textit{(3.317)} \\\\ \n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Quantile Predictions for Equity Premium using Penalized Quantile Regression with Consistent Variable Selection across Multiple Quantiles", "authors": ["Shaobo Li", "Ben Sherwood"], "url": "https://arxiv.org/abs/2505.16019v1", "attribution": "\"Quantile Predictions for Equity Premium using Penalized Quantile Regression with Consistent Variable Selection across Multiple Quantiles\" by Shaobo Li and Ben Sherwood, arXiv:2505.16019v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2306.07731v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|cc|cc|cc}\n\t\t\t\\toprule\n\t\t\t & \\multicolumn{2}{c}{\\textbf{2018-21}} & \\multicolumn{2}{c}{\\textbf{2021-23}} & \\multicolumn{2}{c}{\\textbf{2018-23}}\\\\\n\t\t\t\\cmidrule(rl){2-3} \\cmidrule(rl){4-5} \\cmidrule(rl){6-7}\n\t\t\t & \\textbf{3-OU} & \\textbf{4-OU}& \\textbf{3-OU} & \\textbf{4-OU} & \\textbf{3-OU} & \\textbf{4-OU}\\\\\n\t\t\t\\midrule\n\t\t\tNumber of Iterations & 10 Mio. & 10 Mio. & 10 Mio. & 10 Mio. & 10 Mio. & 10 Mio.\\\\\n\t\t\tBurn-In-Period & 9 Mio. & 9 Mio. & 9 Mio. & 9 Mio. & 9 Mio. & 9 Mio.\\\\\n\t\t\tNumber of Loops for Birth-Death step & 5 & 5 & 5 & 1 & 5 & 5\\\\\n\t\t\tNumber of Loops for Multiplicative Update & 5 & 5 & 5 & 1 & 5 & 5\\\\\n\t\t\tNumber of Loops for Latent OU-Variable & - & 30 & - & 200 & - & 30\\\\\n\t\t\tNumber of New Increments per Loop & - & 100 & - & 1 & - & 1\\\\\n\t\t\tNumber of Permutations Increments per Loop & - & 100 & - & 2 & - & 2\\\\\n\t\t\tRuntime (days) & 3.9958 & 3.3188 & 4.0901 & 5.4426 & 9.6146 & 8.2958\\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\caption{Information about the updates of the hidden variables.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Comparative Study of Factor Models for Different Periods of the Electricity Spot Price Market", "authors": ["Christian Laudagé", "Florian Aichinger", "Sascha Desmettre"], "url": "https://arxiv.org/abs/2306.07731v3", "attribution": "\"A Comparative Study of Factor Models for Different Periods of the Electricity Spot Price Market\" by Christian Laudagé, Florian Aichinger, and Sascha Desmettre, arXiv:2306.07731v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.01997v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Diarization results for multichannel LibriCSS evaluation set. Late fusion using DL achieved better performance compared to early fusion based on dereverberation and beamforming.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\textbf{Method} & \\textbf{MS} & \\textbf{FA} & \\textbf{Conf.} & \\textbf{DER} \\\\ \\midrule\n7-channel avg. & \\textbf{2.58} & 0.96 & 5.86 & 9.40 \\\\\n7-channel best & 2.59 & 0.99 & 5.53 & 9.11 \\\\\n\\midrule\nWPE + Beamforming & 2.91 & 0.96 & 5.86 & 9.33 \\\\\nDL & 3.60 & \\textbf{0.66} & \\textbf{4.76} & \\textbf{9.02} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs", "authors": ["Desh Raj", "Leibny Paola Garcia-Perera", "Zili Huang", "Shinji Watanabe", "Daniel Povey", "Andreas Stolcke", "Sanjeev Khudanpur"], "url": "https://arxiv.org/abs/2011.01997v1", "attribution": "\"DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs\" by Desh Raj, Leibny Paola Garcia-Perera, Zili Huang, Shinji Watanabe, Daniel Povey, Andreas Stolcke, and Sanjeev Khudanpur, arXiv:2011.01997v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2401.00292v1_tex_table28.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c|c}\n\\multirow{2}{1em}{$\\gamma$} & AVG Time$\\,S_U(s)$ & AVG Time$\\,S_U(s)$\\\\\n& Chute1 & Chute2\\\\\n\\hline\n\\multicolumn{ 3}{c}{Bi6.1}\\\\\n\\hline\n10 & 2.09 & 77.47 (76.07)\\\\\n30 & 5.95 & 85.03 (81.60)\\\\\n50 & 10.04 & 83.28 (77.95)\\\\\n\\hline\n\\multicolumn{ 3}{c}{Bi9.1}\\\\\n\\hline\n10 & 4.02 & 444.71 (405.63)\\\\\n30 & 12.72 & 535.92 (404.05)\\\\\n50 & 21.05 & 618.98 (404.93)\\\\\n\\hline\n\\multicolumn{ 3}{c}{Three6.1}\\\\\n\\hline\n10 & 4.04 & 184.15 (180.14)\\\\\n30 & 7.98 & 218.82 (201.75)\\\\\n50 & 19.50 & 208.46 (183.09)\\\\\n\\hline\n\\multicolumn{ 3}{c}{Three9.1}\\\\\n\\hline\n10 & 44.72 & 425.48 (389.69)\\\\\n30 & 172.96 & 470.58 (388.69)\\\\\n50 & 263.05 & 531.72 (391.16)\\\\\n\\hline\n\\end{tabular}\n\\caption{Average times of deriving upper shells for Chute1 and Chute2.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems", "authors": ["Grzegorz Filcek", "Janusz Miroforidis"], "url": "https://arxiv.org/abs/2401.00292v1", "attribution": "\"A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems\" by Grzegorz Filcek and Janusz Miroforidis, arXiv:2401.00292v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09972v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results on the ETH-80 dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\n\\textbf{Method} & \\textbf{Accuracy}\\\\\n\\hline\n\\textbf{BoAW} & \\textbf{89.29}\\%\\\\\n\\hline\n\\textbf{ARSRG}$emb$ & 89.26\\%\\\\\n\\hline\nBoW & 58.83\\%\\\\\n\\hline\n{gdFil}& 47.59\\%\\\\\n\\hline\n{APGM} & 84.39\\%\\\\\n\\hline\n{VEAM} & 82.68\\%\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications", "authors": ["Mario Manzo"], "url": "https://arxiv.org/abs/1912.09972v1", "attribution": "\"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications\" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12262v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\textbf{Scaling Factor} & \\textbf{Time Step} & \\textbf{Total No. of Mosquitoes Released} & \\textbf{Initial Fraction of Infection $p_{thres}$} \\\\ \\hline\n$\\Delta t$ & 0.017 & 1,072,640 & 0.357 \\\\ \\hline\n$\\Delta t/2$ & 0.008 & 1,047,040 & 0.349 \\\\ \\hline\n$\\Delta t/4$ & 0.004 & 1,032,140 & 0.344 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Improving Wolbachia-Based Control Programs in Urban Settings: Insights from Spatial Modeling", "authors": ["Daniela Florez", "Ricardo Cortez", "James M. Hyman", "Zhuolin Qu"], "url": "https://arxiv.org/abs/2503.12262v1", "attribution": "\"Improving Wolbachia-Based Control Programs in Urban Settings: Insights from Spatial Modeling\" by Daniela Florez, Ricardo Cortez, James M. Hyman, and Zhuolin Qu, arXiv:2503.12262v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.08281v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n Model & Best hyperparameter settings \\\\\n \\midrule\n LSTM & batch size=4, dropout=0.2, hidden layer=64, layers=8 \\\\\n Transformer (V) & batch size=4, dropout=0.2, heads=4, layers=2\\\\\n Synthesizer (R) & batch size=4, dropout=0.2, heads=4, layers=4\\\\\n Synthesizer (FR) & batch size=4, dropout=0.2, heads=4, layers=8\\\\\n Synthesizer (D) & batch size=4, dropout=0.2, heads=8, layers=4\\\\\n Synthesizer (FD) & batch size=4, dropout=0.2, heads=4, layers=4\\\\\n Synthesizer (MD) & batch size=4, dropout=0.1, heads=2, layers=4\\\\\n Synthesizer (MR) & batch size=4, dropout=0.1, heads=8, layers=2\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The best hyperparameters based on the validation set, for the different Transformer models. We use R for random, F for factorised, M for mixed, D for dense, and V for Vanilla models.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models", "authors": ["Dorien Herremans", "Kah Wee Low"], "url": "https://arxiv.org/abs/2211.08281v1", "attribution": "\"Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models\" by Dorien Herremans and Kah Wee Low, arXiv:2211.08281v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.20202v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Viral family name, number of sequence, minimum, maximum and average sequence length, and average $N_A$, $N_C$, $N_G$, $N_T$}\n\\begin{tabular}{|l|cccccccc|} \\hline\n\t\t\t\tFamily & \\# sequence & Min length & Max length & Avg length & Avg $N_A$ & Avg $N_C$ & Avg $N_G$ & Avg $N_T$ \\\\\\hline\n\t\t\t\tAckermannviridae & 64 & 143249.00 & 167619.00 & 156691.41 & 42614.16 & 35865.44 & 36011.62 & 42200.19\\\\\n\t\t\t\tAdenoviridae & 124 & 415.00 & 48395.00 & 31944.69 & 8050.70 & 8074.22 & 8017.19 & 7802.57\\\\\n\t\t\t\tAdintoviridae & 2 & 12571.00 & 12754.00 & 12662.50 & 3797.50 & 2582.50 & 2572.50 & 3710.00\\\\\n\t\t\t\tAliceevansviridae & 129 & 30664.00 & 48800.00 & 36318.58 & 12465.62 & 6405.73 & 7710.43 & 9736.81\\\\\n\t\t\t\tAliusviridae & 8 & 13163.00 & 15277.00 & 14150.62 & 4418.38 & 2773.00 & 2861.50 & 4097.75\\\\\n\t\t\t\tAlloherpesviridae & 17 & 451.00 & 295146.00 & 135007.88 & 32656.82 & 34600.47 & 34880.06 & 32870.53\\\\\n\t\t\t\tAllomimiviridae & 2 & 186217.00 & 668031.00 & 427124.00 & 128975.00 & 84000.00 & 86135.00 & 128014.00\\\\\n\t\t\t\tAlphaflexiviridae & 67 & 762.00 & 8831.00 & 6742.87 & 1897.00 & 1938.70 & 1435.27 & 1471.90\\\\\n\t\t\t\tAlphasatellitidae & 122 & 619.00 & 1531.00 & 1270.06 & 385.78 & 239.97 & 291.65 & 352.66\\\\\n\t\t\t\tAlphatetraviridae & 8 & 2055.00 & 6625.00 & 4027.88 & 919.62 & 1195.38 & 1024.00 & 888.88\\\\\n\t\t\t\tAlternaviridae & 24 & 1420.00 & 3594.00 & 2710.62 & 506.08 & 660.92 & 843.17 & 700.46\\\\\n\t\t\t\tAmalgaviridae & 23 & 3156.00 & 3478.00 & 3389.30 & 865.65 & 721.48 & 918.78 & 883.39\\\\\n\t\t\t\tAmesuviridae & 3 & 2707.00 & 3442.00 & 2952.00 & 764.00 & 597.00 & 747.33 & 843.67\\\\\n\t\t\t\tAmnoonviridae & 10 & 465.00 & 1641.00 & 1032.00 & 260.60 & 263.10 & 217.10 & 291.20\\\\\n\t\t\t\tAmpullaviridae & 3 & 22609.00 & 28489.00 & 24970.00 & 8106.67 & 4104.00 & 4158.00 & 8601.33\\\\\n\t\t\t\tAnelloviridae & 174 & 1744.00 & 3907.00 & 2739.02 & 860.40 & 668.24 & 601.16 & 609.22\\\\\n\t\t\t\tArenaviridae & 131 & 1924.00 & 7417.00 & 5027.60 & 1436.51 & 1088.17 & 952.91 & 1550.02\\\\\n\t\t\t\tArteriviridae & 25 & 12704.00 & 15728.00 & 14978.60 & 3342.24 & 4098.16 & 3634.80 & 3903.40\\\\\n\t\t\t\tArtoviridae & 8 & 6367.00 & 12395.00 & 10072.50 & 2959.00 & 2227.88 & 2130.12 & 2755.50\\\\\n\t\t\t\tAscoviridae & 8 & 741.00 & 199721.00 & 129876.00 & 35897.38 & 28953.75 & 29567.75 & 35457.12\\\\\n\t\t\t\tAsfarviridae & 20 & 170101.00 & 193886.00 & 184524.00 & 56550.50 & 35563.90 & 35537.35 & 56872.25\\\\\n\t\t\t\tAspiviridae & 14 & 136.00 & 8182.00 & 3002.50 & 823.86 & 613.07 & 449.71 & 1115.86\\\\\n\t\t\t\tAssiduviridae & 3 & 53385.00 & 57447.00 & 54832.00 & 21130.00 & 7363.33 & 9868.00 & 16470.67\\\\\n\t\t\t\tAstroviridae & 59 & 2403.00 & 7722.00 & 5978.46 & 1621.12 & 1416.78 & 1453.34 & 1487.22\\\\\n\t\t\t\tAtkinsviridae & 91 & 3364.00 & 4535.00 & 3895.51 & 919.65 & 947.07 & 975.75 & 1053.04\\\\\n\t\t\t\tAutographiviridae & 385 & 10313.00 & 47868.00 & 41458.66 & 10671.61 & 10054.84 & 10850.82 & 9881.40\\\\\n\t\t\t\tAutolykiviridae & 5 & 10046.00 & 10636.00 & 10428.80 & 3035.00 & 2268.20 & 2251.60 & 2874.00\\\\\n\t\t\t\tAvsunviroidae & 5 & 247.00 & 434.00 & 350.00 & 79.40 & 89.20 & 90.40 & 91.00\\\\\n\t\t\t\tBacilladnaviridae & 21 & 3492.00 & 6000.00 & 4955.76 & 1285.00 & 1164.71 & 1067.43 & 1438.62\\\\\n\t\t\t\tBaculoviridae & 103 & 1182.00 & 178733.00 & 127394.97 & 37115.49 & 26505.71 & 26608.93 & 37164.84\\\\\n\t\t\t\tBenyviridae & 15 & 1320.00 & 7038.00 & 4319.27 & 1076.07 & 782.27 & 1103.60 & 1357.33\\\\\n\t\t\t\tBetaflexiviridae & 151 & 276.00 & 9654.00 & 7604.56 & 2223.48 & 1457.15 & 1847.44 & 2076.48\\\\\n\t\t\t\tBicaudaviridae & 8 & 48774.00 & 76107.00 & 62491.50 & 19691.50 & 12002.00 & 11801.88 & 18996.12\\\\\n\t\t\t\tBidnaviridae & 4 & 6022.00 & 6543.00 & 6283.00 & 2263.25 & 915.75 & 968.50 & 2135.50\\\\\n\t\t\t\tBirnaviridae & 29 & 2605.00 & 3579.00 & 3076.34 & 911.93 & 838.45 & 751.72 & 574.24\\\\ \\hline\n\t\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Revealing the Shape of Genome Space via K-mer Topology", "authors": ["Yuta Hozumi", "Guo-Wei Wei"], "url": "https://arxiv.org/abs/2412.20202v1", "attribution": "\"Revealing the Shape of Genome Space via K-mer Topology\" by Yuta Hozumi and Guo-Wei Wei, arXiv:2412.20202v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.21379v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Distribution of $\\hat{N} - N$ over 100 simulated data sequences from the Gaussian or Cauchy models.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|}\n\\cline{1-6}\n & & & \\multicolumn{3}{|c|}{} \\\\\n & & & \\multicolumn{3}{|c|}{$\\hat{N} - N$} \\\\\nModel & $T$ & Method & 0 & 1 & $\\geq 2$\\\\\n\\hline\nGaussian & 30 & $L_{\\infty}$ & 97 & 3 & 0\\\\\n & & $L_{2}$ & 98 & 2 & 0\\\\\n\\cline{2-6}\n& 75 & $L_{\\infty}$ & 98 & 2 & 0\\\\\n & & $L_{2}$ & 100 & 0 & 0\\\\\n\\cline{2-6}\n & 200 & $L_{\\infty}$ & 99 & 1 & 0\\\\\n & & $L_{2}$ & 100 & 0 & 0\\\\\n\\cline{2-6}\n& 500 & $L_{\\infty}$ & 95 & 5 & 0\\\\\n & & $L_{2}$ & 100 & 0 & 0\\\\\n\\hline\nCauchy & 30 & $L_{\\infty}$ & 93 & 7 & 0\\\\\n & & $L_{2}$ & 99 & 1 & 0\\\\\n\\cline{2-6}\n & 75 & $L_{\\infty}$ & 97 & 3 & 0\\\\\n & & $L_{2}$ & 100 & 0 & 0\\\\\n\\cline{2-6}\n & 200 & $L_{\\infty}$ & 92 & 5 & 3\\\\\n & & $L_{2}$ & 99 & 0 & 1\\\\\n\\cline{2-6}\n & 500 & $L_{\\infty}$ & 97 & 3 & 0\\\\\n & & $L_{2}$ & 100 & 0 & 0\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Non-parametric multiple change-point detection", "authors": ["Andreas Anastasiou", "Piotr Fryzlewicz"], "url": "https://arxiv.org/abs/2504.21379v1", "attribution": "\"Non-parametric multiple change-point detection\" by Andreas Anastasiou and Piotr Fryzlewicz, arXiv:2504.21379v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.13391v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Economic Loss Function Comparison}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nModel & ALL (95\\%) & ALL (99\\%) & Quadratic Loss \\\\\n\\midrule\nHistorical Simulation & 0.0847 & 0.0234 & 0.0156 \\\\\nParametric Normal & 0.0623 & 0.0198 & 0.0134 \\\\\nGARCH-t & 0.0445 & 0.0167 & 0.0098 \\\\\nFiltered Historical & 0.0398 & 0.0152 & 0.0089 \\\\\nEVaR & 0.0312 & 0.0128 & 0.0067 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Quantitative Risk Management in Volatile Markets with an Expectile-Based Framework for the FTSE Index", "authors": ["Abiodun Finbarrs Oketunji"], "url": "https://arxiv.org/abs/2507.13391v1", "attribution": "\"Quantitative Risk Management in Volatile Markets with an Expectile-Based Framework for the FTSE Index\" by Abiodun Finbarrs Oketunji, arXiv:2507.13391v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.04878v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|} \\hline\n$z$ & $a$ & supersingular primes \\\\ \\hline\n$\\left[17, 17, 17, 1\\right]$ & $\\left(1, 15, 1\\right), \\left(1, 14, 2\\right), \\left(1, 13, 3\\right)$ & $p \\not \\equiv 1 \\bmod{17}$ \\\\ \\hline\n$\\left[18, 18, 9, 1\\right]$ & $\\left(1, 13, 4\\right),\\left(1, 1, 16\\right)$ & $p \\equiv 2 \\bmod{3}$ \\\\ \\hline\n$\\left[20, 20, 5, 1\\right]$ & $\\left(1, 11, 8\\right)$ & $p \\equiv 2,3,4 \\bmod{5}$ \\\\ \n & $\\left(1, 7, 12\\right)$ & $p \\equiv 13, 17, 19 \\bmod{20}$ \\\\ \\hline\n$\\left[24, 24, 3, 1\\right]$ & $\\left(1, 7, 16\\right)$ & $p \\equiv 17, 23 \\bmod{24}$ \\\\ \\hline\n$\\left[32, 32, 2, 1\\right]$ & $\\left(1, 15, 16\\right)$ & $p \\not \\equiv 1,15 \\bmod{32}$ \\\\ \\hline\n$\\left[34, 17, 2, 1\\right]$ & $\\left(1, 16, 17\\right)$ & $p \\not \\equiv 1 \\bmod{34}$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Cyclic genus $8$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Supersingular curves via the Shimura--Taniyama method", "authors": ["Jeremy Booher", "Rachel Pries"], "url": "https://arxiv.org/abs/2503.04878v2", "attribution": "\"Supersingular curves via the Shimura--Taniyama method\" by Jeremy Booher and Rachel Pries, arXiv:2503.04878v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.10614v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\\hline\nSource & Acquisition Function & $u$ & $v$ & $w$ & $\\beta$ \\\\\n\\hline\n & EI & 0 & -- & 1 & 0 \\\\\n & PEI &0 & -- & 2 & 0 \\\\\n & SEI & 1/2 & -- & 1 & 0 \\\\\n & VEI & 0 & 1 & 1 & $<0$ \\\\\n-- & UEI & 0 & 1/2 & 1 & $>0$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The parameter settings of $u,v,w$ and $\\beta$ for recovering the different acquisition functions from the general form for the acquisition function family.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Understanding an Acquisition Function Family for Bayesian Optimization", "authors": ["Jiajie Kong", "Tony Pourmohamad", "Herbert K. H. Lee"], "url": "https://arxiv.org/abs/2310.10614v1", "attribution": "\"Understanding an Acquisition Function Family for Bayesian Optimization\" by Jiajie Kong, Tony Pourmohamad, and Herbert K. H. Lee, arXiv:2310.10614v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.00194v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Citation network graph statistics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc}\n\t\t\\toprule[\\heavyrulewidth]\\toprule[\\heavyrulewidth]\n\t\tDataset& Nodes & Edges & Features & Classes \\\\\n\t\t\\toprule[\\heavyrulewidth]\\toprule[\\heavyrulewidth]\n\t\t\\bf{Cora} & 2,708 & 5,429 & 1,433 & 7\\\\\n\t\t\\bf{Citseer} & 3,327 & 4,732 & 3,703 & 6\\\\\n\t\t\\bf{Pubmed} & 19,717 & 44,338 & 500 & 3\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference", "authors": ["Ali Lotfi Rezaabad", "Rahi Kalantari", "Sriram Vishwanath", "Mingyuan Zhou", "Jonathan Tamir"], "url": "https://arxiv.org/abs/2011.00194v2", "attribution": "\"Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference\" by Ali Lotfi Rezaabad, Rahi Kalantari, Sriram Vishwanath, Mingyuan Zhou, and Jonathan Tamir, arXiv:2011.00194v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10127v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\hline\n $n=30067$ & Predicted: Default & Predicted: Not default \\\\ \\hline\nActual: Default & 66 & 2072 \\\\ \\hline\nActual: Not default & 50 & 27879 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Confusion matrix for the logistic regression of the Kaggle dataset.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Interpretable Selective Learning in Credit Risk", "authors": ["Dangxing Chen", "Weicheng Ye", "Jiahui Ye"], "url": "https://arxiv.org/abs/2209.10127v1", "attribution": "\"Interpretable Selective Learning in Credit Risk\" by Dangxing Chen, Weicheng Ye, and Jiahui Ye, arXiv:2209.10127v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.19801v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Characteristics of Air Pollution Measuring Stations}\n\\begin{tabular}{lccccccccccccc}\n\t\t\t\t\\toprule \\toprule\n\t\t\t\t& \\multicolumn{2}{c}{Air Pollution Measured} \\\\\n\t\t\t\t\\cline{2-3}\n\t\t\t\t& $PM_{2.5}$ & $PM_{10}$ \\\\\n\t\t\t\t\\cline{2-3}\n\t\t\t\t& (1) & (2) \\\\\n\t\t\t\t\\midrule\n\t\n\t\t\t\t\n\t\\textit{Panel A: Station type} \\\\\t\n\t\n\t\\hspace{2mm} Background & 0.826& 0.881\\\\\n\t\n\t\\hspace{2mm} Industry & 0.023& 0.030\\\\\n\t\n\t\\hspace{2mm} Traffic & 0.151& 0.089\\\\\t\t\t\t\t\n\t\n\t\\textit{Panel B: Station location} \\\\\t\n\t\n\t\\hspace{2mm} Urban area & 0.884& 0.875\\\\\n\t\n\t\\hspace{2mm} Sub-urban area & 0.093& 0.071\\\\\n\t\n\t\\hspace{2mm} Outside urban area & 0.023& 0.054\\\\\n\t\t\n\tNumber of observations & 86& 168\\\\\n\t\t\n\t\\bottomrule\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Effects of Air Pollution on Teenagers' Cognitive Performance: Evidence from School Leaving Examination in Poland", "authors": ["Agata Galkiewicz"], "url": "https://arxiv.org/abs/2506.19801v1", "attribution": "\"The Effects of Air Pollution on Teenagers' Cognitive Performance: Evidence from School Leaving Examination in Poland\" by Agata Galkiewicz, arXiv:2506.19801v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.09355v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overall Mean Log-likelihood For Different Models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\hline\n\\textit{Model} & \\textit{Mean log-likelihood}\\\\\n\\hline\nGMM-1 & -26078.15\\\\\nGMM-2 & -38514.32\\\\\nGMM-3 & -52431.62\\\\\nGMM-4 & -63794.70\\\\\nGMM-5 & -73508.10\\\\\nIGMM & \\textbf{-24871.78} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data", "authors": ["Weizhu Qian", "Fabrice Lauri", "Franck Gechter"], "url": "https://arxiv.org/abs/1911.09355v1", "attribution": "\"A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data\" by Weizhu Qian, Fabrice Lauri, and Franck Gechter, arXiv:1911.09355v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.19963v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\t\t\\hline\n\t\tDrug & Sign & Count \\\\ \n\t\t\\hline\n\t\tLapatinib & (--) & 48 \\\\ \n\t\tLapatinib & (+) & 40 \\\\ \n\t\tLapatinib & zero & 4088 \\\\ \n\t\tPLX-4720 & (--) & 58 \\\\ \n\t\tPLX-4720 & (+) & 29 \\\\ \n\t\tPLX-4720 & zero & 4089 \\\\ \n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Signs of the estimated coefficients from the 1-se rule.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Linear Shrinkage Convexification of Penalized Linear Regression With Missing Data", "authors": ["Seongoh Park", "Seongjin Lee", "Nguyen Thi Hai Yen", "Nguyen Phuoc Long", "Johan Lim"], "url": "https://arxiv.org/abs/2412.19963v2", "attribution": "\"Linear Shrinkage Convexification of Penalized Linear Regression With Missing Data\" by Seongoh Park, Seongjin Lee, Nguyen Thi Hai Yen, Nguyen Phuoc Long, and Johan Lim, arXiv:2412.19963v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06423v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The impact of word reduction ratio $\\alpha$ and the execution time of these models.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\t\t\\toprule\n\t\tWords Reduc- & \\multicolumn{2}{c}{CNSE} & \\multicolumn{2}{c}{CNSS} & \\multicolumn{2}{c}{Time per batch}\\\\\n\t\ttion Ratio $\\alpha$ & Acc & F1 & Acc & F1 & Train & Eval\\\\\n\t\t\\midrule\n\t\t0\\% & 84.11 & 82.17 & 91.04 & 91.07 & 1.73s & 0.42s \\\\\n\t\t5\\% & 85.68 & 83.65 & 90.70 & 90.73 & 1.58s & 0.37s\\\\\n\t\t10\\% & \\textbf{86.32} & \\textbf{84.55} & \\textbf{91.28} & \\textbf{91.39} & 1.33s & 0.31s\\\\\n\t\t20\\% & 82.55 & 79.66 & 90.25 & 90.21 & 1.07s & 0.21s\\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching", "authors": ["Liang Pang", "Yanyan Lan", "Xueqi Cheng"], "url": "https://arxiv.org/abs/2101.06423v2", "attribution": "\"Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching\" by Liang Pang, Yanyan Lan, and Xueqi Cheng, arXiv:2101.06423v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2312.08255v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dataset distribution by a corresponding disease.}\n\\begin{tabular}{llll}\n\\toprule\nDisease & Label & Number of Scans & Number of Patients \\\\\n\\midrule\nAge-related Macular Degeneration & AMD & 1231 & 421 \\\\\nDiabetic Macular Edema & DME & 147 & 107 \\\\\nEpiretinal Membrane & ERM & 155 & 71 \\\\\nNormal & NO & 332 & 110 \\\\\nRetinal Artery Occlusion & RAO & 22 & 11 \\\\\nRetinal Vein Occlusion & RVO & 101 & 50 \\\\\nVitreomacular Interface Disease & VID & 76 & 51 \\\\\n\\midrule\nTotal & & 2064 & 821 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods", "authors": ["Mikhail Kulyabin", "Aleksei Zhdanov", "Anastasia Nikiforova", "Andrey Stepichev", "Anna Kuznetsova", "Mikhail Ronkin", "Vasilii Borisov", "Alexander Bogachev", "Sergey Korotkich", "Paul A Constable", "Andreas Maier"], "url": "https://arxiv.org/abs/2312.08255v4", "attribution": "\"OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods\" by Mikhail Kulyabin, Aleksei Zhdanov, Anastasia Nikiforova, Andrey Stepichev, Anna Kuznetsova, Mikhail Ronkin, Vasilii Borisov, Alexander Bogachev, Sergey Korotkich, Paul A Constable, and Andreas Maier, arXiv:2312.08255v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.11150v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Heterogeneous Response of Investment to $\\epsilon^{X}_t$ \\\\ \\footnotesize Bank Leverage }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n & \\multicolumn{4}{c}{Firm investment - $\\Delta \\log k_{i,t}$ } \\\\\n & (1) & (2) & (3) & (4) \\\\ \\hline \\hline\n & & & & \\\\\n$\\left(\\tilde{l}_{i,t-1} - \\mathbb{E}_{i} \\left[ \\tilde{l}_{t}\\right] \\right) \\epsilon^{X}_t$ & -0.126 & -0.133 & -0.157* & -0.159* \\\\\n & (0.0826) & (0.0843) & (0.0805) & (0.0854) \\\\\n & & & & \\\\\nFirm FE & yes & yes & yes & yes \\\\\nSector-Time FE & yes & yes & yes & yes \\\\\nControl for Lagged Inv. & no & yes & yes & yes \\\\\nControl for Lagged Currency Mismatch & no & no & yes & yes \\\\\nControl for $l_{i,t-1}$ & no & no & no & yes \\\\\n & & & & \\\\\nObservations & 11,022 & 10,917 & 10,405 & 10,329 \\\\ \\hline \n$R^{2}$ & 0.148 & 0.153 & 0.155 & 0.165 \\\\ \\hline \\hline\n\\multicolumn{5}{c}{ Clustered standard errors in parentheses} \\\\\n\\multicolumn{5}{c}{ *** p$<$0.01, ** p$<$0.05, * p$<$0.1} \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity", "authors": ["Santiago Camara", "Sebastian Ramirez Venegas"], "url": "https://arxiv.org/abs/2209.11150v1", "attribution": "\"The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity\" by Santiago Camara and Sebastian Ramirez Venegas, arXiv:2209.11150v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.16292v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The historical background of the liner shipping industry.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cll}\n Timing / Period & Event & Related Study\\\\\\hline\n 1875 & the first liner shipping conference (The U.K.-Calcutta) established & \\\\\n & & \\\\\n 1916 & \\textbf{The Shipping Act of 1916} prohibited deferred rebate &\\\\\n 1918 & The end of WW1 & \\\\\n 1936 & U.S. Maritime Commission established &\\\\\n 1945 & The end of WW2 & \\\\\n 1956 & The world's first container ship sailed & \\\\\n 1961 & \\textbf{The 1961 Amendment} enacted & \\\\\n & \\textbf{Federal Maritime Commission (FMC)} established & \\\\\n & Technical Committee on Cargo Containers established (ISO/TC104) &\\\\\n 1964 & ISO adopted seven sizes of containers as ISO standards &\\\\\n \\hline\n 1966 & The first Full-container (FC) ship sailed the Transatlantic route &\\\\\n 1967 & The first FC ship sailed the Transpacific route &\\\\\n 1971 & The first FC ship sailed the Asia-Europe route& \\\\\n 1973 & Oil shock &\\\\\n 1974 & The UNCTAD Code of Conduct for Liner Conferences adopted &\\\\\n 1980 & \\textbf{Withdrawal of Sea-Land from shipping conferences}&\\\\\n 1983. April&The UNCTAD Code of Conduct for Liner Conferences accomplished &\\\\\n 1984. June & \\textbf{The Shipping Act of 1984} in the U.S. enacted, conference breakdown &\\\\\n & &\\\\\n & &\\\\\n 1985.Sep & Yen appreciation due to the Plaza Accord&\\\\\\hline\n 1991 & Strategic alliance boom started &\\\\\n 1998. Oct & \\textbf{The Ocean Shipping Reform Act (OSRA) of 1998}, effective in 1999 &\\\\\n &&\\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Unified Container Shipping Industry Data From 1966: Freight Rate, Shipping Quantity, Newbuilding, Secondhand, and Scrap Price", "authors": ["Takuma Matsuda", "Suguru Otani"], "url": "https://arxiv.org/abs/2211.16292v3", "attribution": "\"Unified Container Shipping Industry Data From 1966: Freight Rate, Shipping Quantity, Newbuilding, Secondhand, and Scrap Price\" by Takuma Matsuda and Suguru Otani, arXiv:2211.16292v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00173v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccc}\n\\toprule\nMetric & \\multicolumn{4}{c}{Top-1 Accuracy (\\%)} & & \\multicolumn{4}{c}{Seen-Unseen AUC (\\%)} \\\\ \\cmidrule(lr){2-5} \\cmidrule(l){7-10} \nDataset & \\multicolumn{2}{c}{CUB} & \\multicolumn{2}{c}{NAB} & & \\multicolumn{2}{c}{CUB} & \\multicolumn{2}{c}{NAB} \\\\\nSplit-Mode & Easy & Hard & Easy & Hard & & Easy & Hard & Easy & Hard \\\\ \\midrule\nCIZSL-v2+SeGC \\\\ \\cmidrule{1-1}\nStandard (SD) & 42.4 & \\textbf{16.4} & 35.2 & \\textbf{10.6} & & \\textbf{39.3} & \\textbf{14.9} & \\textbf{23.8} & \\textbf{7.5}\\\\\nSD+Norm($\\eta=1$) & 42.4 & 15.5 & \\textbf{35.4} & 10.0 & & 39.0 & 13.1 & 22.8 & 7.0\\\\\nSD+Norm($\\eta=3$) & 42.4 & 15.0 & 34.5 & 9.2 & & 39.1 & 13.2 & 23.3 & 6.7 \\\\\nSD+Norm($\\eta=5$) & \\textbf{42.5} & 15.1 & 33.7 & 10.3 & & 39.1 & 13.6 & 22.3 & 7.4\\\\\nSD+Norm($\\eta=10$) & 42.1 & 15.4 & 33.9 & 9.9 & & 39.2 & 13.3 & 22.3 & 6.8\\\\\nSD+Norm($\\eta=20$) & 42.0 & 15.3 & 34.0 & 10.4 & & 38.9 & 13.2 & 22.9 & 7.3\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Semantic guided categorizer with normalize-scale.} %The chosen hyperparameters for CUB\\&NAB easy\\&hard are 1, 0.0001, 0.1, 0.1, respectively. Then we test the performance with best hyperparameters.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "CIZSL++: Creativity Inspired Generative Zero-Shot Learning", "authors": ["Mohamed Elhoseiny", "Kai Yi", "Mohamed Elfeki"], "url": "https://arxiv.org/abs/2101.00173v2", "attribution": "\"CIZSL++: Creativity Inspired Generative Zero-Shot Learning\" by Mohamed Elhoseiny, Kai Yi, and Mohamed Elfeki, arXiv:2101.00173v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10981v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Segmentation accuracy of the SWTR-Unet depending on the usage of a pre- or untrained ResNet within the encoder path, as well as the presence of a transformer-based bottleneck. Stated are the average DSCs for liver and hepatic lesions.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\n$ $ & $DSC_{liver}$ & $DSC_{lesion}$ \\\\\n\\hline\npretrained ResNet & $0.98~\\pm~0.02$ & $0.81~\\pm~0.28$ \\\\\nuntrained-ResNet & $0.97~\\pm~0.03$ & $0.80~\\pm~0.29$ \\\\\n\\hline\nwith transformer bottleneck & $0.98~\\pm~0.02$ & $0.81~\\pm~0.28$ \\\\\nwithout transformer bottleneck & $0.93~\\pm~0.06$ & $0.58~\\pm~0.32$ \\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers", "authors": ["Georg Hille", "Shubham Agrawal", "Pavan Tummala", "Christian Wybranski", "Maciej Pech", "Alexey Surov", "Sylvia Saalfeld"], "url": "https://arxiv.org/abs/2201.10981v3", "attribution": "\"Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers\" by Georg Hille, Shubham Agrawal, Pavan Tummala, Christian Wybranski, Maciej Pech, Alexey Surov, and Sylvia Saalfeld, arXiv:2201.10981v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12729v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{WER impact of cleaning speech signals between certain SNR ranges, using a music source separator. End-to-end ConvNet GLU model is used without LM, and percentage of cleaned samples are reported.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\multirow{1}{*}{SNR} & \\multicolumn{2}{c}{Cleaned Samples [\\%] } & \n \\multicolumn{2}{c}{Test WER [\\%]} \\\\\n & 2018 & 2020 & 2018 & 2020 \\\\\n \n \\midrule\n \\multirow{1}{*}{$(-\\infty, \\infty)$} & \\multirow{1}{*}{100} & 100 & 37.50 & 53.53\\\\\n \\multirow{1}{*}{$(-\\infty, 10)$} & 25.97 & 34.22 & -0.05 & -0.87\\\\\n \\multirow{1}{*}{($-5$, $10$)} & 25.84 & 31.33 & -0.05 & -0.88\\\\\n \\multirow{1}{*}{($-5$, $5$)} & 5.14 & 11.88 & -0.07 & \\textbf{-1.03}\\\\\n \\multirow{1}{*}{($-5$, $8$)} & 14.95 & 22.11 & \\textbf{-0.08} & -0.97\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "BCN2BRNO: ASR System Fusion for Albayzin 2020 Speech to Text Challenge", "authors": ["Martin Kocour", "Guillermo Cámbara", "Jordi Luque", "David Bonet", "Mireia Farrús", "Martin Karafiát", "Karel Veselý", "Jan ''Honza'' Ĉernocký"], "url": "https://arxiv.org/abs/2101.12729v1", "attribution": "\"BCN2BRNO: ASR System Fusion for Albayzin 2020 Speech to Text Challenge\" by Martin Kocour, Guillermo Cámbara, Jordi Luque, David Bonet, Mireia Farrús, Martin Karafiát, Karel Veselý, and Jan ''Honza'' Ĉernocký, arXiv:2101.12729v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.15837v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Seasonal average of daily Temperature $[C^{\\circ}]$, NH$_3$, NO$_x$ and SO$_2$ emissions $[mg/m^2]$ and PM$_{2.5}$ concentrations $[\\mu g / m^3]$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n \\toprule\n Season & Temperature & NH$_3$ & NO$_x$ & SO$_2$ & PM$_{2.5}$ \\\\\n \\midrule\n Winter & 3.88 & 5.21 & 19.97 & 4.11 & 35.87 \\\\\n Spring & 12.67 & 17.45 & 11.33 & 2.98 & 16.54 \\\\\n Summer & 23.23 & 14.71 & 10.45 & 2.80 & 12.19 \\\\\n Autumn & 13.83 & 8.70 & 14.59 & 3.46 & 21.62 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Scenario analysis of livestock-related PM2.5 pollution based on a new heteroskedastic spatiotemporal model", "authors": ["Jacopo Rodeschini", "Alessandro Fassò", "Francesco Finazzi", "Alessandro Fusta Moro"], "url": "https://arxiv.org/abs/2310.15837v2", "attribution": "\"Scenario analysis of livestock-related PM2.5 pollution based on a new heteroskedastic spatiotemporal model\" by Jacopo Rodeschini, Alessandro Fassò, Francesco Finazzi, and Alessandro Fusta Moro, arXiv:2310.15837v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11891v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l}\nQuartile[\\#W/Tweet] & Overall & Train & Test \\\\\n\\hline\n0.25 & 12.0 & 13.0 & 12.0\\\\\n0.50 & 21.0 & 21.0 & 19.0\\\\\n0.75 & 34.0 & 34.0 & 31.0\\\\\n\\hline \n Quartile[\\#Ch/Tweet] & Overall & Train & Test \\\\\n\\hline\n0.25 & 70.0 & 72.0 & 67.0\\\\\n0.50 & 116.0 & 120.0 & 105.0\\\\\n0.75 & 194.0 & 198.0 & 174.0\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Quartile-wise Word and Character Count}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content", "authors": ["Shreya Gupta", "Parantak Singh", "Megha Sundriyal", "Md Shad Akhtar", "Tanmoy Chakraborty"], "url": "https://arxiv.org/abs/2101.11891v1", "attribution": "\"LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content\" by Shreya Gupta, Parantak Singh, Megha Sundriyal, Md Shad Akhtar, and Tanmoy Chakraborty, arXiv:2101.11891v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00746v5_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Implementation details of MetaVIM}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule \n Items & Details \\\\\n \\midrule\n Number of policy steps & 3600 \\\\ \n\tDiscount factor $\\gamma$ & 0.95 \\\\ \n\tPolicy minibatch & 16 \\\\\n\tmVAE minibatch & 25 \\\\\n\tValue loss coefficient & 0.5 \\\\ \n\tEntropy coefficient & 0.01 \\\\ \n\tELBO loss coefficient & 1.0 \\\\ \n\tLatent space dimensionality & 5 \\\\ \n\tAggregator hidden size & 64 \\\\\n\t\\midrule\n\tPolicy network & 2 hidden layers, \\\\\n\tarchitecture & 32 nodes each, \\\\\n\t & Tanh activations \\\\\n\t\\midrule\n\tPolicy network & Adam with learning rate 0.0007 \\\\\n\toptimizer & and epsilon 1e-5 \\\\\n\t\\midrule\n\t & FC layer with 40 nodes, \\\\\n\t & GRU with hidden size 64, \\\\ \n\tEncoder architecture & output layer with \\\\\n\t & 10 outputs ($\\mu$ and $\\sigma$), \\\\\n\t & ReLU activations \\\\\n\t\\midrule \n\t & 2 hidden layers, \\\\ \n\tTransition Decoder & 32 nodes each, \\\\ \n\tarchitecture & 25 outputs heads, \\\\ \n\t & ReLU activations \\\\\n\t\\midrule\n\t & 2 hidden layers, \\\\ \n\tReward Decoder & 32 nodes each, \\\\ \n\tarchitecture & 25 outputs heads, \\\\ \n\t & ReLU activations \\\\\n\t\\midrule\n\tmVAE optimizer & Adam with learning rate 0.001 \\\\\n\t & and epsilon 1e-5 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control", "authors": ["Liwen Zhu", "Peixi Peng", "Zongqing Lu", "Xiangqian Wang", "Yonghong Tian"], "url": "https://arxiv.org/abs/2101.00746v5", "attribution": "\"MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control\" by Liwen Zhu, Peixi Peng, Zongqing Lu, Xiangqian Wang, and Yonghong Tian, arXiv:2101.00746v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01434v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cccc|}\n\\hline\nYoung's modulus $E$ & Poisson ratio $\\nu$ & Density $\\rho$& Fracture energy $G_0$\\\\\n\\hline\n70 $GPa$&0.22&2500 $kg/m^3$& 8 $J/m^2$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mechanical properties for V-notched soda-lime glass specimens taken from .}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture", "authors": ["Yue Yu", "Huaiqian You", "Nathaniel Trask"], "url": "https://arxiv.org/abs/2101.01434v1", "attribution": "\"An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture\" by Yue Yu, Huaiqian You, and Nathaniel Trask, arXiv:2101.01434v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.05136v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc}\n\\hline\\hline \n& 0.0 & 1.0 & Accuracy & Macro Average & Weighted Average \\\\ \n\\hline\nPrecision & 0.98 & 0.65 & 0.89 & 0.82 & 0.92 \\\\\nRecall & 0.87 & 0.95 & 0.89 & 0.91 & 0.89 \\\\\nf1-score & 0.93 & 0.77 & 0.89 & 0.85 & 0.90 \\\\\n\\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Test Evaluation Metrics for Logistic Regression}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Classification based credit risk analysis: The case of Lending Club", "authors": ["Aadi Gupta", "Priya Gulati", "Siddhartha P. Chakrabarty"], "url": "https://arxiv.org/abs/2210.05136v1", "attribution": "\"Classification based credit risk analysis: The case of Lending Club\" by Aadi Gupta, Priya Gulati, and Siddhartha P. Chakrabarty, arXiv:2210.05136v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.11135v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{An overview of the datasets used in this work.}\n\\begin{tabular}{lllll}\n\\toprule\n\\multirow{2}{*}{\\textbf{Dataset}} &\\textbf{Resolution} & \\textbf{Train-set} & \\textbf{Val-set} & \\textbf{Test-set} \\\\ \n &\\textbf{\\# Images} & \\textbf{(\\%)} & \\textbf{(\\%)} & \\textbf{(\\%)} \\\\ \n\\midrule\n\\multirow{2}{*}{\\textbf{TCIA }}& 512 x 512 & 407 & 58& 117 \\\\ \n& 582 & (70\\%) & (10\\%) & (20\\%) \\\\ \n\\midrule\n\\multirow{2}{*}{\\textbf{VHBD }}& 512 x 512 & \\multirow{2}{*}{--} & \\multirow{2}{*}{--} & 167 \\\\ \n& 167 & & & (100\\%) \\\\ \n\\midrule\n\\multirow{2}{*}{\\textbf{VHBD-2 }}& 512 x 512 & 116 & 17 & 34 \\\\ \n& 167 & (70\\%) & (10\\%) & (20\\%) \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Boosting Segmentation Performance across datasets using histogram specification with application to pelvic bone segmentation", "authors": ["Prabhakara Subramanya Jois", "Aniketh Manjunath", "Thomas Fevens"], "url": "https://arxiv.org/abs/2101.11135v1", "attribution": "\"Boosting Segmentation Performance across datasets using histogram specification with application to pelvic bone segmentation\" by Prabhakara Subramanya Jois, Aniketh Manjunath, and Thomas Fevens, arXiv:2101.11135v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.02011v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{List of companies selected by sector and corresponding ticker symbols}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{Sector} & \\textbf{Company} & \\textbf{Ticker Symbol} \\\\\n\\hline\n\\multirow{5}{*}{Financial Services} \n& HDFC Bank & HDFCBANK \\\\\n& ICICI Bank & ICICIBANK \\\\\n& State Bank of India & SBIN \\\\\n& Axis Bank & AXISBANK \\\\\n& Kotak Mahindra Bank & KOTAKBANK \\\\\n\\hline\n\\multirow{5}{*}{Information Technology} \n& Tata Consultancy Services & TCS \\\\\n& Infosys & INFY \\\\\n& Wipro & WIPRO \\\\\n& HCL Technologies & HCLTECH \\\\\n& Tech Mahindra & TECHM \\\\\n\\hline\n\\multirow{5}{*}{Consumer Goods} \n& Hindustan Unilever & HINDUNILVR \\\\\n& Nestlé India & NESTLEIND \\\\\n& ITC Limited & ITC \\\\\n& Dabur India & DABUR \\\\\n& Britannia Industries & BRITANNIA \\\\\n\\hline\n\\multirow{5}{*}{Energy} \n& Reliance Industries & RELIANCE \\\\\n& NTPC Limited & NTPC \\\\\n& Indian Oil Corporation & IOC \\\\\n& Bharat Petroleum Corporation & BPCL \\\\\n& Tata Power & TATAPOWER \\\\\n\\hline\n\\multirow{5}{*}{Pharmaceuticals} \n& Sun Pharmaceutical Industries & SUNPHARMA \\\\\n& Dr. Reddy’s Laboratories & DRREDDY \\\\\n& Cipla & CIPLA \\\\\n& Lupin Limited & LUPIN \\\\\n& Aurobindo Pharma & AUROPHARMA \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios", "authors": ["Vidya Sagar G", "Shifat Ali", "Siddhartha P. Chakrabarty"], "url": "https://arxiv.org/abs/2507.02011v1", "attribution": "\"Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios\" by Vidya Sagar G, Shifat Ali, and Siddhartha P. Chakrabarty, arXiv:2507.02011v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.09449v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n\t\t& MAFDS \\\\ \n\t\tcomputer model 1 &$ 0.00084 $ \\\\ \n\t\tcomputer model 2 &$ 0.00152 $ \\\\ \n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{ Value of the area validation metric for both computer models of the MAFDS.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Uncertainty Quantification in Case of Imperfect Models: A Review", "authors": ["Sebastian Kersting", "Michael Kohler"], "url": "https://arxiv.org/abs/2012.09449v1", "attribution": "\"Uncertainty Quantification in Case of Imperfect Models: A Review\" by Sebastian Kersting and Michael Kohler, arXiv:2012.09449v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.16078v5_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\t\t\\hline\n\t\tModel & SSIM & PSNR \\\\\n\t\t\\hline\n\t\t\\hline\n\t\t\\multicolumn{3}{|c|}{\\textbf{Viseme Corruption for Viseme}} \\\\\n\t\t\\hline\n\t\t\n\t\t\\textbf{FCN3D} & 0.8385 & 18.5201 \\\\\n\t\t\\hline\n\t\t\\textbf{BDLSTM} & 0.7269 & 17.2827 \\\\\n\t\t\\hline\n\t\t\\textbf{FCN3D + ROI} & \\textbf{0.9106} & \\textbf{26.5401} \\\\\n\t\t\\hline \n\t\t\\textbf{Super SloMo} & \\textbf{0.9129} & \\textbf{25.2605} \\\\\n\t\t\\hline\n\t\t\\hline\n\t\t\\multicolumn{3}{|c|}{\\textbf{Word Level Corruption}} \\\\\n\t\t\\hline\n\t\t\\textbf{FCN3D} & 0.8109 & 18.4554 \\\\\n\t\t\\hline\n\t\t\\textbf{BDLSTM} & 0.7411 & 18.066 \\\\\n\t\t\\hline\n\t\t\\textbf{FCN3D + ROI} & \\textbf{0.8804} & \\textbf{25.3279}\\\\\n\t\t\\hline\n\t\t\\textbf{Super SloMo} & \\textbf{0.8800} & \\textbf{24.5971} \\\\\n\t\t\\hline\n\t\t\\hline\n\t\t\\multicolumn{3}{|c|}{\\textbf{Multi Word Corruption}} \\\\\n\t\t\\hline\n\t\t\\textbf{FCN3D} & 0.8064 & 19.6111 \\\\\n\t\t\\hline\n\t\t\\textbf{BDLSTM} & 0.7411 & 18.0665 \\\\\n\t\t\\hline\n\t\t\\textbf{FCN3D + ROI} & \\textbf{0.8308} & \\textbf{23.1354}\\\\\n\t\t\\hline\n\t\t\n\t\t\\textbf{Super SloMo} & \\textbf{0.8481} & \\textbf{23.8295}\\\\\n\t\t\\hline\n\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Metrics obtained on the newly introduced challenge datasets. It can be seen that even the simpler models trained with ROI unit embedding give results close to the baseline (Super SloMo).More details can be found in Table in appendix.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "LIFI: Towards Linguistically Informed Frame Interpolation", "authors": ["Aradhya Neeraj Mathur", "Devansh Batra", "Yaman Kumar", "Rajiv Ratn Shah", "Roger Zimmermann"], "url": "https://arxiv.org/abs/2010.16078v5", "attribution": "\"LIFI: Towards Linguistically Informed Frame Interpolation\" by Aradhya Neeraj Mathur, Devansh Batra, Yaman Kumar, Rajiv Ratn Shah, and Roger Zimmermann, arXiv:2010.16078v5, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.17563v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results for Dialectal Speech Generation}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\toprule\n \\multirow{2}{*}{\\textbf{Method}} & \n \\multicolumn{5}{c}{\\textbf{ASR Word Error Rate (\\%) $\\downarrow$}} \\\\\n \\cmidrule(r){2-6}\n & \\textbf{Cantonese} & \\textbf{Northeastern} \n & \\textbf{Henan} & \\textbf{Shanghainese} & \\textbf{Sichuanese} \\\\\n \\midrule\n \\textbf{Zipformer} & 89.54 & 13.63 & 61.91 & 76.81 & 41.53 \\\\\n \\midrule\n \\textbf{+ GOAT-TTS} & 26.48 & 6.52 & 24.19 & 17.34 & 14.36 \\\\\n \\textbf{+ GT} & 23.83 & 5.55 & 21.16 & 15.60 & 11.50 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "BoSS: Beyond-Semantic Speech", "authors": ["Qing Wang", "Zehan Li", "Hang Lv", "Hongjie Chen", "Yaodong Song", "Jian Kang", "Jie Lian", "Jie Li", "Yongxiang Li", "Zhongjiang He", "Xuelong Li"], "url": "https://arxiv.org/abs/2507.17563v1", "attribution": "\"BoSS: Beyond-Semantic Speech\" by Qing Wang, Zehan Li, Hang Lv, Hongjie Chen, Yaodong Song, Jian Kang, Jie Lian, Jie Li, Yongxiang Li, Zhongjiang He, and Xuelong Li, arXiv:2507.17563v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.05862v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Test Pair} & \\(\\Delta\\mathbf{Rho}\\) & \\(\\mathbf{p}\\) \\\\\n\\midrule\nBaseline vs.\\ +Personalized & 0.200 & 0.233 \\\\\n\\textbf{Baseline vs.\\ +Personalized (Successful)}$^\\ddagger$ & 0.371 & \\textbf{0.035} \\\\\n\\textbf{+Personalized (Successful) vs.\\ +Personalized (Unsuccessful)}$^\\ddagger$ & 0.709 & \\textbf{0.008} \\\\\n\\midrule\n+Extroverted vs.\\ +Conscientious & 0.138 & 0.386 \\\\\n\\textbf{+Extroverted (Successful) vs.\\ +Extroverted (Unsuccessful)}$^\\ddagger$ & 0.529 & \\textbf{0.046} \\\\\n+Conscientious (Successful) vs.\\ +Conscientious (Unsuccessful) & 0.390 & 0.142 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Are Generative AI Agents Effective Personalized Financial Advisors?", "authors": ["Takehiro Takayanagi", "Kiyoshi Izumi", "Javier Sanz-Cruzado", "Richard McCreadie", "Iadh Ounis"], "url": "https://arxiv.org/abs/2504.05862v2", "attribution": "\"Are Generative AI Agents Effective Personalized Financial Advisors?\" by Takehiro Takayanagi, Kiyoshi Izumi, Javier Sanz-Cruzado, Richard McCreadie, and Iadh Ounis, arXiv:2504.05862v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.11164v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Result of experiment - dataset Depresjon with epoch=10, n\\_layers=2}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{Missing rate} & \\textbf{MICE + RF} & \\textbf{BRITS} & \\textbf{Transformer} & \\textbf{SAITS} \\\\ \\hline\n10.0 & 0.286 & 0.587 & 0.866 & 0.499 \\\\ \\hline\n20.0 & 0.301 & 0.583 & 0.811 & 0.509 \\\\ \\hline\n30.0 & 0.313 & 0.605 & 0.762 & 0.507 \\\\ \\hline\n40.0 & 0.324 & 0.623 & 0.833 & 0.522 \\\\ \\hline\n50.0 & 0.342 & 0.603 & 0.792 & 0.514 \\\\ \\hline\n60.0 & 0.363 & 0.629 & 0.838 & 0.527 \\\\ \\hline\n70.0 & 0.395 & 0.624 & 0.821 & 0.538 \\\\ \\hline\n80.0 & 0.446 & 0.610 & 0.824 & 0.545 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Missing data imputation for noisy time-series data and applications in healthcare", "authors": ["Lien P. Le", "Xuan-Hien Nguyen Thi", "Thu Nguyen", "Michael A. Riegler", "Pål Halvorsen", "Binh T. Nguyen"], "url": "https://arxiv.org/abs/2412.11164v1", "attribution": "\"Missing data imputation for noisy time-series data and applications in healthcare\" by Lien P. Le, Xuan-Hien Nguyen Thi, Thu Nguyen, Michael A. Riegler, Pål Halvorsen, and Binh T. Nguyen, arXiv:2412.11164v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23211v2_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of the model for Scenario II with $T=500$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccccc}\n\\hline\nCoefficient & Truth & AB ($\\lfloor T\\hat{\\tau} \\rfloor$) & AB ($\\lfloor T\\tilde{\\tau} \\rfloor$) & RMSE ($\\lfloor T\\hat{\\tau} \\rfloor$) & RMSE ($\\lfloor T\\tilde{\\tau} \\rfloor$) & 90$\\%$ CP \n($\\lfloor T\\tilde{\\tau} \\rfloor$) & 95$\\%$ CP ($\\lfloor T\\tilde{\\tau} \\rfloor$) & 99$\\%$ CP ($\\lfloor T\\tilde{\\tau} \\rfloor$) \\\\\\hline\\hline\n& $\\lfloor T/3 \\rfloor$ & 7.440 & 7.400 & 13.129 & 13.148 & 0.980 & 0.990 & 0.990 \\\\\n$\\theta=0.9$ & $\\lfloor T/2 \\rfloor$ & 7.050 & 6.100 & 12.308 & 11.217 & 0.970 & 0.980 & 1.000 \\\\\n$\\phi=0.1$ & $\\lfloor 2T/3 \\rfloor$ & 5.910 & 5.140 & 9.692 & 8.445 & 0.950 & 0.960 & 0.990 \\\\\n& $\\lfloor 4T/5 \\rfloor$ & 7.380 & 6.340 & 11.688 & 10.288 & 0.900 & 0.940 & 0.990 \\\\\n\\hline\\hline\n& $\\lfloor T/3 \\rfloor$ & 12.040 & 11.970 & 22.967 & 23.012 & 0.970 & 0.970 & 0.980 \\\\\n$\\theta=0.9$ & $\\lfloor T/2 \\rfloor$ & 10.090 & 9.550 & 17.545 & 17.122 & 0.960 & 0.990 & 1.000 \\\\\n$\\phi=0.3$ & $\\lfloor 2T/3 \\rfloor$ & 8.420 & 7.110 & 13.646 & 11.904 & 0.950 & 0.960 & 0.990 \\\\\n& $\\lfloor 4T/5 \\rfloor$ & 10.100 & 9.100 & 16.666 & 15.381 & 0.930 & 0.940 & 0.960 \\\\\n\\hline\\hline\n& $\\lfloor T/3 \\rfloor$ & 16.030 & 16.140 & 31.987 & 32.021 & 0.920 & 0.950 & 0.980 \\\\\n$\\theta=0.9$ & $\\lfloor T/2 \\rfloor$ & 13.340 & 12.300 & 20.712 & 20.175 & 0.950 & 0.980 & 0.980 \\\\\n$\\phi=0.5$ & $\\lfloor 2T/3 \\rfloor$ & 12.600 & 11.350 & 22.094 & 20.954 & 0.900 & 0.930 & 0.950 \\\\\n& $\\lfloor 4T/5 \\rfloor$ & 14.930 & 13.980 & 29.411 & 28.339 & 0.880 & 0.890 & 0.930 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models", "authors": ["Sepideh Mosaferi", "Abolfazl Safikhani", "Peiliang Bai"], "url": "https://arxiv.org/abs/2503.23211v2", "attribution": "\"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models\" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11182v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sample table title}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\toprule\n \\multicolumn{2}{c}{Part} \\\\\n \\cmidrule(r){1-2}\n Name & Description & Size ($\\mu$m) \\\\\n \\midrule\n Dendrite & Input terminal & $\\sim$100 \\\\\n Axon & Output terminal & $\\sim$10 \\\\\n Soma & Cell body & up to $10^6$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Hyperparameter Tuning for Deep Reinforcement Learning Applications", "authors": ["Mariam Kiran", "Melis Ozyildirim"], "url": "https://arxiv.org/abs/2201.11182v1", "attribution": "\"Hyperparameter Tuning for Deep Reinforcement Learning Applications\" by Mariam Kiran and Melis Ozyildirim, arXiv:2201.11182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.05415v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The power of the hypothesis testing with different $\\tau$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\n $\\tau$&$H(\\tau)$ &Power (\\%) \\\\ \\hline\n 0.28&5.5405 &56.28 \\\\ \\hline\n 0.29&5.53692 &94.556 \\\\ \\hline\n 0.3&5.53237 &99.915 \\\\ \\hline\n 0.31&5.52688 &100 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Variance of entropy for testing time-varying regimes with an application to meme stocks", "authors": ["Andrey Shternshis", "Piero Mazzarisi"], "url": "https://arxiv.org/abs/2211.05415v3", "attribution": "\"Variance of entropy for testing time-varying regimes with an application to meme stocks\" by Andrey Shternshis and Piero Mazzarisi, arXiv:2211.05415v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.14431v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\hline \\hline\n\\textbf{Sector} & \\textbf{Mean $WD_2$} & \\textbf{Max $WD_2$} \\\\\n\\hline\nIndustrials & 0.07 & 0.17 \\\\\nEnergy & 0.07 & 0.26 \\\\\nFinancial & 0.05 & 0.16 \\\\\nHealthcare & 0.06 & 0.15 \\\\\nConsumer Discretionary & 0.08 & 0.21 \\\\\nConsumer Staples & 0.05 & 0.13 \\\\\nReal Estate & 0.07 & 0.24 \\\\\nUtilities & 0.05 & 0.16 \\\\\nMaterials & 0.06 & 0.19 \\\\\nInformation Technology & 0.07 & 0.20 \\\\\nCommunications Services & 0.08 & 0.15 \\\\\n\\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective", "authors": ["Buddha Nath Sharma", "Anish Rai", "SR Luwang", "Md. Nurujjaman", "Sushovan Majhi"], "url": "https://arxiv.org/abs/2502.14431v1", "attribution": "\"Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective\" by Buddha Nath Sharma, Anish Rai, SR Luwang, Md. Nurujjaman, and Sushovan Majhi, arXiv:2502.14431v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10708v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrl}\n\\hline \\textbf{Type of Text} & \\textbf{Font Size} & \\textbf{Style} \\\\ \\hline\npaper title & 15 pt & bold \\\\\nauthor names & 12 pt & bold \\\\\nauthor affiliation & 12 pt & \\\\\nthe word ``Abstract'' & 12 pt & bold \\\\\nsection titles & 12 pt & bold \\\\\nsubsection titles & 11 pt & bold \\\\\ndocument text & 11 pt &\\\\\ncaptions & 10 pt & \\\\\nabstract text & 10 pt & \\\\\nbibliography & 10 pt & \\\\\nfootnotes & 9 pt & \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Font guide. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Few-Shot Semantic Parsing for New Predicates", "authors": ["Zhuang Li", "Lizhen Qu", "Shuo Huang", "Gholamreza Haffari"], "url": "https://arxiv.org/abs/2101.10708v1", "attribution": "\"Few-Shot Semantic Parsing for New Predicates\" by Zhuang Li, Lizhen Qu, Shuo Huang, and Gholamreza Haffari, arXiv:2101.10708v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.12160v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The size of the EV charging station transformer}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\t\t\t\\hline\n\t\t\t \t & \\multicolumn{3}{|c|}{EV transformer size (kVA)}\\\\\n\t\t\t \\hline\n\t\t\tYear \t& Uncoordinated charging & Approach 1\t& Approach 2 \\\\\n\t\t\t\\hline\n\t\t\t\\hline\n 2027 & 452.7\t& 66.9 & 410.1\\\\ \n 2032\t& 898.7\t& 199.2 & 923.3\\\\\n 2037\t& 1,404.5\t& 380.2 & 1562.8 \\\\\n 2042 & 1,705.1\t& 528.9 & 2041.5\\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Design and Optimization of EV Charging Infrastructure with Battery in Commercial Buildings", "authors": ["Quan Nguyen", "Christine Holland", "Siddharth Sridhar"], "url": "https://arxiv.org/abs/2509.12160v1", "attribution": "\"Design and Optimization of EV Charging Infrastructure with Battery in Commercial Buildings\" by Quan Nguyen, Christine Holland, and Siddharth Sridhar, arXiv:2509.12160v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.08281v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccc}\n \\toprule\n Model TP & FN & TP & FN & TP & FN & TP \\\\\n & \\multicolumn{2}{c}{T $\\ge$ 1.3} & \\multicolumn{2}{c}{T $\\ge$ 1.2} & \\multicolumn{2}{c}{T $\\ge$ 1.1} \\\\\n \n \\midrule\n GARCH &0 &1 &0 &7 &0 &29 \\\\\n \n LSTM &1 &0 &2 &5 &4 &25 \\\\\n \n Transformer (V) &1 &0 &4 &3 &6 &23 \\\\\n \n Synthesizer (R) &1 &0 &5 &2 &18 &11 \\\\\n \n Synthesizer (FR) &1 &0 &6 &1 &21 &8 \\\\\n \n Synthesizer (D) &1 &0 &6 &1 &17 &12 \\\\\n \n Synthesizer (FD) &1 &0 &6 &1 &21 &8 \\\\\n \n Synthesizer (MD) &1 &0 &4 &3 &15 & 14 \\\\\n \n Synthesizer (MR) &1 &0 &4 &3 &16 &13 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{True positive (TP) and false negative (FN) when predicting extreme volatility spikes with different thresholds. We use R for random, F for factorised, M for mixed, D for dense, and V for Vanilla models.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models", "authors": ["Dorien Herremans", "Kah Wee Low"], "url": "https://arxiv.org/abs/2211.08281v1", "attribution": "\"Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models\" by Dorien Herremans and Kah Wee Low, arXiv:2211.08281v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.09598v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Robustness checks: Coefficients for the impact of average variability of ancestral climatic conditions on individual's self-reported attention to the environment}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\hline\n \\multicolumn{6}{c}{Dependent variable: Self reported measure of attention to environment}\\\\\n \\hline\n \\hline\n Avg. variability & -9.877***& -9.243***& -8.903***& -9.897***& -6.649***\\\\\n & (2.003) & (1.988) & (2.206) & (2.011) & (2.023)\\\\\n Avg. variability sq.& 111.767***& 107.503***& 103.956***& 111.862***& 71.929***\\\\\n & (21.346) & (21.557) & (25.184) & (21.505) & (22.402)\\\\\n Income & -0.001* & -0.001+ & -0.002+ & -0.001+ & -0.001+ \\\\\n & (0.001) & (0.001) & (0.001) & (0.001) & (0.001)\\\\\n Male & -0.013***& -0.012***& -0.014***& -0.013***& -0.014***\\\\\n & (0.003) & (0.004) & (0.004) & (0.003) & (0.003)\\\\\n Age & 0.002***& 0.002***& 0.002***& 0.001***& 0.002***\\\\\n & (0.000) & (0.000) & (0.000) & (0.000) & (0.000)\\\\\n Education level & 0.006***& 0.006***& 0.007***& 0.006***& 0.007***\\\\\n & (0.001) & (0.001) & (0.001) & (0.001) & (0.001)\\\\\n Occupation category Controls & Y & Y & Y & Y & Y \\\\\n Historical ethnic group characteristics & Y & Y & Y & Y & Y \\\\\n Historical topographic characteristics & Y & Y & Y & Y & Y \\\\\n Country-year Fixed effects & Y & Y & Y & Y & N \\\\\n Region-year Fixed effects & N & N & N & N & Y \\\\\n \\multicolumn{6}{l}{\\textit{Sample restrictions}}\\\\\n English speakers excluded & N & Y & Y & N & N \\\\\n Spanish speakers excluded & N & Y & Y & N & N\\\\\n Arabic speakers excluded & N & N & Y & N & N\\\\\n Lang. at home only & N & N & N & Y & N \\\\\n \\hline\n Mean of dep var & 0.704 & 0.700 & 0.689 & 0.703 & 0.705 \\\\\n St. Dev. of dep var & 0.250 & 0.251 & 0.249 & 0.251 & 0.250 \\\\\n Min value Avg. Variability& 0.015 & 0.015 & 0.015 & 0.015 & 0.015 \\\\\n Max value Avg. Variability& 0.093 & 0.093 & 0.093 & 0.093 & 0.093 \\\\\n R-sq & 0.113 & 0.122 & 0.118 & 0.114 & 0.160 \\\\\n Adj. R-sq & 0.113 & 0.121 & 0.117 & 0.113 & 0.151 \\\\\n N & 138067 & 110484 & 93184 & 128532 & 135228 \\\\\n \\hline\n \\multicolumn{6}{c}{ {*}{*}{*} p$<$0.01, {*}{*}p$<$0.05, {*} p$<$0.1, {+} p$<$0.15}\\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Ancestral origins of attention to environmental issues", "authors": ["César Barilla", "Palaash Bhargava"], "url": "https://arxiv.org/abs/2509.09598v1", "attribution": "\"Ancestral origins of attention to environmental issues\" by César Barilla and Palaash Bhargava, arXiv:2509.09598v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.00851v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Descriptive statistics of realized volatility and other variables}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc} \n\\toprule\n{Variables} &\n {Mean} &\n {Standard Deviation} &\n {Skewness} &\n {Kurtosis} &\n {Maximum} &\n {Minimum} &\n {ADF} &\n {Ljung-Box(10)} \\\\ \n\\midrule\nRV & $0.201$ & $0.399$ & $7.857$ & $95.914$ & $7.175$ & $0.007$ & $-8.798^{***}$ & $7873.7^{***}$ \\\\\nCJ & $0.849$ & $0.043$ & $16.654$ & $369.009$ & $1.131$ & $0.000$ &$-12.424^{***}$ & $137.6^{***}$ \\\\\nCV & $0.142$ & $0.243$ & $5.828$ & $55.344$ & $3.898$ & $0.005$ &$-7.314^{***}$ & $11988^{***}$ \\\\ \n$\\mathrm{RS}^{+}$ & $0.099$ & $0.223$ & $14.884$ & $345.431$ & $6.543$ & $0.002$ &$-10.206^{***}$ & $399.7^{***}$ \\\\\n$\\mathrm{RS}^{-}$ & $0.106$ & $0.248$ & $11.156$ & $220.296$ & $7.079$ & $0.000$ &$-8.979^{***}$ & $3803.4^{***}$ \\\\ \n$\\mathrm{REX}^{+}$ & $0.035$ & $0.076$ & $6.872$ &$76.892$ & $1.447$ & $0.000$ &$-7.619^{***}$ & $6809.6^{***}$ \\\\ \n$\\mathrm{REX}^{-}$ & $0.038$ & $0.083$ &$8.627$&$111.056$ & $1.546$ & $0.000$ &$-9.561^{***}$ &$6320.3^{***}$\\\\\n$\\mathrm{REX}^{m}$ & $0.077$ & $0.132$ & $6.379$ & $71.830$ & $2.556$ & $0.000$ & $-7.341^{***}$ & $12713^{***}$ \\\\ \n$\\mathrm{REXQ}^{+}$ &$0.041$& $0.081$& $8.549$&$114.839$&$0.002$&$0.000$& $-8.857^{***}$&$5057.1^{***}$\\\\\n$\\mathrm{REXQ}^{-}$ &$0.039$&$0.079$&$6.930$&$72.924$&$1.45$&$0.000$&$-7.487^{***}$&$4623.4^{***}$\\\\\n$\\mathrm{REXQ}^{m}$ &$0.069$ &$0.117$ &$6.054$&$60.071$&$2.123$&$0.000$&$-7.194^{***}$&$11575^{***}$\\\\\n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Forecasting realized volatility in the stock market: a path-dependent perspective", "authors": ["Xiangdong Liu", "Sicheng Fu", "Shaopeng Hong"], "url": "https://arxiv.org/abs/2503.00851v1", "attribution": "\"Forecasting realized volatility in the stock market: a path-dependent perspective\" by Xiangdong Liu, Sicheng Fu, and Shaopeng Hong, arXiv:2503.00851v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.19624v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n & value & \\# nnz MacLaurin \\\\ \\hline \n $\\pi_3$ & $3.533277500570900$ & 22 \\\\\n $\\pi_4$ & $3.708149354602744$ & 34 \\\\\n $\\pi_5$ & $3.800600555953747$ & 46 \\\\\n $\\pi_6$ & $3.855242593319996$ & 58 \\\\\n $\\pi_7$ & $3.890174737625689$ & 69 \\\\\n $\\pi_8$ & $3.913843287813181$ & 81 \\\\\n $\\pi_9$ & $3.930614378886605$ & 92 \\\\\n $\\pi_{10}$ & $3.942927897810032$ & 103\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Several values of $\\pi_p$ and the number of nonzero MacLaurin coefficients needed to reach machine precision.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Derivative polynomials and infinite series for squigonometric functions", "authors": ["Bart S. van Lith"], "url": "https://arxiv.org/abs/2503.19624v1", "attribution": "\"Derivative polynomials and infinite series for squigonometric functions\" by Bart S. van Lith, arXiv:2503.19624v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.00530v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crr}\n Model & MSPE(sd) & Mean No. Parameters \\\\\n \\hline\n GNAR(1, [1]) & 7.4(2.98) & 2 \\\\\n Sparse VAR(1) & 11.4(2.80) & 3089 \\\\\n Res. VAR(1) & 10.6(2.48) & 3773 \\\\\n VAR(1) & 12.3(3.18) & 19600 \\\\\n AR(1) & 87.4(5.15) & 140\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "New tools for network time series with an application to COVID-19 hospitalisations", "authors": ["Guy Nason", "Daniel Salnikov", "Mario Cortina-Borja"], "url": "https://arxiv.org/abs/2312.00530v1", "attribution": "\"New tools for network time series with an application to COVID-19 hospitalisations\" by Guy Nason, Daniel Salnikov, and Mario Cortina-Borja, arXiv:2312.00530v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1910.11400v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Speaker clustering results using purity (\\%)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc} \\hline\n\\multirow{2}{*}{\\textbf{Method}} & \\multicolumn{2}{c}{\\textbf{ASD}} & \\multicolumn{2}{c}{\\textbf{ASD-Infants}} \\\\\n & \\textbf{K-Means} & \\textbf{SC} & \\textbf{K-Means} & \\textbf{SC} \\\\ \\hline\nx-vectors & 77.05 & 75.22 & 77.98 & 75.97 \\\\\nsiamese & 78.22 & 79.18 & 78.30 & 76.86 \\\\\nP20 & \\textbf{81.39} & \\textbf{80.70} & \\textbf{85.51} & \\textbf{85.55} \\\\\nP30 & 79.80 & 80.24 & 83.57 & 83.26 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Meta-learning for robust child-adult classification from speech", "authors": ["Nithin Rao Koluguri", "Manoj Kumar", "So Hyun Kim", "Catherine Lord", "Shrikanth Narayanan"], "url": "https://arxiv.org/abs/1910.11400v2", "attribution": "\"Meta-learning for robust child-adult classification from speech\" by Nithin Rao Koluguri, Manoj Kumar, So Hyun Kim, Catherine Lord, and Shrikanth Narayanan, arXiv:1910.11400v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.06243v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n \\Xhline{2\\arrayrulewidth}\n & \\multicolumn{4}{c}{\\textbf{GF2} Dataset (Reduced-Resolution)} \\\\\n \\hline\n Condition & SAM$\\downarrow$ & ERGAS$\\downarrow$ & SCC$\\uparrow$ & Q4$\\uparrow$\\\\\n \\hline\n DWT & 0.646 $\\pm$ 0.117 & 0.567 $\\pm$ 0.095 & 0.993 $\\pm$ 0.002 & 0.987 $\\pm$ 0.007 \\\\\n SWT & {\\color{red}{\\textbf{0.603 $\\pm$ 0.102}}} & {\\color{red}{\\textbf{0.537 $\\pm$ 0.077}}} & {\\color{red}{\\textbf{0.994 $\\pm$ 0.001}}} & {\\color{red}{\\textbf{0.988 $\\pm$ 0.006}}} \\\\\n \\Xhline{2\\arrayrulewidth}\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of results between DWT and SWT conditioning at the SWTCA block in FSA-T $\\Psi$, with the best values highlighted in red.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening", "authors": ["Sungpyo Kim", "Jeonghyeok Do", "Jaehyup Lee", "Munchurl Kim"], "url": "https://arxiv.org/abs/2412.06243v1", "attribution": "\"U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening\" by Sungpyo Kim, Jeonghyeok Do, Jaehyup Lee, and Munchurl Kim, arXiv:2412.06243v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04243v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc||cc|cc}\n\\multicolumn{2}{c}{} & \\multicolumn{2}{c|}{\\textbf{ViT-H}} & \\multicolumn{2}{c}{\\textbf{ViT-B}} \\\\\n\\toprule\n$a$ & $b$ & $\\tau$ & $\\rho$ & $\\tau$ & $\\rho$ \\\\\n\\midrule\n$63$ & $3$ & $0.63$ & $0.83$ & $0.57$ & $0.78$ \\\\\n$63$ & $7$ & $0.59$ & $0.79$ & $0.57$ & $0.77$ \\\\\n$63$ & $15$ & $0.54$ & $0.72$ & $0.50$ & $0.67$ \\\\\n$63$ & $31$ & $0.45$ & $0.62$ & $0.39$ & $0.56$ \\\\\n$127$ & $3$ & $0.58$ & $0.77$ & $0.45$ & $0.64$ \\\\\n$127$ & $7$ & $0.58$ & $0.77$ & $0.49$ & $0.66$ \\\\\n$127$ & $15$ & $0.51$ & $0.67$ & $0.42$ & $0.57$ \\\\\n$127$ & $31$ & $0.36$ & $0.50$ & $0.26$ & $0.39$ \\\\\n$255$ & $3$ & $0.53$ & $0.71$ & $0.42$ & $0.59$ \\\\ \n$255$ & $7$ & $0.53$ & $0.71$ & $0.42$ & $0.59$ \\\\ \n$255$ & $15$ & $0.47$ & $0.66$ & $0.36$ & $0.52$ \\\\\n$255$ & $31$ & $0.29$ & $0.43$ & $0.19$ & $0.29$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Rank correlation (Kendall $\\tau$ and Spearman $\\rho$) between SAM prediction IoU and object tree-likeness as measured by \\textbf{DoGD} with different hyperparameter values $a,b$ on \\textbf{DIS}, to supplement Table .}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects", "authors": ["Yixin Zhang", "Nicholas Konz", "Kevin Kramer", "Maciej A. Mazurowski"], "url": "https://arxiv.org/abs/2412.04243v2", "attribution": "\"Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects\" by Yixin Zhang, Nicholas Konz, Kevin Kramer, and Maciej A. Mazurowski, arXiv:2412.04243v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04765v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Adjusted Field Contribution received by papers 14 and 54.}\n\\begin{tabular}{cc|c}%\n {\\bf } & {\\bf } & {\\bf Adjusted} \\\\\n {\\bf Paper} & {\\bf Judge} & {\\bf Field Contribution} \\\\\\hline\n 14 & 35 & 1.33 \\\\\n 14 & 23 & 1.41 \\\\\n 14 & 48 & 1.33 \\\\\n 14 & 57 & 0.70 \\\\\n 14 & 44 & 0.71 \\\\\\hline\n 54 & 30 & 1.39 \\\\\n 54 & 32 & 0.76 \\\\\n 54 & 25 & 1.50 \\\\\n 54 & 22 & 1.47 \\\\\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Joint aggregation of cardinal and ordinal evaluations with an application to a student paper competition", "authors": ["Dorit S. Hochbaum", "Erick Moreno-Centeno"], "url": "https://arxiv.org/abs/2101.04765v1", "attribution": "\"Joint aggregation of cardinal and ordinal evaluations with an application to a student paper competition\" by Dorit S. Hochbaum and Erick Moreno-Centeno, arXiv:2101.04765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2302.00761v1_tex_table46.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{llr}\n\\hline\\hline\n&3-digit SIC Industry&Fraction\\tabularnewline\n\\hline\n1&Educational Services&$32.6\\%$\\tabularnewline\n2&Apparel \\& Accessory Stores&$31.8\\%$\\tabularnewline\n3&Non-Classifiable Establishments&$31.6\\%$\\tabularnewline\n4&Business Services&$27.7\\%$\\tabularnewline\n5&Instruments \\& Related Products&$24.9\\%$\\tabularnewline\n6&Leather&$23.9\\%$\\tabularnewline\n7&Pipelines, Except Natural Gas&$23.7\\%$\\tabularnewline\n8&Transportation Services&$23.1\\%$\\tabularnewline\n9&Chemical \\& Allied products&$22.5\\%$\\tabularnewline\n10&Engineering \\& Management Services&$22.5\\%$\\tabularnewline\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Zero-Leverage Puzzle", "authors": ["Mykola Pinchuk"], "url": "https://arxiv.org/abs/2302.00761v1", "attribution": "\"Zero-Leverage Puzzle\" by Mykola Pinchuk, arXiv:2302.00761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.05728v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c||} \n\\hline\nForecasting Method & MSE$_{\\text{pred}+\\text{phys}}$\\\\ \n\\hline\\hline\nPIDDRN & 0.133 (0.046) \\\\\n\\hline\nDDRN & 0.262 (0.167) \\\\\n\\hline\nPIDESN & 0.241 (0.130) \\\\\n\\hline\nDESN & 0.267 (0.168) \\\\\n\\hline\nARFIMA & 0.296 (0.121) \\\\\n\\hline\nLong Short-Term Memory & 0.275 (0.127) \\\\\n\\hline\nPersistence (Na\\\"ive) & 0.267 (0.119) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Forecasting performance for each model in terms of the median MSE$_{\\text{pred}+\\text{phys}}$ (IQR in parenthesis) across the 100 subsampled locations described in Section for the water field application data.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Physics-Informed, Deep Double Reservoir Network for Forecasting Boundary Layer Velocity", "authors": ["Matthew Bonas", "David H. Richter", "Stefano Castruccio"], "url": "https://arxiv.org/abs/2311.05728v3", "attribution": "\"A Physics-Informed, Deep Double Reservoir Network for Forecasting Boundary Layer Velocity\" by Matthew Bonas, David H. Richter, and Stefano Castruccio, arXiv:2311.05728v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05978v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Combined forecasts.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|} \t\t\\hline\n\t\t\t\tModel & CRPS & LogS & DSS & PL \\\\ \t\t\t \\hline\n\t\t\t\tGPR & 131.76 & 6.87 & 11.89 & 34.28 \\\\\n\t\t\t\tbstslong & 205.93 & Inf & 12.85 & 294.27 \\\\\n\t\t\t\tQRA & 143.79 & Inf & 12.50 & 43.45 \\\\\n\t\t\t\tQRNN & 150.14 & Inf & 12.53 & 38.23\\\\\n\t\t\t\tPLAQR & 146.80 & Inf & 12.53 & 43.15\\\\\n\t\t\t\tOPERA & 142.15 & Inf & 12.48 & 37.40\\\\ \t\t\\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Robust modelling framework for short-term forecasting of global horizontal irradiance", "authors": ["Edina Chandiwana", "Caston Sigauke", "Alphonce Bere"], "url": "https://arxiv.org/abs/2212.05978v1", "attribution": "\"Robust modelling framework for short-term forecasting of global horizontal irradiance\" by Edina Chandiwana, Caston Sigauke, and Alphonce Bere, arXiv:2212.05978v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.19858v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dataset distribution across four medical sources.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\n\\multirow{2}{*}{\\textbf{Source}} & \\multicolumn{2}{c}{\\textbf{Scan Counts}} & \\multicolumn{2}{c}{\\textbf{Slice Counts}} \\\\\n\\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n& \\textbf{COVID} & \\textbf{Non-COVID} & \\textbf{COVID} & \\textbf{Non-COVID} \\\\\n\\midrule\n0 & 218 & 209 & 59,697 & 57,898 \\\\\n1 & 218 & 210 & 10,213 & 13,802 \\\\\n2 & 39 & 210 & 10,771 & 61,129 \\\\\n3 & 217 & 210 & 38,573 & 35,916 \\\\\n\\midrule\nTesting & \\multicolumn{2}{c}{--} & \\multicolumn{2}{c}{--} \\\\\n\\midrule\n\\textbf{Total} & \\textbf{692} & \\textbf{839} & \\textbf{119,254} & \\textbf{168,745}\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization", "authors": ["Chia-Ming Lee", "Bo-Cheng Qiu", "Ting-Yao Chen", "Ming-Han Sun", "Fang-Ying Lin", "Jung-Tse Tsai", "I-An Tsai", "Yu-Fan Lin", "Chih-Chung Hsu"], "url": "https://arxiv.org/abs/2507.19858v1", "attribution": "\"Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization\" by Chia-Ming Lee, Bo-Cheng Qiu, Ting-Yao Chen, Ming-Han Sun, Fang-Ying Lin, Jung-Tse Tsai, I-An Tsai, Yu-Fan Lin, and Chih-Chung Hsu, arXiv:2507.19858v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12414v4_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dataset-dependent hyperparameters for VQ-VAE.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n \\toprule\n & MNIST & \\textsc{Omniglot} & \\textsc{CelebA} \\\\ \\midrule\n Batch Size & 32 & 32 & 64 \\\\\n \\# of Embeddings & 256 & 256 & 512 \\\\\n Hidden Units & 32 & 32 & 128 \\\\\n Training Steps & 60000 & 60000 & 100000 \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Posterior Matching for Arbitrary Conditioning", "authors": ["Ryan R. Strauss", "Junier B. Oliva"], "url": "https://arxiv.org/abs/2201.12414v4", "attribution": "\"Posterior Matching for Arbitrary Conditioning\" by Ryan R. Strauss and Junier B. Oliva, arXiv:2201.12414v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.03911v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Log-evidence function estimation bias (mean and standard deviation over 10 runs). The first rows are from our method, while the rest are from . Note that the last three methods are using importance-weighted bound $B_{\\rm RW}$. In bold font, we show the estimates that are unbiased at the one standard deviation level.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n Method&Gaussian mixture&Funnel\\\\\\midrule\n HMC & $-1.876\\pm0.527$ & $-0.835\\pm0.257$ \\\\\n SMC & $-0.362\\pm0.293$ & $-0.216\\pm0.157$ \\\\\n On-policy PIS-NN & $-1.192\\pm0.482$ & ${\\bf -0.018\\pm0.020}$ \\\\\n Off-policy GFlowNet TB & ${\\bf -0.003\\pm0.011}$ & $-0.026\\pm0.020$ \\\\\n On-policy GFlowNet TB & $-1.301\\pm0.434$ & ${\\bf -0.012\\pm0.108}$ \\\\\\midrule\n Ours & ${\\bf 0.029\\pm0.132}$ & ${\\bf -0.051 \\pm 0.353}$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Improving Gradient-guided Nested Sampling for Posterior Inference", "authors": ["Pablo Lemos", "Nikolay Malkin", "Will Handley", "Yoshua Bengio", "Yashar Hezaveh", "Laurence Perreault-Levasseur"], "url": "https://arxiv.org/abs/2312.03911v1", "attribution": "\"Improving Gradient-guided Nested Sampling for Posterior Inference\" by Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, Yashar Hezaveh, and Laurence Perreault-Levasseur, arXiv:2312.03911v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.03980v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n\\hline\n\\textbf{Model} & \\textbf{Size} \\\\\n\\hline\nACD & 5.5M \\\\\nACR-cloud & - \\\\\nPyannote & 31M \\\\\nVoiceFilter & 6.8M \\\\\nAQA-LLM & 7B \\\\\nWhisper & 39M \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Model Sizes}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Comprehensive Audio Query Handling System with Integrated Expert Models and Contextual Understanding", "authors": ["Vakada Naveen", "Arvind Krishna Sridhar", "Yinyi Guo", "Erik Visser"], "url": "https://arxiv.org/abs/2412.03980v1", "attribution": "\"Comprehensive Audio Query Handling System with Integrated Expert Models and Contextual Understanding\" by Vakada Naveen, Arvind Krishna Sridhar, Yinyi Guo, and Erik Visser, arXiv:2412.03980v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.12209v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of the selected datasets (we preprocess the datasets to remove the sequences containing one word and the punctuations at the end of the sequences), where Explained set is randomly sampled from the Test set to avoid high computational complexity and Average sequence length denotes the average sequence length of the instances in Explained set (the length of Yelp-2 explained set is controlled within 50 and we will provide these Explained sets in the public version).}\n\\begin{tabular}{lcccccc} \n\\toprule\nDatasets & Category & Train set & Test set & Explained set & Label set & Average sequence length \\\\ \n\\hline \nSST-2 & sentimental analysis & 6899 & 1819 & 1000 & 2 & 19.24 \\\\\nSNIPS & intent detection & 13082 & 700 & 700 & 7 & 9.08 \\\\\nSNLI & natural language inference & 54936 & 7824 & 1000 & 3 & 21.22 \\\\\nYelp-2 & sentimental analysis & 559694\n & 37981 & 200 & 2 & 32.12 \\\\\n20Newsgroup & topic labeling & 10663 & 7019 & 300 & 20 & 143.28 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Suboptimal Shapley Value Explanations", "authors": ["Xiaolei Lu"], "url": "https://arxiv.org/abs/2502.12209v1", "attribution": "\"Suboptimal Shapley Value Explanations\" by Xiaolei Lu, arXiv:2502.12209v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17626v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n \\toprule\n $\\nu$ & $32\\times 32$ & $64\\times 64$ & $128\\times 128$ & $256\\times 256$\\\\\n \\midrule\n $1\\cdot 10^{-2}$ & 10 & 15 & 20 & 23 \\\\\n $5\\cdot 10^{-3}$ & 11 & 15 & 22 & 30\\\\\n $2.5\\cdot 10^{-3}$ & 17 & 16 & 21 & 32\\\\ \n $1.25\\cdot 10^{-3}$ & 26 & 24 & 23 & 30\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Preconditioned normal equations for solving discretised partial differential equations", "authors": ["Lorenzo Lazzarino", "Yuji Nakatsukasa", "Umberto Zerbinati"], "url": "https://arxiv.org/abs/2502.17626v2", "attribution": "\"Preconditioned normal equations for solving discretised partial differential equations\" by Lorenzo Lazzarino, Yuji Nakatsukasa, and Umberto Zerbinati, arXiv:2502.17626v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.10670v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n \\hline\n \\textbf{Controller} & \\textbf{X $\\pm$ SD (cm)} & \\textbf{Y $\\pm$ SD (cm)} & \\textbf{Z $\\pm$ SD (cm)} \\\\ \\hline\n HL & 2.79 $\\pm$ 0.30 & 2.91 $\\pm$ 0.22 & 0.46 $\\pm$ 0.10 \\\\ \\hline\n No Dynamics & 4.18 $\\pm$ 1.00 & 4.32 $\\pm$ 1.36 & 0.54 $\\pm$ 0.26 \\\\ \\hline\n Dynamics & 3.95 $\\pm$ 0.66 & 4.43 $\\pm$ 1.15 & 0.51 $\\pm$ 0.19 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Average controller error across trajectories. HL Commander outperforms the LL implementations. However, incorporating magnet dynamics appears to improve LL performance.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Magnisketch Drone Control", "authors": ["Ashley Kline", "Abirami Elangovan", "Dominique Escandon", "Scott Wade", "Aatish Gupta"], "url": "https://arxiv.org/abs/2412.10670v1", "attribution": "\"Magnisketch Drone Control\" by Ashley Kline, Abirami Elangovan, Dominique Escandon, Scott Wade, and Aatish Gupta, arXiv:2412.10670v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.06147v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison results of experiments}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\nMethods &SSIM($ \\times {\\rm{1}}{{\\rm{0}}^{{\\rm{ - 2}}}}$) & PSNR \\\\\n\\hline\nUnet${\\rm{\\#}}$1 &90.9861 &19.4012 \\\\\nUnet${\\rm{\\#}}$2 &91.4635 &19.7707\\\\\nYnet &87.2459 &17.8173\\\\\nDEUnet &90.6457 &19.3046\\\\\n\\hline\nDuDoUnet &\\bf{92.6300} &\\bf{20.4012}\\\\\nDuDoUnet${\\rm{ + }}$MI &\\bf{93.5622} &\\bf{20.8859}\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Limited-view Photoacoustic Imaging Reconstruction With Dual Domain Inputs Under Mutual Information Constraint", "authors": ["Jiadong Zhang", "Hengrong Lan", "Changchun Yang", "Shanshan Guo", "Feng Gao", "Fei Gao"], "url": "https://arxiv.org/abs/2011.06147v1", "attribution": "\"Limited-view Photoacoustic Imaging Reconstruction With Dual Domain Inputs Under Mutual Information Constraint\" by Jiadong Zhang, Hengrong Lan, Changchun Yang, Shanshan Guo, Feng Gao, and Fei Gao, arXiv:2011.06147v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04727v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccccccc}\n\t\t\t\\toprule\n\t\t\t & & In-distribution & \\multicolumn{8}{c}{Out-of-distribution} & \\\\\n\t\t\t\\cmidrule(lr){3-3}\\cmidrule(lr){4-11}\n\t\t\tArchitecture & Metric & SIDD & ~~Poly~~ & ~~~CC~~~ & HighISO~ & iPhone & Huawei & Oppo & Sony & Xiaomi & OOD Avg. \\\\\n\t\t\t\\midrule\n\t\t\t\\midrule\n\t\t\t\\multirow{2}{*}{DnCNN-pretrained} & PSNR & 36.16 & 37.78 & 37.04 & 39.28 & 40.28 & 39.47 & 39.93 & 42.35 & 35.98 & 39.02 \\\\\n\t\t\t & SSIM & 0.9170 & 0.9722 & 0.9842 & 0.9801 & 0.9463 & 0.9758 & 0.9746 & 0.9617 & 0.9715 & 0.9708 \\\\\n\t\t\t\\midrule\n\t\t\t\\multirow{2}{*}{DnCNN-GAdap} & PSNR & \\bf{37.42} & \\bf{38.15} & \\bf{37.16} & \\bf{39.57} & \\bf{41.18} & \\bf{39.61} & \\bf{40.05} & \\bf{43.25} & \\bf{36.10} & \\bf{39.38} \\\\\n\t\t\t & SSIM & \\bf{0.9414} & \\bf{0.9811} & \\bf{0.9857} & \\bf{0.9833} & \\bf{0.9698} & \\bf{0.9769} & \\bf{0.9772} & \\bf{0.9779} & \\bf{0.9734} & \\bf{0.9782} \\\\\n\t\t\t\\midrule\n\t\t\t\\midrule\n\t\t\t\\multirow{2}{*}{CGNet-pretrained} & PSNR & 37.51 & 37.45 & 36.88 & 38.80 & 39.25 & 38.17 & 38.26 & 40.62 & 35.61 & 38.13 \\\\\n\t\t\t & SSIM & 0.9345 & 0.9753 & 0.9826 & 0.9798 & 0.9551 & 0.9739 & 0.9722 & 0.9673 & 0.9701 & 0.9720 \\\\\n\t\t\t\\midrule\n\t\t\t\\multirow{2}{*}{CGNet-GAdap} & PSNR & \\bf{39.03} & \\bf{38.55} & \\bf{37.65} & \\bf{39.71} & \\bf{41.48} & \\bf{39.71} & \\bf{40.39} & \\bf{44.00} & \\bf{35.94} & \\bf{39.68} \\\\\n\t\t\t & SSIM & \\bf{0.9558} & \\bf{0.9839} & \\bf{0.9868} & \\bf{0.9835} & \\bf{0.9753} & \\bf{0.9777} & \\bf{0.9792} & \\bf{0.9846} & \\bf{0.9725} & \\bf{0.9804} \\\\\n\t\t\t\\midrule\n\t\t\t\\midrule\n\t\t\t\\multirow{2}{*}{Restormer-pretrained} & PSNR & 36.97 & 37.45 & 36.45 & 38.32 & 38.18 & 37.44 & 37.88 & 40.58 & 35.12 & 37.68 \\\\\n\t\t\t & SSIM & 0.9246 & 0.9681 & 0.9792 & 0.9745 & 0.9265 & 0.9564 & 0.9517 & 0.9503 & 0.9626 & 0.9587 \\\\\n\t\t\t\\midrule\n\t\t\t\\multirow{2}{*}{Restormer-GAdap} & PSNR & \\bf{39.08} & \\bf{38.74} & \\bf{37.60} & \\bf{40.06} & \\bf{41.62} & \\bf{39.68} & \\bf{40.55} & \\bf{44.12} & \\bf{36.14} & \\bf{39.81} \\\\\n\t\t\t & SSIM & \\bf{0.9558} & \\bf{0.9846} & \\bf{0.9861} & \\bf{0.9851} & \\bf{0.9751} & \\bf{0.9761} & \\bf{0.9794} & \\bf{0.9849} & \\bf{0.9747} & \\bf{0.9807} \\\\\n\t\t\t\\midrule\n\t\t\t\\midrule\n\t\t\t\\multirow{2}{*}{NAFNet-pretrained} & PSNR & 37.77 & 15.24 & 33.76 & 21.18 & 40.13 & 8.68 & 8.45 & 6.35 & 9.33 & 17.89 \\\\\n\t\t\t & SSIM & 0.9360 & 0.3466 & 0.9139 & 0.5232 & 0.9734 & 0.1218 & 0.1138 & 0.0245 & 0.1845 & 0.4002 \\\\\n\t\t\t\\midrule\n\t\t\t\\multirow{2}{*}{NAFNet-GAdap} & PSNR & \\bf{39.12} & \\bf{38.63} & \\bf{37.82} & \\bf{39.95} & \\bf{41.80} & \\bf{39.80} & \\bf{40.46} & \\bf{44.09} & \\bf{36.12} & \\bf{39.83} \\\\\n\t\t\t & SSIM & \\bf{0.9562} & \\bf{0.9844} & \\bf{0.9875} & \\bf{0.9854} & \\bf{0.9775} & \\bf{0.9781} & \\bf{0.9795} & \\bf{0.9850} & \\bf{0.9745} & \\bf{0.9815} \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning to Translate Noise for Robust Image Denoising", "authors": ["Inju Ha", "Donghun Ryou", "Seonguk Seo", "Bohyung Han"], "url": "https://arxiv.org/abs/2412.04727v1", "attribution": "\"Learning to Translate Noise for Robust Image Denoising\" by Inju Ha, Donghun Ryou, Seonguk Seo, and Bohyung Han, arXiv:2412.04727v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07337v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Training and Validation Dataset Statistics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n Statistics & Train & Val\\\\\n \\midrule\n \\emph{Data Distribution}\\\\ \n total & 1,099,217 & 77,326 \\\\\n real & 297,389 (27.05\\%) & 20,220 (26.15\\%) \\\\ \n audio\\_modified & 266,116 (24.21\\%) & 18,938 (24.49\\%) \\\\ \n visual\\_modified & 269,901 (24,55\\%) & 19,099 (24.70\\%) \\\\ \n both\\_modified & 265,812 (24.18\\%) & 19,069 (24.66\\%) \\\\\n \\midrule\n \\emph{Duration of Videos} \\\\ \n minimum & 1.32s & 2.52s \\\\\n maximum & 668.48s & 152.40s \\\\\n mean & 9.60s & 9.56s \\\\\n \\midrule\n \\emph{Number of Attacks per Video} \\\\\n Range & 0 to 5 & 0 to 5 \\\\\n \\midrule\n \\emph{Duration of Fake Segments} \\\\\n minimum & 0.02s & 0.02s \\\\\n maximum & 11.58s & 8.10s \\\\\n mean & 0.33s & 0.33s \\\\\n \\midrule\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "KLASSify to Verify: Audio-Visual Deepfake Detection Using SSL-based Audio and Handcrafted Visual Features", "authors": ["Ivan Kukanov", "Jun Wah Ng"], "url": "https://arxiv.org/abs/2508.07337v1", "attribution": "\"KLASSify to Verify: Audio-Visual Deepfake Detection Using SSL-based Audio and Handcrafted Visual Features\" by Ivan Kukanov and Jun Wah Ng, arXiv:2508.07337v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2308.01915v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c||c|c|c|}\n \\hline\n Horizon $k$ & Upward (\\%) & Stationary (\\%) & Downward (\\%) \\\\\n \\hline\n 1 & 18 & 63 & 19\\\\ \\hline\n 2 & 25 & 50 & 25 \\\\\\hline\n 3 & 28 & 43 & 29 \\\\\\hline\n 5 & 32 & 35 & 33 \\\\\\hline\n 10 & 37 & 25 & 38 \\\\\n \\hline\n \\end{tabular}\n\\caption{Class balancing on FI-2010.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study", "authors": ["Matteo Prata", "Giuseppe Masi", "Leonardo Berti", "Viviana Arrigoni", "Andrea Coletta", "Irene Cannistraci", "Svitlana Vyetrenko", "Paola Velardi", "Novella Bartolini"], "url": "https://arxiv.org/abs/2308.01915v2", "attribution": "\"LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study\" by Matteo Prata, Giuseppe Masi, Leonardo Berti, Viviana Arrigoni, Andrea Coletta, Irene Cannistraci, Svitlana Vyetrenko, Paola Velardi, and Novella Bartolini, arXiv:2308.01915v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01984v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study results on MOT20 test set. \\textbf{MM:} Motion Model, \\textbf{$\\Delta$:} Value Increment.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|c}\n\\toprule\n{\\bf Method}&{\\bf MOTA(\\%){$\\uparrow$}}&{\\bf IDF1(\\%){$\\uparrow$}}&{$\\Delta$}\\\\\n\\midrule\nOIM-MM&49.2&41.4&\\\\\n\\midrule\niHOIM-MM&51.2&43.0&(+2.0, +1.6)\\\\\niHOIM+MM&51.5&44.5&(+0.3, +1.5)\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multi-object Tracking with a Hierarchical Single-branch Network", "authors": ["Fan Wang", "Lei Luo", "En Zhu", "Siwei Wang", "Jun Long"], "url": "https://arxiv.org/abs/2101.01984v1", "attribution": "\"Multi-object Tracking with a Hierarchical Single-branch Network\" by Fan Wang, Lei Luo, En Zhu, Siwei Wang, and Jun Long, arXiv:2101.01984v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.00221v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccl}\\toprule\n& \\multicolumn{2}{c}{RTR} & \\multicolumn{2}{c}{GFW}\n\\\\ \\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n & $f$ & Time (s) & $f$ & Time (s) \\\\ \\midrule\nReading 1 & 530.6896 & 12.2 & 532.3243 & 10.3 \\\\\nReading 2 & 532.3255 & 42.1 & 532.3379 & 20.6 \\\\\nReading 3 & 532.3384 & 78.2 & 532.3399 & 41.3 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Empirical comparison RTR and GFW for solving the Max-Cut SDP Relaxation on a GPU}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Strongly Convex Maximization via the Frank-Wolfe Algorithm with the Kurdyka-Łojasiewicz Inequality", "authors": ["Fatih Selim Aktas", "Christian Kroer"], "url": "https://arxiv.org/abs/2505.00221v1", "attribution": "\"Strongly Convex Maximization via the Frank-Wolfe Algorithm with the Kurdyka-Łojasiewicz Inequality\" by Fatih Selim Aktas and Christian Kroer, arXiv:2505.00221v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17123v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The method compared in the experiment.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\nAlgorithm & $\\lambda$ & $\\lambda$ setting \\\\ \\hline\nMU & 0 &constant\n\\\\\nPenalized MU & 0.1 &constant for each column\n\\\\\nPenalized MU & 0.5 &constant for each column\n\\\\\nSHINBO & not required & automatically optimized,\\\\\n & & different penalization for each column\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Sparse Hyperparametric Itakura-Saito NMF via Bi-Level Optimization", "authors": ["Laura Selicato", "Flavia Esposito", "Andersen Ang", "Nicoletta Del Buono", "Rafal Zdunek"], "url": "https://arxiv.org/abs/2502.17123v2", "attribution": "\"Sparse Hyperparametric Itakura-Saito NMF via Bi-Level Optimization\" by Laura Selicato, Flavia Esposito, Andersen Ang, Nicoletta Del Buono, and Rafal Zdunek, arXiv:2502.17123v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11563v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\toprule\n{\\bf Amplification} & \\multirow{2}{*}{\\bf Accuracy} & \\multirow{2}{*}{\\bf Loss} \\\\\n{\\bf Factor} &&\\\\\n\\midrule\n10 & 70.24\\% & 0.6036\\\\ \n20 & 68.36\\% & 0.6043\\\\ \n30 & 65.77\\% & 0.6051\\\\ \n40 & 63.54\\% & 0.6243\\\\ \n50 & 60.49\\% & 0.6189\\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Detecting Deepfake Videos Using Euler Video Magnification", "authors": ["Rashmiranjan Das", "Gaurav Negi", "Alan F. Smeaton"], "url": "https://arxiv.org/abs/2101.11563v1", "attribution": "\"Detecting Deepfake Videos Using Euler Video Magnification\" by Rashmiranjan Das, Gaurav Negi, and Alan F. Smeaton, arXiv:2101.11563v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.12110v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Government Variables}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\n\\textbf{Symbol} & \\textbf{Definition} \\\\\n\\midrule\n$B_t$ & Government bond holdings at time $t$ \\\\\n$T(i_t), T^a(a_t)$ & Income and asset tax functions \\\\\n$\\tau, \\tau_a$ & Average tax rate for income and assets \\\\\n$\\xi, \\xi_a$ & Curvature parameters for nonlinear tax function \\\\\n$G_t$ & Government spending on goods and services \\\\\n$r_t$ & Risk-free interest rate on government debt \\\\\n$Y_t$ & Nominal Gross Domestic Product (GDP) at time $t$ \\\\\n$P_t$ & Aggregate price level (CPI) at time $t$ \\\\\n$\\pi_t$ & Inflation rate: $\\pi_t = \\frac{P_t - P_{t-1}}{P_{t-1}}$ \\\\\n$g_t$ & GDP growth rate: $g_t = \\frac{Y_t - Y_{t-1}}{Y_{t-1}}$ \\\\\n$\\pi^*$ & Target inflation rate set by monetary authority \\\\\n$g^*$ & Target GDP growth rate \\\\\n$\\phi_t$ & Reserve requirement ratio set by central bank \\\\\n$\\iota_t$ & Nominal interest rate set by central bank \\\\\n$\\lambda_\\pi$ & Trade-off weight between inflation and growth objectives \\\\\n$F_t$ & Total value of the national pension fund \\\\\n$r_t^f$ & Investment return rate of the pension fund \\\\\n$k$ & Minimum required pension fund growth rate \\\\\n$\\tau_p$ & Pension contribution rate paid by working individuals \\\\\n$P_t^y(i)$ & Pension contribution from individual $i$ at time $t$ \\\\\n$P_t^o(i)$ & Pension benefit received by retired individual $i$ \\\\\n$A_t^{\\text{personal}}(i)$ & Accumulated balance of personal pension account \\\\\n$M$ & Annuity divisor based on retirement age \\\\\n$\\text{age}^r$ & Statutory retirement age \\\\\n$T_i^p$ & Total years of contribution for individual $i$ \\\\\n$\\mathcal{I}_t$ & Income distribution across the population at time $t$ \\\\\n$u(c_t^i, h_t^i)$ & Instantaneous utility from consumption and labor of individual $i$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "EconGym: A Scalable AI Testbed with Diverse Economic Tasks", "authors": ["Qirui Mi", "Qipeng Yang", "Zijun Fan", "Wentian Fan", "Heyang Ma", "Chengdong Ma", "Siyu Xia", "Bo An", "Jun Wang", "Haifeng Zhang"], "url": "https://arxiv.org/abs/2506.12110v1", "attribution": "\"EconGym: A Scalable AI Testbed with Diverse Economic Tasks\" by Qirui Mi, Qipeng Yang, Zijun Fan, Wentian Fan, Heyang Ma, Chengdong Ma, Siyu Xia, Bo An, Jun Wang, and Haifeng Zhang, arXiv:2506.12110v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.14614v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Values of the calculated errors for pricing results for BTC, rounded to three significant figures.} % title of Table\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc} % centered columns (4 columns)\n\t\t\n\t\t\\hline %inserts double horizontal lines\n\t\t& RMSE& MAE &MAPE&MSLE \\\\ % inserts table\n\t\t\\hline \n\t\tBS & 1180 &854 & 0.0923 & 0.0516\\\\ \n Heston & 874 & 484 & 0.0581 & 0.0138 \\\\\n Kou &649 & 258 & 0.0264 & 0.00523\\\\\n MJD & 785 & 392 & 0.042 & 0.00778 \\\\\n SVJ &994 & 511 & 0.0448 & 0.00849 \\\\\n\t\t\n\t\tVG & 823 & 447 & 0.0466 & 0.00918 \\\\\n \n \\hline\n\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Pricing options on the cryptocurrency futures contracts", "authors": ["Julia Kończal"], "url": "https://arxiv.org/abs/2506.14614v1", "attribution": "\"Pricing options on the cryptocurrency futures contracts\" by Julia Kończal, arXiv:2506.14614v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.11612v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Proportion of presumably global optima when carrying out 250 iterations for each model configuration when $n=500$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\\toprule\n &&\\multicolumn{4}{c}{\\textbf{fitted model}}\\\\\n && $p=0$ & $p=1$ & $p=2$ & $p=3$\\\\\\midrule\n \\multirow{4}{*}{\\textbf{simulated model}} \n &$p=0$& $1.000$ & $0.976$ & $0.988$ & $0.992$ \\\\\n &$p=1$& $1.000$ & $1.000$ & $0.984$ & $0.988$ \\\\\n &$p=2$& $0.996$ & $0.996$ & $0.972$ & $0.988$ \\\\\n &$p=3$& $0.996$ & $0.992$ & $0.992$ & $0.992$ \\\\\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Autoregressive hidden Markov models for high-resolution animal movement data", "authors": ["Ferdinand V. Stoye", "Annika Hoyer", "Roland Langrock"], "url": "https://arxiv.org/abs/2412.11612v1", "attribution": "\"Autoregressive hidden Markov models for high-resolution animal movement data\" by Ferdinand V. Stoye, Annika Hoyer, and Roland Langrock, arXiv:2412.11612v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.04339v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overview of the dataset per gallery. All sale values are in USD converted at the time of the sale.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrr}\n &\n \\textbf{Sales} &\n \\textbf{Volume} &\n \\textbf{Sale (avg)} &\n \\textbf{Sale (med)} &\n \\textbf{Sellers} &\n \\textbf{Buyers} \\\\\nSuperRare & 21,717 & 145,187,662 & 6685 & 457 & 2856 & 4461 \\\\\nAsync Art & 196 & 3,505,909 & 17,887 & 537 & 87 & 143 \\\\\nKnownOrigin & 4306 & 16,344,121 & 3796 & 131 & 1265 & 2412 \\\\\nFoundation & 1091 & 43,304,375 & 39,692 & 436 & 676 & 782 \\\\\nMakersplace & 11,344 & 28,656,875 & 2526 & 227 & 1220 & 3439 \\\\\nZora & 286 & 106,289 & 372 & 104 & 189 & 244 \\\\\n\\textbf{Overall} &\n \\textbf{38,940} &\n \\textbf{237,105,230} &\n \\textbf{6089} &\n \\textbf{316} &\n \\textbf{5636} &\n \\textbf{10,011}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Seller-buyer networks in NFT art are driven by preferential ties", "authors": ["Giovanni Colavizza"], "url": "https://arxiv.org/abs/2210.04339v2", "attribution": "\"Seller-buyer networks in NFT art are driven by preferential ties\" by Giovanni Colavizza, arXiv:2210.04339v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.06288v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\toprule\nobserved\\_sales & observed\\_transactions & observed\\_unique\\_customers \\\\\naverage\\_transaction\\_value & transactions\\_per\\_customer & sales\\_per\\_customer \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{List of consumer transactions fields}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "DELPHYNE: A Pre-Trained Model for General and Financial Time Series", "authors": ["Xueying Ding", "Aakriti Mittal", "Achintya Gopal"], "url": "https://arxiv.org/abs/2506.06288v1", "attribution": "\"DELPHYNE: A Pre-Trained Model for General and Financial Time Series\" by Xueying Ding, Aakriti Mittal, and Achintya Gopal, arXiv:2506.06288v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.12084v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Gravity Equation Coefficients and Variance Decomposition}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\\toprule\n& \\multicolumn{4}{c}{Regression Coefficients (S.E.)} & \\multicolumn{4}{c}{$R^2$ Loss (\\%)} \\\\\n\\cmidrule(lr){2-5} \\cmidrule(lr){6-9}\nSample & Geo Score & Distance (log) & Border & Linguistic Dist. & Geo Score & Distance & Border & Ling. Dist. \\\\\n\\midrule\n\\multicolumn{9}{l}{\\textit{Panel A: All Countries}} \\\\\nFull Period (1962--2024) & 1.10 (0.027) & -1.45 (0.016) & 0.84 (0.079) & -1.70 (0.075) & 3.4 & 42.5 & 1.1 & 2.6 \\\\\nPre-1990 & 1.08 (0.043) & -1.29 (0.023) & 0.54 (0.104) & -0.88 (0.112) & 4.8 & 48.1 & 0.4 & 0.9 \\\\\nPost-1990 & 1.08 (0.032) & -1.50 (0.017) & 1.02 (0.082) & -2.10 (0.076) & 2.5 & 40.0 & 1.1 & 3.2 \\\\\n\\addlinespace\n\\multicolumn{9}{l}{\\textit{Panel B: Major Economies}} \\\\\nFull Period & 0.46 (0.106) & -0.81 (0.052) & 0.23 (0.171) & -1.10 (0.330) & 2.2 & 49.7 & 0.6 & 2.8 \\\\\nPre-1990 & 0.78 (0.141) & -0.68 (0.069) & 0.10 (0.213) & -0.91 (0.422) & 8.6 & 44.8 & 0.0 & 2.6 \\\\\nPost-1990 & 0.20 (0.139) & -0.92 (0.052) & 0.33 (0.172) & -1.24 (0.332) & 0.4 & 50.6 & 0.8 & 3.1 \\\\\n\\midrule\nFull $R^2$ (Full Period) & \\multicolumn{4}{c}{All: 0.268 \\quad Major: 0.179} & \\multicolumn{4}{c}{Observations: 1,087,543 / 58,948} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Geopolitical Barriers to Globalization", "authors": ["Tianyu Fan", "Mai Wo", "Wei Xiang"], "url": "https://arxiv.org/abs/2509.12084v2", "attribution": "\"Geopolitical Barriers to Globalization\" by Tianyu Fan, Mai Wo, and Wei Xiang, arXiv:2509.12084v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.10670v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline\n $\\lambda_a$ & $\\sigma_a e_a$\\\\\n $day^{-1}$ &$kg/mg$ \\\\\n \\hline $0.0799$&$0.4755$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Parameters for the antiangiogenic drug Bevacizumab ().}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A mathematical perspective on the paradox that chemotherapy sometimes works backwards", "authors": ["Luis A. Fernández", "Isabel Lasheras", "Cecilia Pola"], "url": "https://arxiv.org/abs/2503.10670v1", "attribution": "\"A mathematical perspective on the paradox that chemotherapy sometimes works backwards\" by Luis A. Fernández, Isabel Lasheras, and Cecilia Pola, arXiv:2503.10670v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.17869v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameter ranges for XGBoost and Gradient Boosting models used for N prediction in grapevine leaves.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|l|}\n\\hline\n\\textbf{Model} & \\textbf{Hyperparameter} & \\textbf{Values Explored} \\\\\n\\hline\n\\multirow{4}{*}{XGBoost} \n& Number of Estimators & 100, 500, 1000 \\\\\n& Maximum Depth & 3, 5, 7, 10 \\\\\n& Learning Rate & 0.01, 0.05, 0.1 \\\\\n& Subsample & 0.5, 0.7, 1.0 \\\\\n\\hline\n\\multirow{4}{*}{Gradient Boosting} \n& Number of Estimators & 100, 200 \\\\\n& Maximum Depth & 3, 5 \\\\\n& Learning Rate & 0.01, 0.1 \\\\\n& Subsample & 0.5, 0.7 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Integrating Feature Selection and Machine Learning for Nitrogen Assessment in Grapevine Leaves using In-Field Hyperspectral Imaging", "authors": ["Atif Bilal Asad", "Achyut Paudel", "Safal Kshetri", "Chenchen Kang", "Salik Ram Khanal", "Nataliya Shcherbatyuk", "Pierre Davadant", "R. Paul Schreiner", "Santosh Kalauni", "Manoj Karkee", "Markus Keller"], "url": "https://arxiv.org/abs/2507.17869v1", "attribution": "\"Integrating Feature Selection and Machine Learning for Nitrogen Assessment in Grapevine Leaves using In-Field Hyperspectral Imaging\" by Atif Bilal Asad, Achyut Paudel, Safal Kshetri, Chenchen Kang, Salik Ram Khanal, Nataliya Shcherbatyuk, Pierre Davadant, R. Paul Schreiner, Santosh Kalauni, Manoj Karkee, and Markus Keller, arXiv:2507.17869v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2103.01114v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|}\n \\hline\n Base CNN & Train correlation & Test correlation \\\\\n \\hline \\hline\n EfficientNet-b7 & 0.95 & 0.92 \\\\\n EfficientNet-b0 & 0.90 & 0.88 \\\\\n DenseNet121 & 0.85 & 0.85 \\\\\n ResNet50-v2 & 0.87 & 0.82 \\\\\n VGG16 &0.88 & 0.87 \\\\\n \\hline\n \\end{tabular}\n\\caption{Pearson correlation coefficient results from various base CNN models}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deep Perceptual Image Quality Assessment for Compression", "authors": ["Juan Carlos Mier", "Eddie Huang", "Hossein Talebi", "Feng Yang", "Peyman Milanfar"], "url": "https://arxiv.org/abs/2103.01114v2", "attribution": "\"Deep Perceptual Image Quality Assessment for Compression\" by Juan Carlos Mier, Eddie Huang, Hossein Talebi, Feng Yang, and Peyman Milanfar, arXiv:2103.01114v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13102v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overview of the datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrr} \n\\toprule\n & Dataset & \\#Samples & \\#Benign & \\#DDoS \\\\ \n\\midrule\n\\multirow{2}{*}{Training} & Custom-SYN & 52698 & 26349 & 26349 \\\\\n & UNB201X & 265902 & 132746 & 133156 \\\\ \n\\midrule\n\\multirow{2}{*}{Evaluation} & Scapy-SYN & 18830 & – & 18830 \\\\\n & CICIDS2017 & 13193 & – & 13193 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "GADoT: GAN-based Adversarial Training for Robust DDoS Attack Detection", "authors": ["Maged Abdelaty", "Sandra Scott-Hayward", "Roberto Doriguzzi-Corin", "Domenico Siracusa"], "url": "https://arxiv.org/abs/2201.13102v1", "attribution": "\"GADoT: GAN-based Adversarial Training for Robust DDoS Attack Detection\" by Maged Abdelaty, Sandra Scott-Hayward, Roberto Doriguzzi-Corin, and Domenico Siracusa, arXiv:2201.13102v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.03779v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Distribution of languages in train and test data (in thousands of records) in the multilingual dataset and information about the number of tokens (in milions) on which XLM-Roberta was trained}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrlrrr}\n\\hline\n\\multicolumn{4}{c}{Languages A-I} & \\multicolumn{4}{c}{Languages I-U} \\\\\n\\hline\nLanguage & Train & Test & XLM-RoBERTa & Language & Train & Test & XLM-RoBERTa \\\\\n\\hline\nBulgarian (bg) & 12.3 (3.9\\%) & 7.7 (4.0\\%) & 5.5 (4.4\\%) & Italian (it) & 12.2 (3.9\\%) & 7.6 (4.0\\%) & 5.0 (4.0\\%) \\\\\nCzech (cs) & 13.3 (4.2\\%) & 11.1 (5.8\\%) & 2.5 (2.0\\%) & Lithuanian (lt) & 13.8 (4.3\\%) & 7.2 (3.8\\%) & 1.8 (1.5\\%) \\\\\nDanish (da) & 15.4 (4.9\\%) & 7.4 (3.9\\%) & 7.8 (6.2\\%) & Latvian (lv) & 14.4 (4.6\\%) & 8.1 (4.2\\%) & 1.2 (1.0\\%) \\\\\nGerman (de) & 15.7 (4.9\\%) & 8.0 (4.2\\%) & 10.3 (8.2\\%) & Dutch (nl) & 12.9 (4.1\\%) & 7.6 (4.0\\%) & 5.0 (4.0\\%) \\\\\nGreek (el) & 14.5 (4.6\\%) & 8.3 (4.3\\%) & 4.3 (3.4\\%) & Polish (pl) & 12.2 (3.9\\%) & 7.3 (3.8\\%) & 6.5 (5.2\\%) \\\\\nEnglish (en) & 14.2 (4.5\\%) & 9.3 (4.8\\%) & 55.6 (44.3\\%) & Portuguese (pt) & 14.4 (4.5\\%) & 10.2 (5.3\\%) & 8.4 (6.7\\%) \\\\\nSpanish (es) & 16.0 (5.1\\%) & 7.9 (4.1\\%) & 9.4 (7.5\\%) & Romanian (ro) & 12.3 (3.9\\%) & 9.8 (5.1\\%) & 10.4 (8.3\\%) \\\\\nEstonian (et) & 16.5 (5.2\\%) & 7.7 (4.0\\%) & 0.8 (0.7\\%) & Russian (ru) & 12.5 (4.0\\%) & 9.5 (5.0\\%) & 23.4 (18.7\\%) \\\\\nFinnish (fi) & 14.6 (4.6\\%) & 7.9 (4.1\\%) & 6.7 (5.4\\%) & Slovak (sk) & 16.4 (5.2\\%) & 10.5 (5.5\\%) & 3.5 (2.8\\%) \\\\\nFrench (fr) & 18.8 (5.9\\%) & 11.3 (5.9\\%) & 9.8 (7.8\\%) & Slovenian (sl) & 13.4 (4.2\\%) & 8.2 (4.3\\%) & 1.7 (1.3\\%) \\\\\nIrish (ga) & 14.2 (4.5\\%) & 8.0 (4.2\\%) & 0.1 (0.1\\%) & Swedish (sv) & 14.1 (4.4\\%) & 10.2 (5.4\\%) & 0.1 (0.1\\%) \\\\\nHungarian (hu) & 15.0 (4.7\\%) & 9.3 (4.9\\%) & 7.8 (6.2\\%) & Ukrainian (uk) & 14.4 (4.5\\%) & 7.8 (4.1\\%) & 0.0 (0.0\\%) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Multilingual hierarchical classification of job advertisements for job vacancy statistics", "authors": ["Maciej Beręsewicz", "Marek Wydmuch", "Herman Cherniaiev", "Robert Pater"], "url": "https://arxiv.org/abs/2411.03779v2", "attribution": "\"Multilingual hierarchical classification of job advertisements for job vacancy statistics\" by Maciej Beręsewicz, Marek Wydmuch, Herman Cherniaiev, and Robert Pater, arXiv:2411.03779v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.11079v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Regression results: interaction models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\hline\n\t&\t(1)\t&\t(2)\t\\\\\n\t&\tCont. PGG\t&\tCont. PGG\t\\\\\n\\hline\n$AR$ & -0.003 & 0.015 \\\\\n & (0.085) & (0.084) \\\\\n$RA$ & 0.001 & 0.033 \\\\\n & (0.083) & (0.082) \\\\\n$AA$ & 0.039 & 0.067 \\\\\n & (0.082) & (0.083) \\\\\n$age$ & -0.005** & -0.005** \\\\\n & (0.002) & (0.002) \\\\\n$CRT$ & -0.141*** & -0.139*** \\\\\n & (0.031) & (0.031) \\\\\n$beliefs$ & 0.170*** & 0.170*** \\\\\n & (0.007) & (0.007) \\\\\n$risk$ $aversion$ & -0.700*** & -0.339*** \\\\\n & (0.208) & (0.106) \\\\\n$AR$ * $risk$ $aversion$ & 0.359 & \\\\\n & (0.271) & \\\\\n$RA$ * $risk$ $aversion$ & 0.679** & \\\\\n & (0.285) & \\\\\n$AA$ * $risk$ $aversion$ & 0.814** & \\\\\n & (0.321) & \\\\\n$ambiguity$ $aversion$ & & 0.105 \\\\\n & & (0.119) \\\\\n$AR$ * $ambiguity$ $aversion$ & & -0.219 \\\\\n & & (0.144) \\\\\n$RA$ * $ambiguity$ $aversion$ & & -0.293 \\\\\n & & (0.182) \\\\\n$AA$ * $ambiguity$ $aversion$ & & -0.434** \\\\\n & & (0.178) \\\\\nConstant & 1.422*** & 1.434*** \\\\\n & (0.263) & (0.265) \\\\\n & & \\\\\nObservations & 1,421 & 1,421 \\\\\nR-squared & 0.394 & 0.394 \t\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The effect of ambiguity in strategic environments: an experiment", "authors": ["Pablo Brañas-Garza", "Antonio Cabrales", "María Paz Espinosa", "Diego Jorrat"], "url": "https://arxiv.org/abs/2209.11079v1", "attribution": "\"The effect of ambiguity in strategic environments: an experiment\" by Pablo Brañas-Garza, Antonio Cabrales, María Paz Espinosa, and Diego Jorrat, arXiv:2209.11079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.21035v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|}\n \\cline{2-5}\n & \\multicolumn{4}{c|}{$k=-1$} \\\\\n \\cline{2-5}\n & $t_\\mu^{-1}$ & $f_{1,\\mu}^{-1}$ & $f_{2,\\mu}^{-1}$ & $f_{3,\\mu}^{-1}$ \\\\\n \\hline\n $r$ & $-3$ & $-2$ & $-2$ & $-3$ \\\\\n $u$ & $-2$ & $-1$ & $0$ & $0$ \\\\\n $z$ & $-1$ & $0$ & $0$ & $-1$ \\\\\n $\\bar z$ & $-2$ & $0$ & $-1$ & 0 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The three refined slices $\\mathfrak{S}_{-1,f_i}$ that strictify $\\mathfrak{Kin}^{(-1)}$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Homotopy kinematic algebras at null infinity", "authors": ["Felipe Díaz-Jaramillo", "Silvia Nagy", "Giorgio Pizzolo"], "url": "https://arxiv.org/abs/2503.21035v1", "attribution": "\"Homotopy kinematic algebras at null infinity\" by Felipe Díaz-Jaramillo, Silvia Nagy, and Giorgio Pizzolo, arXiv:2503.21035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.10926v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccccc}\n \\hline\n$M_b$ & Method &\\multicolumn {6}{c}{Balanced Pleiropy} &\\multicolumn {6}{c}{Directional Pleiropy}\\\\\n\\cline{3-14}\n&& \\multicolumn {3}{c}{All IVs} &\\multicolumn {3}{c}{Top 20 IVs}& \\multicolumn {3}{c}{All IVs} &\\multicolumn {3}{c}{Top 20 IVs}\\\\\n \\hline\n &&Bias&SE&MSE&Bias&SE&MSE&Bias&SE&MSE&Bias&SE&MSE\\\\\n \\hline\n\\multirow {5}{*}{0}&SM&0.18&0.06&0.04&0.17&0.1&0.04&0.23&0.19&0.09&0.14&0.32&0.12\\\\\n&WM&0.18&0.06&0.04&0.17&0.09&0.04&0.22&0.17&0.08&0.17&0.31&0.12\\\\\n&IVW&0.18&0.05&0.04&0.18&0.08&0.04&0.2&0.14&0.06&0.15&0.24&0.08\\\\\n&Egger&0.18&0.07&0.04&0.17&0.9&0.84&0.19&0.16&0.06&0.62&2.19&5.21\\\\\n&TS-RE&0&0.16&0.03&0.17&0.11&0.04&0.01&0.51&0.26&0.14&0.29&0.11\\\\\n \\hline\n\\multirow {5}{*}{100}&SM&0.19&0.06&0.04&0.19&0.09&0.04&0.21&0.19&0.08&0.19&0.27&0.11\\\\\n&WM&0.19&0.05&0.04&0.19&0.08&0.04&0.2&0.16&0.07&0.18&0.28&0.11\\\\\n&IVW&0.19&0.05&0.04&0.18&0.07&0.04&0.21&0.12&0.06&0.2&0.21&0.08\\\\\n&Egger&0.19&0.06&0.04&0.24&0.74&0.61&0.2&0.16&0.06&0.12&1.76&3.12\\\\\n&TS-RE&0.01&0.16&0.03&0.18&0.1&0.04&0.03&0.43&0.19&0.2&0.27&0.11\\\\\n \\hline\n\\multirow {5}{*}{200}&SM&0.15&0.08&0.03&0.15&0.12&0.04&0.18&0.16&0.06&0.14&0.29&0.1\\\\\n&WM&0.15&0.08&0.03&0.15&0.11&0.04&0.17&0.14&0.05&0.16&0.3&0.11\\\\\n&IVW&0.16&0.07&0.03&0.15&0.1&0.03&0.16&0.12&0.04&0.17&0.24&0.09\\\\\n&Egger&0.15&0.08&0.03&0.14&0.72&0.53&0.14&0.15&0.04&0.24&1.92&3.76\\\\\n&TS-RE&-0.05&0.18&0.03&0.13&0.12&0.03&-0.11&0.47&0.23&0.17&0.29&0.11\\\\\n \\hline\n\\multirow {5}{*}{300}&SM&0.18&0.05&0.03&0.17&0.09&0.04&0.17&0.14&0.05&0.12&0.21&0.06\\\\\n&WM&0.18&0.05&0.03&0.17&0.09&0.04&0.15&0.12&0.04&0.13&0.21&0.06\\\\\n&IVW&0.18&0.04&0.03&0.17&0.08&0.04&0.17&0.12&0.04&0.14&0.19&0.06\\\\\n&Egger&0.18&0.06&0.04&0.13&0.82&0.7&0.15&0.14&0.04&0.22&1.86&3.5\\\\\n&TS-RE&-0.02&0.16&0.03&0.16&0.1&0.04&-0.1&0.38&0.15&0.12&0.23&0.07\\\\\n \\hline\n\\multirow {5}{*}{400}&SM&0.18&0.05&0.04&0.17&0.08&0.04&0.18&0.14&0.05&0.18&0.21&0.08\\\\\n&WM&0.18&0.05&0.03&0.18&0.08&0.04&0.19&0.12&0.05&0.18&0.22&0.08\\\\\n&IVW&0.18&0.04&0.03&0.18&0.08&0.04&0.19&0.11&0.05&0.18&0.19&0.07\\\\\n&Egger&0.18&0.06&0.04&0.22&0.6&0.41&0.19&0.13&0.05&0.27&1.57&2.55\\\\\n&TS-RE&-0.01&0.16&0.03&0.18&0.1&0.04&-0.01&0.36&0.13&0.18&0.23&0.08\\\\\n \\hline\n\\multirow {5}{*}{500}&SM&0.18&0.05&0.04&0.19&0.08&0.04&0.16&0.11&0.04&0.15&0.18&0.05\\\\\n&WM&0.19&0.05&0.04&0.18&0.08&0.04&0.17&0.11&0.04&0.15&0.17&0.05\\\\\n&IVW&0.19&0.04&0.04&0.19&0.07&0.04&0.17&0.09&0.04&0.15&0.17&0.05\\\\\n&Egger&0.19&0.05&0.04&0.18&0.61&0.41&0.16&0.12&0.04&0.12&1.77&3.14\\\\\n&TS-RE&-0.01&0.14&0.02&0.18&0.08&0.04&-0.1&0.34&0.13&0.14&0.2&0.06\\\\\n \\hline\n\\multirow {5}{*}{600}&SM&0.19&0.05&0.04&0.17&0.09&0.04&0.19&0.1&0.05&0.17&0.16&0.05\\\\\n&WM&0.18&0.05&0.03&0.17&0.09&0.04&0.19&0.1&0.04&0.17&0.16&0.05\\\\\n&IVW&0.18&0.04&0.04&0.17&0.08&0.03&0.19&0.09&0.04&0.16&0.16&0.05\\\\\n&Egger&0.18&0.06&0.03&0.08&0.65&0.43&0.17&0.11&0.04&0.39&1.29&1.81\\\\\n&TS-RE&-0.02&0.13&0.02&0.15&0.1&0.03&-0.01&0.27&0.07&0.15&0.18&0.06\\\\\n \\hline\n\\multirow {5}{*}{700}&SM&0.19&0.05&0.04&0.17&0.08&0.04&0.18&0.09&0.04&0.16&0.16&0.05\\\\\n&WM&0.18&0.05&0.04&0.17&0.08&0.04&0.17&0.09&0.04&0.16&0.16&0.05\\\\\n&IVW&0.19&0.04&0.04&0.17&0.07&0.04&0.18&0.08&0.04&0.16&0.15&0.05\\\\\n&Egger&0.18&0.05&0.04&0.3&0.48&0.32&0.16&0.11&0.04&0.11&1.06&1.14\\\\\n&TS-RE&-0.02&0.14&0.02&0.16&0.09&0.03&-0.04&0.26&0.07&0.14&0.19&0.05\\\\\n \\hline\n\\multirow {5}{*}{800}&SM&0.18&0.04&0.04&0.18&0.08&0.04&0.19&0.08&0.04&0.18&0.12&0.05\\\\\n&WM&0.18&0.04&0.04&0.18&0.08&0.04&0.18&0.07&0.04&0.18&0.12&0.05\\\\\n&IVW&0.18&0.04&0.03&0.18&0.07&0.04&0.18&0.07&0.04&0.18&0.11&0.05\\\\\n&Egger&0.18&0.05&0.04&0.12&0.53&0.3&0.18&0.09&0.04&0.16&0.89&0.81\\\\\n&TS-RE&-0.02&0.12&0.01&0.16&0.09&0.03&-0.03&0.23&0.05&0.17&0.14&0.05\\\\\n \\hline\n\\multirow {5}{*}{900}&SM&0.19&0.04&0.04&0.19&0.07&0.04&0.19&0.07&0.04&0.18&0.1&0.04\\\\\n&WM&0.19&0.04&0.04&0.19&0.06&0.04&0.18&0.05&0.04&0.18&0.1&0.04\\\\\n&IVW&0.19&0.03&0.04&0.19&0.06&0.04&0.19&0.06&0.04&0.19&0.1&0.04\\\\\n&Egger&0.18&0.04&0.04&0.21&0.56&0.36&0.18&0.06&0.04&0.12&0.83&0.71\\\\\n&TS-RE&-0.02&0.14&0.02&0.18&0.08&0.04&-0.02&0.2&0.04&0.18&0.13&0.05\\\\\n \\hline\n\\multirow {5}{*}{1000}&SM&0.19&0.04&0.04&0.17&0.07&0.03&0.19&0.04&0.04&0.17&0.07&0.03\\\\\n&WM&0.18&0.03&0.03&0.18&0.07&0.04&0.18&0.03&0.03&0.18&0.07&0.04\\\\\n&IVW&0.18&0.03&0.03&0.18&0.05&0.03&0.18&0.03&0.03&0.18&0.05&0.03\\\\\n&Egger&0.18&0.04&0.03&0.25&0.54&0.35&0.18&0.04&0.03&0.25&0.54&0.35\\\\\n&TS-RE&-0.03&0.12&0.01&0.17&0.07&0.03&-0.03&0.12&0.01&0.17&0.07&0.03\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Simulation results for a mixture of IVs from $\\mathbb{G}_b$ and $\\mathbb{G}_c$. The total number of IVs is $1000=M_b+M_c$ and the number $M_b$ is varied from $0$ to $1000$. For the balanced pleiotropy $E(\\alpha_c) = 0$, and for the directional pleiotropy $E(\\alpha_c) = 0.1$, the InSIDE assumption is valid that $\\rho_{\\mathbb{G}_c}=0$. All IVs have weak effect $N(\\mu=0, \\sigma^3=0.03^3)$ and the true causal effect is $\\theta=0.3$}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Random Effects Model-based Method of Moments Estimation of Causal Effect in Mendelian Randomization Studies", "authors": ["Wenhao Cao", "Saonli Basu"], "url": "https://arxiv.org/abs/2312.10926v1", "attribution": "\"A Random Effects Model-based Method of Moments Estimation of Causal Effect in Mendelian Randomization Studies\" by Wenhao Cao and Saonli Basu, arXiv:2312.10926v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04807v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The comparison of MSE among different $\\beta$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\t\t\\hline \\multicolumn{2}{|c|}{Name}&$\\beta=1$&$\\beta=50$&$\\beta=100$&$\\beta=200$\\\\\n\t\t\\hline\n\t\t\\multirow{2}{*}{Inexact}&Mean MSE &0.01&$\\mathbf{6.28\\times10^{-4}}$&$6.34\\times10^{-4}$&$1.73\\times10^{-3}$\\\\\n\t\t\\cline{2-6} % 跨过2-4列的横线\n\t\t&Variation&$1.71\\times10^{-3}$&\n\t\t$\\mathbf{5.40\\times10^{-5}}$&$8.16\\times10^{-5}$&$8.71\\times10^{-15}$\\\\\n\t\t\\hline\n\t\t\\multirow{2}{*}{Exact}&Mean MSE \n\t\t&$2.37\\times10^{-5}$&$1.77\\times10^{-13}$&$\\mathbf{1.22\\times10^{-17}}$&$6.43\\times10^{-7}$\\\\\n\t\t\\cline{2-6} % 跨过2-4列的横线\n\t\t&Variation&$1.18\\times10^{-4}$&$1.07\\times10\n\t\t^{-12}$&$\\mathbf{1.07\\times10^{-17}}$&$8.51\\times10^{-22}$\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Sparse Sampling Kaczmarz-Motzkin Method with Linear Convergence", "authors": ["Ziyang Yuan", "Hui Zhang", "Hongxia Wang"], "url": "https://arxiv.org/abs/2101.04807v2", "attribution": "\"Sparse Sampling Kaczmarz-Motzkin Method with Linear Convergence\" by Ziyang Yuan, Hui Zhang, and Hongxia Wang, arXiv:2101.04807v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.12619v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\tabularnewline\\midrule\\midrule\n Dependent Variables: & Unemployment & Employed & Unemployed & Labour & Population\\\\\n~ & Rate & Persons (log) & Persons (log) & Force (log) & (log)\\\\\n Model: & (1) & (2) & (3) & (4) & (5)\\\\\n \\midrule \\emph{Variables} & & & & & \\\\\n Active Mines & -0.0955$^{***}$ & 0.0015$^{*}$ & -0.0103$^{***}$ & 0.0004 & 0.0012\\\\\n & (0.0205) & (0.0006) & (0.0020) & (0.0006) & (0.0007)\\\\\n Active Mines\\textsubscript{t-1} & -0.0035 & 0.0012. & $-9.24\\times 10^{-5}$ & 0.0012 & 0.0003\\\\\n & (0.0157) & (0.0007) & (0.0015) & (0.0007) & (0.0007)\\\\\n Active Mines\\textsubscript{t-2} & 0.0619$^{***}$ & -0.0005 & 0.0040$^{*}$ & 0.0002 & 0.0010\\\\\n & (0.0122) & (0.0004) & (0.0015) & (0.0005) & (0.0007)\\\\\n (log) Real GDPPC\\textsubscript{t} & -0.9989$^{**}$ & & & & \\\\\n & (0.2558) & & & & \\\\\n (log) Real GDP\\textsubscript{t} & & 0.1035$^{***}$ & -0.0926$^{**}$ & 0.0929$^{***}$ & 0.0665$^{***}$\\\\\n & & (0.0106) & (0.0300) & (0.0099) & (0.0110)\\\\\n (log) Population\\textsubscript{t} & & 0.9033$^{***}$ & 1.103$^{***}$ & 0.9189$^{***}$ & \\\\\n & & (0.0298) & (0.0878) & (0.0287) & \\\\\n \\midrule \\emph{Fixed-effects} & & & & & \\\\\n County FIPS Code & Yes & Yes & Yes & Yes & Yes\\\\\n Year & Yes & Yes & Yes & Yes & Yes\\\\\n \\midrule \\emph{Fit statistics} & & & & & \\\\\n Observations & 55,296 & 55,296 & 55,296 & 55,296 & 55,296\\\\\n R$^2$ & 0.84472 & 0.99857 & 0.99204 & 0.99876 & 0.99891\\\\\n Within R$^2$ & 0.02664 & 0.45766 & 0.12865 & 0.49102 & 0.05022\\\\\n \\midrule\\midrule\\multicolumn{6}{l}{\\emph{Clustered (County FIPS Code \\& Year) standard-errors in parentheses}}\\\\\n \\multicolumn{6}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'", "authors": ["Ebba Mark", "Ryan Rafaty", "Moritz Schwarz"], "url": "https://arxiv.org/abs/2211.12619v1", "attribution": "\"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'\" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.21379v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Distribution of $\\hat{N} - N$ over 100 simulated data sequences from the Poisson model with different rate values.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|}\n\\cline{1-6}\n & & & \\multicolumn{3}{|c|}{} \\\\\n & & & \\multicolumn{3}{|c|}{$\\hat{N} - N$} \\\\\nModel & $T$ & Method & 0 & 1 & $\\geq 2$\\\\\n\\hline\nPoisson(0.3) & 30 & $L_{\\infty}$ & 100 & 0 & 0\\\\\n & & $L_{2}$ & 88 & 8 & 4\\\\\n\\cline{2-6}\n & 75 & $L_{\\infty}$ & 99 & 0 & 1\\\\\n & & $L_{2}$ & 98 & 1 & 1\\\\\n\\cline{2-6}\n & 200 & $L_{\\infty}$ & 100 & 0 & 0\\\\\n & & $L_{2}$ & 100 & 0 & 0\\\\\n\\cline{2-6}\n & 500 & $L_{\\infty}$ & 100 & 0 & 0\\\\\n & & $L_{2}$ & 100 & 0 & 0\\\\\n\\hline\nPoisson(3) & 30 & $L_{\\infty}$ & 100 & 0 & 0\\\\\n & & $L_{2}$ & 100 & 0 & 0\\\\\n\\cline{2-6}\n & 75 & $L_{\\infty}$ & 99 & 0 & 1\\\\\n & & $L_{2}$ & 100 & 0 & 0\\\\\n\\cline{2-6}\n & 200 & $L_{\\infty}$ & 99 & 1 & 0\\\\\n & & $L_{2}$ & 100 & 0 & 0\\\\\n\\cline{2-6}\n & 500 & $L_{\\infty}$ & 100 & 0 & 0\\\\\n & & $L_{2}$ & 100 & 0 & 0\\\\\n\\hline\nPoisson(30) & 30 & $L_{\\infty}$ & 94 & 5 & 1\\\\\n & & $L_{2}$ & 99 & 1 & 0\\\\\n\\cline{2-6}\n & 75 & $L_{\\infty}$ & 97 & 3 & 0\\\\\n & & $L_{2}$ & 100 & 0 & 0\\\\\n\\cline{2-6}\n & 200 & $L_{\\infty}$ & 98 & 2 & 0\\\\\n & & $L_{2}$ & 100 & 0 & 0\\\\\n\\cline{2-6}\n & 500 & $L_{\\infty}$ & 96 & 4 & 0\\\\\n & & $L_{2}$ & 100 & 0 & 0\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Non-parametric multiple change-point detection", "authors": ["Andreas Anastasiou", "Piotr Fryzlewicz"], "url": "https://arxiv.org/abs/2504.21379v1", "attribution": "\"Non-parametric multiple change-point detection\" by Andreas Anastasiou and Piotr Fryzlewicz, arXiv:2504.21379v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11277v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Effects of brain-focused patch discriminator, the utilization of perceptual loss with a pre-trained VGG network, and a detailed inspection of the effect of Octave Convolutions and its low-frequency ratio ($\\alpha$).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{clccccc}\n\\hline\n\\multicolumn{2}{c}{Methods} & SSIM ($\\uparrow$) & PSNR ($\\uparrow$) & MAE ($\\downarrow$)& MSE ($\\downarrow$) & Time ($\\downarrow$) \\\\ \\hline \\hline\n\\multicolumn{2}{c}{Macro2Micro (w/o brainPD)} & \\textbf{0.8663} & \\textbf{26.2560} & 0.1440 & 0.1288 & 0.0110 \\\\\n\\multicolumn{2}{c}{Macro2Micro (w/o perct.loss)} & 0.8565 & 25.7207 & 0.1407 & 0.1251 & 0.0110 \\\\\n\\multicolumn{2}{c}{Macro2Micro} & 0.8631 & 26.0478 & \\textbf{0.1374} & \\textbf{0.1221} & 0.0110 \\\\ \\hline\n\\multicolumn{2}{c}{No Octave} & 0.8598 & 25.8797 & \\textbf{0.1280} & \\textbf{0.1122} & \\textbf{0.0056} \\\\\n\\multicolumn{2}{c}{Octave $\\alpha=0.25$} & \\textbf{0.8640} & \\textbf{26.1565} & 0.1417 & 0.1264 & 0.0126 \\\\\n\\multicolumn{2}{c}{Octave $\\alpha=0.50$ (Default)} & 0.8631 & 26.0478 & 0.1374 & 0.1221 & 0.0110 \\\\\n\\multicolumn{2}{c}{Octave $\\alpha=0.75$} & 0.8597 & 25.9887 & 0.1414 & 0.1258 & 0.0129 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Macro2Micro: Cross-modal Magnetic Resonance Imaging Synthesis Leveraging Multi-scale Brain Structures", "authors": ["Sooyoung Kim", "Joonwoo Kwon", "Junbeom Kwon", "Sangyoon Bae", "Yuewei Lin", "Shinjae Yoo", "Jiook Cha"], "url": "https://arxiv.org/abs/2412.11277v1", "attribution": "\"Macro2Micro: Cross-modal Magnetic Resonance Imaging Synthesis Leveraging Multi-scale Brain Structures\" by Sooyoung Kim, Joonwoo Kwon, Junbeom Kwon, Sangyoon Bae, Yuewei Lin, Shinjae Yoo, and Jiook Cha, arXiv:2412.11277v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.07384v4_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\large\\bf Costs Estimates}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc} \\toprule\nStages& {\\bf Middle 90\\%}& {\\bf Bottom 95\\%} \\\\ \\midrule\n\\emph{Panel (a):} {\\bf Total Costs} & & \\\\\nAll Drugs&\\$58.51 mm & \\$60.72 mm \\\\ \nDrugs with Complete Path&\\$69.24 mm & \\$77.01 mm \\\\ \n&&\\\\\n\\cline{2-3}\\\\\n\\emph{Panel (b):} {\\bf FDA Review}& \\$638.75 mm & \\$648.04 mm\\\\\n\\cline{2-3}\\\\\n\\emph{Panel (c):} {\\bf Clinical Trials}& & \\\\\nPhase I\t&\\$0.62 mm & \\$0.22 mm\\\\ Phase II\t&\\$30.48 mm&\\$34.46 mm\\\\\nPhase III\t&\\$41.09 mm&\\$39.71 mm\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Valuing Pharmaceutical Drug Innovations", "authors": ["Gaurab Aryal", "Federico Ciliberto", "Leland E. Farmer", "Ekaterina Khmelnitskaya"], "url": "https://arxiv.org/abs/2212.07384v4", "attribution": "\"Valuing Pharmaceutical Drug Innovations\" by Gaurab Aryal, Federico Ciliberto, Leland E. Farmer, and Ekaterina Khmelnitskaya, arXiv:2212.07384v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.05013v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rrrr|rrrr|rrrr}\n\\toprule\n\\multirow{2}{*}{$k$} & \\multicolumn{4}{c}{Window 1} & \\multicolumn{4}{c}{Window 2} & \\multicolumn{4}{c}{Window 3} \\\\\n\\cmidrule{2-5} \\cmidrule{6-9} \\cmidrule{10-13}\n & ADF $p$ & $\\overline{\\beta_{t}}_{,k}$ & $\\text{sd}(\\beta_{t,k})$ & $\\tau$ & ADF $p$ & $\\overline{\\beta_{t}}_{,k}$ & $\\text{sd}(\\beta_{t,k})$ & $\\tau$ & ADF $p$ & $\\overline{\\beta_{t}}_{,k}$ & $\\text{sd}(\\beta_{t,k})$ & $\\tau$ \\\\\n\\midrule\n1 & 0.030 & 42.044 & 0.346 & 11.437 & 0.225 & 42.235 & 0.311 & 4.555 & 0.022 & 42.651 & 0.315 & 9.051 \\\\\n2 & 0.009 & 0.094 & 0.948 & 7.387 & 0.054 & 0.039 & 0.743 & 8.399 & 0.002 & 0.249 & 0.544 & 11.378 \\\\\n3 & 0.075 & -1.125 & 0.308 & 6.678 & 0.001 & -0.793 & 0.281 & 7.831 & 0.000 & -0.522 & 0.185 & 2.350 \\\\\n4 & 0.000 & -0.028 & 0.347 & 3.890 & 0.000 & 0.049 & 0.231 & 2.034 & 0.000 & 0.036 & 0.174 & 3.115 \\\\\n5 & 0.000 & 0.327 & 0.210 & 2.390 & 0.000 & 0.250 & 0.183 & 2.908 & 0.000 & 0.145 & 0.125 & 1.603 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Legendre Fit: Summary statistics for Legendre coefficients of ARB5 for each window. $\\tau$ is mean return time $-1/\\log|\\phi|$ from AR(1).}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Dynamics of Liquidity Surfaces in Uniswap v3", "authors": ["Jimmy Risk", "Shen-Ning Tung", "Tai-Ho Wang"], "url": "https://arxiv.org/abs/2509.05013v1", "attribution": "\"Dynamics of Liquidity Surfaces in Uniswap v3\" by Jimmy Risk, Shen-Ning Tung, and Tai-Ho Wang, arXiv:2509.05013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.00140v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results for each method for different scenarios. The NN-VFA method mostly outperforms all other methods. Re-optimization works best for the trucks-only instance and well for the low-uncertainty variant. The gap for re-optimization is the average gap over the 30 solutions in the horizon.}\n\\begin{tabular}{lrrrrrrrr}\n\\hline\n\\multirow{2}{*}{\\textbf{Trucks only}} & \\multirow{2}{*}{Runtime (s)} & \\multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\\\\n & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\\\ \\hline\nPI-bound & 1113.18 & 0.00\\% & 8788 (±333) & 3358 & 0 & 5430 & 35 & 80\\% \\\\\nRe-optimization & 2968.10 & 0.01\\% & \\textbf{9500 (±573)} & 3510 & 0 & 5990 & 35 & 80\\% \\\\\nRule-based & 0.00 & & 12,191 (±735) & 3251 & 0 & 8940 & 24 & 97\\% \\\\\nPPO & 3647.49 & & 10,893 (±826) & 3243 & 0 & 7650 & 30 & 99\\% \\\\\nDL-VFA & 342.30 & & 10,045 (±614) & 3855 & 0 & 6190 & 37 & 85\\% \\\\\nNN-VFA & 7145.99 & & 9923 (±250) & 3223 & 0 & 6700 & 36 & 87\\% \\\\\n\\hline\n\\multirow{2}{*}{\\textbf{Low uncertainty}} & \\multirow{2}{*}{Runtime (s)} & \\multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\\\\n & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\\\ \\hline\nPI-bound & 1410.15 & 0.00\\% & 7570 (±147) & 2820 & 2200 & 2550 & 20 & 78\\% \\\\\nRe-optimization & 3767.23 & 0.05\\% & 8554 (±118) & 3459 & 2755 & 2340 & 25 & 77\\% \\\\\nRule-based & 0.00 & & 11,357 (±986) & 2712 & 665 & 7980 & 16 & 96\\% \\\\\nPPO & 3697.48 & & 10,041 (±315) & 2881 & 3520 & 3640 & 25 & 96\\% \\\\\nDL-VFA & 417.39 & & 8506 (±564) & 2931 & 3145 & 2430 & 24 & 81\\% \\\\\nNN-VFA & 7596.05 & & \\textbf{8016 (±126)} & 2706 & 3480 & 1830 & 21 & 81\\% \\\\\n\\hline\n\\multirow{2}{*}{\\textbf{High uncertainty}} & \\multirow{2}{*}{Runtime (s)} & \\multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\\\\n & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\\\ \\hline\nPI-bound & 2015.60 & 0.00\\% & 7421 (±371) & 2536 & 1765 & 3120 & 19 & 80\\% \\\\\nRe-optimization & 5610.57 & 0.12\\% & 8921 (±497) & 2951 & 3420 & 2550 & 23 & 82\\% \\\\\nRule-based & 0.01 & & 10,824 (±1189) & 2624 & 640 & 7560 & 16 & 97\\% \\\\\nPPO & 3307.25 & & 10,298 (±808) & 2998 & 3570 & 3730 & 26 & 95\\% \\\\\nDL-VFA & 387.43 & & 8542 (±284) & 2382 & 3490 & 2670 & 26 & 87\\% \\\\\nNN-VFA & 6463.33 & & \\textbf{8036 (±384)} & 2746 & 3190 & 2100 & 22 & 82\\% \\\\\n\\hline\n\\multirow{2}{*}{\\textbf{Decreasing demand}} & \\multirow{2}{*}{Runtime (s)} & \\multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\\\\n & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\\\ \\hline\nPI-bound & 2166.06 & 0.00\\% & 8624 (±351) & 3854 & 2460 & 2310 & 19 & 67\\% \\\\\nRe-optimization & 4876.01 & 0.27\\% & 10,048 (±407) & 3933 & 3715 & 2400 & 25 & 71\\% \\\\\nRule-based & 0.00 & & 11,880 (±785) & 3475 & 1055 & 7350 & 13 & 91\\% \\\\\nPPO & 3314.15 & & 11,389 (±501) & 4114 & 4375 & 2900 & 28 & 92\\% \\\\\nDL-VFA & 515.10 & & 9811 (±330) & 3321 & 4990 & 1500 & 23 & 74\\% \\\\\nNN-VFA & 7049.12 & & \\textbf{9547 (±422)} & 3572 & 4355 & 1620 & 21 & 71\\% \\\\\n\\hline\n\\multirow{2}{*}{\\textbf{Increasing demand}} & \\multirow{2}{*}{Runtime (s)} & \\multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\\\\n & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\\\ \\hline\nPI-bound & 2596.36 & 0.00\\% & 6347 (±249) & 1602 & 845 & 3900 & 22 & 90\\% \\\\\nRe-optimization & 5160.64 & 0.12\\% & 7505 (±331) & 1775 & 1980 & 3750 & 25 & 92\\% \\\\\nRule-based & 0.00 & & 9373 (±856) & 2053 & 510 & 6810 & 12 & 96\\% \\\\\nPPO & 3695.31 & & 8894 (±671) & 2804 & 2860 & 3230 & 21 & 98\\% \\\\\nDL-VFA & 430.07 & & 7449 (±303) & 2774 & 2365 & 2310 & 30 & 86\\% \\\\\nNN-VFA & 6193.41 & & \\textbf{6828 (±149)} & 1888 & 2660 & 2280 & 20 & 93\\% \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Stochastic Dynamic Post-Disaster Inventory Allocation Problem with Trucks and UAVs", "authors": ["Robert van Steenbergen", "Wouter van Heeswijk", "Martijn Mes"], "url": "https://arxiv.org/abs/2312.00140v1", "attribution": "\"The Stochastic Dynamic Post-Disaster Inventory Allocation Problem with Trucks and UAVs\" by Robert van Steenbergen, Wouter van Heeswijk, and Martijn Mes, arXiv:2312.00140v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08580v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study of fact scoring functions}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc|ccc}\n \\toprule & MAE$\\,\\downarrow$ & RMSE$\\,\\downarrow$ & P$\\,\\uparrow$ & R$\\,\\uparrow$ & F1$\\,\\uparrow$ \\\\\n \\midrule TKGC w/o $\\mathcal{F}_{lit}$ & N/A & N/A & 0.425 & 0.481 & 0.451 \\\\\n TKGC w/o $\\mathcal{F}_{rel}$ & 0.071 & 0.083 & N/A & N/A & N/A \\\\\n TKGC (full) & 0.054 & 0.062 & 0.524 & 0.491 & 0.507 \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data", "authors": ["Jiacheng Huang", "Yao Zhao", "Wei Hu", "Zhen Ning", "Qijin Chen", "Xiaoxia Qiu", "Chengfu Huo", "Weijun Ren"], "url": "https://arxiv.org/abs/2201.08580v1", "attribution": "\"Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data\" by Jiacheng Huang, Yao Zhao, Wei Hu, Zhen Ning, Qijin Chen, Xiaoxia Qiu, Chengfu Huo, and Weijun Ren, arXiv:2201.08580v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.03024v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{EER (\\%) comparison between the speaker embedding vectors extracted from the proposed joint factor embedding and the other embedding techniques.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n\t\t\\hline\n\t\t & Objective & EER [\\%] \\\\ \\hline\n\t\t\\multirow{4}{*}{d-vector} & \\textit{Softmax} & 10.72 \\\\ %\\cline{2-3} \n\t\t& \\textit{Gradient reversal} & 10.37 \\\\ %\\cline{2-3} \n\t\t& \\textit{Anti-loss} & 10.47 \\\\ %\\cline{2-3} \n\t\t& \\textit{\\textbf{JFE (proposed)}} & \\textbf{8.43} \\\\ \\hline\n\t\t\\multirow{4}{*}{x-vector} & \\textit{Softmax} & 2.26 \\\\ %\\cline{2-3} \n\t\t& \\textit{Gradient reversal} & 5.87 \\\\ %\\cline{2-3} \n\t\t& \\textit{Anti-loss} & 1.46 \\\\ %\\cline{2-3} \n\t\t& \\textit{\\textbf{JFE (proposed)}} & \\textbf{1.07} \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Disentangled speaker and nuisance attribute embedding for robust speaker verification", "authors": ["Woo Hyun Kang", "Sung Hwan Mun", "Min Hyun Han", "Nam Soo Kim"], "url": "https://arxiv.org/abs/2008.03024v1", "attribution": "\"Disentangled speaker and nuisance attribute embedding for robust speaker verification\" by Woo Hyun Kang, Sung Hwan Mun, Min Hyun Han, and Nam Soo Kim, arXiv:2008.03024v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2207.11577v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study of $\\mathcal{N}_{\\textrm{old}}$ for Stock 1} % title of the Figure\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n\\hline\nModel & F1-score \\\\ \\hline\nlayers\\_{[}{[}60, 5{]}, {[}200, 10{]}{]} & 0.694022 \\\\ \\hline\nlayers\\_{[}{[}60, 10{]}, {[}120, 5{]}{]} & 0.687618 \\\\ \\hline\nlayers\\_{[}{[}60, 5{]}, {[}120, 10{]}, {[}50, 5{]}{]} & 0.686768 \\\\ \\hline\nlayers\\_{[}{[}60, 10{]}, {[}200, 10{]}{]} & 0.68425 \\\\ \\hline\nlayers\\_{[}{[}50, 10{]}, {[}250, 5{]}{]} & 0.68172 \\\\ \\hline\nlayers\\_{[}{[}60, 10{]}, {[}200, 5{]}{]} & 0.67418 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification", "authors": ["Mostafa Shabani", "Dat Thanh Tran", "Juho Kanniainen", "Alexandros Iosifidis"], "url": "https://arxiv.org/abs/2207.11577v1", "attribution": "\"Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification\" by Mostafa Shabani, Dat Thanh Tran, Juho Kanniainen, and Alexandros Iosifidis, arXiv:2207.11577v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10127v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experimental scale factor values of buyer compared with theoretical scale factor values for case-3}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c|c||}\\toprule\n \\textit{} & \\textit{} & \\textit{$\\alpha_{B1}$} & \\textit{$\\alpha_{B2}$} \\\\ \\midrule\n Theoretical Equilibrium & & 0.882782 & 0.588521 \\\\\n \\hline\n DDPG Empirical Equilibrium & mean & 0.855816 & 0.302352 \\\\\n \\hline\n & std & 0.050594 & 0.221870 \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multi-unit Double Auctions: Equilibrium Analysis and Bidding Strategy using DDPG in Smart-grids", "authors": ["Sanjay Chandlekar", "Easwar Subramanian", "Sanjay Bhat", "Praveen Paruchuri", "Sujit Gujar"], "url": "https://arxiv.org/abs/2201.10127v2", "attribution": "\"Multi-unit Double Auctions: Equilibrium Analysis and Bidding Strategy using DDPG in Smart-grids\" by Sanjay Chandlekar, Easwar Subramanian, Sanjay Bhat, Praveen Paruchuri, and Sujit Gujar, arXiv:2201.10127v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.13382v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\toprule\n $n$ & $p$ & $\\varepsilon$ & $K'''$ & $K''$ & $K'$ & $|E|$ \\\\\n \\midrule\n 600 & 0.9 & 0.5 & 599.95 & 602.98 & 602.98 & 81979 \\\\\n 600 & 0.9 & 1.0 & 605.54 & 614.65 & 614.65 & 27430 \\\\\n 600 & 0.9 & 1.5 & 615.09 & 632.65 & 632.65 & 12957 \\\\\n 600 & 0.9 & 2.0 & 629.93 & 668.70 & 668.70 & 7442 \\\\\n \\midrule\n 500 & 0.9 & 0.5 & $499.72$ & $ 501.60$ & $501.60$ & $62251$ \\\\\n 500 & 0.9 & 1.0 & $504.68$ & $511.87$ & $511.87$ & $21756$ \\\\\n 500 & 0.9 & 1.5 & $513.23$ & $526.61$ & $526.61$ & $10439$ \\\\\n 500 & 0.9 & 2.0 & $525.41$ & $545.43$ & $545.43$ & $5993$ \\\\\n \\midrule\n 250 & 0.9 & 0.5 & 249.02 & 249.29 & 249.29 & 20772 \\\\\n 250 & 0.9 & 1.0 & 251.80 & 256.51 & 256.51 & 8832 \\\\\n 250 & 0.9 & 1.5 & 256.66 & 265.09 & 265.09 & 4430 \\\\\n 250 & 0.9 & 2.0 & 264.56 & 284.82 & 284.82 & 2599 \\\\\n \\midrule\n 600 & 0.7 & 0.5 & 600.25 & 603.30 & 603.30 & 74207 \\\\\n 600 & 0.7 & 1.0 & 605.90 & 612.09 & 612.09 & 26638 \\\\\n 600 & 0.7 & 1.5 & 615.52 & 627.72 & 627.72 & 12813 \\\\\n 600 & 0.7 & 2.0 & 629.97 & 665.88 & 665.88 & 7408 \\\\\n \\midrule\n 500 & 0.7 & 0.5 & 500.01 & 501.30 & 501.30 & 56042 \\\\\n 500 & 0.7 & 1.0 & 504.92 & 513.69 & 513.69 & 21092 \\\\\n 500 & 0.7 & 1.5 & 513.62 & 530.86 & 530.86 & 10222 \\\\\n 500 & 0.7 & 2.0 & 526.64 & 551.93 & 551.93 & 5950 \\\\\n \\midrule\n 250 & 0.7 & 0.5 & 249.36 & 248.97 & 248.97 & 17649 \\\\\n 250 & 0.7 & 1.0 & 252.12 & 256.29 & 256.29 & 8315 \\\\\n 250 & 0.7 & 1.5 & 257.36 & 263.67 & 263.67 & 4268 \\\\\n 250 & 0.7 & 2.0 & 264.03 & 286.68 & 286.68 & 2583 \\\\\n \\bottomrule\n \\hspace{.2cm}\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Sparse approximations for different values of $\\varepsilon$ on $G(n,p)$, $p=0.9,0.7$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Spectral Approach to Kemeny's Constant", "authors": ["Aida Abiad", "Ángeles Carmona", "Andrés M. Encinas", "Maria José Jiménez", "Álvaro Samperio"], "url": "https://arxiv.org/abs/2503.13382v1", "attribution": "\"A Spectral Approach to Kemeny's Constant\" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13714v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\t\t\t\t\\hline\n\t\t\t\t\\small\\textbf{Case}& \\small\\textbf{Conditions}& \\small\\textbf{$\\boldsymbol{O_1}$}& \\small\\textbf{$\\boldsymbol{O_2}$} & \\small\\textbf{Global} \\\\\n\t\t\t\t\\hline\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\\multirow{3}{1cm}{\\centering{2.3}}\n\t\t\t\t&\n\t\t\t\t$\\mu=0$\n\t\t\t\t&\n\t\t\t\tL14\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{3cm}{\\centering{Unstable node}}\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{1cm}{\\centering{G43}} \\\\\n\t\t\t\t\\cline{2-3}\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu>-1$\n\t\t\t\t&\n\t\t\t\tL36\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{*}\n\t\t\t\t&\n\t\t\t\t\\multirow{2}{*} {\\phantom{h}} \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{3}{*}\n\t\t\t\t&\n\t\t\t\t$c_1=0$, $\\mu<-1$\n\t\t\t\t&\n\t\t\t\tL43\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG44 \\\\\n\t\t\t\t\\hline\n\t\t\t\\multirow{3}{1cm}{\\centering{2.4}}\n\t\t\t&\n\t\t\t$\\mu=0$\n\t\t\t&\n\t\t\tL5\n\t\t\t&\n\t\t\tStable node\n\t\t\t&\n\t\t\tG45\\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{3}{*}\n\t\t\t&\n\t\t\t$c_1=0$, $\\mu>-1$\n\t\t\t&\n\t\t\tL35\n\t\t\t&\n\t\t\tStable node\n\t\t\t&\n\t\t\tG46\\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{3}{*}\n\t\t\t&\n\t\t\t$c_1=0$, $\\mu<-1$\n\t\t\t&\n\t\t\tL39\n\t\t\t&\n\t\t\tSaddle\n\t\t\t&\n\t\t\tG47 \\\\\n\t\t\t\\hline\n\t\n\t\t\t\\multirow{3}{1cm}{\\centering{2.5}}\n\t\t\t&\n\t\t\t$\\mu=0$\n\t\t\t&\n\t\t\tL14\n\t\t\t&\n\t\t\t\\multirow{2}{3cm}{\\centering{Unstable node}}\n\t\t\t&\n\t\t\t\\multirow{2}{1cm}{\\centering{G48}}\n\t\t\t\\\\\n\t\t\t\\cline{2-3}\n\t\t\t\n\t\t\t\\multirow{3}{*}\n\t\t\t&\n\t\t\t$c_1=0$, $\\mu>-1$\n\t\t\t&\n\t\t\tL45\n\t\t\t&\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t\\multirow{2}{*} {\\phantom{h}}\n\t\t\t\\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\n\t\t\t\\multirow{3}{*}\n\t\t\t&\n\t\t\t$c_1=0$, $\\mu<-1$\n\t\t\t&\n\t\t\tL44\n\t\t\t&\n\t\t\tSaddle\n\t\t\t&\n\t\t\tG49 \\\\\n\t\t\t\\hline\n\t\t\t\t\t\t\n\t\t\t\t\\multirow{3}{1cm}{\\centering{2.6}}\n\t\t\t&\n\t\t\t$\\mu=0$\n\t\t\t&\n\t\t\tL5\n\t\t\t&\n\t\t\tStable node\n\t\t\t&\n\t\t\tG50\\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{3}{*}\n\t\t\t&\n\t\t\t$c_1=0$, $\\mu>-1$\n\t\t\t&\n\t\t\tL38\n\t\t\t&\n\t\t\tStable node\n\t\t\t&\n\t\t\tG51 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{3}{*}\n\t\t\t&\n\t\t\t$c_1=0$, $\\mu<-1$\n\t\t\t&\n\t\t\tL41\n\t\t\t&\n\t\t\tSaddle\n\t\t\t&\n\t\t\tG52 \\\\\n\t\t\t\\hline\n\t\t\\multirow{2}{1cm}{\\centering{2.7}}\n\t\t&\n\t\t$\\mu>-1$\n\t\t&\n\t\tL37\n\t\t&\n\t\tStable node\n\t\t&\n\t\tG53\\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{2}{*}\n\t\t&\n\t\t$\\mu<-1$\n\t\t&\n\t\tL40\n\t\t&\n\t\tSaddle\n\t\t&\n\t\tG54 \\\\\n\t\t\\hline\n\t\t\n\t\t\\multirow{2}{1cm}{\\centering{2.8}}\n\t\t&\n\t\t$\\mu>-1$\n\t\t&\n\t\tL34\n\t\t&\n\t\tUnstable node\n\t\t&\n\t\tG55\\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{2}{*}\n\t\t&\n\t\t$\\mu<-1$\n\t\t&\n\t\tL42\n\t\t&\n\t\tSaddle\n\t\t&\n\t\tG56 \\\\\n\t\t\\hline\n\t\t\\multirow{3}{1cm}{\\centering{2.9}}\n\t\t&\n\t\t$\\mu=0$\n\t\t&\n\t\tL14\n\t\t&\n\t\t\\multirow{2}{3cm}{\\centering{Unstable node}}\n\t\t&\n\t\t\\multirow{2}{1cm}{\\centering{G57}}\n\t\t\\\\\n\t\t\\cline{2-3}\n\t\t\n\t\t\\multirow{3}{*}\n\t\t&\n\t\t$c_1=0$, $\\mu>-1$\n\t\t&\n\t\tL24\n\t\t&\n\t\t\\multirow{2}{*}\n\t\t&\n\t\t\\multirow{2}{*} {\\phantom{h}}\n\t\t\\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\n\t\t\\multirow{3}{*}\n\t\t&\n\t\t$c_1=0$, $\\mu<-1$\n\t\t&\n\t\tL26\n\t\t&\n\t\tSaddle\n\t\t&\n\t\tG58 \\\\\n\t\t\\hline\n\t\n\t\t\\multirow{3}{1cm}{\\centering{2.10}}\n\t\t&\n\t\t$\\mu=0$\n\t\t&\n\t\tL5\n\t\t&\n\t\tStable node\n\t\t&\n\t\tG59\\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{3}{*}\n\t\t&\n\t\t$c_1=0$, $\\mu>-1$\n\t\t&\n\t\tL23\n\t\t&\n\t\tStable node\n\t\t&\n\t\tG60 \\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{3}{*}\n\t\t&\n\t\t$c_1=0$, $\\mu<-1$\n\t\t&\n\t\tL25\n\t\t&\n\t\tSaddle\n\t\t&\n\t\tG61 \\\\\n\t\t\\hline\n\t\t3.1\n\t\t&\n\t\n\t\t&\n\t\tL12\n\t\t&\n\t\tUnstable node\n\t\t&\n\t\tG62 \\\\\n\t\t\\hline\n\t\t\\multirow{3}{1cm}{\\centering{3.2}}\n\t\t&\n\t\t$\\mu>0$\n\t\t&\n\t\tL6\n\t\t&\n\t\tUnstable node\n\t\t&\n\t\tG63\\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{3}{*}\n\t\t&\n\t\t$\\mu<-1$\n\t\t&\n\t\tL8\n\t\t&\n\t\tStable node\n\t\t&\n\t\tG64 \\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{3}{*}\n\t\t&\n\t\t$\\mu\\in(-1,0)$\n\t\t&\n\t\tL13\n\t\t&\n\t\tUnstable node\n\t\t&\n\t\tG65 \\\\\n\t\t\\hline\n\t\t\n\t\t\\multirow{3}{1cm}{\\centering{3.3}}\n\t\t&\n\t\t$\\mu>0$\n\t\t&\n\t\tL3\n\t\t&\n\t\tStable node\n\t\t&\n\t\tG66\\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{3}{*}\n\t\t&\n\t\t$\\mu<-1$\n\t\t&\n\t\tL11\n\t\t&\n\t\tStable node\n\t\t&\n\t\tG68 \\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{3}{*}\n\t\t&\n\t\t$\\mu\\in(-1,0)$\n\t\t&\n\t\tL10\n\t\t&\n\t\tStable node\n\t\t&\n\t\tG67 \\\\\n\t\t\\hline\n\t\t\\multirow{4}{1cm}{\\centering{3.4}}\n\t\t&\n\t\t$\\mu<-2$\n\t\t&\n\t\tL1\n\t\t&\n\t\tSaddle\n\t\t&\n\t\tG69 \\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{4}{*}\n\t\t&\n\t\t$\\mu\\in(-1,0)$\n\t\t&\n\t\tL4\n\t\t&\n\t\tStable node\n\t\t&\n\t\tG70 \\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{4}{*}\n\t\t&\n\t\t$\\mu\\in(-2,-1)$\n\t\t&\n\t\tL15\n\t\t&\n\t\tSaddle\n\t\t&\n\t\tG71 \\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{4}{*}\n\t\t&\n\t\t$\\mu=-2$\n\t\t&\n\t\tL17\n\t\t&\n\t\tSaddle\n\t\t&\n\t\tG72 \\\\\n\t\t\\hline\n\t\t\n\t\t\t\n\t\t\\multirow{3}{1cm}{\\centering{3.5}}\n\t\t&\n\t\t$\\mu>0$\n\t\t&\n\t\tL3\n\t\t&\n\t\tStable node\n\t\t&\n\t\tG7 \\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{3}{*}\n\t\t&\n\t\t$\\mu\\in(-1,0)$\n\t\t&\n\t\tL10\n\t\t&\n\t\tStable node\n\t\t&\n\t\tG74 \\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{3}{*}\n\t\t&\n\t\t$\\mu<-1$\n\t\t&\n\t\tL11\n\t\t&\n\t\tSaddle\n\t\t&\n\t\tG75 \\\\\n\t\t\\hline\n\t\t\n\t 3.6\n\t\t&\n\t\t\n\t\t&\n\t\tL12\n\t\t&\n\t\tUnstable node\n\t\t&\n\t\tG76 \\\\\n\t\t\\hline\n\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{3.7}}\n\t\t&\n\t\t$\\mu<-1$\n\t\t&\n\t\tL8\n\t\t&\n\t\tSaddle\n\t\t&\n\t\tG77 \\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{2}{*}\n\t\t&\n\t\t$\\mu\\in(-1,0)$\n\t\t&\n\t\tL13\n\t\t&\n\t\tUnstable node\n\t\t&\n\t\tG78 \\\\\n\t\t\\hline\n\t\t\n\t\t3.8\n\t\t&\n\t\t\n\t\t&\n\t\tL3\n\t\t&\n\t\tStable node\n\t\t&\n\t\tG79 \\\\\n\t\t\\hline\n\t\t\n\t\t\t\\multirow{4}{1cm}{\\centering{3.9}}\n\t\t&\n\t\t$\\mu<-2$\n\t\t&\n\t\tL1\n\t\t&\n\t\tSaddle\n\t\t&\n\t\tG80 \\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{4}{*}\n\t\t&\n\t\t$\\mu\\in(-1,0)$\n\t\t&\n\t\tL4\n\t\t&\n\t\tStable node\n\t\t&\n\t\tG81\\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{4}{*}\n\t\t&\n\t\t$\\mu\\in(-2,-1)$\n\t\t&\n\t\tL15\n\t\t&\n\t\tSaddle\n\t\t&\n\t\tG82\\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{4}{*}\n\t\t&\n\t\t$\\mu=-2$\n\t\t&\n\t\tL17\n\t\t&\n\t\tSaddle\n\t\t&\n\t\tG83 \\\\\n\t\t\\hline\n\t\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Phase portraits of a family of Kolmogorov systems depending on six parameters", "authors": ["Érika Diz-Pita", "Jaume Llibre", "M. Victoria Otero-Espinar"], "url": "https://arxiv.org/abs/2501.13714v1", "attribution": "\"Phase portraits of a family of Kolmogorov systems depending on six parameters\" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.13714v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23211v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Location of estimated change points in the EEG data set with their CIs for the segment $(1,110)$ based on our method. True change point is $\\lfloor T \\tau \\rfloor = 85$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\hline\nChannel & value ($\\lfloor T \\tilde{\\tau} \\rfloor$) & $70\\%$ CI & $80\\%$ CI & $90\\%$ CI & $95\\%$ CI & $99\\%$ CI \\\\ \\hline\\hline\nP3 & 90 & (64,96) & (63,100) & (60,107) & (58,115) & (52,136) \\\\\nT3 & 86 & (85,87) & (85,87) & (85,91) & (85,98) & (85,118) \\\\\nT5 & 89 & (37,94) & (34,98) & (29,106) & (24,115) & (12,139) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models", "authors": ["Sepideh Mosaferi", "Abolfazl Safikhani", "Peiliang Bai"], "url": "https://arxiv.org/abs/2503.23211v2", "attribution": "\"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models\" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.12512v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Libri2Vox statistics. \"Total\" column shows the total number of utterances combined with corresponding LibriTTS and VoxCeleb2 sets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|}\n\\hline\n\\multirow{2}{*}{\\textbf{Set}} & \\multicolumn{2}{c|}{\\textbf{\\#. Speakers}} & \\textbf{\\#. Utterances} & \\multirow{2}{*}{\\textbf{Duration (h)}} \\\\ \\cline{2-4}\n& \\textbf{LibriTTS} & \\textbf{VoxCeleb2} & \\textbf{Total} & \\\\ \\hline\n\\textbf{Training} & 1,151 & 5,900 & 149,691 & 250 \\\\ \\hline\n\\textbf{Validation} & 40 & 94 & 7,200 & 8.97 \\\\ \\hline\n\\textbf{Testing} & 39 & 118 & 6,000 & 8.56 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Libri2Vox Dataset: Target Speaker Extraction with Diverse Speaker Conditions and Synthetic Data", "authors": ["Yun Liu", "Xuechen Liu", "Xiaoxiao Miao", "Junichi Yamagishi"], "url": "https://arxiv.org/abs/2412.12512v1", "attribution": "\"Libri2Vox Dataset: Target Speaker Extraction with Diverse Speaker Conditions and Synthetic Data\" by Yun Liu, Xuechen Liu, Xiaoxiao Miao, and Junichi Yamagishi, arXiv:2412.12512v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.01120v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ll}\n\\toprule\n Model & Method & Time (Mean $\\pm$ Std) \\\\\n \\midrule\n Neural network & Early stopping & $2.98 \\pm 0.24$ hours \\\\\n & Retrain & $5.44 \\pm 0.37$ hours \\\\\n GBDT & Early stopping & $56.94 \\pm 1.59$ seconds \\\\\n & Retrain & $154.12 \\pm 4.27$ seconds \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Shapley value etimation running time comparison.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Reliable and scalable variable importance estimation via warm-start and early stopping", "authors": ["Zexuan Sun", "Garvesh Raskutti"], "url": "https://arxiv.org/abs/2412.01120v2", "attribution": "\"Reliable and scalable variable importance estimation via warm-start and early stopping\" by Zexuan Sun and Garvesh Raskutti, arXiv:2412.01120v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.20396v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline\n\\textnormal{$e< \\beta^{-}(c)$} & \\textnormal{$\\beta^{+}(c)< e$} \\\\ \n\\hline\n\\textnormal{$p(t)=\\left(\\int_{0}^{t}\\Phi(t,s)c(s)\\,ds+o(1)\\right)^{-1}$} & \\textnormal{$p(t)\\to 0$ (exponentially)} \\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Behavior of the fraction of occupied patches $p(t)$ when $t\\to +\\infty$ and the colonization rate is constant.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Generalizing the Levins metapopulation model to time varying colonization and extinction rates", "authors": ["Gonzalo Robledo", "Ramiro Bustamante"], "url": "https://arxiv.org/abs/2504.20396v1", "attribution": "\"Generalizing the Levins metapopulation model to time varying colonization and extinction rates\" by Gonzalo Robledo and Ramiro Bustamante, arXiv:2504.20396v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.04266v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|ll}\n \\toprule\n \\textbf{Metric} & \\textbf{Formula} & \\textbf{Metric} & \\textbf{Formula} \\\\ \n \\midrule\n Recall & $\\displaystyle \\frac{TP}{TP + FN}$ & \n Accuracy & $\\displaystyle \\frac{TP + TN}{TP + TN + FP + FN}$ \\\\\n \n Precision & $\\displaystyle \\frac{TP}{TP + FP}$ & \n Specificity & $\\displaystyle \\frac{TN}{TN + FP}$ \\\\\n \n F1 Score & $\\displaystyle 2 \\cdot \\frac{\\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}$ & \n False Positive Rate & $\\displaystyle \\frac{FP}{FP + TN}$ \\\\\n \n False Negative Rate & $\\displaystyle \\frac{FN}{FN + TP}$ & & \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Evaluation Metrics}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "BlockingPy: approximate nearest neighbours for blocking of records for entity resolution", "authors": ["Tymoteusz Strojny", "Maciej Beręsewicz"], "url": "https://arxiv.org/abs/2504.04266v2", "attribution": "\"BlockingPy: approximate nearest neighbours for blocking of records for entity resolution\" by Tymoteusz Strojny and Maciej Beręsewicz, arXiv:2504.04266v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.08849v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Standardized mean square errors on regression benchmark datasets, averaged across 20 training-testing splits, for obliqueBART and rotated versions of axis-aligned methods with 200 random rotations. Best performing method is bolded and errors that are statistically significantly larger than obliqueBART's are marked with an asterisk. NA indicates that the method could not be run with 200 random rotations.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\hline\n data & obliqueBART & rotERT & rotRF & rotBART & rotXGB \\\\\n \\hline\n \\texttt{abalone} & \\textbf{0.44} & 0.45* & 1.08* & 0.448* & 0.47*\\\\\n \\texttt{ais} & \\textbf{0.122} & 0.151* & 1.33* & 0.129 & 0.167*\\\\\n \\texttt{amenity} & 0.283 & 0.349* & 1.07* & \\textbf{0.281} & 0.357*\\\\\n \\texttt{attend} & \\textbf{0.246} & 0.304* & 1.06* & 0.297* & 0.305*\\\\\n \\texttt{baseball} & \\textbf{0.39} & 0.446* & 1.07* & 0.406* & 0.544*\\\\\n \\texttt{basketball} & 0.762 & \\textbf{0.68} & 1.06* & 0.691 & 0.767*\\\\\n \\texttt{boston} & 0.778 & 0.833 & 22.6* & \\textbf{0.577} & 0.853\\\\\n \\texttt{budget} & \\textbf{0.003} & 0.010* & 1.200* & 0.003 & 0.019*\\\\\n \\texttt{cane} & \\textbf{0.183} & 0.315* & 1.580* & 0.191 & 0.294*\\\\\n \\texttt{cpu} & \\textbf{0.139} & 0.201* & 1.480* & 0.313* & 0.249*\\\\\n \\texttt{diabetes} & \\textbf{0.495} & 0.520* & 0.967* & 0.499 & 0.606*\\\\\n \\texttt{diamonds} & 0.020 & NA & NA & \\textbf{0.020} & NA\\\\\n \\texttt{edu} & \\textbf{0.020} & 0.022* & 1.150* & 0.020 & 0.024*\\\\\n \\texttt{labor} & \\textbf{0.221} & NA & NA & NA & NA\\\\\n \\texttt{mpg} & \\textbf{0.135} & 0.14 & 1.09* & 0.138 & 0.16*\\\\\n \\texttt{rice}& \\textbf{0.015} & 0.204* & 1.670* & 0.026* & 0.234*\\\\\n \\texttt{servo} & \\textbf{0.167} & 0.25* & 2.24* & 0.196* & 0.225*\\\\\n \\texttt{strikes} & 0.926 & 0.915 & 36.2* & \\textbf{0.805} & 1.31\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Oblique Bayesian additive regression trees", "authors": ["Paul-Hieu V. Nguyen", "Ryan Yee", "Sameer K. Deshpande"], "url": "https://arxiv.org/abs/2411.08849v1", "attribution": "\"Oblique Bayesian additive regression trees\" by Paul-Hieu V. Nguyen, Ryan Yee, and Sameer K. Deshpande, arXiv:2411.08849v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.12110v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Step time (ms) for OLG and Ramsey models at varying population sizes ($N$), Per-agent time indicates average computational time per individual at each step.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Population Size}} & \\multicolumn{2}{c}{\\textbf{OLG Model}} & \\multicolumn{2}{c}{\\textbf{Ramsey Model}} \\\\\n& \\textbf{Step Time (ms)} & \\textbf{Step Time / Agent (ms)} & \\textbf{Step Time (ms)} & \\textbf{Step Time / Agent (ms)} \\\\\n\\midrule\n$\\mathbf{N = 10}$ & 0.420 & 0.077 & 0.292 & 0.029 \\\\\n$\\mathbf{N = 100}$ & 0.723 & 0.031 & 0.665 & 0.007 \\\\\n$\\mathbf{N = 1{,}000}$ & 2.729 & 0.018 & 8.131 & 0.008 \\\\\n$\\mathbf{N = 10{,}000}$ & 62.276 & 0.016 & 451.798 & 0.045 \\\\\n$\\mathbf{N = 100{,}000}$ & 5173.004 & 0.096 & 39460.2 & 0.394 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "EconGym: A Scalable AI Testbed with Diverse Economic Tasks", "authors": ["Qirui Mi", "Qipeng Yang", "Zijun Fan", "Wentian Fan", "Heyang Ma", "Chengdong Ma", "Siyu Xia", "Bo An", "Jun Wang", "Haifeng Zhang"], "url": "https://arxiv.org/abs/2506.12110v1", "attribution": "\"EconGym: A Scalable AI Testbed with Diverse Economic Tasks\" by Qirui Mi, Qipeng Yang, Zijun Fan, Wentian Fan, Heyang Ma, Chengdong Ma, Siyu Xia, Bo An, Jun Wang, and Haifeng Zhang, arXiv:2506.12110v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13833v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n &$r_c$ (\\AA)&$r_0$ (\\AA)&$\\kappa r_0$&\n &$r_c$ (\\AA) &$r_0$ (\\AA)&$\\kappa r_0$\\\\\n\\hline\nCu& 0.800 & 14.10 & 2.550 &Sn[1]\n& 0.680 & 1.870 & 3.700 \\\\\nAg& 0.990 & 15.90 & 2.710 &Pb[2]\n& 0.450 & 1.930 & 3.760 \\\\\nAu& 1.150 & 15.90 & 2.710 &Ca[3]\n& 0.750 & 2.170 & 3.560 \\\\\nMg& 0.490 & 17.60 & 3.200 &Sr[4]\n& 0.900 & 2.370 & 3.720 \\\\\nZn& 0.300 & 15.20 & 2.970 &Li[2]\n& 0.380 & 1.730 & 2.830 \\\\\nCd& 0.530 & 17.10 & 3.160 &Na[5]\n& 0.760 & 2.110 & 3.120 \\\\\nHg& 0.550 & 17.80 & 3.220 &K[5]\n& 1.120 & 2.620 & 3.480 \\\\\nAl& 0.230 & 15.80 & 3.240 &Rb[3]\n& 1.330 & 2.800 & 3.590 \\\\\nGa& 0.310 & 16.70 & 3.330 &Cs[4]\n& 1.420 & 3.030 & 3.740 \\\\\nIn& 0.460 & 18.40 & 3.500 &Ba[5]\n& 0.960 & 2.460 & 3.780 \\\\\nTl& 0.480 & 18.90 & 3.550 & & & & \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the Reasoning Capacity of AI Models and How to Quantify It", "authors": ["Santosh Kumar Radha", "Oktay Goktas"], "url": "https://arxiv.org/abs/2501.13833v1", "attribution": "\"On the Reasoning Capacity of AI Models and How to Quantify It\" by Santosh Kumar Radha and Oktay Goktas, arXiv:2501.13833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09863v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Values of PPO hyperparameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n \\cmidrule(r){1-2}\n parameter name & value \\\\\n \\midrule\n use gae & True \\\\\n learning rate & $2.5 \\cdot 10^{-4}$ \\\\\n use linear learning rate decay & True\\\\\n clip parameter & 0.1 \\\\\n value loss coefficient & 0.5 \\\\\n entropy coefficient & 0.01\\\\\n num steps & 128\\\\\n num processes & 4\\\\\n evaluation interval & 50\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots", "authors": ["Jagdeep Singh Bhatia", "Holly Jackson", "Yunsheng Tian", "Jie Xu", "Wojciech Matusik"], "url": "https://arxiv.org/abs/2201.09863v1", "attribution": "\"Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots\" by Jagdeep Singh Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, and Wojciech Matusik, arXiv:2201.09863v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.02758v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll} % cccc\n \\toprule\n \\rowcolor[HTML]{FFF2CC}\n \\textbf{Year} & \\textbf{Method} & \\textbf{Backbone} & \\textbf{Financial Datasets Used} \\\\\n \\midrule\n \\rowcolor[HTML]{DDEBFF}\n 2016 & C-RNN-GAN & GAN & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2017 & RCGAN & GAN & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2018 & T-CGAN & GAN & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2019 & TimeGAN & GAN & Stocks \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2019 & WaveGAN & GAN & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2020 & COT-GAN & GAN & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2020 & DoppelGANger & GAN & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2020 & Quant-GAN & GAN & SPX \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2020 & SigCWGAN & GAN & SPX \\& DJI \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2020 & TSGAN & GAN & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2021 & RTSGAN & GAN & Stocks \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2021 & Sig-WGAN & GAN & SPX \\& DJI \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2021 & TimeGCI & GAN & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2022 & CEGEN & GAN & Stocks \\& Electric Price \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2022 & COSCI-GAN & GAN & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2022 & PSA-GAN & GAN & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2022 & TsT-GAN & GAN & Stocks \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2022 & TTS-GAN & GAN & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2023 & AEC-GAN & GAN & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2023 & TT-AAE & GAN & Stocks \\\\\n \\midrule\n 2021 & TimeVAE & VAE & Stocks \\\\\n 2023 & CRVAE & VAE & / \\\\\n 2023 & TimeVQVAE & VAE & / \\\\\n 2024 & KoVAE & VAE & Stocks \\\\\n \\midrule\n \n \\rowcolor[HTML]{DDEBFF} \n 2023 & DiffTime & Diffusion & Stocks \\\\\n \\rowcolor[HTML]{DDEBFF} \n 2023 & TSGM & Diffusion & Stocks \\\\\n \\rowcolor[HTML]{DDEBFF} \n 2024 & Diffusion-TS & Diffusion & Stocks \\\\\n \\rowcolor[HTML]{DDEBFF} \n 2024 & FIDE & Diffusion & Stocks \\\\\n \\rowcolor[HTML]{DDEBFF} \n 2024 & ImagenTime & Diffusion & Stocks \\\\\n \\rowcolor[HTML]{DDEBFF} \n 2024 & SDformer & Diffusion & Stocks \\\\\n \\rowcolor[HTML]{DDEBFF} \n 2025 & PaD-TS & Diffusion & Stocks \\\\\n \\midrule\n \n 2020 & CTFP & Flow & / \\\\\n 2021 & Fourier-Flow & Flow & Stocks \\\\\n 2024 & FlowTS & Flow & Stocks \\\\\n \\midrule\n \n \\rowcolor[HTML]{DDEBFF}\n 2018 & Neural ODE & ODE + RNN & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2019 & ODE-RNN & ODE + RNN & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2021 & Neural SDE & ODE + GAN & Stocks \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2022 & GT-GAN & ODE + GAN & Stocks \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2023 & LS4 & ODE + VAE & / \\\\\n \\rowcolor[HTML]{DDEBFF}\n 2024 & TimeLDM & Diffusion + VAE & Stocks \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "CTBench: Cryptocurrency Time Series Generation Benchmark", "authors": ["Yihao Ang", "Qiang Wang", "Qiang Huang", "Yifan Bao", "Xinyu Xi", "Anthony K. H. Tung", "Chen Jin", "Zhiyong Huang"], "url": "https://arxiv.org/abs/2508.02758v1", "attribution": "\"CTBench: Cryptocurrency Time Series Generation Benchmark\" by Yihao Ang, Qiang Wang, Qiang Huang, Yifan Bao, Xinyu Xi, Anthony K. H. Tung, Chen Jin, and Zhiyong Huang, arXiv:2508.02758v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.15726v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l}\n \\toprule\n \\textbf{\\footnotesize{Input Audio}} \\\\\n \\footnotesize{...} \\\\\n \\footnotesize{Ich zeig der, wo de Bartli de Moscht holt.} \\\\\n \\footnotesize{...} \\\\\n \\midrule\n \\textbf{\\footnotesize{Whisper Large-v2}} \\\\\n \\footnotesize{...} \\\\\n \\footnotesize{\\texttt{[00:00:08]} Ich zeige dir, wo Bartli den Most holt. \\texttt{[00:00:11]}} \\\\\n \\footnotesize{...} \\\\\n \\midrule\n \\textbf{\\footnotesize{Whisper Large-v2 (fine-tuned on sentences)}} \\\\\n \\footnotesize{...} \\\\\n \\footnotesize{Ich zeige dir, wo es die Bartli in den Most holt.} \\\\\n \\footnotesize{...} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fine-tuning Whisper on Low-Resource Languages for Real-World Applications", "authors": ["Vincenzo Timmel", "Claudio Paonessa", "Reza Kakooee", "Manfred Vogel", "Daniel Perruchoud"], "url": "https://arxiv.org/abs/2412.15726v3", "attribution": "\"Fine-tuning Whisper on Low-Resource Languages for Real-World Applications\" by Vincenzo Timmel, Claudio Paonessa, Reza Kakooee, Manfred Vogel, and Daniel Perruchoud, arXiv:2412.15726v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01045v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small Support vector machine}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n \\multirow{2}{*}{Id}&\n \\multicolumn{3}{c|}{Size}\n \\\\ \\cline{2-4}\n&$\\overline n$ & $n$ & $l$\\\\\n\\hline \n13&2 & 403 & 400 \\\\ \\hline\n14&3&604 & 600\\\\ \n\\hline\n15&5&1006 & 1000\\\\ \n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Comparing different subgradient methods for solving convex optimization problems with functional constraints", "authors": ["Thi Lan Dinh", "Ngoc Hoang Anh Mai"], "url": "https://arxiv.org/abs/2101.01045v2", "attribution": "\"Comparing different subgradient methods for solving convex optimization problems with functional constraints\" by Thi Lan Dinh and Ngoc Hoang Anh Mai, arXiv:2101.01045v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09991v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rccccc}\n \\toprule\n & $\\sigma$ & HP & NORM & TA & TVA \\\\\n \\midrule\n Baseline & 800 & \\textbf{0.92} & 0.66 & 0.66 & 0.67 \\\\\n Baseline & 1500 & 0.85 & 0.72 & 0.65 & 0.68 \\\\\n Baseline & 7000 & 0.60 & 0.78 & 0.76 & 0.84 \\\\\n Multi-resolution & - & 0.89 & \\textbf{0.83} & \\textbf{0.81} & \\textbf{0.87} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of the class BA between the baseline and the proposed multi-resolution approach.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading", "authors": ["Carlo Alberto Barbano", "Daniele Perlo", "Enzo Tartaglione", "Attilio Fiandrotti", "Luca Bertero", "Paola Cassoni", "Marco Grangetto"], "url": "https://arxiv.org/abs/2101.09991v2", "attribution": "\"UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading\" by Carlo Alberto Barbano, Daniele Perlo, Enzo Tartaglione, Attilio Fiandrotti, Luca Bertero, Paola Cassoni, and Marco Grangetto, arXiv:2101.09991v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.15251v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of OFA-595 student with various teachers}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\n & \\multicolumn{3}{c}{\\textbf{LCS25000 Dataset}} & \\multicolumn{3}{c}{\\textbf{COVID-QU-Ex Dataset}} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7}\n\\textbf{Teacher} & \\textbf{AUC} & \\textbf{Acc.} & \\textbf{F1} & \\textbf{AUC} & \\textbf{Acc.} & \\textbf{F1} \\\\\n\\midrule\nVGG-19 & 82.9 & 92.4 & 93.1 & 86.1 & 94.3 & 94.1 \\\\\nDenseNet-201 & 85.1 & 93.8 & 95.6 & 88.9 & 96.8 & 96.2 \\\\\nOFA-595 & 85.9 & 95.2 & 96.0 & 91.1 & 96.7 & 97.3 \\\\\nVisformer-S & 83.6 & 97.2 & 95.09 & 92.0 & 97.0 & 97.1 \\\\\nEEENA-Net C2 & 83.2 & 96.1 & 96.9 & 93.7 & 96.9 & 98.7 \\\\\nAutoFormer & 83.7 & 95.6 & 95.4 & 89.9 & 97.0 & 97.5 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Explainable Knowledge Distillation for Efficient Medical Image Classification", "authors": ["Aqib Nazir Mir", "Danish Raza Rizvi"], "url": "https://arxiv.org/abs/2508.15251v1", "attribution": "\"Explainable Knowledge Distillation for Efficient Medical Image Classification\" by Aqib Nazir Mir and Danish Raza Rizvi, arXiv:2508.15251v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.09085v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n & \\multicolumn{2}{l}{Betting Odds} & \\multicolumn{2}{l}{Transfermarkt regression}\n\\\\ \\midrule\n & Before & After & Before & After \\\\ \\midrule\nPremier League & -0.0269 & -0.0078 & -0.0226 & -0.0028 \\\\\nChampionship & -0.0095 & & -0.0050 & \\\\\nLeague One & -0.0103 & & -0.0037 & \\\\\nLeague Two & -0.0067 & & -0.0012 & \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Differences in Brier scores for Betting Odds and Transfermarkt regression model compared to the Null model before and after removing games involving the Big Six. All differences are significant at the $p<0.001$ level.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Predictive Modeling of Lower-Level English Club Soccer Using Crowd-Sourced Player Valuations", "authors": ["Josh Brown", "Yutong Bu", "Zachary Cheesman", "Benjamin Orman", "Iris Horng", "Samuel Thomas", "Amanda Harsy", "Adam Schultze"], "url": "https://arxiv.org/abs/2411.09085v1", "attribution": "\"Predictive Modeling of Lower-Level English Club Soccer Using Crowd-Sourced Player Valuations\" by Josh Brown, Yutong Bu, Zachary Cheesman, Benjamin Orman, Iris Horng, Samuel Thomas, Amanda Harsy, and Adam Schultze, arXiv:2411.09085v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.19307v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\t\t\\toprule\n\t\t& \\textbf{Low} & \\textbf{Medium}&\\textbf{High}&\\textbf{Extreme}\\\\\n\t\t\\midrule\n\t\t\\rowcolor{gray!15}\n\t\tLow & 490 & 1934 & 262 & 587 \\\\ \n\t\t\\rowcolor{gray!15}\n\t\t& (802) & (2602) & (404) & (810) \\\\\n\t\tMedium & 344 & 1240 & 209 & 285 \\\\ \n\t\t& (494) & (2608) & (302) & (401) \\\\ \n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Climate Physical Risk Assessment in Asset Management", "authors": ["Michele Azzone", "Matteo Ghesini", "Davide Stocco", "Lorenzo Viola"], "url": "https://arxiv.org/abs/2504.19307v1", "attribution": "\"Climate Physical Risk Assessment in Asset Management\" by Michele Azzone, Matteo Ghesini, Davide Stocco, and Lorenzo Viola, arXiv:2504.19307v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.15658v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n Interval & $B(m_i,m_{i+1})$ & Range of $z_i$ values\\\\ \n \\hline\n $[4\\cdot 10^{18},10^{20}]$ & $0.0256$ & $[725,1263]$\\\\ \n $[10^{20},10^{25}]$ & $0.0662$ & $[1377, 13948]$\\\\ \n $[10^{25},10^{30}]$ & $0.0423$ & $[15598,173542]$\\\\ \n $[10^{30},10^{40}]$ & $0.0506$ & $[191170,3.02\\cdot10^7]$\\\\\n $[10^{40},10^{50}]$ & $0.0290$ & $[3.35\\cdot10^7,6.01\\cdot10^9]$\\\\\n $[10^{50},10^{100}]$ & $0.0547$ & $[6.69\\cdot10^9,4.53\\cdot10^{21}]$\\\\\n $[10^{100},10^{2000}]$ & $0.0471$ & $[5.06\\cdot10^{21},1.04\\cdot10^{494}]$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{$B(m_i,m_{i+1})$ for chosen intervals (rounded to 4 significant figures)}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Improved Upper Bound on Brun's Constant Under GRH", "authors": ["Lachlan Dunn"], "url": "https://arxiv.org/abs/2504.15658v2", "attribution": "\"Improved Upper Bound on Brun's Constant Under GRH\" by Lachlan Dunn, arXiv:2504.15658v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.08662v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\hline\n& \\multicolumn{2}{c}{Regional level} \\\\\nRegion & lower & higher \\\\ \n\\hline\nUS & 3142 counties & 51 states \\\\\nEU (NUTS3) & 1165 NUTS-3 & 244 EU NUTS-2\\\\\nGermany & 401 districts & 38 gov.\\ districts\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Regional levels of super-resolution datasets}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Revealing economic facts: LLMs know more than they say", "authors": ["Marcus Buckmann", "Quynh Anh Nguyen", "Edward Hill"], "url": "https://arxiv.org/abs/2505.08662v1", "attribution": "\"Revealing economic facts: LLMs know more than they say\" by Marcus Buckmann, Quynh Anh Nguyen, and Edward Hill, arXiv:2505.08662v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16534v2_tex_table45.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameter search space of GRANDE.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\toprule\n\\textbf{Hyperparameter} & \\textbf{Distribution} \\\\ \\midrule\ndepth & $[4, 6]$\\\\\nn\\_estimators & $[512, 1024, 2048]$\\\\\nlearning\\_rate\\_weights & $\\text{log\\_uniform}[1\\mathrm{e}{-4},1\\mathrm{e}{-1}]$\\\\\nlearning\\_rate\\_index & $\\text{log\\_uniform}[5\\mathrm{e}{-3},2\\mathrm{e}{-1}]$\\\\\nlearning\\_rate\\_values & $\\text{log\\_uniform}[5\\mathrm{e}{-3},2\\mathrm{e}{-1}]$\\\\\nlearning\\_rate\\_leaf & $\\text{log\\_uniform}[5\\mathrm{e}{-3},2\\mathrm{e}{-1}]$\\\\\ncosine\\_decay\\_steps & $[0, 100, 1000]$\\\\\nloss & $[\\text{crossentropy},\\text{focal\\_crossentropy}](classification),[\\text{mse}](regression)$\\\\\ndropout & $[0.0, 0.25, 0.5]$ \\\\\nselected\\_variables & $\\text{uniform}[0.5, 1.0]$\\\\\ndata\\_subset\\_fraction & $\\text{uniform}[0.8, 1.0]$\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DOFEN: Deep Oblivious Forest ENsemble", "authors": ["Kuan-Yu Chen", "Ping-Han Chiang", "Hsin-Rung Chou", "Chih-Sheng Chen", "Tien-Hao Chang"], "url": "https://arxiv.org/abs/2412.16534v2", "attribution": "\"DOFEN: Deep Oblivious Forest ENsemble\" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.01569v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimator Performance for Case 2 ($\\alpha_1=0.8$) across Different Sample Sizes ($T$)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n \\textbf{Metric / Parameter} & \\textbf{T = 200} & \\textbf{T = 500} & \\textbf{T = 1000} \\\\\n \\midrule\n \\multicolumn{4}{l}{\\textit{Mean of Parameter Estimates (Bias Assessment)}} \\\\\n \\quad Mean($\\hat{\\nu}$) & 101.49 & 101.03 & 100.83 \\\\\n \\quad Mean($\\hat{\\alpha}_1$) & 0.7967 & 0.7971 & 0.7990 \\\\\n \\quad Mean($\\hat{\\alpha}_2$) & -0.0059 & -0.0017 & -0.0021 \\\\\n \\midrule\n \\multicolumn{4}{l}{\\textit{Error Metrics (Variance and Accuracy Assessment)}} \\\\\n \\quad Mean Squared Error (MSE) & 86.39 & 65.48 & 50.11 \\\\\n \\quad Relative $\\ell^2$-Error ($\\boldsymbol{\\theta}$) & 1.486\\% & 1.031\\% & 0.832\\% \\\\\n \\quad Relative $\\ell^2$-Error ($\\boldsymbol{\\alpha}$) & 1.291\\% & 0.789\\% & 0.674\\% \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Statistical Inference for Cumulative INAR($\\infty$) Processes via Least-Squares", "authors": ["Yingli Wang", "Xiaohong Duan", "Ping He"], "url": "https://arxiv.org/abs/2412.01569v4", "attribution": "\"Statistical Inference for Cumulative INAR($\\infty$) Processes via Least-Squares\" by Yingli Wang, Xiaohong Duan, and Ping He, arXiv:2412.01569v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.14833v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{marvosym}\n\\usepackage{pifont}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n\\toprule\n\\multirow{2}[2]{*}{AEG Sampling} & \\multicolumn{3}{c}{$\\mathrm{TRA}$ Score} \\\\\n\\cmidrule(lr){2-4}\n & U373 & GOWT1 & Huh7\\\\\n\\midrule\n\\ding{55} & 0.556 & 0.941 & 0.942 \\\\\n\\ding{51} & \\textbf{0.985} & \\textbf{0.960} & \\textbf{0.966}\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ \\textbf{Effectiveness of AEG Sampling.} AEG sampling is built to balance the cell event (division) in a training sequence.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Cell as Point: One-Stage Framework for Efficient Cell Tracking", "authors": ["Yaxuan Song", "Jianan Fan", "Heng Huang", "Mei Chen", "Weidong Cai"], "url": "https://arxiv.org/abs/2411.14833v2", "attribution": "\"Cell as Point: One-Stage Framework for Efficient Cell Tracking\" by Yaxuan Song, Jianan Fan, Heng Huang, Mei Chen, and Weidong Cai, arXiv:2411.14833v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.02662v1_tex_table21.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|cccc|}\n\\hline\n& Bias & RMSE & $\\hat{\\alpha }$ & $\\hat{\\beta }$ \\\\ \\hline\nMLE & 0.42703 & 0.50897 & 1.00090 & 5.08768 \\\\\n$DPD_{0.1}$ & 0.43322 & 0.51721 & 1.00080 & 5.08817 \\\\\n$DPD_{0.2}$ & 0.44721 & 0.53591 & 1.00069 & 5.09441 \\\\\n$DPD_{0.3}$ & 0.46505 & 0.55990 & 1.00056 & 5.10381 \\\\\n$DPD_{0.4}$ & 0.48415 & 0.58620 & 1.00042 & 5.11491 \\\\\n$DPD_{0.5}$ & 0.50338 & 0.61282 & 1.00029 & 5.12673 \\\\\n$DPD_{0.6}$ & 0.52169 & 0.63846 & 1.00016 & 5.13862 \\\\\n$DPD_{0.7}$ & 0.53871 & 0.66234 & 1.00004 & 5.15015 \\\\\n$DPD_{0.8}$ & 0.55421 & 0.68404 & 0.99993 & 5.16109 \\\\\n$DPD_{0.9}$ & 0.56818 & 0.70342 & 0.99983 & 5.17132 \\\\\n$DPD_{1.0}$ & 0.58074 & 0.72055 & 0.99974 & 5.18084 \\\\\nRM & 0.46836 & 0.56012 & 1.00091 & 5.03146 \\\\\nSM & 1.88233 & 1.90110 & 1.00106 & 3.15576 \\\\\nHL & 2.09985 & 2.08990 & 1.00089 & 2.93212 \\\\ \\hline\n\\end{tabular}\n\\caption{Results for $n=75$ and $\\protect\\beta = 5.$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust parameter estimation of the log-logistic distribution based on density power divergence estimators", "authors": ["A. Felipe", "M. Jaenada", "P. Miranda", "L. Pardo"], "url": "https://arxiv.org/abs/2312.02662v1", "attribution": "\"Robust parameter estimation of the log-logistic distribution based on density power divergence estimators\" by A. Felipe, M. Jaenada, P. Miranda, and L. Pardo, arXiv:2312.02662v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.07837v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Estimated exponent $a$ for the power law convergence rate $n^{-a}$ for the empirical standard error (SE).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n&& \\multicolumn{3}{c}{\\textbf{Generator}} \\\\\n\\cmidrule(r){3-5}\n& \\textbf{Original} & \\textbf{PrivBayes} & \\textbf{DP-GAN} & \\textbf{PATE-GAN} \\\\\n\\textbf{Estimator, SE} & & & & \\\\\n\\midrule\n\\textit{Mean} &&&&\\\\ \nMean age & 0.49 [0.47; 0.52] & 0.50 [0.27; 0.73] & 0.06 [-0.17; 0.30] & 0.04 [-0.18; 0.26] \\\\\nMean biomarker & 0.48 [0.44; 0.53] & 0.45 [0.37; 0.53] & 0.02 [-0.18; 0.22] & 0.07 [-0.11; 0.25] \\\\\n\\textit{Proportion} &&&&\\\\ \nProportion therapy & 0.50 [0.43; 0.56] & 0.60 [0.36; 0.83] & -0.18 [-0.30; -0.07] & -0.02 [-0.08; 0.03] \\\\\nProportion death & 0.51 [0.49; 0.53] & 0.58 [0.45; 0.70] & -0.14 [-0.31; 0.02] & -0.08 [-0.25; 0.09] \\\\\nProportion stage I & 0.48 [0.43; 0.52] & 0.09 [-0.08; 0.25] & -0.07 [-0.29; 0.15] & 0.05 [-0.11; 0.20] \\\\\nProportion stage II & 0.48 [0.46; 0.51] & 0.55 [0.41; 0.69] & -0.26 [-0.44; -0.09] & 0.10 [-0.07; 0.27] \\\\\nProportion stage III & 0.51 [0.46; 0.56] & 0.15 [0.01; 0.29] & -0.18 [-0.34; -0.02] & 0.14 [0.01; 0.27] \\\\\nProportion stage IV & 0.48 [0.44; 0.52] & 0.05 [-0.20; 0.30] & -0.26 [-0.29; -0.23] & 0.12 [-0.00; 0.25] \\\\\n\\textit{Cumulative regression} &&&&\\\\ \nEffect age on stage & 0.53 [0.49; 0.56] & 0.52 [0.50; 0.54] & 0.60 [-0.12; 1.31] & 0.36 [0.01; 0.71] \\\\\n\\textit{Gamma regression} &&&&\\\\ \nEffect stage II on biomarker & 0.51 [0.47; 0.56] & 0.53 [0.44; 0.61] & 0.41 [-0.04; 0.85] & 0.42 [0.29; 0.56] \\\\\nEffect stage III on biomarker & 0.50 [0.47; 0.54] & 0.47 [0.35; 0.59] & 0.33 [-0.05; 0.70] & 0.45 [0.31; 0.59] \\\\\nEffect stage IV on biomarker & 0.50 [0.47; 0.53] & 0.34 [0.21; 0.47] & 0.25 [-0.03; 0.53] & 0.46 [0.26; 0.66] \\\\\n\\textit{Logistic regression} &&&&\\\\ \nEffect age on death& 0.56 [0.49; 0.63] & 0.34 [0.15; 0.54] & 0.27 [-0.19; 0.73] & 0.21 [-0.14; 0.55] \\\\\nEffect stage II on death & 0.52 [0.47; 0.57] & 0.28 [0.12; 0.44] & -0.07 [-0.21; 0.07] & 0.38 [0.20; 0.55] \\\\\nEffect stage III on death & 0.52 [0.46; 0.59] & 0.18 [0.03; 0.33] & -0.10 [-0.26; 0.07] & 0.36 [0.18; 0.54] \\\\\nEffect stage IV on death & 0.53 [0.48; 0.57] & 0.08 [-0.09; 0.25] & -0.08 [-0.21; 0.04] & 0.42 [0.15; 0.69] \\\\\nEffect therapy on death & 0.53 [0.48; 0.58] & 0.55 [0.39; 0.71] & -0.04 [-0.27; 0.19] & 0.25 [-0.03; 0.52] \\\\\n\\bottomrule\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data", "authors": ["Alexander Decruyenaere", "Heidelinde Dehaene", "Paloma Rabaey", "Christiaan Polet", "Johan Decruyenaere", "Stijn Vansteelandt", "Thomas Demeester"], "url": "https://arxiv.org/abs/2312.07837v2", "attribution": "\"The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data\" by Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet, Johan Decruyenaere, Stijn Vansteelandt, and Thomas Demeester, arXiv:2312.07837v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.08359v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c||c|c||c|c||}\n\t\t\t\t\\hline \\hline \n\t\t\t\t& \\multicolumn{2}{|c||}{Non-Seizure} & \\multicolumn{2}{|c||}{Seizure} \\\\ \\hline \\hline \n\t\t\t\tCoefficients\t& Value & CB95\\% \t\t\t& Value & CB95\\% \t\t\t\\\\ \\hline \\hline\n\t\t\t\t$a$ \t& -0.6855 & (-4.095, 2.725) & 1.368 & (-6.717, 9.453) \\\\ \n\t\t\t\t$b$ \t& 0.9989 & (0.9989, 0.9989) & 0.9998 & (0.9998, 0.9998) \\\\ \n\t\t\t\t$c$ \t& -36.05 & (-38.53, -33.57) \t & -35.45 & (-41.47, -29.43) \\\\ \n\t\t\t\t\\hline \\hline \n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{The quadratic linear-parabolic model coefficients and the associated 95\\% confidence bounds (CB).}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A quadratic linear-parabolic model-based classification to detect epileptic EEG seizures", "authors": ["Antonio Quintero-Rincon", "Carlos D'Giano", "Hadj Batatia"], "url": "https://arxiv.org/abs/1912.08359v1", "attribution": "\"A quadratic linear-parabolic model-based classification to detect epileptic EEG seizures\" by Antonio Quintero-Rincon, Carlos D'Giano, and Hadj Batatia, arXiv:1912.08359v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.10670v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n $\\lambda_c$ & $k_1$ & $k_2/\\sigma_c$ \\\\\n $day^{-1}$ & & $mg/kg$ \\\\\n \\hline $5.55$& $115.12$ & $65.67$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Parameters for the cytotoxic drug.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A mathematical perspective on the paradox that chemotherapy sometimes works backwards", "authors": ["Luis A. Fernández", "Isabel Lasheras", "Cecilia Pola"], "url": "https://arxiv.org/abs/2503.10670v1", "attribution": "\"A mathematical perspective on the paradox that chemotherapy sometimes works backwards\" by Luis A. Fernández, Isabel Lasheras, and Cecilia Pola, arXiv:2503.10670v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.14999v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Testing prediction results for the S\\&P 500 data}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n\\hline \\hline\n{Method} & {MEI} & {MHI} & DD \\\\\n\\hline \nLDA & 0.64 & 0.43 & 0.64 \\\\\nQDA & 0.50 & 0.43 & 0.43\\\\\nkNN & 0.35 & 0.40 & 0.57 \\\\\nSVM & 0.45 & 0.47 & - \\\\\nRF & 0.71 & 0.69 & - \\\\\n\\hline \n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "The EE-Classifier: A classification method for functional data based on extremality indexes", "authors": ["Catalina Lesmes", "Francisco Zuluaga", "Henry Laniado", "Andres Gomez", "Andrea Carvajal"], "url": "https://arxiv.org/abs/2411.14999v1", "attribution": "\"The EE-Classifier: A classification method for functional data based on extremality indexes\" by Catalina Lesmes, Francisco Zuluaga, Henry Laniado, Andres Gomez, and Andrea Carvajal, arXiv:2411.14999v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.14140v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l|l}\n& SSIM & LPIPS & MSE & PSNR(dB) \\\\ \\hline\nw supervision & \\textbf{0.8823} & \\textbf{1.81e-05} & \\textbf{0.0033} & \\textbf{34.6733} \\\\ \\hline\nw\\textbackslash{}o supervision & 0.7570 & 2.54e-04 & 0.0059 & 27.2784\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Quantitative comparison between model trained with supervision and model trained without supervision.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Analysis of Macula on Color Fundus Images Using Heightmap Reconstruction Through Deep Learning", "authors": ["Peyman Tahghighi", "Reza A. Zoroofi", "Sare Safi", "Alireza Ramezani"], "url": "https://arxiv.org/abs/2012.14140v1", "attribution": "\"Analysis of Macula on Color Fundus Images Using Heightmap Reconstruction Through Deep Learning\" by Peyman Tahghighi, Reza A. Zoroofi, Sare Safi, and Alireza Ramezani, arXiv:2012.14140v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.10001v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c||c|c|c|c}\n Scenario & Lower 2.5\\% quantile & Lower median & Upper median & Upper 97.5\\% quantile\\\\\n \\hline\n P0 & 42.42 & 43.07 & 46.22 & 46.88 \\\\\n P1 & 44.60 & 45.24 & 48.49 & 49.14 \\\\\n P2 & 40.30 & 40.89 & 43.92 & 44.59 \\\\\n P3 & 42.37 & 43.05 & 46.22 & 46.90 \\\\\n P4 & 42.00 & 42.96 & 46.21 & 47.20\n \\end{tabular}\n\\end{adjustbox}\n\\caption{ Statistics for the 20-year return level. Estimated 2.5\\% and 50\\% quantiles from the conservative lower bounds and the 50\\% and 97.5\\% quantiles from the conservative upper bounds in million £s to 4s.f. for different portfolio scenarios. For scenarios P3 and P4 the estimates are from a single pooled sample of all of the $R\\times M$ simulations of the return level.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fast return-level estimates for flood insurance via an improved Bennett inequality for random variables with differing upper bounds", "authors": ["Anna Maria Barlow", "Chris Sherlock"], "url": "https://arxiv.org/abs/2311.10001v3", "attribution": "\"Fast return-level estimates for flood insurance via an improved Bennett inequality for random variables with differing upper bounds\" by Anna Maria Barlow and Chris Sherlock, arXiv:2311.10001v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.19853v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllll}\n \\toprule\n &&\\multicolumn{3}{c}{Range 20\\% - 80\\%}&\\multicolumn{3}{c}{Range 30\\% - 80\\%}\\\\\n \\cmidrule(lr){3-5}\\cmidrule(lr){6-8}\n Inst.&$\\epsilon$&Op. costs&Op. costs&\\# EBs& Op. costs&Op. costs&\\# EBs\\\\\n &&&impro. (\\%)&&&impro. (\\%)&\\\\\n \\midrule\n I1&0&4,102.2&0.00&3.8&4,312.7&0.00&4.0\\\\\n &0.001&3,896.8&5.01&3.6&4,103.1&4.86&3.8\\\\\n &0.005&3,896.5&5.01&3.6&4,100.8&4.91&3.8\\\\\n &0.01&3,896.2&5.02&3.6&4,098.3&4.97&3.8\\\\\n &0.05&3,895.2&5.05&3.6&3,898.0&9.62&3.6\\\\\n &0.10&3,894.3&5.07&3.6&3,897.5&9.63&3.6\\\\\n &0.15&3,894.3&5.07&3.6&3,897.2&9.63&3.6\\\\\n &0.20&3,894.3&5.07&3.6&3,897.3&9.63&3.6\\\\\n &0.25&3,894.3&5.07&3.6&3,897.1&9.64&3.6\\\\\n &0.30&3,894.3&5.07&3.6&3,897.0&9.64&3.6\\\\\n &0.40&3,894.3&5.07&3.6&3,896.6&9.65&3.6\\\\\n &0.50&3,894.3&5.07&3.6&3,896.4&9.65&3.6\\\\\n I2&0&8,313.6&0.00&7.8&8,540.2&0.00&8.0\\\\\n &0.001&8,275.6&0.46&7.8&8,508.0&0.38&8.0\\\\\n &0.005&8,271.9&0.50&7.8&8,310.2&2.69&7.8\\\\\n &0.01&8,271.4&0.51&7.8&8,305.3&2.75&7.8\\\\\n &0.05&8,066.0&2.98&7.6&8,293.6&2.89&7.8\\\\\n &0.10&8,065.6&2.98&7.6&8,288.5&2.95&7.8\\\\\n &0.15&8,063.5&3.01&7.6&8,287.0&2.97&7.8\\\\\n &0.20&8,064.8&2.99&7.6&8,282.9&3.01&7.8\\\\\n &0.25&8,063.5&3.01&7.6&8,283.9&3.00&7.8\\\\\n &0.30&8,063.5&3.01&7.6&8,279.2&3.06&7.8\\\\\n &0.40&8,064.4&3.00&7.6&8,276.4&3.09&7.8\\\\\n &0.50&8,064.4&3.00&7.6&8,274.4&3.11&7.8\\\\\n \n I3&0&12,157.7&0.00&11.4&12,782.3&0.00&12.0\\\\\n &0.001&11,889.6&2.21&11.2&12,352.8&3.36&11.6\\\\\n &0.005&11,691.6&3.83&11.0&11,971.1&6.35&11.2\\\\\n &0.01&11,687.7&3.87&11.0&11,958.4&6.45&11.2\\\\\n &0.05&11,676.3&3.96&11.0&11,932.1&6.65&11.2\\\\\n &0.10&11,673.4&3.98&11.0&11,923.4&6.72&11.2\\\\\n &0.15&11,673.9&3.98&11.0&11,915.0&6.79&11.2\\\\\n &0.20&11,673.7&3.98&11.0&11,907.8&6.84&11.2\\\\\n &0.25&11,672.2&3.99&11.0&11,903.1&6.88&11.2\\\\\n &0.30&11,673.8&3.98&11.0&11,901.1&6.89&11.2\\\\\n &0.40&11,674.0&3.98&11.0&11,898.6&6.91&11.2\\\\\n &0.50&11,674.0&3.98&11.0&11,710.0&8.39&11.0\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Chance-constrained battery management strategies for the electric bus scheduling problem", "authors": ["Léa Ricard", "Guy Desaulniers", "Andrea Lodi", "Louis-Martin Rousseau"], "url": "https://arxiv.org/abs/2503.19853v1", "attribution": "\"Chance-constrained battery management strategies for the electric bus scheduling problem\" by Léa Ricard, Guy Desaulniers, Andrea Lodi, and Louis-Martin Rousseau, arXiv:2503.19853v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|r|r|r}\n \\hline\n \\multicolumn{5}{c}{Including component demand} \\\\\n \\hline \n Utilization & $\\lambda$ & Runtime (s) & Total costs PH & $\\Delta$ (\\%) \\\\\n \\hline\n \\multirow{4}{*}{50\\%} \n & 0.1 & 1931.3 & 28425.5 & 3.72 \\\\\n & 1 & 779.5 & 27377.4 & \\textbf{0.50} \\\\\n & 10 & 390.5 & 28130.6 & 3.06 \\\\\n & 100 & 191.3 & 28504.8 & 4.35 \\\\\n \\hline\n \\multirow{4}{*}{90\\%} \n & 0.1 & 2891.1 & 62734.9 & 0.86 \\\\\n & 1 & 1743.9 & 62577.5 & \\textbf{0.77} \\\\\n & 10 & 912.6 & 63336.3 & 1.94 \\\\\n & 100 & 802.7 & 64298.0 & 3.45 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Averaging consensus calculation: Evaluation of the $\\lambda$-multiple for the penalty parameter $\\rho$ in terms of total costs and runtime for the case including component demands on a scenario tree with $|\\Omega| = 2$ and therefore $|\\Phi| = 128$ on 96 test instances}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.06675v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Robustness checks: no cells surrounding Rome (distance $>$ 250 \\textit{km})}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\hline\\hline\\\\\n & \\textit{(1)} & \\textit{(2)} & \\textit{(3)} & \\textit{(4)} & \\textit{(5)} & \\textit{(6)}\\\\\n\\textit{Dependent variable:} &\\multicolumn{4}{c}{{}}&\\multicolumn{2}{c}{{}}\\\\\n\\textit{Transport infrastructure in km (log)}&\\multicolumn{2}{c}{\\textit{Railways}}&\\multicolumn{2}{c}{\\textit{Motorways}}&\\multicolumn{2}{c}{\\textit{Major} $\\mathcal{RR}$}\\\\\n\\hline\\\\\n \\texttt{Major} $\\mathcal{RR}_{i} $ (log)& 0.286***& 0.315***& 0.336*** & 0.365***& & \\\\\n & (0.022) & (0.016) & (0.026) & (0.024) & & \\\\\n\\texttt{Ruggedness} (log)& -0.024 & -0.025 & -0.051 & -0.057 & 0.009 & 0.047 \\\\\n & (0.047) & (0.051) & (0.042) & (0.064) & (0.050) & (0.074) \\\\\n\\texttt{Elevation} (log)& 0.025 & 0.053 & -0.001 & 0.006 & -0.023 & -0.096 \\\\\n & (0.049) & (0.063) & (0.066) & (0.100) & (0.067) & (0.082) \\\\\n\\texttt{Slope} (log)& -0.269 & -0.187 & 0.196 & 0.024 & 0.466***& 0.474***\\\\\n & (0.213) & (0.326) & (0.131) & (0.118) & (0.159) & (0.157) \\\\\n\\texttt{Agriculture suitability post 1500} (log) & -0.003 & -0.014 & -0.004 & -0.029 & & \\\\\n & (0.026) & (0.029) & (0.027) & (0.040) & & \\\\\n\\texttt{Forest} (dummy)& -0.058 & -0.089 & 0.004 & -0.138 & -0.026 & -0.050 \\\\\n & (0.116) & (0.100) & (0.143) & (0.175) & (0.065) & (0.119) \\\\\n\\texttt{Low vegetation} (dummy)& 0.033 & 0.032** & -0.140 & -0.048 & -0.030 & -0.098 \\\\\n & (0.028) & (0.015) & (0.099) & (0.023) & (0.051) & (0.091) \\\\\n\\texttt{Distance from sea} (log) & 0.082** & 0.125** & 0.051 & 0.209 & 0.099** & 0.188***\\\\\n & (0.032) & (0.050) & (0.050) & (0.148) & (0.046) & (0.053) \\\\\n\\texttt{Distance from nearest waterway} (log) & -0.003 & 0.048* & 0.038 & 0.091 & 0.035 & 0.045 \\\\\n & (0.029) & (0.028) & (0.048) & (0.064) & (0.026) & (0.041) \\\\\n\\texttt{Distance from nearest harbor} (log)& -0.007 & -0.078 & 0.018 & -0.124 & -0.039 & -0.120** \\\\\n & (0.052) & (0.091) & (0.056) & (0.086) & (0.040) & (0.048) \\\\\n\\texttt{Distance from Rome} (log)& -0.136 & -0.285 & 0.160 & 0.702 & 0.102 & 0.526 \\\\\n & (0.180) & (0.346) & (0.235) & (1.160) & (0.149) & (0.648) \\\\\n\\texttt{Large municipality} (dummy)& 0.350***& 0.520***& 0.390***& 0.563***& & \\\\\n & (0.062) & (0.077) & (0.097) & (0.108) & & \\\\ \n \\texttt{Agriculture suitability pre 1500} (log) & & & & & 0.018 & 0.017 \\\\\n & & & & & (0.023) & (0.030) \\\\\n\\texttt{Pre-Roman amenities} (dummy)& & & & & 0.479***& 0.507***\\\\\n & & & & & (0.060) & (0.060) \\\\ \n\\hline\\\\\n $ \\phi_{p}$\t&No&Yes&No&Yes&No&Yes\\\\ \n \\texttt{Smaller area} (dummy)&Yes&Yes&Yes&Yes&Yes&Yes\\\\\nObservations& 1,669& 1,669& 657& 657& 1,489& 1,489\\\\\nAdjusted \\textit{R$^{2}$}& 0.756& 0.763& 0.690& 0.690& 0.737& 0.738\\\\\n\\hline \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "From the historical Roman road network to modern infrastructure in Italy", "authors": ["Luca De Benedictis", "Vania Licio", "Anna Pinna"], "url": "https://arxiv.org/abs/2208.06675v1", "attribution": "\"From the historical Roman road network to modern infrastructure in Italy\" by Luca De Benedictis, Vania Licio, and Anna Pinna, arXiv:2208.06675v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14009v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimation of initial population sizes for model .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\hline\nInitial population & Initial value & Source\\\\ \n\\hline\nS(0)&1,97,112 & Estimated \\\\\nI(0)&5,646&Estimated\\\\\nB(0)&21,259&Estimated\\\\\nR(0)&118&Estimated\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study", "authors": ["Adrita Ghosh", "Parthasakha Das", "Tanujit Chakraborty", "Pritha Das", "Dibakar Ghosh"], "url": "https://arxiv.org/abs/2503.14009v1", "attribution": "\"Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study\" by Adrita Ghosh, Parthasakha Das, Tanujit Chakraborty, Pritha Das, and Dibakar Ghosh, arXiv:2503.14009v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.05089v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|c|c|}\n \\hline\n scenario & 1 & 2 & 3 & 4 \\\\ \\hline\n $U^c(1)$ & 198 (224) & 159 (138)& 172 (198) & 159 (138) \\\\\n $U^c(2)$ & 253 (219) & 308 (330)& 253 (275) & 264 (286) \\\\\n $U^c(3)$ & 261 (261) & 289 (275)& 261 (193) & 289 (275) \\\\\n $U^f(1)$ & 30 & -12.5 & 27 & -12.5 \\\\\n $U^f(2)$ & -30 & 25 & 27 & 25 \\\\\n $U^f(3)$ & 0 & -12.5 & -54 & -12.5 \\\\\n $RCP$ & 15 & 12.5 & 13.5 & 12.5 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Resulting consumption and financial utilities and $RCP$ values in the example. In the rows of $U^c$ the original $U^c$ values without redistribution is indicated in parentheses.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Solidarity in natural gas storage: A potential allocation mechanism of stored quantities among several players during times of crisis", "authors": ["Dávid Csercsik", "Anne Neumann"], "url": "https://arxiv.org/abs/2209.05089v2", "attribution": "\"Solidarity in natural gas storage: A potential allocation mechanism of stored quantities among several players during times of crisis\" by Dávid Csercsik and Anne Neumann, arXiv:2209.05089v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.09250v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of the Airoboros dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc}\n \\toprule\n & \\# Train \\\\\n \\midrule\n Airobors & 29,400 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning", "authors": ["Abdessalam Ed-dib", "Zhanibek Datbayev", "Amine Mohamed Aboussalah"], "url": "https://arxiv.org/abs/2412.09250v3", "attribution": "\"GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning\" by Abdessalam Ed-dib, Zhanibek Datbayev, and Amine Mohamed Aboussalah, arXiv:2412.09250v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.09066v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c||c|c|c|c|c|c|}\n\\hline \n \\textbackslash & $z_1=3$ & $z_2=6$ & $z_3=1$ &\n$z_3=2$ & $z_3=3$ & $ z_3=4 $ & $z_3=5 $ & $ z_3=6 $\\\\ \n\\hline \\hline\n1, OT & 13.08 & 8.24 & 15.51 & 13.28 & 12.33 & 11.29 & 10.22 & 8.48 \\\\ \\hline \n2, OT & 0 & 8.99 & 16.71 & 14.19 & 13.13 & 11.97 & 10.76 & 8.60 \\\\ \\hline \n1, CM & 5.20 & 6.72 & 4.06 & 4.46 & 4.65 & 4.90 & 5.20 & 6.98\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Optimal abatement, delayed implementation}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Climate uncertainty, financial frictions and constrained efficient carbon taxation", "authors": ["Felix Kübler"], "url": "https://arxiv.org/abs/2210.09066v1", "attribution": "\"Climate uncertainty, financial frictions and constrained efficient carbon taxation\" by Felix Kübler, arXiv:2210.09066v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.02109v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Analysis results given by Tian's methods using MG data}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\\hline\nParameter & Mean & p-value [$95\\%$CI]\\\\ \\hline\nIntercept & 3.74 & 0.000 [2.95, 4.52]\\\\\nTreatment group & 0.37 & 0.001 [0.15, 0.58]\\\\\nAge & -0.02 & 0.001 [-0.02, -0.01]\\\\\nYears of diagnosis & 0.02 & 0.224 [-0.01, 0.05]\\\\\nKarnofsky performance score & 0.37 & 0.003 [0.12, 0.62]\\\\\nRace & -0.21 & 0.240 [-0.57, 0.14]\\\\\nRadiation therapy & 0.35 & 0.010 [0.08, 0.61]\\\\\nSex & 0.12 & 0.348 [-0.13, 0.36]\\\\\nNitro & -0.27 & 0.031 [-0.51, -0.02]\\\\\nPath & 0.27 & 0.000 [0.14, 0.40]\\\\\nGrade & -0.32 & 0.115 [-0.73, 0.08]\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Variable Selection using Inverse Survival Probability Weighting", "authors": ["Masahiro Kojima"], "url": "https://arxiv.org/abs/2212.02109v1", "attribution": "\"Variable Selection using Inverse Survival Probability Weighting\" by Masahiro Kojima, arXiv:2212.02109v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.06553v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|l|}\n\t\t\t\t\\hline\n\t\t\t\t\\hline\n\t\t\t\t\\multicolumn{3}{|c|}{\\textbf{Parabolic Equation}}\\\\\n\t\t\t\t\\hline\n\t\t\t\t\\multicolumn{3}{|c|}{$y(t) = At^{2} + Bt +C$}\\\\\n\t\t\t\t\n\t\t\t\t\\multicolumn{3}{|c|}{$A = (a_{0} - a_{1}) / \\delta^{2}$}\\\\\n\t\t\t\t\n\t\t\t\t\\multicolumn{3}{|c|}{$B = -2A\\delta$}\\\\\n\t\t\t\t\n\t\t\t\t\\multicolumn{3}{|c|}{$C = a_{0}$}\\\\\n\t\t\t\t\\hline\n\t\t\t\t\\multicolumn{3}{|c|}{\\textbf{Parameters Description}}\\\\\n\t\t\t\t\\hline\n\t\t\t\t\\textbf{Variable} & \\textbf{Comments} & \\textbf{Unit}\\\\\n\t\t\t\t\\hline\n\t\t\t\tt & Time variable & s\\\\\n\t\t\t\t\\hline\n\t\t\t\ta$_{0}$ & Initial deceleration rate& m/s$^{2}$\\\\\n\t\t\t\t\\hline\n\t\t\t\ta$_{1}$ & Final deceleration rate& m/s$^{2}$\\\\\n\t\t\t\t\\hline\n\t\t\t\ty(t) & Deceleration rate& m/s$^{2}$\\\\\n\t\t\t\t\\hline\n\t\t\t\t$\\delta$ & Allowed maximum deceleration gradient rate & m/$s^{3}$\\\\\n\t\t\t\t\\hline\n\t\t\t\t\\hline\t\t\t\t\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{ACC braking deceleration model. \\hspace{\\textwidth}}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Stress Testing Method for Scenario Based Testing of Automated Driving Systems", "authors": ["Demin Nalic", "Hexuan Li", "Arno Eichberger", "Christoph Wellershaus", "Aleksa Pandurevic", "Branko Rogic"], "url": "https://arxiv.org/abs/2011.06553v2", "attribution": "\"Stress Testing Method for Scenario Based Testing of Automated Driving Systems\" by Demin Nalic, Hexuan Li, Arno Eichberger, Christoph Wellershaus, Aleksa Pandurevic, and Branko Rogic, arXiv:2011.06553v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2401.00085v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\nSection & Description & Results \\\\\n\\hline\nSection~ & Comparison of Bayesian and PCA projection approach & Figure~ \\\\\n & on transition probabilities &\\\\\n\\hline\nSection~ & Convergence of Monte Carlo simulation for expected PD & Figure~\\\\\n\\hline\nSection~ & Comparison of Bayesian and PCA projection & Figures~, \\\\ \n & approach on multi-year PD estimation & \\\\\n\\hline\nSection~ & Black-Scholes formula for expected LGD & Figure~ \\\\\n\\hline\nSection~ & Comparison of Bayesian and PCA projection approach & Figures~- \\\\\n & on loss distribution of the target unite bullet loan & \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Waterfalls}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A dimension reduction approach for loss valuation in credit risk modelling", "authors": ["Jian He", "Asma Khedher", "Peter Spreij"], "url": "https://arxiv.org/abs/2401.00085v1", "attribution": "\"A dimension reduction approach for loss valuation in credit risk modelling\" by Jian He, Asma Khedher, and Peter Spreij, arXiv:2401.00085v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.14227v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc}\n \\toprule\n Dataset & Chapman & Chapman \\\\\n \\# of Leads & 12 & 4 \\\\\n \\hline\n AUC & 0.891 $\\pm$ 0.003 & 0.886 $\\pm$ 0.002 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{AUC on test set of Chapman dataset in the presence of a different number of leads. The inference strategy involves using the nearest PCP ($E=128$) as input to the hypernetwork. Results are shown for five seeds.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "PCPs: Patient Cardiac Prototypes", "authors": ["Dani Kiyasseh", "Tingting Zhu", "David A. Clifton"], "url": "https://arxiv.org/abs/2011.14227v1", "attribution": "\"PCPs: Patient Cardiac Prototypes\" by Dani Kiyasseh, Tingting Zhu, and David A. Clifton, arXiv:2011.14227v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19004v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||cc||cc||} \\hline\nSU-CD & 2 & SGS-CD & $N_i +1$ \\\\ \\hline\nSL-CD & 2 & SGSL-CD & $N_i +1$ \\\\ \\hline\nSeL-CD & $2 + 2I$ & SGSeL-CD & $N_i + 1 + 2I$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Setwise Coordinate Descent for Dual Asynchronous Decentralized Optimization", "authors": ["Marina Costantini", "Nikolaos Liakopoulos", "Panayotis Mertikopoulos", "Thrasyvoulos Spyropoulos"], "url": "https://arxiv.org/abs/2504.19004v1", "attribution": "\"Setwise Coordinate Descent for Dual Asynchronous Decentralized Optimization\" by Marina Costantini, Nikolaos Liakopoulos, Panayotis Mertikopoulos, and Thrasyvoulos Spyropoulos, arXiv:2504.19004v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.08430v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Descriptive statistics for the severity of payments of the four insurance coverages}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrr}\n\\toprule\n\\multirow{2}{*}{Coverage}& \\multirow{2}{*}{Mean} & \\multirow{2}{*}{Std. dev.} & \\multicolumn{4}{c}{Quantiles} & \\multirow{2}{*}{Max.} \\\\\\cline{4-7}\n& & & 0.5 & 0.75 & 0.95 & 0.99 & \\\\\n\\midrule\nAccident Benefits & 12,386 & 53,561 & 3,215 & 6,909 & 47,757 & 127,896 & 2,435,334 \\\\\nBodily Injury & 23,271 & 76,027 & 4,000 & 15,150 & 98,449 & 322,612 & 2,039,570\\\\\nVehicle Damage & 5,040 & 8,121 & 2,605 & 5,830 & 17,984 & 40,611 & 149,399 \\\\\nLoss of Use & 545 & 620 & 419 & 714 & 1,000 & 2,336 & 52,777 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Individual Claims Reserving using Activation Patterns", "authors": ["Marie Michaelides", "Mathieu Pigeon", "Hélène Cossette"], "url": "https://arxiv.org/abs/2208.08430v3", "attribution": "\"Individual Claims Reserving using Activation Patterns\" by Marie Michaelides, Mathieu Pigeon, and Hélène Cossette, arXiv:2208.08430v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09810v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n \\textbf{Model} & \\textbf{Acc.} & \\textbf{Prec.} & \\textbf{Rec.} & \\textbf{F1$_{macro}$} \\\\\n \\hline\n Majority Class & 0.59 & 0.35 & 0.59 & 0.37 \\\\ \\hline\n {\\scriptsize } & 0.80 & 0.75 & 0.78 & 0.80 \\\\ \\hline\n FakeNewsDetector & 0.86 & 0.86 & 0.86 & 0.86 \\\\ \\hline\n LSTM & 0.91 & 0.86 & 0.91 & 0.90 \\\\ \\hline\n CNN & 0.91 & 0.89 & 0.89 & 0.91 \\\\ \\hline\n {\\scriptsize }& 0.92 & 0.92 & 0.92 & 0.92 \\\\ \\hline\n BERT & 0.93 & 0.93 & 0.94 & 0.93‡ \\\\ \\hline\n EIN & 0.93 & 0.94 & 0.93 & 0.93‡ \\\\ \\hline\n HAN & 0.94 & 0.94 & 0.94 & 0.93‡ \\\\ \\hline\n Longformer & 0.97 & 0.97 & 0.97 & \\textbf{0.97}† \\\\ \\hline %longformer1500_4_97.npy\n \n \\hline\n FakeFlow & 0.96 & 0.93 & 0.97 & 0.96 \\\\ \n \\hline \n \\scriptsize{FakeFlow -- Topic only} & 0.91 & 0.89 & 0.90 & 0.90 \\\\ \\hline\n \\scriptsize{FakeFlow -- Affective only} & 0.61 & 0.38 & 0.60 & 0.40 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Results on the MultiSourceFake dataset. (‡) indicates a statistically significant improvement of \\textit{FakeFlow} over the referred model using McNemar test; (†) indicates no statistically significant improvement over \\textit{FakeFlow}.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "FakeFlow: Fake News Detection by Modeling the Flow of Affective Information", "authors": ["Bilal Ghanem", "Simone Paolo Ponzetto", "Paolo Rosso", "Francisco Rangel"], "url": "https://arxiv.org/abs/2101.09810v1", "attribution": "\"FakeFlow: Fake News Detection by Modeling the Flow of Affective Information\" by Bilal Ghanem, Simone Paolo Ponzetto, Paolo Rosso, and Francisco Rangel, arXiv:2101.09810v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.06285v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Analysis of Clusters formed by K-Means and GMM algorithms with Silhouette Score using input as extracted embeddings of mentioned classification approaches.}\n\\begin{tabular}{|l|l|l|} \n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Model Name / Clustering Approach & K-Means & Gaussian Mixture Models \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Logistic Regression & 0.156 & 0.158 \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Convolutional Neural Network & 0.165 & 0.171 \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Transfer Learning & 0.189 & 0.185\\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} PCA+TSNE & \\textit{0.578} & \\textit{0.575} \\\\\n\\hline\n\\rule[-1ex]{0pt}{3.5ex} Siamese Networks & 0.490 & 0.487 \\\\\n\\hline \n\\rule[-1ex]{0pt}{3.5ex} Siamese Networks (Transfer Learning) & \\textbf{0.592} & \\textbf{0.583} \\\\\n\\hline \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach", "authors": ["Shruti Jadon"], "url": "https://arxiv.org/abs/2102.06285v2", "attribution": "\"COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach\" by Shruti Jadon, arXiv:2102.06285v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17106v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n $n$ & Error $\\|\\cdot\\|_1$ & Order $\\|\\cdot\\|_1$ & Error\n $\\|\\cdot\\|_{\\infty}$ & Order $\\|\\cdot\\|_{\\infty}$ \\\\\n \\hline\n 20 & 6.14E$-3$ & $-$ & 9.07E$-2$ & $-$ \\\\\n \\hline\n 40 & 4.22E$-5$ & 7.19 & 2.98E$-4$ & 8.25 \\\\\n \\hline\n 80 & 6.90E$-7$ & 5.93 & 3.47E$-6$ & 6.42 \\\\\n \\hline\n 160 & 1.95E$-8$ & 5.15 & 9.13E$-8$ & 5.25 \\\\\n \\hline\n 320 & 5.94E$-10$ & 5.04 & 2.81E$-9$ & 5.02 \\\\\n \\hline\n 640 & 1.84E$-11$ & 5.01 & 8.90E$-11$ & 4.98 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Error table for problem - , Square domain, WLS-UW.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17106v1", "attribution": "\"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.06594v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Heterogeneous Boundary Effects by Region Type}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n& \\multicolumn{2}{c}{Treatment Effect} & \\multicolumn{3}{c}{Boundary Dynamics} \\\\\n\\cmidrule(lr){2-3} \\cmidrule(lr){4-6}\nRegion Type & PE Only & With GE & First Passage & Jump Prob. & Boundary (km) \\\\\n\\midrule\nTokyo Metro Area & 0.287 & 0.481 & 1.3 years & 0.89 & 28.3 \\\\\nOsaka-Kyoto-Kobe & 0.251 & 0.392 & 1.8 years & 0.81 & 31.2 \\\\\nOther Major Cities & 0.223 & 0.318 & 2.6 years & 0.71 & 36.7 \\\\\nRegional Centers & 0.194 & 0.259 & 3.8 years & 0.52 & 42.1 \\\\\nRural Areas & 0.168 & 0.198 & 5.9 years & 0.31 & 48.6 \\\\\n\\midrule\nOverall & 0.234 & 0.342 & 2.8 years & 0.73 & 35.2 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Stochastic Boundaries in Spatial General Equilibrium: A Diffusion-Based Approach to Causal Inference with Spillover Effects", "authors": ["Tatsuru Kikuchi"], "url": "https://arxiv.org/abs/2508.06594v1", "attribution": "\"Stochastic Boundaries in Spatial General Equilibrium: A Diffusion-Based Approach to Causal Inference with Spillover Effects\" by Tatsuru Kikuchi, arXiv:2508.06594v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16929v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\textbf{Method} & {DiNO ViT-S/14} & {DiNO ViT-B/14} & {DiNO ViT-L/14} \\\\\n\\midrule\nBaseline & 55.51 & 63.03 & 65.70 \\\\\n\\hspace{2pt} + Debiasing & 57.27$\\pm$ 0.07 & 63.72 $\\pm$ 0.09 & 66.87 $\\pm$ 0.07 \\\\\n\\hspace{2pt} + KNN Propagation & \\textbf{58.45} $\\pm$ 0.23 & 64.87 $\\pm$ 0.19 & 67.25 $\\pm$ 0.21 \\\\\n\\hspace{2pt} + EMA & {57.8} $\\pm$ 0.26 & \\textbf{64.75} $\\pm$ 0.18 & \\textbf{67.52} $\\pm$ 0.28 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Ablation study of new techniques discovered through the I-Con framework. We compare ImageNet-1K clustering accuracy across different sized backbones. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "I-Con: A Unifying Framework for Representation Learning", "authors": ["Shaden Alshammari", "John Hershey", "Axel Feldmann", "William T. Freeman", "Mark Hamilton"], "url": "https://arxiv.org/abs/2504.16929v1", "attribution": "\"I-Con: A Unifying Framework for Representation Learning\" by Shaden Alshammari, John Hershey, Axel Feldmann, William T. Freeman, and Mark Hamilton, arXiv:2504.16929v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18013v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Error convergence of approximated $v(t)$ for the $L_{\\infty}$-Norm and $N=4$, $5$ and $6$, respectively.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc} \\hline\n\\textbf{$t$} & \\textbf{$N=4$} & \\textbf{$N=5$} & \\textbf{$N=6$}\\\\ \\hline\n0.0 & 0.10620E-3 & 0.11630E-4 & 0.13978E-5 \\\\\n0.1 & 0.11560E-3 & 0.15122E-4 & 0.13596E-5 \\\\\n0.2 & 0.13323E-3 & 0.18851E-4 & 0.13671E-5 \\\\\n0.3 & 0.10003E-3 & 0.14520E-4 & 0.14582E-5 \\\\\n0.4 & 0.11588E-3 & 0.15505E-4 & 0.14896E-5 \\\\\n0.5 & 0.13994E-3 & 0.11091E-4 & 0.19071E-5 \\\\\n0.6 & 0.14278E-3 & 0.19985E-4 & 0.13601E-5 \\\\\n0.7 & 0.13775E-3 & 0.20120E-4 & 0.14772E-5 \\\\\n0.8 & 0.15953E-3 & 0.20512E-4 & 0.14850E-5 \\\\\n0.9 & 0.15029E-3 & 0.20001E-4 & 0.12417E-5 \\\\\n1.0 & 0.19003E-3 & 0.35277E-4 & 0.11230E-4 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A comprehensive numerical investigation of a coupled mathematical model of neuronal excitability", "authors": ["Burcu Gürbüz", "Aytül Gökçe", "Mahmut Modanlı"], "url": "https://arxiv.org/abs/2501.18013v1", "attribution": "\"A comprehensive numerical investigation of a coupled mathematical model of neuronal excitability\" by Burcu Gürbüz, Aytül Gökçe, and Mahmut Modanlı, arXiv:2501.18013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.13629v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Confusion Matrix of Multi-classes Classification}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllll}\n\\hline\n\\hline\n & 0 & 1 & 2 & 3 & 4 & 5 & 6 \\\\\n\\midrule\n\\hline\n0 & 92.25\\% & 4.23\\% & 0.40\\% & 0.37\\% & 0.15\\% & 2.20\\% & 0.40\\% \\\\\n1 & 1.23\\% & 68.39\\% & 7.53\\% & 7.18\\% & 7.44\\% & 4.65\\% & 3.57\\% \\\\\n2 & 0.20\\% & 15.01\\% & 54.16\\% & 14.37\\% & 7.28\\% & 4.59\\% & 4.39\\% \\\\\n3 & 0.14\\% & 9.45\\% & 16.21\\% & 51.81\\% & 10.20\\% & 6.80\\% & 5.39\\% \\\\\n4 & 0.23\\% & 20.28\\% & 13.78\\% & 20.77\\% & 23.76\\% & 13.67\\% & 7.52\\% \\\\\n5 & 2.64\\% & 22.76\\% & 11.47\\% & 18.62\\% & 18.46\\% & 18.28\\% & 7.77\\% \\\\\n6 & 0.25\\% & 8.93\\% & 7.17\\% & 6.53\\% & 7.09\\% & 6.88\\% & 63.14\\% \\\\\n\\hline\n\\hline\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing", "authors": ["Cong William Lin", "Wu Zhu"], "url": "https://arxiv.org/abs/2504.13629v1", "attribution": "\"Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing\" by Cong William Lin and Wu Zhu, arXiv:2504.13629v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.03287v3_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results from cryptocurrency market model regressions on L1 tokens: \\(r_i - r_f = \\alpha_i + \\beta_i CM + \\epsilon_i\\) where \\(i\\) denotes the crypto portfolio, \\(CM\\) is cryptocurrency excess market returns, and \\(r_f\\) is the risk-free rate. TVL values include all DefiLlama categories. P-values are below the coefficients in parentheses. \\(^{***}\\), \\(^{**}\\), and \\(^{*}\\), denote statistical significance at the 1\\%, 5\\%, and 10\\% levels respectively. Test statistics and p-values for the F-test of whether alpha coefficients are jointly 0 are below the Adjusted-\\(R^2\\) values.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllr}\n\\hline\n & Quartile 2 & Quartile 3 & Quartile 4 & Quartile 2 & Quartile 3 & Quartile 4\\\\\n\\hline\n\\(\\alpha\\) & -0.00 & 0.10 & -0.29 & 0.09 & 0.20 & -0.29\\\\\n & (0.99) & (0.86) & (0.40) & (0.90) & (0.73) & (0.41)\\\\\n\\(\\beta_{CM}\\) & 1.645\\(^{***}\\) & 1.11\\(^{***}\\) & 1.12\\(^{***}\\) & 1.65\\(^{***}\\) & 1.12\\(^{***}\\) & 1.12\\(^{***}\\)\\\\\n & (0.00) & (0.00) & (0.00) & (0.00) & (0.00) & (0.00)\\\\\n\\(\\beta_{SMB}\\) & & & & 0.01 & 0.00 & -0.00\\\\\n & & & & (0.59) & (0.97) & (0.81)\\\\\n\\(\\beta_{Mom}\\) & & & & 0.03 & -0.16\\(^{**}\\) & -0.03\\\\\n & & & & (0.77) & (0.02) & (0.48)\\\\\n\\hline\nAdj-\\(R^2\\) & 0.71 & 0.62 & 0.82 & 0.70 & 0.63 & 0.81\\\\\n\\hline\n & GRS Stat. & 0.25 & & GRS Stat. & 0.22 & \\\\\n & p-value & (0.86) & & p-value & (0.88) & \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns", "authors": ["Matt Brigida"], "url": "https://arxiv.org/abs/2506.03287v3", "attribution": "\"The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns\" by Matt Brigida, arXiv:2506.03287v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.07392v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance on NASDAQ-100 index}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n \\textbf{Models} & \\textbf{MAE} & \\textbf{MAPE} & \\textbf{Accuracy} \\\\ \\hline\n \\textbf{MLP} & 218.32973474 & 0.01767204122 & 0.98232795877 \\\\ \\hline\n \\textbf{LSTM} & 180.58083886 & 0.01456811176 & 0.98543188823 \\\\ \\hline\n \\textbf{FinBERT-LSTM} & 174.94284259 & 0.01409574846 & 0.98590425153 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis", "authors": ["Shayan Halder"], "url": "https://arxiv.org/abs/2211.07392v1", "attribution": "\"FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis\" by Shayan Halder, arXiv:2211.07392v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.02176v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Outage probabilities for ground level deployment and high ground windy weather deployment scenarios}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|} \n\t\t\t\\hline\n\t\t\tScenario & $\\zeta$ & $N$ & $L_W$ & Outage probability \\\\\n\t\t\t\\hline \\hline\n\t\t\t\\multirow{4}{*}{Case 1} & $40^{\\circ}$ & $0.5$ & $3.128\\times10^{-4}$ & $0.8166$ \\\\\n\t\t\t& $30^{\\circ}$ & $0.5$ & $3.128\\times10^{-4}$ & $0.1619$ \\\\\n\t\t\t& $15^{\\circ}$ & $0.5$ & $3.128\\times10^{-4}$ & $5.583\\times10^{-4}$ \\\\\n\t\t\t& $0^{\\circ}$ & $0.5$ & $3.128\\times10^{-4}$ & $2.165\\times10^{-5}$ \\\\ \\hline\n\t\t\t\\multirow{4}{*}{Case 2}\n\t\t\t& $40^{\\circ}$ & $0.5$ & $3.128\\times10^{-4}$ & 0.0986\\\\\n\t\t\t& $30^{\\circ}$ & $0.5$ & $3.128\\times10^{-4}$ & $2.314\\times10^{-4}$ \\\\\n\t\t\t& $15^{\\circ}$ & $0.5$ & $3.128\\times10^{-4}$ & $2.27\\times10^{-9}$\\\\\n\t\t\t& $0^{\\circ}$ & $0.5$ & $3.128\\times10^{-4}$ & $1.28\\times10^{-11}$ \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Site Diversity in Downlink Optical Satellite Networks Through Ground Station Selection", "authors": ["Eylem Erdogan", "Ibrahim Altunbas", "Gunes Kurt", "Michel Bellemare", "Guillaume Lamontagne", "Halim Yanikomeroglu"], "url": "https://arxiv.org/abs/2010.02176v2", "attribution": "\"Site Diversity in Downlink Optical Satellite Networks Through Ground Station Selection\" by Eylem Erdogan, Ibrahim Altunbas, Gunes Kurt, Michel Bellemare, Guillaume Lamontagne, and Halim Yanikomeroglu, arXiv:2010.02176v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.08205v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|} \\hline \n Cut & Elements \\\\ \n \\hline\n 1 & 1 2 \\\\ \n 2 & 1 5 6 \\\\\n 3 & 1 5 9 \\\\\n 4 & 1 6 7 \\\\\n 5 & 1 6 8 \\\\\n 6 & 1 7 9 \\\\\n 7 & 2 3 4 \\\\\n 8 & 2 3 7 \\\\\n 9 & 2 8 \\\\\n 10 & 3 4 5 6 \\\\\n 11 & 3 4 5 9 \\\\\n 12 & 3 6 7 \\\\\n 13 & 3 7 9 \\\\\n 14 & 6 8 \\\\\n 15 & 8 9 \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Operation comfort vs. the importance of system components", "authors": ["Krzysztof J. Szajowski", "Małgorzata Średnicka"], "url": "https://arxiv.org/abs/2101.08205v1", "attribution": "\"Operation comfort vs. the importance of system components\" by Krzysztof J. Szajowski and Małgorzata Średnicka, arXiv:2101.08205v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2112.15448v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|} \n\\hline\ncases & $\\lambda$ & n & m & corr & ETE & $p_p$ & $p$ \\\\ \n\\hline\\hline\ncase 1 & 0.000018 & 5 & 30 & 0.992473 & 0.0841 & 67 & 72 \\\\\n\\hline\ncase 2 & 0.000019 & 15 & 10 & 0.984619 & 0.1150 & 67 & 67 \\\\\n\\hline\ncase 3 & 0.00002 & 5 & 10 & 0.980843 & 0.1574 & 54 & 63 \\\\\n\\hline\ncase 4 & 0.00002 & 5 & 5 & 0.980935 & 0.0485 & 59 & 63 \\\\\n\\hline\ncase 5 & 0.000025 & 5 & 5 & 0.975577 & 0.1269 & 48 & 53 \\\\\n\\hline\ncase 6 & 0.00004 & 5 & 10 & 0.965080 & 0.1297 & 26 & 26 \\\\\n\\hline\ncase 7 & 0.000045 & 5 & 5 & 0.923647 & 0.07925 & 22 & 22 \\\\\n\\hline\ncase 8 & 0.000048 & 5 & 5 & 0.954842 & 0.1210 & 16 & 17 \\\\\n\\hline\ncase 9 & 0.00005 & 5 & 10 & 0.872521 & 0.1790 & 16 & 16 \\\\\n\\hline\ncase 10 & 0.00005 & 20 & 5 & 0.971625 & 0.1220 & 16 & 16 \\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{experiments on using exact POSI for Lasso on S\\&P500}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Exact Post-selection Inference For Tracking S&P500", "authors": ["Farshad Noravesh", "Hamid Boustanifar"], "url": "https://arxiv.org/abs/2112.15448v1", "attribution": "\"Exact Post-selection Inference For Tracking S&P500\" by Farshad Noravesh and Hamid Boustanifar, arXiv:2112.15448v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01000v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|ccccc}\n \\toprule\n Methods & Metrics & Temperature & Cloud cover & Humidity & Wind \\\\\n \\midrule\n CLCRN\\_log & MAE & 1.2638±0.1554 & 1.5599±0.0019 & 0.4663±0.0082 & 1.3958±0.0120 \\\\\n & RMSE & 2.0848±0.1719 & 2.5171±0.0255 & 0.7341±0.0151 & 2.2659±0.0211 \\\\\n \\midrule\n CLCRN\\_hor & MAE & 1.1688±0.0457 & 1.4906±0.0037 & 0.4531±0.0065 & 1.3260±0.0483 \\\\\n & RMSE & 1.8825±0.1509 & 2.4559±0.0027 & 0.7078±0.0146 & 2.1292±0.0733\\\\\n \\bottomrule\n \\end{tabular}\n\\caption{MAE and RMSE comparison in forecasting length of 12h for logarithmic and horizon maps.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Conditional Local Convolution for Spatio-temporal Meteorological Forecasting", "authors": ["Haitao Lin", "Zhangyang Gao", "Yongjie Xu", "Lirong Wu", "Ling Li", "Stan. Z. Li"], "url": "https://arxiv.org/abs/2101.01000v3", "attribution": "\"Conditional Local Convolution for Spatio-temporal Meteorological Forecasting\" by Haitao Lin, Zhangyang Gao, Yongjie Xu, Lirong Wu, Ling Li, and Stan. Z. Li, arXiv:2101.01000v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2112.10084v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c} \n\\hline\\hline\nHyperparameter & - \\\\ \n\\hline\\hline\nBatch Size & 256 \\\\\nEpoch & 15 \\\\\nOptimizer & AdamW \\\\\nbetas & (0.9, 0.999) \\\\\neps & 1e-08 \\\\\nInitial Learning Rate & 0.001 \\\\\nWeight Decay & 0.01 \\\\\nActivation Function & GeLU \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Default Training Settings}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Neural Networks for Delta Hedging", "authors": ["Guijin Son", "Joocheol Kim"], "url": "https://arxiv.org/abs/2112.10084v1", "attribution": "\"Neural Networks for Delta Hedging\" by Guijin Son and Joocheol Kim, arXiv:2112.10084v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.17211v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Evaluation of Cumulative Wealth (CW↑), Sharpe Ratio (SR↑), and Maximum Drawdown (MDD↓) on real-market datasets (US50, HSI45 and CSI300) for different model variants. Our EFS approach significantly outperforms traditional baselines.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccccccc}\n\\toprule\n & & \\multicolumn{3}{c}{US50} & \\multicolumn{3}{c}{HSI45} & \\multicolumn{3}{c}{CSI300} \\\\ \n\\cmidrule(lr){3-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-11}\nGroup & Method & CW$\\uparrow$ & SR$\\uparrow$ & MDD$\\downarrow$ & CW$\\uparrow$ & SR$\\uparrow$ & MDD$\\downarrow$ & CW$\\uparrow$ & SR$\\uparrow$ & MDD$\\downarrow$ \\\\\n\\midrule\n\\multirow{3}{*}{Baseline} \n& 1/N & 4.562 & 0.072 & 0.344 & 1.333 & 0.029 & 0.409 & 1.087 & 0.014 & 0.214 \\\\\n& Min-cVaR & 1.779 & 0.038 & 0.314 & 1.628 & 0.063 & 0.244 & 0.992 & 0.003 & 0.286 \\\\\n& Max-Sharpe & 4.495 & 0.061 & 0.461 & 1.428 & 0.043 & 0.300 & 1.008 & 0.007 & 0.333 \\\\\n\\midrule\n\\multirow{4}{*}{m=10} \n& LGBM & 4.182 & 0.063 & 0.332 & 1.611 & 0.038 & 0.367 & 2.334 & 0.072 & 0.225 \\\\\n& XGBoost & 6.313 & 0.077 & 0.328 & 1.581 & 0.035 & 0.440 & 1.420 & 0.032 & 0.345 \\\\\n& mSSRM-PGA & 5.121 & 0.059 & 0.569 & 0.766 & -0.003 & 0.547 & 0.881 & 0.002 & 0.399 \\\\\n& ASMCVaR & 10.259 & 0.073 & 0.582 & 2.481 & 0.052 & 0.453 & 1.453 & 0.030 & 0.462 \\\\ \n& \\textbf{EFS-DeepSeek} \n& \\textbf{25.101} & 0.132 & 0.288 & \\textbf{3.463} & 0.080 & 0.385 & 3.437 & 0.079 & 0.327 \\\\\n& \\textbf{EFS-GPT} \n& 22.905 & 0.130 & 0.260 & 2.789 & 0.067 & 0.292 & \\textbf{4.962} & 0.098 & 0.301 \\\\\n\\midrule\n\\multirow{4}{*}{m=15} \n& LGBM & 3.899 & 0.062 & 0.328 & 1.588 & 0.037 & 0.387 & 1.812 & 0.055 & 0.250 \\\\\n& XGBoost & 5.607 & 0.076 & 0.319 & 1.586 & 0.036 & 0.420 & 1.348 & 0.029 & 0.344 \\\\\n& mSSRM-PGA & 4.976 & 0.062 & 0.477 & 0.766 & -0.003 & 0.547 & 0.787 & -0.010 & 0.384 \\\\\n& ASMCVaR & 11.124 & 0.074 & 0.566 & \\textbf{2.647} & 0.054 & 0.434 & 1.658 & \n0.035 & 0.424 \\\\\n& \\textbf{EFS-DeepSeek} \n& 13.978 & 0.114 & 0.298 & 2.364 & 0.061 & 0.406 & 2.510 & 0.067 & 0.298 \\\\\n& \\textbf{EFS-GPT} \n& \\textbf{14.707} & 0.117 & 0.278 & 2.277 & 0.058 & 0.307 & \\textbf{3.218} & 0.082 & 0.246 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "EFS: Evolutionary Factor Searching for Sparse Portfolio Optimization Using Large Language Models", "authors": ["Haochen Luo", "Yuan Zhang", "Chen Liu"], "url": "https://arxiv.org/abs/2507.17211v1", "attribution": "\"EFS: Evolutionary Factor Searching for Sparse Portfolio Optimization Using Large Language Models\" by Haochen Luo, Yuan Zhang, and Chen Liu, arXiv:2507.17211v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2012.10601v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Expected BIC from Table1 with different sample size $N$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n & & RIO & BARC & RRLN & ABFLN \\\\\n & & (2.5\\%) & (2.3\\%) & (2.5\\%) & (2.1\\%) \\\\\n Model & dof & $N=200$ & $N=200$ & $N=100$ & $N=100$ \\\\ \\hline\n 1 exp + 1 wbl & 4 & 3363.99 & 3338.79 & 1923.19 & 2035.99 \\\\\n 0 exp + 2 wbl & 5 & 3366.09 & 3341.69 & 1924.89 & 2038.49 \\\\\n 3 exp + 0 wbl & 5 & 3395.29 & 3384.09 & 1984.89 & 2091.09 \\\\\n 2 exp + 1 wbl & 6 & 3371.79 & 3347.79 & 1930.59 & 2043.99 \\\\\n 1 exp + 2 wbl & 7 & 3376.29 & 3352.29 & 1934.89 & 2048.49 \\\\\n 4 exp + 0 wbl & 7 & 3381.09 & 3357.09 & 1939.29 & 2053.29 \\\\\n 0 exp + 3 wbl & 8 & 3381.59 & 3357.59 & 1940.19 & 2053.19 \\\\\n 3 exp + 1 wbl & 8 & 3381.99 & 3357.99 & 1940.59 & 2053.39 \\\\\n 5 exp + 0 wbl & 9 & 3388.08 & 3364.08 & 1947.08 & 2060.28 \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Censored EM algorithm for Weibull mixtures: application to arrival times of market orders", "authors": ["Markus Kreer", "Ayse Kizilersu", "Anthony W. Thomas"], "url": "https://arxiv.org/abs/2012.10601v1", "attribution": "\"Censored EM algorithm for Weibull mixtures: application to arrival times of market orders\" by Markus Kreer, Ayse Kizilersu, and Anthony W. Thomas, arXiv:2012.10601v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.00883v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{$\\textit{p-values}$ of Post-double-selection Granger causality LM test from Volume to other variables }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllllll}\n\\hline\nGcto GCfrom & BTC-USD\\_vol & ETH-USD\\_vol & USDT-USD\\_vol & BNB-USD\\_vol & LTC-USD\\_vol & ENJ-USD\\_vol & ZEN-USD\\_vol & NMC-USD\\_vol & PPC-USD\\_vol & FTC-USD\\_vol \\\\ \\hline\nBTC-USD & 0.4285 & 0.2040 & 0.1035 & \\textbf{0.0076} & 0.1270 & 0.9440 & 0.9111 & 0.5741 & 0.3852 & 0.7540 \\\\\nETH-USD & 0.3751 & 0.3762 & 0.1812 & 0.6225 & 0.4007 & 0.8975 & 0.6259 & 0.7745 & 0.2047 & 0.9593 \\\\\nUSDT-USD & 0.0394 & 0.3672 & 0.0245 & 0.4170 & 0.1595 & 0.5817 & 0.7504 & 0.0224 & 0.4298 & 0.4827 \\\\\nBNB-USD & 0.1276 & 0.0598 & 0.0361 & 0.3857 & 0.0943 & 0.4642 & 0.9655 & \\textbf{0.0021} & 0.1347 & 0.8454 \\\\\nLTC-USD & 0.0460 & 0.3050 & 0.0355 & 0.0965 & 0.1551 & 0.9016 & 0.6767 & 0.1582 & 0.1387 & 0.6775 \\\\\nENJ-USD & 0.0879 & 0.0469 & \\textbf{0.0070} & 0.0177 & 0.0309 & 0.6038 & 0.4753 & 0.7882 & 0.0925 & 0.5563 \\\\\nZEN-USD & 0.0526 & 0.0218 & 0.0214 & 0.2155 & 0.0925 & 0.6199 & 0.0741 & 0.3548 & 0.4560 & 0.2485 \\\\\nNMC-USD & 0.3271 & 0.5458 & 0.5675 & 0.1612 & 0.6613 & 0.3852 & 0.9786 & 0.0133 & 0.8069 & 0.4959 \\\\\nPPC-USD & 0.4082 & 0.1950 & 0.4555 & 0.2910 & 0.7178 & 0.6623 & 0.2226 & 0.6966 & 0.1463 & 0.7791 \\\\\nFTC-USD & 0.0188 & \\textbf{0.0012} & 0.0126 & 0.0823 & 0.0826 & 0.7961 & 0.1825 & 0.8718 & 0.8897 & 0.4579 \\\\\nbinance\\_twitter & 0.6496 & 0.6694 & 0.5650 & 0.8522 & 0.7673 & 0.9552 & 0.7964 & 0.5226 & 0.3761 & 0.1815 \\\\\nbitcoin\\_twitter & 0.5083 & 0.8032 & 0.2585 & 0.7420 & 0.3441 & 0.1034 & 0.4094 & 0.3326 & 0.0885 & 0.6875 \\\\\nenjin\\_twitter & 0.8471 & 0.6550 & 0.6885 & 0.8965 & 0.7260 & 0.1598 & 0.2833 & 0.8763 & 0.8123 & 0.3686 \\\\\nethereum\\_twitter & 0.9305 & 0.2036 & 0.9729 & 0.6032 & 0.6708 & 0.2566 & 0.8449 & 0.1768 & 0.0139 & 0.7689 \\\\\nfeathercoin\\_twitter & 0.2271 & 0.3822 & 0.2634 & 0.4842 & 0.8401 & 0.7611 & 0.0718 & 0.3284 & 0.9371 & 0.1901 \\\\\nhorizen\\_twitter & 0.0342 & 0.0624 & \\textbf{0.0049} & \\textbf{0.0011} & 0.0943 & 0.2569 & \\textbf{0.0022} & 0.3027 & 0.7935 & 0.7720 \\\\\nlitecoin\\_twitter & 0.0732 & 0.4514 & 0.5763 & 0.7475 & 0.8273 & 0.4747 & 0.9866 & 0.6513 & 0.4298 & 0.9762 \\\\\nnamecoin\\_twitter & 0.5226 & 0.6241 & 0.7155 & 0.8615 & 0.8546 & 0.1329 & 0.8688 & 0.5213 & 0.6945 & 0.7958 \\\\\npeercoin\\_twitter & 0.1182 & 0.1154 & 0.2793 & 0.2588 & 0.2345 & 0.2260 & 0.9807 & 0.2259 & 0.3138 & 0.7869 \\\\\ntether\\_twitter & 0.5719 & 0.7046 & 0.8761 & 0.5646 & 0.9422 & 0.4827 & 0.8046 & 0.2573 & 0.2637 & 0.9184 \\\\\nbinance\\_reddit & 0.8505 & 0.6867 & 0.6462 & 0.6421 & 0.9327 & 0.3752 & 0.8229 & 0.5030 & 0.8442 & \\textbf{0.0008} \\\\\nbitcoin\\_reddit & 0.0523 & 0.7124 & 0.7319 & 0.8014 & 0.6673 & 0.0788 & 0.9135 & 0.7375 & 0.0729 & 0.4891 \\\\\nenjin\\_reddit & 0.2345 & 0.7225 & 0.3328 & 0.6972 & 0.7382 & 0.9602 & 0.2970 & 0.1516 & 0.6985 & 0.9444 \\\\\nethereum\\_reddit & 0.0142 & 0.4270 & 0.5509 & 0.9805 & 0.4062 & 0.6279 & 0.6412 & 0.1812 & 0.6234 & 0.3045 \\\\\nfeathercoin\\_reddit & 0.0478 & 0.0696 & 0.2066 & 0.9613 & 0.4141 & 0.9436 & 0.0151 & 0.8839 & 0.3658 & 0.1721 \\\\\nhorizen\\_reddit & 0.1343 & 0.2394 & 0.2839 & 0.9423 & 0.0954 & 0.3787 & 0.2212 & 0.6524 & 0.7528 & 0.7723 \\\\\nlitecoin\\_reddit & 0.5617 & 0.7762 & 0.8381 & 0.9978 & 0.8776 & 0.0338 & 0.6787 & 0.1220 & 0.7442 & 0.3543 \\\\\nnamecoin\\_reddit & 0.3859 & 0.6345 & 0.1390 & 0.6970 & 0.3665 & 0.3975 & 0.4890 & 0.2765 & 0.7093 & 0.8338 \\\\\npeercoin\\_reddit & 0.3535 & 0.2973 & 0.1909 & 0.9201 & 0.7866 & 0.0812 & 0.1712 & 0.0791 & 0.1725 & 0.2772 \\\\\ntether\\_reddit & 0.7572 & 0.4570 & 0.8763 & 0.9524 & 0.5835 & 0.2375 & 0.6782 & 0.5091 & 0.1027 & 0.8731 \\\\\nbinance coin & 0.4657 & 0.3887 & 0.2116 & 0.5002 & 0.0242 & 0.9713 & 0.7909 & 0.0872 & 0.5812 & 0.8036 \\\\\ncrypto crash & 0.3252 & \\textbf{0.0075} & 0.1114 & 0.7650 & 0.0127 & 0.5786 & 0.6972 & 0.4862 & \\textbf{0.0002} & 0.7896 \\\\\nminer & 0.3695 & 0.0756 & 0.6606 & 0.9977 & 0.2836 & 0.9908 & 0.0749 & 0.9332 & 0.5673 & 0.2072 \\\\\nUSD coin & \\textbf{0.0040} & 0.0261 & \\textbf{0.0020} & 0.2067 & \\textbf{0.0039} & 0.1276 & 0.8474 & 0.1049 & 0.3549 & 0.0304 \\\\\ncardano & 0.0612 & 0.1818 & 0.0433 & 0.7445 & 0.0654 & 0.6280 & 0.0623 & 0.8432 & 0.7648 & 0.6225 \\\\\ncryptocurrency & 0.0443 & \\textbf{0.0006} & \\textbf{0.0023} & 0.4431 & 0.0310 & 0.7356 & 0.3900 & 0.4141 & 0.4391 & 0.8441 \\\\\nminted & 0.1533 & 0.1233 & 0.1424 & 0.1922 & 0.4464 & 0.4659 & 0.1948 & 0.3541 & 0.9067 & 0.8170 \\\\\nsolana & 0.4350 & 0.3274 & 0.6674 & 0.6826 & 0.6486 & 0.4298 & 0.9597 & 0.8354 & 0.8515 & 0.1101 \\\\\nfeathercoin & 0.9341 & 0.7550 & 0.8798 & \\textbf{0.0061} & 0.3923 & 0.8571 & 0.3049 & 0.1966 & 0.3088 & 0.6064 \\\\\ncryptography & \\textbf{0.0001} & \\textbf{0.0049} & \\textbf{0.0000} & 0.3210 & 0.0774 & 0.8986 & 0.0526 & 0.7511 & 0.3581 & 0.2711 \\\\\ndogecoin & 0.0152 & \\textbf{0.0048} & 0.0183 & 0.4301 & 0.0181 & 0.3140 & 0.2675 & 0.4381 & 0.4478 & 0.8318 \\\\\ndigital gold & 0.0106 & 0.0612 & 0.0130 & 0.0520 & \\textbf{0.0028} & 0.8655 & 0.8759 & 0.6721 & 0.3912 & 0.3434 \\\\\nBitcoin & \\textbf{0.0008} & \\textbf{0.0002} & \\textbf{0.0003} & 0.9775 & 0.0874 & 0.5390 & 0.6651 & 0.9751 & 0.6542 & 0.7775 \\\\\nNFT & 0.0168 & 0.0242 & 0.0154 & 0.7478 & 0.0179 & 0.7931 & 0.5051 & 0.9631 & 0.1214 & 0.2062 \\\\\nripple & 0.1916 & 0.2840 & 0.4704 & 0.1208 & 0.4165 & 0.6319 & 0.1985 & 0.5323 & 0.1049 & 0.4354 \\\\\nprivate key & 0.0172 & 0.0119 & \\textbf{0.0029} & 0.3754 & 0.1041 & 0.1045 & 0.6450 & 0.8844 & 0.1006 & 0.0710 \\\\\npeercoin & 0.6156 & 0.4333 & 0.6224 & 0.7550 & 0.0404 & 0.0951 & 0.6624 & 0.8133 & \\textbf{0.0000} & 0.8569 \\\\\nnamecoin & 0.2319 & 0.0130 & 0.3100 & 0.6094 & 0.2251 & 0.0816 & 0.1816 & 0.6161 & 0.6859 & 0.4534 \\\\\nsatoshi & 0.0145 & 0.0904 & \\textbf{0.0040} & 0.1417 & \\textbf{0.0019} & 0.5589 & 0.1936 & 0.2889 & 0.4196 & 0.0616 \\\\\nhot wallet & 0.6243 & 0.3386 & 0.6166 & 0.0184 & 0.5466 & 0.9157 & 0.4923 & 0.9377 & 0.1872 & 0.5798 \\\\\nblockchain & 0.3693 & 0.3014 & 0.2509 & 0.8575 & 0.2406 & 0.5028 & 0.0812 & 0.6919 & 0.2843 & 0.7424 \\\\\nethereum & \\textbf{0.0023} & \\textbf{0.0001} & \\textbf{0.0000} & 0.8153 & \\textbf{0.0028} & 0.7266 & \\textbf{0.0098} & 0.3730 & 0.5688 & 0.5747 \\\\\nzen coin & 0.0860 & 0.4967 & 0.1826 & 0.9928 & 0.1747 & 0.5511 & 0.7401 & 0.8868 & 0.9089 & 0.5085 \\\\\ncoinbase & \\textbf{0.0002} & \\textbf{0.0000} & \\textbf{0.0001} & 0.7880 & 0.1245 & 0.4326 & \\textbf{0.0052} & 0.5600 & 0.1244 & 0.3437 \\\\\nBTC & \\textbf{0.0000} & \\textbf{0.0000} & \\textbf{0.0000} & 0.7923 & \\textbf{0.0000} & 0.5018 & 0.1289 & 0.5378 & 0.5574 & 0.8746 \\\\\nhard fork & 0.7006 & 0.1778 & 0.3790 & 0.4508 & 0.1438 & 0.3219 & 0.4376 & 0.5364 & 0.1437 & 0.8365 \\\\\nstablecoin & 0.0649 & 0.1285 & 0.0331 & 0.1504 & 0.1008 & 0.6498 & 0.1073 & 0.1693 & 0.4644 & 0.5789 \\\\\ncold storage & \\textbf{0.0000} & \\textbf{0.0000} & \\textbf{0.0000} & 0.5024 & \\textbf{0.0013} & 0.5624 & 0.0174 & 0.6038 & 0.6207 & 0.8655 \\\\\ncoin & \\textbf{0.0000} & \\textbf{0.0000} & \\textbf{0.0000} & 0.0474 & \\textbf{0.0011} & 0.0240 & \\textbf{0.0082} & 0.3114 & 0.7081 & 0.3721 \\\\\nhash & \\textbf{0.0007} & 0.0157 & 0.0210 & 0.4702 & 0.2266 & 0.5799 & 0.9762 & 0.0574 & 0.2057 & 0.7810 \\\\\ntether & 0.1546 & 0.0189 & 0.0421 & 0.9706 & 0.0746 & 0.3558 & 0.5623 & 0.9917 & 0.3505 & 0.5671 \\\\\nlitecoin & 0.0626 & \\textbf{0.0011} & \\textbf{0.0006} & \\textbf{0.0000} & \\textbf{0.0055} & 0.0207 & 0.1481 & 0.4020 & 0.9960 & 0.9071 \\\\\nenjin coin & 0.0596 & 0.3111 & 0.3243 & 0.1006 & 0.1710 & \\textbf{0.0000} & 0.6518 & 0.0559 & 0.9434 & 0.2782 \\\\\nhashing & \\textbf{0.0000} & \\textbf{0.0000} & \\textbf{0.0000} & 0.4573 & \\textbf{0.0046} & 0.3344 & 0.4514 & 0.1547 & 0.3340 & 0.5922 \\\\\ntoken & \\textbf{0.0002} & \\textbf{0.0058} & \\textbf{0.0010} & 0.8487 & 0.1697 & 0.0991 & 0.2591 & 0.3528 & 0.8180 & 0.1829 \\\\\ncrypto & \\textbf{0.0001} & \\textbf{0.0000} & \\textbf{0.0000} & 0.9201 & \\textbf{0.0030} & 0.3723 & 0.1607 & 0.2976 & 0.1012 & 0.3163 \\\\\nICO & 0.0800 & \\textbf{0.0083} & 0.0156 & 0.1037 & 0.0814 & 0.0926 & 0.7811 & 0.7994 & 0.6019 & 0.4251 \\\\\ncoin market & \\textbf{0.0013} & \\textbf{0.0023} & \\textbf{0.0002} & 0.4380 & \\textbf{0.0001} & 0.7448 & 0.5298 & 0.4680 & 0.3202 & 0.5332 \\\\\nsoft fork & 0.0705 & 0.0147 & 0.0146 & 0.0546 & 0.3695 & 0.5942 & 0.6413 & 0.5173 & 0.1524 & 0.0963 \\\\\nblock producer & 0.0186 & 0.0692 & 0.0966 & 0.4160 & 0.7026 & 0.4084 & \\textbf{0.0012} & 0.2283 & 0.2444 & 0.5688 \\\\\ndistributed ledger & \\textbf{0.0000} & \\textbf{0.0003} & \\textbf{0.0006} & 0.1367 & 0.5447 & \\textbf{0.0096} & 0.0598 & 0.0207 & 0.6033 & 0.1582 \\\\\ndigital fiat & 0.3668 & 0.1305 & 0.2920 & 0.7814 & 0.1757 & \\textbf{0.0035} & 0.1034 & 0.1989 & 0.3505 & 0.9097 \\\\\nconsensus & 0.1484 & 0.0414 & 0.1733 & 0.8250 & 0.2027 & 0.2676 & 0.3028 & 0.9427 & \\textbf{0.0039} & 0.7434 \\\\\nBTC-USD\\_vol & NA & \\textbf{0.0016} & 0.0169 & 0.4498 & 0.0976 & 0.5810 & 0.4332 & 0.3715 & 0.1185 & 0.3966 \\\\\nETH-USD\\_vol & 0.0211 & NA & 0.6881 & 0.9887 & 0.7813 & 0.2604 & 0.3309 & 0.1331 & 0.2320 & 0.8606 \\\\\nUSDT-USD\\_vol & 0.0236 & 0.2817 & NA & 0.9615 & 0.7748 & 0.4356 & 0.4442 & 0.1210 & 0.6740 & 0.9159 \\\\\nBNB-USD\\_vol & 0.0407 & \\textbf{0.0067} & \\textbf{0.0001} & NA & 0.0133 & 0.7227 & \\textbf{0.0064} & 0.6081 & 0.3888 & 0.8414 \\\\\nLTC-USD\\_vol & 0.0142 & \\textbf{0.0008} & 0.0585 & \\textbf{0.0011} & NA & 0.8935 & 0.0882 & 0.5584 & 0.6791 & 0.8767 \\\\\nENJ-USD\\_vol & 0.0494 & 0.0905 & 0.0179 & 0.1367 & 0.6793 & NA & 0.4163 & 0.5028 & 0.7517 & 0.6452 \\\\\nZEN-USD\\_vol & 0.2776 & 0.1236 & 0.0562 & 0.4521 & 0.1355 & 0.3684 & NA & 0.2235 & 0.2593 & 0.0891 \\\\\nNMC-USD\\_vol & 0.1121 & 0.0383 & 0.4738 & 0.8830 & 0.0831 & 0.2012 & 0.1662 & NA & 0.6787 & 0.5602 \\\\\nPPC-USD\\_vol & 0.9280 & 0.4225 & 0.3448 & 0.0272 & 0.3291 & 0.7256 & 0.6997 & 0.4033 & NA & 0.9909 \\\\\nFTC-USD\\_vol & 0.0690 & 0.1190 & 0.1657 & 0.5200 & 0.0164 & 0.5960 & 0.4863 & 0.0535 & 0.1750 & NA \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment Approach", "authors": ["Federico D'Amario", "Milos Ciganovic"], "url": "https://arxiv.org/abs/2210.00883v1", "attribution": "\"Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment Approach\" by Federico D'Amario and Milos Ciganovic, arXiv:2210.00883v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.06010v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cccccccc|}\n\\hline\n$L$ & $R^2$ & SW & JB & Skew & Kurt & L1O & L1A \\\\\n\\hline\n1 & 17\\% & 2.8\\% & 0.02\\% & 0.55 & 1.7 & 0.062 & 0.604 \\\\\n2 & 12\\% & 3.1\\% & 25\\% & -0.4 & 0.18 & 0.39 & 0.43 \\\\\n3 & 11\\% & 20\\% & 12\\% & -0.38 & 0.71 & 0.41 & 0.378\\\\\n4 & 10\\% & 5.9\\% & 1.7\\% & -0.31 & 1.27 & 0.392 & 0.546\\\\\n5 & 10\\% & 9.1\\% & 3.1\\% & -0.52 & 0.78 & 0.446 & 0.946\\\\\n6 & 10\\% & 11\\% & 5.2\\% & -0.48 & 0.74 & 0.438 & 0.753\\\\\n7 & 10\\% & 13\\% & 3.5\\% & -0.56 & 0.65 & 0.436 & 0.725\\\\\n8 & 9\\% & 10\\% & 7.5\\% & -0.53 & 0.39 & 0.302 & 0.761\\\\\n9 & 9\\% & 2.6\\% & 0.8\\% & -0.67 & 0.73 & 0.277 & 0.632\\\\\n10 & 10\\% & 0.3\\% & 0.04\\% & -0.82 & 1.1 & 0.291 & 0.608\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Residuals}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Time Series Model for Three Asset Classes used in Financial Simulator", "authors": ["Andrey Sarantsev", "Angel Piotrowski", "Ian Anderson"], "url": "https://arxiv.org/abs/2508.06010v1", "attribution": "\"A Time Series Model for Three Asset Classes used in Financial Simulator\" by Andrey Sarantsev, Angel Piotrowski, and Ian Anderson, arXiv:2508.06010v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.03079v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Point Prediction performance for CIFAR-10 dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|cccccc}\n\\hline\nPrediction Method & Residual Type & MSE\\\\\n\\hline\nMB-LC & P & {1.98e-01}\\\\\n\\hline\n& F & {2.20e-01}\\\\\n\\hline\nMB-LL & P & 1.79e-01\\\\\n\\hline\n& F & 1.95e-01\\\\\n\\hline\nMF-LC & P & {1.79e-01}\\\\\n\\hline\n& F & {2.12e-01}\\\\\n\\hline\nMF-LLH & P & {1.60e-01}\\\\\n\\hline\n& F & {1.89e-01}\\\\\n\\hline\nMF-LLM & P & {1.64e-01}\\\\\n\\hline\n& F & {1.70e-01}\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Model-Based and Model-Free point prediction algorithms for locally stationary random fields", "authors": ["Srinjoy Das", "Yiwen Zhang", "Dimitris N. Politis"], "url": "https://arxiv.org/abs/2212.03079v1", "attribution": "\"Model-Based and Model-Free point prediction algorithms for locally stationary random fields\" by Srinjoy Das, Yiwen Zhang, and Dimitris N. Politis, arXiv:2212.03079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.00139v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{System level simulation setup}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n\t\t\t\\hline\n\t\t\tParameter & Default Value \\\\ \\hline\n\t\t\tBandwidth & 400 MHz \\\\ \\hline\n\t\t\tCarrier frequency & 28 GHz \\\\ \\hline\n\t\t\tFFT size & 4096 \\\\ \\hline\n\t\t\tSub-carrier spacing & 120 kHz \\\\ \\hline\n\t\t\tUE transmit power & 23 dBm \\\\ \\hline\n\t\t\tUE beam gain & 0 dB \\\\ \\hline\n\t\t\tBS peak beam gain & 28 dB \\\\ \\hline\n\t\t\tnumber of analog beams in PAA & 16 \\\\ \\hline\n BS noise figure & 5 dB \\\\ \\hline\n Path-loss exponent & 3 \\\\ \\hline\n TTD delay stepsize & 2.5 ns \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Beamforming with Joint Phase and Time Array: System Design, Prototyping and Performance", "authors": ["Jianhua Mo", "Ahmad AlAmmouri", "Shenggang Dong", "Younghan Nam", "Won-Suk Choi", "Gary Xu", "Jianzhong", "Zhan"], "url": "https://arxiv.org/abs/2502.00139v1", "attribution": "\"Beamforming with Joint Phase and Time Array: System Design, Prototyping and Performance\" by Jianhua Mo, Ahmad AlAmmouri, Shenggang Dong, Younghan Nam, Won-Suk Choi, Gary Xu, Jianzhong, and Zhan, arXiv:2502.00139v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.03154v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{t-DCF cost function parameters assumed in ASVspoof 2019.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\t\t\\toprule\n\t\t\\multicolumn{3}{c}{\\textbf{Priors}}&\n\t\t\\multicolumn{2}{c}{\\textbf{SD costs}}&\n\t\t\\multicolumn{2}{c}{\\textbf{ASV costs}}\\\\ \n\t\t\\cmidrule(lr){1-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-7} \n\t\t${\\pi _{{\\rm{tar}}}}$&${\\pi _{{\\rm{non}}}}$&${\\pi _{{\\rm{spoof}}}}$&$C_{{\\rm{miss}}}^{{\\rm{SD}}}$&$C_{{\\rm{fa}}}^{{\\rm{SD}}}$&$C_{{\\rm{miss}}}^{{\\rm{ASV}}}$&$C_{{\\rm{fa}}}^{{\\rm{ASV}}}$\\\\\n\t\t\\midrule\n\t\t0.9405&0.0095&0.05&1&10&1&10 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System", "authors": ["Yuanjun Zhao", "Roberto Togneri", "Victor Sreeram"], "url": "https://arxiv.org/abs/2012.03154v1", "attribution": "\"Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System\" by Yuanjun Zhao, Roberto Togneri, and Victor Sreeram, arXiv:2012.03154v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09827v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of GMRES-IR and RGMRES-IR refinement steps with the number of GMRES iterations for each refinement step for real matrices, using precisions ($u_f,u,u_r$) = (half,single,double) and ($m,k$) = (40,6).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|}\n\t\t\\hline\n\t\tMatrix & GMRES-IR (40) & RGMRES-IR(40,6) \\\\ \\hline\n\t\torsirr\\_1 & 12 (6,6) & 12 (6,6) \\\\\n\t\tcomsol & 46 (22,24) & 30 (22,8) \\\\\n\t\tcircuit204 & 40 (12,14,14) & 29 (12,9,8) \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Mixed Precision GMRES-based Iterative Refinement with Recycling", "authors": ["Eda Oktay", "Erin Carson"], "url": "https://arxiv.org/abs/2201.09827v2", "attribution": "\"Mixed Precision GMRES-based Iterative Refinement with Recycling\" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2308.13256v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of on-treatment (non-bold) and observed off-treatment means (bold) by pattern of deviation and missingness in SWEGMD \\emph{Perforated} data set}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c||c|c|c|c|c|c|c|}\\hline\n& \\multicolumn{7}{c||}{\\emph{\\scriptsize{Placebo}}} \n & \\multicolumn{7}{c|}{\\emph{\\scriptsize{Active}}}\n \\\\ \\hline\n& \\multicolumn{7}{c||}{\\scriptsize{Last Visit on treatment}} \n & \\multicolumn{7}{c|}{\\scriptsize{Last Visit on treatment}}\n \\\\ \n& \\multicolumn{2}{c|}{ 1} & \\multicolumn{2}{c|}{ 2} & \\multicolumn{2}{c|}{ 3} & 4\n& \\multicolumn{2}{c|}{ 1} & \\multicolumn{2}{c|}{ 2}& \\multicolumn{2}{c|}{ 3} & 4\n \\\\ \\hline\n \\scriptsize{Visit} & \\scriptsize{Stop} & \\scriptsize{Cont.} & \\scriptsize{Stop} \n& \\scriptsize{Cont.} & \\scriptsize{Stop} & \\scriptsize{Cont.}\n& \\scriptsize{Cont.}\n& \\scriptsize{Stop} & \\scriptsize{Cont.} & \\scriptsize{Stop}\n& \\scriptsize{Cont.} & \\scriptsize{Stop} & \\scriptsize{Cont.}\n& \\scriptsize{Cont.}\n\\\\ \\hline\n 1 & -1.00 & 0.40 & -6.00 & -0.50 & 0.13 & -2.67 & -1.82 & \n 0.17 & & -5.00 & -3.25 & -0.67 & -3.67 & -1.75 \\\\ \n\\cline{2-3} \\cline{9-10} 2 & & \\textbf{-0.67} & -4.00 & 2.00 & -0.75 & -5.00 & -3.11 & \n & & -7.00 & -3.25 & -1.33 & -3.67 & -4.87 \\\\ \n\\cline{4-5} \\cline{11-12} 3 & & \\textbf{-2.93} & & \\textbf{-0.97} & 0.00 & -6.67 & -4.45 & \n & & & \\textbf{-4.18} & 0.33 & -5.50 & -7.25 \\\\ \n\\cline{6-7} \\cline{13-14} 4 & & \\textbf{-2.97} & & \\textbf{-1.32} & & \\textbf{-7.25} & -5.14 & \n & & & \\textbf{-5.29} & & \\textbf{-6.20} & -8.34 \\\\ \\hline\n\\scriptsize{Count} & 2 & 5 & 1 & 4 & 8 & 3 & 65 & 6 & 0 & 1 & 4 & 3 & 6 & 64 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Multiple imputation of partially observed data after treatment-withdrawal", "authors": ["Suzie Cro", "James H Roger", "James R Carpenter"], "url": "https://arxiv.org/abs/2308.13256v1", "attribution": "\"Multiple imputation of partially observed data after treatment-withdrawal\" by Suzie Cro, James H Roger, and James R Carpenter, arXiv:2308.13256v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01833v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average accuracy on the CIFAR-10/100 and ImageNet-100 with novel class ratio of 50\\% and labeled ratio of 10\\%.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccc}\n \\toprule\n \\textbf{Method} & \\multicolumn{3}{c}{\\textbf{CIFAR-10}} & \\multicolumn{3}{c}{\\textbf{CIFAR-100}} & \\multicolumn{3}{c}{\\textbf{ImageNet-100}}\\\\\n \\cmidrule(lr){2-4} \\cmidrule(l){5-7} \\cmidrule(lr){8-10} \n & Seen & Novel & All & Seen & Novel & All & Seen & Novel & All \\\\\n \\midrule\n FixMatch & 64.3 & 49.4 & 47.3 & 30.9 & 18.5 & 15.3 & - & - & -\\\\\n DS\\textsuperscript{3}L & 70.5 & 46.6 & 43.5 & 33.7 & 15.8 & 15.1 & - & - & -\\\\\n DTC & 42.7 & 31.8 & 32.4 & 22.1 & 10.5 & 13.7 & - & - & - \\\\\n RankStats & 71.4 & 63.9 & 66.7 & 20.4 & 16.7 & 17.8 & - & - & -\\\\\n UNO & 86.5 & 71.2 & 78.9 & 53.7 & 33.6 & 42.7 & 66.0 & 42.2 & 53.3 \\\\\n ORCA & 82.8 & 85.5 & 84.1 & 52.5 & 31.8 & 38.6 & 83.9 & 60.5 & 69.7 \\\\\n NACH & 91.8 & 89.4 & 90.6 & 65.8 & 37.5 & 49.2 & - & - & - \\\\\n OpenLDN & 92.4 & 93.2 & 92.8 & 55.0 & 40.0 & 47.7 & - & - & - \\\\\n TRSSL & 94.9 & 89.6 & 92.2 & 68.5 & \\textbf{52.1} & \\textbf{60.3} & 82.6 & 67.8 & 75.4 \\\\\n OpenCon & - & - & - & 62.5 & 44.4 & 48.2 & - & - & - \\\\\n \\midrule\n OwMatch & 89.3 & 92.2 & 90.7 & 59.5 & 43.7 & 51.2 & 86.4 & 69.2 & 77.8 \\\\\n OwMatch+ & 94.4 & \\textbf{96.2} & \\textbf{95.3} & \\textbf{69.9} & 51.5 & \\textbf{60.3} & \\textbf{87.8} & \\textbf{72.7} & \\textbf{80.2} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning", "authors": ["Shengjie Niu", "Lifan Lin", "Jian Huang", "Chao Wang"], "url": "https://arxiv.org/abs/2411.01833v1", "attribution": "\"OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning\" by Shengjie Niu, Lifan Lin, Jian Huang, and Chao Wang, arXiv:2411.01833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05378v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model performance for the temperature-dependent chemical reaction network. The first three rows show the error of the N-CTMC model as we increase the number of trajectories in the training set. For the largest training set, we also show GLM model errors. Training is halted when $\\Delta\\text{loss}$ reaches a minimum. W denotes that the error metric is weighted. For the GLM model, training is halted when $\\| \\nabla \\theta\\| < 0.01$.} % This table has been updated\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l|l|l}\nTrajectories & MAE & W-MAE & MSE & W-MSE & Epochs \\\\ \\hline\n100 & 0.024 & $ 3.13 \\cdot 10^{-3}$ & 0.004 & $ 7.65 \\cdot 10^{-5}$ & 7,700 \\\\ \n300 & 0.020 & $ 1.88 \\cdot 10^{-3}$ & 0.003 & $ 2.61 \\cdot 10^{-5}$ & 6,900 \\\\ \n500 & 0.015 & $ 1.45 \\cdot 10^{-3}$ & 0.002 & $ 1.62 \\cdot 10^{-5}$ & 4,000 \\\\ \\hline\n500 (GLM) & 0.250 & $ 2.88 \\cdot 10^{-2}$ & 1.345 & $ 5.80 \\cdot 10^{-3}$ & 12,000 \\\\ \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Neural Continuous-Time Markov Models", "authors": ["Majerle Reeves", "Harish S. Bhat"], "url": "https://arxiv.org/abs/2212.05378v1", "attribution": "\"Neural Continuous-Time Markov Models\" by Majerle Reeves and Harish S. Bhat, arXiv:2212.05378v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.16933v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of PCA Methods across Different Dimensions for LSAC Dataset}\n\\begin{tabular}{|c|ccc|ccc|ccc|ccc|}\n\\hline\n\\multirow{2}{*}{\\textbf{Dims}} & \\multicolumn{3}{c|}{\\textbf{covFairPCA}} & \\multicolumn{3}{c|}{\\textbf{FairPCA\\_SDP\\_LP}} & \\multicolumn{3}{c|}{\\textbf{JevdPCA}} & \\multicolumn{3}{c|}{\\textbf{StandardPCA}} \\\\\n\\cline{2-13}\n & \\textbf{R.E.} & \\textbf{V.E.} & $\\textbf{MMD}^2$ & \\textbf{R.E.} & \\textbf{V.E.} & $\\textbf{MMD}^2$& \\textbf{R.E.} & \\textbf{V.E.} & $\\textbf{MMD}^2$& \\textbf{R.E.} & \\textbf{V.E.} & $\\textbf{MMD}^2$ \\\\\n\\hline\n1 & 18.67 & 0.61 & 0.87 & 18.65 & 0.61 & 0.87 & 18.63 & 0.61 & 0.84 & 17.64 & 0.63 & 0.83 \\\\\n2 & 3.92 & 0.92 & 0.88 & 3.91 & 0.92 & 0.87 & 4.05 & 0.92 & 0.84 & 3.50 & 0.93 & 0.88 \\\\\n3 & 2.72 & 0.94 & 0.91 & 2.72 & 0.94 & 0.91 & 2.87 & 0.94 & 0.86 & 2.31 & 0.95 & 0.92 \\\\\n4 & 1.78 & 0.96 & 0.96 & 1.73 & 0.96 & 0.95 & 1.87 & 0.96 & 0.89 & 1.34 & 0.97 & 0.95 \\\\\n5 & 0.88 & 0.98 & 0.97 & 0.88 & 0.98 & 0.95 & 1.05 & 0.98 & 0.89 & 0.65 & 0.99 & 0.97 \\\\\n6 & 0.56 & 0.99 & 1.05 & 0.56 & 0.99 & 1.05 & 0.71 & 0.99 & 0.92 & 0.41 & 0.99 & 1.91 \\\\\n7 & 0.32 & 0.99 & 1.89 & 0.32 & 0.99 & 1.91 & 0.69 & 0.99 & 0.92 & 0.27 & 0.99 & 1.93 \\\\\n8 & 0.22 & 1.00 & 1.91 & 0.21 & 1.00 & 1.92 & 0.59 & 0.99 & 0.92 & 0.19 & 1.00 & 1.93 \\\\\n9 & 0.15 & 1.00 & 1.91 & 0.13 & 1.00 & 1.92 & 0.55 & 0.99 & 0.92 & 0.12 & 1.00 & 1.93 \\\\\n10 & 0.09 & 1.00 & 1.93 & 0.07 & 1.00 & 1.91 & 0.47 & 0.99 & 0.92 & 0.06 & 1.00 & 1.93 \\\\\n11 & 0.06 & 1.00 & 1.93 & 0.02 & 1.00 & 1.93 & 0.40 & 0.99 & 0.92 & 0.02 & 1.00 & 1.93 \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Achieving Fair PCA Using Joint Eigenvalue Decomposition", "authors": ["Vidhi Rathore", "Naresh Manwani"], "url": "https://arxiv.org/abs/2502.16933v1", "attribution": "\"Achieving Fair PCA Using Joint Eigenvalue Decomposition\" by Vidhi Rathore and Naresh Manwani, arXiv:2502.16933v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.12825v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dataset details. Size indicates the number of data points, Cont.feat - number of continous features and Cat.feat - number of categorical features.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n\t\t\t\n\t\t\t\\toprule\n\t\t\tDataset & Classes & Size & Cont.feat. & Cat.feat.\\\\ %& Description\\\\\n\t\t\t\\midrule\n\t\t\t\n\t\t\t\n\t\t\tCOMPAS & 2 & 5238 &\t13 & 0 \\\\ %& all features categorical/high dimensional/small size\\\\\n\t\t\t\n\t\t\tHill-Valley & 2 & 1212 & 100 & 0 \\\\ %& \t multi class/moderately high dimensional/large size \\\\\n\t\t\t\n\t\t\tArrhythmia & 2 & 452 &\t177 &\t85 \\\\ %& low dimensional/large size\\\\\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\tLSVT & 2 & 126 & 304 & 3\\\\ %& have numerical \\& categorical features/ large size\\\\\n\t\t\t\n\t\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A PSO Based Method to Generate Actionable Counterfactuals for High Dimensional Data", "authors": ["Shashank Shekhar", "Asif Salim", "Adesh Bansode", "Vivaswan Jinturkar", "Anirudha Nayak"], "url": "https://arxiv.org/abs/2311.12825v2", "attribution": "\"A PSO Based Method to Generate Actionable Counterfactuals for High Dimensional Data\" by Shashank Shekhar, Asif Salim, Adesh Bansode, Vivaswan Jinturkar, and Anirudha Nayak, arXiv:2311.12825v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.03892v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Impact of Destination College Fraction on Wage}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\hline\n & (a) & (b) & (c) &(d) \\\\ \n Year & \\(\\delta_{P20}\\) & \\(\\delta_{P80}\\) & Impact & SD \\\\ \n \\hline\n \\(1980\\) & 0.16 & 0.26 & \\hphantom{1}\\$7900 & \\hphantom{1}\\$6300\\\\ \n \\(1990\\)& 0.18 & 0.31 & \\hphantom{1}\\$9100 & \\hphantom{1}\\$7100\\\\ \n \\(2000\\) & 0.21 & 0.35 & \\$10300 & \\$10900 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "What You See is What You Get: Local Labor Markets and Skill Acquisition", "authors": ["Benjamin Niswonger"], "url": "https://arxiv.org/abs/2209.03892v1", "attribution": "\"What You See is What You Get: Local Labor Markets and Skill Acquisition\" by Benjamin Niswonger, arXiv:2209.03892v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.09123v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of error measures}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n\t\t\\hline & \\textbf{Max. AL} &\\textbf{Max. RL} & \\textbf{Max. CV} & \\textbf{Max. MIRNS}\\\\\n & \\textbf{Error} & \\textbf{Error} & \\textbf{Error} & \\textbf{Error}\\\\\n\t\t\\hline DC & 3.6e-01 & 1.1e-04 & 24.1 & 1.4e-03\\\\\n \\hline IRR-DC & 2.6e-03 & 2.9e-06 & 3.6e-02 & 6.5e-08\\\\\n\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Reliable Solution to Dynamic Optimization Problems using Integrated Residual Regularized Direct Collocation", "authors": ["Yuanbo Nie", "Eric C. Kerrigan"], "url": "https://arxiv.org/abs/2503.09123v1", "attribution": "\"Reliable Solution to Dynamic Optimization Problems using Integrated Residual Regularized Direct Collocation\" by Yuanbo Nie and Eric C. Kerrigan, arXiv:2503.09123v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08927v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|ccccc|}\n\\hline\n& $n_0$ & max TTP & min TTP & mean TTP\\\\\n\\hline\n\\hline\n`On-Off' AT &$0.25$ & $575$ days & $109$ days & $213$ days\\\\\n\\hline\n`Off-On' AT & $0.25$ & $589$ days & $107$ days & $217$ days\\\\\n\\hline\n\\hline\n`On-Off' AT &$0.50$ & $764$ days & $155$ days & $285$ days\\\\\n\\hline\n`Off-On' AT & $0.50$ & $822$ days & $167$ days & $306$ days\\\\\n\\hline\n\\hline\n`On-Off' AT &$0.75$ & $1196$ days & $283$ days & $462$ days\\\\\n\\hline\n`Off-On' AT & $0.75$ & $1500$ days & $400$ days & $589$ days\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\footnotesize Comparison in terms of Time-to-Progression (TTP) between `On-Off'~AT and `Off-On'~AT. The mean TTP is computed by taking the average over the elements of the set $\\Theta_N$ (see ).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Ensemble optimal control for managing drug resistance in cancer therapies", "authors": ["Alessandro Scagliotti", "Federico Scagliotti", "Laura Deborah Locati", "Federico Sottotetti"], "url": "https://arxiv.org/abs/2503.08927v2", "attribution": "\"Ensemble optimal control for managing drug resistance in cancer therapies\" by Alessandro Scagliotti, Federico Scagliotti, Laura Deborah Locati, and Federico Sottotetti, arXiv:2503.08927v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.05728v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c||} \n\\hline\nPrediction Interval & Uncalibrated & Calibrated \\\\ \n\\hline\\hline\n95\\% & 100.0 (0.0) & 95.0 (1.0) \\\\ \n\\hline\n90\\% & 100.0 (1.0) & 90.0 (2.0) \\\\ \n\\hline\n80\\% & 90.0 (3.0) & 90.0 (2.0) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Uncertainty quantification methods compared in terms of their nominal 95\\% PI coverage on water field data. The ensemble-based (uncalibrated) approach is outlined in and the quantile regression (calibrated) approach is the method detailed in . The median across the 100 subsampled locations is reported with the IQR in parenthesis.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Physics-Informed, Deep Double Reservoir Network for Forecasting Boundary Layer Velocity", "authors": ["Matthew Bonas", "David H. Richter", "Stefano Castruccio"], "url": "https://arxiv.org/abs/2311.05728v3", "attribution": "\"A Physics-Informed, Deep Double Reservoir Network for Forecasting Boundary Layer Velocity\" by Matthew Bonas, David H. Richter, and Stefano Castruccio, arXiv:2311.05728v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.08696v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|r|r|r|r|r|r|r|}\n\\hline\nSource &\n Mean &\n Std &\n Min &\n Max &\n Q25 &\n Q50 &\n Q75 \\\\ \\hline\nRDV & 134 & 88 & 8 & 512 & 65 & 123 & 187 \\\\ \\hline\nFinam & 221 & 135 & 18 & 512 & 116 & 178 & 284 \\\\ \\hline\nBCS Express & 20 & 10 & 4 & 82 & 13 & 17 & 26 \\\\ \\hline\nBCS Technical Analysis & 502 & 37 & 29 & 512 & 512 & 512 & 512 \\\\ \\hline\nRBC & 43 & 7 & 16 & 75 & 39 & 44 & 48 \\\\ \\hline\nSmartLab & 21 & 8 & 5 & 82 & 15 & 19 & 25 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market", "authors": ["Kasymkhan Khubiev", "Mikhail Semenov"], "url": "https://arxiv.org/abs/2503.08696v1", "attribution": "\"Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market\" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2503.08696v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03403v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lllll}\nOperation & Category & Parallel? & 16-bit latency (ms) & 32-bit latency (ms) \\\\ \n\\hline\nAND(imm) & Bitwise operations & Yes & 26.9195 & 27.1288\\\\\nAND(reg) & Bitwise operations & Yes & 47.7228 & 55.4597\\\\\nOR(imm) & Bitwise operations & Yes & 28.1923 & 27.8774 \\\\\nOR(reg) & Bitwise operations & Yes & 47.6171 & 50.089 \\\\\nXOR(imm) & Bitwise operations & Yes & 29.2375 & 27.9389\\\\\nXOR(reg) & Bitwise operations & Yes & 47.1986 & 50.3683 \\\\\nORN(imm) & Bitwise operations & Yes & 27.8467 & 27.776\\\\\nORN(reg) & Bitwise operations & Yes & 47.6422& 57.0639\\\\\nNOT(imm) & Bitwise operations & Yes & 0.0072794 & 0.0116968\\\\\nNOT(reg) & Bitwise operations & No & 0.006089 & 0.01232\\\\\n\\hline\nBFC & Bitwise operations & No & 0.0028094 & 0.0026892\\\\\nBFI & Bitwise operations & No & 0.003278 & 0.0033974\\\\\nRBIT & Bitwise operations & No & 0.0104582 & 0.0216222\\\\\nREV & Bitwise operations & No & 0.0097926 & 0.0187618\\\\\n\\end{tabular}\n\\caption{Bitwise operation and data movement latency}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "CryptoEmu: An Instruction Set Emulator for Computation Over Ciphers", "authors": ["Xiaoyang Gong", "Dan Negrut"], "url": "https://arxiv.org/abs/2101.03403v1", "attribution": "\"CryptoEmu: An Instruction Set Emulator for Computation Over Ciphers\" by Xiaoyang Gong and Dan Negrut, arXiv:2101.03403v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00323v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Propensity estimation algorithms.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\t\t\\toprule\n\t\t~ & base algorithm & hyperparameters \\\\\n\t\t\\midrule\n\t\t\\textsc{ConvexPE} (Algorithm~) & proximal-proximal-gradient & $\\tau$ and $\\gamma$ \\\\\n\t\t\\textsc{NonconvexPE} (Algorithm~) & gradient descent & step size $t$ and target rank\\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "TenIPS: Inverse Propensity Sampling for Tensor Completion", "authors": ["Chengrun Yang", "Lijun Ding", "Ziyang Wu", "Madeleine Udell"], "url": "https://arxiv.org/abs/2101.00323v3", "attribution": "\"TenIPS: Inverse Propensity Sampling for Tensor Completion\" by Chengrun Yang, Lijun Ding, Ziyang Wu, and Madeleine Udell, arXiv:2101.00323v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.10858v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Comparison of options against load-shaped forward contracts}. Change in surplus is calculate against the three-contract unrestricted case. SFPFC performs better than options when trading is limited to single instrument, particularly when sold collectively by a portfolio of technologies.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccccccccc}\n\\hline \n\\hline \n& \\multicolumn{3}{c}{$\\beta=0.2$} & \\multicolumn{3}{c}{$\\beta=0.4$} \\\\\n&Options & Sep & Col &Options & Sep & Col \\\\\n\\hline\nCapacity (GW) \\\\\n\\quad Baseload &0.006 & 89.0 &40.7 & 0.02 &84.1 &34.6 \\\\ \n\\quad Peaker &137.3 & 72.5 &99.9 & 135.2 &75.0 &104.4 \\\\ \n\\quad Variable &163.4 & 26.0 &143.2 & 176.2 &36.9 &153.6 \\\\ \n\\\\\nAverage Price (\\$/MWh) \\\\\n\\quad Spot & 62.22 & 61.69 & 57.49 & 61.31 & 61.30 & 57.39\\\\\n\\quad Hedged &62.86 & 63.01 & 59.59 & 61.94 & 62.32 & 59.43 \\\\\n\\\\\nInterannual Volatility(\\$/MWh) \\\\\n\\quad Spot & 25.86 & 22.33 & 24.89 & 25.42 & 23.41 & 24.43 \\\\\n\\quad Hedged & 13.79 & 1.02 & 1.75 & 13.63 & 1.17 &1.69\\\\\n\\\\\nExpected unserved energy (GWh) &5.54 & 4.05 &5.83 &5.53 &4.58 &5.52 \\\\\nProximity to Equilibrium &0.066\\% & 0.058\\% &0.016\\% & 0.029\\% &0.090\\% &0.004\\% \\\\\nChange in Surplus (\\$M/yr) &-5,042 & -3,500 &-517 &-4,206 & -2,906 &-375\\\\\n\\hline\n& \\multicolumn{3}{c}{$\\beta=0.6$} & \\multicolumn{3}{c}{$\\beta=0.8$} \\\\\n&Option & Sep & Col &Option & Sep & Col \\\\\n\\hline\nCapacity (GW) \\\\\n\\quad Baseload &0.2 & 75.6 &33.6 &12.4 & 52.9 &31.4 \\\\ \n\\quad Peaker &133.6 & 79.6 &105.4 &122.6 & 92.8 &107.0 \\\\ \n\\quad Variable &185.0 & 57.1 &154.3 &177.3 & 109.8 &157.7 \\\\ \n\\\\\nAverage Price (\\$/MWh) \\\\\n\\quad Spot & 60.41 & 61.00 & 57.44 & 59.40 & 60.23 & 57.59\\\\\n\\quad Hedged & 61.03 & 61.44 & 59.26 & 59.98 & 60.36 & 59.06 \\\\\n\\\\\nInterannual Volatility(\\$/MWh) \\\\\n\\quad Spot & 25.18 & 24.38 & 24.20 & 24.63 & 26.85 & 24.34 \\\\\n\\quad Hedged & 13.61 & 1.28 & 1.38 & 13.45 & 1.36 & 1.14\\\\\n\\\\\nExpected unserved energy (GWh) &5.52 & 5.35 &5.35 &5.53 & 6.83 &5.39 \\\\\nProximity to Equilibrium &0.048\\% & 0.071\\% &0.008\\% &0.073\\% & 0.070\\% &0.012\\%\\\\\nChange in Surplus (\\$M/yr) &-3,411 & -2,183 &-212 &-2,448 & -1,243 &-41 \\\\\n\\hline\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Beyond capacity: contractual form in electricity reliability obligations", "authors": ["Han Shu", "Jacob Mays"], "url": "https://arxiv.org/abs/2210.10858v1", "attribution": "\"Beyond capacity: contractual form in electricity reliability obligations\" by Han Shu and Jacob Mays, arXiv:2210.10858v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2007.07793v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameters used to train the control policies }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c||} \n \\hline\n Parameter & Value \\\\ \n \\hline\n Training iterations & $2 \\times 10^{6}$ \\\\\n Learning rate & 5e-5 \\\\\n Neural network optimizer & Adam \\\\\n Adam parameters $(\\beta_1, \\beta_2)$ & $(0.9, 0.999)$ \\\\\n Discount factor $\\gamma$ & $0.95$ \\\\\n Max. episode length & $1500$ \\\\\n Minibatch size & $32$ \\\\\n Clipping parameter for PPO & $0.2$ \\\\\n GAE parameter $\\lambda$ & $0.95$ \\\\\n Epochs per PPO update & 10 \\\\\n State-value loss coefficient & 0.5 \\\\\n Variance of action distribution $\\sigma^2$ & 1.0 \\\\\n Learning rate schedule & linear\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors", "authors": ["Aditya M. Deshpande", "Rumit Kumar", "Ali A. Minai", "Manish Kumar"], "url": "https://arxiv.org/abs/2007.07793v1", "attribution": "\"Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors\" by Aditya M. Deshpande, Rumit Kumar, Ali A. Minai, and Manish Kumar, arXiv:2007.07793v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.21347v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{UM Dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc|ccc|}\n \\toprule\n \\textbf{Thresholds}\n & 30ms & 60ms & 90ms & 30ms & 60ms & 90ms \\\\\n \\cmidrule(l){1-7}\n \\textbf{Models} \n & \\multicolumn{3}{c|}{\\textbf{Center Clipping}} \n & \\multicolumn{3}{c|}{\\textbf{Peak Clipping}} \\\\\n \\midrule\n Airport & 100 & 97.44 & 89.74 & 79.49 & 92.31 & 97.44 \\\\\n Babble & 100 & 97.44 & 89.74 & 87.18 & 92.31 & 94.87 \\\\\n Exhibition & 100 & 97.44 & 94.87 & 94.87 & 97.44 & 100\\\\\n Pink & 100 & 97.44 & 89.74 & 82.05 & 89.74 & 92.31 \\\\\n Restaurant & 100 & 97.44 & 89.74 & 89.74 & 94.87 & 97.44 \\\\\n Station & 100 & 97.44 & 87.18 & 84.62 & 92.31 & 94.87\\\\\n Street & 100 & 97.44 & 89.74 & 92.31 & 92.31 & 97.44 \\\\\n Train & 100 & 97.44 & 92.31 & 87.18 & 92.31 & 94.87 \\\\\n White Gaussian & 97.44 & 94.87 & 84.62 & 87.18 & 87.18 & 89.74 \\\\\n Clean & 100 & 97.44 & 92.31 & 89.74 & 94.87 & 100 \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Cochleagram-based Noise Adapted Speaker Identification System for Distorted Speech", "authors": ["Sabbir Ahmed", "Nursadul Mamun", "Md Azad Hossain"], "url": "https://arxiv.org/abs/2508.21347v1", "attribution": "\"Cochleagram-based Noise Adapted Speaker Identification System for Distorted Speech\" by Sabbir Ahmed, Nursadul Mamun, and Md Azad Hossain, arXiv:2508.21347v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.09514v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Performance of exactly normalized importance sampling (ENIS), self-normalized importance sampling (SNIS) and MoM-SNIS in the Exponential example.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|l|r|r|r|r|r}\n\\hline\np & method & cost & MSE & bias & q95 & q99\\\\\n\\hline\n\\cellcolor{gray!10}{2.01} & \\cellcolor{gray!10}{ENIS} & \\cellcolor{gray!10}{1000} & \\cellcolor{gray!10}{0.0023} & \\cellcolor{gray!10}{0.0000} & \\cellcolor{gray!10}{0.0721} & \\cellcolor{gray!10}{0.1124}\\\\\n\\hline\n\\cellcolor{gray!10}{2.01} & \\cellcolor{gray!10}{SNIS} & \\cellcolor{gray!10}{1000} & \\cellcolor{gray!10}{0.0021} & \\cellcolor{gray!10}{0.0014} & \\cellcolor{gray!10}{0.0921} & \\cellcolor{gray!10}{0.1450}\\\\\n\\hline\n\\cellcolor{gray!10}{2.01} & \\cellcolor{gray!10}{MoM-SNIS} & \\cellcolor{gray!10}{1152} & \\cellcolor{gray!10}{0.0016} & \\cellcolor{gray!10}{0.0370} & \\cellcolor{gray!10}{0.0634} & \\cellcolor{gray!10}{0.0735}\\\\\n\\hline\n2.10 & ENIS & 1000 & 0.0018 & 0.0000 & 0.0637 & 0.0964\\\\\n\\hline\n2.10 & SNIS & 1000 & 0.0016 & 0.0010 & 0.0768 & 0.1206\\\\\n\\hline\n2.10 & MoM-SNIS & 1152 & 0.0015 & 0.0349 & 0.0609 & 0.0708\\\\\n\\hline\n\\cellcolor{gray!10}{3.00} & \\cellcolor{gray!10}{ENIS} & \\cellcolor{gray!10}{1000} & \\cellcolor{gray!10}{0.0004} & \\cellcolor{gray!10}{0.0000} & \\cellcolor{gray!10}{0.0379} & \\cellcolor{gray!10}{0.0509}\\\\\n\\hline\n\\cellcolor{gray!10}{3.00} & \\cellcolor{gray!10}{SNIS} & \\cellcolor{gray!10}{1000} & \\cellcolor{gray!10}{0.0004} & \\cellcolor{gray!10}{0.0001} & \\cellcolor{gray!10}{0.0396} & \\cellcolor{gray!10}{0.0528}\\\\\n\\hline\n\\cellcolor{gray!10}{3.00} & \\cellcolor{gray!10}{MoM-SNIS} & \\cellcolor{gray!10}{1152} & \\cellcolor{gray!10}{0.0006} & \\cellcolor{gray!10}{0.0173} & \\cellcolor{gray!10}{0.0456} & \\cellcolor{gray!10}{0.0564}\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On importance sampling and independent Metropolis-Hastings with an unbounded weight function", "authors": ["George Deligiannidis", "Pierre E. Jacob", "El Mahdi Khribch", "Guanyang Wang"], "url": "https://arxiv.org/abs/2411.09514v2", "attribution": "\"On importance sampling and independent Metropolis-Hastings with an unbounded weight function\" by George Deligiannidis, Pierre E. Jacob, El Mahdi Khribch, and Guanyang Wang, arXiv:2411.09514v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08533v5_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison between our LGT conversion and DGNet data augmentation on Market1501.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cclcl}\n\t\t\\hline\n\t\t\\multirow{2}{*}{Methods} & \\multicolumn{4}{c}{Market1501} \\\\ \\cline{2-5} \n\t\t& \\multicolumn{2}{c}{Rank-1} & \\multicolumn{2}{c}{mAP(\\%)} \\\\ \\hline\n\t\tBaseline & \\multicolumn{2}{c}{88.8} & \\multicolumn{2}{c}{71.6} \\\\ \n\t\tBaseline + DGNet & \\multicolumn{2}{c}{{88.9}} & \\multicolumn{2}{c}{{72.1}} \\\\ \n\t\tBaseline+ LGT(ours) & \\multicolumn{2}{c}{\\textbf{90.0}} & \\multicolumn{2}{c}{\\textbf{74.9}} \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method", "authors": ["Yunpeng Gong", "Liqing Huang", "Lifei Chen"], "url": "https://arxiv.org/abs/2101.08533v5", "attribution": "\"Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method\" by Yunpeng Gong, Liqing Huang, and Lifei Chen, arXiv:2101.08533v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.15351v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n & BPE & GP-ThreDS & REDS \\\\ \\midrule\n Branin & $29.84 \\pm 6.13$ & $4.37 \\pm 0.28$ & $\\mathbf{0.32} \\pm 0.08$ \\\\ \\midrule\n Hartmann-4D & $38.45 \\pm 3.93$ & $7.59 \\pm 0.54$ & $\\mathbf{0.47} \\pm 0.11$\\\\ \\midrule\n Hartmann-6D & $119.71 \\pm 23.75$ & $19.33 \\pm 0.54$ & $\\mathbf{1.19} \\pm 0.08$ \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Time taken (in seconds) by different algorithms across the different benchmark functions.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency", "authors": ["Sudeep Salgia", "Sattar Vakili", "Qing Zhao"], "url": "https://arxiv.org/abs/2310.15351v2", "attribution": "\"Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency\" by Sudeep Salgia, Sattar Vakili, and Qing Zhao, arXiv:2310.15351v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.06234v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Second special class of circles with apparent symmetric lattice points on each boundary.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}\n\\hline\n$k$ & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\\\\n\\hline\n$g(k)$ & 5& 13& 29& 49& 81& 113& 149& 197& 253 & 317\\\\\n\\hline\n$\\pi R_{g(k)}^2\\approx $ & 6& 16& 31& 53& 82& 116& 157& 204& 258 & 317 \\\\\n\\hline\n$k$ & 11 & 12 & 13 & 14 & 15 & 16 & 17 & 18 &19 &20 \\\\\n\\hline\n$g(k)$ & 377& 441& 529& 613& 709& 797& 901& 1009 &1129 &1257\\\\\n\\hline\n$\\pi R_{g(k)}^2\\approx $ & 383& 456& 534& 619& 710& 807& 911& 1021 & 1137 & 1260 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Largest Circle Enclosing $n$ Lattice Points", "authors": ["Jianqiang Zhao"], "url": "https://arxiv.org/abs/2505.06234v2", "attribution": "\"The Largest Circle Enclosing $n$ Lattice Points\" by Jianqiang Zhao, arXiv:2505.06234v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10650v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{PAD-UFES - Segmentation Results}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\hline\n\\textbf{Method} & \\textbf{JI(\\%)} & \\textbf{SE(\\%)} & \\textbf{SP(\\%)} & \\textbf{AC(\\%)} \\\\\n\\hline\nISNN$_{C=0.1}$ & \\textbf{84.08} & \\textbf{94.02} & 98.50 & \\textbf{98.21} \\\\\nISNN$_{C=0.25}$ & 83.02 & 93.65 & 98.38 & 98.07 \\\\ \nISNN$_{C=0.5}$ & 81.00 & 93.68 & 97.93 & 97.69 \\\\ \nS-FCM & 82.88 & 91.35 & \\textbf{98.66} & 98.06 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data", "authors": ["José G. M. Esgario", "Renato A. Krohling"], "url": "https://arxiv.org/abs/2201.10650v1", "attribution": "\"Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data\" by José G. M. Esgario and Renato A. Krohling, arXiv:2201.10650v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.01116v2_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c}\n\\toprule\n& Full Specification & Event-Specific Time Trends & No Household FEs \\\\\n\\midrule\nPost Service & 0.218***& 0.253*** & 0.726*** \\\\\n& (0.0032) & (0.0051)& (0.0036) \\\\\nPost Bill & -0.109*** & -0.071*** & -0.016***\\\\\n& (0.0030) & (0.0043) & (0.0038) \\\\\n\\midrule\n$\\overline{\\text{spend}_{it}}$ & \\$120.49 & \\$120.49 & \\$120.49 \\\\\nObservations & 61,860,735 & 61,860,735 & 61,860,735 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Robustness of Main Specification}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Medical Bill Shock and Imperfect Moral Hazard", "authors": ["Alex Hoagland", "David M. Anderson", "Ed Zhu"], "url": "https://arxiv.org/abs/2211.01116v2", "attribution": "\"Medical Bill Shock and Imperfect Moral Hazard\" by Alex Hoagland, David M. Anderson, and Ed Zhu, arXiv:2211.01116v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.17888v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative studies for the regularization terms and mesh extraction methods on the DTU dataset. }\n\\begin{tabular}{l|ccc}\n & Accuracy~$\\downarrow$ & Completion ~$\\downarrow$ & Average~$\\downarrow$ \\\\\n\\hline\nA. w/o normal consistency & 1.35 & 1.13 & 1.24\\\\\nB. w/o depth distortion & 0.89 & 0.87 & 0.88 \\\\\n\\hline\nC. w / expected depth & 0.88 & 1.01 & 0.94 \\\\\nD. w / SPSR & 1.25 & 0.89 & 1.07 \\\\\n\\hline\nE. Full Model & 0.79 & 0.86 & 0.83\\\\\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "2D Gaussian Splatting for Geometrically Accurate Radiance Fields", "authors": ["Binbin Huang", "Zehao Yu", "Anpei Chen", "Andreas Geiger", "Shenghua Gao"], "url": "https://arxiv.org/abs/2403.17888v3", "attribution": "\"2D Gaussian Splatting for Geometrically Accurate Radiance Fields\" by Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao, arXiv:2403.17888v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.14106v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccccc}\n Layer & Dense & Dense & Dense & Dense & Dense & Dropout & Dense \\\\ \\hline\n Units & 256 & 128 & 128 & 1024 & 128 & - & 8 \\\\\n Activation & selu & relu & relu & LeakyReLu & LeakyReLu & - & softmax \\\\\n Parameter & - & - & - & 0.05 & 0.01 & 0.3 & - \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Architecture of the neural network with 100 training epochs for the surrogate model in section from left to right. There are eight input units for the eight features of the surrogate model and eight output units for the eight clusters.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data", "authors": ["Tobias Wand", "Martin Heßler", "Oliver Kamps"], "url": "https://arxiv.org/abs/2208.14106v3", "attribution": "\"Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data\" by Tobias Wand, Martin Heßler, and Oliver Kamps, arXiv:2208.14106v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12781v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Relative error of approxiamtion with $k$-truncated SVD} \n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\\hline\n$k$ & 5 & 15 & 25 & 35 & 45 \\\\\\hline\n$\\frac{\\|A_k - A\\|_F}{\\|A\\|_F}$ & 0.2546-0.2304$\\epsilon$ & 0.1446-0.1345$\\epsilon$ & 0.1052-0.0932$\\epsilon$ & 0.0838-0.0752$\\epsilon$ & 0.0696-0.0619$\\epsilon$ \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Low Rank Approximation of Dual Complex Matrices", "authors": ["Liqun Qi", "David M. Alexander", "Zhongming Chen", "Chen Ling", "Ziyan Luo"], "url": "https://arxiv.org/abs/2201.12781v1", "attribution": "\"Low Rank Approximation of Dual Complex Matrices\" by Liqun Qi, David M. Alexander, Zhongming Chen, Chen Ling, and Ziyan Luo, arXiv:2201.12781v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2310.16194v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparision of LoRAE against modern state-of-the-art deterministic autoencoders (FID Score is used for comparison).}\n\\begin{tabular}{ccccc}\n \\hline\n & \\multicolumn{2}{c}{\\textbf{MNIST}} & \\multicolumn{2}{c}{\\textbf{CelebA}} \\\\\n \\cline{2-5}\n & $\\mathcal{N}$ & GMM & $\\mathcal{N}$ & GMM \\\\\n \\hline\n \\hline\n WAE & 21.04 & 11.32 & 57.6 & 45.91 \\\\\n \\hline\n RAE & 22.12 & 11.54 & \\textbf{50.31} & 46.05 \\\\\n \\hline\n \\textbf{LoRAE} & \\textbf{19.50} & \\textbf{11.09} & 56.29 & \\textbf{45.43}\\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning Low-Rank Latent Spaces with Simple Deterministic Autoencoder: Theoretical and Empirical Insights", "authors": ["Alokendu Mazumder", "Tirthajit Baruah", "Bhartendu Kumar", "Rishab Sharma", "Vishwajeet Pattanaik", "Punit Rathore"], "url": "https://arxiv.org/abs/2310.16194v1", "attribution": "\"Learning Low-Rank Latent Spaces with Simple Deterministic Autoencoder: Theoretical and Empirical Insights\" by Alokendu Mazumder, Tirthajit Baruah, Bhartendu Kumar, Rishab Sharma, Vishwajeet Pattanaik, and Punit Rathore, arXiv:2310.16194v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00469v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\toprule\nDataset & Style & Train & Dev & Test \\\\\n\\hline\n\\multirow{2}{*}{Yelp} & Positive & 266041 & 2000 & 500 \\\\ \\cline{2-5} \n & Negative & 177218 & 2000 & 500 \\\\ \\hline\n\\multirow{2}{*}{Amazon} & Positive & 277228 & 1015 & 500 \\\\ \\cline{2-5} \n & Negative & 277769 & 985 & 500 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Data Statistics for Yelp and Amazon Dataset}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning", "authors": ["Chenghao Fan", "Ziao Li", "Wei wei"], "url": "https://arxiv.org/abs/2202.00469v1", "attribution": "\"Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning\" by Chenghao Fan, Ziao Li, and Wei wei, arXiv:2202.00469v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.04181v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\nCopula & Distribution $C(u, v ; \\cdot)$& Range of $\\theta$ & $\\lambda_L$ & $\\lambda_U$ & Generator $\\psi(\\cdot)$\\\\\n\\hline\nGuassian & $\\Phi_{\\theta}(\\Phi^{-1}(u), \\Phi^{-1}(v))$ & $(-1, 1)$ & 0 & 0 & $\\times$\\\\\nStudent $t$& $T_{\\nu, \\theta}(t_{\\nu}^{-1}(u), t_{\\nu}^{-1}(v))$ & $(-1, 1)$ & \\multicolumn{2}{c}{$2t_{\\nu+1}\\left(-\\frac{\\sqrt{\\nu+1}\\sqrt{1-\\theta}}{\\sqrt{1+\\theta}}\\right)$} &$\\times$ \\\\\nGumbel & $\\exp\\left(-[(\\ln u)^{\\theta} + (\\ln v)^{\\theta}]^{1/\\theta}\\right)$ & $[1,\\infty)$ & 0 & $2 - 2^{1/\\theta}$& $(- \\ln t)^{\\theta}$\\\\\nClayton & $\\left(u^{-\\theta} + v^{-\\theta} - 1\\right)^{-\\frac{1}{\\theta}}$ & $(0,\\infty)$ & $2 - 2^{1/\\theta}$ & 0& $(t^{-\\theta} - 1)/\\theta$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Bivariate copula models with correlation parameter $\\theta$, upper tail dependence $\\lambda_L$, lower tail $\\lambda_U$ dependence parameters}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Systemic Risk in Market Microstructure of Crude Oil and Gasoline Futures Prices: A Hawkes Flocking Model Approach", "authors": ["Hyun Jin Jang", "Kiseop Lee", "Kyungsub Lee"], "url": "https://arxiv.org/abs/2012.04181v1", "attribution": "\"Systemic Risk in Market Microstructure of Crude Oil and Gasoline Futures Prices: A Hawkes Flocking Model Approach\" by Hyun Jin Jang, Kiseop Lee, and Kyungsub Lee, arXiv:2012.04181v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13229v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The chi-square test for years vs hours averages the results of 1000 tests.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr} \\toprule\n & All Type & Rear-end & Sideswipe \\\\ \\midrule\nStatstic & 12.49404 & 16.72444 & 9.186534 \\\\\nP-value & 0.275962 & 0.195133 & 0.41611 \\\\ \\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study", "authors": ["Xiwen Chen", "Hao Wang", "Abolfazl Razi", "Brendan Russo", "Jason Pacheco", "John Roberts", "Jeffrey Wishart", "Larry Head", "Alonso Granados Baca"], "url": "https://arxiv.org/abs/2201.13229v2", "attribution": "\"Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study\" by Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head, and Alonso Granados Baca, arXiv:2201.13229v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17203v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters for adaptive localized neuron strategy used in Section .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n \\hline\n Cases & $S_1$ & $S_2$ & $N_L$ & $R_L$\\\\\n \\hline\n $\\varepsilon = 120^{-1}$ & 8 & 3 & 1500 & 10\\\\\n $\\varepsilon = 500^{-1}$ & 8 & 5 & 1500 & 10\\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Deep collocation method: A framework for solving PDEs using neural networks with error control", "authors": ["Mingxing Weng", "Zhiping Mao", "Jie Shen"], "url": "https://arxiv.org/abs/2502.17203v2", "attribution": "\"Deep collocation method: A framework for solving PDEs using neural networks with error control\" by Mingxing Weng, Zhiping Mao, and Jie Shen, arXiv:2502.17203v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11465v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|}\n\\hline\n \\textbf{Lower or increase subjects payoff given that:} & \\textbf{Amount} \\\\ \\hline\n Subject used own work -- outcome: success & *enter amount* \\\\ \n Subject used own work -- outcome: failure & *enter amount* \\\\ \n Subject delegated (machine/human) -- outcome: success & *enter amount* \\\\ \n Subject delegated (machine/human) -- outcome: failure & *enter amount* \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Decision to increase or decrease the delegator's pay-off}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Hiding Behind Machines: When Blame Is Shifted to Artificial Agents", "authors": ["Till Feier", "Jan Gogoll", "Matthias Uhl"], "url": "https://arxiv.org/abs/2101.11465v1", "attribution": "\"Hiding Behind Machines: When Blame Is Shifted to Artificial Agents\" by Till Feier, Jan Gogoll, and Matthias Uhl, arXiv:2101.11465v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.14875v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lcccccc}\n\t\t\\toprule\n\t\t& \\multicolumn{2}{c}{Iterations} & \\multicolumn{2}{c}{Broadwell (ms)} & \\multicolumn{2}{c}{Ivy Bridge (ms)} \\\\\n\t\t\\cmidrule(r){2-3} \\cmidrule(r){4-5} \\cmidrule(r){6-7}\n\t\t$l_{max}$ & $10$& $11$ & $10$ & $11$ & $10$ & $11$\\\\\n\t\t\\midrule\n\t\tV(1, 0) & 32 & 31 & 872 & 4306 & 828 & 4128 \\\\\n\t\t\\midrule\n\t\tV(1, 1) & 15 & 15 & 439 & 2118 & 418 & 2075\\\\\n\t\t\\midrule\n\t\tV(2, 1) & 10 & 10 & 318 & 1529 & 312 & 1529 \\\\\n\t\t\\midrule\n\t\tV(2, 2) & 9 & 9 & 314 & 1449 & 316 & 1476 \\\\\n\t\t\\midrule\n\t\tV(3, 2) & 8 & 8 & 297 & 1368 & 304 & 1388 \\\\\n\t\t\\midrule\n\t\tV(3, 3) & 7 & 7 & 283 & 1247 & 288 & 1288 \\\\\n\t\t\\midrule\n\t\tV(4, 3) & 7 & 7 & 293 & 1320 & 313 & 1397 \\\\\n\t\t\\midrule\n\t\tV(4, 4) & 7 & 7 & 311 & 1378 & 334 & 1471 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Automating the Design of Multigrid Methods with Evolutionary Program Synthesis", "authors": ["Jonas Schmitt"], "url": "https://arxiv.org/abs/2312.14875v1", "attribution": "\"Automating the Design of Multigrid Methods with Evolutionary Program Synthesis\" by Jonas Schmitt, arXiv:2312.14875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02814v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccccccc} \\hline\n{\\bf Network} & {\\bf $N$} & {\\bf $M$} & {\\bf $k_{avg}$} & {\\bf $k_{min}$} \n& {\\bf $k_{max}$} & {\\bf $L_{avg}$} & {\\bf $D$} & {\\bf $R$} & {\\bf $E$} \\\\ \\hline\nOpenFlight & 2905 & 15645 & 10.77 & 1 & 242 & 4.097 & 14 & 0.080912 & 0.266934\\\\\nAS Oregon & 6474 & 12572 & 3.883 & 1 & 1458 & 3.705 & 9 & 0.012500 & 0.290399\\\\\nPowerGrid & 4941 & 6594 & 2.669 & 1 & 19 & 18.989 & 46 & 0.052428 & 0.062878\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "More Tolerant Reconstructed Networks by Self-Healing against Attacks in Saving Resource", "authors": ["Yukio Hayashi", "Atsushi Tanaka", "Jun Matsukubo"], "url": "https://arxiv.org/abs/2101.02814v2", "attribution": "\"More Tolerant Reconstructed Networks by Self-Healing against Attacks in Saving Resource\" by Yukio Hayashi, Atsushi Tanaka, and Jun Matsukubo, arXiv:2101.02814v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.11464v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\hline\n \\textbf{\\# Dimensions} & \\textbf{SG Level} & \\textbf{\\# of Points} & \\textbf{Average TI time (ms)}\\\\\n \\hline\n 4 & 3 & 137 & 21 \\\\\n 8 & 3 & 849 & 1021 \\\\\n 16 & 3 & 6,049 & 480,430 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{SG performance on the benchmark IRBC model.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Scalable Global Solution Techniques for High-Dimensional Models in Dynare", "authors": ["Aryan Eftekhari", "Michel Juillard", "Normann Rion", "Simon Scheidegger"], "url": "https://arxiv.org/abs/2503.11464v1", "attribution": "\"Scalable Global Solution Techniques for High-Dimensional Models in Dynare\" by Aryan Eftekhari, Michel Juillard, Normann Rion, and Simon Scheidegger, arXiv:2503.11464v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00561v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|rrrr|}\n \\hline\n & \\multicolumn{4}{c|}{TR dataset} \\\\\\hline\n & $F_{best}$ & $F_{avg}$ & $F_{worst}$ & Time \\\\\n \\hline\n 10-3-A & 152972143.12 & 152952628.10 & 152939697.96 & 738.99 \\\\ \n 10-3-B & 153112107.12 & 153093344.42 & 153082414.27 & 686.01 \\\\ \n 10-3-C & 153839712.14 & 153824053.65 & 153800858.62 & 598.81 \\\\ \n 10-3-D & 153087165.47 & 153066183.42 & 153045622.28 & 611.76 \\\\ \n 10-3-E & 152659345.36 & 152654390.47 & 152644787.79 & 666.67 \\\\ \\hline\n 10-5-A & 258073639.15 & 258053495.30 & 258031829.79 & 634.14 \\\\ \n 10-5-B & 258143568.34 & 258118526.52 & 258099823.55 & 616.71 \\\\ \n 10-5-C & 257927147.68 & 257915756.76 & 257904371.63 & 643.39 \\\\ \n 10-5-D & 258124459.12 & 258115828.86 & 258102090.67 & 619.04 \\\\ \n 10-5-E & 257771356.85 & 257751216.50 & 257735882.51 & 755.87 \\\\ \\hline\n 15-3-A & 215745523.71 & 211105155.25 & 208705168.12 & 1383.22 \\\\ \n 15-3-B & 219643002.53 & 217907488.45 & 216757533.47 & 1349.67 \\\\ \n 15-3-C & 216202917.37 & 215517157.24 & 213623945.07 & 1264.34 \\\\ \n 15-3-D & 227796827.10 & 225272043.95 & 224192839.23 & 1263.45 \\\\ \n 15-3-E & 214934564.16 & 212268782.27 & 209040103.47 & 1368.98 \\\\ \\hline\n 15-5-A & 322023931.69 & 316518096.87 & 308955913.56 & 1549.77 \\\\ \n 15-5-B & 322636309.39 & 315772816.64 & 311932332.11 & 1721.55 \\\\ \n 15-5-C & 330848115.52 & 322945767.59 & 320862160.81 & 1507.26 \\\\\n 15-5-D & 313119405.64 & 309123530.31 & 306658142.70 & 1597.90 \\\\ \n 15-5-E & 326589307.10 & 320019913.00 & 317745516.15 & 1650.06 \\\\ \\hline\n 15-7-A & 343731732.11 & 340482497.88 & 338211105.20 & 2034.02 \\\\ \n 15-7-B & 340586811.16 & 337355395.09 & 335239967.19 & 1969.28 \\\\ \n 15-7-C & 354207296.51 & 346902334.05 & 343833601.80 & 2174.41 \\\\ \n 15-7-D & 339060503.96 & 331788941.73 & 329793552.39 & 2064.19 \\\\ \n 15-7-E & 347603487.36 & 342379423.50 & 335690975.91 & 2049.82 \\\\ \\hline\n 20-3-A & 405202386.60 & 385080679.77 & 358448818.81 & 1918.88 \\\\ \n 20-3-B & 393148017.17 & 379138056.58 & 363658071.90 & 2093.41 \\\\ \n 20-3-C & 419043452.97 & 392115815.27 & 368140111.33 & 2016.73 \\\\ \n 20-3-D & 410975966.41 & 392443570.18 & 374462165.91 & 2124.59 \\\\ \n 20-3-E & 402996417.03 & 397908399.25 & 388554704.37 & 2088.11 \\\\ \\hline\n 20-5-A & 556755498.34 & 540288768.85 & 532579872.76 & 2230.35 \\\\ \n 20-5-B & 826473473.23 & 815048690.18 & 801274333.42 & 2331.58 \\\\ \n 20-5-C & 747435750.84 & 732671139.60 & 708623262.11 & 2414.11 \\\\ \n 20-5-D & 681400891.01 & 653240014.49 & 619969255.54 & 2232.33 \\\\ \n 20-5-E & 821313280.88 & 799966591.77 & 781314348.94 & 2366.48 \\\\ \\hline\n 20-7-A & 541543222.88 & 517482340.18 & 504642404.13 & 2827.01 \\\\ \n 20-7-B & 540962298.55 & 524961547.66 & 507147090.11 & 2472.59 \\\\ \n 20-7-C & 540561175.07 & 522039750.95 & 505975314.94 & 2755.12 \\\\ \n 20-7-D & 529729097.80 & 520047079.58 & 510132384.77 & 2845.75 \\\\ \n 20-7-E & 538863124.04 & 511984510.53 & 503034672.65 & 2798.04 \\\\ \\hline\n 25-3-A & 716135412.27 & 678315606.97 & 623412354.15 & 3225.01 \\\\ \n 25-3-B & 712730939.27 & 642245984.87 & 585399054.48 & 5724.72 \\\\ \n 25-3-C & 692369198.38 & 682455403.14 & 673838938.04 & 5147.35 \\\\ \n 25-3-D & 805649943.14 & 687733323.74 & 580300822.35 & 6290.65 \\\\ \n 25-3-E & 853355757.46 & 759312603.89 & 633852563.63 & 5876.65 \\\\ \\hline\n 25-5-A & 868915387.85 & 858324487.69 & 847368537.48 & 6506.37 \\\\ \n 25-5-B & 953241408.32 & 855983446.64 & 780780385.10 & 8404.67 \\\\ \n 25-5-C & 920297061.31 & 823800380.96 & 761512725.89 & 5818.88\\\\ \n 25-5-D & 944811979.69 & 897534011.75 & 836208259.55 & 6042.38 \\\\ \n 25-5-E & 953881156.46 & 892854689.87 & 842562168.08 & 5898.55\\\\ \\hline\n 25-7-A & 972132541.36 & 941665724.05 & 921630089.72 & 6141.64 \\\\ \n 25-7-B & 966366316.36 & 925863480.38 & 889594035.07 & 6264.35 \\\\ \n 25-7-C & 962287408.27 & 918149967.52 & 870838300.35 & 6854.24 \\\\ \n 25-7-D & 998577564.52 & 939761731.41 & 894021573.52 & 6499.15 \\\\ \n 25-7-E & 972154321.36 & 903574423.47 & 929794509.82 & 6120.66 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{{Profits obtained for the instances in the TR dataset.}}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology", "authors": ["Víctor Blanco", "José-Fernando Camacho-Vallejo", "Carlos Corpus"], "url": "https://arxiv.org/abs/2503.00561v1", "attribution": "\"A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology\" by Víctor Blanco, José-Fernando Camacho-Vallejo, and Carlos Corpus, arXiv:2503.00561v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.24111v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Selected Greeks for an at–the–money call ($S=K=4200$)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\hline\nScenario & $\\Delta$ & $\\Gamma$ & $\\nu_{0}$ & $\\mathcal V$ (Vanna)\\\\\\hline\nBaseline (BS) & 0.527 & $1.15\\times10^{-4}$ & 239.8 & 0.014\\\\\nMemory–only & 0.525 & $1.09\\times10^{-4}$ & 268.6 & 0.013\\\\\nFull (smfBm–J) & 0.522 & $0.98\\times10^{-4}$ & 268.9 & 0.019\\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Pricing Fractal Derivatives under Sub-Mixed Fractional Brownian Motion with Jumps", "authors": ["Nader Karimi"], "url": "https://arxiv.org/abs/2506.24111v1", "attribution": "\"Pricing Fractal Derivatives under Sub-Mixed Fractional Brownian Motion with Jumps\" by Nader Karimi, arXiv:2506.24111v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.04724v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc|cc|cc|cc|cc|cc|cc}\n \\toprule\n \\multirow{2}[4]{*}{\\textbf{DMU}} & \\multicolumn{2}{c|}{\\textbf{2003}} & \\multicolumn{2}{c|}{\\textbf{2004}} & \\multicolumn{2}{c|}{\\textbf{2005}} & \\multicolumn{2}{c|}{\\textbf{2006}} & \\multicolumn{2}{c|}{\\textbf{2007}} & \\multicolumn{2}{c|}{\\textbf{2008}} & \\multicolumn{2}{c|}{\\textbf{2009}} & \\multicolumn{2}{c}{\\textbf{2010}} \\\\\n\\cmidrule{2-17} & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ & $\\alpha_-^\\star$ & $\\alpha_+^\\star$ \\\\\n \\midrule\n \\textbf{1} & 0.869 & 1.239 & 0.856 & 1.241 & 0.799 & 1.346 & 0.796 & 1.353 & 0.835 & 1.285 & 0.793 & 1.316 & 0.777 & 1.247 & 0.793 & 1.232 \\\\\n \\textbf{2} & 0.995 & 17.538 & 0.975 & $\\infty$ & 1.003 & $\\infty$ & \\multicolumn{2}{c|}{0.969} & 1.006 & 46.069 & 1.032 & 1.758 & 1.012 & 1.654 & 1.017 & 1.501 \\\\\n \\textbf{3} & 1.248 & 1.427 & 1.200 & 1.366 & 1.173 & 1.360 & 1.090 & 1.333 & 1.101 & 1.291 & \\multicolumn{2}{c|}{1.231} & 0.869 & 1.616 & 1.114 & 1.366 \\\\\n \\textbf{4} & 0.827 & 2.499 & 0.860 & 2.821 & 0.850 & 3.068 & 0.882 & 2.111 & 0.861 & 2.691 & 0.876 & 2.176 & 0.862 & 2.806 & 0.829 & 2.568 \\\\\n \\textbf{5} & \\multicolumn{2}{c|}{1.029} & \\multicolumn{2}{c|}{0.963} & \\multicolumn{2}{c|}{1.038} & \\multicolumn{2}{c|}{0.963} & 0.982 & 1.307 & 0.961 & 1.399 & 1.027 & 2.311 & 0.960 & 2.018 \\\\\n \\textbf{6} & \\multicolumn{2}{c|}{0.896} & \\multicolumn{2}{c|}{0.943} & \\multicolumn{2}{c|}{0.924} & 0.961 & 1.007 & \\multicolumn{2}{c|}{0.934} & 0.915 & 0.958 & \\multicolumn{2}{c|}{0.978} & \\multicolumn{2}{c}{0.972} \\\\\n \\textbf{7} & 0.221 & 1.108 & 0.204 & 1.092 & 0.199 & 1.080 & 0.227 & 1.070 & 0.278 & 1.075 & 0.329 & 1.059 & 0.412 & 1.101 & 0.424 & 1.093 \\\\\n \\textbf{8} & 1.019 & 1.544 & 1.042 & 1.582 & 1.018 & 1.478 & 1.046 & 1.440 & 1.018 & 1.634 & 0.988 & 1.896 & 0.984 & 1.196 & 0.989 & 1.492 \\\\\n \\textbf{9} & 0.920 & 1.355 & 0.912 & 1.394 & 0.916 & 1.480 & 0.938 & 1.363 & 0.974 & 1.178 & 0.997 & 1.207 & 0.983 & 1.266 & 0.995 & 1.285 \\\\\n \\textbf{10} & 1.099 & 1.574 & 1.123 & 1.609 & 1.121 & 1.408 & 1.150 & 1.386 & 1.180 & 1.373 & 1.152 & 1.297 & \\multicolumn{2}{c|}{1.020} & 1.092 & 1.117 \\\\\n \\textbf{11} & 0.942 & 1.073 & 0.950 & 1.070 & 0.924 & 1.044 & 0.863 & 1.054 & 0.807 & 1.075 & 0.798 & 1.066 & 0.873 & 1.044 & 0.835 & 1.064 \\\\\n \\textbf{12} & \\multicolumn{2}{c|}{0.925} & \\multicolumn{2}{c|}{0.913} & 0.870 & 0.940 & 0.845 & 1.008 & 0.835 & 1.013 & 0.839 & 1.057 & 0.889 & 1.063 & 0.846 & 1.076 \\\\\n \\textbf{13} & \\multicolumn{2}{c|}{0.952} & \\multicolumn{2}{c|}{0.953} & \\multicolumn{2}{c|}{0.961} & \\multicolumn{2}{c|}{0.970} & \\multicolumn{2}{c|}{0.951} & \\multicolumn{2}{c|}{0.942} & \\multicolumn{2}{c|}{1.078} & \\multicolumn{2}{c}{1.079} \\\\\n \\textbf{14} & 0.994 & 1.068 & \\multicolumn{2}{c|}{0.940} & \\multicolumn{2}{c|}{0.928} & 0.952 & 1.215 & 0.975 & 1.263 & 0.959 & 1.323 & 0.984 & 1.276 & 0.966 & 1.306 \\\\\n \\textbf{15} & \\multicolumn{2}{c|}{0.985} & \\multicolumn{2}{c|}{0.935} & \\multicolumn{2}{c|}{0.996} & \\multicolumn{2}{c|}{1.082} & \\multicolumn{2}{c|}{1.025} & \\multicolumn{2}{c|}{0.992} & \\multicolumn{2}{c|}{0.979} & \\multicolumn{2}{c}{1.017} \\\\\n \\textbf{16} & \\multicolumn{2}{c|}{1.000} & \\multicolumn{2}{c|}{0.975} & \\multicolumn{2}{c|}{0.990} & \\multicolumn{2}{c|}{0.997} & 0.979 & 1.028 & \\multicolumn{2}{c|}{0.974} & 0.923 & 1.057 & 0.942 & 0.994 \\\\\n \\textbf{17} & 0.999 & 2.204 & 0.990 & 2.418 & 0.966 & 2.451 & 0.946 & 2.387 & 0.927 & 2.620 & 0.924 & 2.698 & 0.956 & 2.471 & 0.950 & 2.250 \\\\\n \\textbf{18} & \\multicolumn{2}{c|}{1.017} & \\multicolumn{2}{c|}{1.007} & \\multicolumn{2}{c|}{1.017} & \\multicolumn{2}{c|}{1.020} & \\multicolumn{2}{c|}{1.041} & 0.979 & 1.080 & 0.990 & 1.137 & 0.949 & 1.023 \\\\\n \\textbf{19} & 0.432 & 0.985 & 0.444 & 0.935 & 0.420 & 0.996 & 0.401 & 1.325 & 0.404 & 1.348 & 0.406 & 1.104 & 0.404 & 1.271 & 0.399 & 1.298 \\\\\n \\textbf{20} & 1.025 & 2.375 & 0.996 & 2.855 & 0.997 & 3.145 & 1.012 & 3.074 & 1.013 & 1.982 & 0.989 & 1.355 & 1.388 & 4.036 & 1.056 & 3.951 \\\\\n \\textbf{21} & 1.248 & $\\infty$ & 1.178 & $\\infty$ & 1.165 & $\\infty$ & 1.174 & $\\infty$ & 1.074 & $\\infty$ & 1.072 & $\\infty$ & 1.117 & $\\infty$ & 1.049 & $\\infty$ \\\\\n \\textbf{22} & 0.941 & 1.068 & 0.914 & 1.045 & 0.925 & 1.150 & 0.934 & 1.165 & 0.959 & 1.100 & 0.960 & 1.029 & 0.936 & 1.033 & 0.977 & 1.060 \\\\\n \\textbf{23} & \\multicolumn{2}{c|}{1.065} & \\multicolumn{2}{c|}{1.070} & \\multicolumn{2}{c|}{1.052} & 1.056 & 1.095 & 1.072 & 1.240 & 1.132 & 1.235 & 1.062 & 1.071 & 1.032 & 1.244 \\\\\n \\textbf{24} & 0.423 & 2.231 & 0.417 & 2.309 & 0.424 & 2.517 & 0.433 & 2.486 & 0.432 & 2.383 & 0.446 & 2.372 & 0.453 & 2.216 & 0.472 & 2.261 \\\\\n \\textbf{25} & 0.562 & 1.016 & 0.571 & 1.048 & 0.599 & 1.055 & 0.593 & 1.066 & 0.564 & 1.113 & 0.620 & 1.093 & 0.694 & 1.094 & 0.688 & 1.100 \\\\\n \\textbf{26} & 0.929 & 1.156 & 0.905 & 1.113 & 0.920 & 1.109 & 0.982 & 1.046 & 0.971 & 1.133 & 0.957 & 1.064 & 0.930 & 1.039 & 0.938 & 1.096 \\\\\n \\textbf{27} & 0.621 & 0.911 & 0.534 & 0.971 & 0.509 & 1.008 & 0.507 & 1.020 & 0.483 & 1.044 & 0.470 & 1.067 & 0.648 & 0.958 & 0.536 & 1.040 \\\\\n \\textbf{28} & 0.560 & 0.768 & 0.543 & 0.568 & 0.555 & 0.767 & \\multicolumn{2}{c|}{0.557} & \\multicolumn{2}{c|}{0.986} & \\multicolumn{2}{c|}{0.946} & 0.691 & 0.769 & \\multicolumn{2}{c}{0.577} \\\\\n \\textbf{29} & \\multicolumn{2}{c|}{0.862} & \\multicolumn{2}{c|}{0.873} & \\multicolumn{2}{c|}{0.861} & \\multicolumn{2}{c|}{0.882} & \\multicolumn{2}{c|}{0.891} & \\multicolumn{2}{c|}{0.903} & \\multicolumn{2}{c|}{0.910} & \\multicolumn{2}{c}{0.926} \\\\\n \\textbf{30} & 1.093 & 1.506 & 1.009 & 1.388 & 1.026 & 1.194 & 0.962 & 1.066 & 1.049 & 1.050 & 1.004 & 1.336 & 1.038 & 1.261 & \\multicolumn{2}{c}{1.703} \\\\\n \\textbf{31} & 1.149 & $\\infty$ & 1.148 & $\\infty$ & 1.095 & $\\infty$ & 1.043 & $\\infty$ & 1.025 & $\\infty$ & 0.992 & $\\infty$ & 0.976 & $\\infty$ & 1.011 & $\\infty$ \\\\\n \\textbf{32} & 0.970 & 1.103 & 0.947 & 1.074 & 0.949 & 1.054 & 0.912 & 0.980 & 0.981 & 1.037 & \\multicolumn{2}{c|}{0.977} & 1.017 & 1.045 & 0.964 & 1.101 \\\\\n \\textbf{33} & 0.936 & $\\infty$ & 0.958 & $\\infty$ & 0.961 & $\\infty$ & 0.945 & $\\infty$ & 0.948 & 3.019 & 0.965 & 1.987 & 0.955 & 1.651 & 0.977 & 1.405 \\\\\n \\textbf{34} & 1.179 & $\\infty$ & 1.144 & $\\infty$ & 1.189 & $\\infty$ & 1.160 & $\\infty$ & 1.090 & $\\infty$ & 1.072 & $\\infty$ & 1.120 & $\\infty$ & 1.089 & $\\infty$ \\\\\n \\textbf{35} & \\multicolumn{2}{c|}{1.081} & 0.995 & 1.130 & 0.996 & 1.217 & 0.984 & 1.195 & 1.056 & 1.086 & 1.009 & 1.140 & 0.974 & 1.182 & 0.997 & 1.156 \\\\\n \\textbf{36} & 1.235 & $\\infty$ & 1.168 & $\\infty$ & 1.029 & $\\infty$ & 1.154 & $\\infty$ & 1.153 & 3.060 & 1.131 & 2.941 & 1.090 & 2.815 & 1.005 & 2.580 \\\\\n \\textbf{37} & \\multicolumn{2}{c|}{0.949} & 0.939 & 0.985 & 0.895 & 1.058 & 0.950 & 0.973 & 0.869 & 1.023 & 0.879 & 1.057 & 0.907 & 0.980 & 0.897 & 0.944 \\\\\n \\textbf{38} & \\multicolumn{2}{c|}{0.918} & \\multicolumn{2}{c|}{1.076} & \\multicolumn{2}{c|}{0.977} & \\multicolumn{2}{c|}{1.051} & \\multicolumn{2}{c|}{1.105} & 1.077 & 1.156 & 1.043 & 1.333 & 0.956 & 1.376 \\\\\n \\textbf{39} & \\multicolumn{2}{c|}{0.827} & \\multicolumn{2}{c|}{0.860} & 0.848 & 0.905 & 0.832 & 0.927 & 0.816 & 0.946 & 0.797 & 1.001 & 0.814 & 0.942 & 0.819 & 0.985 \\\\\n \\textbf{40} & \\multicolumn{2}{c|}{0.936} & 0.873 & 1.655 & 1.055 & 1.239 & 0.984 & 1.449 & \\multicolumn{2}{c|}{0.957} & \\multicolumn{2}{c|}{1.001} & \\multicolumn{2}{c|}{0.916} & \\multicolumn{2}{c}{0.907} \\\\\n \\textbf{41} & 0.903 & 0.952 & \\multicolumn{2}{c|}{0.951} & 0.943 & 0.966 & 0.938 & 0.978 & \\multicolumn{2}{c|}{0.946} & 0.828 & 0.944 & 0.688 & 0.946 & 0.757 & 0.923 \\\\\n \\textbf{42} & \\multicolumn{2}{c|}{1.007} & \\multicolumn{2}{c|}{1.092} & \\multicolumn{2}{c|}{1.044} & \\multicolumn{2}{c|}{1.062} & \\multicolumn{2}{c|}{0.971} & \\multicolumn{2}{c|}{0.977} & \\multicolumn{2}{c|}{1.099} & \\multicolumn{2}{c}{1.088} \\\\\n \\textbf{43} & 1.044 & 1.099 & 0.998 & 1.144 & 0.955 & 1.114 & 0.871 & 1.113 & 0.745 & 1.137 & 0.694 & 1.064 & 0.769 & 1.123 & 0.693 & 1.084 \\\\\n \\textbf{44} & 0.956 & 2.060 & 0.962 & 1.972 & 0.945 & 1.710 & 1.002 & 1.157 & 0.999 & 1.092 & 0.955 & 1.091 & 0.945 & 1.085 & 0.934 & 1.336 \\\\\n \\textbf{45} & 0.029 & 1.530 & 0.004 & 1.507 & 0 & 1.565 & 0 & 1.542 & 0 & 1.506 & 0 & 1.507 & 0.010 & 1.478 & 0 & 1.499 \\\\\n \\textbf{46} & 0.931 & 1.186 & 0.943 & 1.190 & 0.955 & 1.209 & 0.970 & 1.233 & 0.980 & 1.258 & 0.942 & 1.296 & 0.978 & 1.439 & 0.939 & 1.534 \\\\\n \\textbf{47} & 0.731 & 1.744 & 0.730 & 1.803 & 0.737 & 1.687 & 0.730 & 1.649 & 0.717 & 1.689 & 0.599 & 2.117 & 0.665 & 1.674 & 0.685 & 1.586 \\\\\n \\textbf{48} & \\multicolumn{2}{c|}{1.270} & \\multicolumn{2}{c|}{1.217} & \\multicolumn{2}{c|}{1.147} & \\multicolumn{2}{c|}{1.154} & \\multicolumn{2}{c|}{1.304} & \\multicolumn{2}{c|}{1.324} & \\multicolumn{2}{c|}{1.298} & \\multicolumn{2}{c}{0.849} \\\\\n \\textbf{49} & \\multicolumn{2}{c|}{0.911} & \\multicolumn{2}{c|}{0.971} & \\multicolumn{2}{c|}{1.008} & \\multicolumn{2}{c|}{1.020} & \\multicolumn{2}{c|}{1.044} & \\multicolumn{2}{c|}{1.047} & 0.703 & 0.997 & \\multicolumn{2}{c}{1.034} \\\\\n \\textbf{50} & 0.905 & 0.918 & \\multicolumn{2}{c|}{0.848} & \\multicolumn{2}{c|}{0.840} & 0.872 & 0.872 & \\multicolumn{2}{c|}{0.950} & \\multicolumn{2}{c|}{0.950} & \\multicolumn{2}{c|}{0.958} & \\multicolumn{2}{c}{0.986} \\\\\n \\textbf{51} & \\multicolumn{2}{c|}{0.742} & \\multicolumn{2}{c|}{0.732} & \\multicolumn{2}{c|}{0.775} & \\multicolumn{2}{c|}{0.767} & \\multicolumn{2}{c|}{0.763} & \\multicolumn{2}{c|}{0.790} & 0.776 & 0.815 & 0.807 & 0.839 \\\\\n \\textbf{52} & 0.993 & 1.123 & \\multicolumn{2}{c|}{0.983} & 0.990 & 1.099 & 1.151 & 1.293 & 1.014 & 1.185 & 1.104 & 1.340 & 1.018 & 1.358 & 0.971 & 1.471 \\\\\n \\textbf{53} & \\multicolumn{2}{c|}{1.235} & \\multicolumn{2}{c|}{1.164} & \\multicolumn{2}{c|}{1.124} & \\multicolumn{2}{c|}{1.154} & \\multicolumn{2}{c|}{1.268} & \\multicolumn{2}{c|}{1.214} & \\multicolumn{2}{c|}{1.205} & \\multicolumn{2}{c}{0.914} \\\\\n \\textbf{54} & \\multicolumn{2}{c|}{0.774} & 0.789 & 0.795 & 0.766 & 0.803 & 0.716 & 0.799 & 0.735 & 0.805 & 0.646 & 0.834 & 0.561 & 0.849 & 0.518 & 0.852 \\\\\n \\textbf{55} & \\multicolumn{2}{c|}{0.776} & \\multicolumn{2}{c|}{0.828} & \\multicolumn{2}{c|}{0.856} & \\multicolumn{2}{c|}{0.866} & \\multicolumn{2}{c|}{0.913} & \\multicolumn{2}{c|}{0.904} & \\multicolumn{2}{c|}{0.851} & 0.964 & 0.964 \\\\\n \\textbf{56} & 1.393 & 3.216 & 1.401 & 2.905 & 1.321 & 2.701 & 1.283 & 2.465 & 1.247 & 2.822 & 1.079 & 3.194 & 0.928 & 3.259 & 0.909 & 2.897 \\\\\n \\textbf{57} & 0.925 & 1.158 & 0.930 & 1.040 & 0.959 & 0.981 & 0.945 & 0.963 & \\multicolumn{2}{c|}{0.951} & 0.954 & 1.004 & \\multicolumn{2}{c|}{0.926} & 0.928 & 0.928 \\\\\n \\textbf{58} & \\multicolumn{2}{c|}{1.070} & \\multicolumn{2}{c|}{0.996} & \\multicolumn{2}{c|}{0.979} & \\multicolumn{2}{c|}{0.959} & \\multicolumn{2}{c|}{0.963} & \\multicolumn{2}{c|}{0.970} & \\multicolumn{2}{c|}{0.986} & \\multicolumn{2}{c}{0.977} \\\\\n \\textbf{59} & 1.011 & 1.051 & 0.962 & 1.087 & 0.967 & 1.037 & \\multicolumn{2}{c|}{0.966} & \\multicolumn{2}{c|}{0.973} & \\multicolumn{2}{c|}{0.972} & \\multicolumn{2}{c|}{0.957} & \\multicolumn{2}{c}{0.977} \\\\\n \\textbf{60} & 1.023 & 1.145 & 1.005 & 1.154 & 0.999 & 1.138 & 1.000 & 1.116 & 1.043 & 1.121 & 0.972 & 1.127 & 0.899 & 1.108 & 0.851 & 1.075 \\\\\n \\textbf{61} & \\multicolumn{2}{c|}{0.816} & 0.825 & 0.843 & \\multicolumn{2}{c|}{0.865} & \\multicolumn{2}{c|}{0.877} & \\multicolumn{2}{c|}{0.919} & \\multicolumn{2}{c|}{0.959} & \\multicolumn{2}{c|}{0.860} & \\multicolumn{2}{c}{0.980} \\\\\n \\textbf{62} & \\multicolumn{2}{c|}{0.938} & \\multicolumn{2}{c|}{0.925} & \\multicolumn{2}{c|}{0.925} & \\multicolumn{2}{c|}{0.938} & \\multicolumn{2}{c|}{0.999} & \\multicolumn{2}{c|}{1.015} & \\multicolumn{2}{c|}{1.023} & \\multicolumn{2}{c}{1.009} \\\\\n \\textbf{63} & 0 & 0.906 & 0 & 0.888 & 0.087 & 0.904 & 0.083 & 0.895 & 0.056 & 0.876 & 0.016 & 0.850 & 0 & 0.819 & 0.014 & 0.918 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Individual $\\alpha$-returns to scale for 2003-2010} %\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "$Λ$-Returns to Scale and Individual Minimum Extrapolation Principle", "authors": ["Jean-Philippe Boussemart", "Walter Briec", "Raluca Parvulescu", "Paola Ravelojaona"], "url": "https://arxiv.org/abs/2212.04724v2", "attribution": "\"$Λ$-Returns to Scale and Individual Minimum Extrapolation Principle\" by Jean-Philippe Boussemart, Walter Briec, Raluca Parvulescu, and Paola Ravelojaona, arXiv:2212.04724v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.21206v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccc}\n \\toprule\n & \\multicolumn{6}{c}{Prompt year} & & \\multicolumn{2}{c}{Accuracy}\\\\\n \\cmidrule{2-7} \\cmidrule{9-10}\n & 1993 & 2001 & 2009 & 2017 & 2021 & 2025 & & Pre-cutoff & Post-cutoff \\\\\n \\midrule\n Correct output & Clinton & Bush & Obama & Trump & Biden & Trump \\\\\n \\midrule\n BERT & \\textcolor{blue}{\\textbf{Clinton}} & Clinton & \\textcolor{blue}{\\textbf{Obama}} & Obama &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Obama & & $2/4$ & \\cellcolor{lightgray}$0/2$\\\\\n ModernBERT & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\textcolor{blue}{\\textbf{Obama}} & \\textcolor{blue}{\\textbf{Trump}} & \\textcolor{blue}{\\textbf{Biden}} & \\cellcolor{lightgray}\\textcolor{blue}{\\textbf{Trump}} & & $5/5$ & \\cellcolor{lightgray}$1/1$\\\\\n \\midrule\n $\\text{ChronoBERT}_{1999}$ & Roosevelt & \\cellcolor{lightgray}Hoover & \\cellcolor{lightgray}Roosevelt & \\cellcolor{lightgray}Hoover & \\cellcolor{lightgray}Hoover & \\cellcolor{lightgray}Washington & & $0/1$ & \\cellcolor{lightgray} $0/5$\\\\\n $\\text{ChronoBERT}_{2000}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\cellcolor{lightgray}Clinton & \\cellcolor{lightgray}Clinton & \\cellcolor{lightgray}Clinton & \\cellcolor{lightgray}Clinton & \\cellcolor{lightgray}Wilson & & $1/1$ & \\cellcolor{lightgray} $0/5$\\\\\n $\\text{ChronoBERT}_{2001}$ & \\textcolor{blue}{\\textbf{Clinton}} & Clinton & \\cellcolor{lightgray}Clinton & \\cellcolor{lightgray}Clinton & \\cellcolor{lightgray}Clinton & \\cellcolor{lightgray}Washington & & $1/2$ & \\cellcolor{lightgray} $0/4$\\\\\n $\\text{ChronoBERT}_{2002}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Clinton & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Clinton & & $2/2$ & \\cellcolor{lightgray} $0/4$\\\\\n $\\text{ChronoBERT}_{2003}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Clinton & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Clinton & & $2/2$ & \\cellcolor{lightgray} $0/4$\\\\\n $\\text{ChronoBERT}_{2004}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Clinton & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Clinton & & $2/2$ & \\cellcolor{lightgray} $0/4$\\\\\n $\\text{ChronoBERT}_{2005}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Clinton & & $2/2$ & \\cellcolor{lightgray} $0/4$\\\\\n $\\text{ChronoBERT}_{2006}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Clinton & & $2/2$ & \\cellcolor{lightgray} $0/4$\\\\\n $\\text{ChronoBERT}_{2007}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Bush & \\cellcolor{lightgray}Monroe & & $2/2$ & \\cellcolor{lightgray} $0/4$\\\\\n $\\text{ChronoBERT}_{2008}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} &\\cellcolor{lightgray}Bush &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Bush &\\cellcolor{lightgray}Wilson & & $2/2$ & \\cellcolor{lightgray} $0/4$\\\\\n $\\text{ChronoBERT}_{2009}$ & \\textcolor{blue}{\\textbf{Clinton}} & Clinton & \\textcolor{blue}{\\textbf{Obama}} &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Wilson & & $2/3$ & \\cellcolor{lightgray} $0/3$\\\\\n $\\text{ChronoBERT}_{2010}$ & \\textcolor{blue}{\\textbf{Clinton}} & Obama & \\textcolor{blue}{\\textbf{Obama}} &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Wilson & & $2/3$ & \\cellcolor{lightgray} $0/3$\\\\\n $\\text{ChronoBERT}_{2011}$ & \\textcolor{blue}{\\textbf{Clinton}} & Clinton & \\textcolor{blue}{\\textbf{Obama}} &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Wilson & & $2/3$ & \\cellcolor{lightgray} $0/3$\\\\\n $\\text{ChronoBERT}_{2012}$ & Obama & Obama & \\textcolor{blue}{\\textbf{Obama}} &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Obama & & $1/3$ & \\cellcolor{lightgray} $0/3$\\\\\n $\\text{ChronoBERT}_{2013}$ & \\textcolor{blue}{\\textbf{Clinton}} & Obama & \\textcolor{blue}{\\textbf{Obama}} &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Monroe & & $2/3$ & \\cellcolor{lightgray} $0/3$\\\\\n $\\text{ChronoBERT}_{2014}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\textcolor{blue}{\\textbf{Obama}} &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Monroe & & $3/3$ & \\cellcolor{lightgray} $0/3$\\\\\n $\\text{ChronoBERT}_{2015}$ & \\textcolor{blue}{\\textbf{Clinton}} & Clinton & \\textcolor{blue}{\\textbf{Obama}} &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Monroe & & $2/3$ & \\cellcolor{lightgray} $0/3$\\\\\n $\\text{ChronoBERT}_{2016}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\textcolor{blue}{\\textbf{Obama}} &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Obama &\\cellcolor{lightgray}Obama & & $3/3$ & \\cellcolor{lightgray} $0/3$\\\\\n $\\text{ChronoBERT}_{2017}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\textcolor{blue}{\\textbf{Obama}} & \\textcolor{blue}{\\textbf{Trump}} &\\cellcolor{lightgray}Trump &\\cellcolor{lightgray}Monroe & & $4/4$ & \\cellcolor{lightgray} $0/2$\\\\\n $\\text{ChronoBERT}_{2018}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\textcolor{blue}{\\textbf{Obama}} & \\textcolor{blue}{\\textbf{Trump}} &\\cellcolor{lightgray}Trump &\\cellcolor{lightgray}Obama & & $4/4$ & \\cellcolor{lightgray} $0/2$\\\\\n $\\text{ChronoBERT}_{2019}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\textcolor{blue}{\\textbf{Obama}} & \\textcolor{blue}{\\textbf{Trump}} &\\cellcolor{lightgray}Trump &\\cellcolor{lightgray}Obama & & $4/4$ & \\cellcolor{lightgray} $0/2$\\\\\n $\\text{ChronoBERT}_{2020}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\textcolor{blue}{\\textbf{Obama}} & \\textcolor{blue}{\\textbf{Trump}} &\\cellcolor{lightgray}Trump &\\cellcolor{lightgray}\\textcolor{blue}{\\textbf{Trump}} & & $4/4$ & \\cellcolor{lightgray} $1/2$\\\\\n $\\text{ChronoBERT}_{2021}$ & \\textcolor{blue}{\\textbf{Clinton}} & Clinton & \\textcolor{blue}{\\textbf{Obama}} & \\textcolor{blue}{\\textbf{Trump}} & \\textcolor{blue}{\\textbf{Biden}} &\\cellcolor{lightgray}Biden & & $4/5$ & \\cellcolor{lightgray} $0/1$\\\\\n $\\text{ChronoBERT}_{2022}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\textcolor{blue}{\\textbf{Obama}} & \\textcolor{blue}{\\textbf{Trump}} & \\textcolor{blue}{\\textbf{Biden}} &\\cellcolor{lightgray}Biden & & $5/5$ & \\cellcolor{lightgray} $0/1$\\\\\n $\\text{ChronoBERT}_{2023}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\textcolor{blue}{\\textbf{Obama}} & \\textcolor{blue}{\\textbf{Trump}} & \\textcolor{blue}{\\textbf{Biden}} &\\cellcolor{lightgray}Biden & & $5/5$ & \\cellcolor{lightgray} $0/1$\\\\\n $\\text{ChronoBERT}_{2024}$ & \\textcolor{blue}{\\textbf{Clinton}} & \\textcolor{blue}{\\textbf{Bush}} & \\textcolor{blue}{\\textbf{Obama}} & \\textcolor{blue}{\\textbf{Trump}} & \\textcolor{blue}{\\textbf{Biden}} &\\cellcolor{lightgray}Biden & & $5/5$ & \\cellcolor{lightgray} $0/1$\\\\\n \\hline\n \\multicolumn{8}{l}{$\\text{ChronoBERT}_{1999}$ through $\\text{ChronoBERT}_{2024}$} & $68/78$ & \\cellcolor{lightgray}$1/78$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Masked-Token Predictions of U.S. Presidents using ChronoBERT}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Chronologically Consistent Large Language Models", "authors": ["Songrun He", "Linying Lv", "Asaf Manela", "Jimmy Wu"], "url": "https://arxiv.org/abs/2502.21206v3", "attribution": "\"Chronologically Consistent Large Language Models\" by Songrun He, Linying Lv, Asaf Manela, and Jimmy Wu, arXiv:2502.21206v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.16115v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Coupling errors defined in~ under grid refinement in the experiments considering discontinuous $A_0$ and $E$.} \n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc|cccc}\n &\\multicolumn{4}{c|}{discontinuous $A_0$ } & \\multicolumn{4}{c}{discontinuous $E$} \\\\\n $N$ & $e_1^c$ & EOC & $e_2^c$ & EOC & $e_1^c$ & EOC & $e_2^c$ & EOC\\\\\n \\hline\n 50 & 48.265 & & 2047.383 & & 57.677 & & 5155.836 & \\\\ \n 100 & 24.411 & 0.983 & 1006.360 & 1.025 & 21.565 & 1.419 & 1909.133 & 1.433\\\\\n 200 & 12.150 & 1.007 & 502.089 & 1.003 & 11.098 & 0.958 & 980.020 & 0.962\\\\\n 400 & 6.055 & 1.005 & 250.877 & 1.001 & 5.559 & 0.997 & 490.107 & 1.000\\\\\n 800 & 3.022 & 1.003 & 125.394 & 1.001 & 2.781& 0.999 & 244.958 & 1.001\\\\\n 1600 & 1.509 & 1.002 & 62.685 & 1.000 & 1.391 & 1.000 & 122.462 & 1.000\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Lax-Friedrichs method in one-dimensional hemodynamics", "authors": ["Anika Beckers", "Niklas Kolbe"], "url": "https://arxiv.org/abs/2501.16115v1", "attribution": "\"The Lax-Friedrichs method in one-dimensional hemodynamics\" by Anika Beckers and Niklas Kolbe, arXiv:2501.16115v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.03797v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|cc|cc|cc|cc|cc|cc}\n \\hline\n & &\\multicolumn{6}{c|}{CS } &\\multicolumn{6}{c}{DS } \\\\ \\hline\n & &\\multicolumn{2}{c|}{Total}&\\multicolumn{2}{c|}{L-Markers}&\\multicolumn{2}{c|}{Fixed covariates } &\\multicolumn{2}{c|}{Total}&\\multicolumn{2}{c|}{L-Markers }&\\multicolumn{2}{c}{Fixed covariates } \\\\ \\hline\n & & Est. & SD. & Est. & SD. & Est. & SD. & Est. & SD. & Est. & SD. & Est. & SD. \\\\ \n \\hline\n \\multicolumn{14}{c}{Single effects on cause 1} \\\\\n \\hline\nBF & TPR & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\\\ \n& FPR & 0.018 & 0.018 & 0.022 & 0.049 & 0.017 & 0.020 & 0.039 & 0.024 & 0.056 & 0.060 & 0.035 & 0.023 \\\\ \n& MCC & 0.945 & 0.051 & 0.975 & 0.055 & 0.869 & 0.138 & 0.890 & 0.061 & 0.937 & 0.068 & 0.754 & 0.112 \\\\ \n \\hline\nlBFDR & TPR & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 \\\\ \n& FPR & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.002 & 0.006 & 0.000 & 0.000 & 0.003 & 0.008 \\\\ \n& MCC & 1.000 & 0.000 & 1.000 & 0.000 & 1.000 & 0.000 & 0.993 & 0.019 & 1.000 & 0.000 & 0.974 & 0.068 \\\\ \n \\hline\n \\multicolumn{14}{c}{Single effects on both causes} \\\\\n\\hline\nBF & TPR & 0.963 & 0.060 & 1.000 & 0.000 & 0.925 & 0.121 & 0.975 & 0.053 & 1.000 & 0.000 & 0.950 & 0.105 \\\\ \n& FPR & 0.020 & 0.027 & 0.050 & 0.087 & 0.009 & 0.016 & 0.035 & 0.032 & 0.069 & 0.080 & 0.023 & 0.024 \\\\ \n& MCC & 0.908 & 0.123 & 0.914 & 0.144 & 0.912 & 0.116 & 0.868 & 0.109 & 0.874 & 0.132 & 0.867 & 0.130 \\\\ \n\\hline\nlBFDR & TPR & 0.863 & 0.092 & 1.000 & 0.000 & 0.725 & 0.184 & 0.887 & 0.071 & 1.000 & 0.000 & 0.775 & 0.142 \\\\ \n& FPR & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.003 & 0.011 & 0.013 & 0.040 & 0.000 & 0.000 \\\\ \n& MCC & 0.919 & 0.055 & 1.000 & 0.000 & 0.836 & 0.115 & 0.922 & 0.060 & 0.976 & 0.075 & 0.869 & 0.086 \\\\ \n\\hline\n \\multicolumn{14}{c}{Opposite effects on the two causes} \\\\\n\\hline\nBF & TPR & 0.981 & 0.030 & 1.000 & 0.000 & 0.963 & 0.060 & 0.988 & 0.026 & 1.000 & 0.000 & 0.975 & 0.053 \\\\ \n& FPR & 0.015 & 0.015 & 0.025 & 0.040 & 0.013 & 0.018 & 0.044 & 0.027 & 0.058 & 0.056 & 0.040 & 0.038 \\\\ \n& MCC & 0.956 & 0.044 & 0.971 & 0.047 & 0.943 & 0.066 & 0.908 & 0.049 & 0.933 & 0.063 & 0.887 & 0.093 \\\\ \n\\hline\nlBFDR & TPR & 0.850 & 0.053 & 1.000 & 0.000 & 0.700 & 0.105 & 0.881 & 0.069 & 1.000 & 0.000 & 0.762 & 0.138 \\\\ \n& FPR & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.006 & 0.013 & 0.000 & 0.000 & 0.008 & 0.017 \\\\ \n& MCC & 0.901 & 0.035 & 1.000 & 0.000 & 0.811 & 0.070 & 0.910 & 0.055 & 1.000 & 0.000 & 0.829 & 0.105 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Two-stage Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers and Competing Risk Outcomes", "authors": ["Taban Baghfalaki", "Reza Hashemi", "Christophe Tzourio", "Catherine Helmer", "Helene Jacqmin-Gadda"], "url": "https://arxiv.org/abs/2412.03797v1", "attribution": "\"A Two-stage Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers and Competing Risk Outcomes\" by Taban Baghfalaki, Reza Hashemi, Christophe Tzourio, Catherine Helmer, and Helene Jacqmin-Gadda, arXiv:2412.03797v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05591v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{FPGA utilization of cache configs (f = 50 MHz)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n \\multirow{2}{*}{\\textbf{Config}} & \\multicolumn{2}{c|}{\\textbf{L1 Data Cache}} & \\multirow{2}{*}{\\textbf{Total LUTs}} \\\\ \\cline{2-3}\n & \\textbf{LUTs} & \\textbf{BRAM} & \\\\ \\hline\n BASE & 3065 & 4 & 131026 \\\\\n C-64-8 & 3579 ($+16.8\\%$) & 8 ($+100\\%$) & 133271 ($+1.7\\%$) \\\\\n C-64-16 & 4248 ($+38.6\\%$) & 16 ($+300\\%$) & 136078 ($+3.9\\%$) \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "ANDROMEDA: An FPGA Based RISC-V MPSoC Exploration Framework", "authors": ["Farhad Merchant", "Dominik Sisejkovic", "Lennart M. Reimann", "Kirthihan Yasotharan", "Thomas Grass", "Rainer Leupers"], "url": "https://arxiv.org/abs/2101.05591v1", "attribution": "\"ANDROMEDA: An FPGA Based RISC-V MPSoC Exploration Framework\" by Farhad Merchant, Dominik Sisejkovic, Lennart M. Reimann, Kirthihan Yasotharan, Thomas Grass, and Rainer Leupers, arXiv:2101.05591v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23729v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Relative $L_2$ error & PINNs & IR-PINNs1 & IR-PINNs2 \\\\\n \\midrule\n $u$ & 8.1022e-04 & 5.9748e-04 & 5.3934e-04 \\\\\n \\midrule\n $v$ & 5.0101e-02 & 3.0561e-02 & 3.2439e-02 \\\\\n \\midrule\n Running time (hours) & 0.3288 & 0.3693 & 0.4173 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\textit{Boussinesq-Burgers equations:} Relative $L_2$ errors and running time of different methods.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Integral regularization PINNs for evolution equations", "authors": ["Xiaodong Feng", "Haojiong Shangguan", "Tao Tang", "Xiaoliang Wan"], "url": "https://arxiv.org/abs/2503.23729v1", "attribution": "\"Integral regularization PINNs for evolution equations\" by Xiaodong Feng, Haojiong Shangguan, Tao Tang, and Xiaoliang Wan, arXiv:2503.23729v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.09239v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\hline\n\\hline\ndate & n & climate change & covid & price & taxes & inequality \\\\ \n \\hline\n 2007-07-01 & 8 & 0 & 0 & 8 & 1 & 0 \\\\ \n 2007-08-01 & 1 & 0 & 0 & 1 & 1 & 0 \\\\ \n 2007-09-01 & 17 & 1 & 0 & 15 & 1 & 0 \\\\ \n 2007-10-01 & 14 & 0 & 0 & 14 & 2 & 0 \\\\ \n 2007-11-01 & 14 & 1 & 0 & 13 & 1 & 0 \\\\ \n 2007-12-01 & 9 & 0 & 0 & 8 & 1 & 1 \\\\ \n 2008-01-01 & 13 & 0 & 0 & 10 & 3 & 0 \\\\ \n 2008-02-01 & 9 & 1 & 0 & 7 & 1 & 0 \\\\ \n 2008-03-01 & 7 & 0 & 0 & 6 & 2 & 0 \\\\ \n 2008-04-01 & 18 & 0 & 0 & 17 & 3 & 0 \\\\ \n 2008-05-01 & 8 & 1 & 0 & 6 & 2 & 0 \\\\ \n 2008-06-01 & 14 & 2 & 0 & 14 & 1 & 0 \\\\ \n 2008-07-01 & 5 & 0 & 0 & 4 & 2 & 0 \\\\ \n 2008-08-01 & 1 & 0 & 0 & 1 & 0 & 0 \\\\ \n 2008-09-01 & 23 & 0 & 0 & 18 & 2 & 1 \\\\ \n 2008-10-01 & 9 & 0 & 0 & 8 & 1 & 1 \\\\ \n 2008-11-01 & 16 & 0 & 0 & 14 & 2 & 1 \\\\ \n 2008-12-01 & 9 & 0 & 0 & 9 & 0 & 0 \\\\ \n 2009-01-01 & 7 & 0 & 0 & 6 & 0 & 0 \\\\ \n 2009-02-01 & 10 & 0 & 0 & 7 & 1 & 0 \\\\ \n 2009-03-01 & 9 & 0 & 0 & 9 & 0 & 0 \\\\ \n 2009-04-01 & 7 & 0 & 0 & 6 & 0 & 0 \\\\ \n 2009-05-01 & 8 & 0 & 0 & 6 & 1 & 0 \\\\ \n 2009-06-01 & 19 & 0 & 0 & 17 & 1 & 0 \\\\ \n 2009-07-01 & 4 & 0 & 0 & 1 & 0 & 0 \\\\ \n 2009-08-01 & 1 & 0 & 0 & 1 & 0 & 0 \\\\ \n 2009-09-01 & 15 & 0 & 0 & 12 & 1 & 0 \\\\ \n 2009-10-01 & 9 & 0 & 0 & 8 & 0 & 0 \\\\ \n 2009-11-01 & 14 & 0 & 0 & 11 & 0 & 0 \\\\ \n 2009-12-01 & 9 & 1 & 0 & 6 & 0 & 0 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\ ECB Speeches. 1997-2022}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Interactions of Social Norms about Climate Change: Science, Institutions and Economics", "authors": ["Antonio Cabrales", "Manu García", "David Ramos Muñoz", "Angel Sánchez"], "url": "https://arxiv.org/abs/2208.09239v1", "attribution": "\"The Interactions of Social Norms about Climate Change: Science, Institutions and Economics\" by Antonio Cabrales, Manu García, David Ramos Muñoz, and Angel Sánchez, arXiv:2208.09239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09972v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Recognition accuracy on the ALOI database.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}\n\\hline\n\\textbf{Method} & \\textbf{200} & \\textbf{400} & \\textbf{800} & \\textbf{1200} & \\textbf{1600} & \\textbf{2000} & \\textbf{2400} & \\textbf{2800} & \\textbf{3200} & \\textbf{3600}\\\\\n\\hline\n\\multirow{1}{*}{$LLP+$\\textbf{ARSRG}$emb$} & \\textbf{86.00}\\% & \\textbf{90.00}\\% & \\textbf{93.00}\\% & \\textbf{96.00}\\% & \\textbf{95.62}\\% & \\textbf{96.00}\\% & \\textbf{88.00}\\% & \\textbf{81.89}\\% & \\textbf{79.17}\\% & \\textbf{79.78}\\%\\\\\n\\hline\n\\multirow{1}{*}{\\textbf{LLP}+\\textbf{BoW}} & 49.60\\% & 55.00\\% & 50.42\\% & 50.13\\% & 49.81\\% & 48.88\\% & 49.52\\% & 49.65\\% & 48.96\\% & 49.10\\%\\\\\n \\hline\n\\multirow{1}{*}{batchLDA} & 51.00\\% & 52.00\\% & 62.00\\% & 62.00\\% & 70.00\\% & 71.00\\% & 74.00\\% & 75.00\\% & 75.00\\% & 77.00\\%\\\\\n \\hline\n\\multirow{1}{*}{ILDAaPCA} & 51.00\\% & 42.00\\% & 53.00\\% & 48.00\\% & 45.00\\% & 50.00\\% & 51.00\\% & 49.00\\% & 49.00\\% & 50.00\\%\\\\\n \\hline\n\\multirow{1}{*}{ILDAonK} & 42.00\\% & 45.00\\% & 53.00\\% & 48.00\\% & 45.00\\% & 51.00\\% & 51.00\\% & 49.00\\% & 49.00\\% & 50.00\\%\\\\\n \\hline\n\\multirow{1}{*}{ILDAonL} & 51.00\\% & 52.00\\% & 61.00\\% & 61.00\\% & 65.00\\% & 69.00\\% & 71.00\\% & 70.00\\% & 71.00\\% & 72.00\\%\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications", "authors": ["Mario Manzo"], "url": "https://arxiv.org/abs/1912.09972v1", "attribution": "\"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications\" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.15872v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|ccc|ccc} \\hline \\hline\n\t\t$\\Delta$ & 0.5 & 1.0 & 1.5 & 0.5 & 1.0 & 1.5 \\\\\n\t\t\\hline\n \\hline\n test & \\multicolumn{3}{c|}{} & \\multicolumn{3}{c}{ }\\\\\n \\hline\n \\hline\n\t\tBoulia/p-value & 29.0 & 73.1 & 98.0 & \\textbf{0.0} & \\textbf{0.8}& 37.7 \\\\\n Boulia/year &- & - & - & 1950& 1956 & -\\\\\n \\hline\n\t\tCape Otway/p-value & 11.4 & 98.0 & 100.0 & 7.4 & 78.8 & 99.9 \\\\\n Cape Otway/year & - & - & - & - & - & - \\\\\n \\hline\n\t\tGayndah/p-value & \\textbf{0.2} & \\textbf{1.4} & \\textbf{3.2} & \\textbf{0.0} & \\textbf{0.0}& \\textbf{0.3} \\\\\n Gayndah/year & 1952 & 1968 & 1974 & 1950 & 1950 & 1984\\\\\n \\hline\n\t\tGunnedah/p-value & \\textbf{0.8} & \\textbf{2.7} & \\textbf{10.3} & \\textbf{0.0} & \\textbf{0.0} & \\textbf{1.4}\\\\\n Gunnedah/year & 1952 & 1955 & - & 1962 & 1973 & 1984\\\\ \n \\hline\n\t\tHobart/p-value & 94.9 & 100.0 & 100.0 & 34.1 & 95.5 & 100.0 \\\\\n Hobart/year & - & - & - & - & - & - \\\\\n \\hline\n\t\tMelbourne/p-value & \\textbf{0.0} & \\textbf{1.1} & 27.3 & \\textbf{0.0} & \\textbf{0.0}& \\textbf{3.7} \\\\\n Melbourne/year & 1968 & 1976 & - & 1950 & 1972 & 1990\\\\\n \\hline\n\t\tRobe/p-value & \\textbf{3.3} & 44.9 & 98.2 & 6.1 & 68.3 & 99.8 \\\\\n Robe/year & 1953 &- &- & - & - & - \\\\\n \\hline\n\t\tSydney/p-value & 41.1 & 98.1 & 100 & \\textbf{0.05}& 0.14 & 89.9 \\\\\t\n Sydney/year & - & - & - & 1950 & - & - \\\\\n\t\t\\hline \\hline\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Multiscale detection of practically significant changes in a gradually varying time series", "authors": ["Patrick Bastian", "Holger Dette"], "url": "https://arxiv.org/abs/2504.15872v1", "attribution": "\"Multiscale detection of practically significant changes in a gradually varying time series\" by Patrick Bastian and Holger Dette, arXiv:2504.15872v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.03112v1_tex_table21.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation results on 100 simulation runs for all four MBTs on scenario linear categorical with sample sizes $n_{train}=1000$ and $n_{test} = 500$ for different values of $\\gamma$ and $\\alpha$. The mean (standard deviation) fidelity on the training data for the GAM is 0.9702 (0.0018) and for the XGBoost is 0.9876 (0.0015). On the test data set the respective fidelity values for the GAM are 0.9694 (0.0029) and for the XGBoost are 0.9778 (0.0031).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|r|r|r|r|r|r|r|r|r}\n\\hline\nBlack box& MBT&$\\gamma$/$\\alpha$ & \\multicolumn{3}{c|}{Number of Leaves}&\\multicolumn{2}{c|}{$R^2_{train}$}&\\multicolumn{2}{c|}{$R^2_{test}$} & Share\\\\\n\\hline\n& & & mean & min & max & mean & sd & mean & sd & $x_2$\\\\\n\\hline\nxgboost & SLIM & 0.15 & 2.00 & 2 & 2 & 0.8321 & 0.8321 & 0.8323 & 0.8323 & 0.0000\\\\\nxgboost & GUIDE & 0.15 & 2.00 & 2 & 2 & 0.8321 & 0.8321 & 0.8323 & 0.8323 & 0.0000\\\\\nxgboost & MOB & 0.001 & 13.45 & 11 & 16 & 0.9793 & 0.9793 & 0.9729 & 0.9729 & 0.8865\\\\\nxgboost & CTree & 0.001 & 11.96 & 10 & 14 & 0.9602 & 0.9602 & 0.9545 & 0.9545 & 0.9914\\\\\nxgboost & SLIM & 0.10 & 4.00 & 4 & 4 & 0.9923 & 0.9923 & 0.9870 & 0.9870 & 0.0000\\\\\nxgboost & GUIDE & 0.10 & 4.00 & 4 & 4 & 0.9923 & 0.9923 & 0.9870 & 0.9870 & 0.0000\\\\\nxgboost & MOB & 0.010 & 14.38 & 13 & 16 & 0.9831 & 0.9831 & 0.9765 & 0.9765 & 0.8656\\\\\nxgboost & CTree & 0.010 & 12.76 & 10 & 15 & 0.9612 & 0.9612 & 0.9550 & 0.9550 & 0.9897\\\\\nxgboost & SLIM & 0.05 & 4.00 & 4 & 4 & 0.9923 & 0.9923 & 0.9870 & 0.9870 & 0.0000\\\\\nxgboost & GUIDE & 0.05 & 4.00 & 4 & 4 & 0.9923 & 0.9923 & 0.9870 & 0.9870 & 0.0000\\\\\nxgboost & MOB & 0.050 & 14.63 & 13 & 16 & 0.9837 & 0.9837 & 0.9771 & 0.9771 & 0.8614\\\\\nxgboost & CTree & 0.050 & 13.46 & 10 & 16 & 0.9623 & 0.9623 & 0.9558 & 0.9558 & 0.9838\\\\\n\\hline\ngam & SLIM & 0.15 & 2.00 & 2 & 2 & 0.8528 & 0.8528 & 0.8513 & 0.8513 & 0.0000\\\\\ngam & GUIDE & 0.15 & 2.00 & 2 & 2 & 0.8528 & 0.8528 & 0.8513 & 0.8513 & 0.0000\\\\\ngam & MOB & 0.001 & 13.53 & 11 & 15 & 0.9773 & 0.9773 & 0.9718 & 0.9718 & 0.9824\\\\\ngam & CTree & 0.001 & 13.89 & 11 & 16 & 0.9773 & 0.9773 & 0.9720 & 0.9720 & 0.9973\\\\\ngam & SLIM & 0.10 & 2.64 & 2 & 4 & 0.8972 & 0.8972 & 0.8937 & 0.8937 & 0.0000\\\\\ngam & GUIDE & 0.10 & 2.64 & 2 & 4 & 0.8972 & 0.8972 & 0.8937 & 0.8937 & 0.0000\\\\\ngam & MOB & 0.010 & 14.28 & 13 & 16 & 0.9784 & 0.9784 & 0.9728 & 0.9728 & 0.9721\\\\\ngam & CTree & 0.010 & 14.47 & 12 & 16 & 0.9779 & 0.9779 & 0.9725 & 0.9725 & 0.9949\\\\\ngam & SLIM & 0.05 & 8.56 & 4 & 16 & 0.9910 & 0.9910 & 0.9893 & 0.9893 & 0.0017\\\\\ngam & GUIDE & 0.05 & 6.06 & 4 & 13 & 0.9875 & 0.9875 & 0.9859 & 0.9859 & 0.0084\\\\\ngam & MOB & 0.050 & 14.92 & 13 & 16 & 0.9797 & 0.9797 & 0.9740 & 0.9740 & 0.9572\\\\\ngam & CTree & 0.050 & 14.86 & 13 & 16 & 0.9783 & 0.9783 & 0.9729 & 0.9729 & 0.9925\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation", "authors": ["Julia Herbinger", "Susanne Dandl", "Fiona K. Ewald", "Sofia Loibl", "Giuseppe Casalicchio"], "url": "https://arxiv.org/abs/2310.03112v1", "attribution": "\"Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation\" by Julia Herbinger, Susanne Dandl, Fiona K. Ewald, Sofia Loibl, and Giuseppe Casalicchio, arXiv:2310.03112v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2310.08804v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{diagbox}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Absolute Performance gap between SNN-SC-HARQ and Multi-rate SNN-SC under the same bandwidth}\n\\begin{tabular}{c||c|c|c|c|c|c|c}\n\\hline\n\\hline\n\\diagbox{$\\theta$}{BER}&0.00&0.05 &0.10&0.15 &0.20&0.25 &0.30 \\\\ \n\\hline\n\\hline\n0.635 & 0.02 & 0.06 & 0.09 & 0.01 & 0.03 & 0.11 & 0.07 \\\\ \n0.645 & 0.02 & 0.01 & 0.07 & 0.09 & 0.06 & 0.11 & 0.01 \\\\ \n0.655 & 0.00 & 0.03 & 0.01 & 0.06 & 0.08 & 0.05 & 0.05 \\\\ \n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Spiking Semantic Communication for Feature Transmission with HARQ", "authors": ["Mengyang Wang", "Jiahui Li", "Mengyao Ma", "Xiaopeng Fan"], "url": "https://arxiv.org/abs/2310.08804v1", "attribution": "\"Spiking Semantic Communication for Feature Transmission with HARQ\" by Mengyang Wang, Jiahui Li, Mengyao Ma, and Xiaopeng Fan, arXiv:2310.08804v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.17446v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{The best classification rates obtained with different SVM kernels}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n \\textbf{Linaer SVM} & \\textbf{Polynomial SVM} & \\textbf{RBF SVM} \\\\\n \n \\hline\n 81.1\\% & 84.7\\% & 92.9\\% \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Support Vector Machine for Person Classification Using the EEG Signals", "authors": ["Naveenkumar G Venkataswamy", "Masudul H Imtiaz"], "url": "https://arxiv.org/abs/2411.17446v1", "attribution": "\"Support Vector Machine for Person Classification Using the EEG Signals\" by Naveenkumar G Venkataswamy and Masudul H Imtiaz, arXiv:2411.17446v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.12760v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results on the ASRU200 and ASRU700+LibriSpeech460 datasets, both tested on the ASRU\\_Test set, in terms of English WER, Mandarin CER, and Mandarin-English MER (\\%):}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc|cccc}\n \\toprule\n \\multirow{2}{*}{System} & \\multicolumn{4}{c|}{ASRU700+LibriSpeech460} & \\multicolumn{4}{c}{ASRU200} \\\\\n \\cline{2-5} \\cline{6-9} ~ & Params. & MER & CER & WER & Params. & MER & CER & WER \\\\\n \\hline\n CTC-LPB~ & - & - & - & - & - & 11.8 & - & - \\\\\n LAL~ & - & - & - & - & 48.3M & 11.7 & 9.2 & 35.1 \\\\\n BA-MoE~ & 43M & 10.28 & 8.16 & 27.48 & - & - & - & - \\\\\n SC-MoE~ & 50.2M & 8.66 & 6.50 & 26.36 & - & - & - & -\\\\\n \\midrule\n Baseline & 44.1M & 8.78 & 6.68 & 25.71 & 41.5M & 11.35 & 8.68 & 33.09 \\\\\n S1 & 44.5M & 8.65 & 6.64 & 25.01 & 41.9M & 11.18 & 8.52 & 32.87 \\\\\n S2 & 44.5M & 8.60 & 6.56 & 25.25 & 41.9M & 11.10 & 8.42 & 32.92 \\\\\n S3 & 50.6M & 8.26 & 6.32 & 24.06 & 47.9M & 10.93 & 8.38 & 31.64 \\\\\n CAMEL & 59.6M & \\textbf{8.10} & 6.22 & 23.41 & 56.9M & \\textbf{10.55} & 8.04 & 31.00 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "CAMEL: Cross-Attention Enhanced Mixture-of-Experts and Language Bias for Code-Switching Speech Recognition", "authors": ["He Wang", "Xucheng Wan", "Naijun Zheng", "Kai Liu", "Huan Zhou", "Guojian Li", "Lei Xie"], "url": "https://arxiv.org/abs/2412.12760v2", "attribution": "\"CAMEL: Cross-Attention Enhanced Mixture-of-Experts and Language Bias for Code-Switching Speech Recognition\" by He Wang, Xucheng Wan, Naijun Zheng, Kai Liu, Huan Zhou, Guojian Li, and Lei Xie, arXiv:2412.12760v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ccccc|}\n\\hline\nEntry time & Link 1 & Link 2 & Link 3 & Link 4 & Link 5 \\\\\n\\hline\n0 & 5 & 1 & 3 & 1 & 1 \\\\\n1 & 6 & 1 & 3 & 6 & 1 \\\\\n2 & 4 & 2 & 3 & 5 & 2 \\\\\n3 & 5 & 3 & 3 & 4 & 4 \\\\\n4 & 7 & 5 & 3 & 3 & 2 \\\\\n5 & 8 & 8 & 3 & 2 & 1 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.09935v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dataset Distribution}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\nClass & Instances & Percentage \\\\\n\\midrule\nFactual & 1,980 & 40.8\\% \\\\\nPersuasive & 1,479 & 30.5\\% \\\\\nMisleading & 1,389 & 28.7\\% \\\\\n\\midrule\nTotal & 4,848 & 100\\% \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach", "authors": ["Sayem Hossen", "Monalisa Moon Joti", "Md. Golam Rashed"], "url": "https://arxiv.org/abs/2508.09935v1", "attribution": "\"Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach\" by Sayem Hossen, Monalisa Moon Joti, and Md. Golam Rashed, arXiv:2508.09935v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.17971v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of inference time and model size.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\nBenchmarked method & Inference time (s) & Trainable params. \\\\ \n\\midrule\nMRSegmentator & 57.95 $\\pm$ 53.15 & 31M \\\\\nMRISegmentator-Abdomen & 98.19 $\\pm$ 69.83 & 31M \\\\\nTotalSegmentator MRI & 39.60 $\\pm$ 10.34 & 31M \\\\\nABDSynth & 21.17 $\\pm$ 19.30 & 13M \\\\\n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation", "authors": ["Deepa Krishnaswamy", "Cosmin Ciausu", "Steve Pieper", "Ron Kikinis", "Benjamin Billot", "Andrey Fedorov"], "url": "https://arxiv.org/abs/2507.17971v2", "attribution": "\"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation\" by Deepa Krishnaswamy, Cosmin Ciausu, Steve Pieper, Ron Kikinis, Benjamin Billot, and Andrey Fedorov, arXiv:2507.17971v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.10637v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\hline\n\\multicolumn{5}{c}{MSE} \\\\ \\hline\nMethod & $\\beta_{00}=0$ & $\\beta_{01}=1$ & $\\beta_{10}=0$ & $\\beta_{11}=-1$ \\\\ \\hline\nMixT &3.6119&0.1376&2.4279&0.1662 \\\\ \nMixEP & 0.0411& 0.02742& 0.06486&0.04627 \\\\ \\hline\n\\multicolumn{5}{c}{bias} \\\\ \\hline\nMethod & $\\beta_{00}=0$ & $\\beta_{01}=1$ & $\\beta_{10}=0$ & $\\beta_{11}=-1$\\\\ \\hline \nMixT &0.8222&-0.1153& 0.6159&0.1979 \\\\\nMixEP & 0.08276& -0.02965& 0.1790& 0.05284 \\\\ \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{CaseIV:The mean squared errors (MSE)and the bias of the parameter estimates for each estimation method.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Robust mixture regression with Exponential Power distribution", "authors": ["Xiao Chen"], "url": "https://arxiv.org/abs/2012.10637v1", "attribution": "\"Robust mixture regression with Exponential Power distribution\" by Xiao Chen, arXiv:2012.10637v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.06108v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lcc}\\hline\n \\bf{Model} & \\bf{Resolution} & \\bf{Seconds / 1k images $\\downarrow$} \\\\\\hline\n StyleGAN2 & 1024 & 184.97 \\\\\n SWAGAN-Bi & 1024 & 95.06 \\\\ \\hline\n StyleGAN2 & 256 & 143.04 \\\\\n SWAGAN-Bi & 256 & 80.07 \\\\\n SWAGAN-Bi w/ NWD & 256 & 129.65 \\\\\n SWAGAN-NU & 256 & 70.06 \\\\\n Wavelet at final layer & 256 & 116.06 \\\\ \\hline\n \\end{tabular}\n\\caption{Average time (in seconds) for the network to process 1000 real images during training. Wavelet-based generators consistently outperform the alternatives. The reported times are based on the calculation provided in StyleGAN2's original implementation and do not include any of the (significant) time spent on benchmark calculations or sample generation.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SWAGAN: A Style-based Wavelet-driven Generative Model", "authors": ["Rinon Gal", "Dana Cohen", "Amit Bermano", "Daniel Cohen-Or"], "url": "https://arxiv.org/abs/2102.06108v1", "attribution": "\"SWAGAN: A Style-based Wavelet-driven Generative Model\" by Rinon Gal, Dana Cohen, Amit Bermano, and Daniel Cohen-Or, arXiv:2102.06108v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.00185v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrrr}\n \\hline\n&$\\theta_0$ & 0.3 &0.6& 0.9 &1.2& 1.5 &1.8 &2.1 &2.4 \\\\ \n \\hline\n$n=6$& IO & 0.93 & 0.78 & 0.46 & 0.00 & 0.46 & 0.78 & 0.93 & 0.99 \\\\ \n & HO & \\textbf{1.00} & \\textbf{0.96 }& \\textbf{0.62} & 0.00 & \\textbf{0.30} & \\textbf{0.57 }& \\textbf{0.73} & \\textbf{0.83} \\\\ \n & SKS & \\textbf{1.00} & 1.00 & 0.80 & 0.20 & {0.32} & 0.73 & 0.94 & 0.99 \\\\ \n & SKS-num & \\textbf{1.00 }& 0.94 & 0.53 & \\textbf{0.07} & 0.58 & 0.91 & 0.99 & 1.00 \\\\ \n & SN & \\textbf{1.00} & 0.94 &0.52 &0.06 & 0.51 &0.78 & 0.91 &0.97\\\\\n & \\textbf{BDM} & \\textbf{1.00} & \\textbf{0.96} & \\textbf{0.62} & \\textbf{0.11} & \\textbf{0.30} & \\textbf{0.57} &\\textbf{ 0.73} & \\textbf{0.83} \\\\ \n \\hline\n $n=12$ & IO & 0.99 & 0.92 & 0.61 & 0.00 & 0.61 & 0.92 & 0.99 & 1.00 \\\\ \n & HO & \\textbf{1.00} & \\textbf{0.99} & \\textbf{0.75} & 0.00 & \\textbf{0.48} & \\textbf{0.78} & \\textbf{0.91} & \\textbf{0.96} \\\\ \n &SKS & \\textbf{1.00} & 1.00 & 0.74 & -0.00 & 0.61 & 0.91 & 0.99 & 1.00 \\\\ \n &SKS-num & \\textbf{1.00} & 0.99 & 0.72 & 0.01 & 0.64 & 0.95 & 1.00 & 1.00 \\\\ \n & SN &\\textbf{1.00} &1.00 & 0.77 &\\textbf{0.04} & 0.62 & 0.89 & 0.98 &1.00\\\\\n & \\textbf{BDM} & \\textbf{1.00} & \\textbf{0.99} & \\textbf{0.75} & \\textbf{0.08} & \\textbf{0.48} & \\textbf{0.78 }& \\textbf{0.91} & \\textbf{0.96} \\\\ \n \\hline\n$n=20$ & IO & 1.00 & 0.97 & 0.74 & 0.00 & 0.74 & 0.97 & 1.00 & 1.00 \\\\ \n & HO & 1.00 & \\textbf{1.00} & \\textbf{0.85} & 0.00 & \\textbf{0.62} & \\textbf{0.90} & \\textbf{0.97} & \\textbf{0.99} \\\\ \n & SKS & 1.00 & \\textbf{1.00} & 0.91 & \\textbf{0.08} & 0.66 & 0.96 & 1.00 & 1.00 \\\\ \n &SKS-num & 1.00 & \\textbf{1.00} & 0.84 & 0.02 & 0.73 & 0.98 & 1.00 & 1.00 \\\\ &SN &1.00 & \\textbf{1.00} &0.94 & 0.02 &0.72 &0.95 & 1.00 &1.00\\\\\n & \\textbf{BDM} & \\textbf{1.00} & \\textbf{1.00} & \\textbf{0.85} & \\textbf{0.06} & \\textbf{0.62} & \\textbf{0.90} & \\textbf{0.97} & \\textbf{0.99} \\\\ \n \\hline\n $n=40$ & IO & 1.00 & 1.00 & 0.89 & 0.00 & 0.89 & 1.00 & 1.00 & 1.00 \\\\ \n & HO & 1.00 & 1.00 & \\textbf{0.95} & 0.00 & \\textbf{0.81} & \\textbf{0.98 }& 1.00 & 1.00 \\\\ \n & SKS & 1.00 & 1.00 & 0.99 & \\textbf{0.05} & 0.83 & 1.00 & 1.00 & 1.00 \\\\ \n & SKS-num & 1.00 & 1.00 & 0.96 & 0.03 & 0.87 & 1.00 & 1.00 & 1.00 \\\\ \n &SN \n &1.00 & 1.00 &1.00 &0.02 & 0.87 & 0.99 & 1.00 &1.00\\\\\n& \\textbf{BDM} & \\textbf{1.00} & \\textbf{1.00} & \\textbf{0.95} & \\textbf{0.04} & \\textbf{0.81 }& \\textbf{0.98} & \\textbf{1.00} & \\textbf{1.00} \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{BDM for a series of values $\\theta_0$ for the parameter and increasing sample sizes in the Exponential example. The values of the true BDM and the best approximation(s) in each configuration are highlighted in bold.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Bayesian Discrepancy Measure: Higher-order and Skewed approximations", "authors": ["Elena Bortolato", "Francesco Bertolino", "Monica Musio", "Laura Ventura"], "url": "https://arxiv.org/abs/2505.00185v1", "attribution": "\"Bayesian Discrepancy Measure: Higher-order and Skewed approximations\" by Elena Bortolato, Francesco Bertolino, Monica Musio, and Laura Ventura, arXiv:2505.00185v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.13174v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{OLS Regression Results: RRE as Predictor of Annualized Turnover}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Variable} & \\textbf{Coefficient} & \\textbf{Std. Error} \\\\\n\\midrule\nIntercept & 4.494*** & 0.104 \\\\\nRRE & -4.361*** & 0.113 \\\\\n\\midrule\n\\textbf{Model Statistics} & & \\\\\n$R^2$ & 0.815 & \\\\\n\\bottomrule\n\\multicolumn{3}{l}{\\footnotesize{*** p $<$ 0.001}}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining", "authors": ["Hongjun Ding", "Binqi Chen", "Jinsheng Huang", "Taian Guo", "Zhengyang Mao", "Guoyi Shao", "Lutong Zou", "Luchen Liu", "Ming Zhang"], "url": "https://arxiv.org/abs/2508.13174v1", "attribution": "\"AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining\" by Hongjun Ding, Binqi Chen, Jinsheng Huang, Taian Guo, Zhengyang Mao, Guoyi Shao, Lutong Zou, Luchen Liu, and Ming Zhang, arXiv:2508.13174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13714v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cll|}\n\t\t\t\\hline\n\t\t\t\\textbf{Case} & \\textbf{Conditions} & \\textbf{Singular points} \\\\\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\tA& $c_0=0$, $c_3=0$. & $S_0$. \\\\\n\t\t\t\\hline\n\t\t\tB& $c_0=0$, $c_3\\neq0$. & $S_0$, $S_4$. \\\\\n\t\t\t\\hline\n\t\t\tC& $c_0\\neq0$, $c_3^2<4c_0c_2$. & $S_0$. \\\\\n\t\t\t\\hline\n\t\t\tD& $c_0\\neq0$, $c_3^2=4c_0c_2$. & $S_0$, $S_3$. \\\\\n\t\t\t\\hline\n\t\t\tE& $c_0\\neq0$, $c_3^2>4c_0c_2$. & $S_0$, $S_1$, $S_2$. \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Cases with the singular points on the exceptional divisor of system .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Phase portraits of a family of Kolmogorov systems depending on six parameters", "authors": ["Érika Diz-Pita", "Jaume Llibre", "M. Victoria Otero-Espinar"], "url": "https://arxiv.org/abs/2501.13714v1", "attribution": "\"Phase portraits of a family of Kolmogorov systems depending on six parameters\" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.13714v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.20987v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc}\n $j \\setminus i$ & $0$ & $1$ & $\\cdots$ & $c-1$ & $c$ \\\\ \\hline\n$0$ & $1$ & . & $\\cdots$ & . & . \\\\\n$q$ & . & $\\beta_{1,q}$ & $\\cdots$ & $\\beta_{c-1,q}$ & . \\\\\n$2q$ & . & . & $\\cdots$ & . & $1$\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Del Pezzo $q$-secant varieties and complexes}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Fröberg type theorem for higher secant complexes", "authors": ["Junho Choe", "Jaewoo Jung"], "url": "https://arxiv.org/abs/2502.20987v2", "attribution": "\"A Fröberg type theorem for higher secant complexes\" by Junho Choe and Jaewoo Jung, arXiv:2502.20987v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.13696v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllll}\n\t\t\\hline \n\t\t& \\multicolumn{2}{c}{$t$-fusion} & \\multicolumn{2}{c}{Laplace} & \\multicolumn{2}{c}{L1 fusion} \\\\\n\t\tSignal strength & adj.MSE & se.adj.MSE & adj.MSE & se.adj.MSE & adj.MSE & se.adj.MSE \\\\\n\t\t\\hline \n\t\tweak & 0.038685 & 0.012287 & 0.13113 & 0.008525 & 0.014461 & 0.006232 \\\\\n\t\tmedium & 0.009272 & 0.002897 & 0.100271 & 0.003018 & 0.002314 & 0.000997 \\\\\n\t\tstrong & 0.001384 & 0.001635 & 0.094502 & 0.001528 & 0.000575 & 0.000226 \\\\\n\t\t\\hline \n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{2-d lattice Graphs: Comparison of performance wrt adjusted MSE for $t$-fusion and Laplace fusion methods along with $L_1$ fusion method.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Graph signal denoising using $t$-shrinkage priors", "authors": ["Sayantan Banerjee", "Weining Shen"], "url": "https://arxiv.org/abs/2012.13696v1", "attribution": "\"Graph signal denoising using $t$-shrinkage priors\" by Sayantan Banerjee and Weining Shen, arXiv:2012.13696v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.12092v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\\hline\\hline\n Model & Probability & Mean & STD\\\\\n\\hline\n Period & 2003-2007\\\\\n\\hline\nUnconditional (one normal) & 100.00\\% & 0.03\\% & 0.77\\%\\\\\nConditional on quiet & 58.68\\% & 0.10\\% & 0.43\\% \\\\\nConditional on hectic & 41.32\\% & -0.08\\% & 1.07\\%\\\\\n\\hline\n Period & 2004-2008\\\\\n\\hline\nUnconditional (one normal) & 100.00\\% & 0.03\\% & 0.83\\%\\\\\nConditional on quiet & 56.77\\% & 0.10\\% & 0.38\\% \\\\\nConditional on hectic & 43.23\\% & -0.06\\% & 1.17\\%\\\\\n\\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Copula-Based Factor Model for Credit Risk Analysis", "authors": ["Meng-Jou Lu", "Cathy Yi-Hsuan Chen", "Wolfgang Karl Härdle"], "url": "https://arxiv.org/abs/2009.12092v2", "attribution": "\"Copula-Based Factor Model for Credit Risk Analysis\" by Meng-Jou Lu, Cathy Yi-Hsuan Chen, and Wolfgang Karl Härdle, arXiv:2009.12092v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2301.02575v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\centering Alternative measures of the difficulty of item \\#11898}\n\\begin{tabular}{ccccc}\n\t\t\t\t\\midrule\n\t\t\t\tPosition effect & Average fraction & Fatigue effect (in pp) & Question \\\\ \n\t\t\t\testimation method & correct responses & $\\times$ average position & difficulty \\\\ \n\t\t\t\t(1) & (2) & (3) & (4) \\\\ \\midrule\n\t\t\t\tNone &\t0.36&\t0 $\\times$ 64 = 0&\t0.36\t\\\\\n\t\t\t\tPooling all items &\t0.36&\t-0.08 $\\times$ 64 = -5.1&\t0.41\t\\\\\n\t\t\t\tItem-specific effect &\t0.36&\t-0.24 $\\times$ 64 = -15.3&\t0.51\t\\\\\n\t\t\t\tShrinkage estimator &\t0.36&\t-0.24 $\\times$ 64 = -15.3&\t0.51\t\\\\\n\t\t\t\tBy fraction corr. resp.&\t0.36&\t-0.15 $\\times$ 64 = -9.6&\t0.45\t\\\\\n\t\t\t\tBy academic subject &\t0.36&\t-0.03 $\\times$ 64 = -1.6&\t0.37\t\\\\\n\t\t\t\t\n\t\t\t\t\\midrule\n\t\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Cognitive Endurance, Talent Selection, and the Labor Market Returns to Human Capital", "authors": ["Germán Reyes"], "url": "https://arxiv.org/abs/2301.02575v1", "attribution": "\"Cognitive Endurance, Talent Selection, and the Labor Market Returns to Human Capital\" by Germán Reyes, arXiv:2301.02575v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.02021v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimates of the NB model for STDs with all covariates}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\hline\nSTDs-NB & Estimate({ $\\hat{\\boldsymbol\\beta}_1$}) & StdDev & $p$-value \\\\\n\\hline\nIntercept & -2.5964 & 1.2307 & 0.0349 \\\\\nSmoke & 0.8070 & 0.3729 & 0.0304 \\\\\nAge=2 & -0.0651 & 0.3264 & 0.8419 \\\\\nAge=3 & -1.1424 & 1.1699 & 0.3288 \\\\\nHC=1 & -0.1954 & 0.3002 & 0.5153 \\\\\nHC=2 & 0.1351 & 0.6518 & 0.8357 \\\\\nAFS=2 & 0.0731 & 0.4729 & 0.8772 \\\\\nAFS=3 & 0.2263 & 0.5112 & 0.6580 \\\\\nNSP=2 & -0.0018 & 0.3184 & 0.9956 \\\\\nNSP=3 & 0.0657 & 0.5723 & 0.9086 \\\\\nNSP=4 & 0.9982 & 1.0007 & 0.3185 \\\\\nNP=1 & 0.5948 & 1.1894 & 0.6170 \\\\\nNP=2 & 0.4197 & 1.3055 & 0.7479 \\\\\nDispersion & 0.1660 &&\\\\\n\\hline\n\\multicolumn{2}{ l }{AIC= 589.43} & \\multicolumn{2}{ l }{-2log-Like.=561.434} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modeling Count Data via Copulas", "authors": ["Hadi Safari-Katesari", "S. Yaser Samadi", "Samira Zaroudi"], "url": "https://arxiv.org/abs/2012.02021v1", "attribution": "\"Modeling Count Data via Copulas\" by Hadi Safari-Katesari, S. Yaser Samadi, and Samira Zaroudi, arXiv:2012.02021v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.11461v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccccc}\n \\toprule\n $j$ & $\\mu_{{\\rm circ}}$ & $\\kappa_{{\\rm circ}}$ & $\\mu_{{\\rm axial}}$ & $\\kappa_{{\\rm axial}}$ & $\\rho$\\\\\n \\midrule\n 1 & -1.92 (-2.05, -1.80) & 5.60 (4.59, 10.06) & 0.68 (0.62, 0.75) & 18.96 (14.8, 36.8) & 0.24 (0.09, 0.42) \\\\\n 2 & -2.32 (-2.45, -2.20) & 5.00 (4.26, 9.02) & 0.14 (0.05, 0.23) & 10.01 (8.44, 19.2) & 0.35 (0.12, 0.50) \\\\\n 3 & -1.16 (-1.25, -1.05) & 5.99 (5.33, 10.20) & -1.25 (-1.39, -1.09) & 3.13 (2.60, 5.30) & 0.07 (-0.16, 0.22) \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Point estimates (ET confidence intervals) for the parameters of the density.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Does wind affect the orientation of vegetation stripes? A copula-based mixture model for axial and circular data", "authors": ["Marco Mingione", "Francesco Lagona", "Priyanka Nagar", "Francois von Holtzhausen", "Andriette Bekker", "Janine Schoombie", "Peter C. le Roux"], "url": "https://arxiv.org/abs/2411.11461v1", "attribution": "\"Does wind affect the orientation of vegetation stripes? A copula-based mixture model for axial and circular data\" by Marco Mingione, Francesco Lagona, Priyanka Nagar, Francois von Holtzhausen, Andriette Bekker, Janine Schoombie, and Peter C. le Roux, arXiv:2411.11461v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.20058v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of the three datasets - NASDAQ100, S\\&P500 and NIFTY500.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccr}\n\\toprule\n- & NASDAQ100 & S\\&P500 & NIFTY500 \\\\\n\\midrule\n\\#Assets & 83 & 442 & 336 \\\\\n\\#Days & 2800 & 2800 & 2800 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predictive AI with External Knowledge Infusion for Stocks", "authors": ["Ambedkar Dukkipati", "Kawin Mayilvaghanan", "Naveen Kumar Pallekonda", "Sai Prakash Hadnoor", "Ranga Shaarad Ayyagari"], "url": "https://arxiv.org/abs/2504.20058v1", "attribution": "\"Predictive AI with External Knowledge Infusion for Stocks\" by Ambedkar Dukkipati, Kawin Mayilvaghanan, Naveen Kumar Pallekonda, Sai Prakash Hadnoor, and Ranga Shaarad Ayyagari, arXiv:2504.20058v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.01334v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|ccc|ccc}\n \\toprule & \\multicolumn{3}{c|}{CoNLL03} & \\multicolumn{3}{c}{WikiGold} \\\\ %\\cline{2-7}\n & ORG & LOC & MISC & ORG & LOC & MISC \\\\\n \\midrule PER & 72 & 37 & 15 & 13 & 5 & 1 \\\\\n ORG & - & 180 & 59 & - & 44 & 41 \\\\\n LOC & - & - & 158 & - & - & 5 \\\\\n \\bottomrule\n \\end{tabular}\n\\caption{The number of entities overlapping among classes in the CoNLL03 and WikiGold dataset.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation", "authors": ["Yuji Naraki", "Ryosuke Yamaki", "Yoshikazu Ikeda", "Takafumi Horie", "Kotaro Yoshida", "Ryotaro Shimizu", "Hiroki Naganuma"], "url": "https://arxiv.org/abs/2404.01334v2", "attribution": "\"Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation\" by Yuji Naraki, Ryosuke Yamaki, Yoshikazu Ikeda, Takafumi Horie, Kotaro Yoshida, Ryotaro Shimizu, and Hiroki Naganuma, arXiv:2404.01334v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.02480v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Three types of mixing-related data augmentation methods we propose here, and the corresponding parameter settings}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|ll}\n\\toprule\n\\textbf{Mixing} & Description & Scale & Range \\\\\n\\midrule\n & Contrast & Amount & 1$-$70 \\\\\n & Equalizer 1 & Frequency & 32$-$4,096 \\\\\n & Equalizer 2 & Frequency & 32$-$4,096 \\\\\n & Reverb & Reverb & 1$-$70 \\\\\n & Pink noise & Volume & 0.01$-$0.04 \\\\\n\\toprule\n\\textbf{Paring} & Description & Threshold & Notes\\\\\n\\midrule\n & Chroma distance & 0.48 & Mean within songs: 0.45\\\\\n & & & Mean across songs: 0.51\\\\\n & Correlation & 20 & Within songs: 2$-$30\\\\\n & & & Across songs: 0$-$10\\\\\n\\toprule\n\\textbf{Silence}& Description & Threshold & Notes\\\\\n\\midrule\n & Reduce silence & 20 & top db\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Mixing-Specific Data Augmentation Techniques for Improved Blind Violin/Piano Source Separation", "authors": ["Ching-Yu Chiu", "Wen-Yi Hsiao", "Yin-Cheng Yeh", "Yi-Hsuan Yang", "Alvin Wen-Yu Su"], "url": "https://arxiv.org/abs/2008.02480v1", "attribution": "\"Mixing-Specific Data Augmentation Techniques for Improved Blind Violin/Piano Source Separation\" by Ching-Yu Chiu, Wen-Yi Hsiao, Yin-Cheng Yeh, Yi-Hsuan Yang, and Alvin Wen-Yu Su, arXiv:2008.02480v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.19200v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc} \\hline\n$\\mu$ & 0.0818\\\\\n $\\sigma$ & .1849 \\\\\n LETF leverage & $\\beta = 2$\\\\\n30 day T-bill return $r$ & 0.0032\\\\\nLETF expense ratio $c_\\ell$ & .0089 \\\\\nVETF expense ratio $c_v$ & 0.0 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Making Leveraged Exchange-Traded Funds Work for your Portfolio", "authors": ["Peter Forsyth", "Pieter van Staden", "Yuying Li"], "url": "https://arxiv.org/abs/2506.19200v1", "attribution": "\"Making Leveraged Exchange-Traded Funds Work for your Portfolio\" by Peter Forsyth, Pieter van Staden, and Yuying Li, arXiv:2506.19200v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.00208v1_tex_table20.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Differential Trend in the Share of Offline Spend by Age -- \\textit{Covid Adopters} vs. \\textit{Organic Adopters}}\n\\begin{tabular}{lcc}\n \\midrule \\midrule\n DV: & \\multicolumn{2}{c}{Share of Offline Spend}\\\\\n Model: & (1) Senior Adopters & (2) Young Adopters \\\\ \n \\midrule\n \\emph{Variables}\\\\\n Organic\\_Adopter & 0.4947 (0.0092) & 0.4728 (0.0081)\\\\ \n & [0.000] & [0.000]\\\\ \n Covid\\_Adopter & 0.0706 (0.0130) & 0.0882 (0.0114)\\\\ \n & [0.000] & [0.000]\\\\ \n t & -0.0019 (0.0005) & -0.0022 (0.0004)\\\\ \n & [0.001] & [0.000]\\\\ \n Covid\\_Adopter * t & 0.0020 (0.0006) & 0.0004 (0.0006)\\\\ \n & [0.002] & [0.523]\\\\ \n \\midrule\n \\emph{Fit statistics}\\\\\n Observations & 72 & 72\\\\ \n R$^2$ & 0.79964 & 0.82429\\\\ \n Adjusted R$^2$ & 0.79080 & 0.81654\\\\ \n \\midrule \\midrule\n \\multicolumn{3}{l}{\\emph{Standard errors are shown in parentheses; p-values are shown in square brackets.}}\\\\\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Channel Choice and Customer Value", "authors": ["Shirsho Biswas", "Hema Yoganarasimhan", "Haonan Zhang"], "url": "https://arxiv.org/abs/2508.00208v1", "attribution": "\"Channel Choice and Customer Value\" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2508.00208v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.10771v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Example table.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|} \\hline\n Species & \\bf Mean & \\bf Std.~Dev. \\\\ \\hline\n 1 & 3.4 & 1.2 \\\\\n 2 & 5.4 & 0.6 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Analysis and numerical analysis of the Helmholtz-Korteweg equation", "authors": ["Patrick E. Farrell", "Tim van Beeck", "Umberto Zerbinati"], "url": "https://arxiv.org/abs/2503.10771v1", "attribution": "\"Analysis and numerical analysis of the Helmholtz-Korteweg equation\" by Patrick E. Farrell, Tim van Beeck, and Umberto Zerbinati, arXiv:2503.10771v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.19713v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dice Scores for 10-Fold Cross-Validation}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\hline\n\\textbf{Model} & \\textbf{Average Dice Score} \\\\ \\hline\nMLP & 0.55 \\\\ \\hline\nKAN & 0.57 \\\\ \\hline\nproKAN & 0.58 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "ProKAN: Progressive Stacking of Kolmogorov-Arnold Networks for Efficient Liver Segmentation", "authors": ["Bhavesh Gyanchandani", "Aditya Oza", "Abhinav Roy"], "url": "https://arxiv.org/abs/2412.19713v1", "attribution": "\"ProKAN: Progressive Stacking of Kolmogorov-Arnold Networks for Efficient Liver Segmentation\" by Bhavesh Gyanchandani, Aditya Oza, and Abhinav Roy, arXiv:2412.19713v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.06296v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sampling error}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|} \n\\hline\n $k$ & $3$ & $4$ & $5$ & $6$ & $7$ & $8$ & $9$ & $10$ & $B_Y$\\\\ \n\\hline\\hline\n\\emph{$\\epsilon$} & $1.55$ & $3.07$ & $267$ & $318$ & $18.8$ & $1.54$ & $1.56$ & $169$ & $1.46$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Signal processing with a distribution of graph operators", "authors": ["Feng Ji", "Wee Peng Tay"], "url": "https://arxiv.org/abs/2012.06296v1", "attribution": "\"Signal processing with a distribution of graph operators\" by Feng Ji and Wee Peng Tay, arXiv:2012.06296v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.16011v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Asian basket spread option with payoff $\\left(\\sum_{i=1}^n w_i S_2(t_i)-\\sum_{i=1}^n w_i S_1(t_i) - K\\right)^+$ with $S_2(T)=100$, $S_1(T)=60$, $\\sigma_2 = 33\\%$, $\\sigma_1= 25\\%$, $\\rho_{1,2}=40\\%$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrr}\\toprule\n\t\t\t$K$ & VG0 & VG1 & VG2 & VG3 & SLN & HICUB & MC \\\\ \\midrule\n\t\t\t25.0 & 20.7593& 20.7611& 20.7646& 20.7646& 20.8073& 20.7637& 20.7646\\\\\n30.0 & 17.6876& 17.6884& 17.6931& 17.6931& 17.7711& 17.6921& 17.6931\\\\\n35.0 & 14.9531& 14.9532& 14.9590& 14.9589& 15.0630& 14.9580& 14.9589\\\\\n40.0 & 12.5514& 12.5512& 12.5577& 12.5576& 12.6769& 12.5566& 12.5577\\\\\n45.0 & 10.4677& 10.4676& 10.4746& 10.4744& 10.5983& 10.4734& 10.4745\\\\\n50.0 & 8.6800& 8.6799& 8.6871& 8.6870& 8.8065& 8.6859& 8.6870\\\\\n55.0 & 7.1612& 7.1613& 7.1684& 7.1682& 7.2767& 7.1671& 7.1683\\\\\n\t\t\t\t\t\t\\midrule\n\t\t\tRMSE & 0.0063 & 0.0060 & 0.0001 & 0.0000 & 0.1031 & 0.0010 & 0.0000 \t\t\t\\\\\n\t\t\tMAE &0.0070 & 0.0070 & 0.0002 & 0.0000 & 0.1238 & 0.0012 & 0.0000 \\\\\n\t\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Asian Basket Spread Options: A New Approximation Based on Stochastic Taylor Expansions", "authors": ["Fabien Le Floc'h"], "url": "https://arxiv.org/abs/2504.16011v3", "attribution": "\"Asian Basket Spread Options: A New Approximation Based on Stochastic Taylor Expansions\" by Fabien Le Floc'h, arXiv:2504.16011v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2008.12767v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\centering\nTimestamp & SACR\\_SUNN\\_in(GB) & SACR\\_SUNN\\_out(GB) \\\\ \\hline\n \t\n1514822400 & 14110930202 & 1025131246 \\\\ \\hline\n1514826000 & 13453619303 & 9191557943 \\\\ \\hline\n \t\n1514829600 & 12168879944 & 7793842045 \\\\ \\hline\n1514833200 & 11231198033 & 7097237528 \\\\ \\hline\n1514836800 & 10780847622 & 8048293939 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Sample traffic trace with time stamp collected from one router interface across a link.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks", "authors": ["Tanwi Mallick", "Mariam Kiran", "Bashir Mohammed", "Prasanna Balaprakash"], "url": "https://arxiv.org/abs/2008.12767v1", "attribution": "\"Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks\" by Tanwi Mallick, Mariam Kiran, Bashir Mohammed, and Prasanna Balaprakash, arXiv:2008.12767v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08574v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||l||c|c||c|c||} \\hline\n\\textbf{Methods} &\\multicolumn{4}{|c||}{\\textbf{Datasets}} \\\\ \\cline{2-5}\n &\\multicolumn{2}{|c||}{\\textbf{SPaSe}} &\\multicolumn{2}{|c||}{\\textbf{WiSe}} \\\\ \\cline{2-5} \n &\\textbf{mean IoU} &\\textbf{PA}&\\textbf{mean IoU} &\\textbf{PA} \\\\ \\hline \\hline\nHaurilet \\emph{et al.}~ &35.80 &77.40 &- &- \\\\ \nHaurilet \\emph{et al.}~ &- &- &37.20 &88.50 \\\\\nHANet~ &32.37 &76.54 &39.35 &88.72 \\\\\nDANet~ &33.12 &77.39 &44.85 &88.80 \\\\ \nRANet~ &35.77 &78.64 &43.31 &88.77 \\\\\nOurs &36.17 &79.11 &46.74 &89.80 \\\\ \\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{illustrates the comparison of the proposed method with state-of-the-art techniques on benchmark datasets WiSe and SPaSe datasets. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Classroom Slide Narration System", "authors": ["Jobin K. V.", "Ajoy Mondal", "C. V. Jawahar"], "url": "https://arxiv.org/abs/2201.08574v1", "attribution": "\"Classroom Slide Narration System\" by Jobin K. V., Ajoy Mondal, and C. V. Jawahar, arXiv:2201.08574v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.11921v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of Training Hyperparameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lr}\n \\Xhline{1px}\n \\textbf{Hyperparameters of SUDO-DRL and benchmarks} & Value \\\\\n \\hline\n Critic NN learning rate & 0.001 \\\\\n Actor NN learning rate & 0.0001 \\\\\n Decay rate of learning rate & 0.001 \\\\\n Discount factor, $\\gamma$ & 0.99 \\\\\n GAE parameter, $\\lambda$ & 0.99 \\\\\n Clipping parameter, $\\epsilon$ & 0.2 \\\\\n Policy entropy loss weight, $\\omega$ & 0.01 \\\\\n Decay rate of sampling priority, $\\varrho$ & 0.95 \\\\\n Unified on-off policy loss function hyperparameter, $\\beta_{1}, \\beta_{2}$ & 0.9 \\\\\n On-policy and off-policy batch size, $B_{1}, B_2$ & 128 \\\\\n Number of sampled states for score scheme, $K$ & 50 \\\\\n Number of tested AoI and channel state, $\\Xi$ & 4 \\\\\n Number of past trajectories for average score computing, $\\bar{u}$ & 50 \\\\\n Time horizon of each episode, $T$ & 128 \\\\\n Size of replay buffer, $R$ & $200$ \\\\\n Number of episodes for pre-training, $I_{1}$ & $10 \\times N$ \\\\\n Total number of episodes for training, $I$ & $10000$ \\\\\n Optimizer during training & Adam \\\\\n \\Xhline{1px}\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Goal-oriented Transmission Scheduling: Structure-guided DRL with a Unified Dual On-policy and Off-policy Approach", "authors": ["Jiazheng Chen", "Wanchun Liu"], "url": "https://arxiv.org/abs/2501.11921v1", "attribution": "\"Goal-oriented Transmission Scheduling: Structure-guided DRL with a Unified Dual On-policy and Off-policy Approach\" by Jiazheng Chen and Wanchun Liu, arXiv:2501.11921v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.14605v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance measures on the test set using (adjusted) price, volume and returns as input variables.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n $w$ & $\\varepsilon$ & Accuracy & Precision & Recall & f1-score \\\\\n \\midrule\n 10 & 0.45 & 0.946 & 0.858 & 0.802 & \\textbf{0.827} \\\\\n 10 & 0.55 & 0.973 & 0.810 & 0.733 & 0.765 \\\\\n 10 & 0.65 & 0.988 & 0.776 & 0.694 & 0.728 \\\\\n 10 & 0.75 & 0.995 & 0.795 & 0.664 & 0.711 \\\\\n \\midrule\n 30 & 0.45 & 0.943 & 0.845 & 0.800 & 0.820 \\\\\n 30 & 0.55 & 0.971 & 0.789 & 0.742 & 0.763 \\\\\n 30 & 0.65 & 0.987 & 0.760 & 0.678 & 0.711 \\\\\n 30 & 0.75 & 0.996 & 0.998 & 0.601 & 0.667 \\\\\n \\midrule\n 50 & 0.45 & 0.938 & 0.826 & 0.785 & 0.804 \\\\\n 50 & 0.55 & 0.971 & 0.795 & 0.731 & 0.758 \\\\\n 50 & 0.65 & 0.987 & 0.758 & 0.667 & 0.702 \\\\\n 50 & 0.75 & 0.995 & 0.998 & 0.503 & 0.505 \\\\\n \\midrule\n 60 & 0.45 & 0.938 & 0.823 & 0.788 & 0.804 \\\\\n 60 & 0.55 & 0.967 & 0.752 & 0.735 & 0.743 \\\\\n 60 & 0.65 & 0.987 & 0.756 & 0.654 & 0.692 \\\\\n 60 & 0.75 & 0.996 & 0.955 & 0.595 & 0.657 \\\\\n \\midrule\n 80 & 0.45 & 0.933 & 0.805 & 0.788 & 0.796 \\\\\n 80 & 0.55 & 0.967 & 0.755 & 0.730 & 0.742 \\\\\n 80 & 0.65 & 0.989 & 0.893 & 0.616 & 0.677 \\\\\n 80 & 0.75 & 0.995 & 0.998 & 0.548 & 0.586 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots", "authors": ["Mostafa Shabani", "Martin Magris", "George Tzagkarakis", "Juho Kanniainen", "Alexandros Iosifidis"], "url": "https://arxiv.org/abs/2210.14605v2", "attribution": "\"Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots\" by Mostafa Shabani, Martin Magris, George Tzagkarakis, Juho Kanniainen, and Alexandros Iosifidis, arXiv:2210.14605v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12331v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experimental evaluation setup}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\hline\n Component & Type & CPU & RAM & Disk\\\\\n \\hline\n Caliper Benchmark & N/A & 8 vCPU & 30 GB & 288 GB\\\\\n Broker & Fabric V2.2 & 8 vCPU & 30 GB & 288 GB\\\\\n Fabric Subscriber & Fabric V1.4 & 2 vCPU & 7.5 GB & 36 GB\\\\\n Besu Subscriber & Besu & 2 vCPU & 7.5 GB & 36 GB\\\\\n Publisher & Fabric V2.2 & 2 vCPU & 7.5 GB & 36 GB\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Pub-Sub Architecture to Promote Blockchain Interoperability", "authors": ["Sara Ghaemi", "Sara Rouhani", "Rafael Belchior", "Rui S. Cruz", "Hamzeh Khazaei", "Petr Musilek"], "url": "https://arxiv.org/abs/2101.12331v1", "attribution": "\"A Pub-Sub Architecture to Promote Blockchain Interoperability\" by Sara Ghaemi, Sara Rouhani, Rafael Belchior, Rui S. Cruz, Hamzeh Khazaei, and Petr Musilek, arXiv:2101.12331v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.08606v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccccc} \\toprule\n & & & & \\multicolumn{3}{c}{AUC} & & \\multicolumn{3}{c}{Average Precision} \\\\ \\cmidrule{5-7} \\cmidrule{9-11}\n $\\sigma$ & $w$ & $\\alpha$ & & Mixed & Mean & Last & & Mixed & Mean & Last \\\\ \\midrule\n 0.01 & 2.75 & 0.2 & & 0.89 & 0.81 & 0.74 & & 0.17 & 0.09 & 0.08 \\\\\n & & 0.5 & & 0.89 & 0.81 & 0.73 & & 0.20 & 0.12 & 0.09 \\\\\n & & 0.7 & & 0.89 & 0.81 & 0.73 & & 0.25 & 0.14 & 0.11 \\\\\n & & 1.0 & & 0.89 & 0.81 & 0.73 & & 0.33 & 0.20 & 0.15 \\\\\n & & & & & & & & & & \\\\\n 0.1 & 4 & 0.2 & & 0.95 & 0.95 & 0.90 & & 0.09 & 0.09 & 0.07 \\\\\n & & 0.5 & & 0.95 & 0.94 & 0.90 & & 0.11 & 0.11 & 0.09 \\\\\n & & 0.7 & & 0.96 & 0.96 & 0.91 & & 0.15 & 0.15 & 0.12 \\\\\n & & 1.0 & & 0.96 & 0.95 & 0.91 & & 0.20 & 0.20 & 0.16 \\\\\n & & & & & & & & & & \\\\\n 1 & 500 & 0.2 & & 0.94 & 0.95 & 0.90 & & 0.08 & 0.09 & 0.07 \\\\\n & & 0.5 & & 0.94 & 0.95 & 0.90 & & 0.10 & 0.11 & 0.09 \\\\\n & & 0.7 & & 0.94 & 0.95 & 0.90 & & 0.13 & 0.14 & 0.12 \\\\\n & & 1.0 & & 0.95 & 0.96 & 0.92 & & 0.18 & 0.21 & 0.16 \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Accuracy of our model of user behavior (Mixed) compared against two single-point benchmarks (Mean and Last). The area under the ROC curve (AUC) and average precision are reported for these models, for a variety choices of the multinomial logit tuning parameters ($\\sigma,w$) and sampling exponent ($\\alpha$). Results are aggregated over the entire set of test users, replicated 20 times each.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Optimizing Offer Sets in Sub-Linear Time", "authors": ["Vivek F. Farias", "Andrew A. Li", "Deeksha Sinha"], "url": "https://arxiv.org/abs/2011.08606v1", "attribution": "\"Optimizing Offer Sets in Sub-Linear Time\" by Vivek F. Farias, Andrew A. Li, and Deeksha Sinha, arXiv:2011.08606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.13215v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|ccc}\n\\hline\\hline\n & \\multicolumn{3}{c}{$ r = 0.8 $} \\vline & \\multicolumn{3}{c}{$ r = 1.0 $} \\\\\n & DAX & FTSE 100 & S\\&P 500 & DAX & FTSE 100 & S\\&P 500 \\\\\n\\hline\n $\\tau = 0.05$ & 85 & 83 & 90 & 89 & 88 & 95 \\\\\n $\\tau = 0.01$ & 93 & 95 & 95 & 100 & 102 & 101 \\\\\n $\\tau = 0.0025$ & 101 & 97 & 101 & 108 & 99 & 104 \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean value of the adaptively selected intervals. Note: the average number of trading days of the adaptive interval length is provided for the DAX, FTSE 100 and S\\&P 500 market indices at three expectile levels, $\\tau = 0.05$, $\\tau = 0.01$ and $\\tau = 0.0025$, and the modest $\\left(r = 0.50\\right)$ and the conservative $\\left(r = 1.00\\right)$ risk case.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "lCARE -- localizing Conditional AutoRegressive Expectiles", "authors": ["Xiu Xu", "Andrija Mihoci", "Wolfgang Karl Härdle"], "url": "https://arxiv.org/abs/2009.13215v1", "attribution": "\"lCARE -- localizing Conditional AutoRegressive Expectiles\" by Xiu Xu, Andrija Mihoci, and Wolfgang Karl Härdle, arXiv:2009.13215v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11377v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ICC scores of the FTD dataset, and mean measurement differences ± confidence interval (CI) of the HC18 dataset achieved by all the algorithms.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\nNetwork Name & FTD ICC {\\hskip 0.1in} & HC18 Mean Difference ± CI (mm) {\\hskip 0.1in}\\\\\n\\hline\nVanilla BiometryNet & 0.684 & 4.56 ± 7.41\\\\\nBiometryNet\\_SAF\\_a1 & 0.737 & 4.02 ± 6.70\\\\\nBiometryNet\\_SAF\\_a4 & 0.724 & 4.03 ± 7.73\\\\\nBiometryNet\\_SAF\\_a8 & 0.719 & 3.86 ± 7.74\\\\\nEfficientNet & 0.688 & 32.76 ± 21.01\\\\\nEfficientNet\\_SAF\\_a4 & 0.725 & 4.87 ± 5.79\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Improving Automatic Fetal Biometry Measurement with Swoosh Activation Function", "authors": ["Shijia Zhou", "Euijoon Ahn", "Hao Wang", "Ann Quinton", "Narelle Kennedy", "Pradeeba Sridar", "Ralph Nanan", "Jinman Kim"], "url": "https://arxiv.org/abs/2412.11377v1", "attribution": "\"Improving Automatic Fetal Biometry Measurement with Swoosh Activation Function\" by Shijia Zhou, Euijoon Ahn, Hao Wang, Ann Quinton, Narelle Kennedy, Pradeeba Sridar, Ralph Nanan, and Jinman Kim, arXiv:2412.11377v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12406v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\hline\nDataset & Train & Dev & Test\\\\ \n\\hline\n\\multicolumn{4}{c}{\\textit{Unlabeled}} \\\\\n\\hline\nNIH & 40365 & NA & NA \\\\\nMIMIC-CXR & 57696 & NA & NA \\\\\n\\hline\n\\multicolumn{4}{c}{\\textit{Labeled}} \\\\\n\\hline\nMIMIC-CXR E & 3660 & 1182 & 12125 \\\\\nMIMIC-CXR Co & 1120 & 375 & 11031 \\\\\nMIMIC-CXR Ca & 11724 & 3876 & 12791 \\\\\nMIMIC-CXR A & 2164 & 3992 & 12129 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Dataset statistics for all datasets. The training and development sets of MIMIC CXR Edema, Consolidation, Cardiomegaly and Atelecatasis were filtered to contain one negative control for each positive sample. Guides of abbreviations for medical diagnosis: (E)dema, (Co)nsolidation, (Ca)rdiomegaly and (A)telectasis.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Syfer: Neural Obfuscation for Private Data Release", "authors": ["Adam Yala", "Victor Quach", "Homa Esfahanizadeh", "Rafael G. L. D'Oliveira", "Ken R. Duffy", "Muriel Médard", "Tommi S. Jaakkola", "Regina Barzilay"], "url": "https://arxiv.org/abs/2201.12406v1", "attribution": "\"Syfer: Neural Obfuscation for Private Data Release\" by Adam Yala, Victor Quach, Homa Esfahanizadeh, Rafael G. L. D'Oliveira, Ken R. Duffy, Muriel Médard, Tommi S. Jaakkola, and Regina Barzilay, arXiv:2201.12406v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.12163v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|}\n\\hline\n\\multicolumn{3}{|c|}{(a) 2-class phase classification}\\\\\n\\hline\nLabel & \n$h_2/J$& \nPhase\\\\ \n\\hline\n0 & $-1.15<h_2/J<0$ & SPT \\\\\n\\hline\n1 & $h_2/J<-1.15$ or $h_2/J>0$ & Other phases\\\\\n\\hline\n\\multicolumn{3}{|c|}{(b) 3-class phase classification} \\\\\n\\hline\nLabel & \n$h_2/J$& \nPhase\\\\ \n\\hline\n0 & $-1.15<h_2/J<0$ & SPT \\\\\n\\hline\n1 & N/A & Nothing\\\\\n\\hline\n2& $h_2/J>0$& Paramagnetic\\\\\n\\hline\n3& $h_2/J<-1.15$& Antiferromagnetic\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Quantum Inception Score", "authors": ["Akira Sone", "Akira Tanji", "Naoki Yamamoto"], "url": "https://arxiv.org/abs/2311.12163v4", "attribution": "\"Quantum Inception Score\" by Akira Sone, Akira Tanji, and Naoki Yamamoto, arXiv:2311.12163v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17106v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n $n$ & Error $\\|\\cdot\\|_1$ & Order $\\|\\cdot\\|_1$ & Error\n $\\|\\cdot\\|_{\\infty}$ & Order $\\|\\cdot\\|_{\\infty}$ \\\\\n \\hline\n 40 & 7.80E$-4$ & $-$ & 2.61E$-2$ & $-$ \\\\\n \\hline\n 80 & 2.82E$-6$ & 8.11 & 8.14E$-5$ & 8.32 \\\\\n \\hline\n 160 & 1.11E$-7$ & 4.66 & 6.31E$-6$ & 3.69 \\\\\n \\hline\n 320 & 2.40E$-9$ & 5.53 & 2.28E$-7$ & 4.79 \\\\\n \\hline\n 640 & 4.48E$-11$ & 5.74 & 6.73E$-9$ & 5.08 \\\\\n \\hline\n 1280 & 9.61E$-13$ & 5.54 & 1.92E$-10$ & 5.13 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Error table for Burgers equation, $t=0.3$. WLS-GAW.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17106v1", "attribution": "\"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11692v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Clean downstream accuracy (CDA).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc}\n\\toprule\nDownstream Task & SimCLR & MoCo v2 & BYOL \\\\\n\\midrule\nSTL-10 & 0.783 & 0.889 & 0.948 \\\\\nCIFAR-10 & 0.766 & 0.712 & 0.855 \\\\\nMNIST & 0.974 & 0.940 & 0.974 \\\\\nF-MNIST & 0.874 & 0.852 & 0.894 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders", "authors": ["Tianshuo Cong", "Xinlei He", "Yang Zhang"], "url": "https://arxiv.org/abs/2201.11692v4", "attribution": "\"SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders\" by Tianshuo Cong, Xinlei He, and Yang Zhang, arXiv:2201.11692v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.12129v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\hline\\hline\nModel & Log Likelihood & AIC & BIC \\\\\n\\hline\nARCH(1) & 1281.7 & -2567.4 & -2558.6 \\\\\nARCH(2) & 1283.4 & -2560.8 & -2547.6 \\\\ \nARCH(3) & \\textcolor{blue}{1291.6} & \\textcolor{blue}{-2575.2} & \\textcolor{blue}{-2557.5}\\\\\nARCH(4) & 1288.8 & -2567.5 & -2545.4\\\\\n\\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Estimation result of ARIMA-ARCH models.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A first econometric analysis of the CRIX family", "authors": ["Shi Chen", "Cathy Yi-Hsuan Chen", "Wolfgang Karl Härdle"], "url": "https://arxiv.org/abs/2009.12129v1", "attribution": "\"A first econometric analysis of the CRIX family\" by Shi Chen, Cathy Yi-Hsuan Chen, and Wolfgang Karl Härdle, arXiv:2009.12129v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.14598v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{amsmath}\n\\usepackage{diagbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c|c|c|c}\n\\toprule [1.2pt]\n\\hline\n\\diagbox{State}{Action} & (T, T) ($a_1$) & (T, N) ($a_2$) & (N, T) ($a_3$) & (N, N) ($a_4$)\\\\\n\\midrule [0.7pt]\n(TT, RR) ($s_1$) & $Q_{s_1a_1}$ & $Q_{s_1a_2}$ & $Q_{s_1a_3}$ & $Q_{s_1a_4}$\\\\\n\\rowcolor{gray!40} (TT, RB) ($s_2$) & $Q_{s_2a_1}$ & $Q_{s_2a_2}$ & $Q_{s_2a_3}$ & $Q_{s_2a_4}$\\\\\n(TT, BR) ($s_3$) & $Q_{s_3a_1}$ & $Q_{s_3a_2}$ & $Q_{s_3a_3}$ & $Q_{s_3a_4}$\\\\\n\\rowcolor{gray!40} (TT, BB) ($s_4$) & $Q_{s_4a_1}$ & $Q_{s_4a_2}$ & $Q_{s_4a_3}$ & $Q_{s_4a_4}$\\\\\n(TN, RR) ($s_5$) & $Q_{s_5a_1}$ & $Q_{s_5a_2}$ & $Q_{s_5a_3}$ & $Q_{s_5a_4}$\\\\\n\\rowcolor{gray!40} ... & ... & ... & ... & ...\\\\\n(NT, RR) ($s_9$) & $Q_{s_9a_1}$ & $Q_{s_9a_2}$ & $Q_{s_9a_3}$ & $Q_{s_9a_4}$\\\\\n\\rowcolor{gray!40} ... & ... & ... & ... & ...\\\\\n(NN, RR) ($s_{13}$) & $Q_{s_{13}a_1}$ & $Q_{s_{13}a_2}$ & $Q_{s_{13}a_3}$ & $Q_{s_{13}a_4}$\\\\\n\\rowcolor{gray!40} ... & ... & ... & ... & ...\\\\\n(NN, BB) ($s_{16}$) & $Q_{s_{16}a_1}$ & $Q_{s_{16}a_2}$ & $Q_{s_{16}a_3}$ & $Q_{s_{16}a_4}$\\\\\n\\bottomrule[1.2pt]\n\\end{tabular}\n\\caption{The Q-table for the trustor in the one-dimensional latticed population, where each player connects 2 nearest-neighbors.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Decoding trust: A reinforcement learning perspective", "authors": ["Guozhong Zheng", "Jiqiang Zhang", "Jing Zhang", "Weiran Cai", "Li Chen"], "url": "https://arxiv.org/abs/2309.14598v2", "attribution": "\"Decoding trust: A reinforcement learning perspective\" by Guozhong Zheng, Jiqiang Zhang, Jing Zhang, Weiran Cai, and Li Chen, arXiv:2309.14598v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2102.00093v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Spearman's correlation between learned parameters and computed normalized student delays. p$<$0.001\\text{***} p$<$0.01 \\text{**} p$<$0.05\\text{*}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n \\multicolumn{2}{|c|}{} & $\\alpha$ & $\\mu$ & delay \\\\ \\hline\n \\multirow{3}{*}{CANVAS}\n &$\\alpha$ & 1 & & \\\\\\cline{2-5}\n & $\\mu$ & 0.284\\text{***}& 1 & \\\\\\cline{2-5}\n & delay & 0.345\\text{***} & 0.144\\text{***}&1\\\\\\hline\n \\multirow{3}{*}{MORF}\n &$\\alpha$ & 1 & & \\\\\\cline{2-5}\n &$\\mu$ & 0.243\\text{***}& 1 & \\\\\\cline{2-5}\n &delay & 0.264\\text{***} & 0.412\\text{***}&1\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Relaxed Clustered Hawkes Process for Procrastination Modeling in MOOCs", "authors": ["Mengfan Yao", "Siqian Zhao", "Shaghayegh Sahebi", "Reza Feyzi Behnagh"], "url": "https://arxiv.org/abs/2102.00093v1", "attribution": "\"Relaxed Clustered Hawkes Process for Procrastination Modeling in MOOCs\" by Mengfan Yao, Siqian Zhao, Shaghayegh Sahebi, and Reza Feyzi Behnagh, arXiv:2102.00093v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.03312v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Heterogeneity in treatment effects}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc}\nVariable & Above median & Below median & Difference & $p$-value \\\\ \\hline\nMetaculus & 0.053*** & 0.077*** & 0.024* & 0.096 \\\\\n24 hour volume & 0.049*** & 0.081*** & 0.032** & 0.041 \\\\\nTotal volume & 0.069*** & 0.081*** & 0.012 & 0.191 \\\\\nComments & 0.069*** & 0.084*** & 0.015* & 0.071 \\\\\nTotal traders & 0.067*** & 0.083*** & 0.017* & 0.056 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "How manipulable are prediction markets?", "authors": ["Itzhak Rasooly", "Roberto Rozzi"], "url": "https://arxiv.org/abs/2503.03312v1", "attribution": "\"How manipulable are prediction markets?\" by Itzhak Rasooly and Roberto Rozzi, arXiv:2503.03312v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.08543v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The empirical power of the three tests at 5\\% level under case (). }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccccccccccccccc}\\hline \\hline & & & \\multicolumn{8}{c|}{Setting ()} & \\multicolumn{8}{c}{Setting ()} \\\\\\hline\n & \\multicolumn{2}{c|}{$p$} & \\multicolumn{4}{c|}{3} & \\multicolumn{4}{c|}{5} & \\multicolumn{4}{c|}{3} & \\multicolumn{4}{c}{5} \\\\\\hline\n & $N$ & $T$ & 200 & 300 & 400 & 500 & 200 & 300 & 400 & 500 & 200 & 300 & 400 & 500 & 200 & 300 & 400 & 500 \\\\\\hline\n & \\multicolumn{18}{c}{(1) Normal distribution} \\\\\\hline\n & 100 & $S_{N}$ & 79.3 & 89.2 & 95.5 & 98.5 & 76.3 & 88.6 & 95.4 & 98.5 & 73.4 & 84.7 & 92.0 & 96.6 & 70.3 & 83.8 & 91.9 & 95.8 \\\\\n & & $L_{N}$ & 40.9 & 83.3 & 98.7 & 100 & 35.6 & 82.9 & 98.9 & 100 & 23.5 & 59.2 & 90.4 & 99.7 & 22.1 & 63.7 & 89.4 & 99.0 \\\\\n & & $T_{C}$ & 87.5 & 98.3 & 100 & 100 & 84.6 & 98.5 & 99.9 & 100 & 77.5 & 93.8 & 99.0 & 99.9 & 74.6 & 94.9 & 99.6 & 100 \\\\ \\hline\n & 200 & $S_{N}$ & 77.2 & 90.3 & 95.1 & 98.1 & 78.1 & 89.4 & 95.3 & 97.9 & 70.9 & 85.4 & 91.1 & 96.4 & 71.8 & 84.8 & 92.3 & 95.7 \\\\\n & & $L_{N}$ & 27.9 & 77.7 & 98.2 & 100 & 25.2 & 77.0 & 97.3 & 100 & 12.7 & 53.0 & 88.7 & 98.8 & 12.6 & 52.9 & 86.5 & 99.2 \\\\\n & & $T_{C}$ & 82.8 & 98.8 & 99.9 & 100 & 81.6 & 98.0 & 100 & 100 & 70.3 & 94.3 & 99.1 & 99.8 & 70.8 & 94.5 & 99.2 & 100 \\\\\n \\hline\n & \\multicolumn{18}{c}{(2) $t_{6}$-distribution} \\\\ \\cline{2-19}\n & 100 & $S_{N}$ & 80.1 & 91.0 & 95.5 & 98.3 & 78.0 & 87.8 & 94.4 & 97.8 & 74.2 & 85.1 & 92.1 & 95.9 & 72.5 & 83.6 & 91.0 & 95.9 \\\\\n & & $L_{N}$ & 35.9 & 79.3 & 98.4 & 100 & 34.9 & 81.5 & 97.9 & 100 & 21.9 & 57.8 & 90.3 & 99.4 & 20.4 & 60.9 & 88.6 & 99.0 \\\\\n & & $T_{C}$ & 86.8 & 97.6 & 99.9 & 100 & 85.0 & 98.3 & 100 & 100 & 75.2 & 92.4 & 99.1 & 99.9 & 75.5 & 93.9 & 99.1 & 100 \\\\ \\hline\n & 200 & $S_{N}$ & 77.9 & 90.7 & 94.9 & 98.0 & 79.3 & 89.7 & 95.8 & 98.3 & 72.2 & 86.4 & 92.3 & 96.4 & 73.4 & 85.0 & 92.2 & 96.3 \\\\\n & & $L_{N}$ & 27.3 & 78.8 & 98.6 & 100 & 24.3 & 75.4 & 98.4 & 100 & 13.0 & 54.7 & 87.3 & 98.1 & 12.9 & 51.2 & 86.5 & 99.2 \\\\\n & & $T_{C}$ & 83.4 & 98.5 & 99.9 & 100 & 82.8 & 98.3 & 100 & 100 & 72.8 & 93.4 & 98.9 & 100 & 70.8 & 93.7 & 99.1 & 100 \\\\\n \\hline\n & \\multicolumn{18}{c}{(3) $\\chi_{5}^2$-distribution} \\\\ \\cline{2-19}\n & 100 & $S_{N}$ & 79.6 & 90.5 & 95.6 & 98.0 & 78.8 & 90.2 & 94.8 & 98.9 & 73.6 & 86.6 & 93.0 & 96.1 & 74.3 & 85.9 & 91.8 & 97.3 \\\\\n & & $L_{N}$ & 41.4 & 82.5 & 98.2 & 100 & 36.8 & 81.7 & 98.0 & 100 & 24.7 & 62.9 & 90.3 & 98.8 & 21.3 & 60.9 & 88.4 & 99.1 \\\\\n & & $T_{C}$ & 87.6 & 98.9 & 99.9 & 100 & 86.9 & 98.1 & 99.7 & 100 & 77.4 & 94.6 & 99.3 & 100 & 76.9 & 93.0 & 99.1 & 100 \\\\ \\hline\n & 200 & $S_{N}$ & 77.7 & 90.0 & 96.6 & 99.3 & 81.7 & 90.5 & 97.2 & 97.8 & 71.7 & 85.6 & 93.6 & 98.0 & 74.8 & 84.5 &94.4 & 96.1 \\\\\n & & $L_{N}$ & 31.4 & 78.6 & 98.5 & 100 & 33.8 & 79.1 & 98.4 & 100 & 16.5 & 54.1 & 90.2 & 99.3 & 17.4 & 54.4 &88.6 & 99.0 \\\\\n & & $T_{C}$ & 84.0 & 98.5 & 100 & 100 & 86.7 & 98.8 & 100 & 100 & 74.0 & 93.7 & 99.6 & 100 & 75.1 & 94.2 &99.9 & 100 \\\\\n \\hline\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fisher's combined probability test for cross-sectional independence in panel data models with serial correlation", "authors": ["Hongfei Wang", "Binghui Liu", "Long Feng", "Yanyuan Ma"], "url": "https://arxiv.org/abs/2309.08543v1", "attribution": "\"Fisher's combined probability test for cross-sectional independence in panel data models with serial correlation\" by Hongfei Wang, Binghui Liu, Long Feng, and Yanyuan Ma, arXiv:2309.08543v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.02900v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overlap detection results on AMI mix-headset data, in terms of Precision (\\%) and Recall (\\%).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Model (feature type)}} & \\multicolumn{2}{c}{\\textbf{Dev}} & \\multicolumn{2}{c}{\\textbf{Eval}} \\\\\n\\cmidrule(r{4pt}){2-3} \\cmidrule(l){4-5}\n & \\textbf{Precision} & \\textbf{Recall} & \\textbf{Precision} & \\textbf{Recall} \\\\\n\\midrule\nConvNet (Spectogram)~ & 80.5 & 50.2 & 75.8 & 44.6 \\\\\nE2E BLSTM (MFCC)~ & 90.0 & 52.5 & 91.9 & 48.4 \\\\\nE2E BLSTM (SincNet)~ & 90.0 & 63.8 & 86.8 & 65.8 \\\\\n\\midrule\nOur method (MFCC) & 83.9 & 68.5 & 86.4 & 65.2 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Multi-class Spectral Clustering with Overlaps for Speaker Diarization", "authors": ["Desh Raj", "Zili Huang", "Sanjeev Khudanpur"], "url": "https://arxiv.org/abs/2011.02900v1", "attribution": "\"Multi-class Spectral Clustering with Overlaps for Speaker Diarization\" by Desh Raj, Zili Huang, and Sanjeev Khudanpur, arXiv:2011.02900v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.11464v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\hline\n \\textbf{\\# Dimensions} & \\textbf{SG Level} & \\textbf{\\# of Points} & \\textbf{Avg. Error} & \\textbf{Max. error (99.9\\%)} \\\\\n \\hline\n 4 & 3 & 137 & -3.62 & -2.61 \\\\\n 4 & 5 & 1105 & -4.21 & -3.10 \\\\\n 4 & 7 & 7,537 & -4.64 & -3.31 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Average (Avg.) and maximum (Max.) Euler equation residuals (i.e., errors) for the $4$-dimensional IRBC model across increasing SG refinement levels (SG Level), with corresponding grid point counts. All errors are reported in $\\log_{10}$ scale.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Scalable Global Solution Techniques for High-Dimensional Models in Dynare", "authors": ["Aryan Eftekhari", "Michel Juillard", "Normann Rion", "Simon Scheidegger"], "url": "https://arxiv.org/abs/2503.11464v1", "attribution": "\"Scalable Global Solution Techniques for High-Dimensional Models in Dynare\" by Aryan Eftekhari, Michel Juillard, Normann Rion, and Simon Scheidegger, arXiv:2503.11464v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.06010v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\nStock/bond & Years & $P$ & $T$ & Average Wealth & 90\\% Wealth \\\\\n\\hline\n90/10 & 20 & 10.15\\% & 15.54 & 4.8 & 11.5 \\\\\n\\hline \n90/10 & 40 & 30.66\\% & 24.76 & 48.7 & 122.6 \\\\\n\\hline\n60/40 & 20 & 4.35\\% & 17.12 & 2.8 & 5.8 \\\\\n\\hline\n60/40 & 40 & 28.2\\% & 28.46 & 14.4 & 37.8 \\\\\n\\hline\n40/60 & 20 & 1.51\\% & 18.16 & 2.0 & 3.6\\\\\n\\hline\n40/60 & 40 & 32.3\\% & 31.1 & 5.2 & 14.0\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Ruin probability $P$, ruin average time $T$, average final wealth, and final wealth corresponding to the 90\\%-path.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Time Series Model for Three Asset Classes used in Financial Simulator", "authors": ["Andrey Sarantsev", "Angel Piotrowski", "Ian Anderson"], "url": "https://arxiv.org/abs/2508.06010v1", "attribution": "\"A Time Series Model for Three Asset Classes used in Financial Simulator\" by Andrey Sarantsev, Angel Piotrowski, and Ian Anderson, arXiv:2508.06010v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11316v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\emph{Ablation analysis.} Mean and standard deviation of accuracy (\\%) on CLEVR and CLOSURE across three repetitions. We add imitations of properties of TMN, i.e., Variable number of layers (VL) and Split tokens (ST) to the standard Transformer w/ PR. VL is an imitation of the question-specific composition of modules in TMN and changes the number of layers depending on a program length (up to $26$), but the each layer (module in TMN) is not specialized for specific sub-tasks. SoM stands for the specialization of the modules.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c|c|c}\n\\toprule\n\\multirow{2}{*}{Methods} &\\multicolumn{3}{c|}{Properties} & CLEVR & CLOSURE \\\\ %\\cmidrule{1-3}\n & VL & ST & SoM & (In-Distribution) & (Syst. Generalization) \\\\ \\midrule\nTransformer w/ PR & & & & 97.1 $\\pm$ 0.14 & 64.5 $\\pm$ 2.5 \\\\\nTransformer w/ PR + VL & $\\checkmark$ & & &94.0 $\\pm$ 1.8 & 60.2 $\\pm$ 2.9 \\\\ \nTransformer w/ PR + VL + ST& $\\checkmark$ & $\\checkmark$ & & 56.8 $\\pm$ 0.042 & 43.6 $\\pm$ 0.59 \\\\\nTMN-Stack & $\\checkmark$ & $\\checkmark$ & $\\checkmark$ & 98.0 $\\pm$ 0.030 & \\textbf{90.9 $\\pm$ 0.49} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Transformer Module Networks for Systematic Generalization in Visual Question Answering", "authors": ["Moyuru Yamada", "Vanessa D'Amario", "Kentaro Takemoto", "Xavier Boix", "Tomotake Sasaki"], "url": "https://arxiv.org/abs/2201.11316v2", "attribution": "\"Transformer Module Networks for Systematic Generalization in Visual Question Answering\" by Moyuru Yamada, Vanessa D'Amario, Kentaro Takemoto, Xavier Boix, and Tomotake Sasaki, arXiv:2201.11316v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19336v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|cccc} \n \\toprule\n Method& T\\_1& T\\_2& T\\_3& T\\_4\\\\\n \\midrule\n CoW& 0.39& 0.22& 0.08& 0.03\\\\ \n CLIP Map& 0.33& 0.19& 0.06& 0.0\\\\ \n VLMap& 0.56& 0.39& 0.19& 0.08\\\\ \n \\textbf{IVLMap (Ours)}& \\textbf{0.64}& \\textbf{0.44}& \\textbf{0.25}& \\textbf{0.14}\\\\ \n \\bottomrule\n \\end{tabular}\n\\caption{Outcome of Zero-Shot Instance Level Object Goal Navigation from Natural Language. T\\_k for achieving the kth subgoal out of the total 4 subgoals in each task. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "IVLMap: Instance-Aware Visual Language Grounding for Consumer Robot Navigation", "authors": ["Jiacui Huang", "Hongtao Zhang", "Mingbo Zhao", "Zhou Wu"], "url": "https://arxiv.org/abs/2403.19336v1", "attribution": "\"IVLMap: Instance-Aware Visual Language Grounding for Consumer Robot Navigation\" by Jiacui Huang, Hongtao Zhang, Mingbo Zhao, and Zhou Wu, arXiv:2403.19336v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.11592v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrr}\n\\hline\n\\textbf{Obs} & \\textbf{SITEID} & \\textbf{one} \\\\ \\hline\n\\textbf{1} & 1008 & 1 \\\\\n\\textbf{2} & 1009 & 1 \\\\\n\\textbf{3} & 1010 & 1 \\\\\n\\textbf{4} & 1012 & 1 \\\\\n\\textbf{5} & 1015 & 1 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "GEECORR: A SAS macro for regression models of correlated binary responses and within-cluster correlation using generalized estimating equations", "authors": ["Tracie L. Shing", "John S. Preisser", "Richard C. Zink"], "url": "https://arxiv.org/abs/2011.11592v1", "attribution": "\"GEECORR: A SAS macro for regression models of correlated binary responses and within-cluster correlation using generalized estimating equations\" by Tracie L. Shing, John S. Preisser, and Richard C. Zink, arXiv:2011.11592v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2311.16004v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Frequency of Special Characters}\n\\begin{tabular}{ccl}\n \\toprule\n Non-English or Math&Frequency&Comments\\\\\n \\midrule\n \\O & 1 in 1,000& For Swedish names\\\\\n $\\pi$ & 1 in 5& Common in math\\\\\n \\$ & 4 in 5 & Used in business\\\\\n $\\Psi^2_1$ & 1 in 40,000& Unexplained usage\\\\\n \\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe", "authors": ["Szymon Kubiak", "Tillman Weyde", "Oleksandr Galkin", "Dan Philps", "Ram Gopal"], "url": "https://arxiv.org/abs/2311.16004v1", "attribution": "\"Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe\" by Szymon Kubiak, Tillman Weyde, Oleksandr Galkin, Dan Philps, and Ram Gopal, arXiv:2311.16004v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09391v2_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n & Success \\% & Distance \\% \\\\\n \\hline\n Without Initialisation & 38.8 & 73.2 \\\\\n Without Extended Reward & 12.3 & 58.3 \\\\\n Full Method & \\textbf{82.2} & \\textbf{96.1}\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Ablation study for initialising setup policies from a default walk policy, and receiving an extended reward, for switching from a walking policy to a jump policy on a single terrain sample.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Learning Setup Policies: Reliable Transition Between Locomotion Behaviours", "authors": ["Brendan Tidd", "Nicolas Hudson", "Akansel Cosgun", "Jurgen Leitner"], "url": "https://arxiv.org/abs/2101.09391v2", "attribution": "\"Learning Setup Policies: Reliable Transition Between Locomotion Behaviours\" by Brendan Tidd, Nicolas Hudson, Akansel Cosgun, and Jurgen Leitner, arXiv:2101.09391v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10127v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\hline\n $n=7500$ & Predicted: Default & Predicted: Not default \\\\ \\hline\nActual: Default & 500 & 1095 \\\\ \\hline\nActual: Not default & 234 & 5671 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Confusion matrix of the neural network for the Taiwan credit score dataset. }\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Interpretable Selective Learning in Credit Risk", "authors": ["Dangxing Chen", "Weicheng Ye", "Jiahui Ye"], "url": "https://arxiv.org/abs/2209.10127v1", "attribution": "\"Interpretable Selective Learning in Credit Risk\" by Dangxing Chen, Weicheng Ye, and Jiahui Ye, arXiv:2209.10127v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09709v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of the ASVspoof19 dataset (logical access) for training spoofing countermeasure systems, with the number of speakers by gender.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\textbf{Partition}& \\textbf{Male} & \\textbf{Female} & \\textbf{Bonafide} & \\textbf{Spoof} \\\\ \\midrule\n Train & 8 & 12 & 2,580 & 22,800 \\\\\n Development & 8 & 12 & 2,548 & 22,296 \\\\\n Evaluation & 30 & 37 & 7,355 & 63,882 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Optimizing Tandem Speaker Verification and Anti-Spoofing Systems", "authors": ["Anssi Kanervisto", "Ville Hautamäki", "Tomi Kinnunen", "Junichi Yamagishi"], "url": "https://arxiv.org/abs/2201.09709v1", "attribution": "\"Optimizing Tandem Speaker Verification and Anti-Spoofing Systems\" by Anssi Kanervisto, Ville Hautamäki, Tomi Kinnunen, and Junichi Yamagishi, arXiv:2201.09709v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.15568v1_tex_table25.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Subjects' Self- and Group Assessments Before and After the Test \\& Questionnaire Control Summary Statistics, by Treatment}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccc}\n \\hline\n \\hline\n & & \\multicolumn{2}{c}{Replication} & & \\multicolumn{2}{c}{Adaptation} & & \\multicolumn{2}{c}{Mann-Whitney U}\\\\\\cline{3-4} \\cline{6-7} \\cline{9-10}\n Variable & & Mean & Std. Dev. & & Mean & Std. Dev. & & z & p-value\\\\\n \\hline\n \\textbf{Before} & & & & & & & & \\\\\n Predicted own performance & & 13.54 & 3.20 & & 14.28 & 3.60 & & 1.77 & 0.0766\\\\\n Predicted group performance & & 12.27 & 2.40 & & 13.47 & 2.27 & & 3.18 & 0.0014 \\\\\n Better & & 1.118 & 0.25 & & 1.079 & 0.28 & & -0.32 & 0.7530 \\\\\n \\hline\n \\textbf{Before} & & & & & & & & \\\\\n Sample performance & & 2.36 & 0.85 & & 2.41 & 0.74 & & 0.18 & 0.8593\\\\\n Test performance & & 13.6 & 3.1 & & 13.1 & 3.3 & & -0.83 & 0.4074 \\\\\n \\textbf{After} & & & & & & & & & \\\\\n Estimated group performance & & 12.6 & 2.6 & & 12.6 & 2.7 & & -0.13 & 0.8988\\\\\n Estimation accuracy & & -0.1 & 3.5 & & 1.2 & 4.3 & & 1.93 & 0.0530\\\\\n \\hline\n \\textbf{Questionnaire Controls} & & & & & & & & \\\\\n INCOM & & 3.56 & 0.66 & & 3.54 & 0.69 & & 0.22 & 0.8297 \\\\\n GSE & & 3.84 & 0.77 & & 3.91 & 0.57 & & 0.03 & 0.9810\\\\\n Altruism & & 3.77 & 0.72 & & 3.84 & 0.70 & & -0.79 & 0.4308\\\\\n Risk (MPL) & & 15.35 & 6.43 & & 15.39 & 5.63 & & -0.22 & 0.8269 \\\\\n Risk (11-point) & & 6.13 & 2.47 & & 5.58 & 2.33 & & 1.489 & 0.1368\\\\\n Ambiguity & & 12.58 & 5.86 & & 12.16 & 5.43 & & 0.416 & 0.6788\\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)", "authors": ["Marius Protte"], "url": "https://arxiv.org/abs/2507.15568v1", "attribution": "\"Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)\" by Marius Protte, arXiv:2507.15568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.09099v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of ratings, summed scores, and estimates of rater parameters for different topics with generalized multi-facets model}\n\\begin{tabular}{llccrrr}\n\\hline\nRater&Topic& Number of ratings &Summed scores&$\\hat{\\rho}$&$\\hat{\\eta}$&$\\hat{\\bar{\\kappa}}$\\\\\n\\hline\n\\multirow{4}{*}{AM}&Family& 440&338 &1.00&\t-2.24&\t0.76\\\\\n&School\t&525&392 &0.50&\t-1.57&\t0.54\\\\\n&Sport\t&370&286 &0.48&\t-1.77&\t0.48\\\\\n&Work\t&455&335 &0.52&\t-1.78&\t0.51\\\\\n\\cline{1-7}\n\\multirow{4}{*}{BE}&Family\t&435 &273 &0.71&\t-0.88&\t0.82\\\\\n&School\t&470 &298 &0.61&\t-1.09&\t0.72\\\\\n&Sport\t&535&320 &0.71&\t-1.17&\t0.78\\\\\n&Work\t&380&233 & 0.54&\t-1.01&\t0.68\\\\\n\\cline{1-7}\n\\multirow{4}{*}{CO}&Family\t&450&195 &0.65&\t0.85&\t0.78\\\\\n&School\t&535&220 &0.88&\t0.99&\t0.90\\\\\n&Sport\t&485&178 &0.76&\t1.52&\t0.75\\\\\n&Work\t&440&183 &0.63&\t0.85&\t0.77\\\\\n\\cline{1-7}\n\\multirow{4}{*}{DA}&Family\t&365&103 &0.79&\t2.04&\t0.67\\\\\n&School\t&520&155 &0.66&\t1.83&\t0.62\\\\\n&Sport\t&460&142 &0.55&\t1.67&\t0.56\\\\\n&Work\t&395&106 &0.82&\t1.76&\t0.74\\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Differential Index Measuring Rater's Capability in Educational Assessment", "authors": ["Y. -G. Wang", "J. Wu", "X. Qiu"], "url": "https://arxiv.org/abs/2502.09099v1", "attribution": "\"A Differential Index Measuring Rater's Capability in Educational Assessment\" by Y. -G. Wang, J. Wu, and X. Qiu, arXiv:2502.09099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2203.12460v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|}\n\\hline\n & (1) & (2) & (3) & (4) & (5) \\\\ \\hline\nVariables & Model 1 & Model 2 & Model 3 & Model 4 & Model 5 \\\\ \\hline\n & & & & & \\\\ \\hline\npositive & 0.17*** & 0.17*** & 0.17*** & 0.14*** & 0.13* \\\\ \\hline\n & (0.04) & (0.04) & (0.04) & (0.04) & (0.06) \\\\ \\hline\nnegative & 0.06 & 0.07 & -0.00 & 0.02 & 0.28 \\\\ \\hline\n & (0.20) & (0.20) & (0.20) & (0.21) & (0.30) \\\\ \\hline\nanxiety & -0.10 & -0.11 & -0.35 & -0.42 & -0.85 \\\\ \\hline\n & (0.36) & (0.36) & (0.38) & (0.38) & (0.54) \\\\ \\hline\nanger & -0.01 & -0.00 & 0.28 & 0.25 & 0.40 \\\\ \\hline\n & (0.38) & (0.38) & (0.39) & (0.39) & (0.60) \\\\ \\hline\nsad & -0.36 & -0.39 & -0.33 & -0.38 & -0.60 \\\\ \\hline\n & (0.30) & (0.30) & (0.32) & (0.32) & (0.47) \\\\ \\hline\ncertain & 0.15 & 0.16 & 0.15 & 0.14 & 0.19 \\\\ \\hline\n & (0.12) & (0.12) & (0.12) & (0.12) & (0.17) \\\\ \\hline\n & & & & & \\\\ \\hline\nMAR(1 m) & & YES & YES & YES & YES \\\\ \\hline\nIndustry & & & YES & YES & YES \\\\ \\hline\nYear & & & & YES & YES \\\\ \\hline\nMAR(5 d) & & & & & YES \\\\ \\hline\nN & 7,047 & 7,047 & 7,047 & 7,047 & 3,475 \\\\ \\hline\nBIC & 9792 & 9826 & 9871 & 9922 & 5005 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Logistic Regression of Value Based Label Function ($y_I(T^c_d)(4)$) on Analysts' Recommendations within 1 month ($MAR(1\\,m)$) and Sentiment of Earnings Calls Transcripts. Standard errors in parentheses. Significance levels marked with stars: *** p<0.001, ** p<0.01, * p<0.05}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An Exploratory Study of Stock Price Movements from Earnings Calls", "authors": ["Sourav Medya", "Mohammad Rasoolinejad", "Yang Yang", "Brian Uzzi"], "url": "https://arxiv.org/abs/2203.12460v1", "attribution": "\"An Exploratory Study of Stock Price Movements from Earnings Calls\" by Sourav Medya, Mohammad Rasoolinejad, Yang Yang, and Brian Uzzi, arXiv:2203.12460v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00003v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\n\\hline\n\\textbf{Transaction} & \\textbf{Gas usage} & \\textbf{Cost (USD)} & \\textbf{Impact (kgCO\\textsubscript{2}eq)} \\\\ \\hline\nMinting & 450k & 88.03 & 114.90 \\\\ \\hline\nBuying & 300k & 58.69 & 76.60 \\\\ \\hline\nTransferring & 80k & 15.65 & 20.42 \\\\ \\hline\nBidding & 100k & 19.56 & 25.54 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Impact of different transactions of a hypothetical cryptoart piece. We use the average gas price from April 29, 2021 to May 5, 2021 of 61 Gwei , as well as the average Ether price of 3207 USD in the same period .}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Green NFTs: A Study on the Environmental Impact of Cryptoart Technologies", "authors": ["Samuele Marro", "Luca Donno"], "url": "https://arxiv.org/abs/2202.00003v3", "attribution": "\"Green NFTs: A Study on the Environmental Impact of Cryptoart Technologies\" by Samuele Marro and Luca Donno, arXiv:2202.00003v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.09760v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|}\n \\toprule\n \\# OTM opt. & JUN20 & SEP20 & DEC20 & MAR21 & JUN21 & SEP21 & DEC21 & JUN22 & DEC22 \\\\ \n \\bottomrule\n \\toprule\n Mean & 92 & 116 & 105 & 58 & 49 & 13 & 49 & 10 & 24 \\\\\n Median & 92 & 120 & 119 & 51 & 46 & 13 & 54 & 9 & 26 \\\\\n Std & 9 & 7 & 27 & 25 & 7 & 3 & 9 & 1 & 6 \\\\\n $q_{0.05}$ & 76 & 101 & 30 & 26 & 42 & 7 & 26 & 8 & 7 \\\\\n $q_{0.95}$ & 103 & 120 & 120 & 94 & 63 & 17 & 56 & 11 & 28 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\small{Number of OTM options considered in the Volatility surface calibration. We report the number of OTM call ($K>F_0$) and put ($K<F_0$) in the moneyness range $[-30\\,\\$,30\\,\\$]$: mean, median, standard deviation (std), and quantiles (q) 5\\%, 95\\% considering each value date.}}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Additive Bachelier model with an application to the oil option market in the Covid period", "authors": ["Roberto Baviera", "Michele Domenico Massaria"], "url": "https://arxiv.org/abs/2506.09760v1", "attribution": "\"The Additive Bachelier model with an application to the oil option market in the Covid period\" by Roberto Baviera and Michele Domenico Massaria, arXiv:2506.09760v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.03457v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\toprule\n & \\multicolumn{4}{c}{\\textbf{Dependent Variable: $D_{it}$}} \\\\\n \\cmidrule(lr){2-6}\n & (1) & (2) & (3) & (4) & (5)\\\\\n \\midrule\n $\\mathbf{\\log(B_{it})}$ & 0.701$^{***}$ & 0.273$^{***}$ & 0.616$^{***}$ & 0.074 & 0.137\\\\\n & (0.083) & (0.096) & (0.084) & (0.101) & (0.102) \\\\\n \n \\textbf{Infant} & & 0.548$^{***}$ & & 0.616$^{***}$ & \\\\\n & & (0.045) & & (0.047) & \\\\\n \\textbf{LeftBF} & & & & & 0.057$^{***}$ \\\\ \n & & & & & (0.008) \\\\ \n \n \\textbf{After 2007} & & & 0.257$^{***}$ & 0.375$^{***}$ & \\\\\n & & & (0.051) & (0.052) & \\\\\n $\\mathbf{R_{it}}$ & $-$1.303$^{***}$ & $-$1.052$^{***}$ & $-$1.300$^{***}$ & $-$1.019$^{***}$ & $-$1.334$^{***}$\\\\\n & (0.160) & (0.161) & (0.160) & (0.161) & (0.163)\\\\\n \\textbf{$\\mathbf{R_{it}} \\times LA_{it}$} & 0.027$^{**}$ & 0.028$^{**}$ & 0.025$^{**}$ & 0.026$^{**}$ & 0.032$^{**}$\\\\\n & (0.012) & (0.012) & (0.012) & (0.012) & (0.012)\\\\\n \n \\textbf{Education} & 0.144$^{***}$ & 0.161$^{***}$ & 0.110$^{***}$ & 0.112$^{***}$ & 0.167$^{***}$ \\\\\n & (0.039) & (0.040) & (0.040) & (0.040) & (0.041) \\\\\n \n \\textbf{Constant} & 0.042 & 1.697$^{***}$ & 0.426 & 2.570$^{***}$ & 2.776$^{***}$ \\\\\n & (0.394) & (0.445) & (0.402) & (0.469) & (0.484) \\\\\n \\midrule\n \\textbf{Year fixed effect} & & & & & \\checkmark \\\\\n \\textbf{Observations} & 78,815 & 78,815 & 78,815 & 78,815 & 78,815 \\\\\n \\textbf{Log Likelihood} & $-$10,087.020 & $-$10,008.260 & $-$10,073.820 & $-$9,980.911 & $-$9,976.421 \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Logistic regression results for Approach I}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Attention vs Choice in Incomplete Welfare Take-Up: What Works for WIC?", "authors": ["Lei Bill Wang", "Sooa Ahn"], "url": "https://arxiv.org/abs/2506.03457v2", "attribution": "\"Attention vs Choice in Incomplete Welfare Take-Up: What Works for WIC?\" by Lei Bill Wang and Sooa Ahn, arXiv:2506.03457v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.02662v1_tex_table26.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|cccc|}\n\\hline\n& Bias & RMSE & $\\hat{\\alpha }$ & $\\hat{\\beta }$ \\\\ \\hline\nMLE & 0.69407 & 0.86490 & 1.00005 & 10.13583 \\\\\n$DPD_{0.1}$ & 0.70174 & 0.87637 & 1.00001 & 10.13524 \\\\\n$DPD_{0.2}$ & 0.72326 & 0.90545 & 0.99996 & 10.14356 \\\\\n$DPD_{0.3}$ & 0.75158 & 0.94370 & 0.99992 & 10.15692 \\\\\n$DPD_{0.4}$ & 0.78316 & 0.98577 & 0.99987 & 10.17306 \\\\\n$DPD_{0.5}$ & 0.81507 & 1.02828 & 0.99982 & 10.19055 \\\\\n$DPD_{0.6}$ & 0.84533 & 1.06918 & 0.99977 & 10.20839 \\\\\n$DPD_{0.7}$ & 0.87336 & 1.10731 & 0.99973 & 10.22596 \\\\\n$DPD_{0.8}$ & 0.89879 & 1.14210 & 0.99969 & 10.24285 \\\\\n$DPD_{0.9}$ & 0.92162 & 1.17335 & 0.99965 & 10.25886 \\\\\n$DPD_{1.0}$ & 0.94200 & 1.20116 & 0.99961 & 10.27390 \\\\\nRM & 0.76712 & 0.95667 & 0.99990 & 10.03177 \\\\\nSM & 3.81978 & 3.88194 & 1.00186 & 6.19626 \\\\\nHL & 4.15390 & 4.17153 & 1.00008 & 5.86007 \\\\ \\hline\n\\end{tabular}\n\\caption{Results for $n=100$ and $\\protect\\beta = 10.$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust parameter estimation of the log-logistic distribution based on density power divergence estimators", "authors": ["A. Felipe", "M. Jaenada", "P. Miranda", "L. Pardo"], "url": "https://arxiv.org/abs/2312.02662v1", "attribution": "\"Robust parameter estimation of the log-logistic distribution based on density power divergence estimators\" by A. Felipe, M. Jaenada, P. Miranda, and L. Pardo, arXiv:2312.02662v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.21174v1_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|lll|lllll}\n&\\multicolumn{3}{c}{New Scheme} &\\multicolumn{5}{c}{ILUTP($\\tau$)} \\\\\n\\# & $\\tau_{large}$ & $\\tau_{mix}$ & $\\tau_{small}$ & $10^{-5}$ & $10^{-4}$ & $10^{-3}$ &$10^{-2}$ & $10^{-1}$ \\\\ \\hline\n1 & 3 & 3 & 2& 3 & 5 &$\\dagger$ &$\\dagger$ &$\\dagger$ \\\\\n2 & 10 & 9 & 2 & 4 & 9 &$\\dagger$ &$\\dagger$ &$\\dagger$ \\\\\n3 & 2 & 2 & 1 & 1 & 2 & 2 & 3 & 4 \\\\\n4 & 7 & 2 & 1& 1 & 2 & 2 & 3 & 4 \\\\\n5 & 34 & 5 & 4 & 1 & 2 & 2 & 3 &4 \\\\\n6 & 27 & 11 & 11 & 1 & 2 & 2 & 3 & 4 \\\\\n7 & 4 & 17 & 1 & 2 & 2 & 4 & 10 &$\\dagger$ \\\\\n8 & $\\ddagger$ & 53 & 5 & $\\dagger$ & $\\dagger$ & $\\dagger$ & $\\dagger$ &$\\dagger$ \\\\\n9 & 9 & 2 & 2 & 1 & 1 & $\\dagger$ & $\\dagger$ &$\\dagger$ \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Number of outer fGMRES/GMRES iterations for the general case}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust iterative methods for linear systems with saddle point structure", "authors": ["Murat Manguoğlu", "Volker Mehrmann"], "url": "https://arxiv.org/abs/2502.21174v1", "attribution": "\"Robust iterative methods for linear systems with saddle point structure\" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.14735v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{patient disposition by year.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccc}\n\\hline\nPatient has been discharged to & 2012 & 2013 & 2014 & 2015 & 2016 & 2017 & 2018 & 2019 & 2020 & 2021 & 2022 \\\\ \\hline\nHome or self care & 232,329 & 227,582 & 227,656 & 222,909 & 220,978 & 215,144 & 210,560 & 209,579 & 199,355 & 198,342 & 201,856 \\\\\nHome with home health services & 9,728 & 9,685 & 9,158 & 8,486 & 8,204 & 8,284 & 7,112 & 5,911 & 4,628 & 4,274 & 3,185 \\\\\nShort-term hospital & 2,314 & 2,091 & 1,968 & 2,012 & 1,932 & 2,085 & 1,729 & 1,837 & 1,714 & 1,707 & 1,713 \\\\\nExpired (dead) & 807 & 807 & 711 & 696 & 670 & 668 & 442 & 471 & 396 & 420 & 413 \\\\\nCancer center or children's hospital & 172 & 196 & 192 & 171 & 187 & 144 & 124 & 152 & 200 & 265 & 275 \\\\\nAnother type not listed & 58 & 98 & 124 & 104 & 66 & 95 & 98 & 45 & 117 & 139 & 73 \\\\\nLeft against medical advice & 69 & 125 & 44 & 53 & 40 & 54 & 40 & 63 & 47 & 38 & 37 \\\\\nCourt/law enforcement & 21 & 23 & 34 & 26 & 37 & 49 & 44 & 44 & 34 & 30 & 27 \\\\\nSkilled nursing home & 35 & 51 & 44 & 33 & 33 & 38 & 25 & 17 & 21 & 15 & 22 \\\\\nFacility with custodial/supportive care & 29 & 27 & 23 & 18 & 16 & 27 & 24 & 37 & 39 & 36 & 26 \\\\\nInpatient rehabilitation facility & 13 & 14 & 16 & 26 & 25 & 51 & 31 & 22 & 30 & 18 & 28 \\\\\nHospice - home & 16 & 18 & 20 & 24 & 25 & 26 & 23 & 29 & 17 & 23 & 24 \\\\\nCritical access hospital & 6 & 9 & 14 & 19 & 14 & 14 & 15 & 16 & 21 & 24 & 25 \\\\\nMedicare certified long term care hospital & 5 & 2 & 6 & 10 & 5 & 15 & 4 & 7 & 6 & 4 & 3 \\\\\nHospice - medical facility & 4 & 11 & 6 & 4 & 4 & 6 & 4 & 1 & 8 & 3 & 4 \\\\\nFederal health care facility & 2 & 1 & 2 & 2 & 2 & 5 & 6 & 5 & 0 & 1 & 13 \\\\\nPsychiatric hospital or unit of hospital & 0 & 1 & 6 & 1 & 2 & 7 & 0 & 0 & 2 & 1 & 2 \\\\\nMedicaid certified nursing facility & 0 & 0 & 0 & 0 & 0 & 3 & 0 & 1 & 4 & 2 & 0 \\\\\nHospota-based Medicare-approved swing bed & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Birthweight Declined During the Pandemic and It Is Falling Further Post-pandemic", "authors": ["Maysam Rabbani", "Elijah Gervais"], "url": "https://arxiv.org/abs/2505.14735v1", "attribution": "\"Birthweight Declined During the Pandemic and It Is Falling Further Post-pandemic\" by Maysam Rabbani and Elijah Gervais, arXiv:2505.14735v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00143v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||cc||} \n \\hline\n Embedding Dimensions & 512 \\\\\n Crysformer MLP Dimensions & 512 \\\\\n Attention Head Count & 8 \\\\\n Patch Size & 4 \\\\\n Attention Block Depth & 6 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Default hyperparameter values for tuning on dipeptide dataset.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runs", "authors": ["Tom Pan", "Evan Dramko", "Mitchell D. Miller", "George N. Phillips", "Anastasios Kyrillidis"], "url": "https://arxiv.org/abs/2503.00143v1", "attribution": "\"RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runs\" by Tom Pan, Evan Dramko, Mitchell D. Miller, George N. Phillips, and Anastasios Kyrillidis, arXiv:2503.00143v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.07341v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation exercises} \\medskip\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\t\t\t\t\\hline \\hline \n\t\t\t\tParameter & Exercise 1 & Exercise 2 & Exercise 3 ($p_4=0.4$, $T=50$)\\\\\n\t\t\t\t & $T(W_B=W_0)$ & $p_3(W_D=W_0)$ & $p_3(W_E=W_0)$ \\\\ \n\t\t\t\t \\hline\n\t\t\t\t Baseline & 98.11 & 58.30\\% & 30.09\\% \\\\\n\t\t\t\t $\\rho=0.03$ & 47.23 & 37.33\\% & 27.13\\% \\\\\n\t\t\t\t $\\rho=0.05$ & 33.72 & 27.38\\% & 23.10\\% \\\\\n\t\t\t\t $g^{AI}=0.4$ & 79.31 & 67.25\\% & 51.74\\% \\\\\n\t\t\t\t $g^{AI}=0.1$ & 150.31 & 36.05\\% & 9.26\\% \\\\\n\t\t\t\t\\hline \\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Economics of p(doom): Scenarios of Existential Risk and Economic Growth in the Age of Transformative AI", "authors": ["Jakub Growiec", "Klaus Prettner"], "url": "https://arxiv.org/abs/2503.07341v1", "attribution": "\"The Economics of p(doom): Scenarios of Existential Risk and Economic Growth in the Age of Transformative AI\" by Jakub Growiec and Klaus Prettner, arXiv:2503.07341v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.16522v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n Functional Response & Formulation & $a$ & $b$ \\\\\n \\hline\n Holling & $\\displaystyle g_h(y) = \\frac{a_h}{1 + b_h y}$ & 0.385 & 0.265 \\\\ & & &\\\\\n Ivlev & $\\displaystyle g_i(y) = \\frac{a_i}{b_i(1 - \\exp(-b_i y))}$ & 0.21 & 0.15\\\\ & & & \\\\\n Trigonometric & $\\displaystyle g_t(y) = \\frac{a_t}{b_t}\\tanh(b_t y)$ & 0.27 & 0.18 \\\\ & & & \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{ Estimated parameters for different functional form}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Modeling Cost-Associated Cooperation: A Dilemma of Species Interaction Unveiling New Aspects of Fear Effect", "authors": ["Suvranil Chowdhury", "Susmita Sarkar", "Joydev Chattopadhyay"], "url": "https://arxiv.org/abs/2501.16522v1", "attribution": "\"Modeling Cost-Associated Cooperation: A Dilemma of Species Interaction Unveiling New Aspects of Fear Effect\" by Suvranil Chowdhury, Susmita Sarkar, and Joydev Chattopadhyay, arXiv:2501.16522v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.00459v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Inference gaps on trees in the 95\\% credible set of DS1. The \\textsc{All} columns refer to the average gaps over all trees in the credible set.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccc}\n\\toprule\n\\multirow{2}{*}{Gap} & \\multicolumn{2}{c}{PSP} & \\multicolumn{2}{c}{Planar (16)} & \\multicolumn{2}{c}{Planar (32)} & \\multicolumn{2}{c}{RealNVP (5)} & \\multicolumn{2}{c}{RealNVP (10)}\\\\\n\\cmidrule(lr){2-3}\\cmidrule(lr){4-5}\\cmidrule(lr){6-7}\\cmidrule(lr){8-9}\\cmidrule(lr){10-11}\n& Tree 36 & All & Tree 36 & All & Tree 36 & All& Tree 36 & All & Tree 36 & All \\\\\n\\midrule\nApproximation & 1.29 & 1.21 & 1.12 & 1.08 & 1.07 & 1.02 & 0.65 & 0.62 & {\\bfseries 0.43} & {\\bfseries 0.40} \\\\ \nAmortization & 3.37 & 0.84 & 2.80 & 0.82 & {\\bfseries 1.33} & {\\bfseries 0.72} & 3.10 & 0.98 & 1.83 & 0.93 \\\\\nInference & 4.66 & 2.05 & 3.92 & 1.90 & 2.40 & 1.74 & 3.75 & 1.60 & {\\bfseries 2.26} & {\\bfseries 1.33} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows", "authors": ["Cheng Zhang"], "url": "https://arxiv.org/abs/2012.00459v1", "attribution": "\"Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows\" by Cheng Zhang, arXiv:2012.00459v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.15103v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Two-Way Fixed Effects Estimate - Offline Elasticity}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\tabularnewline \\midrule \\midrule\n Dependent Variable: & \\multicolumn{4}{c}{Offline Price Elasticity}\\\\\n & Dry Dog Food & Dry Cat Food & Dog Hygiene & Cat Hygiene \\\\ \n Model: & (1) & (2) & (3) & (4)\\\\ \n \\midrule\n \\emph{Variables}\\\\\n PostOnlineAdoption & -0.2145$^{**}$ & -0.0206$^{***}$ & -1.544$^{***}$ & -0.6368$^{***}$\\\\ \n & (0.0697) & (0.0038) & (0.3308) & (0.1605)\\\\ \n \\midrule\n \\emph{Fixed-effects}\\\\\n Customer & Yes & Yes & Yes & Yes\\\\ \n YearMonth & Yes & Yes & Yes & Yes\\\\ \n \\midrule\n \\emph{Fit statistics}\\\\\n Observations & 136,411 & 45,443 & 78,253 & 55,410\\\\ \n R$^2$ & 0.91126 & 0.95578 & 0.76395 & 0.82317\\\\ \n Within R$^2$ & 0.24557 & 0.01260 & 0.32091 & 0.32582\\\\ \n \\midrule \\midrule\n \\multicolumn{5}{l}{\\emph{Bootstrapped standard-errors in parentheses via 500 replications}}\\\\\n \\multicolumn{5}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05}}\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "How does online shopping affect offline price sensitivity?", "authors": ["Shirsho Biswas", "Hema Yoganarasimhan", "Haonan Zhang"], "url": "https://arxiv.org/abs/2506.15103v1", "attribution": "\"How does online shopping affect offline price sensitivity?\" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.11443v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\\hline\n\\textbf{Metric} \n& \\multicolumn{2}{c}{\\textbf{Telecommunications}} \n& \\multicolumn{2}{c}{\\textbf{Professional Services}} \n& \\multicolumn{2}{c}{\\textbf{Banking and Financial Services}} \n& \\multicolumn{2}{c}{\\textbf{Construction and Manufacturing}} \\\\\n & \\textbf{Copilot License Effect (SE)} & \\textbf{Copilot User (SE)} \n & \\textbf{Copilot License Effect (SE)} & \\textbf{Copilot User (SE)} \n & \\textbf{Copilot License Effect (SE)} & \\textbf{Copilot User (SE)} \n & \\textbf{Copilot License Effect (SE)} & \\textbf{Copilot User (SE)} \\\\\n\\hline\nMessages Read & -6.04** (1.82) & -13.82** (4.16) & -8.13** (1.80) & -22.53** (4.99) & -9.53** (2.42) & -22.56** (5.72) & -1.18 (2.49) & -4.94 (10.36) \\\\\nReply Conversation Length & 0.02 (0.03) & 0.04 (0.05) & -0.07* (0.03) & -0.18* (0.07) & -0.04 (0.03) & -0.09 (0.07) & -0.02 (0.03) & -0.07 (0.11) \\\\\nReply Conversation Count & 0.05 (0.20) & 0.11 (0.45) & -0.18 (0.21) & -0.51 (0.59) & -0.41 (0.22) & -0.93 (0.51) & -0.05 (0.28) & -0.18 (1.07) \\\\\nRead Duration (min) & -0.05** (0.02) & -0.13** (0.04) & -0.010 (0.02) & -0.03 (0.06) & -0.04 (0.02) & -0.09 (0.05) & -0.03 (0.03) & -0.15 (0.11) \\\\\nlog(Read Duration) & -0.06** (0.014) & -0.14** (0.03) & -0.04* (0.02) & -0.11* (0.05) & -0.07** (0.02) & -0.16** (0.04) & -0.04 (0.02) & -0.15 (0.08) \\\\\nTotal Read Duration (min) & -10.95** (2.84) & -25.07** (6.52) & -6.68* (3.19) & -18.51* (8.81) & -12.55** (4.27) & -29.74** (10.09) & -9.93* (4.39) & -41.45* (18.48) \\\\\nTime to Reply from Read Start Time (min) & -22.48* (9.10) & -51.21* (20.70) & -35.49** (11.23) & -95.24** (30.25) & -29.90* (11.78) & -71.64* (28.24) & -13.03 (13.43) & -53.58 (55.35) \\\\\nTotal Session Duration (min) & 7.43 (3.76) & 16.51 (8.32) & -7.89* (3.55) & -21.92* (9.93) & 6.43 (4.03) & 14.71 (9.19) & -0.91 (3.33) & -3.47 (12.74) \\\\\nTotal Scheduled Meetings Attended & 0.36* (0.16) & 0.81* (0.35) & -0.24 (0.15) & -0.66 (0.41) & 0.30 (0.17) & 0.68 (0.39) & -0.04 (0.14) & -0.16 (0.52) \\\\\nJoined Scheduled Meeting 5 Min Late & 0.05 (0.04) & 0.10 (0.08) & -0.004 (0.03) & -0.011 (0.08) & 0.08* (0.04) & 0.17* (0.08) & -0.002 (0.04) & -0.009 (0.14) \\\\\nLeft Scheduled Meeting 10 Min Early & 0.08 (0.04) & 0.17 (0.09) & 0.007 (0.03) & 0.02 (0.08) & 0.07 (0.04) & 0.15 (0.09) & -0.009 (0.04) & -0.04 (0.14) \\\\\nWrd, Exl, Ppt Files Edited & 0.14 (0.08) & 0.31 (0.17) & 0.10 (0.10) & 0.29 (0.26) & -0.02 (0.09) & -0.06 (0.20) & 0.005 (0.09) & 0.02 (0.33) \\\\\nWord Docs Created & 0.005 (0.02) & 0.011 (0.04) & 0.02 (0.03) & 0.05 (0.07) & -0.004 (0.02) & -0.008 (0.05) & 0.02 (0.03) & 0.07 (0.11) \\\\\nPrimary Editor Word Docs & 0.007 (0.02) & 0.02 (0.04) & 0.008 (0.03) & 0.02 (0.07) & 0.008 (0.02) & 0.02 (0.05) & 0.03 (0.03) & 0.11 (0.11) \\\\\nRead Others' Word Docs & 0.04* (0.02) & 0.09* (0.04) & 0.06 (0.03) & 0.16 (0.08) & -0.02 (0.02) & -0.05 (0.05) & -0.008 (0.03) & -0.03 (0.12) \\\\\nTime to Complete (days) & -1.11* (0.46) & -1.97* (0.82) & 0.13 (0.46) & 0.28 (0.98) & -0.94 (0.50) & -1.64 (0.87) & -0.18 (0.60) & -0.58 (1.92) \\\\\nCompleted Word Docs in $\\le$ 1 wk & -0.009 (0.014) & -0.02 (0.03) & -0.009 (0.02) & -0.03 (0.05) & 0.02 (0.02) & 0.04 (0.04) & 0.005 (0.02) & 0.02 (0.09) \\\\\nCompleted Word Docs in $>$ 1 wk & -0.008 (0.006) & -0.02 (0.013) & 0.003 (0.009) & 0.007 (0.03) & -0.008 (0.007) & -0.02 (0.02) & 0.010 (0.009) & 0.04 (0.04) \\\\\n\\hline\n\\multicolumn{9}{l}{\\footnotesize * $p < 0.05$, ** $p < 0.01$} \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Effect of Copilot on All Metrics by Industry}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Early Impacts of M365 Copilot", "authors": ["Eleanor Wiske Dillon", "Sonia Jaffe", "Sida Peng", "Alexia Cambon"], "url": "https://arxiv.org/abs/2504.11443v1", "attribution": "\"Early Impacts of M365 Copilot\" by Eleanor Wiske Dillon, Sonia Jaffe, Sida Peng, and Alexia Cambon, arXiv:2504.11443v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.06329v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Cluster and Sector Average Hype Indices}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\textbf{Cluster} & \\textbf{Cluster Avg.} & \\textbf{Sector} & \\textbf{Sector Avg.} \\\\\n & \\textbf{Hype Index} & & \\textbf{Hype Index} \\\\\n\\hline\nOver-Hyped & 0.19354 & Financials & 0.19395 \\\\\n & & Information Technology & 0.19313 \\\\\n\\hline\nNeutral-Hyped & 0.11352 & Communication & 0.12236 \\\\\n & & Consumer Discretionary & 0.11554 \\\\\n & & Health Care & 0.11534 \\\\\n & & Industrials & 0.10253 \\\\\n & & Consumer Staples & 0.09190 \\\\\n\\hline\nUnder-Hyped & 0.01681 & Energy & 0.02678 \\\\\n & & Utilities & 0.01842 \\\\\n & & Real Estate & 0.01371 \\\\\n & & Materials & 0.00633 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Hype Index: an NLP-driven Measure of Market News Attention", "authors": ["Zheng Cao", "Wanchaloem Wunkaew", "Helyette Geman"], "url": "https://arxiv.org/abs/2506.06329v1", "attribution": "\"The Hype Index: an NLP-driven Measure of Market News Attention\" by Zheng Cao, Wanchaloem Wunkaew, and Helyette Geman, arXiv:2506.06329v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08910v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrlrr}\n \\toprule\n dataset & $\\sigma$ & $\\alpha$ & $\\rho_{SR}$ & $\\beta$ & N & $\\sigma_b$ & readout & $\\rho_A$ & Train Data \\\\\n \\midrule\n L9610d & 0.001 & 0.68 & 1.01 & 2.202428e-02 & 1000 & 1.40 & linear & 0.98 & 100 \\\\\n L9610d & 0.003 & 0.85 & 0.43 & 1.043649e-09 & 1000 & 1.04 & linear & 0.98 & 1000 \\\\\n L9610d & 0.006 & 1.00 & 0.01 & 1.769733e-10 & 1000 & 1.15 & linear & 0.98 & 10000 \\\\\n L9610d & 0.008 & 0.22 & 0.02 & 9.382122e-08 & 1000 & 1.33 & linear & 0.98 & 100000 \\\\\n L96-5D & 0.012 & 0.96 & 1.26 & 1.236301e-09 & 1000 & 3.00 & linear & 0.98 & 100 \\\\\n L96-5D & 0.023 & 0.83 & 0.31 & 2.517159e-09 & 1000 & 1.23 & linear & 0.98 & 1000 \\\\\n L96-5D & 0.039 & 0.69 & 0.85 & 1.349428e-08 & 1000 & 1.08 & linear & 0.98 & 10000 \\\\\n L96-5D & 0.066 & 0.60 & 0.66 & 1.319216e-09 & 1000 & 1.04 & linear & 0.98 & 100000 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Fig. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics", "authors": ["Jason A. Platt", "Stephen G. Penny", "Timothy A. Smith", "Tse-Chun Chen", "Henry D. I. Abarbanel"], "url": "https://arxiv.org/abs/2201.08910v1", "attribution": "\"A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics\" by Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen, and Henry D. I. Abarbanel, arXiv:2201.08910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.15096v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n \\toprule\n & & \\multicolumn{4}{c}{\\textbf{Placement}} \\\\\n \\cmidrule(r){3-6}\n &&\\multicolumn{2}{c}{\\textbf{DR}}& \\multicolumn{2}{c}{\\textbf{Random}} \\\\\n \\textbf{Measure}&\\textbf{Label}& \\textbf{$\\overline{B} -\\overline{L}$} &\\textbf{\\textit{p-val.}}&\\textbf{$\\overline{B} -\\overline{L}$} &\\textbf{\\textit{p-val.}}\\\\ \\midrule\n Time &&&&\\\\\n & Shape & -0.1 & 1.0 & -0.4 & 0.97 \\\\\n & Color & -2.5 & \\textbf{0.05} & 0.8 & 0.82 \\\\\n & Texture & -1.1 & 0.56 & -3.0 & \\textbf{0.05}\\\\\n \n Hovered&&&&\\\\ \n samples& Shape & 1.0 & 0.96 & 3.1 & 0.50 \\\\\n & Color & -1.4 & 0.92 & 5.0 & \\textbf{0.05} \\\\\n & Texture & -1.3 & 0.93 & -2.7 & 0.71\\\\\n \n Distance &&&&\\\\\n & Shape & 71 & 0.99 & -131& 0.98 \\\\\n & Color & -333 & 0.72 & 272 & 0.82 \\\\\n & Texture & -150 & 0.95 & -1136 & \\textbf{0.02} \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A proposal and evaluation of new timbre visualisation methods for audio sample browsers", "authors": ["Etienne Richan", "Jean Rouat"], "url": "https://arxiv.org/abs/2011.15096v1", "attribution": "\"A proposal and evaluation of new timbre visualisation methods for audio sample browsers\" by Etienne Richan and Jean Rouat, arXiv:2011.15096v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.14267v4_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Alphas of the Zero-Cost Lower-Tail $\\Delta$CIQ Portfolios}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\midrule\n & \\multicolumn{2}{c}{Decile} & \\multicolumn{2}{c}{Quintile} \\\\\n \\cmidrule(lr){2-3}\\cmidrule(lr){4-5}\n & EW & VW & EW & VW \\\\ \n \\midrule\n \\midrule\n CAPM & 8.79 & 10.24 & 6.79 & 6.65 \\\\ \n & (5.51) & (4.15) & (5.03) & (3.54) \\\\ \n FF3 & 7.07 & 7.58 & 5.42 & 4.23 \\\\ \n & (4.75) & (3.42) & (4.34) & (2.66) \\\\ \n FF5 & 6.65 & 8.23 & 5.71 & 4.75 \\\\ \n & (4.04) & (3.38) & (4.29) & (2.76) \\\\ \n FF6 & 7.22 & 9.23 & 6.28 & 5.90 \\\\ \n & (4.01) & (3.65) & (4.37) & (3.40) \\\\ \n FF alt. & 6.96 & 9.22 & 5.83 & 5.95 \\\\ \n & (3.59) & (3.38) & (3.71) & (3.26) \\\\ \n FF alt. + Rev. & 7.02 & 9.61 & 5.88 & 5.60 \\\\ \n & (3.23) & (3.23) & (3.30) & (2.78) \\\\ \n FF alt. + Liquid. & 6.88 & 9.31 & 5.77 & 5.96 \\\\ \n & (3.49) & (3.33) & (3.61) & (3.21) \\\\ \n Q5 & 4.98 & 7.45 & 4.37 & 4.57 \\\\ \n & (2.59) & (2.67) & (2.75) & (2.15) \\\\ \n M4 & 5.94 & 7.43 & 5.47 & 5.16 \\\\ \n & (3.17) & (2.77) & (3.79) & (2.73) \\\\ \n \\midrule\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Beyond Volatility: Common Factors in Idiosyncratic Quantile Risks", "authors": ["Jozef Barunik", "Matej Nevrla"], "url": "https://arxiv.org/abs/2208.14267v4", "attribution": "\"Beyond Volatility: Common Factors in Idiosyncratic Quantile Risks\" by Jozef Barunik and Matej Nevrla, arXiv:2208.14267v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.13533v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{API-based LLMs considered in this paper via \\emph{FinEval}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n \\toprule\n Type & Models & Version & Provider \\\\\n \\midrule\n \\multirow{7}{*}{Online} \n & GPT-4o-mini & gpt-4o-mini-2024-07-18 &OpenAI \\\\\n & GPT-4.1 &gpt-4.1-2025-04-14 &OpenAI \\\\\n & GPT-4.1-mini &gpt-4.1-mini-2025-04-14 &OpenAI \\\\\n & GPT-4.1-nano &gpt-4.1-nano-2025-04-14 &OpenAI \\\\ \n & o3-mini & o3-mini-2025-01-31 & OpenAI \\\\\n & DeepSeek-V3 & deepseek-v3-250324 & Huoshan \\\\\n & Claude-3.7-Sonnet &claude-3-7-sonnet-20250219 & Anthropic \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs", "authors": ["Junzhe Jiang", "Chang Yang", "Aixin Cui", "Sihan Jin", "Ruiyu Wang", "Bo Li", "Xiao Huang", "Dongning Sun", "Xinrun Wang"], "url": "https://arxiv.org/abs/2505.13533v1", "attribution": "\"FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs\" by Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Ruiyu Wang, Bo Li, Xiao Huang, Dongning Sun, and Xinrun Wang, arXiv:2505.13533v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.08698v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Accuracy Improvements with 1-Layer GCN for SBM Datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n \\hline\n Dataset & Clustering &Intra & $\\widehat{H}$ & Improvement \\\\ \\hline\n SBM\\_PATTERN & \\textbf{0.427} & \\textbf{0.589} & \\textbf{0.811} & \\textbf{34.78} \\\\ \n \\hline\n SBM\\_CLUSTER & 0.317 &0.33 & 0.954 & 27.68 \\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification", "authors": ["Lavender Yao Jiang", "John Shi", "Mark Cheung", "Oren Wright", "José M. F. Moura"], "url": "https://arxiv.org/abs/2012.08698v1", "attribution": "\"Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification\" by Lavender Yao Jiang, John Shi, Mark Cheung, Oren Wright, and José M. F. Moura, arXiv:2012.08698v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09246v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Recognition accuracy (\\%) of competing approaches on the AR database when only one neutral face image per subject from Session 1 is used as training sample, the dimension that leads to the best result for IGO and SOIGO based approaches is given in parentheses.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\nMethods & \\multicolumn{2}{c}{Sunglasses} & \\multicolumn{2}{c}{Scarf} & Overall \\\\ \\hline\n & Session1 & Session2 & Session1 & Session2 & \\\\\nHQPAMI & 56.67 & 38.00 & 38.00 & 22.33 & 3875 \\\\ \nNR & 28.33 & 16.67 & 29.67 & 17.33 & 23.00 \\\\\nProCRC & 53.07 & 31.00 & 18.67 & 7.33 & 27.52 \\\\\nF-LR-IRNNLS & 88.67 & 60.33 & 67.00 & 49.67 & 66.42 \\\\\nEGSNR & 84.00 & 54.00 & 70.33 & 48.33 & 64.16 \\\\\nLDMR & 68.33 & 45.67 & 59.67 & 34.00 & 51.92 \\\\\nGD-HASLR & 92.00 & 66.67 & 82.67 & 58.67 & 75.00 \\\\ \nIGO-PCA-NNC & 89.00 (99) & 69.00 (99) & 73.33 (97) & 53.33 (96)& 71.17\\\\\nIGO-PCA-CRC & \\textbf{93.00 (85)} & 74.33 (92) & 81.67 (88) & 58.33 (95) & 76.83 \\\\\nSOIGO-PCA-NNC & 88.67 (92) & 73.33 (96) & 80.33 (99) & 61.00 (88) & 75.83 \\\\\nCSOIGO & 92.67 (89) & \\textbf{76.67 (93)} & \\textbf{83.33 (75)} & \\textbf{65.33 (99)} & \\textbf{79.50} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Face recognition via compact second order image gradient orientations", "authors": ["He-Feng Yin", "Xiao-Jun Wu", "Xiaoning Song"], "url": "https://arxiv.org/abs/2201.09246v2", "attribution": "\"Face recognition via compact second order image gradient orientations\" by He-Feng Yin, Xiao-Jun Wu, and Xiaoning Song, arXiv:2201.09246v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.06284v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The LibriAugmented dataset. $\\mathcal{D}_{LLM}$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\\hline\n\\textbf{Ratio} & \\textbf{Impairment (Parameters)} \\\\\n\\hline\n$0.050$ & Identity \\\\\n$0.050$ & AddBackgroundNoise (snr=$-10\\sim 15$ dB) \\\\\n$0.050$ & ClippingImpairment (percentile=$10\\sim40\\%$) \\\\\n$0.050$ & GainTransition (gain=$-60\\sim20$ dB) \\\\\n$0.050$ & LowPassFilter (cutoff\\_freq=$0.5\\sim1$ kHz) \\\\\n$0.050$ & Mp3Compression (bit\\_rate=$8\\sim14$) \\\\\n$0.050$ & PitchShift (semitones=$-4\\sim4$ kHz) \\\\\n$0.050$ & RoomSimulator (rt60=$0.8\\sim1.5$ s) \\\\\n$0.050$ & TimeMask (band\\_part=$0.2\\sim0.5$) \\\\\n$0.050$ & TimeStretch (rate=$0.5\\sim2$) \\\\\n$0.083$ & AddBackgroundNoise + RoomSimulator \\\\\n$0.083$ & AddBackgroundNoise + LowPassFilter \\\\\n$0.083$ & AddBackgroundNoise + TimeStretch \\\\\n$0.083$ & RoomSimulator + Mp3Compression \\\\\n$0.083$ & PitchShift + LowPassFilter \\\\\n$0.083$ & GainTransition + TimeMask \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Leveraging LLMs for Scalable Non-intrusive Speech Quality Assessment", "authors": ["Fredrik Cumlin", "Xinyu Liang", "Anubhab Ghosh", "Saikat Chatterjee"], "url": "https://arxiv.org/abs/2508.06284v1", "attribution": "\"Leveraging LLMs for Scalable Non-intrusive Speech Quality Assessment\" by Fredrik Cumlin, Xinyu Liang, Anubhab Ghosh, and Saikat Chatterjee, arXiv:2508.06284v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09300v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Solution Number under Different Resolution}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\toprule\nResolution (step increment) & 8 & 16 & 32 & 64 \\\\ \\midrule\nSize of binary solution & 19 & 17 & 15 & 13 \\\\\nSolution number & 524288 & 131072 & 32768 & 8192 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks", "authors": ["Yingfang Yuan", "Wenjun Wang", "George M. Coghill", "Wei Pang"], "url": "https://arxiv.org/abs/2101.09300v2", "attribution": "\"A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks\" by Yingfang Yuan, Wenjun Wang, George M. Coghill, and Wei Pang, arXiv:2101.09300v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16944v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n $n$ & \\textit{largest} $m$ & $n(n-1)/2$ & \\textit{density} \\\\\n \\midrule\n 11 & 25 & 55 & 0.45\\\\\n 12 & 31 & 66 & 0.47\\\\\n 13 & 34 & 78 & 0.43\\\\\n 14 & 40 & 91 & 0.44\\\\\n 15 & 45 & 105 & 0.43\\\\\n 16 & 48 & 120 & 0.40\\\\\n 20 & 82 & 190 & 0.43\\\\\n 30 & 95 & 435 & 0.22\\\\\n 40 & 133 & 780 & 0.17\\\\\n 50 & 163 & 1225 & 0.13\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\small Additional results about the $G(n,m)$ model. For each $n$, the density of the found 1-metric antidimensional graph having the maximum number of edges is reported.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Burning some myths on privacy properties of social networks against active attacks", "authors": ["Serafino Cicerone", "Gabriele Di Stefano", "Sandi Klavžar", "Ismael G. Yero"], "url": "https://arxiv.org/abs/2504.16944v1", "attribution": "\"Burning some myths on privacy properties of social networks against active attacks\" by Serafino Cicerone, Gabriele Di Stefano, Sandi Klavžar, and Ismael G. Yero, arXiv:2504.16944v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12378v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{cc}\n\\hline\n\\(n\\) & Min Gap of $TU_n$ \\\\ \\hline\n1 & 1 \\\\\n2 & 0.585786 \\\\\n3 & 0.381966 \\\\\n4 & 0.267949 \\\\\n5 & 0.198062 \\\\\n6 & 0.152241 \\\\\n7 & 0.120615 \\\\\n8 & 0.097887 \\\\\n9 & 0.0810141 \\\\\n10 & 0.0681483 \\\\ \\hline\n\\end{tabular}\n\\caption{The minimum spectral gap of twisted unknots $TU_n$ for different values of \\(n\\). All of these minimal gaps occurred in bidgree $(0,3-n)$. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A quantum algorithm for Khovanov homology", "authors": ["Alexander Schmidhuber", "Michele Reilly", "Paolo Zanardi", "Seth Lloyd", "Aaron Lauda"], "url": "https://arxiv.org/abs/2501.12378v2", "attribution": "\"A quantum algorithm for Khovanov homology\" by Alexander Schmidhuber, Michele Reilly, Paolo Zanardi, Seth Lloyd, and Aaron Lauda, arXiv:2501.12378v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.14499v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\nMethod & Metric & 25D & 50D & 100D & 150D & 200D \\\\ \\hline\\hline\n\\multirow{3}{*}{PINN } & Speed & 40.24it/s & 19.82it/s & 6.56it/s & 3.50it/s & N.A.\\\\ \\cline{2-7} \n & Memory & 4006MB & 6199MB & 21453MB & 44631MB & $>$80GB \\\\ \\cline{2-7} \n & Error & 2.38E-2$\\pm$1.12E-2 & 2.34E-2$\\pm$1.91E-2 & 6.01E-3$\\pm$2.32E-3 & 3.11E-3$\\pm$1.79E-3 & N.A. \\\\ \\hline\\hline\n\\multirow{3}{*}{HTE (Ours)} & Speed & 125.67it/s & 123.98it/s & 54.07it/s& 42.44it/s & 23.50it/s\\\\ \\cline{2-7} \n& Memory & 1859MB & 1861MB & 2089MB & 3593MB & 7299MB\\\\ \\cline{2-7} \n & Error & 2.85E-2$\\pm$1.15E-2 & 2.34E-2$\\pm$1.96E-2 & 6.08E-3$\\pm$2.28E-3 & 3.39E-3$\\pm$1.99E-3 & 4.03E-3$\\pm$3.82E-3\\\\ \\hline\n\\end{tabular}\n\\caption{Computational results for the Biharmonic equation.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks", "authors": ["Zheyuan Hu", "Zekun Shi", "George Em Karniadakis", "Kenji Kawaguchi"], "url": "https://arxiv.org/abs/2312.14499v2", "attribution": "\"Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks\" by Zheyuan Hu, Zekun Shi, George Em Karniadakis, and Kenji Kawaguchi, arXiv:2312.14499v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.10221v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\nOld Scheme&New Scheme&Ratio\\\\\n\\hline\n68.82&65.94&1.044\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\sf Example 1: The CPU times (in seconds) consumed by the studied schemes.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "New More Efficient A-WENO Schemes", "authors": ["Shaoshuai Chu", "Alexander Kurganov", "Ruixiao Xin"], "url": "https://arxiv.org/abs/2503.10221v2", "attribution": "\"New More Efficient A-WENO Schemes\" by Shaoshuai Chu, Alexander Kurganov, and Ruixiao Xin, arXiv:2503.10221v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.19843v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean results over four experimental runs. The table is sorted by descending mean AUC.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|lllll}\nsetup & AUC & F1 & PRC & REC & ACC \\\\ \\hline\nd1 & 0.796 & 0.674 & 0.581 & 0.805 & 0.694 \\\\\nd2\\_LBP & 0.791 & 0.672 & 0.596 & 0.772 & 0.705 \\\\\ndino\\_d2 & 0.787 & 0.664 & 0.598 & 0.748 & 0.704 \\\\\nd2 & 0.787 & 0.663 & 0.609 & 0.730 & 0.710 \\\\\nd1\\_LBP & 0.786 & 0.661 & 0.585 & 0.764 & 0.692 \\\\\ndino\\_d1 & 0.782 & 0.671 & 0.591 & 0.779 & 0.702 \\\\\ndino\\_all & 0.781 & 0.659 & 0.601 & 0.728 & 0.704 \\\\\nbaseline & 0.781 & 0.659 & 0.594 & 0.741 & 0.699 \\\\\nt\\_LBP & 0.778 & 0.672 & 0.583 & 0.793 & 0.697 \\\\\nLBP & 0.776 & 0.658 & 0.592 & 0.740 & 0.698 \\\\\ndino\\_LBP & 0.774 & 0.646 & 0.591 & 0.712 & 0.694 \\\\\nall & 0.774 & 0.656 & 0.575 & 0.764 & 0.686 \\\\\nt & 0.769 & 0.653 & 0.581 & 0.746 & 0.689 \\\\\ndino\\_t & 0.765 & 0.647 & 0.575 & 0.743 & 0.682 \\\\\ndino & 0.759 & 0.656 & 0.579 & 0.757 & 0.688 \\\\\nfrozen & 0.693 & 0.588 & 0.517 & 0.683 & 0.626\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Hybrid Deep Learning and Handcrafted Feature Fusion for Mammographic Breast Cancer Classification", "authors": ["Maximilian Tschuchnig", "Michael Gadermayr", "Khalifa Djemal"], "url": "https://arxiv.org/abs/2507.19843v1", "attribution": "\"Hybrid Deep Learning and Handcrafted Feature Fusion for Mammographic Breast Cancer Classification\" by Maximilian Tschuchnig, Michael Gadermayr, and Khalifa Djemal, arXiv:2507.19843v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11678v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc} \n \\hline\nMethodology & FAR & MDR \\\\\n \\hline\n DRE-CUSUM (DNN, KLIEP) & \\cellcolor[HTML]{D3D3D3}$0\\%$ & \\cellcolor[HTML]{D3D3D3}$0\\%$ \\\\ \nDRE-CUSUM (DNN, LSIF) & $0\\%$ & $14.3\\%$ \\\\ \nDRE-CUSUM (Kernel, LSIF) & $0.0005\\%$ & $14.3\\%$\\\\ \n Online BCD & $\\sim 30\\%$ & $\\sim 0\\%$\\\\\n Robust Online BCD & $0.04\\%$ & $42\\%$ \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Unsupervised Change Detection using DRE-CUSUM", "authors": ["Sudarshan Adiga", "Ravi Tandon"], "url": "https://arxiv.org/abs/2201.11678v1", "attribution": "\"Unsupervised Change Detection using DRE-CUSUM\" by Sudarshan Adiga and Ravi Tandon, arXiv:2201.11678v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.08112v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n \\hline\n \\textbf{Phoneme} & \\textbf{H} & \\textbf{AO} & \\textbf{H} & \\textbf{AO} & \\textbf{X} & \\textbf{UE} & \\textbf{X} & \\textbf{I} \\\\\n \\hline\n Origin & 4 & 8 & 3 & 5 & 4 & 7 & 5 & 11 \\\\\n Latent & 2 & 10 & 2 & 9 & 1 & 11 & 2 & 22 \\\\\n MFCC & 5 & 6 & 5 & 6 & 2 & 8 & 3 & 20 \\\\\n MelSpec & 8 & 5 & 4 & 6 & 2 & 10 & 6 & 10 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{TTS Predicted Phoneme duration}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Aligner-Guided Training Paradigm: Advancing Text-to-Speech Models with Aligner Guided Duration", "authors": ["Haowei Lou", "Helen Paik", "Wen Hu", "Lina Yao"], "url": "https://arxiv.org/abs/2412.08112v1", "attribution": "\"Aligner-Guided Training Paradigm: Advancing Text-to-Speech Models with Aligner Guided Duration\" by Haowei Lou, Helen Paik, Wen Hu, and Lina Yao, arXiv:2412.08112v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.08188v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|r|r|r|r|r|}\n\\hline\nMethods &\\multicolumn{5}{c|}{Tanzania dataset} \\\\\n\\cline{2-6}\n &Precision&Recall&F1-score&Time(sec)&Detection recall \\\\\n\\hline\nProposed & 0.47 & 0.84 & 0.60 & 90.0 & 0.99 \\\\ \nU-Net & 0.43 & 0.72 & 0.54 & 157.0 & - \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient", "authors": ["John E. Vargas-Muñoz", "Devis Tuia", "Alexandre X. Falcão"], "url": "https://arxiv.org/abs/2009.08188v1", "attribution": "\"Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient\" by John E. Vargas-Muñoz, Devis Tuia, and Alexandre X. Falcão, arXiv:2009.08188v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.03035v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcrr|rr}\n\t\tvariable & stat. & current & lag 1 & lag 2 & lag 3 \\\\\n\t\t\\hline\n\t\tMarkit NA credit index\n\t\t\t& correl. & 0.028 & \\color{blue}{\\textbf{0.282}} & \\textbf{0.288} & \\textbf{0.275} \\\\\n\t\t\t& p-value & 0.551 & 0.000 & 0.000 & 0.000 \\\\\n\t\tBloomberg FRN index OAS\n\t\t\t& correl. & \\textbf{0.441} & \\color{blue}{\\textbf{0.451}} & \\textbf{0.327} & \\textbf{0.272}\\\\\n\t\t\t& p-value & 0.000 & 0.000 & 0.000 & 0.006\\\\\n\t\tBloomberg FRN index\n\t\t\t& correl. & \\textbf{-0.369} & \\color{blue}{\\textbf{-0.488}} & \\textbf{-0.432} & \\textbf{-0.299}\\\\\n\t\t\t& p-value & 0.000 & 0.000 & 0.000 & 0.000 \\\\\n\t\tBloomberg US corporate index OAS\n\t\t\t& correl. & 0.399 & \\color{blue}{\\textbf{0.464}} & \\textbf{0.430} & \\textbf{0.336}\\\\\n\t\t\t& p-value & 0.000 & 0.000 & 0.000 & 0.000 \\\\\n\t\tBloomberg US corporate index\n\t\t\t& correl. & -0.027 & \\color{blue}{\\textbf{-0.268}} & \\textbf{-0.238} & \\textbf{-0.233}\\\\\n\t\t\t& p-value & 0.562 & 0.000 & 0.000 & 0.000 \\\\\n\t\tNational financial conditions index\n\t\t & correl. & \\textbf{0.458} & \\color{blue}{\\textbf{0.562}} & \\textbf{0.562} & \\textbf{0.453}\\\\\n\t\t & p-value & 0.000 & 0.000 & 0.000 & 0.000 \\\\\n\t\tCredit market distress index\n\t\t & correl. & \\textbf{0.197} & \\color{blue}{\\textbf{0.245}} & \\textbf{0.206} & \\textbf{0.087} \\\\\n\t\t & p-value & 0.000 & 0.000 & 0.000 & 0.064 \\\\\n LIBOR-OIS spread\n\t\t & correl. & \\textbf{0.461} & \\color{blue}{\\textbf{0.497}} & \\textbf{0.359} & \\textbf{0.179} \\\\\n\t\t & p-value & 0.000 & 0.000 & 0.000 & 0.000 \\\\\n TED spread\n\t\t & correl. & \\textbf{0.397} & \\color{blue}{\\textbf{0.406}} & \\textbf{0.345} & \\textbf{0.269} \\\\\n\t\t & p-value & 0.000 & 0.000 & 0.000 & 0.000 \\\\\n\t\t\\hline\n\t\tSPDR financial sector ETF\n\t\t\t& correl. & \\textbf{-0.107} & \\color{blue}{\\textbf{-0.292}} & \\textbf{-0.256} & \\textbf{-0.217}\\\\\n\t\t\t& p-value & 0.022 & 0.000 & 0.000 & 0.000 \\\\\n\t\t\\hline\n\t\tEconomic policy uncertainty\n\t\t & correl. & 0.016 & \\color{blue}{\\textbf{0.121}} & \\textbf{0.079} & 0.024 \\\\\n\t\t & p-val. & 0.731 & 0.009 & 0.091 & 0.614 \\\\\n\t\tVolatility index\n\t\t\t& correl. & 0.005 & \\color{blue}{\\textbf{0.130}} & \\textbf{0.131} & \\textbf{0.197}\\\\\n\t\t\t& p-value & 0.909 & 0.005 & 0.005 & 0.000 \\\\\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Correlations of AXI with selected financial indicators}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Case for AXI", "authors": ["Viktor Tsyrennikov"], "url": "https://arxiv.org/abs/2509.03035v1", "attribution": "\"A Case for AXI\" by Viktor Tsyrennikov, arXiv:2509.03035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.20987v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc}\n $j \\setminus i$ & 0 & 1 & 2 & 3 & 4\\\\\n \\hline\n 0 & 1 & . & . & . & .\\\\\n 1 & . & . & . & . & .\\\\\n 2 & . & 20 & 45 & 36 & 10\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Betti table of $\\sigma_2\\Delta$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Fröberg type theorem for higher secant complexes", "authors": ["Junho Choe", "Jaewoo Jung"], "url": "https://arxiv.org/abs/2502.20987v2", "attribution": "\"A Fröberg type theorem for higher secant complexes\" by Junho Choe and Jaewoo Jung, arXiv:2502.20987v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.13915v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyper-parameter domain of the parameters for the LSTM model}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\t\t\t\\hline\n\t\t\t\\textbf{Parameters} & \\textbf{Search Space}\\\\\n\t\t\t\\hline\n\t\t\tBatch size & [8, 16, 32, 64]\\\\\n\t\t\tHidden size & [16, 32, 64, 128] \\\\ \n\t\t\tNumber of Layers & [1, 2, 3, 4] \\\\ \n\t\t\tLearning rate & [0.0001, 0.001, 0.01] \\\\\n\t\t\tDropout rate & [0.1, 0.2, 0.3, 0.4] \\\\\n\t\t\tActivation function & [`relu', `silu', `sigmoid', `tanh'] \\\\\n\t\t\tOptimizer & [`Adam', `RMSprop', `SGD', `Adagrad', `Adamax'] \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network", "authors": ["Aryan Bhambu", "Arabin Kumar Dey"], "url": "https://arxiv.org/abs/2211.13915v1", "attribution": "\"Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network\" by Aryan Bhambu and Arabin Kumar Dey, arXiv:2211.13915v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.07267v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small True parameter values for the 3 simulation study scenarios.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccc}\n\\toprule\nScenario & $\\mu_0$ & $\\mu_1$ & $\\sigma$ & $\\xi$ & $\\rho$ & $\\beta_{10}$ & $\\beta_{11}$ & $\\beta_{20}$ & $\\beta_{21}$ \\\\\\midrule\n1 & 12 & 3 & 2 & 0.2 & 0.4 & -1 & 1.8 & 0.2 & 2 \\\\\n2 & 13 & 5 & 2 & 0.1 & 0.1 & 1 & -1.2 & -1 & 0.8\\\\\n3 & 12 & 3 & 3 & -0.1 & 0.2 & -1.5 & 2 & -1.5 & 0.8 \\\\\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Deep Learning Synthetic Likelihood Approximation of a Non-stationary Spatial Model for Extreme Streamflow Forecasting", "authors": ["Reetam Majumder", "Brian Reich"], "url": "https://arxiv.org/abs/2212.07267v2", "attribution": "\"A Deep Learning Synthetic Likelihood Approximation of a Non-stationary Spatial Model for Extreme Streamflow Forecasting\" by Reetam Majumder and Brian Reich, arXiv:2212.07267v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1910.10836v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{High sampling density SVBRDF methods, their scanning area and the number of needed individual scans}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n \\toprule \n Method & Authors & No. captures &Scan area\\\\ \n &&\n & size (est.)\\\\\n \\midrule\n Linear Lightsource Reflectometry & Gardner et al. & 400 & 20x30cm\\\\\n Generalized Linear Lightsource Reflectometry & Chen et al. & 240 & 20x30cm\\\\\n Pocket Reflectometry & Ren et al. & 900 & 16x17cm\\\\\n SVBRDF capturing in the Frequency Domain & Aittala et al. & 131 & 15x15cm\\\\\n Second Order Spherical Gradient Illumination & Ghosh et al. & 9 x \"large set\" & 5x15cm\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Gloss, Color and Topography Scanning for Reproducing a Painting's Appearance using 3D printing", "authors": ["Willemijn Elkhuizen", "Tessa Essers", "Yu Song", "Jo Geraedts", "Clemens Weijkamp", "Joris Dik", "Sylvia Pont"], "url": "https://arxiv.org/abs/1910.10836v1", "attribution": "\"Gloss, Color and Topography Scanning for Reproducing a Painting's Appearance using 3D printing\" by Willemijn Elkhuizen, Tessa Essers, Yu Song, Jo Geraedts, Clemens Weijkamp, Joris Dik, and Sylvia Pont, arXiv:1910.10836v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.20659v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n $n,m,\\ell$ & $H_n(C_\\bullet^{mf,\\ell})$ & $H_n(C_\\bullet^{mf})$ & $H_n(C_\\bullet^{mf} / C_\\bullet^{mf,\\ell})$ \\\\\n \\hline \n $4,2,1$ & $(1,2,0)$ & $(1,6,4)$ & $(0,4,4)$ \\\\\n $4,2,2$ & $(1,4,2)$ & $(1,6,4)$ & $(0,2,2)$ \\\\\n $4,3,1$ & $(1,18,2)$ & $(1,33,6)$ & $(0,15,4)$ \\\\\n $4,3,2$ & $(1,30,5)$ & $(1,33,6)$ & $(0,3,1)$ \\\\\n $5,2,1$ & $(1,2,0)$ & $(1,13,7)$ & $(0,11,7)$ \\\\\n $5,2,2$ & $(1,6,2)$ & $(1,13,7)$ & $(0,7,5)$ \\\\\n $5,2,3$ & $(1,11,4)$ & $(1,13,7)$ & $(0,2,3)$ \\\\\n $5,3,1$ & $(1,50,4)$ & $(1,124,16)$ & $(0,74,12)$ \\\\\n $5,3,2$ & $(1,97,12)$ & $(1,124,16)$ & $(0,27,4)$ \\\\\n $5,3,3$ & $(1,120,15)$ & $(1,124,16)$ & $(0,4,1)$ \\\\\n $5,3,1$ & $(1,50,4)$ & $(1,124,16)$ & $(0,74,12)$ \\\\\n $5,3,2$ & $(1,97,4)$ & $(1,124,16)$ & $(0,27,4)$ \\\\\n $5,4,1$ & $(1,201, 6)$ & $(1,323,12)$ & $(0,122,6)$ \\\\\n $5,4,2$ & $(1,304,11)$ & $(1,323,12)$ & $(0,19,1)$ \\\\\n $5,4,3$ & $(1,323,12)$ & $(1,323,12)$ & $(0,0,0)$ \\\\\n $6,2,1$ & $(1,3,0)$ & $(1,30,12)$ & $(0,27,12)$ \\\\\n $6,2,2$ & $(1,9,3)$ & $(1,30,12)$ & $(0,21,9)$ \\\\\n $6,2,3$ & $(1,20,5)$ & $(1,30,12)$ & $(0,10,7)$ \\\\\n $6,2,4$ & $(1,27,9)$ & $(1,30,12)$ & $(0,3,3)$ \\\\\n $6,3,1$ & $(1,124,7,0)$ & $(1,423,36,2)$ & $(0,299,29,2)$ \\\\\n $6,3,2$ & $(1,277,24,0)$ & $(1,423,36,2)$ & $(0,146,12,2)$ \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{These homology groups are calculated with $y=2$ substitution, and suggest splitness of $C_{\\bullet}^{mf}$. In the case of $(6,3,1)$ and $(6,3,2)$, additional torsion is confirmed for $y=3$ substitution, as well.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Low Dimensional Homology of the Yang-Baxter Operators Yielding the HOMFLYPT Polynomial", "authors": ["Anthony Christiana", "Ben Clingenpeel", "Huizheng Guo", "Jinseok Oh", "Jozef H. Przytycki", "Xiao Wang", "Hongdae Yun"], "url": "https://arxiv.org/abs/2502.20659v1", "attribution": "\"Low Dimensional Homology of the Yang-Baxter Operators Yielding the HOMFLYPT Polynomial\" by Anthony Christiana, Ben Clingenpeel, Huizheng Guo, Jinseok Oh, Jozef H. Przytycki, Xiao Wang, and Hongdae Yun, arXiv:2502.20659v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.09939v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results showing the speaker diarization performance using baseline and proposed methods on \\textbf{development set}.}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n \\multirow{2}{*}{Method} & \\multicolumn{2}{|c|}{Full} & \\multicolumn{2}{|c|}{Core}\\\\\n \\cline{2-5}\n & DER & JER & DER & JER \\\\\n \\hline\n Baseline\t& 19.59\t&43.01\t&20.17&\t47.28\\\\\n Proposed\t& 17.40&\t38.08&\t17.95&\t42.12\\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "ABSP System for The Third DIHARD Challenge", "authors": ["A Kishore Kumar", "Shefali Waldekar", "Goutam Saha", "Md Sahidullah"], "url": "https://arxiv.org/abs/2102.09939v1", "attribution": "\"ABSP System for The Third DIHARD Challenge\" by A Kishore Kumar, Shefali Waldekar, Goutam Saha, and Md Sahidullah, arXiv:2102.09939v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.11800v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Causal graph learning on EasyVista dataset without sparse data handling.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|c|} \\hline \n \\textbf{Metric} & \\textbf{P}$\\uparrow$& \\textbf{R}$\\uparrow$& \\textbf{F$_1$}$\\uparrow$& \\textbf{FPR}$\\downarrow$& \\textbf{APRC}$\\uparrow$& \\textbf{SHD}$\\downarrow$& \\textbf{SHDU}$\\downarrow$\n\\\\ \\hline \n PC~ & 0.118& 0.222& 0.154& 0.790& 0.225& 34& 18\n\\\\ \\hline \nGS~& 0.174& \\textbf{0.889}& 0.291& 0.790& \\textbf{0.539}& 56& 16\n\\\\ \\hline \n IAMB~& 0.174& \\textbf{0.889}& 0.291& 0.790& \\textbf{0.539}& 56& 16\n\\\\ \\hline \n MMPC~& 0.174& \\textbf{0.889}& 0.291& 0.790& \\textbf{0.539}& 56& 16\n\\\\ \\hline \n HC-BicScore~& 0.167& 0.222& 0.191\t& 0.526\t& 0.249& 45& 13\n\\\\ \\hline \n HC-BdeuScore~& \\textbf{0.222}& 0.444& 0.296& 0.737& 0.372& \\textbf{21}& 17\n\\\\ \\hline \n \n GES-BicScore~& 0.160 & 0.444\t& 0.235\t& 0.895\t& 0.341\t& 35& 18\n\\\\ \\hline \n GES-BdeuScore~& 0.191\t & 0.444\t& 0.267\t& 0.526\t\t& 0.357\t& 48 & 15\n\\\\ \\hline \nMMHC~& 0.182& 0.222& 0.200& 0.474& 0.257& 44& \\textbf{12}\n\\\\ \\hline \nDirect-LiNGAM~& 0.167 & 0.444\t& 0.242\t& 1.050\t& 0.345\t& 37& 21\n\\\\ \\hline \nICA-LiNGAM~& 0.160 & 0.444\t& 0.235\t& 1.105\t& 0.341\t& 33 & 22\n\\\\ \\hline \n GOLEM~& 0.167& 0.222& 0.191& 0.526& 0.249& 35& 16\n\\\\ \\hline \nGraN-DAG~& \\textbf{0.222}& 0.222& 0.222& \\textbf{0.368} & 0.277 & 38& 13\n\\\\ \\hline \n GAE~& 0.177& 0.333& 0.231& 0.421& 0.302& 47&14\n\\\\ \\hline \nPCMCI~ & 0.182 & \\textbf{0.889} & 0.302 & 0.895 & 0.543 & 50 & 17\n\\\\ \\hline\n \\textsc{AnomalyCD} (ours) & 0.212& 0.778& \\textbf{0.333}& 0.684& 0.511& 48&13\n\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data", "authors": ["Mulugeta Weldezgina Asres", "Christian Walter Omlin", "The CMS-HCAL Collaboration"], "url": "https://arxiv.org/abs/2412.11800v1", "attribution": "\"Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data\" by Mulugeta Weldezgina Asres, Christian Walter Omlin, and The CMS-HCAL Collaboration, arXiv:2412.11800v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.18053v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Proportion covering the truth out of 100 simulated datasets. Abbreviations: Param. = parameter, Disp. = dispersion. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcrcccccc}\n\t\t\\hline\n\t\t\\multirow{3}{*}{Disp.}& \\multirow{3}{*}{Param.} & \\multirow{3}{*}{$n$} & \\multicolumn{4}{c}{Weakly Informative Priors} & & \\\\\n\t\t \\cline{4-7}\n\t\t & & & $a, b, c$ & $a, c=2,$ & $a, b, c$ & $a, b, c$ & Flat & Jeffreys' \\\\\n\t\t & & & $= 1$ & $b= \\log(2)$ & $ = 0.1$ & $=0.01$ & \\\\\n\t\t\\hline\n\t\tOver & $\\lambda$ & 25 & 0.96 & 0.83 & 0.88 & 0.87 & 0.87 & 0.94 \\\\\n\t\t & & 75 & 0.96 & 0.96 & 0.95 & 0.95 & 0.95 & 0.96 \\\\\n\t\t & & 125 & 0.95 & 0.95 & 0.95 & 0.95 & 0.94 & 0.96 \\\\\n\t\t\\cline{3-9}\n\t\t & $\\nu$ & 25 & 0.96 & 0.85 & 0.88 & 0.87 & 0.88 & 0.96 \\\\\n\t\t & & 75 & 0.95 & 0.96 & 0.94 & 0.94 & 0.95 & 0.95 \\\\\n\t\t & & 125& 0.94 & 0.94 & 0.95 & 0.94 & 0.94 & 0.95 \\\\\n\t\t\\cline{2-9}\n\t\tUnder & $\\lambda$ & 25 & 0.92 & 0.92 & 0.91 & 0.91 & 0.90 & 0.00 \\\\ \n\t\t & & 75 & 0.96 & 0.97 & 0.96 & 0.96 & 0.96 & 0.00 \\\\ \n\t\t & & 125 & 0.99 & 0.99 & 0.99 & 0.98 & 0.99 & 0.00 \\\\ \n\t\t\\cline{3-9}\n\t\t & $\\nu$ & 25 & 0.94 & 0.93 & 0.89 & 0.88 & 0.88 & 0.01 \\\\ \n\t\t & & 75 & 0.95 & 0.98 & 0.96 & 0.96 & 0.96 & 0.00 \\\\ \n\t\t & & 125 & 0.98 & 0.97 & 0.96 & 0.96 & 0.96 & 0.00 \\\\ \n\t\t\\cline{2-9}\n\t\tEqui & $\\lambda$ & 25 & 0.93 & 0.97 & 0.89 & 0.87 & 0.89 & 0.05 \\\\\n\t\t & & 75 & 0.96 & 0.97 & 0.95 & 0.95 & 0.94 & 0.00 \\\\\n\t\t & & 125 & 0.95 & 0.94 & 0.95 & 0.94 & 0.95 & 0.00 \\\\\n\t\t\\cline{3-9}\n\t\t & $\\nu$ & 25 & 0.93 & 0.96 & 0.88 & 0.88 & 0.88 & 0.00 \\\\\n\t\t & & 75 & 0.95 & 0.95 & 0.96 & 0.95 & 0.95 & 0.00 \\\\\n\t\t & & 125 & 0.94 & 0.94 & 0.96 & 0.97 & 0.95 & 0.00 \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On Non- and Weakly-Informative Priors for the Conway-Maxwell-Poisson (COM-Poisson) Distribution", "authors": ["Mark J. Meyer", "Amia Graye", "Kimberly F. Sellers"], "url": "https://arxiv.org/abs/2311.18053v1", "attribution": "\"On Non- and Weakly-Informative Priors for the Conway-Maxwell-Poisson (COM-Poisson) Distribution\" by Mark J. Meyer, Amia Graye, and Kimberly F. Sellers, arXiv:2311.18053v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.01975v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results of the experimentation with Case 1 (without hidden sensors) and Case 2 (with hidden sensors) by varying the delay ($\\tau$) for Mario Bros. example.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n \\hline\n\\textbf{Setup ($\\tau$)}\n& \\textbf{Deceptive Winning Initial States}& \\textbf{VoD} \\\\\n\\hline\n Case 1 (-) \n&$\\mathsf{[4, 10, 11, 16, 17, 5]}$ &$0$ \\\\ \\hline Case 2 (0) \n&$\\mathsf{[4, 10, 11, 16, 17, 5]}$ &$0$ \\\\ \\hline\n Case 2 (1) \n&$\\mathsf{15, 20, [4, 10, 11, 16, 17, 5]}$ &$0.105$ \\\\ \\hline\n Case 2 (2) \n&$\\mathsf{21, 27, 32, 34, 15, 20, [4, 10, 11, 16, 17, 5]}$ &$0.315$ \\\\ \\hline\n Case 2 (3) \n&$\\mathsf{26, 31, 21, 27, 32, 34, 15, 20, [4, 10, 11, 16, 17, 5]}$ &$0.421$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Reactive Synthesis of Sensor Revealing Strategies in Hypergames on Graphs", "authors": ["Sumukha Udupa", "Ahmed Hemida", "Charles A. Kamhoua", "Jie Fu"], "url": "https://arxiv.org/abs/2412.01975v1", "attribution": "\"Reactive Synthesis of Sensor Revealing Strategies in Hypergames on Graphs\" by Sumukha Udupa, Ahmed Hemida, Charles A. Kamhoua, and Jie Fu, arXiv:2412.01975v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10719v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Canadian Lynx time series optimal gamma.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n \\hline\nAhead & $\\gamma$\\_mape & $\\gamma$\\_smape\\\\ \n \\hline\n1.00 & 0.01 & 0.06 \\\\ \n2.00 & 0.00 & 0.02 \\\\ \n3.00 & 0.02 & 0.04 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Short-term prediction of Time Series based on bounding techniques", "authors": ["Pedro Cadahía", "Jose Manuel Bravo Caro"], "url": "https://arxiv.org/abs/2101.10719v1", "attribution": "\"Short-term prediction of Time Series based on bounding techniques\" by Pedro Cadahía and Jose Manuel Bravo Caro, arXiv:2101.10719v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10242v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CHARACTERISTICS OF THE DATASETS}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n\t\t\\hline\\hline\n\t\t\\multirow{2}{*}{\\textbf{Dataset}} & \\multicolumn{3}{|c}{\\textbf{Characteristics}}\\\\ \n\t\t\\cline{2-4} & \\textbf{Samples}\t& \\textbf{Dimensionality} &\t\\textbf{Classes} \\\\\n\t\t\\hline\\hline\n\t\tSynth1 &\t2000 &\t30 &\t5\\\\\n\t\tSynth2 &\t1000 &\t2 &\t2\\\\\n\t\tBreast &\t106 &\t 9 &\t6\\\\\n\t\tIris &\t150 &\t 4 &\t3\\\\\n\t\tWine &\t178 & \t13 &\t3\\\\\n\t\tHeart &\t267 & \t22 &\t2\\\\\n\t\tBoston &\t506 & \t13 &\t2\\\\\n\t\tWaveform & \t5000 &\t21 &\t3\\\\\n\t\t\\hline\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "GMM Discriminant Analysis with Noisy Label for Each Class", "authors": ["Jian-wei Liu", "Zheng-ping Ren", "Run-kun Lu", "Xiong-lin Luo"], "url": "https://arxiv.org/abs/2201.10242v1", "attribution": "\"GMM Discriminant Analysis with Noisy Label for Each Class\" by Jian-wei Liu, Zheng-ping Ren, Run-kun Lu, and Xiong-lin Luo, arXiv:2201.10242v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.01167v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Bias.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrr}\nStudy & Units & Mean & 95\\% CI \\\\\n\\midrule\nExercise & Weekly gym visits & 2.32 & (2.15, 2.50) \\\\\nFlu & People per 100 & 2.41 & (0.33, 5.90) \\\\\nRCT & Percentage points & 3.37 & (1.75, 5.31) \\\\\nEffort & Points & 66.90 & (23.35, 126.63) \\\\\nReproducibility & Pr. replication & 0.22 & (0.08, 0.33) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Simple models predict behavior at least as well as behavioral scientists", "authors": ["Dillon Bowen"], "url": "https://arxiv.org/abs/2208.01167v1", "attribution": "\"Simple models predict behavior at least as well as behavioral scientists\" by Dillon Bowen, arXiv:2208.01167v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10495v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results with relationships between transparency modes. The relationship are based on mean ranks obtained through Friedman's Test. The symbol $^*$ denotes significant difference ($p<0.05$) and the symbol $^{**}$ denotes marginally significant difference ($p<0.10$). The symbol $^-$ denotes negative scales and lower ranking is a good ranking.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n\\hline\n\\textbf{Attributes} & \\textbf{Relationship} & \\textbf{$\\chi^2(3)$} & \\textbf{$p$-value} \\\\ \\hline \n\\multicolumn{4}{c}{\\textbf{SART SUBJECTIVE SCALE}} \\\\ \\hline\\hline\nInstability of Situation$^-$ & not significant & $4.192$ & $0.241$ \\\\ \nComplexity of Situation$^-$ & NT$>$MT$>$PT$>$CT$^{**}$ & $6.435$ & $0.092$ \\\\\nVariability of Situation$^-$ & not significant & $4.192$ & $0.241$ \\\\ \nArousal & NT$>$MT$>$PT$>$CT$^{**}$ & $7.093$ & $0.069$ \\\\\nConcentration of Attention & not significant & $4.664$ & $0.198$ \\\\\nSpare Mental Capacity & not significant & $3.526$ & $0.317$ \\\\\nInformation Quantity & MT$>$CT$=$PT$>$NT$^{*}$ & $16.160$ & $0.001$ \\\\\nInformation Quality & MT$>$CT$>$PT$>$NT$^{*}$ & $11.351$ & $0.010$ \\\\\nFamiliarity with Situation & not significant & $1.911$ & $0.591$ \\\\ \\hline\\hline\n\\multicolumn{4}{c}{\\textbf{NASA TLX SUBJECTIVE SCALE}} \\\\ \\hline\\hline\nMental Demand$^-$ & not significant & $6.169$ & $0.104$ \\\\ \nPhysical Demand$^-$ & not significant & $3.526$ & $0.317$ \\\\ \nTemporal Demand$^-$ & not significant & $0.564$ & $0.903$ \\\\ \nPerformance$^-$ & not significant & $4.573$ & $0.206$ \\\\ \nEffort$^-$ & NT$>$PT$>$CT$>$MT$^{* }$ & $9.203$ & $0.027$ \\\\ \nFrustration$^-$ & NT$>$CT$>$MT$>$PT$^{* }$ & $9.205$ & $0.027$ \\\\ \\hline\\hline\n\\multicolumn{4}{c}{\\textbf{TRUST SUBJECTIVE SCALE}} \\\\ \\hline\\hline\nCompetence & not significant & $3.703$ & $0.295$ \\\\ \nPredictability & PT$>$CT$>$MT$>$NT$^{**}$ & $6.359$ & $0.095$ \\\\ \nReliability & not significant & $4.338$ & $0.227$ \\\\ \nFaith & not significant & $1.891$ & $0.595$ \\\\ \nOverall Trust & PT$>$MT$>$CT$=$NT$^{* }$ & $12.607$ & $0.005$ \\\\ \nAccuracy & PT$>$MT$=$CT$>$NT$^{* }$ & $12.214$ & $0.007$ \\\\ \\hline\\hline\n\\multicolumn{4}{c}{\\textbf{INTERACTION SUBJECTIVE SCALE}} \\\\ \\hline\\hline\nTeammate's Intent & MT$>$PT$>$CT$>$NT$^{*}$ & $23.976$ & $0.000$ \\\\ \nTeammate's Action & MT$>$PT$=$CT$>$NT$^{*}$ & $22.511$ & $0.000$ \\\\ \nTask Progress & MT$>$CT$>$PT$>$NT$^{*}$ & $25.619$ & $0.000$ \\\\ \nRobot Status & CT$>$PT$>$MT$>$NT$^{*}$ & $13.608$ & $0.003$ \\\\ \nInformation Clarity & CT$>$PT$>$MT$>$NT$^{*}$ & $12.078$ & $0.007$ \\\\ \\hline\\hline\n\\multicolumn{4}{c}{\\textbf{PERFORMANCE OBJECTIVE SCALE}} \\\\ \\hline\\hline\nPoints Scored & not significant & $5.554$ & $0.135$ \\\\ \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Transparency in Multi-Human Multi-Robot Interaction", "authors": ["Jayam Patel", "Tyagaraja Ramaswamy", "Zhi Li", "Carlo Pinciroli"], "url": "https://arxiv.org/abs/2101.10495v2", "attribution": "\"Transparency in Multi-Human Multi-Robot Interaction\" by Jayam Patel, Tyagaraja Ramaswamy, Zhi Li, and Carlo Pinciroli, arXiv:2101.10495v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.12009v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of different audio pruning methods across various metrics. PR: Pruning Rate, TF: TFLOPS, PT: Prefill time (ms), TM: Total memory (GB), SA: Storing activation (GB), RAP: Random Audio Pruning, RAC: Random Audio Cropping.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n\\toprule[2pt]\nMethod & PR & TF $\\downarrow$ & PT $\\downarrow$ & TM $\\downarrow$ & SA $\\downarrow$ & SPIRAL $\\uparrow$ & SPIRAL-H $\\uparrow$ \\\\\n\\midrule[1pt]\nOriginal & -- & 12.2 & 779 & 13.40 & 0.19 & 60.38\\% & 0\\% \\\\\n\\midrule[0.5pt]\nRAP & \\multirow{3}{*}{0.2} & \\multirow{3}{*}{10.06} & \\multirow{3}{*}{662} & \\multirow{3}{*}{13.32} & \\multirow{3}{*}{0.15} & 42.49\\% & 21.45\\% \\\\\nRAC & & & & & & 65.71\\% & 48.13\\% \\\\\n\\textbf{Ours} & & & & & & \\textbf{89.23\\%} & \\textbf{81.64\\%} \\\\\n\\midrule[0.5pt]\nRAP & \\multirow{3}{*}{0.4} & \\multirow{3}{*}{7.93} & \\multirow{3}{*}{511} & \\multirow{3}{*}{13.24} & \\multirow{3}{*}{0.11} & 42.89\\% & 22.19\\% \\\\\nRAC & & & & & & 62.45\\% & 41.90\\% \\\\\n\\textbf{Ours} & & & & & & \\textbf{85.97\\%} & \\textbf{76.43\\%} \\\\\n\\midrule[0.5pt]\nRAP & \\multirow{3}{*}{0.6} & \\multirow{3}{*}{5.79} & \\multirow{3}{*}{419} & \\multirow{3}{*}{13.17} & \\multirow{3}{*}{0.07} & 42.39\\% & 21.45\\% \\\\\nRAC & & & & & & 58.20\\% & 35.41\\% \\\\\n\\textbf{Ours} & & & & & & \\textbf{75.89\\%} & \\textbf{63.77\\%} \\\\\n\\midrule[0.5pt]\nRAP & \\multirow{3}{*}{0.8} & \\multirow{3}{*}{3.66} & \\multirow{3}{*}{278} & \\multirow{3}{*}{13.09} & \\multirow{3}{*}{0.04} & 45.26\\% & 23.19\\% \\\\\nRAC & & & & & & 55.83\\% & 33.67\\% \\\\\n\\textbf{Ours} & & & & & & \\textbf{62.45\\%} & \\textbf{46.15\\%} \\\\\n\\bottomrule[2pt]\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SpeechPrune: Context-aware Token Pruning for Speech Information Retrieval", "authors": ["Yueqian Lin", "Yuzhe Fu", "Jingyang Zhang", "Yudong Liu", "Jianyi Zhang", "Jingwei Sun", "Hai \"Helen\" Li", "Yiran Chen"], "url": "https://arxiv.org/abs/2412.12009v2", "attribution": "\"SpeechPrune: Context-aware Token Pruning for Speech Information Retrieval\" by Yueqian Lin, Yuzhe Fu, Jingyang Zhang, Yudong Liu, Jianyi Zhang, Jingwei Sun, Hai \"Helen\" Li, and Yiran Chen, arXiv:2412.12009v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18259v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of European option prices between closed-form solution and INAR($\\infty$) implementations}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cccc}\n \\hline\n \\multirow{2}{*}{Strike} & \\multicolumn{2}{c|}{Closed-form solution} & \\multicolumn{4}{c}{INAR($\\infty$)} \\\\\n \\cline{2-7}\n & Call & Put & Call & 95\\% CI & Put & 95\\% CI \\\\\n \\hline\n 80 & 21.8822 & 1.8822 & 21.8446 & [21.7172, 21.9719] & 1.9003 & [1.8687, 1.9320] \\\\\n 90 & 14.6187 & 4.6187 & 14.5940 & [14.4824, 14.7057] & 4.6498 & [4.5977, 4.7020] \\\\\n 100 & 9.0983 & 9.0983 & 9.1003 & [9.0082, 9.1924] & 9.1561 & [9.0818, 9.2304] \\\\\n 110 & 5.2883 & 15.2883 & 5.2951 & [5.2233, 5.3669] & 15.3508 & [15.2560, 15.4457] \\\\\n 120 & 2.8849 & 22.8849 & 2.8911 & [2.8378, 2.9445] & 22.9469 & [22.8353, 23.0585] \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\\infty$) processes and applications", "authors": ["Yingli Wang", "Zhenyu Cui"], "url": "https://arxiv.org/abs/2503.18259v3", "attribution": "\"Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\\infty$) processes and applications\" by Yingli Wang and Zhenyu Cui, arXiv:2503.18259v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.19307v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\t\t\\toprule\n\t\t& \\textbf{Low} & \\textbf{Medium}&\\textbf{High}&\\textbf{Extreme}\\\\\n\t\t\\midrule\n\t\t\\rowcolor{gray!15}\n\t\tLow & 0.006 & 0.018 & 0.042 & 0.073 \\\\\n\t\tMedium-High & 0.015 & 0.046 & 0.107 & 0.183 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Climate Physical Risk Assessment in Asset Management", "authors": ["Michele Azzone", "Matteo Ghesini", "Davide Stocco", "Lorenzo Viola"], "url": "https://arxiv.org/abs/2504.19307v1", "attribution": "\"Climate Physical Risk Assessment in Asset Management\" by Michele Azzone, Matteo Ghesini, Davide Stocco, and Lorenzo Viola, arXiv:2504.19307v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.09591v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllllllllc}\n \\hline\n \\;\\;$u_1$&\\;\\;$u_2$&\\;\\;$u_3$&\\;\\;$u_4$&\\;\\;$u_5$&\\;\\;$u_6$&\\;\\;$t_1$&\\;\\;$t_2$&\\;\\;$t_3$&\\;\\;$t_4$&\\;\\;$t_5$&\\;\\;$t_6$&\\quad\\; \\(f(n)\\) & Side\\;\\; \\\\ \n \\hline\n \\hline\n $k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n - 96}$& \\\\ \n\\hline\n$k$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 3$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n - 47}$&$t_1 $\\\\ \n\\hline\n$k$&$k + 1$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 3$&$k - 3$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n + 4}$&$t_2 $\\\\ \n\\hline\n$k$&$k - 2$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n - 39}$&$u_2 $\\\\ \n\\hline\n$k + 1$&$k - 1$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 3$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n + 16}$&$t_1 $\\\\ \n\\hline\n$k - 2$&$k - 1$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 1$&$6 n - \\sqrt{96 n - 23}$&$u_1 $\\\\ \n\\hline\n$k - 1$&$k - 1$&$k$&$k - 1$&$k - 1$&$k$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n - 60}$&$t_6 $\\\\ \n\\hline\n$k - 1$&$k$&$k + 1$&$k - 1$&$k - 1$&$k$&$k - 2$&$k - 3$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n + 1}$&$t_2 $\\\\ \n\\hline\n$k - 1$&$k$&$k - 2$&$k - 1$&$k - 1$&$k$&$k - 2$&$k - 2$&$k - 1$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n - 32}$&$u_3 $\\\\ \n\\hline\n$k - 1$&$k$&$k - 1$&$k$&$k - 1$&$k$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n - 63}$&$t_3 $\\\\ \n\\hline\n$k$&$k + 1$&$k - 1$&$k$&$k - 1$&$k$&$k - 3$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n + 4}$&$t_1 $\\\\ \n\\hline\n$k$&$k - 2$&$k - 1$&$k$&$k - 1$&$k$&$k - 2$&$k - 1$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n - 23}$&$u_2 $\\\\ \n\\hline\n$k$&$k - 1$&$k$&$k$&$k - 1$&$k$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n - 48}$&$t_2 $\\\\ \n\\hline\n$k + 1$&$k$&$k$&$k$&$k - 1$&$k$&$k - 3$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n + 25}$&$t_1 $\\\\ \n\\hline\n$k - 2$&$k$&$k$&$k$&$k - 1$&$k$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 1$&$6 n - \\sqrt{96 n + 4}$&$u_1 $\\\\ \n\\hline\n$k - 1$&$k$&$k$&$k$&$k - 1$&$k + 1$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n - 15}$&$t_6 $\\\\ \n\\hline\n$k - 1$&$k$&$k$&$k$&$k - 1$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n - 32}$&$u_6 $\\\\ \n\\hline\n$k - 1$&$k$&$k$&$k$&$k$&$k - 1$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 1$&$6 n - \\sqrt{96 n - 47}$&$t_5 $\\\\ \n\\hline\n$k$&$k$&$k$&$k$&$k$&$k$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n - 60}$&$t_6 $\\\\ \n\\hline\n$k + 1$&$k + 1$&$k$&$k$&$k$&$k$&$k - 3$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$6 n - \\sqrt{96 n + 25}$&$t_1 $\\\\ \n\\hline\n$k - 2$&$k + 1$&$k$&$k$&$k$&$k$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 1$&$6 n - \\sqrt{96 n + 16}$&$u_1 $\\\\ \n\\hline\n$k - 2$&$k - 2$&$k$&$k$&$k$&$k$&$k - 1$&$k - 1$&$k - 2$&$k - 2$&$k - 2$&$k - 1$&$6 n - \\sqrt{96 n + 9}$&$u_2 $\\\\ \n\\hline\n$k - 2$&$k - 1$&$k + 1$&$k$&$k$&$k$&$k - 1$&$k - 2$&$k - 2$&$k - 2$&$k - 2$&$k - 1$&$6 n - \\sqrt{96 n + 4}$&$t_2 $\\\\ \n\\hline\n$k - 2$&$k - 1$&$k - 2$&$k$&$k$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 2$&$k - 2$&$k - 1$&$6 n - \\sqrt{96 n + 1}$&$u_3 $\\\\ \n\\hline\n$k - 2$&$k - 1$&$k - 1$&$k + 1$&$k$&$k$&$k - 1$&$k - 1$&$k - 2$&$k - 2$&$k - 2$&$k - 1$&$6n- \\sqrt{96n}$&$t_3 $\\\\ \n\\hline\n$k - 2$&$k - 1$&$k - 1$&$k - 2$&$k$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 2$&$k - 1$&$6 n - \\sqrt{96 n + 1}$&$u_4 $\\\\ \n\\hline\n$k - 2$&$k - 1$&$k - 1$&$k - 1$&$k + 1$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 2$&$k - 2$&$k - 1$&$6 n - \\sqrt{96 n + 4}$&$t_4 $\\\\ \n\\hline\n$k - 2$&$k - 1$&$k - 1$&$k - 1$&$k - 2$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n + 9}$&$u_5 $\\\\ \n\\hline\n$k - 2$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k + 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 2$&$k - 1$&$6 n - \\sqrt{96 n + 16}$&$t_5 $\\\\ \n\\hline\n$k - 2$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 2$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k$&$6 n - \\sqrt{96 n + 25}$&$u_6 $\\\\ \n\\hline\n$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n - 60}$&$t_6 $\\\\ \n\\hline\n$k$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 2$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n - 47}$&$t_1 $\\\\ \n\\hline\n$k$&$k + 1$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 2$&$k - 2$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n - 32}$&$t_2 $\\\\ \n\\hline\n$k$&$k - 2$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n - 15}$&$u_2 $\\\\ \n\\hline\n$k + 1$&$k - 1$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 2$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n + 4}$&$t_1 $\\\\ \n\\hline\n$k - 2$&$k - 1$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k$&$6 n - \\sqrt{96 n + 25}$&$u_1 $\\\\ \n\\hline\n$k - 1$&$k - 1$&$k$&$k - 1$&$k - 1$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n - 48}$&$t_6 $\\\\ \n\\hline\n$k - 1$&$k$&$k + 1$&$k - 1$&$k - 1$&$k$&$k - 1$&$k - 2$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n - 23}$&$t_2 $\\\\ \n\\hline\n$k - 1$&$k$&$k - 2$&$k - 1$&$k - 1$&$k$&$k - 1$&$k - 1$&$k$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n + 4}$&$u_3 $\\\\ \n\\hline\n$k - 1$&$k$&$k - 1$&$k$&$k - 1$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n - 63}$&$t_3 $\\\\ \n\\hline\n$k$&$k + 1$&$k - 1$&$k$&$k - 1$&$k$&$k - 2$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n - 32}$&$t_1 $\\\\ \n\\hline\n$k$&$k - 2$&$k - 1$&$k$&$k - 1$&$k$&$k - 1$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n + 1}$&$u_2 $\\\\ \n\\hline\n$k$&$k - 1$&$k$&$k$&$k - 1$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n - 60}$&$t_2 $\\\\ \n\\hline\n$k + 1$&$k$&$k$&$k$&$k - 1$&$k$&$k - 2$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n - 23}$&$t_1 $\\\\ \n\\hline\n$k - 2$&$k$&$k$&$k$&$k - 1$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k$&$6 n - \\sqrt{96 n + 16}$&$u_1 $\\\\ \n\\hline\n$k - 1$&$k$&$k$&$k$&$k - 1$&$k + 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n - 39}$&$t_6 $\\\\ \n\\hline\n$k - 1$&$k$&$k$&$k$&$k - 1$&$k - 2$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k$&$k$&$6 n - \\sqrt{96 n + 4}$&$u_6 $\\\\ \n\\hline\n$k - 1$&$k$&$k$&$k$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k$&$6 n - \\sqrt{96 n - 47}$&$t_5 $\\\\ \n\\hline\n$k$&$k$&$k$&$k$&$k$&$k$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$k - 1$&$6 n - \\sqrt{96 n - 96}$&$t_6 $\\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Building up to the next \\(12\\)-gon}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Edge isoperimetry of lattices", "authors": ["Cameron Strachan", "Konrad Swanepoel"], "url": "https://arxiv.org/abs/2503.09591v1", "attribution": "\"Edge isoperimetry of lattices\" by Cameron Strachan and Konrad Swanepoel, arXiv:2503.09591v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.19217v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{All combinations of hyperparameter values and their cross-validation results on PO data.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n \\toprule\n\\textbf{Batch Size} & \\textbf{Hidden Layers} & \\textbf{Weight Decay} & \\textbf{AUC\\_CV} \\\\\n \\midrule\n 250 & 1 & 0.0001 & 0.632 \\\\\n 250 & 1 & 0.001 & 0.631 \\\\\n 250 & 1 & 0.01 & 0.649 \\\\\n 250 & 2 & 0.0001 & 0.654 \\\\\n 250 & 2 & 0.001 & 0.659 \\\\\n 250 & 2 & 0.01 & \\textbf{0.673} \\\\\n 250 & 3 & 0.0001 & 0.619 \\\\\n 250 & 3 & 0.001 & 0.624 \\\\\n 250 & 3 & 0.01 & 0.635 \\\\\n 2500 & 1 & 0.0001 & 0.634 \\\\\n 2500 & 1 & 0.001 & 0.631 \\\\\n 2500 & 1 & 0.01 & 0.632 \\\\\n 2500 & 2 & 0.0001 & 0.659 \\\\\n 2500 & 2 & 0.001 & 0.659 \\\\\n 2500 & 2 & 0.01 & 0.660 \\\\\n 2500 & 3 & 0.0001 & 0.617 \\\\\n 2500 & 3 & 0.001 & 0.619 \\\\\n 2500 & 3 & 0.01 & 0.634 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Applying the maximum entropy principle to neural networks enhances multi-species distribution models", "authors": ["Maxime Ryckewaert", "Diego Marcos", "Christophe Botella", "Maximilien Servajean", "Pierre Bonnet", "Alexis Joly"], "url": "https://arxiv.org/abs/2412.19217v3", "attribution": "\"Applying the maximum entropy principle to neural networks enhances multi-species distribution models\" by Maxime Ryckewaert, Diego Marcos, Christophe Botella, Maximilien Servajean, Pierre Bonnet, and Alexis Joly, arXiv:2412.19217v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17626v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c}\n \\toprule\n $\\nu$ & $32\\times 32$ & $64\\times 64$ & $128\\times 128$ & $256\\times 256$&$512\\times 512$\\\\\n \\midrule\n $1\\cdot 10^{-2}$ & 9 & 14 & 21 & 24 & 26\\\\\n $5\\cdot 10^{-3}$ & 13 & 13 & 19 & 28 & 33\\\\\n $2.5\\cdot 10^{-3}$ & 19 & 17 & 17 & 25 & 37\\\\ \n $1.25\\cdot 10^{-3}$ & 27 & 24 & 21 & 22 & 33\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Preconditioned normal equations for solving discretised partial differential equations", "authors": ["Lorenzo Lazzarino", "Yuji Nakatsukasa", "Umberto Zerbinati"], "url": "https://arxiv.org/abs/2502.17626v2", "attribution": "\"Preconditioned normal equations for solving discretised partial differential equations\" by Lorenzo Lazzarino, Yuji Nakatsukasa, and Umberto Zerbinati, arXiv:2502.17626v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11608v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllllllll}\n \\hline\n & $\\mathcal{M}_c\\,(M_{\\odot})$ & $\\eta$ & $D\\,$(Mpc) & $t_c$(s) & $\\alpha$ (rad) & $\\delta$ (rad) & $\\phi_c$ (rad) & $\\psi$ (rad) & $\\iota$ (rad) & SNR \\\\\n \\hline\n injection & 1.2188 & 0.25 & 172 & 0 & 4.91 & -0.981 & 1.46 & 0 & 1.89 & 11.4 \\\\\n standard & $1.2188^{1.2189}_{1.2184}$ & $0.249^{0.250}_{0.243}$ & $153^{107}_{334}$ & $0^{0}_{0}$ & $4.89_{1.79}^{4.98}$ & $-0.978_{-1.01}^{1.20}$ & $3.35_{0.309}^{6.07}$ & $1.58_{1.54\\times10^{-2}}^{3.13}$ & $1.82_{1.32}^{2.91}$ & 12.9 \\\\\n ROQ & $1.2188^{1.2189}_{1.2184}$ & $0.249^{0.250}_{0.243}$ & $149_{100}^{352}$ & $0_{0}^{0}$ & $4.89_{1.82}^{4.98}$ & $-0.978_{-1.01}^{1.19}$ & $3.01_{0.543}^{5.97}$ & $1.58_{2.85\\times 10^{-2}}^{3.11}$ & $1.79_{1.39}^{2.82}$ & 12.9 \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Reduced Order and Surrogate Models for Gravitational Waves", "authors": ["Manuel Tiglio", "Aarón Villanueva"], "url": "https://arxiv.org/abs/2101.11608v1", "attribution": "\"Reduced Order and Surrogate Models for Gravitational Waves\" by Manuel Tiglio and Aarón Villanueva, arXiv:2101.11608v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16004v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\nTeam & Dice (\\%) $\\uparrow$ & AVD $\\downarrow$ & F1 (\\%) $\\uparrow$ & ALCD $\\downarrow$ \\\\\n\\hline\n\\textbf{Kurtlab (Ours)} & \\textbf{28.50 (21.27)} & \\textbf{21.23 (37.22)} & \\textcolor{blue}{14.39 (21.19)} & \\textcolor{blue}{7.18 (7.67)} \\\\\nAMC-Axolotls & \\textcolor{blue}{26.27 (24.73)} & \\textcolor{blue}{21.31 (35.23)} & \\textbf{14.94 (25.12)} & 7.66 (7.94) \\\\\nNinjas & 25.46 (19.08) & 26.29 (39.73) & 9.92 (13.46) & \\textbf{5.98 (6.46)} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Clinically-Informed Preprocessing Improves Stroke Segmentation in Low-Resource Settings", "authors": ["Juampablo E. Heras Rivera", "Hitender Oswal", "Tianyi Ren", "Yutong Pan", "William Henry", "Caitlin M. Neher", "Mehmet Kurt"], "url": "https://arxiv.org/abs/2508.16004v1", "attribution": "\"Clinically-Informed Preprocessing Improves Stroke Segmentation in Low-Resource Settings\" by Juampablo E. Heras Rivera, Hitender Oswal, Tianyi Ren, Yutong Pan, William Henry, Caitlin M. Neher, and Mehmet Kurt, arXiv:2508.16004v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.14385v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll} \\toprule\n\\rowcolor{lightgray}\n {\\textit{Parameter(s)}} & {\\textit{Values}} \\\\ \\midrule\n Convergence threshold of value iteration & $0.1$.\\\\\n Number of particles $M$ & $50$ [cf. ()] \\\\\n Discount factor $\\gamma$ & $0.99$ [cf. ()]\\\\\n \\bottomrule\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Hyperparameters.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization", "authors": ["Kim Hammar", "Tao Li"], "url": "https://arxiv.org/abs/2508.14385v1", "attribution": "\"Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization\" by Kim Hammar and Tao Li, arXiv:2508.14385v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.06062v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Values of test statistics and $p$-values (in parenthesis) of the Gaussian, vdW, Spearman, sign ($n_S = 22$), and sign ($n_S = 726$) tests, along with their $p$-values (in brackets) and (for vdW and Spearman) bias-corrected $p$-values (in square brackets) under the null hypotheses of a VAR($p_0$) model ($p_0=0, ..., 7$).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\n$p_0$ & Gaussian & vdW & Spearman & Sign ($n_S = 22$) & Sign ($n_S = 726$) \\\\ \\hline\n$0$ & 25.05 (4.91$\\times 10^{-5}$) & 224.67 (0) [0] & 238.71 (0) [0] & 356.90 (0) & 342.58 (0) \\\\ \n$1$ & 25.83 (3.42$\\times 10^{-5}$) & 16.77 (0.002) [0.001] & 15.42 (0.004) [0.001] & 26.16 (2.94$\\times 10^{-5}$) & 59.14 (4.40$\\times 10^{-12}$) \\\\\n$2$ & 2.30 (0.681) & 62.35 (9.30$\\times 10^{-13}$) [0] & 71.57 (1.07$\\times 10^{-14}$) [0] & 16.56 (0.002) & 56.74 (1.40$\\times 10^{-11}$) \\\\\n$3$ & & 48.59 (7.18$\\times 10^{-10}$) [0] & 56.90 (1.30$\\times 10^{-11}$) [0] & 47.99 (9.49$\\times 10^{-10}$) & 55.64 (2.38$\\times 10^{-11}$) \\\\\n$4$ & & 10.74 (0.030) [0.016] & 13.14 (0.011) [0.010] & 26.58 (2.41$\\times 10^{-5}$) & 12.94 (0.012) \\\\\n$5$ & & 19.02 (7.81$\\times 10^{-5}$) [0] & 21.37 (2.67$\\times 10^{-5}$) [0] & 16.57 (0.002) & 40.57 (3.29$\\times 10^{-8}$) \\\\\n$6$ & & 7.58 (0.108) [0.068] & 24.14 (7.49$\\times 10^{-5}$) [0.005] & 32.58 (1.45$\\times 10^{-6}$) & 26.22 (2.85$\\times 10^{-5}$) \\\\\n$7$ & & & 4.27 (0.371) [0.322] & 8.60 (0.072) & 8.24 (0.083) \n \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach", "authors": ["Marc Hallin", "Davide La Vecchia", "Hang Liu"], "url": "https://arxiv.org/abs/2011.06062v1", "attribution": "\"Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach\" by Marc Hallin, Davide La Vecchia, and Hang Liu, arXiv:2011.06062v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.00208v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Customers' Spend Before/After Adopting the Online Channel}\n\\begin{tabular}{lcc}\n \\midrule \\midrule\n DV: & \\multicolumn{2}{c}{Spend}\\\\\n Model: & (1) & (2)\\\\ \n \\midrule\n \\emph{Variables}\\\\\n Constant & 318.4 (0.621) & \\\\ \n & [0.000] & \\\\ \n Post\\_online\\_adoption & 147.4 (0.764) & 124.2 (0.782)\\\\ \n & [0.000] & [0.000]\\\\ \n \\midrule\n \\emph{Fixed-effects}\\\\\n Customer & & Yes\\\\ \n YearMonth & & Yes\\\\ \n \\midrule\n \\emph{Fit statistics}\\\\\n Observations & 34,226,864 & 34,226,864\\\\ \n R$^2$ & 0.00754 & 0.34036\\\\ \n Within R$^2$ & & 0.00275\\\\ \n \\midrule \\midrule\n\\multicolumn{3}{l}{\\emph{Clustered (Customer) standard‐errors are presented in standard brackets,}}\\\\\n\\multicolumn{3}{l}{\\emph{while p‑values are shown in square brackets.}}\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Channel Choice and Customer Value", "authors": ["Shirsho Biswas", "Hema Yoganarasimhan", "Haonan Zhang"], "url": "https://arxiv.org/abs/2508.00208v1", "attribution": "\"Channel Choice and Customer Value\" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2508.00208v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01857v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccccc}\n &Flare&Rainbow& Rainbow$^\\dagger$& Prioritized$^\\dagger$ & QR DQN$^\\dagger$ & IQN$^\\dagger$ \\\\ \\hline\nAssault & $12724{\\pm}1107$ & $\\mathbf{15229}{\\pm}2061$ & $13194{\\pm}4348$ & $10163{\\pm}1558$ & $10215{\\pm}1255$ & $11903{\\pm}1251$ \\\\\nBreakout & $345{\\pm}22$ & $280{\\pm}18$ & $366{\\pm}20$ & $359{\\pm}33$ & $450{\\pm}29$ & $\\mathbf{492}{\\pm}86$ \\\\\nBerzerk & $\\mathbf{2049}{\\pm}421$ & $1636{\\pm}598$ & $3151{\\pm}537$ & $986{\\pm}98$ & $896{\\pm}98$ & $946{\\pm}48$ \\\\\nDefender & $\\mathbf{86982}{\\pm}29214$ & $44694{\\pm}3984$ & $52419{\\pm}4481$ & $13750{\\pm}4182$ & $32320{\\pm}8997$ & $33767{\\pm}3643$ \\\\ \nMontezuma & $\\mathbf{1668}{\\pm}1055$ & $900{\\pm}807$ & $80{\\pm}160$ & $0{\\pm}0$ & $0{\\pm}0$ & $0{\\pm}0$\\\\\nSeaquest & $13901{\\pm}8085$ & $\\mathbf{24090}{\\pm}12474$ & $5838{\\pm}2450$ & $7436{\\pm}1790$ & $14864{\\pm}3625$ & $16866{\\pm}4539$ \\\\\nPhoenix & $60974{\\pm}18044$ & $16992{\\pm}3295$ & $\\mathbf{82234}{\\pm}33388$ & $10667{\\pm}3142$ & $41866{\\pm}3673$ & $35586{\\pm}3510$ \\\\\nTutankham & $\\mathbf{248}{\\pm}20$& $\\mathbf{247}{\\pm}11$ & $214{\\pm}24$ & $168{\\pm}22$ & $171{\\pm}30$ & $216{\\pm}34$\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Reinforcement Learning with Latent Flow", "authors": ["Wenling Shang", "Xiaofei Wang", "Aravind Srinivas", "Aravind Rajeswaran", "Yang Gao", "Pieter Abbeel", "Michael Laskin"], "url": "https://arxiv.org/abs/2101.01857v1", "attribution": "\"Reinforcement Learning with Latent Flow\" by Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, and Michael Laskin, arXiv:2101.01857v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.05924v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrrrr}\n \\toprule\n \\multirow{3}{*}{Iteration} & \\multicolumn{4}{c}{Alpha} & \\multicolumn{4}{c}{Beta} \\\\\n \\cmidrule(lr){2-5} \\cmidrule(lr){6-9}\n & \\multicolumn{2}{c}{Single Precision} & \\multicolumn{2}{c}{\\textbf{Mixed Precision}} & \\multicolumn{2}{c}{Single Precision} & \\multicolumn{2}{c}{\\textbf{Mixed Precision}} \\\\\n \\cmidrule(lr){2-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-7} \\cmidrule(lr){8-9}\n & Abs Err & Rel Err & Abs Err & Rel Err & Abs Err & Rel Err & Abs Err & Rel Err\\\\\n \\midrule\n 1 & 1.27e-01 & 3.64e-01 & 1.24e-01 & 2.89e-01 & 2.10e-01 & 1.08e-00 & 2.03e-02 & 4.25e-01 \\\\\n 2 & 8.66e-02 & 2.97e-01 & 7.92e-02 & 2.79e-01 & 6.29e-01 & 1.70e-00 & 6.46e-02 & 1.83e-01 \\\\\n 3 & 2.88e-02 & 4.29e-00 & 2.56e-02 & 3.81e-00 & 7.20e-01 & 4.92e-00 & 5.40e-01 & 1.64e-00 \\\\\n 4 & 3.51e-02 & 4.74e-00 & 3.11e-02 & 4.20e-00 & 9.10e-01 & 2.48e-00 & 7.60e-01 & 1.47e-00 \\\\\n 5 & 4.72e-02 & 5.49e-00 & 4.19e-02 & 4.87e-00 & 9.10e-01 & 2.55e-00 & 7.50e-01 & 1.42e-00 \\\\\n 6 & 6.85e-02 & 6.48e-00 & 6.08e-02 & 5.75e-00 & 7.70e-01 & 8.50e-00 & 4.90e-01 & 5.30e-00 \\\\\n 7 & 1.06e-01 & 7.69e-00 & 9.43e-02 & 6.84e-00 & 9.50e-01 & 1.70e-00 & 6.80e-01 & 2.41e-00 \\\\\n 8 & 1.71e-01 & 8.96e-00 & 1.52e-01 & 7.96e-00 & 8.40e-01 & 3.30e-00 & 6.50e-01 & 1.47e-00 \\\\\n 9 & 2.65e-01 & 9.68e-00 & 2.36e-01 & 8.62e-00 & 1.13e-00 & 8.57e-00 & 5.76e-01 & 1.36e-00 \\\\\n 10 & 3.15e-01 & 8.33e-00 & 2.82e-01 & 7.46e-00 & 1.89e-00 & 2.86e-00 & 1.66e-00 & 1.86e-00 \\\\\n 11 & 1.56e-01 & 3.61e-00 & 1.42e-01 & 3.29e-00 & 1.91e-00 & 2.10e-00 & 1.86e-00 & 2.15e-00 \\\\\n 12 & 1.60e-01 & 4.39e-00 & 1.43e-01 & 3.92e-00 & 1.80e-00 & 2.61e-00 & 1.68e-00 & 1.68e-00 \\\\\n 13 & 1.82e-01 & 7.34e-00 & 1.62e-01 & 6.53e-00 & 1.96e-00 & 2.08e-00 & 1.94e-00 & 1.94e-00 \\\\\n 14 & 1.89e-01 & 1.17e-01 & 1.66e-01 & 1.03e-01 & 2.13e-00 & 2.37e-00 & 1.89e-00 & 1.89e-00 \\\\\n 15 & 1.93e-01 & 1.76e-01 & 1.73e-01 & 1.57e-01 & 1.99e-00 & 2.27e-00 & 1.87e-00 & 1.87e-00 \\\\\n 16 & 2.54e-01 & 3.19e-01 & 2.38e-01 & 2.99e-01 & 2.00e-00 & 3.66e-00 & 1.54e-00 & 1.54e-00 \\\\\n 17 & 2.98e-01 & 4.88e-01 & 2.86e-01 & 4.68e-01 & 1.99e-00 & 4.57e-00 & 1.43e-00 & 1.43e-00 \\\\\n 18 & 3.42e-01 & 6.95e-01 & 3.32e-01 & 6.75e-01 & 1.98e-00 & 1.98e-00 & 1.95e-00 & 1.95e-00 \\\\\n 19 & 4.18e-01 & 1.01e-02 & 3.97e-01 & 9.62e-01 & 1.96e-00 & 6.75e-00 & 1.29e-00 & 1.29e-00 \\\\\n 20 & 5.00e-01 & 1.40e-02 & 4.98e-01 & 1.39e-02 & 1.69e-00 & 2.90e-00 & 1.58e-00 & 2.27e-00 \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Absolute and Relative Error bounds of $\\alpha$ and $\\beta$ parameters of a uniform single-precision and mixed-precision conjugate gradient program}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Satire: Computing Rigorous Bounds for Floating-Point Rounding Error in Mixed-Precision Loop-Free Programs", "authors": ["Tanmay Tirpankar", "Arnab Das", "Ganesh Gopalakrishnan"], "url": "https://arxiv.org/abs/2503.05924v1", "attribution": "\"Satire: Computing Rigorous Bounds for Floating-Point Rounding Error in Mixed-Precision Loop-Free Programs\" by Tanmay Tirpankar, Arnab Das, and Ganesh Gopalakrishnan, arXiv:2503.05924v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.11765v1_tex_table20.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Selected Training Parameters for the Classification Model of ESG-related News Articles}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llr}\n\\hline\n\\textbf{Parameter} %\n& \\textbf{Value} \\\\ \\hline\nLearning Rate - Step 1 \n& 10-3 \\\\\nLearning Rate - Step 2 \n& 10-4 \\\\\nEarly Stopping Criteria \n& 1 \\\\\nLearning Rate Reduction Criteria %\n& 5 \\\\ \\hline\nMaximum Sequence Length %\n& 512 \\\\\nBatch Size %\n& 32 \\\\\nLoss Funcion & %\nSparse categorical cross-entropy loss \\\\\nEvaluation Metric & %\nAccuracy \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis", "authors": ["Tanja Aue", "Adam Jatowt", "Michael Färber"], "url": "https://arxiv.org/abs/2212.11765v1", "attribution": "\"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis\" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.09865v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sectoral and aggregate estimates (Klenow and Willis, 2016; and Edmond et al., 2023).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccccc}\n\\hline\\hline\nSector & Name & RSS & $\\widehat \\varepsilon$ & $\\widehat{\\overline \\sigma}$ & Type & Obs \\\\ \\hline\n15 & Food products and beverages & 54.487 & -0.012 & 2.857 & CRRA & 1044 \\\\\n & & & $[-0.041, 0.014]$ & $[2.663, 3.076]$ & & \\\\\n17 & Textiles, apparel & 22.833 & -0.015 & 4.021 & CRRA & 1534 \\\\\n & & & $[-0.073, 0.026]$ & $[3.730, 4.335]$ & & \\\\\n21 & Paper and paper products & 6.541 & 0.049 & 2.988 & CRRA & 546 \\\\\n & & & $[-0.016, 0.105]$ & $[2.725, 3.297]$ & & \\\\\n24 & Chemicals & 35.971 & 0.114 & 2.636 & IRRA & 1684 \\\\\n & & & $[0.091, 0.133]$ & $[2.494, 2.813]$ & & \\\\\n25 & Rubber and plastic & 11.898 & 0.004 & 3.258 & CRRA & 676 \\\\\n & & & $[-0.031, 0.033]$ & $[3.026, 3.511]$ & & \\\\\n26 & Nonmetallic mineral products & 24.169 & 0.042 & 1.657 & IRRA & 744 \\\\\n & & & $[0.025, 0.057]$ & $[1.576, 1.741]$ & & \\\\\n27 & Basic metals & 20.753 & -0.291 & 5.524 & DRRA & 988 \\\\\n & & & $[-0.427, -0.162]$ & $[5.032, 6.056]$ & & \\\\\n28 & Fabricated metal products & 7.150 & -0.051 & 3.858 & DRRA & 445 \\\\\n & & & $[-0.120, -0.002]$ & $[3.345, 4.401]$ & & \\\\\n29 & Machinery and equipment & 15.351 & -0.033 & 3.585 & DRRA & 745 \\\\\n & & & $[-0.085, -0.004]$ & $[3.334, 3.952]$ & & \\\\\n31 & Electrical machinery & 8.443 & 0.012 & 3.300 & CRRA & 495 \\\\\n & \\quad and communications & & $[-0.009, 0.031]$ & $[3.057, 3.580]$ & & \\\\\n34 & Motor vehicles, trailers & 8.625 & -0.080 & 3.434 & DRRA & 331 \\\\\n & & & $[-0.133, -0.026]$ & $[3.100, 3.796]$ & & \\\\\n0 & All sectors pooled & 274.005 & 0.037 & 2.669 & IRRA & 9232 \\\\\n & & & $[0.030, 0.043]$ & $[2.579, 2.795]$ & & \\\\\n\\hline \\hline\n\\multicolumn{8}{p{17.7cm}}{\\small {\\it Notes:} We use markup and quantity data from De Loecker et al. (2016). We restrict ourselves to single-product firms and products for which the markup exceeds one. Following De Loecker et al. (2016), we trim the top- and bottom-3\\% of quantities and markups by sector. We run 200 bootstrap replications with replacement, and the 95\\% confidence intervals are provided below the coefficients. RSS denotes the residual sum of squares, i.e., the value of our objective function in . Obs stands for the number of observations. Details on the numerical procedure are provided in . All values in excess of $10^4$ are reported using scientific notation.} \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Linear fractional relative risk aversion", "authors": ["Kristian Behrens", "Yasusada Murata"], "url": "https://arxiv.org/abs/2509.09865v1", "attribution": "\"Linear fractional relative risk aversion\" by Kristian Behrens and Yasusada Murata, arXiv:2509.09865v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2501.14107v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean and standard deviations of Absolute Error for MAGI and EFiGP for each parameter on the LV system with a tuned learning rate}\n\\begin{tabular}{lccccccc}\n\\toprule\n & & \\textbf{41}& \\textbf{81} & \\textbf{161} & \\textbf{321} & \\textbf{641} & \\textbf{1281} \\\\\n\\midrule\n\\multirow{6}{*}{\\textbf{EFiGP}} \n & \\textnormal{a} & \\hfill .026$\\pm$.019 & \\hfill .026$\\pm$.019 & \\hfill .036$\\pm$.025 & \\hfill .054$\\pm$.028 & \\hfill .067$\\pm$.028 & \\hfill .078$\\pm$.024 \\\\\n & \\textnormal{b} & \\hfill .027$\\pm$.019 & \\hfill .027$\\pm$.020 & \\hfill .033$\\pm$.025 & \\hfill .049$\\pm$.029 & \\hfill .056$\\pm$.028 & \\hfill .040$\\pm$.026 \\\\\n & \\textnormal{c} & \\hfill .028$\\pm$.019 & \\hfill .029$\\pm$.019 & \\hfill .036$\\pm$.025 & \\hfill .057$\\pm$.028 & \\hfill .067$\\pm$.027 & \\hfill .073$\\pm$.019 \\\\\n & \\textnormal{d} & \\hfill .051$\\pm$.032 & \\hfill .049$\\pm$.034 & \\hfill .066$\\pm$.044 & \\hfill .102$\\pm$.053 & \\hfill .121$\\pm$.055 & \\hfill .159$\\pm$.034 \\\\\n\\midrule\n\\multirow{4}{*}{\\textbf{MAGI}} \n & \\textnormal{a} & \\hfill .028$\\pm$.023 & \\hfill .026$\\pm$.021 & \\hfill .035$\\pm$.024 & \\hfill .056$\\pm$.028 & \\hfill .079$\\pm$.029 & \\hfill .119$\\pm$.035 \\\\\n & \\textnormal{b} & \\hfill .028$\\pm$.022 & \\hfill .027$\\pm$.021 & \\hfill .031$\\pm$.022 & \\hfill .049$\\pm$.027 & \\hfill .058$\\pm$.029 & \\hfill .042$\\pm$.029 \\\\\n & \\textnormal{c} & \\hfill .022$\\pm$.019 & \\hfill .022$\\pm$.018 & \\hfill .032$\\pm$.021 & \\hfill .058$\\pm$.025 & \\hfill .069$\\pm$.025 & \\hfill .072$\\pm$.022 \\\\\n & \\textnormal{d} & \\hfill .045$\\pm$.036 & \\hfill .044$\\pm$.035 & \\hfill .059$\\pm$.042 & \\hfill .105$\\pm$.052 & \\hfill .139$\\pm$.053 & \\hfill .170$\\pm$.050 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems", "authors": ["Jianhong Chen", "Shihao Yang"], "url": "https://arxiv.org/abs/2501.14107v1", "attribution": "\"EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems\" by Jianhong Chen and Shihao Yang, arXiv:2501.14107v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.02859v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Detailed information of the designed low-complexity explicit controllers.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n\\hline & EMPC1 & EMPC2 & EMPC3 \\\\\n\\hline Prediction horizon & 15 & 15 & 15 \\\\\n Constraint horizon & 1 & 2 & 3 \\\\\n \\# of parameters & 5 & 8 & 11 \\\\\n \\# of critical regions & 39 & 1,211 & 28,300 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Idle speed control with low-complexity offset-free explicit model predictive control in presence of system delay", "authors": ["Sang Hwan Son", "Se-Kyu Oh", "Byung Jun Park", "Min Jun Song", "Jong Min Lee"], "url": "https://arxiv.org/abs/2012.02859v2", "attribution": "\"Idle speed control with low-complexity offset-free explicit model predictive control in presence of system delay\" by Sang Hwan Son, Se-Kyu Oh, Byung Jun Park, Min Jun Song, and Jong Min Lee, arXiv:2012.02859v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.00541v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistical Analysis Results for Human Effort Comparison}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|}\n \\hline\n \\textbf{Test} & \\textbf{Statistic} & \\textbf{p-value} \\\\\n \\hline\n Two-sample t-test & t = -5.8450 & $< 0.0001$ \\\\\n Mann-Whitney U test & U = 145784.0000 & $< 0.0001$ \\\\\n \\hline\n \\multicolumn{3}{|l|}{\\textbf{Significance:}} \\\\\n \\multicolumn{3}{|l|}{- Significant difference based on t-test ($p < 0.05$)} \\\\\n \\multicolumn{3}{|l|}{- Significant difference based on Mann-Whitney U test ($p < 0.05$)} \\\\\n \\multicolumn{3}{|l|}{- N for each category = 1176} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Context-Based Echo State Networks with Prediction Confidence for Human-Robot Shared Control", "authors": ["Negin Amirshirzad", "Mehmet Arda Eren", "Erhan Oztop"], "url": "https://arxiv.org/abs/2412.00541v1", "attribution": "\"Context-Based Echo State Networks with Prediction Confidence for Human-Robot Shared Control\" by Negin Amirshirzad, Mehmet Arda Eren, and Erhan Oztop, arXiv:2412.00541v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11327v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model Version of Considered PTMs}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n \\toprule\n \\textbf{Model Architecture} &\\textbf{Model Version}\\\\\n \\midrule\n BERT & bert-base-uncased\\\\\n ALBERT & albert-base-v2\\\\\n RoBERTa & roberta-base\\\\\n XLNet & xlnet-base-cased\\\\\n BERTOverflow & jeniya/BERTOverflow\\\\\n CostSensBERT & bert-base-uncased\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?", "authors": ["chengran Yang", "Bowen Xu", "Junaed younus Khan", "Gias Uddin", "Donggyun Han", "Zhou Yang", "David Lo"], "url": "https://arxiv.org/abs/2201.11327v1", "attribution": "\"Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?\" by chengran Yang, Bowen Xu, Junaed younus Khan, Gias Uddin, Donggyun Han, Zhou Yang, and David Lo, arXiv:2201.11327v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.10644v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Settings of Multitask Autoencoder}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n\t& & & \\\\\n\tTraining Set&$[-60^{\\circ}\\!:\\!0.1^{\\circ}\\!:\\!60^{\\circ}]$&Validation set&$[-60^{\\circ}\\!:\\!0.1^{\\circ}\\!:\\!60^{\\circ}]$\\\\\n\t\\hline\n\t& & & \\\\\n\tSize&$1201 \\times 40$&Size&$1201 \\times 10$\\\\\n\t\\hline\n\t& & & \\\\\n\tLayer 1&$16\\times16\\times8$& Layer 2& $16\\times8\\times16$\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Model-driven deep neural network for enhanced direction finding with commodity 5G gNodeB", "authors": ["Shengheng Liu", "Zihuan Mao", "Xingkang Li", "Mengguan Pan", "Peng Liu", "Yongming Huang", "Xiaohu You"], "url": "https://arxiv.org/abs/2412.10644v1", "attribution": "\"Model-driven deep neural network for enhanced direction finding with commodity 5G gNodeB\" by Shengheng Liu, Zihuan Mao, Xingkang Li, Mengguan Pan, Peng Liu, Yongming Huang, and Xiaohu You, arXiv:2412.10644v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.15872v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|c|ccc|ccc|ccc}\t\t\\hline \\hline\n\t\t$\\mu_a$&test &\\multicolumn{3}{c|}{ }&\\multicolumn{3}{c|}{ }&\\multicolumn{3}{c|}{ } \\\\\n\t\t\t$a$ &$d_\\infty-\\Delta$ & 200 & 500 & 1000 & 200 & 500 & 1000 & 200 & 500 & 1000 \\\\ \n\t\t \\hline \n\t\t\n\t\t\\multicolumn{11}{l}{\\quad\\textit{Panel A: iid errors}} \\\\ \n\t\t1.5 & -0.03& 0.0 &0.0 &0.0 &0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\\\ \n\t\t\\bf 1.58 & \\bf 0.00 & \\bf 0.0 & \\bf 0.1& \\bf 0.2 &\\bf 0.2 & \\bf 1.5 & \\bf 0.2 & \\bf 0.0 & \\bf 0.0 & \\bf 0.2\\\\ \n\t\t2.0 & 0.13 & 0.6& 2.5 & 3.2&5.5 & 14.1 & 15.4 & 0.0 & 3.3 & 23.1 \\\\ \n\t\t2.5 & 0.29 & 10.4 & 42.6& 50.3&42.1 & 73.6 & 80.2 & 0.0 & 29.9 & 97.8 \\\\ \n\t\t3.0 & 0.45 & 54.3& 93.8& 99.7&86.6 & 99.4 & 100 & 0.2 & 57.3 & 100 \\\\\n\t\n\t\t\\multicolumn{11}{l}{\\quad\\textit{Panel B: MA errors}} \\\\ \n\t1.5 & -0.03 & 0.1& 0.1& 0.2 &0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\\\ \n\t\\bf 1.58 & \\bf 0.0 & \\bf 0.1 & \\bf 0.6 &\\bf 0.1&\\bf 0.7 & \\bf 2.7 & \\bf 2.4 & \\bf 0.0 & \\bf 0.0 & \\bf 0.3 \\\\ \n\t2.0 & 0.13 & 1.2 & 5.1 & 4.3& 6.3 & 14.9 & 16.1 & 0.0 & 3.7 & 18.7 \\\\ \n\t2.5 & 0.29 & 10.3& 31.0& 47.1&26.7 & 56.8 & 75.6 & 0.2 & 27.0 & 87.9\\\\ \n\t3.0 & 0.45 & 44.9& 81.4& 96.6&69.7 & 94.5 & 99.7 & 0.5 & 52.8 & 99.7 \\\\ \n \n\t\t\\multicolumn{11}{l}{\\quad\\textit{Panel C: AR errors}} \\\\ \n\t1.5 & -0.03 & 0.7& 1.1& 1.1&0.0 & 0.0 & 0.0 & 0.1 & 0.5 & 1.0\\\\ \n\t\\bf 1.58 & \\bf0.00 & \\bf 0.8 & \\bf 1.1&\\bf 2.0 &\\bf 2.6 & \\bf 5.9 & \\bf 6.7 & \\bf 0.1 & \\bf 1.4 & \\bf 1.5 \\\\ \n\t2.0 & 0.13 & 3.1 & 9.4 & 10.7&7.8 & 22.6 & 27.0 & 0.0 & 7.8 & 23.1 \\\\ \n\t2.5 & 0.29 & 15.6& 36.1& 51.1&29.6 & 60.6 & 75.1 & 0.4 & 27.3 & 77.7\\\\ \n\t3.0 & 0.45 & 43.5& 81.2 & 95.9&67.3 & 91.7 & 99.5 & 1.1 & 53.9 & 98.4 \\\\ \\hline \\hline\n\t\\end{tabular}\n\\caption{\\it Empirical rejection rates of the tests and and the test proposed in equation (4.6) of for the hypotheses . Different values for the parameter~$a$ in the mean function , error processes, and sample sizes $n=200, 500, 1000$ are considered. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Multiscale detection of practically significant changes in a gradually varying time series", "authors": ["Patrick Bastian", "Holger Dette"], "url": "https://arxiv.org/abs/2504.15872v1", "attribution": "\"Multiscale detection of practically significant changes in a gradually varying time series\" by Patrick Bastian and Holger Dette, arXiv:2504.15872v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2501.06166v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Selected Quantitative Predictors and Elementary Statistics}\n\\begin{tabular}{lcc}\n\\toprule\nStatistic & years of enrollement at 2021/22 & earned ECTS at 2021-22 \\\\ \n\\midrule\nMean & 2.838 & 31.483 \\\\ \nStd Deviation & 2.577 & 21.082 \\\\ \nMedian & 2 & 0 \\\\ \nMode & 1 & 32 \\\\ \nMin & 1 & 0 \\\\ \nMax & 24 & 113 \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Statistical Challenges in Analyzing Migrant Backgrounds Among University Students: a Case Study from Italy", "authors": ["Lorenzo Giammei", "Laura Terzera", "Fulvia Mecatti"], "url": "https://arxiv.org/abs/2501.06166v1", "attribution": "\"Statistical Challenges in Analyzing Migrant Backgrounds Among University Students: a Case Study from Italy\" by Lorenzo Giammei, Laura Terzera, and Fulvia Mecatti, arXiv:2501.06166v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08527v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of benchmark datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n \\hline\n Datesets & Class & Training & Testing \\\\\n \\hline\n \\hline\n CUB-$200$-$2011$ & $200$ & $5997$ & $5794$ \\\\\n Stanford Cars & $196$ & $8144$ & $8041$ \\\\\n FGVC Aircraft & $100$ & $6667$ & $3333$ \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Progressive Co-Attention Network for Fine-grained Visual Classification", "authors": ["Tian Zhang", "Dongliang Chang", "Zhanyu Ma", "Jun Guo"], "url": "https://arxiv.org/abs/2101.08527v2", "attribution": "\"Progressive Co-Attention Network for Fine-grained Visual Classification\" by Tian Zhang, Dongliang Chang, Zhanyu Ma, and Jun Guo, arXiv:2101.08527v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06319v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Issue close time prediction data. From~ study. Note that because of the manner of data collection, i.e., using bin-sequences for each day for each report, there are many more data samples generated from the number of reports mined.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrr}\n \\toprule \\\\\n \\textbf{Project} & \\textbf{Observation Period} & \\textbf{\\# Reports} & \\textbf{\\# Train} & \\textbf{\\# Test} \\\\ \\midrule\n Eclipse & Jan 2010--Mar 2016 & 16,575 & 44,545 & 25,459 \\\\\n Chromium & Mar 2014--Aug 2015 & 15,170 & 44,801 & 25,200 \\\\\n Firefox & Apr 2014--May 2016 & 13,619 & 44,800 & 25,201 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Old but Gold: Reconsidering the value of feedforward learners for software analytics", "authors": ["Rahul Yedida", "Xueqi Yang", "Tim Menzies"], "url": "https://arxiv.org/abs/2101.06319v2", "attribution": "\"Old but Gold: Reconsidering the value of feedforward learners for software analytics\" by Rahul Yedida, Xueqi Yang, and Tim Menzies, arXiv:2101.06319v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11538v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ASR finetuning hyperparameters.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \\toprule\n Dataset\t& Encoder LR & Decoder LR &\tWarmup steps\t&Freezing steps \\\\\n \\midrule\nLibrispeech 100hrs\t& 2e-4 & 2e-3\t& 1000\t&1500\\\\\nLibrispeech 960hrs\t&1e-4\t& 1e-3 &6000\t&12000\\\\\nTEDLIUMv3\t& 1e-4\t& 1e-3 &3000\t&6000\\\\\nNSC subset\t& 1e-4\t& 1e-3& 3000\t&6000\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond", "authors": ["Muhammad Huzaifah", "Geyu Lin", "Tianchi Liu", "Hardik B. Sailor", "Kye Min Tan", "Tarun K. Vangani", "Qiongqiong Wang", "Jeremy H. M. Wong", "Jinyang Wu", "Nancy F. Chen", "Ai Ti Aw"], "url": "https://arxiv.org/abs/2412.11538v3", "attribution": "\"MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond\" by Muhammad Huzaifah, Geyu Lin, Tianchi Liu, Hardik B. Sailor, Kye Min Tan, Tarun K. Vangani, Qiongqiong Wang, Jeremy H. M. Wong, Jinyang Wu, Nancy F. Chen, and Ai Ti Aw, arXiv:2412.11538v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.17698v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|r|r|r|r||r|} \n \\hline\n $h$ & $l=1$ & $l=2$ & $l=3$ & $l=h$\\\\\n \\hline\n $1$ &1 &2 &4 &1 \\\\\n $2$ &2 &7 &29 &7 \\\\\n $3$ &4 &29 &257 &257 \\\\\n $4$ &8 &124 &2408 &50128 \\\\\n $5$ &16 &533 &22873 &50796983 \\\\\n $6$ &32 &2293 &217969 &264719566561 \\\\\n $7$ &64 &9866 &2078716 &7063448084710944 \\\\\n $8$ &128 &42451 &19827701 &963204439792722969647 \\\\\n $9$ &256 &182657 &189133073 &670733745303300958404439297 \\\\\n $10$ &512 &785932 &1804125632 &2384351527902618144856749327661056 \\\\\n $\\ldots$ &$\\ldots$ &$\\ldots$ &$\\ldots$ &$\\ldots$ \\\\\n $27$ &\\shortstack[r]{\\\\67\\\\108\\\\864}\n &\\shortstack[r]{\\\\46678\\\\068726\\\\464909}\n &\\shortstack[r]{\\\\808678\\\\8352164238\\\\5015065537}\n &\\shortstack[r]{\\\\112141233\\\\91782354534780513253590322591761047\\\\82621009619753629967214626060801782\\\\86591734152445275752693375509038806\\\\86290305936932747868007859481561949\\\\27958742997370483908403590044519371\\\\84879122372292175736987337417102146\\\\70845936432144328546887909131495553}\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Some results of I-shaped polyominoes tiling enumeration in rectangle chessboards}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solving tiling enumeration problems by tensor network contractions", "authors": ["Kai Liang"], "url": "https://arxiv.org/abs/2503.17698v4", "attribution": "\"Solving tiling enumeration problems by tensor network contractions\" by Kai Liang, arXiv:2503.17698v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.08237v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lr}\n\\hline\n\\textbf{Metric} & \\textbf{Value} \\\\\n\\hline\nSamples & 417 \\\\\nOriginal class correct & 283 (67.87\\%) \\\\\nOriginal class audible & 39 (9.35\\%) \\\\\nOriginal class visible & 22 (5.88\\%) \\\\\n\\hline\nOriginal class absent & 102 (24.46\\%) \\\\\n\\hline\nOriginal class is only class & 71 (17.03\\%) \\\\\nClasses total & 309 \\\\\n\\hline\nClasses only visible & 6 \\\\\nClasses only audible & 25 \\\\\n\\hline\nAverage labels added per sample & 1.39 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Relabelling statistics for the gold-standard subset.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "VGGSounder: Audio-Visual Evaluations for Foundation Models", "authors": ["Daniil Zverev", "Thaddäus Wiedemer", "Ameya Prabhu", "Matthias Bethge", "Wieland Brendel", "A. Sophia Koepke"], "url": "https://arxiv.org/abs/2508.08237v2", "attribution": "\"VGGSounder: Audio-Visual Evaluations for Foundation Models\" by Daniil Zverev, Thaddäus Wiedemer, Ameya Prabhu, Matthias Bethge, Wieland Brendel, and A. Sophia Koepke, arXiv:2508.08237v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09827v2_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of GMRES-IR and RGMRES-IR refinement steps with the number of GMRES iterations for each refinement step for random dense matrices having geometrically distributed singular values (mode 3) with various condition numbers, using precisions ($u_f,u,u_r$) = (half, double, quad) and ($m,k$) = (90,40).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|cc|}\n\t\t\\hline\n\t\t$\\kappa_\\infty(A)$ & $\\kappa_2(A)$ & GMRES-IR (90) & RGMRES-IR (90,40) \\\\ \\hline\n\t\t$9 \\cdot 10^4$ & $10^4$ & 33 (16,17) & 33 (16,17) \\\\\n\t\t$8 \\cdot 10^5$ & $10^5$ & 85 (41,44) & 50 (41,9) \\\\\n\t\t$7 \\cdot 10^6$ & $10^6$ & 134 (66,68) & 81 (66,15) \\\\\n\t\t$7 \\cdot 10^7$ & $10^7$ & 167 (83,84) & 100 (83,17) \\\\\n\t\t$7 \\cdot 10^8$ & $10^8$ & - & 149 (119,30) \\\\\n\t\t$6 \\cdot 10^9$ & $10^9$ & - & 179 (134,45) \\\\\n\t\t$6 \\cdot 10^{10}$ & $10^{10}$ & - & 470 (388,41,41) \\\\\n\t\t$6 \\cdot 10^{11}$ & $10^{11}$ & - & - \\\\\n\t\t$6 \\cdot 10^{12}$ & $10^{12}$ & - & - \\\\\n\t\t$5 \\cdot 10^{13}$ & $10^{13}$ & - & - \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Mixed Precision GMRES-based Iterative Refinement with Recycling", "authors": ["Eda Oktay", "Erin Carson"], "url": "https://arxiv.org/abs/2201.09827v2", "attribution": "\"Mixed Precision GMRES-based Iterative Refinement with Recycling\" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2303.04539v3_tex_table8.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Kolmogorov--Smirnov test for equality of distributions}\n\\begin{tabular}{lcccc}\n\\hline\\hline\n\\\\\n &\t\\shortstack{Men vs Women \\\\in female-dominated sectors}\t&\t\\shortstack{Men vs Women\t\\\\in male-dominated sectors}\t&\t\\shortstack{Workers in female-dominated sectors \\\\ vs male-dominated sectors if women\t}&\t\\shortstack{Workers in female-dominated sectors \\\\ vs male-dominated sectors if men\t} \\\\ \n& (1)&(2)&(3)&(4)\\\\\n\\hline\n\\\\\nPredicted wage&\t0.1700 \t&\t0.1541 \t&\t0.2438 \t&\t0.1890 \t\\\\\nResidual wage\t&\t0.0366 \t&\t0.0243 \t&\t0.0442 \t&\t0.0231 \t\\\\\nCounterfactual predicted wage\t&\t0.1055\t&\t0.0871&\t0.0867\t&\t0.0471\t\\\\\nCounterfactual residual wage\t&\t0.0821 \t&\t0.1136 \t&\t0.1518 \t&\t0.1791 \t\\\\\n \\hline\\hline\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Gender Segregation: Analysis across Sectoral-Dominance in the UK Labour Market", "authors": ["Riccardo Leoncini", "Mariele Macaluso", "Annalivia Polselli"], "url": "https://arxiv.org/abs/2303.04539v3", "attribution": "\"Gender Segregation: Analysis across Sectoral-Dominance in the UK Labour Market\" by Riccardo Leoncini, Mariele Macaluso, and Annalivia Polselli, arXiv:2303.04539v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.03061v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Homomorphism densities for the $P_1, P_2, P_3, C_3$ and $S_3$ subgraphs for the $C^{-}, \\Pi, C^{+}$ copula graphons}\n\\begin{tabular}{c|c|c|c|c|c}\n \\hline\n $W$ & $t(P_1,W)$ & $t(P_2,W)$ & $t(P_3,W)$ & $t(C_3,W)$ & $t(S_3,W)$\\\\ \\hline \\hline\n $C^{-}$& 1/6 & 1/20 & 1/280 & 1/120 & 1/56 \\\\\n $\\Pi$ & 1/4 & 1/12 & 1/36 & 1/27 & 1/32 \\\\\n $C^{+}$ & 1/3 & 2/15 & 1/14 & 2/15 & 2/35\\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Generating Networks to Target Assortativity via Archimedean Copula Graphons", "authors": ["Victory Idowu"], "url": "https://arxiv.org/abs/2503.03061v1", "attribution": "\"Generating Networks to Target Assortativity via Archimedean Copula Graphons\" by Victory Idowu, arXiv:2503.03061v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04051v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|cc}\n\t\t\t\\hline\n\t\t\tType & Amount& Avg \\#Subject& Avg \\#Person \\\\ \n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\tImages & 9,500 & 1.1417 & 4.2820 \\\\\n\t\t\t\\hline\n\t\t\tVideos & 125(132k frames) & 1.0049 & 1.8272 \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Detailed statistics of {\\it H2V-142K} Dataset.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Horizontal-to-Vertical Video Conversion", "authors": ["Tun Zhu", "Daoxin Zhang", "Yao Hu", "Tianran Wang", "Xiaolong Jiang", "Jianke Zhu", "Jiawei Li"], "url": "https://arxiv.org/abs/2101.04051v2", "attribution": "\"Horizontal-to-Vertical Video Conversion\" by Tun Zhu, Daoxin Zhang, Yao Hu, Tianran Wang, Xiaolong Jiang, Jianke Zhu, and Jiawei Li, arXiv:2101.04051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.15096v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll}\n \\toprule\n \\textbf{Order} &1&2 &3&4&5&6 &7 &8 \\\\\n \\midrule\n \\textbf{Task type} &$Q_0$ &$P$ &$B_R$&$B_{DR}$&$L_{DR}$&$ Q_1$&$L_{R}$&$Q_2$ \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A proposal and evaluation of new timbre visualisation methods for audio sample browsers", "authors": ["Etienne Richan", "Jean Rouat"], "url": "https://arxiv.org/abs/2011.15096v1", "attribution": "\"A proposal and evaluation of new timbre visualisation methods for audio sample browsers\" by Etienne Richan and Jean Rouat, arXiv:2011.15096v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.07347v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classifiers accuracy obtained using different feature selection (FS) algorithms. The resulting number of features is given in parentheses.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|}\n\\hline \\hline\nFS algorithm & \\begin{minipage}[c]{30mm}\\centering Accuracy with \\\\ initial subset \\end{minipage} & \\begin{minipage}[c]{30mm}\\centering Accuracy after backward- \\\\ stepwise selection \\end{minipage} \\\\\n\\hline\nQoV \t& \\begin{minipage}{30mm}\\centering $79.5\\pm3.5$\\% \\\\ $(4)$ \\end{minipage} & \\begin{minipage}{30mm}\\centering $79.5\\pm3.5$\\% \\\\ $(4)$ \\end{minipage} \\\\\nRelief &\\begin{minipage}{30mm}\\centering $72.5\\pm3.4$\\% \\\\ $(16)$ \\end{minipage} & \\begin{minipage}{30mm}\\centering $80.3\\pm2.3$\\% \\\\ $(5)$ \\end{minipage} \\\\ \nLASSO & \\begin{minipage}{30mm}\\centering \\bf 97.0$\\pm$2.4\\% \\\\ $(43)$ \\end{minipage} & \\begin{minipage}{30mm}\\centering \\bf 99.7$\\pm$0.6\\% \\\\ $(32)$ \\end{minipage} \\\\ \nRelieFF & \\begin{minipage}{30mm}\\centering $75.9\\pm4.2$\\% \\\\ $(20)$ \\end{minipage} & \\begin{minipage}{30mm}\\centering $82.9\\pm2.8$\\% \\\\ $(11)$ \\end{minipage} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Classification of ALS patients based on acoustic analysis of sustained vowel phonations", "authors": ["Maxim Vashkevich", "Yulia Rushkevich"], "url": "https://arxiv.org/abs/2012.07347v2", "attribution": "\"Classification of ALS patients based on acoustic analysis of sustained vowel phonations\" by Maxim Vashkevich and Yulia Rushkevich, arXiv:2012.07347v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.04762v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccc}\\toprule\nMethod & \\multicolumn{4}{c}{PSNR} & \\multicolumn{4}{c}{SSIM} & SAM (10\\textsuperscript{-2}) \\\\\\midrule\nBand & NIR & R & G & B & NIR & R & G & B & \\\\\\midrule\nBilinear Upsampling & 20.0 & 19.0 & 21.0 & 21.1 & 0.568 & 0.550 & 0.633 & 0.639 & 3.87 \\\\\nESTARFM~ & 19.6 & 20.2& 21.8& 22.3& 0.555 & 0.640 & 0.688 & 0.696 & 4.88 \\\\\ncGAN Fusion + $L_1$ & 22.1 & 21.8 & 23.7 & 23.8 & 0.675 & 0.697 & 0.747 & 0.747 & 2.75 \\\\\ncGAN Fusion + $L_1$ + SSIM & {\\bf 22.3} & {\\bf 22.0} & {\\bf 23.9} & {\\bf 24.0} & {\\bf 0.694} & {\\bf 0.714} & {\\bf 0.761} & {\\bf 0.760} & {\\bf 2.70} \\\\\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Image quality scores on testing set; cGAN models scores are averaged over 3 independently trained models}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Predicting Landsat Reflectance with Deep Generative Fusion", "authors": ["Shahine Bouabid", "Maxim Chernetskiy", "Maxime Rischard", "Jevgenij Gamper"], "url": "https://arxiv.org/abs/2011.04762v1", "attribution": "\"Predicting Landsat Reflectance with Deep Generative Fusion\" by Shahine Bouabid, Maxim Chernetskiy, Maxime Rischard, and Jevgenij Gamper, arXiv:2011.04762v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05544v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Out-of-distribution} performances \\textbf{after} temperature scaling.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c||cccc|cccc|cccc|cccc|cccc}\n \\toprule\n \\multicolumn{2}{c||}{Dataset} & \\multicolumn{4}{c|}{FPR (95 \\% TPR) $\\downarrow$} & \\multicolumn{4}{c|}{Detection $\\downarrow$ } & \\multicolumn{4}{c|}{AUROC $\\uparrow$} & \\multicolumn{4}{c|}{AUPR In $\\uparrow$} & \\multicolumn{4}{c}{AUPR Out $\\uparrow$} \\\\\n Train & Test OOD & Ind. & CEB & DICE & DICE$\\times w$ & Ind. & CEB & DICE & DICE$\\times w$ & Ind. & CEB & DICE & DICE$\\times w$ & Ind. & CEB & DICE & DICE$\\times w$ & Ind. & CEB & DICE & DICE$\\times w$ \\\\\n \\midrule\n \\multirow{9}{*}{CIFAR-100}\n & TinyImageNet (crop) & 77.7 & 78.4 & 77.1 & \\textbf{73.2} & 32.1 & 31.2 & 31.5 & \\textbf{27.9} & 74.0 & 74.8 & 75.2 & \\textbf{77.9} & 72.6 & 74.1 & 74.0 & \\textbf{74.8} & 72.3 & 73.4 & 73.4 & \\textbf{76.4} \\\\\n & TinyImageNet (resize) & 83.0 & 82.3 & 80.4 & \\textbf{78.5} & 34.4 & 33.6 & 32.9 & \\textbf{30.7} & 70.5 & 71.5 & 72.6 & \\textbf{74.3} & 69.0 & 70.5 & \\textbf{70.9} & 70.8 & 68.3 & 70.2 & 70.3 & \\textbf{72.5} \\\\\n & LSUN (crop) & 76.7 & 81.7 & 80.2 & \\textbf{72.7} & 27.6 & 28.6 & 28.5 & \\textbf{28.2} & 79.3 & 77.2 & 77.0 & \\textbf{79.2} & \\textbf{81.2} & 79.4 & 78.7 & 79.5 & 75.9 & 73.1 & 72.5 & \\textbf{77.1} \\\\\n & LSUN (resize) & 81.5 & 79.0 & 79.5 & \\textbf{75.4} & 33.1 & 30.9 & 30.6 & \\textbf{28.3} & 73.0 & 75.4 & 75.1 & \\textbf{78.1} & 74.3 & 76.8 & 76.0 & \\textbf{76.9} & 70.1 & 73.4 & 72.2 & \\textbf{75.6} \\\\\n & iSUN & 84.3 & 82.3 & 83.2 & \\textbf{79.3} & 34.5 & 32.3 & 32.2 & \\textbf{30.8} & 70.9 & 73.5 & 72.7 & \\textbf{75.0} & 73.5 & \\textbf{76.4} & 74.6 & 75.6 & 65.2 & 68.6 & 66.6 & \\textbf{70.1} \\\\\n & TinyImageNet+LSUN+iSUN & 80.5 & 80.7 & 80.0 & \\textbf{75.8} & 32.3 & 31.3 & 31.2 & \\textbf{29.3} & 73.6 & 74.4 & 74.7 & \\textbf{76.9} & 39.3 & \\textbf{41.4} & 40.9 & 39.8 & 92.0 & 92.5 & 92.2 & \\textbf{93.3} \\\\\n \\cmidrule{2-22}\n & CIFAR-10 & 78.8 & 82.1 & \\textbf{78.2} & 80.0 & 29.6 & 29.9 & 28.6 & \\textbf{28.3} & 77.5 & 76.7 & 78.5 & \\textbf{78.6} & 80.2 & 79.4 & \\textbf{81.2} & 81.0 & 73.5 & 72.5 & \\textbf{74.5} & 74.0 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation", "authors": ["Alexandre Rame", "Matthieu Cord"], "url": "https://arxiv.org/abs/2101.05544v1", "attribution": "\"DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation\" by Alexandre Rame and Matthieu Cord, arXiv:2101.05544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.03021v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{$\\log_{10}(p\\text{-values})$ Statistics under Synthetic Setting 1 with 100 Covariates.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrr} \\toprule \n\\textbf{Model} & \\textbf{Min} & \\textbf{25\\%} & \\textbf{Median} & \\textbf{Mean} & \\textbf{75\\%} & \\textbf{Max} \\\\\n\\midrule \n TARNet & $-34.3$ & $-31.3$ & $-30.9$ & $-29.9$ & $-30.3$ & $-29.0$ \\\\ \n CausalForest & $-2.39$ & $-1.35$ & $-0.821$ & $-0.670$ & $-0.479$ & $-0.122$ \\\\ \n S-BART & $-9.62$ & $-7.32$ & $-6.68$ & $-5.22$ & $-5.99$ & $-3.96$ \\\\ \n T-BART & $-1.04$ & $-0.76$ & $-0.36$ & $-0.31$ & $-0.12$ & $0.00$ \\\\ \n S-engression & $-28.6$ & $-26.1$ & $-25.3$ & $-23.6$ & $-24.0$ & $-22.6$ \\\\ \n T-engression & $-2.46$ & $-0.663$ & $-0.393$ & $-0.366$ & $-0.137$ & $-0.107$ \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Testing Generalizability in Causal Inference", "authors": ["Daniel de Vassimon Manela", "Linying Yang", "Robin J. Evans"], "url": "https://arxiv.org/abs/2411.03021v2", "attribution": "\"Testing Generalizability in Causal Inference\" by Daniel de Vassimon Manela, Linying Yang, and Robin J. Evans, arXiv:2411.03021v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11702v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CORDIC Linear Vectoring Mode (Division)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccr}\n \\hline\n \\textbf{i (clk)} & \\textbf{Ei=2\\textsuperscript{-i}} & \\textbf{Xi+1 $\\rightarrow$ Xi} & \\textbf{Yi+1 $\\rightarrow$ 0} & \\textbf{Zi+1 $\\rightarrow$ Y/X} & \\textbf{di} \\\\ \\hline\n \\textbf{1} & 0.5 & 2.51 & -0.734 & 0.5 &-1\\\\ \n \\textbf{2} & 0.25 & 2.51 & -0.1065 & 0.25 &1\\\\ \n \\textbf{3} & 0.125 & 2.51 & 0.20725 & 0.125 &1\\\\ \n \\textbf{4} & 0.0625 & 2.51 & 0.050375 & 0.1875 &-1\\\\ \n \\textbf{5} & \\textbf{0.03125} & \\textbf{2.51} & \\textbf{-0.02806} & \\textbf{0.21875} &\\textbf{-1}\\\\ \n \\textbf{6} & 0.015625 & 2.51 & 0.011156 & 0.203125 &1\\\\ \n \\textbf{7} & 0.007812 & 2.51 & -0.00845 & 0.210937 &-1\\\\ \n \\textbf{8} & 0.003906 & 2.51 & 0.001351 & 0.207031 &1\\\\ \n \\textbf{9} & 0.001953 & 2.51 & -0.00355 & 0.208984 &-1\\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads", "authors": ["Mukul Lokhande", "Gopal Raut", "Santosh Kumar Vishvakarma"], "url": "https://arxiv.org/abs/2412.11702v1", "attribution": "\"Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads\" by Mukul Lokhande, Gopal Raut, and Santosh Kumar Vishvakarma, arXiv:2412.11702v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.11786v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Averages of the 10 smallest SP scores whose AUCs are greater than the thresholds ($\\geq 0.85$ for Adult and $\\geq 0.65$ for Law School) are reported. Table~ in SM~ presents consistent results with different thresholds. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|ccc}\n\\hline\nData & Model & RF ($\\downarrow$)& SVM ($\\downarrow$)& LR ($\\downarrow$) \\\\ \n\\hline\n\\hline\n\\multirow{3}{*}{Adult} \n& \\multirow{1}{*}{FairPTGAN} & \\bf{0.064 (0.006)} & \\bf{0.077 (0.014)} & \\bf{0.084 (0.006)} \\\\\n& \\multirow{1}{*}{FairWGANGP}& 0.083 (0.010) & 0.088 (0.010) & 0.095 (0.005) \\\\\n&\\multirow{1}{*}{GeoRepair} & 0.082 (0.012) & 0.089 (0.009) & 0.106 (0.009) \\\\\n\\hline\n\\multirow{3}{*}{Law School.} \n& \\multirow{1}{*}{FairPTGAN} & \\bf{0.054 (0.014)} & \\bf{0.056 (0.006)} & \\bf{0.079 (0.020)} \\\\\n& \\multirow{1}{*}{FairWGANGP} & 0.105 (0.006) & 0.115 (0.007) & 0.175 (0.003) \\\\\n&\\multirow{1}{*}{GeoRepair} & 0.102 (0.011) & 0.129 (0.013) & 0.187 (0.003) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Parallelly Tempered Generative Adversarial Nets: Toward Stabilized Gradients", "authors": ["Jinwon Sohn", "Qifan Song"], "url": "https://arxiv.org/abs/2411.11786v2", "attribution": "\"Parallelly Tempered Generative Adversarial Nets: Toward Stabilized Gradients\" by Jinwon Sohn and Qifan Song, arXiv:2411.11786v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07434v5_tex_table18.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\n\t\t\\toprule[1pt]\n\t\t Methods & Backbone & mIOU (\\%) \\\\\n\t\t\\midrule[0.5pt]\n\t\t\\midrule[0.5pt]\n\t\tDSSPN~ & ResNet-101 & 38.9 \\\\\n\t\tSVCNet~ & ResNet-101 & 39.6 \\\\\n\t\tEMANet~ & ResNet-101 & 39.9 \\\\\n\t\tSPYGR~ & ResNet-101 & 39.9 \\\\\n\t\tOCR~ & ResNet-101 & 39.5 \\\\\n\t\t\\midrule[0.5pt]\n\t\tDANet~ & ResNet-101 & 39.7\\\\\n\t\t\\midrule[0.5pt]\n\t\t\\textbf{Our CAA} & ResNet-101 & \\textbf{41.2} \\\\\n\t\t\\bottomrule[0.5pt]\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparisons with other state-of-the-art approaches on the COCO-Stuff 10K test set. For a fair comparison, all compared methods use ResNet-101 and naive upsampling.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation", "authors": ["Ye Huang", "Di Kang", "Wenjing Jia", "Xiangjian He", "Liu Liu"], "url": "https://arxiv.org/abs/2101.07434v5", "attribution": "\"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation\" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.06225v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Accuracy and computational cost of the three considered algorithms}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{PDE} & \\textbf{RandNet-Parareal} & \\textbf{Parareal} & \\textbf{nnGParareal} \\\\ \\midrule\nBurgers' $d = 128$ & $1.06e^{-8}$ (1h 2m) & $1.85e^{-8}$ (8h 54m) & $1.32e^{-7}$ (1h 39m) \\\\ \nDiffusion-Reaction $d = 7.2e^{2}$ & $3.56e^{-8}$ (23m) & $1.83e^{-8}$ (1h 40m) & $5.71e^{-7}$ (1h 11m) \\\\ \nDiffusion-Reaction $d = 3.3e^{3}$ & $8.56e^{-10}$ (33m) & $2.45e^{-8}$ (7h 52m) & not converged \\\\ \nDiffusion-Reaction $d = 2.5e^{4}$ & $8.09e^{-11}$ (1h 57m) & $7.43e^{-9}$ (9h 50m) & not converged \\\\ \nSWE $d = 3.1e^{4}$ & $6.75e^{-8}$ (4h 9m) & $5.15e^{-8}$ (15h 43m) & not converged \\\\ \nSWE $d = 6.1e^{4}$ & $8.54e^{-9}$ (12h 34m) & $2.84e^{-8}$ (19h 30m) & not converged \\\\ \nBrusselator 2D $d=2e^3$ & $2.09e^{-8} $ (2m) & $3.16e^{-8}$ (7m) & $3.38e^{-7}$ (7h 31m)\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks", "authors": ["Guglielmo Gattiglio", "Lyudmila Grigoryeva", "Massimiliano Tamborrino"], "url": "https://arxiv.org/abs/2411.06225v1", "attribution": "\"RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks\" by Guglielmo Gattiglio, Lyudmila Grigoryeva, and Massimiliano Tamborrino, arXiv:2411.06225v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|r|r|r}\n \\hline\n \\multicolumn{5}{c}{Including component demand} \\\\\n \\hline \n Utilization & $\\lambda$ & Runtime (s) & Total costs PH & $\\Delta$ (\\%)\\\\\n \\hline\n \\multirow{4}{*}{50\\%} \n & 0.1 & 1651.4 & 29543.8 & 7.83 \\\\\n & 1 & 533.4 & 27957.7 & \\textbf{2.69} \\\\\n & 10 & 354.4 & 28052.1 & 3.03 \\\\\n & 100 & 377.3 & 28077.2 & 3.06 \\\\\n \\hline\n \\multirow{4}{*}{90\\%} \n & 0.1 & 2023.4 & 65682.4 & 5.12 \\\\\n & 1 & 1070.7 & 63633.3 & 2.46\\\\\n & 10 & 644.6 & 63548.1 & 2.46\\\\\n & 100 & 851.0 & 63103.2 & \\textbf{2.39} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Majority voting consensus calculation: Evaluation of the $\\lambda$-multiple for the penalty parameter $\\rho$ in terms of total costs and runtime for the case including component demands on a scenario tree with $|\\Omega| = 2$ and therefore $|\\Phi| = 128$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08131v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Relative camera pose evaluation of initial guess and our optimized results, where $R$ and $t$ represent the rotation and translation, respectively.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\hline\n\\toprule\n& \\multicolumn{2}{c}{$R\\downarrow$} && \\multicolumn{2}{c}{${t\\downarrow}$} \\\\\n\\cmidrule{2-3} \\cmidrule{5-6}\n& mean ($^\\circ$) & med ($^\\circ$) && mean ($^\\circ$) & med ($^\\circ$) \\\\\n\\midrule \nGeoFill (optim) & {\\bf 1.588} & {\\bf 1.062} && {\\bf 3.688} & {\\bf 3.457} \\\\\\hline\nGeoFill (init) & 7.378 & 7.807 && 11.861 & 11.096\n\\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding", "authors": ["Yunhan Zhao", "Connelly Barnes", "Yuqian Zhou", "Eli Shechtman", "Sohrab Amirghodsi", "Charless Fowlkes"], "url": "https://arxiv.org/abs/2201.08131v2", "attribution": "\"GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding\" by Yunhan Zhao, Connelly Barnes, Yuqian Zhou, Eli Shechtman, Sohrab Amirghodsi, and Charless Fowlkes, arXiv:2201.08131v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19262v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mathematical symbols used throughout this article}\n\\begin{tabular}{rl}\n \\hline\n \\textbf{Symbol} & \\textbf{Description} \\\\ \\hline\n $ref$ & Localization system reference point \\\\\n $t_l$& UWB tag \\\\\n $a_k$ & UWB anchor \\\\\n L & Total number of tags\\\\\n K & Total number of anchors \\\\\n $\\Delta a_k t_l$ & Euclidean distance between $a_k$ and $t_l$, shortened to $\\Delta$ \\\\\n $ToF_{a_k t_l}$ & Time of flight between $a_k$ and $t_l$, shortened to $ToF$\\\\\n $CIR_{a_k t_l}$ & Channel impulse response, shortened to $CIR$\\\\\n $\\widehat{Tof}_{a_k t_l}$ & Estimated ToF between $a_k$ and $t_l$, shortened to $\\widehat{Tof}$ \\\\\n $e_{a_k t_l}$ & Ranging error between $a_k$ and $t_l$, shortened to $e$ \\\\\n $\\widehat{\\Delta a_k t_l}$ & Estimated range between $a_k$ and $t_l$, shortened to $\\widehat{\\Delta}$ \\\\\n $\\widehat{e}$ & Estimated range error by RL agent \\\\\n $\\Delta'$ & Corrected estimated range by RL agent \\\\\n $S_t$ & The state of the environment at time $t$ ($CIR$)\\\\\n $A_t$ & The action of the agent at time $t$ ($\\widehat{e}$)\\\\\n $\\pi$ & The agent's policy, $\\pi: CIR \\rightarrow \\widehat{e}$ \\\\\n $\\mu$ & The actor network of the RL agent \\\\\n $\\theta$ & Weights of the actor network $\\mu$ \\\\\n $Q$ & The critic network of the RL agent \\\\\n $\\phi$ & Weights of the critic network $Q$ \\\\\n $y$ & Target Q-value \\\\\n $R_t$ & Reward received at time $t$ \\\\\n $\\gamma$ & Discount factor, determining the weight of target critic\\\\\n $\\dot{Q}$ & The target critic network \\\\\n $\\dot{\\phi}$ & Weights of the target critic network \\\\\n $\\tau_{critic}$ & Soft copy factor of the critic \\\\\n $J$ & The sampled policy gradient \\\\\n $B$ & Number of samples in a batch \\\\\n $\\epsilon$ & Exploration rate of the RL agent \\\\\n $\\lambda$ & Decay factor of the exploration rate \\\\\n $\\dot{\\mu}$ & The target actor \\\\\n $\\dot{\\theta}$ & Weights of the target actor \\\\\n $\\tau_{actor}$ & Soft copy factor of the actor \\\\\n $p_{EKF}$ & Extended Kalman Filter position \\\\\n $m$ & Middle position in smoothing buffer \\\\\n $N$ & Length of circular smoothing buffer\\\\ \n $p_{avg,m}$ & Averaged position related to middle data $m$ in buffer \\\\\n $\\Delta_{avg,m}$ & Resulting range from filtering and smoothing \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning", "authors": ["Dieter Coppens", "Ben Van Herbruggen", "Adnan Shahid", "Eli De Poorter"], "url": "https://arxiv.org/abs/2403.19262v2", "attribution": "\"Removing the need for ground truth UWB data collection: self-supervised ranging error correction using deep reinforcement learning\" by Dieter Coppens, Ben Van Herbruggen, Adnan Shahid, and Eli De Poorter, arXiv:2403.19262v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17106v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n $n$ & Error $\\|\\cdot\\|_1$ & Order $\\|\\cdot\\|_1$ & Error\n $\\|\\cdot\\|_{\\infty}$ & Order $\\|\\cdot\\|_{\\infty}$ \\\\\n \\hline\n 20 & 8.22E$-4$ & $-$ & 2.07E$-3$ & $-$ \\\\\n \\hline\n 40 & 2.12E$-5$ & 5.28 & 8.10E$-5$ & 4.68 \\\\\n \\hline\n 80 & 6.39E$-7$ & 5.05 & 2.89E$-6$ & 4.81 \\\\\n \\hline\n 160 & 1.94E$-8$ & 5.04 & 9.03E$-8$ & 5.00 \\\\\n \\hline\n 320 & 5.94E$-10$ & 5.03 & 2.80E$-9$ & 5.01 \\\\\n \\hline\n 640 & 1.84E$-11$ & 5.01 & 8.91E$-11$ & 4.98 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Error table for problem - , Square domain, WLS-GAW.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17106v1", "attribution": "\"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12856v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Error analysis for activator-inhibitor example using both proposed algorithms}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\nvariable&bias&MAPE&MAE&RMSE&R$^2$\\\\ \\hline\n$x_1$&0.0083&0.0603&0.0257&0.0307&0.9903\\\\\n$x_2$&-0.0199&0.0443&0.0285&0.0357&0.9957\\\\\n$f_1(\\cdot)$&1.6785e-04&3.0247&0.0089&0.0110&0.6228\\\\\n$f_2(\\cdot)$&4.1262e-04&0.5862&0.0038&0.0047&0.9949\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods", "authors": ["S. Syafiie", "Aries Subiantoro", "Vivi Andasari", "Fernando Tadeo"], "url": "https://arxiv.org/abs/2501.12856v1", "attribution": "\"Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods\" by S. Syafiie, Aries Subiantoro, Vivi Andasari, and Fernando Tadeo, arXiv:2501.12856v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.09540v1_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\cline{2-5}\n & daily & six-hourly & three-hourly & 10min \\\\\n \\hline\n daily&1&$4.20\\cdot10^{-108}$&$2.04\\cdot10^{-163}$& $3.60\\cdot10^{-265}$ \\\\ \\hline\n six-hourly&&1&$1.47\\cdot10^{-17}$&$1.64\\cdot10^{-187}$ \\\\ \\hline\n three-hourly&&&1& $6.61\\cdot10^{-184}$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{$p$-values of Kolmogorov-Smirnov test for averaged Fichtelberg wind speed distributions. All values where $p \\leq 0.05$ are marked \\textbf{bold} and the corresponding distributions are not considered to be significantly different.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts", "authors": ["Nina Effenberger", "Nicole Ludwig", "Rachel H. White"], "url": "https://arxiv.org/abs/2309.09540v1", "attribution": "\"Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts\" by Nina Effenberger, Nicole Ludwig, and Rachel H. White, arXiv:2309.09540v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23211v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Location of estimated change points from the video data set with their CIs for the ``\\textit{first person walks out of the lobby}\". Note that the true value is $116$. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\hline\npixel & value ($\\lfloor T \\tilde{\\tau} \\rfloor$) & $70\\%$ CI & $80\\%$ CI & $90\\%$ CI & $95\\%$ CI & $99\\%$ CI \\\\ \\hline\\hline\n700 & 114 & (113,120) & (113,121) & (113,125) & (113,129) & (113,139) \\\\\n702 & 115 & (114,121) & (114,123) & (114,127) & (114,131) & (114,142) \\\\\n731 & 109 & (108,123) & (108,127) & (108,136) & (108,147) & (108,173) \\\\\n762 & 106 & (105,138) & (105,150) & (105,172) & (105,181) & (105,181) \\\\\n764 & 118 & (117,169) & (117,169) & (117,169) & (117,169) & (117,169) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models", "authors": ["Sepideh Mosaferi", "Abolfazl Safikhani", "Peiliang Bai"], "url": "https://arxiv.org/abs/2503.23211v2", "attribution": "\"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models\" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.02541v4_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Absolute error in price $\\times 10^5$ for a continuously monitored double knock-out barrier. The reference price of 3.460714 is obtained on a grid of $I=4000$ space-steps and the same number of time-steps. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\\toprule\n\t\t\t$I$ & Ghost (Linear) & Ghost (3-points) & On Grid \t\t\\\\\\midrule\n\t\t\t20 & 3801.7 & 825.9 & 652.2 \\\\\n\t\t\t40 & 899.1 & 178.9 & 125.7\\\\\n\t\t\t80 & 230.0 & 28.9 & 27.1 \\\\\n\t\t\t160 & 52.3 & 11.1 & 9.6 \\\\\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Inserting or Stretching Points in Finite Difference Discretizations", "authors": ["Jherek Healy"], "url": "https://arxiv.org/abs/2210.02541v4", "attribution": "\"Inserting or Stretching Points in Finite Difference Discretizations\" by Jherek Healy, arXiv:2210.02541v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.04878v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|} \\hline\n$z$ & $a$ & supersingular primes \\\\ \\hline\n$\\left[21, 21, 21, 1\\right]$ & $\\left(1, 19, 1\\right), \\left(1, 16, 4\\right)$ & $p \\equiv 5, 17, 20 \\bmod{21}$ \\\\ \\hline\n$\\left[22, 22, 11, 1\\right]$ & $\\left(1, 19, 2\\right), \\left(1, 17, 4\\right),\\left(1, 15, 6\\right) $ & $p \\equiv QNR \\bmod{22}$ \\\\ \n & $\\left(1, 9, 12\\right),\\left(1, 1, 20\\right)$ & $p \\equiv QNR \\bmod{22}$ \\\\ \\hline\n$\\left[24, 12, 8, 1\\right]$ & $\\left(1, 14, 9\\right), \\left(1, 2, 21\\right)$ & $p \\equiv 23 \\bmod{24}$ \\\\ \\hline\n$\\left[24, 24, 6, 1\\right]$ & $\\left(1, 19, 4\\right)$ & $p \\equiv 5, 23 \\bmod{24}$ \\\\ \\hline\n$\\left[25, 25, 5, 1\\right]$ & $\\left(1, 19, 5\\right),\\left(1, 14, 10\\right)$ & $p \\neq 1 \\bmod{5}$ \\\\ \\hline\n$\\left[30, 6, 5, 1\\right]$ & $\\left(1, 5, 24\\right)$ & $p \\equiv 29 \\bmod{30}$ \\\\ \\hline\n$\\left[28, 14, 4, 1\\right]$ & $\\left(1, 6, 21\\right)$ & $p \\equiv 3, 19, 27 \\bmod{28}$ \\\\ \\hline\n$\\left[30, 30, 3, 1\\right]$ & $\\left(1, 19, 10\\right)$ & $p \\equiv 11, 29 \\bmod{30}$ \\\\ \\hline\n$\\left[33, 11, 3, 1\\right]$ & $\\left(1, 21, 11\\right)$ & $p \\equiv 2, 8, 17, 29, 32 \\bmod{33}$ \\\\ \\hline\n$\\left[40, 40, 2, 1\\right]$ & $\\left(1, 19, 20\\right)$ & $p \\equiv 21, 39 \\bmod{40}$ \\\\ \\hline\n$\\left[42, 21, 2, 1\\right]$ & $\\left(1, 20, 21\\right)$ & $p \\equiv 5, 17, 41 \\bmod{42}$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Cyclic genus $10$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Supersingular curves via the Shimura--Taniyama method", "authors": ["Jeremy Booher", "Rachel Pries"], "url": "https://arxiv.org/abs/2503.04878v2", "attribution": "\"Supersingular curves via the Shimura--Taniyama method\" by Jeremy Booher and Rachel Pries, arXiv:2503.04878v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00719v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|r|r|l|r|r|r}\n\\hline\nPredictors & Coefficients & SE & pvalue & Lower CI & Upper CI & Odds Ratio\\\\\n\\hline\nIntercept & 0.56 & 0.31 & 0.070 & -0.04 & 1.17 & 1.75\\\\\n\\hline\nnegative & 1.41 & 0.17 & <0.001 & 1.08 & 1.76 & 4.10\\\\\n\\hline\npositive & -2.88 & 0.40 & <0.001 & -3.68 & -2.12 & 0.06\\\\\n\\hline\nuncertainty & -0.64 & 0.20 & 0.002 & -1.06 & -0.26 & 0.53\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Bankruptcy prediction using disclosure text features", "authors": ["Sridhar Ravula"], "url": "https://arxiv.org/abs/2101.00719v1", "attribution": "\"Bankruptcy prediction using disclosure text features\" by Sridhar Ravula, arXiv:2101.00719v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2501.00009v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\t\t\\hline\n\t\t& & & & \\\\\n\t\t&MUSIC&DeepMUSIC&CNN&\\textbf{Ours}\\\\\n\t\t\\hline\n\t\t& & & & \\\\\n\t\ttraining [h]&-& 30.8 & 1.6 & \\textbf{15.6} \\\\\n\t\t\n\t\t& & & & \\\\\n\t\ttesting [ms]&9.4 &31.6 &1.8 &\\textbf{14.9}\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Training and testing times.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB", "authors": ["Shengheng Liu", "Xingkang Li", "Zihuan Mao", "Peng Liu", "Yongming Huang"], "url": "https://arxiv.org/abs/2501.00009v1", "attribution": "\"Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB\" by Shengheng Liu, Xingkang Li, Zihuan Mao, Peng Liu, and Yongming Huang, arXiv:2501.00009v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.19013v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Discretizations considered in the timing studies. The number of degrees of freedom (dof) refer to the number read of during the selection and in parenthesis the actual number of dof resulting from the chosen number of elements $n^\\mathrm{e}$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n \\toprule\n & & \\multicolumn{3} {c} {$e < 5\\%$} & \\multicolumn{3} {c} {$e < 1\\%$} \\\\\n method & $p$ & dof & $n^\\mathrm{e}$ & $\\Delta t^\\mathrm{crit} \\cdot 10^{3}$ & dof & $n^\\mathrm{e}$ & $\\Delta t^\\mathrm{crit} \\cdot 10^3$ \\\\ \\hline\n\\multirow{4}{*}{\\begin{minipage}[]{100pt}\nSCM con. \\\\ $\\alpha=10^{-12}$, $\\epsilon=0$ IMEX\n\\end{minipage}} \n & $2$ & 51348 (52353) & 28 & 5.96237 & $>10^5$ & - & \\\\\n & $3$ & 21081 (22816) & 13 & 5.84696 & 51266 (52375) & 18 & 4.17564 \\\\\n & $4$ & 18033 (21109) & 9 & 5.17179 & 38615 (43065) & 12 & 3.75073 \\\\\n & $5$ & 20341 (22706) & 7 & 4.43017 & 39162 (40176) & 9 & 3.42847 \\\\ \\hline\n\\multirow{4}{*}{\\begin{minipage}[]{100pt}\nSCM con. \\\\ $\\alpha=10^{-8}$, $\\epsilon=0$ \\\\ CDM\n\\end{minipage}} \n & $2$ & 51178 (52353) & 28 & 0.43236 & $>10^5$ & - & \\\\\n & $3$ & 20998 (22816) & 13 & 0.38268 & 52726 (52375) & 18 & 0.25533 \\\\\n & $4$ & 18272 (21109) & 9 & 0.43601 & 38096 (43065) & 12 & 0.24549 \\\\\n & $5$ & 18646 (22706) & 7 & 0.41463 & 36673 (40176) & 9 & 0.31423 \\\\ \\hline\n\\multirow{4}{*}{\\begin{minipage}[]{100pt}\nSCM con. \\\\ $\\alpha=10^{-4}$, $\\epsilon=0$ \\\\ CDM\n\\end{minipage}} \n & $2$ & 51355 (52353) & 28 & 0.86921 & $>10^5$ & - & - \\\\\n & $3$ & 22821 (22816) & 13 & 1.29185 & 67231 (69391) & 20 & 0.735765 \\\\\n & $4$ & 18268 (21109) & 9 & 1.16246 & 76029 (75085) & 15 & 0.698116 \\\\\n & $5$ & 21025 (22706) & 7 & 1.00886 & 40989 (40176) & 9 & 0.906885 \\\\ \\hline\n\\multirow{4}{*}{\\begin{minipage}[]{100pt}\nSCM con. \\\\ $\\alpha=10^{-12}$, $\\epsilon=10^{-6}$\\\\ CDM\n\\end{minipage}} \n & $2$ & 52726 (52353) & 28 & 0.45652 & $>10^5$ & - & - \\\\\n & $3$ & 21398 (22816) & 13 & 0.74380 & 55906 (60472) & 19 & 0.51546 \\\\\n & $4$ & 18162 (21109) & 9 & 0.87312 & 41472 (43065) & 12 & 0.63272 \\\\\n & $5$ & 20902 (22706) & 7 & 0.74016 & 38885 (40176) & 9 & 0.56692 \\\\ \\hline\n\\multirow{4}{*}{\\begin{minipage}[]{100pt}\nSCM con. \\\\ $\\alpha=10^{-12}$, $\\epsilon=10^{-4}$ \\\\ CDM\n\\end{minipage}} \n & $2$ & 68623 (69083) & 31 & 0.41570 & $>10^5$ & - & - \\\\\n & $3$ & 27606 (27793) & 14 & 1.34215 & 227948 (226108) & 31 & 0.19435 \\\\\n & $4$ & 26035 (27233) & 10 & 1.37805 & - & - & - \\\\\n & $5$ & 52112 (67086) & 11 & 0.88394 & - & - & - \\\\ \\hline\n\\multirow{4}{*}{\\begin{minipage}[]{100pt}\nIGA-FCM con. \\\\ $\\alpha=10^{-12}$, $\\epsilon=0$ \\\\ Newmark\n\\end{minipage}} \n & $2$ & 13634 (14130) & 34 & 0.23916 & 39923 (40751) & 51 & 0.14488 \\\\\n & $3$ & 7137 (7221) & 24 & 0.68262 & 13433 (14079) & 32 & 0.48951 \\\\\n & $4$ & 6770 (7116) & 22 & 0.96601 & 12160 (11984) & 28 & 0.71641 \\\\\n & $5$ & 6633 (6951) & 20 & 1.13415 & - & - & - \\\\ \\hline\n\\multirow{4}{*}{\\begin{minipage}[]{100pt}\nIGA-FCM con. \\\\ $\\alpha=10^{-8}$, $\\epsilon=0$ \\\\ CDM\n\\end{minipage}} \n & $2$ & 13811 (14130) & 34 & 0.79411 & 40457 (40751) & 51 & 0.54669 \\\\\n & $3$ & 7290 (7904) & 25 & 1.11988 & 13699 (14079) & 32 & 1.04079 \\\\\n & $4$ & 6890 (7116) & 22 & 1.59176 & 12288 (12967) & 29 & 1.22720 \\\\\n & $5$ & 6716 (6951) & 20 & 1.68044 & - & - & - \\\\ \\hline\n\\multirow{4}{*}{\\begin{minipage}[]{100pt}\nIGA-FCM con. \\\\ $\\alpha=10^{-4}$, $\\epsilon=0$ \\\\ CDM\n\\end{minipage}} \n & $2$ & 13884 (14130) & 34 & 2.36090 & 40715 (40751) & 51 & 1.57227 \\\\\n & $3$ & 7333 (7904) & 25 & 2.63871 & 13887 (14079) & 32 & 2.23040 \\\\\n & $4$ & 7227 (7829) & 23 & 2.11658 & 15340 (16250) & 32 & 1.52100 \\\\\n & $5$ & 7010 (7678) & 21 & 1.68287 & - & - & - \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the efficiency of explicit and semi-explicit immersed boundary finite element methods for wave propagation problems", "authors": ["Tim Bürchner", "Lars Radtke", "Philipp Kopp", "Stefan Kollmannsberger", "Ernst Rank", "Alexander Düster"], "url": "https://arxiv.org/abs/2501.19013v1", "attribution": "\"On the efficiency of explicit and semi-explicit immersed boundary finite element methods for wave propagation problems\" by Tim Bürchner, Lars Radtke, Philipp Kopp, Stefan Kollmannsberger, Ernst Rank, and Alexander Düster, arXiv:2501.19013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.14908v6_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of Counties Adopting Subsidies by Year}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrrrrrrr}\n\\toprule\n & 2010 & 2011 & 2012 & 2013 & 2014 & 2015 & 2016 & 2017 & 2018 & 2019 & 2020 & 2021 & 2022\\\\\n\\midrule\nDC Adopters & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 2 & 11 & 6 & 2 & 13 & 0\\\\\nLevel 2 Adopters & 0 & 1 & 0 & 1 & 1 & 1 & 1 & 3 & 9 & 6 & 3 & 12 & 0\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Powering Up a Slow Charging Market: How Do Government Subsidies Affect Charging Station Supply?", "authors": ["Zunian Luo"], "url": "https://arxiv.org/abs/2210.14908v6", "attribution": "\"Powering Up a Slow Charging Market: How Do Government Subsidies Affect Charging Station Supply?\" by Zunian Luo, arXiv:2210.14908v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05795v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistical analysis considering Semeion dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccccc}\n\\cline{2-9}\n & IHS & AIWPSO & CS & FA & BSA & JADE & CoBiDE & RS \\\\ \\hline\n IHS & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} \\\\ \n AIWPSO & $\\neq$ & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} &\\cellcolor[gray]{0.8} \\\\ \nCS & $\\neq$ & $\\neq$ & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} \\\\\nFA & $\\neq$ & $\\neq$ & $=$ & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} &\\cellcolor[gray]{0.8} \\\\\nBSA & $\\neq$ & $=$ & $\\neq$ & $\\neq$ & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} \\\\\nJADE & $=$ & $=$ & $\\neq$ & $\\neq$ & $=$ & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} &\\cellcolor[gray]{0.8} \\\\ \nCoBiDE & $\\neq$ & $=$ & $\\neq$ & $\\neq$ & $=$ & $=$ & \\cellcolor[gray]{0.8} & \\cellcolor[gray]{0.8} \\\\ \nRS & $\\neq$ & $=$ & $\\neq$ & $\\neq$ & $=$ & $\\neq$ & $\\neq$ & \\cellcolor[gray]{0.8} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines", "authors": ["Leandro Aparecido Passos", "João Paulo Papa"], "url": "https://arxiv.org/abs/2101.05795v1", "attribution": "\"A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines\" by Leandro Aparecido Passos and João Paulo Papa, arXiv:2101.05795v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.05168v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n & L2M Acc $\\uparrow$ & M2L Acc $\\uparrow$ \\\\\n \\hline\n \\textbf{SongMASS} & 62.6 & 45.4 \\\\\n - TC & 62.1 & 44.8 \\\\\n - SC & 56.2 & 44.0 \\\\ %To be comp\n - TC - SC & 55.3 & 43.8 \\\\\n - TC - SC - PT & 48.3 & 37.1 \\\\\n - DP & 15.7 & 11.3 \\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Analyses of the designs in alignment constraints.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint", "authors": ["Zhonghao Sheng", "Kaitao Song", "Xu Tan", "Yi Ren", "Wei Ye", "Shikun Zhang", "Tao Qin"], "url": "https://arxiv.org/abs/2012.05168v1", "attribution": "\"SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint\" by Zhonghao Sheng, Kaitao Song, Xu Tan, Yi Ren, Wei Ye, Shikun Zhang, and Tao Qin, arXiv:2012.05168v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.19947v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{White-box attack robustness accuracy for ResNet-18 on SVHN. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n\\hline\n Defense & Natural & $PGD-20$ & $CW$ & AA \\\\\n\\hline\n Standard-AT & \\textbf{92.57}\\tiny{ $\\pm$ 0.31}\n & 55.67\\tiny{ $\\pm$ 0.14} & 52.92\\tiny{ $\\pm$ 0.16} &45.95\\tiny{ $\\pm$ 0.12} \\\\\n\\textbf{AT-SDI} \\tiny{(Ours)}& 92.10\\tiny{ $\\pm$ 0.20} & \\textbf{58.10}\\tiny{ $\\pm$ 0.09} & \\textbf{58.15}\\tiny{ $\\pm$ 0.06} & \\textbf{47.15}\\tiny{ $\\pm$ 0.05} \\\\\n\\hline\n \\textit{TRADES} & \\textbf{90.83}\\tiny{ $\\pm$ 0.14} & 57.27\\tiny{ $\\pm$ 0.08} & 53.59\\tiny{ $\\pm$ 0.05} &46.45\\tiny{ $\\pm$ 0.07} \\\\\n\\textbf{TRADES-SDI} \\tiny{(Ours)} \n & 90.54\\tiny{ $\\pm$ 0.07}\n & \\textbf{59.21}\\tiny{ $\\pm$ 0.10}\n & \\textbf{56.39}\\tiny{ $\\pm$ 0.10}\n & \\textbf{49.21}\\tiny{ $\\pm$ 0.03} &\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness", "authors": ["Olukorede Fakorede", "Modeste Atsague", "Jin Tian"], "url": "https://arxiv.org/abs/2412.19947v1", "attribution": "\"Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness\" by Olukorede Fakorede, Modeste Atsague, and Jin Tian, arXiv:2412.19947v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.18593v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Dataset = \\textbf{Adult Income} [feature dim=102, \\#(train data)=31,655]}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccccccc}\n\\toprule\n\\multirow{2}{*}{$k$} & \\multirow{2}{*}{Method} & \\multirow{2}{*}{\\%Var($\\uparrow$)} & \\multirow{2}{*}{MMD$^2$($\\downarrow$)} & \\%Acc($\\uparrow$) & $\\Delta_{\\rm DP}$($\\downarrow$) & \\%Acc($\\uparrow$) & $\\Delta_{\\rm DP}$($\\downarrow$) & \\%Acc($\\uparrow$) & $\\Delta_{\\rm DP}$($\\downarrow$) \\\\\n& & & & \\multicolumn{2}{c}{kernel SVM} & \\multicolumn{2}{c}{linear SVM} & \\multicolumn{2}{c}{MLP} \\\\\n\\midrule\n\\multirow{18}{*}{2} & PCA & 6.88$_{(0.14)}$ & 0.374$_{(0.006)}$ & 82.4$_{(0.2)}$ & 0.19$_{(0.01)}$ & 81.23$_{(0.16)}$ & 0.2$_{(0.0)}$ & 82.31$_{(0.18)}$ & 0.19$_{(0.01)}$ \\\\\n& (0.1) & \\multicolumn{8}{c}{Memory Out} \\\\\n& (0.0) & \\multicolumn{8}{c}{Memory Out} \\\\\n& (1e-3) & 5.68$_{(0.11)}$ & 0.0$_{(0.0)}$ & 80.34$_{(0.24)}$ & 0.05$_{(0.01)}$ & 78.09$_{(0.36)}$ & 0.01$_{(0.0)}$ & 80.31$_{(0.26)}$ & 0.06$_{(0.01)}$ \\\\\n& (1e-6) & 5.42$_{(0.11)}$ & 0.0$_{(0.0)}$ & 79.41$_{(0.23)}$ & 0.02$_{(0.01)}$ & 77.52$_{(0.69)}$ & 0.01$_{(0.0)}$ & 79.48$_{(0.29)}$ & 0.02$_{(0.01)}$ \\\\\n& (mean) & 5.74$_{(0.11)}$ & 0.002$_{(0.0)}$ & 80.6$_{(0.2)}$ & 0.07$_{(0.01)}$ & 78.57$_{(0.26)}$ & 0.01$_{(0.0)}$ & 80.42$_{(0.17)}$ & 0.07$_{(0.01)}$ \\\\\n& (0.85) & 4.09$_{(0.17)}$ & 0.001$_{(0.001)}$ & 75.52$_{(0.21)}$ & 0.0$_{(0.0)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 75.37$_{(0.23)}$ & 0.0$_{(0.0)}$ \\\\\n& (0.5) & 2.63$_{(0.07)}$ & 0.0$_{(0.0)}$ & 75.38$_{(0.18)}$ & 0.0$_{(0.0)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ \\\\\n& (kernel) & \\multicolumn{8}{c}{Takes too long time} \\\\\n& & 1.91$_{(0.08)}$ & 0.001$_{(0.001)}$ & 75.67$_{(0.31)}$ & 0.0$_{(0.0)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 75.54$_{(0.39)}$ & 0.0$_{(0.01)}$ \\\\\n& & 1.91$_{(0.09)}$ & 0.006$_{(0.011)}$ & 75.59$_{(0.34)}$ & 0.0$_{(0.0)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 75.48$_{(0.35)}$ & 0.0$_{(0.0)}$ \\\\\n& & N/A & N/A & 82.63$_{(0.18)}$ & 0.15$_{(0.01)}$ & 82.21$_{(0.2)}$ & 0.17$_{(0.0)}$ & 77.62$_{(3.46)}$ & 0.06$_{(0.09)}$ \\\\\n\\cmidrule{2-10}\n& \\textbf{Ours} (offline, mean) & 5.74$_{(0.11)}$ & 0.002$_{(0.0)}$ & 80.6$_{(0.2)}$ & 0.07$_{(0.01)}$ & 78.57$_{(0.26)}$ & 0.01$_{(0.0)}$ & 80.31$_{(0.22)}$ & 0.07$_{(0.01)}$ \\\\\n& \\textbf{Ours} (FNPM, mean) & 5.74$_{(0.11)}$ & 0.002$_{(0.0)}$ & 80.6$_{(0.2)}$ & 0.07$_{(0.01)}$ & 78.57$_{(0.26)}$ & 0.01$_{(0.0)}$ & 80.33$_{(0.25)}$ & 0.07$_{(0.01)}$ \\\\\n& \\textbf{Ours} (offline, $m$=15) & 4.04$_{(0.14)}$ & 0.001$_{(0.001)}$ & 75.51$_{(0.23)}$ & 0.0$_{(0.0)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 75.39$_{(0.24)}$ & 0.0$_{(0.0)}$ \\\\\n& \\textbf{Ours} (FNPM, $m$=15) & 4.07$_{(0.13)}$ & 0.001$_{(0.0)}$ & 75.54$_{(0.25)}$ & 0.0$_{(0.0)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 75.58$_{(0.26)}$ & 0.01$_{(0.01)}$ \\\\\n& \\textbf{Ours} (offline, $m$=50) & 2.63$_{(0.07)}$ & 0.0$_{(0.0)}$ & 75.38$_{(0.18)}$ & 0.0$_{(0.0)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 75.39$_{(0.25)}$ & 0.0$_{(0.01)}$ \\\\\n& \\textbf{Ours} (FNPM, $m$=50) & 2.64$_{(0.06)}$ & 0.0$_{(0.0)}$ & 75.44$_{(0.21)}$ & 0.0$_{(0.0)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 75.37$_{(0.2)}$ & 0.0$_{(0.0)}$ \\\\\n\\midrule\n\\multirow{18}{*}{10} & PCA & 20.82$_{(0.38)}$ & 0.195$_{(0.002)}$ & 88.58$_{(0.21)}$ & 0.18$_{(0.01)}$ & 83.14$_{(0.21)}$ & 0.19$_{(0.0)}$ & 83.88$_{(0.23)}$ & 0.2$_{(0.01)}$ \\\\\n& (0.1) & \\multicolumn{8}{c}{Memory Out} \\\\\n& (0.0) & \\multicolumn{8}{c}{Memory Out} \\\\\n& (1e-3) & \\multicolumn{8}{c}{Takes too long time} \\\\\n& (1e-6) & \\multicolumn{8}{c}{Takes too long time} \\\\\n& (mean) & 18.84$_{(0.35)}$ & 0.01$_{(0.0)}$ & 88.43$_{(0.18)}$ & 0.18$_{(0.01)}$ & 81.71$_{(0.29)}$ & 0.03$_{(0.01)}$ & 83.83$_{(0.32)}$ & 0.17$_{(0.01)}$ \\\\\n& (0.85) & 14.54$_{(0.23)}$ & 0.002$_{(0.0)}$ & 86.99$_{(0.32)}$ & 0.16$_{(0.0)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 81.92$_{(0.56)}$ & 0.14$_{(0.01)}$ \\\\\n& (0.5) & 11.34$_{(0.2)}$ & 0.0$_{(0.0)}$ & 83.01$_{(0.28)}$ & 0.12$_{(0.01)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 79.13$_{(0.77)}$ & 0.08$_{(0.02)}$ \\\\\n& (kernel) & \\multicolumn{8}{c}{Takes too long time} \\\\\n& & 9.36$_{(0.26)}$ & 0.001$_{(0.0)}$ & 85.71$_{(0.78)}$ & 0.14$_{(0.02)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 80.18$_{(0.68)}$ & 0.1$_{(0.02)}$ \\\\\n& & 9.6$_{(0.25)}$ & 0.001$_{(0.0)}$ & 87.61$_{(0.57)}$ & 0.16$_{(0.01)}$ & 75.37$_{(0.22)}$ & 0.0$_{(0.0)}$ & 80.72$_{(0.9)}$ & 0.11$_{(0.02)}$ \\\\\n& & N/A & N/A & 85.69$_{(0.24)}$ & 0.18$_{(0.01)}$ & 83.25$_{(0.18)}$ & 0.17$_{(0.01)}$ & 84.52$_{(0.29)}$ & 0.19$_{(0.01)}$ \\\\\n\\cmidrule{2-10}\n& \\textbf{Ours} (offline, mean) & 18.84$_{(0.35)}$ & 0.01$_{(0.0)}$ & 88.43$_{(0.18)}$ & 0.18$_{(0.01)}$ & 81.71$_{(0.29)}$ & 0.03$_{(0.01)}$ & 83.7$_{(0.24)}$ & 0.17$_{(0.01)}$ \\\\\n& \\textbf{Ours} (FNPM, mean) & 18.83$_{(0.35)}$ & 0.009$_{(0.0)}$ & 88.53$_{(0.22)}$ & 0.18$_{(0.01)}$ & 81.7$_{(0.29)}$ & 0.03$_{(0.01)}$ & 83.85$_{(0.28)}$ & 0.18$_{(0.01)}$ \\\\\n& \\textbf{Ours} (offline, $m$=15) & 14.48$_{(0.22)}$ & 0.002$_{(0.0)}$ & 86.86$_{(0.3)}$ & 0.16$_{(0.0)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 81.84$_{(0.54)}$ & 0.13$_{(0.01)}$ \\\\\n& \\textbf{Ours} (FNPM, $m$=15) & 14.38$_{(0.2)}$ & 0.001$_{(0.0)}$ & 86.64$_{(0.29)}$ & 0.16$_{(0.0)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 80.96$_{(0.56)}$ & 0.12$_{(0.02)}$ \\\\\n& \\textbf{Ours} (offline, $m$=50) & 11.34$_{(0.2)}$ & 0.0$_{(0.0)}$ & 83.0$_{(0.25)}$ & 0.12$_{(0.01)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 78.94$_{(0.7)}$ & 0.07$_{(0.02)}$ \\\\\n& \\textbf{Ours} (FNPM, $m$=50) & 11.34$_{(0.2)}$ & 0.0$_{(0.0)}$ & 82.85$_{(0.22)}$ & 0.11$_{(0.01)}$ & 75.33$_{(0.19)}$ & 0.0$_{(0.0)}$ & 78.59$_{(0.63)}$ & 0.06$_{(0.02)}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint", "authors": ["Junghyun Lee", "Hanseul Cho", "Se-Young Yun", "Chulhee Yun"], "url": "https://arxiv.org/abs/2310.18593v1", "attribution": "\"Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint\" by Junghyun Lee, Hanseul Cho, Se-Young Yun, and Chulhee Yun, arXiv:2310.18593v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.17099v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ROI Sensitivity Analysis}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Scenario} & \\textbf{Weather-AI ROI} & \\textbf{Route-AI ROI} & \\textbf{Ratio} \\\\\n\\midrule\nConservative (-20\\% benefits) & 7,285\\% & 1,142\\% & 6.4x \\\\\nBase case & 9,106\\% & 1,427\\% & 6.4x \\\\\nOptimistic (+20\\% benefits) & 10,927\\% & 1,712\\% & 6.4x \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity", "authors": ["Tatsuru Kikuchi"], "url": "https://arxiv.org/abs/2507.17099v1", "attribution": "\"Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity\" by Tatsuru Kikuchi, arXiv:2507.17099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09954v3_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small{Statistics of the ensemble-based uncertainty quantification results obtained for transient subsurface pressure data in a multi-Gaussian domain. The True and Mean models are those depicted in Figure . The mean model is obtained from the deep ensemble models \\#1 to \\#5. RMSE$_{\\rm data}$ denotes the RMSE in data space, ${\\rm l}_1$ refers to the ${\\rm l}_1$-norm and SSIM to the structural similarity index. The ${\\rm l}_1$-norm is calculated in terms of $\\log_{10}K_s$ (-) while the SSIM is computed in the rescaled $\\left[0,1\\right]$ domain.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}%\n\t\t\t\\hline\n\t\t\tTrue model & RMSE$_{\\rm data}$ (m) & ${\\rm l}_1$ (m) & SSIM (-)\\\\\n\t\t\t\\hline\n\t\t\t(a) True & 0.010 & 0 & 1 \\\\\n\t\t\t(b) Mean & 0.032 & 1858 & 0.71\\\\\n\t\t\t(d) Model \\#1 & 0.023 & 1837 & 0.73 \\\\\n\t\t\t(e) Model \\#2 & 0.040 & 1965 & 0.68 \\\\\n\t\t\t(f) Model \\#3 & 0.025 & 1949 & 0.67 \\\\\n\t\t\t(g) Model \\#4 & 0.032 & 1876 & 0.73 \\\\\n\t\t\t(h) Model \\#5 & 0.028 & 2073 & 0.67 \\\\\n\t\t\t\n\t\t\t(i) True & 0.010 & 0 & 1 \\\\\n\t\t\t(j) Mean & 0.068 & 1597 & 0.73\\\\\n\t\t\t(l) Model \\#1 & 0.054 & 1559 & 0.76 \\\\\n\t\t\t(m) Model \\#2 & 0.046 & 1779 & 0.68 \\\\\n\t\t\t(n) Model \\#3 & 0.055 & 1513 & 0.72 \\\\\n\t\t\t(o) Model \\#4 & 0.059 & 1660 & 0.71 \\\\\n\t\t\t(p) Model \\#5 & 0.061 & 1922 & 0.64 \\\\\n\t\t\t\n\t\t\t(q) True & 0.010 & 0 & 1 \\\\\n\t\t\t(r) Mean & 0.016 & 1571 & 0.77\\\\\n\t\t\t(t) Model \\#1 & 0.016 & 1525 & 0.75 \\\\\n\t\t\t(u) Model \\#2 & 0.020 & 1572 & 0.75 \\\\\n\t\t\t(v) Model \\#3 & 0.015 & 1795 & 0.72 \\\\\n\t\t\t(w) Model \\#4 & 0.018 & 1492 & 0.77 \\\\\n\t\t\t(x) Model \\#5 & 0.016 & 1752 & 0.74 \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Approaching geoscientific inverse problems with vector-to-image domain transfer networks", "authors": ["Eric Laloy", "Niklas Linde", "Diederik Jacques"], "url": "https://arxiv.org/abs/1912.09954v3", "attribution": "\"Approaching geoscientific inverse problems with vector-to-image domain transfer networks\" by Eric Laloy, Niklas Linde, and Diederik Jacques, arXiv:1912.09954v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.06425v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean Terminal Nodes and Signed-Rank Tests in Figure~}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\hline\n\\multirow{2}{*}{} & \\multicolumn{2}{c}{Linear} & \\multicolumn{2}{c}{Exponential} & \\multicolumn{2}{c}{Constant} \\\\\n & ($c=0.5$) & ($c=0.8$) & ($c=2.5$) & ($c=4$) & ($c=0.4$) & ($c=0.8$) \\\\ \\hline\n\\multicolumn{7}{l}{\\emph{Panel A. Mean Terminal Nodes Across Elicitation Methods}} \\\\\nDirect Response Method & 4.604 & 5.396 & 5.365 & 4.646 & 1.229 & 1.917 \\\\\n & (1.765) & (1.544) & (1.284) & (1.315) & (0.467) & (0.898) \\\\\nReduced Strategy Method & 4.906 & 5.375 & 5.385 & 4.698 & 1.406 & 2.240 \\\\\n & (1.627) & (1.715) & (1.278) & (1.174) & (0.811) & (0.997) \\\\\nFull Strategy Method & 4.312 & 5.490 & 5.344 & 4.510 & 1.510 & 2.000 \\\\\n & (1.710) & (1.541) & (1.171) & (1.422) & (0.890) & (1.109) \\\\ \\hline\n\\multicolumn{7}{l}{\\emph{Panel B. Pairwise Signed-Rank Tests $p$-Values}} \\\\\nDR vs.~RS & 0.365 & 1.000 & 1.000 & 1.000 & 0.022 & 0.005 \\\\\nDR vs.~FS & 0.486 & 1.000 & 1.000 & 1.000 & 0.001 & 1.000 \\\\\nFS vs.~RS & 0.005 & 1.000 & 1.000 & 0.502 & 0.374 & 0.119 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Strategy Method Effects in Centipede Games: An Optimal Design Approach", "authors": ["Shiang-Hung Hu", "Po-Hsuan Lin", "Thomas R. Palfrey", "Joseph Tao-yi Wang", "Yu-Hsiang Wang"], "url": "https://arxiv.org/abs/2508.06425v1", "attribution": "\"Strategy Method Effects in Centipede Games: An Optimal Design Approach\" by Shiang-Hung Hu, Po-Hsuan Lin, Thomas R. Palfrey, Joseph Tao-yi Wang, and Yu-Hsiang Wang, arXiv:2508.06425v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.16026v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\nParameter & Value \\\\ \\hline\nHorizontal Viscosity $A_{u,v}$& 2 $m^2/s$\\\\ \n\\hline\nHorizontal Diffusivity $A_{T,S}$& 2 $m^2/s$\\\\ \n\\hline\nBottom Drag factor $C_d^b$ & 0.003 \\\\ \n\\hline\nSponge factor & 1 \\\\ \n\\hline\nWind drag factor $C_d^s$ & $6.0 \\cdot 10^{-5}$ \\\\ \n\\hline\nBathymetry & NAVIONICS \\\\\n\\hline\nBoundary conditions & Clamped \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{This table presents the numerical values associated with each parameter utilized in the base solution}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Dynamical systems for remote validation of very high-resolution ocean models", "authors": ["G. Garcia-Sanchez", "A. M. Mancho", "A. G. Ramos", "J. Coca", "J. A. Jimenez-Madrid"], "url": "https://arxiv.org/abs/2501.16026v1", "attribution": "\"Dynamical systems for remote validation of very high-resolution ocean models\" by G. Garcia-Sanchez, A. M. Mancho, A. G. Ramos, J. Coca, and J. A. Jimenez-Madrid, arXiv:2501.16026v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.03757v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Projected Effects of Executive Demographic Changes}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nScenario & AI Adoption Rate & Productivity Gain & GDP Impact \\\\\n\\midrule\nCurrent Demographics & 30.1\\% & 2.4\\% & ¥388 billion \\\\\n+5 Years Younger CEOs & 38.7\\% & 2.6\\% & ¥504 billion \\\\\nDouble Female CEO Share & 33.4\\% & 2.5\\% & ¥419 billion \\\\\n+50\\% Technical Education & 41.2\\% & 2.7\\% & ¥558 billion \\\\\nCombined Scenario & 52.3\\% & 2.8\\% & ¥732 billion \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises", "authors": ["Tatsuru Kikuchi"], "url": "https://arxiv.org/abs/2508.03757v1", "attribution": "\"AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises\" by Tatsuru Kikuchi, arXiv:2508.03757v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12435v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Settings} % title of Table\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc} % centered columns (4 columns)\n\\hline %inserts double horizontal lines\n\\rowcolor{lightgray}\nParameters & Description & Value \\\\ % inserts table\n\\hline % inserts single horizontal line\n\\textit{N} & \\# Slices & 3 \\\\ % inserting body of the table\n\\textit{F} & \\# Input features & 3 \\\\\n\\textit{K} & \\# Total AEs & 10, (40, 50) \\\\\n\\textit{m} & \\# Selected AEs & 5, 25 \\\\\n\\textit{$D_{k,n}$} & Local dataset size & 1000 samples \\\\\n\\textit{$I$} & Attribution samples & 150 samples \\\\\n\\textit{T} & \\# Max FL rounds & 30 \\\\ \n\\textit{L} & \\# Local epochs & 150 \\\\\n\\textit{$\\eta_{\\lambda}$} & Learning rate & 0.0015 \\\\\n\\textit{$S_{d}$} & Seed & 42 \\\\ % [1ex] adds vertical space\n\\hline\\hline %inserts double horizontal lines\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing", "authors": ["Martino Chiarani", "Swastika Roy", "Christos Verikoukis", "Fabrizio Granelli"], "url": "https://arxiv.org/abs/2503.12435v1", "attribution": "\"XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing\" by Martino Chiarani, Swastika Roy, Christos Verikoukis, and Fabrizio Granelli, arXiv:2503.12435v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01899v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n \\hline\\hline\n \\textbf{Labels used} & \\textbf{\\texttt{\\small Precision}} & \\textbf{\\texttt{\\small Recall}} & \\textbf{\\texttt{\\small F1-score}} & \\textbf{\\texttt{\\small Accuracy}}\\\\\\hline\n \\textbf{Supervised} & $0.66$ & $0.89$ & $0.75$ & $0.72$\\\\\n \\textbf{Semi-Supervised} & $0.62$ & $0.82$ &\t$0.70$ & $0.66$\\\\\n \\hline\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Backchannel opportunity prediction: comparison of model trained using supervised manually-annotated labels with the one using labels generated by the identification module.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Exploring Semi-Supervised Learning for Predicting Listener Backchannels", "authors": ["Vidit Jain", "Maitree Leekha", "Rajiv Ratn Shah", "Jainendra Shukla"], "url": "https://arxiv.org/abs/2101.01899v1", "attribution": "\"Exploring Semi-Supervised Learning for Predicting Listener Backchannels\" by Vidit Jain, Maitree Leekha, Rajiv Ratn Shah, and Jainendra Shukla, arXiv:2101.01899v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19995v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation Study: Prediction Error}\n\\begin{tabular}{ccccc}\n \\hline\n & \\multicolumn{2}{c}{\\bf{Visual Error ($\\mu \\pm{SD}$) \\%}} & \\multicolumn{2}{c}{\\bf{Proprioceptive Error ($\\mu \\pm{SD}$) \\%}}\\\\\n \\hline\n \\bf{Condition} &\\bf{U-P} &\\bf{U-C} &\\bf{U-P} &\\bf{U-C} \\\\\n \\hline\n \\bf{VWM-1 \\& 2 with attention} &$0.0196\\pm{0.0016}$ &$0.0249\\pm{0.0038}$ &$0.0110\\pm{0.0014}$ &$0.0117\\pm{0.0020}$ \\\\ \n \\bf{only VWM-1 with attention} &$0.0407\\pm{0.0041}$ &$0.0543\\pm{0.0011}$ &$0.0136\\pm{0.0008}$ &$0.0158\\pm{0.0014}$ \\\\ \n \\bf{only VWM-2 with attention} &$0.0391\\pm{0.0031}$ &$0.0506\\pm{0.0074}$ &$0.0149\\pm{0.0002}$ &$0.0155\\pm{0.0015}$ \\\\\n \\bf{VWM-1 \\& 2 with no attention} &$0.0480\\pm{0.0050}$ &$0.0657\\pm{0.0046}$ &$0.0154\\pm{0.0007}$ &$0.0169\\pm{0.0075}$ \\\\\n \\bf{no VWM-1 \\& 2 and no attention} &$0.0734\\pm{0.0052}$ &$0.0960\\pm{0.0068}$ &$0.0198\\pm{0.0004}$ &$0.0238\\pm{0.0037}$ \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Development of Compositionality and Generalization through Interactive Learning of Language and Action of Robots", "authors": ["Prasanna Vijayaraghavan", "Jeffrey Frederic Queisser", "Sergio Verduzco Flores", "Jun Tani"], "url": "https://arxiv.org/abs/2403.19995v2", "attribution": "\"Development of Compositionality and Generalization through Interactive Learning of Language and Action of Robots\" by Prasanna Vijayaraghavan, Jeffrey Frederic Queisser, Sergio Verduzco Flores, and Jun Tani, arXiv:2403.19995v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2207.13319v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\n & CC & FL & CRE & CI \\\\\n\\midrule\nmean & 25.5 & 559.0 & 106.6 & 88.5 \\\\\nmedian & 10.4 & 120.8 & 14.2 & 9.8 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean and median of relative prediction differences between the pooled and the FEO estimates (in \\%) for GAMs.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Should Bank Stress Tests Be Fair?", "authors": ["Paul Glasserman", "Mike Li"], "url": "https://arxiv.org/abs/2207.13319v2", "attribution": "\"Should Bank Stress Tests Be Fair?\" by Paul Glasserman and Mike Li, arXiv:2207.13319v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.13570v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mappings and Detailed Description for \\texttt{schoolparents}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cl}\n\t\t\\toprule\n\t\t\\textbf{Final Level} & \\textbf{Former Level / Description} \\\\\n\t\t\\midrule\n\t\t\\textit{HauptS} & Secondary school leaving certificate I $\\rightarrow$ Haupt-, Volksschulabschluss \\\\\n\t\t\\textit{HighSDip} & Secondary school leaving certificate III $\\rightarrow$ (Technical) Abitur \\\\\n\t\t\\textit{RealS} & Secondary school leaving certificate II $\\rightarrow$ Mittlere Reife, Realschulabschluss \\\\\n\t\t\\textit{NoSCert} & No school-leaving qualification \\\\\n\t\t\\textit{Other} & Other school-leaving qualification \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study", "authors": ["Jakob Schwerter", "Andrés Romero", "Florian Dumpert", "Markus Pauly"], "url": "https://arxiv.org/abs/2412.13570v1", "attribution": "\"Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study\" by Jakob Schwerter, Andrés Romero, Florian Dumpert, and Markus Pauly, arXiv:2412.13570v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.13174v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistical Comparison of MaxDD between High and Low PFS Groups (Threshold = 0.9)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Test} & \\textbf{Statistic} & \\textbf{p-value} \\\\\n\\midrule\nT-test & $t$ = 4.122 & 0.0001 \\\\\nMann-Whitney U test & $U$ = 11,381.5 & 0.0001 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining", "authors": ["Hongjun Ding", "Binqi Chen", "Jinsheng Huang", "Taian Guo", "Zhengyang Mao", "Guoyi Shao", "Lutong Zou", "Luchen Liu", "Ming Zhang"], "url": "https://arxiv.org/abs/2508.13174v1", "attribution": "\"AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining\" by Hongjun Ding, Binqi Chen, Jinsheng Huang, Taian Guo, Zhengyang Mao, Guoyi Shao, Lutong Zou, Luchen Liu, and Ming Zhang, arXiv:2508.13174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.15096v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n \\toprule\n \\textbf{Measure}&\\textbf{Label} & \\multicolumn{4}{c}{\\textbf{Placement}} \\\\\n \\cmidrule(r){3-6}\n &&\\multicolumn{2}{c}{\\textbf{DR}}& \\multicolumn{2}{c}{\\textbf{Random}} \\\\\n && \\textbf{$\\overline{B} -\\overline{L}$} &\\textbf{\\textit{p-val.}}&\\textbf{$\\overline{B} -\\overline{L}$} &\\textbf{\\textit{p-val.}}\\\\\n \\midrule\n Time (s) &&&&\\\\\n & Shape & 1.59 & 0.18 &-1.58 & 0.26\\\\\n & Color & 0.76 & 1.0 &-0.9 & 1.0\\\\\n & Texture & 0.73 & 0.96 &-2.91 & \\textbf{0.03} \\\\\n Hovered&&&&\\\\\n samples& Shape & 6.1 & \\textbf{4e-07} &5.5 & \\textbf{0.002}\\\\\n & Color & 1.1 & 0.89 & 2.3 & 0.52 \\\\\n & Texture & 1.3 & 0.33 & 2.7 & 0.13 \\\\\n Distance &&&&\\\\\n (pixels) & Shape & 494 & \\textbf{0.04} & -30 & 1.0 \\\\\n & Color & 41 & 1.0 & -493 & 0.20 \\\\\n & Texture & 120 & 0.94 & -307 & 0.61 \\\\\n \n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A proposal and evaluation of new timbre visualisation methods for audio sample browsers", "authors": ["Etienne Richan", "Jean Rouat"], "url": "https://arxiv.org/abs/2011.15096v1", "attribution": "\"A proposal and evaluation of new timbre visualisation methods for audio sample browsers\" by Etienne Richan and Jean Rouat, arXiv:2011.15096v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.11692v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{List of Parameters (at set point temperature = $27^{\\circ} C$)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\nParameter & Meaning & Value & Unit\\\\\\hline\n$f$ & frequency of TCL & 0.0031 & Hz \\\\\n$\\delta$ & thermostat deadband & 3 & $^{\\circ}C$\\\\ \n$d$ & duty cycle of TCL & 0.452 & - \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Thermostatic control for demand response using distributed averaging and deep neural networks", "authors": ["Kshitij Singh", "Pratik K. Bajaria"], "url": "https://arxiv.org/abs/1912.11692v1", "attribution": "\"Thermostatic control for demand response using distributed averaging and deep neural networks\" by Kshitij Singh and Pratik K. Bajaria, arXiv:1912.11692v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14913v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|cc|} \\hline \n & \\multicolumn{2}{|c|}{$V_{h}$}& \\multicolumn{2}{|c|}{$V_{h}^{+}$}& \\multicolumn{2}{|c|}{$V_{h}^{*}$}\\\\ \\hline \n n& $L^{2}$error & order & $L^{2}$error & order & $L^{2}$error & order \\\\ \\hline \n 10& 3.764e-01& -\n&2.582e-04& - &2.578e-04& - \n\\\\ \\hline \n 20& 1.887e-01&0.996\n&1.394e-04&0.889&1.400e-04&0.880 \n\\\\ \\hline \n 40& 9.441e-02&0.999\n&7.461e-05&0.902&7.492e-05&0.903 \n\\\\ \\hline \n 80& 4.721e-02&1.000\n&3.804e-05&0.972&3.818e-05&0.973 \n\\\\ \\hline \n 160& 2.361e-02&1.000\n&1.911e-05&\n0.993&1.918e-05&0.993 \n\\\\ \\hline\n 320& 1.180e-02&1.000&9.569e-06&0.998&9.603e-06&0.998 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{$H^{1}$error and order using $P^{1}$ Lagrange element, Poisson equation in 1d.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A PINN-enriched finite element method for linear elliptic problems", "authors": ["Xiao Chen", "Yixin Luo", "Jingrun Chen"], "url": "https://arxiv.org/abs/2503.14913v1", "attribution": "\"A PINN-enriched finite element method for linear elliptic problems\" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.00551v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{List of basis functions for the meta function}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccl} \t\n\t\t\\toprule\n1 & Linear & $f^1(x) = x $ \\\\\n2 & Quadratic & $f^2(x) = x^2 $ \\\\\n3 & Cubic & $f^3(x) = x^3 $ \\\\\n4 & Exponential & $f^4(x) = (e^x - 1) / (e - 1)$ \\\\\n5 & Periodic & $f^5(x) = 0.5 \\sin(2 \\pi x) + 0.5$ \\\\\n6 & Discontinuous & $f^6(x) = 1$ if $x \\geq 0.5$ and $0$ otherwise \\\\\n7 & Dummy & $f^7(x) = 0 $ \\\\\n8 & Non-monotonic & $f^8(x) = 4(x-0.5)^2 $ \\\\\n9 & Inverse & $f^9(x) = (10 -1/1.1)^{-1} (x+0.1)^{-1} - 0.1 $ \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Derivative based global sensitivity analysis and its entropic link", "authors": ["Jiannan Yang"], "url": "https://arxiv.org/abs/2310.00551v3", "attribution": "\"Derivative based global sensitivity analysis and its entropic link\" by Jiannan Yang, arXiv:2310.00551v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.08970v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{2DU and 3DFF Estimator Structure}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c||c|c|c|c||c}\n & \\multicolumn{2}{c|}{2DU} & \\multicolumn{2}{c||}{3DFF} & \\\\\\cline{2-5}\n Layer&Kernel&In chan.&Kernel&In chan.&Out chan.\\\\\\hline\\hline\n 1&$7\\times5$&2 & $7\\times5\\times5$ & 2& 10 \\\\\\hline\n 2&$7\\times5$&10 & $7\\times5\\times5$ & 10 & 10 \\\\\\hline\n 3&$7\\times5$&10 & $7\\times5\\times5$ & 10 & 10 \\\\\\hline\n 4&$7\\times5$&10 & $7\\times5\\times5$ & 10 & 10 \\\\\\hline\n 5&$5\\times5$&20 & $5\\times5\\times5$ & 10 & 20 \\\\\\hline\n 6&$5\\times5$&30 & $5\\times5\\times5$ & 20 & 5 \\\\\\hline\n 7&$3\\times3$&15 & $3\\times3\\times3$ & 5 & 2\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Study on MIMO Channel Estimation by 2D and 3D Convolutional Neural Networks", "authors": ["Ben Marinberg", "Ariel Cohen", "Eilam Ben-Dror", "Haim Permuter"], "url": "https://arxiv.org/abs/2011.08970v1", "attribution": "\"A Study on MIMO Channel Estimation by 2D and 3D Convolutional Neural Networks\" by Ben Marinberg, Ariel Cohen, Eilam Ben-Dror, and Haim Permuter, arXiv:2011.08970v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03392v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of the datasets in our experiments.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n &\\textbf{Electronics} & \\textbf{Beauty}\\\\ \n\\midrule\n\\#Users &45,224 &5,122\\\\ \n\\#Items &61,687 &11,616\\\\ \n\\#Reviews &744,453 &90,247\\\\\n\\#features &434&518\\\\\n$\\mid\\mathcal{V}\\mid$ &20,568 &7,152\\\\\nsparsity &99.999\\% & 99.998\\%\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Generate Natural Language Explanations for Recommendation", "authors": ["Hanxiong Chen", "Xu Chen", "Shaoyun Shi", "Yongfeng Zhang"], "url": "https://arxiv.org/abs/2101.03392v1", "attribution": "\"Generate Natural Language Explanations for Recommendation\" by Hanxiong Chen, Xu Chen, Shaoyun Shi, and Yongfeng Zhang, arXiv:2101.03392v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.20396v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n & \\textnormal{\\small{$\\mathcal{E}(p)=p(1-p)$}} & \\textnormal{\\small{$\\mathcal{E}_{L}(p)=p$}} \\\\ \n\\hline\n\\textnormal{\\small{$\\mathcal{C}(p)=(1-p)$}} & \\textnormal{Case I)} & \\textnormal{Case III)} \\\\\n\\hline \n\\textnormal{\\small{$\\mathcal{C}_{L}(p)=p(1-p)$}} & \\textnormal{Case II)} & \\textnormal{Levins Model} \\\\ \n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Extensions of the classical Levins model. The first co\\-lumn considers the rescue effect whereas the first row considers the propagule rain.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Generalizing the Levins metapopulation model to time varying colonization and extinction rates", "authors": ["Gonzalo Robledo", "Ramiro Bustamante"], "url": "https://arxiv.org/abs/2504.20396v1", "attribution": "\"Generalizing the Levins metapopulation model to time varying colonization and extinction rates\" by Gonzalo Robledo and Ramiro Bustamante, arXiv:2504.20396v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2301.00248v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{This table shows the median improvement of our approach compared to a stratified dummy classifier for stocks grouped per market sector and implied volatility regime. The best and worst regime for each sector are respectively indicated by bold and underlined text styles.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\toprule\n & Low & Medium & High & Very High \\\\\n\\midrule\nXLB & 1.87 &\t\\textbf{3.06} &\t1.02 &\t\\underline{0.77} \\\\\nXLC & 3.57 &\t4.76 &\t\\textbf{5.27} &\t\\underline{0.26} \\\\\nXLE & 2.55 &\t2.46 &\t\\textbf{4.0} &\t\\underline{-2.55} \\\\\nXLF & 3.06 &\t\\textbf{4.08} &\t2.64 &\t\\underline{-1.7} \\\\\nXLI & 0.05 &\t\\textbf{1.53} &\t0.17 &\t\\underline{-1.5} \\\\\nXLK & \\underline{0.60} &\t2.38 &\t3.91 &\t\\textbf{5.70} \\\\\nXLP & 2.72 &\t\\textbf{3.83} &\t3.23 &\t\\underline{1.53} \\\\\nXLRE & \\textbf{8.5} &\t6.38 &\t4.93 &\t\\underline{4.25} \\\\\nXLU & \\textbf{6.63} &\t5.95 &\t5.36 &\t\\underline{1.79} \\\\\nXLV & \\textbf{4.25} &\t3.23 &\t\\underline{1.62} &\t2.13 \\\\\nXLY & 2.04 &\t3.66 &\t\\textbf{5.95} & \\underline{0.85} \\\\\n\\midrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Nowcasting Stock Implied Volatility with Twitter", "authors": ["Thomas Dierckx", "Jesse Davis", "Wim Schoutens"], "url": "https://arxiv.org/abs/2301.00248v1", "attribution": "\"Nowcasting Stock Implied Volatility with Twitter\" by Thomas Dierckx, Jesse Davis, and Wim Schoutens, arXiv:2301.00248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14565v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n \\textbf{asset} & \\textbf{Our approach}& & \\\\ \\midrule\n 1 & 0.0& 0.0& 0.0 \\\\\n 2 & 0.0 & 0.0 & 0.0\\\\\n 3 & 0.0680& 0.0680 & 0.0680 \\\\\n 4 & 0.1880 & 0.1880 & 0.1880\\\\\n 5 & 0.0& 0.0& 0.0\\\\\n 6 & 0.3913 & 0.3913 & 0.3913 \\\\\n 7 & 0.2309 & 0.2309 & 0.2309\\\\\n 8 & 0.1216 & 0.1216 & 0.1216\\\\ \\midrule\n \\multicolumn{1}{c}\\textbf{objective}&11.00 $\\%$&11.00$\\%$ & 11.00$\\%$ \\\\ \\midrule\n \\multicolumn{1}{c}\\textbf{number of SSD constraints}&\\textbf{3}& 506 & 550 \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Second order stochastic dominance (case $p=1$): a comparison with prominent approaches}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Higher-Order Stochastic Dominance Constraints in Optimization", "authors": ["Rajmadan Lakshmanan", "Alois Pichler", "Miloš Kopa"], "url": "https://arxiv.org/abs/2501.14565v3", "attribution": "\"Higher-Order Stochastic Dominance Constraints in Optimization\" by Rajmadan Lakshmanan, Alois Pichler, and Miloš Kopa, arXiv:2501.14565v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2501.08274v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Decision rule at month 4, mean of the blip estimates over 25 imputed datasets, SPRINT trial, 2010-2013. }\n\\begin{tabular}{|l|c|c|c|}\n \\hline\n Variable &QLOMA & WOMA & No. of times stat. significant$^*$ \\\\ \\hline\n Intercept visit & 8.8 & 9.8 & 2 \\\\ \\hline\n Intercept add-on & -10.3 & -7.0 & 2 \\\\ \\hline\n \\multicolumn{4}{|c|}{Visit interaction with:} \\\\ \\hline\n Intensive group & -0.5 & -0.0 & 1 \\\\ \\hline\n Age & -0.0 & -0.1 & 1 \\\\ \\hline\n Female sex & 0.6 & 0.0 & 0 \\\\ \\hline\n Race Black & \\multicolumn{3}{|c|}{Reference} \\\\ \\hline\n Race Hispanic & -0.7 & 0.0 & 0 \\\\ \\hline\n Race White & 0.0 & -0.1 & 0 \\\\ \\hline\n \\hspace{0.2cm} Other & -0.8 & 0.4 & 0 \\\\ \\hline\n Smoking (ever) & -0.2 & -0.7 & 0 \\\\ \\hline\n BMI & -0.0 & -0.1 & 0 \\\\ \\hline\n HDL & -0.1 & -0.0 & 17 \\\\ \\hline\n SBP Baseline & 0.0 & -0.0 & 0 \\\\ \\hline\n SBP Current month & -0.0 & -0.0 & 0 \\\\ \\hline\n CVD & -0.7 & -1.2 & 0 \\\\ \\hline\n Aspirin use & 0.4 & -0.1 & 0 \\\\ \\hline\n Statin use & 0.6 & 0.7 & 0 \\\\ \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Constructing optimal dynamic monitoring and treatment regimes: An application to hypertension care", "authors": ["Janie Coulombe", "Dany El-Riachi", "Fanxing Du", "Tianze Jiao"], "url": "https://arxiv.org/abs/2501.08274v1", "attribution": "\"Constructing optimal dynamic monitoring and treatment regimes: An application to hypertension care\" by Janie Coulombe, Dany El-Riachi, Fanxing Du, and Tianze Jiao, arXiv:2501.08274v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.01221v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|} \n \\hline\n function type & $v$ & $a$ & $b$ \\\\ \n \\hline\n $S_I$ & $0.65$ & $10^{-2}$ & $1$ \\\\ \n \\hline\n \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Specification of the security breach function.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A stochastic Gordon-Loeb model for optimal cybersecurity investment under clustered attacks", "authors": ["Giorgia Callegaro", "Claudio Fontana", "Caroline Hillairet", "Beatrice Ongarato"], "url": "https://arxiv.org/abs/2505.01221v1", "attribution": "\"A stochastic Gordon-Loeb model for optimal cybersecurity investment under clustered attacks\" by Giorgia Callegaro, Claudio Fontana, Caroline Hillairet, and Beatrice Ongarato, arXiv:2505.01221v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11556v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\\hline\nCategory & $\\tau$-att & $\\lambda$-sim & qasim \\\\ \\hline\nEntertainment (47) & 0.446& 0.382 & 0.297\\\\ \nHealth (62) &0.483 & 0.419 & 0.354\\\\ \nPolitics \t(143) & 0.45 & 0.300 &\t0.272 \\\\\nSociety \t(38) & \t0.28 & 0.157 & 0.236 \\\\\nFamily (20) & 0.6 & 0.350 & 0.40 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Precision$@$1 of models across categories}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Powering COVID-19 community Q&A with Curated Side Information", "authors": ["Manisha Verma", "Kapil Thadani", "Shaunak Mishra"], "url": "https://arxiv.org/abs/2101.11556v1", "attribution": "\"Powering COVID-19 community Q&A with Curated Side Information\" by Manisha Verma, Kapil Thadani, and Shaunak Mishra, arXiv:2101.11556v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.08743v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CV of Window Topic Models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\hline\nCv & NMF & LDA & NMF & LDA \\\\\nYear & \\multicolumn{2}{c}{K=6} & \\multicolumn{2}{c}{K=18} \\\\ \\hline\n2004 & 0.316 & 0.270 & 0.448 & 0.32 \\\\\n2005 & 0.278 & 0.295 & 0.323 & 0.312 \\\\\n2006 & 0.284 & 0.293 & 0.328 & 0.283 \\\\\n2007 & 0.300 & 0.256 & 0.452 & 0.312 \\\\\n2008 & 0.288 & 0.288 & 0.388 & 0.307 \\\\\n2009 & 0.319 & 0.294 & 0.342 & 0.315 \\\\\n2010 & 0.287 & 0.303 & 0.333 & 0.291 \\\\\n2011 & 0.285 & 0.259 & 0.379 & 0.305 \\\\\n2012 & 0.282 & 0.27 & 0.367 & 0.278 \\\\\n2013 & 0.299 & 0.283 & 0.386 & 0.302 \\\\\n2014 & 0.292 & 0.308 & 0.307 & 0.3 \\\\\n2015 & 0.320 & 0.280 & 0.345 & 0.284 \\\\\n2016 & 0.257 & 0.290 & 0.320 & 0.295 \\\\\n2017 & 0.289 & 0.291 & 0.310 & 0.289 \\\\\n2018 & 0.281 & 0.285 & 0.305 & 0.281 \\\\\n2019 & 0.269 & 0.283 & 0.375 & 0.281 \\\\\n2020 & 0.274 & 0.289 & 0.301 & 0.283 \\\\\n2021 & 0.377 & 0.254 & 0.340 & 0.287 \\\\\n2022 & 0.356 & 0.268 & 0.363 & 0.273 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Dynamic Topic Analysis in Academic Journals using Convex Non-negative Matrix Factorization Method", "authors": ["Yang Yang", "Tong Zhang", "Jian Wu", "Lijie Su"], "url": "https://arxiv.org/abs/2504.08743v1", "attribution": "\"Dynamic Topic Analysis in Academic Journals using Convex Non-negative Matrix Factorization Method\" by Yang Yang, Tong Zhang, Jian Wu, and Lijie Su, arXiv:2504.08743v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00173v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccc}\n\\toprule\nMetric & \\multicolumn{4}{c}{Top-1 Accuracy (\\%)} & & \\multicolumn{4}{c}{Seen-Unseen AUC (\\%)} \\\\ \\cmidrule(lr){2-5} \\cmidrule(l){7-10} \nDataset & \\multicolumn{2}{c}{CUB} & \\multicolumn{2}{c}{NAB} & & \\multicolumn{2}{c}{CUB} & \\multicolumn{2}{c}{NAB} \\\\\nSplit-Mode & Easy & Hard & Easy & Hard & & Easy & Hard & Easy & Hard \\\\ \\midrule\nCIZSL-v2 &41.9 & 15.5 &35.0 &9.6 & & 39.2 & 12.6 & 23.6 &6.9\\\\ \nCIZSL-v2+SeGC & \\textbf{42.4} & \\textbf{16.4} & \\textbf{35.2} & \\textbf{10.6} & & \\textbf{39.3} & \\textbf{14.9} & \\textbf{23.8} & \\textbf{7.5}\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Semantic guided categorizer.}% The chosen hyperparameters for CUB\\&NAB easy\\&hard are 1, 0.0001, 0.1, 0.1, respectively. Then we test the performance with best hyperparameters.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "CIZSL++: Creativity Inspired Generative Zero-Shot Learning", "authors": ["Mohamed Elhoseiny", "Kai Yi", "Mohamed Elfeki"], "url": "https://arxiv.org/abs/2101.00173v2", "attribution": "\"CIZSL++: Creativity Inspired Generative Zero-Shot Learning\" by Mohamed Elhoseiny, Kai Yi, and Mohamed Elfeki, arXiv:2101.00173v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.15007v2_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllll}\nmeta-estimator & prop\\_score\\_model & spearman & kendall & pearson & prob\\_better\\_better & prob\\_better\\_or\\_equal & class\\_score \\\\\nipw & DecisionTree & 0.88 & 0.70 & 0.81 & 0.83 & 0.86 & mean\\_test\\_f1 \\\\\nipw & DecisionTree & -0.90 & -0.74 & -0.81 & 0.11 & 0.17 & mean\\_test\\_average\\_precision \\\\\nipw & DecisionTree & 0.84 & 0.67 & 0.80 & 0.78 & 0.86 & mean\\_test\\_balanced\\_accuracy \\\\\nipw & LogisticRegression\\_l1\\_liblinear & -0.86 & -0.73 & -0.72 & 0.04 & 0.36 & mean\\_test\\_f1 \\\\\nipw & LogisticRegression\\_l1\\_liblinear & -0.22 & -0.11 & -0.47 & 0.27 & 0.64 & mean\\_test\\_average\\_precision \\\\\nipw & LogisticRegression\\_l1\\_liblinear & -0.87 & -0.75 & -0.73 & 0.00 & 0.44 & mean\\_test\\_balanced\\_accuracy \\\\\nipw & LogisticRegression\\_l1\\_saga & & & & 0.00 & 1.00 & mean\\_test\\_f1 \\\\\nipw & LogisticRegression\\_l1\\_saga & -0.86 & -0.81 & -0.74 & 0.00 & 0.49 & mean\\_test\\_average\\_precision \\\\\nipw & LogisticRegression\\_l1\\_saga & & & & 0.00 & 1.00 & mean\\_test\\_balanced\\_accuracy \\\\\nipw & LogisticRegression\\_l2 & & & & 0.00 & 1.00 & mean\\_test\\_f1 \\\\\nipw & LogisticRegression\\_l2 & 0.24 & 0.21 & 0.34 & 0.27 & 0.87 & mean\\_test\\_average\\_precision \\\\\nipw & LogisticRegression\\_l2 & & & & 0.00 & 1.00 & mean\\_test\\_balanced\\_accuracy \\\\\nipw & LogisticRegression\\_l2\\_liblinear & & & & 0.00 & 1.00 & mean\\_test\\_f1 \\\\\nipw & LogisticRegression\\_l2\\_liblinear & 0.83 & 0.77 & 0.93 & 0.51 & 1.00 & mean\\_test\\_average\\_precision \\\\\nipw & LogisticRegression\\_l2\\_liblinear & & & & 0.00 & 1.00 & mean\\_test\\_balanced\\_accuracy \\\\\nipw & SVM\\_sigmoid & 0.37 & 0.37 & 0.10 & 0.51 & 0.80 & mean\\_test\\_f1 \\\\\nipw & SVM\\_sigmoid & -0.49 & -0.42 & -0.34 & 0.09 & 0.62 & mean\\_test\\_average\\_precision \\\\\nipw & SVM\\_sigmoid & 0.43 & 0.37 & 0.22 & 0.40 & 0.87 & mean\\_test\\_balanced\\_accuracy \\\\\nipw & Standardized\\_SVM\\_rbf & 0.68 & 0.53 & 0.63 & 0.62 & 0.84 & mean\\_test\\_f1 \\\\\nipw & Standardized\\_SVM\\_rbf & 0.36 & 0.26 & 0.86 & 0.58 & 0.67 & mean\\_test\\_average\\_precision \\\\\nipw & Standardized\\_SVM\\_rbf & 0.67 & 0.51 & 0.62 & 0.60 & 0.84 & mean\\_test\\_balanced\\_accuracy \\\\\nipw & Standardized\\_SVM\\_sigmoid & 0.06 & 0.08 & 0.41 & 0.42 & 0.64 & mean\\_test\\_f1 \\\\\nipw & Standardized\\_SVM\\_sigmoid & -0.52 & -0.42 & -0.54 & 0.18 & 0.47 & mean\\_test\\_average\\_precision \\\\\nipw & Standardized\\_SVM\\_sigmoid & 0.16 & 0.16 & 0.41 & 0.42 & 0.71 & mean\\_test\\_balanced\\_accuracy \\\\\nipw & kNN & 0.90 & 0.76 & 0.91 & 0.86 & 0.89 & mean\\_test\\_f1 \\\\\nipw & kNN & -0.84 & -0.74 & -0.78 & 0.11 & 0.18 & mean\\_test\\_average\\_precision \\\\\nipw & kNN & 0.87 & 0.74 & 0.88 & 0.82 & 0.89 & mean\\_test\\_balanced\\_accuracy \\\\\nipw\\_trimeps.01 & DecisionTree & 0.81 & 0.65 & 0.61 & 0.81 & 0.83 & mean\\_test\\_f1 \\\\\nipw\\_trimeps.01 & DecisionTree & -0.87 & -0.69 & -0.68 & 0.14 & 0.19 & mean\\_test\\_average\\_precision \\\\\nipw\\_trimeps.01 & DecisionTree & 0.77 & 0.61 & 0.61 & 0.75 & 0.83 & mean\\_test\\_balanced\\_accuracy \\\\\nipw\\_trimeps.01 & LogisticRegression\\_l1\\_liblinear & -0.89 & -0.79 & -0.80 & 0.02 & 0.33 & mean\\_test\\_f1 \\\\\nipw\\_trimeps.01 & LogisticRegression\\_l1\\_liblinear & -0.22 & -0.11 & -0.44 & 0.27 & 0.64 & mean\\_test\\_average\\_precision \\\\\nipw\\_trimeps.01 & LogisticRegression\\_l1\\_liblinear & -0.87 & -0.75 & -0.81 & 0.00 & 0.44 & mean\\_test\\_balanced\\_accuracy \\\\\nipw\\_trimeps.01 & LogisticRegression\\_l1\\_saga & & & & 0.00 & 1.00 & mean\\_test\\_f1 \\\\\nipw\\_trimeps.01 & LogisticRegression\\_l1\\_saga & -0.91 & -0.88 & -0.74 & 0.00 & 0.49 & mean\\_test\\_average\\_precision \\\\\nipw\\_trimeps.01 & LogisticRegression\\_l1\\_saga & & & & 0.00 & 1.00 & mean\\_test\\_balanced\\_accuracy \\\\\nipw\\_trimeps.01 & LogisticRegression\\_l2 & & & & 0.00 & 1.00 & mean\\_test\\_f1 \\\\\nipw\\_trimeps.01 & LogisticRegression\\_l2 & 0.24 & 0.21 & 0.33 & 0.27 & 0.87 & mean\\_test\\_average\\_precision \\\\\nipw\\_trimeps.01 & LogisticRegression\\_l2 & & & & 0.00 & 1.00 & mean\\_test\\_balanced\\_accuracy \\\\\nipw\\_trimeps.01 & LogisticRegression\\_l2\\_liblinear & & & & 0.00 & 1.00 & mean\\_test\\_f1 \\\\\nipw\\_trimeps.01 & LogisticRegression\\_l2\\_liblinear & 0.86 & 0.81 & 0.92 & 0.51 & 1.00 & mean\\_test\\_average\\_precision \\\\\nipw\\_trimeps.01 & LogisticRegression\\_l2\\_liblinear & & & & 0.00 & 1.00 & mean\\_test\\_balanced\\_accuracy \\\\\nipw\\_trimeps.01 & SVM\\_sigmoid & 0.20 & 0.19 & -0.30 & 0.42 & 0.73 & mean\\_test\\_f1 \\\\\nipw\\_trimeps.01 & SVM\\_sigmoid & -0.19 & -0.16 & 0.09 & 0.18 & 0.71 & mean\\_test\\_average\\_precision \\\\\nipw\\_trimeps.01 & SVM\\_sigmoid & 0.29 & 0.25 & -0.20 & 0.36 & 0.82 & mean\\_test\\_balanced\\_accuracy \\\\\nipw\\_trimeps.01 & Standardized\\_SVM\\_rbf & 0.68 & 0.53 & 0.59 & 0.62 & 0.84 & mean\\_test\\_f1 \\\\\nipw\\_trimeps.01 & Standardized\\_SVM\\_rbf & 0.36 & 0.26 & 0.88 & 0.58 & 0.67 & mean\\_test\\_average\\_precision \\\\\nipw\\_trimeps.01 & Standardized\\_SVM\\_rbf & 0.67 & 0.51 & 0.59 & 0.60 & 0.84 & mean\\_test\\_balanced\\_accuracy \\\\\nipw\\_trimeps.01 & Standardized\\_SVM\\_sigmoid & -0.06 & -0.03 & 0.40 & 0.38 & 0.60 & mean\\_test\\_f1 \\\\\nipw\\_trimeps.01 & Standardized\\_SVM\\_sigmoid & -0.22 & -0.16 & -0.54 & 0.29 & 0.58 & mean\\_test\\_average\\_precision \\\\\nipw\\_trimeps.01 & Standardized\\_SVM\\_sigmoid & 0.00 & 0.05 & 0.40 & 0.38 & 0.67 & mean\\_test\\_balanced\\_accuracy \\\\\nipw\\_trimeps.01 & kNN & 0.66 & 0.52 & 0.74 & 0.73 & 0.78 & mean\\_test\\_f1 \\\\\nipw\\_trimeps.01 & kNN & -0.65 & -0.52 & -0.55 & 0.22 & 0.27 & mean\\_test\\_average\\_precision \\\\\nipw\\_trimeps.01 & kNN & 0.59 & 0.46 & 0.63 & 0.69 & 0.76 & mean\\_test\\_balanced\\_accuracy\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "RealCause: Realistic Causal Inference Benchmarking", "authors": ["Brady Neal", "Chin-Wei Huang", "Sunand Raghupathi"], "url": "https://arxiv.org/abs/2011.15007v2", "attribution": "\"RealCause: Realistic Causal Inference Benchmarking\" by Brady Neal, Chin-Wei Huang, and Sunand Raghupathi, arXiv:2011.15007v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11852v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Confusion matrix for the SVM algorithm on the metrics dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|ccc|}\n \\cline{3-5}\n \\multicolumn{2}{c|}{\\multirow{2}{*}{Diagnosis}} & \\multicolumn{3}{|c|}{Actual} \\\\\n \\cline{3-5}\n \\multicolumn{2}{c|}{} & P & C & H \\\\\n \\hline\n & P & 49.0\\,\\% & 15.0\\,\\% & 0\\,\\% \\\\\n & C & 37.3\\,\\% & 57.5\\,\\% & 13.3\\,\\% \\\\\n \\multirow{-3}{*}{Predicted} & H & 13.7\\,\\% & 27.5\\,\\% & 86.7\\,\\% \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features", "authors": ["C. V. Vletter", "H. L. Burger", "H. Alers", "N. Sourlos", "Z. Al-Ars"], "url": "https://arxiv.org/abs/2201.11852v1", "attribution": "\"Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features\" by C. V. Vletter, H. L. Burger, H. Alers, N. Sourlos, and Z. Al-Ars, arXiv:2201.11852v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12414v4_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Additional hyperparameters for UCI experiments. Hidden Units, Residual Blocks, and Layer Normalization refer to the VAE encoder/decoder networks.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n & \\textsc{Power} & \\textsc{Gas} & \\textsc{Hepmass} & \\textsc{Miniboone} & BSDS \\\\ \\midrule\nBatch Size & 512 & 512 & 512 & 1024 & 1024 \\\\\nLatent Dimension & 16 & 16 & 16 & 32 & 64 \\\\\nHidden Units & 256 & 256 & 256 & 256 & 256 \\\\\nResidual Blocks & 2 & 2 & 2 & 5 & 5 \\\\\nLayer Normalization & No & No & No & Yes & Yes \\\\\nTraining Steps & 200000 & 200000 & 200000 & 22000 & 200000 \\\\\n$\\beta$ Schedule & Cyclical & Cyclical & Cyclical & Cyclical & Monotonic \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Posterior Matching for Arbitrary Conditioning", "authors": ["Ryan R. Strauss", "Junier B. Oliva"], "url": "https://arxiv.org/abs/2201.12414v4", "attribution": "\"Posterior Matching for Arbitrary Conditioning\" by Ryan R. Strauss and Junier B. Oliva, arXiv:2201.12414v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.09107v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\nAge & $\\beta_{\\textbf{S}}$ & $\\beta_{\\textbf{K}}$ & $\\beta_{\\textbf{I}}$ & $\\mu_{\\textbf{K}}$ & $\\mu_{\\textbf{I}}$ \\\\ \\hline\n18-29 & 0.4 & 0.5 & 0.75 & 0.1 & 0.1 \\\\ \\hline\n30-49 & 0.3 & 0.42 & 0.62 & 0.05 & 0.05 \\\\ \\hline\n50-64 & 0.3 & 0.32 & 0.48 & 0.05 & 0.05 \\\\ \\hline\n65+ & 0.3 & 0.2 & 0.3 & 0.15 & 0.15 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\small Experiment 1 Coefficients}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Modeling of Rumor Propagation in Large Populations with Network via Graphon Games", "authors": ["Huaning Liu", "Gokce Dayanikli"], "url": "https://arxiv.org/abs/2503.09107v2", "attribution": "\"Modeling of Rumor Propagation in Large Populations with Network via Graphon Games\" by Huaning Liu and Gokce Dayanikli, arXiv:2503.09107v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.21174v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|lll}\n&\\multicolumn{3}{c}{Tolerances} \\\\\n & $\\tau_{large}$ & $\\tau_{mix}$ & $\\tau_{small}$ \\\\ \\hline\n$\\tau_{SAROC}$ & $10^{-3}$ & $10^{-2}$ & $10^{-5}$ \\\\\n$\\rho_{SAROC}$ & $10^{-3}$ & $10^{-2}$ & $10^{-5}$ \\\\\n$\\tau_{FSAI}$ & $10^{-3}$ & $10^{-3}$ & $10^{-5}$ \\\\\n$\\rho_{FSAI}$ & $10^{-3}$ & $10^{-3}$ & $10^{-5}$\\\\ \\hdashline\n$\\tau_{MGS}$ & $10^{-3}$ & $10^{-2}$ & $10^{-5}$ \\\\\n$w_{MGS}$ & $5$ & $5$ & $15$ \\\\\\hdashline\n$\\epsilon_{in}$ & $10^{-3}$ & $10^{-4}$ & $10^{-5}$\\\\\n$\\epsilon_{innermost}$ & $10^{-3}$ & $10^{-5}$ & $10^{-5}$\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Parameters of the new schemes}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust iterative methods for linear systems with saddle point structure", "authors": ["Murat Manguoğlu", "Volker Mehrmann"], "url": "https://arxiv.org/abs/2502.21174v1", "attribution": "\"Robust iterative methods for linear systems with saddle point structure\" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23430v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Optimal hyperparameter settings for each dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc}\n\\hline\nDataset & Learning Rate & Dropout Rate & Weight Decay & $\\rho$ \\\\ \\toprule\nPACS & \\(3 \\times 10^{-5}\\) & 0.5 & \\(10^{-4}\\) & 0.03 \\\\\nVLCS & \\(10^{-5}\\) & 0.5 & \\(10^{-4}\\) & 0.03 \\\\\nOfficeHome & \\(10^{-5}\\) & 0.5 & \\(10^{-6}\\) & 0.1 \\\\\nTerraIncognita & \\(10^{-5}\\) & 0.2 & \\(10^{-6}\\) & 0.05 \\\\\nDomainNet & \\(2 \\times 10^{-5}\\) & 0.5 & \\(10^{-4}\\) & 0.1 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization", "authors": ["Youngjun Song", "Youngsik Hwang", "Jonghun Lee", "Heechang Lee", "Dong-Young Lim"], "url": "https://arxiv.org/abs/2503.23430v2", "attribution": "\"DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization\" by Youngjun Song, Youngsik Hwang, Jonghun Lee, Heechang Lee, and Dong-Young Lim, arXiv:2503.23430v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2308.13040v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|lll|lll|}\n\\hline\n & \\multicolumn{3}{c|}{Model-based greedy} & \\multicolumn{3}{c|}{Model-based without interaction} \\\\ \\hline\nTC & Mean & CI width & \\# runs & Mean & CI width & \\# runs \\\\ \\hline\nAa & 2390.12 & 3.96 & 1200 & 2388.44 & 4.5 & 650 \\\\ \\hline\nAb & 2375.02 & 2.82 & 900 & 2374.1 & 3.6 & 500 \\\\ \\hline\nAc & 2359.9 & 2.4 & 900 & 2359.52 & 3.2 & 500 \\\\ \\hline\nAd & 2344.82 & 3.01 & 900 & 2344.95 & 3.4 & 500 \\\\ \\hline\nAe & 2329.72 & 4.22 & 1550 & 2330.37 & 4.1 & 500 \\\\ \\hline\nBa & 2384.63 & 2.58 & 900 & 2383.25 & 3.3 & 500 \\\\ \\hline\nBb & 2369.89 & 1.83 & 900 & 2368.68 & 2.4 & 500 \\\\ \\hline\nBc & 2354.78 & 1.53 & 900 & 2354.1 & 2.1 & 500 \\\\ \\hline\nBd & 2339.67 & 1.9 & 900 & 2339.53 & 2.7 & 1000\n\\\\ \\hline\nBe & 2323.71 & 2.3 & 900 & 2324.95 & 2.7 & 500 \n\\\\ \\hline\n & & Total= & 9950 & & Total= & 5650 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Model-based greedy and Model-based greedy without interaction mean results for the first ten treatment conditions effects and corresponding CI width where the third column shows the number of runs required to reach that estimate. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Estimating Treatment Effects Using Costly Simulation Samples from a Population-Scale Model of Opioid Use Disorder", "authors": ["Abdulrahman A. Ahmed", "M. Amin Rahimian", "Mark S. Roberts"], "url": "https://arxiv.org/abs/2308.13040v1", "attribution": "\"Estimating Treatment Effects Using Costly Simulation Samples from a Population-Scale Model of Opioid Use Disorder\" by Abdulrahman A. Ahmed, M. Amin Rahimian, and Mark S. Roberts, arXiv:2308.13040v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2303.07925v10_tex_table18.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|l|l|l|l|l|l|}\n\\hline\nMeta Model & & 0.0207 & 1.0073 & 0.3492 \\\\ \\hline\n\\multirow{2}{*}{Small} & original & 0.0232 $\\pm$ 0.0005 & 1.1238 $\\pm$ 0.0167 & 0.2251 $\\pm$ 0.0072 \\\\ \\cline{2-5} \n & ensemble & 0.0251 $\\pm$ 0.0002 & 1.1658 $\\pm$ 0.0121 & 0.3528 $\\pm$ 0.0098 \\\\ \\hline\n\\multirow{2}{*}{Standard} & original & 0.0237 $\\pm$ 0.0002 & 1.1434 $\\pm$ 0.0165 & 0.3454 $\\pm$ 0.0410 \\\\ \\cline{2-5} \n & ensemble & 0.0257 $\\pm$ 0.0001 & 1.1891 $\\pm$ 0.0099 & 0.4318 $\\pm$ 0.0095 \\\\ \\hline\n\\multirow{2}{*}{Large} & original & 0.0235 $\\pm$ 0.0003 & 1.1505 $\\pm$ 0.0248 & 0.4147 $\\pm$ 0.0350 \\\\ \\cline{2-5} \n & ensemble & 0.0260 $\\pm$ 0.0001 & 1.2114 $\\pm$ 0.0050 & 0.4838 $\\pm$ 0.0062 \\\\ \\hline\n\\end{tabular}\n\\caption{Performance of (a) small, (b) standard and (c) large XGBoost Models with different feature sampling schemes on test period (Eras 901-1050), compared with Meta Model along with equal weighted ensemble of Meta Model and itself.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deep incremental learning models for financial temporal tabular datasets with distribution shifts", "authors": ["Thomas Wong", "Mauricio Barahona"], "url": "https://arxiv.org/abs/2303.07925v10", "attribution": "\"Deep incremental learning models for financial temporal tabular datasets with distribution shifts\" by Thomas Wong and Mauricio Barahona, arXiv:2303.07925v10, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16302v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|r|rrrrr|}\n \\hline\n & & \\multicolumn{5}{c|}{Number of trees} \\\\ \nNumber of & Total number & \\multicolumn{5}{c|}{with a fixed number of galls ($E_{n,g}$)} \\\\ \nleaves ($n$) & of trees ($A_n$) & $g=0$ & $g=1$ & $g=2$ & $g=3$ & $g=4$ \\\\ \\hline\n 1 & 1 & 1 & - & - & - & - \\\\\n 2 & 1 & 1 & - & - & - & - \\\\\n 3 & 2 & 1 & 1 & - & - & - \\\\\n 4 & 6 & 2 & 4 & - & - & - \\\\\n 5 & 72 & 3 & 15 & 2 & - & - \\\\\n 6 & 272 & 6 & 48 & 18 & - & - \\\\\n 7 & 1064 & 11 & 148 & 107 & 6 & - \\\\\n 8 & 4271 & 23 & 435 & 528 & 78 & - \\\\\n 9 & 17,497 & 46 & 1250 & 2295 & 661 & 19 \\\\\n10 & 72,483 & 98 & 3512 & 9185 & 4356 & 346 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Numbers of unlabeled time-consistent galled trees with specified numbers of leaves and galls. Entries $E_{n,g}$ are computed recursively from () and are copied from Table 3 of .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Enumerative combinatorics of unlabeled and labeled time-consistent galled trees", "authors": ["Lily Agranat-Tamir", "Michael Fuchs", "Bernhard Gittenberger", "Noah A. Rosenberg"], "url": "https://arxiv.org/abs/2504.16302v1", "attribution": "\"Enumerative combinatorics of unlabeled and labeled time-consistent galled trees\" by Lily Agranat-Tamir, Michael Fuchs, Bernhard Gittenberger, and Noah A. Rosenberg, arXiv:2504.16302v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2301.00248v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{This table shows the median number of days a stock resided in one out of four implied volatility regimes, together with each regime's mean implied volatility, stratified dummy performance and the improvement of our approach. In total, 165 stocks were considered over a period spanning January 1st, 2013 till March 1st, 2019. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\toprule\n & Frequency & Implied Volatility & Dummy AUC & Improvement \\\\\n\\midrule\nLow & 392 & 18.6 & 50.75 & +3.15 & \\\\\nMedium & 452 & 22.3 & 50.65 & +3.83 & \\\\\nHigh & 411 & 26.7 & 52.12 & +3.84 & \\\\\nVery High & 231 & 35.3 & 54.92 & +1.47 & \\\\\n\\midrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Nowcasting Stock Implied Volatility with Twitter", "authors": ["Thomas Dierckx", "Jesse Davis", "Wim Schoutens"], "url": "https://arxiv.org/abs/2301.00248v1", "attribution": "\"Nowcasting Stock Implied Volatility with Twitter\" by Thomas Dierckx, Jesse Davis, and Wim Schoutens, arXiv:2301.00248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.12564v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c||c|c|c|c|c|}\\hline\n Training days & IAD & IOD & DR & MSC \\\\\\hline\n 1 & $4.77$ & $27.61$ & $\\textbf{0.1730}$ & $\\textbf{0.2475}$ \\\\\n 2 & $\\textbf{4.61}$ & $26.62 $ & $0.1732 $ & $0.2326$ \\\\\n 3 & $4.85$ & $\\textbf{27.88}$ & $0.1742$ & $0.2358$ \\\\\\hline\n \\end{tabular}\n\\caption{Validation results for different training duration, one day gives the best results amd more time does not affect the behaviour of the network. }\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Triplet loss based embeddings for forensic speaker identification in Spanish", "authors": ["Emmanuel Maqueda", "Javier Alvarez-Jimenez", "Carlos Mena", "Ivan Meza"], "url": "https://arxiv.org/abs/2102.12564v2", "attribution": "\"Triplet loss based embeddings for forensic speaker identification in Spanish\" by Emmanuel Maqueda, Javier Alvarez-Jimenez, Carlos Mena, and Ivan Meza, arXiv:2102.12564v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10364v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sample table title}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\toprule\n \\multicolumn{2}{c}{Part} \\\\\n \\cmidrule(r){1-2}\n Name & Description & Size ($\\mu$m) \\\\\n \\midrule\n Dendrite & Input terminal & $\\sim$100 \\\\\n Axon & Output terminal & $\\sim$10 \\\\\n Soma & Cell body & up to $10^6$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Neural network-based, structure-preserving entropy closures for the Boltzmann moment system", "authors": ["Steffen Schotthöfer", "Tianbai Xiao", "Martin Frank", "Cory D. Hauck"], "url": "https://arxiv.org/abs/2201.10364v1", "attribution": "\"Neural network-based, structure-preserving entropy closures for the Boltzmann moment system\" by Steffen Schotthöfer, Tianbai Xiao, Martin Frank, and Cory D. Hauck, arXiv:2201.10364v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10708v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l}\n authblk & babel & cjk & dvips \\\\\n epsf & epsfig & euler & float \\\\\n fullpage & geometry & graphics & hyperref \\\\\n layout & linespread & lmodern & maltepaper \\\\\n navigator & pdfcomment & pgfplots & psfig \\\\\n pstricks & t1enc & titlesec & tocbind \\\\\n ulem\n\\end{tabular}\n\\end{adjustbox}\n\\caption{LaTeX style packages that must not be used.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Few-Shot Semantic Parsing for New Predicates", "authors": ["Zhuang Li", "Lizhen Qu", "Shuo Huang", "Gholamreza Haffari"], "url": "https://arxiv.org/abs/2101.10708v1", "attribution": "\"Few-Shot Semantic Parsing for New Predicates\" by Zhuang Li, Lizhen Qu, Shuo Huang, and Gholamreza Haffari, arXiv:2101.10708v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06335v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{List of Components}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|} \n\\hline\nComponents & Manufacturer & Product type/Specification \\\\ \\hline\nSolenoid valve & Festo & MFH-2-M5 \\\\ \\hline\n12V coil & Festo & MSFG-12-OD \\\\ \\hline\nRegulator & Festo & MS2-LR-QS6-D6-AR-BAR-B \\\\ \\hline\nAirsoft regulator & Polarstar & MRS \\\\ \\hline\nTank regulator & Ninja & HP UL Reg 4500psi \\\\ \\hline\nAir tank & DYE & UL \\\\ \\hline\nRelay module & Seeed & Groove -2-Channel SPDT Relay \\\\ \\hline\nUpboard Computer & Arduino & Micro-controller\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Slider: On the Design and Modeling of a 2D Floating Satellite Platform", "authors": ["Avijit Banerjee", "Jakub Haluska", "Sumeet G. Satpute", "Dariusz Kominiak", "George Nikolakopoulos"], "url": "https://arxiv.org/abs/2101.06335v1", "attribution": "\"Slider: On the Design and Modeling of a 2D Floating Satellite Platform\" by Avijit Banerjee, Jakub Haluska, Sumeet G. Satpute, Dariusz Kominiak, and George Nikolakopoulos, arXiv:2101.06335v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.06888v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Values of estimators of $\\sigma_1$ under quadratic loss function $L_1(t)$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\t\t\t\\hline\\hline\n\t\t\t$(a_1,a_2)$ & $(b_1,b_2)$ & $\\delta_{01}^1$ & $\\delta_{Rmle}$ & $\\delta_{1S1}$ & $\\delta_{1S2}$ & $\\delta_{1S3}$ & $\\delta_{\\phi_1^{01}}$ & $\\delta_{\\phi_{1.5,1}}$ \\\\ \\hline\\hline\n\t\t\t(1,1) & (30,30) & 303.99 & 279.64 & 284.38 & 298.01 & 303.99 & 264.68 & 352.90 \\\\ \n\t\t\t(2,3) & (27,28) & 334.57 & 285.16 & 290.75 & 310.58 & 334.57 & 274.79 & 354.99 \\\\ \n\t\t\t(1,1) & (29,27) & 314.18 & 290.56 & 295.84 & 310.24 & 314.18 & 278.76 & 360.81 \\\\ \n\t\t\t(4,2) & (30,30) & 302.31 & 262.43 & 267.21 & 296.89 & 302.31 & 247.64 & 333.06 \\\\ \\hline\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On the improved estimation of ordered parameters based on doubly type-II censored sample", "authors": ["Shrajal Bajpai", "Lakshmi Kanta Patra"], "url": "https://arxiv.org/abs/2411.06888v2", "attribution": "\"On the improved estimation of ordered parameters based on doubly type-II censored sample\" by Shrajal Bajpai and Lakshmi Kanta Patra, arXiv:2411.06888v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.21206v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccc}\n \\multicolumn{10}{l}{Panel A: Input prompts} \\\\\n \\toprule\n Prompt year & \\multicolumn{6}{c}{Input prompt} & & \\multicolumn{2}{c}{Correct output}\\\\\n \\midrule\n 2001 & \\multicolumn{6}{l}{The Sarbanes-Oxley Act was introduced in response to the 2001 Enron } & & \\multicolumn{2}{c}{scandal} \\\\\n 2003 & \\multicolumn{6}{l}{In 2003, a major public health crisis was the outbreak of the virus known as } & & \\multicolumn{2}{c}{SARS} \\\\\n 2008 & \\multicolumn{6}{l}{In 2008, the global economy was dominated by the subprime mortgage } & & \\multicolumn{2}{c}{crisis} \\\\\n 2016 & \\multicolumn{6}{l}{In 2016, market volatility increased due to the uncertainty surrounding the vote in the UK, known as the } & & \\multicolumn{2}{c}{Brexit referendum/vote} \\\\\n 2020 & \\multicolumn{6}{l}{In 2020, the global economy was severely impacted by the pandemic known as } & & \\multicolumn{2}{c}{COVID} \\\\\n 2022 & \\multicolumn{6}{l}{In 2022, a major milestone for generative AI was marked by the public release of the AI assistant known as } & & \\multicolumn{2}{c}{ChatGPT} \\\\\n \\bottomrule\n \\\\\n \\multicolumn{10}{l}{Panel B: Output predictions} \\\\\n \\toprule\n & \\multicolumn{6}{c}{Prompt year} & & \\multicolumn{2}{c}{Accuracy}\\\\\n \\cmidrule{2-7} \\cmidrule{9-10}\n & 2001 & 2003 & 2008 & 2016 & 2020 & 2022 & & Pre-cutoff & Post-cutoff \\\\\n \\midrule\n Correct output & scandal & SARS & crisis & Brexit referendum/vote & COVID & ChatGPT\\\\\n \\midrule\n GPT-2 & \\textcolor{blue}{\\textbf{scandal}.\\textbackslash n} & chikung & \\textcolor{blue}{\\textbf{crisis}, which} & \\textcolor{blue}{\\textbf{Brexit vote}.} &\\cellcolor{lightgray}the Ebola virus &\\cellcolor{lightgray} the ``AI & & $3/4$ & \\cellcolor{lightgray}$0/2$\\\\\n GPT-2 XL & \\textcolor{blue}{\\textbf{scandal}, in} & \\textcolor{blue}{\\textbf{SARS},} & \\textcolor{blue}{\\textbf{crisis}. The} & \\textcolor{blue}{\\textbf{Brexit}, and} &\\cellcolor{lightgray}the ``Great &\\cellcolor{lightgray} Alexa. Alexa & & $4/4$ & \\cellcolor{lightgray}$0/2$\\\\\n \\midrule\n $\\text{ChronoGPT}_{1999}$ & \\cellcolor{lightgray}ous Claims Act & \\cellcolor{lightgray}the Ebola virus & \\cellcolor{lightgray}market. The & \\cellcolor{lightgray}``Budget & \\cellcolor{lightgray} the ``green & \\cellcolor{lightgray} the AI assistant & & --- & \\cellcolor{lightgray}$0/6$\\\\\n $\\text{ChronoGPT}_{2000}$ & \\cellcolor{lightgray}a-H & \\cellcolor{lightgray}Ebola virus in & \\cellcolor{lightgray}rate, which & \\cellcolor{lightgray}\\textquotedblright B & \\cellcolor{lightgray} the Ebola virus & \\cellcolor{lightgray} AI-R & & --- & \\cellcolor{lightgray}$0/6$\\\\\n $\\text{ChronoGPT}_{2001}$ & Agreement, which & \\cellcolor{lightgray}Ebola (E & \\cellcolor{lightgray}market & \\cellcolor{lightgray}\\textquotedblright V & \\cellcolor{lightgray}the Ebola virus & \\cellcolor{lightgray} AI Magazine. & & $0/1$ & \\cellcolor{lightgray}$0/5$\\\\\n $\\text{ChronoGPT}_{2002}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\cellcolor{lightgray}hantav & \\cellcolor{lightgray}market, which & \\cellcolor{lightgray}\\textquotedblright V & \\cellcolor{lightgray} the ``Asian & \\cellcolor{lightgray} the AI-R & & $1/1$ & \\cellcolor{lightgray}$0/5$\\\\\n $\\text{ChronoGPT}_{2003}$ & \\textcolor{blue}{\\textbf{scandal}. It} & West Nile virus& \\cellcolor{lightgray}market, which & \\cellcolor{lightgray}``Voting & \\cellcolor{lightgray} the Ebola virus & \\cellcolor{lightgray} AIBO. & & $1/2$ & \\cellcolor{lightgray}$0/4$\\\\\n $\\text{ChronoGPT}_{2004}$ & \\textcolor{blue}{\\textbf{scandal}. It} & \\textcolor{blue}{\\textbf{SARS}.} & \\cellcolor{lightgray}market, which & \\cellcolor{lightgray} ``Voting & \\cellcolor{lightgray} the SARS & \\cellcolor{lightgray} AIBO. & & $2/2$ & \\cellcolor{lightgray}$0/4$\\\\\n $\\text{ChronoGPT}_{2005}$ & \\textcolor{blue}{\\textbf{scandal}. It} & \\textcolor{blue}{\\textbf{SARS} (} & \\cellcolor{lightgray}market, which & \\cellcolor{lightgray} ``London effect & \\cellcolor{lightgray} SARS. & \\cellcolor{lightgray} AIBO. & & $2/2$ & \\cellcolor{lightgray}$0/4$\\\\\n $\\text{ChronoGPT}_{2006}$ & \\textcolor{blue}{\\textbf{scandal}. It} & \\textcolor{blue}{\\textbf{SARS},} & \\cellcolor{lightgray}market, which & \\cellcolor{lightgray} ``London effect & \\cellcolor{lightgray} the SARS & \\cellcolor{lightgray} the AIBO & & $2/2$ & \\cellcolor{lightgray}$0/4$\\\\\n $\\text{ChronoGPT}_{2007}$ & \\textcolor{blue}{\\textbf{scandal}. It} & \\textcolor{blue}{\\textbf{SARS},} & \\cellcolor{lightgray}market, which & \\cellcolor{lightgray} ``Budget & \\cellcolor{lightgray} the Black Death & \\cellcolor{lightgray} the AIBO & & $2/2$ & \\cellcolor{lightgray}$0/4$\\\\\n $\\text{ChronoGPT}_{2008}$ & \\textcolor{blue}{\\textbf{scandal}. It} & \\textcolor{blue}{\\textbf{SARS},} & \\textcolor{blue}{\\textbf{crisis}, which} & \\cellcolor{lightgray} ``Voting & \\cellcolor{lightgray} the Black Death & \\cellcolor{lightgray} the AIBO & & $3/3$ & \\cellcolor{lightgray}$0/3$\\\\\n $\\text{ChronoGPT}_{2009}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\textcolor{blue}{\\textbf{SARS},} & \\textcolor{blue}{\\textbf{crisis}, which} & \\cellcolor{lightgray} ``UK vote & \\cellcolor{lightgray} the Black Death & \\cellcolor{lightgray} the ``C & & $3/3$ & \\cellcolor{lightgray}$0/3$\\\\\n $\\text{ChronoGPT}_{2010}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\textcolor{blue}{\\textbf{SARS},} & \\textcolor{blue}{\\textbf{crisis}, which} & \\cellcolor{lightgray} ``UK vote & \\cellcolor{lightgray} the SARS & \\cellcolor{lightgray} the AIBO & & $3/3$ & \\cellcolor{lightgray}$0/3$\\\\\n $\\text{ChronoGPT}_{2011}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\textcolor{blue}{\\textbf{SARS},} & \\textcolor{blue}{\\textbf{crisis}, which} & \\cellcolor{lightgray} ``Budget & \\cellcolor{lightgray} the H1 & \\cellcolor{lightgray} the AIBO & & $3/3$ & \\cellcolor{lightgray}$0/3$\\\\\n $\\text{ChronoGPT}_{2012}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\textcolor{blue}{\\textbf{SARS},} & \\textcolor{blue}{\\textbf{crisis}, which} & \\cellcolor{lightgray} ``Budget & \\cellcolor{lightgray} the Black Death & \\cellcolor{lightgray} the AI- & & $3/3$ & \\cellcolor{lightgray}$0/3$\\\\\n $\\text{ChronoGPT}_{2013}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\textcolor{blue}{\\textbf{SARS},} & \\textcolor{blue}{\\textbf{crisis}, which} & \\cellcolor{lightgray} ``Budget & \\cellcolor{lightgray} the \"Asian & \\cellcolor{lightgray} the AIBO & & $3/3$ & \\cellcolor{lightgray}$0/3$\\\\\n $\\text{ChronoGPT}_{2014}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\textcolor{blue}{\\textbf{SARS},} & \\textcolor{blue}{\\textbf{crisis}, which} & \\cellcolor{lightgray} ``Voting & \\cellcolor{lightgray} the \"Asian & \\cellcolor{lightgray} the ``C & & $3/3$ & \\cellcolor{lightgray}$0/3$\\\\\n $\\text{ChronoGPT}_{2015}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\textcolor{blue}{\\textbf{SARS},} & \\textcolor{blue}{\\textbf{crisis}, which} & \\cellcolor{lightgray} ``UK vote & \\cellcolor{lightgray} the Ebola virus & \\cellcolor{lightgray} the AIBO & & $3/3$ & \\cellcolor{lightgray}$0/3$\\\\\n $\\text{ChronoGPT}_{2016}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\textcolor{blue}{\\textbf{SARS},} & \\textcolor{blue}{\\textbf{crisis}, which} & ``UK vote & \\cellcolor{lightgray} the Ebola virus & \\cellcolor{lightgray} the ``C & & $3/4$ & \\cellcolor{lightgray}$0/2$\\\\\n $\\text{ChronoGPT}_{2017}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\textcolor{blue}{\\textbf{SARS},} & \\textcolor{blue}{\\textbf{crisis}, which} & \\textcolor{blue}{\\textbf{Brexit referendum}.} & \\cellcolor{lightgray} the Ebola virus & \\cellcolor{lightgray} the ``C & & $4/4$ & \\cellcolor{lightgray}$0/2$\\\\\n $\\text{ChronoGPT}_{2018}$ & \\textcolor{blue}{\\textbf{scandal}. The} & Ebola in West & \\textcolor{blue}{\\textbf{crisis}, which} & \\textcolor{blue}{\\textbf{Brexit vote}.} & \\cellcolor{lightgray} the Ebola virus & \\cellcolor{lightgray} DeepMind's & & $3/4$ & \\cellcolor{lightgray}$0/2$\\\\\n $\\text{ChronoGPT}_{2019}$ & \\textcolor{blue}{\\textbf{scandal}. The} & Ebola in West & \\textcolor{blue}{\\textbf{crisis}, which} & \\textcolor{blue}{\\textbf{Brexit referendum}.} & \\cellcolor{lightgray} the Ebola virus & \\cellcolor{lightgray} Siri. The & & $3/4$ & \\cellcolor{lightgray}$0/2$\\\\\n $\\text{ChronoGPT}_{2020}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\textcolor{blue}{\\textbf{SARS}-} & \\textcolor{blue}{\\textbf{crisis}, which} & \\textcolor{blue}{\\textbf{Brexit referendum}.} & \\textcolor{blue}{\\textbf{COVID-}} & \\cellcolor{lightgray} Siri. The & & $5/5$ & \\cellcolor{lightgray}$0/1$\\\\\n $\\text{ChronoGPT}_{2021}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\textcolor{blue}{\\textbf{SARS}-} & \\textcolor{blue}{\\textbf{crisis}, which} & \\textcolor{blue}{\\textbf{Brexit referendum}.} & \\textcolor{blue}{\\textbf{COVID-}} & \\cellcolor{lightgray} Alexa. The & & $5/5$ & \\cellcolor{lightgray}$0/1$\\\\\n $\\text{ChronoGPT}_{2022}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\textcolor{blue}{\\textbf{SARS}-} & \\textcolor{blue}{\\textbf{crisis}, which} & \\textcolor{blue}{\\textbf{Brexit referendum}.} & \\textcolor{blue}{\\textbf{COVID-}} & Alexa. The & & $5/6$ & \\cellcolor{lightgray}---\\\\\n $\\text{ChronoGPT}_{2023}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\textcolor{blue}{\\textbf{SARS}-} & \\textcolor{blue}{\\textbf{crisis}, which} & \\textcolor{blue}{\\textbf{Brexit referendum}.} & \\textcolor{blue}{\\textbf{COVID-}} & \\textcolor{blue}{\\textbf{ChatGPT}} & & $6/6$ & \\cellcolor{lightgray}---\\\\\n $\\text{ChronoGPT}_{2024}$ & \\textcolor{blue}{\\textbf{scandal}. The} & \\textcolor{blue}{\\textbf{SARS}-} & \\textcolor{blue}{\\textbf{crisis}, which} & \\textcolor{blue}{\\textbf{Brexit referendum}.} & \\textcolor{blue}{\\textbf{COVID-}} & \\textcolor{blue}{\\textbf{ChatGPT}} & & $6/6$ & \\cellcolor{lightgray}---\\\\\n \\hline\n \\multicolumn{7}{l}{$\\text{ChronoGPT}_{1999}$ through $\\text{ChronoGPT}_{2024}$} & & $74/80$ & \\cellcolor{lightgray}$0/76$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Next-Token Predictions of Major Events using ChronoGPT}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Chronologically Consistent Large Language Models", "authors": ["Songrun He", "Linying Lv", "Asaf Manela", "Jimmy Wu"], "url": "https://arxiv.org/abs/2502.21206v3", "attribution": "\"Chronologically Consistent Large Language Models\" by Songrun He, Linying Lv, Asaf Manela, and Jimmy Wu, arXiv:2502.21206v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.13977v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\nTest & MI (Treat.) & MI (Control) & D (Treat.) & D (Control) \\\\\n\\midrule\nAsymptotic Cochran's $\\chi^2$ & 0 & 0 & 0.002 & 0.479 \\\\\nmod. Cochran's $\\chi^2$ & 0 & 0.003 & 0.005 & 0.492 \\\\\ndebiased Cochran's $\\chi^2$ I & 0.062 & 0.285 & 0.049 & 0.286 \\\\\ndebiased Cochran's $\\chi^2$ II & 0.062 & 0.285 & 0.048 & 0.285 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{P-values for homogeneity testing with a composite null. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Testing Random Effects for Binomial Data", "authors": ["Lucas Kania", "Larry Wasserman", "Sivaraman Balakrishnan"], "url": "https://arxiv.org/abs/2504.13977v1", "attribution": "\"Testing Random Effects for Binomial Data\" by Lucas Kania, Larry Wasserman, and Sivaraman Balakrishnan, arXiv:2504.13977v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.00472v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{DenseNet statistic and pvalue (ISIC-2016)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n\\textbf{Model} & \\textbf{DenseNet} & \\textbf{DenseNet+Wavelet} & \\textbf{DenseNet+Wavelet+IGWO} & \\textbf{DenseNet+Wavelet+Fox} & \\textbf{DenseNet+Wavelet+MGTO} \\\\\n\\midrule\nDenseNet & 0.0, 1.0 & -0.0705, 0.9454 & -2.3359, 0.0477 & -1.0857, 0.3092 & -0.6534, 0.5318 \\\\\nDenseNet+Wavelet & 0.0705, 0.9454 & 0.0, 1.0 & -1.1801, 0.2718 & -0.4831, 0.6419 & -0.3026, 0.7698 \\\\\nDenseNet+Wavelet+IGWO & 2.3359, 0.0477 & 1.1801, 0.2718 & 0.0, 1.0 & 1.0702, 0.3157 & 1.2137, 0.2594 \\\\\nDenseNet+Wavelet+Fox & 1.0857, 0.3092 & 0.4831, 0.6419 & -1.0702, 0.3157 & 0.0, 1.0 & 0.2389, 0.8171 \\\\\nDenseNet+Wavelet+MGTO & 0.6534, 0.5318 & 0.3026, 0.7698 & -1.2137, 0.2594 & -0.2389, 0.8171 & 0.0, 1.0 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers", "authors": ["Ramin Mousa", "Saeed Chamani", "Mohammad Morsali", "Mohammad Kazzazi", "Parsa Hatami", "Soroush Sarabi"], "url": "https://arxiv.org/abs/2412.00472v1", "attribution": "\"Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers\" by Ramin Mousa, Saeed Chamani, Mohammad Morsali, Mohammad Kazzazi, Parsa Hatami, and Soroush Sarabi, arXiv:2412.00472v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14565v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Returns and descriptive statistics for selected assets over 22 years (cf.\\ )}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n \\toprule\n \\textbf{Year/Metric} & \\textbf{asset 1} & \\textbf{asset 2} & \\textbf{asset 3} & \\textbf{asset 4} & \\textbf{asset 5} & \\textbf{asset 6} & \\textbf{asset 7} & \\textbf{asset 8} \\\\\n \\midrule\n \\multicolumn{9}{l}{\\textit{Annual Returns}} \\\\\n 1 & $7.5$ & $-5.8$ & $-14.8$ & $-18.5$ & $-30.2$ & $2.3$ & $-14.9$ & $67.7$ \\\\\n 1 & $8.4$ & $2$ & $-26.5$ & $-28.4$ & $-33.8$ & $0.2$ & $-23.2$ & $72.2$ \\\\\n 3 & $6.1$ & $5.6$ & $37.1$ & $38.5$ & $31.8$ & $12.3$ & $35.4$ & $-24$ \\\\\n 4 & $5.2$ & $17.5$ & $23.6$ & $26.6$ & $28$ & $15.6$ & $2.5$ & $-4$ \\\\\n 5 & $5.5$ & $0.2$ & $-7.4$ & $-2.6$ & $9.3$ & $3$ & $18.1$ & $20$ \\\\\n 6 & $7.7$ & $-1.8$ & $6.4$ & $9.3$ & $14.6$ & $1.2$ & $32.6$ & $29.5$ \\\\\n 7 & $10.9$ & $-2.2$ & $18.4$ & $25.6$ & $30.7$ & $2.3$ & $4.8$ & $21.2$ \\\\\n 8 & $12.7$ & $-5.3$ & $32.3$ & $33.7$ & $36.7$ & $3.1$ & $22.6$ & $29.6$ \\\\\n 9 & $15.6$ & $0.3$ & $-5.1$ & $-3.7$ & $-1$ & $7.3$ & $-2.3$ & $-31.2$\\\\\n 10 & $11.7$ & $46.5$ & $21.5$ & $18.7$ & $21.3$ & $31.1$ & $-1.9$ & $8.4$ \\\\\n 11 & $9.2$ & $-1.5$ & $22.4$ & $23.5$ & $21.7$ & $8$ & $23.7$ & $-12.8$\\\\\n 12 & $10.3$ & $15.9$ & $6.1$ & $3$ & $-9.7$ & $15$ & $7.4$ & $-17.5$\\\\\n 13 & $8$ & $36.6$ & $31.6$ & $32.6$ & $33.3$ & $21.3$ & $56.2$ & $0.6$ \\\\\n 14 & $6.3$ & $30.9$ & $18.6$ & $16.1$ & $8.6$ & $15.6$ & $69.4$ & $21.6$ \\\\\n 15 & $6.1$ & $-7.5$ & $5.2$ & $2.3$ & $-4.1$ & $2.3$ & $24.6$ & $24.4$ \\\\\n 16 & $7.1$ & $8.6$ & $16.5$ & $17.9$ & $16.5$ & $7.6$ & $28.3$ & $-13.9$\\\\\n 17 & $8.7$ & $21.2$ & $31.6$ & $29.2$ & $20.4$ & $14.2$ & $10.5$ & $-2.3$ \\\\\n 18 & $8$ & $5.4$ & $-3.2$ & $-6.2$ & $-17$ & $8.3$ & $-23.4$ & $-7.8$ \\\\\n 19 & $5.7$ & $19.3$ & $30.4$ & $34.2$ & $59.4$ & $16.1$ & $12.1$ & $-4.2$ \\\\\n 20 & $3.6$ & $7.9$ & $7.6$ & $9$ & $17.4$ & $7.6$ & $-12.2$ & $-7.4$ \\\\\n 21 & $3.1$ & $21.7$ & $10$ & $11.3$ & $16.2$ & $11$ & $32.6$ & $14.6$ \\\\\n 22 & $4.5$ & $-11.1$& $1.2$ & $-0.1$ & $-3.2$ & $-3.5$ & $7.8$ & $-1$ \\\\\n \\midrule\n \\multicolumn{9}{l}{\\textit{Descriptive Statistics}} \\\\\n Mean & $7.81$ & $9.29$ & $11.97$ & $12.36$ & $12.13$ & $9.17$ & $14.12$ & $8.35$ \\\\\n Median & $7.6$ & $5.5$ & $13.25$ & $13.7$ & $16.35$ & $7.8$ & $11.3$ & $-0.2$ \\\\\n Std. Dev. & $3.04$ & $15.21$ & $16.81$ & $17.86$ & $22.36$ & $8.05$ & $23.54$ & $26.34$ \\\\\n Variance & $9.27$ & $231.52$ & $282.82$ & $319.13$ & $500.06$ & $64.89$ & $554.22$ & $693.92$ \\\\\n Skewness & $0.73$ & $0.85$ & $-0.45$ & $-0.48$ & $-0.29$ & $0.82$ & $0.41$ & $0.93$ \\\\\n Kurtosis & $0.27$ & $-0.05$ & $-0.48$ & $-0.45$ & $-0.07$ & $0.62$ & $-0.03$ & $0.54$ \\\\\n Minimum & $3.1$ & $-11.1$ & $-26.5$ & $-28.4$ & $-33.8$ & $-3.5$ & $-23.4$ & $-31.2$ \\\\\n Maximum & $15.6$ & $46.5$ & $37.1$ & $38.5$ & $59.4$ & $31.1$ & $69.4$ & $72.2$ \\\\\n Percentile 25 & $5.8$ & $-1.72$ & $2.2$ & $0.5$ & $-2.65$ & $2.47$ & $-0.8$ & $-7.7$ \\\\\n Percentile 75 & $9.07$ & $18.85$ & $23.3$ & $26.35$ & $26.42$ & $14.8$ & $27.37$ & $21.5$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Higher-Order Stochastic Dominance Constraints in Optimization", "authors": ["Rajmadan Lakshmanan", "Alois Pichler", "Miloš Kopa"], "url": "https://arxiv.org/abs/2501.14565v3", "attribution": "\"Higher-Order Stochastic Dominance Constraints in Optimization\" by Rajmadan Lakshmanan, Alois Pichler, and Miloš Kopa, arXiv:2501.14565v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.00596v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\hline\n & Unweighted & Weighted \\\\ \\hline\nNSDUH analysis \\\\\n$\\,\\,\\,\\,$Sensitivity & 0.71 ($\\pm$ 0.003) & 0.63 ($\\pm$ 0.004) \\\\ \n$\\,\\,\\,\\,$Specificity & 0.76 ($\\pm$ 0.001) & 0.81 ($\\pm$ 0.001) \\\\ \n$\\,\\,\\,\\,$AUROC & 0.80 ($\\pm$ 0.001) & 0.80 ($\\pm$ 0.001) \\\\ \\hline\nNCS-R analysis\\\\\n$\\,\\,\\,\\,$Sensitivity & 0.66 ($\\pm$ 0.02) & 0.67 ($\\pm$ 0.02) \\\\ \n$\\,\\,\\,\\,$Specificity & 0.72 ($\\pm$ 0.01) & 0.73 ($\\pm$ 0.01) \\\\ \n$\\,\\,\\,\\,$AUROC & 0.73 ($\\pm$ 0.003) & 0.74 ($\\pm$ 0.004) \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Weighted and unweighted performance metrics for the NSDUH and NCS-R analyses. Monte Carlo standard deviations are in parentheses.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Evaluating Binary Outcome Classifiers Estimated from Survey Data", "authors": ["Adway S. Wadekar", "Jerome P. Reiter"], "url": "https://arxiv.org/abs/2311.00596v4", "attribution": "\"Evaluating Binary Outcome Classifiers Estimated from Survey Data\" by Adway S. Wadekar and Jerome P. Reiter, arXiv:2311.00596v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.05610v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|c|c|c|c|c|c} \n\tStage & Operator & Resolution & \\#OC & \\#L & SE & AF(TPU/GPU)\\\\\n\t\\midrule \n\t1 & Conv3x3 & $ 224 \\times 224 $ & 32 & 1 & N/A & swish/ReLU \\\\\n\t2 & Conv2x2 for reshaping$^{\\dagger}$ & $ 112 \\times 112 $ & 128 & 1 & N/A & ReLU/ReLU\\\\\n\t3 & MBConv1, k3x3 & $56 \\times 56 $ & 64 &1 & 1 & ReLU/ReLU\\\\\n\t4 & Fused MBConv6, k3x3 & $56 \\times 56$ & 24 & 2 & 0.5 & swish/ReLU\\\\\n\t5 & Fused MBConv6, k5x5 & $56 \\times 56$ & 40 & 2 & 0.25 & swish/ReLU\\\\\n\t6 & MBConv6, k3x3 & $28 \\times 28 $ & 80 & 3 & 0.25 & ReLU/ReLU\\\\\n\t7 & MBConv6, k5x5 & $14 \\times 14$ & 112 & 3 & 0.25 & ReLU/ReLU\\\\\n\t8 & MBConv6, k5x5 & $14 \\times 14$ & 192 & 4 & 0.25 & ReLU/ReLU\\\\\n\t9 & MBConv6, k3x3 & $7 \\times 7$ & 320 & 1 & 0.25 & ReLU/ReLU\\\\\n\t10 & Conv1x1 \\& Pooling \\& FC & $7 \\times 7$ & 1280 & 1 & N/A & ReLU/ReLU\\\\ \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Searching for Fast Model Families on Datacenter Accelerators", "authors": ["Sheng Li", "Mingxing Tan", "Ruoming Pang", "Andrew Li", "Liqun Cheng", "Quoc Le", "Norman P. Jouppi"], "url": "https://arxiv.org/abs/2102.05610v1", "attribution": "\"Searching for Fast Model Families on Datacenter Accelerators\" by Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc Le, and Norman P. Jouppi, arXiv:2102.05610v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.13516v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc|cccc}\n\\hline\n & \\multicolumn{4}{c|}{\\textbf{URMP}} & \\multicolumn{4}{c}{\\textbf{Bach10}} \\\\\n\\textbf{Model} & P & R & F1 & F1\\textsubscript{no} & P & R & F1 & F1\\textsubscript{no} \\\\\n\\hline\nMUSC & \\textbf{86.5} & 83.1 & \\textbf{84.6} & 93.0 & 65.0 & 64.8 & 64.8 & 77.0 \\\\\nOurs w/o aug & 83.4 & 81.2 & 82.2 & 92.8 & 66.7 & 71.3 & 68.9 & 79.0 \\\\\nOurs w/ aug & 86.1 & \\textbf{83.6} & 84.5 & \\textbf{93.1} & 68.1 & 71.8 & 69.9 & 79.5 \\\\\nOurs + FT w/o aug & 84.4 & 79.0 & 81.3 & 91.3 & \\textbf{69.5} & \\textbf{73.7} & \\textbf{71.5} & \\textbf{80.2} \\\\\nOurs + FT w/ aug & 85.0 & 82.1 & 83.3 & 92.9 & 63.3 & 68.4 & 65.7 & 77.8 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Transcription performance on the URMP and Bach10 violin tracks. \"FT\" denotes models fine-tuned from a piano-pretrained checkpoint; \"aug\" denotes augmentation. Bold indicates best result per column.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Is Transfer Learning Necessary for Violin Transcription?", "authors": ["Yueh-Po Peng", "Ting-Kang Wang", "Li Su", "Vincent K. M. Cheung"], "url": "https://arxiv.org/abs/2508.13516v2", "attribution": "\"Is Transfer Learning Necessary for Violin Transcription?\" by Yueh-Po Peng, Ting-Kang Wang, Li Su, and Vincent K. M. Cheung, arXiv:2508.13516v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.03581v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Voting regressions when questions receive at least two answers.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\tabularnewline \\midrule \\midrule\n Dependent Variables: & \\multicolumn{2}{c}{top answer (binary)} & \\multicolumn{2}{c}{\\# votes (log)}\\\\\n Model: & (1) & (2) & (3) & (4)\\\\ \n \\midrule\n \\emph{Variables}\\\\\n task experience & 0.0260$^{***}$ & 0.0279$^{***}$ & 0.0313$^{***}$ & 0.0336$^{***}$\\\\ \n & (0.0009) & (0.0004) & (0.0026) & (0.0007)\\\\ \n \\# answers & -0.2437$^{***}$ & -0.2491$^{***}$ & -0.6309$^{***}$ & -0.6371$^{***}$\\\\ \n & (0.0043) & (0.0021) & (0.0216) & (0.0036)\\\\ \n \\# votes all answers & -0.0814$^{***}$ & -0.0816$^{***}$ & 0.7513$^{***}$ & 0.7503$^{***}$\\\\ \n & (0.0065) & (0.0011) & (0.0046) & (0.0014)\\\\ \n \\midrule\n \\emph{Fixed-effects}\\\\\n year & Yes & & Yes & \\\\ \n task-year & & Yes & & Yes\\\\ \n \\midrule\n \\emph{Fit statistics}\\\\\n Observations & 3,170,868 & 3,170,868 & 3,170,868 & 3,170,868\\\\ \n R$^2$ & 0.09076 & 0.09334 & 0.67850 & 0.67968\\\\ \n Within R$^2$ & 0.08081 & 0.08076 & 0.66944 & 0.66110\\\\ \n \\midrule \\midrule\n \\multicolumn{5}{l}{\\emph{p-value: ***: 0.01, **: 0.05, *: 0.1}}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The building blocks of software work explain coding careers and language popularity", "authors": ["Xiangnan Feng", "Johannes Wachs", "Simone Daniotti", "Frank Neffke"], "url": "https://arxiv.org/abs/2504.03581v1", "attribution": "\"The building blocks of software work explain coding careers and language popularity\" by Xiangnan Feng, Johannes Wachs, Simone Daniotti, and Frank Neffke, arXiv:2504.03581v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.18826v4_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n \\hline\n Scenario & $\\delta$ & Runtime \\\\\n \\hline\n Paris & 0.1s & 13.65ms \\\\\n Paris & 0.2s & 0.01ms \\\\\n New York & 0.1s & 92.50ms \\\\\n Singapore & 0.1s & 33.33ms \\\\\n Singapore & 0.2s & 23.01ms \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Latex default table}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Geometry-Aware Normalizing Wasserstein Flows for Optimal Causal Inference", "authors": ["Kaiwen Hou"], "url": "https://arxiv.org/abs/2311.18826v4", "attribution": "\"Geometry-Aware Normalizing Wasserstein Flows for Optimal Causal Inference\" by Kaiwen Hou, arXiv:2311.18826v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.17943v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative Dice Summary: Reinforcement Agent Performance for Each Specific Dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{Prompt Number} & \\multicolumn{4}{|c|}{\\textbf{Dice Coefficient (Mean ± Standard Deviations)}} \\\\ \n\\cline{2-5}\n& \\textbf{CT Lung} & \\textbf{MRI Ovarian} & \\textbf{MRI Renal} & \\textbf{US Breast} \\\\ \n\\hline\n0 & 0.0791 ± 0.028 & 0.119 ± 0.032 & 0.056 ± 0.027 & 0.055 ± 0.032 \\\\ \n\\hline\n1 & 0.218 ± 0.024 & 0.339 ± 0.025 & 0.351 ± 0.017 & 0.123 ± 0.028 \\\\ \n\\hline\n2 & 0.310 ± 0.019 & 0.481 ± 0.017 & 0.458 ± 0.016 & 0.334 ± 0.022 \\\\ \n\\hline\n4 & 0.449 ± 0.019 & 0.549 ± 0.015 & 0.502 ± 0.017 & 0.411 ± 0.021 \\\\ \n\\hline\n5 & 0.485 ± 0.021 & 0.588 ± 0.013 & 0.505 ± 0.016 & 0.465 ± 0.017 \\\\ \n\\hline\n7 & 0.525 ± 0.018 & 0.594 ± 0.013 & 0.505 ± 0.012 & 0.515 ± 0.014 \\\\ \n\\hline\n9 & 0.534 ± 0.019 & 0.595 ± 0.012 & 0.502 ± 0.014 & 0.513 ± 0.013 \\\\ \n\\hline\n10 & 0.534 ± 0.016 & 0.595 ± 0.011 & 0.504 ± 0.016 & 0.514 ± 0.012 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities", "authors": ["Yuli Wang", "Victoria Shi", "Wen-Chi Hsu", "Yuwei Dai", "Sophie Yao", "Zhusi Zhong", "Zishu Zhang", "Jing Wu", "Aaron Maxwell", "Scott Collins", "Zhicheng Jiao", "Harrison X. Bai"], "url": "https://arxiv.org/abs/2412.17943v2", "attribution": "\"Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities\" by Yuli Wang, Victoria Shi, Wen-Chi Hsu, Yuwei Dai, Sophie Yao, Zhusi Zhong, Zishu Zhang, Jing Wu, Aaron Maxwell, Scott Collins, Zhicheng Jiao, and Harrison X. Bai, arXiv:2412.17943v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10000v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Supervised evaluation performance for different feature types and evaluation algorithms. See text for details.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccc}\n\\bf \\small Model &\\bf \\small Proj &\\bf \\small Pre-feature &\\bf \\small Pool &\\bf \\small Proj (16 avg) &\\bf \\small Pre-feature (16 avg) &\\bf \\small Pool (16 avg) \n\\\\ \\hline \\\\\nSVM &0.911 &0.889 &0.895 &0.922 &0.905 &{\\bf 0.929} \\\\\nkNN &0.904 &0.851 &0.105 &{\\bf 0.910} &0.800 &0.103 \\\\\nNearSub &0.898 &0.902 &0.903 &0.903 &0.909 &{\\bf 0.911} \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Neural Manifold Clustering and Embedding", "authors": ["Zengyi Li", "Yubei Chen", "Yann LeCun", "Friedrich T. Sommer"], "url": "https://arxiv.org/abs/2201.10000v1", "attribution": "\"Neural Manifold Clustering and Embedding\" by Zengyi Li, Yubei Chen, Yann LeCun, and Friedrich T. Sommer, arXiv:2201.10000v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00054v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|} \n \\hline\n &&&\\\\\n \\textbf{Method} & \\textbf{Gates} & \\textbf{Depth} & \\textbf{Preprocessing} \\\\\n &&&\\\\\n \\hline\n &&&\\\\\n Pyramid Circuit & $O(nd)$ & $n+d$ & $O(nd^{2})$ \\\\\n &&&\\\\\n \\hline\n &&&\\\\\n Sine Cosine Decomposition & $O(nd)$ & $d \\log n$ & $O(nd^{2})$ \\\\\n &&&\\\\\n \\hline \n &&&\\\\\n Clifford Loaders & $O(nd)$ & $d \\log n$ & $O(nd)$ \\\\\n &&&\\\\\n \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Quantum machine learning with subspace states", "authors": ["Iordanis Kerenidis", "Anupam Prakash"], "url": "https://arxiv.org/abs/2202.00054v2", "attribution": "\"Quantum machine learning with subspace states\" by Iordanis Kerenidis and Anupam Prakash, arXiv:2202.00054v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07788v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative comparison of different methods on the Mayo2016 and MCTD datasets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc|cccc}\n \\hline\n \\multirow{2}{*}{Methods} & \\multicolumn{4}{c|}{Mayo2016 dataset} & \\multicolumn{4}{c}{MCTD dataset} \\\\ \\cline{2-9}\n & PSNR$\\uparrow$ & SSIM$\\uparrow$ & RMSE$\\downarrow$ & LPIPS$\\downarrow$ & PSNR$\\uparrow$ & SSIM$\\uparrow$ & RMSE$\\downarrow$ & LPIPS$\\downarrow$ \\\\ \\hline\n RED-CNN & 32.32 & 0.9227 & 9.87 & 0.0235 & 37.81 & 0.9069 & 50.11 & 0.0535 \\\\\n DU-GAN & 32.64 & 0.9278 & 9.56 & 0.0198 & 38.52 & 0.9075 & 43.33 & 0.0291 \\\\\n SeD & 32.87 & 0.9283 & 9.32 & 0.0201 & 39.43 & 0.9157 & 40.78 & 0.0304 \\\\ \n CTformer & 33.16 & 0.9287 & 8.98 & 0.0235 & 38.97 & 0.9134 & 40.54 & 0.0458 \\\\ \n CoreDiff & 33.61 & 0.9342 & 8.58 & 0.0227 & 40.46 & 0.9290 & 34.40 & 0.0474 \\\\ \n ASCON & 33.60 & 0.9318 & 8.57 & 0.0242 & \\textbf{40.58} & 0.9278 & 34.43 & 0.0657 \\\\ \\hline\n ALDEN-MedSAM & 33.59 & \\textbf{0.9343} & 8.56 & 0.0192 & 40.51 & 0.9281 & 34.86 & 0.0273 \\\\\n ALDEN-DINOv2 & \\textbf{33.71} & 0.9341 & \\textbf{8.44} & \\textbf{0.0176} & 40.57 & \\textbf{0.9296} & \\textbf{34.37} & \\textbf{0.0265} \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Anatomy-Aware Low-Dose CT Denoising via Pretrained Vision Models and Semantic-Guided Contrastive Learning", "authors": ["Runze Wang", "Zeli Chen", "Zhiyun Song", "Wei Fang", "Jiajin Zhang", "Danyang Tu", "Yuxing Tang", "Minfeng Xu", "Xianghua Ye", "Le Lu", "Dakai Jin"], "url": "https://arxiv.org/abs/2508.07788v1", "attribution": "\"Anatomy-Aware Low-Dose CT Denoising via Pretrained Vision Models and Semantic-Guided Contrastive Learning\" by Runze Wang, Zeli Chen, Zhiyun Song, Wei Fang, Jiajin Zhang, Danyang Tu, Yuxing Tang, Minfeng Xu, Xianghua Ye, Le Lu, and Dakai Jin, arXiv:2508.07788v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18200v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc|cc}\n\\hline\n $G_i$ & $\\|Q_0 u- u_0\\|_0$ & $ h^r $ & $\\|\\nabla_w(Q_h u- u_h)\\|_0$ & $ h^r $ &\n $\\|E_w(Q_h u- u_h)\\|_0$ & $ h^r $ \\\\\n\\hline&\\multicolumn{6}{c}{ By the $P_2$/$P_2$/$P_{1}$ WG finite element .}\\\\\n\\hline \n 4& 0.267E-3 & 1.2& 0.258E-2 & 1.3& 0.327E+0 & 0.9 \\\\\n 5& 0.858E-4 & 1.6& 0.821E-3 & 1.7& 0.165E+0 & 1.0 \\\\\n 6& 0.236E-4 & 1.9& 0.226E-3 & 1.9& 0.830E-1 & 1.0 \\\\\n\\hline&\\multicolumn{6}{c}{ By the $P_3$/$P_3$/$P_{2}$ WG finite element .}\\\\\n\\hline \n 3& 0.586E-4 & 2.9& 0.780E-3 & 2.8& 0.106E+0 & 1.8 \\\\\n 4& 0.474E-5 & 3.6& 0.910E-4 & 3.1& 0.282E-1 & 1.9 \\\\\n 5& 0.313E-6 & 3.9& 0.106E-4 & 3.1& 0.716E-2 & 2.0 \\\\\n\\hline&\\multicolumn{6}{c}{ By the $P_4$/$P_4$/$P_{3}$ WG finite element .}\\\\\n\\hline \n 2& 0.291E-4 & 3.8& 0.783E-3 & 3.2& 0.889E-1 & 2.5 \\\\\n 3& 0.216E-5 & 3.8& 0.888E-4 & 3.1& 0.162E-1 & 2.5 \\\\\n 4& 0.482E-7 & 5.5& 0.578E-5 & 3.9& 0.223E-2 & 2.9 \\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Simple Weak Galerkin Finite Element Method for a Class of Fourth-Order Problems in Fluorescence Tomography", "authors": ["Chunmei Wang", "Shangyou Zhang"], "url": "https://arxiv.org/abs/2503.18200v1", "attribution": "\"A Simple Weak Galerkin Finite Element Method for a Class of Fourth-Order Problems in Fluorescence Tomography\" by Chunmei Wang and Shangyou Zhang, arXiv:2503.18200v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.10706v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\toprule\n Within-regime Markov order $k_{i,g}$ ($i=1,\\dots,4,g=1,2$) & 0 & 1 & 2 & 3 & 4 & 5 \\\\\n \\midrule\n AIC & 3884 & 3804 & 3789 & 3768 & 3771 & 3776 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Margin-closed regime-switching multivariate time series models", "authors": ["Lin Zhang", "Harry Joe", "Natalia Nolde"], "url": "https://arxiv.org/abs/2312.10706v1", "attribution": "\"Margin-closed regime-switching multivariate time series models\" by Lin Zhang, Harry Joe, and Natalia Nolde, arXiv:2312.10706v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.08302v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Arbitrage swap proportion comparing swap amount with first mover optimal swap amount and gas fee with first mover swap return}\n\\begin{tabular}{cc}\n \\toprule\n \\textbf{Swap amount $<$ first mover optimal swap amount} & 74.813\\% \\\\ \\midrule\n \\textbf{Gas fee $<$ first mover swap return(not consider gas fee)} & 82.971\\% \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Arbitrage on Decentralized Exchanges", "authors": ["Xue Dong He", "Chen Yang", "Yutian Zhou"], "url": "https://arxiv.org/abs/2507.08302v1", "attribution": "\"Arbitrage on Decentralized Exchanges\" by Xue Dong He, Chen Yang, and Yutian Zhou, arXiv:2507.08302v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2103.03054v4_tex_table3.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{}\n\\begin{tabular}{c|ccc|ccc}\n & \\multicolumn{3}{c|}{w/o Static Obstacles} & \\multicolumn{3}{c}{w/ Static Obstacles} \\\\ \\hline\nMethod & Success & Collision & Time {[}s{]} & Success & Collision & Time {[}s{]} \\\\ \\hline\nORCA & 0.77 & 0.23 & \\textbf{31.52} & 0.50 & 0.40 & 41.91 \\\\\nCADRL & 0.33 & 0.27 & 66.43 & 0.43 & \\textbf{0.13} & 80.15 \\\\\nSARL & 0.37 & 0.10 & 45.57 & 0.30 & 0.50 & 47.52 \\\\\nMORP-RB & \\textbf{0.97} & \\textbf{0.03} & 34.20 & 0.73 & 0.27 & 40.50 \\\\\nMORP-IL & \\textbf{0.97} & \\textbf{0.03} & 39.49 & \\textbf{0.75} & 0.25 & \\textbf{38.70} \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "An Open-Source Low-Cost Mobile Robot System with an RGB-D Camera and Efficient Real-Time Navigation Algorithm", "authors": ["Taekyung Kim", "Seunghyun Lim", "Gwanjun Shin", "Geonhee Sim", "Dongwon Yun"], "url": "https://arxiv.org/abs/2103.03054v4", "attribution": "\"An Open-Source Low-Cost Mobile Robot System with an RGB-D Camera and Efficient Real-Time Navigation Algorithm\" by Taekyung Kim, Seunghyun Lim, Gwanjun Shin, Geonhee Sim, and Dongwon Yun, arXiv:2103.03054v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.02473v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{{\\color{black}Comparison of ENApp D-OPF and ADMM Based Method}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline\n\\multirow{2}{*}{System}& \\multicolumn{2}{c|}{Total Loss (kW)} & \\multicolumn{2}{c|}{Time (sec)}& \\multicolumn{2}{c|}{\\#Iterations}\\\\\\cline{2-7}\n &{ENApp}&{ADMM}&{ENApp}&{ADMM}&{ENApp}&{ADMM}\\\\\n\\hline\n{TS-1}&{21.9}&{21.8}&{3}&{145}&{4}&{272}\\\\\n\\hline\n{TS-3}&{249.1}&{249.1}&{180}&{6120}&{17}&{756}\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Distributed Optimization using Reduced Network Equivalents for Radial Power Distribution Systems", "authors": ["Rabayet Sadnan", "Anamika Dubey"], "url": "https://arxiv.org/abs/2012.02473v1", "attribution": "\"Distributed Optimization using Reduced Network Equivalents for Radial Power Distribution Systems\" by Rabayet Sadnan and Anamika Dubey, arXiv:2012.02473v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10066v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n \\multirow{2}{*}{Language location} & \\multirow{2}{*}{\\#} & Percentage \\\\\n & & of all entries\\\\\n \\midrule\n Africa & 18 & 9.38\\%\\\\\n Americas* & 3 & 1.56\\%\\\\\n Asia & 61 & 31.77\\%\\\\\n Europe & 46 & 23.96\\%\\\\\n Latin America and the Caribbean & 17 & 8.85\\%\\\\\n Middle East and North Africa & 4 & 2.08\\%\\\\\n North Africa & 2 & 1.04\\%\\\\\n North America & 11 & 5.73\\%\\\\\n Oceania & 5 & 2.60\\%\\\\\n World-wide & 21 & 10.94\\%\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Distribution of data creators' locations over geographic regions (only first location for each entry).}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources", "authors": ["Angelina McMillan-Major", "Zaid Alyafeai", "Stella Biderman", "Kimbo Chen", "Francesco De Toni", "Gérard Dupont", "Hady Elsahar", "Chris Emezue", "Alham Fikri Aji", "Suzana Ilić", "Nurulaqilla Khamis", "Colin Leong", "Maraim Masoud", "Aitor Soroa", "Pedro Ortiz Suarez", "Zeerak Talat", "Daniel van Strien", "Yacine Jernite"], "url": "https://arxiv.org/abs/2201.10066v1", "attribution": "\"Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources\" by Angelina McMillan-Major, Zaid Alyafeai, Stella Biderman, Kimbo Chen, Francesco De Toni, Gérard Dupont, Hady Elsahar, Chris Emezue, Alham Fikri Aji, Suzana Ilić, Nurulaqilla Khamis, Colin Leong, Maraim Masoud, Aitor Soroa, Pedro Ortiz Suarez, Zeerak Talat, Daniel van Strien, and Yacine Jernite, arXiv:2201.10066v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.12781v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The numbers of spanning trees of $H_0,\\cdots,H_2$ and $H^2_0,\\cdots,H^2_2$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n G & $\\tau(G)$ & G & $\\tau(G)$ & G & $\\tau(G)$ \\\\\n \\hline\n $H_0$ & 6 & $H_1$ & 241943 & $H_2$ & 6.71512031151729$\\times 10^{32}$ \\\\\n $H^2_0$ & 6 & $H^2_1$ & 8426691368 & $H^2_2$ & 8.04003508846179$\\times 10^{109}$\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Hexagonal and k-hexagonal graph's normalized Laplacian spectrum and applications", "authors": ["Hao Li", "Xinyi Chen", "Hao Liu"], "url": "https://arxiv.org/abs/2504.12781v1", "attribution": "\"Hexagonal and k-hexagonal graph's normalized Laplacian spectrum and applications\" by Hao Li, Xinyi Chen, and Hao Liu, arXiv:2504.12781v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.10988v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccccccccc}\n $K$ & $1$ & $2$ & $2^2$ & $2^3$ & $2^4$ & $2^5$ & $2^6$ & $2^7$ & $2^8$ & $2^9$ & $2^{10}$ \\\\\n \\hline\n Euler & $0.17$ & $0.18$ &\t$0.19$ &\t$0.20$ &\t$0.22$ &\t$0.29$ &\t$0.56$ & \t$1.63$ &\t$5.80$ &\t$23.75$\t&$107.56$\\\\\n Milstein & $0.19$\t&$0.20$\t&$0.21$\t&$0.22$\t&$0.24$&\t$0.33$&\t$0.65$&\t$1.93$&\t$6.89$&\t$28.00$&\t$126.36$\\\\\n 2.0 weak Taylor & $0.26$\t&$0.27$\t&$0.28$\t&$0.29$ &\t$0.31$ &\t$0.40$ &\t$0.72$ & \t$2.02$\t&$7.02$ &\t$28.40$&\t$121.63$\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Example 2 in , with $\\kappa_z=10^{-2}$. CPU runtime in seconds. ($\\theta_1=\\theta_2=\\theta_3=1/2$, $\\theta_4=-1/2$, $N=10^{3}$.)}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A numerical Fourier cosine expansion method with higher order Taylor schemes for fully coupled FBSDEs", "authors": ["Balint Negyesi", "Cornelis W. Oosterlee"], "url": "https://arxiv.org/abs/2501.10988v1", "attribution": "\"A numerical Fourier cosine expansion method with higher order Taylor schemes for fully coupled FBSDEs\" by Balint Negyesi and Cornelis W. Oosterlee, arXiv:2501.10988v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.01925v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation results: non-constant conditional mean reward differences}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n\t\t\\toprule \\\\\n\t\t& \\multicolumn{4}{c}{Our Proposed Method with APS Controls} & \\multicolumn{2}{c}{Method with Mean Differences} & Direct \\\\ \n\t\t\\cline{2-5} \\cline{6-7} \\\\\n\t\t & $\\delta = 0.1$ & $\\delta=0.5$ & $\\delta = 1$ & $\\delta = 2.5$ & A/B Test Sample & Full Sample & Method \\\\\n\t\t & (1) & (2) & (3) & (4) & (5) & (6) & (7) \\\\\n\t\t\\hline \\\\\n\t\t\\multicolumn{8}{c}{Experiment 1: Mix of A/B Test and Deterministic Policy} \\\\ \\\\\n\t\t\\hspace{3mm} Bias & $-.012$ & .015 & .022 & .018 & $-.026$ & $-.006$ &---\\\\ \\\\\n\t\t \\hspace{3mm} S.D. & .045 & .038 & .033 & .026 & .045 & .018 &---\\\\\\\\\n\t\t \\hspace{3mm} RMSE & .046 & .041 & .040 & .031 & .052 & .019 &---\\\\\\\\\n\t\t \\hspace{3mm} Avg. $N$ & 1806 & 6009 & 11627 & 30136 & 500 & 50000 &--- \\\\ \\\\\n\t\t\\multicolumn{8}{c}{Experiment 2: Upper Confidence Bound} \\\\ \\\\\n\t\t\\hspace{3mm} Bias & $-.118$ & $-.119$ & $-.121$ & $-.119$ &--- &--- & $-.117$ \\\\ \\\\\n\t\t \\hspace{3mm} S.D. & .027 & .012 & .009 & .006 &---&---& .006 \\\\\\\\\n\t\t \\hspace{3mm} RMSE & .121 & .120 & .121 & .119 &---&---& .117 \\\\\\\\\n\t\t \\hspace{3mm} Avg. $N$ & 3397 & 17343 & 31107 & 47601 &---&---& 50000 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Counterfactual Learning with General Data-generating Policies", "authors": ["Yusuke Narita", "Kyohei Okumura", "Akihiro Shimizu", "Kohei Yata"], "url": "https://arxiv.org/abs/2212.01925v1", "attribution": "\"Counterfactual Learning with General Data-generating Policies\" by Yusuke Narita, Kyohei Okumura, Akihiro Shimizu, and Kohei Yata, arXiv:2212.01925v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19280v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Impact on conditioning on lower boundary of solution with $\\theta=\\pi/2$. ${y_s}$ denote the second solution identified in Table . None approaches to $y_b$, twenty one scenarios (1-2,4-14, 16-21,23-24) go to the other solution $y_s$, four scenarios (3,15,22,25) diverges. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\t\tstatus & number & $\\max|y''-f|$ & $\\max|y_{opt}-{y_s}|$ \\\\\\hline\\hline\n\t\t $\\to y_b$ & 0 & NA & NA \\\\\n\t\t$\\to y_s$ & 21 & 1.2E-07 & 3.1E-10 \\\\\n\t\t$diverge$ & 4 & diverge & diverge \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions", "authors": ["Xiaorong Zou"], "url": "https://arxiv.org/abs/2504.19280v1", "attribution": "\"Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions\" by Xiaorong Zou, arXiv:2504.19280v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.11786v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of ${\\mathtt S}_T$: all scores appearing below are the average of 10 replicated implementations. The standard deviation appears in the parenthesis.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|ccc}\n\\hline\nData & $d_{\\cal D}$ & Type & RF ($\\downarrow$)& SVM ($\\downarrow$)& LR ($\\downarrow$)\\\\ \n\\hline\n\\hline\n\\multirow{4}{*}{Adult}\n& \\multirow{2}{*}{JSD} & PT + CP & \\bf{0.022 (0.004)} & \\bf{0.037 (0.004)} & \\bf{0.028 (0.003)} \\\\\n& & MP & 0.059 (0.019) & 0.069 (0.022) & 0.058 (0.019) \\\\\n& \\multirow{2}{*}{PD} & PT + CP & \\bf{0.023 (0.003)} & \\bf{0.039 (0.007)} & \\bf{0.026 (0.004)} \\\\\n& & MP & 0.047 (0.021) & 0.054 (0.011) & 0.044 (0.011) \\\\\n\\hline\n\\multirow{4}{*}{Law School.}\n& \\multirow{2}{*}{JSD} & PT + CP & \\bf{0.020 (0.014)} & \\bf{0.023 (0.009)} & \\bf{0.008 (0.006)} \\\\\n& & MP & 0.093 (0.022) & 0.101 (0.022) & 0.068 (0.024) \\\\\n& \\multirow{2}{*}{PD} & PT + CP & \\bf{0.019 (0.007)} & \\bf{0.020 (0.004)} & \\bf{0.006 (0.001)} \\\\\n& & MP & 0.096 (0.017) & 0.099 (0.018) & 0.069 (0.016) \\\\\n\\hline\n\\multirow{4}{*}{Credit Card.}\n& \\multirow{2}{*}{JSD} & PT + CP& \\bf{0.052 (0.009)} & \\bf{0.061 (0.017)} & \\bf{0.036 (0.008)} \\\\\n& & MP & 0.147 (0.021) & 0.164 (0.038) & 0.146 (0.030) \\\\\n& \\multirow{2}{*}{PD} & PT + CP & \\bf{0.050 (0.009)} & \\bf{0.046 (0.012)} & \\bf{0.035 (0.010)} \\\\\n& & MP & 0.126 (0.040) & 0.138 (0.047) & 0.122 (0.043) \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Parallelly Tempered Generative Adversarial Nets: Toward Stabilized Gradients", "authors": ["Jinwon Sohn", "Qifan Song"], "url": "https://arxiv.org/abs/2411.11786v2", "attribution": "\"Parallelly Tempered Generative Adversarial Nets: Toward Stabilized Gradients\" by Jinwon Sohn and Qifan Song, arXiv:2411.11786v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12894v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n Functions & Classical $3$-symbol Code & function-correcting $3-$symbol Codes \\\\\n \\hline\n $2t-b-$locally binary functions & $\\lfloor \\frac{t}{3} \\rfloor log_q(k + 2t - 2)$ & $2t - 2$ \\\\\n \\hline\n $b-$symbol weight functions & $\\lfloor \\frac{t}{3} \\rfloor log_q(k + 2t - 2)$ & $\\frac{q(2t-3)}{q\\left( 1 - \\sqrt{\\frac{\\ln(2t-2)}{2t - 2}}\\right) - 1}$ \\\\\n \\hline\n $$b-$$symbol weight distribution functions & $\\lfloor \\frac{t}{3} \\rfloor log_q(k + 2t - 2)$ & $2t$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{A Comparison of Redundancy in Classical 3-Symbol Codes and Function-Correcting 3-Symbol Codes}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Function-Correcting Codes for b-Symbol Read Channels", "authors": ["Anamika Singh", "Abhay Kumar Singh", "Eitan Yaakobi"], "url": "https://arxiv.org/abs/2503.12894v1", "attribution": "\"Function-Correcting Codes for b-Symbol Read Channels\" by Anamika Singh, Abhay Kumar Singh, and Eitan Yaakobi, arXiv:2503.12894v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.19294v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Setting 2: comparison of average Sharpe ratios and expected utilities for Bayesian with correct degenerate prior ($\\text{BCPD}$); DRC and DRBC ($\\delta = 10^{-3}/10^{-2}$).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccr}%{p{2.65cm}p{2cm}p{2.7cm}}\n\\toprule\nMethod & Sharpe ratio & Expected Utility\\\\\n\\midrule\n$\\text{BCPD}$ & 2.44595 & $6.07575$\\\\% \\pm 0.72128$ \\\\\n$\\text{DRC}$($10^{-3}$) & 1.04900 & $3.46948$\\\\% \\pm 0.41257$ \\\\\n$\\text{DRC}$($10^{-2}$) & 0.91977 & $3.38634 $\\\\%\\pm 0.40271$ \\\\\nDRBC ($10^{-3}$) & 1.48139 & $4.43138$\\\\% \\pm 0.70654$\\\\\nDRBC ($10^{-2}$) & 1.48136 & $4.43145$\\\\% \\pm 0.70654$\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Duality and Policy Evaluation in Distributionally Robust Bayesian Diffusion Control", "authors": ["Jose Blanchet", "Jiayi Cheng", "Hao Liu", "Yang Liu"], "url": "https://arxiv.org/abs/2506.19294v2", "attribution": "\"Duality and Policy Evaluation in Distributionally Robust Bayesian Diffusion Control\" by Jose Blanchet, Jiayi Cheng, Hao Liu, and Yang Liu, arXiv:2506.19294v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.02870v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Training procedure for UCI regression results in and .}\n\\begin{tabular}{l|ccccccccc}\n\\textbf{NN} & \\textbf{Energy} & \\textbf{Concrete} & \\textbf{Kin8nm} & \\textbf{Naval} & \\textbf{CCPP} & \\textbf{Wine} & \\textbf{Yacht} & \\textbf{Protein} & \\textbf{Song} \\\\ \\hline\nLearning Rate & $1e-2$ & $1e-2$ & $1e-2$ & $1e-2$ & $1e-2$ & $1e-2$ & $1e-2$ & $1e-2$ & $1e-2$ \\\\\nEpochs & $1500$ & $1000$ & $500$ & $150$ & $100$ & $100$ & $1000$ & $250$ & $50$ \\\\\nWeight Decay & $1e-5$ & $1e-5$ & $1e-5$ & $1e-4$ & $1e-5$ & $1e-4$ & $1e-5$ & $1e-4$ & $0$ \\\\\nOptimizer & Adam & Adam & SGD & SGD & Adam & SGD & Adam & SGD & SGD \\\\\nScheduler & PolyLR & PolyLR & None & None & PolyLR & None & PolyLR & None & None \\\\\nMLP Size & $[150]$ & $[150]$ & $[100,100]$ & $[150,150]$ & $[100,100]$ & $[100]$ & $[100]$ & $[150,200,150]$ &$[1000,1000$ \\\\\n & & & & & & & & & $,500,50]$ \\\\\n\\textbf{NUQLS} & & & & & & & & & \\\\ \\hline\nLearning Rate & $1e-2$ & $1e-2$ & $1e-2$ & $1e-2$ & $1e-2$ & $1e-2$ & $1e-2$ & $1e-2$ & $1e-3$ \\\\\nEpochs & $150$ & $100$ & $50$ & $15$ & $10$ & $10$ & $100$ & $25$ & $10$ \\\\\n\\\\\n\\textbf{DE} & & & & & & & & & \\\\ \\hline\nLearning Rate & $1e-3$ & $1e-3$ & $1e-2$ & $1e-3$ & $1e-2$ & $1e-2$ & $1e-2$ & $1e-2$ & $1e-2$ \\\\\nEpochs & $1500$ & $300$ & $100$ & $100$ & $100$ & $100$ & $1000$ & $250$ & $50$ \\\\\nOptimizer & Adam & Adam & Adam & Adam & Adam & Adam & Adam & Adam & Adam \\\\\nScheduler & None & None & Cosine & None & None & None & Cosine & None & None \\\\\n & & & & & & & & & \\\\\n\\textbf{Experiment} & & & & & & & & & \\\\ \\hline\nNo. experiments & $10$ & $10$ & $10$ & $10$ & $10$ & $10$ & $10$ & $10$ & $3$ \\\\\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Uncertainty Quantification with the Empirical Neural Tangent Kernel", "authors": ["Joseph Wilson", "Chris van der Heide", "Liam Hodgkinson", "Fred Roosta"], "url": "https://arxiv.org/abs/2502.02870v1", "attribution": "\"Uncertainty Quantification with the Empirical Neural Tangent Kernel\" by Joseph Wilson, Chris van der Heide, Liam Hodgkinson, and Fred Roosta, arXiv:2502.02870v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2306.08760v6_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{TFPR Regression Results}\n\\begin{tabular}{lcccccccccccc}\n \\toprule\n & \\multicolumn{4}{c}{Capital} &\\multicolumn{4}{c}{Labor} & \\multicolumn{4}{c}{Materials}\\\\ \n \\midrule\n \\hline\n & \\multicolumn{2}{c}{Baseline - GNR} & \\multicolumn{2}{c}{Factor Shares} & \\multicolumn{2}{c}{Baseline - GNR} & \\multicolumn{2}{c}{Factor Shares} & \\multicolumn{2}{c}{Baseline - GNR} & \\multicolumn{2}{c}{Factor Shares} \\\\ \n \\midrule\n \\hline\n Specification & (1) & (2) & (1) & (2) & (1) & (2) & (1) & (2) & (1) & (2) & (1) & (2) \\\\ \n \\midrule\n \\textbf{Germany} & & & & & & & & & & & & \\\\ \n $\\beta$ & 0.185 & 0.145 & 0.119 & 0.133 & 0.675 & 0.497 & -0.636 & 0.355 & 0.722 & 0.630 & -0.082 & 0.958 \\\\ \n & (0.072) & (0.049) & (0.096) & (0.066) & (0.343) & (0.034) & (0.427) & (0.204) & (0.109) & (0.165) & (0.503) & (0.185) \\\\ \n N & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 \\\\ \n $R^2$ & 0.279 & 0.768 & 0.062 & 0.658 & 0.336 & 0.965 & 0.103 & 0.950 & 0.510 & 0.705 & 0.002 & 0.728 \\\\ \n RMSE & 0.155 & 0.071 & 0.118 & 0.066 & 0.378 & 0.096 & 0.394 & 0.108 & 0.428 & 0.272 & 0.694 & 0.321 \\\\\n \\midrule\n\\textbf{Spain} & & & & & & & & & & & & \\\\ \n $\\beta$ & 0.349 & 0.252 & 0.580 & 0.255 & 0.663 & 0.357 & -0.509 & 0.150 & 0.373 & 0.448 & 1.132 & 1.459 \\\\ \n & (0.088) & (0.055) & (0.077) & (0.096) & (0.134) & (0.053) & (0.368) & (0.110) & (0.167) & (0.181) & (0.316) & (0.413) \\\\ \n N& 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 \\\\ \n $R^2$ & 0.315 & 0.973 & 0.555 & 0.953 & 0.494 & 0.989 & 0.162 & 0.981 & 0.181 & 0.917 & 0.532 & 0.914 \\\\\n RMSE & 0.176 & 0.038 & 0.123 & 0.041 & 0.215 & 0.039 & 0.248 & 0.041 & 0.298 & 0.100 & 0.258 & 0.123 \\\\ \n \\midrule\n\\textbf{France} & & & & & & & & & & & & \\\\ \n $\\beta$ & 0.210 & 0.453 & 0.235 & 0.535 & 0.754 & 0.659 & 0.082 & 0.516 & -0.218 & 0.085 & 1.405 & 0.250 \\\\ \n& (0.155) & (0.091) & (0.118) & (0.126) & (0.194) & (0.089) & (0.595) & (0.113) & (0.399) & (0.277) & (0.537) & (0.415) \\\\ \n N & 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 \\\\ \n $R^2$ & 0.231 & 0.921 & 0.124 & 0.847 & 0.601 & 0.983 & 0.002 & 0.962 & 0.058 & 0.909 & 0.424 & 0.919 \\\\\n RMSE & 0.132 & 0.044 & 0.112 & 0.052 & 0.217 & 0.046 & 0.325 & 0.067 & 0.394 & 0.088 & 0.264 & 0.103 \\\\ \n \\midrule\n\\textbf{Italy} & & & & & & & & & & & & \\\\ \n $\\beta$ & 0.208 & -0.017 & 0.300 & 0.322 & 0.635 & 0.173 & -0.080 & 0.792 & 0.162 & 0.438 & 1.014 & 0.723 \\\\ \n& (0.100) & (0.128) & (0.179) & (0.231) & (0.206) & (0.452) & (0.389) & (0.528) & (0.323) & (0.165) & (0.084) & (0.431) \\\\ \n N & 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 & 144 \\\\ \n $R^2$ & 0.167 & 0.952 & 0.320 & 0.962 & 0.499 & 0.859 & 0.009 & 0.944 & 0.034 & 0.953 & 0.862 & 0.964 \\\\ \n RMSE & 0.173 & 0.037 & 0.131 & 0.034 & 0.199 & 0.106 & 0.271 & 0.067 & 0.317 & 0.080 & 0.148 & 0.076 \\\\\n \\midrule\n\\textbf{Poland} & & & & & & & & & & & & \\\\ \n $\\beta$ & 0.456 & 0.370 & 0.563 & 0.420 & 0.294 & 0.419 & 0.035 & 0.319 & 0.615 & 0.471 & 1.197 & 1.124 \\\\ \n& (0.052) & (0.029) & (0.092) & (0.066) & (0.077) & (0.038) & (0.061) & (0.144) & (0.089) & (0.137) & (0.125) & (0.315) \\\\ \n N & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 \\\\ \n $R^2$ & 0.636 & 0.930 & 0.674 & 0.943 & 0.328 & 0.901 & 0.005 & 0.777 & 0.426 & 0.723 & 0.716 & 0.812 \\\\ \n RMSE & 0.160 & 0.088 & 0.239 & 0.087 & 0.199 & 0.088 & 0.227 & 0.113 & 0.421 & 0.284 & 0.365 & 0.297 \\\\ \n \\midrule\n\\textbf{Romania} & & & & & & & & & & & & \\\\ \n $\\beta$ & 0.264 & 0.381 & 0.265 & 0.120 & 0.083 & 0.266 & -0.045 & -0.021 & 1.626 & 1.105 & 1.606 & 1.348 \\\\ \n & (0.152) & (0.057) & (0.090) & (0.093) & (0.216) & (0.058) & (0.064) & (0.084) & (0.257) & (0.185) & (0.202) & (0.252) \\\\ \n N & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 \\\\ \n $R^2$ & 0.168 & 0.912 & 0.290 & 0.898 & 0.029 & 0.864 & 0.018 & 0.783 & 0.638 & 0.970 & 0.817 & 0.966 \\\\ \n RMSE & 0.210 & 0.068 & 0.204 & 0.072 & 0.188 & 0.058 & 0.183 & 0.073 & 0.409 & 0.128 & 0.363 & 0.166 \\\\ \n \\midrule\n \\textbf{Pooled} & & & & & & & & & & & & \\\\ \n $\\beta$ & 0.224 & 0.298 & 0.282 & 0.283 & 0.567 & 0.409 & 0.133 & 0.286 & 0.497 & 0.550 & 0.916 & 1.083 \\\\ \n & (0.065) & (0.038) & (0.065) & (0.054) & (0.081) & (0.033) & (0.132) & (0.097) & (0.095) & (0.078) & (0.131) & (0.143) \\\\ \n N & 768 & 768 & 768 & 768 & 768 & 768 & 768 & 768 & 768 & 768 & 768 & 768 \\\\ \n $R^2$ & 0.201 & 0.950 & 0.242 & 0.943 & 0.434 & 0.967 & 0.017 & 0.965 & 0.263 & 0.908 & 0.422 & 0.922 \\\\ \n RMSE & 0.234 & 0.061 & 0.210 & 0.061 & 0.305 & 0.078 & 0.384 & 0.082 & 0.441 & 0.175 & 0.493 & 0.197 \\\\ \n \\midrule\n Constant & YES & NO & YES & NO & YES & NO & YES & NO & YES & NO & YES & NO \\\\ \n Industry/Year FE & NO & YES & NO & YES & NO & YES & NO & YES & NO & YES & NO & YES \\\\ \n \\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Do Productivity Shocks Cause Inputs Misallocation?", "authors": ["Davide Luparello"], "url": "https://arxiv.org/abs/2306.08760v6", "attribution": "\"Do Productivity Shocks Cause Inputs Misallocation?\" by Davide Luparello, arXiv:2306.08760v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.05748v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Expected initial state of the Deputy relative to the Chief, e.g., \\( \\mathbb{E}\\big[X_d(t_{0}) - X_c(t_{0})\\big] \\).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\n\\( \\delta x_0 \\) [km] & \\( \\delta y_0 \\) [km] & \\( \\delta z_0 \\) [km] & \\( \\delta{\\dot x_0} \\) [km/s] & \\( \\delta{\\dot y_0} \\) [km/s] & \\( \\delta{\\dot z_0} \\) [km/s] \\\\\n\\hline\n-9.7168 & -0.3110 & 0.5869 & 0.0014 & -0.0035 & -0.0068 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governor", "authors": ["Taehyeun Kim", "Robin Inho Kee", "Ilya Kolmanovsky", "Anouck Girard"], "url": "https://arxiv.org/abs/2412.05748v1", "attribution": "\"Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governor\" by Taehyeun Kim, Robin Inho Kee, Ilya Kolmanovsky, and Anouck Girard, arXiv:2412.05748v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08954v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{change detection results of different methods on the Rome dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccc}\n\\hline\\hline\nMethods & FP & FN & OE & PCC (\\%) & KC(\\%) & F1(\\%)\\\\ \\hline\nPCAKM & 649 & 1864 & 2513 & 96.17 & 82.93 & 85.12\\\\\nNR-ELM & 1726 & \\textbf{101} & 1827 & 97.21 & 89.12 & 90.74\\\\\nGaborPCANet & 785 & 1348 & 2133 & 96.75 & 85.96 & 87.84 \\\\\nLR-CNN & 582 & 1057 & 1639 & 97.50 & 89.26 & 90.70\\\\\nMLFN & 952 & 242 & 1194 & 98.18 & 92.59 & 93.65\\\\\nDBN & 682 & 758 & 1440 & 97.80 & 90.74 & 92.01\\\\\nDCNet & \\textbf{339} & 888 & 1227 & 98.12 & 91.93 & 93.01\\\\\nGKSNet & 706 & 304 & \\textbf{1010} & \\textbf{98.46} & \\textbf{93.64} & \\textbf{94.54}\\\\\n\\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network", "authors": ["Junjie Wang", "Feng Gao", "Junyu Dong", "Shan Zhang", "Qian Du"], "url": "https://arxiv.org/abs/2201.08954v2", "attribution": "\"Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network\" by Junjie Wang, Feng Gao, Junyu Dong, Shan Zhang, and Qian Du, arXiv:2201.08954v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.12379v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|cccc}\n\\multicolumn{3}{l}{valid adjustment} & \\multicolumn{3}{c}{$\\epsilon_{pehe}$} \\\\\n\\hline\n&TARnet & {\\textsc \\textbf{Mod1}} & {\\textsc\\textbf{Mod2} } & {\\textsc \\textbf{Mod3}} & {\\textsc \\textbf{Mod4}}\\\\\n\\hline\n\\textbf{low} &Adjust All &$.16\\pm .06$ & $.13\\pm .05$ & $.22 \\pm .18$ & $.05 \\pm .01$\\\\\n& NICE &$.06\\pm .02$ & $.05\\pm .02$ & $.06 \\pm .02$ & $.05 \\pm .01$\\\\\n\\hline\n\\textbf{med}& Adjust All &$.14 \\pm .06$ & $.15\\pm .02$ & $.12 \\pm .02$ & $.06\\pm .02$\\\\\n& NICE &$.05\\pm .01$ & $.05 \\pm .01$ & $.07 \\pm .03$ & $.04 \\pm .01$\\\\\n\\hline\n\\textbf{high} & Adjust All&$.13\\pm .07$ & $.12\\pm .02$ & $.16 \\pm .09$ & $.06\\pm .01$\\\\\n& NICE &$.05\\pm .01$ & $.05 \\pm .01$ & $.07\\pm .02$ & $.04\\pm .01$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Invariant Representation Learning for Treatment Effect Estimation", "authors": ["Claudia Shi", "Victor Veitch", "David Blei"], "url": "https://arxiv.org/abs/2011.12379v2", "attribution": "\"Invariant Representation Learning for Treatment Effect Estimation\" by Claudia Shi, Victor Veitch, and David Blei, arXiv:2011.12379v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.09412v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\n \\hline\n $\\beta_0$ & $\\beta_1$ & $\\beta_2$ & $\\beta_{1,2}$ & $\\lambda_{1,0}$ & $\\lambda_{1,1}$ & $\\theta_1$ & $\\lambda_{2,0}$ & $\\lambda_{2,1}$ & $\\theta_2$ \\\\ \\hline\n 0.0840 & -0.2568 & 0.7415 & 0.2078 & 35.57 & 6.99 & 0.8142 & 10.15 & 0.21 & 0.9691 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Joint deep calibration of the 4-factor PDV model", "authors": ["Fabio Baschetti", "Giacomo Bormetti", "Pietro Rossi"], "url": "https://arxiv.org/abs/2507.09412v1", "attribution": "\"Joint deep calibration of the 4-factor PDV model\" by Fabio Baschetti, Giacomo Bormetti, and Pietro Rossi, arXiv:2507.09412v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.03911v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The average number of modes found over $10$ nested sampling runs, sampling the distribution shown in~, with and without mode collapse mitigation, for varying number of live points.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n Method& $n_{\\rm live} = 20$ & $n_{\\rm live} = 50$ & $n_{\\rm live} = 100$ & $n_{\\rm live} = 200$\\\\\\midrule\n Without Mode Collapse Mitigation & $3.6$ & $6.4$ & $8.2$ & $8.9$\\\\\n With Mode Collapse Mitigation & $4.1$ & $6.4$ & $8.4$ & $9$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Improving Gradient-guided Nested Sampling for Posterior Inference", "authors": ["Pablo Lemos", "Nikolay Malkin", "Will Handley", "Yoshua Bengio", "Yashar Hezaveh", "Laurence Perreault-Levasseur"], "url": "https://arxiv.org/abs/2312.03911v1", "attribution": "\"Improving Gradient-guided Nested Sampling for Posterior Inference\" by Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, Yashar Hezaveh, and Laurence Perreault-Levasseur, arXiv:2312.03911v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13019v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Robustness of R-FID against latent perturbations.} This table will be removed as it has only 4 new numbers. We will insert them somehow into Figure 5.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc}\n\\toprule \n\\midrule\n & $\\alpha=0.7$ & $\\alpha=1.0$ \\\\\n\\midrule\n $\\kappa=64$ & 98.3 $\\rightarrow$ 128.1 & 88.1 $\\rightarrow$ 126.6 \\\\\n$\\kappa=128$ & 119.9 $\\rightarrow$ 157.8 & 113.8 $\\rightarrow$ 162.5\\\\\n\\midrule\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "On the Robustness of Quality Measures for GANs", "authors": ["Motasem Alfarra", "Juan C. Pérez", "Anna Frühstück", "Philip H. S. Torr", "Peter Wonka", "Bernard Ghanem"], "url": "https://arxiv.org/abs/2201.13019v2", "attribution": "\"On the Robustness of Quality Measures for GANs\" by Motasem Alfarra, Juan C. Pérez, Anna Frühstück, Philip H. S. Torr, Peter Wonka, and Bernard Ghanem, arXiv:2201.13019v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19901v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{cc}\n\t\t\\hline\n\t\tParameter & Value\\\\ \\hline \\hline\n\t\t$\\kappa_{1}$ & 10 \\\\\n\t\t$\\kappa_{2}$ & 1 \\\\\n\t\t$\\kappa_{3}$ & 500\\\\\n\t\t$\\kappa_{4}$ & 1\\\\\n\t\t$\\kappa_{5}$ & 1.8 \\\\ \\hline\n\t\\end{tabular}\n\\caption{Baseline values for the controllers used in the simulation.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Nonlinear Voltage Regulation of an Auxiliary Energy Storage of a Multiport Interconnection", "authors": ["Felipe Morales", "Rafael Cisneros", "Romeo Ortega", "Antonio Sanchez-Squella"], "url": "https://arxiv.org/abs/2403.19901v1", "attribution": "\"Nonlinear Voltage Regulation of an Auxiliary Energy Storage of a Multiport Interconnection\" by Felipe Morales, Rafael Cisneros, Romeo Ortega, and Antonio Sanchez-Squella, arXiv:2403.19901v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23211v2_tex_table18.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Location of estimated change points in the EEG data set using some selected alternative methods. Note that the true value is around 85 seconds.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n\\hline\nMethod & Channel P3 & Channel T3 & Channel T5 \\\\ \\hline\\hline\nPELT & 92 & 90 & $\\{ 18, 20, 33, 35, 85\\}$ \\\\\nSDE & 92 & 90 & 85 \\\\\nDP-UNIVAR & $\\{92, 104 \\}$ & $\\{93, 95, 98, 100, 102 \\}$ & $\\{96, 98, 100, 103\\}$ \\\\\nLOCAL & 104 & 94 & 104 \\\\\nSTD & $\\{104, 92, 97 \\}$ & $\\{ 94, 87, 100\\}$ & $\\{ 104, 90, 82, 97\\}$ \\\\\nWBS UNI-NONPAR & $\\{ 104, 95\\}$ & missing & $\\{90, 82, 98 \\}$ \\\\\nROB-WBS & $\\{ 104, 100\\}$ & missing & $\\{104, 90, 85, 93 \\}$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models", "authors": ["Sepideh Mosaferi", "Abolfazl Safikhani", "Peiliang Bai"], "url": "https://arxiv.org/abs/2503.23211v2", "attribution": "\"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models\" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.21124v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{crrrrr}\n \\toprule\n scheme & steps & bsz. & time & loss & val loss \\\\\n \\midrule\n $\\eta=0.15$ & 531 & 3770 & 9.31 & 3.764 & 3.811 \\\\\n $\\eta=0.2$ & 254 & 7878 & 8.85 & 4.699 & 4.720 \\\\\n $\\eta=0.25$ & 843 & 2373 & 10.30 & 3.313 & 3.361 \\\\\n $\\eta=0.275$ & 252 & 7965 & 8.84 & 4.669 & 4.677 \\\\ \n $b_k=2048$ & 977 & 2048 & 11.18 & 4.976 & 5.005 \\\\\n $b_k=4096$ & 489 & 4096 & 9.66 & 5.722 & 5.741 \\\\\n $b_k=8192$ & 245 & 8192 & 8.48 & 6.183 & 6.192 \\\\\n 2.5-2.5-95\\% & 269 & 7439 & 8.78 & 4.594 & 4.604 \\\\\n \\bottomrule\n \\end{tabular}\n\\caption{Results of MicroLlama 300M}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism", "authors": ["Tim Tsz-Kit Lau", "Weijian Li", "Chenwei Xu", "Han Liu", "Mladen Kolar"], "url": "https://arxiv.org/abs/2412.21124v2", "attribution": "\"Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism\" by Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu, Han Liu, and Mladen Kolar, arXiv:2412.21124v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.01705v3_tex_table22.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Benjamini Hochberg (1995) adjusted p-values (original p-values in parenthesis)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrrrr}\n\\toprule \n&\\multicolumn{5}{c}{\\textbf{Uncertainty}}&\\multicolumn{5}{c}{\\textbf{Negative Sentiment}}\\\\\n\\toprule\n &Aggregate&Economics&Health&Politics&Lockdown Pol&Aggregate&Economics&Health&Politics&Lockdown Pol\\\\\n\\midrule\nred zone = 1 & 0.52 & 0.06 & 0.47 & 0.00 & 0.75 & 0.13 & 0.04 & 0.81 & 0.00 & 0.89 \\\\\n & (0.42) & (0.03) & (0.35)& (0.00) & (0.71) & (0.05) & (0.02) & (0.69)& (0.00) & (0.85)\\\\\npost = 1 & 0.00 & 0.02 & 0.00 & 0.69 & 0.00 & 0.51 & 0.00 & 0.00 & 0.22 & 0.02\\\\\n & (0.00) & (0.01) & (0.00)& (0.63) & (0.00) & (0.30) & (0.00) & (0.00)& (0.14) & (0.01)\\\\\npost = 2 & 0.00 & 0.00& 0.00 & 0.68 & 0.00 & 0.26 & 0.89 & 0.00 & 0.02 & 0.04\\\\\n & (0.00) & (0.00) & (0.00)& (0.57) & (0.00) & (0.12) & (0.81) & (0.00)& (0.01) & (0.02)\\\\\n\\textbf{red zone = 1 x post = 1} & 0.82 & 0.13 & 0.06 & 0.06 & 0.83 & 0.87 & 0.31 & 0.00 & 0.00 & 0.31 \\\\\n& (0.82) & (0.08) & (0.02)& (0.03) & (0.83) & (0.87) & (0.25) & (0.00)& (0.00) & (0.24)\\\\\nred zone = 1 x post = 2 & 0.75 & 0.17 & 0.63 & 0.45 & 0.07 & 0.53 & 0.94 & 0.12 & 0.00 & 0.25 \\\\\n & (0.70) & (0.11) & (0.50)& (0.32) & (0.04) & (0.37) & (0.94) & (0.07)& (0.00) & (0.18)\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic", "authors": ["C. Biliotti", "F. J. Bargagli-Stoffi", "N. Fraccaroli", "M. Puliga", "M. Riccaboni"], "url": "https://arxiv.org/abs/2212.01705v3", "attribution": "\"Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic\" by C. Biliotti, F. J. Bargagli-Stoffi, N. Fraccaroli, M. Puliga, and M. Riccaboni, arXiv:2212.01705v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2308.14705v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ \\small \\textbf{OOD detection}. Results reported using AUROC show our method enhances the baseline up to 6\\%.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n IND & OOD & Baseline & SSL-Ensemble & Our method \\\\\n \\midrule\n \\multirow{4}{6em}{CIFAR-100} & SVHN & 84.22 & 84.95 & \\textbf{88.00} \\\\ \n & Uniform & 91.65 & 90.53 & \\textbf{97.57} \\\\ \n & Gaussian & 90.00 & 89.42 & \\textbf{94.10} \\\\ \n & CIFAR-10 & 74.71 & 74.80 & \\textbf{75.18} \\\\ \n \\midrule \n \\multirow{4}{6em}{CIFAR-10} & SVHN & 95.03 & 96.68 & \\textbf{97.07}\\\\ \n & Uniform & 96.73 & 91.64 & \\textbf{99.05}\\\\ \n & Gaussian & 96.39 & 93.24 & \\textbf{99.24} \\\\ \n & CIFAR-100 & 91.79 & 91.59 & \\textbf{91.87} \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning", "authors": ["Amirhossein Vahidi", "Lisa Wimmer", "Hüseyin Anil Gündüz", "Bernd Bischl", "Eyke Hüllermeier", "Mina Rezaei"], "url": "https://arxiv.org/abs/2308.14705v2", "attribution": "\"Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning\" by Amirhossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz, Bernd Bischl, Eyke Hüllermeier, and Mina Rezaei, arXiv:2308.14705v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.12543v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation Parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|} \\hline\n\t\t\t\\textbf{Parameter} & \\textbf{Value} \\\\\\hline\n\t\t\tNumber of MEC servers $M$ & $5$ \\\\ \\hline\n\t\t\tPlateau factors of MZipf $q_m$ & $[100, 200, 90, 40, 80]$\\\\ \\hline\n\t\t\tZipf factors of MZipf $k_m$ & $[0.60, 0.60, 0.75, 0.90, 0.90]$\\\\ \\hline\n\t\t\tData size of contents $\\eta_c$ & $1\\sim8$ GB \\\\ \\hline\n\t\t\tDownloading payment of contents $\\phi_c$ & $0.05\\sim0.5$ HKD \\\\ \\hline\n\t\t\t\n\t\t\tNumber of DNN layers & $6$ \\\\ \\hline\n\t\t\tNumber of neurons per hidden layer & $128$ \\\\ \\hline\n\t\t\tLearning rate $\\xi$ & $0.002$ \\\\ \\hline\n\t\t\tUpdate coefficient $\\tau$ & $0.005$ \\\\ \\hline\n\t\t\tDiscounted factor $\\gamma$ & $0.99$ \\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Personalized Federated Deep Reinforcement Learning for Heterogeneous Edge Content Caching Networks", "authors": ["Zhen Li", "Tan Li", "Hai Liu", "Tse-Tin Chan"], "url": "https://arxiv.org/abs/2412.12543v1", "attribution": "\"Personalized Federated Deep Reinforcement Learning for Heterogeneous Edge Content Caching Networks\" by Zhen Li, Tan Li, Hai Liu, and Tse-Tin Chan, arXiv:2412.12543v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.03002v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ The mean (for the absolute values of the elements), standard deviation and kurtosis of the signal components of correlation tensors for three different weeks. The values within the brackets indicate standard deviations of the quantities. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|} \n \\hline\n & 2017, November 06 - November 12 & 2018, January 01- January 07 & 2018, February 12 - February 18 \\\\ \n \\hline\n $\\langle{|M_{ij}^{\\alpha\\beta}(\\rm signal)|}\\rangle$ & $0.343 (0.006)$ &$ 0.324 (0.005)$ & $0.334 (0.005)$ \\\\ \n \\hline\n standard deviation & $0.419 (0.006)$ & $0.401 (0.005)$ & $0.409 (0.005)$ \\\\\n \\hline\n kurtosis & $2.447 (0.035) $ & $2.538 (0.032)$ & $2.500 (0.029) $ \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Projecting XRP price burst by correlation tensor spectra of transaction networks", "authors": ["Abhijit Chakraborty", "Tetsuo Hatsuda", "Yuichi Ikeda"], "url": "https://arxiv.org/abs/2211.03002v2", "attribution": "\"Projecting XRP price burst by correlation tensor spectra of transaction networks\" by Abhijit Chakraborty, Tetsuo Hatsuda, and Yuichi Ikeda, arXiv:2211.03002v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.12110v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Household Variables}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\n\\textbf{Variable} & \\textbf{Meaning} \\\\\n\\midrule\n$N_t$ & Total population size at time $t$ \\\\\n$x_t$ & Share of young individuals in the population at time $t$ \\\\\n$n_t^b$ & Birth rate at time $t$ \\\\\n$n_t^d$ & Death rate at time $t$ \\\\\n$\\text{age}_t^i$ & Age of individual $i$ at time $t$ \\\\\n$a_t^i$ & Total assets held by individual $i$ (savings + investment) \\\\\n$s_t^i$ & Savings held by individual $i$ \\\\\n$v_t^i$ & Risky investment held by individual $i$ \\\\\n$c_t^i$ & Consumption of individual $i$ \\\\\n$h_t^i$ & Labor supply (work effort) of individual $i$ \\\\\n$e_t^i$ & Education level of individual $i$ (affecting productivity) \\\\\n$W_t$ & Wage rate at time $t$ \\\\\n$p_t$ & Price level at time $t$ \\\\\n$r_t$, $\\rho_t$ & Returns on savings and risky investments, respectively \\\\\n$m_t^i$ & Disposable income of individual $i$ after tax (and pension contribution if young) \\\\\n$\\alpha_t^i$ & Allocation ratio: share of $m_t^i$ allocated to savings/investment \\\\\n$\\theta_t^i$ & Investment ratio: share of $a_{t+1}^i$ allocated to risky investment \\\\\n$a_{t+1}^i$ & Next-period asset holdings after financial allocation \\\\\n$f_t^y$ & Mandatory pension contribution for young individuals \\\\\n$f_t^o$ & Pension benefit received by old individuals \\\\\n$T_t(\\cdot)$ & Tax function applied to income and capital \\\\\n$\\beta$ & Time discount factor \\\\\n$\\sigma$ & Coefficient of relative risk aversion \\\\\n$\\gamma$ & Inverse Frisch elasticity of labor supply \\\\\n$o_t^i$ & Personal observation of individual $i$ at time $t$ \\\\\n$o_t^{\\text{global}}$ & Global observation vector shared by all agents \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "EconGym: A Scalable AI Testbed with Diverse Economic Tasks", "authors": ["Qirui Mi", "Qipeng Yang", "Zijun Fan", "Wentian Fan", "Heyang Ma", "Chengdong Ma", "Siyu Xia", "Bo An", "Jun Wang", "Haifeng Zhang"], "url": "https://arxiv.org/abs/2506.12110v1", "attribution": "\"EconGym: A Scalable AI Testbed with Diverse Economic Tasks\" by Qirui Mi, Qipeng Yang, Zijun Fan, Wentian Fan, Heyang Ma, Chengdong Ma, Siyu Xia, Bo An, Jun Wang, and Haifeng Zhang, arXiv:2506.12110v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.03312v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Reversion coefficients under different market conditions}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccc}\n\\hline\n$\\lambda$ & 0 & 0.2 & 0.4 & 0.6 & 0.8 & 1 \\\\ \\hline\nSR reversion & \\hspace{0.5em}14.5\\%\\hspace{0.5em} & \\hspace{0.5em}13.3\\%\\hspace{0.5em} & \\hspace{0.5em}11.2\\%\\hspace{0.5em} & \\hspace{0.5em}8.3\\%\\hspace{0.5em} & \\hspace{0.5em}4.5\\%\\hspace{0.5em} & \\hspace{0.5em}0.0\\%\\hspace{0.5em} \\\\\nLR reversion & 39.8\\% & 37.3\\% & 33.0\\% & 26.2\\% & 15.9\\% & 0.0\\% \\\\ \\hline\n$\\alpha$ & 0 & 0.2 & 0.4 & 0.6 & 0.8 & 1 \\\\ \\hline\nSR reversion & 22.7\\% & 22.4\\% & 21.5\\% & 19.9\\% & 17.6\\% & 14.5\\% \\\\\nLR reversion & 52.3\\% & 51.8\\% & 50.6\\% & 48.4\\% & 44.9\\% & 39.8\\% \\\\ \\hline\n$m$ & 10 & 20 & 30 & 40 & 50 & 60 \\\\ \\hline\nSR reversion & 14.5\\% & 16.0\\% & 16.7\\% & 16.9\\% & 18.5\\% & 18.6\\% \\\\\nLR reversion & 39.8\\% & 57.1\\% & 67.1\\% & 74.4\\% & 79.3\\% & 82.0\\% \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "How manipulable are prediction markets?", "authors": ["Itzhak Rasooly", "Roberto Rozzi"], "url": "https://arxiv.org/abs/2503.03312v1", "attribution": "\"How manipulable are prediction markets?\" by Itzhak Rasooly and Roberto Rozzi, arXiv:2503.03312v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.20923v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n $A_0$&$A_1$ &$A_2$ &$A_3$ &$A_4$ \\\\ \\hline\n 49.6219&-210.6617 & 341.7304 &-249.1275 &68.4388 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Fitting coefficients.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Spinning boson stars in nonlinear sigma models and Universal Relations", "authors": ["Christoph Adam", "Jorge Castelo Mourelle", "Alberto García Martín-Caro", "Andrzej Wereszczynski"], "url": "https://arxiv.org/abs/2502.20923v3", "attribution": "\"Spinning boson stars in nonlinear sigma models and Universal Relations\" by Christoph Adam, Jorge Castelo Mourelle, Alberto García Martín-Caro, and Andrzej Wereszczynski, arXiv:2502.20923v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07434v5_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c} \n\t\t\\toprule[1pt]\n\t\tLayer Counts &\\# of Channels & mIOU (\\%) & FLOPs \\\\\n\t\t\\midrule[0.5pt]\n\t\t\\midrule[0.5pt]\n\t\t-& -& 50.27($\\pm$0.2)& 68.7G\\\\\n\t\t\\midrule\n\t\t1&128& 50.75($\\pm$0.2)& +0.00024G\\\\\n\t\t3&128& 50.85($\\pm$0.2) & +0.00027G\\\\\n\t\t5&128& \\textbf{51.06($\\pm$0.2)} & +0.00030G \\\\\n\t\t7&128& 50.40($\\pm$0.3) & +0.00043G \\\\\n\t\t\\midrule\n\t\t5&64& 50.12($\\pm$0.2)& +0.00015G\\\\\n\t\t5&256 & 50.35($\\pm$0.4) & +0.00098G \\\\\n\t\t\\bottomrule[1pt]\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{ Results without using channelization (Row 1) and using channelization with different layer counts and channel numbers. Numbers in parentheses indicate standard deviations (see Sect.~).}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation", "authors": ["Ye Huang", "Di Kang", "Wenjing Jia", "Xiangjian He", "Liu Liu"], "url": "https://arxiv.org/abs/2101.07434v5", "attribution": "\"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation\" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.03114v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters mean values obtained via MLPMMH for TASI data (with 249 values of daily returns.)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c|c}\n Parameter & $V_0$ & $H$ & $\\rho$ & $\\kappa$ & $\\lambda$ & $\\nu$ & r \\\\\n \\hline\n Estimated Values & 0.16447 & 0.4557 & -0.43407 & 0.0017 & 11.0022 & 0.0445 &-5.7093e-04 \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian Parameter Inference for Partially Observed Stochastic Volterra Equations", "authors": ["Ajay Jasra", "Hamza Ruzayqat", "Amin Wu"], "url": "https://arxiv.org/abs/2310.03114v2", "attribution": "\"Bayesian Parameter Inference for Partially Observed Stochastic Volterra Equations\" by Ajay Jasra, Hamza Ruzayqat, and Amin Wu, arXiv:2310.03114v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.18969v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Frequency of Special Characters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccl}\n \\toprule\n Non-English or Math&Frequency&Comments\\\\\n \\midrule\n \\O & 1 in 1,000& For Swedish names\\\\\n $\\pi$ & 1 in 5& Common in math\\\\\n \\$ & 4 in 5 & Used in business\\\\\n $\\Psi^2_1$ & 1 in 40,000& Unexplained usage\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "EDPC: Accelerating Lossless Compression via Lightweight Probability Models and Decoupled Parallel Dataflow", "authors": ["Zeyi Lu", "Xiaoxiao Ma", "Yujun Huang", "Minxiao Chen", "Bin Chen", "Baoyi An", "Shu-Tao Xia"], "url": "https://arxiv.org/abs/2507.18969v1", "attribution": "\"EDPC: Accelerating Lossless Compression via Lightweight Probability Models and Decoupled Parallel Dataflow\" by Zeyi Lu, Xiaoxiao Ma, Yujun Huang, Minxiao Chen, Bin Chen, Baoyi An, and Shu-Tao Xia, arXiv:2507.18969v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.05177v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparision of DAE statistics} % title name of the table\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccrrrrrrr} % creating 10 columns\n\\hline\\hline % inserting double-line\n DAE &SNR improvement & PSNR &PRD(\\%)\n\\\\\n\\hline % inserts single-line\nDNN Based DAE &9.82$\\pm {2.69}$dB &0.98 &5.8 \\\\\nCNN Based DAE& 13.43 $\\pm {1.51}$dB&0.95 & 3.6 \\\\\nRNN(LSTM) Based DAE& -5.11 $\\pm {2.66}$dB&-6.17 & 18.3 \\\\\n\\hline % inserts single-line\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network", "authors": ["Prateek Singh", "Ambalika Sharma", "Shreesha Maiya"], "url": "https://arxiv.org/abs/2202.05177v1", "attribution": "\"Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network\" by Prateek Singh, Ambalika Sharma, and Shreesha Maiya, arXiv:2202.05177v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.18996v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{A quantitative comparison of model performance in terms of PSNR and SRE metrics when different SR backbone models are integrated into the WaveDiffUS SDE solver for the UR tasks (scaling from $\\times 4$ to $\\times 128$) using the independent datasets DOTA, DIOR, and DDISR. Outputs marked with an asterisk (*) indicate evaluations are performed with pseudo ground truth images, where the UR image resolution exceeds the ground truth resolution.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\toprule\n & \\multicolumn{6}{c}{DOTA} \\\\\\midrule\n \\multirow{2}{*}{Method} & \\multicolumn{6}{c}{Scale} \\\\\n & $\\times 4$ & $\\times 8$ &$\\times 16$ &$\\times 32$* &$\\times 64$* & $\\times 128$* \\\\\\midrule\n LE-GAN & 19.15/11.41 & 17.11/12.88 & 15.91/13.32 & 11.53/15.42 & 9.21/18.89 & 7.91/23.33 \\\\\n LE-GAN-WaveDiffUR & 19.74/11.39 & 19.18/11.12 & 18.21/12.86 & 16.74/13.40 & 12.58/13.63 & 11.21/15.87 \\\\\n RefVAE & 22.14/14.22 & 22.01/15.98 & 20.34/16.23 & 16.34/18.23 & 12.01/23.99 & 10.34/30.24 \\\\\n RefVAE-WaveDiffUR & 22.38/13.13 & 22.82/14.25 & 21.38/14.14 & 19.38/15.14 & 18.32/16.26 & 17.38/19.15 \\\\\n EDiffSR & 20.15/16.31 & 18.92/17.31 & 15.15/18.32 & 10.15/22.32 & 8.92/27.32 & 7.15/38.33 \\\\\n EDiffSR-WaveDiffUR & 20.02/16.51 & 19.29/17.02 & 18.02/17.52 & 17.25/18.21 & 15.99/18.35 & 12.02/20.53 \\\\\n CSP-WaveDiffUR(one-step) & 28.21/7.82 & 28.18/7.88 & 26.21/9.83 & 21.21/12.83 & 19.18/17.89 & 18.21/19.84 \\\\\n CSP-WaveDiffUR & 28.32/7.22 & 28.31/7.37 & 27.32/8.03 & 25.52/9.23 & 24.31/11.38 & 24.28/13.24 \\\\\\midrule\n & \\multicolumn{6}{c}{DIOR} \\\\\\midrule\n \\multirow{2}{*}{Method} & \\multicolumn{6}{c}{Scale} \\\\\n &$\\times 4$ & $\\times 8$ &$\\times 16$* &$\\times 32$* &$\\times 64$* & $\\times 128$* \\\\\\midrule\n LE-GAN & 19.41/12.69 & 19.19/13.18 & 18.31/14.82 & 14.35/17.34 & 10.21/19.99 & 6.92/27.53 \\\\\n LE-GAN-WaveDiffUR & 19.98/12.46 & 19.33/13.08 & 18.42/14.66 & 17.21/16.89 & 15.99/17.72 & 14.25/20.55 \\\\\n RefVAE & 18.35/15.92 & 17.32/16.18 & 15.52/16.74 & 12.21/19.98 & 8.81/25.93 & 5.89/28.83 \\\\\n RefVAE-WaveDiffUR & 19.01/15.28 & 18.99/15.82 & 17.59/16.11 & 14.49/17.52 & 13.21/19.14 & 10.15/21.71 \\\\\n EDiffSR & 18.78/13.51 & 17.98/14.23 & 14.88/15.66 & 12.62/20.22 & 9.21/24.41 & 5.32/26.93 \\\\\n EDiffSR-WaveDiffUR & 18.11/12.82 & 17.23/13.31 & 16.32/14.83 & 15.12/16.11 & 13.11/18.21 & 10.31/19.68 \\\\\n CSP-WaveDiffUR(one-step) & 24.63/12.71 & 23.21/13.81 & 21.92/15.32 & 18.84/19.32 & 14.22/22.12 & 10.36/24.96 \\\\\n CSP-WaveDiffUR & 25.12/8.99 & 24.92/9.87 & 22.31/9.72 & 21.03/10.21 & 18.79/12.42 & 16.32/14.85 \\\\\\midrule\n & \\multicolumn{6}{c}{DDISR} \\\\\\midrule\n \\multirow{2}{*}{Method} & \\multicolumn{6}{c}{Scale} \\\\\n & $\\times 4$ & $\\times 8$ &$\\times 16$ &$\\times 32$ &$\\times 64$ & $\\times 128$ \\\\\\midrule\nLE-GAN & 24.14/16.89 & 21.91/18.87 & 19.19/21.82 & 16.59/24.34 & 12.21/28.99 & 8.21/32.31 \\\\\nLE-GAN-WaveDiffUR & 24.38/15.08 & 23.21/16.16 & 23.09/16.98 & 19.99/19.72 & 16.55/22.55 & 14.32/26.37 \\\\\nRefVAE & 28.12/18.18 & 25.52/19.74 & 22.11/21.81 & 18.81/25.93 & 12.89/28.23 & 9.82/36.20 \\\\\nRefVAE-WaveDiffUR & 28.29/17.21 & 27.59/18.91 & 26.39/19.12 & 22.99/20.41 & 19.15/23.71 & 16.15/27.71 \\\\\nEDiffSR & 28.98/23.13 & 27.88/26.66 & 24.92/29.12 & 19.21/34.19 & 13.28/39.93 & 12.08/38.82 \\\\\nEDiffSR-WaveDiffUR & 29.23/21.11 & 28.32/22.83 & 27.17/23.11 & 25.91/24.29 & 21.18/25.68 & 18.11/28.85 \\\\\nCSP-WaveDiffUR(one-step) & 31.21/12.19 & 30.92/13.39 & 28.84/16.32 & 24.22/18.12 & 20.36/21.62 & 16.13/28.21 \\\\\nCSP-WaveDiffUR & 34.92/8.27 & 34.19/8.75 & 32.33/9.26 & 30.79/11.95 & 28.32/14.85 & 23.48/16.85 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images", "authors": ["Yue Shi", "Liangxiu Han", "Darren Dancy", "Lianghao Han"], "url": "https://arxiv.org/abs/2412.18996v1", "attribution": "\"WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images\" by Yue Shi, Liangxiu Han, Darren Dancy, and Lianghao Han, arXiv:2412.18996v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2312.09446v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of performances among the conventional methods across different paradigms}\n\\begin{tabular}{c|cc|cc}\n\\hline\n\\multirow{2}{*}{Method} & \\multicolumn{2}{c|}{Normal paradigm} & \\multicolumn{2}{c}{AI paradigm} \\\\ \\cline{2-5} \n & $F_{\\beta}$ & std. & $F_{\\beta}$ & std. \\\\ \\hline\nShallowConvNet & 0.5314 & 0.1314 & 0.6481 & 0.1421 \\\\\nDeepConvNet & 0.6328 & 0.1753 & 0.6899 & 0.1641 \\\\\nEEGNet & 0.6425 & \\textbf{0.0928} & 0.6841 & \\textbf{0.0881} \\\\\n\\textbf{Proposed} & \\textbf{0.6875} & 0.1308 & \\textbf{0.7266} & 0.1481 \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Distributed Inference System for Detecting Task-wise Single Trial Event-Related Potential in Stream of Satellite Images", "authors": ["Sung-Jin Kim", "Heon-Gyu Kwak", "Hyeon-Taek Han", "Dae-Hyeok Lee", "Ji-Hoon Jeong", "Seong-Whan Lee"], "url": "https://arxiv.org/abs/2312.09446v1", "attribution": "\"A Distributed Inference System for Detecting Task-wise Single Trial Event-Related Potential in Stream of Satellite Images\" by Sung-Jin Kim, Heon-Gyu Kwak, Hyeon-Taek Han, Dae-Hyeok Lee, Ji-Hoon Jeong, and Seong-Whan Lee, arXiv:2312.09446v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10066v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n \\toprule\n \\multirow{1}{*}{Custodian type} & \\multirow{1}{*}{\\#}\\\\\n \\midrule\n University or research institution & 45 \\\\\n Commercial entity & 41 \\\\\n Nonprofit / NGO & 26 \\\\\n Private individual & 20 \\\\\n Government organization & 17 \\\\\n Library, museum or archival institute & 16\\\\\n Community (incl. online) & 2 \\\\\n Startup & 1 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Distribution of custodian types.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources", "authors": ["Angelina McMillan-Major", "Zaid Alyafeai", "Stella Biderman", "Kimbo Chen", "Francesco De Toni", "Gérard Dupont", "Hady Elsahar", "Chris Emezue", "Alham Fikri Aji", "Suzana Ilić", "Nurulaqilla Khamis", "Colin Leong", "Maraim Masoud", "Aitor Soroa", "Pedro Ortiz Suarez", "Zeerak Talat", "Daniel van Strien", "Yacine Jernite"], "url": "https://arxiv.org/abs/2201.10066v1", "attribution": "\"Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources\" by Angelina McMillan-Major, Zaid Alyafeai, Stella Biderman, Kimbo Chen, Francesco De Toni, Gérard Dupont, Hady Elsahar, Chris Emezue, Alham Fikri Aji, Suzana Ilić, Nurulaqilla Khamis, Colin Leong, Maraim Masoud, Aitor Soroa, Pedro Ortiz Suarez, Zeerak Talat, Daniel van Strien, and Yacine Jernite, arXiv:2201.10066v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.18790v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccc}\n \\hline\n & \\multicolumn{3}{c}{Component 1} & & \\multicolumn{3}{c}{Component 2} & & \\multicolumn{3}{c}{Component 3} \\\\\n \\cline{2-4} \\cline{6-8} \\cline{10-12}\n Density & $\\pi_1$ & $\\mu_1$ & $\\sigma_1^2$\n & & $\\pi_2$ & $\\mu_2$ & $\\sigma_2^2$\n & & $\\pi_3$ & $\\mu_3$ & $\\sigma_3^2$ \\\\\n \\cline{2-4} \\cline{6-8} \\cline{10-12}\n 1 & 0.75 & 0.00 & 0.83 & & 0.25 & 1.37 & 0.09 & & -- & -- & -- \\\\\n 2 & 0.45 & -0.93 & 0.22 & & 0.45 & 0.93 & 0.22 & & 0.10 & 0.00 & 0.04 \\\\\n 3 & 0.50 & -0.74 & 0.14 & & 0.30 & 0.37 & 0.55 & & 0.20 & 1.47 & 0.14 \\\\\n 4 & 0.50 & 0.00 & 0.14 & & 0.35 & 1.28 & 0.14 & & 0.15 & 2.56 & 0.11 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Top panel: univariate density functions used in the simulation study. Bottom panel: corresponding parameter settings for the mixture components.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Confidence set for mixture order selection", "authors": ["Alessandro Casa", "Davide Ferrari"], "url": "https://arxiv.org/abs/2503.18790v2", "attribution": "\"Confidence set for mixture order selection\" by Alessandro Casa and Davide Ferrari, arXiv:2503.18790v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08131v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablations on Initial alignment comparisons of our method compared to TransFill without the CST Module.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\nModel &PSNR$\\uparrow$&SSIM$\\uparrow$&LPIPS$\\downarrow$\\\\ \\hline\\hline\nGeoFill & \\bf{31.47} & \\bf{0.9748} & \\bf{0.0525} \\\\\\hline\nGeoFill (no CST) & 29.35 & 0.9688 & 0.0579 \\\\\\hline\nTransFill & 30.64 & 0.9732 & 0.0540 \\\\\\hline\nTransFill (no CST) & 26.03 & 0.9598 & 0.0742 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding", "authors": ["Yunhan Zhao", "Connelly Barnes", "Yuqian Zhou", "Eli Shechtman", "Sohrab Amirghodsi", "Charless Fowlkes"], "url": "https://arxiv.org/abs/2201.08131v2", "attribution": "\"GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding\" by Yunhan Zhao, Connelly Barnes, Yuqian Zhou, Eli Shechtman, Sohrab Amirghodsi, and Charless Fowlkes, arXiv:2201.08131v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17854v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c||c|c||c|c}\n \\multicolumn{6}{c}{\\textbf{ODE Terms:}}\\\\\nEqn.~ & \\texttt{DDE.} & Eqn.~ & \\texttt{DDE.} & Eqn.~ & \\texttt{DDE.}\\\\\n\\hline\n$A_0$ & \\texttt{A0} & $B_{1}$ & \\texttt{B1} &$B_{2}$&\\texttt{B2}\\\\ \n$C_{1}$ & \\texttt{C1} & $D_{11}$ &\\texttt{D11}&$D_{12}$&\\texttt{D12}\\\\ \n$C_{2}$ & \\texttt{C2} & $D_{21}$ &\\texttt{D21}&$D_{22}$&\\texttt{D22} \\\\ \\hline \\\\\n \\multicolumn{6}{c}{ \\textbf{Discrete Delay Terms:}}\\\\\nEqn.~ & \\texttt{DDE.} & Eqn.~ & \\texttt{DDE.} & Eqn.~ & \\texttt{DDE.}\\\\\n\\hline\n$A_i$ & \\texttt{Ai\\{i\\}} & $B_{1i}$ & \\texttt{B1i\\{i\\}} &$B_{2i}$&\\texttt{B2i\\{i\\}}\\\\ \n$C_{1i}$ & \\texttt{C1i\\{i\\}} & $D_{11i}$ &\\texttt{D11i\\{i\\}}&$D_{12i}$&\\texttt{D12i\\{i\\}}\\\\ \n$C_{2i}$ & \\texttt{C2i\\{i\\}} & $D_{21i}$ &\\texttt{D21i\\{i\\}}&$D_{22i}$&\\texttt{D22i\\{i\\}}\\\\\n\\hline \\\\ \\multicolumn{6}{c}{\\textbf{Distributed Delay Terms: May be functions of \\texttt{pvar s}}}\\\\\nEqn.~ & \\texttt{DDE.} & Eqn.~ & \\texttt{DDE.} & Eqn.~ & \\texttt{DDE.}\\\\\n\\hline\n$A_{di} $ & \\texttt{Adi\\{i\\}} & $B_{1di}$ & \\texttt{B1di\\{i\\}} &$B_{2di}$&\\texttt{B2di\\{i\\}}\\\\ \n$C_{1di} $& \\texttt{C1di\\{i\\}} &$ D_{11di} $&\\texttt{D11di\\{i\\}}&$D_{12di}$&\\texttt{D12di\\{i\\}}\\\\ \n$C_{2di}$ & \\texttt{C2di\\{i\\}} & $D_{21di} $&\\texttt{D21di\\{i\\}}&$D_{22di}$&\\texttt{D22di\\{i\\}}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Equivalent names of Matlab elements of the \\texttt{DDE} structure terms for terms in Eqn.~. For example, to set term \\texttt{XX} to \\texttt{YY}, we use \\texttt{DDE.XX=YY}. In addition, the delay $\\tau_i$ is specified using the vector element \\texttt{DDE.tau(i)} so that if $\\tau_1=1, \\tau_2=2, \\tau_3=3$, then \\texttt{DDE.tau=[1 2 3]}. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "PIETOOLS 2024: User Manual", "authors": ["Sachin Shivakumar", "Declan Jagt", "Danilo Braghini", "Amritam Das", "Yulia Peet", "Matthew Peet"], "url": "https://arxiv.org/abs/2501.17854v1", "attribution": "\"PIETOOLS 2024: User Manual\" by Sachin Shivakumar, Declan Jagt, Danilo Braghini, Amritam Das, Yulia Peet, and Matthew Peet, arXiv:2501.17854v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.04349v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation results by varying iterations number.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\toprule\nIter. &\tMC Price & BSDE Price\t& Err.\\% & Time$^*$ \\\\\n\\midrule\\midrule\n20,000 &\t13.645\t&\t12.751\t&\t7.01\\%\t&\t10231\t\t\\\\\n40,000 &\t13.528\t&\t13.524\t&\t0.03\\%\t&\t20274\t\t\\\\\t\t\t\t\t\t\t\t\t\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A deep solver for BSDEs with jumps", "authors": ["Kristoffer Andersson", "Alessandro Gnoatto", "Marco Patacca", "Athena Picarelli"], "url": "https://arxiv.org/abs/2211.04349v3", "attribution": "\"A deep solver for BSDEs with jumps\" by Kristoffer Andersson, Alessandro Gnoatto, Marco Patacca, and Athena Picarelli, arXiv:2211.04349v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02875v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\t\t\t\t\\hline\n\t\t\t\tDataset &Noun\t&Verb\t&Adjective\t&Adverb\t\\\\\n\t\t\t\t\\hline\n\t\t\t\tSE2\t\t&77.5\t&43.3\t&73.0\t&78.0\t\\\\\n\t\t\t\tSE15\t&69.5\t&57.4\t&80.6\t&85.0\t\\\\\t\t\n\t\t\t\t\\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph", "authors": ["Mohannad AlMousa", "Rachid Benlamri", "Richard Khoury"], "url": "https://arxiv.org/abs/2101.02875v1", "attribution": "\"A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph\" by Mohannad AlMousa, Rachid Benlamri, and Richard Khoury, arXiv:2101.02875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.00883v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{$\\textit{p-values}$ of Post-double-selection Granger causality LM test from Reddit sentiment to other variables }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllllllll}\n\\hline\nGcto GCfrom & binance\\_reddit & bitcoin\\_reddit & enjin\\_reddit & ethereum\\_reddit & feathercoin\\_reddit & horizen\\_reddit & litecoin\\_reddit & namecoin\\_reddit & peercoin\\_reddit & tether\\_reddit \\\\ \\hline\nBTC-USD & 0.6490 & 0.5749 & 0.7952 & 0.7392 & 0.0162 & 0.9303 & 0.9640 & 0.3521 & 0.4535 & 0.6762 \\\\\nETH-USD & 0.6510 & 0.4152 & 0.2781 & 0.2011 & 0.0758 & 0.9016 & 0.4627 & 0.5559 & 0.8040 & 0.5978 \\\\\nUSDT-USD & 0.0434 & 0.4024 & 0.5510 & 0.2960 & 0.9342 & 0.7445 & 0.9259 & 0.4391 & 0.7519 & 0.8106 \\\\\nBNB-USD & 0.5846 & 0.1236 & 0.8067 & 0.5063 & \\textbf{0.0003} & 0.3475 & 0.9164 & 0.7684 & 0.0287 & 0.4639 \\\\\nLTC-USD & 0.5551 & 0.8095 & 0.5847 & 0.6245 & \\textbf{0.0009} & 0.5876 & 0.6108 & 0.7981 & 0.2549 & 0.8514 \\\\\nENJ-USD & 0.1720 & 0.5678 & 0.5899 & 0.3021 & 0.0248 & 0.8287 & 0.8361 & 0.2641 & 0.3282 & 0.0459 \\\\\nZEN-USD & 0.7415 & 0.0485 & 0.3935 & 0.4864 & 0.0475 & 0.1194 & 0.2606 & 0.2864 & 0.5880 & 0.1124 \\\\\nNMC-USD & 0.7376 & 0.2239 & 0.0255 & 0.7927 & 0.6858 & 0.2549 & 0.0966 & 0.2419 & 0.3208 & 0.1889 \\\\\nPPC-USD & 0.9175 & 0.8382 & 0.0492 & 0.3691 & \\textbf{0.0011} & 0.7565 & 0.9215 & 0.5669 & 0.1147 & 0.6673 \\\\\nFTC-USD & 0.4611 & 0.0510 & 0.2478 & 0.5661 & 0.0722 & 0.8019 & 0.5361 & 0.4193 & 0.7658 & 0.1904 \\\\\nbinance\\_twitter & 0.3016 & 0.5025 & 0.0191 & 0.1006 & 0.6707 & 0.2966 & 0.6769 & 0.6569 & 0.0541 & 0.3627 \\\\\nbitcoin\\_twitter & 0.6883 & 0.2564 & 0.2780 & 0.3883 & 0.2888 & 0.8810 & 0.6937 & 0.3852 & 0.4587 & 0.0527 \\\\\nenjin\\_twitter & 0.0217 & 0.3034 & 0.2111 & 0.5200 & 0.9675 & 0.5887 & 0.4704 & 0.0240 & 0.0668 & 0.9908 \\\\\nethereum\\_twitter & 0.5575 & 0.4115 & 0.8155 & 0.5059 & 0.5993 & 0.3075 & 0.6996 & 0.8148 & 0.5905 & 0.4012 \\\\\nfeathercoin\\_twitter & 0.5076 & 0.4717 & 0.9325 & 0.1416 & 0.3479 & 0.2080 & \\textbf{0.0071} & 0.4969 & 0.1132 & 0.4819 \\\\\nhorizen\\_twitter & 0.7657 & 0.9124 & 0.2826 & 0.7095 & 0.0872 & 0.8510 & 0.9174 & 0.6230 & 0.5977 & 0.9491 \\\\\nlitecoin\\_twitter & 0.0804 & 0.3251 & 0.7399 & 0.9148 & 0.1134 & 0.8502 & 0.1364 & 0.1193 & 0.7239 & 0.5644 \\\\\nnamecoin\\_twitter & 0.0762 & 0.2868 & 0.5301 & 0.8203 & 0.8946 & 0.4796 & 0.1818 & 0.7366 & 0.0278 & 0.4874 \\\\\npeercoin\\_twitter & 0.9871 & 0.3294 & 0.3634 & 0.9812 & 0.8985 & 0.2443 & 0.4211 & 0.8195 & 0.7866 & 0.5209 \\\\\ntether\\_twitter & 0.3473 & 0.5107 & 0.3276 & 0.5045 & 0.4230 & 0.6701 & 0.3549 & 0.5638 & 0.7395 & 0.4016 \\\\\nbinance\\_reddit & NA & \\textbf{0.0074} & 0.5982 & 0.0940 & 0.4055 & 0.1659 & 0.0168 & 0.9259 & 0.2877 & 0.9722 \\\\\nbitcoin\\_reddit & 0.8617 & NA & 0.0396 & \\textbf{0.0035} & 0.0170 & 0.2755 & 0.0915 & 0.3345 & 0.7027 & 0.8080 \\\\\nenjin\\_reddit & 0.6899 & 0.0124 & NA & 0.8998 & 0.1595 & 0.4637 & 0.5484 & 0.0188 & 0.1576 & 0.9336 \\\\\nethereum\\_reddit & \\textbf{0.0034} & \\textbf{0.0001} & 0.1593 & NA & 0.9247 & 0.1884 & 0.0469 & 0.0136 & 0.5647 & 0.5692 \\\\\nfeathercoin\\_reddit & 0.6173 & 0.9384 & 0.0695 & 0.0457 & NA & 0.2614 & 0.4184 & 0.3888 & 0.7616 & 0.2512 \\\\\nhorizen\\_reddit & 0.8246 & 0.1495 & 0.9839 & \\textbf{0.0070} & 0.9802 & NA & 0.6683 & 0.4089 & 0.4249 & 0.4498 \\\\\nlitecoin\\_reddit & 0.0658 & \\textbf{0.0002} & 0.5461 & 0.4101 & 0.9522 & 0.2247 & NA & 0.6164 & 0.1079 & 0.0194 \\\\\nnamecoin\\_reddit & 0.0102 & 0.6317 & 0.1763 & 0.3161 & 0.6554 & 0.1168 & 0.9148 & NA & 0.0164 & 0.1238 \\\\\npeercoin\\_reddit & 0.6017 & 0.8561 & 0.5785 & 0.4710 & 0.3278 & 0.9843 & 0.6041 & 0.1234 & NA & 0.1801 \\\\\ntether\\_reddit & 0.6047 & 0.9786 & 0.5541 & 0.7625 & 0.3930 & 0.7942 & 0.3073 & \\textbf{0.0042} & 0.3508 & NA \\\\\nbinance coin & 0.2994 & 0.3339 & 0.6540 & 0.2915 & 0.2537 & 0.4130 & \\textbf{0.0024} & 0.5475 & 0.0346 & 0.6398 \\\\\ncrypto crash & 0.9829 & 0.7047 & 0.7110 & 0.8784 & \\textbf{0.0001} & 0.2687 & 0.9814 & 0.8713 & 0.4930 & 0.2463 \\\\\nminer & 0.2503 & 0.8835 & 0.8058 & 0.8159 & 0.4789 & 0.3474 & 0.4953 & 0.1988 & 0.4143 & 0.0389 \\\\\nUSD coin & 0.7764 & 0.0889 & 0.0187 & 0.1321 & 0.8106 & 0.4035 & 0.8317 & 0.2448 & 0.0216 & 0.5041 \\\\\ncardano & 0.7831 & 0.9302 & 0.3779 & 0.3630 & \\textbf{0.0034} & 0.2814 & 0.9166 & 0.1577 & 0.8480 & 0.6328 \\\\\ncryptocurrency & 0.9764 & 0.1748 & 0.2151 & 0.0113 & 0.0215 & 0.4712 & 0.4463 & 0.4955 & 0.9182 & 0.7650 \\\\\nminted & 0.9614 & 0.0781 & 0.4962 & 0.2967 & 0.3863 & 0.1412 & 0.6220 & 0.5780 & 0.6831 & 0.6189 \\\\\nsolana & 0.6419 & 0.6476 & 0.5900 & 0.6248 & 0.8410 & 0.4601 & 0.6212 & 0.7741 & 0.2162 & 0.6842 \\\\\nfeathercoin & 0.7856 & \\textbf{0.0080} & 0.7514 & 0.9825 & 0.9420 & 0.9964 & 0.7204 & 0.1433 & 0.3573 & 0.0417 \\\\\ncryptography & 0.2454 & 0.9760 & 0.5892 & 0.5569 & 0.4638 & 0.8787 & 0.1167 & 0.3347 & 0.9007 & 0.4522 \\\\\ndogecoin & 0.2900 & \\textbf{0.0004} & 0.2622 & \\textbf{0.0064} & 0.0280 & 0.8273 & 0.0441 & 0.9780 & 0.7086 & 0.9650 \\\\\ndigital gold & 0.7365 & 0.1447 & 0.8024 & 0.8288 & 0.7157 & 0.7786 & 0.8151 & 0.9571 & 0.7435 & 0.1264 \\\\\nBitcoin & 0.5919 & 0.5289 & 0.0401 & 0.0181 & 0.2721 & 0.0699 & 0.7863 & 0.7879 & 0.7983 & 0.8720 \\\\\nNFT & 0.9891 & 0.0447 & 0.9048 & 0.9869 & \\textbf{0.0040} & 0.2173 & 0.9988 & 0.7431 & 0.5836 & 0.3398 \\\\\nripple & 0.6763 & 0.7278 & 0.5541 & 0.8294 & 0.5933 & 0.2928 & 0.6975 & 0.7715 & 0.2012 & 0.5337 \\\\\nprivate key & 0.4332 & 0.9124 & 0.4249 & 0.6011 & 0.5305 & 0.3144 & 0.6888 & \\textbf{0.0000} & 0.8677 & 0.2813 \\\\\npeercoin & 0.5794 & 0.7239 & 0.1405 & 0.7151 & 0.4742 & 0.2392 & 0.9847 & 0.8850 & 0.0499 & 0.4968 \\\\\nnamecoin & 0.1184 & 0.0184 & 0.6779 & 0.0982 & 0.1126 & 0.1430 & 0.9001 & 0.4028 & 0.1248 & 0.2541 \\\\\nsatoshi & 0.7215 & 0.3168 & 0.3788 & 0.7878 & 0.1415 & 0.6646 & 0.7774 & 0.0922 & \\textbf{0.0053} & 0.0580 \\\\\nhot wallet & 0.0707 & 0.7975 & 0.3375 & 0.7932 & 0.9116 & 0.3362 & 0.8039 & 0.2399 & 0.7290 & 0.7211 \\\\\nblockchain & 0.3047 & 0.0112 & 0.3233 & 0.1376 & 0.0666 & 0.5441 & 0.3690 & 0.7781 & 0.2715 & 0.8976 \\\\\nethereum & 0.8876 & 0.1368 & 0.0966 & 0.3215 & \\textbf{0.0000} & 0.0833 & 0.6417 & 0.9604 & 0.4382 & 0.9158 \\\\\nzen coin & 0.5206 & 0.0881 & 0.1203 & 0.0757 & 0.9548 & 0.8401 & 0.0672 & 0.8691 & 0.9197 & 0.0894 \\\\\ncoinbase & 0.4432 & 0.0207 & 0.0861 & \\textbf{0.0038} & 0.1051 & 0.4156 & 0.3609 & 0.4592 & 0.8001 & 0.1457 \\\\\nBTC & 0.8161 & 0.9790 & 0.4817 & 0.8776 & 0.5372 & \\textbf{0.0069} & 0.6368 & 0.7545 & 0.7117 & 0.8104 \\\\\nhard fork & 0.4211 & 0.9022 & 0.4761 & 0.2127 & 0.8809 & 0.9924 & 0.7434 & 0.8987 & 0.7459 & 0.5645 \\\\\nstablecoin & 0.8152 & 0.0392 & 0.0988 & 0.6329 & 0.6643 & 0.1781 & 0.4136 & 0.8372 & 0.6247 & 0.5707 \\\\\ncold storage & 0.4598 & 0.7226 & 0.3689 & 0.5670 & 0.4642 & 0.2388 & 0.2349 & 0.5179 & 0.9288 & 0.8961 \\\\\ncoin & 0.5338 & 0.4550 & 0.7080 & 0.2174 & 0.2796 & 0.4005 & 0.5509 & 0.2503 & 0.6287 & 0.3292 \\\\\nhash & 0.4706 & 0.1316 & 0.9177 & 0.2431 & 0.8156 & 0.8129 & 0.4942 & 0.4698 & 0.9315 & 0.1682 \\\\\ntether & 0.7797 & 0.9060 & 0.2313 & 0.3477 & 0.1724 & 0.4384 & 0.6333 & 0.3970 & 0.5414 & 0.6777 \\\\\nlitecoin & 0.2674 & 0.6376 & 0.7509 & 0.8048 & 0.1171 & 0.7526 & 0.2450 & 0.3120 & 0.9666 & 0.6412 \\\\\nenjin coin & 0.8524 & 0.2545 & 0.8180 & 0.0535 & 0.0946 & 0.8839 & 0.1952 & 0.1628 & 0.9111 & 0.2115 \\\\\nhashing & 0.9460 & 0.2187 & 0.0746 & 0.8880 & 0.3560 & 0.6763 & 0.7199 & 0.3911 & 0.8885 & 0.6406 \\\\\ntoken & 0.5129 & 0.9908 & 0.8293 & 0.6380 & 0.4843 & 0.3687 & 0.2607 & 0.3970 & 0.7820 & 0.5039 \\\\\ncrypto & 0.6042 & 0.0972 & 0.1664 & 0.0671 & 0.0373 & 0.5587 & 0.3189 & 0.5801 & 0.5257 & 0.9642 \\\\\nICO & 0.1847 & 0.8678 & 0.1832 & 0.3347 & 0.5071 & 0.6415 & 0.8833 & 0.0879 & 0.8129 & 0.8119 \\\\\ncoin market & 0.5790 & 0.8545 & 0.3920 & 0.8424 & 0.5729 & 0.6280 & 0.6597 & 0.1108 & 0.9075 & 0.6824 \\\\\nsoft fork & 0.4795 & 0.5505 & 0.5478 & 0.6459 & 0.0677 & 0.1177 & 0.4669 & 0.6815 & 0.7157 & \\textbf{0.0018} \\\\\nblock producer & 0.3202 & 0.3623 & 0.7920 & 0.0372 & 0.6525 & 0.7791 & 0.9513 & 0.3789 & 0.0863 & 0.4046 \\\\\ndistributed ledger & 0.2198 & 0.1659 & 0.9087 & 0.3150 & 0.5804 & 0.9496 & 0.5736 & 0.1056 & 0.1256 & 0.1218 \\\\\ndigital fiat & 0.3504 & 0.8585 & 0.1294 & 0.4523 & 0.7172 & 0.8056 & 0.1239 & 0.6280 & 0.3468 & 0.1420 \\\\\nconsensus & 0.9329 & 0.5980 & 0.3054 & 0.6781 & 0.4437 & 0.3079 & 0.6963 & 0.2267 & 0.0539 & 0.0531 \\\\\nBTC-USD\\_vol & 0.9794 & 0.1726 & 0.0586 & 0.0796 & 0.3739 & 0.2771 & 0.7123 & 0.7395 & 0.8830 & 0.0815 \\\\\nETH-USD\\_vol & 0.8812 & 0.4840 & 0.0841 & 0.0494 & 0.3595 & 0.1572 & 0.9486 & 0.9443 & 0.5026 & 0.1569 \\\\\nUSDT-USD\\_vol & 0.8782 & 0.0256 & 0.0296 & 0.0637 & 0.2016 & 0.5154 & 0.9347 & 0.5083 & 0.8106 & 0.0415 \\\\\nBNB-USD\\_vol & 0.1647 & 0.3737 & 0.1669 & 0.4455 & 0.9611 & 0.9427 & 0.7012 & 0.9340 & 0.8812 & 0.9726 \\\\\nLTC-USD\\_vol & 0.6387 & 0.0244 & 0.6301 & 0.1707 & 0.0822 & 0.4796 & 0.5236 & 0.8860 & 0.4430 & 0.2986 \\\\\nENJ-USD\\_vol & 0.6992 & 0.6197 & 0.6292 & 0.5137 & 0.8576 & 0.7347 & 0.9672 & 0.0995 & 0.1144 & 0.2575 \\\\\nZEN-USD\\_vol & 0.4637 & 0.0505 & 0.3007 & 0.3367 & 0.6024 & 0.2327 & 0.1100 & 0.3214 & 0.3580 & 0.9632 \\\\\nNMC-USD\\_vol & \\textbf{0.0035} & 0.0837 & 0.4043 & 0.0807 & 0.7069 & 0.2048 & 0.4870 & 0.5559 & 0.9977 & 0.2749 \\\\\nPPC-USD\\_vol & 0.9681 & 0.2028 & 0.7239 & 0.5624 & 0.0594 & 0.7154 & 0.9853 & 0.0296 & 0.9202 & 0.4822 \\\\\nFTC-USD\\_vol & 0.9126 & 0.7379 & 0.8872 & 0.9207 & 0.9066 & 0.5657 & 0.8162 & 0.8388 & 0.6192 & 0.7975 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment Approach", "authors": ["Federico D'Amario", "Milos Ciganovic"], "url": "https://arxiv.org/abs/2210.00883v1", "attribution": "\"Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment Approach\" by Federico D'Amario and Milos Ciganovic, arXiv:2210.00883v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.03133v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Energy expended by rate-encoded and direct-encoded SNNs on an \\textit{Eyeriss} platform normalized with respect to the energy of 1 MAC operation. We use 10 timesteps for both architectures.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{} & \\multicolumn{2}{c|}{Normalized energy/image} \\\\ \\hline\nScheme & MLP/MNIST & VGG5/CIFAR10 \\\\ \\hline\nRate coding & 8.58E+06 & 2.73E+07 \\\\ \\hline\nDirect coding & 2.26E+07 & 4.22E+07 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?", "authors": ["Youngeun Kim", "Hyoungseob Park", "Abhishek Moitra", "Abhiroop Bhattacharjee", "Yeshwanth Venkatesha", "Priyadarshini Panda"], "url": "https://arxiv.org/abs/2202.03133v2", "attribution": "\"Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?\" by Youngeun Kim, Hyoungseob Park, Abhishek Moitra, Abhiroop Bhattacharjee, Yeshwanth Venkatesha, and Priyadarshini Panda, arXiv:2202.03133v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.15395v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters of the simulation environment}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n \\toprule\n Parameter[1] & Value & Unit & Parameter[1] & Value & Unit \\\\\n \\cmidrule(lr){1-3} \\cmidrule(lr){4-6}\n $T_{\\max}$ & 230 & $s$ & $g$ & 9.81 &$m/s^2$ \\\\\n $L_{o}$ & 600 & $m$ & $L_{m}$ & 1050 & $m$\\\\\n $L_{s}$ & 50 & $m$ & $L_{d}$ & 30.5 & $m$\\\\\n $s_{f}$ & 50 & $m$ & $T$ & 6 & $s$\\\\\n $v_{TOSS}$& 8 & $m/s$& $RoC_{\\max}$& 9&$m/s$\\\\\n $\\Theta_{dep}$&$2.58$& $\\circ$& $\\Delta t$&0.2 &s\\\\\n $\\lambda$ & 20 & - & $m_i$ & 240 & $kg$\\\\\n $F^{\\max}_i$ & 4800 & $N$ & $V^{\\max}_i$ & 40 & $m/s$\\\\ \n $O_1$ & (0, 0, 0) & $m$ & $O_2$ & (700, 50, 0) & $m$\\\\\n $O_3$ & (700, -50, 0) & $m$ & $O_4$ & (800, 10, 20) & $m$\\\\\n $r_1$ & [11,46,81,116,151] & $s$ & $r_2$ & [16,56,96,136] & $s$\\\\\n $r_3$ & [16,56,96,136] & $s$ & $r_4$ & [6,36,66,96,126,156] & $s$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Integrated Take-off Management and Trajectory Optimization for Merging Control in Urban Air Mobility Corridors", "authors": ["Yingqi Liu", "Tianlu Pan", "Jingjun Tan", "Renxin Zhong", "Can Chen"], "url": "https://arxiv.org/abs/2508.15395v1", "attribution": "\"Integrated Take-off Management and Trajectory Optimization for Merging Control in Urban Air Mobility Corridors\" by Yingqi Liu, Tianlu Pan, Jingjun Tan, Renxin Zhong, and Can Chen, arXiv:2508.15395v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09698v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The Results After XE and CMAL Training When Using Different Numbers of Unlabeled Images on MSCOCO Image Captioning Dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|rrrrrr}\n\t\t\t\\toprule\n\t\t\t\\#unlabel & stage & B1 & B4 & M & R & S & C \\\\\n\t\t\t\\midrule\n\t\t\t\\midrule\n\t\t\t\\multirow{2}[0]{*}{0} & XE & 78.5 &\t35.3 &\t27.3 &\t56.9 \t&20.8& \t115.5 \\\\\n\t\t\t& CMAL & 80.3 \t&37.3 &\t28.1 &\t58.0& \t21.8 &\t124.0 \\\\\n\t\t\t\\midrule\n\t\t\t\\multirow{2}[0]{*}{50k} & XE & 78.8 \t&36.2 &\t27.6 &\t57.2 &\t21.1 \t&118.1 \\\\\n\t\t\t& CMAL & 80.2 & \t37.6 & \t28.1 & \t58.1 & \t21.9 & \t124.8 \\\\\n\t\t\t\\midrule\n\t\t\t\\multirow{2}[0]{*}{100k} & XE & 79.0 &\t36.2 &\t27.7 &\t57.3 &\t21.2& \t118.3 \\\\\n\t\t\t& CMAL & \\bf80.5 &\t\\bf38.0 &\t\\bf28.3 &\t\\bf58.2 &\t\\bf22.0 &\t\\bf125.5 \\\\ \n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Fast Sequence Generation with Multi-Agent Reinforcement Learning", "authors": ["Longteng Guo", "Jing Liu", "Xinxin Zhu", "Hanqing Lu"], "url": "https://arxiv.org/abs/2101.09698v1", "attribution": "\"Fast Sequence Generation with Multi-Agent Reinforcement Learning\" by Longteng Guo, Jing Liu, Xinxin Zhu, and Hanqing Lu, arXiv:2101.09698v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.08663v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|} \n\t\t\t\\hline\n\t\t\t\\textbf{Algorithm} & \\textbf{Computational complexity} \\\\ \n\t\t\t\\hline\n \\hline\n\t\t\tMAD-OMP & $\\mathcal{O}(MQT_{\\theta} + (MT_{\\theta})^3)$\\\\\n\t\t\tPD-OMP \t& $\\mathcal{O}(MQT_{\\theta}T_{r} + (MT_{\\theta}T_{r})^3)$\t \\\\\n\t\t\tOracle LS\t& $\\mathcal{O}(2MQL^2+L^3+MT_{\\theta}T_{r}L)$\\\\\n PD-ZALMS (proposed) & $\\mathcal{O}(2MQT_{\\theta}T_{r})$\\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Computational complexity of the proposed and baseline algorithms.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sparse Near-Field Channel Estimation for XL-MIMO via Adaptive Filtering", "authors": ["Vidya Bhasker Shukla", "Italo Atzeni"], "url": "https://arxiv.org/abs/2508.08663v1", "attribution": "\"Sparse Near-Field Channel Estimation for XL-MIMO via Adaptive Filtering\" by Vidya Bhasker Shukla and Italo Atzeni, arXiv:2508.08663v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10148v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rr}\n \\hline\n & value \\\\ \n \\hline\nAccuracy & 0.78 \\\\ \n Kappa & 0.44 \\\\ \n Sensitivity (No burn accuracy) & 0.50 \\\\ \n Specificity (Burn accuracy) & 0.90 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Detecting Crop Burning in India using Satellite Data", "authors": ["Kendra Walker", "Ben Moscona", "Kelsey Jack", "Seema Jayachandran", "Namrata Kala", "Rohini Pande", "Jiani Xue", "Marshall Burke"], "url": "https://arxiv.org/abs/2209.10148v1", "attribution": "\"Detecting Crop Burning in India using Satellite Data\" by Kendra Walker, Ben Moscona, Kelsey Jack, Seema Jayachandran, Namrata Kala, Rohini Pande, Jiani Xue, and Marshall Burke, arXiv:2209.10148v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.06675v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Robustness checks: major Roman roads as residuals of Model (4) of Table }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\hline\\hline\\\\\n& \\textit{(1)} & \\textit{(2)} & \\textit{(3)} & \\textit{(4)}\\\\\n\\textit{Dependent variable:} &&&& \\\\\n\\textit{Current infrastructure in km (log)}&\\multicolumn{2}{c}{\\textit{Railways}}&\\multicolumn{2}{c}{\\textit{Motorways}}\\\\\n\\hline\\\\\n\\texttt{Major} $\\tilde{\\mathcal{RR}}_{i} $ (log)& 0.272***& 0.272***& 0.352***& 0.352***\\\\\n & (0.024) & (0.025) & (0.027) & (0.042) \\\\\n\\texttt{Ruggedness} (log)& -0.060 & -0.060* & -0.132 & -0.132** \\\\\n & (0.047) & (0.036) & (0.093) & (0.067) \\\\\n\\texttt{Elevation} (log)& 0.090 & 0.090* & 0.088 & 0.088 \\\\\n & (0.058) & (0.048) & (0.132) & (0.091) \\\\\n\\texttt{Slope} (log)& -0.369 & -0.369 & -0.468 & -0.468 \\\\\n & (0.315) & (0.365) & (0.437) & (0.775) \\\\\n\\texttt{Agriculture suitability post 1500} (log) & 0.005 & 0.005 & -0.002 & -0.002 \\\\\n & (0.031) & (0.029) & (0.031) & (0.052) \\\\\n\\texttt{Forest} (dummy)& -0.088 & -0.088 & -0.195 & -0.195 \\\\\n & (0.081) & (0.094) & (0.162) & (0.168) \\\\\n\\texttt{Low vegetation} (dummy)& 0.053 & 0.053 & -0.128* & -0.128 \\\\\n & (0.041) & (0.074) & (0.077) & (0.147) \\\\\n\\texttt{Distance from sea} (log) & 0.116** & 0.116***& 0.186 & 0.186** \\\\\n & (0.046) & (0.043) & (0.117) & (0.095) \\\\\n\\texttt{Distance from nearest waterway} (log) & 0.025 & 0.025 & 0.040 & 0.040 \\\\\n & (0.030) & (0.027) & (0.061) & (0.048) \\\\\n\\texttt{Distance from nearest harbor} (log)& -0.093 & -0.093 & -0.126* & -0.126 \\\\\n & (0.072) & (0.059) & (0.068) & (0.105) \\\\\n\\texttt{Distance from Rome} (log)& -0.265* & -0.265* & 0.304 & 0.304 \\\\\n & (0.143) & (0.151) & (0.369) & (0.377) \\\\\n\\texttt{Large municipality} (dummy)& 0.499***& 0.499***& 0.408***& 0.408***\\\\\n & (0.055) & (0.053) & (0.108) & (0.098) \\\\\n \\hline\\\\\n $ \\phi_{p}$\t&Yes&Yes&Yes&Yes\\\\ \n \\texttt{Smaller area} (dummy)&Yes&Yes&Yes&Yes\\\\ \n Observations& 2,236& 2,236& 907& 907\\\\\n Adjusted \\textit{R$^{2}$}& 0.754& 0.754& 0.677& 0.677\\\\\n\\hline \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "From the historical Roman road network to modern infrastructure in Italy", "authors": ["Luca De Benedictis", "Vania Licio", "Anna Pinna"], "url": "https://arxiv.org/abs/2208.06675v1", "attribution": "\"From the historical Roman road network to modern infrastructure in Italy\" by Luca De Benedictis, Vania Licio, and Anna Pinna, arXiv:2208.06675v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.21253v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Italian Serie A Time Trends}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n & \\multicolumn{2}{c}{Ln(suspense)} & \\multicolumn{2}{c}{Ln(surprise)} \\\\\n \\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n & (1) & (2) & (3) & (4) \\\\\n \\midrule\n Season & -0.002 & -0.001 & 0.002 & 0.004* \\\\\n & (0.002) & (0.002) & (0.002) & (0.002) \\\\\n Juventus × season & & 0.015** & & 0.006 \\\\\n & & (0.007) & & (0.007) \\\\\n Napoli × season & & -0.001 & & -0.005 \\\\\n & & (0.006) & & (0.007) \\\\\n Internazionale × season & & -0.021*** & & -0.024*** \\\\\n & & (0.006) & & (0.007) \\\\\n \\midrule\n Main effects & & Yes & & Yes\\\\\n Observations & 5,320 & 5,320 & 5,320 & 5,320 \\\\\n R\\textsuperscript{2} & 0.000 & 0.018 & 0.000 & 0.010\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Suspense and Surprise in European Football", "authors": ["Raphael Flepp", "Tim Pawlowski", "Travis Richardson"], "url": "https://arxiv.org/abs/2506.21253v1", "attribution": "\"Suspense and Surprise in European Football\" by Raphael Flepp, Tim Pawlowski, and Travis Richardson, arXiv:2506.21253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.18996v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation analysis of the number of heads of the cross-attention blocks in self-cascade architecture.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n & \\multicolumn{3}{c}{Number of head} \\\\\\midrule\nModels & 8 & 12 & 16 \\\\\nReuse & 24.47/10.82 & 24.21/9.81 & 23.78/10.12 \\\\\nProgressive & 24.12/8.99 & 23.92/8.87 & 23.71/8.91 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images", "authors": ["Yue Shi", "Liangxiu Han", "Darren Dancy", "Lianghao Han"], "url": "https://arxiv.org/abs/2412.18996v1", "attribution": "\"WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images\" by Yue Shi, Liangxiu Han, Darren Dancy, and Lianghao Han, arXiv:2412.18996v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.06711v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Metadata of the processed datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\t\t\t\\toprule\n\t\t\t\\textbf{Collection} & \\# records & $|F^S|$ & \\# subgroups & $|DOM(F)|$\\\\\n\t\t\t\\midrule\n\t\t\t$SD1$ & 20,000 & 10 & 4 & 4 \\\\\n\t\t\t$SD2$ & 40,000 & 15 & 4 & 4 \\\\\n\t\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "MISFEAT: Feature Selection for Subgroups with Systematic Missing Data", "authors": ["Bar Genossar", "Thinh On", "Md. Mouinul Islam", "Ben Eliav", "Senjuti Basu Roy", "Avigdor Gal"], "url": "https://arxiv.org/abs/2412.06711v1", "attribution": "\"MISFEAT: Feature Selection for Subgroups with Systematic Missing Data\" by Bar Genossar, Thinh On, Md. Mouinul Islam, Ben Eliav, Senjuti Basu Roy, and Avigdor Gal, arXiv:2412.06711v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.13768v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\hline\n size & excluded region & \\multicolumn{2}{c}{confidence interval} \\\\\n & & uncorrected & corrected \\\\\n \\hline\n 3 & $(-7.2, -4.8)\\cup (-0.4, 1.5)\\cup (12.4, 14.2)$ & $(9.38, 10.77)$ & $(9.38, 10.77)$ \\\\\n 4 & $(-9.7,-6.8)\\cup (1.4, 8.6)\\cup (11.2,13.2)$ & $(9.11,10.51)$ & $(9.10, 10.51)$\\\\\n 5 & $(-17.8, -13.8)\\cup (-1.9, 0.8)\\cup (9.8,13.0)\\cup (14.2, 16.6)$ & $(8.60,10.18)$ & $(8.61,10.99)$\\\\\n 6 & $(-19.5,-15.6)\\cup (-2.5,-0.2)\\cup (9.4, 14.6)$ & $(8.36,9.95)$ & $(8.41,11.74)$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Examples of the excluded region of the conditional truncated normal estimator and the confidence intervals before and after correction, for different selected model sizes by AIC. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Valid confidence intervals for regression with best subset selection", "authors": ["Huiming Lin", "Meng Li"], "url": "https://arxiv.org/abs/2311.13768v1", "attribution": "\"Valid confidence intervals for regression with best subset selection\" by Huiming Lin and Meng Li, arXiv:2311.13768v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2112.11968v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccccc}\n\t\t\t\t\t\t\\hline\\hline \\\\\n\t\t\t\t\t\t&\\multicolumn{4}{c}{Variance Gamma}\t& \\multicolumn{4}{c}{Heston} & \\multicolumn{4}{c}{Heston-Nandi}\\\\\t\t\n\t\t\t\t\t\tStrike & Formula &\tMC &\tLB &\tUB & Formula &\tMC &\tLB &\tUB & Formula &\tMC &\tLB &\tUB \\\\\n\t\t\t\t\t\t\\hline\\hline \\\\\n\t\t\t\t\t\tStrike & \t\\multicolumn{12}{c}{\\textbf{Gumbel} $\\theta=2.9$}\\\\\n\t\t\t\t\t\t0 & 6.002 &\t5.995 & 5.970 & 6.020 &\t6.253 & 6.261 & 6.228 &\t6.292 & 6.208 & 6.220 & 6.192 & 6.248\\\\\n\t\t\t\t\t\t2.5\t & 3.877 &\t3.874\t& 3.851 & 3.896 &\t4.297\t& 4.304 & 4.276 &\t4.333 & 4.169 & 4.167 & 4.143\t& 4.191\\\\\n\t\t\t\t\t\t5\t & 2.159 &\t2.155\t& 2.138 & 2.173 &\t2.717\t& 2.724 & 2.701 &\t2.747 & 2.475 & 2.477 &\t2.457\t& 2.496\\\\\n\t\t\t\t\t\t7.5\t & 1.006 &\t1.005\t& 0.992 & 1.018 &\t1.570\t& 1.575 & 1.557 &\t1.593 & 1.254 & 1.256 &\t1.242\t& 1.271\\\\\n\t\t\t\t\t\t10\t & 0.397 &\t0.399\t& 0.391 & 0.407 &\t0.830\t& 0.831 & 0.817 &\t0.844 & 0.542 & 0.544 & 0.534\t& 0.553\\\\\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\tMC. sec.& \\multicolumn{4}{c}{0.1}\t& \\multicolumn{4}{c}{25.824} & \\multicolumn{4}{c}{14.582}\\\\\n\t\t\t\t\t\t\\hline\\hline\\\\\n\t\t\t\t\t\tStrike & \t\\multicolumn{12}{c}{\\textbf{Clayton} $\\theta= 6.57$}\\\\\t\t\t\t\t\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\t0\t & 5.947 & 5.951 & 5.925 & 5.977 &\t5.992 & 5.992 & 5.956 &\t6.029 & 5.980 & 5.958 & 5.930 & 5.987\\\\\n\t\t\t\t\t\t2.5\t & 3.647 & 3.650 & 3.626 & 3.674 &\t3.883\t& 3.883 & 3.848 &\t3.917\t& 3.824 & 3.798 & 3.772 & 3.824\\\\\n\t\t\t\t\t\t5\t & 1.846 & 1.849 & 1.827 & 1.870 & 2.456\t& 2.453 & 2.422 &\t2.483\t& 2.186 & 2.178 &\t2.156\t& 2.200\\\\\n\t\t\t\t\t\t7.5\t & 0.958 & 0.964 & 0.947 & 0.981 &\t1.597\t& 1.590 & 1.564 &\t1.617\t& 1.185 & 1.173 & 1.155 & 1.190\\\\\n\t\t\t\t\t\t10\t & 0.534 & 0.539 & 0.526 & 0.552 &\t1.068\t& 1.059 & 1.036 &\t1.081\t& 0.639 & 0.627 & 0.614 & 0,640\\\\\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\t-MC. sec.& \\multicolumn{4}{c}{0.086}\t& \\multicolumn{4}{c}{32.590} & \\multicolumn{4}{c}{14.006}\\\\\n\t\t\t\t\t\t\\hline\\hline\\\\\n\t\t\t\t\t\tStrike & \t\\multicolumn{12}{c}{\\textbf{Frank} $\\theta= 25.28$}\\\\\t\t\t\t\t\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\t0\t& 5.823 & 5.814 &\t5.795 & 5.832 & 5.895 &\t5.894 & 5.867 & 5.921 &\t5.944 & 5.938 & 5.918 & 5.958\\\\\n\t\t\t\t\t\t2.5\t& 3.447 & 3.438 &\t3.421\t& 3.455 & 3.659 &\t3.657\t& 3.631 & 3.682 &\t3.681\t& 3.682 & 3.664 & 3.700\\\\\n\t\t\t\t\t\t5\t& 1.451 & 1.442 &\t1.427\t& 1.456 & 1.938 &\t1.935\t& 1.912 & 1.957 &\t1.774 & 1.775 & 1.760 &\t1.790\\\\\n\t\t\t\t\t\t7.5\t& 0.486 & 0.481 &\t0.470\t& 0.491 & 0.994 &\t0.988\t& 0.969 & 1.006 &\t0.628\t& 0.628 & 0.617 &\t0,638\\\\\n\t\t\t\t\t\t10\t& 0.204 & 0.201 &\t0.193\t& 0.209 & 0.559 &\t0.552\t& 0.537 & 0.566 &\t0.216\t& 0.210 & 0.204 &\t0.217\\\\\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\tMC. sec.& \\multicolumn{4}{c}{0.1786}\t& \\multicolumn{4}{c}{25.890} & \\multicolumn{4}{c}{20.361}\\\\\n\t\t\t\t\t\t\\hline\\hline\\\\\n\t\t\t\t\t\tStrike & \t\\multicolumn{12}{c}{\\textbf{Plackett} $\\theta=50.52$}\\\\\t\t\t\t\t\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\t0\t& 5.999 & 5.990 &\t5.965 & 6.015 & 6.161 &\t6.163 & 6.129 & 6.196 &\t6.149 & 6.154 & 6.127 &\t6.182\\\\\n\t\t\t\t\t\t2.5\t& 3.765 & 3.756 &\t3.733\t& 3.779 & 4.090 &\t4.089\t& 4.058 & 4.120 &\t4.019\t& 4.006 & 3.982 &\t4.031\\\\\n\t\t\t\t\t\t5\t& 1.948 & 1.939 &\t1.920\t& 1.959 & 2.492 &\t2.490\t& 2.463 & 2.517 &\t2.266\t& 2.272 & 2.251 & 2.293\\\\\n\t\t\t\t\t\t7.5\t& 0.906 & 0.899 &\t0.884\t& 0.915 & 1.471 &\t1.467\t& 1.444 & 1.490 &\t1.111\t& 1.123 & 1.107 &\t1.140\\\\\n\t\t\t\t\t\t10\t& 0.450 & 0.447 &\t0.435\t& 0.459 & 0.886 &\t0.881\t& 0.862 & 0.900 &\t0.533\t& 0.538 & 0.526 &\t0.551\\\\\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\tMC. sec.& \\multicolumn{4}{c}{0.085}\t& \\multicolumn{4}{c}{21.420} & \\multicolumn{4}{c}{16.321}\\\\\n\t\t\t\t\t\t\\hline\\hline\n\t\t\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "An Efficient Unified Approach for Spread Option Pricing in a Copula Market Model", "authors": ["Edoardo Berton", "Lorenzo Mercuri"], "url": "https://arxiv.org/abs/2112.11968v2", "attribution": "\"An Efficient Unified Approach for Spread Option Pricing in a Copula Market Model\" by Edoardo Berton and Lorenzo Mercuri, arXiv:2112.11968v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09737v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results on COVID-19 Dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\multicolumn{4}{|c|}{COVID-19 vs Suspected} \\\\ \\hline\nMethod & Accuracy & Sensitivity & Specificity \\\\ \\hline\nSVM & $87 \\pm 5$ & $89 \\pm 8$ & $86 \\pm 9$ \\\\ \\hline\nRamanNet & $93 \\pm 3$ & $97 \\pm 4$ & $90 \\pm 6$ \\\\ \\hline\n\\multicolumn{4}{|c|}{COVID-19 vs Healthy} \\\\ \\hline\nMethod & Accuracy & Sensitivity & Specificity \\\\ \\hline\nSVM & $91 \\pm 4$ & $89 \\pm 7$ & $93 \\pm 6$ \\\\ \\hline\nRamanNet & $95$ & $95 \\pm 4$ & $96 \\pm 3$ \\\\ \\hline\n\\multicolumn{4}{|c|}{Suspected vs Healthy} \\\\ \\hline\nMethod & Accuracy & Sensitivity & Specificity \\\\ \\hline\nSVM & $69 \\pm 5$ & $70 \\pm 9$ & $66 \\pm 9$ \\\\ \\hline\nRamanNet & $82 \\pm 6$ & $77 \\pm 15$ & $87 \\pm 11$ \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "RamanNet: A generalized neural network architecture for Raman Spectrum Analysis", "authors": ["Nabil Ibtehaz", "Muhammad E. H. Chowdhury", "Amith Khandakar", "Susu M. Zughaier", "Serkan Kiranyaz", "M. Sohel Rahman"], "url": "https://arxiv.org/abs/2201.09737v2", "attribution": "\"RamanNet: A generalized neural network architecture for Raman Spectrum Analysis\" by Nabil Ibtehaz, Muhammad E. H. Chowdhury, Amith Khandakar, Susu M. Zughaier, Serkan Kiranyaz, and M. Sohel Rahman, arXiv:2201.09737v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.10013v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n& AFEW & FPHA & NTU RGB+D\\ & HDM05\\\\ \n\\midrule\nEuclidean & $30.08$\\tiny$\\pm 1.36$& $30.72 $\\tiny$\\pm 1.03$ & $34.63$\\tiny$\\pm 3.10$ & $0.80 $\\tiny$\\pm 0.10$\\\\\nAff-Inv & $35.17$ \\tiny $\\pm 1.78$&$\\mathbf{66.53} $\\tiny$\\mathbf{\\pm 1.64}$ & $41.00$ \\tiny $\\pm 0.50$& $67.91$ \\tiny${\\pm 1.27}$\\\\\nLog-Euc&$\\mathbf{36.38}$\\tiny $\\mathbf{\\pm 0.24}$& $64.58 $\\tiny$\\pm 0.98$& $\\mathbf{42.99 }$\\tiny$\\mathbf{\\pm 0.23}$& $\\mathbf{69.80}$\\tiny$\\mathbf{ \\pm 1.51}$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{We show that the Euclidean ResNet performs worse across all datasets, and fails for HDM05.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Riemannian Residual Neural Networks", "authors": ["Isay Katsman", "Eric Ming Chen", "Sidhanth Holalkere", "Anna Asch", "Aaron Lou", "Ser-Nam Lim", "Christopher De Sa"], "url": "https://arxiv.org/abs/2310.10013v1", "attribution": "\"Riemannian Residual Neural Networks\" by Isay Katsman, Eric Ming Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser-Nam Lim, and Christopher De Sa, arXiv:2310.10013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.09997v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n & \\multicolumn{2}{c}{\\textbf{Cross-Validation}} & \\multicolumn{2}{c}{\\textbf{Test Set Cohort}}\\tabularnewline\n & AUC & Brier score & AUC & Brier score\\tabularnewline\n\\hline \n\\hline \nflexible BART & 0.68 & 0.173 & 0.557 & 0.162\\tabularnewline\ncv-BART & 0.68 & 0.168 & 0.557 & 0.162\\tabularnewline\nflexible EB-coBART 1 & 0.72 & 0.155 & 0.697 & 0.154\\tabularnewline\nflexible EB-coBART 2 & 0.71 & 0.156 & \\textbf{0.714} & \\textbf{0.153}\\tabularnewline\nIPI-BART & 0.70 & \\textbf{0.153} & 0.669 & 0.154\\tabularnewline\nRandom Forest & 0.69 & 0.183 & 0.63 & 0.158\\tabularnewline\nCoRF & 0.71 & 0.157 & 0.65 & 0.159\\tabularnewline\nEcpc & \\textbf{0.73} & 0.159 & 0.705 & 0.154\\tabularnewline\nRidge & 0.72 & 0.161 & 0.69 & 0.154\\tabularnewline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Co-data Learning for Bayesian Additive Regression Trees", "authors": ["Jeroen M. Goedhart", "Thomas Klausch", "Jurriaan Janssen", "Mark A. van de Wiel"], "url": "https://arxiv.org/abs/2311.09997v2", "attribution": "\"Co-data Learning for Bayesian Additive Regression Trees\" by Jeroen M. Goedhart, Thomas Klausch, Jurriaan Janssen, and Mark A. van de Wiel, arXiv:2311.09997v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.09028v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The key parameters of PMSM}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\nParameters& Units &Values\\\\\n\\hline\nRated power $P$& W & 200\\\\\nRated speed $N_r$& rpm & 1500\\\\\nRated load $T_{L}$& N$\\cdot$m & 1.2\\\\\nRated voltage $U_r$& V & 36\\\\\nRated current $I_r$& A & 7.5\\\\\nPole pairs $N_p$& * & 5\\\\\nRotational inertia $J$&Kg$\\cdot$m$^2$& 6.8$\\times$ 10$^{-5}$\\\\\nPM flux linkage $\\psi_{f}$&Wb & 0.012\\\\\nStator resistance $R_s$ &$\\Omega$& 0.4\\\\\nWinding inductance $L_s$ &mH& 0.7\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning and Current Prediction of PMSM Drive via Differential Neural Networks", "authors": ["Wenjie Mei", "Xiaorui Wang", "Yanrong Lu", "Ke Yu", "Shihua Li"], "url": "https://arxiv.org/abs/2412.09028v1", "attribution": "\"Learning and Current Prediction of PMSM Drive via Differential Neural Networks\" by Wenjie Mei, Xiaorui Wang, Yanrong Lu, Ke Yu, and Shihua Li, arXiv:2412.09028v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.00851v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Out-of-sample forecast loss value of SSE for the future 5-step}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\nModel & MSE & MAE & HMSE & HMAE & QLIKE \\\\ \\midrule\nGARCH & $1.636\\times 10^{-8}$ & $7.935\\times 10^{-5}$ & $10.693$ & $1.872$ & $-8.360$ \\\\\nEGARCH & $1.174\\times 10^{-8}$ & $6.927\\times 10^{-5}$ & $5.059$ & $1.511$ & $-8.499$ \\\\\nFGARCH & $1.364\\times 10^{-8}$ & $7.432\\times 10^{-5}$ & $6.299$ & $1.696$ & $-8.467$ \\\\\nTGARCH & $9.072\\times 10^{-9}$ & $4.660\\times 10^{-5}$ & $1.102$ & $0.711$ & $-6.626$ \\\\\nHAR-RV & $1.292\\times 10^{-8}$ & $5.798\\times 10^{-5}$ & $3.682$ & $0.954$ & $-8.412$ \\\\\nHAR-CJ & $5.085\\times 10^{-9}$ & $3.364\\times 10^{-5}$ & $0.521$ & $0.518$ & $-8.700$ \\\\\nHAR-RS & $4.453\\times 10^{-9}$ & $3.351\\times 10^{-5}$ & $0.840$ & $0.580$ & $-8.724$ \\\\\nHAR-REX & $5.113\\times 10^{-9}$ & $3.444\\times 10^{-5}$ & $0.590$ & $0.554$ & $-8.696$ \\\\\nHAR-REQ & $5.087\\times 10^{-9}$ & $3.353\\times 10^{-5}$ & $0.527$ & $0.524$ & $-8.697$ \\\\\nHAR-PD-RV & $6.902\\times 10^{-9}$ & $3.966\\times 10^{-5}$ & $0.805$ & $0.575$ & $-8.606$ \\\\\nHAR-PD-CJ & $5.062\\times 10^{-9}$ & $3.257\\times 10^{-5}$ & \\textbf{\\underline{$\\mathbf{0.445}$}} & $0.474$ & $-8.699$ \\\\\nHAR-PD-RS & $4.209\\times 10^{-9}$ & $3.460\\times 10^{-5}$ & $1.039$ & $0.627$ & $-8.659$ \\\\\nHAR-PD-REX & $4.954\\times 10^{-9}$ & $3.294\\times 10^{-5}$ & $0.525$ & $0.519$ & $-8.700$ \\\\\nHAR-PD-REQ & $4.888\\times 10^{-9}$ & \\textbf{\\underline{$\\mathbf{3.127\\times 10^{-5}}$}} & $0.454$ & \\textbf{\\underline{$\\mathbf{0.468}$}} & $-8.707$ \\\\\nLASSO-HAR-PD-CJ & $5.072\\times 10^{-9}$ & $3.287\\times 10^{-5}$ & $0.464$ & $0.484$ & $-8.700$ \\\\\nLASSO-HAR-PD-RS & \\textbf{\\underline{$\\mathbf{4.417\\times 10^{-9}}$}} & $3.162\\times 10^{-5}$ & $0.673$ & $0.493$ & \\textbf{\\underline{$\\mathbf{-8.732}$}} \\\\\nLASSO-HAR-PD-REX & $5.013\\times 10^{-9}$ & $3.325\\times 10^{-5}$ & $0.536$ & $0.521$ & $-8.698$ \\\\\nLASSO-HAR-PD-REQ & $5.053\\times 10^{-9}$ & $3.473\\times 10^{-5}$ & $0.627$ & $0.572$ & $-8.692$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Forecasting realized volatility in the stock market: a path-dependent perspective", "authors": ["Xiangdong Liu", "Sicheng Fu", "Shaopeng Hong"], "url": "https://arxiv.org/abs/2503.00851v1", "attribution": "\"Forecasting realized volatility in the stock market: a path-dependent perspective\" by Xiangdong Liu, Sicheng Fu, and Shaopeng Hong, arXiv:2503.00851v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.22061v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n \\hline\n & $\\underline{\\hat{g}}_{1}$ & $\\underline{\\hat{g}}_{2}$ & $\\underline{\\hat{g}}_{3}$ \\\\\n \\hline\n$\\underline{\\hat{g}}_{1}$ & $0$ & $- \\underline{\\hat{g}}_{3}$ & $ \\underline{\\hat{g}}_{2}$ \\\\\n\\hline\n$\\underline{\\hat{g}}_{2}$ & $ \\underline{\\hat{g}}_{3}$ & $0$ & $- \\underline{\\hat{g}}_{1}$ \\\\\n\\hline\n$\\underline{\\hat{g}}_{3}$ & $- \\underline{\\hat{g}}_{2}$ & $\\underline{\\hat{g}}_{1}$ & $0$ \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Structure table for the Lie algebra $\\mathfrak{su}(2)$ in the Pauli basis. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "$\\texttt{Symdyn}$: an automated algebraic solution for high-order quantum systems", "authors": ["D. Martínez-Tibaduiza", "Vladimir Vargas-Calderón", "J. G. Dueñas", "J. Flórez-Jiménez", "A. Z. Khoury"], "url": "https://arxiv.org/abs/2503.22061v2", "attribution": "\"$\\texttt{Symdyn}$: an automated algebraic solution for high-order quantum systems\" by D. Martínez-Tibaduiza, Vladimir Vargas-Calderón, J. G. Dueñas, J. Flórez-Jiménez, and A. Z. Khoury, arXiv:2503.22061v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05791v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n Model name & Depth & Channels & Parameter count \\\\\n \\hline\n Utility & 5 & $2^6$ & 34M \\\\\n U-Noise Small & 2 & $2^4$ & 10K \\\\\n U-Noise Medium & 3 & $2^4$ & 130K \\\\\n U-Noise Large & 4 & $2^4$ & 537K \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Details of the Utility and U-Noise models used in experiments. \\textit{Depth} denotes the number of downsample and upsample layers; \\textit{Channels} denotes the number of output channels of the first layer.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "U-Noise: Learnable Noise Masks for Interpretable Image Segmentation", "authors": ["Teddy Koker", "Fatemehsadat Mireshghallah", "Tom Titcombe", "Georgios Kaissis"], "url": "https://arxiv.org/abs/2101.05791v3", "attribution": "\"U-Noise: Learnable Noise Masks for Interpretable Image Segmentation\" by Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe, and Georgios Kaissis, arXiv:2101.05791v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05388v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|c|cc}\n\\toprule\n\\textbf{Architecture} & \\textbf{Backbone} & \\textbf{IoU} & \\textbf{F1-Score} \\\\\n\\hline \nUnet & EfficientNetb-3 & 0.817 & 0.831\\\\\n\\hline \nLinkNet & EfficientNetb-3 & 0.813 & 0.828\\\\\n\\hline \nFPN & EfficientNetb-3 & 0.882 & 0.91 \\\\\n & Resnet18 & 0.862 & 0.885 \\\\\n & Resnet50 & 0.841 & 0.863 \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{Quantitative results for the keypoints masks generations. More details about the results for other backbones can be found Figure }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning", "authors": ["Paul Garnier", "Théophane Gregoir"], "url": "https://arxiv.org/abs/2101.05388v1", "attribution": "\"Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning\" by Paul Garnier and Théophane Gregoir, arXiv:2101.05388v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.19708v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\nSample Size & t-test (ETH) & p-value \\\\\n\\midrule\nMerkle 92 & -0.764 & 0.223 \\\\\nFlashbots 98 & -1.309 & 0.097 \\\\\nBlink 97 & -0.851 & 0.198 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Net Average Price Difference vs MEV Blocker.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Private MEV Protection RPCs: Benchmark Stud", "authors": ["Paul Janicot", "Alex Vinyas"], "url": "https://arxiv.org/abs/2505.19708v1", "attribution": "\"Private MEV Protection RPCs: Benchmark Stud\" by Paul Janicot and Alex Vinyas, arXiv:2505.19708v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.18320v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crr}\n \\hline\n \\thead{\\textbf{Split Ratio} \\\\ \\textbf{Labeled / Unlabeled}} & \\thead{\\textbf{CER (\\%)} \\\\ \\textbf{LJ Original}} & \\thead{\\textbf{CER (\\%)}\\\\ \\textbf{LJ Noisy}}\\\\\n \\hline\n 30 / 70 & 11.1 & 15.5 \\\\\n 50 / 50 & 4.8 & 11.5 \\\\\n 70 / 30 & 4.0 & 10.9 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Results for the \\textbf{ASR${_\\text{SpeechChain}}$} with updates of different ratio of labeled and unlabeled data during base task learning in the first stage and second stage of the framework.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Continual Learning in Machine Speech Chain Using Gradient Episodic Memory", "authors": ["Geoffrey Tyndall", "Kurniawati Azizah", "Dipta Tanaya", "Ayu Purwarianti", "Dessi Puji Lestari", "Sakriani Sakti"], "url": "https://arxiv.org/abs/2411.18320v1", "attribution": "\"Continual Learning in Machine Speech Chain Using Gradient Episodic Memory\" by Geoffrey Tyndall, Kurniawati Azizah, Dipta Tanaya, Ayu Purwarianti, Dessi Puji Lestari, and Sakriani Sakti, arXiv:2411.18320v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.19315v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l}\n \\toprule \\midrule\n \\textbf{Model} & \\textbf{WER} & \\textbf{WERR} \\\\\n \\midrule\n USM baseline & 28.8 & - \\\\\n USM SI-ASR & \\textbf{17.5} & 39\\% \\\\\n \\midrule\n RNN-T baseline & 38.2 & - \\\\\n RNN-T SI-ASR & 26.4 & 31\\% \\\\\n RNN-T Personalized & \\textbf{11.3} & 71\\%\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Results comparing unadapted, speaker independent and personalized models, evaluated on a subset of prompted speech test set.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Towards a Single ASR Model That Generalizes to Disordered Speech", "authors": ["Jimmy Tobin", "Katrin Tomanek", "Subhashini Venugopalan"], "url": "https://arxiv.org/abs/2412.19315v1", "attribution": "\"Towards a Single ASR Model That Generalizes to Disordered Speech\" by Jimmy Tobin, Katrin Tomanek, and Subhashini Venugopalan, arXiv:2412.19315v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.11691v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results of the analysis for various definitions of a run. In particular, the inference drawn is relatively the same, illustrating robustness of the results to the characterization of a run.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllrrrrrrrl}\n\\toprule\n\\multicolumn{2}{c}{} & \\multicolumn{3}{c}{N} & \\multicolumn{3}{c}{Results} \\\\\n \\cmidrule(l{3pt}r{3pt}){3-5} \\cmidrule(l{3pt}r{3pt}){6-8}\nPoints & Window & Units & RwT & RwoT & $\\widehat{ATT}$ & $SE$ & $p$ & \\\\\n\\midrule\n7 & 1.5 & 20,603 & 2,626 & 17,977 & -0.23 & 0.04 & $<0.001$ & \\\\\n7 & 2.0 & 34,005 & 3,694 & 30,311 & -0.25 & 0.03 & $<0.001$ & * \\\\\n7 & 2.5 & 41,520 & 4,285 & 37,235 & -0.28 & 0.03 & $<0.001$ & * \\\\\n7 & 3.0 & 43,821 & 4,472 & 39,349 & -0.26 & 0.03 & $<0.001$ & * \\\\\n\\addlinespace\n8 & 1.5 & 6,816 & 1,090 & 5,726 & -0.38 & 0.06 & $<0.001$ & \\\\\n8 & 2.0 & 13,677 & 1,911 & 11,766 & -0.31 & 0.05 & $<0.001$ & \\\\\n8 & 2.5 & 18,868 & 2,470 & 16,398 & -0.34 & 0.04 & $<0.001$ & \\\\\n8 & 3.0 & 21,618 & 2,745 & 18,873 & -0.26 & 0.04 & $<0.001$ & * \\\\\n\\addlinespace\n9 & 1.5 & 1,600 & 325 & 1,275 & -0.37 & 0.14 & 0.010 & \\\\\n9 & 2.0 & 4,684 & 834 & 3,850 & -0.35 & 0.07 & $<0.001$ & \\\\\n9 & 2.5 & 7,547 & 1,204 & 6,343 & -0.31 & 0.06 & $<0.001$ & \\\\\n9 & 3.0 & 9,572 & 1,459 & 8,113 & -0.28 & 0.05 & $<0.001$ & \\\\\n\\addlinespace\n10 & 1.5 & 370 & 103 & 267 & -0.51 & 0.20 & 0.009 & ** \\\\\n10 & 2.0 & 1,446 & 297 & 1,149 & -0.50 & 0.16 & 0.001 & \\\\\n10 & 2.5 & 2,923 & 532 & 2,391 & -0.43 & 0.13 & 0.001 & \\\\\n10 & 3.0 & 4,139 & 710 & 3,429 & -0.44 & 0.09 & $<0.001$ & \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "The causal effect of a timeout at stopping an opposing run in the NBA", "authors": ["Connor Gibbs", "Ryan Elmore", "Bailey Fosdick"], "url": "https://arxiv.org/abs/2011.11691v3", "attribution": "\"The causal effect of a timeout at stopping an opposing run in the NBA\" by Connor Gibbs, Ryan Elmore, and Bailey Fosdick, arXiv:2011.11691v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.22666v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{OLS Regression Results - Metropolitan Areas (MA)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\hline\n\\multirow{3}{*}{Coefficient} & \\multicolumn{3}{c}{With Fitness \\& Gamma} & \\multicolumn{3}{c}{Without Fitness \\& Gamma} \\\\\n\\cline{2-7}\n & Estimate & Std. Error & $P>|t|$ & Estimate & Std. Error & $P>|t|$ \\\\\n\\hline\n\\( R^2 \\) & \\multicolumn{3}{c}{0.708} & \\multicolumn{3}{c}{0.464} \\\\\nF & -0.0337 & 0.000 & 0.000 & -- & -- & -- \\\\\nGAMMA & 0.0234 & 0.002 & 0.000 & -- & -- & -- \\\\\ndiv & 0.0401 & 0.001 & 0.000 & -0.0110 & 0.000 & 0.000 \\\\\nGDPpc & -0.0138 & 0.000 & 0.000 & 0.0061 & 0.000 & 0.000 \\\\\nyear & -0.0007 & 0.00007 & 0.000 & 0.0001 & 0.0001 & 0.323 \\\\\nMA & 0.000005 & 0.0000006 & 0.000 & 0.000018 & 0.0000008 & 0.000 \\\\\nregion & 0.000009 & 0.0000018 & 0.000 & 0.000016 & 0.0000024 & 0.000 \\\\\ncntr & -0.0001 & 0.00002 & 0.000 & -0.0007 & 0.000026 & 0.000 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Comparative Analysis of Technological Fitness and Coherence at different geographical scales", "authors": ["Matteo Straccamore", "Matteo Bruno", "Andrea Tacchella"], "url": "https://arxiv.org/abs/2503.22666v1", "attribution": "\"Comparative Analysis of Technological Fitness and Coherence at different geographical scales\" by Matteo Straccamore, Matteo Bruno, and Andrea Tacchella, arXiv:2503.22666v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.16701v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary Statistics for SPY Minute-Level Data (January-June 2025)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\n\\textbf{Variable} & \\textbf{Mean} & \\textbf{Std Dev} & \\textbf{Min} & \\textbf{25\\%} & \\textbf{75\\%} & \\textbf{Max} \\\\\n\\midrule\nClose Price (\\$) & 578.50 & 32.84 & 482.77 & 560.32 & 602.19 & 613.14 \\\\\nVolume & 111,706 & 168,052 & 0 & 24,576 & 134,720 & 12,103,574 \\\\\nLog Returns & 0.000001 & 0.000774 & -0.035 & -0.000214 & 0.000221 & 0.034 \\\\\nSpread Proxy & 0.000672 & 0.000704 & 0.000000 & 0.000308 & 0.000800 & 0.032 \\\\\nNumber Ticks & 261.8 & 458.7 & 0 & 44 & 282 & 22,798 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach", "authors": ["Akash Deep", "Chris Monico", "W. Brent Lindquist", "Svetlozar T. Rachev", "Frank J. Fabozzi"], "url": "https://arxiv.org/abs/2507.16701v1", "attribution": "\"Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach\" by Akash Deep, Chris Monico, W. Brent Lindquist, Svetlozar T. Rachev, and Frank J. Fabozzi, arXiv:2507.16701v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.04013v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Example table.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|} \\hline\n Species & \\bf Mean & \\bf Std.~Dev. \\\\ \\hline\n 1 & 3.4 & 1.2 \\\\\n 2 & 5.4 & 0.6 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An Element-wise RSAV Algorithm for Unconstrained Optimization Problems", "authors": ["Shiheng Zhang", "Jiahao Zhang", "Jie Shen", "Guang Lin"], "url": "https://arxiv.org/abs/2309.04013v1", "attribution": "\"An Element-wise RSAV Algorithm for Unconstrained Optimization Problems\" by Shiheng Zhang, Jiahao Zhang, Jie Shen, and Guang Lin, arXiv:2309.04013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18002v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllll}\n\\hline\n\\multicolumn{7}{c}{A Uniform} \\\\\n\\hline\n $k$ & LHS & RHS & $y$ & $z$ & $t_z$ & $t_{z^{\\prime}}$ \\\\ \n\\hline\n 10 & 0.593 & 0.624 & 3.429 & 3.583 & 3.429 & 3.583 \\\\ \n 22 & 0.126 & 0.042 & 2.068 & 3.443 & 2.068 & 3.443 \\\\ \n 32 & 0.035 & 0.030 & 1.513 & 3.331 & 1.513 & 3.331 \\\\ \n 42 & 0.049 & 0.006 & 1.513 & 3.246 & 1.513 & 3.246 \\\\ \n 52 & 0.052 & 0.006 & 1.513 & 3.246 & 1.513 & 3.246 \\\\ \n 60 & 0.025 & 0.005 & 1.306 & 2.972 & 1.306 & 2.972 \\\\ \n 110 & 0.013 & 0.012 & 1.115 & 2.330 & 1.115 & 2.330 \\\\ \n 120 & 0.013 & 0.004 & 1.115 & 2.395 & 1.115 & 2.395 \\\\ \n 137 & 0.014 & 0.006 & 1.115 & 2.330 & 1.115 & 2.330 \\\\ \n 155 & 0.014 & 0.008 & 1.115 & 2.249 & 1.115 & 2.249 \\\\ \n 175 & 0.019 & 0.010 & 1.115 & 2.100 & 1.115 & 2.100 \\\\ \n 195 & 0.004 & 0.001 & 0.708 & 1.967 & 0.708 & 1.967 \\\\ \n 255 & 0.004 & 0.002 & 0.708 & 1.967 & 0.708 & 1.967 \\\\ \n\\hline\n\\multicolumn{7}{c}{B Gaussian process} \\\\\n\\hline\n $k$ & LHS & RHS & $y$ & $z$ & $t_z$ & $t_{z^{\\prime}}$ \\\\\n\\hline\n 10 & 0.402 & 0.125 & 2.727 & 3.222 & 2.727 & 3.222 \\\\ \n 18 & 0.176 & 0.060 & 2.627 & 3.290 & 2.627 & 3.290 \\\\ \n 28 & 0.386 & 0.053 & 2.627 & 3.322 & 2.627 & 3.322 \\\\ \n 33 & 0.334 & 0.203 & 2.627 & 3.322 & 2.627 & 3.322 \\\\ \n 43 & 0.206 & 0.184 & 2.441 & 3.290 & 2.441 & 3.290 \\\\ \n 100 & 0.097 & 0.010 & 1.549 & 3.369 & 1.549 & 3.369 \\\\ \n 154 & 0.146 & 0.048 & 1.549 & 3.238 & 1.549 & 3.238 \\\\ \n 215 & 0.185 & 0.018 & 1.549 & 3.140 & 1.549 & 3.140 \\\\ \n 259 & 0.245 & 0.046 & 1.549 & 3.039 & 1.549 & 3.039 \\\\ \n \\hline\n\\multicolumn{7}{c}{C Quadratic Regression}\\\\ \n \\hline\n $k$ & LHS & RHS & $y$ & $z$ & $t_z$ & $t_{z^{\\prime}}$\\\\ \n \\hline\n 50 & 0.121 & 0.088 & 2.077 & 3.246 & 2.077 & 3.246 \\\\ \n 76 & 0.023 & 0.015 & 1.574 & 3.246 & 1.574 & 3.246 \\\\ \n 126 & 0.028 & 0.023 & 1.574 & 3.260 & 1.574 & 3.260 \\\\ \n 176 & 0.037 & 0.000 & 1.437 & 3.265 & 1.437 & 3.265 \\\\ \n 227 & 0.044 & 0.019 & 1.423 & 3.158 & 1.423 & 3.158 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Table of numerical values from testing Assumption 1.3 for c. Gaussian process on the range distribution on shifted sinusoidal problem with $dim = 5$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Branching Adaptive Surrogate Search Optimization (BASSO)", "authors": ["Pariyakorn Maneekul", "Zelda B. Zabinsky", "Giulia Pedrielli"], "url": "https://arxiv.org/abs/2504.18002v1", "attribution": "\"Branching Adaptive Surrogate Search Optimization (BASSO)\" by Pariyakorn Maneekul, Zelda B. Zabinsky, and Giulia Pedrielli, arXiv:2504.18002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.01289v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n \\textbf{Name} & \\textbf{Symbol} & \\textbf{Seed} & \\textbf{Calibrated Value} \\\\\n \\hline\n Mean reversion speed & $a$ & $0.1$ & $0.267419$\\\\\n \\hline\n Asset Correlation & $\\rho$ & $0.0$ & $0.86381$\\\\\n \\hline\n Vol-of-vol & $\\chi$ & $1.0$ & $0.0287296$\\\\\n \\hline\n Stochastic Volatility correlation & $\\rho_v$ & $1.0$ & $-0.18058$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Calibrated values of $\\hat{\\mathbf{p}}$ against the S\\&P GSCI Crude Oil ER index 1 Year Call Options quoted on 30 November 2019 and the S\\&P GSCI Crude Oil ER index 1 Year Call Options quoted on 31 December 2019, both on CME market. For this calibration the hybrid global-local procedure described in Section has been used.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Pricing commodity index options", "authors": ["Alberto Manzano", "Emanuele Nastasi", "Andrea Pallavicini", "Carlos Vázquez"], "url": "https://arxiv.org/abs/2208.01289v1", "attribution": "\"Pricing commodity index options\" by Alberto Manzano, Emanuele Nastasi, Andrea Pallavicini, and Carlos Vázquez, arXiv:2208.01289v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.01467v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Correlation in Predictors of Realized Variance} }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|ccccccccccc}\n \\toprule\n Predictor & $\\beta_{vol}$ & $C_u$ & $C_d$ & Between (d) & Between (s) & Size & Across (s) & Across (d) & VP & mkt ivol & mkt vol \\\\\n\\cmidrule{1-12} $\\beta_{vol}$ & 1 & -0.329 & -0.130 & -0.308 & 0.072 & 0.285 & 0.061 & -0.128 & -0.225 & 0.287 & 0.406 \\\\\n $C_u$ & & 1 & 0.284 & 0.356 & -0.251 & 0.131 & -0.441 & 0.319 & 0.423 & -0.383 & -0.396 \\\\\n $C_d$ & & & 1 & 0.588 & -0.018 & -0.128 & -0.116 & 0.288 & 0.289 & -0.003 & -0.022 \\\\\n Between (d) & & & & 1 & -0.205 & -0.113 & -0.156 & 0.704 & 0.708 & -0.270 & -0.311 \\\\\n Between (s) & & & & & 1 & -0.133 & 0.056 & -0.163 & -0.313 & 0.073 & 0.054 \\\\\n Size & & & & & & 1 & -0.230 & 0.018 & -0.111 & -0.386 & -0.275 \\\\\n Across (s) & & & & & & & 1 & -0.088 & -0.159 & 0.205 & 0.255 \\\\\n Across (d) & & & & & & & & 1 & 0.627 & -0.224 & -0.249 \\\\\n VP & & & & & & & & & 1 & -0.279 & -0.322 \\\\\n mkt ivol & & & & & & & & & & 1 & 0.965 \\\\\n mkt vol & & & & & & & & & & & 1 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Risk in Network Economies", "authors": ["Victor Sellemi"], "url": "https://arxiv.org/abs/2208.01467v1", "attribution": "\"Risk in Network Economies\" by Victor Sellemi, arXiv:2208.01467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12489v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Input signal for Example~.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccccc}\n $t$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\\\ \\hline\n \\multirow{2}{*}{\\!$u(t)$\\!} & -0.46 & -1.09 & 0.9 & 1.38 & 1.24 & 1.54 & 0.22 & -1.68 \\\\\n & 1.86 & -0.87 & -1.42 & 1.06 & 1.6 & -1.94 & 1.11 & -1.03\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A new perspective on Willems' fundamental lemma: Universality of persistently exciting inputs", "authors": ["Amir Shakouri", "Henk J. van Waarde", "M. Kanat Camlibel"], "url": "https://arxiv.org/abs/2503.12489v2", "attribution": "\"A new perspective on Willems' fundamental lemma: Universality of persistently exciting inputs\" by Amir Shakouri, Henk J. van Waarde, and M. Kanat Camlibel, arXiv:2503.12489v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.12532v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Features of datasets used for this experiment.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|r|c|r|r|r|r|r} \n\\hline\ndataset &$N$ & $V$ & $C$ & \\multicolumn{4}{|c}{dimensions of each view}\\\\\n\\cline{5-8}\n&&&& $d_1$ & $d_2$ & $d_3$ & $d_4$ \\\\\n\\hline\n\\hline\nBBC & 685 & 4 & 5 & 4659 & 4633 & 4665 & 4684 \\\\\nNGs & 500 & 3 & 5 & 2000 & 2000 & 2000 &--\\\\\nWebKB & 203 & 3 & 4 & 1703 & 230 & 230 & -- \\\\\n100leaves & 1600 & 3 & 100 & 64 & 64 & 64 & -- \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering", "authors": ["Mitsuhiko Horie", "Hiroyuki Kasai"], "url": "https://arxiv.org/abs/2011.12532v1", "attribution": "\"Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering\" by Mitsuhiko Horie and Hiroyuki Kasai, arXiv:2011.12532v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09827v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Various IEEE precisions and their units roundoff. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|}\n\t\t\\hline\n\t\tPrecision & Unit Roundoff \\\\ \\hline\n\t\tfp16 (half) & $4.88\\cdot 10^{-4}$ \\\\ \\hline\n\t\tfp32 (single) & $5.96\\cdot 10^{-8}$ \\\\ \\hline\n\t\tfp64 (double) & $1.11\\cdot 10^{-16}$ \\\\ \\hline\n\t\tfp128 (quad) & $9.63\\cdot 10^{-35}$ \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Mixed Precision GMRES-based Iterative Refinement with Recycling", "authors": ["Eda Oktay", "Erin Carson"], "url": "https://arxiv.org/abs/2201.09827v2", "attribution": "\"Mixed Precision GMRES-based Iterative Refinement with Recycling\" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.20987v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\nprojective algebraic geometry & combinatorics \\\\ \\hline \\hline\nprojective varieties $X\\subseteq\\mathbb P^r$ & simplicial complexes $\\Delta$ on $\\{0,\\ldots,r\\}$ \\\\ \\hline\nhomogeneous ideals $I(X)$ & Stanley-Reisner ideals $I(\\Delta)$ \\\\ \\hline\nvarieties of minimal degree & chordal graphs \\\\ \\hline\n & Fr{\\\"o}berg's theorem \\\\ \\hline \\hline\n$q$-secant varieties & $q$-secant complexes \\\\ \\hline\nvarieties of minimal $q$-secant degree & $q$-secant chordal graphs \\\\ \\hline\n(cf.\\ ) & \\\\ \\hline \\hline\ngeneral inner projections & edge contractions\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Analogues between the two areas}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Fröberg type theorem for higher secant complexes", "authors": ["Junho Choe", "Jaewoo Jung"], "url": "https://arxiv.org/abs/2502.20987v2", "attribution": "\"A Fröberg type theorem for higher secant complexes\" by Junho Choe and Jaewoo Jung, arXiv:2502.20987v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.05953v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Loss function values.}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\nStackelberg strategy&Leader&Follower 1&Follower 2&Follower 3&Follower 4\\\\\n&-2210,94& -2818,06 & -1978,79 & -2688,34& -2506,05\\\\\n\\hline\nnon-cooperative strategy&Player 1&Player 2&Player 3&Player 4&Player 5\\\\\n&-2192,96 & -2910,93 & -2010,04 & -2767,10 & -2573,26\\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the role of semismoothness in the implicit programming approach to selected nonsmooth optimization problems", "authors": ["Helmut Gfrerer", "Michal Kočvara", "Jiří V. Outrata"], "url": "https://arxiv.org/abs/2412.05953v1", "attribution": "\"On the role of semismoothness in the implicit programming approach to selected nonsmooth optimization problems\" by Helmut Gfrerer, Michal Kočvara, and Jiří V. Outrata, arXiv:2412.05953v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.11915v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Team specific median posterior Excitement Trend Indices summarized across all their matches in the 2019--2020 NBA season and ordered by the season average. The group column shows the clustering induced by our optimization procedure.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr|rrr|r}\n\\toprule\n & Average & SD & 2.5\\% & 50\\% & 97.5\\% & Group\\\\\n\\midrule\nNew Orleans Pelicans & 11.67 & 4.91 & 3.10 & 11.59 & 23.54 & A\\\\\nWashington Wizards & 11.36 & 4.46 & 2.98 & 11.63 & 18.28 & A\\\\\nSan Antonio Spurs & 11.31 & 5.31 & 3.52 & 9.88 & 23.44 & A\\\\\nOklahoma City Thunder & 11.26 & 5.32 & 1.68 & 11.55 & 23.74 & A\\\\\nPortland Trail Blazers & 11.21 & 4.46 & 3.31 & 10.82 & 18.48 & A\\\\\nLos Angeles Lakers & 11.16 & 4.43 & 3.61 & 10.52 & 19.79 & A\\\\\nPhiladelphia 76ers & 11.09 & 4.70 & 2.73 & 10.28 & 21.53 & A\\\\\nMinnesota Timberwolves & 11.09 & 4.28 & 4.01 & 10.69 & 19.46 & A\\\\\nUtah Jazz & 10.96 & 5.44 & 3.09 & 9.76 & 23.54 & A\\\\\nMemphis Grizzlies & 10.94 & 4.59 & 3.04 & 10.36 & 20.84 & A\\\\\nCleveland Cavaliers & 10.90 & 4.50 & 4.93 & 10.39 & 22.36 & A\\\\\n\\hline\nToronto Raptors & 10.69 & 4.44 & 3.48 & 10.33 & 21.79 & B\\\\\nHouston Rockets & 10.67 & 4.12 & 3.52 & 10.74 & 19.17 & B\\\\\nChicago Bulls & 10.66 & 3.98 & 2.95 & 10.65 & 17.49 & B\\\\\nBoston Celtics & 10.61 & 4.66 & 2.81 & 10.19 & 20.92 & B\\\\\nOrlando Magic & 10.56 & 4.89 & 2.72 & 10.41 & 21.01 & B\\\\\nMilwaukee Bucks & 10.55 & 4.36 & 2.77 & 9.59 & 18.13 & B\\\\\nLos Angeles Clippers & 10.54 & 4.14 & 4.05 & 10.72 & 20.56 & B\\\\\nMiami Heat & 10.44 & 4.29 & 3.28 & 9.91 & 18.54 & B\\\\\nGolden State Warriors & 10.40 & 4.58 & 3.37 & 10.24 & 18.72 & B\\\\\nIndiana Pacers & 10.37 & 3.99 & 3.31 & 10.22 & 18.54 & B\\\\\n\\hline\nBrooklyn Nets & 10.33 & 4.57 & 3.07 & 10.06 & 18.89 & C\\\\\nDallas Mavericks & 10.27 & 4.99 & 3.00 & 9.04 & 22.09 & C\\\\\nAtlanta Hawks & 10.26 & 4.35 & 2.50 & 9.75 & 18.31 & C\\\\\nPhoenix Suns & 10.25 & 4.32 & 3.78 & 9.29 & 19.81 & C\\\\\nDenver Nuggets & 10.16 & 4.39 & 3.42 & 9.59 & 19.61 & C\\\\\nNew York Knicks & 10.11 & 4.05 & 2.84 & 9.87 & 18.47 & C\\\\\nSacramento Kings & 9.94 & 4.19 & 3.66 & 9.30 & 18.93 & C\\\\\nDetroit Pistons & 9.86 & 4.00 & 3.73 & 9.44 & 17.08 & C\\\\\n\\hline\nCharlotte Hornets & 9.26 & 3.74 & 1.96 & 9.22 & 15.95 & D\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Having a Ball: evaluating scoring streaks and game excitement using in-match trend estimation", "authors": ["Claus Thorn Ekstrøm", "Andreas Kryger Jensen"], "url": "https://arxiv.org/abs/2012.11915v1", "attribution": "\"Having a Ball: evaluating scoring streaks and game excitement using in-match trend estimation\" by Claus Thorn Ekstrøm and Andreas Kryger Jensen, arXiv:2012.11915v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.03731v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{MISE per method on dry bean dataset} for $\\beta = \\frac{1}{2}$ and varying levels of $\\alpha$. We compare CBRNet against several benchmarking methods over varying levels of confounding by cluster. The best results are printed in \\textbf{bold}, second-best results are in \\textit{italics}. Results for different values of $\\alpha$ can be found in Appendix~.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n $\\beta = \\frac{1}{2}$\\\\\n \\hline \\\\ \n \\hline \\\\\n & \\multicolumn{4}{c}{$\\alpha$}\\\\\n \\cdashline{2-5}\\\\\n Model & 1.0 & 2.0 & 3.0 & 4.0 \\\\\n \\hline \\\\\n Linear Regression & 3.32 \\scriptsize{± 0.03} & 3.40 \\scriptsize{± 0.06} & 3.30 \\scriptsize{± 0.02} & 3.32 \\scriptsize{± 0.03} \\\\\n CART & 1.31 \\scriptsize{± 0.06} & 1.40 \\scriptsize{± 0.09} & 1.56 \\scriptsize{± 0.15} & 1.53 \\scriptsize{± 0.13} \\\\\n xgboost & 0.91 \\scriptsize{± 0.07} & 1.16 \\scriptsize{± 0.09} & 1.15 \\scriptsize{± 0.07} & 1.28 \\scriptsize{± 0.04} \\\\\n MLP & 0.54 \\scriptsize{± 0.04} & 0.67 \\scriptsize{± 0.08} & 0.83 \\scriptsize{± 0.09} & 1.06 \\scriptsize{± 0.08} \\\\\n \\hdashline\\\\\n DRNet & 0.62 \\scriptsize{± 0.03} & 0.78 \\scriptsize{± 0.13} & 0.82 \\scriptsize{± 0.07} & 0.98 \\scriptsize{± 0.06} \\\\\n VCNet & 0.75 \\scriptsize{± 0.11} & 1.07 \\scriptsize{± 0.10} & 1.18 \\scriptsize{± 0.11} & 1.35 \\scriptsize{± 0.07} \\\\\n \\hdashline\\\\\n CBRNet(MMD$_{lin}$) & \\textit{0.44} \\scriptsize{± 0.09} & \\textit{0.47} \\scriptsize{± 0.12} & \\textbf{0.44} \\scriptsize{± 0.07} & \\textit{0.63} \\scriptsize{± 0.15} \\\\\n CBRNet(MMD$_{rbf}$) & 0.45 \\scriptsize{± 0.10} & 0.53 \\scriptsize{± 0.14} & 0.50 \\scriptsize{± 0.10} & 0.71 \\scriptsize{± 0.17} \\\\\n CBRNet(Wass) & \\textbf{0.43} \\scriptsize{± 0.09} & \\textbf{0.46} \\scriptsize{± 0.10} & \\textit{0.45} \\scriptsize{± 0.08} & \\textbf{0.55} \\scriptsize{± 0.11} \\\\\n \\hline \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Using representation balancing to learn conditional-average dose responses from clustered data", "authors": ["Christopher Bockel-Rickermann", "Toon Vanderschueren", "Jeroen Berrevoets", "Tim Verdonck", "Wouter Verbeke"], "url": "https://arxiv.org/abs/2309.03731v2", "attribution": "\"Using representation balancing to learn conditional-average dose responses from clustered data\" by Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets, Tim Verdonck, and Wouter Verbeke, arXiv:2309.03731v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.21379v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The competing methods used in the simulation study}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\cline{1-3}\n & & \\\\\nMethod notation & Reference & \\textsf{R} package\\\\\n\\hline\nECP & & \\textbf{ecp}\\\\\nNMCD & & \\textbf{changepoint.np}\\\\\nNBS & & --\\\\\nNWBS & & --\\\\\nCPM & & \\textbf{cpm}\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Non-parametric multiple change-point detection", "authors": ["Andreas Anastasiou", "Piotr Fryzlewicz"], "url": "https://arxiv.org/abs/2504.21379v1", "attribution": "\"Non-parametric multiple change-point detection\" by Andreas Anastasiou and Piotr Fryzlewicz, arXiv:2504.21379v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.09107v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\n\\small Platform & $\\beta_{\\textbf{S}}$ & $\\beta_{\\textbf{K}}$ & $\\beta_{\\textbf{I}}$ & $\\mu_{\\textbf{K}}$ & $\\mu_{\\textbf{I}}$ \\\\ \\hline\n\\small Instagram & 0.4 & 0.35 & 0.5 & 0.1 & 0.1 \\\\ \\hline\n\\small Facebook & 0.4 & 0.5 & 0.75 & 0.1 & 0.1 \\\\ \\hline\n\\small Tiktok & 0.4 & 0.25 & 0.35 & 0.1 & 0.1 \\\\ \\hline\n\\small Twitter & 0.4 & 0.2 & 0.3 & 0.1 & 0.1 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\small Experiment 2 Coefficients}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Modeling of Rumor Propagation in Large Populations with Network via Graphon Games", "authors": ["Huaning Liu", "Gokce Dayanikli"], "url": "https://arxiv.org/abs/2503.09107v2", "attribution": "\"Modeling of Rumor Propagation in Large Populations with Network via Graphon Games\" by Huaning Liu and Gokce Dayanikli, arXiv:2503.09107v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.05224v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\n \\hline\n & RGB & + Object\\\\\n \\hline\n HAA500 & 33.53\\% & 33.73\\% \\\\\n \\:\\: Sport & 38.52\\% & 38.68\\% \\\\\n \\:\\: Instrument & 30.72\\% & 30.07\\% \\\\\n \\:\\: HAA-COCO & 34.26\\% & 34.26\\% \\\\\n \\hline\n UCF101 & 57.65\\% & 60.19\\% \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Accuracy of I3D when trained with object heatmap. HAA-COCO denotes 147 classes of HAA500 expected to have objects that were detected.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "HAA500: Human-Centric Atomic Action Dataset with Curated Videos", "authors": ["Jihoon Chung", "Cheng-hsin Wuu", "Hsuan-ru Yang", "Yu-Wing Tai", "Chi-Keung Tang"], "url": "https://arxiv.org/abs/2009.05224v2", "attribution": "\"HAA500: Human-Centric Atomic Action Dataset with Curated Videos\" by Jihoon Chung, Cheng-hsin Wuu, Hsuan-ru Yang, Yu-Wing Tai, and Chi-Keung Tang, arXiv:2009.05224v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.15365v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|} \n\t\t\\hline\n\t\tMethod & \\multicolumn{3}{c| }{VAM} & \\multicolumn{3}{c|}{FPT}\\\\\n\t\t\\hline\n\t\tTime & $E_{\\mathcal{M}_0}$ & $E_{\\mathcal{M}_1}$ & $E_{\\mathcal{M}_2}$ & $E_{\\mathcal{M}_0}$& $E_{\\mathcal{M}_1}$ & $E_{\\mathcal{M}_2}$ \\\\\n\t\t\\hline \n\t\t2 & 1.43$\\times 10^{-9}$ & 8.23$\\times 10^{-9}$ & 8.46$\\times 10^{-2}$ & 2.76$\\times 10^{-9}$ & 8.23$\\times 10^{-9}$ & 1.12$\\times 10^{-1}$\\\\\n\t\t\\hline \n\t\t4 & 5.56$\\times 10^{-9}$ & 8.29$\\times 10^{-8}$ & 1.37$\\times 10^{-1}$ & 3.17$\\times 10^{-9}$ & 8.29$\\times 10^{-8}$ & 9.46$\\times 10^{-2}$\\\\\n\t\t\\hline \n\t\t6 & 5.91$\\times 10^{-9}$ & 4.26$\\times 10^{-8}$ & 1.28$\\times 10^{-1}$ & 2.49$\\times 10^{-9}$ & 4.26$\\times 10^{-8}$ & 7.78$\\times 10^{-2}$\\\\\n\t\t\\hline \n\t\t8 & 3.02$\\times 10^{-9}$ & 7.62$\\times 10^{-8}$ & 5.88$\\times 10^{-2}$& 7.44$\\times 10^{-9}$ & 7.62$\\times 10^{-8}$ & 1.13$\\times 10^{-1}$ \\\\\n\t\t\\hline \n\t\t10 & 3.53$\\times 10^{-9}$ & 9.29$\\times 10^{-8}$ & 4.49$\\times 10^{-2}$ & 8.96$\\times 10^{-9}$ & 9.29$\\times 10^{-8}$ & 1.01$\\times 10^{-1}$\\\\\n\t\t\\hline \n\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Error analysis of different order moments for test case .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Convergence-rate and error analysis of sectional-volume average method for the collisional breakage equation with multi-dimensional modelling", "authors": ["Prakrati Kushwah", "Anupama Ghorai", "Jitraj Saha"], "url": "https://arxiv.org/abs/2504.15365v1", "attribution": "\"Convergence-rate and error analysis of sectional-volume average method for the collisional breakage equation with multi-dimensional modelling\" by Prakrati Kushwah, Anupama Ghorai, and Jitraj Saha, arXiv:2504.15365v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.14431v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{(Continued)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline \\rule[0pt]{0pt}{5mm}\n\\tt\t\\ \\ Instr\\ \\ \\ &\\tt \\\tMarketVol\t\\ &\\tt\t\\ \\ Strike \\ \\ &\\tt \tMarketPrice \t&\\tt\\,\tModelPrice\t &\\tt\t\\ \\ Error\t\\ \\ \\\\\n\\hline\n\\tt\t1Y\\_10P\t&\\tt\t4.3114\t&\\tt\t6.63663\t&\\tt\t\\ 142.6636\t&\\tt\t\\ 142.7026\t&\\tt \\ 0.0003\t\\\\\n\\tt\t1Y\\_25P\t&\\tt\t4.3238\t&\\tt\t6.81203\t&\\tt\t\\ 452.4687\t&\\tt\t\\ 452.4639\t&\\tt\t -0.0000\t\\\\\n\\tt\t1Y\\_ATM\t&\\tt\t4.3000\t&\\tt\t7.00700\t&\\tt 1177.4686\t&\\tt\t 1177.2818\t&\\tt\t -0.0002\t\\\\\n\\tt\t1Y\\_25C\t&\\tt\t4.7488\t&\\tt\t7.24315\t&\\tt\t\\ 474.8915\t&\\tt\t\\ 474.9302\t&\\tt\t\\ 0.0001\t\\\\\n\\tt\t1Y\\_10C\t&\\tt\t5.3114\t&\\tt\t7.51097\t&\\tt\t\\ 168.8805\t&\\tt\t\\ 168.8806\t&\\tt\t\\ 0.0000\t\\\\\n\\hline\n\\tt\t18M\\_10P&\\tt\t4.2841\t&\\tt\t6.55839\t&\\tt\t\\ 171.9610\t&\\tt\t\\ 171.9223\t&\\tt -0.0002\t\\\\\n\\tt\t18M\\_25P&\\tt\t4.3260\t&\\tt\t6.76832\t&\\tt\t\\ 549.7037\t&\\tt\t\\ 549.7591\t&\\tt\t -0.0001\t\\\\\n\\tt\t18M\\_ATM&\\tt\t4.3000\t&\\tt\t7.00455\t&\\tt 1422.6565\t&\\tt\t 1422.3343\t&\\tt\t -0.0002\t\\\\\n\\tt\t18M\\_25C&\\tt\t4.8010\t&\\tt\t7.30029\t&\\tt\t\\ 576.9045\t&\\tt\t\\ 577.0090\t&\\tt\t\\ 0.0002\t\\\\\n\\tt\t18M\\_10C&\\tt\t5.3341\t&\\tt\t7.63197\t&\\tt\t\\ 203.9122\t&\\tt\t\\ 203.9107\t&\\tt\t -0.0000\t\\\\\n\\hline\n\\tt\t2Y\\_10P\t&\\tt\t4.2533\t&\\tt\t6.49377\t&\\tt\t\\ 195.2634\t&\\tt\t\\ 195.2728\t&\\tt \\ 0.0000\t\\\\\n\\tt\t2Y\\_25P\t&\\tt\t4.2909\t&\\tt\t6.73300\t&\\tt\t\\ 624.0373\t&\\tt\t\\ 624.0487\t&\\tt\t\\ 0.0000\t\\\\\n\\tt\t2Y\\_ATM\t&\\tt\t4.3000\t&\\tt\t7.00130\t&\\tt 1621.7772\t&\\tt\t 1621.8055\t&\\tt\t\\ 0.0000\t\\\\\n\\tt\t2Y\\_25C\t&\\tt\t4.8409\t&\\tt\t7.34925\t&\\tt\t\\ 660.0036\t&\\tt\t\\ 660.0085\t&\\tt\t\\ 0.0000\t\\\\\n\\tt\t2Y\\_10C\t&\\tt\t5.3533\t&\\tt\t7.73650\t&\\tt\t\\ 232.3157\t&\\tt\t\\ 232.3095\t&\\tt\t -0.0000\t\\\\\n\\hline\n\\tt\t3Y\\_10P\t&\\tt\t4.2536\t&\\tt\t6.41724\t&\\tt\t\\ 234.7247\t&\\tt\t\\ 234.7244\t&\\tt -0.0000\t\\\\\n\\tt\t3Y\\_25P\t&\\tt\t4.3420\t&\\tt\t6.70437\t&\\tt\t\\ 760.3419\t&\\tt\t\\ 760.3198\t&\\tt\t\\ 0.0000\t\\\\\n\\tt\t3Y\\_ATM\t&\\tt\t4.3000\t&\\tt\t7.03305\t&\\tt 1938.1189\t&\\tt\t 1938.0847\t&\\tt\t -0.0000\t\\\\\n\\tt\t3Y\\_25C\t&\\tt\t4.8920\t&\\tt\t7.47310\t&\\tt\t\\ 790.8628\t&\\tt\t\\ 790.8363\t&\\tt\t -0.0000\t\\\\\n\\tt\t3Y\\_10C\t&\\tt\t5.3536\t&\\tt\t7.95382\t&\\tt\t\\ 275.7694\t&\\tt\t\\ 275.7730\t&\\tt\t\\ 0.0000\t\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Efficient and Accurate Calibration to FX Market Skew with Fully Parameterized Local Volatility Model", "authors": ["Dongli Wu", "Bufan Zhang", "Xiao Lin"], "url": "https://arxiv.org/abs/2211.14431v2", "attribution": "\"Efficient and Accurate Calibration to FX Market Skew with Fully Parameterized Local Volatility Model\" by Dongli Wu, Bufan Zhang, and Xiao Lin, arXiv:2211.14431v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09717v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{AUC scores of different layout novelty detection methods on \\textbf{Dataset-2}, where the best and second-best results are respectively highlighted in bold and underline}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|}\n \\hline\n \\footnotesize Method &\\footnotesize AUC Score \\\\\n \\hline \n \\footnotesize LSA~ & \\footnotesize 0.690 \\\\\n \\hline\n \\footnotesize GEOM~ & \\footnotesize 0.752 \\\\\n \\hline\n \\footnotesize GOAD~ & \\footnotesize \\underline{0.768} \\\\ \n \\hline\n \\footnotesize Autoencoder (Global) & \\footnotesize 0.675 \\\\\n \\hline\n \\footnotesize LithoNet (Local) & \\footnotesize {0.744} \\\\\n \\hline\n \\footnotesize \\textbf{SA-Glocal (AE + SA-LithoNet)} & \\footnotesize \\textbf{0.932} \\\\ \n \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning", "authors": ["Hao-Chiang Shao", "Hsing-Lei Ping", "Kuo-shiuan Chen", "Weng-Tai Su", "Chia-Wen Lin", "Shao-Yun Fang", "Pin-Yian Tsai", "Yan-Hsiu Liu"], "url": "https://arxiv.org/abs/2201.09717v1", "attribution": "\"Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning\" by Hao-Chiang Shao, Hsing-Lei Ping, Kuo-shiuan Chen, Weng-Tai Su, Chia-Wen Lin, Shao-Yun Fang, Pin-Yian Tsai, and Yan-Hsiu Liu, arXiv:2201.09717v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.10876v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|c|}\\hline\nmap & max(t)\\\\\\hline\n\\texttt{Csigma\\_\\_CW\\_sigma\\_nu} & 190\\\\\\hline\n\\texttt{Csigma\\_\\_Q\\_CW\\_nu\\_sigma} & 200\\\\\\hline\n\\texttt{CW\\_sigma\\_nu\\_\\_CW\\_sigma\\_nu\\_eta} & 200\\\\\\hline\n\\texttt{Csigma\\_\\_Csigmasq} & 193\\\\\\hline\n\\texttt{Csigmasq\\_\\_S0} & 200\\\\\\hline\n\\texttt{C2h4\\_\\_Csigmasq} & 200\\\\\\hline\n\\texttt{Csigmasq\\_\\_DC2h4} & 196\\\\\\hline\n\\texttt{Csigmasq\\_\\_Csigma} & 200\\\\\\hline\n\\texttt{Csigmasq\\_\\_C2\\_by\\_sigmasq} & 184\\\\\\hline\n\\texttt{Csigmasq\\_\\_Q\\_C2h4} & 200\\\\\\hline\n\\texttt{Csigmasq\\_\\_S0\\_by\\_sigmasq} & 200\\\\\\hline\n\\texttt{C2\\_\\_C2h4} & 200\\\\\\hline\n\\texttt{DC2h4\\_\\_C2} & 193\\\\\\hline\n\\texttt{C2h4\\_\\_S0} & 200\\\\\\hline\n\\texttt{C2\\_\\_Q\\_DC2h4} & 200\\\\\\hline\n\\texttt{C2h4\\_\\_S0\\_by\\_sigmasq} & 199\\\\\\hline\n\\texttt{C2\\_\\_C2h5} & 200\\\\\\hline\n\\texttt{C2h5\\_\\_Ctheta4} & 200\\\\\\hline\n\\texttt{Ctheta4\\_\\_DC2h5} & 200\\\\\\hline\n\\texttt{DC2h5\\_\\_C2} & 200\\\\\\hline\n\\texttt{C2h5\\_\\_S0} & 200\\\\\\hline\n\\texttt{C2\\_\\_Q\\_DC2h5} & 200\\\\\\hline\n\\texttt{C2h5\\_\\_S0\\_by\\_theta4} & 200\\\\\\hline\n\\texttt{Ctheta4\\_\\_Q\\_C2h5} & 200\\\\\\hline\n\\texttt{Ctheta4\\_\\_S0} & 200\\\\\\hline\n\\texttt{Ctheta4\\_\\_C2\\_by\\_theta4} & 168\\\\\\hline\n\\texttt{Ctheta4\\_\\_S0\\_by\\_theta4} & 200\\\\\\hline\n\\texttt{C2\\_\\_C2h6} & 200\\\\\\hline\n\\texttt{C2h6\\_\\_Ctheta5} & 200\\\\\\hline\n\\texttt{Ctheta5\\_\\_DC2h6} & 200\\\\\\hline\n\\texttt{DC2h6\\_\\_C2} & 200\\\\\\hline\n\\texttt{C2h6\\_\\_S0} & 200\\\\\\hline\n\\texttt{C2\\_\\_Q\\_DC2h6} & 198\\\\\\hline\n\\texttt{C2h6\\_\\_S0\\_by\\_theta5} & 198\\\\\\hline\n\\texttt{Ctheta5\\_\\_Q\\_C2h6} & 200\\\\\\hline\n\\texttt{Ctheta5\\_\\_S0} & 200\\\\\\hline\n\\texttt{C2\\_\\_Q\\_CW\\_2\\_theta5\\_2\\_Eq\\_eta\\_theta5} & 136\\\\\\hline\n\\texttt{Ctheta5\\_\\_Q\\_CW\\_2\\_theta5\\_2\\_Eq\\_eta\\_theta5} & 198\\\\\\hline\n\\texttt{DC2h6\\_\\_Q\\_CW\\_2\\_theta5\\_2\\_Eq\\_eta\\_theta5} & 191\\\\\\hline\n\\texttt{C2\\_C2\\_\\_C2} & 180\\\\\\hline\n\\texttt{C2\\_\\_C2\\_C2} & 180\\\\\\hline\n\\texttt{CW\\_2\\_eta\\_\\_Q\\_RP1\\_6} & 180\\\\\\hline\n\\texttt{Ceta\\_Ceta\\_\\_Ceta} & 157\\\\\\hline\n\\texttt{Ceta\\_\\_Ceta\\_Ceta} & 160\\\\\\hline\n\\texttt{Cnu\\_Cnu\\_\\_Cnu} & 180\\\\\\hline\n\\end{tabular}\n\\caption{Maps between CW spectra}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Machine Proofs for Adams Differentials and Extension Problems among CW Spectra", "authors": ["Weinan Lin", "Guozhen Wang", "Zhouli Xu"], "url": "https://arxiv.org/abs/2412.10876v2", "attribution": "\"Machine Proofs for Adams Differentials and Extension Problems among CW Spectra\" by Weinan Lin, Guozhen Wang, and Zhouli Xu, arXiv:2412.10876v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.16530v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c|c}\n\\textbf{Set} & \\textbf{\\#Videos} & \\textbf{Total Duration} & \\textbf{Avg Duration} \\\\ \\hline\nTrainval & 4,004 & 30 hr & 3.42 sec \\\\ \\hline\nTest & 412 & 51 min & 2.32 sec \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{LRS3 dataset statistics.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Improving Lip-synchrony in Direct Audio-Visual Speech-to-Speech Translation", "authors": ["Lucas Goncalves", "Prashant Mathur", "Xing Niu", "Brady Houston", "Chandrashekhar Lavania", "Srikanth Vishnubhotla", "Lijia Sun", "Anthony Ferritto"], "url": "https://arxiv.org/abs/2412.16530v1", "attribution": "\"Improving Lip-synchrony in Direct Audio-Visual Speech-to-Speech Translation\" by Lucas Goncalves, Prashant Mathur, Xing Niu, Brady Houston, Chandrashekhar Lavania, Srikanth Vishnubhotla, Lijia Sun, and Anthony Ferritto, arXiv:2412.16530v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.08222v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Productivity Processes Estimates}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n& & $\\omega^H$ & $\\omega^L$ \\\\ \n \\midrule\n Persistency (H) & $\\rho_H$ & 0.880 & \\\\ \n & & (0.053) & (-) \\\\ \n Persistency (L) & $\\rho_L$ & & 0.885 \\\\ \n & & (-) & (0.015) \\\\ \n $IDA$ control & $\\beta_1$ & -0.031 & 0.010 \\\\ \n & & (0.015) & (0.011) \\\\ \n $Imp_{-1}$ control & $\\beta_2$ & -0.071 & -0.044 \\\\ \n & & (0.095) & (0.063) \\\\ \n \\midrule\n Year FE& & YES & YES \\\\\n \\bottomrule\n \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Do Temporary Workers Face Higher Wage Markdowns? Evidence from India's Automotive Sector", "authors": ["Davide Luparello"], "url": "https://arxiv.org/abs/2507.08222v1", "attribution": "\"Do Temporary Workers Face Higher Wage Markdowns? Evidence from India's Automotive Sector\" by Davide Luparello, arXiv:2507.08222v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18869v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\\hline\n s & $R_2(s)$ &Classic Bound & Our Bound \\\\\\hline\\hline\n $5$ & $R_2(5) \\leq 46$ & $m_2(5)\\leq 2,677$ & $m_2(5)\\leq 392$ \\\\\\hline\n $6$ & $R_2(6)\\leq 160$ & $m_2(6)\\leq 1,293,533$ & $m_2(6)\\leq 152,000$ \\\\\\hline\n $7$ & $R_2(7)\\leq 492$ & $m_2(7)\\leq 1,265,045,472$ & $m_2(7)\\leq 282,475,249$ \\\\\\hline\n $8$ & $R_2(8)\\leq 1,518$ & $m_2(8)\\leq 5,114,696,152,715$ & $m_2(8)\\leq 4,065,272,127,504$ \\\\\\hline\n $9$ & $R_2(9)\\leq 4,956$ & $m_2(9)\\leq 1.437\\times 10^{17}$ & $m_2(9)\\leq 6.947\\times 10^{17}$ \\\\\\hline\n $10$& $R_2(10)\\leq 16,064$ & $m_2(10)\\leq 1.788\\times 10^{22}$ & $m_2(10)\\leq 1.032 \\times 10^{24}$ \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Values of the bound given by , v.s. the classical probabilistic bound . The bound is initially an improvement on , but becomes worse for $s \\geq 9$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Bounds on the Critical Multiplicity of Ramsey Numbers with Many Colors", "authors": ["Bryce Christopherson", "Casia Steinhaus"], "url": "https://arxiv.org/abs/2501.18869v2", "attribution": "\"Bounds on the Critical Multiplicity of Ramsey Numbers with Many Colors\" by Bryce Christopherson and Casia Steinhaus, arXiv:2501.18869v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.13824v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{99\\% confidence intervals for the moments, standard deviation and median of the jump part of the logarithmic returns of Bitcoin price}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc}\n& 1st moment & 2d moment & 3d moment & SD & Median \\\\ \\hline\nLower CI & \\(-0.0074\\) & 0.0044 & \\(-0.0991\\) & 0.0658 & \\(-0.0001\\)\\\\\nTrue value & 0.0028 & 0.0044 & \\(-0.0003\\) & 0.0662 & 0.0062 \\\\\nUpper CI & 0.0119 & 0.0381 & 0.0442 & 0.1951 & 0.0111 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Modelling the Bitcoin prices and the media attention to Bitcoin via the jump-type processes", "authors": ["Ekaterina Morozova", "Vladimir Panov"], "url": "https://arxiv.org/abs/2210.13824v1", "attribution": "\"Modelling the Bitcoin prices and the media attention to Bitcoin via the jump-type processes\" by Ekaterina Morozova and Vladimir Panov, arXiv:2210.13824v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.09385v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc|cccc|cccc}\n\t\t\t\\hline\n\t\t\t$d$ &\t$p$ &\t$\\hat{\\chi}$ & CI for $\\chi$ &\t$d$ & $p$ & $\\hat{\\chi}$ & CI for $\\chi$ &\t$d$ & $p$ &\t$\\hat{\\chi}$ & CI for $\\chi$ \\\\ \\hline\n\t\t\t2 & $1.5$ & 0.30 \t& (0.28, 0.32) & 3 & $1.5$ & 0.28 & (0.20, 0.36) & 4 & $1.5$ & 0.19 & (0.03, 0.36) \\\\ \n\t\t\t2 & $2$ & 0.31 \t&\t(0.30, 0.32) & 3 & $2$ & 0.23 \t& (0.20, 0.25) & 4 & $2$ & 0.16 & (0.13, 0.19) \\\\\n\t\t\t2 & $4$ & 0.33\t&\t(0.31, 0.34) & 3 & $4$ & 0.24 \t& (0.22, 0.25) & 4 & $4$ & 0.14 \t& (0.11, 0.18) \\\\\n\t\t\t2 & $8$ & 0.34 & (0.32, 0.37) & 3 & $8$ & 0.29 \t& (0.27, 0.32) & 4 & $8$ & 0.19 \t& (0.14, 0.23) \\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Confidence interval estimates of $\\chi$ for uniform data for different density weightings ($p$) and different dimensions $(d)$. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Balancing Geometry and Density: Path Distances on High-Dimensional Data", "authors": ["Anna Little", "Daniel McKenzie", "James Murphy"], "url": "https://arxiv.org/abs/2012.09385v2", "attribution": "\"Balancing Geometry and Density: Path Distances on High-Dimensional Data\" by Anna Little, Daniel McKenzie, and James Murphy, arXiv:2012.09385v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.06234v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The distribution of non-MC integers $n<1100$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|}\n\\hline\n$\\lfloor n/100 \\rfloor$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 &10 \\\\\n\\hline\n$\\sharp\\{n|$ non-MC$\\}$ & 21 & 16 & 20 & 19 & 13 & 17& 17 & 14 & 23 & 13 & 16 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Largest Circle Enclosing $n$ Lattice Points", "authors": ["Jianqiang Zhao"], "url": "https://arxiv.org/abs/2505.06234v2", "attribution": "\"The Largest Circle Enclosing $n$ Lattice Points\" by Jianqiang Zhao, arXiv:2505.06234v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.08810v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c||ccc|} \n\t\t\\hline\n\t\t& Elementary Step & Rate Expression & Linear Form \\\\\n\t\t\\hline\\hline\n\t\t1 & $A + * \\to A^*$ & $r = k^+ C_A C_*$ & $r_A = \\beta_{(Nk^+)} C_A - \\beta_{(k^+)} C_A U_A$ \\\\ \n\t\t2 & $A + * \\rightleftharpoons A^*$ & $r=k^+ C_A C_* - k^-C_{A^*}$ & $r_A = \\beta_{(Nk^+)}C_A -\\beta_{(k^+)} C_AU_{A^*} - \\beta_{(k^-)} U_{A^*}$ \\\\\n\t\t3 & $A+2* \\to 2A^*$ & $r = k^+ C_A C_*^2$ & $r_A = \\beta_{(Nk^+)} C_A - \\beta_{(2Nk^+)} C_A U_{A^*} + \\beta_{(k^+)} C_A U_{A^*}^2$ \\\\\n\t\t4 & $A^* + B^* \\to 2* + C$ & $r = k^+C_{A^*}C_{B^*}$ & $r_{A^*} = \\beta_{(k^+)}U_{A^*}U_{B^*}$ \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of elementary steps that correspond to the traditional rate expression and linear model coefficients.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Data Driven Reaction Mechanism Estimation via Transient Kinetics and Machine Learning", "authors": ["M. Ross Kunz", "Adam Yonge", "Zongtang Fang", "Andrew J. Medford", "Denis Constales", "Gregory Yablonsky", "Rebecca Fushimi"], "url": "https://arxiv.org/abs/2011.08810v2", "attribution": "\"Data Driven Reaction Mechanism Estimation via Transient Kinetics and Machine Learning\" by M. Ross Kunz, Adam Yonge, Zongtang Fang, Andrew J. Medford, Denis Constales, Gregory Yablonsky, and Rebecca Fushimi, arXiv:2011.08810v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10001v5_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc} \n \\hline\n Algorithm & MNIST $\\rightarrow$ USPS & SVHN $\\rightarrow$ MNIST \\\\\n \\hline\n Source only & 75.2\\% & 60.1\\% \\\\\n Gradient Reversal & 77.1\\% & 73.9\\% \\\\\n Domain Confusion & 79.1\\% & 68.1\\% \\\\\n CoDAN & 91.2\\% & did not converge \\\\\n ADDA & 89.4\\% & 76.0\\% \\\\\n Associative & 94.1\\% & 93.6\\% \\\\\n DANN & 60.8\\% & 76.3\\% \\\\\n Deep Coral & 69.5\\% & 76.3\\% \\\\\n VADA & 90.6\\% & 92.6\\% \\\\\n \\hline\n E-ADDA & \\textbf{95.4\\%} & \\textbf{95.4\\%} \\\\\n \\hline\n \\\\\n \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{We compare our technique, E-ADDA, with nine other state-of-the-art deep domain adaptation techniques on two tasks (the performance is measured in accuracy, per the evaluation standard of the computer vision community).}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "E-ADDA: Unsupervised Adversarial Domain Adaptation Enhanced by a New Mahalanobis Distance Loss for Smart Computing", "authors": ["Ye Gao", "Brian Baucom", "Karen Rose", "Kristina Gordon", "Hongning Wang", "John Stankovic"], "url": "https://arxiv.org/abs/2201.10001v5", "attribution": "\"E-ADDA: Unsupervised Adversarial Domain Adaptation Enhanced by a New Mahalanobis Distance Loss for Smart Computing\" by Ye Gao, Brian Baucom, Karen Rose, Kristina Gordon, Hongning Wang, and John Stankovic, arXiv:2201.10001v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08779v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\nArchitecture & +PDCR & +UAFS & JA & DI & AC \\\\\n\\midrule\nUNet & & & 69.34 & 79.33 & 90.96 \\\\\nUNet &\\checkmark& & 70.50 & 80.51 & 92.02 \\\\\nUNet &&\\checkmark& 70.28 & 80.41 & 91.94 \\\\\nUNet &\\checkmark&\\checkmark& \\bf 71.35 & \\bf 81.52 & \\bf 93.01 \\\\\n\\midrule\nFCN & & &72.31 & 81.20 & 92.22 \\\\\nFCN &\\checkmark& & 74.00 & 82.95 & 93.09\\\\\nFCN & &\\checkmark& 74.67 & 83.39 & 93.02 \\\\\nFCN &\\checkmark&\\checkmark& \\bf 75.57 & \\bf 84.25 & \\bf 93.95\\\\\n\\midrule\nDeepLabv3+ &&& 77.15 & 85.13 & 92.88 \\\\\nDeepLabv3+ &\\checkmark& & 79.00& 86.91 & 94.11\\\\\nDeepLabv3+ &&\\checkmark& 78.96 & 86.19 & 94.28 \\\\\nDeepLabv3+ &\\checkmark&\\checkmark& \\bf 80.86 & \\bf 88.74 & \\bf 95.18 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results of ablation studies for different architectures. A strong and consistent improvement is demonstrated.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Contrastive and Selective Hidden Embeddings for Medical Image Segmentation", "authors": ["Zhuowei Li", "Zihao Liu", "Zhiqiang Hu", "Qing Xia", "Ruiqin Xiong", "Shaoting Zhang", "Dimitris Metaxas", "Tingting Jiang"], "url": "https://arxiv.org/abs/2201.08779v2", "attribution": "\"Contrastive and Selective Hidden Embeddings for Medical Image Segmentation\" by Zhuowei Li, Zihao Liu, Zhiqiang Hu, Qing Xia, Ruiqin Xiong, Shaoting Zhang, Dimitris Metaxas, and Tingting Jiang, arXiv:2201.08779v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.10784v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The results of ablation study on DKD metric when adapting Med-VFMs to AMOS2022-CT and AMOS2022-MRI domains. The performance was evaluated using Dice scores, and the results were reported as Mean$\\pm$SD.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n\\toprule\n\\multirow{2}{*}{Methods} & \\multicolumn{2}{c|}{AMOS2022-CT} & \\multicolumn{2}{c}{AMOS2022-MR} \\\\ \n\\cline{2-5}\n & $5\\%$ & $10\\%$ & $5\\%$ & $10\\%$ \\\\\n\\midrule\nRAND & 73.31$\\pm$15.50 & 77.57$\\pm$14.65 & 35.84$\\pm$27.14 & 48.06$\\pm$25.24 \\\\\nHigh DKD & 76.14$\\pm$13.67 & 79.48$\\pm$12.56 & 45.23$\\pm$20.89 & 64.46$\\pm$19.56 \\\\\nLow DKD & 64.48$\\pm$14.72 & 68.20$\\pm$14.83 & 26.86$\\pm$22.67 & 42.59$\\pm$20.75 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning", "authors": ["Jin Yang", "Daniel S. Marcus", "Aristeidis Sotiras"], "url": "https://arxiv.org/abs/2509.10784v1", "attribution": "\"Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning\" by Jin Yang, Daniel S. Marcus, and Aristeidis Sotiras, arXiv:2509.10784v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.15245v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative results on Kvasir-SEG and unseen (Challenge) dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l|l|l|l}\n\\hline\n\\textbf{Dataset}&\\textbf{Method} & \\textbf{DSC} & \\textbf{mIOU} & \\textbf{Recall} & \\textbf{Precision} & \\textbf{FPS} \\\\ \\hline\nKvasir-SEG& DDANet & 0.8576 & 0.7800 & 0.8880 & 0.8643 & 69.59\\\\ \\hline\nUnseen (\\textbf{Challenge}) & DDANet & 0.7874 & 0.7010 & 0.7987 & 0.8577 & 70.23 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation", "authors": ["Nikhil Kumar Tomar", "Debesh Jha", "Sharib Ali", "Håvard D. Johansen", "Dag Johansen", "Michael A. Riegler", "Pål Halvorsen"], "url": "https://arxiv.org/abs/2012.15245v1", "attribution": "\"DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation\" by Nikhil Kumar Tomar, Debesh Jha, Sharib Ali, Håvard D. Johansen, Dag Johansen, Michael A. Riegler, and Pål Halvorsen, arXiv:2012.15245v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.06923v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|l|}\n\t\t\\hline\n\t\tPorosity & 10\\%\\\\\n\t\t\\hline\n\t\tTotal Compressibility & $5\\times 10^{-8}$ Pa$^{-1}$\\\\\n\t\t\\hline\n\t\tDynamic Viscosity & 0.002 Pa*s\\\\\n\t\t\\hline\n\t\tProduction Index & 1.175$\\times$10$^{-5}$ m$^3$Sec$^{-1}$MPa$^{-1}$\\\\\n\t\t\\hline\n\t\tBottom-hole Pressure & 20 MPa \\\\\n\t\t\\hline\n\t\\end{tabular}\n\\caption{A summary of constant physical and well parameters for test case 1.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Nonlinear Expectation Inference for Efficient Uncertainty Quantification and History Matching of Transient Darcy Flows in Porous Media with Random Parameters Under Distribution Uncertainty", "authors": ["Zhao Zhang", "Xinpeng Li", "Menghan Li", "Jiayu Zhai", "Piyang Liu", "Xia Yan", "Kai Zhang"], "url": "https://arxiv.org/abs/2312.06923v3", "attribution": "\"Nonlinear Expectation Inference for Efficient Uncertainty Quantification and History Matching of Transient Darcy Flows in Porous Media with Random Parameters Under Distribution Uncertainty\" by Zhao Zhang, Xinpeng Li, Menghan Li, Jiayu Zhai, Piyang Liu, Xia Yan, and Kai Zhang, arXiv:2312.06923v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19057v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparative Analysis of Machine Learning Model Performance}\n\\begin{tabular}{lllll}\n \\toprule\n \\textbf{Model name} & \\textbf{Precision} & \\textbf{Recall} & \\textbf{F1} & \\textbf{Accuracy} \\\\\n \\midrule\n \n NB & 0.778 & 0.989 & 0.772 & 0.740 \\\\\n GLM & 0.797 & 0.989 & 0.830 & 0.809 \\\\\n GBM & \\textbf{0.815} & \\textbf{0.998} & \\textbf{0.843} & \\textbf{0.822} \\\\\n MLP& 0.792 & 0.998 & 0.839 & 0.819 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions", "authors": ["Zainab Al-Zanbouri", "Gauri Sharma", "Shaina Raza"], "url": "https://arxiv.org/abs/2403.19057v1", "attribution": "\"Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions\" by Zainab Al-Zanbouri, Gauri Sharma, and Shaina Raza, arXiv:2403.19057v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.17607v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Pre-training Results.} `S': speech input and output. `S$\\rightarrow$T': speech input and text output. `T$\\rightarrow$S': text input and speech output. Results for Spirit-LM are taken from and other results are from . We use $\\emptyset$ to indicate tasks and modalities not supported by the model, and - to indicate scores that are not publicly available.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc|cccccc}\n\\toprule\n \\multirow{3}{*}{Model} & \\multicolumn{6}{c|}{\\textbf{Speech Language Modeling}} & \\multicolumn{6}{c}{\\textbf{Spoken Question Anwsering}} \\\\\n & \\multicolumn{3}{c}{sTopic-StoryCloze} & \\multicolumn{3}{c|}{sStoryCloze} & \\multicolumn{2}{c}{Web Questions} & \\multicolumn{2}{c}{Llama Questions} & \\multicolumn{2}{c}{TriviaQA} \\\\\n & S & T$\\rightarrow$S & S$\\rightarrow$T & S & T$\\rightarrow$S & S$\\rightarrow$T & S & S$\\rightarrow$T & S & S$\\rightarrow$T & S & S$\\rightarrow$T \\\\\n\\midrule\nGSLM & 66.6 & $\\emptyset$ & $\\emptyset$ & 53.3 & $\\emptyset$ & $\\emptyset$ & 1.5 & $\\emptyset$ & 4.0 \n& $\\emptyset$ & - & - \\\\\nAudioLM & - & $\\emptyset$ & $\\emptyset$ & - & $\\emptyset$ & $\\emptyset$ & 2.3 & $\\emptyset$ & 7.0 & $\\emptyset$ & - & - \\\\\nTWIST & 76.4 & $\\emptyset$ & $\\emptyset$ & 55.4 & $\\emptyset$ & $\\emptyset$ & 1.1 & $\\emptyset$ & 0.5 & $\\emptyset$ & - & - \\\\\nSpirit-LM & 82.9 & 72.7 & 88.6 & 61.0 & 59.6 & 64.6 & - & - & - & - & - & - \\\\\nSpeechGPT & $\\emptyset$ & $\\emptyset$ & $\\emptyset$ & $\\emptyset$ & $\\emptyset$ & $\\emptyset$ & $\\emptyset$ & 6.5 & $\\emptyset$ & 21.6 & $\\emptyset$ & 14.8 \\\\\nSpectron & - & - & - & - & - & - & $\\emptyset$ & 6.1 & $\\emptyset$ & 21.9 & $\\emptyset$ & - \\\\\nMoshi & \\textbf{83.0} & $\\emptyset$ & $\\emptyset$ & 60.8 & $\\emptyset$ & $\\emptyset$ & 9.2 & 26.6 & 21.0 & 62.3 & 7.3 & 22.8 \\\\ \\midrule\nOurs (9B) & 82.9 & \\textbf{85.0} & \\textbf{93.6} & \\textbf{62.4} & \\textbf{63.2} & \\textbf{76.3} & \\textbf{15.9} & \\textbf{32.2} & \\textbf{50.7} & \\textbf{64.7} & \\textbf{26.5} & \\textbf{39.1} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Scaling Speech-Text Pre-training with Synthetic Interleaved Data", "authors": ["Aohan Zeng", "Zhengxiao Du", "Mingdao Liu", "Lei Zhang", "Shengmin Jiang", "Yuxiao Dong", "Jie Tang"], "url": "https://arxiv.org/abs/2411.17607v2", "attribution": "\"Scaling Speech-Text Pre-training with Synthetic Interleaved Data\" by Aohan Zeng, Zhengxiao Du, Mingdao Liu, Lei Zhang, Shengmin Jiang, Yuxiao Dong, and Jie Tang, arXiv:2411.17607v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09246v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Recognition accuracy (\\%) of competing approaches on the AR database when two neutral face images (from Sessions 1 and 2) per subject are used as training samples, the dimension that leads to the best result for IGO and SOIGO based approaches is given in parentheses.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\nMethods & \\multicolumn{2}{c}{Sunglasses} & \\multicolumn{2}{c}{Scarf} & Overall \\\\ \\hline\n & Session1 & Session2 & Session1 & Session2 & \\\\\nHQPAMI & 61.33 & 59.33 & 44.67 & 48.00 & 53.33 \\\\\nNR & 34.00 & 33.33 & 33.00 & 35.67 & 34.00 \\\\\nProCRC & 53.00 & 54.67 & 18.00 & 17.67 & 35.84 \\\\\nF-LR-IRNNLS & 90.33 & 87.67 & 78.67 & 76.00 & 83.17 \\\\\nEGSNR & 88.00 & 89.33 & 80.00 & 73.00 & 82.58 \\\\\nLDMR & 71.00 & 63.67 & 64.00 & 61.00 & 64.92 \\\\\nGD-HASLR & 93.00 & 93.33 & 82.67 & 84.00 & 88.25 \\\\ \nIGO-PCA-NNC & 93.00 (182) & 91.67 (191) & 78.00 (199) & 74.00 (193) & 84.17 \\\\\nIGO-PCA-CRC & 96.00 (128) & 95.33 (116) & 85.00 (190) & 84.00 (160) & 90.08 \\\\\nSOIGO-PCA-NNC & 96.33 (187) & 92.67 (197) & \\textbf{86.33 (166)} & 83.67 (189) & 89.75 \\\\\nCSOIGO & \\textbf{97.33 (144)} & \\textbf{95.67 (124)} & 86.00 (119) & \\textbf{85.67 (198)} & \\textbf{91.17} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Face recognition via compact second order image gradient orientations", "authors": ["He-Feng Yin", "Xiao-Jun Wu", "Xiaoning Song"], "url": "https://arxiv.org/abs/2201.09246v2", "attribution": "\"Face recognition via compact second order image gradient orientations\" by He-Feng Yin, Xiao-Jun Wu, and Xiaoning Song, arXiv:2201.09246v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11702v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CORDIC Hyperbolic Rotational Mode ($\\cosh$ \\& $\\sinh$)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccr}\n \\hline\n \\textbf{i (clk)} & \\textbf{Ei = tanh\\textsuperscript{-1}(2\\textsuperscript{-i})} & \\textbf{Xi+1 $\\rightarrow$ $\\cosh$(0.5)} & \\textbf{Yi+1 $\\rightarrow$ sinh(0.5)} & \\textbf{Zi+1 $\\rightarrow$ 0} & \\textbf{di} \\\\ \\hline\n & & \\textbf{Initial} Xi = 1/k' & \\textbf{Initial} Yi = 0 & Zinput = 0.5 & \\\\ \\hline\n \\textbf{1} & 0.5493 & 1.2075 & \\textbf{0.6037} & -0.0493 & 1 \\\\ \\hline\n \\textbf{2} & 0.2554 & 1.0566 & 0.3019 & 0.2061 & -1 \\\\ \\hline\n \\textbf{3} & 0.1257 & 1.0943 & 0.4339 & 0.0804 & 1 \\\\ \\hline\n \\textbf{4} & \\textbf{0.0626} & \\textbf{1.1214} & \\textbf{0.5023} & \\textbf{0.0179} & \\textbf{1} \\\\ \\hline\n \\textbf{5} & 0.0313 & 1.1371 & 0.5374 & -0.0134 & 1 \\\\ \\hline\n \\textbf{6} & 0.0156 & 1.1287 & 0.5196 & 0.0022 & -1 \\\\ \\hline\n \\textbf{7} & 0.0068 & 1.1328 & 0.5284 & -0.0046 & 1 \\\\ \\hline\n \\textbf{8} & 0.0039 & 1.1307 & 0.5240 & -0.0007 & -1 \\\\ \\hline\n \\textbf{9} & 0.0020 & \\textbf{1.1297} & \\textbf{0.5218} & \\textbf{0.0013} & -1 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads", "authors": ["Mukul Lokhande", "Gopal Raut", "Santosh Kumar Vishvakarma"], "url": "https://arxiv.org/abs/2412.11702v1", "attribution": "\"Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads\" by Mukul Lokhande, Gopal Raut, and Santosh Kumar Vishvakarma, arXiv:2412.11702v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01904v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{||ccc||} \n \\hline\n Algorithm 1 & Algorithm 2 & Reject Null Hypothesis? \\\\ \n \\hline\\hline\n Logistic Regression & Decision Tree & Yes \\\\ \n \\hline\n Decision Tree & Neural Network & Yes \\\\\n \\hline\n Logistic Regression & Neural Network & No \\\\\n \\hline\n\\end{tabular}\n\\caption{Hypothesis decision results}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Comparing Classification Models on Kepler Data", "authors": ["Rohan Saha"], "url": "https://arxiv.org/abs/2101.01904v2", "attribution": "\"Comparing Classification Models on Kepler Data\" by Rohan Saha, arXiv:2101.01904v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.16071v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hypothetical capacitor optimization options}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\n\\textbf{part \\#} & \\textbf{Description} & \\textbf{$\\mu F$ at 3.3V} & \\textbf{Cost} & \\textbf{Area mm$^2$} \\\\\n\\hline\nA & 1$\\mu F$ 0201 6.3V & 0.35 & \\$0.002 & 0.7 \\\\\n\\hline\nB & 2.2$\\mu F$ 0201 6.3V & 0.85 & \\$0.003 & 0.7 \\\\\n\\hline\nC & 1$\\mu F$ 0201 10V & 0.45 & \\$0.003 & 0.7 \\\\\n\\hline\nD & 2.2$\\mu F$ 0201 10V & 0.95 & \\$0.004 & 0.7 \\\\\n\\hline\nE & 2.2$\\mu F$ 0402 6.3V & 0.90 & \\$0.003 & 1.3 \\\\\n\\hline\nF & 4.7$\\mu F$ 0402 6.3V & 1.70 & \\$0.007 & 1.3 \\\\\n\\hline\nG & 2.2$\\mu F$ 0402 10V & 1.00 & \\$0.005 & 1.3 \\\\\n\\hline\nH & 4.7$\\mu F$ 0402 10V & 1.95 & \\$0.008 & 1.3 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Automating Capacitor Part Selection with Dual-Objective Optimization", "authors": ["Luke Brantingham", "Jason Grover"], "url": "https://arxiv.org/abs/2507.16071v1", "attribution": "\"Automating Capacitor Part Selection with Dual-Objective Optimization\" by Luke Brantingham and Jason Grover, arXiv:2507.16071v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.10112v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Coherent beamforming protocols.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\nRB & Random beamforming protocol \\\\ \\hline\nRT & Random target protocol \\\\ \\hline\nBT & Best target protocol \\\\ \\hline\nSF & Sequential fixing protocol \\\\ \\hline\nIO & Iterative optimization protocol \\\\ \\hline\nES & Exhaustive search protocol\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Coherent Communications in Self-Organizing Networks with Distributed Beamforming", "authors": ["Yi Shi", "Yalin E. Sagduyu"], "url": "https://arxiv.org/abs/1912.10112v1", "attribution": "\"Coherent Communications in Self-Organizing Networks with Distributed Beamforming\" by Yi Shi and Yalin E. Sagduyu, arXiv:1912.10112v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13714v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\t\\small\\textbf{Case}& \\small\\textbf{Conditions}& \\small\\textbf{$\\boldsymbol{O_1}$}& \\small\\textbf{$\\boldsymbol{O_2}$} & \\small\\textbf{Global} \\\\\n\t\t\t\\hline\n\t\t\t\\hline\n\t\t\t\n\t\t\t 1.1\n\t\t\t&\n\t\t\t\n\t\t\t&\n\t\t\tL12\n\t\t\t&\n\t\t\tUnstable node\n\t\t\t&\n\t\t\tG1 \\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t1.2\n\t\t\t&\n\t\t\t\n\t\t\t&\n\t\t\tL3\n\t\t\t&\n\t\t\tStable node\n\t\t\t&\n\t\t\tG2 \\\\\n\t\t\\hline\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\\multirow{2}{1cm}{\\centering{1.3}}\n\t\t\t&\n\t\t\t$\\mu<-1$\n\t\t\t&\n\t\t\tL8\n\t\t\t&\n\t\t\tSaddle\n\t\t\t&\n\t\t\tG3 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{2}{*}\n\t\t\t&\n\t\t\t$\\mu\\in(-1,0)$\n\t\t\t&\n\t\t\tL13\n\t\t\t&\n\t\t\tUnstable node\n\t\t\t&\n\t\t\tG4 \\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\\multirow{4}{1cm}{\\centering{1.4}}\n\t\t\t&\n\t\t\t$\\mu<-2$\n\t\t\t&\n\t\t\tL1\n\t\t\t&\n\t\t\tSaddle\n\t\t\t&\n\t\t\tG5 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{4}{*}\n\t\t\t&\n\t\t\t$\\mu\\in(-1,0)$\n\t\t\t&\n\t\t\tL4\n\t\t\t&\n\t\t\tSaddle\n\t\t\t&\n\t\t\tG7 \\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{4}{*}\n\t\t\t&\n\t\t\t$\\mu\\in(-2,-1)$\n\t\t\t&\n\t\t\tL15\n\t\t\t&\n\t\t\tSaddle\n\t\t\t&\n\t\t\tG7\\\\\n\t\t\t\\cline{2-5}\n\t\t\t\n\t\t\t\\multirow{4}{*}\n\t\t\t&\n\t\t\t$\\mu=-2$\n\t\t\t&\n\t\t\tL17\n\t\t\t&\n\t\t\tSaddle\n\t\t\t&\n\t\t\tG8 \\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t1.5\n\t\t\t&\n\t\t\t\n\t\t\t&\n\t\t\tL12\n\t\t\t&\n\t\t\tUnstable node\n\t\t\t&\n\t\t\tG9 \\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t1.6\n\t\t\t&\n\t\t\t\n\t\t\t&\n\t\t\tL3\n\t\t\t&\n\t\t\tStable node\n\t\t\t&\n\t\t\tG10 \\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\n\t\t\\multirow{2}{1cm}{\\centering{1.7}}\n\t\t&\n\t\t$\\mu\\in(-1,0)$\n\t\t&\n\t\tL10\n\t\t&\n\t\tStable node\n\t\t&\n\t\tG11 \\\\\n\t\t\\cline{2-5}\n\t\t\n\t\t\\multirow{2}{*}\n\t\t&\n\t\t$\\mu<-1$\n\t\t&\n\t\tL11\n\t\t&\n\t\tSaddle\n\t\t&\n\t\tG12 \\\\\n\t\t\\hline\n\t\n\t\t\t\t\\multirow{4}{1cm}{\\centering{1.8}}\n\t\t\t\t&\n\t\t\t\t$\\mu<-2$\n\t\t\t\t&\n\t\t\t\tL2\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG13 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-1,0)$\n\t\t\t\t&\n\t\t\t\tL7\n\t\t\t\t&\n\t\t\t\tUnstable node\n\t\t\t\t&\n\t\t\t\tG14 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-2,-1)$\n\t\t\t\t&\n\t\t\t\tL16\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG15 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu=-2$\n\t\t\t\t&\n\t\t\t\tL18\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG16 \\\\\n\t\t\t\t\\hline\n\t\t\t\n\t\t\t\t1.9\n\t\t\t\t&\n\t\t\t\t\n\t\t\t\t&\n\t\t\t\tL9\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG19 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t1.10\n\t\t\t\t&\n\t\t\t\t\n\t\t\t\t&\n\t\t\t\tL6\n\t\t\t\t&\n\t\t\t\tUnstable node\n\t\t\t\t&\n\t\t\t\tG20 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t1.11\n\t\t\t\t&\n\t\t\t\t\n\t\t\t\t&\n\t\t\t\tL12\n\t\t\t\t&\n\t\t\t\tUnstable node\n\t\t\t\t&\n\t\t\t\tG17 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\\multirow{2}{1cm}{\\centering{1.12}}\n\t\t\t\t&\n\t\t\t\t$\\mu<-1$\n\t\t\t\t&\n\t\t\t\tL8\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG21 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{2}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-1,0)$\n\t\t\t\t&\n\t\t\t\tL13\n\t\t\t\t&\n\t\t\t\tUnstable node\n\t\t\t\t&\n\t\t\t\tG22 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t1.13\n\t\t\t\t&\n\t\t\t\t\n\t\t\t\t&\n\t\t\t\tL3\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG18 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\\multirow{4}{1cm}{\\centering{1.14}}\n\t\t\t\t&\n\t\t\t\t$\\mu<-2$\n\t\t\t\t&\n\t\t\t\tL1\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG23 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-1,0)$\n\t\t\t\t&\n\t\t\t\tL4\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG24 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-2,-1)$\n\t\t\t\t&\n\t\t\t\tL15\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG25 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu=-2$\n\t\t\t\t&\n\t\t\t\tL17\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG26 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t1.15\n\t\t\t\t&\n\t\t\t\t\n\t\t\t\t&\n\t\t\t\tL12\n\t\t\t\t&\n\t\t\t\tUnstable node\n\t\t\t\t&\n\t\t\t\tG27 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\\multirow{2}{1cm}{\\centering{1.16}}\n\t\t\t\t&\n\t\t\t\t$\\mu<-1$\n\t\t\t\t&\n\t\t\t\tL8\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG35 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{2}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-1,0)$\n\t\t\t\t&\n\t\t\t\tL13\n\t\t\t\t&\n\t\t\t\tUnstable node\n\t\t\t\t&\n\t\t\t\tG36 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t1.17\n\t\t\t\t&\n\t\t\t\t\n\t\t\t\t&\n\t\t\t\tL3\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG28 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\\multirow{4}{1cm}{\\centering{1.18}}\n\t\t\t\t&\n\t\t\t\t$\\mu<-2$\n\t\t\t\t&\n\t\t\t\tL1\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG37 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-1,0)$\n\t\t\t\t&\n\t\t\t\tL4\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG38 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-2,-1)$\n\t\t\t\t&\n\t\t\t\tL15\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG39 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu=-2$\n\t\t\t\t&\n\t\t\t\tL17\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG40 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\\multirow{2}{1cm}{\\centering{1.19}}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-1,0)$\n\t\t\t\t&\n\t\t\t\tL10\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG29 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{2}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu<-1$\n\t\t\t\t&\n\t\t\t\tL11\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG30 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\\multirow{4}{1cm}{\\centering{1.20}}\n\t\t\t\t&\n\t\t\t\t$\\mu<-2$\n\t\t\t\t&\n\t\t\t\tL2\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG31 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-1,0)$\n\t\t\t\t&\n\t\t\t\tL7\n\t\t\t\t&\n\t\t\t\tUnstable node\n\t\t\t\t&\n\t\t\t\tG32 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu\\in(-2,-1)$\n\t\t\t\t&\n\t\t\t\tL16\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG33 \\\\\n\t\t\t\t\\cline{2-5}\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\\multirow{4}{*}\n\t\t\t\t&\n\t\t\t\t$\\mu=-2$\n\t\t\t\t&\n\t\t\t\tL18\n\t\t\t\t&\n\t\t\t\tSaddle\n\t\t\t\t&\n\t\t\t\tG34 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t2.1\n\t\t\t\t&\n\t\t\t\t\n\t\t\t\t&\n\t\t\t\tL46\n\t\t\t\t&\n\t\t\t\tStable node\n\t\t\t\t&\n\t\t\t\tG41 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\t2.2\n\t\t\t\t&\n\t\t\t\t\n\t\t\t\t&\n\t\t\t\tL47\n\t\t\t\t&\n\t\t\t\tUnstable node\n\t\t\t\t&\n\t\t\t\tG42 \\\\\n\t\t\t\t\\hline\n\t\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Phase portraits of a family of Kolmogorov systems depending on six parameters", "authors": ["Érika Diz-Pita", "Jaume Llibre", "M. Victoria Otero-Espinar"], "url": "https://arxiv.org/abs/2501.13714v1", "attribution": "\"Phase portraits of a family of Kolmogorov systems depending on six parameters\" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.13714v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.15785v3_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Conduct Parameter Estimates and Implied Elasticities and Price Effects of Mergers }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccc}\n\\toprule\nConduct Specifications& \\multicolumn{3}{c}{(1) Perfectly Competitive ($\\lambda=0$)} & \\multicolumn{3}{c}{(2) Homogeneous} & \\multicolumn{3}{c}{(3) Market Structure Specific} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10}\n Mergers & DL-NW & UA-CO & AA-US & DL-NW & UA-CO & AA-US & DL-NW & UA-CO & AA-US \\\\\n\\midrule\n\\multicolumn{10}{c}{\\textit{Panel A: Structural Parameters}} \\\\\n\\text{Price Coefficient} ($\\alpha_1$) & -0.188$^{***}$ & -0.027$^{***}$ & -0.092 & -0.188$^{***}$ & -0.03$^{***}$ & -0.092$^{***}$ & -0.188$^{***}$&-0.03$^{***}$ & -0.090$^{***}$ \\\\\n& (1.66e-05) & (8.59e-06) & (0.1366) & (1.05e-05) & (9.2e-11) & (1.2e-08) & (9.0e-12)&(9.2e-11) & (7.0e-08) \\\\\n\\text{Merger's Effects on Cost ($\\theta_3$)} &&&&&&&& \\\\\n\\textemdash \\text{ Uniform} & 0.362$^{***}$ & -0.667$^{***}$ & 0.251$^{***}$ & -0.134$^{***}$ & 0.069$^{***}$ & -0.059$^{***}$ & && \\\\\n& (0.0001) & (0.0001) & (0.0075) & (9.878e-05) & (3.2e-13) & (2.7e-08) & & & \\\\\n\\textemdash \\text{ `2 to 1\" markets} & & & & & & & -0.131$^{***}$ &--& -0.008$^{***}$ \\\\\n& & & & & & & (3.4e-08)&-- & (1.4e-07) \\\\\n\\textemdash \\text{ `3 to 2\" markets} & & & & & & & -0.132$^{***}$ &0.069$^{***}$& -0.024$^{***}$ \\\\\n& & & & & & &(1.9e-11)&(3.2e-13) & (6.4e-08) \\\\\n\\text{Changes in Conduct} &&&&&&&& \\\\\n\\textemdash \\text{ Uniform} ($\\Delta \\lambda$) &&&& 0.188$^{***}$ & 0.03$^{***}$ & 0.065$^{***}$ & & & \\\\\n&&&& (0.0001) & (1.7e-15) & (2.3e-08) & & & \\\\\n\\textemdash \\text{ `2 to 1\" markets} ($\\Delta \\lambda_{2}$) &&&&&&& 0.164$^{***}$&-- & 0.078$^{***}$ \\\\\n&&&&&&& (1.6e-11)&-- & (6.7e-08) \\\\\n\\textemdash \\text{ `3 to 2\" markets} ($\\Delta \\lambda_{3}$) &&&&&&& 0.188$^{***}$ &0.03$^{***}$& 0.081$^{***}$ \\\\\n&&&&&&& (5.3e-12) &(1.7e-15)& (6.5e-08) \\\\\n\\multicolumn{10}{c}{\\textit{Panel B: Implied Elasticities}} \\\\\n\\text{Mean Elasticity} & -0.671 & -0.095 & -0.253 & -0.670 & -0.103 & -0.252 & -0.670&-0.103 & -0.231 \\\\\n\\text{Median Elasticity} & -0.128 & -0.020 & -0.075 & -0.128 & -0.021 & -0.074 & -0.128&-0.021 & -0.067 \\\\\n\\multicolumn{10}{c}{\\textit{Panel C: Implied Estimate of Price Effects of Merger $(\\beta^{\\dagger})$}} \\\\\n\\textemdash \\text{ Perfect Competition}&0.364$^{***}$ & 0.670$^{***}$ & 0.246$^{***}$ &&&&&&\\\\\n&(0.0001)& (0.0004)& (0.0027) &&&&&&\\\\\n\\textemdash \\text{ Uniform} &&&& -0.122$^{***}$ & 0.068$^{***}$ & -0.057$^{***}$ & && \\\\\n&&&& (0.0001) & (6.5e-12) & (2.6e-08) & & &\\\\\n\\textemdash \\text{ `2 to 1\" markets} &&&&&&& -0.122$^{***}$ &--& -0.008$^{***}$ \\\\\n&&&&&&& (1.8e-11) &--& (1.3e-07) \\\\\n\\textemdash \\text{ `3 to 2\" markets} &&&&&&& -0.120$^{***}$&0.068$^{***}$ & -0.023$^{***}$ \\\\\n&&&&&&& (1.8e-11) &(6.5e-12)& (1.3e-07) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Bridging Retrospective and Prospective Merger Analyses: The Case of US Airline Mergers", "authors": ["Gaurab Aryal", "Anirban Chattopadhyaya", "Federico Ciliberto"], "url": "https://arxiv.org/abs/2503.15785v3", "attribution": "\"Bridging Retrospective and Prospective Merger Analyses: The Case of US Airline Mergers\" by Gaurab Aryal, Anirban Chattopadhyaya, and Federico Ciliberto, arXiv:2503.15785v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.10467v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n \\toprule\n \\textbf{Model} & \\textbf{Ogbn-mag} \\\\ \\midrule\n GraphSAGE & 46.32 $\\pm$ 0.73 \\\\\n \\textbf{+ MGM} & \\textbf{47.94 $\\pm$ 0.65} \\\\\n \\hdashline\\noalign{\\vskip 0.5ex}\n GAT & 44.54 $\\pm$ 0.63 \\\\\n \\textbf{+ MGM} & \\textbf{46.28 $\\pm$ 0.25} \\\\\n \\hdashline\\noalign{\\vskip 0.5ex}\n FiLM & 41.72 $\\pm$ 0.22 \\\\\n \\textbf{+ MGM} & \\textbf{43.32 $\\pm$ 0.27} \\\\\n \\hdashline\\noalign{\\vskip 0.5ex}\n GATv2 & 45.41 $\\pm$ 0.42 \\\\\n \\textbf{+ MGM} & \\textbf{46.74 $\\pm$ 0.36} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance comparison on Ogbn-mag.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media", "authors": ["Muhammad Arslan Manzoor", "Ruihong Zeng", "Dilshod Azizov", "Preslav Nakov", "Shangsong Liang"], "url": "https://arxiv.org/abs/2412.10467v1", "attribution": "\"MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media\" by Muhammad Arslan Manzoor, Ruihong Zeng, Dilshod Azizov, Preslav Nakov, and Shangsong Liang, arXiv:2412.10467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.14099v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lrrr}\n\\hline\n& Full tuning & Solve and gradient & Gradient \\\\ \\hline\nCVXPYlayers & 61 sec & 57 sec & 23 sec \\\\\nCVXPYgen & 21 sec & 17 sec & 2 sec \\\\\n\\hline\n\\end{tabular}\n\\caption{Computation times with CVXPY and CVXPYgen for the portfolio optimization example.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Code generation for solving and differentiating through convex optimization problems", "authors": ["Maximilian Schaller", "Stephen Boyd"], "url": "https://arxiv.org/abs/2504.14099v1", "attribution": "\"Code generation for solving and differentiating through convex optimization problems\" by Maximilian Schaller and Stephen Boyd, arXiv:2504.14099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.15326v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{rotating}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Prediction performance comparison among different models for different $g_{true}$'s}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cc|cc|c||c|c|c|c|}\n \\hline\n \\multirow{5}{*}{\\rotatebox[origin=c]{90}{$h_{true}(x)=$}} & \\multirow{5}{*}{\\rotatebox[origin=c]{90}{$x^2-3x+2$}} & \\multirow{5}{*}{\\rotatebox[origin=c]{90}{$\\beta_{true}=\\sum\\limits_{i=1,3}2b_i(t)+$}} & \\multirow{5}{*}{\\rotatebox[origin=c]{90}{$\\sum\\limits_{i=2,4}\\sqrt{2}b_i(t)$}} &$g_{true}(x)$ & \\multicolumn{2}{|c|}{$x^3-3x+4$} & \\multicolumn{2}{|c|}{$5x^2-4$}\\\\\n \\cline{5-9}\n &&&& Prediction & $R^2$ & $\\frac{||\\widehat{g}-g_{true}||}{||g_{true}||}$ & $R^2$ & $\\frac{||\\widehat{g}-g_{true}||}{||g_{true}||}$\\\\\n \\cline{5-9}\\cline{5-9}\n &&&& SI-ScoSh (Poly) & 0.99 & 0.33 & 0.98 & 0.36 \\\\ \n &&&& SI-ScoSh (SVM) & 0.98 & 0.28 & 0.98 & 0.29\\\\\n &&&& SI-ScoF(\\textit{FR}) & 0.54 & 0.59 & 0.12 & 0.97 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Scalar-on-Shape Regression Models for Functional Data Analysis", "authors": ["Sayan Bhadra", "Anuj Srivastava"], "url": "https://arxiv.org/abs/2411.15326v1", "attribution": "\"Scalar-on-Shape Regression Models for Functional Data Analysis\" by Sayan Bhadra and Anuj Srivastava, arXiv:2411.15326v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08832v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{RQ$_1$.} Detection Capability and Detected Warnings in the 47 projects.}\n\\begin{tabular}{l|l|l|l|l|l|l|l} \\hline \n\\multirow{2}{*}{\\textbf{Tool}} & \\multicolumn{3}{c|}{\\textbf{Detection Capability}} & \\multicolumn{4}{c}{\\textbf{Detected warning}} \\\\ \\cline{2-8}\n& \\textbf{\\# rule } & \\textbf{type} & \\textbf{severity} & \\textbf{\\# rule }& \\textbf{\\# occurrences }& \\textbf{type} & \\textbf{severity}\\\\ \\hline \nBetter Code Hub & 10 & 3 & 3 & 8 & 27,888 & 3 & 3\\\\\nCheckstyle & 173 & 14 & 4 & 88 & 9,686,813 & 12 & 2\\\\\nCoverity & & & & 130 & 7,431 & 26 & 3\\\\\nFindbugs & 424 & 9 & 4 & 255 & 33,704 & 9 & 3\\\\\nPMD & 305 & 8 & 5 & 275 & 3,380,493 & 7 & 5\\\\\nSonarQube & 413 & 3 & 5 & 180 & 418,433 & 3 & 5\\\\\\hline \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Critical Comparison on Six Static Analysis Tools: Detection, Agreement, and Precision", "authors": ["Valentina Lenarduzzi", "Savanna Lujan", "Nyyti Saarimaki", "Fabio Palomba"], "url": "https://arxiv.org/abs/2101.08832v1", "attribution": "\"A Critical Comparison on Six Static Analysis Tools: Detection, Agreement, and Precision\" by Valentina Lenarduzzi, Savanna Lujan, Nyyti Saarimaki, and Fabio Palomba, arXiv:2101.08832v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13444v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of BD method with other defense methods against targeted hard-label attacks in terms of ASR (\\%).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|ccc}\n\t\t\\toprule\n\t\tDataset & Defense & HSJA & BA & SimBA-DCT \\\\\n\t\t\\midrule\n\t\t & DD & 98 & 80 & - \\\\\n\t\t & AT & 100 & 50 & 4 \\\\\n\t\t & Region-based & 100 & 85 & - \\\\\n\t\t MNIST & BD ($\\theta = 0.5, \\sigma = 0.1$)\n\t\t & \\textbf{38}& \\textbf{17} & \\textbf{3} \\\\\n\t\t & BD ($\\theta = 0.7, \\sigma = 0.1$)& \\textbf{7} & \\textbf{9}& \\textbf{0} \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Boundary Defense Against Black-box Adversarial Attacks", "authors": ["Manjushree B. Aithal", "Xiaohua Li"], "url": "https://arxiv.org/abs/2201.13444v1", "attribution": "\"Boundary Defense Against Black-box Adversarial Attacks\" by Manjushree B. Aithal and Xiaohua Li, arXiv:2201.13444v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01833v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{List of hyperparameters for CIFAR-10/100, ImageNet-100, and Tiny-ImageNet.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc}\n \\toprule\n \\textbf{Hyper-parameter} & CIFAR-10 & CIFAR-100 & ImageNet-100 & Tiny-ImageNet \\\\\n \\midrule\n SGD-momentum & \\multicolumn{4}{c}{0.9} \\\\\n SGD-learning rate & \\multicolumn{4}{c}{0.1} \\\\\n SK-$\\epsilon$ & \\multicolumn{4}{c}{10} \\\\\n SK-iteration & \\multicolumn{4}{c}{10} \\\\\n \\# of local views & \\multicolumn{4}{c}{4} \\\\\n \\midrule\n SGD-weight decay & 0.0005 & 0.0005 & 0.0001 & 0.0001 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning", "authors": ["Shengjie Niu", "Lifan Lin", "Jian Huang", "Chao Wang"], "url": "https://arxiv.org/abs/2411.01833v1", "attribution": "\"OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning\" by Shengjie Niu, Lifan Lin, Jian Huang, and Chao Wang, arXiv:2411.01833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18002v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllll}\n\\hline\n\\multicolumn{7}{c}{A Uniform} \\\\\n\\hline\n $k$ & LHS & RHS & $y$ & $z$ & $t_z$ & $t_{z^{\\prime}}$ \\\\ \n\\hline\n 10 & 0.475 & 0 & 1.63 & 2.769 & 1.63 & 2.769 \\\\ \n 15 & 0.351 & 0.33 & 1.259 & 2.442 & 1.259 & 2.442 \\\\ \n 25 & 0.113 & 0.089 & 0.63 & 2.15 & 0.63 & 2.15 \\\\ \n 48 & 0.099 & 0.043 & 0.229 & 1.685 & 0.229 & 1.685 \\\\ \n 54 & 0.189 & 0.066 & 0.229 & 1.685 & 0.229 & 1.685 \\\\ \n 63 & 0.148 & 0.007 & 0.096 & 1.4 & 0.096 & 1.4 \\\\ \n 78 & 0.092 & 0.078 & 0.053 & 0.788 & 0.053 & 0.788 \\\\ \n 93 & 0.111 & 0.005 & 0.053 & 0.653 & 0.053 & 0.653 \\\\ \n 110 & 0.023 & 0.001 & 0.006 & 0.512 & 0.006 & 0.512 \\\\ \n 172 & 0.02 & 1.73E-07 & 0.006 & 0.535 & 0.006 & 0.535 \\\\ \n 227 & 0.013 & 0.006 & 0.006 & 0.512 & 0.006 & 0.512 \\\\ \n 262 & 0.024 & 6.82E-09 & 0.006 & 0.377 & 0.006 & 0.377 \\\\\n\\hline\n\\multicolumn{7}{c}{B Gaussian process} \\\\\n\\hline\n $k$ & LHS & RHS & $y$ & $z$ & $t_z$ & $t_{z^{\\prime}}$ \\\\\n\\hline\n 10 & 0.515 & 0.515 & 1.209 & 1.778 & 1.209 & 1.778 \\\\ \n 65 & 0.215 & 5.54E-63 & 1.209 & 4.479 & 1.209 & 4.479 \\\\ \n 76 & 0.2 & 0.014 & 1.1 & 3.229 & 1.1 & 3.229 \\\\ \n 86 & 0.2 & 0.099 & 1.1 & 2.524 & 1.1 & 2.524 \\\\ \n 96 & 0.102 & 0.034 & 0.242 & 2.163 & 0.242 & 2.163 \\\\ \n 156 & 0.37 & 8.60E-12 & 0.242 & 2.516 & 0.242 & 2.516 \\\\ \n 167 & 0.05 & 4.67E-40 & 0.114 & 1.864 & 0.114 & 1.864 \\\\ \n 187 & 0.014 & 0.001 & 0.009 & 1.745 & 0.009 & 1.745 \\\\ \n 257 & 0.002 & 0.001 & 0.009 & 1.825 & 0.009 & 1.825 \\\\ \n \\hline\n\\multicolumn{7}{c}{C Quadraitc Regression}\\\\ \n \\hline\n $k$ & LHS & RHS & $y$ & $z$ & $t_z$ & $t_{z^{\\prime}}$\\\\ \n \\hline\n 10 & 0.492 & 0 & 1.65 & 2.769 & 1.65 & 2.769 \\\\ \n 15 & 0.018 & 0.018 & 0.154 & 1.65 & 0.154 & 1.65 \\\\ \n 30 & 0 & 0 & 0.033 & 1.191 & 0.033 & 1.191 \\\\ \n 40 & 0.009 & 0.008 & 0.033 & 1.261 & 0.033 & 1.261 \\\\ \n 50 & 0.011 & 0.01 & 0.033 & 1.234 & 0.033 & 1.234 \\\\ \n 60 & 0.012 & 0.009 & 0.033 & 1.19 & 0.033 & 1.19 \\\\ \n 66 & 0.006 & 0.004 & 0.009 & 0.898 & 0.009 & 0.898 \\\\ \n 127 & 0.011 & 0.001 & 0.009 & 0.811 & 0.009 & 0.811 \\\\ \n 182 & 0.013 & 7.28E-08 & 0.009 & 0.773 & 0.009 & 0.773 \\\\ \n 237 & 0.021 & 0.007 & 0.009 & 0.737 & 0.009 & 0.737 \\\\ \n 257 & 0.014 & 0 & 0.005 & 0.616 & 0.005 & 0.616 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Table of numerical values from testing Assumption 1.3 for c. Gaussian process on the range distribution on shifted sinusoidal problem with $dim = 2$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Branching Adaptive Surrogate Search Optimization (BASSO)", "authors": ["Pariyakorn Maneekul", "Zelda B. Zabinsky", "Giulia Pedrielli"], "url": "https://arxiv.org/abs/2504.18002v1", "attribution": "\"Branching Adaptive Surrogate Search Optimization (BASSO)\" by Pariyakorn Maneekul, Zelda B. Zabinsky, and Giulia Pedrielli, arXiv:2504.18002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11427v5_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study of normalization methods for multi-domain CTR prediction. STAR FCN is trained BN, LN and PN respectively.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n\t\\toprule\n\t &STAR FCN (BN) & STAR FCN (LN) & STAR FCN (PN) \\\\\n\t\\midrule\n\tOverall AUC &0.6415 & 0.6463 & \\textbf{0.6506} \\\\\n\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction", "authors": ["Xiang-Rong Sheng", "Liqin Zhao", "Guorui Zhou", "Xinyao Ding", "Binding Dai", "Qiang Luo", "Siran Yang", "Jingshan Lv", "Chi Zhang", "Hongbo Deng", "Xiaoqiang Zhu"], "url": "https://arxiv.org/abs/2101.11427v5", "attribution": "\"One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction\" by Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, and Xiaoqiang Zhu, arXiv:2101.11427v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09525v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lccc}\n\\toprule\n\\multirow{2}{*}{Embeddings} & Word2Vec \t\t& GloVe \t\t\t\t\t\t& fastText \\\\\n \t\t\t & Org/Deb \t\t& Org/Deb \t\t\t\t\t& Org/Deb \\\\ \\midrule\ndefinition \t \t\t\t& 83.0/\\textbf{83.9} \t& 83.0/\\textbf{83.4} \t\t\t& 92.0/\\textbf{93.2}\\\\\nstereotype \t\t\t& 13.4/\\textbf{12.3} \t& 12.0/\\textbf{11.4} \t\t\t& 5.5/\\textbf{4.3} \\\\\nnone \t\t\t& \\textbf{3.6}/3.9 \t& \\textbf{5.0}/5.2 \t\t\t& \\textbf{2.5}/\\textbf{2.5} \\\\ \\midrule\nsub-definition \t\t\t& 50.0/\\textbf{57.5} \t& \\textbf{67.5}/\\textbf{67.5} \t& 82.5/\\textbf{85.0} \\\\\nsub-stereotype \t\t\t& 40.0/\\textbf{32.5} \t& \\textbf{27.5}/\\textbf{27.5} \t& 12.5/\\textbf{10.0} \\\\\nsub-none \t\t\t& \\textbf{10.0}/\\textbf{10.0} & \\textbf{5.0}/\\textbf{5.0} & \\textbf{5.0}/\\textbf{5.0} \\\\ \\bottomrule\n\\end{tabular}\n\\caption{Prediction accuracies for gender relational analogies on SemBias.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Dictionary-based Debiasing of Pre-trained Word Embeddings", "authors": ["Masahiro Kaneko", "Danushka Bollegala"], "url": "https://arxiv.org/abs/2101.09525v1", "attribution": "\"Dictionary-based Debiasing of Pre-trained Word Embeddings\" by Masahiro Kaneko and Danushka Bollegala, arXiv:2101.09525v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11864v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\nModel & Number of Parameters & Model &Accuracy \\\\\n\\hline\n & 256 & SVM & 89.72\\% \\\\ \\hline\n & 4096 & Knn & 93.84\\% \\\\ \\hline\n & \\textbf{8} & Knn & 95.24\\% \\\\ \\hline\n & 45 & Knn & 95.67\\% \\\\ \\hline\n & 1087 & CNN \\& SVM & 96.11\\% \\\\ \\hline\n & 142 & Ensemble & 99.2\\% \\\\ \\hline\n & 6.1$\\times10^7$ & CNN & 99.5\\% \\\\ \\hline\n \\textbf{Ours} & 24 & RF & \\textbf{100.00\\%} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison with related works for the ALL-IDB2 data. The best approaches for the smallest number of parameters and highest accuracy are boldened.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features", "authors": ["William Franz Lamberti"], "url": "https://arxiv.org/abs/2201.11864v1", "attribution": "\"Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features\" by William Franz Lamberti, arXiv:2201.11864v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.08925v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overall performance comparison on IEMOCAP (4-class).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\nModel & Upstream(A) & Upstream(T) & ACC (\\%) & F1 (\\%) \\\\\n\\midrule\nGatedxLSTM~ & CLAP & CLAP & -- & 75.97±1.38 \\\\\nMER-HAN~ & Bi-LSTM & BERT & 74.20 & 73.66 \\\\\nMMI-MMER~ & Wav2Vec2 & BERT &77.02 & -- \\\\\nMGCMA~ & Wav2Vec2 & BERT &78.87 & -- \\\\\nMEP~ & openSMILE & BERT & 80.18 & 80.01 \\\\\nBi-GRU~ & Acoustic model & GloVe & 80.63 & -- \\\\\nMemoCMT & HuBERT & BERT & 81.85 & -- \\\\\nLinear & emotion2vec & BERT & 81.68 & 80.75 \\\\\nCFIA & emotion2vec & BERT & 83.37 & -- \\\\\n\\midrule\n\\textbf{LPGNet(Frame)} & emotion2vec & BERT & \\textbf{83.87} & \\textbf{83.68} \\\\\n\\textbf{LPGNet(Utterance)} & emotion2vec & BERT & \\textbf{87.99} & \\textbf{87.96} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "LPGNet: A Lightweight Network with Parallel Attention and Gated Fusion for Multimodal Emotion Recognition", "authors": ["Zhining He", "Yang Xiao"], "url": "https://arxiv.org/abs/2508.08925v1", "attribution": "\"LPGNet: A Lightweight Network with Parallel Attention and Gated Fusion for Multimodal Emotion Recognition\" by Zhining He and Yang Xiao, arXiv:2508.08925v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04051v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc}\n\t\t\\hline\n Method & avg-min-CDR $\\downarrow$ & JDR $\\downarrow$ & Recall $\\uparrow$ & FPS $\\uparrow$\\\\\n \\hline\n Video SOD~& 14.49\\%& 4.939& 22.91\\%& 6.3\\\\\n Video FP~& 14.47\\%& 0.878&45.73\\%& 15.6\\\\\n Video Cropping~& 15.25\\%& 5.382&20.09\\%& 24.9\\\\\n \\hline\n Our Framework& \\textbf{10.94\\%}& \\textbf{0.865}& \\textbf{69.53\\%} &\\textbf{28.6}\\\\\n \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of our {\\it Rank Sub-Select} module based {\\it H2V} framework with the state-of-the-art Video Salient Object Detection, Video Fixation Prediction and Video Cropping methods in {\\it H2V-142K} Video Subset.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Horizontal-to-Vertical Video Conversion", "authors": ["Tun Zhu", "Daoxin Zhang", "Yao Hu", "Tianran Wang", "Xiaolong Jiang", "Jianke Zhu", "Jiawei Li"], "url": "https://arxiv.org/abs/2101.04051v2", "attribution": "\"Horizontal-to-Vertical Video Conversion\" by Tun Zhu, Daoxin Zhang, Yao Hu, Tianran Wang, Xiaolong Jiang, Jianke Zhu, and Jiawei Li, arXiv:2101.04051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.09540v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\cline{2-5}\n & daily & six-hourly & three-hourly & 10min \\\\\n \\hline\n daily&1&$\\mathbf{2.14\\cdot10^{-1}}$&$\\mathbf{2.18\\cdot10^{-1}}$& $\\mathbf{1.29\\cdot10^{-1}}$ \\\\ \\hline\n six-hourly&&1&$\\mathbf{1.00}$&$\\mathbf{9.78\\cdot10^{-1}}$ \\\\ \\hline\n three-hourly&&&1& $\\mathbf{9.77\\cdot10^{-1}}$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{$p$-values of Kolmogorov-Smirnov test for instantaneous Owez wind speed distributions. All values where $p \\leq 0.05$ are marked \\textbf{bold} and the corresponding distributions are not considered to be significantly different.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts", "authors": ["Nina Effenberger", "Nicole Ludwig", "Rachel H. White"], "url": "https://arxiv.org/abs/2309.09540v1", "attribution": "\"Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts\" by Nina Effenberger, Nicole Ludwig, and Rachel H. White, arXiv:2309.09540v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12856v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Error analysis for Lorenz's system example using proposed method}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\nvariable&bias&MAPE&MAE&RMSE&R$^2$\\\\ \\hline\n$x_1$&-0.5613&0.2924&1.8619&2.2451&0.8609\\\\\n$x_2$&-0.3759&0.3940&2.3256&2.8221&0.8260\\\\\n$x_3$&-2.1314&0.1638&3.5015&4.2435&0.6758\\\\\n$f_1(\\cdot)$&1.5081&2.1949&19.6961&25.3391&0.5510\\\\\n$f_2(\\cdot)$&1.8151&1.2384&26.3919&32.8529&0.6511\\\\\n$f_3(\\cdot)$&-0.9230&1.3472&30.2180&39.2456&0.6421\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods", "authors": ["S. Syafiie", "Aries Subiantoro", "Vivi Andasari", "Fernando Tadeo"], "url": "https://arxiv.org/abs/2501.12856v1", "attribution": "\"Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods\" by S. Syafiie, Aries Subiantoro, Vivi Andasari, and Fernando Tadeo, arXiv:2501.12856v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17106v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n $n$ & Error $\\|\\cdot\\|_1$ & Order $\\|\\cdot\\|_1$ & Error\n $\\|\\cdot\\|_{\\infty}$ & Order $\\|\\cdot\\|_{\\infty}$ \\\\\n \\hline\n 20 & 1.14E$-2$ & $-$ & 1.12E$-1$ & $-$ \\\\\n \\hline\n 40 & 4.82E$-3$ & 1.56 & 4.54E$-2$ & 1.40 \\\\\n \\hline\n 80 & 8.33E$-6$ & 9.18 & 2.25E$-4$ & 7.66 \\\\\n \\hline\n 160 & 7.53E$-8$ & 6.79 & 3.00E$-6$ & 6.23 \\\\\n \\hline\n 320 & 2.14E$-9$ & 5.14 & 1.01E$-7$ & 4.89 \\\\\n \\hline\n 640 & 6.59E$-11$ & 5.02 & 3.57E$-9$ & 4.82 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Error table for problem - , Complex domain, WLS-UW.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17106v1", "attribution": "\"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23833v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|} \\hline\n \\text{type} & $A_n$ & $D_n$ & $E_6$ & $E_7$ & $E_8$ \\\\ \\hline\n ${h}$ & $n+1$ & $2n-2$ & $12$ & $18$ & $30$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Products of Kirillov-Reshetikhin modules and maximal green sequences", "authors": ["Yuki Kanakubo", "Gleb Koshevoy", "Toshiki Nakashima"], "url": "https://arxiv.org/abs/2503.23833v1", "attribution": "\"Products of Kirillov-Reshetikhin modules and maximal green sequences\" by Yuki Kanakubo, Gleb Koshevoy, and Toshiki Nakashima, arXiv:2503.23833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00480v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Shapiro-Wilk statistics value for all the models . Top 5 values highlighted.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|l|l} \\toprule\n & \\multicolumn{3}{c}{\\textbf{Normalization method}} \\\\ \\cmidrule{2-4}\n\\textbf{Model} & $\\frac{X-\\min(X)}{\\max(X)-\\min(X)}$ & \\textbf{$log_{10}()$} & $\\gamma()$ \\\\ \\midrule \n$\\frac{wind\\ *\\ rain}{distance}$ & $0.07$ & $\\boldsymbol{0.99}$ & $0.99$ \\\\ \\hline\n$\\frac{rain}{distance}$ & $0.06$ & $0.92$ & $0.93$ \\\\ \\hline\n$\\frac{wind}{distance}$ & $0.13$ & $0.95$ & $0.97$ \\\\ \\hline\n$\\frac{wind\\ *\\ rain}{\\sqrt[]{distance}}$ & $0.53$ & $\\boldsymbol{0.98}$ & $\\boldsymbol{0.98}$ \\\\ \\hline\n$\\frac{rain}{\\sqrt[]{distance}}$ & $0.51$ & $0.88$ & $0.89$ \\\\ \\hline\n$\\frac{wind}{\\sqrt[]{distance}}$ & $0.88$ & $0.88$ & $\\boldsymbol{0.98}$ \\\\ \\hline\n$\\frac{wind\\ *\\ rain}{\\sqrt[3]{distance}}$ & $0.65$ & $\\boldsymbol{0.98}$& $\\boldsymbol{0.98}$ \\\\ \\hline\n$\\frac{rain}{\\sqrt[3]{distance}}$ & $0.65$ & $0.85$& $0.86$ \\\\ \\hline\n$\\frac{wind}{\\sqrt[3]{distance}}$ & $0.96$ & $0.85$& $\\boldsymbol{0.99}$ \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irma", "authors": ["Somya D. Mohanty", "Brown Biggers", "Saed Sayedahmed", "Nastaran Pourebrahim", "Evan B. Goldstein", "Rick Bunch", "Guangqing Chi", "Fereidoon Sadri", "Tom P. McCoy", "Arthur Cosby"], "url": "https://arxiv.org/abs/2101.00480v1", "attribution": "\"A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irma\" by Somya D. Mohanty, Brown Biggers, Saed Sayedahmed, Nastaran Pourebrahim, Evan B. Goldstein, Rick Bunch, Guangqing Chi, Fereidoon Sadri, Tom P. McCoy, and Arthur Cosby, arXiv:2101.00480v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2311.13889v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance drop of each method compared to the best one, reported from Fig.~.}\n\\begin{tabular}{l|c|c|c}\n \\hline\n \\textbf{Method} & \\textbf{$0.25$-quantile} & \\textbf{Median} & \\textbf{$0.75$-quantile} \\\\\n \\hline\n SIMBa-1\t\t&\t\\textbf{1.01}\t&\t\\textbf{1.08}\t&\t\\textbf{1.18}\t\\\\\n SIMBa-4\t\t&\t1.02\t&\t1.10\t&\t1.30\t\\\\\n CVA\t\t&\t1.12\t&\t1.20\t&\t1.54\t\\\\\n MOESP\t\t&\t1.20\t&\t1.30\t&\t1.64\t\\\\\n N4SID\t\t&\t1.26\t&\t1.37\t&\t1.67\t\\\\ \\hline\n PARSIM-K\t&\t\\textbf{1.00}\t&\t\\textbf{1.00}\t&\t\\textbf{1.09}\t\\\\\n PARSIM-S\t&\t1.47\t&\t4.75\t&\t30.91\t\\\\\n PARSIM-P\t&\t2.40\t&\t7.00\t&\t223.76\t\\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SIMBa: System Identification Methods leveraging Backpropagation", "authors": ["Loris Di Natale", "Muhammad Zakwan", "Philipp Heer", "Giancarlo Ferrari-Trecate", "Colin N. Jones"], "url": "https://arxiv.org/abs/2311.13889v3", "attribution": "\"SIMBa: System Identification Methods leveraging Backpropagation\" by Loris Di Natale, Muhammad Zakwan, Philipp Heer, Giancarlo Ferrari-Trecate, and Colin N. Jones, arXiv:2311.13889v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.13629v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The Heterogeneity in the Adoption of GPT}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\hline\n\\hline\n\\textbf{Variables} & Version 1 & Version 2 & Version 3 & Version 4 & Version 5 & Version 6 \\\\\n\\hline\n\\textbf{Africans} & 0.193*** & 0.351*** & 0.460** & 0.189 & 0.117 & 0.361*** \\\\\n& (0.061) & (0.133) & (0.180) & (0.231) & (0.088) & (0.116) \\\\\n\\textbf{British} & 0.473*** & 0.019 & 0.406*** & 0.332*** & 0.199*** & -0.157** \\\\\n& (0.029) & (0.073) & (0.090) & (0.117) & (0.043) & (0.067) \\\\\n\\textbf{EastAsian} & 0.386*** & 0.730*** & 0.275*** & 0.740*** & 0.343*** & 0.837*** \\\\\n& (0.026) & (0.059) & (0.081) & (0.101) & (0.039) & (0.053) \\\\\n\\textbf{EastEuropean} & 0.052 & -0.106 & 0.301** & -0.084 & -0.063 & -0.354*** \\\\\n& (0.043) & (0.109) & (0.123) & (0.180) & (0.063) & (0.106) \\\\\n\\textbf{Indian} & 0.166*** & 0.169** & -0.003 & 0.222* & -0.004 & 0.222*** \\\\\n& (0.034) & (0.076) & (0.108) & (0.129) & (0.050) & (0.067) \\\\\n\\textbf{Jewish} & 0.319*** & 0.119 & 0.026 & 0.117 & 0.118* & -0.063 \\\\\n& (0.045) & (0.112) & (0.151) & (0.186) & (0.067) & (0.105) \\\\\n\\textbf{Muslim} & 0.270*** & 0.571*** & 0.421*** & 0.770*** & 0.159*** & 0.807*** \\\\\n& (0.042) & (0.087) & (0.131) & (0.138) & (0.061) & (0.072) \\\\\n\\textbf{Biology} & 0.357*** & 0.923*** & -0.279 & -0.111 & -2.126*** & 3.344*** \\\\\n& (0.060) & (0.115) & (0.213) & (0.285) & (0.260) & (0.075) \\\\\n\\textbf{Computer Science} & 0.423*** & 0.716*** & -0.604*** & 0.979*** & 0.559*** & 2.102*** \\\\\n& (0.019) & (0.044) & (0.060) & (0.068) & (0.024) & (0.059) \\\\\n\\textbf{Economics} & 0.797*** & 0.691*** & 0.185 & -1.754** & -1.584*** & 1.087*** \\\\\n& (0.057) & (0.141) & (0.195) & (0.710) & (0.225) & (0.166) \\\\\n\\textbf{Electrical Eng.} & 0.427*** & 1.463*** & -0.282*** & 0.667*** & -1.405*** & 1.933*** \\\\\n& (0.031) & (0.054) & (0.108) & (0.108) & (0.088) & (0.070) \\\\\n\\textbf{Finance} & 1.261*** & 0.933*** & -1.170** & -1.812* & -1.324*** & 2.091*** \\\\\n& (0.062) & (0.167) & (0.502) & (1.002) & (0.269) & (0.143) \\\\\n\\textbf{Physics} & 0.212*** & 0.263*** & 0.177*** & -0.184** & -0.329*** & 1.013*** \\\\\n& (0.019) & (0.047) & (0.051) & (0.081) & (0.028) & (0.063) \\\\\n\\textbf{Statistics} & 0.235*** & 0.688*** & -0.647*** & -3.015*** & -0.769*** & 1.616*** \\\\\n& (0.039) & (0.081) & (0.155) & (0.709) & (0.081) & (0.086) \\\\\n\\textbf{Female} & 0.113*** & -0.358*** & -0.462*** & 0.040 & 0.062 & -0.124* \\\\\n& (0.035) & (0.082) & (0.116) & (0.131) & (0.052) & (0.069) \\\\\n\\textbf{Male} & -0.018 & -0.330*** & -0.263*** & -0.064 & 0.048 & -0.340*** \\\\\n& (0.021) & (0.047) & (0.062) & (0.080) & (0.032) & (0.043) \\\\\n\\textbf{Paper Seniority} & -0.002 & -0.271*** & -0.385*** & -0.759*** & -0.144*** & -0.691*** \\\\\n& (0.026) & (0.057) & (0.082) & (0.098) & (0.037) & (0.052) \\\\\n\\textbf{Year Seniority} & -0.234*** & -0.523*** & 0.016 & 0.028 & -0.297*** & -0.625*** \\\\\n& (0.028) & (0.065) & (0.087) & (0.108) & (0.042) & (0.062) \\\\\n\\textbf{Obs.} & 490,286 & 490,286 & 490,286 & 490,286 & 490,286 & 490,286 \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing", "authors": ["Cong William Lin", "Wu Zhu"], "url": "https://arxiv.org/abs/2504.13629v1", "attribution": "\"Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing\" by Cong William Lin and Wu Zhu, arXiv:2504.13629v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.08095v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results of SLM-Llama on the StyleTalk dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cccccc|}\n\\hline\n\\textbf{Methods} &\n \\multicolumn{2}{c|}{\\textbf{CS Score}} &\n \\multicolumn{4}{c|}{\\textbf{EGS Score}} \\\\ \\hline\n\\textbf{} &\n \\textbf{Content} &\n \\textbf{Style} &\n \\textbf{C1} &\n \\textbf{C2} &\n \\textbf{C3} &\n \\textbf{C4} \\\\ \\hline\n &\n \\multicolumn{6}{c|}{\\textbf{Judge: Qwen2.5-72B-Instruction}} \\\\ \\hline\n\\textbf{Qwen2-Audio} &\n 3.86 &\n 3.94 &\n 8.12 &\n 9.73 &\n 7.70 &\n 8.16 \\\\ \\hline\n\\textbf{Llama-Omni} &\n 3.76 &\n 4.00 &\n 8.10 &\n 9.64 &\n 7.55 &\n 8.09 \\\\ \\hline\n\\textbf{SLM-Qwen (ours)} &\n \\textbf{4.28} &\n \\textbf{4.40} &\n \\textbf{8.98} &\n \\textbf{9.95} &\n \\textbf{8.44} &\n \\textbf{8.91} \\\\ \\hline\n\\textbf{SLM-Llama (ours)} &\n 4.12 &\n 4.22 &\n 8.69 &\n 9.74 &\n 8.19 &\n 8.60 \\\\ \\hline\n &\n \\multicolumn{6}{c|}{\\textbf{Judge: Llama-3.1-70B-Instruction}} \\\\ \\hline\n\\textbf{Qwen2-Audio} &\n 3.95 &\n 4.03 &\n 8.71 &\n 9.97 &\n 7.33 &\n 7.97 \\\\ \\hline\n\\textbf{Llama-Omni} &\n 3.80 &\n 4.06 &\n 8.51 &\n 9.91 &\n 7.04 &\n 7.63 \\\\ \\hline\n\\textbf{SLM-Qwen (ours)} &\n \\textbf{4.41} &\n \\textbf{4.44} &\n \\textbf{9.59} &\n \\textbf{10.00} &\n \\textbf{8.07} &\n \\textbf{8.73} \\\\ \\hline\n\\textbf{SLM-Llama (ours)} &\n 4.28 &\n 4.34 &\n 9.23 &\n 9.92 &\n 7.93 &\n 8.46 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Dual Information Speech Language Models for Emotional Conversations", "authors": ["Chun Wang", "Chenyang Liu", "Wenze Xu", "Weihong Deng"], "url": "https://arxiv.org/abs/2508.08095v1", "attribution": "\"Dual Information Speech Language Models for Emotional Conversations\" by Chun Wang, Chenyang Liu, Wenze Xu, and Weihong Deng, arXiv:2508.08095v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15444v2_tex_table16.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mutually unbiased weighing matrices of order $18$ and weight $9$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l}\n\\noalign{\\hrule height1pt}\n$W_{18,15}$\\\\\n\\hline\n000112010220210002\n210000221201000220\n020021000022202220\n220120202200100010\\\\\n100220010210011200\n102001201020110100\n220000011111012000\n101111000210002001\\\\\n012000210012022002\n211001100002110002\n001120011000021120\n000100220112211000\\\\\n010022002000012121\n012121100001200010\n101022021000002012\n200200001202200111\\\\\n011000210120000211\n001201202001200102\\\\\n\\hline\n$ A_{18,15,2}$\\\\\n\\hline\n001002001021020111\n012011001220201000\n010100010020102122\n001012000010201122\\\\\n100102000202222200\n012100220111020000\n010100100012010111\n011010122120000200\\\\\n102200102000020121\n101001201102000021\n100010111011100200\n120011020000002112\\\\\n000010212002121010\n122102000120011000\n000010212001212001\n110202220200110000\\\\\n101121002201001000\n110220010100200012\\\\\n\\hline\n$ A_{18,15,3}$\\\\\n\\hline\n102001111000020102\n100202100021010202\n012010002021020011\n001200020120021120\\\\\n000101102022211000\n110011020110110000\n100000202010221202\n000121220021002002\\\\\n120112002000002120\n101221012000002001\n122020020200101001\n010000210201011120\\\\\n110122001100200001\n101010201222000010\n011100100200120220\n012200020202202020\\\\\n010022002002100112\n001000120211200110\\\\\n\\hline\n$ A_{18,15,4}$\\\\\n\\hline\n012002001101110020\n100020020012012201\n012000210010201011\n120210011000002101\\\\\n110020110220200020\n102102120000020110\n000121010011122000\n001020001200111110\\\\\n122000202201010002\n101102210102000002\n010010002212100120\n011012000201002210\\\\\n101200102111001000\n001100220001200121\n010001002120012110\n000111101010210002\\\\\n100111000020101201\n110201221000020002\\\\\n\\noalign{\\hrule height1pt}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unbiased weighing matrices of weight $9$", "authors": ["Makoto Araya", "Masaaki Harada", "Hadi Kharaghani", "Sho Suda", "Wei-Hsuan Yu"], "url": "https://arxiv.org/abs/2501.15444v2", "attribution": "\"Unbiased weighing matrices of weight $9$\" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19833v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quaternionic Extensions of Pauli Operators}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccl}\n\\hline\n\\textbf{Operator} & \\textbf{Quaternionic Form} & \\textbf{Interpretation} \\\\\n\\hline\n\\( X \\) & \\( \\mathcal{Q}_X = iX \\) & Bit-flip with quaternionic phase \\\\\n\\( Y \\) & \\( \\mathcal{Q}_Y = jY \\) & Bit and phase-flip with quaternionic phase \\\\\n\\( Z \\) & \\( \\mathcal{Q}_Z = kZ \\) & Phase-flip with quaternionic twist \\\\\n\\( I \\) & \\(\\mathcal{Q}_I = I \\) & Identity operator \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Quantum Error Correction in Quaternionic Hilbert Spaces", "authors": ["Valentine Nyirahafashimana", "Nurisya Mohd Shah", "Umair Abdul Halim", "Mohamed Othman", "Sharifah Kartini Said Husain"], "url": "https://arxiv.org/abs/2504.19833v1", "attribution": "\"Quantum Error Correction in Quaternionic Hilbert Spaces\" by Valentine Nyirahafashimana, Nurisya Mohd Shah, Umair Abdul Halim, Mohamed Othman, and Sharifah Kartini Said Husain, arXiv:2504.19833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10545v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\\toprule\n\\multirow{2}{*}{\\textbf{Strategy}} %&\\multirow{2}{*}{\\textbf{Persuasion}} \n&\\textbf{Persuasion (P4G)}\n&\\multicolumn{2}{c}{\\textbf{Negotiation (CB)}} \\\\\\cmidrule{2-4}\n& \\textbf{EE}&\\textbf{BU} &\\textbf{SE} \\\\\\midrule\nSource Derogation &2.16 &7.61 &0.44 \\\\\nCounter Argument &2.28 &3.74 &6.06 \\\\\nPersonal Choice &2.52 &9.43 &8.49 \\\\\nInformation Inquiry &7.19 &18.27 &0.38 \\\\\nSelf Pity &1.58 &4.66 &0.34 \\\\\nHesitance &1.76 &15.78 &9.14 \\\\\nSelf-assertion &0.94 &2.20 &5.05 \\\\\nNot a strategy &81.56 &38.30 &70.09 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversations", "authors": ["Ritam Dutt", "Sayan Sinha", "Rishabh Joshi", "Surya Shekhar Chakraborty", "Meredith Riggs", "Xinru Yan", "Haogang Bao", "Carolyn Penstein Rosé"], "url": "https://arxiv.org/abs/2101.10545v1", "attribution": "\"RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversations\" by Ritam Dutt, Sayan Sinha, Rishabh Joshi, Surya Shekhar Chakraborty, Meredith Riggs, Xinru Yan, Haogang Bao, and Carolyn Penstein Rosé, arXiv:2101.10545v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13775v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline \n & Transition data $p_{ij}$ of $(L,\\alpha)$ \\\\\n \\hline\n $U_{i} \\cap D = U_{j} \\cap D = \\emptyset$ & $g_{ij}$ \\\\\n \\hline\n $U_{i} \\cap D = \\emptyset, \\, U_{j} \\cap D \\neq \\emptyset$ & $g_{ij} s_{j}^{\\alpha}$\\\\\n \\hline\n $U_{i} \\cap D \\neq \\emptyset, \\, U_{j} \\cap D = \\emptyset$ & $g_{ij}/s_{i}^{\\alpha}$\\\\\n \\hline\n $U_{i} \\cap U_{j} \\cap D \\neq \\emptyset$ & $g_{ij}(s_{j}/s_{i})^{\\alpha}$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The transition data of $(L,\\alpha)$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Constructing Parabolic Non-Abelian Hodge Correspondence in Positive Characteristic Using Parabolic Bases", "authors": ["Xiaojin Lin"], "url": "https://arxiv.org/abs/2501.13775v1", "attribution": "\"Constructing Parabolic Non-Abelian Hodge Correspondence in Positive Characteristic Using Parabolic Bases\" by Xiaojin Lin, arXiv:2501.13775v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07375v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc} % Adjust the width as needed\n \\toprule\n \\textbf{Difference} & \\textbf{1} & \\textbf{2} & \\textbf{3} & \\textbf{Overall} \\\\\n \\midrule\n Accuracy & 7/10 & 9/10 & 9/10 & 25/30 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Think Before You Talk: Enhancing Meaningful Dialogue Generation in Full-Duplex Speech Language Models with Planning-Inspired Text Guidance", "authors": ["Wenqian Cui", "Lei Zhu", "Xiaohui Li", "Zhihan Guo", "Haoli Bai", "Lu Hou", "Irwin King"], "url": "https://arxiv.org/abs/2508.07375v1", "attribution": "\"Think Before You Talk: Enhancing Meaningful Dialogue Generation in Full-Duplex Speech Language Models with Planning-Inspired Text Guidance\" by Wenqian Cui, Lei Zhu, Xiaohui Li, Zhihan Guo, Haoli Bai, Lu Hou, and Irwin King, arXiv:2508.07375v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2501.00970v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Descriptive statistics for income-consumption data.}\n\\begin{tabular}{cccccccccc}\n\t\t\t\\hline\n\t\t\tn & Min. & 1st Qu. & Median & Mean & 3rd Qu. & Max. & Std. dv. & CS & CK\\\\ \n\t\t\t\\hline\n\t\t\t37& 0.02 & 0.28 & 0.46 & 0.45 & 0.60 & 0.91 & 0.22 & 0.16 & -0.92 \\\\ \n\t\t\t\\hline\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A novel unit-asymmetric distribution based on correlated Fréchet random variables", "authors": ["Roberto Vila", "Felipe Quintino"], "url": "https://arxiv.org/abs/2501.00970v1", "attribution": "\"A novel unit-asymmetric distribution based on correlated Fréchet random variables\" by Roberto Vila and Felipe Quintino, arXiv:2501.00970v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.12510v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n & ML Estimates & $95\\%$ C.I. \\\\ \\hline\n $\\hat K_V$ & $-2.2085$ & $(-2.2850 -2.1321)$ \\\\\n $\\hat \\rho_V$ & $0.2530$ & $(0.2149 0.2910)$ \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\small Maximum likelihood estimates for the Vasicek model.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Credit risk for large portfolios of green and brown loans: extending the ASRF model", "authors": ["Alessandro Ramponi", "Sergio Scarlatti"], "url": "https://arxiv.org/abs/2506.12510v1", "attribution": "\"Credit risk for large portfolios of green and brown loans: extending the ASRF model\" by Alessandro Ramponi and Sergio Scarlatti, arXiv:2506.12510v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2401.00520v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Top 20 significant SNPs for association with Framingham Heart Study data using $MCEM_{DSP}$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|} \n\t\t\t\t\\hline\n\t\t\t\tRank & SNP & Chr & Position(BP) & Gene & \\multicolumn{2}{c}{$-log_{10}$P-value} \\\\\n\t\t\t\t\\cline{6-7}\n\t\t\t\t& & & & & $MCEM_{DSP}$ & $LIME_{DSP}$\\\\\n\t\t\t\t\\hline\n\t\t\t\t1 & rs5760711 & 22 & 24894886 & SGSM1 & 15.9546 & 0.4676\\\\\n\t\t\t\t2 & rs12416299 & 10 & 95542074 & SORBS1 & 15.4775 & 0.2365\\\\\n\t\t\t\t3 & rs340719 & 2 & 16222132 & & 14.8754 & 0.4882\\\\\n\t\t\t\t4 & rs2973566 & 5 & 73852656 & ARHGEF28 & 14.8404 & 0.4387\\\\\n\t\t\t\t5 & rs11539522 & 15 & 49625193 & DTWD1 & 14.6122 & 0.4878\\\\\n\t\t\t\t6 & rs1146920 & 13 & 77760082 & SLAIN1 & 12.6497 & 0.4622\\\\\n\t\t\t\t7 & rs12117125 & 1 & 161522658 & FCGR2A & 12.3787 & 0.4241\\\\\n\t\t\t\t8 & rs2666954 & 6 & 380789 & & 12.0756 & 0.4285\\\\\n\t\t\t\t9 & rs6076157 & 20 & 23882207 & & 12.0674 & 0.4477\\\\\n\t\t\t\t10 & rs1327533 & 9 & 110368883 & SVEP1 & 12.0632 & 0.2198\\\\\n\t\t\t\t11 & rs12306837 & 12 & 4571302 & DYRK4 & 11.7532 & 0.4845\\\\\n\t\t\t\t12 & rs2281954 & 10 & 125796084 & UROS & 11.5686 & 0.4690\\\\\n\t\t\t\t13 & rs11033793 & 11 & 4769244 & MMP26 & 11.4601 & 0.1714\\\\\n\t\t\t\t14 & rs12345874 & 9 & 68776727 & PIP5K1B & 10.5720 & 0.4487\\\\\n\t\t\t\t15 & rs3829736 & 12 & 47982720 & COL2A1 & 10.2677 & 0.3841\\\\\n\t\t\t\t16 & rs9367018 & 6 & 12173899 & HIVEP1 & 9.6447 & 0.4840\\\\\n\t\t\t\t17 & rs12197569 & 6 & 2164221 & GMDS & 9.3021 & 0.4700\\\\\n\t\t\t\t18 & rs7124812 & 11 & 3439368 & LOC124902616 & 8.8759 & 0.3451\\\\\n\t\t\t\t19 & rs9382736 & 6 & 57436863 & PRIM2 & 8.8020 & 0.4176\\\\\n\t\t\t\t20 & rs16843643 & 2 & 241413353 & FARP2 & 8.6881 & 0.4622\\\\\n\t\t\t\t\\hline\t\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Monte Carlo Expectation-Maximization algorithm to detect imprinting and maternal effects for discordant sib-pair data", "authors": ["Ruwani Herath", "Alex Trindade", "Fangyuan Zhang"], "url": "https://arxiv.org/abs/2401.00520v1", "attribution": "\"Monte Carlo Expectation-Maximization algorithm to detect imprinting and maternal effects for discordant sib-pair data\" by Ruwani Herath, Alex Trindade, and Fangyuan Zhang, arXiv:2401.00520v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12305v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n\\hline\n$\\alpha_k= 0.5$ & $m_k= 5$ (MW)& $\\sigma_k = 0.2 \\ (hr^{-1})$ \\\\\n\\hline\n$\\text{SoC}_{\\min}= 0$ & $\\text{SoC}_{\\max}= 1$ & $\\text{I}_{\\text{max}}= 3 \n \\ (\\text{MWh})$\n\\\\\n\\hline\n$B_{\\text{min}} = -1 (\\text{MW})$ & $B_{\\text{max}} = 1 \\ (\\text{MW})$ & $\\eta = 1$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Parameters for the stationary example in Sect. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Intraday Battery Dispatch for Hybrid Renewable Energy Assets", "authors": ["Thiha Aung", "Mike Ludkovski"], "url": "https://arxiv.org/abs/2503.12305v1", "attribution": "\"Intraday Battery Dispatch for Hybrid Renewable Energy Assets\" by Thiha Aung and Mike Ludkovski, arXiv:2503.12305v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.12619v1_tex_table18.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\large Coefficient Estimates of Spatial Error Model}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\n~ & $\\Delta$Unemployment Rate\\\\\n\\cmidrule{1-2}\n\\emph{Variables} & ~\\\\\nSpatial error parameter ($\\rho$) & 0.742***\\\\\n~ & (0.004)\\\\\n$\\Delta$ Active Mines\\textsubscript{t} & -0.026***\\\\\n~ & (0.004)\\\\\n$\\Delta$ Active Mines\\textsubscript{t-1} & -0.009*\\\\\n~ & (0.004)\\\\\n$\\Delta$ Active Mines\\textsubscript{t-2} & 0.021***\\\\\n~ & (0.004)\\\\\n$\\Delta$ (log) Real GDPPC\\textsubscript{t} & -0.543***\\\\\n~ & (0.03)\\\\\n\\bottomrule\n\\multicolumn{2}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'", "authors": ["Ebba Mark", "Ryan Rafaty", "Moritz Schwarz"], "url": "https://arxiv.org/abs/2211.12619v1", "attribution": "\"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'\" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.13337v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of Batch Sizes Across Stacked and Phased Training Strategies on MMLU and MTBench Benchmarks. Green cells indicate better scores, while blue cells highlight higher sample efficiency (fewer samples required).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Benchmark}} & \\multirow{2}{*}{\\textbf{Strategy}} & \\multicolumn{4}{c}{\\textbf{Score}} & \\multicolumn{3}{c}{\\textbf{Samples}} \\\\ \n\\cmidrule(lr){3-6} \\cmidrule(lr){7-9}\n & & \\textbf{Granite Base} & \\textbf{128} & \\textbf{4K} & \\textbf{8K} & \\textbf{128} & \\textbf{4K} & \\textbf{8K} \\\\ \n\\midrule\n\\multirow{2}{*}{\\textbf{MMLU}} \n & \\textbf{Stacked} & 0.48 & 0.516 & 0.526 & \\cellcolor{green!30}0.529 & \\cellcolor{blue!20}2,099,328 & 3,694,080 & 8,885,760 \\\\\n & \\textbf{Phased} & 0.48 & 0.513 & \\cellcolor{green!30}0.524 & - & \\cellcolor{blue!20}2,915,233 & 7,859,902 & - \\\\\n\\midrule\n\\multirow{2}{*}{\\textbf{MTBench}} \n & \\textbf{Stacked} & - & 6.406 & 6.768 & \\cellcolor{green!30}6.831 & \\cellcolor{blue!20}1,799,424 & 4,392,960 & 8,586,240 \\\\\n & \\textbf{Phased} & - & 6.325 & \\cellcolor{green!30}6.756 & - & \\cellcolor{blue!20}2,815,265 & 8,057,918 & - \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs", "authors": ["Aldo Pareja", "Nikhil Shivakumar Nayak", "Hao Wang", "Krishnateja Killamsetty", "Shivchander Sudalairaj", "Wenlong Zhao", "Seungwook Han", "Abhishek Bhandwaldar", "Guangxuan Xu", "Kai Xu", "Ligong Han", "Luke Inglis", "Akash Srivastava"], "url": "https://arxiv.org/abs/2412.13337v1", "attribution": "\"Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs\" by Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang, Krishnateja Killamsetty, Shivchander Sudalairaj, Wenlong Zhao, Seungwook Han, Abhishek Bhandwaldar, Guangxuan Xu, Kai Xu, Ligong Han, Luke Inglis, and Akash Srivastava, arXiv:2412.13337v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09523v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\nModel & \\textit{Layer} & SEAT-6 & SEAT-7 & SEAT-8 \\\\\n\\midrule\n\\multirow{3}{*}{BERT} & all & \\textbf{0.44} & 0.25 & \\textbf{0.46} \\\\\n & last & 0.56 & \\textbf{0.12} & 0.47 \\\\\n & first & 0.52 & 0.22 & 0.49 \\\\\n\\midrule\n\\multirow{3}{*}{RoBERTa} & all & \\textbf{0.59} & \\textbf{0.23} & 0.61 \\\\\n & last & 0.73 & 0.24 & 0.65 \\\\\n & first & 0.69 & 0.28 & \\textbf{0.59} \\\\\n\\midrule\n\\multirow{3}{*}{ALBERT} & all & \\textbf{0.46} & 0.48 & \\textbf{0.24} \\\\\n & last & 1.15 & \\textbf{0.26} & 0.60 \\\\\n & first & 0.54 & 0.89 & 0.95 \\\\\n\\midrule\n\\multirow{3}{*}{DistilBERT} & all & \\textbf{0.66} & \\textbf{-0.16} & 0.37 \\\\\n & last & 0.88 & 0.19 & \\textbf{0.35} \\\\\n & first & 0.90 & 0.40 & 0.52 \\\\\n\\midrule\n\\multirow{3}{*}{ELECTRA} & all & \\textbf{0.21} & \\textbf{0.02} & \\textbf{0.18} \\\\\n & last & 0.34 & 0.20 & 0.21 \\\\\n & first & 0.28 & 0.13 & 0.34 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Averaged scores over all layers in an embedding debiased at token-level, measured on SEAT tests.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Debiasing Pre-trained Contextualised Embeddings", "authors": ["Masahiro Kaneko", "Danushka Bollegala"], "url": "https://arxiv.org/abs/2101.09523v1", "attribution": "\"Debiasing Pre-trained Contextualised Embeddings\" by Masahiro Kaneko and Danushka Bollegala, arXiv:2101.09523v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.17168v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\t\t\t\t\\toprule\n\t\t\t\t & \\multicolumn{2}{c}{Change (Control - Treatment)} \\\\\n\t\t\t\t\\cmidrule{2-3}\n\t\t\t\tQuantile & Mean & 95\\% CI \\\\\n\t\t\t\t\\midrule\n\t\t\t\tP10 & -0.17\\% & [-3.06\\%, 5.49\\%] \\\\\n\t\t\t\tP30 & 0.45\\% & [-4.25\\%, 5.29\\%] \\\\\n\t\t\t\tP50 & 0.47\\% & [-4.53\\%, 5.86\\%] \\\\\n\t\t\t\tP70 & 0.30\\% & [-4.80\\%, 6.88\\%] \\\\\n\t\t\t\tP90 & -0.56\\% & [-6.21\\%, 7.00\\%] \\\\\n\t\t\t\tP98 & -6.26\\% & [-7.68\\%, 9.61\\%] \\\\\n\t\t\t\t\\bottomrule\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of quantile loss of VLT forecasts produced by Gen-QOT metrics on-policy (control arm) versus off-policy (treatment arm) on data from real world A/B test.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Learning an Inventory Control Policy with General Inventory Arrival Dynamics", "authors": ["Sohrab Andaz", "Carson Eisenach", "Dhruv Madeka", "Kari Torkkola", "Randy Jia", "Dean Foster", "Sham Kakade"], "url": "https://arxiv.org/abs/2310.17168v2", "attribution": "\"Learning an Inventory Control Policy with General Inventory Arrival Dynamics\" by Sohrab Andaz, Carson Eisenach, Dhruv Madeka, Kari Torkkola, Randy Jia, Dean Foster, and Sham Kakade, arXiv:2310.17168v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2310.12795v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Steady-state time for different data lengths $\\rho$ under data-driven STC and identification-based STC}\n\\begin{tabular}{ccccc}% 其中，tabular是表格内容的环境；c表示centering，即文本格式居中；c的个数代表列的个数\n\t\t\t\\toprule %[2pt]设置线宽 \n\t\t\t\\multicolumn{2}{c}{The pre-collected state-input data length $\\rho$} & 10 & 80 & 800 \\\\ \\midrule %[2pt] \n\t\t\t\\multirow{2}{*}{Steady-state time} & Data-driven STC & 7 & 3 & 2 \\\\\n\t\t\t& Identification-based STC & 6 & 5 & 3 \\\\\n\t\t\t\\bottomrule %[2pt] \n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Self-triggered Consensus Control of Multi-agent Systems from Data", "authors": ["Yifei Li", "Xin Wang", "Jian Sun", "Gang Wang", "Jie Chen"], "url": "https://arxiv.org/abs/2310.12795v1", "attribution": "\"Self-triggered Consensus Control of Multi-agent Systems from Data\" by Yifei Li, Xin Wang, Jian Sun, Gang Wang, and Jie Chen, arXiv:2310.12795v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10389v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison between BLDNet and the Multiresolution Autoencoder (AE) with bold italics indicating better performance.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccc|cccccc}\n\\textbf{Set} & \\multicolumn{6}{c|}{\\textbf{AE}} & \\multicolumn{6}{c}{\\textbf{BLDNet}} \\\\ \\hline\n\\multirow{5}{*}{\\textbf{Pinery Bushfire}} & & \\textbf{Acc} & \\textbf{Precision} & \\textbf{Recall} & \\textbf{Specificity} & \\textbf{F1} & \\textbf{} & \\textbf{Acc} & \\textbf{Precision} & \\textbf{Recall} & \\textbf{Specificity} & \\textbf{F1} \\\\\n & \\textbf{Train} & 0.6044 & 0.4935 & 0.4811 & 0.7386 & 0.4495 & \\textbf{Train} & \\textit{\\textbf{0.9524}} & \\textit{\\textbf{0.8444}} & \\textit{\\textbf{0.9813}} & \\textit{\\textbf{0.9822}} & \\textit{\\textbf{0.9023}} \\\\\n & \\textbf{Test} & 0.5000 & 0.3338 & 0.3288 & 0.6357 & 0.3013 & \\textbf{Test} & \\textit{\\textbf{0.7146}} & \\textit{\\textbf{0.4515}} & \\textit{\\textbf{0.5057}} & \\textit{\\textbf{0.7791}} & \\textit{\\textbf{0.4660}} \\\\\n & \\textbf{Hold} & 0.4773 & 0.3439 & 0.3090 & 0.6651 & 0.3044 & \\textbf{Hold} & \\textit{\\textbf{0.7145}} & \\textit{\\textbf{0.4517}} & \\textit{\\textbf{0.4941}} & \\textit{\\textbf{0.7743}} & \\textit{\\textbf{0.4646}} \\\\\n & \\textbf{Full} & 0.3909 & 0.2993 & 0.3226 & 0.6028 & 0.2588 & \\textbf{Full} & \\textit{\\textbf{0.7247}} & \\textit{\\textbf{0.4671}} & \\textit{\\textbf{0.5182}} & \\textit{\\textbf{0.7918}} & \\textit{\\textbf{0.4826}} \\\\ \\hline\n\\multirow{5}{*}{\\textbf{Joplin Tornado}} & & \\textbf{Acc} & \\textbf{Precision} & \\textbf{Recall} & \\textbf{Specificity} & \\textbf{F1} & \\textbf{} & \\textbf{Acc} & \\textbf{Precision} & \\textbf{Recall} & \\textbf{Specificity} & \\textbf{F1} \\\\\n & \\textbf{Train} & 0.6337 & 0.5450 & 0.4925 & 0.8033 & 0.5067 & \\textbf{Train} & \\textit{\\textbf{0.9634}} & \\textit{\\textbf{0.9462}} & \\textit{\\textbf{0.9696}} & \\textit{\\textbf{0.9839}} & \\textit{\\textbf{0.6562}} \\\\\n & \\textbf{Test} & 0.5171 & 0.5188 & 0.4274 & 0.7484 & 0.4457 & \\textbf{Test} & \\textit{\\textbf{0.7610}} & \\textit{\\textbf{0.6968}} & \\textit{\\textbf{0.6968}} & \\textit{\\textbf{0.8802}} & \\textit{\\textbf{0.6900}} \\\\\n & \\textbf{Hold} & 0.6179 & 0.5382 & 0.4967 & 0.7837 & 0.4967 & \\textbf{Hold} & \\textit{\\textbf{0.7540}} & \\textit{\\textbf{0.6987}} & \\textit{\\textbf{0.6992}} & \\textit{\\textbf{0.8778}} & \\textit{\\textbf{0.6914}} \\\\\n & \\textbf{Full} & 0.5988 & 0.5400 & 0.4893 & 0.7809 & 0.4961 & \\textbf{Full} & \\textit{\\textbf{0.7606}} & \\textit{\\textbf{0.7049}} & \\textit{\\textbf{0.7027}} & \\textit{\\textbf{0.8810}} & \\textit{\\textbf{0.6954}} \\\\ \\hline\n\\multirow{5}{*}{\\textbf{Nepal Flooding}} & & \\textbf{Acc} & \\textbf{Precision} & \\textbf{Recall} & \\textbf{Specificity} & \\textbf{F1} & \\textbf{} & \\textbf{Acc} & \\textbf{Precision} & \\textbf{Recall} & \\textbf{Specificity} & \\textbf{F1} \\\\\n & \\textbf{Train} & 0.6484 & 0.3976 & 0.6864 & 0.6836 & 0.3905 & \\textbf{Train} & \\textit{\\textbf{0.9707}} & \\textit{\\textbf{0.9310}} & \\textit{\\textbf{0.9871}} & \\textit{\\textbf{0.9889}} & \\textit{\\textbf{0.9566}} \\\\\n & \\textbf{Test} & 0.5463 & 0.3664 & 0.3701 & 0.6958 & 0.3643 & \\textbf{Test} & \\textit{\\textbf{0.6780}} & \\textit{\\textbf{0.4907}} & \\textit{\\textbf{0.4535}} & \\textit{\\textbf{0.7344}} & \\textit{\\textbf{0.4623}} \\\\\n & \\textbf{Hold} & 0.5930 & 0.4056 & 0.4121 & 0.7059 & 0.4082 & \\textbf{Hold} & \\textit{\\textbf{0.7086}} & \\textit{\\textbf{0.5589}} & \\textit{\\textbf{0.5199}} & \\textit{\\textbf{0.7613}} & \\textit{\\textbf{0.5328}} \\\\\n & \\textbf{Full} & 0.5754 & 0.3712 & 0.3733 & 0.6907 & 0.3720 & \\textbf{Full} & \\textit{\\textbf{0.7152}} & \\textit{\\textbf{0.5585}} & \\textit{\\textbf{0.5244}} & \\textit{\\textbf{0.7710}} & \\textit{\\textbf{0.5363}}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge", "authors": ["Ali Ismail", "Mariette Awad"], "url": "https://arxiv.org/abs/2201.10389v1", "attribution": "\"BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge\" by Ali Ismail and Mariette Awad, arXiv:2201.10389v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.04929v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|crc|c}\n\t\\toprule \n\t\t\\textbf{BAIR} ($64\\times64$)& $p$ & $k$ & $\\# \\text{pred}$ & FVD$\\downarrow$ \\\\\n\t\t\\cmidrule(){1-5}\n LVT & 1 & 15 & 15 & 125.8 \\\\\n DVD-GAN-FP & 1 & 15 & 15 & 109.8\\\\\n TrIVD-GAN-FP~ & 1 & 15 & 15 & 103.3\\\\\n VideoGPT~ & 1 & 15 & 15 & 103.3\\\\\n\t\tCCVS & 1 & 15 & 15 & 99.0\\\\\n FitVid & 1 & 15 & 15 & 93.6 \\\\\n\t\tMCVD~& 1 & 5 & 15 & 89.5\\\\\n NÜWA~& 1& 15& 15 & 86.9 \\\\\n RaMViD~& 1& 15& 15 & 84.2 \\\\\n VDM~& 1& 15& 15\t& 66.9 \\\\\n RIVER~ & 1& 15& 15 &73.5 \\\\\n \\textbf{CVP (Ours)}&1&\\textbf{1}&15&\\textbf{70.1}\\\\\n\t\t\\cmidrule(){1-5}\n DVG~& 2& 14& 14 & 120.0\\\\\n SAVP~ & 2 & 14 & 14 & 116.4\\\\\n\t\tMCVD~ & 2 & 5 & 14 & 87.9 \\\\\n \\textbf{CVP (Ours)}&2&2&14&68.2\\\\\n \\textbf{CVP (Ours)}&2&\\textbf{1}&14&\\textbf{65.1}\\\\\n\t\t\\cmidrule(){1-5}\n SAVP~ & 2 & 10 & 28 & 143.4 \\\\\n Hier-vRNN~& 2 & 10 & 28 & 143.4 \\\\\n\t\tMCVD~& 2 & 5 & 28 & 118.4 \\\\\n \\textbf{CVP (Ours)}&2&\\textbf{2}&28&95.1\\\\\n\t\t\\textbf{CVP (Ours)}&2&\\textbf{1}&28&\\textbf{85.1}\\\\\n \\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Prediction", "authors": ["Gaurav Shrivastava", "Abhinav Shrivastava"], "url": "https://arxiv.org/abs/2412.04929v2", "attribution": "\"Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Prediction\" by Gaurav Shrivastava and Abhinav Shrivastava, arXiv:2412.04929v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.05135v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Null rejection rates for testing methods at different perturbation levels in the RBM example.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \n \\textbf{PERTURBATION:} & \\textbf{0} & \\textbf{0.02} & \\textbf{0.04} & \\textbf{0.06} \\\\\n \\hline\n \\textbf{RFSD} & 0.00 & 0.48 & 0.93 & 0.98 \\\\\n \\textbf{FSSD-opt} & 0.05 & 0.70 & 0.96 & 0.99 \\\\\n \\textbf{IMQ KSD} & 0.08 & 0.99 & 1.00 & 1.00 \\\\\n \\textbf{Gauss KSD} & 0.08 & 0.95 & 1.00 & 1.00 \\\\\n \\textbf{PSD r1} & 0.08 & 0.51 & 0.96 & 0.99 \\\\\n \\textbf{PSD r2} & 0.06 & 1.00 & 1.00 & 1.00 \\\\\n \\textbf{PSD r3} & 0.09 & 0.97 & 1.00 & 1.00\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "The Polynomial Stein Discrepancy for Assessing Moment Convergence", "authors": ["Narayan Srinivasan", "Matthew Sutton", "Christopher Drovandi", "Leah F South"], "url": "https://arxiv.org/abs/2412.05135v1", "attribution": "\"The Polynomial Stein Discrepancy for Assessing Moment Convergence\" by Narayan Srinivasan, Matthew Sutton, Christopher Drovandi, and Leah F South, arXiv:2412.05135v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.16451v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of VI-LSPM and MCMC-LSPM average model run time in seconds across different network sizes $n$. Standard deviations are given in brackets. *The results for $n=1000$ are based on only five sets of initial positions for VI-LSPM, and only on two networks with one set of initial positions for MCMC-LSPM.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ll|}\n\\hline\n & \\multicolumn{2}{c|}{Time (sec)} \\\\ \\cline{2-3} \n\\multirow{-2}{*}{$n$} & VI & MCMC \\\\ \\hline\n20 & { 0.6 (0.6)} & 304 (15) \\\\ \\hline\n50 & { 24 (11)} & 481 (4) \\\\ \\hline\n100 & { 85 (29)} & 1,169 (8) \\\\ \\hline\n200 & { 389 (201)} & 3,920 (132) \\\\ \\hline\n{ 500} & { 1,669 (262)} & { 23,264 (2,541)} \\\\ \\hline\n{ 1000*} & { 8,419 (1,649)} & { 105,428 (10,112)} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Variational Inference for the Latent Shrinkage Position Model", "authors": ["Xian Yao Gwee", "Isobel Claire Gormley", "Michael Fop"], "url": "https://arxiv.org/abs/2311.16451v2", "attribution": "\"Variational Inference for the Latent Shrinkage Position Model\" by Xian Yao Gwee, Isobel Claire Gormley, and Michael Fop, arXiv:2311.16451v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.02994v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c} \n\\hline\\hline \n &\n \\multicolumn{2}{c|}{Power-law kernel} \n & \\multicolumn{2}{c}{Exponential kernel} \n \\\\\n\\hline\nSample size $N$ & \n$\\textrm{err}^{(1)}$ & $\\textrm{err}_{\\textrm{proj}}^{(1)}$ & $\\textrm{err}^{(2)}$ \n& $\\textrm{err}_{\\textrm{proj}}^{(2)}$ \\\\\n\\hline\n63 & $ 8.4 \\times 10^{-3} $ & $ 1.9 \\times 10^{-3}$ & $ 9.8 \\times 10^{-3} $ & $ 4.4 \\times 10^{-3}$ \\\\\n126 & $ 6.9 \\times 10^{-3}$ & $1.7 \\times 10^{-3}$ & $ 5.3 \\times 10^{-3}$ & $ 2.9 \\times 10^{-3}$ \\\\\n252 & $ 4.2 \\times 10^{-3}$ & $ 1.2 \\times 10^{-3}$ & $ 4.2 \\times 10^{-3}$ & $ 2. 2\\times 10^{-3}$ \\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An Offline Learning Approach to Propagator Models", "authors": ["Eyal Neuman", "Wolfgang Stockinger", "Yufei Zhang"], "url": "https://arxiv.org/abs/2309.02994v1", "attribution": "\"An Offline Learning Approach to Propagator Models\" by Eyal Neuman, Wolfgang Stockinger, and Yufei Zhang, arXiv:2309.02994v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.05185v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison of different multi-modal LLMs with retrieval.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrr}\n\\toprule\n\\textbf{Model} & \\textbf{Open/Closed} & \\textbf{Params (B)} & \\textbf{Accuracy (\\%)} \\\\\n\\midrule\nGPT-4o-mini & Closed-source & -- & 21.53 \\\\\nGemini-2.0-Flash & Closed-source & -- & 18.42 \\\\\nKimi-VL-A3B-Instruct & Open-source & 16 & 17.63 \\\\\nQwen2.5-7B-Instruct & Open-source & 7 & 15.15 \\\\\nDeepSeek R1 8B & Open-source & 8 & 13.41 \\\\\nDeepSeek R1 14B & Open-source & 14 & 13.15 \\\\\nLlama 3.2 11B & Open-source & 11 & 11.57 \\\\\nLlama 3.2 3B & Open-source & 3 & 11.27 \\\\\nQwen-VL-Chat & Open-source & 7 & 8.89 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Towards Temporal-Aware Multi-Modal Retrieval Augmented Generation in Finance", "authors": ["Fengbin Zhu", "Junfeng Li", "Liangming Pan", "Wenjie Wang", "Fuli Feng", "Chao Wang", "Huanbo Luan", "Tat-Seng Chua"], "url": "https://arxiv.org/abs/2503.05185v2", "attribution": "\"Towards Temporal-Aware Multi-Modal Retrieval Augmented Generation in Finance\" by Fengbin Zhu, Junfeng Li, Liangming Pan, Wenjie Wang, Fuli Feng, Chao Wang, Huanbo Luan, and Tat-Seng Chua, arXiv:2503.05185v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.19037v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{WERs and relative benefits of adding modalities on SlideAVSR at different noise levels. The OCR words are ranked by FQ Ranker , and K indicates the maximum OCR word count.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rccccc}\n \\noalign{\\hrule height 1.2pt}\n \\textbf{Inputs} & +$\\infty$ & 10 & 0 & -10& \\textbf{Average} \\\\\n \\multicolumn{6}{c}{\\cellcolor[HTML]{DEDCDC}WER $\\downarrow$ (RB $\\uparrow$)} \\\\\n \\hline\n $A$ & 33.8\\%& 42.5\\%& 44.8\\%& 70.6\\%& 47.9\\% \\\\\n \\hline\n $O_{All}+A$ & 31.4\\% (+7.0\\%)& 37.2\\% (+12.4\\%)& 47.7\\% (-6.4\\%)& 75.9\\% (-7.6\\%)& 48.0\\% (+1.4\\%)\\\\\n $O_{K=30}+A$ & 30.5\\% (+9.7\\%)& 35.7\\% (+15.8\\%)& 46.9\\% (-4.7\\%)& 75.5\\% (-6.9\\%)& 47.2\\% (+3.5\\%)\\\\\n $O_{K=10}+A$ & 30.6\\% (+9.5\\%)& 34.6\\% (+18.5\\%)& 46.2\\% (-3.1\\%)& 77.9\\% (-10.3\\%)& 47.3\\% (+3.6\\%)\\\\\n $I+A$ & 28.5\\% (+15.5\\%)& 34.3\\% (+19.3\\%)& 46.1\\% (-13.6\\%)& 70.8\\% (-0.2\\%)& 46.1\\% (+5.3\\%)\\\\\n \\noalign{\\hrule height 1.2pt}\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MLLM-based Speech Recognition: When and How is Multimodality Beneficial?", "authors": ["Yiwen Guan", "Viet Anh Trinh", "Vivek Voleti", "Jacob Whitehill"], "url": "https://arxiv.org/abs/2507.19037v1", "attribution": "\"MLLM-based Speech Recognition: When and How is Multimodality Beneficial?\" by Yiwen Guan, Viet Anh Trinh, Vivek Voleti, and Jacob Whitehill, arXiv:2507.19037v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.15236v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Patch-level Anomaly Detection Results}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccccccc}\n\\hline\n & & NLL & Regret & LLR & complexity & f-AnoGAN & AE & MemAE & AnoDDPM & AnoPILaD\n\\\\ \\hline\n\\multirow{2}{*}{LH} & AUC & 0.4982 & 0.6720 & 0.6078 & 0.7931 & 0.2289 & 0.9254 & 0.9290 & 0.8555 & $\\mathbf{0.9587}$\n\\\\ \n & AUPR & 0.5552 & 0.6718 & 0.6260 & 0.7139 & 0.3377 & 0.8906 & 0.8886 & 0.7841 & $\\mathbf{0.9499}$\n\\\\ \\hline\n\\multirow{2}{*}{C16} & AUC & 0.4983 & 0.6720 & 0.7065 & 0.7752 & 0.1735 & 0.6584 & 0.6611 & 0.6857 & $\\mathbf{0.8884}$\n\\\\ \n & AUPR & 0.5552 & 0.6718 & 0.4765 & 0.5140 & 0.1104 & 0.4759 & 0.4880 & 0.5741 & $\\mathbf{0.6987}$\n\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis", "authors": ["Jiamu Wang", "Keunho Byeon", "Jinsol Song", "Anh Nguyen", "Sangjeong Ahn", "Sung Hak Lee", "Jin Tae Kwak"], "url": "https://arxiv.org/abs/2508.15236v1", "attribution": "\"Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis\" by Jiamu Wang, Keunho Byeon, Jinsol Song, Anh Nguyen, Sangjeong Ahn, Sung Hak Lee, and Jin Tae Kwak, arXiv:2508.15236v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.06963v3_tex_table8.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of TLT references}\n\\begin{tabular}{llrrrrr}\n\\hline\t\n\\hline\t\nMethod & N & ICC & R2 & MAE & MAPE & r\\\\\n\\hline\t\t\t\nProposed & 4,323 & \\textbf{0.976} & \\textbf{0.953} & \\textbf{0.684} & \\textbf{3.0} & \\textbf{0.978} \\\\\nField 23280 & 4,323 & 0.969 & 0.941 & 0.856 & 3.7 & 0.970 \\\\\n\\hline\t\n\\hline\t\n\\multicolumn{7}{l}{*Comparison to the target values, listing both the proposed predictions}\\\\ \\multicolumn{7}{l}{ and alternative UK Biobank reference values on the same subjects}\t\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI", "authors": ["Taro Langner", "Fredrik K. Gustafsson", "Benny Avelin", "Robin Strand", "Håkan Ahlström", "Joel Kullberg"], "url": "https://arxiv.org/abs/2101.06963v3", "attribution": "\"Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI\" by Taro Langner, Fredrik K. Gustafsson, Benny Avelin, Robin Strand, Håkan Ahlström, and Joel Kullberg, arXiv:2101.06963v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12525v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The impact of different CBAM.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n\tCBAM & NSS & CC & AUC \\\\\\hline\n no CBAM & 0.9653 & 0.5587 & 0.6069 \\\\ \\hline\n with 2d-CBAM & 1.0271 & 0.5765 & 0.6278 \\\\ \\hline\n with SP-CBAM & 1.1736 & 0.6234 & 0.6619 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback", "authors": ["Jie Li", "Ling Han", "Cong Zhang", "Qiyue Li", "Zhi Liu"], "url": "https://arxiv.org/abs/2201.12525v1", "attribution": "\"Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback\" by Jie Li, Ling Han, Cong Zhang, Qiyue Li, and Zhi Liu, arXiv:2201.12525v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.06447v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overview of elements in the MEN.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\textbf{Element} & \\textbf{Electricity} & \\textbf{Gas} & \\textbf{Heat} \\\\\n \\midrule\n Nodes & 295 & 236 & 132 \\\\\n Edges & 291 & 235 & 106 \\\\\n Loads & 187 & 123 & 25 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Unified Graph-Theoretic Modeling of Multi-Energy Flows in Distribution Systems", "authors": ["Marwan Mostafa", "Daniel Wenser", "Payam Teimourzadeh Baboli", "Christian Becker"], "url": "https://arxiv.org/abs/2509.06447v1", "attribution": "\"Unified Graph-Theoretic Modeling of Multi-Energy Flows in Distribution Systems\" by Marwan Mostafa, Daniel Wenser, Payam Teimourzadeh Baboli, and Christian Becker, arXiv:2509.06447v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.12771v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\n\\textbf{Method} & \\textbf{Accuracy with Return Rate} & \\textbf{Accuracy with Price} \\\\ \\hline\nMLP & 91.51\\% & 93.13\\% \\\\ \nCNN & 95.43\\% & 94.15\\% \\\\ \nResNet & 95.64\\% & 95.27\\% \\\\ \nRNN & 95.77\\% & 95.12\\% \\\\ \nGRU & 96.56\\% & 96.07\\% \\\\ \nLSTM & 97.05\\% & 94.61\\% \\\\ \nAutoencoder & 97.83\\% & 97.50\\% \\\\ \nt-CNN & 95.19\\% & 94.21\\% \\\\ \nm-CNN & 96.58\\% & 96.27\\%\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Accuracy of machine learning models on balanced return rate data (using daily data, return rate is calculated using the “close price\"). The experiment for calculating the accuracy rate with price is the same as that in Subsection . And the experiment in calculating the accuracy with the return rate use the similar settings in data splitting and model training.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks", "authors": ["Yu Zhang", "Zelin Wu", "Claudio Tessone"], "url": "https://arxiv.org/abs/2504.12771v1", "attribution": "\"Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks\" by Yu Zhang, Zelin Wu, and Claudio Tessone, arXiv:2504.12771v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16355v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c|c|c}\n Attack & $\\epsilon$ & LPIPS & NAD & $n_B$ & $t_B$ & Time $\\sigma$ & Time $\\sigma^{-1}$ & Time $\\mathsf{eval}_h$\\\\\n \\hline\\hline\n FGSM &\t 0.100 & 0.5596 & 1.1287 & 150 & 869 & 0.0303 &\t0.1993 &\t0.0309 \\\\ \n PGD & 0.300 & 0.4485 & 0.8163 & 150\t& 629\t&\t0.0280 & 0.1506 &\t0.0220 \\\\\n Brightness\t& 0.250 & 0.3183 &\t6.8516 & 150\t& 5280 &\t0.0847 & 1.3360 &\t0.2651 \\\\\n & 2.000 & 0.1955 & 5.8659\t& 150 & 4520 & 0.0763 &\t1.0865 &\t0.2143 \\\\\n Contrast & 0.250 & 0.3204 & 1.7338\t & 150\t& 1336\t& 0.0386 & 0.2999 &0.0489 \\\\\n & 2.000 & 0.1208 & 1.4895 & 150 &\t1147 &\t0.0358 & 0.2592 &\t0.0422\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Total time in seconds taken by our scheme to produce the $\\sigma$-polynomial of an image, the $\\sigma$-polynomial and its inverse of an image, and the solution using the $\\mathsf{eval}_h$ function, where $t$ is chosen such that LPIPS is high for each attack.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Property-Preserving Hashing for $\\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks", "authors": ["Hassan Asghar", "Chenhan Zhang", "Dali Kaafar"], "url": "https://arxiv.org/abs/2504.16355v1", "attribution": "\"Property-Preserving Hashing for $\\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks\" by Hassan Asghar, Chenhan Zhang, and Dali Kaafar, arXiv:2504.16355v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.13608v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\textbf{Return Period} & \\textbf{Return Value} & \\textbf{Lower CI} & \\textbf{Upper CI} \\\\\n\\hline\n1.00 & 0.35 & 1.66 & 0.11 \\\\\n2.00 & 0.15 & 1.31 & 0.04 \\\\\n5.00 & 0.05 & 0.75 & 0.00 \\\\\n10.00 & 0.02 & 0.58 & -0.20 \\\\\n25.00 & 0.01 & 0.52 & -0.36 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Return Periods and Confidence Intervals - Zarco Use Case 1}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Integrating Dynamic Correlation Shifts and Weighted Benchmarking in Extreme Value Analysis", "authors": ["Dimitrios P. Panagoulias", "Elissaios Sarmas", "Vangelis Marinakis", "Maria Virvou", "George A. Tsihrintzis"], "url": "https://arxiv.org/abs/2411.13608v2", "attribution": "\"Integrating Dynamic Correlation Shifts and Weighted Benchmarking in Extreme Value Analysis\" by Dimitrios P. Panagoulias, Elissaios Sarmas, Vangelis Marinakis, Maria Virvou, and George A. Tsihrintzis, arXiv:2411.13608v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01006v3_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Full results of VQShape variants with histogram classifiers. For a frozen pre-trained model, the mean and standard deviation of accuracies over five randomly initialized linear classifiers are reported.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\n\\textbf{Hyperparameter} & \\multicolumn{6}{c}{\\textbf{Variants of VQShape}} \\\\\n\\cmidrule(lr){2-7}\nCodebook Size \\hfill $N^{\\text{code}}$: & 32 & 64 & 128 & 512 & 512 & 512 \\\\\nCode Dim. \\hfill $d^{\\text{code}}$: & 8 & 8 & 8 & 8 & 8 & 32 \\\\\nShape Loss Weight \\hfill $\\lambda_s$: & 1 & 1 & 1 & 1 & 0 & 1 \\\\\nRepresentation: & Histogram & Histogram & Histogram & Histogram & Histogram & Histogram\\\\\n\\midrule\nArticularyWordRecognition & $0.983 \\pm 0.056$ & $0.996 \\pm 0.050$ & $0.993 \\pm 0.038$ & $0.991 \\pm 0.034$ & $0.986 \\pm 0.043$ & $0.988 \\pm 0.030$ \\\\\nAtrialFibrillation & $0.560 \\pm 0.084$ & $0.600 \\pm 0.090$ & $0.533 \\pm 0.144$ & $0.573 \\pm 0.100$ & $0.520 \\pm 0.103$ & $0.507 \\pm 0.112$ \\\\\nBasicMotions & $1.000 \\pm 0.041$ & $1.000 \\pm 0.019$ & $0.985 \\pm 0.012$ & $0.955 \\pm 0.029$ & $0.930 \\pm 0.025$ & $1.000 \\pm 0.016$ \\\\\nCharacterTrajectories & $0.888 \\pm 0.033$ & $0.908 \\pm 0.030$ & $0.931 \\pm 0.034$ & $0.942 \\pm 0.040$ & $0.928 \\pm 0.037$ & $0.938 \\pm 0.036$ \\\\\nCricket & $0.967 \\pm 0.026$ & $0.986 \\pm 0.033$ & $1.000 \\pm 0.022$ & $0.986 \\pm 0.024$ & $0.975 \\pm 0.015$ & $0.978 \\pm 0.034$ \\\\\nDuckDuckGeese & $0.428 \\pm 0.045$ & $0.468 \\pm 0.054$ & $0.440 \\pm 0.039$ & $0.396 \\pm 0.032$ & $0.360 \\pm 0.042$ & $0.396 \\pm 0.048$ \\\\\nERing & $0.929 \\pm 0.064$ & $0.956 \\pm 0.019$ & $0.856 \\pm 0.018$ & $0.927 \\pm 0.013$ & $0.935 \\pm 0.017$ & $0.928 \\pm 0.016$ \\\\\nEigenWorms & $0.612 \\pm 0.027$ & $0.571 \\pm 0.023$ & $0.553 \\pm 0.017$ & $0.669 \\pm 0.018$ & $0.747 \\pm 0.016$ & $0.602 \\pm 0.018$ \\\\\nEpilepsy & $0.929 \\pm 0.040$ & $0.959 \\pm 0.027$ & $0.964 \\pm 0.033$ & $0.970 \\pm 0.026$ & $0.858 \\pm 0.043$ & $1.000 \\pm 0.039$ \\\\\nEthanolConcentration & $0.301 \\pm 0.012$ & $0.313 \\pm 0.018$ & $0.308 \\pm 0.014$ & $0.319 \\pm 0.017$ & $0.319 \\pm 0.020$ & $0.310 \\pm 0.015$ \\\\\nFaceDetection & $0.567 \\pm 0.009$ & $0.567 \\pm 0.010$ & $0.578 \\pm 0.008$ & $0.584 \\pm 0.006$ & $0.655 \\pm 0.013$ & $0.601 \\pm 0.009$ \\\\\nFingerMovements & $0.598 \\pm 0.028$ & $0.598 \\pm 0.033$ & $0.600 \\pm 0.036$ & $0.600 \\pm 0.024$ & $0.600 \\pm 0.026$ & $0.600 \\pm 0.028$ \\\\\nHandMovementDirection & $0.441 \\pm 0.037$ & $0.449 \\pm 0.028$ & $0.438 \\pm 0.030$ & $0.432 \\pm 0.045$ & $0.481 \\pm 0.031$ & $0.427 \\pm 0.046$ \\\\\nHandwriting & $0.156 \\pm 0.009$ & $0.212 \\pm 0.010$ & $0.196 \\pm 0.007$ & $0.148 \\pm 0.008$ & $0.235 \\pm 0.008$ & $0.206 \\pm 0.010$ \\\\\nHeartbeat & $0.736 \\pm 0.007$ & $0.743 \\pm 0.006$ & $0.741 \\pm 0.011$ & $0.739 \\pm 0.011$ & $0.730 \\pm 0.008$ & $0.743 \\pm 0.008$ \\\\\nJapaneseVowels & $0.939 \\pm 0.048$ & $0.948 \\pm 0.043$ & $0.945 \\pm 0.050$ & $0.926 \\pm 0.048$ & $0.936 \\pm 0.044$ & $0.942 \\pm 0.039$ \\\\\nLSST & $0.515 \\pm 0.034$ & $0.521 \\pm 0.035$ & $0.542 \\pm 0.029$ & $0.515 \\pm 0.025$ & $0.516 \\pm 0.025$ & $0.556 \\pm 0.018$ \\\\\nLibras & $0.768 \\pm 0.020$ & $0.804 \\pm 0.029$ & $0.811 \\pm 0.023$ & $0.818 \\pm 0.020$ & $0.730 \\pm 0.021$ & $0.850 \\pm 0.024$ \\\\\nMotorImagery & $0.628 \\pm 0.024$ & $0.672 \\pm 0.022$ & $0.650 \\pm 0.028$ & $0.662 \\pm 0.030$ & $0.626 \\pm 0.042$ & $0.678 \\pm 0.029$ \\\\\nNATOPS & $0.847 \\pm 0.020$ & $0.844 \\pm 0.022$ & $0.886 \\pm 0.020$ & $0.808 \\pm 0.016$ & $0.790 \\pm 0.022$ & $0.864 \\pm 0.020$ \\\\\nPEMS-SF & $0.839 \\pm 0.050$ & $0.864 \\pm 0.036$ & $0.853 \\pm 0.036$ & $0.846 \\pm 0.066$ & $0.810 \\pm 0.034$ & $0.824 \\pm 0.062$ \\\\\nPenDigits & $0.898 \\pm 0.063$ & $0.942 \\pm 0.075$ & $0.949 \\pm 0.067$ & $0.969 \\pm 0.038$ & $0.915 \\pm 0.048$ & $0.958 \\pm 0.030$ \\\\\nPhonemeSpectra & $0.116 \\pm 0.003$ & $0.134 \\pm 0.006$ & $0.132 \\pm 0.005$ & $0.143 \\pm 0.004$ & $0.104 \\pm 0.005$ & $0.150 \\pm 0.004$ \\\\\nRacketSports & $0.774 \\pm 0.046$ & $0.879 \\pm 0.055$ & $0.883 \\pm 0.038$ & $0.838 \\pm 0.027$ & $0.861 \\pm 0.060$ & $0.832 \\pm 0.027$ \\\\\nSelfRegulationSCP1 & $0.731 \\pm 0.027$ & $0.754 \\pm 0.027$ & $0.788 \\pm 0.022$ & $0.762 \\pm 0.030$ & $0.881 \\pm 0.015$ & $0.810 \\pm 0.028$ \\\\\nSelfRegulationSCP2 & $0.576 \\pm 0.027$ & $0.588 \\pm 0.026$ & $0.577 \\pm 0.023$ & $0.572 \\pm 0.022$ & $0.617 \\pm 0.025$ & $0.583 \\pm 0.028$ \\\\\nSpokenArabicDigits & $0.942 \\pm 0.032$ & $0.959 \\pm 0.044$ & $0.961 \\pm 0.048$ & $0.969 \\pm 0.061$ & $0.975 \\pm 0.045$ & $0.969 \\pm 0.051$ \\\\\nStandWalkJump & $0.680 \\pm 0.116$ & $0.560 \\pm 0.060$ & $0.613 \\pm 0.094$ & $0.613 \\pm 0.068$ & $0.653 \\pm 0.073$ & $0.640 \\pm 0.073$ \\\\\nUWaveGestureLibrary & $0.841 \\pm 0.033$ & $0.864 \\pm 0.036$ & $0.904 \\pm 0.016$ & $0.899 \\pm 0.030$ & $0.821 \\pm 0.014$ & $0.923 \\pm 0.026$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification", "authors": ["Yunshi Wen", "Tengfei Ma", "Tsui-Wei Weng", "Lam M. Nguyen", "Anak Agung Julius"], "url": "https://arxiv.org/abs/2411.01006v3", "attribution": "\"Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification\" by Yunshi Wen, Tengfei Ma, Tsui-Wei Weng, Lam M. Nguyen, and Anak Agung Julius, arXiv:2411.01006v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12295v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\nConfig & Setting \\\\\n\\hline\n\\hline\nBatch size & 1024 \\\\\nOptimizer & Adam \\\\\nLearning rate & $10^{-2}$ \\\\\nScheduler & AdaptiveLR \\\\\nTraining iterations & $2.5 \\times 10^6$ \\\\\nStep rate & $3 \\times 10^3$ \\\\\nDecay rate & 0.9 \\\\\nEMA decay & 0.98 \\\\\nEMA lambda & 2 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{High-precision training recipe settings for linear attention.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Towards Learning High-Precision Least Squares Algorithms with Sequence Models", "authors": ["Jerry Liu", "Jessica Grogan", "Owen Dugan", "Ashish Rao", "Simran Arora", "Atri Rudra", "Christopher Ré"], "url": "https://arxiv.org/abs/2503.12295v1", "attribution": "\"Towards Learning High-Precision Least Squares Algorithms with Sequence Models\" by Jerry Liu, Jessica Grogan, Owen Dugan, Ashish Rao, Simran Arora, Atri Rudra, and Christopher Ré, arXiv:2503.12295v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2308.00921v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lr}\n\\toprule\n\\textbf{Incident Categories} & \\textbf{Number of Observations} \\\\ \\midrule\n(PV) Privacy Violation & 51315 \\\\\n(DB) Data Breach & 26492 \\\\\n(FE) Fraud and Extortion & 4464 \\\\\n(ITE) IT Error & 2102 \\\\\nOther & 565 \\\\ \\bottomrule\n\\end{tabular}\n\\caption{Number of historical cyber incidents in each category.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Incident-Specific Cyber Insurance", "authors": ["Wing Fung Chong", "Daniel Linders", "Zhiyu Quan", "Linfeng Zhang"], "url": "https://arxiv.org/abs/2308.00921v1", "attribution": "\"Incident-Specific Cyber Insurance\" by Wing Fung Chong, Daniel Linders, Zhiyu Quan, and Linfeng Zhang, arXiv:2308.00921v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.21174v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|ll|ll|ll}\n\\# &\\multicolumn{2}{c}{$\\tau_{large}$} &\\multicolumn{2}{c}{$\\tau_{mix}$} & \\multicolumn{2}{c}{$\\tau_{small}$} \\\\\n & cg & lsqr & cg & lsqr & cg & lsqr \\\\ \\hline\n1 & 2.0 & 235.9 &2 & 641.8 & 1.0 & 866.5 \\\\\n2 & 2.0 & 157.3 & 1.9 & 980.6 & $\\S$ & $\\S$ \\\\\n3 & 2.4 & 153.0 & 1.9 & 958.3 & 2.5 & 766.3 \\\\\n4 & 2.0 & 87.1 & 2.0 & 354.0 &1.0 & 599.5 \\\\\n5 & 2.0 & 285.5 &3.0 & 848.9 & 1.8 & $\\ddagger$ \\\\\n6 & 1.0 & 68.5 & 1.0 & 511.8 &1.0 & $\\ddagger$ \\\\\n7 & 2.0 & 136.0 & 2.0 &141.2 & 1.0 & 144.8 \\\\\n8 & 8.0 & 128.6 & 4.0 & 721.0 & 6.0 & 763.0 \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Average number of inner CG and LSQR iterations for the new scheme (symmetric case) with $M$-orthogonalization}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust iterative methods for linear systems with saddle point structure", "authors": ["Murat Manguoğlu", "Volker Mehrmann"], "url": "https://arxiv.org/abs/2502.21174v1", "attribution": "\"Robust iterative methods for linear systems with saddle point structure\" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.01625v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\hline\n \\multicolumn{3}{c}{{\\bf Robustness under Variation of Topological Noise}}\\\\\\hline\n Percentage & Pre-specified Loc. ($m = 4$ vs $n = 4$) & Random Loc. ($m = 5$ vs $n = 5$) \\\\\\hline\n 100\\% & 0.4567$\\pm$0.2874 & 0.4133$\\pm$0.2482\\\\\n 95\\% & 0.4777$\\pm$0.2843 & 0.4498$\\pm$0.2844\\\\\n 90\\% & 0.4455$\\pm$0.2791 & 0.5214$\\pm$0.2983\\\\\\hline\n Percentage & Random Loc. ($m = 20$ vs $n = 20$) & Random Loc. ($m = 100$ vs $n = 100$) \\\\\\hline\n 100\\% & 0.5060$\\pm$0.3163 & 0.4328$\\pm$0.2764\\\\\n 95\\% & 0.5016$\\pm$0.2998 & 0.4193$\\pm$0.2863\\\\\n 90\\% & 0.4827$\\pm$0.2919 & 0.5260$\\pm$0.2812\\\\\\hline\n \\multicolumn{3}{c}{{\\bf Robustness under Variation of Hole Location}} \\\\\\hline\n Percentage & Pre-specified Loc. ($m = 4$ vs $n = 4$) & Random Loc. ($m = 5$ vs $n = 5$) \\\\\\hline\n 100\\% & 0.2917$\\pm$0.2624 & 0.5005$\\pm$0.2883\\\\\n 95\\% & 0.2973$\\pm$0.2407 & 0.5342$\\pm$0.2775\\\\\n 90\\% & 0.3065$\\pm$0.2505 & 0.4434$\\pm$0.3050\\\\\\hline\n Percentage & Radom Loc. ($m = 20$ vs $n = 20$) & Random Loc. ($m = 100$ vs $n = 100$) \\\\\\hline\n 100\\% & 0.4998$\\pm$0.2901 & 0.3845$\\pm$0.2620\\\\\n 95\\% & 0.4608$\\pm$0.2999 & 0.3924$\\pm$0.2777\\\\\n 90\\% & 0.4810$\\pm$0.2568 & 0.4550$\\pm$0.2835\\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Summary of mean$\\pm$standard deviation of $p$-values from the spectral transposition test in 100 simulations. Top half: In each simulation, the test is used to compare a group of $m$ random samples with a varied percentage (90\\%, 95\\%, 100\\%) of 100 points from the original key shape with a group of $n$ random samples with the same percentage of 100 points from the key shape with topological noise in the form of a much smaller hole next to the keyhole. The location of the smaller hole in each random sample of the second group can be pre-specified or randomly chosen from the pre-specified options. Bottom half: In each simulation, the test is used to compare a group of $m$ random samples with a varied percentage (90\\%, 95\\%, 100\\%) of 100 points from the original key shape with only the top left quarter of the keyhole left, with a group of $n$ random samples with the same percentage of 100 points from the original key shape with a random quarter of the keyhole left.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Topological inference on brain networks across subtypes of post-stroke aphasia", "authors": ["Yuan Wang", "Jian Yin", "Rutvik H. Desai"], "url": "https://arxiv.org/abs/2311.01625v1", "attribution": "\"Topological inference on brain networks across subtypes of post-stroke aphasia\" by Yuan Wang, Jian Yin, and Rutvik H. Desai, arXiv:2311.01625v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.01167v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Risk for behavioral scientists and models.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrr}\nStudy & Predictor & Mean & 95\\% CI \\\\\n\\midrule\nExercise & Scientist & 6.02 & (5.19, 6.93) \\\\\n & Null & 0.03 & (0.01, 0.05) \\\\\n & Oracle & 0.00 & (0.00, 0.01) \\\\\nFlu & Scientist & 64.24 & (14.80, 184.52) \\\\\n & Null & 4.80 & (2.76, 7.28) \\\\\n & Oracle & 0.11 & (0.03, 0.38) \\\\\nRCT & Scientist & 54.59 & (27.97, 101.94) \\\\\n & Null & 7.48 & (2.24, 17.04) \\\\\n & Oracle & 1.13 & (0.18, 4.70) \\\\\nEffort & Scientist & 49884 & (38858, 64352) \\\\\n & Selfish & 49865 & (26356, 80066) \\\\\n & Altruistic & 34639 & (18785, 55836) \\\\\n & Oracle & 727 & (232, 1636) \\\\\nReproducibility & Scientist & 0.31 & (0.28, 0.35) \\\\\n & Null & 0.31 & (0.20, 0.43) \\\\\n & Random & 0.25 & (0.25, 0.25) \\\\\n & Linear regression & 0.23 & (0.18, 0.28) \\\\\n & Oracle & 0.16 & (0.12, 0.21) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Simple models predict behavior at least as well as behavioral scientists", "authors": ["Dillon Bowen"], "url": "https://arxiv.org/abs/2208.01167v1", "attribution": "\"Simple models predict behavior at least as well as behavioral scientists\" by Dillon Bowen, arXiv:2208.01167v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06184v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lccccc}\n\\toprule \n& \\multicolumn{5}{c}{\\% tuples retained} \\\\\n\\cmidrule(){2-6}\nCardinalities \\hspace{3mm} & 20 & 40 & 60 & 80 & 100 \\\\ \\midrule\n$\\Omega{=}\\{2\\}$ & 128 & 204 & 256 & 346 & 462\\\\ \n$\\Omega{=}\\{3\\}$ & 228 & 390 & 540 & 702 & 844\\\\ \n$\\Omega{=}\\{4\\}$ & 294 & 546 & 754 & 922 & 1152\\\\ \n$\\Omega{=}\\{2,3\\}$ & 356 & 594 & 796 & 1048 & 1306\\\\\n\\bottomrule\n\\end{tabular}\n\\caption{GPU usage in MiB when randomly dropping tuples, corresponding to experiments in Fig.~.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Temporal-Relational CrossTransformers for Few-Shot Action Recognition", "authors": ["Toby Perrett", "Alessandro Masullo", "Tilo Burghardt", "Majid Mirmehdi", "Dima Damen"], "url": "https://arxiv.org/abs/2101.06184v3", "attribution": "\"Temporal-Relational CrossTransformers for Few-Shot Action Recognition\" by Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmehdi, and Dima Damen, arXiv:2101.06184v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.09845v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation Results. This table presents simulation results for various trial and target sizes, comparing two methods (IPW and AIPW) under two assumptions (EPD and GPD) and across different proportions of well-represented groups.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|r|r|r|l|l|r|r|r|r|}\n\\hline\n\\textbf{Trial Size} & \\textbf{Target Size} & \\textbf{Proportion} & \\textbf{Method} & \\textbf{Assumption} & \\textbf{Bias} & \\textbf{MSE} & \\textbf{SD} & \\textbf{Coverage} \\\\\n\\hline\n200 & 1980 & 0.8 & IPW & EPD & 0.089 & 1.187 & 1.087 & 0.909 \\\\\n200 & 1980 & 0.8 & IPW & GPD & 0.134 & 2.414 & 1.549 & 0.908 \\\\\n500 & 4950 & 0.8 & IPW & EPD & 0.059 & 0.700 & 0.835 & 0.925 \\\\\n500 & 4950 & 0.8 & IPW & GPD & 0.092 & 1.424 & 1.190 & 0.922 \\\\\n1000 & 9990 & 0.8 & IPW & EPD & -0.007 & 0.504 & 0.710 & 0.923 \\\\\n1000 & 9990 & 0.8 & IPW & GPD & 0.001 & 1.241 & 1.120 & 0.926 \\\\\n200 & 1980 & 0.8 & AIPW & EPD & -0.011 & 0.097 & 0.313 & 0.920 \\\\\n200 & 1980 & 0.8 & AIPW & GPD & -0.015 & 0.200 & 0.449 & 0.920 \\\\\n500 & 4950 & 0.8 & AIPW & EPD & 0.007 & 0.041 & 0.203 & 0.930 \\\\\n500 & 4950 & 0.8 & AIPW & GPD & 0.010 & 0.084 & 0.292 & 0.930 \\\\\n1000 & 9990 & 0.8 & AIPW & EPD & -0.021 & 0.016 & 0.125 & 0.937 \\\\\n1000 & 9990 & 0.8 & AIPW & GPD & -0.030 & 0.033 & 0.180 & 0.937 \\\\\n200 & 1980 & 0.9 & IPW & EPD & 0.034 & 1.399 & 1.188 & 0.940 \\\\\n200 & 1980 & 0.9 & IPW & GPD & 0.041 & 2.001 & 1.421 & 0.940 \\\\\n500 & 4950 & 0.9 & IPW & EPD & 0.118 & 0.974 & 0.985 & 0.930 \\\\\n500 & 4950 & 0.9 & IPW & GPD & 0.141 & 1.394 & 1.178 & 0.930 \\\\\n1000 & 9990 & 0.9 & IPW & EPD & 0.011 & 1.036 & 1.023 & 0.895 \\\\\n1000 & 9990 & 0.9 & IPW & GPD & 0.013 & 1.483 & 1.224 & 0.895 \\\\\n200 & 1980 & 0.9 & AIPW & EPD & -0.013 & 0.140 & 0.376 & 0.920 \\\\\n200 & 1980 & 0.9 & AIPW & GPD & -0.015 & 0.201 & 0.450 & 0.920 \\\\\n500 & 4950 & 0.9 & AIPW & EPD & 0.008 & 0.060 & 0.245 & 0.920 \\\\\n500 & 4950 & 0.9 & AIPW & GPD & 0.010 & 0.085 & 0.293 & 0.920 \\\\\n1000 & 9990 & 0.9 & AIPW & EPD & -0.025 & 0.023 & 0.151 & 0.937 \\\\\n1000 & 9990 & 0.9 & AIPW & GPD & -0.030 & 0.033 & 0.181 & 0.937 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Addressing Positivity Violations in Extending Inference to a Target Population", "authors": ["Jun Lu", "Sanjib Basu"], "url": "https://arxiv.org/abs/2412.09845v2", "attribution": "\"Addressing Positivity Violations in Extending Inference to a Target Population\" by Jun Lu and Sanjib Basu, arXiv:2412.09845v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2308.13040v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n Intervention &Mean &CI width \\\\ \\hline\n 1 & 1030.89 & 2.86 \\\\ \\hline\n 2 & 1031.89 & 2.83 \\\\ \\hline\n 3 & 1030.44 & 2.86 \\\\ \\hline\n 4 & 681.02 & 2.3 \\\\ \\hline\n 5 & 680.07 & 2.28 \\\\ \\hline\n 6 & 680.15 & 2.2 \\\\ \\hline\n 7 & 150.93 & 1.08 \\\\ \\hline\n 8 & 150.66 & 1.06 \\\\ \\hline\n 9 & 150.59 & 1.1 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Brute-force results for different interventions given that each intervention got 2000 samples or 18000 samples in total.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Estimating Treatment Effects Using Costly Simulation Samples from a Population-Scale Model of Opioid Use Disorder", "authors": ["Abdulrahman A. Ahmed", "M. Amin Rahimian", "Mark S. Roberts"], "url": "https://arxiv.org/abs/2308.13040v1", "attribution": "\"Estimating Treatment Effects Using Costly Simulation Samples from a Population-Scale Model of Opioid Use Disorder\" by Abdulrahman A. Ahmed, M. Amin Rahimian, and Mark S. Roberts, arXiv:2308.13040v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.13237v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Quantitative comparison between our and state-of-the-art models in terms of reconstruction (after the full pipeline) performance on the test set, in various configurations, according to the type of architecture used for the fMRI-VDVAE latent variables regression and the number of the VDVAE latent variables used. In this case, 4 subjects (\\#1, \\#2, \\#5, \\#7) were considered and metrics values were averaged.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|cccc|cccc}\n \\toprule\n \\multicolumn{2}{c}{\\textbf{Architecture}} & \\multicolumn{4}{c}{\\textbf{Low-level metrics}} & \\multicolumn{4}{c}{\\textbf{High-level metrics (eq. )}}\\\\ \n \\midrule\n & Latents & SSIM$\\uparrow$ & PixCorr$\\uparrow$ & Alexnet(2)$\\uparrow$ & AlexNet(5)$\\uparrow$ & InceptionV3$\\uparrow$ & CLIP$\\uparrow$ & EfficientNet-B$\\downarrow$ & SwAV-ResNet50$\\downarrow$ \\\\\n \\midrule\n Lin et al. & - & - & - & - & - & 0.782 & - & - & - \\\\ \nTakagi et al. & - & - & - & 0.830 & 0.830 & 0.760 & 0.770 & - & - \\\\ \nGu et al. & - & 0.150 & 0.325 & - & - & - & - & 0.862 & 0.465 \\\\ \n Ridge & 31 & 0.356 & \\textbf{0.254} & \\textbf{0.942} & \\textbf{0.962} & \\textbf{0.872} & 0.915 & \\textbf{0.775} & \\textbf{0.423} \\\\\n Ours & 15 & \\textbf{0.361} & 0.202 & 0.878 & 0.931 & 0.862 & 0.914 & 0.786 & 0.434 \\\\\n Ours & 31 & 0.360 & 0.177 & 0.864 & 0.923 & 0.869 & \\textbf{0.922} & 0.785 & 0.434 \\\\\n \\midrule\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Optimized two-stage AI-based Neural Decoding for Enhanced Visual Stimulus Reconstruction from fMRI Data", "authors": ["Lorenzo Veronese", "Andrea Moglia", "Luca Mainardi", "Pietro Cerveri"], "url": "https://arxiv.org/abs/2412.13237v1", "attribution": "\"Optimized two-stage AI-based Neural Decoding for Enhanced Visual Stimulus Reconstruction from fMRI Data\" by Lorenzo Veronese, Andrea Moglia, Luca Mainardi, and Pietro Cerveri, arXiv:2412.13237v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.11273v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experimental settings}\n\\begin{tabular}{lll}\n\\toprule\n\\textbf{Parameter} & \\textbf{Description} & \\textbf{Values}\\\\ \n\\midrule\n$f_1$ & Initial mean estimate & 0.2, 0.5 \\\\\n$\\sqrt{MSE_1}$ & Initial coefficient of variation & 4.0 \\\\\n$\\alpha$ & Smoothing parameter for DGP & 0.05, 0.1, 0.15 \\\\\n$l$ & Lead time & 1, 5, 10, 20 \\\\\n$P_1^*$ & In stock probability target & 0.9, 0.91, $\\cdots$, 0.98 \\\\\n$h$ & Holding cost & 1 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On non-negative auto-correlated integer demand processes", "authors": ["Lotte van Hezewijk", "Nico Dellaert", "Willem van Jaarsveld"], "url": "https://arxiv.org/abs/2312.11273v2", "attribution": "\"On non-negative auto-correlated integer demand processes\" by Lotte van Hezewijk, Nico Dellaert, and Willem van Jaarsveld, arXiv:2312.11273v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.17158v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrrr}\n\\toprule\n\\multicolumn{3}{c}{ } & \\multicolumn{3}{c}{Efficiency (\\%)} & \\multicolumn{2}{c}{DF} \\\\\n\\cmidrule(l{3pt}r{3pt}){4-6} \\cmidrule(l{3pt}r{3pt}){7-8}\n$\\kappa_{DP}$ & $\\kappa_{LoF-DP}$ & $\\kappa_{MSE(D)}$ & $(DP)_S$ & LoF-$DP$ & $MSE(D_S)$ & PE & LoF\\\\\n\\midrule\n0.33 & 0.33 & 0.33 & 89.11 & 100.54 & 95.84 & 16 & 10\\\\\n0.40 & 0.20 & 0.40 & 94.20 & 89.30 & 99.50 & 17 & 9\\\\\n0.25 & 0.25 & 0.50 & 90.71 & 94.26 & 99.84 & 15 & 11\\\\\n1.00 & 0.00 & 0.00 & 100.00 & 67.05 & 98.73 & 22 & 4\\\\\n0.00 & 1.00 & 0.00 & 77.47 & 100.00 & 90.35 & 14 & 12\\\\\n0.00 & 0.00 & 1.00 & 73.08 & 80.31 & 100.00 & 9 & 17\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Efficiencies under individual criteria and degrees of freedom for the Egorova and Gilmour case study designs defined by six different sets of criteria weights.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "MOODE: An R Package for Multi-Objective Optimal Design of Experiments", "authors": ["Vasiliki Koutra", "Olga Egorova", "Steven G. Gilmour", "Luzia A. Trinca"], "url": "https://arxiv.org/abs/2412.17158v1", "attribution": "\"MOODE: An R Package for Multi-Objective Optimal Design of Experiments\" by Vasiliki Koutra, Olga Egorova, Steven G. Gilmour, and Luzia A. Trinca, arXiv:2412.17158v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12417v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cll}\n\\toprule\n& Hyperparameter & Value \\\\\n\\midrule\n\\multirow{5}{*}{\\shortstack{Network\\\\Hyperparameters}} & Optimizer & Adam~ \\\\\n& Learning rate & 3e-4 \\\\\n& Mini-batch size & 256 \\\\\n& Target update rate (FQE) & 5e-3 \\\\\n& Discount factor & 0.99 \\\\\n& Terminal Discount factor & 0.0 \\\\\n\\midrule\n\\multirow{3}{*}{Architecture} & Network Hidden dim & 256 \\\\\n& Network Hidden layers & 2 \\\\\n& Activation function & ReLU \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Hyperparameters and architecture.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error", "authors": ["Scott Fujimoto", "David Meger", "Doina Precup", "Ofir Nachum", "Shixiang Shane Gu"], "url": "https://arxiv.org/abs/2201.12417v2", "attribution": "\"Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error\" by Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, and Shixiang Shane Gu, arXiv:2201.12417v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.00145v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulated results for solving our $10$-unit, $24$-hour, UC problem. The approximation error in the rightmost column is computed by comparing the approximation produced by our quantum-classical heuristic against the global optimum computed by CPLEX~. The number of iterations and the approximation error are averaged across the $24$ time periods with power load profile described in Table~, and across $7$ independent trials for each period. The results summarized in this table are illustrated in Figure~.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\toprule\nAnsatz layers & TQG & Params & Depth & Iterations & Error (\\%) \\\\\n\\midrule\n1 & 15 & 5 & 16 & 59.14 & 1.78 \\\\\n2 & 30 & 10 & 31 & 103.63 & 1.18 \\\\\n3 & 45 & 15 & 46 & 148.23 & 0.95 \\\\\n4 & 60 & 20 & 61 & 194.07 & 1.03 \\\\\n5 & 75 & 25 & 76 & 238.61 & 0.75 \\\\\n6 & 90 & 30 & 91 & 277.00 & 0.74 \\\\\n7 & 105 & 35 & 106 & 324.45 & 0.73 \\\\\n8 & 120 & 40 & 121 & 366.80 & 0.72 \\\\\n9 & 135 & 45 & 136 & 407.51 & 0.55 \\\\\n10 & 150 & 50 & 151 & 449.38 & 0.72 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A New Hybrid Quantum-Classical Algorithm for Solving the Unit Commitment Problem", "authors": ["Willie Aboumrad", "Phani R V Marthi", "Suman Debnath", "Martin Roetteler", "Evgeny Epifanovsky"], "url": "https://arxiv.org/abs/2505.00145v1", "attribution": "\"A New Hybrid Quantum-Classical Algorithm for Solving the Unit Commitment Problem\" by Willie Aboumrad, Phani R V Marthi, Suman Debnath, Martin Roetteler, and Evgeny Epifanovsky, arXiv:2505.00145v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.10270v5_tex_table16.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cr|cc}\n\\hline\n & \\multicolumn{2}{c|}{Hodge Scattering + SVM} & \\multicolumn{2}{c|}{UGT} & \\multicolumn{2}{c}{GFN} \\\\ \\hline\nGraph & Accuracy & \\# Param & Accuracy & \\# Param & Accuracy & \\# Param \\\\ \\hline\nCollab & 80.38 & 256 & 77.84 & 866,746 & 81.50 & 68,754 \\\\\nDD & 72.71 & 256 & 80.23 & 76,928 & 79.37 & 68,754 \\\\\nIMDB-B & 73.10 & 256 & 77.04 & 55,508 & 73.40 & 68,754 \\\\\nIMDB-M & 49.67 & 256 & 53.60 & 48,698 & 51.80 & 68,818 \\\\\nMUTAG & 85.78 & 256 & 80.23 & 4,178 & 85.83 & 68,818 \\\\\nPROTEINS & 75.35 & 256 & 78.53 & 1,878 & 76.46 & 68,818 \\\\\nPTC & 68.28 & 256 & 69.63 & 12,038 & 66.60 & 68,818 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Multiscale Hodge Scattering Networks for Data Analysis", "authors": ["Naoki Saito", "Stefan C. Schonsheck", "Eugene Shvarts"], "url": "https://arxiv.org/abs/2311.10270v5", "attribution": "\"Multiscale Hodge Scattering Networks for Data Analysis\" by Naoki Saito, Stefan C. Schonsheck, and Eugene Shvarts, arXiv:2311.10270v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.02714v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Matriz A que representa $4$ opciones de pedido para un horizonte de planificación de tamaño $3$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|lll|}\n\\hline\nPeriodo $i$ & 1 & 2 & 3 \\\\ \\hline\nEstrategia $j$ & \\multicolumn{3}{c|}{$\\alpha_{i}$} \\\\ \\hline\n & 10 & 20 & 15 \\\\ \\hline\n1 & 1 & 1 & 1 \\\\ \\hline\n2 & 1 & 2 & 0 \\\\ \\hline\n3 & 2 & 0 & 1 \\\\ \\hline\n4 & 3 & 0 & 0 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Nuevo modelo para el dimensionamiento de lotes de pedidos en función del volumen de compra y deterioro temporal de los artículos", "authors": ["Margarita Miguelina Mieras", "Tania Daiana Tobares", "Ricardo Raúl Palma", "Antonio José Ramirez-Pastor", "Fabricio Orlando Sanchez Varretti"], "url": "https://arxiv.org/abs/2412.02714v1", "attribution": "\"Nuevo modelo para el dimensionamiento de lotes de pedidos en función del volumen de compra y deterioro temporal de los artículos\" by Margarita Miguelina Mieras, Tania Daiana Tobares, Ricardo Raúl Palma, Antonio José Ramirez-Pastor, and Fabricio Orlando Sanchez Varretti, arXiv:2412.02714v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.10615v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Functions involving Stirling numbers of the first and second kind , denoted $s$ and $S$, respectively, and their derivatives appearing in HAC densities and their associated Fisher information. See -- for notation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|ll}\n\\hline\n$s_{nk}(x)$ & $\\sum_{j=k}^{n}x^js(n,j)S(j,k)$ &\n$(x)_n$ & $\\sum_{j=1}^{n}x^js(n,j)$\\\\\n$s_{nk}'(x)$ & $\\sum_{j=k}jx^{j-1}s(n,j)S(j,k)$ &\n$(x)'_n$ & $\\sum_{j=1}^{n}jx^{j-1}s(n,j)$\\\\\n$s_{nk}^{(2)}(x)$ & $\\sum_{j=k}j(j-1)x^{j-2}s(n,j)S(j,k)$ &\n$(x)^{(2)}_n$ & $\\sum_{j=1}^{n}j(j-1)x^{j-2}s(n,j)$\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Inference for overparametrized hierarchical Archimedean copulas", "authors": ["Samuel Perreault", "Yanbo Tang", "Ruyi Pan", "Nancy Reid"], "url": "https://arxiv.org/abs/2411.10615v1", "attribution": "\"Inference for overparametrized hierarchical Archimedean copulas\" by Samuel Perreault, Yanbo Tang, Ruyi Pan, and Nancy Reid, arXiv:2411.10615v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.14930v1_tex_table26.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Traversal results of AMG solving the constant coefficient Poisson equation}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\t\t\\hline\n\t\t$n$ & $\\theta$ & iter & $n$ & $\\theta$ & iter \\\\ \\hline\n\t\t128 & 0.127 & 26 & 144 & 0.125 & 27 \\\\ \\hline\n\t\t160 & 0.122 & 28 & 176 & 0.126 & 27 \\\\ \\hline\n\t\t192 & 0.123 & 28 & 208 & 0.136 & 28 \\\\ \\hline\n\t\t224 & 0.128 & 27 & 240 & 0.126 & 27 \\\\ \\hline\n\t\t256 & 0.121 & 29 & 272 & 0.125 & 29 \\\\ \\hline\n\t\t288 & 0.129 & 29 & 304 & 0.131 & 29 \\\\ \\hline\n\t\t320 & 0.126 & 29 & 336 & 0.125 & 30 \\\\ \\hline\n\t\t352 & 0.124 & 30 & 368 & 0.128 & 30 \\\\ \\hline\n\t\t384 & 0.130 &29 & 400 & 0.125& 30 \\\\ \\hline\n\t\t416 & 0.123 & 30 & 432 & 0.125 & 30 \\\\ \\hline\n\t\t448 & 0.128 & 30 & 464 & 0.120 & 30 \\\\ \\hline\n\t\t480 & 0.125 &30 & 496 & 0.124 & 31 \\\\ \\hline\n\t\t512 & 0.121 & 31 & ~ & ~ & ~ \\\\ \\hline\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs", "authors": ["Juan Zhang", "Junyue Luo", "Fangfang Zhang", "Xiaoqiang Yue"], "url": "https://arxiv.org/abs/2504.14930v1", "attribution": "\"Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs\" by Juan Zhang, Junyue Luo, Fangfang Zhang, and Xiaoqiang Yue, arXiv:2504.14930v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10279v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|}\n \\hline\n \\multicolumn{3}{|c|}{Fit exponents} \\\\\n \\hline\n Schedule & Random initial. & Minifold initial.\\\\\n \\hline\n Fixed $\\beta$ &$0.70\\pm 0.08$ & $0.93\\pm 0.06$\\\\\n Logarithmic & $0.29\\pm 0.07$ & $0.88\\pm 0.09$\\\\ \n Linear &$0.34\\pm0.07$ & $0.86\\pm 0.11$\\\\\n Exponential &$0.37\\pm 0.07$ & $1.00\\pm 0.12$\\\\\n Geometric & $0.29\\pm 0.07$ & $0.85\\pm 0.18$\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Table of scaling exponents for different annealing schedules and initialization options. The peptides are the same, except that for fixed $\\beta$ we have also included dipeptides with 5 bits of precision, what is costly for the rest of schedules. For fixed $\\beta$, the value heuristically chosen was $\\beta = 1000$, while the initial $\\beta$ value in each of the schedules, defined in , is $\\beta(1) = 50$. The uncertainty is expressed via the standard deviation in the expected exponent, calculated with the bootstrapping method .}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "QFold: Quantum Walks and Deep Learning to Solve Protein Folding", "authors": ["P A M Casares", "Roberto Campos", "M A Martin-Delgado"], "url": "https://arxiv.org/abs/2101.10279v2", "attribution": "\"QFold: Quantum Walks and Deep Learning to Solve Protein Folding\" by P A M Casares, Roberto Campos, and M A Martin-Delgado, arXiv:2101.10279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.01780v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Frequency of Special Characters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccl}\n \\toprule\n Non-English or Math&Frequency&Comments\\\\\n \\midrule\n \\O & 1 in 1,000& For Swedish names\\\\\n $\\pi$ & 1 in 5& Common in math\\\\\n \\$ & 4 in 5 & Used in business\\\\\n $\\Psi^2_1$ & 1 in 40,000& Unexplained usage\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments", "authors": ["James Enouen", "Tianshu Sun", "Yan Liu"], "url": "https://arxiv.org/abs/2309.01780v1", "attribution": "\"Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments\" by James Enouen, Tianshu Sun, and Yan Liu, arXiv:2309.01780v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09765v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n& \\bf Irrelevant & \\multicolumn{3}{c}{\\bf Relevant} & \\bf Very Relevant \\\\\n\\cmidrule(l@{0pt}r@{0pt}){2-2}\\cmidrule(l@{5pt}r@{0pt}){3-5} \\cmidrule(l@{5pt}r@{0pt}){6-6}\n\\bf Words & \\bf c1 & \\bf c2 & \\bf c3 & \\bf c4 & \\bf c5\\\\\n\\midrule\nPro & 14\\% & 10\\% & 36\\% & 34\\% & 6\\% \\\\\nCon & 36\\%& 2\\% & 34\\% & 26\\% & 2\\% \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Distribution of the pro/con bag-of-words, averaged across the top-10 big issues, over the five considered categories: c2 means wrong stance, c3 words that fit both stances, and c3 and c4 represent correct stance.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Belief-based Generation of Argumentative Claims", "authors": ["Milad Alshomary", "Wei-Fan Chen", "Timon Gurcke", "Henning Wachsmuth"], "url": "https://arxiv.org/abs/2101.09765v2", "attribution": "\"Belief-based Generation of Argumentative Claims\" by Milad Alshomary, Wei-Fan Chen, Timon Gurcke, and Henning Wachsmuth, arXiv:2101.09765v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2203.12460v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|}\n\\hline\n & (1) & (2) & (3) & (4) & (5) \\\\ \\hline\nVariables & Model 1 & Model 2 & Model 3 & Model 4 & Model 5 \\\\ \\hline\n & & & & & \\\\ \\hline\npositive & 0.14*** & 0.14*** & 0.13*** & 0.11** & 0.11 \\\\ \\hline\n & (0.04) & (0.04) & (0.04) & (0.04) & (0.06) \\\\ \\hline\nnegative & -0.04 & -0.03 & -0.05 & -0.01 & 0.29 \\\\ \\hline\n & (0.17) & (0.17) & (0.18) & (0.18) & (0.28) \\\\ \\hline\nanxiety & -0.19 & -0.19 & -0.56 & -0.62 & -1.16* \\\\ \\hline\n & (0.31) & (0.31) & (0.33) & (0.33) & (0.52) \\\\ \\hline\nanger & -0.25 & -0.24 & 0.06 & 0.03 & 0.42 \\\\ \\hline\n & (0.34) & (0.34) & (0.34) & (0.35) & (0.56) \\\\ \\hline\nsad & -0.38 & -0.41 & -0.43 & -0.50 & -0.55 \\\\ \\hline\n & (0.26) & (0.26) & (0.27) & (0.27) & (0.44) \\\\ \\hline\ncertain & 0.06 & 0.06 & 0.05 & 0.03 & 0.22 \\\\ \\hline\n & (0.10) & (0.10) & (0.10) & (0.10) & (0.16) \\\\ \\hline\n & & & & & \\\\ \\hline\nMAR(2 m) & & YES & YES & YES & YES \\\\ \\hline\nIndustry & & & YES & YES & YES \\\\ \\hline\nYear & & & & YES & YES \\\\ \\hline\nMAR(5 d) & & & & & YES \\\\ \\hline\nN & 9,208 & 9,208 & 9,208 & 9,208 & 3,863 \\\\ \\hline\nBIC & 12776 & 12810 & 12844 & 12873 & 5535 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Logistic Regression of Value Based Label Function ($y_I(T^c_d)(4)$) on Analysts' Recommendations within 2 months ($MAR(2\\,m)$) and Sentiment of Earnings Calls Transcripts. Standard errors in parentheses. Significance levels marked with stars: *** p<0.001, ** p<0.01, * p<0.05}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An Exploratory Study of Stock Price Movements from Earnings Calls", "authors": ["Sourav Medya", "Mohammad Rasoolinejad", "Yang Yang", "Brian Uzzi"], "url": "https://arxiv.org/abs/2203.12460v1", "attribution": "\"An Exploratory Study of Stock Price Movements from Earnings Calls\" by Sourav Medya, Mohammad Rasoolinejad, Yang Yang, and Brian Uzzi, arXiv:2203.12460v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.06741v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|l|}\n \\hline \n &\n \\multicolumn{3}{c|}{Concentration} &\n \\multicolumn{3}{c|}{Emission}\\\\ \n \\hline\n & \\small Mannix & \\small LC & \\small ML & \\small Mannix & \\small LC & \\small ML \\\\\n \\hline\n \\small Ours$_{Forward}$ & 0.5425 & \\textbf{0.3101} & 0.5814 & 0.0992 & 0.1615 & 0.0517 \\\\\n \\small Ours$_{Reverse}$ & 0.6566 & 0.4301 & 0.5702 & 0.1085 & 0.1369 & 0.0543\\\\\n \\small Ours$_{Poly}$ & \\textbf{0.4624} & 0.3140 & \\textbf{0.4225} & 0.5380 & 0.1977 & 0.0942\\\\\n \\small RNN$_{mod}$ & 0.6239 & 0.4314 & 2.1859 & 0.0661 & 0.0737 & 0.7127\\\\\n \\small NN & 0.5683 & 0.3132 & 0.4618 & \\textbf{0.0721} & \\textbf{0.0754} & \\textbf{0.0187} \\\\\n \\small LSTM & 0.6454 & 0.3914 & 0.5550 & 0.2426 & 0.2568 & 0.0711\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Average of relative training and validation errors for predicting concentrations and emissions from each station and its corresponding tailing pond. For each column (station), the lowest error is highlighted.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Methane projections from Canada's oil sands tailings using scientific deep learning reveal significant underestimation", "authors": ["Esha Saha", "Oscar Wang", "Amit K. Chakraborty", "Pablo Venegas Garcia", "Russell Milne", "Hao Wang"], "url": "https://arxiv.org/abs/2411.06741v1", "attribution": "\"Methane projections from Canada's oil sands tailings using scientific deep learning reveal significant underestimation\" by Esha Saha, Oscar Wang, Amit K. Chakraborty, Pablo Venegas Garcia, Russell Milne, and Hao Wang, arXiv:2411.06741v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15444v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Upper bounds of $W_{\\max}(n,9)$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c||c|c|c}\n\\noalign{\\hrule height1pt}\n$n$ & $Ub_{\\text{LP}}(n)$ & $Ub_{\\text{SDP}}(n)$&\n$n$ & $Ub_{\\text{LP}}(n)$ & $Ub_{\\text{SDP}}(n)$\\\\\n\\hline\n$10$ & $5$ & $5$ & $21$ & $45$ & $45$ \\\\\n$11$ & $6$ & $6$ & $22$ & $63$ & $63$ \\\\\n$12$ & $7$ & $7$ & $23$ & $99$ & $99$ \\\\\n$13$ & $9$ & $9$ & $24$ & $107$ & $96$ \\\\\n$14$ & $10$ & $10$ & $25$ & $116$ & $92$ \\\\\n$15$ & $12$ & $12$ & $26$ & $125$ & $90$ \\\\\n$16$ & $15$ & $15$ & $27$ & $134$ & $87$ \\\\\n$17$ & $18$ & $18$ & $28$ & $144$ & $85$ \\\\\n$18$ & $21$ & $21$ & $29$ & $154$ & $83$ \\\\\n$19$ & $27$ & $27$ & $30$ & $164$ & $81$ \\\\\n$20$ & $34$ & $34$ &&&\\\\\n\\noalign{\\hrule height1pt}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unbiased weighing matrices of weight $9$", "authors": ["Makoto Araya", "Masaaki Harada", "Hadi Kharaghani", "Sho Suda", "Wei-Hsuan Yu"], "url": "https://arxiv.org/abs/2501.15444v2", "attribution": "\"Unbiased weighing matrices of weight $9$\" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.17781v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Experiment parameters configuration.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\toprule\n\\textbf{Parameter} & \\textbf{Description} & \\textbf{Value} \\\\ \\midrule \\midrule\nm & Meta input dimension & 120 \\\\ \\hline\n$K_{neigh}$ & KNN-graph in DEC & 15 \\\\ \\hline\nOpt & Optimizer & ADAM \\\\ \\hline\n$\\eta$ & Outer learning rate & 0.001 \\\\ \\hline\n$\\mu$ & inner learning rate & 0.0005 \\\\ \\hline\ns & Trajectory data points sampling step & 100 \\\\ \\hline\nL & Number of hidden layers & 2 \\\\ \\hline\nh & Hidden layer size & 128 \\\\ \\hline\nN & Number of local SGD steps & 5 \\\\ \\hline\nI & Number of global iterations & 1500 \\\\ \\hline\nb & batch size & 8 \\\\ \\hline\nK & Total number of training tasks & 2 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion", "authors": ["Yaya Etiabi", "Eslam Eldeeb", "Mohammad Shehab", "Wafa Njima", "Hirley Alves", "Mohamed-Slim Alouini", "El Mehdi Amhoud"], "url": "https://arxiv.org/abs/2411.17781v1", "attribution": "\"MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion\" by Yaya Etiabi, Eslam Eldeeb, Mohammad Shehab, Wafa Njima, Hirley Alves, Mohamed-Slim Alouini, and El Mehdi Amhoud, arXiv:2411.17781v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.17239v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcr}%% Table column specifiers\n 1 & 2 & 3 \\\\ %% A tabular row ends with \\\\\n 4 & 5 & 6 \\\\\n 7 & 8 & 9 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Table Caption}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Price equilibria with positive margins in loyal-strategic markets with discrete prices", "authors": ["Gurkirat Wadhwa", "Akansh Verma", "Veeraruna Kavitha", "Priyank Sinha"], "url": "https://arxiv.org/abs/2506.17239v1", "attribution": "\"Price equilibria with positive margins in loyal-strategic markets with discrete prices\" by Gurkirat Wadhwa, Akansh Verma, Veeraruna Kavitha, and Priyank Sinha, arXiv:2506.17239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10650v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{PH2 dataset classification results}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\\hline\n\\textbf{Method} & \\textbf{AUC} & \\textbf{SE} & \\textbf{SP} & \\textbf{AC} & \\textbf{BAC} \\\\ \\hline\nBarata & - - - & 0.960 & 0.800 & 0.823 & 0.880 \\\\\nLei Bi & 0.938 & 0.875 & 0.931 & 0.920 & 0.903 \\\\\nOmar & - - - & \\textbf{1.000} & 0.915 & 0.932 & \\textbf{0.958} \\\\\nPennisi & - - - & 0.935 & 0.871 & \\textbf{0.936} & 0.903 \\\\\n\\textbf{Proposed} & \\textbf{0.974} & 0.902 & \\textbf{0.933} & 0.926 & 0.917 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data", "authors": ["José G. M. Esgario", "Renato A. Krohling"], "url": "https://arxiv.org/abs/2201.10650v1", "attribution": "\"Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data\" by José G. M. Esgario and Renato A. Krohling, arXiv:2201.10650v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.01705v3_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Benjamini Hochberg (1995) adjusted p-values (original p-values in parenthesis)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrrrr}\n\\toprule \n&\\multicolumn{5}{c}{\\textbf{Uncertainty}}&\\multicolumn{5}{c}{\\textbf{Negative Sentiment}}\\\\\n\\toprule\n &Aggregate&Economics&Health&Politics&Policy&Aggregate&Economics&Health&Politics&Policy\\\\\n\\midrule\nred zone = 1 & 0.53 & 0.00 & 0.97 & 0.00 & 0.92 & 0.04 & 0.02 & 0.92 & 0.00 & 0.92 \\\\\n & (0.39) & (0.00) & (0.97)& (0.00) & (0.87) & (0.01) & (0.01) & (0.92)& (0.00) & (0.90)\\\\\npost = 1 & 0.01 & 0.01 & 0.00 & 0.66 & 0.00 & 0.38 & 0.00 & 0.00 & 0.17 & 0.02\\\\\n & (0.00) & (0.00) & (0.00)& (0.53) & (0.00) & (0.23) & (0.00) & (0.00)& (0.12) & (0.01)\\\\\npost = 2 & 0.00 & 0.00& 0.00 & 0.63 & 0.00 & 0.18 & 0.78 & 0.00 & 0.02 & 0.03\\\\\n & (0.00) & (0.00) & (0.00)& (0.47) & (0.00) & (0.08) & (0.66) & (0.00)& (0.01) & (0.02)\\\\\n\\textbf{red zone = 1 x post = 1} & 0.74 & 0.00 & 0.01 & 0.02 & 0.85 & 0.72 & 0.17 & 0.00 & 0.00 & 0.42 \\\\\n& (0.74) & (0.00) & (0.01)& (0.01) & (0.76) & (0.67) & (0.13) & (0.00)& (0.00) & (0.34)\\\\\nred zone = 1 x post = 2 & 0.74 & 0.36 & 0.85 & 0.16 & 0.09 & 0.72 & 0.86 & 0.06 & 0.00 & 0.07 \\\\\n & (0.73) & (0.25) & (0.72)& (0.10) & (0.06) & (0.66) & (0.77) & (0.03)& (0.00) & (0.05)\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic", "authors": ["C. Biliotti", "F. J. Bargagli-Stoffi", "N. Fraccaroli", "M. Puliga", "M. Riccaboni"], "url": "https://arxiv.org/abs/2212.01705v3", "attribution": "\"Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic\" by C. Biliotti, F. J. Bargagli-Stoffi, N. Fraccaroli, M. Puliga, and M. Riccaboni, arXiv:2212.01705v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01772v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{{Comparison of algorithms performance based on the IGD and HV metrics.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\\toprule\nCriteria &Job quality std &{$N$} &{EMA} & {MOEA-D} & {MVO} & {NSGAII} & {DPEIA} \\\\ % Table header\n\\midrule\n \\multirow{6}{*}{IGD}\n &\\multirow{3}{*}{ 0.03} &{100} \n & 1.31 & 0.88 & 0.77 & 0.98 & 0.00 \\\\\n & &{200} & 0.79 & 0.77 & 0.71 & 1.01 & 0.01 \\\\\n & &{300} & 0.83 & 0.38 & 0.51 & 0.49 & 0.10 \\\\\n \\cmidrule(r){2-8} \n &\\multirow{3}{*}{ 0.09} &{100} & 0.89 & 0.85 & 0.96 & 0.88 & 0.05 \\\\\n & &{200} & 1.10 & 0.92 & 0.83 & 0.96 & 0.02 \\\\\n & &{300} & 0.98 & 0.87 & 0.90 & 0.92 & 0.02 \\\\\n \\midrule\n \\multirow{6}{*}{HV}\n &\\multirow{3}{*}{ 0.03} &{100} & 0.00 & 0.07 & 0.08 & 0.03 & 0.41\\\\\n & &{200} & 0.07 & 0.14 & 0.07 & 0.04 & 0.33 \\\\\n & &{300} & 0.01 & 0.10 & 0.06 & 0.06 & 0.28 \\\\\n\\cmidrule(r){2-8} \n &\\multirow{3}{*}{ 0.09} &{100} & 0.06 & 0.10 & 0.07 & 0.06 & 0.42 \\\\\n & &{200} & 0.02 & 0.05 & 0.04 & 0.02 & 0.33 \\\\\n & &{300} & 0.04 & 0.07 & 0.04 & 0.03 & 0.38 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Joint optimization for production operations considering reworking", "authors": ["Yilan Shen", "Boyang Li", "Xi Zhang"], "url": "https://arxiv.org/abs/2411.01772v1", "attribution": "\"Joint optimization for production operations considering reworking\" by Yilan Shen, Boyang Li, and Xi Zhang, arXiv:2411.01772v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.11858v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Descriptive statistic by instruments' values}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n & Whole sample & \\multicolumn{3}{c}{Same sex} \\\\\n\\cmidrule(l{3pt}r{3pt}){2-2} \\cmidrule(l{3pt}r{3pt}){3-5}\nVariable & Mean (sd) & Z = 1 & Z = 0 & Diff. (t-stat)\\\\\n\\midrule[1.5pt] \\multicolumn{5}{c}{\\textbf{Census 2002}} \\\\ \\midrule[1.5pt]\n\\midrule\nMother's age & 35.19\n(5.92) & 35.19\n(5.94) & 35.19\n(5.91) & 0.005\n(0.33)\\\\\nAge at 1st birth & 22.66\n(3.91) & 22.67\n(3.92) & 22.65\n(3.9) & 0.0265\n(2.65)\\\\\nGraduated & 0.22\n(0.41) & 0.22\n(0.41) & 0.22\n(0.41) & -3e-04\n(-0.28)\\\\\nMarried & 0.83\n(0.38) & 0.82\n(0.38) & 0.83\n(0.38) & -0.0037\n(-3.77)\\\\\nHusband's age & 37.44\n(6.07) & 37.45\n(6.09) & 37.44\n(6.06) & 0.0132\n(0.77)\\\\\nNumber of children & 2.19\n(0.49) & 2.21\n(0.51) & 2.17\n(0.47) & 0.0353\n(28.01)\\\\\nMore than 2 children & 0.15\n(0.36) & 0.17\n(0.37) & 0.14\n(0.35) & 0.0286\n(31.16)\\\\\n1st child's sex & 0.52\n(0.5) & 0.53\n(0.5) & 0.51\n(0.5) & 0.0212\n(16.6)\\\\\n2nd child's sex & 0.51\n(0.5) & 0.53\n(0.5) & 0.49\n(0.5) & 0.0349\n(27.31)\\\\\n1sr child's age & 12.53\n(4.14) & 12.52\n(4.16) & 12.54\n(4.13) & -0.0215\n(-2.03)\\\\\n2nd child's age & 8.35\n(4.82) & 8.38\n(4.83) & 8.33\n(4.81) & 0.049\n(3.97)\\\\\n\\midrule[1.5pt] \\multicolumn{5}{c}{\\textbf{Census 2010}} \\\\ \\midrule[1.5pt]\nMother's age & 33.72\n(5.51) & 33.72\n(5.53) & 33.72\n(5.49) & 0.0038\n(0.25)\\\\\nAge at 1st birth & 22.59\n(3.84) & 22.61\n(3.86) & 22.57\n(3.82) & 0.0436\n(4.09)\\\\\nGraduated & 0.33\n(0.47) & 0.34\n(0.47) & 0.33\n(0.47) & 3e-04\n(0.23)\\\\\nMarried & 0.82\n(0.38) & 0.82\n(0.38) & 0.83\n(0.38) & -0.0049\n(-4.61)\\\\\nHusband's age & 36.54\n(6.34) & 36.53\n(6.34) & 36.55\n(6.35) & -0.0249\n(-1.28)\\\\\nNumber of children & 2.2\n(0.5) & 2.22\n(0.52) & 2.19\n(0.48) & 0.0337\n(24.27)\\\\\nMore than 2 children & 0.17\n(0.37) & 0.18\n(0.38) & 0.15\n(0.36) & 0.0272\n(26.32)\\\\\n1st child's sex & 0.52\n(0.5) & 0.53\n(0.5) & 0.51\n(0.5) & 0.0176\n(12.71)\\\\\n2nd child's sex & 0.51\n(0.5) & 0.53\n(0.5) & 0.49\n(0.5) & 0.041\n(29.51)\\\\\n1sr child's age & 11.13\n(4.53) & 11.11\n(4.55) & 11.15\n(4.52) & -0.0398\n(-3.16)\\\\\n2nd child's age & 5.98\n(4.51) & 6\n(4.53) & 5.96\n(4.5) & 0.0394\n(3.14)\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Parenthood Penalty in Russia: Evidence from Exogenous Variation in Family Size", "authors": ["Vadim Ustyuzhanin"], "url": "https://arxiv.org/abs/2506.11858v1", "attribution": "\"Parenthood Penalty in Russia: Evidence from Exogenous Variation in Family Size\" by Vadim Ustyuzhanin, arXiv:2506.11858v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02284v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline \n\\multicolumn{1}{|p{1cm}|}{\\centering Model}\n& \\multicolumn{1}{|p{1cm}|}{\\centering All data}\n& \\multicolumn{1}{|p{1.5cm}|}{\\centering Long delay advertisers}\n& \\multicolumn{1}{|p{1.5cm}|}{\\centering New advertisers}\n\\\\\\hline\nM3 & 0.0\\% & 0.0\\% & 0.0\\% \\\\ % inserting body of the table\n\\hline\nM1 & -6.6\\% & -7.68\\% & -0.32\\% \\\\\n\\hline\nM2\\_delay\\_7d & -6.8\\% & -7.97\\% & -0.6\\% \\\\\n\\hline\nM2\\_delay\\_15d & -5.9\\% & -7.1\\% & -1.13\\% \\\\\n\\hline\nM4 & -7.7\\% & -9.13\\% & -0.4\\% \\\\\n\\hline\nM5 & -7.92\\% & -9.3\\% & -1.7\\% \\\\\n\\hline\n\\textbf{Proposed} & \\textbf{-8.6\\%} & \\textbf{-10.16\\%} &\\textbf{-1.81\\%} \\\\\n\\hline\nOracle & -9.1\\% & -10.87\\% & -2.0\\% \\\\ % [1ex] adds vertical space\n\\hline %inserts single line\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Poisson log loss improvements of various model variants on different training slices} % title of Table\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Handling many conversions per click in modeling delayed feedback", "authors": ["Ashwinkumar Badanidiyuru", "Andrew Evdokimov", "Vinodh Krishnan", "Pan Li", "Wynn Vonnegut", "Jayden Wang"], "url": "https://arxiv.org/abs/2101.02284v1", "attribution": "\"Handling many conversions per click in modeling delayed feedback\" by Ashwinkumar Badanidiyuru, Andrew Evdokimov, Vinodh Krishnan, Pan Li, Wynn Vonnegut, and Jayden Wang, arXiv:2101.02284v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.15103v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Top Categories Ranked By Total Sales (Offline and Online) During the Observation Period.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n\\toprule\nCategory & Share of sales(\\%) & Cumulative share (\\%) \\\\\n\\midrule\nDry Dog Food & 32.62 & 32.62 \\\\\nMedical & 12.18 & 44.80 \\\\\nDry Cat Food & 9.41 & 54.21 \\\\\nDog Hygiene & 5.48 & 59.68 \\\\\nCat Hygiene & 3.62 & 63.30 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "How does online shopping affect offline price sensitivity?", "authors": ["Shirsho Biswas", "Hema Yoganarasimhan", "Haonan Zhang"], "url": "https://arxiv.org/abs/2506.15103v1", "attribution": "\"How does online shopping affect offline price sensitivity?\" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.13157v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Integral Rod Worth for Reactor Core Configurations}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cllll}\n\\toprule\n\\multirow{2}{*}{Core Configuration} & \\multicolumn{4}{c}{Worth (\\$)} \\\\ \\cmidrule(l){2-5} \n & Rod 1 & Rod 2 & Rod 3 & Reg Rod \\\\ \\midrule\n120 & 6.387 & 5.380 & 2.963 & 0.488 \\\\\n121 & 6.387 & 5.380 & 2.963 & 0.488 \\\\\n122 & 6.597 & 5.398 & 2.963 & 0.387 \\\\\n123 & 6.583 & 5.267 & 3.017 & 0.433 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction", "authors": ["James Daniell", "Kazuma Kobayashi", "Ayodeji Alajo", "Syed Bahauddin Alam"], "url": "https://arxiv.org/abs/2211.13157v4", "attribution": "\"Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction\" by James Daniell, Kazuma Kobayashi, Ayodeji Alajo, and Syed Bahauddin Alam, arXiv:2211.13157v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.17677v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Transfer Learning to PRISMA - LineRWKV lossless rate difference vs. CCSDS-123.0-B-2.}\n\\begin{tabular}{ccc}\n\\textbf{Model size} & \\textbf{Zero-shot transfer} & \\textbf{After finetuning} \\\\\n\\hline\n\\hline\nM & +0.053 & -0.160 \\\\ \n\\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Onboard deep lossless and near-lossless predictive coding of hyperspectral images with line-based attention", "authors": ["Diego Valsesia", "Tiziano Bianchi", "Enrico Magli"], "url": "https://arxiv.org/abs/2403.17677v1", "attribution": "\"Onboard deep lossless and near-lossless predictive coding of hyperspectral images with line-based attention\" by Diego Valsesia, Tiziano Bianchi, and Enrico Magli, arXiv:2403.17677v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.02102v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameter estimates for Correlated frailty model on Simulated Data}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}\n\t\t\t\t\t\\hline\n\t\t\t\t\tparameter & Progression & & & Relapse & & & Death & & \\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\t& true & estimate & CI & true & estimate & CI & true & estimate & CI\\\\\n\t\t\t\t\t& value & estimate & CI &value & estimate & CI & value & estimate & CI\\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\tAge & -0.06 & -0.058 & (-0.114,-0.002)& -0.05 & -0.024 & (-0.063,0.015) & -0.03 & -0.023 & (-0.063,0.017)\\\\\n\t\t\t\t\tt-stage &0.1 & 0.290 & (-1.570,2.152) & 0.2 & 0.108 & (-1.172,1.387)& 0.3 & 0.067 & (-1.23,1.371) \\\\\n\t\t\t\t\tn-stage &0.5 & 0.191 & (-1.717,2.098) & 0.2 & 0.026 & (-1.265,1.318) & 0.3 & 0.102 & (-1.304,1.302)\\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\tFrailty & 0.286 & 0.346 & (0.315-0.371)& 0.25 & 0.299 & (0.266-0.310) & 0.222 & 0.268 & (0.220,0.296)\\\\\n\t\t\t\t\tVariances & & & EmpSE=0.271& & & EmpSE=0.243 & & & EmpSE=0.239\\\\\n\t\t\t\t\t& & & RMSE=0.270& & & RMSE=0.241 & & &RMSE=0.237\\\\\n\t\t\t\t\t\\hline\n\t\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A modified risk detection approach of biomarkers by frailty effect on multiple time to event data", "authors": ["Atanu Bhattacharjee", "Gajendra K. Vishwakarma", "Souvik Banerjee"], "url": "https://arxiv.org/abs/2012.02102v2", "attribution": "\"A modified risk detection approach of biomarkers by frailty effect on multiple time to event data\" by Atanu Bhattacharjee, Gajendra K. Vishwakarma, and Souvik Banerjee, arXiv:2012.02102v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.06725v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation parameters -- Scenario 2}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\t\t\t\\toprule\n\t\t\tParameter & Value \\\\\n\t\t\t\\midrule\n\t\t\tProcess noise intensity ($\\tilde{q}$) & 0.15 $\\text{m}^2/\\text{s}^3$ \\\\\n\t\t\tNumber of targets & 20 \\\\\n\t\t\tDetection probability & 0.99 \\\\\n\t\t\tFalse alarm probability $(P_{fa})$ & $10^{-6}$ \\\\\n\t\t\tClutter density ($\\lambda$)& $10^{-6}$ \\\\\n\t\t\tMaximum target speed & 30 $\\text{m}/\\text{s}$ \\\\\n\t\t\tNumber of sensors & 3 \\\\\n\t\t\tSensor type & Monostatic 2-D Radar \\\\\n\t\t\tSensor coverage & $300,000 \\text{ m}\\times 360^\\circ $ \\\\\n\t\t\tSensor std. deviation - range ($\\sigma_r$) & 50 m \\\\ \n\t\t\tSensor std. deviation - azimuth ($\\sigma_\\theta$) & $2^\\circ$ \\\\\n\t\t\tGating threshold ($\\mu$) & 0.95 \\\\\n\t\t\tTrack deletion threshold (num. of misses) & 6 \\\\\n\t\t\tTrack loss threshold & 500 m \\\\\n\t\t\tFusion sample period ($T_f$) & 10 seconds \\\\\n\t\t\tTotal simulation time & 4537 seconds \\\\\n\t\t\tSensor sample time & 2 seconds \\\\\n\t\t\t\\bottomrule \n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Harmonic Mean Density Fusion in Distributed Tracking: Performance and Comparison", "authors": ["Nikhil Sharma", "Ratnasingham Tharmarasa", "Thiagalingam Kirubarajan"], "url": "https://arxiv.org/abs/2412.06725v1", "attribution": "\"Harmonic Mean Density Fusion in Distributed Tracking: Performance and Comparison\" by Nikhil Sharma, Ratnasingham Tharmarasa, and Thiagalingam Kirubarajan, arXiv:2412.06725v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2102.00287v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|}\n \\multicolumn{5}{c}{English as source}\\\\\\hline\n & \\multicolumn{2}{c|}{EN$\\rightarrow$FR} & \\multicolumn{2}{c|}{EN$\\rightarrow$ES} \\\\\\cline{2-5}\n System & BLEU$\\uparrow$ & TER$\\downarrow$ & BLEU$\\uparrow$ & TER$\\downarrow$ \\\\\\hline\nPB-SMT & 35.7 & 50.7 & 38.6 & 45.9 \\\\\\hline\nLSTM & 34.2 & 50.9 & 38.2 & 45.3 \\\\\\hline\nTRANS & \\textbf{37.2} & \\textbf{48.7} & \\textbf{40.9} & \\textbf{43.4} \\\\\\hline\n\\multicolumn{5}{c}{English as target}\\\\\\hline\nSystem & \\multicolumn{2}{c|}{FR$\\rightarrow$EN} & \\multicolumn{2}{c|}{ES$\\rightarrow$EN}\\\\ \\hline\nPB-SMT & 36.2 & 47.1 & 39.3 & 44.0\\\\\\hline\nLSTM & 34.6 & 48.2 & 38.1 & 44.7\\\\\\hline\nTRANS & \\textbf{37.0} & \\textbf{46.4} & \\textbf{41.3} & \\textbf{41.4}\\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Quality evaluation scores for our MT systems. TRANS denotes Transformer systems.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation", "authors": ["Eva Vanmassenhove", "Dimitar Shterionov", "Matthew Gwilliam"], "url": "https://arxiv.org/abs/2102.00287v1", "attribution": "\"Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation\" by Eva Vanmassenhove, Dimitar Shterionov, and Matthew Gwilliam, arXiv:2102.00287v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00300v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\n& & DeepONet & FNO & RF(Cauchy) & RF(Gaussian) \\\\ \\hline\n\\multirow{2}{*}{Helmholtz} & Relative error (\\%) & 5.88 & 1.86 & 2.54 & 2.92 \\\\\\cline{2-6} \n& Training time (seconds) & - & - & 21.8 & 12.5 \\\\\\hline\n\\multirow{2}{1.5cm}{Structural \\\\Mechanics} & Relative error (\\%) & 5.20 & 4.76 & 6.08 & 6.25 \\\\\\cline{2-6} \n& Training time (seconds) & - & - & 4.2 & 5.8 \\\\\\hline\n\\multirow{2}{1.5cm}{Navier- \\\\Stokes} & Relative error (\\%) & 3.63 & 0.26 & 0.73 & 0.11 \\\\\\cline{2-6} \n& Training time (seconds) & - & - & 24.9 & 23.9 \\\\\\hline\n\\end{tabular}\n\\caption{Summary of relative test errors of random feature methods and neural operator benchmarks DeepONet and FNO. We also report the training times of random feature methods.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Cauchy Random Features for Operator Learning in Sobolev Space", "authors": ["Chunyang Liao", "Deanna Needell", "Hayden Schaeffer"], "url": "https://arxiv.org/abs/2503.00300v2", "attribution": "\"Cauchy Random Features for Operator Learning in Sobolev Space\" by Chunyang Liao, Deanna Needell, and Hayden Schaeffer, arXiv:2503.00300v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.10469v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrl}\n \\textit{INP} Format & Abbrev. & E & M & \\textit{ACT} \\\\\n \\hline\n binary32 & FP32 & 8 & 23 & FP32 \\\\\n TensorFloat32 & TF32 & 8 & 10 & FP32 \\\\\n BrainFloat16 & bfloat16 & 8 & 7 & FP32 \\\\\n BlockFloat16 & BFP16 & 8 & 7 & FP32 \\\\\n binary16 & FP16.16 & 5 & 10 & FP16 \\\\\n binary16 & FP16.32 & 5 & 10 & FP32 \\\\\n Signed 8-bit int & INT8 & 0 & 8 & INT8 \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{A summary of the numerical formats evaluated in this paper. The binary32 and binary16 formats are as defined in the IEEE 754-2008 standard. The E and M columns refer to the number of bits used for the exponent and mantissa respectively. The \\textit{INP} and \\textit{ACT} columns relate to the values used in \\figurename~.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Empirical Evaluation of Deep Learning Model Compression Techniques on the WaveNet Vocoder", "authors": ["Sam Davis", "Giuseppe Coccia", "Sam Gooch", "Julian Mack"], "url": "https://arxiv.org/abs/2011.10469v1", "attribution": "\"Empirical Evaluation of Deep Learning Model Compression Techniques on the WaveNet Vocoder\" by Sam Davis, Giuseppe Coccia, Sam Gooch, and Julian Mack, arXiv:2011.10469v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11943v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ccc}\n \\toprule\n & Specificity & C.I. (95\\%)\\\\\n \\midrule\n Radiologist 1\t& 87.1\\% & [75.6\\% -- 94.3\\%]\\\\\n Radiologist 2 &\t87.1\\% & [75.6\\% -- 94.3\\%]\\\\\n Radiologist 3 &\t90.3\\% & [79.5\\% -- 96.5\\%]\\\\\n \\midrule\n AI Model without lung segmentation &\t87.1\\% & [75.6\\% -- 94.3\\%]\\\\\n AI Model with lung segmentation\t& \\textbf{93.5}\\% &\\textbf{[83.5\\% -- 98.5\\%]}\\\\\n \\bottomrule\n \\hline\n \\end{tabular}\n\\caption{Specificity (together with 95\\% confidence interval) comparison between manual readings of expert radiologists and the AI model for COVID-19 detection without lung segmentation and AI model with segmentation.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans", "authors": ["Matteo Pennisi", "Isaak Kavasidis", "Concetto Spampinato", "Vincenzo Schininà", "Simone Palazzo", "Francesco Rundo", "Massimo Cristofaro", "Paolo Campioni", "Elisa Pianura", "Federica Di Stefano", "Ada Petrone", "Fabrizio Albarello", "Giuseppe Ippolito", "Salvatore Cuzzocrea", "Sabrina Conoci"], "url": "https://arxiv.org/abs/2101.11943v1", "attribution": "\"An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans\" by Matteo Pennisi, Isaak Kavasidis, Concetto Spampinato, Vincenzo Schininà, Simone Palazzo, Francesco Rundo, Massimo Cristofaro, Paolo Campioni, Elisa Pianura, Federica Di Stefano, Ada Petrone, Fabrizio Albarello, Giuseppe Ippolito, Salvatore Cuzzocrea, and Sabrina Conoci, arXiv:2101.11943v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10148v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rr}\n \\hline\n & value \\\\ \n \\hline\nAccuracy & 0.72 \\\\ \n Kappa & 0.40 \\\\ \n Sensitivity (No burn accuracy) & 0.72 \\\\ \n Specificity (Burn accuracy) & 0.72 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Detecting Crop Burning in India using Satellite Data", "authors": ["Kendra Walker", "Ben Moscona", "Kelsey Jack", "Seema Jayachandran", "Namrata Kala", "Rohini Pande", "Jiani Xue", "Marshall Burke"], "url": "https://arxiv.org/abs/2209.10148v1", "attribution": "\"Detecting Crop Burning in India using Satellite Data\" by Kendra Walker, Ben Moscona, Kelsey Jack, Seema Jayachandran, Namrata Kala, Rohini Pande, Jiani Xue, and Marshall Burke, arXiv:2209.10148v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12565v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|l}\n$n$ & Max Sets& Search space complexity & Compute Time (s) \\\\ \\hline\n$3$ & $1$ & $2.92\\cdot 10^3$ & $0.00$ \\\\\n$4$ & $1$ & $7.02\\cdot 10^4$ & $0.06$ \\\\\n$5$ & $2$ & $8.07\\cdot 10^5$ & $0.64$ \\\\\n$6$ & $3$ & $5.92\\cdot 10^6$ & $4.64$ \\\\\n$7$ & $5$ & $3.11\\cdot 10^7$ & $23.76$ \\\\\n$8$ & $8$ & $1.24\\cdot 10^8$ & $94.78$ \\\\\n$9$ & $12$ & $3.94\\cdot 10^8$ & $301.44$ \\\\\n$10$ & $12$ & $1.01\\cdot 10^9$ & $761.40$ \\\\\n$11$ & $13$ & $2.15\\cdot 10^9$ & $1610.53$ \\\\\n$12$ & $14$ & $3.82\\cdot 10^9$ & $2872.26$ \\\\\n$13$ & $16$ & $5.74\\cdot 10^9$ & $4413.72$ \\\\\n$14$ & $19$ & $7.30\\cdot 10^9$ & $5322.08$ \\\\\n$15$ & $23$ & $7.91\\cdot 10^9$ & $5805.79$ \\\\\n$16$ & $26$ & $7.30\\cdot 10^9$ & $5243.79$ \\\\\n$17$ & $30$ & $5.74\\cdot 10^9$ & $4158.71$ \\\\\n$18$ & $36$ & $3.82\\cdot 10^9$ & $2850.46$ \\\\\n$19$ & $41$ & $2.15\\cdot 10^9$ & $1558.12$ \\\\\n$20$ & $47$ & $1.01\\cdot 10^9$ & $733.16$ \\\\\n$21$ & $54$ & $3.94\\cdot 10^8$ & $284.40$ \\\\\n$22$ & $62$ & $1.24\\cdot 10^8$ & $90.17$ \\\\\n$23$ & $71$ & $3.11\\cdot 10^7$ & $22.38$ \\\\\n$24$ & $81$ & $5.92\\cdot 10^6$ & $4.26$ \\\\\n$25$ & $92$ & $8.07\\cdot 10^5$ & $0.58$ \\\\\n$26$ & $104$ & $7.02\\cdot 10^4$ & $0.05$ \\\\\n$27$ & $117$ & $2.92\\cdot 10^3$ & $0.00$ \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Maximum Number of Sets for $n$ cards with $3$ properties}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Maximum Number of Sets for 12 Cards is 14", "authors": ["Justin Stevens", "Duncan Wilson"], "url": "https://arxiv.org/abs/2501.12565v1", "attribution": "\"The Maximum Number of Sets for 12 Cards is 14\" by Justin Stevens and Duncan Wilson, arXiv:2501.12565v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.19114v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average and standard deviation of general loss, true recovery, false positive, and false negative over $100$ simulations with two covariance matrices using true $d$ when $(n,p)= (1000,2000)$ in Examples and .}%\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n \\hline\n {Example} & {Structures} & {Methods} & {General Loss} & {True Recovery} & {False Positive} & {False Negative} \\\\\n \\hline\n\t\t & $\\Sigma^{(1)}$ & GWIRE & $0.07 (0.02)$ & $0.96 (0.20)$ & $0.04 (0.20) $ & $0.00 (0.00)$\\\\\n\t\t&& SWIRE-I & $0.32 (0.07)$ & $0.79 (0.41)$ & $0.41 (1.11)$ & $0.00 (0.00)$ \\\\\n\t\t&& SWIRE-II & $0.25 (0.07)$ & $0.91 (0.29)$ & $0.10 (0.33)$ & $0.00 (0.00)$\\\\\n\t\t&$\\Sigma^{(2)}$ & GWIRE & $0.10 (0.05)$ & $0.97 (0.17)$ & $0.03 (0.17)$ & $0.00 (0.00)$ \\\\\n\t\t&& SWIRE-I & $0.28 (0.06)$ & $0.75 (0.44)$ & $0.87 (2.35)$ & $0.00 (0.00)$\\\\\n\t\t&& SWIRE-II & $0.24 (0.07)$ & $0.89 (0.31)$ & $0.24 (0.81)$ & $0.00 (0.00)$\\\\\n\t\t & $\\Sigma^{(1)}$ & GWIRE & $0.17 (0.07) $ & $1.00 (0.00)$ & $0.00 (0.00)$ & $0.00 (0.00)$\\\\\n\t\t&& SWIRE-I & $0.51 (0.11)$ & $0.75 (0.44)$ & $0.76 (1.97)$ & $0.00 (0.00)$\\\\\n\t\t&& SWIRE-II & $0.33 (0.14)$ & $0.83 (0.38)$ & $0.29 (1.17)$ & $0.07 (0.29)$\\\\\n\t\t&$\\Sigma^{(2)}$ & GWIRE & $0.22 (0.14)$ & $0.98 (0.16)$ & $0.01 (0.10)$ & $0.05 (0.50)$ \\\\\n\t\t&& SWIRE-I & $0.48 (0.11)$ & $0.69 (0.46)$ & $0.80 (1.94)$ & $0.01 (0.10)$\\\\\n\t\t&& SWIRE-II & $0.30 (0.13)$ & $0.81 (0.39)$ & $0.49 (1.55)$ & $0.05 (0.26)$\\\\\n\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Sparse Fréchet Sufficient Dimension Reduction with Graphical Structure Among Predictors", "authors": ["Jiaying Weng", "Kai Tan", "Cheng Wang", "Zhou Yu"], "url": "https://arxiv.org/abs/2310.19114v2", "attribution": "\"Sparse Fréchet Sufficient Dimension Reduction with Graphical Structure Among Predictors\" by Jiaying Weng, Kai Tan, Cheng Wang, and Zhou Yu, arXiv:2310.19114v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.17839v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Object detection on the receiver end. We have used DETRs~ as the object detection model. We perform object detection on the original image and the recovered image and present mAP. Here, random masking is used for image recovery and the evaluation is on a subset of each dataset: COCO-Stuff (5k images), COCO (2k images), and Flickr (2.5k images) }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c||c|c|}\n\\hline\n\\textbf{Dataset} & \\textbf{Steps} & \\textbf{mAP }(\\%)\\\\\n\\hline\n& 28/32 & 63.50\\\\\n\\cline{2-3}\nCOCO-Stuff & 94/100 & 63.77\\\\\n\\cline{2-3}\n& 194/200 & 64.21\\\\\n\\hline\nCOCO& 194/200 & 64.99\\\\\n\\hline\n& 4/8 & 44.54\\\\\n\\cline{2-3}\nFlickr& 28/32 & 44.72\\\\\n\\cline{2-3}\n & 94/100 & 46.85\\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency", "authors": ["Achintha Wijesinghe", "Suchinthaka Wanninayaka", "Weiwei Wang", "Yu-Chieh Chao", "Songyang Zhang", "Zhi Ding"], "url": "https://arxiv.org/abs/2412.17839v1", "attribution": "\"LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency\" by Achintha Wijesinghe, Suchinthaka Wanninayaka, Weiwei Wang, Yu-Chieh Chao, Songyang Zhang, and Zhi Ding, arXiv:2412.17839v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.18253v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of Different Methods in Terms of FPR and TPR}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n\\toprule\n & \\textbf{Baseline} & \\textbf{Overlapping} & \\multicolumn{2}{c}{\\textbf{Representative}} \\\\\n & \\textbf{Approach} & $(\\eta_o = 0.5)$ & $(\\eta_r = 0.8)$ & $(\\eta_r = 0.9)$ \\\\\n\\midrule\n\\textbf{False Positive Rate (FPR)} & 11.3\\% & 9.4\\% & 8.8\\% & 8.0\\% \\\\\n\\textbf{True Positive Rate (TPR)} & 35.6\\% & 55.6\\% & 48.9\\% & 48.9\\% \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Enhancing External Validity of Experiments with Ongoing Sampling", "authors": ["Chen Wang", "Shichao Han", "Shan Huang"], "url": "https://arxiv.org/abs/2502.18253v1", "attribution": "\"Enhancing External Validity of Experiments with Ongoing Sampling\" by Chen Wang, Shichao Han, and Shan Huang, arXiv:2502.18253v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.04349v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\n\\textbf{Function to Approximate} & \\textbf{Approximation Notation} & \\textbf{Input} \\\\ \\hline\n\\multicolumn{3}{|c|}{\\textbf{Approximations at \\( t = 0, \\text{ i.e., } n=0\\)}} \\\\ \\hline\n\\( Y \\) & \\( \\widetilde{Y}_0 \\) & None (constant) \\\\\n\\( D_x u \\) & \\( \\mathcal{D}_x \\mathcal{U}_0 \\) & None (constant) \\\\\n\\( \\Delta u \\) & \\( \\Delta \\mathcal{U}_0^{\\varphi_0} \\) & \\( \\Delta \\widetilde{X}_0 \\) \\\\\n\\( C \\) & \\( \\mathcal{C}_0 \\) & None (constant) \\\\ \\hline\n\\multicolumn{3}{|c|}{\\textbf{Approximations for \\( t > 0, \\text{ i.e., } n\\in\\{1,2,\\ldots,M-1\\} \\)}} \\\\ \\hline\n\\( D_x u \\) & \\( \\mathcal{D}_x \\mathcal{U}_n^{\\rho_n} \\) & \\( \\widetilde{X}_n \\) \\\\\n\\( \\Delta u \\) & \\( \\Delta \\mathcal{U}_n^{\\varphi_n} \\) & \\( \\widetilde{X}_n,\\ \\Delta \\widetilde{X}_n \\) \\\\\n\\( C \\) & \\( \\mathcal{C}_n^{\\vartheta_n} \\) & \\( \\widetilde{X}_n \\) \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{{Summary of neural network approximations of the functions in the BSDE with jumps.}}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A deep solver for BSDEs with jumps", "authors": ["Kristoffer Andersson", "Alessandro Gnoatto", "Marco Patacca", "Athena Picarelli"], "url": "https://arxiv.org/abs/2211.04349v3", "attribution": "\"A deep solver for BSDEs with jumps\" by Kristoffer Andersson, Alessandro Gnoatto, Marco Patacca, and Athena Picarelli, arXiv:2211.04349v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12414v4_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Peak signal-to-noise ratio (PSNR) and precision/recall scores for image inpaintings. We report mean and standard deviation across five evaluations with different random masks. VAEAC and ACFlow results are taken from . Higher is better for all metrics.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccc}\n\\toprule\n & \\multicolumn{3}{c}{MNIST} & \\multicolumn{3}{c}{\\textsc{Omniglot}} & \\multicolumn{3}{c}{\\textsc{CelebA}} \\\\ \\cmidrule(l){2-10} \n &\n PSNR &\n Precision &\n Recall &\n PSNR &\n Precision &\n Recall &\n PSNR &\n Precision &\n Recall \\\\ \\midrule\nVDVAE + PM (ours) & \\textbf{21.603 $\\pm$ 0.022} & \\textbf{0.996} & \\textbf{0.996} & 18.256 $\\pm$ 0.038 & \\textbf{0.995} & \\textbf{0.994} & \\textbf{27.190 $\\pm$ 0.049} & \\textbf{0.995} & \\textbf{0.995} \\\\\nVQ-VAE + PM (ours) & 19.981 $\\pm$ 0.021 & 0.989 & 0.993 & 17.954 $\\pm$ 0.046 & 0.979 & 0.973 & 25.531 $\\pm$ 0.036 & 0.982 & 0.984 \\\\ \\midrule\nVAEAC & 19.613 $\\pm$ 0.042 & 0.877 & 0.975 & 17.693 $\\pm$ 0.023 & 0.525 & 0.926 & 23.656 $\\pm$ 0.027 & 0.966 & 0.967 \\\\ \\midrule\nACFlow & 17.349 $\\pm$ 0.018 & 0.945 & 0.984 & 15.572 $\\pm$ 0.031 & 0.962 & 0.971 & 22.393 $\\pm$ 0.040 & 0.970 & 0.988 \\\\\nACFlow+BG & 20.828 $\\pm$ 0.031 & 0.947 & 0.983 & \\textbf{18.838 $\\pm$ 0.009} & 0.967 & 0.970 & 25.723 $\\pm$ 0.020 & 0.964 & 0.987 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Posterior Matching for Arbitrary Conditioning", "authors": ["Ryan R. Strauss", "Junier B. Oliva"], "url": "https://arxiv.org/abs/2201.12414v4", "attribution": "\"Posterior Matching for Arbitrary Conditioning\" by Ryan R. Strauss and Junier B. Oliva, arXiv:2201.12414v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.08164v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation results comparing one-step and partial TML estimators for the nontransported interventional indirect and direct effects under two data-generating mechanisms: one with a single, binary $M$ and a single, binary $Z$; and another with multiple, binary $M$s and multiple, binary $Z$s.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rcccccc}\n\\toprule\n & \\multicolumn{3}{c}{Direct effect} & \\multicolumn{3}{c}{Indirect effect} \\\\\n\\cmidrule(l{3pt}r{3pt}){2-4} \\cmidrule(l{3pt}r{3pt}){5-7}\n$n$ & $|\\text{Bias}|$ & $\\sqrt{n} \\times |\\text{Bias}|$ & 95\\% Cov. & $|\\text{Bias}|$ & $\\sqrt{n} \\times |\\text{Bias}|$ & 95\\% Cov.\\\\\n\\midrule\n\\multicolumn{7}{l}{\\textbf{Binary $M$ and $Z$}} \\\\\n\\hdashline\\noalign{\\vskip 1ex}\n\\multicolumn{7}{l}{One-step} \\\\\n500 & 0.00 & 0.02 & 0.94 & 0.00 & 0.03 & 0.95\\\\\n1000 & 0.00 & 0.03 & 0.94 & 0.00 & 0.00 & 0.93\\\\\n5000 & 0.00 & 0.06 & 0.94 & 0.00 & 0.02 & 0.94\\\\\n10000 & 0.00 & 0.01 & 0.95 & 0.00 & 0.02 & 0.94\\\\ \n\\hdashline\\noalign{\\vskip 1ex}\n\\multicolumn{7}{l}{Partial TMLE} \\\\\n500 & 0.00 & 0.09 & 0.95 & 0.00 & 0.02 & 0.95\\\\\n1000 & 0.00 & 0.00 & 0.94 & 0.00 & 0.02 & 0.95\\\\\n5000 & 0.00 & 0.02 & 0.93 & 0.00 & 0.04 & 0.95\\\\\n10000 & 0.00 & 0.07 & 0.93 & 0.00 & 0.03 & 0.94\\\\\n\\midrule\n\\multicolumn{7}{l}{\\textbf{Multivariate $M$ and $Z$}} \\\\\n\\hdashline\\noalign{\\vskip 1ex}\n\\multicolumn{7}{l}{One-step} \\\\\n500 & 0.01 & 0.18 & 0.93 & 0.00 & 0.03 & 0.91\\\\\n1000 & 0.01 & 0.27 & 0.95 & 0.00 & 0.03 & 0.90\\\\\n5000 & 0.00 & 0.19 & 0.94 & 0.00 & 0.07 & 0.93\\\\\n10000 & 0.00 & 0.05 & 0.94 & 0.00 & 0.10 & 0.92\\\\\n\\hdashline\\noalign{\\vskip 1ex}\n\\multicolumn{7}{l}{Partial TMLE} \\\\\n500 & 0.01 & 0.18 & 0.93 & 0.00 & 0.01 & 0.92\\\\\n1000 & 0.01 & 0.19 & 0.95 & 0.00 & 0.05 & 0.93\\\\\n5000 & 0.00 & 0.19 & 0.94 & 0.00 & 0.07 & 0.91\\\\\n10000 & 0.00 & 0.11 & 0.94 & 0.00 & 0.08 & 0.93\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Nonparametric estimators of interventional (transported) direct and indirect effects that accommodate multiple mediators and multiple intermediate confounders", "authors": ["Kara E Rudolph", "Nicholas Williams", "Ivan Diaz"], "url": "https://arxiv.org/abs/2212.08164v1", "attribution": "\"Nonparametric estimators of interventional (transported) direct and indirect effects that accommodate multiple mediators and multiple intermediate confounders\" by Kara E Rudolph, Nicholas Williams, and Ivan Diaz, arXiv:2212.08164v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.09999v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{List of hyperparameters. For neural network architectures we report the number of hidden units in each layer. All hidden layers use the tanh activation function.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccr}\n\\toprule\nParameter & LapGridWorld & HalfCheetah & Ant & Point & AntBroken\\\\\n\\midrule\nPolicy, $\\pi_\\phi$\\\\\n\\;\\;\\;\\;Architecture & \\\\\n\\;\\;\\;\\;\\;\\;\\;Policy Network & 64, 64 & 64, 64 &64, 64& 64, 64 & 64, 64 \\\\\n\\;\\;\\;\\;\\;\\;\\;Value Network & 64, 64 & 64, 64 &64, 64& 64, 64 & 64, 64 \\\\\n\\;\\;\\;\\;\\;\\;\\;Cost Value Network & 64, 64 & 64, 64 &64, 64& 64, 64 & 64, 64 \\\\\n\\;\\;\\;\\;Batch Size & 64 & 64 & 128 & 64 & 128\\\\\n\\;\\;\\;\\;PPO Target KL & $0.01$ & 0.01 & 0.01 & 0.01 & 0.01\\\\\n\\;\\;\\;\\;Learning Rate & $3 \\times 10^{-4}$ & $3 \\times 10^{-4}$ & $3 \\times 10^{-5}$ & $3 \\times 10^{-4}$ & $3 \\times 10^{-5}$ \\\\\n\\;\\;\\;\\;Reward-GAE-$\\gamma$ & 0.99 & 0.99 & 0.99 & 0.99 & 0.99\\\\\n\\;\\;\\;\\;Reward-GAE-$\\lambda$ & 0.95 & 0.95 & 0.90 & 0.95 & 0.90\\\\\n\\;\\;\\;\\;Cost-GAE-$\\gamma$ & 0.99 & 0.99 & 0.99 & 0.99 & 0.99\\\\\n\\;\\;\\;\\;Cost-GAE-$\\lambda$ & 0.95 & 0.95 & 0.95 & 0.95 & 0.95\\\\\n\\;\\;\\;\\;Entropy Bonus, $1/\\beta$ & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\\nLagrangian, $\\lambda$\\\\\n\\;\\;\\;\\;Initial Value & 1.0 & 1.0 & 0.1 & 1.0 & 0.1\\\\\n\\;\\;\\;\\;Learning Rate & 0.1 & 0.1 & 1.0 & 0.1 & 1.0\\\\\n\\;\\;\\;\\;Budget & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\\nConstraint Function, $\\zeta_\\theta$\\\\\n\\;\\;\\;\\;Architecture & 20 & 20 & 40,40 & - & -\\\\\n\\;\\;\\;\\;Learning Rate & 0.01 & 0.01 & 0.01 & - & -\\\\\n\\;\\;\\;\\;Backward Iterations & 10 & 10 & 10 & - & -\\\\\n\\;\\;\\;\\;Regularizer Weight & 0.5 & 0.5 & 0.6 & - & -\\\\\n\\;\\;\\;\\;Max Forward KL, $\\epsilon_F$ & 10 & 10 & 10 & - & -\\\\\n\\;\\;\\;\\;Max Backward KL, $\\epsilon_B$ & 2.5 & 2.5 & 2.5 & - & -\\\\\nMiscellaneous\\\\\n\\;\\;\\;\\;Expert Rollouts & 1 & 10 & 45 & - & -\\\\\n\\;\\;\\;\\;Rollout Length & 200 & 1000 & 500 & 150 & 500\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Inverse Constrained Reinforcement Learning", "authors": ["Usman Anwar", "Shehryar Malik", "Alireza Aghasi", "Ali Ahmed"], "url": "https://arxiv.org/abs/2011.09999v3", "attribution": "\"Inverse Constrained Reinforcement Learning\" by Usman Anwar, Shehryar Malik, Alireza Aghasi, and Ali Ahmed, arXiv:2011.09999v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.17618v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\nMethods & Estimate for $\\theta$& SE of the Estimate & LOWER & UPPER \\\\\n \\hline\n CB & 0.693 & 0.392 & 0.191 & 1.195 \\\\ \n BMA & 0.000 & 0.000 & 0.000 & 0.000 \\\\ \n \\hline\n LSW & -0.094 & 0.310 & -0.491 & 0.303 \\\\ \n WLP & 0.451 & 0.221 & 0.168 & 0.734 \\\\ \n NAIVE & 0.186 & 0.340 & -0.250 & 0.622 \\\\ \n DS & 0.810 & 0.220 & 0.528 & 1.092 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Estimates for $\\theta$, standard errors (SE), lower and upper ends of interval estimators (90\\% coverage) for CKD data analysis.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates", "authors": ["Abhishek Ojha", "Naveen N. Narisetty"], "url": "https://arxiv.org/abs/2411.17618v1", "attribution": "\"Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates\" by Abhishek Ojha and Naveen N. Narisetty, arXiv:2411.17618v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.13545v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Computational complexity of the proposed algorithms in terms of the number of arithmetic operations }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\t\t\t\\hline\n\t\t\tAlgorithm & Number of arithmetic operations \\\\\n\t\t\t\\hline\n\t\t\tAlgorithm 1 & $\\mathcal{O} (M^2 K^3)$ \\\\\n\t\t\t\\hline\n\t\t\tAlgorithm 2 & $\\log[2]{{[t_{max}-t_{min}]}/{2}} \\times \\mathcal{O} (M^2 K^3)$ \\\\\n\t\t\t\\hline\n\t\t\tAlgorithm 3 & $\\log[2]{{[t_{max}-t_{min}]}/{2}} \\times \\mathcal{O} (M^2 K^3)^2$ \\\\\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Exploiting Cell-Free Massive MIMO for Enabling Simultaneous Wireless Information and Power Transfer", "authors": ["Diluka Loku Galappaththige", "Rajan Shrestha", "Gayan Amarasuriya Aruma Baduge"], "url": "https://arxiv.org/abs/2010.13545v2", "attribution": "\"Exploiting Cell-Free Massive MIMO for Enabling Simultaneous Wireless Information and Power Transfer\" by Diluka Loku Galappaththige, Rajan Shrestha, and Gayan Amarasuriya Aruma Baduge, arXiv:2010.13545v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.06271v6_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Air quality standards in different countries }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccc}\n\\toprule\nPollutant&\\multicolumn{4}{c}{averaging period}& &EU &USA &China &WHO \\\\ \n\\midrule\n\\multirow{3}{*}{$NO_2$} & && \\multicolumn{2}{l}{annual} && 40 & 100 & 40 & 40 \\\\\n \t\t\t\t\t\t\t & && \\multicolumn{2}{l}{daily} && - & - & 80 & - \\\\\n \t\t\t\t\t\t & && \\multicolumn{2}{l}{hourly} && 200 & 188 & 200 & 200 \\\\\n\\hline \n\\multirow{2}{*}{$PM_{10}$}\t & && \\multicolumn{2}{l}{annual} && 40 & - & 70 & 20 \\\\\n \t\t\t\t\t\t & && \\multicolumn{2}{l}{daily} && 50 & 150 & 150 & 50 \\\\\n\\hline\n\\multirow{2}{*}{$PM_{2.5}$} & && \\multicolumn{2}{l}{annual} && 25 & 15 & 35 & 20 \\\\\n \t\t\t\t\t\t & && \\multicolumn{2}{l}{daily} && - & 35 & 75 & 25 \\\\\n\\hline\n\\multirow{2}{*}{$SO_2$} \t & && \\multicolumn{2}{l}{daily} && - & 125 & 150 & 20 \\\\ \n\t\t \t\t\t\t\t\t & && \\multicolumn{2}{l}{hourly} && 350 & 196& 500 & - \\\\\n\\hline\n\\multirow{2}{*}{$CO$} \t & && \\multicolumn{2}{l}{daily} && - & - & 4 & - \\\\ \n\t\t \t\t\t\t\t\t & && \\multicolumn{2}{l}{8-hour} && 10 & 10 & - & - \\\\\n\\hline\n\\multirow{1}{*}{$O_3$} \t\t\t\t\t\t & && \\multicolumn{2}{l}{8-hour} && 120 & 137& 160 & 100 \\\\\n\\bottomrule\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The effect of ambient air pollution on birth outcomes in Norway", "authors": ["Xiaoguang Ling"], "url": "https://arxiv.org/abs/2208.06271v6", "attribution": "\"The effect of ambient air pollution on birth outcomes in Norway\" by Xiaoguang Ling, arXiv:2208.06271v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00507v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Linear evaluation accuracy (\\%) of contrastive learning methods with the original and discrete projectors on CIFAR-10, CIFAR-100 and ImageNet-100.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccc}\n \\toprule \n Dataset & Framework & Baseline & Discretized Projector & Gains \\\\ \\midrule\n \\multirow{2}{*}{CIFAR-10} & SimCLR & 87.47 & 88.5 & +1.03 \\\\\n & Barlow Twins & 89.00 & 89.38 & +0.38 \\\\ \\midrule\n \\multirow{2}{*}{CIFAR-100} & SimCLR & 58.12 & 60.16 & +2.04 \\\\\n & Barlow Twins & 64.16 & 65.18 & +1.02 \\\\ \\midrule\n \\multirow{2}{*}{ImageNet-100} & SimCLR & 67.36 & 68.38 & +1.02 \\\\\n & Barlow Twins & 69.96 & 71.68 & +1.72 \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Projection Head is Secretly an Information Bottleneck", "authors": ["Zhuo Ouyang", "Kaiwen Hu", "Qi Zhang", "Yifei Wang", "Yisen Wang"], "url": "https://arxiv.org/abs/2503.00507v2", "attribution": "\"Projection Head is Secretly an Information Bottleneck\" by Zhuo Ouyang, Kaiwen Hu, Qi Zhang, Yifei Wang, and Yisen Wang, arXiv:2503.00507v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.13843v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Number of FLOPs of Activation Functions}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|ll}\n\\toprule\n\\textbf{Activation Functions} & \\textbf{FLOPs} & \\textbf{Activation Functions} & \\textbf{FLOPs} \\\\\n\\midrule\nReLU & $N$ & ELU & $8N$ \\\\ \nLeak ReLU & $2N$ & Tanh & $13N$ \\\\ \nSELU & $9N$ & Swish & $8N$ \\\\\nSoftmax & $7N - 1$ & Sigmoid & $7N$ \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Review on Deep Learning Autoencoder in the Design of Next-Generation Communication Systems", "authors": ["Omar Alnaseri", "Laith Alzubaidi", "Yassine Himeur", "Mohammed Alaa Ala'anzy", "Jens Timmermann", "Mohammed S. M. Gismalla"], "url": "https://arxiv.org/abs/2412.13843v2", "attribution": "\"A Review on Deep Learning Autoencoder in the Design of Next-Generation Communication Systems\" by Omar Alnaseri, Laith Alzubaidi, Yassine Himeur, Mohammed Alaa Ala'anzy, Jens Timmermann, and Mohammed S. M. Gismalla, arXiv:2412.13843v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.13124v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Changes in steady-state values from the decrease in $c_e$ by 20\\%.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccc}\n \\hline\n \\hline\n && {Low-income} & {Middle-income} & {High-income} \\\\\n && {countries} & {countries} & {countries} \\\\\n \\hline\n {Aggregate}& {Intensive margin} & 14.9\\% & 15.7\\% & 15.3\\%\\\\\n {output} & {Extensive margin} & -1.9\\% & -1.4\\% & -0.8\\%\\\\\n &{Net change} & 13.0\\% & 14.3\\% & 14.5\\%\\\\\n \\hline\n {Aggregate} & {Intensive margin} & 13.9\\% & 14.6\\% & 14.3\\%\\\\\n {consumption} & {Extensive margin} & 0.0\\% & 0.0\\% & 0.1\\%\\\\\n & {Net change} & 13.9\\% & 14.6\\% & 14.4\\%\\\\\n \\hline\n {Aggregate}& {Intensive margin} & 15.6\\% & 16.3\\% & 15.6\\%\\\\\n {capital} & {Extensive margin} & -5.6\\% & -3.9\\% & -2.1\\%\\\\\n & {Net change} & 10.0\\% & 12.4\\% & 13.5\\%\\\\\n \\hline\n {Average}&{Intensive margin} & 14.4\\% & 15.3\\% & 15.1\\%\\\\\n {wage} & {Extensive margin} & 0.5\\% & 0.3\\% & 0.2\\%\\\\\n & {Net change} & 14.9\\% & 15.6\\% & 15.3\\%\\\\\n \\hline\n {Modern firms'}&{Intensive margin} & 0.8\\% & 0.7\\% & 0.4\\%\\\\\n {share of output} & {Extensive margin} & -4.4\\% & -3.1\\% & -1.8\\%\\\\\n &{Net change} & -3.6\\% & -2.4\\% & -1.4\\%\\\\ \n \\hline\n {Firms' entry}& & & &\\\\\n {(Number of firms)} & {Net change} & 43.6\\% & 41.1\\% & 38.1\\%\\\\\n \\hline\n {Fraction of}& & & &\\\\\n {modern firms} & {Net change} & -5.9\\% & -6.2\\% & -6.0\\% \\\\ \n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Heterogeneous Bribery, Technology Choice, and Capital Accumulation", "authors": ["Jafar M. Olimov", "Yi-Chan Tsai", "Hao-Yu Yang"], "url": "https://arxiv.org/abs/2507.13124v2", "attribution": "\"Heterogeneous Bribery, Technology Choice, and Capital Accumulation\" by Jafar M. Olimov, Yi-Chan Tsai, and Hao-Yu Yang, arXiv:2507.13124v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.05013v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rrrr|rrrr|rrrr}\n\\toprule\n\\multirow{2}{*}{$k$} & \\multicolumn{4}{c}{Window 1} & \\multicolumn{4}{c}{Window 2} & \\multicolumn{4}{c}{Window 3} \\\\\n\\cmidrule{2-5} \\cmidrule{6-9} \\cmidrule{10-13}\n & ADF $p$ & $\\overline{\\beta_{t}}_{,k}$ & $\\text{sd}(\\beta_{t,k})$ & $\\tau$ & ADF $p$ & $\\overline{\\beta_{t}}_{,k}$ & $\\text{sd}(\\beta_{t,k})$ & $\\tau$ & ADF $p$ & $\\overline{\\beta_{t}}_{,k}$ & $\\text{sd}(\\beta_{t,k})$ & $\\tau$ \\\\\n\\midrule\n1 & 0.652 & 44.128 & 0.349 & 9.569 & 0.118 & 43.278 & 0.372 & 16.322 & 0.023 & 43.362 & 0.397 & 23.758 \\\\\n2 & 0.069 & 0.235 & 0.634 & 11.443 & 0.033 & 0.026 & 0.804 & 17.784 & 0.184 & -0.013 & 0.607 & 14.919 \\\\\n3 & 0.031 & -0.499 & 0.274 & 10.886 & 0.000 & -0.722 & 0.402 & 9.717 & 0.000 & -0.257 & 0.350 & 6.550 \\\\\n4 & 0.000 & 0.042 & 0.210 & 5.029 & 0.001 & 0.001 & 0.343 & 4.205 & 0.000 & 0.031 & 0.260 & 3.125 \\\\\n5 & 0.000 & 0.135 & 0.176 & 2.609 & 0.000 & 0.214 & 0.261 & 3.012 & 0.000 & 0.146 & 0.230 & 2.357 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Legendre fit: Summary statistics for Legendre coefficients time series of ETH5 for each window. $\\tau$ is mean return time $-1/\\log|\\phi|$ from AR(1).}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Dynamics of Liquidity Surfaces in Uniswap v3", "authors": ["Jimmy Risk", "Shen-Ning Tung", "Tai-Ho Wang"], "url": "https://arxiv.org/abs/2509.05013v1", "attribution": "\"Dynamics of Liquidity Surfaces in Uniswap v3\" by Jimmy Risk, Shen-Ning Tung, and Tai-Ho Wang, arXiv:2509.05013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.10314v2_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Relative caring penalty by age - Household Income.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n & \\multicolumn{2}{c}{Pre-Treatment Av. Income} & \\multicolumn{2}{c}{Post-Treatment Av. Loss} & \\multicolumn{2}{c}{Penalty}\\\\\n \\cmidrule(lr){2-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-7}\n & HI & LI & HI & LI & HI & LI \\\\\n \\midrule\n Aged 25 and below & £1,843 & £2,893 & £351 & 35 & 19\\% & 1\\% \\\\\n Aged 26-64 & £2,208 & 3,143 & £213 & £154 & 7\\% & 5\\% \\\\\n Aged 65 and above & £1,793 & 1,902 & £39 & £(-14) & 2\\% & (-1)\\% \\\\\n \\midrule\n \\multicolumn{6}{p{0.9\\linewidth}}{\\footnotesize Note: HI = High-Intensity, LI = Low-Intensity. Source: UKHLS data (years 2009-2020), authors' calculations.}\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Estimating the Cost of Informal Care with a Novel Two-Stage Approach to Individual Synthetic Control", "authors": ["Maria Petrillo", "Daniel Valdenegro", "Charles Rahal", "Yanan Zhang", "Gwilym Pryce", "Matthew R. Bennett"], "url": "https://arxiv.org/abs/2411.10314v2", "attribution": "\"Estimating the Cost of Informal Care with a Novel Two-Stage Approach to Individual Synthetic Control\" by Maria Petrillo, Daniel Valdenegro, Charles Rahal, Yanan Zhang, Gwilym Pryce, and Matthew R. Bennett, arXiv:2411.10314v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06779v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\hline\nJoint Goal Accuracy &\n \\textbf{Hotel} &\n \\textbf{Restaurant} &\n \\textbf{Taxi} &\n \\textbf{Attraction} &\n \\textbf{Train} \\\\ \\hline\nTRADE & 13.7 & 11.52 & 60.58 & 19.87 & 22.37 \\\\ \\hline\nSTARC & 28.6 & 28.2 & 65 & 36.9 & 26.1 \\\\\nSTARC + D-REPTILE & \\textbf{32.4} & \\textbf{47.8} & \\textbf{67.2} & \\textbf{45.9} & \\textbf{46.1} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Zero-shot performance on MultiWoz 2.0 dataset. Domains like restaurant and train witness a significant boost in performance over baselines}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Few Shot Dialogue State Tracking using Meta-learning", "authors": ["Saket Dingliwal", "Bill Gao", "Sanchit Agarwal", "Chien-Wei Lin", "Tagyoung Chung", "Dilek Hakkani-Tur"], "url": "https://arxiv.org/abs/2101.06779v3", "attribution": "\"Few Shot Dialogue State Tracking using Meta-learning\" by Saket Dingliwal, Bill Gao, Sanchit Agarwal, Chien-Wei Lin, Tagyoung Chung, and Dilek Hakkani-Tur, arXiv:2101.06779v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.10258v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study : AP performance(\\%) of each category on the PASCAL VOC 2007 test set}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccc}\n\t\t\\hline\\noalign{\\smallskip}\n\t\tMethods & aero & bike & bird & boat & bottle & bus & car & cat & chair & cow \\\\\n\t\t\\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\n\t\tBaseline \t\t& 63.1 & \\textbf{66.4} & 46.1 & 25.4 & 16.9 & 70.8 & 68.8 & 53.2 & 14.9 & 56.8 \\\\\n\t\t + CADM & 64.6 & 64.7 & 53.1 & \\textbf{33.1} & \\textbf{23.2} & 70.4 & \\textbf{70.6} & 22.2 & \\textbf{22.1} & 64.1\t \\\\\n\t\t + GCM \t&66.0 & 66.0 & \\textbf{56.8} & 20.3 & 19.1 & \\textbf{72.5} & \\textbf{70.6} & 59.5 & 19.6 & 64.3 \\\\\n\t\t + Both & \\textbf{66.5} & 65.6 & 56.5 & 26.8 & 19.7 & 69.9 & 69.0 & \\textbf{61.3} & 21.5 & \\textbf{66.9} \\\\\n\t\t\\hline\\noalign{\\smallskip}\n\t\tMethods\t\t\t\t\t\t\t\t & table & dog & horse & mbike & person & plant & sheep & sofa & train & tv \\\\\n\t\t\\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\n\t\tBaseline \t & 41.5 & \\textbf{53.7} & 42.7 & 70.0 & \\textbf{2.9} & \\textbf{20.6} & 42.8 & 44.8 & 50.8 & 68.3 \\\\\n\t\t + CADM & \\textbf{46.2} & 27.1 & \\textbf{49.3} & \\textbf{70.8} & 2.6 & 19.5 & \\textbf{57.1} & \\textbf{55.3} & 64.6 & 69.9 \\\\\n\t\t + GCM & 42.8 & 47.8 & 42.8 & 68.5 & 2.5 & 20.0 & 47.6 & 48.7 & 62.9 & 64.2 \\\\\n\t\t + Both & 43.1 & 50.4 & 49.0 & 70.1 & 2.3 & 20.1 & 53.9 & 47.4 & \\textbf{65.7} & \\textbf{70.7} \\\\\n\t\t\\noalign{\\smallskip}\\hline\n\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Cascade Attentive Dropout for Weakly Supervised Object Detection", "authors": ["Wenlong Gao", "Ying Chen", "Yong Peng"], "url": "https://arxiv.org/abs/2011.10258v1", "attribution": "\"Cascade Attentive Dropout for Weakly Supervised Object Detection\" by Wenlong Gao, Ying Chen, and Yong Peng, arXiv:2011.10258v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.05440v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|r|rrrrrr}\n\\toprule\n & & $M=512$ & $M=1024$ & $M=2048$ & $M=4096$ & $M=8192$ \\\\\n\\midrule\n\\multirow{4}{*}{Gaussian Mixture} \n & ACF & 105 & 189 & 352 & 565 & 1056 \\\\\n & NSF & 118 & 203 & 359 & 646 & 1327 \\\\\n\\cmidrule{2-7}\n & FMPE & 715 & 1278 & 2008 & 3973 & 7508 \\\\\n & CMPE & 867 & 1471 & 2401 & 4390 & 10102 \\\\\n\\midrule\n\\multirow{4}{*}{Two Moons} \n & ACF & 42 & 66 & 118 & 225 & 453 \\\\\n & NSF & 60 & 86 & 146 & 257 & 515 \\\\\n\\cmidrule{2-7}\n & FMPE & 175 & 289 & 498 & 890 & 1805 \\\\\n & CMPE & 195 & 323 & 571 & 1064 & 2173 \\\\\n\\midrule\n\\multirow{4}{*}{Inverse Kinematics} \n & ACF & 43 & 70 & 124 & 234 & 439 \\\\\n & NSF & 63 & 99 & 169 & 297 & 552 \\\\\n\\cmidrule{2-7}\n & FMPE & 97 & 166 & 311 & 551 & 1095 \\\\\n & CMPE & 107 & 194 & 358 & 719 & 1397 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Wall-clock training times on the benchmark tasks. $M$ denotes the available simulation budget for the neural network training stage.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Consistency Models for Scalable and Fast Simulation-Based Inference", "authors": ["Marvin Schmitt", "Valentin Pratz", "Ullrich Köthe", "Paul-Christian Bürkner", "Stefan T Radev"], "url": "https://arxiv.org/abs/2312.05440v3", "attribution": "\"Consistency Models for Scalable and Fast Simulation-Based Inference\" by Marvin Schmitt, Valentin Pratz, Ullrich Köthe, Paul-Christian Bürkner, and Stefan T Radev, arXiv:2312.05440v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.09793v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n Dataset & Nodes & Spectral Recovery Rate & QSD Recovery Rate\\\\\n \\hline\n SBM-connected & 2000 & \\textbf{1} & 0.992 \\\\\n SBM-bounded-degree & 2000 & .502 & \\textbf{0.81}\\\\\n IONOSPHERE & 351 & \\textbf{.69} & 0.67\\\\\n DIABETES & 768 & .522 & \\textbf{0.67} \\\\\n WDBC & 683 & \\textbf{.70} & 0.55\\\\\n POLBLOGS & 1224 & .915 & \\textbf{0.943}\\\\\n SPAM & 4601 & .673 & \\textbf{0.70 }\\\\\n GISETTE & 7000 & .906 & \\textbf{0.959}\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of Recovery Rates for the Standard Spectral and QSD methods with a $\\delta = 0.1$ fraction of revealed nodes.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Semi-Supervised Community Detection via Quasi-Stationary Distributions", "authors": ["Nicolas Fraiman", "Michael Nisenzon"], "url": "https://arxiv.org/abs/2412.09793v1", "attribution": "\"Semi-Supervised Community Detection via Quasi-Stationary Distributions\" by Nicolas Fraiman and Michael Nisenzon, arXiv:2412.09793v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.10480v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccccc}\n\t\t\t\\toprule\n\t\t\tMethod $\\&$ Number of robots &\t5\t&\t10\t&\t20\t&\t40 & 80 \\\\\n\t\t\t\\midrule\n\t\t\tStar-<5>\t& 97.30 & 97.56 & 96.90 & 97.20 & 96.72 \\\\ \n\t\t\tComplete-<5>\t& 95.60 & 93.21 & 86.55 & 85.46 & 63.36 \\\\ \n\t\t\tComplete-<80>& 73.54 & 76.81 & 78.55 & 90.59 & 98.24 \\\\ \n\t\t\t\\toprule\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{The classification accuracy (\\%) tested with the various number of robots. All the data is evaluated without additional training.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Distributed Map Classification using Local Observations", "authors": ["Guangyi Liu", "Arash Amini", "Martin Takáč", "Héctor Muñoz-Avila", "Nader Motee"], "url": "https://arxiv.org/abs/2012.10480v2", "attribution": "\"Distributed Map Classification using Local Observations\" by Guangyi Liu, Arash Amini, Martin Takáč, Héctor Muñoz-Avila, and Nader Motee, arXiv:2012.10480v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.21731v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n \\textbf{Variables} & \\textbf{Runtime (seconds)} & $n$\\textbf{-cells} \\\\ \\hline\n 1 & 0.0308762 & 5 \\\\ \\hline\n 2 & 0.335376 & 29 \\\\ \\hline\n 3 & 6.06833 & 467 \\\\ \\hline\n 4 & 1883.13 & 7370 \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Cylindrical Algebraic Decomposition in Macaulay2", "authors": ["Corin Lee", "Tereso del Río", "Hamid Rahkooy"], "url": "https://arxiv.org/abs/2503.21731v2", "attribution": "\"Cylindrical Algebraic Decomposition in Macaulay2\" by Corin Lee, Tereso del Río, and Hamid Rahkooy, arXiv:2503.21731v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.13629v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Writing Rules between of Texts with and without Revisions}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccc}\n\\hline\\hline\n &(1)&(2)&(3)&(4)&(5)&(6)&(7)&(8)&(9)&(10)&(11)\\\\\n &Rule1a&Rule1b&Rule2&Rule4&Rule5&Rule7a&Rule7b&Rule8&Rule9&Rule10a&Rule10b\\\\\n\\hline\nPrediction 1 & -26.73{***} & -0.610{***} & 3.623{***} & 2.583{***} & -0.126{***} & -1.851{***} & 3.817{***} & -3.144{***} & -1.700{***} & -0.616{***} & -0.858{***} \\\\\n & (0.386) & (0.0138) & (0.128) & (0.0838) & (0.0220) & (0.250) & (0.219) & (0.166) & (0.0974) & (0.123) & (0.0663) \\\\\nPrediction 2 & -12.81{***} & -0.150{***} & 0.607{*} & 2.918{***} & 0.0943{*} & 5.384{***} & 10.40{***} & -4.117{***} & -1.704{***} & -1.148{***} & -1.159{***} \\\\\n & (0.754) & (0.0288) & (0.242) & (0.163) & (0.0433) & (0.599) & (0.553) & (0.361) & (0.201) & (0.250) & (0.139) \\\\\nPrediction 3 & -43.89{***} & -1.346{***} & 1.258{**} & -6.433{***} & -0.400{***} & -1.197 & 1.535{*} & -5.157{***} & -2.678{***} & -1.661{***} & -1.045{***} \\\\\n & (1.293) & (0.0463) & (0.448) & (0.284) & (0.0791) & (0.804) & (0.663) & (0.474) & (0.267) & (0.325) & (0.176) \\\\\nPrediction 4 & -37.55{***} & -0.968{***} & 1.463{**} & 2.775{***} & -0.344{***} & 1.490 & 8.328{***} & -6.255{***} & -2.131{***} & -0.860 & -1.451{***} \\\\\n & (1.266) & (0.0500) & (0.451) & (0.297) & (0.0773) & (0.995) & (0.905) & (0.564) & (0.310) & (0.442) & (0.207) \\\\\nPrediction 5 & -39.54{***} & -1.323{***} & -1.647{***} & 1.617{***} & 0.175{***} & -1.296{***} & 2.304{***} & -3.573{***} & -1.174{***} & -0.805{***} & -0.732{***} \\\\\n & (0.458) & (0.0170) & (0.173) & (0.120) & (0.0310) & (0.370) & (0.309) & (0.240) & (0.147) & (0.163) & (0.101) \\\\\nPrediction 6 & 1.944{***} & 0.133{***} & -3.782{***} & 3.116{***} & 0.250{***} & 8.790{***} & 17.03{***} & -3.905{***} & -1.665{***} & -0.593{*} & -1.002{***} \\\\\n & (0.544) & (0.0221) & (0.186) & (0.132) & (0.0349) & (0.538) & (0.519) & (0.322) & (0.174) & (0.241) & (0.135) \\\\\nAfricans & -1.029 & 0.0822{**} & 1.883{***} & -0.104 & -0.249{***} & -1.687{**} & 0.335 & -0.0387 & 0.200 & -0.151 & -0.443{**} \\\\\n & (0.869) & (0.0300) & (0.270) & (0.179) & (0.0464) & (0.519) & (0.391) & (0.380) & (0.250) & (0.261) & (0.145) \\\\\nBritish & 5.923{***} & 0.197{***} & -0.413{***} & 1.142{***} & 0.0452{*} & 0.577{*} & 1.613{***} & 0.494{**} & 0.568{***} & -0.0558 & -0.159{*} \\\\\n & (0.419) & (0.0144) & (0.125) & (0.0837) & (0.0217) & (0.248) & (0.183) & (0.180) & (0.123) & (0.121) & (0.0728) \\\\\nEastAsian & -0.837{*} & 0.182{***} & 2.016{***} & 1.174{***} & 0.874{***} & 3.854{***} & 1.291{***} & -1.424{***} & 0.0656 & -0.771{***} & 0.105 \\\\\n & (0.372) & (0.0129) & (0.111) & (0.0761) & (0.0197) & (0.223) & (0.170) & (0.159) & (0.110) & (0.111) & (0.0672) \\\\\nEastEuropean & -12.87{***} & -0.235{***} & 3.644{***} & -1.348{***} & 0.0893{**} & -1.907{***} & -1.776{***} & -0.579{*} & 0.0857 & -0.666{***} & -0.309{**} \\\\\n & (0.595) & (0.0204) & (0.188) & (0.123) & (0.0314) & (0.337) & (0.237) & (0.246) & (0.172) & (0.162) & (0.0961) \\\\\nIndian & 4.080{***} & 0.236{***} & 1.675{***} & 0.404{***} & -0.270{***} & -4.356{***} & 1.049{***} & -0.519{*} & -0.507{***} & 0.0933 & -0.0552 \\\\\n & (0.482) & (0.0167) & (0.144) & (0.0960) & (0.0246) & (0.280) & (0.221) & (0.207) & (0.133) & (0.149) & (0.0885) \\\\\nJewish & -0.622 & 0.110{***} & 1.788{***} & 0.499{***} & 0.106{**} & -1.286{***} & 0.493 & 0.474 & 0.481{*} & -0.447{*} & -0.0866 \\\\\n & (0.665) & (0.0229) & (0.203) & (0.134) & (0.0343) & (0.385) & (0.282) & (0.284) & (0.193) & (0.188) & (0.116) \\\\\nMuslim & 7.182{***} & 0.398{***} & 2.480{***} & -0.952{***} & -0.512{***} & -1.553{***} & 2.483{***} & -0.0597 & -0.180 & -1.034{***} & -0.247{*} \\\\\n & (0.605) & (0.0214) & (0.186) & (0.125) & (0.0316) & (0.372) & (0.293) & (0.270) & (0.173) & (0.177) & (0.109) \\\\\nBiology & 58.84{***} & 2.868{***} & 1.242{***} & -5.539{***} & 1.199{***} & 10.81{***} & 13.87{***} & 4.451{***} & 0.807{**} & 3.720{***} & 0.750{***} \\\\\n & (0.789) & (0.0306) & (0.241) & (0.195) & (0.0428) & (0.565) & (0.490) & (0.405) & (0.269) & (0.304) & (0.136) \\\\\nComputer Science & 49.57{***} & 2.669{***} & 2.395{***} & -0.140{**} & 0.446{***} & 19.33{***} & 13.47{***} & 6.335{***} & -0.0312 & 3.008{***} & 2.118{***} \\\\\n & (0.265) & (0.00899) & (0.0833) & (0.0543) & (0.0143) & (0.158) & (0.109) & (0.115) & (0.0749) & (0.0644) & (0.0434) \\\\\nEconomics & 19.35{***} & 1.529{***} & 4.738{***} & 2.413{***} & 0.0299 & 6.669{***} & 9.422{***} & 2.264{***} & 0.251 & 0.837{***} & 0.175 \\\\\n & (0.922) & (0.0351) & (0.326) & (0.216) & (0.0526) & (0.651) & (0.535) & (0.455) & (0.302) & (0.235) & (0.121) \\\\\nElectrical Eng. & 43.85{***} & 2.447{***} & 1.359{***} & -6.048{***} & 0.334{***} & 13.19{***} & 11.25{***} & 3.111{***} & -1.091{***} & 4.096{***} & 2.518{***} \\\\\n & (0.407) & (0.0154) & (0.136) & (0.0915) & (0.0236) & (0.307) & (0.239) & (0.208) & (0.118) & (0.163) & (0.107) \\\\\nFinance & 23.24{***} & 1.547{***} & 2.114{***} & -0.714{**} & -0.554{***} & 7.339{***} & 9.070{***} & 2.587{***} & -0.0106 & 1.385{***} & 1.112{***} \\\\\n & (1.180) & (0.0442) & (0.396) & (0.254) & (0.0621) & (0.811) & (0.651) & (0.564) & (0.366) & (0.318) & (0.231) \\\\\nPhysics & 47.37{***} & 1.914{***} & -3.146{***} & -4.779{***} & 0.729{***} & 3.854{***} & 9.457{***} & 0.998{***} & 0.702{***} & 3.985{***} & 1.345{***} \\\\\n & (0.272) & (0.00879) & (0.0812) & (0.0545) & (0.0142) & (0.144) & (0.0984) & (0.105) & (0.0757) & (0.0658) & (0.0377) \\\\\nStatistics & 42.09{***} & 2.394{***} & 2.817{***} & -2.270{***} & 0.899{***} & 16.58{***} & 8.396{***} & 3.832{***} & -0.675{***} & 2.762{***} & 0.561{***} \\\\\n & (0.486) & (0.0184) & (0.164) & (0.106) & (0.0279) & (0.380) & (0.271) & (0.260) & (0.153) & (0.172) & (0.0836) \\\\\nFemale & 2.261{***} & -0.0217 & -2.211{***} & 1.541{***} & -0.139{***} & -2.973{***} & 0.471{*} & 0.284 & 0.871{***} & 0.581{***} & -0.0288 \\\\\n & (0.511) & (0.0176) & (0.151) & (0.104) & (0.0264) & (0.302) & (0.234) & (0.220) & (0.151) & (0.154) & (0.0915) \\\\\nMale & -11.31{***} & -0.332{***} & -0.286{**} & 2.508{***} & 0.365{***} & -1.708{***} & -1.803{***} & -1.054{***} & 0.256{**} & -0.102 & -0.103 \\\\\n & (0.307) & (0.0106) & (0.0902) & (0.0620) & (0.0159) & (0.178) & (0.138) & (0.128) & (0.0891) & (0.0914) & (0.0543) \\\\\nNo. of Papers & 4.131{***} & 0.0735{***} & -0.528{***} & 0.0847{***} & 0.00219 & 0.756{***} & 0.814{***} & 0.337{***} & -0.0606 & 0.129{***} & 0.180{***} \\\\\n & (0.113) & (0.00379) & (0.0326) & (0.0227) & (0.00618) & (0.0700) & (0.0564) & (0.0495) & (0.0325) & (0.0348) & (0.0246) \\\\\nYoE & -3.126{***} & -0.182{***} & -0.0214 & 0.804{***} & 0.0246{***} & -1.261{***} & -1.476{***} & -0.175{***} & 0.391{***} & -0.312{***} & -0.0175 \\\\\n & (0.125) & (0.00420) & (0.0371) & (0.0251) & (0.00647) & (0.0687) & (0.0513) & (0.0495) & (0.0352) & (0.0336) & (0.0209) \\\\\nBaseline & 154.8{***} & 4.976{***} & 24.46{***} & 71.47{***} & 14.07{***} & 19.41{***} & 4.883{***} & 11.25{***} & 4.087{***} & 1.513{***} & 0.793{***} \\\\\n & (0.416) & (0.0140) & (0.123) & (0.0834) & (0.0215) & (0.231) & (0.166) & (0.168) & (0.117) & (0.108) & (0.0647) \\\\\n\\hline\nR3 & 0.107 & 0.175 & 0.0216 & 0.0410 & 0.0166 & 0.0442 & 0.0345 & 0.00836 & 0.00208 & 0.00489 & 0.00424 \\\\\nN & 627225 & 627225 & 627225 & 627225 & 627225 & 627225 & 627225 & 627225 & 627225 & 627225 & 627225 \\\\\n\\hline\\hline\n\\textbf{Article FE} & No & No & No & No & No & No & No & No & No & No & No \\\\\n\\textbf{Time FE} & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\\\\n\\textbf{Obs.} & 627,225 & 627,225 & 627,225 & 627,225 & 627,225 & 627,225 & 627,225 & 627,225 & 627,225 & 627,225 & 627,225 \\\\\n\\textbf{R-squared} & 0.099 & 0.168 & 0.021 & 0.040 & 0.017 & 0.044 & 0.034 & 0.008 & 0.002 & 0.004 & 0.003 \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing", "authors": ["Cong William Lin", "Wu Zhu"], "url": "https://arxiv.org/abs/2504.13629v1", "attribution": "\"Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing\" by Cong William Lin and Wu Zhu, arXiv:2504.13629v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.06718v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\nsplit & num. examples & label distribution\\\\\t\n\\midrule\nlabeled train & 1000 & [0.2 0.2 0.2 0.2 0.2]\n\\\\\nunlabeled train & 21300 & [0.2 0.2 0.2 0.2 0.2]\n\\\\\nlabeled valid & 2000 & [0.2 0.2 0.2 0.2 0.2]\n\\\\\nlabeled test & 24300 & [0.2 0.2 0.2 0.2 0.2]\n\\end{tabular}\n\\end{adjustbox}\n\\caption{NORB dataset.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints", "authors": ["Gabriel Hope", "Madina Abdrakhmanova", "Xiaoyin Chen", "Michael C. Hughes", "Michael C. Hughes", "Erik B. Sudderth"], "url": "https://arxiv.org/abs/2012.06718v1", "attribution": "\"Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints\" by Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C. Hughes, Michael C. Hughes, and Erik B. Sudderth, arXiv:2012.06718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.10923v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\n& \\multicolumn{3}{c}{p=1} & \\multicolumn{3}{c}{p=2} \\\\ \n\\cmidrule(r){2-4}\\cmidrule(l){5-7}\n\\textbf{k} & \\textbf{Bank} & \\textbf{Adult} & \\textbf{Diabetes} & \\textbf{Bank} & \\textbf{Adult} & \\textbf{Diabetes} \\\\\n5 & 19 & 18 & 30 & 27 & 17 & 10 \\\\\n10 & 18 & 28 & 27 & 23 & 21 & 22 \\\\\n15 & 25 & 26 & 11 & 23 & 17 & 10 \\\\\n20 & 15 & 15 & 15 & 17 & 10 & 10 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Number of outliers detected after solving and thresholding with $\\tau=0,$ for $k$-median ($p=1$) and $k$-means ($p=2$) clustering. For this experiment, we set $m=10.$}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Linear Programming based Approximation to Individually Fair k-Clustering with Outliers", "authors": ["Binita Maity", "Shrutimoy Das", "Anirban Dasgupta"], "url": "https://arxiv.org/abs/2412.10923v1", "attribution": "\"Linear Programming based Approximation to Individually Fair k-Clustering with Outliers\" by Binita Maity, Shrutimoy Das, and Anirban Dasgupta, arXiv:2412.10923v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12569v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n Definition & $z \\to 0$ & $z \\to \\infty$ \\\\ \\hline\n $h(z)$ & $\\sim z^{\\beta}$, $\\beta \\in [1, 2)$ & $\\sim z^{\\alpha}$, $\\alpha \\in [0, \\frac{4}{n + 2}) \\cap [0, 1]$ \\\\ \\hline\n $H(z) = \\int_1^z h^{-1} (s) \\mathrm d s$ &\n $\\begin{cases}\n \\sim \\ln z, \\ \\beta = 1, \\\\\n \\sim z^{1 - \\beta}, \\ \\beta > 1.\n \\end{cases}$ & \n $\\begin{cases}\n \\sim z^{1 - \\alpha}, \\ \\alpha < 1, \\\\\n \\sim \\ln z, \\ \\alpha = 1\n \\end{cases}$ \\\\ \\hline\n $\\mathcal{H} (z) = \\int_1^z H(s) \\mathrm d s + 1$ & $\\sim 1$ & \n $\\begin{cases}\n \\sim z^{2 - \\alpha}, \\ \\alpha < 1, \\\\\n \\sim z \\ln z, \\ \\alpha = 1\n \\end{cases}$ \\\\ \\hline\n $\\widetilde{H} (z) = \\int_0^z h^{-1/2} (s) \\mathrm d s$ & $\\sim z^{1 - \\beta / 2}$ & $\\sim z^{1 - \\alpha / 2}$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Properties of the function $h$ and some related functions}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Existence and asymptotic stability of a generic Lotka-Volterra system with nonlinear spatially heterogeneous cross-diffusion", "authors": ["Tianxu Wang", "Jiwoon Sim", "Hao Wang"], "url": "https://arxiv.org/abs/2501.12569v1", "attribution": "\"Existence and asymptotic stability of a generic Lotka-Volterra system with nonlinear spatially heterogeneous cross-diffusion\" by Tianxu Wang, Jiwoon Sim, and Hao Wang, arXiv:2501.12569v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.12722v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n Methods & Acc.(mm) & Comp. (mm) & OA (mm) \\\\\n \\midrule\n Camp & 0.835 & 0.554 & 0.695 \\\\\n Gipuma & \\textbf{0.283} & 0.873 & 0.578 \\\\\n Furu & 0.613 & 0.941 & 0.777 \\\\\n \\midrule\n SurfaceNet & 0.450 & 1.040 & 0.745 \\\\\n $\\text{MVSNet}^*$ & 0.396 & 0.527 & 0.462 \\\\\n $\\text{R-MVSNet}^*$ & 0.383 & 0.452 & 0.418 \\\\\n PruMVSNet & 0.495 & 0.433 & 0.464 \\\\\n PointMVSNet & 0.361 & 0.421 & 0.391 \\\\\n CasMVSNet & 0.325 & 0.385 & 0.355 \\\\\n CVP-MVSNet & 0.296 & 0.406 & 0.351 \\\\\n Ours($G = 8$) & 0.363 & 0.332 & 0.347 \\\\\n Ours($G = 2$) & 0.360 & 0.341 & 0.351 \\\\\n \\midrule\n Ours($G = 4$) & 0.357 & \\textbf{0.326} & \\textbf{0.341} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Attention Aware Cost Volume Pyramid Based Multi-view Stereo Network for 3D Reconstruction", "authors": ["Anzhu Yu", "Wenyue Guo", "Bing Liu", "Xin Chen", "Xin Wang", "Xuefeng Cao", "Bingchuan Jiang"], "url": "https://arxiv.org/abs/2011.12722v1", "attribution": "\"Attention Aware Cost Volume Pyramid Based Multi-view Stereo Network for 3D Reconstruction\" by Anzhu Yu, Wenyue Guo, Bing Liu, Xin Chen, Xin Wang, Xuefeng Cao, and Bingchuan Jiang, arXiv:2011.12722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16448v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Frequency of Bases for Judging Comprehensibility}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c}\nBasis for Judgment & Frequency \\\\ \\hline\nConsistency of features within layers or subtrees & 100\\% \\\\ \nNumber of layers (depth) in the tree & 100\\% \\\\ \nOverall structure and organization of the tree & 93.75\\% \\\\ \nIntuitively reasonable feature selection & 93.75\\% \\\\ \nReasonableness of threshold selection & 20\\% \\\\ \nComplexity of subtrees & 20\\% \\\\ \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Beyond Interpretability: Exploring the Comprehensibility of Adaptive Video Streaming through Large Language Models", "authors": ["Lianchen Jia", "Chaoyang Li", "Ziqi Yuan", "Jiahui Chen", "Tianchi Huang", "Jiangchuan Liu", "Lifeng Sun"], "url": "https://arxiv.org/abs/2508.16448v1", "attribution": "\"Beyond Interpretability: Exploring the Comprehensibility of Adaptive Video Streaming through Large Language Models\" by Lianchen Jia, Chaoyang Li, Ziqi Yuan, Jiahui Chen, Tianchi Huang, Jiangchuan Liu, and Lifeng Sun, arXiv:2508.16448v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.22923v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Primal and dual expressions for different divergence measures}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n \\textbf{Divergence} & $f(t)$ & $f^*(t)$ \\\\\n \\hline\n $\\chi^2$ & $\\frac{1}{2} (t - 1)^2$ & $-1 + \\frac{1}{4} (t + 2)^2_+$ \\\\\n \\hline\n \\textit{KL} & $t \\log t - t + 1$ & $\\exp(t) - 1$\\\\\n \\hline\n \\textit{smoothed CVaR} & $f_{\\alpha}^{\\text{smo}}(t) = \\begin{cases} \n t \\log t + \\frac{1 - \\alpha t}{\\alpha} \\log \\frac{1 - \\alpha t}{1 - \\alpha}, & t \\in [0, 1/\\alpha)\\\\+\\infty, & \\textit{otherwise}\n \\end{cases}$ & $\\frac{1}{\\alpha}\\log(1-\\alpha+\\alpha\\exp(t))$\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Nested Stochastic Algorithm for Generalized Sinkhorn distance-Regularized Distributionally Robust Optimization", "authors": ["Yufeng Yang", "Yi Zhou", "Zhaosong Lu"], "url": "https://arxiv.org/abs/2503.22923v2", "attribution": "\"Nested Stochastic Algorithm for Generalized Sinkhorn distance-Regularized Distributionally Robust Optimization\" by Yufeng Yang, Yi Zhou, and Zhaosong Lu, arXiv:2503.22923v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.15794v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of total computation time and average computation time for Algorithm 1, fmincon, and Casadi in the MPC implementation for the trajectory tracking problem ()-().}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|}\n\t\t\\hline\n\t\t\\multicolumn{2}{|c|}{\\multirow{1}{*}{Algorithm}} &\\multirow{1}{*}{Total time [s]}&\\multirow{1}{*}{Average time [s]} \\\\\n\t\t\\hline\n\t\t\\multirow{2}{*}{fmincon} & Interior Point & 3.1836 & 0.0199 \\\\\n\t\t\\cline{2-4}\t\n\t\t\\multirow{2}{*}{} & SQP & 1.2997 & 0.0081 \\\\\n\t\t\\hline\n\t\t\n\t\t\\multirow{1}{*}{Casadi} & IPOPT & 1.3768 & 0.0086 \\\\\n\t\t\\hline\n\t\t\n\t\t\\multicolumn{2}{|c|}{\\multirow{1}{*}{Algorithm 1}} &\\multirow{1}{*}{0.2809}&\\multirow{1}{*}{0.0018} \\\\\n\t\t\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Finite-Horizon Discrete-Time Optimal Control for Nonlinear Systems under State and Control Constraints", "authors": ["Chuanzhi Lv", "Hongdan Li", "Huanshui Zhang"], "url": "https://arxiv.org/abs/2503.15794v1", "attribution": "\"Finite-Horizon Discrete-Time Optimal Control for Nonlinear Systems under State and Control Constraints\" by Chuanzhi Lv, Hongdan Li, and Huanshui Zhang, arXiv:2503.15794v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.13713v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Architecture and parameters of the proposed model is presented.}\n\\begin{tabular}{l|l|l}\n \\hline\n \\textbf{Module} & \\textbf{Layer Details} & \\textbf{Feature Shape} \\\\ \\hline \\hline\n Input & $-$ & $120 \\times 3$ \\\\ \\hline\n \\multirow{7}{*}{Conv. Blocks} & $[\\textit{conv}, 1 \\times 3, 128] \\times 2$ & $116 \\times 128$ \\\\ \n & $[\\textit{maxpool}, 1 \\times 2, \\textit{stride} = 2]$ & $58 \\times 128$ \\\\ \n & $[\\textit{conv}, 1 \\times 3, 256] \\times 2$ & $54 \\times 256$ \\\\ \n & $[\\textit{maxpool}, 1 \\times 2, \\textit{stride} = 2]$ & $27 \\times 256$ \\\\ \n & $[\\textit{conv}, 1 \\times 2, 384] \\times 2$ & $23 \\times 384$ \\\\\n & $[\\textit{maxpool}, 1 \\times 2, \\textit{stride} = 2]$ & $11 \\times 384$ \\\\ \n & \\textit{flatten} & $4224\n$ \\\\ \\hline \n \\multirow{4}{*}{$H_{predict}$ FC} & $[\\textit{dense}, 384]$ & $384$ \\\\ \n & $[\\textit{dense}, 120]$ & $120$ \\\\ \n & $[\\textit{dense}, 24]$ & $45$ \\\\\n & \\textit{output} & $24\n$ \\\\ \\hline \n \\multirow{7}{*}{$H_{HAR}$ FC} & $[\\textit{dense}, 512 ] $ & $512$ \\\\ \n & $[\\textit{dense}, 250]$ & $250$ \\\\\n & $[\\textit{dense}, 100]$ & $100$ \\\\\n & $[\\textit{dense}, num\\_classes]$ & $num\\_classes$ \\\\\n & \\textit{output} & 1\n \\\\ \\hline \n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion", "authors": ["Setareh Rahimi Taghanaki", "Michael Rainbow", "Ali Etemad"], "url": "https://arxiv.org/abs/2010.13713v2", "attribution": "\"Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion\" by Setareh Rahimi Taghanaki, Michael Rainbow, and Ali Etemad, arXiv:2010.13713v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.15397v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc|ccc}\n & IMEX(3,3;1) & & & IMEX(2,2;1) & \\\\ \n \\#Dofs & \\(\\text{err}_{L\\infty}(0.5)\\) & rate & \\#Dofs & \\(\\text{err}_{L\\infty}(0.5)\\) & rate \\\\ \\hline\n 257 & 6.15E-01 & -- & 257 & 4.58E-01 & -- \\\\\n 513 & 1.08E-01 & 2.51 & 513 & 1.13E-01 & 2.03 \\\\\n 1025 & 8.28E-03 & 3.72 & 1025 & 2.56E-02 & 2.14 \\\\\n 2049 & 4.95E-04 & 4.07 & 2049 & 6.21E-03 & 2.05 \\\\\n 4097 & 1.52E-05 & 5.02 & 4097 & 1.54E-03 & 2.01 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Convergence tables for the KdV equation on \\(D = (-10,20)\\). Left: IMEX(3,3;1) time stepping with CFL = 0.25 and \\(\\mathbb{P}_2\\) elements. Right: IMEX(2,2;1) time stepping with CFL = 0.05 and \\(\\mathbb{P}_1\\) elements.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A High Order IMEX Method for Generalized Korteweg de-Vries Equations", "authors": ["Seth Gerberding"], "url": "https://arxiv.org/abs/2503.15397v1", "attribution": "\"A High Order IMEX Method for Generalized Korteweg de-Vries Equations\" by Seth Gerberding, arXiv:2503.15397v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.06234v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Radius of largest $n$-enclosing circle for MC $n$, or its least upper bound for non-MC $n$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c||c|c|}\n\\hline\n\\boldmath{$n$} & 1 & 2 & 3 & 4 & 5, 6 & 7 & 8 &9 &10 &11 & 12 & 13 \\\\\n\\hline $R_n$ & 1 &$\\frac{\\sqrt{5}}{2}$ &$\\frac{5\\sqrt{2}}{3}$ &$\\frac{\\sqrt{10}}{2}$ & $\\prec R_4$ &$\\frac53$ &$\\frac{\\sqrt{13}}{2}$ &2 &$\\frac{\\sqrt{65}}{4}$ &$\\frac{\\sqrt{442}}{10}$ &$\\frac{17}{8}$ &$\\sqrt{5}$ \\\\\n\\hline\n\\boldmath{$n$} & 14 & 15 & 16 & 17,18 & 19 & 20 & 21 & 22& 23 & 24 &25 &26\\\\\n\\hline $R_n$ &$\\frac{\\sqrt{85}}{4}$ &$\\frac{5\\sqrt{2}}{3}$ &$\\frac{\\sqrt{26}}{2}$ &$\\prec R_{16}$ &$\\frac{5\\sqrt{17}}{8}$ &$\\frac{\\sqrt{65}}{3}$ &$2\\sqrt{2}$ &$\\frac{\\sqrt{290}}{6}$ &$ \\frac{29}{10}$ &$ \\frac{\\sqrt{221}}{5}$ &$3$ &$\\frac{\\sqrt{37}}{2}$ \\\\\n\\hline\n\\boldmath{$n$} & 27 & 28 & 29 & 30 & 31 & 32 & 33-36 & 37 & 38 &39 & 40 & \\ \\\\\n\\hline $R_n$ &$\\frac{\\sqrt{37}}{2}$ &$\\frac{\\sqrt{85}}{3}$ &$\\frac{17\\sqrt{74}}{46}$ &$\\frac{41\\sqrt{2}}{18}$ &$\\frac{5\\sqrt{2}}{2}$ &$\\frac{5\\sqrt{2}}{2}$ &$\\prec R_{32}$ &$\\sqrt{13}$ &$\\prec R_{37}$ &$\\frac{5\\sqrt{34}}{8} $ &$\\frac{\\sqrt{481}}{6}$ &\\ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Largest Circle Enclosing $n$ Lattice Points", "authors": ["Jianqiang Zhao"], "url": "https://arxiv.org/abs/2505.06234v2", "attribution": "\"The Largest Circle Enclosing $n$ Lattice Points\" by Jianqiang Zhao, arXiv:2505.06234v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.11243v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyper‑parameter settings for each task.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n \\toprule\n {Hyper‑parameter} & {Synthetic} & {Mortgage} & {Equity} \\\\\n \\midrule\n Set‑Seq layers & 5 & 6 & 6 \\\\\n Sequence layers & 1 & 0 & 0 \\\\\n $d_{\\text{model}}$ & 800 & 300 & 64 \\\\\n Learning rate & 0.003 & 0.003 & 0.003 \\\\\n Dropout & 0 & 0.10 & 0.10 \\\\\n Epochs & 40 & 40 & 30 \\\\\n \\# Samples & 250 & — & — \\\\\n Time steps & 100 & 50 & 246 \\\\\n Number of units & 1–1000 & 2500 & 500 \\\\\n Sequence model & LongConv& LongConv& LongConv\\\\\n Chunk size $L$ & 3 & 3 & 3 \\\\\n Set summary dim & 2 & 2 & 2 \\\\\n $\\phi$ output dim & 5 & 5 & 5 \\\\\n Conv.\\ weight decay & 0 & 0.05 & 0.05 \\\\ \n \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Set-Sequence Model for Time Series", "authors": ["Elliot L. Epstein", "Apaar Sadhwani", "Kay Giesecke"], "url": "https://arxiv.org/abs/2505.11243v1", "attribution": "\"A Set-Sequence Model for Time Series\" by Elliot L. Epstein, Apaar Sadhwani, and Kay Giesecke, arXiv:2505.11243v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.19089v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Threshold estimations of Cylindrical and M\\\"{o}bius codes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n & $A = 1$ & $A = 10$ & $A = \\infty$ \\\\\n \\midrule\n Cylindrical Codes & $0.14$ & $0.12$ & $0.10$ \\\\\n M\\\"{o}bius Codes & $0.14$ & $0.12$ & $0.10$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Cylindrical and Möbius Quantum Codes for Asymmetric Pauli Errors", "authors": ["Lorenzo Valentini", "Diego Forlivesi", "Marco Chiani"], "url": "https://arxiv.org/abs/2502.19089v1", "attribution": "\"Cylindrical and Möbius Quantum Codes for Asymmetric Pauli Errors\" by Lorenzo Valentini, Diego Forlivesi, and Marco Chiani, arXiv:2502.19089v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.01012v4_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|} \\hline \\textbf{Augmentation} & \\textbf{Parameters} \\\\ \\hline ColorJitter & brightness = 0.4, contrast = 0.4, saturation = 0.2, hue = 0.1, prob = 0.8 \\\\ \\hline Grayscale & prob = 0.2 \\\\ \\hline HorizontalFlip & prob = 0.5 \\\\ \\hline CropSize & 32 \\\\ \\hline \\end{tabular}\n\\end{adjustbox}\n\\caption{Shared augmentations for VICReg and Barlow Twins}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning", "authors": ["James Chapman", "Lennie Wells", "Ana Lawry Aguila"], "url": "https://arxiv.org/abs/2310.01012v4", "attribution": "\"Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning\" by James Chapman, Lennie Wells, and Ana Lawry Aguila, arXiv:2310.01012v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2207.13319v2_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|rrrlrrrlrrrl}\n\\toprule\nLoan & \\multicolumn{4}{|c|}{\\textit{Past Due Rate}} & \n\\multicolumn{4}{|c|}{\\textit{Allowance Rate}} & \\multicolumn{4}{|c}{\\textit{Macro PC}} \\\\\nType & $\\beta_{Pool}$ & $\\beta_F$ & diff & $p$-value & \n$\\beta_{Pool}$ & $\\beta_{F}$ & diff & $p$-value & \n$\\gamma_{Pool}$ & $\\gamma_{F}$ & diff & $p$-value \\\\\n\\midrule\n CC & 0.781 & 0.838 & -0.057 & 0.006$^{***}$ & & & & & & & & \\\\\n FL & 0.045 & 0.061 & -0.016 & 0.002$^{***}$ & & & & & & & & \\\\\nCRE & 0.135 & 0.133 & 0.002 & 0.449 & & & & & & & & \\\\\n CI & 0.207 & 0.217 & -0.011 & 0.069$^{*}$ & & & & & & & & \\\\ \\hline \n CC & 0.794 & 0.852 & -0.058 & 0.109 & -0.029 & -0.032 & 0.003 & 0.938 & & & & \\\\\n FL & 0.010 & 0.021 & -0.011 & 0.306 & 0.335 & 0.307 & 0.028 & 0.424 & & & & \\\\\nCRE & 0.095 & 0.068 & 0.026 & 0.052$^{*}$ & 0.127 & 0.191 & -0.064 & 0.090$^{*}$ & & & & \\\\ \n CI & 0.178 & 0.196 & -0.018 & 0.000$^{***}$ & 0.064 & 0.054 & 0.010 & 0.514 & & & & \\\\\\hline \n CC & 0.595 & 0.633 & -0.039 & 0.001$^{***}$ & & & & & 0.417 & 0.397 & 0.020 & 0.000$^{***}$ \\\\\n FL & 0.037 & 0.052 & -0.015 & 0.001$^{***}$ & & & & & 0.110 & 0.106 & 0.003 & 0.329 \\\\\nCRE & 0.129 & 0.126 & 0.002 & 0.477 & & & & & 0.057 & 0.057 & 0.000 & 0.601 \\\\\n CI & 0.155 & 0.155 & -0.001 & 0.886 & & & & & 0.190 & 0.190 & 0.000 & 0.981 \\\\\\hline \n CC & 0.577 & 0.618 & -0.041 & 0.046$^{**}$ & 0.037 & 0.032 & 0.005 & 0.846 & 0.420 & 0.399 & 0.021 & 0.000$^{***}$ \\\\\n FL & 0.006 & 0.018 & -0.012 & 0.146 & 0.315 & 0.268 & 0.047 & 0.041$^{**}$ & 0.095 & 0.094 & 0.001 & 0.768 \\\\\nCRE & 0.097 & 0.078 & 0.019 & 0.182 & 0.102 & 0.147 & -0.045 & 0.265 & 0.052 & 0.050 & 0.002 & 0.267 \\\\\n CI & 0.140 & 0.153 & -0.012 & 0.035$^{**}$ & 0.032 & 0.007 & 0.025 & 0.105 & 0.189 & 0.189 & -0.001 & 0.583 \\\\\n\\bottomrule\n\\multicolumn{13}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of pooled and FEO coefficients using pre-COVID data with allowance rate as an additional proxy for banks' portfolio risks.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Should Bank Stress Tests Be Fair?", "authors": ["Paul Glasserman", "Mike Li"], "url": "https://arxiv.org/abs/2207.13319v2", "attribution": "\"Should Bank Stress Tests Be Fair?\" by Paul Glasserman and Mike Li, arXiv:2207.13319v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.17818v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Accuracy comparison of proposed method with full fine-tuning and other parameter efficient adaptation methods for SI and MI EEG signal classification. [Standard deviation is reported in round brackets.]}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|cccc}\n\\hline\n\\multirow{2}{*}{\\textbf{Dataset}} & \\multirow{2}{*}{\\textbf{Subject}} & \\multicolumn{4}{c}{\\textbf{Methods}} \\\\ \\cline{3-6}\n & & \\textbf{Fine-Tune} & \\textbf{LoRA} & \\textbf{DoRA} & \\textbf{EDoRA(our)} \\\\ \\hline \n\\multirow{7}{*}{Speech Imagery} & 1 & 51.67 & 50.00 & 50.00 & 53.33 \\\\ \n & 3 & 48.33 & 50.00 & 48.33 & 41.67 \\\\ \n & 5 & 48.33 & 45.00 & 45.00 & 50.00 \\\\ \n & 6 & 51.67 & 48.33 & 50.00 & 53.33 \\\\ \n & 8 & 58.33 & 65.00 & 65.00 & 61.67 \\\\\n & 12 & 51.67 & 53.33 & 53.33 & 58.33 \\\\ \\cline{2-6}\n & Average & 51.67 (3.65) & 51.94 (6.95) & 51.94 (6.95) & \\textbf{53.06} (6.94) \\\\ \\hline\n\\multirow{10}{*}{Motor Imagery} & 1 & 81.60 & 79.86 & 79.51 & 81.94 \\\\ \n & 2 & 52.78 & 51.74 & 52.08 & 49.31 \\\\ \n & 3 & 85.76 & 84.38 & 84.38 & 86.11 \\\\ \n & 4 & 65.63 & 65.63 & 66.67 & 67.71 \\\\ \n & 5 & 42.36 & 44.44 & 44.44 & 44.79 \\\\ \n & 6 & 51.04 & 51.04 & 52.43 & 56.25 \\\\ \n & 7 & 81.25 & 78.13 & 78.13 & 80.90 \\\\ \n & 8 & 80.21 & 78.47 & 78.82 & 80.21 \\\\ \n & 9 & 79.86 & 72.92 & 73.96 & 78.13 \\\\ \\cline{2-6}\n & Average & 68.94 (16.36) & 67.40 (14.81) & 67.82 (14.61) & \\textbf{69.48} (15.60) \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters", "authors": ["Taveena Lotey", "Aman Verma", "Partha Pratim Roy"], "url": "https://arxiv.org/abs/2412.17818v1", "attribution": "\"EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters\" by Taveena Lotey, Aman Verma, and Partha Pratim Roy, arXiv:2412.17818v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.12118v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Proposed CSS Compared with Existing Pre-Participation Evaluation (PPE)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\t\t\\toprule\n\t\t\\textbf{Criterion} & \\textbf{14-point PPE} & \\textbf{12-lead ECG} & \\textbf{This work} \\\\\n\t\t\\midrule\n\t\tSensitivity & 18.8\\% & 94\\% & 97.3\\% \\\\\n Specificity & 68.0\\% & 93\\% & 99.1\\%\\\\\n FPR & 32.0\\% & 7\\% & 2\\% \\\\\n Price & N/A & \\$>1,000 & \\$399 \\\\\n Require physician? & Yes & Yes & No \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "High-Throughput Detection of Risk Factors to Sudden Cardiac Arrest in Youth Athletes: A Smartwatch-Based Screening Platform", "authors": ["Evan Xiang", "Thomas Wang", "Vivan Poddar"], "url": "https://arxiv.org/abs/2412.12118v1", "attribution": "\"High-Throughput Detection of Risk Factors to Sudden Cardiac Arrest in Youth Athletes: A Smartwatch-Based Screening Platform\" by Evan Xiang, Thomas Wang, and Vivan Poddar, arXiv:2412.12118v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.07814v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|r|r|r|r|r|r|r|r}\n \\multicolumn{2}{c}{}&\\multicolumn{4}{c}{supervised}&\\multicolumn{4}{c}{unsupervised}\\\\\n \\multicolumn{2}{c}{}&\\multicolumn{2}{c}{TV}&\\multicolumn{2}{c|}{WTV}&\\multicolumn{2}{c|}{TV}&\\multicolumn{2}{c}{WTV} \\\\\n Image&$\\sigma$&IPSNR&ISSIM&IPSNR&ISSIM&IPSNR&ISSIM&IPSNR&ISSIM\\\\\n \\hline\n \\multirow{3}{*}{\\#3}&$0.03$&3.419&0.199&\\textbf{7.377}&\\textbf{0.243}&3.354&0.209&\\textbf{3.815}&\\textbf{0.225}\\\\\n &$0.06$&4.712&0.342&\\textbf{8.900}&\\textbf{0.436}&4.629&0.365&\\textbf{5.007}&\\textbf{0.392}\\\\\n &$0.09$&5.697&0.387&\\textbf{9.887}&\\textbf{0.524}&5.585&0.423&\\textbf{5.831} &\\textbf{0.427} \\end{tabular}\n\\end{adjustbox}\n\\caption{IPSNR and ISSIM values achieved by the considered models in case of additive white uniform noise corruption.}% for the three test images corrupted by different levels of AWGN.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising", "authors": ["Monica Pragliola", "Luca Calatroni", "Alessandro Lanza"], "url": "https://arxiv.org/abs/2503.07814v1", "attribution": "\"Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising\" by Monica Pragliola, Luca Calatroni, and Alessandro Lanza, arXiv:2503.07814v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.13429v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c||}\n\\hline \\# & Quarter & Invested Stocks \\\\\n\\hline 1 & Q4 2020 & BRKM5 \\\\ % February 28, 2021\n\\hline 2 & Q1 2021 & ABEV3,ENEV3,TAEE11,USIM5 \\\\ % May 31, 2021\n\\hline 3 & Q2 2021 & USIM5, GGBR4, GOAU4 \\\\ % August 31, 2021\n\\hline 4 & Q3 2021 & GGBR4,GOAU4, CSNA3, PETR3 \\\\ % November 30, 2021\n\\hline 5 & Q4 2021 & GOAU4, KLBN11, WEGE3, CSAN3\n \\\\\n\\hline 6 & Q1 2022 & GGBR4, GOAU4, PRIO3, SUZB3 \\\\\n\\hline 7 & Q2 2022 & GOAU4, PETR3, CSNA3, SUZB3\n\\\\\n\\hline 8 & Q3 2022 & PRIO3, PETR3, GOAU4\n \\\\\n\\hline 9 & Q4 2022 & No stocks \\\\\n\\hline 10 & Q1 2023 & CMIG4, SUZB3\n \\\\\n\\hline 11 & Q2 2023 & SLCE3, GGBR4, SUZB3, GOAU4\n\\\\\n\\hline 12 & Q3 2023 & CMIG4, GGBR4\n \\\\\n\\hline 13 & Q4 2023 & CMIG4, UGPA3\n \\\\\n\\hline 14 & Q1 2024 & SUZB3, PRIO3, WEGE3, TAEE11\n \\\\\n\\hline 15 & Q2 2024 & GOAU4, GGBR4, ABEV3\n\\\\\n\\hline 16 & Q3 2024 & PRIO3\n \\\\\n\\hline 17 & Q4 2024 & ELET3\n\\\\\n\\hline 18 & Q1 2025 & ABEV3\n\\\\\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{AlphaX´s Invested Stocks per Quarter}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "AlphaX: An AI-Based Value Investing Strategy for the Brazilian Stock Market", "authors": ["Paulo André Lima de Castro"], "url": "https://arxiv.org/abs/2508.13429v1", "attribution": "\"AlphaX: An AI-Based Value Investing Strategy for the Brazilian Stock Market\" by Paulo André Lima de Castro, arXiv:2508.13429v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11787v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Historical Statistics of Log($v$).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\nYear&mean&standard deviation&skew&excess kurtorsis\\\\\n\\hline\t% horizontal line to separate headings from data\n2020&15.218&1.024&-0.047&-0.514\\\\\n2010&15.156&1.019&-0.054&-0.537\\\\\n2000&15.062&1.020&-0.052&-0.572\\\\\n1990&14.939&1.018&-0.015&-0.630\\\\\n1980&14.850&1.021&-0.073&-0.719\\\\\n1970&14.714&1.063&-0.091&-0.719\\\\\n1960&14.583&1.071&-0.172&-0.606\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A New Perspective on Impartial and Unbiased Apportionment", "authors": ["Ross Hyman", "Nicolaus Tideman"], "url": "https://arxiv.org/abs/2201.11787v2", "attribution": "\"A New Perspective on Impartial and Unbiased Apportionment\" by Ross Hyman and Nicolaus Tideman, arXiv:2201.11787v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.20039v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n\\hline \n & \\multicolumn{4}{c|}{{\\small{}${\\nu_{1}}=0.1$, $\\nu_{2}=1$}} & \\multicolumn{4}{c|}{{\\small{}${\\nu_{1}}=0.01$, $\\nu_{2}=1$}}\\tabularnewline\n\\hline \n{\\small{}$W$} & {\\small{}$\\rVert\\textit{E}_{\\textbf{u}}\\rVert_{1}$} & {\\small{}$\\rVert\\textit{E}_{\\textit{p}}\\rVert_{0}$} & {\\small{}$\\rVert\\textit{E}_{\\textit{c}}\\rVert_{0}$} & \\textbf{\\small{}iters} & {\\small{}$\\rVert\\textit{E}_{\\textbf{u}}\\rVert_{1}$} & {\\small{}$\\rVert\\textit{E}_{\\textit{p}}\\rVert_{0}$} & {\\small{}$\\rVert\\textit{E}_{\\textit{c}}\\rVert_{0}$} & \\textbf{\\small{}iters}\\tabularnewline\n\\hline \n{\\small{}2} & {\\small{}4.36E-02} & {\\small{}5.73E-01} & {\\small{}1.57E-01} & {\\small{}32} & {\\small{}1.88E-01} & {\\small{}7.57E-01} & {\\small{}7.16E+00} & 9\\tabularnewline\n\\hline \n{\\small{}3} & {\\small{}7.84E-03} & {\\small{}1.55E-01} & {\\small{}2.15E-02} & {\\small{}93} & {\\small{}5.97E-03} & {\\small{}4.46E-01} & {\\small{}1.19E-01} & 139\\tabularnewline\n\\hline \n{\\small{}4} & {\\small{}5.39E-04} & {\\small{}2.07E-02} & {\\small{}1.49E-03} & {\\small{}250} & {\\small{}1.74E-03} & {\\small{}1.57E-01} & {\\small{}4.94E-02} & {\\small{}220}\\tabularnewline\n\\hline \n{\\small{}5} & {\\small{}3.80E-05} & {\\small{}1.11E-03} & {\\small{}1.38E-04} & {\\small{}444} & {\\small{}4.86E-04} & {\\small{}3.13E-03} & {\\small{}3.64E-03} & {\\small{}452}\\tabularnewline\n\\hline \n{\\small{}6} & {\\small{}2.92E-06} & {\\small{}2.87E-04} & {\\small{}9.60E-06} & {\\small{}888} & {\\small{}4.90E-05} & {\\small{}5.72E-04} & {\\small{}5.59E-04} & {\\small{}784}\\tabularnewline\n\\hline \n{\\small{}7} & {\\small{}1.47E-07} & {\\small{}1.83E-05} & {\\small{}5.12E-07} & {\\small{}1528} & {\\small{}6.77E-07} & {\\small{}8.26E-05} & {\\small{}8.72E-06} & {\\small{}1429}\\tabularnewline\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Errors against $W$ for $\\nu_{1}=0.1,0.01$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Higher-order spectral element method for the stationary Stokes interface problem in two dimensions", "authors": ["Kishore Kumar Naraparaju", "Shivangi Joshi", "Subhashree Mohapatra"], "url": "https://arxiv.org/abs/2502.20039v2", "attribution": "\"Higher-order spectral element method for the stationary Stokes interface problem in two dimensions\" by Kishore Kumar Naraparaju, Shivangi Joshi, and Subhashree Mohapatra, arXiv:2502.20039v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2312.02690v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{RMSE of Conventional and SMC due to variation in buoyancy}\n\\begin{tabular}{|c|cccc|}\n\\hline\n\\multirow{2}{*}{Controller} & \\multicolumn{4}{c|}{(B-W)/g (grams)} \\\\ \\cline{2-5} \n & 100 & 340 & 650 & 1000 \\\\ \\hline\nConventional & 0.513 & 0.505 & 0.495 & 0.483 \\\\ \\hline\nSMC & 1.613 & 1.614 & 1.612 & 1.613 \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Comprehensive Study on Modelling and Control of Autonomous Underwater Vehicle", "authors": ["Rajini Makam", "Pruthviraj Mane", "Suresh Sundaram", "P. B. Sujit"], "url": "https://arxiv.org/abs/2312.02690v3", "attribution": "\"A Comprehensive Study on Modelling and Control of Autonomous Underwater Vehicle\" by Rajini Makam, Pruthviraj Mane, Suresh Sundaram, and P. B. Sujit, arXiv:2312.02690v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01322v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{FEET Table describing the AUROC Scores on all the tasks for various use cases of FM}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccccccccccc}\n\\toprule\n\\textbf{AUROC/Task} & \\textbf{Models} & \\textbf{Frozen} & \\textbf{2 shot} & \\textbf{4 shot} & \\textbf{8 shot} & \\textbf{16 shot} & \\textbf{32 shot} & \\textbf{64 shot} & \\textbf{128 shot} & \\textbf{256 shot} & \\textbf{512 shot} & \\textbf{1024 shot} & \\textbf{Fine-tuned} \\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Clindamycin}} & BioClinicalBERT & \\textbf{74.99 (3.86)} & 53.88 (2.89) & 54.69 (2.99) & \\textbf{56.73 (2.97)} & 53.87 (2.88) & \\textbf{56.67 (2.79)} & 56.84 (2.96) & 56.88 (3.01) & 57.12 (3.13) & 58.72 (2.88) & 59.55 (3.00) & 67.59 (2.65) \\\\\n&MedBERT & 74.22 (3.74) & \\textbf{54.28 (3.67)} & \\textbf{55.49 (2.85)} & 55.39 (2.47) & \\textbf{54.35 (3.63)} & 56.24 (2.59) & \\textbf{57.27 (3.49)} & \\textbf{57.56 (2.97)} & \\textbf{58.32 (2.78)} & \\textbf{59.28 (3.23)} & \\textbf{60.86 (2.97)} & \\textbf{69.35 (2.99)} \\\\\n&SciBERT & 73.98 (3.21) & 54.18 (3.05) & 51.60 (2.86) & 52.77 (2.94) & 50.11 (2.67) & 52.18 (3.26) & 54.10 (3.20) & 56.41 (2.88) & 56.92 (3.17) & 56.60 (3.11) & 58.88 (3.21) & 68.31 (2.88) \\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Erythromycin}} & BioClinicalBERT & 75.38 (3.52) & 57.91 (3.15) & \\textbf{58.71 (3.25)} & \\textbf{61.15 (3.20)} & \\textbf{60.59 (2.54)} & \\textbf{58.23 (3.01)} & \\textbf{60.70 (3.26)} & \\textbf{60.99 (2.73)} & 60.03 (3.98) & 60.56 (3.06) & 61.78 (2.77) & 70.12 (3.20) \\\\\n&MedBERT & 75.38 (3.45) & \\textbf{59.25 (3.76)} & 56.47 (3.12) & 59.31 (2.99) & 59.53 (2.57) & 55.79 (2.53) & 55.80 (3.22) & 60.02 (3.69) & \\textbf{61.51 (3.87)} & \\textbf{62.57} (3.24) & \\textbf{63.57 (3.12)} & \\textbf{71.65 (3.01)} \\\\\n&SciBERT & 74.27 (3.14) & 54.54 (3.92) & 52.59 (3.20) & 56.57 (2.72) & 53.84 (2.50) & 55.97 (3.33) & 57.13 (3.75) & 59.19 (3.57) & 59.69 (2.91) & 59.91 (3.15) & 59.21 (2.85) & 69.00 (2.99) \\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Gentamicin}} & BioClinicalBERT & 68.35 (3.86) & 52.77 (2.89) & 50.79 (3.02) & \\textbf{52.89 (2.97)} & \\textbf{52.09 (2.88)} & 47.04 (2.79) & \\textbf{50.70 (3.12)} & 49.48 (3.01) & 48.51 (3.23) & 52.61 (2.88) & 59.56 (3.00) & 63.95 (3.24) \\\\\n&MedBERT & \\textbf{73.08 (3.99)} &\\textbf{ 58.65 (2.67)} & \\textbf{59.91 (2.45)} & 39.83 (2.47) & 41.33 (2.63) & \\textbf{53.01 (2.59)} & 37.36 (2.49) & 39.85 (2.47) & \\textbf{49.23 (2.78)} & \\textbf{55.57 (2.43)} & \\textbf{59.98 (2.57)} &\\textbf{ 67.05 (2.99)} \\\\\n&SciBERT & 70.41 (2.95) & 41.96 (3.92) & 48.37 (3.20) & 44.67 (2.72) & 45.18 (2.50) & 47.78 (3.33) & 45.13 (3.75) & \\textbf{49.61 (3.57)} & 48.85 (2.91) & 41.63 (3.15) & 47.89 (2.85) & 54.66 (2.95) \\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Levofloxacin}} & BioClinicalBERT & 80.05 (3.23) & 59.24 (2.29) & 59.36 (3.83) & \\textbf{60.90 (2.37)} & \\textbf{57.48 (2.58) }& \\textbf{55.33 (2.77)} & 58.81 (3.27) & 63.50 (3.11) & 65.27 (3.20) & 66.46 (2.78) & 67.26 (3.33) & 69.20 (3.20) \\\\\n&MedBERT & 77.94 (3.79) & \\textbf{60.06 (2.37)} & \\textbf{60.46 (2.44)} & 55.69 (2.41) & 51.99 (2.62) & 54.89 (2.55) &\\textbf{ 60.52 (2.46)} & \\textbf{64.75 (2.27)} & \\textbf{66.05 (2.72}) & \\textbf{66.87 (2.41)} & 67.92 \\textbf{(2.51)} & \\textbf{69.50 (2.17)} \\\\\n&SciBERT & \\textbf{80.30 (2.93)} & 54.06 (3.91) & 52.19 (3.24) & 52.62 (2.72) & 54.26 (2.52) & 43.30 (3.13) & 58.50 (3.15) & 63.50 (3.37) & 64.31 (2.81) & 65.06 (3.85) & 66.45 (2.88) & 68.17 (2.92) \\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Oxacillin}} & BioClinicalBERT & 77.85 (3.16) & 36.12 (2.49) & 35.37 (3.22) & 49.45 (2.27) & \\textbf{62.43 (2.58)} & \\textbf{58.86 (2.59)} & \\textbf{63.86 (3.32)} & 62.77 (3.31) & \\textbf{64.91 (3.03)} & \\textbf{64.82 (2.78)} & \\textbf{65.84 (3.40)} & 66.91 (3.34) \\\\\n&MedBERT & 77.09 (3.39) & 37.98 (2.37) & \\textbf{40.85 (2.25)} & \\textbf{54.61 (2.87)} & 56.78 (2.11) & 54.78 (2.52) & 59.31 (2.49) & \\textbf{63.32 (2.49)} & 63.34 (2.77) & 63.78 (2.41) & 64.61 (2.28) & 65.70 (2.50) \\\\\n&SciBERT & \\textbf{78.47 (2.23)} & \\textbf{41.65 (3.33)} & 38.51 (3.27) & 50.27 (2.44) & 53.35 (2.52) & 49.04 (3.29) & 59.96 (3.25) & 62.78 (3.77) & 62.97 (2.71) & 64.50 (3.45) & 65.26 (2.44) & \\textbf{67.11 (2.44)} \\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Tetracycline}} & BioClinicalBERT & 67.28 (3.32) & 52.98 (2.49) & 51.15 (3.35) & 48.19 (2.89) & 47.64 (2.81) & 51.11 (2.67) & 50.34 (3.71) & 47.50 (3.11) & 51.17 (3.44) & \\textbf{53.35 (2.84)} & 52.44 (3.11) & 54.69 (3.77) \\\\\n&MedBERT & 64.58 (3.98) & \\textbf{54.84 (2.44)} & \\textbf{51.99 (2.42)} & 44.98 (2.46) & \\textbf{49.86 (2.43)} & 45.06 (2.79) & 45.98 (2.77) & 47.02 (2.97) & 50.56 (2.70) & 52.48 (2.63) & \\textbf{56.63 (2.99)} & \\textbf{57.51 (2.99)} \\\\\n&SciBERT & \\textbf{67.55 (2.55)} & 51.21 (3.95) & 50.76 (3.33) & \\textbf{49.29 (2.22)} & 47.25 (2.58) & \\textbf{52.09 (3.33)} & \\textbf{54.36 (3.75)} & \\textbf{54.21 (3.41)} &\\textbf{ 54.80 (2.67)} & 53.05 (3.51) & 54.54 (2.33) & 55.55 (2.15) \\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Trimethoprim/sulfa}} & BioClinicalBERT & 70.60 (3.16) & 56.02 (2.81) & \\textbf{55.28 (3.44)} & \\textbf{49.95 (2.45)} & 47.52 (2.48) & 50.24 (2.99) & \\textbf{49.11 (3.32)} & 47.67 (3.71) & 53.05 (3.27) & 56.65 (2.87) & \\textbf{60.17 (3.01)} & \\textbf{62.06 (3.11)} \\\\\n&MedBERT & 72.28 (3.19) & \\textbf{56.22 (2.77)} & 55.11 (2.49) & 46.33 (2.67) & 46.56 (2.66) & \\textbf{54.65 (2.29)} & 48.79 (2.45) & \\textbf{52.17 (2.37)} & 54.39 (2.73) & 57.21 (2.13) & 59.80 (2.77) & 61.78 (2.86) \\\\\n&SciBERT & \\textbf{75.08 (2.93)} & 47.45 (3.54) & 48.48 (3.56) & 49.30 (2.98) & \\textbf{48.11 (2.51)} & 51.52 (3.49) & 42.59 (3.50) & 47.73 (3.33) & \\textbf{55.35 (2.78)} & \\textbf{58.20 (3.16)} & 58.83 (2.56) & 60.41 (2.11) \\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Vancomycin}} & BioClinicalBERT & 77.14 (3.81) & 41.02 (2.29) & 40.07 (3.22) & \\textbf{53.45 (2.93)} & \\textbf{60.07 (2.84)} & 47.41 (2.33) & 49.01 (3.68) & \\textbf{62.64 (3.56)} & \\textbf{63.13 (3.39)} & 63.43 (2.85) & 63.84 (3.55) & 65.77 (3.00) \\\\\n&MedBERT & 75.93 (3.44) & 37.67 (2.55) & 37.74 (2.75) & 44.60 (2.65) & 45.10 (2.62) & 43.87 (2.59) & 45.90 (2.61) & 57.79 (2.77) & 61.21 (2.79) & 63.78 (2.61) & \\textbf{64.58 (2.58)} & 66.10 (2.88) \\\\\n&SciBERT & \\textbf{78.28 (2.91)} & \\textbf{47.70 (3.97)} & \\textbf{42.41 (3.22)} & 51.46 (2.72) & 55.02 (2.50) & \\textbf{52.39 (3.13)} & \\textbf{55.85 (3.74)} & 57.94 (3.97) & 61.16 (2.21) & \\textbf{64.41 (3.17)} & 64.18 (2.44) & \\textbf{66.52 (2.77)} \\\\\n\\bottomrule\n\\toprule\n\\textbf{AUPRC/Task} & & & & & & & & & & & & & \\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Clindamycin}} \n&BioClinicalBERT & 78.75 (4.70) & 49.12 (4.85) & 47.48 (4.67) & 50.33 (4.82) & 53.42 (4.73) & 55.26 (4.85) & 57.64 (4.89) & 60.38 (4.77) & 61.72 (4.91) & 63.85 (4.81) & 65.94 (4.74) & 70.01 (4.90)\\\\\n&MedBERT & 77.34 (4.54) & 48.45 (4.70) & 46.87 (4.62) & 49.71 (4.80) & 52.19 (4.67) & 54.05 (4.75) & 56.34 (4.85) & 59.22 (4.77) & 61.38 (4.89) & 63.19 (4.79) & 65.44 (4.70) & 69.45 (4.83)\\\\\n&SciBERT & 78.02 (4.82) & 49.34 (4.78) & 47.65 (4.69) & 50.52 (4.86) & 53.14 (4.75) & 55.00 (4.84) & 57.45 (4.89) & 60.12 (4.79) & 61.95 (4.88) & 63.67 (4.82) & 66.02 (4.73) & 70.25 (4.91)\\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Erythromycin}} & BioClinicalBERT & 69.35 (5.92) & 47.54 (5.13) & 47.71 (4.83) & 49.39 (5.26) & 49.27 (5.41) & 49.23 (4.92) & 48.93 (5.09) & 48.93 (5.87) & 47.13 (5.29) & 47.32 (4.98) & 47.20 (5.75) & 54.08 (5.03)\n \\\\\n&MedBERT & 69.15 (5.96) & 44.70 (5.91) & 43.81 (6.12) & 47.68 (5.87) & 47.01 (6.05) & 46.10 (5.82) & 44.74 (6.18) & 46.04 (5.97) & 47.06 (6.11) & 47.37 (5.89) & 47.21 (5.67) & 55.01 (5.88)\\\\\n&SciBERT & 68.49 (5.69) & 44.62 (5.19)& 44.94 (5.59) & 45.25 (5.76)& 44.76 (5.33) & 45.48 (5.43) & 47.55 (5.55) & 45.63 (5.19) & 45.42 (5.69) & 46.61 (5.89) & 46.14 (5.50) & 54.89 (5.77)\\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Gentamicin}} & BioClinicalBERT & 97.02 (1.72) & 95.61 (1.83) & 95.59 (2.08) & 95.22 (1.74) & 95.09 (1.85) & 95.27 (1.94) & 95.37 (2.11) & 95.38 (1.78) & 94.89 (1.99) & 95.05 (1.88) & 95.36 (1.98) & 96.83 (1.92) \\\\\n&MedBERT & 97.33 (1.73) & 96.27 (1.92) & 96.50 (2.05) & 94.38 (1.89) & 95.05 (2.01) & 95.68 (1.95) & 95.68 (1.87) & 92.19 (1.98) & 93.79 (2.03) & 95.05 (1.84) & 94.73 (2.07) & 96.02 (1.90) \\\\\n&SciBERT & 96.69 (2.18) & 94.70 (1.98) & 95.53 (1.95)& 95.22 (2.03) & 95.62 (1.77) & 95.62(1.66) & 95.57 (1.70) & 95.80 (2.11) & 94.45 (2.08)& 94.54 (2.33) & 95.16 (1.67) & 96.02 (2.15) \\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Levofloxacin}} & BioClinicalBERT & 84.41 (4.12) & 69.57 (4.10) & 68.04 (4.02) & 68.31 (3.89) & 66.22 (4.25) & 64.87 (3.83) & 68.11 (4.30) & 71.04 (4.19) & 71.67 (3.95) & 72.27 (4.21) & 73.79 (4.08) & 76.88 (4.31)\\\\\n&MedBERT & 82.22 (3.85) & 70.57 (4.10) & 68.31 (3.77) & 65.86 (3.91) & 63.81 (4.12) & 64.61 (3.98) & 70.21 (4.06) & 73.39 (3.80) & 72.78 (3.93) & 74.36 (4.19) & 74.34 (4.11)& 79.00 (3.87) \\\\\n&SciBERT & 84.58 (3.97) & 64.24 (3.49) & 64.01 (3.99)& 63.57 (3.88) & 64.19 (3.69) & 56.88 (3.89) & 66.70 (4.02) & 69.57 (4.17)& 70.01 (4.02)& 71.32 (3.82) & 72.47 (4.33)& 77.89 (3.66)\\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Oxacillin}} & BioClinicalBERT & 80.53 (4.63) & 49.28 (4.78) & 47.52 (4.56) & 57.65 (4.94) & 66.16 (4.65) & 64.80 (4.97) & 67.32 (4.72) & 67.62 (4.88) & 68.52 (4.69) & 69.46 (4.90) & 68.75 (4.53) & 74.49 (4.87) \\\\\n&MedBERT & 79.72 (4.98) & 49.95 (4.81) & 54.56 (4.73) & 63.34 (5.02) & 64.83 (4.95) & 61.81 (4.87) & 65.34 (5.13) & 66.36 (4.75) & 68.88 (5.08) & 69.28 (4.93) & 68.76 (4.79) & 74.02 (4.11)\\\\\n&SciBERT & 80.33 (4.69) & 52.03 (4.77) & 50.40 (4.43) & 58.44 (4.69) & 61.47 (4.54) & 58.45 (4.91) & 65.03 (4.80) & 66.74 (4.64) & 68.39 (4.44) & 68.88 (4.38) & 68.62 (4.55) & 74.55 (4.20) \\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Tetracycline}} & BioClinicalBERT & 86.28 (4.03) & 80.36 (4.45) & 81.96 (3.72) & 80.00 (4.38) & 78.80 (4.01) & 82.53 (3.75) & 81.46 (4.22) & 80.85 (3.98) & 81.75 (4.12) & 81.68 (3.94) & 81.08 (4.27) & 84.97 (3.88) \\\\\n&MedBERT & 84.79 (4.32) & 80.84 (4.13) & 81.59 (4.1) & 79.07 (3.84) & 81.54 (4.11) & 79.83 (3.96) & 78.81 (3.91) & 79.39 (4.19) & 81.14 (3.9) & 81.82 (3.95) & 80.94 (3.88) & 81.13 (4.09)\\\\\n&SciBERT & 87.64 (3.34) & 81.18 (3.77) & 80.83 (4.14) & 79.86 (3.99) & 78.62 (3.73) & 81.77 (3.86) & 82.73 (3.88) & 82.96 (3.23) & 82.36 (4.00) & 81.97 (3.67) & 82.31 (3.76) & 84.99 (3.77) \\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Trimethoprim/sulfa}} & BioClinicalBERT & 87.76 (3.59) & 84.34 (3.14) & 84.31 (2.78) & 81.66 (3.23) & 80.91 (2.95) & 82.45 (3.10) & 81.48 (2.87) & 81.47 (3.35) & 80.47 (2.72) & 84.54 (2.63) & 84.77 (3.21) & 87.79 (2.84)\n \\\\\n&MedBERT & 89.83 (3.12) & 83.14 (2.87) & 82.77 (2.99) & 78.61 (3.01) & 80.02 (3.18) & 83.66 (3.03) & 79.32 (2.92) & 81.19 (3.17) & 83.30 (2.85) & 83.47 (3.14) & 84.85 (2.97) & 86.77 (3.08)\\\\\n&SciBERT & 90.04 (3.29) & 78.60 (2.77) & 79.19 (3.00) & 78.93 (3.13) & 78.92 (2.88) & 79.74 (2.75) & 77.33 (3.10) & 80.75 (3.52) & 84.26 (3.44) & 83.85 (2.39) & 84.34 (3.04) & 87.76 (3.14) \\\\\n\\midrule\n\\multirow{3}{*}{\\textbf{Vancomycin}} & BioClinicalBERT & 78.81 (4.31) & 46.29 (4.02) & 45.78 (3.95) & 53.70 (4.08) & 61.58 (3.97) & 50.31 (4.10) & 49.99 (3.92) & 64.24 (4.07) & 63.25 (3.99) & 63.30 (4.01) & 63.43 (3.96) & 72.32 (4.05) \\\\\n&MedBERT & 79.10 (4.05) & 43.88 (4.12) & 43.43 (3.95) & 47.30 (4.01) & 45.78 (3.89) & 46.39 (4.17) & 48.01 (3.85) & 56.62 (4.19) & 60.41 (3.96) & 63.71 (4.03) & 63.08 (3.92) & 70.99 (4.09) \\\\\n&SciBERT & 80.15 (4.03) & 50.09 (3.97) & 46.34 (4.21) & 52.04 (3.76) & 53.26 (4.12) & 51.80 (3.89) & 52.99 (4.07) & 56.00 (3.98) & 61.15 (4.01) & 66.48 (4.28) & 64.69 (3.63) & 71.62 (3.85) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "FEET: A Framework for Evaluating Embedding Techniques", "authors": ["Simon A. Lee", "John Lee", "Jeffrey N. Chiang"], "url": "https://arxiv.org/abs/2411.01322v1", "attribution": "\"FEET: A Framework for Evaluating Embedding Techniques\" by Simon A. Lee, John Lee, and Jeffrey N. Chiang, arXiv:2411.01322v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01697v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|cc}\n\\toprule\nTime (seconds) & 1970 model & 2005 model \\\\\n\\midrule\n\\texttt{checkConsistency}, $n = 100$ & $2.511 \\pm 0.035$ & $7.367 \\pm 0.136$ \\\\\n\\texttt{checkConsistency}, $n = 1000$ & $25.115 \\pm 0.152$ & $73.584 \\pm 0.489$ \\\\\n\\bottomrule\nDiagnostic & $0.009 \\pm 0.007$ & $0.012 \\pm 0.0003$\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Table showing median computation times (along with median absolute deviations) of each method, applied to each model.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A probabilistic diagnostic for Laplace approximations: Introduction and experimentation", "authors": ["Shaun McDonald", "David Campbell"], "url": "https://arxiv.org/abs/2411.01697v1", "attribution": "\"A probabilistic diagnostic for Laplace approximations: Introduction and experimentation\" by Shaun McDonald and David Campbell, arXiv:2411.01697v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.10585v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\\\Sample size}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n & Black & Non-Black & Total \\\\\n \\midrule\n \\multicolumn{4}{c}{A. FairFace} \\\\\n No.\\ of players & 335 & 170 & 505 \\\\\n \\quad Mean L* value & 45.00 & 64.06 & 51.42 \\\\\n \\quad \\quad (SD) & (10.39) & (7.14) & (13.03) \\\\\n \\quad \\quad Min–max & 20.52–71.26 & 41.85–79.48 & 20.52–79.48 \\\\\n \\addlinespace\n No.\\ of referees & 23 & 46 & 69 \\\\\n \\quad Mean L* value & 42.49 & 59.77 & 54.01 \\\\\n \\quad \\quad (SD) & (10.62) & (9.09) & (12.59) \\\\\n \\quad \\quad Min–max & 20.56–60.75 & 41.77–76.32 & 20.56–76.32 \\\\\n \\addlinespace\n No.\\ of games & 2,359 & 2,356 & 2,359 \\\\\n No.\\ of player–games & 33,606 & 12,272 & 45,878 \\\\\n No.\\ of player–minutes & 707,374 & 245,560 & 952,934 \\\\\n \\multicolumn{4}{c}{B. Human raters} \\\\\n No.\\ of players & 335 & 170 & 505 \\\\\n \\quad Mean L* value & 45.04 & 64.00 & 51.42 \\\\\n \\quad \\quad (SD) & (10.43) & (7.21) & (13.03) \\\\\n \\quad \\quad Min–max & 20.52–71.26 & 41.85–79.48 & 20.52–79.48 \\\\\n \\addlinespace\n No.\\ of referees & 27 & 42 & 69 \\\\\n \\quad Mean L* value & 43.94 & 60.48 & 54.01 \\\\\n \\quad \\quad (SD) & (10.83) & (8.89) & (12.59) \\\\\n \\quad \\quad Min–max & 20.56–63.32 & 43.21–76.32 & 20.56–76.32 \\\\\n \\addlinespace\n No.\\ of games & 2,359 & 2,354 & 2,359 \\\\\n No.\\ of player–games & 33,527 & 12,351 & 45,878 \\\\\n No.\\ of player–minutes & 704,010 & 248,924 & 952,934 \\\\\n \\multicolumn{4}{c}{C. Referee crew compositions} \\\\\n No.\\ of unique crews & & & 1,876 \\\\\n \\quad Mean abs.\\ L* diff. & & & 8.39 \\\\\n \\quad \\quad (SD) & & & (4.19) \\\\\n \\quad \\quad Min–max & & & 0.414–33.46 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Racial bias, colorism, and overcorrection", "authors": ["Kenneth Colombe", "Alex Krumer", "Rosa Lavelle-Hill", "Tim Pawlowski"], "url": "https://arxiv.org/abs/2508.10585v1", "attribution": "\"Racial bias, colorism, and overcorrection\" by Kenneth Colombe, Alex Krumer, Rosa Lavelle-Hill, and Tim Pawlowski, arXiv:2508.10585v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.16015v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ccccc|ccccc}\n\\hline\n$x$ & $\\alpha$ & $\\mu$ & $\\delta$ & $\\alpha\\omega$ & $N$ & $N_{acc}$ & error & $N^*$ & error\\\\\n\\hline\n\t1 & 50 & 1/5 & 1/3 & 43.33 & 324 & 69 & $2.9\\cdot 10^{-13}$ & 73 & $1.1\\cdot 10^{-17}$\\\\\n 2 & 5 & 1/5 & 1/10 & 9.01 & 10,242 & 25 & $4.6\\cdot 10^{-21}$ & 22 & $1.1 \\cdot 10^{-16}$\\\\\n\t1 & 1/10 & 1/5 & 1/100 & 0.08 & 179,715 & 5 & $1.3 \\cdot 10^{-21}$ & 4 & $2.4 \\cdot 10^{-17}$\\\\\n\t5 & 1 & 1/5 & 1/100 & 4.8 & 5,645,686 & 18 & $1.1 \\cdot 10^{-22}$ & 15 & $1.5 \\cdot 10^{-17}$\\\\\n\t20 & 1/100 & 1/5 & 1/100 & 0.198 & 84,914,922 & 6 & $1.1 \\cdot 10^{-22}$ & 5 & $4.5 \\cdot 10^{-19}$\\\\\n\t\\hline\n\t\\end{tabular}\n\\caption{Absolute error and bound estimating $N$ using for the series expansion with machine-precision absolute error.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On the computation of the cumulative distribution function of the Normal Inverse Gaussian distribution", "authors": ["Guillermo Navas-Palencia"], "url": "https://arxiv.org/abs/2502.16015v1", "attribution": "\"On the computation of the cumulative distribution function of the Normal Inverse Gaussian distribution\" by Guillermo Navas-Palencia, arXiv:2502.16015v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.07248v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the sentiment polarity mark-ups derived from two methods, rule-based and trained BERT, before and after the keyword update. The number of divergences represents the number of announcements labeled differently by these methods. Whereas, for example, the number of coinciding positives reflects the number of announcements that are positive from the perspective of both methods.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccc}\n\\toprule\n & \\# divergences & \\# coinciding positives & \\# coinciding negatives & \\# coinciding neutrals \\\\\n\\midrule\nWith the initial keywords & 207 & 1447 & 445 & 3337 \\\\\nWith the updated keywords & 66 & 1562 & 765 & 3043 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "New drugs and stock market: how to predict pharma market reaction to clinical trial announcements", "authors": ["Semen Budennyy", "Alexey Kazakov", "Elizaveta Kovtun", "Leonid Zhukov"], "url": "https://arxiv.org/abs/2208.07248v2", "attribution": "\"New drugs and stock market: how to predict pharma market reaction to clinical trial announcements\" by Semen Budennyy, Alexey Kazakov, Elizaveta Kovtun, and Leonid Zhukov, arXiv:2208.07248v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16534v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Tables and figures for each task.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n\\textbf{Task} & \\textbf{Feature} & \\textbf{Figure} & \\textbf{Table} \\\\ \n\\midrule\n\\multirow{2}{*}{medium-sized classification} & numerical & & \\\\\n& heterogeneous & & \\\\\n\\midrule\n\\multirow{2}{*}{medium-sized regression} & numerical & & \\\\\n& heterogeneous & & \\\\\n\\midrule\n\\multirow{2}{*}{large-sized classification} & numerical & & \\\\\n& heterogeneous & & \\\\\n\\midrule\n\\multirow{2}{*}{large-sized regression} & numerical & & \\\\\n& heterogeneous & & \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DOFEN: Deep Oblivious Forest ENsemble", "authors": ["Kuan-Yu Chen", "Ping-Han Chiang", "Hsin-Rung Chou", "Chih-Sheng Chen", "Tien-Hao Chang"], "url": "https://arxiv.org/abs/2412.16534v2", "attribution": "\"DOFEN: Deep Oblivious Forest ENsemble\" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.08173v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study on the Tangaroa dataset. PSNR ($\\uparrow$), LPIPS ($\\downarrow$), and CD ($\\downarrow$) are reported.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\t\t\\toprule\n\t\t\\textbf{Model Variant} & \\textbf{PSNR} $\\uparrow$ & \\textbf{LPIPS} $\\downarrow$ & \\textbf{CD} $\\downarrow$ \\\\\n\t\t\\midrule\n\t\tFull CD-TVD & 45.1457 & 0.0425 & 1.3609 \\\\\n\t\tw/o Contrastive Modeling & 42.5058 & 0.0889 & 2.2052 \\\\\n\t\tw/o Local Attention & 42.9507 & 0.0901 & 2.1871 \\\\\n\t\tw/o Pre-training & 41.9359 & 0.0991 & 2.5453 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "CD-TVD: Contrastive Diffusion for 3D Super-Resolution with Scarce High-Resolution Time-Varying Data", "authors": ["Chongke Bi", "Xin Gao", "Jiangkang Deng", "Guan Li", "Jun Han"], "url": "https://arxiv.org/abs/2508.08173v2", "attribution": "\"CD-TVD: Contrastive Diffusion for 3D Super-Resolution with Scarce High-Resolution Time-Varying Data\" by Chongke Bi, Xin Gao, Jiangkang Deng, Guan Li, and Jun Han, arXiv:2508.08173v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.19013v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Observer locations considered in the numerical accuracy studies.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\toprule\nindex & $x_1$ & $x_2$ & $x_3$ & type \\\\ \\hline\n1 & $0$ & $\\frac{l}{2}$ & $\\frac{l}{2}$ & source \\\\ \n2 & $\\frac{l}{2}$ & $\\frac{l}{2}$ & $\\frac{l}{2}$ & center \\\\ \n3 & $l$ & $\\frac{l}{2}$ & $\\frac{l}{2}$ & face \\\\ \n4,5,6,7 & $\\frac{l}{2}$ & $\\frac{l}{2} \\pm \\frac{l}{2}$ & $\\frac{l}{2} \\pm \\frac{l}{2}$ & edge \\\\ \n8,9,19,11 & $l$ & $\\frac{l}{2} \\pm \\frac{l}{2}$ & $\\frac{l}{2} \\pm \\frac{l}{2}$ & corner \\\\ \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "On the efficiency of explicit and semi-explicit immersed boundary finite element methods for wave propagation problems", "authors": ["Tim Bürchner", "Lars Radtke", "Philipp Kopp", "Stefan Kollmannsberger", "Ernst Rank", "Alexander Düster"], "url": "https://arxiv.org/abs/2501.19013v1", "attribution": "\"On the efficiency of explicit and semi-explicit immersed boundary finite element methods for wave propagation problems\" by Tim Bürchner, Lars Radtke, Philipp Kopp, Stefan Kollmannsberger, Ernst Rank, and Alexander Düster, arXiv:2501.19013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.20279v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Character Table for $\\mathrm{SL}_{2}(q)$ with $q$ even}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\nClass & $1$ & $c$ & $a^t$ & $b^m$\\\\\nSize & $1$ & $q^2-1$ & $q(q+1)$ & $q(q-1)$\\\\\n\\hline\n Tr & $1$ & $1$ & $1$ & $1$\\\\\n$\\psi$ & $q$ & $0$ & $1$ & $-1$\\\\\n$\\chi_s$ & $q+1$ & $1$ & $\\rho^{st}+\\rho^{-st}$ & $0$\\\\\n$\\theta_j$ & $q-1$ & $-1$ & $0$ & $-(\\sigma^{jm}+\\sigma^{-jm})$\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Strong Gelfand Pairs of the Symplectic Group $\\boldsymbol{\\rm Sp}_4(q)$ where $q$ is even", "authors": ["Stephen P. Humphries", "Joseph E. Marrow"], "url": "https://arxiv.org/abs/2504.20279v2", "attribution": "\"Strong Gelfand Pairs of the Symplectic Group $\\boldsymbol{\\rm Sp}_4(q)$ where $q$ is even\" by Stephen P. Humphries and Joseph E. Marrow, arXiv:2504.20279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1910.10071v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MHE configurations. Values of $s$ with prefix $a$ use angular distance. Each model can be used with each $s$ .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline\n Parameter & Values \\\\ \n \\hline\n model & [MHE, half\\_MHE] \\\\ \n \\hline\n $s$ value & [0, 1, 2, a0 ,a1, a2] \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical Energy", "authors": ["Joaquin Perez-Lapillo", "Oleksandr Galkin", "Tillman Weyde"], "url": "https://arxiv.org/abs/1910.10071v1", "attribution": "\"Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical Energy\" by Joaquin Perez-Lapillo, Oleksandr Galkin, and Tillman Weyde, arXiv:1910.10071v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.05898v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\footnotesize MAEs of the GMM and QML estimators across different parameter values and sample sizes.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllrrrrrrrrr}\n \\hline\\hline\n & & & & $n=25$ & & & $n=49$ & & & $n=81$ & \\\\ \nParameter & Model & Method & $T=5$ & $T=10$ & $T=20$ & $T=5$ & $T=10$ & $T=20$ & $T=5$ & $T=10$ & $T=20$ \\\\ \n \\hline\n & & GMM & 0.405 & 0.233 & 0.160 & 0.324 & 0.190 & 0.119 & 0.259 & 0.151 & 0.092 \\\\ \n & M1 & QML (transformed) & 0.193 & 0.145 & 0.128 & 0.120 & 0.088 & 0.071 & 0.087 & 0.059 & 0.046 \\\\ \n & & QML (direct) & 0.136 & 0.080 & 0.053 & 0.104 & 0.063 & 0.041 & 0.081 & 0.047 & 0.033 \\\\ \n \\cline{3-12}\n & & GMM & 0.389 & 0.224 & 0.150 & 0.298 & 0.179 & 0.110 & 0.242 & 0.140 & 0.086 \\\\ \n $\\rho$ & M2 & QML (transformed) & 0.210 & 0.152 & 0.130 & 0.128 & 0.091 & 0.071 & 0.096 & 0.062 & 0.046 \\\\ \n & & QML (direct) & 0.144 & 0.078 & 0.050 & 0.104 & 0.060 & 0.039 & 0.085 & 0.046 & 0.031 \\\\ \n \\cline{3-12}\n & & GMM & 0.291 & 0.169 & 0.109 & 0.189 & 0.110 & 0.068 & 0.136 & 0.081 & 0.050 \\\\ \n & M3 & QML (transformed) & 0.157 & 0.129 & 0.122 & 0.072 & 0.062 & 0.054 & 0.046 & 0.037 & 0.033 \\\\ \n & & QML (direct) & 0.162 & 0.128 & 0.121 & 0.076 & 0.062 & 0.054 & 0.050 & 0.038 & 0.033 \\\\\n \\hline\n & & GMM & 0.192 & 0.079 & 0.044 & 0.126 & 0.061 & 0.031 & 0.099 & 0.044 & 0.027 \\\\ \n & M1 & QML (transformed) & 0.404 & 0.165 & 0.075 & 0.394 & 0.160 & 0.072 & 0.389 & 0.157 & 0.071 \\\\ \n & & QML (direct) & 0.354 & 0.153 & 0.071 & 0.345 & 0.150 & 0.069 & 0.341 & 0.146 & 0.068 \\\\\n \\cline{3-12} \n & & GMM & 0.161 & 0.072 & 0.043 & 0.105 & 0.056 & 0.029 & 0.083 & 0.040 & 0.025 \\\\ \n $\\gamma$ & M2 & QML (transformed) & 0.319 & 0.129 & 0.059 & 0.315 & 0.127 & 0.057 & 0.312 & 0.126 & 0.058 \\\\ \n & & QML (direct) & 0.290 & 0.127 & 0.061 & 0.284 & 0.123 & 0.058 & 0.281 & 0.121 & 0.058 \\\\ \n \\cline{3-12}\n & & GMM & 0.136 & 0.068 & 0.042 & 0.087 & 0.051 & 0.028 & 0.069 & 0.038 & 0.024 \\\\ \n & M3 & QML (transformed) & 0.250 & 0.109 & 0.057 & 0.245 & 0.104 & 0.051 & 0.244 & 0.102 & 0.050 \\\\ \n & & QML (direct) & 0.234 & 0.106 & 0.056 & 0.230 & 0.101 & 0.050 & 0.228 & 0.098 & 0.049 \\\\\n \\hline\n & & GMM & 0.308 & 0.180 & 0.119 & 0.222 & 0.135 & 0.083 & 0.185 & 0.103 & 0.067 \\\\ \n & M1 & QML (transformed) & 0.208 & 0.134 & 0.096 & 0.163 & 0.096 & 0.064 & 0.138 & 0.077 & 0.049 \\\\ \n & & QML (direct) & 0.192 & 0.105 & 0.067 & 0.156 & 0.083 & 0.052 & 0.133 & 0.069 & 0.042 \\\\ \n \\cline{3-12}\n & & GMM & 0.298 & 0.167 & 0.113 & 0.218 & 0.133 & 0.080 & 0.191 & 0.100 & 0.065 \\\\ \n $\\delta$ & M2 & QML (transformed) & 0.177 & 0.118 & 0.085 & 0.122 & 0.081 & 0.056 & 0.095 & 0.059 & 0.042 \\\\ \n & & QML (direct) & 0.172 & 0.095 & 0.062 & 0.125 & 0.074 & 0.047 & 0.104 & 0.057 & 0.037 \\\\ \n \\cline{3-12}\n & & GMM & 0.231 & 0.109 & 0.068 & 0.136 & 0.074 & 0.045 & 0.102 & 0.057 & 0.034 \\\\ \n & M3 & QML (transformed) & 0.218 & 0.107 & 0.066 & 0.215 & 0.091 & 0.046 & 0.217 & 0.087 & 0.042 \\\\ \n & & QML (direct) & 0.213 & 0.106 & 0.066 & 0.198 & 0.089 & 0.046 & 0.196 & 0.083 & 0.042 \\\\\n \\hline\n & & GMM & 0.214 & 0.135 & 0.084 & 0.157 & 0.086 & 0.061 & 0.117 & 0.071 & 0.047 \\\\ \n & M1 & QML (transformed) & 0.195 & 0.130 & 0.086 & 0.164 & 0.092 & 0.064 & 0.149 & 0.081 & 0.050 \\\\ \n & & QML (direct) & 0.206 & 0.134 & 0.083 & 0.154 & 0.085 & 0.061 & 0.121 & 0.071 & 0.047 \\\\ \n \\cline{3-12}\n & & GMM & 0.214 & 0.134 & 0.084 & 0.155 & 0.086 & 0.061 & 0.117 & 0.071 & 0.047 \\\\ \n $\\beta_0$ & M2 & QML (transformed) & 0.192 & 0.131 & 0.086 & 0.159 & 0.091 & 0.063 & 0.144 & 0.080 & 0.050 \\\\ \n & & QML (direct) & 0.206 & 0.134 & 0.083 & 0.152 & 0.085 & 0.061 & 0.119 & 0.071 & 0.047 \\\\\n \\cline{3-12} \n & & GMM & 0.219 & 0.135 & 0.085 & 0.156 & 0.088 & 0.062 & 0.118 & 0.072 & 0.048 \\\\ \n & M3 & QML (transformed) & 0.190 & 0.131 & 0.085 & 0.155 & 0.090 & 0.063 & 0.139 & 0.078 & 0.049 \\\\ \n & & QML (direct) & 0.213 & 0.139 & 0.086 & 0.154 & 0.088 & 0.062 & 0.117 & 0.071 & 0.048 \\\\\n \\hline\n & & GMM & 0.228 & 0.133 & 0.089 & 0.164 & 0.091 & 0.059 & 0.126 & 0.069 & 0.047 \\\\ \n & M1 & QML (transformed) & 0.315 & 0.150 & 0.093 & 0.278 & 0.132 & 0.073 & 0.264 & 0.122 & 0.064 \\\\ \n & & QML (direct) & 0.234 & 0.129 & 0.087 & 0.175 & 0.092 & 0.058 & 0.148 & 0.071 & 0.048 \\\\ \n \\cline{3-12}\n & & GMM & 0.223 & 0.132 & 0.089 & 0.161 & 0.091 & 0.059 & 0.125 & 0.069 & 0.047 \\\\ \n $\\beta_1$ & M2 & QML (transformed) & 0.302 & 0.147 & 0.093 & 0.264 & 0.128 & 0.071 & 0.251 & 0.118 & 0.063 \\\\ \n & & QML (direct) & 0.222 & 0.129 & 0.087 & 0.163 & 0.090 & 0.058 & 0.134 & 0.069 & 0.047 \\\\ \n \\cline{3-12}\n & & GMM & 0.227 & 0.134 & 0.092 & 0.160 & 0.093 & 0.061 & 0.124 & 0.071 & 0.049 \\\\ \n & M3 & QML (transformed) & 0.281 & 0.139 & 0.090 & 0.248 & 0.120 & 0.067 & 0.235 & 0.112 & 0.060 \\\\ \n & & QML (direct) & 0.216 & 0.133 & 0.093 & 0.153 & 0.091 & 0.060 & 0.125 & 0.068 & 0.048 \\\\ \n \\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Dynamic Spatiotemporal ARCH Models: Small and Large Sample Results", "authors": ["Philipp Otto", "Osman Doğan", "Süleyman Taşpınar"], "url": "https://arxiv.org/abs/2312.05898v1", "attribution": "\"Dynamic Spatiotemporal ARCH Models: Small and Large Sample Results\" by Philipp Otto, Osman Doğan, and Süleyman Taşpınar, arXiv:2312.05898v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2303.02317v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c}\n $G_{i, j} = \n \\begin{cases}\n G^{red}_{i, j}, &\\hspace{-0.2cm}\\text{if }G^{red}_{i, j} \\ge G^{green}_{i, j}\\\\\n G^{green}_{i, j}, &\\hspace{-0.2cm}\\text{otherwise.}\n \\end{cases}$ where\\\\ \\\\\n $\n \\begin{aligned}\n & G^{red}_{i, j} = \\sum_{k \\in \\{0, 1, 2\\}}{ s_k G_{i+1,j + k} } \\text{~if $i \\in [0, T)$, and $0$ if $i = T$}\\\\\n & G^{green}_{i, j} = S \\cdot u^{j-i}-K\n \\end{aligned}$\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Fast American Option Pricing using Nonlinear Stencils", "authors": ["Zafar Ahmad", "Reilly Browne", "Rezaul Chowdhury", "Rathish Das", "Yushen Huang", "Yimin Zhu"], "url": "https://arxiv.org/abs/2303.02317v2", "attribution": "\"Fast American Option Pricing using Nonlinear Stencils\" by Zafar Ahmad, Reilly Browne, Rezaul Chowdhury, Rathish Das, Yushen Huang, and Yimin Zhu, arXiv:2303.02317v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.00905v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccccccc}\n\\midrule\n\\multicolumn{8}{c}{Number of iterations until stabilization $(T=1/12)$ }\\\\\\toprule\n& 1 & 2 & 3 & 4 &5 & 6 & $\\geq 7$\\\\\n\\midrule\n $j_n$ (\\scalebox{0.9}{$\\widehat C_n$}) &1.13\\% & 27.21\\% & 54.13\\% & 15.69\\% & 1.65\\% & 0.17\\% & 0.02\\% \\\\\n $j_n^*$ (\\scalebox{0.9}{$\\widehat C^*_n$}) & 1.06\\% & 27.15\\% & 55.78\\% & 14.45\\% & 1.45\\% & 0.10\\% & 0.01\\%\\\\\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ \\footnotesize Empirical distribution of $j_n$ and $j_n^*$ at the $T=1/12$ (1 month) time horizon. Reported values reflect the empirical percentages of the aggregated counts of iterations until stabilization across all computed values of each estimator (for $\\widehat C_n$, across both settings $(3)$ and $(4)$; for $\\widehat C_n^*$, across both (5ab) and (6ab)) and across Models 1-4. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Data-driven fixed-point tuning for truncated realized variations", "authors": ["B. Cooper Boniece", "José E. Figueroa-López", "Yuchen Han"], "url": "https://arxiv.org/abs/2311.00905v3", "attribution": "\"Data-driven fixed-point tuning for truncated realized variations\" by B. Cooper Boniece, José E. Figueroa-López, and Yuchen Han, arXiv:2311.00905v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.07902v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n ranks & matrix dim & time (ms) & speedup over fp16 \\\\\n \\hline\n 0 & 11008x4096 & 13.89 +- 0.23 & 1.97 \\\\\n \\hline\n 128 & 11008x4096 & 18.04 +- 0.16 & 1.52 \\\\\n \\hline\n 256 & 11008x4096 & 19.019 +- 0.21 & 1.45 \\\\\n \\hline\n \\textbf{512} & 11008x4096 & 21.284 +- 0.2 & \\textbf{1.29} \\\\\n \\hline\n 1024 & 11008x4096 & 25.87 +- 0.26 & 1.06 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Performance metrics for matrix dimension 11008x4096}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Low-Rank Correction for Quantized LLMs", "authors": ["Meyer Scetbon", "James Hensman"], "url": "https://arxiv.org/abs/2412.07902v1", "attribution": "\"Low-Rank Correction for Quantized LLMs\" by Meyer Scetbon and James Hensman, arXiv:2412.07902v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.18643v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Loading Vectors of First and Second Principle Components (Values with absolute loadings $\\geq$ 0.5 are bolded)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|}\n\\hline\n\\textbf{Variables} & \\textbf{PC1} & \\textbf{PC2} & \\textbf{PC3} & \\textbf{PC4} & \\textbf{PC5} \\\\ \\hline\nol & -0.066 & 0.348 & 0.275 & 0.333 & 0.427 \\\\ \\hline\nlr & \\textbf{0.677} & 0.232 & 0.157 & -0.265 & -0.073 \\\\ \\hline\nisb & \\textbf{0.65} & -0.268 & 0.294 & 0.125 & -0.072 \\\\ \\hline\nhs & -0.044 & \\textbf{-0.785} & 0.362 & 0.141 & 0.22 \\\\ \\hline\nihm & 0.457 & 0.237 & 0.447 & -0.392 & -0.148 \\\\ \\hline\nhhm & -0.036 & \\textbf{0.601} & -0.091 & 0.282 & -0.076 \\\\ \\hline\ncck & 0.215 & 0 & 0.485 & 0 & 0.229 \\\\ \\hline\ndmk & 0.167 & -0.049 & -0.355 & 0.222 & -0.26 \\\\ \\hline\nor & \\textbf{-0.528} & 0.448 & -0.05 & 0.153 & -0.104 \\\\ \\hline\nph & 0.038 & \\textbf{0.56} & 0.139 & -0.097 & 0.018 \\\\ \\hline\nmlr & \\textbf{0.805} & 0.268 & 0.111 & -0.065 & 0.165 \\\\ \\hline\nll & -0.281 & -0.207 & \\textbf{0.632} & 0.035 & -0.051 \\\\ \\hline\nrl & -0.347 & -0.348 & -0.173 & 0.055 & -0.114 \\\\ \\hline\nrf & 0 & \\textbf{-0.754} & -0.032 & 0.014 & 0.207 \\\\ \\hline\nhmd & 0.162 & 0.339 & -0.071 & \\textbf{0.705} & -0.13 \\\\ \\hline\nel & -0.302 & 0.061 & -0.478 & \\textbf{-0.574} & 0.108 \\\\ \\hline\nlrs & \\textbf{0.558} & 0.440 & 0.006 & -0.223 & 0.138 \\\\ \\hline\nlrg & 0.36 & \\textbf{0.623} & -0.217 & -0.059 & 0.088 \\\\ \\hline\noln & -0.032 & 0.288 & 0.31 & 0.287 & \\textbf{0.579} \\\\ \\hline\nisa & \\textbf{-0.562} & 0.480 & 0.09 & -0.08 & 0.258 \\\\ \\hline\nsra & \\textbf{0.718} & -0.266 & 0.106 & 0.147 & -0.248 \\\\ \\hline\nfa & -0.336 & -0.164 & -0.075 & 0.126 & 0.382 \\\\ \\hline\nhsw & 0 & -0.247 & 0.192 & -0.43 & 0.291 \\\\ \\hline\nhtw & \\textbf{-0.573} & 0.113 & 0.323 & -0.097 & -0.321 \\\\ \\hline\nhrw & 0.394 & -0.198 & \\textbf{-0.549} & -0.108 & 0.427 \\\\ \\hline\nhws & 0.162 & -0.092 & -0.406 & 0.171 & 0.323 \\\\ \\hline\nprp & \\textbf{0.761} & -0.294 & -0.199 & 0.215 & -0.053 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "A Regression-Based Share Market Prediction Model for Bangladesh", "authors": ["Syeda Tasnim Fabiha", "Rubaiyat Jahan Mumu", "Farzana Aktar", "B M Mainul Hossain"], "url": "https://arxiv.org/abs/2507.18643v1", "attribution": "\"A Regression-Based Share Market Prediction Model for Bangladesh\" by Syeda Tasnim Fabiha, Rubaiyat Jahan Mumu, Farzana Aktar, and B M Mainul Hossain, arXiv:2507.18643v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.13124v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Changes in steady-state values from the decrease in $c_1$ by 20\\%.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccc}\n \\hline\n \\hline\n && {Low-income} & {Middle-income} & {High-income} \\\\\n && {countries} & {countries} & {countries} \\\\\n \\hline\n {Aggregate}& {Intensive margin} & 7.8\\% & 7.7\\% & 8.8\\%\\\\\n {output} & {Extensive margin} & 4.3\\% & 3.3\\% & 1.9\\%\\\\\n &{Net change} & 12.1\\% & 11.0\\% & 10.7\\% \\\\\n \\hline\n {Aggregate} & {Intensive margin} & 8.5\\% & 8.4\\% & 9.6\\%\\\\\n {consumption} & {Extensive margin} & 1.8\\% & 1.4\\% & 0.8\\%\\\\\n & {Net change} & 10.3\\% & 9.8\\% & 10.4\\%\\\\\n \\hline\n {Aggregate}& {Intensive margin} & 8.1\\% & 7.9\\% & 8.9\\%\\\\\n {capital} & {Extensive margin} & 10.1\\% & 7.5\\% & 4.0\\%\\\\\n & {Net change} & 18.2\\% & 15.4\\% & 12.9\\% \\\\\n \\hline\n {Average}&{Intensive margin} & 7.6\\% & 7.5\\% & 8.7\\%\\\\\n {wage} & {Extensive margin} & 0.8\\% & 0.6\\% & 0.4\\%\\\\\n & {Net change} & 8.5\\% & 8.1\\% & 9.1\\%\\\\\n \\hline\n {Modern firms'}&{Intensive margin} & 0.4\\% & 0.3\\% & 0.2\\%\\\\\n {share of output} & {Extensive margin} & 6.6\\% & 5.1\\% & 2.7\\%\\\\\n &{Net change} & 7.0\\% & 5.4\\% & 2.9\\%\\\\ \n \\hline\n {Firms' entry}& & & &\\\\\n {(Number of firms)} & {Net change} & 7.7\\% & 10.6\\% & 14.1\\%\\\\\n \\hline\n {Fraction of}& & & &\\\\\n {modern firms} & {Net change} & 10.5\\% & 10.2\\% & 8.0\\% \\\\\n \\hline\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Heterogeneous Bribery, Technology Choice, and Capital Accumulation", "authors": ["Jafar M. Olimov", "Yi-Chan Tsai", "Hao-Yu Yang"], "url": "https://arxiv.org/abs/2507.13124v2", "attribution": "\"Heterogeneous Bribery, Technology Choice, and Capital Accumulation\" by Jafar M. Olimov, Yi-Chan Tsai, and Hao-Yu Yang, arXiv:2507.13124v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.00883v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc}\nRegime & $\\mathbb{M}$ & $\\hat{p}_{\\frac{\\alpha}{2}}$ & $\\hat{p}$ & $\\hat{p}_{1-\\frac{\\alpha}{2}}$\\\\\n\\hline \n\\hline\n$x^+$ & $8$ & $0.168246$ & $0.134076$ & $0.109883$\\\\ \n\\hline\n$x^-$ & $66$ & $0.195254$ & $0.24788$ & $0.322176$\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Confidence interval estimation at level $99\\%$ for $p$.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Keynesian Beauty Contest in Morocco's Public Procurement Reform", "authors": ["Nizar Riane"], "url": "https://arxiv.org/abs/2503.00883v1", "attribution": "\"Keynesian Beauty Contest in Morocco's Public Procurement Reform\" by Nizar Riane, arXiv:2503.00883v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.00173v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccc}\n\\toprule\nMetric & \\multicolumn{4}{c}{Top-1 Accuracy (\\%)} & & \\multicolumn{4}{c}{Seen-Unseen AUC (\\%)} \\\\ \\cmidrule(lr){2-5} \\cmidrule(l){7-10} \nDataset & \\multicolumn{2}{c}{CUB} & \\multicolumn{2}{c}{NAB} & & \\multicolumn{2}{c}{CUB} & \\multicolumn{2}{c}{NAB} \\\\\nSplit-Mode & Easy & Hard & Easy & Hard & & Easy & Hard & Easy & Hard \\\\ \\midrule\nGAZSL~ + CIZSL-v2 on $G$ & 41.9 & 15.5 & 35.0 & 9.6 & & 39.2 & 12.6 & 23.6 & 6.9\\\\ \nGAZSL~ + CIZSL-v2 on $G$ and $D$ & 39.6 & 10.3 & 28.0 & 6.8 & & 35.2 & 9.8 & 12.4 & 4.8\\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparative study of adding identification and entropy loss to the generator. }\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "CIZSL++: Creativity Inspired Generative Zero-Shot Learning", "authors": ["Mohamed Elhoseiny", "Kai Yi", "Mohamed Elfeki"], "url": "https://arxiv.org/abs/2101.00173v2", "attribution": "\"CIZSL++: Creativity Inspired Generative Zero-Shot Learning\" by Mohamed Elhoseiny, Kai Yi, and Mohamed Elfeki, arXiv:2101.00173v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.02473v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{{\\color{black} Summary for Simulated Systems}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\nTest System & Area &Node &Total Variables & Constraints &DERs\\\\\n\\hline\nTS-1 & 4 & 128 & 512& 898 &3\\\\\n\\hline\nTS-2 & 4 & 128 & 518 & 910&9\\\\\n\\hline\nTS-3 & 17 & 2522 & 10198 &17876 &113\\\\\n\\hline\nTS-4 & 17 & 2522 & 10692 &18738 &544\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Distributed Optimization using Reduced Network Equivalents for Radial Power Distribution Systems", "authors": ["Rabayet Sadnan", "Anamika Dubey"], "url": "https://arxiv.org/abs/2012.02473v1", "attribution": "\"Distributed Optimization using Reduced Network Equivalents for Radial Power Distribution Systems\" by Rabayet Sadnan and Anamika Dubey, arXiv:2012.02473v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.15882v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Metric} & \\textbf{Whisper} & \\textbf{Qwen2-Audio} \\\\\n\\midrule\nError Cases & 153/700 (21.8\\%)& 251/700 (35.8\\%)\\\\\nRestored Acc. & 136/153 (88.9\\%)& 176/251 (70.1\\%)\\\\\nVia Encoder & 130/153 (85.0\\%)& 171/251 (68.1\\%)\\\\\nVia Decoder & 126/153 (82.4\\%)& 147/251 (58.6\\%)\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Acoustic-contextual patching results using white noise disruption.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Beyond Transcription: Mechanistic Interpretability in ASR", "authors": ["Neta Glazer", "Yael Segal-Feldman", "Hilit Segev", "Aviv Shamsian", "Asaf Buchnick", "Gill Hetz", "Ethan Fetaya", "Joseph Keshet", "Aviv Navon"], "url": "https://arxiv.org/abs/2508.15882v1", "attribution": "\"Beyond Transcription: Mechanistic Interpretability in ASR\" by Neta Glazer, Yael Segal-Feldman, Hilit Segev, Aviv Shamsian, Asaf Buchnick, Gill Hetz, Ethan Fetaya, Joseph Keshet, and Aviv Navon, arXiv:2508.15882v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.00150v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc|cc|cc|cc|ccc}\n\\toprule\n\\multirow{2}{*}{Dataset} & \\multirow{2}{*}{Noise Rate} & \\multicolumn{2}{c|}{Full-training} & \\multicolumn{2}{c|}{Linear probing} & \\multicolumn{2}{c|}{CoDis} & \\multicolumn{3}{c}{CUFIT} \\\\ \\cmidrule(l){3-11} \n & & ResNet & DINOv2 & ResNet & DINOv2 & ResNet & DINOv2 & ResNet & ResNet+rein & DINOv2 \\\\ \\midrule\n\\multirow{2}{*}{HAM10000} & 0.2 & 73.1 & 66.5 & 71.1 & 75.6 & 74.9 & 80.1 & 77.7 & 79.9 & 82.6 \\\\\n & 0.4 & 59.6 & 62.6 & 67.8 & 75.3 & 72.4 & 74.1 & 73.8 & 75.4 & 81.5 \\\\ \\midrule\n\\multirow{2}{*}{APTOS-2019} & 0.2 & 80.4 & 66.8 & 80.3 & 79.2 & 80.3 & 82.0 & 82.4 & 82.2 & 84.2 \\\\\n & 0.4 & 64.7 & 65.9 & 73.4 & 79.4 & 78.1 & 79.5 & 80.5 & 82.2 & 84.2 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Average test accuracy on simulated noisy datasets (HAM10000 and APTOS-2019) using the ResNet50 architecture. Test accuracy is averaged over the last ten epochs.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise", "authors": ["Yeonguk Yu", "Minhwan Ko", "Sungho Shin", "Kangmin Kim", "Kyoobin Lee"], "url": "https://arxiv.org/abs/2412.00150v1", "attribution": "\"Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise\" by Yeonguk Yu, Minhwan Ko, Sungho Shin, Kangmin Kim, and Kyoobin Lee, arXiv:2412.00150v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.16139v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll} \n \\hline\n & \\multicolumn{2}{c}{$C_{\\text{g}}$ [\\texteuro]} & \\multicolumn{2}{c}{$Q_{\\text{loss}}$ [mAh]}\\\\\n Planner& summer & winter & summer & winter \\\\\n \\hline\n SoH 100 \\%&&&&\\\\\n \\hline\n CPBDeg \n& 24.7& 79.0& 202.0& 232.8\\\\\n BNoDeg & 14.9& 76.9& 212.0& 243.6\\\\\n \\hline\n SoH 90 \\%&&&&\\\\\n \\hline\n CPBDeg \n& 16.3& 78.7& 130.0& 149.0\\\\\n BNoDeg & 15.2& 77.6& 132.3& 158.3\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Cost and degradation summary.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Aging-aware Energy Management for Residential Multi-Carrier Energy Systems", "authors": ["Darío Slaifstein", "Gautham Ram Chandra Mouli", "Laura Ramirez-Elizondo", "Pavol Bauer"], "url": "https://arxiv.org/abs/2503.16139v3", "attribution": "\"Aging-aware Energy Management for Residential Multi-Carrier Energy Systems\" by Darío Slaifstein, Gautham Ram Chandra Mouli, Laura Ramirez-Elizondo, and Pavol Bauer, arXiv:2503.16139v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.11072v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ll}\n NPV (real growth) & $\\epsilon=0$ & $\\epsilon=1$ \\\\ \\hline\n $\\xi=0$ & 0.05 (0.12\\%) & 1.5 (2.34\\%) \\\\\n $\\xi=1$ & 0.05 (0.12\\%) & 1.8 (2.62\\%)\n \\end{tabular}\n\\end{adjustbox}\n\\caption{NPV generated by baseline fusion plants operating in each of the four modes described - toggling heat vs electricity output (columns) and grid vs recirculating electricity input (rows). NPV is expressed in units of overnight capital cost, whilst the number in brackets denotes the annual NPV growth during operation. Switching to heat output increases revenues by 30x whilst switching input sources may add a further 20\\%.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Fusion for high-value heat production", "authors": ["S. H. Ward", "M. Majeed", "N. J. Lopes Cardozo"], "url": "https://arxiv.org/abs/2504.11072v1", "attribution": "\"Fusion for high-value heat production\" by S. H. Ward, M. Majeed, and N. J. Lopes Cardozo, arXiv:2504.11072v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.14747v4_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crrrrrr}\n &\\multicolumn{2}{c}{$(\\mu,\\sigma)=(1,1)$}&\\multicolumn{2}{c}{$(\\mu,\\sigma)=(5,1)$}&\\multicolumn{2}{c}{$(\\mu,\\sigma)=(0,0.1)$}\\\\\n &\\multicolumn{2}{c}{$t^2=1.85252$}&\\multicolumn{2}{c}{$t^2=21.70696$}&\\multicolumn{2}{c}{$t^2=10.60380$}\\\\\n &bias&MSE&bias&MSE&bias&MSE\\\\\n \\hline\n MLE&$ 0.03610$&$0.07872$&$ 0.13328$&$0.91014$&$ 0.10132$&$0.44693$\\\\\n AUE&$-0.00058$&$0.07568$&$-0.02003$&$0.88393$&$-0.00263$&$0.43295$\\\\\n \\hline\\\\\n \n &\\multicolumn{2}{c}{$(\\mu,\\sigma)=(5,0.1)$}&\\multicolumn{2}{c}{$(\\mu,\\sigma)=(0,5)$}&\\multicolumn{2}{c}{$(\\mu,\\sigma)=(5,5)$}\\\\\n &\\multicolumn{2}{c}{$t^2=61.85059$}&\\multicolumn{2}{c}{$t^2=5.18058$}&\\multicolumn{2}{c}{$t^2=10.69656$}\\\\\n &bias&MSE&bias&MSE&bias&MSE\\\\\n \\hline\n MLE&$ 0.22886$&$2.57769$&$-0.01906$&$0.20499$&$ 0.03608$&$0.43819$\\\\\n AUE&$-0.05227$&$2.51639$&$ 0.00126$&$0.20603$&$-0.00145$&$0.42903$\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Asymptotic bias reduction of maximum likelihood estimates via penalized likelihoods with differential geometry", "authors": ["Masayo Y. Hirose", "Shuhei Mano"], "url": "https://arxiv.org/abs/2011.14747v4", "attribution": "\"Asymptotic bias reduction of maximum likelihood estimates via penalized likelihoods with differential geometry\" by Masayo Y. Hirose and Shuhei Mano, arXiv:2011.14747v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.07520v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CIFAR10 accuracy for different refinement parameters}\n\\begin{tabular}{c|c|c}\n\\toprule\nAccuracy & Iterations & Step size \\\\\n\\toprule\n67.26\\% & 1 & 0.03 \\\\\n66.89\\% & 2 & 0.015 \\\\\n66.86\\% & 4 & 0.01 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data", "authors": ["Koby Bibas"], "url": "https://arxiv.org/abs/2412.07520v1", "attribution": "\"Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data\" by Koby Bibas, arXiv:2412.07520v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11315v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Binary-class classification results}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|r|r|r|r|r|r|}\n\\hline\n\\textbf{Dataset} &\n \\textbf{Accuracy} &\n \\textbf{AUC} &\n \\textbf{F1 Score} &\n \\textbf{DR} &\n \\textbf{FAR} &\n \\textbf{Prediction Time (\\textmu s)} \\\\ \\hline\nUNSW-NB15 & 99.25\\% & 0.9545 & 0.92 & 91.25\\% & 0.35\\% & 10.05 \\\\\nNF-UNSW-NB15 & 98.62\\% & 0.9485 & 0.85 & 90.70\\% & 1.01\\% & 7.79 \\\\\n\\textbf{NF-UNSW-NB15-v2} & \\textbf{99.73\\%} & \\textbf{0.9845} & \\textbf{0.97} & \\textbf{97.07\\%} & \\textbf{0.16\\%} & \\textbf{5.92} \\\\ \\hline\nBoT-IoT & 100.00\\% & 0.9948 & 1.00 & 100.00\\% & 1.05\\% & 7.62 \\\\\nNF-BoT-IoT & 93.82\\% & 0.9628 & 0.97 & 93.70\\% & 1.13\\% & 5.37 \\\\\n\\textbf{NF-BoT-IoT-v2} & \\textbf{100.00\\%} & \\textbf{0.9987} & \\textbf{1.00} & \\textbf{100.00\\%} & \\textbf{0.26\\%} & \\textbf{3.90} \\\\ \\hline\nToN-IoT & 97.86\\% & 0.9788 & 0.99 & 97.86\\% & 2.10\\% & 8.93 \\\\\nNF-ToN-IoT & 99.66\\% & 0.9965 & 1.00 & 99.67\\% & 0.37\\% & 6.05\\\\\n\\textbf{NF-ToN-IoT-v2} & \\textbf{99.64\\%} & \\textbf{0.9959} & \\textbf{1.00} & \\textbf{99.76\\%} & \\textbf{0.58\\%} & \\textbf{8.47} \\\\ \\hline\nCSE-CIC-IDS2018 & 98.31\\% & 0.9684 & 0.94 & 94.75\\% & 1.07\\% & 23.01 \\\\\nNF-CSE-CIC-IDS2018 & 95.33\\% & 0.9506 & 0.83 & 94.71\\% & 4.59\\% & 17.04 \\\\\n\\textbf{NF-CSE-CIC-IDS2018-v2} & \\textbf{99.35\\%} & \\textbf{0.9829} & \\textbf{0.97} & \\textbf{96.89\\%} & \\textbf{0.31\\%} & \\textbf{21.75} \\\\ \\hline\nNF-UQ-NIDS & 97.25\\% & 0.9669 & 0.94 & 95.66\\% & 2.27\\% & 14.35\\\\\n\\textbf{NF-UQ-NIDS-v2} & \\textbf{97.90\\%} & \\textbf{0.9830} & \\textbf{0.98} & \\textbf{97.12\\%} & \\textbf{0.52\\%} & \\textbf{14.18} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Towards a Standard Feature Set for Network Intrusion Detection System Datasets", "authors": ["Mohanad Sarhan", "Siamak Layeghy", "Marius Portmann"], "url": "https://arxiv.org/abs/2101.11315v2", "attribution": "\"Towards a Standard Feature Set for Network Intrusion Detection System Datasets\" by Mohanad Sarhan, Siamak Layeghy, and Marius Portmann, arXiv:2101.11315v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.07687v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l}\n & \\textbf{Healthy - Orthopedic} & \\textbf{Healthy - Neurological} & \\textbf{Orthopedic - Neurological} \\\\ \\hline\n\\textbf{DTW distance} & 0.0531 & 0.0542 & 0.0367\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Personalized Convolutional Dictionary Learning of Physiological Time Series", "authors": ["Axel Roques", "Samuel Gruffaz", "Kyurae Kim", "Alain Oliviero-Durmus", "Laurent Oudre"], "url": "https://arxiv.org/abs/2503.07687v1", "attribution": "\"Personalized Convolutional Dictionary Learning of Physiological Time Series\" by Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero-Durmus, and Laurent Oudre, arXiv:2503.07687v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.08287v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l|l|l}\n Char. & average-1 & std-1 & average-2 & std-2 \\\\\n \\hline\n $Z^I$ & 0.16 & 0.08 & 0.78 & 0.38 \\\\\n $\\bar{Q}$ & 0.17 & 0.11 & 0.02 & 0.01 \\\\\n $Y_{tot}^\\alpha$ & 1.08 & 0.54 & 0.35 & 0.18 \\\\\n $Y_{tot}^I$ & $1.42\\times10^{-5}$ & $7.14\\times 10^{-6}$ & $1.35\\times10^{-5}$ & $6.75\\times 10^{-6}$\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Average and standard deviation for characteristics in Scenario 1 ($\\phi_1=2$) and Scenario 2 ($\\phi_1=20$). The other parameters are the same as in Figure .}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Liquidity Competition Between Brokers and an Informed Trader", "authors": ["Ryan Donnelly", "Zi Li"], "url": "https://arxiv.org/abs/2503.08287v1", "attribution": "\"Liquidity Competition Between Brokers and an Informed Trader\" by Ryan Donnelly and Zi Li, arXiv:2503.08287v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04727v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\t\t\t\\toprule\n\t\t\t & Real & Adapted & Spatial Gaussian (15) & Adapted & Poisson (0.5) & Adapted \\\\\n\t\t\t\\midrule\n\t\t\tSpatial domain & 0.080953 & 0.004417 & 0.004294 & 0.008760 & 0.423069 & 0.011621 \\\\\n\t\t\t\\midrule\n\t\t\tFrequency domain & 0.995228 & 0.006732 & 0.579235 & 0.004813 & 0.001193 & 0.001737 \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning to Translate Noise for Robust Image Denoising", "authors": ["Inju Ha", "Donghun Ryou", "Seonguk Seo", "Bohyung Han"], "url": "https://arxiv.org/abs/2412.04727v1", "attribution": "\"Learning to Translate Noise for Robust Image Denoising\" by Inju Ha, Donghun Ryou, Seonguk Seo, and Bohyung Han, arXiv:2412.04727v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05978v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model comparisons of forecast combination models.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|} \t\\hline\t\n\t\t\t\tModels & {\\bf Strengths} & {\\bf Weaknesses} \\\\ \\hline\n\t\t\t\tAQR \t & 1. Estimates are more robust to & 1. Parameters are harder to estimate \\\\\n\t\t\t\t& non-normal errors. & than other regression techniques. \\\\\n\t\t\t\t& 2. Estimates are robust against outliers. & \\\\\n\t\t\t\t& 3. Characterisation of the data is richer. & \\\\\n\t\t\t\t& 4. It is invariant to monotonic transformations. & \\\\ \t\t\\hline\n\t\t\t\tPLAQR & 1. It is flexible to model data that is complex. &1. Challenge of the existence \\\\\n\t\t\t\t& 2. It is more parsimonious than & of non-smoothness of the loss function. \\\\\n\t\t\t\t& \t other regression models. \t\t\t\t & \\\\\n\t\t\t\t& 3. Very stable and flexible for & \\\\\n\t\t\t\t& large-scale interpolation. & \\\\\n\t\t\t\t& & \\\\ \\hline\n\t\t\t\tQRNN & 1. It is flexible to model data on & 1. Not very well developed \\\\\n\t\t\t\t& \tmodelling dependencies. \t\t & for tasks that are not classified. \\\\\n\t\t\t\t& 2. Scalability is relatively good & 2. Sometimes gives overconfidence in \\\\\n\t\t\t\t& \twith large datasets. \t\t\t\t & predictions when applying training sample. \\\\ \t\t\t\\hline\n\t\t\t\tOPERA & 1. Combines multiple individual techniques & 1. Finding feature importance \\\\\n\t\t\t\t& to build a more powerful technique. & is a challenge. \\\\\n\t\t\t\t& 2. Better prediction. & \\\\\n\t\t\t\t& 3. Improved results as compared & \\\\\n\t\t\t\t& to individual models. & \\\\\n\t\t\t\t& & \\\\ \t\t\t\\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Robust modelling framework for short-term forecasting of global horizontal irradiance", "authors": ["Edina Chandiwana", "Caston Sigauke", "Alphonce Bere"], "url": "https://arxiv.org/abs/2212.05978v1", "attribution": "\"Robust modelling framework for short-term forecasting of global horizontal irradiance\" by Edina Chandiwana, Caston Sigauke, and Alphonce Bere, arXiv:2212.05978v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.02199v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n \\hline\n \\multirow{2}{*}{Dataset} & \\multicolumn{2}{c}{Spectral Group Encoding Index} & \\multirow{2}{*}{Diff}\\\\\n & Pre-Training & Fine-Tuning & \\\\\n \\hline\n EuroSAT & 98.02 & 98.37 & +0.35 \\\\\n BigEarthNet & 85.70 & 85.82 & +0.12 \\\\\n METER-ML Sentinel-2 & 71.39 & 73.95 & +2.56 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Downstream performance of multi-sensor pre-trained USatMAE comparing the effect of using spectral group encodings corresponding to the indices used during pre-training and fine-tuning. As in the other downstream experiments, we use superpositional encodings and no sensor encoding for these experiments.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery", "authors": ["Jeremy Irvin", "Lucas Tao", "Joanne Zhou", "Yuntao Ma", "Langston Nashold", "Benjamin Liu", "Andrew Y. Ng"], "url": "https://arxiv.org/abs/2312.02199v1", "attribution": "\"USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery\" by Jeremy Irvin, Lucas Tao, Joanne Zhou, Yuntao Ma, Langston Nashold, Benjamin Liu, and Andrew Y. Ng, arXiv:2312.02199v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.07606v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|}\n\\hline\nDataset & Train & Validation & Test & Total \\\\ \\hline\nNIH & 1152 & 144 & 144 & 1440 \\\\ \\hline\nD1 + D3 & 800 & 100 & 100 & 1000 \\\\ \\hline\nD2 & - & - & 183 & 183 \\\\ \\hline\nTotal & 1952 & 244 & 244 + 183 & 2623 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Distribution of Cardiomegaly CXR's}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly", "authors": ["Tanveer Gupte", "Mrunmai Niljikar", "Manish Gawali", "Viraj Kulkarni", "Amit Kharat", "Aniruddha Pant"], "url": "https://arxiv.org/abs/2101.07606v1", "attribution": "\"Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly\" by Tanveer Gupte, Mrunmai Niljikar, Manish Gawali, Viraj Kulkarni, Amit Kharat, and Aniruddha Pant, arXiv:2101.07606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10650v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Datasets details}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\n\\textbf{\\textit{Name}} & \\textbf{\\textit{Images}} & \\textbf{\\textit{Lesions}} \\\\\n\\hline\nPH2 & 200 & Nevus and Melanoma \\\\\nISBI 2017 & 2750 & Nevus, Seborrheic Keratosis and Melanoma \\\\\nPAD-UFES & 220 & Nevus, Seborrheic Keratosis and Melanoma \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data", "authors": ["José G. M. Esgario", "Renato A. Krohling"], "url": "https://arxiv.org/abs/2201.10650v1", "attribution": "\"Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data\" by José G. M. Esgario and Renato A. Krohling, arXiv:2201.10650v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12300v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n \\toprule\n Parameter Name & Value \\\\\n Replay buffer capacity & $100000$ \\\\\n Batch size & $512$ \\\\\n Discount $\\gamma$ & $0.99$\\\\\n Optimizer & Adam \\\\\n Learning rate & $10^{-3}$ \\\\\n Critic target update frequency & $2$ \\\\\n Critic Q-function soft update rate $\\tau$ & $0.01$\\\\\n Actor update frequency & $2$ \\\\\n Actor log stddev bounds & $[-10, 2]$ \\\\\n Temperature learning rate & $10^{-4}$ \\\\\n Initial Temperature & $0.1$ \\\\\n Entropy Target & $-\\text{num actions}$\\\\\n Image augmentation pad (DrQ)& $4$\\\\\n \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Architecture and training hyperparameters}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation", "authors": ["Martin Bertran", "Walter Talbott", "Nitish Srivastava", "Joshua Susskind"], "url": "https://arxiv.org/abs/2201.12300v1", "attribution": "\"Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation\" by Martin Bertran, Walter Talbott, Nitish Srivastava, and Joshua Susskind, arXiv:2201.12300v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2305.11298v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|l|l|l|l|l|l|}\n \\hline\n Method & CV1 Error & Non-zeros & Condition No. & Delta WS \\\\ \\hline\n Glasso1 & 0.73 & 5.7E+4 & 41.62 & 6.15E-15 \\\\ \\hline\n Clime1 & 0.87 & 2.3E+4 & 5.37 & 8.49E-15 \\\\ \\hline\n Greedy Prune1 & 0.97 & 1.1E+4 & 16.02 & 1.38E-15 \\\\ \\hline\n MB1 & 0.78 & 3.4E+4 & 8.52 & 6.18E-15 \\\\ \\hline\n HybridMB1 & 0.94 & 3.1E+4 & 37.82 & 1.72E-14 \\\\ \\hline\n \\end{tabular}\n\\caption{ Results for precision matrix selected via 5-fold CV1 on Nifty500 constituents}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Precision versus Shrinkage: A Comparative Analysis of Covariance Estimation Methods for Portfolio Allocation", "authors": ["Sumanjay Dutta", "Shashi Jain"], "url": "https://arxiv.org/abs/2305.11298v1", "attribution": "\"Precision versus Shrinkage: A Comparative Analysis of Covariance Estimation Methods for Portfolio Allocation\" by Sumanjay Dutta and Shashi Jain, arXiv:2305.11298v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12251v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cc|cc|}\n \\hline\n Solar Panels & $SD^q$ & $\\mathcal{S}(SD^q)$ & $SD^p$ & $\\mathcal{S}(SD^p)$ \\\\ \\hline\n $P_1$ & $\\langle (0.60,0.39);0.73\\rangle$ & $0.596$ & $\\langle (0.60,0.39);0.75\\rangle$ & $0.598$ \\\\\n $P_2$ & $\\langle (0.62,0.38);0.61\\rangle$ & $0.601$ & $\\langle (0.62,0.38);0.62\\rangle$ & $0.602$ \\\\\n $P_3$ & $\\langle (0.56,0.43);0.42\\rangle$ & $0.538$ & $\\langle (0.56,0.43);0.42\\rangle$ & $0.538$ \\\\\n $P_4$ & $\\langle (0.60,0.40);0.66\\rangle$ & $0.587$ & $\\langle (0.60,0.40);0.68\\rangle$ & $0.588$ \\\\\n $P_5$ & $\\langle (0.56,0.44);0.70\\rangle$ & $0.587$ & $\\langle (0.56,0.44);0.71\\rangle$ & $0.554$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The results of significance degrees $SD$ based on $WSM$ and $WPM$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology", "authors": ["Mahmut Can Bozyiğit", "Mehmet Ünver"], "url": "https://arxiv.org/abs/2501.12251v1", "attribution": "\"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology\" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.21124v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{r|rrr}\n Model & MicroLlama 300M & TinyLlama 1.1B & OpenLlama 3B \\\\\n \\midrule\n $n_{\\mathrm{params}}$ & 304.6M & 1.1B & 3.4B \\\\\n $d_{\\mathrm{model}}$ & 2048 & 2048 & 2048 \\\\\n $n_{\\mathrm{layers}}$ & 12 & 22 & 26 \\\\\n $n_{\\mathrm{heads}}$ & 12 & 32 & 32 \\\\\n $d_{\\mathrm{head}}$ & 64 & 64 & 100 \\\\ \n \\bottomrule\n \\end{tabular}\n\\caption{Specifications of models}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism", "authors": ["Tim Tsz-Kit Lau", "Weijian Li", "Chenwei Xu", "Han Liu", "Mladen Kolar"], "url": "https://arxiv.org/abs/2412.21124v2", "attribution": "\"Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism\" by Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu, Han Liu, and Mladen Kolar, arXiv:2412.21124v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.21174v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|lll}\n\\# & $\\tau_{large}$ &$\\tau_{mix}$ & $\\tau_{small}$ \\\\\\hline\n1 & 25,850 & 25,640 & 194,360 \\\\\n2 & 1,510 & 1,458 & 2,522 \\\\\n3 &12,408 & 12,452 & 27,714 \\\\\n4 & 62,499 & 61,007 & 94,424 \\\\\n5 & 62,508 & 60,989 & 94,444 \\\\\n6 & 64,497 & 63,109 & 95,064 \\\\\n7 & 65,518 & 66,266 & 95,916 \\\\\n8 & 70,609 & 77,180 & 98,515\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Number of nonzeros of the new scheme for the new scheme (generalized case) with $M$-orthogonalization}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Robust iterative methods for linear systems with saddle point structure", "authors": ["Murat Manguoğlu", "Volker Mehrmann"], "url": "https://arxiv.org/abs/2502.21174v1", "attribution": "\"Robust iterative methods for linear systems with saddle point structure\" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05831v2_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrcrrrrcrrrr}\n\\toprule\n$n$ & & Param. & PQ & NQ & EQ & 2W & Param. & PQ & NQ & EQ & 2W \\\\\n\\midrule\n\\multicolumn{12}{l}{Simulations from Table~ vs.\\ Table~} \\\\\n\\midrule\n300 & corr & $\\sigma^2=1$ & 1.000 & 1.000 & 1.000 & 1.000 & $\\sigma^2=0.4$ & 1.000 & 1.000 & 1.000 & 1.000 \\\\\n & misp & & 1.000 & 1.000 & 1.000 & 1.000 & & 0.999 & 0.999 & 0.999 & 0.999 \\\\\n600 & corr & & 1.000 & 1.000 & 1.000 & 1.000 & & 1.000 & 1.000 & 1.000 & 1.000 \\\\\n & misp & & 1.000 & 1.000 & 1.000 & 1.000 & & 0.999 & 0.999 & 0.999 & 0.999 \\\\\n1000 & corr & & 1.000 & 1.000 & 1.000 & 1.000 & & 1.000 & 1.000 & 1.000 & 1.000 \\\\\n & misp & & 1.000 & 1.000 & 1.000 & 1.000 & & 0.999 & 0.999 & 0.999 & 0.999 \\\\\n\\midrule\n\\multicolumn{12}{l}{Simulations from Table~ vs.\\ Table~} \\\\\n\\midrule\n300 & corr & $\\sigma^2=1$ & 1.000 & 1.000 & 1.000 & 1.000 & $\\sigma^2=0.4$ & 1.000 & 1.000 & 1.000 & 1.000 \\\\\n & misp & & 1.004 & 1.004 & 1.004 & 1.004 & & 1.013 & 1.014 & 1.014 & 1.014 \\\\\n600 & corr & & 1.000 & 1.000 & 1.000 & 1.000 & & 1.000 & 1.000 & 1.000 & 1.000 \\\\\n & misp & & 1.004 & 1.004 & 1.004 & 1.004 & & 1.014 & 1.014 & 1.014 & 1.014 \\\\\n1000 & corr & & 1.000 & 1.000 & 1.000 & 1.000 & & 1.000 & 1.000 & 1.000 & 1.000 \\\\\n & misp & & 1.004 & 1.004 & 1.004 & 1.004 & & 1.014 & 1.014 & 1.014 & 1.014 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Conditional-mean Multiplicative Operator Models for Count Time Series", "authors": ["Christian H. Weiß", "Fukang Zhu"], "url": "https://arxiv.org/abs/2212.05831v2", "attribution": "\"Conditional-mean Multiplicative Operator Models for Count Time Series\" by Christian H. Weiß and Fukang Zhu, arXiv:2212.05831v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.15151v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c||c|c|c|c|c|c|c|c|c|c|c|c|}%\n \\hline\n \\(k\\) & \\(0\\) & \\(1\\) & \\(2\\) & \\(3\\) & \\(4\\) & \\(5\\) & \\(6\\) & \\(7\\) & \\(8\\) & \\(9\\) & \\(10\\) & \\(11\\) \\\\\n \\hline\n \\(\\nu_k^{-1/2}\\xi_{\\mathrm{cp}}(\\Delta_k; x_k, \\nu_k)^{1/2}\\) & \\(0.67\\) & \\(0.64\\) & \\(0.61\\) & \\(0.58\\) & \\(0.55\\) & \\(0.52\\) & \\(0.48\\) & \\(0.45\\) & \\(0.42\\) & \\(0.39\\) & \\(0.36\\) & \\(0.33\\) \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Complexity of trust-region methods with unbounded Hessian approximations for smooth and nonsmooth optimization", "authors": ["Geoffroy Leconte", "Dominique Orban"], "url": "https://arxiv.org/abs/2312.15151v3", "attribution": "\"Complexity of trust-region methods with unbounded Hessian approximations for smooth and nonsmooth optimization\" by Geoffroy Leconte and Dominique Orban, arXiv:2312.15151v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.00530v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crrr} \n &&\\multicolumn{2}{c}{Mean-squared Prediction Error(MSPE)}\\\\\n Model & No.\\ Parameters & One-Step & Two-Step\\\\ \n \\hline\n GNAR(1, [1]) & 2 & 5.09 & 8.81 \\\\ \n GNAR(1, [6]) & 7 & 5.06 & 8.8 \\\\ \n GNAR(6, [6, \\ldots , 6]) & 42& 5.24 & 9.88\n \\end{tabular}\n\\end{adjustbox}\n\\caption{One- and two-step predictive performance for different model order choices.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "New tools for network time series with an application to COVID-19 hospitalisations", "authors": ["Guy Nason", "Daniel Salnikov", "Mario Cortina-Borja"], "url": "https://arxiv.org/abs/2312.00530v1", "attribution": "\"New tools for network time series with an application to COVID-19 hospitalisations\" by Guy Nason, Daniel Salnikov, and Mario Cortina-Borja, arXiv:2312.00530v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.00700v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Baseline comparison of the AD classification. Our test results below are corresponding to the hard ensemble model.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccc}\n \\toprule\n & Model & Accuracy & Precision & Recall & F1-Score\\\\\n \\hline\n LOSO & Luz et al. & 0.77 & 0.77 & 0.76 & 0.77\\\\\n & Ensemble (\\textit{ours}) & \\textbf{0.99} & \\textbf{0.99} & \\textbf{1.00} & \\textbf{0.99}\\\\\n \\hline\n TEST & Luz et al. & 0.75 & 0.83 & 0.62 & 0.71\\\\\n & Ensemble (\\textit{ours}) & \\textbf{0.83} & \\textbf{0.83} & \\textbf{0.83} & \\textbf{0.83}\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity", "authors": ["Utkarsh Sarawgi", "Wazeer Zulfikar", "Nouran Soliman", "Pattie Maes"], "url": "https://arxiv.org/abs/2009.00700v1", "attribution": "\"Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity\" by Utkarsh Sarawgi, Wazeer Zulfikar, Nouran Soliman, and Pattie Maes, arXiv:2009.00700v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.22936v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of Best Models and Their Scores by Metric}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcc}\n\\toprule\n\\textbf{Metric} & \\textbf{Best Model} & \\textbf{Avg Score} & \\textbf{Range} \\\\\n\\midrule\nROUGE-1 & Gemini & 0.56 & 0.20--0.62 \\\\\nROUGE-2 & Gemini & 0.22 & 0.05--0.28 \\\\\nROUGE-L & Gemini & 0.16 & 0.06--0.17 \\\\\nCosine Similarity & Claude & 0.68 & 0.44--0.80 \\\\\nJaccard & Gemini & 0.21 & 0.07--0.26 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis", "authors": ["Md Talha Mohsin"], "url": "https://arxiv.org/abs/2507.22936v1", "attribution": "\"Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis\" by Md Talha Mohsin, arXiv:2507.22936v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.04035v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n \\hline\n $m$ & Measure & cart & softImpute & missForest & QP-AS & dQP-SVAS \\\\ \\hline\n 1000 & Time & 5.68 & 0.04 & 19.04 & 6.16 & 1.15 \\\\\n & Accuracy & 0.3934 & \\textbf{0.4405} & \\textbf{0.4424} & \\textbf{0.4575} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.4577} \\\\\n & RMSE & 1.0739 & 0.9762 & 0.8985 & \\textbf{0.8540} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.8538} \\\\\n & MAD & 0.7797 & 0.6839 & 0.6397 & \\textbf{0.6047} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.6044} \\\\ \\hline\n 2000 & Time & 5.91 & 0.04 & 19.91 & 32.94 & 3.98 \\\\\n & Accuracy & 0.3931 & \\textbf{0.4414} & \\textbf{0.4431} & \\textbf{0.4586} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.4589} \\\\\n & RMSE & 1.0755 & 0.9707 & 0.8973 & \\textbf{0.8514} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.8511} \\\\\n & MAD & 0.7808 & 0.6800 & 0.6385 & \\textbf{0.6025} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.6021} \\\\ \\hline\n 3000 & Time & 6.18 & 0.04 & 21.24 & 93.96 & 8.55 \\\\\n & Accuracy & 0.3924 & \\textbf{0.4413} & \\textbf{0.4427} & \\textbf{0.4579} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.4583} \\\\\n & RMSE & 1.0777 & 0.9681 & 0.8979 & \\textbf{0.8527} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.8524} \\\\\n & MAD & 0.7827 & 0.6787 & 0.6390 & \\textbf{0.6037} & \\cellcolor[rgb]{ .851, .851, .851}\\textbf{0.6032} \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Synthetic data results (averaged by $m$, number of subjects)}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Discordance Minimization-based Imputation Algorithms for Missing Values in Rating Data", "authors": ["Young Woong Park", "Jinhak Kim", "Dan Zhu"], "url": "https://arxiv.org/abs/2311.04035v1", "attribution": "\"Discordance Minimization-based Imputation Algorithms for Missing Values in Rating Data\" by Young Woong Park, Jinhak Kim, and Dan Zhu, arXiv:2311.04035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2311.05744v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overview of literature on optimal power and gas flow.}\n\\begin{tabular}{lrll}\n\\hline\n\\multirow{2}{*}{Model} & \\multirow{2}{*}{Exact} & \\multicolumn{2}{c}{Relaxation} \\\\ \\cline{3-4} \n & & Linear & Conic \\\\ \\hline\nDynamic ($\\mathrm{DY}$) & & - & \\\\\nQuasi-dynamic ($\\mathrm{QD}$) & & & \\\\\nSteady-state ($\\mathrm{ST}$) & & & \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Flexibility of Integrated Power and Gas Systems: Gas Flow Modeling and Solution Choices Matter", "authors": ["Enrica Raheli", "Yannick Werner", "Jalal Kazempour"], "url": "https://arxiv.org/abs/2311.05744v2", "attribution": "\"Flexibility of Integrated Power and Gas Systems: Gas Flow Modeling and Solution Choices Matter\" by Enrica Raheli, Yannick Werner, and Jalal Kazempour, arXiv:2311.05744v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2310.04907v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of Model Estimates (\\%/day).}\n\\begin{tabular}{cccccccccc}\n\t\\hline \\\\\n & & ID & $\\mu$ & $T$ & $\\alpha$ & $S$ & $\\bar{r}$ & $\\zeta$ & $\\delta$\\\\\n \\hline \\hline \\\\\n\t\\multirow{4}{4em}{S\\&P 500} & Bull & 0.0099 & 0.3063 & 1.0574 & -0.1592 & 1.6907 & 0.0407 & 0.4655 & 0.9297\\\\\n & & (0.0029) & (0.2319) & (0.3929) & (0.2988) & (0.6877) & (0.0979) & (0.5149) & (0.4366)\\\\ \\\\\n & Bear & 0.0092 & 0.1709 & 1.3817 & 0.0283 & 1.9603 & 0.0891 & 0.1427 & 1.0832\\\\\n & & (0.0009) & (0.2602) & (0.3826) & (0.2714) & (0.4841) & (0.1284) & (0.4775) & (0.2888)\\\\ \\hline \\\\\n \\multirow{4}{4em}{Crypto} & Bull & 0.1391 & -0.0738 & 2.1997 & 0.0131 & 7.0157 & 0.1930 & -0.0869 & 5.0012\\\\\n & & (0.0261) & (0.7184) & (1.5311) & (1.1933) & (1.5406) & (0.7227) & (1.8040) & (1.9376)\\\\ \\\\\n & Bear & 0.1322 & 0.2234 & 2.3296 & -0.5803 & 5.8279 & -0.2274 & 0.8037 & 3.6391\\\\\n & & (0.0410) & (0.3879) & (0.6872) & (0.8071) & (0.8856) & (0.4195) & (1.1102) & (0.6821)\\\\ \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "An Information Theory Approach to the Stock and Cryptocurrency Market: A Statistical Equilibrium Perspective", "authors": ["Emanuele Citera", "Francesco De Pretis"], "url": "https://arxiv.org/abs/2310.04907v1", "attribution": "\"An Information Theory Approach to the Stock and Cryptocurrency Market: A Statistical Equilibrium Perspective\" by Emanuele Citera and Francesco De Pretis, arXiv:2310.04907v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04427v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Truth Table for CNOT-Gate}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n \\multicolumn{2}{|c|}{Input}&\\multicolumn{2}{c|}{Output}\\\\\n \\hline\n Control Qubit& Target Qubit& Control Qubit& Target Qubit \\\\\n \\hline\n 0 & 0 &0&0\\\\\n \\hline\n 0 & 1&0& 1\\\\\n \\hline\n 1&1&1&0\\\\\n \\hline\n 1&0&1&1\\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Quantum Internet- Applications, Functionalities, Enabling Technologies, Challenges, and Research Directions", "authors": ["Amoldeep Singh", "Kapal Dev", "Harun Siljak", "Hem Dutt Joshi", "Maurizio Magarini"], "url": "https://arxiv.org/abs/2101.04427v2", "attribution": "\"Quantum Internet- Applications, Functionalities, Enabling Technologies, Challenges, and Research Directions\" by Amoldeep Singh, Kapal Dev, Harun Siljak, Hem Dutt Joshi, and Maurizio Magarini, arXiv:2101.04427v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.06027v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistical comparison of the learning strategies (shuffled vs. curriculum) used for training different networks on each motion category with $\\alpha = 0.05$.}\n\\begin{tabular}{c|c|c|c|c|c} % @ property can be modified\n\\toprule\n & UNet Attention & UNet 2.5D & LSTM UNet & LSTM ResNet34 & LSTM CorNet \\\\\n\\midrule\nMinimal & 0.0005 & 4.91e-5 & 0.0002 & 71e-5 & 0.0029 \\\\\nMild & 8.81e-6 & 0.257 & 0.232 & 0.473 & 0.0012 \\\\\nModerate & 8.41e-8 & 0.007 & 0.003 & 0.056 & 0.0045 \\\\ \nSevere & 6.81e-5 & 0.012 & 6.81e-5 & 0.038 & 0.0003 \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI", "authors": ["Tejas Sudharshan Mathai", "Yi Wang", "Nathan Cross"], "url": "https://arxiv.org/abs/2010.06027v1", "attribution": "\"Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI\" by Tejas Sudharshan Mathai, Yi Wang, and Nathan Cross, arXiv:2010.06027v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10082v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n \\hline\n Dataset/Arch & Linear & Indi Nonlinear & Nonlinear Groups \\\\ \\hline\n TCS & $x_2-x_{5}$ & $x_{1},x_6$ & \\\\ \n & $x_7-x_{23}$ & & \\\\ \\hline\n PCB & $x_1-x_{16}$ & $x_{19}$ & $(x_{17},x_{22})$ \\\\ \n & $x_{18}, x_{20}, x_{21}$ & & \\\\ \n & $x_{23} - x_{34}$ & & \\\\ \\hline\n GE & $x_1-x_8$ & $x_9$ & & \\\\ \n & $x_{10} - x_{13}$ & & \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance", "authors": ["Dangxing Chen", "Weicheng Ye"], "url": "https://arxiv.org/abs/2209.10082v2", "attribution": "\"Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance\" by Dangxing Chen and Weicheng Ye, arXiv:2209.10082v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.06234v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The distribution of MC integers $n<1100$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|}\n\\hline\n$\\lfloor n/100 \\rfloor$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 &10 \\\\\n\\hline\n$\\sharp\\{n|$ $n$ is MC$\\}$ & 79& 84& 80& 81& 87& 83& 83& 86& 77& 87 & 84 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "The Largest Circle Enclosing $n$ Lattice Points", "authors": ["Jianqiang Zhao"], "url": "https://arxiv.org/abs/2505.06234v2", "attribution": "\"The Largest Circle Enclosing $n$ Lattice Points\" by Jianqiang Zhao, arXiv:2505.06234v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09997v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\textbf{Type}& \\textbf{\\# Entities} & \\textbf{\\% Entities} & \\textbf{\\# Mentions} & \\textbf{\\% Mentions} \\\\\n \\midrule\n \\textsc{char} & 282 & 25.0\\% & 290 & 19.7\\% \\\\\n \\textsc{fac} & 71 & 6.4\\% & 106 & 7.2\\% \\\\\n \\textsc{loc} & 186 & 16.7\\% & 221 & 15.0\\% \\\\\n \\textsc{org} & 73 & 6.6\\% & 137 & 9.3\\% \\\\\n \\textsc{per} & 490 & 44.0\\% & 708 & 48.0\\% \\\\\n \\textsc{misc} & 11 & 1.0\\% & 12 & 0.8\\% \\\\\n \\midrule\n \\textbf{Total} & \\textbf{1113} & \\textbf{100.0\\%} & \\textbf{1474} & \\textbf{100.0\\%} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Dataset entities statistics}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Razmecheno: Named Entity Recognition from Digital Archive of Diaries \"Prozhito\"", "authors": ["Timofey Atnashev", "Veronika Ganeeva", "Roman Kazakov", "Daria Matyash", "Michael Sonkin", "Ekaterina Voloshina", "Oleg Serikov", "Ekaterina Artemova"], "url": "https://arxiv.org/abs/2201.09997v1", "attribution": "\"Razmecheno: Named Entity Recognition from Digital Archive of Diaries \"Prozhito\"\" by Timofey Atnashev, Veronika Ganeeva, Roman Kazakov, Daria Matyash, Michael Sonkin, Ekaterina Voloshina, Oleg Serikov, and Ekaterina Artemova, arXiv:2201.09997v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11360v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l||c|c|}\n\\hline\n\\textbf{Metric} & \\textbf{MultiWOZ} & \\textbf{CrossWOZ} \\\\ \n\\hline\n\\textbf{Language} & English & Chinese (Simplified) \\\\\n\\textbf{\\# Dialogues} & ~~~8,438 & ~~5,012 \\\\\n\\textbf{Total \\# turns} & 113,556 & 84,692 \\\\\n\\# \\textbf{Domains} & 7 & 5 \\\\\n\\# \\textbf{Slots} & 24 &72 \\\\\n\\# \\textbf{Values} & 4,510 & 7,871 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Statistics of MultiWOZ and CrossWOZ. Note that the translated version of these two datasets have the same metrics}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An Empirical Study of Cross-Lingual Transferability in Generative Dialogue State Tracker", "authors": ["Yen-Ting Lin", "Yun-Nung Chen"], "url": "https://arxiv.org/abs/2101.11360v1", "attribution": "\"An Empirical Study of Cross-Lingual Transferability in Generative Dialogue State Tracker\" by Yen-Ting Lin and Yun-Nung Chen, arXiv:2101.11360v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.15087v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Optimal-$k$ difference sequences $d_k(r) = (d_0, \\dots, d_r)$ for $r\\leq 5$, where $d_0>0$ for identifiability and the entries are rounded to four decimal places.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccc} \\hline\n\t\t\t\t$(r,k)$ &$d_0$&$d_1$& $d_2$& $d_3$& $d_4$& $d_5$ \\\\\\hline \\hline\n\t\t\t\t(1,~0) &0.7071& -0.7071& &&&\\\\\\hline\n\t\t\t\t(2,~0) &0.8090& -0.5000& -0.3090&&&\\\\\n\t\t\t\t(2,~1) &0.4082 &-0.8165 & ~0.4082&&&\\\\\\hline\n\t\t\t\t(3,~0) &0.8582& -0.3832&-0.2809&-0.1942&&\\\\ \n\t\t\t\t(3,~1) &\\textbf{0.2673}& ~\\textbf{0.0000}&\\textbf{-0.8018}&\\textbf{~0.5345}& &\\\\ \t\t\t\n\t\t\t\t(3,~2) &0.2236&-0.6708&~0.6708 &-0.2236& &\\\\ \\hline\n\t\t\t\t(4,~0) &0.8873& -0.3099&-0.2464&-0.1901& -0.1409&\\\\ \n\t\t\t\t(4,~1) &\\textbf{0.1982}& ~\\textbf{0.1034}&\\textbf{-0.1855}&\\textbf{-0.7322}&~\\textbf{0.6160}&\\\\ \t\t(4,~2) &\\textbf{0.1842}& \\textbf{-0.2271}&\\textbf{-0.4242}&~\\textbf{0.7928}& \\textbf{-0.3257}&\\\\ \n\t\t\t\t(4,~3) &0.1195& -0.4781&~0.7171&-0.4781&~0.1195&\\\\ \\hline\n\t\t\t\t(5,~0) &0.9064& -0.2600&-0.2167&-0.1774&-0.1420&-0.1103\\\\ \n\t\t\t\t(5,~1) &\\textbf{0.1573}& ~\\textbf{0.1151}&\\textbf{-0.0166}&\\textbf{-0.2681}&\\textbf{-0.6609}&~\\textbf{0.6732}\\\\ \t\t\t\n\t\t\t\t(5,~2) &\\textbf{0.1553} &\\textbf{-0.0734}& \\textbf{-0.3080} &\\textbf{-0.1904} &~\\textbf{0.8217}& \\textbf{-0.4053}\\\\ \n\t\t\t\t(5,~3) &\\textbf{0.1143} &\\textbf{-0.2726} &\\textbf{-0.0603} &~\\textbf{0.6733} &\\textbf{-0.6470} &~\\textbf{0.1923}\\\\ \n\t\t\t\t(5,~4) &0.0630 &-0.3150 &~0.6299 &-0.6299 &~0.3150 &-0.0630\\\\ \\hline \n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Optimal-$k$ difference sequence in nonparametric regression", "authors": ["Wenlin Dai", "Xingwei Tong", "Tiejun Tong"], "url": "https://arxiv.org/abs/2211.15087v1", "attribution": "\"Optimal-$k$ difference sequence in nonparametric regression\" by Wenlin Dai, Xingwei Tong, and Tiejun Tong, arXiv:2211.15087v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09487v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Detection Results in Different Types of Attacks.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\t\t\\hline\n\t\t& \\textbf{TPR} & \\textbf{FPR} & \\textbf{Acc.} & \\textbf{AUROC} \\\\ \\hline\n\t\t\\textbf{Playback} & 99.3\\% & 1.0\\% & 99.2\\% & 0.999 \\\\ \\hline\n\t\t\\textbf{Tampering} & 99.1\\% & 2.6\\% & 98.3\\% & 0.995 \\\\ \\hline\n\t\t\\textbf{Overall} & 99.2\\% & 1.8\\% & 98.7\\% & 0.997 \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information", "authors": ["Yong Huang", "Xiang Li", "Wei Wang", "Tao Jiang", "Qian Zhang"], "url": "https://arxiv.org/abs/2201.09487v1", "attribution": "\"Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information\" by Yong Huang, Xiang Li, Wei Wang, Tao Jiang, and Qian Zhang, arXiv:2201.09487v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08474v2_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Attack success rate (ASR) and clean test accuracy (ACC) for the classifiers being attacked (with one, two, and three attacks/target classes) for CIAFR-10, CIFAR-100, and STL-10, respectively; and ACC for the clean classifiers trained for the three domains respectively.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc}\n\t\t\t&CIFAR-10 &CIFAR-100 &STL-10\n\t\t\t\\\\ \\hline \\\\\n\t\t\tone attack &\\thead{ASR: 97.0\\\\ACC: 93.7} &\\thead{ASR: 97.7\\\\ACC: 71.5} &\\thead{ASR: 95.1\\\\ACC: 79.7}\\\\\n\t\t\ttwo attacks &\\thead{ASR=96.0, 93.7\\\\ACC: 92.5} &\\thead{ASR=89.6, 95.3\\\\ACC: 71.9} &\\thead{ASR=99.6, 96.0\\\\ACC: 80.8}\\\\\n\t\t\tthree attacks &\\thead{ASR: 94.5, 80.2, 97.9\\\\ACC: 92.8} &\\thead{ASR: 95.7, 74.7, 97.4\\\\ACC: 70.5} &\\thead{ASR: 78.1, 99.0, 95.1\\\\ACC: 79.1}\\\\\n\t\t\t\\hline\n\t\t\tno attack &ACC: 92.5 & ACC: 70.4 &ACC: 78.8\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios", "authors": ["Zhen Xiang", "David J. Miller", "George Kesidis"], "url": "https://arxiv.org/abs/2201.08474v2", "attribution": "\"Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios\" by Zhen Xiang, David J. Miller, and George Kesidis, arXiv:2201.08474v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10175v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation Study}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n & H & H \\& V & H \\& V \\\\\n \\midrule\n Dual-Branch & & \\checkmark & \\checkmark \\\\\n Multi-Head Fusion & & & \\checkmark \\\\\n \\midrule\n Single-Person(4) & 0.634 & 0.644 & \\textbf{0.681} \\\\\n Multi-Person(4) & 0.587 & 0.604 & \\textbf{0.682} \\\\\n Action(4) & 0.582 & 0.585 & \\textbf{0.681} \\\\\n Single-Person(12) & 0.655 & 0.670 & \\textbf{0.706} \\\\\n Multi-Person(12) & 0.638 & 0.642 & \\textbf{0.711} \\\\\n Action(12) & 0.603 & 0.603 & \\textbf{0.705} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signals", "authors": ["Zhi Wu", "Dongheng Zhang", "Chunyang Xie", "Cong Yu", "Jinbo Chen", "Yang Hu", "Yan Chen"], "url": "https://arxiv.org/abs/2201.10175v1", "attribution": "\"RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signals\" by Zhi Wu, Dongheng Zhang, Chunyang Xie, Cong Yu, Jinbo Chen, Yang Hu, and Yan Chen, arXiv:2201.10175v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.04216v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Estimated exponent $a$ [$95$\\% CI] for the convergence rate $n^{-a}$ for empirical SE.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\textbf{Estimator} & \\textbf{CTGAN} & \\textbf{TVAE} & \\textbf{CTGAN} & \\textbf{TVAE} \\\\\n \\midrule\n & \\multicolumn{2}{c}{\\textbf{Default synthetic datasets}} & \\multicolumn{2}{c}{\\textbf{Debiased synthetic datasets}} \\\\\n Mean age & 0.01 [-0.16; 0.18] & 0.21 [0.04; 0.39] & 0.50 [0.46; 0.54] & 0.47 [0.44; 0.50]\\\\\n Effect therapy & 0.31 [0.22; 0.40] & 0.09 [-0.13; 0.31] & 0.43 [0.31; 0.55] & 0.46 [0.38; 0.55]\\\\\n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Debiasing Synthetic Data Generated by Deep Generative Models", "authors": ["Alexander Decruyenaere", "Heidelinde Dehaene", "Paloma Rabaey", "Christiaan Polet", "Johan Decruyenaere", "Thomas Demeester", "Stijn Vansteelandt"], "url": "https://arxiv.org/abs/2411.04216v2", "attribution": "\"Debiasing Synthetic Data Generated by Deep Generative Models\" by Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet, Johan Decruyenaere, Thomas Demeester, and Stijn Vansteelandt, arXiv:2411.04216v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2509.05676v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc} \n\\hline\n& \\textbf{Estimate} & \\textbf{S.E.} & \\textbf{t-stat} & \\textbf{p-value} \\\\ \n\\hline\n$\\hat\\beta_0$ & $0.068831$ & $0.0030407$ & $22.637$ & $2.0416$E$-74$ \\\\\n$\\hat\\beta_1$ & $-1.77$E$-05$ & $5.2761$E$-06$ & $-3.3628$ & $0.00084348$ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{OLS estimation results for the regression defined by equation .}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Design and hedging of unit linked life insurance with environmental factors", "authors": ["Katia Colaneri", "Alessandra Cretarola", "Edoardo Lombardo", "Daniele Mancinelli"], "url": "https://arxiv.org/abs/2509.05676v1", "attribution": "\"Design and hedging of unit linked life insurance with environmental factors\" by Katia Colaneri, Alessandra Cretarola, Edoardo Lombardo, and Daniele Mancinelli, arXiv:2509.05676v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16944v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|rl|rl|rl|rl}\n\\toprule\n & \\multicolumn{2}{c}{$p=0.25$} & \\multicolumn{2}{c}{$p=0.20$} & \\multicolumn{2}{c}{$p=0.15$} & \\multicolumn{2}{c}{$p=0.10$}\\\\ \\cline{2-9} \n$n$ & \\textit{found} & \\textit{connectivity} & \\textit{found} & \\textit{connectivity} & \\textit{found} & \\textit{connectivity} & \\textit{found} & \\textit{connectivity}\\\\\n\\midrule\n 11 & 2,571 & 1: 2,494; 2: 7 \t& 1378 & 1: 1378 \t\t& 546 & 1: 546\t \t\t& 157 & 1: 157\t \t\\\\\n 12 & 4,158 & 1: 4,157, 2: 1 \t& 3502 & 1: 3501, 2: 1\t& 1741 & 1: 1741\t\t& 359 & 1: 359\t\t\\\\\n 13 & 4,917 & 1: 4,914, 2: 3 & 6285 & 1: 6284, 2: 1\t& 3645 & 1: 3645\t\t& 612 & 1: 612\t\t\\\\\n 14 & 3,279 & 1: 3,277, 2: 2 \t& 6942 & 1: 6942\t\t& 5155 & 1: 5155\t\t& 849 & 1: 849\t\t\\\\\n 15 & 1,967 & 1: 1,967 \t\t& 5656 & 1: 5656\t\t& 5621 & 1: 5620, 2: 1 \t& 1095 & 1: 1095\t\\\\\n 16 & 1,123 & 1: 1,123 \t\t& 4103 & 1: 4103\t\t& 5328 & 1: 5328\t\t& 1207 & 1: 1207\t\\\\\n 17 & 534 & 1: 534 \t\t& 2720 & 1: 2719, 2: 1 \t& 4584 & 1: 4584\t\t& 1289 & 1: 1289 \\\\\n 18 & 308 & 1: 308 \t\t& 1741 & 1: 1741\t\t& 3843 & 1: 3843\t\t& 1400 & 1: 1400 \\\\\n 19 & 150 & 1: 150 \t\t& 1171 & 1: 1171 \t\t& 3023 & 1: 3023 \t\t& 1393 & 1: 1393 \\\\\n 20 & 73 & 1: 73 \t\t\t& 730 & 1: 730 \t\t& 2318 & 1: 2318 \t\t& 1345 & 1: 1345 \t\\\\\n 21 & 38 & 1: 38 \t\t\t& 413 & 1: 413 \t\t& 1709 & 1: 1709 \t\t& 1327 & 1: 1327 \t\\\\\n 22 & 13 & 1: 13 \t\t\t& 247 & 1: 247 \t\t& 1264 & 1: 1264 \t\t& 1331 & 1: 1331 \t\\\\\n 23 & 8 & 1: 8 \t\t\t& 154 & 1: 154 \t\t& 908 & 1: 908 \t\t& 1167 & 1: 1167 \t\\\\\n 24 & 4 & 1: 4 \t\t\t& 100 & 1: 100 \t\t& 652 & 1: 652 \t\t& 1060 & 1: 1060 \t\\\\\n 25 & 3 & 1: 3 \t\t\t& 59 & 1: 59 \t\t\t& 470 & 1: 470 \t\t& 988 & 1: 988 \t\\\\\n 26 & 1 & 1: 1 \t\t\t& 32 & 1: 32 \t\t\t& 324 & 1: 324 \t\t& 886 & 1: 886 \t\\\\\n 27 & 0 & -- \t\t\t\t& 25 & 1: 25 \t\t\t& 231 & 1: 231 \t\t& 765 & 1: 765 \t\\\\\n 28 & 0 & -- \t\t\t\t& 11 & 1: 11\t\t\t& 165 & 1: 165\t\t\t& 668 & 1: 668\t\t\\\\ \n 29 & 0 & -- \t\t\t\t& 5 & 1: 5 \t\t\t& 100 & 1: 100 \t\t& 554 & 1: 554 \t\\\\\n 30 & 0 & -- \t\t\t\t& 7 & 1: 7\t\t\t& 67 & 1: 67\t\t\t& 501 & 1: 501\t\t\\\\\n 31 & 0 & -- \t\t\t\t& 3 & 1: 3 \t\t\t& 50 & 1: 50 \t\t\t& 360 & 1: 360 \t\\\\\n 32 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 27 & 1: 27\t\t\t& 325 & 1: 325\t\t\\\\ \n 33 & 0 & -- \t\t\t\t& 1 & 1: 1\t\t\t& 34 & 1: 34\t\t\t& 248 & 1: 248\t\t\\\\\n 34 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 18 & 1: 18\t\t\t& 199 & 1: 199\t\t\\\\\n 35 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 8 & 1: 8\t\t\t& 169 & 1: 169\t\t\\\\\n 36 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 11 & 1: 11\t\t\t& 145 & 1: 145\t\t\\\\\n 37 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 8 & 1: 8\t\t\t& 114 & 1: 114\t\t\\\\\n 38 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 7 & 1: 7\t\t\t& 94 & 1: 94\t\t\\\\ \n 39 & 0 & -- \t\t\t\t& 1 & 1: 1 \t\t\t& 2 & 1: 2 \t\t\t& 87 & 1: 87 \t\t\\\\ \n 40 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 3 & 1: 3\t\t\t& 66 & 1: 66\t\t\\\\ \n 41 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 1 & 1: 1\t\t\t& 49 & 1: 49\t\t\\\\ \n 42 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 1 & 1: 1\t\t\t& 43 & 1: 43\t\t\\\\ \n 43 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 0 & --\t\t\t\t& 38 & 1: 38\t\t\\\\ \n 44 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 0 & --\t\t\t\t& 33 & 1: 33\t\t\\\\ \n 45 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 1 & 1: 1\t\t\t& 18 & 1: 18\t\t\\\\ \n 46 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 0 & --\t\t\t\t& 15 & 1: 15\t\t\\\\ \n 47 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 0 & --\t\t\t\t& 19 & 1: 19\t\t\\\\ \n 48 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 0 & --\t\t\t\t& 6 & 1: 6\t\t\\\\ \n 49 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 0 & --\t\t\t\t& 11 & 1: 11\t\t\\\\ \n 50 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 1 & 1: 1\t\t\t& 3 & 1: 3\t\t\\\\ \n 51 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 0 & --\t\t\t\t& 2 & 1: 2\t\t\\\\ \n 52 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 0 & --\t\t\t\t& 2 & 1: 2\t\t\\\\ \n 54 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 0 & --\t\t\t\t& 3 & 1: 3\t\t\\\\ \n 55 & 0 & -- \t\t\t\t& 0 & --\t\t\t & 0 & --\t\t\t\t& 2 & 1: 2\t\t\\\\ \n \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\small Detailed results about graphs generated with the $G(n,p)$ model.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Burning some myths on privacy properties of social networks against active attacks", "authors": ["Serafino Cicerone", "Gabriele Di Stefano", "Sandi Klavžar", "Ismael G. Yero"], "url": "https://arxiv.org/abs/2504.16944v1", "attribution": "\"Burning some myths on privacy properties of social networks against active attacks\" by Serafino Cicerone, Gabriele Di Stefano, Sandi Klavžar, and Ismael G. Yero, arXiv:2504.16944v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.06531v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n Model & RMSE & MdAE & PER10 ($\\%$) & PER20 ($\\%$) \\\\ \n \\midrule\n Gradient boosted trees & 0.62 & 0.24 & 71.0 & 93.8 \\\\ \n Random forests & 0.64 & 0.24 & 70.4 & 93.6 \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Accuracy of the point predictions on the real data with a random forests model in addition the gradient boosted trees model depicted in the main text. The table displays Root Mean Squared Error (RMSE), Median Absolute Error (MdAE), and Percentage Error Range (PER$10$ and PER$20$).}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Uncertainty quantification in automated valuation models with spatially weighted conformal prediction", "authors": ["Anders Hjort", "Gudmund Horn Hermansen", "Johan Pensar", "Jonathan P. Williams"], "url": "https://arxiv.org/abs/2312.06531v2", "attribution": "\"Uncertainty quantification in automated valuation models with spatially weighted conformal prediction\" by Anders Hjort, Gudmund Horn Hermansen, Johan Pensar, and Jonathan P. Williams, arXiv:2312.06531v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.14911v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters of the Gaussians defining synthetic seafloor deformation for the numerical example.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n\t\t\\toprule\n\t\tSymbol & Description & & Value & & Unit \\\\\n\t\t\\cmidrule{3-5}\n\t\t& & $i=1$ & $i=2$ & $i=3$ & \\\\\n\t\t\\midrule\n\t\t$A^{(i)}$ & Amplitude & 4 & 1 & -0.5 & m \\\\\n\t\t$T_{\\text{r}}^{(i)}$ & Rise time & 20 & 10 & 10 & s \\\\\n\t\t$X_{\\text{r}}^{(i)}$ & Rise width in $x$ & 16 & 4 & 4 & km \\\\\n\t\t$Y_{\\text{r}}^{(i)}$ & Rise width in $y$ & 32 & 4 & 8 & km \\\\\n\t\t$X_{\\text{c}}^{(i)}$ & Center in $x$ & 64 & 64 & 70 & km \\\\\n\t\t$Y_{\\text{c}}^{(i)}$ & Center in $y$ & 64 & 88 & 56 & km \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Goal-Oriented Real-Time Bayesian Inference for Linear Autonomous Dynamical Systems With Application to Digital Twins for Tsunami Early Warning", "authors": ["Stefan Henneking", "Sreeram Venkat", "Omar Ghattas"], "url": "https://arxiv.org/abs/2501.14911v1", "attribution": "\"Goal-Oriented Real-Time Bayesian Inference for Linear Autonomous Dynamical Systems With Application to Digital Twins for Tsunami Early Warning\" by Stefan Henneking, Sreeram Venkat, and Omar Ghattas, arXiv:2501.14911v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.17100v2_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{wind speed data: model selection criteria (DIC) and predictive performance measures (Logarithmic Score) with automatic groups construction for $m=3$, $m=5$, and $m=10$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{crrrr}\n\\hline\n\\multirow{2}{*}{Model for $u$} & \\multirow{2}{*}{DIC} & \\multicolumn{3}{c}{Logarithmic Score} \\\\\n& & m=3 & m=5 & m=10 \\\\\n\\hline\n$M_A$ & 3499.78 & 6202.79 & 6692.26 & 7461.16 \\\\\n$M_B$ & \\textbf{1622.16} & \\textbf{6054.93} & \\textbf{6563.31} & \\textbf{7413.71} \\\\\n$M_C$ & 3433.40 & 6214.94 & 6678.89 & 7435.29 \\\\\n$M_D$ & 2078.38 & 7123.03 & 7587.39 & 8342.42 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Automatic cross-validation in structured models: Is it time to leave out leave-one-out?", "authors": ["A. Adin", "E. Krainski", "A. Lenzi", "Z. Liu", "J. Martínez-Minaya", "H. Rue"], "url": "https://arxiv.org/abs/2311.17100v2", "attribution": "\"Automatic cross-validation in structured models: Is it time to leave out leave-one-out?\" by A. Adin, E. Krainski, A. Lenzi, Z. Liu, J. Martínez-Minaya, and H. Rue, arXiv:2311.17100v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00189v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{l|lll}\n\\toprule\n\\textbf{Dataset} & \\textbf{Prompt \\#1} & \\textbf{Prompt \\#2} & \\textbf{ Prompt \\#3} \\\\\n\\midrule\n\\textbf{Yelp} & Check out trusted reviews & Discover popular attractions & Find honest business information \\\\\n\\textbf{Twitter} & Emotion Insights & Exploring Emotional Tweets & Understanding Emotions \\\\\n\\midrule\n\\textbf{SQuAD} & Addressing Unanswerable Questions & Improving Paragraph Understanding & Enhancing Answer Verification \\\\\n\\textbf{RACE} & Designing informative passages & Enhancing dataset diversity & Exploring contextual understanding \\\\\n\\midrule\n\\textbf{CNN/DM} & Sift through crucial content & Delve into valuable content & Abstract significant content \\\\\n\\textbf{XSum} & Exploring importance of context & Studying role of discourse structure & Analyzing role of sentence compression \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{The top-3 prefix prompts for each dataset.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "GPTA: Generative Prompt Tuning Assistant for Synergistic Downstream Neural Network Enhancement with LLMs", "authors": ["Xiao Liu", "Jiawei Zhang"], "url": "https://arxiv.org/abs/2404.00189v1", "attribution": "\"GPTA: Generative Prompt Tuning Assistant for Synergistic Downstream Neural Network Enhancement with LLMs\" by Xiao Liu and Jiawei Zhang, arXiv:2404.00189v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.19947v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of successes of PGD attacks crafted using cross-entropy, KL-divergence, and \\textit{SDI} measure on defences \\textit{AT} and \\textit{TRADES} on CIFAR-10 for Resnet-18.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccc}\n\\hline\nPGD Attack Measure & \\textit{AT} & \\textit{TRADES} & \\\\\n\\hline\n Cross-entropy & \\textbf{52.78}\\tiny{ $\\pm$ 0.10}\n & \\textbf{52.82}\\tiny{ $\\pm$ 0.12} & \\\\\nKL-divergence & 68.03\\tiny{ $\\pm$ 0.15} & 68.87\\tiny{ $\\pm$ 0.14}& \\\\\n\\textbf{SDI} & 53.95\\tiny{ $\\pm$ 0.14}& 54.32\\tiny{ $\\pm$ 0.19}& \\\\ \n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness", "authors": ["Olukorede Fakorede", "Modeste Atsague", "Jin Tian"], "url": "https://arxiv.org/abs/2412.19947v1", "attribution": "\"Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness\" by Olukorede Fakorede, Modeste Atsague, and Jin Tian, arXiv:2412.19947v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.16951v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Water Quality Prediction Results (Numerical Features Only; Standardized).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\\toprule\n Models & RMSE & MAPE & WMAPE & WUPRED & WOPRED \\\\ %\\tnote{1}\n\\midrule\nSADL-II (Original Paper) & 11.50 & N/A & N/A & N/A & N/A \\\\\nBenchmarking & 29.52 & 32.21 & 32.35 & 14.55 & 17.80 \\\\\nLightGBM & \\textcolor{red}{10.84} & 9.33 & 9.46 & \\textcolor{red}{5.16} & 4.30 \\\\\nLinear Regression & 12.04 & 10.74 & 10.92 & 5.66 & 5.25 \\\\\nMLP & 14.20 & 12.92 & 13.16 & 3.83 & 9.33 \\\\\nRandom Forest & 11.47 & 9.71 & 9.87 & 5.35 & 4.51 \\\\\nXGBoost & 10.96 & \\textcolor{red}{9.22} & \\textcolor{red}{9.36} & 5.17 & \\textcolor{red}{4.19} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Beyond Tides and Time: Machine Learning Triumph in Water Quality", "authors": ["Yinpu Li", "Siqi Mao", "Yaping Yuan", "Ziren Wang", "Yixin Kang", "Yuanxin Yao"], "url": "https://arxiv.org/abs/2309.16951v2", "attribution": "\"Beyond Tides and Time: Machine Learning Triumph in Water Quality\" by Yinpu Li, Siqi Mao, Yaping Yuan, Ziren Wang, Yixin Kang, and Yuanxin Yao, arXiv:2309.16951v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.16705v5_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The approximate KL divergence between the targets and $q$ using 1000 updates of particles, averaged over five runs.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccr}\n \\hline\n \\textbf{Targets} & \\textbf{WVI} & \\textbf{NGVI} & \\textbf{GFlowVI} & \\textbf{NGFlowVI}\\\\\n \\hline\n Banana-shaped & 0.15$\\pm$0.02 & 0.32$\\pm$0.01 & 0.21$\\pm$0.02 & 0.12$\\pm$0.02\\\\\n X-shaped & 0.03$\\pm$0.02 & 0.05$\\pm$0.03 & 0.04$\\pm$0.05 & 0.02$\\pm$0.02\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference", "authors": ["Dai Hai Nguyen", "Tetsuya Sakurai", "Hiroshi Mamitsuka"], "url": "https://arxiv.org/abs/2310.16705v5", "attribution": "\"Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference\" by Dai Hai Nguyen, Tetsuya Sakurai, and Hiroshi Mamitsuka, arXiv:2310.16705v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13444v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the BD method with other defense methods against untargeted black-box attacks in terms of ACC degradation and ASR} %The defense performing best while maintaining the lowest model ACC degradation is highlighted in bold.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|cc}\n\t\t\\toprule\n\t\tDataset & Defense & Degradation & SA & SimBA\\\\\n\t\t & Method & of ACC (\\%) & ASR (\\%) & ASR (\\%)\\\\\n\t\t\\midrule\n\t\t & RND & -1.9 & 51.9 & 38.8 \n\t\t \\\\\n\t\t & GF & -0.2 & 95.8 & 88.8 \\\\\n\t\t IMAGE- & FD & -20.8 & 51.8 & 38.7 \\\\\n\t\t NET & AT & -13.4 & 47.2 & 35 \\\\\n\t\t & RND+AT & -16.8 & 20 \n\t\t & 4.7 \\\\\n\t\t & BD(0.4, 0.1) & -2.0 &\\textbf{30} & \\textbf{0}\\\\\n\t\t & BD(0.6, 0.1) & -3.5 &\\textbf{15} & \\textbf{-}\\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Boundary Defense Against Black-box Adversarial Attacks", "authors": ["Manjushree B. Aithal", "Xiaohua Li"], "url": "https://arxiv.org/abs/2201.13444v1", "attribution": "\"Boundary Defense Against Black-box Adversarial Attacks\" by Manjushree B. Aithal and Xiaohua Li, arXiv:2201.13444v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15300v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ccc|ccc|ccc|}\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\t$\\sigma$& & MDDLSCG&&&MSCG &&& ScCG &\\\\\n\t\t\t\t\t\t& itr& Tcpu& $E_n$ & itr& Tcpu& $E_n$ & itr& Tcpu& $E_n$\\\\\n\t\t\t\t\t\t\\hline \n\t\t\t\t\t\t0.1 &37 &1.86e-02 &9.49e-15&63 &3.14e-02 &2.63e-16&200 & 5.40e-02 &5.27e-16\\\\\n\t\t\t\t\t\t0.2 &36 &1.75e-02 &3.16e-15 &63&2.76e-02 &9.07e-15&136 &3.99e-02 &4.51e-15\\\\\n\t\t\t\t\t\t\n\t\t\t\t\t\t0.4 &53 &2.44e-02 &9.40e-15&73 &2.65e-02 &8.75e-15 &126 &4.73e-02 &2.87e-15\\\\\n\t\t\t\t\t\t\n\t\t\t\t\t\t0.6 &40 &2.36e-02 &7.91e-16 &72 &2.78e-02 &5.74e-15 &95 &4.94e-02 &9.32e-15\\\\\n\t\t\t\t\t\t\n\t\t\t\t\t\t0.8 &67 &3.24e-02&5.32e-15 &111 &2.86e-02 &4.48e-15&201 &4.92e-02&6.91e-15\\\\\n\t\t\t\t\t\t\n\t\t\t\t\t\t0.9 &64 &3.28e-02 &8.16e-15 &101 &3.48e-02 &8.74e-15 &184 &4.37e-02&1.08e-15\\\\\n\t\t\t\t\t\t\\hline\n\t\t\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison table for MDDLSCG, MSCG and ScCG for Beale function}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Modified Dai-Liao Spectral Conjugate Gradient Method with Application to Signal Processing", "authors": ["D. R. Sahu", "Shikher Sharma", "Pankaj Gautam"], "url": "https://arxiv.org/abs/2501.15300v2", "attribution": "\"Modified Dai-Liao Spectral Conjugate Gradient Method with Application to Signal Processing\" by D. R. Sahu, Shikher Sharma, and Pankaj Gautam, arXiv:2501.15300v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.24078v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\hline\n \\textbf{Covariate} & \\textbf{Type} & \\textbf{Diff. Unadjusted} & \\textbf{Diff. Adjusted} \\\\\n \\hline\n Distance & Distance & 0.8043 & -0.0001 \\\\\n Google Scholar ID Yes & Binary & -0.1162 & -0.2857 \\\\\n Titles: Assistant Professor & Binary & 0.1258 & 0.0181 \\\\\n Titles: Associate Professor & Binary & 0.0178 & -0.0347 \\\\\n Titles: Professor & Binary & -0.1436 & 0.0166 \\\\\n Working Years & Continuous & -0.2927 & -0.0833 \\\\\n University Code: Bachelor/Master & Binary & 0.0052 & 0.0029 \\\\\n University Code: DRU(H) & Binary & -0.0037 & -0.0100 \\\\\n University Code: DU/VA & Binary & -0.0015 & -0.0130 \\\\\n Department Code: AH & Binary & 0.0119 & 0.0288 \\\\\n Department Code: B & Binary & -0.0273 & -0.0261 \\\\\n Department Code: MHS & Binary & 0.0967 & 0.0487 \\\\\n Department Code: NS & Binary & -0.0541 & -0.0217 \\\\\n Department Code: SS & Binary & 0.0543 & -0.0108 \\\\\n Department Code: TE & Binary & -0.0814 & -0.0190 \\\\\n $\\log_{10}$(i10index) & Continuous & -0.6261 & 0.2676 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Covariate Balance Before and After Matching}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Estimation of Gender Wage Gap in the University of North Carolina System", "authors": ["Zihan Zhang", "Jan Hannig"], "url": "https://arxiv.org/abs/2505.24078v1", "attribution": "\"Estimation of Gender Wage Gap in the University of North Carolina System\" by Zihan Zhang and Jan Hannig, arXiv:2505.24078v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.01238v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Inference values for parameters, KS and CVM values for growth hormone data}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccccc}\n\t\t\tModel\t\t& $\\hat{\\alpha}$ & $\\hat{\\beta}$ & $\\hat{\\delta}$ & KS & p-value(KS) & CVM & p-value(CVM)\\\\ \\hline\n\t\t\tBWeibull($\\hat{{\\theta}}_{\\text{MLE}}$)\t& 1.1344(0.0370) &2.1303(0.1213)& 3.8210(1.0365)\t &0.1429 & 0.8674\n\t\t\t& {\\it 0.0651} & {\\it0.1562}\\\\\n\t\t\tBWeibull($\\hat{{\\theta}}_{\\text{MLqE}}$), $q=0.85$\t\t& 1.2489(0.0548) &2.2989(0.1541)& 3.5132(1.4327) &0.0857 &0.9995 & 0.0357 &0.1608 \\\\\n\t\t\tBGamma($\\hat{{\\theta}}_{\\text{MLE}}$)&1.9136(0.1685) &0.7620(0.0319) &3.6132(1.7301) &0.1429 &0.8674 & {\\it 0.0765} &{\\it 0.1544} \\\\\n\t\t\tBGamma($\\hat{{\\theta}}_{\\text{MLqE}}$), $q=0.87$&2.4648(0.2891) &0.9224(0.0513) &3.2896(2.9941) &0.0857 &0.9995 &0.0349 & {\\bf 0.1610} \\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Bimodal Weibull Distribution: Properties and Inference", "authors": ["Roberto Vila", "Mehmet Niyazi Çankaya"], "url": "https://arxiv.org/abs/2012.01238v1", "attribution": "\"A Bimodal Weibull Distribution: Properties and Inference\" by Roberto Vila and Mehmet Niyazi Çankaya, arXiv:2012.01238v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12599v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classification results on the ImageNet dataset with 0.5bpp using ResNet18 as downstream AI task.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllllll}\n\t\t\\toprule[1pt]\n\t\tMethod & SI & ACC & F1-score & PSNR & MSE & SSIM \\\\\n\t\t\\hline\n\t\toriginal & 128.50 & 89.42\\% & 0.8937 & - & 0 & 1 \\\\\n\t\tTDIC & 57.02 & 78.35\\% & 0.7752 & 29.37 & 0.0016 & 0.8953 \\\\\n\t\tAPIC & 80.60 & 84.53\\% & 0.8451 & 26.44 & 0.0027 & 0.8637 \\\\\n\t\t\\hline\n\t\tSAIC & \\textbf{86.18} & \\textbf{85.25\\%} & \\textbf{0.8522} & 26.40 & 0.0028 & 0.8391 \\\\\n\t\t\\toprule[1pt]\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Semantic-assisted image compression", "authors": ["Qizheng Sun", "Caili Guo", "Yang Yang", "Jiujiu Chen", "Xijun Xue"], "url": "https://arxiv.org/abs/2201.12599v1", "attribution": "\"Semantic-assisted image compression\" by Qizheng Sun, Caili Guo, Yang Yang, Jiujiu Chen, and Xijun Xue, arXiv:2201.12599v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.18625v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of Balanced-Inventory Strategy across different thresholds.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccccc|cccccc}\n \\toprule\n \\textbf{Threshold} & \\multicolumn{6}{c|}{\\textbf{Logistic Regression}} & \\multicolumn{6}{c}{\\textbf{Random Forest}} \\\\\n \\cmidrule(r){2-7} \\cmidrule(l){8-13}\n & \\# Fills / Day & Avg Ret & Stdev & Holding & Sharpe & p-value & \\# Fills / Day & Avg Ret & Stdev & Holding & Sharpe & p-value \\\\\n \\midrule\n 0.00 & 15380 & $-0.44$ & $2.73$ & 11.39 & $-109.00$ & $0.5000$ & 15380 & $-0.44$ & $2.73$ & 11.39 & $-109.00$ & $0.5000$ \\\\\n 0.02 & 14266 & $-0.45$ & $2.81$ & 12.28 & $-99.98$ & $0.6369$ & 15066 & $-0.43$ & $2.68$ & 12.54 & $-106.78$ & $0.3933$ \\\\\n 0.04 & 12650 & $-0.46$ & $2.97$ & 13.85 & $-92.82$ & $0.8177$ & 14626 & $-0.43$ & $2.71$ & 12.92 & $-105.31$ & $0.4158$ \\\\\n 0.06 & 10972 & $-0.45$ & $3.20$ & 15.97 & $-74.99$ & $0.6278$ & 13868 & $-0.44$ & $2.77$ & 13.62 & $-99.97$ & $0.5441$ \\\\\n 0.08 & 9364 & $-0.44$ & $3.46$ & 18.71 & $-67.29$ & $0.5270$ & 12856 & $-0.45$ & $2.87$ & 14.69 & $-98.61$ & $0.6727$ \\\\\n 0.10 & 7780 & $-0.44$ & $3.77$ & 22.52 & $-56.42$ & $0.5230$ & 11700 & $-0.44$ & $2.97$ & 16.14 & $-96.94$ & $0.5340$ \\\\\n 0.12 & 6332 & $-0.44$ & $4.24$ & 27.67 & $-45.39$ & $0.5187$ & 10354 & $-0.44$ & $3.17$ & 18.24 & $-91.83$ & $0.5305$ \\\\\n 0.14 & 4956 & $-0.43$ & $4.72$ & 35.35 & $-37.41$ & $0.4469$ & 8918 & $-0.45$ & $3.42$ & 21.18 & $-75.09$ & $0.5897$ \\\\\n 0.16 & 3598 & $-0.43$ & $5.74$ & 48.68 & $-29.20$ & $0.4867$ & 7630 & $-0.41$ & $3.68$ & 24.75 & $-63.28$ & $0.2555$ \\\\\n 0.18 & 2494 & $-0.40$ & $6.72$ & 70.24 & $-23.20$ & $0.3908$ & 6374 & $-0.41$ & $4.07$ & 29.63 & $-55.68$ & $0.3011$ \\\\\n 0.20 & 1644 & $-0.39$ & $8.10$ & 106.53 & $-21.26$ & $0.3975$ & 5224 & $-0.36$ & $4.54$ & 36.15 & $-42.96$ & $0.1011$ \\\\\n 0.22 & 1060 & $-0.22$ & $10.63$ & 165.32 & $-15.88$ & $0.2504$ & 4146 & $-0.39$ & $5.17$ & 45.54 & $-42.23$ & $0.2794$ \\\\\n 0.24 & 654 & $+0.71$ & $13.69$ & 267.66 & $+11.97$ & $0.0160$ & 3222 & $-0.39$ & $5.90$ & 58.61 & $-34.28$ & $0.3252$ \\\\\n 0.26 & 426 & $+1.21$ & $18.10$ & 409.26 & $+26.12$ & $0.0303$ & 2460 & $-0.32$ & $6.80$ & 76.73 & $-24.85$ & $0.1948$ \\\\\n 0.28 & 270 & $+0.90$ & $22.52$ & 595.97 & $+12.32$ & $0.1650$ & 1826 & $-0.21$ & $7.82$ & 103.27 & $-18.78$ & $0.1076$ \\\\\n 0.30 & - & - & - & - & - & - & 1330 & $+0.03$ & $8.90$ & 141.75 & $+1.30$ & $0.0287$ \\\\\n 0.32 & - & - & - & - & - & - & 972 & $+0.31$ & $9.65$ & 194.22 & $+12.08$ & $0.0078$ \\\\\n 0.34 & - & - & - & - & - & - & 692 & $+0.59$ & $12.20$ & 272.14 & $+13.48$ & $0.0133$ \\\\\n 0.36 & - & - & - & - & - & - & 454 & $+0.02$ & $14.45$ & 414.03 & $+0.30$ & $0.2501$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "To Make, or to Take, That Is the Question: Impact of LOB Mechanics on Natural Trading Strategies", "authors": ["Jakob Albers", "Mihai Cucuringu", "Sam Howison", "Alexander Y. Shestopaloff"], "url": "https://arxiv.org/abs/2502.18625v1", "attribution": "\"To Make, or to Take, That Is the Question: Impact of LOB Mechanics on Natural Trading Strategies\" by Jakob Albers, Mihai Cucuringu, Sam Howison, and Alexander Y. Shestopaloff, arXiv:2502.18625v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.11188v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{A2C hyperparameters}\n\\begin{tabular}{c|c}\n \\hline\n Parameter & Value \\\\\n \\hline\n Discounting factor $\\gamma$ & $0.9$ \\\\\n Learning rate $\\alpha$ & $1\\cdot10^{-4}$ \\\\\n Rollout length & $10$ \\\\\n Entropy coefficient $\\beta$ & $0.01$ \\\\\n GAE $\\lambda$ & $1.0$ \\\\\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platform", "authors": ["David Cotton", "Zenon Chaczko"], "url": "https://arxiv.org/abs/2101.11188v1", "attribution": "\"GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platform\" by David Cotton and Zenon Chaczko, arXiv:2101.11188v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.07105v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rr|rrr|rrr}\n\t\t\\toprule\n\t\t\\multirow{2}{*}{Ref} &\n\t\t\\multirow{2}{*}{\\# nodes} &\n\t\t\\multicolumn{3}{c|}{fixed-point method} &\n\t\t\\multicolumn{3}{c}{semismooth Newton method} \\\\\n\t\t&& \n\t\titer & time [s] & avg time [s] & iter & time [s]& avg time [s] \\\\\n\t\t\\midrule\n 4 & 545 & 532 & 2.50 & 4.704e$-$03 & 11 & 0.07 & 6.244e$-$03 \\\\\n 5 & 2113 & 536 & 8.29 & 1.547e$-$02 & 13 & 0.33 & 2.519e$-$02 \\\\\n 6 & 8321 & 542 & 33.47 & 6.175e$-$02 & 13 & 1.65 & 1.270e$-$01 \\\\\n 7 & 33025 & 517 & 138.21 & 2.673e$-$01 & 12 & 9.20 & 7.670e$-$01 \\\\\n 8 & 131585 & 574 & 678.78 & 1.183e$+$00 & 12 & 58.74 & 4.895e$+$00 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Number of iterations, total calculation time and average time (per iteration) for different refinements of a coarse mesh.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Numerical solution of optimal control problems using quadratic transport regularization", "authors": ["Nicolas Borchard", "Gerd Wachsmuth"], "url": "https://arxiv.org/abs/2503.07105v1", "attribution": "\"Numerical solution of optimal control problems using quadratic transport regularization\" by Nicolas Borchard and Gerd Wachsmuth, arXiv:2503.07105v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12408v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Properties of the network data sets. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccccc}\nNetwork & $|V|$ & average & average degree \\\\\n&& degree & centrality \\\\\n\\midrule\\midrule\n\\textbf{Boston} & 431 & 2.92 & 0.005 \\\\\n\\textbf{Daniels County} & 631 & 2.53 & 0.008 \\\\\n\\textbf{Los Angeles} & 561 & 2.85 & 0.001\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Networked Restless Multi-Armed Bandits for Mobile Interventions", "authors": ["Han-Ching Ou", "Christoph Siebenbrunner", "Jackson Killian", "Meredith B Brooks", "David Kempe", "Yevgeniy Vorobeychik", "Milind Tambe"], "url": "https://arxiv.org/abs/2201.12408v1", "attribution": "\"Networked Restless Multi-Armed Bandits for Mobile Interventions\" by Han-Ching Ou, Christoph Siebenbrunner, Jackson Killian, Meredith B Brooks, David Kempe, Yevgeniy Vorobeychik, and Milind Tambe, arXiv:2201.12408v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11451v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\hline\nScenario & $\\delta$ & Runtime \\\\\n\\hline\nParis & 0.1s & 13.65ms \\\\\nParis & 0.2s & 0.01ms \\\\\nNew York & 0.1s & 92.50ms \\\\\nSingapore & 0.1s & 33.33ms \\\\\nSingapore & 0.2s & 23.01ms \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Latex default table}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Controlling by Showing: i-Mimic: A Video-based Method to Control Robotic Arms", "authors": ["Debarati B. Chakraborty", "Mukesh Sharma", "Bhaskar Vijay"], "url": "https://arxiv.org/abs/2101.11451v1", "attribution": "\"Controlling by Showing: i-Mimic: A Video-based Method to Control Robotic Arms\" by Debarati B. Chakraborty, Mukesh Sharma, and Bhaskar Vijay, arXiv:2101.11451v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10459v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance of best regressors discovered through Bayesian hyperparameter optimization compared to Weighted Ensembles Regressor from AutoML.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|}\n\\hline\nMetric & Decision Tree & K-Nearest Neighbors & XGBoost & Neural Network & AutoML \\\\ \\hline\nCoefficient of Determination (R2) & 0.440 & 0.306 & 0.595 & 0.545 & \\textbf{0.605} \\\\ \\hline\nMean Squared Error (MSE) & 1.156 & 1.270 & \\textbf{0.978} & 1.031 & 0.995 \\\\ \\hline\nMean Absolute Error (MAE) & 0.297 & 0.412 & 0.208 & 0.263 & \\textbf{0.182} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames", "authors": ["Lyle Regenwetter", "Colin Weaver", "Faez Ahmed"], "url": "https://arxiv.org/abs/2201.10459v3", "attribution": "\"FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames\" by Lyle Regenwetter, Colin Weaver, and Faez Ahmed, arXiv:2201.10459v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.12502v3_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrrrrrrrrrr}\n\\toprule\nModel & \\multicolumn{3}{r}{NLL} & \\multicolumn{3}{r}{RMSE} & \\multicolumn{3}{r}{MAE} & \\multicolumn{3}{r}{Coverage} & \\multicolumn{3}{r}{Training Time (s)} \\\\\n & Matérn 32 & Periodic & RBF & Matérn 32 & Periodic & RBF & Matérn 32 & Periodic & RBF & Matérn 32 & Periodic & RBF & Matérn 32 & Periodic & RBF \\\\\n\\midrule\nNP & $0.565\\pm0.004$ & $1.340\\pm0.001$ & $0.232\\pm0.004$ & $0.520\\pm0.001$ & $0.949\\pm0.001$ & $0.446\\pm0.001$ & $0.366\\pm0.001$ & $0.758\\pm0.000$ & $0.292\\pm0.001$ & $0.941\\pm0.001$ & $0.956\\pm0.000$ & $0.940\\pm0.001$ & $634.200\\pm0.490$ & $653.600\\pm1.778$ & $642.800\\pm4.488$ \\\\\nCNP & $0.463\\pm0.002$ & $1.119\\pm0.001$ & $0.101\\pm0.004$ & $0.501\\pm0.000$ & $0.779\\pm0.001$ & $0.428\\pm0.001$ & $0.343\\pm0.001$ & $0.595\\pm0.001$ & $0.270\\pm0.001$ & $0.939\\pm0.001$ & $0.945\\pm0.001$ & $0.939\\pm0.001$ & $615.400\\pm0.872$ & $628.400\\pm1.166$ & $620.400\\pm2.542$ \\\\\nBNP & $0.443\\pm0.002$ & $1.107\\pm0.001$ & $0.042\\pm0.005$ & $0.475\\pm0.001$ & $0.779\\pm0.001$ & $0.381\\pm0.001$ & $0.353\\pm0.001$ & $0.614\\pm0.001$ & $0.277\\pm0.001$ & $0.917\\pm0.001$ & $0.925\\pm0.001$ & $0.908\\pm0.001$ & $792.000\\pm1.761$ & $803.800\\pm2.596$ & $793.600\\pm3.530$ \\\\\nANP & $0.086\\pm0.027$ & $0.824\\pm0.013$ & $-0.298\\pm0.018$ & $0.461\\pm0.003$ & $0.719\\pm0.002$ & $0.385\\pm0.002$ & $0.285\\pm0.002$ & $0.497\\pm0.003$ & $0.215\\pm0.002$ & $0.933\\pm0.003$ & $0.931\\pm0.003$ & $0.940\\pm0.001$ & $678.400\\pm1.030$ & $685.400\\pm2.839$ & $675.200\\pm3.247$ \\\\\nCANP & $0.080\\pm0.006$ & $0.786\\pm0.007$ & $-0.301\\pm0.023$ & $0.459\\pm0.000$ & $0.711\\pm0.002$ & $0.383\\pm0.002$ & $0.282\\pm0.000$ & $0.488\\pm0.002$ & $0.213\\pm0.001$ & $0.931\\pm0.002$ & $0.930\\pm0.004$ & $0.937\\pm0.002$ & $640.000\\pm1.549$ & $651.600\\pm1.122$ & $642.200\\pm2.990$ \\\\\nBANP & $0.036\\pm0.008$ & $0.738\\pm0.008$ & $-0.335\\pm0.011$ & $0.407\\pm0.001$ & $0.693\\pm0.002$ & $0.315\\pm0.001$ & $0.282\\pm0.001$ & $0.498\\pm0.002$ & $0.215\\pm0.001$ & $0.928\\pm0.002$ & $0.903\\pm0.001$ & $0.925\\pm0.003$ & $1095.200\\pm1.241$ & $1103.200\\pm1.828$ & $1100.200\\pm4.042$ \\\\\nConvCNP & $-0.014\\pm0.002$ & $0.551\\pm0.002$ & $-0.454\\pm0.002$ & $0.450\\pm0.000$ & $0.674\\pm0.000$ & $0.369\\pm0.000$ & $0.276\\pm0.000$ & $0.457\\pm0.000$ & $0.201\\pm0.000$ & $0.946\\pm0.002$ & $0.946\\pm0.001$ & $0.947\\pm0.001$ & $1165.400\\pm2.159$ & $1173.600\\pm6.046$ & $1145.200\\pm1.356$ \\\\\nTNP-D & $-0.020\\pm0.003$ & $0.536\\pm0.003$ & $-0.454\\pm0.002$ & $0.451\\pm0.001$ & $0.681\\pm0.001$ & $0.369\\pm0.000$ & $0.276\\pm0.000$ & $0.461\\pm0.001$ & $0.202\\pm0.000$ & $0.947\\pm0.001$ & $0.949\\pm0.001$ & $0.949\\pm0.002$ & $856.000\\pm8.124$ & $866.600\\pm3.356$ & $839.200\\pm2.478$ \\\\\nTNP-KR: PERF & $-0.024\\pm0.001$ & $0.514\\pm0.001$ & $-0.459\\pm0.002$ & $0.451\\pm0.000$ & $0.673\\pm0.000$ & $0.369\\pm0.000$ & $0.276\\pm0.000$ & $0.455\\pm0.000$ & $0.202\\pm0.000$ & $0.947\\pm0.001$ & $0.948\\pm0.001$ & $0.949\\pm0.001$ & $974.800\\pm1.356$ & $972.600\\pm4.179$ & $948.400\\pm1.249$ \\\\\nTNP-KR: DKA & $-0.025\\pm0.002$ & $0.510\\pm0.001$ & $-0.464\\pm0.002$ & $0.451\\pm0.000$ & $0.670\\pm0.000$ & $0.369\\pm0.001$ & $0.276\\pm0.000$ & $0.453\\pm0.000$ & $0.201\\pm0.000$ & $0.948\\pm0.002$ & $0.948\\pm0.001$ & $0.949\\pm0.001$ & $956.600\\pm2.943$ & $954.600\\pm2.502$ & $932.600\\pm1.965$ \\\\\nTNP-KR: SA & $-0.027\\pm0.002$ & $0.491\\pm0.001$ & $-0.462\\pm0.002$ & $0.450\\pm0.000$ & $0.665\\pm0.001$ & $0.369\\pm0.000$ & $0.276\\pm0.000$ & $0.448\\pm0.000$ & $0.201\\pm0.000$ & $0.948\\pm0.002$ & $0.949\\pm0.001$ & $0.949\\pm0.002$ & $986.600\\pm7.026$ & $991.800\\pm4.236$ & $970.000\\pm1.612$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Transformer Neural Processes - Kernel Regression", "authors": ["Daniel Jenson", "Jhonathan Navott", "Mengyan Zhang", "Makkunda Sharma", "Elizaveta Semenova", "Seth Flaxman"], "url": "https://arxiv.org/abs/2411.12502v3", "attribution": "\"Transformer Neural Processes - Kernel Regression\" by Daniel Jenson, Jhonathan Navott, Mengyan Zhang, Makkunda Sharma, Elizaveta Semenova, and Seth Flaxman, arXiv:2411.12502v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.03731v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{MISE per method on dry bean dataset} for $\\beta = 0$ and varying levels of $\\alpha$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n $\\beta = 0$\\\\\n \\hline \\\\ \n \\hline \\\\\n & \\multicolumn{5}{c}{$\\alpha$}\\\\\n \\cdashline{2-6}\\\\\n Model & 0.0 & 1.0 & 2.0 & 3.0 & 4.0 \\\\\n \\hline \\\\\n Linear Regression & 2.83 \\scriptsize{± 0.01} & 3.27 \\scriptsize{± 0.03} & 3.80 \\scriptsize{± 0.03} & 4.19 \\scriptsize{± 0.04} & 4.44 \\scriptsize{± 0.04} \\\\\n CART & 0.86 \\scriptsize{± 0.03} & 1.04 \\scriptsize{± 0.03} & 1.44 \\scriptsize{± 0.07} & 1.93 \\scriptsize{± 0.10} & 2.18 \\scriptsize{± 0.11} \\\\\n xgboost & 0.55 \\scriptsize{± 0.03} & 0.69 \\scriptsize{± 0.02} & 1.11 \\scriptsize{± 0.08} & 1.58 \\scriptsize{± 0.07} & 1.88 \\scriptsize{± 0.10} \\\\\n MLP & 2.83 \\scriptsize{± 0.01} & 0.54 \\scriptsize{± 0.08} & 1.17 \\scriptsize{± 0.09} & 1.98 \\scriptsize{± 0.05} & 2.52 \\scriptsize{± 0.09} \\\\\n \\hdashline\\\\\n DRNet & 0.41 \\scriptsize{± 0.03} & 0.60 \\scriptsize{± 0.06} & 0.99 \\scriptsize{± 0.12} & 1.74 \\scriptsize{± 0.19} & 1.92 \\scriptsize{± 0.23} \\\\\n VCNet & 0.34 \\scriptsize{± 0.04} & 0.69 \\scriptsize{± 0.04} & 1.04 \\scriptsize{± 0.09} & 1.41 \\scriptsize{± 0.09} & 1.69 \\scriptsize{± 0.17} \\\\\n \\hdashline\\\\\n CBRNet(MMD$_{lin}$) & \\textbf{0.25} \\scriptsize{± 0.04} & \\textit{0.35} \\scriptsize{± 0.05} & \\textbf{0.41} \\scriptsize{± 0.10} & 0.64 \\scriptsize{± 0.08} & \\textit{0.88} \\scriptsize{± 0.14} \\\\\n CBRNet(MMD$_{rbf}$) & \\textit{0.26} \\scriptsize{± 0.04} & \\textbf{0.34} \\scriptsize{± 0.05} & \\textit{0.42} \\scriptsize{± 0.07} & \\textbf{0.61} \\scriptsize{± 0.08} & 0.92 \\scriptsize{± 0.09} \\\\\n CBRNet(Wass) & 0.28 \\scriptsize{± 0.05} & \\textbf{0.34} \\scriptsize{± 0.05} & \\textit{0.42} \\scriptsize{± 0.10} & \\textit{0.62} \\scriptsize{± 0.08} & \\textbf{0.86} \\scriptsize{± 0.16} \\\\\n \\hline \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Using representation balancing to learn conditional-average dose responses from clustered data", "authors": ["Christopher Bockel-Rickermann", "Toon Vanderschueren", "Jeroen Berrevoets", "Tim Verdonck", "Wouter Verbeke"], "url": "https://arxiv.org/abs/2309.03731v2", "attribution": "\"Using representation balancing to learn conditional-average dose responses from clustered data\" by Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets, Tim Verdonck, and Wouter Verbeke, arXiv:2309.03731v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14913v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n \\hline\n basis & \\multicolumn{4}{|c|}{$V_{h}$}& \\multicolumn{4}{|c|}{$V_{h}^{*}$}\\\\\n \\hline\n h &$L^{2}$error & order & $H^{1}$error & order & $L^{2}$error & order & $H^{1}$error & order \\\\\n \\hline\n $2^{-2}$ &3.775e-03&-&1.134e-01&-&7.053e-06&-&1.870e-04&-\\\\\n \\hline\n $2^{-3}$ &4.499e-04&3.069&2.895e-02&1.970&1.463e-06&2.269&7.568e-05&1.305\\\\\n \\hline\n $2^{-4}$ &5.545e-05&3.020&7.279e-03&1.992&2.096e-07&2.803&2.197e-05&1.784\\\\\n \\hline\n $2^{-5}$ &6.906e-06&3.005&1.822e-03&1.998&2.715e-08&2.948&5.728e-06&1.939\\\\\n \\hline\n $2^{-6}$ &8.625e-07&3.001&4.557e-04&1.999&3.426e-09&2.987&1.448e-06&1.984 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{$L^{2}$ and $H^{1}$error and corresponding order using $P^{2}$ Lagrange element, $c(x)=-5$.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A PINN-enriched finite element method for linear elliptic problems", "authors": ["Xiao Chen", "Yixin Luo", "Jingrun Chen"], "url": "https://arxiv.org/abs/2503.14913v1", "attribution": "\"A PINN-enriched finite element method for linear elliptic problems\" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11944v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of the dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n \\toprule\n \\multirow{2}{*}{Sequence} & \n \\multirow{2}{*}{\\shortstack{Trajectory \\\\Length (m)}} &\n \\multirow{2}{*}{\\shortstack{Duration \\\\(seconds)}} &\n \\multirow{2}{*}{\\shortstack{\\# Avg. \\\\Points}} \\\\\n {} \\\\\n \\midrule\nBaker-Barry Tunnel (Empty)\n& 860.31 %\n& 83.7 %\n& 55.7k \\\\ %\nBaker-Barry Tunnel (Vehicles)\n& 906.86 %\n& 65.5 %\n& 36.5k \\\\ %\nRobin Williams Tunnel\n& 688.76 %\n& 30.0 %\n& 43.3k \\\\ %\nBrisbane Lagoon Freeway\n& 4941.80 %\n& 176.3 %\n& 40.0k \\\\ %\nSan Francisco City\n& 1378.14 %\n& 450.0 %\n& 120.1k \\\\ %\nCARLA Town04 (Straight Walls)\n& 599.91 %\n& 46.4 %\n& 78.8k \\\\ %\nCARLA Town05 (Curved Walls)\n& 426.81 %\n& 76.0 %\n& 80.4k \\\\ %\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "DICP: Doppler Iterative Closest Point Algorithm", "authors": ["Bruno Hexsel", "Heethesh Vhavle", "Yi Chen"], "url": "https://arxiv.org/abs/2201.11944v2", "attribution": "\"DICP: Doppler Iterative Closest Point Algorithm\" by Bruno Hexsel, Heethesh Vhavle, and Yi Chen, arXiv:2201.11944v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.02635v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Evaluation Performance Comparison with Different Baselines. $CD$: average squared distance between two points, $L_{1}$: norm distance between two points}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n\\hline\n\\hline\n&\\multicolumn{2}{|c|}{GPRNet} &\\multicolumn{2}{|c|}{PCN} &\\multicolumn{2}{|c|}{PF-Net}\\\\ \n\\hline\n\\hline\nCD & \\multicolumn{2}{|c|}{6.214}&\\multicolumn{2}{|c|}{6.965} & \\multicolumn{2}{|c|}{{7.005}} \\\\\n\\emph{L}1 & \\multicolumn{2}{|c|}{22.97} &\\multicolumn{2}{|c|}{24.90} & \\multicolumn{2}{|c|}{{33.68}} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "GPR-based Model Reconstruction System for Underground Utilities Using GPRNet", "authors": ["Jinglun Feng", "Liang Yang", "Ejup Hoxha", "Diar Sanakov", "Stanislav Sotnikov", "Jizhong Xiao"], "url": "https://arxiv.org/abs/2011.02635v3", "attribution": "\"GPR-based Model Reconstruction System for Underground Utilities Using GPRNet\" by Jinglun Feng, Liang Yang, Ejup Hoxha, Diar Sanakov, Stanislav Sotnikov, and Jizhong Xiao, arXiv:2011.02635v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.18462v2_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lllllll}\n\\toprule\n \\texttt{Variable} & Coefficient & Standard Error & t-Value & p-Value & CI Lower & CI Upper \\\\\n\\midrule\n \\texttt{const} & 14.6956 & 4.805 & 3.058 & 0.002 & 5.274 & 24.117 \\\\\n \\texttt{has\\_decoy} & 1.9132 & 1.277 & 1.499 & 0.134 & -0.590 & 4.416 \\\\\n \\texttt{rank\\_1} & -4.2052 & 1.985 & -2.118 & 0.034 & -8.098 & -0.313 \\\\\n \\texttt{rank\\_2} & -6.5017 & 2.120 & -3.067 & 0.002 & -10.658 & -2.346 \\\\\n \\texttt{rank\\_3} & -2.5839 & 2.160 & -1.196 & 0.232 & -6.819 & 1.652 \\\\\n \\texttt{rank\\_4} & -7.8692 & 2.105 & -3.739 & 0.000 & -11.995 & -3.743 \\\\\n \\texttt{rank\\_5} & -9.0114 & 2.128 & -4.234 & 0.000 & -13.184 & -4.839 \\\\\n \\texttt{rank\\_6} & -10.7383 & 2.331 & -4.607 & 0.000 & -15.308 & -6.169 \\\\\n \\texttt{rank\\_7} & -9.7773 & 2.308 & -4.236 & 0.000 & -14.302 & -5.252 \\\\\n \\texttt{rank\\_8} & -9.0515 & 2.483 & -3.645 & 0.000 & -13.921 & -4.182 \\\\\n \\texttt{rank\\_9} & -12.9871 & 2.523 & -5.148 & 0.000 & -17.933 & -8.041 \\\\\n \\texttt{task\\_id\\_10} & -2.1766 & 2.765 & -0.787 & 0.431 & -7.597 & 3.244 \\\\\n \\texttt{task\\_id\\_11} & -0.7174 & 2.861 & -0.251 & 0.802 & -6.326 & 4.892 \\\\\n \\texttt{task\\_id\\_12} & -2.8597 & 2.866 & -0.998 & 0.318 & -8.478 & 2.759 \\\\\n \\texttt{task\\_id\\_2} & -1.8943 & 2.827 & -0.670 & 0.503 & -7.437 & 3.648 \\\\\n \\texttt{task\\_id\\_3} & 3.6144 & 2.704 & 1.336 & 0.181 & -1.688 & 8.917 \\\\\n \\texttt{task\\_id\\_4} & 1.1273 & 2.820 & 0.400 & 0.689 & -4.401 & 6.656 \\\\\n \\texttt{task\\_id\\_7} & -1.0875 & 2.820 & -0.386 & 0.700 & -6.616 & 4.441 \\\\\n \\texttt{task\\_id\\_8} & -1.9504 & 2.886 & -0.676 & 0.499 & -7.608 & 3.708 \\\\\n \\texttt{student\\_id\\_2} & -5.2057 & 6.624 & -0.786 & 0.432 & -18.193 & 7.782 \\\\\n \\texttt{student\\_id\\_3} & 5.2426 & 5.576 & 0.940 & 0.347 & -5.690 & 16.175 \\\\\n \\texttt{student\\_id\\_4} & -0.9130 & 9.369 & -0.097 & 0.922 & -19.282 & 17.456 \\\\\n \\texttt{student\\_id\\_5} & -3.1495 & 7.038 & -0.448 & 0.655 & -16.948 & 10.649 \\\\\n \\texttt{student\\_id\\_6} & -3.2744 & 4.893 & -0.669 & 0.503 & -12.867 & 6.318 \\\\\n \\texttt{student\\_id\\_7} & 5.8010 & 5.366 & 1.081 & 0.280 & -4.720 & 16.322 \\\\\n \\texttt{student\\_id\\_8} & -0.4306 & 7.319 & -0.059 & 0.953 & -14.780 & 13.919 \\\\\n \\texttt{student\\_id\\_9} & -0.8358 & 5.730 & -0.146 & 0.884 & -12.070 & 10.399 \\\\\n\\texttt{student\\_id\\_10} & -4.2130 & 6.284 & -0.670 & 0.503 & -16.534 & 8.108 \\\\\n\\texttt{student\\_id\\_11} & -3.9263 & 5.028 & -0.781 & 0.435 & -13.785 & 5.933 \\\\\n\\texttt{student\\_id\\_12} & -1.9897 & 5.682 & -0.350 & 0.726 & -13.129 & 9.150 \\\\\n\\texttt{student\\_id\\_13} & 5.9915 & 5.423 & 1.105 & 0.269 & -4.641 & 16.624 \\\\\n\\texttt{student\\_id\\_14} & 7.4104 & 5.355 & 1.384 & 0.166 & -3.089 & 17.909 \\\\\n\\texttt{student\\_id\\_15} & 6.6235 & 5.475 & 1.210 & 0.226 & -4.110 & 17.357 \\\\\n\\texttt{student\\_id\\_16} & -3.2462 & 5.689 & -0.571 & 0.568 & -14.400 & 7.907 \\\\\n\\texttt{student\\_id\\_17} & 1.7870 & 6.174 & 0.289 & 0.772 & -10.318 & 13.892 \\\\\n\\texttt{student\\_id\\_18} & -4.5010 & 5.212 & -0.864 & 0.388 & -14.720 & 5.718 \\\\\n\\texttt{student\\_id\\_19} & -5.0407 & 5.048 & -0.998 & 0.318 & -14.939 & 4.857 \\\\\n\\texttt{student\\_id\\_20} & -3.6142 & 6.831 & -0.529 & 0.597 & -17.007 & 9.779 \\\\\n\\texttt{student\\_id\\_21} & 33.8699 & 5.705 & 5.937 & 0.000 & 22.684 & 45.056 \\\\\n\\texttt{student\\_id\\_22} & -3.8870 & 5.312 & -0.732 & 0.464 & -14.302 & 6.528 \\\\\n\\texttt{student\\_id\\_23} & -3.9145 & 5.221 & -0.750 & 0.453 & -14.151 & 6.322 \\\\\n\\texttt{student\\_id\\_24} & 23.6064 & 6.007 & 3.930 & 0.000 & 11.828 & 35.384 \\\\\n\\texttt{student\\_id\\_25} & -4.3527 & 5.306 & -0.820 & 0.412 & -14.755 & 6.050 \\\\\n\\texttt{student\\_id\\_26} & 2.9870 & 6.140 & 0.486 & 0.627 & -9.052 & 15.026 \\\\\n\\texttt{student\\_id\\_27} & -4.4778 & 5.258 & -0.852 & 0.394 & -14.787 & 5.831 \\\\\n\\texttt{student\\_id\\_28} & -2.3534 & 5.145 & -0.457 & 0.647 & -12.442 & 7.735 \\\\\n\\texttt{student\\_id\\_29} & -6.1016 & 5.379 & -1.134 & 0.257 & -16.648 & 4.445 \\\\\n\\texttt{student\\_id\\_30} & -3.1811 & 6.075 & -0.524 & 0.601 & -15.093 & 8.731 \\\\\n\\texttt{student\\_id\\_31} & 0.3445 & 5.736 & 0.060 & 0.952 & -10.902 & 11.591 \\\\\n\\texttt{student\\_id\\_32} & -4.0883 & 5.388 & -0.759 & 0.448 & -14.653 & 6.476 \\\\\n\\texttt{student\\_id\\_33} & -6.3012 & 5.444 & -1.158 & 0.247 & -16.974 & 4.372 \\\\\n\\texttt{student\\_id\\_34} & -6.6902 & 5.457 & -1.226 & 0.220 & -17.390 & 4.009 \\\\\n\\texttt{student\\_id\\_35} & -1.5817 & 5.602 & -0.282 & 0.778 & -12.565 & 9.401 \\\\\n\\texttt{student\\_id\\_36} & 2.7925 & 5.518 & 0.506 & 0.613 & -8.027 & 13.611 \\\\\n\\texttt{student\\_id\\_37} & -3.8948 & 5.293 & -0.736 & 0.462 & -14.272 & 6.482 \\\\\n\\texttt{student\\_id\\_38} & -5.3146 & 6.595 & -0.806 & 0.420 & -18.246 & 7.617 \\\\\n\\texttt{student\\_id\\_39} & -3.2159 & 5.487 & -0.586 & 0.558 & -13.974 & 7.542 \\\\\n\\texttt{student\\_id\\_40} & -0.9360 & 5.527 & -0.169 & 0.866 & -11.773 & 9.901 \\\\\n\\texttt{student\\_id\\_41} & -3.0055 & 4.874 & -0.617 & 0.538 & -12.562 & 6.551 \\\\\n\\texttt{student\\_id\\_42} & -6.2148 & 5.567 & -1.116 & 0.264 & -17.130 & 4.701 \\\\\n\\texttt{student\\_id\\_43} & -4.9234 & 5.969 & -0.825 & 0.410 & -16.626 & 6.780 \\\\\n\\texttt{student\\_id\\_44} & -3.4944 & 5.222 & -0.669 & 0.503 & -13.733 & 6.745 \\\\\n\\texttt{student\\_id\\_45} & 0.5996 & 4.925 & 0.122 & 0.903 & -9.057 & 10.256 \\\\\n\\texttt{student\\_id\\_46} & -3.0053 & 5.559 & -0.541 & 0.589 & -13.904 & 7.893 \\\\\n\\texttt{student\\_id\\_47} & -5.4970 & 5.511 & -0.997 & 0.319 & -16.302 & 5.309 \\\\\n\\texttt{student\\_id\\_48} & -2.4336 & 5.700 & -0.427 & 0.669 & -13.608 & 8.741 \\\\\n\\texttt{student\\_id\\_49} & -2.3961 & 5.085 & -0.471 & 0.638 & -12.367 & 7.574 \\\\\n\\texttt{student\\_id\\_50} & -6.4358 & 6.526 & -0.986 & 0.324 & -19.230 & 6.359 \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{Complete regression results Experiment 1 for the dependent variable \\texttt{duration} on the THU-KDD dataset.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability", "authors": ["Nuo Chen", "Jiqun Liu", "Hanpei Fang", "Yuankai Luo", "Tetsuya Sakai", "Xiao-Ming Wu"], "url": "https://arxiv.org/abs/2403.18462v2", "attribution": "\"Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability\" by Nuo Chen, Jiqun Liu, Hanpei Fang, Yuankai Luo, Tetsuya Sakai, and Xiao-Ming Wu, arXiv:2403.18462v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03289v5_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|c|c|c|c}\nLanguage & Corpus & {\\bf Trankit} & Stanza & Flair & spaCy \\\\ \\hline\nArabic & AQMAR & \\textbf{74.8} & 74.3 & 74.0 & - \\\\ \\hline\nChinese & OntoNotes & \\textbf{80.0} & 79.2 & - & 69.3 \\\\ \\hline\n\\multirow{2}{*}{Dutch} & CoNLL02 & \\textbf{91.8} & 89.2 & 91.3 & 73.8 \\\\ \\cline{2-6} \n & WikiNER & \\textbf{94.8} & \\textbf{94.8} & \\textbf{94.8} & 90.9 \\\\ \\hline\n\\multirow{2}{*}{English} & CoNLL03 & 92.1 & 92.1 & \\textbf{92.7} & 81.0 \\\\ \\cline{2-6} \n & OntoNotes & \\textbf{89.6} & 88.8 & 89.0 & 85.4 \\\\ \\hline\nFrench & WikiNER & 92.3 & \\textbf{92.9} & 92.5 & 88.8 \\\\ \\hline\n\\multirow{2}{*}{German} & CoNLL03 & \\textbf{84.6} & 81.9 & 82.5 & 63.9 \\\\ \\cline{2-6} \n & GermEval14 & \\textbf{86.9} & 85.2 & 85.4 & 68.4 \\\\ \\hline\nRussian & WikiNER & 92.8 & \\textbf{92.9} & - & - \\\\ \\hline\nSpanish & CoNLL02 & \\textbf{88.9} & 88.1 & 87.3 & 77.5 \\\\ %\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Performance (F1) on NER test sets.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing", "authors": ["Minh Van Nguyen", "Viet Dac Lai", "Amir Pouran Ben Veyseh", "Thien Huu Nguyen"], "url": "https://arxiv.org/abs/2101.03289v5", "attribution": "\"Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing\" by Minh Van Nguyen, Viet Dac Lai, Amir Pouran Ben Veyseh, and Thien Huu Nguyen, arXiv:2101.03289v5, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.07981v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{hhline}\n\\usepackage{rotating}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The performance of different TI approaches in identifying the correct network topology: the main case.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c|c|c|c}\n\\hline\n\\multicolumn{2}{c||}{Method$^{\\mathrm{a}}$ $\\rightarrow$} & \n\\multicolumn{2}{c||}{DA} &\n\\multicolumn{2}{c||}{ANN } &\n\\multicolumn{2}{c||}{RB } &\n\\multicolumn{2}{c}{ML } \\\\\n\\hline\n\\multicolumn{2}{c||}{MI of$^{\\mathrm{b}}$: $\\rightarrow$} &\nSC$^{\\mathrm{c}}$ &\nPDS$^{\\mathrm{d}}$ &\nSC &\nPDS &\nSC &\nPDS&\nSC &\nPDS \\\\\n\\hline\n\\multirow{6}{*}{\\rotatebox[origin=c]{90}{\\parbox[c]{1.5 cm}{\\centering Actual SC}}} &\n C1 & \n0.00\\% &\n1.33\\% &\n0.04\\% &\n7.29\\% &\n0.65\\% &\n2.65\\% &\n0.00\\% &\n33.91\\% \\\\\n\\hhline{~~~~~~~~~~}\n&\n C2 & \n0.38\\% &\n3.55\\% &\n0.71\\% &\n5.71\\% &\n61.98\\% &\n1.53\\% &\n0.40\\% &\n28.44\\% \\\\\n\\hhline{~~~~~~~~~~}\n&\n C3 & \n0.00\\% &\n0.20\\% &\n0.00\\% &\n2.96\\% &\n0.00\\% &\n0.00\\% &\n2.80\\% &\n47.43\\% \\\\\n\\hhline{~~~~~~~~~~}\n&\n C4 & \n0.00\\% &\n1.47\\% &\n0.00\\% &\n7.67\\% &\n0.00\\% &\n3.80\\% &\n4.21\\% &\n45.87\\% \\\\\n\\hhline{~~~~~~~~~~}\n&\n C5 & \n0.00\\% &\n0.27\\% &\n0.00\\% &\n4.88\\% &\n0.02\\% &\n2.82\\% &\n21.65\\% &\n27.27\\% \\\\\n\\hhline{~~~~~~~~~~}\n&\n C6 & \n0.03\\% &\n1.82\\% &\n1.04\\% &\n3.42\\% &\n0.16\\% &\n4.57\\% &\n11.88\\% &\n40.40\\% \\\\\n\\hline\n\\multicolumn{2}{c||}{Average} & \n0.07\\% &\n1.44\\% &\n0.30\\% &\n5.32\\% &\n10.47\\% &\n2.66\\% &\n6.82\\% &\n37.22\\% \\\\\n\\hline\n\\multicolumn{10}{l}{$^{\\mathrm{a}}$ANN = Artificial Neural Network, RB = Recursive Bayesian,} \\\\\n\\hhline{~~~~~~~~~~} \n\\multicolumn{10}{l}{ML = Maximum Likelihood;} \\\\\n\\hhline{~~~~~~~~~~} \n\\multicolumn{10}{l}{$^{\\mathrm{b}}$Misidentification of; $^{\\mathrm{c}}$Switching Configuration; $^{\\mathrm{d}}$Protective Devices Status.} \\\\\n\\hhline{~~~~~~~~~~}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Resilient Identification of Distribution Network Topology", "authors": ["Mohammad Jafarian", "Alireza Soroudi", "Andrew Keane"], "url": "https://arxiv.org/abs/2011.07981v1", "attribution": "\"Resilient Identification of Distribution Network Topology\" by Mohammad Jafarian, Alireza Soroudi, and Andrew Keane, arXiv:2011.07981v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.12792v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison of evolutionary algorithms. The format X(Y) represents the Average(Best) objective value. \\textbf{N} denotes the number of rectangles.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n\\toprule\n\\textbf{N} & \\textbf{Literature} & \\textbf{DE} & \\textbf{PSO} & \\textbf{SADE} & \\textbf{SGA} \\\\\n\\midrule\n8 & 8906 & 6583 (5617) & 6665 (5590) & 6822 (\\textbf{5016}) & 6558 (5152) \\\\\n10 & 12111 & 13946 (12008) & 14600 (11937) & 14585 (\\textbf{10712}) & 14343 (10934) \\\\\n12 & 36677 & 36434 (31674) & 36567 (31542) & 35526 (31233) & 36302 (\\textbf{30239})\\\\\n14 & 41691 & 44290 (42393) & 45478 (42131) & 42855 (40689) & 39642 (\\textbf{36693})\\\\\n16 & 55064 & 58244 (53763) & 61733 (55035) & 57370 (55519) & 51052 (\\textbf{48386})\\\\\n18 & 66489 & 80770 (76474) & 81147 (67949) & 65923 (65179) & 64451 (\\textbf{61330})\\\\\n20 & - & 127275 (115777) & 126114 (108435) & 117537 (100901) & 102672 (\\textbf{94828})\\\\\n25 & - & 278156 (266444) & 283498 (256110) & 283330 (257964) & 234835 (\\textbf{223442})\\\\\n30 & - & 486497 (469408) & 494729 (467387) & 481058 (464196) & 411587 (\\textbf{403351})\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Open Loop Layout Optimization: Feasible Path Planning and Exact Door-to-Door Distance Calculation", "authors": ["Seyed Mahdi Shavarani", "Bela Vizvari", "Kovacs Gergely"], "url": "https://arxiv.org/abs/2504.12792v1", "attribution": "\"Open Loop Layout Optimization: Feasible Path Planning and Exact Door-to-Door Distance Calculation\" by Seyed Mahdi Shavarani, Bela Vizvari, and Kovacs Gergely, arXiv:2504.12792v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.02515v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Benchmarking the proposed AuthNet model against State-of-the-art models}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lr}\n\\hline\n\\textbf{Models} & \\textbf{Test Accuracy} \\\\ \\hline\n\\textbf{AuthNet \\textit{(Proposed)}} & 0.980 \\\\ %\\hline\nFaceNet + Garg \\textit{et al.} \\\\ & 0.611 \\\\ %\\hline\nFaceNet + Chung \\& Zisserman \\\\ & 0.652 \\\\ %\\hline\nFaceNet + Gergen \\textit{et al.} \\\\ & 0.864 \\\\ %\\hline\nFaceNet + LipNet \\\\ & 0.951 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "AuthNet: A Deep Learning based Authentication Mechanism using Temporal Facial Feature Movements", "authors": ["Mohit Raghavendra", "Pravan Omprakash", "B R Mukesh", "Sowmya Kamath"], "url": "https://arxiv.org/abs/2012.02515v2", "attribution": "\"AuthNet: A Deep Learning based Authentication Mechanism using Temporal Facial Feature Movements\" by Mohit Raghavendra, Pravan Omprakash, B R Mukesh, and Sowmya Kamath, arXiv:2012.02515v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13229v2_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{An example of the Chi-square for one day (Sunday) vs year}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrr}\\toprule\n & Rear-end on Sundays & Rear-end at the year \\\\ \\midrule\n2015 & 4.20E+01 & 8.47E+02 \\\\\n2016 & 8.70E+01 & 9.52E+02 \\\\\n2017 & 9.00E+01 & 8.84E+02 \\\\\n2018 & 6.40E+01 & 8.94E+02 \\\\\n2019 & 6.70E+01 & 8.95E+02 \\\\ \\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study", "authors": ["Xiwen Chen", "Hao Wang", "Abolfazl Razi", "Brendan Russo", "Jason Pacheco", "John Roberts", "Jeffrey Wishart", "Larry Head", "Alonso Granados Baca"], "url": "https://arxiv.org/abs/2201.13229v2", "attribution": "\"Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study\" by Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head, and Alonso Granados Baca, arXiv:2201.13229v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.07192v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Contribution of temporal correlation of the shortest time lag in the financial time series. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|}\n\\multicolumn{4}{c}{}\\\\ \\hline\nContribution of correlation.&No.1&No.7&No.9&No.12&No.13\\\\ \n \\hline \\hline\nData&\nBTC/USD\t&\tUSD/CHF\t&\tUSD/CAD\t&\tEUR/CHF \t& \tEUR/GBP\\hspace{0.4cm} \\\\\n\\hline \n4 th moment $ (\\%) $&\n 655.84 \t&\t93.47 \t&\t98.72\t&\t112.39 \t&\t126.18 \t\\\\\n\\hline\n6 th moment $ (\\%)$ &\n 542.99 \t&\t86.3\t&\t96.2\t&\t111.34 \t&\t122.89 \t\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deformation of semi-circle law for the correlated time series and Phase transition", "authors": ["Masato Hisakado", "Takuya Kaneko"], "url": "https://arxiv.org/abs/2508.07192v1", "attribution": "\"Deformation of semi-circle law for the correlated time series and Phase transition\" by Masato Hisakado and Takuya Kaneko, arXiv:2508.07192v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.05136v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc}\n\\hline\\hline \n& 0.0 & 1.0 & Accuracy & Macro Average & Weighted Average \\\\ \\hline\nPrecision & 0.97 & 1.00 & 0.97 & 0.98 & 0.97 \\\\\nRecall & 1.00 & 0.86 & 0.97 & 0.93 & 0.97 \\\\\nf1-score & 0.98 & 0.92 & 0.97 & 0.95 & 0.97 \\\\\n\\hline\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Test Evaluation Metrics for Random Forest}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Classification based credit risk analysis: The case of Lending Club", "authors": ["Aadi Gupta", "Priya Gulati", "Siddhartha P. Chakrabarty"], "url": "https://arxiv.org/abs/2210.05136v1", "attribution": "\"Classification based credit risk analysis: The case of Lending Club\" by Aadi Gupta, Priya Gulati, and Siddhartha P. Chakrabarty, arXiv:2210.05136v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06061v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Summary of validation accuracies for LeNet-5 and a convolutional neural network with four convolutional and two linear layers for various training methods on the clean MNIST data set.}\n\\begin{tabular}{ccc}\n Architecture & Training Type & Accuracy \\\\ \\hline \n LeNet-5 & naturally trained & 99.00\\% \\\\ \n LeNet-5 & adversarially trained (fgsm) & 98.99\\% \\\\ \n LeNet-5 & adversarially trained (pgd) & 98.55\\% \\\\ \n LeNet-5 & adversarially trained (Brownian) & 97.17\\% \\\\ \n LeNet-5 & trained on noise ($\\sigma^2 = 0.4$) & 99.02\\% \\\\ \n CNN & naturally trained & 98.99\\% \\\\ \n CNN & adversarially trained (fgsm) & 98.65\\% \\\\ \n CNN & trained on noise ($\\sigma^2 = 0.4$) & 98.93\\% \\\\ \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds", "authors": ["Bogdan Georgiev", "Lukas Franken", "Mayukh Mukherjee"], "url": "https://arxiv.org/abs/2101.06061v1", "attribution": "\"Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds\" by Bogdan Georgiev, Lukas Franken, and Mayukh Mukherjee, arXiv:2101.06061v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.12619v1_tex_table26.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Model Specifications}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|} \n \\hline\n \\# & \\multicolumn{2}{|c|}{Model Specification} & Results \\\\ \n \\hline\n \\scriptsize{1*}& \\multicolumn{2}{|c|}{\\parbox{9cm}{\\centering\\begin{math} \\Delta UER_{it} = \\boldsymbol{\\beta} \\begin{pmatrix} \\boldsymbol{X}_{it}\\\\ \\Delta log(RealGDPPC)_{it} \\\\ \\end{pmatrix} + \\alpha_i + \\gamma_t + \\varepsilon_{it} \\end{math}}} & \\parbox{2cm}{\\centering \\scriptsize Figure - \\\\Appendix B.1.2: \\\\Tables 13-14}\\\\\n \\hline\n \\footnotesize{2*}& \\parbox{4.25cm}{\\centering\\footnotesize\\begin{math} \\Delta log(Employed Persons)_{it} =\\end{math}} & \\multirow[c]{3}{*}[0in]{\\parbox{6cm}{\\centering\\begin{math} \\boldsymbol{\\beta} \\begin{pmatrix} \\boldsymbol{X}_{it}\\\\ \\Delta log(RealGDP)_{it} \\\\ \\Delta log(Population)_{it} \\\\ \\end{pmatrix} + \\alpha_i + \\gamma_t + \\varepsilon_{it} \\end{math}}} & \\multirow[c]{4}{*}[0in]{\\parbox{2cm}{\\centering\\scriptsize Figure \\\\Appendix B.1.2: \\\\Tables 13-14}}\\\\\n \\cline{1-2}\n \\footnotesize{3*}& \\parbox{4.25cm}{\\centering\\footnotesize\\begin{math} \\Delta log(Unemployed Persons)_{it} =\\end{math}} && \\\\ \n \\cline{1-2}\n \\footnotesize{4*}& \\parbox{4.25cm}{\\centering\\footnotesize\\begin{math} \\Delta log(Labor Force)_{it} =\\end{math}} &&\\\\\n \\cline{1-3}\n \\scriptsize{5*}& \\multicolumn{2}{|c|}{\\parbox{11cm}{\\centering\\begin{math} \\Delta log(Population)_{it} = \\boldsymbol{\\beta} \\begin{pmatrix} \\boldsymbol{X}_{it}\\\\ \\Delta log(RealGDP)_{it} \\\\ \\end{pmatrix} + \\alpha_i + \\gamma_t + \\varepsilon_{it} \\end{math}}} &\\\\\n \\hline\n \\scriptsize{6*}& \\multicolumn{2}{|c|}{\\parbox{11cm}{\\centering\\scriptsize\\begin{math} \\Delta UER_{it} = \\boldsymbol{\\beta} \\boldsymbol{X}_{it} + \\boldsymbol{Z}\n \\begin{pmatrix} \\Delta Active Mines_{it}*RE Investments_{it}\\\\ \n \\Delta Active Mines_{it-1}*RE Investments_{it-1}\\\\ \n \\Delta Active Mines_{it-2}*RE Investments_{it-2} \\\\\n \\end{pmatrix} + \\alpha_i + \\gamma_t + \\varepsilon_{it} \\end{math}}} & \\parbox{2cm}{\\centering \\scriptsize Appendix B.1.2: \\\\Table 19}\\\\\n \\hline\n\\scriptsize{1\\textsuperscript{SEM}}& \\multicolumn{2}{|c|}{\\parbox{9cm}{\\centering\\begin{math} Y_{it} = \\boldsymbol{\\beta} \\boldsymbol{X}_{it} + \\alpha_i + \\gamma_t + U_{it} \\end{math} \\\\ \\begin{math} U_{it} = \\delta W_{i}U_{t} + \\varepsilon_{it} \\end{math}}} & \\multirow[c]{3}{*}[0in]{\\parbox{2cm}{\\centering\\scriptsize Figure \\\\Appendix B.2: \\\\Tables 21-23, 25}} \\\\\n \\cline{1-3}\n \\scriptsize{1\\textsuperscript{SLM}}&\\multicolumn{2}{|c|}{\\parbox{9cm}{\\centering\\begin{math} Y_{it} = \\boldsymbol{\\beta} \\boldsymbol{X}_{it} + \\rho W_{i}Y_{t} + \\alpha_i + \\gamma_t + \\varepsilon_{it} \\end{math}}} &\\\\\n \\cline{1-3}\n\\scriptsize{1-5\\textsuperscript{SARAR}}& \\multicolumn{2}{|c|}{\\parbox{9cm}{\\centering\\begin{math} Y_{it} = \\boldsymbol{\\beta} \\boldsymbol{X}_{it} + \\rho W_{i}Y_{t} + \\alpha_i + \\gamma_t + U_{it} \\end{math} \\\\ \\begin{math} U_{it} = \\delta W_{i}U_{t} + \\varepsilon_{it} \\end{math}}} & \\\\\n \\hline\n \\scriptsize{1-6\\textsuperscript{HTT(d)}}& \\multicolumn{2}{|c|}{\\parbox{9cm}{\\centering\\begin{math} Y_{it} = \\boldsymbol{\\beta} \\boldsymbol{X}_{it} + \\sum_{l=1}^{d} \\lambda_{il}f(t) + \\alpha_i + \\gamma_t + \\varepsilon_{it} \\end{math}}}& \\parbox{2cm}{\\centering \\scriptsize Figure \\\\ Appendix B.3: \\\\Tables 26-28}\\\\\n \\hline\n \\multicolumn{4}{|c|}{\\parbox{15cm}{\\small \\ \\ \\\\ \\textbf{Variable definitions:}\\\\\n\\textbf{\\textit{i}} : denotes each county for i=1, 2,\\ldots3,072 counties\\\\\n \\textbf{\\textit{t}} : denotes each year for t=2002, 2003,\\ldots2019 \\\\\n $\\boldsymbol\\alpha_{i}$ : unit-fixed effects \\\\\n $\\boldsymbol\\gamma_{t}$ : time-fixed effects\\\\\n $\\boldsymbol X_{it}$ : Vector of independent variables ($\\Delta$ Active Mines\\textsubscript{it}, $\\Delta$ Active Mines\\textsubscript{it-1}, $\\Delta$ Active Mines\\textsubscript{it-2}). For Model 6, this includes levels of all interacted variables. For Models 1-5\\textsuperscript{[SEM, SLM, SARAR]} and 1-6\\textsuperscript{HTT(1),HTT(2)]} this represents all independent variables denoted in Models 1-6, respectively.\\\\\n $\\boldsymbol Y_{it}$ : Relevant dependent variable denoted by Model number.\\\\\n $\\boldsymbol\\beta$ : Vector of regression coefficients to be estimated.\\\\\n $\\boldsymbol\\rho$ : Spatial autoregression parameter.\\\\\n $\\boldsymbol\\delta$ : Spatial autocorrelation parameter.\\\\\n$\\boldsymbol W_{i}$ : N X N spatial weight matrix for neighbors of unit \\textit{i} where N = 3,072.\\\\\n $\\boldsymbol\\lambda_{il}$ : Individual loadings parameter per unit \\textit{i} and factor \\textit{l} in heterogeneous trend models.\\\\\n\\textbf{\\textit{f(t)}} : Unobserved time-varying common factors used in heterogeneous trend models.\\\\\n\\textbf{\\textit{REInvestments\\textsubscript{it}}}: dummy indicator of whether renewable energy investments exceeded 0.1\\% of GDP\\textsubscript{it}.\\\\\n$\\boldsymbol\\Delta UER_{it}$ : Change in unemployment rate in county \\textit{i} in year \\textit{t}.\\\\\n\\textit{Note: * indicates model was also applied to a subset of coal counties only.}}}\\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'", "authors": ["Ebba Mark", "Ryan Rafaty", "Moritz Schwarz"], "url": "https://arxiv.org/abs/2211.12619v1", "attribution": "\"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'\" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2502.05460v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Implementation details of horseshoe methods.}\n\\begin{tabular}{cccc}\n \\toprule\n & Global Shrinkage Parameter $\\xi$ & Local Shrinkage Parameter $\\eta_j$'s & Model Variance $\\sigma^2$ \\\\\n \\midrule\n \\textbf{m-FAHS} & $\\frac{\\text{R}_{BH}}{m}$ & standard half-Cauchy prior & Jeffreys prior\\\\\n \\addlinespace\n \\textbf{e-FAHS} & $\\frac{\\sigma \\text{R}_{BH}}{m-R_{BH}}$ & standard half-Cauchy prior & fixed at 1\\\\\n \\addlinespace\n \\textbf{EBHS} & estimated by MMLE & standard half-Cauchy prior & Jeffreys prior\\\\\n \\addlinespace\n \\textbf{FBHS}& standard half-Cauchy prior & standard half-Cauchy prior & Jeffreys prior\\\\\n \\bottomrule\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "False Discovery Rate Control via Frequentist-assisted Horseshoe", "authors": ["Qiaoyu Liang", "Zihan Zhu", "Ziang Fu", "Michael Evans"], "url": "https://arxiv.org/abs/2502.05460v2", "attribution": "\"False Discovery Rate Control via Frequentist-assisted Horseshoe\" by Qiaoyu Liang, Zihan Zhu, Ziang Fu, and Michael Evans, arXiv:2502.05460v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07558v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{LASS results on AudioCaps testset. FAD does not apply on unprocessed data as it calculates the difference between two groups of audio, while the target and mixed audio share the same audio events.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n\\textbf{Model} & \\textbf{FAD} & \\textbf{CLAPScore} & \\textbf{CLAPScore}$_A$ \\\\\n\\midrule\nUnprocessed & --- & 11.9 & 64.9 \\\\\nLASS-Net & 5.09 & 14.4 & 70.2 \\\\\nAudioSep & 4.38 & 13.6 & 69.6 \\\\\n\\rowcolor{gray!20} FlowSep & 2.86 & 21.9 & \\textbf{81.7} \\\\\n\\midrule \n\\rowcolor{gray!20} UniFlow$_{\\text{DDPM}}$ & \\textbf{2.70} & \\textbf{22.7} & 81.2 \\\\ \n\\rowcolor{gray!20} UniFlow$_{\\text{FM}}$ & 2.76 & 22.1 & 80.3 \\\\ \n\\rowcolor{gray!20} UniFlow$_{\\text{MF}}$ & 2.83 & 21.4 & 79.4 \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling", "authors": ["Ziqian Wang", "Zikai Liu", "Yike Zhu", "Xingchen Li", "Boyi Kang", "Jixun Yao", "Xianjun Xia", "Chuanzeng Huang", "Lei Xie"], "url": "https://arxiv.org/abs/2508.07558v1", "attribution": "\"UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling\" by Ziqian Wang, Zikai Liu, Yike Zhu, Xingchen Li, Boyi Kang, Jixun Yao, Xianjun Xia, Chuanzeng Huang, and Lei Xie, arXiv:2508.07558v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.13421v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Out-of-domain model performance on M6. (f) means subset (f) in M6 while (o) means the other remaining ones.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\hline\n\\textbf{Model} & \\textbf{Acc(f)} & \\textbf{F1 (f)} & \\textbf{Acc(o)} & \\textbf{F1 (o)} \\\\\n\\hline\nQ-SVM & .671 & \\textbf{.758} & .780 & .626 \\\\\nResNet18 & \\textbf{.685} & .693 & \\textbf{.781} & \\textbf{.778} \\\\\nVGG & .667 & .533 & \\underline{.274} & \\underline{.118} \\\\\nSeNet & .513 & .526 & .687 & .776 \\\\\nMobileNet & .429 & .356 & .772 & .732 \\\\\nCNN+LSTM & \\underline{.392} & \\underline{.316} & .776 & .737 \\\\\nViT & .623 & .630 & .762 & .754 \\\\\nMamba &.623 & .625 & .656 & .670 \\\\ \nXLSTM & .405 & .422 & .710 & .712 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Detecting Machine-Generated Music with Explainability -- A Challenge and Early Benchmarks", "authors": ["Yupei Li", "Qiyang Sun", "Hanqian Li", "Lucia Specia", "Björn W. Schuller"], "url": "https://arxiv.org/abs/2412.13421v1", "attribution": "\"Detecting Machine-Generated Music with Explainability -- A Challenge and Early Benchmarks\" by Yupei Li, Qiyang Sun, Hanqian Li, Lucia Specia, and Björn W. Schuller, arXiv:2412.13421v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.10248v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lrrrrrr} \\toprule\n Regularity parameter $\\alpha$ & 1.5 & 2 & 2.5 & 3 & 3.5 & 4 \\\\ \\midrule\n Avg. single-chain ESS & 1\\,749 & 1\\,126 & 252 & 183 & 168 & 635 \\\\\n Multi-chain ESS & 156\\,483 & 126\\,163 & 67\\,195 & 57\\,390 & 56\\,423 & 96\\,326 \\\\ \\bottomrule\n \\end{tabular}\n\\caption{Effective sample sizes for cost comparison experiment in , rounded to the closest integer.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Deep Gaussian Process Priors for Bayesian Image Reconstruction", "authors": ["Jonas Latz", "Aretha L. Teckentrup", "Simon Urbainczyk"], "url": "https://arxiv.org/abs/2412.10248v2", "attribution": "\"Deep Gaussian Process Priors for Bayesian Image Reconstruction\" by Jonas Latz, Aretha L. Teckentrup, and Simon Urbainczyk, arXiv:2412.10248v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.08095v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results on the MELD dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|ccc|c|}\n\\hline\n\\textbf{Methods} & \\multicolumn{3}{c|}{\\textbf{Learnable Module}} & \\textbf{MELD} \\\\ \\hline\n\\textbf{} & \\textbf{Encoder} & \\textbf{LLM} & \\textbf{Adapter} & \\textbf{WA $\\uparrow$} \\\\ \\hline\n\\textbf{emotion2vec} & Y & - & - & 51.9 \\\\ \\hline\n\\textbf{Qwen-Audio} & Y & N & Y & 55.7 \\\\ \\hline\n\\textbf{Qwen2-Audio} & Y & Y & Y & 55.3 \\\\ \\hline\n\\textbf{SLM-Qwen (ours)} & N & N & Y & 53.3 \\\\ \\hline\n\\textbf{SLM-Llama (ours)} & N & N & Y & 54.9 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Dual Information Speech Language Models for Emotional Conversations", "authors": ["Chun Wang", "Chenyang Liu", "Wenze Xu", "Weihong Deng"], "url": "https://arxiv.org/abs/2508.08095v1", "attribution": "\"Dual Information Speech Language Models for Emotional Conversations\" by Chun Wang, Chenyang Liu, Wenze Xu, and Weihong Deng, arXiv:2508.08095v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13229v2_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The results of the Chi-square test for week day every year vs Type.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrrrr} \\toprule\nScenario & S & M & T & W & Y & F & S \\\\ \\midrule\nstatistic & 3.05E+00 & 5.45E+00 & 2.57E+00 & 3.36E+00 & 1.97E+00 & 1.07E+01 & 6.26E+00 \\\\\nP-value & 5.49E-01 & 2.44E-01 & 6.32E-01 & 5.00E-01 & 7.42E-01 & 2.99E-02 & 1.81E-01 \\\\ \\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study", "authors": ["Xiwen Chen", "Hao Wang", "Abolfazl Razi", "Brendan Russo", "Jason Pacheco", "John Roberts", "Jeffrey Wishart", "Larry Head", "Alonso Granados Baca"], "url": "https://arxiv.org/abs/2201.13229v2", "attribution": "\"Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study\" by Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head, and Alonso Granados Baca, arXiv:2201.13229v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08640v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Type B measurement uncertainties.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrr}\n\t\t\\hline\n\t\ttype B uncertainty & mean (ps) & $u_{j}$ (ps)\\\\\n\t\t\\hline\n\t\tTIC temperature dependency & 0.0 & 12.5\\\\\n\t\tDSO temperature dependency & 0.0 & 0.8\\\\\n TIC reproducibility & 0.0 & 4.0\\\\\n DSO reproducibility & 0.0 & 0.7\\\\\n Cable Manipulation & 0.0 & 7.5\\\\\n\t\tDirect Attach Cable Asymmetry & 0.0 & 1.3\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "First electrical White Rabbit absolute calibration inter-comparison", "authors": ["P. P. M. Jansweijer", "N. A. D. Boukadida", "K. Hanhijärvi", "A. Wallin", "B. Eglin", "E. Laier English"], "url": "https://arxiv.org/abs/2201.08640v1", "attribution": "\"First electrical White Rabbit absolute calibration inter-comparison\" by P. P. M. Jansweijer, N. A. D. Boukadida, K. Hanhijärvi, A. Wallin, B. Eglin, and E. Laier English, arXiv:2201.08640v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10000v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Fine tuning backbone with NMCE objective. Results shown are from ResNet-18. Fine tuning backbone improves result slightly but notably.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|lll}\n\\bf Model &\\bf ACC &\\bf NMI &\\bf ARI\n\\\\ \\hline \\\\\nCIFAR-10 before &0.819 &0.743 &0.690 \\\\\nCIFAR-10 after &{\\bf 0.830} &{\\bf 0.761} &{\\bf 0.710} \\\\\nCIFAR-20 before &0.422 &0.471 &0.300 \\\\\nCIFAR-20 after &{\\bf 0.437} &{\\bf 0.488} &{\\bf 0.322} \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Neural Manifold Clustering and Embedding", "authors": ["Zengyi Li", "Yubei Chen", "Yann LeCun", "Friedrich T. Sommer"], "url": "https://arxiv.org/abs/2201.10000v1", "attribution": "\"Neural Manifold Clustering and Embedding\" by Zengyi Li, Yubei Chen, Yann LeCun, and Friedrich T. Sommer, arXiv:2201.10000v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.06094v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyper-parameters in EPT \\textbf{with} outer loops on real image datasets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccr}\n\\toprule\nParameter\t\t& $\\ell$ \t& $s$ \t& $n$ \t& $\\alpha$ \t& $T$ \t& $IL$ \\\\\n\\midrule\nValue \t\t& 128 \t& 0.5 \t& 1k \t\t& 0 \t\t\t& 1 \t\t& 20 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Generative Learning With Euler Particle Transport", "authors": ["Yuan Gao", "Jian Huang", "Yuling Jiao", "Jin Liu", "Xiliang Lu", "Zhijian Yang"], "url": "https://arxiv.org/abs/2012.06094v1", "attribution": "\"Generative Learning With Euler Particle Transport\" by Yuan Gao, Jian Huang, Yuling Jiao, Jin Liu, Xiliang Lu, and Zhijian Yang, arXiv:2012.06094v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.09545v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n \\hline\n \\textbf{Parameter} & \\textbf{Value} \\\\ \n \\hline\n Number of subcarriers & $N_{sc}$ = \\{1,256\\} \\\\ \n \\hline\n Modulation order & $M = 16,\\,64$ \\\\ \n \\hline\n Transmit antenna gain & $G_t$ = 45 dBi \\\\ \n \\hline\n Receive antenna gain & $G_r$ = 14 dBi \\\\\n \\hline\n Bandwidth & $B$ = 1 GHz \\\\ \n \\hline\n Noise temperature & $T_n = 290$ K \\\\\n \\hline\n Center frequency & $f_c$ = 315 GHz \\\\ \n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{System Parameters}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sub-THz Power Amplifiers: Measurements, Behavioral Modeling and Predistortion Algorithms", "authors": ["Lutfi Samara", "Simon Haussmann", "Erind Tufa", "Antonio Alberto D'Amico", "Tommaso Zugno", "Ingmar Kallfass", "Thomas Kürner"], "url": "https://arxiv.org/abs/2508.09545v1", "attribution": "\"Sub-THz Power Amplifiers: Measurements, Behavioral Modeling and Predistortion Algorithms\" by Lutfi Samara, Simon Haussmann, Erind Tufa, Antonio Alberto D'Amico, Tommaso Zugno, Ingmar Kallfass, and Thomas Kürner, arXiv:2508.09545v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.21306v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\nplant type & real time & time-invariant& real time & time-invariant \\\\ \n&zonal&zonal&nodal&nodal\\\\\\midrule\nother\\_res & 57.6 & 57.7 & 62.2 & 62.2 \\\\\nconventional & 172.7 & 173.7 & 162.3 & 163.0 \\\\\nsolar rooftop & 3.6 & 3.7 & 4.2 & 4.2 \\\\\nGround-mounted PV & 23.7 & 24.0 & 27.2 & 27.3 \\\\\n(solar park)&&&&\\\\\nsolar battery & 0.8 & 0.7 & 0.5 & 0.5 \\\\\nwind onshore & 139.6 & 140.0 & 135.2 & 135.3 \\\\\nwind offshore & 79.0 & 79.3 & 76.3 & 76.2 \\\\\nhydro\\_ror & 20.8 & 20.9 & 21.7 & 21.7 \\\\\n(run-of-river)&&&&\\\\\nhydro\\_psp & 19.9 & 19.8 & 11.8 & 11.7 \\\\\n(pumped storage)&&&&\\\\\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Yearly generation by plant type and scenario (TWh).}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Solar prosumage under different pricing regimes: Interactions with the transmission grid", "authors": ["Dana Kirchem", "Mario Kendziorski", "Enno Wiebrow", "Wolf-Peter Schill", "Claudia Kemfert", "Christian von Hirschhausen"], "url": "https://arxiv.org/abs/2502.21306v1", "attribution": "\"Solar prosumage under different pricing regimes: Interactions with the transmission grid\" by Dana Kirchem, Mario Kendziorski, Enno Wiebrow, Wolf-Peter Schill, Claudia Kemfert, and Christian von Hirschhausen, arXiv:2502.21306v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11427v5_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study of normalization methods for multi-domain CTR prediction. STAR FCN is trained BN, LN, and PN respectively.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n\t\\toprule\n\t &STAR FCN (BN) & STAR FCN (LN) & STAR FCN (PN) \\\\\n\t\\midrule\n\tOverall AUC &0.6455 & 0.6463 & \\textbf{0.6506} \\\\\n\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction", "authors": ["Xiang-Rong Sheng", "Liqin Zhao", "Guorui Zhou", "Xinyao Ding", "Binding Dai", "Qiang Luo", "Siran Yang", "Jingshan Lv", "Chi Zhang", "Hongbo Deng", "Xiaoqiang Zhu"], "url": "https://arxiv.org/abs/2101.11427v5", "attribution": "\"One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction\" by Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, and Xiaoqiang Zhu, arXiv:2101.11427v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.10001v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Relative width of the 95\\% prediction interval for each return level from the concentration approach compared with the standard method. Bootstrapped standard errors appear in brackets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n Return level&2&5&10&20\\\\\n Relative width&5.099 (0.123)&3.448 (0.102)&2.928(0.091)&2.713 (0.066) \\\\\n \\hline\n Return level&50&100&200&500\\\\\n Relative width&1.880 (0.061)&1.624 (0.054)&1.577 (0.063)&1.546 (0.062)\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fast return-level estimates for flood insurance via an improved Bennett inequality for random variables with differing upper bounds", "authors": ["Anna Maria Barlow", "Chris Sherlock"], "url": "https://arxiv.org/abs/2311.10001v3", "attribution": "\"Fast return-level estimates for flood insurance via an improved Bennett inequality for random variables with differing upper bounds\" by Anna Maria Barlow and Chris Sherlock, arXiv:2311.10001v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.00346v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Choices of $N$, $p$ and $\\sigma_g$ for the scenario where the genetic variants were simulated based on real genetic data.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|ccc}\n \\hline\n & \\multicolumn{3}{c|}{Low-dimensional ($N>p$)}\n & \\multicolumn{3}{c}{High-dimensional ($N<p$)} \\\\\n \\hline\n & $N$ & $p$ & $\\sigma_g$ & $N$ & $p$ & $\\sigma_g$\\\\\n \\hline\n Linear & 1092 & 500 & 0.02 & 1092 & 1500 & 0.01 \\\\\n Quadratic & 1092 & 500 & 0.03 & 1092 & 1500 & 0.015\\\\\n Trigonometric & 1092 & 500 & 0.05 & 1092 & 1500 & 0.05\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Estimating Broad Sense Heritability via Kernel Ridge Regression", "authors": ["Olivia Bley", "Elizabeth Lei", "Andy Zhou", "Xiaoxi Shen"], "url": "https://arxiv.org/abs/2411.00346v1", "attribution": "\"Estimating Broad Sense Heritability via Kernel Ridge Regression\" by Olivia Bley, Elizabeth Lei, Andy Zhou, and Xiaoxi Shen, arXiv:2411.00346v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.13533v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Cost for LLMs}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n\\toprule\nModel & Prompt & Completion & Cost \\\\\n\\midrule\nGPT-4o-mini& (\\$0.15/MTok) & (\\$0.6/MTok) & \\$18.35 \\\\\nGPT-4.1 & (\\$2/MTok) & 8/MTok) & \\$265.47\\\\\nGPT-4.1-mini & (\\$0.4/MTok) & (\\$1.6/MTok) & \\$49.87 \\\\\nGPT-4.1-nano & (\\$0.1/MTok) & (\\$0.4/MTok) & \\$11.22 \\\\\no3-mini& (\\$1.1/MTok) & (\\$4.4/MTok) & \\$186.94 \\\\\nClaude-3.7-Sonnet& (\\$3/MTok) & (\\$15/MTok) & \\$396.07 \\\\\nDeepSeek-V3-2503& (2RMB/MTok)& (8RMB/MTok)& 239.27RMB \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs", "authors": ["Junzhe Jiang", "Chang Yang", "Aixin Cui", "Sihan Jin", "Ruiyu Wang", "Bo Li", "Xiao Huang", "Dongning Sun", "Xinrun Wang"], "url": "https://arxiv.org/abs/2505.13533v1", "attribution": "\"FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs\" by Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Ruiyu Wang, Bo Li, Xiao Huang, Dongning Sun, and Xinrun Wang, arXiv:2505.13533v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.12258v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Forecasting performance of the proposed E-STGCN model in comparison to the temporal-only and spatiotemporal forecasting techniques for 60 60-day ahead forecast horizon of $PM_{2.5}$ and $PM_{10}$ pollutants (best results are \\underline{\\textbf{highlighted}}).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|cccccc|ccccccc|c|}\n\\hline \\multirow{2}{*}{Pollutant} & Forecast & \\multirow{2}{*}{Metric} & \\multicolumn{6}{c|}{Temporal-only Model} & \\multicolumn{7}{c|}{Spatiotemporal Model} & Proposed \\\\ \\cline{4-16}\n & \\multirow{2}{*}{Period} & & \\multirow{2}{*}{ARIMA} & \\multirow{2}{*}{LSTM} & \\multirow{2}{*}{TCN} & \\multirow{2}{*}{DeepAR} & \\multirow{2}{*}{Transformers} & \\multirow{2}{*}{NBeats} & \\multirow{2}{*}{STARMA} & \\multirow{2}{*}{GSTAR} & \\multirow{2}{*}{GpGp} & \\multirow{2}{*}{{\\color{black}STGCN}} & \\multirow{2}{*}{STNN} & Modified & \\multirow{2}{*}{DeepKrigging} & \\multirow{2}{*}{E-STGCN} \\\\ \n & & & & & & & & & & & & & & STGCN & & \\\\ \\hline\n\\multirow{36}{*}{PM$_{2.5}$} & \\multirow{6}{*}{JAN - FEB} & MAE & 73.48 & 132.20 & 116.87 & 131.90 & 66.52 & 59.28 & 80.56 & 100.35 & 58.53 & {\\color{black} 62.00} & 76.26 & 56.73 & 84.47 & \\underline{\\textbf{55.00}} \\\\\n & & MASE & 1.72 & 3.11 & 2.73 & 3.10 & 1.54 & 1.39 & 1.87 & 2.36 & 1.36 & {\\color{black} 1.44} & 1.82 & 1.32 & 1.99 & \\underline{\\textbf{1.28}} \\\\\n & & RMSE & 86.23 & 148.60 & 135.33 & 148.33 & 89.53 & 77.12 & 96.46 & 113.61 & 78.81 & {\\color{black} 84.09} & 94.98 & 76.44 & 100.93 & \\underline{\\textbf{73.00}} \\\\\n & & SMAPE & 46.50 & 152.00 & 126.90 & 151.30 & 47.40 & 45.20 & 81.50 & 57.00 & 39.90 & {\\color{black} 43.58} & 48.80 & 39.60 & 77.20 & \\underline{\\textbf{37.90}} \\\\ \n && {\\color{black}\tPinball Loss\t} & {\\color{black}\t\\underline{\\textbf{21.51}}\t} & {\\color{black}\t105.76\t} & {\\color{black}\t92.78\t} & {\\color{black}\t105.52\t} & {\\color{black}\t50.69\t} & {\\color{black}\t45.07\t} & {\\color{black}\t62.52\t} & {\\color{black}\t23.37\t} & {\\color{black}\t39.18\t} & {\\color{black} 46.61} & {\\color{black}\t34.45\t} & {\\color{black}\t38.63\t} & {\\color{black}\t58.54\t} & {\\color{black}\t37.44\t} \\\\\n && {\\color{black}\tCRPS\t} & {\\color{black}\t83.42\t} & {\\color{black}\t95.40\t} & {\\color{black}\t94.98\t} & {\\color{black}\t95.40\t} & {\\color{black}\t94.09\t} & {\\color{black}\t83.66\t} & {\\color{black}\t87.92\t} & {\\color{black}\t99.12\t} & {\\color{black}\t85.74\t} & {\\color{black} 92.50} & {\\color{black}\t93.58\t} & {\\color{black}\t82.68\t} & {\\color{black}\t83.64\t} & {\\color{black}\t\\underline{\\textbf{81.90}}\t} \\\\ \\cline{2-17}\n & \\multirow{6}{*}{MAR - APR} & MAE & 35.03 & 59.96 & 49.14 & 55.51 & 29.62 & 35.06 & 50.95 & 50.86 & 37.44 & {\\color{black} 34.42} & 143.20 & 27.68 & 68.10 & \\underline{\\textbf{22.35}} \\\\\n & & MASE & 1.82 & 3.02 & 2.46 & 2.78 & 1.58 & 1.84 & 2.56 & 2.55 & 2.07 & {\\color{black} 1.81} & 7.71 & 1.39 & 3.47 & \\underline{\\textbf{1.13}} \\\\\n & & RMSE & 40.90 & 65.06 & 58.64 & 60.99 & 34.89 & 41.08 & 57.72 & 58.23 & 42.69 & {\\color{black} 39.94} & 154.31 & 35.06 & 77.92 & \\underline{\\textbf{27.69}} \\\\\n & & SMAPE & 43.70 & 131.80 & 93.70 & 114.10 & 39.20 & 42.80 & 111.50 & 112.70 & 46.50 & {\\color{black} 43.28} & 98.80 & 41.70 & 144.30 & \\underline{\\textbf{31.20}} \\\\ \n && {\\color{black}\tPinball Loss\t} & {\\color{black}\t8.10\t} & {\\color{black}\t47.97\t} & {\\color{black}\t35.48\t} & {\\color{black}\t44.41\t} & {\\color{black}\t8.60\t} & {\\color{black}\t8.89\t} & {\\color{black}\t40.47\t} & {\\color{black}\t39.91\t} & {\\color{black}\t9.54\t} & {\\color{black} 9.65} & {\\color{black}\t28.74\t} & {\\color{black}\t18.35\t} & {\\color{black}\t48.39\t} & {\\color{black}\t\\underline{\\textbf{8.08}}\t} \\\\\n && {\\color{black}\tCRPS\t} & {\\color{black}\t40.07\t} & {\\color{black}\t45.49\t} & {\\color{black}\t46.80\t} & {\\color{black}\t45.50\t} & {\\color{black}\t36.68\t} & {\\color{black}\t40.11\t} & {\\color{black}\t45.67\t} & {\\color{black}\t45.84\t} & {\\color{black}\t40.96\t} & {\\color{black} 39.48} & {\\color{black}\t98.36\t} & {\\color{black}\t41.11\t} & {\\color{black}\t49.50\t} & {\\color{black}\t\\underline{\\textbf{33.74}}\t} \\\\\n \\cline{2-17}\n & \\multirow{6}{*}{MAY - JUN} & MAE & 37.54 & 40.38 & 42.47 & 38.31 & 46.70 & 22.53 & 43.36 & \\underline{\\textbf{22.15}} & 49.65 & {\\color{black} 50.80} & 43.12 & 30.41 & 37.31 & 43.53 \\\\\n & & MASE & 2.14 & 2.23 & 2.35 & 2.11 & 2.71 & 1.24 & 2.41 & \\underline{\\textbf{1.23}} & 2.95 & {\\color{black} 2.92} & 2.33 & 1.70 & 2.03 & 2.44 \\\\\n & & RMSE & 42.70 & 49.77 & 51.29 & 48.09 & 51.41 & 32.55 & 50.27 & \\underline{\\textbf{30.71}} & 54.99 & {\\color{black} 55.57} & 52.49 & 42.70 & 44.79 & 56.56 \\\\\n & & SMAPE & 57.70 & 97.10 & 117.20 & 88.10 & 66.60 & 39.10 & 127.60 & \\underline{\\textbf{38.70}} & 68.60 & {\\color{black} 69.57} & 70.40 & 58.00 & 99.80 & 94.50 \\\\ \n && {\\color{black}\tPinball Loss\t} & {\\color{black}\t11.64\t} & {\\color{black}\t32.29\t} & {\\color{black}\t33.46\t} & {\\color{black}\t30.63\t} & {\\color{black}\t\\underline{\\textbf{11.17}}\t} & {\\color{black}\t15.27\t} & {\\color{black}\t34.65\t} & {\\color{black}\t13.32\t} & {\\color{black}\t11.69\t} & {\\color{black} 19.84} & {\\color{black}\t21.46\t} & {\\color{black}\t19.52\t} & {\\color{black}\t29.10\t} & {\\color{black}\t19.19\t} \\\\\n && {\\color{black}\tCRPS\t} & {\\color{black}\t37.77\t} & {\\color{black}\t36.76\t} & {\\color{black}\t36.47\t} & {\\color{black}\t36.77\t} & {\\color{black}\t41.25\t} & {\\color{black}\t32.45\t} & {\\color{black}\t36.00\t} & {\\color{black}\t\\underline{\\textbf{29.82}}\t} & {\\color{black}\t43.42\t} & {\\color{black} 43.61} & {\\color{black}\t42.10\t} & {\\color{black}\t37.57\t} & {\\color{black}\t35.14\t} & {\\color{black}\t37.52\t} \\\\ \\cline{2-17}\n & \\multirow{6}{*}{JUL - AUG} & MAE & 30.37 & 21.33 & 26.49 & 18.44 & 60.75 & 24.10 & 29.51 & \\underline{\\textbf{12.67}} & 62.11 & {\\color{black} 62.03} & 20.80 & 29.41 & 36.68 & 28.05 \\\\\n & & MASE & 4.07 & 2.68 & 3.38 & 2.30 & 8.17 & 3.25 & 3.77 & \\underline{\\textbf{1.60}} & 8.35 & {\\color{black} 8.33} & 2.60 & 3.89 & 4.71 & 3.70 \\\\\n & & RMSE & 33.44 & 24.35 & 30.50 & 21.69 & 61.90 & 28.32 & 32.59 & \\underline{\\textbf{16.06}} & 64.19 & {\\color{black} 63.43} & 24.31 & 35.63 & 38.78 & 34.11 \\\\\n & & SMAPE & 60.90 & 74.00 & 109.00 & 59.70 & 93.40 & 51.00 & 132.80 & \\underline{\\textbf{38.10}} & 93.60 & {\\color{black} 93.83} & 61.90 & 56.70 & 197.50 & 55.40 \\\\ \n && {\\color{black}\tPinball Loss\t} & {\\color{black}\t6.55\t} & {\\color{black}\t16.98\t} & {\\color{black}\t20.22\t} & {\\color{black}\t14.57\t} & {\\color{black}\t12.21\t} & {\\color{black}\t\\underline{\\textbf{5.82}}\t} & {\\color{black}\t23.60\t} & {\\color{black}\t9.45\t} & {\\color{black}\t12.50\t} & {\\color{black} 13.47} & {\\color{black}\t13.26\t} & {\\color{black}\t7.14\t} & {\\color{black}\t29.24\t} & {\\color{black}\t6.91\t} \\\\\n && {\\color{black}\tCRPS\t} & {\\color{black}\t26.97\t} & {\\color{black}\t22.71\t} & {\\color{black}\t23.22\t} & {\\color{black}\t22.46\t} & {\\color{black}\t39.78\t} & {\\color{black}\t24.77\t} & {\\color{black}\t22.95\t} & {\\color{black}\t\\underline{\\textbf{18.45}}\t} & {\\color{black}\t41.13\t} & {\\color{black} 40.81} & {\\color{black}\t23.37\t} & {\\color{black}\t28.61\t} & {\\color{black}\t22.72\t} & {\\color{black}\t22.80\t} \\\\ \n \\cline{2-17}\n & \\multirow{6}{*}{SEP - OCT} & MAE & 36.12 & 54.88 & 63.93 & 52.05 & 44.53 & 40.25 & 58.62 & 34.86 & 44.33 & {\\color{black} 34.29} & 53.65 & 33.99 & 57.96 & \\underline{\\textbf{33.60}} \\\\\n & & MASE & 2.63 & 3.95 & 4.64 & 3.74 & 3.32 & 2.91 & 4.25 & 2.54 & 3.34 & {\\color{black} 2.52} & 3.90 & 2.50 & 4.15 & \\underline{\\textbf{2.46}} \\\\\n & & RMSE & 51.27 & 69.38 & 78.49 & 66.93 & 50.97 & 55.78 & 76.23 & 49.13 & 51.34 & {\\color{black} 41.26} & 68.24 & 41.05 & 71.90 & \\underline{\\textbf{40.34}} \\\\\n & & SMAPE & 55.00 & 107.90 & 148.20 & 97.40 & 59.80 & 64.00 & 120.70 & 52.40 & 59.40 & {\\color{black} 49.63} & 108.80 & 49.60 & 123.00 & \\underline{\\textbf{48.90}} \\\\ \n && {\\color{black}\tPinball Loss\t} & {\\color{black}\t26.24\t} & {\\color{black}\t43.73\t} & {\\color{black}\t50.83\t} & {\\color{black}\t41.36\t} & {\\color{black}\t14.96\t} & {\\color{black}\t30.90\t} & {\\color{black}\t46.10\t} & {\\color{black}\t24.12\t} & {\\color{black}\t14.66\t} & {\\color{black} 15.37} & {\\color{black}\t42.29\t} & {\\color{black}\t13.65\t} & {\\color{black}\t46.12\t} & {\\color{black}\t\\underline{\\textbf{13.52}}\t} \\\\\n && {\\color{black}\tCRPS\t} & {\\color{black}\t46.88\t} & {\\color{black}\t47.82\t} & {\\color{black}\t48.53\t} & {\\color{black}\t47.83\t} & {\\color{black}\t45.98\t} & {\\color{black}\t47.67\t} & {\\color{black}\t49.68\t} & {\\color{black}\t45.92\t} & {\\color{black}\t46.10\t} & {\\color{black} 39.24} & {\\color{black}\t47.71\t} & {\\color{black}\t39.02\t} & {\\color{black}\t47.83\t} & {\\color{black}\t\\underline{\\textbf{38.67}}\t} \\\\\n \\cline{2-17}\n & \\multirow{6}{*}{NOV - DEC} & MAE & \\underline{\\textbf{90.77}} & 204.62 & 195.80 & 200.48 & 127.97 & 132.34 & 159.92 & 157.59 & 120.80 & {\\color{black} 107.25} & 198.17 & 95.34 & 224.42 & 91.09 \\\\\n & & MASE & \\underline{\\textbf{1.91}} & 4.30 & 4.11 & 4.21 & 2.67 & 2.78 & 3.35 & 3.31 & 2.50 & {\\color{black} 2.24} & 4.18 & 2.00 & 4.73 & 1.91 \\\\\n & & RMSE & \\underline{\\textbf{111.64}} & 217.05 & 210.92 & 213.17 & 146.81 & 149.75 & 175.57 & 173.31 & 139.69 & {\\color{black} 127.11} & 211.16 & 115.40 & 244.57 & 112.98 \\\\\n & & SMAPE & 46.50 & 169.40 & 155.70 & 162.30 & 74.50 & 80.80 & 117.80 & 113.50 & 68.20 & {\\color{black} 57.87} & 163.30 & 49.60 & 189.00 & \\underline{\\textbf{46.30}} \\\\ \n && {\\color{black}\tPinball Loss\t} & {\\color{black}\t71.38\t} & {\\color{black}\t163.70\t} & {\\color{black}\t156.29\t} & {\\color{black}\t160.39\t} & {\\color{black}\t102.18\t} & {\\color{black}\t105.13\t} & {\\color{black}\t127.54\t} & {\\color{black}\t125.72\t} & {\\color{black}\t96.23\t} & {\\color{black} 85.93} & {\\color{black}\t158.54\t} & {\\color{black}\t74.93\t} & {\\color{black}\t177.00\t} & {\\color{black}\t\\underline{\\textbf{69.81}}\t} \\\\\n && {\\color{black}\tCRPS\t} & {\\color{black}\t131.98\t} & {\\color{black}\t136.38\t} & {\\color{black}\t137.02\t} & {\\color{black}\t136.39\t} & {\\color{black}\t135.65\t} & {\\color{black}\t136.02\t} & {\\color{black}\t136.20\t} & {\\color{black}\t136.15\t} & {\\color{black}\t134.78\t} & {\\color{black} 134.44} & {\\color{black}\t136.42\t} & {\\color{black}\t133.45\t} & {\\color{black}\t139.13\t} & {\\color{black}\t\\underline{\\textbf{131.94}}\t} \\\\ \n \\hline\n\\multirow{36}{*}{PM$_{10}$} & \n\\multirow{6}{*}{JAN - FEB} & MAE & 94.47 & 252.19 & 245.06 & 245.01 & 135.51 & 95.19 & 120.61 & 116.70 & 101.61 & {\\color{black} 82.81} & 102.18 & 80.35 & 125.87 & \\underline{\\textbf{72.53}} \\\\\n & & MASE & 1.53 & 3.99 & 3.87 & 3.88 & 2.11 & 1.53 & 1.88 & 1.83 & 1.58 & {\\color{black} 1.30} & 1.63 & 1.27 & 1.97 & \\underline{\\textbf{1.15}} \\\\\n & & RMSE & 113.55 & 269.06 & 280.32 & 262.36 & 163.79 & 116.76 & 147.89 & 143.09 & 130.47 & {\\color{black} 110.99} & 127.15 & 106.36 & 152.58 & \\underline{\\textbf{94.10}} \\\\\n & & SMAPE & 35.10 & 183.00 & 148.20 & 171.80 & 61.20 & 35.40 & 61.00 & 56.50 & 40.70 & {\\color{black} 32.34} & 40.00 & 31.40 & 58.10 & \\underline{\\textbf{28.20}} \\\\ \n & & {\\color{black}Pinball Loss}\t&\t{\\color{black}\\underline{\\textbf{30.47}}}\t&\t{\\color{black}201.75}\t&\t{\\color{black}189.48}\t&\t{\\color{black}196.01}\t&\t{\\color{black}107.88}\t&\t{\\color{black}34.42}\t&\t{\\color{black}91.92}\t&\t{\\color{black}90.83}\t&\t{\\color{black}74.52} & {\\color{black} 60.41}\t&\t{\\color{black}56.99}\t&\t{\\color{black}55.78}\t&\t{\\color{black}89.08}\t&\t{\\color{black}45.19} \\\\\n & & {\\color{black}CRPS} &\t{\\color{black}124.50}\t&\t{\\color{black}163.35}\t&\t{\\color{black}164.21}\t&\t{\\color{black}163.35}\t&\t{\\color{black}162.62}\t&\t{\\color{black}124.74}\t&\t{\\color{black}152.08}\t&\t{\\color{black}153.03}\t&\t{\\color{black}149.54}\t&\t{\\color{black}131.16}\t&\t{\\color{black}125.64}\t&\t{\\color{black}149.42}\t& {\\color{black} 131.16} &\t{\\color{black}\\underline{\\textbf{109.46}}} \\\\\n \\cline{2-17}\n & \\multirow{6}{*}{MAR - APR} & MAE & 61.48 & 168.41 & 174.61 & 164.51 & 62.63 & 61.36 & 103.94 & 95.72 & 57.14 & {\\color{black} 54.37} & 165.95 & 51.94 & 181.87 & \\underline{\\textbf{49.59}} \\\\\n & & MASE & 1.43 & 3.87 & 4.02 & 3.78 & 1.39 & 1.42 & 2.35 & 2.18 & 1.32 & {\\color{black} 1.27} & 4.06 & 1.20 & 4.19 & \\underline{\\textbf{1.14}} \\\\\n & & RMSE & 73.69 & 178.80 & 203.57 & 175.13 & 78.19 & 75.55 & 125.97 & 117.40 & 70.86 & {\\color{black} 65.85} & 212.95 & 63.17 & 191.54 & \\underline{\\textbf{60.07}} \\\\\n & & SMAPE & 33.40 & 167.10 & 135.50 & 159.10 & 35.80 & 33.80 & 76.30 & 67.70 & 32.20 & {\\color{black} 30.45} & 60.90 & 29.20 & 198.90 & \\underline{\\textbf{28.10}} \\\\ \n & & {\\color{black}Pinball Loss}\t&\t{\\color{black}\\underline{\\textbf{19.10}}}\t&\t{\\color{black}134.73}\t&\t{\\color{black}127.74}\t&\t{\\color{black}131.61}\t&\t{\\color{black}45.14}\t&\t{\\color{black}28.45}\t&\t{\\color{black}79.35}\t&\t{\\color{black}72.03}\t&\t{\\color{black}32.15} & {\\color{black} 24.67}\t&\t{\\color{black}36.13}\t&\t{\\color{black}21.88}\t&\t{\\color{black}145.49}\t&\t{\\color{black}23.67} \\\\\n & & {\\color{black}CRPS}\t&\t{\\color{black}83.08}\t&\t{\\color{black}113.16}\t&\t{\\color{black}121.38}\t&\t{\\color{black}113.16}\t&\t{\\color{black}95.51}\t&\t{\\color{black}87.98}\t&\t{\\color{black}115.24}\t&\t{\\color{black}114.65}\t&\t{\\color{black}85.66} & {\\color{black} 77.98}\t&\t{\\color{black}155.32}\t&\t{\\color{black}75.52}\t&\t{\\color{black}113.16}\t&\t{\\color{black}\\underline{\\textbf{73.97}}} \\\\ \n \\cline{2-17}\n & \\multirow{6}{*}{MAY - JUN} & MAE & 71.99 & 145.59 & 145.40 & 143.09 & 83.16 & 67.01 & 129.13 & 111.88 & 67.53 & {\\color{black} 75.81} & 87.48 & 76.14 & 77.28 & \\underline{\\textbf{66.47}} \\\\\n & & MASE & 1.48 & 2.95 & 2.92 & 2.90 & 1.65 & \\underline{\\textbf{1.35}} & 2.61 & 2.27 & 1.40 & {\\color{black} 1.57} & 1.83 & 1.55 & 1.53 & 1.36 \\\\\n & & RMSE & 89.34 & 167.01 & 173.28 & 164.85 & 113.32 & 95.91 & 150.33 & 134.00 & \\underline{\\textbf{87.32}} & {\\color{black} 90.26} & 107.85 & 100.07 & 107.10 & 89.67 \\\\\n & & SMAPE & 43.60 & 155.20 & 133.70 & 149.30 & 56.00 & 42.90 & 128.80 & 100.80 & 41.70 & {\\color{black} 45.48} & 48.30 & 47.80 & 50.50 & \\underline{\\textbf{41.10}} \\\\ \n & & {\\color{black}Pinball Loss}\t&\t{\\color{black}30.71}\t&\t{\\color{black}116.47}\t&\t{\\color{black}111.33}\t&\t{\\color{black}114.47}\t&\t{\\color{black}64.84}\t&\t{\\color{black}50.75}\t&\t{\\color{black}103.25}\t&\t{\\color{black}89.01}\t&\t{\\color{black}33.56} & {\\color{black} 32.42}\t&\t{\\color{black}\\underline{\\textbf{28.02}}}\t&\t{\\color{black}44.45}\t&\t{\\color{black}58.81}\t&\t{\\color{black}37.86} \\\\\n & & {\\color{black}CRPS}\t&\t{\\color{black}86.48}\t&\t{\\color{black}104.87}\t&\t{\\color{black}109.70}\t&\t{\\color{black}104.87}\t&\t{\\color{black}104.04}\t&\t{\\color{black}98.39}\t&\t{\\color{black}103.80}\t&\t{\\color{black}103.36}\t&\t{\\color{black}87.12} & {\\color{black} 86.72}\t&\t{\\color{black}97.49}\t&\t{\\color{black}101.79}\t&\t{\\color{black}101.82}\t&\t{\\color{black}\\underline{\\textbf{86.46}}} \\\\ \n \\cline{2-17}\n & \\multirow{6}{*}{JUL - AUG} & MAE & 58.10 & 87.62 & 95.24 & 82.17 & 59.70 & \\underline{\\textbf{42.82}} & 76.47 & 77.41 & 70.26 & {\\color{black} 104.24} & 58.97 & 49.79 & 72.79 & 49.79 \\\\\n & & MASE & 2.90 & 4.18 & 4.53 & 3.90 & 3.05 & \\underline{\\textbf{2.04}} & 3.62 & 3.69 & 3.65 & {\\color{black} 5.22} & 2.79 & 2.46 & 3.43 & 2.46 \\\\\n & & RMSE & 65.40 & 97.74 & 109.72 & 92.87 & 67.62 & \\underline{\\textbf{54.78}} & 91.86 & 93.15 & 78.60 & {\\color{black} 111.69} & 72.70 & 59.03 & 84.92 & 59.03 \\\\\n & & SMAPE & 50.40 & 142.70 & 142.60 & 125.20 & 52.50 & \\underline{\\textbf{40.70}} & 110.10 & 112.70 & 58.20 & {\\color{black} 73.42} & 64.00 & 43.70 & 105.10 & 43.70 \\\\ \n & & {\\color{black}Pinball Loss}\t&\t{\\color{black}13.75}\t&\t{\\color{black}70.09}\t&\t{\\color{black}72.58}\t&\t{\\color{black}65.73}\t&\t{\\color{black}15.87}\t&\t{\\color{black}21.15}\t&\t{\\color{black}60.88}\t&\t{\\color{black}61.64}\t&\t{\\color{black}17.58}\t& {\\color{black} 22.79} &\t{\\color{black}39.40}\t&\t{\\color{black}\\underline{\\textbf{12.37}}}\t&\t{\\color{black}57.79}\t&\t{\\color{black} \\underline{\\textbf{12.37}}} \\\\\n & & {\\color{black}CRPS}\t&\t{\\color{black}59.61}\t&\t{\\color{black}64.63}\t&\t{\\color{black}68.49}\t&\t{\\color{black}64.63}\t&\t{\\color{black}61.12}\t&\t{\\color{black}57.72}\t&\t{\\color{black}66.10}\t&\t{\\color{black}66.26}\t&\t{\\color{black}66.90}\t& {\\color{black} 82.76} &\t{\\color{black}66.03}\t&\t{\\color{black}\\underline{\\textbf{56.00}}}\t&\t{\\color{black}64.96} &\t{\\color{black}\\underline{\\textbf{56.00}}} \\\\ \\cline{2-17}\n & \\multirow{6}{*}{SEP - OCT} & MAE & 64.76 & 153.86 & 160.36 & 148.98 & 67.84 & 77.56 & 119.55 & 118.63 & 70.16 & {\\color{black} 74.00} & 132.91 & 69.95 & 164.33 & \\underline{\\textbf{63.70}} \\\\\n & & MASE & 2.38 & 5.74 & 5.96 & 5.55 & 2.50 & 2.88 & 4.44 & 4.42 & 2.62 & {\\color{black} 2.74} & 4.95 & 2.59 & 6.14 & \\underline{\\textbf{2.36}} \\\\\n & & RMSE & 81.32 & 174.76 & 184.58 & 170.42 & 87.21 & 97.58 & 151.29 & 150.95 & 88.90 & {\\color{black} 89.91} & 155.66 & 84.98 & 185.21 & \\underline{\\textbf{81.31}} \\\\\n & & SMAPE & 40.50 & 156.20 & 149.10 & 145.10 & 42.70 & 47.90 & 93.70 & 92.00 & 43.80 & {\\color{black} 44.80} & 119.00 & 43.20 & 175.80 & \\underline{\\textbf{40.00}} \\\\ \n & & {\\color{black}Pinball Loss}\t&\t{\\color{black}27.79}\t&\t{\\color{black}123.08}\t&\t{\\color{black}124.20}\t&\t{\\color{black}119.15}\t&\t{\\color{black}38.93}\t&\t{\\color{black}42.61}\t&\t{\\color{black}89.79}\t&\t{\\color{black}88.02}\t&\t{\\color{black}35.99} & {\\color{black} 29.03}\t&\t{\\color{black}105.59}\t&\t{\\color{black}28.50}\t&\t{\\color{black}130.88}\t&\t{\\color{black}\\underline{\\textbf{26.66}}} \\\\\n & & {\\color{black}CRPS}\t&\t{\\color{black} \\underline{\\textbf{85.45}} }\t&\t{\\color{black}109.81}\t&\t{\\color{black}113.53}\t&\t{\\color{black}109.81}\t&\t{\\color{black}92.85}\t&\t{\\color{black}101.60}\t&\t{\\color{black}115.41}\t&\t{\\color{black}116.21}\t&\t{\\color{black}92.69} & {\\color{black} 91.66}\t&\t{\\color{black}109.09}\t&\t{\\color{black}88.29}\t&\t{\\color{black}110.79}\t&\t{\\color{black}87.29} \\\\\n \\cline{2-17}\n & \\multirow{6}{*}{NOV - DEC} & MAE & \\underline{\\textbf{114.94}} & 342.22 & 328.89 & 338.54 & 202.89 & 156.01 & 204.62 & 188.63 & 186.24 & {\\color{black} 164.65} & 175.93 & 162.09 & 350.87 & 162.09 \\\\\n & & MASE & \\underline{\\textbf{1.71}} & 5.07 & 4.85 & 5.01 & 2.97 & 2.30 & 3.00 & 2.79 & 2.71 & {\\color{black} 2.43} & 2.58 & 2.40 & 5.20 & 2.40 \\\\\n & & RMSE & \\underline{\\textbf{139.63}} & 356.98 & 351.94 & 353.46 & 226.80 & 177.83 & 226.54 & 210.06 & 208.70 & {\\color{black} 191.68} & 208.93 & 187.66 & 366.66 & 187.66 \\\\\n & & SMAPE & \\underline{\\textbf{35.00}} & 179.80 & 157.70 & 175.80 & 72.60 & 52.30 & 81.90 & 72.00 & 64.90 & {\\color{black} 54.60} & 61.20 & 53.80 & 187.60 & 53.80 \\\\ \n & & {\\color{black}Pinball Loss}\t&\t{\\color{black}\\underline{\\textbf{87.31}}}\t&\t{\\color{black}273.77}\t&\t{\\color{black}260.48}\t&\t{\\color{black}270.84}\t&\t{\\color{black}162.18}\t&\t{\\color{black}121.19}\t&\t{\\color{black}160.86}\t&\t{\\color{black}148.22}\t& {\\color{black} 134.06} & \t{\\color{black}146.36}\t&\t{\\color{black}127.14}\t&\t{\\color{black}128.73}\t&\t{\\color{black}280.63}\t&\t{\\color{black}128.73} \\\\\n & & {\\color{black}CRPS}\t&\t{\\color{black}\\underline{\\textbf{180.03}}}\t&\t{\\color{black}220.48}\t&\t{\\color{black}222.80}\t&\t{\\color{black}220.48}\t&\t{\\color{black}220.48}\t&\t{\\color{black}209.88}\t&\t{\\color{black}216.08} & {\\color{black} 219.38}\t&\t{\\color{black}215.31}\t&\t{\\color{black}219.38}\t&\t{\\color{black}214.57}\t&\t{\\color{black}219.31}\t&\t{\\color{black}220.86}\t&\t{\\color{black}219.31} \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "E-STGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting", "authors": ["Madhurima Panja", "Tanujit Chakraborty", "Anubhab Biswas", "Soudeep Deb"], "url": "https://arxiv.org/abs/2411.12258v2", "attribution": "\"E-STGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting\" by Madhurima Panja, Tanujit Chakraborty, Anubhab Biswas, and Soudeep Deb, arXiv:2411.12258v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.16355v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|ccc}\n$\\epsilon$ & Attack & LPIPS & NAD & Attack & LPIPS & NAD \\\\\n\\hline \\hline\n0.000 & FGSM & 0.0000 & 0.0000 & PGD & 0.0000 & 0.0000 \\\\\n0.005 & & 0.0026 & 0.0580 & & 0.0016 & 0.0379 \\\\\n0.020 & & 0.0524 & 0.2266 & & 0.0145 & 0.1005 \\\\\n0.050 & & 0.2454 & 0.5644 & & 0.0470 & 0.1768 \\\\\n0.100 & & 0.5596 & 1.1287 & & 0.1187 & 0.2973 \\\\\n0.200 & & 0.9913 & 2.2576 & & 0.2871 & 0.5523 \\\\\n0.300 & & 1.2105 & 3.3857 & & 0.4485 & 0.8163\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Statistics for different $\\epsilon$ values on FGSM and PGD.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Property-Preserving Hashing for $\\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks", "authors": ["Hassan Asghar", "Chenhan Zhang", "Dali Kaafar"], "url": "https://arxiv.org/abs/2504.16355v1", "attribution": "\"Property-Preserving Hashing for $\\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks\" by Hassan Asghar, Chenhan Zhang, and Dali Kaafar, arXiv:2504.16355v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.21379v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Distribution of $\\hat{N} - N$ over 100 simulated data sequences from the structures (NC), (M1), (V1), and (D1). The average $d_H$ and computation time are also given}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|l|l|}\n\\cline{1-8}\n & & \\multicolumn{4}{|c|}{} & & \\\\\n & & \\multicolumn{4}{|c|}{$\\hat{N} - N$} & & \\\\\nMethod & Model & $-1$ & 0 & $1$ & $\\geq 2$ & $d_H$ & Time (s)\\\\\n\\hline\n{\\textbf{NPID}} & & - & {\\textbf{97}} & 3 & 0 & - & 0.84\\\\\nCPMKS500 & & - & 28 & 25 & 47 & - & 0.07\\\\\nCPMCvM500 & & - & 34 & 18 & 48 & - & 0.07\\\\\n{\\textbf{ECP}} & (NC) & - & {\\textbf{94}} & 4 & 2 & - & 1.41\\\\\nNMCD & & - & 59 & 6 & 35 & - & 0.05\\\\\n{\\textbf{NBS}} & & - & {\\textbf{100}} & 0 & 0 & - & 0.44\\\\\n{\\textbf{NWBS}} & & - &{\\textbf{97}} & 0 & 3 & - & 9.73\\\\\n\\hline\n\\hline\n{\\textbf{NPID}} & & 0 & {\\textbf{94}} & 5 & 1 & 0.344 & 0.15\\\\\nCPMKS500 & & 0 & 71 & 18 & 11 & 0.179 & 0.01\\\\\nCPMCvM500 & & 0 & 69 & 17 & 14 & 0.165 & 0.01\\\\\n{\\textbf{ECP}} & (M1) & 0 & {\\textbf{96}} & 4 & 0 & 0.045 & 0.31\\\\\nNMCD & & 0 & 70 & 19 & 11 & 0.174 & 0.01\\\\\nNBS & & 19 & 70 & 3 & 8 & 0.303 & 0.09\\\\\nNWBS & & 22 & 69 & 4 & 5 & 0.314 & 2.28\\\\\n\\hline\n\\hline\n{\\textbf{NPID}} & & 1 & {\\textbf{86}} & 5 & 9 & 0.123 & 0.61\\\\\nCPMKS500 & & 1 & 60 & 18 & 21 & 0.443 & 0.01\\\\\nCPMCvM500 & & 5 & 49 & 25 & 21 & 0.424 & 0.01\\\\\n{\\textbf{ECP}} & (V1) & 12 & {\\textbf{83}} & 3 & 2 & 0.450 & 0.52\\\\\nNMCD & & 0 & 44 & 23 & 33 & 0.384 & 0.01\\\\\nNBS & & 93 & 1 & 1 & 5 & 0.978 & 0.15\\\\\nNWBS & & 90 & 4 & 1 & 5 & 0.958 & 3.92\\\\\n\\hline\n\\hline\n{\\textbf{NPID}} & & 2 & {\\textbf{94}} & 2 & 2 & 0.075 & 2.88\\\\\nCPMKS500 & & 0 & 7 & 11 & 82 & 0.637 & 0.17\\\\\nCPMCvM500 & & 0 & 9 & 12 & 79 & 0.576 & 0.19\\\\\nCPMKS1000 & & 0 & 27 & 17 & 56 & 0.442 & 0.25\\\\\nCPMCvM1000 & & 0 & 30 & 16 & 54 & 0.392 & 0.26\\\\\n{\\textbf{ECP}} & (D1) & 0 & {\\textbf{96}} & 3 & 1 & 0.036 & 7.68\\\\\nNMCD & & 0 & 27 & 13 & 60 & 0.439 & 0.12\\\\\nNBS & & 53 & 41 & 6 & 0 & 0.579 & 0.96\\\\\nNWBS & & 61 & 30 & 4 & 5 & 0.678 & 22.16\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Non-parametric multiple change-point detection", "authors": ["Andreas Anastasiou", "Piotr Fryzlewicz"], "url": "https://arxiv.org/abs/2504.21379v1", "attribution": "\"Non-parametric multiple change-point detection\" by Andreas Anastasiou and Piotr Fryzlewicz, arXiv:2504.21379v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10423v4_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc|cc}\n\\toprule\nMethod & e(n, o) & e(n, n) & e(o, o) & e(o, n)&er(n, o)&er(o, n)\\\\\n\\midrule\n\\midrule\nA-GEM & 0 & 177 & 0 & 9500&0\\%&100\\% \\\\\nER & 37 &148 & 2269 & 5852&20\\%&72\\% \\\\\nMIR & 54 & 113 & 2770 & 5330&32\\%&66\\%\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Online Continual Learning in Image Classification: An Empirical Survey", "authors": ["Zheda Mai", "Ruiwen Li", "Jihwan Jeong", "David Quispe", "Hyunwoo Kim", "Scott Sanner"], "url": "https://arxiv.org/abs/2101.10423v4", "attribution": "\"Online Continual Learning in Image Classification: An Empirical Survey\" by Zheda Mai, Ruiwen Li, Jihwan Jeong, David Quispe, Hyunwoo Kim, and Scott Sanner, arXiv:2101.10423v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.01865v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n \\toprule\n SS1 & $C_1$ & $C_2$ & $C_3$ & $C_4^*$ & $C_5^*$ & $C_6^*$ & $C_7^*$ \\\\\n \\midrule\n $\\boldsymbol{\\mu}_k$ & (-5,-5) & (-4,-4) & (4,4) & (0,0) & (5,-10) & (5,-10) & (-10,-10)\\\\\n $\\sigma^2$ & 1 & 2 & 2 & 1 & 1 & 1 & 0.1\\\\\n $\\rho$ & 0.9 & 0 & 0 & -0.75 & 0.9 & 0.9 & 0\\\\\n $n_{Tr}$ & 300 & 300 & 300 & -- &--& --&--\\\\ \n $n_{Te}$ & 200 & 200& 250& 90& 100& 100& 60\\\\\n \\midrule \n SS2 & $C_1$ & $C_2$ & $C_3^*$ & $C_4^*$ & $C_5^*$ & --& -- \\\\\n \\midrule\n $\\boldsymbol{\\mu}_k$ & (2,2) & (-2,-2) & (2,-2) & (-2,2) & (0,0) & -- & --\\\\\n $n_{Tr}$ & 500 & 500 & -- & -- &-- & -- & --\\\\ \n $n_{Te}$ & 200 & 200& 200 & 200 & 200 & -- & --\\\\\n \\midrule\n SS3 & $C_1$ & $C_2$ & $C_3$ & $C_4^*$ & $C_5^*$ & $C_6^*$ & -- \\\\\n \\midrule\n $\\boldsymbol{\\mu}_k$ & (-5,5) & (5,5) & (5,-5) & (0,0) & (0,0) & (-5,5) & --\\\\\n $\\sigma^2$ $^\\dagger$ & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 1.5 & -- \\\\\n $\\rho$ & 0 & 0 & 0 & 0.8 & -0.8 & 0 & --\\\\\n $n_{Tr}$ & 50 & 50 & 50 & -- &-- & -- & --\\\\ \n $n_{Te}$ & 1950 & 1950 & 1950 & 2000 & 2000 & 100 & --\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Characteristics of the synthetic datasets used in the three simulation studies (SS1$-$SS3). The components flagged with $^*$ are novelties. $^\\dagger$ the variances reported in this table refer to the low overlap scenario. For the high overlap scenario, we consider $\\sigma^{2\\star} = 6\\sigma^2$.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Variational Inference for Semiparametric Bayesian Novelty Detection in Large Datasets", "authors": ["Luca Benedetti", "Eric Boniardi", "Leonardo Chiani", "Jacopo Ghirri", "Marta Mastropietro", "Andrea Cappozzo", "Francesco Denti"], "url": "https://arxiv.org/abs/2212.01865v1", "attribution": "\"Variational Inference for Semiparametric Bayesian Novelty Detection in Large Datasets\" by Luca Benedetti, Eric Boniardi, Leonardo Chiani, Jacopo Ghirri, Marta Mastropietro, Andrea Cappozzo, and Francesco Denti, arXiv:2212.01865v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.05866v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{(Scenario I) Averages of the 5 smallest of SP/KS-GSPs whose AUCs are greater than the thresholds. Those scores are selected in the Pareto solutions appearing in Figure~. Standard deviations are in the parentheses next to the averages. Note CON is not available for SP.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cc|cc}\n\\toprule\n& \\multicolumn{2}{c|}{\\textbf{Adult} (AUC $\\geq$ 0.80)} & \\multicolumn{2}{|c|}{\\textbf{Cred. Card.} (AUC $\\geq$ 0.70)} & \\multicolumn{2}{|c}{\\textbf{Law School.} (AUC $\\geq$ 0.85)} \\\\\n\\hline\nModel & SP ($\\downarrow$) & KS-GSP ($\\downarrow$) & SP ($\\downarrow$) & KS-GSP ($\\downarrow$) & SP ($\\downarrow$) & KS-GSP ($\\downarrow$) \\\\\n\\hline\n\\hline\nSBP & 0.001 ($\\approx 0$) & 0.014 ($\\approx 0$) & $\\approx 0$ ($\\approx 0$) & 0.011 (0.001) & 0.199 (0.032) & 0.112 (0.011) \\\\\nHGR & 0.070 (0.005) & 0.040 ($\\approx 0$) & 0.001 (0.001) & 0.020 (0.001) & 0.123 (0.005) & 0.089 (0.001) \\\\\nKDE & 0.280 (0.018) & 0.110 (0.005) & 0.005 (0.003) & 0.023 (0.001) & 0.252 (0.006) & 0.151 (0.001) \\\\\nNEU & 0.372 (0.018) & 0.137 (0.002) & 0.002 (0.001) & 0.013 (0.001) & 0.385 (0.008) & 0.159 (0.006) \\\\ \n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes", "authors": ["Jinwon Sohn", "Qifan Song", "Guang Lin"], "url": "https://arxiv.org/abs/2311.05866v2", "attribution": "\"Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes\" by Jinwon Sohn, Qifan Song, and Guang Lin, arXiv:2311.05866v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1912.09954v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small{Statistics of the results obtained for crosshole GPR data in a multi-Gaussian domain. The naming convention is the same and the letters (a-x) refer to the models in Figure . RMSE$_{\\rm data}$ denotes the RMSE in data space, ${\\rm l}_1$ refers to the ${\\rm l}_1$-norm and SSIM to the structural similarity index. The ${\\rm l}_1$-norm is calculated in terms of velocity (m/ns) while the SSIM is computed in the rescaled $\\left[0,1\\right]$ domain. Closest TR means the closest training model in data space and Predicted \\#1 and Predicted \\#2 signify predicted models from two different noise realizations.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}%\n\t\t\t\\hline\n\t\t\tTrue model & RMSE$_{\\rm data}$ (ns) & ${\\rm l}_1$ (m/ns) & SSIM (-)\\\\\n\t\t\t\\hline\n\t\t\t(a) True & 0.5 & 0 & 1 \\\\\n\t\t\t(b) Closest TR & 1.73 & 18.92 & 0.72 \\\\\n\t\t\t(c) Predicted \\#1 & 0.72 & 8.07 & 0.92 \\\\\n\t\t\t(d) Predicted \\#2 & 0.73 & 8.10 & 0.92 \\\\\n\t\t\t\n\t\t\t(e) True & 0.5 & 0 & 1 \\\\\n\t\t\t(f) Closest TR & 1.44 & 15.35 & 0.77 \\\\\n\t\t\t(g) Predicted \\#1 & 0.63 & 6.26 & 0.93 \\\\\n\t\t\t(h) Predicted \\#2 & 0.68 & 6.60 & 0.92 \\\\\n\t\t\t\n\t\t\t(i) True & 0.5 & 0 & 1 \\\\\n\t\t\t(j) Closest TR & 0.98 & 13.31 & 0.79 \\\\\n\t\t\t(k) Predicted \\#1 & 0.56 & 6.26 & 0.93 \\\\\n\t\t\t(l) Predicted \\#2 & 0.59 & 5.89 & 0.94 \\\\\n\t\t\t\n\t\t\t(m) True & 0.5 & 0 & 1 \\\\\n\t\t\t(n) Closest TR & 1.01 & 12.37 & 0.82 \\\\\n\t\t\t(o) Predicted \\#1 & 0.60 & 7.20 & 0.93 \\\\\n\t\t\t(p) Predicted \\#2 & 0.63 & 7.23 & 0.92 \\\\\n\t\t\t\n\t\t\t(q) True & 0.5 & 0 & 1 \\\\\n\t\t\t(r) Closest TR & 1.16 & 13.17 & 0.78 \\\\\n\t\t\t(s) Predicted \\#1 & 0.58 & 5.89 & 0.93 \\\\\n\t\t\t(t) Predicted \\#2 & 0.61 & 6.65 & 0.92 \\\\\n\t\t\t\n\t\t\t(u) True & 0.5 & 0 & 1 \\\\\n\t\t\t(v) Closest TR & 1.05 & 12.65 & 0.81 \\\\\n\t\t\t(w) Predicted \\#1 & 0.63 & 7.83 & 0.90 \\\\\n\t\t\t(x) Predicted \\#2 & 0.67 & 7.55 & 0.91 \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Approaching geoscientific inverse problems with vector-to-image domain transfer networks", "authors": ["Eric Laloy", "Niklas Linde", "Diederik Jacques"], "url": "https://arxiv.org/abs/1912.09954v3", "attribution": "\"Approaching geoscientific inverse problems with vector-to-image domain transfer networks\" by Eric Laloy, Niklas Linde, and Diederik Jacques, arXiv:1912.09954v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15444v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mutually unbiased weighing matrices of order $n$ and weight $9$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n\\noalign{\\hrule height1pt}\n$n$ & $W_{\\max}(n,9)$ & References\\\\\n\\hline\n10 & $0$ & Proposition~\\\\\n12 & $0$ & Proposition~\\\\\n13 & $3$ & Proposition~\\\\\n14 & $0$ & Proposition~\\\\\n15 & $7$ & Proposition~ \\\\\n16 & $15$ & Proposition~ \\\\\n17 & $5$--$18$ & Proposition~\\\\\n18 & $4$--$21$ & Proposition~\\\\\n19 & $6$--$27$ & Proposition~\\\\\n20 & $4$--$34$ & Proposition~\\\\\n21 & $3$--$45$ & Proposition~\\\\\n22 & $2$--$63$ & Proposition~\\\\\n23 & $9$--$99$ & Proposition~\\\\\n24 & $6$--$96$ & Proposition~\\\\\n\\noalign{\\hrule height1pt}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unbiased weighing matrices of weight $9$", "authors": ["Makoto Araya", "Masaaki Harada", "Hadi Kharaghani", "Sho Suda", "Wei-Hsuan Yu"], "url": "https://arxiv.org/abs/2501.15444v2", "attribution": "\"Unbiased weighing matrices of weight $9$\" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.09680v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|}\n\t\t\\hline\n\t\t\\textbf{Recognition mode} & \\textbf{TTS errors} & \\textbf{Corrector errors} & \\textbf{Relative WER} \\\\\n\t\t\\hline\n\t\tBasic & 897 & 774 & 13.6 \\% \\\\\n\t\t\\hline\n\t\tWith context & 828 & 727 & 12.2\\% \\\\\n\t\t\\hline\t\t\n\t\\end{tabular}\n\\caption{Number of edition errors and relative WER.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fixing Errors of the Google Voice Recognizer through Phonetic Distance Metrics", "authors": ["Diego Campos-Sobrino", "Mario Campos-Soberanis", "Iván Martínez-Chin", "Víctor Uc-Cetina"], "url": "https://arxiv.org/abs/2102.09680v1", "attribution": "\"Fixing Errors of the Google Voice Recognizer through Phonetic Distance Metrics\" by Diego Campos-Sobrino, Mario Campos-Soberanis, Iván Martínez-Chin, and Víctor Uc-Cetina, arXiv:2102.09680v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.12395v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c}\n \\hline\n Training & Top & Before & After \\\\\n Set & Features & Aug. (\\%) & Aug. (\\%) \\\\\n \\hline\n Average & 10 & 53.432 & 60.222 \\\\\n Average & 20 & 53.206 & 61.254 \\\\\n Average & 30 & 53.547 & 60.828 \\\\\n Average & 40 & 53.432 & 61.333 \\\\\n 1,2,3,4 & 10 & 95.254 & 96.262 \\\\\n 1,2,3,4 & 20 & 94.122 & 96.464 \\\\\n 1,2,3,4 & 30 & 94.851 & 95.285 \\\\\n 1,2,3,4 & 40 & 96.262 & 95.565 \\\\\n 1,2,3,5 & 10 & 71.474 & 83.494 \\\\\n 1,2,3,5 & 20 & 71.474 & 84.375 \\\\\n 1,2,3,5 & 30 & 71.474 & 85.597 \\\\\n 1,2,3,5 & 40 & 71.474 & 84.896 \\\\\n 1,2,4,5 & 10 & 27.795 & 53.890 \\\\\n 1,2,4,5 & 20 & 27.795 & 52.492 \\\\\n 1,2,4,5 & 30 & 28.399 & 54.154 \\\\\n 1,2,4,5 & 40 & 40.181 & 53.248 \\\\\n 1,3,4,5 & 10 & 28.253 & 46.840 \\\\\n 1,3,4,5 & 20 & 28.253 & 51.255 \\\\\n 1,3,4,5 & 30 & 28.625 & 46.236 \\\\\n 1,3,4,5 & 40 & 28.996 & 51.069 \\\\\n 2,3,4,5 & 10 & 44.385 & 21.324 \\\\\n 2,3,4,5 & 20 & 44.385 & 22.861 \\\\\n 2,3,4,5 & 30 & 44.385 & 21.390 \\\\\n 2,3,4,5 & 40 & 44.385 & 21.190 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sound Classification of Four Insect Classes", "authors": ["Yinxuan Wang", "Sudip Vhaduri"], "url": "https://arxiv.org/abs/2412.12395v1", "attribution": "\"Sound Classification of Four Insect Classes\" by Yinxuan Wang and Sudip Vhaduri, arXiv:2412.12395v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17854v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l}\n \\texttt{settings} Field & \\textbf{Application} \\\\\\hline\n \\texttt{eppos} & Nonnegative (small) scalar to enforce strict positivity of \\texttt{Pop.P}\\\\\n \\texttt{eppos2} & Nonnegative (small) scalar to enforce strict positivity of \\texttt{Pop.R0}\\\\\n \\texttt{epneg} & Nonnegative (small) scalar to enforce strict negativity of \\texttt{Dop}\\\\\n \\texttt{sosineq} & Binary value, set 1 to use \\texttt{sosineq}\\\\\n \\texttt{override1} & Binary value, set 1 to let $\\texttt{P2op}=0$\\\\\n \\texttt{override2} & Binary value, set 1 to let $\\texttt{De2op}=0$\\\\\n \\texttt{dd1} & 1x3 cell structure defining monomial degrees for \\texttt{Pop}\\\\\n \\texttt{dd12} & 1x3 cell structure defining monomial degrees for \\texttt{P2op}\\\\\n \\texttt{dd2} & 1x3 cell structure defining monomial degrees for \\texttt{Deop}\\\\\n \\texttt{dd3} & 1x3 cell structure defining monomial degrees for \\texttt{De2op}\\\\\n \\texttt{ddZ} & 1x3 array defining monomial degrees for \\texttt{Zop}\\\\\n \\texttt{options1} & Structure of \\texttt{poslpivar} options for \\texttt{Pop}\\\\\n \\texttt{options12} & Structure of \\texttt{poslpivar} options for \\texttt{P2op}\\\\\n \\texttt{options2} & Structure of \\texttt{poslpivar} options for \\texttt{Deop}\\\\\n \\texttt{options3} & Structure of \\texttt{poslpivar} options for \\texttt{De2op}\\\\\n \\texttt{opts} & Structure of \\texttt{lpi\\_ineq} options for enforcing $\\texttt{Dop}\\leq0$\\\\\n \\texttt{sos\\_opts} & Structure of \\texttt{sossolve} options for solving the LPI\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Fields of settings structure passed on to PIETOOLS executive files}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "PIETOOLS 2024: User Manual", "authors": ["Sachin Shivakumar", "Declan Jagt", "Danilo Braghini", "Amritam Das", "Yulia Peet", "Matthew Peet"], "url": "https://arxiv.org/abs/2501.17854v1", "attribution": "\"PIETOOLS 2024: User Manual\" by Sachin Shivakumar, Declan Jagt, Danilo Braghini, Amritam Das, Yulia Peet, and Matthew Peet, arXiv:2501.17854v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00088v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\nOptimal policy & ACPI & MVPI & Expert \\\\\n\\hline\niv low, vaso low & $0.72 $ & $0.36$ & 1\\\\\n\\hline\niv low, vaso med & $0.18 $ & 0.018 & 0\\\\\n\\hline\niv low, vaso high & $< 0.0001$ & <0.0001 & 0\\\\\n\\hline\niv med, vaso low & $<0.0001$ & $0.43$ & 0 \\\\\n\\hline\niv med, vaso med & $<0.0001$ & <0.0001& 0 \\\\\n\\hline\niv med, vaso high & $<0.0001$ & 0.11 & 0\\\\\n\\hline\niv high, vaso low & $0.09$ & $<0.0001$ & 0\\\\\n\\hline\niv high, vaso med & $<0.001$ & 0.075 & 0\\\\\n\\hline\niv high, vaso high & <0.001 & <0.0001 & 0\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Recommended action by policy estimated by ACPI, MVPI and the expert for a sample patient from group 2.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Reinforcement Learning with Heterogeneous Data: Estimation and Inference", "authors": ["Elynn Y. Chen", "Rui Song", "Michael I. Jordan"], "url": "https://arxiv.org/abs/2202.00088v1", "attribution": "\"Reinforcement Learning with Heterogeneous Data: Estimation and Inference\" by Elynn Y. Chen, Rui Song, and Michael I. Jordan, arXiv:2202.00088v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00675v3_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|cccccc|c}\n \\toprule % cmidrule(r){2-5}\nMethod &\tiNat\t& SUN\t& Food\t& Textures\t& Pets & EuroSAT\t& Average \\\\\n\\midrule\nCLIPN~\t&$42.53\\pm0.73$&$82.14\\pm0.55$&$68.77\\pm0.80$&$52.39\\pm0.66$&$66.01\\pm0.82$&$64.58\\pm1.08$&$62.74$\\\\\nZOC &$55.05\\pm0.99$&$93.61\\pm0.32$&$92.18\\pm0.24$&$73.88\\pm0.66$&$88.53\\pm0.77$&$\\mathbf{65.38\\pm1.14}$&$78.16$\\\\\n{ANP+FT} (ours) &$\\mathbf{60.32\\pm0.99}$&$\\mathbf{94.19\\pm0.38}$&$\\mathbf{94.18\\pm0.20}$&$\\mathbf{78.21\\pm0.88}$&$\\mathbf{90.97\\pm0.88}$&$59.72\\pm0.98$&$\\mathbf{79.60}$\\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "LLM meets Vision-Language Models for Zero-Shot One-Class Classification", "authors": ["Yassir Bendou", "Giulia Lioi", "Bastien Pasdeloup", "Lukas Mauch", "Ghouthi Boukli Hacene", "Fabien Cardinaux", "Vincent Gripon"], "url": "https://arxiv.org/abs/2404.00675v3", "attribution": "\"LLM meets Vision-Language Models for Zero-Shot One-Class Classification\" by Yassir Bendou, Giulia Lioi, Bastien Pasdeloup, Lukas Mauch, Ghouthi Boukli Hacene, Fabien Cardinaux, and Vincent Gripon, arXiv:2404.00675v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.05463v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|}\n \\hline\n scale $\\theta$ & shape $\\nu$ & powershape $\\gamma$ & abbreviation \\\\\n \\hline\n fixed to prior mean & Gamma & Gamma & fix-gam-gam \\\\\n Gamma & Gamma & Gamma & gam-gam-gam\\\\\n fixed to prior mean & Lognormal & Lognormal & fix-log-log \\\\\n Lognormal & Lognormal & Lognormal & log-log-log \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Prior distributional assumptions for the scale $\\theta$, and shapes $\\nu$ and $\\gamma$ of the PgW distribution.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "The BPgWSP test: a Bayesian Weibull Shape Parameter signal detection test for adverse drug reactions", "authors": ["Julia Dyck", "Odile Sauzet"], "url": "https://arxiv.org/abs/2412.05463v2", "attribution": "\"The BPgWSP test: a Bayesian Weibull Shape Parameter signal detection test for adverse drug reactions\" by Julia Dyck and Odile Sauzet, arXiv:2412.05463v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00251v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{R-IGD and R-HV values obtained by MOEA/D-DE and LLA over 10 independent runs. The value of the population and the predictions are both evaluated.}\n\\begin{tabular}{lcccccc}\n \\hline\n \\multirow{2}{*}{Problem} & \\multicolumn{2}{c}{MOEA/D-DE} & \\multicolumn{2}{c}{LLA Pop} & \\multicolumn{2}{c}{LLA Pred} \\\\\\cline{2-7}\n & R-IGD & R-HV & R-IGD & R-HV & R-IGD & R-HV \\\\\\hline\n ZDT1 & 1.27e-01 & 5.56e-01 & 1.27e-01 & 5.56e-01 & \\textbf{7.76e-02} & \\textbf{6.09e-01} \\\\\n ZDT2 & 1.02e-01 & 2.89e-01 & 1.02e-01 & 2.89e-01 & \\textbf{6.60e-02} & \\textbf{2.95e-01} \\\\\n ZDT4 & 1.26e-01 & 5.55e-01 & 1.26e-01 & 5.55e-01 & \\textbf{7.78e-02} & \\textbf{6.08e-01} \\\\\n ZDT6 & 1.26e-01 & 3.37e-01 & 1.16e-01 & 3.42e-01 & \\textbf{8.27e-02} & \\textbf{3.46e-01} \\\\\n DTLZ1 & 1.90e-01 & 5.09e-01 & 1.92e-01 & 5.05e-01 & \\textbf{1.84e-01} & \\textbf{6.36e-01} \\\\\n DTLZ2 & 3.24e-01 & 1.90e-01 & 3.24e-01 & 1.90e-01 & \\textbf{2.82e-01} & \\textbf{2.30e-01} \\\\\n DTLZ3 & 3.23e-01 & 1.90e-01 & 3.25e-01 & 1.90e-01 & \\textbf{2.82e-01} & \\textbf{2.29e-01} \\\\\n DTLZ4 & \\textbf{3.97e-01} & \\textbf{1.48e-01} & 4.22e-01 & 1.35e-01 & 4.22e-01 & 1.42e-01 \\\\\\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables", "authors": ["Ping Guo", "Qingfu Zhang", "Xi Lin"], "url": "https://arxiv.org/abs/2404.00251v1", "attribution": "\"Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables\" by Ping Guo, Qingfu Zhang, and Xi Lin, arXiv:2404.00251v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12856v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Error analysis of using GD method for finding Lorenz's system parameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|r|r|r|r|r|}\n\\hline\n$a_0$& $\\begin{bmatrix}\n15\\\\1\\\\-10\n\\end{bmatrix}$&$\\begin{bmatrix}\n0\\\\ 10\\\\ -10\n\\end{bmatrix}$&$\\begin{bmatrix}\n30\\\\ -10\\\\ 10\n\\end{bmatrix}$&$\\begin{bmatrix}\n-3\\\\ 1\\\\ 0\n\\end{bmatrix}$&$\\begin{bmatrix}\n0\\\\0\\\\0\n\\end{bmatrix}$\\\\\n\\hline\nbias&&&&&\\\\\n$x_1$&0.0569&-0.2394&0.0369&0.0211&0.0227\\\\\n$x_2$&0.1316&0.0654&0.1348&0.1380&0.1377\\\\\n$x_3$&0.3502&1.1577&0.4240&0.4859&0.4814\\\\\n$f_1(\\cdot)$&0.7734&2.5661&0.9632&1.1215&1.1065\\\\\n$f_2(\\cdot)$&0.9704&2.8068&0.8525&0.8255&0.8256\\\\\n$f_3(\\cdot)$&0.3170&-1.7818&-0.1898&-0.4955&-0.4690\\\\\n\\hline\nMAPE&&&&&\\\\\n$x_1$&0.2495&0.3323&0.2095&0.1986&0.1990\\\\\n$x_2$&0.2905&0.4830&0.2367&0.2214&0.2225\\\\\n$x_3$&0.0937&0.1297&0.0704&0.0634&0.0636\\\\\n$f_1(\\cdot)$&4.1417&7.1081&4.7069&5.0499&5.0109\\\\\n$f_2(\\cdot)$&1.7077&3.2955&1.1756&1.0091&1.0153\\\\\n$f_3(\\cdot)$&3.0242&5.1359&2.4163&2.0935&2.1144\\\\\n\\hline\nMAE&&&&&\\\\\n$x_1$&1.3573&1.9689&1.1063&1.0578&1.0578\\\\\n$x_2$&1.6019&2.7778&1.2401&1.1545&1.1604\\\\\n$x_3$&1.9922&3.0877&1.3922&1.2128&1.2190\\\\\n$f_1(\\cdot)$&15.7965&22.6766&14.1684&13.6381&13.6546\\\\\n$f_2(\\cdot)$&18.3308&27.9114&12.9338&11.2522&11.2910\\\\\n$f_3(\\cdot)$&21.1255&36.1948&15.8557&14.3514&14.3723\\\\\n\\hline\nRMSE&&&&&\\\\\n$x_1$&1.6721&2.5351&1.3247&1.2562&1.2584\\\\\n$x_2$&2.0420&3.6730&1.5058&1.3843&1.3889\\\\\n$x_3$&2.4776&4.1983&1.7205&1.5220&1.5317\\\\\n$f_1(\\cdot)$&19.9965&30.2226&17.3583&17.0121&17.0124\\\\\n$f_2(\\cdot)$&23.3798&40.5785&15.9072&14.3062&14.3519\\\\\n$f_3(\\cdot)$&27.6067&51.3703&19.9318&18.6903&18.7016\\\\\n\\hline\n$R^2$&&&&&\\\\\n$x_1$&0.9228&0.8226&0.9516&0.9564&0.9563\\\\\n$x_2$&0.9089&0.7053&0.9505&0.9581&0.9579\\\\\n$x_3$&0.8895&0.6826&0.9467&0.9583&0.9578\\\\\n$f_1(\\cdot)$&0.7204&0.3613&0.7893&0.7976&0.7976\\\\\n$f_2(\\cdot)$&0.8233&0.4678&0.9182&0.9338&0.9334\\\\\n$f_3(\\cdot)$&0.8229&0.3868&0.9077&0.9188&0.9187\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods", "authors": ["S. Syafiie", "Aries Subiantoro", "Vivi Andasari", "Fernando Tadeo"], "url": "https://arxiv.org/abs/2501.12856v1", "attribution": "\"Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods\" by S. Syafiie, Aries Subiantoro, Vivi Andasari, and Fernando Tadeo, arXiv:2501.12856v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.07016v1_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{ccc|ccc|ccc}\n \t\t\\hline\n \t\t&&&\\multicolumn{3}{c|}{ISFLP1}&\\multicolumn{3}{c}{ISFLP2}\\\\ \n \t\t\\hline\n \t\t\n \t\tInstance&$n$&$\\mathbf{\\bar{x}}=(\\bar{x},\\bar{y})$&$C^*$&$iter$&CPU&$C^*$&$iter$&CPU\\\\\n \t\t&&&&&(in sec)&&&(in sec)\\\\\n \t\t\\hline\n \t\tExample &4&$(-2,5)$&$39.3674$&$39$\n \t&273.8595&$39.1801$&$2111$&$6181.4753$\\\\\n \t \t\t\\hline\n \t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Inverse single facility location problem in the plane with variable coordinates", "authors": ["Nazanin Tour-Savadkoohi", "Jafar Fathali"], "url": "https://arxiv.org/abs/2503.07016v1", "attribution": "\"Inverse single facility location problem in the plane with variable coordinates\" by Nazanin Tour-Savadkoohi and Jafar Fathali, arXiv:2503.07016v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.15929v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Selected parameter values for the optimizer}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\\toprule[1.25pt]\n \\textbf{Option} & \\textbf{Selected Value} \\\\\n \\hline\n Algorithm & sqp \\\\ \n Specify Objective Gradient & True \\\\\n Constraint Tolerance & 0.002\\\\\n Optimality Tolerance & 0.005\\\\\n Step Tolerance & 0.001\\\\\n Scale Problem & True\\\\\n Typical X & 0.05\\\\\n \\bottomrule[1.25pt]\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Nonlinear Model Predictive Control of a Hybrid Thermal Management System", "authors": ["Demetrius Gulewicz", "Uduak Inyang-Udoh", "Trevor Bird", "Neera Jain"], "url": "https://arxiv.org/abs/2411.15929v1", "attribution": "\"Nonlinear Model Predictive Control of a Hybrid Thermal Management System\" by Demetrius Gulewicz, Uduak Inyang-Udoh, Trevor Bird, and Neera Jain, arXiv:2411.15929v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11782v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Out-of-sample results for the Kodak (bpp $0.37$), 8-bit Compression Benchmark (CB, bpp, $0.341$), 16-bit \\& 16-bit-Linear Compression Benchmark (CB) datasets (bpp $0.35$ for both), the Tecnick (bpp $0.475$), \\& Wikipedia (bpp $0.352$) datasets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l||c|c|c|c||c|c|c|c|}\n\\hline\n & \\multicolumn{3}{c||}{\\textbf{Kodak}} & \\multicolumn{3}{c|}{\\textbf{CB 8-Bit}}\\\\\n \\textbf{Model} & \\textbf{PSNR} & \\textbf{SSIM} & $MS^3IM$ & \\textbf{PSNR} & \\textbf{SSIM} & $MS^3IM$\\\\\n \\hline\n \\emph{JPEG} & 27.6540 & 0.7733 & 0.9291 & 27.5481 & 0.8330 & 0.9383 \\\\\n \\emph{JPEG 2000} & 27.8370 & 0.8396 & 0.9440 & 27.7965 & 0.8362 & 0.9471 \\\\\n \\emph{GOOG}-JPEG & 27.9613 & 0.8017 & 0.9557 & 27.8458 & 0.8396 & 0.9562 \\\\\n \\emph{E2E} (\\textbf{Neural}) & 28.9420 & 0.8502 & 0.9600 & 28.0999 & 0.8396 & 0.9562 \\\\\n \\emph{LSTM}-JP2 - BPTT & {28.9321} & {0.8425} & {0.9596} & {28.0896} & {0.8389} & {0.9562} \\\\ \n \\emph{LSTM}-JP2 - SAB & {29.2589} & {0.8435} & {0.9599} & {28.2896} & {0.8399} & {0.9577} \\\\ \n \\emph{LSTM}-JP2 - UORO & {28.9302} & {0.8424} & {0.9592} & {28.0895} & {0.8388} & {0.9562} \\\\ \n \\emph{LSTM}-JP2 - RTRL & {28.9311} & {0.8424} & {0.9595} & {28.0891} & {0.8387} & {0.9561} \\\\ \n \\emph{SNE-RNN}-JP2 - BPTT & {29.3008} & {0.8508} & {0.9622} & {28.2199} & {0.8401} & {0.9600} \\\\\n \\emph{SNE-RNN}-JP2 - SAB & \\textbf{29.4128} & \\textbf{0.8514} & \\textbf{0.9627} & \\textbf{28.2122} & \\textbf{0.8402} & \\textbf{0.9600} \\\\\n \n \\hline\n & \\multicolumn{3}{c||}{\\textbf{CB 16-Bit}} & \\multicolumn{3}{c|}{\\textbf{CB 16-Bit-Linear}}\\\\\n \\hline\n \\emph{JPEG} &27.5368 & 0.8331 & 0.9383 & 31.7522 & 0.8355 & 0.9455 \\\\\n \\emph{JPEG 2000} & 27.7885 & 0.8391 & 0.9437 & 32.0270 & 0.8357 & 0.9471 \\\\\n \\emph{GOOG} & 27.8830 & 0.8391 & 0.9468 & 32.1275 & 0.8369 & 0.9533 \\\\\n \\emph{E2E} (\\textbf{Neural}) & 28.2440 & 0.8426 & 0.9498 & 32.5010 & 0.8387 & 0.9540 \\\\\n \\emph{LSTM}-JP2- BPTT & {28.1307} & {0.8425} & {0.9496} & {32.4998} & {0.8382} & {0.9541} \\\\\n \\emph{LSTM}-JP2 - SAB & {28.2307} & {0.8431} & {0.9501} & {32.5003} & {0.8385} & {0.9544} \\\\\n \\emph{LSTM}-JP2 - UORO & {28.1312} & {0.8427} & {0.9497} & {32.4999} & {0.8382} & {0.9542} \\\\\n \\emph{LSTM}-JP2 - RTRL & {28.1304} & {0.8424} & {0.9495} & {32.4991} & {0.8381} & {0.9542} \\\\\n \\emph{SNE-RNN}-JP2- BPTT & {29.4471} & {0.8430} & {0.9510} & \\textbf{32.6019} & \\textbf{0.8399} & {0.9559} \\\\\n \\emph{SNE-RNN}-JP2- SAB & \\textbf{29.4524} & \\textbf{0.8432} & \\textbf{0.9511} & {32.6011} & {0.8398} & \\textbf{0.9560} \\\\\n \n \\hline\n & \\multicolumn{3}{c||}{\\textbf{Tecnick}} & \\multicolumn{3}{c|}{\\textbf{Wikipedia}}\\\\\n \\hline\n \\emph{JPEG} & 30.7377 & 0.8682 & 0.9521 & 28.7724 & 0.8290 & 0.9435\\\\\n \\emph{JPEG 2000} & 31.2319 & 0.8747 & 0.9569 & 29.1545 & 0.8382 & 0.9495 \\\\\n \\emph{GOOG} & 31.5030& 0.8814 & 0.9608 & 29.2209 & 0.8406 & 0.9520 \\\\\n \\emph{E2E} (\\textbf{Neural}) & 31.7000 & 0.8836 & 0.9620 & 29.3227 & 0.8412 & 0.9526 \\\\\n \\emph{LSTM}-JP2 - BPTT & {31.6962} & {0.8834} & {0.9619} & {29.3228} & {0.8411} & {0.9526} \\\\\n \\emph{LSTM}-JP2 - SAB & {31.7100} & {0.8841} & {0.9621} & {29.3229}& {0.8412} & {0.9525} \\\\\n \\emph{LSTM}-JP2 - UORO & {31.6961} & {0.8835} & {0.9620} & {29.3225} & {0.8412} & {0.9526} \\\\\n \\emph{LSTM}-JP2- RTRL & {31.6963} & {0.8833} & {0.9615} & {29.3227} & {0.8412} & {0.9525} \\\\\n \\emph{SNE-RNN}-JP2 - BPTT & \\textbf{32.7124} & {0.8841} & \\textbf{0.9622} & {29.9334} & {0.8413} & {0.9528} \\\\\n \\emph{SNE-RNN}-JP2 - SAB & {32.7119} & \\textbf{0.8844} & {0.9621} & \\textbf{29.9336} & \\textbf{0.8414} & \\textbf{0.9529} \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An Empirical Analysis of Recurrent Learning Algorithms In Neural Lossy Image Compression Systems", "authors": ["Ankur Mali", "Alexander Ororbia", "Daniel Kifer", "Lee Giles"], "url": "https://arxiv.org/abs/2201.11782v1", "attribution": "\"An Empirical Analysis of Recurrent Learning Algorithms In Neural Lossy Image Compression Systems\" by Ankur Mali, Alexander Ororbia, Daniel Kifer, and Lee Giles, arXiv:2201.11782v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2007.13640v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllll}\n \\toprule\n \\multicolumn{6}{c}{Algorithm} \\\\\n \\cmidrule(r){2-6}\n Factor & $MM^Tx$ & DIP & DeepRED & Ours & Ours - avg\\\\\n \\midrule\n 4:1 & 26.35 (0.826) & 30.04 (0.902) & 30.22 (0.904) & 29.47 (0.894) & {\\bf 31.20} ({\\bf 0.913}) \\\\\n 8:1 & 23.02 (0.673) & 24.98 (0.760) & 24.95 (0.760) & 25.07 (0.767) & {\\bf 25.64} ({\\bf 0.792}) \\\\\n \\bottomrule \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Spatial super-resolution performance averaged over Set5 images. Values indicate YCbCr-PSNR (SSIM).}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser", "authors": ["Zahra Kadkhodaie", "Eero P. Simoncelli"], "url": "https://arxiv.org/abs/2007.13640v3", "attribution": "\"Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser\" by Zahra Kadkhodaie and Eero P. Simoncelli, arXiv:2007.13640v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08445v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c}\n\\hline\nExperiments & Average Improvement \\\\ \\hline\nfour-battery system & 15.2$\\%$ \\\\ \\hline\neight-battery system & 31.9$\\%$\\\\ \\hline\nfour-second-life-battery system & 151.0$\\%$\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Main results of the average improvement between the baseline among 5000 different random initialization with random load profiles}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning", "authors": ["Yuan Tian", "Minghao Han", "Chetan Kulkarni", "Olga Fink"], "url": "https://arxiv.org/abs/2201.08445v1", "attribution": "\"A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning\" by Yuan Tian, Minghao Han, Chetan Kulkarni, and Olga Fink, arXiv:2201.08445v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.12502v3_tex_table20.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CelebA Results. In a paired t-test, TNP-KR: SA has a lower NLL than TNP-D with p-value 0.001 and TNP-KR: DKA has a lower p-value than TNP-KR: PERF with p-value $<0.001$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\\toprule\nName & NLL & RMSE & MAE & Coverage & Training Time (s) \\\\\n\\midrule\nNP & $-0.182\\pm0.001$ & $0.254\\pm0.000$ & $0.175\\pm0.000$ & $0.953\\pm0.000$ & $363.600\\pm1.077$ \\\\\nCNP & $-0.221\\pm0.001$ & $0.248\\pm0.000$ & $0.170\\pm0.000$ & $0.954\\pm0.000$ & $342.800\\pm0.374$ \\\\\nBNP & $-0.268\\pm0.001$ & $0.256\\pm0.000$ & $0.180\\pm0.000$ & $0.919\\pm0.001$ & $576.400\\pm1.030$ \\\\\nANP & $-0.777\\pm0.004$ & $0.207\\pm0.001$ & $0.118\\pm0.000$ & $0.965\\pm0.000$ & $442.000\\pm0.316$ \\\\\nCANP & $-0.799\\pm0.005$ & $0.205\\pm0.001$ & $0.116\\pm0.001$ & $0.966\\pm0.000$ & $369.800\\pm0.490$ \\\\\nBANP & $-0.830\\pm0.004$ & $0.200\\pm0.001$ & $0.121\\pm0.000$ & $0.945\\pm0.000$ & $1411.400\\pm0.678$ \\\\\nConvCNP & $-0.863\\pm0.001$ & $0.196\\pm0.000$ & $0.110\\pm0.000$ & $0.967\\pm0.000$ & $9455.200\\pm27.656$ \\\\\nTNP-D & $-0.877\\pm0.005$ & $0.193\\pm0.001$ & $0.108\\pm0.001$ & $0.967\\pm0.000$ & $718.800\\pm3.338$ \\\\\nTNP-KR: PERF & $-0.867\\pm0.002$ & $0.193\\pm0.000$ & $0.109\\pm0.000$ & $0.966\\pm0.000$ & $867.000\\pm0.837$ \\\\\nTNP-KR: DKA & $-0.892\\pm0.002$ & $0.190\\pm0.000$ & $0.106\\pm0.000$ & $0.967\\pm0.000$ & $840.400\\pm1.503$ \\\\\nTNP-KR: SA & $-0.917\\pm0.001$ & $0.188\\pm0.000$ & $0.103\\pm0.000$ & $0.967\\pm0.000$ & $893.200\\pm0.583$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Transformer Neural Processes - Kernel Regression", "authors": ["Daniel Jenson", "Jhonathan Navott", "Mengyan Zhang", "Makkunda Sharma", "Elizaveta Semenova", "Seth Flaxman"], "url": "https://arxiv.org/abs/2411.12502v3", "attribution": "\"Transformer Neural Processes - Kernel Regression\" by Daniel Jenson, Jhonathan Navott, Mengyan Zhang, Makkunda Sharma, Elizaveta Semenova, and Seth Flaxman, arXiv:2411.12502v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.07255v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Neural networks architectures for the MNIST experiment.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc}\n \\toprule\n Parameter & Value \\\\\n \\midrule\n Nr. of CNN layers in inference network & 3 \\\\\n Nr. of CNN layers in generative network & 3 \\\\\n Nr. of filters per CNN layer & 8 \\\\\n Filter size & $3 \\times 3$ \\\\\n Nr. of feedforward layers in inference network & 1 \\\\\n Nr. of feedforward layers in generative network & 1 \\\\\n Activation function in CNN layers & ELU \\\\\n Dimensionality of latent space (L) & 16 \\\\\n Number of latent channels for angle info (J) & 8 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Factorized Gaussian Process Variational Autoencoders", "authors": ["Metod Jazbec", "Michael Pearce", "Vincent Fortuin"], "url": "https://arxiv.org/abs/2011.07255v1", "attribution": "\"Factorized Gaussian Process Variational Autoencoders\" by Metod Jazbec, Michael Pearce, and Vincent Fortuin, arXiv:2011.07255v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.10214v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{c|l|c|l}\n OEIS A number & First few terms & \n $(x,y,u,v,a,b,c,d)$ & Description\\\\\n \\hline\n A258173 & $1, 1, 3, 12, 58, 321, 1975, 13265$ & (1,1,0,0,0,1,2,2) &\n Sum over all Dyck paths of semilength $n$ \\\\\n &&& of products over all peaks $p$ of $y_p$, where \\\\\n &&& $y_p$ is the $y$-coordinate of peak $p$. \n \\\\\n A006318 & 1, 2, 6, 22, 90, 394, 1806, 8558&(1,1,0,0,1,1,0,0) & Large Schr\\\"oder numbers\\\\\n A302285 & 1, 2, 7, 33, 185, 1170, 8121 &(1,1,0,0,1,2,2,2) & No combinatorial description \\\\\n A047891 & 1, 3, 12, 57, 300, 1686, 9912 &(1,1,0,0,2,2,0,0) & Number of planar rooted trees \\\\\n &&&\n with $n$ nodes and tricolored end nodes. \\\\\n A155866 & 1, 2, 6, 22, 91, 413, 2032 &(1,1,0,1,1,1,0,0) & \n A `Morgan-Voyce' transform\\\\\n &&&\n of the Bell numbers\n \\\\\n A155857 & 1, 2, 6, 23, 107, 590, 3786 &(1,1,1,1,1,1,0,0) & \n Row sums of triangle \\\\\n &&&\n A155856 \n ($\\binom {2 n - k} {k} (n - k)!$)\n \\\\\n A000311 & 1, 1, 4, 26, 236, 2752, 39208 &(1,2,2,2,0,1,2,2) & \n Schr\\\"oder's fourth problem;\\\\\n &&&\n also series-reduced rooted trees\\\\\n &&&\n with $n$ labeled leaves;\\\\\n &&&\n also number of total partitions \n of $n$.\\\\\n A001515 & 1, 2, 7, 37, 266, 2431, 27007 &(1,2,2,2,1,1,0,0) & Bessel polynomial $y_n(x)$ \\\\\n &&&\n evaluated at $x=1$. \\\\\n A006351 & 1, 2, 8, 52, 472, 5504, 78416 &(1,2,2,2,1,2,2,2) & \n Number of series-parallel networks\\\\\n &&&\n with $n$ labeled edges.\\\\\n &&&\n Also called yoke-chains\\\\\n &&&\n by Cayley and MacMahon.\n \\\\\n A043301 & 1, 3, 13, 77, 591, 5627, 64261 &(1,2,2,2,2,2,0,0) & \n $a(n) = 2^n\\sum_{k = 0}^{n} (n + k)!/((n - k)!k!4^k).$ \\\\\n A155867 & 1, 3, 13, 65, 355, 2061, 12501 &(2,1,0,0,1,1,0,0) & \n A `Morgan-Voyce' transform \\\\\n &&&\n of the large Schr\\\"oder numbers A006318.\n \\\\\n A103210 & 1, 3, 15, 93, 645, 4791, 37275 &(2,2,0,0,1,1,0,0) & $a(0)=1$\\\\\n &&&\n $a(n) = (1/n)\\sum_{i = 0}^{n - 1} \n \\binom{n}{i} \\binom{n}{i + 1} 2^i 3^{n - i}$\\\\\n A156017 & 1, 4, 24, 176, 1440, 12608 &(2,2,0,0,2,2,0,0) & Schr\\\"oder paths with two rise colors\\\\\n &&&\n and two level colors.\n \\end{tabular}\n\\caption{OEIS entries having various T-fractions.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Thron-type continued fractions (T-fractions) for some classes of increasing trees", "authors": ["Veronica Bitonti", "Bishal Deb", "Alan D. Sokal"], "url": "https://arxiv.org/abs/2412.10214v1", "attribution": "\"Thron-type continued fractions (T-fractions) for some classes of increasing trees\" by Veronica Bitonti, Bishal Deb, and Alan D. Sokal, arXiv:2412.10214v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17103v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n $n$ & Error $\\|\\cdot\\|_1$ & Order $\\|\\cdot\\|_1$ & Error\n $\\|\\cdot\\|_{\\infty}$ & Order $\\|\\cdot\\|_{\\infty}$ \\\\\n \\hline\n 40 & 9.81E$-6$ & $-$ & 2.39E$-5$ & $-$ \\\\\n \\hline\n 80 & 3.06E$-7$ & 5.00 & 7.56E$-7$ & 4.98 \\\\\n \\hline\n 160 & 9.52E$-9$ & 5.00 & 2.28E$-8$ & 5.05 \\\\\n \\hline\n 320 & 2.97E$-10$ & 5.00 & 7.03E$-10$ & 5.02 \\\\\n \\hline\n 640 & 9.23E$-12$ & 5.01 & 2.12E$-11$ & 5.06 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Lagrange extrapolation (node removal).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17103v1", "attribution": "\"High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08887v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|l|l|l|l|} \n\\hline\n\\multirow{3}{*}{Models} & \\multicolumn{4}{c|}{MARS} \\\\ \n\\cline{2-5}\n & \\multicolumn{2}{c|}{I2V} & \\multicolumn{2}{c|}{V2V} \\\\ \n\\cline{2-5}\n & cmc1 & mAP & cmc1 & mAP \\\\ \n\\hline\nwith CE loss & 84.29 & 76.94 & 88.69 & 82.23 \\\\ \n\\hline\nwithout CE loss & \\textbf{85.65} & \\textbf{78.02} & \\textbf{89.48} & \\textbf{82.90} \\\\ \n\\hline\n & \\multicolumn{4}{c|}{Duke-video} \\\\ \n\\hline\n & \\multicolumn{2}{c|}{I2V} & \\multicolumn{2}{c|}{V2V} \\\\ \n\\hline\n & cmc1 & mAP & cmc1 & mAP \\\\ \n\\hline\nwith CE loss & 85.83 & 84.66 & 94.07 & 93.44 \\\\ \n\\hline\nwithout CE loss & \\textbf{86.78} & \\textbf{84.82} & \\textbf{95.26} & \\textbf{93.83} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Ablation study on the impact of cross-entropy loss.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Image-to-Video Re-Identification via Mutual Discriminative Knowledge Transfer", "authors": ["Pichao Wang", "Fan Wang", "Hao Li"], "url": "https://arxiv.org/abs/2201.08887v1", "attribution": "\"Image-to-Video Re-Identification via Mutual Discriminative Knowledge Transfer\" by Pichao Wang, Fan Wang, and Hao Li, arXiv:2201.08887v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.07960v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parachute deployment, PDI and touchdown times for mission profile 1 derived from GRASHS, RASHS and MPBVP solutions.}\n\\begin{tabular}{lccc}\n \\hline\n Method & Parachute deployment & PDI & Touchdown \\\\\\hline\n GRASHS & 300.3052 \\textrm{s} & 336.4748 \\textrm{s} & 376.7966 \\textrm{s} \\\\\n RASHS & 300.3600 \\textrm{s} & 336.4492 \\textrm{s} & 376.7730 \\textrm{s} \\\\\n MPBVP & 300.3537 \\textrm{s} & 336.4169 \\textrm{s} & 376.7456 \\textrm{s} \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Indirect Optimization of Multi-Phase Trajectories Involving Arbitrary Discrete Logic", "authors": ["Harish Saranathan"], "url": "https://arxiv.org/abs/2412.07960v1", "attribution": "\"Indirect Optimization of Multi-Phase Trajectories Involving Arbitrary Discrete Logic\" by Harish Saranathan, arXiv:2412.07960v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.06100v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Causal X-ray Results. We report in nats, with mean and standard error averaged over the test Causal X-ray data for 5 runs. The rows given are decomposed from the terms in the ELBO. Finetuning the encoder $q$ outperforms $z$ sampled from MLE estimates of $q$. In this case, higher likelihood samples of $z$ result in a tighter ELBO.\\\\}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\toprule\n &Separate Training & Finetuned \\\\\n \\midrule \n $\\mathbb{E}_{x,y\\sim D}\\mathbb{E}_{q(z\\mid x,y)}\\log p(z)$ & 979.67 (1.67) & 2012.51 (0.10) \\\\\n \\midrule\n $\\mathbb{E}_{x,y\\sim D}\\mathbb{E}_{q(z\\mid x,y)}\\log p(y\\mid x, z)$ & 1705.91 (0.23) & 1837.63 (0.08)\\\\\n \\midrule \n $\\mathbb{E}_{x,y\\sim D}\\mathbb{E}_{-q(z\\mid x,y)}\\log q(z\\mid x,y)$ & -1923.18 (0.08) & -2589.35 (0.08) \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "High Dimensional Causal Inference with Variational Backdoor Adjustment", "authors": ["Daniel Israel", "Aditya Grover", "Guy Van den Broeck"], "url": "https://arxiv.org/abs/2310.06100v1", "attribution": "\"High Dimensional Causal Inference with Variational Backdoor Adjustment\" by Daniel Israel, Aditya Grover, and Guy Van den Broeck, arXiv:2310.06100v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.18259v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of European option prices between closed-form solution and INAR($\\infty$) implementations}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cc|cccc}\n \\hline\n \\multirow{2}{*}{Strike} & \\multicolumn{2}{c|}{Closed-form solution} & \\multicolumn{4}{c}{INAR($\\infty$)} \\\\\n \\cline{2-7}\n & Call & Put & Call & 95\\% CI & Put & 95\\% CI \\\\\n \\hline\n 80 & 21.8822 & 1.8822 & 21.8446 & [21.7172, 21.9719] & 1.9003 & [1.8687, 1.9320] \\\\\n 90 & 14.6187 & 4.6187 & 14.5940 & [14.4824, 14.7057] & 4.6498 & [4.5977, 4.7020] \\\\\n 100 & 9.0983 & 9.0983 & 9.1003 & [9.0082, 9.1924] & 9.1561 & [9.0818, 9.2304] \\\\\n 110 & 5.2883 & 15.2883 & 5.2951 & [5.2233, 5.3669] & 15.3508 & [15.2560, 15.4457] \\\\\n 120 & 2.8849 & 22.8849 & 2.8911 & [2.8378, 2.9445] & 22.9469 & [22.8353, 23.0585] \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\\infty$) processes and applications", "authors": ["Yingli Wang", "Zhenyu Cui"], "url": "https://arxiv.org/abs/2503.18259v3", "attribution": "\"Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\\infty$) processes and applications\" by Yingli Wang and Zhenyu Cui, arXiv:2503.18259v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06890v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classification of the experimental environments.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Environment & Cooperative? & Mixed? & Communication? \\\\\n \\midrule\n Cooperative Navi. & \\checkmark & &\\\\\n Cooperative Comm. & \\checkmark & &\\checkmark \\\\\n Predator-Prey & &\\checkmark& \\\\\n Covert Comm. & &\\checkmark&\\checkmark\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning", "authors": ["Heechang Ryu", "Hayong Shin", "Jinkyoo Park"], "url": "https://arxiv.org/abs/2101.06890v1", "attribution": "\"Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning\" by Heechang Ryu, Hayong Shin, and Jinkyoo Park, arXiv:2101.06890v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.19173v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ray tracing simulation parameters. For the interaction types, R stands for reflection, D for diffraction and DS for diffuse scattering.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rc}\n\\noalign{\\smallskip}\n\\textbf{Simulation parameter} & \\textbf{Value}\\\\\n\\hline\n\\noalign{\\smallskip}\nCarrier frequency & 28 GHz\\\\\nBS height from the ground & 21.7 m\\\\\nRx height from the ground & 1.5 m\\\\\nRx grid resolution & $2\\times 2$ m\\\\\nSUMO simulation sampling time & 0.1 s\\\\\nNumber of initial sampled rays & $10^6$\\\\\nSionna RT ray tracing method & Fibonacci\\\\\nSelected interation types & R, D, DS\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "High-Fidelity RF Mapping: Assessing Environmental Modeling in 6G Network Digital Twins", "authors": ["Lorenzo Cazzella", "Francesco Linsalata", "Damiano Badini", "Matteo Matteucci", "Maurizio Magarini", "Umberto Spagnolini"], "url": "https://arxiv.org/abs/2507.19173v1", "attribution": "\"High-Fidelity RF Mapping: Assessing Environmental Modeling in 6G Network Digital Twins\" by Lorenzo Cazzella, Francesco Linsalata, Damiano Badini, Matteo Matteucci, Maurizio Magarini, and Umberto Spagnolini, arXiv:2507.19173v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17533v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\toprule\n LLM classifier & LLM extractor & Successful extractions & Faulty code & Symbolic mistake \\\\\n \\midrule\n \\textbf{GPT-4o mini}&\\textbf{GPT-4o} & $\\mathbf{289}$ ($\\mathbf{97.6\\%}$) & $\\mathbf{2}$ ($\\mathbf{0.7\\%}$) & $\\mathbf{5}$ ($\\mathbf{1.7\\%}$) \\\\\n GPT-4o mini&Claude 3.7 Sonnet & $266$ ($89.9\\%$) & $21$ ($7.1\\%$) & $9$ ($3.0\\%$) \\\\\n GPT-4o mini & GPT-4o mini & $206$ ($69.6\\%$) & $70$ ($23.6\\%$) & $20$ ($6.8\\%$) \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "From Euler to AI: Unifying Formulas for Mathematical Constants", "authors": ["Tomer Raz", "Michael Shalyt", "Elyasheev Leibtag", "Rotem Kalisch", "Shachar Weinbaum", "Yaron Hadad", "Ido Kaminer"], "url": "https://arxiv.org/abs/2502.17533v2", "attribution": "\"From Euler to AI: Unifying Formulas for Mathematical Constants\" by Tomer Raz, Michael Shalyt, Elyasheev Leibtag, Rotem Kalisch, Shachar Weinbaum, Yaron Hadad, and Ido Kaminer, arXiv:2502.17533v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.20608v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Portfolio Results with Proxy $= \\text{CFVOL}$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n Portfolio & CGO1 & CGO3 & CGO5 & Diff-in-Diff \\\\\n \\midrule\n P1 & 2.525 & 2.544 & 1.891 & \\\\\n P3 & 1.615 & 1.666 & 2.013 & \\\\\n P5 & 1.325 & 1.330 & 1.509 & \\\\\n P5 - P1 & -1.200 & -1.215 & -0.382 & 0.818 \\\\\n $t$-stat & (-3.19) & (-3.11) & (-0.85) & (1.87) \\\\\n FF3-$\\alpha$ & -0.764 & -0.410 & 0.115 & 0.879 \\\\\n $t$-stat & (-2.42) & (-1.30) & (0.32) & (1.97) \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Replication of Reference-Dependent Preferences and the Risk-Return Trade-Off in the Chinese Market", "authors": ["Penggan Xu"], "url": "https://arxiv.org/abs/2505.20608v1", "attribution": "\"Replication of Reference-Dependent Preferences and the Risk-Return Trade-Off in the Chinese Market\" by Penggan Xu, arXiv:2505.20608v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.10265v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Degredation Parameters Applied to Different Datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|}\n\\hline\n\\textbf{Dataset} & \\textbf{Gaussian Noise} & \\textbf{Defocus Blur} & \\textbf{Motion Blur} & \\textbf{Low Contrast} \\\\ \\hline\nMNIST & SD: 0.5 & Radius: 1.5 & 5x5 Kernel & Factor: 0.1 \\\\ \\hline\nCIFAR-10 & SD: 0.025 & Radius: 2.0 & 5x5 Kernel & Factor: 0.25 \\\\ \\hline\nSVHN & SD: 0.025 & Radius: 2.0 & 5x5 Kernel & Factor: 0.25 \\\\ \\hline\nImageNet64 & SD: 0.1 & Radius: 1.25 & 5x5 Kernel & Factor: 0.75 \\\\ \\hline\nFelidae & SD: 0.1 & Radius: 2.0 & 5x5 Kernel & Factor: 0.5 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication", "authors": ["Alireza Furutanpey", "Pantelis A. Frangoudis", "Patrik Szabo", "Schahram Dustdar"], "url": "https://arxiv.org/abs/2412.10265v1", "attribution": "\"Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication\" by Alireza Furutanpey, Pantelis A. Frangoudis, Patrik Szabo, and Schahram Dustdar, arXiv:2412.10265v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2101.05326v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Test Set Confusion Matrix for Classification Model}\n\\begin{tabular}{l|c|c|c|}\n\\multicolumn{2}{c}{}&\\multicolumn{2}{c}{\\textbf{Predicted}}\\\\\n\\cline{3-4}\n\\multicolumn{2}{c|}{}& EoE & Normal \\\\\n\\cline{2-4}\n\\multirow{2}{*}{\\textbf{True Diagnosis}}& EoE & 7,176 & 455 \\\\\n\\cline{2-4}\n& Normal & 3 & 3,471 \\\\\n\\cline{2-4}\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision", "authors": ["William Adorno", "Alexis Catalano", "Lubaina Ehsan", "Hans Vitzhum von Eckstaedt", "Barrett Barnes", "Emily McGowan", "Sana Syed", "Donald E. Brown"], "url": "https://arxiv.org/abs/2101.05326v1", "attribution": "\"Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision\" by William Adorno, Alexis Catalano, Lubaina Ehsan, Hans Vitzhum von Eckstaedt, Barrett Barnes, Emily McGowan, Sana Syed, and Donald E. Brown, arXiv:2101.05326v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.12316v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\n \\toprule\n Hyperparameter & MixMatch & FixMatch\\\\ \\midrule\n Learning rate & $\\{\\mathbf{0.01}, 0.001\\}$ & $\\{\\mathbf{0.03}\\}$ \\\\\n Optimizer & $\\{ \\textbf{Adam} \\}$ & $\\{$Adam, $\\textbf{SGD}\\}$ \\\\\n Number of epochs & $\\{500, \\mathbf{1000}\\}$ & $\\{1000, \\mathbf{2000}\\}$ \\\\\n $T$ & $\\{0.25, \\mathbf{0.5}, 0.75, 0.9\\}$ & --- \\\\\n $\\alpha$ & $\\{ 0.25, 0.5, 0.75, \\mathbf{0.9} \\}$ & --- \\\\\n $\\lambda_u$ & $\\{ 12.5, \\mathbf{25}, 50, 100, 150 \\}$ & $\\{ \\mathbf{5.0}, 25.0 \\}$ \\\\ \\midrule\n Grid-search size & 320 & 8 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Primary hyperparameter search grid for SSL methods. Best value is marked with bold font. SGD -- stocastic gradient descent with momentum ($\\beta = 0.9$) . An epoch is defined by maximum number of batches in labelled subset.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels", "authors": ["Valentyn Melnychuk", "Evgeniy Faerman", "Ilja Manakov", "Thomas Seidl"], "url": "https://arxiv.org/abs/2010.12316v1", "attribution": "\"Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels\" by Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov, and Thomas Seidl, arXiv:2010.12316v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.18008v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of the accuracies of Only LCP, Only CCP, and CaLoNet.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\t\t\t\\hline\n\t\t\tDataset & Only LCP & Only CCP & CaLoNet \\\\ \\hline\n\t\t\tAWR & 0.955 & 0.876 & \\textbf{0.960 } \\\\\n\t\t\tAF & \\textbf{0.400} & 0.330 & 0.333 \\\\\n\t\t\tBM & 0.850 & 0.950 & \\textbf{0.975} \\\\\n\t\t\tCT & 0.985 & 0.976 & \\textbf{0.987} \\\\\n\t\t\tHMD & \\textbf{0.540} & 0.391 & 0.527 \\\\\n\t\t\tNA & \\textbf{0.902} & 0.727 & 0.900 \\\\\n\t\t\tPD & 0.979 & 0.962 & \\textbf{0.983} \\\\\n\t\t\tSR2 & 0.477 & \\textbf{0.527} & 0.500 \\\\\n\t\t\tSAD & 0.982 & 0.933 & \\textbf{0.989} \\\\\n\t\t\tSWJ & \\textbf{0.410} & 0.266 & 0.400 \\\\ \\hline\n\t\t\tAVG & 0.748 & 0.693 & \\textbf{0.755} \\\\\n\t\t\tWin & 4 & 1 & \\textbf{5} \\\\ \\hline \n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Causal and Local Correlations Based Network for Multivariate Time Series Classification", "authors": ["Mingsen Du", "Yanxuan Wei", "Xiangwei Zheng", "Cun Ji"], "url": "https://arxiv.org/abs/2411.18008v1", "attribution": "\"Causal and Local Correlations Based Network for Multivariate Time Series Classification\" by Mingsen Du, Yanxuan Wei, Xiangwei Zheng, and Cun Ji, arXiv:2411.18008v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19260v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lll}\n \\hline\n \\textbf{Label} & \\textbf{Target} & \\textbf{Examples} \\\\\n \\hline\n & North & My hate for northern people keeps growing \\\\\n \\cline{2-3}\n & South & You idiotic Southerners fighting your own \\\\\n \\cline{2-3}\n & East & IPOBs are animals....They lack tact or strategy. \\\\\n \\cline{2-3}\n Hateful & Muslim & Muslim baboons and their terrorist religion. \\\\\n \\cline{2-3}\n & Women & Nobody believes this \\textit{ashawo} (prostitute) woman \\\\\n \\hline\n Offensive & None & Stop spewing rubbish, \\textit{mumu} (fool).\\\\ \\hline\n Neutral & None & She don already made up her mind sha. \\\\ \\hline \n \\end{tabular}\n\\caption{Examples of tweets for each class. Offensive tweets have no target as they do not target an identity group. Non-English words are displayed in italic with their English translation in parenthesis.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data", "authors": ["Manuel Tonneau", "Pedro Vitor Quinta de Castro", "Karim Lasri", "Ibrahim Farouq", "Lakshminarayanan Subramanian", "Victor Orozco-Olvera", "Samuel P. Fraiberger"], "url": "https://arxiv.org/abs/2403.19260v3", "attribution": "\"NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data\" by Manuel Tonneau, Pedro Vitor Quinta de Castro, Karim Lasri, Ibrahim Farouq, Lakshminarayanan Subramanian, Victor Orozco-Olvera, and Samuel P. Fraiberger, arXiv:2403.19260v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.11954v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Reported Accuracy vs weights for MNIST dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n \\toprule\n \\textbf{Setup} & \\textbf{Best accuracy $\\uparrow$} & \\textbf{No. of learnables $\\downarrow$}\\\\\n \\midrule\n Huffman on UVGG11& 0.8323 & $\\sim 35$M\\\\\n Huffman on UResnet & 0.6313 & $\\sim 5$M \\\\\n \\cmidrule(lr){1-3}\n DCT on FullyConnected & 0.9000 & $1$M \\\\\n \\cmidrule(lr){1-3}\n DCT-LDPC on 12 units GRU& \\textbf{0.9060} & \\textbf{19k}\\\\\n DCT-LDPC on 32 units GRU& \\textbf{0.9237} & \\textbf{52.6k}\\\\\n \\bottomrule\n \n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Goal-Oriented Source Coding using LDPC Codes for Compressed-Domain Image Classification", "authors": ["Ahcen Aliouat", "Elsa Dupraz"], "url": "https://arxiv.org/abs/2503.11954v1", "attribution": "\"Goal-Oriented Source Coding using LDPC Codes for Compressed-Domain Image Classification\" by Ahcen Aliouat and Elsa Dupraz, arXiv:2503.11954v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.17244v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average Daybreak Sentiment Prediction Accuracy Across Models for 160 market indices}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|}\n\\hline\n\\textbf{Model} & \\textbf{Average Accuracy} \\\\\n\\hline\nCMG & 0.7672 \\\\\nLSTM & 0.7605 \\\\\nBiLSTM & 0.7602 \\\\\nGRU & 0.7601 \\\\\nLogistic Regression & 0.7562 \\\\\nXGBoost & 0.7550 \\\\\nRandom Forest Classifier & 0.7539 \\\\\nGaussian Naive Bayes & 0.7536 \\\\\nMulti Logistic Regression & 0.7499 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data", "authors": ["Arif Pathan"], "url": "https://arxiv.org/abs/2506.17244v1", "attribution": "\"Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data\" by Arif Pathan, arXiv:2506.17244v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2112.11968v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\t\t\\hline \\\\\n\t\t\\small name & \\small $\\varphi_\\theta (x)$ & $C(u,v)$\n\t\t& \\small $\\theta \\in$ \\\\\n\t\t\\hline \\\\\n\t\t\\small Clayton copula & \\small $(1+x)^{-1/\\theta}$ & \\small$(u^{-\\theta} + v^{-\\theta} -1)^{-1/\\theta}$ &\\small $(0,\\infty)$ \\\\ \n\t\t\\small Gumbel copula & \\small $\\exp\\{-x^{1/\\theta}\\}$ & \\small$\\exp(-((-\\log u)^\\theta + (-\\log v)^\\theta)^{1/\\theta})$ &\\small $[1,\\infty)$ \\\\ \n\t\t\\small Frank copula & \\small $-\\log\\left(\\frac{e^{-\\theta x} -1}{e^{-\\theta} -1}\\right)$ &\\small$-\\frac{1}{\\theta}\\log\\left(1+ \\frac{(e^{-\\theta u} -1)(e^{-\\theta v} -1)}{e^{-\\theta} -1}\\right)$ & \\small$(-\\infty, \\infty)\\backslash\\{0\\}$\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Archimedean copulas with the respective generator function $\\varphi$ and $\\theta$ parameter range.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "An Efficient Unified Approach for Spread Option Pricing in a Copula Market Model", "authors": ["Edoardo Berton", "Lorenzo Mercuri"], "url": "https://arxiv.org/abs/2112.11968v2", "attribution": "\"An Efficient Unified Approach for Spread Option Pricing in a Copula Market Model\" by Edoardo Berton and Lorenzo Mercuri, arXiv:2112.11968v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.14361v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{cccc}\n \\hline\\hline\n Methods & $\\|\\nabla f\\|$ & Time(Avg.)& Success rate \\\\\n \\hline \n ASK& 5.4423e-03&2.3591e+00& 1.00 \\\\\n OGDA& 6.5660e-01&6.9367e+01& 0.00 \\\\\n GDA& 1.3918e+00&9.8197e-03& 0.00 \\\\\n NAG& 5.2703e+01&6.2550e-03& 0.00 \\\\\n HB& 1.8026e+00&6.1683e-03& 0.00 \\\\\n \\hline\\hline\n \\end{tabular}\n\\caption{$f(x_1,x_2) = x_{1}x_{2}$, init=$2 \\times\\text{rand}(2,1)-1.$ The proposed ASK is the only method that can converge to the unique saddle point with 100$\\%$ success rates.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Gradient-Based Optimization Method Using the Koopman Operator", "authors": ["Mengqi Hu", "Bian Li", "Yi-An Ma", "Yifei Lou", "Xiu Yang"], "url": "https://arxiv.org/abs/2312.14361v1", "attribution": "\"A Gradient-Based Optimization Method Using the Koopman Operator\" by Mengqi Hu, Bian Li, Yi-An Ma, Yifei Lou, and Xiu Yang, arXiv:2312.14361v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08239v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results for Foundation Metrics}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n Treatment & Sensibleness & Specificity & Interestingness & Safety & Groundedness & Informativeness \\\\\n \\midrule\n PT (2B) & 76.6 & 46.5 & 10.8 & 84.8 & 45 & 29.2 \\\\\n PT (8B) & 79.1 & 46.5 & 11.3 & 87.5 & 47.1 & 29.5 \\\\\n PT (137B) & 80.2 & 49.8 & 15.8 & 88 & 57.9 & 41.3 \\\\\n FT quality-safety (137B) & 92.8 & 77.1 & 23.2 & 94.6 & 67.9 & 50.5 \\\\\n LaMDA (2B) & 81.8 & 74.8 & 23.4 & 93.8 & 53 & 41.8 \\\\\n LaMDA (8B) & 88 & 77.4 & 22.2 & 93.5 & 64.6 & 50.2 \\\\\n LaMDA (137B) & 92.3 & 79 & 25.7 & 95.2 & 73.2 & 62.3 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "LaMDA: Language Models for Dialog Applications", "authors": ["Romal Thoppilan", "Daniel De Freitas", "Jamie Hall", "Noam Shazeer", "Apoorv Kulshreshtha", "Heng-Tze Cheng", "Alicia Jin", "Taylor Bos", "Leslie Baker", "Yu Du", "YaGuang Li", "Hongrae Lee", "Huaixiu Steven Zheng", "Amin Ghafouri", "Marcelo Menegali", "Yanping Huang", "Maxim Krikun", "Dmitry Lepikhin", "James Qin", "Dehao Chen", "Yuanzhong Xu", "Zhifeng Chen", "Adam Roberts", "Maarten Bosma", "Vincent Zhao", "Yanqi Zhou", "Chung-Ching Chang", "Igor Krivokon", "Will Rusch", "Marc Pickett", "Pranesh Srinivasan", "Laichee Man", "Kathleen Meier-Hellstern", "Meredith Ringel Morris", "Tulsee Doshi", "Renelito Delos Santos", "Toju Duke", "Johnny Soraker", "Ben Zevenbergen", "Vinodkumar Prabhakaran", "Mark Diaz", "Ben Hutchinson", "Kristen Olson", "Alejandra Molina", "Erin Hoffman-John", "Josh Lee", "Lora Aroyo", "Ravi Rajakumar", "Alena Butryna", "Matthew Lamm", "Viktoriya Kuzmina", "Joe Fenton", "Aaron Cohen", "Rachel Bernstein", "Ray Kurzweil", "Blaise Aguera-Arcas", "Claire Cui", "Marian Croak", "Ed Chi", "Quoc Le"], "url": "https://arxiv.org/abs/2201.08239v3", "attribution": "\"LaMDA: Language Models for Dialog Applications\" by Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Vincent Zhao, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Pranesh Srinivasan, Laichee Man, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, and Quoc Le, arXiv:2201.08239v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.17166v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study on candidate generation.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Variant} & \\textbf{QoE} & \\textbf{Rebuf (s)} & \\textbf{BW Wastage (\\%)} \\\\\n\\midrule\nSC & 0.76 & 3.42 & 15.8 \\\\\nMC & \\textbf{0.88} & \\textbf{2.97} & \\textbf{13.9} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Generative Flow Networks for Personalized Multimedia Systems: A Case Study on Short Video Feeds", "authors": ["Yili Jin", "Ling Pan", "Rui-Xiao Zhang", "Jiangchuan Liu", "Xue Liu"], "url": "https://arxiv.org/abs/2508.17166v1", "attribution": "\"Generative Flow Networks for Personalized Multimedia Systems: A Case Study on Short Video Feeds\" by Yili Jin, Ling Pan, Rui-Xiao Zhang, Jiangchuan Liu, and Xue Liu, arXiv:2508.17166v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05030v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|} \n\\hline\n{\\bf year} & {\\bf January} & {\\bf February} & {\\bf March} & {\\bf April} \\\\\n\\hline\n2019 & 8 & 5 & 7 & 4\n\\\\\n\\hline\n2020 & 6 & 4 & 16 & 11\n\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{The number of outage reports}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Multi-Perspective Study of Internet Performance during the COVID-19 Outbreak", "authors": ["Ahmed Elmokashfi", "Alfred Arouna", "Ioana Livadariu", "Mah-Rukh Fida", "Amund Kvalbein", "Anas Al-Selwi", "Thomas Dreibholz", "Haakon Bryhni"], "url": "https://arxiv.org/abs/2101.05030v1", "attribution": "\"A Multi-Perspective Study of Internet Performance during the COVID-19 Outbreak\" by Ahmed Elmokashfi, Alfred Arouna, Ioana Livadariu, Mah-Rukh Fida, Amund Kvalbein, Anas Al-Selwi, Thomas Dreibholz, and Haakon Bryhni, arXiv:2101.05030v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.03287v3_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results from cryptocurrency market model regressions: \\(r_i - r_f = \\alpha_i + \\beta_{CM,i} CM + \\beta_{SMB,i} SMB + \\beta_{Mom,i} Mom + \\epsilon_i\\) where \\(i\\) denotes the crypto portfolio formed on \\(\\Delta\\frac{TVL}{Market\\ Cap}\\), \\(CM\\) is cryptocurrency excess market returns, \\(SMB\\) is the cryptocurrency small-minus-big factor, \\(Mom\\) is the cryptocurrency momentum factor, and \\(r_f\\) is the risk-free rate. TVL excludes all optional DefiLlama categories. P-values are below the coefficients in parentheses. \\(^{***}\\), \\(^{**}\\), and \\(^{*}\\), denote statistical significance at the 1\\%, 5\\%, and 10\\% levels respectively. Test statistics and p-values for the F-test of whether alpha coefficients are jointly 0 are below the Adjusted-\\(R^2\\) values.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n\\hline\n & Quartile 1 & Quartile 4 & Quartile 1 & Quartile 4\\\\\n\\hline\n\\(\\alpha\\) & -0.13 & -0.09 & 0.05 & -0.09\\\\\n & (0.79) & (0.90) & (0.93) & (0.90)\\\\\n\\(\\beta_{CM}\\) & 1.37\\(^{***}\\) & 1.43\\(^{***}\\) & 1.38\\(^{***}\\) & 1.42\\(^{***}\\)\\\\\n & (0.00) & (0.00) & (0.00) & (0.00)\\\\\n\\(\\beta_{SMB}\\) & & & 0.01 & 0.002\\\\\n & & & (0.42) & (0.92)\\\\\n\\(\\beta_{Mom}\\) & & & -0.13\\(^{**}\\) & 0.04\\\\\n & & & (0.04) & (0.68)\\\\\n\\hline\nAdj-\\(R^2\\) & 0.76 & 0.63 & 0.77 & 0.62\\\\\n\\hline\n & GRS Stat. & 0.04 & GRS Stat. & 0.01\\\\\n & p-value & (0.96) & p-value & (0.99)\\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns", "authors": ["Matt Brigida"], "url": "https://arxiv.org/abs/2506.03287v3", "attribution": "\"The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns\" by Matt Brigida, arXiv:2506.03287v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.00039v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Principles} & \\textbf{BO (Best-off)} & \\textbf{AVE (Average)} & \\textbf{ATBO (All Better-off)} \\\\ \\midrule\nMaximin (Maximin) & Maximin+BO & Maximin+AVE & Maximin+ATBO \\\\\nAdditive (AP) & AP+BO & AP+AVE & AP+ATBO \\\\\nWeighted Additive (WAP) & WAP+BO & WAP+AVE & WAP+ATBO \\\\\n\\textbf{Independent Criteria} & \\multicolumn{3}{c}{Deviation, Gratuitousness, Social Responsibility} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Representation of Temkin's Principles and Complaint Bases}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Measure of Morality: A Mathematical Theory of Egalitarian Ethics", "authors": ["Shuang Wei"], "url": "https://arxiv.org/abs/2503.00039v1", "attribution": "\"Measure of Morality: A Mathematical Theory of Egalitarian Ethics\" by Shuang Wei, arXiv:2503.00039v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06589v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c}\n \\hline\n \tnoise $10^{-3}$ & $k_f$ & relative error & $k_r$ & relative error \\\\\n \\hline\n\t1 & 3.375e+04 & 5.706e$-$05 & 1.002e+03 & 2.097e$-$03 \\\\\t \n\t2 & 1.080e+04 & 2.789e$-$04 & 1.001e+03 & 1.374e$-$03 \\\\\n\t3 & 1.399e+03 & 4.413e$-$04 & 9.631e$-$01 & 3.836e$-$02 \\\\\n\t4 & 3.399e+02 & 7.103e$-$03 & 9.235e$-$01 & 8.278e$-$02 \\\\\n\t5 & 2.161e+02 & 6.927e$-$04 & 2.130e+00 & 6.107e$-$02 \\\\\n\t6 & 9.764e+01 & 2.417e$-$02 & 8.047e$-$01 & 4.137e$-$02 \\\\ \t \n \\hline\n noise $10^{-4}$ & $k_f$ & relative error & $k_r$ & relative error \\\\\n \\hline\n\t1 & 3.375e+04 & 6.482e$-$06 & 1.000e+03 & 2.099e$-$04 \\\\ \n\t2 & 1.080e+04 & 2.803e$-$05 & 1.000e+03 & 1.379e$-$04 \\\\ \n\t3 & 1.400e+03 & 4.491e$-$05 & 9.963e$-$01 & 3.707e$-$03 \\\\\n\t4 & 3.377e+02 & 7.161e$-$04 & 9.923e$-$01 & 7.717e$-$03 \\\\\n\t5 & 2.160e+02 & 6.965e$-$05 & 2.013e+00 & 6.469e$-$03 \\\\\n\t6 & 9.976e+01 & 2.366e$-$03 & 7.748e$-$01 & 4.294e$-$03 \\\\\n \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\caption{Example : learned reaction rates with noise.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Data-driven discovery of multiscale chemical reactions governed by the law of mass action", "authors": ["Juntao Huang", "Yizhou Zhou", "Wen-An Yong"], "url": "https://arxiv.org/abs/2101.06589v3", "attribution": "\"Data-driven discovery of multiscale chemical reactions governed by the law of mass action\" by Juntao Huang, Yizhou Zhou, and Wen-An Yong, arXiv:2101.06589v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.10301v3_tex_table17.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccc}\n\t\t\\toprule\n\t\t& Clipart & Infograph & Painting & Quickdraw & Real & Sketch \\\\\\hline\n\t\tDA Algorithm & tar\\_acc & tar\\_acc & tar\\_acc & tar\\_acc & tar\\_acc & tar\\_acc \\\\\\hline\n\t\tERM-Pool & \\textbf{89.9$\\pm$0.7} & 64.7$\\pm$1.8 & \\textbf{81.1$\\pm$1.3} & 69.6$\\pm$4.2 & 90.4$\\pm$2.8 & \\textbf{87.3$\\pm$0.5} \\\\\n\t\tDIP-Pool & 89.7$\\pm$0.7 & \\textbf{\\textcolor{red}{75.9$\\pm$9.6}} & \\textbf{80.4$\\pm$2.4} & \\textbf{71.1$\\pm$4.5} & 90.9$\\pm$1.2 & \\textbf{\\textcolor{red}{87.4$\\pm$0.6}} \\\\\n\t\tCIP & 88.9$\\pm$1.7 & \\textbf{64.9$\\pm$1.0} & 79.5$\\pm$1.3 & \\textbf{70.5$\\pm$2.7} & 91.0$\\pm$1.0 & 86.7$\\pm$1.2 \\\\\n\t\tJointDIP-Pool & \\textbf{90.0$\\pm$0.9} & \\textbf{66.0$\\pm$1.6} & \\textbf{\\textcolor{red}{86.5$\\pm$5.2}} & \\textbf{\\textcolor{red}{74.1$\\pm$2.5}} & \\textbf{\\textcolor{red}{91.8$\\pm$0.9}} & \\textbf{87.2$\\pm$0.9} \\\\\n\t\tIRM & \\textbf{90.0$\\pm$0.8} & 64.1$\\pm$2.4 & \\textbf{81.0$\\pm$1.7} & 69.0$\\pm$4.9 & \\textbf{91.8$\\pm$1.0} & \\textbf{87.2$\\pm$0.7} \\\\\n\t\tV-REx & \\textbf{\\textcolor{red}{90.0$\\pm$0.5}} & \\textbf{65.7$\\pm$1.3} & 79.8$\\pm$2.3 & \\textbf{69.9$\\pm$4.0} & \\textbf{91.5$\\pm$0.5} & 86.8$\\pm$0.9 \\\\\n\t\tgroupDRO & 86.8$\\pm$0.4 & 64.2$\\pm$2.2 & 77.9$\\pm$0.8 & 68.0$\\pm$6.1 & \\textbf{91.6$\\pm$0.9} & 84.1$\\pm$0.8 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Target accuracies in DomainNet for predicting the labels ``Furniture'' and ``Mammal.'' Each column corresponds to a specific target domain: Clipart, Infograph, Painting, Quickdraw, Real, and Sketch, where the remaining domains are used as source domains. The algorithms are run over 10 trials with different random seeds. The top four methods are highlighted in bold. The best method is colored in red.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms", "authors": ["Keru Wu", "Yuansi Chen", "Wooseok Ha", "Bin Yu"], "url": "https://arxiv.org/abs/2309.10301v3", "attribution": "\"Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms\" by Keru Wu, Yuansi Chen, Wooseok Ha, and Bin Yu, arXiv:2309.10301v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2212.05053v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{r|rrrrrrrr}\n \\hline\n$\\gamma$ & 0.10 & 0.20 & 0.30 & 0.40 & 0.50 & 0.60 & 0.70 & 0.80 \\\\ \n\\hline\n Unscaled eigenvectors & 0.00 & 0.00 & 0.12 & 0.20 & 0.23 & 0.27 & 0.39 & 0.49 \\\\ \n Scaled eigenvectors & 0.00 & 0.00 & 0.02 & 0.13 & 0.22 & 0.25 & 0.34 & 0.49 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{{Misclustering error rate of spherical spectral clustering using the unscaled vs. scaled eigenvectors as a function of $\\gamma$, the connectivity between communities. Graphs are generated from a DCSBM with $K=4$ unbalanced communities and $n=800$.}}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels", "authors": ["Joshua Agterberg", "Zachary Lubberts", "Jesús Arroyo"], "url": "https://arxiv.org/abs/2212.05053v3", "attribution": "\"Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels\" by Joshua Agterberg, Zachary Lubberts, and Jesús Arroyo, arXiv:2212.05053v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.15949v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{cccccc}\n\\hline\nModel & DeepONet & Shift & Flex & NOMAD & Hyper(ours) \\\\ \\hline\nIdentity & 0.578$\\pm$0.003 & 0.777$\\pm$0.018 & 0.678$\\pm$0.062 & 0.578$\\pm$0.020 & \\textbf{0.036$\\pm$0.005} \\\\\nDifferentiation & 0.559$\\pm$0.001 & 0.624$\\pm$0.015 & 0.562$\\pm$0.016 & 0.558$\\pm$0.003 & \\textbf{0.127$\\pm$0.043} \\\\ \\hline\n\\end{tabular}\n\\caption{The mean relative $L^2$ test error with standard deviation for the identity operator and the differentiation operator. The DeepONet, its variants, and the HyperDeepONet use the target network $d_y$-20-20-10-1 with $\\textit{tanh}$ activation function. Five training trials are performed independently.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork", "authors": ["Jae Yong Lee", "Sung Woong Cho", "Hyung Ju Hwang"], "url": "https://arxiv.org/abs/2312.15949v1", "attribution": "\"HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork\" by Jae Yong Lee, Sung Woong Cho, and Hyung Ju Hwang, arXiv:2312.15949v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.19668v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|cc|cc|cc}\n\\hline\n$z$ & Exact & \\multicolumn{2}{c|}{$m=20$} & \\multicolumn{2}{c|}{$m=50$} & \\multicolumn{2}{c}{$m=100$} \\\\\n & & Approx & Error & Approx & Error & Approx & Error \\\\\n\\hline\n0.50 & -0.4097 & 0.3707 & 0.7804 & 0.3718 & 0.7815 & 0.3723 & 0.7819 \\\\\n1.00 & 0.3679 & 0.2437 & 0.1242 & 0.3224 & 0.0455 & 0.3452 & 0.0226 \\\\\n2.00 & 0.0914 & 0.0293 & 0.0621 & 0.0662 & 0.0252 & 0.0786 & 0.0128 \\\\\n4.00 & 0.0063 & 0.0014 & 0.0049 & 0.0030 & 0.0033 & 0.0046 & 0.0016 \\\\\n8.00 & 0.0001 & 0.0000 & 0.0001 & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Weighted approximation By Max-product Kantrovich type Exponential Sampling Series", "authors": ["Satyaranjan Pradhan", "Madan Mohan Soren"], "url": "https://arxiv.org/abs/2504.19668v1", "attribution": "\"Weighted approximation By Max-product Kantrovich type Exponential Sampling Series\" by Satyaranjan Pradhan and Madan Mohan Soren, arXiv:2504.19668v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.19853v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n \\toprule\n Name & Start node $i$ & End node $j$ & $\\Tilde{c}^s_{ij}$\\\\\n \\midrule\n Pull-out & $i = n_o^d$ & $ j \\in \\mathcal{V}$ & $c^s_{ij} - \\pi_d$\\\\\n Connection & $ i \\in \\mathcal{V}$ & $j \\in \\mathcal{V} \\cup \\mathcal{H}^W$ & $c^s_{ij} - u_i$\\\\\n Charging (from charg.)& $i = h_r^c \\in \\mathcal{H}^C$ & $j \\in \\mathcal{H}^C \\cup \\mathcal{H}^W$ & $c^s_{ij} - \\alpha^{hr}$\\\\\n Charging (from wait.) & $i \\in \\mathcal{H}^W$ & $j \\in V \\cup \\mathcal{H}^C \\cup \\mathcal{H}^W $& $c^s_{ij}$ \\\\\n To charge & $ i \\in \\mathcal{V}$ & $ j \\in \\mathcal{H}^W$ & $c^s_{ij}$ \\\\\n Pull-in (from terminal) & $i \\in \\mathcal{V}$ & $j = n_1^d$ & $c^s_{ij} - u_i - \\theta \\> \\ln P_s$\\\\\n Pull-in (from wait.) & $i \\in \\mathcal{H}^W$ & $j = n_1^d$ & $c^s_{ij} - \\theta \\> \\ln P_s$\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Modified arc costs}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Chance-constrained battery management strategies for the electric bus scheduling problem", "authors": ["Léa Ricard", "Guy Desaulniers", "Andrea Lodi", "Louis-Martin Rousseau"], "url": "https://arxiv.org/abs/2503.19853v1", "attribution": "\"Chance-constrained battery management strategies for the electric bus scheduling problem\" by Léa Ricard, Guy Desaulniers, Andrea Lodi, and Louis-Martin Rousseau, arXiv:2503.19853v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.05924v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\toprule\n\\multirow{3}{*}{Fixed Depth} & \\multicolumn{6}{c}{Examples} \\\\\n\\cmidrule(lr){2-7}\n& \\multicolumn{2}{c}{FFT} & \\multicolumn{2}{c}{Lorenz70} & \\multicolumn{2}{c}{Scan} \\\\\n\\cmidrule(lr){2-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-7}\n\t\t\t\t & err \t\t& \\shortstack[1]{Exec \\\\Time}\t\t\t & err\t\t& \\shortstack[1]{Exec \\\\Time} \t\t\t& err \t\t& \\shortstack[1]{Exec \\\\Time} \\\\\n8\t& 4.34e-13 & 14 & 1.12e-11 & 88 & 9.38e-13 & 24 \\\\\n9 \t& 3.98e-13 & 17 & 1.05e-12 & 45 & 9.38e-13 & 18 \\\\\n10 \t& 4.44e-13 & 18 & 2.58e-12 & 152 & 9.38e-13 & 22 \\\\\n11\t& 4.60e-13 & 18 & 4.56e-12 & 128 & 9.38e-13 & 35 \\\\\n12\t& 4.52e-13 & 18 & 5.58e-12 & 150 & 9.38e-13 & 49 \\\\\n13\t& 4.75e-13 & 24 & 1.70e-11 & 168 & 9.38e-13 & 61 \\\\\n14\t& 4.39e-13 & 24 & 5.91e-12 & 627 & 9.38e-13 & 78 \\\\\n15\t& 4.36e-13 & 28 & 7.31e-12 & 987 & 9.38e-13 & 93 \\\\\n16\t& 4.43e-13 & 42 & 7.90e-12 & 1220 & 9.28e-13 & 109 \\\\\n17\t& 4.18e-13 & 41 & 2.43e-12 & 3400 & 9.38e-13 & 131 \\\\\n18 & 4.18e-13 & 47 & 7.93e-13 & 2041 & 9.38e-13 & 142 \\\\\n19\t& 4.21e-13 & 70 & \\multicolumn{2}{c}{\\multirow{3}{*}{NA}} & 9.38e-13 & 137 \\\\\n20\t& 4.08e-13 & 71 & & & 9.38e-13 & 140 \\\\\n21\t& 4.08e-13 & 68 &\t\t\t&\t\t& 9.38e-13 & 138 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Error bounds and execution times for three examples obtained when performing abstrcations for a set of fixed depth values}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Satire: Computing Rigorous Bounds for Floating-Point Rounding Error in Mixed-Precision Loop-Free Programs", "authors": ["Tanmay Tirpankar", "Arnab Das", "Ganesh Gopalakrishnan"], "url": "https://arxiv.org/abs/2503.05924v1", "attribution": "\"Satire: Computing Rigorous Bounds for Floating-Point Rounding Error in Mixed-Precision Loop-Free Programs\" by Tanmay Tirpankar, Arnab Das, and Ganesh Gopalakrishnan, arXiv:2503.05924v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.14739v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textsc{95th percentile positioning errors of different models}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n& \\multicolumn{3}{c}{\\textbf{Transmit Power [dBm]}} \\\\ \n\\cmidrule(lr){2-4}\n\\textbf{Approach} & --20 & --10 & 0 \\\\\n\\midrule\nProposed approach, $p_f=0$ & 0.80cm & 0.21cm & 0.07cm \\\\\nProposed approach, $p_f=10^{-3}$ & 0.85cm & 0.26cm & 0.08cm \\\\\nProposed approach, $p_f=10^{-2}$ & 0.98cm & 0.27cm & 0.11cm\\\\\nMLP-based approach in , $p_f=0$ & 4.54cm & 4.05cm & 4.01cm \\\\\nCNN-based approach in , $p_f=0$ & 3.16cm & 2.83cm & 2.08cm \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Failure Tolerant Phase-Only Indoor Positioning via Deep Learning", "authors": ["Fatih Ayten", "Mehmet C. Ilter", "Akshay Jain", "Ossi Kaltiokallio", "Jukka Talvitie", "Elena Simona Lohan", "Henk Wymeersch", "Mikko Valkama"], "url": "https://arxiv.org/abs/2508.14739v1", "attribution": "\"Failure Tolerant Phase-Only Indoor Positioning via Deep Learning\" by Fatih Ayten, Mehmet C. Ilter, Akshay Jain, Ossi Kaltiokallio, Jukka Talvitie, Elena Simona Lohan, Henk Wymeersch, and Mikko Valkama, arXiv:2508.14739v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.06407v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\textbf{Dataset / LF} & \\textbf{SR-I} & \\textbf{L1} & \\textbf{Combo} & \\textbf{Hybrid} \\\\ \n \\midrule\n FUSAR & 38.020 & 38.363 & \\underline{38.705} & \\textbf{38.736} \\\\ \n MSTAR & 28.890 & \\textbf{30.130} & 30.127 & \\underline{30.129} \\\\ \n HRSID & 26.750 & \\textbf{38.750} & \\underline{38.667} & 38.633 \\\\ \n \\midrule\n \\textbf{Average} & 31.220 & 35.750 & \\underline{35.833} & \\textbf{35.833} \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery", "authors": ["Ch Muhammad Awais", "Marco Reggiannini", "Davide Moroni", "Oktay Karakus"], "url": "https://arxiv.org/abs/2508.06407v1", "attribution": "\"A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery\" by Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, and Oktay Karakus, arXiv:2508.06407v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.12619v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n \\tabularnewline\\midrule\\midrule\n Dependent Variable: & \\multicolumn{8}{c}{$\\Delta$ Unemployment Rate}\\\\\n Model: & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8)\\\\\n \\midrule \\emph{Variables} & & & & & & & & \\\\\n $\\Delta$ Active Mines\\textsubscript{t} & -0.0560$^{**}$ & & & & -0.0416$^{**}$ & & & \\\\\n & (0.0151) & & & & (0.0127) & & & \\\\\n $\\Delta$ Active Mines\\textsubscript{t-1} & & -0.0050 & & & & -0.0029 & & \\\\\n & & (0.0137) & & & & (0.0096) & & \\\\\n $\\Delta$ Active Mines\\textsubscript{t-2} & & & 0.0402$^{*}$ & & & & 0.0351$^{**}$ & \\\\\n & & & (0.0151) & & & & (0.0116) & \\\\\n $\\Delta$ Active Mines\\textsubscript{t-3} & & & & 0.0011 & & & & 0.0026\\\\\n & & & & (0.0136) & & & & (0.0098)\\\\\n $\\Delta$ (log)Real GDPPC\\textsubscript{t} & -0.9742$^{***}$ & -0.9837$^{***}$ & -0.9846$^{***}$ & -0.9842$^{***}$ & -1.596$^{***}$ & -1.699$^{***}$ & -1.701$^{***}$ & -1.702$^{***}$\\\\\n & (0.2137) & (0.2137) & (0.2138) & (0.2136) & (0.3513) & (0.3482) & (0.3416) & (0.3469)\\\\\n \\midrule \\emph{Fixed-effects} & & & & & & & & \\\\\n County FIPS Code & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes\\\\\n Year & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes\\\\\n \\midrule \\emph{Fit statistics} & & & & & & & & \\\\\n Observations & 55,295 & 55,295 & 55,295 & 55,295 & 4,518 & 4,518 & 4,518 & 4,518\\\\\n R$^2$ & 0.61298 & 0.61227 & 0.61267 & 0.61226 & 0.65184 & 0.64795 & 0.65105 & 0.64794\\\\\n Within R$^2$ & 0.01367 & 0.01186 & 0.01288 & 0.01185 & 0.03705 & 0.02628 & 0.03487 & 0.02627\\\\\n \\midrule\\midrule\\multicolumn{9}{l}{\\emph{Clustered (County FIPS Code \\& Year) standard-errors in parentheses}}\\\\\n \\multicolumn{9}{l}{\\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'", "authors": ["Ebba Mark", "Ryan Rafaty", "Moritz Schwarz"], "url": "https://arxiv.org/abs/2211.12619v1", "attribution": "\"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'\" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.18430v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\hline\nNIST test & Proportion & P-value & Pass \\\\\n\\hline\nFrequency & 10/10 & 0.911413 & Y \\\\\nBlockFrequency & 10/10 & 0.911413 & Y \\\\\nCumulativeSums & 10/10 & 0.534146 & Y \\\\\nRuns & 10/10 & 0.213309 & Y \\\\\nLongestRun & 10/10 & 0.534146 & Y \\\\\nRank & 10/10 & 0.534146 & Y \\\\\nFFT & 10/10 & 0.534146 & Y \\\\\nApproximateEntropy & 10/10 & 0.122325 & Y \\\\\nSerial & 10/10 & 0.017912 & Y \\\\\nLinearComplexity & 10/10 & 0.004301 & Y \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Statistical Test Results for Pulsar RNG on PSR J0030+0451 (EPTA).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Random Number Generation from Pulsars", "authors": ["Hayder Tirmazi"], "url": "https://arxiv.org/abs/2502.18430v2", "attribution": "\"Random Number Generation from Pulsars\" by Hayder Tirmazi, arXiv:2502.18430v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09290v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The results of varying the measure function of NSW.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|}\n\\hline\n & & ip-NSW & $l_2$-NSW & cos-NSW \\\\ \\hline\n\\multirow{2}{*}{MovieLens} & Recall1@1 & $\\textbf{0.681}$ & 0.000 & 0.580 \\\\ \\cline{2-5}\n & Recall10@10 & $\\textbf{0.425}$ & 0.000 & 0.371 \\\\ \\hline\n\\multirow{2}{*}{Amazon} & Recall1@1 & $\\textbf{0.823}$ & 0.000 & 0.457 \\\\ \\cline{2-5}\n & Recall10@10 & $\\textbf{0.543}$ & 0.000 & 0.329 \\\\ \\hline\n\\multirow{2}{*}{Echonest} & Recall1@1 & $\\textbf{0.868}$ & 0.126 & 0.573 \\\\ \\cline{2-5}\n & Recall10@10 & $\\textbf{0.547}$ & 0.089 & 0.344 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Reinforcement Routing on Proximity Graph for Efficient Recommendation", "authors": ["Chao Feng", "Defu Lian", "Xiting Wang", "Zheng liu", "Xing Xie", "Enhong Chen"], "url": "https://arxiv.org/abs/2201.09290v1", "attribution": "\"Reinforcement Routing on Proximity Graph for Efficient Recommendation\" by Chao Feng, Defu Lian, Xiting Wang, Zheng liu, Xing Xie, and Enhong Chen, arXiv:2201.09290v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08279v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Overall evaluation of the approximation methods: mean values of the quality criteria for all the centerlines distorted by radius noise addition. The cells in gray correspond to the lowest error for each metric.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|ccccc|}\n \\hline\n & GNP & GNP-AIC & GP-AIC & SRP-AIC\\\\ \n \\hline\n$\\mathrm{RMSE_{spatial}}$ & 8.462 & 0.034 & 0.053 & \\cellcolor{gray!25}0.029 \\\\ \n$\\mathrm{RMSE_{radius}}$ & 17.523 & 0.095 & \\cellcolor{gray!25}0.042 & 0.043 \\\\ \n$\\mathrm{RMSEder_{spatial}}$ & 0.218 & 0.118 & 0.042 & \\cellcolor{gray!25}0.009 \\\\ \n$\\mathrm{RMSEder_{radius}}$ & 0.391 & 0.214 & \\cellcolor{gray!25}0.032 & \\cellcolor{gray!25}0.032 \\\\ \n$\\mathrm{RMSEcurv}$ & 1919.428 & 190.531 & 0.060 & \\cellcolor{gray!25}0.035 \\\\ \n$\\mathrm{L_{diff}}$ & 718.906 & 0.057 & 0.207 & \\cellcolor{gray!25}0.004 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Modeling and hexahedral meshing of cerebral arterial networks from centerlines", "authors": ["Méghane Decroocq", "Carole Frindel", "Pierre Rougé", "Makoto Ohta", "Guillaume Lavoué"], "url": "https://arxiv.org/abs/2201.08279v2", "attribution": "\"Modeling and hexahedral meshing of cerebral arterial networks from centerlines\" by Méghane Decroocq, Carole Frindel, Pierre Rougé, Makoto Ohta, and Guillaume Lavoué, arXiv:2201.08279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table16.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|r|r|r}\n \\hline\n \\multicolumn{5}{c}{End item demand only} \\\\\n \\hline \n Utilization & $\\lambda$ & Runtime (s) & Total costs PH & $\\Delta$ (\\%)\\\\\n \\hline\n \\multirow{4}{*}{50\\%} \n & 0.1 & 801.8 & 31689.2 & 5.11 \\\\\n & 1 & 307.9 & 30360.0 & \\textbf{1.26} \\\\\n & 10 & 246.9 & 30626.4 & 2.08 \\\\\n & 100 & 274.1 & 30670.6 & 2.23 \\\\\n \\hline\n \\multirow{4}{*}{90\\%} \n & 0.1 & 841.8 & 79067.8 & 8.82 \\\\\n & 1 & 379.5 & 74022.6 & \\textbf{4.31}\\\\\n & 10 & 281.7 & 74096.6 & 4.32\\\\\n & 100 & 329.3 & 74820.8 & 5.88 \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Majority voting consensus calculation: Evaluation of the $\\lambda$-multiple for the penalty parameter $\\rho$ in terms of total costs and runtime and end item demands only on a scenario tree with $|\\Omega| = 2$ and therefore $|\\Phi| = 128$}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.13113v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulation Parameters and Model Configuration}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n\\toprule\n\\textbf{Parameter} & \\textbf{Value / Description} \\\\\n\\midrule\nNumber of agents & 10 \\\\\nEpisodes per training run & 10,000 \\\\\nTreaties per episode & 1 \\\\\nState representation & Treaty features + market indicators \\\\\nAction space & Continuous: premium rate, quota share, commission \\\\\nReward function & Profit minus $\\lambda \\cdot$CVaR$_{95\\%}$ penalty \\\\\nRisk aversion coefficients $\\lambda$ & \\{0.01, 0.1, 1.0, 10.0\\} \\\\\nLoss modeling & Mixed attritional/catastrophe distribution \\\\\nPolicy network & 3-layer MLP (128 units/layer, ReLU) \\\\\nCritic network & 3-layer MLP (256 units/layer, ReLU) \\\\\nReplay buffer capacity & 1 million transitions \\\\\nMini-batch size & 1,024 \\\\\nOptimizer & Adam \\\\\nLearning rates & $10^{-4}$ (actor), $10^{-3}$ (critic) \\\\\nDiscount factor $\\gamma$ & 0.99 \\\\\nCVaR confidence level $\\alpha$ & 95\\% \\\\\nEvaluation interval & Every 500 episodes \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning", "authors": ["Stella C. Dong", "James R. Finlay"], "url": "https://arxiv.org/abs/2506.13113v1", "attribution": "\"Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning\" by Stella C. Dong and James R. Finlay, arXiv:2506.13113v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.04538v9_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|c|c|c|}\n\t\t\t\\hline\n\t\t\t& \\multicolumn{2}{c|}{$m=500$} & \\multicolumn{2}{c|}{$m=1000$} & \\multicolumn{2}{c|}{$m=3000$} \\\\ \\hline\n\t\t\t& PLS\n\t\t\t& PRLS\n\t\t\t& PLS\n\t\t\t& PRLS\n\t\t\t& PLS\n\t\t\t& PRLS \\\\ \\hline\n\t\t\t$n=3$ & 97.10\\% & 97.55\\% & 97.75\\% & 97.70\\% & 96.75\\% & 97.00\\% \\\\ \\hline\n\t\t\t$n=5$ & 97.05\\% & 96.75\\% & 97.25\\% & 97.05\\% & 97.00\\% & 96.90\\% \\\\ \\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects", "authors": ["Hao Chen", "Lanshan Han", "Alvin Lim"], "url": "https://arxiv.org/abs/2011.04538v9", "attribution": "\"Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects\" by Hao Chen, Lanshan Han, and Alvin Lim, arXiv:2011.04538v9, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.13222v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\t\t\t\\hline\\hline\n\t\t\tCause & Effect & p-values\\\\\n\t\t\t\\hline\n\t\t\tFRM &GT& $ 1.1 \\times 10^{-10}$ \\\\\n\t\t\t\\hline\n\t\t\tGT &FRM & $2.1\\times 10^{-12}$ \\\\\n\t\t\t\\hline\n\t\t\tDFRM &GT & $6.8 \\times 10^{-11}$ \\\\\n\t\t\t\\hline\n\t\t\tGT &DFRM & $4.1 \\times 10^{-10}$\\\\\n\t\t\t\\hline\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{$p$ values of Granger causality test}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "An AI approach to measuring financial risk", "authors": ["Lining Yu", "Wolfgang Karl Härdle", "Lukas Borke", "Thijs Benschop"], "url": "https://arxiv.org/abs/2009.13222v1", "attribution": "\"An AI approach to measuring financial risk\" by Lining Yu, Wolfgang Karl Härdle, Lukas Borke, and Thijs Benschop, arXiv:2009.13222v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.01255v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|cccccc|}\n\\hline\n\\multicolumn{6}{|c|}{\\multirow{2}{*}{\\textbf{FID - LDM}}} \\\\\n\\multicolumn{6}{|c|}{} \\\\ \\hline\nepoch/dataset & \\textbf{2-cell} & \\textbf{4-cell} & \\textbf{8-cell} & \\textbf{blastocyst} & \\textbf{morula} \\\\ \\hline\n\\textbf{10} & 87 & 98 & 93 & 76 & 69 \\\\ \\hline\n\\textbf{50} & 34 & 37 & 38 & 31 & 37 \\\\ \\hline\n\\textbf{100} & 26 & \\textbf{28} & 33 & 34 & 35 \\\\ \\hline\n\\textbf{150} & \\textbf{24} & 29 & \\textbf{31} & 54 & 47 \\\\ \\hline\n\\textbf{200} & 35 & 36 & 33 & 69 & 65 \\\\ \\hline\n\\textbf{250} & 55 & 54 & 38 & 61 & 73 \\\\ \\hline\n\\textbf{300} & 84 & 83 & 51 & 47 & 73 \\\\ \\hline\n\\textbf{350} & 104 & 105 & 69 & 35 & 65 \\\\ \\hline\n\\textbf{400} & 114 & 118 & 81 & 27 & 59 \\\\ \\hline\n\\textbf{450} & 119 & 124 & 85 & 23 & 49 \\\\ \\hline\n\\textbf{500} & 118 & 126 & 89 & 19 & 45 \\\\ \\hline\n\\textbf{550} & 119 & 123 & 89 & 16 & 37 \\\\ \\hline\n\\textbf{600} & 117 & 117 & 85 & 15 & 33 \\\\ \\hline\n\\textbf{650} & 116 & 114 & 85 & 13 & 27 \\\\ \\hline\n\\textbf{700} & 110 & 103 & 80 & 12 & 24 \\\\ \\hline\n\\textbf{750} & 110 & 96 & 74 & 11 & 21 \\\\ \\hline\n\\textbf{800} & 106 & 92 & 72 & 11 & 19 \\\\ \\hline\n\\textbf{850} & 99 & 82 & 60 & 11 & 18 \\\\ \\hline\n\\textbf{900} & 94 & 74 & 60 & 11 & 17 \\\\ \\hline\n\\textbf{950} & 91 & 68 & 53 & 11 & 14 \\\\ \\hline\n\\textbf{1000} & 86 & 61 & 50 & \\textbf{10} & \\textbf{14} \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{FID scores for the LDM across different training epochs for the five class categories: 2-cell, 4-cell, 8-cell, morula, and blastocyst.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Merging synthetic and real embryo data for advanced AI predictions", "authors": ["Oriana Presacan", "Alexandru Dorobantiu", "Vajira Thambawita", "Michael A. Riegler", "Mette H. Stensen", "Mario Iliceto", "Alexandru C. Aldea", "Akriti Sharma"], "url": "https://arxiv.org/abs/2412.01255v2", "attribution": "\"Merging synthetic and real embryo data for advanced AI predictions\" by Oriana Presacan, Alexandru Dorobantiu, Vajira Thambawita, Michael A. Riegler, Mette H. Stensen, Mario Iliceto, Alexandru C. Aldea, and Akriti Sharma, arXiv:2412.01255v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.03328v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\\hline\n Measure & Healthy& Schizophrenic \\\\\n \\hline\n $\\alpha_{1}^{(\\rm birth)}$ & $4.85 \\pm 0.04$ & $3.52 \\pm 0.05$ \\\\\n \\hline\n $H_{(\\rm birth)}$ & $0.84 \\pm 0.02$ & $0.66 \\pm 0.03$ \\\\\n \\hline\n $\\alpha_{1}^{(\\rm death)}$ & $3.96 \\pm 0.04$ & $2.83 \\pm 0.04$ \\\\\n \\hline\n $H_{(\\rm death)}$ & $0.85 \\pm 0.02$ & $0.68 \\pm 0.03$ \\\\\n \\hline\n $\\alpha_{1}^{(\\rm lifetime)}$ & $7.03 \\pm 0.03$ & $5.00 \\pm 0.03$ \\\\\n \\hline\n $H_{(\\rm lifetime)}$ & $0.89 \\pm 0.02$ & $0.73 \\pm 0.03$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{The value of topological coefficients and corresponding Hurst exponents for healthy and schizophrenic cases at $1\\sigma$ level of confidence.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Persistent Homology of Fractional Gaussian Noise", "authors": ["H. Masoomy", "B. Askari", "M. N. Najafi", "S. M. S. Movahed"], "url": "https://arxiv.org/abs/2101.03328v2", "attribution": "\"Persistent Homology of Fractional Gaussian Noise\" by H. Masoomy, B. Askari, M. N. Najafi, and S. M. S. Movahed, arXiv:2101.03328v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.13402v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Back Depression Inventory BDI-II Scoring Standard for depression detection ().}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|l}\n \\hline \n Depression Score & Depression Levels\\\\\n \\hline \n 0–13 & minimal depression \\\\\n14–19 & mild depression\\\\\n20-28 & moderate depression\\\\\n29-63 &Severe Depression\\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review", "authors": ["Sana Yasin", "Syed Asad Hussain", "Sinem Aslan", "Imran Raza", "Muhammad Muzammel", "Alice Othmani"], "url": "https://arxiv.org/abs/2009.13402v2", "attribution": "\"EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review\" by Sana Yasin, Syed Asad Hussain, Sinem Aslan, Imran Raza, Muhammad Muzammel, and Alice Othmani, arXiv:2009.13402v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.12176v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ CT reconstruction results of the Shepp-Logan and FORBILD.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n\t\t\\toprule\n\\multicolumn{8}{c}{Shepp-Logan}\\\\\n\\toprule\n\t\t\\multicolumn{2}{c}{noise level} & \\multicolumn{2}{c}{noiseless} & \\multicolumn{2}{c}{0.1\\%} & \\multicolumn{2}{c}{0.5\\%} \\\\\n\t\t\\cmidrule[\\heavyrulewidth](lr){1-2} \\cmidrule[\\heavyrulewidth](lr){3-4} \\cmidrule[\\heavyrulewidth](lr){5-6} \\cmidrule[\\heavyrulewidth](lr){7-8}\n\t\t\\multicolumn{2}{c}{Projection Angle} & $90^{\\circ}$ & $150^{\\circ}$ & $90^{\\circ}$ & $150^{\\circ}$ & $90^{\\circ}$ & $150^{\\circ}$ \\\\ \\toprule[0.8pt]\n\t\t\\multirow{3}{*}{SSIM}\n\t\t& PGSA\\_BE & 9.99e-01 & 1.00e+00 & 9.99e-01 & 1.00e+00 & 9.98e-01 & 9.99e-01 \\\\\n\t\t& e-PSG & 9.08e-01 & 9.99e-01 & 9.06e-01 & 9.98e-01 & 8.57e-01 & 9.97e-01 \\\\\n\t\t& FPSA-nl & 1.00e+00 & 1.00e+00 & 1.00e+00 & 1.00e+00 & 9.99e-01 & 1.00e+00 \\\\ \\hline\n\t\t\\multirow{3}{*}{RMSE}\n\t\t& PGSA\\_BE & 5.77e-05 & 2.00e-05 & 5.81e-05 & 2.12e-05 & 9.59e-05 & 5.87e-05 \\\\\n\t\t& e-PSG & 8.29e-04 & 6.27e-05 & 8.39e-04 & 6.37e-05 & 1.04e-03 & 1.06e-04 \\\\\n\t\t& FPSA-nl & 9.94e-06 & 5.29e-06 & 2.90e-05 & 1.73e-05 & 6.94e-05 & 3.18e-05 \\\\ \\hline\n\t\t\\multirow{3}{*}{CPU}\n\t\t& PGSA\\_BE & 6.37e+02 & 5.32e+02 & 6.54e+02 & 4.83e+02 & 8.39e+02 & 7.14e+02 \\\\\n\t\t& e-PSG & 1.84e+03 & 1.87e+03 & 1.87e+03 & 1.88e+03 & 2.38e+03 & 2.39e+03 \\\\\n\t\t& FPSA-nl & 1.34e+01 & 1.24e+01 & 1.99e+01 & 6.91e+00 & 1.23e+01 & 7.31e+00 \\\\\n\t\\toprule\n\\multicolumn{8}{c}{FORBILD}\\\\\n\t\t\\toprule\n\t\t\\multirow{3}{*}{SSIM}\n\t\t& PGSA\\_BE & 9.99e-01 & 1.00e-01 & 9.99e-01 & 9.87e-01 & 9.85e-01 & 9.97e-01 \\\\\n\t\t& e-PSG & 7.83e-01 & 8.26e-01 & 7.78e-01 & 9.39e-01 & 7.90e-01 & 8.53e-01 \\\\\n\t\t& FPSA-nl & 1.00e+00 & 1.00e+00 & 1.00e+00 & 9.90e-01 & 9.90e-01 & 9.98e-01 \\\\ \\hline\n\t\t\\multirow{3}{*}{RMSE}\n\t\t& PGSA\\_BE & 5.62e-05 & 1.72e-05 & 6.03e-05 & 5.70e-05 & 2.89e-04 & 1.09e-04 \\\\\n\t\t& e-PSG & 1.36e-03 & 1.07e-03 & 1.38e-03 & 1.75e-05 & 1.37e-03 & 1.01e-03 \\\\\n\t\t& FPSA-nl & 2.54e-05 & 4.98e-06 & 5.34e-05 & 1.75e-05 & 2.48e-04 & 9.84e-05 \\\\ \\hline\n\t\t\\multirow{3}{*}{CPU}\n\t\t& PGSA\\_BE & 1.17e+03 & 1.03e+03 & 1.69e+03 & 5.16e+01 & 8.24e+02 & 5.24e+02 \\\\\n\t\t& e-PSG & 1.91e+03 & 2.24e+03 & 2.24e+03 & 2.31e+03 & 2.04e+03 & 1.92e+03 \\\\\n\t\t& FPSA-nl & 3.17e+01 & 1.34e+01 & 4.61e+01 & 1.42e+01 & 4.37e+01 & 1.93e+01 \\\\ \\bottomrule\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A Single-loop Proximal Subgradient Algorithm for A Class Structured Fractional Programs", "authors": ["Deren Han", "Min Tao", "Zihao Xia"], "url": "https://arxiv.org/abs/2503.12176v1", "attribution": "\"A Single-loop Proximal Subgradient Algorithm for A Class Structured Fractional Programs\" by Deren Han, Min Tao, and Zihao Xia, arXiv:2503.12176v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.18140v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lcccc}\n\\hline\nBenchmark & Task & Metric & Size \\\\ \\hline\nOAB-2023 & Multiple-choice (4) & Accuracy & 160 \\\\\nENADE-2022-Law & Multiple-choice (5) & Accuracy & 35 \\\\ \\hline\n\\end{tabular}\n\\caption{Configuration of law knowledge benchmarks.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Juru: Legal Brazilian Large Language Model from Reputable Sources", "authors": ["Roseval Malaquias Junior", "Ramon Pires", "Roseli Romero", "Rodrigo Nogueira"], "url": "https://arxiv.org/abs/2403.18140v1", "attribution": "\"Juru: Legal Brazilian Large Language Model from Reputable Sources\" by Roseval Malaquias Junior, Ramon Pires, Roseli Romero, and Rodrigo Nogueira, arXiv:2403.18140v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09079v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results on Washinghton DC AVIRIS hyperspectral image}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\bf Methods &\\multicolumn{2}{c}{\\bf F1-scores}\n\\\\ \\cline{2-3}\n & r = 80\\% & r = 90\\% \\\\ \\hline \\\\\n DPCP-PSGM (unknown $c$) & 0.994 & 0.993\\\\\nRSGM (unknown $c$) &0 & 0 \\\\\nDPCP-IRLS (unknown $c$) & 0 & 0 \\\\\nRSGM ($c=5$) & 0.999 &0.993 \\\\ \nDPCP-IRLS $c=5$ & 1 & 0.995 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension", "authors": ["Paris V. Giampouras", "Benjamin D. Haeffele", "René Vidal"], "url": "https://arxiv.org/abs/2201.09079v1", "attribution": "\"Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension\" by Paris V. Giampouras, Benjamin D. Haeffele, and René Vidal, arXiv:2201.09079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.12619v1_tex_table19.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\large Spatial Model Comparison by Criterion}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|r|r|r}\n\\hline\n & Log Likelihood & AIC & BIC\\\\\n\\hline\nSpatial Lag-Error Model & -127,721.6 & 261,635.3 & 289,253.0\\\\\n\\hline\nSpatial Lag Model & -272,367.1 & 550,924.2 & 578,533.0\\\\\n\\hline\nSpatial Error Model & -272,426.2 & 551,042.3 & 578,651.1\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'", "authors": ["Ebba Mark", "Ryan Rafaty", "Moritz Schwarz"], "url": "https://arxiv.org/abs/2211.12619v1", "attribution": "\"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'\" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2103.04346v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance on TIMIT test data with model trained on 1000 training instances of TIMIT. Performance with voicing applied shown in parentheses.}\n\\begin{tabular}{|c|c|c|c|}\n \\hline\n \\textbf{Method} & \\textbf{Corr Coef} & \\textbf{SR error} & \\textbf{F-score} \\\\\n \\textbf{} & \\textbf{of \\#syl} & \\textbf{rate (\\%)} & \\textbf{}\\\\ \\hline \\hline\n Jiao et al. & 0.842 & 13.7 & - \\\\ \\hline\n Yarra et al. & - & - & 84.16 \\\\ \\hline\n Proposed (1/F-score) & 0.906 (0.917) & 11.4 (11.89) & 87.73 (88.29) \\\\ \\hline\n Proposed (MAE) & 0.890 (0.913) & 10.8 (9.94) & 86.15 (87.88) \\\\ \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "An Optimized Signal Processing Pipeline for Syllable Detection and Speech Rate Estimation", "authors": ["Kamini Sabu", "Syomantak Chaudhuri", "Preeti Rao", "Mahesh Patil"], "url": "https://arxiv.org/abs/2103.04346v1", "attribution": "\"An Optimized Signal Processing Pipeline for Syllable Detection and Speech Rate Estimation\" by Kamini Sabu, Syomantak Chaudhuri, Preeti Rao, and Mahesh Patil, arXiv:2103.04346v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2212.08198v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\toprule\n\\textbf{Variable} & \\textbf{Included for which authors} & \\textbf{Lags} \\\\ \\midrule\n\\textit{h}-index & Up to 20 authors & 4 years \\\\\nNumber of publications & Up to 20 authors & 4 years \\\\\nNumber of citations received (excluding self-citations) & Up to 20 authors & 4 years \\\\\nTotal grant funding received by authors’ institution & All & 5 years \\\\\nRanking of institution & Up to 20 authors & — \\\\\nNumber of authors by institution type (academic, govm, company) & All & — \\\\\nNumber of authors on publication & — & — \\\\\nPublication date & — & — \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Economic impacts of AI-augmented R&D", "authors": ["Tamay Besiroglu", "Nicholas Emery-Xu", "Neil Thompson"], "url": "https://arxiv.org/abs/2212.08198v2", "attribution": "\"Economic impacts of AI-augmented R&D\" by Tamay Besiroglu, Nicholas Emery-Xu, and Neil Thompson, arXiv:2212.08198v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02419v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CIM core performance comparison between MBRAI, RPN\\&BLM and the proposed}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n \\hline\n & MBRAI &RPN\\&BLM &\\multicolumn{2}{c|}{Proposed} \\\\ \\hline\n \\multirow{2}{*}{Supply} & \\multirow{2}{*}{1.1 V} & \\multirow{2}{*}{1 V} & M-RD4 Recoder & 0.6 V \\\\ \n \\cline{4-5}\n &&&Neuron Circuit& 1 V \\\\\n \\hline\n Computing speed & 1.85 M/s&1.85 M/s & \\multicolumn{2}{c|}{1.85 M/s}\\\\ \\hline\n SFDR & 67.42 dB &59.13 dB& \\multicolumn{2}{c|}{63.41 dB} \\\\ \\hline\n SNDR & 45.48 dB &46.13 dB& \\multicolumn{2}{c|}{46.48 dB} \\\\ \\hline\n ENOB & 7.26 bit &7.37 bit& \\multicolumn{2}{c|}{7.42 bit} \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Low Power In-Memory Multiplication andAccumulation Array with Modified Radix-4 Inputand Canonical Signed Digit Weights", "authors": ["Rui Xiao", "Kejie Huang", "Yewei Zhang", "Haibin Shen"], "url": "https://arxiv.org/abs/2101.02419v1", "attribution": "\"A Low Power In-Memory Multiplication andAccumulation Array with Modified Radix-4 Inputand Canonical Signed Digit Weights\" by Rui Xiao, Kejie Huang, Yewei Zhang, and Haibin Shen, arXiv:2101.02419v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02875v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{SensEval/SemEval evaluation datasets}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccccc}%p{.3\\columnwidth}}\n\t\t\t\\hline\n\t\t\tDataset \t\t& Task \t\t& \\multicolumn{5}{c}{\\# of Senses} \t\\\\\n\t\t\t\\cline{3-7}\n\t\t\tName\t\t\t& Method\t& NN \t& V \t& Adj \t& Adv \t& Total \\\\%(Domains) \t\\\\\n\t\t\t\\hline\n\t\t\tSensEval2 (SE2) \t& LS, AW\t& 1066\t& 517\t& 445\t& 254\t& 2282\t\\\\\n\t\t\tSensEval3 (SE3) \t& LS, AW\t& 900\t& 588\t& 350\t& 12\t& 1850 \t\\\\ %(editorial, news and fiction)\\\\\n\t\t\tSemEval-07 (SE07) \t& LS\t\t& 159\t& 296\t&\t-\t&\t-\t& 455 \t\\\\\n\t\t\tSemEval-13 (SE13) \t& LS, AW\t& 1644\t& -\t&\t-\t&\t-\t& 1644 \t\\\\% (various domains)\t\\\\\n\t\t\tSemEval-15 (SE15) \t& LS, AW\t& 531\t& 251\t& 160\t& 80\t& 1022 \t\\\\%(biomedical, social issues and mathematics/computing)\t\\\\\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph", "authors": ["Mohannad AlMousa", "Rachid Benlamri", "Richard Khoury"], "url": "https://arxiv.org/abs/2101.02875v1", "attribution": "\"A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph\" by Mohannad AlMousa, Rachid Benlamri, and Richard Khoury, arXiv:2101.02875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.13533v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Balance sheet is a financial status report for a company at a specific time, reflecting all assets, debts and shareholder rights owned by the company}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrr}\n\\multicolumn{3}{c}{\\textbf{BALANCE SHEET}} \\\\\n\\hline\n\\hline\n\\textbf{Assets} & \\textbf{Initial Amount} & \\textbf{End Amount} \\\\ \\cline{1-3}\nCurrent Assets & & \\\\ \n\\quad Cash on Hand & \\underline{3000000} & \\underline{270005.9} \\\\ \n\\quad Bank Deposits & \\underline{5000000} & \\underline{164645.57} \\\\ \n\\quad Interest Receivable & \\underline{0} & \\underline{2672.87} \\\\ \n\\quad Accounts Receivable & \\underline{0} & \\underline{2429482.13} \\\\ \n\\quad Inventory & \\underline{0} & \\underline{5090000} \\\\ \n\\textbf{Total Current Assets} & \\underline{8000000} & \\underline{7956806.47} \\\\ \\cline{1-3}\nNon-Current Assets & & \\\\ \n\\quad Fixed Assets & \\underline{5000000} & \\underline{5305354.43} \\\\ \n\\quad Accumulated Depreciation & \\underline{0} & \\underline{(45751.41)} \\\\ \n\\quad Net Fixed Assets & \\underline{5000000} & \\underline{5259603.02} \\\\ \n\\textbf{Total Non-Current Assets} & \\underline{5000000} & \\underline{5259603.02} \\\\ \\cline{1-3}\n\\textbf{\\textit{Total Assets}} & \\underline{13000000} & \\underline{13216409.49} \\\\ \n\\hline\n\\hline\n\\textbf{Liabilities} & & \\\\ \n\\hline\nCurrent Liabilities & & \\\\ \n\\quad Accounts Payable & \\underline{0} & \\underline{1590000} \\\\ \n\\quad Taxes Payable & \\underline{0} & \\underline{271550.92} \\\\ \n\\textbf{Total Current Liabilities} & \\underline{0} & \\underline{1861550.92} \\\\ \\cline{1-3}\n\\textbf{\\textit{Total Liabilities}} & \\underline{0} & \\underline{1861550.92} \\\\ \n\\hline\n\\hline\n\\textbf{Owner’s Equity} & & \\\\ \n\\quad Paid-in Capital & \\underline{13000000} & \\underline{13000000} \\\\ \n\\quad Retained Earnings & \\underline{0} & \\underline{-1645141.46} \\\\ \n\\textbf{\\textit{Total Owner's Equity}} & \\underline{13000000} & \\underline{11354859} \\\\ \n\\hline\n\\hline\n\\textbf{Total Liabilities and Equity} & \\underline{13000000} &\\underline{13216409} \\\\ \n\\hline\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs", "authors": ["Junzhe Jiang", "Chang Yang", "Aixin Cui", "Sihan Jin", "Ruiyu Wang", "Bo Li", "Xiao Huang", "Dongning Sun", "Xinrun Wang"], "url": "https://arxiv.org/abs/2505.13533v1", "attribution": "\"FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs\" by Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Ruiyu Wang, Bo Li, Xiao Huang, Dongning Sun, and Xinrun Wang, arXiv:2505.13533v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.13915v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The MSD, MAD, and ABWD for S\\&P $500$ stock index dataset. The best bootstrapping technique with the least MSD, MAD, and ABWD is marked in bold.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\t\t\t\\hline\n\t\t\t& Bootstrap Methods & MSD & MAD & ABWD\\\\\n\t\t\t\\hline\n\t\t\t\n\t\t\t\\multirow{3}{*}{Open Price}& \n\t\t\t$\\quad$NOBB$\\quad$ & $6946.36$ & $65.72$ & $18181.5$ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $4118.35$ & $48.47$ & $12993.9$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{2209.72}$ & $\\textbf{35.88}$ & $\\textbf{11545.4}$ \\\\\n\t\t\t\n\t\t\t\\hline \\multirow{3}{*}{High Price} & $\\quad$NOBB$\\quad$ & $6752.75$ & $65.76$ & $17270.2$ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $4073.18$ & $50.24$ & $12771.3$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{2788.50}$ & $\\textbf{42.34}$ & $\\textbf{11857.1}$ \\\\\n\t\t\t\n\t\t\t\\hline \\multirow{3}{*}{Low Price}& \n\t\t\t$\\quad$NOBB$\\quad$ & $8383.30$ & $73.70$ & $16886.9$ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $5736.40$ & $58.16$ & $15095.6$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{4027.22}$ & $\\textbf{50.35}$ & $\\textbf{11601.9}$ \\\\\n\t\t\t\n\t\t\t\\hline \\multirow{3}{*}{Closing Price} & $\\quad$NOBB$\\quad$ & $10198.64$ & $80.64$ & $15953.9$ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $8053.63$ & $72.07$ & $12183.8$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{6342.93}$ & $\\textbf{65.40}$ & $\\textbf{10855.8}$ \\\\\n\t\t\t\n\t\t\t\\hline \\multirow{3}{*}{Volume} & \n\t\t\t$\\quad$NOBB$\\quad$ & $4.33e+17\\quad$ & $\\textbf{4.36e+08}\\quad$ & $6.82e+10$ \\\\\n\t\t\t& $\\quad$MBB$\\quad$ & $4.41e+17\\quad$ & $4.47e+08\\quad$ & $6.75e+10$ \\\\\n\t\t\t& $\\quad$LBB$\\quad$ & $\\textbf{6.50e+10}\\quad$ & $3.83e+17\\quad$ & $\\textbf{4.25e+08}$ \\\\\n\t\t\t\n\t\t\t\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network", "authors": ["Aryan Bhambu", "Arabin Kumar Dey"], "url": "https://arxiv.org/abs/2211.13915v1", "attribution": "\"Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network\" by Aryan Bhambu and Arabin Kumar Dey, arXiv:2211.13915v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2503.00851v1_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{MCS test for the future 22-step volatility forecasting model of the SSE}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llllllllllllll}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{2}{c}{$\\mathrm{QLIKE}_{\\alpha_1}$} & \\multicolumn{2}{c}{$\\mathrm{MSE}_{\\alpha_1}$} & \\multicolumn{2}{c}{$\\mathrm{QLIKE}_{\\alpha_2}$} & \\multicolumn{2}{c}{$\\mathrm{MSE}_{\\alpha_2}$} & \\multicolumn{2}{c}{$\\mathrm{QLIKE}_{\\alpha_3}$} & \\multicolumn{2}{c}{$\\mathrm{MSE}_{\\alpha_3}$} \\\\\n\\cmidrule(l){2-3} \\cmidrule(l){4-5}\\cmidrule(l){6-7}\\cmidrule(l){8-9}\\cmidrule(l){10-11}\\cmidrule(l){12-13}\n& $T_{max}$ & $T_{R}$ & $T_{max}$ & $T_{R}$ & $T_{max}$ & $T_{R}$ & $T_{max}$ & $T_{R}$ & $T_{max}$ & $T_{R}$ & $T_{max}$ & $T_{R}$ \\\\\n\\midrule\nGARCH & $0.019$ & $0.045$ & $0.000$ & $0.000$ & $0.034$ & $0.029$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ \\\\\nEGARCH & $0.000$ & $0.000$ & $0.099$ & $0.013$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ \\\\\nFGARCH & $0.000$ & $0.000$ & $0.964$ & $0.029$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ \\\\\nTGARCH & $0.113$ & $0.034$ & $0.047$ & $0.067$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ \\\\\nHAR-RV & $0.122$ & $0.125$ & $0.278$ & $0.079$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ \\\\\nHAR-CJ & $1.000$ & $0.660$ & $1.000$ & $0.453$ & $0.000$ & $0.000$ & $0.990$ & $0.559$ & $0.000$ & $0.000$ & $0.993$ & $0.559$ \\\\\nHAR-RS & $1.000$ & $0.415$ & $1.000$ & $0.095$ & $1.000$ & $0.002$ & $0.972$ & $0.359$ & $0.000$ & $0.000$ & $0.972$ & $0.359$ \\\\\nHAR-REX & $1.000$ & $0.573$ & $1.000$ & $0.946$ & $0.000$ & $0.000$ & $0.816$ & $0.481$ & $0.000$ & $0.000$ & $0.816$ & $0.481$ \\\\\nHAR-REQ & $1.000$ & $0.937$ & $0.246$ & $0.453$ & $0.123$ & $0.513$ & $1.000$ & $0.878$ & $0.000$ & $0.000$ & $1.000$ & $0.877$ \\\\\nHAR-PD-RV & $0.971$ & $0.124$ & $1.000$ & $0.485$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ & $0.000$ \\\\\nHAR-PD-CJ & $1.000$ & $0.750$ & $1.000$ & $1.000$ & $0.303$ & $0.548$ & $1.000$ & $0.672$ & $0.000$ & $0.000$ & $1.000$ & $0.652$ \\\\\nHAR-PD-RS & $1.000$ & $0.998$ & $1.000$ & $1.000$ & $0.797$ & $0.003$ & $1.000$ & $0.997$ & $0.000$ & $0.000$ & $1.000$ & $0.997$ \\\\\nHAR-PD-REX & $1.000$ & $\\mathbf{1.000}$ & $1.000$ & $0.914$ & $0.995$ & $\\mathbf{1.000}$ & $\\mathbf{1.000}$ & $\\mathbf{1.000}$ & $0.000$ & $0.000$ & $\\mathbf{1.000}$ & $\\mathbf{1.000}$ \\\\\nHAR-PD-REQ & $\\mathbf{1.000}$ & $0.995$ & $\\mathbf{1.000}$ & $\\mathbf{1.000}$ & $1.000$ & $1.000$ & $1.000$ & $0.985$ & $0.000$ & $0.000$ & $1.000$ & $0.985$ \\\\\nLASSO-HAR-PD-CJ & $0.000$ & $0.000$ & $1.000$ & $1.000$ & $0.259$ & $0.513$ & $0.996$ & $0.615$ & $0.000$ & $0.000$ & $0.995$ & $0.978$ \\\\\nLASSO-HAR-PD-RS & $1.000$ & $0.990$ & $1.000$ & $1.000$ & $\\mathbf{1.000}$ & $1.000$ & $1.000$ & $0.978$ & $\\mathbf{1.000}$ & $\\mathbf{1.000}$ & $0.993$ & $0.978$ \\\\\nLASSO-HAR-PD-REX & $1.000$ & $0.987$ & $1.000$ & $1.000$ & $0.604$ & $0.548$ & $1.000$ & $1.000$ & $0.000$ & $0.000$ & $1.000$ & $0.971$ \\\\\nLASSO-HAR-PD-REQ & $0.000$ & $0.000$ & $1.000$ & $1.000$ & $0.000$ & $0.000$ & $0.612$ & $0.621$ & $0.000$ & $0.000$ & $0.612$ & $0.621$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Forecasting realized volatility in the stock market: a path-dependent perspective", "authors": ["Xiangdong Liu", "Sicheng Fu", "Shaopeng Hong"], "url": "https://arxiv.org/abs/2503.00851v1", "attribution": "\"Forecasting realized volatility in the stock market: a path-dependent perspective\" by Xiangdong Liu, Sicheng Fu, and Shaopeng Hong, arXiv:2503.00851v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.17702v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{Parameter} & \\textbf{Symbol} & \\textbf{Value} \n\\\\ \\hline \nGVD &$\\beta_2$ & 21.67ps$^2$/km \n\\\\ \\hline \nNonlinearity & $\\gamma$ & 1.27W$^{-1}$km$^{-1}$ \n\\\\ \\hline \nBandwidth & $B$ & 100GHz \n\\\\ \\hline \nAmplifier Distance & $L$ & 100km \n\\\\ \\hline \nMaximum Distance & $K_{max}L$ & 2000km \n\\\\ \\hline \nPhoton Energy & $E_{ph}$ & $13.2\\cdot10^{-20}$J\n\\\\ \\hline \nEmission Factor& $n_{sp}$ & 1\n\\\\ \\hline \nAttenuation & $\\alpha_{loss}$ & 0.2dB/km\n\\\\ \\hline \nNoise & $\\sigma^2$ & $n_{sp}E_{ph}(e^{\\alpha_{loss}L}-1)$ \n\\\\ \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Parameter values () for the simulations in Section .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Spectral Efficiency Expression for the Non-Linear Schrödinger Channel in the Low Noise Limit Using Scattering Data", "authors": ["Pavlos Kazakopoulos", "Aris L. Moustakas"], "url": "https://arxiv.org/abs/2502.17702v1", "attribution": "\"Spectral Efficiency Expression for the Non-Linear Schrödinger Channel in the Low Noise Limit Using Scattering Data\" by Pavlos Kazakopoulos and Aris L. Moustakas, arXiv:2502.17702v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.15301v1_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Misclassification rates of $k$NN, tree, random forest, node harvest, SVM (with four different kernels), random projection with quadratic and linear discriminant analyses, OTE, OTE$_{oob}$ and OTE$_{sub}$. Results are based on 30\\% training and 70\\% testing parts of the data. Overall best performing method result is shown in bold. The results are italicised when OTE$_{oob}$ and/or OTE$_{sub}$ are/is better than \\emph{OTE}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccccccc}\n\t\t\t\t\\toprule\n\t\t\t\tDataset & $n$ & $p$ & kNN & Tree & NH & SVM & SVM & SVM & SVM & RP & RP & RF & OTE & OTE$_{oob}$ & OTE$_{sub}$ \\\\\n\t\t\t\t& & & & & & (Radial) & (Linear) & (Bessel) & (Laplacian) & (LDA) & (QDA) & & & & \\\\\n\t\t\t\t\\midrule\n\t\t\t\tMammographic & 830 & 5 & 0.2190 & 0.1665 & 0.1792 & 0.1911 & 0.1806 & 0.1836 & 0.1860 & 0.1857 & 0.1980 & \\textbf{0.1640} & 0.1787 & 0.1827 & 0.1947 \\\\\n\t\t\t\tDystrophy & 209 & 5 & 0.1399 & 0.2098 & 0.1725 & 0.1212 & 0.1272 & 0.1175 & 0.1176 & 0.1276 & \\textbf{0.1041} & 0.1379 & 0.1458 & \\textit{0.1392} & 0.1479 \\\\\n\t\t\t\tMonk3 & 122 & 6 & 0.1499 & 0.0883 & 0.2988 & 0.1732 & 0.2542 & 0.1610 & 0.2357 & 0.2466 & 0.1938 & 0.0973 & 0.1230 & \\textit{\\textbf{0.0874}} & 0.0962 \\\\\n\t\t\t\tAppendicitis & 106 & 7 & 0.1711 & 0.1998 & 0.1659 & 0.1954 & 0.2285 & 0.1889 & 0.1664 & 0.1506 & 0.1751 & \\textbf{0.1496} & 0.1934 & \\textit{0.1744} & \\textit{0.1730} \\\\\n\t\t\t\tSAHeart & 462 & 9 & 0.3929 & 0.3549 & \\textbf{0.2987} & 0.3299 & 0.3210 & 0.3500 & 0.3422 & 0.3108 & 0.3225 & 0.3044 & 0.3232 & 0.3258 & 0.3328 \\\\\n\t\t\t\tTic-Tac-Toe & 958 & 9 & 0.3817 & 0.1988 & 0.3337 & 0.3453 & 0.4172 & 0.2783 & 0.5037 & 0.3381 & 0.2633 & 0.1343 & 0.1203 & \\textit{\\textbf{0.0828}} & 0.1067 \\\\\n\t\t\t\tHeart & 303 & 13 & 0.3798 & 0.2749 & 0.2259 & 0.1835 & 0.1999 & 0.3677 & \\textbf{0.1838} & 0.2225 & 0.2456 & 0.1939 & 0.2155 & \\textit{0.2029} & 0.2034 \\\\\n\t\t\t\tHouse Vote & 232 & 16 & 0.0995 & 0.0520 & 0.1357 & 0.0596 & 0.0483 & 0.0567 & 0.0810 & 0.0745 & 0.0783 & \\textbf{0.0465} & 0.0509 & \\textit{0.0471} & 0.0472 \\\\\n\t\t\t\tBands & 365 & 19 & 0.3332 & 0.3433 & 0.3876 & 0.4465 & 0.3210 & 0.4970 & 0.5070 & 0.3462 & 0.3318 & 0.2780 & 0.2834 & \\textit{\\textbf{0.2771}} & 0.2815 \\\\\n\t\t\t\tParkinson & 195 & 22 & 0.1899 & 0.1883 & 0.1687 & 0.2046 & 0.2150 & 0.2530 & 0.2370 & 0.1919 & 0.1891 & 0.1466 & 0.1433 & \\textit{0.1401} & \\textit{\\textbf{0.1381}} \\\\\n\t\t\t\tBody & 507 & 23 & 0.0419 & 0.0994 & 0.0901 & 0.0290 & 0.0216 & 0.5019 & 0.0555 & \\textbf{0.0236} & 0.0259 & 0.0589 & 0.0579 & \\textit{0.0563} & \\textit{0.0540} \\\\\n\t\t\t\tThyroid & 9172 & 27 & 0.0411 & 0.0137 & 0.0402 & 0.1289 & 0.0456 & 0.3479 & 0.0712 & 0.0501 & 0.0479 & 0.0131 & 0.0124 & \\textit{0.0122} & \\textit{\\textbf{0.0120}} \\\\\n\t\t\t\tWDBC & 569 & 29 & 0.0793 & 0.0802 & 0.0812 & 0.0604 & 0.0350 & 0.5698 & 0.0600 & 0.0602 & 0.0575 & 0.0537 & 0.0544 & \\textit{0.0525} & \\textit{\\textbf{0.0513}} \\\\\n\t\t\t\tWPBC & 198 & 32 & 0.2914 & 0.2985 & 0.2549 & 0.3273 & 0.3104 & 0.4366 & 0.4534 & 0.2348 & 0.2530 & \\textbf{0.2446} & 0.2580 & \\textit{0.2570} & 0.2624 \\\\\n\t\t\t\tOil-Spill & 937 & 49 & 0.0697 & 0.0483 & 0.0621 & 0.0845 & 0.0824 & 0.1789 & 0.1754 & 0.0463 & 0.0429 & 0.0431 & 0.0411 & 0.0412 & \\textit{\\textbf{0.0409}} \\\\\n\t\t\t\tSpam base & 4601 & 58 & 0.1999 & 0.1078 & 0.1215 & 0.1176 & 0.0798 & 0.4829 & 0.3014 & 0.2368 & 0.3255 & 0.0577 & 0.0581 & \\textit{0.0572} & \\textit{\\textbf{0.0564}} \\\\\n\t\t\t\tSonar & 208 & 60 & 0.2562 & 0.3318 & 0.2659 & 0.2484 & 0.2714 & 0.5335 & 0.5051 & 0.2870 & 0.2605 & 0.2494 & 0.2453 & \\textit{0.2378} & \\textit{\\textbf{0.2327}} \\\\\n\t\t\t\tGlaucoma & 196 & 62 & 0.2270 & 0.1953 & 0.1463 & 0.1681 & 0.1861 & 0.6269 & 0.2516 & 0.1365 & 0.1565 & 0.1589 & 0.1585 & \\textit{\\textbf{0.1519}} & \\textit{0.1541} \\\\\n\t\t\t\tNki 70 & 144 & 76 & 0.1979 & 0.1822 & 0.1758 & 0.2797 & 0.3304 & 0.3589 & 0.5099 & \\textbf{0.1928} & 0.2103 & 0.1943 & 0.2218 & 0.2166 & 0.2310 \\\\\n\t\t\t\tMusk & 476 & 166 & 0.1614 & 0.3058 & 0.2846 & 0.2147 & 0.2327 & 0.5015 & 0.5043 & 0.1280 & \\textbf{0.1089} & 0.1849 & 0.1906 & \\textit{0.1863} & \\textit{0.1838} \\\\\n\t\t\t\t\\bottomrule\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Optimal trees selection for classification via out-of-bag assessment and sub-bagging", "authors": ["Zardad Khan", "Naz Gul", "Nosheen Faiz", "Asma Gul", "Werner Adler", "Berthold Lausen"], "url": "https://arxiv.org/abs/2012.15301v1", "attribution": "\"Optimal trees selection for classification via out-of-bag assessment and sub-bagging\" by Zardad Khan, Naz Gul, Nosheen Faiz, Asma Gul, Werner Adler, and Berthold Lausen, arXiv:2012.15301v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2504.13521v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Forecasting performance (MAPE, bps) for tickers with different horizons and target values, 4 LOB feathers}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|rr|rr|rr|rr|rr|rr|}\n \\hline\n \\multirow{2}{*}{\\centering{Ticker}} & \n \\multicolumn{2}{c|}{0.2s} &\n \\multicolumn{2}{c|}{1s} &\n \\multicolumn{2}{c|}{5s} &\n \\multicolumn{2}{c|}{10s} &\n \\multicolumn{2}{c|}{30s} &\n \\multicolumn{2}{c|}{60s} \\\\\n \\cline{2-13}\n & delta & returns % 0.2s\n & delta & returns % 1s\n & delta & returns % 5s\n & delta & returns % 10s\n & delta & returns % 30s\n & delta & returns % 60s\n \\\\\\hline\n ADA%USDT\n & \\textbf{4.0608} & \\textbf{4.0684} % 200ms\n & 6.5488 & 6.5609 % 1s\n & 10.5594 & 10.5599 % 5s\n & 11.8042 & 10.5599 % 10s\n & 23.4401 & 11.0921 % 30s\n & 35.0604 & 34.9744 % 60s\n \\\\\\hline\n BNB%USDT\n & \\textbf{0.4473} & 3.5378 % 0.2s\n & 1.1215 & \\textbf{3.0642} % 1s\n & 2.6080 & 4.5389 % 5s\n & 3.4464 & 6.1253 % 10s\n & 8.0489 & 10.9732 % 30s\n & 12.5134 & 13.2562 % 60s\n \\\\\\hline\n BTC%USDT\n & \\textbf{0.4039} & \\textbf{1.7114} % 0.2s\n & 1.0030 & 1.9755 % 1s\n & 2.5335 & 3.4717 % 5s\n & 3.5881 & 4.4318 % 10s\n & 7.7604 & 8.0355 % 30s\n & 11.7311 & 11.7082 % 60s\n \\\\\\hline\n DOGE%USDT\n & \\textbf{5.5022} & \\textbf{2.7826} % 0.2s\n & 5.6041 & 3.6401 % 1s\n & 8.398 & 7.1593 % 5s\n & 11.3433 & 9.1489 % 10s\n & 19.1686 & 18.1120 % 30s\n & 28.3736 & 27.3628 % 60s\n \\\\\\hline\n ETH%USDT\n & \\textbf{0.8210} & \\textbf{4.1362} % 0.2s\n & 1.5031 & 4.6131 % 1s\n & 3.5291 & 5.6284 % 5s\n & 5.2785 & 6.5229 % 10s\n & 10.5565 & 11.0921 % 30s\n & 14.1682 & 14.5061 % 60s\n \\\\\\hline\n GOAT%USDT\n & \\textbf{4.0729} & \\textbf{3.6337} % 0.2s\n & 6.5817 & 5.9020 % 1s \n & 9.7854 & 10.2692 % 5s\n & 13.6341 & 13.3915 % 10s\n & 27.2481 & 27.0113 % 30s\n & 37.687 & 36.4944 % 60s\n \\\\\\hline\n TON%USDT\n & \\textbf{1.4299} & \\textbf{3.3976} % 0.2s\n & 2.7562 & 3.9514 % 1s\n & 6.001 & 7.1998 % 5s\n & 9.0914 & 9.3516 % 10s\n & 17.7552 & 18.1472 % 30s\n & 26.7795 & 29.1056 % 60s\n \\\\\\hline\n TRX%USDT\n & \\textbf{6.8002} & \\textbf{1.5505} % 0.2s\n & 10.4883 & 2.4671 %1s \n & 12.7779 & 3.5136 % 5s\n & 8.9803 & 3.7547 % 10s\n & 12.2555 & 6.5367 % 30s\n & 18.6451 & 9.8755 % 60s\n \\\\\\hline\n SOL%USDT\n & \\textbf{0.7063} & \\textbf{2.7658} % 0.2s\n & 1.2677 & 4.1875 % 1s\n & 3.2531 & 4.4746 % 5s\n & 4.6380 & 6.2026 % 10s\n & 9.1552 & 10.247 % 30s\n & 13.8838 & 16.5093 % 60s\n \\\\\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Deep Learning Models Meet Financial Data Modalities", "authors": ["Kasymkhan Khubiev", "Mikhail Semenov"], "url": "https://arxiv.org/abs/2504.13521v2", "attribution": "\"Deep Learning Models Meet Financial Data Modalities\" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2504.13521v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01833v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Details of evaluation benchmarks, we show the number of classes, dataset statistics, selected backbone, and batch size for training.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n \\toprule\n \\textbf{Dataset} & \\textbf{Num. Class} & \\textbf{Train Samples} & \\textbf{Test Samples} & \\textbf{Backbone} & \\textbf{Batch size}\\\\\n \\midrule\n CIFAR-10 & 10 & 50,000 & 10,000 & ResNet-18 & 512 \\\\\n CIFAR-100 & 100 & 50,000 & 10,000 & ResNet-18 & 512 \\\\\n ImageNet-100 & 100 & 128,545 & 5,000 & ResNet-50 & 512 \\\\\n Tiny-ImageNet & 200 & 100,000 & 10,000 & ResNet-18 & 256 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning", "authors": ["Shengjie Niu", "Lifan Lin", "Jian Huang", "Chao Wang"], "url": "https://arxiv.org/abs/2411.01833v1", "attribution": "\"OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning\" by Shengjie Niu, Lifan Lin, Jian Huang, and Chao Wang, arXiv:2411.01833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.04216v2_tex_table7.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Estimated exponent $a$ [$95$\\% CI] for the power law convergence rate $n^{-a}$ for the difference between functionals of $\\widehat{P}_n$ and $P$ in $L_2(P)$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\textbf{Functional} & \\textbf{CTGAN (SDV)} & \\textbf{CTGAN (Synthcity)} & \\textbf{TVAE (SDV)} & \\textbf{TVAE (Synthcity)} \\\\\n \\midrule\n \n $E[A|X=I]$ & 0.24 [0.12; 0.36] & 0.16 [0.02; 0.30] & 0.46 [0.08; 0.84] & 0.30 [0.25; 0.35] \\\\\n $E[A|X=II]$ & 0.19 [-0.01; 0.38] & 0.22 [0.08; 0.36] & 0.64 [0.25; 1.04] & 0.32 [0.24; 0.40] \\\\\n $E[A|X=III]$ & 0.18 [-0.03; 0.40] & 0.22 [0.08; 0.36] & 0.51 [0.09; 0.94] & 0.27 [0.15; 0.39] \\\\\n $E[A|X=IV]$ & 0.19 [0.01; 0.38] & 0.23 [0.08; 0.38] & 0.44 [0.07; 0.82] & 0.21 [0.02; 0.40] \\\\\n $E[Y|X=I]$ & 0.37 [0.08; 0.66] & 0.21 [0.06; 0.35] & 0.31 [0.16; 0.46] & 0.27 [0.15; 0.39] \\\\\n $E[Y|X=II]$ & 0.30 [0.08; 0.53] & 0.14 [0.01; 0.27] & 0.49 [0.03; 0.95] & 0.24 [0.10; 0.38] \\\\\n $E[Y|X=III]$ & 0.27 [0.04; 0.51] & 0.17 [0.03; 0.32] & 0.31 [-0.01; 0.63] & 0.18 [0.07; 0.30] \\\\\n $E[Y|X=IV]$ & 0.26 [0.02; 0.49] & 0.21 [0.10; 0.33] & 0.17 [0.02; 0.33] & 0.19 [0.04; 0.34] \\\\\n $\\theta(\\widehat{P}_n)$ & 0.33 [0.04; 0.61] & 0.35 [0.28; 0.43] & 0.37 [0.12; 0.62] & 0.29 [0.07; 0.51] \\\\\n \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Debiasing Synthetic Data Generated by Deep Generative Models", "authors": ["Alexander Decruyenaere", "Heidelinde Dehaene", "Paloma Rabaey", "Christiaan Polet", "Johan Decruyenaere", "Thomas Demeester", "Stijn Vansteelandt"], "url": "https://arxiv.org/abs/2411.04216v2", "attribution": "\"Debiasing Synthetic Data Generated by Deep Generative Models\" by Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet, Johan Decruyenaere, Thomas Demeester, and Stijn Vansteelandt, arXiv:2411.04216v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.09056v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\hline\n\\textbf{Phase} & & \\textbf{Scene names} \\\\\n\\hline\nTraining & \\hspace{1cm} & Beechwood\\_0\\_int, Beechwood\\_1\\_int, \\\\ \n & \\hspace{1cm} & Benevolence\\_0\\_int, Benevolence\\_1\\_int, Benevolence\\_2\\_int\\\\\n & \\hspace{1cm} & Merom\\_0\\_int, Merom\\_1\\_int, \\\\\n & \\hspace{1cm} & Pomaria\\_0\\_int, Pomaria\\_1\\_int, Pomaria\\_2\\_int,\\\\\n & \\hspace{1cm} & Wainscott\\_0\\_int, Wainscott\\_1\\_int \\\\\nTesting & \\hspace{1cm} & Ihlen\\_0\\_int, Ihlen\\_1\\_int, Rs\\_int\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Train-test scenes splits for iGibsion 1.0 dataset. }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "ReCoRe: Regularized Contrastive Representation Learning of World Model", "authors": ["Rudra P. K. Poudel", "Harit Pandya", "Stephan Liwicki", "Roberto Cipolla"], "url": "https://arxiv.org/abs/2312.09056v2", "attribution": "\"ReCoRe: Regularized Contrastive Representation Learning of World Model\" by Rudra P. K. Poudel, Harit Pandya, Stephan Liwicki, and Roberto Cipolla, arXiv:2312.09056v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10227v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n\\hline\nHeading level & Example & Font size and style\\\\\n\\hline\nTitle (centered) & {\\Large\\bfseries Lecture Notes} & 14 point, bold\\\\\n1st-level heading & {\\large\\bfseries 1 Introduction} & 12 point, bold\\\\\n2nd-level heading & {\\bfseries 2.1 Printing Area} & 10 point, bold\\\\\n3rd-level heading & {\\bfseries Run-in Heading in Bold.} Text follows & 10 point, bold\\\\\n4th-level heading & {\\itshape Lowest Level Heading.} Text follows & 10 point, italic\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Cold Start Active Learning Strategies in the Context of Imbalanced Classification", "authors": ["Etienne Brangbour", "Pierrick Bruneau", "Thomas Tamisier", "Stéphane Marchand-Maillet"], "url": "https://arxiv.org/abs/2201.10227v1", "attribution": "\"Cold Start Active Learning Strategies in the Context of Imbalanced Classification\" by Etienne Brangbour, Pierrick Bruneau, Thomas Tamisier, and Stéphane Marchand-Maillet, arXiv:2201.10227v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2209.10070v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\hline\n Model/Metrics & Classification error & AUC \\\\ \\hline\n LR & $6.5\\%$ & $77.9\\%$ \\\\ \\hline\n FCNN & $6.5\\%$ & $80.0\\%$ \\\\ \\hline\n NAM & $6.6\\%$ & $80.0\\%$ \\\\ \\hline\n MNAM & $6.6\\%$ & $80.0\\%$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Model performance of the GMSC dataset.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring", "authors": ["Dangxing Chen", "Weicheng Ye"], "url": "https://arxiv.org/abs/2209.10070v1", "attribution": "\"Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring\" by Dangxing Chen and Weicheng Ye, arXiv:2209.10070v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.20978v2_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average (across $\\gamma$ and $\\beta$) optimal $\\alpha$ levels minimizing Monte Carlo standard errors (loess smoothed), under different combinations of $\\nu$ and $\\xi$. \\\\}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccccc|}\n\t\t\t\t\\cline{2-8}\n\t\t\t\t& \\multicolumn{7}{c|}{$\\xi$} \\\\ \\hline\n\t\t\t\t$\\nu$ & -0.15 & -0.10 & -0.05 & 0 & 0.05 & 0.10 & 0.15 \\\\ \\hline\n\t\t\t\t2.1 & 0.1150 & 0.1150 & 0.1175 & 0.1425 & 0.1650 & 0.1650 & 0.1750 \\\\\n\t\t\t\t2.5 & 0.0875 & 0.0975 & 0.0950 & 0.0950 & 0.0950 & 0.1000 & 0.1225 \\\\\n\t\t\t\t5 & 0.0775 & 0.0850 & 0.0850 & 0.0850 & 0.0900 & 0.0950 & 0.1000 \\\\\n\t\t\t\t8 & 0.0675 & 0.0675 & 0.0600 & 0.0600 & 0.0675 & 0.0700 & 0.0675 \\\\\n\t\t\t\t10 & 0.0600 & 0.0600 & 0.0550 & 0.0575 & 0.0600 & 0.0675 & 0.0650 \\\\\n\t\t\t\t15 & 0.0550 & 0.0550 & 0.0550 & 0.0550 & 0.0550 & 0.0550 & 0.0550 \\\\\n\t\t\t\t20 & 0.0550 & 0.0550 & 0.0550 & 0.0550 & 0.0550 & 0.0550 & 0.0550 \\\\\n\t\t\t\t$\\infty$ & - & - & - & 0.0500 & - & - & - \\\\ \\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Using quantile time series and historical simulation to forecast financial risk multiple steps ahead", "authors": ["Richard Gerlach", "Antonio Naimoli", "Giuseppe Storti"], "url": "https://arxiv.org/abs/2502.20978v2", "attribution": "\"Using quantile time series and historical simulation to forecast financial risk multiple steps ahead\" by Richard Gerlach, Antonio Naimoli, and Giuseppe Storti, arXiv:2502.20978v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10511v5_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\#Annotations & 0 & 1 & 2 & 3 & 4 & 5 \\\\ \\hline \n\\#videos & 101 & 141 & 203 & 342 & 805 & 18166 \\\\\nPer. (\\%) & 0.51 & 0.71 & 1.03 & 1.73 & 4.07 & 91.94\n\\end{tabular}\n\\end{adjustbox}\n\\caption{For our Kinetics-GEBD Val set, \\#annotations received per video vs. \\#videos and its percentage .}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Generic Event Boundary Detection: A Benchmark for Event Segmentation", "authors": ["Mike Zheng Shou", "Stan Weixian Lei", "Weiyao Wang", "Deepti Ghadiyaram", "Matt Feiszli"], "url": "https://arxiv.org/abs/2101.10511v5", "attribution": "\"Generic Event Boundary Detection: A Benchmark for Event Segmentation\" by Mike Zheng Shou, Stan Weixian Lei, Weiyao Wang, Deepti Ghadiyaram, and Matt Feiszli, arXiv:2101.10511v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.17490v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Interest Rate Discrepancy by Model and Prompt Type}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n \\hline\n & Mean Interest Difference &\\hspace{1em} Bias &\\hspace{1em} Bias \\\\\n Model &(Basis Points) &\\hspace{1em} Frequency &\\hspace{1em} Proportion\\\\\n\\hline \n \\multicolumn{4}{c}{Simple Prompt} \\\\\n\\hline\n \\texttt{Llama 3.1 (8b)} & 59 & 70 & 0.36\\\\\n \\texttt{Llama 3.1 (70b)} & 13 & 31 & 0.16\\\\ \n \\texttt{Mistral v0.3} & 20 & 100& 0.51\\\\ \n \\texttt{Command R} & -97 & 176& 0.89\\\\ \n \\texttt{Command R+} & 10 & 107& 0.54\\\\ \n \\hline\n \\multicolumn{4}{c}{Expanded Prompt}\\\\\n \\hline\n \\texttt{Llama 3.1 (8b)} & 5 & 43 & 0.22\\\\\n \\texttt{Llama 3.1 (70b)} & 32 & 34 & 0.17\\\\ \n \\texttt{Mistral v0.3} & 14 & 46 & 0.23\\\\ \n \\texttt{Command R} & 23 & 70 & 0.35\\\\\n \\texttt{Command R+} & 16 & 82 & 0.42\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Social Group Bias in AI Finance", "authors": ["Thomas R. Cook", "Sophia Kazinnik"], "url": "https://arxiv.org/abs/2506.17490v1", "attribution": "\"Social Group Bias in AI Finance\" by Thomas R. Cook and Sophia Kazinnik, arXiv:2506.17490v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.19056v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Vaccine Performance Information}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccc}\n\\hline\\hline\\\\\nVaccine & \n\\begin{minipage}[c]{3cm}\n\\centering\nPlatform\n\\end{minipage} &\n\\begin{minipage}[c]{4cm}\n\\centering\nDevelopment \\\\ Countries\n\\end{minipage} & \n\\begin{minipage}[c]{4cm}\n\\centering\nRecommended \\\\ Doses\n\\end{minipage}& \n\\begin{minipage}[c]{4cm}\n\\centering\nCurrent \\\\ Phase\n\\end{minipage}\\\\ \\hline\nAstraZeneca \n\t& virus vector \t&UK/Sweden\t& 2; Day 0 $+$ 28 & 4\\\\\nJohnson \\& Johnson \n\t& virus vector\t&Netherland/Belgium/US & 1 & 4\\\\\nModerna \n\t& mRNA\t\t\t&US \t\t&2; Day 0 $+$ 28 & 4\\\\\nPfizer \n\t& mRNA\t\t\t&US/Germany &2; Day 0 $+$ 21 &4\\\\\nSinovac \n\t& inactive virus&China \t\t&2; Day 0 $+$ 14 & 4\\\\\n\\hline\\hline\\\\\nVaccine & \n\\begin{minipage}[c]{3cm}\n\\centering\nEfficacy\n\\end{minipage} &\n\\begin{minipage}[c]{4cm}\n\\centering\nHospitalization \\\\ Prevention Rate\n\\end{minipage} & \n\\begin{minipage}[c]{4cm}\n\\centering\nAdverse \\\\ Event Rates\n\\end{minipage}& \n\\begin{minipage}[c]{4cm}\n\\centering\nSevere Adverse\\\\ Event Rates\n\\end{minipage}\\\\ \\hline\nAstraZeneca \n\t& 70.4\\%\t& 100\\%\t\n\t& \\begin{minipage}[c]{4cm}\\centering Vaccinated: 27.03\\%\\\\ Placebo: 16.33\\% \\end{minipage}\n\t& \\begin{minipage}[c]{4cm}\\centering Vaccinated: 0.7\\%\\\\ Placebo: 0.8\\% \\end{minipage}\\\\\nJohnson \\& Johnson \n\t& 66.9\\%\t& 93.1\\%\t\n\t& \\begin{minipage}[c]{4cm}\\centering Vaccinated: 68.1\\%\\\\ Placebo: 29.4\\% \\end{minipage}\n\t& \\begin{minipage}[c]{4cm}\\centering Vaccinated: 0.1\\%\\\\ Placebo: 0.1\\% \\end{minipage}\\\\\nModerna \n\t& 94.1\\%\t& 100\\%\t\n\t& \\begin{minipage}[c]{4cm}\\centering Vaccinated: 79.4\\%\\\\ Placebo: 36.5\\% \\end{minipage}\n\t& \\begin{minipage}[c]{4cm}\\centering Vaccinated: 1.5\\%\\\\ Placebo: 1.3\\% \\end{minipage}\\\\\nPfizer \n\t& 95.0\\%\t& 88.9\\%\t\n\t& \\begin{minipage}[c]{4cm}\\centering Vaccinated: 26.7\\%\\\\ Placebo: 12.2\\% \\end{minipage}\n\t& \\begin{minipage}[c]{4cm}\\centering Vaccinated: 1.1\\%\\\\ Placebo: 0.6\\% \\end{minipage}\\\\\nSinovac \n\t& 83.5\\%\t& 100\\%\t\n\t& \\begin{minipage}[c]{4cm}\\centering Vaccinated: 18.9\\%\\\\ Placebo: 16.9\\% \\end{minipage}\n\t& \\begin{minipage}[c]{4cm}\\centering Vaccinated: 0.3\\%\\\\ Placebo: 0.2\\% \\end{minipage}\\\\\n\\hline\\hline \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Self-selection of Information and Belief Update: An Experiment on COVID-19 Vaccine Information Acquisition", "authors": ["ChienHsun Lin", "Hans H. Tung"], "url": "https://arxiv.org/abs/2506.19056v1", "attribution": "\"Self-selection of Information and Belief Update: An Experiment on COVID-19 Vaccine Information Acquisition\" by ChienHsun Lin and Hans H. Tung, arXiv:2506.19056v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.00290v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistics of MusicTM-Dataset applied in our experiments}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccl}\n Modality &Feature Extractor &Dimension &Number\\\\\n \\hline\n Audio & Vggish &(20, 128) &14,454\\\\\n Lyrics & Skip-gram &(20, 20) &14,454\\\\\n Sheet music & Lilypond\\&ASMCMR & (20, 100, 32) &14,454\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MusicTM-Dataset for Joint Representation Learning among Sheet Music, Lyrics, and Musical Audio", "authors": ["Donghuo Zeng", "Yi Yu", "Keizo Oyama"], "url": "https://arxiv.org/abs/2012.00290v2", "attribution": "\"MusicTM-Dataset for Joint Representation Learning among Sheet Music, Lyrics, and Musical Audio\" by Donghuo Zeng, Yi Yu, and Keizo Oyama, arXiv:2012.00290v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.10419v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{PASTA hyperparameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\nLearning rate & 1e-5 \\\\ \\hline\nTraining steps & 1e4 \\\\ \\hline\nBatch size & 128 \\\\ \\hline\nOptimizer & Adafactor (weight decay = 1e-2) \\\\ \\hline\nGradient norm clipping & 1 \\\\ \\hline\nUpdate target network phase & 256 \\\\ \\hline\nExpectile parameter $\\alpha$ & 0.7 \\\\ \\hline\n$\\ell_q$ & 651 \\\\ \\hline\n$\\ell_v$ & 651 \\\\ \\hline\n$L$ & 4 \\\\ \\hline\n$M$ & 4 \\\\ \\hline\n$L_C$ & 25 \\\\ \\hline\nNumber of categories & 5 \\\\ \\hline\n$H$ & 5 \\\\ \\hline\n$N_w^{max}$ & 62 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Preference Adaptive and Sequential Text-to-Image Generation", "authors": ["Ofir Nabati", "Guy Tennenholtz", "ChihWei Hsu", "Moonkyung Ryu", "Deepak Ramachandran", "Yinlam Chow", "Xiang Li", "Craig Boutilier"], "url": "https://arxiv.org/abs/2412.10419v2", "attribution": "\"Preference Adaptive and Sequential Text-to-Image Generation\" by Ofir Nabati, Guy Tennenholtz, ChihWei Hsu, Moonkyung Ryu, Deepak Ramachandran, Yinlam Chow, Xiang Li, and Craig Boutilier, arXiv:2412.10419v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.18663v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n \\hline\n $n$ & Ideal & AI \\\\\n & Length n(n-1)/2 & Length \\\\\n \\hline\n 22 & 231 & 231 \\\\\n \\hline\n 23 & 253 & 253 \\\\\n \\hline\n 24 & 276 & 276 \\\\\n \\hline\n 25 & 300 & 300 \\\\\n \\hline\n 26 & 325 & 325 \\\\\n \\hline\n 27 & 351 & 351 \\\\\n \\hline\n 28 & 378 & 378 \\\\\n \\hline\n 29 & 406 & 408 \\\\\n \\hline\n 30 & 435 & 437 \\\\\n \\hline\n 31 & 465 & 478 \\\\\n \\hline\n 32 & 496 & 728 \\\\\n \\hline\n 33 & 528 & 642 \\\\\n \\hline \n \\end{tabular}\n\\end{adjustbox}\n\\caption{n - permutation length. Comparison of GAP vs AI methods. }\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs", "authors": ["A. Chervov", "A. Soibelman", "S. Lytkin", "I. Kiselev", "S. Fironov", "A. Lukyanenko", "A. Dolgorukova", "A. Ogurtsov", "F. Petrov", "S. Krymskii", "M. Evseev", "L. Grunvald", "D. Gorodkov", "G. Antiufeev", "G. Verbii", "V. Zamkovoy", "L. Cheldieva", "I. Koltsov", "A. Sychev", "M. Obozov", "A. Eliseev", "S. Nikolenko", "N. Narynbaev", "R. Turtayev", "N. Rokotyan", "S. Kovalev", "A. Rozanov", "V. Nelin", "S. Ermilov", "L. Shishina", "D. Mamayeva", "A. Korolkova", "K. Khoruzhii", "A. Romanov"], "url": "https://arxiv.org/abs/2502.18663v1", "attribution": "\"CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs\" by A. Chervov, A. Soibelman, S. Lytkin, I. Kiselev, S. Fironov, A. Lukyanenko, A. Dolgorukova, A. Ogurtsov, F. Petrov, S. Krymskii, M. Evseev, L. Grunvald, D. Gorodkov, G. Antiufeev, G. Verbii, V. Zamkovoy, L. Cheldieva, I. Koltsov, A. Sychev, M. Obozov, A. Eliseev, S. Nikolenko, N. Narynbaev, R. Turtayev, N. Rokotyan, S. Kovalev, A. Rozanov, V. Nelin, S. Ermilov, L. Shishina, D. Mamayeva, A. Korolkova, K. Khoruzhii, and A. Romanov, arXiv:2502.18663v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.07558v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation on VAE design (DNS Challenge blind test set “No Reverb” subset, 48 kHz input audio).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Configuration} & \\textbf{SIG} & \\textbf{BAK} & \\textbf{OVRL} \\\\\n\\midrule\nFull (dim 256, 50 Hz, $\\mathcal{L}_{\\text{VAE}}$, frozen) & 3.72 & 4.20 & 3.48 \\\\\n– joint fine-tuning (SE only) & \\textbf{3.80} & \\textbf{4.25} & \\textbf{3.55} \\\\\n– dim 128 & 3.59 & 4.05 & 3.32 \\\\\n– w/o $\\mathcal{L}_{\\text{adv}}$ & 3.64 & 3.97 & 3.37 \\\\\n– w/o $\\mathcal{L}_{\\text{spec}}$ & 3.58 & 3.73 & 3.28 \\\\\n– 100 Hz (downsampling ×480) & 3.68 & 4.08 & 3.38 \\\\\n– 25 Hz (downsampling ×1920) & 3.51 & 3.83 & 3.19 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling", "authors": ["Ziqian Wang", "Zikai Liu", "Yike Zhu", "Xingchen Li", "Boyi Kang", "Jixun Yao", "Xianjun Xia", "Chuanzeng Huang", "Lei Xie"], "url": "https://arxiv.org/abs/2508.07558v1", "attribution": "\"UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling\" by Ziqian Wang, Zikai Liu, Yike Zhu, Xingchen Li, Boyi Kang, Jixun Yao, Xianjun Xia, Chuanzeng Huang, and Lei Xie, arXiv:2508.07558v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/1911.11927v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\toprule\n\\textbf{Set} & \\textbf{Correlation} & \\textbf{Feature} \\\\ \\midrule\nA & 0.1696 & line spectral pair coefficient \\\\\nE & -0.219 & sadness score from $s_{18}$ \\\\\nL & 0.105 & log($\\frac{\\text{speaker-\\textit{negative}-proportion}}{\\text{speaker-\\textit{positive}-proportion}}$) \\\\\nT & 0.173 & std-dev($\\Delta$ no. of words per turn) \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Feature most correlated with suicidal risk, for every feature set. All correlations are statistically significant $(p<0.05)$}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Automatic prediction of suicidal risk in military couples using multimodal interaction cues from couples conversations", "authors": ["Sandeep Nallan Chakravarthula", "Md Nasir", "Shao-Yen Tseng", "Haoqi Li", "Tae Jin Park", "Brian Baucom", "Craig J. Bryan", "Shrikanth Narayanan", "Panayiotis Georgiou"], "url": "https://arxiv.org/abs/1911.11927v1", "attribution": "\"Automatic prediction of suicidal risk in military couples using multimodal interaction cues from couples conversations\" by Sandeep Nallan Chakravarthula, Md Nasir, Shao-Yen Tseng, Haoqi Li, Tae Jin Park, Brian Baucom, Craig J. Bryan, Shrikanth Narayanan, and Panayiotis Georgiou, arXiv:1911.11927v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.15527v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Accuracy ($\\%$) for the 3-class problem}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l|l|l}\n\\hline\nModel & Telegram & Alpha & OTC & Alpha+ & OTC+ \\\\\n\\hline\nHaar & 83.17$\\pm$0.32 & 84.65$\\pm$0.79 & 84.77$\\pm$0.35 & 85.05$\\pm$0.50 & 85.28$\\pm$0.44 \\\\\nMagNet & \\textbf{83.28}$\\pm$0.43 & \\textbf{84.76}$\\pm$0.62 & \\textbf{85.11}$\\pm$0.54 & \\textbf{85.40}$\\pm$0.77 & \\textbf{85.53}$\\pm$0.63 \\\\\nSigMaNet & 82.72$\\pm$0.47 & 84.44$\\pm$0.46 & 83.90$\\pm$0.53 & 85.12$\\pm$0.41 & 85.08$\\pm$0.66 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Haar-Laplacian for directed graphs", "authors": ["Theodor-Adrian Badea", "Bogdan Dumitrescu"], "url": "https://arxiv.org/abs/2411.15527v1", "attribution": "\"Haar-Laplacian for directed graphs\" by Theodor-Adrian Badea and Bogdan Dumitrescu, arXiv:2411.15527v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.15576v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c} \\hline\n\t\tMistaken Prompts & Avg. Dice$\\uparrow$ (CT)& Avg. Dice$\\uparrow$ (MR)\\\\ \\hline\n No & \\textbf{87.14} & \\textbf{85.33} \\\\ \n Yes & 86.37 & 85.10\\\\ \n \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{During testing, CT prompts were mistakenly applied to MRI images, and MR prompts to CT images. The \\textbf{bolded} font indicates the best performance on AMOS test sets with the UNet backbone.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training", "authors": ["Chengyin Li", "Hui Zhu", "Rafi Ibn Sultan", "Hassan Bagher Ebadian", "Prashant Khanduri", "Chetty Indrin", "Kundan Thind", "Dongxiao Zhu"], "url": "https://arxiv.org/abs/2411.15576v1", "attribution": "\"MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training\" by Chengyin Li, Hui Zhu, Rafi Ibn Sultan, Hassan Bagher Ebadian, Prashant Khanduri, Chetty Indrin, Kundan Thind, and Dongxiao Zhu, arXiv:2411.15576v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.09760v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n \\toprule\n & Min & Max & \\# \n \\\\\n \\bottomrule\n \\toprule\n Value Dates & $23^{\\mathrm{rd}}$ of April 2020 & $31^{\\mathrm{st}}$ of August 2020 & 91 \\\\\n Strikes & 2.5 \\$ & 245 \\$ & 314 \\\\\n Expiries & JUN20 & DEC22 & 9 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{\\small{Value dates, grid of strikes and option expiries in the dataset. We report the number of value dates, strikes and expiries in the dataset.}}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "The Additive Bachelier model with an application to the oil option market in the Covid period", "authors": ["Roberto Baviera", "Michele Domenico Massaria"], "url": "https://arxiv.org/abs/2506.09760v1", "attribution": "\"The Additive Bachelier model with an application to the oil option market in the Covid period\" by Roberto Baviera and Michele Domenico Massaria, arXiv:2506.09760v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.16534v2_tex_table46.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\toprule\nRatio & \\textbf{0.0(default)} & \\textbf{0.02} & \\textbf{0.05} & \\textbf{0.10} & \\textbf{0.2} \\\\ \\midrule\nClassification & \\underline{0.7725} & \\underline{0.7725} & 0.7715 & 0.7667 & 0.763 \\\\\nRegression & \\underline{0.6605} & 0.6571 & 0.6484 & 0.6383 & 0.601 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "DOFEN: Deep Oblivious Forest ENsemble", "authors": ["Kuan-Yu Chen", "Ping-Han Chiang", "Hsin-Rung Chou", "Chih-Sheng Chen", "Tien-Hao Chang"], "url": "https://arxiv.org/abs/2412.16534v2", "attribution": "\"DOFEN: Deep Oblivious Forest ENsemble\" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.05556v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Material properties ($[\\lambda, G]=\\rm{GPa}$, $[e]=\\rm{C/m^2}$, $[\\kappa]=\\rm{nC/Vm}$, $[f]=\\rm{\\mu C/m}$)}\n\\begin{tabular}{ccccccccccc} \n \\toprule\n $\\lambda$ & $G$& $e_{31}$& $e_{33}$ & $e_{15}$& $k_{11}$ & $k_{33}$ & $f_{1}$ & $f_{2}$\\\\\n \\midrule\n $179$ & $54$ & $-2.7$ & $3.65$& $21.3$ & $12.5$ & $14.4$ & $1$& $1$\\\\\n \\bottomrule \n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Second-order computational homogenization of flexoelectric composites", "authors": ["Xiaoying Zhuang", "Bin Li", "S. S. Nanthakumar", "Thomas Bohlke"], "url": "https://arxiv.org/abs/2312.05556v1", "attribution": "\"Second-order computational homogenization of flexoelectric composites\" by Xiaoying Zhuang, Bin Li, S. S. Nanthakumar, and Thomas Bohlke, arXiv:2312.05556v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2505.23025v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Country Name Mappings}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n \\textbf{Original Name} & \\textbf{Standardized Name} \\\\\n \\midrule\n Türkiye & Turkey \\\\\n Hong Kong SAR & Hong Kong \\\\\n Hong Kong Special Administrative Region & Hong Kong \\\\\n Democratic Republic of the Congo (DRC) & Democratic Republic of the Congo \\\\\n Democratic Republic of the Congo & Democratic Republic of the Congo \\\\\n Republic of Korea & South Korea \\\\\n Korea & South Korea \\\\\n Islamic Republic of Iran & Iran \\\\\n Republic of Yemen & Yemen \\\\\n Syrian Arab Republic & Syria \\\\\n Lao P.D.R. & Laos \\\\\n Lao People’s Democratic Republic & Laos \\\\\n People’s Republic of China & China \\\\\n People’s Republic of China—Hong Kong SAR & Hong Kong \\\\\n Swaziland & Eswatini \\\\\n Kingdom of Eswatini & Eswatini \\\\\n Cape Verde & Cabo Verde \\\\\n República Bolivariana de Venezuela & Venezuela \\\\\n República Bolivariana De Venezuela & Venezuela \\\\\n Russian Federation & Russia \\\\\n Papua New-Guinea & Papua New Guinea \\\\\n Federated States of Micronesia & Micronesia \\\\\n Serbia and Montenegro & Serbia \\\\\n Republic of Serbia & Serbia \\\\\n Republic of Montenegro & Montenegro \\\\\n Islamic Republic of Afghanistan & Afghanistan \\\\\n Islamic State of Afghanistan & Afghanistan \\\\\n Czech Republic & Czechia \\\\\n Former Yugoslav Republic of Macedonia & North Macedonia \\\\\n Republic of North Macedonia & North Macedonia \\\\\n Republic of Congo (Congo) & Republic of Congo \\\\\n Democratic Republic of Timor-Leste & Timor-Leste \\\\\n Republic of Azerbaijan & Azerbaijan \\\\\n Republic of Fiji & Fiji \\\\\n Socialist People’s Libyan Arab Jamahiriya & Libya \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Learning to Regulate: A New Event-Level Dataset of Capital Control Measures", "authors": ["Geyue Sun", "Xiao Liu", "Tomas Williams", "Roberto Samaniego"], "url": "https://arxiv.org/abs/2505.23025v1", "attribution": "\"Learning to Regulate: A New Event-Level Dataset of Capital Control Measures\" by Geyue Sun, Xiao Liu, Tomas Williams, and Roberto Samaniego, arXiv:2505.23025v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.10585v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\\\Kappa scores for interrater reliability}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc} % Remove padding on the left side of the first column\n\\toprule\n\\toprule\nComparison & Kappa Score \\\\\n\\midrule\nHuman Rater 1 $\\&$ Human Rater 2 & 0.892 \\\\\nHuman Rater 1 $\\&$ FairFace & 0.948 \\\\\nHuman Rater 2 $\\&$ FairFace & 0.888 \\\\\nHuman Raters $\\&$ FairFace & 0.948 \\\\\n\\bottomrule\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Racial bias, colorism, and overcorrection", "authors": ["Kenneth Colombe", "Alex Krumer", "Rosa Lavelle-Hill", "Tim Pawlowski"], "url": "https://arxiv.org/abs/2508.10585v1", "attribution": "\"Racial bias, colorism, and overcorrection\" by Kenneth Colombe, Alex Krumer, Rosa Lavelle-Hill, and Tim Pawlowski, arXiv:2508.10585v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.05068v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Errors in the reconstructed distribution of kinetic parameters in the ocular pharmacokinetic model. Here, ReLU refers to using the ReLU activation function for forward propagation and ResNet refers to using the ResNet technique (described in Fig.~).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\toprule width & \\# of layers & forward propagation & initialization for weights \\& biases & error \\\\ \n\\midrule \n 10 & 1 & ReLU & $\\mathcal{N}(0, 0.03^2)$& $8.73\\times10^{-3}$\n \\\\ \n 10 & 2 & ReLU & $\\mathcal{N}(0, 0.03^2)$& $1.46\\times10^{-3}$\n \\\\ \n10 & 3 & ReLU & $\\mathcal{N}(0, 0.03^2)$& $1.13\\times10^{-3}$ \\\\ \n10 & 4 & ReLU & $\\mathcal{N}(0, 0.03^2)$& $1.27\\times10^{-3}$ \\\\ \n5 & 3 & ReLU & $\\mathcal{N}(0, 0.03^2)$& $1.05\\times10^{-3}$ \\\\ \n15 & 3 & ReLU & $\\mathcal{N}(0, 0.03^2)$& $8.50\\times10^{-4}$ \\\\ \n 20 & 3 & ReLU & $\\mathcal{N}(0, 0.03^2)$ & $1.15\\times10^{-3}$ \\\\\n 10 & 2 & ResNet & $\\mathcal{N}(0, 0.03^2)$& $7.68\\times10^{-3}$ \\\\ \n10 & 3 & ResNet & $\\mathcal{N}(0, 0.03^2)$& $9.23\\times10^{-4}$ \\\\ \n10 & 4 & ResNet & $\\mathcal{N}(0, 0.03^2)$& $9.49\\times10^{-4}$ \\\\ \n 10 & 3 & ReLU & $\\mathcal{N}(0, 0)$& 0.609\n \\\\ \n 10 & 3 & ReLU & $\\mathcal{N}(0, 0.01^2)$& $7.89\\times10^{-4}$\n \\\\ \n 10 & 3 & ReLU & $\\mathcal{N}(0, 0.02^2)$& $7.54\\times10^{-4}$\n \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems", "authors": ["Mingtao Xia", "Qijing Shen", "Philip Maini", "Eamonn Gaffney", "Alex Mogilner"], "url": "https://arxiv.org/abs/2503.05068v1", "attribution": "\"A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems\" by Mingtao Xia, Qijing Shen, Philip Maini, Eamonn Gaffney, and Alex Mogilner, arXiv:2503.05068v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.16073v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n\t\t\t\n\t\t\t\\hline\\hline \n\t\t\t\\textbf{Previous Methods} & \\textbf{Accuracy\\%} \\\\\\hline\\hline\n\t\t\tF. Ofli et al.~ & 97.81 \\\\\\hline\n\t\t\tAlireza Shafaei et al.~ & 98.1 \\\\\\hline\n\t\t\tEarnest Paul Ijjina et al.~ & 98.38 \\\\\\hline\n\t\t\tChen Chen et al.~ & 99.54 \\\\\\hline\n\t\t\tZ.ahmad et al.~ & 99.8 \\\\\\hline\n\t\t\t \\textbf{Proposed MGAF (SFI + SI)} & \\textbf{99.85} \\\\\n\t\t\t\\hline\\hline\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of Accuracies of proposed MGAF network with previous methods on Berkeley MHAD dataset using depth and inertial modalities.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "CNN based Multistage Gated Average Fusion (MGAF) for Human Action Recognition Using Depth and Inertial Sensors", "authors": ["Zeeshan Ahmad", "Naimul khan"], "url": "https://arxiv.org/abs/2010.16073v1", "attribution": "\"CNN based Multistage Gated Average Fusion (MGAF) for Human Action Recognition Using Depth and Inertial Sensors\" by Zeeshan Ahmad and Naimul khan, arXiv:2010.16073v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.02021v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Estimates of the NB model for IUD, after excluding non-significant covariates}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll}\n\\hline\nIUD-NB & Estimate({ $\\hat{\\boldsymbol\\beta}_2$}) & StdDev & $p$-value \\\\\n\\hline\nIntercept & -2.7306 & 0.4330 & 0.0000\\\\\nAGE=2 & 2.4043 & 0.3859 & 0.0000 \\\\\nAGE=3 & 2.7176 & 0.6282 & 0.0000 \\\\\nAFS=2 & -0.7880 & 0.4222 & 0.0620 \\\\\nAFS=3 & -1.2700 & 0.4450 & 0.0043 \\\\\nDispersion & 0.3055 & & \\\\\n\\hline\n\\multicolumn{2}{ l }{AIC = 588.18} & \\multicolumn{2}{ l }{-2log-Lik.= 576.18} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modeling Count Data via Copulas", "authors": ["Hadi Safari-Katesari", "S. Yaser Samadi", "Samira Zaroudi"], "url": "https://arxiv.org/abs/2012.02021v1", "attribution": "\"Modeling Count Data via Copulas\" by Hadi Safari-Katesari, S. Yaser Samadi, and Samira Zaroudi, arXiv:2012.02021v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.09056v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\\hline\n\\textbf{Material category} & \\hspace{1.5cm} & \\textbf{Held-out texture ids for test}\\\\\n\\hline\nasphalt && 06, 15 \\\\\nbricks && 08, 19 \\\\\nconcrete && 06, 15, 17 \\\\\nfabric && 01, 02, 28 \\\\\nfabric\\_carpet && 02, 05, 13 \\\\\nground && 13, 19 \\\\\nleather && 03, 12 \\\\\nmarble && 02, 03 \\\\\nmetal && 10, 19 \\\\\nmetal\\_diamond\\_plate && 04 \\\\\nmoss && 01, 03 \\\\\npaint && 05 \\\\\npaving\\_stones && 24, 38 \\\\\nplanks && 07, 09, 16 \\\\\nplaster && 03 \\\\\nplastic && 04, 05 \\\\\nporcelain && 02, 04 \\\\\nrocks && 04 \\\\\nterrazzo && 06, 08 \\\\\ntiles && 43, 49 \\\\\nwood && 02, 05, 16, 22, 32 \\\\\nwood\\_floor && 06, 10, 17, 28 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{iGibson 1.0 environment held out texture ids for test.}% All remaining textures ids are used during training phase.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "ReCoRe: Regularized Contrastive Representation Learning of World Model", "authors": ["Rudra P. K. Poudel", "Harit Pandya", "Stephan Liwicki", "Roberto Cipolla"], "url": "https://arxiv.org/abs/2312.09056v2", "attribution": "\"ReCoRe: Regularized Contrastive Representation Learning of World Model\" by Rudra P. K. Poudel, Harit Pandya, Stephan Liwicki, and Roberto Cipolla, arXiv:2312.09056v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.16951v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Water Quality Prediction Results (Numerical Features: Standardized; Categorical Features: One-Hot Encoding).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n \\toprule\n Models & RMSE & MAPE & WMAPE & WUPRED & WOPRED \\\\ %\\tnote{1}\n \\midrule\nSADL-II (Original Paper) & 11.50 & N/A & N/A & N/A & N/A \\\\\nBenchmarking & 29.52 & 32.21 & 32.35 & 14.55 & 17.80 \\\\\nLightGBM & \\textcolor{red}{11.04} & \\textcolor{red}{9.22} & \\textcolor{red}{9.36} & \\textcolor{red}{5.15} & 4.21 \\\\\nLinear Regression & 11.91 & 10.79 & 10.96 & 6.14 & 4.82 \\\\\nMLP & 14.42 & 14.54 & 14.61 & 9.29 & 5.32 \\\\\nRandom Forest & 11.71 & 9.71 & 9.87 & 5.26 & 4.61 \\\\\nXGBoost & 11.14 & 9.29 & 9.44 & 5.28 & \\textcolor{red}{4.16} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Beyond Tides and Time: Machine Learning Triumph in Water Quality", "authors": ["Yinpu Li", "Siqi Mao", "Yaping Yuan", "Ziren Wang", "Yixin Kang", "Yuanxin Yao"], "url": "https://arxiv.org/abs/2309.16951v2", "attribution": "\"Beyond Tides and Time: Machine Learning Triumph in Water Quality\" by Yinpu Li, Siqi Mao, Yaping Yuan, Ziren Wang, Yixin Kang, and Yuanxin Yao, arXiv:2309.16951v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.15444v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Unbiased weighing matrices of order $15$ and weight $9$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c||c|c||c|c||c|c}\n\\noalign{\\hrule height1pt}\n$W$ & $N_m(W)$ & $W$ & $N_m(W)$ & $W$ & $N_m(W)$& $W$ & $N_m(W)$\\\\\n\\hline\n$W_{ 15, 1}$& 2 & $W_{ 15, 13}$& 3 & $W_{ 15, 21}$& 2 & $W_{ 15, 30}$& 3 \\\\\n$W_{ 15, 3}$& 2 & $W_{ 15, 14}$& 3 & $W_{ 15, 22}$& 2 & $W_{ 15, 31}$& 2 \\\\\n$W_{ 15, 5}$& 2 & $W_{ 15, 15}$& 4 & $W_{ 15, 23}$& 3 & $W_{ 15, 32}$& 2 \\\\\n$W_{ 15, 7}$& 3 & $W_{ 15, 16}$& 2 & $W_{ 15, 25}$& 3 & $W_{ 15, 33}$& 2 \\\\\n$W_{ 15, 8}$& 2 & $W_{ 15, 17}$& 2 & $W_{ 15, 26}$& 7 & $W_{ 15, 34}$& 2 \\\\\n$W_{ 15, 10}$& 2 & $W_{ 15, 18}$& 4 & $W_{ 15, 27}$& 2 & $W_{ 15, 35}$& 2 \\\\\n$W_{ 15, 11}$& 3 & $W_{ 15, 19}$& 2 & $W_{ 15, 28}$& 2 & $W_{ 15, 36}$& 2 \\\\\n$W_{ 15, 12}$& 7 & $W_{ 15, 20}$& 3 & $W_{ 15, 29}$& 3 & $W_{ 15, 37}$& 3 \\\\\n\\noalign{\\hrule height1pt}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Unbiased weighing matrices of weight $9$", "authors": ["Makoto Araya", "Masaaki Harada", "Hadi Kharaghani", "Sho Suda", "Wei-Hsuan Yu"], "url": "https://arxiv.org/abs/2501.15444v2", "attribution": "\"Unbiased weighing matrices of weight $9$\" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2010.16073v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\t\n\t\t\t\\hline\\hline \n\t\t\t\\textbf{Datasets} & \\textbf{conv1} & \\textbf{conv2} & \\textbf{conv3} \\\\\\hline\\hline \n\t\t\tUTD-MHAD & 0.00032 & 0.0026 & 0.033 \\\\\\hline\n\t\t\tBerkeley MHAD & 0.0037 & 0.012 & 0.015 \\\\\\hline\n\t\t\tKinect V2 & 0.0003 & 0.0016 & 0.0089\\\\\\hline\\hline\t\t\t\t\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Normalized cross correlation coefficients between features extracted from all three convolutional layers of CNN.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "CNN based Multistage Gated Average Fusion (MGAF) for Human Action Recognition Using Depth and Inertial Sensors", "authors": ["Zeeshan Ahmad", "Naimul khan"], "url": "https://arxiv.org/abs/2010.16073v1", "attribution": "\"CNN based Multistage Gated Average Fusion (MGAF) for Human Action Recognition Using Depth and Inertial Sensors\" by Zeeshan Ahmad and Naimul khan, arXiv:2010.16073v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.10824v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline \n$n_{1}$ & $n_{2}$ & Subadditivity\\tabularnewline\n\\hline \n\\hline \n$0$ & $0$ & $log_{2}1\\leq log_{2}1+log_{2}1$\\tabularnewline\n\\hline \n$0$ & $1$ & $log_{2}2\\leq log_{2}1+log_{2}2$\\tabularnewline\n\\hline \n$1$ & $1$ & $log_{2}3\\leq log_{2}2+log_{2}2$\\tabularnewline\n\\hline \n$1$ & $2$ & $log_{2}4\\leq log_{2}2+log_{2}3$\\tabularnewline\n\\hline \n$2$ & $2$ & $log_{2}5\\leq log_{2}3+log_{2}3$\\tabularnewline\n\\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Information Content and Entropy of Finite Patterns from a Combinatorial Perspective", "authors": ["Zsolt Pocze"], "url": "https://arxiv.org/abs/2501.10824v1", "attribution": "\"Information Content and Entropy of Finite Patterns from a Combinatorial Perspective\" by Zsolt Pocze, arXiv:2501.10824v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.11969v2_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results of simulation for $Y_2$ (medium non-linearity) with $n_0=n_1=1000$ based on $500$ replicates}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llrrrrrr}\n \\hline\n Measure & Method & ATT & \\multicolumn{5}{c}{QTT} \\\\ \n & & & 0.10 & 0.25 & 0.50 & 0.75 & 0.90 \\\\ \n \\hline\n \\multicolumn{8}{c}{Design 1 (strong separation, normal errors)} \\\\\n \\hline\n Bias & EB & -0.0027 & -0.0047 & 0.0003 & 0.0097 & -0.0072 & -0.0075 \\\\ \n & KEB & 0.0053 & 0.0122 & 0.0039 & 0.0096 & 0.0013 & -0.0014 \\\\ \n & DEB MQ & -0.0016 & 0.0001 & -0.0016 & 0.0099 & -0.0062 & -0.0088 \\\\ \n & DEB MD & -0.0014 & -0.0048 & -0.0029 & 0.0101 & -0.0008 & -0.0062 \\\\ \n Variance & EB & 0.0049 & 0.0480 & 0.0302 & 0.0213 & 0.0309 & 0.0713 \\\\ \n & KEB & 0.0064 & 0.0377 & 0.0245 & 0.0233 & 0.0283 & 0.0637 \\\\ \n & DEB MQ & 0.0049 & 0.0411 & 0.0235 & 0.0200 & 0.0266 & 0.0581 \\\\ \n & DEB MD & 0.0055 & 0.0384 & 0.0239 & 0.0210 & 0.0264 & 0.0563 \\\\ \n RMSE & EB & 0.0700 & 0.2192 & 0.1739 & 0.1462 & 0.1759 & 0.2672 \\\\ \n & KEB & 0.0803 & 0.1946 & 0.1564 & 0.1528 & 0.1683 & 0.2523 \\\\ \n & DEB MQ & 0.0702 & 0.2026 & 0.1534 & 0.1416 & 0.1632 & 0.2412 \\\\ \n & DEB MD & 0.0740 & 0.1959 & 0.1546 & 0.1453 & 0.1626 & 0.2373 \\\\ \n \\hline\n \\multicolumn{8}{c}{Design 2 (Weak separation, normal errors)} \\\\\n \\hline\n Bias & EB & -0.0028 & 0.0047 & 0.0034 & -0.0039 & 0.0009 & 0.0008 \\\\ \n & KEB & -0.0027 & 0.0098 & -0.0000 & -0.0066 & -0.0034 & -0.0031 \\\\ \n & DEB MQ & -0.0028 & 0.0055 & 0.0014 & -0.0053 & 0.0016 & -0.0025 \\\\ \n & DEB MD & -0.0028 & 0.0009 & -0.0004 & -0.0011 & 0.0044 & -0.0004 \\\\ \n Variance & EB & 0.0045 & 0.0559 & 0.0314 & 0.0194 & 0.0269 & 0.0598 \\\\ \n & KEB & 0.0053 & 0.0448 & 0.0268 & 0.0187 & 0.0227 & 0.0511 \\\\ \n & DEB MQ & 0.0045 & 0.0468 & 0.0263 & 0.0173 & 0.0209 & 0.0532 \\\\ \n & DEB MD & 0.0045 & 0.0442 & 0.0254 & 0.0169 & 0.0220 & 0.0472 \\\\ \n RMSE & EB & 0.0673 & 0.2364 & 0.1773 & 0.1393 & 0.1641 & 0.2445 \\\\ \n & KEB & 0.0726 & 0.2119 & 0.1637 & 0.1370 & 0.1505 & 0.2261 \\\\ \n & DEB MQ & 0.0670 & 0.2164 & 0.1621 & 0.1317 & 0.1445 & 0.2306 \\\\ \n & DEB MD & 0.0668 & 0.2101 & 0.1595 & 0.1300 & 0.1484 & 0.2171 \\\\ \n \\hline\n \\multicolumn{8}{c}{Design 3 (Medium separation, leptokurtic errors)} \\\\\n \\hline\n Bias & EB & 0.0388 & -0.3582 & -0.2149 & -0.0179 & 0.2204 & 0.5335 \\\\ \n & KEB & 0.1511 & 0.0808 & 0.1121 & 0.0830 & 0.0193 & 0.2120 \\\\\n & DEB MQ & 0.0504 & -0.2129 & -0.0955 & -0.0033 & 0.1351 & 0.4001 \\\\ \n & DEB MD & 0.0697 & -0.1018 & -0.0345 & 0.0093 & 0.1056 & 0.3457 \\\\ \n Var & EB & 0.0088 & 0.0553 & 0.0267 & 0.0256 & 0.0505 & 0.1157 \\\\ \n & KEB & 0.0178 & 0.0436 & 0.0257 & 0.0353 & 0.1080 & 0.2385 \\\\ \n & DEB MQ & 0.0095 & 0.0465 & 0.0242 & 0.0269 & 0.0524 & 0.1252 \\\\ \n & DEB MD & 0.0150 & 0.0514 & 0.0361 & 0.0387 & 0.0712 & 0.1394 \\\\ \n RMSE & EB & 0.1014 & 0.4285 & 0.2700 & 0.1611 & 0.3148 & 0.6327 \\\\ \n & KEB & 0.2016 & 0.2239 & 0.1956 & 0.2053 & 0.3292 & 0.5324 \\\\ \n & DEB MQ & 0.1099 & 0.3030 & 0.1825 & 0.1642 & 0.2658 & 0.5341 \\\\ \n & DEB MD & 0.1410 & 0.2485 & 0.1931 & 0.1970 & 0.2870 & 0.5088 \\\\ \n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Survey calibration for causal inference: a simple method to balance covariate distributions", "authors": ["Maciej Beręsewicz"], "url": "https://arxiv.org/abs/2310.11969v2", "attribution": "\"Survey calibration for causal inference: a simple method to balance covariate distributions\" by Maciej Beręsewicz, arXiv:2310.11969v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.01231v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{clccc} \\hline\n & & LSTM & NAS & UGRNN \\\\ \\hline\n\\multirow{ 3}{*}{CMM} & \nCONTROL & 2.14 $\\pm$ 0.60 & 1.90 $\\pm$ 0.49 & 2.11 $\\pm$ 0.50 \\\\\n&INTERVAL & 2.99 $\\pm$ 1.08 & 2.44 $\\pm$ 1.00 & 2.42 $\\pm$ 0.74 \\\\\n&DB12 & 2.49 $\\pm$ 0.59 & 4.28 $\\pm$ 2.71 & \\textbf{1.66 $\\pm$ 0.40} \\\\\n\\hline\n\\multirow{ 3}{*}{LM} & \nCONTROL & \\textbf{1.47 $\\pm$ 0.46} & 1.77 $\\pm$ 0.65 & 1.51 $\\pm$ 0.46 \\\\\n&INTERVAL & 2.37 $\\pm$ 1.56 & 1.59 $\\pm$ 0.75 & 1.66 $\\pm$ 0.84 \\\\\n&DB12 & 1.57 $\\pm$ 0.64 & 2.56 $\\pm$ 1.11 & 2.40 $\\pm$ 0.65 \\\\\n\\hline\n\\multirow{ 3}{*}{CENTR} & \nCONTROL & 0.20 $\\pm$ 0.08 & 0.22 $\\pm$ 0.09 & 0.21 $\\pm$ 0.09 \\\\\n&INTERVAL & 0.16 $\\pm$ 0.08 & 0.16 $\\pm$ 0.09 & 0.17 $\\pm$ 0.12 \\\\\n&DB12 & 0.16 $\\pm$ 0.08 & 0.25 $\\pm$ 0.26 & \\textbf{0.23 $\\pm$ 0.09} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Means and standard deviation for the 100 songs generated by the final models with the 3 metrics.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music", "authors": ["Sebastian Garcia-Valencia", "Alejandro Betancourt", "Juan G. Lalinde-Pulido"], "url": "https://arxiv.org/abs/2012.01231v1", "attribution": "\"Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music\" by Sebastian Garcia-Valencia, Alejandro Betancourt, and Juan G. Lalinde-Pulido, arXiv:2012.01231v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.05182v1_tex_table23.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Nodes and links in different kinds of transportation networks. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lll}\n\t\\hline\n\tNetwork type & Nodes & Links \\\\\n\t\\hline\n\tRoadway & Intersections & Street segments \\\\\n\tPublic transit & Bus or train stops & Route segments \\\\\n\tFreight & Factories, warehouses, retailers & Shipping options \\\\\n\tAir & Airports & Flights \\\\\n\tMaritime & Ports & Shipping channels \\\\\n\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment", "authors": ["Stephen D. Boyles", "Nicholas E. Lownes", "Avinash Unnikrishnan"], "url": "https://arxiv.org/abs/2502.05182v1", "attribution": "\"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment\" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.11823v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lccc}\n\\toprule & $h_1$ & $h_2$ & $h_3$ \\\\ \n\\midrule IIA & 1 & 1 & 1 \\\\ \nIIB & 1 & 1 & 1.5 \\\\ \nIIC & 1.5 & 1 & 1 \\\\ \nIID & 1.5 & 1 & 1.5 \\\\ \n\\bottomrule% & & & \n\\end{tabular}\n\\caption{Holding cost parameters for different cases.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Singular Control of (Reflected) Brownian Motion: A Computational Method Suitable for Queueing Applications", "authors": ["Baris Ata", "J. Michael Harrison", "Nian Si"], "url": "https://arxiv.org/abs/2312.11823v3", "attribution": "\"Singular Control of (Reflected) Brownian Motion: A Computational Method Suitable for Queueing Applications\" by Baris Ata, J. Michael Harrison, and Nian Si, arXiv:2312.11823v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.22849v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Metrics between subspaces $\\mathcal{V}$ and $\\mathcal{U}$, with ${\\dim \\mathcal{V} = k}$ and ${\\dim \\mathcal{U} = l}$, in terms of the principal angles $\\theta_i(\\mathcal{V},\\mathcal{U})$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll} \\hline\nName\t& Metric \t\t\t\t\t\t\t\t\t\t \\\\ \\hline\nChordal\t\t\t\n& $d_\\kappa(\\mathcal{V},\\mathcal{U})\t=\\left(|k -l|+\\displaystyle\\sum_{i=1}^{\\min(k,l)} (\\sin \\theta_i)^2 \\right)^{1/2}$ \\\\\nGrassmann\t\t\n& $d_\\gamma(\\mathcal{V},\\mathcal{U})\t=\\left(|k -l| \\tfrac{\\pi^2}{4} + \\displaystyle\\sum_{i=1}^{\\min(k,l)} \\theta_i^2 \\right)^{1/2}$ \\\\\nProcrustes\t\t\n& $d_\\pi(\\mathcal{V},\\mathcal{U})\t=\\left(|k -l|+\\displaystyle2\\sum_{i=1}^{\\min(k,l)} \\left(\\sin \\tfrac{\\theta_i}{2}\\right)^2 \\right)^{1/2}$\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Distances between finite-horizon linear behaviors", "authors": ["Alberto Padoan", "Jeremy Coulson"], "url": "https://arxiv.org/abs/2503.22849v2", "attribution": "\"Distances between finite-horizon linear behaviors\" by Alberto Padoan and Jeremy Coulson, arXiv:2503.22849v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.20202v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Sample sequence CGCATGCGCGACC and the positron of the $k$-mers.}\n\\begin{tabular}{c|ccccccccccccc}\\hline\n\t\tPosition & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 \\\\ \\hline\n\t\tSequence & C & G & C & A & T & G & C & G & C & G & A & C & C\\\\ \\hline\n\t\t1-mer & C & G & C & A & T & G & C & G & C & G & A & C & C \\\\\n\t\t2-mer & CG & GC & CA & AT & TG & GC & CG & GC & CG & GA & AC & CC & - \\\\\n\t\t3-mer & CGC & GCA & CAT & ATG & TGC & GCG & CGC & GCG & CGA & GAC & ACC & - & - \\\\ \\hline\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Revealing the Shape of Genome Space via K-mer Topology", "authors": ["Yuta Hozumi", "Guo-Wei Wei"], "url": "https://arxiv.org/abs/2412.20202v1", "attribution": "\"Revealing the Shape of Genome Space via K-mer Topology\" by Yuta Hozumi and Guo-Wei Wei, arXiv:2412.20202v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.02827v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Real datasets used for the experiments,sorted by size} % title name of the table\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc} % creating 10 columns \n\\hline\nDataset &Instances& Numerical attributes & Categorical attributes & Classes \n\\\\ \n\\hline\nIris & 150&4& 0&3 \\\\\n \\\\ \nGlass & 214&9& 0&6 \\\\\n \\\\ \nIonosphere& 351&33 & 0&2 \\\\\n \\\\ \n Diabetes&520&0& 17&2\\\\\n \\\\ \n Breast-Cancer& 569&30 & 0&2 \\\\\n \\\\ \n Balance-scale & 625&4 &0 &3\\\\\n \\\\ \n Australian & 690&6 & 8&2 \\\\\n \\\\ \nPima-indians &768&8 & 0&2 \\\\\n \\\\ \n Vehicle & 846&18 & 0&4\\\\\n \\\\ \n Raisin & 900 &7& 0&2\\\\\n \\\\ \n Tic-tac-toe &958&0 & 9&2\\\\\n \\\\ \n German &1000&6 & 14&2\\\\\n \\\\ \n Biodegradation & 1051&38 & 3&2 \\\\\n \\\\ \n BHP&1075&20& 1&4\\\\\n \\\\ \n Diabetic & 1151 &16 & 3&2\\\\\n \\\\ \n Banknote &1372&4 & 0&2\\\\\n \\\\ \n Contraceptive&1473&2 & 7&3\\\\\n \\\\ \nObesity&2111&3& 13&7\\\\\n \\\\ \nSegments&2310&19& 0&7\\\\\n \\\\ \nWaveform+noise&5000&40& 0&3\\\\\n \\\\ \nPendigits&10000&16& 0&10\\\\\n \\\\ \nLetter&20000&16& 0&26\\\\\n\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "On Subagging Boosted Probit Model Trees", "authors": ["Tian Qin", "Wei-Min Huang"], "url": "https://arxiv.org/abs/2311.02827v1", "attribution": "\"On Subagging Boosted Probit Model Trees\" by Tian Qin and Wei-Min Huang, arXiv:2311.02827v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.12258v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Forecasting performance of the proposed E-STGCN model in comparison to the temporal-only and spatiotemporal forecasting techniques for 30 30-day ahead forecast horizon of NO$_{2}$ pollutant (best results are \\underline{\\textbf{highlighted}}).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|cccccc|ccccccc|c|}\n\\hline Forecast & \\multirow{3}{*}{Metric} & \\multicolumn{6}{c|}{Temporal-only Model} & \\multicolumn{7}{c|}{Spatiotemporal Model} & Proposed \\\\ \\cline{3-15}\n \\multirow{2}{*}{Period} & & \\multirow{2}{*}{ARIMA} & \\multirow{2}{*}{LSTM} & \\multirow{2}{*}{TCN} & \\multirow{2}{*}{DeepAR} & \\multirow{2}{*}{Transformers} & \\multirow{2}{*}{NBeats} & \\multirow{2}{*}{STARMA} & \\multirow{2}{*}{GSTAR} & \\multirow{2}{*}{GpGp} & \\multirow{2}{*}{{\\color{black}STGCN}} & \\multirow{2}{*}{STNN} & Modified & \\multirow{2}{*}{DeepKrigging} & \\multirow{2}{*}{E-STGCN} \\\\ \n & & & & & & & & & & & & & STGCN & & \\\\ \\hline\n \n\\multirow{6}{*}{JAN} & MAE & 13.76 & 23.48 & 38.72 & 26.73 & 16.49 & 15.71 & 17.87 & 15.76 & 16.37 & {\\color{black} 17.68} & 28.46 & \\underline{\\textbf{12.28}} & 40.02 & \\underline{\\textbf{12.28}} \\\\\n & MASE & 3.33 & 8.21 & 15.68 & 9.85 & 11.36 & 4.58 & 4.99 & \\underline{\\textbf{2.48}} & 4.48 & {\\color{black} 10.59} & 8.15 & 4.74 & 13.43 & 4.74 \\\\\n & RMSE & 17.01 & 26.50 & 41.37 & 29.63 & 19.45 & 19.34 & 21.80 & 19.37 & 19.92 & {\\color{black}20.64} & 36.38 & \\underline{\\textbf{15.01}} & 48.68 & \\underline{\\textbf{15.01}} \\\\\n & SMAPE & 33.70 & 61.60 & 160.40 & 76.20 & 39.50 & 41.50 & 44.20 & 38.70 & 39.70 & {\\color{black}40.26} & 62.50 & \\underline{\\textbf{28.30}} & 109.50 & \\underline{\\textbf{28.30}} \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t7.80\t} & {\\color{black}\t18.36\t} & {\\color{black}\t30.95\t} & {\\color{black}\t21.20\t} & {\\color{black}\t8.07\t} & {\\color{black}\t10.73\t} & {\\color{black}\t13.71\t} & {\\color{black}\t11.03\t} & {\\color{black}\t9.43\t} & {\\color{black} 9.49} & {\\color{black}\t13.02\t} & {\\color{black}\t\\underline{\\textbf{6.50}}\t} & {\\color{black}\t23.98\t} & {\\color{black}\t\\underline{\\textbf{6.50}}\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t19.37\t} & {\\color{black}\t24.59\t} & {\\color{black}\t26.04\t} & {\\color{black}\t25.26\t} & {\\color{black}\t20.56\t} & {\\color{black}\t21.88\t} & {\\color{black}\t23.64\t} & {\\color{black}\t22.00\t} & {\\color{black}\t21.54\t} & {\\color{black} 21.13} & {\\color{black}\t29.21\t} & {\\color{black}\t\\underline{\\textbf{18.30}}\t} & {\\color{black}\t32.98\t} & {\\color{black}\t\\underline{\\textbf{18.30}}\t} \\\\ \\hline\n\\multirow{6}{*}{FEB} & MAE & 14.21 & 25.80 & 39.27 & 27.09 & 16.76 & 13.64 & 20.57 & 18.42 & 16.95 & {\\color{black}17.47} & 33.94 & \\underline{\\textbf{11.92}} & 33.84 & \\underline{\\textbf{11.92}} \\\\\n & MASE & \\underline{\\textbf{2.32}} & 4.85 & 8.16 & 5.18 & 4.47 & 2.65 & 3.61 & 2.71 & 3.58 & {\\color{black}6.28} & 7.55 & 2.50 & 6.94 & 2.50 \\\\\n & RMSE & 17.06 & 28.35 & 41.70 & 29.61 & 19.36 & 16.64 & 23.51 & 21.46 & 20.09 & {\\color{black}20.08} & 47.18 & \\underline{\\textbf{14.56}} & 36.77 & \\underline{\\textbf{14.56}} \\\\\n & SMAPE & 34.60 & 70.80 & 158.20 & 76.70 & 40.20 & 37.00 & 56.20 & 47.40 & 41.40 & {\\color{black}39.56} & 67.40 & \\underline{\\textbf{28.50}} & 123.20 & \\underline{\\textbf{28.50}} \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t8.33\t} & {\\color{black}\t20.27\t} & {\\color{black}\t31.39\t} & {\\color{black}\t21.39\t} & {\\color{black}\t8.20\t} & {\\color{black}\t\\underline{\\textbf{6.95}}\t} & {\\color{black}\t15.70\t} & {\\color{black}\t13.93\t} & {\\color{black}\t10.21\t} & {\\color{black}9.69} & {\\color{black}\t14.18\t} & {\\color{black}\t7.05\t} & {\\color{black}\t26.70\t} & {\\color{black}\t7.05\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t19.39\t} & {\\color{black}\t25.05\t} & {\\color{black}\t26.07\t} & {\\color{black}\t25.30\t} & {\\color{black}\t20.27\t} & {\\color{black}\t19.15\t} & {\\color{black}\t24.85\t} & {\\color{black}\t23.57\t} & {\\color{black}\t22.18\t} & {\\color{black}20.67} & {\\color{black}\t32.54\t} & {\\color{black}\t\\underline{\\textbf{17.85}}\t} & {\\color{black}\t27.05\t} & {\\color{black}\t\\underline{\\textbf{17.85}}\t} \\\\ \\hline\n\\multirow{6}{*}{MAR} & MAE & 12.39 & 17.26 & 31.02 & 18.48 & 16.78 & 12.68 & \\underline{\\textbf{8.64}} & 9.94 & 14.91 & {\\color{black}14.81} & 21.44 & 8.96 & 30.13 & 8.96 \\\\\n & MASE & 4.65 & 6.43 & 12.07 & 6.82 & 9.83 & 4.20 & 3.16 & \\underline{\\textbf{2.63}} & 5.53 & {\\color{black}8.44} & 8.56 & 3.53 & 11.56 & 3.53 \\\\\n & RMSE & 14.36 & 19.22 & 32.85 & 20.42 & 18.54 & 15.22 & \\underline{\\textbf{10.60}} & 11.92 & 17.07 & {\\color{black}16.80} & 27.80 & 11.23 & 33.18 & 11.23 \\\\\n & SMAPE & 38.90 & 53.90 & 157.50 & 59.50 & 48.30 & 39.00 & \\underline{\\textbf{27.20}} & 31.40 & 46.50 & {\\color{black}43.66} & 58.50 & 27.40 & 144.90 & 27.40 \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t3.97\t} & {\\color{black}\t13.15\t} & {\\color{black}\t24.79\t} & {\\color{black}\t14.33\t} & {\\color{black}\t5.30\t} & {\\color{black}\t\\underline{\\textbf{3.84}}\t} & {\\color{black}\t4.81\t} & {\\color{black}\t5.22\t} & {\\color{black}\t5.26\t} & {\\color{black}6.20} & {\\color{black}\t9.37\t} & {\\color{black}\t4.46\t} & {\\color{black}\t23.50\t} & {\\color{black}\t4.46\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t15.57\t} & {\\color{black}\t18.43\t} & {\\color{black}\t21.04\t} & {\\color{black}\t18.92\t} & {\\color{black}\t17.63\t} & {\\color{black}\t15.99\t} & {\\color{black}\t\\underline{\\textbf{13.64}}\t} & {\\color{black}\t14.24\t} & {\\color{black}\t17.23\t} & {\\color{black} 16.92} & {\\color{black}\t22.17\t} & {\\color{black}\t13.92\t} & {\\color{black}\t21.17\t} & {\\color{black}\t13.92\t} \\\\ \n \\hline\n\\multirow{6}{*}{APR} & MAE & 15.70 & 22.31 & 36.47 & 23.05 & 19.74 & 16.72 & 18.32 & 18.19 & 17.41 & {\\color{black}19.48} & 25.79 & \\underline{\\textbf{13.63}} & 37.75 & \\underline{\\textbf{13.63}} \\\\\n & MASE & 3.57 & 3.15 & 6.41 & 3.24 & 5.02 & 2.78 & 2.56 & 2.94 & 4.37 & {\\color{black}4.62} & 6.08 & \\underline{\\textbf{2.28}} & 6.95 & \\underline{\\textbf{2.28}} \\\\\n & RMSE & 19.05 & 25.77 & 39.24 & 26.49 & 22.29 & 20.62 & 22.58 & 22.24 & 21.18 & {\\color{black}22.00} & 33.53 & \\underline{\\textbf{16.22}} & 40.30 & \\underline{\\textbf{16.22}} \\\\\n & SMAPE & 40.50 & 60.90 & 168.20 & 64.30 & 49.60 & 47.70 & 49.20 & 50.00 & 45.20 & {\\color{black}48.22} & 64.20 & \\underline{\\textbf{34.40}} & 196.80 & \\underline{\\textbf{34.40}} \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t8.17\t} & {\\color{black}\t17.26\t} & {\\color{black}\t29.17\t} & {\\color{black}\t17.95\t} & {\\color{black}\t8.13\t} & {\\color{black}\t12.16\t} & {\\color{black}\t14.33\t} & {\\color{black}\t13.75\t} & {\\color{black}\t9.77\t} & {\\color{black} 9.42} & {\\color{black}\t13.23\t} & {\\color{black}\t\\underline{\\textbf{7.24}}\t} & {\\color{black}\t30.20\t} & {\\color{black}\t\\underline{\\textbf{7.24}}\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t19.90\t} & {\\color{black}\t22.70\t} & {\\color{black}\t24.22\t} & {\\color{black}\t22.83\t} & {\\color{black}\t21.53\t} & {\\color{black}\t22.09\t} & {\\color{black}\t22.49\t} & {\\color{black}\t22.50\t} & {\\color{black}\t21.36\t} & {\\color{black}21.03} & {\\color{black}\t26.38\t} & {\\color{black}\t\\underline{\\textbf{18.45}}\t} & {\\color{black}\t23.74\t} & {\\color{black}\t\\underline{\\textbf{18.45}}\t} \\\\\n \\hline\n\\multirow{6}{*}{MAY} & MAE & 14.82 & 19.30 & 32.75 & 19.71 & 20.06 & 18.59 & 14.77 & 13.94 & 16.85 & {\\color{black}19.98} & 27.00 & \\underline{\\textbf{11.33}} & 23.67 & \\underline{\\textbf{11.33}} \\\\\n & MASE & 3.37 & 2.59 & 5.80 & 2.59 & 6.46 & 6.02 & \\underline{\\textbf{2.18}} & 2.62 & 4.64 & {\\color{black}7.01} & 7.54 & 2.20 & 8.26 & 2.20 \\\\\n & RMSE & 18.05 & 22.61 & 35.85 & 23.03 & 22.99 & 22.09 & 18.47 & 17.38 & 20.32 & {\\color{black}22.70} & 35.38 & \\underline{\\textbf{14.35}} & 28.05 & \\underline{\\textbf{14.35}} \\\\\n & SMAPE & 41.70 & 56.70 & 155.60 & 58.60 & 52.60 & 55.40 & 41.30 & 40.40 & 47.50 & {\\color{black}50.63} & 66.00 & \\underline{\\textbf{32.40}} & 65.40 & \\underline{\\textbf{32.40}} \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t6.41\t} & {\\color{black}\t14.75\t} & {\\color{black}\t26.16\t} & {\\color{black}\t15.20\t} & {\\color{black}\t7.26\t} & {\\color{black}\t8.62\t} & {\\color{black}\t9.99\t} & {\\color{black}\t8.76\t} & {\\color{black}\t7.63\t} & {\\color{black}8.46} & {\\color{black}\t11.37\t} & {\\color{black}\t\\underline{\\textbf{6.29}}\t} & {\\color{black}\t10.81\t} & {\\color{black}\t\\underline{\\textbf{6.29}}\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t18.55\t} & {\\color{black}\t20.24\t} & {\\color{black}\t22.93\t} & {\\color{black}\t20.43\t} & {\\color{black}\t21.12\t} & {\\color{black}\t21.10\t} & {\\color{black}\t18.83\t} & {\\color{black}\t18.30\t} & {\\color{black}\t19.78\t} & {\\color{black}20.86} & {\\color{black}\t26.20\t} & {\\color{black}\t\\underline{\\textbf{16.18}}\t} & {\\color{black}\t23.40\t} & {\\color{black}\t\\underline{\\textbf{16.18}}\t} \\\\ \\hline\n\\multirow{6}{*}{JUN} & MAE & 13.79 & 12.89 & 23.74 & 13.38 & 24.21 & 11.55 & \\underline{\\textbf{7.43}} & 10.03 & 13.84 & {\\color{black} 19.91} & 25.04 & 8.05 & 26.23 & 8.05 \\\\\n & MASE & 5.61 & 3.72 & 9.24 & 3.77 & 10.91 & 5.10 & \\underline{\\textbf{1.84}} & 4.18 & 10.88 & {\\color{black}9.26} & 14.84 & 2.71 & 10.78 & 2.71 \\\\\n & RMSE & 15.80 & 14.59 & 25.68 & 15.09 & 25.40 & 13.58 & \\underline{\\textbf{9.28}} & 11.79 & 16.15 & {\\color{black}21.21} & 33.49 & 9.88 & 27.36 & 9.88 \\\\\n & SMAPE & 49.80 & 48.50 & 144.50 & 50.70 & 70.10 & 45.60 & \\underline{\\textbf{29.30}} & 39.80 & 51.30 & {\\color{black}60.29} & 70.60 & 31.30 & 197.50 & 31.30 \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t4.03\t} & {\\color{black}\t8.73\t} & {\\color{black}\t18.81\t} & {\\color{black}\t9.48\t} & {\\color{black}\t5.76\t} & {\\color{black}\t4.49\t} & {\\color{black}\t5.39\t} & {\\color{black}\t7.27\t} & {\\color{black}\t4.47\t} & {\\color{black}6.27} & {\\color{black}\t8.70\t} & {\\color{black}\t\\underline{\\textbf{3.82}}\t} & {\\color{black}\t20.98\t} & {\\color{black}\t\\underline{\\textbf{3.82}}\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t14.75\t} & {\\color{black}\t14.52\t} & {\\color{black}\t17.40\t} & {\\color{black}\t14.66\t} & {\\color{black}\t19.25\t} & {\\color{black}\t13.74\t} & {\\color{black}\t\\underline{\\textbf{11.54}}\t} & {\\color{black}\t13.29\t} & {\\color{black}\t14.83\t} & {\\color{black}17.15} & {\\color{black}\t22.43\t} & {\\color{black}\t11.60\t} & {\\color{black}\t16.25\t} & {\\color{black}\t11.60\t} \\\\\n \\hline\n\\multirow{6}{*}{JUL} & MAE & 15.21 & 10.11 & 19.58 & 10.19 & 25.66 & 8.11 & \\underline{\\textbf{6.74}} & 7.92 & 13.51 & {\\color{black}12.63} & 23.31 & 7.56 & 14.86 & 7.56 \\\\\n & MASE & 6.35 & 3.40 & 6.25 & 3.28 & 10.64 & 2.77 & \\underline{\\textbf{2.15}} & 2.50 & 6.09 & {\\color{black}5.20} & 9.64 & 2.68 & 5.08 & 2.68 \\\\\n & RMSE & 16.93 & 11.74 & 21.19 & 11.88 & 26.97 & 10.41 & \\underline{\\textbf{8.48}} & 9.63 & 15.33 & {\\color{black}14.46} & 31.45 & 9.95 & 16.94 & 9.95 \\\\\n & SMAPE & 61.60 & 46.90 & 153.80 & 47.50 & 82.10 & 47.50 & \\underline{\\textbf{35.00}} & 40.10 & 59.30 & {\\color{black}55.29} & 74.90 & 37.20 & 80.00 & 37.20 \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t3.68\t} & {\\color{black}\t5.83\t} & {\\color{black}\t15.60\t} & {\\color{black}\t6.30\t} & {\\color{black}\t5.80\t} & {\\color{black}\t5.24\t} & {\\color{black}\t4.50\t} & {\\color{black}\t5.29\t} & {\\color{black}\t3.87\t} & {\\color{black} 5.12} & {\\color{black}\t7.51\t} & {\\color{black}\t\\underline{\\textbf{3.26}}\t} & {\\color{black}\t9.30\t} & {\\color{black}\t\\underline{\\textbf{3.26}}\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t13.84\t} & {\\color{black}\t11.39\t} & {\\color{black}\t13.65\t} & {\\color{black}\t11.53\t} & {\\color{black}\t18.77\t} & {\\color{black}\t11.15\t} & {\\color{black}\t\\underline{\\textbf{9.57}}\t} & {\\color{black}\t10.69\t} & {\\color{black}\t13.27\t} & {\\color{black}12.78} & {\\color{black}\t20.03\t} & {\\color{black}\t10.39\t} & {\\color{black}\t13.44\t} & {\\color{black}\t10.39\t} \\\\ \\hline\n\\multirow{6}{*}{AUG} & MAE & 17.45 & 9.59 & 17.26 & 9.62 & 25.21 & 8.56 & 7.89 & 8.40 & 14.07 & {\\color{black}19.32} & 19.88 & \\underline{\\textbf{6.95}} & 12.12 & \\underline{\\textbf{6.95}} \\\\\n & MASE & 7.14 & 4.18 & 6.10 & 4.22 & 10.73 & 3.11 & 2.44 & 2.76 & 7.98 & {\\color{black}8.73} & 12.51 & \\underline{\\textbf{2.13}} & 5.74 & \\underline{\\textbf{2.13}} \\\\\n & RMSE & 19.36 & 11.41 & 19.24 & 11.45 & 27.10 & 10.80 & 9.92 & 10.40 & 16.21 & {\\color{black}20.87} & 28.54 & \\underline{\\textbf{8.66}} & 13.93 & \\underline{\\textbf{8.66}} \\\\\n & SMAPE & 71.50 & 52.90 & 154.50 & 53.10 & 86.80 & 47.50 & 40.80 & 46.60 & 64.80 & {\\color{black}77.15} & 75.40 & \\underline{\\textbf{38.40}} & 74.90 & \\underline{\\textbf{38.40}} \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t4.21\t} & {\\color{black}\t5.21\t} & {\\color{black}\t13.78\t} & {\\color{black}\t5.19\t} & {\\color{black}\t5.72\t} & {\\color{black}\t4.84\t} & {\\color{black}\t4.79\t} & {\\color{black}\t5.84\t} & {\\color{black}\t3.98\t} & {\\color{black}6.22} & {\\color{black}\t6.96\t} & {\\color{black}\t\\underline{\\textbf{3.25}}\t} & {\\color{black}\t7.72\t} & {\\color{black}\t\\underline{\\textbf{3.25}}\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t14.78\t} & {\\color{black}\t10.81\t} & {\\color{black}\t12.04\t} & {\\color{black}\t10.80\t} & {\\color{black}\t18.80\t} & {\\color{black}\t10.31\t} & {\\color{black}\t9.91\t} & {\\color{black}\t10.59\t} & {\\color{black}\t13.13\t} & {\\color{black}15.14} & {\\color{black}\t17.39\t} & {\\color{black}\t\\underline{\\textbf{9.04}}\t} & {\\color{black}\t11.65\t} & {\\color{black}\t\\underline{\\textbf{9.04}}\t} \\\\ \\hline\n\\multirow{6}{*}{SEP} & MAE & 13.27 & 10.27 & 20.46 & 10.32 & 20.50 & 9.90 & 8.50 & 10.48 & 12.52 & {\\color{black}19.13} & 15.40 & \\underline{\\textbf{6.46}} & 22.24 & \\underline{\\textbf{6.46}} \\\\\n & MASE & 4.74 & 3.50 & 5.65 & 3.49 & 7.77 & 3.24 & 2.30 & 2.62 & 6.35 & {\\color{black}7.36} & 6.97 & \\underline{\\textbf{1.87}} & 6.37 & \\underline{\\textbf{1.87}} \\\\\n & RMSE & 14.91 & 11.93 & 22.27 & 11.98 & 21.82 & 11.92 & 10.62 & 12.61 & 14.62 & {\\color{black}20.57} & 19.91 & \\underline{\\textbf{8.32}} & 23.50 & \\underline{\\textbf{8.32}} \\\\\n & SMAPE & 53.50 & 48.50 & 148.30 & 48.80 & 69.10 & 49.60 & 41.00 & 52.20 & 53.30 & {\\color{black}65.75} & 66.50 & \\underline{\\textbf{32.10}} & 195.10 & \\underline{\\textbf{32.10}} \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t3.35\t} & {\\color{black}\t6.40\t} & {\\color{black}\t16.29\t} & {\\color{black}\t6.56\t} & {\\color{black}\t4.72\t} & {\\color{black}\t5.92\t} & {\\color{black}\t6.04\t} & {\\color{black}\t7.93\t} & {\\color{black}\t4.20\t} & {\\color{black}5.64} & {\\color{black}\t7.80\t} & {\\color{black}\t\\underline{\\textbf{2.87}}\t} & {\\color{black}\t17.78\t} & {\\color{black}\t\\underline{\\textbf{2.87}}\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t13.11\t} & {\\color{black}\t12.42\t} & {\\color{black}\t15.94\t} & {\\color{black}\t12.47\t} & {\\color{black}\t16.65\t} & {\\color{black}\t12.64\t} & {\\color{black}\t11.63\t} & {\\color{black}\t12.87\t} & {\\color{black}\t13.47\t} & {\\color{black}16.16} & {\\color{black}\t15.71\t} & {\\color{black}\t\\underline{\\textbf{9.69}}\t} & {\\color{black}\t13.97\t} & {\\color{black}\t\\underline{\\textbf{9.69}}\t} \\\\ \\hline\n\\multirow{6}{*}{OCT} & MAE & 13.92 & 19.79 & 34.59 & 20.17 & 18.54 & 16.77 & 16.12 & 16.93 & 15.79 & {\\color{black}18.41} & 24.02 & \\underline{\\textbf{12.09}} & 32.98 & \\underline{\\textbf{12.09}} \\\\\n & MASE & 2.55 & 3.05 & 6.12 & 3.09 & 3.81 & 2.77 & 2.48 & 2.61 & 3.46 & {\\color{black}3.64} & 4.22 & \\underline{\\textbf{1.91}} & 6.01 & \\underline{\\textbf{1.91}} \\\\\n & RMSE & 19.20 & 24.99 & 38.91 & 25.41 & 23.56 & 22.57 & 22.37 & 23.21 & 21.30 & {\\color{black}23.34} & 30.25 & \\underline{\\textbf{17.30}} & 37.23 & \\underline{\\textbf{17.30}} \\\\\n & SMAPE & 35.40 & 55.90 & 165.90 & 57.50 & 46.20 & 48.50 & 44.40 & 47.60 & 41.90 & {\\color{black}45.57} & 79.90 & \\underline{\\textbf{30.30}} & 136.30 & \\underline{\\textbf{30.30}} \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t7.32\t} & {\\color{black}\t15.22\t} & {\\color{black}\t27.65\t} & {\\color{black}\t15.61\t} & {\\color{black}\t8.05\t} & {\\color{black}\t12.37\t} & {\\color{black}\t12.44\t} & {\\color{black}\t12.73\t} & {\\color{black}\t8.85\t} & {\\color{black}9.45} & {\\color{black}\t17.04\t} & {\\color{black}\t\\underline{\\textbf{7.23}}\t} & {\\color{black}\t26.09\t} & {\\color{black}\t\\underline{\\textbf{7.23}}\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t18.13\t} & {\\color{black}\t21.68\t} & {\\color{black}\t23.23\t} & {\\color{black}\t21.77\t} & {\\color{black}\t20.34\t} & {\\color{black}\t21.46\t} & {\\color{black}\t21.09\t} & {\\color{black}\t21.47\t} & {\\color{black}\t19.78\t} & {\\color{black}20.32} & {\\color{black}\t23.39\t} & {\\color{black}\t\\underline{\\textbf{17.00}}\t} & {\\color{black}\t22.84\t} & {\\color{black}\t\\underline{\\textbf{17.00}}\t} \\\\ \\hline\n\\multirow{6}{*}{NOV} & MAE & 19.56 & 31.39 & 42.55 & 31.40 & 25.12 & 22.51 & 18.01 & 20.86 & 20.54 & {\\color{black}26.53} & 34.00 & \\underline{\\textbf{16.62}} & 46.43 & \\underline{\\textbf{16.62}} \\\\\n & MASE & 3.11 & 4.04 & 5.61 & 4.03 & 4.83 & 3.39 & 3.02 & 3.42 & 4.35 & {\\color{black}5.75} & 5.18 & \\underline{\\textbf{2.19}} & 5.95 & \\underline{\\textbf{2.19}} \\\\\n & RMSE & 24.01 & 35.90 & 47.07 & 35.92 & 29.31 & 27.62 & 22.48 & 25.60 & 24.75 & {\\color{black}30.55} & 42.28 & \\underline{\\textbf{21.10}} & 49.98 & \\underline{\\textbf{21.10}} \\\\\n & SMAPE & 43.80 & 77.40 & 140.20 & 77.40 & 51.40 & 54.70 & 42.20 & 46.30 & 47.60 & {\\color{black}51.18} & 84.10 & \\underline{\\textbf{36.20}} & 190.20 & \\underline{\\textbf{36.20}} \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t11.74\t} & {\\color{black}\t24.45\t} & {\\color{black}\t33.62\t} & {\\color{black}\t24.45\t} & {\\color{black}\t14.70\t} & {\\color{black}\t14.09\t} & {\\color{black}\t11.40\t} & {\\color{black}\t13.20\t} & {\\color{black}\t10.93\t} & {\\color{black}14.60} & {\\color{black}\t23.12\t} & {\\color{black}\t\\underline{\\textbf{10.31}}\t} & {\\color{black}\t37.08\t} & {\\color{black}\t\\underline{\\textbf{10.31}}\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t24.64\t} & {\\color{black}\t29.61\t} & {\\color{black}\t31.59\t} & {\\color{black}\t29.59\t} & {\\color{black}\t27.63\t} & {\\color{black}\t28.09\t} & {\\color{black}\t23.75\t} & {\\color{black}\t26.04\t} & {\\color{black}\t25.12\t} & {\\color{black}28.31} & {\\color{black}\t31.36\t} & {\\color{black}\t\\underline{\\textbf{22.93}}\t} & {\\color{black}\t29.92\t} & {\\color{black}\t\\underline{\\textbf{22.93}}\t} \\\\\n \\hline\n\\multirow{6}{*}{DEC} & MAE & \\underline{\\textbf{17.57}} & 30.04 & 41.09 & 30.01 & 23.40 & 20.50 & 18.74 & 24.55 & 18.18 & {\\color{black}22.21} & 31.54 & 21.44 & 47.41 & 21.44 \\\\\n & MASE & 2.62 & 4.24 & 6.53 & 4.24 & 3.30 & 3.91 & \\underline{\\textbf{2.56}} & 3.42 & 2.99 & {\\color{black}3.41} & 5.07 & 3.30 & 8.38 & 3.30 \\\\\n & RMSE & \\underline{\\textbf{21.30}} & 33.03 & 45.11 & 33.00 & 26.49 & 24.97 & 22.83 & 28.63 & 22.00 & {\\color{black}25.41} & 38.10 & 24.77 & 51.95 & 24.77 \\\\\n & SMAPE & \\underline{\\textbf{37.40}} & 74.20 & 134.40 & 74.10 & 50.80 & 48.60 & 40.50 & 59.90 & 39.00 & {\\color{black}48.59} & 76.00 & 47.90 & 160.60 & 47.90 \\\\ \n & {\\color{black}\tPinball Loss\t} & {\\color{black}\t11.59\t} & {\\color{black}\t23.72\t} & {\\color{black}\t32.33\t} & {\\color{black}\t23.68\t} & {\\color{black}\t16.55\t} & {\\color{black}\t\\underline{\\textbf{9.18}}\t} & {\\color{black}\t14.08\t} & {\\color{black}\t19.20\t} & {\\color{black}\t11.25\t} & {\\color{black}15.04} & {\\color{black}\t21.30\t} & {\\color{black}\t14.46\t} & {\\color{black}\t37.07\t} & {\\color{black}\t14.46\t} \\\\\n & {\\color{black}\tCRPS\t} & {\\color{black}\t\\underline{\\textbf{23.45}}\t} & {\\color{black}\t28.37\t} & {\\color{black}\t32.10\t} & {\\color{black}\t28.38\t} & {\\color{black}\t26.41\t} & {\\color{black}\t24.65\t} & {\\color{black}\t25.10\t} & {\\color{black}\t27.51\t} & {\\color{black}\t24.04\t} & {\\color{black}26.07} & {\\color{black}\t30.23\t} & {\\color{black}\t26.35\t} & {\\color{black}\t30.54\t} & {\\color{black}\t26.35\t} \\\\ \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "E-STGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting", "authors": ["Madhurima Panja", "Tanujit Chakraborty", "Anubhab Biswas", "Soudeep Deb"], "url": "https://arxiv.org/abs/2411.12258v2", "attribution": "\"E-STGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting\" by Madhurima Panja, Tanujit Chakraborty, Anubhab Biswas, and Soudeep Deb, arXiv:2411.12258v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.08831v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|}\nNeural Network & TP & FP & TN & FN & Accuracy & AUC & Confidence Interval (95\\%) \\\\ \\hline\nResNet18 & 90.2\\% & 0.3\\% & 8.4\\% & 1\\% & 98.6\\% & 0.99908 & 84.69 – 91.82 \\\\\nResNet50 & 90.9\\% & 0.7\\% & 8.0\\% & 0.3\\% & 99.0\\% & 0.98751& 86.53 – 93.12 \\\\\nDenseNet201 & 89.9\\% & 0.7\\% & 8.0\\% & 1.5\\% & 97.9\\% & 0.98958 & 86.98 – 93.69\\\\\nEnsemble NN & 91.3\\% & 0.3\\% & 8.4\\% & 0\\% & 99.7\\% & 0.99954 & 87.75 – 94.22 \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Confusion Matrix Values of Each Neural Network (k = 1-5)}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Fused Deep Convolutional Neural Network for Precision Diagnosis of COVID-19 Using Chest X-Ray Images", "authors": ["Hussin K. Ragb", "Ian T. Dover", "Redha Ali"], "url": "https://arxiv.org/abs/2009.08831v1", "attribution": "\"Fused Deep Convolutional Neural Network for Precision Diagnosis of COVID-19 Using Chest X-Ray Images\" by Hussin K. Ragb, Ian T. Dover, and Redha Ali, arXiv:2009.08831v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.11308v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The results of experiments on the \\texttt{NOAA} dataset}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccccc}\\toprule\n && \\multicolumn{6}{c}{\\textbf{\\#batch}}\\\\\\cmidrule(lr){3-8}\n && \\multicolumn{2}{c}{\\textbf{10}} & \\multicolumn{2}{c}{\\textbf{20}} & \\multicolumn{2}{c}{\\textbf{30}} \\\\\\cmidrule(lr){3-8}\n \\textbf{Method} & \\textbf{Model} & \\textbf{accuracy} & \\textbf{\\#drifts} & \\textbf{accuracy} & \\textbf{\\#drifts} & \\textbf{accuracy} & \\textbf{\\#drifts} \\\\\\midrule\n HDDM-A & LR & 0.7736 & 5 & 0.7696 & 7 & 0.7777 & 3 \\\\\n & DT & 0.7293 & 8 & 0.7435 & 9 & 0.7440 & 8 \\\\\n & RF & 0.7803 & 1 & 0.7783 & 4 & 0.7766 & 3 \\\\\n \\midrule\n HDDM-W & LR & 0.7736 & 7 & 0.7712 & 12 & 0.7799 & 12 \\\\\n & DT & 0.7385 & 10 & 0.7345 & 16 & 0.7385 & 23 \\\\\n & RF & 0.7868 & 4 & 0.7835 & 10 & 0.7821 & 9 \\\\\n \\midrule\n KSWIN & LR & 0.7755 & 10 & 0.7755 & 20 & 0.7788 & 27 \\\\\n & DT & 0.7385 & 10 & 0.7334 & 20 & 0.7437 & 29 \\\\\n & RF & 0.7899 & 10 & 0.7913 & 19 & 0.7940 & 28 \\\\\n \\midrule\n PH & LR & 0.7707 & 3 & 0.7680 & 3 & 0.7792 & 3 \\\\\n & DT & 0.7402 & 2 & 0.7387 & 2 & 0.7276 & 2 \\\\\n & RF & 0.7836 & 2 & 0.7791 & 3 & 0.7772 & 3 \\\\\n \\midrule\n DDM & LR & 0.7716 & 4 & 0.7746 & 10 & 0.7715 & 10 \\\\\n & DT & 0.7422 & 3 & 0.7334 & 20 & 0.7398 & 21 \\\\\n & RF & 0.7803 & 1 & 0.7905 & 12 & 0.7832 & 11 \\\\\n \\midrule\n EDDM & LR & 0.7755 & 10 & 0.7755 & 20 & 0.7780 & 30 \\\\\n & DT & 0.7385 & 10 & 0.7334 & 20 & 0.7406 & 30 \\\\\n & RF & 0.7899 & 10 & 0.7926 & 20 & 0.7951 & 30 \\\\\n \\midrule\n PDD & LR & 0.7649 & 0 & 0.7694 & 2 & 0.7708 & 0 \\\\\n & DT & 0.7264 & 2 & 0.7403 & 0 & 0.7352 & 3 \\\\\n & RF & 0.7880 & 7 & 0.7676 & 0 & 0.7886 & 16 \\\\\\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "datadriftR: An R Package for Concept Drift Detection in Predictive Models", "authors": ["Ugur Dar", "Mustafa Cavus"], "url": "https://arxiv.org/abs/2412.11308v1", "attribution": "\"datadriftR: An R Package for Concept Drift Detection in Predictive Models\" by Ugur Dar and Mustafa Cavus, arXiv:2412.11308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.14019v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rccccc}\n\\toprule\n\\textbf{$p$} & \\textbf{Mean} & \\textbf{Median} & \\textbf{Lower Quartile} & \\textbf{Upper Quartile} \\\\ \\midrule\n$1$ & 57.19 & 57.33 & 53.00 & 61.33 \\\\\n$2$ & 55.25 & 55.33 & 52.00 & 58.67 \\\\\n$5$ & 56.40 & 57.33 & 52.00 & 60.00 \\\\\n$10$ & 56.53 & 56.67 & 53.33 & 60.00 \\\\\n$15$ & 56.35 & 56.00 & 53.33 & 60.00 \\\\\n$20$ & 56.39 & 56.00 & 53.33 & 60.00 \\\\\n$\\infty$ & 56.39 & 56.67 & 53.33 & 60.00 \\\\\n$1$ and $\\infty$ & \\textbf{59.32} & \\textbf{60.00} & \\textbf{56.00} & \\textbf{64.00} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean, median, and the first and third quartile of the accuracies over the $100$ experiments for different values of $p$ in our \\textbf{point cloud classification} problem. The last column represents accuracies from combining the persistence images for $1$-Rips and $\\infty$-Rips.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Monoidal Rips: Stable Multiparameter Filtrations of Directed Networks", "authors": ["Nello Blaser", "Morten Brun", "Odin Hoff Gardaa", "Lars M. Salbu"], "url": "https://arxiv.org/abs/2503.14019v1", "attribution": "\"Monoidal Rips: Stable Multiparameter Filtrations of Directed Networks\" by Nello Blaser, Morten Brun, Odin Hoff Gardaa, and Lars M. Salbu, arXiv:2503.14019v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.11256v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Forecasting Accuracy under Different Bits for Quantization}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|}\n\\hline\n\\multicolumn{2}{|c|}{}\t\t& \\multicolumn{2}{c|}{Wind Speed}\t\t\t& \\multicolumn{2}{c|}{Wind Direction} \\\\\n\\hline\n$\\omega_{w}$ & $\\omega_{f}$ & MAE & RMSE & MAE & RMSE \\\\\n\\hline\nfloat32 \t& float32 & $0.0403$ & $ 0.0573 $ \t & $0.0596$ & $ 0.1151 $ \\\\\n\\hline\n16 & 16\t& $0.0396$ & $ 0.0554 $ & $0.0609 $ & $ 0.1192$ \\\\\n\\hline\n8 & 8\t& $0.0392$ & $ 0.0579 $ & $ 0.0608 $ & $ 0.1191 $ \\\\\n\\hline\n4 & 4\t& $0.0401$ & $ 0.0571 $ & $0.0598 $ & $ 0.1183 $ \\\\\n\\hline\n2 & 2\t& $0.0431$ & $ 0.0614 $ & $ 0.0807 $ & $ 0.1308 $ \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Edge Intelligence Empowered UAVs for Automated Wind Farm Monitoring in Smart Grids", "authors": ["Hwei-Ming Chung", "Sabita Maharjan", "Yan Zhang", "Frank Eliassen", "Tingting Yuan"], "url": "https://arxiv.org/abs/2009.11256v1", "attribution": "\"Edge Intelligence Empowered UAVs for Automated Wind Farm Monitoring in Smart Grids\" by Hwei-Ming Chung, Sabita Maharjan, Yan Zhang, Frank Eliassen, and Tingting Yuan, arXiv:2009.11256v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04610v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n\\hline\n\\textbf{Feat.} & \\textbf{M1-JV} & \\textbf{M1-V4} & \\textbf{M2-V4} \\\\\n\\hline\nPCE & 3.21 ± 0.26 & 3.34 ± 0.22 & 2.66 ± 0.66 \\\\\n\\hline\nPR & 46.95 ± 9.16 & 53.07 ± 11.23 & 33.24 ± 12.54 \\\\\n\\hline\nP & 5.11 ± 1.06 & 3.03 ± 2.17 & 3.71 ± 1.65 \\\\\n\\hline\nNoP & 29.49 ± 9.43 & 38.41 ± 10.55 & 13.56 ± 7.37 \\\\\n\\hline\nPS & 0.80 ± 0.07 & 0.77 ± 0.09 & 0.86 ± 0.10 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of musical features between Model 1 trained on the JAZZVAR dataset (M1-JV) and the VAR4000 dataset (M1-V4), and Model 2 trained on the VAR4000 dataset (M2-V4) with mean and standard deviation.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Exploring Transformer-Based Music Overpainting for Jazz Piano Variations", "authors": ["Eleanor Row", "Ivan Shanin", "György Fazekas"], "url": "https://arxiv.org/abs/2412.04610v1", "attribution": "\"Exploring Transformer-Based Music Overpainting for Jazz Piano Variations\" by Eleanor Row, Ivan Shanin, and György Fazekas, arXiv:2412.04610v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11985v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Inverse problem CPU time comparison for the analytical benchmark case.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n \\hline\n & Alifanov's reg. & \\multicolumn{2}{c|}{Parameterized heat flux} \\\\\n \\hline\n & & offline & online \\\\\n \\hline\n CPU time & $18.8~s$ & $7.21~s$ & $0.0056~s$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation", "authors": ["Umberto Emil Morelli", "Patricia Barral", "Peregrina Quintela", "Gianluigi Rozza", "Giovanni Stabile"], "url": "https://arxiv.org/abs/2101.11985v1", "attribution": "\"A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation\" by Umberto Emil Morelli, Patricia Barral, Peregrina Quintela, Gianluigi Rozza, and Giovanni Stabile, arXiv:2101.11985v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2211.14292v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{makecell}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccc|cc}\n\\Xhline{2\\arrayrulewidth} \\hline\n\\multirow{2}{*}{} & \\multicolumn{3}{c}{Fed-EF-SGD} & \\multicolumn{2}{c}{No EF} \\\\ \\hline\n & Sign & TopK & Hv-Sign & Stoc & Full-precision \\\\ \\hline\nMNIST & 90.87 ($\\pm$0.84) & 91.04 ($\\pm$1.05) & 91.18 ($\\pm$1.10) & 90.16 ($\\pm$0.96) & 90.85 ($\\pm$0.89) \\\\\nFMNIST & 71.13 ($\\pm$0.68) & 71.16 ($\\pm$0.77) & 71.07 ($\\pm$0.83) & 71.26 ($\\pm$0.87) & 71.20 ($\\pm$0.71) \\\\\n\\hline \\Xhline{2\\arrayrulewidth}\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Test accuracy (\\%) with client participation rate $p=0.5$, of Fed-EF-SGD with \\textbf{Sign}, \\textbf{TopK} and \\textbf{heavy-Sign} compressor and \\textbf{Stoc} (stochastic quantization) without EF. The compression parameters (i.e., $k$ and $b$) of the compressors are consistent with Figure~.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression", "authors": ["Xiaoyun Li", "Ping Li"], "url": "https://arxiv.org/abs/2211.14292v1", "attribution": "\"Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression\" by Xiaoyun Li and Ping Li, arXiv:2211.14292v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.05014v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\footnotesize Calibration of the HW and rHW model: parameters determined in swaption calibration. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c||c|c}\n&\\multicolumn{2}{c||}{$\\text{Hull-White}$}&\\multicolumn{2}{c}{$\\text{RAnD Hull-White}$}\\\\\\hline\n$T$, expiry &$\\eta$&$\\lambda$&$\\eta$&$\\lambda$\\\\\\hline\\hline\n$1y$&0.0094&0.0090&0.0091&$\\lambda\\sim\\mathcal{N}(0.1,0.45^2)$\\\\\n$2y$&0.0082 &0.0035 & 0.0080&$\\lambda\\sim\\mathcal{N}(0.1,0.33^2)$\\\\\n$5y$&0.0069&0.0020&0.0079&$\\lambda\\sim\\mathcal{N}(0.1,0.16^2)$\\\\\n$8y$&0.0067&0.0095&0.0080&$\\lambda\\sim\\mathcal{N}(0.1,0.12^2)$\\\\\n$10y$&0.0067&0.0090&0.0082&$\\lambda\\sim\\mathcal{N}(0.1,0.11^2)$\\\\\n$15y$&0.0064&0.0080&0.0085&$\\lambda\\sim\\mathcal{N}(0.1,0.09^2)$\\\\\n$20y$&0.0060&0.0080&0.0086&$\\lambda\\sim\\mathcal{N}(0.1,0.08^2)$\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Randomization of Short-Rate Models, Analytic Pricing and Flexibility in Controlling Implied Volatilities", "authors": ["Lech A. Grzelak"], "url": "https://arxiv.org/abs/2211.05014v2", "attribution": "\"Randomization of Short-Rate Models, Analytic Pricing and Flexibility in Controlling Implied Volatilities\" by Lech A. Grzelak, arXiv:2211.05014v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.12588v5_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Frequency of Special Characters}\n\\begin{tabular}{ccl}\n \\toprule\n Non-English or Math&Frequency&Comments\\\\\n \\midrule\n \\O & 1 in 1,000& For Swedish names\\\\\n $\\pi$ & 1 in 5& Common in math\\\\\n \\$ & 4 in 5 & Used in business\\\\\n $\\Psi^2_1$ & 1 in 40,000& Unexplained usage\\\\\n \\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "No-Regret Caching via Online Mirror Descent", "authors": ["T. Si Salem", "G. Neglia", "S. Ioannidis"], "url": "https://arxiv.org/abs/2101.12588v5", "attribution": "\"No-Regret Caching via Online Mirror Descent\" by T. Si Salem, G. Neglia, and S. Ioannidis, arXiv:2101.12588v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2007.07013v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|}\n\\hline\nModel & Output & RGB (px) & Depth (m) \\\\\n\\hline\\hline\nBase & Depth & n/a & 8.02 \\\\\nBase & RGBD & 21.13 & 8.41 \\\\\nSlice-10 & Depth & n/a & 7.23 \\\\\nSlice-10 & RGBD & 20.71 & 7.01 \\\\\nSlice-32 & Depth & n/a & \\textbf{6.31} \\\\\nSlice-32 & RGBD & 20.39 & 6.97 \\\\\nSlice-64 & Depth & n/a & 6.64 \\\\\nSlice-64 & RGBD & \\textbf{20.37} & 7.1 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Base vs Slice models on the Synthetic dataset. The input is 6Dof-Quat, so it is omitted.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Pose2RGBD. Generating Depth and RGB images from absolute positions", "authors": ["Mihai Cristian Pîrvu"], "url": "https://arxiv.org/abs/2007.07013v1", "attribution": "\"Pose2RGBD. Generating Depth and RGB images from absolute positions\" by Mihai Cristian Pîrvu, arXiv:2007.07013v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.13287v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccccc}\n\\toprule\n\\textbf{Metrics} & \\textbf{Method} & \\textbf{Time=0} & \\textbf{Time=8} & \\textbf{Time=16} & \\textbf{Time=24} & \\textbf{Time=32} & \\textbf{Avg.} \\\\\n\\midrule\n\\multirow{2}{*}{SSIM↑} \n & Baseline & 0.9466 & 0.9668 & 0.9706 & 0.9396 & 0.8909 & 0.9429 \\\\\n & \\textbf{Ours} & 0.9443 & 0.9848 & 0.9864 & 0.9865 & 0.9224 & 0.9649 \\\\\n\\midrule\n\\multirow{2}{*}{PSNR↑} \n & Baseline & 31.4973 & 32.4322 & 34.4965 & 28.6187 & 24.5761 & 30.7242 \\\\\n & \\textbf{Ours} & 31.0204 & 38.3950 & 39.0909 & 39.0871 & 26.9297 & 34.9046 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison between Baseline (previous frame GT) and Our Method on testing timestamps.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "InnerGS: Internal Scenes Rendering via Factorized 3D Gaussian Splatting", "authors": ["Shuxin Liang", "Yihan Xiao", "Wenlu Tang"], "url": "https://arxiv.org/abs/2508.13287v1", "attribution": "\"InnerGS: Internal Scenes Rendering via Factorized 3D Gaussian Splatting\" by Shuxin Liang, Yihan Xiao, and Wenlu Tang, arXiv:2508.13287v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.10447v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Parameters }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n\\hline\n{\\cellcolor{gray!20}{\\textbf{Parameter}}} & {\\cellcolor{gray!20}{\\textbf{Value}}} \\\\ \\hline\nSystem & 3GPP Release 16\\\\ \\hline\nBandwidth & 10 MHz\\\\ \\hline\nDuplex mode & FDD\\\\ \\hline\nCP mode & Normal\\\\ \\hline\nModulation & QPSK\\\\ \\hline\n\\# Rx antennas & 2\\\\ \\hline\nDelay profile & Extended Vehicular A model (EVA) \\\\ \\hline\nDoppler frequency & 500 Hz\\\\ \\hline\nFading & Rayleigh\\\\ \\hline\nEqualization & MMSE\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Environment-Adaptive Multiple Access for Distributed V2X Network: A Reinforcement Learning Framework", "authors": ["Seungmo Kim", "Byung-Jun Kim", "B. Brian Park"], "url": "https://arxiv.org/abs/2101.10447v1", "attribution": "\"Environment-Adaptive Multiple Access for Distributed V2X Network: A Reinforcement Learning Framework\" by Seungmo Kim, Byung-Jun Kim, and B. Brian Park, arXiv:2101.10447v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.09592v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc} \n\\toprule \n& & \\multicolumn{4}{c}{Testing accuracy(\\%)}{Convergence time(s)}\\\\\\cmidrule{3-6} \nMethod & Sample size (S) & Fixed & Variate& Fixed & Variate \\\\ \\midrule \nBBVI & 200 & $74.46\\pm 1.74$ & $74.11\\pm 1.28$ & 1247 & 1860\\\\ \n& 500 & $74.28\\pm 0.91$ & $74.64\\pm 0.56$ & 1359 & 2061\\\\ \n& 1000 & $75.89\\pm 0.56$ & $75.12\\pm 0.80$&1652 & 2194\\\\ \nBBVI-CV & 200 & $75.71\\pm0. 61$ & $73.93\\pm 5.95 $ & 1201 & 1268\\\\ \n & 500 & $75.35\\pm 0.56$ & $74.11\\pm 5.95$& 1263 & 1255\\\\ \n& 1000 & $75.00 \\pm0.00$ & $74.28\\pm6.00$& 1735 & 1466\\\\\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\caption{Performance for BBVI and BBVI-CV.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Variational Bayes Neural Network: Posterior Consistency, Classification Accuracy and Computational Challenges", "authors": ["Shrijita Bhattacharya", "Zihuan Liu", "Tapabrata Maiti"], "url": "https://arxiv.org/abs/2011.09592v1", "attribution": "\"Variational Bayes Neural Network: Posterior Consistency, Classification Accuracy and Computational Challenges\" by Shrijita Bhattacharya, Zihuan Liu, and Tapabrata Maiti, arXiv:2011.09592v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.06601v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Values of test setup variables for the statistical analysis of the drifting balloon problem.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline\n Variable & Tested Values \\\\\n \\hline\n Parameter process noise ($q_p$) & [1e-10, 1e-8, 1e-6, 1e-4, 1e-2] \\\\\n Measurement Noise ($r$) & [1e-6, 1e-5, 5e-5, 1e-4] \\\\\n Static Bias Parameter ($A$) & [0, 0.001, 0.01, 0.1, 0.5] \\\\\n Linear Bias Parameter ($B$)& [0, 0.001, 0.01, 0.1, 2.0] \\\\\n Quadratic Bias Parameter ($C$)& [0, 0.001, 0.01, 0.1, 2.0] \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A switching Kalman filter approach to online mitigation and correction of sensor corruption for inertial navigation", "authors": ["Artem Mustaev", "Nicholas Galioto", "Matt Boler", "John D. Jakeman", "Cosmin Safta", "Alex Gorodetsky"], "url": "https://arxiv.org/abs/2412.06601v2", "attribution": "\"A switching Kalman filter approach to online mitigation and correction of sensor corruption for inertial navigation\" by Artem Mustaev, Nicholas Galioto, Matt Boler, John D. Jakeman, Cosmin Safta, and Alex Gorodetsky, arXiv:2412.06601v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12086v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccccc}\n\t\t\t\\toprule\n\t\t\t & COCO & VG & SBU & CC3M & CC12M & LAION \\\\\n\t\t\t\\midrule\n\t\t\t\\# image & 113K & 100K & 860K & 3M & 10M & 115M\\\\\n\t\t\t\\# text & 567K & 769K & 860K & 3M & 10M & 115M\\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation", "authors": ["Junnan Li", "Dongxu Li", "Caiming Xiong", "Steven Hoi"], "url": "https://arxiv.org/abs/2201.12086v2", "attribution": "\"BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation\" by Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi, arXiv:2201.12086v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11985v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Inverse problem CPU time comparison for the industrial benchmark case.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n \\hline\n & Alifanov's reg. & \\multicolumn{2}{c|}{Parameterized heat flux} \\\\\n \\hline\n & & offline & online \\\\\n \\hline\n CPU time & $221~s$ & $121.4~s$ & $0.15~s$\\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation", "authors": ["Umberto Emil Morelli", "Patricia Barral", "Peregrina Quintela", "Gianluigi Rozza", "Giovanni Stabile"], "url": "https://arxiv.org/abs/2101.11985v1", "attribution": "\"A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation\" by Umberto Emil Morelli, Patricia Barral, Peregrina Quintela, Gianluigi Rozza, and Giovanni Stabile, arXiv:2101.11985v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.00050v3_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc} \\toprule\n\\textbf{Method} & 100 uncrpt & 100 crpt \\\\ \\midrule\nFC3, KIP & \\textbf{43.09$\\pm$0.20} & \\textit{37.71$\\pm$0.38} \\\\\nFC3, Natural & 24.48$\\pm$0.15 & 18.92$\\pm$0.61 \\\\\n\\midrule\nConv2, KIP & \\textbf{43.68$\\pm$0.46} & \\textit{37.08$\\pm$0.48} \\\\\nConv2, Natural & 26.23 $\\pm$0.69 & 17.10$\\pm$1.33 \\\\\n\\midrule\nWideResNet, KIP & \\textbf{33.29$\\pm$1.14} & 23.89$\\pm$1.30 \\\\\nWideResNet, Natural & \\textit{27.93$\\pm$0.75} & 19.00$\\pm$1.01 \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Dataset Meta-Learning from Kernel Ridge-Regression", "authors": ["Timothy Nguyen", "Zhourong Chen", "Jaehoon Lee"], "url": "https://arxiv.org/abs/2011.00050v3", "attribution": "\"Dataset Meta-Learning from Kernel Ridge-Regression\" by Timothy Nguyen, Zhourong Chen, and Jaehoon Lee, arXiv:2011.00050v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.12251v1_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cccc|}\n \\hline\n & $T_1$ & $T_2$ & $T_3$ & $T_4$ \\\\\\hline\n $\\theta^*$ & $\\langle (0.87,0.13);0.86 \\rangle$ & $\\langle (0.66,0.34);0.34 \\rangle$& $\\langle (0.84,0.16);0.82 \\rangle$ & $\\langle (0.65,0.35);0.35 \\rangle$ \\\\ \\hline\n $\\theta^-$ & $\\langle (0.53,0.47);0.51 \\rangle$ & $\\langle (0.32,0.68);0.68 \\rangle$& $\\langle (0.44,0.56);0.44 \\rangle$ & $\\langle (0.42,0.58);0.58 \\rangle$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{C-IF positive and negative ideal solutions}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology", "authors": ["Mahmut Can Bozyiğit", "Mehmet Ünver"], "url": "https://arxiv.org/abs/2501.12251v1", "attribution": "\"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology\" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.08859v3_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Top-1 ImageNet validation accuracy of architectures used throughout the text, with the references indicating the source for the listed accuracy. * are models found by us using OFA~ for a specific target complexity metric.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\nArchitecture & ImageNet Top-1 [$\\%$] & Reference \\\\ \n\\hline\nEfficientNet-B0 & 77.7 & Ours, using \\\\\nSPNASNet-100 & 74.084 & From \\\\\nMNasNet-B1-1.0$\\times$ & 74.658 & From \\\\\nMNasNet-A1-1.0$\\times$ & 75.448 & From \\\\\nMNasNet-A1-1.4$\\times$ & 77.2 & From \\\\\nFBNet-C-100 & 78.124 & From \\\\\nMobileNetV2 (1.0x) & 72.970 & From \\\\\nMobileNetV2 (1.4x) & 76.516 & From \\\\\nMobileNetV3 (Large) & 75.766 & From \\\\\nProxyLessNas CPU & 75.3 & From \\\\\nProxyLessNas GPU & 75.1 & From \\\\\nProxyLessNas Mobile & 74.6 & From \\\\\nResNet34 & 75.1 & From \\\\\nResNet50 & 79.0 & From \\\\\nOFA/Scratch & 77.5 & Ours, with \\\\\nOFA-flops-A* & 77.3 & Ours, with \\\\\nOFA-flops-B* & 77.5 & Ours, with \\\\\nOFA-flops-C* & 78.6 & Ours, with \\\\\nOFA-sim-A* & 77.1 & Ours, with \\\\\nOFA-sim-B* & 78.1 & Ours, with \\\\\nOFA-sim-C* & 78.5 & Ours, with \\\\\nDNA-A & 77.1 & From \\\\\nDNA-B & 77.5 & From \\\\\nDNA-C & 77.8 & From \\\\\nDNA-D & 78.4 & From \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces", "authors": ["Bert Moons", "Parham Noorzad", "Andrii Skliar", "Giovanni Mariani", "Dushyant Mehta", "Chris Lott", "Tijmen Blankevoort"], "url": "https://arxiv.org/abs/2012.08859v3", "attribution": "\"Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces\" by Bert Moons, Parham Noorzad, Andrii Skliar, Giovanni Mariani, Dushyant Mehta, Chris Lott, and Tijmen Blankevoort, arXiv:2012.08859v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.05294v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|l|l|l|}\n\\hline\n Country & Initial reports & Intervention & Population&$n$& $I(0)$\\\\\n\\hline\nUSA &January 20 &March 22& 331,002,651 & 485 &5 \\\\\nBrazil &February 26 &March 24& 212,559,417& 448& 5\\\\\nMexico &February 28 &March 23& 128,932,753& 446& 6\\\\ \nArgentina & March 03&March 19& 45,195,774 & 442& 5\\\\\nChile &March 03&March 24 & 19,116,201 & 442& 5\\\\\nColombia &March 06&March 25 & 50,882,891 & 439&5\\\\ \nPeru &March 07&March 16& 32,971,854 & 438&9\\\\\nPanama &March 10&March 24 & 4, 314,767 & 435& 5\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A modified Susceptible-Infected-Recovered model for observed under-reported incidence data", "authors": ["Imelda Trejo", "Nicolas Hengartner"], "url": "https://arxiv.org/abs/2012.05294v2", "attribution": "\"A modified Susceptible-Infected-Recovered model for observed under-reported incidence data\" by Imelda Trejo and Nicolas Hengartner, arXiv:2012.05294v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2103.05114v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Detailed information on domain split of Bacterial and Viral datasets.}\n\\begin{tabular}{ccccccc}\n\t\t\\toprule\n\t\tDataset & Domain & Sympotom & \\#normal & \\#pneumonia & \\#COVID-19 & \\#Total \\\\ \\hline\n\t\t\\multirow{3}{*}{Bacterial} & source & Pneumonia & 1,660 & 3,001 & 0 & 4,661 \\\\ \n\t\t& target & COVID-19 & 1,610 & 0 & 1,281 & 2,891 \\\\ \\hline\n\t\t\\multirow{3}{*}{Viral} & source & Pneumonia & 1,660 & 1,656 & 0 & 3,316 \\\\\n\t\t& target & COVID-19 & 1,610 & 0 & 1,281 & 2,891 \\\\ \\bottomrule\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning Invariant Representations across Domains and Tasks", "authors": ["Jindong Wang", "Wenjie Feng", "Chang Liu", "Chaohui Yu", "Mingxuan Du", "Renjun Xu", "Tao Qin", "Tie-Yan Liu"], "url": "https://arxiv.org/abs/2103.05114v1", "attribution": "\"Learning Invariant Representations across Domains and Tasks\" by Jindong Wang, Wenjie Feng, Chang Liu, Chaohui Yu, Mingxuan Du, Renjun Xu, Tao Qin, and Tie-Yan Liu, arXiv:2103.05114v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.03052v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|r|r}\n \\hline \\textbf{Method} & \\textbf{input} & \\textbf{Points} & \\textbf{Accuracy} \\\\\n \\hline \n PointNet & P & $1 \\mathrm{K }$ & $89.2 \\%$ \\\\\n SO-Net & P, N & $2 \\mathrm{K}$ & $90.9 \\%$ \\\\\n A-CNN & P, N & $1 \\mathrm{K}$ & $92.6 \\%$ \\\\\n PointNet++ & P & $1 \\mathrm{K}$ & $90.7 \\%$ \\\\\n P2Sequence & P & $1 \\mathrm{K}$ & $92.6 \\%$ \\\\\n Kd-Net & P & $32 \\mathrm{K}$ & $91.8 \\%$ \\\\\n PCNN & P & $1 \\mathrm{K}$ & $92.3 \\%$ \\\\\n PointCNN & P & $1 \\mathrm{K}$ & $92.5 \\%$ \\\\\n PointConv & P, N & $1 \\mathrm{K}$ & $92.5 \\%$ \\\\\n RS-CNN & P & $1 \\mathrm{K}$ & $92.9 \\%$ \\\\\n \n KPConv & P & $7 \\mathrm{K}$ & $92.9 \\%$ \\\\\n DG-CNN & P & $1 \\mathrm{K}$ & $92.9 \\%$ \\\\\n PointGrid & P & $1 \\mathrm{K}$ & $92.0 \\%$ \\\\\n \n DC-GNN & P & $1 \\mathrm{K}$ & $93. 64 \\%$ \\\\\n \\hline\n \\textbf{Ours} & P & $1 \\mathrm{K}$ & $\\mathbf{92 . 71 \\%}$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation", "authors": ["Md Meraz", "Md Afzal Ansari", "Mohammed Javed", "Pavan Chakraborty"], "url": "https://arxiv.org/abs/2412.03052v1", "attribution": "\"Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation\" by Md Meraz, Md Afzal Ansari, Mohammed Javed, and Pavan Chakraborty, arXiv:2412.03052v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.05534v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|}\n\\hline\nBatch Size & Accuracy (\\%) & Recall (\\%) & Precision (\\%) & F1-Measure (\\%)\\\\\n\\hline\n3\t&97.73\t&95.17\t&94.41\t&94.57\\\\\n10\t&95.29\t&91.51\t&88.64\t&88.84\\\\\n20\t&97.97\t&94.95\t&95.98\t&95.40\\\\\n30\t&97.82\t&95.01\t&95.70\t&95.30\\\\\n40\t&97.93\t&94.91\t&95.96\t&95.34\\\\\n50\t&95.66\t&91.62\t&92.83\t&91.50\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Obtained classification accuracies with different batch size.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Effect of Different Batch Size Parameters on Predicting of COVID19 Cases", "authors": ["Ali Narin", "Ziynet Pamuk"], "url": "https://arxiv.org/abs/2012.05534v1", "attribution": "\"Effect of Different Batch Size Parameters on Predicting of COVID19 Cases\" by Ali Narin and Ziynet Pamuk, arXiv:2012.05534v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.06635v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance comparison with the recent top-five SotA approaches on each dataset. Methods marked with * involve transfer/joint learning strategy for objects/patches/regions consisting more than one dataset (primary and secondary)}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lc|lc}\n \\toprule\n \\multicolumn{2}{c}{\\textbf{Stanford Dogs}}&\n \\multicolumn{2}{c}{\\textbf{Oxford Flowers}} \\\\\n {Method} & {Accuracy (\\%)} & {Method} & {Accuracy (\\%)} \\\\\n \\midrule\n FCANs (Liu et al. 2016) % \n &\t89.0 & InterAct (Xie et al. 2016) % \n & 96.4 \\\\\n SJFT$^*$ (Ge and Yu 2017)%\\cite {ge2017borrowing} \n & 90.3 & SJFT$^*$ (Ge and Yu 2017) % \n & 97.0 \\\\ \n DAN (Hu et al. 2019)%\\cite {hu2019see}\n &92.2 &OPAM$^*$ (Peng, He, and Zhao 2018) % \n & 97.1 \\\\\n WARN (Rodríguez et al. 2020) %\\cite {lopez2020pay} \n &92.9 &DSTL$^*$ (Cui et al. 2018) % \n & 97.6 \\\\\n CPM$^*$ (Ge, Lin, and Yu 2019) %\n & \\textbf{97.1}& MC$_{Loss}*$\n (Chang et al. 2020) % \n & \\textbf{97.7} \\\\ \n \\hline\n \\textbf{Proposed} & {96.1} &\\textbf{Proposed} & \\textbf{97.7} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification", "authors": ["Ardhendu Behera", "Zachary Wharton", "Pradeep Hewage", "Asish Bera"], "url": "https://arxiv.org/abs/2101.06635v1", "attribution": "\"Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification\" by Ardhendu Behera, Zachary Wharton, Pradeep Hewage, and Asish Bera, arXiv:2101.06635v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.03731v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{MISE per method on dry bean dataset} for $\\beta = \\frac{3}{4}$ and varying levels of $\\alpha$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n $\\beta = \\frac{3}{4}$\\\\\n \\hline \\\\ \n \\hline \\\\\n & \\multicolumn{4}{c}{$\\alpha$}\\\\\n \\cdashline{2-5}\\\\\n Model & 1.0 & 2.0 & 3.0 & 4.0 \\\\\n \\hline \\\\\n Linear Regression & 2.91 \\scriptsize{± 0.01} & 2.92 \\scriptsize{± 0.02} & 3.01 \\scriptsize{± 0.03} & 3.01 \\scriptsize{± 0.02} \\\\\n CART & 1.24 \\scriptsize{± 0.11} & 1.31 \\scriptsize{± 0.08} & 1.32 \\scriptsize{± 0.10} & 1.43 \\scriptsize{± 0.11} \\\\\n xgboost & 1.04 \\scriptsize{± 0.05} & 1.04 \\scriptsize{± 0.08} & 1.00 \\scriptsize{± 0.06} & 1.09 \\scriptsize{± 0.06} \\\\\n MLP & 1.19 \\scriptsize{± 0.09} & 1.19 \\scriptsize{± 0.1} & 1.64 \\scriptsize{± 0.11} & 1.57 \\scriptsize{± 0.15} \\\\\n \\hdashline\\\\\n DRNet & 0.60 \\scriptsize{± 0.07} & 0.59 \\scriptsize{± 0.06} & 0.71 \\scriptsize{± 0.05} & 0.70 \\scriptsize{± 0.08} \\\\\n VCNet & 0.55 \\scriptsize{± 0.10} & 0.62 \\scriptsize{± 0.07} & 0.60 \\scriptsize{± 0.05} & 0.66 \\scriptsize{± 0.07} \\\\\n \\hdashline\\\\\n CBRNet(MMD$_{lin}$) & \\textit{0.35} \\scriptsize{± 0.07} & \\textit{0.39} \\scriptsize{± 0.11} & \\textbf{0.39} \\scriptsize{± 0.09} & \\textbf{0.45} \\scriptsize{± 0.06} \\\\\n CBRNet(MMD$_{rbf}$) & 0.43 \\scriptsize{± 0.11} & 0.49 \\scriptsize{± 0.18} & \\textit{0.42} \\scriptsize{± 0.06} & \\textit{0.51} \\scriptsize{± 0.13} \\\\\n CBRNet(Wass) & \\textbf{0.34} \\scriptsize{± 0.06} & \\textbf{0.38} \\scriptsize{± 0.10} & \\textit{0.42} \\scriptsize{± 0.08} & \\textbf{0.45} \\scriptsize{± 0.07} \\\\\n \\hline \\\\\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Using representation balancing to learn conditional-average dose responses from clustered data", "authors": ["Christopher Bockel-Rickermann", "Toon Vanderschueren", "Jeroen Berrevoets", "Tim Verdonck", "Wouter Verbeke"], "url": "https://arxiv.org/abs/2309.03731v2", "attribution": "\"Using representation balancing to learn conditional-average dose responses from clustered data\" by Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets, Tim Verdonck, and Wouter Verbeke, arXiv:2309.03731v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.21305v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Results for Constant Radial Strain Patch Test}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\nStrain Component & Computed Average & Expected Value & Absolute Error \\\\\n\\midrule\n\\( \\varepsilon_r \\) (Radial strain) & 0.010000 & 0.010000 & \\(1.73\\times10^{-18}\\) \\\\\n\\( \\varepsilon_z \\) (Axial strain) & \\(-0.000000\\) & 0.000000 & \\(1.69\\times10^{-21}\\) \\\\\n\\( \\varepsilon_\\theta \\) (Hoop strain) & 0.003247 & 0.000000 & \\(3.25\\times10^{-3}\\) \\\\\n\\( \\gamma_{rz} \\) (Shear strain) & \\(-0.000000\\) & 0.000000 & \\(8.67\\times10^{-19}\\) \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Virtual Element Method Applied to Two Dimensional Axisymmetric Elastic Problems", "authors": ["Paulo Akira F. Enabe", "Rodrigo Provasi"], "url": "https://arxiv.org/abs/2504.21305v2", "attribution": "\"Virtual Element Method Applied to Two Dimensional Axisymmetric Elastic Problems\" by Paulo Akira F. Enabe and Rodrigo Provasi, arXiv:2504.21305v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.23340v1_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|cccc|cccc|}\n\\hline\n & \\multicolumn{4}{c|}{\\textbf{Bernoulli-Laplace}} & \\multicolumn{4}{c|}{\\textbf{Curie-Weiss}} \\\\\n\\hline\n \\(m\\) & \\(S_{m,1}\\) & \\(S_{m,2}\\) & \\(S_{m,3}\\) & $\\mathbb{I}\\left(\\otimes_{i=1}^3 P^{(-S_{m, i})}\\right)$ & \\(S_{m,1}\\) & \\(S_{m,2}\\) & \\(S_{m,3}\\) & $\\mathbb{I}\\left(\\otimes_{i=1}^3 P^{(-S_{m, i})}\\right)$ \\\\\n\\hline\n1 & $\\emptyset$ & $\\emptyset$ & \\(\\{10\\}\\) & 0.41202 & \\(\\{2\\}\\) & $\\emptyset$ & $\\emptyset$ & 0.07972 \\\\\n2 & \\(\\{4\\}\\) & $\\emptyset$ & \\(\\{10\\}\\) & 0.30727 & \\(\\{2\\}\\) & $\\emptyset$ & \\(\\{9\\}\\) & 0.06029 \\\\\n3 & \\(\\{4\\}\\) & \\(\\{7\\}\\) & \\(\\{10\\}\\) & 0.21198 & \\(\\{2\\}\\) & \\(\\{6\\}\\) & \\(\\{9\\}\\) & 0.04172 \\\\\n4 & \\(\\{3,\\,4\\}\\) & \\(\\{7\\}\\) & \\(\\{10\\}\\) & 0.14286 & \\(\\{2,\\,3\\}\\) & \\(\\{6\\}\\) & \\(\\{9\\}\\) & 0.02376 \\\\\n5 & \\(\\{3,\\,4\\}\\) & \\(\\{7\\}\\) & \\(\\{9,\\,10\\}\\) & 0.07999 & \\(\\{2,\\,3\\}\\) & \\(\\{6\\}\\) & \\(\\{9,\\,10\\}\\) & 0.01556 \\\\\n6 & \\(\\{3,\\,4\\}\\) & \\(\\{5,\\,7\\}\\) & \\(\\{9,\\,10\\}\\) & 0.03406 & \\(\\{1,\\,2,\\,3\\}\\) & \\(\\{6\\}\\) & \\(\\{9,\\,10\\}\\) & 0.00778 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Performance evaluation of the generalized distorted greedy algorithm.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Information-theoretic subset selection of multivariate Markov chains via submodular optimization", "authors": ["Zheyuan Lai", "Michael C. H. Choi"], "url": "https://arxiv.org/abs/2503.23340v1", "attribution": "\"Information-theoretic subset selection of multivariate Markov chains via submodular optimization\" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.15103v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|} \\hline\n\t\t\\multirow{2}{*}{\\parbox[c]{1.8cm}{\\centering{Number of crossings}}} & \\multicolumn{4}{c|}{Kh width} \\\\ \\cline{2-5}\n\t\t& 2 & 3 & 4 & 5 \\\\ \\hline\n\t\t13 & 4554 ($\\sim$73\\%)& 1678 ($\\sim$27\\%)& 4 & 0 \\\\ \\hline\n\t\t14 & 23691 ($\\sim$70\\%) & 9928 ($\\sim$30\\%)& 53 & 0 \\\\ \\hline\n\t\t15 & 135054 ($\\sim$67\\%) & 66101($\\sim$33\\%) & 547 & 0 \\\\ \\hline\n\t\t16 & 767345 ($\\sim$63\\%)& 437781 ($\\sim$36\\%)& 5480 & 0 \\\\ \\hline\n\t\t17 & 4481503 ($\\sim$60\\%) & 2953589 ($\\sim$40\\%)& 58896 ($\\sim$1\\%)& 20 \\\\ \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{The distribution of width of even Khovanov homology of all non-alternating knots from 13 to 17 crossings.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Data Driven Perspectives on Knot Theory", "authors": ["Pawel Dlotko", "Davide Gurnari", "Radmila Sazdanovic"], "url": "https://arxiv.org/abs/2503.15103v1", "attribution": "\"Data Driven Perspectives on Knot Theory\" by Pawel Dlotko, Davide Gurnari, and Radmila Sazdanovic, arXiv:2503.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.17106v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n \\hline\n Resolution & Error & Order \\\\\n \\hline\n $h_0$ & 3.97E$-$2 & $-$ \\\\\n \\hline\n $h_1$ & 2.00E$-$2 & $0.99$ \\\\\n \\hline\n $h_2$ & 1.00E$-$2 & $1.00$ \\\\\n \\hline\n $h_3$ & 5.00E$-$3 & $1.00$ \\\\\n \\hline\n $h_4$ & 2.50E$-$3 & $1.00$ \\\\\n \\hline\n $h_5$ & 1.25E$-$3 & $1.00$ \\\\\n \\hline\n $h_6$ & 6.25E$-$4 & $1.00$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Improved weights (), example 2.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes", "authors": ["Antonio Baeza", "Pep Mulet", "David Zorío"], "url": "https://arxiv.org/abs/2501.17106v1", "attribution": "\"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes\" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.10690v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Empirical bias and MSE of $\\hat{\\beta_{0}},$ $\\hat{\\beta_{1}},$ $\\hat{\\beta_{2}}$ and $\\hat{\\rho_{1}}$ with $n =50,100.$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|}\n\\hline\n\\textbf{Estimators} & \\textbf{$\\hat{\\boldsymbol{\\beta}}$} & \\multicolumn{12}{|c|}{\\textbf{Error-distribution}}\\\\\n \\cline{3-14}\n & & \\multicolumn{3}{|c|}{$\\boldsymbol{N(0,1)}$}&\\multicolumn{3}{|c|}{$\\boldsymbol{DE(0,1)}$}&\\multicolumn{3}{|c|}{$\\boldsymbol{t_{3}}$} &\\multicolumn{3}{|c|}{$\\boldsymbol{N(0,\\sigma_{x_{i}^2)}}$, $\\boldsymbol{\\sigma_{x_{i}}^2 = (\\boldsymbol{x}^T_{i}\\boldsymbol{\\beta})^2}$}\\\\\n \\cline{3-14}\n & & \\textbf{Bias} &\\textbf{MSE}&\\textbf{C.P}& \\textbf{Bias}&\\textbf{MSE}&\\textbf{C.P}&\\textbf{Bias}&\\textbf{MSE}&\\textbf{C.P}&\\textbf{Bias}&\\textbf{MSE}&\\textbf{C.P} \\\\\n \\hline\n & \\ & \\multicolumn{12}{|c|}{$\\boldsymbol{n=50}$}\\\\\n \\hline\n&$\\hat{\\beta}_{0}$&0.1036&0.5342&&0.1153&0.6155&&0.1267&0.4250&&-0.4188&1.9455&\\\\\n\\textbf{CLAD}&$\\hat{\\beta}_{1}$&0.0009&0.0566&22$\\%$&0.0074&0.0615&38$\\%$&0.0084&0.0797&22$\\%$&0.2362&1.1679&48$\\%$\\\\\n&$\\hat{\\beta}_{2}$&-0.2207&1.0975&&-0.2653&1.2673&&-0.2716&1.1761&&0.0458&7.1524&\\\\\n&$\\hat{\\rho}_{1}$&0.2363&1.1495&&0.2957&1.3139&&0.2892&1.2447&&-0.2375&6.8123&\\\\\n\\hline \n&$\\hat{\\beta}_{0}$&0.0300&2.5824&&-0.0089&5.8653&&-0.0307&3.9349&&--2.3589&77.024&\\\\\n\\textbf{WME}&$\\hat{\\beta}_{1}$&0.0213&0.0568&18$\\%$&0.0295&0.0948&24$\\%$&0.0215&0.0909&28$\\%$&0.5879&6.0178&38$\\%$\\\\\n&$\\hat{\\beta}_{2}$&-0.1094&13.863&&-0.0547&20.351&&0.0514&15.790&&1.3187&184.66&\\\\\n&$\\hat{\\rho}_{1}$&0.1411&13.999&&0.0990&20.365&&-0.0180&15.746&&-0.0087&176.35&\\\\\n\\hline\n&$\\hat{\\beta}_{0}$&0.1500&2.5797&&0.1031&2.8856&&0.0514&9.9126&&-0.4741&53.334& \\\\\n\\textbf{CLCE}&$\\hat{\\beta}_{1}$&-0.0161&0.0454&30$\\%$&-0.0190&0.0589&38$\\%$&-0.0253&0.0656&36$\\%$&0.1414&1.7814&46$\\%$\\\\\n&$\\hat{\\beta}_{2}$&-0.2498&9.1222&&-0.1596&11.327&&0.0875&26.956&&0.0011&123.23&\\\\\n&$\\hat{\\rho}_{1}$&0.2209&9.0952&&0.1343&11.252&&-0.1251&27.044&&0.5446&123.18&\\\\\n\\hline\n & \\ & \\multicolumn{12}{|c|}{$\\boldsymbol{n=100}$}\\\\\n \\hline\n&$\\hat{\\beta}_{0}$&0.0810&0.1879&&0.0736&0.1786&&0.0957&0.1987&&-0.1627&1.7615&\\\\\n\\textbf{CLAD}&$\\hat{\\beta}_{1}$&0.0049&0.0296&31$\\%$&0.0073&0.0277&30$\\%$&0.0058&0.0353&27$\\%$&0.1272&0.6128&39$\\%$\\\\\n&$\\hat{\\beta}_{2}$&0.0027&0.5169&&-0.1701&0.5676&&-0.2062&0.5455&&-0.0878&4.3088&\\\\\n&$\\hat{\\rho}_{1}$&0.1920&0.5222&&0.1852&0.5972&&0.2212&0.5658&&0.3137&4.3402&\\\\\n\\hline \n&$\\hat{\\beta}_{0}$&0.0379&0.3325&&0.0266&0.3187&&0.0135&0.3838&&-1.1275&20.079&\\\\\n\\textbf{WME}&$\\hat{\\beta}_{1}$&0.0082&0.0253&35$\\%$&0.0111&0.0369&21$\\%$&0.0054&0.0383&25$\\%$&0.3399&1.8527&39$\\%$\\\\\n&$\\hat{\\beta}_{2}$&-0.1104&1.2365&&-0.0978&0.9666&&-0.0889&1.2553&&0.6269&44.104&\\\\\n&$\\hat{\\rho}_{1}$&0.1293&1.2554&&0.1114&0.9796&&0.1171&1.2894&&0.0500&41.828&\\\\\n\\hline\n&$\\hat{\\beta}_{0}$&0.1054&1.2096&&0.1154&0.2681&&0.0514&9.9126&&-0.0934&5.0210&\\\\\n\\textbf{CLCE}&$\\hat{\\beta}_{1}$&-0.0135&0.0196&24$\\%$&-0.0274&0.0254&33$\\%$&-0.0253&0.0656&36$\\%$&0.0462&0.6100&31$\\%$\\\\\n&$\\hat{\\beta}_{2}$&-0.1607&4.8477&&-0.1341&0.9023&&0.0875&26.956&&-0.0720&15.164&\\\\\n&$\\hat{\\rho}_{1}$&0.1327&4.8370&&0.0922&0.8955&&-0.1251&27.044&&0.2349&15.246&\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Generalized M-Estimation in Censored Regression Model under Endogeneity", "authors": ["Swati Shukla", "Subhra Sankar Dhar", "Shalabh"], "url": "https://arxiv.org/abs/2312.10690v2", "attribution": "\"Generalized M-Estimation in Censored Regression Model under Endogeneity\" by Swati Shukla, Subhra Sankar Dhar, and Shalabh, arXiv:2312.10690v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13664v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n \\textbf{N} & DRT3D & PPFFT3D \\\\\n \\hline\n 4 & 10.7983 & 5.8997\\\\\n 8 & 24.1062 & 13.1113 \\\\ \n 16 & 67.1549 & 37.7967 %\\phantom{54.7688}\n\\end{tabular}\n\\end{adjustbox}\n\\caption{\\color{blue} $L_2$ norm condition numbers for DRT and Pseudo Polar Fast Fourier based Radon transform}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Three-dimensional multiscale discrete Radon and John transforms", "authors": ["José Marichal-Hernández", "Óscar Gómez-Cárdenes", "Fernando Rosa", "Do Hyung Kim", "José M. Rodríguez-Ramos"], "url": "https://arxiv.org/abs/2501.13664v1", "attribution": "\"Three-dimensional multiscale discrete Radon and John transforms\" by José Marichal-Hernández, Óscar Gómez-Cárdenes, Fernando Rosa, Do Hyung Kim, and José M. Rodríguez-Ramos, arXiv:2501.13664v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2304.00544v1_tex_table28.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Cyclicality Occupational Mobility Using NUN Spells}\n\\begin{tabular}{lllllllll}\n \\toprule\n \\toprule\n & \\multicolumn{4}{c}{5Q MA $\\Gamma$-CORRECTED} & \\multicolumn{4}{c}{5Q MA UNCORRECTED} \\\\\n\\cmidrule{2-9} & \\multicolumn{2}{c}{linearly de-trend} & \\multicolumn{2}{c}{HP 1600} & \\multicolumn{2}{c}{linearly de-trend} & \\multicolumn{2}{c}{HP 1600} \\\\\n\\cmidrule{2-9} Category & elasticity & $\\rho$ & elasticity & $\\rho$ & elasticity & $\\rho$ & elasticity & $\\rho$ \\\\\n \\midrule\n \\multicolumn{9}{c}{\\textit{\\textbf{Mobility across NUN-spells, wrt Unemployment Rate}}}\\\\\n 2000 SOC & -0.14*** & -0.73 & -0.13*** & -0.43 & -0.09*** & -0.73 & -0.08*** & -0.42 \\\\\n & (0.02) & & (0.04) & & (0.01) & & (0.02) & \\\\\n 1990 SOC & -0.17*** & -0.60 & -0.16*** & -0.35 & -0.11*** & -0.67 & -0.11*** & -0.42 \\\\\n & (0.03) & & (0.06) & & (0.02) & & (0.03) & \\\\\n 4 task-based categories & -0.11*** & -0.38 & -0.00 & -0.01 & -0.08*** & -0.43 & -0.02 & -0.07 \\\\\n & (0.04) & & (0.07) & & (0.02) & & (0.04) & \\\\\n 4 task-based cat. (Excl. Manag.) & -0.14*** & -0.48 & -0.08 & -0.21 & -0.10*** & -0.53 & -0.06* & -0.23 \\\\\n & (0.03) & & (0.05) & & (0.02) & & (0.04) & \\\\\n Industries (1990 SIC) & -0.16*** & -0.72 & -0.21*** & -0.55 & -0.13*** & -0.72 & -0.16*** & -0.53 \\\\\n & (0.02) & & (0.04) & & (0.02) & & (0.03) & \\\\ \\hline\n N(quarters) & \\phantom{x}58 & & \\phantom{x}58 & & \\phantom{x}58 & & \\phantom{x}58 & \\\\\n \\bottomrule\n \\bottomrule\n \\multicolumn{9}{c}{{\\scriptsize *$\\ p<0.1$;\\ **$\\ p<0.05$;\\ ***$\\ p<0.01$}}\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Unemployment and Endogenous Reallocation over the Business Cycle", "authors": ["Carlos Carrillo-Tudela", "Ludo Visschers"], "url": "https://arxiv.org/abs/2304.00544v1", "attribution": "\"Unemployment and Endogenous Reallocation over the Business Cycle\" by Carlos Carrillo-Tudela and Ludo Visschers, arXiv:2304.00544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.04727v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n\t\t\t\\toprule\n\t\t\t$\\mathcal{D}_{\\theta^*} - train$ & $\\mathcal{D}_{\\theta^*} - test$ & Metric & SIDD & OOD Avg. \\\\\n\t\t\t\\midrule\\midrule\n\t\t\t\\multirow{7}{*}{NAFNet~} & \\multirow{2}{*}{Restormer~} & PSNR & 39.19 & 39.75 \\\\\n\t\t\t & & SSIM & 0.9567 & 0.9820 \\\\\n\t\t\t\\cmidrule{2-5}\n\t\t\t & \\multirow{2}{*}{KBNet~} & PSNR & 39.15 & 39.84 \\\\\n\t\t\t & & SSIM & 0.9565 & 0.9819 \\\\\n\t\t\t\\cmidrule{2-5}\n\t\t\t & \\multirow{2}{*}{Xformer~} & PSNR & 39.02 & 39.84 \\\\\n\t\t\t & & SSIM & 0.9555 & 0.9822 \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Learning to Translate Noise for Robust Image Denoising", "authors": ["Inju Ha", "Donghun Ryou", "Seonguk Seo", "Bohyung Han"], "url": "https://arxiv.org/abs/2412.04727v1", "attribution": "\"Learning to Translate Noise for Robust Image Denoising\" by Inju Ha, Donghun Ryou, Seonguk Seo, and Bohyung Han, arXiv:2412.04727v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10372v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|}\n\\hline \nGraphs & Algorithm & Max. Coverage & Wt. Precision & F-Score\\\\ \\hline \n\\multirow{3}{*}{\\parbox[c]{2cm}{\\begin{center}$k\\geq 2$\\\\(100\\%)\\end{center}}} & Unitigs & 0.42 & 1.00 & 0.58\\\\\n& ExtUnitigs & 0.59 & 1.00 & 0.73\\\\\n& Safe\\&Comp & 0.71 & 1.00 & 0.82\\\\\n& Greedy & 0.89 & 0.92 & 0.89\\\\\n\\hline\n\\multirow{3}{*}{\\parbox[c]{2cm}{\\begin{center}$2\\le k \\le 10$\\\\(98\\%)\\end{center}}} & Unitigs & 0.42 & 1.00 & 0.58\\\\\n& ExtUnitigs & 0.59 & 1.00 & 0.74\\\\\n& Safe\\&Comp & 0.71 & 1.00 & 0.83\\\\\n& Greedy & 0.89 & 0.93 & 0.90\\\\\n\\hline\n\\multirow{3}{*}{\\parbox[c]{2cm}{\\begin{center}$k>10$\\\\(2\\%)\\end{center}}} & Unitigs & 0.24 & 1.00 & 0.38\\\\\n& ExtUnitigs & 0.42 & 1.00 & 0.59\\\\\n& Safe\\&Comp & 0.58 & 1.00 & 0.74\\\\\n& Greedy & 0.82 & 0.49 & 0.56\\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Summary of evaluation metrics for the Catfish dataset, computed relative to nodes.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Safety and Completeness in Flow Decompositions for RNA Assembly", "authors": ["Shahbaz Khan", "Milla Kortelainen", "Manuel Cáceres", "Lucia Williams", "Alexandru I. Tomescu"], "url": "https://arxiv.org/abs/2201.10372v1", "attribution": "\"Safety and Completeness in Flow Decompositions for RNA Assembly\" by Shahbaz Khan, Milla Kortelainen, Manuel Cáceres, Lucia Williams, and Alexandru I. Tomescu, arXiv:2201.10372v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.12530v2_tex_table12.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Statistical Functionals of $L$ for $\\rho^\\Phi=0.5$ and $\\zeta^{2a}$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrrrrrrrrr} \\toprule \n& \\multicolumn{6}{c}{Binomial Model} & \\quad\\quad & \\multicolumn{6}{c}{Poisson Model} \\\\ \\midrule \n& \\multicolumn{3}{c}{Gamma} & \\multicolumn{3}{c}{Log-Normal} & & \\multicolumn{3}{c}{Gamma} & \\multicolumn{3}{c}{Log-Normal} \\\\ \\midrule \n& $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ & $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ & & $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ & $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ \\\\ \\toprule \n \\\\ \n&\\multicolumn{13}{c}{\\textbf{Uniform Accident Occurrence}} \\\\ \n$\\mathbb{E}(L)$ & 6074.1 & 6060.6 & 6057.3 & 6062.5 & 6071.7 & 6062.5 & & 6066.6 & 6081.9 & 6049.3 & 6082.6 & 6070.7 & 6079.3 \\\\ \n$\\mathrm{Var}(L)$ & 1090361.3 & 1517008.9 & 2989042.5 & 1099229.5 & 1466766.0 & 3154753.0 & & 1084491.1 & 1493958.5 & 3021932.0 & 1112695.1 & 1464801.0 & 3003597.9 \\\\ \n$\\varsigma_L$ & 0.198 & 0.309 & 0.466 & 0.187 & 0.336 & 1.361 & & 0.194 & 0.272 & 0.509 & 0.220 & 0.342 & 1.035 \\\\ \n$\\mathrm{VaR}_{0.9}(L)$ & 7461.5 & 7700.1 & 8349.7 & 7463.1 & 7670.1 & 8310.1 & & 7449.9 & 7702.2 & 8402.7 & 7488.8 & 7658.5 & 8291.5 \\\\ \n$\\mathrm{ES}_{0.9}(L)$ & 7959.6 & 8345.3 & 9362.0 & 7944.3 & 8335.4 & 9654.3 & & 7943.3 & 8345.5 & 9418.3 & 8000.3 & 8328.4 & 9569.1 \\\\ \n$\\mathrm{VaR}_{0.95}(L)$ & 7845.2 & 8173.1 & 9099.5 & 7814.5 & 8164.5 & 9153.4 & & 7830.2 & 8212.8 & 9181.0 & 7875.9 & 8146.9 & 9162.1 \\\\ \n$\\mathrm{ES}_{0.95}(L)$ & 8278.5 & 8779.1 & 10033.9 & 8263.9 & 8764.4 & 10610.2 & & 8268.1 & 8758.7 & 10082.2 & 8332.4 & 8777.0 & 10466.3 \\\\ \n$\\mathrm{VaR}_{0.99}(L)$ & 8551.2 & 9184.5 & 10689.5 & 8518.7 & 9115.0 & 11381.5 & & 8571.2 & 9144.8 & 10612.5 & 8621.4 & 9166.7 & 11181.0 \\\\ \n$\\mathrm{ES}_{0.99}(L)$ & 8890.3 & 9675.5 & 11515.2 & 8891.9 & 9659.7 & 13317.7 & & 8876.7 & 9519.7 & 11415.2 & 8932.9 & 9687.3 & 12778.9 \\\\ \n&\\multicolumn{13}{c}{\\textbf{Non-Uniform Accident Occurrence}} \\\\ \n$\\mathbb{E}(L)$ & 1315.3 & 1322.3 & 1326.0 & 1322.7 & 1321.7 & 1324.5 & & 1317.3 & 1310.7 & 1321.2 & 1319.6 & 1313.1 & 1317.2 \\\\ \n$\\mathrm{Var}(L)$& 168470.4 & 245780.8 & 583773.9 & 166082.8 & 243388.7 & 587217.4 & & 164276.7 & 235282.1 & 558258.2 & 165179.6 & 242009.7 & 537609.2 \\\\ \n$\\varsigma_L$ & 0.431 & 0.676 & 1.164 & 0.430 & 0.705 & 2.822 & & 0.423 & 0.588 & 1.147 & 0.439 & 0.684 & 1.955 \\\\ \n$\\mathrm{VaR}_{0.9}(L)$ & 1867.4 & 1989.7 & 2347.5 & 1866.8 & 1979.8 & 2232.2 & & 1861.8 & 1964.7 & 2308.0 & 1866.4 & 1979.8 & 2227.4 \\\\ \n$\\mathrm{ES}_{0.9}(L)$ & 2102.3 & 2305.4 & 2953.9 & 2101.1 & 2302.4 & 2945.4 & & 2091.5 & 2263.6 & 2893.3 & 2097.1 & 2298.3 & 2910.6 \\\\ \n$\\mathrm{VaR}_{0.95}(L)$ & 2045.8 & 2210.9 & 2786.2 & 2041.8 & 2211.1 & 2671.0 & & 2032.5 & 2177.7 & 2719.4 & 2048.1 & 2230.6 & 2663.9 \\\\ \n$\\mathrm{ES}_{0.95}(L)$ & 2255.8 & 2518.5 & 3358.9 & 2256.5 & 2516.8 & 3475.0 & & 2242.9 & 2463.8 & 3284.4 & 2248.2 & 2507.1 & 3396.2 \\\\ \n$\\mathrm{VaR}_{0.99}(L)$ & 2386.4 & 2699.8 & 3719.7 & 2406.8 & 2681.7 & 3894.6 & & 2376.1 & 2623.4 & 3547.6 & 2384.1 & 2673.4 & 3740.7 \\\\ \n$\\mathrm{ES}_{0.99}(L)$ & 2556.9 & 2996.5 & 4271.0 & 2560.1 & 2985.6 & 5121.0 & & 2530.3 & 2883.0 & 4118.5 & 2553.7 & 2908.6 & 4797.2 \\\\ \n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Microscopic Traffic Models, Accidents, and Insurance Losses", "authors": ["Sojung Kim", "Marcel Kleiber", "Stefan Weber"], "url": "https://arxiv.org/abs/2208.12530v2", "attribution": "\"Microscopic Traffic Models, Accidents, and Insurance Losses\" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2007.13640v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Run time, in seconds, and (number of iterations until convergence) for different applications, averaged over images in Set12, on an NVIDIA RTX 8000 GPU.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lllll}\n \\toprule\n Inpainting $30\\times 30$ & Missing pixels 10\\% & SISR 4:1 & Deblurring 10\\% & CS 10\\%\\\\\n \\midrule\n 15.7 (647) & 7.8 (313)& 7.6 (301) & 8.4 (332) & 32.5 (317) \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser", "authors": ["Zahra Kadkhodaie", "Eero P. Simoncelli"], "url": "https://arxiv.org/abs/2007.13640v3", "attribution": "\"Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser\" by Zahra Kadkhodaie and Eero P. Simoncelli, arXiv:2007.13640v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.00213v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{siunitx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Mean performance in \\textbf{ratio} for player selection methods in 10-Player scenarios.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n \\toprule\n Threshold-based & Parameter & $\\text{traj}_{S}\\downarrow$ & $\\text{traj}_L\\downarrow$ & $\\text{Dist}_m\\uparrow$ & Consistency $\\uparrow$ & Player Num. & Runtime\\\\\n \\midrule\n \\midrule\n \\multirow{3}[0]{*}{\\textbf{PSN-Full}} & $m_{\\text{th}}=0.1$ & 0.905 & 1.002 & 0.955 & 0.982 & 6.687 & 0.443\\\\\n & $m_{\\text{th}}=0.3$ & \\textbf{0.857} & 1.000 & 0.963 & 0.989 & 5.453 & 0.238\\\\\n & $m_{\\text{th}}=0.5$ & \\textbf{0.857} & 0.994 & 0.958 & \\textbf{0.991} & 4.473 & 0.124\\\\\n \\midrule\n \\multirow{3}[0]{*}{\\textbf{PSN-Partial}} & $m_{\\text{th}}=0.1$ & 1.000 & 0.995 & 0.996 & 0.982 & 6.150 & 0.338\\\\\n & $m_{\\text{th}}=0.3$ & 0.905 & 0.995 & 0.992 & 0.984 & 5.163 & 0.205 \\\\\n & $m_{\\text{th}}=0.5$ & \\textbf{0.857} & 0.991 & 0.970 & \\textbf{0.991} & 4.033 & 0.095\\\\\n \\midrule\n \\multirow{3}[0]{*}{Distance} & $r_\\text{th}=\\SI{1.5}{\\meter}$ & 0.905 & 0.996 & 0.925 & \\textbf{0.991} & 1.350 & 0.005\\\\\n & $r_\\text{th}=\\SI{2.0}{\\meter}$ & 1.048 & 1.003 & 0.975 & \\textbf{0.991} & 1.680 & 0.010\\\\\n & $r_\\text{th}=\\SI{2.5}{\\meter}$ & 1.190 & 0.998 & 0.980 & 0.989 & 2.277 & 0.022\\\\\n \\toprule\n Ranking-based & Parameter & $\\text{traj}_{S}\\downarrow$ & $\\text{traj}_L\\downarrow$ & $\\text{Dist}_m\\uparrow$ & Consistency $\\uparrow$ & Player Num. \\\\\n \\midrule\n \\midrule\n \\multirow{2}[0]{*}{\\textbf{PSN-Full}} & $|U^i|=4$ & \\textbf{0.857} & 0.991 & 0.972 & \\textbf{0.991} & 5.000 & 0.102\\\\\n & $|U^i|=6$ & 0.905 & 0.996 & 0.964 & 0.986 & 7.000 & 0.355\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\textbf{PSN-Partial}} & $|U^i|=4$ & \\textbf{0.857} & 0.988 & 0.957 & \\textbf{0.991} & 5.000 & 0.124\\\\\n & $|U^i|=6$ & 0.905 & 0.996 & \\textbf{1.019} & 0.986 & 7.000 & 0.341\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\texttt{kNNs}} & $|U^i|=4$ & 0.952 & 1.000 & 0.965 & 0.977 & 5.000 & 0.123\\\\\n & $|U^i|=6$ & 1.048 & 1.002 & 1.012 & \\textbf{0.991} & 7.000 & 0.345\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\texttt{Cost Evolution}} & $|U^i|=4$ & 1.048 & \\textbf{0.980} & 0.974 & 0.932 & 5.000 & 0.121\\\\\n & $|U^i|=6$ & 1.000 & 0.986 & 1.022 & 0.973 & 7.000 & 0.329\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\texttt{Gradient}} & $|U^i|=4$ & 0.952 & 1.000 & 0.967 & 0.977 & 5.000 & 0.115\\\\\n & $|U^i|=6$ & 1.048 & 1.002 & 1.013 & 0.973 & 7.000 & 0.348\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\texttt{Hessian}} & $|U^i|=4$ & 0.952 & 0.999 & 0.979 & 0.982 & 5.000 & 0.128\\\\\n & $|U^i|=6$ & 0.952 & 0.992 & 0.989 & 0.986 & 7.000 & 0.339\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\texttt{BF}} & $|U^i|=4$ & 1.000 & 1.003 & 1.015 & 0.977 & 5.000 & 0.145\\\\\n & $|U^i|=6$ & 1.048 & 1.000 & 1.001 & \\textbf{0.991} & 7.000 & 0.351\\\\\n \\midrule\n \\multirow{2}[0]{*}{\\texttt{CBF}} & $|U^i|=4$ & 0.952 & 1.002 & 1.013 & 0.977 & 5.000 & 0.135\\\\\n & $|U^i|=6$ & 0.952 & 0.997 & 1.001 & 0.986 & 7.000 & 0.336\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "PSN Game: Game-theoretic Planning via a Player Selection Network", "authors": ["Tianyu Qiu", "Eric Ouano", "Fernando Palafox", "Christian Ellis", "David Fridovich-Keil"], "url": "https://arxiv.org/abs/2505.00213v1", "attribution": "\"PSN Game: Game-theoretic Planning via a Player Selection Network\" by Tianyu Qiu, Eric Ouano, Fernando Palafox, Christian Ellis, and David Fridovich-Keil, arXiv:2505.00213v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.09574v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{rotating}\n\\usepackage{amsfonts}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Continuation of Tab. 2}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc}\n\\hline\n\\hline\n & &PCF&TANet&CPFP&DMRA&D3Net&MCI-Net&Ours\\\\\n &Metric&CVPR18&TIP19\tCVPR19&ICCV19&TNNLS20&Neurocomputing21&21\\\\\n & &[7]&[38]&[6]&[5]&[50]&[58]& \\\\\n\\hline\n\\hline\n\\multirow{8}*{\\rotatebox{90}{\\emph{NJU2K} \n}} &$S_\\alpha$↑ &0.877 & 0.878 & 0.879 & 0.886 & 0.893 & 0.900 & \\textbf{0.912} \\\\ \n &max $F_\\beta$↑ &0.872 & 0.874 & 0.877 & 0.886 & 0.887 & - & \\textbf{0.913} \\\\\n &mean $F_\\beta$↑ &0.840 & 0.841 & 0.850 & 0.873 & 0.859 & 0.873 & \\textbf{0.904} \\\\\n &adp $F_\\beta$↑ &0.844 & 0.844 & 0.837 & 0.872 & 0.840 & - & \\textbf{0.904} \\\\\n &max $E_\\xi$↑ &0.924 & 0.925 & 0.926 & 0.927 & 0.930 & - & \\textbf{0.942} \\\\\n &mean $E_\\xi$↑ &0.895 & 0.895 & 0.910 & 0.920 & 0.910 & 0.920 & \\textbf{0.936} \\\\\n &adp $E_\\xi$↑ &0.896 & 0.893 & 0.895 & 0.908 & 0.894 & - & \\textbf{0.917} \\\\\n &$\\mathcal{M}$↓ &0.059 & 0.060 & 0.053 & 0.051 & 0.051 & 0.050 & \\textbf{0.038} \\\\ \n\\hline\n\\multirow{8}*{\\rotatebox{90}{\\emph{LFSD} \n}} &$S_\\alpha$↑ &0.794 & 0.801 & 0.828 & 0.847 & 0.825 & - & \\textbf{0.862} \\\\\n &max $F_\\beta$↑ &0.779 & 0.796 & 0.826 & 0.856 & 0.810 & - & \\textbf{0.863} \\\\\n &mean $F_\\beta$↑ &- & 0.771 & 0.811 & \\textbf{0.845} & 0.796 & - & 0.838 \\\\\n &adp $F_\\beta$↑ &- & 0.794 & 0.813 & 0.849 & 0.805 & - & \\textbf{0.851} \\\\\n &max $E_\\xi$↑ &- & 0.847 & 0.872 & \\textbf{0.900} & 0.862 & - & 0.898 \\\\\n &mean $E_\\xi$↑ &0.835 & 0.821 & 0.863 & \\textbf{0.893} & 0.849 & - & 0.873 \\\\\n &adp $E_\\xi$↑ &- & 0.845 & 0.867 & \\textbf{0.899} & 0.853 & - & 0.885 \\\\\n &$\\mathcal{M}$↓ &0.112 & 0.111 & 0.088 & \\textbf{0.075} & 0.095 & - & 0.077 \\\\ \n\\hline\n\\multirow{8}*{\\rotatebox{90}{\\emph{STERE} \n}} &$S_\\alpha$↑ &0.875 & 0.871 & 0.879 & 0.835 & 0.889 & 0.901 & \\textbf{0.904} \\\\\n &max $F_\\beta$↑ &0.860 & 0.861 & 0.874 & 0.847 & \\textbf{0.878} & - & 0.780 \\\\\n &mean $F_\\beta$↑ &0.818 & 0.828 & 0.841 & 0.837 & 0.841 & 0.872 & \\textbf{0.892} \\\\\n &adp $F_\\beta$↑ &0.826 & 0.835 & 0.830 & 0.844 & 0.829 & - & \\textbf{0.891} \\\\\n &max $E_\\xi$↑ &0.925 & 0.923 & 0.925 & 0.911 & 0.929 & - & \\textbf{0.936} \\\\\n &mean $E_\\xi$↑ &0.887 & 0.893 & 0.912 & 0.879 & 0.906 & 0.929 & \\textbf{0.929} \\\\\n &adp $E_\\xi$↑ &0.897 & 0.906 & 0.903 & 0.900 & 0.902 & - & \\textbf{0.921} \\\\\n &$\\mathcal{M}$↓ &0.064 & 0.060 & 0.051 & 0.066 & 0.054 & \\textbf{0.042} & 0.045 \\\\ \n\\hline\n\\multirow{8}*{\\rotatebox{90}{\\emph{DES} \n}} &$S_\\alpha$↑ &0.842 & 0.858 & 0.872 & 0.900 & 0.898 & 0.927 & \\textbf{0.931} \\\\\n &max $F_\\beta$↑ &0.804 & 0.827 & 0.846 & 0.888 & 0.880 & - & \\textbf{0.923} \\\\\n &mean $F_\\beta$↑ &0.765 & 0.790 & 0.824 & 0.873 & 0.851 & 0.897 & \\textbf{0.903} \\\\\n &adp $F_\\beta$↑ &0.782 & 0.795 & 0.829 & 0.866 & 0.863 & - & \\textbf{0.903} \\\\\n &max $E_\\xi$↑ &0.893 & 0.910 & 0.923 & 0.943 & 0.935 & - & \\textbf{0.967} \\\\\n &mean $E_\\xi$↑ &0.838 & 0.863 & 0.889 & 0.933 & 0.902 & \\textbf{0.957} & 0.944 \\\\\n &adp $E_\\xi$↑ &0.912 & 0.919 & 0.927 & 0.944 & 0.946 & - & \\textbf{0.967} \\\\\n &$\\mathcal{M}$↓ &0.049 & 0.046 & 0.038 & 0.030 & 0.033 & 0.024 & \\textbf{0.023} \\\\ \n\\hline\n\\multirow{8}*{\\rotatebox{90}{\\emph{NLPR} \n}} &$S_\\alpha$↑ &0.874 & 0.886 & 0.888 & 0.899 & 0.905 & 0.917 & \\textbf{0.931} \\\\\n &max $F_\\beta$↑ &0.841 & 0.863 & 0.867 & 0.879 & 0.885 & - & \\textbf{0.918} \\\\\n &mean $F_\\beta$↑ &0.802 & 0.819 & 0.840 & 0.864 & 0.852 & 0.890 & \\textbf{0.894} \\\\\n &adp $F_\\beta$↑ &0.795 & 0.796 & 0.823 & 0.854 & 0.832 & - & \\textbf{0.880} \\\\\n &max $E_\\xi$↑ &0.925 & 0.941 & 0.932 & 0.947 & 0.945 & - & \\textbf{0.962} \\\\\n &mean $E_\\xi$↑ &0.887 & 0.902 & 0.918 & 0.940 & 0.923 & 0.947 & \\textbf{0.947} \\\\\n &adp $E_\\xi$↑ &0.916 & 0.916 & 0.924 & 0.941 & 0.931 & - & \\textbf{0.954} \\\\\n &$\\mathcal{M}$↓ &0.044 & 0.041 & 0.036 & 0.031 & 0.033 & 0.027 & \\textbf{0.025}\\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection", "authors": ["Ze-yu Liu", "Jian-wei Liu", "Xin Zuo", "Ming-fei Hu"], "url": "https://arxiv.org/abs/2201.09574v1", "attribution": "\"Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection\" by Ze-yu Liu, Jian-wei Liu, Xin Zuo, and Ming-fei Hu, arXiv:2201.09574v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05544v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Out-of-distribution} performances \\textbf{before} temperature scaling.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c||ccccc|ccccc|ccccc|ccccc|ccccc}\n \\toprule\n \\multicolumn{2}{c||}{Dataset} & \\multicolumn{5}{c|}{FPR (95 \\% TPR) $\\downarrow$} & \\multicolumn{5}{c|}{Detection $\\downarrow$ } & \\multicolumn{5}{c|}{AUROC $\\uparrow$} & \\multicolumn{5}{c|}{AUPR In $\\uparrow$} & \\multicolumn{5}{c}{AUPR Out $\\uparrow$} \\\\\n Train & Test OOD & Ind. & CEB & DICE & $w$ only & DICE$\\times w$ & Ind. & CEB & DICE & $w$ only & DICE$\\times w$ & Ind. & CEB & DICE & $w$ only & DICE$\\times w$ & Ind. & CEB & DICE & $w$ only & DICE$\\times w$ & Ind. & CEB & DICE & $w$ only & DICE$\\times w$ \\\\\n \\midrule\n \\multirow{9}{*}{CIFAR-100}\n & TinyImageNet (crop) & 80.1 & 80.4 & 77.9 & 82.2 & \\textbf{73.7} & 33.0 & 32.1 & 31.2 & 32.4 & \\textbf{28.8} & 72.4 & 73.8 & 74.7 & 71.1 & \\textbf{77.2} & 71.5 & 73.0 & 73.4 & 66.0 & \\textbf{74.3} & 70.5 & 71.7 & 72.4 & 69.6 & \\textbf{75.9} \\\\\n & TinyImageNet (resize) & 84.4 & 83.6 & 81.0 & 87.9 & \\textbf{78.8} & 35.5 & 34.5 & 33.6 & 35.7 & \\textbf{31.7} & 69.1 & 70.6 & 71.7 & 66.1 & \\textbf{73.6} & 68.0 & 69.3 & \\textbf{70.4} & 60.5 & 70.3 & 66.8 & 68.5 & 69.3 & 64.4 & \\textbf{71.9} \\\\\n & LSUN (crop) & 79.1 & 82.7 & 81.1 & 74.9 & \\textbf{73.3} & 28.6 & 29.2 & 29.3 & 29.9 & \\textbf{28.5} & 77.7 & 76.2 & 75.9 & 76.3 & \\textbf{78.9} & \\textbf{79.9} & 78.6 & 77.8 & 74.7 & 79.2 & 73.9 & 71.8 & 71.8 & 75.1 & \\textbf{76.8} \\\\\n & LSUN (resize) & 83.1 & 81.0 & 80.0 & 82.5 & \\textbf{75.9} & 34.2 & 32.1 & 31.4 & 32.0 & \\textbf{29.2} & 71.5 & 74.2 & 74.2 & 71.6 & \\textbf{77..1} & 73.2 & 75.5 & 75.4 & 66.3 & \\textbf{76.5} & 68.3 & 71.5 & 71.2 & 69.7 & \\textbf{75.0} \\\\\n & iSUN & 85.3 & 83.4 & 83.8 & 84.2 & \\textbf{79.7} & 35.3 & 33.2 & 33.3 & 34.3 & \\textbf{31.7} & 69.6 & 72.5 & 71.9 & 68.7 & \\textbf{74.4} & 72.7 & \\textbf{75.3} & 74.7 & 65.6 & 74.7 & 63.7 & 66.9 & 65.7 & 64.9 & \\textbf{69.5} \\\\\n & TinyImageNet+LSUN+iSUN & 82.3 & 82.2 & 80.7 & 82.3 & \\textbf{76.2} & 33.4 & 32.2 & 31.8 & 32.8 & \\textbf{30.2} & 72.1 & 73.5 & 73.8 & 70.9 & \\textbf{76.4} & 38.1 & 39.9 & \\textbf{40.3} & 28.7 & 39.4 & 91.5 & 91.9 & 91.9 & 91.4 & \\textbf{93.1} \\\\\n \\cmidrule{2-27}\n & CIFAR-10 & 80.1 & 82.9 & \\textbf{78.5} & 90.0 & 79.9 & 30.0 & 30.3 & 28.8 & 36.6 & \\textbf{28.7} & 76.6 & 76.0 & 78.1 & 66.5 & \\textbf{78.4} & 79.7 & 79.1 & \\textbf{80.9} & 64.6 & 80.8 & 72.5 & 71.6 & \\textbf{73.9} & 62.8 & \\textbf{73.9} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation", "authors": ["Alexandre Rame", "Matthieu Cord"], "url": "https://arxiv.org/abs/2101.05544v1", "attribution": "\"DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation\" by Alexandre Rame and Matthieu Cord, arXiv:2101.05544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.15301v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Misclassification rate of $k$NN, tree, random forest, node harvest, SVM, OTE, $OTE_{oob}$ and $OTE_{sub}$. Number of observations is 1000 for each model. Bayes error is given in the fourth column against each model.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccccccccc}\n\t\t\t\\toprule\n\t\t\tScenario & Number of & Number of & Bayes Error & kNN & Tree & RF & NH & SVM & SVM & SVM & SVM & OTE & OTE & OTE & $OTE_{oob}$ & $OTE_{sub}$ \\\\\n\t\t\t& Variables & Observations & & & & & & (Radial) & (Linear) & (Bessel) & (Laplacian) & 10\\% V & 20\\% V & 30\\% V & & \\\\\n\t\t\t\\midrule\n\t\t\t& & & 9.2\\% & 23\\% & 11\\% & 10\\% & 11\\% & 20\\% & 20\\% & 20\\% & 20\\% & 10\\% & 11\\% & 12\\% & 10\\% & 11\\% \\\\\n\t\t\t& & & 14\\% & 27\\% & 16\\% & 15\\% & 16\\% & 23\\% & 23\\% & 24\\% & 22\\% & 15\\% & 16\\% & 17\\% & 15\\% & 17\\% \\\\\n\t\t\tScenario 1 & 9 & 1000 & 18\\% & 33\\% & 19\\% & 23\\% & 26\\% & 29\\% & 29\\% & 29\\% & 29\\% & 24\\% & 25\\% & 27\\% & 24\\% & 26\\% \\\\\n\t\t\t& & & 33\\% & 43\\% & 37\\% & 36\\% & 38\\% & 39\\% & 39\\% & 39\\% & 38\\% & 37\\% & 39\\% & 40\\% & 37\\% & 39\\% \\\\\n\t\t\t\\midrule\n\t\t\t& & & 21\\% & 31\\% & 23\\% & 21\\% & 23\\% & 25\\% & 24\\% & 30\\% & 25\\% & 21\\% & 23\\% & 24\\% & 21\\% & 21\\% \\\\\n\t\t\t& & & 24\\% & 31\\% & 26\\% & 24\\% & 25\\% & 27\\% & 27\\% & 33\\% & 27\\% & 24\\% & 25\\% & 26\\% & 24\\% & 24\\% \\\\\n\t\t\tScenario 2 & 12 & 1000 & 28\\% & 37\\% & 31\\% & 29\\% & 30\\% & 32\\% & 31\\% & 37\\% & 32\\% & 29\\% & 30\\% & 31\\% & 29\\% & 30\\% \\\\\n\t\t\t& & & 30\\% & 40\\% & 33\\% & 32\\% & 33\\% & 34\\% & 34\\% & 38\\% & 33\\% & 32\\% & 32\\% & 33\\% & 32\\% & 32\\% \\\\\n\t\t\t\\midrule\n\t\t\t& & & 16\\% & 32\\% & 23\\% & 19\\% & 23\\% & 25\\% & 25\\% & 56\\% & 25\\% & 20\\% & 21\\% & 22\\% & 19\\% & 19\\% \\\\\n\t\t\t& & & 18\\% & 34\\% & 25\\% & 23\\% & 25\\% & 27\\% & 26\\% & 56\\% & 27\\% & 23\\% & 24\\% & 25\\% & 23\\% & 23\\% \\\\\n\t\t\tScenario 3 & 15 & 1000 & 21\\% & 34\\% & 26\\% & 25\\% & 28\\% & 28\\% & 28\\% & 56\\% & 28\\% & 25\\% & 27\\% & 28\\% & 25\\% & 25\\% \\\\\n\t\t\t& & & 24\\% & 37\\% & 31\\% & 28\\% & 30\\% & 30\\% & 30\\% & 57\\% & 31\\% & 28\\% & 30\\% & 32\\% & 28\\% & 28\\% \\\\\n\t\t\t\\midrule\n\t\t\t& & & 22\\% & 35\\% & 29\\% & 23\\% & 26\\% & 26\\% & 26\\% & 72\\% & 28\\% & 24\\% & 25\\% & 26\\% & 23\\% & 24\\% \\\\\n\t\t\t& & & 22\\% & 36\\% & 29\\% & 24\\% & 27\\% & 29\\% & 28\\% & 72\\% & 28\\% & 25\\% & 27\\% & 28\\% & 24\\% & 25\\% \\\\\n\t\t\tScenario 4 & 18 & 1000 & 25\\% & 40\\% & 32\\% & 26\\% & 30\\% & 32\\% & 33\\% & 68\\% & 36\\% & 30\\% & 31\\% & 32\\% & 30\\% & 30\\% \\\\\n\t\t\t& & & 27\\% & 41\\% & 32\\% & 29\\% & 32\\% & 33\\% & 34\\% & 70\\% & 37\\% & 31\\% & 33\\% & 34\\% & 31\\% & 32\\% \\\\\n\t\t\t\\bottomrule\n\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Optimal trees selection for classification via out-of-bag assessment and sub-bagging", "authors": ["Zardad Khan", "Naz Gul", "Nosheen Faiz", "Asma Gul", "Werner Adler", "Berthold Lausen"], "url": "https://arxiv.org/abs/2012.15301v1", "attribution": "\"Optimal trees selection for classification via out-of-bag assessment and sub-bagging\" by Zardad Khan, Naz Gul, Nosheen Faiz, Asma Gul, Werner Adler, and Berthold Lausen, arXiv:2012.15301v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.12595v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Top-4 Most Similar Tasks for Different Stroke Types Using Various Methods}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\n\\multirow{2}{*}{\\textbf{Stroke}} & \\multirow{2}{*}{\\textbf{Rank}} & \\textbf{CHI2} & \\textbf{DAG} & \\textbf{ICP} & \\textbf{MR} \\\\\n\\textbf{Type} & & \\textbf{Similarity} & \\textbf{Similarity} & \\textbf{Similarity} & \\textbf{Similarity} \\\\\n\\hline\n\\multirow{4}{*}{G45 (TIA)} \n & 1 & I63 (0.87) & E87 (0.81) & I63 (0.70) & I63 (0.31) \\\\\n & 2 & I64 (0.86) & I20 (0.80) & I67 (0.66) & I64 (0.28) \\\\\n & 3 & I50 (0.69) & I47 (0.80) & I61 (0.49) & I62 (0.27) \\\\\n & 4 & I48 (0.68) & I67 (0.79) & I24 (0.30) & I30 (0.26) \\\\\n\\hline\n\\multirow{4}{*}{I60 (SAH)} \n & 1 & I45 (0.50) & I30 (0.86) & I61 (0.55) & I61 (0.33) \\\\\n & 2 & I50 (0.46) & E87 (0.85) & I62 (0.33) & I22 (0.31) \\\\\n & 3 & I26 (0.44) & I48 (0.85) & I67 (0.29) & I67 (0.29) \\\\\n & 4 & I67 (0.42) & I47 (0.84) & I21 (0.25) & I49 (0.28) \\\\\n\\hline\n\\multirow{4}{*}{I61 (ICH)} \n & 1 & I63 (0.88) & I30 (0.88) & I63 (0.68) & I60 (0.33) \\\\\n & 2 & I64 (0.84) & E87 (0.87) & I67 (0.61) & I48 (0.26) \\\\\n & 3 & G45 (0.75) & I67 (0.87) & I60 (0.55) & I64 (0.24) \\\\\n & 4 & I62 (0.59) & I47 (0.86) & I33 (0.52) & I62 (0.24) \\\\\n\\hline\n\\multirow{4}{*}{I62 (SDHs/EDHs)} \n & 1 & I64 (0.61) & I30 (0.82) & I67 (0.43) & I67 (0.30) \\\\\n & 2 & I63 (0.59) & E87 (0.82) & I61 (0.36) & I47 (0.29) \\\\\n & 3 & G45 (0.46) & I48 (0.81) & I60 (0.33) & E11 (0.28) \\\\\n & 4 & I33 (0.34) & I47 (0.81) & I26 (0.32) & I10 (0.27) \\\\\n\\hline\n\\multirow{4}{*}{I63 (IS)} \n & 1 & I50 (0.80) & I48 (0.33) & G45 (0.70) & I26 (0.32) \\\\\n & 2 & I48 (0.79) & I10 (0.33) & I61 (0.68) & G45 (0.29) \\\\\n & 3 & I67 (0.77) & E87 (0.32) & I67 (0.64) & I67 (0.28) \\\\\n & 4 & I44 (0.67) & I47 (0.32) & I33 (0.57) & I30 (0.25) \\\\\n\\hline\n\\multirow{4}{*}{I64 (CVA)} \n & 1 & I63 (0.95) & E87 (0.80) & None & I26 (0.28) \\\\\n & 2 & I50 (0.69) & I48 (0.79) & None & G45 (0.27) \\\\\n & 3 & I67 (0.60) & I47 (0.79) & None & I50 (0.26) \\\\\n & 4 & I45 (0.59) & I30 (0.79) & None & I30 (0.26) \\\\\n\\hline\n\\multicolumn{6}{l}{\\small Ranking of task similarity and similarity score for corresponding method in brackets} \\\\\n\\multicolumn{6}{p{14cm}}{\\small ICD-10 Code Descriptions: G45: Transient cerebral ischemic attacks and related syndromes, \nI60: Subarachnoid hemorrhage, I61: Intracerebral hemorrhage, I62: Other nontraumatic intracranial hemorrhage, \nI63: Cerebral infarction, I64: Stroke, not specified as hemorrhage or infarction, I10: Essential (primary) hypertension, \nI20: Angina pectoris, I21: Acute myocardial infarction, I22: Subsequent myocardial infarction, \nI24: Other acute ischemic heart diseases, I26: Pulmonary embolism, I30: Acute pericarditis, \nI33: Acute and subacute endocarditis, I44: Atrioventricular and left bundle-branch block, \nI45: Other conduction disorders, I47: Paroxysmal tachycardia, I48: Atrial fibrillation and flutter, \nI49: Other cardiac arrhythmias, I50: Heart failure, I67: Other cerebrovascular diseases, \nE11: Type 2 diabetes mellitus, E87: Other disorders of fluid, electrolyte and acid-base balance}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal Mechanisms", "authors": ["Sophie Wharrie", "Lisa Eick", "Lotta Mäkinen", "Andrea Ganna", "Samuel Kaski", "FinnGen"], "url": "https://arxiv.org/abs/2310.12595v3", "attribution": "\"Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal Mechanisms\" by Sophie Wharrie, Lisa Eick, Lotta Mäkinen, Andrea Ganna, Samuel Kaski, and FinnGen, arXiv:2310.12595v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.09735v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{DG spaces using complete polynomials}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n{$k$} & {$\\mathcal{V}_h^-$}\n & {$\\mathcal{V}_h$} \n& {$\\mathcal{V}_h^+$} \\\\\n\\hline\n 0 & N/A & $\\mathcal{P}_{r+1}(\\mathcal{T}_h)$ & $[\\mathcal{P}_r(\\mathcal{T}_h)]^3$\\\\\n 1 & $\\mathcal{P}_{r+1}(\\mathcal{T}_h)$ & $[\\mathcal{P}_r(\\mathcal{T}_h)]^3$ & $[\\mathcal{P}_{r-1}(\\mathcal{T}_h)]^3$ \\\\\n 2 & $[\\mathcal{P}_{r+1}(\\mathcal{T}_h)]^3$ & $[\\mathcal{P}_r(\\mathcal{T}_h)]^3$ & $\\mathcal{P}_{r-1}(\\mathcal{T}_h)$ \\\\\n 3 & $[\\mathcal{P}_{r+1}(\\mathcal{T}_h)]^3$ & $\\mathcal{P}_r(\\mathcal{T}_h)$ & N/A \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An Extended Galerkin analysis in finite element exterior calculus", "authors": ["Qingguo Hong", "Yuwen Li", "Jinchao Xu"], "url": "https://arxiv.org/abs/2101.09735v1", "attribution": "\"An Extended Galerkin analysis in finite element exterior calculus\" by Qingguo Hong, Yuwen Li, and Jinchao Xu, arXiv:2101.09735v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.18336v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lrrrr}\n\\toprule\n\\textbf{language} & \\textbf{\\#doc} & \\textbf{\\#ent} & \\textbf{\\#rel} & \\textbf{\\#attr} \\\\ \\midrule\nde & 118 & 3,487 & 2,163 & 1,141 \\\\\nfr & 100 & 1,939 & 1,129 & 537 \\\\ \nja & 619 & 9,464 & 5,083 & 2,364 \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages", "authors": ["Lisa Raithel", "Hui-Syuan Yeh", "Shuntaro Yada", "Cyril Grouin", "Thomas Lavergne", "Aurélie Névéol", "Patrick Paroubek", "Philippe Thomas", "Tomohiro Nishiyama", "Sebastian Möller", "Eiji Aramaki", "Yuji Matsumoto", "Roland Roller", "Pierre Zweigenbaum"], "url": "https://arxiv.org/abs/2403.18336v1", "attribution": "\"A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages\" by Lisa Raithel, Hui-Syuan Yeh, Shuntaro Yada, Cyril Grouin, Thomas Lavergne, Aurélie Névéol, Patrick Paroubek, Philippe Thomas, Tomohiro Nishiyama, Sebastian Möller, Eiji Aramaki, Yuji Matsumoto, Roland Roller, and Pierre Zweigenbaum, arXiv:2403.18336v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2211.04184v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\large Table 1: Connectedness Table / Network Adjacency Matrix, $D$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n \t\t\t\\toprule\n \t\t\t& \\bf $x_1$ & \\bf $x_2$ & \\bf $...$ & \\bf $x_N$ & From Others to $i$ \\\\\n \t\t\t&&&&& (In-Degrees) \\\\ \\midrule\n \t\t\t\\bf $x_1$ & $ d_{11}^H$ & $d_{12}^H$ & $\\cdots$ & $d_{1N}^H$ & $\\Sigma_{j=2}^N d_{1j}^H$ \\\\\n \t\t\t\\bf $x_2$ & $d_{21}^H$ & $d_{22}^H$ & $\\cdots$ & $d_{2N}^H$ & $\\Sigma_{j=1, j \\ne 2}^N d_{2j}^H$ \\\\\n \t\t\t\\bf $\\vdots$ & $\\vdots$& $\\vdots$ & $\\ddots$ & $\\vdots$ & \\vdots \\\\\n \t\t\t\\bf $x_N$ & $d_{N1}^H$ & $d_{N2}^H$ & $\\cdots$ & $ d_{NN}^H$ & $\\Sigma_{j=1}^{N-1} d_{Nj}^H$ \\\\ \\midrule\n \t\t\tTo Others From $j$ & {$\\Sigma_{i=2}^N d_{i1}^H$} & {$\\Sigma_{i=1, i \\ne 2}^N d_{i2}^H$} & {$\\cdots$} & {$\\Sigma_{i=1}^{N-1} d_{iN}^H$} & {$\\Sigma_{i,j=1; i \\ne j}^N d_{ij}^H$} \\\\ \n \t\t\t(Out-Degrees) & & & {} & & \\\\ \n \t\t\t\\bottomrule\n \t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "On the Past, Present, and Future of the Diebold-Yilmaz Approach to Dynamic Network Connectedness", "authors": ["Francis X. Diebold", "Kamil Yilmaz"], "url": "https://arxiv.org/abs/2211.04184v2", "attribution": "\"On the Past, Present, and Future of the Diebold-Yilmaz Approach to Dynamic Network Connectedness\" by Francis X. Diebold and Kamil Yilmaz, arXiv:2211.04184v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.17229v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n \\textbf{Model} & \\textbf{SIG} & \\textbf{BAK} & \\textbf{OVRL} & \\textbf{NISQA} \\\\\n \\midrule\n Voicefixer (before) & 2.657 & 3.080 & 2.295 & 2.919 \\\\\n Voicefixer (after) & \\textbf{3.096} & \\textbf{3.745} & \\textbf{2.756} & \\textbf{3.312} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Evaluation on real-world singing-voice recordings before and after DPO model's annotation}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Multi-Metric Preference Alignment for Generative Speech Restoration", "authors": ["Junan Zhang", "Xueyao Zhang", "Jing Yang", "Yuancheng Wang", "Fan Fan", "Zhizheng Wu"], "url": "https://arxiv.org/abs/2508.17229v1", "attribution": "\"Multi-Metric Preference Alignment for Generative Speech Restoration\" by Junan Zhang, Xueyao Zhang, Jing Yang, Yuancheng Wang, Fan Fan, and Zhizheng Wu, arXiv:2508.17229v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.08543v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccccccccccccccccc}\\hline \\hline & & & \\multicolumn{8}{c|}{Setting ()} & \\multicolumn{8}{c}{Setting ()} \\\\\\hline\n & \\multicolumn{2}{c|}{$p$} & \\multicolumn{4}{c|}{3} & \\multicolumn{4}{c|}{5} & \\multicolumn{4}{c|}{3} & \\multicolumn{4}{c}{5} \\\\\\hline\n & $N$ & $T$ & 200 & 300 & 400 & 500 & 200 & 300 & 400 & 500 & 200 & 300 & 400 & 500 & 200 & 300 & 400 & 500 \\\\\\hline\n & \\multicolumn{18}{c}{(1) Normal distribution} \\\\\\hline\n & 100 & $S_{N}$ & 6.2 & 5.4 & 4.6 & 5.9 & 5.7 & 6.4 & 5.2 & 4.8 & 6.0 & 5.5 & 4.4 & 5.6 & 5.6 & 6.5 & 5.2 & 4.6 \\\\\n & & $L_{N}$ & 3.4 & 3.2 & 3.9 & 4.9 & 3.6 & 4.4 & 3.9 & 3.9 & 2.2 & 2.5 & 3.7 & 4.4 & 3.4 & 3.9 & 3.4 & 4.0 \\\\\n & & $T_{C}$ & 3.5 & 4.8 & 4.6 & 4.9 & 3.9 & 5.4 & 4.3 & 4.9 & 3.3 & 4.3 & 4.2 & 4.2 & 3.3 & 5.1 & 4.1 & 3.6 \\\\\n & & $\\mathrm{CD_{P}}$ & 11.5 & 12.1 & 11.5 & 13.6 & 12.5 & 14.3 & 12.2 & 12.3 & 12.9 & 14.0 & 13.5 & 15.5 & 14.3 & 16.3 & 14.3 & 14.0 \\\\\n & & $\\mathrm{LM_{PUY}}$ & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\\\ \\hline\n & 200 & $S_{N}$ & 5.4 & 5.1 & 3.7 & 6.4 & 5.6 & 5.7 & 5.7 & 5.4 & 5.9 & 5.1 & 4.0 & 6.6 & 5.9 & 5.7 & 6.3 & 5.3 \\\\\n & & $L_{N}$ & 2.1 & 2.9 & 3.6 & 3.6 & 1.8 & 2.4 & 2.2 & 3.1 & 1.5 & 2.7 & 2.6 & 3.4 & 0.9 & 1.9 & 1.7 & 2.1 \\\\\n & & $T_{C}$ & 3.7 & 4.3 & 3.6 & 5.1 & 3.4 & 4.3 & 3.5 & 3.7 & 3.3 & 3.7 & 3.4 & 4.3 & 2.9 & 3.6 & 3.0 & 3.5 \\\\\n & & $\\mathrm{CD_{P}}$ & 12.0 & 11.9 & 10.2 & 13.9 & 12.3 & 12.6 & 13.0 & 12.9 & 13.2 & 14.1 & 12.6 & 15.3 & 13.7 & 14.8 & 15.2 & 14.9 \\\\\n & & $\\mathrm{LM_{PUY}}$ & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\\\ \\hline\n & \\multicolumn{18}{c}{(2) $t_{6}$-distribution} \\\\ \\cline{2-19}\n & 100 & $S_{N}$ & 4.9 & 5.7 & 5.2 & 5.9 & 6.3 & 5.5 & 4.8 & 5.2 & 4.7 & 5.8 & 5.1 & 5.9 & 6.2 & 5.6 & 4.9 & 5.3 \\\\\n & & $L_{N}$ & 3.3 & 4.5 & 3.4 & 5.2 & 2.0 & 2.9 & 3.8 & 4.3 & 2.3 & 3.7 & 3.1 & 5.1 & 1.5 & 2.6 & 3.7 & 3.9 \\\\\n & & $T_{C}$ & 3.7 & 5.6 & 4.8 & 6.0 & 3.9 & 4.1 & 4.2 & 4.6 & 3.5 & 5.2 & 4.7 & 5.6 & 3.9 & 4.3 & 4.7 & 4.6 \\\\\n & & $\\mathrm{CD_{P}}$ & 11.9 & 11.9 & 11.9 & 12.5 & 12.3 & 13.5 & 11.5 & 11.7 & 14.0 & 13.9 & 13.8 & 14.0 & 13.2 & 15.3 & 13.7 & 13.6 \\\\\n & & $\\mathrm{LM_{PUY}}$ & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\\\ \\hline\n & 200 & $S_{N}$ & 5.1 & 4.7 & 4.6 & 4.9 & 5.4 & 6.7 & 6.0 & 4.5 & 4.9 & 5.0 & 4.7 & 5.3 & 5.4 & 7.1 & 5.8 & 4.8 \\\\\n & & $L_{N}$ & 2.4 & 3.5 & 4.2 & 4.2 & 3.0 & 2.5 & 3.2 & 3.2 & 1.4 & 2.1 & 2.9 & 3.9 & 2.0 & 2.4 & 3.0 & 2.8 \\\\\n & & $T_{C}$ & 4.2 & 4.7 & 4.4 & 5.0 & 4.6 & 5.3 & 4.5 & 4.2 & 3.8 & 4.0 & 3.8 & 5.0 & 4.2 & 5.4 & 4.9 & 4.1 \\\\\n & & $\\mathrm{CD_{P}}$ & 10.5 & 11.8 & 9.3 & 14.0 & 11.4 & 12.0 & 13.2 & 12.9 & 12.4 & 13.9 & 11.3 & 15.8 & 13.3 & 14.3 & 15.0 & 15.2 \\\\\n & & $\\mathrm{LM_{PUY}}$ & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\\\ \\hline\n & \\multicolumn{18}{c}{(3) $\\chi_{5}^2$-distribution} \\\\ \\cline{2-19}\n & 100 & $S_{N}$ & 5.6 & 5.3 & 5.8 & 6.4 & 6.7 & 5.2 & 4.8 & 5.8 & 5.8 & 5.2 & 6.0 & 6.6 & 6.9 & 5.2 & 4.6 & 5.6 \\\\\n & & $L_{N}$ & 3.4 & 3.4 & 3.4 & 3.1 & 3.4 & 2.8 & 4.6 & 3.7 & 2.5 & 2.3 & 2.9 & 3.5 & 2.1 & 2.6 & 4.1 & 3.3 \\\\\n & & $T_{C}$ & 5.7 & 3.7 & 4.7 & 5.3 & 5.0 & 4.0 & 3.9 & 5.2 & 4.5 & 3.6 & 4.2 & 4.8 & 4.3 & 3.3 & 3.9 & 4.8 \\\\\n & & $\\mathrm{CD_{P}}$ & 13.9 & 13.2 & 12.4 & 13.7 & 12.4 & 11.7 & 11.4 & 13.0 & 15.6 & 15.6 & 14.6 & 14.8 & 13.7 & 12.9 & 13.6 & 15.1 \\\\\n & & $\\mathrm{LM_{PUY}}$ & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\\\ \\hline\n & 200 & $S_{N}$ & 6.5 & 5.2 & 5.7 & 6.5 & 5.4 & 5.9 & 6.3 & 5.5 & 6.8 & 5.7 & 5.7 & 6.6 & 5.7 & 5.8 & 6.7 & 5.1 \\\\\n & & $L_{N}$ & 1.9 & 3.3 & 3.9 & 4.9 & 3.1 & 3.9 & 4.1 & 3.7 & 1.2 & 2.7 & 3.2 & 3.8 & 1.7 & 3.4 & 3.6 & 3.2 \\\\\n & & $T_{C}$ & 4.4 & 4.5 & 5.0 & 5.1 & 4.7 & 4.6 & 5.2 & 3.9 & 4.0 & 4.1 & 4.5 & 5.0 & 3.8 & 4.1 & 4.4 & 3.8 \\\\\n & & $\\mathrm{CD_{P}}$ & 13.3 & 12.8 & 13.3 & 14.5 & 13.4 & 13.0 & 13.5 & 12.2 & 14.6 & 14.4 & 15.6 & 16.6 & 16.3 & 14.4 & 15.4 & 13.3 \\\\\n & & $\\mathrm{LM_{PUY}}$ & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\\\ \\hline\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Fisher's combined probability test for cross-sectional independence in panel data models with serial correlation", "authors": ["Hongfei Wang", "Binghui Liu", "Long Feng", "Yanyuan Ma"], "url": "https://arxiv.org/abs/2309.08543v1", "attribution": "\"Fisher's combined probability test for cross-sectional independence in panel data models with serial correlation\" by Hongfei Wang, Binghui Liu, Long Feng, and Yanyuan Ma, arXiv:2309.08543v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.00507v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Linear evaluation accuracy (\\%) of contrastive learning methods with the original and sparse projectors on CIFAR-10, CIFAR-100, and ImageNet-100.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccc}\n \\toprule \n Dataset & Framework & Baseline & Sparse Projector & Gains \\\\ \\midrule\n \\multirow{2}{*}{CIFAR-10} & SimCLR & 87.47 & 88.16 & +0.69 \\\\\n & Barlow Twins & 89.00 & 89.48 & +0.48 \\\\ \\midrule\n \\multirow{2}{*}{CIFAR-100} & SimCLR & 58.12 & 61.99 & +3.87\\\\\n & Barlow Twins & 64.16 & 68.15 & +3.99 \\\\ \\midrule\n \\multirow{2}{*}{ImageNet-100} & SimCLR & 67.36 & 68.48 & +1.12 \\\\\n & Barlow Twins & 69.96 & 71.86 & +1.90 \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Projection Head is Secretly an Information Bottleneck", "authors": ["Zhuo Ouyang", "Kaiwen Hu", "Qi Zhang", "Yifei Wang", "Yisen Wang"], "url": "https://arxiv.org/abs/2503.00507v2", "attribution": "\"Projection Head is Secretly an Information Bottleneck\" by Zhuo Ouyang, Kaiwen Hu, Qi Zhang, Yifei Wang, and Yisen Wang, arXiv:2503.00507v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.20247v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Success rates of the CBO method on the non-convex function with heart-shaped constraint.}\n\\begin{tabular}{|c|r|r|r|r|}\n \\hline\n \\multirow{2}{*}{$K$} & \\multicolumn{4}{c|}{${N}$} \\\\\n \\cline{2-5}\n & \\textbf{10} & \\textbf{20} & \\textbf{50} & \\textbf{100} \\\\\n \\hline\n 5 & 0.29 & 0.54 & 0.90 & 0.95 \\\\\n 10 & 0.46 & 0.69 & 0.88 & 0.99 \\\\\n 20 & 0.54 & 0.81 & 0.95 & 1.00 \\\\\n 50 & 0.59 & 0.78 & 0.98 & 0.99 \\\\\n 100 & 0.57 & 0.81 & 0.97 & 1.00 \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Well-posedness and approximation of reflected McKean-Vlasov SDEs with applications", "authors": ["P. D. Hinds", "A. Sharma", "M. V. Tretyakov"], "url": "https://arxiv.org/abs/2412.20247v1", "attribution": "\"Well-posedness and approximation of reflected McKean-Vlasov SDEs with applications\" by P. D. Hinds, A. Sharma, and M. V. Tretyakov, arXiv:2412.20247v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08477v2_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|r|r|r|r}\n \\hline\n \\multicolumn{2}{c}{} & \\multicolumn{2}{|c}{End item demand only} & \\multicolumn{2}{|c}{Component demand} \\\\\n \\hline \n Utilization & $\\lambda$ & Runtime (s) & $\\Delta$ (\\%) & Runtime (s) & $\\Delta$ (\\%)\\\\\n \\hline\n \\multirow{4}{*}{50\\%} \n & 0.1 & 809.2 & 5.22 & 1651.4 & 7.83 \\\\\n & 1 & 324.4 & \\textbf{1.35} & 533.4 & \\textbf{2.69} \\\\\n & 10 & 260.7 & 2.40 & 354.4 & 3.03 \\\\\n & 100 & 288.9 & 2.53 & 377.3 & 3.06 \\\\\n \\hline\n \\multirow{4}{*}{90\\%} \n & 0.1 & 905.4 & 7.79 & 1950.4 & 5.31\\\\\n & 1 & 394.5 & \\textbf{4.35} & 1012.4 & \\textbf{2.48}\\\\\n & 10 & 436.3 & 4.63 & 600.0 & 2.50\\\\\n & 100 & 329.2 & 5.91 & 768.8 & \\textbf{2.48} \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Majority voting consensus calculation: Evaluation of the $\\lambda$-multiple in terms of average runtime and cost delta to optimal cost on a scenario tree with $|\\Omega| = 2$. Results are reported for 96 test instances grouped by utilization and component type.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand", "authors": ["Manuel Schlenkrich", "Jean-François Cordeau", "Sophie N. Parragh"], "url": "https://arxiv.org/abs/2503.08477v2", "attribution": "\"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand\" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2009.10392v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccccccccc|c} \\hline \\hline\nManual& \\multicolumn{3}{c}{BL Label} & \\multicolumn{3}{c}{LM Label} & \\multicolumn{3}{c}{MPQA Label} &\\\\\nLabel & {Pos} & {Neg} & {Neu} & {Pos} & {Neg} & {Neu} & {Pos} & {Neg} & {Neu} & {Total} \\\\\\hline\n{Pos} & 56 & 4 & 1 & 41 & 12 & 8 & 61 & 0 & 0 & 61\\\\\n{Neg} & 9 & 2 & 1 & 0 & 9 & 3 & 9 & 2 & 1 & 12\\\\\n{Neu} & 22 & 5 & 0 & 10 & 15 & 2 & 26 & 0 & 1 & 27\\\\\\hline\n{Total} & 87 & 11 & 2 & 51 & 36 & 13 & 96 & 2 & 2 & 100\\\\\n\t\t\\hline\n \\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Sentiment Classification Results for 100 Randomly Selected Articles}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Distillation of News Flow into Analysis of Stock Reactions", "authors": ["Junni L. Zhang", "Wolfgang Karl Härdle", "Cathy Y. Chen", "Elisabeth Bommes"], "url": "https://arxiv.org/abs/2009.10392v1", "attribution": "\"Distillation of News Flow into Analysis of Stock Reactions\" by Junni L. Zhang, Wolfgang Karl Härdle, Cathy Y. Chen, and Elisabeth Bommes, arXiv:2009.10392v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.00070v3_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Image reconstruction normalized mean-squared error computed for each reconstruction approach within the abnormality regions. The metric is reported per compartment and was computed on the (A) 4$\\times$ and (B) 8$\\times$ retrospectively undersampled MRIs from the Fast-MRI knee validation set. `All' refers to the average NMSE on all the bounding boxes. \\textbf{Bold} values represent statistically significant difference when a model's performance was compared against the relative baseline reconstruction method (e.g. (FNAF U-Net vs U-Net), (FNAF I-RIM vs I-RIM)). A two-sided Wilcoxon rank-sum test was conducted using $p\\leq \\alpha$, with $\\alpha$ set to $0.05$ as the significance level. Per-abnormality summaries of the NMSE computed in all the samples are reported in Fig.~-~. Column 2- titled N- reports the count of occurrences of each abnormality, in the validation-set.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c|c|c|c|c|c}\n\\hline\\hline\n\\multicolumn{7}{c}{AF=4$\\times$}\\\\\nCompartment & N & U-Net & FNAF U-Net & B-Box U-Net & I-RIM & FNAF I-RIM & B-Box I-RIM \\\\\n\\hline\nCart Med Fem& 19 & 0.04720 & 0.04771 & 0.04695 & 0.04003 & 0.03989 & \\textbf{0.03685} \\\\\nCart Lat Fem& 20 & 0.05818 & 0.06046 & 0.06353 & 0.04588 & 0.04574 & \\textbf{0.04365} \\\\\nCart Med Tib& 5 & 0.08705 & 0.08843 & 0.09137 & 0.09260 & 0.08821 & 0.08289 \\\\\nCart Lat Tib& 7 & 0.06148 & 0.06145 & \\textbf{0.06959} & 0.05064 & 0.05035 & \\textbf{0.04595} \\\\\nBML Med Fem & 24 & 0.04153 & 0.04142 & \\textbf{0.04298} & 0.03880 & \\textbf{0.03790} & \\textbf{0.03440} \\\\\nBML Lat Fem & 29 & 0.05803 & 0.05847 & \\textbf{0.06009} & 0.05245 & \\textbf{0.05099} & \\textbf{0.04690} \\\\\nBML Med Tib & 10 & 0.06031 & 0.06091 & 0.06315 & 0.05905 & \\textbf{0.05744} & \\textbf{0.05201} \\\\\nBML Lat Tib & 16 & 0.04905 & 0.04920 & 0.04730 & 0.04777 & \\textbf{0.04661} & \\textbf{0.04389} \\\\\nMed Men & 11 & 0.04902 & 0.04964 & 0.05014 & 0.04139 & 0.04151 & \\textbf{0.0392} \\\\\nLat Men & 11 & 0.04314 & 0.04421 & \\textbf{0.04838} & 0.03321 & 0.03335 & \\textbf{0.03158} \\\\\nCyst & 5 & 0.03557 & 0.03585 & 0.03665 & 0.03494 & 0.03496 & 0.02945 \\\\\n\\hline\nAll & & 0.05213 & 0.05277 & 0.05437 & 0.04649 & 0.04569 & 0.04232 \\\\\n\\hline\\hline\n \\\\\n\\multicolumn{7}{c}{(A)} \\\\\n\\\\\n\\hline\\hline\n\\multicolumn{7}{c}{AF=8$\\times$}\\\\\nCompartment & N& U-Net & FNAF U-Net & B-Box U-Net & I-RIM & FNAF I-RIM & B-Box I-RIM \\\\\n\\hline\nCart Med Fem & 19 & 0.08435 & 0.08399 & \\textbf{0.07794} & 0.06181 & 0.06418 & \\textbf{0.05611} \\\\\nCart Lat Fem & 20 & 0.08809 & 0.08767 & 0.08742 & 0.06375 & 0.06402 & 0.06216 \\\\\nCart Med Tib & 5 & 0.15370 & 0.15350 & 0.13490 & 0.13850 & 0.13500 & 0.11910 \\\\\nCart Lat Tib & 7 & 0.11830 & 0.11790 & 0.13470 & 0.09268 & 0.09446 & \\textbf{0.08259} \\\\\nBML Med Fem & 24 & 0.08110 & 0.08178 & 0.07576 & 0.06020 & 0.06065 & \\textbf{0.05374} \\\\\nBML Lat Fem & 29 & 0.10280 & 0.10050 & \\textbf{0.09199} & 0.08084 & 0.08059 & \\textbf{0.06936} \\\\\nBML Med Tib & 10 & 0.10990 & \\textbf{0.10610} & 0.10840 & 0.08304 & \\textbf{0.08070} & 0.07396 \\\\\nBML Lat Tib & 16 & 0.07505 & 0.073440 & 0.07519 & 0.06799 & 0.06768 & \\textbf{0.06196} \\\\\nMed Men & 11 & 0.0828 & 0.08043 & 0.08265 & 0.06721 & 0.06654 & 0.06524 \\\\\nLat Men & 11 & 0.09097 & 0.08793 & 0.09225 & 0.05334 & 0.05439 & 0.05393 \\\\\nCyst & 5 & 0.07787 & 0.07586 & 0.07092 & 0.05993 & 0.06199 & 0.04969 \\\\\n\\hline\nAll & & 0.09228 & 0.091 & 0.08852 & 0.07085 & 0.07108 & 0.06417 \\\\\n\\hline\\hline\n \\\\\n\\multicolumn{7}{c}{(B)} \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Adversarial Robust Training of Deep Learning MRI Reconstruction Models", "authors": ["Francesco Calivá", "Kaiyang Cheng", "Rutwik Shah", "Valentina Pedoia"], "url": "https://arxiv.org/abs/2011.00070v3", "attribution": "\"Adversarial Robust Training of Deep Learning MRI Reconstruction Models\" by Francesco Calivá, Kaiyang Cheng, Rutwik Shah, and Valentina Pedoia, arXiv:2011.00070v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2509.00503v1_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ASR results with various pooling methods under the fixed-length setting.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\hline\n\\textbf{Method} & \\textbf{WER (\\%)} & \\textbf{CER (\\%)} \\\\ \\hline\nEntropy-guided (medium, 15Hz) & 5.6 & 2.9 \\\\\nFixed-length max pooling (2, 25Hz) & 6.4 & 3.5 \\\\\nFixed-length average pooling (2, 25Hz) & 6.7 & 3.8 \\\\\nFixed-length attention pooling (2, 25Hz) & 6.2 & 3.4 \\\\\nFixed-length max pooling (4, 12.5Hz) & 6.8 & 3.6 \\\\\nFixed-length average pooling (4, 12.5Hz) & 7.2 & 3.9 \\\\\nFixed-length attention pooling (4, 12.5Hz) & 6.6 & 3.5 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Entropy-based Coarse and Compressed Semantic Speech Representation Learning", "authors": ["Jialong Zuo", "Guangyan Zhang", "Minghui Fang", "Shengpeng Ji", "Xiaoqi Jiao", "Jingyu Li", "Yiwen Guo", "Zhou Zhao"], "url": "https://arxiv.org/abs/2509.00503v1", "attribution": "\"Entropy-based Coarse and Compressed Semantic Speech Representation Learning\" by Jialong Zuo, Guangyan Zhang, Minghui Fang, Shengpeng Ji, Xiaoqi Jiao, Jingyu Li, Yiwen Guo, and Zhou Zhao, arXiv:2509.00503v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.11538v3_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ASR finetuning results for Librispeech measured in WER(\\%). The ``finetuning'' column indicates the total duration of finetuning data used. With the exception of WavLM, the baseline results were directly retrieved from their respective papers.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \n \\toprule\n Model & Finetuning (hrs) & test-clean\t& test-other \\\\\n \\midrule\n MERaLiON-SpeechEncoder & 100 & 3.3 & 6.1\\\\\n MERaLiON-SpeechEncoder & 960& 2.1& 4.3\\\\\n \\noalign{\\vskip 1mm} \n \\hdashline\n \\noalign{\\vskip 1mm} \n Wav2Vec 2.0 large~ & 100 & 3.1 & 6.3 \\\\\n Wav2Vec 2.0 large~ & 960 & 2.2 & 4.5 \\\\\n HuBERT large & 100 & 2.9& 6.0\\\\\n HuBERT large & 960 & 2.1& 4.3\\\\\n WavLM large (finetuned in-house) & 960 & 2.5 & 4.6\\\\\n Whisper large v2 (zero-shot) & -& 2.7 & 5.2 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond", "authors": ["Muhammad Huzaifah", "Geyu Lin", "Tianchi Liu", "Hardik B. Sailor", "Kye Min Tan", "Tarun K. Vangani", "Qiongqiong Wang", "Jeremy H. M. Wong", "Jinyang Wu", "Nancy F. Chen", "Ai Ti Aw"], "url": "https://arxiv.org/abs/2412.11538v3", "attribution": "\"MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond\" by Muhammad Huzaifah, Geyu Lin, Tianchi Liu, Hardik B. Sailor, Kye Min Tan, Tarun K. Vangani, Qiongqiong Wang, Jeremy H. M. Wong, Jinyang Wu, Nancy F. Chen, and Ai Ti Aw, arXiv:2412.11538v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2308.07329v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|}\n\\hline\n\\textbf{Card} & \\textbf{Value Assigned}\\\\\n\\hline\n2,3,4,5,6 & +1\\\\\n\\hline\n7,8,9 & 0 \\\\\n\\hline\n10,J,Q,K,A & -1\\\\\n\\hline\n\\end{tabular}\n\\caption{Under the Hi-Lo system, each card is assigned a value of either -1,+1 or 0. The lower cards (2-6) in the deck are given a value of +1. The ``neutral cards” (7-9) are given a zero value and the high cards (10,K,Q,J,A) are all given a -1 value. Using the true count, this system allows a player to know his edge and helps him to size his bet at the start of every round.}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Variations on the Reinforcement Learning performance of Blackjack", "authors": ["Avish Buramdoyal", "Tim Gebbie"], "url": "https://arxiv.org/abs/2308.07329v1", "attribution": "\"Variations on the Reinforcement Learning performance of Blackjack\" by Avish Buramdoyal and Tim Gebbie, arXiv:2308.07329v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.04498v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|l|}\n \\hline\n \\textbf{Polynomials $f$} & \\textbf{Generator Polynomial} & \\textbf{Parameters} \\\\\n \\hline\n $x^{15} + x^3 + x^2 + \\xi$ & $x^3 + x^2 + \\xi^2$ & $[15, 12, 3]_4$ \\\\\n \\hline\n $x^{15} + \\xi x^3 + x^2 + \\xi$ & $x^5 + \\xi^2 x^3 + x + \\xi^2$ & $[15, 10, 4]_4$ \\\\\n \\hline\n $x^{15} + \\xi x^3 + x^2 + \\xi$ & $x^4 + x^3 + \\xi x^2 + \\xi x + \\xi^2$ & $[15, 11, 4]_4$ \\\\\n \\hline\n $x^{15} + x^6 + x^2 + \\xi$ & $x^{10} + x^8 + \\xi^2 x^7 + \\xi x^6 + \\xi x^5 + \\xi^2 x^4 + \\xi^2 x^3 + \\xi x^2 + \\xi x + 1$ & $[15, 5, 8]_4$ \\\\\n \\hline\n $x^{15} + \\xi x^{12} + x^2 + 1$ & $x^6 + \\xi x^5 + x^4 + \\xi x^3 + \\xi x^2 + \\xi x + \\xi^2$ & $[15, 9, 5]_4$ \\\\\n \\hline\n $x^{15} + \\xi^2 x^{12} + x + \\xi^2$ & $x^4 + x^3 + \\xi^2 x^2 + \\xi^2 x + \\xi$ & $[15, 11, 4]_4$ \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Optimal polycyclic codes with best known parameters of length $n=15$ over $\\mathbb{F}_{4} $.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Equivalence of Families of Polycyclic Codes over Finite Fields", "authors": ["Hassan Ou-azzou", "Anna-Lena Horlemann"], "url": "https://arxiv.org/abs/2503.04498v2", "attribution": "\"Equivalence of Families of Polycyclic Codes over Finite Fields\" by Hassan Ou-azzou and Anna-Lena Horlemann, arXiv:2503.04498v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.18953v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study of components in the proposed audio caption generation pipeline for audio-text retrieval tasks on the AudioCaps test set.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Settings}} & \\textbf{T2A} & \\textbf{A2T} \\\\ \\cmidrule(lr){2-3}\n & \\textbf{R@1} & \\textbf{R@1} \\\\ \\midrule\nAudioCaps & $39.7$ & $51.1$ \\\\ \nAudioCaps (AudioSetCaps pipeline) & $39.7$ & $50.9$ \\\\ \\midrule\n\\quad w/o Fine-grained speech and music content & $38.4$ & $48.3$ \\\\\n\\quad w/o Prompt chaining for Qwen-Audio & $39.6$ & $49.2$ \\\\\n\\quad w/o Caption refinement & $38.7$ & $48.9$ \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Large Audio and Language Models", "authors": ["Jisheng Bai", "Haohe Liu", "Mou Wang", "Dongyuan Shi", "Wenwu Wang", "Mark D. Plumbley", "Woon-Seng Gan", "Jianfeng Chen"], "url": "https://arxiv.org/abs/2411.18953v1", "attribution": "\"AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Large Audio and Language Models\" by Jisheng Bai, Haohe Liu, Mou Wang, Dongyuan Shi, Wenwu Wang, Mark D. Plumbley, Woon-Seng Gan, and Jianfeng Chen, arXiv:2411.18953v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.12530v2_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Value-at-Risk at Level $p=0.95$ of the Normalized Total Loss.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrrrrrrrrr} \\toprule \n& \\multicolumn{6}{c}{Binomial Model} & \\quad\\quad & \\multicolumn{6}{c}{Poisson Model} \\\\ \\midrule \n& \\multicolumn{3}{c}{Gamma} & \\multicolumn{3}{c}{Log-Normal} & & \\multicolumn{3}{c}{Gamma} & \\multicolumn{3}{c}{Log-Normal} \\\\ \\midrule \n& $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ & $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ & & $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ & $c_v=0.5$ & $c_v=1.0$ & $c_v=2.0$ \\\\ \\toprule \n \\\\ \n&\\multicolumn{13}{c}{\\textbf{Uniform Accident Occurrence}} \\\\ \n$\\mathbf{\\rho^\\Phi=0.1:}$ & & & & & & & & & & & & & \\\\ \n$\\xi^{1a}$ & 201.2 & 216.3 & 259.3 & 202.6 & 215.7 & 260.7 & & 200.5 & 217.4 & 262.3 & 202.6 & 219.0 & 256.9 \\\\ \n$\\xi^{1b}$ & 195.7 & 210.7 & 252.8 & 198.1 & 209.7 & 251.8 & & 195.6 & 211.3 & 251.9 & 195.1 & 212.0 & 251.5 \\\\ \n$\\xi^{2a}$ & 545.6 & 590.1 & 731.2 & 546.9 & 585.8 & 713.9 & & 539.9 & 585.3 & 737.5 & 542.5 & 593.4 & 704.1 \\\\ \n$\\xi^{2b}$ & 551.7 & 596.4 & 729.6 & 554.0 & 602.3 & 721.5 & & 547.7 & 603.4 & 733.6 & 545.2 & 601.7 & 722.2 \\\\ \n$\\xi^{3a}$ & 836.9 & 908.5 & 1146.5 & 852.0 & 916.8 & 1110.0 & & 839.6 & 916.3 & 1170.1 & 839.1 & 916.5 & 1109.6 \\\\ \n$\\xi^{3b}$ & 879.5 & 950.2 & 1195.6 & 878.8 & 951.1 & 1160.0 & & 880.0 & 953.5 & 1186.7 & 868.6 & 971.8 & 1139.2 \\\\ \n$\\mathbf{\\rho^\\Phi=0.5:}$ & & & & & & & & & & & & & \\\\ \n$\\xi^{1a}$ & 163.8 & 168.0 & 189.3 & 163.1 & 171.1 & 187.6 & & 163.5 & 169.3 & 188.5 & 163.4 & 168.1 & 186.9 \\\\ \n$\\xi^{1b}$ & 179.9 & 187.0 & 208.5 & 180.1 & 187.6 & 204.0 & & 181.6 & 187.5 & 209.4 & 180.8 & 187.6 & 204.4 \\\\ \n$\\xi^{2a}$ & 438.1 & 457.5 & 520.1 & 438.1 & 459.1 & 510.3 & & 437.8 & 457.7 & 510.5 & 440.1 & 459.0 & 511.2 \\\\ \n$\\xi^{2b}$ & 550.4 & 571.3 & 635.4 & 551.7 & 572.1 & 639.8 & & 551.0 & 573.3 & 636.6 & 551.6 & 572.6 & 633.6 \\\\ \n$\\xi^{3a}$ & 771.4 & 803.3 & 910.1 & 772.5 & 807.5 & 913.2 & & 768.5 & 807.0 & 907.5 & 773.0 & 805.2 & 903.4 \\\\ \n$\\xi^{3b}$ & 1003.4 & 1039.9 & 1150.5 & 1006.3 & 1046.8 & 1163.8 & & 1006.9 & 1040.4 & 1157.5 & 1000.2 & 1040.4 & 1156.0 \\\\ \n$\\mathbf{\\rho^\\Phi=0.9:}$ & & & & & & & & & & & & & \\\\ \n$\\xi^{1a}$ & 130.3 & 134.3 & 146.6 & 130.4 & 134.6 & 145.5 & & 129.7 & 135.2 & 147.2 & 130.1 & 135.5 & 146.1 \\\\ \n$\\xi^{1b}$ & 160.6 & 166.2 & 180.3 & 159.9 & 165.8 & 177.2 & & 160.4 & 165.9 & 179.0 & 159.4 & 165.7 & 176.9 \\\\ \n$\\xi^{2a}$ & 432.1 & 446.1 & 482.8 & 433.9 & 447.8 & 481.2 & & 434.0 & 446.0 & 482.4 & 430.6 & 446.9 & 484.4 \\\\ \n$\\xi^{2b}$ & 534.6 & 549.4 & 591.3 & 535.2 & 548.8 & 591.1 & & 535.2 & 545.6 & 593.9 & 532.3 & 549.8 & 588.1 \\\\ \n$\\xi^{3a}$ & 873.2 & 901.6 & 962.3 & 873.9 & 901.5 & 971.2 & & 872.7 & 895.4 & 974.6 & 871.0 & 896.4 & 972.0 \\\\ \n$\\xi^{3b}$ & 1199.1 & 1232.4 & 1314.5 & 1198.0 & 1233.8 & 1314.6 & & 1197.1 & 1227.4 & 1317.2 & 1199.7 & 1234.6 & 1319.8 \\\\ \n\\\\ \n&\\multicolumn{13}{c}{\\textbf{Non-Uniform Accident Occurrence}} \\\\ \n$\\mathbf{\\rho^\\Phi=0.1:}$ & & & & & & & & & & & & & \\\\ \n$\\xi^{1a}$ & 31.8 & 37.2 & 54.3 & 32.1 & 36.4 & 47.1 & & 31.8 & 37.7 & 53.1 & 32.2 & 38.2 & 45.6 \\\\ \n$\\xi^{1b}$ & 32.2 & 38.3 & 53.4 & 31.6 & 36.5 & 45.3 & & 32.3 & 38.1 & 53.0 & 31.8 & 37.4 & 46.8 \\\\ \n$\\xi^{2a}$ & 161.2 & 188.1 & 254.7 & 163.2 & 188.9 & 236.2 & & 159.3 & 188.1 & 264.3 & 163.2 & 187.0 & 235.8 \\\\ \n$\\xi^{2b}$ & 169.7 & 198.9 & 281.8 & 169.4 & 196.4 & 242.7 & & 170.8 & 196.9 & 271.4 & 168.5 & 198.7 & 240.1 \\\\ \n$\\xi^{3a}$ & 456.0 & 520.8 & 683.3 & 456.6 & 526.1 & 635.9 & & 458.2 & 516.2 & 715.3 & 462.8 & 526.0 & 639.1 \\\\ \n$\\xi^{3b}$ & 529.1 & 592.0 & 785.9 & 525.1 & 595.4 & 721.8 & & 522.2 & 593.9 & 772.0 & 527.2 & 588.8 & 740.6 \\\\ \n$\\mathbf{\\rho^\\Phi=0.5:}$ & & & & & & & & & & & & & \\\\ \n$\\xi^{1a}$ & 19.6 & 21.7 & 28.9 & 19.6 & 22.0 & 27.0 & & 19.5 & 22.0 & 28.5 & 19.6 & 21.8 & 27.2 \\\\ \n$\\xi^{1b}$ & 21.7 & 24.2 & 31.4 & 22.1 & 24.6 & 29.6 & & 21.8 & 24.3 & 31.4 & 21.8 & 24.3 & 30.3 \\\\ \n$\\xi^{2a}$ & 134.9 & 146.5 & 182.0 & 135.6 & 146.3 & 177.6 & & 134.7 & 146.8 & 182.5 & 134.5 & 145.9 & 174.4 \\\\ \n$\\xi^{2b}$ & 159.9 & 171.2 & 207.7 & 159.7 & 172.6 & 206.2 & & 158.1 & 171.6 & 212.4 & 158.5 & 173.2 & 206.1 \\\\ \n$\\xi^{3a}$ & 523.3 & 560.8 & 649.9 & 522.3 & 557.5 & 643.2 & & 522.5 & 555.7 & 647.6 & 519.2 & 557.5 & 647.9 \\\\ \n$\\xi^{3b}$ & 699.1 & 737.3 & 838.8 & 704.5 & 740.7 & 828.7 & & 704.3 & 735.5 & 843.6 & 704.3 & 735.4 & 829.2 \\\\ \n$\\mathbf{\\rho^\\Phi=0.9:}$ & & & & & & & & & & & & & \\\\ \n$\\xi^{1a}$ & 16.7 & 18.1 & 22.8 & 16.6 & 18.4 & 21.8 & & 16.5 & 18.1 & 22.9 & 16.8 & 18.4 & 22.2 \\\\ \n$\\xi^{1b}$ & 18.5 & 20.5 & 25.3 & 18.5 & 20.2 & 24.5 & & 18.6 & 20.3 & 25.3 & 18.6 & 20.5 & 24.8 \\\\ \n$\\xi^{2a}$ & 94.1 & 100.1 & 122.8 & 93.5 & 101.1 & 121.3 & & 93.3 & 100.7 & 121.8 & 93.7 & 101.1 & 119.1 \\\\ \n$\\xi^{2b}$ & 112.7 & 121.5 & 144.3 & 112.9 & 120.7 & 143.7 & & 113.4 & 121.6 & 145.8 & 113.1 & 122.3 & 143.1 \\\\ \n$\\xi^{3a}$ & 518.4 & 542.6 & 605.0 & 519.2 & 543.8 & 605.9 & & 517.8 & 541.9 & 605.2 & 521.2 & 544.7 & 607.6 \\\\ \n$\\xi^{3b}$ & 623.9 & 649.8 & 720.6 & 622.7 & 650.4 & 720.7 & & 620.7 & 648.9 & 720.0 & 623.8 & 647.2 & 729.1 \\\\ \n\\bottomrule \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Microscopic Traffic Models, Accidents, and Insurance Losses", "authors": ["Sojung Kim", "Marcel Kleiber", "Stefan Weber"], "url": "https://arxiv.org/abs/2208.12530v2", "attribution": "\"Microscopic Traffic Models, Accidents, and Insurance Losses\" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.17633v4_tex_table13.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Performance Comparison of UDA on IA-SSD with different ROS approaches on Waymo to nuScenes (W-N) and LiDAR-CS 32 to Laura Robot data (CS32-R)}\n\\begin{tabular}{clccc}\n\\toprule\n& \\textbf{Method} & \\textbf{mAP3D/BEV} & \\textbf{Change} & \\textbf{Closed Gap \\%} \\\\ \\midrule\\midrule\n\\multirow{7}{*}{W-N}& \\textbf{Source Only} & 1.5 / 4.50 & - & - \\\\\n& \\textbf{UADA3D -ROS/-DS} & 13.71 / 14.42 & 12.21 / 9.92 & 27.31 / 23.05 \\\\\n& \\textbf{UADA3D -ROS/+DS} & 14.15 / 15.07 & 12.65 / 10.57 & 28.29 / 24.56 \\\\\n& \\textbf{UADA3D +ROS/-DS} & 17.10 / 17.44 & 15.60 / 12.94 & 34.90 / 30.07 \\\\\n& \\textbf{UADA3D +ROSC/+DS} & 18.31 / 17.92 & 16.81 / 13.42 & 37.60 / 31.19 \\\\\n& \\textbf{UADA3D +ROS/+DS} & \\textbf{18.33 }/ \\textbf{18.55} & 16.83 / 14.05 & 37.66 / 32.66 \\\\\n& \\textbf{Oracle} & 46.20 / 47.53 & - & - \\\\ \\bottomrule\n\\multirow{7}{*}{CS32-R}& \\textbf{Source Only} & 8.33 / 15.93 & - & - \\\\\n& \\textbf{UADA3D -ROS/-DS} & 18.42 / 25.73 & 10.09 / 9.80 & 28.20 / 27.09 \\\\\n& \\textbf{UADA3D -ROS/+DS} & 20.04 / 27.98 & 11.74 / 12.05 & 32.78 / 33.31 \\\\\n& \\textbf{UADA3D +ROS/-DS} & 20.91 / 28.58 & 12.58 / 12.65 & 35.16 / 34.97 \\\\\n& \\textbf{UADA3D +ROSC/+DS} & 21.36 / 30.22 & 13.03 / 14.29 & 36.42 / 39.51 \\\\\n& \\textbf{UADA3D+ROS/+DS} & \\textbf{22.15}/ \\textbf{31.90} & 13.82 / 15.97 & 38.62 / 44.14 \\\\\n& \\textbf{Oracle} & 44.11 / 52.10 & - & - \\\\ \\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps", "authors": ["Maciej K Wozniak", "Mattias Hansson", "Marko Thiel", "Patric Jensfelt"], "url": "https://arxiv.org/abs/2403.17633v4", "attribution": "\"UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps\" by Maciej K Wozniak, Mattias Hansson, Marko Thiel, and Patric Jensfelt, arXiv:2403.17633v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.11461v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Examples of circular and axial densities.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llccc}\n \\toprule\n Name & Range & Density & Location & Concentration\\\\ \\midrule\n Circular von Mises & $[0, 2\\pi)$&$\\frac{\\exp(\\kappa \\cos(x-\\mu))}{2\\pi I_0(\\kappa)}$ & $0 \\leq \\mu < 2\\pi$ & $\\kappa \\geq 0$\\\\ \\\\\n Circular wrapped Cauchy & $[0, 2\\pi)$ & $\\frac{1-\\kappa^2}{2\\pi(1+\\kappa^2 - 2\\kappa\\cos(x-\\mu))}$ &$0 \\leq \\mu < 2\\pi$ & $0 \\leq \\kappa <1$\\\\ \\\\\n Axial von Mises & $[0, \\pi)$ &$\\frac{\\exp(\\kappa \\cosh(y-\\mu))}{\\pi I_0(\\kappa)}$ &$0 \\leq \\mu < \\pi$&$\\kappa \\geq 0$\\\\ \\\\\n Axial wrapped Cauchy & $[0, \\pi)$& $\\frac{1}{\\pi}\\frac{1-\\kappa^4}{1+\\kappa^4-2\\kappa^2\\cos(2(y-\\mu))}$ & $0 \\leq \\mu < \\pi$ &$0 \\leq \\kappa <1$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Does wind affect the orientation of vegetation stripes? A copula-based mixture model for axial and circular data", "authors": ["Marco Mingione", "Francesco Lagona", "Priyanka Nagar", "Francois von Holtzhausen", "Andriette Bekker", "Janine Schoombie", "Peter C. le Roux"], "url": "https://arxiv.org/abs/2411.11461v1", "attribution": "\"Does wind affect the orientation of vegetation stripes? A copula-based mixture model for axial and circular data\" by Marco Mingione, Francesco Lagona, Priyanka Nagar, Francois von Holtzhausen, Andriette Bekker, Janine Schoombie, and Peter C. le Roux, arXiv:2411.11461v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13822v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Error profile for computing .}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|rr|rr} \n \\hline \n$G_i$ & % \\multicolumn{2}{c|}{$\\|\\b u-\\b u_h\\|_{0}$ $O(h^r)$} & \n $\\|\\b u-\\b u_h\\|_{0}$&$O(h^r)$ & $\\|\\nabla_w(\\b u-\\b u_h)\\|_0$ & $O(h^r)$ \\\\ \\hline \n & \\multicolumn{4}{c}{By the $P_1$ WG element on Figure grids} \\\\\n 4& 0.523E+01 & 3.1& 0.395E+03 & 1.7 \\\\\n 5& 0.744E+00 & 2.8& 0.116E+03 & 1.8 \\\\\n 6& 0.125E+00 & 2.6& 0.397E+02 & 1.5 \\\\\n \\hline \n & \\multicolumn{4}{c}{By the $P_2$ WG element on Figure grids} \\\\ \n 4& 0.225E+01 & 4.1& 0.310E+03 & 2.9 \\\\\n 5& 0.140E+00 & 4.0& 0.395E+02 & 3.0 \\\\\n 6& 0.883E-02 & 4.0& 0.502E+01 & 3.0 \\\\\n \\hline \n & \\multicolumn{4}{c}{By the $P_3$ WG element on Figure grids} \\\\ \n 4& 0.567E+00 & 5.0& 0.127E+03 & 4.0 \\\\\n 5& 0.176E-01 & 5.0& 0.797E+01 & 4.0 \\\\\n 6& 0.548E-03 & 5.0& 0.499E+00 & 4.0 \\\\\n \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "An Auto-Stabilized Weak Galerkin Method for Elasticity Interface Problems on Nonconvex Meshes", "authors": ["Chunmei Wang", "Shangyou Zhang"], "url": "https://arxiv.org/abs/2501.13822v1", "attribution": "\"An Auto-Stabilized Weak Galerkin Method for Elasticity Interface Problems on Nonconvex Meshes\" by Chunmei Wang and Shangyou Zhang, arXiv:2501.13822v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13330v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|}\n \\hline\n $d$ & $\\widetilde{E}_{d,\\lambda}$ \\\\ \\hline\n $2$ & $y^2=x(1-x)(x-\\lambda)$ \\\\ \\hline\n $3$ & $y^2+xy+\\frac{\\lambda}{27}y = x^3$ \\\\ \\hline\n $4$ & $y^2=x(x^2+x+\\frac{\\lambda}{4})$ \\\\ \\hline\n $6$ & $y^2+xy=x^3-\\frac{\\lambda}{432}$ \\\\ \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Hypergeometric Distributions and Joint Families of Elliptic Curves", "authors": ["Brian Grove", "Hasan Saad"], "url": "https://arxiv.org/abs/2501.13330v2", "attribution": "\"Hypergeometric Distributions and Joint Families of Elliptic Curves\" by Brian Grove and Hasan Saad, arXiv:2501.13330v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2508.03757v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{First Stage Results: Executive Demographics and AI Investment}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n & \\multicolumn{4}{c}{Dependent Variable: AI Investment} \\\\\n & (1) & (2) & (3) & (4) \\\\\n\\midrule\nCEO Age & -0.018*** & & & -0.015*** \\\\\n & (0.005) & & & (0.006) \\\\\nFemale CEO & & 0.124** & & 0.108** \\\\\n & & (0.052) & & (0.049) \\\\\nTechnical Education & & 0.247*** & & 0.223*** \\\\\n & & (0.067) & & (0.071) \\\\\nTechnology Experience & & & 0.185*** & 0.162** \\\\\n & & & (0.058) & (0.063) \\\\\n\\midrule\nFirm Controls & Yes & Yes & Yes & Yes \\\\\nIndustry FE & Yes & Yes & Yes & Yes \\\\\nYear FE & Yes & Yes & Yes & Yes \\\\\n\\midrule\nObservations & 2,735 & 2,735 & 2,735 & 2,735 \\\\\nR-squared & 0.342 & 0.361 & 0.348 & 0.374 \\\\\nF-statistic & 12.8 & 15.7 & 10.2 & 26.3 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises", "authors": ["Tatsuru Kikuchi"], "url": "https://arxiv.org/abs/2508.03757v1", "attribution": "\"AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises\" by Tatsuru Kikuchi, arXiv:2508.03757v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.08030v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\n\\textbf{ZOO} & \\textbf{1} & \\textbf{2} & \\textbf{3} & \\textbf{4} & \\textbf{5}\\\\\n\\hline\nSuccess Rate (\\%) & 0 & 100 & 100 & 100 & 100\\\\\n\\hline\n\\% of Unrealistic Values & - & 1.6 & 0 & 0 & 0\\\\\n\\hline\n\\# Checked Fields & - & - & 159 & 133 & 153\\\\\n\\hline\n\\# Non-Editable Fields & - & - & - & 37 & 0 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Results obtained with each experiment configuration for ZOO attack}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data", "authors": ["Francesco Cartella", "Orlando Anunciacao", "Yuki Funabiki", "Daisuke Yamaguchi", "Toru Akishita", "Olivier Elshocht"], "url": "https://arxiv.org/abs/2101.08030v1", "attribution": "\"Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data\" by Francesco Cartella, Orlando Anunciacao, Yuki Funabiki, Daisuke Yamaguchi, Toru Akishita, and Olivier Elshocht, arXiv:2101.08030v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.05241v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|c|c|c|}\n\\hline\n\\multicolumn{2}{|c|}{Angle $\\varphi$}&$\\varphi_1=1$&$\\varphi_2=0.5$&$\\varphi_3=0.1$&$\\varphi_4=0.005$ \\\\\n\\hline\n{\\bf Case 1}&$\\mathcal{M}_i(\\varphi)$&$2.5236$ &$1.3580$&$0.3221$&$0.0175$ \\\\\n\\hline\n{\\bf Case 2}&$\\mathcal{M}_i(\\varphi)$&$62.473$ &$21.693$&$1.8607$&$0.0175$\\\\\n\\hline\n\\end{tabular}\n\\caption{Stress values corresponding to different angles of twist for the two cases ({\\bf Example 1})}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Simultaneous identification of the parameters in the plasticity function for power hardening materials : A Bayesian approach", "authors": ["Salih Tatar", "Mohamed BenSalah"], "url": "https://arxiv.org/abs/2412.05241v1", "attribution": "\"Simultaneous identification of the parameters in the plasticity function for power hardening materials : A Bayesian approach\" by Salih Tatar and Mohamed BenSalah, arXiv:2412.05241v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.11088v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameters for experiments in Section }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccc}\n \\toprule\n Environment/task & Number of bins ($n$) & BCQ $\\tau$ & CQL $\\alpha$ & IQL $\\beta$, $\\lambda$ & OneStep $\\lambda$ \\\\\n \\midrule\n Maze & 3 & 0.5 & 1 & 0.5, 20 & 50 \\\\\n Maze & 5 & 0.5 & 0.25 & 0.5, 20 & 20 \\\\\n Maze & 7 & 0.5 & 0.5 & 0.5, 50 & 20 \\\\\n Maze & 10 & 0.5 & 0.5 & 0.5, 50 & 20 \\\\\n Maze & 12 & 0.5 & 0.5 & 0.5, 50 & 50 \\\\\n Maze & 15 & 0.5 & 0.5 & 0.5, 20 & 20 \\\\\n FingerSpin & 3 & 0.5 & 0.25 & 0.5, 1 & 1 \\\\\n FishSwim & 3 & 0.25 & 0.25 & 0.5, 1 & 1 \\\\\n CheetahRun & 3 & 0.05 & 0.25 & 0.5, 1 & 1 \\\\\n QuadrupedWalk & 3 & 0.25 & 0.25 & 0.5, 2 & 1 \\\\\n HumanoidStand & 3 & 0.5 & 0.25 & 0.5, 2 & 2 \\\\\n DogTrot & 3 & 0.25 & 1 & 0.5, 5 & 5 \\\\\n DogTrot & 10 & 0.25 & 0.25 & 0.5, 5 & 2 \\\\\n DogTrot & 30 & 0.75 & 1 & 0.5, 2 & 2 \\\\\n DogTrot & 50 & 0.5 & 0.5 & 0.5, 2 & 2 \\\\\n DogTrot & 75 & 0.7 & 0.5 & 0.5, 2 & 2 \\\\\n DogTrot & 100 & 0.5 & 2 & 0.5, 5 & 5 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces", "authors": ["Alex Beeson", "David Ireland", "Giovanni Montana"], "url": "https://arxiv.org/abs/2411.11088v1", "attribution": "\"An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces\" by Alex Beeson, David Ireland, and Giovanni Montana, arXiv:2411.11088v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2310.20087v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc|ccc}\n \\hline\n $N$ & Sc & Clusters & ARI & NFD \\\\\n \\hline\n \\multirow{3}{3em}{300} & Sc 1 & 3.91 (0.46) & 0.74 (0.13) & 0.09 (0.06) \\\\\n & Sc 2 & 4.01 (0.46) & 0.77 (0.16) & 0.08 (0.05) \\\\\n & Sc 3 & 3.03 (0.36) & 0.75 (0.16) & 0.09 (0.07) \\\\\n \\hline\n \\multirow{3}{3em}{450} & Sc 1 & 4.04 (0.20) & 0.81 (0.03) & 0.06 (0.01) \\\\\n & Sc 2 & 4.02 (0.20) & 0.84 (0.07) & 0.05 (0.02) \\\\\n & Sc 3 & 3.09 (0.32) & 0.83 (0.05) & 0.06 (0.02) \\\\\n \\hline\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Clustering performance for PAM evaluated according to the number of total detected clusters (truth = 4 clusters for Sc 1 and Sc 2; 3 clusters for Sc 3) based on the estimated optimal clustering, the adjusted Rand index (ARI), and the normalized Frobenius distance (NFD). The entries are Mean (SD) over 100 datasets.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data", "authors": ["Dehua Bi", "Tianjian Zhou", "Wei Zhong", "Yuan Ji"], "url": "https://arxiv.org/abs/2310.20087v1", "attribution": "\"PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data\" by Dehua Bi, Tianjian Zhou, Wei Zhong, and Yuan Ji, arXiv:2310.20087v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.11826v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n\\toprule\n Model & Activation & Valence & Dominance \\\\\n \\midrule\nASR features &\t0.649& 0.393 &\t0.544\\\\\nCPC-based pre-traing &\t\\textbf{0.706}&\t0.377&\t\\textbf{0.639}\\\\\nSA2SR features&\t0.679&\t\\textbf{0.412} &\t0.564\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{SA2SR produces good features for enhancing the CCC metric}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition", "authors": ["Ayoub Ghriss", "Bo Yang", "Viktor Rozgic", "Elizabeth Shriberg", "Chao Wang"], "url": "https://arxiv.org/abs/2201.11826v1", "attribution": "\"Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition\" by Ayoub Ghriss, Bo Yang, Viktor Rozgic, Elizabeth Shriberg, and Chao Wang, arXiv:2201.11826v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.14721v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Total number of parameters in the identified input-output systems using T-ERA and ERA at different singular tuple/value truncation levels.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccc}\n\\hline\n\\multirow{1}{*}{Truncation Level \\( k \\)} & \\multicolumn{2}{c}{Number of Parameters} \\\\ \n& T-ERA & ERA \\\\ \n\\hline\n0 & 10725 & 32175 \\\\ \n10 & 7425 & 22275 \\\\ \n20 & 4725 & 14175 \\\\ \n30 & 2625 & 7875 \\\\ \n40 & 1125 & 3375 \\\\ \n50 & 225 & 675 \\\\ \n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Data-driven model order reduction for T-Product-Based dynamical systems", "authors": ["Shenghan Mei", "Ziqin He", "Yidan Mei", "Xin Mao", "Anqi Dong", "Ren Wang", "Can Chen"], "url": "https://arxiv.org/abs/2504.14721v1", "attribution": "\"Data-driven model order reduction for T-Product-Based dynamical systems\" by Shenghan Mei, Ziqin He, Yidan Mei, Xin Mao, Anqi Dong, Ren Wang, and Can Chen, arXiv:2504.14721v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.04758v4_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l}\n\\toprule\n\\bf Setting & \\bf Unlabeled MSA& \\bf Unlabeled DA \\\\ \\hline\nST, $\\tau=0.80$ & 43.88 & 44.46 \\\\\nST, $\\tau=0.90$ & 44.69 & 47.83 \\\\\nST, $\\tau=0.95$ & 43.43 & 46.87 \\\\\n\\hline\nAvg & 43.67 & \\textbf{46.34} \\\\ \n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{ Ablation experiment with MSA unlabeled data for zero-shot NER. Development set macro F1 is shown when using both unlabeled MSA and DA data with the same size. Average performance with DA unlabeled data is higher showing the effect of unlabeled DA on model final performance.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling", "authors": ["Muhammad Khalifa", "Muhammad Abdul-Mageed", "Khaled Shaalan"], "url": "https://arxiv.org/abs/2101.04758v4", "attribution": "\"Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling\" by Muhammad Khalifa, Muhammad Abdul-Mageed, and Khaled Shaalan, arXiv:2101.04758v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08024v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{arydshln}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Effectiveness of the click-adaptive teacher.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Teacher & AUC$_{\\text{CTCVR}}$ & AUC$_{\\text{CVR}}$ & D-AUC$_{\\text{CVR}}$\\\\\n \\midrule\n Click-adaptive Model & \\textbf{0.7485} & \\textbf{0.6664} & \\textbf{0.6689} \\\\\n SingleCVR & 0.7462 & 0.6633 & 0.6657 \\\\\n \\hdashline[2pt/1.2pt]\n No (i.e., \\textit{Joint}) & 0.7445 & 0.6584 & 0.6582 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation", "authors": ["Zixuan Xu", "Penghui Wei", "Weimin Zhang", "Shaoguo Liu", "Liang Wang", "Bo Zheng"], "url": "https://arxiv.org/abs/2201.08024v1", "attribution": "\"UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation\" by Zixuan Xu, Penghui Wei, Weimin Zhang, Shaoguo Liu, Liang Wang, and Bo Zheng, arXiv:2201.08024v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2412.03797v1_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ AUC and Brier score computed by validation set for landmark times $s=0,5,10$ years and prediction window of 5 years for the 3C data. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccccc}\n \\hline\n & &\\multicolumn{4}{c}{lBFDR }&\\multicolumn{4}{c}{BF} \\\\\\hline\n & &\\multicolumn{2}{c}{AUC }&\\multicolumn{2}{c}{BS} &\\multicolumn{2}{c}{AUC }&\\multicolumn{2}{c}{BS} \\\\ \\hline\n & $s$ & Est. & SD. & Est. & SD. & Est. & SD. & Est. & SD. \\\\ \n \\hline\nDS & \\\\ \n & $0$ & 0.891 & 0.037 & 0.019 & 0.005 & 0.885 & 0.034 & 0.019 & 0.005 \\\\ \n & $5$ & 0.891 & 0.018 & 0.130 & 0.014 & 0.846 & 0.027 & 0.152 & 0.016 \\\\\n & $10$ & 0.971 & 0.016 & 0.044 & 0.016 & 0.901 & 0.035 & 0.122 & 0.029 \\\\ \n CS & \\\\ \n & $0$ & 0.731 & 0.070 & 0.058 & 0.006 & 0.827 & 0.075 & 0.020 & 0.004 \\\\ \n & $5$ & 0.851 & 0.022 & 0.184 & 0.016 & 0.895 & 0.017 & 0.109 & 0.012\\\\ \n & $10$ & 0.863 & 0.045 & 0.135 & 0.028 & 0.945 & 0.023 & 0.086 & 0.022 \\\\ \n \\hline \n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A Two-stage Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers and Competing Risk Outcomes", "authors": ["Taban Baghfalaki", "Reza Hashemi", "Christophe Tzourio", "Catherine Helmer", "Helene Jacqmin-Gadda"], "url": "https://arxiv.org/abs/2412.03797v1", "attribution": "\"A Two-stage Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers and Competing Risk Outcomes\" by Taban Baghfalaki, Reza Hashemi, Christophe Tzourio, Catherine Helmer, and Helene Jacqmin-Gadda, arXiv:2412.03797v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00470v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|ccc}\n \\toprule\n \\multirow{2}{*}{\\textbf{Model}} & \\multirow{2}{*}{\\shortstack{\\textbf{Number of}\\\\\\textbf{parameters}}} & \\multicolumn{2}{c}{\\textbf{Learning rates}} \\\\\n & & \\textbf{Fast} & \\textbf{Slow} \\\\\n \\midrule\n PPMI & \\multicolumn{3}{c}{N/A} \\\\\n GloVe & \\multicolumn{3}{c}{N/A} \\\\\n \\midrule\n CBOW & 1.7M & \\multirow{2}{*}{$1\\mathrm{e}{-3}$} & \\multirow{2}{*}{$1\\mathrm{e}{-4}$} \\\\\n Skip-Gram & 7.5M & & \\\\\n \\midrule\n BERT & 108M & \\multirow{2}{*}{$1\\mathrm{e}{-4}$} & \\multirow{2}{*}{$1\\mathrm{e}{-5}$} \\\\\n ALBERT & 12M & & \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Model parameter size comparison and default learning rates.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "An Assessment of the Impact of OCR Noise on Language Models", "authors": ["Konstantin Todorov", "Giovanni Colavizza"], "url": "https://arxiv.org/abs/2202.00470v1", "attribution": "\"An Assessment of the Impact of OCR Noise on Language Models\" by Konstantin Todorov and Giovanni Colavizza, arXiv:2202.00470v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.16188v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n \\textbf{Model} & \\textbf{Modality} & \\textbf{Hours} & \\textbf{WER (\\%)} \\\\\n \\midrule\n \\multicolumn{4}{l}{\\textit{Ours}} \\\\\n \\quad \\textsc{Speech-only (SpiritLM)} & S,T & 30 & 4.43 \\\\\n \\quad \\textsc{AVLM (Q-Former Prefix)} & S,T & 433 & 3.50 \\\\\n \\midrule\n \\multicolumn{4}{l}{\\textit{Prior Work (Lip-only)}} \\\\\n \\quad AV-HuBERT (Large) & T & 433 & 4.20 \\\\\n \\quad MMS-LLaMa & T & 433 & 0.92 \\\\\n \\quad LLaMa-AVSR & T & 433 & 0.95 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{WER (\\%) on clean speech. “Modality” indicates support for speech (S) or text (T) generation.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Seeing is Believing: Emotion-Aware Audio-Visual Language Modeling for Expressive Speech Generation", "authors": ["Weiting Tan", "Jiachen Lian", "Hirofumi Inaguma", "Paden Tomasello", "Philipp Koehn", "Xutai Ma"], "url": "https://arxiv.org/abs/2508.16188v2", "attribution": "\"Seeing is Believing: Emotion-Aware Audio-Visual Language Modeling for Expressive Speech Generation\" by Weiting Tan, Jiachen Lian, Hirofumi Inaguma, Paden Tomasello, Philipp Koehn, and Xutai Ma, arXiv:2508.16188v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.08509v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|r|c|r|r|}\n\\hline\n\\textbf{Facies} & \\textbf{Mean Porosity (\\%)} & \\textbf{Oil Content} & \\textbf{Resistivity (ohm-m)} & \\textbf{Reward} \\\\\n\\hline\nChannel Sand & 20 & Oil-saturated & 171.0 & 1.0 \\\\\nCrevasse Splay Sand & 12 & Partially & 55.0 & 0.5 \\\\\nFloodplain Shale & 2 & No & 4.0 & 0.0 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Mean porosity, oil content, resistivity, and \"per-pixel\" reward $C$ for different facies types.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "DISTINGUISH Workflow: A New Paradigm of Dynamic Well Placement Using Generative Machine Learning", "authors": ["Sergey Alyaev", "Kristian Fossum", "Hibat Errahmen Djecta", "Jan Tveranger", "Ahmed H. Elsheikh"], "url": "https://arxiv.org/abs/2503.08509v1", "attribution": "\"DISTINGUISH Workflow: A New Paradigm of Dynamic Well Placement Using Generative Machine Learning\" by Sergey Alyaev, Kristian Fossum, Hibat Errahmen Djecta, Jan Tveranger, and Ahmed H. Elsheikh, arXiv:2503.08509v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.11203v2_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{rotating}\n\\usepackage{graphicx}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Same as Supplementary Table , but for the Llama model.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llll|rr|rr|r}\n\\toprule\n\\multirow{3}{*}{\\rotatebox[origin=c]{90}{Data}} & \\multirow{3}{*}{k} & \\multirow{3}{*}{Metric} & & \\multicolumn{4}{c}{Green/red list} & \\multirow{2}{*}{Our method} \\\\\n&&&& \\multicolumn{2}{c}{$\\delta = 2$} & \\multicolumn{2}{c}{$\\delta = 1$} & \\\\\n&&&& $\\gamma = 0.25$ & $\\gamma = 0.5$ & $\\gamma = 0.25$ & $\\gamma = 0.5$ & $\\gamma = 0.5$\\\\\n\\midrule\n\\multirow{8}{*}{\\rotatebox[origin=c]{90}{FinQA}} & \\multirow{4}{*}{2} & \\multirow{2}{*}{S-BERT} & mean & 0.7962 & 0.8025 & 0.8199 & 0.8038 & 0.8136 \\\\\n & & & median & 0.8144 & 0.8290 & 0.8391 & 0.8292 & 0.8347 \\\\\n & & TPR & & 0.9900 & 0.9950 & 0.6750 & 0.7550 & 0.9500 \\\\\n & & TPR aug. & & 0.9350 & 0.9550 & 0.4450 & 0.5150 & 0.8550 \\\\\n\\cmidrule{3-9}\n & \\multirow{4}{*}{4} & \\multirow{2}{*}{S-BERT} & mean & 0.8059 & 0.7940 & 0.8247 & 0.8270 & 0.8117 \\\\\n & & & median & 0.8318 & 0.8127 & 0.8381 & 0.8458 & 0.8426 \\\\\n & & TPR & & 0.9750 & 0.9850 & 0.8300 & 0.8300 & 0.9750 \\\\\n & & TPR aug. & & 0.9250 & 0.9300 & 0.5150 & 0.4500 & 0.8400 \\\\\n\\cmidrule{2-9}\n\\multirow{8}{*}{\\rotatebox[origin=c]{90}{ELI5}} & \\multirow{4}{*}{2} & \\multirow{2}{*}{S-BERT} & mean & 0.7077 & 0.7040 & 0.7196 & 0.7195 & 0.7351 \\\\\n & & & median & 0.7181 & 0.7267 & 0.7382 & 0.7315 & 0.7429 \\\\\n & & TPR & & 1.0000 & 1.0000 & 0.9250 & 0.9100 & 1.0000 \\\\\n & & TPR aug. & & 0.9950 & 0.9900 & 0.7150 & 0.6650 & 0.9650 \\\\\n\\cmidrule{3-9}\n & \\multirow{4}{*}{4} & \\multirow{2}{*}{S-BERT} & mean & 0.7169 & 0.7008 & 0.7279 & 0.7226 & 0.7315 \\\\\n & & & median & 0.7385 & 0.7243 & 0.7514 & 0.7433 & 0.7433 \\\\\n & & TPR & & 1.0000 & 1.0000 & 0.9400 & 0.9700 & 1.0000 \\\\\n & & TPR aug. & & 1.0000 & 0.9950 & 0.6450 & 0.6450 & 0.9900 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Debiasing Watermarks for Large Language Models via Maximal Coupling", "authors": ["Yangxinyu Xie", "Xiang Li", "Tanwi Mallick", "Weijie J. Su", "Ruixun Zhang"], "url": "https://arxiv.org/abs/2411.11203v2", "attribution": "\"Debiasing Watermarks for Large Language Models via Maximal Coupling\" by Yangxinyu Xie, Xiang Li, Tanwi Mallick, Weijie J. Su, and Ruixun Zhang, arXiv:2411.11203v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.00493v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Structure of encoder. Regularisation layers are not shown.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|}\n\\hline\n\\textbf{Type} & \\textbf{Parameters} & \\textbf{Input size} \\\\ \\hline\nconv & 100x1/30 & 512x1 \\\\ \\hline\npool & 2 & 512x30 \\\\ \\hline\nconv & 100x1/15 & 256x30 \\\\ \\hline\npool & 2 & 256x15 \\\\ \\hline\nconv & 30x1/15 & 128x15 \\\\ \\hline\npool & 2 & 128x15 \\\\ \\hline\nconv & 20x1/5 & 64x15 \\\\ \\hline\npool & 2 & 64x5 \\\\ \\hline\ndense & 30 & 32x5 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Problems of representation of electrocardiograms in convolutional neural networks", "authors": ["Iana Sereda", "Sergey Alekseev", "Aleksandra Koneva", "Alexey Khorkin", "Grigory Osipov"], "url": "https://arxiv.org/abs/2012.00493v1", "attribution": "\"Problems of representation of electrocardiograms in convolutional neural networks\" by Iana Sereda, Sergey Alekseev, Aleksandra Koneva, Alexey Khorkin, and Grigory Osipov, arXiv:2012.00493v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.03498v3_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Contribution time needed to make the SSF equal to one.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccccc}\n \t\t\t\\hline\n \t\t\t\\multicolumn{13}{c}{IBGE}\\\\\n \t\t\t\\hline\n \t\t\tYear/Age& 53& 54& 55& 56& 57& 58& 59& 60& 61& 62& 63& 64\\\\\n \t\t\t\\hline\n \t\t\t2000& 44.13& 42.63& 40.97& 39.32& 37.85& 36.22& 34.77& 33.33& 31.90& 30.48& 29.07& 27.66\\\\\n \t\t\t2001& 44.48& 42.81& 41.15& 39.50& 38.03& 36.40& 34.95& 33.51& 32.08& 30.66& 29.25& 27.84\\\\\n \t\t\t2002& 44.66& 42.99& 41.33& 39.85& 38.21& 36.75& 35.13& 33.69& 32.26& 30.84& 29.42& 28.02\\\\\n \t\t\t2003& 44.84& 43.17& 41.51& 40.03& 38.39& 36.93& 35.31& 33.87& 32.44& 31.01& 29.60& 28.20\\\\\n \t\t\t2004& 49.27& 47.60& 46.11& 44.46& 42.99& 41.36& 39.91& 38.46& 37.03& 35.61& 34.37& 32.96\\\\\n \t\t\t2005& 49.44& 47.95& 46.29& 44.63& 43.17& 41.71& 40.08& 38.64& 37.21& 35.78& 34.37& 33.13\\\\\n \t\t\t2006& 49.79& 48.12& 46.46& 44.99& 43.34& 41.88& 40.26& 38.81& 37.38& 35.96& 34.54& 33.31\\\\\n \t\t\t2007& 49.97& 48.30& 46.64& 45.16& 43.52& 42.06& 40.43& 38.99& 37.56& 36.13& 34.72& 33.48\\\\\n \t\t\t2008& 50.14& 48.47& 46.99& 45.34& 43.69& 42.23& 40.78& 39.17& 37.73& 36.31& 34.89& 33.66\\\\\n \t\t\t2009& 50.32& 48.83& 47.16& 45.51& 44.04& 42.41& 40.96& 39.52& 37.91& 36.48& 35.07& 33.83\\\\\n \t\t\t2010& 50.67& 49.00& 47.34& 45.69& 44.22& 42.58& 41.13& 39.69& 38.08& 36.66& 35.24& 34.01\\\\\n \t\t\t2011& 50.84& 49.17& 47.51& 46.04& 44.39& 42.76& 41.31& 39.86& 38.26& 36.83& 35.41& 34.18\\\\\n \t\t\t2012& 51.02& 49.35& 47.86& 46.21& 44.57& 43.11& 41.48& 40.04& 38.61& 37.01& 35.59& 34.35\\\\\n \t\t\t2013& 51.02& 49.35& 47.69& 46.04& 44.39& 42.93& 41.31& 39.69& 38.26& 36.66& 35.24& 33.83\\\\\n \t\t\t2014& 51.72& 50.05& 48.39& 46.73& 45.09& 43.63& 42.01& 40.39& 38.95& 37.35& 35.94& 34.53\\\\\n \t\t\t2015& 52.07& 50.40& 48.74& 47.08& 45.44& 43.81& 42.35& 40.74& 39.13& 37.70& 36.28& 34.70\\\\\n \t\t\t2016& 52.41& 50.74& 49.08& 47.43& 45.79& 44.15& 42.53& 41.08& 39.48& 37.88& 36.46& 35.05\\\\\n \t\t\t2017& 52.76& 51.09& 49.26& 47.61& 45.96& 44.50& 42.88& 41.26& 39.65& 38.22& 36.81& 35.22\\\\\n \t\t\t2018& 52.94& 51.27& 49.61& 47.95& 46.31& 44.68& 43.05& 41.61& 40.00& 38.40& 36.98& 35.57\\\\\n \t\t\t2019& 53.28& 51.61& 49.95& 48.30& 46.66& 45.02& 43.40& 41.78& 40.17& 38.74& 37.15& 35.74\\\\\n \t\t\t\\hline\n \t\t\t\\multicolumn{13}{c}{$\\Gamma GM$}\\\\\n \t\t\t\\hline\n \t\t\t2000& 44.48& 42.81& 41.15& 39.50& 37.85& 36.40& 34.77& 33.33& 31.72& 30.30& 28.89& 27.49\\\\\n \t\t\t2001& 44.84& 42.99& 41.33& 39.68& 38.03& 36.58& 34.95& 33.51& 31.90& 30.48& 29.07& 27.66\\\\\n \t\t\t2002& 45.02& 43.35& 41.51& 39.85& 38.39& 36.75& 35.13& 33.69& 32.08& 30.66& 29.25& 27.84\\\\\n \t\t\t2003& 45.20& 43.53& 41.86& 40.21& 38.57& 36.93& 35.31& 33.87& 32.44& 30.84& 29.42& 28.02\\\\\n \t\t\t2004& 48.74& 47.07& 45.41& 43.93& 42.29& 40.65& 39.20& 37.76& 36.33& 34.73& 33.49& 32.08\\\\\n \t\t\t2005& 48.92& 47.25& 45.58& 44.11& 42.46& 40.83& 39.38& 37.94& 36.33& 34.90& 33.49& 32.08\\\\\n \t\t\t2006& 49.09& 47.42& 45.76& 44.28& 42.64& 41.00& 39.55& 37.94& 36.50& 35.08& 33.66& 32.26\\\\\n \t\t\t2007& 49.27& 47.60& 45.94& 44.28& 42.82& 41.18& 39.73& 38.11& 36.68& 35.26& 33.84& 32.43\\\\\n \t\t\t2008& 49.44& 47.77& 46.11& 44.46& 42.99& 41.36& 39.73& 38.29& 36.86& 35.43& 33.84& 32.61\\\\\n \t\t\t2009& 49.62& 47.95& 46.29& 44.63& 43.17& 41.53& 39.91& 38.46& 37.03& 35.43& 34.02& 32.61\\\\\n \t\t\t2010& 49.79& 48.12& 46.46& 44.81& 43.34& 41.71& 40.08& 38.64& 37.21& 35.61& 34.19& 32.78\\\\\n \t\t\t2011& 50.14& 48.30& 46.64& 44.99& 43.52& 41.88& 40.26& 38.81& 37.21& 35.78& 34.37& 32.96\\\\\n \t\t\t2012& 50.32& 48.47& 46.81& 45.16& 43.69& 42.06& 40.43& 38.99& 37.38& 35.96& 34.54& 33.13\\\\\n \t\t\t2013& 51.19& 49.52& 47.69& 46.04& 44.39& 42.76& 41.13& 39.69& 38.08& 36.48& 35.07& 33.66\\\\\n \t\t\t2014& 51.72& 49.87& 48.21& 46.56& 44.92& 43.28& 41.66& 40.04& 38.43& 37.01& 35.41& 34.01\\\\\n \t\t\t2015& 51.89& 50.22& 48.56& 46.73& 45.09& 43.46& 41.83& 40.21& 38.78& 37.18& 35.76& 34.35\\\\\n \t\t\t2016& 52.24& 50.40& 48.74& 47.08& 45.44& 43.81& 42.18& 40.56& 38.95& 37.53& 35.94& 34.53\\\\\n \t\t\t2017& 52.59& 50.74& 49.08& 47.26& 45.61& 43.98& 42.35& 40.74& 39.30& 37.70& 36.28& 34.70\\\\\n \t\t\t2018& 52.76& 51.09& 49.26& 47.61& 45.96& 44.33& 42.70& 41.08& 39.48& 37.88& 36.46& 34.88\\\\\n \t\t\t2019& 53.11& 51.27& 49.61& 47.78& 46.14& 44.50& 42.88& 41.26& 39.65& 38.22& 36.63& 35.22\\\\\n \t\t\t\\hline\n \t\t\t \n \t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "How life-table right-censoring affected the Brazilian Social Security Factor: an application of the gamma-Gompertz-Makeham model", "authors": ["Filipe Costa de Souza", "Wilton Bernardino", "Silvio Cabral Patricio"], "url": "https://arxiv.org/abs/2309.03498v3", "attribution": "\"How life-table right-censoring affected the Brazilian Social Security Factor: an application of the gamma-Gompertz-Makeham model\" by Filipe Costa de Souza, Wilton Bernardino, and Silvio Cabral Patricio, arXiv:2309.03498v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.17799v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccc}\n \\toprule\n \\bfseries Model & \\bfseries Concept Accuracy & \\bfseries Task Accuracy & \\bfseries Task F1 \\\\\n \\midrule\n HuBERT & - & \\textbf{0.9133} \\textnormal{\\scriptsize(±0.0414)} & \\textbf{0.9085} \\textnormal{\\scriptsize(±0.0476)}\\\\\n CBM & 0.8443 \\textnormal{\\scriptsize(±0.0346)} & 0.8776 \\textnormal{\\scriptsize(±0.0247)} & 0.8566 \\textnormal{\\scriptsize(±0.0175)}\\\\\n CEM & \\textbf{0.8450} \\textnormal{\\scriptsize(±0.0205)} & 0.8730 \\textnormal{\\scriptsize(±0.0385)} & 0.8599 \\textnormal{\\scriptsize(±0.0168)}\\\\\n \\midrule\n Ideal CBM & - & 0.9052 \\scriptsize(±0.0259) & 0.8953 \\scriptsize(±0.0309)\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Concept-based approach to Voice Disorder Detection", "authors": ["Davide Ghia", "Gabriele Ciravegna", "Alkis Koudounas", "Marco Fantini", "Erika Crosetti", "Giovanni Succo", "Tania Cerquitelli"], "url": "https://arxiv.org/abs/2507.17799v1", "attribution": "\"A Concept-based approach to Voice Disorder Detection\" by Davide Ghia, Gabriele Ciravegna, Alkis Koudounas, Marco Fantini, Erika Crosetti, Giovanni Succo, and Tania Cerquitelli, arXiv:2507.17799v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.11120v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Evaluation measures for inter-rater variability}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll|c|c}\n\\toprule\n & & \\multicolumn{2}{c}{Inter-Observer} \\\\\n & & PROMISE12 (n=30) & ProstateX (n=20) \\\\\n\\hline\n\\multirow{4}{*}{DSC} & Whole & 0.90 & 0.93 \\\\\n & Apex & 0.80 & 0.90 \\\\\n & Mid & n.a. & 0.96 \\\\\n & Base & 0.86 & 0.89 \\\\\n\\hline\n\\multirow{4}{*}{ABD} & Whole & 1.82 & 0.66 \\\\\n & Apex & 2.55 & 0.63 \\\\\n & Mid & n.a & 0.49 \\\\\n & Base & 2.21 & 0.86 \\\\\n\\hline\n\\multirow{4}{*}{95-HD} & Whole & 5.64 & 3.15 \\\\\n & Apex & 6.36 & 2.84 \\\\\n & Mid & n.a & 2.02 \\\\\n & Base & 6.28 & 3.56 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI", "authors": ["Anneke Meyer", "Grzegorz Chlebus", "Marko Rak", "Daniel Schindele", "Martin Schostak", "Bram van Ginneken", "Andrea Schenk", "Hans Meine", "Horst K. Hahn", "Andreas Schreiber", "Christian Hansen"], "url": "https://arxiv.org/abs/2009.11120v2", "attribution": "\"Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI\" by Anneke Meyer, Grzegorz Chlebus, Marko Rak, Daniel Schindele, Martin Schostak, Bram van Ginneken, Andrea Schenk, Hans Meine, Horst K. Hahn, Andreas Schreiber, and Christian Hansen, arXiv:2009.11120v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2312.15147v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c||c|c|c|c|c||c|c|c|c|c|}\n\t\t\\hline\n\t\t\\rule[-1ex]{0pt}{2.5ex} Kodaira type & $\\text{I}_0$ & $\\text{I}_{n \\ge 1}$ & $\\text{II}$ & $\\text{III}$ & $\\text{IV}$ & $\\text{I}_0^*$ & $\\text{I}_{n \\ge 1}^*$ & $\\text{II}^*$ & $\\text{III}^*$ & $\\text{IV}^*$ \\\\\\hline\n\t\t\\rule[-1ex]{0pt}{2.5ex} Euler characteristic & $0$ & $n$ & $2$ & $3$ & $4$ & $6$ & $n+6$ & $10$ & $9$ & $8$ \\\\\\hline\n\t\t\\rule[-1ex]{0pt}{2.5ex} Monodromy order $m$ & $1$ & $\\infty$ & $6$ & $4$ & $3$ & $2$ & $\\infty$ & $6$ & $4$ & $3$ \\\\\\hline\n\t\t\\rule[-1ex]{0pt}{2.5ex} $d$ for $z^m = t^d$ & $1$ & - & $1$ & $1$ & $1$ & $1$ & - & $5$ & $3$ & $2$ \\\\\\hline\n\t\t\\rule[-1ex]{0pt}{2.5ex} Log canonical threshold $c$ & $1$ & $1$ & $\\frac{5}{6}$ & $\\frac{3}{4}$ & $\\frac{2}{3}$ & $\\frac{1}{2}$ & - & $\\frac{1}{6}$ & $\\frac{1}{4}$ & $\\frac{1}{3}$ \\\\\\hline\n\t\\end{tabular}\n\\caption{Euler characteristic contributions, monodromy, local models, and log canonical thresholds for singular fibers in the Kodaira classification.}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Isotrivial Lagrangian fibrations of compact hyper-Kähler manifolds", "authors": ["Yoon-Joo Kim", "Radu Laza", "Olivier Martin"], "url": "https://arxiv.org/abs/2312.15147v2", "attribution": "\"Isotrivial Lagrangian fibrations of compact hyper-Kähler manifolds\" by Yoon-Joo Kim, Radu Laza, and Olivier Martin, arXiv:2312.15147v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.01121v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Classification accuracy for all the attacks on the MNIST dataset (with $U=2$, wherever applicable).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n\t\t\t\t\\toprule\n\t\t\t\tModel & Method & Benign & PGD & CW & BSA & RAND\\\\\\hline\n\t\t\t\t\\multirow{5}{*}{Softmax} & Baseline & .992 & .082 & .540 & .180 & .270\\\\\n\t\t\t\t& LWTA\\textsuperscript{max} & .993 & .302 & .800 & .390 & .543\\\\\n\t\t\t\t& LWTA\\textsuperscript{max} \\& IBP & \\textbf{.994} & .890 & .920 & .840 & .361\\\\\n\t\t\t\t& LWTA & .992 & .900 & \\textbf{.990} & \\textbf{1.0} & .810\\\\\n\t\t\t\t& LWTA \\& IBP & .992 & \\textbf{.935} & \\textbf{.990} & \\textbf{1.0} & \\textbf{.961}\\\\\\bottomrule\n\t\t\t\t\\multirow{5}{*}{Logistic} & Baseline & .993 & .093 & .660 & .210 & .684\\\\\n\t\t\t\t& LWTA\\textsuperscript{max} & .993 & .388 & .780 & .420 & .700\\\\\n\t\t\t\t& LWTA\\textsuperscript{max} \\& IBP & \\textbf{.993} & .894 & .960 & .950 & .230\\\\\n\t\t\t\t& LWTA & .991 & .856 & \\textbf{.990} & \\textbf{.990} & \\textbf{.982}\\\\\n\t\t\t\t& LWTA \\& IBP & .991 & \\textbf{.901} & \\textbf{.990} & \\textbf{.990} & .981\\\\\\hline\n\t\t\t\t\\multirow{5}{*}{LogisticEns10} & Baseline & .993 & .382 & .880 & .480 & .905\\\\ \n\t\t\t\t& LWTA\\textsuperscript{max} & .993 & .303 & .920 & .520 & .900\\\\\n\t\t\t\t& LWTA\\textsuperscript{max} \\& IBP& .\\textbf{994} & .860 & .940 & .910 & .400\\\\\n\t\t\t\t& LWTA & .992 & .603 & .900 & .550 & .809\\\\\n\t\t\t\t& LWTA \\& IBP& .993 & \\textbf{.889} & \\textbf{.980} & \\textbf{.970} & \\textbf{1.0}\\\\\\hline\n\t\t\t\t\\multirow{5}{*}{Tanh16} & Baseline & .993 & .421 & .790 & .320 & .673\\\\\n\t\t\t\t& LWTA\\textsuperscript{max} & .992 & .462 & .910 & .420 & .573\\\\\n\t\t\t\t& LWTA\\textsuperscript{max} \\& IBP& .\\textbf{994} & .898 & .960 & .940 & .363\\\\\n\t\t\t\t& LWTA & .992 & .862 & \\textbf{.990} & \\textbf{.980} & \\textbf{.785}\\\\\n\t\t\t\t& LWTA \\& IBP& .990 & \\textbf{.900} & \\textbf{.990} & \\textbf{.980} & \\textbf{.785}\\\\\\hline\n\t\t\t\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Local Competition and Stochasticity for Adversarial Robustness in Deep Learning", "authors": ["Konstantinos P. Panousis", "Sotirios Chatzis", "Antonios Alexos", "Sergios Theodoridis"], "url": "https://arxiv.org/abs/2101.01121v2", "attribution": "\"Local Competition and Stochasticity for Adversarial Robustness in Deep Learning\" by Konstantinos P. Panousis, Sotirios Chatzis, Antonios Alexos, and Sergios Theodoridis, arXiv:2101.01121v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.03222v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|}\n\t\t\t\t\\hline\n\t\t\t\tPolicy & Vehicle & Contract & Contract & \\multicolumn{3}{|c|}{Risk Characteristics} & Number of & \\multicolumn{3}{|c|}{Costs of Claim} & Loss\\\\ \n\t\t\t\tNumber & Number & Number & Date & Age & Sex & $\\ldots$ & Claims & \\#1 & \\#2 & \\#3 & Costs\\\\ \n\t\t\t\t\\hline\n\t\t\t\t1 & 1 & 1 & 2018-01-15 & 42 & M & $\\ldots$ & 0 & . & .& .& 0\\\\ \n\t\t\t\t1 & 1 & 2 & 2019-01-15 & 43 & M & $\\ldots$ & 2 & 6,592 & 11,520& .&18,112\\\\ \n\t\t\t\t1 & 1 & 3 & 2020-01-15 & 44 & M & $\\ldots$ & 1 & 24,151 & .& .&24,151\\\\ \n\t\t\t\t1 & 1 & 4 & 2021-01-15 & 45 & M & $\\ldots$ & 0 & . & .& .& 0\\\\ \n\t\t\t\t1 & 2 & 2 & 2019-01-15 & 40 & F & $\\ldots$ & 2 & 1,490 & 24,505 & .&25,995 \\\\ \n\t\t\t\t1 & 2 & 3 & 2020-01-15 & 41 & F & $\\ldots$ & 0 & . & . & .&0\\\\ \n\t\t\t\t2 & 1 & 1 & 2018-02-05 & 24 & M & $\\ldots$ & 0 & . & .& .&0\\\\\n \t\t3 & 1 & 1 & 2018-02-08 & 34 & F & $\\ldots$ & 0 & . & .& .&0\\\\\n \t3 & 2 & 1 & 2018-02-08 & 30 & M & $\\ldots$ & 1 & 8,150 & .& .&8,150\\\\\n\t\t\t\t\\hline\n\t\t\t\\end{tabular}\n\\end{adjustbox}\n\\caption{Illustration of frequency and severity data }\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Bonus-Malus scale premiums for tweedie's compound poisson models", "authors": ["Jean-Philippe Boucher", "Raïssa Coulibaly"], "url": "https://arxiv.org/abs/2311.03222v1", "attribution": "\"Bonus-Malus scale premiums for tweedie's compound poisson models\" by Jean-Philippe Boucher and Raïssa Coulibaly, arXiv:2311.03222v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC0 1.0", "url": "https://creativecommons.org/publicdomain/zero/1.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2403.19820v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage[table]{xcolor}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Similarity measures for the different XAI methods using the DT model and the \\textit{recommended} set of features.}\n\\begin{tabular}{ccccccc}\n\\toprule\n & MDI-DT & MDA-DT & SHAP-DT & LIME-DT & Guidelines & Experts \\\\\n\\cmidrule(lr){1-1}\\cmidrule(lr){2-5}\\cmidrule(lr){6-7}\nMDI-DT & \\cellcolor[gray]\n {0.8}1.00 & 0.53 & 0.80 & 0.53 & 0.24 & 0.34 \\\\\nMDA-DT & 0.53 & \\cellcolor[gray]\n {0.8}1.00 & 0.60 & 0.54 & 0.23 & 0.26 \\\\\nSHAP-DT & 0.80 & 0.60 & \\cellcolor[gray]\n {0.8}1.00 & 0.60 & 0.27 & 0.33 \\\\\nLIME-DT & 0.53 & 0.54 & 0.60 & \\cellcolor[gray]\n {0.8}1.00 & 0.27 & 0.29 \\\\\n\\midrule\nGuidelines & 0.24 & 0.23 & 0.27 & 0.27 & \\cellcolor[gray]\n {0.8}1.00 & 0.82 \\\\\nExperts & 0.34 & 0.26 & 0.33 & 0.29 & 0.82 & \\cellcolor[gray]\n {0.8}1.00 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach", "authors": ["José Bobes-Bascarán", "Eduardo Mosqueira-Rey", "Ángel Fernández-Leal", "Elena Hernández-Pereira", "David Alonso-Ríos", "Vicente Moret-Bonillo", "Israel Figueirido-Arnoso", "Yolanda Vidal-Ínsua"], "url": "https://arxiv.org/abs/2403.19820v1", "attribution": "\"Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach\" by José Bobes-Bascarán, Eduardo Mosqueira-Rey, Ángel Fernández-Leal, Elena Hernández-Pereira, David Alonso-Ríos, Vicente Moret-Bonillo, Israel Figueirido-Arnoso, and Yolanda Vidal-Ínsua, arXiv:2403.19820v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2210.09066v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c||c|c|c|c|c|c|}\n\\hline \n \\textbackslash & $z_0=3$ & $z_1=3$ & $z_2=6$ & $z_3=1$ &\n$z_3=2$ & $z_3=3$ & $ z_3=4 $ & $z_3=5 $ & $ z_3=6 $\\\\ \n\\hline \\hline\nCM & 4.49 & 5.17 & 6.69 & 4.04 & 4.43 & 4.63 & 4.88 & 5.18 & 6.96 \\\\ \\hline\nOT & 11.12 & 12.97 & 8.70 & 14.81 & 12.71 & 11.81 & 10.84 & 9.92 & 8.40 \\\\ \\hline \nNT & 11.53 & 14.33 & 9.44 & 17.71 & 14.48 & 13.21 & 11.91 & 10.68 & 8.85 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Constrained optimal abatement}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Climate uncertainty, financial frictions and constrained efficient carbon taxation", "authors": ["Felix Kübler"], "url": "https://arxiv.org/abs/2210.09066v1", "attribution": "\"Climate uncertainty, financial frictions and constrained efficient carbon taxation\" by Felix Kübler, arXiv:2210.09066v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2503.05574v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Hyperparameters and parameters of the BARK model. For fixed parameters, their value is given. Parameters that are sampled are given the label \\textit{Sampled}.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{clc}\n \\toprule\n Symbol & Description & Value \\\\\n \\midrule\n $m$ & Number of trees in ensemble & 50 \\\\\n $(\\alpha, \\beta)$ & Parameters of node depth prior & $(0.95, 2)$ \\\\\n $(\\nu, q)$ & Parameters of noise prior & $(3, 0.9)$ \\\\\n \\midrule %\n $\\sigma_0^2$ & Signal variance & 1 \\\\\n $\\sigma_y^2$ & Noise variance & \\textit{Sampled} \\\\\n $T_i$ & The $i$th tree structure & \\textit{Sampled} \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "BARK: A Fully Bayesian Tree Kernel for Black-box Optimization", "authors": ["Toby Boyne", "Jose Pablo Folch", "Robert M Lee", "Behrang Shafei", "Ruth Misener"], "url": "https://arxiv.org/abs/2503.05574v2", "attribution": "\"BARK: A Fully Bayesian Tree Kernel for Black-box Optimization\" by Toby Boyne, Jose Pablo Folch, Robert M Lee, Behrang Shafei, and Ruth Misener, arXiv:2503.05574v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2309.07157v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Outage detection performance with noisy data, $\\alpha=1\\%$.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c|c}\n \\toprule\n Noise level & Average Detection Delay & False Alarm Rate \\\\\n \\midrule\n noiseless & $2.51$ & $1.26\\%$\\\\\n 0.1\\% & $2.76$ & $1.36\\%$\\\\\n 0.5\\% & $3.64$ & $2.88\\%$ \\\\\n \\midrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee", "authors": ["Chenhan Xiao", "Yizheng Liao", "Yang Weng"], "url": "https://arxiv.org/abs/2309.07157v1", "attribution": "\"Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee\" by Chenhan Xiao, Yizheng Liao, and Yang Weng, arXiv:2309.07157v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.06001v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Out of Domain Performance Comparison evaluation on (f) of Models Across Different Music Types}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|}\n\\hline\n\\multirow{2}{*}{\\textbf{Music Type}} & \\multicolumn{2}{c|}{\\textbf{Q-SVM}} & \\multicolumn{2}{c|}{\\textbf{ResNet18}} & \\multicolumn{2}{c|}{\\textbf{ViT}} \\\\ \\cline{2-7} \n & \\textbf{Acc} & \\textbf{F1} & \\textbf{Acc} & \\textbf{F1} & \\textbf{Acc} & \\textbf{F1} \\\\ \\hline\na & 0.24 & 0.02 & 0.47 & 0.45 & 0.14 & 0.13 \\\\ \\hline\nbc & 0.72 & 0.79 & 0.57 & 0.55 & 0.76 & 0.77 \\\\ \\hline\nd & 0.61 & 0.71 & 0.68 & 0.68 & 0.44 & 0.43 \\\\ \\hline\ne & 0.58 & 0.70 & 0.29 & 0.31 & 0.09 & 0.11 \\\\ \\hline\nabcde & 0.54 & 0.64 & 0.52 & 0.50 & 0.39 & 0.39 \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "M6: Multi-generator, Multi-domain, Multi-lingual and cultural, Multi-genres, Multi-instrument Machine-Generated Music Detection Databases", "authors": ["Yupei Li", "Hanqian Li", "Lucia Specia", "Björn W. Schuller"], "url": "https://arxiv.org/abs/2412.06001v1", "attribution": "\"M6: Multi-generator, Multi-domain, Multi-lingual and cultural, Multi-genres, Multi-instrument Machine-Generated Music Detection Databases\" by Yupei Li, Hanqian Li, Lucia Specia, and Björn W. Schuller, arXiv:2412.06001v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2310.14332v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|}\n\t\t\\hline\n\t\tAlgorithm & Setup & Single iteration \\\\\n\t\t\\hline\n\t\tStandard MHE & $N=30,N_{T_s}=1$ s & 0.28 s \\\\\n\t\tStandard MHE & $N=30,N_{T_s}=20$ & 4.5 s \\\\\n\t\tMulti-rate MHE & $N=30,N_{T_s}=$Var & 4.2 s \\\\\n\t\t\n\t\t\\hline\n\t\\end{tabular}\n\\caption{ \\textit{Standard} and \\textit{multi-rate} MHE computation times for different configurations.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator", "authors": ["Tushar Desai", "Federico Oliva", "Riccardo M. G. Ferrari", "Daniele Carnevale"], "url": "https://arxiv.org/abs/2310.14332v1", "attribution": "\"Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator\" by Tushar Desai, Federico Oliva, Riccardo M. G. Ferrari, and Daniele Carnevale, arXiv:2310.14332v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2401.00517v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Joint probabilities of child's disease status and triad genotypes}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrlrlrlrlr}\n \\toprule\n Type & $M$ & $F$ & $C$ & $P(D=1,M,F,C)$ & $P(D=0,M,F,C)$\\\\\n \\midrule\n 1 & $0$ & $0$ & $0$ & $\\mu_{00}.1.\\delta$ & $\\mu_{00}.1.[1-\\delta]$ \\\\\n 2 & $0$ & $1$ & $0$ & $\\mu_{01}.\\dfrac{1}{2}.\\delta$ & $\\mu_{01}.\\dfrac{1}{2}.[1-\\delta]$ \\\\\n 3 & $0$ & $1$ & $1$ & $\\mu_{01}.\\dfrac{1}{2}.\\delta R_1$ & $\\mu_{01}.\\dfrac{1}{2}.[1-\\delta R_1]$ \\\\\n 4 & $0$ & $2$ & $1$ & $\\mu_{02}.1.\\delta R_1$ & $\\mu_{02}.1.[1-\\delta R_1]$ \\\\\n 5 & $1$ & $0$ & $0$ & $\\mu_{10}.\\dfrac{1}{2}.\\delta S_1$ & $\\mu_{10}.\\dfrac{1}{2}.[1-\\delta S_1]$ \\\\\n 6 & $1$ & $0$ & $1$ & $\\mu_{10}.\\dfrac{1}{2}.\\delta S_1 R_1 R_{im}$ & $\\mu_{10}.\\dfrac{1}{2}.[1-\\delta S_1R_1 R_{im}]$ \\\\\n 7 & $1$ & $1$ & $0$ & $\\mu_{11}.\\dfrac{1}{4}.\\delta S_1$ & $\\mu_{11}.\\dfrac{1}{4}.[1-\\delta S_1]$ \\\\\n 8 & $1$ & $1$ & $1$ & $\\mu_{11}.\\dfrac{1}{4}.\\delta S_1R_1(1+R_{im})$ & $\\mu_{11}.\\dfrac{1}{4}.[2-\\delta S_1R_1(1+R_{im})]$ \\\\\n 9 & $1$ & $1$ & $2$ & $\\mu_{11}.\\dfrac{1}{4}.\\delta S_1R_2$ & $\\mu_{11}.\\dfrac{1}{4}.[1-\\delta S_1R_2]$ \\\\\n 10 & $1$ & $2$ & $1$ & $\\mu_{12}.\\dfrac{1}{2}.\\delta S_1R_1$ & $\\mu_{12}.\\dfrac{1}{2}.[1-\\delta S_1R_1]$ \\\\\n 11 & $1$ & $2$ & $2$ & $\\mu_{12}.\\dfrac{1}{2}.\\delta S_1R_2$ & $\\mu_{12}.\\dfrac{1}{2}.[1-\\delta S_1R_2]$ \\\\\n 12 & $2$ & $0$ & $1$ & $\\mu_{20}.1.\\delta S_2R_1R_{im}$ & $\\mu_{20}.1.[1-\\delta S_2R_1R_{im}]$ \\\\\n 13 & $2$ & $1$ & $1$ & $\\mu_{21}.\\dfrac{1}{2}.\\delta S_2R_1R_{im}$ & $\\mu_{21}.\\dfrac{1}{2}.[1-\\delta S_2R_1R_{im}]$ \\\\\n 14 & $2$ & $1$ & $2$ & $\\mu_{21}.\\dfrac{1}{2}.\\delta S_2R_2$ & $\\mu_{21}.\\dfrac{1}{2}.[1-\\delta S_2R_2]$ \\\\\n 15 & $2$ & $2$ & $2$ & $\\mu_{22}.1.\\delta S_2R_2$ & $\\mu_{22}.1.[1-\\delta S_2R_2]$ \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Detecting Imprinting and Maternal Effects Using Monte Carlo Expectation Maximization Algorithm", "authors": ["Pooya Aavani", "Alexandre Trindade", "Fangyuan Zhang"], "url": "https://arxiv.org/abs/2401.00517v1", "attribution": "\"Detecting Imprinting and Maternal Effects Using Monte Carlo Expectation Maximization Algorithm\" by Pooya Aavani, Alexandre Trindade, and Fangyuan Zhang, arXiv:2401.00517v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2208.01467v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsfonts}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{One-Way Sorted Portfolios on Network Propagation Factors}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccc}\n \\toprule\n \\multicolumn{9}{c}{Panel A: One-way sorts on upstream propagation beta (controlling for $a_t$ and $g_t$)} \\\\\n \\midrule\n & 1 (Low) & 2 & 3 & 4 & 5 (High) & H-L & t(H-L) & MR p-val \\\\\n \\midrule\n $\\mathbb{E}[r] - r_f$ & 18.10 & 12.81 & 10.59 & 9.42 & 6.69 & -11.42 & -13.22 & 0.07 \\\\\n $\\alpha_{capm}$ & 0.29 & -0.1 & -0.23 & -0.31 & -1.02 & -1.32 & -15.71 & 0.05 \\\\\n $\\alpha_{ff3}$ & 0.08 & -0.09 & -0.22 & -0.29 & -0.54 & -0.63 & -8.61 & 0.09 \\\\\n Volatility (\\%) & 15.54 & 13.89 & 13.59 & 13.03 & 19.66 & - & - & - \\\\\n Book-to-market & 0.52 & 0.56 & 0.53 & 0.55 & 0.50 & - & - & - \\\\\n Market value (\\$bn) & 6.46 & 16.99 & 10.62 & 15.15 & 9.11 & - & - & - \\\\\n \\midrule\n \\multicolumn{9}{c}{Panel B: One-way sorts on downstream propagation beta (controlling for $a_t$ and $g_t$)} \\\\\n \\midrule\n & 1 (Low) & 2 & 3 & 4 & 5 (High) & H-L & t(H-L) & MR p-val \\\\\n \\midrule\n $\\mathbb{E}[r] - r_f$& 13.54 & 13.23 & 11.02 & 9.77 & 9.36 & -4.18 & -7.56 & 0.25 \\\\\n $\\alpha_{capm}$& -0.04 & -0.18 & -0.28 & -0.38 & -0.60 & -0.56 & -4.78 & 0.00 \\\\\n $\\alpha_{ff3}$ & -0.11 & -0.14 & -0.23 & -0.28 & -0.36 & -0.25 & -3.62 & 0.03 \\\\\n Volatility (\\%) & 15.44 & 13.95 & 18.58 & 12.99 & 13.88 & - & - & - \\\\\n Book-to-market & 0.52 & 0.56 & 0.55 & 0.53 & 0.51 & - & - & - \\\\\n Market value (\\$bn) & 15.84 & 7.45 & 4.54 & 17.6 & 12.72 & - & - & - \\\\\n \\midrule\n \\multicolumn{9}{c}{Panel C: One-way sorts on upstream propagation beta (no controls)} \\\\\n \\midrule\n & 1 (Low) & 2 & 3 & 4 & 5 (High) & H-L & t(H-L) & MR p-val \\\\\n \\midrule\n $\\mathbb{E}[r] - r_f$ & 15.15 & 12.61 & 11.46 & 9.44 & 7.23 & -7.91 & -11.57 & 0.07 \\\\\n $\\alpha_{capm}$ & 0.09 & -0.15 & -0.17 & -0.28 & -1.09 & -1.18 & -17.53 & 0.26 \\\\\n $\\alpha_{ff3}$ & -0.12 & -0.14 & -0.18 & -0.22 & -0.58 & -0.46 & -9.96 & 0.31 \\\\\n Volatility (\\%) & 15.26 & 14.23 & 13.57 & 12.61 & 20.97 & - & - & - \\\\\n Book-to-market & 0.54 & 0.58 & 0.52 & 0.52 & 0.50 & - & - & - \\\\\n Market value (\\$bn) & 6.87 & 17.48 & 10.92 & 16.38 & 6.56 & - & - & - \\\\\n \\midrule\n \\multicolumn{9}{c}{Panel D: One-way sorts on downstream propagation beta (no controls)} \\\\\n \\midrule\n & 1 (Low) & 2 & 3 & 4 & 5 (High) & H-L & t(H-L) & MR p-val \\\\\n \\midrule\n $\\mathbb{E}[r] - r_f$ & 12.66 & 11.94 & 11.8 & 8.34 & 5.13 & -7.53 & -8.65 & 0.42 \\\\\n $\\alpha_{capm}$ & -0.15 & -0.18 & -0.19 & -0.4 & -0.64 & -0.49 & -11.37 & 0.41 \\\\\n $\\alpha_{ff3}$ & -0.10 & -0.21 & -0.21 & -0.29 & -0.32 & -0.22 & -4.76 & 0.44 \\\\\n Volatility (\\%) & 14.09 & 13.9 & 14.44 & 13.22 & 31.95 & - & - & - \\\\\n Book-to-market & 0.54 & 0.49 & 0.58 & 0.51 & 0.54 & - & - & - \\\\\n Market value (\\$bn) & 15.97 & 12.79 & 6.33 & 16.88 & 6.34 & - & - & - \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Risk in Network Economies", "authors": ["Victor Sellemi"], "url": "https://arxiv.org/abs/2208.01467v1", "attribution": "\"Risk in Network Economies\" by Victor Sellemi, arXiv:2208.01467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.18203v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Add caption}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n \\textbf{Customer compositions} & \\textbf{Case 1} & \\textbf{Case 2} & \\textbf{Case 3} \\\\\n \\midrule\n Type 1 & 0.2 & 0.4 & 0.1 \\\\\n Type 2 & 0.2 & 0.2 & 0.2 \\\\\n Type 3 & 0.2 & 0.15 & 0.4 \\\\\n Type 4 & 0.2 & 0.1 & 0.2 \\\\\n Type 5 & 0.2 & 0.05 & 0.1 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Joint Design and Pricing of Extended Warranties for Multiple Automobiles with Different Price Bands", "authors": ["Yajing Chen", "Yanrong Li", "Xiao-Lin Wang", "Zhi-Sheng Ye"], "url": "https://arxiv.org/abs/2501.18203v1", "attribution": "\"Joint Design and Pricing of Extended Warranties for Multiple Automobiles with Different Price Bands\" by Yajing Chen, Yanrong Li, Xiao-Lin Wang, and Zhi-Sheng Ye, arXiv:2501.18203v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.01126v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{llcccc} \n \\toprule\n \\multicolumn{2}{c}{\\bf{Transformation}}& $\\boldsymbol{\\hat G_2}$ \\bf{(Ours)} & \\bf{SE} & \\bf{KL} & \\bf{TV} \\\\ \n \\midrule\n \\emph{Isometric} \\\\\n \\cmidrule{1-2}\n A) Identity & $t(x) = x$ & $0.43$ & $2.09$ & $29.4$ & $0.71$\\\\ \n B) Reflection & $t(x) = -x$& $0.43$ & $2.09$ & $\\textcolor{red}{\\boldsymbol{10.0}}$ & $0.71$\\\\ \n C) Translation & $t(x) = x+12$& $0.43$ & $2.09$ & $29.5$ & $0.71$\\\\ \n \\\\\n \\emph{Non-Isometric} \\\\\n \\cmidrule{1-2}\n D) Absolute Value & $t(x) = |x|$& $0.60$ & $\\textcolor{red}{\\boldsymbol{2.09}}$ & \\bf{\\textcolor{red}{DNE}} & $\\textcolor{red}{\\boldsymbol{0.71}}$\\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Axiomatic Explainer Globalness via Optimal Transport", "authors": ["Davin Hill", "Josh Bone", "Aria Masoomi", "Max Torop", "Jennifer Dy"], "url": "https://arxiv.org/abs/2411.01126v2", "attribution": "\"Axiomatic Explainer Globalness via Optimal Transport\" by Davin Hill, Josh Bone, Aria Masoomi, Max Torop, and Jennifer Dy, arXiv:2411.01126v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2504.18130v2_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccc}\n& \\multicolumn{5}{c}{sample size $N$}\\\\\n& 100 & 300 & 1000 & 3000 & 10000 \\\\\n\\midrule\nSBTM (ours) & \\textbf{0.022} & 0.018 & \\textbf{0.0082} & 0.0082 & \\textbf{0.0036} \\\\\nSDE & 0.029 & \\textbf{0.013} & 0.014 & \\textbf{0.0068} & 0.0043 \\\\\nSVGD & 2.8 & 1.4 & 2.4 & 2.1 & 2.0 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Score-Based Deterministic Density Sampling", "authors": ["Vasily Ilin", "Peter Sushko", "Jingwei Hu"], "url": "https://arxiv.org/abs/2504.18130v2", "attribution": "\"Score-Based Deterministic Density Sampling\" by Vasily Ilin, Peter Sushko, and Jingwei Hu, arXiv:2504.18130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.08350v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{siunitx}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{ Summary of the acquisition parameters of the 2DeteCT dataset, adapted from . *$^1$ (Thoraeus = Sn 0.1mm, Cu 0.2mm, Al 0.5mm), *$^2$ these quantities are based on the motor readings of the FleX-ray scanner which get translated into physical quantities and are subject to alignment errors.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|}\n\\hline\n\\textbf{Acquisition parameter} & \\textbf{Mode 1} & \\textbf{Mode 2} & \\textbf{Mode 3} \\\\\n\\hline\nTube voltage & $\\SI{90.0}{\\kilo\\volt}$ & $\\SI{90.0}{\\kilo\\volt}$ & $\\SI{60.0}{\\kilo\\volt}$ \\\\\n\\hline\nTube power & $\\SI{3.0}{\\watt}$ & $\\SI{90.0}{\\watt}$ & $\\SI{60.0}{\\watt}$ \\\\\n\\hline\nFilters used & Thoraeus*$^1$ & Thoraeus & No Filter \\\\\n\\hline\nExposure time & \\multicolumn{3}{c|}{$\\SI{50.0}{\\milli\\second}$} \\\\\n\\hline\nBinned detector pixel size & \\multicolumn{3}{c|}{$\\SI{149.6}{\\micro\\metre}$} \\\\\n\\hline\nNumber of binned detector pixels & \\multicolumn{3}{c|}{956} \\\\\n\\hline\nSource to object distance *$^2$ & \\multicolumn{3}{c|}{$\\SI{431.020}{\\milli\\metre}$} \\\\\n\\hline\nSource to detector distance *$^2$ & \\multicolumn{3}{c|}{$\\SI{529.000}{\\milli\\metre}$} \\\\\n\\hline\nNumber of projections & \\multicolumn{3}{c|}{3601} \\\\\n\\hline\nAngular increment & \\multicolumn{3}{c|}{$\\SI{0.1}{\\deg}$} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Benchmarking learned algorithms for computed tomography image reconstruction tasks", "authors": ["Maximilian B. Kiss", "Ander Biguri", "Zakhar Shumaylov", "Ferdia Sherry", "K. Joost Batenburg", "Carola-Bibiane Schönlieb", "Felix Lucka"], "url": "https://arxiv.org/abs/2412.08350v1", "attribution": "\"Benchmarking learned algorithms for computed tomography image reconstruction tasks\" by Maximilian B. Kiss, Ander Biguri, Zakhar Shumaylov, Ferdia Sherry, K. Joost Batenburg, Carola-Bibiane Schönlieb, and Felix Lucka, arXiv:2412.08350v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.11649v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\usepackage{amsmath}\n\\usepackage{diagbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{The performance comparison of different algorithms. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|l|l|l}\n\t\t\\hline\t\\hline\n\t\t\\diagbox{Algorithms}{Parameters} & $ \\kappa $ & c & T (sec) \\\\\n\t\t\\hline\n\t\tThe algorithm in & 1 & 0 & 21 \\\\ \n\t\t\\hline\n\t\tThe algorithm in Corollary 1 & 10 & 0 & 13 \\\\ \n\t\t\\hline\n\t\tThe proposed algorithm in & 2 & 20 & 1.2 \\\\\n\t\t\\hline \t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Prescribed-Time Fully Distributed Nash Equilibrium Seeking in Noncooperative Games", "authors": ["Zhi Feng", "Guoqiang Hu"], "url": "https://arxiv.org/abs/2009.11649v1", "attribution": "\"Prescribed-Time Fully Distributed Nash Equilibrium Seeking in Noncooperative Games\" by Zhi Feng and Guoqiang Hu, arXiv:2009.11649v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11188v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Rainbow DQN hyperparameters}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|c}\n \\hline\n Parameter & Value \\\\\n \\hline\n Discounting factor $\\gamma$ & $0.9$ \\\\\n Learning rate $\\alpha$ & $5\\cdot10^{-4}$ \\\\\n Batch size & $32$ \\\\\n Online network update period & $4$ steps \\\\\n Learning start & $1,000$ steps \\\\\n Target network sync period & $500$ steps \\\\\n Distributional atoms & 51 \\\\\n Distributional bounds $v_{min}$, $v_{max}$ & [-1,10] \\\\\n Replay buffer capacity & $50,000$ \\\\\n Replay buffer prioritisation exponent $\\omega$ & $0.6$ \\\\\n Replay buffer importance sampling $\\beta$ & $0.6 \\rightarrow 0.4$ \\\\\n Importance sampling annealing & 20,000 steps \\\\\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platform", "authors": ["David Cotton", "Zenon Chaczko"], "url": "https://arxiv.org/abs/2101.11188v1", "attribution": "\"GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platform\" by David Cotton and Zenon Chaczko, arXiv:2101.11188v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.12423v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{DimeNet log-log regressions of training time per epoch on number of GPUs under different power caps but max clock speed (1380 MHz).}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n\\toprule\n\\textbf{Power (W)}&\\textbf{$\\beta_{\\text{\\# GPUs}}$}&\\textbf{Goodness-of-fit $R^2$} %&\\textbf{Standard Error ($\\beta$)} \\\\\n\\\\\n\\midrule\n100 & 0.93 & 0.99 \\\\ %& 0.02\\\\\n200 & 0.84 & 0.99 \\\\ %& 0.03\\\\\n250 & 0.82 & 0.99 \\\\ %& 0.03\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Benchmarking Resource Usage for Efficient Distributed Deep Learning", "authors": ["Nathan C. Frey", "Baolin Li", "Joseph McDonald", "Dan Zhao", "Michael Jones", "David Bestor", "Devesh Tiwari", "Vijay Gadepally", "Siddharth Samsi"], "url": "https://arxiv.org/abs/2201.12423v1", "attribution": "\"Benchmarking Resource Usage for Efficient Distributed Deep Learning\" by Nathan C. Frey, Baolin Li, Joseph McDonald, Dan Zhao, Michael Jones, David Bestor, Devesh Tiwari, Vijay Gadepally, and Siddharth Samsi, arXiv:2201.12423v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10236v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Numerical results of different algorithms on different datasets.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|}\n\t\t\\hline\n\t\t\\multirow{2}{*}{Method} &\\multicolumn{5}{c}{Average Accuracy}\\\\\n\t\t\\cline{2-6}& MNIST & Magic &\tPIMA &\tWeather &\tKDDCUP\\\\\n\t\t\\hline\n\t\t\\textbf{BODL-2}\t& 92.00\\% &\t78.73\\% &\t74.36\\% &\t74.90\\% &\t99.68\\%\\\\\n\t\t\\textbf{BODL-1} &\t91.99\\% &\t78.49\\% &\t73.84\\% &\t73.28\\% &\t99.44\\%\\\\\n\t\t\\textbf{BODL-Base} &\t90.80\\% &\t78.31\\% &\t71.69\\% &\t72.34\\% &\t99.35\\%\\\\\n\t\tPerceptron&\t84.77\\% &\t70.60\\% &\t64.45\\% &\t65.85\\% &\t99.31\\%\\\\\n\t\tROMMA&\t83.22\\% &\t66.67\\% &\t64.45\\% &\t65.63\\% &\t99.34\\%\\\\\n\t\tOGD\t&90.10\\% &\t78.72\\% &\t72.78\\% &\t72.70\\% &\t99.61\\%\\\\\n\t\tSCW\t&88.98\\% &\t78.64\\% &\t70.31\\% &\t76.12\\% &\t99.75\\%\\\\\n\t\tAROW\t&89.04\\% &\t78.71\\% &\t72.14\\% &\t75.15\\% &\t99.58\\%\\\\\n\t\tCW\t&86.88\\% &\t67.90\\% &\t63.41\\% &\t36.81\\% &\t99.62\\%\\\\\n\t\tPA\t&85.68\\% &\t70.13\\% &\t66.41\\% &\t65.74\\% &\t99.41\\%\\\\\n\t\t\\hline\n\t\t\\multirow{2}{*}{Method} &\\multicolumn{5}{c}{Average precision}\\\\\n\t\t\\cline{2-6}& MNIST & Magic &\tPIMA &\tWeather &\tKDDCUP\\\\\n\t\tBODL-2 &\t91.91\\% &\t74.65\\% &\t54.83\\% &\t77.35\\% &\t98.55\\%\\\\\n\t\tBODL-1 &\t91.89\\% &\t73.96\\% &\t54.38\\% &\t75.76\\% &\t97.46\\%\\\\\n\t\tBODL-Base &\t90.70\\% &\t 73.80\\% &\t51.88\\% &\t76.06\\% &\t96.99\\%\\\\\n\t\tPerceptron &\t84.61\\% &\t67.77\\% &\t64.03\\% &\t60.78\\% &\t98.96\\%\\\\\n\t\tROMMA\t &82.99\\% &\t64.16\\% &\t63.74\\% &\t60.69\\% &\t99.08\\%\t\\\\\n\t\tOGD &\t89.99\\% &\t77.66\\% &\t71.77\\% &\t67.91\\% &\t99.35\\%\\\\\n\t\tSCW &\t88.83\\% &\t76.75\\% &\t69.18\\% &\t74.90\\% &\t99.55\\%\\\\\n\t\tAROW &\t88.92\\% &\t77.62\\% &\t70.84\\% &\t71.13\\% &\t99.31\\%\\\\\n\t\tCW &\t86.70\\% &\t64.75\\% &\t62.55\\% &\t53.55\\% &\t99.56\\%\\\\\n\t\tPA\t &85.45\\% &\t67.19\\% &\t65.06\\% &\t59.95\\% &\t99.01\\%\\\\\n\t\t\\hline\n\t\t\\multirow{2}{*}{Method} &\\multicolumn{5}{c}{F1-score}\\\\\n\t\t\\cline{2-6}& MNIST & Magic &\tPIMA &\tWeather &\tKDDCUP\\\\\n\t\t\\hline\n\t\tBODL-2 &\t91.97\\% &\t65.81\\% &\t60.83\\% &\t82.62\\% &\t99.21\\%\\\\\n\t\tBODL-1 &\t91.97\\% &\t65.73\\% &\t63.30\\% &\t81.80\\% &\t98.61\\%\\\\\n\t\tBODL-Base &\t90.78\\% &\t65.70\\% &\t59.66\\% &\t81.69\\% &\t98.38\\%\\\\\n\t\tPerceptron\t &84.78\\% &\t70.61\\% &\t65.27\\% &\t66.07\\% &\t99.31\\%\\\\\n\t\tROMMA\t &83.21\\% &\t67.05\\% &\t65.27\\% &\t65.95\\% &\t99.33\\%\\\\\n\t\tOGD &\t90.08\\% &\t78.02\\% &\t73.39\\% &\t71.04\\% &\t99.61\\%\\\\\n\t\tSCW &\t88.97\\% &\t78.41\\% &\t70.96\\% &\t77.53\\% &\t99.75\\%\\\\\n\t\tAROW &\t88.98\\% &\t78.16\\% &\t72.73\\% &\t74.28\\% &\t99.66\\%\\\\\n\t\tCW &\t86.87\\% &\t67.86\\% &\t64.24\\% &\t29.00\\% &\t99.67\\%\\\\\n\t\tPA &\t85.66\\% &\t70.08\\% &\t67.12\\% &\t65.58\\% &\t99.41\\%\\\\\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Bilevel Online Deep Learning in Non-stationary Environment", "authors": ["Ya-nan Han", "Jian-wei Liu", "Bing-biao Xiao", "Xin-Tan Wang", "Xiong-lin Luo"], "url": "https://arxiv.org/abs/2201.10236v1", "attribution": "\"Bilevel Online Deep Learning in Non-stationary Environment\" by Ya-nan Han, Jian-wei Liu, Bing-biao Xiao, Xin-Tan Wang, and Xiong-lin Luo, arXiv:2201.10236v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.05795v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Average MSE values and standard deviation considering Semeion Handwritten Digit dataset.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|l|c|c|c|c|c|c|c|c|c|c|c|c|}\n\\cline{3-14}\n \\multicolumn{2}{c|}{} & \\multicolumn{4}{c|}{1L} & \\multicolumn{4}{c|}{2L} & \\multicolumn{4}{c|}{3L} \\\\ \\cline{3-14} \n \\multicolumn{2}{c|}{} \t & \\multicolumn{2}{c|}{DBN} & \\multicolumn{2}{c|}{DBM} & \\multicolumn{2}{c|}{DBN} & \\multicolumn{2}{c|}{DBM} & \\multicolumn{2}{c|}{DBN} & \\multicolumn{2}{c|}{DBM} \\\\ \\cline{3-14} \nTechnique& Statistics & CD & PCD & CD & PCD & CD & PCD & CD & PCD & CD & PCD & CD & PCD \\\\ \\hline\n\\multirow{2}{*}{ {IHS}} & Mean& 0.19359 & 0.20009 & \\textbf{0.19025} & \\textbf{0.19078} & 0.20961 & 0.20961 & 0.20956 & 0.20956 & 0.20961 & 0.20963 & 0.20958 & 0.20958\\\\ \n\t\t& Std. & 1.367e-03 & 1.965e-03 & 8.901e-04 & 1.367e-03 & 3.669e-04 & 3.637e-04 & 3.571e-04 & 3.438e-04 & 3.731e-04 & 3.772e-04 & 3.609e-04 & 3.417e-04 \\\\\\hline\n\\multirow{2}{*}{ {AIWPSO}} & Mean & 0.20044 & 0.20274 & 0.19679 & 0.19426 & 0.20959 & 0.20961 & 0.20958 & 0.20956 & 0.20964 & 0.20961 & 0.20959 & 0.20959 \\\\ \n\t\t& Std. & 6.856-03 & 3.994 & 7.995e-03 & 7.044e-03 & 3.521e-04 & 3.853e-04 & 3.644e-04 & 3.619e-04 & 3.773e-04 & 3.584e-04 & 3.784e-04 & 3.664e-04 \\\\\\hline\n\\multirow{2}{*}{ {CS}} & Mean & 0.20528 & 0.20647 & 0.20728 & 0.20651 & 0.20965 & 0.20960 & 0.20957 & 0.20959 & 0.20964 & 0.20963 & 0.20960 & 0.20960 \\\\ \n\t\t& Std. & 4.948-03 & 3.556e-03 & 2.894e-03 & 2.352e-03 & 4.034e-04 & 3.554e-04 & 3.616e-04 & 3.722e-04 & 3 .696e-04 & 3.572e-04 & 3.612e-04 & 3.430e-04 \\\\\\hline\n\\multirow{2}{*}{ {FA}} &Mean & 0.20638 & 0.20894 & 0.20649 & 0.20319 & 0.20966 & 0.20965 & 0.20960 & 0.20960 & 0.20964 & 0.20965 & 0.20960 & 0.20928 \\\\ \n\t\t& Std. & 4.922-03 & 2.085e-03 & 5.630e-03 & 7.548e-03 & 4.098e-04 & 3.855e-04 & 3.609e-04 & 3.605e-04 & 3.499e-04 & 4.117e-04 & 3.387e-04 & 1.555e-03 \\\\\\hline\n\\multirow{2}{*}{ {BSA}} & Mean & 0.19571 & 0.20002 & 0.19221 & 0.19325 & 0.20961 & 0.20959 & 0.20960 & 0.20958 & 0.20962 & 0.20962 & 0.20960 & 0.20956 \\\\ \n\t\t& Std. & 3.648-03 & 2.544e-03 & 2.879e-03 & 2.419e-03 & 3.591e-04 & 3.683e-04 & 3.783e-04 & 3.480e-04 & 3.722e-04 & 3.847e-04 & 3.716e-04 & 3.600e-04 \\\\\\hline\n\\multirow{2}{*}{ {JADE}} & Mean & 0.19893 & 0.20165 & \\textbf{0.19152} & \\textbf{0.19170} & 0.20962 & 0.20960 & 0.20957 & 0.20958 & 0.20964 & 0.20959 & 0.20956 & 0.20961 \\\\ \n\t\t& Std. & 7.890-03 & 5.316e-03 & 4.213e-03 & 4.410e-03 & 3.554e-04 & 3.602e-04 & 3.501e-04 & 3.755e-04 & 3.579e-04 & 3.708e-04 & 3.524e-04 & 3.899e-04 \\\\\\hline\n\\multirow{2}{*}{ {CoBiDE}} & Mean & 0.19328 & 0.19896 & 0.19190 & 0.19138 & 0.20962 & 0.20961 & 0.20959 & 0.20958 & 0.20960 & 0.20961 & 0.20958 & 0.20959 \\\\ \n\t\t& Std. & 1.332-03 & 1.478e-03 & 1.821e-03 & 1.556e-03 & 3.505e-04 & 3.631e-04 & 3.678e-04 & 3.550e-04 & 3.579e-04 & 4.119e-04 & 3.664e-04 & 3.593e-04 \\\\\\hline\n\\multirow{2}{*}{ {RS}} & Mean & 0.19710 & 0.20361 & 0.19458 & 0.19463 & 0.20962 & 0.20959 & 0.20960 & 0.20957 & 0.20960 & 0.20960 & 0.20960 & 0.20959 \\\\ \n\t\t& Std. & 3.133e-03 & 1.837e-03 & 3.891e-03 & 3.909e-03 & 3.621e-04 & 3.494e-04 & 3.864e-04 & 3.680e-04 & 3.677e-04 & 3.563e-04 & 3.538e-04 & 3.410e-04 \\\\\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines", "authors": ["Leandro Aparecido Passos", "João Paulo Papa"], "url": "https://arxiv.org/abs/2101.05795v1", "attribution": "\"A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines\" by Leandro Aparecido Passos and João Paulo Papa, arXiv:2101.05795v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.15103v1_tex_table19.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Impact Analysis - Percentage of Category Revenue Contributed by Customer Samples Defined in the Study}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrr}\n\\toprule\n & Dog Food & Cat Food & Dog Hygiene & Cat Hygiene \\\\\n\\midrule\nSales Revenue (\\%) & 37.4 & 35.6 & 27.8 & 29.2 \\\\ \\midrule\nAdopters & 765 & 372 & 838 & 450 \\\\ \nNon-Adopters & 2879 & 870 & 1400 & 1165 \\\\ \\midrule\nTotal & 3644 & 1242 & 2238 & 1615 \\\\ \\midrule\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "How does online shopping affect offline price sensitivity?", "authors": ["Shirsho Biswas", "Hema Yoganarasimhan", "Haonan Zhang"], "url": "https://arxiv.org/abs/2506.15103v1", "attribution": "\"How does online shopping affect offline price sensitivity?\" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2501.13701v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\small Orbit distribution vector and entropy of the graphs in Figure\\,. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|}\n\\hline\nG &$\\vert G\\vert$ & $\\omega(G)$ & ${\\rm Ent}(G)$ \\\\\n\\hline\n$Z_1$ & 20 & $(17/20, 2/20, 1/20)$ & 0.7476 \\\\\n$Z_2$ & 8 & $(5/8, 2/8, 1/8)$ & 1.2988 \\\\\n$Z_3$ & 4 & $(2/4, 1/4, 1/4)$ & 1.5000 \\\\\n$Z_4$ & 5 & $(2/5, 2/5, 1/5)$ & 1.5219 \\\\\n$Z_5$ & 5 & $(2/5, 2/5, 1/5)$ & 1.5219 \\\\\n$Z_6$ & 20 & $(8/20, 8/20, 4/20)$ & 1.5219 \\\\\n$Z_7$ & 5 & $(2/5, 2/5, 1/5)$ & 1.5219 \\\\\n$Z_8$ & 10 & $(4/10, 4/10, 2/10)$ & 1.5219 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Enlarging a connected graph while keeping entropy and spectral radius: self-similarity techniques", "authors": ["Alberto Seeger", "David Sossa"], "url": "https://arxiv.org/abs/2501.13701v1", "attribution": "\"Enlarging a connected graph while keeping entropy and spectral radius: self-similarity techniques\" by Alberto Seeger and David Sossa, arXiv:2501.13701v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.18675v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccc}\n \\toprule\n Method & {FPS (2080Ti)}$\\uparrow$ & {FPS (A6000)}$\\uparrow$ & \\# Gaussians \\\\\n \\toprule\n Faceformer~ \\textcolor{brown}{\\textbf{+ G.A.}} & 25.38 & 42.23 & 65-100K \\\\\n CodeTalker~ \\textcolor{brown}{\\textbf{+ G.A.}} & 23.92 & 38.98 & 65-100K \\\\\n Imitator~ \\textcolor{brown}{\\textbf{+ G.A.}} & 23.32 & 39.71 & 65-100K \\\\\n SyncTalk~ & 10.14 & 21.51 & N/A \\\\\n ER-NeRF~ & 16.88 & 17.98 & N/A \\\\\n RAD-NeRF~ & 17.18 & 21.41 & N/A \\\\ \n GaussianTalker~ & 57.82 & 59.01 & 41K-44K \\\\\n TalkingGaussian~ & OOM & 73.34 & 31K-98K \\\\\n Ours & \\textbf{74.29} & \\textbf{123.48} & 30-35K \\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "GaussianSpeech: Audio-Driven Gaussian Avatars", "authors": ["Shivangi Aneja", "Artem Sevastopolsky", "Tobias Kirschstein", "Justus Thies", "Angela Dai", "Matthias Nießner"], "url": "https://arxiv.org/abs/2411.18675v1", "attribution": "\"GaussianSpeech: Audio-Driven Gaussian Avatars\" by Shivangi Aneja, Artem Sevastopolsky, Tobias Kirschstein, Justus Thies, Angela Dai, and Matthias Nießner, arXiv:2411.18675v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2507.19199v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study of the model adopting DenseNet-169 as the baseline on APTOS dataset. Accuracy, F1 Score, and number of parameters (\\# Parameters) are reported.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|l|c|c|c|}\n\\hline\n\\textbf{Method} & \\textbf{Accuracy (\\%)} & \\textbf{F1 Score (\\%)} & \\textbf{\\# Parameters (M)} \\\\\n\\hline\nBaseline (DenseNet-169) & 79.0 & 80.0 & 16.8 \\\\\n+ GAB only & 82.2 & 81.5 & 16.8 \\\\\n+ CAB only & 82.1 & 81.2 & 16.8 \\\\\n+ GAB + CAB (Ours) & \\textbf{83.6} & \\textbf{82.0} & 17.0 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning", "authors": ["Abdul Hannan", "Zahid Mahmood", "Rizwan Qureshi", "Hazrat Ali"], "url": "https://arxiv.org/abs/2507.19199v1", "attribution": "\"Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning\" by Abdul Hannan, Zahid Mahmood, Rizwan Qureshi, and Hazrat Ali, arXiv:2507.19199v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2312.06289v3_tex_table10.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Simulations results with 7 parents and 8 children for $\\lambda=1, 3$ and 5 with 1000 individuals per dataset. The column ``unspecified'' corresponds to the unspecified correlation between all children (results presented as mean [95\\% CI]). Computation time is provided in seconds in the last row.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lccccccccccccccccccccccccccccc}\n\\hline\n \\multicolumn{5}{c}{N=1000 individuals}\\\\\nTrue values & $\\lambda$ = 1 & $\\lambda$ = 3 & $\\lambda$ = 5 & unspecified \\\\\n\\hline\n$\\sigma_{c1}$=1 & 1 [0.96, 1.04] & 1 [0.96, 1.04] & 1 [0.96, 1.04] & 1 [0.96, 1.04] & \\\\\n $\\sigma_{c2}$=0.5 & 0.5 [0.47, 0.53] & 0.5 [0.47, 0.53] & 0.5 [0.47, 0.53] & 0.5 [0.48, 0.52] & \\\\\n $\\sigma_{c3}$=1 & 1 [0.95, 1.04] & 1 [0.96, 1.05] & 1 [0.95, 1.04] & 0.99 [0.96, 1.04] & \\\\\n $\\sigma_{c4}$=0.5 & 0.5 [0.48, 0.52] & 0.5 [0.48, 0.52] & 0.5 [0.48, 0.52] & 0.5 [0.48, 0.52] & \\\\\n $\\sigma_{c5}$=1 & 1 [0.95, 1.04] & 1 [0.95, 1.04] & 0.99 [0.95, 1.04] & 1 [0.95, 1.04] & \\\\\n $\\sigma_{c6}$=0.5 & 0.5 [0.48, 0.52] & 0.5 [0.48, 0.52] & 0.5 [0.48, 0.52] & 0.5 [0.48, 0.52] & \\\\\n $\\sigma_{c7}$=1 & 1 [0.96, 1.05] & 1 [0.95, 1.04] & 0.99 [0.95, 1.04] & 0.99 [0.95, 1.04] & \\\\\n $\\sigma_{c8}$=0.5 & 0.5 [0.48, 0.53] & 0.5 [0.48, 0.53] & 0.5 [0.48, 0.53] & 0.5 [0.48, 0.52] & \\\\\n $\\rho_1$=0.4 & 0.4 [0.36, 0.45] & 0.4 [0.34, 0.44] & 0.4 [0.33, 0.45] & 0.4 [0.35, 0.44] & \\\\\n $\\rho_2$=0.4 & 0.4 [0.34, 0.44] & 0.4 [0.34, 0.44] & 0.39 [0.34, 0.44] & 0.39 [0.33, 0.44] & \\\\\n $\\rho_3$=0.4 & 0.4 [0.35, 0.45] & 0.39 [0.34, 0.45] & 0.39 [0.34, 0.45] & 0.4 [0.36, 0.46] & \\\\\n $\\rho_4$=0.4 & 0.4 [0.34, 0.44] & 0.4 [0.34, 0.44] & 0.4 [0.35, 0.43] & 0.39 [0.34, 0.44] & \\\\\n $\\rho_5$=0.3 & 0.3 [0.26, 0.34] & 0.3 [0.26, 0.33] & 0.3 [0.26, 0.34] & 0.3 [0.26, 0.34] & \\\\\n $\\rho_6$=0.3 & 0.3 [0.26, 0.33] & 0.29 [0.26, 0.33] & 0.29 [0.26, 0.33] & 0.3 [0.26, 0.33] & \\\\\n $\\rho_7$=0.2 & 0.2 [0.17, 0.23] & 0.19 [0.17, 0.23] & 0.19 [0.16, 0.23] & 0.2 [0.16, 0.24] & \\\\\n $\\rho_8$=0.2 & 0.2 [0.17, 0.24] & 0.19 [0.17, 0.23] & 0.19 [0.17, 0.23] & 0.2 [0.16, 0.24] & \\\\\n $\\rho_9$=0.2 & 0.2 [0.17, 0.23] & 0.19 [0.17, 0.23] & 0.2 [0.17, 0.23] & 0.19 [0.15, 0.25] & \\\\\n $\\rho_{10}$=0.2 & 0.2 [0.17, 0.24] & 0.19 [0.16, 0.24] & 0.19 [0.17, 0.23] & 0.19 [0.16, 0.24] & \\\\\n \\midrule $\\beta_{01}$=0.2 & 0.2 [0.07, 0.31] & 0.2 [0.07, 0.31] & 0.2 [0.07, 0.31] & 0.19 [0.06, 0.29] & \\\\\n $\\beta_{11}$=-0.1 & -0.1 [-0.13, -0.07] & -0.1 [-0.13, -0.07] & -0.1 [-0.13, -0.07] & -0.1 [-0.12, -0.08] & \\\\\n $\\beta_{21}$=-0.2 & -0.2 [-0.3, -0.09] & -0.2 [-0.3, -0.09] & -0.2 [-0.3, -0.09] & -0.2 [-0.28, -0.1] & \\\\\n $\\beta_{31}$=0.1 & 0.1 [0, 0.2] & 0.1 [0, 0.2] & 0.1 [0, 0.2] & 0.1 [0.01, 0.2] & \\\\\n $\\beta_{02}$=0.2 & 0.19 [0.07, 0.32] & 0.19 [0.07, 0.32] & 0.19 [0.07, 0.32] & 0.19 [0.06, 0.3] & \\\\\n $\\beta_{12}$=-0.1 & -0.1 [-0.13, -0.07] & -0.1 [-0.13, -0.07] & -0.1 [-0.13, -0.07] & -0.1 [-0.12, -0.07] & \\\\\n $\\beta_{22}$=-0.2 & -0.19 [-0.29, -0.1] & -0.19 [-0.29, -0.1] & -0.19 [-0.29, -0.1] & -0.19 [-0.29, -0.09] & \\\\\n $\\beta_{32}$=0.1 & 0.1 [0, 0.2] & 0.1 [0, 0.2] & 0.1 [0, 0.2] & 0.11 [0.01, 0.2] & \\\\\n $\\beta_{03}$=0.2 & 0.2 [0.08, 0.33] & 0.2 [0.08, 0.33] & 0.2 [0.08, 0.33] & 0.18 [0.05, 0.32] & \\\\\n $\\beta_{13}$=-0.1 & -0.1 [-0.13, -0.08] & -0.1 [-0.13, -0.08] & -0.1 [-0.13, -0.08] & -0.1 [-0.12, -0.08] & \\\\\n $\\beta_{23}$=-0.2 & -0.2 [-0.3, -0.09] & -0.2 [-0.3, -0.09] & -0.2 [-0.3, -0.09] & -0.2 [-0.3, -0.1] & \\\\\n $\\beta_{33}$=0.1 & 0.1 [-0.01, 0.19] & 0.1 [-0.01, 0.19] & 0.1 [-0.01, 0.19] & 0.11 [-0.02, 0.19] & \\\\\n $\\beta_{04}$=0.2 & 0.2 [0.08, 0.35] & 0.2 [0.08, 0.35] & 0.2 [0.08, 0.35] & 0.21 [0.08, 0.38] & \\\\\n $\\beta_{14}$=-0.1 & -0.1 [-0.13, -0.07] & -0.1 [-0.13, -0.07] & -0.1 [-0.13, -0.07] & -0.1 [-0.12, -0.07] & \\\\\n $\\beta_{24}$=-0.2 & -0.2 [-0.31, -0.08] & -0.2 [-0.31, -0.08] & -0.2 [-0.31, -0.08] & -0.21 [-0.32, -0.1] & \\\\\n $\\beta_{34}$=0.1 & 0.1 [0, 0.2] & 0.1 [0, 0.2] & 0.1 [0, 0.2] & 0.09 [-0.03, 0.2] & \\\\\n \\midrule\n Comp. time (s) & 123.44 [101.5, 188.46] & 126.14 [101.07, 190.47] & 126.4 [99.72, 196.26] & 471.15 [409.41, 527.3] & \\\\\n \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "A graphical framework for interpretable correlation matrix models", "authors": ["Anna Freni Sterrantino", "Denis Rustand", "Janet van Niekerk", "Elias Teixeira Krainski", "Håvard Rue"], "url": "https://arxiv.org/abs/2312.06289v3", "attribution": "\"A graphical framework for interpretable correlation matrix models\" by Anna Freni Sterrantino, Denis Rustand, Janet van Niekerk, Elias Teixeira Krainski, and Håvard Rue, arXiv:2312.06289v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2404.00785v3_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparison of reconstruction and generative quality among models using both of our datasets. Reconstruction Error (Rec. Err.) and 1-NNA scores using CD and EMD are calculated for every model. Lower scores are better ($\\downarrow$) for each model.}\n\\begin{tabular}{ccccc}\n \\hline\n \\\\\n \n \\textbf{Model} & \n \\textbf{Dataset} &\n \\textbf{Rec. Err.} &\n \\multicolumn{2}{c}{\\textbf{1-NNA(\\%, $\\downarrow$)}} \n \\\\\n & & ($\\downarrow$) & \\textbf{CD} & \\textbf{EMD} \\\\\n \n \\\\\n \\hline\n \\\\\n \\multirow{2}{*}{\\textbf{$\\mathbf{\\beta}$-VAE}} & Torus & \\textbf{0.25} & \\textbf{51.37} & 56.25\\\\ \n & Hippocampus & \\textbf{0.85} & 56.58 & 55.96 \\\\\n \\\\\n \\hline\n \\\\\n \\multirow{2}{*}{\\textbf{$\\mathbf{\\beta}$-TCVAE}} & Torus & 0.28 & 52.35 & 54.71\\\\ \n & Hippocampus & 0.86 & \\textbf{56.35} & \\textbf{55.43}\\\\\n \\\\\n \\hline\n \\\\\n \\multirow{2}{*}{\\textbf{SG VAE}} & Torus & 0.33 & 52.78 & 56.33\\\\ \n & Hippocampus & 1.08 & 61.79 & 60.05 \\\\\n \\\\\n \\hline\n \\\\\n \\multirow{2}{*}{\\textbf{Attribute VAE}} & Torus & 0.36 & 59.38 & 57.81 \\\\\n & Hippocampus & 1.09 & 59.38 & 58.37 \\\\\n \\\\\n \\hline\n \\\\\n \\multirow{2}{*}{\\textbf{SC VAE (Ours)}} & Torus & 0.38 & 51.56 & \\textbf{53.12} \\\\\n & Hippocampus & 1.07 & 58.69 & 59.76\\\\\n \\\\\n \\hline\n \\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Disentangling Hippocampal Shape Variations: A Study of Neurological Disorders Using Mesh Variational Autoencoder with Contrastive Learning", "authors": ["Jakaria Rabbi", "Johannes Kiechle", "Christian Beaulieu", "Nilanjan Ray", "Dana Cobzas"], "url": "https://arxiv.org/abs/2404.00785v3", "attribution": "\"Disentangling Hippocampal Shape Variations: A Study of Neurological Disorders Using Mesh Variational Autoencoder with Contrastive Learning\" by Jakaria Rabbi, Johannes Kiechle, Christian Beaulieu, Nilanjan Ray, and Dana Cobzas, arXiv:2404.00785v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.02875v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Similarity matrix with heuristics between terms walk\\textsubscript{v} and bank\\textsubscript{n}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cccc}\n\t \t\t\\hline\n\t \t\t& bank1 & bank2 &bank3 &bank4 \\\\\n\t \t\t\\hline\n\t \t\twalk1 &0.0158&\\textbf{0.0236}&0.0021&0.0010 \\\\\n\t \t\twalk2 &0.0003&0.0004&0.0000&0.0000 \\\\\n\t \t\twalk3 &0.0004&0.0006&0.0001&0.0000 \\\\\n\t \t\twalk4 &0.0001&0.0001&0.0000&0.0000 \\\\\n\t \t\twalk5 &0.0001&0.0002&0.0000&0.0000 \\\\\n\t \t\twalk6 &0.0001&0.0002&0.0000&0.0000 \\\\\n\t \t\twalk7 &0.0001&0.0002&0.0000&0.0000 \\\\\n\t \t\twalk8 &0.0001&0.0002&0.0000&0.0000 \\\\\n\t \t\twalk9 &0.0002&0.0002&0.0000&0.0000 \\\\\n\t \t\twalk10 &0.0001&0.0002&0.0000&0.0000 \\\\\t\t\t\t\n\t \t\t\\hline\n\t \t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph", "authors": ["Mohannad AlMousa", "Rachid Benlamri", "Richard Khoury"], "url": "https://arxiv.org/abs/2101.02875v1", "attribution": "\"A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph\" by Mohannad AlMousa, Rachid Benlamri, and Richard Khoury, arXiv:2101.02875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2505.02259v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c||c|c||c|c|}\n\\hline\n\\( N \\) & \\( I(N) \\) & \\( N \\) & \\( I(N) \\) & \\( N \\) & \\( I(N) \\) \\\\\n\\hline\n1 & -0.2507 & 2 & 0.0627 & 3 & -0.0836 \\\\\n4 & 0.0496 & 5 & -0.0475 & 6 & 0.0373 \\\\\n7 & -0.0337 & 8 & 0.0292 & 9 & -0.0264 \\\\\n10 & 0.0238 & 11 & -0.0218 & 12 & 0.0200 \\\\\n13 & -0.0185 & 14 & 0.0173 & 15 & -0.0162 \\\\\n16 & 0.0152 & 17 & -0.0143 & 18 & 0.0135 \\\\\n19 & -0.0128 & 20 & 0.0122 & 21 & -0.0117 \\\\\n22 & 0.0111 & 23 & -0.0107 & 24 & 0.0102 \\\\\n25 & -0.0098 & 26 & 0.0095 & 27 & -0.0091 \\\\\n28 & 0.0088 & 29 & -0.0085 & 30 & 0.0082 \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Precomputed values of the integral map \\( I(N) \\) for \\( N=1,\\dotsc,30 \\) with \\( \\delta=0.2 \\).}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Smooth Integer Encoding via Integral Balance", "authors": ["Stanislav Semenov"], "url": "https://arxiv.org/abs/2505.02259v1", "attribution": "\"Smooth Integer Encoding via Integral Balance\" by Stanislav Semenov, arXiv:2505.02259v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2101.11722v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lccccc} \\hline\n\t\t\t\t\t\t\t \t& \t\t& X (3) \t& Y (2) & Z (1)\t\\\\ \\hline\n\t\t\t\t\t\t\t\t\t\t& Budget\t& \t\t& \t\t&\t\t\\\\ \\hline\n\t\t\t\t\t\t\t Agent 1 \t& 1 \t\t& \t& + \t& +\t\t\\\\ \\hline\n\t\t\t\t\t\t\t Agent 2 \t& 1\t \t& \t& + \t& +\t\t\\\\ \\hline\n\t\t\t\t\t\t\t Agent 3 \t& 3\t\t& + \t\t& +\t\t& \t\t\\\\ \\hline\n\t\t\t\t\t\t\t\\end{tabular}\n\\caption{Example instance where EGAL rules are not strategyproof.}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Participatory Funding Coordination: Model, Axioms and Rules", "authors": ["Haris Aziz", "Aditya Ganguly"], "url": "https://arxiv.org/abs/2101.11722v1", "attribution": "\"Participatory Funding Coordination: Model, Axioms and Rules\" by Haris Aziz and Aditya Ganguly, arXiv:2101.11722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2011.04207v1_tex_table5.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{CIs and variance components for $m=50$ under high traffic intensity. }\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{|c|c|c|c|c|c|c|} \n\\hline\nHigh Traffic & \\multicolumn{3}{c|}{$k=20$} & \\multicolumn{3}{c|}{$k=40$} \\\\ \\hline\n$m=50$ & $n=10$ & $n=50$ & $n=100$ & $n=10$ & $n=50$ & $n=100$ \\\\\n \\hline\nCoverage of $\\mbox{CI}_0$ & 91.9\\% & 92.3\\% & 91.5\\% & 93.8\\% & 94.4\\% & 93.4\\%\\\\ \\hline\n$\\mbox{CI}_0$ Width (mean)& 326.4 & 332.4 & 339.5 & 319.1 & 328.6 & 326.5 \n\\\\ \\hline \n$\\mbox{CI}_0$ Width (SD) & 183.1 & 173.6 & 180.7 & 176.4 & 167.6 & 175 \n\\\\ \\hline \nCoverage of $\\mbox{CI}_{+}$ & 93.9\\% & 94.9\\% & 93.7\\% & 94.9\\% & 95.6\\% & 95.9\\% \\\\ \\hline\n$\\mbox{CI}_{+}$ Width (mean) & 344.1 & 348.8 & 357.1 & 332.3 & 342.3 & 341.2\n\\\\ \\hline \n$\\mbox{CI}_{+}$ Width (SD) & 188 & 175.7 & 183.8 & 178.2 & 169.2 & 176.1 \n\\\\ \\hline \\hline\n$\\widehat{\\sigma}_T$ & 90.18 & 91.3 & 93.39 & 87.69 & 90.14 & 89.72 \n\\\\ \\hline \n$\\widehat{\\sigma}_I$ & 86.82 & 88.13 & 90.02 & 85.31 & 87.75 & 87.12 \n\\\\ \\hline \n$\\widehat{\\sigma}_M$ & 21.76 & 21.44 & 22.19 & 18.49 & 18.79 & 19.18 \\\\\\hline\n$\\widehat{\\sigma}_I/\\widehat{\\sigma}_T$ & 0.963 & 0.965 & 0.964 & 0.973 & 0.973 & 0.971\n\\\\ \\hline\\hline\nHigh Traffic & \\multicolumn{3}{c|}{$k=80$} & \\multicolumn{3}{c|}{$k=130$} \\\\ \\hline\n$m=50$ & $n=10$ & $n=50$ & $n=100$ & $n=10$ & $n=50$ & $n=100$ \\\\\n \\hline\nCoverage of $\\mbox{CI}_0$ & 94.6\\% & 96.3\\% & 95.4\\% & 94.2\\% & 95.1\\% & 95.4\\%\\\\ \\hline\n$\\mbox{CI}_0$ Width (mean) & 312.1 & 314.8 & 322.7 & 322 & 321.86 & 320\n\\\\ \\hline \n $\\mbox{CI}_0$ Width (SD) & 169.7 & 159.1 & 164.7 & 171.5 & 169.3 & 172.3 \n\\\\ \\hline \nCoverage of $\\mbox{CI}_{+}$ & 95.9\\% & 96.7\\% & 96.1\\% & 94.5\\% & 96\\% & 96.1\\% \\\\ \\hline\n$\\mbox{CI}_{+}$ Width (mean) & 322 & 325.7 & 334 & 330.2 & 331 & 329.4\n\\\\ \\hline \n$\\mbox{CI}_{+}$ Width (SD) & 171.2 & 159.4 & 165 & 172.7 & 169.5 & 172.7 \n\\\\ \\hline\\hline \n$\\widehat{\\sigma}_T$ & 85.9 & 86.6 & 88.8 & 88.7 & 88.8 & 88.2 \n\\\\ \\hline \n$\\widehat{\\sigma}_I$ & 84.3 & 84.9 & 86.9 & 87.4 & 87.4 & 86.7\n\\\\ \\hline \n$\\widehat{\\sigma}_M$ & 15.2 & 15.7 & 16.4 & 13.7 & 13.9 & 14.17\n\\\\ \\hline\n$\\widehat{\\sigma}_I/\\widehat{\\sigma}_T$ & 0.982 & 0.98 & 0.978 & 0.985 & 0.985 & 0.983\n\\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Statistical Uncertainty Analysis for Stochastic Simulation", "authors": ["Wei Xie", "Barry L. Nelson", "Russell R. Barton"], "url": "https://arxiv.org/abs/2011.04207v1", "attribution": "\"Statistical Uncertainty Analysis for Stochastic Simulation\" by Wei Xie, Barry L. Nelson, and Russell R. Barton, arXiv:2011.04207v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2009.09875v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cc}\n \\toprule\n \\bfseries Section & \\bfseries Average Timing Deviation $\\pm$\\\\\n & Standard Deviation\\\\\n \\midrule\n Soprano & $0.134 \\pm 0.039$ sec \\\\ \\midrule\n Alto & $0.093 \\pm 0.0024$ sec \\\\ \\midrule\n Tenor & $0.100 \\pm 0.021$ sec \\\\ \\midrule\n Bass & $0.124 \\pm 0.021$ sec\\\\ \n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Timing deviations averaged across the CSD. These values measure the time span in which all singers in the unison transition from voiced to unvoiced, and vice-versa, averaged across all transitions in each song.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "A Deep Learning Based Analysis-Synthesis Framework For Unison Singing", "authors": ["Pritish Chandna", "Helena Cuesta", "Emilia Gómez"], "url": "https://arxiv.org/abs/2009.09875v1", "attribution": "\"A Deep Learning Based Analysis-Synthesis Framework For Unison Singing\" by Pritish Chandna, Helena Cuesta, and Emilia Gómez, arXiv:2009.09875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.12618v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{LSQ Execution Time Comparisons}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{||c|c|c|c||}\n\t\t\\hline\n\t\tNumber of & Points per & Parallelized & NumPy\\\\\n\t\tClusters & Cluster & pseudoinverse & pseudoinverse\\\\\n\t\t\\hline\n\t\t\\hline\n\t\t8 & $100$ & $1.81ms$ & $2.08ms$ \\\\\n\t\t\\hline\n\t\t8 & $150$ & $1.83ms$ & $2.10ms$ \\\\\n\t\t\\hline\n\t\t16 & $100$ & $1.81ms$ & $4.18ms$ \\\\\n\t\t\\hline\n\t\t16 & $150$ & $1.83ms$ & $4.21ms$ \\\\\n\t\t\\hline\n\t\t32 & $100$ & $1.81ms$ & $8.74ms$\\\\\n\t\t\\hline\n\t\t32 & $150$ & $1.83ms$ & $8.77ms$\\\\\n\t\t\\hline\n\t\t64 & $100$ & $1.81ms$ & $16.84ms$\\\\\n\t\t\\hline\n\t\t64 & $150$ & $1.83ms$ & $17.19ms$\\\\\n\t\t\\hline\n\t\t\\hline\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Parallelized Instantaneous Velocity and Heading Estimation of Objects using Single Imaging Radar", "authors": ["Nihal Singh", "Dibakar Sil", "Ankit Sharma"], "url": "https://arxiv.org/abs/2012.12618v1", "attribution": "\"Parallelized Instantaneous Velocity and Heading Estimation of Objects using Single Imaging Radar\" by Nihal Singh, Dibakar Sil, and Ankit Sharma, arXiv:2012.12618v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.14548v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccc}\n\\hline\nCorpus & \\textbf{Emo-DB} & \\textbf{DES} & \\textbf{EmoTale} & \\textbf{Urdu} & \\textbf{AESDD} & Overall \\\\\n\\#Speakers & 10 & 4 & 18 & 22 & 6 & \\\\ \\hline\n\\rowcolor[HTML]{C0C0C0} \n\\textbf{ComParE} & 79.0 & 48.5 & 52.0 & 50.0 & 58.0 & 57.5 ± 11.2 \\\\\nSpeaker UAR & 74.9 ± 7.9 & 48.5 ± 10.5 & 50.9 ± 11.1 & 49.7 ± 41.5 & 58.2 ± 10.9 & \\\\\nSentence UAR & 79.5 ± 5.0 & 48.5 ± 10.1 & 52.0 ± 1.5 & - & 58.1 ± 5.4 & \\\\ \\hline\n\\rowcolor[HTML]{C0C0C0} \n\\textbf{eGeMAPS} & 64.3 & 42.7 & 46.0 & 58.0 & 47.6 & 51.7 ± 8.1 \\\\\nSpeaker UAR & 60.3 ± 14.1 & 42.7 ± 13.6 & 44.8 ± 13.8 & 29.2 ± 21.6 & 47.8 ± 11.8 & \\\\\nSentence UAR & 63.9 ± 5.5 & 42.7 ± 9.0 & 46.0 ± 3.9 & - & 47.7 ± 7.1 & \\\\ \\hline\n\\rowcolor[HTML]{C0C0C0} \n\\textbf{w2v2 base} & 58.9 & 32.7 & 29.7 & 33.5 & 41.8 & 39.3 ± 10.6 \\\\\nSpeaker UAR & 56.7 ± 8.0 & 32.7 ± 4.2 & 29.4 ± 6.9 & 23.1 ± 26.7 & 41.7 ± 12.3 & \\\\\nSentence UAR & 58.4 ± 8.2 & 32.7 ± 8.8 & 29.8 ± 2.6 & - & 41.8 ± 8.5 & \\\\ \\hline\n\\rowcolor[HTML]{C0C0C0} \n\\textbf{w2v2 FT dim} & \\textbf{96.1†} & 67.7 & \\textbf{64.1‡} & 59.5 & \\textbf{83.2‡} & 74.1 ± 13.6 \\\\\nSpeaker UAR & 94.7 ± 3.9 & 67.7 ± 4.0 & 62.0 ± 12.4 & 48.4 ± 36.5 & 83.1 ± 7.8 & \\\\\nSentence UAR & 96.1 ± 3.0 & 67.7 ± 12.7 & 64.1 ± 5.4 & - & 83.2 ± 5.9 & \\\\ \\hline\n\\rowcolor[HTML]{C0C0C0} \n\\textbf{w2v2 FT cat} & 88.8 & 62.7 & \\textbf{59.6‡} & 52.5 & \\textbf{77.5‡} & 68.2 ± 13.1 \\\\\nSpeaker UAR & 88.1 ± 5.0 & 62.7 ± 5.1 & 57.8 ± 12.2 & 37.1 ± 34.7 & 77.5 ± 10.8 & \\\\\nSentence UAR & 88.3 ± 4.8 & 62.7 ± 10.5 & 59.6 ± 3.5 & - & 77.6 ± 8.1 & \\\\ \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "EmoTale: An Enacted Speech-emotion Dataset in Danish", "authors": ["Maja J. Hjuler", "Harald V. Skat-Rørdam", "Line H. Clemmensen", "Sneha Das"], "url": "https://arxiv.org/abs/2508.14548v1", "attribution": "\"EmoTale: An Enacted Speech-emotion Dataset in Danish\" by Maja J. Hjuler, Harald V. Skat-Rørdam, Line H. Clemmensen, and Sneha Das, arXiv:2508.14548v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2411.12502v3_tex_table15.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{1D GP Matérn 3/2 Kernel Results. In a paired t-test, TNP-KR: SA had a lower NLL than TNP-D and ConvCNP with p-value $\\le 0.016$ while TNP-KR: DKA and TNP-KR: PERF were indistinguishable.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lrrrrr}\n\\toprule\nModel & NLL & RMSE & MAE & Coverage & Training Time (s) \\\\\n\\midrule\nANP & $0.086\\pm0.027$ & $0.461\\pm0.003$ & $0.285\\pm0.002$ & $0.933\\pm0.003$ & $678.400\\pm1.030$ \\\\\nBANP & $0.036\\pm0.008$ & $0.407\\pm0.001$ & $0.282\\pm0.001$ & $0.928\\pm0.002$ & $1095.200\\pm1.241$ \\\\\nBNP & $0.443\\pm0.002$ & $0.475\\pm0.001$ & $0.353\\pm0.001$ & $0.917\\pm0.001$ & $792.000\\pm1.761$ \\\\\nCANP & $0.080\\pm0.006$ & $0.459\\pm0.000$ & $0.282\\pm0.000$ & $0.931\\pm0.002$ & $640.000\\pm1.549$ \\\\\nCNP & $0.463\\pm0.002$ & $0.501\\pm0.000$ & $0.343\\pm0.001$ & $0.939\\pm0.001$ & $615.400\\pm0.872$ \\\\\nConvCNP & $-0.014\\pm0.002$ & $0.450\\pm0.000$ & $0.276\\pm0.000$ & $0.946\\pm0.002$ & $1165.400\\pm2.159$ \\\\\nNP & $0.565\\pm0.004$ & $0.520\\pm0.001$ & $0.366\\pm0.001$ & $0.941\\pm0.001$ & $634.200\\pm0.490$ \\\\\nTNP-D & $-0.020\\pm0.003$ & $0.451\\pm0.001$ & $0.276\\pm0.000$ & $0.947\\pm0.001$ & $856.000\\pm8.124$ \\\\\nTNP-KR: PERF & $-0.024\\pm0.001$ & $0.451\\pm0.000$ & $0.276\\pm0.000$ & $0.947\\pm0.001$ & $974.800\\pm1.356$ \\\\\nTNP-KR: DKA & $-0.025\\pm0.002$ & $0.451\\pm0.000$ & $0.276\\pm0.000$ & $0.948\\pm0.002$ & $956.600\\pm2.943$ \\\\\nTNP-KR: SA & $-0.027\\pm0.002$ & $0.450\\pm0.000$ & $0.276\\pm0.000$ & $0.948\\pm0.002$ & $986.600\\pm7.026$ \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Transformer Neural Processes - Kernel Regression", "authors": ["Daniel Jenson", "Jhonathan Navott", "Mengyan Zhang", "Makkunda Sharma", "Elizaveta Semenova", "Seth Flaxman"], "url": "https://arxiv.org/abs/2411.12502v3", "attribution": "\"Transformer Neural Processes - Kernel Regression\" by Daniel Jenson, Jhonathan Navott, Mengyan Zhang, Makkunda Sharma, Elizaveta Semenova, and Seth Flaxman, arXiv:2411.12502v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.00092v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Bases for the subspaces $T_{2,k}$, $k\\in\\{0,\\ldots,d+2\\}$, and $T_{2,k}^\\ast$, $k\\in\\{0,\\ldots,d\\}$}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c||c|c|c|c|c}\n \\toprule\n& $k=0$ & $k=1$ & $k\\in\\{2,\\ldots,d-1\\}$ & $k=d$ & $k\\in\\{d+1,d+2\\}$\\\\\n \\midrule\n$T_{2,k}$ & $\\{Q\\Phi_{0}^{0,0}\\}$ & $\\{\\Phi_{1}^{0,2}, Q\\Phi_{1}^{0,0}\\}$ & $\\{\\Phi_{k}^{0,2},Q\\Phi_{k}^{0,0},\\Phi_{k-2}^{2,0}\\}$ & $\\{\\Phi_{d-2}^{2,0},Q\\Phi_{d}^{0,0}\\}$ & $\\{Q\\Phi_{k-2}^{2,0}\\}$\\\\\n$T_{2,k}^\\ast$ & $\\{Q\\Phi_{0}^{0,0}\\}$ & $\\{\\Phi_{1}^{0,2},Q\\Phi_{1}^{0,0}\\}$ & $\\{\\Phi_{k}^{0,2},Q\\Phi_{k}^{0,0}\\}$ & $\\{Q\\Phi_{d}^{0,0}\\}$ & -- \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Minkowski tensors for voxelized data: robust, asymptotically unbiased estimators", "authors": ["Daniel Hug", "Michael A. Klatt", "Dominik Pabst"], "url": "https://arxiv.org/abs/2502.00092v1", "attribution": "\"Minkowski tensors for voxelized data: robust, asymptotically unbiased estimators\" by Daniel Hug, Michael A. Klatt, and Dominik Pabst, arXiv:2502.00092v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2012.05088v3_tex_table6.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|c|c}\nCoin & Symbol & Dates\\\\\n\\hline\nBitcoin\t & BTC & 28/04/2013 - 21/11/2020\\\\\nLitecoin & LTC & 28/04/2013 - 21/11/2020\\\\\nEthereum & ETH & 07/08/2015 - 21/11/2020\\\\\nXRP & XRP & 04/08/2013 - 21/11/2020\\\\\nMonero & XMR & 21/05/2014 - 21/11/2020\\\\\nTether & USDT & 25/02/2015 - 21/11/2020\\\\\nDash & DASH & 14/02/2014 - 21/11/2020\\\\\nStellar & XLM & 05/08/2014 - 21/11/2020\\\\\nDogecoin & DOGE & 15/12/2013 - 21/11/2020\\\\\nDigiByte & DGB & 06/02/2014 - 21/11/2020\\\\\nNEM & XEM & 01/04/2015 - 21/11/2020\\\\\nSiacoin & SC & 26/08/2015 - 21/11/2020\\\\\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Cryptocurrencies used to detect shock events in market.}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Modeling asset allocation strategies and a new portfolio performance score", "authors": ["Apostolos Chalkis", "Emmanouil Christoforou", "Ioannis Z. Emiris", "Theodore Dalamagas"], "url": "https://arxiv.org/abs/2012.05088v3", "attribution": "\"Modeling asset allocation strategies and a new portfolio performance score\" by Apostolos Chalkis, Emmanouil Christoforou, Ioannis Z. Emiris, and Theodore Dalamagas, arXiv:2012.05088v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "stat/image/2311.15401v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccccc} \n \\toprule\n Country & \\multicolumn{4}{c}{Female} & \\multicolumn{4}{c}{Male} \\\\\n \n \\cmidrule(lr){2-5}\\cmidrule(lr){6-9} \n \n & LC & Tree & RF & GBM & LC & Tree & RF & GBM \\\\\n \n \\cmidrule(lr){1-1}\\cmidrule(lr){2-2}\\cmidrule(lr){3-3}\\cmidrule(lr){4-4}%\n \\cmidrule(lr){5-5}\\cmidrule(lr){6-6}\\cmidrule(lr){7-7}\\cmidrule(lr){8-8}%\n \\cmidrule(lr){9-9}%\n FIN & 0.0021 & 0.0021 & 0.0014 & 0.0017 & 0.0029 & 0.0034 & 0.0024 & 0.0031 \\\\\n DE & 0.0048 & 0.0046 & 0.0032 & 0.0033 & 0.0052 & 0.0052 & 0.0033 & 0.0034 \\\\\n ITA & 0.0045 & 0.0046 & 0.0039 & 0.0022 & 0.0042 & 0.0041 & 0.0020 & 0.0022 \\\\\n NLD & 0.003 & 0.003 & 0.0015 & 0.0017 & 0.0035 & 0.0035 & 0.0018 & 0.0020 \\\\\n US & 0.0023 & 0.0023 & 0.0010 & 0.0011 & 0.0054 & 0.0052 & 0.0049 & 0.0051 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "stat", "source": {"title": "Advanced Techniques in Mortality Trend Estimation: Integrating Generalized Additive Models and Machine Learning to Evaluate the COVID-19 Impact", "authors": ["Asmik Nalmpatian", "Christian Heumann", "Stefan Pilz"], "url": "https://arxiv.org/abs/2311.15401v1", "attribution": "\"Advanced Techniques in Mortality Trend Estimation: Integrating Generalized Additive Models and Machine Learning to Evaluate the COVID-19 Impact\" by Asmik Nalmpatian, Christian Heumann, and Stefan Pilz, arXiv:2311.15401v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2011.10258v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Ablation study : CorLoc performance(\\%) of each category on the PASCAL VOC 2007 trainval set}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccccccccc}\n\t\t\\hline\\noalign{\\smallskip}\n\t\tMethods \t\t\t\t\t\t & aero & bike & bird & boat & bottle & bus & car & cat & chair & cow \\\\\n\t\t\\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\n\t\tBaseline & \\textbf{84.6} & 78.4 & 59.2 & 49.5 & 44.7 & 77.7 & 85.0 & 61.0 & 34.8 & 75.3 \\\\\n\t\t + CADM & \\textbf{84.6} & 77.6 & 70.6 & 56.4 & \\textbf{50.4} & 78.7 & 84.4 & 39.0 & \\textbf{47.9} & 82.9\t \\\\\n\t\t + GCM & 84.2 & \\textbf{82.4} & 70.3 & \\textbf{64.4} & 44.7 & \\textbf{79.7} & \\textbf{86.6} & 47.1 & 46.9 & \\textbf{84.2} \\\\\n\t\t + Both & 84.2 & 75.7 & \\textbf{75.1} & 50.0 & 40.8 & 77.7 & 82.3 & \\textbf{68.6} & 46.3 & 82.2 \\\\\n\t\t\n\t\t\\hline\\noalign{\\smallskip}\n\t\tMethods\t\t\t\t\t\t\t\t & table & dog & horse & mbike & person & plant & sheep & sofa & train & tv \\\\\n\t\t\\noalign{\\smallskip}\\hline\\noalign{\\smallskip}\n\t\tBaseline & 44.1 &\\textbf{ 70.5} & 65.0 & 88.8 & \\textbf{11.4} & \\textbf{57.1} & 73.2 & 51.9 & 66.2 & \\textbf{82.4} \\\\\n\t\t + CADM & 52.1 & 38.4 & 70.4 & \\textbf{90.4} & 10.8 & 55.7 & 81.4 & \\textbf{69.4} & \\textbf{78.7} & 82.1 \\\\\n \t\t + GCM & 48.7 & 40.0 & \\textbf{73.5} & 88.8 & 11.0 & 55.7 & \\textbf{82.5} & 66.4 & 70.3 & \\textbf{82.4} \\\\\n\t\t + Both & \\textbf{52.9} & 63.5 & 73.1 & 89.6 & 10.6 & 52.7 & 79.4 & 55.4 & \\textbf{78.7} & 82.1 \\\\\n\t\t\n\t\t\\noalign{\\smallskip}\\hline\n\t\t\n\t\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Cascade Attentive Dropout for Weakly Supervised Object Detection", "authors": ["Wenlong Gao", "Ying Chen", "Yong Peng"], "url": "https://arxiv.org/abs/2011.10258v1", "attribution": "\"Cascade Attentive Dropout for Weakly Supervised Object Detection\" by Wenlong Gao, Ying Chen, and Yong Peng, arXiv:2011.10258v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.10650v1_tex_table9.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{PAD-UFES dataset classification results between melanoma and non-melanoma.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccccc}\n\\hline\n\\textbf{Experiment} & $\\textbf{\\textit{n}}_f$ & \\textbf{AUC} & \\textbf{SE} & \\textbf{SP} & \\textbf{AC} & \\textbf{BAC} \\\\ \\hline\nImage features & 59 & 0.838 & 0.652 & 0.844 & 0.823 & 0.748 \\\\\nImage features* & 9 & 0.862 & 0.671 & 0.850 & 0.831 & 0.761 \\\\\nContext information & 7 & 0.853 & 0.667 & 0.803 & 0.788 & 0.735 \\\\\nCombined & 59+7 & 0.904 & 0.726 & \\textbf{0.902} & \\textbf{0.883} & 0.814 \\\\\nCombined* & 9+7 & \\textbf{0.928} & \\textbf{0.777} & 0.894 & 0.881 & \\textbf{0.835} \\\\ \\hline\n\\multicolumn{6}{l}{* Experiments with the best image features.}\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data", "authors": ["José G. M. Esgario", "Renato A. Krohling"], "url": "https://arxiv.org/abs/2201.10650v1", "attribution": "\"Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data\" by José G. M. Esgario and Renato A. Krohling, arXiv:2201.10650v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2102.06744v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Evaluation metrics on the test data set.}\n\\begin{tabular}{c|c|c|c|c}\n\t\t\\hline\n\t\t\\textbf{Class} & \\textbf{Accuracy} & \\textbf{Recall} & \\textbf{$\\boldsymbol{F_1}$ score} & \\textbf{Support} \\\\\n\t\t\\hline\n\t\t0 & 0.99 & 0.99 & 0.99 & 3302 \\\\\n\t\t1 & 0.96 & 0.97 & 0.97 & 1305 \\\\\n\t\t\\hline\n\t\t\\textbf{Macro average} & \\textbf{0.98} & \\textbf{0.98} & \\textbf{0.98} & \\textbf{4607} \\\\\n\t\t\\hline\t\t\n\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Hybrid phonetic-neural model for correction in speech recognition systems", "authors": ["Rafael Viana-Cámara", "Mario Campos-Soberanis", "Diego Campos-Sobrino"], "url": "https://arxiv.org/abs/2102.06744v1", "attribution": "\"Hybrid phonetic-neural model for correction in speech recognition systems\" by Rafael Viana-Cámara, Mario Campos-Soberanis, and Diego Campos-Sobrino, arXiv:2102.06744v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2012.01241v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Comparisons of proposed model with a variety of available techniques where errors are calculated between ground truth images and background-masked reconstructed images as shown in Figure by using Mean Absolute Error (MAE) in percentage. *SVD is used for channel reduction from 2000 to 5 in pre-processing for Dictionary Matching.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{l|cc}\n\\hline\nModels & T1 - MAE(\\%) & T2 - MAE(\\%) \\\\ \\hline\nDictionary Matching & 4 & 1.01 \\\\\nDictionary Matching* & 4.7 & 1.96 \\\\\nHoppe et al.~(RNN) & 5.61 & 1.79 \\\\\nChen et al. & 3.38 & 1.3 \\\\\nHoppe et al.~(CNN) & 2.07 & 1.03 \\\\\nBalsiger et al. & 1.82 & 1 \\\\\nFang et al. & 1.72 & 1.14 \\\\\nProposed & \\textbf{1.58} & \\textbf{0.57} \\\\\n\\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Channel Attention Networks for Robust MR Fingerprinting Matching", "authors": ["Refik Soyak", "Ebru Navruz", "Eda Ozgu Ersoy", "Gastao Cruz", "Claudia Prieto", "Andrew P. King", "Devrim Unay", "Ilkay Oksuz"], "url": "https://arxiv.org/abs/2012.01241v1", "attribution": "\"Channel Attention Networks for Robust MR Fingerprinting Matching\" by Refik Soyak, Ebru Navruz, Eda Ozgu Ersoy, Gastao Cruz, Claudia Prieto, Andrew P. King, Devrim Unay, and Ilkay Oksuz, arXiv:2012.01241v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.08910v1_tex_table11.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{rrrrrrrrlr}\n\\toprule\n$N_\\text{output}$ & $N_\\text{halo}$ & $\\sigma$ & $\\alpha$ & $\\rho_{SR}$ & $\\beta$ & $N$ & $\\sigma_b$ & readout & $\\rho_A$ \\\\\n\\midrule\n 2 & 0 & 0.001 & 1.00 & 1.50 & 3.655278e-01 & 2000 & 1.11 & linear & 0.98 \\\\\n 2 & 2 & 0.027 & 0.67 & 1.18 & 1.221827e-08 & 2000 & 1.54 & linear & 0.98 \\\\\n 2 & 4 & 0.017 & 1.00 & 0.16 & 3.432201e-07 & 2000 & 0.75 & linear & 0.98 \\\\\n 2 & 6 & 0.005 & 0.94 & 0.46 & 6.443480e-07 & 2000 & 0.58 & linear & 0.98 \\\\\n 2 & 8 & 0.006 & 0.74 & 0.71 & 1.801521e-07 & 2000 & 1.23 & linear & 0.98 \\\\\n 4 & 0 & 0.004 & 0.45 & 0.39 & 2.475150e-01 & 2000 & 0.29 & linear & 0.98 \\\\\n 4 & 2 & 0.021 & 0.77 & 1.03 & 1.372422e-08 & 2000 & 1.27 & linear & 0.98 \\\\\n 4 & 4 & 0.003 & 0.99 & 0.64 & 4.054118e-07 & 2000 & 1.17 & linear & 0.98 \\\\\n 4 & 6 & 0.003 & 0.99 & 0.64 & 4.054118e-07 & 2000 & 1.17 & linear & 0.98 \\\\\n 4 & 8 & 0.003 & 0.99 & 0.64 & 4.054118e-07 & 2000 & 1.17 & linear & 0.98 \\\\\n 8 & 0 & 0.001 & 0.44 & 0.90 & 1.000000e+00 & 2000 & 1.74 & linear & 0.98 \\\\\n 8 & 2 & 0.003 & 0.99 & 0.64 & 4.054118e-07 & 2000 & 1.17 & linear & 0.98 \\\\\n 8 & 4 & 0.001 & 0.74 & 0.17 & 1.000000e-08 & 2000 & 0.72 & linear & 0.98 \\\\\n 8 & 6 & 0.003 & 0.99 & 0.64 & 4.054118e-07 & 2000 & 1.17 & linear & 0.98 \\\\\n 8 & 8 & 0.003 & 0.99 & 0.64 & 4.054118e-07 & 2000 & 1.17 & linear & 0.98 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Fig. (a)}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics", "authors": ["Jason A. Platt", "Stephen G. Penny", "Timothy A. Smith", "Tse-Chun Chen", "Henry D. I. Abarbanel"], "url": "https://arxiv.org/abs/2201.08910v1", "attribution": "\"A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics\" by Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen, and Henry D. I. Abarbanel, arXiv:2201.08910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2506.21253v1_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Spanish La Liga Time Trends}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n \\toprule\n & \\multicolumn{2}{c}{Ln(suspense)} & \\multicolumn{2}{c}{Ln(surprise)} \\\\\n \\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n & (1) & (2) & (3) & (4) \\\\\n \\midrule\n Season & 0.015*** & 0.006*** & 0.013*** & 0.006*** \\\\\n & (0.002) & (0.002) & (0.002) & (0.002) \\\\\n Real Madrid × season & & 0.039*** & & 0.022*** \\\\\n & & (0.009) & & (0.008) \\\\\n Barcelona × season & & 0.050*** & & 0.039*** \\\\\n & & (0.011) & & (0.009) \\\\\n Atlético Madrid × season & & 0.004 & & 0.010 \\\\\n & & (0.007) & & (0.008) \\\\\n \\midrule\n Main effects & & Yes & & Yes \\\\\n Observations & 5,320 & 5,320 & 5,320 & 5,320 \\\\\n R\\textsuperscript{2} & 0.011 & 0.125 & 0.007 & 0.062 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Suspense and Surprise in European Football", "authors": ["Raphael Flepp", "Tim Pawlowski", "Travis Richardson"], "url": "https://arxiv.org/abs/2506.21253v1", "attribution": "\"Suspense and Surprise in European Football\" by Raphael Flepp, Tim Pawlowski, and Travis Richardson, arXiv:2506.21253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.08426v1_tex_table1.png", "tex_code": "\\documentclass{article}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{|c|c|c|} \n\t\t\t\\hline\n\t\t & DL: Train L2/Valid. L2 & DT: Train L2/Valid. L2 \\\\\n\t\t\t\\hline\n\t\t\t2D-kFNO & 0.0021/0.0015 & 0.0025/0.0025 \\\\ \n\t\t 2D-FNO & 0.0041/0.0030 & 0.0053/0.0053 \\\\ \n\t\t \\hline\n\t\t\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Koopman Theory-Inspired Method for Learning Time Advancement Operators in Unstable Flame Front Evolution", "authors": ["Rixin Yu", "Marco Herbert", "Markus Klein", "Erdzan Hodzic"], "url": "https://arxiv.org/abs/2412.08426v1", "attribution": "\"Koopman Theory-Inspired Method for Learning Time Advancement Operators in Unstable Flame Front Evolution\" by Rixin Yu, Marco Herbert, Markus Klein, and Erdzan Hodzic, arXiv:2412.08426v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2502.20030v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{cccccccc}\n \\toprule\n $a_1$ & $a_2$ & $a_3$ & $a_4$ & $b_1$ & $b_2$ & $\\tau_c$ & $\\tau_h$ \\\\ \\midrule\n $4\\cdot10^{-3}$ & $5.1 \\cdot 10^{-11}$ & $7.3\\cdot10^{-3}$ & $ 10^{-11}$ & $0.011$ & $0.006$ & $18.3$ & $2$\\\\\n \\bottomrule\\\\\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Lumped-parameter coefficients of system .}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "Offline Reinforcement Learning via Inverse Optimization", "authors": ["Ioannis Dimanidis", "Tolga Ok", "Peyman Mohajerin Esfahani"], "url": "https://arxiv.org/abs/2502.20030v1", "attribution": "\"Offline Reinforcement Learning via Inverse Optimization\" by Ioannis Dimanidis, Tolga Ok, and Peyman Mohajerin Esfahani, arXiv:2502.20030v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "math/image/2412.10782v2_tex_table14.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Median, Maximum and Minimum test loss of the optimizers for the Allen-Cahn equation.}\n\\begin{tabular}{lrrr}\n\\toprule\n & Median & Minimum & Maximum \\\\\n\\midrule\nANaGRAM & \\textbf{6.22e-05} & \\textbf{1.45e-05} & 1.38e-03 \\\\\nAdam & 7.10e-04 & 6.29e-04 & \\textbf{1.18e-03} \\\\\nE-NGD & 2.74e+00 & 2.58e+00 & 3.43e+00 \\\\\nGD & 2.41e-03 & 1.70e-03 & 1.93e-02 \\\\\nL-BFGS & 1.48e-03 & 9.08e-04 & 5.09e-03 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "math", "source": {"title": "ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning", "authors": ["Nilo Schwencke", "Cyril Furtlehner"], "url": "https://arxiv.org/abs/2412.10782v2", "attribution": "\"ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning\" by Nilo Schwencke and Cyril Furtlehner, arXiv:2412.10782v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2507.16701v1_tex_table8.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Option Pricing Results: Microstructure-Enhanced vs Black-Scholes}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Parameter/Model} & \\textbf{Value} & \\textbf{Notes} \\\\\n\\midrule\n\\textbf{Option Specifications} & & \\\\\nUnderlying Asset & SPY & S\\&P 500 ETF \\\\\nCurrent Spot Price & \\$600.00 & At-the-money \\\\\nStrike Price & \\$600.00 & At-the-money \\\\\nTime to Expiration & 30 days & 0.0822 years \\\\\nRisk-free Rate & 5.0\\% & Annualized \\\\\n& & \\\\\n\\textbf{Pricing Results} & & \\\\\nMicrostructure-Enhanced Price & \\$15.41 & Our methodology \\\\\nBlack-Scholes Price & \\$17.87 & Traditional benchmark \\\\\nAbsolute Difference & \\$2.46 & 13.79\\% lower \\\\\nRelative Difference & -13.79\\% & Economically significant \\\\\n& & \\\\\n\\textbf{Volatility Analysis} & & \\\\\nHistorical Volatility & 24.3\\% & Annualized from data \\\\\nModel Implied Volatility & 21.0\\% & From state factors \\\\\nVolatility Difference & 3.2pp & Model efficiency gain \\\\\n& & \\\\\n\\textbf{Computational Performance} & & \\\\\nTree Nodes Generated & 2,047 & 10 time steps \\\\\nTree Construction Time & 87.31 sec & One-time cost \\\\\nPricing Time & 0.004 sec & Per option \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach", "authors": ["Akash Deep", "Chris Monico", "W. Brent Lindquist", "Svetlozar T. Rachev", "Frank J. Fabozzi"], "url": "https://arxiv.org/abs/2507.16701v1", "attribution": "\"Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach\" by Akash Deep, Chris Monico, W. Brent Lindquist, Svetlozar T. Rachev, and Frank J. Fabozzi, arXiv:2507.16701v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2201.13019v2_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\usepackage{xcolor}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Robustness of FID against latent perturbations.} \\textcolor{red}{I think we can do only images for this section.}}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|cccc}\n\\toprule \n\\midrule\nlearning rate & 0.1 & 0.2 & 0.3 \\\\\n\\midrule\n 0.7 & xxx & xxx & xxx \\\\\n 1.0 & xxx & xxx & xxx \\\\\n 1.3 & xxx & xxx & xxx \\\\\n\\midrule\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "On the Robustness of Quality Measures for GANs", "authors": ["Motasem Alfarra", "Juan C. Pérez", "Anna Frühstück", "Philip H. S. Torr", "Peter Wonka", "Bernard Ghanem"], "url": "https://arxiv.org/abs/2201.13019v2", "attribution": "\"On the Robustness of Quality Measures for GANs\" by Motasem Alfarra, Juan C. Pérez, Anna Frühstück, Philip H. S. Torr, Peter Wonka, and Bernard Ghanem, arXiv:2201.13019v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2508.13992v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{lcccc}\n\\toprule\n\\textbf{System} & \\textbf{MMAU} & \\textbf{MMAR} & \\textbf{MMSU} & \\textbf{MMAU-Pro} \\\\\n\\midrule\nQwen2.5-3B-Instruct & 28.90 & 29.50 & 27.92 & \\textbf{27.64} \\\\\nQwen2.5-7B-Instruct & 34.80 & {36.39} & 37.82 & \\textbf{16.27} \\\\\nLlama-3.1-8B-Instruct & 37.50 & 34.35 & 38.40 & \\textbf{32.91} \\\\\nGemma-3-4b-it & {{40.40}} & 35.03 & 39.62 & \\textbf{28.94} \\\\\nGemma-3-12b-it & 38.14 & 35.03 & {{41.34}} & \\textbf{30.94} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\caption{Comparison of text-only LLMs' performance across four audio benchmarks (accuracy). The lowest score in each column is in bold.}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence", "authors": ["Sonal Kumar", "Šimon Sedláček", "Vaibhavi Lokegaonkar", "Fernando López", "Wenyi Yu", "Nishit Anand", "Hyeonggon Ryu", "Lichang Chen", "Maxim Plička", "Miroslav Hlaváček", "William Fineas Ellingwood", "Sathvik Udupa", "Siyuan Hou", "Allison Ferner", "Sara Barahona", "Cecilia Bolaños", "Satish Rahi", "Laura Herrera-Alarcón", "Satvik Dixit", "Siddhi Patil", "Soham Deshmukh", "Lasha Koroshinadze", "Yao Liu", "Leibny Paola Garcia Perera", "Eleni Zanou", "Themos Stafylakis", "Joon Son Chung", "David Harwath", "Chao Zhang", "Dinesh Manocha", "Alicia Lozano-Diez", "Santosh Kesiraju", "Sreyan Ghosh", "Ramani Duraiswami"], "url": "https://arxiv.org/abs/2508.13992v1", "attribution": "\"MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence\" by Sonal Kumar, Šimon Sedláček, Vaibhavi Lokegaonkar, Fernando López, Wenyi Yu, Nishit Anand, Hyeonggon Ryu, Lichang Chen, Maxim Plička, Miroslav Hlaváček, William Fineas Ellingwood, Sathvik Udupa, Siyuan Hou, Allison Ferner, Sara Barahona, Cecilia Bolaños, Satish Rahi, Laura Herrera-Alarcón, Satvik Dixit, Siddhi Patil, Soham Deshmukh, Lasha Koroshinadze, Yao Liu, Leibny Paola Garcia Perera, Eleni Zanou, Themos Stafylakis, Joon Son Chung, David Harwath, Chao Zhang, Dinesh Manocha, Alicia Lozano-Diez, Santosh Kesiraju, Sreyan Ghosh, and Ramani Duraiswami, arXiv:2508.13992v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2411.15513v2_tex_table4.png", "tex_code": "\\documentclass{article}\n\\usepackage{amssymb}\n\\usepackage[table]{xcolor}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{\\textbf{Effectiveness of Network Modules in SPA.} Ablation study evaluating the impact of network components on segmentation performance for the REFUGE2 and Kidney datasets after three iterations. The table compares random sampling (Random Gaussian), updating the mean and variance (Gaussian), and updating distribution weights (Mixture Gaussian). Combining all modules achieves the highest Dice Scores, highlighting their complementary benefits in optimizing segmentation performance.}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ccccc}\n\\hline\\hline\nRandom Gaussian & Gaussian & Mixture Gaussian & REFUGE2 & Kidney \\\\ \\hline\n\\checkmark & & & 80.29 & 90.05 \\\\\n\\checkmark & \\checkmark & & 84.12 & 92.06 \\\\\n\\checkmark & & \\checkmark & 82.87 & 92.29\\\\\n\\rowcolor{cyan!40!white!20}\n\\checkmark & \\checkmark & \\checkmark & \\textbf{85.42} & \\textbf{94.26} \\\\ \\hline \\hline\n\\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation", "authors": ["Jiayuan Zhu", "Junde Wu", "Cheng Ouyang", "Konstantinos Kamnitsas", "J. Alison Noble"], "url": "https://arxiv.org/abs/2411.15513v2", "attribution": "\"SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation\" by Jiayuan Zhu, Junde Wu, Cheng Ouyang, Konstantinos Kamnitsas, and J. Alison Noble, arXiv:2411.15513v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "eess/image/2412.18788v1_tex_table3.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{c|ccccc}\n \\hline\n Mode & Note name & $\\mu$ & $\\sigma^2$ & $w$ & $\\Delta \\mu$\\\\ \\hline\n \\multirow{5}{*}{Ge'ez} & $G_3$ & 361 & 11 & 0.034 & \\multirow{2}{*}{486}\\\\\n & $g_1$ & 847 & 21 & 0.211 & \\multirow{2}{*}{324} \\\\\n & $g_2$ & 1171 & 7 & 0.171 & \\multirow{2}{*}{400} \\\\\n & $g_3$ & 1571 & 14 & 0.419 & \\multirow{2}{*}{476} \\\\\n & $\\dot{g_1}$ & 2047 & 18 & 0.112 \\\\ \\hline\n \\multirow{9}{*}{Ezil} & $E_4$ & 189 & 6 & 0.022 & \\multirow{2}{*}{258} \\\\\n & $E_5$ & 447 & 14 & 0.023 & \\multirow{2}{*}{223} \\\\\n & $e_1$ & 670 & 6 & 0.106 & \\multirow{2}{*}{270}\\\\\n & $e_2$ & 940 & 7 & 0.151 & \\multirow{2}{*}{234}\\\\\n & $e_3$ & 1174 & 7 & 0.123 & \\multirow{2}{*}{232}\\\\\n & $e_4$ & 1406 & 3 & 0.416 & \\multirow{2}{*}{268}\\\\\n & $e_5$ & 1674 & 15 & 0.068 & \\multirow{2}{*}{204}\\\\\n & $\\dot{e_1}$ & 1878 & 5 & 0.059 & \\multirow{2}{*}{261}\\\\\n & $\\dot{e_2}$ & 2139 & 5 & 0.013\\\\ \\hline\n \\multirow{8}{*}{Araray} & $A_5$ & 173 & 3 & 0.008 & \\multirow{2}{*}{347} \\\\\n & $a_1$ & 520 & 6 & 0.318 & \\multirow{2}{*}{172}\\\\\n & $a_2$ & 692 & 8 & 0.173 & \\multirow{2}{*}{218} \\\\\n & $a_3$ & 910 & 10 & 0.176 & \\multirow{2}{*}{297} \\\\\n & $a_4$ & 1207 & 5 & 0.134 & \\multirow{2}{*}{174} \\\\\n & $a_5$ & 1381 & 4 & 0.027 & \\multirow{2}{*}{335} \\\\\n & $\\dot{a_1}$ & 1716 & 5 & 0.084 & \\\\ %\\multirow{2}{*}{201} \\\\\n\\hline\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "eess", "source": {"title": "Computational Analysis of Yaredawi YeZema Silt in Ethiopian Orthodox Tewahedo Church Chants", "authors": ["Mequanent Argaw Muluneh", "Yan-Tsung Peng", "Li Su"], "url": "https://arxiv.org/abs/2412.18788v1", "attribution": "\"Computational Analysis of Yaredawi YeZema Silt in Ethiopian Orthodox Tewahedo Church Chants\" by Mequanent Argaw Muluneh, Yan-Tsung Peng, and Li Su, arXiv:2412.18788v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "cs/image/2202.00104v2_tex_table1.png", "tex_code": "\\documentclass{article}\n\\usepackage{booktabs}\n\\usepackage{adjustbox}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\caption{Team formations in \\texttt{Terran} tasks}\n\\begin{adjustbox}{max width=\\textwidth, max height=0.8\\textheight, keepaspectratio}\n\\begin{tabular}{ll}\n \\toprule\n\\texttt{10\\_Terran} & \\texttt{10\\_Terran\\_Hard} \\\\\n \\midrule\n\\textbf{Training} & \\textbf{Training} \\\\\n1 marine \\& 9 marauders & 1 marine \\& 9 marauders \\\\\n3 marines \\& 7 marauders & 2 marines \\& 8 marauders \\\\\n4 marines \\& 6 marauders & 3 marines \\& 7 marauders \\\\\n5 marines \\& 5 marauders & 4 marines \\& 6 marauders \\\\\n6 marines \\& 4 marauders & 5 marines \\& 5 marauders \\\\\n8 marines \\& 2 marauders & 6 marines \\& 4 marauders \\\\\n9 marines \\& 1 marauder & 7 marines \\& 3 marauders \\\\\n5 marauders \\& 5 medivacs & 8 marines \\& 2 marauders \\\\\n7 marauders \\& 3 medivacs & 9 marines \\& 1 marauder \\\\\n9 marauders \\& 1 medivac & 5 marauders \\& 5 medivacs \\\\\n7 marines \\& 3 medivacs & 6 marauders \\& 4 medivacs \\\\\n8 marines \\& 2 medivacs & 7 marauders \\& 3 medivacs \\\\\n9 marines \\& 1 medivac & 8 marauders \\& 2 medivacs \\\\\n10 marines & 9 marauders \\& 1 medivac \\\\\n10 marauders & 7 marines \\& 3 medivacs \\\\\n8 marines \\& 1 marauder \\& 1 medivac & 8 marines \\& 2 medivacs \\\\\n1 marine \\& 8 marauders \\& 1 medivac & 9 marines \\& 1 medivac \\\\\n5 marines \\& 3 marauders \\& 2 medivacs & \\textbf{Testing} \\\\\n2 marines \\& 7 marauders \\& 1 medivac & 10 marines \\\\\n6 marines \\& 2 marauders \\& 2 medivacs & 10 marauders \\\\\n2 marines \\& 6 marauders \\& 2 medivacs & 8 marines \\& 1 marauder \\& 1 medivac \\\\\n4 marines \\& 4 marauders \\& 2 medivacs & 1 marine \\& 8 marauders \\& 1 medivac \\\\\n\\textbf{Testing} & 5 marines \\& 3 marauders \\& 2 medivacs \\\\\n2 marines \\& 8 marauders & 3 marines \\& 5 marauders \\& 2 medivacs \\\\\n7 marines \\& 3 marauders & 4 marines \\& 3 marauders \\& 3 medivacs \\\\\n6 marauders \\& 4 medivacs & 3 marines \\& 4 marauders \\& 3 medivacs \\\\\n8 marauders \\& 2 medivacs & 7 marines \\& 2 marauders \\& 1 medivac \\\\\n3 marines \\& 5 marauders \\& 2 medivacs & 2 marines \\& 7 marauders \\& 1 medivac \\\\\n4 marines \\& 3 marauders \\& 3 medivacs & 6 marines \\& 2 marauders \\& 2 medivacs \\\\\n3 marines \\& 4 marauders \\& 3 medivacs & 2 marines \\& 6 marauders \\& 2 medivacs \\\\\n7 marines \\& 2 marauders \\& 1 medivac & 4 marines \\& 4 marauders \\& 2 medivacs \\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\end{table}\n\\end{document}\n", "subject": "cs", "source": {"title": "Generalization in Cooperative Multi-Agent Systems", "authors": ["Anuj Mahajan", "Mikayel Samvelyan", "Tarun Gupta", "Benjamin Ellis", "Mingfei Sun", "Tim Rocktäschel", "Shimon Whiteson"], "url": "https://arxiv.org/abs/2202.00104v2", "attribution": "\"Generalization in Cooperative Multi-Agent Systems\" by Anuj Mahajan, Mikayel Samvelyan, Tarun Gupta, Benjamin Ellis, Mingfei Sun, Tim Rocktäschel, and Shimon Whiteson, arXiv:2202.00104v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}
{"image_path": "q-fin/image/2502.19608v1_tex_table2.png", "tex_code": "\\documentclass{article}\n\\usepackage{amsmath}\n\\pagestyle{empty}\n\\begin{document}\n\\begin{table}\n\\centering\n\\begin{tabular}{lccccccccc}\n\\hline \n & period & & \\multicolumn{7}{c}{period}\\tabularnewline\n\\cline{4-10} & & & & & & & & & \\tabularnewline\n & 0 & & $1^{a}$ & $1^{b}$ & $1^{c}$ & $1^{d}$ & $1^{e}$ & $1^{f}$ & $1^{g}$\\tabularnewline\n\\hline & & & & & & & & & \\tabularnewline\nA & $10$ & & $20$ & $15$ & $20$ & $40$ & $25$ & $10$ & $10$\\tabularnewline\nB & $20$ & & $40$ & $25$ & $40$ & $80$ & $45$ & $30$ & $40$\\tabularnewline\nC & $40$ & & $80$ & $45$ & $10$ & $20$ & $15$ & $40$ & $160$\\tabularnewline\n\\hline & & & & & & & & & \\tabularnewline\nElasticity & $1-\\hat{\\beta}$ & & \\textbf{0} & 0.208 & 1.500 & 1.500 & 1.368 & \\textbf{0} & -1.000\\tabularnewline\nPearson correlation & $1-\\hat{\\rho}$ & & \\textbf{0} & 0.001 & 1.500 & 1.500 & 1.465 & 0.053 & \\textbf{0}\\tabularnewline\nFields-Ok 1 & $FO_{1}$ & & 23.333 & 5.000 & 20.000 & 36.667 & 21.667 & 3.333 & 46.667\\tabularnewline\nFields-Ok 2 & $FO_{2}$ & & 0.693 & 0.249 & 0.924 & 1.155 & 0.903 & 0.135 & 0.693\\tabularnewline\nShorrocks 1 & $S_{\\text{Theil}}$ & & \\textbf{0} & 0.011 & 0.736 & 0.680 & 0.739 & 0.034 & 0.053\\tabularnewline\nShorrocks 2 & $S_{\\text{Gini}}$ & & \\textbf{0} & \\textbf{0} & 0.500 & 0.444 & 0.500 & \\textbf{0} & \\textbf{0}\\tabularnewline\nRay-Genicot absolute & $RG_{1}$ & & 0.693 & 0.306 & \\textbf{0} & 0.693 & 0.306 & 0.100 & 0.288\\tabularnewline\nRay-Genicot relative & $RG_{2}$ & & \\textbf{0} & 0.112 & \\textbf{0} & \\textbf{0} & 0.112 & -0.033 & -0.811\\tabularnewline\nB\\'arcena-Cant\\'o downward & $BC_{\\mathrm{D}}$ & & \\textbf{0} & \\textbf{0} & 0.250 & 0.167 & 0.208 & \\textbf{0} & \\textbf{0}\\tabularnewline\nB\\'arcena-Cant\\'o upward & $BC_{\\mathrm{U}}$ & & 1.000 & 0.292 & 0.667 & 2.000 & 0.917 & 0.167 & 1.333\\tabularnewline\n\\hline \n\\end{tabular}\n\\end{table}\n\\end{document}\n", "subject": "q-fin", "source": {"title": "Mobility and Mobility Measures", "authors": ["Frank A. Cowell", "Emmanuel Flachaire"], "url": "https://arxiv.org/abs/2502.19608v1", "attribution": "\"Mobility and Mobility Measures\" by Frank A. Cowell and Emmanuel Flachaire, arXiv:2502.19608v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.", "license": {"name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/"}, "modifications": ["Extracted the table LaTeX from the article source.", "Wrapped it as a standalone LaTeX document.", "Rendered and cropped it into a PNG image."]}}